From 01c23b3acf3223afdfb2a7eb8aed6d75863d4978 Mon Sep 17 00:00:00 2001
From: Marcos Arancibia <marcos.arancibia@oracle.com>
Date: Wed, 17 Dec 2025 14:51:33 -0500
Subject: [PATCH] Large update to hundreds of Notebooks, dozens of Notebook
 additions, adjustments to folder names, updates to copyright

Large update to hundreds of Notebooks, dozens of Notebook additions, adjustments to folder names, updates to copyright.

Adding all Spatial AI notebooks as new OML4Py notebooks.
---
 ...ed_with_OML4Py_on_Autonomous_Database.json |    2 +-
 ...ate_data_using_the_Transparency_Layer.json |   66 +-
 ...Use_in_database_algorithms_and_models.json |   56 +-
 ...on_objects_and_user_defined_functions.json |   40 +-
 ...tions_using_Embedded_Python_Execution.json |   26 +-
 .../oml4py-live-labs/Lab6_Use_AutoML.json     |   10 +-
 .../labs/oml4py-live-labs/README.md           |   14 +-
 .../python/OML Run-me-first.json              |    0
 ...Party Packages - Environment Creation.json |    0
 ... Packages - Python Environment Usage .json |    0
 .../python/OML4Py -0- Tour.json               |    0
 .../python/OML4Py -1- Introduction.json       |    0
 ...y -2- Data Selection and Manipulation.json |    0
 ...y -3- Datastore and Script Repository.json |    0
 .../OML4Py -4- Embedded Python Execution.json |    0
 .../python/OML4Py -5- AutoML.json             |    0
 .../python/OML4Py Anomaly Detection SVM.json  |    0
 .../OML4Py Association Rules Apriori.json     |    0
 .../OML4Py Attribute Importance MDL.json      |    0
 .../python/OML4Py Classification DT.json      |    0
 .../python/OML4Py Classification GLM.json     |    0
 .../python/OML4Py Classification NB.json      |    0
 .../python/OML4Py Classification NN.json      |    0
 .../python/OML4Py Classification RF.json      |    0
 .../python/OML4Py Classification SVM.json     |    0
 .../python/OML4Py Clustering EM.json          |    0
 .../python/OML4Py Clustering KM.json          |    0
 ...L4Py Data Cleaning Duplicates Removal.json |    0
 .../OML4Py Data Cleaning Missing Data.json    |    0
 .../OML4Py Data Cleaning Outlier Removal.json |    0
 ...ata Cleaning Recode Synonymous Values.json |    0
 .../OML4Py Data Transformation Binning.json   |    0
 ...ML4Py Data Transformation Categorical.json |    0
 ...nsformation Normalization and Scaling.json |    0
 ... Data Transformation One Hot Encoding.json |    0
 .../python/OML4Py Dataset Creation.json       |    0
 .../python/OML4Py Date and Time Classes.json  |    0
 ...ML4Py Feature Engineering Aggregation.json |    0
 ...4Py Feature Extraction ESA Wiki Model.json |    0
 .../python/OML4Py Feature Extraction ESA.json |    0
 .../python/OML4Py Feature Extraction SVD.json |    0
 ...4Py Feature Selection Algorithm-based.json |    0
 ...re Selection Using Summary Statistics.json |    0
 .../OML4Py Importing Wide Datasets.json       |    0
 .../python/OML4Py Partitioned Model SVM.json  |    0
 .../python/OML4Py REST API.json               |    0
 .../python/OML4Py Regression GLM.json         |    0
 .../python/OML4Py Regression NN.json          |    0
 .../python/OML4Py Regression SVM.json         |    0
 .../python/OML4Py Statistical Functions.json  |    0
 .../python/OML4Py Text Mining SVM.json        |    0
 .../python/OML4Py Time Series ESM.json        |    0
 ...rs_OML4Py_Cross_Validation_AUC_ML_101.json |    0
 ..._execution_using_third_party_packages.json |    0
 ...ours_OML4Py_Weight_of_Evidence_ML_101.json |    0
 ...eHours_OML_Datastore_for_R_and_Python.json |    0
 ...loy_an_XGBoost_model_in_OML_Services.ipynb |    0
 .../notebooks-classic/python/README.md        |    0
 .../r/OML Import Wiki ESA Model.json          |    0
 .../notebooks-classic/r/OML Run-me-first.json |    0
 ...Party Packages - Environment Creation.json |    0
 ...-Party Packages - R Environment Usage.json |    0
 .../r/OML4R -1- Introduction.json             |    0
 ...R -2- Data Selection and Manipulation.json |    0
 ...R -3- Datastore and Script Repository.json |    0
 .../r/OML4R -4- Embedded R Execution.json     |    0
 .../r/OML4R Anomaly Detection SVM.json        |    0
 .../r/OML4R Association Rules Apriori.json    |    0
 .../r/OML4R Attribute Importance MDL.json     |    0
 .../r/OML4R Classification DT.json            |    0
 .../r/OML4R Classification GLM.json           |    0
 .../r/OML4R Classification NB.json            |    0
 .../r/OML4R Classification RF.json            |    0
 .../r/OML4R Classification SVM.json           |    0
 .../r/OML4R Clustering EM.json                |    0
 .../r/OML4R Clustering KM.json                |    0
 .../r/OML4R Clustering OC.json                |    0
 ...OML4R Data Cleaning Duplicate Removal.json |    0
 .../r/OML4R Data Cleaning Missing Data.json   |    0
 .../OML4R Data Cleaning Outlier Removal.json  |    0
 ...ata Cleaning Recode Synonymous Values.json |    0
 .../r/OML4R Data Transformation Binning.json  |    0
 ...ata Transformation Categorical Recode.json |    0
 ...4R Data Transformation Date Datatypes.json |    0
 ...nsformation Normalization and Scaling.json |    0
 ... Data Transformation One-Hot Encoding.json |    0
 .../r/OML4R Dataset Creation.json             |    0
 ...OML4R Feature Engineering Aggregation.json |    0
 ...L4R Feature Extraction ESA Wiki Model.json |    0
 .../r/OML4R Feature Extraction ESA.json       |    0
 .../r/OML4R Feature Extraction SVD.json       |    0
 ...L4R Feature Selection Algorithm-based.json |    0
 ...re Selection Using Summary Statistics.json |    0
 .../r/OML4R Partitioned Model SVM.json        |    0
 .../notebooks-classic/r/OML4R REST API.json   |    0
 .../r/OML4R Regression GLM.json               |    0
 .../r/OML4R Regression NN .json               |    0
 .../r/OML4R Regression SVM.json               |    0
 .../r/OML4R Statistical Functions .json       |    0
 .../r/OML4R Text Mining SVM.json              |    0
 .../r/OfficeHours_OML4R_Demonstration.json    |    0
 ...eHours_OML_Datastore_for_R_and_Python.json |    0
 .../notebooks-classic/r/README.md             |    0
 .../Credit_Scoring_100K_SQL_Create_Table.sql  |    0
 ... Export and Import  Serialized Models.json |    0
 ...SQL 21c or 23c Anomaly Detection MSET.json |    0
 ...SQL 21c or 23c Classification XGBoost.json |    0
 ...OML4SQL 21c or 23c Regression XGBoost.json |    0
 .../sql/OML4SQL Anomaly Detection SVM.json    |    0
 .../OML4SQL Association Rules Apriori.json    |    0
 .../sql/OML4SQL Attribute Importance MDL.json |    0
 .../sql/OML4SQL Classification DT.json        |    0
 .../sql/OML4SQL Classification GLM.json       |    0
 .../sql/OML4SQL Classification NB.json        |    0
 .../sql/OML4SQL Classification NN.json        |    0
 .../sql/OML4SQL Classification RF.json        |    0
 .../sql/OML4SQL Classification SVM.json       |    0
 .../sql/OML4SQL Clustering EM.json            |    0
 .../sql/OML4SQL Clustering KM.json            |    0
 .../sql/OML4SQL Clustering OC.json            |    0
 ...4SQL Data Cleaning Duplicates Removal.json |    0
 .../OML4SQL Data Cleaning Missing Data.json   |    0
 ...OML4SQL Data Cleaning Outlier Removal.json |    0
 ...ata Cleaning Recode Synonymous Values.json |    0
 .../OML4SQL Data Transformation Binning.json  |    0
 ...L4SQL Data Transformation Categorical.json |    0
 ...ransformation Normalization and Scale.json |    0
 .../OML4SQL Exporting Serialized Models.json  |    0
 ...ture Engineering Aggregation and Time.json |    0
 ...SQL Feature Extraction ESA Wiki Model.json |    0
 .../sql/OML4SQL Feature Extraction NMF.json   |    0
 .../sql/OML4SQL Feature Extraction SVD.json   |    0
 ...SQL Feature Selection Algorithm Based.json |    0
 ...ion Unsupervised Attribute Importance.json |    0
 ...re Selection Using Summary Statistics.json |    0
 .../sql/OML4SQL Nested Columns.json           |    0
 .../sql/OML4SQL Partitioned Model SVM.json    |    0
 ...L Procedure for Importing Data to ADB.json |    0
 .../sql/OML4SQL Regression GLM.json           |    0
 .../sql/OML4SQL Regression NN.json            |    0
 .../sql/OML4SQL Regression SVM.json           |    0
 .../sql/OML4SQL Statistical Functions.json    |    0
 .../sql/OML4SQL Text Mining SVM.json          |    0
 .../sql/OML4SQL Time Series ESM.json          |    0
 ...OML4SQL_Automated_Text_Mining_Example.json |    0
 .../sql/OML4SQL_Credit_Score_Predictions.json |    0
 .../sql/OML4SQL_Insurance_Claims_Fraud.json   |    0
 .../sql/OML4SQL_My_First_Notebook.json        |    0
 ...Good_Wine_for_20_dollars_with_ADW_OML.json |    0
 ...redicting_Customer_Lifetime_Value_LTV.json |    0
 .../OML4SQL_SQLFORMAT_and_Forms_Examples.json |    0
 .../sql/OML4SQL_Targeting_Top_Customers.json  |    0
 .../OML4SQL_Targeting_Top_Customers_10k.json  |    0
 ...ng_Top_Customers_Desktop_Viz_companion.dva |  Bin
 .../notebooks-classic/sql/README.md           |    0
 .../python/OML Run-me-first.dsnb              |    1 +
 ...Party Packages - Environment Creation.dsnb | 2512 +++++++++++++++++
 ...y Packages - Python Environment Usage.dsnb | 1689 +++++++++++
 .../notebooks-oml/python/OML4Py -0- Tour.dsnb |    1 +
 .../python/OML4Py -1- Introduction.dsnb       |    1 +
 ...y -2- Data Selection and Manipulation.dsnb |    1 +
 ...y -3- Datastore and Script Repository.dsnb |    1 +
 .../OML4Py -4- Embedded Python Execution.dsnb |    1 +
 .../python/OML4Py -5- AutoML.dsnb             |    1 +
 .../python/OML4Py Anomaly Detection SVM.dsnb  |    1 +
 .../OML4Py Association Rules Apriori.dsnb     |    1 +
 .../OML4Py Attribute Importance MDL.dsnb      |    1 +
 .../python/OML4Py Classification DT.dsnb      |    1 +
 .../python/OML4Py Classification GLM.dsnb     |    1 +
 .../python/OML4Py Classification NB.dsnb      |    1 +
 .../python/OML4Py Classification NN.dsnb      |    1 +
 .../python/OML4Py Classification RF.dsnb      |    1 +
 .../python/OML4Py Classification SVM.dsnb     |    1 +
 .../python/OML4Py Clustering EM.dsnb          |    1 +
 .../python/OML4Py Clustering KM.dsnb          | 1046 +++++++
 ...L4Py Data Cleaning Duplicates Removal.dsnb |    1 +
 .../OML4Py Data Cleaning Missing Data.dsnb    |    1 +
 .../OML4Py Data Cleaning Outlier Removal.dsnb |    1 +
 ...ata Cleaning Recode Synonymous Values.dsnb |    1 +
 .../OML4Py Data Transformation Binning.dsnb   |    1 +
 ...ML4Py Data Transformation Categorical.dsnb |    1 +
 ...Py Data Transformation Date Datatypes.dsnb |    1 +
 ...nsformation Normalization and Scaling.dsnb |    1 +
 ... Data Transformation One Hot Encoding.dsnb |    1 +
 .../python/OML4Py Dataset Creation.dsnb       |    1 +
 ...ML4Py Feature Engineering Aggregation.dsnb |    1 +
 ...4Py Feature Extraction ESA Wiki Model.dsnb |    1 +
 .../python/OML4Py Feature Extraction ESA.dsnb |    1 +
 .../python/OML4Py Feature Extraction NMF.dsnb |    1 +
 .../python/OML4Py Feature Extraction SVD.dsnb |    1 +
 ...4Py Feature Selection Algorithm-based.dsnb |    1 +
 ...re Selection Using Summary Statistics.dsnb |    1 +
 .../OML4Py Importing Wide Datasets.dsnb       |    1 +
 .../python/OML4Py Partitioned Model SVM.dsnb  |    1 +
 .../notebooks-oml/python/OML4Py REST API.dsnb |    1 +
 .../python/OML4Py Regression GLM.dsnb         |    1 +
 .../python/OML4Py Regression NN.dsnb          |    1 +
 .../python/OML4Py Regression SVM.dsnb         |    1 +
 ...SQL API for Embedded Python Execution.dsnb |    1 +
 ...rative Clustering and Regionalization.dsnb |    1 +
 ...patial AI Categorical Lag Transformer.dsnb |    1 +
 .../OML4Py Spatial AI DBSCAN Accidents.dsnb   |    1 +
 ...Spatial AI Embedded Execution for SQL.dsnb |  832 ++++++
 ...ML4Py Spatial AI Exploratory Analysis.dsnb |    1 +
 .../OML4Py Spatial AI GWR Regressor.dsnb      |    1 +
 ...Py Spatial AI Geographical Classifier.dsnb |    1 +
 ...4Py Spatial AI Geographical Regressor.dsnb |    1 +
 .../OML4Py Spatial AI Hotspot Clustering.dsnb |    1 +
 .../OML4Py Spatial AI KMeans Clustering.dsnb  |    1 +
 .../OML4Py Spatial AI LOF Accidents.dsnb      |    1 +
 .../OML4Py Spatial AI OLS Regressor.dsnb      |    1 +
 .../OML4Py Spatial AI PAR Object Store.dsnb   |    1 +
 .../OML4Py Spatial AI Run Me First.dsnb       |    1 +
 .../OML4Py Spatial AI SLX Classifier.dsnb     |    1 +
 .../OML4Py Spatial AI Save Load Run.dsnb      |  862 ++++++
 .../OML4Py Spatial AI Scoord Transformer.dsnb |    1 +
 ...al AI Spatial Fixed Effects Regressor.dsnb |    1 +
 .../OML4Py Spatial AI Spatial Imputer.dsnb    |    1 +
 ...Py Spatial AI Spatial Lag Transformer.dsnb |    1 +
 ...l AI Spatial Lag and Error Regressors.dsnb |    1 +
 .../OML4Py Spatial AI Spatial Operations.dsnb |    1 +
 .../OML4Py Spatial AI Spatial Pipeline.dsnb   |    1 +
 ... AI SpatialDataFrame to OML DataFrame.dsnb |    1 +
 .../python/OML4Py Statistical Functions.dsnb  |    1 +
 .../python/OML4Py Text Mining SVM.dsnb        |    1 +
 .../python/OML4Py Time Series ESM.dsnb        |    1 +
 .../python/README.md                          |    2 +-
 .../notebooks-oml/r/OML Run-me-first.dsnb     |    1 +
 ...Party Packages - Environment Creation.dsnb | 2512 +++++++++++++++++
 ...-Party Packages - R Environment Usage.dsnb | 1456 ++++++++++
 .../notebooks-oml/r/OML4R -0- Tour.dsnb       |    1 +
 .../r/OML4R -1- Introduction.dsnb             |    1 +
 ...R -2- Data Selection and Manipulation.dsnb |    1 +
 ...R -3- Datastore and Script Repository.dsnb |    1 +
 .../r/OML4R -4- Embedded R Execution.dsnb     |    1 +
 .../r/OML4R Anomaly Detection SVM.dsnb        |    1 +
 .../r/OML4R Association Rules Apriori.dsnb    |    1 +
 .../r/OML4R Attribute Importance MDL.dsnb     |    1 +
 .../r/OML4R Classification DT.dsnb            |    1 +
 .../r/OML4R Classification GLM.dsnb           |    1 +
 .../r/OML4R Classification NB.dsnb            |    1 +
 .../r/OML4R Classification NN.dsnb            |    1 +
 .../r/OML4R Classification RF.dsnb            |    1 +
 .../r/OML4R Classification SVM.dsnb           |    1 +
 .../notebooks-oml/r/OML4R Clustering EM.dsnb  |    1 +
 .../notebooks-oml/r/OML4R Clustering KM.dsnb  | 1145 ++++++++
 .../r/OML4R Clustering OC.dsnb                |    2 +-
 ...OML4R Data Cleaning Duplicate Removal.dsnb |    1 +
 .../r/OML4R Data Cleaning Missing Data.dsnb   |    1 +
 .../OML4R Data Cleaning Outlier Removal.dsnb  |    1 +
 ...ata Cleaning Recode Synonymous Values.dsnb |    1 +
 .../r/OML4R Data Transformation Binning.dsnb  |    1 +
 ...ata Transformation Categorical Recode.dsnb |    1 +
 ...4R Data Transformation Date Datatypes.dsnb |    1 +
 ...nsformation Normalization and Scaling.dsnb |    1 +
 ... Data Transformation One-Hot Encoding.dsnb |    1 +
 .../r/OML4R Dataset Creation.dsnb             |    1 +
 ...OML4R Feature Engineering Aggregation.dsnb |    1 +
 ...L4R Feature Extraction ESA Wiki Model.dsnb |    1 +
 .../r/OML4R Feature Extraction ESA.dsnb       |    1 +
 .../r/OML4R Feature Extraction SVD.dsnb       |    1 +
 ...L4R Feature Selection Algorithm-based.dsnb |    1 +
 ...re Selection Using Summary Statistics.dsnb |    1 +
 .../r/OML4R Partitioned Model SVM.dsnb        |    1 +
 .../notebooks-oml/r/OML4R REST API.dsnb       |    1 +
 .../notebooks-oml/r/OML4R Regression GLM.dsnb |    1 +
 .../notebooks-oml/r/OML4R Regression NN.dsnb  |    1 +
 .../notebooks-oml/r/OML4R Regression SVM.dsnb |    1 +
 .../r/OML4R Statistical Functions.dsnb        |    1 +
 .../r/OML4R Text Mining SVM.dsnb              |    1 +
 .../r/OML4R Time Series ESM.dsnb              |    1 +
 .../{notebooks => notebooks-oml}/r/README.md  |    2 +-
 .../rest/OML Batch Scoring REST.dsnb          |    1 +
 .../rest/OML Data Bias Detector REST.dsnb     |    1 +
 .../rest/OML Data Drift Monitoring REST.dsnb  |    1 +
 .../rest/OML Data Monitoring REST.dsnb        |    1 +
 .../rest/OML Model Drift Monitoring REST.dsnb |    0
 .../rest/OML Model Monitoring REST.dsnb       |    1 +
 .../rest/OML Monitoring Model Drift REST.dsnb |    1 +
 .../rest/README.md                            |    2 +-
 ...L Export and Import Serialized Models.dsnb |    1 +
 ... Vector Search - Sentence Transformer.dsnb |    1 +
 .../sql/OML Import Wiki ESA Model.dsnb        |    1 +
 .../notebooks-oml/sql/OML Run-me-first.dsnb   |    1 +
 ... AI SpatialDataFrame to OML DataFrame.dsnb |    1 +
 .../sql/OML4SQL Anomaly Detection EM.dsnb     |    1 +
 .../sql/OML4SQL Anomaly Detection MSET.dsnb   |    1 +
 .../sql/OML4SQL Anomaly Detection SVM.dsnb    |    1 +
 .../OML4SQL Association Rules Apriori.dsnb    |    1 +
 .../sql/OML4SQL Attribute Importance MDL.dsnb |    1 +
 .../sql/OML4SQL Classification DT.dsnb        |    1 +
 .../sql/OML4SQL Classification GLM.dsnb       |    1 +
 .../sql/OML4SQL Classification NB.dsnb        |    1 +
 .../sql/OML4SQL Classification NN.dsnb        |    1 +
 .../sql/OML4SQL Classification RF.dsnb        |    1 +
 .../sql/OML4SQL Classification SVM.dsnb       |    1 +
 .../sql/OML4SQL Classification XGBoost.dsnb   |    1 +
 .../sql/OML4SQL Clustering EM.dsnb            | 1521 ++++++++++
 .../sql/OML4SQL Clustering KM.dsnb            |    1 +
 .../sql/OML4SQL Clustering OC.dsnb            |    1 +
 ...4SQL Data Cleaning Duplicates Removal.dsnb |    1 +
 .../OML4SQL Data Cleaning Missing Data.dsnb   |    1 +
 ...OML4SQL Data Cleaning Outlier Removal.dsnb |    1 +
 ...L4SQL Data Cleaning Outlier Removal_1.dsnb |    1 +
 ...ata Cleaning Recode Synonymous Values.dsnb |    1 +
 .../OML4SQL Data Transformation Binning.dsnb  |    2 +-
 ...L4SQL Data Transformation Categorical.dsnb |    1 +
 ...ransformation Normalization and Scale.dsnb |    1 +
 ...ture Engineering Aggregation and Time.dsnb |    1 +
 ...SQL Feature Extraction ESA Wiki Model.dsnb |    1 +
 .../sql/OML4SQL Feature Extraction NMF.dsnb   |    1 +
 .../sql/OML4SQL Feature Extraction SVD.dsnb   |    1 +
 ...SQL Feature Selection Algorithm Based.dsnb |    1 +
 ...ion Unsupervised Attribute Importance.dsnb |    1 +
 ...re Selection Using Summary Statistics.dsnb |    1 +
 .../sql/OML4SQL Nested Columns.dsnb           |    1 +
 .../sql/OML4SQL Partitioned Model SVM.dsnb    |    1 +
 ...L Procedure for Importing Data to ADB.dsnb |    1 +
 .../sql/OML4SQL Regression GLM.dsnb           |    1 +
 .../sql/OML4SQL Regression NN.dsnb            |    1 +
 .../sql/OML4SQL Regression SVM.dsnb           |    1 +
 .../sql/OML4SQL Regression XGBoost.dsnb       |    1 +
 .../sql/OML4SQL Statistical Functions.dsnb    |    1 +
 .../sql/OML4SQL Text Mining SVM.dsnb          |    1 +
 .../sql/OML4SQL Time Series ESM.dsnb          |    1 +
 ...me Series Regression ESM plus GLM XGB.dsnb |    1 +
 .../sql/README.md                             |    0
 ...y -2- Data Selection and Manipulation.dsnb |    1 -
 .../OML4Py -4- Embedded Python Execution.dsnb |    1 -
 .../notebooks/python/OML4Py -5- AutoML.dsnb   |    1 -
 .../OML4Py Association Rules Apriori.dsnb     |    1 -
 .../OML4Py Attribute Importance MDL.dsnb      |    1 -
 .../python/OML4Py Classification SVM.dsnb     |    1 -
 .../python/OML4Py Clustering EM.dsnb          |    1 -
 .../python/OML4Py Clustering KM.dsnb          |    1 -
 ...L4Py Data Cleaning Duplicates Removal.dsnb |    1 -
 .../OML4Py Data Cleaning Missing Data.dsnb    |    1 -
 .../OML4Py Data Cleaning Outlier Removal.dsnb |    1 -
 ...ata Cleaning Recode Synonymous Values.dsnb |    1 -
 .../OML4Py Data Transformation Binning.dsnb   |    1 -
 ...ML4Py Data Transformation Categorical.dsnb |    1 -
 ...Py Data Transformation Date Datatypes.dsnb |    1 -
 ...nsformation Normalization and Scaling.dsnb |    1 -
 ... Data Transformation One Hot Encoding.dsnb |    1 -
 .../python/OML4Py Dataset Creation.dsnb       |    1 -
 ...ML4Py Feature Engineering Aggregation.dsnb |    1 -
 ...4Py Feature Extraction ESA Wiki Model.dsnb |    1 -
 .../python/OML4Py Feature Extraction ESA.dsnb |    1 -
 ...4Py Feature Selection Algorithm-based.dsnb |    1 -
 ...re Selection Using Summary Statistics.dsnb |    1 -
 .../OML4Py Importing Wide Datasets.dsnb       |    1 -
 .../python/OML4Py Partitioned Model SVM.dsnb  |    1 -
 .../notebooks/python/OML4Py REST API.dsnb     |    1 -
 .../python/OML4Py Regression GLM.dsnb         |    1 -
 .../python/OML4Py Regression NN.dsnb          |    1 -
 .../python/OML4Py Regression SVM.dsnb         |    1 -
 .../python/OML4Py Time Series ESM.dsnb        |    1 -
 ...R -2- Data Selection and Manipulation.dsnb |    1 -
 ...R -3- Datastore and Script Repository.dsnb |    1 -
 .../r/OML4R -4- Embedded R Execution.dsnb     |    1 -
 .../r/OML4R Anomaly Detection SVM.dsnb        |    1 -
 .../r/OML4R Association Rules Apriori.dsnb    |    1 -
 .../r/OML4R Attribute Importance MDL.dsnb     |    1 -
 .../notebooks/r/OML4R Classification DT.dsnb  |    1 -
 .../notebooks/r/OML4R Classification GLM.dsnb |    1 -
 .../notebooks/r/OML4R Classification NB.dsnb  |    1 -
 .../notebooks/r/OML4R Classification NN.dsnb  |    1 -
 .../notebooks/r/OML4R Classification RF.dsnb  |    1 -
 .../notebooks/r/OML4R Classification SVM.dsnb |    1 -
 .../notebooks/r/OML4R Clustering EM.dsnb      |    1 -
 .../notebooks/r/OML4R Clustering KM.dsnb      |    1 -
 ...OML4R Data Cleaning Duplicate Removal.dsnb |    1 -
 .../r/OML4R Data Cleaning Missing Data.dsnb   |    1 -
 .../OML4R Data Cleaning Outlier Removal.dsnb  |    1 -
 ...ata Cleaning Recode Synonymous Values.dsnb |    1 -
 .../r/OML4R Data Transformation Binning.dsnb  |    1 -
 ...ata Transformation Categorical Recode.dsnb |    1 -
 ...4R Data Transformation Date Datatypes.dsnb |    1 -
 ...nsformation Normalization and Scaling.dsnb |    1 -
 ... Data Transformation One-Hot Encoding.dsnb |    1 -
 .../notebooks/r/OML4R Dataset Creation.dsnb   |    1 -
 ...OML4R Feature Engineering Aggregation.dsnb |    1 -
 ...L4R Feature Extraction ESA Wiki Model.dsnb |    1 -
 .../r/OML4R Feature Extraction ESA.dsnb       |    1 -
 .../r/OML4R Feature Extraction SVD.dsnb       |    1 -
 ...L4R Feature Selection Algorithm-based.dsnb |    1 -
 ...re Selection Using Summary Statistics.dsnb |    1 -
 .../r/OML4R Partitioned Model SVM.dsnb        |    1 -
 .../notebooks/r/OML4R REST API.dsnb           |    1 -
 .../notebooks/r/OML4R Regression GLM.dsnb     |    1 -
 .../notebooks/r/OML4R Regression NN.dsnb      |    1 -
 .../notebooks/r/OML4R Regression SVM.dsnb     |    1 -
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diff --git a/machine-learning/labs/oml4py-live-labs/Lab1_Get_Started_with_OML4Py_on_Autonomous_Database.json b/machine-learning/labs/oml4py-live-labs/Lab1_Get_Started_with_OML4Py_on_Autonomous_Database.json
index 5e7adf71..a9651ff8 100755
--- a/machine-learning/labs/oml4py-live-labs/Lab1_Get_Started_with_OML4Py_on_Autonomous_Database.json
+++ b/machine-learning/labs/oml4py-live-labs/Lab1_Get_Started_with_OML4Py_on_Autonomous_Database.json
@@ -1 +1 @@
-{"paragraphs":[{"text":"%md\n## **Initiate a call to the Python interpreter**\nTo run Python commands in a notebook, you must first connect to the Python interpreter. This occurs as a result of running your first `%python` paragraph. To use OML4Py, you must import the `oml` module, which automatically establishes a connection to your database. In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as SQL or Python. This example shows you how to:\n\n* Connect to a Python interpreter to run Python commands in a notebook\n* Import the Python modules—oml, pandas, numpy, and matplotlib\n* Check if the oml module is connected to the database\n\nNote: `z` is a reserved keyword and must not be used as a variable in %python paragraphs in Oracle Machine Learning Notebooks. You will see `z.show()` fucntion used in the examples to display Python object and proxy object content.\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:30+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown","editorHide":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2><strong>Initiate a call to the Python interpreter</strong></h2>\n<p>To run Python commands in a notebook, you must first connect to the Python interpreter. This occurs as a result of running your first <code>%python</code> paragraph. To use OML4Py, you must import the <code>oml</code> module, which automatically establishes a connection to your database. In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as SQL or Python. This example shows you how to:</p>\n<ul>\n<li>Connect to a Python interpreter to run Python commands in a notebook</li>\n<li>Import the Python modules—oml, pandas, numpy, and matplotlib</li>\n<li>Check if the oml module is connected to the database</li>\n</ul>\n<p>Note: <code>z</code> is a reserved keyword and must not be used as a variable in %python paragraphs in Oracle Machine Learning Notebooks. You will see <code>z.show()</code> fucntion used in the examples to display Python object and proxy object content.</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986446_2142145532","id":"20211001-190306_2044056072","dateCreated":"2021-09-21T20:48:10+0000","dateStarted":"2021-09-22T20:18:31+0000","dateFinished":"2021-09-22T20:18:33+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"focus":true,"$$hashKey":"object:376"},{"text":"%python\n\nimport oml","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:33+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true,"editorHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"interrupted":false,"jobName":"paragraph_1633114986450_1302574953","id":"20211001-190306_576066479","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:34+0000","dateFinished":"2021-09-22T20:18:42+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:377"},{"text":"%md\n## **Verify Connection to the Autonomous Database**\nUsing the default interpreter bindings, OML Notebooks automatically establishes a database connection for the notebook.  \n\nTo verify the Python interpreter has established a database connection through the `oml` module, run the command shown below.  If the notebook is connected, the command returns `True`.         \n\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:42+0000","config":{"tableHide":false,"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2><strong>Verify Connection to the Autonomous Database</strong></h2>\n<p>Using the default interpreter bindings, OML Notebooks automatically establishes a database connection for the notebook.</p>\n<p>To verify the Python interpreter has established a database connection through the <code>oml</code> module, run the command shown below.  If the notebook is connected, the command returns <code>True</code>.</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_2092135881","id":"20211001-190306_1271780091","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:43+0000","dateFinished":"2021-09-22T20:18:43+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:378"},{"text":"%python\n\noml.isconnected()","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:43+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"True\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_371292639","id":"20211001-190306_2058125440","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:43+0000","dateFinished":"2021-09-22T20:18:44+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:379"},{"text":"%md\n\n## **View Help Files**\nThe Python help function is used to display the documentation of packages, modules, functions, classes, and keywords. The help function has the following syntax:","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:44+0000","config":{"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2><strong>View Help Files</strong></h2>\n<p>The Python help function is used to display the documentation of packages, modules, functions, classes, and keywords. The help function has the following syntax:</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_563552517","id":"20211001-190306_759056463","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:44+0000","dateFinished":"2021-09-22T20:18:44+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:380"},{"text":"%python\n\nhelp([object])","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:44+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on list object:\n\nclass list(object)\n |  list(iterable=(), /)\n |  \n |  Built-in mutable sequence.\n |  \n |  If no argument is given, the constructor creates a new empty list.\n |  The argument must be an iterable if specified.\n |  \n |  Methods defined here:\n |  \n |  __add__(self, value, /)\n |      Return self+value.\n |  \n |  __contains__(self, key, /)\n |      Return key in self.\n |  \n |  __delitem__(self, key, /)\n |      Delete self[key].\n |  \n |  __eq__(self, value, /)\n |      Return self==value.\n |  \n |  __ge__(self, value, /)\n |      Return self>=value.\n |  \n |  __getattribute__(self, name, /)\n |      Return getattr(self, name).\n |  \n |  __getitem__(...)\n |      x.__getitem__(y) <==> x[y]\n |  \n |  __gt__(self, value, /)\n |      Return self>value.\n |  \n |  __iadd__(self, value, /)\n |      Implement self+=value.\n |  \n |  __imul__(self, value, /)\n |      Implement self*=value.\n |  \n |  __init__(self, /, *args, **kwargs)\n |      Initialize self.  See help(type(self)) for accurate signature.\n |  \n |  __iter__(self, /)\n |      Implement iter(self).\n |  \n |  __le__(self, value, /)\n |      Return self<=value.\n |  \n |  __len__(self, /)\n |      Return len(self).\n |  \n |  __lt__(self, value, /)\n |      Return self<value.\n |  \n |  __mul__(self, value, /)\n |      Return self*value.\n |  \n |  __ne__(self, value, /)\n |      Return self!=value.\n |  \n |  __repr__(self, /)\n |      Return repr(self).\n |  \n |  __reversed__(self, /)\n |      Return a reverse iterator over the list.\n |  \n |  __rmul__(self, value, /)\n |      Return value*self.\n |  \n |  __setitem__(self, key, value, /)\n |      Set self[key] to value.\n |  \n |  __sizeof__(self, /)\n |      Return the size of the list in memory, in bytes.\n |  \n |  append(self, object, /)\n |      Append object to the end of the list.\n |  \n |  clear(self, /)\n |      Remove all items from list.\n |  \n |  copy(self, /)\n |      Return a shallow copy of the list.\n |  \n |  count(self, value, /)\n |      Return number of occurrences of value.\n |  \n |  extend(self, iterable, /)\n |      Extend list by appending elements from the iterable.\n |  \n |  index(self, value, start=0, stop=9223372036854775807, /)\n |      Return first index of value.\n |      \n |      Raises ValueError if the value is not present.\n |  \n |  insert(self, index, object, /)\n |      Insert object before index.\n |  \n |  pop(self, index=-1, /)\n |      Remove and return item at index (default last).\n |      \n |      Raises IndexError if list is empty or index is out of range.\n |  \n |  remove(self, value, /)\n |      Remove first occurrence of value.\n |      \n |      Raises ValueError if the value is not present.\n |  \n |  reverse(self, /)\n |      Reverse *IN PLACE*.\n |  \n |  sort(self, /, *, key=None, reverse=False)\n |      Sort the list in ascending order and return None.\n |      \n |      The sort is in-place (i.e. the list itself is modified) and stable (i.e. the\n |      order of two equal elements is maintained).\n |      \n |      If a key function is given, apply it once to each list item and sort them,\n |      ascending or descending, according to their function values.\n |      \n |      The reverse flag can be set to sort in descending order.\n |  \n |  ----------------------------------------------------------------------\n |  Class methods defined here:\n |  \n |  __class_getitem__(...) from builtins.type\n |      See PEP 585\n |  \n |  ----------------------------------------------------------------------\n |  Static methods defined here:\n |  \n |  __new__(*args, **kwargs) from builtins.type\n |      Create and return a new object.  See help(type) for accurate signature.\n |  \n |  ----------------------------------------------------------------------\n |  Data and other attributes defined here:\n |  \n |  __hash__ = None\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_1398026865","id":"20211001-190306_1063775532","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:45+0000","dateFinished":"2021-09-22T20:18:45+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:381"},{"text":"%md\n---\nFor example,\n\n- To view the help files for the `oml.create` function, type the below code.","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:45+0000","config":{"tableHide":false,"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<hr />\n<p>For example,</p>\n<ul>\n<li>To view the help files for the <code>oml.create</code> function, type the below code.</li>\n</ul>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_-1567926762","id":"20211001-190306_437824049","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:46+0000","dateFinished":"2021-09-22T20:18:46+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:382"},{"text":"%python\n\nhelp(oml.create)","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:46+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on cython_function_or_method in module oml.core.methods:\n\ncreate(x, table, oranumber=True, dbtypes=None, append=False)\n    Creates a table in Oracle Database from a Python data set.\n    \n    Parameters\n    ----------\n    x : pandas.DataFrame or a list of tuples of equal size\n      If ``x`` is a list of tuples of equal size, each tuple represents\n      a row in the table. The column names are set to COL1, COL2, ... and so on.\n    table : str\n      A name for the table.\n    oranumber : bool, True (default)\n      If True, use SQL NUMBER for numeric columns. Otherwise, use BINARY_DOUBLE.\n      Ignored if ``append`` is True.\n    dbtypes : dict mapping str to str or list of str\n      A list of SQL types to use on the new table. If a list, its length should\n      be equal to the number of columns. If a dict, the keys are the names of the\n      columns. Ignored if ``append`` is True.\n    append : bool, False (default)\n      Indicates whether to append the data to the existing table.\n    \n    Notes\n    -----\n     * When creating a new table, for columns whose SQL types are not specified in\n       ``dbtypes``, NUMBER is used for numeric columns when ``oranumber`` is True\n       and BINARY_DOUBLE is used when ``oranumber`` is False. Users should set\n       ``oranumber`` to False when the data contains NaN values. For string columns,\n       the default type is VARCHAR2(4000), and for bytes columns, the default type\n       is BLOB.\n     * When ``x`` is specified with an empty pandas.DataFrame, OML creates an\n       empty table. NUMBER is used for numeric columns when ``oranumber`` is True\n       and BINARY_DOUBLE is used when ``oranumber`` is False. VARCHAR2(4000) is\n       used for columns of object dtype in the pandas.DataFrame.\n     * OML does not support columns containing values of multiple data types,\n       data conversion is needed or a TypeError may be raised.\n     * OML determines default column types by looking at 20 random rows sampled\n       from the table. For tables with less than 20 rows, all rows are used\n       in column type determination. NaN values are considered as float type.\n       If a column has all Nones, or has inconsistent data types that are not\n       None in the sampled rows, a default column type cannot be determined,\n       and a ValueError is raised unless a SQL type for the column is specified\n       in ``dbtypes``.\n    \n    Returns\n    -------\n    new_table : oml.DataFrame\n      A proxy object that represents the newly-created table.\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_904721421","id":"20211001-190306_70160754","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:46+0000","dateFinished":"2021-09-22T20:18:46+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:383"},{"text":"%md\n---\nTo view the help files for `oml` module, type the code below.","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:46+0000","config":{"tableHide":false,"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<hr />\n<p>To view the help files for <code>oml</code> module, type the code below.</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_-1816292524","id":"20211001-190306_1536023944","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:47+0000","dateFinished":"2021-09-22T20:18:47+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:384"},{"text":"%python\n\nhelp(oml)","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:47+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on package oml:\n\nNAME\n    oml - Oracle Machine Learning for Python\n\nDESCRIPTION\n    A component of the Oracle Advanced Analytics Option, Oracle Machine Learning\n    for Python makes the open source Python programming language and environment\n    ready for enterprise in-database data. Designed for problems involving both\n    large and small volumes of data, Oracle Machine Learning for Python integrates\n    Python with Oracle Database. Python users can run Python commands and scripts\n    for statistical, machine learning, and graphical analyses on data stored in\n    Oracle Database. Python users can develop, refine, and deploy Python scripts\n    that leverage the parallelism and scalability of Oracle Database to automate\n    data analysis. Data analysts and data scientists can run Python modules and\n    develop and operationalize Python scripts for machine learning applications\n    in one step without having to learn SQL. Oracle Machine Learning for Python\n    performs function pushdown for in-database execution of core Python and\n    popular Python module functions. Being integrated with Oracle Database,\n    Oracle Machine Learning for Python can run any Python module via embedded\n    Python while the database manages the data served to the Python engines.\n\nPACKAGE CONTENTS\n    algo (package)\n    automl (package)\n    core (package)\n    ds (package)\n    embed (package)\n    graphics (package)\n    mlx (package)\n    script (package)\n\nCLASSES\n    oml.algo.model.odmModel(builtins.object)\n        oml.algo.ai.ai\n        oml.algo.ar.ar\n        oml.algo.dt.dt\n        oml.algo.em.em\n        oml.algo.esa.esa\n        oml.algo.glm.glm\n        oml.algo.km.km\n        oml.algo.nb.nb\n        oml.algo.nn.nn\n        oml.algo.rf.rf\n        oml.algo.svd.svd\n        oml.algo.svm.svm\n    oml.core.number._Number(oml.core.series._Series)\n        oml.core.float.Float\n    oml.core.series._Series(oml.core.vector._Vector)\n        oml.core.boolean.Boolean\n        oml.core.bytes.Bytes\n        oml.core.string.String\n    oml.core.vector._Vector(builtins.object)\n        oml.core.frame.DataFrame\n    \n    class Boolean(oml.core.series._Series)\n     |  Boolean series data class.\n     |  \n     |  Represents a single column of 0, 1, and NULL values in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      Boolean\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __and__(self, other)\n     |  \n     |  __init__(self)\n     |  \n     |  __invert__(self)\n     |  \n     |  __or__(self, other)\n     |  \n     |  all(self)\n     |      Checks whether all elements in the Boolean series data object are True.\n     |      \n     |      Returns\n     |      =======\n     |      all: bool\n     |  \n     |  any(self)\n     |      Checks whether any elements in the Boolean series data object are True.\n     |      \n     |      Returns\n     |      -------\n     |      any: bool\n     |  \n     |  pull(self)\n     |      Pulls data represented by this object from Oracle Database into an\n     |      in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list of bool and None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  describe(self)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of an OML series data distribution.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |        Includes ``count`` (number of non-null entries), ``unique``\n     |        (number of unique entries), ``top`` (most common value), ``freq``,\n     |        (frequency of the most common value).\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class Bytes(oml.core.series._Series)\n     |  Bytes(other, dbtype)\n     |  \n     |  Binary series data class.\n     |  \n     |  Represents a single column of RAW or BLOB data in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      Bytes\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, other, dbtype)\n     |      Convert underlying Oracle Database type.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Bytes\n     |      dbtype : 'raw' or 'blob'\n     |  \n     |  len(self)\n     |      Computes the length of each byte string.\n     |      \n     |      Returns\n     |      -------\n     |      length : oml.Float\n     |  \n     |  pull(self)\n     |      Pulls data represented by this object from Oracle Database into an\n     |      in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list of bytes and None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  describe(self)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of an OML series data distribution.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |        Includes ``count`` (number of non-null entries), ``unique``\n     |        (number of unique entries), ``top`` (most common value), ``freq``,\n     |        (frequency of the most common value).\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class DataFrame(oml.core.vector._Vector)\n     |  DataFrame(other)\n     |  \n     |  Tabular dataframe class.\n     |  \n     |  Represents multiple columns of Boolean, Bytes, Float, and/or String data.\n     |  \n     |  Method resolution order:\n     |      DataFrame\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, strata_cols=None, use_hash=True, hash_cols=None, nvl=None)\n     |      Splits the oml.DataFrame object randomly into k consecutive folds.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      strata_cols : a list of string values or None (default)\n     |        Names of the columns used for stratification. If None, stratification\n     |        is not performed. Must be None when ``use_hash`` is False.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      hash_cols : a list of string values or None (default)\n     |        If a list of string values, use the values from these named columns\n     |        to hash to split the data. If None, use the values from the 1st 10\n     |        columns to hash.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of k 2-tuples of oml.DataFrame objects\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``hash_cols`` refers to a single LOB column.\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean, str, list of str, 2-tuple\n     |        * oml.Boolean : select only the rows satisfying the condition. Must be from the same data\n     |          source as self.\n     |        * str : select the column of the same name\n     |        * list of str : select the columns whose names matches the elements in the list.\n     |        * 2-tuple : The first element in the tuple denotes which rows to select.\n     |          It can be either a oml.Boolean or ``slice(None)`` (this selects all\n     |          rows). The second element in the tuple denotes which columns to select.\n     |          It can be either ``slice(None)`` (this selects all columns), str, list\n     |          of str, int, or list of int. If int or list of int, selects the\n     |          column(s) in the corresponding position(s).\n     |      \n     |      Returns\n     |      -------\n     |      subset : OML data object\n     |        Is a oml.DataFrame if has more than one column, otherwise is a OML series data object.\n     |  \n     |  __init__(self, other)\n     |      Convert OML series data object(s) to oml.DataFrame.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object or dict mapping str to OML series data objects\n     |        * OML series data object : initializes a single-column oml.DataFrame containing the\n     |          same data. \n     |        * dict : initializes a oml.DataFrame that comprises all the OML series data objects\n     |          in the dict in an arbitrary order. Each column in the resulting oml.DataFrame has as\n     |          its column name its corresponding key in the dict.\n     |  \n     |  corr(self, method='pearson', min_periods=1, skipna=True)\n     |      Computes pairwise correlation between all columns where possible,\n     |      given the type of coefficient.\n     |      \n     |      Parameters\n     |      ----------\n     |      method : 'pearson' (default), 'kendall', or 'spearman'\n     |        * pearson : Uses Pearson's correlation coefficient. Can only calculate\n     |          correlations between Float or Boolean columns.\n     |        * kendall : Uses Kendall's tau-b coefficient.\n     |        * spearman : Uses Spearman's rho coefficient.\n     |      min_periods : int, optional, 1 (default)\n     |        The minimum number of observations required per pair of columns to \n     |        have a valid result.\n     |      skipna : bool, True (default)\n     |        If True, NaN and (+/-)Inf values are mapped to NULL.\n     |      \n     |      Returns\n     |      -------\n     |      y : :py:class:`pandas.DataFrame`\n     |  \n     |  count(self, numeric_only=False)\n     |      Returns the number of elements that are not NULL for each column.\n     |      \n     |      Parameters\n     |      ----------\n     |      numeric_only : boolean, False (default)\n     |        Includes only Float and Boolean columns.\n     |      \n     |      Returns\n     |      -------\n     |      count : :py:class:`pandas.Series`\n     |  \n     |  crosstab(self, index, columns=None, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False, pivot=False)\n     |      Computes a simple cross-tabulation of two or more columns. By default,\n     |      computes a frequency table for the columns unless a column and\n     |      an aggregation function have been passed.\n     |      \n     |      Parameters\n     |      ----------\n     |      index : str or list of str\n     |        Names of the column(s) of the DataFrame to group by. If ``pivot`` is\n     |        True, these columns are displayed in the rows of the result table.\n     |      columns : str or list of str, optional\n     |        Names of the other column(s) of the Dataframe to group by. If ``pivot``\n     |        is True, these columns are displayed in the columns of the result\n     |        table.\n     |      values : str, optional\n     |        The name of the column to aggregate according to the grouped columns.\n     |        Requires ``aggfunc`` to be specified.\n     |      aggfunc : OML DataFrame aggregation function object, optional\n     |        The supported oml.DataFrame aggregation functions include: count, \n     |        max, mean, median, min, nunique, std and sum. To use ``aggfunc``, \n     |        specify the function object using its full name, for example, \n     |        ``oml.DataFrame.sum``, ``oml.DataFrame.nunique``, and so on.\n     |        If specified, requires ``values`` to also be specified.\n     |      rownames : str or list of str, None (default)\n     |        If specified, must match number of names in ``index``. If None, names in\n     |        ``index`` are used. \n     |      colnames : str or list of str, None (default)\n     |        If specified, must match number of strings in ``columns``. If None,\n     |        names in ``columns`` are used. Ignored if ``pivot`` is True.\n     |      margins : bool, False (default)\n     |        Includes row and column margins (subtotals)\n     |      margins_name : str, 'All' (default)\n     |        Names of the row and column that contain the totals when ``margins``\n     |        is True. Should be a value not contained in any of the columns specified\n     |        by ``index`` and ``columns``. \n     |      dropna : bool, True (default)\n     |        In addition, if ``pivot`` is True, drops columns from the result\n     |        table if all the entries of the column are NaN.\n     |      normalize : boolean, {'all', 'index', 'columns'} or {0, 1}, False (default)\n     |        Normalizes by dividing the values by their sum.\n     |      \n     |        * If 'all' or True, normalizes over all values.\n     |        * If 'index' or 0, normalizes over each row.\n     |        * If 'columns' or 1, normalizes over each column.\n     |        * If ``margins`` is True, also normalizes margin values.\n     |      pivot : bool, False (default)\n     |        If True, returns results in pivot table format. Else, returns results in\n     |        relational table format.\n     |      \n     |      Returns\n     |      -------\n     |      crosstab : oml.DataFrame\n     |      \n     |      See Also\n     |      --------\n     |      DataFrame.pivot_table\n     |  \n     |  cumsum(self, by, ascending=True, na_position='last', skipna=True)\n     |      Gets the cumulative sum of each ``Float`` or ``Boolean`` column after the\n     |      ``DataFrame`` object is sorted.\n     |      \n     |      Parameters\n     |      ----------\n     |      by : str or list of str\n     |        A single column name or list of column names by which to sort the \n     |        DataFrame object. Columns in ``by`` do not have to be ``Float`` or \n     |        ``Boolean``.\n     |      ascending : bool or list of bool, True (default)\n     |        If True, sort is in ascending order, otherwise descending. Specify \n     |        list for multiple sort orders. If this is a list of bools, must match\n     |        the length of ``by``.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NaN and None at the beginning, ``last`` places them \n     |        at the end.\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      cumsum : oml.DataFrame\n     |  \n     |  describe(self, percentiles=None, include=None, exclude=None)\n     |      Generates descriptive statistics that summarize the central tendency,\n     |      dispersion, and shape of the data in each column\n     |      \n     |      Parameters\n     |      ----------\n     |      percentiles : bool, list-like of numbers, or None (default), optional \n     |        The percentiles to include in the output for `Float` columns. All\n     |        must be between 0 and 1. If ``percentiles`` is None or True,\n     |        ``percentiles`` is set to ``[.25, .5, .75]``, which corresponds\n     |        to the 25th, 50th, and 75th percentiles. If `percentiles` is False,\n     |        only ``min`` and ``max`` stats and no other percentiles is\n     |        included.\n     |      include : 'all', list-like of OML column types or None (default), optional\n     |        Types of columns to include in the result. Available options:\n     |      \n     |        - 'all': Includes all columns.\n     |        - List of OML column types : Only includes specified types in\n     |          the results.\n     |        - None (default) : If ``Float`` columns exist and ``exclude`` is\n     |          None, only includes ``Float`` columns. Otherwise, includes all\n     |          columns.\n     |      exclude : list of OML column types or None (default), optional\n     |        Types of columns to exclude from the result. Available options:\n     |      \n     |        - List of OML column types : Excludes specified types from\n     |          the results.\n     |        - None (default) : Result excludes nothing.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.DataFrame`\n     |         The concatenation of the summary statistics for each column.\n     |      \n     |      See Also\n     |      --------\n     |      DataFrame.count\n     |      DataFrame.max\n     |      DataFrame.min\n     |      DataFrame.mean\n     |      DataFrame.std\n     |      DataFrame.select_types\n     |  \n     |  drop(self, columns)\n     |      Drops specified columns.\n     |      \n     |      Parameters\n     |      ----------\n     |      columns : str or list of str\n     |        Columns to drop from the object.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : oml.DataFrame\n     |  \n     |  drop_duplicates(self, subset=None)\n     |      Removes duplicated rows from oml.DataFrame object.\n     |      \n     |      Use ``subset`` to consider a set of rows duplicates if they have\n     |      identical values for only a subset of the columns. In this case, after\n     |      deduplication, each of the other columns contains the minimum value\n     |      found across the set.\n     |      \n     |      Parameters\n     |      ----------\n     |      subset : str or list of str, optional\n     |        Columns to consider for identifying duplicates. If None, use all\n     |        columns.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : oml.DataFrame\n     |  \n     |  dropna(self, how='any', thresh=None, subset=None)\n     |      Removes rows containing missing values.\n     |      \n     |      Parameters\n     |      ----------\n     |      how : {'any', 'all'}, 'any' (default)\n     |        Determines if row is removed from DataFrame when at least one or all\n     |        values are missing.\n     |      thresh : int, optional\n     |        Requires that many of missing values to drop a row from DataFrame.\n     |      subset : list, optional\n     |        The names of the columns to check for missing values.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : oml.DataFrame\n     |        DataFrame without missing values.\n     |  \n     |  kurtosis(self, skipna=True)\n     |      Returns the sample kurtosis of the values for each ``Float``\n     |      column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      kurt : :py:class:`pandas.Series`\n     |  \n     |  max(self, skipna=True, numeric_only=False)\n     |      Returns the maximum value in each column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      numeric_only : boolean, False (default)\n     |        Includes only ``Float`` and ``Boolean`` columns. \n     |      \n     |      Returns\n     |      -------\n     |      max : :py:class:`pandas.Series`\n     |  \n     |  mean(self, skipna=True)\n     |      Returns the mean of the values for each ``Float`` or ``Boolean`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      mean : :py:class:`pandas.Series`\n     |  \n     |  median(self, skipna=True)\n     |      Returns the median of the values for each ``Float`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Exclude NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      median : :py:class:`pandas.Series`\n     |  \n     |  merge(self, other, on=None, left_on=None, right_on=None, how='left', suffixes=('_l', '_r'), nvl=True)\n     |      Joins data sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data set object\n     |      on : str or list of str, optional\n     |        Column names to join on. Must be found in both ``self`` and ``other``.\n     |      left_on : str or list of str, optional\n     |        Column names of ``self`` to join on.\n     |      right_on : str or list of str, optional\n     |        Column names of ``other`` to join on. If specified, must specify the same\n     |        number of columns as ``left_on``.\n     |      how : 'left' (default), 'right', 'inner', 'full'\n     |        * left : left outer join\n     |        * right : right outer join\n     |        * full : full outer join\n     |        * inner : inner join\n     |      \n     |        If ``on`` and ``left_on`` are both None, then ``how`` is ignored,\n     |        and a cross join is performed.\n     |      suffixes : sequence of length 2\n     |        Suffix to apply to column names on the left and right side,\n     |        respectively.\n     |      nvl : True (default), False, dict \n     |        * True : join condition includes NULL value\n     |        * False : join condition excludes NULL value\n     |        * dict : specifies the values that join columns use in replacement of NULL value with column names as keys\n     |      \n     |      Returns\n     |      -------\n     |      merged : oml.DataFrame\n     |  \n     |  min(self, skipna=True, numeric_only=False)\n     |      Returns the minimum value in each column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      numeric_only : boolean, False (default)\n     |        Includes only ``Float`` and ``Boolean`` columns\n     |      \n     |      Returns\n     |      -------\n     |      min : :py:class:`pandas.Series`\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns number of unique values for each column of DataFrame.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : pandas.Series\n     |  \n     |  pivot_table(self, index, columns=None, values=None, aggfunc=<cyfunction DataFrame.mean at 0x7f81c5e39110>, margins=False, dropna=True, margins_name='All')\n     |      Converts data set to a spreadsheet-style pivot table. Due to the Oracle\n     |      1000 column limit, pivot tables with more than 1000 columns are\n     |      automatically truncated to display the categories with the most entries\n     |      for each value column.\n     |      \n     |      Parameters\n     |      ----------\n     |      index : str or list of str\n     |        Names of columns containing the keys to group by on the pivot table\n     |        index.\n     |      columns : str or list of str, optional\n     |        Names of columns containing the keys to group by on the pivot table\n     |        columns. \n     |      values : str or list of str, optional\n     |        Names of columns to aggregate on. If None, values are inferred \n     |        as all columns not in ``index`` or ``columns``.\n     |      aggfunc : OML DataFrame aggregation function or a list of them, oml.DataFrame.mean (default)\n     |        The supported oml.DataFrame aggregation functions include: count, max,\n     |        mean, median, min, nunique, std and sum. When using aggregation\n     |        functions, specify the function object using its full name, for example,\n     |        ``oml.DataFrame.sum``, ``oml.DataFrame.nunique``, and so on.\n     |        If ``aggfunc`` contains more than one function, each function is \n     |        applied to each column in ``values``. If the function does not apply to\n     |        the type of a column in ``values``, the result table skips applying \n     |        the function to the particular column. \n     |      margins : bool, False (default)\n     |        Include row and column margins (subtotals)\n     |      dropna : bool, True (default)\n     |        Unless ``columns`` is None, drop column labels from the result table if\n     |        all the entries corresponding to the column label are NaN for all\n     |        aggregations.\n     |      margins_name : string, 'All' (default)\n     |        Names of the row and column that contain the totals when ``margins``\n     |        is True. Should be a value not contained in any of the columns specified\n     |        by ``index`` and ``columns``. \n     |      \n     |      Returns\n     |      -------\n     |      pivoted : oml.DataFrame\n     |      \n     |      See Also\n     |      --------\n     |      DataFrame.crosstab\n     |  \n     |  pull(self, aslist=False)\n     |      Pulls data represented by the DataFrame from Oracle Database\n     |      into an in-memory Python object.\n     |      \n     |      Parameters\n     |      ----------\n     |      aslist : bool\n     |        If False, returns a pandas.DataFrame. Otherwise, returns the data\n     |        as a list of tuples.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : :py:class:`pandas.DataFrame` or list of tuples\n     |  \n     |  rename(self, columns)\n     |      Renames columns.\n     |      \n     |      Parameters\n     |      ----------\n     |      columns : dict or list\n     |        ``dict`` contains old and new column names.\n     |        ``list`` contains the new names for all the columns in order.\n     |      \n     |      Notes\n     |      -----\n     |      The method changes the column names of the caller DataFrame object too.\n     |      \n     |      Returns\n     |      -------\n     |      renamed : DataFrame\n     |  \n     |  replace(self, old, new, default=None, columns=None)\n     |      Replace values given in `old` with `new` in specified columns.\n     |      \n     |      Parameters\n     |      ----------\n     |      columns : list of str or None (default)\n     |        Columns to look for values in `old`. If None, then all columns\n     |        of DataFrame will be replaced.\n     |      old : list of float, or list of str\n     |        Specifying the old values. When specified with a list of float, it \n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      new : list of float, or list of str\n     |        A list of the same length as argument `old` specifying \n     |        the new values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      default : float, str, or None (default)\n     |        A single value to use for the non-matched elements in argument\n     |        `old`. If None, non-matched elements will preserve their\n     |        original values. If not None, data type should be consistent\n     |        with values in `new`. Must be set when `old` and `new` contain \n     |        values of different data types.\n     |      \n     |      Returns\n     |      -------\n     |      replaced : oml.DataFrame\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * if values in `old` have data types inconsistent with original values\n     |          in the target columns\n     |        * if `default` is specifed with a non-None value which has data type\n     |          inconsistent with values in `new`\n     |        * if `default` is None when `old` and `new` contain values of different \n     |          data types\n     |  \n     |  round(self, decimals=0)\n     |      Rounds oml.Float values in the oml.DataFrame object to \n     |      the specified decimal place.\n     |      \n     |      Parameters\n     |      ----------\n     |      decimals : non-negative int\n     |      \n     |      Returns\n     |      -------\n     |      rounded: oml.DataFrame\n     |  \n     |  sample(self, frac=None, n=None, random_state=None)\n     |      Return a random sample data sets from an oml.DataFrame object.\n     |      \n     |      Parameters\n     |      ----------\n     |      frac : a float value\n     |        Fraction of data sets to return. The value should be between 0 and 1.\n     |        Cannot be used with n.\n     |      n : an integer value\n     |        Number of rows to return. Default = 1 if frac = None.\n     |        Cannot be used with frac. \n     |      random_state : int or 12345 (default)\n     |        The seed to use for random sampling.\n     |      \n     |      Returns\n     |      -------\n     |      sample_data : an oml.DataFrame objects\n     |        It contains the random sample rows from an oml.DataFrame object.\n     |        The fraction of returned data sets is specified by the frac parameter.\n     |  \n     |  select_types(self, include=None, exclude=None)\n     |      Returns the subset of columns include/excluding columns based on their OML\n     |      type.\n     |      \n     |      Parameters\n     |      ----------\n     |      include, exclude : list of OML column types\n     |        A selection of OML column types to be included/excluded. At least one of\n     |        these parameters must be supplied. \n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If both of ``include`` and ``exclude`` are None.\n     |        * If ``include`` and ``exclude`` have overlapping elements.\n     |      \n     |      Returns\n     |      -------\n     |      subset : oml.DataFrame\n     |  \n     |  skew(self, skipna=True)\n     |      Returns the sample skewness of the values for each ``Float``\n     |      column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      skew : :py:class:`pandas.Series`\n     |  \n     |  sort_values(self, by, ascending=True, na_position='last')\n     |      Specifies the order in which rows appear in the result set.\n     |      \n     |      Parameters\n     |      ----------\n     |      by : str or list of str\n     |        Column names or list of column names.\n     |      ascending : bool or list of bool, True (default)\n     |        If True, sort is in ascending order. Sort is in descending order\n     |        otherwise. Specify list for multiple sort orders. If this is a list of\n     |        bools, must match the length of ``by``.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them \n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : oml.DataFrame\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, strata_cols=None, use_hash=True, hash_cols=None, nvl=None)\n     |      Splits the oml.DataFrame object randomly into multiple data sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) default\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      strata_cols: a list of string values or None (default):\n     |        Names of the columns used for stratification. If None, stratification\n     |        is not performed. Must be None when use_hash is False.\n     |      use_hash: boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use random\n     |        number to split the data.\n     |      hash_cols: a list of string values or None (default):\n     |        If a list of string values, use the values from these named columns\n     |        to hash to split the data. If None, use the values from the 1st 10\n     |        columns to hash.\n     |      nvl: numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of oml.DataFrame objects\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``hash_cols`` refers to a single LOB column.\n     |  \n     |  std(self, skipna=True)\n     |      Returns the sample standard deviation of the values of each ``Float`` or\n     |      ``Boolean`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      std : :py:class:`pandas.Series`\n     |  \n     |  sum(self, skipna=True)\n     |      Returns the sum of the values of each ``Float`` or ``Boolean`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Exclude NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      sum : :py:class:`pandas.Series`\n     |  \n     |  t_dot(self, other=None, skipna=True, pull_from_db=True)\n     |      Calculates the matrix cross-product of self with other.\n     |      \n     |      Equivalent to transposing self first, then multiplying it with other. \n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.DataFrame, optional\n     |        If not specified, self is used.\n     |      skipna : bool, True (default)\n     |        Treats NaN entries as 0.\n     |      pull_from_db : bool, True (default)\n     |        If True, returns a pandas.DataFrame. If False, returns a\n     |        oml.DataFrame consisting of three columns:\n     |      \n     |        - ROWID: the row number of the resulting matrix \n     |        - COLID: the column number of the resulting matrix \n     |        - VALUE: the value at the corresponding position of the matrix \n     |      \n     |      Returns\n     |      -------\n     |      prod : float, :py:class:`pandas.Series`, or :py:class:`pandas.DataFrame`\n     |      \n     |      See Also\n     |      --------\n     |      oml.Float.dot\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties defined here:\n     |  \n     |  columns\n     |      The column names of the data set.\n     |  \n     |  dtypes\n     |      The types of the columns of the data set.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class Float(oml.core.number._Number)\n     |  Float(other, dbtype=None)\n     |  \n     |  Numeric series data class.\n     |  \n     |  Represents a single column of NUMBER, BINARY_DOUBLE or BINARY_FLOAT data \n     |  in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      Float\n     |      oml.core.number._Number\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __abs__(self)\n     |      Return the absolute value of every element in ``self``.\n     |      \n     |      Equivalent to ``abs(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      absval : oml.Float\n     |  \n     |  __add__(self, other)\n     |      Equivalent to ``self + other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : add the scalar to each element in ``self``. \n     |        * oml.Float : must come from the same data source. Add corresponding\n     |          elements in ``self`` and ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      sum : oml.Float\n     |  \n     |  __contains__(self, item)\n     |      Check whether all elements in ``item`` exists in the Float series\n     |      \n     |      Equivalent to ``item in self``.\n     |      \n     |      Parameters\n     |      ----------\n     |      item : int/float, list of int/float, oml.Float\n     |        Values to check in series\n     |      \n     |      Returns\n     |      -------\n     |      contains : bool\n     |        Returns `True` if all elements exists, otherwise `False`\n     |  \n     |  __divmod__(self, other)\n     |      Equivalent to ``divmod(self, other)``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : Find the quotient and remainder when each element in ``self`` is\n     |          divided by the scalar.\n     |        * oml.Float : must come from the same data source. Find the quotient and\n     |          remainder when each element in ``self`` is divided by the corresponding element\n     |          in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      divrem : oml.DataFrame\n     |        The first column contains the floor of the quotient, and the second column\n     |        contains the remainder.\n     |  \n     |  __floordiv__(self, other)\n     |      Equivalent to ``self // other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : divide each element in ``self`` by the scalar. \n     |        * oml.Float : must come from the same data source. Divide each element in\n     |          ``self`` by the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      quotient : oml.Float\n     |  \n     |  __init__(self, other, dbtype=None)\n     |      Convert to oml.Float, or convert underlying Oracle Database type.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Boolean or oml.Float\n     |        * oml.Boolean : initialize a oml.Float object that has value 1 (resp. 0)\n     |          wherever ``other`` has value True (resp. False).\n     |        * oml.Float : initialize a oml.Float object with the same data as \n     |          ``other``, except the underlying Oracle Database type has been converted\n     |          to the one specified by ``dbtype``. \n     |      dbtype : 'number' or 'binary_double'\n     |        Ignored if ``other`` is type ``oml.Boolean``. Must be specified if ``other``\n     |        is type ``oml.Float``.\n     |  \n     |  __matmul__(self, other)\n     |      Equivalent to ``self @ other`` and ``self.dot(other)``.\n     |      \n     |      Returns the inner product with an oml.Float. Matrix multiplication with a\n     |      oml.DataFrame.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Float or oml.DataFrame\n     |      \n     |      Returns\n     |      -------\n     |      matprod : oml.Float\n     |      \n     |      See Also\n     |      --------\n     |      Float.dot\n     |  \n     |  __mod__(self, other)\n     |      Equivalent to ``self % other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : Find the remainder when each element in ``self`` is divided by the\n     |          scalar.\n     |        * oml.Float : must come from the same data source. Find the remainder when each\n     |          element in ``self`` is divided by the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      remainder : oml.Float\n     |  \n     |  __mul__(self, other)\n     |      Equivalent to ``self * other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : multiply the scalar with each element in ``self``. \n     |        * oml.Float : must come from the same data source. Multiply corresponding\n     |          elements in ``self`` and ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      product : oml.Float\n     |  \n     |  __neg__(self)\n     |      Return the negation of every element in ``self``. Equivalent to ``-self``.\n     |      \n     |      Returns\n     |      -------\n     |      negation : oml.Float\n     |  \n     |  __pow__(self, other)\n     |      Equivalent to ``self ** other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : Raise each element in ``self`` to the power of the scalar. \n     |        * oml.Float : must come from the same data source. Raise each element in \n     |          ``self`` to the power of the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      power : oml.Float\n     |  \n     |  __sub__(self, other)\n     |      Equivalent to ``self - other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : subtract the scalar from each element in ``self``. \n     |        * oml.Float : must come from the same data source. From each element in\n     |          ``self``, subtract the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      difference : oml.Float\n     |  \n     |  __truediv__(self, other)\n     |      Equivalent to ``self / other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : divide each element in ``self`` by the scalar. \n     |        * oml.Float : must come from the same data source. Divide each element in\n     |          ``self`` by the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      quotient : oml.Float\n     |  \n     |  ceil(self)\n     |      Returns the ceiling of each element in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      ceil : oml.Float\n     |  \n     |  cut(self, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)\n     |      Returns the indices of half-open bins to which each value belongs.\n     |      \n     |      Parameters\n     |      ----------\n     |      bins : int or strictly monotonically increasing sequence of float/int\n     |        If int, defines number of equal-width bins in the range of this column.\n     |        In this case, to include the min and max value, the range is extended by\n     |        .1% on each side where the bin does not include the endpoint.\n     |        If a sequence, defines bin edges allowing for non-uniform bin-widths. In \n     |        this case, the range of x is not extended.\n     |      right : bool, True (default)\n     |        Indicates whether the bins include the rightmost edge or the leftmost\n     |        edge.\n     |      labels : sequence of unique str, int, or float values, False, or None (default)\n     |        If a sequence, must be the same length as the resulting number of bins\n     |        and must have values of same type. If False, bins are sequentially\n     |        labeled with integers. If None, bins are labeled with the intervals\n     |        they correspond to.\n     |      retbins : bool, False (default)\n     |        Indicates whether to return the bin edges or not.  \n     |      precision : int, 3 (default)\n     |        When ``labels`` is None, determines the precision of the bin labels.\n     |      include_lowest : bool, False (default) \n     |        Indicates whether the first interval should be left-inclusive.\n     |      \n     |      Returns\n     |      -------\n     |      out : oml.Float or oml.String\n     |        If labels are ints or floats, return oml.Float.\n     |        If labels are str, return oml.String.\n     |      bins : :py:class:`numpy.ndarray` of floats\n     |        Returned only if ``retbins`` is True.\n     |  \n     |  describe(self, percentiles=None)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of the OML series data distribution.\n     |      \n     |      Parameters\n     |      ----------\n     |      percentiles : list-like of numbers, optional \n     |        The percentiles to include in the output. All must be between 0 and 1.\n     |        The default is [.25, .5, .75], which corresponds to the inclusion of \n     |        the 25th, 50th, and 75th percentiles.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |         Includes ``count`` (number of non-null entries), ``mean``, ``std``,\n     |         ``min``, ``max``, and the specified ``percentiles``. The 50th\n     |         percentile is always included.\n     |  \n     |  dot(self, other=None, skipna=True)\n     |      Returns the inner product with an oml.Float. Matrix multiplication with a\n     |      oml.DataFrame.\n     |      \n     |      Can be called using self @ other.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Float or oml.DataFrame, optional\n     |        If not specified, self is used.\n     |      skipna : bool, True (default)\n     |        Treats NaN entries as 0.\n     |      \n     |      Returns\n     |      -------\n     |      dot_product : :py:class:`pandas.Series` or float\n     |  \n     |  exp(self)\n     |      Returns element-wise e to the power of values in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      exp : oml.Float\n     |  \n     |  floor(self)\n     |      Returns the floor of each element in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      floor : oml.Float\n     |  \n     |  isinf(self)\n     |      Detects infinite values element-wise in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      isinf : oml.Boolean\n     |  \n     |  isnan(self)\n     |      Detects a NaN (not a number) element from Float object.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates NaN for each element.\n     |  \n     |  log(self, base=None)\n     |      Returns element-wise logarithm, to the given ``base``, of values\n     |      in the Float series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      base : int, float, optional\n     |        The base of the logarithm, by default natural logarithm\n     |      \n     |      Returns\n     |      -------\n     |      log : oml.Float\n     |  \n     |  replace(self, old, new, default=None)\n     |      Replace values given in `old` with `new`.\n     |      \n     |      Parameters\n     |      ----------\n     |      old : list of float, or list of str\n     |        Specifying the old values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      new : list of float, or list of str\n     |        A list of the same length as argument `old` specifying\n     |        the new values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      default : float, str, or None (default)\n     |        A single value to use for the non-matched elements in argument\n     |        `old`. If None, non-matched elements will preserve their\n     |        original values. If not None, data type should be consistent\n     |        with values in `new`. Must be set when `old` and `new` contain\n     |        values of different data types.\n     |      \n     |      Returns\n     |      -------\n     |      replaced : oml.Float\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * if values in `old` have data types inconsistent with original values\n     |        * if `default` is specifed with a non-None value which has data type \n     |          inconsistent with values in `new`\n     |        * if `default` is None when `old` and `new` contain values of different\n     |          data types\n     |  \n     |  round(self, decimals=0)\n     |      Rounds oml.Float values to the specified decimal place.\n     |      \n     |      Parameters\n     |      ----------\n     |      decimals : non-negative int\n     |      \n     |      Returns\n     |      -------\n     |      rounded : oml.Float\n     |  \n     |  sqrt(self)\n     |      Returns the square root of each element in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      sqrt : oml.Float\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.number._Number:\n     |  \n     |  cumsum(self, ascending=True, na_position='last', skipna=True)\n     |      Gets the cumulative sum after the OML series data object is sorted.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        Sorts ascending, otherwise descending.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NaN and None at the beginning, ``last`` places them \n     |        at the end.\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      cumsum : oml.Float\n     |  \n     |  kurtosis(self, skipna=True)\n     |      Returns the sample kurtosis of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      kurt : float or nan\n     |  \n     |  mean(self, skipna=True)\n     |      Returns the mean of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      mean : float or numpy.nan\n     |  \n     |  median(self, skipna=True)\n     |      Returns the median of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      median : float or numpy.nan\n     |  \n     |  skew(self, skipna=True)\n     |      Returns the sample skewness of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      skew : float or nan\n     |  \n     |  std(self, skipna=True)\n     |      Returns the sample standard deviation of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      std : float or numpy.nan\n     |  \n     |  sum(self, skipna=True)\n     |      Returns the sum of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      sum : float or numpy.nan\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  pull(self)\n     |      Pulls data represented by the series data object from Oracle Database\n     |      into an in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class String(oml.core.series._Series)\n     |  String(other, dbtype)\n     |  \n     |  Character series data class.\n     |  \n     |  Represents a single column of VARCHAR2, CHAR, or CLOB data in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      String\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __contains__(self, item)\n     |      Check whether all elements in ``item`` exists in the String series\n     |      \n     |      Equivalent to ``item in self``.\n     |      \n     |      Parameters\n     |      ----------\n     |      item : str, list of str, oml.String\n     |        Values to check in series\n     |      \n     |      Returns\n     |      -------\n     |      contains : bool\n     |        Returns ``True`` if all elements exist, otherwise ``False``.\n     |  \n     |  __init__(self, other, dbtype)\n     |      Convert underlying Oracle Database type.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.String\n     |      dbtype : 'varchar2' or 'clob'\n     |  \n     |  count_pattern(self, pat, flags=0)\n     |      Counts the number of occurrences of the pattern in each string. \n     |      \n     |      Parameters\n     |      ----------\n     |      pat : str that is a valid regular expression conforming to the POSIX standard\n     |      flags : int, 0 (default, no flags)\n     |        The following :py:mod:`python:re` module flags are supported:\n     |      \n     |        - :py:data:`python:re.I`/:py:data:`python:re.IGNORECASE` : Performs case-insensitive matching.\n     |        - :py:data:`python:re.M`/:py:data:`python:re.MULTILINE` : Treats the source string as multiple lines.\n     |          Interprets the caret (^) and dollar sign ($) as the start and end,\n     |          respectively, of any line anywhere in source string. Without this flag,\n     |          the caret and dollar sign match only the start and end, respectively, of\n     |          the source string.\n     |        - :py:data:`python:re.S`/:py:data:`python:re.DOTALL` : Allows the period (.) to match all characters,\n     |          including the newline character. Without this flag, the period matches all\n     |          characters except the newline character.\n     |      \n     |        Multiple flags can be specifed by bitwise OR-ing them.\n     |      \n     |      Returns\n     |      -------\n     |      counts : oml.Float\n     |  \n     |  find(self, sub, start=0)\n     |      Returns the lowest index in each string where substring is found that is\n     |      greater than or equal to ``start``. Returns -1 on failure.\n     |      \n     |      Parameters\n     |      ----------\n     |      sub : str\n     |        The text expression to search.\n     |      start : int\n     |        A nonnegative integer indicating when the function begins the search.   \n     |      \n     |      Returns\n     |      -------\n     |      found : oml.Float\n     |  \n     |  len(self)\n     |      Computes the length of each string.\n     |      \n     |      Returns\n     |      -------\n     |      length : oml.Float\n     |  \n     |  pull(self)\n     |      Pulls data represented by this object from Oracle Database into an\n     |      in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list of str and None\n     |  \n     |  replace(self, old, new, default=None)\n     |      Replace values given in `old` with `new`.\n     |      \n     |      Parameters\n     |      ----------\n     |      old : list of float, or list of str\n     |        Specifying the old values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      new : list of float, or list of str\n     |        A list of the same length as argument `old` specifying\n     |        the new values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      default : float, str, or None (default)\n     |        A single value to use for the non-matched elements in argument\n     |        `old`. If None, non-matched elements will preserve their\n     |        original values. If not None, data type should be consistent\n     |        with values in `new`. Must be set when `old` and `new` contain \n     |        values of different data types.\n     |      \n     |      Returns\n     |      -------\n     |      replaced : oml.String\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * if values in `old` have data types inconsistent with original values\n     |        * if `default` is specifed with a non-None value which has data type\n     |          inconsistent with values in `new`\n     |        * if `default` is None when `old` and `new` contain values of different\n     |          data types\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  describe(self)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of an OML series data distribution.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |        Includes ``count`` (number of non-null entries), ``unique``\n     |        (number of unique entries), ``top`` (most common value), ``freq``,\n     |        (frequency of the most common value).\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class ai(oml.algo.model.odmModel)\n     |  ai(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Attribute Importance <https://www.oracle.com/machine-learning/attribute-importance>`_ Model\n     |  \n     |  Computes the relative importance of variables (aka attributes or columns) when predicting\n     |  a target variable (numeric or categorical column). This function exposes the \n     |  corresponding Oracle Machine Learning in-database algorithm. \n     |  Oracle Machine Learning does not support the prediction functions\n     |  for attribute importance. The results of attribute importance are the attributes\n     |  of the build data ranked according to their predictive influence. The ranking and\n     |  the measure of importance can be used for selecting attributes.\n     |  \n     |  :Attributes:\n     |  \n     |      **importance** : oml.DataFrame\n     |  \n     |          Relative importance of predictor variables for predicting a response variable.\n     |          It includes the following components:\n     |              \n     |              - variable: The name of the predictor variable\n     |              - importance: The importance of the predictor variable\n     |              - rank: The predictor variable rank based on the importance value.\n     |  \n     |  Method resolution order:\n     |      ai\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of Attribute Importance object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Attribute Importance model to create an oml.ai object from.\n     |          The specified database model is not dropped when the oml.ai object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Attribute Importance model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings or Algorithm-specific Settings are not\n     |          applicable to Attribute Importance model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None)\n     |      Fits an Attribute Importance Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.ai object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.ai object is deleted\n     |          unless oml.ai object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class ar(oml.algo.model.odmModel)\n     |  ar(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Association Rules <https://www.oracle.com/machine-learning/association-rules>`_ Model\n     |  \n     |  Builds an Association Rules Model used to discover the probability of item co-occurrence\n     |  in a collection. This function exposes the corresponding Oracle Machine Learning \n     |  in-database algorithm. The relationships between co-occurring items are expressed as \n     |  association rules.\n     |  Oracle Machine Learning does not support the prediction functions for association modeling.\n     |  The results of an association model are the rules that identify patterns of\n     |  association within the data. Association rules can be ranked by support\n     |  (How often do these items occur together in the data?) and confidence\n     |  (How likely are these items to occur together in the data?).\n     |  \n     |  :Attributes:\n     |  \n     |      **rules** : oml.DataFrame\n     |  \n     |          Details of each rule that shows \n     |          how the appearance of a set of items in a transactional\n     |          record implies the existence of another set of items.\n     |          It includes the following components:\n     |  \n     |          - rule_id: The identifier of the rule\n     |          - number_of_items: The total number of attributes referenced in the antecedent and consequent of the rule\n     |          - lhs_name: The name of the antecedent.\n     |          - lhs_value: The value of the antecedent.\n     |          - rhs_name: The name of the consequent.\n     |          - rhs_value: The value of the consequent.\n     |          - support: The number of transactions that satisfy the rule.\n     |          - confidence: The likelihood of a transaction satisfying the rule.\n     |          - revconfidence: The number of transactions in which the rule occurs divided by the number of transactions in which the consequent occurs.\n     |          - lift: The degree of improvement in the prediction over random chance when the rule is satisfied.\n     |  \n     |      **itemsets** : oml.DataFrame\n     |  \n     |          Description of the item sets from the model built.\n     |          It includes the following components:\n     |  \n     |          - itemset_id: The itemset identifier\n     |          - support: The support of the itemset\n     |          - number_of_items: The number of items in the itemset\n     |          - item_name: The name of the item\n     |          - item_value: The value of the item\n     |  \n     |  Method resolution order:\n     |      ar\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of Association Rules object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Association Rules model to create an oml.ar object from.\n     |          The specified database model is not dropped when the oml.ar object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Association Rules model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic\n     |          Data Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Association are applicable.\n     |          No algorithm-specific Settings are applicable to Association model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, model_name=None, case_id=None)\n     |      Fits an Association Rules Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.ar object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.ar object is deleted\n     |          unless oml.ar object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class dt(oml.algo.model.odmModel)\n     |  dt(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Decision Tree <https://www.oracle.com/machine-learning/decision-tree>`_ Model\n     |  \n     |  Builds a Decision Tree Model used to generate rules (conditional statements \n     |  that can easily be understood by humans and be used within a database to identify \n     |  a set of records) to predict a target value (numeric or categorical column). This \n     |  function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  A decision tree predicts a target value by asking a sequence of questions. \n     |  At a given stage in the sequence, the question that is asked depends upon the \n     |  answers to the previous questions. The goal is to ask questions that, taken \n     |  together, uniquely identify specific target values. Graphically, this process \n     |  forms a tree structure. During the training process, the Decision Tree algorithm \n     |  must repeatedly find the most efficient way to split a set of cases (records) \n     |  into two child nodes. The model offers two homogeneity metrics, gini and entropy, \n     |  for calculating the splits. The default metric is gini.\n     |  \n     |  \n     |  :Attributes:\n     |  \n     |      **nodes** : oml.DataFrame\n     |      \n     |        The node summary information with tree node details.\n     |        It includes the following components:\n     |            \n     |        - parent: The node ID of the parent\n     |        - node.id: The node ID\n     |        - row.count: The number of records in the training set that belong to the node\n     |        - prediction: The predicted Target value\n     |        - split: The main split\n     |        - surrogate: The surrogate split\n     |        - full.splits: The full splitting criterion\n     |        \n     |      **distributions** : oml.DataFrame\n     |  \n     |        The target class distributions at each tree node.\n     |        It includes the following components:\n     |            \n     |        - node_id: The node ID\n     |        - target_value: The target value\n     |        - target_count: The number of rows for a given target_value\n     |  \n     |  Method resolution order:\n     |      dt\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of dt object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Decision Tree model to create an oml.dt object from.\n     |          The specified database model is not dropped when the oml.dt object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Decision Tree model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566>`_\n     |          are applicable to Decision Tree model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, cost_matrix=None, case_id=None)\n     |      Fits a decision tree model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.dt object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.dt object is deleted\n     |          unless oml.dt object is saved into a datastore.\n     |      cost_matrix : OML DataFrame, list of ints, floats or None (default)\n     |          An optional numerical matrix that specifies the costs for incorrectly\n     |          predicting the target values. The first value represents the actual target value.\n     |          The second value represents the predicted target value. The third value is the cost.\n     |          In general, the diagonal entries of the matrix are zeros. Refer to `Oracle Data\n     |          Mining User's Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-89E0DB46-BA9A-4420-8216-FD6C96C74D6D>`_\n     |          for more details about cost matrix.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if proba is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols``, and the\n     |        results. The results include the most likely target class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data and returns the mean accuracy.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class em(oml.algo.model.odmModel)\n     |  em(n_clusters=None, model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Expectation Maximization <https://www.oracle.com/machine-learning/expectation-maximization>`_ Model\n     |  \n     |  Builds an Expectation Maximization (EM) Model used to performs probabilistic \n     |  clustering based on a density estimation algorithm. This function exposes the \n     |  corresponding Oracle Machine Learning in-database algorithm. In density estimation, \n     |  the goal is to construct a density function that captures how a given population is \n     |  distributed. The density estimate is based on observed data that represents a \n     |  sample of the population.\n     |  \n     |  :Attributes:\n     |  \n     |      **clusters** : oml.DataFrame\n     |      \n     |        The general per-cluster information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - cluster_name: The name of a cluster in the model\n     |            - record_count: The number of rows used in the build\n     |            - parent: The ID of the parent\n     |            - tree_level: The number of splits from the root\n     |            - left_child_id: The ID of the left child\n     |            - right_child_id: The ID of the right child\n     |            \n     |      **taxonomy**: oml.DataFrame\n     |      \n     |        The parent/child cluster relationship.\n     |        It includes the following components:\n     |            \n     |            - parent_cluster_id: The ID of the parent cluster\n     |            - child_cluster_id: The ID of the child cluster\n     |            \n     |      **centroids**: oml.DataFrame\n     |      \n     |        Per cluster-attribute center (centroid) information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - attribute_name: The attribute name\n     |            - mean: The average value of a numeric attribute\n     |            - mode_value: The most frequent value of a categorical attribute\n     |            - variance: The variance of a numeric attribute\n     |            \n     |      **leaf_cluster_counts**: pandas.DataFrame\n     |      \n     |        Leaf clusters with support.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a leaf cluster in the model\n     |            - cnt: The number of records in a leaf cluster\n     |            \n     |      **attribute_importance**: oml.DataFrame\n     |      \n     |        Attribute importance of the fitted model.\n     |        It includes the following components:\n     |            \n     |            - attribute_name: The attribute name\n     |            - attribute_importance_value: The attribute importance for an attribute\n     |            - attribute_rank: The rank of the attribute based on importance\n     |            \n     |      **projection**: oml.DataFrame\n     |      \n     |        The coefficients used by random projections to map nested columns to a lower dimensional space.\n     |        It exists only when nested or text data is present in the build data.\n     |        It includes the following components:\n     |            \n     |            - feature_name: The name of feature\n     |            - attribute_name: The attribute name\n     |            - attribute_value: The attribute value\n     |            - coefficient: The projection coefficient for an attribute\n     |            \n     |      **components**: oml.DataFrame\n     |      \n     |        EM components information about their prior probabilities and what cluster they map to.\n     |        It includes the following components:\n     |            \n     |            - component_id: The unique identifier of a component\n     |            - cluster_id: The ID of a cluster in the model\n     |            - prior_probability: The component prior probability\n     |            \n     |      **cluster_hists**: oml.DataFrame\n     |        Cluster histogram information.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - variable: The attribute name\n     |            - bin.id: The ID of a bin\n     |            - lower.bound: The numeric lower bin boundary\n     |            - upper.bound: The numeric upper bin boundary\n     |            - label: The label of the cluster\n     |            - count: The histogram count\n     |            \n     |      **rules**: oml.DataFrame\n     |      \n     |        Conditions for a case to be assigned with some probability to a cluster.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - rhs.support: The record count\n     |            - rhs.conf: The record confidence\n     |            - lhr.support: The rule support\n     |            - lhs.conf: The rule confidence\n     |            - lhs.var: The attribute predicate name\n     |            - lhs.var.support: The attribute predicate support\n     |            - lhs.var.conf: The attribute predicate confidence\n     |            - predicate: The attribute predicate\n     |  \n     |  Method resolution order:\n     |      em\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, n_clusters=None, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of em object.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_clusters : positive integer, None (default)\n     |          The number of clusters. If n_clusters is None, the number of clusters will be determined\n     |          either by current setting parameters or automatically by the algorithm.\n     |      model_name : string or None (default)\n     |          The name of an existing database Expectation Maximization model to create an oml.em object from.\n     |          The specified database model is not dropped when the oml.em object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Expectation Maximization model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Clustering and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8>`_\n     |          are applicable to Expectation Maximization model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, model_name=None, case_id=None)\n     |      Fits an Expectation Maximization Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.em object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.em object is deleted\n     |          unless oml.em object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. If the mode is 'class', the results include the most likely\n     |        target class and its probability. If mode is 'raw', the results\n     |        include for each target class, the probability belonging\n     |        to that class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each cluster on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned clusters to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each cluster, the probability\n     |        belonging to that cluster.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class esa(oml.algo.model.odmModel)\n     |  esa(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Explicit Semantic Analysis <https://www.oracle.com/machine-learning/explicit-semantic-analysis>`_ Model\n     |  \n     |  Builds an Explicit Semantic Analysis (ESA) Model to be used for feature extraction. \n     |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  ESA uses concepts of an existing knowledge base as features rather than latent\n     |  features derived by latent semantic analysis methods such as Singular\n     |  Value Decomposition and Latent Dirichlet Allocation. Each row, for example,\n     |  a document in the training data maps to a feature, that is, a concept.\n     |  ESA works best with concepts represented by text documents.\n     |  It has multiple applications in the area of text processing, most\n     |  notably semantic relatedness (similarity) and explicit topic modeling.\n     |  Text similarity use cases might involve, for example, resume matching, searching\n     |  for similar blog postings, and so on.\n     |  \n     |  :Attributes:\n     |  \n     |      **features** : oml.DataFrame\n     |      \n     |        Description of each feature extracted. \n     |        It includes the following components:\n     |            \n     |            - feature_id: The unique identifier of a feature as it appears in the training data\n     |            - attribute_name: The attribute name\n     |            - attribute_value: The attribute value\n     |            - coefficient: The coefficient (weight) associated with the attribute in a particular feature.\n     |  \n     |  Method resolution order:\n     |      esa\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of esa object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Explicit Semantic Analysis model to create an oml.esa object from.\n     |          The specified database model is not dropped when the oml.esa object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Explicit Semantic Analysis model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Feature Extraction and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-91FC543B-24E4-4D93-9D79-E95B1846F3B7>`_\n     |          are applicable to Explicit Semantic Analysis model.\n     |  \n     |  __repr__(self)\n     |  \n     |  feature_compare(self, x, compare_cols=None, supplemental_cols=None)\n     |      Compares features of data and generates relatedness.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          The data used to measure relatedness.\n     |      compare_cols : str, a list of str or None (default)\n     |          The column(s) used to measure data relatedness.\n     |          If None, all the columns of ``x`` are compared to measure relatedness.\n     |      supplemental_cols : a list of str or None (default)\n     |          A list of columns to display along with the resulting 'SIMILARITY' column.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |          Contains a 'SIMILARITY' column that measures relatedness and supplementary columns if specified.\n     |  \n     |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits an ESA Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.esa object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.esa object is deleted\n     |          unless oml.esa object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include the most likely feature and its probability.\n     |  \n     |  transform(self, x, supplemental_cols=None, topN=None)\n     |      Make predictions and return relevancy for each feature on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned values to\n     |          the specified number of features that have the highest topN values.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the relevancy for each feature on new data and the specified ``supplemental_cols``.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class glm(oml.algo.model.odmModel)\n     |  glm(mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Generalized Linear Models <https://www.oracle.com/machine-learning/generalized-linear-models>`_\n     |  \n     |  Builds Generalized Linear Models (GLM), which include and extend the class of \n     |  linear models (linear regression), to be used for classification or regression. \n     |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  Generalized linear models relax the restrictions on linear models, which are often \n     |  violated in practice. For example, binary (yes/no or 0/1) responses do not have same \n     |  variance across classes. This model uses a parametric modeling technique. Parametric \n     |  models make assumptions about the distribution of the data. When the assumptions are \n     |  met, parametric models can be more efficient than non-parametric models.\n     |  \n     |  :Attributes:\n     |  \n     |      **coef** : oml.DataFrame\n     |      \n     |        The coefficients of the GLM model, one for each predictor variable.\n     |        It includes the following components:\n     |            \n     |            - nonreference: The target value used as nonreference\n     |            - attribute name: The attribute name\n     |            - attribute value: The attribute value\n     |            - coefficient: The estimated coefficient\n     |            - std error: The standard error\n     |            - t value: The test statistics\n     |            - p value: The statistical significance\n     |            \n     |      **fit_details**: oml.DataFrame\n     |      \n     |        The model fit details such as adjusted_r_square, error_mean_square and so on.\n     |        It includes the following components:\n     |            \n     |            - name: The fit detail name\n     |            - value: The fit detail value\n     |            \n     |      **deviance**: float\n     |      \n     |        Minus twice the maximized log-likelihood, up to a constant.\n     |        \n     |      **null_deviance**: float\n     |      \n     |        The deviance for the null (intercept only) model.\n     |        \n     |      **aic**: float\n     |      \n     |        Akaike information criterion.\n     |        \n     |      **rank**: integer\n     |      \n     |        The numeric rank of the fitted model.\n     |        \n     |      **df_residual**: float\n     |      \n     |        The residual degrees of freedom.\n     |        \n     |      **df_null**: float\n     |      \n     |        The residual degrees of freedom for the null model.\n     |        \n     |      **converged**: bool\n     |      \n     |        The indicator for whether the model converged.\n     |        \n     |      **nonreference**: int or str\n     |      \n     |        For logistic regression, the response values that represents success.\n     |  \n     |  Method resolution order:\n     |      glm\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |      Initializes an instance of glm object.\n     |      \n     |      Parameters\n     |      ----------\n     |      mining_function : 'CLASSIFICATION' or 'REGRESSION', 'CLASSIFICATION' (default)\n     |          Type of model mining functionality\n     |      model_name : string or None (default)\n     |          The name of an existing database Generalized Linear Model to create an oml.glm object from.\n     |          The specified database model is not dropped when the oml.glm object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Generalized Linear Model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C>`_\n     |          are applicable to Generalized Linear Model model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits a GLM Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.glm object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.glm object is deleted\n     |          unless oml.glm object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None, confint=None, level=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      confint : bool, False (default)\n     |          A logical indicator for whether to produce confidence intervals.\n     |          for the predicted values.\n     |      level : float between 0 and 1 or None (default)\n     |          A numeric value within [0, 1] to use for the confidence level.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True for classification.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. For a classification model, the results include the most\n     |        likely target class and optionally its probability and confidence\n     |        intervals. For a linear regression model, the results consist of a column\n     |        for the prediction and optionally its confidence intervals.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  residuals(self, x, y)\n     |      Return the deviance residuals, which includes the following components:\n     |        - deviance: The deviance residual\n     |        - pearson: The Pearson residual\n     |        - response: The residual of the working response.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      \n     |      Return: oml.DataFrame\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data, returns the mean accuracy for classifications\n     |      or the coefficient of determination R^2 of the prediction for regressions.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications or the coefficient of\n     |              determination R^2 of the prediction for regressions.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class km(oml.algo.model.odmModel)\n     |  km(n_clusters=None, model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `k-means <https://www.oracle.com/machine-learning/k-means>`_ Model\n     |  \n     |  Builds a K-Means (KM) Model that uses a distance-based clustering algorithm to \n     |  partition data into a specified number of clusters. This function exposes the \n     |  corresponding Oracle Machine Learning in-database algorithm. Distance-based \n     |  algorithms rely on a distance function to measure the similarity between cases. \n     |  Cases are assigned to the nearest cluster according to the distance function used.\n     |  \n     |  :Attributes:\n     |  \n     |      **clusters** : oml.DataFrame\n     |      \n     |        The general per-cluster information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - row_cnt: The number of rows used in the build\n     |            - parent_cluster_id: The ID of the parent\n     |            - tree_level: The number of splits from the root\n     |            - dispersion: The measure of the quality of the cluster, and computationally, the sum of square errors\n     |            \n     |      **taxonomy**: oml.DataFrame\n     |      \n     |        The parent/child cluster relationship.\n     |        It includes the following components:\n     |            \n     |            - parent_cluster_id: The ID of the parent cluster\n     |            - child_cluster_id: The ID of the child cluster\n     |            \n     |      **centroids**: oml.DataFrame\n     |      \n     |        Per cluster-attribute center (centroid) information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - attribute_name: The attribute name\n     |            - mean: The average value of a numeric attribute\n     |            - mode_value: The most frequent value of a categorical attribute\n     |            - variance: The variance of a numeric attribute\n     |            \n     |      **leaf_cluster_counts**: pandas.DataFrame\n     |      \n     |        Leaf clusters with support.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a leaf cluster in the model\n     |            - cnt: The number of records in a leaf cluster\n     |            \n     |      **cluster_hists**: oml.DataFrame\n     |      \n     |        Cluster histogram information.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - variable: The attribute name\n     |            - bin.id: The ID of a bin\n     |            - lower.bound: The numeric lower bin boundary\n     |            - upper.bound: The numeric upper bin boundary\n     |            - label: The label of the cluster\n     |            - count: The histogram count\n     |            \n     |      **rules**: oml.DataFrame\n     |      \n     |        Conditions for a case to be assigned with some probability to a cluster.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - rhs.support: The record count\n     |            - rhs.conf: The record confidence\n     |            - lhr.support: The rule support\n     |            - lhs.conf: The rule confidence\n     |            - lhs.var: The attribute predicate name\n     |            - lhs.var.support: The attribute predicate support\n     |            - lhs.var.conf: The attribute predicate confidence\n     |            - predicate: The attribute predicate\n     |  \n     |  Method resolution order:\n     |      km\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, n_clusters=None, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of km object.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_clusters : positive integer, default None\n     |          Number of clusters. If n_clusters is None, the number of clusters will be determined\n     |          either by current setting parameters or automatically by the internal algorithm.\n     |      model_name : string or None (default)\n     |          The name of an existing database K-Means model to create an oml.km object from.\n     |          The specified database model is not dropped when the oml.km object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing K-Means model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Clustering and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C>`_\n     |          are applicable to K-Means model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits a K-Means Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.km object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.km object is deleted\n     |          unless oml.km object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |      ctx_settings : dict or None(default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each cluster on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer\n     |          A positive integer that restricts the returned clusters to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each cluster, the probability\n     |        belonging to that cluster.\n     |  \n     |  score(self, x)\n     |      Calculates the score value based on the input data ``x``.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          A new data set used to calculate score value.\n     |      \n     |      Returns\n     |      -------\n     |      pred : float\n     |             Score values, that is, opposite of the value of ``x`` on the K-means objective.\n     |  \n     |  transform(self, x)\n     |      Transforms ``x`` to a cluster-distance space.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the distance to each cluster.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class nb(oml.algo.model.odmModel)\n     |  nb(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Naive Bayes <https://www.oracle.com/machine-learning/naive-bayes>`_ Model\n     |  \n     |  Builds a Naive Bayes Model that uses conditional probabilities to predict a target \n     |  variable (numeric or categorical column). Naive Bayes looks at the historical data \n     |  and calculates conditional probabilities for the target values by observing the \n     |  frequency of attribute values and of combinations of attribute values. Naive Bayes \n     |  assumes that each predictor is conditionally independent of the others. (Bayes' \n     |  Theorem requires that the predictors be independent.)\n     |  \n     |  :Attributes:\n     |  \n     |      **priors** : oml.DataFrame\n     |      \n     |        An optional named numerical vector that specifies the priors for the target classes.\n     |        It includes the following components:\n     |            \n     |            - target_name: The name of the target column\n     |            - target_value: The target value\n     |            - prior_probability: The prior probability for a given target_value\n     |            - count: The number of rows for a given target_value\n     |            \n     |      **conditionals** : oml.DataFrame\n     |      \n     |        Conditional probabilities for each predictor variable.\n     |        It includes the following components:\n     |            \n     |            - target_name: The name of the target column\n     |            - target_value: The target value\n     |            - attribute_name: The column name\n     |            - attribute_subname: The nested column subname.\n     |            - attribute_value: The mining attribute value\n     |            - conditional_probability: The conditional probability of a mining attribute for a given target\n     |            - count: The number of rows for a given mining attribute and a given target\n     |  \n     |  Method resolution order:\n     |      nb\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of nb object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Naive Bayes model to create an oml.nb object from.\n     |          The specified database model is not dropped when the oml.nb object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Naive Bayes model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-A04C5F4E-1303-44DC-A7DA-185C969330C8>`_\n     |          are applicable to Naive Bayes model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, priors=None, case_id=None)\n     |      Fits a Naive Bayes Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.nb object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.nb object is deleted\n     |          unless oml.nb object is saved into a datastore.\n     |      priors : OML DataFrame or dict or list of ints or floats or None (default)\n     |          The priors represent the overall distribution of the target in the\n     |          population. By default, the priors are computed from the sample.\n     |          If the sample is known to be a distortion of the population target\n     |          distribution, then the user can override the default by providing\n     |          a priors table as a setting for model creation. For OML DataFrame\n     |          input, the first value represents the target value. The second value\n     |          represents the prior probability. For dictionary type input, the key\n     |          represents the target value. The value represents the prior probability.\n     |          For list type input, the first value represents target value. The\n     |          second value represents the prior probability. See `Oracle Data\n     |          Mining Concepts Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-4303E423-3613-4ABC-B077-450C20755F4C>`_\n     |          for more details.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include the most likely target class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      TopN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data and returns the mean accuracy.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class nn(oml.algo.model.odmModel)\n     |  nn(mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Neural Network <https://www.oracle.com/machine-learning/neural-network>`_ Model\n     |  \n     |  Builds a Neural Network (NN) Model that uses an algorithm inspired from biological\n     |  neural network for classification and regression. Neural Network is used to to estimate\n     |  or approximate functions that depend on a large number of generally unknown inputs.\n     |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  An artificial neural network is composed of a large number of interconnected neurons\n     |  which exchange messages between each other to solve specific problems. They learn by\n     |  examples and tune the weights of the connections among the neurons during the learning\n     |  process. Neural Network is capable of solving a wide variety of tasks such as computer\n     |  vision, speech recognition, and various complex business problems.\n     |  \n     |  :Attributes:\n     |  \n     |      **weights** : oml.DataFrame\n     |  \n     |        Weights of fitted model between nodes in different layers.\n     |        It includes the following components:\n     |  \n     |            - layer: The layer ID, 0 as an input layer\n     |            - idx_from: The node index that the weight connects from (attribute id for input layer)\n     |            - idx_to: The node index that the weights connects to\n     |            - attribute_name: The attribute name (only for the input layer)\n     |            - attribute_subname: The attribute subname\n     |            - attribute_value: The attribute value\n     |            - target_value: The target value.\n     |            - weight: The value of weight\n     |  \n     |      **topology** : oml.DataFrame\n     |  \n     |        Topology of the fitted model including number of nodes and hidden layers.\n     |        It includes the following components:\n     |  \n     |            - hidden_layer_id: The id number of the hidden layer\n     |            - num_node: The number of nodes in each layer\n     |            - activation_function: The activation function in each layer\n     |  \n     |  Method resolution order:\n     |      nn\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |      Initializes an instance of nn object.\n     |      \n     |      Parameters\n     |      ----------\n     |      mining_function : 'CLASSIFICATION' or 'REGRESSION', 'CLASSIFICATION' (default)\n     |          Type of model mining functionality\n     |      model_name : string or None (default)\n     |          The name of an existing database Neural Network model to create an oml.nn object from.\n     |          The specified database model is not dropped when the oml.nn object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Neural Network model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4>`_\n     |          are applicable to Neural Network model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None, class_weight=None)\n     |      Fits a Neural Network Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.nn object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.nn object is deleted\n     |          unless oml.nn object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |      class_weight : OML DataFrame or dict or list of ints or floats or None (default)\n     |          An optional matrix that is used to influence the weighting of\n     |          target classes during model creation. For OML DataFrame input, the first\n     |          value represents the target value. The second value represents the class weight.\n     |          For dictionary type input, the key represents the target value. The value\n     |          represents the class weight. For list type input, the first value represents\n     |          target value. The second value represents the predicted target value.\n     |          Refer to `Oracle Data Mining  User's Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-44E40062-CE46-4C10-BE10-BB96B562BA20>`_\n     |          for more details about class weights.\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches settings of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If params is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. For a classification model, the results include the most\n     |        likely target class and its probability. For a regression\n     |        model, the results consist of a column for the prediction. For an\n     |        anomaly detection model, the results include a prediction and its\n     |        probability. If the prediction is 1, the case is considered typical.\n     |        If the prediction is 0, the case is considered anomalous. This\n     |        behavior reflects the fact that the model is trained with normal data.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data, returns the mean accuracy for classifications\n     |      or the coefficient of determination R^2 of the prediction for regressions.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications or the coefficient of\n     |              determination R^2 of the prediction for regressions.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class rf(oml.algo.model.odmModel)\n     |  rf(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Random Forest <https://www.oracle.com/machine-learning/random-forest>`_ Model\n     |  \n     |  Builds a Random Forest (RF) Model that uses an ensemble (also called forest) of trees \n     |  for classification. This function exposes the corresponding Oracle Machine Learning \n     |  in-database algorithm. Random Forest is a popular ensemble learning technique for \n     |  classification. By combining the ideas of bagging and random selection of variables, \n     |  the algorithm produces collection of decision trees with controlled variance, while \n     |  avoiding overfitting - a common problem for decision trees.\n     |  \n     |  :Attributes:\n     |  \n     |      **importance** : oml.DataFrame\n     |      \n     |        Attribute importance of the fitted model.\n     |        It includes the following components:\n     |            \n     |            - attribute_name: The attribute name\n     |            - attribute_subname: The attribute subname \n     |            - attribute_importance: The attribute importance for an attribute in the forest\n     |  \n     |  Method resolution order:\n     |      rf\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of rf object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Random Forest model to create an oml.rf object from.\n     |          The specified database model is not dropped when the oml.rf object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Random Forest model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117>`_\n     |          are applicable to Random Forest model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, cost_matrix=None, case_id=None)\n     |      Fits a Random Forest Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.rf object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.rf object is deleted\n     |          unless oml.rf object is saved into a datastore.\n     |      cost_matrix : OML DataFrame or list of ints or floats or None (default)\n     |          An optional numerical square matrix that specifies the costs for incorrectly\n     |          predicting the target values. The first value represents the actual target value.\n     |          The second value represents the predicted target value. The third value is the cost.\n     |          In general, the diagonal entries of the matrix are zeros. Refer to `Oracle Data\n     |          Mining User's Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-89E0DB46-BA9A-4420-8216-FD6C96C74D6D>`_\n     |          for more details about cost matrix.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols``, and the most likely\n     |        target class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data and returns the mean accuracy.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class svd(oml.algo.model.odmModel)\n     |  svd(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Singular Value Decomposition <https://www.oracle.com/machine-learning/singular-value-decomposition>`_ Model\n     |  \n     |  Builds a Singular Value Decomposition (SVD) Model that can be used for feature \n     |  extraction. SVD provides orthogonal linear transformations that capture the \n     |  underlying variance of the data by decomposing a rectangular matrix into three \n     |  matrixes: U, D, and V. Matrix D is a diagonal matrix and its singular values \n     |  reflect the amount of data variance captured by the bases. Columns of matrix V \n     |  contain the right singular vectors and columns of matrix U contain the left singular\n     |  vectors.\n     |  \n     |  :Attributes:\n     |  \n     |      **features** : oml.DataFrame\n     |      \n     |        Features extracted by the fitted model including feature id and associated coefficient.\n     |        It includes the following components:\n     |            \n     |            - feature_id: The ID of a feature in the model\n     |            - attribute_name: The attribute name\n     |            - attribute_value: The attribute value\n     |            - value: The matrix entry value\n     |            \n     |      **u** : oml.DataFrame\n     |      \n     |        A dataframe whose columns contain the left singular vectors.\n     |        The column name is the corresponding feature id.\n     |        \n     |      **v** : oml.DataFrame\n     |      \n     |        A dataframe whose columns contain the right singular vectors.\n     |        The column name is the corresponding feature id.\n     |        \n     |      **d** : oml.DataFrame\n     |      \n     |        A dataframe containing the singular values of the input data.\n     |        It includes the following components:\n     |            \n     |            - feature_id: The ID of a feature in the model\n     |            - value: The singular values of the input data\n     |  \n     |  Method resolution order:\n     |      svd\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of svd object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Singular Value Decomposition model to create an oml.svd object from.\n     |          The specified database model is not dropped when the oml.svd object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Singular Value Decomposition model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Feature Extraction and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117>`_\n     |          are applicable to Singular Value Decomposition model.\n     |  \n     |  __repr__(self)\n     |  \n     |  feature_compare(self, x, compare_cols=None, supplemental_cols=None)\n     |      Compares features of data and generates relatedness.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          The data used to measure relatedness.\n     |      compare_cols : str, a list of str or None (default)\n     |          The column(s) used to measure data relatedness.\n     |          If None, all the columns of ``x`` are compared to measure relatedness.\n     |      supplemental_cols : a list of str or None (default)\n     |          A list of columns to display along with the resulting 'SIMILARITY' column.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |          Contains a 'SIMILARITY' column that measures relatedness and supplementary columns if specified.\n     |  \n     |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits an SVD Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.svd object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.svd object is deleted\n     |          unless oml.svd object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model. \n     |          ``case_id`` and SVDS_U_MATRIX_OUTPUT in ``odm_settings`` \n     |          must be specified in order to produce matrix U.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the predicted feature index on the new data and the specified ``supplemental_cols``.\n     |  \n     |  transform(self, x, supplemental_cols=None, topN=None)\n     |      Performs dimensionality reduction and returns value for each feature on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned values to\n     |          the specified number of features that have the highest topN values.\n     |          If None, all features will be returned.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the values of new data after the SVD transform and the specified ``supplemental_cols``.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class svm(oml.algo.model.odmModel)\n     |  svm(mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Support Vector Machine <https://www.oracle.com/machine-learning/support-vector-machines>`_ Model\n     |  \n     |  Builds a Support Vector Machine (SVM) Model to be used for regression, classification, \n     |  or anomaly detection. This function exposes the corresponding Oracle Machine Learning \n     |  in-database algorithm. SVM is a powerful, state-of-the-art algorithm with strong \n     |  theoretical foundations based on the Vapnik-Chervonenkis theory. SVM has strong \n     |  regularization properties. Regularization refers to the generalization of the model to \n     |  new data.\n     |  \n     |  \n     |  :Attributes:\n     |  \n     |      **coef** : oml.DataFrame\n     |      \n     |        The coefficients of the SVM model, one for each predictor variable.\n     |        It includes the following components:\n     |          \n     |            - target_value: The target value\n     |            - attribute_name: The attribute name\n     |            - attribute_subname: The attribute subname\n     |            - attribute_value: The attribute value\n     |            - coef: The projection coefficient value\n     |  \n     |  Method resolution order:\n     |      svm\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |      Initializes an instance of svm object.\n     |      \n     |      Parameters\n     |      ----------\n     |      mining_function : 'CLASSIFICATION' (default), 'REGRESSION' or 'ANOMALY_DETECTION'\n     |          Type of model mining functionality.\n     |      model_name : string or None (default)\n     |          The name of an existing database Support Vector Machine model to create an oml.svm object from.\n     |          The specified database model is not dropped when the oml.svm object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Support Vector Machine model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB>`_\n     |          are applicable to Support Vector Machine model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits an SVM Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object or None, or string\n     |          Target values.\n     |          Must be specified when SVM algorithm is used for classification\n     |          or regression and must be None when used for anomaly detection.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.svm object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.svm object is deleted\n     |          unless oml.svm object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : oml.DataFrame\n     |          Predictor values used by the model to generate scores\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. For a classification model, the results include the most\n     |        likely target class and its probability. For a regression\n     |        model, the results consist of a column for the prediction. For an\n     |        anomaly detection model, the results include a prediction and its\n     |        probability. If the prediction is 1, the case is considered normal.\n     |        If the prediction is 0, the case is considered anomalous. This\n     |        behavior reflects the fact that the model is trained with normal data.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data, returns the mean accuracy for classifications\n     |      or the coefficient of determination R^2 of the prediction for regressions.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications or the coefficient of\n     |              determination R^2 of the prediction for regressions.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n\nFUNCTIONS\n    boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_ticks=True, autorange=False, zorder=None)\n        Makes a box and whisker plot.\n        \n        For every column of ``x`` or for every column object in ``x``, makes a box and whisker plot.\n        \n        Parameters\n        ----------\n        x : oml.DataFrame or oml.Float or list of oml.Float\n          The data to plot.\n        notch : bool, False (default), optional\n          If True, produces a notched box plot. Otherwise, a rectangular\n          boxplot is produced. By default, the confidence intervals are approximated\n          as ``median +/-1.57 IQR/sqrt(n)`` where ``n`` is the number of not-null/NA\n          values in the column. \n        conf_intervals : array-like, optional\n          Array or sequence whose first dimension is equal to the number of columns\n          in ``x`` and whose second dimension is 2.\n        labels : sequence, optional\n          Length must be equal to the number of columns in ``x``. When an element of\n          ``labels`` is not None, the default label of the column, which is the name\n          of the column, is overridden.\n        \n        Notes\n        -----\n        For information on the other parameters, see documentation for :py:func:`matplotlib.pyplot.boxplot`.\n        \n        Returns\n        -------\n        ax : :py:class:`matplotlib.axes.Axes`\n          The :py:class:`matplotlib.axes.Axes` instance of the boxplot figure.\n        result : dict\n          A dict mapping each component of the boxplot to the corresponding list of\n          :py:class:`matplotlib.lines.Lines2D` instances created.\n    \n    check_embed()\n        Indicates whether embedded Python is set up in the connected Oracle Database.\n        \n        Returns\n        -------\n        embed_status : bool or None\n          None when not connected.\n    \n    connect(user=None, password=None, host=None, port=None, sid=None, service_name=None, dsn=None, encoding='UTF-8', nencoding='UTF-8', automl=None, **kwargs)\n        Establishes an Oracle Database connection.\n        \n        Just as with :py:func:`cx_Oracle.connect`, the user, password, and data\n        source name can be provided separately or with host, port, sid or\n        service_name.\n        \n        There can be only one active connection. Calling this method when an\n        active connection already exists replaces the active connection with\n        a new one. This results in the previous connection being implicitly\n        disconnected with the corresponding release of resources.\n        \n        Parameters\n        ----------\n        user : str or None (default)\n        password : str or None (default)\n        host : str or None (default)\n          Host name of the Oracle Database.\n        port : int, str or None (default)\n          The Oracle Database port number.\n        sid : str or None (default)\n          The Oracle Database SID.\n        service_name : str or None (default)\n          The service name to be used in the connection identifier for\n          the Oracle Database.\n        dsn : str or None (default)\n          Data source name. The TNS entry of the database, or an TNS\n          alias in the Oracle Wallet.\n        encoding : str, 'UTF-8' (default)\n          Encoding to use for regular database strings.\n        nenconding : str, 'UTF-8' (default)\n          Encoding to use for national character set database strings.\n        automl : str, or bool or None (default)\n          To enable automl, specify:\n            * True: if ``host``, ``port``, ``sid`` or ``service_name``\n              are specified and a connection pool is running for this\n              (``host``, ``port``, ``sid`` or ``service_name``).\n            * Data source name: for a running connection pool\n              if ``dsn`` is specified with a data source name.\n            * TNS alias in an Oracle Wallet: for a running connection pool\n              if ``dsn`` is also specified with Wallet TNS alias.\n          Otherwise, automl is disabled.\n        \n        Notes\n        -----\n         * Parameters ``sid`` and ``service_name`` are exclusive.\n         * Parameters (``host``, ``port``, ``sid`` or ``service_name``),\n           and ``dsn`` can only be specified exclusively.\n         * Parameter ``user`` and ``password`` must be provided when\n           (``host``, ``port``, ``sid`` or ``service_name``) is specified,\n           or ``dsn`` (and optionally ``automl``) is specified with\n           a data source name.\n         * Parameter ``user`` and ``password`` should be set to empty str \"\",\n           when ``dsn`` (and optionally ``automl``) is specified with\n           Wallet TNS alias, to establish connection with Oracle Wallet.\n         * Automl requires `Database Resident Connection Pooling (DRCP)\n           <https://docs.oracle.com/en/database/oracle/oracle-database/18/jjdbc/database-resident-connection-pooling.html#GUID-80365FCC-7053-46BD-819F-E051065E4C4E>`_\n           running on the Database server.\n    \n    create(x, table, oranumber=True, dbtypes=None, append=False)\n        Creates a table in Oracle Database from a Python data set.\n        \n        Parameters\n        ----------\n        x : pandas.DataFrame or a list of tuples of equal size\n          If ``x`` is a list of tuples of equal size, each tuple represents\n          a row in the table. The column names are set to COL1, COL2, ... and so on.\n        table : str\n          A name for the table.\n        oranumber : bool, True (default)\n          If True, use SQL NUMBER for numeric columns. Otherwise, use BINARY_DOUBLE.\n          Ignored if ``append`` is True.\n        dbtypes : dict mapping str to str or list of str\n          A list of SQL types to use on the new table. If a list, its length should\n          be equal to the number of columns. If a dict, the keys are the names of the\n          columns. Ignored if ``append`` is True.\n        append : bool, False (default)\n          Indicates whether to append the data to the existing table.\n        \n        Notes\n        -----\n         * When creating a new table, for columns whose SQL types are not specified in\n           ``dbtypes``, NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. Users should set\n           ``oranumber`` to False when the data contains NaN values. For string columns,\n           the default type is VARCHAR2(4000), and for bytes columns, the default type\n           is BLOB.\n         * When ``x`` is specified with an empty pandas.DataFrame, OML creates an\n           empty table. NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. VARCHAR2(4000) is\n           used for columns of object dtype in the pandas.DataFrame.\n         * OML does not support columns containing values of multiple data types,\n           data conversion is needed or a TypeError may be raised.\n         * OML determines default column types by looking at 20 random rows sampled\n           from the table. For tables with less than 20 rows, all rows are used\n           in column type determination. NaN values are considered as float type.\n           If a column has all Nones, or has inconsistent data types that are not\n           None in the sampled rows, a default column type cannot be determined,\n           and a ValueError is raised unless a SQL type for the column is specified\n           in ``dbtypes``.\n        \n        Returns\n        -------\n        new_table : oml.DataFrame\n          A proxy object that represents the newly-created table.\n    \n    cursor()\n        Returns a cx_Oracle cursor object of the current OML database connection.\n        It can be used to execute queries against Oracle Database.\n        \n        Returns\n        -------\n        cursor_obj : a cx_Oracle :ref:`cx:cursorobj`.\n    \n    dir()\n        Returns the names of OML objects in the workspace.\n        \n        Returns\n        -------\n        obj_names : list of str\n    \n    disconnect(cleanup=True)\n        Terminates the Oracle Database connection. By default, the OML\n        objects created through this connection will be deleted.\n        \n        Parameters\n        ----------\n        cleanup : bool, True (default)\n          Cleans up OML objects defined in Python's main module before\n          disconnecting from the database.\n    \n    do_eval(func, func_owner=None, graphics=False, **kwargs)\n        Runs the user-defined Python function using a Python engine spawned and \n        controlled by the database environment.\n        \n        Parameters\n        ----------\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : Python object or oml.embed.data_image._DataImage\n          If no image is rendered in the script, returns whatever Python object returned\n          by the function. Otherwise, returns an oml.embed.data_image._DataImage object.\n          See :ref:`more-output`.\n    \n    drop(table=None, view=None, model=None)\n        Drops a database table, view, or model.\n        \n        Parameters\n        ----------\n        table : str or None (default)\n          The name of the table to drop.\n        view : str or None (default)\n          The name of the view to drop.\n        model : str or None (default)\n          The name of the model to drop.\n    \n    grant(name, typ='datastore', user=None)\n        Grants read privilege for a Python script or datastore.\n        Requires the user to have the `PYQADMIN` Oracle Database role.\n        \n        Parameters\n        ----------\n        name : str\n          The name of Python script in the Python script repository or the name of\n          a datastore. The current user must be the owner of the Python script or\n          datastore.\n        typ : 'datastore' (default) or 'pyqscript'\n          A str specifying either 'datastore' or 'pyqscript' to grant the\n          read privilege. 'pyqscript' requires Embedded Python.\n        user : str or None (default)\n          The user to grant read privilege of the named Python script or datastore\n          to. Treated as case-sensitive if wrapped in double quotes. Treated as\n          case-insensitive otherwise. If None, grant read privilege to public.\n    \n    group_apply(data, index, func, func_owner=None, parallel=None, orderby=None, graphics=False, **kwargs)\n        Partitions database data by the column(s) specified in ``index``\n        and runs the user-defined Python function on each partition using \n        Python engines spawned and controlled by the database environment.\n        \n        Parameters\n        ----------\n        data : oml.DataFrame\n          The OML DataFrame that represents the in-database data that ``func`` is\n          applied on.\n        index : OML data object\n          The columns to partition the ``data`` before sending it to ``func``.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        parallel : bool or int or None (default)\n          A preferred degree of parallelism to use in the embedded Python job;\n          either a positive integer greater than or equal to 1\n          for a specific degree of parallelism,\n          a value of 'None', 'False' or '0' for no parallelism,\n          a value of 'True' for the ``data`` default parallelism.\n          Cannot exceed the degree of parallelism limit controlled by \n          service level in ADW.\n        orderby : oml.DataFrame, oml.Float, or oml.String\n          An optional argument used to specify the ordering of group partitions.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : dict \n          If no image is rendered in the script, returns a dict of Python objects\n          returned by the function. Otherwise, returns a dict of \n          oml.embed.data_image._DataImage objects. See :ref:`more-output`.\n    \n    hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, **kwargs)\n        Plots a histogram.\n        \n        Computes and draws a histogram for every data set column contained in ``x``.\n        \n        Parameters\n        ----------\n        x : oml.Float\n        bins : int, strictly monotonic increasing sequence, 'auto', 'doane', 'fd', 'rice', 'scott', 'sqrt', or 'sturges', optional\n          * If an integer, denotes the number of equal width bins to generate.\n          * If a sequence, denotes bin edges and overrides the values of ``range``.\n          * If a string, denotes the estimator to use calculate the optimal number\n            of bins. 'auto' is the maximum of the 'fd' and 'sturges' estimators.\n          * Default is taken from the matplotlib rcParam ``hist.bins``.\n        weights : oml.Float\n          Must come from the same table as ``x``.\n        cumulative : int, float, or boolean, False (Default)\n          If greater than zero, then a histogram is computed where each bin gives\n          the counts in that bin plus all bins for smaller values. If ``density``\n          is also True, then the histogram is normalized so the last bin equals 1.\n          If less than zero, the direction of accumulation is reversed. In this\n          case, if ``density`` is True, then the histogram is normalized so that the\n          first bin equals 1. \n        rwidth : int, float, or None (default)\n          Ratio of the width of the bars to the bin widths. Values less than 0\n          is treated as 0. Values more than 1 is treated as 1. If None,\n          defaults to 1.\n        color : str that indicates a color spec or None (default)\n          If None, use the standard line color sequence. \n        label : str or None (default) \n          The label that is applied to the first patch of the histogram.\n        \n        Notes\n        -----\n        For information on the other parameters, see documentation for :py:func:`matplotlib.pyplot.hist`.\n        \n        Returns\n        -------\n        n : :py:class:`numpy.ndarray`\n          The values of the histogram bins. \n        bins : :py:class:`numpy.ndarray`\n          The edges of the bins. An array of length #bins + 1.\n        patches : list of :py:class:`matplotlib.patches.Rectangle`\n          Individual patches used to create the histogram.\n    \n    index_apply(times, func, func_owner=None, parallel=None, graphics=False, **kwargs)\n        Runs the user-defined Python function multiple times, passing the \n        run index as first argument, using Python engines spawned and controlled \n        by the database environment.\n        \n        Parameters\n        ----------\n        times : int\n          The number of times to execute the function.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        parallel : bool or int or None (default)\n          A preferred degree of parallelism to use in the embedded Python job;\n          either a positive integer greater than or equal to 1\n          for a specific degree of parallelism,\n          a value of 'None', 'False' or '0' for no parallelism,\n          a value of 'True' for the ``data`` default parallelism.\n          Cannot exceed the degree of parallelism limit controlled by\n          service level in ADW.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : list\n          If no image is rendered in the script, returns a list of Python objects\n          returned by the function. Otherwise, returns a list of \n          oml.embed.data_image._DataImage objects. See :ref:`more-output`.\n    \n    isconnected(check_automl=False)\n        Indicates whether an active Oracle Database connection exists.\n        \n        Parameters\n        ----------\n        check_automl: bool, False (default)\n            Indicates whether to check the connection is automl-enabled.\n        \n        Returns\n        -------\n        connected : bool\n    \n    push(x, oranumber=True, dbtypes=None)\n        Pushes data into Oracle Database.\n        \n        Creates an internal table in Oracle Database and inserts the data\n        into the table. The table exists as long as an OML object (either\n        in the Python client or saved in the datastore) references the table.\n        \n        Parameters\n        ----------\n        x : pandas.DataFrame or a list of tuples of equal size\n          If ``x`` is a list of tuples of equal size, each tuple represents\n          a row in the table. The column names are set to COL1, COL2, ... and so on.\n        oranumber : bool\n          If True (default), use SQL NUMBER for numeric columns. Otherwise\n          use BINARY_DOUBLE. Ignored if ``append`` is True.\n        dbtypes : dict or list of str\n          The SQL data types to use in the table.\n        \n        Notes\n        -----\n         * When creating a new table, for columns whose SQL types are not specified in\n           ``dbtypes``, NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. Users should set\n           ``oranumber`` to False when the data contains NaN values. For string columns,\n           the default type is VARCHAR2(4000), and for bytes columns, the default type\n           is BLOB.\n         * When ``x`` is specified with an empty pandas.DataFrame, OML creates an\n           empty table. NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. VARCHAR2(4000) is\n           used for columns of object dtype in the pandas.DataFrame.\n         * OML does not support columns containing values of multiple data types,\n           data conversion is needed or a TypeError may be raised.\n         * OML determines default column types by looking at 20 random rows sampled\n           from the table. For tables with less than 20 rows, all rows are used\n           in column type determination. NaN values are considered as float type.\n           If a column has all Nones, or has inconsistent data types that are not\n           None in the sampled rows, a default column type cannot be determined,\n           and a ValueError is raised unless a SQL type for the column is specified\n           in ``dbtypes``.\n        \n        Returns\n        -------\n        temp_table : oml.DataFrame\n    \n    revoke(name, typ='datastore', user=None)\n        Revokes read privilege for a Python script or datastore.\n        Requires the user to have the `PYQADMIN` Oracle Database role.\n        \n        Parameters\n        ----------\n        name : str\n          The name of Python script in the Python script repository or the name of\n          a datastore. The current user must be the owner of the Python script or\n          datastore.\n        typ : 'datastore' (default) or 'pyqscript'\n          A str specifying either 'datastore' or 'pyqscript' to revoke the\n          read privilege. 'pyqscript' requires Embedded Python.\n        user : str or None (default)\n          The user to revoke read privilege of the named Python script or datastore\n          from. Treated as case-sensitive if wrapped in double quotes. Treated as\n          case-insensitive otherwise. If None, revoke read privilege from public.\n    \n    row_apply(data, func, func_owner=None, rows=1, parallel=None, graphics=False, **kwargs)\n        Partitions database data into chunks of rows and runs the user-defined \n        Python function on each chunk using Python engines spawned and controlled \n        by the database environment.\n        \n        Parameters\n        ----------\n        data : oml.DataFrame\n          The OML DataFrame that represents the in-database data that ``func``\n          is applied on.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        rows : int, 1 (default)\n          The maximum number of rows in each chunk.\n        parallel : bool or int or None (default)\n          A preferred degree of parallelism to use in the embedded Python job;\n          either a positive integer greater than or equal to 1\n          for a specific degree of parallelism,\n          a value of 'None', 'False' or '0' for no parallelism,\n          a value of 'True' for the ``data`` default parallelism.\n          Cannot exceed the degree of parallelism limit controlled by \n          service level in ADW.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : pandas.DataFrame or a list of oml.embed.data_image._DataImage\n          If no image is rendered in the script, returns a :py:class:`pandas.DataFrame`.\n          Otherwise, returns a list of oml.embed.data_image._DataImage objects.\n          See :ref:`more-output`.\n    \n    sync(schema=None, regex_match=False, **kwargs)\n        Creates a DataFrame proxy object in Python that represents an Oracle\n        Database data set.\n        \n        The data set can be one of the following: a database table, view, or query.\n        \n        Parameters\n        ----------\n        schema : str or None (default)\n          The name of the schema where the database object exists;\n          if None, then the current schema is used.\n        regex_match : bool, False (default)\n          Synchronizes tables or views that match a regular expression.\n          Ignored if ``query`` is used.\n        table, view, query : str or None (default)\n          The name of a table, of a view, or of an Oracle SQL query to select\n          from the database. When ``regex_match`` is True, this specifies the\n          name pattern. Exactly one of these parameters must be a str and the\n          other two must be None.\n        \n        Notes\n        -----\n        When ``regex_match`` is True, synchronizes the matched tables or views\n        to a dict with the table or view name as the key.\n        \n        Returns\n        -------\n        data_set : oml.DataFrame, or if ``regex_match`` is used, returns\n          a dict of oml.DataFrame\n    \n    table_apply(data, func, func_owner=None, graphics=False, **kwargs)\n        Runs the user-defined Python function with data pulled from \n        a database table or view using a Python engine spawned and \n        controlled by the database environment.\n        \n        Parameters\n        ----------\n        data : oml.DataFrame\n          The oml.DataFrame that represents the data ``func`` is applied on.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : Python object or oml.embed.data_image._DataImage\n          If no image is rendered in the script, returns whatever Python object returned\n          by the function. Otherwise, returns an oml.embed.data_image._DataImage object.\n          See :ref:`more-output`.\n\nDATA\n    __all__ = ['connect', 'disconnect', 'isconnected', 'check_embed', 'cur...\n    __build_serial__ = '1.0_08122021_1858'\n\nVERSION\n    1.0\n\nFILE\n    /usr/local/lib/python3.9/site-packages/oml/__init__.py\n\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_-1634618100","id":"20211001-190306_1526941016","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:47+0000","dateFinished":"2021-09-22T20:18:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:385"},{"text":"%md\n## Learn More\n\n* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html');\">Get Started with OML4Py and OML Notebooks</a>\n* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/');\">Oracle Machine Learning Notebooks</a>\n \n**Last Updated Date** - September 2021\n \nCopyright (c) 2021 Oracle Corporation \n###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0</a>\n---","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:48+0000","config":{"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"results":{},"enabled":true,"editorHide":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2>Learn More</h2>\n<ul>\n<li><a href=\"https: //docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html');\">Get Started with OML4Py and OML Notebooks</a></li>\n<li><a href=\"https: //docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/');\">Oracle Machine Learning Notebooks</a></li>\n</ul>\n<p><strong>Last Updated Date</strong> - September 2021</p>\n<p>Copyright (c) 2021 Oracle Corporation</p>\n<h6><a href=\"https: //oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n<hr />\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_1016371808","id":"20211001-190306_68494635","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:48+0000","dateFinished":"2021-09-22T20:18:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:386"},{"text":"%md\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:48+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown"},"settings":{"params":{},"forms":{}},"interrupted":false,"jobName":"paragraph_1633114986450_-1060179910","id":"20211001-190306_1958773210","dateCreated":"2021-04-23T13:27:18+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":false,"$$hashKey":"object:387"}],"id":"79628","noteParams":{},"noteForms":{},"angularObjects":{"ORA221A1BC345:OMLUSER:78556":[],"ORA36CEFD120D:OMLUSER:78556":[],"ORA9405AD2E1E:OMLUSER:78556":[],"MDW276C5A6A4D:shared_process":[]},"config":{"looknfeel":"default","personalizedMode":"false"},"info":{},"name":"Lab 1: Get Started with OML4Py on Autonomous Database"}
\ No newline at end of file
+{"paragraphs":[{"text":"%md\n## **Initiate a call to the Python interpreter**\nTo run Python commands in a notebook, you must first connect to the Python interpreter. This occurs as a result of running your first `%python` paragraph. To use OML4Py, you must import the `oml` module, which automatically establishes a connection to your database. In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as SQL or Python. This example shows you how to:\n\n* Connect to a Python interpreter to run Python commands in a notebook\n* Import the Python modules—oml, pandas, numpy, and matplotlib\n* Check if the oml module is connected to the database\n\nNote: `z` is a reserved keyword and must not be used as a variable in %python paragraphs in Oracle Machine Learning Notebooks. You will see `z.show()` fucntion used in the examples to display Python object and proxy object content.\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:30+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown","editorHide":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2><strong>Initiate a call to the Python interpreter</strong></h2>\n<p>To run Python commands in a notebook, you must first connect to the Python interpreter. This occurs as a result of running your first <code>%python</code> paragraph. To use OML4Py, you must import the <code>oml</code> module, which automatically establishes a connection to your database. In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as SQL or Python. This example shows you how to:</p>\n<ul>\n<li>Connect to a Python interpreter to run Python commands in a notebook</li>\n<li>Import the Python modules—oml, pandas, numpy, and matplotlib</li>\n<li>Check if the oml module is connected to the database</li>\n</ul>\n<p>Note: <code>z</code> is a reserved keyword and must not be used as a variable in %python paragraphs in Oracle Machine Learning Notebooks. You will see <code>z.show()</code> fucntion used in the examples to display Python object and proxy object content.</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986446_2142145532","id":"20211001-190306_2044056072","dateCreated":"2021-09-21T20:48:10+0000","dateStarted":"2021-09-22T20:18:31+0000","dateFinished":"2021-09-22T20:18:33+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"focus":true,"$$hashKey":"object:376"},{"text":"%python\n\nimport oml","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:33+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true,"editorHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"interrupted":false,"jobName":"paragraph_1633114986450_1302574953","id":"20211001-190306_576066479","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:34+0000","dateFinished":"2021-09-22T20:18:42+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:377"},{"text":"%md\n## **Verify Connection to the Autonomous Database**\nUsing the default interpreter bindings, OML Notebooks automatically establishes a database connection for the notebook.  \n\nTo verify the Python interpreter has established a database connection through the `oml` module, run the command shown below.  If the notebook is connected, the command returns `True`.         \n\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:42+0000","config":{"tableHide":false,"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2><strong>Verify Connection to the Autonomous Database</strong></h2>\n<p>Using the default interpreter bindings, OML Notebooks automatically establishes a database connection for the notebook.</p>\n<p>To verify the Python interpreter has established a database connection through the <code>oml</code> module, run the command shown below.  If the notebook is connected, the command returns <code>True</code>.</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_2092135881","id":"20211001-190306_1271780091","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:43+0000","dateFinished":"2021-09-22T20:18:43+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:378"},{"text":"%python\n\noml.isconnected()","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:43+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"True\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_371292639","id":"20211001-190306_2058125440","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:43+0000","dateFinished":"2021-09-22T20:18:44+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:379"},{"text":"%md\n\n## **View Help Files**\nThe Python help function is used to display the documentation of packages, modules, functions, classes, and keywords. The help function has the following syntax:","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:44+0000","config":{"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2><strong>View Help Files</strong></h2>\n<p>The Python help function is used to display the documentation of packages, modules, functions, classes, and keywords. The help function has the following syntax:</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_563552517","id":"20211001-190306_759056463","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:44+0000","dateFinished":"2021-09-22T20:18:44+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:380"},{"text":"%python\n\nhelp([object])","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:44+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on list object:\n\nclass list(object)\n |  list(iterable=(), /)\n |  \n |  Built-in mutable sequence.\n |  \n |  If no argument is given, the constructor creates a new empty list.\n |  The argument must be an iterable if specified.\n |  \n |  Methods defined here:\n |  \n |  __add__(self, value, /)\n |      Return self+value.\n |  \n |  __contains__(self, key, /)\n |      Return key in self.\n |  \n |  __delitem__(self, key, /)\n |      Delete self[key].\n |  \n |  __eq__(self, value, /)\n |      Return self==value.\n |  \n |  __ge__(self, value, /)\n |      Return self>=value.\n |  \n |  __getattribute__(self, name, /)\n |      Return getattr(self, name).\n |  \n |  __getitem__(...)\n |      x.__getitem__(y) <==> x[y]\n |  \n |  __gt__(self, value, /)\n |      Return self>value.\n |  \n |  __iadd__(self, value, /)\n |      Implement self+=value.\n |  \n |  __imul__(self, value, /)\n |      Implement self*=value.\n |  \n |  __init__(self, /, *args, **kwargs)\n |      Initialize self.  See help(type(self)) for accurate signature.\n |  \n |  __iter__(self, /)\n |      Implement iter(self).\n |  \n |  __le__(self, value, /)\n |      Return self<=value.\n |  \n |  __len__(self, /)\n |      Return len(self).\n |  \n |  __lt__(self, value, /)\n |      Return self<value.\n |  \n |  __mul__(self, value, /)\n |      Return self*value.\n |  \n |  __ne__(self, value, /)\n |      Return self!=value.\n |  \n |  __repr__(self, /)\n |      Return repr(self).\n |  \n |  __reversed__(self, /)\n |      Return a reverse iterator over the list.\n |  \n |  __rmul__(self, value, /)\n |      Return value*self.\n |  \n |  __setitem__(self, key, value, /)\n |      Set self[key] to value.\n |  \n |  __sizeof__(self, /)\n |      Return the size of the list in memory, in bytes.\n |  \n |  append(self, object, /)\n |      Append object to the end of the list.\n |  \n |  clear(self, /)\n |      Remove all items from list.\n |  \n |  copy(self, /)\n |      Return a shallow copy of the list.\n |  \n |  count(self, value, /)\n |      Return number of occurrences of value.\n |  \n |  extend(self, iterable, /)\n |      Extend list by appending elements from the iterable.\n |  \n |  index(self, value, start=0, stop=9223372036854775807, /)\n |      Return first index of value.\n |      \n |      Raises ValueError if the value is not present.\n |  \n |  insert(self, index, object, /)\n |      Insert object before index.\n |  \n |  pop(self, index=-1, /)\n |      Remove and return item at index (default last).\n |      \n |      Raises IndexError if list is empty or index is out of range.\n |  \n |  remove(self, value, /)\n |      Remove first occurrence of value.\n |      \n |      Raises ValueError if the value is not present.\n |  \n |  reverse(self, /)\n |      Reverse *IN PLACE*.\n |  \n |  sort(self, /, *, key=None, reverse=False)\n |      Sort the list in ascending order and return None.\n |      \n |      The sort is in-place (i.e. the list itself is modified) and stable (i.e. the\n |      order of two equal elements is maintained).\n |      \n |      If a key function is given, apply it once to each list item and sort them,\n |      ascending or descending, according to their function values.\n |      \n |      The reverse flag can be set to sort in descending order.\n |  \n |  ----------------------------------------------------------------------\n |  Class methods defined here:\n |  \n |  __class_getitem__(...) from builtins.type\n |      See PEP 585\n |  \n |  ----------------------------------------------------------------------\n |  Static methods defined here:\n |  \n |  __new__(*args, **kwargs) from builtins.type\n |      Create and return a new object.  See help(type) for accurate signature.\n |  \n |  ----------------------------------------------------------------------\n |  Data and other attributes defined here:\n |  \n |  __hash__ = None\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_1398026865","id":"20211001-190306_1063775532","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:45+0000","dateFinished":"2021-09-22T20:18:45+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:381"},{"text":"%md\n\nFor example,\n\n- To view the help files for the `oml.create` function, type the below code.","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:45+0000","config":{"tableHide":false,"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<hr />\n<p>For example,</p>\n<ul>\n<li>To view the help files for the <code>oml.create</code> function, type the below code.</li>\n</ul>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_-1567926762","id":"20211001-190306_437824049","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:46+0000","dateFinished":"2021-09-22T20:18:46+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:382"},{"text":"%python\n\nhelp(oml.create)","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:46+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on cython_function_or_method in module oml.core.methods:\n\ncreate(x, table, oranumber=True, dbtypes=None, append=False)\n    Creates a table in Oracle Database from a Python data set.\n    \n    Parameters\n    ----------\n    x : pandas.DataFrame or a list of tuples of equal size\n      If ``x`` is a list of tuples of equal size, each tuple represents\n      a row in the table. The column names are set to COL1, COL2, ... and so on.\n    table : str\n      A name for the table.\n    oranumber : bool, True (default)\n      If True, use SQL NUMBER for numeric columns. Otherwise, use BINARY_DOUBLE.\n      Ignored if ``append`` is True.\n    dbtypes : dict mapping str to str or list of str\n      A list of SQL types to use on the new table. If a list, its length should\n      be equal to the number of columns. If a dict, the keys are the names of the\n      columns. Ignored if ``append`` is True.\n    append : bool, False (default)\n      Indicates whether to append the data to the existing table.\n    \n    Notes\n    -----\n     * When creating a new table, for columns whose SQL types are not specified in\n       ``dbtypes``, NUMBER is used for numeric columns when ``oranumber`` is True\n       and BINARY_DOUBLE is used when ``oranumber`` is False. Users should set\n       ``oranumber`` to False when the data contains NaN values. For string columns,\n       the default type is VARCHAR2(4000), and for bytes columns, the default type\n       is BLOB.\n     * When ``x`` is specified with an empty pandas.DataFrame, OML creates an\n       empty table. NUMBER is used for numeric columns when ``oranumber`` is True\n       and BINARY_DOUBLE is used when ``oranumber`` is False. VARCHAR2(4000) is\n       used for columns of object dtype in the pandas.DataFrame.\n     * OML does not support columns containing values of multiple data types,\n       data conversion is needed or a TypeError may be raised.\n     * OML determines default column types by looking at 20 random rows sampled\n       from the table. For tables with less than 20 rows, all rows are used\n       in column type determination. NaN values are considered as float type.\n       If a column has all Nones, or has inconsistent data types that are not\n       None in the sampled rows, a default column type cannot be determined,\n       and a ValueError is raised unless a SQL type for the column is specified\n       in ``dbtypes``.\n    \n    Returns\n    -------\n    new_table : oml.DataFrame\n      A proxy object that represents the newly-created table.\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_904721421","id":"20211001-190306_70160754","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:46+0000","dateFinished":"2021-09-22T20:18:46+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:383"},{"text":"%md\n\nTo view the help files for `oml` module, type the code below.","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:46+0000","config":{"tableHide":false,"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<hr />\n<p>To view the help files for <code>oml</code> module, type the code below.</p>\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_-1816292524","id":"20211001-190306_1536023944","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:47+0000","dateFinished":"2021-09-22T20:18:47+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:384"},{"text":"%python\n\nhelp(oml)","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:47+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on package oml:\n\nNAME\n    oml - Oracle Machine Learning for Python\n\nDESCRIPTION\n    A component of the Oracle Advanced Analytics Option, Oracle Machine Learning\n    for Python makes the open source Python programming language and environment\n    ready for enterprise in-database data. Designed for problems involving both\n    large and small volumes of data, Oracle Machine Learning for Python integrates\n    Python with Oracle Database. Python users can run Python commands and scripts\n    for statistical, machine learning, and graphical analyses on data stored in\n    Oracle Database. Python users can develop, refine, and deploy Python scripts\n    that leverage the parallelism and scalability of Oracle Database to automate\n    data analysis. Data analysts and data scientists can run Python modules and\n    develop and operationalize Python scripts for machine learning applications\n    in one step without having to learn SQL. Oracle Machine Learning for Python\n    performs function pushdown for in-database execution of core Python and\n    popular Python module functions. Being integrated with Oracle Database,\n    Oracle Machine Learning for Python can run any Python module via embedded\n    Python while the database manages the data served to the Python engines.\n\nPACKAGE CONTENTS\n    algo (package)\n    automl (package)\n    core (package)\n    ds (package)\n    embed (package)\n    graphics (package)\n    mlx (package)\n    script (package)\n\nCLASSES\n    oml.algo.model.odmModel(builtins.object)\n        oml.algo.ai.ai\n        oml.algo.ar.ar\n        oml.algo.dt.dt\n        oml.algo.em.em\n        oml.algo.esa.esa\n        oml.algo.glm.glm\n        oml.algo.km.km\n        oml.algo.nb.nb\n        oml.algo.nn.nn\n        oml.algo.rf.rf\n        oml.algo.svd.svd\n        oml.algo.svm.svm\n    oml.core.number._Number(oml.core.series._Series)\n        oml.core.float.Float\n    oml.core.series._Series(oml.core.vector._Vector)\n        oml.core.boolean.Boolean\n        oml.core.bytes.Bytes\n        oml.core.string.String\n    oml.core.vector._Vector(builtins.object)\n        oml.core.frame.DataFrame\n    \n    class Boolean(oml.core.series._Series)\n     |  Boolean series data class.\n     |  \n     |  Represents a single column of 0, 1, and NULL values in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      Boolean\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __and__(self, other)\n     |  \n     |  __init__(self)\n     |  \n     |  __invert__(self)\n     |  \n     |  __or__(self, other)\n     |  \n     |  all(self)\n     |      Checks whether all elements in the Boolean series data object are True.\n     |      \n     |      Returns\n     |      =======\n     |      all: bool\n     |  \n     |  any(self)\n     |      Checks whether any elements in the Boolean series data object are True.\n     |      \n     |      Returns\n     |      -------\n     |      any: bool\n     |  \n     |  pull(self)\n     |      Pulls data represented by this object from Oracle Database into an\n     |      in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list of bool and None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  describe(self)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of an OML series data distribution.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |        Includes ``count`` (number of non-null entries), ``unique``\n     |        (number of unique entries), ``top`` (most common value), ``freq``,\n     |        (frequency of the most common value).\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class Bytes(oml.core.series._Series)\n     |  Bytes(other, dbtype)\n     |  \n     |  Binary series data class.\n     |  \n     |  Represents a single column of RAW or BLOB data in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      Bytes\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, other, dbtype)\n     |      Convert underlying Oracle Database type.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Bytes\n     |      dbtype : 'raw' or 'blob'\n     |  \n     |  len(self)\n     |      Computes the length of each byte string.\n     |      \n     |      Returns\n     |      -------\n     |      length : oml.Float\n     |  \n     |  pull(self)\n     |      Pulls data represented by this object from Oracle Database into an\n     |      in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list of bytes and None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  describe(self)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of an OML series data distribution.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |        Includes ``count`` (number of non-null entries), ``unique``\n     |        (number of unique entries), ``top`` (most common value), ``freq``,\n     |        (frequency of the most common value).\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class DataFrame(oml.core.vector._Vector)\n     |  DataFrame(other)\n     |  \n     |  Tabular dataframe class.\n     |  \n     |  Represents multiple columns of Boolean, Bytes, Float, and/or String data.\n     |  \n     |  Method resolution order:\n     |      DataFrame\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, strata_cols=None, use_hash=True, hash_cols=None, nvl=None)\n     |      Splits the oml.DataFrame object randomly into k consecutive folds.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      strata_cols : a list of string values or None (default)\n     |        Names of the columns used for stratification. If None, stratification\n     |        is not performed. Must be None when ``use_hash`` is False.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      hash_cols : a list of string values or None (default)\n     |        If a list of string values, use the values from these named columns\n     |        to hash to split the data. If None, use the values from the 1st 10\n     |        columns to hash.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of k 2-tuples of oml.DataFrame objects\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``hash_cols`` refers to a single LOB column.\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean, str, list of str, 2-tuple\n     |        * oml.Boolean : select only the rows satisfying the condition. Must be from the same data\n     |          source as self.\n     |        * str : select the column of the same name\n     |        * list of str : select the columns whose names matches the elements in the list.\n     |        * 2-tuple : The first element in the tuple denotes which rows to select.\n     |          It can be either a oml.Boolean or ``slice(None)`` (this selects all\n     |          rows). The second element in the tuple denotes which columns to select.\n     |          It can be either ``slice(None)`` (this selects all columns), str, list\n     |          of str, int, or list of int. If int or list of int, selects the\n     |          column(s) in the corresponding position(s).\n     |      \n     |      Returns\n     |      -------\n     |      subset : OML data object\n     |        Is a oml.DataFrame if has more than one column, otherwise is a OML series data object.\n     |  \n     |  __init__(self, other)\n     |      Convert OML series data object(s) to oml.DataFrame.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object or dict mapping str to OML series data objects\n     |        * OML series data object : initializes a single-column oml.DataFrame containing the\n     |          same data. \n     |        * dict : initializes a oml.DataFrame that comprises all the OML series data objects\n     |          in the dict in an arbitrary order. Each column in the resulting oml.DataFrame has as\n     |          its column name its corresponding key in the dict.\n     |  \n     |  corr(self, method='pearson', min_periods=1, skipna=True)\n     |      Computes pairwise correlation between all columns where possible,\n     |      given the type of coefficient.\n     |      \n     |      Parameters\n     |      ----------\n     |      method : 'pearson' (default), 'kendall', or 'spearman'\n     |        * pearson : Uses Pearson's correlation coefficient. Can only calculate\n     |          correlations between Float or Boolean columns.\n     |        * kendall : Uses Kendall's tau-b coefficient.\n     |        * spearman : Uses Spearman's rho coefficient.\n     |      min_periods : int, optional, 1 (default)\n     |        The minimum number of observations required per pair of columns to \n     |        have a valid result.\n     |      skipna : bool, True (default)\n     |        If True, NaN and (+/-)Inf values are mapped to NULL.\n     |      \n     |      Returns\n     |      -------\n     |      y : :py:class:`pandas.DataFrame`\n     |  \n     |  count(self, numeric_only=False)\n     |      Returns the number of elements that are not NULL for each column.\n     |      \n     |      Parameters\n     |      ----------\n     |      numeric_only : boolean, False (default)\n     |        Includes only Float and Boolean columns.\n     |      \n     |      Returns\n     |      -------\n     |      count : :py:class:`pandas.Series`\n     |  \n     |  crosstab(self, index, columns=None, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False, pivot=False)\n     |      Computes a simple cross-tabulation of two or more columns. By default,\n     |      computes a frequency table for the columns unless a column and\n     |      an aggregation function have been passed.\n     |      \n     |      Parameters\n     |      ----------\n     |      index : str or list of str\n     |        Names of the column(s) of the DataFrame to group by. If ``pivot`` is\n     |        True, these columns are displayed in the rows of the result table.\n     |      columns : str or list of str, optional\n     |        Names of the other column(s) of the Dataframe to group by. If ``pivot``\n     |        is True, these columns are displayed in the columns of the result\n     |        table.\n     |      values : str, optional\n     |        The name of the column to aggregate according to the grouped columns.\n     |        Requires ``aggfunc`` to be specified.\n     |      aggfunc : OML DataFrame aggregation function object, optional\n     |        The supported oml.DataFrame aggregation functions include: count, \n     |        max, mean, median, min, nunique, std and sum. To use ``aggfunc``, \n     |        specify the function object using its full name, for example, \n     |        ``oml.DataFrame.sum``, ``oml.DataFrame.nunique``, and so on.\n     |        If specified, requires ``values`` to also be specified.\n     |      rownames : str or list of str, None (default)\n     |        If specified, must match number of names in ``index``. If None, names in\n     |        ``index`` are used. \n     |      colnames : str or list of str, None (default)\n     |        If specified, must match number of strings in ``columns``. If None,\n     |        names in ``columns`` are used. Ignored if ``pivot`` is True.\n     |      margins : bool, False (default)\n     |        Includes row and column margins (subtotals)\n     |      margins_name : str, 'All' (default)\n     |        Names of the row and column that contain the totals when ``margins``\n     |        is True. Should be a value not contained in any of the columns specified\n     |        by ``index`` and ``columns``. \n     |      dropna : bool, True (default)\n     |        In addition, if ``pivot`` is True, drops columns from the result\n     |        table if all the entries of the column are NaN.\n     |      normalize : boolean, {'all', 'index', 'columns'} or {0, 1}, False (default)\n     |        Normalizes by dividing the values by their sum.\n     |      \n     |        * If 'all' or True, normalizes over all values.\n     |        * If 'index' or 0, normalizes over each row.\n     |        * If 'columns' or 1, normalizes over each column.\n     |        * If ``margins`` is True, also normalizes margin values.\n     |      pivot : bool, False (default)\n     |        If True, returns results in pivot table format. Else, returns results in\n     |        relational table format.\n     |      \n     |      Returns\n     |      -------\n     |      crosstab : oml.DataFrame\n     |      \n     |      See Also\n     |      --------\n     |      DataFrame.pivot_table\n     |  \n     |  cumsum(self, by, ascending=True, na_position='last', skipna=True)\n     |      Gets the cumulative sum of each ``Float`` or ``Boolean`` column after the\n     |      ``DataFrame`` object is sorted.\n     |      \n     |      Parameters\n     |      ----------\n     |      by : str or list of str\n     |        A single column name or list of column names by which to sort the \n     |        DataFrame object. Columns in ``by`` do not have to be ``Float`` or \n     |        ``Boolean``.\n     |      ascending : bool or list of bool, True (default)\n     |        If True, sort is in ascending order, otherwise descending. Specify \n     |        list for multiple sort orders. If this is a list of bools, must match\n     |        the length of ``by``.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NaN and None at the beginning, ``last`` places them \n     |        at the end.\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      cumsum : oml.DataFrame\n     |  \n     |  describe(self, percentiles=None, include=None, exclude=None)\n     |      Generates descriptive statistics that summarize the central tendency,\n     |      dispersion, and shape of the data in each column\n     |      \n     |      Parameters\n     |      ----------\n     |      percentiles : bool, list-like of numbers, or None (default), optional \n     |        The percentiles to include in the output for `Float` columns. All\n     |        must be between 0 and 1. If ``percentiles`` is None or True,\n     |        ``percentiles`` is set to ``[.25, .5, .75]``, which corresponds\n     |        to the 25th, 50th, and 75th percentiles. If `percentiles` is False,\n     |        only ``min`` and ``max`` stats and no other percentiles is\n     |        included.\n     |      include : 'all', list-like of OML column types or None (default), optional\n     |        Types of columns to include in the result. Available options:\n     |      \n     |        - 'all': Includes all columns.\n     |        - List of OML column types : Only includes specified types in\n     |          the results.\n     |        - None (default) : If ``Float`` columns exist and ``exclude`` is\n     |          None, only includes ``Float`` columns. Otherwise, includes all\n     |          columns.\n     |      exclude : list of OML column types or None (default), optional\n     |        Types of columns to exclude from the result. Available options:\n     |      \n     |        - List of OML column types : Excludes specified types from\n     |          the results.\n     |        - None (default) : Result excludes nothing.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.DataFrame`\n     |         The concatenation of the summary statistics for each column.\n     |      \n     |      See Also\n     |      --------\n     |      DataFrame.count\n     |      DataFrame.max\n     |      DataFrame.min\n     |      DataFrame.mean\n     |      DataFrame.std\n     |      DataFrame.select_types\n     |  \n     |  drop(self, columns)\n     |      Drops specified columns.\n     |      \n     |      Parameters\n     |      ----------\n     |      columns : str or list of str\n     |        Columns to drop from the object.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : oml.DataFrame\n     |  \n     |  drop_duplicates(self, subset=None)\n     |      Removes duplicated rows from oml.DataFrame object.\n     |      \n     |      Use ``subset`` to consider a set of rows duplicates if they have\n     |      identical values for only a subset of the columns. In this case, after\n     |      deduplication, each of the other columns contains the minimum value\n     |      found across the set.\n     |      \n     |      Parameters\n     |      ----------\n     |      subset : str or list of str, optional\n     |        Columns to consider for identifying duplicates. If None, use all\n     |        columns.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : oml.DataFrame\n     |  \n     |  dropna(self, how='any', thresh=None, subset=None)\n     |      Removes rows containing missing values.\n     |      \n     |      Parameters\n     |      ----------\n     |      how : {'any', 'all'}, 'any' (default)\n     |        Determines if row is removed from DataFrame when at least one or all\n     |        values are missing.\n     |      thresh : int, optional\n     |        Requires that many of missing values to drop a row from DataFrame.\n     |      subset : list, optional\n     |        The names of the columns to check for missing values.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : oml.DataFrame\n     |        DataFrame without missing values.\n     |  \n     |  kurtosis(self, skipna=True)\n     |      Returns the sample kurtosis of the values for each ``Float``\n     |      column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      kurt : :py:class:`pandas.Series`\n     |  \n     |  max(self, skipna=True, numeric_only=False)\n     |      Returns the maximum value in each column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      numeric_only : boolean, False (default)\n     |        Includes only ``Float`` and ``Boolean`` columns. \n     |      \n     |      Returns\n     |      -------\n     |      max : :py:class:`pandas.Series`\n     |  \n     |  mean(self, skipna=True)\n     |      Returns the mean of the values for each ``Float`` or ``Boolean`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      mean : :py:class:`pandas.Series`\n     |  \n     |  median(self, skipna=True)\n     |      Returns the median of the values for each ``Float`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Exclude NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      median : :py:class:`pandas.Series`\n     |  \n     |  merge(self, other, on=None, left_on=None, right_on=None, how='left', suffixes=('_l', '_r'), nvl=True)\n     |      Joins data sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data set object\n     |      on : str or list of str, optional\n     |        Column names to join on. Must be found in both ``self`` and ``other``.\n     |      left_on : str or list of str, optional\n     |        Column names of ``self`` to join on.\n     |      right_on : str or list of str, optional\n     |        Column names of ``other`` to join on. If specified, must specify the same\n     |        number of columns as ``left_on``.\n     |      how : 'left' (default), 'right', 'inner', 'full'\n     |        * left : left outer join\n     |        * right : right outer join\n     |        * full : full outer join\n     |        * inner : inner join\n     |      \n     |        If ``on`` and ``left_on`` are both None, then ``how`` is ignored,\n     |        and a cross join is performed.\n     |      suffixes : sequence of length 2\n     |        Suffix to apply to column names on the left and right side,\n     |        respectively.\n     |      nvl : True (default), False, dict \n     |        * True : join condition includes NULL value\n     |        * False : join condition excludes NULL value\n     |        * dict : specifies the values that join columns use in replacement of NULL value with column names as keys\n     |      \n     |      Returns\n     |      -------\n     |      merged : oml.DataFrame\n     |  \n     |  min(self, skipna=True, numeric_only=False)\n     |      Returns the minimum value in each column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      numeric_only : boolean, False (default)\n     |        Includes only ``Float`` and ``Boolean`` columns\n     |      \n     |      Returns\n     |      -------\n     |      min : :py:class:`pandas.Series`\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns number of unique values for each column of DataFrame.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : pandas.Series\n     |  \n     |  pivot_table(self, index, columns=None, values=None, aggfunc=<cyfunction DataFrame.mean at 0x7f81c5e39110>, margins=False, dropna=True, margins_name='All')\n     |      Converts data set to a spreadsheet-style pivot table. Due to the Oracle\n     |      1000 column limit, pivot tables with more than 1000 columns are\n     |      automatically truncated to display the categories with the most entries\n     |      for each value column.\n     |      \n     |      Parameters\n     |      ----------\n     |      index : str or list of str\n     |        Names of columns containing the keys to group by on the pivot table\n     |        index.\n     |      columns : str or list of str, optional\n     |        Names of columns containing the keys to group by on the pivot table\n     |        columns. \n     |      values : str or list of str, optional\n     |        Names of columns to aggregate on. If None, values are inferred \n     |        as all columns not in ``index`` or ``columns``.\n     |      aggfunc : OML DataFrame aggregation function or a list of them, oml.DataFrame.mean (default)\n     |        The supported oml.DataFrame aggregation functions include: count, max,\n     |        mean, median, min, nunique, std and sum. When using aggregation\n     |        functions, specify the function object using its full name, for example,\n     |        ``oml.DataFrame.sum``, ``oml.DataFrame.nunique``, and so on.\n     |        If ``aggfunc`` contains more than one function, each function is \n     |        applied to each column in ``values``. If the function does not apply to\n     |        the type of a column in ``values``, the result table skips applying \n     |        the function to the particular column. \n     |      margins : bool, False (default)\n     |        Include row and column margins (subtotals)\n     |      dropna : bool, True (default)\n     |        Unless ``columns`` is None, drop column labels from the result table if\n     |        all the entries corresponding to the column label are NaN for all\n     |        aggregations.\n     |      margins_name : string, 'All' (default)\n     |        Names of the row and column that contain the totals when ``margins``\n     |        is True. Should be a value not contained in any of the columns specified\n     |        by ``index`` and ``columns``. \n     |      \n     |      Returns\n     |      -------\n     |      pivoted : oml.DataFrame\n     |      \n     |      See Also\n     |      --------\n     |      DataFrame.crosstab\n     |  \n     |  pull(self, aslist=False)\n     |      Pulls data represented by the DataFrame from Oracle Database\n     |      into an in-memory Python object.\n     |      \n     |      Parameters\n     |      ----------\n     |      aslist : bool\n     |        If False, returns a pandas.DataFrame. Otherwise, returns the data\n     |        as a list of tuples.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : :py:class:`pandas.DataFrame` or list of tuples\n     |  \n     |  rename(self, columns)\n     |      Renames columns.\n     |      \n     |      Parameters\n     |      ----------\n     |      columns : dict or list\n     |        ``dict`` contains old and new column names.\n     |        ``list`` contains the new names for all the columns in order.\n     |      \n     |      Notes\n     |      -----\n     |      The method changes the column names of the caller DataFrame object too.\n     |      \n     |      Returns\n     |      -------\n     |      renamed : DataFrame\n     |  \n     |  replace(self, old, new, default=None, columns=None)\n     |      Replace values given in `old` with `new` in specified columns.\n     |      \n     |      Parameters\n     |      ----------\n     |      columns : list of str or None (default)\n     |        Columns to look for values in `old`. If None, then all columns\n     |        of DataFrame will be replaced.\n     |      old : list of float, or list of str\n     |        Specifying the old values. When specified with a list of float, it \n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      new : list of float, or list of str\n     |        A list of the same length as argument `old` specifying \n     |        the new values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      default : float, str, or None (default)\n     |        A single value to use for the non-matched elements in argument\n     |        `old`. If None, non-matched elements will preserve their\n     |        original values. If not None, data type should be consistent\n     |        with values in `new`. Must be set when `old` and `new` contain \n     |        values of different data types.\n     |      \n     |      Returns\n     |      -------\n     |      replaced : oml.DataFrame\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * if values in `old` have data types inconsistent with original values\n     |          in the target columns\n     |        * if `default` is specifed with a non-None value which has data type\n     |          inconsistent with values in `new`\n     |        * if `default` is None when `old` and `new` contain values of different \n     |          data types\n     |  \n     |  round(self, decimals=0)\n     |      Rounds oml.Float values in the oml.DataFrame object to \n     |      the specified decimal place.\n     |      \n     |      Parameters\n     |      ----------\n     |      decimals : non-negative int\n     |      \n     |      Returns\n     |      -------\n     |      rounded: oml.DataFrame\n     |  \n     |  sample(self, frac=None, n=None, random_state=None)\n     |      Return a random sample data sets from an oml.DataFrame object.\n     |      \n     |      Parameters\n     |      ----------\n     |      frac : a float value\n     |        Fraction of data sets to return. The value should be between 0 and 1.\n     |        Cannot be used with n.\n     |      n : an integer value\n     |        Number of rows to return. Default = 1 if frac = None.\n     |        Cannot be used with frac. \n     |      random_state : int or 12345 (default)\n     |        The seed to use for random sampling.\n     |      \n     |      Returns\n     |      -------\n     |      sample_data : an oml.DataFrame objects\n     |        It contains the random sample rows from an oml.DataFrame object.\n     |        The fraction of returned data sets is specified by the frac parameter.\n     |  \n     |  select_types(self, include=None, exclude=None)\n     |      Returns the subset of columns include/excluding columns based on their OML\n     |      type.\n     |      \n     |      Parameters\n     |      ----------\n     |      include, exclude : list of OML column types\n     |        A selection of OML column types to be included/excluded. At least one of\n     |        these parameters must be supplied. \n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If both of ``include`` and ``exclude`` are None.\n     |        * If ``include`` and ``exclude`` have overlapping elements.\n     |      \n     |      Returns\n     |      -------\n     |      subset : oml.DataFrame\n     |  \n     |  skew(self, skipna=True)\n     |      Returns the sample skewness of the values for each ``Float``\n     |      column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      skew : :py:class:`pandas.Series`\n     |  \n     |  sort_values(self, by, ascending=True, na_position='last')\n     |      Specifies the order in which rows appear in the result set.\n     |      \n     |      Parameters\n     |      ----------\n     |      by : str or list of str\n     |        Column names or list of column names.\n     |      ascending : bool or list of bool, True (default)\n     |        If True, sort is in ascending order. Sort is in descending order\n     |        otherwise. Specify list for multiple sort orders. If this is a list of\n     |        bools, must match the length of ``by``.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them \n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : oml.DataFrame\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, strata_cols=None, use_hash=True, hash_cols=None, nvl=None)\n     |      Splits the oml.DataFrame object randomly into multiple data sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) default\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      strata_cols: a list of string values or None (default):\n     |        Names of the columns used for stratification. If None, stratification\n     |        is not performed. Must be None when use_hash is False.\n     |      use_hash: boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use random\n     |        number to split the data.\n     |      hash_cols: a list of string values or None (default):\n     |        If a list of string values, use the values from these named columns\n     |        to hash to split the data. If None, use the values from the 1st 10\n     |        columns to hash.\n     |      nvl: numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of oml.DataFrame objects\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``hash_cols`` refers to a single LOB column.\n     |  \n     |  std(self, skipna=True)\n     |      Returns the sample standard deviation of the values of each ``Float`` or\n     |      ``Boolean`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      std : :py:class:`pandas.Series`\n     |  \n     |  sum(self, skipna=True)\n     |      Returns the sum of the values of each ``Float`` or ``Boolean`` column.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Exclude NaN values when computing the result\n     |      \n     |      Returns\n     |      -------\n     |      sum : :py:class:`pandas.Series`\n     |  \n     |  t_dot(self, other=None, skipna=True, pull_from_db=True)\n     |      Calculates the matrix cross-product of self with other.\n     |      \n     |      Equivalent to transposing self first, then multiplying it with other. \n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.DataFrame, optional\n     |        If not specified, self is used.\n     |      skipna : bool, True (default)\n     |        Treats NaN entries as 0.\n     |      pull_from_db : bool, True (default)\n     |        If True, returns a pandas.DataFrame. If False, returns a\n     |        oml.DataFrame consisting of three columns:\n     |      \n     |        - ROWID: the row number of the resulting matrix \n     |        - COLID: the column number of the resulting matrix \n     |        - VALUE: the value at the corresponding position of the matrix \n     |      \n     |      Returns\n     |      -------\n     |      prod : float, :py:class:`pandas.Series`, or :py:class:`pandas.DataFrame`\n     |      \n     |      See Also\n     |      --------\n     |      oml.Float.dot\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties defined here:\n     |  \n     |  columns\n     |      The column names of the data set.\n     |  \n     |  dtypes\n     |      The types of the columns of the data set.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class Float(oml.core.number._Number)\n     |  Float(other, dbtype=None)\n     |  \n     |  Numeric series data class.\n     |  \n     |  Represents a single column of NUMBER, BINARY_DOUBLE or BINARY_FLOAT data \n     |  in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      Float\n     |      oml.core.number._Number\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __abs__(self)\n     |      Return the absolute value of every element in ``self``.\n     |      \n     |      Equivalent to ``abs(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      absval : oml.Float\n     |  \n     |  __add__(self, other)\n     |      Equivalent to ``self + other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : add the scalar to each element in ``self``. \n     |        * oml.Float : must come from the same data source. Add corresponding\n     |          elements in ``self`` and ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      sum : oml.Float\n     |  \n     |  __contains__(self, item)\n     |      Check whether all elements in ``item`` exists in the Float series\n     |      \n     |      Equivalent to ``item in self``.\n     |      \n     |      Parameters\n     |      ----------\n     |      item : int/float, list of int/float, oml.Float\n     |        Values to check in series\n     |      \n     |      Returns\n     |      -------\n     |      contains : bool\n     |        Returns `True` if all elements exists, otherwise `False`\n     |  \n     |  __divmod__(self, other)\n     |      Equivalent to ``divmod(self, other)``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : Find the quotient and remainder when each element in ``self`` is\n     |          divided by the scalar.\n     |        * oml.Float : must come from the same data source. Find the quotient and\n     |          remainder when each element in ``self`` is divided by the corresponding element\n     |          in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      divrem : oml.DataFrame\n     |        The first column contains the floor of the quotient, and the second column\n     |        contains the remainder.\n     |  \n     |  __floordiv__(self, other)\n     |      Equivalent to ``self // other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : divide each element in ``self`` by the scalar. \n     |        * oml.Float : must come from the same data source. Divide each element in\n     |          ``self`` by the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      quotient : oml.Float\n     |  \n     |  __init__(self, other, dbtype=None)\n     |      Convert to oml.Float, or convert underlying Oracle Database type.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Boolean or oml.Float\n     |        * oml.Boolean : initialize a oml.Float object that has value 1 (resp. 0)\n     |          wherever ``other`` has value True (resp. False).\n     |        * oml.Float : initialize a oml.Float object with the same data as \n     |          ``other``, except the underlying Oracle Database type has been converted\n     |          to the one specified by ``dbtype``. \n     |      dbtype : 'number' or 'binary_double'\n     |        Ignored if ``other`` is type ``oml.Boolean``. Must be specified if ``other``\n     |        is type ``oml.Float``.\n     |  \n     |  __matmul__(self, other)\n     |      Equivalent to ``self @ other`` and ``self.dot(other)``.\n     |      \n     |      Returns the inner product with an oml.Float. Matrix multiplication with a\n     |      oml.DataFrame.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Float or oml.DataFrame\n     |      \n     |      Returns\n     |      -------\n     |      matprod : oml.Float\n     |      \n     |      See Also\n     |      --------\n     |      Float.dot\n     |  \n     |  __mod__(self, other)\n     |      Equivalent to ``self % other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : Find the remainder when each element in ``self`` is divided by the\n     |          scalar.\n     |        * oml.Float : must come from the same data source. Find the remainder when each\n     |          element in ``self`` is divided by the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      remainder : oml.Float\n     |  \n     |  __mul__(self, other)\n     |      Equivalent to ``self * other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : multiply the scalar with each element in ``self``. \n     |        * oml.Float : must come from the same data source. Multiply corresponding\n     |          elements in ``self`` and ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      product : oml.Float\n     |  \n     |  __neg__(self)\n     |      Return the negation of every element in ``self``. Equivalent to ``-self``.\n     |      \n     |      Returns\n     |      -------\n     |      negation : oml.Float\n     |  \n     |  __pow__(self, other)\n     |      Equivalent to ``self ** other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : Raise each element in ``self`` to the power of the scalar. \n     |        * oml.Float : must come from the same data source. Raise each element in \n     |          ``self`` to the power of the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      power : oml.Float\n     |  \n     |  __sub__(self, other)\n     |      Equivalent to ``self - other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : subtract the scalar from each element in ``self``. \n     |        * oml.Float : must come from the same data source. From each element in\n     |          ``self``, subtract the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      difference : oml.Float\n     |  \n     |  __truediv__(self, other)\n     |      Equivalent to ``self / other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : int, float, or oml.Float\n     |        * scalar : divide each element in ``self`` by the scalar. \n     |        * oml.Float : must come from the same data source. Divide each element in\n     |          ``self`` by the corresponding element in ``other``.\n     |      \n     |      Returns\n     |      -------\n     |      quotient : oml.Float\n     |  \n     |  ceil(self)\n     |      Returns the ceiling of each element in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      ceil : oml.Float\n     |  \n     |  cut(self, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)\n     |      Returns the indices of half-open bins to which each value belongs.\n     |      \n     |      Parameters\n     |      ----------\n     |      bins : int or strictly monotonically increasing sequence of float/int\n     |        If int, defines number of equal-width bins in the range of this column.\n     |        In this case, to include the min and max value, the range is extended by\n     |        .1% on each side where the bin does not include the endpoint.\n     |        If a sequence, defines bin edges allowing for non-uniform bin-widths. In \n     |        this case, the range of x is not extended.\n     |      right : bool, True (default)\n     |        Indicates whether the bins include the rightmost edge or the leftmost\n     |        edge.\n     |      labels : sequence of unique str, int, or float values, False, or None (default)\n     |        If a sequence, must be the same length as the resulting number of bins\n     |        and must have values of same type. If False, bins are sequentially\n     |        labeled with integers. If None, bins are labeled with the intervals\n     |        they correspond to.\n     |      retbins : bool, False (default)\n     |        Indicates whether to return the bin edges or not.  \n     |      precision : int, 3 (default)\n     |        When ``labels`` is None, determines the precision of the bin labels.\n     |      include_lowest : bool, False (default) \n     |        Indicates whether the first interval should be left-inclusive.\n     |      \n     |      Returns\n     |      -------\n     |      out : oml.Float or oml.String\n     |        If labels are ints or floats, return oml.Float.\n     |        If labels are str, return oml.String.\n     |      bins : :py:class:`numpy.ndarray` of floats\n     |        Returned only if ``retbins`` is True.\n     |  \n     |  describe(self, percentiles=None)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of the OML series data distribution.\n     |      \n     |      Parameters\n     |      ----------\n     |      percentiles : list-like of numbers, optional \n     |        The percentiles to include in the output. All must be between 0 and 1.\n     |        The default is [.25, .5, .75], which corresponds to the inclusion of \n     |        the 25th, 50th, and 75th percentiles.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |         Includes ``count`` (number of non-null entries), ``mean``, ``std``,\n     |         ``min``, ``max``, and the specified ``percentiles``. The 50th\n     |         percentile is always included.\n     |  \n     |  dot(self, other=None, skipna=True)\n     |      Returns the inner product with an oml.Float. Matrix multiplication with a\n     |      oml.DataFrame.\n     |      \n     |      Can be called using self @ other.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.Float or oml.DataFrame, optional\n     |        If not specified, self is used.\n     |      skipna : bool, True (default)\n     |        Treats NaN entries as 0.\n     |      \n     |      Returns\n     |      -------\n     |      dot_product : :py:class:`pandas.Series` or float\n     |  \n     |  exp(self)\n     |      Returns element-wise e to the power of values in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      exp : oml.Float\n     |  \n     |  floor(self)\n     |      Returns the floor of each element in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      floor : oml.Float\n     |  \n     |  isinf(self)\n     |      Detects infinite values element-wise in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      isinf : oml.Boolean\n     |  \n     |  isnan(self)\n     |      Detects a NaN (not a number) element from Float object.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates NaN for each element.\n     |  \n     |  log(self, base=None)\n     |      Returns element-wise logarithm, to the given ``base``, of values\n     |      in the Float series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      base : int, float, optional\n     |        The base of the logarithm, by default natural logarithm\n     |      \n     |      Returns\n     |      -------\n     |      log : oml.Float\n     |  \n     |  replace(self, old, new, default=None)\n     |      Replace values given in `old` with `new`.\n     |      \n     |      Parameters\n     |      ----------\n     |      old : list of float, or list of str\n     |        Specifying the old values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      new : list of float, or list of str\n     |        A list of the same length as argument `old` specifying\n     |        the new values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      default : float, str, or None (default)\n     |        A single value to use for the non-matched elements in argument\n     |        `old`. If None, non-matched elements will preserve their\n     |        original values. If not None, data type should be consistent\n     |        with values in `new`. Must be set when `old` and `new` contain\n     |        values of different data types.\n     |      \n     |      Returns\n     |      -------\n     |      replaced : oml.Float\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * if values in `old` have data types inconsistent with original values\n     |        * if `default` is specifed with a non-None value which has data type \n     |          inconsistent with values in `new`\n     |        * if `default` is None when `old` and `new` contain values of different\n     |          data types\n     |  \n     |  round(self, decimals=0)\n     |      Rounds oml.Float values to the specified decimal place.\n     |      \n     |      Parameters\n     |      ----------\n     |      decimals : non-negative int\n     |      \n     |      Returns\n     |      -------\n     |      rounded : oml.Float\n     |  \n     |  sqrt(self)\n     |      Returns the square root of each element in the Float series data object.\n     |      \n     |      Returns\n     |      -------\n     |      sqrt : oml.Float\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.number._Number:\n     |  \n     |  cumsum(self, ascending=True, na_position='last', skipna=True)\n     |      Gets the cumulative sum after the OML series data object is sorted.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        Sorts ascending, otherwise descending.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NaN and None at the beginning, ``last`` places them \n     |        at the end.\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      cumsum : oml.Float\n     |  \n     |  kurtosis(self, skipna=True)\n     |      Returns the sample kurtosis of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      kurt : float or nan\n     |  \n     |  mean(self, skipna=True)\n     |      Returns the mean of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      mean : float or numpy.nan\n     |  \n     |  median(self, skipna=True)\n     |      Returns the median of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      median : float or numpy.nan\n     |  \n     |  skew(self, skipna=True)\n     |      Returns the sample skewness of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      skew : float or nan\n     |  \n     |  std(self, skipna=True)\n     |      Returns the sample standard deviation of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      std : float or numpy.nan\n     |  \n     |  sum(self, skipna=True)\n     |      Returns the sum of the values.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      sum : float or numpy.nan\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  pull(self)\n     |      Pulls data represented by the series data object from Oracle Database\n     |      into an in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class String(oml.core.series._Series)\n     |  String(other, dbtype)\n     |  \n     |  Character series data class.\n     |  \n     |  Represents a single column of VARCHAR2, CHAR, or CLOB data in Oracle Database.\n     |  \n     |  Method resolution order:\n     |      String\n     |      oml.core.series._Series\n     |      oml.core.vector._Vector\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __contains__(self, item)\n     |      Check whether all elements in ``item`` exists in the String series\n     |      \n     |      Equivalent to ``item in self``.\n     |      \n     |      Parameters\n     |      ----------\n     |      item : str, list of str, oml.String\n     |        Values to check in series\n     |      \n     |      Returns\n     |      -------\n     |      contains : bool\n     |        Returns ``True`` if all elements exist, otherwise ``False``.\n     |  \n     |  __init__(self, other, dbtype)\n     |      Convert underlying Oracle Database type.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : oml.String\n     |      dbtype : 'varchar2' or 'clob'\n     |  \n     |  count_pattern(self, pat, flags=0)\n     |      Counts the number of occurrences of the pattern in each string. \n     |      \n     |      Parameters\n     |      ----------\n     |      pat : str that is a valid regular expression conforming to the POSIX standard\n     |      flags : int, 0 (default, no flags)\n     |        The following :py:mod:`python:re` module flags are supported:\n     |      \n     |        - :py:data:`python:re.I`/:py:data:`python:re.IGNORECASE` : Performs case-insensitive matching.\n     |        - :py:data:`python:re.M`/:py:data:`python:re.MULTILINE` : Treats the source string as multiple lines.\n     |          Interprets the caret (^) and dollar sign ($) as the start and end,\n     |          respectively, of any line anywhere in source string. Without this flag,\n     |          the caret and dollar sign match only the start and end, respectively, of\n     |          the source string.\n     |        - :py:data:`python:re.S`/:py:data:`python:re.DOTALL` : Allows the period (.) to match all characters,\n     |          including the newline character. Without this flag, the period matches all\n     |          characters except the newline character.\n     |      \n     |        Multiple flags can be specifed by bitwise OR-ing them.\n     |      \n     |      Returns\n     |      -------\n     |      counts : oml.Float\n     |  \n     |  find(self, sub, start=0)\n     |      Returns the lowest index in each string where substring is found that is\n     |      greater than or equal to ``start``. Returns -1 on failure.\n     |      \n     |      Parameters\n     |      ----------\n     |      sub : str\n     |        The text expression to search.\n     |      start : int\n     |        A nonnegative integer indicating when the function begins the search.   \n     |      \n     |      Returns\n     |      -------\n     |      found : oml.Float\n     |  \n     |  len(self)\n     |      Computes the length of each string.\n     |      \n     |      Returns\n     |      -------\n     |      length : oml.Float\n     |  \n     |  pull(self)\n     |      Pulls data represented by this object from Oracle Database into an\n     |      in-memory Python object.\n     |      \n     |      Returns\n     |      -------\n     |      pulled_obj : list of str and None\n     |  \n     |  replace(self, old, new, default=None)\n     |      Replace values given in `old` with `new`.\n     |      \n     |      Parameters\n     |      ----------\n     |      old : list of float, or list of str\n     |        Specifying the old values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      new : list of float, or list of str\n     |        A list of the same length as argument `old` specifying\n     |        the new values. When specified with a list of float, it\n     |        can contain float('nan') and None. When specified with a list of str, it\n     |        can contain None.\n     |      default : float, str, or None (default)\n     |        A single value to use for the non-matched elements in argument\n     |        `old`. If None, non-matched elements will preserve their\n     |        original values. If not None, data type should be consistent\n     |        with values in `new`. Must be set when `old` and `new` contain \n     |        values of different data types.\n     |      \n     |      Returns\n     |      -------\n     |      replaced : oml.String\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * if values in `old` have data types inconsistent with original values\n     |        * if `default` is specifed with a non-None value which has data type\n     |          inconsistent with values in `new`\n     |        * if `default` is None when `old` and `new` contain values of different\n     |          data types\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.series._Series:\n     |  \n     |  KFold(self, n_splits=3, seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into k consecutive folds \n     |      for use with k-fold cross validation.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_splits : int, 3 (default)\n     |        The number of folds. Must be greater than or equal to 2.\n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default):\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      kfold_data : a list of pairs of series objects of the same type as caller\n     |        Each pair within the list is a fold. The first element of the pair is the\n     |        train set, and the second element is the test set, which consists of all\n     |        elements not in the train set.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  __eq__(self, other)\n     |      Equivalent to ``self == other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      equal : oml.Boolean\n     |  \n     |  __ge__(self, other)\n     |      Equivalent to ``self >= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterequal : oml.Boolean\n     |  \n     |  __gt__(self, other)\n     |      Equivalent to ``self > other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      greaterthan : oml.Boolean\n     |  \n     |  __le__(self, other)\n     |      Equivalent to ``self <= other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessequal : oml.Boolean\n     |  \n     |  __lt__(self, other)\n     |      Equivalent to ``self < other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      lessthan : oml.Boolean\n     |  \n     |  __ne__(self, other)\n     |      Equivalent to ``self != other``.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : OML series data object of compatible type or corresponding built-in python scalar\n     |        * scalar : every element in ``self`` will be compared to the scalar.\n     |        * a OML series : must come from the same data source. Every element in ``self`` will be\n     |          compared to the corresponding element in ``other``. oml.Float and oml.Boolean series\n     |          can be compared to each other. oml.String and oml.Bytes series can be compared to each\n     |          other.\n     |      \n     |      Returns\n     |      -------\n     |      notequal : oml.Boolean\n     |  \n     |  count(self)\n     |      Returns the number of elements that are not NULL.\n     |      \n     |      Returns\n     |      -------\n     |      nobs : int\n     |  \n     |  describe(self)\n     |      Generates descriptive statistics that summarize the central tendency, \n     |      dispersion, and shape of an OML series data distribution.\n     |      \n     |      Returns\n     |      -------\n     |      summary : :py:class:`pandas.Series`\n     |        Includes ``count`` (number of non-null entries), ``unique``\n     |        (number of unique entries), ``top`` (most common value), ``freq``,\n     |        (frequency of the most common value).\n     |  \n     |  drop_duplicates(self)\n     |      Removes duplicated elements.\n     |      \n     |      Returns\n     |      -------\n     |      deduplicated : type of caller\n     |  \n     |  dropna(self)\n     |      Removes missing values.\n     |      \n     |      Missing values include None and/or nan if applicable.\n     |      \n     |      Returns\n     |      -------\n     |      dropped : type of caller\n     |  \n     |  isnull(self)\n     |      Detects the missing value None.\n     |      \n     |      Returns\n     |      -------\n     |      isnull : oml.Boolean\n     |        Indicates missing value None for each element.\n     |  \n     |  max(self, skipna=True)\n     |      Returns the maximum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      max : Python type corresponding to the column or numpy.nan\n     |  \n     |  min(self, skipna=True)\n     |      Returns the minimum value.\n     |      \n     |      Parameters\n     |      ----------\n     |      skipna : boolean, True (default)\n     |        Excludes NaN values when computing the result.\n     |      \n     |      Returns\n     |      -------\n     |      min : Python type corresponding to the column or numpy.nan\n     |  \n     |  nunique(self, dropna=True)\n     |      Returns the number of unique values.\n     |      \n     |      Parameters\n     |      ----------\n     |      dropna : bool, True (default)\n     |        If True, NULL values are not included in the count. \n     |      \n     |      Returns\n     |      -------\n     |      nunique : int\n     |  \n     |  sort_values(self, ascending=True, na_position='last')\n     |      Sorts the values in the series data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      ascending : bool, True (default)\n     |        If True, sorts in ascending order. Otherwise, sorts in descending order.\n     |      na_position : {'first', 'last'}, 'last' (default)\n     |        ``first`` places NANs and Nones at the beginning; ``last`` places them\n     |        at the end.\n     |      \n     |      Returns\n     |      -------\n     |      sorted_obj : type of caller\n     |  \n     |  split(self, ratio=(0.7, 0.3), seed=12345, use_hash=True, nvl=None)\n     |      Splits the series data object randomly into multiple sets.\n     |      \n     |      Parameters\n     |      ----------\n     |      ratio : a list of float values or (0.7, 0.3) (default)\n     |        All the numbers must be positive and the sum of them are no more than \n     |        1. Each number represents the ratio of split data in one set. \n     |      seed : int or 12345 (default)\n     |        The seed to use for random splitting.\n     |      use_hash : boolean, True (default)\n     |        If True, use hashing to randomly split the data. If False, use a random\n     |        number to split the data.\n     |      nvl : numeric value, str, or None (default)\n     |        If not None, the specified values are used to hash in place of Null.\n     |      \n     |      Returns\n     |      -------\n     |      split_data : a list of series objects of the same type as caller\n     |        Each of which contains the portion of data by the specified ratio.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |        * If ``use_hash`` is True, and the underlying database column type of\n     |          ``self`` is a LOB.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data and other attributes inherited from oml.core.series._Series:\n     |  \n     |  __hash__ = None\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.core.vector._Vector:\n     |  \n     |  __del__(self)\n     |  \n     |  __getitem__(self, key)\n     |      Index self. Equivalent to ``self[key]``.\n     |      \n     |      Parameters\n     |      ----------\n     |      key : oml.Boolean\n     |        Must be from the same data source as self.\n     |      \n     |      Returns\n     |      -------\n     |      subset : same type as self\n     |        Contains only the rows satisfying the condition in ``key``.\n     |  \n     |  __len__(self)\n     |      Returns number of rows. Equivalent to ``len(self)``.\n     |      \n     |      Returns\n     |      -------\n     |      rownum : int\n     |  \n     |  __repr__(self)\n     |  \n     |  append(self, other, all=True)\n     |      Appends the ``other`` OML data object of the same class to this data object.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : An OML data object of the same class\n     |      all : boolean, True (default)\n     |        Keeps the duplicated elements from the two data objects.\n     |      \n     |      Returns\n     |      -------\n     |      appended : type of caller\n     |        A new data object containing the elements from both objects.\n     |  \n     |  concat(self, other, auto_name=False)\n     |      Combines current OML data object with the ``other`` data objects column-wise.\n     |      \n     |      Current object and the ``other`` data objects must be combinable, that is,\n     |      they both represent data from the same underlying database table, view, or query.\n     |      \n     |      Parameters\n     |      ----------\n     |      other : an OML data object, a list of OML data objects, or a dict mapping str to OML data objects.\n     |        * OML data object: an OML series data object or an oml.DataFrame\n     |        * list: a sequence of OML series and DataFrame objects to concat.\n     |        * dict: a dict mapping str to OML series and DataFrame objects,\n     |          the column name of concatenated OML series object is replaced with str,\n     |          column names of concatenated OML DataFrame object is prefixed with str.\n     |          Need to specify a :py:obj:`python:collections.OrderedDict` if the \n     |          concatenation order is expected to follow the key insertion order.\n     |      auto_name : boolean, False (default)\n     |        Indicates whether to automatically resolve conflict column names.\n     |        If True, append duplicated column names with suffix ``[column_index]``.\n     |      \n     |      Notes\n     |      -----\n     |      After concatenation is done, if there is any empty column names in the resulting \n     |      oml.DataFrame, they will be renamed with ``COL[column_index]``.\n     |      \n     |      Raises\n     |      ------\n     |      ValueError\n     |        * If ``other`` is not a single nor a list/dict of OML objects, or if ``other`` is empty.\n     |        * If objects in ``other`` are not from same data source.\n     |        * If ``auto_name`` is False and duplicated column names are detected.\n     |      \n     |      Returns\n     |      -------\n     |      concat_table : oml.DataFrame\n     |        An oml.DataFrame data object with its original columns followed by the \n     |        columns of ``other``.\n     |  \n     |  create_view(self, view=None, use_colname=False)\n     |      Creates an Oracle Database view for the data represented by the OML data \n     |      object.\n     |      \n     |      Parameters\n     |      ----------\n     |      view : str or None (default)\n     |        The name of a database view. If ``view`` is None, the created view is managed\n     |        by OML and the view is automatically dropped when no longer needed.\n     |        If a ``view`` is specified, then it is up to the user to drop the view.\n     |      use_colname : bool, False (default)\n     |        Indicates whether to create the view with the same column names as the\n     |        DataFrame object. Ignored if ``view`` is specified.\n     |      \n     |      Raises\n     |      ------\n     |      TypeError\n     |       * If the object represents data from a temporary table or view, and the view\n     |         to create is meant to persist past the current session.\n     |      \n     |      Returns\n     |      -------\n     |      new_view : oml.DataFrame\n     |  \n     |  head(self, n=5)\n     |      Returns the first n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_head : type of caller\n     |  \n     |  materialize(self, table=None)\n     |      Pushes the contents represented by an OML proxy object (a view, a table and so on) \n     |      into a table in Oracle Database.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : str or None (default)\n     |        The name of a table. If ``table`` is None, an OML-managed table is \n     |        created, that is, OML drops the table when it is no longer used by \n     |        any OML object or when you invoke ``oml.disconnect(cleanup=True)`` to \n     |        terminate the database connection. If a table is specified, \n     |        then it's up to the user to drop the named table.\n     |      \n     |      Returns\n     |      -------\n     |      new_table : same type as self\n     |  \n     |  tail(self, n=5)\n     |      Returns the last n elements.\n     |      \n     |      Parameters\n     |      ----------\n     |      n : int, 5 (default)\n     |        The number of elements to return.\n     |      \n     |      Returns\n     |      -------\n     |      obj_tail : type of caller\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.core.vector._Vector:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  shape\n     |      The dimensions of the data set.\n     |      The first element is the number of rows and the second is the number of\n     |      columns.\n    \n    class ai(oml.algo.model.odmModel)\n     |  ai(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Attribute Importance <https://www.oracle.com/machine-learning/attribute-importance>`_ Model\n     |  \n     |  Computes the relative importance of variables (aka attributes or columns) when predicting\n     |  a target variable (numeric or categorical column). This function exposes the \n     |  corresponding Oracle Machine Learning in-database algorithm. \n     |  Oracle Machine Learning does not support the prediction functions\n     |  for attribute importance. The results of attribute importance are the attributes\n     |  of the build data ranked according to their predictive influence. The ranking and\n     |  the measure of importance can be used for selecting attributes.\n     |  \n     |  :Attributes:\n     |  \n     |      **importance** : oml.DataFrame\n     |  \n     |          Relative importance of predictor variables for predicting a response variable.\n     |          It includes the following components:\n     |              \n     |              - variable: The name of the predictor variable\n     |              - importance: The importance of the predictor variable\n     |              - rank: The predictor variable rank based on the importance value.\n     |  \n     |  Method resolution order:\n     |      ai\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of Attribute Importance object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Attribute Importance model to create an oml.ai object from.\n     |          The specified database model is not dropped when the oml.ai object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Attribute Importance model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings or Algorithm-specific Settings are not\n     |          applicable to Attribute Importance model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None)\n     |      Fits an Attribute Importance Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.ai object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.ai object is deleted\n     |          unless oml.ai object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class ar(oml.algo.model.odmModel)\n     |  ar(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Association Rules <https://www.oracle.com/machine-learning/association-rules>`_ Model\n     |  \n     |  Builds an Association Rules Model used to discover the probability of item co-occurrence\n     |  in a collection. This function exposes the corresponding Oracle Machine Learning \n     |  in-database algorithm. The relationships between co-occurring items are expressed as \n     |  association rules.\n     |  Oracle Machine Learning does not support the prediction functions for association modeling.\n     |  The results of an association model are the rules that identify patterns of\n     |  association within the data. Association rules can be ranked by support\n     |  (How often do these items occur together in the data?) and confidence\n     |  (How likely are these items to occur together in the data?).\n     |  \n     |  :Attributes:\n     |  \n     |      **rules** : oml.DataFrame\n     |  \n     |          Details of each rule that shows \n     |          how the appearance of a set of items in a transactional\n     |          record implies the existence of another set of items.\n     |          It includes the following components:\n     |  \n     |          - rule_id: The identifier of the rule\n     |          - number_of_items: The total number of attributes referenced in the antecedent and consequent of the rule\n     |          - lhs_name: The name of the antecedent.\n     |          - lhs_value: The value of the antecedent.\n     |          - rhs_name: The name of the consequent.\n     |          - rhs_value: The value of the consequent.\n     |          - support: The number of transactions that satisfy the rule.\n     |          - confidence: The likelihood of a transaction satisfying the rule.\n     |          - revconfidence: The number of transactions in which the rule occurs divided by the number of transactions in which the consequent occurs.\n     |          - lift: The degree of improvement in the prediction over random chance when the rule is satisfied.\n     |  \n     |      **itemsets** : oml.DataFrame\n     |  \n     |          Description of the item sets from the model built.\n     |          It includes the following components:\n     |  \n     |          - itemset_id: The itemset identifier\n     |          - support: The support of the itemset\n     |          - number_of_items: The number of items in the itemset\n     |          - item_name: The name of the item\n     |          - item_value: The value of the item\n     |  \n     |  Method resolution order:\n     |      ar\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of Association Rules object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Association Rules model to create an oml.ar object from.\n     |          The specified database model is not dropped when the oml.ar object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Association Rules model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic\n     |          Data Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Association are applicable.\n     |          No algorithm-specific Settings are applicable to Association model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, model_name=None, case_id=None)\n     |      Fits an Association Rules Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.ar object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.ar object is deleted\n     |          unless oml.ar object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class dt(oml.algo.model.odmModel)\n     |  dt(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Decision Tree <https://www.oracle.com/machine-learning/decision-tree>`_ Model\n     |  \n     |  Builds a Decision Tree Model used to generate rules (conditional statements \n     |  that can easily be understood by humans and be used within a database to identify \n     |  a set of records) to predict a target value (numeric or categorical column). This \n     |  function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  A decision tree predicts a target value by asking a sequence of questions. \n     |  At a given stage in the sequence, the question that is asked depends upon the \n     |  answers to the previous questions. The goal is to ask questions that, taken \n     |  together, uniquely identify specific target values. Graphically, this process \n     |  forms a tree structure. During the training process, the Decision Tree algorithm \n     |  must repeatedly find the most efficient way to split a set of cases (records) \n     |  into two child nodes. The model offers two homogeneity metrics, gini and entropy, \n     |  for calculating the splits. The default metric is gini.\n     |  \n     |  \n     |  :Attributes:\n     |  \n     |      **nodes** : oml.DataFrame\n     |      \n     |        The node summary information with tree node details.\n     |        It includes the following components:\n     |            \n     |        - parent: The node ID of the parent\n     |        - node.id: The node ID\n     |        - row.count: The number of records in the training set that belong to the node\n     |        - prediction: The predicted Target value\n     |        - split: The main split\n     |        - surrogate: The surrogate split\n     |        - full.splits: The full splitting criterion\n     |        \n     |      **distributions** : oml.DataFrame\n     |  \n     |        The target class distributions at each tree node.\n     |        It includes the following components:\n     |            \n     |        - node_id: The node ID\n     |        - target_value: The target value\n     |        - target_count: The number of rows for a given target_value\n     |  \n     |  Method resolution order:\n     |      dt\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of dt object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Decision Tree model to create an oml.dt object from.\n     |          The specified database model is not dropped when the oml.dt object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Decision Tree model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566>`_\n     |          are applicable to Decision Tree model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, cost_matrix=None, case_id=None)\n     |      Fits a decision tree model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.dt object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.dt object is deleted\n     |          unless oml.dt object is saved into a datastore.\n     |      cost_matrix : OML DataFrame, list of ints, floats or None (default)\n     |          An optional numerical matrix that specifies the costs for incorrectly\n     |          predicting the target values. The first value represents the actual target value.\n     |          The second value represents the predicted target value. The third value is the cost.\n     |          In general, the diagonal entries of the matrix are zeros. Refer to `Oracle Data\n     |          Mining User's Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-89E0DB46-BA9A-4420-8216-FD6C96C74D6D>`_\n     |          for more details about cost matrix.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if proba is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols``, and the\n     |        results. The results include the most likely target class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data and returns the mean accuracy.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class em(oml.algo.model.odmModel)\n     |  em(n_clusters=None, model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Expectation Maximization <https://www.oracle.com/machine-learning/expectation-maximization>`_ Model\n     |  \n     |  Builds an Expectation Maximization (EM) Model used to performs probabilistic \n     |  clustering based on a density estimation algorithm. This function exposes the \n     |  corresponding Oracle Machine Learning in-database algorithm. In density estimation, \n     |  the goal is to construct a density function that captures how a given population is \n     |  distributed. The density estimate is based on observed data that represents a \n     |  sample of the population.\n     |  \n     |  :Attributes:\n     |  \n     |      **clusters** : oml.DataFrame\n     |      \n     |        The general per-cluster information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - cluster_name: The name of a cluster in the model\n     |            - record_count: The number of rows used in the build\n     |            - parent: The ID of the parent\n     |            - tree_level: The number of splits from the root\n     |            - left_child_id: The ID of the left child\n     |            - right_child_id: The ID of the right child\n     |            \n     |      **taxonomy**: oml.DataFrame\n     |      \n     |        The parent/child cluster relationship.\n     |        It includes the following components:\n     |            \n     |            - parent_cluster_id: The ID of the parent cluster\n     |            - child_cluster_id: The ID of the child cluster\n     |            \n     |      **centroids**: oml.DataFrame\n     |      \n     |        Per cluster-attribute center (centroid) information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - attribute_name: The attribute name\n     |            - mean: The average value of a numeric attribute\n     |            - mode_value: The most frequent value of a categorical attribute\n     |            - variance: The variance of a numeric attribute\n     |            \n     |      **leaf_cluster_counts**: pandas.DataFrame\n     |      \n     |        Leaf clusters with support.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a leaf cluster in the model\n     |            - cnt: The number of records in a leaf cluster\n     |            \n     |      **attribute_importance**: oml.DataFrame\n     |      \n     |        Attribute importance of the fitted model.\n     |        It includes the following components:\n     |            \n     |            - attribute_name: The attribute name\n     |            - attribute_importance_value: The attribute importance for an attribute\n     |            - attribute_rank: The rank of the attribute based on importance\n     |            \n     |      **projection**: oml.DataFrame\n     |      \n     |        The coefficients used by random projections to map nested columns to a lower dimensional space.\n     |        It exists only when nested or text data is present in the build data.\n     |        It includes the following components:\n     |            \n     |            - feature_name: The name of feature\n     |            - attribute_name: The attribute name\n     |            - attribute_value: The attribute value\n     |            - coefficient: The projection coefficient for an attribute\n     |            \n     |      **components**: oml.DataFrame\n     |      \n     |        EM components information about their prior probabilities and what cluster they map to.\n     |        It includes the following components:\n     |            \n     |            - component_id: The unique identifier of a component\n     |            - cluster_id: The ID of a cluster in the model\n     |            - prior_probability: The component prior probability\n     |            \n     |      **cluster_hists**: oml.DataFrame\n     |        Cluster histogram information.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - variable: The attribute name\n     |            - bin.id: The ID of a bin\n     |            - lower.bound: The numeric lower bin boundary\n     |            - upper.bound: The numeric upper bin boundary\n     |            - label: The label of the cluster\n     |            - count: The histogram count\n     |            \n     |      **rules**: oml.DataFrame\n     |      \n     |        Conditions for a case to be assigned with some probability to a cluster.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - rhs.support: The record count\n     |            - rhs.conf: The record confidence\n     |            - lhr.support: The rule support\n     |            - lhs.conf: The rule confidence\n     |            - lhs.var: The attribute predicate name\n     |            - lhs.var.support: The attribute predicate support\n     |            - lhs.var.conf: The attribute predicate confidence\n     |            - predicate: The attribute predicate\n     |  \n     |  Method resolution order:\n     |      em\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, n_clusters=None, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of em object.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_clusters : positive integer, None (default)\n     |          The number of clusters. If n_clusters is None, the number of clusters will be determined\n     |          either by current setting parameters or automatically by the algorithm.\n     |      model_name : string or None (default)\n     |          The name of an existing database Expectation Maximization model to create an oml.em object from.\n     |          The specified database model is not dropped when the oml.em object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Expectation Maximization model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Clustering and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8>`_\n     |          are applicable to Expectation Maximization model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, model_name=None, case_id=None)\n     |      Fits an Expectation Maximization Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.em object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.em object is deleted\n     |          unless oml.em object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. If the mode is 'class', the results include the most likely\n     |        target class and its probability. If mode is 'raw', the results\n     |        include for each target class, the probability belonging\n     |        to that class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each cluster on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned clusters to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each cluster, the probability\n     |        belonging to that cluster.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class esa(oml.algo.model.odmModel)\n     |  esa(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Explicit Semantic Analysis <https://www.oracle.com/machine-learning/explicit-semantic-analysis>`_ Model\n     |  \n     |  Builds an Explicit Semantic Analysis (ESA) Model to be used for feature extraction. \n     |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  ESA uses concepts of an existing knowledge base as features rather than latent\n     |  features derived by latent semantic analysis methods such as Singular\n     |  Value Decomposition and Latent Dirichlet Allocation. Each row, for example,\n     |  a document in the training data maps to a feature, that is, a concept.\n     |  ESA works best with concepts represented by text documents.\n     |  It has multiple applications in the area of text processing, most\n     |  notably semantic relatedness (similarity) and explicit topic modeling.\n     |  Text similarity use cases might involve, for example, resume matching, searching\n     |  for similar blog postings, and so on.\n     |  \n     |  :Attributes:\n     |  \n     |      **features** : oml.DataFrame\n     |      \n     |        Description of each feature extracted. \n     |        It includes the following components:\n     |            \n     |            - feature_id: The unique identifier of a feature as it appears in the training data\n     |            - attribute_name: The attribute name\n     |            - attribute_value: The attribute value\n     |            - coefficient: The coefficient (weight) associated with the attribute in a particular feature.\n     |  \n     |  Method resolution order:\n     |      esa\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of esa object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Explicit Semantic Analysis model to create an oml.esa object from.\n     |          The specified database model is not dropped when the oml.esa object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Explicit Semantic Analysis model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Feature Extraction and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-91FC543B-24E4-4D93-9D79-E95B1846F3B7>`_\n     |          are applicable to Explicit Semantic Analysis model.\n     |  \n     |  __repr__(self)\n     |  \n     |  feature_compare(self, x, compare_cols=None, supplemental_cols=None)\n     |      Compares features of data and generates relatedness.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          The data used to measure relatedness.\n     |      compare_cols : str, a list of str or None (default)\n     |          The column(s) used to measure data relatedness.\n     |          If None, all the columns of ``x`` are compared to measure relatedness.\n     |      supplemental_cols : a list of str or None (default)\n     |          A list of columns to display along with the resulting 'SIMILARITY' column.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |          Contains a 'SIMILARITY' column that measures relatedness and supplementary columns if specified.\n     |  \n     |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits an ESA Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.esa object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.esa object is deleted\n     |          unless oml.esa object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include the most likely feature and its probability.\n     |  \n     |  transform(self, x, supplemental_cols=None, topN=None)\n     |      Make predictions and return relevancy for each feature on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned values to\n     |          the specified number of features that have the highest topN values.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the relevancy for each feature on new data and the specified ``supplemental_cols``.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class glm(oml.algo.model.odmModel)\n     |  glm(mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Generalized Linear Models <https://www.oracle.com/machine-learning/generalized-linear-models>`_\n     |  \n     |  Builds Generalized Linear Models (GLM), which include and extend the class of \n     |  linear models (linear regression), to be used for classification or regression. \n     |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  Generalized linear models relax the restrictions on linear models, which are often \n     |  violated in practice. For example, binary (yes/no or 0/1) responses do not have same \n     |  variance across classes. This model uses a parametric modeling technique. Parametric \n     |  models make assumptions about the distribution of the data. When the assumptions are \n     |  met, parametric models can be more efficient than non-parametric models.\n     |  \n     |  :Attributes:\n     |  \n     |      **coef** : oml.DataFrame\n     |      \n     |        The coefficients of the GLM model, one for each predictor variable.\n     |        It includes the following components:\n     |            \n     |            - nonreference: The target value used as nonreference\n     |            - attribute name: The attribute name\n     |            - attribute value: The attribute value\n     |            - coefficient: The estimated coefficient\n     |            - std error: The standard error\n     |            - t value: The test statistics\n     |            - p value: The statistical significance\n     |            \n     |      **fit_details**: oml.DataFrame\n     |      \n     |        The model fit details such as adjusted_r_square, error_mean_square and so on.\n     |        It includes the following components:\n     |            \n     |            - name: The fit detail name\n     |            - value: The fit detail value\n     |            \n     |      **deviance**: float\n     |      \n     |        Minus twice the maximized log-likelihood, up to a constant.\n     |        \n     |      **null_deviance**: float\n     |      \n     |        The deviance for the null (intercept only) model.\n     |        \n     |      **aic**: float\n     |      \n     |        Akaike information criterion.\n     |        \n     |      **rank**: integer\n     |      \n     |        The numeric rank of the fitted model.\n     |        \n     |      **df_residual**: float\n     |      \n     |        The residual degrees of freedom.\n     |        \n     |      **df_null**: float\n     |      \n     |        The residual degrees of freedom for the null model.\n     |        \n     |      **converged**: bool\n     |      \n     |        The indicator for whether the model converged.\n     |        \n     |      **nonreference**: int or str\n     |      \n     |        For logistic regression, the response values that represents success.\n     |  \n     |  Method resolution order:\n     |      glm\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |      Initializes an instance of glm object.\n     |      \n     |      Parameters\n     |      ----------\n     |      mining_function : 'CLASSIFICATION' or 'REGRESSION', 'CLASSIFICATION' (default)\n     |          Type of model mining functionality\n     |      model_name : string or None (default)\n     |          The name of an existing database Generalized Linear Model to create an oml.glm object from.\n     |          The specified database model is not dropped when the oml.glm object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Generalized Linear Model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C>`_\n     |          are applicable to Generalized Linear Model model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits a GLM Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.glm object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.glm object is deleted\n     |          unless oml.glm object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None, confint=None, level=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      confint : bool, False (default)\n     |          A logical indicator for whether to produce confidence intervals.\n     |          for the predicted values.\n     |      level : float between 0 and 1 or None (default)\n     |          A numeric value within [0, 1] to use for the confidence level.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True for classification.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. For a classification model, the results include the most\n     |        likely target class and optionally its probability and confidence\n     |        intervals. For a linear regression model, the results consist of a column\n     |        for the prediction and optionally its confidence intervals.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  residuals(self, x, y)\n     |      Return the deviance residuals, which includes the following components:\n     |        - deviance: The deviance residual\n     |        - pearson: The Pearson residual\n     |        - response: The residual of the working response.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      \n     |      Return: oml.DataFrame\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data, returns the mean accuracy for classifications\n     |      or the coefficient of determination R^2 of the prediction for regressions.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications or the coefficient of\n     |              determination R^2 of the prediction for regressions.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class km(oml.algo.model.odmModel)\n     |  km(n_clusters=None, model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `k-means <https://www.oracle.com/machine-learning/k-means>`_ Model\n     |  \n     |  Builds a K-Means (KM) Model that uses a distance-based clustering algorithm to \n     |  partition data into a specified number of clusters. This function exposes the \n     |  corresponding Oracle Machine Learning in-database algorithm. Distance-based \n     |  algorithms rely on a distance function to measure the similarity between cases. \n     |  Cases are assigned to the nearest cluster according to the distance function used.\n     |  \n     |  :Attributes:\n     |  \n     |      **clusters** : oml.DataFrame\n     |      \n     |        The general per-cluster information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - row_cnt: The number of rows used in the build\n     |            - parent_cluster_id: The ID of the parent\n     |            - tree_level: The number of splits from the root\n     |            - dispersion: The measure of the quality of the cluster, and computationally, the sum of square errors\n     |            \n     |      **taxonomy**: oml.DataFrame\n     |      \n     |        The parent/child cluster relationship.\n     |        It includes the following components:\n     |            \n     |            - parent_cluster_id: The ID of the parent cluster\n     |            - child_cluster_id: The ID of the child cluster\n     |            \n     |      **centroids**: oml.DataFrame\n     |      \n     |        Per cluster-attribute center (centroid) information.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a cluster in the model\n     |            - attribute_name: The attribute name\n     |            - mean: The average value of a numeric attribute\n     |            - mode_value: The most frequent value of a categorical attribute\n     |            - variance: The variance of a numeric attribute\n     |            \n     |      **leaf_cluster_counts**: pandas.DataFrame\n     |      \n     |        Leaf clusters with support.\n     |        It includes the following components:\n     |            \n     |            - cluster_id: The ID of a leaf cluster in the model\n     |            - cnt: The number of records in a leaf cluster\n     |            \n     |      **cluster_hists**: oml.DataFrame\n     |      \n     |        Cluster histogram information.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - variable: The attribute name\n     |            - bin.id: The ID of a bin\n     |            - lower.bound: The numeric lower bin boundary\n     |            - upper.bound: The numeric upper bin boundary\n     |            - label: The label of the cluster\n     |            - count: The histogram count\n     |            \n     |      **rules**: oml.DataFrame\n     |      \n     |        Conditions for a case to be assigned with some probability to a cluster.\n     |        It includes the following components:\n     |            \n     |            - cluster.id: The ID of a cluster in the model\n     |            - rhs.support: The record count\n     |            - rhs.conf: The record confidence\n     |            - lhr.support: The rule support\n     |            - lhs.conf: The rule confidence\n     |            - lhs.var: The attribute predicate name\n     |            - lhs.var.support: The attribute predicate support\n     |            - lhs.var.conf: The attribute predicate confidence\n     |            - predicate: The attribute predicate\n     |  \n     |  Method resolution order:\n     |      km\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, n_clusters=None, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of km object.\n     |      \n     |      Parameters\n     |      ----------\n     |      n_clusters : positive integer, default None\n     |          Number of clusters. If n_clusters is None, the number of clusters will be determined\n     |          either by current setting parameters or automatically by the internal algorithm.\n     |      model_name : string or None (default)\n     |          The name of an existing database K-Means model to create an oml.km object from.\n     |          The specified database model is not dropped when the oml.km object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing K-Means model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Clustering and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C>`_\n     |          are applicable to K-Means model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits a K-Means Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.km object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.km object is deleted\n     |          unless oml.km object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |      ctx_settings : dict or None(default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each cluster on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer\n     |          A positive integer that restricts the returned clusters to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each cluster, the probability\n     |        belonging to that cluster.\n     |  \n     |  score(self, x)\n     |      Calculates the score value based on the input data ``x``.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          A new data set used to calculate score value.\n     |      \n     |      Returns\n     |      -------\n     |      pred : float\n     |             Score values, that is, opposite of the value of ``x`` on the K-means objective.\n     |  \n     |  transform(self, x)\n     |      Transforms ``x`` to a cluster-distance space.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the distance to each cluster.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class nb(oml.algo.model.odmModel)\n     |  nb(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Naive Bayes <https://www.oracle.com/machine-learning/naive-bayes>`_ Model\n     |  \n     |  Builds a Naive Bayes Model that uses conditional probabilities to predict a target \n     |  variable (numeric or categorical column). Naive Bayes looks at the historical data \n     |  and calculates conditional probabilities for the target values by observing the \n     |  frequency of attribute values and of combinations of attribute values. Naive Bayes \n     |  assumes that each predictor is conditionally independent of the others. (Bayes' \n     |  Theorem requires that the predictors be independent.)\n     |  \n     |  :Attributes:\n     |  \n     |      **priors** : oml.DataFrame\n     |      \n     |        An optional named numerical vector that specifies the priors for the target classes.\n     |        It includes the following components:\n     |            \n     |            - target_name: The name of the target column\n     |            - target_value: The target value\n     |            - prior_probability: The prior probability for a given target_value\n     |            - count: The number of rows for a given target_value\n     |            \n     |      **conditionals** : oml.DataFrame\n     |      \n     |        Conditional probabilities for each predictor variable.\n     |        It includes the following components:\n     |            \n     |            - target_name: The name of the target column\n     |            - target_value: The target value\n     |            - attribute_name: The column name\n     |            - attribute_subname: The nested column subname.\n     |            - attribute_value: The mining attribute value\n     |            - conditional_probability: The conditional probability of a mining attribute for a given target\n     |            - count: The number of rows for a given mining attribute and a given target\n     |  \n     |  Method resolution order:\n     |      nb\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of nb object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Naive Bayes model to create an oml.nb object from.\n     |          The specified database model is not dropped when the oml.nb object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Naive Bayes model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-A04C5F4E-1303-44DC-A7DA-185C969330C8>`_\n     |          are applicable to Naive Bayes model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, priors=None, case_id=None)\n     |      Fits a Naive Bayes Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.nb object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.nb object is deleted\n     |          unless oml.nb object is saved into a datastore.\n     |      priors : OML DataFrame or dict or list of ints or floats or None (default)\n     |          The priors represent the overall distribution of the target in the\n     |          population. By default, the priors are computed from the sample.\n     |          If the sample is known to be a distortion of the population target\n     |          distribution, then the user can override the default by providing\n     |          a priors table as a setting for model creation. For OML DataFrame\n     |          input, the first value represents the target value. The second value\n     |          represents the prior probability. For dictionary type input, the key\n     |          represents the target value. The value represents the prior probability.\n     |          For list type input, the first value represents target value. The\n     |          second value represents the prior probability. See `Oracle Data\n     |          Mining Concepts Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-4303E423-3613-4ABC-B077-450C20755F4C>`_\n     |          for more details.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include the most likely target class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      TopN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data and returns the mean accuracy.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class nn(oml.algo.model.odmModel)\n     |  nn(mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Neural Network <https://www.oracle.com/machine-learning/neural-network>`_ Model\n     |  \n     |  Builds a Neural Network (NN) Model that uses an algorithm inspired from biological\n     |  neural network for classification and regression. Neural Network is used to to estimate\n     |  or approximate functions that depend on a large number of generally unknown inputs.\n     |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n     |  An artificial neural network is composed of a large number of interconnected neurons\n     |  which exchange messages between each other to solve specific problems. They learn by\n     |  examples and tune the weights of the connections among the neurons during the learning\n     |  process. Neural Network is capable of solving a wide variety of tasks such as computer\n     |  vision, speech recognition, and various complex business problems.\n     |  \n     |  :Attributes:\n     |  \n     |      **weights** : oml.DataFrame\n     |  \n     |        Weights of fitted model between nodes in different layers.\n     |        It includes the following components:\n     |  \n     |            - layer: The layer ID, 0 as an input layer\n     |            - idx_from: The node index that the weight connects from (attribute id for input layer)\n     |            - idx_to: The node index that the weights connects to\n     |            - attribute_name: The attribute name (only for the input layer)\n     |            - attribute_subname: The attribute subname\n     |            - attribute_value: The attribute value\n     |            - target_value: The target value.\n     |            - weight: The value of weight\n     |  \n     |      **topology** : oml.DataFrame\n     |  \n     |        Topology of the fitted model including number of nodes and hidden layers.\n     |        It includes the following components:\n     |  \n     |            - hidden_layer_id: The id number of the hidden layer\n     |            - num_node: The number of nodes in each layer\n     |            - activation_function: The activation function in each layer\n     |  \n     |  Method resolution order:\n     |      nn\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |      Initializes an instance of nn object.\n     |      \n     |      Parameters\n     |      ----------\n     |      mining_function : 'CLASSIFICATION' or 'REGRESSION', 'CLASSIFICATION' (default)\n     |          Type of model mining functionality\n     |      model_name : string or None (default)\n     |          The name of an existing database Neural Network model to create an oml.nn object from.\n     |          The specified database model is not dropped when the oml.nn object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Neural Network model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4>`_\n     |          are applicable to Neural Network model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None, class_weight=None)\n     |      Fits a Neural Network Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.nn object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.nn object is deleted\n     |          unless oml.nn object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |      class_weight : OML DataFrame or dict or list of ints or floats or None (default)\n     |          An optional matrix that is used to influence the weighting of\n     |          target classes during model creation. For OML DataFrame input, the first\n     |          value represents the target value. The second value represents the class weight.\n     |          For dictionary type input, the key represents the target value. The value\n     |          represents the class weight. For list type input, the first value represents\n     |          target value. The second value represents the predicted target value.\n     |          Refer to `Oracle Data Mining  User's Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-44E40062-CE46-4C10-BE10-BB96B562BA20>`_\n     |          for more details about class weights.\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches settings of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If params is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. For a classification model, the results include the most\n     |        likely target class and its probability. For a regression\n     |        model, the results consist of a column for the prediction. For an\n     |        anomaly detection model, the results include a prediction and its\n     |        probability. If the prediction is 1, the case is considered typical.\n     |        If the prediction is 0, the case is considered anomalous. This\n     |        behavior reflects the fact that the model is trained with normal data.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data, returns the mean accuracy for classifications\n     |      or the coefficient of determination R^2 of the prediction for regressions.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications or the coefficient of\n     |              determination R^2 of the prediction for regressions.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class rf(oml.algo.model.odmModel)\n     |  rf(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Random Forest <https://www.oracle.com/machine-learning/random-forest>`_ Model\n     |  \n     |  Builds a Random Forest (RF) Model that uses an ensemble (also called forest) of trees \n     |  for classification. This function exposes the corresponding Oracle Machine Learning \n     |  in-database algorithm. Random Forest is a popular ensemble learning technique for \n     |  classification. By combining the ideas of bagging and random selection of variables, \n     |  the algorithm produces collection of decision trees with controlled variance, while \n     |  avoiding overfitting - a common problem for decision trees.\n     |  \n     |  :Attributes:\n     |  \n     |      **importance** : oml.DataFrame\n     |      \n     |        Attribute importance of the fitted model.\n     |        It includes the following components:\n     |            \n     |            - attribute_name: The attribute name\n     |            - attribute_subname: The attribute subname \n     |            - attribute_importance: The attribute importance for an attribute in the forest\n     |  \n     |  Method resolution order:\n     |      rf\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of rf object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Random Forest model to create an oml.rf object from.\n     |          The specified database model is not dropped when the oml.rf object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Random Forest model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each dict element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117>`_\n     |          are applicable to Random Forest model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, cost_matrix=None, case_id=None)\n     |      Fits a Random Forest Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object, or string\n     |          Target values.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.rf object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.rf object is deleted\n     |          unless oml.rf object is saved into a datastore.\n     |      cost_matrix : OML DataFrame or list of ints or floats or None (default)\n     |          An optional numerical square matrix that specifies the costs for incorrectly\n     |          predicting the target values. The first value represents the actual target value.\n     |          The second value represents the predicted target value. The third value is the cost.\n     |          In general, the diagonal entries of the matrix are zeros. Refer to `Oracle Data\n     |          Mining User's Guide <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-89E0DB46-BA9A-4420-8216-FD6C96C74D6D>`_\n     |          for more details about cost matrix.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols``, and the most likely\n     |        target class.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data and returns the mean accuracy.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class svd(oml.algo.model.odmModel)\n     |  svd(model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Singular Value Decomposition <https://www.oracle.com/machine-learning/singular-value-decomposition>`_ Model\n     |  \n     |  Builds a Singular Value Decomposition (SVD) Model that can be used for feature \n     |  extraction. SVD provides orthogonal linear transformations that capture the \n     |  underlying variance of the data by decomposing a rectangular matrix into three \n     |  matrixes: U, D, and V. Matrix D is a diagonal matrix and its singular values \n     |  reflect the amount of data variance captured by the bases. Columns of matrix V \n     |  contain the right singular vectors and columns of matrix U contain the left singular\n     |  vectors.\n     |  \n     |  :Attributes:\n     |  \n     |      **features** : oml.DataFrame\n     |      \n     |        Features extracted by the fitted model including feature id and associated coefficient.\n     |        It includes the following components:\n     |            \n     |            - feature_id: The ID of a feature in the model\n     |            - attribute_name: The attribute name\n     |            - attribute_value: The attribute value\n     |            - value: The matrix entry value\n     |            \n     |      **u** : oml.DataFrame\n     |      \n     |        A dataframe whose columns contain the left singular vectors.\n     |        The column name is the corresponding feature id.\n     |        \n     |      **v** : oml.DataFrame\n     |      \n     |        A dataframe whose columns contain the right singular vectors.\n     |        The column name is the corresponding feature id.\n     |        \n     |      **d** : oml.DataFrame\n     |      \n     |        A dataframe containing the singular values of the input data.\n     |        It includes the following components:\n     |            \n     |            - feature_id: The ID of a feature in the model\n     |            - value: The singular values of the input data\n     |  \n     |  Method resolution order:\n     |      svd\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, model_name=None, model_owner=None, **params)\n     |      Initializes an instance of svd object.\n     |      \n     |      Parameters\n     |      ----------\n     |      model_name : string or None (default)\n     |          The name of an existing database Singular Value Decomposition model to create an oml.svd object from.\n     |          The specified database model is not dropped when the oml.svd object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Singular Value Decomposition model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Feature Extraction and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117>`_\n     |          are applicable to Singular Value Decomposition model.\n     |  \n     |  __repr__(self)\n     |  \n     |  feature_compare(self, x, compare_cols=None, supplemental_cols=None)\n     |      Compares features of data and generates relatedness.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          The data used to measure relatedness.\n     |      compare_cols : str, a list of str or None (default)\n     |          The column(s) used to measure data relatedness.\n     |          If None, all the columns of ``x`` are compared to measure relatedness.\n     |      supplemental_cols : a list of str or None (default)\n     |          A list of columns to display along with the resulting 'SIMILARITY' column.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |          Contains a 'SIMILARITY' column that measures relatedness and supplementary columns if specified.\n     |  \n     |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits an SVD Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.svd object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.svd object is deleted\n     |          unless oml.svd object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The column name used as case id for building the model. \n     |          ``case_id`` and SVDS_U_MATRIX_OUTPUT in ``odm_settings`` \n     |          must be specified in order to produce matrix U.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the predicted feature index on the new data and the specified ``supplemental_cols``.\n     |  \n     |  transform(self, x, supplemental_cols=None, topN=None)\n     |      Performs dimensionality reduction and returns value for each feature on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned values to\n     |          the specified number of features that have the highest topN values.\n     |          If None, all features will be returned.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the values of new data after the SVD transform and the specified ``supplemental_cols``.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n    \n    class svm(oml.algo.model.odmModel)\n     |  svm(mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |  \n     |  In-database `Support Vector Machine <https://www.oracle.com/machine-learning/support-vector-machines>`_ Model\n     |  \n     |  Builds a Support Vector Machine (SVM) Model to be used for regression, classification, \n     |  or anomaly detection. This function exposes the corresponding Oracle Machine Learning \n     |  in-database algorithm. SVM is a powerful, state-of-the-art algorithm with strong \n     |  theoretical foundations based on the Vapnik-Chervonenkis theory. SVM has strong \n     |  regularization properties. Regularization refers to the generalization of the model to \n     |  new data.\n     |  \n     |  \n     |  :Attributes:\n     |  \n     |      **coef** : oml.DataFrame\n     |      \n     |        The coefficients of the SVM model, one for each predictor variable.\n     |        It includes the following components:\n     |          \n     |            - target_value: The target value\n     |            - attribute_name: The attribute name\n     |            - attribute_subname: The attribute subname\n     |            - attribute_value: The attribute value\n     |            - coef: The projection coefficient value\n     |  \n     |  Method resolution order:\n     |      svm\n     |      oml.algo.model.odmModel\n     |      builtins.object\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, mining_function='CLASSIFICATION', model_name=None, model_owner=None, **params)\n     |      Initializes an instance of svm object.\n     |      \n     |      Parameters\n     |      ----------\n     |      mining_function : 'CLASSIFICATION' (default), 'REGRESSION' or 'ANOMALY_DETECTION'\n     |          Type of model mining functionality.\n     |      model_name : string or None (default)\n     |          The name of an existing database Support Vector Machine model to create an oml.svm object from.\n     |          The specified database model is not dropped when the oml.svm object is deleted.\n     |      model_owner: string or None (default)\n     |          The owner name of the existing Support Vector Machine model\n     |          The current database user by default\n     |      params : key-value pairs or dict\n     |          Oracle Machine Learning parameter settings. Each list element's name and\n     |          value refer to the parameter setting name and value, respectively.\n     |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n     |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n     |          for applicable parameters and valid values. Global and Automatic Data\n     |          Preparation Settings in Table 5-5 apply generally to the model.\n     |          Mining Function Settings for Classification and `Algorithm-specific\n     |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB>`_\n     |          are applicable to Support Vector Machine model.\n     |  \n     |  __repr__(self)\n     |  \n     |  fit(self, x, y, model_name=None, case_id=None, ctx_settings=None)\n     |      Fits an SVM Model according to the training data and parameter settings.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object or None, or string\n     |          Target values.\n     |          Must be specified when SVM algorithm is used for classification\n     |          or regression and must be None when used for anomaly detection.\n     |          If y is a single column OML object, target values specified by y must be combinable with x.\n     |          If y is a string, y is the name of the column in x that specifies the target values.\n     |      model_name : string or None (default)\n     |          User-specified model name.\n     |          The user-specified database model is not dropped when oml.svm object is deleted.\n     |          If None, a system-generated model name will be used.\n     |          The system-generated model is dropped when oml.svm object is deleted\n     |          unless oml.svm object is saved into a datastore.\n     |      case_id : string or None (default)\n     |          The name of a column that contains unique case identifiers.\n     |      ctx_settings : dict or None (default)\n     |          A list to specify Oracle Text attribute-specific settings.\n     |          This argument is applicable to building models in Oracle Database 12.2 or later.\n     |          The name of each list element refers to the text column while the list value\n     |          is a scalar string specifying the attribute-specific text transformation.\n     |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n     |          MAX_FEATURES.\n     |  \n     |  predict(self, x, supplemental_cols=None, proba=False, topN_attrs=False)\n     |      Makes predictions on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : oml.DataFrame\n     |          Predictor values used by the model to generate scores\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      proba : boolean, False (default)\n     |          Returns prediction probability if ``proba`` is True.\n     |      topN_attrs : boolean, positive integer, False (default)\n     |          Returns the top N most influence attributes of the predicted target value\n     |          for regression if topN_attrs is not False.\n     |          Returns the top N most influence attributes of the highest probability class\n     |          for classification if topN_attrs is not False.\n     |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. For a classification model, the results include the most\n     |        likely target class and its probability. For a regression\n     |        model, the results consist of a column for the prediction. For an\n     |        anomaly detection model, the results include a prediction and its\n     |        probability. If the prediction is 1, the case is considered normal.\n     |        If the prediction is 0, the case is considered anomalous. This\n     |        behavior reflects the fact that the model is trained with normal data.\n     |  \n     |  predict_proba(self, x, supplemental_cols=None, topN=None)\n     |      Makes predictions and returns probability for each class on new data.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |        Attribute values used by the model to generate scores.\n     |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n     |          Data set presented with the prediction result.\n     |          It must be concatenatable with ``x``.\n     |      topN : positive integer or None (default)\n     |          A positive integer that restricts the returned target classes to\n     |          the specified number of those that have the highest probability.\n     |      \n     |      Returns\n     |      -------\n     |      pred : oml.DataFrame\n     |        Contains the features specified by ``supplemental_cols`` and the\n     |        results. The results include for each target class, the probability\n     |        belonging to that class.\n     |  \n     |  score(self, x, y)\n     |      Makes predictions on new data, returns the mean accuracy for classifications\n     |      or the coefficient of determination R^2 of the prediction for regressions.\n     |      \n     |      Parameters\n     |      ----------\n     |      x : an OML object\n     |          Attribute values for building the model.\n     |      y : a single column OML object\n     |          Target values.\n     |      \n     |      Returns\n     |      -------\n     |      score : float\n     |              Mean accuracy for classifications or the coefficient of\n     |              determination R^2 of the prediction for regressions.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Methods inherited from oml.algo.model.odmModel:\n     |  \n     |  export_sermodel(self, table=None, partition=None)\n     |      Export model.\n     |      \n     |      Parameters\n     |      ----------\n     |      table : string or None (default)\n     |          A name for the new table where the serialized model is saved.\n     |          If None, the serialized model will be saved to a temporary table.\n     |      partition : string or None (default)\n     |          Name of the partition that needs to be exported.\n     |          If partition is None, all partitions are exported\n     |      \n     |      Returns\n     |      -------\n     |      oml_bytes : an oml.Bytes object\n     |        Contains the BLOB content from the model export\n     |  \n     |  get_params(self, params=None, deep=False)\n     |      Fetches parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : iterable of strings, None (default)\n     |          Names of parameters to fetch. If ``params`` is None,\n     |          fetches all settings.\n     |      deep : boolean, False (default)\n     |          Includes the computed and default parameters or not.\n     |      \n     |      Returns\n     |      -------\n     |      settings : dict mapping str to str\n     |  \n     |  set_params(self, **params)\n     |      Changes parameters of the model.\n     |      \n     |      Parameters\n     |      ----------\n     |      params : dict object mapping str to str\n     |          The key should be the name of the setting, and the value should be\n     |          the new setting.\n     |      \n     |      Returns\n     |      -------\n     |      model : the model itself.\n     |  \n     |  ----------------------------------------------------------------------\n     |  Readonly properties inherited from oml.algo.model.odmModel:\n     |  \n     |  model_owner\n     |      The owner name of database mining model\n     |  \n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from oml.algo.model.odmModel:\n     |  \n     |  __dict__\n     |      dictionary for instance variables (if defined)\n     |  \n     |  __weakref__\n     |      list of weak references to the object (if defined)\n     |  \n     |  model_name\n     |      The given name of database mining model\n     |  \n     |  pivot_limit\n     |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n\nFUNCTIONS\n    boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_ticks=True, autorange=False, zorder=None)\n        Makes a box and whisker plot.\n        \n        For every column of ``x`` or for every column object in ``x``, makes a box and whisker plot.\n        \n        Parameters\n        ----------\n        x : oml.DataFrame or oml.Float or list of oml.Float\n          The data to plot.\n        notch : bool, False (default), optional\n          If True, produces a notched box plot. Otherwise, a rectangular\n          boxplot is produced. By default, the confidence intervals are approximated\n          as ``median +/-1.57 IQR/sqrt(n)`` where ``n`` is the number of not-null/NA\n          values in the column. \n        conf_intervals : array-like, optional\n          Array or sequence whose first dimension is equal to the number of columns\n          in ``x`` and whose second dimension is 2.\n        labels : sequence, optional\n          Length must be equal to the number of columns in ``x``. When an element of\n          ``labels`` is not None, the default label of the column, which is the name\n          of the column, is overridden.\n        \n        Notes\n        -----\n        For information on the other parameters, see documentation for :py:func:`matplotlib.pyplot.boxplot`.\n        \n        Returns\n        -------\n        ax : :py:class:`matplotlib.axes.Axes`\n          The :py:class:`matplotlib.axes.Axes` instance of the boxplot figure.\n        result : dict\n          A dict mapping each component of the boxplot to the corresponding list of\n          :py:class:`matplotlib.lines.Lines2D` instances created.\n    \n    check_embed()\n        Indicates whether embedded Python is set up in the connected Oracle Database.\n        \n        Returns\n        -------\n        embed_status : bool or None\n          None when not connected.\n    \n    connect(user=None, password=None, host=None, port=None, sid=None, service_name=None, dsn=None, encoding='UTF-8', nencoding='UTF-8', automl=None, **kwargs)\n        Establishes an Oracle Database connection.\n        \n        Just as with :py:func:`cx_Oracle.connect`, the user, password, and data\n        source name can be provided separately or with host, port, sid or\n        service_name.\n        \n        There can be only one active connection. Calling this method when an\n        active connection already exists replaces the active connection with\n        a new one. This results in the previous connection being implicitly\n        disconnected with the corresponding release of resources.\n        \n        Parameters\n        ----------\n        user : str or None (default)\n        password : str or None (default)\n        host : str or None (default)\n          Host name of the Oracle Database.\n        port : int, str or None (default)\n          The Oracle Database port number.\n        sid : str or None (default)\n          The Oracle Database SID.\n        service_name : str or None (default)\n          The service name to be used in the connection identifier for\n          the Oracle Database.\n        dsn : str or None (default)\n          Data source name. The TNS entry of the database, or an TNS\n          alias in the Oracle Wallet.\n        encoding : str, 'UTF-8' (default)\n          Encoding to use for regular database strings.\n        nenconding : str, 'UTF-8' (default)\n          Encoding to use for national character set database strings.\n        automl : str, or bool or None (default)\n          To enable automl, specify:\n            * True: if ``host``, ``port``, ``sid`` or ``service_name``\n              are specified and a connection pool is running for this\n              (``host``, ``port``, ``sid`` or ``service_name``).\n            * Data source name: for a running connection pool\n              if ``dsn`` is specified with a data source name.\n            * TNS alias in an Oracle Wallet: for a running connection pool\n              if ``dsn`` is also specified with Wallet TNS alias.\n          Otherwise, automl is disabled.\n        \n        Notes\n        -----\n         * Parameters ``sid`` and ``service_name`` are exclusive.\n         * Parameters (``host``, ``port``, ``sid`` or ``service_name``),\n           and ``dsn`` can only be specified exclusively.\n         * Parameter ``user`` and ``password`` must be provided when\n           (``host``, ``port``, ``sid`` or ``service_name``) is specified,\n           or ``dsn`` (and optionally ``automl``) is specified with\n           a data source name.\n         * Parameter ``user`` and ``password`` should be set to empty str \"\",\n           when ``dsn`` (and optionally ``automl``) is specified with\n           Wallet TNS alias, to establish connection with Oracle Wallet.\n         * Automl requires `Database Resident Connection Pooling (DRCP)\n           <https://docs.oracle.com/en/database/oracle/oracle-database/18/jjdbc/database-resident-connection-pooling.html#GUID-80365FCC-7053-46BD-819F-E051065E4C4E>`_\n           running on the Database server.\n    \n    create(x, table, oranumber=True, dbtypes=None, append=False)\n        Creates a table in Oracle Database from a Python data set.\n        \n        Parameters\n        ----------\n        x : pandas.DataFrame or a list of tuples of equal size\n          If ``x`` is a list of tuples of equal size, each tuple represents\n          a row in the table. The column names are set to COL1, COL2, ... and so on.\n        table : str\n          A name for the table.\n        oranumber : bool, True (default)\n          If True, use SQL NUMBER for numeric columns. Otherwise, use BINARY_DOUBLE.\n          Ignored if ``append`` is True.\n        dbtypes : dict mapping str to str or list of str\n          A list of SQL types to use on the new table. If a list, its length should\n          be equal to the number of columns. If a dict, the keys are the names of the\n          columns. Ignored if ``append`` is True.\n        append : bool, False (default)\n          Indicates whether to append the data to the existing table.\n        \n        Notes\n        -----\n         * When creating a new table, for columns whose SQL types are not specified in\n           ``dbtypes``, NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. Users should set\n           ``oranumber`` to False when the data contains NaN values. For string columns,\n           the default type is VARCHAR2(4000), and for bytes columns, the default type\n           is BLOB.\n         * When ``x`` is specified with an empty pandas.DataFrame, OML creates an\n           empty table. NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. VARCHAR2(4000) is\n           used for columns of object dtype in the pandas.DataFrame.\n         * OML does not support columns containing values of multiple data types,\n           data conversion is needed or a TypeError may be raised.\n         * OML determines default column types by looking at 20 random rows sampled\n           from the table. For tables with less than 20 rows, all rows are used\n           in column type determination. NaN values are considered as float type.\n           If a column has all Nones, or has inconsistent data types that are not\n           None in the sampled rows, a default column type cannot be determined,\n           and a ValueError is raised unless a SQL type for the column is specified\n           in ``dbtypes``.\n        \n        Returns\n        -------\n        new_table : oml.DataFrame\n          A proxy object that represents the newly-created table.\n    \n    cursor()\n        Returns a cx_Oracle cursor object of the current OML database connection.\n        It can be used to execute queries against Oracle Database.\n        \n        Returns\n        -------\n        cursor_obj : a cx_Oracle :ref:`cx:cursorobj`.\n    \n    dir()\n        Returns the names of OML objects in the workspace.\n        \n        Returns\n        -------\n        obj_names : list of str\n    \n    disconnect(cleanup=True)\n        Terminates the Oracle Database connection. By default, the OML\n        objects created through this connection will be deleted.\n        \n        Parameters\n        ----------\n        cleanup : bool, True (default)\n          Cleans up OML objects defined in Python's main module before\n          disconnecting from the database.\n    \n    do_eval(func, func_owner=None, graphics=False, **kwargs)\n        Runs the user-defined Python function using a Python engine spawned and \n        controlled by the database environment.\n        \n        Parameters\n        ----------\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : Python object or oml.embed.data_image._DataImage\n          If no image is rendered in the script, returns whatever Python object returned\n          by the function. Otherwise, returns an oml.embed.data_image._DataImage object.\n          See :ref:`more-output`.\n    \n    drop(table=None, view=None, model=None)\n        Drops a database table, view, or model.\n        \n        Parameters\n        ----------\n        table : str or None (default)\n          The name of the table to drop.\n        view : str or None (default)\n          The name of the view to drop.\n        model : str or None (default)\n          The name of the model to drop.\n    \n    grant(name, typ='datastore', user=None)\n        Grants read privilege for a Python script or datastore.\n        Requires the user to have the `PYQADMIN` Oracle Database role.\n        \n        Parameters\n        ----------\n        name : str\n          The name of Python script in the Python script repository or the name of\n          a datastore. The current user must be the owner of the Python script or\n          datastore.\n        typ : 'datastore' (default) or 'pyqscript'\n          A str specifying either 'datastore' or 'pyqscript' to grant the\n          read privilege. 'pyqscript' requires Embedded Python.\n        user : str or None (default)\n          The user to grant read privilege of the named Python script or datastore\n          to. Treated as case-sensitive if wrapped in double quotes. Treated as\n          case-insensitive otherwise. If None, grant read privilege to public.\n    \n    group_apply(data, index, func, func_owner=None, parallel=None, orderby=None, graphics=False, **kwargs)\n        Partitions database data by the column(s) specified in ``index``\n        and runs the user-defined Python function on each partition using \n        Python engines spawned and controlled by the database environment.\n        \n        Parameters\n        ----------\n        data : oml.DataFrame\n          The OML DataFrame that represents the in-database data that ``func`` is\n          applied on.\n        index : OML data object\n          The columns to partition the ``data`` before sending it to ``func``.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        parallel : bool or int or None (default)\n          A preferred degree of parallelism to use in the embedded Python job;\n          either a positive integer greater than or equal to 1\n          for a specific degree of parallelism,\n          a value of 'None', 'False' or '0' for no parallelism,\n          a value of 'True' for the ``data`` default parallelism.\n          Cannot exceed the degree of parallelism limit controlled by \n          service level in ADW.\n        orderby : oml.DataFrame, oml.Float, or oml.String\n          An optional argument used to specify the ordering of group partitions.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : dict \n          If no image is rendered in the script, returns a dict of Python objects\n          returned by the function. Otherwise, returns a dict of \n          oml.embed.data_image._DataImage objects. See :ref:`more-output`.\n    \n    hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, **kwargs)\n        Plots a histogram.\n        \n        Computes and draws a histogram for every data set column contained in ``x``.\n        \n        Parameters\n        ----------\n        x : oml.Float\n        bins : int, strictly monotonic increasing sequence, 'auto', 'doane', 'fd', 'rice', 'scott', 'sqrt', or 'sturges', optional\n          * If an integer, denotes the number of equal width bins to generate.\n          * If a sequence, denotes bin edges and overrides the values of ``range``.\n          * If a string, denotes the estimator to use calculate the optimal number\n            of bins. 'auto' is the maximum of the 'fd' and 'sturges' estimators.\n          * Default is taken from the matplotlib rcParam ``hist.bins``.\n        weights : oml.Float\n          Must come from the same table as ``x``.\n        cumulative : int, float, or boolean, False (Default)\n          If greater than zero, then a histogram is computed where each bin gives\n          the counts in that bin plus all bins for smaller values. If ``density``\n          is also True, then the histogram is normalized so the last bin equals 1.\n          If less than zero, the direction of accumulation is reversed. In this\n          case, if ``density`` is True, then the histogram is normalized so that the\n          first bin equals 1. \n        rwidth : int, float, or None (default)\n          Ratio of the width of the bars to the bin widths. Values less than 0\n          is treated as 0. Values more than 1 is treated as 1. If None,\n          defaults to 1.\n        color : str that indicates a color spec or None (default)\n          If None, use the standard line color sequence. \n        label : str or None (default) \n          The label that is applied to the first patch of the histogram.\n        \n        Notes\n        -----\n        For information on the other parameters, see documentation for :py:func:`matplotlib.pyplot.hist`.\n        \n        Returns\n        -------\n        n : :py:class:`numpy.ndarray`\n          The values of the histogram bins. \n        bins : :py:class:`numpy.ndarray`\n          The edges of the bins. An array of length #bins + 1.\n        patches : list of :py:class:`matplotlib.patches.Rectangle`\n          Individual patches used to create the histogram.\n    \n    index_apply(times, func, func_owner=None, parallel=None, graphics=False, **kwargs)\n        Runs the user-defined Python function multiple times, passing the \n        run index as first argument, using Python engines spawned and controlled \n        by the database environment.\n        \n        Parameters\n        ----------\n        times : int\n          The number of times to execute the function.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        parallel : bool or int or None (default)\n          A preferred degree of parallelism to use in the embedded Python job;\n          either a positive integer greater than or equal to 1\n          for a specific degree of parallelism,\n          a value of 'None', 'False' or '0' for no parallelism,\n          a value of 'True' for the ``data`` default parallelism.\n          Cannot exceed the degree of parallelism limit controlled by\n          service level in ADW.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : list\n          If no image is rendered in the script, returns a list of Python objects\n          returned by the function. Otherwise, returns a list of \n          oml.embed.data_image._DataImage objects. See :ref:`more-output`.\n    \n    isconnected(check_automl=False)\n        Indicates whether an active Oracle Database connection exists.\n        \n        Parameters\n        ----------\n        check_automl: bool, False (default)\n            Indicates whether to check the connection is automl-enabled.\n        \n        Returns\n        -------\n        connected : bool\n    \n    push(x, oranumber=True, dbtypes=None)\n        Pushes data into Oracle Database.\n        \n        Creates an internal table in Oracle Database and inserts the data\n        into the table. The table exists as long as an OML object (either\n        in the Python client or saved in the datastore) references the table.\n        \n        Parameters\n        ----------\n        x : pandas.DataFrame or a list of tuples of equal size\n          If ``x`` is a list of tuples of equal size, each tuple represents\n          a row in the table. The column names are set to COL1, COL2, ... and so on.\n        oranumber : bool\n          If True (default), use SQL NUMBER for numeric columns. Otherwise\n          use BINARY_DOUBLE. Ignored if ``append`` is True.\n        dbtypes : dict or list of str\n          The SQL data types to use in the table.\n        \n        Notes\n        -----\n         * When creating a new table, for columns whose SQL types are not specified in\n           ``dbtypes``, NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. Users should set\n           ``oranumber`` to False when the data contains NaN values. For string columns,\n           the default type is VARCHAR2(4000), and for bytes columns, the default type\n           is BLOB.\n         * When ``x`` is specified with an empty pandas.DataFrame, OML creates an\n           empty table. NUMBER is used for numeric columns when ``oranumber`` is True\n           and BINARY_DOUBLE is used when ``oranumber`` is False. VARCHAR2(4000) is\n           used for columns of object dtype in the pandas.DataFrame.\n         * OML does not support columns containing values of multiple data types,\n           data conversion is needed or a TypeError may be raised.\n         * OML determines default column types by looking at 20 random rows sampled\n           from the table. For tables with less than 20 rows, all rows are used\n           in column type determination. NaN values are considered as float type.\n           If a column has all Nones, or has inconsistent data types that are not\n           None in the sampled rows, a default column type cannot be determined,\n           and a ValueError is raised unless a SQL type for the column is specified\n           in ``dbtypes``.\n        \n        Returns\n        -------\n        temp_table : oml.DataFrame\n    \n    revoke(name, typ='datastore', user=None)\n        Revokes read privilege for a Python script or datastore.\n        Requires the user to have the `PYQADMIN` Oracle Database role.\n        \n        Parameters\n        ----------\n        name : str\n          The name of Python script in the Python script repository or the name of\n          a datastore. The current user must be the owner of the Python script or\n          datastore.\n        typ : 'datastore' (default) or 'pyqscript'\n          A str specifying either 'datastore' or 'pyqscript' to revoke the\n          read privilege. 'pyqscript' requires Embedded Python.\n        user : str or None (default)\n          The user to revoke read privilege of the named Python script or datastore\n          from. Treated as case-sensitive if wrapped in double quotes. Treated as\n          case-insensitive otherwise. If None, revoke read privilege from public.\n    \n    row_apply(data, func, func_owner=None, rows=1, parallel=None, graphics=False, **kwargs)\n        Partitions database data into chunks of rows and runs the user-defined \n        Python function on each chunk using Python engines spawned and controlled \n        by the database environment.\n        \n        Parameters\n        ----------\n        data : oml.DataFrame\n          The OML DataFrame that represents the in-database data that ``func``\n          is applied on.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        rows : int, 1 (default)\n          The maximum number of rows in each chunk.\n        parallel : bool or int or None (default)\n          A preferred degree of parallelism to use in the embedded Python job;\n          either a positive integer greater than or equal to 1\n          for a specific degree of parallelism,\n          a value of 'None', 'False' or '0' for no parallelism,\n          a value of 'True' for the ``data`` default parallelism.\n          Cannot exceed the degree of parallelism limit controlled by \n          service level in ADW.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : pandas.DataFrame or a list of oml.embed.data_image._DataImage\n          If no image is rendered in the script, returns a :py:class:`pandas.DataFrame`.\n          Otherwise, returns a list of oml.embed.data_image._DataImage objects.\n          See :ref:`more-output`.\n    \n    sync(schema=None, regex_match=False, **kwargs)\n        Creates a DataFrame proxy object in Python that represents an Oracle\n        Database data set.\n        \n        The data set can be one of the following: a database table, view, or query.\n        \n        Parameters\n        ----------\n        schema : str or None (default)\n          The name of the schema where the database object exists;\n          if None, then the current schema is used.\n        regex_match : bool, False (default)\n          Synchronizes tables or views that match a regular expression.\n          Ignored if ``query`` is used.\n        table, view, query : str or None (default)\n          The name of a table, of a view, or of an Oracle SQL query to select\n          from the database. When ``regex_match`` is True, this specifies the\n          name pattern. Exactly one of these parameters must be a str and the\n          other two must be None.\n        \n        Notes\n        -----\n        When ``regex_match`` is True, synchronizes the matched tables or views\n        to a dict with the table or view name as the key.\n        \n        Returns\n        -------\n        data_set : oml.DataFrame, or if ``regex_match`` is used, returns\n          a dict of oml.DataFrame\n    \n    table_apply(data, func, func_owner=None, graphics=False, **kwargs)\n        Runs the user-defined Python function with data pulled from \n        a database table or view using a Python engine spawned and \n        controlled by the database environment.\n        \n        Parameters\n        ----------\n        data : oml.DataFrame\n          The oml.DataFrame that represents the data ``func`` is applied on.\n        func : function, str or :py:func:`oml.script.Callable <oml.script.load>`\n          ``func`` can be one of the following:\n        \n          * A Python function.\n          * Name of a registered script that defines a Python function.\n          * A string that if evaluated, defines a Python function.\n          * A callable object returned from ``script_load`` function.\n        func_owner : str or None (default)\n          An optional value specifying the owner of the registered script\n          when argument ``func`` is set to a registered script name.\n        graphics : bool, False (default)\n          If True, images rendered from extant :py:class:`matplotlib.figure.Figure`\n          objects are included in the result.\n        **kwargs :\n          Contains any combinaton of the following:\n        \n          * :ref:`special-control`\n          * additional arguments to ``func``.\n        \n        Returns\n        -------\n        result : Python object or oml.embed.data_image._DataImage\n          If no image is rendered in the script, returns whatever Python object returned\n          by the function. Otherwise, returns an oml.embed.data_image._DataImage object.\n          See :ref:`more-output`.\n\nDATA\n    __all__ = ['connect', 'disconnect', 'isconnected', 'check_embed', 'cur...\n    __build_serial__ = '1.0_08122021_1858'\n\nVERSION\n    1.0\n\nFILE\n    /usr/local/lib/python3.9/site-packages/oml/__init__.py\n\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_-1634618100","id":"20211001-190306_1526941016","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:47+0000","dateFinished":"2021-09-22T20:18:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:385"},{"text":"%md\n## Learn More\n\n* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html');\">Get Started with OML4Py and OML Notebooks</a>\n* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/');\">Oracle Machine Learning Notebooks</a>\n \n**Last Updated Date** - September 2021\n \nCopyright (c) 2021 Oracle Corporation \n###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0</a>\n---","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:48+0000","config":{"editorSetting":{"language":"md","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"results":{},"enabled":true,"editorHide":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<h2>Learn More</h2>\n<ul>\n<li><a href=\"https: //docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-notebooks-data-analysis-and-data-visualization1.html');\">Get Started with OML4Py and OML Notebooks</a></li>\n<li><a href=\"https: //docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/');\">Oracle Machine Learning Notebooks</a></li>\n</ul>\n<p><strong>Last Updated Date</strong> - September 2021</p>\n<p>Copyright (c) 2021 Oracle Corporation</p>\n<h6><a href=\"https: //oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n<hr />\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_1016371808","id":"20211001-190306_68494635","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:48+0000","dateFinished":"2021-09-22T20:18:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:386"},{"text":"%md\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:48+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown"},"settings":{"params":{},"forms":{}},"interrupted":false,"jobName":"paragraph_1633114986450_-1060179910","id":"20211001-190306_1958773210","dateCreated":"2021-04-23T13:27:18+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":false,"$$hashKey":"object:387"}],"id":"79628","noteParams":{},"noteForms":{},"angularObjects":{"ORA221A1BC345:OMLUSER:78556":[],"ORA36CEFD120D:OMLUSER:78556":[],"ORA9405AD2E1E:OMLUSER:78556":[],"MDW276C5A6A4D:shared_process":[]},"config":{"looknfeel":"default","personalizedMode":"false"},"info":{},"name":"Lab 1: Get Started with OML4Py on Autonomous Database"}
\ No newline at end of file
diff --git a/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json b/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json
index 1ea73aba..5d2a3527 100755
--- a/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json
+++ b/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json
@@ -84,7 +84,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n1.2. Here, you load the IRIS data and combine the target and predictors into a single DataFrame, which matches the form the data would have as a database table. You use the `oml.push` function to load this Pandas DataFrame into the database, which creates a temporary table and returns a proxy object that you assign to IRIS_TMP.\n\nSuch temporary tables will be automatically deleted when the database connection is terminated unless saved in a datastore. You learn more about datastore in lab 4.\nIn OML Notebooks, you use the notebook-context `z.show` method to display Python objects and proxy object content. \n  \nDisplay the first few rows of IRIS_TMP using `z.show` for displaying DataFrame results in the notebook viewer.",
+      "text": "%md\n\n1.2. Here, you load the IRIS data and combine the target and predictors into a single DataFrame, which matches the form the data would have as a database table. You use the `oml.push` function to load this Pandas DataFrame into the database, which creates a temporary table and returns a proxy object that you assign to IRIS_TMP.\n\nSuch temporary tables will be automatically deleted when the database connection is terminated unless saved in a datastore. You learn more about datastore in lab 4.\nIn OML Notebooks, you use the notebook-context `z.show` method to display Python objects and proxy object content. \n  \nDisplay the first few rows of IRIS_TMP using `z.show` for displaying DataFrame results in the notebook viewer.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:18+0000",
       "config": {
@@ -338,7 +338,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n1.4. Use the transparency layer function `shape` to view the shape, or number of rows and columns, of the IRIS table.",
+      "text": "%md\n\n1.4. Use the transparency layer function `shape` to view the shape, or number of rows and columns, of the IRIS table.",
       "user": "OMLUSER",
       "dateUpdated": "2021-10-01T19:04:33+0000",
       "config": {
@@ -416,7 +416,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n1.5. Use the transparency layer function `head()` to view the first three rows of the IRIS table.\n",
+      "text": "%md\n\n1.5. Use the transparency layer function `head()` to view the first three rows of the IRIS table.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:23+0000",
       "config": {
@@ -493,7 +493,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n1.6. Use the transparency layer function `describe()` to calculate descriptive statistics that summarize the central tendency, dispersion, and shape of the IRIS table in each numeric column.\n",
+      "text": "%md\n\n1.6. Use the transparency layer function `describe()` to calculate descriptive statistics that summarize the central tendency, dispersion, and shape of the IRIS table in each numeric column.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:25+0000",
       "config": {
@@ -706,7 +706,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.2 Run the following script to select specific columns by name and return the first three records with the specified column names.\n",
+      "text": "%md\n\n2.2 Run the following script to select specific columns by name and return the first three records with the specified column names.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:29+0000",
       "config": {
@@ -812,7 +812,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.3. Select the first four columns in `CUST_DF` using an index range. Note that Python uses `0` as a starting element for indexing. The starting element is the first column, `MARITAL_STATUS`. The second element is the second column, `STATE`, and so on.  The script returns the columns by specified by the index range.",
+      "text": "%md\n\n2.3. Select the first four columns in `CUST_DF` using an index range. Note that Python uses `0` as a starting element for indexing. The starting element is the first column, `MARITAL_STATUS`. The second element is the second column, `STATE`, and so on.  The script returns the columns by specified by the index range.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:30+0000",
       "config": {
@@ -922,7 +922,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.4. Run the following script to select columns by data type, specifying the data type as `oml.string`.  The script returns the MARITAL_STATUS, STATE, CUSTOMER_ID, GENDER,  PROFESSION, REGION, BUY_INSURANCE, LTV_BIN, FIRST_NAME, LAST_NAME columns.",
+      "text": "%md\n\n2.4. Run the following script to select columns by data type, specifying the data type as `oml.string`.  The script returns the MARITAL_STATUS, STATE, CUSTOMER_ID, GENDER,  PROFESSION, REGION, BUY_INSURANCE, LTV_BIN, FIRST_NAME, LAST_NAME columns.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:32+0000",
       "config": {
@@ -1148,7 +1148,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.2. This step shows an example of a compound row selection using the `OR` filtering condition, denoted by the `|` symbol. Run the following scripts to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` OR `STATE` is `equal to MA`:",
+      "text": "%md\n\n3.2. This step shows an example of a compound row selection using the `OR` filtering condition, denoted by the `|` symbol. Run the following scripts to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` OR `STATE` is `equal to MA`:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:35+0000",
       "config": {
@@ -1288,7 +1288,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.3. This step shows an example of a compound row selection using `AND`, denoted by the `\u0026` symbol. Run the following script to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` AND `BANK_FUNDS` is `greater than 5000`:\n",
+      "text": "%md\n\n3.3. This step shows an example of a compound row selection using `AND`, denoted by the `\u0026` symbol. Run the following script to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` AND `BANK_FUNDS` is `greater than 5000`:\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:36+0000",
       "config": {
@@ -1468,7 +1468,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.1.1. In this step, you use the `append()` function to append an `oml.Float` series object to another, and then append an `oml.DataFrame` object to another.\n\n**Note:** An `oml.Float` is numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n",
+      "text": "%md\n\n4.1.1. In this step, you use the `append()` function to append an `oml.Float` series object to another, and then append an `oml.DataFrame` object to another.\n\n**Note:** An `oml.Float` is numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:38+0000",
       "config": {
@@ -1579,7 +1579,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n### 4.2. Use the replace () function\n\n4.2.1. In this step, you use the `replace()` function to replace a particular value in columns of an OML dataframe. ",
+      "text": "%md\n\n### 4.2. Use the replace () function\n\n4.2.1. In this step, you use the `replace()` function to replace a particular value in columns of an OML dataframe. ",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:40+0000",
       "config": {
@@ -1865,7 +1865,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3.2. This step shows how to create an `oml.Float` object by taking product of `MORTGAGE_AMOUNT` column and `NUM_MORTGAGES` column, and then concatenate it with the preivous `oml.DataFrame`, with a new column name `MORTGAGE_TOTAL`.   \n**Note:** An oml.Float is a numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n\n",
+      "text": "%md\n\n4.3.2. This step shows how to create an `oml.Float` object by taking product of `MORTGAGE_AMOUNT` column and `NUM_MORTGAGES` column, and then concatenate it with the preivous `oml.DataFrame`, with a new column name `MORTGAGE_TOTAL`.   \n**Note:** An oml.Float is a numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:44+0000",
       "config": {
@@ -1974,7 +1974,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3.3. Concatenate object CUST_DF with ID2 and turn on automatic name conflict resolution. In this example, `auto_name\u003dTrue` controls whether to call automatic name conflict resolution if one or more column names are duplicates in the two data frames:\n",
+      "text": "%md\n\n4.3.3. Concatenate object CUST_DF with ID2 and turn on automatic name conflict resolution. In this example, `auto_name\u003dTrue` controls whether to call automatic name conflict resolution if one or more column names are duplicates in the two data frames:\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:45+0000",
       "config": {
@@ -2081,7 +2081,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3.4. Run the following script to concatenate multiple OML data objects and perform customized renaming. ",
+      "text": "%md\n\n4.3.4. Run the following script to concatenate multiple OML data objects and perform customized renaming. ",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:47+0000",
       "config": {
@@ -2299,7 +2299,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.4.2. Run the following script to perform a left outer join on SUBSET1 with the `oml.DataFrame` object on the shared column `CUSTOMER_ID` and apply the suffixes `.l` and `.r` to column names on the left and right side, respectively.\n",
+      "text": "%md\n\n4.4.2. Run the following script to perform a left outer join on SUBSET1 with the `oml.DataFrame` object on the shared column `CUSTOMER_ID` and apply the suffixes `.l` and `.r` to column names on the left and right side, respectively.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:55+0000",
       "config": {
@@ -2583,7 +2583,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.5.2 Run the script below to check the number of rows with `NULL` for `MARITAL_STATUS` in the `CUSTOMER_INSURANCE_LTV_NOISE` proxy object.  Then we check the top 5 records.",
+      "text": "%md\n\n4.5.2 Run the script below to check the number of rows with `NULL` for `MARITAL_STATUS` in the `CUSTOMER_INSURANCE_LTV_NOISE` proxy object.  Then we check the top 5 records.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:23:58+0000",
       "config": {
@@ -2779,7 +2779,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.5.3. Run the script to drop rows with any missing values and create a new proxy object `RES_DF`. Verify its shape and first 100 records.",
+      "text": "%md\n\n4.5.3. Run the script to drop rows with any missing values and create a new proxy object `RES_DF`. Verify its shape and first 100 records.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:24:00+0000",
       "config": {
@@ -2976,7 +2976,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.5.4. Run the following script to drop rows with missing values for a particular subset of colummns and create a new proxy object `RES_DF`. Verify its shape and first 100 records.  ",
+      "text": "%md\n\n4.5.4. Run the following script to drop rows with missing values for a particular subset of colummns and create a new proxy object `RES_DF`. Verify its shape and first 100 records.  ",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:24:01+0000",
       "config": {
@@ -3313,7 +3313,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.5.6. Use the `drop_duplicates()` function to drop duplicate rows.  In this case, because we are requesting only the columns `STATE` and `MARITAL_STATUS`, the final unique combination of these columns has a much smaller count than the original data. ",
+      "text": "%md\n\n4.5.6. Use the `drop_duplicates()` function to drop duplicate rows.  In this case, because we are requesting only the columns `STATE` and `MARITAL_STATUS`, the final unique combination of these columns has a much smaller count than the original data. ",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:24:04+0000",
       "config": {
@@ -3452,7 +3452,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.5.7. Run the following script to drop specific columns and create a new proxy object `DROPPED_DF`. Verify its shape and first 100 records.",
+      "text": "%md\n\n4.5.7. Run the following script to drop specific columns and create a new proxy object `DROPPED_DF`. Verify its shape and first 100 records.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:24:06+0000",
       "config": {
@@ -3722,7 +3722,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.2. Run the following script to split the data set into samples of 20% and 80% size.",
+      "text": "%md\n\n5.2. Run the following script to split the data set into samples of 20% and 80% size.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:11:29+0000",
       "config": {
@@ -3800,7 +3800,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.3. Run the following script to perform stratified sampling on the column `BUY_INSURANCE`. In this example, the column in which you perform stratified sampling is `BUY_INSURANCE`. Stratified sampling divides the data into different groups based on shared characteristics prior to sampling, ensuring that members from each subgroup is included in the analysis.",
+      "text": "%md\n\n5.3. Run the following script to perform stratified sampling on the column `BUY_INSURANCE`. In this example, the column in which you perform stratified sampling is `BUY_INSURANCE`. Stratified sampling divides the data into different groups based on shared characteristics prior to sampling, ensuring that members from each subgroup is included in the analysis.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:11:40+0000",
       "config": {
@@ -3878,7 +3878,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.4. Verify that the stratified sampling generates splits in which all of the different categories of `BUY_INSURANCE` (\u0027No\u0027 and \u0027Yes\u0027) are present in each split",
+      "text": "%md\n\n5.4. Verify that the stratified sampling generates splits in which all of the different categories of `BUY_INSURANCE` (\u0027No\u0027 and \u0027Yes\u0027) are present in each split",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:11:48+0000",
       "config": {
@@ -3957,7 +3957,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.5. Split by computing hash on the target column.",
+      "text": "%md\n\n5.5. Split by computing hash on the target column.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:11:56+0000",
       "config": {
@@ -4035,7 +4035,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.6. Verify that the different categories of `BUY_INSURANCE` are present in only one of the splits generated by hashing on the category column:",
+      "text": "%md\n\n5.6. Verify that the different categories of `BUY_INSURANCE` are present in only one of the splits generated by hashing on the category column:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:12:05+0000",
       "config": {
@@ -4113,7 +4113,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.7. Split the data randomly into 4 consecutive folds using the `KFold` function.",
+      "text": "%md\n\n5.7. Split the data randomly into 4 consecutive folds using the `KFold` function.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:12:12+0000",
       "config": {
@@ -4304,7 +4304,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n6.2. Use the `crosstab` function to find the categories that the most entries belonged to. This example shows how to use the `crosstab` function to find the count of state and gender customers in descending order in the CUST_DF OML frame",
+      "text": "%md\n\n6.2. Use the `crosstab` function to find the categories that the most entries belonged to. This example shows how to use the `crosstab` function to find the count of state and gender customers in descending order in the CUST_DF OML frame",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:12:38+0000",
       "config": {
@@ -4412,7 +4412,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n6.3. Use the `crosstab` function to find the ratio of the genders for State and across entries in the `CUST_DF` dataframe.",
+      "text": "%md\n\n6.3. Use the `crosstab` function to find the ratio of the genders for State and across entries in the `CUST_DF` dataframe.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:12:49+0000",
       "config": {
@@ -4520,7 +4520,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n6.4. Use the `pivot_table` function to find the mean bank funds across all State and gender combinations in the `CUST_DF` dataframe.",
+      "text": "%md\n\n6.4. Use the `pivot_table` function to find the mean bank funds across all State and gender combinations in the `CUST_DF` dataframe.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:13:01+0000",
       "config": {
@@ -4628,7 +4628,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n6.5. Use `pivot_table` function to find the maximum and minimum bank funds for every State and gender combination (plus the overall for all genders) in the `CUST_DF` dataframe:\n",
+      "text": "%md\n\n6.5. Use `pivot_table` function to find the maximum and minimum bank funds for every State and gender combination (plus the overall for all genders) in the `CUST_DF` dataframe:\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:13:10+0000",
       "config": {
@@ -4818,7 +4818,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n7.2. Run the following script to render the data in a histogram for `MORTGAGE_AMOUNT`.",
+      "text": "%md\n\n7.2. Run the following script to render the data in a histogram for `MORTGAGE_AMOUNT`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:13:33+0000",
       "config": {
@@ -5010,7 +5010,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n8.1.2. Run the following script to view all persistent database tables that match the name `IRIS`.",
+      "text": "%md\n\n8.1.2. Run the following script to view all persistent database tables that match the name `IRIS`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:14:05+0000",
       "config": {
@@ -5167,7 +5167,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n9.2. To close the cursor, run `cr.close`.\n",
+      "text": "%md\n\n9.2. To close the cursor, run `cr.close`.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-24T17:18:30+0000",
       "config": {
diff --git a/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json b/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json
index ada4e6ec..4018b3dd 100755
--- a/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json
+++ b/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json
@@ -147,7 +147,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.2. Run the following command to display the first few rows of table `CUST_DF`:",
+      "text": "%md\n\n2.2. Run the following command to display the first few rows of table `CUST_DF`:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:09+0000",
       "config": {
@@ -283,7 +283,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.3. Run the following script to randomly split and select the data into 60% for train and 40% for test.",
+      "text": "%md\n\n2.3. Run the following script to randomly split and select the data into 60% for train and 40% for test.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:11+0000",
       "config": {
@@ -356,7 +356,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.4. Now, build a GLM regression model for predicting lifetime value `LTV` using the `oml.glm` function.\n\nThis method runs the `oml.glm` algorithm in-database using the given settings. The settings are supplied as key-value pairs. In this example, feature generation and feature selection are specified.\n\n**Note:** For a complete list of settings, refer to the OML4Py product documentation. You may also refer to the `oml.glm` help file.\n",
+      "text": "%md\n\n2.4. Now, build a GLM regression model for predicting lifetime value `LTV` using the `oml.glm` function.\n\nThis method runs the `oml.glm` algorithm in-database using the given settings. The settings are supplied as key-value pairs. In this example, feature generation and feature selection are specified.\n\n**Note:** For a complete list of settings, refer to the OML4Py product documentation. You may also refer to the `oml.glm` help file.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:13+0000",
       "config": {
@@ -434,7 +434,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.5. Run the following script to view model fit details to understand the key statistics of the model. Locate the values of Root Mean Square Error `ROOT_MEAN_SQ` and R-squared `R_SQ` from the output. RMSE and R-squared are used to evaluate baseline performance of the model.\n\n* RMSE is a measure of the differences between values predicted by a model and the values observed. A good model should have a low RMSE. But at the same time, a model with very low RMSE has the potential to overfit.\n* R-Squared is a statistical measure that represents the goodness of fit of a regression model. The ideal value for R-squared is 1. The closer the value of R-squared is to 1, the better the model fit. For instance, if the R-squared of a model is 0.50, then approximately half of the observed variation can be explained by the model\u0027s inputs",
+      "text": "%md\n\n2.5. Run the following script to view model fit details to understand the key statistics of the model. Locate the values of Root Mean Square Error `ROOT_MEAN_SQ` and R-squared `R_SQ` from the output. RMSE and R-squared are used to evaluate baseline performance of the model.\n\n* RMSE is a measure of the differences between values predicted by a model and the values observed. A good model should have a low RMSE. But at the same time, a model with very low RMSE has the potential to overfit.\n* R-Squared is a statistical measure that represents the goodness of fit of a regression model. The ideal value for R-squared is 1. The closer the value of R-squared is to 1, the better the model fit. For instance, if the R-squared of a model is 0.50, then approximately half of the observed variation can be explained by the model\u0027s inputs",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:30+0000",
       "config": {
@@ -541,7 +541,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.6. Run the following command to display and view the model coefficients:",
+      "text": "%md\n\n2.6. Run the following command to display and view the model coefficients:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:32+0000",
       "config": {
@@ -652,7 +652,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.7. Run the following script to make predictions using the test data and display the results.  From the RES_DF DataFrame proxy object, the predicted values and lifetime value are displayed in the `PREDICTION` and `LTV` columns respectively.\n",
+      "text": "%md\n\n2.7. Run the following script to make predictions using the test data and display the results.  From the RES_DF DataFrame proxy object, the predicted values and lifetime value are displayed in the `PREDICTION` and `LTV` columns respectively.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:34+0000",
       "config": {
@@ -760,7 +760,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.8. Run the following command to plot the predicted versus the actual years of residence and then click the **Scatter Chart** icon to see the visualization. Click **Settings** to see how the plot was specified.",
+      "text": "%md\n\n2.8. Run the following command to plot the predicted versus the actual years of residence and then click the **Scatter Chart** icon to see the visualization. Click **Settings** to see how the plot was specified.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:36+0000",
       "config": {
@@ -868,7 +868,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.9. Using matplotlib, plot the predicted and actual years of residence and visually compare it against the perfect fit line, `y\u003dx` in `red`.  The plot indicates how far the prediction deviated from actual value, which is known as the prediction error. Ideally, the predictions will converge to the perfect fit line.",
+      "text": "%md\n\n2.9. Using matplotlib, plot the predicted and actual years of residence and visually compare it against the perfect fit line, `y\u003dx` in `red`.  The plot indicates how far the prediction deviated from actual value, which is known as the prediction error. Ideally, the predictions will converge to the perfect fit line.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:38+0000",
       "config": {
@@ -908,7 +908,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n\n\n2.10. Run the following script to plot the residuals using matplotlib. Ideally prediction errors (the residuals) would show up as small random noise around the perfect fit `y\u003d0` line in `red`. When there are strange patterns found, usually that means that some other confounding effect is at play, and further investigation is required to identify more attributes that might not be considered currently.",
+      "text": "%md\n\n\n\n2.10. Run the following script to plot the residuals using matplotlib. Ideally prediction errors (the residuals) would show up as small random noise around the perfect fit `y\u003d0` line in `red`. When there are strange patterns found, usually that means that some other confounding effect is at play, and further investigation is required to identify more attributes that might not be considered currently.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:39+0000",
       "config": {
@@ -956,7 +956,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---",
+      "text": "%md\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:40+0000",
       "config": {
@@ -1071,7 +1071,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.11. Run the following script to calculate the RMSE manually on the prediction results on the testing test and the R-  squared on the testing set using the score method.\n\n**Note:** Both the RMSE and R-squared calculations are similar to the values produced by `oml.glm`.",
+      "text": "%md\n\n2.11. Run the following script to calculate the RMSE manually on the prediction results on the testing test and the R-  squared on the testing set using the score method.\n\n**Note:** Both the RMSE and R-squared calculations are similar to the values produced by `oml.glm`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:45+0000",
       "config": {
@@ -1222,7 +1222,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.2. To display and view the K-means model details, run the following command:",
+      "text": "%md\n\n3.2. To display and view the K-means model details, run the following command:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:53+0000",
       "config": {
@@ -1300,7 +1300,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.3. To view the cluster details, run the following command. The command displays the cluster details for all clusters in the hierarchy with row counts and dispersion.\nThe dispersion value is a measure of how compact or how spread out the data is within a cluster. The dispersion value is a number greater than 0.  The lower the dispersion value, the more compact the cluster, that is, the data points are closer to the centroid of the cluster. A larger dispersion value indicates that the data points are more disperse or spread out from the centroid.\n",
+      "text": "%md\n\n3.3. To view the cluster details, run the following command. The command displays the cluster details for all clusters in the hierarchy with row counts and dispersion.\nThe dispersion value is a measure of how compact or how spread out the data is within a cluster. The dispersion value is a number greater than 0.  The lower the dispersion value, the more compact the cluster, that is, the data points are closer to the centroid of the cluster. A larger dispersion value indicates that the data points are more disperse or spread out from the centroid.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:55+0000",
       "config": {
@@ -1410,7 +1410,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.4. Run the following script to display the taxonomy. The taxonomy shows the hierarchy of the child clusters in relationship to the parent clusters.",
+      "text": "%md\n\n3.4. Run the following script to display the taxonomy. The taxonomy shows the hierarchy of the child clusters in relationship to the parent clusters.",
       "user": "OMLUSER",
       "dateUpdated": "2021-10-01T19:24:47+0000",
       "config": {
@@ -1517,7 +1517,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.5. Run the following command to predict the cluster membership.  The `supplemental_cols` argument carries the target column to the output to retain the relationship between the predictions and their original preditor values. These predictors may include a case id, for example to join with other tables, or multiple (or all) columns of the scoring data. You should be aware that unlike Pandas DataFrames, which are explicitly ordered in memory, results from relational databases do not have a specific order unless explicitly specified by an `ORDER BY` clause. As such, you cannot rely on results to maintain the same order across different data sets (tables and DataFrame proxy objects).",
+      "text": "%md\n\n3.5. Run the following command to predict the cluster membership.  The `supplemental_cols` argument carries the target column to the output to retain the relationship between the predictions and their original preditor values. These predictors may include a case id, for example to join with other tables, or multiple (or all) columns of the scoring data. You should be aware that unlike Pandas DataFrames, which are explicitly ordered in memory, results from relational databases do not have a specific order unless explicitly specified by an `ORDER BY` clause. As such, you cannot rely on results to maintain the same order across different data sets (tables and DataFrame proxy objects).",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:50:59+0000",
       "config": {
@@ -1590,7 +1590,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.6. Run the following command to view the pred computed in step 5.",
+      "text": "%md\n\n3.6. Run the following command to view the pred computed in step 5.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:51:01+0000",
       "config": {
@@ -1727,7 +1727,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.7. Run the following command to view the cluster results using a matplotlib scatterplot. We are using a sample of 10% of the total data (by using `sample(0.1)`) to make it more visible.",
+      "text": "%md\n\n3.7. Run the following command to view the cluster results using a matplotlib scatterplot. We are using a sample of 10% of the total data (by using `sample(0.1)`) to make it more visible.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T20:59:36+0000",
       "config": {
@@ -1878,7 +1878,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.2. Build a partitioned model using the SVM algorithm to predict `LTV`, partitioned by `GENDER`.  The script builds an SVM partitioned model. Scroll down the notebook paragraph for complete details of the model.\n",
+      "text": "%md\n\n4.2. Build a partitioned model using the SVM algorithm to predict `LTV`, partitioned by `GENDER`.  The script builds an SVM partitioned model. Scroll down the notebook paragraph for complete details of the model.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:03:34+0000",
       "config": {
@@ -1956,7 +1956,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3. Run the following script to predict on the test set and display prediction result. Note the use of the top level model only.\n\nThe script makes predictions using the test data returning an OML DataFrame proxy object and displays the result in a table. The predicted values are listed in the PREDICTION column.",
+      "text": "%md\n\n4.3. Run the following script to predict on the test set and display prediction result. Note the use of the top level model only.\n\nThe script makes predictions using the test data returning an OML DataFrame proxy object and displays the result in a table. The predicted values are listed in the PREDICTION column.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:03:40+0000",
       "config": {
@@ -2094,7 +2094,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.4. Run the following command to show the model global statistics for each partitioned sub-model. The partition name column contains the values from the partition column. If multiple columns were specified, then there would be one column for each with corresponding value.\n",
+      "text": "%md\n\n4.4. Run the following command to show the model global statistics for each partitioned sub-model. The partition name column contains the values from the partition column. If multiple columns were specified, then there would be one column for each with corresponding value.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:03:42+0000",
       "config": {
@@ -2202,7 +2202,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\nUse Python to score data and display prediction details for every customer by using the `topN_attrs \u003d True` argument to the `.predict` call.  The prediction details return the actual value of attributes used for scoring and the relative importance of the attributes in determining the score of each customer, in descending order of importance. ",
+      "text": "%md\n\nUse Python to score data and display prediction details for every customer by using the `topN_attrs \u003d True` argument to the `.predict` call.  The prediction details return the actual value of attributes used for scoring and the relative importance of the attributes in determining the score of each customer, in descending order of importance. ",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:03:44+0000",
       "config": {
@@ -2325,7 +2325,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.5. Run the following command to materialize the test dataset. The `materialize` method creates a new database table based on the content specified by an OML DataFrame proxy object.\n\nHere, you materialize the data to table `TEST_DATA` so that it can be queried from SQL.",
+      "text": "%md\n\n4.5. Run the following command to materialize the test dataset. The `materialize` method creates a new database table based on the content specified by an OML DataFrame proxy object.\n\nHere, you materialize the data to table `TEST_DATA` so that it can be queried from SQL.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:03:56+0000",
       "config": {
@@ -2398,7 +2398,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.6. Use SQL to score data and display prediction details.",
+      "text": "%md\n\n4.6. Use SQL to score data and display prediction details.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:03:59+0000",
       "config": {
@@ -2581,7 +2581,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.2. Split the data set into train and test sets.",
+      "text": "%md\n\n5.2. Split the data set into train and test sets.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:15:22+0000",
       "config": {
@@ -2654,7 +2654,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.3. Train an SVM model.",
+      "text": "%md\n\n5.3. Train an SVM model.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:15:28+0000",
       "config": {
@@ -2732,7 +2732,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.4. Create the MLX Global Feature Importance explainer gfi, using the `f1_weighted` metric.",
+      "text": "%md\n\n5.4. Create the MLX Global Feature Importance explainer gfi, using the `f1_weighted` metric.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:04:17+0000",
       "config": {
@@ -2805,7 +2805,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.5. Run the explainer `gfi.explain` to generate the global feature importance for the test data. The explainer returns the list of attributes in order of importance to the model.\n\nNote: this can take almost a minute, depending on your configuration.",
+      "text": "%md\n\n5.5. Run the explainer `gfi.explain` to generate the global feature importance for the test data. The explainer returns the list of attributes in order of importance to the model.\n\nNote: this can take almost a minute, depending on your configuration.",
       "user": "OMLUSER",
       "dateUpdated": "2021-10-01T19:26:56+0000",
       "config": {
@@ -2883,7 +2883,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\nAnother way of seeing the Global Feature Importance results with the `z.show` function in Zeppelin",
+      "text": "%md\n\nAnother way of seeing the Global Feature Importance results with the `z.show` function in Zeppelin",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T19:05:02+0000",
       "config": {
diff --git a/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json b/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json
index 3bc56d60..0bedba07 100755
--- a/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json
+++ b/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json
@@ -152,7 +152,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.2. Run the following script to create the temporary Diabetes table:",
+      "text": "%md\n\n2.2. Run the following script to create the temporary Diabetes table:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:30:49+0000",
       "config": {
@@ -230,7 +230,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.3. Run the following script to create the Boston table:",
+      "text": "%md\n\n2.3. Run the following script to create the Boston table:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:30:50+0000",
       "config": {
@@ -381,7 +381,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.2. Save the `DIABETES_TMP` tables into the database.\n\n**Note:** The condition `append\u003dTRUE` adds the object to the datastore, if it already exists.  The default is `append\u003dFalse`, and in that case, you will receive an error stating that the datastore exists and it won\u0027t be able to create it again.",
+      "text": "%md\n\n3.2. Save the `DIABETES_TMP` tables into the database.\n\n**Note:** The condition `append\u003dTRUE` adds the object to the datastore, if it already exists.  The default is `append\u003dFalse`, and in that case, you will receive an error stating that the datastore exists and it won\u0027t be able to create it again.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:00+0000",
       "config": {
@@ -454,7 +454,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.3. Save the `IRIS` table to a new datastore, and then list the datastores. Notice that you see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. The two datastores `ds_iris_data` and `ds_pydata` are present, with the latter containing the three objects you added.",
+      "text": "%md\n\n3.3. Save the `IRIS` table to a new datastore, and then list the datastores. Notice that you see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. The two datastores `ds_iris_data` and `ds_pydata` are present, with the latter containing the three objects you added.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:03+0000",
       "config": {
@@ -637,7 +637,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.2. Run the following script to save the objects `regr1` and `regr2` to the datastore `ds_pymodels`, and allow the read privilege to be granted to them.\n\n**Note:** `overwrite\u003dTrue` indicates that the contents of the datastore should be replaced.",
+      "text": "%md\n\n4.2. Run the following script to save the objects `regr1` and `regr2` to the datastore `ds_pymodels`, and allow the read privilege to be granted to them.\n\n**Note:** `overwrite\u003dTrue` indicates that the contents of the datastore should be replaced.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:08+0000",
       "config": {
@@ -747,7 +747,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3. Now grant the read privilege to all users by specifying `user\u003dNone`. Finally, list the datastores to which the read privilege has been granted.",
+      "text": "%md\n\n4.3. Now grant the read privilege to all users by specifying `user\u003dNone`. Finally, list the datastores to which the read privilege has been granted.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:09+0000",
       "config": {
@@ -903,7 +903,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.2. Run the following script to load the named Python object `regr2` (regression model), from the datastore to the global workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.",
+      "text": "%md\n\n5.2. Run the following script to load the named Python object `regr2` (regression model), from the datastore to the global workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:22+0000",
       "config": {
@@ -981,7 +981,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.3. Run the following script to view the model details",
+      "text": "%md\n\n5.3. Run the following script to view the model details",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:29+0000",
       "config": {
@@ -1059,7 +1059,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.4. Run the following script to load the named Python object `regr1`, from the datastore to the user\u0027s workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.",
+      "text": "%md\n\n5.4. Run the following script to load the named Python object `regr1`, from the datastore to the user\u0027s workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:30+0000",
       "config": {
@@ -1245,7 +1245,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n6.2. Run the following script to list the datastores to which other users have been granted the read privilege:",
+      "text": "%md\n\n6.2. Run the following script to list the datastores to which other users have been granted the read privilege:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:38+0000",
       "config": {
@@ -1512,7 +1512,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n8.2. Run the following script to grant read privilege to `OMLUSER2`.\n\nNote: If you are running this Notebook on your own tenancy, make sure to follow the instructions on creationg a new OMLUSER2, otherwise you will get an error. ",
+      "text": "%md\n\n8.2. Run the following script to grant read privilege to `OMLUSER2`.\n\nNote: If you are running this Notebook on your own tenancy, make sure to follow the instructions on creationg a new OMLUSER2, otherwise you will get an error. ",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:46+0000",
       "config": {
@@ -1772,7 +1772,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n10.2. Run the following script to view the string that you just created:",
+      "text": "%md\n\n10.2. Run the following script to view the string that you just created:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:55+0000",
       "config": {
@@ -1921,7 +1921,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n11.2. Run the `oml.script.dir` script to list the scripts to which the read privilege has been granted, and where `sctype` is set to `grant`.",
+      "text": "%md\n\n11.2. Run the `oml.script.dir` script to list the scripts to which the read privilege has been granted, and where `sctype` is set to `grant`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:57+0000",
       "config": {
@@ -1998,7 +1998,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n11.3. Run the following script to load the named function `MyLM_function` into the Python engine for use as a typical Python function using  `oml.script.load`.",
+      "text": "%md\n\n11.3. Run the following script to load the named function `MyLM_function` into the Python engine for use as a typical Python function using  `oml.script.load`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:31:59+0000",
       "config": {
@@ -2075,7 +2075,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n11.4. Extract the function text string from the function object and use this to save in the script repository using `oml.script_create`.",
+      "text": "%md\n\n11.4. Extract the function text string from the function object and use this to save in the script repository using `oml.script_create`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:32:00+0000",
       "config": {
@@ -2152,7 +2152,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n11.5. Run the script `oml.script.create` to create a test function `MyTEST_function`:",
+      "text": "%md\n\n11.5. Run the script `oml.script.create` to create a test function `MyTEST_function`:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:32:01+0000",
       "config": {
@@ -2224,7 +2224,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n11.6. Use `oml.script.dir` to list all the available scripts.",
+      "text": "%md\n\n11.6. Use `oml.script.dir` to list all the available scripts.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:32:02+0000",
       "config": {
@@ -2333,7 +2333,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n11.7. Call the `table_apply` on `build_lm_str` and `loaded_str` functions. Note that these strings represent the same function `build_lm_str` that was saved to the script repository after assigning the function to a string object. The `loaded_str` is the string representation of the function extracted using `get_source().read()`.",
+      "text": "%md\n\n11.7. Call the `table_apply` on `build_lm_str` and `loaded_str` functions. Note that these strings represent the same function `build_lm_str` that was saved to the script repository after assigning the function to a string object. The `loaded_str` is the string representation of the function extracted using `get_source().read()`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:32:04+0000",
       "config": {
@@ -2410,7 +2410,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\nRun the same function on `loaded_str`:",
+      "text": "%md\n\nRun the same function on `loaded_str`:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:32:05+0000",
       "config": {
@@ -2559,7 +2559,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n12.2. Call the function `build_lm3` to build the model or model `MyGlobalML_function`:",
+      "text": "%md\n\n12.2. Call the function `build_lm3` to build the model or model `MyGlobalML_function`:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T21:32:07+0000",
       "config": {
diff --git a/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json b/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json
index e7f489d9..19749839 100755
--- a/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json
+++ b/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json
@@ -75,7 +75,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n1.2. Run the following script to obtain a proxy object to the `IRIS` table.",
+      "text": "%md\n\n1.2. Run the following script to obtain a proxy object to the `IRIS` table.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:11:38+0000",
       "config": {
@@ -233,7 +233,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.2. Run the script to predict the petal length using the `predict` function, and show the first 10 records.",
+      "text": "%md\n\n2.2. Run the script to predict the petal length using the `predict` function, and show the first 10 records.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:11:42+0000",
       "config": {
@@ -312,7 +312,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.3. Run the following script to assess model quality using mean squared error and R^2:",
+      "text": "%md\n\n2.3. Run the following script to assess model quality using mean squared error and R^2:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:11:44+0000",
       "config": {
@@ -391,7 +391,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.4. Run the following script to generate a scatterplot of the data along with a plot of the regression line:",
+      "text": "%md\n\n2.4. Run the following script to generate a scatterplot of the data along with a plot of the regression line:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:11:46+0000",
       "config": {
@@ -544,7 +544,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.2. Now, call the user-defined function `build_lm_1` to build the model and plot the petal length predictions.:",
+      "text": "%md\n\n3.2. Now, call the user-defined function `build_lm_1` to build the model and plot the petal length predictions.:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:11:51+0000",
       "config": {
@@ -697,7 +697,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.4. By calling the `table_apply`, a Python engine is spawned and the user-defined function `build_lm_1` is called on that engine with the data referenced by IRIS being passed in as a pandas DataFrame. Part of the return value is the image, which is automatically displayed. In this example, we are passing the function object to the `table_apply` function.",
+      "text": "%md\n\n3.4. By calling the `table_apply`, a Python engine is spawned and the user-defined function `build_lm_1` is called on that engine with the data referenced by IRIS being passed in as a pandas DataFrame. Part of the return value is the image, which is automatically displayed. In this example, we are passing the function object to the `table_apply` function.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:11:56+0000",
       "config": {
@@ -810,7 +810,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.5. Run the following script to print the object, model, type and coefficient.",
+      "text": "%md\n\n3.5. Run the following script to print the object, model, type and coefficient.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:01+0000",
       "config": {
@@ -963,7 +963,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n3.7. Use the `row_apply` to call this user-defined function and return a single DataFrame proxy object as the result. The `row_apply` function takes as arguments the proxy object `IRIS`, that we want 10 rows scored at a time (resulting in 15 function calls), the user-defined function, the linear model object, and that we want the result to be returned as a single table by specifying the table definition.",
+      "text": "%md\n\n3.7. Use the `row_apply` to call this user-defined function and return a single DataFrame proxy object as the result. The `row_apply` function takes as arguments the proxy object `IRIS`, that we want 10 rows scored at a time (resulting in 15 function calls), the user-defined function, the linear model object, and that we want the result to be returned as a single table by specifying the table definition.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:05+0000",
       "config": {
@@ -1151,7 +1151,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.2. Change the user-defined function to save the models in a datastore. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in the Python environment. Here, you construct a name concatenating `mod_` as a prefix and the corresponding `Species` value.",
+      "text": "%md\n\n4.2. Change the user-defined function to save the models in a datastore. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in the Python environment. Here, you construct a name concatenating `mod_` as a prefix and the corresponding `Species` value.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:11+0000",
       "config": {
@@ -1225,7 +1225,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3. Use `group_apply` to call the user-defined function and list the resulting models, which are a dictionary of three elements each assigned the model object name. The `group_apply` function takes the data, the index parameter that specifies the column or columns to partition on, the user-defined function, and the database to which you connect from the Python engine. Connecting to the database is necessary when using the datastore functionality.\n\nHere, the model object names are `mod_versicolor`, `mod_virginica`, and `mod_setosa`.\nWhen you load the datastore, you get the three models loaded into the client Python engine, assigned to their respective variables.\n\n**Note:** If the datastore exists, then delete it so that the `group_apply` function completes successfully.\n**Note:** Embedded Python execution can also leverage functions from third-party packages, for example, sklearn and matplotlib, as provided with the Autonomous Database environment. These packages can be used inside the user-defined function as shown here using LinearSVC.",
+      "text": "%md\n\n4.3. Use `group_apply` to call the user-defined function and list the resulting models, which are a dictionary of three elements each assigned the model object name. The `group_apply` function takes the data, the index parameter that specifies the column or columns to partition on, the user-defined function, and the database to which you connect from the Python engine. Connecting to the database is necessary when using the datastore functionality.\n\nHere, the model object names are `mod_versicolor`, `mod_virginica`, and `mod_setosa`.\nWhen you load the datastore, you get the three models loaded into the client Python engine, assigned to their respective variables.\n\n**Note:** If the datastore exists, then delete it so that the `group_apply` function completes successfully.\n**Note:** Embedded Python execution can also leverage functions from third-party packages, for example, sklearn and matplotlib, as provided with the Autonomous Database environment. These packages can be used inside the user-defined function as shown here using LinearSVC.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:13+0000",
       "config": {
@@ -1542,7 +1542,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.2. Use the `oml.do_eval` function to call the function `RandomRedDots` that you created in step 1:",
+      "text": "%md\n\n5.2. Use the `oml.do_eval` function to call the function `RandomRedDots` that you created in step 1:",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:24+0000",
       "config": {
@@ -1664,7 +1664,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n5.3. In this example, you modify the function to use subplots, thereby creating separate figure objects for the scatter plots. Store this in the script repository as `RandomRedDots2` and call the function to see the results. As expected, you get both plots.\nRun the following script to define the `RandomRedDots2` function that generates two scatter plots, and returns a two column DataFrame. Note that you can pass arguments to these functions here, `num_dots_1` and `num_dots_2`.",
+      "text": "%md\n\n5.3. In this example, you modify the function to use subplots, thereby creating separate figure objects for the scatter plots. Store this in the script repository as `RandomRedDots2` and call the function to see the results. As expected, you get both plots.\nRun the following script to define the `RandomRedDots2` function that generates two scatter plots, and returns a two column DataFrame. Note that you can pass arguments to these functions here, `num_dots_1` and `num_dots_2`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:32+0000",
       "config": {
@@ -1743,7 +1743,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n**Note:** When you call `RandomRedDots2` using embedded Python execution, you will get both plots as shown in the result.\n\n5.4. Use the `oml.do_eval` function to call the function `RandomRedDots2`. Here, you are specifying arguments to `do_eval` for `num_dots_1` and `num_dots_2`. These are specified as you would any other argument to `do_eval`. This applies to the other embedded Python functions as well.\n",
+      "text": "%md\n\n**Note:** When you call `RandomRedDots2` using embedded Python execution, you will get both plots as shown in the result.\n\n5.4. Use the `oml.do_eval` function to call the function `RandomRedDots2`. Here, you are specifying arguments to `do_eval` for `num_dots_1` and `num_dots_2`. These are specified as you would any other argument to `do_eval`. This applies to the other embedded Python functions as well.\n",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-22T23:12:34+0000",
       "config": {
diff --git a/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json b/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json
index 95e06c34..26c303dc 100755
--- a/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json
+++ b/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json
@@ -74,7 +74,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n1.2. Use the `oml.sync` function to create an OML Dataframe `CUST_DF` as a proxy for the database table `CUSTOMER_INSURANCE_LTV`.",
+      "text": "%md\n\n1.2. Use the `oml.sync` function to create an OML Dataframe `CUST_DF` as a proxy for the database table `CUSTOMER_INSURANCE_LTV`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-30T04:59:31+0000",
       "config": {
@@ -226,7 +226,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n2.2. Run the following script to select the top `classification` algorithms for predicting whether a customer will buy insurance (`BUY_INSURANCE`). It displays the top 4 ranked algorithms (requested by the `k\u003d4`) and their `accuracy` (the metric we requested in `score_metric`).  \n\nThe script returns Random Forest (rf), Decision Tree (dt), Neural Networks (nn) and Generalized Linear Models (glm) as the Top 4 algorithms, and among these the Random Forest is ranked highest for this problem, and the string `selected_insur_alg` will be used in subsequent AutoML function calls.\n \n**Note**: AutoML also supports regression, and the AlgorithmSelection function has other options and metrics that can be found in the documentation links at the bottom of this notebook.",
+      "text": "%md\n\n2.2. Run the following script to select the top `classification` algorithms for predicting whether a customer will buy insurance (`BUY_INSURANCE`). It displays the top 4 ranked algorithms (requested by the `k\u003d4`) and their `accuracy` (the metric we requested in `score_metric`).  \n\nThe script returns Random Forest (rf), Decision Tree (dt), Neural Networks (nn) and Generalized Linear Models (glm) as the Top 4 algorithms, and among these the Random Forest is ranked highest for this problem, and the string `selected_insur_alg` will be used in subsequent AutoML function calls.\n \n**Note**: AutoML also supports regression, and the AlgorithmSelection function has other options and metrics that can be found in the documentation links at the bottom of this notebook.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-30T04:59:35+0000",
       "config": {
@@ -500,7 +500,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.2. Run the following script to **list the hyperparameters and their values** that were tried for the `top two models`, along with the corresponding model\u0027s score metric value (`accuracy`).",
+      "text": "%md\n\n4.2. Run the following script to **list the hyperparameters and their values** that were tried for the `top two models`, along with the corresponding model\u0027s score metric value (`accuracy`).",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-30T05:01:48+0000",
       "config": {
@@ -578,7 +578,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3. Run the following script to **list the hyperparameters and their values** that were tried for the `top ten models`, along with the corresponding model\u0027s score metric value (`accuracy`) in a better formatted way.",
+      "text": "%md\n\n4.3. Run the following script to **list the hyperparameters and their values** that were tried for the `top ten models`, along with the corresponding model\u0027s score metric value (`accuracy`) in a better formatted way.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-30T05:01:50+0000",
       "config": {
@@ -730,7 +730,7 @@
       "commited": true
     },
     {
-      "text": "%md\n---\n4.3. Run the following script to specify a custom search space to explore for model building using the `param_space` argument to the `tune` function. With this specification, model tuning will narrow the set of important hyperparameter values.\n**Note**: For illustration purposes we are using a different scoring metric, `f1_macro`.",
+      "text": "%md\n\n4.3. Run the following script to specify a custom search space to explore for model building using the `param_space` argument to the `tune` function. With this specification, model tuning will narrow the set of important hyperparameter values.\n**Note**: For illustration purposes we are using a different scoring metric, `f1_macro`.",
       "user": "OMLUSER",
       "dateUpdated": "2021-09-30T05:01:53+0000",
       "config": {
diff --git a/machine-learning/labs/oml4py-live-labs/README.md b/machine-learning/labs/oml4py-live-labs/README.md
index e5109c82..e39a7230 100644
--- a/machine-learning/labs/oml4py-live-labs/README.md
+++ b/machine-learning/labs/oml4py-live-labs/README.md
@@ -1,9 +1,9 @@
 # Oracle Machine Learning for Python
-This set of notebooks from the OML4Py workshop [Introduction to Oracle Machine Learning for Python on Autonomous Database](https://bit.ly/oml4pyhol) introduces you to Oracle Machine Learning for Python (OML4Py) on Oracle Autonomous Database. 
+This set of notebooks from the OML4Py workshop [Introduction to Oracle Machine Learning for Python on Autonomous AI Database](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=786) introduces you to Oracle Machine Learning for Python (OML4Py) on Oracle Autonomous Database. 
 
 Oracle Machine Learning for Python (OML4Py) supports scalable in-database data exploration and preparation using native Python syntax, scalable in-database algorithms for machine learning model building and scoring, and automated machine learning (AutoML). Users can also invoke user-defined Python functions from Python and REST APIs using database-spawned Python engines. OML4Py increases data scientist productivity and reduces solution deployment complexity. Join us for this tour of OML4Py.
 
-Python is a major programming language used for data science and machine learning. OML4Py is a feature on Oracle Autonomous Database that provides Python users access to powerful in-database functionality supporting data scientists for both scalability, performance, and ease of solution deployment. 
+Python is a major programming language used for data science and machine learning. OML4Py is a feature on Oracle Autonomous AI Database that provides Python users access to powerful in-database functionality supporting data scientists for both scalability, performance, and ease of solution deployment. 
 
 Oracle Machine Learning Notebooks is a collaborative user interface for data scientists and business and data analysts who perform machine learning in Oracle Autonomous Database.   
 
@@ -13,7 +13,7 @@ Key Features:
 * Enables sharing of notebooks and templates with permissions and execution scheduling 
 * Access to 30+ parallel, scalable in-Database implementations of machine learning algorithms
 * Python, SQL and PL/SQL scripting language supported
-* Enables and supports deployments of enterprise machine learning methodologies in Autonomous Data Warehouse (ADW), Autonomous Transactional Database (ATP) and Autonomous JSON Database (AJD)
+* Enables and supports deployments of enterprise machine learning methodologies in Autonomous AI Lakehouse, Autonomous AI Transactional Database and Autonomous AI JSON Database
 
 The current folder contains the examples based on Oracle Machine Learning for Python (OML4Py) used for the Live Labs "Python Users: Build intelligent applications faster with Oracle Machine Learning":
 
@@ -25,12 +25,12 @@ The current folder contains the examples based on Oracle Machine Learning for Py
  * **Lab 5 OML4Py Embedded Python Execution** - Examples of using open-source Python scripts and algorithms (like SciKit-Learn) with OML4Py
  * **Lab 6 OML4Py AutoML** - Examples of how to execute OML AutoML processes to identify the ideal algorithms and tune the models.
 
-More information on the Live Labs workshop [Introduction to Oracle Machine Learning for Python on Autonomous Database](https://bit.ly/oml4pyhol)
+More information on the Live Labs workshop [Introduction to Oracle Machine Learning for Python on Autonomous Database](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=786)
 
-See also [Announcing next generation OML Notebooks on Oracle Autonomous Database](https://blogs.oracle.com/machinelearning/post/announcing-next-generation-to-oml-notebooks-on-oracle-autonomous-database) blog post for more information on OML Notebooks.
+See also [Announcing next generation OML Notebooks on Oracle Autonomous AI Database](https://blogs.oracle.com/machinelearning/post/announcing-next-generation-to-oml-notebooks-on-oracle-autonomous-database) blog post for more information on OML Notebooks.
 
-Last updated: April 2023
+Last updated: November 2025
 
-#### Copyright (c) 2023 Oracle Corporation and/or its affilitiates.
+#### Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
 
 ###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)
diff --git a/machine-learning/notebooks/notebooks-classic/python/OML Run-me-first.json b/machine-learning/notebooks-oml/notebooks-classic/python/OML Run-me-first.json
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Run-me-first","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1737138360559,"interpreter":"md.low","endTime":1737138361104,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Run-me-first: Data preparation and creation for examples","","In this notebook, we will load data used in other notebooks that require the `CUSTOMER_INSURANCE_LTV` table, and will also prepare the dataset used by the `Data Preparation` notebooks, in particular those for data cleaning and feature selection.","","The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.","","We will take the original table and will tweak the data, with some original values being replaced by `NULL`, and adding `duplicated rows` artificially to generate a resulting table we will call `CUSTOMER_INSURANCE_LTV_NOISE`. ","","The source of the data is a CSV file stored in <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\"> Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the `DBMS_CLOUD.CREATE_EXTERNAL_TABLE` function among others.","","###### `IMPORTANT` Please run this notebook before running any of the Data Preparation notebooks.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737138361570,"interpreter":"md.low","endTime":1737138362088,"results":[{"message":"<h1 id=\"oml-run-me-first-data-preparation-and-creation-for-examples\">OML Run-me-first: Data preparation and creation for examples<\/h1>\n<p>In this notebook, we will load data used in other notebooks that require the <code>CUSTOMER_INSURANCE_LTV<\/code> table, and will also prepare the dataset used by the <code>Data Preparation<\/code> notebooks, in particular those for data cleaning and feature selection.<\/p>\n<p>The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We will take the original table and will tweak the data, with some original values being replaced by <code>NULL<\/code>, and adding <code>duplicated rows<\/code> artificially to generate a resulting table we will call <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code>.<\/p>\n<p>The source of the data is a CSV file stored in <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\"> Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the <code>DBMS_CLOUD.CREATE_EXTERNAL_TABLE<\/code> function among others.<\/p>\n<h6 id=\"important-please-run-this-notebook-before-running-any-of-the-data-preparation-notebooks\"><code>IMPORTANT<\/code> Please run this notebook before running any of the Data Preparation notebooks.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Cleanup if tables exist","message":["%script","","-- DELETE PREVIOUS DATA IF IT EXISTS","BEGIN ","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE EXT_CUSTOMER_INSURANCE_LTV';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_NOISE';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","   BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_SQL';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_PY';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","END;  "],"enabled":true,"result":{"startTime":1737138362545,"interpreter":"script.low","endTime":1737138365251,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV` table ","","This table will be used in other notebooks and allow us to build the `CUSTOMER_INSURANCE_LTV_NOISE` that will also be used.","","We will pull the data from OML dataset storage in GitHub using the `DBMS_CLOUD` capabilities.","","More details on `DBMS_CLOUD` can be found <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">in the Documentation<\/a>"],"enabled":true,"result":{"startTime":1737138365721,"interpreter":"md.low","endTime":1737138366172,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv-table\">Create the <code>CUSTOMER_INSURANCE_LTV<\/code> table<\/h4>\n<p>This table will be used in other notebooks and allow us to build the <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code> that will also be used.<\/p>\n<p>We will pull the data from OML dataset storage in GitHub using the <code>DBMS_CLOUD<\/code> capabilities.<\/p>\n<p>More details on <code>DBMS_CLOUD<\/code> can be found <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">in the Documentation<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the data from Object Storage","message":["%script","","-- LOADS THE DATA FROM GITHUB BY CREATING AN EXTERNAL TABLE AND THEN CREATING A TABLE WITH THAT","DECLARE","     uri_data        varchar2(1000) := 'https://raw.githubusercontent.com/oracle/oracle-db-examples/master/machine-learning/datasets/CUST_INSUR_LTV_APPLY.csv';","     csv_format      varchar2(1000) := '{\"dateformat\":\"YYYY-MM-DD\", \"skipheaders\":\"1\", \"delimiter\":\",\", \"ignoreblanklines\":\"true\", \"removequotes\":\"true\", \"blankasnull\":\"true\", \"trimspaces\":\"lrtrim\", \"truncatecol\":\"true\", \"ignoremissingcolumns\":\"true\"}';","BEGIN","DBMS_CLOUD.CREATE_EXTERNAL_TABLE(","            TABLE_NAME => 'EXT_CUSTOMER_INSURANCE_LTV',","            FILE_URI_LIST => uri_data,","            FORMAT => csv_format,","            COLUMN_LIST => 'MARITAL_STATUS          VARCHAR2(26),","                            STATE                   CHAR(26),","                            CREDIT_BALANCE          NUMBER(8,0),","                            CUSTOMER_TENURE         NUMBER(3,0),","                            MORTGAGE_AMOUNT         NUMBER(7,0),","                            BANK_FUNDS              NUMBER(7,0),","                            NUM_DEPENDENTS          NUMBER(3,0),","                            HAS_CHILDREN            NUMBER(3,0),","                            INCOME                  NUMBER(7,0),","                            CUSTOMER_ID             CHAR(26),","                            GENDER                  CHAR(26),","                            PROFESSION              VARCHAR2(35),","                            CREDIT_CARD_LIMITS      NUMBER(6,0),","                            REGION                  VARCHAR2(26),","                            HOME_OWNERSHIP          NUMBER(3,0),","                            NUM_ONLINE_TRANS        NUMBER(6,0),","                            BUY_INSURANCE           VARCHAR2(26),","                            MONTHLY_CHECKS          NUMBER(4,0),","                            NUM_TRANS_KIOSK         NUMBER(3,0),","                            AGE                     NUMBER(4,0),","                            MONEY_MONTLY_OVERDRAWN  NUMBER(6,2),","                            LTV                     NUMBER(9,2),","                            TOTAL_AUTOM_PAYMENTS    NUMBER(8,0),","                            NUM_TRANS_TELLER        NUMBER(3,0),","                            CHECKING_BALANCE        NUMBER(7,0),","                            NUM_TRANS_ATM           NUMBER(3,0),","                            LTV_BIN                 VARCHAR2(26),","                            FIRST_NAME              VARCHAR2(26),","                            NUM_MORTGAGES           NUMBER(3,0),","                            CAR_OWNERSHIP           NUMBER(3,0),","                            LAST_NAME               VARCHAR2(26)');","                            ","-- WRITE A TABLE INTO THE CURRENT USER WITH THE NAME \"CUSTOMER_INSURANCE_LTV\"                             ","  EXECUTE IMMEDIATE 'create table CUSTOMER_INSURANCE_LTV as select * from EXT_CUSTOMER_INSURANCE_LTV';","  EXCEPTION WHEN OTHERS THEN NULL;","","END;"],"enabled":true,"result":{"startTime":1737138366645,"interpreter":"script.low","endTime":1737138369552,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the number of records","message":["%sql","","SELECT count(*) FROM CUSTOMER_INSURANCE_LTV;"],"enabled":true,"result":{"startTime":1737138370067,"interpreter":"sql.low","endTime":1737138370613,"results":[{"message":"COUNT(*)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the contents of the CUSTOMER_INSURANCE_LTV table","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737138371093,"interpreter":"sql.low","endTime":1737138371596,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         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\tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV_NOISE` table","","---"],"enabled":true,"result":{"startTime":1737138372065,"interpreter":"md.low","endTime":1737138372531,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv_noise-table\">Create the <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code> table<\/h4>\n<hr 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COMMIT;","END;"],"enabled":true,"result":{"startTime":1737138379481,"interpreter":"script.low","endTime":1737138380033,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV_SQL` and the `CUSTOMER_INSURANCE_LTV_PY` tables ","","---"],"enabled":true,"result":{"startTime":1737138380501,"interpreter":"md.low","endTime":1737138380947,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv_sql-and-the-customer_insurance_ltv_py-tables\">Create the <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> and the <code>CUSTOMER_INSURANCE_LTV_PY<\/code> tables<\/h4>\n<hr 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML Third-Party Packages - Environment Creation.dsnb b/machine-learning/notebooks-oml/python/OML Third-Party Packages - Environment Creation.dsnb
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@@ -0,0 +1,2512 @@
+[
+  {
+    "name" : "OML Third-Party Packages - Environment Creation",
+    "description" : null,
+    "tags" : null,
+    "version" : "7",
+    "layout" : "jupyter",
+    "type" : "medium",
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+          "status" : "SUCCESS",
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+        "dynamicFormParams" : "{}"
+      },
+      {
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+        "message" : [
+          "%md",
+          "",
+          "# OML Third-Party Packages - Environment Creation",
+          "",
+          "Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. ",
+          "",
+          "Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the OML4Py Python, SQL, and REST APIs, and the OML4R R, SQL, and REST APIs.",
+          "",
+          "In this notebook, we demonstrate a typical workflow for third-party environment creation and package installation in OML notebooks. Section 1 contains common commands used by ADMIN while creating and testing conda environments. In section 2, the ADMIN user creates a conda environment, installs packages, and uploads the conda environment to an Object Storage bucket associated with the Autonomous Database. ",
+          "",
+          "In the template notebooks titled, *OML Third-Party Packages - R Environment Usage* and *OML Third-Party Packages - Python Environment Usage*, the OML user downloads and activates the environment, and uses the packages in their notebook session.",
+          "",
+          "---",
+          "Copyright (c) 2025 Oracle Corporation ",
+          "###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988360092,
+          "endTime" : 1739988360575,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h1 id=\"oml-third-party-packages---environment-creation\">OML Third-Party Packages - Environment Creation</h1>\n<p>Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages.</p>\n<p>Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the OML4Py Python, SQL, and REST APIs, and the OML4R R, SQL, and REST APIs.</p>\n<p>In this notebook, we demonstrate a typical workflow for third-party environment creation and package installation in OML notebooks. Section 1 contains common commands used by ADMIN while creating and testing conda environments. In section 2, the ADMIN user creates a conda environment, installs packages, and uploads the conda environment to an Object Storage bucket associated with the Autonomous Database.</p>\n<p>In the template notebooks titled, <em>OML Third-Party Packages - R Environment Usage</em> and <em>OML Third-Party Packages - Python Environment Usage</em>, the OML user downloads and activates the environment, and uses the packages in their notebook session.</p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation</p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Supported conda commands",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Refer to [Using Oracle Machine Learning on Autonomous Database](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-classic.html#GUID-5A206265-9EB0-4E49-A882-BFBF2DB5DB71) for a table of supported conda commands.",
+          "",
+          "This notebook reviews the following conda commands:",
+          "",
+          "-  `--help`",
+          "-  `install`",
+          "-  `info`",
+          "-  `search`",
+          "-  `env list`",
+          "-  `create`",
+          "-  `activate`",
+          "-  `list`",
+          "-  `install`",
+          "-  `uninstall`",
+          "-  `remove`",
+          "-  `list-saved-envs`",
+          "-  `upload`",
+          "-  `delete`"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
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+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Refer to <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-classic.html#GUID-5A206265-9EB0-4E49-A882-BFBF2DB5DB71\">Using Oracle Machine Learning on Autonomous Database</a> for a table of supported conda commands.</p>\n<p>This notebook reviews the following conda commands:</p>\n<ul>\n<li><code>--help</code></li>\n<li><code>install</code></li>\n<li><code>info</code></li>\n<li><code>search</code></li>\n<li><code>env list</code></li>\n<li><code>create</code></li>\n<li><code>activate</code></li>\n<li><code>list</code></li>\n<li><code>install</code></li>\n<li><code>uninstall</code></li>\n<li><code>remove</code></li>\n<li><code>list-saved-envs</code></li>\n<li><code>upload</code></li>\n<li><code>delete</code></li>\n</ul>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
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+      {
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+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "### Section 1: Commands for Creating and Managing Environments"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"section-1-commands-for-creating-and-managing-environments\">Section 1: Commands for Creating and Managing Environments</h3>\n",
+              "type" : "HTML"
+            }
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+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "### Conda Help",
+          "---",
+          "",
+          "To get help for conda commands, run the command name followed by the `--help` flag. The `conda` command is not run explicitly because the %conda interpreter provides the conda context."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
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+        "result" : {
+          "startTime" : 1739988362933,
+          "endTime" : 1739988363373,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>To get help for conda commands, run the command name followed by the <code>--help</code> flag. The <code>conda</code> command is not run explicitly because the %conda interpreter provides the conda context.</p>\n",
+              "type" : "HTML"
+            }
+          ],
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Get help for all conda commands",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "--help"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
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+              "message" : "usage: conda install [-h] [--revision REVISION] [-n ENVIRONMENT | -p PATH]\n                     [-c CHANNEL] [--use-local] [--override-channels]\n                     [--repodata-fn REPODATA_FNS] [--experimental {jlap,lock}]\n                     [--no-lock] [--repodata-use-zst | --no-repodata-use-zst]\n                     [--strict-channel-priority] [--no-channel-priority]\n                     [--no-deps | --only-deps] [--no-pin] [--copy]\n                     [--no-shortcuts] [--shortcuts-only SHORTCUTS_ONLY] [-C]\n                     [-k] [--offline] [--json] [-v] [-q] [-d] [-y]\n                     [--download-only] [--show-channel-urls] [--file FILE]\n                     [--solver {classic,libmamba}] [--force-reinstall]\n                     [--freeze-installed | --update-deps | -S | --update-all | --update-specs]\n                     [-m] [--clobber] [--dev]\n                     [package_spec ...]\n\nInstall a list of packages into a specified conda environment.\n\nThis command accepts a list of package specifications (e.g, bitarray=0.8)\nand installs a set of packages consistent with those specifications and\ncompatible with the underlying environment. If full compatibility cannot\nbe assured, an error is reported and the environment is not changed.\n\nConda attempts to install the newest versions of the requested packages. To\naccomplish this, it may update some packages that are already installed, or\ninstall additional packages. To prevent existing packages from updating,\nuse the --freeze-installed option. This may force conda to install older\nversions of the requested packages, and it does not prevent additional\ndependency packages from being installed.\n\nIf you wish to skip dependency checking altogether, use the '--no-deps'\noption. This may result in an environment with incompatible packages, so\nthis option must be used with great caution.\n\nconda can also be called with a list of explicit conda package filenames\n(e.g. ./lxml-3.2.0-py27_0.tar.bz2). Using conda in this mode implies the\n--no-deps option, and should likewise be used with great caution. Explicit\nfilenames and package specifications cannot be mixed in a single command.\n\npositional arguments:\n  package_spec          List of packages to install or update in the conda\n                        environment.\n\noptions:\n  -h, --help            Show this help message and exit.\n  --revision REVISION   Revert to the specified REVISION.\n  --file FILE           Read package versions from the given file. Repeated\n                        file specifications can be passed (e.g. --file=file1\n                        --file=file2).\n  --dev                 Use `sys.executable -m conda` in wrapper scripts\n                        instead of CONDA_EXE. This is mainly for use during\n                        tests where we test new conda sources against old\n                        Python versions.\n\nTarget Environment Specification:\n  -n ENVIRONMENT, --name ENVIRONMENT\n                        Name of environment.\n  -p PATH, --prefix PATH\n                        Full path to environment location (i.e. prefix).\n\nChannel Customization:\n  -c CHANNEL, --channel CHANNEL\n                        Additional channel to search for packages. These are\n                        URLs searched in the order they are given (including\n                        local directories using the 'file://' syntax or simply\n                        a path like '/home/conda/mychan' or '../mychan').\n                        Then, the defaults or channels from .condarc are\n                        searched (unless --override-channels is given). You\n                        can use 'defaults' to get the default packages for\n                        conda. You can also use any name and the .condarc\n                        channel_alias value will be prepended. The default\n                        channel_alias is https://conda.anaconda.org/.\n  --use-local           Use locally built packages. Identical to '-c local'.\n  --override-channels   Do not search default or .condarc channels. Requires\n                        --channel.\n  --repodata-fn REPODATA_FNS\n                        Specify file name of repodata on the remote server\n                        where your channels are configured or within local\n                        backups. Conda will try whatever you specify, but will\n                        ultimately fall back to repodata.json if your specs\n                        are not satisfiable with what you specify here. This\n                        is used to employ repodata that is smaller and reduced\n                        in time scope. You may pass this flag more than once.\n                        Leftmost entries are tried first, and the fallback to\n                        repodata.json is added for you automatically. For more\n                        information, see conda config --describe repodata_fns.\n  --experimental {jlap,lock}\n                        jlap: Download incremental package index data from\n                        repodata.jlap; implies 'lock'. lock: use locking when\n                        reading, updating index (repodata.json) cache. Now\n                        enabled.\n  --no-lock             Disable locking when reading, updating index\n                        (repodata.json) cache.\n  --repodata-use-zst, --no-repodata-use-zst\n                        Check for/do not check for repodata.json.zst. Enabled\n                        by default.\n\nSolver Mode Modifiers:\n  --strict-channel-priority\n                        Packages in lower priority channels are not considered\n                        if a package with the same name appears in a higher\n                        priority channel.\n  --no-channel-priority\n                        Package version takes precedence over channel\n                        priority. Overrides the value given by `conda config\n                        --show channel_priority`.\n  --no-deps             Do not install, update, remove, or change\n                        dependencies. This WILL lead to broken environments\n                        and inconsistent behavior. Use at your own risk.\n  --only-deps           Only install dependencies.\n  --no-pin              Ignore pinned file.\n  --solver {classic,libmamba}\n                        Choose which solver backend to use.\n  --force-reinstall     Ensure that any user-requested package for the current\n                        operation is uninstalled and reinstalled, even if that\n                        package already exists in the environment.\n  --freeze-installed, --no-update-deps\n                        Do not update or change already-installed\n                        dependencies.\n  --update-deps         Update dependencies that have available updates.\n  -S, --satisfied-skip-solve\n                        Exit early and do not run the solver if the requested\n                        specs are satisfied. Also skips aggressive updates as\n                        configured by the 'aggressive_update_packages' config\n                        setting. Use 'conda info --describe\n                        aggressive_update_packages' to view your setting.\n                        --satisfied-skip-solve is similar to the default\n                        behavior of 'pip install'.\n  --update-all, --all   Update all installed packages in the environment.\n  --update-specs        Update based on provided specifications.\n\nPackage Linking and Install-time Options:\n  --copy                Install all packages using copies instead of hard- or\n                        soft-linking.\n  --no-shortcuts        Don't install start menu shortcuts\n  --shortcuts-only SHORTCUTS_ONLY\n                        Install shortcuts only for this package name. Can be\n                        used several times.\n  -m, --mkdir           Create the environment directory, if necessary.\n  --clobber             Allow clobbering (i.e. overwriting) of overlapping\n                        file paths within packages and suppress related\n                        warnings.\n\nNetworking Options:\n  -C, --use-index-cache\n                        Use cache of channel index files, even if it has\n                        expired. This is useful if you don't want conda to\n                        check whether a new version of the repodata file\n                        exists, which will save bandwidth.\n  -k, --insecure        Allow conda to perform \"insecure\" SSL connections and\n                        transfers. Equivalent to setting 'ssl_verify' to\n                        'false'.\n  --offline             Offline mode. Don't connect to the Internet.\n\nOutput, Prompt, and Flow Control Options:\n  --json                Report all output as json. Suitable for using conda\n                        programmatically.\n  -v, --verbose         Can be used multiple times. Once for detailed output,\n                        twice for INFO logging, thrice for DEBUG logging, four\n                        times for TRACE logging.\n  -q, --quiet           Do not display progress bar.\n  -d, --dry-run         Only display what would have been done.\n  -y, --yes             Sets any confirmation values to 'yes' automatically.\n                        Users will not be asked to confirm any adding,\n                        deleting, backups, etc.\n  --download-only       Solve an environment and ensure package caches are\n                        populated, but exit prior to unlinking and linking\n                        packages into the prefix.\n  --show-channel-urls   Show channel urls. Overrides the value given by `conda\n                        config --show show_channel_urls`.\n\nExamples:\n\nInstall the package 'scipy' into the currently-active environment::\n\n    conda install scipy\n\nInstall a list of packages into an environment, myenv::\n\n    conda install -n myenv scipy curl wheel\n\nInstall a specific version of 'python' into an environment, myenv::\n\n    conda install -p path/to/myenv python=3.11\n\n\n",
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+          "### Conda Info",
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+          "The `info` command provides information about the conda installation, including the conda version and available channels."
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+              "message" : "\n     active environment : base\n    active env location : /opt/conda\n            shell level : 1\n       user config file : /u01/.condarc\n populated config files : /u01/.condarc\n          conda version : 24.1.2\n    conda-build version : not installed\n         python version : 3.12.1.final.0\n                 solver : libmamba (default)\n       virtual packages : __archspec=1=zen3\n                          __conda=24.1.2=0\n                          __glibc=2.17=0\n                          __linux=5.4.17=0\n                          __unix=0=0\n       base environment : /opt/conda  (read only)\n      conda av data dir : /opt/conda/etc/conda\n  conda av metadata url : None\n           channel URLs : https://repo.anaconda.com/pkgs/main/linux-64\n                          https://repo.anaconda.com/pkgs/main/noarch\n                          https://repo.anaconda.com/pkgs/r/linux-64\n                          https://repo.anaconda.com/pkgs/r/noarch\n          package cache : /u01/.conda/pkgs\n       envs directories : /u01/.conda/envs\n                          /opt/conda/envs\n               platform : linux-64\n             user-agent : conda/24.1.2 requests/2.32.3 CPython/3.12.1 Linux/5.4.17-2136.338.4.2.el7uek.x86_64 oracle/7.9 glibc/2.17 solver/libmamba conda-libmamba-solver/23.12.0 libmambapy/1.5.3\n                UID:GID : 1001:1001\n             netrc file : None\n           offline mode : False\n\n\n\n\n",
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+          "### Conda Search",
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+              "message" : "Loading channels: ...working... done\n# Name                       Version           Build  Channel             \nscikit-learn                  0.19.0 py27_nomklh0ffebdf_2  pkgs/main           \nscikit-learn                  0.19.0  py27hd893acb_2  pkgs/main           \nscikit-learn                  0.19.0 py35_nomklh375dd1d_2  pkgs/main           \nscikit-learn                  0.19.0  py35h25e8076_2  pkgs/main           \nscikit-learn                  0.19.0 py36_nomklh41feb14_2  pkgs/main           \nscikit-learn                  0.19.0  py36h97ac459_2  pkgs/main           \nscikit-learn                  0.19.1 py27_nomklh6479e79_0  pkgs/main           \nscikit-learn                  0.19.1 py27_nomklh6cfcb94_0  pkgs/main           \nscikit-learn                  0.19.1  py27h445a80a_0  pkgs/main           \nscikit-learn                  0.19.1  py27hedc7406_0  pkgs/main           \nscikit-learn                  0.19.1 py35_nomklh26d41a3_0  pkgs/main           \nscikit-learn                  0.19.1  py35hbf1f462_0  pkgs/main           \nscikit-learn                  0.19.1 py36_nomklh27f7947_0  pkgs/main           \nscikit-learn                  0.19.1 py36_nomklh6cfcb94_0  pkgs/main           \nscikit-learn                  0.19.1  py36h7aa7ec6_0  pkgs/main           \nscikit-learn                  0.19.1  py36hedc7406_0  pkgs/main           \nscikit-learn                  0.19.1 py37_nomklh6cfcb94_0  pkgs/main           \nscikit-learn                  0.19.1  py37hedc7406_0  pkgs/main           \nscikit-learn                  0.19.2  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py27h4989274_0  pkgs/main           \nscikit-learn                  0.19.2  py35h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py35h4989274_0  pkgs/main           \nscikit-learn                  0.19.2  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py36h4989274_0  pkgs/main           \nscikit-learn                  0.19.2  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py37h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py27h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py27h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py27h4989274_1  pkgs/main           \nscikit-learn                  0.20.0  py35h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py35h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py35h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py35h4989274_1  pkgs/main           \nscikit-learn                  0.20.0  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py36h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py36h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py36h4989274_1  pkgs/main           \nscikit-learn                  0.20.0  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py37h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py37h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py37h4989274_1  pkgs/main           \nscikit-learn                  0.20.1  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.1  py27h4989274_0  pkgs/main           \nscikit-learn                  0.20.1  py27hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.1  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.1  py36h4989274_0  pkgs/main           \nscikit-learn                  0.20.1  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.1  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.1  py37h4989274_0  pkgs/main           \nscikit-learn                  0.20.1  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.2  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.2  py27hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.2  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.2  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.2  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.2  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.3  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.3  py27hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.3  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.3  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.3  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.3  py37hd81dba3_0  pkgs/main           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       1.21.5 py310h5f9d8c6_4  pkgs/main           \nnumpy                         1.21.5 py310hac523dd_3  pkgs/main           \nnumpy                         1.21.5 py310hfa59a62_1  pkgs/main           \nnumpy                         1.21.5 py310hfa59a62_2  pkgs/main           \nnumpy                         1.21.5  py37h6c91a56_3  pkgs/main           \nnumpy                         1.21.5  py37h7a5d4dd_1  pkgs/main           \nnumpy                         1.21.5  py37h7a5d4dd_2  pkgs/main           \nnumpy                         1.21.5  py37he7a7128_1  pkgs/main           \nnumpy                         1.21.5  py37he7a7128_2  pkgs/main           \nnumpy                         1.21.5  py37hf838250_3  pkgs/main           \nnumpy                         1.21.5  py38h6c91a56_3  pkgs/main           \nnumpy                         1.21.5  py38h7a5d4dd_1  pkgs/main           \nnumpy                         1.21.5  py38h7a5d4dd_2  pkgs/main           \nnumpy                         1.21.5  py38he7a7128_1  pkgs/main           \nnumpy                         1.21.5  py38he7a7128_2  pkgs/main           \nnumpy                         1.21.5  py38hf6e8229_4  pkgs/main           \nnumpy                         1.21.5  py38hf838250_3  pkgs/main           \nnumpy                         1.21.5  py39h6c91a56_3  pkgs/main           \nnumpy                         1.21.5  py39h7a5d4dd_1  pkgs/main           \nnumpy                         1.21.5  py39h7a5d4dd_2  pkgs/main           \nnumpy                         1.21.5  py39he7a7128_1  pkgs/main           \nnumpy                         1.21.5  py39he7a7128_2  pkgs/main           \nnumpy                         1.21.5  py39hf6e8229_4  pkgs/main           \nnumpy                         1.21.5  py39hf838250_3  pkgs/main           \nnumpy                         1.21.6 py310h5f9d8c6_0  pkgs/main           \nnumpy                         1.21.6 py310h5f9d8c6_1  pkgs/main           \nnumpy                         1.21.6 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  1.8.2 py36_blas_openblas_201  conda-forge         \nnumpy                          1.9.3 py27_blas_openblas_200  conda-forge         \nnumpy                          1.9.3 py27_blas_openblas_201  conda-forge         \nnumpy                          1.9.3 py27_blas_openblas_202  conda-forge         \nnumpy                          1.9.3 py27_blas_openblas_203  conda-forge         \nnumpy                          1.9.3 py27_blas_openblash1522bff_1006  conda-forge         \nnumpy                          1.9.3 py27_blas_openblash1522bff_1007  conda-forge         \nnumpy                          1.9.3 py27_blas_openblash1522bff_1207  conda-forge         \nnumpy                          1.9.3 py27_blas_openblashb06ca3d_207  conda-forge         \nnumpy                          1.9.3 py27_blas_openblashb06ca3d_6  conda-forge         \nnumpy                          1.9.3 py27_blas_openblashb06ca3d_7  conda-forge         \nnumpy                          1.9.3 py27_blas_openblashd3ea46f_205  conda-forge         \nnumpy                          1.9.3 py27_blas_openblashd3ea46f_206  conda-forge         \nnumpy                          1.9.3 py27h8b7e671_1208  conda-forge         \nnumpy                          1.9.3 py27he5ce36f_1209  conda-forge         \nnumpy                          1.9.3 py34_blas_openblas_200  conda-forge         \nnumpy                          1.9.3 py34_blas_openblas_201  conda-forge         \nnumpy                          1.9.3 py34_blas_openblas_202  conda-forge         \nnumpy                          1.9.3 py34_blas_openblas_203  conda-forge         \nnumpy                          1.9.3 py35_blas_openblas_200  conda-forge         \nnumpy                          1.9.3 py35_blas_openblas_201  conda-forge         \nnumpy                          1.9.3 py35_blas_openblas_202  conda-forge         \nnumpy                          1.9.3 py35_blas_openblas_203  conda-forge         \nnumpy                          1.9.3 py35_blas_openblashd3ea46f_205  conda-forge         \nnumpy                          1.9.3 py35_blas_openblashd3ea46f_206  conda-forge         \nnumpy                          1.9.3 py36_blas_openblas_200  conda-forge         \nnumpy                          1.9.3 py36_blas_openblas_201  conda-forge         \nnumpy                          1.9.3 py36_blas_openblas_202  conda-forge         \nnumpy                          1.9.3 py36_blas_openblas_203  conda-forge         \nnumpy                          1.9.3 py36_blas_openblash1522bff_1006  conda-forge         \nnumpy                          1.9.3 py36_blas_openblash1522bff_1007  conda-forge         \nnumpy                          1.9.3 py36_blas_openblash1522bff_1207  conda-forge         \nnumpy                          1.9.3 py36_blas_openblashb06ca3d_207  conda-forge         \nnumpy                          1.9.3 py36_blas_openblashb06ca3d_6  conda-forge         \nnumpy                          1.9.3 py36_blas_openblashb06ca3d_7  conda-forge         \nnumpy                          1.9.3 py36_blas_openblashd3ea46f_205  conda-forge         \nnumpy                          1.9.3 py36_blas_openblashd3ea46f_206  conda-forge         \nnumpy                          1.9.3 py36h8b7e671_1208  conda-forge         \nnumpy                          1.9.3 py36he5ce36f_1209  conda-forge         \nnumpy                          1.9.3 py37_blas_openblash1522bff_1006  conda-forge         \nnumpy                          1.9.3 py37_blas_openblash1522bff_1007  conda-forge         \nnumpy                          1.9.3 py37_blas_openblash1522bff_1207  conda-forge         \nnumpy                          1.9.3 py37_blas_openblashb06ca3d_207  conda-forge         \nnumpy                          1.9.3 py37_blas_openblashb06ca3d_6  conda-forge         \nnumpy                          1.9.3 py37_blas_openblashb06ca3d_7  conda-forge         \nnumpy                          1.9.3 py37_blas_openblashd3ea46f_206  conda-forge         \nnumpy                          1.9.3 py37h8b7e671_1208  conda-forge         \nnumpy                          1.9.3 py37he5ce36f_1209  conda-forge         \nnumpy                         1.10.4 py27_blas_openblas_200  conda-forge         \nnumpy                         1.10.4 py27_blas_openblas_201  conda-forge         \nnumpy                         1.10.4 py27_blas_openblas_202  conda-forge         \nnumpy                         1.10.4 py27_blas_openblas_203  conda-forge         \nnumpy                         1.10.4 py27_blas_openblas_204  conda-forge         \nnumpy                         1.10.4 py27_blas_openblas_205  conda-forge         \nnumpy                         1.10.4 py34_blas_openblas_200  conda-forge         \nnumpy                         1.10.4 py34_blas_openblas_201  conda-forge         \nnumpy                         1.10.4 py34_blas_openblas_202  conda-forge         \nnumpy                         1.10.4 py34_blas_openblas_203  conda-forge         \nnumpy                         1.10.4 py34_blas_openblas_204  conda-forge         \nnumpy                         1.10.4 py34_blas_openblas_205  conda-forge         \nnumpy                         1.10.4 py35_blas_openblas_200  conda-forge         \nnumpy                         1.10.4 py35_blas_openblas_201  conda-forge         \nnumpy                         1.10.4 py35_blas_openblas_202  conda-forge         \nnumpy                         1.10.4 py35_blas_openblas_203  conda-forge         \nnumpy                         1.10.4 py35_blas_openblas_204  conda-forge         \nnumpy                         1.10.4 py35_blas_openblas_205  conda-forge         \nnumpy                         1.10.4 py36_blas_openblas_205  conda-forge         \nnumpy                         1.11.0 py27_blas_openblas_200  conda-forge         \nnumpy                         1.11.0 py27_blas_openblas_201  conda-forge         \nnumpy                         1.11.0 py34_blas_openblas_200  conda-forge         \nnumpy                         1.11.0 py34_blas_openblas_201  conda-forge         \nnumpy                         1.11.0 py35_blas_openblas_200  conda-forge         \nnumpy                         1.11.0 py35_blas_openblas_201  conda-forge         \nnumpy                         1.11.1 py27_blas_openblas_200  conda-forge         \nnumpy                         1.11.1 py27_blas_openblas_201  conda-forge         \nnumpy                         1.11.1 py27_blas_openblas_202  conda-forge         \nnumpy                         1.11.1 py34_blas_openblas_200  conda-forge         \nnumpy                         1.11.1 py34_blas_openblas_201  conda-forge         \nnumpy                         1.11.1 py34_blas_openblas_202  conda-forge         \nnumpy                         1.11.1 py35_blas_openblas_200  conda-forge         \nnumpy                         1.11.1 py35_blas_openblas_201  conda-forge         \nnumpy                         1.11.1 py35_blas_openblas_202  conda-forge         \nnumpy                         1.11.2 py27_blas_openblas_200  conda-forge         \nnumpy                         1.11.2 py27_blas_openblas_201  conda-forge         \nnumpy                         1.11.2 py27_blas_openblas_202  conda-forge         \nnumpy                         1.11.2 py34_blas_openblas_200  conda-forge         \nnumpy                         1.11.2 py34_blas_openblas_201  conda-forge         \nnumpy                         1.11.2 py34_blas_openblas_202  conda-forge         \nnumpy                         1.11.2 py35_blas_openblas_200  conda-forge         \nnumpy                         1.11.2 py35_blas_openblas_201  conda-forge         \nnumpy                         1.11.2 py35_blas_openblas_202  conda-forge         \nnumpy                         1.11.3 py27_blas_openblas_200  conda-forge         \nnumpy                         1.11.3 py27_blas_openblas_201  conda-forge         \nnumpy                         1.11.3 py27_blas_openblas_202  conda-forge         \nnumpy                         1.11.3 py27_blas_openblas_203  conda-forge         \nnumpy                         1.11.3 py27_blas_openblash1522bff_1205  conda-forge         \nnumpy                         1.11.3 py27_blas_openblashb06ca3d_205  conda-forge         \nnumpy                         1.11.3 py27_blas_openblashd3ea46f_205  conda-forge         \nnumpy                         1.11.3 py27h8b7e671_1206  conda-forge         \nnumpy                         1.11.3 py27he5ce36f_1207  conda-forge         \nnumpy                         1.11.3 py34_blas_openblas_200  conda-forge         \nnumpy                         1.11.3 py34_blas_openblas_201  conda-forge         \nnumpy                         1.11.3 py34_blas_openblas_202  conda-forge         \nnumpy                         1.11.3 py34_blas_openblas_203  conda-forge         \nnumpy                         1.11.3 py35_blas_openblas_200  conda-forge         \nnumpy                         1.11.3 py35_blas_openblas_201  conda-forge         \nnumpy                         1.11.3 py35_blas_openblas_202  conda-forge         \nnumpy                         1.11.3 py35_blas_openblas_203  conda-forge         \nnumpy                         1.11.3 py35_blas_openblashd3ea46f_205  conda-forge         \nnumpy                         1.11.3 py36_blas_openblas_200  conda-forge         \nnumpy                         1.11.3 py36_blas_openblas_201  conda-forge         \nnumpy                         1.11.3 py36_blas_openblas_202  conda-forge         \nnumpy                         1.11.3 py36_blas_openblas_203  conda-forge         \nnumpy                         1.11.3 py36_blas_openblash1522bff_1205  conda-forge         \nnumpy                         1.11.3 py36_blas_openblashb06ca3d_205  conda-forge         \nnumpy                         1.11.3 py36_blas_openblashd3ea46f_205  conda-forge         \nnumpy                         1.11.3 py36h8b7e671_1206  conda-forge         \nnumpy                         1.11.3 py36he5ce36f_1207  conda-forge         \nnumpy                         1.11.3 py37_blas_openblash1522bff_1205  conda-forge         \nnumpy                         1.11.3 py37_blas_openblashb06ca3d_205  conda-forge         \nnumpy                         1.11.3 py37_blas_openblashd3ea46f_205  conda-forge         \nnumpy                         1.11.3 py37h8b7e671_1206  conda-forge         \nnumpy                         1.11.3 py37he5ce36f_1207  conda-forge         \nnumpy                         1.12.0 py27_blas_openblas_200  conda-forge         \nnumpy                         1.12.0 py34_blas_openblas_200  conda-forge         \nnumpy                         1.12.0 py35_blas_openblas_200  conda-forge         \nnumpy                         1.12.0 py36_blas_openblas_200  conda-forge         \nnumpy                         1.12.1 py27_blas_openblas_200  conda-forge         \nnumpy                         1.12.1 py27_blas_openblas_201  conda-forge         \nnumpy                         1.12.1 py27_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.12.1 py27_blas_openblash24bf2e0_201  conda-forge         \nnumpy                         1.12.1 py27_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.12.1 py34_blas_openblas_200  conda-forge         \nnumpy                         1.12.1 py34_blas_openblas_201  conda-forge         \nnumpy                         1.12.1 py35_blas_openblas_200  conda-forge         \nnumpy                         1.12.1 py35_blas_openblas_201  conda-forge         \nnumpy                         1.12.1 py36_blas_openblas_200  conda-forge         \nnumpy                         1.12.1 py36_blas_openblas_201  conda-forge         \nnumpy                         1.12.1 py36_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.12.1 py36_blas_openblash24bf2e0_201  conda-forge         \nnumpy                         1.12.1 py36_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.13.0 py27_blas_openblas_200  conda-forge         \nnumpy                         1.13.0 py34_blas_openblas_200  conda-forge         \nnumpy                         1.13.0 py35_blas_openblas_200  conda-forge         \nnumpy                         1.13.0 py36_blas_openblas_200  conda-forge         \nnumpy                         1.13.1 py27_blas_openblas_200  conda-forge         \nnumpy                         1.13.1 py27_blas_openblas_201  conda-forge         \nnumpy                         1.13.1 py34_blas_openblas_200  conda-forge         \nnumpy                         1.13.1 py34_blas_openblas_201  conda-forge         \nnumpy                         1.13.1 py35_blas_openblas_200  conda-forge         \nnumpy                         1.13.1 py35_blas_openblas_201  conda-forge         \nnumpy                         1.13.1 py36_blas_openblas_200  conda-forge         \nnumpy                         1.13.1 py36_blas_openblas_201  conda-forge         \nnumpy                         1.13.2 py27_blas_openblas_200  conda-forge         \nnumpy                         1.13.2 py35_blas_openblas_200  conda-forge         \nnumpy                         1.13.2 py36_blas_openblas_200  conda-forge         \nnumpy                         1.13.3 py27_blas_openblas_200  conda-forge         \nnumpy                         1.13.3 py27_blas_openblas_201  conda-forge         \nnumpy                         1.13.3 py27_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.13.3 py27_blas_openblash1522bff_1201  conda-forge         \nnumpy                         1.13.3 py27_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.13.3 py27_blas_openblashb06ca3d_201  conda-forge         \nnumpy                         1.13.3 py35_blas_openblas_200  conda-forge         \nnumpy                         1.13.3 py35_blas_openblas_201  conda-forge         \nnumpy                         1.13.3 py36_blas_openblas_200  conda-forge         \nnumpy                         1.13.3 py36_blas_openblas_201  conda-forge         \nnumpy                         1.13.3 py36_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.13.3 py36_blas_openblash1522bff_1201  conda-forge         \nnumpy                         1.13.3 py36_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.13.3 py36_blas_openblashb06ca3d_201  conda-forge         \nnumpy                         1.14.0 py27_blas_openblas_200  conda-forge         \nnumpy                         1.14.0 py34_blas_openblas_200  conda-forge         \nnumpy                         1.14.0 py35_blas_openblas_200  conda-forge         \nnumpy                         1.14.0 py36_blas_openblas_200  conda-forge         \nnumpy                         1.14.1 py27_blas_openblas_200  conda-forge         \nnumpy                         1.14.1 py35_blas_openblas_200  conda-forge         \nnumpy                         1.14.1 py36_blas_openblas_200  conda-forge         \nnumpy                         1.14.2 py27_blas_openblas_200  conda-forge         \nnumpy                         1.14.2 py35_blas_openblas_200  conda-forge         \nnumpy                         1.14.2 py36_blas_openblas_200  conda-forge         \nnumpy                         1.14.3 py27_blas_openblas_200  conda-forge         \nnumpy                         1.14.3 py35_blas_openblas_200  conda-forge         \nnumpy                         1.14.3 py36_blas_openblas_200  conda-forge         \nnumpy                         1.14.4 py27_blas_openblash24bf2e0_200  conda-forge         \nnumpy                         1.14.4 py35_blas_openblash24bf2e0_200  conda-forge         \nnumpy                         1.14.4 py36_blas_openblash24bf2e0_200  conda-forge         \nnumpy                         1.14.5 py27_blas_openblash24bf2e0_200  conda-forge         \nnumpy                         1.14.5 py27_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.5 py27_blas_openblashd3ea46f_201  conda-forge         \nnumpy                         1.14.5 py27_blas_openblashd3ea46f_202  conda-forge         \nnumpy                         1.14.5 py35_blas_openblash24bf2e0_200  conda-forge         \nnumpy                         1.14.5 py35_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.5 py35_blas_openblashd3ea46f_201  conda-forge         \nnumpy                         1.14.5 py35_blas_openblashd3ea46f_202  conda-forge         \nnumpy                         1.14.5 py36_blas_openblash24bf2e0_200  conda-forge         \nnumpy                         1.14.5 py36_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.5 py36_blas_openblashd3ea46f_201  conda-forge         \nnumpy                         1.14.5 py36_blas_openblashd3ea46f_202  conda-forge         \nnumpy                         1.14.5 py37_blas_openblashd3ea46f_202  conda-forge         \nnumpy                         1.14.6 py27_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.14.6 py27_blas_openblash1522bff_1200  conda-forge         \nnumpy                         1.14.6 py27_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.14.6 py27_blas_openblashb06ca3d_200  conda-forge         \nnumpy                         1.14.6 py27_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.6 py27h95a1406_1201  conda-forge         \nnumpy                         1.14.6 py27he5ce36f_1201  conda-forge         \nnumpy                         1.14.6 py35_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.6 py36_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.14.6 py36_blas_openblash1522bff_1200  conda-forge         \nnumpy                         1.14.6 py36_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.14.6 py36_blas_openblashb06ca3d_200  conda-forge         \nnumpy                         1.14.6 py36_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.6 py36h95a1406_1201  conda-forge         \nnumpy                         1.14.6 py36he5ce36f_1201  conda-forge         \nnumpy                         1.14.6 py37_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.14.6 py37_blas_openblash1522bff_1200  conda-forge         \nnumpy                         1.14.6 py37_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.14.6 py37_blas_openblashb06ca3d_200  conda-forge         \nnumpy                         1.14.6 py37_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.14.6 py37h95a1406_1201  conda-forge         \nnumpy                         1.14.6 py37he5ce36f_1201  conda-forge         \nnumpy                         1.14.6 py38h95a1406_1201  conda-forge         \nnumpy                         1.15.0 py27_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.15.0 py35_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.15.0 py36_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.15.0 py37_blas_openblashd3ea46f_200  conda-forge         \nnumpy                         1.15.1 py27_blas_openblashd3ea46f_0  conda-forge         \nnumpy                         1.15.1 py27_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.1 py35_blas_openblashd3ea46f_0  conda-forge         \nnumpy                         1.15.1 py35_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.1 py36_blas_openblashd3ea46f_0  conda-forge         \nnumpy                         1.15.1 py36_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.1 py37_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.2 py27_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.15.2 py27_blas_openblash1522bff_1201  conda-forge         \nnumpy                         1.15.2 py27_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.15.2 py27_blas_openblashb06ca3d_201  conda-forge         \nnumpy                         1.15.2 py27_blas_openblashd3ea46f_0  conda-forge         \nnumpy                         1.15.2 py27_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.2 py35_blas_openblashd3ea46f_0  conda-forge         \nnumpy                         1.15.2 py36_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.15.2 py36_blas_openblash1522bff_1201  conda-forge         \nnumpy                         1.15.2 py36_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.15.2 py36_blas_openblashb06ca3d_201  conda-forge         \nnumpy                         1.15.2 py36_blas_openblashd3ea46f_0  conda-forge         \nnumpy                         1.15.2 py36_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.2 py37_blas_openblash1522bff_1001  conda-forge         \nnumpy                         1.15.2 py37_blas_openblash1522bff_1201  conda-forge         \nnumpy                         1.15.2 py37_blas_openblashb06ca3d_1  conda-forge         \nnumpy                         1.15.2 py37_blas_openblashb06ca3d_201  conda-forge         \nnumpy                         1.15.2 py37_blas_openblashd3ea46f_1  conda-forge         \nnumpy                         1.15.3 py27_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.15.3 py27_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.15.3 py36_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.15.3 py36_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.15.3 py37_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.15.3 py37_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.15.4 py27_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.15.4 py27_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.15.4 py27h8b7e671_1001  conda-forge         \nnumpy                         1.15.4 py27h8b7e671_1002  conda-forge         \nnumpy                         1.15.4 py36_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.15.4 py36_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.15.4 py36h8b7e671_1001  conda-forge         \nnumpy                         1.15.4 py36h8b7e671_1002  conda-forge         \nnumpy                         1.15.4 py37_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.15.4 py37_blas_openblashb06ca3d_0  conda-forge         \nnumpy                         1.15.4 py37h8b7e671_1001  conda-forge         \nnumpy                         1.15.4 py37h8b7e671_1002  conda-forge         \nnumpy                         1.16.0 py27_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.16.0 py36_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.16.0 py37_blas_openblash1522bff_1000  conda-forge         \nnumpy                         1.16.1 py27_blas_openblash1522bff_0  conda-forge         \nnumpy                         1.16.1 py36_blas_openblash1522bff_0  conda-forge         \nnumpy                         1.16.1 py37_blas_openblash1522bff_0  conda-forge         \nnumpy                         1.16.2 py27_blas_openblash1522bff_0  conda-forge         \nnumpy                         1.16.2  py27h7fc5cc3_0  conda-forge         \nnumpy                         1.16.2  py27h8b7e671_1  conda-forge         \nnumpy                         1.16.2 py36_blas_openblash1522bff_0  conda-forge         \nnumpy                         1.16.2  py36h7fc5cc3_0  conda-forge         \nnumpy                         1.16.2  py36h8b7e671_1  conda-forge         \nnumpy                         1.16.2 py37_blas_openblash1522bff_0  conda-forge         \nnumpy                         1.16.2  py37h7fc5cc3_0  conda-forge         \nnumpy                         1.16.2  py37h8b7e671_1  conda-forge         \nnumpy                         1.16.3  py27he5ce36f_0  conda-forge         \nnumpy                         1.16.3  py36he5ce36f_0  conda-forge         \nnumpy                         1.16.3  py37he5ce36f_0  conda-forge         \nnumpy                         1.16.4  py27h95a1406_0  conda-forge         \nnumpy                         1.16.4  py36h95a1406_0  conda-forge         \nnumpy                         1.16.4  py37h95a1406_0  conda-forge         \nnumpy                         1.16.5  py27h95a1406_0  conda-forge         \nnumpy                         1.16.5  py36h2aa4a07_1  conda-forge         \nnumpy                         1.16.5  py36h95a1406_0  conda-forge         \nnumpy                         1.16.5  py37h95a1406_0  conda-forge         \nnumpy                         1.16.5  py37haa41c4c_1  conda-forge         \nnumpy                         1.16.5  py38h18fd61f_1  conda-forge         \nnumpy                         1.16.5  py38h95a1406_0  conda-forge         \nnumpy                         1.16.6  py36h2aa4a07_0  conda-forge         \nnumpy                         1.16.6  py37haa41c4c_0  conda-forge         \nnumpy                         1.16.6  py38h18fd61f_0  conda-forge         \nnumpy                         1.17.0  py36h95a1406_0  conda-forge         \nnumpy                         1.17.0  py37h95a1406_0  conda-forge         \nnumpy                         1.17.1  py36h95a1406_0  conda-forge         \nnumpy                         1.17.1  py37h95a1406_0  conda-forge         \nnumpy                         1.17.2  py36h95a1406_0  conda-forge         \nnumpy                         1.17.2  py37h95a1406_0  conda-forge         \nnumpy                         1.17.3  py36h95a1406_0  conda-forge         \nnumpy                         1.17.3  py37h95a1406_0  conda-forge         \nnumpy                         1.17.3  py38h95a1406_0  conda-forge         \nnumpy                         1.17.5  py36h2aa4a07_1  conda-forge         \nnumpy                         1.17.5  py36h95a1406_0  conda-forge         \nnumpy                         1.17.5  py37h95a1406_0  conda-forge         \nnumpy                         1.17.5  py37haa41c4c_1  conda-forge         \nnumpy                         1.17.5  py38h18fd61f_1  conda-forge         \nnumpy                         1.17.5  py38h95a1406_0  conda-forge         \nnumpy                         1.18.1  py36h7314795_1  conda-forge         \nnumpy                         1.18.1  py36h95a1406_0  conda-forge         \nnumpy                         1.18.1  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+        "message" : [
+          "%md",
+          "",
+          "### Enhanced Conda Commands",
+          "",
+          "A set of enhanced conda commands in the conda environment lifecycle management package `env-lcm` supports the management of environments saved to Object Storage, including uploading, downloading, listing, and deleting available environments."
+        ],
+        "selectedVisualization" : "html",
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+            {
+              "message" : "<h3 id=\"enhanced-conda-commands\">Enhanced Conda Commands</h3>\n<p>A set of enhanced conda commands in the conda environment lifecycle management package <code>env-lcm</code> supports the management of environments saved to Object Storage, including uploading, downloading, listing, and deleting available environments.</p>\n",
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+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Help for conda lifecycle environment commands",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "env-lcm --help "
+        ],
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+          "interpreter" : "conda.conda_medium",
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Usage: conda-env-lcm [OPTIONS] COMMAND [ARGS]...\n\n  ADB-S Command Line Interface (CLI) to manage persistence of conda\n  environments\n\nOptions:\n  -v, --version  Show the version and exit.\n  --help         Show this message and exit.\n\nCommands:\n  delete           Delete a saved conda environment\n  download         Download a saved conda environment\n  import           Create or update a conda environment from saved metadata\n  list-local-envs  List locally available environments for use\n  list-saved-envs  List saved conda environments\n  upload           Save conda environment for later use\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "### Creating Conda Environments",
+          "",
+          "The ADMIN user has the permissions to create conda environments and install packages.",
+          "",
+          "Start by listing the environments available by default. Conda contains default environments with some core system libraries and conda dependencies. The active environment is marked with an asterisk (*)."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"creating-conda-environments\">Creating Conda Environments</h3>\n<p>The ADMIN user has the permissions to create conda environments and install packages.</p>\n<p>Start by listing the environments available by default. Conda contains default environments with some core system libraries and conda dependencies. The active environment is marked with an asterisk (*).</p>\n",
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+          ],
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+        "width" : 12,
+        "title" : "List environments",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "env list"
+        ],
+        "selectedVisualization" : "raw",
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+        "hideCode" : false,
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+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "# conda environments:\n#\nbase                  *  /opt/conda\n\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
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+      },
+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Installing and removing packges from conda environments",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "---",
+          "### Create a Conda Environment",
+          "---",
+          "",
+          "This section demonstrates creating and installing packages to a conda environment, then removes the environment. We illustrate commonly used options available for environment creation and testing. The environment exists for the duration of the notebook session and does not persist between sessions unless it is saved to Object Storage. For instructions that include both creating and persisting an environment for OML users, refer to the section 2, *Create a Conda Environment and Upload to Object Storage*.",
+          "",
+          "As ADMIN user:",
+          "",
+          "- Use the `create` command to create an environment and install the Python *keras* package. ",
+          "- Verify that the new environment is created, and activate the environment.",
+          "- Install, then uninstall an additional Python package into the environment, *pytorch*.",
+          "- Deactivate and remove the environment.",
+          "",
+          "Notes:",
+          "",
+          "- When conda installs a package into an environment it also installs any required dependencies. While we are demonstrating that it's possible to install packages to an existing environment, it is a best practice to install all the packages that you want in a specific environment at the same time to avoid package dependency conflicts.",
+          "",
+          "- The ADMIN user can access the conda environment from Python and R, but does not have the capability to run embedded Python and R execution commands.",
+          "",
+          "For help with the conda `create` command, enter `create --help` in a %conda paragraph."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
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+        "enabled" : true,
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+          "startTime" : 1739988430843,
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+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>This section demonstrates creating and installing packages to a conda environment, then removes the environment. We illustrate commonly used options available for environment creation and testing. The environment exists for the duration of the notebook session and does not persist between sessions unless it is saved to Object Storage. For instructions that include both creating and persisting an environment for OML users, refer to the section 2, <em>Create a Conda Environment and Upload to Object Storage</em>.</p>\n<p>As ADMIN user:</p>\n<ul>\n<li>Use the <code>create</code> command to create an environment and install the Python <em>keras</em> package.</li>\n<li>Verify that the new environment is created, and activate the environment.</li>\n<li>Install, then uninstall an additional Python package into the environment, <em>pytorch</em>.</li>\n<li>Deactivate and remove the environment.</li>\n</ul>\n<p>Notes:</p>\n<ul>\n<li>\n<p>When conda installs a package into an environment it also installs any required dependencies. While we are demonstrating that it's possible to install packages to an existing environment, it is a best practice to install all the packages that you want in a specific environment at the same time to avoid package dependency conflicts.</p>\n</li>\n<li>\n<p>The ADMIN user can access the conda environment from Python and R, but does not have the capability to run embedded Python and R execution commands.</p>\n</li>\n</ul>\n<p>For help with the conda <code>create</code> command, enter <code>create --help</code> in a %conda paragraph.</p>\n",
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+            }
+          ],
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+        },
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+        "width" : 12,
+        "title" : "Create conda environment",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Create conda environment called *myenv* with Python 3.12 for OML4Py compatibility and install the *keras* package."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
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+          "endTime" : 1739988432236,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Create conda environment called <em>myenv</em> with Python 3.12 for OML4Py compatibility and install the <em>keras</em> package.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+        "message" : [
+          "%conda ",
+          "",
+          "create -n myenv python=3.12.6 keras "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+              "message" : "Channels:\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/myenv\n\n  added / updated specs:\n    - keras\n    - python=3.12.6\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _libgcc_mutex-0.1          |             main           3 KB\n    _openmp_mutex-5.1          |            1_gnu          21 KB\n    absl-py-2.1.0              |  py312h06a4308_0         239 KB\n    blas-1.0                   |              mkl           6 KB\n    bzip2-1.0.8                |       h5eee18b_6         262 KB\n    c-ares-1.19.1              |       h5eee18b_0         118 KB\n    ca-certificates-2024.12.31 |       h06a4308_0         128 KB\n    expat-2.6.4                |       h6a678d5_0         180 KB\n    h5py-3.12.1                |  py312h5842655_1         1.4 MB\n    hdf5-1.14.5                |       h2b7332f_2         5.9 MB\n    intel-openmp-2023.1.0      |   hdb19cb5_46306        17.2 MB\n    keras-3.6.0                |  py312h06a4308_0         2.9 MB\n    krb5-1.20.1                |       h143b758_1         1.3 MB\n    ld_impl_linux-64-2.40      |       h12ee557_0         710 KB\n    libcurl-8.12.0             |       hc9e6f67_0         468 KB\n    libedit-3.1.20230828       |       h5eee18b_0         179 KB\n    libev-4.33                 |       h7f8727e_1         111 KB\n    libffi-3.4.4               |       h6a678d5_1         141 KB\n    libgcc-ng-11.2.0           |       h1234567_1         5.3 MB\n    libgfortran-ng-11.2.0      |       h00389a5_1          20 KB\n    libgfortran5-11.2.0        |       h1234567_1         2.0 MB\n    libgomp-11.2.0             |       h1234567_1         474 KB\n    libnghttp2-1.57.0          |       h2d74bed_0         674 KB\n    libssh2-1.11.1             |       h251f7ec_0         308 KB\n    libstdcxx-ng-11.2.0        |       h1234567_1         4.7 MB\n    libuuid-1.41.5             |       h5eee18b_0          27 KB\n    lz4-c-1.9.4                |       h6a678d5_1         156 KB\n    markdown-it-py-2.2.0       |  py312h06a4308_1         134 KB\n    mdurl-0.1.0                |  py312h06a4308_0          22 KB\n    mkl-2023.1.0               |   h213fc3f_46344       171.5 MB\n    mkl-service-2.4.0          |  py312h5eee18b_2          67 KB\n    mkl_fft-1.3.11             |  py312h5eee18b_0         205 KB\n    mkl_random-1.2.8           |  py312h526ad5a_0         324 KB\n    ml_dtypes-0.5.0            |  py312h6a678d5_0         302 KB\n    namex-0.0.7                |  py312h06a4308_0          15 KB\n    ncurses-6.4                |       h6a678d5_0         914 KB\n    numpy-2.0.1                |  py312hc5e2394_1          11 KB\n    numpy-base-2.0.1           |  py312h0da6c21_1         8.5 MB\n    openssl-3.0.15             |       h5eee18b_0         5.2 MB\n    optree-0.12.1              |  py312hdb19cb5_0         326 KB\n    packaging-24.2             |  py312h06a4308_0         195 KB\n    pip-25.0                   |  py312h06a4308_0         2.8 MB\n    pygments-2.15.1            |  py312h06a4308_1         1.7 MB\n    python-3.12.6              |       h5148396_1        34.6 MB\n    readline-8.2               |       h5eee18b_0         357 KB\n    rich-13.9.4                |  py312h06a4308_0         615 KB\n    setuptools-75.8.0          |  py312h06a4308_0         2.2 MB\n    sqlite-3.45.3              |       h5eee18b_0         1.2 MB\n    tbb-2021.8.0               |       hdb19cb5_0         1.6 MB\n    tk-8.6.14                  |       h39e8969_0         3.4 MB\n    typing-extensions-4.12.2   |  py312h06a4308_0           9 KB\n    typing_extensions-4.12.2   |  py312h06a4308_0          79 KB\n    tzdata-2025a               |       h04d1e81_0         117 KB\n    wheel-0.45.1               |  py312h06a4308_0         147 KB\n    xz-5.6.4                   |       h5eee18b_1         567 KB\n    zlib-1.2.13                |       h5eee18b_1         111 KB\n    zstd-1.5.6                 |       hc292b87_0         664 KB\n    ------------------------------------------------------------\n                                           Total:       282.8 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main \n  _openmp_mutex      pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu \n  absl-py            pkgs/main/linux-64::absl-py-2.1.0-py312h06a4308_0 \n  blas               pkgs/main/linux-64::blas-1.0-mkl \n  bzip2              pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6 \n  c-ares             pkgs/main/linux-64::c-ares-1.19.1-h5eee18b_0 \n  ca-certificates    pkgs/main/linux-64::ca-certificates-2024.12.31-h06a4308_0 \n  expat              pkgs/main/linux-64::expat-2.6.4-h6a678d5_0 \n  h5py               pkgs/main/linux-64::h5py-3.12.1-py312h5842655_1 \n  hdf5               pkgs/main/linux-64::hdf5-1.14.5-h2b7332f_2 \n  intel-openmp       pkgs/main/linux-64::intel-openmp-2023.1.0-hdb19cb5_46306 \n  keras              pkgs/main/linux-64::keras-3.6.0-py312h06a4308_0 \n  krb5               pkgs/main/linux-64::krb5-1.20.1-h143b758_1 \n  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0 \n  libcurl            pkgs/main/linux-64::libcurl-8.12.0-hc9e6f67_0 \n  libedit            pkgs/main/linux-64::libedit-3.1.20230828-h5eee18b_0 \n  libev              pkgs/main/linux-64::libev-4.33-h7f8727e_1 \n  libffi             pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1 \n  libgcc-ng          pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 \n  libgfortran-ng     pkgs/main/linux-64::libgfortran-ng-11.2.0-h00389a5_1 \n  libgfortran5       pkgs/main/linux-64::libgfortran5-11.2.0-h1234567_1 \n  libgomp            pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 \n  libnghttp2         pkgs/main/linux-64::libnghttp2-1.57.0-h2d74bed_0 \n  libssh2            pkgs/main/linux-64::libssh2-1.11.1-h251f7ec_0 \n  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 \n  libuuid            pkgs/main/linux-64::libuuid-1.41.5-h5eee18b_0 \n  lz4-c              pkgs/main/linux-64::lz4-c-1.9.4-h6a678d5_1 \n  markdown-it-py     pkgs/main/linux-64::markdown-it-py-2.2.0-py312h06a4308_1 \n  mdurl              pkgs/main/linux-64::mdurl-0.1.0-py312h06a4308_0 \n  mkl                pkgs/main/linux-64::mkl-2023.1.0-h213fc3f_46344 \n  mkl-service        pkgs/main/linux-64::mkl-service-2.4.0-py312h5eee18b_2 \n  mkl_fft            pkgs/main/linux-64::mkl_fft-1.3.11-py312h5eee18b_0 \n  mkl_random         pkgs/main/linux-64::mkl_random-1.2.8-py312h526ad5a_0 \n  ml_dtypes          pkgs/main/linux-64::ml_dtypes-0.5.0-py312h6a678d5_0 \n  namex              pkgs/main/linux-64::namex-0.0.7-py312h06a4308_0 \n  ncurses            pkgs/main/linux-64::ncurses-6.4-h6a678d5_0 \n  numpy              pkgs/main/linux-64::numpy-2.0.1-py312hc5e2394_1 \n  numpy-base         pkgs/main/linux-64::numpy-base-2.0.1-py312h0da6c21_1 \n  openssl            pkgs/main/linux-64::openssl-3.0.15-h5eee18b_0 \n  optree             pkgs/main/linux-64::optree-0.12.1-py312hdb19cb5_0 \n  packaging          pkgs/main/linux-64::packaging-24.2-py312h06a4308_0 \n  pip                pkgs/main/linux-64::pip-25.0-py312h06a4308_0 \n  pygments           pkgs/main/linux-64::pygments-2.15.1-py312h06a4308_1 \n  python             pkgs/main/linux-64::python-3.12.6-h5148396_1 \n  readline           pkgs/main/linux-64::readline-8.2-h5eee18b_0 \n  rich               pkgs/main/linux-64::rich-13.9.4-py312h06a4308_0 \n  setuptools         pkgs/main/linux-64::setuptools-75.8.0-py312h06a4308_0 \n  sqlite             pkgs/main/linux-64::sqlite-3.45.3-h5eee18b_0 \n  tbb                pkgs/main/linux-64::tbb-2021.8.0-hdb19cb5_0 \n  tk                 pkgs/main/linux-64::tk-8.6.14-h39e8969_0 \n  typing-extensions  pkgs/main/linux-64::typing-extensions-4.12.2-py312h06a4308_0 \n  typing_extensions  pkgs/main/linux-64::typing_extensions-4.12.2-py312h06a4308_0 \n  tzdata             pkgs/main/noarch::tzdata-2025a-h04d1e81_0 \n  wheel              pkgs/main/linux-64::wheel-0.45.1-py312h06a4308_0 \n  xz                 pkgs/main/linux-64::xz-5.6.4-h5eee18b_1 \n  zlib               pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1 \n  zstd               pkgs/main/linux-64::zstd-1.5.6-hc292b87_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate myenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\n",
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+          "%md",
+          "",
+          "Verify the *myenv* environment is in the list of environments. The asterisk (*) indicates active environments. The new environment is created but not activated."
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+          "forms" : "[]"
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+            {
+              "message" : "# conda environments:\n#\nbase                  *  /opt/conda\nmyenv                    /u01/.conda/envs/myenv\n\n\n\n",
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+        "hasTitle" : true,
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+          "%md",
+          "",
+          "Activate the *myenv* environment and list the environments to verify the activation. The asterisk (*) next to the environment name confirms the activation."
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nConda environment 'myenv' activated\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
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+      {
+        "row" : 0,
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Show the activated environment in the environment listing",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "env list"
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+        "selectedVisualization" : "raw",
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+          "results" : [
+            {
+              "message" : "# conda environments:\n#\nbase                     /opt/conda\nmyenv                 *  /u01/.conda/envs/myenv\n\n\n\n",
+              "type" : "TEXT"
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+          ],
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+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "### Installing and Uninstalling Libraries",
+          "",
+          "The ADMIN user can install and uninstall libraries into an environment using the `install` and `uninstall` commands.",
+          "",
+          "For help with the conda `install` and `uninstall` commmands, type `install --help` and `uninstall --help` in a %conda paragraph."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
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+            {
+              "message" : "<h3 id=\"installing-and-uninstalling-libraries\">Installing and Uninstalling Libraries</h3>\n<p>The ADMIN user can install and uninstall libraries into an environment using the <code>install</code> and <code>uninstall</code> commands.</p>\n<p>For help with the conda <code>install</code> and <code>uninstall</code> commmands, type <code>install --help</code> and <code>uninstall --help</code> in a %conda paragraph.</p>\n",
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+          "forms" : "[]"
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+        "title" : "Install additional packages",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
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+          "Install the *pytorch* package into the activated *myenv* environment. Note, to avoid dependency conflicts, install all packages when the environment is built."
+        ],
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Channels:\n - pytorch\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/myenv\n\n  added / updated specs:\n    - pytorch\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    cuda-cudart-12.4.127       |       h99ab3db_0          21 KB\n    cuda-cudart_linux-64-12.4.127|       hd681fbe_0         207 KB\n    cuda-cupti-12.4.127        |       h6a678d5_1         2.0 MB\n    cuda-libraries-12.4.1      |       h06a4308_1          19 KB\n    cuda-nvrtc-12.4.127        |       h99ab3db_1        19.8 MB\n    cuda-nvtx-12.4.127         |       h6a678d5_1          30 KB\n    cuda-opencl-12.4.127       |       h6a678d5_0          28 KB\n    cuda-runtime-12.4.1        |       hb982923_0          18 KB\n    cuda-version-12.4          |       hbda6634_3          19 KB\n    filelock-3.13.1            |  py312h06a4308_0          24 KB\n    gmp-6.3.0                  |       h6a678d5_0         608 KB\n    gmpy2-2.2.1                |  py312h5eee18b_0         258 KB\n    jinja2-3.1.5               |  py312h06a4308_0         352 KB\n    libcublas-12.4.5.8         |       h99ab3db_1       247.6 MB\n    libcufft-11.2.1.3          |       h99ab3db_1       174.5 MB\n    libcufile-1.9.1.3          |       h99ab3db_1         995 KB\n    libcurand-10.3.5.147       |       h99ab3db_1        42.4 MB\n    libcusolver-11.6.1.9       |       h99ab3db_1        83.5 MB\n    libcusparse-12.3.1.170     |       h99ab3db_1       120.0 MB\n    libnpp-12.2.5.30           |       h99ab3db_1       100.5 MB\n    libnvfatbin-12.4.127       |       h7934f7d_2         857 KB\n    libnvjitlink-12.4.127      |       h99ab3db_1        17.4 MB\n    libnvjpeg-12.3.1.117       |       h6a678d5_1         2.6 MB\n    llvm-openmp-14.0.6         |       h9e868ea_0         4.4 MB\n    markupsafe-3.0.2           |  py312h5eee18b_0          26 KB\n    mpc-1.3.1                  |       h5eee18b_0         129 KB\n    mpfr-4.2.1                 |       h5eee18b_0         821 KB\n    mpmath-1.3.0               |  py312h06a4308_0         988 KB\n    networkx-3.4.2             |  py312h06a4308_0         3.1 MB\n    ocl-icd-2.3.2              |       h5eee18b_1         136 KB\n    pytorch-2.5.1              |py3.12_cuda12.4_cudnn9.1.0_0        1.46 GB  pytorch\n    pytorch-cuda-12.4          |       hc786d27_7           7 KB  pytorch\n    pytorch-mutex-1.0          |             cuda           3 KB  pytorch\n    pyyaml-6.0.2               |  py312h5eee18b_0         217 KB\n    sympy-1.13.3               |  py312h06a4308_1        15.0 MB\n    torchtriton-3.1.0          |            py312       233.6 MB  pytorch\n    yaml-0.2.5                 |       h7b6447c_0          75 KB\n    ------------------------------------------------------------\n                                           Total:        2.51 GB\n\nThe following NEW packages will be INSTALLED:\n\n  cuda-cudart        pkgs/main/linux-64::cuda-cudart-12.4.127-h99ab3db_0 \n  cuda-cudart_linux~ pkgs/main/noarch::cuda-cudart_linux-64-12.4.127-hd681fbe_0 \n  cuda-cupti         pkgs/main/linux-64::cuda-cupti-12.4.127-h6a678d5_1 \n  cuda-libraries     pkgs/main/linux-64::cuda-libraries-12.4.1-h06a4308_1 \n  cuda-nvrtc         pkgs/main/linux-64::cuda-nvrtc-12.4.127-h99ab3db_1 \n  cuda-nvtx          pkgs/main/linux-64::cuda-nvtx-12.4.127-h6a678d5_1 \n  cuda-opencl        pkgs/main/linux-64::cuda-opencl-12.4.127-h6a678d5_0 \n  cuda-runtime       pkgs/main/noarch::cuda-runtime-12.4.1-hb982923_0 \n  cuda-version       pkgs/main/noarch::cuda-version-12.4-hbda6634_3 \n  filelock           pkgs/main/linux-64::filelock-3.13.1-py312h06a4308_0 \n  gmp                pkgs/main/linux-64::gmp-6.3.0-h6a678d5_0 \n  gmpy2     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done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n\n\n",
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           h251f7ec_0  \nlibstdcxx-ng              11.2.0               h1234567_1  \nlibuuid                   1.41.5               h5eee18b_0  \nllvm-openmp               14.0.6               h9e868ea_0  \nlz4-c                     1.9.4                h6a678d5_1  \nmarkdown-it-py            2.2.0           py312h06a4308_1  \nmarkupsafe                3.0.2           py312h5eee18b_0  \nmdurl                     0.1.0           py312h06a4308_0  \nmkl                       2023.1.0         h213fc3f_46344  \nmkl-service               2.4.0           py312h5eee18b_2  \nmkl_fft                   1.3.11          py312h5eee18b_0  \nmkl_random                1.2.8           py312h526ad5a_0  \nml_dtypes                 0.5.0           py312h6a678d5_0  \nmpc                       1.3.1                h5eee18b_0  \nmpfr                      4.2.1                h5eee18b_0  \nmpmath                    1.3.0           py312h06a4308_0  \nnamex                     0.0.7           py312h06a4308_0  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+              "message" : "# packages in environment at /u01/.conda/envs/myenv:\n#\n# Name                    Version                   Build  Channel\n_libgcc_mutex             0.1                        main  \n_openmp_mutex             5.1                       1_gnu  \nabsl-py                   2.1.0           py312h06a4308_0  \nblas                      1.0                         mkl  \nbzip2                     1.0.8                h5eee18b_6  \nc-ares                    1.19.1               h5eee18b_0  \nca-certificates           2024.12.31           h06a4308_0  \nexpat                     2.6.4                h6a678d5_0  \nh5py                      3.12.1          py312h5842655_1  \nhdf5                      1.14.5               h2b7332f_2  \nintel-openmp              2023.1.0         hdb19cb5_46306  \nkeras                     3.6.0           py312h06a4308_0  \nkrb5                      1.20.1               h143b758_1  \nld_impl_linux-64          2.40                 h12ee557_0  \nlibcurl                   8.12.0               hc9e6f67_0  \nlibedit                   3.1.20230828         h5eee18b_0  \nlibev                     4.33                 h7f8727e_1  \nlibffi                    3.4.4                h6a678d5_1  \nlibgcc-ng                 11.2.0               h1234567_1  \nlibgfortran-ng            11.2.0               h00389a5_1  \nlibgfortran5              11.2.0               h1234567_1  \nlibgomp                   11.2.0               h1234567_1  \nlibnghttp2                1.57.0               h2d74bed_0  \nlibssh2                   1.11.1               h251f7ec_0  \nlibstdcxx-ng              11.2.0               h1234567_1  \nlibuuid                   1.41.5               h5eee18b_0  \nlz4-c                     1.9.4                h6a678d5_1  \nmarkdown-it-py            2.2.0           py312h06a4308_1  \nmdurl                     0.1.0           py312h06a4308_0  \nmkl                       2023.1.0         h213fc3f_46344  \nmkl-service               2.4.0           py312h5eee18b_2  \nmkl_fft                   1.3.11          py312h5eee18b_0  \nmkl_random                1.2.8           py312h526ad5a_0  \nml_dtypes                 0.5.0           py312h6a678d5_0  \nnamex                     0.0.7           py312h06a4308_0  \nncurses                   6.4                  h6a678d5_0  \nnumpy                     2.0.1           py312hc5e2394_1  \nnumpy-base                2.0.1           py312h0da6c21_1  \nopenssl                   3.0.15               h5eee18b_0  \noptree                    0.12.1          py312hdb19cb5_0  \npackaging                 24.2            py312h06a4308_0  \npip                       25.0            py312h06a4308_0  \npygments                  2.15.1          py312h06a4308_1  \npython                    3.12.6               h5148396_1  \nreadline                  8.2                  h5eee18b_0  \nrich                      13.9.4          py312h06a4308_0  \nsetuptools                75.8.0          py312h06a4308_0  \nsqlite                    3.45.3               h5eee18b_0  \ntbb                       2021.8.0             hdb19cb5_0  \ntk                        8.6.14               h39e8969_0  \ntyping-extensions         4.12.2          py312h06a4308_0  \ntyping_extensions         4.12.2          py312h06a4308_0  \ntzdata                    2025a                h04d1e81_0  \nwheel                     0.45.1          py312h06a4308_0  \nxz                        5.6.4                h5eee18b_1  \nzlib                      1.2.13               h5eee18b_1  \nzstd                      1.5.6                hc292b87_0  \n\n\n",
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+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "### Removing Environments",
+          "---",
+          "If you don't intend to upload the environment to Object Storage for the OML users in the database, you can simply exit the notebook session and it will go out of scope.  Alternatively, it can be explicitly removed using the `env remove` command. Remove the *myenv* environment and verify it was removed. A best practice is to deactivate the environment prior to removal.",
+          "",
+          "For help on the `env remove` command, type `env remove --help` in the %conda interpreter."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>If you don't intend to upload the environment to Object Storage for the OML users in the database, you can simply exit the notebook session and it will go out of scope.  Alternatively, it can be explicitly removed using the <code>env remove</code> command. Remove the <em>myenv</em> environment and verify it was removed. A best practice is to deactivate the environment prior to removal.</p>\n<p>For help on the <code>env remove</code> command, type <code>env remove --help</code> in the %conda interpreter.</p>\n",
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+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Deactivate the environment ",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "deactivate"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nConda environment 'conda' deactivated\n\n",
+              "type" : "TEXT"
+            }
+          ],
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+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Remove the environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "env remove -n myenv",
+          "",
+          "env list"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nRemove all packages in environment /u01/.conda/envs/myenv:\n\n\n\nusage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n# conda environments:\n#\nbase                  *  /opt/conda\n\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "",
+          "### Specify Packages for Installation",
+          "",
+          "",
+          "",
+          "#### Install Packages from the `conda-forge` Channel",
+          "* **Conda channels** are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The `conda` command searches a set of channels. By default, packages are automatically downloaded and updated from the [`default` channel] (https://repo.anaconda.com/pkgs/).",
+          "* The **conda-forge** channel is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. We use the [`conda-forge` channel] (https://conda-forge.org/), a community channel made up of thousands of contributors, in the following examples.",
+          "",
+          "",
+          "#### Install a Specific Version of a Package",
+          "* To install a specific version of a package, use `<package_name>=<version>`"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "hideInIFrame" : false,
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+          "startTime" : 1739988672882,
+          "endTime" : 1739988673332,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"specify-packages-for-installation\">Specify Packages for Installation</h3>\n<h4 id=\"install-packages-from-the-conda-forge-channel\">Install Packages from the <code>conda-forge</code> Channel</h4>\n<ul>\n<li><strong>Conda channels</strong> are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The <code>conda</code> command searches a set of channels. By default, packages are automatically downloaded and updated from the [<code>default</code> channel] (<a href=\"https://repo.anaconda.com/pkgs/\">https://repo.anaconda.com/pkgs/</a>).</li>\n<li>The <strong>conda-forge</strong> channel is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. We use the [<code>conda-forge</code> channel] (<a href=\"https://conda-forge.org/\">https://conda-forge.org/</a>), a community channel made up of thousands of contributors, in the following examples.</li>\n</ul>\n<h4 id=\"install-a-specific-version-of-a-package\">Install a Specific Version of a Package</h4>\n<ul>\n<li>To install a specific version of a package, use <code>&lt;package_name&gt;=&lt;version&gt;</code></li>\n</ul>\n",
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+            }
+          ],
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create an environment using conda-forge ",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "create -n mychannelenv -c conda-forge python=3.12.6",
+          "",
+          "activate mychannelenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+          "endTime" : 1739988701037,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Channels:\n - conda-forge\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/mychannelenv\n\n  added / updated specs:\n    - python=3.12.6\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n    bzip2-1.0.8                |       h4bc722e_7         247 KB  conda-forge\n    ca-certificates-2025.1.31  |       hbcca054_0         154 KB  conda-forge\n    ld_impl_linux-64-2.43      |       h712a8e2_2         654 KB  conda-forge\n    libexpat-2.6.4             |       h5888daf_0          72 KB  conda-forge\n    libffi-3.4.6               |       h2dba641_0          52 KB  conda-forge\n    libgcc-14.2.0              |       h77fa898_1         829 KB  conda-forge\n    libgcc-ng-14.2.0           |       h69a702a_1          53 KB  conda-forge\n    libgomp-14.2.0             |       h77fa898_1         450 KB  conda-forge\n    liblzma-5.6.4              |       hb9d3cd8_0         109 KB  conda-forge\n    liblzma-devel-5.6.4        |       hb9d3cd8_0         370 KB  conda-forge\n    libnsl-2.0.1               |       hd590300_0          33 KB  conda-forge\n    libsqlite-3.49.1           |       hee588c1_1         894 KB  conda-forge\n    libuuid-2.38.1             |       h0b41bf4_0          33 KB  conda-forge\n    libxcrypt-4.4.36           |       hd590300_1          98 KB  conda-forge\n    libzlib-1.3.1              |       hb9d3cd8_2          60 KB  conda-forge\n    ncurses-6.5                |       h2d0b736_3         871 KB  conda-forge\n    openssl-3.4.1              |       h7b32b05_0         2.8 MB  conda-forge\n    pip-25.0.1                 |     pyh8b19718_0         1.2 MB  conda-forge\n    python-3.12.6              |hc5c86c4_2_cpython        30.1 MB  conda-forge\n    readline-8.2               |       h8228510_1         275 KB  conda-forge\n    setuptools-75.8.0          |     pyhff2d567_0         757 KB  conda-forge\n    tk-8.6.13                  |noxft_h4845f30_101         3.2 MB  conda-forge\n    tzdata-2025a               |       h78e105d_0         120 KB  conda-forge\n    wheel-0.45.1               |     pyhd8ed1ab_1          61 KB  conda-forge\n    xz-5.6.4                   |       hbcc6ac9_0          23 KB  conda-forge\n    xz-gpl-tools-5.6.4         |       hbcc6ac9_0          33 KB  conda-forge\n    xz-tools-5.6.4             |       hb9d3cd8_0          88 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:        43.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_gnu \n  bzip2              conda-forge/linux-64::bzip2-1.0.8-h4bc722e_7 \n  ca-certificates    conda-forge/linux-64::ca-certificates-2025.1.31-hbcca054_0 \n  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.43-h712a8e2_2 \n  libexpat           conda-forge/linux-64::libexpat-2.6.4-h5888daf_0 \n  libffi             conda-forge/linux-64::libffi-3.4.6-h2dba641_0 \n  libgcc             conda-forge/linux-64::libgcc-14.2.0-h77fa898_1 \n  libgcc-ng          conda-forge/linux-64::libgcc-ng-14.2.0-h69a702a_1 \n  libgomp            conda-forge/linux-64::libgomp-14.2.0-h77fa898_1 \n  liblzma            conda-forge/linux-64::liblzma-5.6.4-hb9d3cd8_0 \n  liblzma-devel      conda-forge/linux-64::liblzma-devel-5.6.4-hb9d3cd8_0 \n  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0 \n  libsqlite          conda-forge/linux-64::libsqlite-3.49.1-hee588c1_1 \n  libuuid            conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0 \n  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 \n  libzlib            conda-forge/linux-64::libzlib-1.3.1-hb9d3cd8_2 \n  ncurses            conda-forge/linux-64::ncurses-6.5-h2d0b736_3 \n  openssl            conda-forge/linux-64::openssl-3.4.1-h7b32b05_0 \n  pip                conda-forge/noarch::pip-25.0.1-pyh8b19718_0 \n  python             conda-forge/linux-64::python-3.12.6-hc5c86c4_2_cpython \n  readline           conda-forge/linux-64::readline-8.2-h8228510_1 \n  setuptools         conda-forge/noarch::setuptools-75.8.0-pyhff2d567_0 \n  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101 \n  tzdata             conda-forge/noarch::tzdata-2025a-h78e105d_0 \n  wheel              conda-forge/noarch::wheel-0.45.1-pyhd8ed1ab_1 \n  xz                 conda-forge/linux-64::xz-5.6.4-hbcc6ac9_0 \n  xz-gpl-tools       conda-forge/linux-64::xz-gpl-tools-5.6.4-hbcc6ac9_0 \n  xz-tools           conda-forge/linux-64::xz-tools-5.6.4-hb9d3cd8_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate mychannelenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\nusage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n\nConda environment 'mychannelenv' activated\n\n",
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+        "title" : "Install a package from conda-forge by specifying the channel",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "install seaborn --channel conda-forge"
+        ],
+        "selectedVisualization" : "raw",
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+        "hideCode" : false,
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70 KB  conda-forge\n    lcms2-2.17                 |       h717163a_0         242 KB  conda-forge\n    lerc-4.0.0                 |       h27087fc_0         275 KB  conda-forge\n    libblas-3.9.0              |28_h59b9bed_openblas          16 KB  conda-forge\n    libbrotlicommon-1.1.0      |       hb9d3cd8_2          67 KB  conda-forge\n    libbrotlidec-1.1.0         |       hb9d3cd8_2          32 KB  conda-forge\n    libbrotlienc-1.1.0         |       hb9d3cd8_2         275 KB  conda-forge\n    libcblas-3.9.0             |28_he106b2a_openblas          16 KB  conda-forge\n    libdeflate-1.23            |       h4ddbbb0_0          71 KB  conda-forge\n    libgfortran-14.2.0         |       h69a702a_1          53 KB  conda-forge\n    libgfortran5-14.2.0        |       hd5240d6_1         1.4 MB  conda-forge\n    libjpeg-turbo-3.0.0        |       hd590300_1         604 KB  conda-forge\n    liblapack-3.9.0            |28_h7ac8fdf_openblas          16 KB  conda-forge\n    libopenblas-0.3.28         |pthreads_h94d23a6_1         5.3 MB  conda-forge\n    libpng-1.6.47              |       h943b412_0         282 KB  conda-forge\n    libstdcxx-14.2.0           |       hc0a3c3a_1         3.7 MB  conda-forge\n    libstdcxx-ng-14.2.0        |       h4852527_1          53 KB  conda-forge\n    libtiff-4.7.0              |       hd9ff511_3         418 KB  conda-forge\n    libwebp-base-1.5.0         |       h851e524_0         420 KB  conda-forge\n    libxcb-1.17.0              |       h8a09558_0         387 KB  conda-forge\n    matplotlib-base-3.10.0     |  py312hd3ec401_0         7.8 MB  conda-forge\n    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n    numpy-2.2.3                |  py312h72c5963_0         8.1 MB  conda-forge\n    openjpeg-2.5.3             |       h5fbd93e_0         335 KB  conda-forge\n    packaging-24.2             |     pyhd8ed1ab_2          59 KB  conda-forge\n    pandas-2.2.3               |  py312hf9745cd_1        14.7 MB  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pyhd8ed1ab_0          16 KB  conda-forge\n    statsmodels-0.14.4         |  py312hc0a28a1_0        11.5 MB  conda-forge\n    unicodedata2-16.0.0        |  py312h66e93f0_0         395 KB  conda-forge\n    xorg-libxau-1.0.12         |       hb9d3cd8_0          14 KB  conda-forge\n    xorg-libxdmcp-1.1.5        |       hb9d3cd8_0          19 KB  conda-forge\n    zstd-1.5.6                 |       ha6fb4c9_0         542 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:       119.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n  brotli             conda-forge/linux-64::brotli-1.1.0-hb9d3cd8_2 \n  brotli-bin         conda-forge/linux-64::brotli-bin-1.1.0-hb9d3cd8_2 \n  contourpy          conda-forge/linux-64::contourpy-1.3.1-py312h68727a3_0 \n  cycler             conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_1 \n  fonttools          conda-forge/linux-64::fonttools-4.56.0-py312h178313f_0 \n  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.8-py312h84d6215_0 \n  lcms2              conda-forge/linux-64::lcms2-2.17-h717163a_0 \n  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n  libblas            conda-forge/linux-64::libblas-3.9.0-28_h59b9bed_openblas \n  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.1.0-hb9d3cd8_2 \n  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.1.0-hb9d3cd8_2 \n  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.1.0-hb9d3cd8_2 \n  libcblas           conda-forge/linux-64::libcblas-3.9.0-28_he106b2a_openblas \n  libdeflate         conda-forge/linux-64::libdeflate-1.23-h4ddbbb0_0 \n  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_1 \n  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hd5240d6_1 \n  libjpeg-turbo      conda-forge/linux-64::libjpeg-turbo-3.0.0-hd590300_1 \n  liblapack          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statsmodels        conda-forge/linux-64::statsmodels-0.14.4-py312hc0a28a1_0 \n  unicodedata2       conda-forge/linux-64::unicodedata2-16.0.0-py312h66e93f0_0 \n  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.12-hb9d3cd8_0 \n  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 \n  zstd               conda-forge/linux-64::zstd-1.5.6-ha6fb4c9_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n\n\n",
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+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "install seaborn=0.13.2"
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+            {
+              "message" : "Channels:\n - defaults\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/mychannelenv\n\n  added / updated specs:\n    - seaborn=0.13.2\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    blas-2.128                 |         openblas          16 KB  conda-forge\n    blas-devel-3.9.0           |28_h1ea3ea9_openblas          16 KB  conda-forge\n    liblapacke-3.9.0           |28_he2f377e_openblas          16 KB  conda-forge\n    numpy-1.26.4               |  py312h2809609_0          10 KB\n    numpy-base-1.26.4          |  py312he1a6c75_0         7.7 MB\n    openblas-0.3.28            |pthreads_h6ec200e_1         5.5 MB  conda-forge\n    seaborn-0.13.2             |  py312h06a4308_0         723 KB\n    seaborn-base-0.13.2        |     pyhd8ed1ab_0         229 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:        14.1 MB\n\nThe following NEW packages will be INSTALLED:\n\n  blas               conda-forge/linux-64::blas-2.128-openblas \n  blas-devel         conda-forge/linux-64::blas-devel-3.9.0-28_h1ea3ea9_openblas \n  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-28_he2f377e_openblas \n  numpy-base         pkgs/main/linux-64::numpy-base-1.26.4-py312he1a6c75_0 \n  openblas           conda-forge/linux-64::openblas-0.3.28-pthreads_h6ec200e_1 \n\nThe following packages will be SUPERSEDED by a higher-priority channel:\n\n  numpy              conda-forge::numpy-2.2.3-py312h72c596~ --> pkgs/main::numpy-1.26.4-py312h2809609_0 \n  seaborn            conda-forge/noarch::seaborn-0.13.2-hd~ --> pkgs/main/linux-64::seaborn-0.13.2-py312h06a4308_0 \n\nThe following packages will be DOWNGRADED:\n\n  seaborn-base                          0.13.2-pyhd8ed1ab_3 --> 0.13.2-pyhd8ed1ab_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n\n\n",
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+        "message" : [
+          "%md",
+          "",
+          "",
+          "## Section 2: Create a Conda Environment and Upload to Object Storage",
+          "",
+          "",
+          "This section demonstrates creating two conda environments and uploading them to Object Storage. An environment saved in Object Storage can be used by all of the Autonomous Database users in the database, and will remain in Object Storage until ADMIN deletes it.",
+          "",
+          "As ADMIN user:",
+          "",
+          "- Create one environment for Python packages named *mypyenv*, and one for R packages named *myrenv*.",
+          "- Install the *tensorflow* and *seaborn* packages into the Python environment and the *forecast* and *ggplot2* packages into the R environment.",
+          "- Upload the environments to Object Storage. ",
+          "- Delete the environments from Object Storage. "
+        ],
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+              "message" : "<h2 id=\"section-2-create-a-conda-environment-and-upload-to-object-storage\">Section 2: Create a Conda Environment and Upload to Object Storage</h2>\n<p>This section demonstrates creating two conda environments and uploading them to Object Storage. An environment saved in Object Storage can be used by all of the Autonomous Database users in the database, and will remain in Object Storage until ADMIN deletes it.</p>\n<p>As ADMIN user:</p>\n<ul>\n<li>Create one environment for Python packages named <em>mypyenv</em>, and one for R packages named <em>myrenv</em>.</li>\n<li>Install the <em>tensorflow</em> and <em>seaborn</em> packages into the Python environment and the <em>forecast</em> and <em>ggplot2</em> packages into the R environment.</li>\n<li>Upload the environments to Object Storage.</li>\n<li>Delete the environments from Object Storage.</li>\n</ul>\n",
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+        "message" : [
+          "%md",
+          "",
+          "Create a conda environment named *mypyenv* with Python 3.12 for OML4Py compatibility and install the `tensorflow` and `seaborn` packages."
+        ],
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Create a conda environment named <em>mypyenv</em> with Python 3.12 for OML4Py compatibility and install the <code>tensorflow</code> and <code>seaborn</code> packages.</p>\n",
+              "type" : "HTML"
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create Python conda environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "create -n mypyenv -c conda-forge --strict-channel-priority python=3.12.6 tensorflow seaborn"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":500,\"lastColumns\":[],\"version\":1}}]",
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+              "message" : "Channels:\n - conda-forge\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/mypyenv\n\n  added / updated specs:\n    - python=3.12.6\n    - seaborn\n    - tensorflow\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge\n    absl-py-2.1.0              |     pyhd8ed1ab_1         105 KB  conda-forge\n    astunparse-1.6.3           |     pyhd8ed1ab_3          18 KB  conda-forge\n    brotli-python-1.1.0        |  py312h2ec8cdc_2         342 KB  conda-forge\n    c-ares-1.34.4              |       hb9d3cd8_0         201 KB  conda-forge\n    cached-property-1.5.2      |       hd8ed1ab_1           4 KB  conda-forge\n    cached_property-1.5.2      |     pyha770c72_1          11 KB  conda-forge\n    certifi-2025.1.31          |     pyhd8ed1ab_0         159 KB  conda-forge\n    cffi-1.17.1                |  py312h06ac9bb_0         288 KB  conda-forge\n    charset-normalizer-3.4.1   |     pyhd8ed1ab_0          46 KB  conda-forge\n    flatbuffers-24.12.23       |       h8f4948b_0         1.5 MB  conda-forge\n    gast-0.6.0                 |     pyhd8ed1ab_0          24 KB  conda-forge\n    giflib-5.2.2               |       hd590300_0          75 KB  conda-forge\n    google-pasta-0.2.0         |     pyhd8ed1ab_2          48 KB  conda-forge\n    grpcio-1.67.1              |  py312hacea422_1         879 KB  conda-forge\n    h2-4.2.0                   |     pyhd8ed1ab_0          53 KB  conda-forge\n    h5py-3.13.0                |nompi_py312hedeef09_100         1.3 MB  conda-forge\n    hdf5-1.14.3                |nompi_h2d575fe_109         3.7 MB  conda-forge\n    hpack-4.1.0                |     pyhd8ed1ab_0          30 KB  conda-forge\n    hyperframe-6.1.0           |     pyhd8ed1ab_0          17 KB  conda-forge\n    icu-75.1                   |       he02047a_0        11.6 MB  conda-forge\n    idna-3.10                  |     pyhd8ed1ab_1          49 KB  conda-forge\n    importlib-metadata-8.6.1   |     pyha770c72_0          28 KB  conda-forge\n    keras-3.8.0                |     pyh753f3f9_0         716 KB  conda-forge\n    keyutils-1.6.1             |       h166bdaf_0         115 KB  conda-forge\n    krb5-1.21.3                |       h659f571_0         1.3 MB  conda-forge\n    libabseil-20240722.0       | cxx17_hbbce691_4         1.3 MB  conda-forge\n    libaec-1.1.3               |       h59595ed_0          35 KB  conda-forge\n    libblas-3.9.0              |30_h59b9bed_openblas          17 KB  conda-forge\n    libcblas-3.9.0             |30_he106b2a_openblas          16 KB  conda-forge\n    libcurl-8.12.1             |       h332b0f4_0         417 KB  conda-forge\n    libedit-3.1.20250104       | pl5321h7949ede_0         132 KB  conda-forge\n    libev-4.33                 |       hd590300_2         110 KB  conda-forge\n    libgrpc-1.67.1             |       h25350d4_1         7.4 MB  conda-forge\n    libhwloc-2.11.2            |default_h0d58e46_1001         2.3 MB  conda-forge\n    libiconv-1.18              |       h4ce23a2_0         697 KB  conda-forge\n    liblapack-3.9.0            |30_h7ac8fdf_openblas          16 KB  conda-forge\n    libnghttp2-1.64.0          |       h161d5f1_0         632 KB  conda-forge\n    libopenblas-0.3.29         |pthreads_h94d23a6_0         5.6 MB  conda-forge\n    libprotobuf-5.28.3         |       h6128344_1         2.8 MB  conda-forge\n    libre2-11-2024.07.02       |       hbbce691_2         205 KB  conda-forge\n    libssh2-1.11.1             |       hf672d98_0         297 KB  conda-forge\n    libxml2-2.13.6             |       h8d12d68_0         674 KB  conda-forge\n    llvm-openmp-19.1.7         |       h024ca30_0         3.0 MB  conda-forge\n    markdown-3.6               |     pyhd8ed1ab_0          76 KB  conda-forge\n    markdown-it-py-3.0.0       |     pyhd8ed1ab_1          63 KB  conda-forge\n    markupsafe-3.0.2           |  py312h178313f_1          24 KB  conda-forge\n    mdurl-0.1.2                |     pyhd8ed1ab_1          14 KB  conda-forge\n    mkl-2024.2.2               |      ha957f24_16       118.9 MB  conda-forge\n    ml_dtypes-0.4.0            |  py312hf9745cd_2         163 KB  conda-forge\n    namex-0.0.8                |     pyhd8ed1ab_1          11 KB  conda-forge\n    opt_einsum-3.4.0           |     pyhd8ed1ab_1          61 KB  conda-forge\n    optree-0.14.0              |  py312h68727a3_1         362 KB  conda-forge\n    protobuf-5.28.3            |  py312h2ec8cdc_0         454 KB  conda-forge\n    pycparser-2.22             |     pyh29332c3_1         108 KB  conda-forge\n    pygments-2.19.1            |     pyhd8ed1ab_0         868 KB  conda-forge\n    pysocks-1.7.1              |     pyha55dd90_7          21 KB  conda-forge\n    python-flatbuffers-25.2.10 |     pyhbc23db3_0          34 KB  conda-forge\n    re2-2024.07.02             |       h9925aae_2          26 KB  conda-forge\n    requests-2.32.3            |     pyhd8ed1ab_1          57 KB  conda-forge\n    rich-13.9.4                |     pyhd8ed1ab_1         181 KB  conda-forge\n    snappy-1.2.1               |       h8bd8927_1          42 KB  conda-forge\n    tbb-2021.13.0              |       hceb3a55_1         172 KB  conda-forge\n    tensorboard-2.18.0         |     pyhd8ed1ab_1         4.9 MB  conda-forge\n    tensorboard-data-server-0.7.0|  py312hda17c39_2         3.4 MB  conda-forge\n    tensorflow-2.18.0          |cpu_py312h69ecde4_0          44 KB  conda-forge\n    tensorflow-base-2.18.0     |cpu_py312h099d1c6_0       150.7 MB  conda-forge\n    tensorflow-estimator-2.18.0|cpu_py312hc0a35a6_0         683 KB  conda-forge\n    termcolor-2.5.0            |     pyhd8ed1ab_1          12 KB  conda-forge\n    typing-extensions-4.12.2   |       hd8ed1ab_1          10 KB  conda-forge\n    typing_extensions-4.12.2   |     pyha770c72_1          39 KB  conda-forge\n    urllib3-2.3.0              |     pyhd8ed1ab_0          98 KB  conda-forge\n    werkzeug-3.1.3             |     pyhd8ed1ab_1         238 KB  conda-forge\n    wrapt-1.17.2               |  py312h66e93f0_0          62 KB  conda-forge\n    zipp-3.21.0                |     pyhd8ed1ab_1          21 KB  conda-forge\n    zstandard-0.23.0           |  py312hef9b889_1         410 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:       330.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_kmp_llvm \n  absl-py            conda-forge/noarch::absl-py-2.1.0-pyhd8ed1ab_1 \n  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\n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate mypyenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\n",
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+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "Create a conda environment named *myrenv* with R-4.0.5 for OML4R compatibility and install the `forecast` and `ggplot2` packages."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
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+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Create a conda environment named <em>myrenv</em> with R-4.0.5 for OML4R compatibility and install the <code>forecast</code> and <code>ggplot2</code> packages.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create R conda environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "create -n myrenv -c conda-forge --strict-channel-priority r-base=4.0.5 r-forecast r-ggplot2"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Channels:\n - conda-forge\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/myrenv\n\n  added / updated specs:\n    - r-base=4.0.5\n    - r-forecast\n    - r-ggplot2\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _r-mutex-1.0.1             |      anacondar_1           3 KB  conda-forge\n    binutils_impl_linux-64-2.43|       h4bf12b8_2         5.4 MB  conda-forge\n    bwidget-1.10.1             |       ha770c72_0         126 KB  conda-forge\n    cairo-1.16.0               |    ha61ee94_1014         1.5 MB  conda-forge\n    curl-7.86.0                |       h2283fc2_1          91 KB  conda-forge\n    expat-2.6.4                |       h5888daf_0         135 KB  conda-forge\n    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n    font-ttf-ubuntu-0.83       |       h77eed37_3         1.5 MB  conda-forge\n    fontconfig-2.14.2          |       h14ed4e7_0         266 KB  conda-forge\n    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge\n    fonts-conda-forge-1        |                0           4 KB  conda-forge\n    fribidi-1.0.10             |       h36c2ea0_0         112 KB  conda-forge\n    gcc_impl_linux-64-14.2.0   |       h6b349bd_1        69.1 MB  conda-forge\n    gettext-0.23.1             |       h5888daf_0         473 KB  conda-forge\n    gettext-tools-0.23.1       |       h5888daf_0         2.8 MB  conda-forge\n    gfortran_impl_linux-64-14.2.0|       hc73f493_1        16.4 MB  conda-forge\n    graphite2-1.3.13           |    h59595ed_1003          95 KB  conda-forge\n    gsl-2.7                    |       he838d99_0         3.2 MB  conda-forge\n    gxx_impl_linux-64-14.2.0   |       h2c03514_1        13.7 MB  conda-forge\n    harfbuzz-6.0.0             |       h8e241bc_0         1.2 MB  conda-forge\n    icu-70.1                   |       h27087fc_0        13.5 MB  conda-forge\n    jpeg-9e                    |       h0b41bf4_3         235 KB  conda-forge\n    kernel-headers_linux-64-3.10.0|      he073ed8_18         921 KB  conda-forge\n    krb5-1.19.3                |       h08a2579_0         1.4 MB  conda-forge\n    libasprintf-0.23.1         |       h8e693c7_0          42 KB  conda-forge\n    libasprintf-devel-0.23.1   |       h8e693c7_0          33 KB  conda-forge\n    libcurl-7.86.0             |       h2283fc2_1         351 KB  conda-forge\n    libdeflate-1.14            |       h166bdaf_0          81 KB  conda-forge\n    libgcc-devel_linux-64-14.2.0|     h41c2201_101         2.6 MB  conda-forge\n    libgettextpo-0.23.1        |       h5888daf_0         163 KB  conda-forge\n    libgettextpo-devel-0.23.1  |       h5888daf_0          36 KB  conda-forge\n    libgfortran-ng-14.2.0      |       h69a702a_1          53 KB  conda-forge\n    libglib-2.78.1             |       hebfc3b9_0         2.6 MB  conda-forge\n    libhwloc-2.9.1             |       hd6dc26d_0         2.5 MB  conda-forge\n    libnghttp2-1.58.0          |       h47da74e_1         617 KB  conda-forge\n    libpng-1.6.43              |       h2797004_0         281 KB  conda-forge\n    libsanitizer-14.2.0        |       h2a3dede_1         4.3 MB  conda-forge\n    libssh2-1.11.0             |       h0841786_0         265 KB  conda-forge\n    libstdcxx-devel_linux-64-14.2.0|     h41c2201_101        12.9 MB  conda-forge\n    libtiff-4.4.0              |       h82bc61c_5         473 KB  conda-forge\n    libxcb-1.13                |    h7f98852_1004         391 KB  conda-forge\n    libxml2-2.10.3             |       hca2bb57_4         697 KB  conda-forge\n    libzlib-1.2.13             |       h4ab18f5_6          60 KB  conda-forge\n    make-4.4.1                 |       hb9d3cd8_2         501 KB  conda-forge\n    pango-1.50.14              |       hd33c08f_0         427 KB  conda-forge\n    pcre2-10.40                |       hc3806b6_0         2.3 MB  conda-forge\n    pixman-0.44.2              |       h29eaf8c_0         372 KB  conda-forge\n    r-backports-1.4.1          |    r40hcfec24a_0         110 KB  conda-forge\n    r-base-4.0.5               |       hb87df5d_8        23.8 MB  conda-forge\n    r-brio-1.1.3               |    r40hcfec24a_0          40 KB  conda-forge\n    r-callr-3.7.2              |    r40hc72bb7e_0         427 KB  conda-forge\n    r-cli-3.4.1                |    r40h7525677_0         1.2 MB  conda-forge\n    r-colorspace-2.0_3         |    r40h06615bd_0         2.5 MB  conda-forge\n    r-crayon-1.5.1             |    r40hc72bb7e_0         164 KB  conda-forge\n    r-curl-4.3.2               |    r40hcfec24a_0         777 KB  conda-forge\n    r-desc-1.4.2               |    r40hc72bb7e_0         332 KB  conda-forge\n    r-diffobj-0.3.5            |    r40hcfec24a_0         1.0 MB  conda-forge\n    r-digest-0.6.29            |    r40h03ef668_0         208 KB  conda-forge\n    r-ellipsis-0.3.2           |    r40hcfec24a_0          42 KB  conda-forge\n    r-evaluate-0.16            |    r40hc72bb7e_0          85 KB  conda-forge\n    r-fansi-1.0.3              |    r40h06615bd_0         323 KB  conda-forge\n    r-farver-2.1.1             |    r40h7525677_0         1.4 MB  conda-forge\n    r-forecast-8.17.0          |    r40h37cf8d7_0         1.5 MB  conda-forge\n    r-fracdiff-1.5_1           |    r40hb699f27_1         116 KB  conda-forge\n    r-fs-1.5.2                 |    r40h7525677_1         554 KB  conda-forge\n    r-generics-0.1.3           |    r40hc72bb7e_0          93 KB  conda-forge\n    r-ggplot2-3.3.6            |    r40hc72bb7e_0         4.0 MB  conda-forge\n    r-glue-1.6.2               |    r40h06615bd_0         154 KB  conda-forge\n    r-gtable-0.3.1             |    r40hc72bb7e_0         174 KB  conda-forge\n    r-isoband-0.2.5            |    r40h03ef668_0         1.8 MB  conda-forge\n    r-jsonlite-1.8.0           |    r40h06615bd_0         960 KB  conda-forge\n    r-labeling-0.4.2           |    r40hc72bb7e_1          67 KB  conda-forge\n    r-lattice-0.20_45          |    r40hcfec24a_0         1.1 MB  conda-forge\n    r-lifecycle-1.0.2          |    r40hc72bb7e_0         110 KB  conda-forge\n    r-lmtest-0.9_40            |    r40h8da6f51_0         413 KB  conda-forge\n    r-magrittr-2.0.3           |    r40h06615bd_0         215 KB  conda-forge\n    r-mass-7.3_58.1            |    r40h06615bd_0         1.1 MB  conda-forge\n    r-matrix-1.4_1             |    r40h0154571_0         4.8 MB  conda-forge\n    r-mgcv-1.8_40              |    r40h0154571_0         3.0 MB  conda-forge\n    r-munsell-0.5.0            | r40hc72bb7e_1004         247 KB  conda-forge\n    r-nlme-3.1_159             |    r40h8da6f51_0         2.3 MB  conda-forge\n    r-nnet-7.3_17              |    r40hcfec24a_0         132 KB  conda-forge\n    r-pillar-1.8.1             |    r40hc72bb7e_0         672 KB  conda-forge\n    r-pkgconfig-2.0.3          |    r40hc72bb7e_1          25 KB  conda-forge\n    r-pkgload-1.3.0            |    r40hc72bb7e_0         191 KB  conda-forge\n    r-praise-1.0.0             | r40hc72bb7e_1005          24 KB  conda-forge\n    r-processx-3.7.0           |    r40h06615bd_0         330 KB  conda-forge\n    r-ps-1.7.1                 |    r40h06615bd_0         323 KB  conda-forge\n    r-quadprog-1.5_8           |    r40h742201e_3          47 KB  conda-forge\n    r-quantmod-0.4.20          |    r40hc72bb7e_0         1.0 MB  conda-forge\n    r-r6-2.5.1                 |    r40hc72bb7e_0          89 KB  conda-forge\n    r-rcolorbrewer-1.1_3       |    r40h785f33e_0          65 KB  conda-forge\n    r-rcpp-1.0.9               |    r40h7525677_1         2.0 MB  conda-forge\n    r-rcpparmadillo-0.11.2.3.1 |    r40h9f5de39_0         950 KB  conda-forge\n    r-rematch2-2.1.2           |    r40hc72bb7e_1          52 KB  conda-forge\n    r-rlang-1.0.6              |    r40h7525677_0         1.5 MB  conda-forge\n    r-rprojroot-2.0.3          |    r40hc72bb7e_0         115 KB  conda-forge\n    r-scales-1.2.1             |    r40hc72bb7e_0         611 KB  conda-forge\n    r-testthat-3.1.4           |    r40h7525677_0         1.6 MB  conda-forge\n    r-tibble-3.1.8             |    r40h06615bd_0         695 KB  conda-forge\n    r-timedate-4021.104        |    r40hc72bb7e_0         1.6 MB  conda-forge\n    r-tseries-0.10_51          |    r40h1463581_0         391 KB  conda-forge\n    r-ttr-0.24.3               |    r40h06615bd_0         531 KB  conda-forge\n    r-urca-1.3_0               | r40h8da6f51_1006         1.1 MB  conda-forge\n    r-utf8-1.2.2               |    r40hcfec24a_0         162 KB  conda-forge\n    r-vctrs-0.4.1              |    r40h7525677_0         1.2 MB  conda-forge\n    r-viridislite-0.4.1        |    r40hc72bb7e_0         1.3 MB  conda-forge\n    r-waldo-0.4.0              |    r40hc72bb7e_0         109 KB  conda-forge\n    r-withr-2.5.0              |    r40hc72bb7e_0         240 KB  conda-forge\n    r-xts-0.12.1               |    r40h06615bd_0         906 KB  conda-forge\n    r-zoo-1.8_11               |    r40h06615bd_0        1015 KB  conda-forge\n    sed-4.8                    |       he412f7d_0         264 KB  conda-forge\n    sysroot_linux-64-2.17      |      h0157908_18        14.5 MB  conda-forge\n    tbb-2021.9.0               |       hf52228f_0         1.5 MB  conda-forge\n    tktable-2.10               |       h8bc8fbc_6          89 KB  conda-forge\n    xorg-kbproto-1.0.7         |    hb9d3cd8_1003          30 KB  conda-forge\n    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n    xorg-libx11-1.8.4          |       h0b41bf4_0         810 KB  conda-forge\n    xorg-libxext-1.3.4         |       h0b41bf4_2          49 KB  conda-forge\n    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n    xorg-libxt-1.3.0           |       hd590300_0         371 KB  conda-forge\n    xorg-renderproto-0.11.1    |    hb9d3cd8_1003          12 KB  conda-forge\n    xorg-xextproto-7.3.0       |    hb9d3cd8_1004          30 KB  conda-forge\n    xorg-xproto-7.0.31         |    hb9d3cd8_1008          72 KB  conda-forge\n    zlib-1.2.13                |       h4ab18f5_6          91 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:       256.9 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_kmp_llvm \n  _r-mutex           conda-forge/noarch::_r-mutex-1.0.1-anacondar_1 \n  binutils_impl_lin~ conda-forge/linux-64::binutils_impl_linux-64-2.43-h4bf12b8_2 \n  bwidget            conda-forge/linux-64::bwidget-1.10.1-ha770c72_0 \n  bzip2              conda-forge/linux-64::bzip2-1.0.8-h4bc722e_7 \n  c-ares             conda-forge/linux-64::c-ares-1.34.4-hb9d3cd8_0 \n  ca-certificates    conda-forge/linux-64::ca-certificates-2025.1.31-hbcca054_0 \n  cairo              conda-forge/linux-64::cairo-1.16.0-ha61ee94_1014 \n  curl               conda-forge/linux-64::curl-7.86.0-h2283fc2_1 \n  expat              conda-forge/linux-64::expat-2.6.4-h5888daf_0 \n  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 \n  fontconfig         conda-forge/linux-64::fontconfig-2.14.2-h14ed4e7_0 \n  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 \n  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0 \n  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n  fribidi            conda-forge/linux-64::fribidi-1.0.10-h36c2ea0_0 \n  gcc_impl_linux-64  conda-forge/linux-64::gcc_impl_linux-64-14.2.0-h6b349bd_1 \n  gettext            conda-forge/linux-64::gettext-0.23.1-h5888daf_0 \n  gettext-tools      conda-forge/linux-64::gettext-tools-0.23.1-h5888daf_0 \n  gfortran_impl_lin~ conda-forge/linux-64::gfortran_impl_linux-64-14.2.0-hc73f493_1 \n  graphite2          conda-forge/linux-64::graphite2-1.3.13-h59595ed_1003 \n  gsl                conda-forge/linux-64::gsl-2.7-he838d99_0 \n  gxx_impl_linux-64  conda-forge/linux-64::gxx_impl_linux-64-14.2.0-h2c03514_1 \n  harfbuzz           conda-forge/linux-64::harfbuzz-6.0.0-h8e241bc_0 \n  icu                conda-forge/linux-64::icu-70.1-h27087fc_0 \n  jpeg               conda-forge/linux-64::jpeg-9e-h0b41bf4_3 \n  kernel-headers_li~ conda-forge/noarch::kernel-headers_linux-64-3.10.0-he073ed8_18 \n  keyutils           conda-forge/linux-64::keyutils-1.6.1-h166bdaf_0 \n  krb5               conda-forge/linux-64::krb5-1.19.3-h08a2579_0 \n  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.43-h712a8e2_2 \n  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n  libasprintf        conda-forge/linux-64::libasprintf-0.23.1-h8e693c7_0 \n  libasprintf-devel  conda-forge/linux-64::libasprintf-devel-0.23.1-h8e693c7_0 \n  libblas            conda-forge/linux-64::libblas-3.9.0-30_h59b9bed_openblas \n  libcblas           conda-forge/linux-64::libcblas-3.9.0-30_he106b2a_openblas \n  libcurl            conda-forge/linux-64::libcurl-7.86.0-h2283fc2_1 \n  libdeflate         conda-forge/linux-64::libdeflate-1.14-h166bdaf_0 \n  libedit            conda-forge/linux-64::libedit-3.1.20250104-pl5321h7949ede_0 \n  libev              conda-forge/linux-64::libev-4.33-hd590300_2 \n  libexpat           conda-forge/linux-64::libexpat-2.6.4-h5888daf_0 \n  libffi             conda-forge/linux-64::libffi-3.4.6-h2dba641_0 \n  libgcc             conda-forge/linux-64::libgcc-14.2.0-h77fa898_1 \n  libgcc-devel_linu~ conda-forge/noarch::libgcc-devel_linux-64-14.2.0-h41c2201_101 \n  libgcc-ng          conda-forge/linux-64::libgcc-ng-14.2.0-h69a702a_1 \n  libgettextpo       conda-forge/linux-64::libgettextpo-0.23.1-h5888daf_0 \n  libgettextpo-devel conda-forge/linux-64::libgettextpo-devel-0.23.1-h5888daf_0 \n  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_1 \n  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-14.2.0-h69a702a_1 \n  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hd5240d6_1 \n  libglib            conda-forge/linux-64::libglib-2.78.1-hebfc3b9_0 \n  libgomp            conda-forge/linux-64::libgomp-14.2.0-h77fa898_1 \n  libhwloc           conda-forge/linux-64::libhwloc-2.9.1-hd6dc26d_0 \n  libiconv           conda-forge/linux-64::libiconv-1.18-h4ce23a2_0 \n  liblapack          conda-forge/linux-64::liblapack-3.9.0-30_h7ac8fdf_openblas \n  liblzma            conda-forge/linux-64::liblzma-5.6.4-hb9d3cd8_0 \n  liblzma-devel      conda-forge/linux-64::liblzma-devel-5.6.4-hb9d3cd8_0 \n  libnghttp2         conda-forge/linux-64::libnghttp2-1.58.0-h47da74e_1 \n  libopenblas        conda-forge/linux-64::libopenblas-0.3.29-pthreads_h94d23a6_0 \n  libpng             conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n  libsanitizer       conda-forge/linux-64::libsanitizer-14.2.0-h2a3dede_1 \n  libssh2            conda-forge/linux-64::libssh2-1.11.0-h0841786_0 \n  libstdcxx          conda-forge/linux-64::libstdcxx-14.2.0-hc0a3c3a_1 \n  libstdcxx-devel_l~ conda-forge/noarch::libstdcxx-devel_linux-64-14.2.0-h41c2201_101 \n  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-14.2.0-h4852527_1 \n  libtiff            conda-forge/linux-64::libtiff-4.4.0-h82bc61c_5 \n  libuuid            conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0 \n  libwebp-base       conda-forge/linux-64::libwebp-base-1.5.0-h851e524_0 \n  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1004 \n  libxml2            conda-forge/linux-64::libxml2-2.10.3-hca2bb57_4 \n  libzlib            conda-forge/linux-64::libzlib-1.2.13-h4ab18f5_6 \n  llvm-openmp        conda-forge/linux-64::llvm-openmp-19.1.7-h024ca30_0 \n  make               conda-forge/linux-64::make-4.4.1-hb9d3cd8_2 \n  mkl                conda-forge/linux-64::mkl-2024.2.2-ha957f24_16 \n  ncurses            conda-forge/linux-64::ncurses-6.5-h2d0b736_3 \n  openssl            conda-forge/linux-64::openssl-3.4.1-h7b32b05_0 \n  pango              conda-forge/linux-64::pango-1.50.14-hd33c08f_0 \n  pcre2              conda-forge/linux-64::pcre2-10.40-hc3806b6_0 \n  pixman             conda-forge/linux-64::pixman-0.44.2-h29eaf8c_0 \n  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 \n  r-backports        conda-forge/linux-64::r-backports-1.4.1-r40hcfec24a_0 \n  r-base             conda-forge/linux-64::r-base-4.0.5-hb87df5d_8 \n  r-brio             conda-forge/linux-64::r-brio-1.1.3-r40hcfec24a_0 \n  r-callr            conda-forge/noarch::r-callr-3.7.2-r40hc72bb7e_0 \n  r-cli              conda-forge/linux-64::r-cli-3.4.1-r40h7525677_0 \n  r-colorspace       conda-forge/linux-64::r-colorspace-2.0_3-r40h06615bd_0 \n  r-crayon           conda-forge/noarch::r-crayon-1.5.1-r40hc72bb7e_0 \n  r-curl             conda-forge/linux-64::r-curl-4.3.2-r40hcfec24a_0 \n  r-desc             conda-forge/noarch::r-desc-1.4.2-r40hc72bb7e_0 \n  r-diffobj          conda-forge/linux-64::r-diffobj-0.3.5-r40hcfec24a_0 \n  r-digest           conda-forge/linux-64::r-digest-0.6.29-r40h03ef668_0 \n  r-ellipsis         conda-forge/linux-64::r-ellipsis-0.3.2-r40hcfec24a_0 \n  r-evaluate         conda-forge/noarch::r-evaluate-0.16-r40hc72bb7e_0 \n  r-fansi            conda-forge/linux-64::r-fansi-1.0.3-r40h06615bd_0 \n  r-farver           conda-forge/linux-64::r-farver-2.1.1-r40h7525677_0 \n  r-forecast         conda-forge/linux-64::r-forecast-8.17.0-r40h37cf8d7_0 \n  r-fracdiff         conda-forge/linux-64::r-fracdiff-1.5_1-r40hb699f27_1 \n  r-fs               conda-forge/linux-64::r-fs-1.5.2-r40h7525677_1 \n  r-generics         conda-forge/noarch::r-generics-0.1.3-r40hc72bb7e_0 \n  r-ggplot2          conda-forge/noarch::r-ggplot2-3.3.6-r40hc72bb7e_0 \n  r-glue             conda-forge/linux-64::r-glue-1.6.2-r40h06615bd_0 \n  r-gtable           conda-forge/noarch::r-gtable-0.3.1-r40hc72bb7e_0 \n  r-isoband          conda-forge/linux-64::r-isoband-0.2.5-r40h03ef668_0 \n  r-jsonlite         conda-forge/linux-64::r-jsonlite-1.8.0-r40h06615bd_0 \n  r-labeling         conda-forge/noarch::r-labeling-0.4.2-r40hc72bb7e_1 \n  r-lattice          conda-forge/linux-64::r-lattice-0.20_45-r40hcfec24a_0 \n  r-lifecycle        conda-forge/noarch::r-lifecycle-1.0.2-r40hc72bb7e_0 \n  r-lmtest           conda-forge/linux-64::r-lmtest-0.9_40-r40h8da6f51_0 \n  r-magrittr         conda-forge/linux-64::r-magrittr-2.0.3-r40h06615bd_0 \n  r-mass             conda-forge/linux-64::r-mass-7.3_58.1-r40h06615bd_0 \n  r-matrix           conda-forge/linux-64::r-matrix-1.4_1-r40h0154571_0 \n  r-mgcv             conda-forge/linux-64::r-mgcv-1.8_40-r40h0154571_0 \n  r-munsell          conda-forge/noarch::r-munsell-0.5.0-r40hc72bb7e_1004 \n  r-nlme             conda-forge/linux-64::r-nlme-3.1_159-r40h8da6f51_0 \n  r-nnet             conda-forge/linux-64::r-nnet-7.3_17-r40hcfec24a_0 \n  r-pillar           conda-forge/noarch::r-pillar-1.8.1-r40hc72bb7e_0 \n  r-pkgconfig        conda-forge/noarch::r-pkgconfig-2.0.3-r40hc72bb7e_1 \n  r-pkgload          conda-forge/noarch::r-pkgload-1.3.0-r40hc72bb7e_0 \n  r-praise           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conda-forge/linux-64::readline-8.2-h8228510_1 \n  sed                conda-forge/linux-64::sed-4.8-he412f7d_0 \n  sysroot_linux-64   conda-forge/noarch::sysroot_linux-64-2.17-h0157908_18 \n  tbb                conda-forge/linux-64::tbb-2021.9.0-hf52228f_0 \n  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101 \n  tktable            conda-forge/linux-64::tktable-2.10-h8bc8fbc_6 \n  tzdata             conda-forge/noarch::tzdata-2025a-h78e105d_0 \n  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-hb9d3cd8_1003 \n  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0 \n  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000 \n  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.8.4-h0b41bf4_0 \n  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.12-hb9d3cd8_0 \n  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 \n  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h0b41bf4_2 \n  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003 \n  xorg-libxt         conda-forge/linux-64::xorg-libxt-1.3.0-hd590300_0 \n  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-hb9d3cd8_1003 \n  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-hb9d3cd8_1004 \n  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-hb9d3cd8_1008 \n  xz                 conda-forge/linux-64::xz-5.6.4-hbcc6ac9_0 \n  xz-gpl-tools       conda-forge/linux-64::xz-gpl-tools-5.6.4-hbcc6ac9_0 \n  xz-tools           conda-forge/linux-64::xz-tools-5.6.4-hb9d3cd8_0 \n  zlib               conda-forge/linux-64::zlib-1.2.13-h4ab18f5_6 \n  zstd               conda-forge/linux-64::zstd-1.5.6-ha6fb4c9_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate myrenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\n",
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+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Upload environment to Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Upload the environments to the Object Storage bucket associated with the Autonomous Database instance using the  `upload` command. We provide environment descriptions and two tags, one for the user name and one for the application name, and we overwrite any environment with the same name if it exists.",
+          "",
+          "The application tag is required for use with embedded execution. For example, OML4Py embedded Python execution works with conda environments containing the OML4PY tag, and OML4R embedded R execution works with conda environments containing the OML4R tag.",
+          "",
+          "There is one Object Storage bucket for each data center region. The conda environments are saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and only available to users through OML notebooks. There is a 8G maximum size for a single conda environment, and no size limit on  Object Storage.",
+          "",
+          "To get help for the `upload` command, type `upload --help` in the %conda interpreter."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
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+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Upload the environments to the Object Storage bucket associated with the Autonomous Database instance using the  <code>upload</code> command. We provide environment descriptions and two tags, one for the user name and one for the application name, and we overwrite any environment with the same name if it exists.</p>\n<p>The application tag is required for use with embedded execution. For example, OML4Py embedded Python execution works with conda environments containing the OML4PY tag, and OML4R embedded R execution works with conda environments containing the OML4R tag.</p>\n<p>There is one Object Storage bucket for each data center region. The conda environments are saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and only available to users through OML notebooks. There is a 8G maximum size for a single conda environment, and no size limit on  Object Storage.</p>\n<p>To get help for the <code>upload</code> command, type <code>upload --help</code> in the %conda interpreter.</p>\n",
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+            }
+          ],
+          "forms" : "[]"
+        },
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+      {
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+        "width" : 12,
+        "title" : "Save Python environment to Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "upload mypyenv --overwrite --description 'Install Python seaborn and tensorflow packages' -t user 'OMLUSER' -t application OML4PY"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
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+          "endTime" : 1739988900143,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\nUploading conda environment mypyenv\nUpload successful for conda environment mypyenv\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Save R environment to Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "upload myrenv --overwrite --description 'Install R forecast and ggplot2 packages' -t user 'OMLUSER' -t application 'OML4R'"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988900630,
+          "endTime" : 1739988941791,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\nUploading conda environment myrenv\nUpload successful for conda environment myrenv\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
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+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "The environments are now available for the OML user to download and will remain in Object Storage until it is deleted. ",
+          "",
+          "Verify the environments are saved to Object storage using the `list-saved-envs`,  passing the environment name to the `-e` flag."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
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+          "endTime" : 1739988942795,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>The environments are now available for the OML user to download and will remain in Object Storage until it is deleted.</p>\n<p>Verify the environments are saved to Object storage using the <code>list-saved-envs</code>,  passing the environment name to the <code>-e</code> flag.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Search for the saved environments",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs -e mypyenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "enabled" : true,
+        "forms" : "[]",
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+          "startTime" : 1739988943282,
+          "endTime" : 1739988945466,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\n{\n  \"name\": \"mypyenv\",\n  \"size\": \"2.2 GiB\",\n  \"description\": \"Install Python seaborn and tensorflow packages\",\n  \"tags\": {\n    \"application\": \"OML4PY\",\n    \"user\": \"OMLUSER\"\n  },\n  \"number_of_installed_packages\": 149\n}\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
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+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs -e myrenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988945956,
+          "endTime" : 1739988948142,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\n{\n  \"name\": \"myrenv\",\n  \"size\": \"1.5 GiB\",\n  \"description\": \"Install R forecast and ggplot2 packages\",\n  \"tags\": {\n    \"application\": \"OML4R\",\n    \"user\": \"OMLUSER\"\n  },\n  \"number_of_installed_packages\": 171\n}\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "### Delete Environment from Object Storage",
+          "---",
+          "",
+          "If an environment is no longer in use, it can be deleted from Object Storage using the `delete` command. ",
+          "",
+          "For help on the conda `delete` command, type `delete --help` in a %conda paragraph.",
+          "",
+          "Note, do not run the paragraphs containing the `delete` commands if you want to use them in an OML user session."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988948625,
+          "endTime" : 1739988949077,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>If an environment is no longer in use, it can be deleted from Object Storage using the <code>delete</code> command.</p>\n<p>For help on the conda <code>delete</code> command, type <code>delete --help</code> in a %conda paragraph.</p>\n<p>Note, do not run the paragraphs containing the <code>delete</code> commands if you want to use them in an OML user session.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Delete R environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "delete myrenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1718053975458,
+          "endTime" : 1718053977055,
+          "interpreter" : "conda.conda_medium",
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+          "status" : null,
+          "results" : null,
+          "forms" : null
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Delete Python environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "delete mypyenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
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+          "forms" : null
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Confirm environment deletion",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1718053978796,
+          "endTime" : 1718053980390,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : null,
+          "status" : null,
+          "results" : null,
+          "forms" : null
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Access Control List",
+        "hasTitle" : true,
+        "message" : [
+          "%md\r",
+          "---\r",
+          "## Optional: Add OML user to the Access Control List\r",
+          "---\r",
+          "\r",
+          "A requirement of the SQL API for embedded R and Python execution on Autonomous Database is adding the OML user to the Access Control List (ACL). The ACL provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database. \r",
+          "\r",
+          "As ADMIN, run the network access control list `rqAppendHostAce` function (OML4R) or `pyqAppendHostAce` (OML4Py) to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides.  \r",
+          "\r",
+          "For example, if your username is *OMLUSER* and your Autonomous Database resides in the Ashburn region, the root domain is *adb.us-ashburn-1.oraclecloudapps.com*. \r"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988949566,
+          "endTime" : 1739988950024,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>A requirement of the SQL API for embedded R and Python execution on Autonomous Database is adding the OML user to the Access Control List (ACL). The ACL provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database.</p>\n<p>As ADMIN, run the network access control list <code>rqAppendHostAce</code> function (OML4R) or <code>pyqAppendHostAce</code> (OML4Py) to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides.</p>\n<p>For example, if your username is <em>OMLUSER</em> and your Autonomous Database resides in the Ashburn region, the root domain is <em>adb.us-ashburn-1.oraclecloudapps.com</em>.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "OML4R",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "-- replace username and root domain with your database values",
+          "",
+          "exec rqAppendHostAce('OMLUSER','adb.us-ashburn-1.oraclecloudapps.com')"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739989149503,
+          "endTime" : 1739989150161,
+          "interpreter" : "script.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "OML4Py",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "-- replace username and root domain with your database values",
+          "",
+          "exec pyqAppendHostAce('OMLUSER','adb.us-ashburn-1.oraclecloudapps.com')"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739985092571,
+          "endTime" : 1739985094835,
+          "interpreter" : "script.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "## Use Conda Environments",
+          "---",
+          "",
+          "Refer to the template notebooks, *OML Third-Party Packages - Python Environment Usage* and *OML Third-Party Packages - R Environment Usage* for the steps to download and use the saved environments, *mypyenv* and *myrenv*. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988950510,
+          "endTime" : 1739988950959,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Refer to the template notebooks, <em>OML Third-Party Packages - Python Environment Usage</em> and <em>OML Third-Party Packages - R Environment Usage</em> for the steps to download and use the saved environments, <em>mypyenv</em> and <em>myrenv</em>.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## End of Script"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988951458,
+          "endTime" : 1739988951907,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of Script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML Third-Party Packages - Python Environment Usage.dsnb b/machine-learning/notebooks-oml/python/OML Third-Party Packages - Python Environment Usage.dsnb
new file mode 100644
index 00000000..09361e6f
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML Third-Party Packages - Python Environment Usage.dsnb	
@@ -0,0 +1,1689 @@
+[
+  {
+    "name" : "OML Third-Party Packages - Python Environment Usage",
+    "description" : null,
+    "tags" : null,
+    "version" : "7",
+    "layout" : null,
+    "type" : "low",
+    "snapshot" : false,
+    "isEditable" : true,
+    "isRunnable" : true,
+    "template" : null,
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+    "paragraphs" : [
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+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991635401,
+          "endTime" : 1739991647669,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
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+      {
+        "row" : 0,
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+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "# OML Third-Party Packages - Python Environment Usage",
+          "",
+          "Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. ",
+          "",
+          "Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the Python and the OML4Py Python, SQL, and REST APIs, and R and the OML4R R, SQL, and REST APIs.",
+          "",
+          "In this notebook, we demonstrate a typical workflow for third-party environment usage in OML notebooks using Python and OML4Py. The OML user downloads and uses the packages in conda environments that were previously created and saved to an Object Storage bucket folder associated with the Autonomous Database.",
+          "",
+          "The template notebook titled, *OML Third-Party Packages - Environment Creation*, the ADMIN user creates a conda environment, installs packages, and uploads the conda environments to Object Storage.",
+          "",
+          "",
+          "Copyright (c) 2025 Oracle Corporation ",
+          "<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0</a>"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991648241,
+          "endTime" : 1739991648787,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h1 id=\"oml-third-party-packages---python-environment-usage\">OML Third-Party Packages - Python Environment Usage</h1>\n<p>Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages.</p>\n<p>Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the Python and the OML4Py Python, SQL, and REST APIs, and R and the OML4R R, SQL, and REST APIs.</p>\n<p>In this notebook, we demonstrate a typical workflow for third-party environment usage in OML notebooks using Python and OML4Py. The OML user downloads and uses the packages in conda environments that were previously created and saved to an Object Storage bucket folder associated with the Autonomous Database.</p>\n<p>The template notebook titled, <em>OML Third-Party Packages - Environment Creation</em>, the ADMIN user creates a conda environment, installs packages, and uploads the conda environments to Object Storage.</p>\n<p>Copyright (c) 2025 Oracle Corporation\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0</a></p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "List a named environment in Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Use `list-saved-envs` to list and return the details for a named conda environment in Object Storage.  The environment name, size, and number of packages is returned along with the description and tags provided by the ADMIN when uploading the environment.",
+          "",
+          "The application tag indicates how the environment can be used.  For example, the environment `mypyenv` was created for use with Python and OML4Py."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991649372,
+          "endTime" : 1739991649839,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Use <code>list-saved-envs</code> to list and return the details for a named conda environment in Object Storage.  The environment name, size, and number of packages is returned along with the description and tags provided by the ADMIN when uploading the environment.</p>\n<p>The application tag indicates how the environment can be used.  For example, the environment <code>mypyenv</code> was created for use with Python and OML4Py.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs -e mypyenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991662579,
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+          "interpreter" : "conda.conda_low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\n{\n  \"name\": \"mypyenv\",\n  \"size\": \"2.2 GiB\",\n  \"description\": \"Install Python seaborn and tensorflow packages\",\n  \"tags\": {\n    \"application\": \"OML4PY\",\n    \"user\": \"OMLUSER\"\n  },\n  \"number_of_installed_packages\": 149\n}\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Using a Python environment",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "In this example, we build a machine learning model that accurately predicts an iris flower species when given flower measurements. The Python packages used are *Keras*, *TensorFlow*, *Seaborn*, *Pandas*, *Numpy*, and *Scikit-Learn*, and the conda environment is *mypyenv*. We first run the code and create a UDF using Python, followed by invoking the UDF in OML4Py embedded Python execution from the Python, SQL and REST APIs.",
+          "",
+          "Note, *Pandas*, *Numpy*, and *Scikit-Learn* are included in OML Notebooks in Oracle Autonomous Database so it is not necessary for ADMIN to install them when creating the conda environment."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991667287,
+          "endTime" : 1739991667751,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>In this example, we build a machine learning model that accurately predicts an iris flower species when given flower measurements. The Python packages used are <em>Keras</em>, <em>TensorFlow</em>, <em>Seaborn</em>, <em>Pandas</em>, <em>Numpy</em>, and <em>Scikit-Learn</em>, and the conda environment is <em>mypyenv</em>. We first run the code and create a UDF using Python, followed by invoking the UDF in OML4Py embedded Python execution from the Python, SQL and REST APIs.</p>\n<p>Note, <em>Pandas</em>, <em>Numpy</em>, and <em>Scikit-Learn</em> are included in OML Notebooks in Oracle Autonomous Database so it is not necessary for ADMIN to install them when creating the conda environment.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Download and activate the environment",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Conda environments are available after they are downloaded and activated using the `download` and `activate` functions in a %conda paragraph. An activated environment is available until it is deactivated."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991668405,
+          "endTime" : 1739991668913,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Conda environments are available after they are downloaded and activated using the <code>download</code> and <code>activate</code> functions in a %conda paragraph. An activated environment is available until it is deactivated.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%conda",
+          "",
+          "download mypyenv --overwrite",
+          "",
+          "activate mypyenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991669458,
+          "endTime" : 1739991701105,
+          "interpreter" : "conda.conda_low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\nDownloading conda environment mypyenv\nDownload successful for conda environment mypyenv\n\n\nusage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n\nConda environment 'mypyenv' activated\n\n",
+              "type" : "TEXT"
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             pyha770c72_1    conda-forge\ncertifi                   2025.1.31          pyhd8ed1ab_0    conda-forge\ncffi                      1.17.1          py312h06ac9bb_0    conda-forge\ncharset-normalizer        3.4.1              pyhd8ed1ab_0    conda-forge\ncontourpy                 1.3.1           py312h68727a3_0    conda-forge\ncycler                    0.12.1             pyhd8ed1ab_1    conda-forge\nflatbuffers               24.12.23             h8f4948b_0    conda-forge\nfonttools                 4.56.0          py312h178313f_0    conda-forge\nfreetype                  2.12.1               h267a509_2    conda-forge\ngast                      0.6.0              pyhd8ed1ab_0    conda-forge\ngiflib                    5.2.2                hd590300_0    conda-forge\ngoogle-pasta              0.2.0              pyhd8ed1ab_2    conda-forge\ngrpcio                    1.67.1          py312hacea422_1    conda-forge\nh2                        4.2.0              pyhd8ed1ab_0    conda-forge\nh5py                      3.13.0          nompi_py312hedeef09_100    conda-forge\nhdf5                      1.14.3          nompi_h2d575fe_109    conda-forge\nhpack                     4.1.0              pyhd8ed1ab_0    conda-forge\nhyperframe                6.1.0              pyhd8ed1ab_0    conda-forge\nicu                       75.1                 he02047a_0    conda-forge\nidna                      3.10               pyhd8ed1ab_1    conda-forge\nimportlib-metadata        8.6.1              pyha770c72_0    conda-forge\nkeras                     3.8.0              pyh753f3f9_0    conda-forge\nkeyutils                  1.6.1                h166bdaf_0    conda-forge\nkiwisolver                1.4.8           py312h84d6215_0    conda-forge\nkrb5                      1.21.3               h659f571_0    conda-forge\nlcms2                     2.17                 h717163a_0    conda-forge\nld_impl_linux-64          2.43                 h712a8e2_2    conda-forge\nlerc                      4.0.0                h27087fc_0    conda-forge\nlibabseil                 20240722.0      cxx17_hbbce691_4    conda-forge\nlibaec                    1.1.3                h59595ed_0    conda-forge\nlibblas                   3.9.0           30_h59b9bed_openblas    conda-forge\nlibbrotlicommon           1.1.0                hb9d3cd8_2    conda-forge\nlibbrotlidec              1.1.0                hb9d3cd8_2    conda-forge\nlibbrotlienc              1.1.0                hb9d3cd8_2    conda-forge\nlibcblas                  3.9.0           30_he106b2a_openblas    conda-forge\nlibcurl                   8.12.1               h332b0f4_0    conda-forge\nlibdeflate                1.23                 h4ddbbb0_0    conda-forge\nlibedit                   3.1.20250104    pl5321h7949ede_0    conda-forge\nlibev                     4.33                 hd590300_2    conda-forge\nlibexpat                  2.6.4                h5888daf_0    conda-forge\nlibffi                    3.4.6                h2dba641_0    conda-forge\nlibgcc                    14.2.0               h77fa898_1    conda-forge\nlibgcc-ng                 14.2.0               h69a702a_1    conda-forge\nlibgfortran               14.2.0               h69a702a_1    conda-forge\nlibgfortran5              14.2.0               hd5240d6_1    conda-forge\nlibgrpc                   1.67.1               h25350d4_1    conda-forge\nlibhwloc                  2.11.2          default_h0d58e46_1001    conda-forge\nlibiconv                  1.18                 h4ce23a2_0    conda-forge\nlibjpeg-turbo             3.0.0                hd590300_1    conda-forge\nliblapack                 3.9.0           30_h7ac8fdf_openblas    conda-forge\nliblzma                   5.6.4                hb9d3cd8_0    conda-forge\nliblzma-devel             5.6.4                hb9d3cd8_0    conda-forge\nlibnghttp2                1.64.0               h161d5f1_0    conda-forge\nlibnsl                    2.0.1                hd590300_0    conda-forge\nlibopenblas               0.3.29          pthreads_h94d23a6_0    conda-forge\nlibpng                    1.6.47               h943b412_0    conda-forge\nlibprotobuf               5.28.3               h6128344_1    conda-forge\nlibre2-11                 2024.07.02           hbbce691_2    conda-forge\nlibsqlite                 3.49.1               hee588c1_1    conda-forge\nlibssh2                   1.11.1               hf672d98_0    conda-forge\nlibstdcxx                 14.2.0               hc0a3c3a_1    conda-forge\nlibstdcxx-ng              14.2.0               h4852527_1    conda-forge\nlibtiff                   4.7.0                hd9ff511_3    conda-forge\nlibuuid                   2.38.1               h0b41bf4_0    conda-forge\nlibwebp-base              1.5.0                h851e524_0    conda-forge\nlibxcb                    1.17.0               h8a09558_0    conda-forge\nlibxcrypt                 4.4.36               hd590300_1    conda-forge\nlibxml2                   2.13.6               h8d12d68_0    conda-forge\nlibzlib                   1.3.1                hb9d3cd8_2    conda-forge\nllvm-openmp               19.1.7               h024ca30_0    conda-forge\nmarkdown                  3.6                pyhd8ed1ab_0    conda-forge\nmarkdown-it-py            3.0.0              pyhd8ed1ab_1    conda-forge\nmarkupsafe                3.0.2           py312h178313f_1    conda-forge\nmatplotlib-base           3.10.0          py312hd3ec401_0    conda-forge\nmdurl                     0.1.2              pyhd8ed1ab_1    conda-forge\nmkl                       2024.2.2            ha957f24_16    conda-forge\nml_dtypes                 0.4.0           py312hf9745cd_2    conda-forge\nmunkres                   1.1.4              pyh9f0ad1d_0    conda-forge\nnamex                     0.0.8              pyhd8ed1ab_1    conda-forge\nncurses                   6.5                  h2d0b736_3    conda-forge\nnumpy                     2.2.3           py312h72c5963_0    conda-forge\nopenjpeg                  2.5.3                h5fbd93e_0    conda-forge\nopenssl                   3.4.1                h7b32b05_0    conda-forge\nopt_einsum                3.4.0              pyhd8ed1ab_1    conda-forge\noptree                    0.14.0          py312h68727a3_1    conda-forge\npackaging                 24.2               pyhd8ed1ab_2    conda-forge\npandas                    2.2.3           py312hf9745cd_1    conda-forge\npatsy                     1.0.1              pyhd8ed1ab_1    conda-forge\npillow                    11.1.0          py312h80c1187_0    conda-forge\npip                       25.0.1             pyh8b19718_0    conda-forge\nprotobuf                  5.28.3          py312h2ec8cdc_0    conda-forge\npthread-stubs             0.4               hb9d3cd8_1002    conda-forge\npycparser                 2.22               pyh29332c3_1    conda-forge\npygments                  2.19.1             pyhd8ed1ab_0    conda-forge\npyparsing                 3.2.1              pyhd8ed1ab_0    conda-forge\npysocks                   1.7.1              pyha55dd90_7    conda-forge\npython                    3.12.6          hc5c86c4_2_cpython    conda-forge\npython-dateutil           2.9.0.post0        pyhff2d567_1    conda-forge\npython-flatbuffers        25.2.10            pyhbc23db3_0    conda-forge\npython-tzdata             2025.1             pyhd8ed1ab_0    conda-forge\npython_abi                3.12                    5_cp312    conda-forge\npytz                      2024.1             pyhd8ed1ab_0    conda-forge\nqhull                     2020.2               h434a139_5    conda-forge\nre2                       2024.07.02           h9925aae_2    conda-forge\nreadline                  8.2                  h8228510_1    conda-forge\nrequests                  2.32.3             pyhd8ed1ab_1    conda-forge\nrich                      13.9.4             pyhd8ed1ab_1    conda-forge\nscipy                     1.15.2          py312ha707e6e_0    conda-forge\nseaborn                   0.13.2               hd8ed1ab_3    conda-forge\nseaborn-base              0.13.2             pyhd8ed1ab_3    conda-forge\nsetuptools                75.8.0             pyhff2d567_0    conda-forge\nsix                       1.17.0             pyhd8ed1ab_0    conda-forge\nsnappy                    1.2.1                h8bd8927_1    conda-forge\nstatsmodels               0.14.4          py312hc0a28a1_0    conda-forge\ntbb                       2021.13.0            hceb3a55_1    conda-forge\ntensorboard               2.18.0             pyhd8ed1ab_1    conda-forge\ntensorboard-data-server   0.7.0           py312hda17c39_2    conda-forge\ntensorflow                2.18.0          cpu_py312h69ecde4_0    conda-forge\ntensorflow-base           2.18.0          cpu_py312h099d1c6_0    conda-forge\ntensorflow-estimator      2.18.0          cpu_py312hc0a35a6_0    conda-forge\ntermcolor                 2.5.0              pyhd8ed1ab_1    conda-forge\ntk                        8.6.13          noxft_h4845f30_101    conda-forge\ntyping-extensions         4.12.2               hd8ed1ab_1    conda-forge\ntyping_extensions         4.12.2             pyha770c72_1    conda-forge\ntzdata                    2025a                h78e105d_0    conda-forge\nunicodedata2              16.0.0          py312h66e93f0_0    conda-forge\nurllib3                   2.3.0              pyhd8ed1ab_0    conda-forge\nwerkzeug                  3.1.3              pyhd8ed1ab_1    conda-forge\nwheel                     0.45.1             pyhd8ed1ab_1    conda-forge\nwrapt                     1.17.2          py312h66e93f0_0    conda-forge\nxorg-libxau               1.0.12               hb9d3cd8_0    conda-forge\nxorg-libxdmcp             1.1.5                hb9d3cd8_0    conda-forge\nxz                        5.6.4                hbcc6ac9_0    conda-forge\nxz-gpl-tools              5.6.4                hbcc6ac9_0    conda-forge\nxz-tools                  5.6.4                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+        "width" : 12,
+        "title" : "Import libraries",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "import warnings",
+          "warnings.filterwarnings(\"ignore\")",
+          "",
+          "import keras",
+          "from keras.models import Sequential",
+          "from keras.layers import Dense",
+          "from keras.optimizers import Adam",
+          "",
+          "import seaborn as sns",
+          "import pandas as pd",
+          "import numpy as np",
+          "",
+          "from sklearn.metrics import classification_report, confusion_matrix"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
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+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Load the iris data",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "import pandas as pd",
+          "",
+          "import ssl",
+          "ssl._create_default_https_context = ssl._create_unverified_context",
+          "",
+          "df = sns.load_dataset(\"iris\")",
+          "",
+          "z.show(df)"
+        ],
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Plot the pairwise relationships in the iris dataset",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "sns.set(style=\"ticks\")",
+          "sns.set_palette(\"husl\")",
+          "sns.pairplot(df.iloc[:,0:6], hue=\"species\")"
+        ],
+        "selectedVisualization" : null,
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+        "result" : {
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+          "endTime" : 1739991714466,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<seaborn.axisgrid.PairGrid object at 0x7f383246dbb0>\n",
+              "type" : "TEXT"
+            },
+            {
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+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Split the data into test and train sets",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "X = df.iloc[:,0:4].values",
+          "y = df.iloc[:,4].values",
+          "",
+          "from sklearn.preprocessing import LabelEncoder",
+          "encoder =  LabelEncoder()",
+          "y1 = encoder.fit_transform(y)",
+          "Y = pd.get_dummies(y1).values",
+          "",
+          "from sklearn.model_selection import train_test_split",
+          "X_train,X_test, y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0) "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991714999,
+          "endTime" : 1739991715540,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Build the model",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "model = Sequential()",
+          "",
+          "model.add(Dense(4,input_shape=(4,), activation='relu'))",
+          "model.add(Dense(3,activation='softmax'))",
+          "",
+          "# compile the model using the atom optimizer with a learning rate of 0.04 and a loss function of ",
+          "# categorical cross-entropy and the parameter set to optimize is accuracy.",
+          "model.compile(Adam(learning_rate=0.04),'categorical_crossentropy', metrics=['accuracy'])",
+          "",
+          "model.fit(X_train,y_train, epochs=50)"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":200,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991716075,
+          "endTime" : 1739991719083,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Epoch 1/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m1s\u001B[0m 534ms/step - accuracy: 0.5312 - loss: 2.0064\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 7ms/step - accuracy: 0.3629 - loss: 2.2617  \nEpoch 2/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2500 - loss: 1.5031\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3167 - loss: 1.3985 \nEpoch 3/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.2500 - loss: 1.2553\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.1269 - loss: 1.2588 \nEpoch 4/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - accuracy: 0.0000e+00 - loss: 1.2702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.1183 - loss: 1.1952     \nEpoch 5/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.1394\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3310 - loss: 1.1394 \nEpoch 6/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3125 - loss: 1.1985\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3185 - loss: 1.1562 \nEpoch 7/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.1875 - loss: 1.1558\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.2810 - loss: 1.1411 \nEpoch 8/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3750 - loss: 1.1013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3300 - loss: 1.1194 \nEpoch 9/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3125 - loss: 1.0840\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3371 - loss: 1.1059 \nEpoch 10/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.1178\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3748 - loss: 1.1044 \nEpoch 11/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2500 - loss: 1.1282\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3467 - loss: 1.1058 \nEpoch 12/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.1010\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3665 - loss: 1.0972 \nEpoch 13/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2500 - loss: 1.1225\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3165 - loss: 1.1085 \nEpoch 14/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4062 - loss: 1.0936\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3779 - loss: 1.0957 \nEpoch 15/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3125 - loss: 1.1051\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3446 - loss: 1.0999 \nEpoch 16/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4375 - loss: 1.0853\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3935 - loss: 1.0927 \nEpoch 17/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.5938 - loss: 1.0599\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.4310 - loss: 1.0868 \nEpoch 18/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.2812 - loss: 1.1112\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3310 - loss: 1.1028 \nEpoch 19/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4688 - loss: 1.0822\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3935 - loss: 1.0927 \nEpoch 20/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4062 - loss: 1.0897\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3665 - loss: 1.0961 \nEpoch 21/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3125 - loss: 1.1005\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3519 - loss: 1.0974 \nEpoch 22/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3125 - loss: 1.1019\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3363 - loss: 1.1002 \nEpoch 23/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4375 - loss: 1.0830\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3862 - loss: 1.0925 \nEpoch 24/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4062 - loss: 1.0896\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3852 - loss: 1.0940 \nEpoch 25/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3750 - loss: 1.0930\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3842 - loss: 1.0925 \nEpoch 26/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4375 - loss: 1.0845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3685 - loss: 1.0960 \nEpoch 27/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3750 - loss: 1.0898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3738 - loss: 1.0933 \nEpoch 28/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3750 - loss: 1.0973\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3706 - loss: 1.0966 \nEpoch 29/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.2812 - loss: 1.1060\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3310 - loss: 1.1008 \nEpoch 30/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2500 - loss: 1.1040\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3519 - loss: 1.0969 \nEpoch 31/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3750 - loss: 1.0954\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3633 - loss: 1.0959 \nEpoch 32/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.5000 - loss: 1.0782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.4060 - loss: 1.0910 \nEpoch 33/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 19ms/step - accuracy: 0.3125 - loss: 1.1076\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3540 - loss: 1.0988 \nEpoch 34/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 19ms/step - accuracy: 0.4688 - loss: 1.0789\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3862 - loss: 1.0933 \nEpoch 35/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.0998\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3498 - loss: 1.0989 \nEpoch 36/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3438 - loss: 1.0972\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3727 - loss: 1.0947 \nEpoch 37/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 20ms/step - accuracy: 0.4062 - loss: 1.0876\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3665 - loss: 1.0956 \nEpoch 38/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4688 - loss: 1.0794\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3904 - loss: 1.0914 \nEpoch 39/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3438 - loss: 1.1082\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3602 - loss: 1.0998 \nEpoch 40/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.2500 - loss: 1.1164\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3383 - loss: 1.1012 \nEpoch 41/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4062 - loss: 1.0883\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3696 - loss: 1.0952 \nEpoch 42/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.4688 - loss: 1.0770\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.4123 - loss: 1.0884 \nEpoch 43/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.2500 - loss: 1.1157\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3217 - loss: 1.1035 \nEpoch 44/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3438 - loss: 1.0942\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3529 - loss: 1.0971 \nEpoch 45/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3438 - loss: 1.1020\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3477 - loss: 1.0998 \nEpoch 46/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.3750 - loss: 1.0917\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3706 - loss: 1.0949 \nEpoch 47/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3750 - loss: 1.0921\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3717 - loss: 1.0950 \nEpoch 48/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4375 - loss: 1.0784\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3790 - loss: 1.0931 \nEpoch 49/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - accuracy: 0.3125 - loss: 1.0980\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step - accuracy: 0.3508 - loss: 1.0972 \nEpoch 50/50\n\r\u001B[1m1/4\u001B[0m \u001B[32m━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - accuracy: 0.4688 - loss: 1.0814\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step - accuracy: 0.3967 - loss: 1.0920 \n<keras.src.callbacks.history.History object at 0x7f38328edf70>\n",
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+          "forms" : "[]"
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+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Score the test data using the model",
+        "hasTitle" : true,
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+          "%python",
+          "",
+          "y_pred = model.predict(X_test, verbose=0)",
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+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+              "message" : "array([[0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842],\n       [0.32338503, 0.30626655, 0.37034842]], dtype=float32)\n",
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+            }
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Get the accuracy of the predicted values",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "y_test_class = np.argmax(y_test,axis=1)",
+          "y_pred_class = np.argmax(y_pred,axis=1)",
+          "",
+          "report = classification_report(y_test_class,y_pred_class, output_dict=True)",
+          "",
+          "res = pd.DataFrame(report)",
+          "res"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
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+          "startTime" : 1739991720912,
+          "endTime" : 1739991721422,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "              0     1         2  accuracy  macro avg  weighted avg\nprecision   0.0   0.0  0.200000       0.2   0.066667      0.040000\nrecall      0.0   0.0  1.000000       0.2   0.333333      0.200000\nf1-score    0.0   0.0  0.333333       0.2   0.111111      0.066667\nsupport    11.0  13.0  6.000000       0.2  30.000000     30.000000\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Create Python UDF",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "def build_mod(df):",
+          "  import oml",
+          "  import keras",
+          "  from keras.models import Sequential",
+          "  from keras.layers import Dense",
+          "  from keras.optimizers import Adam",
+          "  import seaborn as sns",
+          "  import pandas as pd",
+          "  import numpy as np",
+          "  import matplotlib.pyplot as plt",
+          "",
+          "  sns.set(style=\"ticks\")",
+          "  sns.set_palette(\"husl\")",
+          "  sns.pairplot(df.iloc[:,0:6], hue=\"species\")",
+          "",
+          "  # split the data into test and train sets",
+          "  X = df.iloc[:,0:4].values",
+          "  y = df.iloc[:,4].values",
+          "",
+          "  from sklearn.preprocessing import LabelEncoder",
+          "  encoder =  LabelEncoder()",
+          "  y1 = encoder.fit_transform(y)",
+          "  Y = pd.get_dummies(y1).values",
+          "",
+          "  from sklearn.model_selection import train_test_split",
+          "  X_train,X_test, y_train,y_test = train_test_split(X,Y,test_size=0.2,",
+          "    random_state=0) ",
+          "",
+          "  model = Sequential()",
+          "  model.add(Dense(4,input_shape=(4,),activation='relu'))",
+          "  model.add(Dense(3,activation='softmax'))",
+          "  model.compile(Adam(learning_rate=0.04),'categorical_crossentropy',metrics=['accuracy'])",
+          "  model.fit(X_train,y_train, epochs=50, verbose=0)",
+          "",
+          "  y_pred = model.predict(X_test, verbose=0)",
+          "  y_test_class = np.argmax(y_test,axis=1)",
+          "  y_pred_class = np.argmax(y_pred,axis=1)",
+          "  ",
+          "  from sklearn.metrics import classification_report,confusion_matrix",
+          "  report = classification_report(y_test_class,y_pred_class, output_dict=True)",
+          "  res = pd.DataFrame(report)",
+          "  return print(res)",
+          "  plt.show()",
+          "  "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991721914,
+          "endTime" : 1739991722407,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Run the UDF in Python",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "build_mod(df)"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+          "startTime" : 1739991722883,
+          "endTime" : 1739991728259,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "              0     1    2  accuracy  macro avg  weighted avg\nprecision   1.0   1.0  1.0       1.0        1.0           1.0\nrecall      1.0   1.0  1.0       1.0        1.0           1.0\nf1-score    1.0   1.0  1.0       1.0        1.0           1.0\nsupport    11.0  13.0  6.0       1.0       30.0          30.0\n",
+              "type" : "TEXT"
+            },
+            {
+              "message" : "<div style='width:auto;height:auto'><img 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+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : " Create a database table from a pandas DataFrame and get a proxy object",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "import oml",
+          "",
+          "try:",
+          "    oml.drop(table=\"DF\")",
+          "except: ",
+          "    pass",
+          "",
+          "MY_DF = oml.create(df, table=\"DF\")",
+          "",
+          "z.show(MY_DF)"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991728763,
+          "endTime" : 1739991729516,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
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+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Run the UDF using the Python API for embedded Python execution",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "oml.table_apply(MY_DF, func=build_mod, graphics=True)"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991730002,
+          "endTime" : 1739991735518,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "              0     1    2  accuracy  macro avg  weighted avg\nprecision   1.0   1.0  1.0       1.0        1.0           1.0\nrecall      1.0   1.0  1.0       1.0        1.0           1.0\nf1-score    1.0   1.0  1.0       1.0        1.0           1.0\nsupport    11.0  13.0  6.0       1.0       30.0          30.0\n",
+              "type" : "TEXT"
+            },
+            {
+              "message" : "<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\nNone\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create a string representation of the UDF",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "build_mod = \"\"\"def build_mod(df):",
+          "  import oml",
+          "  import warnings",
+          "  warnings.filterwarnings(\"ignore\")",
+          "  import keras",
+          "  from keras.models import Sequential",
+          "  from keras.layers import Dense",
+          "  from keras.optimizers import Adam",
+          "  import seaborn as sns",
+          "  import pandas as pd",
+          "  import numpy as np",
+          "  import matplotlib.pyplot as plt",
+          "  ",
+          "  sns.set(style=\"ticks\")",
+          "  sns.set_palette(\"husl\")",
+          "  sns.pairplot(df.iloc[:,0:6], hue=\"species\")",
+          "",
+          "  # split the data into test and train sets",
+          "  X = df.iloc[:,0:4].values",
+          "  y = df.iloc[:,4].values",
+          "",
+          "  from sklearn.preprocessing import LabelEncoder",
+          "  encoder =  LabelEncoder()",
+          "  y1 = encoder.fit_transform(y)",
+          "  Y = pd.get_dummies(y1).values",
+          "",
+          "  from sklearn.model_selection import train_test_split",
+          "  X_train,X_test, y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0) ",
+          "",
+          "  model = Sequential()",
+          "  model.add(Dense(4,input_shape=(4,),activation='relu'))",
+          "  model.add(Dense(3,activation='softmax'))",
+          "  model.compile(Adam(learning_rate=0.04),'categorical_crossentropy',metrics=['accuracy'])",
+          "  model.fit(X_train,y_train, epochs=50, verbose=0)",
+          "",
+          "  y_pred = model.predict(X_test, verbose=0)",
+          "  y_test_class = np.argmax(y_test,axis=1)",
+          "  y_pred_class = np.argmax(y_pred,axis=1)",
+          "",
+          "  from sklearn.metrics import classification_report,confusion_matrix",
+          "  report = classification_report(y_test_class,y_pred_class, output_dict=True)",
+          "  res = pd.DataFrame(report)",
+          "  return(res)",
+          "  plt.show()\"\"\"",
+          "  "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991736012,
+          "endTime" : 1739991736467,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Save the string representation of the UDF to the script repository",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "oml.script.create(\"build_mod\", func=build_mod, overwrite=True)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991736956,
+          "endTime" : 1739991737443,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Run the UDF by referencing it as a named script",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "oml.table_apply(MY_DF, func=\"build_mod\", graphics=True)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991737938,
+          "endTime" : 1739991742923,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<div style='width:auto;height:auto'><img 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fLydOVOMXjJ75Oyoqztmwzx1Pd3OdkcTE3EtkrKyt5//vfTywW40c/+tFsN2dS3fKPUlS9kSNv/DmHe3t5Uh6lxFtJyBXhSGwvI5lBdg1sIewuYmnxGqqDDVMmc1+L0tJSHn30URKJBN/85jenbbuXSwhBUfUGVt73Pxlsf5UTb/0d4wP7Ka7dhGH6LvrYt/pe5ienvsPxsYOsKN3Am73Ps2dwG7HMMCtKN+IxVK6fMr+pAEu5JosXL8Y0TcrKyjAMg1gsds7txcXFFBefmY129sXlai40Mp8/t/eqsCGw7fMDr3nk9LGY6dyc1FgHB178XbyhGhbd+pv8sO2bbO19idaiVWhC4+jofir9tXNqsd25mK8khEAKjf4Tz5CO97C45SHudbdS5q8h4i5BSknaSrGh4nYsx+JP3/517q3/AI82//x5uUbX2o7ZJKVktG8Xh1//cwJFzVQtfh+RitXohueSjy32ltMYaqEx1IKuGRMzLTOEXJFZG45WlOmkAizlqgkhsG2bF154gYULF+JyudB1neXLl8/cTpMZcL9jqEXA5HI5rhuzttCVKlzgkxwe2UN1oGFyrcFceoTkaDu2lcbKpxjLjuDSXVT6a/HoXrb1vYxLd7Ot92VWlG6g2FM26xfxucSx84wN7EfT3ZieMKmxTnTDix+dRyrup4skIVcUQzPI21maI0vpircxnBmgM34CKR24wYKH1Fgn6fFO3L4SypvvI1jUDAisfIrRvj3ogTJO5QcKtbB81ZPnU87O4NY9ZO0MUtpkrDQl3gpsabN78E0qfLUMpvuoDtSrtTKVeUkFWMo1CQQC3HbbbbhcLlwuF1VVVdi2jaZpM/KBKONJhGeKRGGXC5nOIHyX/ub8bvFS5xN87cD/5ubKu/mV1V/C1F2ESltZfu+fYbqDGO4go5khTo4dRgIj6QE+vOhzdCZO8Xd7vsTmqvv5ldW/jyFU0HpaMnaSfc//JqY7hD/axGD7K5TU3Urv0ad4fWQrT+WPsKZ8Mx9Y+CmePPktvnPknwi6Qtxe8xDvX/DzN1TStpQSpIOmuwmVLcNwBdj//H8nUrGKVff/NX3HfsqRN/6C3sYVPJ7ezdKSdfzWhv892TuatbMcG92P2/ACAsvJ0TZ2GCklf7v7DWoCjRyJ7WVDxe382to/wVTnoTLP3DjvdmVW+Hw+bNvmtddewzRNxsfHaW1t5X3ve9+0B1hSSmQ8Ce5zh6+EEEjTKNTCKo5M6z7ns1JvFcXeMir8tZMJw7rhpqR2E/GhI5x86++IagZRVxFRIwxum40VtxOKRdk/9DbVgbpLJiq/22i6C1+4Dt3wgpS4PFEiFSux80mK3D6KjVGq/HU0hhZR6a9lKN1H0BWh0l97w/XCpMe76Nj/7cLMXjsHQsMTqMAXaURoOp5ABZ5AOSX+GkroozrQcE7JhVJvBSXeSmoDTWhCp8xfTYm3gupAA3knS4W/huFMP1WBerR5mlupvLupAEu5ZpZlcdddd+H1eifLNczI2mESyObAf37yq3C7IKlqYZ0mhGBt+WYWRpfiN4OTFzYpJVY2Tn/bi/QdeZLV9bew0LOOqvAyyhrvRtdd3FR5NytLNxF2R1UuzDtYuSTxoSNouonbX05x7c3ULvswVYvfR2M+wQonRbG3HFNz8b4FP8f7FvwcGStF1Ft6wy2NM9q3m+6D3ydQtJD48BHCuotld/8J3lANQjMortnIuvf9C8Lw0JrpJ+opQRc6tmMTz42ypGgVf7r5q3gNH6l8nBUlG/jTzV/Da/hJW0m8ho+0lcJnBtSMQmVeUgGWcs3Wrl3LSy+9RD6f55ZbbsE0TTTt0h+IVxyESaew5uBU23YZyPSZaeqFBWIdssl+XJ4omuF51wVeuqYT9ZSc87fkaBsHX/oDdFeAoobb2OK32Bvfy8+GNmPkhvnb3V+iNtjEF1b+zpxKdJ8rfJF6apZ+iEyil/RYJy5fCZpm0pXr4kfH/42jsX2sKN1Ixkpxavwod9e+j+c7fkRzZCm/sOI3b6hjGq1aR1XLIwRLFjM+eAhNd3Hw5T8gWrWORZt+HU03cXmL2TnwBv928K9YWbKRTyz9z+zof4N/P/jXrCzdyKeW/jr7ht7mawe+zLryW/n4kl/G0IzJGYQe4+IzERVlLlNfC5RrdroIZDqdpru7m9LSy1sG5IoDHkdCLg9TBVi6DpncOX8aan+VniNP0rH/21i561O8c65ypMPx2EH2D22nKxhg2O8nXLWOmDXOSGaI/SO72T+0HbfuIZmPk7dzl97ou4yUDsnRU0Sr1uANVpEe7yKfHkEiSeTGGMkMMpTu59TYUcayMQJmmK5EOyOZIfpT3TjSme2nMK1Md4ji2puJVK5h8a2/SbCkhfR4N8mRk0hpT94vmY9jCJPhzABv9b3CoeFdDGf66Uq08VbfK3TET+DS3YxlY7zV+zI9iY6Z6QFXlOtM9WAp16yrq4u1a9dSVVWF1zuDtWsyWdA1plypxdDP6cHKZ0aJDx8lUrGKbHqYvuPPUrPk/Yg5VEBzuk1elOSZQqyOtNGEzng2xlf2/BERdzGnnOMEnSCuYyPc6itm/bL/yo9PfpPXun9Kub+awVQvlp0t9AJOVCR/t/X+TSWXGubAS7+Hk08TKF5EsHQJ9at+HoTGgsgSPrb4izzX/h/0pbroTXYwnBmYqJb/i6ws3ThZrfxGOZZDHa9z5I2/IFSyhBX3/WUh2b1iFS5vEdJxJs/HtJWiM36SnJPlW4f/nhJvBb+y6kucGj/K3+z+fRaEW+kYP44jHXYOvEFLdDm/tvZPcOuecyri3yjHTXn3UAGWcs2i0SivvvoqgUCA1tZW1q9fPyP7KSS4X2CpEV0v9G45Dug640OHcfvLEJqO21fKeOIA8ZFjBItbbtgPaunk6TzwfZKxkzSt+zx9+RH+49jX2FBxOyvKNlHmq0ITGsXecsKuKNvMDGsjDWyquoddg1sJuaL4DD8ZK8W/H/xrqoKN9CU7ef/CT1ITaLxhj9vl0g0vkYqVJIaPYVsZTE+YE7v/lf6aFo4ljgPg4GBLm7pQMxKJ1/CztHgtfckuvn34H3mg8UMsLlp5QxxLwx3C7SvBE6oGoRH3+njOn2SRtxTtzf9FUeVaKprfg98IUOQppdJXy0h2iJpAIxsq78BnBtjR/zoNoYWkrDjNkaW0jx/FpXv4yp4/YlF0OW1jh1lbfis3Vd6l1sBU5h0VYCnXrKamhkgkwooVK3C7ZybHREqJHE8iPFNvXwiBnBgmlD43iZHj+KNNk7cFihYw3LmVQLQJobsAsB2bjJ3Ca/jnfRKtbWWIDx+jY983yCUHKW28g7dTB3m99zlSVoLm6HLaxo7gNwN0JU4RMEMYmomd9nOH4ec/rf5DHGljS5vvH/4nXul5hu5UB+l8kn1D26kJNM72U5x1ustP5aL3cvi1PyWbGiZkLmEk3sH+WIbtg1sQCJYWr+EXlv83BlN9/L9D/4e9g9t4uOmjPNfxI7b2vkCRt4yWohU3RLCQz8RwbItMvBfpWBzLdPJ2fD8D9jjFQw6p2ClKG+8gkR+nzFeFBE6OHcJn+HEcm5WlG1l48z/i0t3k7Cy60OlJdvB236t85+g/cTS2j4yVJpYZYn35bWgT71tFmS9UgKVcsz179lBeXk4sFiMWi7Fo0aIZ2Y9MpMB9kQ/ZiVINtpHHsTLnfCBrhgdNN4kPHSFUtgxLWrza+RMG071UBurZXHXveQvUzhfScTi162t0H/4R0ar1+CP1hEtb2WzVkrFTtBatodhbxodbfoGeRAdew4/fCBDJWSxzL0RD4NYLkwCklNxW8yBuw8PR0YP0J7sJucKz/RRn1emhLis7TtuO/4ttpQmVLsGWkt0hg/0ju9hYcSdlvirqQ8281PkUr3b9hNVlN1MVqKcuuID3LfhZqv313Fx1z7wPrk4fD8MVIJ+JESxZhNAMFntquSe4hnpPDbWRSkKlS9ANDz4zwKnxYyyMLOUDzZ+ktXgtpu5CExoBVwgAU3OxtfdF/nn/X7AospzHmj9JbWgBPYkOlhStvOFmYCrvDvPziqLMKU1NTTz55JPk83nuv//+GduPTCQRZcUXvoPLRCbTZI0M2juW6hBC4AvXMdKznWDJYvYPvV0IPorXcGr8KAeGd7K8ZP28HLqRSLKpIaxcgmBRcyEvCEEFUX629VfpSrTxStfTrCu/lSp/O8+3/wcRI8jGmE4+a/D8qR+wsepuQu4ihBA0RVupCS/gf27/TfJO/l2d8C6lJJscZLhzC/5oA7n0MI6VRUoHRzoksQi7iyjzVfFo889jaAZv9r5A2krh0b3UBptw6x5aoitoia4A5ncukZQSO59koO1l0uPd2HaW8fQgL7R9n1BijNb2E4TLvDTc99/QJmZMCgQV/hrC7igfbvk8pjb1l6TxXIxUPoGuGXyo5RcueD9FmS9UgKVcNSklBw8eZHR0lJaWFhzHweM5N7AZHh5m9+7dNDc3U19ff/U7s53CLEHzwqes8LggniRFFy5v8XkXskKpBp2hwf0cHdtHS3QlmtCoCy7g4PAumiNL8Zn+q2/jLNE0nQXrf4nS5vcQLm4BxORztxyLbx/+R7b2vsAnWn+Vm72L+aBVg08EKa2o4klOcfDIV8g6OR5e8LHJx7o0N7+w7DcYSvfRFFkyq89vtnUf/iFtO/+ZyoUPsuzuP8WxcwyeeolTu/+VTY2beMJI8MTJb9BatIrlJev41NJfZ2PFHfzbwb/m9Z5n+MOb/5HGcMtsP42rJqVEOtbEBBHBQNuLHHrlf+AN1xKpWMWRiJ8fHP5rWiPL+Pzdf0Q03ACIwuLrQiNtJelNduI3AkimTvIXQnB7zUPUhxZS6q3A1C6Qa6ko88j8TjxRZp3X6yUQCFBeXk5lZSUej4e2tjYcx8FxHJ577jmCwSChUKhQ5NKyyGQy5PP5K5uKnUqDrl/8Q9ftxhkbJ5MYwPSEzrtZCIEvUs/eI98j4iqaLKKpC4Oop5gjI3tmbXq4lJJcLkc2m8W27Us/4JzHOmwf2c4/dfw/jo4fPuc2TWgsjC6lLrgA0b2Pzj1fp9w2iFiQip0k4i4i6i4h6inlnOmZ0iF76k3yO79Nfrx7Gp7h1bNtm2w2Sy6Xm5XXx/REcPvL8AQqCRQ1YZg+UmNdeILVdBs2AkFDaBFvdT1D25EfETbDrCy9iYWRpTRHlhJ0RWasbY7jzPixSY22s//F36Fz/3eR0sYXbiBY0kK4bDmOY1FpllAfaCLkLubfu7/Hc52Ps/+l36PzwHeR0iHkLqLIU0qZr/qihWs9hpfFRSsp9par4Eq5IageLOWqCSFoamo67+/f+973qK2tRQjB0NAQjY2NvPLKKzzyyCO8/fbb7Ny5k+7ubu6+++7L3pdMpMB1idPVbeIkk1ieOFp06pwNzfSSyY5SJfyTH+JCCMp91RyL7WdpydpZKQaZzWb54Q9/yPDwMMeOHbuix9qOxctdT7F98A1qgo20lq6fzPMRCN7b9DHuKLuD/U/9CqOpYZbd82eMDx6kbd/XGY9oODjEssOFBXcTQ+imF1130XvsScb69xGuWIU/umAmnvZl6ejo4Kc//SnJZBKX6/oPG1mZMXBsUmMdpOO9jA8eYKjjVfzlyzkkRmkfP86y4nUciR9hQdKmpuEu/J4Iv7rmj3Ckg1ufufUxe3p6eOqpp0gmk1iWNSP7GB86xEDbi2QSfVS1PEy4fDlrHv4HNN3EsfMI3WS9neHF9h/xtUN/Te/YMR4dtMklB6lqeYTxbAzLyTOc6cd2bJVPpbxrqABLmXanAxdN01i3bh39/f2Ew2GEEGzcuJH169ezY8cOksnkZW9TjsYvayFnx84VyjVcYFZgPDdK1u3FHmlD+s8URNWFgam76U12UB9aeNntmi5ut5sPfehD2LbN1772tSt6rK6ZPNb8Kar99dxV+8g5SdRCCHRhEApU0bTmc+RSQ0QqVuIP12E5OXZbRxhLjeA1vIwPHmL/C7+FP9LIsrv/mMY1n2O0bzflTZcfCM+EhoYGPve5zxGLxXjiiSeu+/7Lmu7GyieJDx9l5xOfp2n9F6hd9lF8kQZuj+2iMgcl3gaeG3yRV3xeNuoGLiGuS6BeXV3NZz7zGRKJBN/5zndmZB9FVetpXP1pQqVL0U0fQggMV2Eo/fREEp9mssxdzz2hddSbZdSES4iUr0Q3vbg0k0RuDFdwgeqZUt5VVIClXDUpJQcOHCAWi03+rbq6mk2bNqFPDOdt2LCBXC6HaZoIUcjv0TQNTbv84pVSSuTIGETPH/Z7J6HpeMzz869Ob6c30Uk4WIOVGERaWYRZCNqEEFT4azk8sofa4ILrXrZBCDF5zC5nmaF3PraleBWLilcizsq/gsJzTsZOMj5yjJ6An7jHoW/4bXxmgJY1n8Hc/T/IOzlwHKRjYWXjhar30qG4dhPFtTfBO7Z5NiklpLPYB08gSqNodZXTfhEVQmAYBrp+/vCSlBIsC+fgCfB40BbWTb2U0lXKZ0ZJx7vxBCrJpWPk0zFMd4jmDb+EY+cZeXkL7u5uRMlGbMciJXM4XL9hzIsdG5g4Ptkc9oETiGgIrbH6il8fTTfxhmpx+YoBQTY1zFDH6/jC9UQqVkwuCO7NZWk9dZRIhZ8Fd//WmYkmUpJzssRzY7zY8TgLIktpjrRe8D06mh1m9+BWGkOLqA8tVEGZMm+pAEu5Jn6/H8dxyOVyOI6DYRhUV1dP3q5p2nmJ71fMtpGZLMJ16aEFW7dxy+CUtznSYSDVQ1WgDpm3ycfacZUumvwA9xl+2seOksonJqePzxdCiPOm/0spyVtpjr/9FYYSXXw3OM5YboyqQB1ZO8P/2PQP/Hzrf+Ke2vfSXLQCU3ex5uGvYLojGO7QxHGZ+iKIlIWli3QNe+8RrO8/h2iowvW5D55TSkNKWZigoAkQFw7UroXT3kP+W09DyI/7ix9D+j2Tley5gkD+TJudiaRuk6HOLXQd+G6hvEfpEiqaH6CoegPSsdE0g0U3/zq18Y9wON9PWXwbPiOALWdmqO5q2QdPYH37J4jqMlyf/SDS6ym8fpf5mgx3beXolv9JoHghq+7/KwbbXuL4W3+DL9LEqvu/jOkOgdAoa7wDT7ASb6Bicu1PKSU317yHCn8Ne4be5p/2/QVLi9fw39b/zwsu4vxGz7N8df//YmnxOn73pv9zQ63fqLy7qABLuWpCCBobGzl+/Dgvv/wysViMjRs3UldXN707yuYLF/PLkMr1YcSDhQv7Oy4cyXx8cukYfMXkY224SpphIvFWIIh4SjgxdogVJRvm9TdnKSXHRw/yvaP/l3q3Q33aw9JgNUP5GH5h4tEinNryV4SCtVhjp8isDuEqWUKotPXSG3ccrFd3IE91Y9y/GVFTjmisRmuuA+PcnhTZM4j1zOtoLY3om1ae95pMB1EcLew7EsQ+fBLnWDt43JDNYTx8OyI8dcA9FenYdB38PiPdb9G45rMEok0ES5agG17y2TjpRB8j3W/Te/THhEqXUrf8Y1immyNHX8KWNhX+Gtz6DC4XdRW0ylJEUw0i6Cf/vWcQJVHkeAKtqgz9trWFVRAu4nSSvy9cj9AMXL4S3P4KfOFaDrz0e0Sr1lO3/GNouotI+fJzHismhkoXl6xBCo2dA1so9Vbyf3b9PqtKb+I9DR9Ef8fyVY3hxbREV7C2/BaMeVqbTlFAzSJUpkFbWxvr1q1j5cqVpNPpy37c5c56konURcsznHVPsvYoWvr8WXhSSgZS3QRPF83UTZBgJwYm7yOEoMRTxsnRw8jrOMwzU/YObmN7/2scsQdwsuO8R1/AJ0seZE1CsCohSfbvZ+DUS8S63ybW/fblbzhn4ew4gLP/GE73AFp1Oa7PPoZxz03nLcTttPfgHDiOvecIWFc2O/JyCCEQkSDmzz+C+cidOAdO4HT24ew+jLP3aGFo+Qo4Tp6BUy8z2P4qg+2v4o82UrnoITTDTWL4CPHBAwx3vUlytJ2Rnu3YVpqueBt7Brfi0tw8suBnZzSp/UoJIRAVJbg+/QG0RQ04+49j7zyIc6ITe8fBwpeXS8ilR0iOthEfOkxi5DipsQ5SY6fIJvsZ7nyTgbYXsK00jm2RiLWRz4xN+d5eXLSC39v0t9SHmulJdnA4tgdLnrt/IQStRav5vU1/x3ubPja5DqGizEfq64FyzVauXEk+n6e7u5vly5df+gETLqeHSEqJHIpBwHfJ+9v5VKGUQ06CLcE4KxcJyWCq95wp4HqwjOzgEfRg+WQeiUv3YMk8Q+k+ynxVl/1c5oozFzbJSr2CYc8yFnqbqWoqYaj9NWI+H/+hd1LiKuZ9ejN+I0hq7BQuX8kltzl5/D0ujPfdhdMziL54Yo3Cdw4LTtxfW74QPZtDa6i6zCD5ygkhkBPbNt5zM/bxDkTAB9k8WlXZFW1L0900rPoU0rEY6doKCHqPPE6kch3R6g24fcWY7hD5dAyPv5yUleKbh79CLDvEhxZ9jlJvxdzs+XSZ6K0L4H13IaLBwqSRipJCT98lFNfcxMKN/4l8Zoy9z/5/BIqaaVr3hcn3jC/ahKYZDLa/wuHX/pRo1TqW3vmH6Ma5Mz6F0PAZAYKuCGPZEaR0pgyghBB4Dd/0PG9FmUUqwFKuWSqV4pVXXkFKieM40759ORa/rGGefGYMzXBD2gHLAePMh3fOzpCx0+cMOQjThzPei52KYfgLFeKFEJR5qzga20epd/oTtmeSdGyGOt8kmxzAcfLkY6dYlRT4TRupWYCcyLnRcJDYuQR5yyab6Ec6U/cuScue6H2y0Ne0IkyjEDg116E11513fKTj4BzrwOkbwtiwDBHwYdxRWPx7Ro9lMo319n6E143e0ogoP/N6XgkhBOGyVgzTTzY1RHq8i2DJEvKZURwrQ6x3J3Y+jdtfjm56AYEjbUzNpDmyFHMurpeXTGO9vQ+tohR98+pzhmkv6/gIUejPFQKXrxjTE0VKh3x2jOToKXTTV+jxlRNFRKUDF+kB1oVe+NHMeb5okKJcnAqwlGt26tQpNm3ahM/nY9euXSxevHhyxuA1cxxkKosojlzyrrlMDN3lQ7oMRDKL9BRObyklg6k+fEaAdyZtG8Fysv0H0RtvmfxGHvUUc2hkDxk7Pa++SecyMY5u+Us8wSrigwfxhmpIx3uwc0mSY6doWPVJlrc8wkrdwSVceHKF9Rrz6RiB4ubzZh/iSJy+IawfvQCWhagsRautgLw12Rsl8xMJ3cZEEdh0FuuJl5H9w2jFYfRlM1/yQkqJvf8Y9k9eRRSFkUuaMB64tRAYTASEV0I3/TSu/Ry7n/5PpMY6CJUtJdazndL626lZ+iGObvlLhGaw5PbfIegr49fW/gkZK025v/rSG7/OpJTYh05i/+R1nIoSXPVV4HUXSpnoOlK/9CSAkc6tnNj2N3iCVdhWGk0z6Dv+E7zBanyhWgpV2yWlDbfhDdXg9pdecGFmIQQ3V91DU2QxEXeRqoml3NBUgKVcM7/fz5NPPjlZeuGHP/wh73//+6cnwMpZYFnn5fa8U2HNuCFc3ih4JGI8gyzyTX5b7091E3SHz2mTEAJcAZx4P3ZyGN1fMjEbTyNghjg1dpTFRSvnTS+W4QpSvuA+rHwK0x3G7SspJCeH6vAEKymquQl/uJbTiwFJr03nge/Sf+JZFt70q4TLz3qujoP1wlacQ22I6nJEOICIhrD3HsF+6S3029ejLajF+sFzoGkYj92DCPjB7UJbvhBZFL74upHTTESChSGvaAhneIz8j19CxsYxH7v3soLzc7YlBL5QLaUNd2BlE/iLmhBAWeNdFFWvI1K5hmyin+GO1wlEmyjylM7Ic5o2kSCipgxRWgSahtM9gPXD59EaqjHu33zJoVt/0QKKa2/BH20kNd6JP9KI6YniC9eSHu8mVLYUzXATHzrC0Te/THHNJhpWfwpxgartpu6iNnh+gWJFudGoAEu5Zs3NzbS1tdHa2orX66WhoWHaghI5Fgev+5Lbk46FnU+hBcqRbhuRyE7eZjl54rkYUc/5eUZCCIxgBdm+ffgW3MHpmk8V/hoOj+xhYXQphpgf37I13UXTus8jnYkhGiEmhm00pHTO61VwHIuR7rcZG9jPaP8+wuUrz9xo2TiH25AdvegP345x65rCxbm9FywH2dmHjIZwjrYXZg7GUxDwIwwd455N4DjnzSicSXI8iUxnEQEL0hlkLo9s70GOjsMVBlgAhjvI4s2/iZT2meOnmSA0Kprv59Arf8TAqZepWfohNH2Onx+nJ4nYdqFHeHAE2d6Lk80h79yAuESAFShqZvm9f4bQdKRjv+O/FkIzEEInPnSEsb49SMemfsXHQZtbsykV5XpTAZZyzXbt2kUoFCIWi3HixAkWLJieZVWklMjYOHgvPSvLttKF2j6IwnBVLDV5Wyw7hKm70S6Q8SFcfpxEP1a8DyNYObnYMUBXvG3eFDssDMsaF5wbLKU8NwFdd9G45tMU19xEWdNd597ZZWI8eBuyewBtzZLCVH4pEZqGMzAMi+rRqsowHru3sL3iyJkEe00g9Ov70aIvbYa8hX3oBLJnEG39MvRVLWhV5Ve1PSEEQjeRsvA8BGfylYqq1tG88VfwhesmK5rPRZOvh2kgu/qhpgI0gd5cB++9A1FejPBdOggSQqAbE8nwp3MYz/rv6f2UNd4B0iFQvOhMkVFFeRdTAZZyzaqrqyfXinvggQemddtyPIG4jAArlx6ZSDoGdFFIcs/bSFOnL9lJyBW9YA0mIQRGqIps3wGMQDmIQj5RTaCBXQNvUhNswhDz+63iOBYDJ18glx6hatF7MdwBhBCESpcSKl0KTJHw7DhIy8I5fBIsB33NEvC4Cr0hHjcYOvraM3WzZM8A9sEThbyripLrFpQKIZA+T6EMwZE2pKahlRWj37Ty0g++hGxykL5jPyFY1kpR1TqE0DDcIaqXfGBy33PWROK/TCQLQ+y6VngPBHyF+ldce/ullOTSw/QeeZJgSQvVrR9gquK0ivJuNL+vGsqckM1mueOOO3j22WfJZrOXfsAVkOPJS84glFKSifdies7kWElTRySyWBEXscwQNZfI+RCGpzDMmBzECFYA4DF86JrO0dhelhStntsX0ylI6WBbZxLZj7/1d+TTIwSKF1FUdeELrHQcSGWwXtyG7OpHlBYh+4YQ1WUYt65FW1hfyGvK5cHlQmgC6ThYr2zH2X4AMoUCn9ebvedwoeJ/XSXStgvJ+ICcCC4utDSLdPITQ6jnD0UPd23l+Ft/S7h8OeGH/g7DvHS5kLnCaevGfupViAbRFtaBpiHTmcJSQrYDulbofdI1xCWKjULhWDlWBqGZaGf1UPafeJ7jb/8d0YrVRCpWnfmioyjvcirAUq7Z4OAgg4OD3HbbbfT09Jxz2+myDVc1qzCXL+Ty6JcoNiht8pkxvOHaM3/zmIjxLHFfmkKN9kvv2wiWkx04gh4oQ4jCBbkuuIA9g29R5a8n7C6aNxdXKSWx3p2cfPsfKGm4jZrWD1K+4D4y8R784QtX2i/MyDuOvXVPYZmZgA8CXkRZEcLtwj54AnvnQYTPixyKYTx0O3pTDQiBvnxRoUL4oobr90TPIiIhRCSETKRw9hzByuRw2nvQKkswHrp9ymRuO5/k6JYvk8vEaLnlN/AGK8+5PVzWWliTsW4z+jxbskVUlqItaQKvG9k3BMUR8t99BlFejOzsKyS9jycQlaWFivwXyZmTUjI+eJDj2/4PkYpVNK753GSQFa1aS3HNJorrbkHMxTIVijJLVIClXLONGzfS0dFBbW0t9fX159z2+OOPo2kamzZtoqzsyoo+ykTqzPT/i7ByiYlk5DMXCOk1EbEUPYF+Iu6iS+5LCAGmD2esByeXRHcXes10YVAfXMBLnU/ynobH8Br+ORFkSccmOdqG0Ax84bqJROxCT14+N04g2sT44CHGBvZheqPULfsIzeu/gEROHicpJcRTOKPjaBUlhR6O3kFkRy9kcpC3kLFxtJpyZEDidPXhHDiBHBpFOiMwPFYoAttUU8jpWtaMa0nTpQPi6T4WtoMcihXOF01DjsZhYAQnFEC2deGk0oWZqFMEWFYuwUj3W+QyoyRjJ8mlhjHcQaxcgkDRAvzRBax8z5cRms58G/oSxWHMT7wPe89h7J4ByObAspEjY4UJAGMJsG1EKo3T3oMoChdmY77j/LatLPGhI4x0b2O0bw8A0smDXiiBEShqZuV7/te8PEaKMpNUgKVcs0AgQGtrIRcnGDx3OM+yLHw+3+TCrwMDAwwPD9Pe3k5R0cUDHxlPnlMhfMr7SElqrBPTEzn3wuDScVIZRuJ9VEQub1ajEALNFyU/fBKtcsVkr1vQFSFrZ3nm1A+4q+69hFzRaQ+yLMuivb2ddDpNLBa75P0TsZPsfvpX0U0vqx/8G7zBKvLZMfa/8FukxjtZcd9fUrXoYXTTR6RiJUIzJ0pQnMV2yD/5Ms7+4xiP3IkoiZD/9x8jyoqQPQOIqlK05Qtx2rrBsrEBMln0TSsL69nFxs6pcyWEmJGZg/F4nK6uLsbGxsjnz11aRUqJ09OP9dybkM4WemZqK6CmvJD4vnwhoiR6wYrlbl8pS277bfLZcZKjpzi545/wh+vJZWI0rvksVS2PTDlL8Lzq9rMkmUzS0dFBMpkkl8udfwddK9S7io0X3oOpDMLnQTTVgtss9DYmUuS/9kNETTmuT73/vPfcUMfrHHrlDwkUt0yW8zg7if30hICpzJXjpCizQS30pMwYKSUPPfQQLS0tbNu2DYBYLMbJkyfp7e299ONH4wjfpRLcJenxLkx36LxbbGyMrES/QD2eqeieMPmx7olq1AVCCEq85ZT5Knm67bucGD2II53LXkvxcjiOQ1dXF21tbYyPj1/y/pruwnSHMN0hxESxRiEMTE8U3fDSf+I5Tna+wiv0cMq+SMCm6+A2cY63Y+86DC6zMGvT4waPG5nLI7zuQnK7riEiIYyNK9CXNGHcvLpQtHKGpVIp2traaG9vx7bPrzgvTBPhnki+Nw0wdGQyhQgH0NcvQ2usvuAFXmg6RTU3Ub7gPnTTi6aZaIYH3fBguKbO/RvLjvCdI//I232v4MjpX1/xSqTTaU6dOkV7ezuWZZ1zm4wnsZ5+DedUdyFo8roRHjf4PJDOINwujA3L0RprwO0qzCjUzj9Op4+Jy1tE1eJHCZUuuXTZFCkZSvfxrcN/z66BLThy+ld4UJS5TvVgKTNGSsnhw4fp7u5m9epCknhLSwstLS1s376dRCJx0cfK0fjkkicXkk0WFmsW76gILYG4maYmV4o1xeMuSDNAaNjJIYzgmSn+QghCriiLosvZM7iNvlQ3N1XeOW01skzT5LbbbkNKSV9f3yXv7wvXsfqhv0UIHdMTAcBw+Vl615cY7nyTQ6/9KQcTu3khd5T2+Alailbiemd+jJRg6GitC3AOnoB0FuNn7kNvaURmc4Uk6LEEIhzAPnAc+0cvQkkUHOe69kiUlZXxwAMPEIvF+NGPfnTObUIIKC/GeO8d2Ft240gJhoG+YTnawnpIZwtlCi4y1Hy6d9XJZzBcfnTTz5LbfgdvaOq1KPcPbef7x/6FumAzrcVrCLjOD+6vl+LiYu6//34SiQTf/OY3z7nNOdmF/cK2QqHRykIxVOOxzYUePdspBFumgbawDtcvf6zwu3H+JcGxc7h9pThWBqR92a/9G93P8/1jX2Vp8RqWFq/FrUo3KO8yKsBSZoymaaxZs4Y1a9ZM/u2yL8yWVZgFdtHEW4f44GHc/vLztiuRdDm9VI4XM3YFbRZCoPuKyMdOTSS7n1v53aW7WRhdRmf8JK91P8Nt1feja9f+Npqc/XiZvWJCCNzvWKBZCIHpDlFUvZGqlvcSCISxrGZWl918zhqMZz0AhMA52l64AGdzaNXlhcR2jxvrqVdxjrZhPHJnIehatwytqrTQy3UdXeqcEUJA0I8I+pC9g2iNNTinerDe3oezbR+iuhzz0bsuWbE8XLGS8aHDlNRtxheuveB+myNLuaP2vSwIL571pZQudmxE0IeoLkOURpEj44igD+s/XkA0VmM+cueZAqNCIKIXDhIDxYsIFDUTqViJblz+DME15TfTET/OkqJVmHO9GKuizAAVYClzUzY/UTh0aoX6OyNYuQTuQMV5t8dzo0hDYKYEwpJI8/J7XDRPkPzwCbDzYJyfA6YJjbrgAk6OHWbf0NusLL1pTuWYmJ4wi276zyBgjSy0d8r2SQkjYxAbg4AXOTKOfegkOBJRFMLp7EX2j+D0j2AsasD8mfsuWEtsNkkpIZfH6RmEeAo5PIocjCG9HmTPQGEmqmVfNMAq1ARrZekdvw/i4pkTZb4qvrDitydy2ube8ZiUzSNHxiDgQ/YPF3ok+4cKw4D2xY/H2XzhWlpv/92J1/7ynm+hjlwjv7Tyd9EmFhhXlHcbFWApc5JMpC7RUyIZH9iP5x3T6qFwwR1I9RJwhbFdAteYRbbYvILgQCB0F1ZyEDN84QV8G0ILOTSyh+pAAyXeijkTZAkhYCLvTL9Ykwwd48HbcNYuRQR9hbpJL7+Fs/sw5mc+gPnoXTiDMfRZKrtwOaSUICX2wRPIzj601Usm167UljWjr1mCCPoLOWSXcPZxu9T9riSvb7ZoC2oxP/ogTt8QTt4q5KRtehCtvPiSk0fOdrnHZarHzYfjpCgzRX2tUOYcKSVyaBQR8F6wOGQ2OYSVS6Gb55dNyDs5ErkxPIYPy6fjGcmft42LEUKg+0vIj7RdcMhOCIGuGdSHmnmz98VZT3a+GkIItKpS9GXNyJ4BZCKFCIcKU/V1DWd4DKetG5nJFhKmn3gZe/uBi/YsXk/SdrDf2of15CuFXiq/F5lIIpPpwnMJ+tBbF6DVzp3g93oSbhda6wK06rJCTTnbwenoxekbmjOvoaLcyFSApcxJhUWeL5QUO9F7FTj/wimlZDjdj2ciN8Z2a5hxu5D1fgWE6cVOjyKti1emD5ghHGlzYuzQtM4qvBpSysIix6nMFbVFJlNYL7+NPNWN7O7H6R5AprPYL7+N88ZO7P3HcI6cwn51RyFpOjNFOYDZkMlgv/I29ms7wTAQRWHk0XZwHPTNawqz4xScWBzZPVCocdbejf3m7kJNLEVRZpQaIlTmHtsp1OspiU55czY5iG1l8JjnJxg70mYg3UuR53SCukTqYCZs8qErOd0FmjtAfrQTV0nzRWeg1QUXsHtgK3XBZjxXkAQ87XJ58t97Btk/gvnRBxFVpZf1MOH3oW9aBfk8srocraIYEQ6i37Iap6wIfcmCwmyzjcvRasqvaHhpRnnc6JtWFXpkbBs5MIKoqyz02jTXzcl8sdmgL6xDrl4ChoZzvBOtorhQnkNRlBmlAixl7kllADllTR7p2IwPHMATrJy69yozgCGMM7kfQpD36XiGc1cUYBWKjhaRHzmJq2QBF0vuNTUXIVeU/UPbWVu+efaGo3IWsmewUKk7mbroXWUyjdPVj/B5kKk0xi2rQdcKFb0D/smFnPXVSyZfB/OxiST3ORK3CF1Hv2UNupTYe4+AZSHKi9FXtCBKIu/KYcF3Oj3DUl/ZgtPTD2NxpN9bGFJVFGVGqQBLmVOklDgDw+A/f1FdKSWZ5ECh92qKniJL5ulJtFP8joRzy6fjHcyBLS+R9X0uobsKCyYnBiYXgJ7yfkJQ6a/h0MhumqOthF2ztGZhwIv5sQeR40m0xgsn5wPY2/djPfsmoiiE7B8uVHIvKyL/1R8iiiO4vvDhQrmGs4/XFRy760ZM/F8uj+weANvB3nEA496b52Z7r6PCBACw9x/H+t5PEWUlGB++H62qbO70QirKDUzlYClzjuwfRgTPH/6T0masfy/eYNV5AYwjHU6NHcNnBs6fuTRxVzNxRSVHEUJgBCvI9h9EOhdPYteETk2gkS3dz89awrsQAlFdjrakCTFFwchz7lsURoT8EAoUqrb7vIiAr/D3ytLLnsI/6/IW1kvbCsv5hAMQDRVqOr27YyugkMdo/fhFnI5eCIcQlcXoyxchKkpU756iXAfz5FNUedeYmLEmys5dp1BKSWL4KEIzzlkHTUqJ5eTpiJ8ga6fPyr06Vy5k4OvLMRYyrig3R5henEQ/+dFOzGj9RXOxwu4iRrJD7Bl8i9VlN81K7Z/LvXBqS5sxq8qxnngJ6XbBWByxqgXXFz5cKI9xnYuJXi05Gsd++W0QAuPjD6NVlSK8HpV/BcjOvsIEgLIizE88glYUBtNQwZWiXCcqwFLmDClloSciFDjnIiClJJPoIz58HF+0gUR+nJSVIGdnSOWTpPJxfGbwgsEVQmB7NNx9OYykjeXXr+gCbIZryPbuRfeG0d65qPQ5uxHUh5o5OrIPl+6itXgNAjEnL2hC06AohL5hOeLwSbTlCwsVvYP+2W7aFRHREMbdNyHzefSGqsKahAoAor4K/ba1iJoKtIqSwmuuKMp1owIsZdY4jjO5eK9wCrWvrPZuRGMNtm0Vhtoci3jsBO3dW8i4feSGtqMLA1NzYeouXLqHgBma7C26WHmCTNQgfDTJ6GI/lltDXnbdUR09XE3yxKt4KpdjhKom1iw8v7K1QNAcaeXIyD664qdYVbaJsCuKoRloaOfcXwgx+XM2KeU5x2ZGtTSgtTQgATlHEp8vdFzgzLGZfJ01gbh1DQJwoFCh/AZ2Ocdmkt+L9vDtAIU1Gm/wY6NNBJBz8QuN8u6kAixlVmiaxmuvvcbx48cpcXu5K1iO0zNA3CXwxUexnSy53DhONkYun8JnRokYPgxhXuAD9BLBgYR4fByv7qP6zRzxKPRHe8gbF69zdc4mbAvr4DNopg/hKSZjumlza9gCbNsmlUoRDAYndic5mTvK6+3PUuQpJWKW4NF95Ds1Mj2Ftq5Zs4Z169aduw8psSyLr3/965jmxYfppJTE43FCoWtbbHhsbIxwOHxN28hmsziOg9d79WUqLMsim80SDod59NFHKS4+d6FvKSVtbW38y7/8yxVtN5PJFNZudLuvum3xeBy/3z95Eb8ayWQSt9uNcYn8uIvxer187GMfO28bUkq6urr46le/ChTOx3Q6TSAQuOp9nS2VSmGa5iXPycuVzWaRUuLxTM8C0PF4nAceeIDW1tZp2Z6iTAchZ7s6ovKulM/nSaVSaJqGAAxNo6Ojg507d/G+971v2lNopJR861vf4v7776eoqJDfJZFIcW2n/+mwbmhoiOeee56PfOTDF/0GLR0mi566XC5cLtd5w6GpVOrcnogLSKVSfPvb3+aTn/zkVV/4Lcviq1/9Kp/+9Kev6cK/a9cuEokEt95661Vvo729nT179vDII4/g8/nQ31GryXEc4vH4FT/XrVu3omkaGzZsuKp2OY7DN77xDR555JFrCkR/+MMfsn79empqrq4AqmVZ/Nu//Rtf/OIXzwt0HMchkUhMnkvd3d1s3bqVxx577Krbe7ZnnnmGhoYGWlpapmV7b7/9NpZlsWnTpmvelpSSb3zjGzz22GOUlpaqHixlzlA9WMqsME3zvIuV2+fD5fXgmaJEw7VyHAfT48Ht8+HxT3+eUdqbwefxEfAFr6ntQgj8l9k+TdPw+/0EAoHzgpHLlc/n8fl8BAKBa+qd8Pv9SCkne/Cudhunn89Ux1DTtKsKcE73PF1t2xzHmTxGV7sNKeXk87vabeTz+Qv2wmmadk5PZiAQuOixvBLT0fZ38vv9WJY1LdtzHAe/349pXqh3W1Fmh+rBUuaMdDpNPB6fkW+hUkr6+vooKSmZtmGOs+VyOYaHh6mouH7r3tm2TV9fH1VV55etuFyO49DT00NVVdU1DX+Nj49j2zbR6NTV9y/HTL3+o6OjhVmeV9n7JKWkt7eX0tLSqz53pJQMDAwQDoeveljsSl6rTCbD2NgYZWUXmPhxBaSUDA8P4/V6Lzv4v5SxsTGklEQikWve1nS8PooyE1SApSiKoiiKMs3UvF1lWuVyObZs2UIulyOfz/P666+zbds2stnLTyZXFEVRlPlOBVjKtJFSsm/fPl577TWy2Sz5fJ6DBw9SXFyMYRhIKbFtm3w+z9jYGGNjY7Pd5Dnj9OzBfD5PX18flnVlVedvZI7jkM/nSafTDAwMXLQUx7vN6WOTyWTo7+9Xx0ZR5hCV5K5Mm1gsxo4dOyb/XVVVxaZNm9i9ezeGYVBfX8+2bdvYsWMHvb293HPPPdx1110z0pazLzTzIfE1m83ygx/8gOHhYdra2vjSl750zeUXbhTt7e08/fTTJJNJvF4vv/RLv3TB13S+ve7XqqenhyeeeIJUKoWUkl/7tV+75ISHd9sxUpTZogIsZdoEAgEeeOABnn/+eTKZDKOjoyQSCWzbnkw+3bhxI+vXr2fHjh2kUqmZa4xlYx89hfB60Bqq5nwVa7fbzYc//GFs2+Zf//VfZ7s5c0p9fT2f+9zniMViPPHEExe8n5QSGRvHaetGb2lA+r03fABRVVXFZz/7WRKJBN/97ncv70G5PM6hk4jSIqhSZQ0UZaaoAEuZNi6Xi9raWj72sY9NznJavnw5K1aswOcrlF7QdX3yZyY/2J2OXqyvPwkBH65f/igiPD3Ty2eKEALDMNA07Zpm812pnJ0lbaUIuS68BNBsO31MDOMS6+g5Eusnr+HsOYy8fzPGXRuvXyNnydnH5nI5xzrIf/MniJoyXJ/7IHinp9inoijnUgGWMu3OrtUzW9OmRTiAqK1ABP2IebJw8fVmOxYvdvyYgVQPDzR+mFJfxWw36dpoAq22AjkwjCi5+nIRNzoRDiCqStHqKuEaissqinJx6t2l3JBEcQTXpz8AmgBTnebv5DgWnW3Pk0310RRdzP6ht7mj9uE524t1ufTNq9E3LAO3a94/l5kiaspx/eKHwNDhKgvUKopyaXM7MUVRrpCUcjKJV3hcCJeq7jyV8cFD7D/xYyIjfQTNEMOZAXLODVBKQ9PA457zOXfX2znvCyEQHjfiUkOuiqJcE/UppNwwpJTI0XHst/cjh0bVlPULkFIS691FxhfC0ExkZgwJJHLjs920qyalRI4nsd/ajxwcUa/9BCklMpPF3nkQp71HHRdFuY5UgKXcUOw392B9+2msF7dNLqqsnCufHWMs1Y9uejG8Uax4H2FXhL5k52w37ZrYb+zE+s7TWC++BY568U+zD53E+s5PsR5/CbK52W6OorxrqABLmdeklMhcHpnLAyCqywsJvPVVoEY/ppSJ9zHmJPGZATSXHzs5RNAVYSDdO697OLS6SkRlCaK8GBVdT9S7yuYQRWFEdTlaY3Uh70pRlOtCZf8q81syTf47T4MjMT/8APryhegtDaBmDk5JSklytI2kJgkaAYThxskmcAmTwVwfEomYh5GpEAKtdQFa7yDOzoPYRWH0FYve1TlGsnuA/A+eRdRU4PrMB8DjBl19p1aU60UFWMq0klKSSCTw+/0IIchms+RyOQKBwIzUd5LJNE5nf+HfmSxayF+4kChTktImk+gnLRxKdC8gQGjotkUyH8eRNpqYpxdhCbJ/BNk7BEOx2W7NrJPjCWTPIEgJmoZQvVeKcl2pAEuZNlJKuru7+bd/+zd+9Vd/FZfLxVNPPYVt22zYsIGGhoZpH4ISJVHMjz4IUiKKw+e2x7ZxjpxC5vLoS5sRc7hcw/UamnOsDMlsDCG0M4GUpoOVwdAMkvk4YXfRdWnL5brUsZG5PPb+4wifB+P+W3AWN6IvbrxOrZtdFzs2WnMdxkcfRPh92PuPIUqiaI3V7+pePUW5nubuFUeZd7LZLLt27aKyshLbtkmlUmiaxqpVq2hvb6e+vp6jR49y4sQJTp48SWtr67XvVBNoi+qB89dVk+MJ8t97FtIZxOd/Br2x5tr3N0Py+Tzbtm1jfHycjo6OGdtPLjNKyslgGm6EEEgp0UwPTiaOx/QSyw7PuQBrYGCAXbt2EY/HyWQy59wmpcRp78H63k8h6Mf9yx9HX1s4r94NgcTIyAjbt28nmUyet/SUcJnoK1uwdx7C+v6ziMoyXF/4kOrhVZTrZJ6OBShzUSaTIZfLcfLkSbq6uhBCkM/n6evrm1y4OBKJUFdXR1lZ2bTsUwgx+SPzeZzRONJ2Crd5PWiLG9GWNCGKwpfY0uzSNI2Kigpqa2sJBAIzsg8pJemxTlJC4jX9wMTxM73YmRhew89YduSc+8tMFuk4M9Key+X1eqmtraW6unrKhYxFSRRtUUNhYsPpHKNsDplMzeuk/cvhdrupra2lpqbmgsvlaNVlaM11aIsbwDCQjsQZSyCzuRv++CjKbFI9WMq0iUQiPPbYY6xevXoyoFqzZg1DQ0O0trYihKC8vJzy8nLS6TSJRGLa9i1tG+vZLdi7j2A8dBv6yhZwuzAfu7eQgzLH808Mw2DhwoU4jsO2bdtmbD+ZRD8J8hQZZ4I4zfBiJ4fwGk2MZwu5S1JKnEMncU71ICpL0Ve2ILTZ6REKhUIsXbqUkZERDh8+fM5tQgiIBNFvWU3+B89hPbcF/e6bsL7/HHJ0HPNn34sovXGXzQkEArS2thKPx9m1a9d5twshoLwY81PvL+Rh6Rp2WzfWd55GNNZgvv9uNSFEUWaICrCUadfU1DT573A4THNz84zs55xv345E9o/AaBw5PHrm77o2I0NFU33zn/NDUtIhnR4ij42pu878XdORdh635qI3VShGKQdHcDr7EAtqkR29yKERRFnx7LX9EuR4EkbjOH3D6KkMsncAmcogU2mkjABz5/W5UK/RjLZv4gtGoSBrAhkbB+8QWNZ1D7DOfv6nh6nP/l1RbhQqwFLmr1Qaa+teRDSMvnIRxntvR960ojBUlM9jv7UfAj705YsQ0zg9XUrJybHD7Ox/nTXlm9k39Db1oYWsLN04p2fg2VaGbC6BI8S5pRiEQDoWJgYpK4FjWzgHTyKqywrHrawI53gHorRoTl4AhRDoyxciQn5kKlNoa1MtWlEY52g7JNNoixthjrQ9bSV5oeNxyn01hNwR9g9t547ahyn2lE378ZVSQiqD/dY+KI4gB0cQoQBiWTNaY811z8eSUhLLDPFi5xO0Fq9mSdEqYpkhXup8giUTv8/Fc0xRroYKsJQr9s5v4LP1gei092I/uwUiQbQFNWilRVBaSNC2j7Vj/fgl8PvQ6qsQ0dC07deSFt89+n/Z3v8qe4be4sjIHprCS2iJLsdnzkz+1HTIJgfIa2Cc97YXIG2EtNHQSA/04rZscE/0cvk8yL4hSGfB57nu7b4cwu1CW1BH/vEXcfYfg7EEoqUBeawdp6wYV2M1eOdG2/cNbeffDv4Vpd5KIu5ijsT2IpF8cOFnZmR/9uE2rJ+8hoiGkNksIhpGdvXhDIzAmiXg887Ifi/kle6f8K3DX2F56Qb++4Yvs63vZb5x+CusLN3If9/wZVy6SsJXbgwqwFKuiJSSgwcPcuDAAaSU3H777VRUVMxKW0RJBNFQVRi6EhrOWBwhNPB7EWVFaEubEeEAYpqDAl3o3FRxJ1krzS3V9+ExfLREl8/5C0M63ksSC6/hPy8oFrqJk88gECQPH8ZTWn/ug71u5FAMUVd5HVt8hTRR6L0cTyBTGbTGaqSmIarLwJw7eUYNoYWsK7+NmkAjRZ5SPIaX5cXrZ2x/WnVZ4X1SXowYTxS+hJgGWlMNuFyX3sA0W1a8jhWlG1lbvhlDmDRHWllZupF15bdiaOqSpNw41NmsXDYpJY7jsGvXLm6//XZcLheRSGT2GpS3YSwBXg/W95/B6RtCKytCW7cMfWUL5scfKgwLTXOBU01o3F77ELdU34ehmdxe8yC6MNC1uZ1In0n0ksLCa5yb9C2EQBgenGycoO3BGhuDGs9kEFZIJA/h9Awgaivm7hCOlMi+IZz2Hoy7NqJvWllYMUfTYJYS9KdS5qviv6z9UzRRyA+8p/5RTM01Y8dVJlLI0Tgi4MP80P04fUPk9x5FjoyB4wDX77wVQtAcaeW/b/gyhmagCf283xXlRqECLOWyZTIZvv/973Pq1ClGRkbw+Xzcf//91NTMbH2pCyUFy2wWGU+Cz4vMZAsJ7gEfjMUBEBeYtn61+z77AqgJbbLHaq73XAE4jkU2NUROQFA7v9eisGTOOHWJEvSI5/wZgx4XcmAYLBvmSMHW884LRyJjYxBPIbM50PU5Ewy+s61nTzJwTVF6Yjr3KRMpiCcLEwF0HfL5Qi/faHwiwLq+hBDnvGfe+ftpKvldme/mxielMi94PB4++tGPsn379snyC/l8fuZ3nMtj7zgIpoG+ajFycAT7wAmEx4V+10b01gWgCeRoAkwdrWp6amxJKelOnGJr70usKruJBeEl8/aDPp8ZRSLI2Okpe9qE4UZmEpi9BgNlPkrPu4Mo9BCl0ohw8Lq0+ZKkxDl4Ajk8ir5uWSGQ8HrQ77kJ/aaVs9268+wdeouTY4e5tfr+GUloP08yhbX9AMLnRVuxqDBU6jLQmusxP/sYIhI8k2c3Bw2l+3i1+6csLlpJa9HqefveU969VIClXDYhBDt37uT73/8+x44dI5fLcffdd8/4fuXgCNYTL4OmoVWXYb2yHeft/YULhs+Dcdu6wjI45SXTu18kT578Fs+0f5/BdC+NK1rQr+NwynTKJPqxdR0sgZiqvrBmoMXiGBQTdxJIKc+5oAkhkAE/cmAEGQrMjYtdOoP1k9eQgzFEUQT7ZCfOG7vQVi9GeN1zo40Tsnaarx/6W06MHsRvBLi3/gMzvk+nrQf7yVcg6AchED2DsH45wudBX1h/6Q3Msq29L/H1Q3/D6tKbWbThy+eWFlGUeUAFWMplsywLj8fDfffdRzQaRQhBcfGZ2kiO47BlyxbGx8e55ZZb8Hg8/OQnPyEUCk3+fjVENIy2ejG4zEJJhhWLkGMJRCSIqChGJpJgGBDwge0gxxOIoB+MaxsiEghurrqHoXQf68pvRZunCx9IKUnG2sgaJm5r6sBDCB3PqIMs8mE5QzjSQX9HPowI+Ao1xprrrlPLL8HtRlu3FNnVj6gsQcvlkQ3ViLpK5FgCIkHEDCwwfjVMzc1dte+lyFNKS9H16V0TVaVoyxZCSRQ5FEOrKEam0oUbzwpApeMgx5MIjwvcM5cLdqWWlaxlXflt3Fx1D7pKflfmIXXWKpfNcRySySS7d+9m0aJFdHV14ff7WbJkCVDo5Vi0aBHPPvssQ0NDVFZWMjAwwMKFCzEMAyklmUyGTCZDIpG4/GU6fB7Mx+4DAQiB1roAPZXBeupVRDKNs/co2A7mpx7FOXgS69kt6JtXY9x90zU9XyEEy0vW01q8Gl3M7FvFcRwSiQSWZZHNZqd129KxyKYGyZgCtzH1lHyBhi8bxA56sFMWtrTO761zGcjeBDgS9Ot3Ec7n8ySTScbGxnDOzhnSNYw7NoB0cI6cKpwPNWU4h05iv7Id86MPzpmeGk1ovKfhg9xb/350YUxbEHP62CQSiXOPDSCKwpg/916c4x3kdx3CcST2338HrboM8+MPg9tVmLhyrIP8955Bq6vE/OgDc2bGZUNoEf9t/V9OBPpzI+hTlCuhAizlsrlcLm666SZOnTpFVVUVjuOcd6EIBAJUVVWRSqVwu9088sgj7NixA7fbTXNzMwcOHGDPnj10dXWxefPmy9qvEAJ5dkeElJC3IJsrrDlnO4hMFiy78A09m4Nk+pxtFII5CQgkhcBOIC55oRNCYIiZv+Dkcjmef/55RkZGaGtrm9Zt57PjSMciaWVx61P3Iup5iUzFkdLGkQ62Y8E7E481rZCLlUhB+PrV++rt7eWFF144b2klIUShhJfUCpMcslnI5SFnQWbi3JgI4qe/gKcDFzh/pJRIJGLiXDs9W1Agpr0Q7eDgIM899xzJZJJkMnnObYX3jYbM5gvvl1wOMllkMl14r7gmvuDkcpCe+Ltz7WsTnv1eu5Lj/s7jJhAY2twI9hTlaqgAS7lsjuNgWRa33nor+/bto7y8nIaGhsnb8/k8W7ZsYXR0lLKyMgYHBzl48CCpVIqiokIB0LVr17JmzRq2b99+2WsRymwO6/WdhSHCoB/nZBfakka0lYuQuTxacRjh8+L0DiIHRjA+cDf60jPL80gpGe3bw8DJ5wk238PzQ6/gM4OE3VHuqHloTuR2uN1uHn30URzH4atf/eq0bbcwPHgCl6+YePwwZb4p6lhJiTluY7kcsDOYmknGTk0uCH2aEALp8+IMj6JfxwCrtraWT3ziE8RiMR5//PHzbhdCoC9bCI6DMzSK7B5A27AMGU8VerWqStFXLZ624cJsaoSOfV8nVLqUsoY7EGdNGpBSsn9oO2/1vUxVoJ5kPsFDTR/Ga/gvssWrV1lZyc/93M+RSCT41re+NeV9tMoSRGM1WlUpTsCHVl1O/rk30eoq0FctRlvchOvzP1PI1ZqGZXOyyQE69n2Toqr1FNfdXKhNdwlSSvpT3fyk7TssjCzlxNhhVpZsYFXZpjkzZKkoV0oFWMply+fz9Pf3c+zYMWKxGKOjozQ1NVFZWbhom6bJXXfdBZzpMSgrK5v8/ey6Smf/filyYAT7+TchMlGNvX8YORQrTD3vG0b6PYWcqxMdyI4+RDgAa8/UcZLSpn3PvzPU/gqx7Cl+lHiTsLuIMm8ly4rXURmonbZjdLXeeXymjyQxcgIzUE5+LHvBHgH3aJ5cwI2ez+DWvcRz40Q9580lLORhDcWQjdXX7cJ3WeeLoSPHk8gDJ5Dd/dBchz0wgjx6ClFahL64adqq0I/27qBjz9fxFzVTVL0e031mlQDLyfOD419jz+BWFkaW0ZVoY1XZTTRHWqdl3+90OcfGOd6BPHACu38EBDhjCeTJLmR5MfqiBkTAN60FZIc736Rj7zcYG9hPpGoNhum7rMe93v0MT578JtWBBkYygxyN7WVpydp5UQZFUaaiAizlsrndbmprazl58iTRaJS6urrJnilgyg/66bgIi6Iw+qZV4HEh/D6cU91oixpwTnZCNITweRHRUKF3q7QIbcUizl1qT6d68aOY7hB1jbcwMFqG5eQJmhGinsLMw5ydpSfRQZGnlKArfMN8a7ayicIQYbAMTVwg6V+CkbLJ+l04Vga3O0wyP37eTEKgUNG9f/i652FdkhBoDdXI2DiiKIQoiYLLREaCiPIScE/fUFOotJWqxe/DG6ohk+hH01xoRiFp3NBM7qv/AMWeMsp8VZT7qtDQSeWTDGf6KfdVYWrXd4ajaKpFLGlCVBQj8lZhTcKiMKKytFBHDgqrH0xTm0JlSymquYmSulvQLxAcSSmxpUVfsouIuxi/GWR9xe30JbuoDTbRk+xgUWQZ3YlTFHvKb6j3pPLuoQIs5YoIIaioqGD79u1s2bKFz3zmM+cME84Inwfj4dtPtwD9phXYe4/h7D2GKA6D0DDeu6ZQA2njctC08z6MS+pvnRiu0PEWNfFnb/06HsNLKh/HY3h5s/cF/nnfX3BT5d18fsVvXpe8q+shPnwEl6+ElJW6YKK+lpcIGzBMpJXFpblJWRcYvj19XJNpCM3MsNfVkj0DODsOoq1aXOi1SaZxfepRRGXZtOZIe4JVtGz+b7Tt+Gd2/PgXaFr3eWqXfXjy9k2Vd7Gx4g5Ojh3h+Y4f8Q97/5j60ELe7H2ej7b8Ig82fmT6GnM5xuLIjp5CAPixBwvDgFLiHG0n9w/fRWuqwfzog9MyPAiQSfQxPngQl7cIR9oXLG3yVu8r/MO+P2Ft2S18cdXvURdcwBdW/jZCaEjp8Fr3M/zels/fcO9J5d1jbsxhVuYNKSWHDx9mdHSUtWvXEgpd/SLKl5pFKKVETs6MOn2FlIVka8ee+JFIxy7U+dEE4kLVu6VETPTgCAoLNufsHLZj4UibvJ3DcvJk7TSOdHCkjZROYZaVtC9/xuMcIh2b8cFDePzlxHNjeC4wg9BM2NgugdANZD6NLnSydmZyMsB5vG6c0fEZbPmVkVIWiqDm8mDbYFmFivO2DVA4L6a590MgcOwsjpPHtgoTKhzpICf+pwkdicRy8uTsLGkrWfi3k4MLHdeZYjtgOUjLmnifaBPvIadwnPLWNW2+8D6deL84No6dn/hvtvC6SInjnHk/FZLZHfJObvL4nHl/FV4nXTOwHWvi9sz1PmKKMi1UD5ZyxUpKShgbG2N4eBjLsrAsC+MqlqW52EVPTqwrZ7+5G7GgDtk/VJgh5nJhbFqJVlGKaK5Da6xBX7UYvBcYinBs+o7/lPjQEepWfAy3v5zKQAO/sfbPebnzKR4/8f+IestYW3YLv7bmT3ir72V+cOyreKXB0pyLfPkiXh94hZur7mFp8dp5NUyRSQ7gODk0w81YdgSvMUUujJSYSQvbXVivz7EymEIna2WYKhAQQkAogOwfQs6BdQmllJDOYO8+DLl8YTisKAy2jbFuGaLi/Dyya3U6iVs3/VQufIj0WCf9Q/t5bvBlou5iepId3Fx1D/WhZtaU3UzQFWU0M8St1Q+wOLqS611yQJRG0RbUImrKC0vlUHgdtZZGXL/0YfD7rnr5IyklmXgPHfu/TSC6gETsBMGSxax979/j9pWh6S564m083fYdWovXcFP1vfQnu/hJ23dojizlDzf9IyW+CgzNpDPexlNt32Jp8Rpuqb6PW6rvpSbYSKm3EmOGy6QoykxQZ61yxVavXs369etxuVzYts0bb7zB7bfffukHXiF712Hs13cheoeQnX0AiPJi7OoyGBpFHjiBk0xj3HrhwMe20nTu/y7xoUMESlqoXPgguqYTcEXY0vci4/lRagML8Ogeou5SXul6irC7GLdm4s6Vsm/4Bd5M7MeSFkuKV8+bSu5SSsYH9uMJVCKRpKwEQVdkyvsaCRvLp08sh+OgAQ4OlmNNvU6ex12YZGA7YMz+8XC6B7D3HIVUBtndjxNPIAdjaK3NCH36O+lH+/fSue9b+CINACRjJzlh5nl2/HWKPaW0x49jSwvLuYc3e1+k3FfNqfGj1AUXUOwppaVoBeI6Bln20XacgycQw6OweTVM9GQKQ0dUl1/z9gdOvULn/m8TKl3K+MB+giUtrH3k/2KYPqSU7Oh/nafav8eR0f2sLr+FLb0v8FTbt1lWso7f3vjXuHUPjnR4vfsZnmv/D7oTp9hQcQcew0dL0Yprbp+izBYVYClXRAiB338m98ZxHAYHB2dkX/qqFmRsDK11Ac6RNrAc8LgRHjcyEkBbsQhtZctFL/K64aVm6QcZ7dtNtHLN5N+j3hJur36AjkQbdaEFLCtZx1hmmE2Vd6MJHbfUqKeCmqqlmL3Pclv1/fOqkrtjZ0mOtRMqW47l5LEc68IJ7mmHXOj0R4FA2jlcmotUPjH1DC5NFIaWMtlC9fxZplWVoS1rhmQaJxwozCgtCqPVVUz7vqR0cPuKKa7bTKB4EdnkAJ5gJcGKTSwz05T7ayj3VdMYWozX8LGm7Gbqgs3UBpuo9NexvuL26xZcSdtGDo8hKkoQrU1odZXgmv6SJMW1NzHcsZ5I5RoM009x3S3oxpkZmytKN7JicAMby+/AbXjZWHEn3fE2VpRuYCDZQ8RTTMAMsaHido7G9rK+4nYGUj0UeUrxm8FZ7yVVlKulAizlskkpsSfyWs52LXlYFyKEgMpSzI88iByKYT/zBnjcEPJjPf0asm8IfdNK9BUtF/0AFppO5aKHqVz4IIgzye9pK8Wh2B6GM/18pOXztI0e5qsHvsyKkg0cHzuEAB5e9/PUhBbQWnHLhWfgzVGZeC+aZiKEhmXncaQ95YVdyzmFESutsJgzQiDtPKbmImnFiVB8/sYB/F6coRjaNM4+uxqF2lwejFtWg2WTf/xFnD1HCsvlDI1CadElt3ElkqPtHHzljzBdQfK+YoZOvUywdAlHjv+Y4+IU8fwYxZ4ynuv4DzJWIZ/vw4s+T1WgsLzQ9TyPnLZu8l9/EtFQhewewLEd2GxNe69jcuQkiZHjCN1FfPgImumhpvUDk0FWV/IU7ePHCLmi3Ot8gOpAPV9c9Xu80fMcv7vlF1hZupFfXvX7dCZO0hE/gUTy45Nfp7VoDV9c9bsquV2Zt1SApVy2TCbD97//fTKZzOTfHnjggcnaV1JKOjs7cRyH2tpaNE1jeHiYeDxOXV0d+lTDTVOYTHiVFIathJisMC0MAykmggFNm6jkXSgncHYF6UKwAKfzXaQobFBKORFonKmqLYRAm6gaXViW43QVaq1QUfqsQomn23Yl1eCvNyklY4MH8QSrEEKQzMdxXaiCe9bB0c5qv2Yg7Swu3UMqP/VMwkIelh85OAL1VTPxFC7b5LkiROF80PXCa3/6b9O5j4lkek0z0AwPQjML54FmIgQYmguX5p5I0J74IlI41RBCm6xQjmTyfD1dsXxGziEhwGXA6WFSce7zudp9nvM+KzwZdNOLphkgnYn7OOfcz6170c5a2/L08XCkzenVFcTEe04T+sT7VFHmNxVgKZfN4/Hw8Y9/nPHxcY4dO0YulyMQCJyT4J7L5di1axf5fJ6amhpeeOEFAoEAmUyGxYsXA5yZSXSRmXnO0VM4xzuRY3H0FS2Yv/BB0AT2gRPIA8fRblqJHBzB3nsUfWULUjoMtL3EcMcblNTfxtjAPqxsHHewkhNhP4dHDxDxFCMQLAy3sr7qDn5tzR/z01Pf58mT32RFYDHV/np8ZoCNFXdxV+3DdCfbebnrJwxn+rmz9mGWl2zAkQ6vdT9NZ7yNWHaIBxo+RHOk9ZovkKePh+M4Fz0ul7UtxyIT7yVUvqxQxX4iwX2qNhopB8d1VgFY3YXMpXEFikjm4xfeictVqIc10es1ky56vuQtrFfengi4deToONqqxchcHlEcma4WMNL9FiNdb5PLjFDWdC9WLk4mNcTY4jt52xmkObiE2qRB2F3CoqJlhF1RhtP9rCy7icdP/D/KvFV4DR8d8ZM8uuDnqArUc3B4Jy90/Jg7ah9iecn6qzqHLnZsRDiAKC9GK4nipLOI6nLyT7yMVlOOvmHFVdUxk1KSGuugfc//IxBtIjnWgS9Uiy9cjz/aVLhPRSv/sP8vaQ0toaL7GP6qVqoDDTRHWtGETnfiFI+f+H9UBxppLV5DbbCRfz3wv6kONFDtr2dt+a18ovU/U+qtmPE1QBVlJqmzV7lspy8ATz/9NP39/Xi9XoqLi4lEIpO3+/1+EokEhmGQzWZxu90sWbKEjo4OFi9ezM6dO9m7dy+dnZ3ccsstU+/Ikdi7jyA7+5A9A5C3CovTWhbOzoPI9l6QII+3I8cS6EubcYRNz5EfM9L5JqnxThwrS3zoMPiLebrCS3uqg4bQIkzNRcf4MdZUbMZnBtg1sIW28SOMl46yf2QHI9khgq4wjyz4OK8e+SldiTY64scxNTdLi9eRshL89NQPSObH6Uq0EXWXsCCy5JrzarLZLE8//TSxWIyjR49e07YyiX6EZiAmegwSuTE8U80gZKJEg/tMD53QXTi5JC6tgtHs8NTFRuFMr0giBaGZXTans7OTl19+mXg8ft6CxnI0jv36TkRRpLC+Xnc/VJUiB2M4i5vQSqPXvH/p2Ay2vcxw5xYyiT5KGu4gmxxgfLyD1yqDnMx0YRsmu4a2EnYVMZ6P0RVvoy/ZiQS29DxHbWgBjmPTmWijOdJKhb+WV7t/yktdT4CApcVrriqY6Ovr4/nnn59c8PlsTlc/8nAbTs5CnugsTALo7CtUcF+1GLxXV9k+1rODnsM/wh0oJMi7PFHiw0dIj3eRz4xySg7wYu4AbZ6dPNiXpJ0Bdo1uYSw3wl117+Wtvld4oeNxqgMNxDJDdCdOIaVDd+IU+4e3I5HcU/e+C5YVUZT5QgVYyhULhUJUV1dz6NChc3KyLMsiHo9TUVHB4OAg4XCYTCbDoUOHaGxsBGDJkiU0NDSwe/fuC39j1wT6uqU4kWBh7bSVLYULuuZCv3Mj8mQnoqwYWVWGtrAODB0NjdqlHyIQbSJSuYbBU6/gDdfiDdXyaFkVR4Z24bbyGK4ANe5yOseOoSG4p+Q2OoOLaCJMZU0pRcFaohPV3O+pf5RTY0cZTPdS4a9hLDdC2F3E+xb8LL3JTsazMW6tvn9akpZdLhd33nkn+Xz+vEV7r1QydhKXrxghBI50SFtJ/K4p8uSkxEjbZKJnclyE4cJJxXBpJjk7iyOdiWHTcwkhkH4vzuAI+gwHWBUVFTz00EOMjo7y0ksvnduOaAjjnk2FHhzLxgl40ZpqIZdHa6q5qv1JKckkeslnxggUL0RoBuUL7kN3+ckmB/EEq3B5orj9ZdwVCtJSup7mouXU+BswDTdN4SXEc6PsHHiDFSUbKPKUUuwtx2f46U60U+QuoSfZzl11j+DVvdxUdfc5w2dXoqSkhIceeohEIsFTTz11zm1aQxX6besgGsJxu9BqymFRPaKiBNxXn+xeXLORhlWfxOUvIz50GF+4DpevhGDJYhLDR1hTtQqhr2VBoImySDslpY30u3VWlm7EY/i4ueoeEvkxKn11dCfbqQ7UM5jqoypQR31oIUuKVqnlcZQbggqwlCsihGDTpk2cOHGCJUuWsHDhwnNuM02TJUuWUFxcjGEY3HPPPYyPj1NfX48QAp/Ph8/nIxQKXXCxZyEEWlNNYfHm9h6csij68kWFwGv5Qli+cIrHaBTX3kxx7c30n3yO9HgX8eEjePzlbGj9G0o7DtJ37Hmi1Rvpy+zjTzu+iUtz8Vi6mE2BWoba/4M7l3+cpuZPcDpZZVXpTawqvYnXun/K3+/5Y/YPbee/rvtzbqq867z2XitN04hEIjiOg8dz9WvmSSlJjp7CXzQxXCMdMlZ66iDJBmHLc8sNCw0p7UK5BqGRtTP4tKkrtougH9k/gmyqndE8NJfLRXFxIWDU3rlgs6Gjby7MDrVe2Y7s6IOK0kLlf+3q2mTlk+x/4XdIjbWz/J4/J1q1lmDpEvqO/5TRvl04XVtx+8sASXBghHuXfZT6ukfO2cZwup/HT3ydwyN7+f2b/o6aYOELRnfiFF/a+kUMYfAHm/6eTyz9NeDqzyHTNCkqKsI0zfOOjQgHMR6+HfvQycL7yLZx/fwj11SxXQiBJ1jFgg1fpP/EcwyceJZg6RJsK0t86BBjfbuJSPj4vX+GprugDl7qfIKjo/uQSO6uex/lvmp+bsl/mnL7t9c8OLkfRZnvVIClXBEpJc8//zxlZWX09fXR3t4+GWTpuj7ZU3Wa1+ulpKTkqvYlXAa4TMREjtelPnRPJw5ruhup6eiuAJorgERimD50w4tuuDHMAB4jhUsz0U0fmu4q3Gb6kI5VGFqbmHEopcSje/GbATyG75yEeltacy5HxMolsO0s2kQPQNpKomvGlL1swpII57y/FirkS3siwErjMy+wJI7HjewZLJRsuMpCldfqzILesnC+GDqY+kRi+dVdpAUCw+VHN/1oRqGnRzoWQjcxTB+20NEMN9Jx0E0/jhDY0iqce0JHExq6puMxvEgkuqZP9iYKBH4jiKEZ6BPV3gvLyRjTX21+4jwVZuG4CNMoVHKfhv1Ix0LTDHRXAN3w4tg5NN2FMH3o7sDkuqRSSkzNjSY0vIa/sCoCEtux0DUdRzpoQseR9uSxk7JQAf/074oyX82tq4MyLyQSCUpKSkin0xw+fBiv10tNzdUNx1yIEAJ9zVK0BXWIgO+KeiOy0Wqei0BT+UbGnTQ7jv8TH1n+GQJFzbT3b+dEuJxK24upuTgc9rHeCrHk9t/FH6nn4Cv/A3+0kfoVP4vQC9/015Rv5jc8/5Mfn/g6/3H8X/lA8yfZMfA6r3Y9zfubP8Gi6PI58407mxw4pwbReG4Utz714sJ61sExpmq3RDoWLt1NKp+YXBB7SrqGHE9MY0L51RFCoG9YjtbSWDhfruH10E0fS+/8Ixw7g9tXymj/HgZPvUwq1kbD6s9geqO07/43fNFGDrhzvJTaxoo2jaOxfVQHGvjAwk/h1n00hBaRzMcnA4vn2n/E3sFtfHrZf6E22ETIHeW17p+ytedFPrjoMzSFF89IkKUtqMX1yx9FuF3XHAgXktzbObn9H/FHm/FHCsntiZFj5EqbeMMXZ2XZQpae1WNaHainOtBApb+Wv9n9BzSFF3Ni9BAt0eUcGz3Awuhyjsb2sq78Vu6ofZj28WN87+g/s678Nu6ofUgFWcq8pQIs5Ypt2LCB3t5empub0XUdy7q2tcwuRBj6VV249w3vYPvIWwwEGollh0lZSW6tvh9z6DBtieMctNIMpnsxhEnUU0JZOkiJrxLHytJ/4ll8oRpqlnwATQ8XFsgVBpaT4+2+Vzhk7uL2mgd4ufNJ3u5/lapAPQujy65rZe4LOT08eDr/CphYg/ACCe5JC9ulnROMCADdhZNL4zX8jOViVNNwwX2KUAA5MIIsCs96kCkMY1oCPSEELm9hO1JKYt1vM9L5JomRE4TLV+CnibGBfdi6zltGN8dSbWi6i10DWyj1VnB/488wnh1lS8/zWE6e9zZ9DI/h47n2/+DE2GFWlG5gecl68k6OFzt+zN6ht2kMt9AUXnzNbZ/y+WgaIhqetu3FenbQf/J5QiXdjA8eJJPow86nOGEk2Z7dx7A1xl2NH8CrFXLzDo3s5sDwTjJWmuOjB2gfP048P0YsO8Sx0QMMpvs4PnqAlJVgc/V72Dv4Fm/2vsBodpjN1fepfCxl3lIBlnLF+vv72b17N3feeSfhcJiGhoZZa4uUDsmRk0gcAtEFIDTWld5MZ8nt1Hlr6XLGyGBT7SolVdTEUpcbT9BLR7INDQ2v4WOJUU9Z3R1ohofG1Z/GG65Bd52buF0XXMjPLPocEXcRpb4qHm3+BNWBBu6qfWROBFcASIdMvHcy/wognhvFbwanuK/ESNo47ne0XQiE7sbJxvF4KoldZCahEAIZ9OMMDKO1NMx4uYbZYOdTuLxFFNXchDdUiy9ST7h8GXXLPoLpK+EhM89bY3uoCzWjoVPhr6E73k65v4oPL/oF0laKmkAjbt3Dh1s+z5GRvawtvxUAU3Px2MJPsyDSym01D8zyM70w28oQHz6K21eKJ1BBUfUGSutvJ1S6FJevlFDpEuLDR9lQtRKdlSwMtZAZPo4IVOD2l7OydCMbym9nUXQ5IVeE1uI1jGVHWBBeQnv8OA2hRZwaP8LS4rWYmslNlXcxnhtleck6TG36K88ryvWiAizlig0ODlJXV0csFsPtvvpvl9da7wkgPd7N7p/+ZxCCNQ99BW+oBrtvPwsPbUOriLPL1UfSirM0JeDwszSt/wJrKjfx1NbvItCIekq4fdmDuP2FKeeNaz/LVIvx+s0AH1j4ycmikEuKVrG4aOWcKjRqWxksK43QCm9rKR0S+fELDvHpOUkueP7wizBcOPkULs2YmEloXzjXzNAhkyssxO258XoaBk+9wtE3v0ygaCGOnePw63/G0ju+RNO6zwMQzo3y461PMZDuoSfZQWf8BFt6n+OWynv5+JIvThbUFEKwrvxW1pZvPuecWV6ynmUl6+bUefROw51bOfDS7xIuX8mK+/6S8cGDjHRvw8olGR/YRzbZTy49TNCx+Njdf8xQ+2vsfu6XiVavZ/k9f8qx0QPsGdpGykpwOLYXTej82to/xqN72TxRYPSW6nsnj0GZr6pw7ObwMVGUy6EGt5UrtmTJEnp6eti7d+95Se1XYlpm3xluXL5iXJ5oYdYSEt30YboCeD1FhFwRgmYYnzuC7vKjuYO4dQ9hdxEBVxCv4cNj+CeTcsVEcvs72yZEofL7ZFI1Eluev2zQbMqlR9B1F6cDxIydASkQU73NZSEHS06R2yY0A5nPIBDYjoXl5C++Y68bOTw6LQHzXCClg2PnkVJiuIMYZgDTHcZw+THdIQyXDykdQGBqLoo8Zbh0DwEzRMgdxaW5CbuLJhLYz8wieOc5dPpvpyuaW441J4+h4Q5guIK4/aUIoWG4gpieCC5vBMMdxOUvBcOL6StC03RMT6hwf18ZQmgEzBA+w0/IFcVn+Im4izGEcc7xOJ1nZTl5JPK846Qo85HqwVKu2IkTJ1ixYgU9PT20t7ezdOnSWWuL21fKyvd8GZC4vMWMdL9Fz5HHqVr8KLnMKO/Pa0jDYEXLz3CwpJlvDbzARr/Jb234q0LPjGYQcV9gvb0LcKTD693PsLX3RR5s/AhLi9fM+sXgdP6V6S06s95iPoGuTV1fSbPkmdVOzrtRR1rZQhCm6WTsNO4LFH0sLJsTwOkZRK8qm6ZnM3ukY9N16IfEerbTsPpTFNduomn9L9J//BnKm+4jVNZK98EfMNy1laY1n8Nr+PmV1X9A1k5j2XnchrdQ2sLw883DXyGZj/Oxxb9E2H3hNRHTVpLvHPknsnaGjy7+AmH3tRdHnU4udwRfuBZfuA6h6ZjuEJ5ABb5wPU3rv0BfboifHv4HlpbXsFjoRKvWs+59/4LpCSE0kzVlt/DHt/wLfjPIWHaEiLsIQzu3VISUkpNjh/mPY//KytIN3FP/6FXXBlOUuUIFWMoVc7lc9PX1sWLFinPWFzx7yY6zp8+f/n26gpB3fst3TQQVUkpGurYydOoVcslhQJKeqOqeWfgIh9NtbOt/FQe4pepeDM08r51nt/1CbGnzRvezhSR3fx2txavnQB6WJJPom6yuDYUZhB7dO2XLjJSNY4oL5E1NHBM7i9fwE8sMEXJFL3xcvG7kwAg4TmEtwHnm7NfecfIMnHyeWO8OQqVLCBYvJD3eRaznbVzeIoKlLfSffB7d9FG95DF8oWoCZoiAWSjkevoYdSfaeaHjx+SdHPfVf+Cix280O8LLXU+RsdLcWfvwnAuw4iPHiPXuxM6nqV36M4wN7me0dxdWLkHdio9zaPgNto/uJKlJ7mv6MG7dgzdUWKOyULpCo8xX+P3sfMB35vbtHniTN3tfIGXFuaP2YVzz8FxSlLOpAEu5IkIIHnjggclg6uzihvl8npdffpmxsTHuuecevF4v3/rWt6itrWXz5s3XVEDzNCkl47kYx0cPsiDUgjPSjstbhD/aAAjKGu8mEWsjUrORtlQ7ZuUSmkLNhEpbuTtchlv3sqxkHcdj+zE1F02RJWgTvTSHhndRG1xAibf8okGWIQw+uOgzNEeWsrn6PXMguCr0vOTSMXzhusLvUhLLDOEzg1MGUZMB1hSEEGC4cLIJfG4/o9kR6i+2c00rlGuIjSNK5lZwcClSOsSHj2HnU4TLlqHpbhrXfo6i/o1ULHwAx8oSKFpIce1mqhY/SrBoIfXrf5ETqQ5iZPFIh3S8l1OZLnTDw8LIUnTNoNxXxWeW/VdimWGG04N4jQCV/qkLspZ6K/jU0l8ja2epD51fRHe2FdfcxIL1X8QfaSDWu4tAdAFN634Rf7SRsf69rAm2km35Ik3BBSR6d+GE6/AGq8naaQ6N7KHUW8Fguo8ybyX9qR7KfJUMpvqoDtZT6q2cPCa31z6EBFqLV6nkduWGoAIs5YrpF/hmaZomd955J8888wzxeByPx4NlWbhcE8UapWRwcJDh4WE6OjqIRq/8YuxIh+8d/Reea/8P7iy+hcVHd+IL1rDqwf+D6Q4z3LWV0d4d9JmSb+Z2EfGU8KVVn8UwvVQYNXxw0WfoGD/GH279Fdy6my9t+gdK/VW80f0s/3ffn7Oh4g5+dc0fYogLV7sWQtAcWUpzZOnk79fKtm3a29vJZDKMjo5e8eNzqWE03YSJXBaJQzIfJ+iKTHl/I+UUSjRcgDDcOLkEpi9cmEk4kYw85X2FgHCgsG7kDARY8Xic7u5uxsbGyOcvkQ92hbKpIfY//1vkMzFWPvDXRMqXE61cQ7SyMOzbd+I5ug/9gPjQYbzBSoqq1zFSWsvXdv49zfva+C8rfpf9+/+dr8VfQdMM/mDTV6gK1GNoJrdW388bPc/yV7t+hwXhJfz2xr+ecn09U3dxe81DV9X+ZDJJV1cXyWRy2o/NaaYnQsOqn6fnyJMc3fKXhMtWsOK+v2C48032PvcbRCvX8b57/4yBky+w9/U/I1K+gpXv+TKv9zzLv+z/nyyKLuNobD+Loss4HNtLfbCZoXQfi6LL+fW1f4Jrok5bibecxxZ+ClCV3JUbgwqwlGm1a9cuvF4v1dXVCCH46Ec/yptvvklbWxuLFy9meHiY48eP09XVdVUBlhCCcl81xZ5SSv01eAK9eEPVaBPfeN3+UlyeKKY7SpleSZGvElN3TT4WwGv4KfVW4NE9uCeKckbcxZR4KyjzVU2dFD5FO6aTbdt0dHQwPj7O2NjYFT8+Nd6J6T5Tiypv5yaqYU+V4C4xUjbZyIXf/sJw42TiuIVJ1s5gO/nJ6vBTCvpxOnrRLBthTO/QTjKZ5MSJE8Tj8WmvuabpbrzhmkICuyuIbWULgSoCx86jGx5c3mJc3mK8oWpAEHJFKPVWUO6vxjR8BP5/9t47zK6rvPf/rF1Ob3PO9D7qI2nUmy25G1eajU1JSDAtgeR3c3MTkpBAcpPATbkhuaQQQg0QIBSDwQZs44ptuahbXRqV0fTeTj+7rN8fZ2akkUZ1qqT9eR49mnPOLmuvXdZ3v++73tcTpdAoQVV13Op4AVXgKaLYW0aJr+KCMUVXej2l02mOHz9OMpkkl8td0TYuBcvM4vJGcfuKcAdKEIqG7ongCZSN3H8qLl8Ub6B0LFYr5i2myFtK1FNMobeUQm8pRekuSnwVmLZBsa/snOvTEVYO1xJCzsVpKw5XJZlMhh/84AdEo1GWLFlCIBDg4MGDZDIZbr75ZgKBwFi8y44dO0gkEtx2222XtQ8pJe3HnuLo3m+yeNWHKaxYP1bqBqD75Auc3PUVyha9nYJFd6Orbjyab9yDW0pJ0hhGoODTAyNlTCziuWG8mg9dcc34g360X6SUfO1rX+M973kPodAEBZrPs27boR+jewvQ3fl1+tM9HOjbSUWg9twZkaYk9uYwqVL3eXNXSWljDbfjrd5Ie7KFRQUNFPnKLtgGeaoDdeUilKLzB3RfCaN9MzAwwE9+8hMeeeSRc2sSTmLblpFC2iaJ/uMc2/4FSuffTcmCu2l89Z/IZQYIFS2lfPHb87PoFA2JJGEM41Y8uFQ3lpkhbacRQsOvB8f1ty1tErlh3KpnzFIzlYz2TSKR4Lvf/S4f+chHzmthvlKGew9zdOvnCMQWEe89TLRyE3VrPoQQCkZmCFX35sW3tDGyw2OfJZJ4bgi36iFrZc75f7buNQeHmcKxYDlMGR6Ph9/8zd8c911paem4z5N9mEppMdi2Dau3keGOXZTV3Y4YmSknpWSo600SfUeJ9x6kevm7x347uw0B1/jM1opQZzW4eKJg+0tF2ga5dD/ecNXYNvoyXecM9qOMpWe44LkQgEAaGfx6kN501wUFFoCIhbGb2hCFFwiIvwKmcwAWIl93UErJUPc+hjrfRNXcFJStobf1VSwjn1dMqNqIZStfqzB0hutV070EmXiWpSIUQu7IhL9NVfunm0RfI4Ndb5JN9WAZaQbat1Oz8jfQdC8u7xn3jFDHfRaIsXtq1DV69v8ODtcyjsBymLNIKcE0sRubEZEgoqwIIVRqV34Ab7ACRXOTjrfhDZ0OHi6quYXUUDOlC+4Zi0c6G9syGOzcjVBcREoaJhRhVxPZVO/IMZwebAczfeeNv1LT1nlqEJ69oI6dS+L3RehMto4U5p24T4UQSL8XebINMlnwTn5Cw6UipYScgd14Ki/uSmJXJDxiVZsZ7jlE6YJ78IarWHzjJzCyw+juCEOde6GkAZc3ykDHLlTNQ7ikAXGV1MmTUoIxci9FQ4jSwkvuo0jpSorr7iRc0oCiaPij81A1N0Z2mP7Wbfgi1QSiCx1LlIPDWVwdTweH6xbr4AmMb/wE4/tPQTaHEAJfpAaXN8rxN/6Vw6/8PfYZiTD727bR2/wKPU2/Ip/o6VySg03se/bPOPD8p8kku2foSKYHKSXJgZO4vKdzeVnSImEMo6sTBOpLiWvYxPJeWFQKIVBcPqxUP5rQsGyTRO4isWEjwe72idYZT5hp7WvE+ObjGD95Howri9MaaN9O98nnaD/yU4RQKJl/F5VLHyI1dJJ9z/0pJ3Z8iURfI/uf+zP2P//nZK+ya8c6dALjG49hfO9JyF56vFa89yg9Tc/T0/QiZYveSkHZWoRQ6Dn1Evuf/xRHtn4Oy0hNY8sdHK5OHIHlMKcRBSFEUQGivHhcjiVPsAxvqJJgbDHijOBh38gUcX/BPMbyOUmJZVtkjCS2baGoLnzhavwF89D0iQshXzVIm+TACVy+0xaJoWx/PsfXeW5vPWFhuS8hkF/zYmeHQUoinijN8eMXFE5CCEQkhN3adVkD+FQgoiFEYQSlNJZPG3EFeIMV+EKVBGOLxjL6A3iClXj8Jfij89E9EXyRWnzh6pHKAfnrK2dlyVnZOZmJfRQRCSKKooiK4svKV+b2F+ENVhCIzkdRtdP94i8Z+16oOra0yZipfDb2OdwPDg4zheMidJizCCFQqkpx/c77QNfyde9GiFVuZN07v46m+8a5aUoW3EWs6ga0ccWaJc82/Yhftf2Cu4vuIHDsVYKFS5i39rfQ3JcWSD5XMbLDWGZ6bLBHSrpTbQRd4QldNoohUUyJvBQNIhSkZSKtHCFXAU3DjVQF5xNyRc7vDlIVCAexDp9EXbl4RtxGQgiU2kpc/9+vgUvLt+EKKKzeTLikAc3lH+deVlUXuieCpgfwBEtZsOF/cHzbv9G897vMW/fbJK0kX9779wgh+GjDnxA8K75vLiCEQKkuw/W778v30WXM9FRUHd1bgOYKcaYbuqBsDWvf/hU03Yei6Bzu28V3D3+R5bG1vGvxRy6Y6sTB4XrAsWA5zGmEEAi/F+E6nXV9tGagyxNBUV3jvlcUDZe3AEU9vbxlW7zZ+waHB/bT2L2dwY5dDHbsQihXd70zKSXDPYdw+U6XqLGkTXeqHb8+sXDU0hb2BPUHz4fQvVipfgSCMn8lu7pe5sTQYRK54XF19saWFwIRCyO7+pA9AzNmyRDKyHWi61d0ToUQCEUduXbGz2xL9DcS7zvCQMdOkJJcup/hngP0t2/HMjMMZPrY17uN/b07GM4NTuFRTS1j99Jl9lFqqIXh7v0MdOzAtk5bJoWi4vbFUPV8wPqpoUYODrzJru5XMayZtWA6OMxFHAuWwzWPqmi8d/HHqC9YxYbizRiFu/FH6s6ycl19SGkR7ztCILZobMDsz3QjhII6Uc4lKXH3G5h+9SIzCPMIIVDcAcxkD1qoDI/qozJYx0Cmh+bhY0TcMZYXrhvLMzZu3YpirD2H0basQXqnPj3BTFJe/wC6J0Ks6kYQCrGKDSy68RP4C+ajufyU69X81oo/RSAoGSkJcy0Rq7qRRTd+gmDhkjExNRE3Vt6Nhc2CSD0e7Sp3vTs4TAGOwHK45hFCUBVaQFVoQf6LUN3Y91czyYETCKGMuQdtaXF88BCF3tKJj02Ca8gkE7uMMiSqjswlkVYORXOjCxcxTzExTzH9mR72dL/GmpLNqMrpR4kQAul2IWJhzO370G5YBa6r010khMDjL6Fq+XvHPmvuABX1D459VhHcUHbH2OdrDc0VoHLpQ8D5j08IQdAV4b5577vgcg4O1xOOi9BhyjBNk507d7Jz504MIx/oeuzYMd544w2y2eyU7ktKGyMbx7YuLaB2tNj0mf+uZmzLoK/ldXyR2rFC1+2JZgw7h0ed2MqgJy2QXFr81QgCgXD5MeOdp78b6b+opwhTGhwfOnTOORBCQCgAbjfWtn3I3PSUcZkObMvAyMaRIy7Qia6ZMz9fK9fUhTBzCeRFgtevh35wcLgcHIHlMGUIIaisrKSzs5OWlhZSqRS7du3CNE0OHjyYn81nWRiGgWVZVxyfI6VkoH0nu372MZr2fBMmiAW62pBSYpomhmFg2xc+nnxC1b0IRUEdmQU5mO3jaP8+Sv2V57FeSXztWXJB7ZLcg2MIgeIJYQ53IC3zrJ/yLrG2eBOD2b4JRZaIhZGaivnGXmTmymbZ2baNYRiYpjntMV22bdK05z/Z9fPfYaB915yfDXdm30wX8d5D7P7573Bi51eR9vTtx8HhWsNxETpMGYqioCgKiUSC0tJScrkcuq5TWlpKa2srAG+88QY7d+6kvb2dO++884r3lRw8SaKvEc0Vomblb6Be7clCs1l+/OMf09vby/Hjx8+7nJSSbLKL/rbthEtXAtCf7mZP9+uU+6vxpBUUw8ByKVgeZWzSl2vARE9apEouwz04ilARmhtjuA09Uj3ekoNCqb+SfT3b2FR+B66z6hUKISAWQQ4MY27djbZxBdLvvSwrx6lTp3jyySdJJpO43ReohzgFSNtkqGsfib6jJPobKShfO637myzt7e088cQTpFIpLMualn2khlqJ9x1FqC5s6wNjGe2vZ0aFt2Otc7gQTi1Chykjm83yxS9+kQULFrBy5Uq8Xi8vvvgitm2zYcMGampqsG0by7LYtWsXqVSK22+//bL3I6XEyAwy0L4Tf3Q+/si59fauNkYtWJZl8c1vfpP3ve9959QizIurHtqO/JRAdAGKy0db/CQnug8wP11GuG8k75cq8qkYBPmAdkCPW6SLdKR2ZUZradtYw224ipegeseXwpFSMpDtxZIWq4puyOfgOut8SCkhmUZ29qIum5/Pa3aJszht28Y0TQYGBvjZz37GBz/4wSmrRXg2UkqS/cdJDjURrdiA5pq43NBcYbRvEokEP/jBD/joRz865bUIjWyc/rZt+MJVTsb2Ebq7uxFCUFRUNNtNcZjDOBYshylD13UeeeQRIF+XUNM07r//fgzDIBDIF1VWVXXs36h740oQWoBo9S0A0+oemU5GLX6jcSu6rqOqKkKIMXehtA0sI4ltpon3HaG/dQ+h8EKyw0maurZB1xArjBj4bFIhFamODn4CYUsU0wQJuaiSd+9MYva8cEfJtO3DFalE8xUiFRU5Ys0IKSH60j282vxLFkWXE9BDeFTf+NI6Lg3KCskdOI44dAKtthJREETqGqgqtlsfc19q2umEloqi4HK50HV9TIhO5yDvDtXgDtUAc+/aGr2HLtQ3F3MxXzaKh2jVzcDc648zGe2XK7k2bNvm5ZdfJplMEg6H6e3tpbi4mIaGBrZu3YrL5WLjxo1s27aNXC7HvHnz0HWdPXv20NzczIYNG2hubqajo4Pbbrvtkgu1O1zbOALLYcpQFIVIJDLuO03T8HrPDbp2uVxs3bqVQ4cOjX2XzWaJx+MUFhZOS/u6u7uJRqNo2tRf9oZhMDAwQHFx8cUXHmH9+vVs3Lhx3HdSSlRV5Zvf/CaaplFVmKUy0o+RHSaTg8rMIpRMLx5DZZEVoXUwjVrlhazAm4VzywONDjYTG6qlLens7KC0rOwSBiYNZBmiLY1CK7aqk9E82AgSiQTCsogFvbS178StuvFbEVR7IneSRBgW6oEjhNwehNeNqWv8qKeJ9qEBSkpKePjhhye8Dtra2vjSl750kXaOZ3h4OD/LLRi8rPXOpKuri1gsNqlrp7e3l2AweMVuTiklQgg+9rGPoevn9mtPTw9f/vKXAcjlcgwPD0/ZvTQwMIDb7cbnm5r0C/F4HCnllAmRrq4uHnroIVauXHlF6+dyOY4cOcKaNWvYv38/q1atYv/+/XR0dJBIJEgkEsTjcTRN45ZbbmHv3r2kUil27NhBfX09Tz31FEIIFi5cOOG5cbg+cQSWw6ywfPlylixZMm5QP3XqFDt37uTBBx+c8v1JKfn2t7/N/fffTzQanfLt9/b28vTTT/Nrv/Zrl/wGPZErR1EUfuM3fuO865y55WQqxRvf+Q4fft+HUK/QZWaYJk985St89N2PoE9CPOzctYtEPM66W2654m3c2tTE7t27eeCBByYcpAoKCvjkJz952RaKV199FUVR2LRp0xW1S0rJN7/5TR544AHC4SvP0v7oo4+yceNGqqqqrmh90zT56le/OuFvgUCAP/mTPxnrm7a2Nl599VUefvjhK27vmTz55JPU1dWxZMmSKdneG2+8gWmabN68edLbklLyrW9964r7FfIvgjfddBOvv/46LS0tLF68GMuycLvd5HI51qxZg6qqtLe3k8lkgLw10ev1UlxczKJFi1BVle3bt+Pz+Vi1atWkj8vh6scRWA6zwqib8Ezcbjderxf9CrNxXwjbtvH5fLhcLlyuKwj0vggul2ts+5NpuxDiktunGwZ+v3/MtXiFO8Tn86Hr+qTevN1uN6Zpok+ib0ctJOc7/0KIK7L+eL3ey+rXs5mKa0dKic/nw+12X/E2Rgf08/12Zt+4XK4pu5eklHi93km1/Ww8Hg+maU7J9kbPz5mu08tFSklfX9+Y66+jo4PVq1ezaNEidu3aNSayAA4ePEhtbW3ewlxVxcmTJykrK6Orq4uSkhIWLFgw6WNyuDZwgtwd5gy5XI5MJkMwOPWBxVJKhoaGCAQC0+IiHA00DocnrgE4Hdi2zdDQEJHIBWoDXgQpJQMDAxQUFEyq3el0Gtu28fv9V7yN6Tr/qVQK4IrdW6PXTjAYvGIhK6UkHo+PiZ4r3calnivDMEilUoRCoSkRWIlEApfLNWWzOCd7Ts5kKs7PmWzdupXFixdPW6iCw/WDI7AcHBwcHBxGcFIwOEwVjovQwcHBwcFhBEdYOUwVTiZ3h1mhv7+f7u7u2W7GnENKyYkTJ644fcW1TC6X4+TJk3M+u/psYBgGx48fd/rmIkgp8//SGaR5/moSUsqxHGNn/n/gwIExd/hoZQHLsrAsa9xyDg7gWLAcZonjx4+TSCQuK63BXEJKicRGcGnJMi9nu8899xzvec97pmy693S1daZJJBK88MILPPLII1f1cUwHmUyGZ599lo985CNTnmh0ppip69Q+1oz5k+dQ6uej3XsTqOfuS0rJk08+iWVZhMNh2traiEaj7N+/n3Xr1pFOp0kmk9TV1XH48GEqKyupq6vj9ddfJxaLcfvttzvXqINjwXJwuFyklOzv3cFX9/0DLfETc9pqIKVkb88bfG3f52hPnJrt5jg4TIiUks5kK1/b9zl2dW/Fnsb6orK7H9nVh93SAfb5ywsVFxeTTqd57bXXiEQi+P1+6urqWLp0Kf39/dx0002cOHGCkpIS+vr6aGxsxOVysW/fvjmdkNVh5nAEloPDZWJKgydOfIenTz3Ki60/R54niedcwJImPzv5PX7R9H3e6HzhtItkEqLw9DbsOScuZc7A7h+ac+1yOM3Z107eciV5pf1pnmz6AU+c+C6GPYmSAxdBXbMU7dffiv7AnXCeGcWjLkIhBJs3byabzeLz+aioqODIkSNUVVXxyiuv0NDQgG3bKIpCKBTCtm2qqqoc65UD4LgIHRwuG1WoLIutZSjbz8LIcgRz92GqCo3bq96GXw+ysmgjCWOYp5sepcBdyC1V96MKFYlEcOklRiSSbZ0vcnLoKHfVPEDMWzLNR3HpyPZurH2NaPffPFZ2x2HuIKWkJ93BM6ceY3HBClYX30BPupNnT/2EgCvEzZX3sqZ4M7oy9bnqIB/ALj0u1FVLxj5PhKqqbNiwgQ0bNlx0m4sXLx77e926dRfcrsP1hSOwHBwuA1ta7O5+je1dL3F0cD+H+/ewsezWOSuyhBBsKruN9aW3oAqVfb3bebTxa0TchawuvpGW+Al2dW/lvrr3UOwrv6SBIW2m+N7h/6A5fpwyfxW3Vt0/A0dyachkGpnNgS0d+/wc5ZW2X/Kjxq+zqKCBfb3bSZtJnmn+CSsK1/PJDf+IW/WOr2E5xVyq+HFEksNkcQSWg8NlkDQSfPfwv+PXg6wq2sSmstuB03Xi5iJCKGgjA1Z1cD43VdxLqb8St+bliRPfYVf3VsLuKA8s+MAlbc+jerm79iEO9O1iSfTKar9NB1JKZM7MW64cF+GcZW3JFo4NHkAVGk82/YAFkaVsLL2NG8vvmHZxBZzjPr7c+/bMPFm2bdPf308sFjtnO4Zh0NfXR2lp6XnXd7i2cQSWg8NFGI0REQh8mp87qt7Bqfgx3rXwQ+iKzg+PfoXq4AI2lt2KIub2DK6wO8rHVvxZ3lUibbaU34VfD7Km+NJrwgkhiHqKKPfX4FKmJrP3lGFZjsCaY5x5/wD4ND9l/mqCehhVqKws2shNlfegCm3aRYeUklyql/ajPyNcsoKCsjUTLmdZFsePH6empoajR4+SyWTQdZ3i4mKampqIRqN0dXVRV1dHd3c3QggOHjzIvHnzGBwcZGhoiNraWl5//XXWrVtHU1MT9fX1tLS0oGkay5Ytu2pnezpcOo7AcnC4AFJK+jPdPHnyh9THVrOm+EbuqXsYW9qoQuWVtqf54dGvURdezMqijfj0wGw3+YIIIVCFSmeylaebfsSG0pv5H5V/eVmDW9pM8t+Hv0hz/Djlgeo55SLEskARYDm5iOYCUkr6Mt08efIHLC9cy8qiTbzc9jSPHfsGDYXr+ZP1n8Oj+S4rBnCydJ14huPbvkC4ZAVr3vrvqJrnnGWEEBw9epS+vj5effVVPB4PiqJQV1eHrutjefyqqqo4fPgwra2trFmzBl3X2b9/P3V1dezduxfDMHj11VdZv349L7zwAoODgzz88MMoV1ic3eHqwhFYDg4X4fWO53ns2DfY2/sGy2KrxwYEkCwoWMbG0tuoj63CrZ77oJ5rjLonXml7mp8c/ybtyVN8Yt3fX/J6kHcR3ln9Tg7272ZxQcO0tfWKMC2EpoFpAnPMunad8kbHCzx27Bvs691OfXQ1q4tv4MjAXjaU3oJHO+0SPPMay1tYx3+eKiKla4hV3UCsajOKMnGuOUVRqK+v59vf/jZ33nknPT09VFdXj9XKLCgooKenh71796IoCoFAgFOnTlFdXY1lWXR1dRGNRkkmkwghOHXqFMFgEGBaaq06zE0cgeUwrViWRSKRwOPxTFmh2KngUuIgpJS0Jk7SmmhiRdEGVKHxesfzbKm4m+ODh9nV/Qq3VN7P/1r7WRShTnvsyFSQMhM8c+oxhnND3FB2JwWeGI8e/Rp3VL+DIu/pWJEzZxZKKYnnBnnm1GNUheaxruRm7pv3Xu6pezfqHHOJStMCXUMa5hyddnD9sTS2hi0Vd1Pur+Ynx77JkugqPrH279AUDXGGuOpNd/Fc809ZFlvD8sK19KQ7eb75CZbG1tBQuG5KRIkQgmDhYlbe/Y8IRYMLXCU1NTV8/OMfJxaL0dvbi5RyrKC0aZqsXbuWaDSKYRh4vV56enoIh8Pcd999ZDIZYrEYmUwGVVUZGBigsLBw7LPD9YEjsBymDSklO3bsYGBggPr6empqauZEfqKsmeH5lsfxqF5uqrwHbeQt9pw3aGy2d77Ekf691IQW8mr7M5wcPsKighX8+NjX2db5K0zb5Nfrf3fS4mqm+uX44CG+d+RLFLhj/Pmmf+Pr+/+RJ7t/gEf18paaB3jm1I8Ju2P0pNtZV3Iz88L56ey7u1/ju4f/nYpgLctia/HrwRkTlJfVN5YFmgrm+RNIXkvMhfvpYoRcYUp9lcRzQzzZ9AOWF66jProKGG+leqn1F/yw8SscGdjI4ugK3uh4kR8c/TKrim+gProSXZ2a1A1CCMQlbEvTtLFKEyUl56YiCQTGhwNUVFScs8xoNQafzwcwp14yHaYfR2A5TBu2bbN9+3YqKysZHBykurqaxsZGTpw4wfHjx6mvr5+VdrUmTvKdw19AV1wsia6i1F8J5K07L7U+RdRTxLqSmzgxdJjD/Xs4FT9GeaCajWW3UugtI+opYkv53RiWwdqSLVOSosEwDLZt20Y8HqelpWXS24NzBSNARaCWDaW3UOKrIOopZH6knt50B/PCSzg13Mi2zl/h0Xz0pjtpGm7kD9f+HapQWVTQwIbSW1lYsAy36p2S9l0qPT097Nmzh+HhYTKZzIUXtizQdTCuj0za/f397Nq1i2QySSqVmu3mAOeKvtc7XuDRxq+xqKCB1cU3sqH0FnRVpy/dxasdz7EgspQl0ZUsLFjOosgKlkRXogoVvx6kzF9NoafUyWnmcFXiCCyHaUMIQWFhIcuXL2fnzp00NDQQCoWoqKhgeHh41tpV7CvjpvK7cWleWhMn2Nr+DKuKN7Gj82V+dOzrlPqqWFSwnEcbv4Zpm9SFFrM8to47qt+BIlRURWVLxd1sKrsdTdGnxHWhKArFxcUEg0H8fv+kt5d3uXTyQssTLC9cT3101djsv99b/VcIFGxpoSCoDNaRNOLoqovGwf2E3VHWFm9hdfGNqEJFCEGpv5I/XPe3s+IK9Xg8lJeX4/f76evru/DClg0eFXmdlCpxu92Ul5eTSCSmTJhPloFML8+3PM7CyHIaitZTFZzHvPASlheu4+FFH0FXXAgEr3e+wH8d+heWFKxkeeE6etOdHBl4E4/mxZIWteFFLIutZVlszZS6oqWU+WtfKHAJwfXxeBzLsohEIuNCC0bTMJSUlIxto62tjbKyMieI3QFwBJbDNKIoCnfeeSeHDx/mlltuyQ/UpaWUlpaSyWRIJBIz2p7Rh2NAD/Phhj8mZ2X4q9d+h+NDh2lLNDGcHWBRpAFN0dne+RIbS2/l2eafcnzoIAuG61EUdexBL4SYMpcF5N0RixYtwrZttm3bNiXbfKn1Kb535EusHzrMgsgy9BFXqCryt70pDQ4P7OXNntepCS5kQ9mt1IQWUuGvxa8HKRzJ0D6a40sTU1N8+nIJhUIsX76c/v5+jhw5cuGFbRs05bpxEQYCAZYtW0Y8HmfPnj2z0oazLaW7e17lu4e/yNLoKhZHV4zEMZ7EpbpBgiIUpJRUBuqoDS3Cq/l59OjXWBJdSUWgjgWRZahCI+KKEvMUUeKvmLJEvlJK2pPNPHbsG6wo3MiWirsm3LZpmuzZs4elS5fy/PPPs2DBAl599VWKiooYHBwkkUjQ0NBAf38/Bw4cIJPJsGTJEjo6OlBVld27d1NdXY2UkpaWFjZu3Eg0Gp2SY3C4enAElsO0UlhYyJYtW2a7GUgp6U51sL3rV8wP19OaOIlpG1QE6gi7Y7hUN7t7XmVeeAkDmR6+efDzfHbzV3jXwg/yxHEXq4puQLnKUoM3FK1jZd8mijylPN30QzaW3YZfD/JS65P4tABpM0mJr4L66GoyVpqXW58iY6aJ5wZ5pf1pjg0e5KaKu9lQeithd3Tuz3yybRAKqOrILEKH6UZKyXBugK1tz1Adms/S2Brmh+tZXXwjq4s2oSsuKgN1lPmrqY+uGot3BBjKDaAKFU3RqY+tQhM68dwAzcONPHHiO3Sn2nmq6YccHdzPJ9f/45S90Ozoepnnmh+nJX6CjaW34p4gTYOqqvT19fHKK6/Q19dHMBiks7OT+vp6+vv78Xq9dHV10dnZydDQEHfeeSc7duwgmUzS19fHqlWrKCsr44UXXuDkyZO43W5uu+22uX8POUwpjsByuC6QSJ5tfoxHG7/O4oIVdKfaKPFV4FY9rC25CUuaVAXnUeQtx/QYrPBspNBbQm1oEQ2F69GnyBU4UwghWBhZzh+v+7/8064/4+dN38u7O8OL+caB/0fQFSHkilAVnMeiggZ+1fpz4rkhNpbeSnmgFkVRyZhpvn7gnzBsg/vq3jPbh3RxLBsUgVAUMJ08WDPFK22/5Ov7P8fS2Bo+tfGfqQkt5JPrP4eqqAgU0maSikAtIDBtA1XJW4EHs32cGDqMlJKjg/upC+VdgqpQ+dbBf2ZRQQPrSm5mfenNqMrUDVUbSm+hK9mav6/PI9qEECxbtoxvfetb3HHHHRiGQSwWIxqN0tfXR39/P+vXr8fj8SClxO/343K5sCyLqqoq9uzZQyqVoquri0gkclU9OxymDkdgOVwXCAQNhes53P8muuJiUUEDRwb2kjISZK0M88JLiLqLMOwsIFgYyQdzCyHyro2rECEEbs3LDWV3kjHT1MdWEXUXsar4BqLuIhJGnI5kC03DjZT7a0i5k7xj/m9SE16AaRv84MhXMaVBdXD+bB/KpWHZ+WBoRYBlzenyRdcS9dHVrCjawOqRIs1nus+llHQkW3i1/RnmR5byroUfHHMpRt1F1IYWUhtahEt1szy2Fl11oQiN/mwvG0tv5a7ad43FbE0FQghKfZV8uOGPLmqRLi8v5/d///dxuVz5bPRSoigKpaWlWJbFokWLWL16NVJKdF3nrrvuGvt78eLFaJpGXV0dlmXhcrmca/E6xBFYDpNGSollWVhWPu5lLj5MhMgLrOHcAP9v16co9pazMLKMwWw/FYFabq9+O0FXmJ8c+y9eaHmcw/17mB9ZSm144Ww3fVIIBLdW3c+N5Xeyr3c7bYlT/O7KP8enBzFtg6ebHuVQ/25a4idImyl2dr9C3BhicbSBQwN7ODZ4gFPxYywvXDfbh3Jx7LwFC0VB2o4FayYQQlAXXsSfbfg8qqJNKIRGg9jrQot5oeVnLIgsZWHBcgayPRwbPIgiVD618Z95uulRvnPoC6wo2sCfrP8cfj2AQJnyZ4kQApWLB80rijKWXmEUKSW33nrr2HbObJvLddoaNhUTVRyufhyB5TBpMpkM3//+94H8jK8HHnhgTuZ7EUJQE1rIuuKbiXmLOdC3i4g7RtgdRVd0Xmt/ljXFN4xYsSDqKZzlFk8eIQQCQcpM8PX9/8hQrp9SXwXLCteiCIUFkWUMZQfQFRcJI84zzY/xYuvP+MyNX2Fj6a0E9TBLCuZOQecLYlmAyFuxrpMg97nAhSZ8CCFYHF3Bn234PC+0/Iyv7f8HGgrX01C4DpfiZk3xZlYV34BP81PiK6cyOI8yfzUu1T0n63qeLaocHC6EI7AcJkUmk6GlpQVVVbnlllvQNG0sud5sIqXElCa7uraSNpPkzBSLrAAlJav4n2v+mlfbn2UoO8CJ4cPs791OS/wEO7teZkXRRm6pvA9VqHg038V3NEeYKOfVmd95VR/10ZX0prs4PnSIjJVmWWwNr3U8y9GBfawruRlVaAznBqgKzMOvB3nb/F/nvrr3XjXxZ9K284m5hchbsxxmjNFg9zc6XqTSW0Gov51gbBGhoqUMZfvZ1vkiUtosiizHsk2+c/gLNMTW84l1f49X96Mg6Eq105vupDVxkuebH2dRQQPzwkuuimvPwWEiHIHlMCkGBwfZv3//WNZ2TdO4++67Z9WCJaXEsHO80fEiP2r8OlkrQ0+6gzsjm7jVyuAvW8HLbU+jKzrVgfmoQqXCW8GQt5oiLcJX9/9fAKpD86kJzX0X4WhJn/29O1lbspkibxkAA5ketrY/y8LIMgq9JRzo28Vgtp+EMUTaTPGXN/w7Hclmjg0eQlNcNMePMy+8mOb4MTJmCo/mvbrKepwVg+Uws7zW8Txf2ft3VHvKeFuPScBXTPmit7HNauE7Td+h1F9Fib+CoB6hKjifUn8luupCFSpS2oRcBUTcMTyql/8+/EVqQgv5803/etXGQDo4OALLYVKUlJSwbt069u/fz8aNG9m2bRuGYcyawMrXzRvi6aZHOT50iEJvKT49gE/z06sLnk3uYdmQzvGhg+iKzh1V78SluqlLW1S2D+I2B4j7F4FQCOuRWTmGy8WSFv99+Iu81vE8D2c+zPuWfAwJPN/yBN89/O+sLdnC7678CxZEltKdaqfAU4RbdePT/NR4q+kJdlHqLsJnSQKuGIsiDfj0wEX3O+cYFVhC5P92mFG8qpcyfzVl/hrCQkc1cxx97Z9QqldSGZxHgbuQg327KPNXU+qrRFdcMGZkFdSGF1EXXkxFoBaJZGXhxnFpHRwcrjYcgeUwKWzbZufOnRw/fpxEIkEsFsPtds/qDK7XOp7jB0e/THmglk2lt3FLcBXbXDvYn2zkQP9u2pLNlPtrEAjurH4HUW8xyZ5DHG3bjwsXt8cFqaFm1KWDSE/hnHdRqEJhdfGN9KQ7WRxdQT60XVKtF1Pjq2G+uxLDytKaaMK0DUqUChShkrNz2KpG1FNEWQ7W9qQpDs6jILIKXdGvvll49mmBJR0L1rRxdikcMztMT/PLeLM9RBUfpfhYtuUPyAy1YWSHiRdUUWBAobeEanMBQVeY3d2vsSS6AsnpzOgLI0v5vdV/hSpULGmhiomD5h0crhaursyJDnMORVG45557xirHnzx5khdffJFcLjdrbZofrmdpbA03lt/JO2rfQ8fe/2Z3x6/Y1b2VIm8ZljQxrCwNkQaCehBdceELVVJYfRNGup/cUAuhgnkMdu7BMtNzvqCuEAp3VL+Dz9z4ZdYUbx79lkW+Gj7gWs1mz0KCrjBLY6upCNSwvesljvS/SXfLa5waOMS+nm0Mm0n8wQra9n6XIy9+hrZDPyE11Dznj30ctmPBmm6klJjZOO1HnmC45yAg6Tz2NId+9Vk49iIP2NVskIX0nvwVQiisvv8L2JFy9vZu59RwI3+64Z94aOGHWFzQwMqiTWPVBSB/HbtUN6qijfyvXl0C38HhLBwLlsOkEEKQy+Vwu93cfvvt/PKXvySdTnP06FEaGhpmpT3zI/X82YbPoykaQkoKSleyPBsmrakskSEOW0kqTTd1x/aQDO4mbhkMde+nZd93CcQWUlhzM4Mdu2h87Z9w+wopqr11xo/jclGEgkcbX4Q5XNxAQ3QhQnWhKCofWv4JWnv3kUn1ENMi9B34EdWRIHFPJcWpDAPtuwkW1SNti2Nv/AvRqhtYfvtnEOIqeUyMJBrNCyzHgjVddDe9wKGXPkswtpiK+gewLYNgbBEuT5S+E8/jCZTQY+bwRWpYdc//GynmvIIVhRsIusJE3DE+tfHzaIo+pQlEHRzmGs7V7TBp3G43tm3z/PPPo2kaixcvPid/zFQhpaQn3cGh/jepj66kyFuGEAIpJcmBEyT6GgkVL2O45xCKqqN7ItSseD+xwZMsPPB9+g4/xfzSlWiqB/Q0J3d/fWTDNt5QJaUL7qWi/kGO7/giqsuPP1I7LccxlUgpSQ+3MtS9n4KyNbj9xQDEc0O82fM61aEFVAfnowmNKB7emo7gcoWQHo2lg0NUJxKEwyVQtBRPoARFcWGZGVzeKN0nniNWuQnNHZr71oQzLVj2VWR5u8oIRBcQKlqK7g5xbNsXCBTMJ953BF+kFm+oEn9BHWYugV64kFc7X6AiOI9Pb/yXvFVKaGMJcB0crnUcgeUwaVwuF6tWreLYsWMUFBQwb948NG16Li1bWvzw6Nd4rvmn3Ff3bj64/A9RUZG2wfHt/06i/zjh4gaGew8BoKguVtz1D5za9XWyw634QpVoCDRFQ/EVQqoflzdCNtVLcd0dRCs2oLn8LNr0+0jbQtHcc15YSGlxbNu/0XPqJcoXv51I6WqKam/muZbH+fahf2FNdAO/EbubkurNWEYCY6gVLVjBcO9hwiUr8GaHiZSuRAiFlgM/wOWJsOKuf+Dwy39L24EfsujGT1C57OHZPsyLIm0bFCWfqsFJ0zBt+MJVlC16K5aRwjIzeILlGNkhXJ4omUQHZjbOYOceTupJnur5b5YXrudPN/yTE7DucN3hCCyHSROPx9m5cyfr169H13UUZeqzL4+iCIWlsdU0DR9lYcFylJEgWCFUCsrWYuYSdB57klDxMnyhajR3AN0dIlK+lkyym4pFb6W/fQeZRBe5VA8F5euxzDSDHbsxs3ES/cdYdttfoqguUK+OAUEIhWjFesxsnJ6mF+k48jgu7+dZGFnK/PBSSjMGja/8PXLD71JceyveUBVuXxF+M43uDpLTvSQHmxho306oaCn+SB2eQCkFFeuR0iYQWzTbh3hpmGe4CG0JUub/dphSek6+yNGt/4A/ugDdFcTMxpHSQtFcaO4ggdhCbCvLwlgDx9Mqy2Jr0K4WN7ODwxTiXPUOk0ZVVXp6ejh06BA+n4+ampppy58khMItlfeysfRWPJoXIfLzNISiUrn83bh8hZi5JOHiFQRii3B5C0gPteD2Rmm48+8Y7jlAX8uruLxR/AXzGOreR2H1FgLRBfgL6ggW1SPmYAbpCyGEQsWSByiqvZ3j2/+d5OBJPIESloar+csbvkD3ocfpHEhimRkSA8dZfvtnaDnwAzobf46UEl+4BqHohIqWsnjzH+MNliEUnbrVH6JmxftRde+ct+IBYFuIEXEvlZFko4ozj2eynD3RwROqIFTSgO4K0df6Kr5wDS5PAZ5AKYXVWwgVLWX+ut9GqG7W27k5m5XdwWG6cQSWw6Txer3cc889DAwMUFJSgnLGoDZap1BRlHHfTwZFqBPmaRJCpWTeHeTS/Qx07CLeewjVFcDlidB76mUWb/ljfJEawiUNBGOLEYoGQlC57N3MX/8xQKBobhBX4aAsFFzeApZs+WNs20TV8qLIqwWoXv4eiqpvYs+Tv0ermaG64X1YRgpvqBLNFaS/bRtVy9/Lkpv+ZGw9AKHqKFeJFQ84HeQOp2cSOk+4SWPlkvQ0v4w/UkuwcAnp4TaGuw8Qq95CsHAJgYIFzFv32/Sc+hVHt/4DoZIG1tz3b6iqju9qun4cHKYY5/HjMGkymQx79uwhlUqRyWQoLy8nEMgLoFwuxze+8Q1uueUWlixZMu1tyab6yKX7UFU3fT0H0dwhouXriVZuJJvsQdoWi2/8BP6CuvybubRRNM/VYaE5D1JKMvF2Brv24vEXY2SHiVXdgG1m6Wt9HU+glET/cbyhcsxcmpM7v4LuLcBfMA9N96HqPlze6DhxdVViWqfFseKkapgq+lpf4+CLf0m4ZAWr7vk8nkAJHn8x0jaI9xxCKBqq7iMQXUAgtpBI6WqEI6wcHByB5TB5bNtG0zRSqRT9/f1jliopJbt37yYSiWCaJlJKWlpaaG9v5+jRo1RWVk5639K2GOjcA9ImUrqKlgPfp3nvd4hWbMAXqUHaFkPd+wgULKDr+NPE+45SVHsby2//DKo2cYHa2cAwDA4dOkQymaSrq+uy1s0HuX+B7pPPESpaSqL/GCvu+hyJ/mM07f46bl8xycETFNXcgidYgWUk0N1hFm36X/Q2v4JppPBFqqfpyCbPwMAAjY2NDA8PXzC/mrSssyxY136qhqGhIY4ePUoikSCTyUzPToSK21eE7o7Q27yV9HArtm1i5pK4A6Xo7hAD7TvwhipY89Yvomqeq87N7uAwHTgCy2FSWJaFpmmsWbOGw4cPs2zZMrze/BRs0zRpbW2lpaUFXddZunQplmWRy+UwDGNK9p8cambfM59ECMHat38Zb7ACT6AUhMATKMO2DbKJLnwFtSiaCykl4eJlY7FbcwnDMMjlctiXOQNOCIVw6UpSwy34I3UgFOJ9RzEyQ7i8MbyhChCCTLKLZOtrFNbcjNtfjCdQQtXy91BR/05U3TdnrVe2bZPL5cjlchdOfGpa4BqxnFwnyUallGN9M114g2X5tAyeCM37vou/YB6KqqNqbkIjMYsHf/VXhIqWsvLuf8xPEHFwcHAElsPk6Ojo4Lnnnhv7vGPHDsrKytB1HV3Xeeihh2htbR2bXVhXV0ddXR1er5dEIjHp/bs8EULFS0HaaK4gUtp4QxWomhdfqIpsuhdV9VBZ/wC6J4yUMh+0rcytN2xd11m7di22bXP06NHLWlcIhcql76Js4X0oisZwz2GOb/8Cqu7DG6qkoHwdi+tu49i2f0OgULvyA/giNSiqCyHEnI+zisVibNmyhf7+fjo7O8+/4GgtQhgrlzM3JePUEYlE2Lx5M/F4nKampmnZhy9cTWHtLaSHWkj0HUVRddz+EsLFDVQ1vJdE/3GSAycIFdZfPUlpHRxmAOducJgUFRUVvP/97x/3XXNzM4lEYiyT+1S4As9m1JKheyI03PE3AKi6j0y8nYG27cSqt1Cy4C68wQrARtX9c9ZCc6WM9oEQAkXRUNzBke9N4n1H0T0RVt39T/giNQhFY8lNf4q0TDR3ECMzSF/rawSjC/EEy6+NvjnHRXjtW7Bmgr6W1zj80mfxBCsIxBYSKlpKzcrfRAjBUOfevGvwbV9Cm4MvLg4Os8nc85M4XFUIIVBVddw/0zTJZrMXXfdK69xJKbGMJN0nns0XZdZ9aC4/AMHCRXjDVVhmmqY93yDRf3ROu7+uFNs26W99g8HO3Uh7fKyRN1hOqGgpoaKluIOlZJLddB1/Bmmb6J58RvaOoz9n7y//iGPbvoCU10iskmWdTstwncRgzQS6twC3v4RI6WpW3fN5Fmz4XVzeKP1t29j7zB9xZOs/oKiuMYuog4NDHseC5TApTNMknU6PfRZCXHKZnMk8jDuP/ZIjW/+egvL1rLrnnxAjcR8l896C219C64Ef0NP0IsPdB1j79q/g8Rdd8b7mIom+RvY996eompe1b/syvvBpK6EnWM7Kuz+HECpC0Tm8/W/pPvEsCzf9L6ob3gvk3T7eUCXBwsVzMh7tSpCWjRgVWIriWLCmiFyqF9vMkEm0o6j6WIyVJ1CGN1RFsHAJilNT0MHhHJy7wmFSdHZ28sILL4x99ng8vO1tb6OsrGxa9ielJJfux8zFCRbWEyxcDCMzloQQIFQipSvRXEEyiU7cvkJUzTMtbZlNdE+EQHTBOOudlJJEfyO5VD9C1VE1D8HCxfgjdfhC1fng/xEKqzcTLmlAcwXgGohUklKOdxEqjgVrqtA9EdyBUnyhasQZ5W7CJQ2se8dX8+k9HIHl4HAOzl3hMCkqKir4tV/7NQ4dOkRjYyMul2tKk4qeg7Q5ueurxHsPEe85mBcI0gJOx34IoRCIzmf1ff+KECrKNSiwPIFSVt3zeRACdaRwrpEZYP9znyI93EYgOh8zl2Tt279M7aoPULX83Wi6f2x9oai4vAWz1PppQnI6yF1V8mkbHCZNLj1AvDef78q2DVTcQP4+c3kis9s4B4c5jCOwHCaFEIJEIsGePXsYGBigoKAAwzBwuaZpqrYQ+CK1ZBJdeAJlBKILxixYZ7crb525Nskfn3/cd4rqzvcHApc3hstfhKK6R9w6c3um4KSx5ThDnFCUfNoGh0nj9hfhCZQTKJiH4hRsdnC4ZByB5TBpVFWlpqYGn89HV1fXtAa6CqFQWf8uSuffhWVmSA7kp4gHoguu+wBbVfdRf8ufY1s5QGCbGYa63sRfMA9PoOza7h95VryV6gisqaKgdBXr3v5lECoDHTvxBivxhauu7evJwWEKuDaiWx1mjdGZgLFYjHvvvZe1a9dO+4NXUTVc3gIy8Q72PfspDr7wvzEyQ9O6z6sBIQSa7sPliaC7Q3SffIE3n/7D/ExBe2oSu85Z7LNmpCoKGObstOUaQygqLl8hfa2v8ubTf8jRV/8B27z4LGEHh+sdx4LlMCls2+aJJ57g4MGDLFy4EMuyxvJfTTe6O4Q3WIo3XO1kj54AT6AUt78Yb7D86ixgfTlMILCk6QisqcTjL8bjL8Ebqjw9W9PBweG8OALLYVIoisLb3vY26urqsCyLurq66Yu/Ogt/dB5r3vpFFNWNqntnZJ9XC0IIimpuIly8HM0duPZrw0k7H+Q+iqJAZnqsLFJKbCs3EtcmrgtXmRCCgrK1rHvH19B037jZhA4ODhPjCCyHSSGEwLZtTpw4QSqVYmhoiC1bthAKhWZg3woub3Ta93O1IhQVt79wtpsxM9iScQprmmKwpJQMde2lt3kr3lAFZYvuv27KwwhFxe2LzXYzHByuGhw7r8Ok0bT8ANPb20s8Hp8xC5aDwxhnF8hWp95FmM8zdpy+1jeIlK0mlxkgOXDiiisSODg4XNs4Asth0rhcrrEZhD6fD1133AcOM8xEQe6mNaXix8gM0n3yOULFSxGKhi9cy2Dnm4z3TTo4ODjkcQSWw6RJJBJks1n+4i/+gkQiQTwed97qHWYUadunk4xC/m8JTNF1KG2LruPP4IvUoqjukTxkPnLpfiwjc3o5KbGljWWbmLbh3AcODtcx10fwgMO0omkax44dY2BggI6ODtLpNPfccw/l5eWz3TSH64UzCz2PIsi7Dqdgxlty8BSmkcBXUHdGULtAcwdJDTcTjC3GtA329e6gI9mMJU1saRNxx9hQeis+3X/B7Ts4OFx7OALLYdL4/X7+8A//cNx3uq6Pvb1LKRHi3NlWztu9w5RhmKCp587oO9t1eAVI22KgfQf+SN2474XIZ8xPDjYRiC3i9Y7nGcoOUB6oRhEKIOjPdPNCyxPcXfsuNGfmnYPDdYXjInSYNEIIPB7PuH+qqiKlZMeOHfz4xz/m6NGjE67n4DAVyJwB6lmpKCRg2RMufzkY2WFy2UFUl/+ca1Z3B8kMt9Ob6qAj2UJNaD4u1Y2m6GiKRpG3DEtaHB3Y57xQODhcZzgWLIdpQwjB6tWr8Xg8dHZ2smjRInp7e+nv76elpYVIJHJZ25NSIoeTMBRHlBbmk0l29oLHjYiFr2rBZlkWzc3NZDIZBgcHZ7s5I32dgKEEoqwIoWvjfxtKQDyBKB3/23SQSCRob29ncHAQ83wzA3MGaGfn+pLnzi68TKSUDPccwu0vRkyQrFUoOra02NP2AuX+6nOWEUJQGahlf+9OFkSW4VLdk2rP2aRSKVpbW0kmkxjGxNn6pWkhO3rA70UUhPLB/529EPAhIsGr+r5xcJjLOBYsh2mls7OTQ4cOsW7dOiCfyuHIkSM0Nzdf/sZMC/PHz5D74vewDh7Hbukk9+UfYnznZ5DKXHz9OYxlWTQ1NXHkyJE5IbAwLcwfPUvui9/HevPIeOtLzsD43i/IffH72Eebpr0piUSCo0ePcvz48fOKCLI5xDkCi3xs1qSQJPqP4fZOnE9MCIGieejsP0jQFZlwGV1x4dP8HB88NOVWrFQqRWNjI42NjeRyuXN+l1JiH20i9x/fx/jh05AzsI+3kPvi9zG+9yRkz13HwcFhanAElsO0IaVk3759CCFob29HCMGSJUt429vexubNmy//zVkI8HlA1xEuHaEp+YSSLh0UgRxOYDd3ILM5pGVjd/Vi9w9eFa4ZXde59dZbefvb305tbe2stUNKicxksdu788kHFAXOtlAJgdD1vAUxnsTu6c/P4psmSkpKuP/++7n77rvxeifO2C8zOdDOtqSJSbcrl+rHtrIo2vktT1lNo0R6RuKuzkUIQXmghkP9uzHl1ObmisVi3Hfffdx77734/ecJpNfUvPtUVbG7+pDpTP6+0VXszl7kkDPr18FhOnBchA7ThqIo3HvvveO+u1J3RN4tFUdduwxlXhWiohjZ0YPSsAiQSMPEfPxF7EPHUe/ZglJTjvGfjyGCflwffQiCc3sW12i/zORAJ6WERAo5GM+LVMsGJNb+Y1gv70TMr0JpWAjZLLK7D4piCGXk/BUVoLh17APHsV7cjv7RhxCxyLS082LXjJQScrm8+D4TTYXc5ATNcN9hXP6i87ZBSklbtgtXNp0v13OekkS64kKg0JFopio4b8rccpe0HZeOsnYpaCrGd36G8HlRViwC28b4+mOImjK0OzYhCkKIUMBxGTo4TBGOwHKY80hbIrv7MB5/AYYTyEwOpbkd2daN7OpDFISRPQOARBQW5GOCFJG3vqjq+PxIDqexbIyf/QrZ3AEFofxMPNtGDicRsQgCCTkD88mtIMD12w9DKIB1qiPf901tiEW1eQuXouQtX939KCUxcOkIIcbif0RBKB8DNB3nQkow7fw5H0EIgdS1SdUjtC2DRO9RgkVLzruMaRsM5gYoU1xIM4NwTSzk81asavb1bqcyWIdg+q9JaVnYbd3YPQPIU+2ISAgRDYNLR7Z3g9cDXjdkDYwv/xBRU47rgw/kxbaDg8OkcVyEDnMWaRjYHT3Iti7M57flvwwFEBXFkDXyA4TPC5qK+eNnsA+dgJA/b4lJZxFuV94V4rg/zkHmDGRXX14cFYRgOInsH8r3b0kMdctqsCV2ezejWsDu7MN4eivmN34C2Sy4dNT6eegfeRciHMB87g2M//g+5iu7kN39+QH+wLF8vM9Pnp+CeKjzYMt8WZyzxJtw6cgrjM2TUpIaagZFuWBh46FsP5qio+g+zGTvBbfp0wKkzATdqfYZsVTaTe2YP30+L6ABkKAIhNuF7OqHbA79N96WP9e6lnf7Ojg4TBmOBcthTiKlxDp4AnvXQSiKYh9vzsf+lMSQx5oRC2vyFpSiKKK8GNnWlY/N8nqwdh/KL2dZeW1wnViwpJR5d18miyiOIdTzvz9ZOw9iPvc6IhZBtnQiSmIIdyjft2VFmI89j4iFwZYoKxfng96feBER8OYH42gY/L68gB11w6kqBP3YOw5gvbQT/TfelrciurSRGX7TdB5sO299OzsGyu1CxpNjedguB2mb9LW+jj9ce0H3YEeyhbA7iipVzKE29Ej1eZcXQlARqGVn1yvcW/cwgondiVOFTKWR3f2ISAjZ3Z8XoukMDMQRIT94PSixAigvRikrQvi858bbOTg4XDHO3eQwN5Eg+waxD59EGGbeCqWNuvtEPqBZAm4dDAMKQiiFBch0FtncgSiO4vrtdyMTqfyg4vMgpiCj95wmncH49hPIngFcH3oAUVtx/mU1Nd+nI1pAWb4AEQ5iPv7CyHcS/F6U8mLs/Y2QNRDhAIQCuN59D/i9yK4+jB88hdA0tAffgrqxAXXVYsyf/wrZ0gmKgrJ0Hq7yX0f4PHkxNg3IZHrEFXzWD24d2TeYt2hONMPwfNuTkoGOXQCo53H5AVjSpD/TQ0WgFgGY8U6QFojzP1aDepjOZCvHBg+yMLJ8WuOdhMedL3qtqacnK6QBtwsC3ryFVxEIRUEURaetHQ4O1yuOwHKYc0gp87Ez2Vx+UDZMlMW1YNnIwWG0R96B7BlA9OTfzrXbNuTdhC/thHgib2EJ+EFVMX/4NDKdzQuO8uLZPrRpZjTuTEFeZOBWV9ejVJRg7diPnc4iigoQI/nE8LjRP/wgIhpB9vQjmzuQCNT7b0GpKEYEfAghsA0LNA2ZMzB/8hyiOIr+6/ejv+deZCqDKIzkB+9pCn6HEatdVy8EfeeKldGZc01tKPOrLknMSCmJ9x5isGsv4ZKGC1qvBjN9KEIZmz0oFBUr1Y96gaB4IQR14UXs7NqKV/NTGaidML/WVKDUlqO96y3IVAY7mUb4vaCqCL8nb9GKhKZlvw4ODnkcgeUw95AS87nXsXYfQt2yBnXN0rwVSpIf3Asj2Nkctsedn/VUVACKgn7PFmQiCZadD6rOmfkBRVWuDzeh1513y2VzFxU1QlOhOIo0rXxslGHmB1yPG+H3opQVI7xuZDiA/qEHwTSRtsxbDUe3EQvj+uhD2KfaMX/+0oiFUOTPUcA3rYd6JnZn74QWGCEEFEbyAd3zqy66HSklif5Gek69TLikAUW58OPx1HAjBZ7CMTGleKMY/SdR/UUXXE8TOgsjy3it/VmKfOWsLNw4bjtThX2qA/NHzyLqKhC6CgEf2ubVefdgzkB4PU5Au4PDNOIILIe5iSLygeqhACgKxnd/gdBV9I8+BEKgLKlDL4nlB3JFyQ9OPk/eFTWCdLvQP/jOvOC6Dt7WhRD5/rocLAs5MAyAUlGC/q67EG4dPK6xbRILYx9pyk/xryrF9YF3gNuV318sgogEUarK8n1/Ga64KSGZzk94OJ9QGA10t6wJ8mSdRkpJcuAE3SdfIFS8/IKB7QAJY4jh3CA1ntMJSBV3EKPvGNLMInTPedcVQuBWPSyJrmIw28cvT/2IeeElrCnZPLX1CoUA28qn4kimEYqCUhLLuwYdHBymHUdgOcw9hEC7YxPKqiUoxVHkQHzk7T7/T4h8DNbF4kaEEIhwcEaafFWiKuj334K9eTVKcRShKojCyAVXmcjGIlQ1b0WcBeye/ounf9C1fOqJaHjCn6W0ifceoafpV4RKlqOorgtuz7ItDvbtptBbOj7dghAonjC53qO4S8/vXswvKhAICtyFRNwxWuInePbUT7ml6j486tSks1DqKnB9/L3YXb2YP34uL0RnID2Eg4NDHkdgOcw5hBDgcaNWlOS/KIyg/9ZDeUtVxBFMU8Wo1U89O0HnBCgLq3H97vvyVqo54laSUiLbui94TQghkF5PPgXFWQJLSollpulreZXk4ClCJStQtfNbd6SUWNLkcN+bWLaFXw+Ocz0LIVD9hRh9x9GCpReMxTpzHYGgOjif7lQ7T538ITdV3kPMUzxpkSUUJV9HsjiKUlo0cu6cR76Dw0zh3G0Os0Y2myWVSl3awr6RUiXp9PQ1aIbRdR1N084ZSG3bJp1Oo13ApTUrhEbiqmbgHHg8HpQJZn2apkk6nUYIgdbRi9XRk3dppi+Q70ra2PsbyRVFsISBkTiFlBbpRBcDHW/i8sbwhivJpgbPWTUnM2SsNBJJ2khyfPAQmqJR5C0jneyecHe25iZ96GcESpaheiLkdA9Z18Qlfs5ERUPHxfcPfZmqUB01oYUoikJADVPsrkAgUBQFt9s9ofga7ZuJ+m0mz91s4XK5UFXVyUTvMGcQ0ilC5TALHDp0iJ/97GcEAqdjhhKJBP39/VRXV0/5/qSUnDx5koqKCtzu89eVu1IymQzt7e3U1dVd8gN+7dq1rF+/ftzylmXx6KOP0tPTg6peOJ7JNE1OnjzJggULrnhQsW2bxsZGFi5cOPHAfIn09fVhmiYlJSVXvI3R819XV8e73vUuYrHYuN8HBwf52te+htfrRRGCFYoPXYJ9kfQPPT09qKZFb1EI3BkWl/aQydoMJSWoXsQF0ipIn4Hlz3Ls6HEqS6rw6B6U85TDORMhbVzSIqxDl+3ipKnTfKqZWGEhfv+FJwBIKcnYKYRPogUFesKHfjKKtCSJRII/+IM/QD8rKWgikeArX/nKmPhKpVL09PRQXX3+vFyXQ1tbG4FAgHB4Yjfr5dLb24tt2xQXT35mr5SSEydO8P73v5+VK1c6AsthzuAILIdZwbZtbNse9zA8deoU27dv56GHHpry/Ukp+da3vsVb3/rWcwbuqaCnp4ennnqK97///Zf8gBdCjP07s532JRYoTiaTfPvb3+ajH/3oFYsjwzD48pe/zG/91m+dM2hfDjt27CCRSHDrrbde8TZOnjzJ7t27efDBB1FGJy6cgZQSy7IuewDdunUriqJwww03XFG7bNvmG9/4Bg8++CCRSOSKtgHwwx/+kI0bN17xC8Toufr4xz9+zrk6u29aW1vZunUr7373u6dEcPziF7+grq6O+vr6SW8L4PXXX8c0TbZs2TLpbUkp+cY3vsEDDzxANBp1BJbDnGGO+SAcrhcURTlHFLhcLkKh0ISD62SxbZtwOIyu6xe1DF0Juq4TCoUm7aIQQlxy+zRNIxKJoCjKFR+TbdtEIhE0TZtUv/h8PmzbntQ23G43wWDwvOdfCHFFblOfzzepPhJCTPrakVISDAbH3FhXuo3zCbyz+2b0XpoKl5mUkkAggNvtnrJ7x+fzYZrmlGxv9N6eyN3u4DCbOBYshzmDZVkYhnHeGJPJIKUkk8ngdrsn5Qo7H7Ztk81m8Xg8M/aQt22bTCaD13vls86klKRSKXy+CRJ1XgaGYWDb9qTcr6ZpYprmlJ//XC4H5EXHlSClJJ1Onzcu7FK3kc1mJy3SLvVcWZZFLpebkutRSkkul0NV1SmLC5zsOTmTqTg/Dg7TgSOwHBwcHBwcHBymGEfuO8wKlmVhmuZsN2POIaXEMAyc955zcfrm/Dh94+Aw93AElsOscOjQIXbu3DnbzZhzSCl57LHHSCaTs92UOcfw8DCPP/64IyImIJVK8eMf/9jpGweHOYQT5O4wZRiGwauvvoppmtx4442oqsqzzz6Lz+dj06ZNeDynE1qm0+lLz4E1xUgpMewcAoGm6PlklFKSs7Joio6qnI6RsaWNYWXRVfdYUd/pZmho6JJnEk4HtrTIWhlcimesL6SU5OwsqtBQxezkGrIsi6GhoRnf75Uydp0JBVWo5KwsAoFLnfoYQ9u2GRwcdASWg8McwrFgOUwZqqqybt06DMOgvb0d0zRpaWmhtLQUXdfHgmWTySSZTGbWBoOhbD+f3/VpvvjmZ0mbeUvRof49/PXr/x8/P/nf2NIC8gPkto4X+avXf5eXWp9EyukTPaMBzMlkEsMwpm0/l9KO19tf4C9f+x2ea/4ptrSRUtISP87fbvsDvnf4i5hyZl27pmmSTCZJpVJXlYDoz/Twjzv/lK/s/TtebPkZX3zzs3z2jd/j2OCBKTsOy7IuqW+klFdV3zk4XAs4FiyHKUMIQU9PD+l0mqqqKlRV5Z577mHPnj1omsb8+fPZvXs3u3fvpq2tbVI5kyZDf7aX/X07cSku4rlBfHqAk0NHONy/B58e4N7ad6OoKhLJ3t5tHO5/k5rQQm6qvIfpKmWczWZ54okn6O/vp7GxcZr2cnEkkqOD+2gc2E+Bu5Dbqt6Gpmg0DTeyv3c7g5k+Hlj4CPpUFiW+CK2trfzyl78kmUxOS4qN6aIj2cKbPa8TchWQs7Ic6t9DX7qLpuFGFkSWTck+Ojs7efLJJ0kmk2Mz8yZC9g0CIApnp2akg8P1iCOwHKaMbDbLT37yE2pqamhtbcXv93PixAkymQyhUAiA9evXs27dOnbu3EkikZjW9li2xdGBfRh2jqWx1WgjoqDUV8mW8rvwaQGinnwm6VVFm7ih/E42l78FTcnfFgLB2+e/nxJfBetKb0aZRoOv2+3m4YcfxrIs/vM//3Pa9pOzshzo20lADzM/Uo8iFGyZ76eslWVZbA331r6boB6h2FfG7p5XWRZbw9qSLXx4+R9RGZyHT/NPW/smoqamhg9/+MMMDAzw+OOPz+i+L5WclWV/706CrjALIvXYIy7nzeV3saZ4M9XBeawc3ETSjLOx7LYp2295eTkf/OAHSSQSfO973zvvcvbxFgAUR2A5OMwYjsBymDLcbje/+7u/ixBiLB/NTTfdBDCW8HA09uTsDObTwUC2h8/v+jQJY5i/2PQFFkcbAOhOtbG1/Rlcipu7ah+k2FfO3t7tvN7xPIadY13pzegin+yyxFfB2+e/f6zN08XZfTNdNA4c4HM7PknUU8RnNn+FiDvKYLaff971FwznBvnzTf/K4oIV3D/vvfzNtt/nUP8ePrL8j7ir5l3cU/vwtLdvIkaTr87VHEdSSg707eRzO/6YEn8lf33jlxjODvCFN/+KnJXlntqHqAzOozI4b2ydqerDS+0bmcqMK0zt4OAw/TgCy2HKEEKcU8JjNgdFj+pjfqSehBGnwFMI5AdDXXVTHZyPR/OiCBUpJeWBGqqC86gNLiRnZbBsE7d6/iSNpm2StdJ4Nd8l1aabLaSUY0HrHs1H1FNIbWghpf4qPKoH0zawbIu68BISuSE8qg/DzqEpOvPD9aSMBGX+fGkXJ0v2+Sn0ljIvUk+Rtwxb2qhCZX54KUPZfjyaj7SZRFN0TNvEq3mB8eWRcnYWKeUFr7lJYVkwRwWqg8O1iiOwHK5Z/HqQ31v9V9jSxjvi1jo13MiX9/0d5f4abGnzb3v+io+v+BTqiKusOXGCf9/zGVyqh/fX/3/EvOcWo7Vsi5+d+G+2tj3NAwse4YbyO+as+JBInm76ES+2/pxfX/I7rCjayJ9t/Dya0NAUnceOfZM3Ol+kwl9DyF3Al/b+DTWhBby//n+gCHVELDiPiYuhCo0yfxWnhhv5xoH/R1viJLdWvpXtXS/x02P/RXP8ODWhhXQmW/i1Jb/DkujposQJY5iv7Pt7Mmaa317xSWLeKy+YPRFSSpASZnFmqoPD9Yjz5HS4ZhFC4NF8477rTLVxdGA/g9l+PKqXjmQL/ZkemoeP05o4iS0lmqJhS4vBbN/EAkuaHOrfzYnhwxwfOsim8tsRzE2BZUuLfb3bOT54kKbhRlYWb8KnBQAw7ByH+9/kxNDhvGVP0Tk2eJDh3ADx3CBHBvbSHD9Oc/wYS2Orx233zBlpc1VcziTdqXbaEqfoTnWQMpN0JJop9VdxfPAgcd8gJ4ePYti5keWaWBJdCeT7MWEMs793JxkrRX+md8oFFlKCUMCykFI658vBYYZwBJbDdcWKwvX8rzWfJeYt4XDfm7QkTtCRbGFV8Q38nuuv8ag+bGnhVj1UnREzcya64uL2qrchpWRT2R1zVlxB3rLy/qX/g01lt7OyeBNZM8MbnS9S4I6xNLaaW6vuR1NcbC6/E6/mJ2kmiHmKKfZV8KHln6At3sSq4k3jtimlpHHwAM3Dx7ix/E58emCWjm5uIISgPrqK9y3+GL2ZLkKuCEPZAUzbGMsltiy2hjXFW4gbQ6ws2ggwlv5if+9OHln2+/i0ALWhhVPfQClBEWA4FiwHh5nEEVgO1xU+PcDmirvoTXfx9Kk/pz/Ty6nhRgw7y10177qkt3tLmrzU+iS7e16lLryI+ZH6OSuyhBBUBGqoCNQAcKB3J1/a+3+Ieor5i03/xtb2Z9jV/QrLYmvYUnH3uOOfF17MvPDic7aZNpN8Ze/fcXL4CG7Vw02V98zY8cxV3JqHhqL1Y587Ei18+tWPkjaTlPgqcKte3rvk43jPsKgaVo5vHfwXdve8xq8v+R0eWPDI9FiX7BFroxB5N+FVlOrCweFqxhFYDtclfj1AfXQ17clTlPmrqQrMI2nEx35XlXyMUtZM49MDY1ncpZSYtkldeDG96S4WRJbOWXE1EVFvMXXhJRT7yvHrQeaF6+lNd1HmryJtJvFqfmxs0kYCr+ZHVc59RLhUNyuKNuJS3WMB8A55pJRYI4lYF0aWE88NEHIVEHHHEIgxF13exSqYF15C3BhiXrh+Ohs14iZ0BJaDw0ziCCyH6xIpJRkrjWEbvH3++xnK9vFnr3yIBZFldCRbcKtu5keWsr3zV3xo+R+yqvgGIB+39M2Dn+dQ3x4+2vAn1MdWXVUxLVLaZM00hpXFljYZM4kqNH524r/58bFv8D9X/zWNgwf43pH/4O6ad3H/vPedUyJIFRrvW/IxTNvArXpn6UjmJhKbxxq/ya/afkFVYD5pM0WZv5qm4aM81fQD3jb//aiogOSltid5pf2X3FP7EA2F66fvOhonsJxs7g4OM4UjsBymDNu26erqQghBcXExQgiGh4dJJpOUlJTMWBbus0uCTDRwpc0UJ4eOMJTrJ2dl6Ey20pZsIuQuoDl+DEUoKEKlLdlEe7KZVYwILCtH48B+2hInSZrxMavE+fYz25zdtoFsH23JU2SsDGkzSXP8BIPZXgw7R8ZM0Z/poTV+gvbEKY4NHsyXBzpLYAkhUNFQVefxAeOvN1vaHBs6SHuimYAeojPVSthdwEC2l5NDR5HSRqIgkTQNHaUz2UJ7snm6GzgisHBmEjo4zCDOE9JhyrAsi87OTo4dO8amTZsoKiriqaeewuPxUF9fz8KF4wN4p6M22uisrNc7nsetegi5CmgoXHeOq6vAU8jvrf5Ldne/xpGBvawquoH/teb/ENDDZK00Hs1HgbuQ5vixsaBkyMdwfXzln7OreysdiRaafcdpHNhPmb+a+tiqKy4IPdoXU9UnUkqGsv280fki88P1+TgxIagM1LK5/C0U+yqIuGPcWP4WdnS62FB6K4pQaEmcpCJQy/9c/ZmR4zlXFEspOTKwl9b4STaW3UbQFZ6SNl/oWOYqtrTZ2/MGnak2Cj0ldKc7uKHsDm4ouwO/HiRrpYl5ShjI9jIvvBhVaGSsNG90PM/8SD0PLvgguupiODeQdyNepki/pL6REiSnXYQODg4zgiOwHKYMXddZsmQJjY2N+Hw+MpkMbrebhoYGmpubWbhwIW+++SYHDhygubmZjRs3XnyjV8DW9mf4yr6/p8xfha64+NTGf6bwrKnvilCoDS3iPw/8P44NHuCDyxTeOu995wxw1aH54z4LIagOzefRo19lW+eL7Opex4G+XcwP1/O/b/jCFc+oy+VyPPvsswwMDExZLcLnW57g24f+lQ2lt/KHa/8WXXXRNNzIy21PUeAu5OaKe3mt/Vl2dL3EooIGqoLz+Jfd/5siXxl/s+VrRNyxCbc7GuTeNNyIS/Vw8zQHube1tfHKK68Qj8fJZrPTuq/LZTg7wFf2/V/6Ml2sKNzIjq6XWVW0iT/Z8DncqmfCdfb37uDf9vw1xb5yCtyFHOrfjSZ0Hlz4yGXvv7u7mxdffJFkMkk8Hp94oTM1mOMidHCYMRyB5TBlmKbJo48+SkVFBZqmYds2mUyGY8eOUV5eDsC8efMoLi5m796909aOBZGlLC5oIOYpwa16kFKOy/8jpSRtJslZ2bF8RAsiSy+4TSklKTOBKjRcqptVxZvoy3SxsfQ2LGmyPLYOl+q+4jbrus769evJ5XL09vZe8XbOZFFBAzWhhSyNrUYRKmkjiU/zMz+8lDJ/FQFXiBVFGxjK9bOooIGIO0Z9bBW1oUVjiVknwqW6WVtyE27NS/V5UllMJYWFhdx8880MDQ3xyiuvTPv+Lgev7mdN8WaODLxJqb+SmtCCCWeVSikx7BxZK0OZv5plsTVUBecT8xRj2gaLCpZf0f4jkQg333wziUSCZ555ZuKFpASkY8FycJhhHIHlMGVIKVmxYgWKopDNZvH7/dxyyy0MDg6yYMEChBCEQiFCoRBtbW3TUuxZCJG3Jm36AkO5Ab6w5zP8485P8nur/5ryQH7GW1+mi3/e9RfY0iKgh/itFZ9kfrj+gu6Z9uQp/mXX/6bQV8rvrPw0b6l5gFsq78elurmj+h1oijapkjmKolBcXIxt2/j9U1NMOWOmMKwc8dwQSSPOv7/5GXrSHUTdRaTNFIoQ3Ff3bu6sfueYteVTG/8ZVWioFzgWVWi8Z/Fv8S77Q5MSlZeKx+OhvLwcj8czY3F8l4pLcfOO+e/n87uPcLh/DwsiyzjQt4vIqce4r+7diBGXsSVNvn3oX9nbu50PLv0D/mzj51GFihAK99a9+4pL5LjdbsrKyojH4+fvm1EXIQJp21fRnFcHh6sbR2A5TBm6rrNy5cpx3/n9fsrKyma0HUII3JoXcgN0pVpJGgmGsv2U+asASBpx2hKnUITA8BhYdn5a/UTB6qPfJXJx2pOnSJkJMmYavx7Eo+Vn0Lm1iV1Bs01Xqo2edAddqTYyVor2xCn6sz0IFCxpkrWyeFTfOJHkUvJ/X2iwF0LkRdgEKRyuR9JWis5kK7a0CLtj9KW76E61Y0sbZUTOWLZJ03AjHYlm+jPduBT3WB+r2jTPxBwNcBc4LkIHhxnEeUI6XDapVIquri5s26asrAyfz3fxlWaBQm8J/3P1Z9jV9Qr7e7fTHD+OQGBLi83ld5Ix01jS4sRQPmHm7u7XWF64lgWRZWO5ijqTrbza/gw+LcBb5/0a60puGiscPddZWbSRtcVbuKHsTiLuGLdU3kdPugPLtoh5S3ix5eeU+StpHj5OWaCalBHHtk0KvSWsLrkJVZlb1qK5hi1tDvbuZH/fTm6vehvtyVPUhZZwe9XbSRlxfnr8v6gLLaY10UTMU0S5v4Y1xZvJWGl+1fokm8vvRFdd095Oadn5Qs9OmgYHhxnFEVgOl4WUkp/+9KcEAgF0XSccDs9ZgaUIlZrQfP5z/+c4MXyEmtAidEUnZSYJ6WFaEydJmUma48fY1fUKO7tfYXXxjXxy/T+iqy4kkp8e/y9+eepHzAsvwaV6eMf837jimYIzzRudL/JG1wtYmMwLL+bZ5p/g1fy0JU4RcAWJeUqQSI4PHmJ54VpODh2hyFtG2kzwN5GlFExQh9HhNDkry38f+Q8O9b/JxtJb2dn9CqeGj/HH6/6Bf9jxx7QlT7Gm6EaODOwj7C6gLdHE+pJb2Nu7DU3RmR9Zct5yTFOKZZ0hsJwYLAeHmcIRWA6XjGEYHDhwgL6+PsrLy/H7/ei6PvPtsHIMZvuIeGJoQr+gO8ulethYeithd4xCXxmKENi2hVvzUuqvImnGiXmKqQstwswOs77oxjHXl0CwofQWetOdFPvKKQ/UoCvTb3GYKpZFV7Mo0kBD4QbC7gJuLH8LGTNFeaCGoB4mZ2UIu8J4pcqi8DJinmJcQifijuJ3hWa7+XMWW1oMZPpwqW42lN6KW/VS56vFLkhTHa0n5i1mc8VdHBs8wIrCjXg0L4XeUkr9lWwufwthdxRFKOedpTnlnGnBsqyZ2aeDg4MjsBwuDyklCxcuxOPxkMlkSKfThMPTmwfpTGxp84uT3+enx7/NuxZ+kHvr3n3BUjWaovPOhY9gSQtGlpto6YGW11BbOil3d0NdPrmmEIJVxTewvHAdAoGiqChcHdYrgMFsPz3pTvozPWiKi19b8nFs7JEJZQLLynFy51eo7YyzsGophUtuBiSKUCcVsH+t0zhwgM/v+jRLY2v4SMMfcVvxbRx4/tNUAytWPoxX8/PQwg9hSQtFKNxT+xBCCGxpoyk6N1bcCeQnC8wIlpUv9qwoebHl4OAwI1w9o4XDrKPrOoWFhezbtw+Xy8XQ0BBdXV0z2gYpJb3pLuLGIP2ZbsYn+ZkYVdHQFRe6oqErOtpZ/3TVhZHux84Mkol3jExrzyMQY+vkZ31dPXOw+jM9DOcG6Et3IbFRhIomzjhuRcdIdGPE2zHTgyN94R4RV3IkvYU9pxN9zgbx3BCD2X66Uq1IKVFtCzPRjZnsBSsH5K85l+pGFRqaoqGK/DWoCGWs/2fsWhq1YCkK0rFgOTjMGI4Fy+GSkVKSSCQoLCxkcHCQgoKCc7KzTzeKUHh40YfZUHoLNaEFiEt4R5BSMti5h95TL1O68D68oQraD/8URXNTvuitCFWnZP5deMNVeIOViBEXoZSSlvgJXm57ik1ltzMvvOSqEVhCCG6rfhvVofmU+CpQUehsfJJsuo+KJe9EdwdB0cguvIkTvixLq9YA+WNODjbRdfwZvMEyMvEOyuvfidtXdNUc+3SzJLCARwKbKfTW0LH3uxTX3srSW/+SjsafM9S9H2+wHISaz6af6+eXTT+mLryYtSVbZqcwuGWBEAhFgOkILAeHmcIRWA6XTDabZc+ePXR3dxOPx0kmk5SVlVFXVweML9sxOgvvzM9TgRCCkLuAhqL1l7yOtE2a9nyTvuaXEYpKcd3tnNjxHyiqi2j5enzhSjSXn2j5uvHrYfPzk9/jl6d+xHBukN9a8cmRQr1XB17Nx/LC/DFlkj0c3/EfGOl+QkX1RMvXYdoGT3c+xc7eVyjsauCdwTpA0nHkCdqPPI7m8pMebkN1BahueN/sHswcItm1H+3oi6R9RSStHJaRoqB0Dd3HnyHefYBY5Q24vBEAtne+xPePfpl5ocUsi63Fp09NjrPLQY5zEToCy8FhpnAElsMl43a7ec973sP3vvc97r//fg4fPjyuPEdvby8//vGPefDBBykqKuLw4cPs2LGD1atXs3z5lWWqvlSklFhmGjM7jO4pwMgMorn8qLofy8wQqljHQK6fQEkDQnXhrliDpnuR0iaT6EL3hDEyQ2iuALaVL8eieyLcUHYHvelO1pVsmdPxV1kzxVA2X89OV/M5lmzbJJfqQ3MH0d0hShfcTTreTsblYSjdi24arC1Yh5lNsNg/n2yyG4RCuGQFqeEWfKFKkoOniFZcupi9VpFSYuYSWEYaf8F8IqVr8ARKSMfbCRbWY/nCKBUrCUQXo7lOz6pdGl3NhpJbqI+twj0DSVknxByZRehYsBwcZhRHYDlcMqNWqCVLlvDYY4/hdru57777xn6PRqNUVVWRy+XjUDKZDIqi4PP5xqxZg4ODY7FbU5neQUqL49u/SPeJ5yiZ/xZ6Tr5ApHwti274Axq3fYGXlC62e7p4a+9rFDb28COtGbdw8+COL2B0H6So5ha6Tz5PQdkaUsOtSGmx4i3/l5VFG1kWWzPtMTOWZdHV1UU2mz1/TbnzYNsWPzzyVV5o/TkPL/ggd897D1JKuk8+T+Nrn6d43h0s3PQ/mb/uY5wcPMjf7P40US3M3UMqRaqXu3IG8Z4vscvM4AtXg7RZeuv/RneHkNJGKNqsugdTqRQ9PT0MDg5imuastMG2shx+5e8Y7j5ARf0DZJNdKKpOaqiZlo5tbO2Pc0RvpVLqLJAGXlwIISgP1PCH6/4WRajT4h7MZDJ0dXWRTCbP3zeWnXcPKgrSEVgODjOGI7AcLplsNktPTw+JRAKXy4WiKGQymbFZhIqioCh5K4+Ukvr6empqanj88cepqqpC0zRaW1s5dOgQp06dYu3atZNu05gbUkqMzBBGZpBsqhcjO4SZjSNtC8tMkRDDxI0h0kacTC5OQh3GUNxkjCHszCC59ABGZpDcyN/SNrBHApZnIhmkaZrs27ePwcFBuru7L2tdic1Qtp+4MUTSTJAP/BeY2cRYPyAlKCqGtEgYw7ikQi6noqs2MpfCtHKYRgrTF8M2s/lZlIqKmAMu0cHBQXbs2EE8HscwjBnd9+j1JaWNmR3GyA5hZPL/60YUMztEzkyRUpMkcsMkjTj2WRMDVDF9AnV4eJidO3eSTCbJZDITLzSaB0tRHAuWg8MMIqQzRcjhErFtG8MwOHjwIG63m3A4TGFhIW533vUxMDDAj370I8rLy1m0aBGKorB//36KiorYtGnTuG3t2LGDRCLBbbfdNqk2WWaW9iOPI22TopqbSSc6CUTnkxw4iTdYgdtfRDbZxVB2gF0Du2mJH2epp4biogZ0oRGTbgbatpEabkHVPFhmFk33AQLV5aNy6bvQ3eFpt+CM3oa2bfP1r3+d97znPYRCl5aLSkrJcHaAU0NHWRBdPlao2TYzDPUc5JTVz4HEEVZrVXhTw5xKnCDgLsCXGMLjL2a45wDBwnqMzABCqHjDVVQtfzfKHCmFM9o3AwMD/OQnP+GRRx4ZE/LTTTreQfvhnxAuWYG/oI7kwEkGOnZjGkmMVD+qK4AtTTzzbyXjC1LkLaHEV4lpG7zY+nOSRpy7ah7EpwempX1jpZwSCb773e/ykY985JyahNabh5G2BE2FVAZt44ppaYuDg8N45sYT1OGqQFEUXC4Xmqbx+uuv09raygc+8AFqamoAKCgo4CMf+ci4debNm95M1amhFo5v+wJCKEQrNxItz1vFXGWrx5bxBEpx+Yvobv8FT7Y8Rk/RjfzRwnfj0jxI26L1wA9oP/wYkdI1DHXvxRPI107MJDrwh6spnnfntB4DnHa/XomQE0IQ9kRZ4RkvYlXdS6h0Bb/Y9gl2dL9MwrOcVcMWcvAEaVcQy1dIovcw8b4j2FaWeO8RPMEy+lq2Urrgbty+GUqEeRFm0z3Z37aNk7u+Sqi4gTVv/Xc0V4C2gz/CG6og3nsIX6SOXKqHBSUrWVh9+mWhO9XOtw/9K4aVY3nhOhZElk5L+y6lb6Rpg6Y4Qe4ODjOMI7AcLps333yTwcFBbrjhBgoLZ7cunzdUTuWyh5G2icdfjGVmyMQ789mxpI3m8uP2FyNQWF+8mY6hRtYGV2IkuzFREEKhsHozycGTFFSswxuqwF+QF4Xp4VbCJVfP276Ukly6DzOXxBeuQtoWmeFWbi17C17Nz0qlgpBnEF+4Epc3BkJB1b2ouo+CsnX4owvyFqxAKbr7+s3kLqXEyAxiGknCxQ0Uz3sLwcIlpAabAUGsejMubwxF8xAuXk4uM0BB+Xh3d8xbwn217yVhDFHiq5idA2HEwmXboGhjLkIppZNyw8FhBnAElsNls2HDBl566SWee+455s+fP+1Wqguhal7mr//4yCdB877/pqfpBTR3iHjPIdy+Qlbe/Y+4fIUEe1u5O+Fj+MQP2Hf4eTRXACT4IjX5dATVbupv+TRiLIt5PpbpahmMjOwQ+5/7FMnBUzTc+XdkEp0cffVzlMx/C7+z5g848NynSdoGmXgHLm8My0jhC1dhZIfwRaqom//hkS1dHcc7XVhGiv3Pf4rUYDPL7/gskdJVdJ98nlNvfpNgbPHIzMHF+Vg1YOnNnx7LnTaKW/Xw8OK8NXdWcl+diW3ny+QoIu8qdHBwmBEcgeVw2SSTSW688UYefPBBgsEgTU1N1NbWzkpb8uInP4BJKbGtHEIoICVSWthWDts2AYm0DaSZAWmfkaE8n97BNjPYtoEYKZEzsvVZOaYrZuT4kdZIkH4WKW1sM4u0TSwzg1DUkX7JggDbMrBtA9syuJrE5PQisS0DaZvYtoltG4Acuabs03/bBpaRHCurdCZCiNkXVqPYMi+wRtsjRz87ODhMJ47AcrgshBCsXn06vskwDHbt2jVrAutsIiUNDHbsIlK6GqGoqJqH9iNPUL38vYSKl9PX8hq1qz5IrOoGQCAUDd0TJruqK5/FXczdXFcXQ/dEWH7H32DmEvgjtYRLVhAqqme45xDHt/070YqNpIaa8ATKcPsKKZ53O/GeI/Q2v0ywcMlsN3/OoOp+lt/+WSwjhVA0Bjv2oLmCFNXeipEdIlK6Gt0dIlK2hqGuvST6jhIsnMNZ/kcFlRj525nX5OAwIzgCy+Gaoqf5FfpatiJti4GOHaiaF0+glHDJCgbattHXshUhlPwsuTPSL7g8M1eweroQQuANlo37zheu5dgb/0pfy6tEytYS7z2MN1jOcPc+qpY9zMmWV0Z+W0MgumCWWj63EELgCZQA0H7kCXpPvUQ63o7m8pOJd+CPLsBI9xMsrKe3+SU8gZK5K1AleRfh2GdHYDk4zBSOwHK4ZAzD4M033xyX0HDx4sVs2LBhVtpj2yapoRY03YfbXwxArHITib6jRCs24g4Uo7mC6K4A4aJ6XN4CjOwwRbW3nBMzcy0hpU1qqAUhVIzsEIXVN+PyRgkWLiFYuBjNFUBzBXD5Cilf9DY8/mKKa2+e7WbPClJKMolObCuHL1w1zoJp2ya6p4BQ8XJ8kRp8kVqyye58XUZFJRBdiMsXo7jujlk8goshz3AR4ggsB4cZ5NodZRymhdEEo6ZpYts2QggqKytnpS3xnsPs/eUn8IarWXn359BcQRJ9jQx17cUy0nhDFcxf9zFUzQ1CIegOUX/Tn04YM3Mtkehr5M1ffiJvqeo5RFHdbSzZ8knUMxOmCoEQCsXz7qC47rZrvk/ORy49wN5f/jG5dC+r7vk8wcLFY78l+o5y+KX/g6p7iVXdQE3Dr522eo7ENBVW3TDn+07a9oi+EnlrlhPo7uAwI1y9AScOM46u6zQ0NDA8PMxTTz3F008/TW9v79jviUSC119/nVQqBUAul2P37t00NzczlflspZRI20KSd33krQ7ijL85/fe44F57zg+Gl0q+D04H64/1yUgA/+gyIPJlWoQ4bcUQylg/CSHyGduvgT65EvLdcsZECds6/U9KJBKEAkLNp/4YzXA/ch3N+b4bcxE6MVgODjONY8FyuGza29tZu3YtQgiSyeTY90IIjh07RlVVFT6fjwMHDpBIJDhy5Ahvf/vb8Xq9+Zl+to1t21ckuqSUDHTsZLj7AImBE5TXP0jFknfkUy4A/uh8QsUNRMvXk463cWLHl9A9YXzhavpbX6d88dsJFS+fc4Pi5faLmYtz6s3/QtU8VDW8j8GOPfS1vIruiZBL9xEt34BQNOav+xipoRaOvvqPeENV5NL91K76TVze6Awc1dQgpcSyLKxpSJKpewpYcdc/YJkZjMwQB1/6P3iDFVhGEiktohUbCJesID3cxtFXP0fVsvcQiC2cM9fP6HVzwb6x7NMTYiWOBcvBYYZwBJbDZbN69WoCgQBbt24dV0/Q7/dTVFQ09rm/v5+FCxfS19dHJpPB6/XyxhtvsGvXLtrb27n99tsve99SWrQffpxsqoeBtm2EipdTs/I3EEIgpaSv9TUG2t7AzMVJD+UTQ3oCpeieMAPtO3D5CgkVL5+KbphSstksjz32GH19fRw7duyiyycHmmje+x0UzUNR7S20HvwhyYETmNk4Ri5OsHAxuWQv1St/naY3v0lf8ysEi5aSGmyisHoL0YqrR2A1NTXx9NNPj9XAnEpGA9qllJw49mU6jvx0pMRSMfG+oyBthFAZ7j1Isv8Ybl8hgdjCKW3DZGhra+PnP/85qVTqPHUa5YjldtR6KZxs7g4OM4QjsBwui+HhYXbs2MGdd97JvHnz6OnpoaQkP+MqkUhw4sSJsTityspKduzYgWEYBAJ5C9O6detYvXo1u3btOn9x2gsghErlsocY7NiDN1hBYdWN+RirEcoW3IuVS1FUeyuDnXsAie4O4S+Yhy9cQ8kMlL25EtxuNw8++CCWZfGtb33rossHovOoWfUIqu7FG6qkesWv033yBbyBMrLJHjR3AFX34fGXUN3w63j8pQSi88mmeggWLpqBI5o6qqureeSRRxgYGOAXv/jFtO2nZN5byCZ78BfMI5vqoaB8PYrmIlqxnv62GjLxdkoX3Dtt+78SysvL+cAHPkAikeDRRx89d4FRi5XI5ziTuobMGXMlQ5eDwzWNI7AcLoumpia6urp4+eWX8fv9bNmyZew3t9vNO9/5TnRdx+v1UlxcTCwWw+PxjFkedF0f+5fNZi97/0IIwsUNhIuW5fOsnxFTJYQgEFvEki2fBCGIVW0aTcYOCGKVN4wNNHMNIQRutxvbts8p1jsRmivAvLUjmcKFQkHZWgpK14xYKuTYcQuhEK1YT7R83dhvV1uuL1VVUVUVt9s9bedOCIG/oI4lN/3pmDX0zISzocL6kT/nVgyfoih4PB4MwzhPu+R4l6CmQuby77vzYZlZhrr24gvX4PYXzam+cXCYbRyB5XBZrFixgkwmw/bt27ntttuIx+OEw/kcUrquU1Y2Pg/TdNQqzAdsqxO+hY8L5h4N7D3945S3ZTYZH9B//uM+5zeHCRFCjJVJOudSERcXvXMS0wL1DEGta8j01AgsKSW9p14mnWhnoGMnlUsfwuWNOSLLwWGEq+tV1mFOcOLECSoqKhgcHBw3i9DBwWGOYVqgnGHl1XVIZaZkVq9lpkkMHCcYW4y/YAGdx55G2ubFV3RwuE5wBJbDZbNgwQJee+019u3bN6uFnh0cHC6MNMzxFiyXhpwiF2Fq8BSaK4hQVHRPGCklw72HpzQli4PD1YwjsBwum87OThoaGlBVlaGhodlujoODw/kwTFDOcNmpIzFYk9RAUkri/Y24A/kKCvkYtloG23c5ViwHhxEcgeVw2QwNDbF+/XrKy8s5efKkI7IcHOYqhgHKGfFjmopMZcbXJ7wCpG2QTfagu0Nj3ymqG4lNJtHpWLEcHHAElsMVUFlZyf79+4nH4/T19dHc3DzjbZBSIk1zJJu58zCfLqSUSMvK/7tK+lnadv7auEraO53I3FkuQiHA48I+1oychMgyMkMjGeFPW8fyOcVKGe45NIkWOzhcOzizCB0uCyEEt95666y2QUqJbO3C/OWrKPXzUDetdCbHTRfZHOaTLyNzJvpbbwG/d7ZbdEGkZWO+vBN5shXt3psQpVM/i/VqQUqZt2Bppy1YQggoLcJuakNEglfUP1JKkkOncHkLzvlN94SJ9xzmjPwo49tzBs5sQ4drHUdgOUwZUkpM08SyLNzufPLP0VxXU53DyD7Zin3gGDKVRl2/HBTnUp4O5FACa+dBMC3sG1aiznGBRc7A3nUQ2daNvXIxynUssADImYiz8qoJTYXiGHZHD6LkStIqSJIDJ3H7i89ZVyg6UtoY6UFcvtPVAqSUNA7uZ1/vdjyqj4bC9VQG61CuspxsDg6XgzMqOUwZlmXxs5/9jFwux+bNm4nFYnz7299myZIlrF+/Ho/HM2X7UlYsRo2nwO1C9g3CFQ0UDmcjpYRUGru9B6W0EBELo731VjDNq0OsuF1o92zBevNI/towrbyguE6RZ1mwxvC5kV39V7RN28ySSw/gC1ef85sQAs0dIjXcOiawpJS0xk+wu+s1FhQsxbQNtnW+yNGBfawvvZmQq8C5dx2uSRyB5TBlJJNJbNtm7dq1nDhxglgshq7rxONxzJGYmFOnTtHS0kJjYyO1tbVXtB8hBIQDKItrMf7zMey9R3D99sPg903tAc0ghmGwb98+EokEHR0ds9oWc+serGdfQ1mzFP3dd6NubABmz6XT19fH4cOHGR4evmj2f6EIRHkx9uMvYB88jvLRhxDVZRdc52pmcHCQgwcPkkwmJy49lc1N7NZVFDDNfF1C7dKHASkl8b7GfHD7eaxPLm8BqaFmwiUNCCGwpMn2rpeZF1mCS3HjVj0sLlhBf6aHJ0/+gBJ/JbWhhRR5y/DrQcBxHzpcGzj2WYcpQ9d1DMNgcHAQt9uNruu8613vQtd1WlpagJE3XE1DUabg0nO7EAVhRFEBqFo+uNkw8jFaUiLNqys4W1EUVFWdmr6ZBCIShKAfEQ3nE1VyesCTUiINM9+vhpkPJjfP38djy09iMoIQYqxczvkG3jPbhQBRWIAI+UHTkNa1OxHizL6ZCJnN5VMznLsiWDaYlx7oLqXEMtP0t2/HG6o477nQ3SEyyS6kzF8XzfHjqELFo3rHlbWKeopYGluDrujs793Bz098j6eafshQbuCaPV8O1xeOBcthyvB6vSxbtoze3l4WL17MwMAAR48exePxUFdXhxCCmpoaampq0DSNRCJxRfuRUmI3tWNt2wcuDXxeQGK+vAv74HG0OzYiiqKYT7yICPrQ7rsZ3K4pPdapRtd1Vq1ahW3bHDx4cNbaIYRAXbsUZX411rZ95P7zMbS7NqPUlgNg7z+GtX0fRELI3kFELAzZHPrbb4PAuRZE2daF8fOXUJfOR928+orKFUWjUTZt2kR/fz+tra3n7kNKZEcv5tNboTCCbOlARCNQXoTx5EsoZUVod22e2FV2lRMOh9m4cSPxeJzGxsZzF8gaEwssyM8CNE3g4veGlJLkYBM9TS/i9hehaBdw9wsFpMTMDqN7CjjYt5tSf+W58VpCIBBE3DEi7hhSSoZyAzx98lHurnuIiDt6nh04OFwdOALLYcoQQtDQ0DDuu6KiovMufylvqee1jLR2Itu6kJ29yEQaecs67MMnkMeasavLUAB7fyMEfKi3bUDMcYE1VYz216W6WM7s3zHrgqqC34t95CSypRN7UW1eYEmJdfgEdmsXtHTCcBKqSpG9A8jbNiAmEFh2Syfy6Cksy0bduAJc02Ods5vbsVs6EANDyNYuqLEgk0UeOondPwS3bbgmBdYFsWyQ9vhEo2fidiFTmQnP25lIKckkOug69jTB4qWomvei15fq8pNJdJEQFikjji+44KLNFUIQdhUggvBy65PcW/duNEW/6HoODnMVR2A5zBoXe0jnp5mb2Eea8rl8vG6E14Pd1YeorUB09CBKC/P/IkG0uzYjl9ShNCxCeNwoG1cgwgFEwD8zBzTLSCnz4mcogbK4FqFf+PaWUiL7BrFbu1AX14L3DKuES0e9YSV2LJwXW4dPoiysQbt1A3ZZETKTxW7pRK2fB7qGKDx3yj6A0rAIzbIRVaVwkfZMBmVhDUr/EGSyiJIYhAIITUUsqEYURWFkVut1hWGMq0N4JkIIpEtHJlJQfHFLUc+plwnEFl6SuBJC4PYVkRg4zimjnSJv2SULfiEEIVcB/ZleDve/ybLYWicey+GqxRFYDnMa69AJzO/+/HRMUM5AtnWjLJuPffA4IhZBDieR65ej1lVAXUV+vROt2DsOQNCPun45Ihyc5SOZARIpzO89iewbRP/wg6gLay68vGVh/vwl7APH4O23oW5efXowM02s3YfBNLF/9iss20b/rYdRq8sQAS+5L/0Q2dEDKxahrV123l0oAR/KljVTeJATY59qRx5rRjZ3IqpKIJ7Ebu3C9VsPo1zDQe4XJGtc0CUrPC5IJJFSXlDE5NL9mNlh/AXzLlnsaO4AvZ2NnLJs6mPrLqvZQggqA7Uc6N3J/Eg9Xu36eEFyuPZwBJbD3EVKhNeNKI5BwIsI+vMDRjYHsTAUFeRFl88Ltp0P6JUSJAi/F1FWhAh4QYiLDiLXBC4dUVEMmoq4SL6qvHXQQsTC+T4M+SGbnyCAqoBpIUpjMBjP97lt58usGPmgdlFeBKaJCAXGLI2oat4dZdt595SuTWufSylP78vvRUQjyEwOCkIITYNSC6mp+cD381hyrmVkLpefLXg+XDoykb7wNqQk3ncUl6/wsvpPCJVEdpCQv/iKcl1pik70/2/vzp6byu5Fj3/31mBJlmUNnjGWMYa2mZoO82EI3SGQ9FCVW3XuSadOnZfzdu59yV+Tl1SlKvclnZyGDlQ3hOEQ6A7EzdQYY9MY7DYYeZYnWbKGvfe6D8Jq3IyWN7QNv08VhQ3S0t5rL2n/tIbf8lRyY/QS22v2v3HXTrweJMASS5JSCutWL8bxr9DWRlGxEdT0TH5SdWUIUhlAQ2k6mkPHOPp31MQ02up61Ld9OP5lM67/+Ajj2N8xPjmB899+kV8d9zpzu3D974P5lWGeZ885s7rvYZy+iFYVBk3DunkX63YfanAMrSqMGhiB2kqYTKCFA6hECuPs12hOJ+rBEFpdFTidaN4S1IMhcp/9D/raRpzvbsc4eQHr/iCufz34cjOpGya541/mg8CaCvB50ABN1/NBZmsTxuHT6A21OD/Yt6B0BK8DNZt59uIOhwM1m35S0vXvy1AWM/E7+CPPn0P1KMPKMWhMUJUt7vprmkZ16Qq64t/QGt5MoOTJQ9BCLGVv1ieOWD6Uyg/7DI1CqRf1YIj8XUCDRBKVzcHQaP5bekUINRyH6RnweVADI1h9MfS3GrHuDUAqjZqYgtc8wNI0DVwueIF5wdbDBQLkDNTIeH5+VKkXFRsGy8xPFDeM/ER2pVAj42guZ344dmA03ys4OoE1Mp4ftr0/hOVwoHZswrpzDzUczyeAfZkBVjaHunMfdA3NU4Ian8qf08P5eqTSqPuDWNlcvoftDQuwSCSfvbjD6cj3BpvmU3dCyKWnMM0M2gImm+eztnfi8oaxEoOgNoG28AUGGjo1pSu5NnKRffW/lKzvYtl5wz5xxHKQn3w9BT4Pjvd2oreuyv9umqjkbH5ibiaLvn87WlUYa3gsP9E9XI4WCsDaRvS1UbRgGa5/PYjKZNFX1vzYp7WkOH6yDs3rQWWzqId70qmcgd7ahBYMQCIJAT9qcjqfwDWVRsWGoGkl+rb1+UntM7M4WprId4EAJW6s3n4cP90GloXeVP9yT8LrwfnRfszOu/mh5LpKrBIXemUIggH0lTWo1ia0SPCNm+SulEJNz0DA/+wHul2oyUQ+l9wTypiJd1Pie7EgWSmFQtE31c1oapD6slWYk/3kJvtxhaILHuaby5X17fh1RlODVPnqZKhQLCsSYImlRymMs19jfX0Dx893oa+qh1X1kDPI/b9jqIlp1OAo+r9sxtFQi/H5edA13P/3N/kVY4/QW5t+pJNY2rRyP46t68n98SjWrV60dAY1MIrrP/8XeuOKxx5vtt/GvNyJVluJ6/98/NgQpGP7RnL/fQrr0g0c7+3E+f7el38OuoYWKce60Q1KoTevRN28i1Vfjf72W+jROojWvfTjWJIsCzWbeerqTvh+RwSrL5b/YuKYP09NKZNEvJvS8PMntyulSOamuRW/TsZMU1/WiK7paIFaMkMdOEsrwF268CALjYayZi4OnOGDpo9xO96sQFksb9LnKmyjlOL69eucOXOGVCqFZVm0t7dz+vRpksnkixekaej11fmNaOurH/6TBg4HWkMtWkUQKkPo9dXg96FH69Dra9C8nnzywqf8Ed/TNC0/tLayGq0qnO/9q6tCKy97Yr1pkSBaXSVaYx24HI8/RtPQo7VoVZH8BPi513jZ5+HzoDfUoq2oQltVn1/4UBHKzy17eAxv5PXPPtwG53nnHfCjZpIYX17B6osV8qIppZideoBSJvqzghqlyJlZ7kx0cmnoPCVOL3X+KPrckKDuwlFWTfK7r7DSUwvO0K5pGqWuMrxOH22DZzEtc0HPF+LHJD1Ywjazs7Pcvn2btWvXcvPmTVpbW+nu7qa5uZnOzk62bduGYRgYhkE2my1safMkjp2b0N9pQfPkP9yVUqBrON/bnp9/ZVpoPg/oOq7/+Cj/JLdrWW6xoT1c5ZjJZDBNs7Bv40s/F13H+bOdqN0/yefMUiqffPIJr6vXV+P6r1/nV+c5HE98jGPbBvRNa+dfs0WYC4pM0ySbzZJOp+cFAAB4Pbj+/YP8KKXLgePtt/KB48OcXsuxPbyI59WNik+C2134/Zll1deAYWJ29+WvXU0FlpFmpO/v+IKNTy3DUiZDqRg9E52UOLxEy5rRHs6TevTxekkATXOQ6v0Sd1UL7nAT6Aubk1Xnj/LdVDdfxf7Gjtp38Tievkr2jQumxZIlAZawjWEYOJ1O/H4/g4ODWJY173eAq1evcu3aNWKxGKWlpcTj8cLzLcvCNE1crpeTvTmXy+F0vpzUAUopDMNY0LG3trayfn0+h1Qmk+Gvf/0r8XicGzdu4Pf78XiesR3Jw9fM5XK43YvLUp/NZhddhvlwz0fnIiaSW5aFZVmUlJTw3nvvEQwGAejv7+fkyZMkEglisRiffvrpgq6haeZ7PZ62X9+LsKPt5HK5Re81GQgEOHDgQOFchoaG+OKLL0gmk/T29nL48GFKnS5+MpEj6dBwjgwv4AAN/Ef70d+q537mLFPpDCWj08DD97ZXx+F2AIqZ3DSDs/cxcjlCJZXoepbxialnFq+UiXXrDF5XCVpZAzg9TGg64y8YAysUXbMdfNlzirciGyl3hcmOKDLDFrlcjh07dtDUJFMCxNKhqdf1K5545UzT5PPPP2d2dpaNGzdSVVVFW1sbMzMz/PSnP6W2trbQM5NKpRgZGZl304vFYnR0dHDo0CHbgyClFJ999hnvvvsuoZD9S77Hx8c5f/48v/rVr1742MvLywkGg4UeLOvhhsijo6Ok0+nnlpNKpTh69Ci//vWvi75pG4bBn/70J37zm98sKjjq6OggmUyyc+fOosvo7++nq6uLX/ziF9TW1haCvrm6MQyDgYGBBbeNq1evous677zzTlHHZVkWhw8f5tChQwQCgaLKUEpx4sQJNm/eTF1dcfPCDMPgyJEj/Pa3v32sbkzTJBaL5feS1DRS8QmuXLnC+x98sKDXcGgauqaRdWQwre9vDefOnWNltJ7mNY+kayjyzqED169fxzQMNm7dSm6h5ej57Q4BrIzCTCsOHz7Mxx9/zIoVT9+EWohXTXqwhG10XefDDz/EsqxC4PT+++8XerIenQtTVlZGWdnjaRMGBgZobGy0/UPSsizC4TArV66kosL+1AGlpaVEIhGi0WhRwY6maYU6q619sczjyWSSUChENBotuncml8sVylhMz+HY2BiJRILGxsaiy7Asi5GRERoaGuZd/7m6cTgcrFq1asHlPnjwAF3Xiz62ubbT0NBQ6FVbKKUUFRUVrFixgmj0ORn2nyKXy+Hz+V6obvp1HUfAT7CuZtHvJaUUvrIKAuW1VFQUd+w/5PUPYBgGNZWLL2/u+vywboT4sUmAJWzz6If9o1705h+JRNiyZcvLODQ0TWPXrl34/c9Ztl4kv9/Pzp07X+kHvNvtZt++fYsacnI4HOzfv39Rw2cA0WiUXC63qDIqKiqK7mV6lubm5kVdF03T2L17N17vs7PjP8+WLVsIh5+/79/TLORahUIhtm5d2BY1z7Jx48YnfiEqVlNTE5Zl2VLW3PUpLZUtdcTSIqsIxZJRWlpKIBB4KROTlVKEw2FSqZTt5c/NhQoEAoX5Pi+bUopEIkEkElnUjcqyLCKRCNPT04uql3A4TElJyaLK0DQNj8fD7Oyzt29ZiLkFA4s5Nk3TWLlyZWFhRrGCwSCZTKboMrLZLJFIhEwm88zHzQ3D2/VeMk0Tp9OJz+dbdFnw/TXxer22HJ9SilAoxPT0tG1BmxB2kABLLAlKKfr6+vjd735n6w12ruy7d+/S1tbG6OiorWXPlf/ZZ59x8eJFOjo6XsnKNaUU/f39XL58mZs3bxZdTi6XIxaLcezYsaLrXSlFZ2cnv//97xfVi3Xu3Dk6OztJpVJFl/FDs7Oz3Lt3rzARvBi5XI6jR49y/vz5oo+jv7+fL7/8kuHh4aLah1KK06dPc/36dU6fPv3MMgzD4IsvvuDChQv09PQUfcxzMpkMf/vb32wpC+Zfk0QisejylFIMDw9z5syZwmIaIZYCCbDEkpBKpWhvb6e2tnbRQ01P0tXVxdTUFD09PbZ/y53bSHpsbGzRQ20vStM0NmzYgMPhWNTQiMfjweFwvNCk+qeJx+P09fURDocX1YNXVVXF5OQk9+7dK7qMH/L7/bS0tKDrelHXRilFe3s7gUBgUe3mzp07jI+P09PTU3Qdud1uBgcHn3seqVQKt9vN5s2bicViRb3Wo3w+H+vWrVt0OXMevSZ2rBjWdZ2ysjKmpqZe2ftPiBchAZZYEgzDwOv1EovFGB8ft7386upqVq9ezdjYGIZh2Fp2LpdDKcWmTZteyrE/zenTp0mn09TUFL8N0OzsLJFIBL/f/9yhp6eZS0/R39/P9PR0UWUopWhubmb9+vX09fUVVcaTJJNJPvnkE9atW1d0KgqHw8HMzAwPHjwouu1UVlayatUqEolEUfWslGJycpIdO3aQSCSe2YPl8XhIp9N89913tqyYNU2TeDzO2NiYLV9OUqkUn3zyCa2trYtODwLfp4eJRqNMTT07VYQQr5KkaRBLSiwWo7q6elEpA54km81y+/ZtQqGQ7Uu5lVLcv3+fyclJ1qxZg9frfemT3S3L4ubNm+RyORobG4lEIkWVk0wm6e7uJhgMFr0CEvJ18ODBA1asWFFUGUopBgYGGBkZoampifLy8qKO44dmZmbo7OzE5/PR0tJSdI9JOp1menqaysrKoq6tYRh0d3fj8/mIRhe+L59Sing8zv3794lGo4TD4aeWoZQiFosxMTGxqHOek8lk6OjoQNd1Nm3atOj35tw18Xq9tLS0LGRpnMgAAAkiSURBVDrIyuVyfPvttzidTtasWWP7Z4cQxZIASwghhBDCZjJEKN4IlmUxNfX9XmimaS545ZxSal4Zr5Pn1YdSqvD/cz/PzSUyDOO5w1bL0Q/rZO73hfhhu3ud5HK5Z05StyxrXv1NT08XhlhzuRwzMzOv5DiF+LFIgCWWJaUUpmkWtmiZ+3suq/VcVvS5nzOZDMeOHSt82CcSCY4fPz7vMU8q0zRNMpkMZ86cIZvNcuTIkcK+b0v5prnQ+pmamuLkyZPznv9odnnLsgpBlGmaHD9+nJ6eHtra2hgfH+fkyZOFxy5lT6oXy7Lo6+ujvb19Xv1MTU1x4sSJwnNnZmb4/PPPX7jNnDx5knQ6zbFjxxad4uFVeVr93Lp1i+7u7nn1Mzo6ytmzZ+c990ltBvLB6cmTJ7l9+zZff/01Q0NDnD17tvA6QryOZLBaLEvpdJpPP/2UUChEWVkZsViMqqoqAO7du4fX6+XAgQNcvHiR6elpfvnLXz42Qdc0TTo6Orh58ybBYJBIJEJHRwcul4tDhw5x+vRpkskka9as4dSpU5SUlDA4OMhf/vIXmpqa2LNnz49x6i8kkUhw5MgRampqcDqdDA8P09DQwPj4OJOTk/h8Pvbu3cuFCxfIZDLs379/3uq2vr4+RkdHaWtr42c/+xnxeJyJiQlWr17NlStXuHv3LuFwmK+++oqPPvqImzdvMjU1xYEDB4rOVP4q9PT0cPz4ccrLy2lpaeHu3bt4PB7cbjcdHR34fD7a29tJpVK8++67T2wz7e3tdHV1EQqFCAQC3Lp1C5fLxcGDBzl16hSpVIq1a9dy4sQJvF4vAwMD/PnPf17ybQagvb2dixcv4vf7WbNmDb29vZSVlZFMJhkeHubDDz/k6tWrWJbFzp0759VPT08P8Xica9eusXv37kJbW7t2LZcvX6avrw+fz0dbWxuHDh2io6ODsbExDhw4QENDw4941kK8HNKDJZYtXdfRdZ2zZ8+ilCrc5Ocytg8MDKDrOn19fQwNDT2xjPPnz2OaJt3d3YyPj/POO+8QDAa5cuUKTU1NrF+/Hl3XWb9+PTt27CAUCvHzn//c1lQCL4vD4UApxblz59A0jRs3bpBIJAqBw1xaiW+//ZaJiYl5zw0EAnzzzTcAfPXVV5SVlTExMcGNGzcKN8Samhq2b9/OqlWrWL16NVu3brV1BeDLkMlkaGxsZNOmTfzhD38o5GTy+/3s3r2biooKnE4nPT09jI2NPfZ80zQL+bBu375daDNer5cbN27Q3NxMa2sruq6zYcMGtm/fTigU4oMPPuDu3btLvrcmnU6zbt06Ghsb+eMf/0gulysE0/v27SMQCOByuejs7HxsuDQSiXDt2jWy2Sz/+Mc/CAQCTExM0NHRwcGDB6mvr6e6upqdO3cSjUZpbm5my5YtS77NCFEsCbDEsqSUIhqN0t/fT11dHdXV1ezbtw9N07h27Rqjo6MkEgni8Tg+nw/Lsh5bdaXrOi0tLVRVVbFnzx58Ph8ejwen00kkEqG3t5eOjg4cDgemadLb20tJSQkul2tZ7HnW2NhIb28vDQ0N1NbWsnfvXkzT5PLly8zMzDA0NEQymcTj8WBZ1rzVf6FQiIGBAbZu3UpPTw8rV65E13Wqq6u5cuUKsViMkpISRkdHGRsbw+v14nK5lnwAAdDd3U1XVxd79uyhsrKSXbt2UVtbS39/Px0dHUxMTFBaWoppmo+tiHQ6nbz11ltUVlayd+9eSktLKSkpweFwEIlEuHv3Ll1dXfPajMfjWTZtxrIsurq66O3tZffu3VRVVRX+7u3t5ZtvvmF2dhav1/vYe8rv9zMxMcHbb79Nf38/NTX5fRCrqqq4dOkSg4ODeL1ehoaGGB8fX1ZtRohiyCpCsSwZhsHAwABOp5NwOMzQ0BClpaVcvHixsKFuMBhkYGAAl8tFKBQilUoVPtB1XccwjEJPV0lJCX6/H6fTSTabxe12c/v2ba5evcquXbuoqqoil8vhdrsJBAIkEgmCweCSvWnOZWj3er2UlZUxPDxMIBDg1KlTbNy4kbq6unnnPlc/c4ka3W43yWSSsrIyJicnqaysZGpqitLSUgYHB3G5XFRWVjIyMoLX68XhcFBSUlKo06Wqs7OTO3fusG3bNqqqqhgaGkLX9cLPc70ubrebYDBIOp3G6XTOazNzdeDxePD5fLhcLjKZDG63mzt37nD58mX27NlDJBLBMAzcbjfl5eVMT08v6TYD0NbWRiKRYMOGDVRUVBSudSQSYWhoiGAwSDwex+PxFOpnrqf00TYzNTVFOBwmkUjg9/sL5VRUVDA8PIzP58PpdOJ2u5d8mxGiWBJgideGUorvvvuOysrKJ25Mm06nuXDhAtlsltraWjZt2vTUnE2mafLPf/4TXdfZtm2bLR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style='width=auto;height:auto'><div>\n              0     1    2  accuracy  macro avg  weighted avg\nprecision   1.0   1.0  1.0       1.0        1.0           1.0\nrecall      1.0   1.0  1.0       1.0        1.0           1.0\nf1-score    1.0   1.0  1.0       1.0        1.0           1.0\nsupport    11.0  13.0  6.0       1.0       30.0          30.0\n",
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+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Access Control List",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "---",
+          "### Running UDFs in the SQL and REST APIs for embedded Python execution",
+          "---",
+          "",
+          "The Access Control List (ACL) provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database. ",
+          "",
+          "As ADMIN, run the network access control list `pyqAppendHostAce` function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides. For example, if your username is *OMLUSER* and your Autonomous Database resides in the Ashburn region, the root domain is *adb.us-ashburn-1.oraclecloudapps.com*.",
+          "",
+          "Then run the token store `pyqSetAuthToken` function to persist the authorization token issued by a cloud host for use with the upcoming SQL calls."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991743405,
+          "endTime" : 1739991743878,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>The Access Control List (ACL) provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database.</p>\n<p>As ADMIN, run the network access control list <code>pyqAppendHostAce</code> function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides. For example, if your username is <em>OMLUSER</em> and your Autonomous Database resides in the Ashburn region, the root domain is <em>adb.us-ashburn-1.oraclecloudapps.com</em>.</p>\n<p>Then run the token store <code>pyqSetAuthToken</code> function to persist the authorization token issued by a cloud host for use with the upcoming SQL calls.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Add user to the Access Control List",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "exec pyqAppendHostAce('OMLUSER','adb.us-ashburn-1.oraclecloudapps.com')",
+          "",
+          "  PL/SQL procedure successfully completed.",
+          "  Elapsed: 00:00:00.370"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Define function to get authorization token",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "To use the SQL and REST APIs for embedded Python execution, the UDF must be stored in the script repository, and an Oracle Machine Learning (OML) cloud account USERNAME, PASSWORD, and URL must be provided to obtain an authentication token. To use a conda environment when calling OML4Py script execution endpoints, specify the conda environment in the `env_name` field when using SQL, and the `envName` field when using REST.",
+          "",
+          "Obtain the token URL from the OML service console in Autonomous Database by first signing into your Oracle Cloud Infrastructure account with your OCI user name and password:",
+          "",
+          "- Click the “three line menu” and select the desired Autonomous Database instance.",
+          "- Click Database Actions and scroll down to the Oracle Machine Learning RESTful Services tile.",
+          "- Click “Copy” to get the REST authentication token for REST APIs URL of the form: /omlusers/api/oauth2/v1/token",
+          "",
+          "The includes the location, tenancy ID, and database name separated by dashes. For example, https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991744377,
+          "endTime" : 1739991744839,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>To use the SQL and REST APIs for embedded Python execution, the UDF must be stored in the script repository, and an Oracle Machine Learning (OML) cloud account USERNAME, PASSWORD, and URL must be provided to obtain an authentication token. To use a conda environment when calling OML4Py script execution endpoints, specify the conda environment in the <code>env_name</code> field when using SQL, and the <code>envName</code> field when using REST.</p>\n<p>Obtain the token URL from the OML service console in Autonomous Database by first signing into your Oracle Cloud Infrastructure account with your OCI user name and password:</p>\n<ul>\n<li>Click the “three line menu” and select the desired Autonomous Database instance.</li>\n<li>Click Database Actions and scroll down to the Oracle Machine Learning RESTful Services tile.</li>\n<li>Click “Copy” to get the REST authentication token for REST APIs URL of the form: /omlusers/api/oauth2/v1/token</li>\n</ul>\n<p>The includes the location, tenancy ID, and database name separated by dashes. For example, <a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com</a>.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Define get_token2 function",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "create or replace function get_token2 (",
+          "  p_url       varchar2,",
+          "  p_username  varchar2,",
+          "  p_password  varchar2,",
+          "  p_verbose   number default 0) ",
+          "  return varchar2",
+          "  authid definer as pragma autonomous_transaction;",
+          "  l_buffer            varchar2(4000);",
+          "  l_request_content   clob;",
+          "  l_response_content  clob;",
+          "  l_request           utl_http.req;",
+          "  l_response          utl_http.resp;",
+          "  l_url               varchar2(1000);",
+          "  l_smt               varchar2(1000);",
+          "  l_access_token      varchar2(4000);",
+          "begin",
+          "  l_url := p_url;",
+          "  l_request_content := q'[{\"grant_type\":\"password\", \"username\":\"]' || p_username || q'[\", \"password\":\"]' || p_password ||",
+          "  q'[\"}]';",
+          "  begin",
+          "    utl_http.set_wallet('');",
+          "    l_request := utl_http.begin_request(l_url,'POST',' HTTP/1.1');",
+          "    utl_http.set_header(l_request,'Content-Type','application/json');",
+          "    utl_http.set_header(l_request,'Accept','application/json');",
+          "    utl_http.set_header(l_request,'Content-Length',length(l_request_content));",
+          "    utl_http.write_text(l_request,l_request_content);",
+          "    l_response := utl_http.get_response(l_request);",
+          "    if (l_response.status_code != utl_http.HTTP_OK) THEN",
+          "      RAISE_APPLICATION_ERROR(-20999, 'Token request failed with status code: '||l_response.status_code||', reason: '||l_response.reason_phrase);",
+          "    end if;",
+          "    if p_verbose > 0 then",
+          "      dbms_output.put_line('HTTP response status code: ' || l_response.status_code);",
+          "      dbms_output.put_line('HTTP response reason phrase: ' || l_response.reason_phrase);",
+          "    end if;",
+          "    begin",
+          "      loop",
+          "        utl_http.read_line(l_response,l_buffer);",
+          "        l_access_token := regexp_substr(l_buffer,'\"accessToken\":\"([^\"]+)\"',1,1,'i',1);",
+          "        if p_verbose > 0 then",
+          "          dbms_output.put_line('l_access_token: [' || l_access_token || ']');",
+          "        end if;",
+          "      end loop;",
+          "      utl_http.end_response(l_response);",
+          "    exception",
+          "      when utl_http.end_of_body then utl_http.end_response(l_response);",
+          "    end;",
+          "  end;",
+          "  return(l_access_token);",
+          "  dbms_output.put_line('Access token is: ' || l_access_token);",
+          "end get_token2;",
+          "/"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991800603,
+          "endTime" : 1739991801083,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nFunction GET_TOKEN2 compiled\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create access details table to support token acquisition",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "BEGIN EXECUTE IMMEDIATE 'DROP TABLE ACCESS_DETAILS';",
+          "EXCEPTION WHEN OTHERS THEN NULL; ",
+          "END;",
+          "/",
+          "",
+          "CREATE TABLE ACCESS_DETAILS (",
+          "  NAME  VARCHAR2(20),",
+          "  VALUE VARCHAR2(1000)",
+          "  );",
+          "/",
+          "",
+          "-- Important! Replace the OML URL, username and password with your database-specific values",
+          "",
+          "INSERT INTO ACCESS_DETAILS VALUES ('URL', '<YOUR OML URL>/omlusers/api/oauth2/v1/token');",
+          "INSERT INTO ACCESS_DETAILS VALUES ('USERNAME', 'OMLUSER');",
+          "INSERT INTO ACCESS_DETAILS VALUES ('PASSWORD', 'YOUR PASSWORD');"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991806248,
+          "endTime" : 1739991806788,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable ACCESS_DETAILS created.\n\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Obtain and set token",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "DECLARE",
+          "   access_token VARCHAR2(2000);",
+          "   is_set BOOLEAN;",
+          "BEGIN",
+          "   with url_string  as (select VALUE from ACCESS_DETAILS where NAME = 'URL'), ",
+          "        username    as (select VALUE from ACCESS_DETAILS where NAME = 'USERNAME'),",
+          "        pswd        as (select VALUE from ACCESS_DETAILS where NAME = 'PASSWORD')",
+          "   SELECT get_token2(url_string.VALUE, username.VALUE, pswd.VALUE) INTO access_token ",
+          "   FROM   url_string, username, pswd ",
+          "   WHERE localtimestamp=localtimestamp;",
+          "",
+          "   pyqSetAuthToken(access_token);",
+          "     is_set := pyqIsTokenSet();",
+          "     IF (is_set) THEN",
+          "      DBMS_OUTPUT.put_line ('token is set');",
+          "       -- enable to return token",
+          "       -- DBMS_OUTPUT.put_line (''|| access_token);",
+          "    END IF;",
+          "END;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991814893,
+          "endTime" : 1739991816375,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "token is set\n\n\nPL/SQL procedure successfully completed.\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Show the list of available OML4Py conda environments",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "SELECT * from table(pyqListEnvs())"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":200,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991822775,
+          "endTime" : 1739991834874,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "name      size      description                                      number_of_installed_packages   \nmypyenv   2.2 GiB   Install Python seaborn and tensorflow packages                              149 \n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the Python UDF using the SQL API for embedded Python execution - synchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "set long 2000",
+          "",
+          "SELECT ID, VALUE FROM table(pyqTableEval( ",
+          "    inp_nam => 'DF', ",
+          "    par_lst => '{\"oml_graphics_flag\":true}', ",
+          "    out_fmt => 'PNG', ",
+          "    scr_name => 'build_mod',",
+          "    scr_owner=> NULL,",
+          "    env_name => 'mypyenv'));"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739991906479,
+          "endTime" : 1739991990282,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nID   VALUE                                                                                                                                                                                                                                                                                                                                                                 \n   1 [{\"0\":0.0,\"1\":0.0,\"2\":0.2,\"accuracy\":0.2,\"macro avg\":0.0666666667,\"weighted avg\":0.04},{\"0\":0.0,\"1\":0.0,\"2\":1.0,\"accuracy\":0.2,\"macro avg\":0.3333333333,\"weighted avg\":0.2},{\"0\":0.0,\"1\":0.0,\"2\":0.3333333333,\"accuracy\":0.2,\"macro avg\":0.1111111111,\"weighted avg\":0.0666666667},{\"0\":11.0,\"1\":13.0,\"2\":6.0,\"accuracy\":0.2,\"macro avg\":30.0,\"weighted avg\":30.0}]   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the Python UDF using the SQL API for embedded Python execution - asynchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "set long 2000",
+          "",
+          "SELECT VALUE FROM table(pyqTableEval( ",
+          "    inp_nam => 'DF', ",
+          "    par_lst => '{\"oml_graphics_flag\":true, \"oml_async_flag\":true}', ",
+          "    out_fmt => 'PNG', ",
+          "    scr_name => 'build_mod',",
+          "    scr_owner=> NULL,",
+          "    env_name => 'mypyenv'));"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992093748,
+          "endTime" : 1739992106356,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nVALUE                                                                                                                                            \nhttps://hmnsuoc10zlzdol-moviestreamworkshop.adb.ap-tokyo-1.oraclecloudapps.com/oml/api/py-scripts/v1/jobs/2a98c846-22db-4f4f-9b01-4f2cb0284555   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Get the job status",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Poll the job status sing the `pyqJobStatus` function. If the job is still running, the return value will be *job is still running*.  When the job completes, a job ID and result location are returned. ",
+          "",
+          "Note, replace your job ID with the one shown here."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992115147,
+          "endTime" : 1739992115596,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Poll the job status sing the <code>pyqJobStatus</code> function. If the job is still running, the return value will be <em>job is still running</em>.  When the job completes, a job ID and result location are returned.</p>\n<p>Note, replace your job ID with the one shown here.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%script",
+          "",
+          "set long 1000",
+          "",
+          "SELECT VALUE from pyqJobStatus(job_id => '2a98c846-22db-4f4f-9b01-4f2cb0284555');"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992209207,
+          "endTime" : 1739992214854,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nVALUE                                                                                                                                                   \nhttps://hmnsuoc10zlzdol-moviestreamworkshop.adb.ap-tokyo-1.oraclecloudapps.com/oml/api/py-scripts/v1/jobs/2a98c846-22db-4f4f-9b01-4f2cb0284555/result   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Retrieve the result",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "set long 500",
+          "",
+          "SELECT NAME, ID, VALUE, dbms_lob.substr(image,100,1) image FROM pyqJobResult(job_id => '2a98c846-22db-4f4f-9b01-4f2cb0284555', out_fmt=>'PNG');"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992224450,
+          "endTime" : 1739992225359,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nNAME   ID   VALUE                                                                                                                                                                                                                                                                                                                                                                 IMAGE                                                                                                                                                                                                      \n          1 [{\"0\":0.0,\"1\":0.0,\"2\":0.2,\"accuracy\":0.2,\"macro avg\":0.0666666667,\"weighted avg\":0.04},{\"0\":0.0,\"1\":0.0,\"2\":1.0,\"accuracy\":0.2,\"macro avg\":0.3333333333,\"weighted avg\":0.2},{\"0\":0.0,\"1\":0.0,\"2\":0.3333333333,\"accuracy\":0.2,\"macro avg\":0.1111111111,\"weighted avg\":0.0666666667},{\"0\":11.0,\"1\":13.0,\"2\":6.0,\"accuracy\":0.2,\"macro avg\":30.0,\"weighted avg\":30.0}]   89504E470D0A1A0A0000000D494844520000046A000003E808060000008668185B0000003A74455874536F667477617265004D6174706C6F746C69622076657273696F6E332E31302E302C2068747470733A2F2F6D6174706C6F746C69622E6F72672F94   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the Python UDF using the REST API for embedded Python execution - synchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "",
+          "$ curl -i -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' \\",
+          "       --header 'Accept: application/json' -d '{\"input\":\"select * from DF\",\"envName\":\"mypyenv\", \"graphicsFlag\":true}' \\",
+          "       \"<omlserver>/oml/api/py-scripts/v1/table-apply/build_mod\"",
+          "",
+          "Returns the result:",
+          "",
+          "[{\"0\":0.0,\"1\":0.0,\"2\":0.2,\"accuracy\":0.2,\"macro avg\":0.0666666667,\"weighted avg\":0.04},...}]   89504E470D0A1A0A0000000D494844520000046A00000..."
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the Python UDF using the REST API for embedded Python execution - asynchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "",
+          "$ curl -i -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' \\",
+          "       --header 'Accept: application/json' -d '{\"input\":\"select * from DF\",\"envName\":\"mypyenv\", \"graphicsFlag\":true, \"asyncFlag\":true}' \\",
+          "       \"<omlserver>/oml/api/py-scripts/v1/table-apply/build_mod\"",
+          "",
+          "Returns the job ID:",
+          "",
+          "Location: <YOUR OML URL>/oml/api/py-scripts/v1/jobs/3887d8a2-9b91-448c-940e-542b39205049"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Retrieve the result",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "$ curl -i -X GET --header \"Authorization: Bearer <token>\" \\",
+          "       --header 'Accept: application/json' \\",
+          "       \"<omlserver>/oml/api/py-scripts/v1/jobs/3887d8a2-9b91-448c-940e-542b39205049/result\"",
+          "",
+          "Returns:",
+          "",
+          "[{\"0\":0.0,\"1\":0.0,\"2\":0.2,\"accuracy\":0.2,\"macro avg\":0.0666666667,\"weighted avg\":0.04},...}]   89504E470D0A1A0A0000000D494844520000046A00000..."
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## End of script"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992251007,
+          "endTime" : 1739992251454,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py -0- Tour.dsnb b/machine-learning/notebooks-oml/python/OML4Py -0- Tour.dsnb
new file mode 100644
index 00000000..72e587d0
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py -0- Tour.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py -0- Tour","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737140124407,"interpreter":"md.low","endTime":1737140124936,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning for Python (OML4Py)","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as automated machine learning capabilities. In this notebook, we highlight the range of OML4Py features.","","* Automated Machine Learning (AutoML)","* Machine learning model building and scoring","* Creating database tables","* Transparency layer functionality","* Embedded Python execution","* REST API invocation of embedded Python execution","","This notebook is the first of a series (0 through 5) that is intended to introduce the range of OML4Py functionality through short examples. ","","Copyright (c) 2025 Oracle Corporation ","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737140125443,"interpreter":"md.low","endTime":1737140125909,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python-oml4py\">Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as automated machine learning capabilities. In this notebook, we highlight the range of OML4Py features.<\/p>\n<ul>\n<li>Automated Machine Learning (AutoML)<\/li>\n<li>Machine learning model building and scoring<\/li>\n<li>Creating database tables<\/li>\n<li>Transparency layer functionality<\/li>\n<li>Embedded Python execution<\/li>\n<li>REST API invocation of embedded Python execution<\/li>\n<\/ul>\n<p>This notebook is the first of a series (0 through 5) that is intended to introduce the range of OML4Py functionality through short examples.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Import libraries supporting OML4Py","","","Note: The Oracle Database connectivity package ***python-oracledb*** is used to support database access. ","You can get access to the underlying connection to use ***python-oracledb*** directly via the function ***oml.cursor***. ","See the <a href=\"https://python-oracledb.readthedocs.io/en/latest/\" target=\"_blank\">general ***cx_Oracle*** documentation <\/a> for more information. ","","For Oracle Autonomous Database, a database connection is automatically established provided you have an appropriate interpreter ","binding specified and import the ***oml*** package. "],"enabled":true,"result":{"startTime":1737140126398,"interpreter":"md.low","endTime":1737140126863,"results":[{"message":"<h1 id=\"import-libraries-supporting-oml4py\">Import libraries supporting OML4Py<\/h1>\n<p>Note: The Oracle Database connectivity package <em><strong>python-oracledb<\/strong><\/em> is used to support database access.\nYou can get access to the underlying connection to use <em><strong>python-oracledb<\/strong><\/em> directly via the function <em><strong>oml.cursor<\/strong><\/em>.\nSee the <a href=\"https://python-oracledb.readthedocs.io/en/latest/\" target=\"_blank\">general <em><strong>cx_Oracle<\/strong><\/em> documentation <\/a> for more information.<\/p>\n<p>For Oracle Autonomous Database, a database connection is automatically established provided you have an appropriate interpreter\nbinding specified and import the <em><strong>oml<\/strong><\/em> package.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","from oml import automl","import warnings","","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737140127410,"interpreter":"python.low","endTime":1737140129899,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Get a proxy object for a database table","Here, we illustrate two ways to get proxy objects to database tables. First, we do so creating a new table with ***oml.create*** based on data from sklearn datasets. The second uses the ***oml.sync*** function to obtain an OML proxy object for the named table. A typical machine learning step is to split the data into train and test sets, followed by separating predictors from the target. This is illustrated on the OML proxy objects using the OML4Py transparency layer functionality. "],"enabled":true,"result":{"startTime":1737140130377,"interpreter":"md.low","endTime":1737140130843,"results":[{"message":"<h2 id=\"get-a-proxy-object-for-a-database-table\">Get a proxy object for a database table<\/h2>\n<p>Here, we illustrate two ways to get proxy objects to database tables. First, we do so creating a new table with <em><strong>oml.create<\/strong><\/em> based on data from sklearn datasets. The second uses the <em><strong>oml.sync<\/strong><\/em> function to obtain an OML proxy object for the named table. A typical machine learning step is to split the data into train and test sets, followed by separating predictors from the target. This is illustrated on the OML proxy objects using the OML4Py transparency layer functionality.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Create table from Python DataFrame","message":["%python","","from sklearn import datasets","import pandas as pd","pd.set_option('display.width',1000)","","breastcancer = datasets.load_breast_cancer()","bc_data = breastcancer.data.astype(float)","X = pd.DataFrame(bc_data, columns = breastcancer.feature_names)","y = pd.DataFrame(breastcancer.target, columns = ['TARGET'])","","try:","    oml.drop(table='BREASTCANCER')","except:","    pass","    ","BREASTCANCER = oml.create(pd.concat([X, y], axis=1), table = 'BREASTCANCER') # returns proxy object","","print(\"Shape:\",BREASTCANCER.shape)","print(\"Description:\\n\", breastcancer.DESCR)"],"enabled":true,"result":{"startTime":1737140131337,"interpreter":"python.low","endTime":1737140132381,"results":[{"message":"Shape: (569, 31)\nDescription:\n .. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 569\n\n:Number of Attributes: 30 numeric, predictive attributes and the class\n\n:Attribute Information:\n    - radius (mean of distances from center to points on the perimeter)\n    - texture (standard deviation of gray-scale values)\n    - perimeter\n    - area\n    - smoothness (local variation in radius lengths)\n    - compactness (perimeter^2 / area - 1.0)\n    - concavity (severity of concave portions of the contour)\n    - concave points (number of concave portions of the contour)\n    - symmetry\n    - fractal dimension (\"coastline approximation\" - 1)\n\n    The mean, standard error, and \"worst\" or largest (mean of the three\n    worst/largest values) of these features were computed for each image,\n    resulting in 30 features.  For instance, field 0 is Mean Radius, field\n    10 is Radius SE, field 20 is Worst Radius.\n\n    - class:\n            - WDBC-Malignant\n            - WDBC-Benign\n\n:Summary Statistics:\n\n===================================== ====== ======\n                                        Min    Max\n===================================== ====== ======\nradius (mean):                        6.981  28.11\ntexture (mean):                       9.71   39.28\nperimeter (mean):                     43.79  188.5\narea (mean):                          143.5  2501.0\nsmoothness (mean):                    0.053  0.163\ncompactness (mean):                   0.019  0.345\nconcavity (mean):                     0.0    0.427\nconcave points (mean):                0.0    0.201\nsymmetry (mean):                      0.106  0.304\nfractal dimension (mean):             0.05   0.097\nradius (standard error):              0.112  2.873\ntexture (standard error):             0.36   4.885\nperimeter (standard error):           0.757  21.98\narea (standard error):                6.802  542.2\nsmoothness (standard error):          0.002  0.031\ncompactness (standard error):         0.002  0.135\nconcavity (standard error):           0.0    0.396\nconcave points (standard error):      0.0    0.053\nsymmetry (standard error):            0.008  0.079\nfractal dimension (standard error):   0.001  0.03\nradius (worst):                       7.93   36.04\ntexture (worst):                      12.02  49.54\nperimeter (worst):                    50.41  251.2\narea (worst):                         185.2  4254.0\nsmoothness (worst):                   0.071  0.223\ncompactness (worst):                  0.027  1.058\nconcavity (worst):                    0.0    1.252\nconcave points (worst):               0.0    0.291\nsymmetry (worst):                     0.156  0.664\nfractal dimension (worst):            0.055  0.208\n===================================== ====== ======\n\n:Missing Attribute Values: None\n\n:Class Distribution: 212 - Malignant, 357 - Benign\n\n:Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n:Donor: Nick Street\n\n:Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass.  They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\nConstruction Via Linear Programming.\" Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree.  Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets\",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n|details-start|\n**References**\n|details-split|\n\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n  for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n  San Jose, CA, 1993.\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n  July-August 1995.\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n  163-171.\n\n|details-end|\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get proxy object for named database table and split data","message":["%python","","BREASTCANCER = oml.sync(table = \"BREASTCANCER\") # returns proxy object","print(\"Full Dataset Type: \", type(BREASTCANCER))","print(\"Full Dataset Shape: \", BREASTCANCER.shape)","","BREASTCANCER_X,BREASTCANCER_y = BREASTCANCER.drop('TARGET'), BREASTCANCER['TARGET']","","BREASTCANCER_train,  BREASTCANCER_test = BREASTCANCER.split(ratio=(0.6, 0.4))","","BREASTCANCER_train_X, BREASTCANCER_train_y = BREASTCANCER_train.drop('TARGET'), BREASTCANCER_train['TARGET']","BREASTCANCER_test_X, BREASTCANCER_test_y = BREASTCANCER_test.drop('TARGET'), BREASTCANCER_test['TARGET']","","print(\"Train Type: \", type(BREASTCANCER_train))","print(\"Train Shape: \", BREASTCANCER_train.shape)"],"enabled":true,"result":{"startTime":1737140132863,"interpreter":"python.low","endTime":1737140133833,"results":[{"message":"Full Dataset Type:  <class 'oml.core.frame.DataFrame'>\nFull Dataset Shape:  (569, 31)\nTrain Type:  <class 'oml.core.frame.DataFrame'>\nTrain Shape:  (343, 31)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(BREASTCANCER.head(10))"],"enabled":true,"result":{"startTime":1737140134295,"interpreter":"python.low","endTime":1737140134772,"results":[{"message":"mean radius\tmean texture\tmean perimeter\tmean area\tmean smoothness\tmean compactness\tmean concavity\tmean concave points\tmean symmetry\tmean fractal dimension\tradius error\ttexture error\tperimeter error\tarea error\tsmoothness error\tcompactness error\tconcavity error\tconcave points error\tsymmetry error\tfractal dimension error\tworst radius\tworst texture\tworst perimeter\tworst area\tworst smoothness\tworst compactness\tworst concavity\tworst concave points\tworst symmetry\tworst fractal dimension\tTARGET\n17.99\t10.38\t122.8\t1001.0\t0.1184\t0.2776\t0.3001\t0.1471\t0.2419\t0.07871\t1.095\t0.9053\t8.589\t153.4\t0.006399\t0.04904\t0.05373\t0.01587\t0.03003\t0.006193\t25.38\t17.33\t184.6\t2019.0\t0.1622\t0.6656\t0.7119\t0.2654\t0.4601\t0.1189\t0.0\n20.57\t17.77\t132.9\t1326.0\t0.08474\t0.07864\t0.0869\t0.07017\t0.1812\t0.05667\t0.5435\t0.7339\t3.398\t74.08\t0.005225\t0.01308\t0.0186\t0.0134\t0.01389\t0.003532\t24.99\t23.41\t158.8\t1956.0\t0.1238\t0.1866\t0.2416\t0.186\t0.275\t0.08902\t0.0\n19.69\t21.25\t130.0\t1203.0\t0.1096\t0.1599\t0.1974\t0.1279\t0.2069\t0.05999\t0.7456\t0.7869\t4.585\t94.03\t0.00615\t0.04006\t0.03832\t0.02058\t0.0225\t0.004571\t23.57\t25.53\t152.5\t1709.0\t0.1444\t0.4245\t0.4504\t0.243\t0.3613\t0.08758\t0.0\n11.42\t20.38\t77.58\t386.1\t0.1425\t0.2839\t0.2414\t0.1052\t0.2597\t0.09744\t0.4956\t1.156\t3.445\t27.23\t0.00911\t0.07458\t0.05661\t0.01867\t0.05963\t0.009208\t14.91\t26.5\t98.87\t567.7\t0.2098\t0.8663\t0.6869\t0.2575\t0.6638\t0.173\t0.0\n20.29\t14.34\t135.1\t1297.0\t0.1003\t0.1328\t0.198\t0.1043\t0.1809\t0.05883\t0.7572\t0.7813\t5.438\t94.44\t0.01149\t0.02461\t0.05688\t0.01885\t0.01756\t0.005115\t22.54\t16.67\t152.2\t1575.0\t0.1374\t0.205\t0.4\t0.1625\t0.2364\t0.07678\t0.0\n12.45\t15.7\t82.57\t477.1\t0.1278\t0.17\t0.1578\t0.08089\t0.2087\t0.07613\t0.3345\t0.8902\t2.217\t27.19\t0.00751\t0.03345\t0.03672\t0.01137\t0.02165\t0.005082\t15.47\t23.75\t103.4\t741.6\t0.1791\t0.5249\t0.5355\t0.1741\t0.3985\t0.1244\t0.0\n18.25\t19.98\t119.6\t1040.0\t0.09463\t0.109\t0.1127\t0.074\t0.1794\t0.05742\t0.4467\t0.7732\t3.18\t53.91\t0.004314\t0.01382\t0.02254\t0.01039\t0.01369\t0.002179\t22.88\t27.66\t153.2\t1606.0\t0.1442\t0.2576\t0.3784\t0.1932\t0.3063\t0.08368\t0.0\n13.71\t20.83\t90.2\t577.9\t0.1189\t0.1645\t0.09366\t0.05985\t0.2196\t0.07451\t0.5835\t1.377\t3.856\t50.96\t0.008805\t0.03029\t0.02488\t0.01448\t0.01486\t0.005412\t17.06\t28.14\t110.6\t897.0\t0.1654\t0.3682\t0.2678\t0.1556\t0.3196\t0.1151\t0.0\n13.0\t21.82\t87.5\t519.8\t0.1273\t0.1932\t0.1859\t0.09353\t0.235\t0.07389\t0.3063\t1.002\t2.406\t24.32\t0.005731\t0.03502\t0.03553\t0.01226\t0.02143\t0.003749\t15.49\t30.73\t106.2\t739.3\t0.1703\t0.5401\t0.539\t0.206\t0.4378\t0.1072\t0.0\n12.46\t24.04\t83.97\t475.9\t0.1186\t0.2396\t0.2273\t0.08543\t0.203\t0.08243\t0.2976\t1.599\t2.039\t23.94\t0.007149\t0.07217\t0.07743\t0.01432\t0.01789\t0.01008\t15.09\t40.68\t97.65\t711.4\t0.1853\t1.058\t1.105\t0.221\t0.4366\t0.2075\t0.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compute summary statistics using the overloaded describe( ) function","message":["%python","","res = BREASTCANCER.describe()","res.index.name = 'statistic'","res.reset_index(inplace=True)","z.show(res.round(4))"],"enabled":true,"result":{"startTime":1737140135250,"interpreter":"python.low","endTime":1737140136371,"results":[{"message":"statistic\tmean radius\tmean texture\tmean perimeter\tmean area\tmean smoothness\tmean compactness\tmean concavity\tmean concave points\tmean symmetry\tmean fractal dimension\tradius error\ttexture error\tperimeter error\tarea error\tsmoothness error\tcompactness error\tconcavity error\tconcave points error\tsymmetry error\tfractal dimension error\tworst radius\tworst texture\tworst perimeter\tworst area\tworst smoothness\tworst compactness\tworst concavity\tworst concave points\tworst symmetry\tworst fractal dimension\tTARGET\ncount\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\t569.0\nmean\t14.1273\t19.2896\t91.969\t654.8891\t0.0964\t0.1043\t0.0888\t0.0489\t0.1812\t0.0628\t0.4052\t1.2169\t2.8661\t40.3371\t0.007\t0.0255\t0.0319\t0.0118\t0.0205\t0.0038\t16.2692\t25.6772\t107.2612\t880.5831\t0.1324\t0.2543\t0.2722\t0.1146\t0.2901\t0.0839\t0.6274\nstd\t3.524\t4.301\t24.299\t351.9141\t0.0141\t0.0528\t0.0797\t0.0388\t0.0274\t0.0071\t0.2773\t0.5516\t2.0219\t45.491\t0.003\t0.0179\t0.0302\t0.0062\t0.0083\t0.0026\t4.8332\t6.1463\t33.6025\t569.357\t0.0228\t0.1573\t0.2086\t0.0657\t0.0619\t0.0181\t0.4839\nmin\t6.981\t9.71\t43.79\t143.5\t0.0526\t0.0194\t0.0\t0.0\t0.106\t0.05\t0.1115\t0.3602\t0.757\t6.802\t0.0017\t0.0023\t0.0\t0.0\t0.0079\t0.0009\t7.93\t12.02\t50.41\t185.2\t0.0712\t0.0273\t0.0\t0.0\t0.1565\t0.055\t0.0\n25%\t11.7\t16.17\t75.17\t420.3\t0.0864\t0.0649\t0.0296\t0.0203\t0.1619\t0.0577\t0.2324\t0.8339\t1.606\t17.85\t0.0052\t0.0131\t0.0151\t0.0076\t0.0152\t0.0022\t13.01\t21.08\t84.11\t515.3\t0.1166\t0.1472\t0.1145\t0.0649\t0.2504\t0.0715\t0.0\n50%\t13.37\t18.84\t86.24\t551.1\t0.0959\t0.0926\t0.0615\t0.0335\t0.1792\t0.0615\t0.3242\t1.108\t2.287\t24.53\t0.0064\t0.0204\t0.0259\t0.0109\t0.0187\t0.0032\t14.97\t25.41\t97.66\t686.5\t0.1313\t0.2119\t0.2267\t0.0999\t0.2822\t0.08\t1.0\n75%\t15.78\t21.8\t104.1\t782.7\t0.1053\t0.1304\t0.1307\t0.074\t0.1957\t0.0661\t0.4789\t1.474\t3.357\t45.19\t0.0081\t0.0324\t0.042\t0.0147\t0.0235\t0.0046\t18.79\t29.72\t125.4\t1084.0\t0.146\t0.3391\t0.3829\t0.1614\t0.3179\t0.0921\t1.0\nmax\t28.11\t39.28\t188.5\t2501.0\t0.1634\t0.3454\t0.4268\t0.2012\t0.304\t0.0974\t2.873\t4.885\t21.98\t542.2\t0.0311\t0.1354\t0.396\t0.0528\t0.079\t0.0298\t36.04\t49.54\t251.2\t4254.0\t0.2226\t1.058\t1.252\t0.291\t0.6638\t0.2075\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Automated Machine Learning (AutoML)","The AutoML workflow consists of ","","- identify the most appropriate algorithm ","- select the features (predictors) that best support that algorithm","- tune the model using algorithm hyperparameters and the selected features","","### Automated Algorithm Selection","","Which algorithm is best to predict my target?","","- Classification: Decision Tree, Generalized Linear Model, Na?ve Bayes, Neural Network, RandomForest, Support Vector Machine","- Regression: Generalized Linear Model, Neural Network, Support Vector Machine","","To use AutoML algorithm selection, we create an ***AlgorithmSelection*** object, here with the 'accuracy' score metric. Then, we invoke ***select*** to get the top 4 predicted algorithms by setting k=4. Algorithm Selection automatically selects the best algorithm from the set of supported in-database algorithms by using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and ***meta-learning*** techniques.","","Users can select from a variety of ***score_metric*** options:","","- binary classification \u2013 accuracy (default), f1, precision, recall, roc_auc, f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted","- multiclass classification \u2013 accuracy (default), f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted","- regression \u2013 r2 (default), neg_mean_squared_error, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error","","Automated Algorithm Selection provides a ranked list of algorithms. The first element produced the highest score metric and likely should be chosen for subsequent steps.","","See the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/algorithm-selection.html\" target=\"_blank\"> documentation for Automated Algorithm Selection<\/a> for more details.","","---"],"enabled":true,"result":{"startTime":1737140136839,"interpreter":"md.low","endTime":1737140137300,"results":[{"message":"<h2 id=\"automated-machine-learning-automl\">Automated Machine Learning (AutoML)<\/h2>\n<p>The AutoML workflow consists of<\/p>\n<ul>\n<li>identify the most appropriate algorithm<\/li>\n<li>select the features (predictors) that best support that algorithm<\/li>\n<li>tune the model using algorithm hyperparameters and the selected features<\/li>\n<\/ul>\n<h3 id=\"automated-algorithm-selection\">Automated Algorithm Selection<\/h3>\n<p>Which algorithm is best to predict my target?<\/p>\n<ul>\n<li>Classification: Decision Tree, Generalized Linear Model, Na?ve Bayes, Neural Network, RandomForest, Support Vector Machine<\/li>\n<li>Regression: Generalized Linear Model, Neural Network, Support Vector Machine<\/li>\n<\/ul>\n<p>To use AutoML algorithm selection, we create an <em><strong>AlgorithmSelection<\/strong><\/em> object, here with the 'accuracy' score metric. Then, we invoke <em><strong>select<\/strong><\/em> to get the top 4 predicted algorithms by setting k=4. Algorithm Selection automatically selects the best algorithm from the set of supported in-database algorithms by using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and <em><strong>meta-learning<\/strong><\/em> techniques.<\/p>\n<p>Users can select from a variety of <em><strong>score_metric<\/strong><\/em> options:<\/p>\n<ul>\n<li>binary classification \u2013 accuracy (default), f1, precision, recall, roc_auc, f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted<\/li>\n<li>multiclass classification \u2013 accuracy (default), f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted<\/li>\n<li>regression \u2013 r2 (default), neg_mean_squared_error, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error<\/li>\n<\/ul>\n<p>Automated Algorithm Selection provides a ranked list of algorithms. The first element produced the highest score metric and likely should be chosen for subsequent steps.<\/p>\n<p>See the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/algorithm-selection.html\" target=\"_blank\"> documentation for Automated Algorithm Selection<\/a> for more details.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","as_bc = automl.AlgorithmSelection(mining_function='classification', score_metric='accuracy', parallel=4)","","alg_ranking = as_bc.select(BREASTCANCER_X, BREASTCANCER_y, k=4) ","","print(\"Ranked algorithms:\\n\", alg_ranking)","","best_alg = alg_ranking[0][0]","","print(\"\\nBest algorithm:\\n\", best_alg)"],"enabled":true,"result":{"startTime":1737140137770,"interpreter":"python.low","endTime":1737140175005,"results":[{"message":"UserWarning:Parallelism exceeds the DOP limit 2 (reverting to parallel=2)\nRanked algorithms:\n [('svm_gaussian', 0.9693819151783754), ('glm_ridge', 0.9687707743017476), ('svm_linear', 0.9652491110455712), ('nn', 0.9623719734339202)]\n\nBest algorithm:\n svm_gaussian\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Automated Feature Selection","","In this example, we define a ***FeatureSelection*** object with score metric 'accuracy' and invoke ***reduce*** specifying the neural network algorithm determined above and the data set proxy object. ","","Automated Feature  Selection provides a list of selected features, or columns. Use this set to produce an OML DataFrame with this column subset."," ","See the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/feature-selection.html\" target=\"_blank\"> documentation for Automated Feature Selection<\/a> for more details.","","We see the set of selected columns. Replace the `'nn'` in the code below with `'svm_linear'` (in single quotes) for example to see if different columns are selected for different algorithms.","","We use the selected set of columns to also reduce the train and test datasets created earlier for use in our manual model building later in this script. "],"enabled":true,"result":{"startTime":1737140175488,"interpreter":"md.low","endTime":1737140175950,"results":[{"message":"<h3 id=\"automated-feature-selection\">Automated Feature Selection<\/h3>\n<p>In this example, we define a <em><strong>FeatureSelection<\/strong><\/em> object with score metric 'accuracy' and invoke <em><strong>reduce<\/strong><\/em> specifying the neural network algorithm determined above and the data set proxy object.<\/p>\n<p>Automated Feature  Selection provides a list of selected features, or columns. Use this set to produce an OML DataFrame with this column subset.<\/p>\n<p>See the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/feature-selection.html\" target=\"_blank\"> documentation for Automated Feature Selection<\/a> for more details.<\/p>\n<p>We see the set of selected columns. Replace the <code>'nn'<\/code> in the code below with <code>'svm_linear'<\/code> (in single quotes) for example to see if different columns are selected for different algorithms.<\/p>\n<p>We use the selected set of columns to also reduce the train and test datasets created earlier for use in our manual model building later in this script.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","fs_bc = automl.FeatureSelection(mining_function = 'classification', score_metric = 'accuracy', parallel=4)","","selected_features = fs_bc.reduce('nn', BREASTCANCER_X, BREASTCANCER_y)","","BREASTCANCER_X_reduced = BREASTCANCER_X[:,selected_features]","","BREASTCANCER_train_X_reduced = BREASTCANCER_train_X[:,selected_features]","BREASTCANCER_test_X_reduced = BREASTCANCER_test_X[:,selected_features]","","print(\"Selected columns:\",BREASTCANCER_train_X_reduced.columns)","","print(\"\\nNumber of columns:\")","\"{} reduced to {}\".format(len(BREASTCANCER_train_X.columns), len(BREASTCANCER_train_X_reduced.columns))"],"enabled":true,"result":{"startTime":1737140176412,"interpreter":"python.low","endTime":1737140275010,"results":[{"message":"UserWarning:Parallelism exceeds the DOP limit 2 (reverting to parallel=2)\nSelected columns: ['mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concave points error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points']\n\nNumber of columns:\n'30 reduced to 23'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Automated Model Tuning","","Using data set proxy object, we define a ***ModelTuning*** object for classification. We then invoke ***tune*** to produce the tuned model using the neural network algorithm specified by the argument `'nn'`, but you should tune the same algorithm type that you used to do Feature Selection.  We also provide the subset of columns determined above when passing the `reduced` attributes to the `X` and `y`. ","","The result of model tuning is a dictionary with the best model and the tuned parameters.","See the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/model-tuning.html\" target=\"_blank\"> documentation for Automated Model Tuning <\/a> for more details."],"enabled":true,"result":{"startTime":1737140275513,"interpreter":"md.low","endTime":1737140275962,"results":[{"message":"<h3 id=\"automated-model-tuning\">Automated Model Tuning<\/h3>\n<p>Using data set proxy object, we define a <em><strong>ModelTuning<\/strong><\/em> object for classification. We then invoke <em><strong>tune<\/strong><\/em> to produce the tuned model using the neural network algorithm specified by the argument <code>'nn'<\/code>, but you should tune the same algorithm type that you used to do Feature Selection.  We also provide the subset of columns determined above when passing the <code>reduced<\/code> attributes to the <code>X<\/code> and <code>y<\/code>.<\/p>\n<p>The result of model tuning is a dictionary with the best model and the tuned parameters.\nSee the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/model-tuning.html\" target=\"_blank\"> documentation for Automated Model Tuning <\/a> for more details.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","at_bc = automl.ModelTuning(mining_function = 'classification', parallel=4)","","results = at_bc.tune('nn', BREASTCANCER_train_X_reduced, BREASTCANCER_train_y)","","tuned_model = results['best_model']","tuned_model"],"enabled":true,"result":{"startTime":1737140276420,"interpreter":"python.low","endTime":1737140391536,"results":[{"message":"UserWarning:Parallelism exceeds the DOP limit 2 (reverting to parallel=2)\n\nAlgorithm Name: Neural Network\n\nMining Function: CLASSIFICATION\n\nTarget: TARGET\n\nSettings: \n                    setting name                                    setting value\n0                      ALGO_NAME                              ALGO_NEURAL_NETWORK\n1          CLAS_WEIGHTS_BALANCED                                               ON\n2       LBFGS_GRADIENT_TOLERANCE                                       .000000001\n3            LBFGS_HISTORY_DEPTH                                               20\n4            LBFGS_SCALE_HESSIAN                       LBFGS_SCALE_HESSIAN_ENABLE\n5               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_TANH','NNET_ACTIVATIONS_TANH'\n6             NNET_HIDDEN_LAYERS                                                2\n7                NNET_ITERATIONS                                              100\n8           NNET_NODES_PER_LAYER                                            26,26\n9               NNET_REGULARIZER                            NNET_REGULARIZER_NONE\n10                NNET_TOLERANCE                                          .000001\n11                  ODMS_DETAILS                                      ODMS_ENABLE\n12  ODMS_MISSING_VALUE_TREATMENT                          ODMS_MISSING_VALUE_AUTO\n13              ODMS_RANDOM_SEED                                                0\n14                 ODMS_SAMPLING                            ODMS_SAMPLING_DISABLE\n15                     PREP_AUTO                                               ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              35\n2     LOSS_VALUE               0\n3       NUM_ROWS             343\n\nAttributes: \narea error\ncompactness error\nconcave points error\nfractal dimension error\nmean area\nmean compactness\nmean concave points\nmean concavity\nmean perimeter\nmean smoothness\nmean texture\nperimeter error\nradius error\nsmoothness error\ntexture error\nworst area\nworst compactness\nworst concave points\nworst concavity\nworst perimeter\nworst radius\nworst smoothness\nworst texture\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE    ACTIVATION_FUNCTION\n0                0        26  NNET_ACTIVATIONS_TANH\n1                1        26  NNET_ACTIVATIONS_TANH\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE  TARGET_VALUE    WEIGHT\n0         0       0.0       0     area error              None            None           NaN -0.792409\n1         0       0.0       1     area error              None            None           NaN  0.619671\n2         0       0.0       2     area error              None            None           NaN -0.522546\n3         0       0.0       3     area error              None            None           NaN -0.131913\n4         0       0.0       4     area error              None            None           NaN -0.111940\n...     ...       ...     ...            ...               ...             ...           ...       ...\n1348      2      22.0       0           None              None            None           1.0 -0.884313\n1349      2      23.0       0           None              None            None           1.0  1.249784\n1350      2      24.0       0           None              None            None           1.0  2.465188\n1351      2      25.0       0           None              None            None           1.0  1.366111\n1352      2       NaN       0           None              None            None           1.0  1.022515\n\n[1353 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### View all evaluated models","","We can also view all the evaluated models - their score metric and hyperparameters. Here, we'll look at the top 2. ","","Notice the diffences in the settings from the `Best` model vs the `Second Best`. "],"enabled":true,"result":{"startTime":1737140392119,"interpreter":"md.low","endTime":1737140392559,"results":[{"message":"<h4 id=\"view-all-evaluated-models\">View all evaluated models<\/h4>\n<p>We can also view all the evaluated models - their score metric and hyperparameters. Here, we'll look at the top 2.<\/p>\n<p>Notice the diffences in the settings from the <code>Best<\/code> model vs the <code>Second Best<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","all_models = results['all_evals']","","print(\"Best tuned model settings:\\n\", all_models[0])","","print(\"\\nNext best tuned model settings:\\n\", all_models[1])"],"enabled":true,"result":{"startTime":1737140393031,"interpreter":"python.low","endTime":1737140393494,"results":[{"message":"Best tuned model settings:\n (0.9713446716542691, {'CLAS_WEIGHTS_BALANCED': 'ON', 'NNET_ALL_ACTIVATIONS': 'NNET_ACTIVATIONS_TANH', 'NNET_HIDDEN_LAYERS': 2, 'NNET_NODES': 26, 'NNET_REGULARIZER': 'NNET_REGULARIZER_NONE'})\n\nNext best tuned model settings:\n (0.969967177902215, {'NNET_ALL_ACTIVATIONS': 'NNET_ACTIVATIONS_TANH', 'NNET_HIDDEN_LAYERS': 1, 'NNET_NODES': 50, 'NNET_REGULARIZER': 'NNET_REGULARIZER_NONE', 'CLAS_WEIGHTS_BALANCED': 'OFF', 'ODMS_DETAILS': 'ODMS_DISABLE'})\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## In-database machine learning algorithms","","Oracle Machine Learning provides over 30 machine learning parallelized algorithms in the database kernel software. These algorithms are exposed through three commonly used languages for data science: Python, R, and SQL. ","","While AutoML does a lot of the work for us, you may wish to build indiviudal models manually. In this section, we show examples illustrating using the in-database machine learning algorithms directly.","","### Compute attribute importance ranking of variables","","Using the IRIS table, which should have already been created by running the notebook \"OML4Py Dataset Creation\", determine which variables (a.k.a., attributes or predictors) are most important in predicting target 'SPECIES'."],"enabled":true,"result":{"startTime":1737140393965,"interpreter":"md.low","endTime":1737140394413,"results":[{"message":"<h2 id=\"in-database-machine-learning-algorithms\">In-database machine learning algorithms<\/h2>\n<p>Oracle Machine Learning provides over 30 machine learning parallelized algorithms in the database kernel software. These algorithms are exposed through three commonly used languages for data science: Python, R, and SQL.<\/p>\n<p>While AutoML does a lot of the work for us, you may wish to build indiviudal models manually. In this section, we show examples illustrating using the in-database machine learning algorithms directly.<\/p>\n<h3 id=\"compute-attribute-importance-ranking-of-variables\">Compute attribute importance ranking of variables<\/h3>\n<p>Using the IRIS table, which should have already been created by running the notebook &quot;OML4Py Dataset Creation&quot;, determine which variables (a.k.a., attributes or predictors) are most important in predicting target 'SPECIES'.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table if needed","message":["%python","","iris = datasets.load_iris()","iris_data = iris.data.astype(float)","X = pd.DataFrame(iris_data,","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)),","                 columns = ['SPECIES'])","","try:","    oml.drop(table='IRIS')","except:","    pass","    ","IRIS = oml.create(pd.concat([X, y], axis=1), table = 'IRIS')","","print(\"Shape:\",IRIS.shape)"],"enabled":true,"result":{"startTime":1737140394886,"interpreter":"python.low","endTime":1737140395598,"results":[{"message":"Shape: (150, 5)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","IRIS_X = IRIS.drop('SPECIES')","IRIS_y = IRIS['SPECIES']","","z.show(IRIS.head(10))"],"enabled":true,"result":{"startTime":1737140396061,"interpreter":"python.low","endTime":1737140396561,"results":[{"message":"SEPAL_LENGTH\tSEPAL_WIDTH\tPETAL_LENGTH\tPETAL_WIDTH\tSPECIES\n4.6\t3.6\t1\t0.2\tsetosa\n5.1\t2.5\t3\t1.1\tversicolor\n6.0\t2.2\t4\t1.0\tversicolor\n5.8\t2.6\t4\t1.2\tversicolor\n5.5\t2.3\t4\t1.3\tversicolor\n5.5\t2.5\t4\t1.3\tversicolor\n6.1\t2.8\t4\t1.3\tversicolor\n5.7\t2.5\t5\t2.0\tvirginica\n6.0\t2.2\t5\t1.5\tvirginica\n6.3\t2.5\t5\t1.9\tvirginica\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Define attribute importance (ai) object and invoke fit","First, we define the attribute importance ***ai*** object, compute attribute importance using function ***fit***, and show the results, which includes the importance ranking.","","Notice that petal width is most predictive of the target species. The importance value produced by this algorithm provides a relative ranking, not an absolute number, to be used to distinguish importance among variables."],"enabled":true,"result":{"startTime":1737140397032,"interpreter":"md.low","endTime":1737140397492,"results":[{"message":"<h3 id=\"define-attribute-importance-ai-object-and-invoke-fit\">Define attribute importance (ai) object and invoke fit<\/h3>\n<p>First, we define the attribute importance <em><strong>ai<\/strong><\/em> object, compute attribute importance using function <em><strong>fit<\/strong><\/em>, and show the results, which includes the importance ranking.<\/p>\n<p>Notice that petal width is most predictive of the target species. The importance value produced by this algorithm provides a relative ranking, not an absolute number, to be used to distinguish importance among variables.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","ai_obj = oml.ai()","ai_obj = ai_obj.fit(IRIS_X, IRIS_y)","","ai_obj"],"enabled":true,"result":{"startTime":1737140397962,"interpreter":"python.low","endTime":1737140399087,"results":[{"message":"\nAlgorithm Name: Attribute Importance\n\nMining Function: ATTRIBUTE_IMPORTANCE\n\nSettings: \n                   setting name            setting value\n0                     ALGO_NAME              ALGO_AI_MDL\n1                  ODMS_DETAILS              ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                       ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS              150\n\nAttributes: \nPETAL_LENGTH\nPETAL_WIDTH\nSEPAL_LENGTH\nSEPAL_WIDTH\n\nPartition: NO\n\nImportance: \n\n       variable  importance  rank\n0   PETAL_WIDTH    1.050935     1\n1  PETAL_LENGTH    1.030633     2\n2  SEPAL_LENGTH    0.454824     3\n3   SEPAL_WIDTH    0.191514     4\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(ai_obj.importance)"],"enabled":true,"result":{"startTime":1737140399570,"interpreter":"python.low","endTime":1737140400054,"results":[{"message":"variable\timportance\trank\nPETAL_WIDTH\t1.0509352930567628\t1\nPETAL_LENGTH\t1.0306333565712642\t2\nSEPAL_LENGTH\t0.45482398694827053\t3\nSEPAL_WIDTH\t0.1915137959145064\t4\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build a Random Forest predictive model and score data","","In this example, we split the data into train and test sets to build a RandomForest model to predict 'Species' from the ***IRIS*** table. Note that ***IRIS_DAT*** is a proxy object and the derived train and test data sets are also proxy objects. "],"enabled":true,"result":{"startTime":1737140400528,"interpreter":"md.low","endTime":1737140400970,"results":[{"message":"<h3 id=\"build-a-random-forest-predictive-model-and-score-data\">Build a Random Forest predictive model and score data<\/h3>\n<p>In this example, we split the data into train and test sets to build a RandomForest model to predict 'Species' from the <em><strong>IRIS<\/strong><\/em> table. Note that <em><strong>IRIS_DAT<\/strong><\/em> is a proxy object and the derived train and test data sets are also proxy objects.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","IRIS_DAT = IRIS.split()","IRIS_train_X = IRIS_DAT[0].drop('SPECIES')","IRIS_train_y = IRIS_DAT[0]['SPECIES']","IRIS_test = IRIS_DAT[1]","","print(\"IRIS_DAT[0]: \", type(IRIS_DAT[0]))","print(\"IRIS_DAT[1]: \", type(IRIS_DAT[1]))","print(\"IRIS_train_X: \", type(IRIS_train_X))","print(\"IRIS_train_y: \", type(IRIS_train_y))"],"enabled":true,"result":{"startTime":1737140401453,"interpreter":"python.low","endTime":1737140402279,"results":[{"message":"IRIS_DAT[0]:  <class 'oml.core.frame.DataFrame'>\nIRIS_DAT[1]:  <class 'oml.core.frame.DataFrame'>\nIRIS_train_X:  <class 'oml.core.frame.DataFrame'>\nIRIS_train_y:  <class 'oml.core.string.String'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Random Forest model specifying a maximum tree depth of 4, and display model details.","","Note that the model also reports attribute importance."],"enabled":true,"result":{"startTime":1737140402756,"interpreter":"md.low","endTime":1737140403196,"results":[{"message":"<h3 id=\"build-a-random-forest-model-specifying-a-maximum-tree-depth-of-4-and-display-model-details\">Build a Random Forest model specifying a maximum tree depth of 4, and display model details.<\/h3>\n<p>Note that the model also reports attribute importance.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","rf_mod = oml.rf(tree_term_max_depth = '4')","rf_mod = rf_mod.fit(IRIS_train_X, IRIS_train_y)","","rf_mod"],"enabled":true,"result":{"startTime":1737140403666,"interpreter":"python.low","endTime":1737140404955,"results":[{"message":"\nAlgorithm Name: Random Forest\n\nMining Function: CLASSIFICATION\n\nTarget: SPECIES\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_RANDOM_FOREST\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5               ODMS_RANDOM_SEED                        0\n6                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                      PREP_AUTO                       ON\n8                 RFOR_NUM_TREES                       20\n9            RFOR_SAMPLING_RATIO                       .5\n10          TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n11           TREE_TERM_MAX_DEPTH                        4\n12         TREE_TERM_MINPCT_NODE                      .05\n13        TREE_TERM_MINPCT_SPLIT                       .1\n14         TREE_TERM_MINREC_NODE                       10\n15        TREE_TERM_MINREC_SPLIT                       20\n\nComputed Settings: \n  setting name setting value\n0    RFOR_MTRY             2\n\nGlobal Statistics: \n  attribute name attribute value\n0      AVG_DEPTH            3.25\n1  AVG_NODECOUNT             5.5\n2      MAX_DEPTH               4\n3  MAX_NODECOUNT               7\n4      MIN_DEPTH               4\n5  MIN_NODECOUNT               5\n6       NUM_ROWS             104\n\nAttributes: \nPETAL_LENGTH\nPETAL_WIDTH\nSEPAL_LENGTH\nSEPAL_WIDTH\n\nPartition: NO\n\nImportance: \n\n  ATTRIBUTE_NAME ATTRIBUTE_SUBNAME  ATTRIBUTE_IMPORTANCE\n0   PETAL_LENGTH              None              0.543437\n1    PETAL_WIDTH              None              0.563783\n2   SEPAL_LENGTH              None              0.082777\n3    SEPAL_WIDTH              None              0.071437\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Use the Random Forest model to predict 'Species'","","When predicting values, users can include supplemental columns from the original data. This is useful to compare actual target values against predicted values, or to include unique keys for subsequent row identification. Including such values is necessary since row order is not implicitly maintained on results from relational databases by default. Ordering is performed only if requested by the user via a sort, e.g., using the overloaded ***sort_values*** function."],"enabled":true,"result":{"startTime":1737140405423,"interpreter":"md.low","endTime":1737140405877,"results":[{"message":"<h3 id=\"use-the-random-forest-model-to-predict-species\">Use the Random Forest model to predict 'Species'<\/h3>\n<p>When predicting values, users can include supplemental columns from the original data. This is useful to compare actual target values against predicted values, or to include unique keys for subsequent row identification. Including such values is necessary since row order is not implicitly maintained on results from relational databases by default. Ordering is performed only if requested by the user via a sort, e.g., using the overloaded <em><strong>sort_values<\/strong><\/em> function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","pred = rf_mod.predict(IRIS_test.drop('SPECIES'), ","                      supplemental_cols = IRIS_test[:, ['SPECIES']])","","print(\"Shape:\",pred.shape)","z.show(pred.head(4))"],"enabled":true,"result":{"startTime":1737140406360,"interpreter":"python.low","endTime":1737140406946,"results":[{"message":"Shape: (46, 2)\n","type":"TEXT"},{"message":"SPECIES\tPREDICTION\nversicolor\tversicolor\nversicolor\tversicolor\nvirginica\tvirginica\nsetosa\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Compute a Confusion Matrix for the predicted results","","Using the overloaded ***crosstab*** function, compute the confusion matrix between the actual values in the 'SPECIES' column and the predictions in the 'PREDICTION' column. Note that the return type from ***crosstab*** is an ***OML DataFrame***. This avoids pulling the result to the client unless required, and allows subsequent in-database manipulation. Since columns are sorted by name after pivoting while the rows are not, sort the rows as a post-processing step."],"enabled":true,"result":{"startTime":1737140407421,"interpreter":"md.low","endTime":1737140407877,"results":[{"message":"<h3 id=\"compute-a-confusion-matrix-for-the-predicted-results\">Compute a Confusion Matrix for the predicted results<\/h3>\n<p>Using the overloaded <em><strong>crosstab<\/strong><\/em> function, compute the confusion matrix between the actual values in the 'SPECIES' column and the predictions in the 'PREDICTION' column. Note that the return type from <em><strong>crosstab<\/strong><\/em> is an <em><strong>OML DataFrame<\/strong><\/em>. This avoids pulling the result to the client unless required, and allows subsequent in-database manipulation. Since columns are sorted by name after pivoting while the rows are not, sort the rows as a post-processing step.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res_ct = pred.crosstab('SPECIES','PREDICTION',pivot=True)","","print(\"Type:\",type(res_ct))","print(\"Columns:\",res_ct.columns)","print()","res_ct.sort_values(by='SPECIES')"],"enabled":true,"result":{"startTime":1737140408346,"interpreter":"python.low","endTime":1737140408971,"results":[{"message":"Type: <class 'oml.core.frame.DataFrame'>\nColumns: ['SPECIES', 'count_(setosa)', 'count_(versicolor)', 'count_(virginica)']\n\n      SPECIES  count_(setosa)  count_(versicolor)  count_(virginica)\n0      setosa              14                   0                  0\n1  versicolor               0                  14                  1\n2   virginica               0                   0                 17\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Transparency Layer functionality","","OML4Py DataFrame proxy objects enable data to be manipulated in-database as database tables and views - without data being pulled to the client - but using familiar Python functions and syntax. Overloaded Python functions translate select DataFrame functionality to equivalent SQL functions for in-database processing, parallelism, and scalability.","","In this first example, we pull the IRIS data from the database to get a Pandas DataFrame."],"enabled":true,"result":{"startTime":1737140409476,"interpreter":"md.low","endTime":1737140409943,"results":[{"message":"<h2 id=\"transparency-layer-functionality\">Transparency Layer functionality<\/h2>\n<p>OML4Py DataFrame proxy objects enable data to be manipulated in-database as database tables and views - without data being pulled to the client - but using familiar Python functions and syntax. Overloaded Python functions translate select DataFrame functionality to equivalent SQL functions for in-database processing, parallelism, and scalability.<\/p>\n<p>In this first example, we pull the IRIS data from the database to get a Pandas DataFrame.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","local_iris = IRIS.pull()","","print(\"Type IRIS: \", type(IRIS))","print(\"Type local_iris: \", type(local_iris))","","z.show(local_iris.head())"],"enabled":true,"result":{"startTime":1737140410421,"interpreter":"python.low","endTime":1737140410922,"results":[{"message":"Type IRIS:  <class 'oml.core.frame.DataFrame'>\nType local_iris:  <class 'pandas.core.frame.DataFrame'>\n","type":"TEXT"},{"message":"SEPAL_LENGTH\tSEPAL_WIDTH\tPETAL_LENGTH\tPETAL_WIDTH\tSPECIES\n4.6\t3.6\t1.0\t0.2\tsetosa\n5.1\t2.5\t3.0\t1.1\tversicolor\n6.0\t2.2\t4.0\t1.0\tversicolor\n5.8\t2.6\t4.0\t1.2\tversicolor\n5.5\t2.3\t4.0\t1.3\tversicolor\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Overloaded aggregate functions","","Perform a few aggregate operations on the IRIS database table 'SEPAL_LENGTH' column. ","","A key observation is that these statistics are computed in-database, leveraging the parallelism and scalability of Oracle Database for the computation. No data moves to the client except the final statistic(s). "],"enabled":true,"result":{"startTime":1737140411398,"interpreter":"md.low","endTime":1737140411856,"results":[{"message":"<h3 id=\"overloaded-aggregate-functions\">Overloaded aggregate functions<\/h3>\n<p>Perform a few aggregate operations on the IRIS database table 'SEPAL_LENGTH' column.<\/p>\n<p>A key observation is that these statistics are computed in-database, leveraging the parallelism and scalability of Oracle Database for the computation. No data moves to the client except the final statistic(s).<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","print(\"\\nMin SEPAL_LENGTH: \",    IRIS[\"SEPAL_LENGTH\"].min())","print(\"\\nMax SEPAL_LENGTH: \",    IRIS[\"SEPAL_LENGTH\"].max())","print(\"\\nMean SEPAL_LENGTH: \",   IRIS[\"SEPAL_LENGTH\"].mean().round(4))","print(\"\\nMedian SEPAL_LENGTH: \", IRIS[\"SEPAL_LENGTH\"].median())","print(\"\\nSkew SEPAL_LENGTH: \",   IRIS[\"SEPAL_LENGTH\"].skew().round(4))"],"enabled":true,"result":{"startTime":1737140412334,"interpreter":"python.low","endTime":1737140413169,"results":[{"message":"\nMin SEPAL_LENGTH:  4.3\n\nMax SEPAL_LENGTH:  7.9\n\nMean SEPAL_LENGTH:  5.8433\n\nMedian SEPAL_LENGTH:  5.8\n\nSkew SEPAL_LENGTH:  0.3118\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Filter data","","Select DataFrame rows using filter criteria."],"enabled":true,"result":{"startTime":1737140413841,"interpreter":"md.low","endTime":1737140414304,"results":[{"message":"<h3 id=\"filter-data\">Filter data<\/h3>\n<p>Select DataFrame rows using filter criteria.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","IRIS_filtered = IRIS[(IRIS[\"PETAL_LENGTH\"] < 1.5) | ","                     (IRIS[\"SEPAL_LENGTH\"] == 5.0), :]","print(\"\\nLength: \", len(IRIS_filtered))","IRIS_filtered.head(3)"],"enabled":true,"result":{"startTime":1737140414790,"interpreter":"python.low","endTime":1737140415343,"results":[{"message":"\nLength:  30\n   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH SPECIES\n0           4.6          3.6           1.0          0.2  setosa\n1           4.3          3.0           1.1          0.1  setosa\n2           5.8          4.0           1.2          0.2  setosa\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Join two DataFrames","","Data tables and views can be joined in-database using the ***merge*** function. This avoids the need to pull the data to the client, while benefitting from Oracle Database scalability and performance.","","Here, we create a temporary table with textual descriptions of each species that we can join with the IRIS data set."],"enabled":true,"result":{"startTime":1737140415810,"interpreter":"md.low","endTime":1737140416333,"results":[{"message":"<h3 id=\"join-two-dataframes\">Join two DataFrames<\/h3>\n<p>Data tables and views can be joined in-database using the <em><strong>merge<\/strong><\/em> function. This avoids the need to pull the data to the client, while benefitting from Oracle Database scalability and performance.<\/p>\n<p>Here, we create a temporary table with textual descriptions of each species that we can join with the IRIS data set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","import pandas as pd","","iris_descr = pd.DataFrame({\"SPECIES\" : [\"setosa\", \"versicolor\",\"virginica\"],","                           \"description\" : [\"Iris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\", ","                                            \"Iris versicolor is commonly known as, e.g., the blue flag, harlequin blueflag, larger blue flag, and in Britain and Ireland as purple iris.\", ","                                            \"Iris virginica is a perennial species of flowering plant native to eastern North America.\"],","                      })","IRIS_DESCR = oml.push(iris_descr)","z.show(IRIS_DESCR)"],"enabled":true,"result":{"startTime":1737140416811,"interpreter":"python.low","endTime":1737140417382,"results":[{"message":"SPECIES\tdescription\nsetosa\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\nversicolor\tIris versicolor is commonly known as, e.g., the blue flag, harlequin blueflag, larger blue flag, and in Britain and Ireland as purple iris.\nvirginica\tIris virginica is a perennial species of flowering plant native to eastern North America.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","IRIS_2 = IRIS.merge(IRIS_DESCR,on=\"SPECIES\")","","z.show(IRIS_2.head(4))"],"enabled":true,"result":{"startTime":1737140417869,"interpreter":"python.low","endTime":1737140418348,"results":[{"message":"SEPAL_LENGTH_l\tSEPAL_WIDTH_l\tPETAL_LENGTH_l\tPETAL_WIDTH_l\tSPECIES\tdescription_r\n4.6\t3.6\t1\t0.2\tsetosa\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\n5.1\t2.5\t3\t1.1\tversicolor\tIris versicolor is commonly known as, e.g., the blue flag, harlequin blueflag, larger blue flag, and in Britain and Ireland as purple iris.\n6.0\t2.2\t4\t1.0\tversicolor\tIris versicolor is commonly known as, e.g., the blue flag, harlequin blueflag, larger blue flag, and in Britain and Ireland as purple iris.\n5.8\t2.6\t4\t1.2\tversicolor\tIris versicolor is commonly known as, e.g., the blue flag, harlequin blueflag, larger blue flag, and in Britain and Ireland as purple iris.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Compute pairwise correlation","","Using only numeric columns, we can compute a correlation matrix on the proxy object for the database table IRIS using the overloaded ***corr*** function. Here we see that petal length and width are highly correlated."],"enabled":true,"result":{"startTime":1737140418824,"interpreter":"md.low","endTime":1737140419268,"results":[{"message":"<h3 id=\"compute-pairwise-correlation\">Compute pairwise correlation<\/h3>\n<p>Using only numeric columns, we can compute a correlation matrix on the proxy object for the database table IRIS using the overloaded <em><strong>corr<\/strong><\/em> function. Here we see that petal length and width are highly correlated.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","corr_res = IRIS.corr()","corr_res"],"enabled":true,"result":{"startTime":1737140419744,"interpreter":"python.low","endTime":1737140420346,"results":[{"message":"              SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH\nSEPAL_LENGTH      1.000000    -0.117570      0.871754     0.817941\nSEPAL_WIDTH      -0.117570     1.000000     -0.428440    -0.366126\nPETAL_LENGTH      0.871754    -0.428440      1.000000     0.962865\nPETAL_WIDTH       0.817941    -0.366126      0.962865     1.000000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Overloaded data visualization functions","","OML4Py overloads select graphics functions. ","#### boxplot","Here, we use the overloaded ***boxplot*** function to show the distribution of the ***IRIS*** table 'length' and 'width' columns. The statistical computations take place in-database - avoiding data movement. The function returns only the summary statistics needed to produce the plot, which enables scalability. No overhead is incurred for moving the data to the client for processing, whether 150 rows (as in IRIS) or 150 million rows.","","#### histogram","Similarly, we use the overloaded ***hist*** function, where the statistics are computed in-database. Only the summary statistics needed for the plot are returned to the client."],"enabled":true,"result":{"startTime":1737140420822,"interpreter":"md.low","endTime":1737140421274,"results":[{"message":"<h3 id=\"overloaded-data-visualization-functions\">Overloaded data visualization functions<\/h3>\n<p>OML4Py overloads select graphics functions.<\/p>\n<h4 id=\"boxplot\">boxplot<\/h4>\n<p>Here, we use the overloaded <em><strong>boxplot<\/strong><\/em> function to show the distribution of the <em><strong>IRIS<\/strong><\/em> table 'length' and 'width' columns. The statistical computations take place in-database - avoiding data movement. The function returns only the summary statistics needed to produce the plot, which enables scalability. No overhead is incurred for moving the data to the client for processing, whether 150 rows (as in IRIS) or 150 million rows.<\/p>\n<h4 id=\"histogram\">histogram<\/h4>\n<p>Similarly, we use the overloaded <em><strong>hist<\/strong><\/em> function, where the statistics are computed in-database. Only the summary statistics needed for the plot are returned to the client.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","import matplotlib.pyplot as plt","plt.style.use('seaborn-v0_8')","plt.figure(figsize=[10,5]) ","","oml.graphics.boxplot(IRIS[:, :4], notch=True,","                     showmeans = True,","                     labels=IRIS.columns[:4])","                     ","plt.title('Distribution of IRIS Attributes')","","plt.ylabel('cm')"],"enabled":true,"result":{"startTime":1737140421753,"interpreter":"python.low","endTime":1737140423051,"results":[{"message":"Text(0, 0.5, 'cm')\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Create derived variables","","We can also create derived variables (add new columns based on other columns) with the transparency layer.","","Here, we identify flowers as having \"large\" petals in a new column, and also compute the square of the sepal length. "],"enabled":true,"result":{"startTime":1737140424850,"interpreter":"md.low","endTime":1737140425300,"results":[{"message":"<h3 id=\"create-derived-variables\">Create derived variables<\/h3>\n<p>We can also create derived variables (add new columns based on other columns) with the transparency layer.<\/p>\n<p>Here, we identify flowers as having &quot;large&quot; petals in a new column, and also compute the square of the sepal length.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","is_large_petal = (IRIS['PETAL_LENGTH'] > 5.0) & (IRIS['PETAL_WIDTH'] > 2.0)","","print(\"Type: \", type(is_large_petal))","","IRIS2 = IRIS.concat({'IS_LARGE_PETAL': is_large_petal,","                     'SEPAL_LEN_SQ'  : IRIS['SEPAL_LENGTH']**2})","                     ","z.show(IRIS2[[\"IS_LARGE_PETAL\",\"SEPAL_LEN_SQ\"]].head())"],"enabled":true,"result":{"startTime":1737140425804,"interpreter":"python.low","endTime":1737140426500,"results":[{"message":"Type:  <class 'oml.core.boolean.Boolean'>\n","type":"TEXT"},{"message":"IS_LARGE_PETAL\tSEPAL_LEN_SQ\nFalse\t21.16\nFalse\t26.01\nFalse\t36.0\nFalse\t33.64\nFalse\t30.25\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compute statistics","Then, we use the overloaded ***describe*** function to compute statistics in-database."],"enabled":true,"result":{"startTime":1737140426963,"interpreter":"md.low","endTime":1737140427402,"results":[{"message":"<h2 id=\"compute-statistics\">Compute statistics<\/h2>\n<p>Then, we use the overloaded <em><strong>describe<\/strong><\/em> function to compute statistics in-database.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","IRIS2.describe()"],"enabled":true,"result":{"startTime":1737140427860,"interpreter":"python.low","endTime":1737140428616,"results":[{"message":"       SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH  SEPAL_LEN_SQ\ncount    150.000000   150.000000    150.000000   150.000000    150.000000\nmean       5.843333     3.057333      3.758000     1.199333     34.825667\nstd        0.828066     0.435866      1.765298     0.762238      9.921721\nmin        4.300000     2.000000      1.000000     0.100000     18.490000\n25%        5.100000     2.800000      1.600000     0.300000     26.010000\n50%        5.800000     3.000000      4.350000     1.300000     33.640000\n75%        6.400000     3.300000      5.100000     1.800000     40.960000\nmax        7.900000     4.400000      6.900000     2.500000     62.410000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Return data and multiple images using embedded Python execution","","Here, we create a function ***RandomRedDots*** that creates a simple DataFrame and generates two plots of random red dots. It also allows for two arguments, namely the number of dots to be generated in each plot. ","","We then create this function in the Python script repository, also named \"RandomRedDots\", and run the native Python function."],"enabled":true,"result":{"startTime":1737140429081,"interpreter":"md.low","endTime":1737140429553,"results":[{"message":"<h2 id=\"return-data-and-multiple-images-using-embedded-python-execution\">Return data and multiple images using embedded Python execution<\/h2>\n<p>Here, we create a function <em><strong>RandomRedDots<\/strong><\/em> that creates a simple DataFrame and generates two plots of random red dots. It also allows for two arguments, namely the number of dots to be generated in each plot.<\/p>\n<p>We then create this function in the Python script repository, also named &quot;RandomRedDots&quot;, and run the native Python function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","def RandomRedDots (num_dots_1=100, num_dots_2=10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt  ","","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","    fig = plt.figure(1)","    ax = fig.add_subplot(111)","    ax.scatter(range(0,int(num_dots_1)), np.random.rand(int(num_dots_1)),c='r')","    fig.suptitle(\"Random Red Dots\")","","    fig2 = plt.figure(2)","    ax2 = fig2.add_subplot(111)","    ax2.scatter(range(0,int(num_dots_2)), np.random.rand(int(num_dots_2)),c='r')","    fig2.suptitle(\"Random Red Dots\")","    return df"],"enabled":true,"result":{"startTime":1737140430015,"interpreter":"python.low","endTime":1737140430478,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","RandomRedDots(30,100)"],"enabled":true,"result":{"startTime":1737140430944,"interpreter":"python.low","endTime":1737140431508,"results":[{"message":"   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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hDfOnuo0Q2AKYeFmBvjAABVsLIQ3wZ6CM2AAacjwAIsJBERh7cLiN9xAbAgOMxBQZYSN/Ig7d5z8BjjDxIylwfsQEw4GyMAAFWwshDfJnqoyMbAHfUN6hj6w511Df05hf5+N4IOAGfZMBiGHmIL6N9xAbAgCN5IpFIxOxGWNn+/QcNfb6CgjzDn9ONXNGPGagDZPt+tEitpLT3o0VeZ7rZ/ny0iOH2Y0FBnoGtsS6mwACr6ht5MOqG58QtHYzuI6uhJhSQNkyBAU7X06PcFUs1YlONsva2KDxhog5XVDGlZgPUhALShxEgwOH6bqLe5j3yhMPRm2juiqVmNw1DoSYUkFYEQICTcRO1LWpCAelFAAQ4WLybqFpbM9wiJIpq1EB6EQABDhbvJqri4gy3CAmjJhSQVgRAgJNxE7W1rhWrFFy4SKGSUkW8XoVKShVcuIgEdsAArAIDHG6oooGEPxZ3pBp115K7XVEHqB8jah+5pH4SUkMABDidm2+iTuGmatRDlW1IdBsSI54DjseZALiFm26isC0jah9RPwmJIAcIAGANRpRtoPQDEuSoAKiurk6zZs1SeXm5qqurBxzft2+frrjiCs2ZM0ezZ8/WK6+8YkIrARtzwnYaTngNTtXaOuzaR8Oqn8S54SqOCYBCoZBWrlypdevWqaamRhs2bNCuXbv6PWbt2rWqqKjQ+vXr9eCDD+qee+4xqbWAzThhTyonvAanKy4edu2jlOoncW64kmMCoMbGRpWWlqqkpETZ2dmqqqpSbW1tv8d4PB4dOnRIknTw4EEVFhaa0VTwLct2nLCdhhNeg+MZUbYhhefg3HAnxwRAgUBARUVfRvZ+v1+BQKDfY66//no9//zzOuecc7Rw4UItW7Ys0810N75l2ZMTciqc8BpcwojaR0k9B+eGa7lqFVhNTY0uvfRSXXPNNdq5c6duu+02bdiwQVlZg8eBY8fmyOfzGtqOgoI8Q5/PNm66SYqxMiNnZLb00ENJP51r+9Fgcfvx/XZpkJwK774WFfQckgr8qf3xYLB3O47i4vQuzU/nazgio+djpvrNBAXFY6Xf/Dr6Gr3FxcrJyUm+ZlWiz5GBc8MMXB/jc0wA5Pf71db2ZXJbIBCQ39//pH366ae1bt06SdK0adN0+PBhdXZ2aty4cYM+b2ensdF/QUGe9u8/aOhz2kIwqPw/PqtYoWToj+vV8ZM7k7qQp70fXVJALaF+9I1S/oSJ8jbvGXAoNH6iOnyjpGTfi0zXaUnHazhKxj7XDq9vM6AfRxdKXSGpaxh9G+850nxumGG456NbgifHTIFNmTJFTU1Nam5uVnd3t2pqalRWVtbvMcXFxdq2bZsk6f3339fhw4eVn59vRnNdxzY7WzNNN1AattPIeM6FQ7YEIVclDRxybiB59v/KcITP59Py5cu1YMEChUIhzZ07V5MnT9aaNWt08skn64ILLtAdd9yhZcuW6Xe/+508Ho9+9rOfyePxmN10V+hbmRHrW5aVdramgFpsQ22nkbQ4ORddS+5Oy03H0NdgBpP6zQ1sf24gJZ5IJBIxuxFWZvSwtmunwCTlLru9X3DRJ7hwUdLBRVr6MRhU/swzYg+Fl5Sqo77BcTeYpPvRgKnBrA92K3/GafKEwwOORbxedWzdkd6K1WmY3szE59r0fssA06+PDpn6ZgosMY4ZAYL1Wf1bViLTdHa/wQybAdtpmD4aaNMtQUzvNzew6bmB1DgmBwg2cGRTzo76BnVs3aGO+obekR+LJG+mVEANySPnIjX0G2Aoa9x54C5W/ZZ15AYTa5qOG4yxrD4aaFX0G2AccoDiIAfImtLWj9FlxjFuMBYZqTKS6ecjORepsUK/2TSXyg3IAUqM867owHAcmabrWnK3+TcYN7DqaKDVmdlvDq9FBPfgbAVi4cYMxESpCDgFSdAAgMSwbxYchAAIAJAQ21R0BxJAAAQASAilIuAkBEBAKoJBZX2wmyF/OFesc5xaRHAQkqCBZLACBk4X5xynFhGcgis2kARWwMDp4p7jlIqAQzAFBiSKFTBwumTO8b5SEQQ/sCkCICBBrICB03GOw00IgIAEsQIGTsc5DjchAAISxQoYOB3nOFyEJGggCayAgdNxjsMt2A0+DnaDtybT+9EKu3EbwPR+dAhH9qMJ57gj+9EE7AafGEaAgFSwWSqcjnMcDkcOEAAAcB0CIAAA4DoEQAAAwHUIgAAAgOsQAAEAANchAAISEQwq64Pd7PcVD/0EN+P8txUCIGAoPT3KXXa78meeofwZpyl/5hnKXXa71NNjdsushX6Cm3H+2xJ1gIAh5K5YqpzqtdGfvc17oj933Xe/Wc2yHPoJbsb5b0+MAAGDCQY1YlNNzEMjNm1kmLsP/QQ34/y3LQIgYBBZgTZl7W2JfWxfi7ICbRlukTXRT3Azzn/7IgCCOyWQrBj2Fyk8YWLsY+Mn9u6RBPoJrsb5b18EQHCXZJIVc3J0uKIq5tMcrqi0/iaomVqRYvd+AoaD89+2SIKGqySbrNi1YpWk3rn8rH0tCo+fqMMVldF/t5S+3bvHfUW5P1+lEZtqlLW3ReEJE3W4oqq3zb70fORt1U+AwTj/7ckTiUQiZjfCyvbvP2jo8xUU5Bn+nG7Urx/7bvz+oqG/bQWDyp95hrzNewYcCpWUqqO+YfDfT/RvmKGnR7krlkYDnkhOjrIOHRrwsODCRQOCPMPPRyv3UxrxuTaG7fvRIuf/cPuxoCDPwNZYF1NgsK8ka28MK1kxJ0fhr51gyZt636iWt3mPPOFwzOBHytCKFAv3E5B2nP+2QgAE2zr2xt83nZW7YmnMx1sqWdGo/JwhluAeixUpAPAlAiDYUyq1N6yQrGhwxdihRrWOxYoUAPgSSdCwp9bWuNNZ4a+dMOCY2cmKRleM7RvVipXXdCxWpDiYRXJPADthBAj2VFyc2nSWz6eu++5XR32DOrbuUEd9Q2/gkabVUf2ko2LsEKNa4VF5ini9CpWU9iZAsyLFediDCkgZI0CwpyM3/qNHU/okNNLRl6yYQYkkYafSpkFHtW5bqqyPP2JUwMHYgwpIHQEQbMvs6axkDTVdNaz8nCOjWl1L7h4wDRIePXo4TYaVxRlR7FpyN4EvMAQCINjXEDd+SxruqFUCz5/pUS2YJ10jioBbEADBfoJB6f12yTeqN2iw0Y3fbqNWsK60jSgCLkESNOzjqIRPfeMb9kz4NDMJG85ihbIOgI1x1YVtOCrh00ajVrAuRhSB1LEXWBxG75NU0HNI+/umbpC44ezjhUHZfu8lizC9Hx1SB8j0fnQI9gJLDFNgmeCEqRuTDWsfL8Dp2IMKSBpTYBngqKkbk5DwCQAwEiNA6ZaO6r9uRMInAMBAjgqA6urqNGvWLJWXl6u6ujrmYzZu3KjKykpVVVXp5ptvTnubmLoxTteKVQouXKRQSanEFg8AgGFwzBRYKBTSypUr9fjjj8vv9+vyyy9XWVmZvv71r0cf09TUpOrqav3Hf/yHxowZo48//jjt7WLqxkBHFT4s6DmkDpLJAQApcswIUGNjo0pLS1VSUqLs7GxVVVWptra232P++7//W9///vc1ZswYSdK4cePS3zCmboyXkyNNmkTfAQBS5pgRoEAgoKKiL0dT/H6/Ghsb+z2mqalJkvS9731P4XBY119/vc4555y0t+3oWh3efS0KUasDAABTOSYASkQoFNKHH36oJ598Um1tbZo/f76ef/55jR5iw8ixY3Pk83mH/8d/8+vehOfWVnmLi5WTkyPGL4bHLbUq0o1+NAb9aIyk+vHINVXFxYwIH4PzMT7HBEB+v19tbV8mFAcCAfn9/gGPOfXUU3XccceppKREf//3f6+mpiadcsopgz5vZ6exq7QKJk3qLVDVRbGv4aBgmjHoR2PQj8ZIuB97epS7YqlGbKpR1t4WhSdM1OGKqt5RdbaVoRBighyTAzRlyhQ1NTWpublZ3d3dqqmpUVlZWb/HXHjhhXrttdckSR0dHWpqalJJSYkZzQUApKivtpq3eY884XC0tlruiqVD/2IwqKwPdlN+BJIcFAD5fD4tX75cCxYsUGVlpSoqKjR58mStWbMmmgw9c+ZMHX/88aqsrNSVV16p2267TWPHjjW55QCAhKVSW+2oavz5M06jGj8ksRdYXEYPazNUbgz60Rj0ozHoR2Mk0o9ZH+xW/ozT5AmHBxyLeL3q2LpjwEbDuctu71eNv09w4SJHVuNnCiwxjhkBAgA4X19ttZjHYtVWoxo/BkEABACwjyRrq1GNH4MhXR4AYCtH11bL2tei8BC11ajGj8EQAAFWEQwqK9DWe0GmpgkwuKO2xYn7mTkyYhQrB4hq/O5GAASYjZomQGpycgYkPMeSzIgR3IOrK2Cyvpomffpqmkhy5AoVIOOSGTHKNEZ+TUMStNtRGMxcrFABMqdvxMgKgQa1iUxHAORWfPgsgRUqgIWl8QtiytWsYRgCIJfiw2cNSdc0AZB+6f6CyMivJRAAuREfPutIsqYJgPRL9xdERn6tgQDIhfjwWUvXilUKLlykUEmpIl6vQiWlvSX6071CJRiU3n+fgBc4Wga+IDLyaw0EQC7Eh89ijqxQ6ahvUMfWHeqob+hd/ZWuJfBHDe/rG98g/ws4Ska+IDLyawkEQG7Eh8+aMrRC5ejhfZH/BfSTqS+Iho38spI3ZQRALmXatAvMRf4XMLRMfUEcbOS3uzuxgIaVvMNGIUS3snJhMKRNIsP7iVTWNQ1F45ABGa0c3TfyeySgSbQi/FAFVPWbXxvfTgciAHK7BEvJwxlsuzEk24Ugk0z4gphURXhGcg3BFBjgJjbN/6JulQ04MRclU5Wjkwxo4o3kqrXV8CY6EQEQ4DJH53/JDvlffNu1NnJRhi3ZlWfxErVVXGx4G52IAAhwm6OSL/WXv6R/2f0wUbfK2hidG76kV57ZdCTXagiAYH1OHFq3gpwcadKk9F4sDXjvqFtlYYzOGSOFgIaVvMNnza98gDR04iuszcik5SM3h6MTRPsY8m2XlWUps/2qQgtJeuUZK3mHjQAIlsUyT/tKakVLAtKyLNnJK8syFNTZdlWhFaUa0LCSN2VMgcGaGFq3r3S8d2nYLsSRuSuZTkgmF8V4mVp5BgIgWJPtlnmSpxSV1qRlo24ODg2wzQjqyEWJY7jXBq4taUMABEuyzTJPlgAPYIekZUeuLDMrqMv0Zr52MdxrA9eWtCMAgjXZZGjdkdMow2WD984OQVqyTA/qmLrpZ7jXBq4t6UcABMuy/NC6Q6dRjGD5984GQVqynBjU2dZwrw1cWzLC5WOUsDSLL/NkCfAQLP7eSRne8DIThioXUF7e/31g6X9aDffawLUlMwiAYH0WXebJEuAEWPS9k2SLIC1ZA4K64gkKHz9GI17crJG/+63C4ycofPzxyvr0E2Xt3euspf8WMtxrA9eWzGAKDEiVA6dRXMlJuSvHJCQfLp+l495+S97m5t48kpbm/j+TV5Iew702cG3JCEJ+YBgcN40CZ8jJUdhfpBEvbU7o4SM2bVTXkru5sRpouNcGri3p54lEIhGzG2Fl+/cfNPT5CgryDH9ON7JcP9o0p8Jy/WgEE94LK/Zj1ge7lT/jNHnC4biPjXi96ti6w/TpSiv247AN93xM4feH248FBXkp/66dMAUGGMFJ0yh2Rd2UfoZaFTbgseSVpM9wrw1cW9KGAAiAI1A35RhD5JEci7wSuBEBEAD7o25KTAPqMU38qr44eYpCJV+1Zn0mIINIgrYbm+aaIA04F6KomzKIwZb6c+70oh9cjREguyC/AX04FwagCnIcx+aRuD2vhM8QRABkG+Q3pJmNdlzmXIiBuilIAp8hSARA9kB+Q/rY7Zsg58KgLL//GKyBzxCOIAfIBiyf32DjefS+b4J9+r4JSlLXffeb1axBWf5cMJMDt7aA8fgMoQ8jQDZg2fwGu42eHMuG3wQtey5YidvzWzAkPkPoQwBkBxbNb7D7PHoi3wQtx6LnAmAbfIZwBFNgNmG5fWHijJ7YYV8hu+64bLlzAbAZPkOQ2AssLsvtBWaRfJuh9hmKZGXpkz88p55vTU9bG43aMyh32e39coD6BBcusmQOUD8GnAuO3HvJBPSjMTLej8l+hixy/Y2HvcASwwiQ3fTlN5hsqNETeTw6/ruXKDxhog5XVPV+q/JZ81Sz9TdBi5wLgG0l+hnq6VHuiqUasalGWXtbbHFtQ3y8c0jNkXn0WKMnnlBIkvVXVEli5RCAuOy2WhSJIQkaKetXdyUrSxGvN+bjrLqiqh9WDgGIxYarRZEYAiCk7sjoSUd9gz75w3PSIOlkllhRZaNKzwBiMOkzbMvVokiIowKguro6zZo1S+Xl5aqurh70cZs3b9Y//MM/6K233spg6xwsJ0c935puzdoadq9VZHUElkg3kz/D1A1yLscEQKFQSCtXrtS6detUU1OjDRs2aNeuXQMed+jQIT3xxBM69dRTTWilg1m0tobdaxVZVqo3JQKmxNBPUaZ/hi16bcPwOSYAamxsVGlpqUpKSpSdna2qqirV1tYOeNyaNWv0ox/9SCNGjDChlc5mub2YmLtPm6RvSozEJYZ+6s8in2HLXdtgCMcEQIFAQEVFXw5F+v1+BQKBfo9555131NbWpvPOOy/DrXOJo3KCOrbuUEd9Q+8KCZOWiTJ3nyYp3JRM/xZvE47upxRGtSzzGbbYtQ3GcM27Fw6H9bOf/Uw//elPk/q9sWNz5PPFXt2UKucXmcqTSv1p/ytx+zF3svTVr0pNTQMOeUpKNO7kyQxfK4Xz8f12aZCbkndfiwp6DkkFR73/waC0eWPMx+e8sEk5Dz7giPdh0H4MBqXWVqm4eOjX6dR+6umRbrlFeu45ac+e3s/kJZdIDzwQM4Do14+W+wxn5tpmBOffZ4bPMQGQ3+9XW9uX3wYCgYD8/i9P1K6uLv31r3/VD37wA0nS/v37tWjRIq1du1ZTpkwZ9Hk7O40dYqVirDES7cfciypiV3q+qEJdXSGpy93vRUrno2+U8gcpghkaP1EdvlHSUc+Z9cFu5Tc3yxPjqSLNzep4+2+2L+gYsx+TLJ7n1H4aUG29qUlas0bBz7oH1NCJ1Y98hpNHJejEOGYKbMqUKWpqalJzc7O6u7tVU1OjsrKy6PG8vDw1NDRoy5Yt2rJli6ZOnRo3+IH9MXefBkkmhbp1FU2y01mO7CcDcnj4DCNdHBMA+Xw+LV++XAsWLFBlZaUqKio0efJkrVmzJmYyNFyCufu0SOqm5MZVNKnc+B3YT4bk8PAZRpqwGWocltsMFZLoR6NkbHPe6HRQjD3XHHAj69ePwaB8O17X8d+9JPZmwV6vOrbuiD2d5bR+CgaVP/OM2NOlJaXqqG/od97wuTYGU2CJseEnCoBlJLqZpBv2XDs656elWcqKPcA+5HSW0/ppiD0D7TqqBecgAAKQOQ7ewf7YDTN1ZFPgYyV043dQP/VNi8Yc1QJMRAAEAMM1RM5P3ybBrr3xO21UC45BAAQAw9XaOmiyryIRffKH59TzrenuvvE7aFQLzuCYVWAAYJri4sGXsE8oIfgBLIgACACGy4FL2AGnYwrMShJdUgzAckj2BeyFAMgKkiyZD8CCSPYFbIW7qwUcu3y2r2S+pAF75TgSI19wEpJ9AVsgB8hsBuyVY1s9PcpddrvyZ56h/BmnKX/mGcpddnvv7tEAAKQRI0AmS2SvHKd+m3T9yBcAwDSMAJnMkTtAJ8LNI18AANMRAJnNpctnDdklGgCAFDEFZgFuXD7bN/IVa5doR498AQAsgQDICty4fJZdogEAJiIAshKXLZ9148gXAMAaCIBgHjeOfAEALIEkaPQXDCrrg92ZXYXVN/JF8AMAyBACIPSiKCEAwEWYAoMkihICANyFESBYryihGdNwAABXIQCCdYoSMg0HAMgQpsBgmaKETMMBADKFESBYYzsOq03DAQAcjREgSDK/KGEi03BuKhIJAEgvAiD0GqwoYTCorOY9aS9SaJVpOACAOzAFhv76ihJmZ2c2IdkK03AAANdgBMjqgkFTtokwIyHZ7Gk4AIB7MAJkVWYuCTcrIfnINFxHfYM6tu5QR31Db7DlI04HABiLAMii+kZgvM175AmHoyMwuSuWpv1vm14XiL3BYGUU6gQcgQDIikxeEt6XkBzzGAnJcCsKdQKOQgBkQVYYgSEhGejPzFFZIIoRSMMQAFmQFUZgulasUnDhIoVKShXxehUqKVVw4SJ7JCRzgXAOq7yXFOqE2RiBNBzZpVZ0ZATm6FVYfTI2AjNYXSAr6+lR7oqlGrGpRll7WxSeMFGHK6p6gzYSqe3FYu9lvFFZtbZKowsz3Cq4CVsFGY8RIIuyzAiMjRKSmaJwDqu9l/FGZVVcnOEWwVUYgUwLAiCrYkl4crhAOIcV30vy4mAi0/NCHYoAyOpsNAJjJi4QzmHV99Iyo7JwHSvkhToRARAcgQuEc1j2vWRUFmZhBDItCIDgDFwgnMPq7+Wxo7LBoPT++0yzIq0YgTQeX13gGOwl5hy2eC+PWqmmvS3KT2almkl7/MHG7Lgy1+I8kUgkYnYjrGz//oOGPl9BQZ7hz+lGQ/YjN5eEWf58tPB7mbvs9pilKoILFw2+LNliy/utxvLno00Mtx8LCvIMbI11MQUG5yFx3Dms+l6muFLNasv7ATcjAAKAJKW0Us2Ky/sBFyMAAoAkpbJSzarL+5FhVtneBQRAAJC0FFaqWXZ5PzKDvbwshwAIAFJw9LJkJbIs2erL+83m8HIC5H9ZD6vA4mAVmDXRj8agHw0QDKqg55D2+0bFD2Kiq8BiLO936yqwo1bGefe2KOTElXHBoPJnniFv854Bh0IlpeqobzA0AGYVWGIccnYBgElycqQCv5TIDYdaLgO4YZfzRPK/wl87IcOtAlNgAJBpVl3en2kuWRlH/pc1OSoAqqur06xZs1ReXq7q6uoBxx9//HFVVlZq9uzZuvLKK7V3714TWgkAkFy0Mo78L0tyTAAUCoW0cuVKrVu3TjU1NdqwYYN27drV7zEnnniinnnmGT3//POaNWuWfvGLX5jUWgCWxTLljHHTyAh7eVmPYwKgxsZGlZaWqqSkRNnZ2aqqqlJtbW2/x5x11lkaOXKkJGnq1Klqa3PItwsAw8cy5cxz08jIkfyvjvoGdWzdoY76ht4cJ6cketuQY3o+EAioqOjLbwt+v1+NjY2DPv7pp5/WOeeck4mmAbABNyTjWtHRG99697UoZMWNb43Ul/8F0zkmAErGc889p7fffltPPfVU3MeOHZsjn89r6N93yxLDdKMfjUE/qne6a/PGmIdyXtiknAcfiDsaQT8Ow29+3fsetLbKW1ysnJwcpTT2c+Q5VFzsrNGjFHA+xueYAMjv9/eb0goEAvL7/QMet3XrVj322GN66qmnlJ2dHfd5OzuNzQOg7oox6Edj0I+9sj7YrfzmZnliHIs0N6vj7b8N+a2dfjRGwaRJvf3YlWRfHlVLKGtvi8JOrCWUBOoAJcYxOUBTpkxRU1OTmpub1d3drZqaGpWVlfV7zLvvvqvly5dr7dq1GjdunEktTTMSOIGkuSkZN2MyeC2iyjJS4ZgAyOfzafny5VqwYIEqKytVUVGhyZMna82aNdFk6J///OcKBoO68cYbdckll+jaa681udUGIoETSJ3ZybhO+uKS6WuRS2oJwXhshRGHXbbCyF12e78Ezj7BhYscmcDJlIMx6MejDGObipT70YFTN8O5FqXSj1kf7Fb+jNPkCYcHHIt4verYusN1ScdMgSXGMSNArsY3IGD4TFim7LipGxOuRUxfIlUEQA7gmmqqQCZkapsKB35xMeVaZPb0JWyLAMgB+AYE2I8Tv7iYdS2iyjJSQQDkBHwDAmzHkV9czLoWUWUZKeDscIijq6kOSOCE+YJBZQXaem9qBKSQosFCrIRhO39xMfVaRJVlJIFVYHHYZRVYlEtutLZZvWTxVT626UeLG/4qsORXnlleCtcizkdjsAosMTb/hGEAvgFZCvtLYUhHpm66ltztvC8uXItgceQAOZ2TCqzZjdGrfHgvnStTK88ARBEAORWVoU1n2Cof3ksAMBxTYA7F1Iv5+lb5eJv3DDyWxCof3ksAMB4jQE7kwAJrtmTEkmDeSwBIC0aAHCiRqReSEzNjuEuCeS8BID0IgBzIqKkXGGCYq3x4LwEgPZgCcyIqQ1tPqqt8eC8BIC0YAXIoKkM7B+8lABiPStBx2K4S9LEcWhnalRVj0/BeurIf04B+NAb9aAwqQSeGESCnoxqrc/BeAoBhyAEC4GxU0AYQAwEQAGeigjaAITAFBsCRqKANYCiMAAFwHipoA4iDAAhAbDbOnTFsI1oAjkUABKA/B+TO9FXQjnmMCtoARAAE4Bh9uTPe5j3yhMPR3JncFUvNblriqKANIA4CILex8bQGMsBBuTNdK1YpuHCRQiWlini9CpWUKrhwERW0AUhiFZh79PQod8VSjdhUo6y9LQpPmKjDFVW9NwMfpwF6OWr3+WFuRAvA2bjzuQRLgpEIR+4+TwVtADEwBeYGDprWQJqROwPAJRgBcgFHTWsg7dh9HoAbEAC5gCOnNZA+5M4AcAGmwNyAaQ2koi93hvMDgAMxAuQSTGsAAPAlAiC3YFoDAIAoAiC3YUkwAADkAAEAAPchAAIAAK5DAAQAiI99BOEwBEAAenGDQyw9PcpddrvyZ56h/BmnKX/mGcpddrvU02N2y4BhIQkacDs2ysUQ2EcQTsUIEOByfTc4b/MeecLh6A0ud8VSs5sGs7GPIByMAAhwM25wGEIi+wgCdkUABLhZays3OAyqbx/BmMfYRxA2RwAEuFlxMTc4DI59BOFgBECAm3GDQxxdK1YpuHCRQiWlini9CpWUKrhwEfsISqyctDmWeAAux0a5GBL7CA7EyklH4J0C3I4bHBLBPoJRlAZwBqbAAPTqu8ER/ACDY+WkYxAAAQCQIEoDOIejAqC6ujrNmjVL5eXlqq6uHnC8u7tbN910k8rLy/Xd735XLS2xT2IAAGKhNIBzOCYACoVCWrlypdatW6eamhpt2LBBu3bt6veYP/zhDxo9erRefPFFXXXVVXrggQdMai0AwJZYOekYjgmAGhsbVVpaqpKSEmVnZ6uqqkq1tbX9HrNlyxZdeumlkqRZs2Zp27ZtikQiZjQXAGBTlAZwBsesAgsEAioq+nLo0e/3q7GxccBjiouLJUk+n095eXnq7OxUfn5+RtsKALAxVk46gmMCoHQZOzZHPp/X0OcsKMgz9Pncin40Bv1oDPrRGPbqxzyp1G92I2KyVz+awzEBkN/vV1vbl9n3gUBAfr9/wGNaW1tVVFSknp4eHTx4UGPHjh3yeTs7jV3SWFCQp/37Dxr6nG5EPxqDfjQG/WgM+tEYw+1HtwRPjskBmjJlipqamtTc3Kzu7m7V1NSorKys32PKysr07LPPSpI2b96ss846Sx6Px4zmAgAAEzlmBMjn82n58uVasGCBQqGQ5s6dq8mTJ2vNmjU6+eSTdcEFF+jyyy/XrbfeqvLyco0ZM0YPPvig2c0GAAAm8ERYBjUko4djGeI1Bv1oDPrRGPSjMehHYzAFlhjHTIEBAAAkigAIAAC4DgEQAABwHXKAAACA6zACBAAAXIcACAAAuA4BEAAAcB0CIAAA4DoEQAAAwHUIgAAAgOsQAGVQXV2dZs2apfLyclVXV5vdHNtobW3VFVdcocrKSlVVVen3v/+9JOmTTz7R1VdfrYsuukhXX321Pv30U5Nban2hUEhz5szRP/3TP0mSmpub9d3vflfl5eW66aab1N3dbXIL7eHAgQO64YYb9J3vfEcVFRXauXMn52MKfve736mqqkoXX3yxFi9erMOHD3NOJuDOO+/UjBkzdPHFF0f/bbDzLxKJ6L777lN5eblmz56td955x6xmWw4BUIaEQiGtXLlS69atU01NjTZs2KBdu3aZ3Sxb8Hq9uuOOO7Rx40b913/9l/793/9du3btUnV1tWbMmKEXXnhBM2bMIKhMwBNPPKFJkyZFf37ggQd01VVX6cUXX9To0aP19NNPm9g6+1i1apVmzpypP/3pT3ruuec0adIkzsckBQIBPfHEE3rmmWe0YcMGhUIh1dTUcE4m4LLLLtO6dev6/dtg519dXZ2ampr0wgsv6N5779WKFStMaLE1EQBlSGNjo0pLS1VSUqLs7GxVVVWptrbW7GbZQmFhoU466SRJ0qhRo3TCCScoEAiotrZWc+bMkSTNmTNHL730komttL62tjb9+c9/1uWXXy6p95vhq6++qlmzZkmSLr30Us7JBBw8eFCvv/56tB+zs7M1evRozscUhEIhff755+rp6dHnn3+ugoICzskETJ8+XWPGjOn3b4Odf33/7vF4NHXqVB04cEDt7e2ZbrIlEQBlSCAQUFFRUfRnv9+vQCBgYovsqaWlRe+9955OPfVUffzxxyosLJQkFRQU6OOPPza5dda2evVq3XrrrcrK6v3Yd3Z2avTo0fL5fJKkoqIizskEtLS0KD8/X3feeafmzJmjpUuXKhgMcj4mye/365prrtH555+vs88+W6NGjdJJJ53EOZmiwc6/Y+899OmXCIBgG11dXbrhhhu0ZMkSjRo1qt8xj8cjj8djUsus7+WXX1Z+fr5OPvlks5tiez09PXr33Xc1b948rV+/XiNHjhww3cX5GN+nn36q2tpa1dbWqr6+Xp999pnq6+vNbpYjcP4lxmd2A9zC7/erra0t+nMgEJDf7zexRfbyxRdf6IYbbtDs2bN10UUXSZLGjRun9vZ2FRYWqr29Xfn5+Sa30rreeOMNbdmyRXV1dTp8+LAOHTqkVatW6cCBA+rp6ZHP51NbWxvnZAKKiopUVFSkU089VZL0ne98R9XV1ZyPSdq6dasmTpwY7aeLLrpIb7zxBudkigY7/46999CnX2IEKEOmTJmipqYmNTc3q7u7WzU1NSorKzO7WbYQiUS0dOlSnXDCCbr66quj/15WVqb169dLktavX68LLrjApBZa380336y6ujpt2bJFv/rVr3TWWWfpl7/8pc4880xt3rxZkvTss89yTiagoKBARUVF2r17tyRp27ZtmjRpEudjksaPH6///d//1WeffaZIJKJt27bp61//OudkigY7//r+PRKJ6M0331ReXl50qszt2A0+g1555RWtXr1aoVBIc+fO1aJFi8xuki1s375d3//+9/WNb3wjmr+yePFinXLKKbrpppvU2tqq8ePH66GHHtLxxx9vbmNtoKGhQb/97W/1m9/8Rs3NzfrJT36iTz/9VCeeeKIeeOABZWdnm91Ey3vvvfe0dOlSffHFFyopKdFPf/pThcNhzsckPfzww9q4caN8Pp9OPPFErVq1SoFAgHMyjsWLF+u1115TZ2enxo0bpx//+Me68MILY55/kUhEK1euVH19vUaOHKnVq1drypQpZr8ESyAAAgAArsMUGAAAcB0CIAAA4DoEQAAAwHUIgAAAgOsQAAEAANchAAIAAK5DAAQAAFyHAAgAALgOARAAAHAdAiAAAOA6BEAAAMB1CIAAAIDrEAABAADXIQACAACuQwAEAABchwAIAAC4DgEQAABwHQIgAADgOgRAAADAdQiAAACA6xAAAQAA1yEAAgAArkMABAAAXIcACAAAuA4BEAAAcB0CIAAA4DoEQAAAwHUIgAAAgOv8fzaLoB8ki+eiAAAAAElFTkSuQmCC style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":100,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","rdd_string = \"\"\"def RandomRedDots (num_dots_1=100, num_dots_2=10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt  ","","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","    fig = plt.figure(1)","    ax = fig.add_subplot(111)","    ax.scatter(range(0,int(num_dots_1)), np.random.rand(int(num_dots_1)),c='r')","    fig.suptitle(\"Random Red Dots\")","","    fig2 = plt.figure(2)","    ax2 = fig2.add_subplot(111)","    ax2.scatter(range(0,int(num_dots_2)), np.random.rand(int(num_dots_2)),c='r')","    fig2.suptitle(\"Random Red Dots\")","    return df\"\"\"","","oml.script.create(\"RandomRedDots\", func=rdd_string, is_global=False, overwrite=True)","print(oml.script.dir('RandomRedDots'))"],"enabled":true,"result":{"startTime":1737140431983,"interpreter":"python.low","endTime":1737140432518,"results":[{"message":"            name                                             script description                date\n0  RandomRedDots  def RandomRedDots (num_dots_1=100, num_dots_2=...        None 2025-01-17 19:00:32\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Use do_eval from embedded Python execution","","We can invoke this function with the embedded Python execution function ***do_eval***. Note, however, that ***do_eval*** does not automatically pass data to the user-defined fucntion, but does allow arguments to be passed.","","Here, we see the two images and DataFrame that are returned, using the default arguments to ***RandomRedDots***."],"enabled":true,"result":{"startTime":1737140432992,"interpreter":"md.low","endTime":1737140433446,"results":[{"message":"<h3 id=\"use-do_eval-from-embedded-python-execution\">Use do_eval from embedded Python execution<\/h3>\n<p>We can invoke this function with the embedded Python execution function <em><strong>do_eval<\/strong><\/em>. Note, however, that <em><strong>do_eval<\/strong><\/em> does not automatically pass data to the user-defined fucntion, but does allow arguments to be passed.<\/p>\n<p>Here, we see the two images and DataFrame that are returned, using the default arguments to <em><strong>RandomRedDots<\/strong><\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots\")","print(\"Type: \", type(res))","","res"],"enabled":true,"result":{"startTime":1737140433905,"interpreter":"python.low","endTime":1737140434423,"results":[{"message":"Type:  <class 'pandas.core.frame.DataFrame'>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Use the OML4Py REST API for invoking embedded Python execution","","Another option is to use the OML4Py Embedded Python Execution REST API to run user-defined Python functions saved in the script repository. The REST API is used when separation between the client and the Database server is beneficial. For example, REST APIs can be called from a web development application, improving the portability of the interface to other platforms, increasing project scalability, and enabling components to evolve independently. Use the OML4Py REST API to deploy and manage scripts. ","","To run a Python script from the REST API, it must reside in the OML4Py script repository. An Oracle Machine Learning cloud service account ***username*** and ***password*** must be provided for authentication.","","To authenticate and obtain a token, use ***cURL*** with the ***-d*** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", ","    \"password\":\"' <yourpassword>'\"}' \"<oml-cloud-service-url>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _- -header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance ","","For example, Oracle Machine Learning user ***OMLUSER*** with password ***OMLPASSWD*** and cloud service URL ***https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com*** authenticates and obtain a REST token using the following approach:"],"enabled":true,"result":{"startTime":1737140434898,"interpreter":"md.low","endTime":1737140435347,"results":[{"message":"<h3 id=\"use-the-oml4py-rest-api-for-invoking-embedded-python-execution\">Use the OML4Py REST API for invoking embedded Python execution<\/h3>\n<p>Another option is to use the OML4Py Embedded Python Execution REST API to run user-defined Python functions saved in the script repository. The REST API is used when separation between the client and the Database server is beneficial. For example, REST APIs can be called from a web development application, improving the portability of the interface to other platforms, increasing project scalability, and enabling components to evolve independently. Use the OML4Py REST API to deploy and manage scripts.<\/p>\n<p>To run a Python script from the REST API, it must reside in the OML4Py script repository. An Oracle Machine Learning cloud service account <em><strong>username<\/strong><\/em> and <em><strong>password<\/strong><\/em> must be provided for authentication.<\/p>\n<p>To authenticate and obtain a token, use <em><strong>cURL<\/strong><\/em> with the <em><strong>-d<\/strong><\/em> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, \n&quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' &quot;&lt;oml-cloud-service-url&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>- -header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>oml-cloud-service-url<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n<p>For example, Oracle Machine Learning user <em><strong>OMLUSER<\/strong><\/em> with password <em><strong>OMLPASSWD<\/strong><\/em> and cloud service URL <em><strong><a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a><\/strong><\/em> authenticates and obtain a REST token using the following approach:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Export the OML cloud service URL to environment variable *omlserver* and exchange Database credentials for an access token."],"enabled":true,"result":{"startTime":1737140435829,"interpreter":"md.low","endTime":1737140436282,"results":[{"message":"<p>Export the OML cloud service URL to environment variable <em>omlserver<\/em> and exchange Database credentials for an access token.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["$ export omlserver=https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com","$ export username=OMLUSER","$ export password=OMLPASSWD","","$ curl -i -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<username>'\", ","        \"password\":\"'<password>'\"}' \"<omlserver>/omlusers/api/oauth2/v1/token\"","","","    HTTP/1.1 200 OK","    Date: Wed, 09 Mar 2022 21:59:16 GMT","    Content-Type: application/json","    Content-Length: 1027","    Connection: keep-alive","    Cache-Control: no-cache, no-store, private","    X-Frame-Options: SAMEORIGIN","    X-XSS-Protection: 1;mode=block","    Strict-Transport-Security: max-age=31536000; includeSubDomains","    X-Content-Type-Options: nosniff"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Copy the *accessToken* field","Copy the *accessToken* field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and ","it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1737140436745,"interpreter":"md.low","endTime":1737140437190,"results":[{"message":"<h3 id=\"copy-the-accesstoken-field\">Copy the <em>accessToken<\/em> field<\/h3>\n<p>Copy the <em>accessToken<\/em> field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and\nit can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["$ export token='eyJhbGciOiJ...'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Invoke the ***RandomRedDots*** function","","Now invoke the ***RandomRedDots*** function in the script repository from the REST API with the ***cURL*** command. Set ***graphicsFlag*** to ***true*** to return the ","PNG image bits from the function in JSON format. The default REST API execution mode is *synchronous*, so code execution will wait for the REST API ","call to return before additional calls may be run."],"enabled":true,"result":{"startTime":1737140437661,"interpreter":"md.low","endTime":1737140438102,"results":[{"message":"<h3 id=\"invoke-the-randomreddots-function\">Invoke the <em><strong>RandomRedDots<\/strong><\/em> function<\/h3>\n<p>Now invoke the <em><strong>RandomRedDots<\/strong><\/em> function in the script repository from the REST API with the <em><strong>cURL<\/strong><\/em> command. Set <em><strong>graphicsFlag<\/strong><\/em> to <em><strong>true<\/strong><\/em> to return the\nPNG image bits from the function in JSON format. The default REST API execution mode is <em>synchronous<\/em>, so code execution will wait for the REST API\ncall to return before additional calls may be run.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -X POST --header \"Authorization: Bearer <token>\" -header 'Content-Type: application/json' \u2013header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### The result is returned as JSON representation","The result of invoking the REST endpoint is a JSON representation of the value returned from the Python script. Dataframes, lists, and dictionaries, strings, tuples, and images are supported. Image bytes are returned in PNG format. A truncated portion of the returned value is:"],"enabled":true,"result":{"startTime":1737140438561,"interpreter":"md.low","endTime":1737140438999,"results":[{"message":"<h3 id=\"the-result-is-returned-as-json-representation\">The result is returned as JSON representation<\/h3>\n<p>The result of invoking the REST endpoint is a JSON representation of the value returned from the Python script. Dataframes, lists, and dictionaries, strings, tuples, and images are supported. Image bytes are returned in PNG format. A truncated portion of the returned value is:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["{\"result\":[{\"IMAGE\":\"iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3de3wU5b3H8e8mQMLFBGwgXBIJReulqFAQjMgBaixFpNBIi4AFKWproQU5pypekOpRvBwVW1GOnlp6VC5KY7GiVAyJl0pFQXqqArYaIEYSoEiCoGA3c/6Y7cDpuCj.........,\"ID\":2}]}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Refer to the ***OML4Py REST API*** notebook for a workflow containing detailed examples."],"enabled":true,"result":{"startTime":1737140439462,"interpreter":"md.low","endTime":1737140439906,"results":[{"message":"<p>Refer to the <em><strong>OML4Py REST API<\/strong><\/em> notebook for a workflow containing detailed examples.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":{"startTime":1737140440367,"interpreter":"md.low","endTime":1737140440804,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737140441265,"interpreter":"md.low","endTime":1737140441729,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":"zeppelin","isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4Py -1- Introduction","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737139191056,"interpreter":"md.low","endTime":1737139191528,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python (OML4Py)  ","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight a few of the OML4Py features:","","* Load OML library","* Create database tables","* Use the transparency layer","* Rank attributes for predictive value using the in-database attribute importance algorithm","* Build predictive models","* Score data using these models","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737139191998,"interpreter":"md.low","endTime":1737139192463,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Import libraries supporting OML4Py","","To use OML4Py, first import the package ***oml***. ","","Note: The Oracle Database connectivity package ***cx_Oracle*** is used to support database access and users can get access to the underlying connection to use ***cx_Oracle*** directly via the function ***oml.cursor***. See","<a href=\"https://cx-oracle.readthedocs.io/en/latest\" target=\"_blank\"> this link to cx_Oracle docs<\/a> for general ***cx_Oracle*** documentation. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1737139192957,"interpreter":"md.low","endTime":1737139193406,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python ","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737139193893,"interpreter":"python.low","endTime":1737139194377,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Look up help on OML4Py and other functions","message":["%python","","help(oml.push)"],"enabled":true,"result":{"startTime":1737139194849,"interpreter":"python.low","endTime":1737139195328,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Transparency Layer functionality","","## Create a Pandas DataFrame and load into the database","","We have a couple of options for loading data into the database from Python. In this first example, we load the **iris** data and combine target and predictors into a single DataFrame, which matches the form the data would have as a database table. This Pandas DataFrame can then be loaded into the database using the ***push*** function, which creates a temporary table and returns a proxy object that we assign to variable IRIS_TMP. Such temporary tables will be automatically deleted when the database connection is terminated unless saved in a datastore (the topic of 'datastore' is covered in another notebook).","","We then invoke standard functions like ***shape*** and ***columns***, as well as ***head***, to begin to explore the data."],"enabled":true,"result":{"startTime":1737139195810,"interpreter":"md.low","endTime":1737139196266,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from sklearn.datasets import load_iris","from sklearn import linear_model","import pandas as pd","","iris = load_iris()","x = pd.DataFrame(iris.data, ","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","iris_df = pd.concat([x,y], axis=1)","","IRIS_TMP = oml.push(iris_df)","print(\"Shape:\",IRIS_TMP.shape)","print(\"Columns:\",IRIS_TMP.columns)","z.show(IRIS_TMP.head(4))"],"enabled":true,"result":{"startTime":1737139196754,"interpreter":"python.low","endTime":1737139197535,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create a persistent table","We can also create a persistent table using the ***create*** function and specifying a tablename, *IRIS*. This table is now accessible both within OML4Py but also directly from SQL.","","Use the function ***z.show*** to automatically pull the desired data to the Python for display in the notebook. By doing this, you can also use the built-in Zeppelin visualization options such as bar, pie, area, line, and scatter plots. "],"enabled":true,"result":{"startTime":1737139198005,"interpreter":"md.low","endTime":1737139198471,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","try:","    oml.drop(table=\"IRIS\")","except:","    pass","IRIS = oml.create(iris_df, table=\"IRIS\")","print(\"Shape:\",IRIS.shape)","z.show(IRIS.head(10))"],"enabled":true,"result":{"startTime":1737139198958,"interpreter":"python.low","endTime":1737139199575,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Transparency layer functions available on an OML DataFrame","","List the functions defined for an OML DataFrame proxy object, in this case \"IRIS\"."],"enabled":true,"result":{"startTime":1737139200043,"interpreter":"md.low","endTime":1737139200491,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = [x for x in IRIS.__dir__() if not x.startswith('_')]","res.sort()","res"],"enabled":true,"result":{"startTime":1737139200951,"interpreter":"python.low","endTime":1737139201414,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Using standard Python syntax on database data","","Perform a few operations on the SEPAL_LENGTH column of the IRIS database table using the transparency layer. Note that standard Python syntax is used. "],"enabled":true,"result":{"startTime":1737139201887,"interpreter":"md.low","endTime":1737139202335,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["                    %python","","print(\"Add 5 to each SEPAL_LENGTH: \",IRIS[\"SEPAL_LENGTH\"] + 5)","print(\"\\nMin SEPAL_LENGTH: \", IRIS[\"SEPAL_LENGTH\"].min())","print(\"\\nMean SEPAL_LENGTH: \",IRIS[\"SEPAL_LENGTH\"].mean())"],"enabled":true,"result":{"startTime":1737139202804,"interpreter":"python.low","endTime":1737139203416,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Data masking","Explore masking using IRIS PETAL_LENGTH column. Note that standard Python syntax is used. "],"enabled":true,"result":{"startTime":1737139203889,"interpreter":"md.low","endTime":1737139204335,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","print(\"PETAL_LENGTH: \", IRIS[\"PETAL_LENGTH\"])","","MASK1 = IRIS[\"PETAL_LENGTH\"] > 5.1","print(\"\\nPETAL_LENGTH > 5.1: \", IRIS[\"PETAL_LENGTH\"][MASK1])","","MASK2 = (IRIS[\"PETAL_LENGTH\"] < 1.4) | (IRIS[\"PETAL_LENGTH\"] > 5.6)","print(\"\\nPETAL_LENGTH < 1.4 | > 5.6: \", IRIS[\"PETAL_LENGTH\"][MASK2])"],"enabled":true,"result":{"startTime":1737139204815,"interpreter":"python.low","endTime":1737139205422,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Explore data by computing pairwise correlation","Using only numeric columns, we can compute a correlation matrix on the proxy object for the database table *IRIS* using the overloaded ***corr*** function. Here we see that petal length and petal width are highly correlated."],"enabled":true,"result":{"startTime":1737139205915,"interpreter":"md.low","endTime":1737139206374,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","IRIS.corr()"],"enabled":true,"result":{"startTime":1737139206844,"interpreter":"python.low","endTime":1737139207418,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Explore distribution of IRIS numeric columns","","OML4Py overloads select graphics functions as well. Here, we illustrate the use of ***boxplot*** to show the distribution of the length and width columns. Here, the statistical computations take place in the database - avoiding data movement - and returns only the summary statistics needed to produce the plot, which enables scalability. No overhead is incurred for moving the data to the client for processing, whether 150 rows (as in *IRIS*) or 150 million rows."],"enabled":true,"result":{"startTime":1737139207900,"interpreter":"md.low","endTime":1737139208373,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","import matplotlib.pyplot as plt","","oml.graphics.boxplot(IRIS[:, :4], notch=True, showmeans = True, ","                     labels=['Sepal Length', 'Sepal Width','Petal Length', 'Petal Width'])","plt.title('Distribution of IRIS Attributes')","plt.ylabel('cm');"],"enabled":true,"result":{"startTime":1737139208842,"interpreter":"python.low","endTime":1737139210307,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Use in-database ML algorithms ","","## Compute attribute importance ranking of variables","","Using the *IRIS* table, determine which variables (a.k.a., attributes or predictors) are most important in predicting target 'SPECIES'.","","Since we're starting with a database table, we split *IRIS* into test and train data sets, and then separate the target from the predictors in the training set.","","In the ***setting*** variable, we disable sampling. Note that OML4Py provides parameters to algorithms as name-value pairs. See the OML4Py Users Guide for available options.","","Then, we define the attribute importance object, compute attribute importance using function fit, and show the results, which includes the importance ranking.","","Notice that petal width is most predictive of the target 'SPECIES'. The importance value produced by this algorithm provides a relative ranking, not an absolute number, to be used to distinguish importance among variables."],"enabled":true,"result":{"startTime":1737139210797,"interpreter":"md.low","endTime":1737139211253,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","from oml import ai","","IRIS_train_dat, IRIS_test_dat = IRIS.split() ","IRIS_train_x = IRIS_train_dat.drop('SPECIES')","IRIS_train_y = IRIS_train_dat['SPECIES']"],"enabled":true,"result":{"startTime":1737139211735,"interpreter":"python.low","endTime":1737139212521,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","setting = {'ODMS_SAMPLING':'ODMS_SAMPLING_DISABLE'}","","ai_obj = ai(**setting)","ai_obj = ai_obj.fit(IRIS_train_x, IRIS_train_y)","","ai_obj"],"enabled":true,"result":{"startTime":1737139212994,"interpreter":"python.low","endTime":1737139214687,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(ai_obj.importance)"],"enabled":true,"result":{"startTime":1737139215166,"interpreter":"python.low","endTime":1737139215669,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build predictive models","","In this example, we set up the data and a ***demo*** cost matrix to build a Random Forest model to predict 'SPECIES' from the *IRIS* table.","","Note that we create the cost matrix as a named database table, *RF_COST*."],"enabled":true,"result":{"startTime":1737139216159,"interpreter":"md.low","endTime":1737139216635,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","cost_matrix = [['setosa', 'setosa', 0],","  ['setosa', 'virginica', 0.2],","  ['setosa', 'versicolor', 0.8],","  ['virginica', 'virginica', 0],","  ['virginica', 'setosa', 0.5],","  ['virginica', 'versicolor', 0.5],","  ['versicolor', 'versicolor', 0],","  ['versicolor', 'setosa', 0.4],","  ['versicolor', 'virginica', 0.6]]","  ","try:","    oml.drop(table='RF_COST')","except:","    pass","","cost_matrix = oml.create(pd.DataFrame(cost_matrix, columns = ['ACTUAL_TARGET_VALUE', 'PREDICTED_TARGET_VALUE', 'COST']), \"RF_COST\")","cost_matrix.pull().pivot(index='ACTUAL_TARGET_VALUE', ","                         columns='PREDICTED_TARGET_VALUE',values='COST')"],"enabled":true,"result":{"startTime":1737139217102,"interpreter":"python.low","endTime":1737139217689,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build a Random Forest model using the Cost Matrix","Build a Random Forest model specifying a maximum tree depth of 4, and display model details.","Note that the model also reports attribute importance."],"enabled":true,"result":{"startTime":1737139218188,"interpreter":"md.low","endTime":1737139218637,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from oml import rf","","rf_mod = rf(tree_term_max_depth = '4')","rf_mod = rf_mod.fit(IRIS_train_x, IRIS_train_y, cost_matrix = cost_matrix)","","rf_mod"],"enabled":true,"result":{"startTime":1737139219121,"interpreter":"python.low","endTime":1737139220428,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the Random Forest model to predict SPECIES","","When predicting values, users can include supplemental columns from the original data. This can be particularly useful to compare actual target values against predicted values, or to include unique keys for subsequent row identification. This is necessary since row order is not implicitly maintained on results from the database. This is consistent with how SQL and relational databases provide ordering only if requested by the user via a sort."],"enabled":true,"result":{"startTime":1737139220889,"interpreter":"md.low","endTime":1737139221340,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","pred = rf_mod.predict(IRIS_test_dat.drop('SPECIES'), ","                      supplemental_cols = IRIS_test_dat[:, ['SEPAL_LENGTH', 'SEPAL_WIDTH', 'PETAL_LENGTH', 'SPECIES']])","print(\"Shape:\",pred.shape)","z.show(pred.head(10))"],"enabled":true,"result":{"startTime":1737139221813,"interpreter":"python.low","endTime":1737139222462,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Generate and display a Confusion Matrix for the predicted results","","Using the overloaded ***crosstab*** function, compute the confusion matrix between the actual values in SPECIES and the predictions in the PREDICTION column."],"enabled":true,"result":{"startTime":1737139222939,"interpreter":"md.low","endTime":1737139223405,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res_ct = pred.crosstab('SPECIES','PREDICTION',pivot=True)","print(\"Type:\",type(res_ct))","print(\"Columns:\",res_ct.columns)"],"enabled":true,"result":{"startTime":1737139223873,"interpreter":"python.low","endTime":1737139224783,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Sort the rows","Since columns are sorted by name after pivoting, but the rows are not, sort the rows as a post-processing step."],"enabled":true,"result":{"startTime":1737139225246,"interpreter":"md.low","endTime":1737139225707,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res_ct.sort_values(by='SPECIES')"],"enabled":true,"result":{"startTime":1737139226178,"interpreter":"python.low","endTime":1737139226656,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build an Anomaly Detection Model with SVM","","The Support Vector Machine algorithms supports a one-class SVM option for anomaly detection. Predictions with value 0 are considered anomalies. Users can specify various settings, e.g., the outlier rate or the type of SVM kernel functions."],"enabled":true,"result":{"startTime":1737139227128,"interpreter":"md.low","endTime":1737139227581,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from oml import svm","","settings = {'svms_outlier_rate' : 0.05}","svm_mod = svm('anomaly_detection', svms_kernel_function = 'dbms_data_mining.svms_linear',**settings)","svm_mod = svm_mod.fit(IRIS, None)","","svm_mod"],"enabled":true,"result":{"startTime":1737139228066,"interpreter":"python.low","endTime":1737139229780,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Predict which cases are anomalies","We can predict which cases are anomalies using the ***predict*** function. If ***proba*** is set to True, we will get the probability associated with the prediction."],"enabled":true,"result":{"startTime":1737139230242,"interpreter":"md.low","endTime":1737139230693,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = svm_mod.predict(IRIS, proba = True)","print(\"Type:\", type(res))","z.show(res.head(4))"],"enabled":true,"result":{"startTime":1737139231154,"interpreter":"python.low","endTime":1737139231696,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Assess how many anomalies were identified","Let's assess how many anomalies were identified. Here we see that roughly 5% are reported as indicated from our setting above."],"enabled":true,"result":{"startTime":1737139232157,"interpreter":"md.low","endTime":1737139232599,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","anomalies = res[res['PREDICTION'] == 0]","","print(\"Type:\", type(anomalies))","print(\"IRIS Shape:\", IRIS.shape)","print(\"Anomalies Shape:\", anomalies.shape)","z.show(anomalies.head(10))"],"enabled":true,"result":{"startTime":1737139233060,"interpreter":"python.low","endTime":1737139234029,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Obtain attribute importance for supervised models using machine learning explainability (MLX)","","Machine Learning Explainability (MLX) aids in explaining machine learning model behavior, by providing model-agnostic explanations for classification and regression algorithms. This treats the model as a black box instead of using properties from the model itself to guide the explanation. The technique used is called _Global Permutation Importance_ (GPI).","","Read more about it in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/model-explainability.html\" target=\"_blank\">the documentation for Model Explainability (MLX) <\/a> "],"enabled":true,"result":{"startTime":1737139234489,"interpreter":"md.low","endTime":1737139234928,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","import oml","from oml.mlx import GlobalFeatureImportance","","IRIS_model = oml.dt().fit(IRIS_train_x, IRIS_train_y)","IRIS_exp = GlobalFeatureImportance(mining_function='classification',score_metric='f1_weighted',random_state=42, parallel=4)","IRIS_res = IRIS_exp.explain(IRIS_model, IRIS_train_x, IRIS_train_y, n_iter=8)","IRIS_res"],"enabled":true,"result":{"startTime":1737139235404,"interpreter":"python.low","endTime":1737139240087,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1737139240548,"interpreter":"md.low","endTime":1737139240987,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737139241448,"interpreter":"md.low","endTime":1737139241893,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py -2- Data Selection and Manipulation.dsnb b/machine-learning/notebooks-oml/python/OML4Py -2- Data Selection and Manipulation.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py -2- Data Selection and Manipulation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734736671122,"interpreter":"md.low","endTime":1734736671659,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python - Data Selection and Manipulation","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight features of the transparency layer involving data selection and manipulation.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734736672127,"interpreter":"md.low","endTime":1734736672605,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python---data-selection-and-manipulation\">Oracle Machine Learning for Python - Data Selection and Manipulation<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight features of the transparency layer involving data selection and manipulation.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Import libraries supporting OML4Py and create a proxy object","","To use OML4Py, first import the package ***oml***. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1734736673068,"interpreter":"md.low","endTime":1734736673531,"results":[{"message":"<h3 id=\"import-libraries-supporting-oml4py-and-create-a-proxy-object\">Import libraries supporting OML4Py and create a proxy object<\/h3>\n<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>.<\/p>\n<p>Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1734736673999,"interpreter":"python.low","endTime":1734736676476,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a temporary table for the IRIS data","message":["%python","","from sklearn.datasets import load_iris","import pandas as pd","","iris = load_iris()","","x = pd.DataFrame(iris.data, columns = ['SEPAL_LENGTH','SEPAL_WIDTH', 'PETAL_LENGTH','PETAL_WIDTH'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","z = pd.concat([x, y], axis=1)","","IRIS_TMP = oml.push(z)"],"enabled":true,"result":{"startTime":1734736676958,"interpreter":"python.low","endTime":1734736677565,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Select table columns using proxy object IRIS_TMP"],"enabled":true,"result":{"startTime":1734736678031,"interpreter":"md.low","endTime":1734736678485,"results":[{"message":"<h3 id=\"select-table-columns-using-proxy-object-iris_tmp\">Select table columns using proxy object IRIS_TMP<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Select specific columns by name","message":["%python","","IRIS_projected1 = IRIS_TMP[:, [\"SEPAL_LENGTH\", \"PETAL_LENGTH\"]]","IRIS_projected1.head(3)"],"enabled":true,"result":{"startTime":1734736678956,"interpreter":"python.low","endTime":1734736679562,"results":[{"message":"   SEPAL_LENGTH  PETAL_LENGTH\n0           4.6             1\n1           5.1             3\n2           6.0             4\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Select specific columns by index ","message":["%python","","IRIS_projected2 = IRIS_TMP[:, 1:4]","IRIS_projected2.head(3)"],"enabled":true,"result":{"startTime":1734736680025,"interpreter":"python.low","endTime":1734736680594,"results":[{"message":"   SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH\n0          3.6             1          0.2\n1          2.5             3          1.1\n2          2.2             4          1.0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Select specific columns by data type","message":["%python","","IRIS_projected3 = IRIS_TMP.select_types(include=[oml.String])","IRIS_projected3.head(3)"],"enabled":true,"result":{"startTime":1734736681064,"interpreter":"python.low","endTime":1734736681674,"results":[{"message":"      SPECIES\n0      setosa\n1  versicolor\n2  versicolor\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Select table rows using proxy object IRIS_TMP","","* Select sepal length and petal length where petal length is less than 1.5","* Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering","* Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0"],"enabled":true,"result":{"startTime":1734736682148,"interpreter":"md.low","endTime":1734736682602,"results":[{"message":"<h3 id=\"select-table-rows-using-proxy-object-iris_tmp\">Select table rows using proxy object IRIS_TMP<\/h3>\n<ul>\n<li>Select sepal length and petal length where petal length is less than 1.5<\/li>\n<li>Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering<\/li>\n<li>Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Simple row selection","message":["%python","","IRIS_filtered1 = IRIS_TMP[IRIS_TMP[\"PETAL_LENGTH\"] < 1.5, ","                                  [\"SEPAL_LENGTH\", \"PETAL_LENGTH\"]]","print(\"Length: \", len(IRIS_filtered1))","IRIS_filtered1.head(3)"],"enabled":true,"result":{"startTime":1734736683075,"interpreter":"python.low","endTime":1734736683645,"results":[{"message":"Length:  24\n   SEPAL_LENGTH  PETAL_LENGTH\n0           4.6           1.0\n1           4.3           1.1\n2           5.8           1.2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compound row selection with 'or'","message":["%python","","IRIS_filtered2 = IRIS_TMP[(IRIS_TMP[\"PETAL_LENGTH\"] < 1.5) | ","                          (IRIS_TMP[\"SEPAL_LENGTH\"] == 5.0), :]","print(\"Length: \", len(IRIS_filtered2))","IRIS_filtered2.head(3)"],"enabled":true,"result":{"startTime":1734736684110,"interpreter":"python.low","endTime":1734736684725,"results":[{"message":"Length:  30\n   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH SPECIES\n0           4.6          3.6           1.0          0.2  setosa\n1           4.3          3.0           1.1          0.1  setosa\n2           5.8          4.0           1.2          0.2  setosa\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compound row selection with 'and'","message":["%python","","IRIS_filtered3 = IRIS_TMP[(IRIS_TMP[\"PETAL_LENGTH\"] < 1.5) & ","                          (IRIS_TMP[\"SEPAL_LENGTH\"] > 5.0), :]","print(\"Length: \", len(IRIS_filtered3))","IRIS_filtered3.head(4)"],"enabled":true,"result":{"startTime":1734736685194,"interpreter":"python.low","endTime":1734736685758,"results":[{"message":"Length:  7\n   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH SPECIES\n0           5.8          4.0           1.2          0.2  setosa\n1           5.5          3.5           1.3          0.2  setosa\n2           5.4          3.9           1.3          0.4  setosa\n3           5.2          3.4           1.4          0.2  setosa\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Use Pandas DataFrame objects","","Let's explore operations involving Pandas DataFrames.","","We'll start by creating a temporary table from a Pandas DataFrame and use the ***append*** function. First, append an oml.Float series object to another, then append an oml.DataFrame object to another."],"enabled":true,"result":{"startTime":1734736686221,"interpreter":"md.low","endTime":1734736686678,"results":[{"message":"<h3 id=\"use-pandas-dataframe-objects\">Use Pandas DataFrame objects<\/h3>\n<p>Let's explore operations involving Pandas DataFrames.<\/p>\n<p>We'll start by creating a temporary table from a Pandas DataFrame and use the <em><strong>append<\/strong><\/em> function. First, append an oml.Float series object to another, then append an oml.DataFrame object to another.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data in temporary table","message":["%python","","my_df = pd.DataFrame({\"id\" : [1, 2, 3, 4, 5],","                      \"val\" : [\"a\", \"b\", \"c\", \"d\", \"e\"],","                      \"ch\" : [\"p\", \"q\", \"r\", \"a\", \"b\"],","                      \"num\" : [4, 3, 6.7, 7.2, 5]})","MY_DF = oml.push(my_df)","","print (my_df.dtypes)","","num1 = MY_DF['id']","num2 = MY_DF['num']","num1.append(num2)"],"enabled":true,"result":{"startTime":1734736687141,"interpreter":"python.low","endTime":1734736687667,"results":[{"message":"id       int64\nval     object\nch      object\nnum    float64\ndtype: object\n[1, 2, 3, 4, 5, 4, 3, 6.7, 7.2, 5]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Using append","message":["%python","","x = MY_DF[['id', 'val']] ","y = MY_DF[['num', 'ch']] ","","print(\"Types x:\\n\", x.dtypes)","print(\"Types y:\\n\", y.dtypes)","","x.append(y)"],"enabled":true,"result":{"startTime":1734736688127,"interpreter":"python.low","endTime":1734736688602,"results":[{"message":"Types x:\n id     <class 'oml.core.integer.Integer'>\nval      <class 'oml.core.string.String'>\ndtype: object\nTypes y:\n num      <class 'oml.core.float.Float'>\nch     <class 'oml.core.string.String'>\ndtype: object\n    id val\n0  1.0   a\n1  2.0   b\n2  3.0   c\n3  4.0   d\n4  5.0   e\n5  4.0   p\n6  3.0   q\n7  6.7   r\n8  7.2   a\n9  5.0   b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Concat","","Next, try a few examples using the ***concat*** function.","","* Create two oml.DataFrame objects and combine the objects column-wise","* Create an oml.Float object with the rounded exponential of two times the values in the num column of the oml_frame object, then concatenate it with the oml.DataFrame object y using a new column name","* Concatenate object x with multiple objects and turn on automatic name conflict resolution","* Concatenate multiple OML data objects and perform customized renaming"],"enabled":true,"result":{"startTime":1734736689070,"interpreter":"md.low","endTime":1734736689508,"results":[{"message":"<h3 id=\"concat\">Concat<\/h3>\n<p>Next, try a few examples using the <em><strong>concat<\/strong><\/em> function.<\/p>\n<ul>\n<li>Create two oml.DataFrame objects and combine the objects column-wise<\/li>\n<li>Create an oml.Float object with the rounded exponential of two times the values in the num column of the oml_frame object, then concatenate it with the oml.DataFrame object y using a new column name<\/li>\n<li>Concatenate object x with multiple objects and turn on automatic name conflict resolution<\/li>\n<li>Concatenate multiple OML data objects and perform customized renaming<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Column-wise concatentation","message":["%python","","from collections import OrderedDict","","x = MY_DF[['id', 'val']]","y = MY_DF[['num', 'ch']]","x.concat(y)"],"enabled":true,"result":{"startTime":1734736689977,"interpreter":"python.low","endTime":1734736690524,"results":[{"message":"   id val  num ch\n0   1   a  4.0  p\n1   2   b  3.0  q\n2   3   c  6.7  r\n3   4   d  7.2  a\n4   5   e  5.0  b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Concatenate new column","message":["%python","","w = (MY_DF['num']*2).exp().round(decimals=2)","y.concat({'round(exp(2*num))':w})"],"enabled":true,"result":{"startTime":1734736691011,"interpreter":"python.low","endTime":1734736691543,"results":[{"message":"   num ch  round(exp(2*num))\n0  4.0  p            2980.96\n1  3.0  q             403.43\n2  6.7  r          660003.22\n3  7.2  a         1794074.77\n4  5.0  b           22026.47\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Concatenation with name resolution","message":["%python","","z = MY_DF[:,'id']","x.concat([z, w, y], auto_name=True)"],"enabled":true,"result":{"startTime":1734736692023,"interpreter":"python.low","endTime":1734736692552,"results":[{"message":"   id val  id3         num  num5 ch\n0   1   a    1     2980.96   4.0  p\n1   2   b    2      403.43   3.0  q\n2   3   c    3   660003.22   6.7  r\n3   4   d    4  1794074.77   7.2  a\n4   5   e    5    22026.47   5.0  b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Concatenation with renaming","message":["%python","","x.concat(OrderedDict([('ID',z), ('round(exp(2*num))',w), ('New_',y)]))"],"enabled":true,"result":{"startTime":1734736693014,"interpreter":"python.low","endTime":1734736693501,"results":[{"message":"   id val  ID  round(exp(2*num))  New_num New_ch\n0   1   a   1            2980.96      4.0      p\n1   2   b   2             403.43      3.0      q\n2   3   c   3          660003.22      6.7      r\n3   4   d   4         1794074.77      7.2      a\n4   5   e   5           22026.47      5.0      b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Merge","","Next, explore merging data using ***merge***. Perform:","","* cross join","* left outer join","* right outer join"],"enabled":true,"result":{"startTime":1734736693964,"interpreter":"md.low","endTime":1734736694405,"results":[{"message":"<h3 id=\"merge\">Merge<\/h3>\n<p>Next, explore merging data using <em><strong>merge<\/strong><\/em>. Perform:<\/p>\n<ul>\n<li>cross join<\/li>\n<li>left outer join<\/li>\n<li>right outer join<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":100,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Cross join","message":["%python","","x = MY_DF[['id', 'val']]","y = MY_DF[['num', 'ch']]","","z = x.merge(y)","z"],"enabled":true,"result":{"startTime":1734736694866,"interpreter":"python.low","endTime":1734736695362,"results":[{"message":"    id_l val_l  num_r ch_r\n0      1     a    4.0    p\n1      2     b    4.0    p\n2      3     c    4.0    p\n3      4     d    4.0    p\n4      5     e    4.0    p\n5      1     a    3.0    q\n6      2     b    3.0    q\n7      3     c    3.0    q\n8      4     d    3.0    q\n9      5     e    3.0    q\n10     1     a    6.7    r\n11     2     b    6.7    r\n12     3     c    6.7    r\n13     4     d    6.7    r\n14     5     e    6.7    r\n15     1     a    7.2    a\n16     2     b    7.2    a\n17     3     c    7.2    a\n18     4     d    7.2    a\n19     5     e    7.2    a\n20     1     a    5.0    b\n21     2     b    5.0    b\n22     3     c    5.0    b\n23     4     d    5.0    b\n24     5     e    5.0    b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Left outer join","message":["%python","","x.head(4).merge(other=MY_DF[['id', 'num']], on=\"id\", suffixes=['.l','.r'])"],"enabled":true,"result":{"startTime":1734736695822,"interpreter":"python.low","endTime":1734736696353,"results":[{"message":"   id val.l  num.r\n0   1     a    4.0\n1   2     b    3.0\n2   3     c    6.7\n3   4     d    7.2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Right outer join","message":["%python","","x.merge(other=y, left_on=\"id\", right_on=\"num\", how=\"right\")"],"enabled":true,"result":{"startTime":1734736696813,"interpreter":"python.low","endTime":1734736697289,"results":[{"message":"   id_l val_l  num_r ch_r\n0   3.0     c    3.0    q\n1   4.0     d    4.0    p\n2   5.0     e    5.0    b\n3   NaN  None    6.7    r\n4   NaN  None    7.2    a\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Dropna, drop_duplicates mand drop","","Next, try the drop functonality with ***dropna***, ***drop_duplicates***, and ***drop***:","","* drop rows with any missing values","* drop rows in which all column values are missing","* drop rows in which any numeric column values are missing","* drop duplicate rows","* drop rows that have the same value in column 'string1' and 'string2'","* drop column 'string2'"],"enabled":true,"result":{"startTime":1734736697750,"interpreter":"md.low","endTime":1734736698197,"results":[{"message":"<h3 id=\"dropna-drop_duplicates-mand-drop\">Dropna, drop_duplicates mand drop<\/h3>\n<p>Next, try the drop functonality with <em><strong>dropna<\/strong><\/em>, <em><strong>drop_duplicates<\/strong><\/em>, and <em><strong>drop<\/strong><\/em>:<\/p>\n<ul>\n<li>drop rows with any missing values<\/li>\n<li>drop rows in which all column values are missing<\/li>\n<li>drop rows in which any numeric column values are missing<\/li>\n<li>drop duplicate rows<\/li>\n<li>drop rows that have the same value in column 'string1' and 'string2'<\/li>\n<li>drop column 'string2'<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data","message":["%python","","my_df2 = pd.DataFrame({'numeric': [1, 1.4, -4, -4, 5.432, None, None],","                   'string1' : [None, None, 'a', 'a', 'a', 'b', None],","                   'string2': ['x', None, 'z', 'z', 'z', 'x', None]})","MY_DF2 = oml.push(my_df2, dbtypes = {'numeric': 'BINARY_DOUBLE',","                                     'string1':'CHAR(1)', ","                                     'string2':'CHAR(1)'})","MY_DF2  "],"enabled":true,"result":{"startTime":1734736698657,"interpreter":"python.low","endTime":1734736699197,"results":[{"message":"   numeric string1 string2\n0    1.400    None    None\n1      NaN    None    None\n2    1.000    None       x\n3      NaN       b       x\n4   -4.000       a       z\n5   -4.000       a       z\n6    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with any missing values ","message":["%python","","MY_DF2.dropna(how='any')"],"enabled":true,"result":{"startTime":1734736699659,"interpreter":"python.low","endTime":1734736700134,"results":[{"message":"   numeric string1 string2\n0   -4.000       a       z\n1   -4.000       a       z\n2    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with all missing values","message":["%python","","MY_DF2.dropna(how='all')"],"enabled":true,"result":{"startTime":1734736700591,"interpreter":"python.low","endTime":1734736701072,"results":[{"message":"   numeric string1 string2\n0    1.400    None    None\n1    1.000    None       x\n2      NaN       b       x\n3   -4.000       a       z\n4   -4.000       a       z\n5    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with any missing numeric values","message":["%python","","MY_DF2.dropna(how='any', subset=['numeric'])"],"enabled":true,"result":{"startTime":1734736701596,"interpreter":"python.low","endTime":1734736702108,"results":[{"message":"   numeric string1 string2\n0    1.400    None    None\n1    1.000    None       x\n2   -4.000       a       z\n3   -4.000       a       z\n4    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop duplicate rows","message":["%python","","MY_DF2.drop_duplicates()"],"enabled":true,"result":{"startTime":1734736702586,"interpreter":"python.low","endTime":1734736703121,"results":[{"message":"   numeric string1 string2\n0    5.432       a       z\n1    1.000    None       x\n2      NaN       b       x\n3   -4.000       a       z\n4    1.400    None    None\n5      NaN    None    None\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with same value in named columns","message":["%python","","MY_DF2.drop_duplicates(subset=['string1', 'string2'])"],"enabled":true,"result":{"startTime":1734736703581,"interpreter":"python.low","endTime":1734736704111,"results":[{"message":"   numeric string1 string2\n0     -4.0       a       z\n1      1.4    None    None\n2      1.0    None       x\n3      NaN       b       x\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop specific column","message":["%python","","MY_DF2.drop('string2')"],"enabled":true,"result":{"startTime":1734736704585,"interpreter":"python.low","endTime":1734736705074,"results":[{"message":"   numeric string1\n0    1.400    None\n1      NaN    None\n2    1.000    None\n3      NaN       b\n4   -4.000       a\n5   -4.000       a\n6    5.432       a\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Split and KFold","","Next, explore the ***split*** and ***KFold*** function using the *digits* data set. After creating an OML DataFrame proxy object for the *digits* data set,","","* split the data into 20% and 80% samples","* split the data into four sets of 25% each","* perform stratification on the target column","* verify that the stratified sampling generates splits in which all of the different categories of digits (digits 0~9) are present in each split","* hash on the target column","* verify that the different categories of digits (digits 0~9) are present in only one of the splits generated by hashing on the category column","* split the data randomly into 4 consecutive folds"],"enabled":true,"result":{"startTime":1734736705542,"interpreter":"md.low","endTime":1734736705991,"results":[{"message":"<h3 id=\"split-and-kfold\">Split and KFold<\/h3>\n<p>Next, explore the <em><strong>split<\/strong><\/em> and <em><strong>KFold<\/strong><\/em> function using the <em>digits<\/em> data set. After creating an OML DataFrame proxy object for the <em>digits<\/em> data set,<\/p>\n<ul>\n<li>split the data into 20% and 80% samples<\/li>\n<li>split the data into four sets of 25% each<\/li>\n<li>perform stratification on the target column<\/li>\n<li>verify that the stratified sampling generates splits in which all of the different categories of digits (digits 0~9) are present in each split<\/li>\n<li>hash on the target column<\/li>\n<li>verify that the different categories of digits (digits 0~9) are present in only one of the splits generated by hashing on the category column<\/li>\n<li>split the data randomly into 4 consecutive folds<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data as temporary table","message":["%python","","import pandas as pd","from sklearn.datasets import load_digits","","digits = load_digits()","pd_digits = pd.DataFrame(digits.data,","                         columns=['IMG'+str(i) for i in","                         range(digits['data'].shape[1])])","pd_digits = pd.concat([pd_digits,","                       pd.Series(digits.target,","                                  name = 'target')],","                                  axis = 1)","DIGITS = oml.push(pd_digits)","print(\"Shape: \", DIGITS.shape)"],"enabled":true,"result":{"startTime":1734736706453,"interpreter":"python.low","endTime":1734736707475,"results":[{"message":"Shape:  (1797, 65)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data 80/20","message":["%python","","splits = DIGITS.split(ratio=(.2, .8), use_hash = False)","print(\"Split lengths: \", [len(split) for split in splits])"],"enabled":true,"result":{"startTime":1734736707933,"interpreter":"python.low","endTime":1734736708509,"results":[{"message":"Split lengths:  [351, 1446]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into 4 even samples","message":["%python","","splits = DIGITS.split(ratio = (.25, .25, .25, .25), use_hash = False)","print(\"Split lengths: \", [len(split) for split in splits])"],"enabled":true,"result":{"startTime":1734736708976,"interpreter":"python.low","endTime":1734736709578,"results":[{"message":"Split lengths:  [432, 460, 451, 454]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Stratified sampling","message":["%python","","splits = DIGITS.split(strata_cols=['target'])","print(\"Split lengths: \", [split.shape for split in splits])"],"enabled":true,"result":{"startTime":1734736710047,"interpreter":"python.low","endTime":1734736710859,"results":[{"message":"Split lengths:  [(1285, 65), (512, 65)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Veryify stratified samples","message":["%python","","print(\"Verify values: \", [split['target'].drop_duplicates().sort_values().pull()","for split in splits])"],"enabled":true,"result":{"startTime":1734736711325,"interpreter":"python.low","endTime":1734736711822,"results":[{"message":"Verify values:  [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Hash on target column","message":["%python","","splits = DIGITS.split(hash_cols=['target'])","print(\"Split lengths: \", [split.shape for split in splits])"],"enabled":true,"result":{"startTime":1734736712317,"interpreter":"python.low","endTime":1734736713223,"results":[{"message":"Split lengths:  [(899, 65), (898, 65)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Verify hashing","message":["%python","","print(\"Verify values: \", [split['target'].drop_duplicates().sort_values().pull()","for split in splits])"],"enabled":true,"result":{"startTime":1734736713687,"interpreter":"python.low","endTime":1734736714164,"results":[{"message":"Verify values:  [[0, 1, 3, 5, 8], [2, 4, 6, 7, 9]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Randomly split into 4 folds","message":["%python","","folds = DIGITS.KFold(n_splits=4)","","print(\"Split lengths: \", [(len(fold[0]), len(fold[1])) for fold in folds])"],"enabled":true,"result":{"startTime":1734736714626,"interpreter":"python.low","endTime":1734736715906,"results":[{"message":"Split lengths:  [(1352, 445), (1336, 461), (1379, 418), (1325, 472)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Correlation analysis","","Next, we explore the ***corr*** function. We construct a trivial, highly correlated DataFrame. First, verify that the correlation between column A and B is 1."],"enabled":true,"result":{"startTime":1734736716363,"interpreter":"md.low","endTime":1734736716796,"results":[{"message":"<h3 id=\"correlation-analysis\">Correlation analysis<\/h3>\n<p>Next, we explore the <em><strong>corr<\/strong><\/em> function. We construct a trivial, highly correlated DataFrame. First, verify that the correlation between column A and B is 1.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","import pandas as pd","","my_df2 = pd.DataFrame({'A': range(4), 'B': [2*i for i in range(4)]})","MY_DF2 = oml.push(my_df2)","MY_DF2"],"enabled":true,"result":{"startTime":1734736717258,"interpreter":"python.low","endTime":1734736717859,"results":[{"message":"   A  B\n0  0  0\n1  1  2\n2  2  4\n3  3  6\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MY_DF2.corr()"],"enabled":true,"result":{"startTime":1734736718320,"interpreter":"python.low","endTime":1734736718882,"results":[{"message":"   A  B\nA  1  1\nB  1  1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Correlation analysis with skipna","","When using ***corr***, by default, 'skipna' is True."],"enabled":true,"result":{"startTime":1734736719351,"interpreter":"md.low","endTime":1734736719788,"results":[{"message":"<h3 id=\"correlation-analysis-with-skipna\">Correlation analysis with skipna<\/h3>\n<p>When using <em><strong>corr<\/strong><\/em>, by default, 'skipna' is True.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Modify data to alter correlation results","message":["%python","","my_df3 = my_df2","","my_df3.loc[2, 'A'] = 1.5","","# Change an entry to NaN (not a number) to test the 'skipna' parameter in corr","my_df3.loc[1, 'B'] = None","","# Push my_df3 to Oracle Database using the floating point column type ","# because NaNs cannot be used in Oracle numbers","MY_DF3 = oml.push(my_df3, oranumber=False)","MY_DF3"],"enabled":true,"result":{"startTime":1734736720252,"interpreter":"python.low","endTime":1734736720754,"results":[{"message":"     A    B\n0  0.0  0.0\n1  1.0  NaN\n2  1.5  4.0\n3  3.0  6.0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Default corr skipping missing values","message":["%python","","MY_DF3.corr()"],"enabled":true,"result":{"startTime":1734736721211,"interpreter":"python.low","endTime":1734736721745,"results":[{"message":"          A         B\nA  1.000000  0.981981\nB  0.981981  1.000000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Not skipping missing values produces NaNs","message":["%python","","MY_DF3.corr(skipna=False)"],"enabled":true,"result":{"startTime":1734736722204,"interpreter":"python.low","endTime":1734736722738,"results":[{"message":"     A    B\nA  1.0  NaN\nB  NaN  1.0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Crosstab and pivot","","Next, explore the ***crosstab*** and ***pivot*** functions on a DataFrame relating gender, ","left or right handedness, and speed and accuracy on some task."],"enabled":true,"result":{"startTime":1734736723200,"interpreter":"md.low","endTime":1734736723636,"results":[{"message":"<h3 id=\"crosstab-and-pivot\">Crosstab and pivot<\/h3>\n<p>Next, explore the <em><strong>crosstab<\/strong><\/em> and <em><strong>pivot<\/strong><\/em> functions on a DataFrame relating gender,\nleft or right handedness, and speed and accuracy on some task.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%python","","my_df4 = pd.DataFrame({","     'GENDER': ['M', 'M', 'F', 'M', 'F', 'M', 'F', 'F', None, 'F', 'M', 'F'],","     'HAND': ['L', 'R', 'R', 'L', 'R', None, 'L', 'R', 'R', 'R', 'R', 'R'],","     'SPEED': [40.5, 30.4, 60.8, 51.2, 54, 29.3, 34.1, 39.6, 46.4, 12, 25.3, 37.5],","     'ACCURACY': [.92, .94, .87, .9, .85, .97, .96, .93, .89, .84, .91, .95]","    })","    ","MY_DF4 = oml.push(my_df4)"],"enabled":true,"result":{"startTime":1734736724102,"interpreter":"python.low","endTime":1734736724602,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the categories that the most entries belong to","message":["%python","","MY_DF4.crosstab('GENDER', 'HAND').sort_values('count', ascending=False)"],"enabled":true,"result":{"startTime":1734736725062,"interpreter":"python.low","endTime":1734736725883,"results":[{"message":"  GENDER  HAND  count\n0      F     R      5\n1      M     L      2\n2      M     R      2\n3      F     L      1\n4      M  None      1\n5   None     R      1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For each gender and across entries, find ratio of entries with different hand values","message":["%python","","MY_DF4.crosstab('GENDER', 'HAND', pivot = True, margins = True, normalize = 0)"],"enabled":true,"result":{"startTime":1734736726347,"interpreter":"python.low","endTime":1734736727225,"results":[{"message":"  GENDER  count_(L)  count_(R)  count_(None)\n0    All   0.250000   0.666667      0.083333\n1      F   0.166667   0.833333      0.000000\n2      M   0.400000   0.400000      0.200000\n3   None   0.000000   1.000000      0.000000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the mean speed across all gender and hand combinations","message":["%python","","MY_DF4.pivot_table('GENDER', 'HAND', 'SPEED')"],"enabled":true,"result":{"startTime":1734736727689,"interpreter":"python.low","endTime":1734736728646,"results":[{"message":"  GENDER  mean(SPEED)_(L)  mean(SPEED)_(R)  mean(SPEED)_(None)\n0      F            34.10            40.78                 NaN\n1      M            45.85            27.85                29.3\n2   None              NaN            46.40                 NaN\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the median vlaues for accuracy and speed by gender and hand","message":["%python","","MY_DF4.pivot_table('GENDER', 'HAND', aggfunc = oml.DataFrame.median)"],"enabled":true,"result":{"startTime":1734736729112,"interpreter":"python.low","endTime":1734736730135,"results":[{"message":"  GENDER  median(ACCURACY)_(L)  ...  median(SPEED)_(R)  median(SPEED)_(None)\n0      F                  0.96  ...              39.60                   NaN\n1      M                  0.91  ...              27.85                  29.3\n2   None                   NaN  ...              46.40                   NaN\n\n[3 rows x 7 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the max and min speeds for every gender and hand combination and across all combinations","message":["%python","","MY_DF4.pivot_table('GENDER', 'HAND', 'SPEED', ","               aggfunc = [oml.DataFrame.max, oml.DataFrame.min],","               margins = True)"],"enabled":true,"result":{"startTime":1734736730601,"interpreter":"python.low","endTime":1734736731624,"results":[{"message":"  GENDER  max(SPEED)_(L)  ...  min(SPEED)_(None)  min(SPEED)_(All)\n0    All            51.2  ...               29.3              12.0\n1      F            34.1  ...                NaN              12.0\n2      M            51.2  ...               29.3              25.3\n3   None             NaN  ...                NaN              46.4\n\n[4 rows x 9 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Create new columns","","Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client Python engine."],"enabled":true,"result":{"startTime":1734736732085,"interpreter":"md.low","endTime":1734736732532,"results":[{"message":"<h3 id=\"create-new-columns\">Create new columns<\/h3>\n<p>Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client Python engine.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data as temporary table","message":["%python","","shopping_cart = pd.DataFrame({","  'Item_name': ['paper_towel', 'ground_pork', 'tofu', 'eggs',","                'pork_loin', 'whole_milk', 'egg_custard'],","  'Item_type': ['grocery', 'meat', 'grocery', 'dairy', 'meat',","                'dairy', 'bakery'],","  'Quantity': [1, 2.6, 4, 1, 1.9, 1, 1],","  'Unit_price': [1.19, 2.79, 0.99, 2.49, 3.19, 2.5, 3.99]","  })","CART = oml.push(shopping_cart)","CART"],"enabled":true,"result":{"startTime":1734736732997,"interpreter":"python.low","endTime":1734736733886,"results":[{"message":"     Item_name Item_type  Quantity  Unit_price\n0  paper_towel   grocery       1.0        1.19\n1  ground_pork      meat       2.6        2.79\n2         tofu   grocery       4.0        0.99\n3         eggs     dairy       1.0        2.49\n4    pork_loin      meat       1.9        3.19\n5   whole_milk     dairy       1.0        2.50\n6  egg_custard    bakery       1.0        3.99\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a column Price = Quantity * Unit_price","message":["%python","","","price = CART['Quantity']*(CART['Unit_price'])","print(\"Type: \", type(price))","","CART = CART.concat({'Price': price.round(2)})","","CART"],"enabled":true,"result":{"startTime":1734736734373,"interpreter":"python.low","endTime":1734736734914,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price\n0  paper_towel   grocery       1.0        1.19   1.19\n1  ground_pork      meat       2.6        2.79   7.25\n2         tofu   grocery       4.0        0.99   3.96\n3         eggs     dairy       1.0        2.49   2.49\n4    pork_loin      meat       1.9        3.19   6.06\n5   whole_milk     dairy       1.0        2.50   2.50\n6  egg_custard    bakery       1.0        3.99   3.99\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the pattern 'egg' in the Item_name column","message":["%python","","egg_pattern = CART['Item_name'].count_pattern('egg')","print(\"Type: \", type(egg_pattern))","CART.concat({'Egg_pattern': egg_pattern})"],"enabled":true,"result":{"startTime":1734736735380,"interpreter":"python.low","endTime":1734736735919,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price  Egg_pattern\n0  paper_towel   grocery       1.0        1.19   1.19            0\n1  ground_pork      meat       2.6        2.79   7.25            0\n2         tofu   grocery       4.0        0.99   3.96            0\n3         eggs     dairy       1.0        2.49   2.49            1\n4    pork_loin      meat       1.9        3.19   6.06            0\n5   whole_milk     dairy       1.0        2.50   2.50            0\n6  egg_custard    bakery       1.0        3.99   3.99            1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the start index of substring 'pork' in the Item_name column","message":["%python","","pork_startInd = CART['Item_name'].find('pork')","print(\"Type: \", type(pork_startInd))","CART.concat({'Pork_startInd': pork_startInd})"],"enabled":true,"result":{"startTime":1734736736391,"interpreter":"python.low","endTime":1734736736921,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price  Pork_startInd\n0  paper_towel   grocery       1.0        1.19   1.19             -1\n1  ground_pork      meat       2.6        2.79   7.25              7\n2         tofu   grocery       4.0        0.99   3.96             -1\n3         eggs     dairy       1.0        2.49   2.49             -1\n4    pork_loin      meat       1.9        3.19   6.06              0\n5   whole_milk     dairy       1.0        2.50   2.50             -1\n6  egg_custard    bakery       1.0        3.99   3.99             -1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check whether items are of grocery category","message":["%python","","is_grocery=CART['Item_type']=='grocery'","print(\"Type: \", type(is_grocery))","CART.concat({'Is_grocery': is_grocery})"],"enabled":true,"result":{"startTime":1734736737392,"interpreter":"python.low","endTime":1734736737924,"results":[{"message":"Type:  <class 'oml.core.boolean.Boolean'>\n     Item_name Item_type  Quantity  Unit_price  Price  Is_grocery\n0  paper_towel   grocery       1.0        1.19   1.19        True\n1  ground_pork      meat       2.6        2.79   7.25       False\n2         tofu   grocery       4.0        0.99   3.96        True\n3         eggs     dairy       1.0        2.49   2.49       False\n4    pork_loin      meat       1.9        3.19   6.06       False\n5   whole_milk     dairy       1.0        2.50   2.50       False\n6  egg_custard    bakery       1.0        3.99   3.99       False\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Calculate the length of item names","message":["%python","","name_length=CART['Item_name'].len()","print(\"Type: \", type(name_length))","CART.concat({'Name_length': name_length})"],"enabled":true,"result":{"startTime":1734736738400,"interpreter":"python.low","endTime":1734736738933,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price  Name_length\n0  paper_towel   grocery       1.0        1.19   1.19           11\n1  ground_pork      meat       2.6        2.79   7.25           11\n2         tofu   grocery       4.0        0.99   3.96            4\n3         eggs     dairy       1.0        2.49   2.49            4\n4    pork_loin      meat       1.9        3.19   6.06            9\n5   whole_milk     dairy       1.0        2.50   2.50           10\n6  egg_custard    bakery       1.0        3.99   3.99           11\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use a few other function to modify the Price column","message":["%python","","print(\"Ceiling: \", CART['Price'].ceil())","print(\"Floor: \", CART['Price'].floor())","print(\"Exponential: \", CART['Price'].exp().round(3))","print(\"logarithm: \", CART['Price'].log().round(3))","print(\"Square Root: \", CART['Price'].sqrt().round(3))"],"enabled":true,"result":{"startTime":1734736739397,"interpreter":"python.low","endTime":1734736739898,"results":[{"message":"Ceiling:  [2, 8, 4, 3, 7, 3, 4]\nFloor:  [1, 7, 3, 2, 6, 2, 3]\nExponential:  [3.287, 1408.105, 52.457, 12.061, 428.375, 12.182, 54.055]\nlogarithm:  [0.174, 1.981, 1.376, 0.912, 1.802, 0.916, 1.384]\nSquare Root:  [1.091, 2.693, 1.99, 1.578, 2.462, 1.581, 1.997]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, using a modified version of the *iris* data set, explore ***sort_values*** and ***tail*** functions.","","We modify the dataset by replacing a few entries with NaNs to test how the ***na_position*** parameter works in the ***sort_values*** method."],"enabled":true,"result":{"startTime":1734736740361,"interpreter":"md.low","endTime":1734736740807,"results":[{"message":"<p>Next, using a modified version of the <em>iris<\/em> data set, explore <em><strong>sort_values<\/strong><\/em> and <em><strong>tail<\/strong><\/em> functions.<\/p>\n<p>We modify the dataset by replacing a few entries with NaNs to test how the <em><strong>na_position<\/strong><\/em> parameter works in the <em><strong>sort_values<\/strong><\/em> method.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%python","","from sklearn.datasets import load_iris","import pandas as pd","","iris = load_iris()","","x = pd.DataFrame(iris.data, columns = ['Sepal_Length', 'Sepal_Width',","                                       'Petal_Length', 'Petal_Width'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor',","                                     2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","                   ","x['Sepal_Width'].replace({3.5: None}, inplace=True)","x['Petal_Length'].replace({1.5: None}, inplace=True)","x['Petal_Width'].replace({2.3: None}, inplace=True)","","iris_df = pd.concat([x, y], axis=1)","IRIS2 = oml.push(iris_df)"],"enabled":true,"result":{"startTime":1734736741274,"interpreter":"python.low","endTime":1734736741773,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Sort data set first by Sepal_Length then by Sepal_Width in descending order","message":["%python","","IRIS2.sort_values(by = ['Sepal_Length', 'Sepal_Width'], ascending=False).head(8)"],"enabled":true,"result":{"startTime":1734736742248,"interpreter":"python.low","endTime":1734736742915,"results":[{"message":"   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width    SPECIES\n0           7.9          3.8           6.4          2.0  virginica\n1           7.7          3.8           6.7          2.2  virginica\n2           7.7          3.0           6.1          NaN  virginica\n3           7.7          2.8           6.7          2.0  virginica\n4           7.7          2.6           6.9          NaN  virginica\n5           7.6          3.0           6.6          2.1  virginica\n6           7.4          2.8           6.1          1.9  virginica\n7           7.3          2.9           6.3          1.8  virginica\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display last 5 rows of the data set","message":["%python","","IRIS2.tail()"],"enabled":true,"result":{"startTime":1734736743381,"interpreter":"python.low","endTime":1734736743911,"results":[{"message":"   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width    SPECIES\n0           7.9          3.8           6.4          2.0  virginica\n1           7.6          3.0           6.6          2.1  virginica\n2           7.7          2.8           6.7          2.0  virginica\n3           7.7          3.8           6.7          2.2  virginica\n4           7.7          2.6           6.9          NaN  virginica\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Sort last 5 rows of the iris data by petal length and petal width","message":["%python","","IRIS2.tail().sort_values(by = ['Petal_Length', 'Petal_Width'], na_position = 'last')"],"enabled":true,"result":{"startTime":1734736744383,"interpreter":"python.low","endTime":1734736744897,"results":[{"message":"   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width    SPECIES\n0           7.9          3.8           6.4          2.0  virginica\n1           7.6          3.0           6.6          2.1  virginica\n2           7.7          2.8           6.7          2.0  virginica\n3           7.7          3.8           6.7          2.2  virginica\n4           7.7          2.6           6.9          NaN  virginica\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Other operations on columns","","Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics."],"enabled":true,"result":{"startTime":1734736745356,"interpreter":"md.low","endTime":1734736745804,"results":[{"message":"<h3 id=\"other-operations-on-columns\">Other operations on columns<\/h3>\n<p>Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data as temporary table","message":["%python","","my_df4 = pd.DataFrame({'numeric_col': [1, 1.4, -4, 3.145, 5, None],","                       'string_col' : [None, None, 'a', 'a', 'a', 'b'],","                       'bytes_col'  : [b'a', b'b', b'c', b'c', b'd', b'e']})","","MY_DF4 = oml.push(my_df4, dbtypes = {'numeric_col': 'BINARY_DOUBLE',","                                     'string_col':'CHAR(1)', ","                                     'bytes_col':'RAW(1)'})","MY_DF4"],"enabled":true,"result":{"startTime":1734736746281,"interpreter":"python.low","endTime":1734736746858,"results":[{"message":"   numeric_col string_col bytes_col\n0        1.000       None      b'a'\n1        1.400       None      b'b'\n2       -4.000          a      b'c'\n3        3.145          a      b'c'\n4        5.000          a      b'd'\n5          NaN          b      b'e'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Combine a Boolean column with MY_DF4","message":["%python","","oml_bool = MY_DF4['numeric_col'] > 3","","MY_DF4 = MY_DF4.concat(oml_bool)","MY_DF4.rename({'COL4':'boolean_col'})"],"enabled":true,"result":{"startTime":1734736747327,"interpreter":"python.low","endTime":1734736747863,"results":[{"message":"   numeric_col string_col bytes_col  boolean_col\n0        1.000       None      b'a'        False\n1        1.400       None      b'b'        False\n2       -4.000          a      b'c'        False\n3        3.145          a      b'c'         True\n4        5.000          a      b'd'         True\n5          NaN          b      b'e'         True\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Describe","","Use the describe function to compute a variety of statistics on all columns. Then, exclude the Float columns."],"enabled":true,"result":{"startTime":1734736748330,"interpreter":"md.low","endTime":1734736748790,"results":[{"message":"<h3 id=\"describe\">Describe<\/h3>\n<p>Use the describe function to compute a variety of statistics on all columns. Then, exclude the Float columns.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MY_DF4.describe(include='all')"],"enabled":true,"result":{"startTime":1734736749262,"interpreter":"python.low","endTime":1734736749964,"results":[{"message":"        numeric_col string_col bytes_col boolean_col\ncount      5.000000          4         6           6\nunique          NaN          2         5           2\ntop             NaN          a      b'c'       False\nfreq            NaN          3         2           3\nmean       1.309000        NaN       NaN         NaN\nstd        3.364655        NaN       NaN         NaN\nmin       -4.000000        NaN       NaN         NaN\n25%        1.000000        NaN       NaN         NaN\n50%        1.400000        NaN       NaN         NaN\n75%        3.145000        NaN       NaN         NaN\nmax        5.000000        NaN       NaN         NaN\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MY_DF4.describe(exclude=[oml.Float])"],"enabled":true,"result":{"startTime":1734736750428,"interpreter":"python.low","endTime":1734736751076,"results":[{"message":"       string_col bytes_col boolean_col\ncount           4         6           6\nunique          2         5           2\ntop             a      b'c'       False\nfreq            3         2           3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute the sum of values in each Float or Boolean column","message":["%python","","MY_DF4.sum()"],"enabled":true,"result":{"startTime":1734736751544,"interpreter":"python.low","endTime":1734736752149,"results":[{"message":"numeric_col    6.545\nboolean_col    3.000\ndtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Other statistics","","Find the cumulative sum of values in each Float or Boolean column after MY_DF4 is sorted by the bytes column in descending order. Then, compute skew, median, and kurtosis for float columns."],"enabled":true,"result":{"startTime":1734736752620,"interpreter":"md.low","endTime":1734736753067,"results":[{"message":"<h3 id=\"other-statistics\">Other statistics<\/h3>\n<p>Find the cumulative sum of values in each Float or Boolean column after MY_DF4 is sorted by the bytes column in descending order. Then, compute skew, median, and kurtosis for float columns.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute cummulate sum","message":["%python","","MY_DF4.cumsum(by = 'bytes_col', ascending = False)"],"enabled":true,"result":{"startTime":1734736753541,"interpreter":"python.low","endTime":1734736754024,"results":[{"message":"   numeric_col  boolean_col\n0          NaN            1\n1        5.000            2\n2        1.000            2\n3        4.145            3\n4        5.545            3\n5        6.545            3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute select statistics","message":["%python","","print(MY_DF4.skew())","print(MY_DF4.median())","print(MY_DF4.kurtosis())"],"enabled":true,"result":{"startTime":1734736754495,"interpreter":"python.low","endTime":1734736757374,"results":[{"message":"numeric_col   -0.683838\ndtype: float64\nnumeric_col    1.4\ndtype: float64\nnumeric_col   -0.582684\ndtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Boxplot and hist","","Next, use the overloaded ***boxplot*** and ***hist*** functions using the *wine* data set. The statistics supporting these plots are computed in-database, so transfer of data or client side memory limitations are avoided."],"enabled":true,"result":{"startTime":1734736757828,"interpreter":"md.low","endTime":1734736758269,"results":[{"message":"<h3 id=\"boxplot-and-hist\">Boxplot and hist<\/h3>\n<p>Next, use the overloaded <em><strong>boxplot<\/strong><\/em> and <em><strong>hist<\/strong><\/em> functions using the <em>wine<\/em> data set. The statistics supporting these plots are computed in-database, so transfer of data or client side memory limitations are avoided.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Boxplot of select columns","message":["%python","","import matplotlib.pyplot as plt","from sklearn.datasets import load_wine","","wine = load_wine()","wine_data = pd.DataFrame(wine.data, columns = wine.feature_names)","WINE = oml.push(wine_data)","","oml.graphics.boxplot(WINE[:,8:12], showmeans=True, ","                     meanline=True, patch_artist=True, ","                     labels=WINE.columns[8:12])","plt.title('Distribution of Wine Attributes')","plt.show()"],"enabled":true,"result":{"startTime":1734736758747,"interpreter":"python.low","endTime":1734736760025,"results":[{"message":"<div style='width:auto;height:auto'><img 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Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1734736762658,"interpreter":"md.low","endTime":1734736763096,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":"zeppelin","template":null,"templateConfig":null,"name":"OML4Py -3- Datastore and Script Repository","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734738342979,"interpreter":"md.medium","endTime":1734738343506,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python - Datastore and Script Repository","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight the datastore and script repository features of OML4Py.","","With a datastore, you can store Python objects in a named datastore entry. This named datastore can then use in subsequent Python sessions and even be made available to other users or programs by granting/revoking read permissions.","","Python objects, including OML4Py proxy objects, exist for the duration of the current Python session unless you explicitly save them. You can save one or more Python objects, including OML proxy objects, to a named datastore and then load those objects in a later Python session. This is also useful when using embedded Python execution. Datastores exist in the user\u2019s Oracle Database schema. A datastore, and the objects it contains, persist in the database until explicitly deleted.","","Using a datastore, you can:","","* Save OML4Py and other Python objects in one Python session and load them in another Python session","* Pass non-scalar arguments to Python functions for use in embedded Python execution from both the Python, and more importantly, the SQL API where only scalar values can be passed to functions","* List available datastores and explore the contents of a datastore","","With the script repository, users can:","","* Create and store user-defined Python functions as scripts in Oracle Database","* Grant or revoke the read/eecute privilege to a script","* List available scripts","* Load a script function into the Python environment","* Drop a script from the script repository","","You can make scripts either private or global. A private script is available only to the owner. A global script is available to any user. For private scripts, the owner of the script may grant the read privilege to other users or revoke that privilege.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734738343964,"interpreter":"md.medium","endTime":1734738344485,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python---datastore-and-script-repository\">Oracle Machine Learning for Python - Datastore and Script Repository<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight the datastore and script repository features of OML4Py.<\/p>\n<p>With a datastore, you can store Python objects in a named datastore entry. This named datastore can then use in subsequent Python sessions and even be made available to other users or programs by granting/revoking read permissions.<\/p>\n<p>Python objects, including OML4Py proxy objects, exist for the duration of the current Python session unless you explicitly save them. You can save one or more Python objects, including OML proxy objects, to a named datastore and then load those objects in a later Python session. This is also useful when using embedded Python execution. Datastores exist in the user\u2019s Oracle Database schema. A datastore, and the objects it contains, persist in the database until explicitly deleted.<\/p>\n<p>Using a datastore, you can:<\/p>\n<ul>\n<li>Save OML4Py and other Python objects in one Python session and load them in another Python session<\/li>\n<li>Pass non-scalar arguments to Python functions for use in embedded Python execution from both the Python, and more importantly, the SQL API where only scalar values can be passed to functions<\/li>\n<li>List available datastores and explore the contents of a datastore<\/li>\n<\/ul>\n<p>With the script repository, users can:<\/p>\n<ul>\n<li>Create and store user-defined Python functions as scripts in Oracle Database<\/li>\n<li>Grant or revoke the read/eecute privilege to a script<\/li>\n<li>List available scripts<\/li>\n<li>Load a script function into the Python environment<\/li>\n<li>Drop a script from the script repository<\/li>\n<\/ul>\n<p>You can make scripts either private or global. A private script is available only to the owner. A global script is available to any user. For private scripts, the owner of the script may grant the read privilege to other users or revoke that privilege.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Import libraries supporting OML4Py and create proxy object","","To use OML4Py, first import the package ***oml***. Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1734738344950,"interpreter":"md.medium","endTime":1734738345423,"results":[{"message":"<h3 id=\"import-libraries-supporting-oml4py-and-create-proxy-object\">Import libraries supporting OML4Py and create proxy object<\/h3>\n<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>. Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and suppress warnings","message":["%python","","import oml","import pandas as pd","","import warnings","warnings.filterwarnings(\"ignore\")"],"enabled":true,"result":{"startTime":1734738345874,"interpreter":"python.medium","endTime":1734738348361,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create Pandas DataFrames and load them into Autonomous Database","","We load three data sets, combining target and predictors into a single DataFrame, before invoking create and displaying the columns for each. These will be used in exploring *datastore* functionality."],"enabled":true,"result":{"startTime":1734738348813,"interpreter":"md.medium","endTime":1734738349264,"results":[{"message":"<h3 id=\"create-pandas-dataframes-and-load-them-into-autonomous-database\">Create Pandas DataFrames and load them into Autonomous Database<\/h3>\n<p>We load three data sets, combining target and predictors into a single DataFrame, before invoking create and displaying the columns for each. These will be used in exploring <em>datastore<\/em> functionality.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create IRIS table","message":["%python","from sklearn import datasets","from sklearn import linear_model","import pandas as pd","","iris = datasets.load_iris()","x = pd.DataFrame(iris.data, columns = ['SEPAL_LENGTH','SEPAL_WIDTH',","                                       'PETAL_LENGTH','PETAL_WIDTH'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor',","                 2:'virginica'}[x], iris.target)), columns = ['SPECIES'])","try:","    oml.drop(table='IRIS')","except:","    pass","IRIS = oml.create(pd.concat([x, y], axis=1), table = 'IRIS')","iris = pd.concat([x, y], axis=1)","print(IRIS.columns)"],"enabled":true,"result":{"startTime":1734738349723,"interpreter":"python.medium","endTime":1734738350422,"results":[{"message":"['SEPAL_LENGTH', 'SEPAL_WIDTH', 'PETAL_LENGTH', 'PETAL_WIDTH', 'SPECIES']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create DIABETES temporary table","message":["%python","","diabetes = datasets.load_diabetes()","x = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)","y = pd.DataFrame(diabetes.target, columns=['disease_progression'])","","DIABETES_TMP = oml.push(pd.concat([x, y], axis=1))","print(DIABETES_TMP.columns)"],"enabled":true,"result":{"startTime":1734738350879,"interpreter":"python.medium","endTime":1734738351493,"results":[{"message":"['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'disease_progression']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create WINE temporary table","message":["%python","","from sklearn.datasets import load_wine","","wine = load_wine()","x = pd.DataFrame(wine.data, columns = wine[\"feature_names\"])","y = pd.DataFrame(list(map(lambda x: {0:'class_0', 1: 'class_1', 2:'class_2'}[x], wine.target)), ","                 columns = ['target'])","","wine_df = pd.concat([x,y], axis=1)","","WINE_TMP = oml.push(wine_df)","print(WINE_TMP.columns)"],"enabled":true,"result":{"startTime":1734738351963,"interpreter":"python.medium","endTime":1734738352667,"results":[{"message":"['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline', 'target']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Save Python objects to datastore","","Save the actual *iris* data set and the temporary WINE object to a datastore named \"ds_pydata\", overwriting if the named datastore already exists. Note that you can store actual data objects in a datastore, but large data objects should remain as database tables for performance and scalability.","","By storing the WINE_TMP object, the temporary table will not be deleted when the session terminates."],"enabled":true,"result":{"startTime":1734738353124,"interpreter":"md.medium","endTime":1734738353567,"results":[{"message":"<h3 id=\"save-python-objects-to-datastore\">Save Python objects to datastore<\/h3>\n<p>Save the actual <em>iris<\/em> data set and the temporary WINE object to a datastore named &quot;ds_pydata&quot;, overwriting if the named datastore already exists. Note that you can store actual data objects in a datastore, but large data objects should remain as database tables for performance and scalability.<\/p>\n<p>By storing the WINE_TMP object, the temporary table will not be deleted when the session terminates.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","oml.ds.save(objs={'iris':iris, 'oml_wine':WINE_TMP},","            name=\"ds_pydata\", description = \"python datasets\", overwrite=True)"],"enabled":true,"result":{"startTime":1734738354031,"interpreter":"python.medium","endTime":1734738354690,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Add a third object, the temporary DIABETES proxy object, to that same datastore","message":["%python","","oml.ds.save(objs={'oml_diabetes':DIABETES_TMP}, name=\"ds_pydata\", append=True)"],"enabled":true,"result":{"startTime":1734738355150,"interpreter":"python.medium","endTime":1734738356441,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Save the *iris* DataFrame to a new datastore, and then list the datastores. Notice we see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. Our two datastores ***ds_iris_data*** and ***ds_pydata*** are present, with the latter containing three objects."],"enabled":true,"result":{"startTime":1734738356895,"interpreter":"md.medium","endTime":1734738357336,"results":[{"message":"<p>Save the <em>iris<\/em> DataFrame to a new datastore, and then list the datastores. Notice we see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. Our two datastores <em><strong>ds_iris_data<\/strong><\/em> and <em><strong>ds_pydata<\/strong><\/em> are present, with the latter containing three objects.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Save the iris data frame to a new datastore and list the datastores","message":["%python","","oml.ds.save(objs={'iris':iris},","            name=\"ds_iris_data\", description = \"iris dataset\", overwrite=True)","","oml.ds.dir()"],"enabled":true,"result":{"startTime":1734738357797,"interpreter":"python.medium","endTime":1734738358358,"results":[{"message":"          datastore_name  ...                                       description\n0   PredictPurchaseModel  ...                                              None\n1         auc_result_ftr  ...                                              None\n2         automl results  ...                                              None\n3                   ds-1  ...                                              None\n4          ds-py-glm-mod  ...                                              None\n5                    ds1  ...                                              None\n6                    ds2  ...                                              None\n7                    ds3  ...                                              None\n8                    ds4  ...                                              None\n9           ds_iris_data  ...                                      iris dataset\n10             ds_pydata  ...                                   python datasets\n11         ds_rf_species  ...  Random Forest model predicting Species from IRIS\n12        my_datastore_1  ...                                              None\n\n[13 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Store model objects in a datastore","","To illustrate storing other types of objects in datastores, we'll create regression models using sklearn and OML4Py."],"enabled":true,"result":{"startTime":1734738358811,"interpreter":"md.medium","endTime":1734738359251,"results":[{"message":"<h3 id=\"store-model-objects-in-a-datastore\">Store model objects in a datastore<\/h3>\n<p>To illustrate storing other types of objects in datastores, we'll create regression models using sklearn and OML4Py.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","regr1 = linear_model.LinearRegression()","regr1.fit(wine.data, wine.target)","","regr2 = oml.glm(\"regression\")","X = WINE_TMP.drop('alcohol')","y = WINE_TMP['alcohol']","regr2 = regr2.fit(X, y)"],"enabled":true,"result":{"startTime":1734738359714,"interpreter":"python.medium","endTime":1734738362294,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save objects to the datastore","message":["%md","","Save objects to a datastore and allow the read privilege to be granted to them. Then grant the read privilege to all users by specifying ***user=None***. Finally list the datastores to which the read privilege has been granted."],"enabled":true,"result":{"startTime":1734738362746,"interpreter":"md.medium","endTime":1734738363191,"results":[{"message":"<p>Save objects to a datastore and allow the read privilege to be granted to them. Then grant the read privilege to all users by specifying <em><strong>user=None<\/strong><\/em>. Finally list the datastores to which the read privilege has been granted.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.ds.save(objs={'regr1':regr1, 'regr2':regr2},","            name=\"ds_pymodel\", grantable=True, overwrite=True)","","oml.ds.dir()"],"enabled":true,"result":{"startTime":1734738363658,"interpreter":"python.medium","endTime":1734738364261,"results":[{"message":"          datastore_name  ...                                       description\n0   PredictPurchaseModel  ...                                              None\n1         auc_result_ftr  ...                                              None\n2         automl results  ...                                              None\n3                   ds-1  ...                                              None\n4          ds-py-glm-mod  ...                                              None\n5                    ds1  ...                                              None\n6                    ds2  ...                                              None\n7                    ds3  ...                                              None\n8                    ds4  ...                                              None\n9           ds_iris_data  ...                                      iris dataset\n10             ds_pydata  ...                                   python datasets\n11            ds_pymodel  ...                                              None\n12         ds_rf_species  ...  Random Forest model predicting Species from IRIS\n13        my_datastore_1  ...                                              None\n\n[14 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Grant read privilege on the datastore","message":["%python","","oml.grant(name=\"ds_pymodel\", typ=\"datastore\", user=None)","","oml.ds.dir(dstype=\"grant\")"],"enabled":true,"result":{"startTime":1734738364722,"interpreter":"python.medium","endTime":1734738366016,"results":[{"message":"  datastore_name grantee\n0     ds_pymodel  PUBLIC\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Load datastore objects into memory","","Load all Python objects from a datastore to the global workspace and sort the result by name. Notice that they have the name specified in the dictionary when saved."],"enabled":true,"result":{"startTime":1734738366471,"interpreter":"md.medium","endTime":1734738366924,"results":[{"message":"<h3 id=\"load-datastore-objects-into-memory\">Load datastore objects into memory<\/h3>\n<p>Load all Python objects from a datastore to the global workspace and sort the result by name. Notice that they have the name specified in the dictionary when saved.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","sorted(oml.ds.load(name=\"ds_pydata\"))"],"enabled":true,"result":{"startTime":1734738367381,"interpreter":"python.medium","endTime":1734738369556,"results":[{"message":"['iris', 'oml_diabetes', 'oml_wine']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the named Python object, regr2, from the datastore to the global workspace","message":["%python","","oml.ds.load(name=\"ds_pymodel\", objs=[\"regr2\"])"],"enabled":true,"result":{"startTime":1734738370008,"interpreter":"python.medium","endTime":1734738372389,"results":[{"message":"['regr2']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the named Python object regr1 from the datastore to the user's workspace","message":["%python","","oml.ds.load(name=\"ds_pymodel\", objs=[\"regr1\"], to_globals=False)"],"enabled":true,"result":{"startTime":1734738372851,"interpreter":"python.medium","endTime":1734738374523,"results":[{"message":"{'regr1': LinearRegression()}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show datastores and other details","message":["%python","","oml.ds.dir(dstype=\"all\")[['owner', 'datastore_name', 'object_count']]"],"enabled":true,"result":{"startTime":1734738374985,"interpreter":"python.medium","endTime":1734738376014,"results":[{"message":"      owner        datastore_name  object_count\n0   OMLUSER  PredictPurchaseModel             1\n1   OMLUSER        auc_result_ftr             1\n2   OMLUSER        automl results             3\n3   OMLUSER                  ds-1             3\n4   OMLUSER         ds-py-glm-mod             1\n5   OMLUSER                   ds1             1\n6   OMLUSER                   ds2             1\n7   OMLUSER                   ds3             1\n8   OMLUSER                   ds4             1\n9   OMLUSER          ds_iris_data             1\n10  OMLUSER             ds_pydata             3\n11  OMLUSER            ds_pymodel             2\n12  OMLUSER         ds_rf_species             1\n13  OMLUSER        my_datastore_1             1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show datastores which other users have been granted the read privilege","message":["%python","","oml.ds.dir(dstype=\"grant\")"],"enabled":true,"result":{"startTime":1734738376474,"interpreter":"python.medium","endTime":1734738377671,"results":[{"message":"  datastore_name grantee\n0     ds_pymodel  PUBLIC\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show datastores to which this user has been granted the read privilege (there are none)","message":["%python","","oml.ds.dir(dstype=\"granted\")"],"enabled":true,"result":{"startTime":1734738378131,"interpreter":"python.medium","endTime":1734738379161,"results":[{"message":"Empty DataFrame\nColumns: [owner, datastore_name, object_count, size, date, description]\nIndex: []\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show datastores whose names match a pattern","message":["%python","","oml.ds.dir(name='pydata', regex_match=True)[['datastore_name', 'object_count']]"],"enabled":true,"result":{"startTime":1734738379617,"interpreter":"python.medium","endTime":1734738380087,"results":[{"message":"  datastore_name  object_count\n0      ds_pydata             3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Describe contents of a datastore","","Let's describe the contents of the ds_pydata datastore. Notice that the three proxy objects are listed."],"enabled":true,"result":{"startTime":1734738380594,"interpreter":"md.medium","endTime":1734738381047,"results":[{"message":"<h3 id=\"describe-contents-of-a-datastore\">Describe contents of a datastore<\/h3>\n<p>Let's describe the contents of the ds_pydata datastore. Notice that the three proxy objects are listed.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.ds.describe(name='ds_pydata')"],"enabled":true,"result":{"startTime":1734738381526,"interpreter":"python.medium","endTime":1734738382100,"results":[{"message":"    object_name                        class  ...  row_count  col_count\n0          iris  pandas.core.frame.DataFrame  ...        150          5\n1  oml_diabetes                oml.DataFrame  ...        442         11\n2      oml_wine                oml.DataFrame  ...        178         14\n\n[3 rows x 6 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### User privileges on a datastore","","Revoke the read privilege from every user, and again show datastores to which read privilege has been granted. The result is empty."],"enabled":true,"result":{"startTime":1734738382574,"interpreter":"md.medium","endTime":1734738383019,"results":[{"message":"<h3 id=\"user-privileges-on-a-datastore\">User privileges on a datastore<\/h3>\n<p>Revoke the read privilege from every user, and again show datastores to which read privilege has been granted. The result is empty.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.revoke(name=\"ds_pymodel\", typ=\"datastore\", user=None)","","oml.ds.dir(dstype=\"grant\")"],"enabled":true,"result":{"startTime":1734738383490,"interpreter":"python.medium","endTime":1734738383985,"results":[{"message":"Empty DataFrame\nColumns: [datastore_name, grantee]\nIndex: []\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Grant read privilege to the user PYQUSER2","message":["%python","","oml.grant(name=\"ds_pymodel\", typ=\"datastore\", user=\"OMLUSER2\")","","oml.ds.dir(dstype=\"grant\")"],"enabled":true,"result":{"startTime":1734738384457,"interpreter":"python.medium","endTime":1734738385007,"results":[{"message":"  datastore_name   grantee\n0     ds_pymodel  OMLUSER2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Delete datastore content","","Delete some objects from the datastore. Delete a datastore. Delete all datastores whose names match a pattern. Show the existing datastores again."],"enabled":true,"result":{"startTime":1734738385469,"interpreter":"md.medium","endTime":1734738385913,"results":[{"message":"<h3 id=\"delete-datastore-content\">Delete datastore content<\/h3>\n<p>Delete some objects from the datastore. Delete a datastore. Delete all datastores whose names match a pattern. Show the existing datastores again.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.ds.delete(name=\"ds_pydata\", objs=[\"iris\", \"oml_boston\"])","","oml.ds.delete(name=\"ds_pydata\")","","oml.ds.delete(name=\"_pymodel\", regex_match=True)","","oml.ds.dir()"],"enabled":true,"result":{"startTime":1734738386372,"interpreter":"python.medium","endTime":1734738387075,"results":[{"message":"          datastore_name  ...                                       description\n0   PredictPurchaseModel  ...                                              None\n1         auc_result_ftr  ...                                              None\n2         automl results  ...                                              None\n3                   ds-1  ...                                              None\n4          ds-py-glm-mod  ...                                              None\n5                    ds1  ...                                              None\n6                    ds2  ...                                              None\n7                    ds3  ...                                              None\n8                    ds4  ...                                              None\n9           ds_iris_data  ...                                      iris dataset\n10         ds_rf_species  ...  Random Forest model predicting Species from IRIS\n11        my_datastore_1  ...                                              None\n\n[12 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Python Script Repository","","To illustrate using the Python Script Repository, we first define a function ***build_lm1*** that will fit a regression model. With this function, we create a script named \"MyLM_function\". OML4Py stores named user-defined functions -- the scripts -- in the repository."],"enabled":true,"result":{"startTime":1734738387537,"interpreter":"md.medium","endTime":1734738387994,"results":[{"message":"<h3 id=\"python-script-repository\">Python Script Repository<\/h3>\n<p>To illustrate using the Python Script Repository, we first define a function <em><strong>build_lm1<\/strong><\/em> that will fit a regression model. With this function, we create a script named &quot;MyLM_function&quot;. OML4Py stores named user-defined functions -- the scripts -- in the repository.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python ","","iris = datasets.load_iris()","x = pd.DataFrame(iris.data, columns = ['SEPAL_LENGTH','SEPAL_WIDTH',","                                       'PETAL_LENGTH','PETAL_WIDTH'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","IRIS2 = oml.push(pd.concat([x, y], axis=1))"],"enabled":true,"result":{"startTime":1734738388454,"interpreter":"python.medium","endTime":1734738388983,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define function object","message":["%python","","def build_lm1(dat):","    from sklearn import linear_model","    regr = linear_model.LinearRegression()","    import pandas as pd","    dat = pd.get_dummies(dat, drop_first=True)","    X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", ","             \"SPECIES_versicolor\", \"SPECIES_virginica\"]]","    y = dat[[\"SEPAL_LENGTH\"]]","    regr.fit(X, y)","    return regr"],"enabled":true,"result":{"startTime":1734738389436,"interpreter":"python.medium","endTime":1734738389888,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define function as string","message":["%python","","build_lm2 = \"\"\"def build_lm2(dat):","    from sklearn import linear_model","    regr = linear_model.LinearRegression()","    import pandas as pd","    dat = pd.get_dummies(dat, drop_first=True)","    X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", ","             \"SPECIES_versicolor\", \"SPECIES_virginica\"]]","    y = dat[[\"SEPAL_LENGTH\"]]","    regr.fit(X, y)","    return regr\"\"\""],"enabled":true,"result":{"startTime":1734738390344,"interpreter":"python.medium","endTime":1734738390805,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View function string","message":["%python","print(build_lm2)"],"enabled":true,"result":{"startTime":1734738391255,"interpreter":"python.medium","endTime":1734738391712,"results":[{"message":"def build_lm2(dat):\n    from sklearn import linear_model\n    regr = linear_model.LinearRegression()\n    import pandas as pd\n    dat = pd.get_dummies(dat, drop_first=True)\n    X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \n             \"SPECIES_versicolor\", \"SPECIES_virginica\"]]\n    y = dat[[\"SEPAL_LENGTH\"]]\n    regr.fit(X, y)\n    return regr\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Create scripts in repository","","Create ***MyLM_function2*** using function string. Note, that one cannot provide the function object when storing a user-defined function. "],"enabled":true,"result":{"startTime":1734738392167,"interpreter":"md.medium","endTime":1734738392606,"results":[{"message":"<h3 id=\"create-scripts-in-repository\">Create scripts in repository<\/h3>\n<p>Create <em><strong>MyLM_function2<\/strong><\/em> using function string. Note, that one cannot provide the function object when storing a user-defined function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","try:","    oml.script.drop(\"MyLM_function2\")","except: ","    pass","oml.script.create(\"MyLM_function2\", func=build_lm2, overwrite=True)"],"enabled":true,"result":{"startTime":1734738393069,"interpreter":"python.medium","endTime":1734738393526,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"List the scripts available only to the current user","message":["%python","","oml.script.dir(sctype='user')"],"enabled":true,"result":{"startTime":1734738393980,"interpreter":"python.medium","endTime":1734738394463,"results":[{"message":"                       name  ...                date\n0            MyLM_function2  ... 2024-12-20 23:46:33\n1           MyTEST_function  ... 2023-05-12 00:21:09\n2             RandomRedDots  ... 2024-06-18 14:24:53\n3                  build_lm  ... 2024-06-18 15:26:16\n4                build_lm_1  ... 2024-05-03 16:31:12\n5             build_lm_rest  ... 2024-12-20 00:44:23\n6                 build_mod  ... 2024-07-24 18:33:37\n7               density_plt  ... 2024-06-18 15:27:03\n8               group_count  ... 2024-12-20 00:43:16\n9                mean_angle  ... 2022-10-04 21:23:19\n10  myLinearRegressionModel  ... 2022-02-09 18:11:59\n11                 score_lm  ... 2023-02-16 01:45:59\n12                score_mod  ... 2024-06-18 15:26:51\n13               sk_svc_fit  ... 2024-05-03 16:31:21\n14                std_angle  ... 2022-10-04 21:23:19\n15          test_noinp_rest  ... 2022-03-08 00:32:47\n\n[16 rows x 4 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Grant the read privilege to the MyLM_function1 script to the user PYQUSER2","message":["%python","","oml.grant(name=\"MyLM_function2\", typ=\"pyqscript\", user=\"OMLUSER2\")"],"enabled":true,"result":{"startTime":1734738394921,"interpreter":"python.medium","endTime":1734738395401,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"List the scripts to which read privilege has been granted","message":["%python","","oml.script.dir(sctype=\"grant\")"],"enabled":true,"result":{"startTime":1734738395876,"interpreter":"python.medium","endTime":1734738396339,"results":[{"message":"             name   grantee  ... description                date\n0  MyLM_function2  OMLUSER2  ...        None 2024-12-20 23:46:33\n\n[1 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View the contents of the script","message":["%python","","import inspect","","MyLM_function2 = oml.script.load(\"MyLM_function2\")","MyLM_function2.get_source()","print(str(MyLM_function2))","print(MyLM_function2.get_source().read())"],"enabled":true,"result":{"startTime":1734738396804,"interpreter":"python.medium","endTime":1734738397257,"results":[{"message":"<oml.script.script.Callable object at 0x7f50c69e6660>\ndef build_lm2(dat):\n    from sklearn import linear_model\n    regr = linear_model.LinearRegression()\n    import pandas as pd\n    dat = pd.get_dummies(dat, drop_first=True)\n    X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \n             \"SPECIES_versicolor\", \"SPECIES_virginica\"]]\n    y = dat[[\"SEPAL_LENGTH\"]]\n    regr.fit(X, y)\n    return regr\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Revoke the read privilege to the MyLM_function script from the user PYQUSER2","message":["%python","","oml.revoke(name=\"MyLM_function2\", typ=\"pyqscript\", user=\"PYQUSER2\")"],"enabled":true,"result":{"startTime":1734738397721,"interpreter":"python.medium","endTime":1734738398179,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Invoke function using embedded Python execution","","We'll use embedded Python execution to invoke this function. First we ***sync*** the IRIS table to get a proxy object, then use ***table_apply***, providing the proxy object, function name and the output type. We'll view the result and then pull the coefficients."],"enabled":true,"result":{"startTime":1734738398639,"interpreter":"md.medium","endTime":1734738399109,"results":[{"message":"<h3 id=\"invoke-function-using-embedded-python-execution\">Invoke function using embedded Python execution<\/h3>\n<p>We'll use embedded Python execution to invoke this function. First we <em><strong>sync<\/strong><\/em> the IRIS table to get a proxy object, then use <em><strong>table_apply<\/strong><\/em>, providing the proxy object, function name and the output type. We'll view the result and then pull the coefficients.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use table_apply to provide full IRIS data set to function","message":["%python","","res = oml.table_apply(IRIS, func=\"MyLM_function2\", oml_input_type=\"pandas.DataFrame\")","res"],"enabled":true,"result":{"startTime":1734738399570,"interpreter":"python.medium","endTime":1734738400085,"results":[{"message":"LinearRegression()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model coefficients","message":["%python","","res.coef_"],"enabled":true,"result":{"startTime":1734738400547,"interpreter":"python.medium","endTime":1734738401002,"results":[{"message":"array([[ 0.49588894,  0.82924391, -0.31515517, -0.72356196, -1.02349781]])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Storing functions as 'global'","","Let's define and save another function ***build_lm3***, but this time as global. We'll then invoke that function to build another model."],"enabled":true,"result":{"startTime":1734738401463,"interpreter":"md.medium","endTime":1734738401909,"results":[{"message":"<h3 id=\"storing-functions-as-global\">Storing functions as 'global'<\/h3>\n<p>Let's define and save another function <em><strong>build_lm3<\/strong><\/em>, but this time as global. We'll then invoke that function to build another model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","build_lm3 = \"\"\"def build_lm3(dat):","  from sklearn import linear_model","  regr = linear_model.LinearRegression()","  X = dat[[\"PETAL_WIDTH\"]]","  y = dat[[\"PETAL_LENGTH\"]]","  regr.fit(X, y)","  return regr\"\"\""],"enabled":true,"result":{"startTime":1734738402369,"interpreter":"python.medium","endTime":1734738402830,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","oml.script.create(\"MyGlobalLM_function\", func=build_lm3, is_global=True, overwrite=True)"],"enabled":true,"result":{"startTime":1734738403280,"interpreter":"python.medium","endTime":1734738403737,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Invoke the function using table_apply","message":["%python","","res = oml.table_apply(IRIS, func=\"MyGlobalLM_function\", ","                      oml_input_type=\"pandas.DataFrame\")","res"],"enabled":true,"result":{"startTime":1734738404191,"interpreter":"python.medium","endTime":1734738404684,"results":[{"message":"LinearRegression()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"List the scripts in the script repository available to the current user only","message":["%python","","oml.script.dir()"],"enabled":true,"result":{"startTime":1734738405142,"interpreter":"python.medium","endTime":1734738405617,"results":[{"message":"                       name  ...                date\n0            MyLM_function2  ... 2024-12-20 23:46:33\n1           MyTEST_function  ... 2023-05-12 00:21:09\n2             RandomRedDots  ... 2024-06-18 14:24:53\n3                  build_lm  ... 2024-06-18 15:26:16\n4                build_lm_1  ... 2024-05-03 16:31:12\n5             build_lm_rest  ... 2024-12-20 00:44:23\n6                 build_mod  ... 2024-07-24 18:33:37\n7               density_plt  ... 2024-06-18 15:27:03\n8               group_count  ... 2024-12-20 00:43:16\n9                mean_angle  ... 2022-10-04 21:23:19\n10  myLinearRegressionModel  ... 2022-02-09 18:11:59\n11                 score_lm  ... 2023-02-16 01:45:59\n12                score_mod  ... 2024-06-18 15:26:51\n13               sk_svc_fit  ... 2024-05-03 16:31:21\n14                std_angle  ... 2022-10-04 21:23:19\n15          test_noinp_rest  ... 2022-03-08 00:32:47\n\n[16 rows x 4 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"List all of the scripts available to the current user","message":["%python","","oml.script.dir(sctype='all')"],"enabled":true,"result":{"startTime":1734738406079,"interpreter":"python.medium","endTime":1734738406606,"results":[{"message":"      owner  ...                date\n0     AKASH  ... 2022-10-23 22:35:27\n1   OMLUSER  ... 2024-12-20 23:46:33\n2   OMLUSER  ... 2023-05-12 00:21:09\n3   OMLUSER  ... 2024-06-18 14:24:53\n4   OMLUSER  ... 2024-06-18 15:26:16\n5   OMLUSER  ... 2024-05-03 16:31:12\n6   OMLUSER  ... 2024-12-20 00:44:23\n7   OMLUSER  ... 2024-07-24 18:33:37\n8   OMLUSER  ... 2024-06-18 15:27:03\n9   OMLUSER  ... 2024-12-20 00:43:16\n10  OMLUSER  ... 2022-10-04 21:23:19\n11  OMLUSER  ... 2022-02-09 18:11:59\n12  OMLUSER  ... 2023-02-16 01:45:59\n13  OMLUSER  ... 2024-06-18 15:26:51\n14  OMLUSER  ... 2024-05-03 16:31:21\n15  OMLUSER  ... 2022-10-04 21:23:19\n16  OMLUSER  ... 2022-03-08 00:32:47\n17   PYQSYS  ... 2022-02-11 06:06:44\n18   PYQSYS  ... 2024-12-20 23:46:43\n19   PYQSYS  ... 2024-05-03 16:31:24\n20   PYQSYS  ... 2024-05-03 16:31:25\n21   PYQSYS  ... 2024-05-03 16:31:26\n22   PYQSYS  ... 2021-08-13 17:37:02\n23   PYQSYS  ... 2021-08-13 17:42:49\n24   PYQSYS  ... 2021-08-13 22:38:54\n25   PYQSYS  ... 2024-10-10 22:12:54\n26   PYQSYS  ... 2021-08-04 00:06:31\n27   PYQSYS  ... 2021-08-04 19:57:55\n28   PYQSYS  ... 2021-08-03 23:39:03\n29   PYQSYS  ... 2022-10-14 19:32:38\n30   PYQSYS  ... 2022-12-15 19:02:44\n31   PYQSYS  ... 2022-09-07 17:41:08\n32   PYQSYS  ... 2024-01-08 21:04:46\n33   PYQSYS  ... 2023-10-10 18:51:20\n34   PYQSYS  ... 2024-04-04 15:44:24\n35   PYQSYS  ... 2024-06-10 14:36:17\n36   PYQSYS  ... 2024-01-08 20:48:52\n37   PYQSYS  ... 2021-04-30 01:02:04\n38   PYQSYS  ... 2021-11-30 22:58:03\n39   PYQSYS  ... 2021-11-30 22:58:15\n40   PYQSYS  ... 2022-11-11 00:42:53\n41   PYQSYS  ... 2023-04-01 01:25:22\n42   PYQSYS  ... 2022-11-11 00:42:51\n43   PYQSYS  ... 2021-06-28 23:03:17\n44   PYQSYS  ... 2021-06-28 15:18:29\n45   PYQSYS  ... 2024-06-20 01:08:00\n46   PYQSYS  ... 2022-11-11 22:35:05\n47   PYQSYS  ... 2024-10-17 15:16:35\n48   PYQSYS  ... 2024-10-17 15:16:36\n49   PYQSYS  ... 2023-10-10 18:59:53\n50   PYQSYS  ... 2021-12-16 21:49:42\n51   PYQSYS  ... 2022-03-29 22:03:10\n52   PYQSYS  ... 2022-03-29 22:10:04\n53   PYQSYS  ... 2022-03-30 04:05:06\n54   PYQSYS  ... 2023-10-02 14:55:44\n\n[55 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"List the scripts available to all users","message":["%python","","oml.script.dir(sctype='global')"],"enabled":true,"result":{"startTime":1734738407064,"interpreter":"python.medium","endTime":1734738407563,"results":[{"message":"                              name  ...                date\n0                            GLBLM  ... 2022-02-11 06:06:44\n1              MyGlobalLM_function  ... 2024-12-20 23:46:43\n2                    RandomRedDots  ... 2024-05-03 16:31:24\n3                   RandomRedDots2  ... 2024-05-03 16:31:25\n4                   RandomRedDots3  ... 2024-05-03 16:31:26\n5                             TEST  ... 2021-08-13 17:37:02\n6                            TEST4  ... 2021-08-13 17:42:49\n7                         TEST_FUN  ... 2021-08-13 22:38:54\n8                     ad_data_test  ... 2024-10-10 22:12:54\n9                       alg_select  ... 2021-08-04 00:06:31\n10                     algo_select  ... 2021-08-04 19:57:55\n11                    build_automl  ... 2021-08-03 23:39:03\n12                   build_glm_mod  ... 2022-10-14 19:32:38\n13                       build_lm2  ... 2022-12-15 19:02:44\n14                  build_lm_mod_2  ... 2022-09-07 17:41:08\n15                      check_path  ... 2024-01-08 21:04:46\n16             compute_random_mean  ... 2023-10-10 18:51:20\n17  eml_order_route_prediction_udf  ... 2024-04-04 15:44:24\n18                     group_count  ... 2024-06-10 14:36:17\n19               install_implicit1  ... 2024-01-08 20:48:52\n20                         my_pred  ... 2021-04-30 01:02:04\n21                      my_predict  ... 2021-11-30 22:58:03\n22                         pred_lm  ... 2021-11-30 22:58:15\n23                     pred_oml_rf  ... 2022-11-11 00:42:53\n24                         pred_rf  ... 2023-04-01 01:25:22\n25                 pred_rf_species  ... 2022-11-11 00:42:51\n26               pred_rf_species_2  ... 2021-06-28 23:03:17\n27               pred_rf_species_3  ... 2021-06-28 15:18:29\n28               smo_table_details  ... 2024-06-20 01:08:00\n29                            test  ... 2022-11-11 22:35:05\n30                         test_15  ... 2024-10-17 15:16:35\n31                         test_16  ... 2024-10-17 15:16:36\n32                       test_func  ... 2023-10-10 18:59:53\n33                  thread_process  ... 2021-12-16 21:49:42\n34                       train_AML  ... 2022-03-29 22:03:10\n35                      train_AML2  ... 2022-03-29 22:10:04\n36              train_automl_embed  ... 2022-03-30 04:05:06\n37             train_automl_string  ... 2023-10-02 14:55:44\n\n[38 rows x 4 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Loading functions from the script repository","","Load the MyLM_function1 and MyGlobalLM_function scripts, and pull the models to the local Python session. For MYLM, build the model on the IRIS data set and pull the coefficients. For GlobalMYLM, build and display the model."],"enabled":true,"result":{"startTime":1734738408023,"interpreter":"md.medium","endTime":1734738408464,"results":[{"message":"<h3 id=\"loading-functions-from-the-script-repository\">Loading functions from the script repository<\/h3>\n<p>Load the MyLM_function1 and MyGlobalLM_function scripts, and pull the models to the local Python session. For MYLM, build the model on the IRIS data set and pull the coefficients. For GlobalMYLM, build and display the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MYLM = oml.script.load(name=\"MyLM_function2\")","GlobalMYLM = oml.script.load(name=\"MyGlobalLM_function\")","","print(\"Coefficients: \", MYLM(IRIS.pull()).coef_)","print(\"Model: \", GlobalMYLM(IRIS.pull()))"],"enabled":true,"result":{"startTime":1734738408925,"interpreter":"python.medium","endTime":1734738409449,"results":[{"message":"Coefficients:  [[ 0.49588894  0.82924391 -0.31515517 -0.72356196 -1.02349781]]\nModel:  LinearRegression()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"List the available scripts","message":["%python","","oml.script.dir(sctype=\"all\")"],"enabled":true,"result":{"startTime":1734738409910,"interpreter":"python.medium","endTime":1734738410415,"results":[{"message":"      owner  ...                date\n0     AKASH  ... 2022-10-23 22:35:27\n1   OMLUSER  ... 2024-12-20 23:46:33\n2   OMLUSER  ... 2023-05-12 00:21:09\n3   OMLUSER  ... 2024-06-18 14:24:53\n4   OMLUSER  ... 2024-06-18 15:26:16\n5   OMLUSER  ... 2024-05-03 16:31:12\n6   OMLUSER  ... 2024-12-20 00:44:23\n7   OMLUSER  ... 2024-07-24 18:33:37\n8   OMLUSER  ... 2024-06-18 15:27:03\n9   OMLUSER  ... 2024-12-20 00:43:16\n10  OMLUSER  ... 2022-10-04 21:23:19\n11  OMLUSER  ... 2022-02-09 18:11:59\n12  OMLUSER  ... 2023-02-16 01:45:59\n13  OMLUSER  ... 2024-06-18 15:26:51\n14  OMLUSER  ... 2024-05-03 16:31:21\n15  OMLUSER  ... 2022-10-04 21:23:19\n16  OMLUSER  ... 2022-03-08 00:32:47\n17   PYQSYS  ... 2022-02-11 06:06:44\n18   PYQSYS  ... 2024-12-20 23:46:43\n19   PYQSYS  ... 2024-05-03 16:31:24\n20   PYQSYS  ... 2024-05-03 16:31:25\n21   PYQSYS  ... 2024-05-03 16:31:26\n22   PYQSYS  ... 2021-08-13 17:37:02\n23   PYQSYS  ... 2021-08-13 17:42:49\n24   PYQSYS  ... 2021-08-13 22:38:54\n25   PYQSYS  ... 2024-10-10 22:12:54\n26   PYQSYS  ... 2021-08-04 00:06:31\n27   PYQSYS  ... 2021-08-04 19:57:55\n28   PYQSYS  ... 2021-08-03 23:39:03\n29   PYQSYS  ... 2022-10-14 19:32:38\n30   PYQSYS  ... 2022-12-15 19:02:44\n31   PYQSYS  ... 2022-09-07 17:41:08\n32   PYQSYS  ... 2024-01-08 21:04:46\n33   PYQSYS  ... 2023-10-10 18:51:20\n34   PYQSYS  ... 2024-04-04 15:44:24\n35   PYQSYS  ... 2024-06-10 14:36:17\n36   PYQSYS  ... 2024-01-08 20:48:52\n37   PYQSYS  ... 2021-04-30 01:02:04\n38   PYQSYS  ... 2021-11-30 22:58:03\n39   PYQSYS  ... 2021-11-30 22:58:15\n40   PYQSYS  ... 2022-11-11 00:42:53\n41   PYQSYS  ... 2023-04-01 01:25:22\n42   PYQSYS  ... 2022-11-11 00:42:51\n43   PYQSYS  ... 2021-06-28 23:03:17\n44   PYQSYS  ... 2021-06-28 15:18:29\n45   PYQSYS  ... 2024-06-20 01:08:00\n46   PYQSYS  ... 2022-11-11 22:35:05\n47   PYQSYS  ... 2024-10-17 15:16:35\n48   PYQSYS  ... 2024-10-17 15:16:36\n49   PYQSYS  ... 2023-10-10 18:59:53\n50   PYQSYS  ... 2021-12-16 21:49:42\n51   PYQSYS  ... 2022-03-29 22:03:10\n52   PYQSYS  ... 2022-03-29 22:10:04\n53   PYQSYS  ... 2022-03-30 04:05:06\n54   PYQSYS  ... 2023-10-02 14:55:44\n\n[55 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Drop scripts from repository","","Drop one of the private scripts.","","Drop the global script.","","List the available scripts again."],"enabled":true,"result":{"startTime":1734738410898,"interpreter":"md.medium","endTime":1734738411354,"results":[{"message":"<h3 id=\"drop-scripts-from-repository\">Drop scripts from repository<\/h3>\n<p>Drop one of the private scripts.<\/p>\n<p>Drop the global script.<\/p>\n<p>List the available scripts again.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.script.drop(\"MyLM_function2\")","oml.script.drop(\"MyGlobalLM_function\", is_global=True)","oml.script.dir(sctype=\"all\")"],"enabled":true,"result":{"startTime":1734738411821,"interpreter":"python.medium","endTime":1734738412352,"results":[{"message":"      owner  ...                date\n0     AKASH  ... 2022-10-23 22:35:27\n1   OMLUSER  ... 2023-05-12 00:21:09\n2   OMLUSER  ... 2024-06-18 14:24:53\n3   OMLUSER  ... 2024-06-18 15:26:16\n4   OMLUSER  ... 2024-05-03 16:31:12\n5   OMLUSER  ... 2024-12-20 00:44:23\n6   OMLUSER  ... 2024-07-24 18:33:37\n7   OMLUSER  ... 2024-06-18 15:27:03\n8   OMLUSER  ... 2024-12-20 00:43:16\n9   OMLUSER  ... 2022-10-04 21:23:19\n10  OMLUSER  ... 2022-02-09 18:11:59\n11  OMLUSER  ... 2023-02-16 01:45:59\n12  OMLUSER  ... 2024-06-18 15:26:51\n13  OMLUSER  ... 2024-05-03 16:31:21\n14  OMLUSER  ... 2022-10-04 21:23:19\n15  OMLUSER  ... 2022-03-08 00:32:47\n16   PYQSYS  ... 2022-02-11 06:06:44\n17   PYQSYS  ... 2024-05-03 16:31:24\n18   PYQSYS  ... 2024-05-03 16:31:25\n19   PYQSYS  ... 2024-05-03 16:31:26\n20   PYQSYS  ... 2021-08-13 17:37:02\n21   PYQSYS  ... 2021-08-13 17:42:49\n22   PYQSYS  ... 2021-08-13 22:38:54\n23   PYQSYS  ... 2024-10-10 22:12:54\n24   PYQSYS  ... 2021-08-04 00:06:31\n25   PYQSYS  ... 2021-08-04 19:57:55\n26   PYQSYS  ... 2021-08-03 23:39:03\n27   PYQSYS  ... 2022-10-14 19:32:38\n28   PYQSYS  ... 2022-12-15 19:02:44\n29   PYQSYS  ... 2022-09-07 17:41:08\n30   PYQSYS  ... 2024-01-08 21:04:46\n31   PYQSYS  ... 2023-10-10 18:51:20\n32   PYQSYS  ... 2024-04-04 15:44:24\n33   PYQSYS  ... 2024-06-10 14:36:17\n34   PYQSYS  ... 2024-01-08 20:48:52\n35   PYQSYS  ... 2021-04-30 01:02:04\n36   PYQSYS  ... 2021-11-30 22:58:03\n37   PYQSYS  ... 2021-11-30 22:58:15\n38   PYQSYS  ... 2022-11-11 00:42:53\n39   PYQSYS  ... 2023-04-01 01:25:22\n40   PYQSYS  ... 2022-11-11 00:42:51\n41   PYQSYS  ... 2021-06-28 23:03:17\n42   PYQSYS  ... 2021-06-28 15:18:29\n43   PYQSYS  ... 2024-06-20 01:08:00\n44   PYQSYS  ... 2022-11-11 22:35:05\n45   PYQSYS  ... 2024-10-17 15:16:35\n46   PYQSYS  ... 2024-10-17 15:16:36\n47   PYQSYS  ... 2023-10-10 18:59:53\n48   PYQSYS  ... 2021-12-16 21:49:42\n49   PYQSYS  ... 2022-03-29 22:03:10\n50   PYQSYS  ... 2022-03-29 22:10:04\n51   PYQSYS  ... 2022-03-30 04:05:06\n52   PYQSYS  ... 2023-10-02 14:55:44\n\n[53 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734738412808,"interpreter":"md.medium","endTime":1734738413242,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1734738413701,"interpreter":"md.medium","endTime":1734738414147,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":"zeppelin","template":null,"templateConfig":null,"name":"OML4Py -4- Embedded Python Execution","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737141736712,"interpreter":"md.medium","endTime":1737141737189,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python - Embedded Python Execution","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight using OML4Py Embedded Python Execution. We first build a linear model in Python directly, then create a function that will use Python engines spawned by the Autonomous Database environment.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737141737665,"interpreter":"md.medium","endTime":1737141738131,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python---embedded-python-execution\">Oracle Machine Learning for Python - Embedded Python Execution<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight using OML4Py Embedded Python Execution. We first build a linear model in Python directly, then create a function that will use Python engines spawned by the Autonomous Database environment.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Import libraries supporting OML4Py and create data table","","To use OML4Py, first import the package ***oml***. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1737141738612,"interpreter":"md.medium","endTime":1737141739080,"results":[{"message":"<h2 id=\"import-libraries-supporting-oml4py-and-create-data-table\">Import libraries supporting OML4Py and create data table<\/h2>\n<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>.<\/p>\n<p>Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import oml"],"enabled":true,"result":{"startTime":1737141739589,"interpreter":"python.medium","endTime":1737141740043,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table for the IRIS data","message":["%python","","from sklearn.datasets import load_iris","from sklearn import linear_model","import pandas as pd","","iris = load_iris()","x = pd.DataFrame(iris.data, ","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","iris_df = pd.concat([x,y], axis=1)","","try:","    oml.drop(table=\"IRIS\")","except:","    pass","IRIS = oml.create(iris_df, table=\"IRIS\")","IRIS.head(4)"],"enabled":true,"result":{"startTime":1737141740540,"interpreter":"python.medium","endTime":1737141741319,"results":[{"message":"   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH     SPECIES\n0           4.6          3.6             1          0.2      setosa\n1           5.1          2.5             3          1.1  versicolor\n2           6.0          2.2             4          1.0  versicolor\n3           5.8          2.6             4          1.2  versicolor\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build and score a linear model from sklearn directly in Python","","A best practice with embedded Python execution is to validate your Python script, and then your user-defined function before invoking the embedded Python APIs. The next few paragraphs exercise the functionality we want."],"enabled":true,"result":{"startTime":1737141741786,"interpreter":"md.medium","endTime":1737141742246,"results":[{"message":"<h2 id=\"build-and-score-a-linear-model-from-sklearn-directly-in-python\">Build and score a linear model from sklearn directly in Python<\/h2>\n<p>A best practice with embedded Python execution is to validate your Python script, and then your user-defined function before invoking the embedded Python APIs. The next few paragraphs exercise the functionality we want.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from sklearn import linear_model","lm = linear_model.LinearRegression()","X = iris_df[[\"PETAL_WIDTH\"]]","y = iris_df[[\"PETAL_LENGTH\"]]","mod = lm.fit(X, y)","print(\"Model:\",mod)"],"enabled":true,"result":{"startTime":1737141742741,"interpreter":"python.medium","endTime":1737141743228,"results":[{"message":"Model: LinearRegression()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","pred = mod.predict(iris_df[[\"PETAL_WIDTH\"]])","pred[0:10]"],"enabled":true,"result":{"startTime":1737141743709,"interpreter":"python.medium","endTime":1737141744170,"results":[{"message":"array([[1.52954613],\n       [1.52954613],\n       [1.52954613],\n       [1.52954613],\n       [1.52954613],\n       [1.97553423],\n       [1.75254018],\n       [1.52954613],\n       [1.52954613],\n       [1.30655208]])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Assess model quality","","Assess model quality using mean squared error and R^2 and generate a scatterplot of the data along with a plot of the regression line."],"enabled":true,"result":{"startTime":1737141744671,"interpreter":"md.medium","endTime":1737141745131,"results":[{"message":"<h2 id=\"assess-model-quality\">Assess model quality<\/h2>\n<p>Assess model quality using mean squared error and R^2 and generate a scatterplot of the data along with a plot of the regression line.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute quality metrics","message":["%python","","from sklearn.metrics import mean_squared_error, r2_score","","print('Coefficients: ', mod.coef_)","print(\"Mean squared error: %.2f\"","      % mean_squared_error(iris_df[[\"PETAL_LENGTH\"]], pred))","","print('Variance score (1 is perfect prediction): %.2f' % r2_score(iris_df[[\"PETAL_LENGTH\"]], ","       pred))"],"enabled":true,"result":{"startTime":1737141745612,"interpreter":"python.medium","endTime":1737141746096,"results":[{"message":"Coefficients:  [[2.2299405]]\nMean squared error: 0.23\nVariance score (1 is perfect prediction): 0.93\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Scatterplot with regression line for petal dimensions","message":["%python","","import matplotlib.pyplot as plt","","plt.scatter(iris_df.loc[:,\"PETAL_WIDTH\"], iris_df.loc[:,\"PETAL_LENGTH\"])","plt.plot(iris_df[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)","plt.grid(True)","plt.title('Prediction of Petal Length')","","plt.xlabel('Petal Width')","plt.ylabel('Petal Length')","","plt.show()"],"enabled":true,"result":{"startTime":1737141746573,"interpreter":"python.medium","endTime":1737141747034,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build this same model using embedded Python execution","","Using embedded Python execution, let's build the same linear model, but spawned Python engines under control of the Autonomous Database environment. First, we'll define our function, ***build_lm_1***, that returns the model as the result. We refer to this as a \"user-defined function.\" As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded Python execution can return images as well as structured content in the Python API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.","","We run this function to ensure it returns what we expect. In this case, both an image and a model."],"enabled":true,"result":{"startTime":1737141747499,"interpreter":"md.medium","endTime":1737141747957,"results":[{"message":"<h2 id=\"build-this-same-model-using-embedded-python-execution\">Build this same model using embedded Python execution<\/h2>\n<p>Using embedded Python execution, let's build the same linear model, but spawned Python engines under control of the Autonomous Database environment. First, we'll define our function, <em><strong>build_lm_1<\/strong><\/em>, that returns the model as the result. We refer to this as a &quot;user-defined function.&quot; As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded Python execution can return images as well as structured content in the Python API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.<\/p>\n<p>We run this function to ensure it returns what we expect. In this case, both an image and a model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define a function to build model, score data, and plot values","message":["%python","","def build_lm_1(dat):","    import oml","    from sklearn import linear_model","    import matplotlib.pyplot as plt","","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    mod = lm.fit(X, y)","    ","    pred = mod.predict(dat[[\"PETAL_WIDTH\"]])","    plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])","    plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)","    plt.grid(True)","    plt.title('Prediction of Petal Length')","    ","    plt.xlabel('Petal Width')","    plt.ylabel('Petal Length')","    plt.show()","    return mod"],"enabled":true,"result":{"startTime":1737141748436,"interpreter":"python.medium","endTime":1737141748899,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke user-defined Python function","message":["%python","","mod = build_lm_1(iris_df)"],"enabled":true,"result":{"startTime":1737141749360,"interpreter":"python.medium","endTime":1737141749852,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View returned model object","message":["%python","","print(\"\\nType:\",type(mod), \"\\n\")","print(\"Model:\",mod)","print(\"Coefficient\", mod.coef_) "],"enabled":true,"result":{"startTime":1737141750331,"interpreter":"python.medium","endTime":1737141750797,"results":[{"message":"\nType: <class 'sklearn.linear_model._base.LinearRegression'> \n\nModel: LinearRegression()\nCoefficient [[2.2299405]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the `table_apply` function","","The ***table_apply*** function takes the proxy object *IRIS* as input data and loads that data to the user-defined function as a pandas DataFrame. The user-defined function is passed as a Python string. We see that the model comes back as an OML object, which we can pull to the client and see that it's the linear model. We also display the image automatically."],"enabled":true,"result":{"startTime":1737141751259,"interpreter":"md.medium","endTime":1737141751729,"results":[{"message":"<h2 id=\"use-the-table_apply-function\">Use the <code>table_apply<\/code> function<\/h2>\n<p>The <em><strong>table_apply<\/strong><\/em> function takes the proxy object <em>IRIS<\/em> as input data and loads that data to the user-defined function as a pandas DataFrame. The user-defined function is passed as a Python string. We see that the model comes back as an OML object, which we can pull to the client and see that it's the linear model. We also display the image automatically.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","mod = oml.table_apply(data=IRIS, func = build_lm_1)","","print(\"Type:\",type(mod))","print(\"Model:\",mod)","print(\"Coefficient\", mod.coef_)"],"enabled":true,"result":{"startTime":1737141752189,"interpreter":"python.medium","endTime":1737141752712,"results":[{"message":"Type: <class 'sklearn.linear_model._base.LinearRegression'>\nModel: LinearRegression()\nCoefficient [[2.2299405]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the in-Database Python script repository","","We can also save this user-defined function in the Python script repository and invoke this function by providing the function name string."],"enabled":true,"result":{"startTime":1737141753180,"interpreter":"md.medium","endTime":1737141753631,"results":[{"message":"<h2 id=\"use-the-in-database-python-script-repository\">Use the in-Database Python script repository<\/h2>\n<p>We can also save this user-defined function in the Python script repository and invoke this function by providing the function name string.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","build_lm_1_str = \"\"\"def build_lm_1(dat):","    import oml","    from sklearn import linear_model","    import matplotlib.pyplot as plt","","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    mod = lm.fit(X, y)","    ","    pred = mod.predict(dat[[\"PETAL_WIDTH\"]])","    plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])","    plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)","    plt.grid(True)","    plt.title('Prediction of Petal Length')","    ","    plt.xlabel('Petal Width')","    plt.ylabel('Petal Length')","    plt.show()","    return mod\"\"\""],"enabled":true,"result":{"startTime":1737141754111,"interpreter":"python.medium","endTime":1737141754586,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overwrite Script and list contents of Python script repository","message":["%python","","oml.script.create(\"build_lm_1\", build_lm_1_str, overwrite = True)","","z.show(oml.script.dir('build_lm_1'))"],"enabled":true,"result":{"startTime":1737141755044,"interpreter":"python.medium","endTime":1737141755536,"results":[{"message":"name\tscript\tdescription\tdate\nbuild_lm_1\tdef build_lm_1(dat):     import oml     from sklearn import linear_model     import matplotlib.pyplot as plt      lm = linear_model.LinearRegression()     X = dat[[\"PETAL_WIDTH\"]]     y = dat[[\"PETAL_LENGTH\"]]     mod = lm.fit(X, y)          pred = mod.predict(dat[[\"PETAL_WIDTH\"]])     plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])     plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)     plt.grid(True)     plt.title('Prediction of Petal Length')          plt.xlabel('Petal Width')     plt.ylabel('Petal Length')     plt.show()     return mod\tNone\t2025-01-17 19:22:35\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## The value passed to `func` can also be a string","","Notice the value to ***func*** is now a string, not a Python function object, however, the results are the same."],"enabled":true,"result":{"startTime":1737141756016,"interpreter":"md.medium","endTime":1737141756469,"results":[{"message":"<h2 id=\"the-value-passed-to-func-can-also-be-a-string\">The value passed to <code>func<\/code> can also be a string<\/h2>\n<p>Notice the value to <em><strong>func<\/strong><\/em> is now a string, not a Python function object, however, the results are the same.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.table_apply(data=IRIS, func = 'build_lm_1', graphics=True)","res"],"enabled":true,"result":{"startTime":1737141756933,"interpreter":"python.medium","endTime":1737141759937,"results":[{"message":"LinearRegression()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","print(\"\\nModel:\",res.dat)","print(\"\\nCoefficients:\", res.dat.coef_)"],"enabled":true,"result":{"startTime":1737141760419,"interpreter":"python.medium","endTime":1737141760876,"results":[{"message":"\nModel: LinearRegression()\n\nCoefficients: [[2.2299405]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `row_apply` and `group_apply` to process chuncks of rows in parallel","","The ***row_apply*** function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native Python models. In this example, the user-defined function ***score_lm_1*** takes a pandas DataFrame and a linear model. The ***row_apply*** invocation takes as arguments the proxy object *IRIS*, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.","","For illustrative purposes, you can also use the ***group_apply*** function to partition the data according to one or more columns and score as well, passing the model as an argument."],"enabled":true,"result":{"startTime":1737141761338,"interpreter":"md.medium","endTime":1737141761796,"results":[{"message":"<h2 id=\"use-row_apply-and-group_apply-to-process-chuncks-of-rows-in-parallel\">Use <code>row_apply<\/code> and <code>group_apply<\/code> to process chuncks of rows in parallel<\/h2>\n<p>The <em><strong>row_apply<\/strong><\/em> function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native Python models. In this example, the user-defined function <em><strong>score_lm_1<\/strong><\/em> takes a pandas DataFrame and a linear model. The <em><strong>row_apply<\/strong><\/em> invocation takes as arguments the proxy object <em>IRIS<\/em>, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.<\/p>\n<p>For illustrative purposes, you can also use the <em><strong>group_apply<\/strong><\/em> function to partition the data according to one or more columns and score as well, passing the model as an argument.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create used-defined Python function to score data","message":["%python","","def score_lm_1(dat, model):","    import pandas as pd","    from sklearn import linear_model","    pred = model.predict(dat[[\"PETAL_WIDTH\"]])","    return pd.concat([dat[['SPECIES', 'PETAL_LENGTH']], ","                      pd.DataFrame(pred, columns=['PRED_PETAL_LENGTH'])], axis=1)"],"enabled":true,"result":{"startTime":1737141762251,"interpreter":"python.medium","endTime":1737141762715,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data in parallel using DataFrame proxy object, returning  scores with data also as proxy object","message":["%python","","res = oml.row_apply(IRIS, rows=10, func=score_lm_1, model=mod, ","                    func_value=pd.DataFrame([('a', 1, 1)], ","                                            columns=['SPECIES', 'PETAL_LENGTH', 'PRED_PETAL_LENGTH']))","z.show(res.head())"],"enabled":true,"result":{"startTime":1737141763189,"interpreter":"python.medium","endTime":1737141764667,"results":[{"message":"SPECIES\tPETAL_LENGTH\tPRED_PETAL_LENGTH\nsetosa\t1\t1\nsetosa\t1\t1\nsetosa\t1\t1\nsetosa\t1\t2\nsetosa\t1\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Illustrate using 'group apply' with same user-defined function","message":["%python","","res = oml.group_apply(IRIS, index=IRIS[['SPECIES']], func=score_lm_1, model=mod, ","                    func_value=pd.DataFrame([('a', 1, 1)], ","                                            columns=['SPECIES', 'PETAL_LENGTH', 'PRED_PETAL_LENGTH']))","","z.show(res.head())"],"enabled":true,"result":{"startTime":1737141765131,"interpreter":"python.medium","endTime":1737141767410,"results":[{"message":"SPECIES\tPETAL_LENGTH\tPRED_PETAL_LENGTH\nsetosa\t1\t1\nsetosa\t1\t1\nsetosa\t1\t1\nsetosa\t1\t2\nsetosa\t1\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build one model per `SPECIES` using `group_apply`","","A better use of ***group_apply*** - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function is then invoked on each group. The function ***group_apply*** makes this easy, as well as specifying the desired number of parallel Python engines using the ***parallel*** argument.","","Here, we build three models, one specific to each species, and return them as a dictionary."],"enabled":true,"result":{"startTime":1737141767874,"interpreter":"md.medium","endTime":1737141768311,"results":[{"message":"<h2 id=\"build-one-model-per-species-using-group_apply\">Build one model per <code>SPECIES<\/code> using <code>group_apply<\/code><\/h2>\n<p>A better use of <em><strong>group_apply<\/strong><\/em> - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function is then invoked on each group. The function <em><strong>group_apply<\/strong><\/em> makes this easy, as well as specifying the desired number of parallel Python engines using the <em><strong>parallel<\/strong><\/em> argument.<\/p>\n<p>Here, we build three models, one specific to each species, and return them as a dictionary.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","","def build_lm_g(dat):","    import oml","    from sklearn import linear_model","","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    mod = lm.fit(X, y)","   ","    return mod","","mod = oml.group_apply(IRIS[:,[\"PETAL_LENGTH\",\"PETAL_WIDTH\",\"SPECIES\"]], ","                      index=oml.DataFrame(IRIS['SPECIES']), ","                      func=build_lm_g, parallel=2,","                      oml_connect = True)","print(\"Type:\",type(mod))","mod"],"enabled":true,"result":{"startTime":1737141768783,"interpreter":"python.medium","endTime":1737141770660,"results":[{"message":"Type: <class 'dict'>\n{'virginica': LinearRegression(), 'setosa': LinearRegression(), 'versicolor': LinearRegression()}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Change the UDF to store the models inside the python datastore","","Next, change the user-defined function to save each model in a datastore - instead of returning it - and return ***True*** instead. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in Python, here \"mod_\" and the corresponding Species value."],"enabled":true,"result":{"startTime":1737141771116,"interpreter":"md.medium","endTime":1737141771558,"results":[{"message":"<h2 id=\"change-the-udf-to-store-the-models-inside-the-python-datastore\">Change the UDF to store the models inside the python datastore<\/h2>\n<p>Next, change the user-defined function to save each model in a datastore - instead of returning it - and return <em><strong>True<\/strong><\/em> instead. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in Python, here &quot;mod_&quot; and the corresponding Species value.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","def build_lm_2(dat, dsname):","    import oml","    from sklearn import linear_model","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    lm.fit(X, y)","    name = \"mod_\" + dat.loc[dat.index[0],'SPECIES']","    oml.ds.save(objs = {name: lm}, name=dsname, append=True) ","    return name"],"enabled":true,"result":{"startTime":1737141772024,"interpreter":"python.medium","endTime":1737141772476,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Replace an existing python datastore with the output of `group_apply`","","If the datastore exists, we delete it so that the ***group_apply*** function completes successfully. The ***group_apply*** function takes the data, the ***index*** parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the Python engine. Connecting to the database is necessary when using the datastore functionality.","","We then print the outcome, which contains a dictionary of three elements each assigned the model object name.","","When we load the datastore, we get the three models loaded into the client Python engine, assigned to their respective variables. For example we could request to see the model specifically built for the \"versicolor\" species."],"enabled":true,"result":{"startTime":1737141772935,"interpreter":"md.medium","endTime":1737141773387,"results":[{"message":"<h2 id=\"replace-an-existing-python-datastore-with-the-output-of-group_apply\">Replace an existing python datastore with the output of <code>group_apply<\/code><\/h2>\n<p>If the datastore exists, we delete it so that the <em><strong>group_apply<\/strong><\/em> function completes successfully. The <em><strong>group_apply<\/strong><\/em> function takes the data, the <em><strong>index<\/strong><\/em> parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the Python engine. Connecting to the database is necessary when using the datastore functionality.<\/p>\n<p>We then print the outcome, which contains a dictionary of three elements each assigned the model object name.<\/p>\n<p>When we load the datastore, we get the three models loaded into the client Python engine, assigned to their respective variables. For example we could request to see the model specifically built for the &quot;versicolor&quot; species.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","try:","    oml.ds.delete('ds-1')","except:","    print(\"Datastore not found\")","    ","res = oml.group_apply(IRIS[:,[\"PETAL_LENGTH\",\"PETAL_WIDTH\",\"SPECIES\"]], ","                      index=oml.DataFrame(IRIS['SPECIES']), ","                      func=build_lm_2, dsname=\"ds-1\",","                      oml_connect = True)","print(\"Outcome:\",res)"],"enabled":true,"result":{"startTime":1737141773849,"interpreter":"python.medium","endTime":1737141777032,"results":[{"message":"Outcome: {'virginica': 'mod_virginica', 'setosa': 'mod_setosa', 'versicolor': 'mod_versicolor'}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Leveraging third-party packages in Embedded Python Execution","","Embedded Python execution can also leverage functions from third-party packages. These packages need to be installed on the database server machine, but can then be used inside the user-defined function as shown here using ***LinearSVC***.","","Again, we create this script in the Python script repository and then invoke it by name using ***table_apply***. We then pull the model to the client and view its type."],"enabled":true,"result":{"startTime":1737141777494,"interpreter":"md.medium","endTime":1737141777937,"results":[{"message":"<h2 id=\"leveraging-third-party-packages-in-embedded-python-execution\">Leveraging third-party packages in Embedded Python Execution<\/h2>\n<p>Embedded Python execution can also leverage functions from third-party packages. These packages need to be installed on the database server machine, but can then be used inside the user-defined function as shown here using <em><strong>LinearSVC<\/strong><\/em>.<\/p>\n<p>Again, we create this script in the Python script repository and then invoke it by name using <em><strong>table_apply<\/strong><\/em>. We then pull the model to the client and view its type.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","fit = \"\"\"def fit(data):","","    from sklearn.svm import LinearSVC","    x = data.drop('SPECIES', axis = 1).values","    y = data['SPECIES']","    mod =  LinearSVC().fit(x, y)","    return mod\"\"\"","","oml.script.create(\"sk_svc_fit\", fit, overwrite = True)","","z.show(oml.script.dir(['build_lm_1','sk_svc_fit']))"],"enabled":true,"result":{"startTime":1737141778399,"interpreter":"python.medium","endTime":1737141778888,"results":[{"message":"name\tscript\tdescription\tdate\nbuild_lm_1\tdef build_lm_1(dat):     import oml     from sklearn import linear_model     import matplotlib.pyplot as plt      lm = linear_model.LinearRegression()     X = dat[[\"PETAL_WIDTH\"]]     y = dat[[\"PETAL_LENGTH\"]]     mod = lm.fit(X, y)          pred = mod.predict(dat[[\"PETAL_WIDTH\"]])     plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])     plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)     plt.grid(True)     plt.title('Prediction of Petal Length')          plt.xlabel('Petal Width')     plt.ylabel('Petal Length')     plt.show()     return mod\tNone\t2025-01-17 19:22:35\nsk_svc_fit\tdef fit(data):      from sklearn.svm import LinearSVC     x = data.drop('SPECIES', axis = 1).values     y = data['SPECIES']     mod =  LinearSVC().fit(x, y)     return mod\tNone\t2025-01-17 19:22:58\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Invoke function on all data in DataFrame proxy object","message":["%python","","mod = oml.table_apply(data=IRIS, ","                      func = 'sk_svc_fit',","                      oml_input_type=\"pandas.DataFrame\")","print(\"Type:\",type(mod))","print('Coefficients: \\n', mod.coef_)"],"enabled":true,"result":{"startTime":1737141779343,"interpreter":"python.medium","endTime":1737141780819,"results":[{"message":"Type: <class 'sklearn.svm._classes.LinearSVC'>\nCoefficients: \n [[ 0.18344977  0.4549369  -0.81495365 -0.43357339]\n [ 0.05524625 -0.9008946   0.40923385 -0.9606271 ]\n [-0.85048831 -0.98657611  1.38090768  1.86511359]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using a third-party package for prediction","","The model can now be used normally, e.g., to predict outcomes."],"enabled":true,"result":{"startTime":1737141781289,"interpreter":"md.medium","endTime":1737141781744,"results":[{"message":"<h2 id=\"using-a-third-party-package-for-prediction\">Using a third-party package for prediction<\/h2>\n<p>The model can now be used normally, e.g., to predict outcomes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","pred = mod.predict(iris_df.drop('SPECIES', axis = 1).values)","pred2 = pred.reshape((-1,1))","pred2[0:10]"],"enabled":true,"result":{"startTime":1737141782206,"interpreter":"python.medium","endTime":1737141782667,"results":[{"message":"array([['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa']], dtype=object)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Assessing model quality with a third-party package","","Let's assess model quality using a confusion matrix computed from the ***sklearn.metrics*** function confusion_matrix."],"enabled":true,"result":{"startTime":1737141783144,"interpreter":"md.medium","endTime":1737141783598,"results":[{"message":"<h2 id=\"assessing-model-quality-with-a-third-party-package\">Assessing model quality with a third-party package<\/h2>\n<p>Let's assess model quality using a confusion matrix computed from the <em><strong>sklearn.metrics<\/strong><\/em> function confusion_matrix.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from sklearn.metrics import confusion_matrix","","conf_mat = confusion_matrix(iris_df[[\"SPECIES\"]], pred2)","conf_mat"],"enabled":true,"result":{"startTime":1737141784070,"interpreter":"python.medium","endTime":1737141784531,"results":[{"message":"array([[50,  0,  0],\n       [ 0, 47,  3],\n       [ 0,  2, 48]])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Simple `group_apply` for counting each species","","The ***group_apply*** function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set."],"enabled":true,"result":{"startTime":1737141785000,"interpreter":"md.medium","endTime":1737141785443,"results":[{"message":"<h2 id=\"simple-group_apply-for-counting-each-species\">Simple <code>group_apply<\/code> for counting each species<\/h2>\n<p>The <em><strong>group_apply<\/strong><\/em> function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","def group_count(dat):","  import pandas as pd","  return pd.DataFrame([(dat[\"SPECIES\"][0], dat.shape[0])],","                      columns = [\"SPECIES\", \"CNT\"])","","index = oml.DataFrame(IRIS['SPECIES'])","res = oml.group_apply(IRIS, index, func=group_count,","                      oml_input_type=\"pandas.DataFrame\",","                      func_value=pd.DataFrame([('a', 1)],","                                              columns=[\"SPECIES\", \"CNT\"]))","z.show(res)"],"enabled":true,"result":{"startTime":1737141785906,"interpreter":"python.medium","endTime":1737141788304,"results":[{"message":"SPECIES\tCNT\nvirginica\t50\nsetosa\t50\nversicolor\t50\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using `index_apply` for repeated invocations","","The ***index_apply*** function allows the same function to invoked a specified number of times. ","The first argument to the user-defined function is an index number for the function execution. ","For example, if the \"times\" argument is 12, each function invocation will receive a distinct value between 1 and 10. ","This can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis."],"enabled":true,"result":{"startTime":1737141788767,"interpreter":"md.medium","endTime":1737141789215,"results":[{"message":"<h2 id=\"using-index_apply-for-repeated-invocations\">Using <code>index_apply<\/code> for repeated invocations<\/h2>\n<p>The <em><strong>index_apply<\/strong><\/em> function allows the same function to invoked a specified number of times.\nThe first argument to the user-defined function is an index number for the function execution.\nFor example, if the &quot;times&quot; argument is 12, each function invocation will receive a distinct value between 1 and 10.\nThis can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","def compute_random_mean(index):","    import numpy as np","    import scipy","    from statistics import mean ","    np.random.seed(index)","    res = np.random.random((100,1))*10","    return mean(res[1])","","print(\"Test: \", compute_random_mean(3))","","res = oml.index_apply(times=12, func=compute_random_mean)","res"],"enabled":true,"result":{"startTime":1737141789684,"interpreter":"python.medium","endTime":1737141790253,"results":[{"message":"Test:  7.081478226181048\n[7.203244934421581, 0.25926231827891333, 7.081478226181048, 5.4723224917572235, 8.707323061773764, 3.3197980530117723, 7.7991879224011464, 9.68540662820932, 5.018745921487388, 0.207519493594015, 0.19475241487624584, 7.400496965154048]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Multiple images returned from embedded Python execution","","Here, we create a function ***RandomRedDots*** that creates a simple DataFrame and generates two plots of random red dots. We create a function in the Python Script Repository named \"RandomRedDots\", and then run the native Python function."],"enabled":true,"result":{"startTime":1737141790724,"interpreter":"md.medium","endTime":1737141791171,"results":[{"message":"<h2 id=\"multiple-images-returned-from-embedded-python-execution\">Multiple images returned from embedded Python execution<\/h2>\n<p>Here, we create a function <em><strong>RandomRedDots<\/strong><\/em> that creates a simple DataFrame and generates two plots of random red dots. We create a function in the Python Script Repository named &quot;RandomRedDots&quot;, and then run the native Python function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","RandomRedDots = \"\"\"def RandomRedDots (num_dots_1 = 100, num_dots_2 = 10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt","       ","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","    plt.scatter(range(0,num_dots_1), np.random.rand(100),c='r')","    plt.title(\"Random Red Dots\")","    plt.show()","    plt.scatter(range(0,num_dots_2), np.random.rand(10),c='r')","    plt.title(\"Random Red Dots\")","    plt.show()","    return df\"\"\"","","oml.script.create(\"RandomRedDots\", func=RandomRedDots, is_global=True, overwrite=True)","","print(RandomRedDots)"],"enabled":true,"result":{"startTime":1737141791635,"interpreter":"python.medium","endTime":1737141792099,"results":[{"message":"def RandomRedDots (num_dots_1 = 100, num_dots_2 = 10):\n    import numpy as np\n    import pandas as pd\n    import matplotlib.pyplot as plt\n       \n    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}\n    df = pd.DataFrame(data=d)\n    plt.scatter(range(0,num_dots_1), np.random.rand(100),c='r')\n    plt.title(\"Random Red Dots\")\n    plt.show()\n    plt.scatter(range(0,num_dots_2), np.random.rand(10),c='r')\n    plt.title(\"Random Red Dots\")\n    plt.show()\n    return df\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use of the `graphics` flag","","If we simply invoke the named function, we see that the result is an OML object. Notice that we do not see images."],"enabled":true,"result":{"startTime":1737141792578,"interpreter":"md.medium","endTime":1737141793017,"results":[{"message":"<h2 id=\"use-of-the-graphics-flag\">Use of the <code>graphics<\/code> flag<\/h2>\n<p>If we simply invoke the named function, we see that the result is an OML object. Notice that we do not see images.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots\")","print(\"Type: \", type(res))","","res"],"enabled":true,"result":{"startTime":1737141793481,"interpreter":"python.medium","endTime":1737141793977,"results":[{"message":"Type:  <class 'pandas.core.frame.DataFrame'>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Returning graphics","","To ensure we get the plots, we need to modify the function using the `graphics` flag."],"enabled":true,"result":{"startTime":1737141794446,"interpreter":"md.medium","endTime":1737141794892,"results":[{"message":"<h2 id=\"returning-graphics\">Returning graphics<\/h2>\n<p>To ensure we get the plots, we need to modify the function using the <code>graphics<\/code> flag.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots\", graphics=True)","print(\"Type: \", type(res))","","res"],"enabled":true,"result":{"startTime":1737141795350,"interpreter":"python.medium","endTime":1737141796003,"results":[{"message":"Type:  <class 'oml.embed.data_image._DataImage'>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Example invocation via REST API with the `graphicsFlag`","","Invoke the *RandomRedDots* function from the REST API with *graphicsFlag* to *true*:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots\""],"enabled":true,"result":{"startTime":1737141796474,"interpreter":"md.medium","endTime":1737141796925,"results":[{"message":"<h2 id=\"example-invocation-via-rest-api-with-the-graphicsflag\">Example invocation via REST API with the <code>graphicsFlag<\/code><\/h2>\n<p>Invoke the <em>RandomRedDots<\/em> function from the REST API with <em>graphicsFlag<\/em> to <em>true<\/em>:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/py-scripts/v1/do-eval/RandomRedDots&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A second option generating separate figures: RandomRedDots2","","Here, we create separate figure objects, add subplots, and then create the scatter plot. We'll store this in the script repository as \"RandomRedDots2\" and ","invoke the function to see the results. As expected, we get both plots.","","Now, when we invoke \"RandomRedDots2\" using embedded Python execution, we get both plots shown in the result."],"enabled":true,"result":{"startTime":1737141797385,"interpreter":"md.medium","endTime":1737141797834,"results":[{"message":"<h2 id=\"a-second-option-generating-separate-figures-randomreddots2\">A second option generating separate figures: RandomRedDots2<\/h2>\n<p>Here, we create separate figure objects, add subplots, and then create the scatter plot. We'll store this in the script repository as &quot;RandomRedDots2&quot; and\ninvoke the function to see the results. As expected, we get both plots.<\/p>\n<p>Now, when we invoke &quot;RandomRedDots2&quot; using embedded Python execution, we get both plots shown in the result.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","RandomRedDots2 = \"\"\"def RandomRedDots2 (num_dots_1 = 100, num_dots_2 = 10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt  ","","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","    fig = plt.figure(1)","    ax = fig.add_subplot(111)","    ax.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')","    fig.suptitle(\"Random Red Dots\")","","    fig2 = plt.figure(2)","    ax2 = fig2.add_subplot(111)","    ax2.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')","    fig2.suptitle(\"Random Red Dots\")","    return df\"\"\"","","oml.script.create(\"RandomRedDots2\", func=RandomRedDots2, is_global=True, overwrite=True)","","print(RandomRedDots2)"],"enabled":true,"result":{"startTime":1737141798314,"interpreter":"python.medium","endTime":1737141798804,"results":[{"message":"def RandomRedDots2 (num_dots_1 = 100, num_dots_2 = 10):\n    import numpy as np\n    import pandas as pd\n    import matplotlib.pyplot as plt  \n\n    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}\n    df = pd.DataFrame(data=d)\n    fig = plt.figure(1)\n    ax = fig.add_subplot(111)\n    ax.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')\n    fig.suptitle(\"Random Red Dots\")\n\n    fig2 = plt.figure(2)\n    ax2 = fig2.add_subplot(111)\n    ax2.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')\n    fig2.suptitle(\"Random Red Dots\")\n    return df\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots2\", num_dots_1 = 600, num_dots_2 = 200, graphics = True)","","print(type(res))","","res"],"enabled":true,"result":{"startTime":1737141799263,"interpreter":"python.medium","endTime":1737141799976,"results":[{"message":"<class 'oml.embed.data_image._DataImage'>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Equivalent invocation via REST API for RandomRedDots2","","Here is the equivalent REST API call","    ","    curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots2\""],"enabled":true,"result":{"startTime":1737141800451,"interpreter":"md.medium","endTime":1737141800893,"results":[{"message":"<h2 id=\"equivalent-invocation-via-rest-api-for-randomreddots2\">Equivalent invocation via REST API for RandomRedDots2<\/h2>\n<p>Here is the equivalent REST API call<\/p>\n<pre><code>curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/py-scripts/v1/do-eval/RandomRedDots2&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A third option with a single plot with subplots: RandomRedDots3","","","Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example."],"enabled":true,"result":{"startTime":1737141801376,"interpreter":"md.medium","endTime":1737141801826,"results":[{"message":"<h2 id=\"a-third-option-with-a-single-plot-with-subplots-randomreddots3\">A third option with a single plot with subplots: RandomRedDots3<\/h2>\n<p>Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","RandomRedDots3 = \"\"\"def RandomRedDots3 (num_dots_1 = 100, num_dots_2 = 10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt","","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","","    plt.subplot(2,1, 1)","    plt.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')","    plt.title(\"Random Red Dots\")","","    plt.subplot(2, 1, 2)","    plt.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')","    plt.title(\"Random Red Dots\")","    return df\"\"\"","","oml.script.create(\"RandomRedDots3\", func=RandomRedDots3, is_global=True, overwrite=True)","oml.do_eval(func=\"RandomRedDots3\", graphics=True)"],"enabled":true,"result":{"startTime":1737141802303,"interpreter":"python.medium","endTime":1737141802911,"results":[{"message":"   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Equivalent invocation via REST API for RandomRedDots3","","Here is the equivalent REST API call:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots3\""],"enabled":true,"result":{"startTime":1737141803378,"interpreter":"md.medium","endTime":1737141803822,"results":[{"message":"<h2 id=\"equivalent-invocation-via-rest-api-for-randomreddots3\">Equivalent invocation via REST API for RandomRedDots3<\/h2>\n<p>Here is the equivalent REST API call:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/py-scripts/v1/do-eval/RandomRedDots3&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Returning only the structured contents","","We can also return only the structured contents as an OML DataFrame, which keeps the result in the database - returning a proxy object to the result. We pull the results to the notebook explicitly to display it locally."],"enabled":true,"result":{"startTime":1737141804295,"interpreter":"md.medium","endTime":1737141804750,"results":[{"message":"<h2 id=\"returning-only-the-structured-contents\">Returning only the structured contents<\/h2>\n<p>We can also return only the structured contents as an OML DataFrame, which keeps the result in the database - returning a proxy object to the result. We pull the results to the notebook explicitly to display it locally.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","import pandas as pd","res = oml.do_eval(func=RandomRedDots, func_value=pd.DataFrame({\"id\":[0],\"val\":[0]}))","type(res)","","res.pull()"],"enabled":true,"result":{"startTime":1737141805229,"interpreter":"python.medium","endTime":1737141806192,"results":[{"message":"   id  val\n0   1    0\n1   2    0\n2   3    0\n3   4    0\n4   5    0\n5   6    0\n6   7    0\n7   8    0\n8   9    0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Loading the python script from the repository","","Finally, note that a function stored in the Python Script Repository can be loaded back in the Python client engine, we can view its source, and finally invoke the function."],"enabled":true,"result":{"startTime":1737141806680,"interpreter":"md.medium","endTime":1737141807127,"results":[{"message":"<h2 id=\"loading-the-python-script-from-the-repository\">Loading the python script from the repository<\/h2>\n<p>Finally, note that a function stored in the Python Script Repository can be loaded back in the Python client engine, we can view its source, and finally invoke the function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","random_red_dots = oml.script.load('RandomRedDots2')","","print(random_red_dots.get_source())","random_red_dots()"],"enabled":true,"result":{"startTime":1737141807600,"interpreter":"python.medium","endTime":1737141808103,"results":[{"message":"def RandomRedDots2 (num_dots_1 = 100, num_dots_2 = 10):\n    import numpy as np\n    import pandas as pd\n    import matplotlib.pyplot as plt  \n\n    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}\n    df = pd.DataFrame(data=d)\n    fig = plt.figure(1)\n    ax = fig.add_subplot(111)\n    ax.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')\n    fig.suptitle(\"Random Red Dots\")\n\n    fig2 = plt.figure(2)\n    ax2 = fig2.add_subplot(111)\n    ax2.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')\n    fig2.suptitle(\"Random Red Dots\")\n    return df\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737141808615,"interpreter":"md.medium","endTime":1737141809061,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737141809543,"interpreter":"md.medium","endTime":1737141809994,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py -5- AutoML","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734739998489,"interpreter":"md.low","endTime":1734739999017,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python (OML4Py)","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automated machine learning (AutoML) features.","","In this notebook, we highlight the AutoML workflow using OML4Py:","","* Automated algorithm selection - identify the most appropriate algorithm","* Automated feature selection - select the features (predictors) that best support that algorithm","* Automated model tuning - tune the model using algorithm hyperparameters and the selected features","","Using the WINE dataset form scikit-learn, we use AutoML for classification on the 'target' column, and for regression on the 'proline' column. ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734739999477,"interpreter":"md.low","endTime":1734739999969,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python-oml4py\">Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automated machine learning (AutoML) features.<\/p>\n<p>In this notebook, we highlight the AutoML workflow using OML4Py:<\/p>\n<ul>\n<li>Automated algorithm selection - identify the most appropriate algorithm<\/li>\n<li>Automated feature selection - select the features (predictors) that best support that algorithm<\/li>\n<li>Automated model tuning - tune the model using algorithm hyperparameters and the selected features<\/li>\n<\/ul>\n<p>Using the WINE dataset form scikit-learn, we use AutoML for classification on the 'target' column, and for regression on the 'proline' column.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries supporting OML4Py ","message":["%md","","To use OML4Py, first import the package ***oml***. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1734740000432,"interpreter":"md.low","endTime":1734740000888,"results":[{"message":"<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>.<\/p>\n<p>Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":{"startTime":1734740001350,"interpreter":"python.low","endTime":1734740003832,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create data sets as Pandas DataFrame and load into Oracle Database","message":["%md","","In this example, we load the **wine** and **iris** data sets, combining target and predictors into a single DataFrame that matches the form the data would have as a database table. These DataFrame objects can then be loaded into Oracle Database using the ***create*** function."],"enabled":true,"result":{"startTime":1734740004283,"interpreter":"md.low","endTime":1734740004739,"results":[{"message":"<p>In this example, we load the <strong>wine<\/strong> and <strong>iris<\/strong> data sets, combining target and predictors into a single DataFrame that matches the form the data would have as a database table. These DataFrame objects can then be loaded into Oracle Database using the <em><strong>create<\/strong><\/em> function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Create WINE data table","message":["%python","","from sklearn.datasets import load_wine","","wine = load_wine()","x = pd.DataFrame(wine.data, columns = wine[\"feature_names\"])","y = pd.DataFrame(list(map(lambda x: {0:'class_0', 1: 'class_1', 2:'class_2'}[x], wine.target)), ","                 columns = ['target'])","","wine_df = pd.concat([x,y], axis=1)","","try:","    oml.drop(table=\"WINE\")","except:","    pass","WINE = oml.create(wine_df, table=\"WINE\")","print(WINE.shape)","print(WINE.dtypes)"],"enabled":true,"result":{"startTime":1734740005202,"interpreter":"python.low","endTime":1734740006088,"results":[{"message":"(178, 14)\nalcohol                           <class 'oml.core.float.Float'>\nmalic_acid                        <class 'oml.core.float.Float'>\nash                               <class 'oml.core.float.Float'>\nalcalinity_of_ash                 <class 'oml.core.float.Float'>\nmagnesium                         <class 'oml.core.float.Float'>\ntotal_phenols                     <class 'oml.core.float.Float'>\nflavanoids                        <class 'oml.core.float.Float'>\nnonflavanoid_phenols              <class 'oml.core.float.Float'>\nproanthocyanins                   <class 'oml.core.float.Float'>\ncolor_intensity                   <class 'oml.core.float.Float'>\nhue                               <class 'oml.core.float.Float'>\nod280/od315_of_diluted_wines      <class 'oml.core.float.Float'>\nproline                           <class 'oml.core.float.Float'>\ntarget                          <class 'oml.core.string.String'>\ndtype: object\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the first 20 rows","message":["%python","","z.show(WINE.head(20))"],"enabled":true,"result":{"startTime":1734740006686,"interpreter":"python.low","endTime":1734740007164,"results":[{"message":"alcohol\tmalic_acid\tash\talcalinity_of_ash\tmagnesium\ttotal_phenols\tflavanoids\tnonflavanoid_phenols\tproanthocyanins\tcolor_intensity\thue\tod280/od315_of_diluted_wines\tproline\ttarget\n14.23\t1.71\t2.43\t15.6\t127\t2.8\t3.06\t0.28\t2.29\t5.64\t1.04\t3.92\t1065\tclass_0\n13.2\t1.78\t2.14\t11.2\t100\t2.65\t2.76\t0.26\t1.28\t4.38\t1.05\t3.4\t1050\tclass_0\n13.16\t2.36\t2.67\t18.6\t101\t2.8\t3.24\t0.3\t2.81\t5.68\t1.03\t3.17\t1185\tclass_0\n14.37\t1.95\t2.5\t16.8\t113\t3.85\t3.49\t0.24\t2.18\t7.8\t0.86\t3.45\t1480\tclass_0\n13.24\t2.59\t2.87\t21.0\t118\t2.8\t2.69\t0.39\t1.82\t4.32\t1.04\t2.93\t735\tclass_0\n14.2\t1.76\t2.45\t15.2\t112\t3.27\t3.39\t0.34\t1.97\t6.75\t1.05\t2.85\t1450\tclass_0\n14.39\t1.87\t2.45\t14.6\t96\t2.5\t2.52\t0.3\t1.98\t5.25\t1.02\t3.58\t1290\tclass_0\n14.06\t2.15\t2.61\t17.6\t121\t2.6\t2.51\t0.31\t1.25\t5.05\t1.06\t3.58\t1295\tclass_0\n14.83\t1.64\t2.17\t14.0\t97\t2.8\t2.98\t0.29\t1.98\t5.2\t1.08\t2.85\t1045\tclass_0\n13.86\t1.35\t2.27\t16.0\t98\t2.98\t3.15\t0.22\t1.85\t7.22\t1.01\t3.55\t1045\tclass_0\n14.1\t2.16\t2.3\t18.0\t105\t2.95\t3.32\t0.22\t2.38\t5.75\t1.25\t3.17\t1510\tclass_0\n14.12\t1.48\t2.32\t16.8\t95\t2.2\t2.43\t0.26\t1.57\t5.0\t1.17\t2.82\t1280\tclass_0\n13.75\t1.73\t2.41\t16.0\t89\t2.6\t2.76\t0.29\t1.81\t5.6\t1.15\t2.9\t1320\tclass_0\n14.75\t1.73\t2.39\t11.4\t91\t3.1\t3.69\t0.43\t2.81\t5.4\t1.25\t2.73\t1150\tclass_0\n14.38\t1.87\t2.38\t12.0\t102\t3.3\t3.64\t0.29\t2.96\t7.5\t1.2\t3.0\t1547\tclass_0\n13.63\t1.81\t2.7\t17.2\t112\t2.85\t2.91\t0.3\t1.46\t7.3\t1.28\t2.88\t1310\tclass_0\n14.3\t1.92\t2.72\t20.0\t120\t2.8\t3.14\t0.33\t1.97\t6.2\t1.07\t2.65\t1280\tclass_0\n13.83\t1.57\t2.62\t20.0\t115\t2.95\t3.4\t0.4\t1.72\t6.6\t1.13\t2.57\t1130\tclass_0\n14.19\t1.59\t2.48\t16.5\t108\t3.3\t3.93\t0.32\t1.86\t8.7\t1.23\t2.82\t1680\tclass_0\n13.64\t3.1\t2.56\t15.2\t116\t2.7\t3.03\t0.17\t1.66\t5.1\t0.96\t3.36\t845\tclass_0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Automated Machine Learning (AutoML)","The AutoML workflow consists of ","","- identify the most appropriate algorithm ","- select the features (predictors) that best support that algorithm","- tune the model using algorithm hyperparameters and the selected features","","### Automated Algorithm Selection","","Which algorithm is best to predict my target among those available with OML4Py?","","- Classification: Decision Tree, Generalized Linear Model, Na?ve Bayes, Neural Network, RandomForest, Support Vector Machine","- Regression: Generalized Linear Model, Neural Network, Support Vector Machine","","To use AutoML algorithm selection, we create an ***AlgorithmSelection*** object, here with the 'accuracy' score metric. Then, we invoke ***select*** to get the top 4 predicted algorithms by setting k=4. Automated algorithm selection ranks the algorithms from the set of supported in-database algorithms likely to produce better models using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and ***meta-learning*** techniques.","","Users can select from a variety of ***score_metric*** options:","","- binary classification \u2013 accuracy (default), f1, precision, recall, roc_auc, f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted","- multiclass classification \u2013 accuracy (default), f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted","- regression \u2013 r2 (default), neg_mean_squared_error, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error"],"enabled":true,"result":{"startTime":1734740007641,"interpreter":"md.low","endTime":1734740008118,"results":[{"message":"<h2 id=\"automated-machine-learning-automl\">Automated Machine Learning (AutoML)<\/h2>\n<p>The AutoML workflow consists of<\/p>\n<ul>\n<li>identify the most appropriate algorithm<\/li>\n<li>select the features (predictors) that best support that algorithm<\/li>\n<li>tune the model using algorithm hyperparameters and the selected features<\/li>\n<\/ul>\n<h3 id=\"automated-algorithm-selection\">Automated Algorithm Selection<\/h3>\n<p>Which algorithm is best to predict my target among those available with OML4Py?<\/p>\n<ul>\n<li>Classification: Decision Tree, Generalized Linear Model, Na?ve Bayes, Neural Network, RandomForest, Support Vector Machine<\/li>\n<li>Regression: Generalized Linear Model, Neural Network, Support Vector Machine<\/li>\n<\/ul>\n<p>To use AutoML algorithm selection, we create an <em><strong>AlgorithmSelection<\/strong><\/em> object, here with the 'accuracy' score metric. Then, we invoke <em><strong>select<\/strong><\/em> to get the top 4 predicted algorithms by setting k=4. Automated algorithm selection ranks the algorithms from the set of supported in-database algorithms likely to produce better models using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and <em><strong>meta-learning<\/strong><\/em> techniques.<\/p>\n<p>Users can select from a variety of <em><strong>score_metric<\/strong><\/em> options:<\/p>\n<ul>\n<li>binary classification \u2013 accuracy (default), f1, precision, recall, roc_auc, f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted<\/li>\n<li>multiclass classification \u2013 accuracy (default), f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted<\/li>\n<li>regression \u2013 r2 (default), neg_mean_squared_error, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare WINE classification data","message":["%python","","WINE_X_cl,WINE_y_cl = WINE.drop('target'), WINE['target']"],"enabled":true,"result":{"startTime":1734740008596,"interpreter":"python.low","endTime":1734740009060,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare WINE regression data","message":["%python","","WINE_X_reg,WINE_y_reg = WINE.drop('proline'), WINE['proline']"],"enabled":true,"result":{"startTime":1734740009514,"interpreter":"python.low","endTime":1734740009973,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Rank classification algorithms for WINE data","message":["%python","","as_wine_cl = automl.AlgorithmSelection(mining_function='classification', ","                                       score_metric='accuracy', parallel=2)","","wine_alg_ranking_cl = as_wine_cl.select(WINE_X_cl, WINE_y_cl, k=4) ","","print(\"Ranked algorithms:\\n\", wine_alg_ranking_cl)","","selected_wine_alg_cl = next(iter(dict(wine_alg_ranking_cl).keys()))","print(\"Best algorithm: \", selected_wine_alg_cl)"],"enabled":true,"result":{"startTime":1734740010437,"interpreter":"python.low","endTime":1734740035964,"results":[{"message":"Ranked algorithms:\n [('svm_linear', 0.9830065359477125), ('svm_gaussian', 0.981992337164751), ('nn', 0.9644515277375317), ('rf', 0.9461170675166619)]\nBest algorithm:  svm_linear\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Rank regression algorithms for WINE data","message":["%python","","as_wine_reg = automl.AlgorithmSelection(mining_function='regression', ","                                        score_metric='r2', parallel=2)","","wine_alg_ranking_reg = as_wine_reg.select(WINE_X_reg, WINE_y_reg, k=4) ","","print(\"Ranked algorithms:\\n\", wine_alg_ranking_reg)","","selected_wine_alg_reg = next(iter(dict(wine_alg_ranking_reg).keys()))","print(\"Best algorithm: \", selected_wine_alg_reg)"],"enabled":true,"result":{"startTime":1734740036418,"interpreter":"python.low","endTime":1734740058016,"results":[{"message":"Ranked algorithms:\n [('svm_gaussian', 0.7233656799402365), ('svm_linear', 0.6905950887761092), ('glm_ridge', 0.6839975942592192), ('glm', 0.683944877749971)]\nBest algorithm:  svm_gaussian\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Automated Feature Selection","","Next, we determine the features that best support the selected algorithm. We first define a ***FeatureSelection*** object with score metric 'accuracy' and invoke ***reduce*** specifying the desired algorithm determined above and the train and test data set proxy objects. ","","We see the set of selected columns. Try other algorithms, e.g., svm_linear, to see if different columns are selected."],"enabled":true,"result":{"startTime":1734740058468,"interpreter":"md.low","endTime":1734740058906,"results":[{"message":"<h3 id=\"automated-feature-selection\">Automated Feature Selection<\/h3>\n<p>Next, we determine the features that best support the selected algorithm. We first define a <em><strong>FeatureSelection<\/strong><\/em> object with score metric 'accuracy' and invoke <em><strong>reduce<\/strong><\/em> specifying the desired algorithm determined above and the train and test data set proxy objects.<\/p>\n<p>We see the set of selected columns. Try other algorithms, e.g., svm_linear, to see if different columns are selected.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Classification Feature Selection","message":["%python","","","fs_wine_cl = automl.FeatureSelection(mining_function = 'classification', ","                                     score_metric = 'accuracy', parallel=2)","","selected_wine_features_cl = fs_wine_cl.reduce(selected_wine_alg_cl, ","                                              WINE_X_cl, WINE_y_cl)","","WINE_X_reduced_cl = WINE_X_cl[:,selected_wine_features_cl]","","print(\"Selected columns:\", WINE_X_reduced_cl.columns)","print(\"Number of columns:\")","\"{}  reduced to {}\".format(len(WINE_X_cl.columns), len(selected_wine_features_cl))"],"enabled":true,"result":{"startTime":1734740059360,"interpreter":"python.low","endTime":1734740094172,"results":[{"message":"Selected columns: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\nNumber of columns:\n'13  reduced to 11'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Regression Feature Selection","message":["%python","","","fs_wine_reg = automl.FeatureSelection(mining_function = 'regression', ","                                     score_metric = 'r2', parallel=2)","","selected_wine_features_reg = fs_wine_reg.reduce(selected_wine_alg_reg, ","                                                WINE_X_reg, WINE_y_reg)","","WINE_X_reduced_reg = WINE_X_reg[:,selected_wine_features_reg]","","print(\"Selected columns:\",WINE_X_reduced_reg.columns)","print(\"Number of columns:\")","\"{}  reduced to {}\".format(len(WINE_X_reg.columns), len(selected_wine_features_reg))"],"enabled":true,"result":{"startTime":1734740094638,"interpreter":"python.low","endTime":1734740138366,"results":[{"message":"Selected columns: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'target']\nNumber of columns:\n'13  reduced to 13'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Automated Model Tuning","","At this point, we're ready to build and tune the models we want to use. First, we define a ***ModelTuning*** object for classification. ","We then invoke ***tune*** to produce the tuned model using the algorithm selected above and the reduced column data. ","","The result of model tuning is a dictionary with the 'best model' and 'all evals', which contains a list of all hyperparameter choices tried and their corresponding score.","","When an in-database model is created without a specific name, it is viewed as temporary, such that when the database connection terminates (or Notebook session closes) the models are dropped from the user schema. Models associated with proxy objects will not be dropped if the proxy object is stored in a datastore or if the model is assinged an explicit name. ","","Assigning an explicit name after a model is created can be accomplished by assigning a name to the `model_name` property. For example, a model proxy object referenced by variable `my_model` could be assigned a name using: ","","    my_model.model_name = 'MY_MODEL'","   "],"enabled":true,"result":{"startTime":1734740138828,"interpreter":"md.low","endTime":1734740139267,"results":[{"message":"<h3 id=\"automated-model-tuning\">Automated Model Tuning<\/h3>\n<p>At this point, we're ready to build and tune the models we want to use. First, we define a <em><strong>ModelTuning<\/strong><\/em> object for classification.\nWe then invoke <em><strong>tune<\/strong><\/em> to produce the tuned model using the algorithm selected above and the reduced column data.<\/p>\n<p>The result of model tuning is a dictionary with the 'best model' and 'all evals', which contains a list of all hyperparameter choices tried and their corresponding score.<\/p>\n<p>When an in-database model is created without a specific name, it is viewed as temporary, such that when the database connection terminates (or Notebook session closes) the models are dropped from the user schema. Models associated with proxy objects will not be dropped if the proxy object is stored in a datastore or if the model is assinged an explicit name.<\/p>\n<p>Assigning an explicit name after a model is created can be accomplished by assigning a name to the <code>model_name<\/code> property. For example, a model proxy object referenced by variable <code>my_model<\/code> could be assigned a name using:<\/p>\n<pre><code>my_model.model_name = 'MY_MODEL'\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Classification Model Tuning","message":["%python","","","mt_wine_cl = automl.ModelTuning(mining_function = 'classification', parallel=2)","","results_cl = mt_wine_cl.tune(selected_wine_alg_cl, WINE_X_reduced_cl, WINE_y_cl)","tuned_model_cl = results_cl['best_model']","tuned_model_cl"],"enabled":true,"result":{"startTime":1734740139717,"interpreter":"python.low","endTime":1734740174537,"results":[{"message":"\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: target\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1         CLAS_WEIGHTS_BALANCED                           OFF\n2                  ODMS_DETAILS                   ODMS_ENABLE\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                     PREP_AUTO                            ON\n6        SVMS_COMPLEXITY_FACTOR                            10\n7           SVMS_CONV_TOLERANCE                         .0001\n8          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n9                   SVMS_SOLVER               SVMS_SOLVER_IPM\n\nComputed Settings: \n          setting name setting value\n0  SVMS_NUM_ITERATIONS            30\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              15\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproline\ntotal_phenols\n\nPartition: NO\n\nCOEFFICIENTS: \n\n   TARGET_VALUE                ATTRIBUTE_NAME  ... ATTRIBUTE_VALUE    COEF\n0       class_0             alcalinity_of_ash  ...            None -1.0241\n1       class_0                       alcohol  ...            None  0.9540\n2       class_0                           ash  ...            None  0.7172\n3       class_0               color_intensity  ...            None -0.1460\n4       class_0                    flavanoids  ...            None  0.6934\n5       class_0                           hue  ...            None -0.0126\n6       class_0                    malic_acid  ...            None  0.3877\n7       class_0          nonflavanoid_phenols  ...            None  0.0100\n8       class_0  od280/od315_of_diluted_wines  ...            None  0.5115\n9       class_0                       proline  ...            None  1.1391\n10      class_0                 total_phenols  ...            None  0.2301\n11      class_0                          None  ...            None -0.8932\n12      class_1             alcalinity_of_ash  ...            None  0.8536\n13      class_1                       alcohol  ...            None -1.0057\n14      class_1                           ash  ...            None -1.1235\n15      class_1               color_intensity  ...            None -3.0848\n16      class_1                    flavanoids  ...            None  0.9363\n17      class_1                           hue  ...            None  0.7513\n18      class_1                    malic_acid  ...            None -0.6123\n19      class_1          nonflavanoid_phenols  ...            None  0.6385\n20      class_1  od280/od315_of_diluted_wines  ...            None -0.1823\n21      class_1                       proline  ...            None -2.5648\n22      class_1                 total_phenols  ...            None  0.0292\n23      class_1                          None  ...            None -1.8943\n24      class_2             alcalinity_of_ash  ...            None -0.0291\n25      class_2                       alcohol  ...            None  0.8029\n26      class_2                           ash  ...            None  0.4917\n27      class_2               color_intensity  ...            None  1.0072\n28      class_2                    flavanoids  ...            None -1.6001\n29      class_2                           hue  ...            None -0.7076\n30      class_2                    malic_acid  ...            None  0.0608\n31      class_2          nonflavanoid_phenols  ...            None -0.3188\n32      class_2  od280/od315_of_diluted_wines  ...            None -0.7787\n33      class_2                       proline  ...            None  0.0669\n34      class_2                 total_phenols  ...            None  0.1863\n35      class_2                          None  ...            None -1.7603\n\n[36 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Regression Model Tuning","message":["%python","","","mt_wine_reg = automl.ModelTuning(mining_function = 'regression', parallel=2)","","results_reg = mt_wine_reg.tune(selected_wine_alg_reg, WINE_X_reduced_reg, WINE_y_reg)","tuned_model_reg = results_reg['best_model']","tuned_model_reg"],"enabled":true,"result":{"startTime":1734740175020,"interpreter":"python.low","endTime":1734740283126,"results":[{"message":"\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: proline\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5         SVMS_COMPLEXITY_FACTOR                             3\n6            SVMS_CONV_TOLERANCE                         .0001\n7                   SVMS_EPSILON           0.33367165666666665\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           203\n10                  SVMS_STD_DEV            2.5495097567963922\n\nComputed Settings: \n          setting name    setting value\n0  SVMS_NUM_ITERATIONS               30\n1          SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              12\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\ntarget\ntotal_phenols\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Specify a model tuning search space","","Explicitly indicate the search space you want to explore for model building using the ***param_space*** argument to the ***tune*** function. Below, we show using parameters for the Random Forest algorithm for classification and Neural Network for regression. "],"enabled":true,"result":{"startTime":1734740283597,"interpreter":"md.low","endTime":1734740284045,"results":[{"message":"<h3 id=\"specify-a-model-tuning-search-space\">Specify a model tuning search space<\/h3>\n<p>Explicitly indicate the search space you want to explore for model building using the <em><strong>param_space<\/strong><\/em> argument to the <em><strong>tune<\/strong><\/em> function. Below, we show using parameters for the Random Forest algorithm for classification and Neural Network for regression.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Classification model tuning using search space","message":["%python","","","search_space={","        'RFOR_SAMPLING_RATIO': {","            'type': 'continuous', 'range': [0.05, 0.5]}, ","        'RFOR_NUM_TREES': {","            'type': 'discrete', ","            'range': [50, 55]}, ","        'TREE_IMPURITY_METRIC': {","            'type': 'categorical', ","            'range': ['TREE_IMPURITY_ENTROPY', 'TREE_IMPURITY_GINI']},","        }","              ","at_wine2_cl = automl.ModelTuning(mining_function='classification', ","                                 score_metric='f1_macro', parallel=2)","results2_cl = at_wine2_cl.tune('rf', WINE_X_cl, WINE_y_cl, ","                               param_space=search_space)","score2_cl, params2_cl = results2_cl['all_evals'][0]","\"{:.2}\".format(score2_cl)","","tuned_model2_cl = results2_cl['best_model']","tuned_model2_cl"],"enabled":true,"result":{"startTime":1734740284499,"interpreter":"python.low","endTime":1734740337528,"results":[{"message":"\nAlgorithm Name: Random Forest\n\nMining Function: CLASSIFICATION\n\nTarget: target\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_RANDOM_FOREST\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5               ODMS_RANDOM_SEED                        0\n6                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                      PREP_AUTO                       ON\n8                 RFOR_NUM_TREES                       50\n9            RFOR_SAMPLING_RATIO                     0.35\n10          TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n11           TREE_TERM_MAX_DEPTH                       16\n12         TREE_TERM_MINPCT_NODE                      .05\n13        TREE_TERM_MINPCT_SPLIT                       .1\n14         TREE_TERM_MINREC_NODE                       10\n15        TREE_TERM_MINREC_SPLIT                       20\n\nComputed Settings: \n  setting name setting value\n0    RFOR_MTRY             6\n\nGlobal Statistics: \n  attribute name attribute value\n0      AVG_DEPTH            3.74\n1  AVG_NODECOUNT            7.88\n2      MAX_DEPTH               5\n3  MAX_NODECOUNT              11\n4      MIN_DEPTH               4\n5  MIN_NODECOUNT               5\n6       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\nproline\ntotal_phenols\n\nPartition: NO\n\nImportance: \n\n                  ATTRIBUTE_NAME ATTRIBUTE_SUBNAME  ATTRIBUTE_IMPORTANCE\n0              alcalinity_of_ash              None              0.087762\n1                        alcohol              None              0.269680\n2                            ash              None              0.045137\n3                color_intensity              None              0.407622\n4                     flavanoids              None              0.294175\n5                            hue              None              0.138300\n6                      magnesium              None              0.027684\n7                     malic_acid              None              0.045442\n8           nonflavanoid_phenols              None              0.027490\n9   od280/od315_of_diluted_wines              None              0.149391\n10               proanthocyanins              None              0.008828\n11                       proline              None              0.223896\n12                 total_phenols              None              0.024040\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Regression model tuning using search space","message":["%python","","search_space={","        'NNET_REGULARIZER': {","            'range': ['NNET_REGULARIZER_HELDASIDE', 'NNET_REGULARIZER_L2', 'NNET_REGULARIZER_NONE'],","            'type': 'categorical',},","        'NNET_ALL_ACTIVATIONS': {","            'range': ['NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_LINEAR',  'NNET_ACTIVATIONS_BIPOLAR_SIG'],","            'type': 'categorical',},","        'NNET_HIDDEN_LAYERS': {","            'range': [1, 2],","            'type': 'discrete',},","        'NNET_NODES': {","            'range': [10, 20],","            'type': 'discrete',},","        }","","at_wine2_reg = automl.ModelTuning(mining_function='regression', ","                                  score_metric='r2', parallel=2)","results2_reg = at_wine2_reg.tune('nn', WINE_X_reg, WINE_y_reg,","                                 param_space=search_space)","score2_reg, params2_reg = results2_reg['all_evals'][0]","\"{:.2}\".format(score2_reg)","","tuned_model2_reg = results2_reg['best_model']","tuned_model2_reg"],"enabled":true,"result":{"startTime":1734740337992,"interpreter":"python.low","endTime":1734740378625,"results":[{"message":"\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: proline\n\nSettings: \n                    setting name                   setting value\n0                      ALGO_NAME             ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                      .000000001\n2            LBFGS_HISTORY_DEPTH                              20\n3            LBFGS_SCALE_HESSIAN      LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_BIPOLAR_SIG'\n5        NNET_HELDASIDE_MAX_FAIL                               6\n6           NNET_HELDASIDE_RATIO                             .25\n7             NNET_HIDDEN_LAYERS                               1\n8                NNET_ITERATIONS                             100\n9           NNET_NODES_PER_LAYER                              50\n10              NNET_REGULARIZER      NNET_REGULARIZER_HELDASIDE\n11                NNET_TOLERANCE                         .000001\n12                  ODMS_DETAILS                     ODMS_ENABLE\n13  ODMS_MISSING_VALUE_TREATMENT         ODMS_MISSING_VALUE_AUTO\n14              ODMS_RANDOM_SEED                               0\n15                 ODMS_SAMPLING           ODMS_SAMPLING_DISABLE\n16                     PREP_AUTO                              ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              25\n2     LOSS_VALUE        0.158361\n3       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\ntarget\ntotal_phenols\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_BIPOLAR_SIG\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0        0       0.0       0  ...            None         None -0.214998\n1        0       0.0       1  ...            None         None  0.142397\n2        0       0.0       2  ...            None         None -0.220107\n3        0       0.0       3  ...            None         None  0.113885\n4        0       0.0       4  ...            None         None  0.214632\n..     ...       ...     ...  ...             ...          ...       ...\n846      1      46.0       0  ...            None         None  0.079437\n847      1      47.0       0  ...            None         None -0.129681\n848      1      48.0       0  ...            None         None  0.391486\n849      1      49.0       0  ...            None         None  0.018201\n850      1       NaN       0  ...            None         None -0.055256\n\n[851 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Automated Model Selection","As a short cut, users may go directly to model selection on the training data. Automated model selection chooses the best algorithm (using Algorithm Selection)","from the set of supported algorithms, then builds, tunes and returns the model."],"enabled":true,"result":{"startTime":1734740379090,"interpreter":"md.low","endTime":1734740379544,"results":[{"message":"<h3 id=\"automated-model-selection\">Automated Model Selection<\/h3>\n<p>As a short cut, users may go directly to model selection on the training data. Automated model selection chooses the best algorithm (using Algorithm Selection)\nfrom the set of supported algorithms, then builds, tunes and returns the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Classification Model Selection","message":["%python","","","ms_wine = automl.ModelSelection(mining_function = 'classification', parallel=2)","","best_model = ms_wine.select(WINE_X_cl, WINE_y_cl, k=1, cv=2)","best_model"],"enabled":true,"result":{"startTime":1734740379997,"interpreter":"python.low","endTime":1734740417903,"results":[{"message":"(\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: target\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1          CLAS_WEIGHTS_BALANCED                           OFF\n2                   ODMS_DETAILS                   ODMS_ENABLE\n3   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                      PREP_AUTO                            ON\n6         SVMS_COMPLEXITY_FACTOR                            10\n7            SVMS_CONV_TOLERANCE                         .0001\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                  SVMS_STD_DEV            2.5495097567963922\n\nComputed Settings: \n          setting name    setting value\n0  SVMS_NUM_ITERATIONS               30\n1          SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              13\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\nproline\ntotal_phenols\n\nPartition: NO\n\n, 'svm_gaussian')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Regression Model Selection","message":["%python","","","ms_wine_reg = automl.ModelSelection(mining_function = 'regression', parallel=2)","","best_model_reg = ms_wine_reg.select(WINE_X_reg, WINE_y_reg, k=1, cv=2)","best_model_reg"],"enabled":true,"result":{"startTime":1734740418390,"interpreter":"python.low","endTime":1734740466315,"results":[{"message":"(\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: proline\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5         SVMS_COMPLEXITY_FACTOR                    0.82624875\n6            SVMS_CONV_TOLERANCE                         .0001\n7                   SVMS_EPSILON                           0.1\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                  SVMS_STD_DEV            2.5495097567963922\n\nComputed Settings: \n          setting name    setting value\n0  SVMS_NUM_ITERATIONS               30\n1          SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              10\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\ntarget\ntotal_phenols\n\nPartition: NO\n\n, 'svm_gaussian')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734740466783,"interpreter":"md.low","endTime":1734740467226,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1734740467689,"interpreter":"md.low","endTime":1734740468129,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":"zeppelin","template":null,"templateConfig":null,"name":"OML4Py Anomaly Detection SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1718666566541,"interpreter":"md.medium","endTime":1718666566598,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Detect Unusual or Rare Occurrences using SVM Anomaly Detection","Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.) in your data using the semi-supervised learning algorithm One-Class Support Vector Machine. In this notebook, you will build a One-Class SVM model and then use it to flag unusual or suspicious records. The data used in this example is from the SH schema. The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737148324149,"interpreter":"md.medium","endTime":1737148324600,"results":[{"message":"<h1 id=\"detect-unusual-or-rare-occurrences-using-svm-anomaly-detection\">Detect Unusual or Rare Occurrences using SVM Anomaly Detection<\/h1>\n<p>Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.) in your data using the semi-supervised learning algorithm One-Class Support Vector Machine. In this notebook, you will build a One-Class SVM model and then use it to flag unusual or suspicious records. The data used in this example is from the SH schema. The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md \r","<dl>\r","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/anomalydetection-5663169.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\r","<\/dl>"],"enabled":true,"result":{"startTime":1737148302201,"interpreter":"md.medium","endTime":1737148302762,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/anomalydetection-5663169.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1737148386586,"interpreter":"md.medium","endTime":1737148387062,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737148387540,"interpreter":"python.medium","endTime":1737148390052,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create OML Data Frames for tables CUSTOMERS and SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","SUPP_DEM  = oml.sync(query = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS')","","type(CUSTOMERS)"],"enabled":true,"result":{"startTime":1737148390533,"interpreter":"python.medium","endTime":1737148391426,"results":[{"message":"<class 'oml.core.frame.DataFrame'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMERS","message":["%python","","z.show(CUSTOMERS.head(10))"],"enabled":true,"result":{"startTime":1737148391902,"interpreter":"python.medium","endTime":1737148392635,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n13350\tF\tNone\t1929\tC: 50,000 - 69,999\t11000\n16905\tF\tmarried\t1975\tC: 50,000 - 69,999\t1500\n20460\tF\tNone\t1976\tC: 50,000 - 69,999\t7000\n24016\tF\tNone\t1933\tC: 50,000 - 69,999\t9000\n27571\tF\tmarried\t1952\tC: 50,000 - 69,999\t1500\n31127\tF\tNone\t1925\tC: 50,000 - 69,999\t9000\n34683\tF\tNone\t1942\tC: 50,000 - 69,999\t9000\n39127\tF\tNone\t1946\tC: 50,000 - 69,999\t7000\n42682\tF\tNone\t1967\tC: 50,000 - 69,999\t9000\n20014\tF\tmarried\t1931\tC: 50,000 - 69,999\t1500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPP_DEM","message":["%python","","z.show(SUPP_DEM.head(10))"],"enabled":true,"result":{"startTime":1737148393104,"interpreter":"python.medium","endTime":1737148393763,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n102547\t10th\t0\t1\tOther\t0\t1\n101050\t10th\t0\t1\tOther\t0\t1\n100040\t11th\t0\t1\tSales\t0\t1\n102117\tHS-grad\t0\t1\tFarming\t0\t1\n101074\t10th\t0\t1\tHandler\t1\t1\n104179\t10th\t0\t1\tHandler\t1\t1\n100417\t11th\t0\t1\tHandler\t1\t1\n101146\t< Bach.\t0\t1\t?\t1\t1\n103420\t< Bach.\t0\t1\t?\t1\t1\n101987\t< Bach.\t0\t1\tOther\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Merge (join) CUSTOMERS and SUPP_DEM OML proxy objects on CUST_ID column ","message":["%python","","CUST_DF = CUSTOMERS.merge(SUPP_DEM, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1737148394246,"interpreter":"python.medium","endTime":1737148394719,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUST_DF","message":["%python","","z.show(CUST_DF.head(10))"],"enabled":true,"result":{"startTime":1737148395185,"interpreter":"python.medium","endTime":1737148395799,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n103383\tM\tNeverM\t1976\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t1\tProtec.\t3\t1\n100924\tM\tNeverM\t1963\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t2\tCrafts\t4\t0\n102237\tM\tNeverM\t1975\tJ: 190,000 - 249,999\t11000\tProfsc\t0\t2\tProf.\t4\t1\n102078\tF\tNeverM\t1975\tJ: 190,000 - 249,999\t9000\tProfsc\t0\t2\tProf.\t6\t1\n104047\tM\tNeverM\t1983\tL: 300,000 and above\t7000\tHS-grad\t0\t1\tOther\t2\t1\n102510\tM\tMarried\t1956\tL: 300,000 and above\t15000\tAssoc-A\t1\t3\tProtec.\t5\t0\n100863\tM\tMarried\t1971\tL: 300,000 and above\t15000\tHS-grad\t0\t3\tCleric.\t3\t0\n103472\tM\tMarried\t1965\tE: 90,000 - 109,999\t3000\tBach.\t1\t3\tSales\t4\t0\n100799\tM\tNeverM\t1977\tG: 130,000 - 149,999\t7000\tHS-grad\t0\t1\tCrafts\t3\t1\n102248\tM\tMarried\t1962\tI: 170,000 - 189,999\t10000\t< Bach.\t1\t3\tCrafts\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"showSeriesX\":[\"mean(YRS_RESIDENCE)\"],\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL_STATUS\",\"mean(YRS_RESIDENCE)\"],\"version\":1},\"pie\":{\"version\":1},\"line\":{\"version\":1},\"funnel\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"CUST_MARITAL_STATUS\",\"CUST_YEAR_OF_BIRTH\"],\"showSeries\":[\"mean(YRS_RESIDENCE)\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"mean(YRS_RESIDENCE)\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL_STATUS\",\"mean(YRS_RESIDENCE)\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Visualize the distribution of birth date, marital status and years of residence ","message":["%python","","DF = CUST_DF","DF = DF.crosstab(['CUST_YEAR_OF_BIRTH', 'CUST_MARITAL_STATUS'], values = 'YRS_RESIDENCE', aggfunc= oml.DataFrame.mean)","z.show(DF.head(10))"],"enabled":true,"result":{"startTime":1737148396278,"interpreter":"python.medium","endTime":1737148397064,"results":[{"message":"CUST_YEAR_OF_BIRTH\tCUST_MARITAL_STATUS\tmean(YRS_RESIDENCE)\n1922\tDivorc.\t4.0\n1922\tWidowed\t1.0\n1923\tNeverM\t4.0\n1924\tMarried\t5.0\n1925\tWidowed\t10.0\n1926\tMabsent\t4.0\n1928\tMarried\t4.0\n1928\tWidowed\t4.8\n1929\tMarried\t6.333333333333333\n1929\tWidowed\t2.6666666666666665\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build the Anomaly Detection Model","message":["%md","After dropping any previous model, specify settings: the outlier rate is the proportion of rows that should be flagged as outliers. Then, create the oml.svm python object indicating this is to be an anomaly detection model with the specified settings. Finally, invoke ***fit*** on the data with 'None' specified for the target. We explicitly provide a model name for future reference and the case id to uniquely identify rows. "],"enabled":true,"result":{"startTime":1737148397527,"interpreter":"md.medium","endTime":1737148397997,"results":[{"message":"<p>After dropping any previous model, specify settings: the outlier rate is the proportion of rows that should be flagged as outliers. Then, create the oml.svm python object indicating this is to be an anomaly detection model with the specified settings. Finally, invoke <em><strong>fit<\/strong><\/em> on the data with 'None' specified for the target. We explicitly provide a model name for future reference and the case id to uniquely identify rows.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build the SVM model using the defaults","message":["%python","","try:","    oml.drop(model=\"ANOMALY_DETECTION_MODEL1\")","except:","    pass","","odm_settings = {'svms_outlier_rate' : 0.01}","","svm_mod = oml.svm(\"anomaly_detection\", **odm_settings)","svm_mod.fit(CUST_DF, None, model_name = 'ANOMALY_DETECTION_MODEL1', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737148398475,"interpreter":"python.medium","endTime":1737148402162,"results":[{"message":"\nModel Name: ANOMALY_DETECTION_MODEL1\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: ANOMALY_DETECTION\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                            ON\n5           SVMS_CONV_TOLERANCE                         .0001\n6          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n7             SVMS_OUTLIER_RATE                          0.01\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR               10\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              16\n2       NUM_ROWS            4500\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n    TARGET_VALUE       ATTRIBUTE_NAME ATTRIBUTE_SUBNAME       ATTRIBUTE_VALUE    COEF\n0              1        AFFINITY_CARD              None                  None  0.5645\n1              1    CUST_CREDIT_LIMIT              None                  None  0.9606\n2              1          CUST_GENDER              None                     F  1.0127\n3              1          CUST_GENDER              None                     M  1.1695\n4              1    CUST_INCOME_LEVEL              None       A: Below 30,000  0.0732\n5              1    CUST_INCOME_LEVEL              None    B: 30,000 - 49,999  0.1590\n6              1    CUST_INCOME_LEVEL              None    C: 50,000 - 69,999  0.1067\n7              1    CUST_INCOME_LEVEL              None    D: 70,000 - 89,999  0.0902\n8              1    CUST_INCOME_LEVEL              None   E: 90,000 - 109,999  0.1872\n9              1    CUST_INCOME_LEVEL              None  F: 110,000 - 129,999  0.2213\n10             1    CUST_INCOME_LEVEL              None  G: 130,000 - 149,999  0.1805\n11             1    CUST_INCOME_LEVEL              None  H: 150,000 - 169,999  0.2187\n12             1    CUST_INCOME_LEVEL              None  I: 170,000 - 189,999  0.2434\n13             1    CUST_INCOME_LEVEL              None  J: 190,000 - 249,999  0.3005\n14             1    CUST_INCOME_LEVEL              None  K: 250,000 - 299,999  0.1674\n15             1    CUST_INCOME_LEVEL              None  L: 300,000 and above  0.2339\n16             1  CUST_MARITAL_STATUS              None               Divorc.  0.4683\n17             1  CUST_MARITAL_STATUS              None               Mabsent  0.0607\n18             1  CUST_MARITAL_STATUS              None                Mar-AF  0.0025\n19             1  CUST_MARITAL_STATUS              None               Married  0.7758\n20             1  CUST_MARITAL_STATUS              None                NeverM  0.6492\n21             1  CUST_MARITAL_STATUS              None                Separ.  0.1099\n22             1  CUST_MARITAL_STATUS              None               Widowed  0.1158\n23             1   CUST_YEAR_OF_BIRTH              None                  None  1.5147\n24             1            EDUCATION              None                  10th  0.0861\n25             1            EDUCATION              None                  11th  0.0894\n26             1            EDUCATION              None                  12th  0.0394\n27             1            EDUCATION              None               1st-4th  0.0125\n28             1            EDUCATION              None               5th-6th  0.0293\n29             1            EDUCATION              None               7th-8th  0.0679\n30             1            EDUCATION              None                   9th  0.0598\n31             1            EDUCATION              None               < Bach.  0.4503\n32             1            EDUCATION              None               Assoc-A  0.1148\n33             1            EDUCATION              None               Assoc-V  0.1374\n34             1            EDUCATION              None                 Bach.  0.3364\n35             1            EDUCATION              None               HS-grad  0.5553\n36             1            EDUCATION              None               Masters  0.1206\n37             1            EDUCATION              None                   PhD  0.0291\n38             1            EDUCATION              None               Presch.  0.0025\n39             1            EDUCATION              None                Profsc  0.0515\n40             1       HOUSEHOLD_SIZE              None                     1  0.3351\n41             1       HOUSEHOLD_SIZE              None                     2  0.5915\n42             1       HOUSEHOLD_SIZE              None                     3  0.6005\n43             1       HOUSEHOLD_SIZE              None                   4-5  0.1649\n44             1       HOUSEHOLD_SIZE              None                   6-8  0.0999\n45             1       HOUSEHOLD_SIZE              None                    9+  0.3904\n46             1           OCCUPATION              None                     ?  0.1502\n47             1           OCCUPATION              None               Armed-F  0.0016\n48             1           OCCUPATION              None               Cleric.  0.2684\n49             1           OCCUPATION              None                Crafts  0.2226\n50             1           OCCUPATION              None                 Exec.  0.2402\n51             1           OCCUPATION              None               Farming  0.0761\n52             1           OCCUPATION              None               Handler  0.0896\n53             1           OCCUPATION              None               House-s  0.0152\n54             1           OCCUPATION              None               Machine  0.1431\n55             1           OCCUPATION              None                 Other  0.2398\n56             1           OCCUPATION              None                 Prof.  0.2754\n57             1           OCCUPATION              None               Protec.  0.0420\n58             1           OCCUPATION              None                 Sales  0.2278\n59             1           OCCUPATION              None               TechSup  0.1035\n60             1           OCCUPATION              None               Transp.  0.0866\n61             1        YRS_RESIDENCE              None                  None  0.8047\n62             1          Y_BOX_GAMES              None                  None  0.6311\n63             1                 None              None                  None -1.0000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/support-vector-machine.html\" target=\"_blank\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> 'SVMS_EPSILON' : '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> 'SVMS_BATCH_ROWS' : '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> 'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> 'SVMS_CONV_TOLERANCE': '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> 'SVMS_NUM_PIVOTS': '200' "],"enabled":true,"result":{"startTime":1737148544569,"interpreter":"md.medium","endTime":1737148545011,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/support-vector-machine.html\" target=\"_blank\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' : '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE': '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS': '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build the SVM model using the specific settings","message":["%python","","try:","    oml.drop(model=\"ANOMALY_DETECTION_MODEL1\")","except:","    pass","","odm_settings = {'SVMS_OUTLIER_RATE'  : '0.01',","                'SVMS_REGULARIZER'   : 'SVMS_REGULARIZER_L1',","                'SVMS_CONV_TOLERANCE': '0.001'","                }","","svm_mod = oml.svm(\"anomaly_detection\", **odm_settings)","svm_mod.fit(CUST_DF, None, model_name = 'ANOMALY_DETECTION_MODEL1', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737148403553,"interpreter":"python.medium","endTime":1737148406331,"results":[{"message":"\nModel Name: ANOMALY_DETECTION_MODEL1\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: ANOMALY_DETECTION\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                            ON\n5           SVMS_CONV_TOLERANCE                         0.001\n6          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n7             SVMS_OUTLIER_RATE                          0.01\n8              SVMS_REGULARIZER           SVMS_REGULARIZER_L1\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR               10\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              14\n2       NUM_ROWS            4500\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n    TARGET_VALUE       ATTRIBUTE_NAME ATTRIBUTE_SUBNAME       ATTRIBUTE_VALUE    COEF\n0              1        AFFINITY_CARD              None                  None  0.5644\n1              1    CUST_CREDIT_LIMIT              None                  None  0.9607\n2              1          CUST_GENDER              None                     F  1.0127\n3              1          CUST_GENDER              None                     M  1.1695\n4              1    CUST_INCOME_LEVEL              None       A: Below 30,000  0.0732\n5              1    CUST_INCOME_LEVEL              None    B: 30,000 - 49,999  0.1590\n6              1    CUST_INCOME_LEVEL              None    C: 50,000 - 69,999  0.1067\n7              1    CUST_INCOME_LEVEL              None    D: 70,000 - 89,999  0.0903\n8              1    CUST_INCOME_LEVEL              None   E: 90,000 - 109,999  0.1872\n9              1    CUST_INCOME_LEVEL              None  F: 110,000 - 129,999  0.2211\n10             1    CUST_INCOME_LEVEL              None  G: 130,000 - 149,999  0.1805\n11             1    CUST_INCOME_LEVEL              None  H: 150,000 - 169,999  0.2187\n12             1    CUST_INCOME_LEVEL              None  I: 170,000 - 189,999  0.2435\n13             1    CUST_INCOME_LEVEL              None  J: 190,000 - 249,999  0.3007\n14             1    CUST_INCOME_LEVEL              None  K: 250,000 - 299,999  0.1674\n15             1    CUST_INCOME_LEVEL              None  L: 300,000 and above  0.2339\n16             1  CUST_MARITAL_STATUS              None               Divorc.  0.4682\n17             1  CUST_MARITAL_STATUS              None               Mabsent  0.0607\n18             1  CUST_MARITAL_STATUS              None                Mar-AF  0.0025\n19             1  CUST_MARITAL_STATUS              None               Married  0.7758\n20             1  CUST_MARITAL_STATUS              None                NeverM  0.6491\n21             1  CUST_MARITAL_STATUS              None                Separ.  0.1099\n22             1  CUST_MARITAL_STATUS              None               Widowed  0.1158\n23             1   CUST_YEAR_OF_BIRTH              None                  None  1.5147\n24             1            EDUCATION              None                  10th  0.0860\n25             1            EDUCATION              None                  11th  0.0894\n26             1            EDUCATION              None                  12th  0.0394\n27             1            EDUCATION              None               1st-4th  0.0125\n28             1            EDUCATION              None               5th-6th  0.0293\n29             1            EDUCATION              None               7th-8th  0.0679\n30             1            EDUCATION              None                   9th  0.0598\n31             1            EDUCATION              None               < Bach.  0.4502\n32             1            EDUCATION              None               Assoc-A  0.1148\n33             1            EDUCATION              None               Assoc-V  0.1374\n34             1            EDUCATION              None                 Bach.  0.3364\n35             1            EDUCATION              None               HS-grad  0.5554\n36             1            EDUCATION              None               Masters  0.1205\n37             1            EDUCATION              None                   PhD  0.0291\n38             1            EDUCATION              None               Presch.  0.0025\n39             1            EDUCATION              None                Profsc  0.0515\n40             1       HOUSEHOLD_SIZE              None                     1  0.3351\n41             1       HOUSEHOLD_SIZE              None                     2  0.5914\n42             1       HOUSEHOLD_SIZE              None                     3  0.6005\n43             1       HOUSEHOLD_SIZE              None                   4-5  0.1648\n44             1       HOUSEHOLD_SIZE              None                   6-8  0.1000\n45             1       HOUSEHOLD_SIZE              None                    9+  0.3904\n46             1           OCCUPATION              None                     ?  0.1501\n47             1           OCCUPATION              None               Armed-F  0.0016\n48             1           OCCUPATION              None               Cleric.  0.2684\n49             1           OCCUPATION              None                Crafts  0.2226\n50             1           OCCUPATION              None                 Exec.  0.2402\n51             1           OCCUPATION              None               Farming  0.0761\n52             1           OCCUPATION              None               Handler  0.0897\n53             1           OCCUPATION              None               House-s  0.0152\n54             1           OCCUPATION              None               Machine  0.1431\n55             1           OCCUPATION              None                 Other  0.2398\n56             1           OCCUPATION              None                 Prof.  0.2754\n57             1           OCCUPATION              None               Protec.  0.0420\n58             1           OCCUPATION              None                 Sales  0.2278\n59             1           OCCUPATION              None               TechSup  0.1035\n60             1           OCCUPATION              None               Transp.  0.0866\n61             1        YRS_RESIDENCE              None                  None  0.8046\n62             1          Y_BOX_GAMES              None                  None  0.6312\n63             1                 None              None                  None -1.0000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke predict() to score data for anomalies","message":["%python","","RES_DF = svm_mod.predict(CUST_DF, supplemental_cols = CUST_DF, proba = True)"],"enabled":true,"result":{"startTime":1737148406842,"interpreter":"python.medium","endTime":1737148407371,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Anomalies are indicated by PREDICTION = 0, display a few rows flagged as anomalies","message":["%python","","z.show(RES_DF[RES_DF['PREDICTION']==0][[\"PREDICTION\",\"PROBABILITY\"] + CUST_DF.columns].head())"],"enabled":true,"result":{"startTime":1737148407847,"interpreter":"python.medium","endTime":1737148408499,"results":[{"message":"PREDICTION\tPROBABILITY\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t0.5736932463708171\t102419\tF\tWidowed\t1940\tF: 110,000 - 129,999\t10000\t7th-8th\t0\t9+\tFarming\t2\t0\n0\t0.5576556568546509\t100930\tF\tWidowed\t1922\tF: 110,000 - 129,999\t5000\tBach.\t0\t2\tProf.\t0\t0\n0\t0.5444671787206659\t102922\tF\tWidowed\t1931\tE: 90,000 - 109,999\t1500\t< Bach.\t0\t2\tTechSup\t4\t0\n0\t0.5092856272191477\t102802\tF\tNeverM\t1939\tI: 170,000 - 189,999\t1500\tAssoc-A\t0\t6-8\tProf.\t5\t0\n0\t0.6276574931464398\t101434\tF\tWidowed\t1927\tD: 70,000 - 89,999\t3000\t7th-8th\t0\t2\tOther\t6\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"CUST_MARITAL_STATUS\",\"Bin(PROBABILITY)\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"Bin(PROBABILITY)\",\"CUST_MARITAL_STATUS\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot the frequency distribution of the anomaly probability brackets against marital status","message":["%python","","RES_DF = RES_DF.concat(RES_DF['PROBABILITY'].cut(10))","DF = RES_DF.crosstab(['Bin(PROBABILITY)', \"CUST_MARITAL_STATUS\"])","z.show(DF.sort_values('Bin(PROBABILITY)'))"],"enabled":true,"result":{"startTime":1737148408962,"interpreter":"python.medium","endTime":1737148409792,"results":[{"message":"Bin(PROBABILITY)\tCUST_MARITAL_STATUS\tcount\n(0.5, 0.546]\tDivorc.\t7\n(0.5, 0.546]\tMabsent\t4\n(0.5, 0.546]\tMar-AF\t1\n(0.5, 0.546]\tMarried\t3\n(0.5, 0.546]\tNeverM\t2\n(0.5, 0.546]\tSepar.\t6\n(0.5, 0.546]\tWidowed\t20\n(0.546, 0.592]\tDivorc.\t7\n(0.546, 0.592]\tMabsent\t11\n(0.546, 0.592]\tMar-AF\t2\n(0.546, 0.592]\tMarried\t3\n(0.546, 0.592]\tNeverM\t1\n(0.546, 0.592]\tSepar.\t18\n(0.546, 0.592]\tWidowed\t23\n(0.592, 0.637]\tDivorc.\t21\n(0.592, 0.637]\tMabsent\t8\n(0.592, 0.637]\tMarried\t3\n(0.592, 0.637]\tNeverM\t3\n(0.592, 0.637]\tSepar.\t13\n(0.592, 0.637]\tWidowed\t24\n(0.637, 0.683]\tDivorc.\t27\n(0.637, 0.683]\tMabsent\t11\n(0.637, 0.683]\tMarried\t13\n(0.637, 0.683]\tNeverM\t10\n(0.637, 0.683]\tSepar.\t11\n(0.637, 0.683]\tWidowed\t21\n(0.683, 0.728]\tDivorc.\t48\n(0.683, 0.728]\tMabsent\t12\n(0.683, 0.728]\tMarried\t30\n(0.683, 0.728]\tNeverM\t23\n(0.683, 0.728]\tSepar.\t13\n(0.683, 0.728]\tWidowed\t17\n(0.728, 0.774]\tDivorc.\t81\n(0.728, 0.774]\tMabsent\t10\n(0.728, 0.774]\tMarried\t66\n(0.728, 0.774]\tNeverM\t65\n(0.728, 0.774]\tSepar.\t34\n(0.728, 0.774]\tWidowed\t24\n(0.774, 0.819]\tDivorc.\t148\n(0.774, 0.819]\tMabsent\t13\n(0.774, 0.819]\tMarried\t168\n(0.774, 0.819]\tNeverM\t151\n(0.774, 0.819]\tSepar.\t21\n(0.774, 0.819]\tWidowed\t6\n(0.819, 0.865]\tDivorc.\t185\n(0.819, 0.865]\tMabsent\t3\n(0.819, 0.865]\tMarried\t413\n(0.819, 0.865]\tNeverM\t390\n(0.819, 0.865]\tSepar.\t18\n(0.819, 0.865]\tWidowed\t1\n(0.865, 0.911]\tDivorc.\t83\n(0.865, 0.911]\tMabsent\t3\n(0.865, 0.911]\tMarried\t770\n(0.865, 0.911]\tNeverM\t590\n(0.911, 0.956]\tDivorc.\t8\n(0.911, 0.956]\tMarried\t565\n(0.911, 0.956]\tNeverM\t268\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the top 5 most anomalous cases","message":["%python","","z.show(RES_DF[RES_DF['PREDICTION']==0][[\"PREDICTION\",\"PROBABILITY\", \"Bin(PROBABILITY)\"] + RES_DF.columns].sort_values('PROBABILITY', ascending = False).head(5))"],"enabled":true,"result":{"startTime":1737148410252,"interpreter":"python.medium","endTime":1737148411138,"results":[{"message":"PREDICTION\tPROBABILITY\tBin(PROBABILITY)\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY\tBin(PROBABILITY)\n0\t0.7574043635917386\t(0.728, 0.774]\t103154\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.7574043635917386\t(0.728, 0.774]\n0\t0.7574043635917386\t(0.728, 0.774]\t100199\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.7574043635917386\t(0.728, 0.774]\n0\t0.7331582224784821\t(0.728, 0.774]\t102948\tF\tWidowed\t1927\tE: 90,000 - 109,999\t1500\t10th\t0\t9+\tSales\t2\t0\t0\t0.7331582224784821\t(0.728, 0.774]\n0\t0.6680564238136353\t(0.637, 0.683]\t101137\tF\tWidowed\t1935\tH: 150,000 - 169,999\t1500\t10th\t0\t9+\tOther\t3\t0\t0\t0.6680564238136353\t(0.637, 0.683]\n0\t0.6559101468486637\t(0.637, 0.683]\t104359\tF\tWidowed\t1927\tD: 70,000 - 89,999\t1500\t7th-8th\t0\t2\tOther\t6\t0\t0\t0.6559101468486637\t(0.637, 0.683]\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View Prediction Details using topN_attrs option in python","message":["%python","","RES_DF = svm_mod.predict(CUST_DF, supplemental_cols = CUST_DF[['CUST_ID']], proba = True, topN_attrs = True)","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1737148411612,"interpreter":"python.medium","endTime":1737148450341,"results":[{"message":"CUST_ID\tPREDICTION\tPROBABILITY\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100100\t1\t0.8080396719171986\tAFFINITY_CARD\t1\t.018\tCUST_CREDIT_LIMIT\t10000\t.016\tOCCUPATION\tProf.\t.01\tCUST_YEAR_OF_BIRTH\t1959\t-.014\tY_BOX_GAMES\t0\t-.024\n100200\t1\t0.9040083901000975\tCUST_YEAR_OF_BIRTH\t1983\t.024\tY_BOX_GAMES\t1\t.009\tCUST_CREDIT_LIMIT\t9000\t.004\tOCCUPATION\tOther\t.002\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-.007\n100300\t1\t0.9103858239407895\tCUST_CREDIT_LIMIT\t10000\t.008\tOCCUPATION\tProf.\t.005\tAFFINITY_CARD\t1\t.005\tCUST_YEAR_OF_BIRTH\t1961\t-.004\tCUST_INCOME_LEVEL\tG: 130,000 - 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 from d,","         XMLTable('/Details'","                  PASSING d.anomalyDetails","                  COLUMNS","                  class varchar2(16) PATH '@class') c,","         XMLTable('/Details/Attribute'","                  PASSING d.anomalyDetails","                  COLUMNS","                  name varchar2(16) PATH '@name',","                  weight varchar2(30) PATH '@weight') x","    WHERE c.class='0'","ORDER BY CUST_ID, name FETCH FIRST 10 ROWS 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":"zeppelin","isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4Py Association Rules Apriori","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738092835015,"interpreter":"md.low","endTime":1738092835162,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Association Rules (Market Basket Analysis)","","The Association Rules machine learning technique, also known as Market Basket Analysis, can  be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing.  In his notebook, you will build association rules models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1738092835265,"interpreter":"md.low","endTime":1738092835373,"results":[{"message":"<h1 id=\"association-rules-market-basket-analysis\">Association Rules (Market Basket Analysis)<\/h1>\n<p>The Association Rules machine learning technique, also known as Market Basket Analysis, can  be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing.  In his notebook, you will build association rules models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1738092835476,"interpreter":"md.low","endTime":1738092835566,"results":[{"message":"<dl>\n<img 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target=\"_blank\">OML Association Rules<\/a>"],"enabled":true,"result":{"startTime":1738092835676,"interpreter":"md.low","endTime":1738092835767,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import oml","import pandas as pd","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1738092835874,"interpreter":"python.low","endTime":1738092840515,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get SALES proxy object and display a few rows of data","message":["%python","","SALES_DF = oml.sync(query = \"SELECT CUST_ID, PROD_ID, QUANTITY_SOLD, AMOUNT_SOLD, CHANNEL_ID, PROMO_ID FROM 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how = \"inner\", on = 'PROD_ID', suffixes = [\"\", \"\"])","SALES_TRANS_CUST = SALES_TRANS_CUST[['CUST_ID', 'PROD_NAME', 'PROD_CATEGORY']].drop_duplicates()"],"enabled":true,"result":{"startTime":1738092841121,"interpreter":"python.low","endTime":1738092841381,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display customer purchases with associated product category","message":["%python","","z.show(SALES_TRANS_CUST.head())"],"enabled":true,"result":{"startTime":1738092841488,"interpreter":"python.low","endTime":1738092841840,"results":[{"message":"CUST_ID\tPROD_NAME\tPROD_CATEGORY\n100001\tKeyboard Wrist Rest\tSoftware/Other\n100001\tO/S Documentation Set - English\tSoftware/Other\n100001\tY Box\tElectronics\n100002\tCD-RW, High Speed Pack of 5\tSoftware/Other\n100002\tO/S Documentation Set - English\tSoftware/Other\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Check the distribution of PROD_CATEGORY ","message":["%python","","z.show(SALES_TRANS_CUST.crosstab(\"PROD_CATEGORY\"))"],"enabled":true,"result":{"startTime":1738092841942,"interpreter":"python.low","endTime":1738092842483,"results":[{"message":"PROD_CATEGORY\tcount\nElectronics\t294\nHardware\t4\nPeripherals and Accessories\t1568\nSoftware/Other\t938\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Association Rules (A Priori)","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3');\">Association Rules - A Priori<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","    ","- Specify the name of a column that contains the items in a transaction. ","   When you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:  (1) `Case ID`, either categorical or numeric, and (2) `Item ID`, either categorical or numeric.","> 'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'    ","","- Specify the Minimum support for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_SUPPORT' : '0.1'","","- Specify the Minimum confidence for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_CONFIDENCE' : '0.1'","","- Specify the Maximum rule length for association rules.","    The default value is 4.  It requires a number between 2 and 20 (inclusive).","> 'ASSO_MAX_RULE_LENGTH': '4'"],"enabled":true,"result":{"startTime":1738092842586,"interpreter":"md.low","endTime":1738092842676,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-association-rules-a-priori\">Examples of possible setting overrides for Association Rules (A Priori)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3');\">Association Rules - A Priori<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the name of a column that contains the items in a transaction.\nWhen you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:  (1) <code>Case ID<\/code>, either categorical or numeric, and (2) <code>Item ID<\/code>, either categorical or numeric.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum support for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_SUPPORT' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum confidence for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_CONFIDENCE' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Maximum rule length for association rules.\nThe default value is 4.  It requires a number between 2 and 20 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MAX_RULE_LENGTH': '4'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify settings and create Association Rules model","message":["%python","","setting = {'ASSO_MIN_SUPPORT':'0.04', ","           'ASSO_MIN_CONFIDENCE':'0.1', ","           'ASSO_MAX_RULE_LENGTH': '2', ","           'ODMS_ITEM_ID_COLUMN_NAME':'PROD_NAME'}","","ar_mod = oml.ar(**setting)","ar_mod = ar_mod.fit(SALES_TRANS_CUST, case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1738092842778,"interpreter":"python.low","endTime":1738092844002,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details","message":["%python","","ar_mod"],"enabled":true,"result":{"startTime":1738092844098,"interpreter":"python.low","endTime":1738092844505,"results":[{"message":"\nAlgorithm Name: Association Rules\n\nMining Function: ASSOCIATION\n\nSettings: \n                    setting name                   setting value\n0                      ALGO_NAME  ALGO_APRIORI_ASSOCIATION_RULES\n1           ASSO_MAX_RULE_LENGTH                               2\n2            ASSO_MIN_CONFIDENCE                             0.1\n3        ASSO_MIN_REV_CONFIDENCE                               0\n4               ASSO_MIN_SUPPORT                            0.04\n5           ASSO_MIN_SUPPORT_INT                               1\n6                   ODMS_DETAILS                     ODMS_ENABLE\n7       ODMS_ITEM_ID_COLUMN_NAME                       PROD_NAME\n8   ODMS_MISSING_VALUE_TREATMENT         ODMS_MISSING_VALUE_AUTO\n9                  ODMS_SAMPLING           ODMS_SAMPLING_DISABLE\n10                     PREP_AUTO                              ON\n\nGlobal Statistics: \n      attribute name attribute value\n0      ITEMSET_COUNT              63\n1        MAX_SUPPORT         0.32234\n2           NUM_ROWS             940\n3         RULE_COUNT             104\n4  TRANSACTION_COUNT             940\n\nAttributes: \nPROD_NAME\n\nPartition: NO\n\nItemsets: \n\n     ITEMSET_ID   SUPPORT  NUMBER_OF_ITEMS                          ITEM_NAME ITEM_VALUE\n0             1  0.310638                1    18\" Flat Panel Graphics Monitor       None\n1             2  0.292553                1        CD-RW, High Speed Pack of 5       None\n2             3  0.311702                1                    Extension Cable       None\n3             4  0.305319                1                 External 8X CD-ROM       None\n4             5  0.188298                1                Keyboard Wrist Rest       None\n5             6  0.217021                1  Model SM26273 Black Ink Cartridge       None\n6             7  0.322340                1                          Mouse Pad       None\n7             8  0.195745                1      Multimedia speakers- 3\" cones       None\n8             9  0.194681                1    O/S Documentation Set - English       None\n9            10  0.318085                1           SIMM- 16MB PCMCIAII card       None\n10           11  0.321277                1                     Standard Mouse       None\n11           12  0.091489                2    18\" Flat Panel Graphics Monitor       None\n12           12  0.091489                2        CD-RW, High Speed Pack of 5       None\n13           13  0.090426                2    18\" Flat Panel Graphics Monitor       None\n14           13  0.090426                2                    Extension Cable       None\n15           14  0.082979                2    18\" Flat Panel Graphics Monitor       None\n16           14  0.082979                2                 External 8X CD-ROM       None\n17           15  0.059574                2    18\" Flat Panel Graphics Monitor       None\n18           15  0.059574                2                Keyboard Wrist Rest       None\n19           16  0.067021                2    18\" Flat Panel Graphics Monitor       None\n20           16  0.067021                2  Model SM26273 Black Ink Cartridge       None\n21           17  0.094681                2    18\" Flat Panel Graphics Monitor       None\n22           17  0.094681                2                          Mouse Pad       None\n23           18  0.055319                2    18\" Flat Panel Graphics Monitor       None\n24           18  0.055319                2      Multimedia speakers- 3\" cones       None\n25           19  0.051064                2    18\" Flat Panel Graphics Monitor       None\n26           19  0.051064                2    O/S Documentation Set - English       None\n27           20  0.154255                2    18\" Flat Panel Graphics Monitor       None\n28           20  0.154255                2           SIMM- 16MB PCMCIAII card       None\n29           21  0.095745                2    18\" Flat Panel Graphics Monitor       None\n30           21  0.095745                2                     Standard Mouse       None\n31           22  0.068085                2        CD-RW, High Speed Pack of 5       None\n32           22  0.068085                2                    Extension Cable       None\n33           23  0.153191                2        CD-RW, High Speed Pack of 5       None\n34           23  0.153191                2                 External 8X CD-ROM       None\n35           24  0.058511                2        CD-RW, High Speed Pack of 5       None\n36           24  0.058511                2                Keyboard Wrist Rest       None\n37           25  0.057447                2        CD-RW, High Speed Pack of 5       None\n38           25  0.057447                2  Model SM26273 Black Ink Cartridge       None\n39           26  0.075532                2        CD-RW, High Speed Pack of 5       None\n40           26  0.075532                2                          Mouse Pad       None\n41           27  0.055319                2        CD-RW, High Speed Pack of 5       None\n42           27  0.055319                2      Multimedia speakers- 3\" cones       None\n43           28  0.056383                2        CD-RW, High Speed Pack of 5       None\n44           28  0.056383                2    O/S Documentation Set - English       None\n45           29  0.087234                2        CD-RW, High Speed Pack of 5       None\n46           29  0.087234                2           SIMM- 16MB PCMCIAII card       None\n47           30  0.070213                2        CD-RW, High Speed Pack of 5       None\n48           30  0.070213                2                     Standard Mouse       None\n49           31  0.081915                2                    Extension Cable       None\n50           31  0.081915                2                 External 8X CD-ROM       None\n51           32  0.047872                2                    Extension Cable       None\n52           32  0.047872                2                Keyboard Wrist Rest       None\n53           33  0.063830                2                    Extension Cable       None\n54           33  0.063830                2  Model SM26273 Black Ink Cartridge       None\n55           34  0.177660                2                    Extension Cable       None\n56           34  0.177660                2                          Mouse Pad       None\n57           35  0.047872                2                    Extension Cable       None\n58           35  0.047872                2      Multimedia speakers- 3\" cones       None\n59           36  0.050000                2                    Extension Cable       None\n60           36  0.050000                2    O/S Documentation Set - English       None\n61           37  0.094681                2                    Extension Cable       None\n62           37  0.094681                2           SIMM- 16MB PCMCIAII card       None\n63           38  0.180851                2                    Extension Cable       None\n64           38  0.180851                2                     Standard Mouse       None\n65           39  0.048936                2                 External 8X CD-ROM       None\n66           39  0.048936                2                Keyboard Wrist Rest       None\n67           40  0.057447                2                 External 8X CD-ROM       None\n68           40  0.057447                2  Model SM26273 Black Ink Cartridge       None\n69           41  0.103191                2                 External 8X CD-ROM       None\n70           41  0.103191                2                          Mouse Pad       None\n71           42  0.063830                2                 External 8X CD-ROM       None\n72           42  0.063830                2      Multimedia speakers- 3\" cones       None\n73           43  0.052128                2                 External 8X CD-ROM       None\n74           43  0.052128                2    O/S Documentation Set - English       None\n75           44  0.089362                2                 External 8X CD-ROM       None\n76           44  0.089362                2           SIMM- 16MB PCMCIAII card       None\n77           45  0.075532                2                 External 8X CD-ROM       None\n78           45  0.075532                2                     Standard Mouse       None\n79           46  0.046809                2                Keyboard Wrist Rest       None\n80           46  0.046809                2                          Mouse Pad       None\n81           47  0.065957                2                Keyboard Wrist Rest       None\n82           47  0.065957                2           SIMM- 16MB PCMCIAII card       None\n83           48  0.054255                2                Keyboard Wrist Rest       None\n84           48  0.054255                2                     Standard Mouse       None\n85           49  0.077660                2  Model SM26273 Black Ink Cartridge       None\n86           49  0.077660                2                          Mouse Pad       None\n87           50  0.044681                2  Model SM26273 Black Ink Cartridge       None\n88           50  0.044681                2      Multimedia speakers- 3\" cones       None\n89           51  0.043617                2  Model SM26273 Black Ink Cartridge       None\n90           51  0.043617                2    O/S Documentation Set - English       None\n91           52  0.064894                2  Model SM26273 Black Ink Cartridge       None\n92           52  0.064894                2           SIMM- 16MB PCMCIAII card       None\n93           53  0.079787                2  Model SM26273 Black Ink Cartridge       None\n94           53  0.079787                2                     Standard Mouse       None\n95           54  0.067021                2                          Mouse Pad       None\n96           54  0.067021                2      Multimedia speakers- 3\" cones       None\n97           55  0.058511                2                          Mouse Pad       None\n98           55  0.058511                2    O/S Documentation Set - English       None\n99           56  0.091489                2                          Mouse Pad       None\n100          56  0.091489                2           SIMM- 16MB PCMCIAII card       None\n101          57  0.184043                2                          Mouse Pad       None\n102          57  0.184043                2                     Standard Mouse       None\n103          58  0.044681                2      Multimedia speakers- 3\" cones       None\n104          58  0.044681                2    O/S Documentation Set - English       None\n105          59  0.059574                2      Multimedia speakers- 3\" cones       None\n106          59  0.059574                2           SIMM- 16MB PCMCIAII card       None\n107          60  0.057447                2      Multimedia speakers- 3\" cones       None\n108          60  0.057447                2                     Standard Mouse       None\n109          61  0.062766                2    O/S Documentation Set - English       None\n110          61  0.062766                2           SIMM- 16MB PCMCIAII card       None\n111          62  0.055319                2    O/S Documentation Set - English       None\n112          62  0.055319                2                     Standard Mouse       None\n113          63  0.091489                2           SIMM- 16MB PCMCIAII card       None\n114          63  0.091489                2                     Standard Mouse       None\n\nRules: \n\n     RULE_ID  NUMBER_OF_ITEMS                           LHS_NAME LHS_VALUE                           RHS_NAME RHS_VALUE   SUPPORT  CONFIDENCE  REVCONFIDENCE      LIFT\n0          1                2        CD-RW, High Speed Pack of 5      None    18\" Flat Panel Graphics Monitor      None  0.091489    0.312727       0.294521  1.006725\n1          2                2    18\" Flat Panel Graphics Monitor      None        CD-RW, High Speed Pack of 5      None  0.091489    0.294521       0.312727  1.006725\n2          3                2                    Extension Cable      None    18\" Flat Panel Graphics Monitor      None  0.090426    0.290102       0.291096  0.933891\n3          4                2    18\" Flat Panel Graphics Monitor      None                    Extension Cable      None  0.090426    0.291096       0.290102  0.933891\n4          5                2                 External 8X CD-ROM      None    18\" Flat Panel Graphics Monitor      None  0.082979    0.271777       0.267123  0.874899\n5          6                2    18\" Flat Panel Graphics Monitor      None                 External 8X CD-ROM      None  0.082979    0.267123       0.271777  0.874899\n6          7                2                Keyboard Wrist Rest      None    18\" Flat Panel Graphics Monitor      None  0.059574    0.316384       0.191781  1.018497\n7          8                2    18\" Flat Panel Graphics Monitor      None                Keyboard Wrist Rest      None  0.059574    0.191781       0.316384  1.018497\n8          9                2  Model SM26273 Black Ink Cartridge      None    18\" Flat Panel Graphics Monitor      None  0.067021    0.308824       0.215753  0.994158\n9         10                2    18\" Flat Panel Graphics Monitor      None  Model SM26273 Black Ink Cartridge      None  0.067021    0.215753       0.308824  0.994158\n10        11                2                          Mouse Pad      None    18\" Flat Panel Graphics Monitor      None  0.094681    0.293729       0.304795  0.945567\n11        12                2    18\" Flat Panel Graphics Monitor      None                          Mouse Pad      None  0.094681    0.304795       0.293729  0.945567\n12        13                2      Multimedia speakers- 3\" cones      None    18\" Flat Panel Graphics Monitor      None  0.055319    0.282609       0.178082  0.909768\n13        14                2    18\" Flat Panel Graphics Monitor      None      Multimedia speakers- 3\" cones      None  0.055319    0.178082       0.282609  0.909768\n14        15                2    O/S Documentation Set - English      None    18\" Flat Panel Graphics Monitor      None  0.051064    0.262295       0.164384  0.844375\n15        16                2    18\" Flat Panel Graphics Monitor      None    O/S Documentation Set - English      None  0.051064    0.164384       0.262295  0.844375\n16        17                2           SIMM- 16MB PCMCIAII card      None    18\" Flat Panel Graphics Monitor      None  0.154255    0.484950       0.496575  1.561140\n17        18                2    18\" Flat Panel Graphics Monitor      None           SIMM- 16MB PCMCIAII card      None  0.154255    0.496575       0.484950  1.561140\n18        19                2                     Standard Mouse      None    18\" Flat Panel Graphics Monitor      None  0.095745    0.298013       0.308219  0.959358\n19        20                2    18\" Flat Panel Graphics Monitor      None                     Standard Mouse      None  0.095745    0.308219       0.298013  0.959358\n20        21                2                    Extension Cable      None        CD-RW, High Speed Pack of 5      None  0.068085    0.218430       0.232727  0.746634\n21        22                2        CD-RW, High Speed Pack of 5      None                    Extension Cable      None  0.068085    0.232727       0.218430  0.746634\n22        23                2                 External 8X CD-ROM      None        CD-RW, High Speed Pack of 5      None  0.153191    0.501742       0.523636  1.715046\n23        24                2        CD-RW, High Speed Pack of 5      None                 External 8X CD-ROM      None  0.153191    0.523636       0.501742  1.715046\n24        25                2                Keyboard Wrist Rest      None        CD-RW, High Speed Pack of 5      None  0.058511    0.310734       0.200000  1.062147\n25        26                2        CD-RW, High Speed Pack of 5      None                Keyboard Wrist Rest      None  0.058511    0.200000       0.310734  1.062147\n26        27                2  Model SM26273 Black Ink Cartridge      None        CD-RW, High Speed Pack of 5      None  0.057447    0.264706       0.196364  0.904813\n27        28                2        CD-RW, High Speed Pack of 5      None  Model SM26273 Black Ink Cartridge      None  0.057447    0.196364       0.264706  0.904813\n28        29                2                          Mouse Pad      None        CD-RW, High Speed Pack of 5      None  0.075532    0.234323       0.258182  0.800960\n29        30                2        CD-RW, High Speed Pack of 5      None                          Mouse Pad      None  0.075532    0.258182       0.234323  0.800960\n30        31                2      Multimedia speakers- 3\" cones      None        CD-RW, High Speed Pack of 5      None  0.055319    0.282609       0.189091  0.966008\n31        32                2        CD-RW, High Speed Pack of 5      None      Multimedia speakers- 3\" cones      None  0.055319    0.189091       0.282609  0.966008\n32        33                2    O/S Documentation Set - English      None        CD-RW, High Speed Pack of 5      None  0.056383    0.289617       0.192727  0.989965\n33        34                2        CD-RW, High Speed Pack of 5      None    O/S Documentation Set - English      None  0.056383    0.192727       0.289617  0.989965\n34        35                2           SIMM- 16MB PCMCIAII card      None        CD-RW, High Speed Pack of 5      None  0.087234    0.274247       0.298182  0.937428\n35        36                2        CD-RW, High Speed Pack of 5      None           SIMM- 16MB PCMCIAII card      None  0.087234    0.298182       0.274247  0.937428\n36        37                2                     Standard Mouse      None        CD-RW, High Speed Pack of 5      None  0.070213    0.218543       0.240000  0.747020\n37        38                2        CD-RW, High Speed Pack of 5      None                     Standard Mouse      None  0.070213    0.240000       0.218543  0.747020\n38        39                2                 External 8X CD-ROM      None                    Extension Cable      None  0.081915    0.268293       0.262799  0.860734\n39        40                2                    Extension Cable      None                 External 8X CD-ROM      None  0.081915    0.262799       0.268293  0.860734\n40        41                2                Keyboard Wrist Rest      None                    Extension Cable      None  0.047872    0.254237       0.153584  0.815642\n41        42                2                    Extension Cable      None                Keyboard Wrist Rest      None  0.047872    0.153584       0.254237  0.815642\n42        43                2  Model SM26273 Black Ink Cartridge      None                    Extension Cable      None  0.063830    0.294118       0.204778  0.943586\n43        44                2                    Extension Cable      None  Model SM26273 Black Ink Cartridge      None  0.063830    0.204778       0.294118  0.943586\n44        45                2                          Mouse Pad      None                    Extension Cable      None  0.177660    0.551155       0.569966  1.768211\n45        46                2                    Extension Cable      None                          Mouse Pad      None  0.177660    0.569966       0.551155  1.768211\n46        47                2      Multimedia speakers- 3\" cones      None                    Extension Cable      None  0.047872    0.244565       0.153584  0.784612\n47        48                2                    Extension Cable      None      Multimedia speakers- 3\" cones      None  0.047872    0.153584       0.244565  0.784612\n48        49                2    O/S Documentation Set - English      None                    Extension Cable      None  0.050000    0.256831       0.160410  0.823962\n49        50                2                    Extension Cable      None    O/S Documentation Set - English      None  0.050000    0.160410       0.256831  0.823962\n50        51                2           SIMM- 16MB PCMCIAII card      None                    Extension Cable      None  0.094681    0.297659       0.303754  0.954947\n51        52                2                    Extension Cable      None           SIMM- 16MB PCMCIAII card      None  0.094681    0.303754       0.297659  0.954947\n52        53                2                     Standard Mouse      None                    Extension Cable      None  0.180851    0.562914       0.580205  1.805935\n53        54                2                    Extension Cable      None                     Standard Mouse      None  0.180851    0.580205       0.562914  1.805935\n54        55                2                Keyboard Wrist Rest      None                 External 8X CD-ROM      None  0.048936    0.259887       0.160279  0.851198\n55        56                2                 External 8X CD-ROM      None                Keyboard Wrist Rest      None  0.048936    0.160279       0.259887  0.851198\n56        57                2  Model SM26273 Black Ink Cartridge      None                 External 8X CD-ROM      None  0.057447    0.264706       0.188153  0.866981\n57        58                2                 External 8X CD-ROM      None  Model SM26273 Black Ink Cartridge      None  0.057447    0.188153       0.264706  0.866981\n58        59                2                          Mouse Pad      None                 External 8X CD-ROM      None  0.103191    0.320132       0.337979  1.048516\n59        60                2                 External 8X CD-ROM      None                          Mouse Pad      None  0.103191    0.337979       0.320132  1.048516\n60        61                2      Multimedia speakers- 3\" cones      None                 External 8X CD-ROM      None  0.063830    0.326087       0.209059  1.068020\n61        62                2                 External 8X CD-ROM      None      Multimedia speakers- 3\" cones      None  0.063830    0.209059       0.326087  1.068020\n62        63                2    O/S Documentation Set - English      None                 External 8X CD-ROM      None  0.052128    0.267760       0.170732  0.876983\n63        64                2                 External 8X CD-ROM      None    O/S Documentation Set - English      None  0.052128    0.170732       0.267760  0.876983\n64        65                2           SIMM- 16MB PCMCIAII card      None                 External 8X CD-ROM      None  0.089362    0.280936       0.292683  0.920140\n65        66                2                 External 8X CD-ROM      None           SIMM- 16MB PCMCIAII card      None  0.089362    0.292683       0.280936  0.920140\n66        67                2                     Standard Mouse      None                 External 8X CD-ROM      None  0.075532    0.235099       0.247387  0.770012\n67        68                2                 External 8X CD-ROM      None                     Standard Mouse      None  0.075532    0.247387       0.235099  0.770012\n68        69                2                          Mouse Pad      None                Keyboard Wrist Rest      None  0.046809    0.145215       0.248588  0.771196\n69        70                2                Keyboard Wrist Rest      None                          Mouse Pad      None  0.046809    0.248588       0.145215  0.771196\n70        71                2           SIMM- 16MB PCMCIAII card      None                Keyboard Wrist Rest      None  0.065957    0.207358       0.350282  1.101223\n71        72                2                Keyboard Wrist Rest      None           SIMM- 16MB PCMCIAII card      None  0.065957    0.350282       0.207358  1.101223\n72        73                2                     Standard Mouse      None                Keyboard Wrist Rest      None  0.054255    0.168874       0.288136  0.896846\n73        74                2                Keyboard Wrist Rest      None                     Standard Mouse      None  0.054255    0.288136       0.168874  0.896846\n74        75                2                          Mouse Pad      None  Model SM26273 Black Ink Cartridge      None  0.077660    0.240924       0.357843  1.110140\n75        76                2  Model SM26273 Black Ink Cartridge      None                          Mouse Pad      None  0.077660    0.357843       0.240924  1.110140\n76        77                2      Multimedia speakers- 3\" cones      None  Model SM26273 Black Ink Cartridge      None  0.044681    0.228261       0.205882  1.051790\n77        78                2  Model SM26273 Black Ink Cartridge      None      Multimedia speakers- 3\" cones      None  0.044681    0.205882       0.228261  1.051790\n78        79                2    O/S Documentation Set - English      None  Model SM26273 Black Ink Cartridge      None  0.043617    0.224044       0.200980  1.032358\n79        80                2  Model SM26273 Black Ink Cartridge      None    O/S Documentation Set - English      None  0.043617    0.200980       0.224044  1.032358\n80        81                2           SIMM- 16MB PCMCIAII card      None  Model SM26273 Black Ink Cartridge      None  0.064894    0.204013       0.299020  0.940062\n81        82                2  Model SM26273 Black Ink Cartridge      None           SIMM- 16MB PCMCIAII card      None  0.064894    0.299020       0.204013  0.940062\n82        83                2                     Standard Mouse      None  Model SM26273 Black Ink Cartridge      None  0.079787    0.248344       0.367647  1.144332\n83        84                2  Model SM26273 Black Ink Cartridge      None                     Standard Mouse      None  0.079787    0.367647       0.248344  1.144332\n84        85                2      Multimedia speakers- 3\" cones      None                          Mouse Pad      None  0.067021    0.342391       0.207921  1.062204\n85        86                2                          Mouse Pad      None      Multimedia speakers- 3\" cones      None  0.067021    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Bach.\t1041\nAssoc-A\t172\nAssoc-V\t196\nBach.\t779\nHS-grad\t1462\nMasters\t190\nPhD\t49\nPresch.\t3\nProfsc\t94\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select the columns to consider for attribute importance analysis","message":["%python","","DATA_DF = SUPP_DF[[\"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", ","                   \"CUST_ID\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                   \"HOUSEHOLD_SIZE\", \"OCCUPATION\",   \"OS_DOC_SET_KANJI\", ","                   \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", 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href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","    ","* Specify wheter to do sampling for the build data.  It requires the options `ODMS_SAMPLING_ENABLE` or  `ODMS_SAMPLING_DISABLE`.   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The default is <code>ODMS_SAMPLING_DISABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_SAMPLING' : 'ODMS_SAMPLING_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Compute Attribute Importance without sampling","message":["%python","","setting = {'ODMS_SAMPLING':'ODMS_SAMPLING_DISABLE'}","ai_mod = oml.ai(**setting)","ai_mod = ai_mod.fit(train_x, train_y, case_id = 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification DT","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737150579461,"interpreter":"md.low","endTime":1737150579927,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Predicting Target Customers using Classification Decision Trees","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","","---"],"enabled":true,"result":{"startTime":1737150580401,"interpreter":"md.low","endTime":1737150580855,"results":[{"message":"<h1 id=\"predicting-target-customers-using-classification-decision-trees\">Predicting Target Customers using Classification Decision Trees<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737150581341,"interpreter":"md.low","endTime":1737150581788,"results":[{"message":"<dl>\n<img 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number of rows and columns","message":["%python","","DEMO.shape"],"enabled":true,"result":{"startTime":1737150585264,"interpreter":"python.low","endTime":1737150585724,"results":[{"message":"(4500, 14)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 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responders","message":["%python","","z.show(DEMO.crosstab('AFFINITY_CARD'))"],"enabled":true,"result":{"startTime":1737150586230,"interpreter":"python.low","endTime":1737150586890,"results":[{"message":"AFFINITY_CARD\tcount\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"selectedId\":\"AFFINITY_CARD\",\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}],\"borderWidth\":0},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\",\"count\"],\"version\":1},\"scatterplot\":{\"groupByColumns\":[],\"showSeriesY\":[],\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\",\"count\"],\"version\":1},\"line\":{\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab(['HOUSEHOLD_SIZE', 'AFFINITY_CARD']))"],"enabled":true,"result":{"startTime":1737150587358,"interpreter":"python.low","endTime":1737150587975,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_CARD\tcount\n1\t0\t681\n1\t1\t11\n2\t0\t1040\n2\t1\t109\n3\t0\t973\n3\t1\t814\n9+\t0\t476\n9+\t1\t29\n4-5\t0\t112\n4-5\t1\t107\n6-8\t0\t146\n6-8\t1\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an OML Data Frame with desired columns for analysis","message":["%python","","DEMO_DF = DEMO[[\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", "," \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", "," \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\"]]"," "],"enabled":true,"result":{"startTime":1737150588432,"interpreter":"python.low","endTime":1737150588904,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first few records of DEMO_DF","message":["%python","","z.show(DEMO_DF.head())"],"enabled":true,"result":{"startTime":1737150589368,"interpreter":"python.low","endTime":1737150589912,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n102547\t0\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n101050\t0\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n100040\t0\t0\t1\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n102117\t0\t1\t0\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n101074\t0\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create train and test data (60/40 split) ","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X = TEST","TEST_Y = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1737150590378,"interpreter":"python.low","endTime":1737150591139,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Decision Tree model to predict AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1737150591597,"interpreter":"md.low","endTime":1737150592040,"results":[{"message":"<h3 id=\"build-a-decision-tree-model-to-predict-affinity_card\">Build a Decision Tree model to predict AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree Model using default settings","message":["%python","","try:","    oml.drop(model = 'DT_CLAS_MODEL') ","except:","    print(\"No such model\")","","setting = dict()","dt_mod = oml.dt(**setting)","dt_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'DT_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737150592501,"interpreter":"python.low","endTime":1737150594897,"results":[{"message":"\nModel Name: DT_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Decision Tree\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_DECISION_TREE\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n6                      PREP_AUTO                       ON\n7           TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n8            TREE_TERM_MAX_DEPTH                        7\n9          TREE_TERM_MINPCT_NODE                      .05\n10        TREE_TERM_MINPCT_SPLIT                       .1\n11         TREE_TERM_MINREC_NODE                       10\n12        TREE_TERM_MINREC_SPLIT                       20\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS             2725\n\nAttributes: \nEDUCATION\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nDistributions: \n\n    NODE_ID  TARGET_VALUE  TARGET_COUNT\n0         0             0          2088\n1         0             1           637\n2         1             0           676\n3         1             1           549\n4         2             0           108\n5         2             1           252\n6         3             0           568\n7         3             1           297\n8         4             0          1412\n9         4             1            88\n10        5             0            50\n11        5             1            23\n12        6             0          1362\n13        6             1            65\n14        7             0            89\n15        7             1           244\n16        8             0            19\n17        8             1             8\n18        9             0           188\n19        9             1            38\n20       10             0           380\n21       10             1           259\n22       11             0            20\n23       12             0            30\n24       12             1            23\n25       13             0           826\n26       13             1             6\n27       14             0           536\n28       14             1            59\n\nNodes: \n\n    parent  ...                                        full.splits\n0      0.0  ...                    (HOUSEHOLD_SIZE IN (\"3\" \"4-5\"))\n1      0.0  ...           (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\"))\n2      1.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n3      1.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n4      2.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n5      2.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n6      3.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n7      3.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n8      4.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n9      4.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n10     5.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n11     5.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n12     6.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n13     6.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n14     NaN  ...                                                  (\n\n[15 rows x 7 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","dt_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737150595363,"interpreter":"python.low","endTime":1737150595980,"results":[{"message":"0.824789\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for DT ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/decision-tree.html\" target=\"_blank\">Decision Tree<\/a> ","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'","* Specify Tree impurity metric for Decision Tree. Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> 'TREE_IMPURITY_METRIC' : 'TREE_IMPURITY_GINI'","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node). For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> 'TREE_TERM_MAX_DEPTH' : '7'","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.  It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> 'TREE_TERM_MINPCT_NODE' : '0.05'","* Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.  It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> 'TREE_TERM_MINPCT_SPLIT' : '0.1'","* Specify The minimum number of rows in a node. It requires a number greater than or equal to zero. The default is 10.      ","> 'TREE_TERM_MINREC_NODE' : '10'","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.  No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","> 'TREE_TERM_MINREC_SPLIT' : '20'","* Specify the maximum number of bins for each attribute. For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","> 'CLAS_MAX_SUP_BINS' : '32'"],"enabled":true,"result":{"startTime":1737150596443,"interpreter":"md.low","endTime":1737150596908,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-dt\">Examples of possible setting overrides for DT<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/decision-tree.html\" target=\"_blank\">Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<\/ul>\n<ul>\n<li>Specify a row weight column<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify Tree impurity metric for Decision Tree. Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_IMPURITY_METRIC' : 'TREE_IMPURITY_GINI'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node). For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MAX_DEPTH' : '7'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.  It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINPCT_NODE' : '0.05'<\/p>\n<\/blockquote>\n<ul>\n<li>Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.  It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINPCT_SPLIT' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node. It requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINREC_NODE' : '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.  No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINREC_SPLIT' : '20'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute. For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>'CLAS_MAX_SUP_BINS' : '32'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree Model using explicit settings","message":["%python","","try:","    oml.drop(model = 'DT_CLAS_MODEL') ","except:","    print(\"No such model\")","","settings = {'TREE_IMPURITY_METRIC': 'TREE_IMPURITY_GINI', ","            'TREE_TERM_MAX_DEPTH': '7',","            'TREE_TERM_MINPCT_NODE': '0.05', ","            'TREE_TERM_MINPCT_SPLIT': '0.1',","            'TREE_TERM_MINREC_NODE': '10',","            'TREE_TERM_MINREC_SPLIT': '20',","            'CLAS_MAX_SUP_BINS': '32'} ","","","dt_mod = oml.dt(**settings)","dt_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'DT_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737150597380,"interpreter":"python.low","endTime":1737150599593,"results":[{"message":"\nModel Name: DT_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Decision Tree\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_DECISION_TREE\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n6                      PREP_AUTO                       ON\n7           TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n8            TREE_TERM_MAX_DEPTH                        7\n9          TREE_TERM_MINPCT_NODE                     0.05\n10        TREE_TERM_MINPCT_SPLIT                      0.1\n11         TREE_TERM_MINREC_NODE                       10\n12        TREE_TERM_MINREC_SPLIT                       20\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS             2725\n\nAttributes: \nEDUCATION\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nDistributions: \n\n    NODE_ID  TARGET_VALUE  TARGET_COUNT\n0         0             0          2088\n1         0             1           637\n2         1             0           676\n3         1             1           549\n4         2             0           108\n5         2             1           252\n6         3             0           568\n7         3             1           297\n8         4             0          1412\n9         4             1            88\n10        5             0            50\n11        5             1            23\n12        6             0          1362\n13        6             1            65\n14        7             0            89\n15        7             1           244\n16        8             0            19\n17        8             1             8\n18        9             0           188\n19        9             1            38\n20       10             0           380\n21       10             1           259\n22       11             0            20\n23       12             0            30\n24       12             1            23\n25       13             0           826\n26       13             1             6\n27       14             0           536\n28       14             1            59\n\nNodes: \n\n    parent  ...                                        full.splits\n0      0.0  ...                    (HOUSEHOLD_SIZE IN (\"3\" \"4-5\"))\n1      0.0  ...           (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\"))\n2      1.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n3      1.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n4      2.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n5      2.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n6      3.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n7      3.0  ...  (HOUSEHOLD_SIZE IN (\"3\" \"4-5\")) AND (OCCUPATIO...\n8      4.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n9      4.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n10     5.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n11     5.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n12     6.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n13     6.0  ...  (HOUSEHOLD_SIZE IN (\"1\" \"2\" \"6-8\" \"9+\")) AND (...\n14     NaN  ...                                                  (\n\n[15 rows x 7 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","","dt_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737150600059,"interpreter":"python.low","endTime":1737150600595,"results":[{"message":"0.824789\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Score test data adding supplemental columns","message":["%python","","case_id = 'CUST_ID'","RES_DF = dt_mod.predict(TEST_X, supplemental_cols = TEST_X)","RES_PROB = dt_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","","# Join the entire result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1737150601066,"interpreter":"python.low","endTime":1737150601785,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display confusion matrix, lift chart and ROC curve ","message":["%python","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions","     and the target column,","     The Settings name (for the charts), ","     The name of the model used (for the charts),","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," ","    # Extract Statistics from the Confusion Matrix","    cf_local = conf_matrix.pull()","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","    ","    # Estimated AUC via Triangle (not very precise) could be","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","    # Compute real AUC using roc_curve by loading the","    # data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","    ","    # Store all statistics to export","    statistics = {'Algorithm' : name,","                  'Algorithm_setting' : settings_name,","                  'TN' : TN,","                  'TP' : TP,","                  'FP' : FP,","                  'FN' : FN,","                  'TPR' : TPR,","                  'FPR' : FPR,","                  'TNR' : TNR,","                  'FNR' : FNR,","                  'Precision' : Precision,","                  'Accuracy' : Accuracy,","                  'NPV' : NPV,","                  'DetectionRate' : DetectionRate,","                  'BalancedAccuracy' : BalancedAccuracy,","                  'AUC' : AUC,","                  'F1Score' : F1Score,","                  'MathewsCorrCoef' : MathewsCorrCoef","                  }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = round((TN/TOTAL*100),2)","    FP_P = round((FP/TOTAL*100),2)","    FN_P = round((FN/TOTAL*100),2)","    TP_P = round((TP/TOTAL*100),2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+","          \"F1Score: \"+str(round(F1Score,4))","          )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT,  color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","                 textcoords='data', ","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","                                 connectionstyle='arc3, rad=0.3'), ","                 horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),","                 xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), ","                 xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13) ","    ax4.legend(loc=\"lower right\")","    ","    return(statistics, pred_local)","_ = evaluate_model(pred_data=RES_DF, settings_name='Gini,Max Depth:7,Min%Node:0.05,Min%Split:0.1', name='Decision Tree', target='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1737150602268,"interpreter":"python.low","endTime":1737150604656,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1288 (72.56%)\tFalse Positive: 259 (14.59%)\nACTUAL 1\tFalse Negative: 52 (2.93%)\tTrue Positive: 176 (9.92%)\nAccuracy: 82.4789%\tAUC: 0.8542\tF1Score: 0.5309\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification GLM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1718721504352,"interpreter":"md.low","endTime":1718721504499,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Generalized Linear Model","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification generalized linear model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737150754308,"interpreter":"md.low","endTime":1737150754854,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-generalized-linear-model\">Classification Modeling to Predict Target Customers using Generalized Linear Model<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification generalized linear model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737150766469,"interpreter":"md.low","endTime":1737150766918,"results":[{"message":"<dl>\n<img 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Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">OML4Py Generalized Linear Model<\/a>"],"enabled":true,"result":{"startTime":1737150849725,"interpreter":"md.low","endTime":1737150850182,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">OML4Py Generalized Linear Model<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', 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number of rows and columns","message":["%python","","DEMO.shape"],"enabled":true,"result":{"startTime":1737150854509,"interpreter":"python.low","endTime":1737150854999,"results":[{"message":"(4500, 14)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab('AFFINITY_CARD'))"],"enabled":true,"result":{"startTime":1737150855432,"interpreter":"python.low","endTime":1737150856050,"results":[{"message":"AFFINITY_CARD\tcount\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"aggregationOption\":\"Last\",\"series\":{\"selectedId\":\"count\",\"availableSeriesElements\":[{\"id\":\"HOUSEHOLD_SIZE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 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train and test data (60/40 split) ","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X = TEST","TEST_Y = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1737150859424,"interpreter":"python.low","endTime":1737150860182,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Logistic Regression model to predict AFFINITY_CARD","","___"],"enabled":true,"result":{"startTime":1737150860725,"interpreter":"md.low","endTime":1737150861132,"results":[{"message":"<h3 id=\"build-a-logistic-regression-model-to-predict-affinity_card\">Build a Logistic Regression model to predict AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Model using default settings","message":["%python","","try:","    oml.drop(model = 'GLM_CLAS_MODEL') ","except:","    pass","","setting = dict()","glm_mod = oml.glm(**setting)","glm_mod .fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'GLM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737150861571,"interpreter":"python.low","endTime":1737150864425,"results":[{"message":"\nModel Name: GLM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name                  setting value\n0                     ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1         CLAS_WEIGHTS_BALANCED                            OFF\n2               GLMS_CONF_LEVEL                            .95\n3           GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n4            GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n5                  ODMS_DETAILS                    ODMS_ENABLE\n6  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n7                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n8                     PREP_AUTO                             ON\n\nComputed Settings: \n            setting name            setting value\n0    GLMS_CONV_TOLERANCE  .0000050000000000000004\n1    GLMS_NUM_ITERATIONS                       30\n2  GLMS_RIDGE_REGRESSION   GLMS_RIDGE_REG_DISABLE\n3            GLMS_SOLVER         GLMS_SOLVER_CHOL\n\nGlobal Statistics: \n             attribute name attribute value\n0             AIC_INTERCEPT         2965.61\n1                 AIC_MODEL         1970.39\n2                 CONVERGED             YES\n3            DEPENDENT_MEAN        0.233761\n4                ITERATIONS              10\n5                 LR_CHI_SQ         1075.22\n6         LR_CHI_SQ_P_VALUE               0\n7                     LR_DF              40\n8         NEG2_LL_INTERCEPT         2963.61\n9             NEG2_LL_MODEL         1888.39\n10               NUM_PARAMS              41\n11                 NUM_ROWS            2725\n12              PCT_CORRECT        0.833394\n13            PCT_INCORRECT        0.166606\n14                 PCT_TIED               0\n15           PSEUDO_R_SQ_CS        0.326035\n16            PSEUDO_R_SQ_N        0.491785\n17          RANK_DEFICIENCY               2\n18             SC_INTERCEPT         2971.52\n19                 SC_MODEL          2212.7\n20  VALID_COVARIANCE_MATRIX              NO\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n    NONREFERENCE           attribute name attribute value  coefficient std error t value p value significance code\n0              1              (Intercept)            None    -8.069182      None    None    None                  \n1              1  BOOKKEEPING_APPLICATION            None     5.847191      None    None    None                  \n2              1      BULK_PACK_DISKETTES            None     0.352406      None    None    None                  \n3              1                EDUCATION            10th     5.943289      None    None    None                  \n4              1                EDUCATION            11th     6.093187      None    None    None                  \n5              1                EDUCATION            12th     5.384745      None    None    None                  \n6              1                EDUCATION         1st-4th    -3.532597      None    None    None                  \n7              1                EDUCATION         5th-6th    -2.438630      None    None    None                  \n8              1                EDUCATION         7th-8th     4.311646      None    None    None                  \n9              1                EDUCATION             9th     4.409045      None    None    None                  \n10             1                EDUCATION         < Bach.     0.369300      None    None    None                  \n11             1                EDUCATION         Assoc-A     0.641607      None    None    None                  \n12             1                EDUCATION         Assoc-V     0.295088      None    None    None                  \n13             1                EDUCATION           Bach.     0.660166      None    None    None                  \n14             1                EDUCATION         Masters     1.278234      None    None    None                  \n15             1                EDUCATION             PhD     1.807601      None    None    None                  \n16             1                EDUCATION         Presch.     0.000000      None    None    None                  \n17             1                EDUCATION          Profsc     1.261547      None    None    None                  \n18             1       FLAT_PANEL_MONITOR            None    -0.269614      None    None    None                  \n19             1     HOME_THEATER_PACKAGE            None     0.294809      None    None    None                  \n20             1           HOUSEHOLD_SIZE               1    -2.664088      None    None    None                  \n21             1           HOUSEHOLD_SIZE               2    -2.217674      None    None    None                  \n22             1           HOUSEHOLD_SIZE             4-5     0.404088      None    None    None                  \n23             1           HOUSEHOLD_SIZE             6-8    -3.657788      None    None    None                  \n24             1           HOUSEHOLD_SIZE              9+    -2.392858      None    None    None                  \n25             1               OCCUPATION               ?    -0.384344      None    None    None                  \n26             1               OCCUPATION         Armed-F     2.707883      None    None    None                  \n27             1               OCCUPATION         Cleric.     0.528068      None    None    None                  \n28             1               OCCUPATION           Exec.     1.174578      None    None    None                  \n29             1               OCCUPATION         Farming    -0.161607      None    None    None                  \n30             1               OCCUPATION         Handler    -0.500757      None    None    None                  \n31             1               OCCUPATION         House-s    -6.533814      None    None    None                  \n32             1               OCCUPATION         Machine     0.299557      None    None    None                  \n33             1               OCCUPATION           Other    -0.401084      None    None    None                  \n34             1               OCCUPATION           Prof.     1.242899      None    None    None                  \n35             1               OCCUPATION         Protec.     0.687687      None    None    None                  \n36             1               OCCUPATION           Sales     0.628732      None    None    None                  \n37             1               OCCUPATION         TechSup     0.782102      None    None    None                  \n38             1               OCCUPATION         Transp.     0.483469      None    None    None                  \n39             1         OS_DOC_SET_KANJI            None    -7.604536      None    None    None                  \n40             1         PRINTER_SUPPLIES            None     0.000000      None    None    None                  \n41             1            YRS_RESIDENCE            None     0.195556      None    None    None                  \n42             1              Y_BOX_GAMES            None    -1.060428      None    None    None                  \n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0             AIC_INTERCEPT  2965.609563\n1                 AIC_MODEL  1970.385429\n2            DEPENDENT_MEAN     0.233761\n3                ITERATIONS    10.000000\n4                 LR_CHI_SQ  1075.224135\n5         LR_CHI_SQ_P_VALUE     0.000000\n6                     LR_DF    40.000000\n7           MODEL_CONVERGED     1.000000\n8         NEG2_LL_INTERCEPT  2963.609563\n9             NEG2_LL_MODEL  1888.385429\n10               NUM_PARAMS    41.000000\n11                 NUM_ROWS  2725.000000\n12              PCT_CORRECT     0.833394\n13            PCT_INCORRECT     0.166606\n14                 PCT_TIED     0.000000\n15           PSEUDO_R_SQ_CS     0.326035\n16            PSEUDO_R_SQ_N     0.491785\n17          RANK_DEFICIENCY     2.000000\n18             SC_INTERCEPT  2971.519787\n19                 SC_MODEL  2212.704601\n20  VALID_COVARIANCE_MATRIX     0.000000\n\nRank: \n\n41\n\nDeviance: \n\n1888.385429\n\nAIC: \n\n1970.385429\n\nNull Deviance: \n\n2963.609563\n\nDF Residual: \n\n2684.0\n\nDF Null: \n\n2724.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","glm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737150864858,"interpreter":"python.low","endTime":1737150865424,"results":[{"message":"0.838873\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">Generalized Linear Model<\/a> ","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","   ","- Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'","    ","- Specify The confidence level for coefficient confidence intervals.  The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> 'GLMS_CONF_LEVEL' : '0.95'","    ","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> 'GLMS_RIDGE_REGRESSION' : 'GLMS_RIDGE_REG_DISABLE'","    ","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> 'GLMS_RIDGE_VALUE' : '2'","    ","- Turn feature selection on or off.  By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below.","> 'GLMS_FTR_SELECTION' : 'GLMS_FTR_SELECTION_ENABLE'","    ","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> 'GLMS_FTR_GENERATION' : 'GLMS_FTR_GENERATION_ENABLE'","    ","- Specify whether feature generation is quadratic or cubic.  When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`.","> 'GLMS_FTR_GEN_METHOD' : 'GLMS_FTR_GEN_QUADRATIC'","    ","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> 'GLMS_FTR_SEL_CRIT' : 'GLMS_FTR_SEL_ALPHA_INV'","    ","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled).  By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> 'GLMS_MAX_FEATURES' : '30'","    ","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> 'GLMS_PRUNE_MODEL' : 'GLMS_PRUNE_MODEL_ENABLE'","","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class.  By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> 'GLMS_REFERENCE_CLASS_NAME' : '<target_value>'","","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> 'GLMS_ROW_DIAGNOSTICS' : 'GLMS_ROW_DIAG_ENABLE'","     ","- Specify the convergence tolerance setting of the GLM algorithm.  The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> 'GLMS_CONV_TOLERANCE' : '0.0001'","     ","- Specify the maximum number of iterations for the GLM algorithm.  The default value is system-determined, and it requires a positive integer.","> 'GLMS_NUM_ITERATIONS' : '200'","     ","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> 'GLMS_BATCH_ROWS' : '2000'","     ","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> 'GLMS_SOLVER' : 'GLMS_SOLVER_SGD'","    ","- Specify whether to use sparse solver if it is available. The default value is `GLMS_SPARSE_SOLVER_DISABLE`.  ","> 'GLMS_SPARSE_SOLVER' : 'GLMS_SPARSE_SOLVER_ENABLE'","","<strong> Available starting with Oracle Database 23ai <\/strong>","- Specify the `link` function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK`, `GLMS_CAUCHIT_LINK` .  For regression, only one option is available: `GLMS_IDENTITY_LINK (default)`.","> 'GLMS_LINK_FUNCTION' : 'GLMS_IDENTITY_LINK'"],"enabled":true,"result":{"startTime":1737151021519,"interpreter":"md.low","endTime":1737151021973,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals.  The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_CONF_LEVEL' : '0.95'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_RIDGE_REGRESSION' : 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_RIDGE_VALUE' : '2'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off.  By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_SELECTION' : 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_GENERATION' : 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic.  When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_GEN_METHOD' : 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_SEL_CRIT' : 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled).  By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_MAX_FEATURES' : '30'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_PRUNE_MODEL' : 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class.  By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_REFERENCE_CLASS_NAME' : '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_ROW_DIAGNOSTICS' : 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm.  The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_CONV_TOLERANCE' : '0.0001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm.  The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_NUM_ITERATIONS' : '200'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_BATCH_ROWS' : '2000'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_SOLVER' : 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is <code>GLMS_SPARSE_SOLVER_DISABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_SPARSE_SOLVER' : 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> Available starting with Oracle Database 23ai <\/strong><\/p>\n<ul>\n<li>Specify the <code>link<\/code> function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code>, <code>GLMS_CAUCHIT_LINK<\/code> .  For regression, only one option is available: <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_LINK_FUNCTION' : 'GLMS_IDENTITY_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Model using feature generation and feature selection","message":["%python","","try:","    oml.drop(model = 'GLM_CLAS_MODEL') ","except:","    print(\"No such model\")","    ","    ","settings = {'GLMS_FTR_GENERATION': 'GLMS_FTR_GENERATION_ENABLE', ","            'GLMS_FTR_SELECTION': 'GLMS_FTR_SELECTION_ENABLE', ","            'GLMS_FTR_GEN_METHOD': 'GLMS_FTR_GEN_CUBIC',","            'GLMS_FTR_SEL_CRIT': 'GLMS_FTR_SEL_AIC',","            'GLMS_MAX_FEATURES': '100'}","","glm_mod = oml.glm(**settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'GLM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737150866741,"interpreter":"python.low","endTime":1737150869389,"results":[{"message":"\nModel Name: GLM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1          CLAS_WEIGHTS_BALANCED                            OFF\n2                GLMS_CONF_LEVEL                            .95\n3            GLMS_FTR_GENERATION     GLMS_FTR_GENERATION_ENABLE\n4            GLMS_FTR_GEN_METHOD             GLMS_FTR_GEN_CUBIC\n5             GLMS_FTR_SELECTION      GLMS_FTR_SELECTION_ENABLE\n6              GLMS_FTR_SEL_CRIT               GLMS_FTR_SEL_AIC\n7              GLMS_MAX_FEATURES                            100\n8               GLMS_PRUNE_MODEL        GLMS_PRUNE_MODEL_ENABLE\n9              GLMS_SELECT_BLOCK      GLMS_SELECT_BLOCK_DISABLE\n10                  ODMS_DETAILS                    ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n12                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n13                     PREP_AUTO                             ON\n\nComputed Settings: \n            setting name            setting value\n0    GLMS_CONV_TOLERANCE  .0000050000000000000004\n1    GLMS_NUM_ITERATIONS                       30\n2  GLMS_RIDGE_REGRESSION   GLMS_RIDGE_REG_DISABLE\n\nGlobal Statistics: \n             attribute name attribute value\n0             AIC_INTERCEPT         2965.61\n1                 AIC_MODEL          2006.4\n2                 CONVERGED             YES\n3            DEPENDENT_MEAN        0.233761\n4                ITERATIONS               7\n5                 LR_CHI_SQ         1003.21\n6         LR_CHI_SQ_P_VALUE               0\n7                     LR_DF              22\n8         NEG2_LL_INTERCEPT         2963.61\n9             NEG2_LL_MODEL          1960.4\n10               NUM_PARAMS              23\n11                 NUM_ROWS            2725\n12              PCT_CORRECT        0.835596\n13            PCT_INCORRECT        0.164404\n14                 PCT_TIED               0\n15           PSEUDO_R_SQ_CS        0.307988\n16            PSEUDO_R_SQ_N        0.464562\n17             SC_INTERCEPT         2971.52\n18                 SC_MODEL         2142.33\n19  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n    NONREFERENCE                            attribute name attribute value  coefficient  std error     t value       p value significance code\n0              1                               (Intercept)            None    -2.783917   0.209411  176.731734  2.506402e-40               ***\n1              1  BOOKKEEPING_APPLICATION*OCCUPATION_Exec.            None     1.125387   0.167174   45.317407  1.675533e-11               ***\n2              1       BULK_PACK_DISKETTES*EDUCATION_Bach.            None     0.690014   0.276848    6.212014  1.268862e-02                 *\n3              1    BULK_PACK_DISKETTES*HOUSEHOLD_SIZE_4-5            None    -0.954386   0.409668    5.427305  1.982425e-02                 *\n4              1                           EDUCATION_Bach.            None     0.528078   0.252473    4.374876  3.647248e-02                 *\n5              1          EDUCATION_Bach.*OCCUPATION_Prof.            None    -0.810472   0.362774    4.991192  2.547664e-02                 *\n6              1     FLAT_PANEL_MONITOR*OCCUPATION_TechSup            None     1.380693   0.362603   14.498783  1.402501e-04               ***\n7              1    HOME_THEATER_PACKAGE*EDUCATION_Masters            None     1.158908   0.271769   18.184346  2.005202e-05               ***\n8              1     HOME_THEATER_PACKAGE*EDUCATION_Profsc            None     1.578869   0.492805   10.264602  1.356070e-03                **\n9              1     HOME_THEATER_PACKAGE*OCCUPATION_Sales            None     0.676069   0.202676   11.126992  8.508047e-04               ***\n10             1          HOUSEHOLD_SIZE_1*EDUCATION_Bach.            None    -2.578937   0.783734   10.827900  9.998196e-04               ***\n11             1          HOUSEHOLD_SIZE_2*EDUCATION_Bach.            None    -1.079278   0.393955    7.505407  6.151404e-03                **\n12             1                        HOUSEHOLD_SIZE_4-5            None     3.408867   0.617581   30.467150  3.395681e-08               ***\n13             1                          OCCUPATION_Prof.            None     1.506893   0.218360   47.623107  5.165601e-12               ***\n14             1        OCCUPATION_Prof.*EDUCATION_Assoc-A            None     2.237579   0.837404    7.139813  7.539101e-03                **\n15             1                             YRS_RESIDENCE            None     0.377439   0.039964   89.198687  3.570858e-21               ***\n16             1            YRS_RESIDENCE*HOUSEHOLD_SIZE_2            None    -0.330292   0.034314   92.651830  6.235291e-22               ***\n17             1          YRS_RESIDENCE*HOUSEHOLD_SIZE_4-5            None    -0.494057   0.131336   14.150999  1.687075e-04               ***\n18             1          YRS_RESIDENCE*HOUSEHOLD_SIZE_6-8            None    -0.911161   0.298741    9.302516  2.288393e-03                **\n19             1           YRS_RESIDENCE*HOUSEHOLD_SIZE_9+            None    -0.425958   0.052883   64.879068  7.963858e-16               ***\n20             1                YRS_RESIDENCE*OCCUPATION_?            None    -0.126435   0.055996    5.098149  2.395138e-02                 *\n21             1            YRS_RESIDENCE*OCCUPATION_Other            None    -0.167678   0.065836    6.486708  1.086840e-02                 *\n22             1                               Y_BOX_GAMES            None    -1.471169   0.223338   43.391133  4.482145e-11               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0             AIC_INTERCEPT  2965.609563\n1                 AIC_MODEL  2006.396413\n2            DEPENDENT_MEAN     0.233761\n3                ITERATIONS     7.000000\n4                 LR_CHI_SQ  1003.213151\n5         LR_CHI_SQ_P_VALUE     0.000000\n6                     LR_DF    22.000000\n7           MODEL_CONVERGED     1.000000\n8         NEG2_LL_INTERCEPT  2963.609563\n9             NEG2_LL_MODEL  1960.396413\n10               NUM_PARAMS    23.000000\n11                 NUM_ROWS  2725.000000\n12              PCT_CORRECT     0.835596\n13            PCT_INCORRECT     0.164404\n14                 PCT_TIED     0.000000\n15           PSEUDO_R_SQ_CS     0.307988\n16            PSEUDO_R_SQ_N     0.464562\n17             SC_INTERCEPT  2971.519787\n18                 SC_MODEL  2142.331558\n19  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n23\n\nDeviance: \n\n1960.396413\n\nAIC: \n\n2006.396413\n\nNull Deviance: \n\n2963.609563\n\nDF Residual: \n\n2702.0\n\nDF Null: \n\n2724.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","","glm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737150869834,"interpreter":"python.low","endTime":1737150870346,"results":[{"message":"0.825352\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Model using ridge regression","message":["%python","","try:","    oml.drop(model = 'GLM_CLAS_MODEL') ","except:","    print(\"No such model\")","settings = {'GLMS_RIDGE_REGRESSION': 'GLMS_RIDGE_REG_ENABLE', ","            'GLMS_RIDGE_VALUE': '2'}","","glm_mod = oml.glm(**settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'GLM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737150870782,"interpreter":"python.low","endTime":1737150873186,"results":[{"message":"\nModel Name: GLM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1          CLAS_WEIGHTS_BALANCED                            OFF\n2                GLMS_CONF_LEVEL                            .95\n3            GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n4             GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n5          GLMS_RIDGE_REGRESSION          GLMS_RIDGE_REG_ENABLE\n6               GLMS_RIDGE_VALUE                              2\n7             GLMS_VIF_FOR_RIDGE         GLMS_VIF_RIDGE_DISABLE\n8                   ODMS_DETAILS                    ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n10                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n11                     PREP_AUTO                             ON\n\nComputed Settings: \n          setting name            setting value\n0  GLMS_CONV_TOLERANCE  .0000050000000000000004\n1  GLMS_NUM_ITERATIONS                       30\n2          GLMS_SOLVER         GLMS_SOLVER_CHOL\n\nGlobal Statistics: \n             attribute name attribute value\n0             AIC_INTERCEPT         2965.61\n1                 AIC_MODEL         1993.44\n2                 CONVERGED             YES\n3            DEPENDENT_MEAN        0.233761\n4                ITERATIONS               8\n5                 LR_CHI_SQ         1054.17\n6         LR_CHI_SQ_P_VALUE               0\n7                     LR_DF              41\n8         NEG2_LL_INTERCEPT         2963.61\n9             NEG2_LL_MODEL         1909.44\n10               NUM_PARAMS              42\n11                 NUM_ROWS            2725\n12              PCT_CORRECT        0.835963\n13            PCT_INCORRECT        0.164037\n14                 PCT_TIED               0\n15           PSEUDO_R_SQ_CS        0.320809\n16            PSEUDO_R_SQ_N        0.483901\n17          RANK_DEFICIENCY               1\n18             SC_INTERCEPT         2971.52\n19                 SC_MODEL         2241.67\n20  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n    NONREFERENCE           attribute name attribute value  coefficient  std error     t value       p value significance code\n0              1              (Intercept)            None    -2.968706   0.447968   43.917736  3.424715e-11               ***\n1              1  BOOKKEEPING_APPLICATION            None     0.923901   0.384691    5.768022  1.632039e-02                 *\n2              1      BULK_PACK_DISKETTES            None     0.253833   0.227489    1.245022  2.645054e-01                  \n3              1                EDUCATION            10th     0.621063   0.474272    1.714812  1.903623e-01                  \n4              1                EDUCATION            11th     0.786414   0.460175    2.920493  8.746114e-02                 .\n5              1                EDUCATION            12th     0.149390   0.565331    0.069829  7.915856e-01                  \n6              1                EDUCATION         1st-4th    -0.413029   0.620772    0.442688  5.058280e-01                  \n7              1                EDUCATION         5th-6th    -0.511143   0.598239    0.730019  3.928770e-01                  \n8              1                EDUCATION         7th-8th    -0.399553   0.495325    0.650682  4.198689e-01                  \n9              1                EDUCATION             9th    -0.317939   0.520942    0.372486  5.416522e-01                  \n10             1                EDUCATION         < Bach.     0.272133   0.151792    3.214128  7.300506e-02                 .\n11             1                EDUCATION         Assoc-A     0.490290   0.261295    3.520835  6.060190e-02                 .\n12             1                EDUCATION         Assoc-V     0.177313   0.256415    0.478184  4.892462e-01                  \n13             1                EDUCATION           Bach.     0.567309   0.159015   12.728083  3.602056e-04               ***\n14             1                EDUCATION         Masters     1.063318   0.248644   18.288155  1.898842e-05               ***\n15             1                EDUCATION             PhD     1.208887   0.376637   10.302101  1.328788e-03                **\n16             1                EDUCATION         Presch.    -0.008385   0.704206    0.000142  9.905000e-01                  \n17             1                EDUCATION          Profsc     0.935072   0.333274    7.872030  5.020526e-03                **\n18             1       FLAT_PANEL_MONITOR            None    -0.186209   0.220708    0.711811  3.988434e-01                  \n19             1     HOME_THEATER_PACKAGE            None     0.276253   0.172567    2.562714  1.094106e-01                  \n20             1           HOUSEHOLD_SIZE               1    -2.003328   0.312804   41.016697  1.509342e-10               ***\n21             1           HOUSEHOLD_SIZE               2    -2.008406   0.155304  167.238407  2.967250e-38               ***\n22             1           HOUSEHOLD_SIZE             4-5     0.473613   0.210160    5.078617  2.422267e-02                 *\n23             1           HOUSEHOLD_SIZE             6-8    -1.821955   0.414773   19.295443  1.119733e-05               ***\n24             1           HOUSEHOLD_SIZE              9+    -2.015944   0.227024   78.852178  6.693560e-19               ***\n25             1               OCCUPATION               ?    -0.405677   0.291782    1.933057  1.644244e-01                  \n26             1               OCCUPATION         Armed-F     0.176248   0.666780    0.069869  7.915276e-01                  \n27             1               OCCUPATION         Cleric.     0.296821   0.221685    1.792741  1.805925e-01                  \n28             1               OCCUPATION           Exec.     0.995936   0.183388   29.493144  5.611486e-08               ***\n29             1               OCCUPATION         Farming    -0.237750   0.335414    0.502433  4.784332e-01                  \n30             1               OCCUPATION         Handler    -0.473328   0.374722    1.595531  2.065377e-01                  \n31             1               OCCUPATION         House-s    -0.219728   0.644495    0.116234  7.331555e-01                  \n32             1               OCCUPATION         Machine     0.129710   0.254556    0.259647  6.103626e-01                  \n33             1               OCCUPATION           Other    -0.549116   0.288432    3.624435  5.693690e-02                 .\n34             1               OCCUPATION           Prof.     1.087352   0.202453   28.846485  7.834797e-08               ***\n35             1               OCCUPATION         Protec.     0.464419   0.322570    2.072873  1.499388e-01                  \n36             1               OCCUPATION           Sales     0.466966   0.194189    5.782556  1.618599e-02                 *\n37             1               OCCUPATION         TechSup     0.535564   0.278577    3.696007  5.454285e-02                 .\n38             1               OCCUPATION         Transp.     0.259341   0.000000    0.000000  1.000000e+00                  \n39             1         OS_DOC_SET_KANJI            None    -3.593939   5.977039    0.361551  5.476463e-01                  \n40             1         PRINTER_SUPPLIES            None     0.000000   0.000000    0.000000  1.000000e+00                  \n41             1            YRS_RESIDENCE            None     0.196504   0.037668   27.214461  1.820916e-07               ***\n42             1              Y_BOX_GAMES            None    -1.115959   0.248111   20.230398  6.865332e-06               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0             AIC_INTERCEPT  2965.609563\n1                 AIC_MODEL  1993.436185\n2            DEPENDENT_MEAN     0.233761\n3                ITERATIONS     8.000000\n4                 LR_CHI_SQ  1054.173378\n5         LR_CHI_SQ_P_VALUE     0.000000\n6                     LR_DF    41.000000\n7           MODEL_CONVERGED     1.000000\n8         NEG2_LL_INTERCEPT  2963.609563\n9             NEG2_LL_MODEL  1909.436185\n10               NUM_PARAMS    42.000000\n11                 NUM_ROWS  2725.000000\n12              PCT_CORRECT     0.835963\n13            PCT_INCORRECT     0.164037\n14                 PCT_TIED     0.000000\n15           PSEUDO_R_SQ_CS     0.320809\n16            PSEUDO_R_SQ_N     0.483901\n17          RANK_DEFICIENCY     1.000000\n18             SC_INTERCEPT  2971.519787\n19                 SC_MODEL  2241.665581\n20  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n42\n\nDeviance: \n\n1909.436185\n\nAIC: \n\n1993.436185\n\nNull Deviance: \n\n2963.609563\n\nDF Residual: \n\n2683.0\n\nDF Null: \n\n2724.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","glm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737150873668,"interpreter":"python.low","endTime":1737150874197,"results":[{"message":"0.841127\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Model using sparse, SGD solver","message":["%python","","try:","    oml.drop(model = 'GLM_CLAS_MODEL') ","except:","    print(\"No such model\")","    ","    ","settings = {'GLMS_SPARSE_SOLVER': 'GLMS_SPARSE_SOLVER_ENABLE',","            'GLMS_SOLVER': 'GLMS_SOLVER_SGD',","            'GLMS_BATCH_ROWS': '2000'}","","glm_mod = oml.glm(**settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'GLM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737150874647,"interpreter":"python.low","endTime":1737150877041,"results":[{"message":"\nModel Name: GLM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1          CLAS_WEIGHTS_BALANCED                            OFF\n2                GLMS_BATCH_ROWS                           2000\n3                GLMS_CONF_LEVEL                            .95\n4            GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n5             GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n6                    GLMS_SOLVER                GLMS_SOLVER_SGD\n7             GLMS_SPARSE_SOLVER      GLMS_SPARSE_SOLVER_ENABLE\n8                   ODMS_DETAILS                    ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n10                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n11                     PREP_AUTO                             ON\n\nComputed Settings: \n            setting name          setting value\n0    GLMS_CONV_TOLERANCE                  .0001\n1    GLMS_NUM_ITERATIONS                    500\n2  GLMS_RIDGE_REGRESSION  GLMS_RIDGE_REG_ENABLE\n3       GLMS_RIDGE_VALUE                    .01\n\nGlobal Statistics: \n             attribute name attribute value\n0             AIC_INTERCEPT         2965.61\n1                 AIC_MODEL         2312.28\n2                 CONVERGED              NO\n3            DEPENDENT_MEAN        0.233761\n4                ITERATIONS             501\n5                 LR_CHI_SQ         737.332\n6         LR_CHI_SQ_P_VALUE               0\n7                     LR_DF              42\n8         NEG2_LL_INTERCEPT         2963.61\n9             NEG2_LL_MODEL         2226.28\n10               NUM_PARAMS              43\n11                 NUM_ROWS            2725\n12              PCT_CORRECT        0.812844\n13            PCT_INCORRECT        0.187156\n14                 PCT_TIED               0\n15           PSEUDO_R_SQ_CS        0.237064\n16            PSEUDO_R_SQ_N        0.357582\n17          RANK_DEFICIENCY               0\n18             SC_INTERCEPT         2971.52\n19                 SC_MODEL         2566.42\n20  VALID_COVARIANCE_MATRIX              NO\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n    NONREFERENCE           attribute name attribute value  coefficient std error t value p value significance code\n0              1              (Intercept)            None    -0.669821      None    None    None                  \n1              1  BOOKKEEPING_APPLICATION            None    -0.141002      None    None    None                  \n2              1      BULK_PACK_DISKETTES            None    -0.138900      None    None    None                  \n3              1                EDUCATION            10th    -0.099036      None    None    None                  \n4              1                EDUCATION            11th    -0.093334      None    None    None                  \n5              1                EDUCATION            12th    -0.054159      None    None    None                  \n6              1                EDUCATION         1st-4th    -0.032721      None    None    None                  \n7              1                EDUCATION         5th-6th    -0.074591      None    None    None                  \n8              1                EDUCATION         7th-8th    -0.151314      None    None    None                  \n9              1                EDUCATION             9th    -0.146047      None    None    None                  \n10             1                EDUCATION         < Bach.    -0.060513      None    None    None                  \n11             1                EDUCATION         Assoc-A     0.047738      None    None    None                  \n12             1                EDUCATION         Assoc-V    -0.033102      None    None    None                  \n13             1                EDUCATION           Bach.     0.298163      None    None    None                  \n14             1                EDUCATION         Masters     0.262989      None    None    None                  \n15             1                EDUCATION             PhD     0.151208      None    None    None                  \n16             1                EDUCATION         Presch.    -0.004397      None    None    None                  \n17             1                EDUCATION          Profsc     0.154942      None    None    None                  \n18             1       FLAT_PANEL_MONITOR            None    -0.080572      None    None    None                  \n19             1     HOME_THEATER_PACKAGE            None    -0.001950      None    None    None                  \n20             1           HOUSEHOLD_SIZE               1    -0.465928      None    None    None                  \n21             1           HOUSEHOLD_SIZE               2    -0.914519      None    None    None                  \n22             1           HOUSEHOLD_SIZE             4-5     0.220039      None    None    None                  \n23             1           HOUSEHOLD_SIZE             6-8    -0.192097      None    None    None                  \n24             1           HOUSEHOLD_SIZE              9+    -0.607532      None    None    None                  \n25             1               OCCUPATION               ?    -0.132962      None    None    None                  \n26             1               OCCUPATION         Armed-F     0.007164      None    None    None                  \n27             1               OCCUPATION         Cleric.    -0.162208      None    None    None                  \n28             1               OCCUPATION           Exec.     0.411764      None    None    None                  \n29             1               OCCUPATION         Farming    -0.102857      None    None    None                  \n30             1               OCCUPATION         Handler    -0.149833      None    None    None                  \n31             1               OCCUPATION         House-s    -0.043487      None    None    None                  \n32             1               OCCUPATION         Machine    -0.147349      None    None    None                  \n33             1               OCCUPATION           Other    -0.423843      None    None    None                  \n34             1               OCCUPATION           Prof.     0.474594      None    None    None                  \n35             1               OCCUPATION         Protec.     0.027153      None    None    None                  \n36             1               OCCUPATION           Sales     0.043241      None    None    None                  \n37             1               OCCUPATION         TechSup     0.015980      None    None    None                  \n38             1               OCCUPATION         Transp.    -0.061911      None    None    None                  \n39             1         OS_DOC_SET_KANJI            None    -1.848425      None    None    None                  \n40             1         PRINTER_SUPPLIES            None     0.000000      None    None    None                  \n41             1            YRS_RESIDENCE            None     0.080093      None    None    None                  \n42             1              Y_BOX_GAMES            None    -1.787249      None    None    None                  \n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name          value\n0             AIC_INTERCEPT   2.965610e+03\n1                 AIC_MODEL   2.312277e+03\n2            DEPENDENT_MEAN   2.337615e-01\n3                ITERATIONS   5.010000e+02\n4                 LR_CHI_SQ   7.373324e+02\n5         LR_CHI_SQ_P_VALUE  7.261162e-128\n6                     LR_DF   4.200000e+01\n7           MODEL_CONVERGED   0.000000e+00\n8         NEG2_LL_INTERCEPT   2.963610e+03\n9             NEG2_LL_MODEL   2.226277e+03\n10               NUM_PARAMS   4.300000e+01\n11                 NUM_ROWS   2.725000e+03\n12              PCT_CORRECT   8.128440e-01\n13            PCT_INCORRECT   1.871560e-01\n14                 PCT_TIED   0.000000e+00\n15           PSEUDO_R_SQ_CS   2.370637e-01\n16            PSEUDO_R_SQ_N   3.575819e-01\n17          RANK_DEFICIENCY   0.000000e+00\n18             SC_INTERCEPT   2.971520e+03\n19                 SC_MODEL   2.566417e+03\n20  VALID_COVARIANCE_MATRIX   0.000000e+00\n\nRank: \n\n43\n\nDeviance: \n\n2226.277203\n\nAIC: \n\n2312.277203\n\nNull Deviance: \n\n2963.609563\n\nDF Residual: \n\n2682.0\n\nDF Null: \n\n2724.0\n\nConverged: \n\nFalse\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","glm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737150877496,"interpreter":"python.low","endTime":1737150878067,"results":[{"message":"0.810141\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Score test data adding supplemental columns","message":["%python","","# Set the case ID column for use below","case_id = 'CUST_ID'","","# Gather the Predictions","RES_DF = glm_mod.predict(TEST_X, supplemental_cols = TEST_X)","","# Additionally collect the PROBABILITY_OF_0 and PROBABILITY_OF_1","RES_PROB = glm_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","","# Join the entire result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1737150878510,"interpreter":"python.low","endTime":1737150879145,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display confusion matrix, lift chart and ROC curve ","message":["%python","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions","     and the target column,","     The Settings name (for the charts), ","     The name of the model used (for the charts),","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," ","    # Extract Statistics from the Confusion Matrix","    cf_local = conf_matrix.pull()","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","    ","    # Estimated AUC via Triangle (not very precise) could be","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","    # Compute real AUC using roc_curve by loading the","    # data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","    ","    # Store all statistics to export","    statistics = {'Algorithm' : name,","                  'Algorithm_setting' : settings_name,","                  'TN' : TN,","                  'TP' : TP,","                  'FP' : FP,","                  'FN' : FN,","                  'TPR' : TPR,","                  'FPR' : FPR,","                  'TNR' : TNR,","                  'FNR' : FNR,","                  'Precision' : Precision,","                  'Accuracy' : Accuracy,","                  'NPV' : NPV,","                  'DetectionRate' : DetectionRate,","                  'BalancedAccuracy' : BalancedAccuracy,","                  'AUC' : AUC,","                  'F1Score' : F1Score,","                  'MathewsCorrCoef' : MathewsCorrCoef","                  }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = round((TN/TOTAL*100),2)","    FP_P = round((FP/TOTAL*100),2)","    FN_P = round((FN/TOTAL*100),2)","    TP_P = round((TP/TOTAL*100),2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+","          \"F1Score: \"+str(round(F1Score,4))","          )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT, color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","                 textcoords='data', ","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","                                 connectionstyle='arc3, rad=0.3'), ","                 horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),","                 xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), ","                 xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13) ","    ax4.legend(loc=\"lower right\")","    ","    return(statistics, pred_local)","_ = evaluate_model(pred_data=RES_DF, settings_name='Sparse SGD Solver', name='Generalized Linear Model', target='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1737150879581,"interpreter":"python.low","endTime":1737150882077,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1303 (73.41%)\tFalse Positive: 300 (16.9%)\nACTUAL 1\tFalse Negative: 37 (2.08%)\tTrue Positive: 135 (7.61%)\nAccuracy: 81.0141%\tAUC: 0.8473\tF1Score: 0.4448\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img src=data:image/png;base64,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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":11,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction results","message":["%python","","z.show(RES_DF[RES_DF['PROBABILITY_OF_1'] > 0.5][['PREDICTION', 'PROBABILITY_OF_1'] + RES_DF.columns].head())"],"enabled":true,"result":{"startTime":1737150882523,"interpreter":"python.low","endTime":1737150883384,"results":[{"message":"PREDICTION\tPROBABILITY_OF_1\tCUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY_OF_0\tPROBABILITY_OF_1\n1\t0.5064267507530414\t101031\t1\t1\t1\tMasters\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.49357324924695856\t0.5064267507530414\n1\t0.5540940040773146\t101990\t0\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.4459059959226853\t0.5540940040773146\n1\t0.5107411705469768\t101822\t1\t1\t1\tMasters\t1\t1\t3\tExec.\t0\t1\t5\t0\t1\t0.4892588294530231\t0.5107411705469768\n1\t0.5194517557949659\t102661\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t6\t0\t1\t0.4805482442050341\t0.5194517557949659\n1\t0.5204546685371887\t104462\t0\t1\t0\t< Bach.\t0\t1\t3\tProf.\t0\t1\t6\t0\t1\t0.4795453314628112\t0.5204546685371887\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the OML4Py interface to score data and display prediction details","message":["%python","","RES_DF = glm_mod.predict(TEST_X, supplemental_cols = TEST_X[['CUST_ID', 'AFFINITY_CARD']], topN_attrs = True)","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1737150883816,"interpreter":"python.low","endTime":1737150896682,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100300\t1\t1\tY_BOX_GAMES\t0\t.135\tOCCUPATION\tProf.\t.118\tEDUCATION\tBach.\t.074\tBULK_PACK_DISKETTES\t0\t.022\tFLAT_PANEL_MONITOR\t0\t.011\n101100\t0\t0\tHOUSEHOLD_SIZE\t2\t.127\tOCCUPATION\tOther\t.049\tBULK_PACK_DISKETTES\t1\t.005\tFLAT_PANEL_MONITOR\t1\t.003\tEDUCATION\tAssoc-V\t.003\n101300\t1\t0\tEDUCATION\t< Bach.\t.013\tBULK_PACK_DISKETTES\t1\t.011\tFLAT_PANEL_MONITOR\t1\t.007\tBOOKKEEPING_APPLICATION\t1\t.003\tYRS_RESIDENCE\t4\t.001\n101400\t1\t0\tOCCUPATION\tMachine\t.034\tEDUCATION\t9th\t.034\tYRS_RESIDENCE\t4\t.001\tOS_DOC_SET_KANJI\t0\t-.001\tFLAT_PANEL_MONITOR\t0\t-.01\n101500\t0\t0\tY_BOX_GAMES\t1\t.089\tHOUSEHOLD_SIZE\t1\t.023\tYRS_RESIDENCE\t2\t.007\tEDUCATION\t< Bach.\t.002\tBULK_PACK_DISKETTES\t1\t.002\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test data into a table","message":["%python","","try:","    oml.drop(table = 'GLM_TEST_TABLE') ","except:","    pass","_ = TEST_X.materialize(table = 'GLM_TEST_TABLE') "],"enabled":true,"result":{"startTime":1737150897135,"interpreter":"python.low","endTime":1737150897819,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(GLM_CLAS_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(GLM_CLAS_MODEL USING *) PD","     FROM GLM_TEST_TABLE","     WHERE CUST_ID < 100015","     ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details'","              PASSING OUT.PD","              COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]',","              \"Attribute3\" XMLType PATH 'Attribute[3]') ","     OUTPRED","     "],"enabled":true,"result":{"startTime":1737150898266,"interpreter":"sql.low","endTime":1737150900210,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".217\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".011\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".007\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".177\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".009\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".008\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".17\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".009\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".007\" \n100007\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".136\" \t\"OCCUPATION\" actualValue=\"Other\" weight=\".053\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".005\" \n100010\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".138\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".006\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".003\" \n100011\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".168\" \t\"EDUCATION\" actualValue=\"9th\" weight=\".022\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\".015\" 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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification NB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737857808378,"interpreter":"md.low","endTime":1737857808881,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Predicting Target Customers using Classification using Naive Bayes","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737857809321,"interpreter":"md.low","endTime":1737857809782,"results":[{"message":"<h1 id=\"predicting-target-customers-using-classification-using-naive-bayes\">Predicting Target Customers using Classification using Naive Bayes<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737857810294,"interpreter":"md.low","endTime":1737857810744,"results":[{"message":"<dl>\n<img 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target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/naive-bayes.html\" target=\"_blank\">OML4Py Naive Bayes<\/a>"],"enabled":true,"result":{"startTime":1737857811185,"interpreter":"md.low","endTime":1737857811614,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle Github<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/naive-bayes.html\" target=\"_blank\">OML4Py Naive Bayes<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737857812089,"interpreter":"python.low","endTime":1737857814539,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","DEMO = oml.sync(table = \"SUPPLEMENTARY_DEMOGRAPHICS\", schema = \"SH\")","z.show(DEMO.head())"],"enabled":true,"result":{"startTime":1737857814957,"interpreter":"python.low","endTime":1737857815636,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNone\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%python","","DEMO.shape"],"enabled":true,"result":{"startTime":1737857816067,"interpreter":"python.low","endTime":1737857816545,"results":[{"message":"(4500, 14)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab('AFFINITY_CARD'))"],"enabled":true,"result":{"startTime":1737857816980,"interpreter":"python.low","endTime":1737857817617,"results":[{"message":"AFFINITY_CARD\tcount\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab(['HOUSEHOLD_SIZE', 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data into train and test sets (60/40)","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X = TEST","TEST_Y = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1737857820895,"interpreter":"python.low","endTime":1737857821619,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Naive Bayes model to predict AFFINITY_CARD","","___"],"enabled":true,"result":{"startTime":1737857822055,"interpreter":"md.low","endTime":1737857822484,"results":[{"message":"<h3 id=\"build-a-naive-bayes-model-to-predict-affinity_card\">Build a Naive Bayes model to predict AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model using default settings","message":["%python","","try:","    oml.drop(model = 'NB_CLAS_MODEL') ","except:","    pass","","setting = dict()","nb_mod = oml.nb(**setting)","nb_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'NB_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737857822910,"interpreter":"python.low","endTime":1737857825388,"results":[{"message":"\nModel Name: NB_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Naive Bayes\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name            setting value\n0                     ALGO_NAME         ALGO_NAIVE_BAYES\n1         CLAS_WEIGHTS_BALANCED                      OFF\n2       NABS_PAIRWISE_THRESHOLD                        0\n3      NABS_SINGLETON_THRESHOLD                        0\n4                  ODMS_DETAILS              ODMS_ENABLE\n5  ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n6                 ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                     PREP_AUTO                       ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS             2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nEDUCATION\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nPriors: \n\n     TARGET_NAME  TARGET_VALUE  PRIOR_PROBABILITY  COUNT\n0  AFFINITY_CARD             0           0.766239   2088\n1  AFFINITY_CARD             1           0.233761    637\n\nConditionals: \n\n      TARGET_NAME  TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME                                    ATTRIBUTE_VALUE  CONDITIONAL_PROBABILITY  COUNT\n0   AFFINITY_CARD             0  BOOKKEEPING_APPLICATION              None                                            ( ; .5]                 0.137452    287\n1   AFFINITY_CARD             0  BOOKKEEPING_APPLICATION              None                                            (.5;  )                 0.862548   1801\n2   AFFINITY_CARD             0                EDUCATION              None  '10th', '11th', '12th', '1st-4th', '5th-6th', ...                 0.822797   1718\n3   AFFINITY_CARD             0                EDUCATION              None                                            'Bach.'                 0.141284    295\n4   AFFINITY_CARD             0                EDUCATION              None                         'Masters', 'PhD', 'Profsc'                 0.035920     75\n5   AFFINITY_CARD             0     HOME_THEATER_PACKAGE              None                                            ( ; .5]                 0.503831   1052\n6   AFFINITY_CARD             0     HOME_THEATER_PACKAGE              None                                            (.5;  )                 0.496169   1036\n7   AFFINITY_CARD             0           HOUSEHOLD_SIZE              None                                         '1', '6-8'                 0.235632    492\n8   AFFINITY_CARD             0           HOUSEHOLD_SIZE              None                                          '2', '9+'                 0.440613    920\n9   AFFINITY_CARD             0           HOUSEHOLD_SIZE              None                                         '3', '4-5'                 0.323755    676\n10  AFFINITY_CARD             0               OCCUPATION              None     '?', 'Cleric.', 'Crafts', 'Farming', 'Machine'                 0.418103    873\n11  AFFINITY_CARD             0               OCCUPATION              None                        'Armed-F', 'Exec.', 'Prof.'                 0.170019    355\n12  AFFINITY_CARD             0               OCCUPATION              None                      'Handler', 'House-s', 'Other'                 0.190613    398\n13  AFFINITY_CARD             0               OCCUPATION              None           'Protec.', 'Sales', 'TechSup', 'Transp.'                 0.221264    462\n14  AFFINITY_CARD             0            YRS_RESIDENCE              None                                           ( ; 2.5]                 0.266284    556\n15  AFFINITY_CARD             0            YRS_RESIDENCE              None                                         (2.5; 3.5]                 0.238506    498\n16  AFFINITY_CARD             0            YRS_RESIDENCE              None                                         (3.5; 6.5]                 0.432471    903\n17  AFFINITY_CARD             0            YRS_RESIDENCE              None                                           (6.5;  )                 0.062739    131\n18  AFFINITY_CARD             0              Y_BOX_GAMES              None                                            ( ; .5]                 0.617337   1289\n19  AFFINITY_CARD             0              Y_BOX_GAMES              None                                            (.5;  )                 0.382663    799\n20  AFFINITY_CARD             1  BOOKKEEPING_APPLICATION              None                                            ( ; .5]                 0.036107     23\n21  AFFINITY_CARD             1  BOOKKEEPING_APPLICATION              None                                            (.5;  )                 0.963893    614\n22  AFFINITY_CARD             1                EDUCATION              None  '10th', '11th', '12th', '1st-4th', '5th-6th', ...                 0.552590    352\n23  AFFINITY_CARD             1                EDUCATION              None                                            'Bach.'                 0.265306    169\n24  AFFINITY_CARD             1                EDUCATION              None                         'Masters', 'PhD', 'Profsc'                 0.182104    116\n25  AFFINITY_CARD             1     HOME_THEATER_PACKAGE              None                                            ( ; .5]                 0.164835    105\n26  AFFINITY_CARD             1     HOME_THEATER_PACKAGE              None                                            (.5;  )                 0.835165    532\n27  AFFINITY_CARD             1           HOUSEHOLD_SIZE              None                                         '1', '6-8'                 0.010989      7\n28  AFFINITY_CARD             1           HOUSEHOLD_SIZE              None                                          '2', '9+'                 0.127159     81\n29  AFFINITY_CARD             1           HOUSEHOLD_SIZE              None                                         '3', '4-5'                 0.861852    549\n30  AFFINITY_CARD             1               OCCUPATION              None     '?', 'Cleric.', 'Crafts', 'Farming', 'Machine'                 0.252747    161\n31  AFFINITY_CARD             1               OCCUPATION              None                        'Armed-F', 'Exec.', 'Prof.'                 0.470958    300\n32  AFFINITY_CARD             1               OCCUPATION              None                      'Handler', 'House-s', 'Other'                 0.031397     20\n33  AFFINITY_CARD             1               OCCUPATION              None           'Protec.', 'Sales', 'TechSup', 'Transp.'                 0.244898    156\n34  AFFINITY_CARD             1            YRS_RESIDENCE              None                                           ( ; 2.5]                 0.023548     15\n35  AFFINITY_CARD             1            YRS_RESIDENCE              None                                         (2.5; 3.5]                 0.105181     67\n36  AFFINITY_CARD             1            YRS_RESIDENCE              None                                         (3.5; 6.5]                 0.676609    431\n37  AFFINITY_CARD             1            YRS_RESIDENCE              None                                           (6.5;  )                 0.194662    124\n38  AFFINITY_CARD             1              Y_BOX_GAMES              None                                            ( ; .5]                 0.952904    607\n39  AFFINITY_CARD             1              Y_BOX_GAMES              None                                            (.5;  )                 0.047096     30\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","","nb_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737857825848,"interpreter":"python.low","endTime":1737857826382,"results":[{"message":"0.80338\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of setting overrides for Naive Bayes","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/naive-bayes.html\" target=\"_blank\">Naive Bayes<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'","* Specify the value of pairwise threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0. ","> 'NABS_PAIRWISE_THRESHOLD' : '0'","* Specify the value of singleton threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0. ","> 'NABS_SINGLETON_THRESHOLD' : '0'"],"enabled":true,"result":{"startTime":1737857826809,"interpreter":"md.low","endTime":1737857827222,"results":[{"message":"<h3 id=\"examples-of-setting-overrides-for-naive-bayes\">Examples of setting overrides for Naive Bayes<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/naive-bayes.html\" target=\"_blank\">Naive Bayes<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of pairwise threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>'NABS_PAIRWISE_THRESHOLD' : '0'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of singleton threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>'NABS_SINGLETON_THRESHOLD' : '0'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes Model using explicit settings","message":["%python","","try:","    oml.drop(model = 'NB_CLAS_MODEL') ","except:","    pass","","setting = {'NABS_PAIRWISE_THRESHOLD' : '0', ","           'NABS_SINGLETON_THRESHOLD': '0'}","nb_mod = oml.nb(**setting)","nb_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'NB_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1737857827720,"interpreter":"python.low","endTime":1737857829693,"results":[{"message":"\nModel Name: NB_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Naive Bayes\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name            setting value\n0                     ALGO_NAME         ALGO_NAIVE_BAYES\n1         CLAS_WEIGHTS_BALANCED                      OFF\n2       NABS_PAIRWISE_THRESHOLD                        0\n3      NABS_SINGLETON_THRESHOLD                        0\n4                  ODMS_DETAILS              ODMS_ENABLE\n5  ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n6                 ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                     PREP_AUTO                       ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS             2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nEDUCATION\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nPriors: \n\n     TARGET_NAME  TARGET_VALUE  PRIOR_PROBABILITY  COUNT\n0  AFFINITY_CARD             0           0.766239   2088\n1  AFFINITY_CARD             1           0.233761    637\n\nConditionals: \n\n      TARGET_NAME  TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME                                    ATTRIBUTE_VALUE  CONDITIONAL_PROBABILITY  COUNT\n0   AFFINITY_CARD             0  BOOKKEEPING_APPLICATION              None                                            ( ; .5]                 0.137452    287\n1   AFFINITY_CARD             0  BOOKKEEPING_APPLICATION              None                                            (.5;  )                 0.862548   1801\n2   AFFINITY_CARD             0                EDUCATION              None  '10th', '11th', '12th', '1st-4th', '5th-6th', ...                 0.822797   1718\n3   AFFINITY_CARD             0                EDUCATION              None                                            'Bach.'                 0.141284    295\n4   AFFINITY_CARD             0                EDUCATION              None                         'Masters', 'PhD', 'Profsc'                 0.035920     75\n5   AFFINITY_CARD             0     HOME_THEATER_PACKAGE              None                                            ( ; .5]                 0.503831   1052\n6   AFFINITY_CARD             0     HOME_THEATER_PACKAGE              None                                            (.5;  )                 0.496169   1036\n7   AFFINITY_CARD             0           HOUSEHOLD_SIZE              None                                         '1', '6-8'                 0.235632    492\n8   AFFINITY_CARD             0           HOUSEHOLD_SIZE              None                                          '2', '9+'                 0.440613    920\n9   AFFINITY_CARD             0           HOUSEHOLD_SIZE              None                                         '3', '4-5'                 0.323755    676\n10  AFFINITY_CARD             0               OCCUPATION              None     '?', 'Cleric.', 'Crafts', 'Farming', 'Machine'                 0.418103    873\n11  AFFINITY_CARD             0               OCCUPATION              None                        'Armed-F', 'Exec.', 'Prof.'                 0.170019    355\n12  AFFINITY_CARD             0               OCCUPATION              None                      'Handler', 'House-s', 'Other'                 0.190613    398\n13  AFFINITY_CARD             0               OCCUPATION              None           'Protec.', 'Sales', 'TechSup', 'Transp.'                 0.221264    462\n14  AFFINITY_CARD             0            YRS_RESIDENCE              None                                           ( ; 2.5]                 0.266284    556\n15  AFFINITY_CARD             0            YRS_RESIDENCE              None                                         (2.5; 3.5]                 0.238506    498\n16  AFFINITY_CARD             0            YRS_RESIDENCE              None                                         (3.5; 6.5]                 0.432471    903\n17  AFFINITY_CARD             0            YRS_RESIDENCE              None                                           (6.5;  )                 0.062739    131\n18  AFFINITY_CARD             0              Y_BOX_GAMES              None                                            ( ; .5]                 0.617337   1289\n19  AFFINITY_CARD             0              Y_BOX_GAMES              None                                            (.5;  )                 0.382663    799\n20  AFFINITY_CARD             1  BOOKKEEPING_APPLICATION              None                                            ( ; .5]                 0.036107     23\n21  AFFINITY_CARD             1  BOOKKEEPING_APPLICATION              None                                            (.5;  )                 0.963893    614\n22  AFFINITY_CARD             1                EDUCATION              None  '10th', '11th', '12th', '1st-4th', '5th-6th', ...                 0.552590    352\n23  AFFINITY_CARD             1                EDUCATION              None                                            'Bach.'                 0.265306    169\n24  AFFINITY_CARD             1                EDUCATION              None                         'Masters', 'PhD', 'Profsc'                 0.182104    116\n25  AFFINITY_CARD             1     HOME_THEATER_PACKAGE              None                                            ( ; .5]                 0.164835    105\n26  AFFINITY_CARD             1     HOME_THEATER_PACKAGE              None                                            (.5;  )                 0.835165    532\n27  AFFINITY_CARD             1           HOUSEHOLD_SIZE              None                                         '1', '6-8'                 0.010989      7\n28  AFFINITY_CARD             1           HOUSEHOLD_SIZE              None                                          '2', '9+'                 0.127159     81\n29  AFFINITY_CARD             1           HOUSEHOLD_SIZE              None                                         '3', '4-5'                 0.861852    549\n30  AFFINITY_CARD             1               OCCUPATION              None     '?', 'Cleric.', 'Crafts', 'Farming', 'Machine'                 0.252747    161\n31  AFFINITY_CARD             1               OCCUPATION              None                        'Armed-F', 'Exec.', 'Prof.'                 0.470958    300\n32  AFFINITY_CARD             1               OCCUPATION              None                      'Handler', 'House-s', 'Other'                 0.031397     20\n33  AFFINITY_CARD             1               OCCUPATION              None           'Protec.', 'Sales', 'TechSup', 'Transp.'                 0.244898    156\n34  AFFINITY_CARD             1            YRS_RESIDENCE              None                                           ( ; 2.5]                 0.023548     15\n35  AFFINITY_CARD             1            YRS_RESIDENCE              None                                         (2.5; 3.5]                 0.105181     67\n36  AFFINITY_CARD             1            YRS_RESIDENCE              None                                         (3.5; 6.5]                 0.676609    431\n37  AFFINITY_CARD             1            YRS_RESIDENCE              None                                           (6.5;  )                 0.194662    124\n38  AFFINITY_CARD             1              Y_BOX_GAMES              None                                            ( ; .5]                 0.952904    607\n39  AFFINITY_CARD             1              Y_BOX_GAMES              None                                            (.5;  )                 0.047096     30\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test data","message":["%python","","nb_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1737857830130,"interpreter":"python.low","endTime":1737857830635,"results":[{"message":"0.80338\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score test data adding supplemental columns","message":["%python","","# Set the case ID attribute","case_id = 'CUST_ID'","# Gather the Predictions","RES_DF = nb_mod.predict(TEST_X, supplemental_cols = TEST_X)","# Additionally collect the PROBABILITY_OF_0 and PROBABILITY_OF_1","RES_PROB = nb_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","# Join the entire result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])","","z.show(RES_DF.head(10))"],"enabled":true,"result":{"startTime":1737857831058,"interpreter":"python.low","endTime":1737857831985,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY_OF_0\tPROBABILITY_OF_1\n101616\t0\t1\t0\tBach.\t0\t0\t1\tTechSup\t0\t1\t2\t1\t0\t0.9998823492272785\t0.00011765077272152528\n101364\t0\t1\t1\t< Bach.\t1\t0\t1\t?\t0\t1\t3\t1\t0\t0.999885393591948\t0.00011460640805204865\n101207\t0\t1\t1\tBach.\t1\t0\t2\tProf.\t0\t1\t1\t1\t0\t0.9981810127212492\t0.001818987278750956\n101845\t0\t1\t0\tBach.\t0\t1\t2\tProf.\t0\t1\t5\t0\t1\t0.3246401635869718\t0.6753598364130282\n100758\t1\t1\t0\tAssoc-V\t0\t1\t3\tCrafts\t0\t1\t4\t0\t1\t0.4003627272975348\t0.5996372727024653\n100442\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.009639158541689678\t0.9903608414583104\n100023\t0\t1\t1\tHS-grad\t1\t1\t3\tSales\t0\t1\t8\t0\t1\t0.1553193924255481\t0.8446806075744518\n104037\t0\t1\t1\tBach.\t1\t0\t9+\tSales\t0\t1\t3\t0\t0\t0.956442859206339\t0.04355714079366095\n102642\t0\t1\t1\tHS-grad\t1\t0\t9+\tTransp.\t0\t1\t3\t1\t0\t0.9987030290990759\t0.0012969709009241258\n102036\t0\t1\t0\tAssoc-V\t0\t1\t9+\tTechSup\t0\t1\t4\t0\t0\t0.7708388008314279\t0.22916119916857214\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display confusion matrix, lift chart and ROC curve ","message":["%python","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions","     and the target column,","     The Settings name (for the charts), ","     The name of the model used (for the charts),","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," ","    # Extract Statistics from the Confusion Matrix","    cf_local = conf_matrix.pull()","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","    ","    # Estimated AUC via Triangle (not very precise) could be","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","    # Compute real AUC using roc_curve by loading the","    # data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","    ","    # Store all statistics to export","    statistics = {'Algorithm' : name,","                  'Algorithm_setting' : settings_name,","                  'TN' : TN,","                  'TP' : TP,","                  'FP' : FP,","                  'FN' : FN,","                  'TPR' : TPR,","                  'FPR' : FPR,","                  'TNR' : TNR,","                  'FNR' : FNR,","                  'Precision' : Precision,","                  'Accuracy' : Accuracy,","                  'NPV' : NPV,","                  'DetectionRate' : DetectionRate,","                  'BalancedAccuracy' : BalancedAccuracy,","                  'AUC' : AUC,","                  'F1Score' : F1Score,","                  'MathewsCorrCoef' : MathewsCorrCoef","                  }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = round((TN/TOTAL*100),2)","    FP_P = round((FP/TOTAL*100),2)","    FN_P = round((FN/TOTAL*100),2)","    TP_P = round((TP/TOTAL*100),2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+","          \"F1Score: \"+str(round(F1Score,4))","          )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT, color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","                 textcoords='data', ","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","                                 connectionstyle='arc3, rad=0.3'), ","                 horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),","                 xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), ","                 xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13) ","    ax4.legend(loc=\"lower right\")","    ","    return(statistics, pred_local)","_ = evaluate_model(pred_data=RES_DF, settings_name='Pairwise and Singleton Thresholds=0', name='Na?ve Bayes', target='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1737857832420,"interpreter":"python.low","endTime":1737857834695,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1094 (61.63%)\tFalse Positive: 103 (5.8%)\nACTUAL 1\tFalse Negative: 246 (13.86%)\tTrue Positive: 332 (18.7%)\nAccuracy: 80.338%\tAUC: 0.858\tF1Score: 0.6555\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img src=data:image/png;base64,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diff --git a/machine-learning/notebooks-oml/python/OML4Py Classification NN.dsnb b/machine-learning/notebooks-oml/python/OML4Py Classification NN.dsnb
new file mode 100644
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+++ b/machine-learning/notebooks-oml/python/OML4Py Classification NN.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification NN","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733787113254,"interpreter":"md.low","endTime":1733787113717,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Neural Network","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification neural network model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733787114179,"interpreter":"md.low","endTime":1733787114617,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-neural-network\">Classification Modeling to Predict Target Customers using Neural Network<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification neural network model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1733787115088,"interpreter":"md.low","endTime":1733787115527,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1733787115984,"interpreter":"md.low","endTime":1733787116422,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1733787116875,"interpreter":"python.low","endTime":1733787117331,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","DEMO = oml.sync(table = \"SUPPLEMENTARY_DEMOGRAPHICS\", schema = \"SH\")","z.show(DEMO.head())"],"enabled":true,"result":{"startTime":1733787117787,"interpreter":"python.low","endTime":1733787118322,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNone\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%python","","DEMO.shape"],"enabled":true,"result":{"startTime":1733787118774,"interpreter":"python.low","endTime":1733787119232,"results":[{"message":"(4500, 14)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Affinity Card Responder\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab('AFFINITY_CARD'))"],"enabled":true,"result":{"startTime":1733787119689,"interpreter":"python.low","endTime":1733787120346,"results":[{"message":"AFFINITY_CARD\tcount\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Household Size split by Affinity Card Responder\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab(['HOUSEHOLD_SIZE', 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\"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\"]]"],"enabled":true,"result":{"startTime":1733787121982,"interpreter":"python.low","endTime":1733787122442,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first few records of DEMO_DF","message":["%python","","z.show(DEMO_DF.head())"],"enabled":true,"result":{"startTime":1733787122898,"interpreter":"python.low","endTime":1733787123437,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n102547\t0\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n101050\t0\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n100040\t0\t0\t1\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n102117\t0\t1\t0\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n101074\t0\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network regression model for predicting AFFINITY CARD","","---"],"enabled":true,"result":{"startTime":1733787123905,"interpreter":"md.low","endTime":1733787124360,"results":[{"message":"<h3 id=\"build-a-neural-network-regression-model-for-predicting-affinity-card\">Build a Neural Network regression model for predicting AFFINITY CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create train and test data (60/40 split) ","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X = TEST","TEST_Y = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1733787124821,"interpreter":"python.low","endTime":1733787125582,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Neural Network model using default settings","message":["%python","","try:","    oml.drop(model = 'NN_CLAS_MODEL') ","except:","    pass","","setting = dict()","nn_mod = oml.nn('classification', **setting)","nn_mod .fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'NN_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1733787126036,"interpreter":"python.low","endTime":1733787130021,"results":[{"message":"\nModel Name: NN_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1          CLAS_WEIGHTS_BALANCED                         OFF\n2       LBFGS_GRADIENT_TOLERANCE                  .000000001\n3            LBFGS_HISTORY_DEPTH                          20\n4            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n5               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n6             NNET_HIDDEN_LAYERS                           1\n7                NNET_ITERATIONS                         200\n8                 NNET_TOLERANCE                     .000001\n9                   ODMS_DETAILS                 ODMS_ENABLE\n10  ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n11              ODMS_RANDOM_SEED                           0\n12                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n13                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS             188\n2     LOSS_VALUE       0.0833814\n3       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        45  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE  TARGET_VALUE     WEIGHT\n0         0       0.0       0  BOOKKEEPING_APPLICATION              None            None           NaN  -6.155214\n1         0       0.0       1  BOOKKEEPING_APPLICATION              None            None           NaN  -1.890929\n2         0       0.0       2  BOOKKEEPING_APPLICATION              None            None           NaN  -8.059902\n3         0       0.0       3  BOOKKEEPING_APPLICATION              None            None           NaN   1.682937\n4         0       0.0       4  BOOKKEEPING_APPLICATION              None            None           NaN   1.139048\n...     ...       ...     ...                      ...               ...             ...           ...        ...\n2111      1      41.0       0                     None              None            None           0.0 -19.212336\n2112      1      42.0       0                     None              None            None           0.0  15.951291\n2113      1      43.0       0                     None              None            None           0.0   2.599071\n2114      1      44.0       0                     None              None            None           0.0  -2.921324\n2115      1       NaN       0                     None              None            None           0.0   0.472860\n\n[2116 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%python","nn_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1733787130498,"interpreter":"python.low","endTime":1733787131112,"results":[{"message":"0.849014\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Neural Networks ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4');\">Neural Networks<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","   ","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'","   ","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS.","> 'NNET_HIDDEN_LAYERS' : '3'","","> 'NNET_NODES_PER_LAYER' : '8,8,2'","","> 'NNET_ACTIVATIONS' : \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","    ","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> 'NNET_REGULARIZER' : 'NNET_REGULARIZER_HELDASIDE'","","> 'NNET_HELDASIDE_RATIO' : '0.25'","","> 'NNET_HELDASIDE_MAX_FAIL' : '6'","    ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> 'NNET_REG_LAMBDA' : '1'","","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> 'NNET_ITERATIONS' : '200'","","> 'NNET_TOLERANCE' : '0.000001'","","> 'ODMS_RANDOM_SEED' : '0'","","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> 'LBFGS_HISTORY_DEPTH' : '10'","","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> 'LBFGS_GRADIENT_TOLERANCE' : '0.0000001'","","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> 'LBFGS_SCALE_HESSIAN' : 'LBFGS_SCALE_HESSIAN_ENABLE'","","> 'LBFGS_SCALE_HESSIAN' : 'LBFGS_SCALE_HESSIAN_DISABLE'","    "],"enabled":true,"result":{"startTime":1733787131573,"interpreter":"md.low","endTime":1733787132057,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-neural-networks\">Examples of possible setting overrides for Neural Networks<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4');\">Neural Networks<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_HIDDEN_LAYERS' : '3'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_NODES_PER_LAYER' : '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_ACTIVATIONS' : &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_REGULARIZER' : 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_HELDASIDE_RATIO' : '0.25'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_HELDASIDE_MAX_FAIL' : '6'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_REG_LAMBDA' : '1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_ITERATIONS' : '200'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_TOLERANCE' : '0.000001'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'ODMS_RANDOM_SEED' : '0'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_HISTORY_DEPTH' : '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_GRADIENT_TOLERANCE' : '0.0000001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_SCALE_HESSIAN' : 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'LBFGS_SCALE_HESSIAN' : 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Neural Network Model using 3 hidden layers and various activation functions ","message":["%python","","try:","    oml.drop(model = 'NN_CLAS_MODEL') ","except:","    pass","","settings = {'NNET_HIDDEN_LAYERS':'3',","            'NNET_NODES_PER_LAYER':'8,8,2',","            'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\"}","","nn_mod = oml.nn('classification', **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'NN_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1733787132523,"interpreter":"python.low","endTime":1733787135509,"results":[{"message":"\nModel Name: NN_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                                      setting value\n0                      ALGO_NAME                                ALGO_NEURAL_NETWORK\n1          CLAS_WEIGHTS_BALANCED                                                OFF\n2       LBFGS_GRADIENT_TOLERANCE                                         .000000001\n3            LBFGS_HISTORY_DEPTH                                                 20\n4            LBFGS_SCALE_HESSIAN                         LBFGS_SCALE_HESSIAN_ENABLE\n5               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BI...\n6             NNET_HIDDEN_LAYERS                                                  3\n7                NNET_ITERATIONS                                                200\n8           NNET_NODES_PER_LAYER                                              8,8,2\n9                 NNET_TOLERANCE                                            .000001\n10                  ODMS_DETAILS                                        ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT                            ODMS_MISSING_VALUE_AUTO\n12              ODMS_RANDOM_SEED                                                  0\n13                 ODMS_SAMPLING                              ODMS_SAMPLING_DISABLE\n14                     PREP_AUTO                                                 ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.216223\n3       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0         8       NNET_ACTIVATIONS_ARCTAN\n1                1         8  NNET_ACTIVATIONS_BIPOLAR_SIG\n2                2         2         NNET_ACTIVATIONS_TANH\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE  TARGET_VALUE     WEIGHT\n0        0       0.0       0  BOOKKEEPING_APPLICATION              None            None           NaN   0.550972\n1        0       0.0       1  BOOKKEEPING_APPLICATION              None            None           NaN   4.022674\n2        0       0.0       2  BOOKKEEPING_APPLICATION              None            None           NaN  -0.831490\n3        0       0.0       3  BOOKKEEPING_APPLICATION              None            None           NaN  -5.051634\n4        0       0.0       4  BOOKKEEPING_APPLICATION              None            None           NaN   5.046059\n5        0       0.0       5  BOOKKEEPING_APPLICATION              None            None           NaN  -3.764502\n6        0       0.0       6  BOOKKEEPING_APPLICATION              None            None           NaN   2.760607\n7        0       0.0       7  BOOKKEEPING_APPLICATION              None            None           NaN  -0.462865\n8        0       1.0       0      BULK_PACK_DISKETTES              None            None           NaN  -4.544186\n9        0       1.0       1      BULK_PACK_DISKETTES              None            None           NaN   5.249557\n10       0       1.0       2      BULK_PACK_DISKETTES              None            None           NaN  -0.222991\n11       0       1.0       3      BULK_PACK_DISKETTES              None            None           NaN   3.329165\n12       0       1.0       4      BULK_PACK_DISKETTES              None            None           NaN   4.954588\n13       0       1.0       5      BULK_PACK_DISKETTES              None            None           NaN   1.185106\n14       0       1.0       6      BULK_PACK_DISKETTES              None            None           NaN   0.350928\n15       0       1.0       7      BULK_PACK_DISKETTES              None            None           NaN   0.826079\n16       0       2.0       0       FLAT_PANEL_MONITOR              None            None           NaN   0.975833\n17       0       2.0       1       FLAT_PANEL_MONITOR              None            None           NaN   2.668065\n18       0       2.0       2       FLAT_PANEL_MONITOR              None            None           NaN   2.149598\n19       0       2.0       3       FLAT_PANEL_MONITOR              None            None           NaN   0.047416\n20       0       2.0       4       FLAT_PANEL_MONITOR              None            None           NaN   2.442907\n21       0       2.0       5       FLAT_PANEL_MONITOR              None            None           NaN   1.906927\n22       0       2.0       6       FLAT_PANEL_MONITOR              None            None           NaN  -6.439255\n23       0       2.0       7       FLAT_PANEL_MONITOR              None            None           NaN   1.771014\n24       0       3.0       0     HOME_THEATER_PACKAGE              None            None           NaN  -1.481844\n25       0       3.0       1     HOME_THEATER_PACKAGE              None            None           NaN  -3.127951\n26       0       3.0       2     HOME_THEATER_PACKAGE              None            None           NaN  -3.115540\n27       0       3.0       3     HOME_THEATER_PACKAGE              None            None           NaN  -2.051754\n28       0       3.0       4     HOME_THEATER_PACKAGE              None            None           NaN  -4.685087\n29       0       3.0       5     HOME_THEATER_PACKAGE              None            None           NaN  -3.579628\n30       0       3.0       6     HOME_THEATER_PACKAGE              None            None           NaN  -0.233580\n31       0       3.0       7     HOME_THEATER_PACKAGE              None            None           NaN   1.738858\n32       0       4.0       0         OS_DOC_SET_KANJI              None            None           NaN   0.953441\n33       0       4.0       1         OS_DOC_SET_KANJI              None            None           NaN   0.016056\n34       0       4.0       2         OS_DOC_SET_KANJI              None            None           NaN  -0.590067\n35       0       4.0       3         OS_DOC_SET_KANJI              None            None           NaN   0.695657\n36       0       4.0       4         OS_DOC_SET_KANJI              None            None           NaN  -0.355026\n37       0       4.0       5         OS_DOC_SET_KANJI              None            None           NaN   0.202667\n38       0       4.0       6         OS_DOC_SET_KANJI              None            None           NaN   0.386489\n39       0       4.0       7         OS_DOC_SET_KANJI              None            None           NaN  -2.483962\n40       0       5.0       0         PRINTER_SUPPLIES              None            None           NaN   0.241342\n41       0       5.0       1         PRINTER_SUPPLIES              None            None           NaN   0.177983\n42       0       5.0       2         PRINTER_SUPPLIES              None            None           NaN  -0.593673\n43       0       5.0       3         PRINTER_SUPPLIES              None            None           NaN   0.297604\n44       0       5.0       4         PRINTER_SUPPLIES              None            None           NaN  -0.093283\n45       0       5.0       5         PRINTER_SUPPLIES              None            None           NaN  -0.019828\n46       0       5.0       6         PRINTER_SUPPLIES              None            None           NaN  -0.445602\n47       0       5.0       7         PRINTER_SUPPLIES              None            None           NaN  -0.523718\n48       0       6.0       0            YRS_RESIDENCE              None            None           NaN   0.639198\n49       0       6.0       1            YRS_RESIDENCE              None            None           NaN  -1.217046\n50       0       6.0       2            YRS_RESIDENCE              None            None           NaN   6.095151\n51       0       6.0       3            YRS_RESIDENCE              None            None           NaN  -3.623359\n52       0       6.0       4            YRS_RESIDENCE              None            None           NaN  -1.316432\n53       0       6.0       5            YRS_RESIDENCE              None            None           NaN   1.764602\n54       0       6.0       6            YRS_RESIDENCE              None            None           NaN  -2.868697\n55       0       6.0       7            YRS_RESIDENCE              None            None           NaN  -0.034549\n56       0       7.0       0              Y_BOX_GAMES              None            None           NaN  -0.310615\n57       0       7.0       1              Y_BOX_GAMES              None            None           NaN   4.085339\n58       0       7.0       2              Y_BOX_GAMES              None            None           NaN  -0.564503\n59       0       7.0       3              Y_BOX_GAMES              None            None           NaN  -1.957860\n60       0       7.0       4              Y_BOX_GAMES              None            None           NaN  -0.953833\n61       0       7.0       5              Y_BOX_GAMES              None            None           NaN  -2.519473\n62       0       7.0       6              Y_BOX_GAMES              None            None           NaN   1.254678\n63       0       7.0       7              Y_BOX_GAMES              None            None           NaN  -4.254691\n64       0       8.0       0                EDUCATION              None            10th           NaN  -2.231140\n65       0       8.0       1                EDUCATION              None            10th           NaN  -0.017877\n66       0       8.0       2                EDUCATION              None            10th           NaN   0.769051\n67       0       8.0       3                EDUCATION              None            10th           NaN  -3.353423\n68       0       8.0       4                EDUCATION              None            10th           NaN  -0.248788\n69       0       8.0       5                EDUCATION              None            10th           NaN  -2.752838\n70       0       8.0       6                EDUCATION              None            10th           NaN   1.425084\n71       0       8.0       7                EDUCATION              None            10th           NaN  -0.236381\n72       0       9.0       0                EDUCATION              None            11th           NaN  -4.781995\n73       0       9.0       1                EDUCATION              None            11th           NaN   0.861193\n74       0       9.0       2                EDUCATION              None            11th           NaN   2.481005\n75       0       9.0       3                EDUCATION              None            11th           NaN  -0.033838\n76       0       9.0       4                EDUCATION              None            11th           NaN  -1.417570\n77       0       9.0       5                EDUCATION              None            11th           NaN   0.711172\n78       0       9.0       6                EDUCATION              None            11th           NaN   0.900808\n79       0       9.0       7                EDUCATION              None            11th           NaN   1.640350\n80       0      10.0       0                EDUCATION              None            12th           NaN  -0.283217\n81       0      10.0       1                EDUCATION              None            12th           NaN   1.446613\n82       0      10.0       2                EDUCATION              None            12th           NaN   0.482450\n83       0      10.0       3                EDUCATION              None            12th           NaN  -1.313002\n84       0      10.0       4                EDUCATION              None            12th           NaN  -0.864781\n85       0      10.0       5                EDUCATION              None            12th           NaN  -3.034506\n86       0      10.0       6                EDUCATION              None            12th           NaN   0.228667\n87       0      10.0       7                EDUCATION              None            12th           NaN   0.063869\n88       0      11.0       0                EDUCATION              None         1st-4th           NaN   0.682317\n89       0      11.0       1                EDUCATION              None         1st-4th           NaN  -0.376695\n90       0      11.0       2                EDUCATION              None         1st-4th           NaN  -0.765374\n91       0      11.0       3                EDUCATION              None         1st-4th           NaN   0.980978\n92       0      11.0       4                EDUCATION              None         1st-4th           NaN   0.444260\n93       0      11.0       5                EDUCATION              None         1st-4th           NaN   1.261375\n94       0      11.0       6                EDUCATION              None         1st-4th           NaN  -0.599409\n95       0      11.0       7                EDUCATION              None         1st-4th           NaN  -0.747764\n96       0      12.0       0                EDUCATION              None         5th-6th           NaN   2.350087\n97       0      12.0       1                EDUCATION              None         5th-6th           NaN  -1.194453\n98       0      12.0       2                EDUCATION              None         5th-6th           NaN  -0.476406\n99       0      12.0       3                EDUCATION              None         5th-6th           NaN   1.755521\n100      0      12.0       4                EDUCATION              None         5th-6th           NaN  -0.062421\n101      0      12.0       5                EDUCATION              None         5th-6th           NaN   2.165119\n102      0      12.0       6                EDUCATION              None         5th-6th           NaN  -0.265784\n103      0      12.0       7                EDUCATION              None         5th-6th           NaN  -0.860173\n104      0      13.0       0                EDUCATION              None         7th-8th           NaN   2.219977\n105      0      13.0       1                EDUCATION              None         7th-8th           NaN  -0.577905\n106      0      13.0       2                EDUCATION              None         7th-8th           NaN  -0.186361\n107      0      13.0       3                EDUCATION              None         7th-8th           NaN   2.749431\n108      0      13.0       4                EDUCATION              None         7th-8th           NaN  -0.255316\n109      0      13.0       5                EDUCATION              None         7th-8th           NaN   1.906492\n110      0      13.0       6                EDUCATION              None         7th-8th           NaN  -1.888525\n111      0      13.0       7                EDUCATION              None         7th-8th           NaN   0.200544\n112      0      14.0       0                EDUCATION              None             9th           NaN   1.560602\n113      0      14.0       1                EDUCATION              None             9th           NaN  -0.859454\n114      0      14.0       2                EDUCATION              None             9th           NaN  -2.378861\n115      0      14.0       3                EDUCATION              None             9th           NaN  -0.214686\n116      0      14.0       4                EDUCATION              None             9th           NaN   0.332187\n117      0      14.0       5                EDUCATION              None             9th           NaN  -0.428085\n118      0      14.0       6                EDUCATION              None             9th           NaN   0.163591\n119      0      14.0       7                EDUCATION              None             9th           NaN  -0.599878\n120      0      15.0       0                EDUCATION              None         < Bach.           NaN  -0.077475\n121      0      15.0       1                EDUCATION              None         < Bach.           NaN  -2.678006\n122      0      15.0       2                EDUCATION              None         < Bach.           NaN  -1.599209\n123      0      15.0       3                EDUCATION              None         < Bach.           NaN   0.475446\n124      0      15.0       4                EDUCATION              None         < Bach.           NaN   1.054764\n125      0      15.0       5                EDUCATION              None         < Bach.           NaN  -1.915758\n126      0      15.0       6                EDUCATION              None         < Bach.           NaN   3.236350\n127      0      15.0       7                EDUCATION              None         < Bach.           NaN   2.816953\n128      0      16.0       0                EDUCATION              None         Assoc-A           NaN   0.687381\n129      0      16.0       1                EDUCATION              None         Assoc-A           NaN  -2.689756\n130      0      16.0       2                EDUCATION              None         Assoc-A           NaN  -0.724088\n131      0      16.0       3                EDUCATION              None         Assoc-A           NaN  -6.363675\n132      0      16.0       4                EDUCATION              None         Assoc-A           NaN  -0.057052\n133      0      16.0       5                EDUCATION              None         Assoc-A           NaN  -1.950162\n134      0      16.0       6                EDUCATION              None         Assoc-A           NaN   2.509943\n135      0      16.0       7                EDUCATION              None         Assoc-A           NaN  -1.745707\n136      0      17.0       0                EDUCATION              None         Assoc-V           NaN   0.489882\n137      0      17.0       1                EDUCATION              None         Assoc-V           NaN   3.823702\n138      0      17.0       2                EDUCATION              None         Assoc-V           NaN  -4.671725\n139      0      17.0       3                EDUCATION              None         Assoc-V           NaN  -1.590698\n140      0      17.0       4                EDUCATION              None         Assoc-V           NaN   2.599469\n141      0      17.0       5                EDUCATION              None         Assoc-V           NaN  -0.966022\n142      0      17.0       6                EDUCATION              None         Assoc-V           NaN  -0.146461\n143      0      17.0       7                EDUCATION              None         Assoc-V           NaN  -1.700121\n144      0      18.0       0                EDUCATION              None           Bach.           NaN   3.003522\n145      0      18.0       1                EDUCATION              None           Bach.           NaN  -4.053027\n146      0      18.0       2                EDUCATION              None           Bach.           NaN   4.810584\n147      0      18.0       3                EDUCATION              None           Bach.           NaN   0.060939\n148      0      18.0       4                EDUCATION              None           Bach.           NaN  -3.494798\n149      0      18.0       5                EDUCATION              None           Bach.           NaN   3.759932\n150      0      18.0       6                EDUCATION              None           Bach.           NaN   1.990793\n151      0      18.0       7                EDUCATION              None           Bach.           NaN   2.821279\n152      0      19.0       0                EDUCATION              None         HS-grad           NaN  -0.577378\n153      0      19.0       1                EDUCATION              None         HS-grad           NaN  -3.055116\n154      0      19.0       2                EDUCATION              None         HS-grad           NaN  -1.199511\n155      0      19.0       3                EDUCATION              None         HS-grad           NaN   2.316586\n156      0      19.0       4                EDUCATION              None         HS-grad           NaN  -3.783206\n157      0      19.0       5                EDUCATION              None         HS-grad           NaN   2.144846\n158      0      19.0       6                EDUCATION              None         HS-grad           NaN  -4.378627\n159      0      19.0       7                EDUCATION              None         HS-grad           NaN   0.075112\n160      0      20.0       0                EDUCATION              None         Masters           NaN  -2.129630\n161      0      20.0       1                EDUCATION              None         Masters           NaN  -0.068145\n162      0      20.0       2                EDUCATION              None         Masters           NaN   1.086933\n163      0      20.0       3                EDUCATION              None         Masters           NaN   3.336988\n164      0      20.0       4                EDUCATION              None         Masters           NaN   1.985902\n165      0      20.0       5                EDUCATION              None         Masters           NaN  -2.872064\n166      0      20.0       6                EDUCATION              None         Masters           NaN   1.714786\n167      0      20.0       7                EDUCATION              None         Masters           NaN   1.332158\n168      0      21.0       0                EDUCATION              None             PhD           NaN  -3.668654\n169      0      21.0       1                EDUCATION              None             PhD           NaN   4.026777\n170      0      21.0       2                EDUCATION              None             PhD           NaN   3.498508\n171      0      21.0       3                EDUCATION              None             PhD           NaN  -3.282219\n172      0      21.0       4                EDUCATION              None             PhD           NaN   2.157389\n173      0      21.0       5                EDUCATION              None             PhD           NaN  -2.227621\n174      0      21.0       6                EDUCATION              None             PhD           NaN  -2.436483\n175      0      21.0       7                EDUCATION              None             PhD           NaN  -2.273367\n176      0      22.0       0                EDUCATION              None         Presch.           NaN   0.233669\n177      0      22.0       1                EDUCATION              None         Presch.           NaN   0.146263\n178      0      22.0       2                EDUCATION              None         Presch.           NaN   0.212361\n179      0      22.0       3                EDUCATION              None         Presch.           NaN   0.037750\n180      0      22.0       4                EDUCATION              None         Presch.           NaN  -0.023505\n181      0      22.0       5                EDUCATION              None         Presch.           NaN  -0.030250\n182      0      22.0       6                EDUCATION              None         Presch.           NaN   0.048143\n183      0      22.0       7                EDUCATION              None         Presch.           NaN  -0.068044\n184      0      23.0       0                EDUCATION              None          Profsc           NaN   1.636102\n185      0      23.0       1                EDUCATION              None          Profsc           NaN   4.441040\n186      0      23.0       2                EDUCATION              None          Profsc           NaN  -0.645189\n187      0      23.0       3                EDUCATION              None          Profsc           NaN   0.638435\n188      0      23.0       4                EDUCATION              None          Profsc           NaN   0.604769\n189      0      23.0       5                EDUCATION              None          Profsc           NaN   1.673764\n190      0      23.0       6                EDUCATION              None          Profsc           NaN   0.610700\n191      0      23.0       7                EDUCATION              None          Profsc           NaN  -1.174869\n192      0      24.0       0           HOUSEHOLD_SIZE              None               1           NaN   6.593012\n193      0      24.0       1           HOUSEHOLD_SIZE              None               1           NaN   1.603573\n194      0      24.0       2           HOUSEHOLD_SIZE              None               1           NaN   0.264902\n195      0      24.0       3           HOUSEHOLD_SIZE              None               1           NaN   2.251463\n196      0      24.0       4           HOUSEHOLD_SIZE              None               1           NaN  -2.323275\n197      0      24.0       5           HOUSEHOLD_SIZE              None               1           NaN   2.372668\n198      0      24.0       6           HOUSEHOLD_SIZE              None               1           NaN  -0.307006\n199      0      24.0       7           HOUSEHOLD_SIZE              None               1           NaN   0.216369\n200      0      25.0       0           HOUSEHOLD_SIZE              None               2           NaN   2.665446\n201      0      25.0       1           HOUSEHOLD_SIZE              None               2           NaN  -1.809309\n202      0      25.0       2           HOUSEHOLD_SIZE              None               2           NaN  -4.050093\n203      0      25.0       3           HOUSEHOLD_SIZE              None               2           NaN  -1.821795\n204      0      25.0       4           HOUSEHOLD_SIZE              None               2           NaN  -3.512369\n205      0      25.0       5           HOUSEHOLD_SIZE              None               2           NaN   0.163726\n206      0      25.0       6           HOUSEHOLD_SIZE              None               2           NaN   0.227448\n207      0      25.0       7           HOUSEHOLD_SIZE              None               2           NaN   0.561015\n208      0      26.0       0           HOUSEHOLD_SIZE              None               3           NaN  -5.041394\n209      0      26.0       1           HOUSEHOLD_SIZE              None               3           NaN  -0.496901\n210      0      26.0       2           HOUSEHOLD_SIZE              None               3           NaN   0.301859\n211      0      26.0       3           HOUSEHOLD_SIZE              None               3           NaN  -2.942151\n212      0      26.0       4           HOUSEHOLD_SIZE              None               3           NaN  -3.179928\n213      0      26.0       5           HOUSEHOLD_SIZE              None               3           NaN  -6.143895\n214      0      26.0       6           HOUSEHOLD_SIZE              None               3           NaN  -1.264661\n215      0      26.0       7           HOUSEHOLD_SIZE              None               3           NaN   1.641789\n216      0      27.0       0           HOUSEHOLD_SIZE              None             4-5           NaN  -7.183694\n217      0      27.0       1           HOUSEHOLD_SIZE              None             4-5           NaN   0.156796\n218      0      27.0       2           HOUSEHOLD_SIZE              None             4-5           NaN   5.893699\n219      0      27.0       3           HOUSEHOLD_SIZE              None             4-5           NaN  -7.138889\n220      0      27.0       4           HOUSEHOLD_SIZE              None             4-5           NaN   3.304240\n221      0      27.0       5           HOUSEHOLD_SIZE              None             4-5           NaN  -4.585369\n222      0      27.0       6           HOUSEHOLD_SIZE              None             4-5           NaN  -0.983136\n223      0      27.0       7           HOUSEHOLD_SIZE              None             4-5           NaN  -0.989369\n224      0      28.0       0           HOUSEHOLD_SIZE              None             6-8           NaN   1.254129\n225      0      28.0       1           HOUSEHOLD_SIZE              None             6-8           NaN  -0.306097\n226      0      28.0       2           HOUSEHOLD_SIZE              None             6-8           NaN  -3.271849\n227      0      28.0       3           HOUSEHOLD_SIZE              None             6-8           NaN   2.963470\n228      0      28.0       4           HOUSEHOLD_SIZE              None             6-8           NaN   1.934052\n229      0      28.0       5           HOUSEHOLD_SIZE              None             6-8           NaN   4.470858\n230      0      28.0       6           HOUSEHOLD_SIZE              None             6-8           NaN   1.997109\n231      0      28.0       7           HOUSEHOLD_SIZE              None             6-8           NaN   1.382087\n232      0      29.0       0           HOUSEHOLD_SIZE              None              9+           NaN   0.675574\n233      0      29.0       1           HOUSEHOLD_SIZE              None              9+           NaN  -1.279190\n234      0      29.0       2           HOUSEHOLD_SIZE              None              9+           NaN   1.636918\n235      0      29.0       3           HOUSEHOLD_SIZE              None              9+           NaN   4.528144\n236      0      29.0       4           HOUSEHOLD_SIZE              None              9+           NaN   2.490247\n237      0      29.0       5           HOUSEHOLD_SIZE              None              9+           NaN   1.942492\n238      0      29.0       6           HOUSEHOLD_SIZE              None              9+           NaN   1.575996\n239      0      29.0       7           HOUSEHOLD_SIZE              None              9+           NaN  -2.022382\n240      0      30.0       0               OCCUPATION              None               ?           NaN  -0.340473\n241      0      30.0       1               OCCUPATION              None               ?           NaN  -1.405698\n242      0      30.0       2               OCCUPATION              None               ?           NaN  -2.834033\n243      0      30.0       3               OCCUPATION              None               ?           NaN  -1.364534\n244      0      30.0       4               OCCUPATION              None               ?           NaN   0.237536\n245      0      30.0       5               OCCUPATION              None               ?           NaN  -0.638941\n246      0      30.0       6               OCCUPATION              None               ?           NaN   0.416838\n247      0      30.0       7               OCCUPATION              None               ?           NaN  -1.698270\n248      0      31.0       0               OCCUPATION              None         Armed-F           NaN  -0.447618\n249      0      31.0       1               OCCUPATION              None         Armed-F           NaN   0.437226\n250      0      31.0       2               OCCUPATION              None         Armed-F           NaN   0.207561\n251      0      31.0       3               OCCUPATION              None         Armed-F           NaN  -0.201549\n252      0      31.0       4               OCCUPATION              None         Armed-F           NaN  -0.826934\n253      0      31.0       5               OCCUPATION              None         Armed-F           NaN  -0.072368\n254      0      31.0       6               OCCUPATION              None         Armed-F           NaN   0.538664\n255      0      31.0       7               OCCUPATION              None         Armed-F           NaN   0.139859\n256      0      32.0       0               OCCUPATION              None         Cleric.           NaN  -3.545373\n257      0      32.0       1               OCCUPATION              None         Cleric.           NaN   1.802669\n258      0      32.0       2               OCCUPATION              None         Cleric.           NaN  -2.851766\n259      0      32.0       3               OCCUPATION              None         Cleric.           NaN  -0.887672\n260      0      32.0       4               OCCUPATION              None         Cleric.           NaN   1.128071\n261      0      32.0       5               OCCUPATION              None         Cleric.           NaN  -5.895855\n262      0      32.0       6               OCCUPATION              None         Cleric.           NaN  -1.926913\n263      0      32.0       7               OCCUPATION              None         Cleric.           NaN   1.895135\n264      0      33.0       0               OCCUPATION              None          Crafts           NaN  -0.031263\n265      0      33.0       1               OCCUPATION              None          Crafts           NaN  -4.990289\n266      0      33.0       2               OCCUPATION              None          Crafts           NaN   1.674400\n267      0      33.0       3               OCCUPATION              None          Crafts           NaN   2.169872\n268      0      33.0       4               OCCUPATION              None          Crafts           NaN  -0.714804\n269      0      33.0       5               OCCUPATION              None          Crafts           NaN  -1.848532\n270      0      33.0       6               OCCUPATION              None          Crafts           NaN  -5.279646\n271      0      33.0       7               OCCUPATION              None          Crafts           NaN   2.161464\n272      0      34.0       0               OCCUPATION              None           Exec.           NaN  -3.865130\n273      0      34.0       1               OCCUPATION              None           Exec.           NaN   1.358623\n274      0      34.0       2               OCCUPATION              None           Exec.           NaN  -1.931001\n275      0      34.0       3               OCCUPATION              None           Exec.           NaN   0.499997\n276      0      34.0       4               OCCUPATION              None           Exec.           NaN  -3.043084\n277      0      34.0       5               OCCUPATION              None           Exec.           NaN   2.562355\n278      0      34.0       6               OCCUPATION              None           Exec.           NaN   4.612208\n279      0      34.0       7               OCCUPATION              None           Exec.           NaN   1.003573\n280      0      35.0       0               OCCUPATION              None         Farming           NaN   0.507339\n281      0      35.0       1               OCCUPATION              None         Farming           NaN  -3.104054\n282      0      35.0       2               OCCUPATION              None         Farming           NaN  -1.932887\n283      0      35.0       3               OCCUPATION              None         Farming           NaN  -0.414322\n284      0      35.0       4               OCCUPATION              None         Farming           NaN   2.285649\n285      0      35.0       5               OCCUPATION              None         Farming           NaN  -0.059162\n286      0      35.0       6               OCCUPATION              None         Farming           NaN  -0.930402\n287      0      35.0       7               OCCUPATION              None         Farming           NaN   1.051785\n288      0      36.0       0               OCCUPATION              None         Handler           NaN   5.059105\n289      0      36.0       1               OCCUPATION              None         Handler           NaN  -0.870008\n290      0      36.0       2               OCCUPATION              None         Handler           NaN  -3.224161\n291      0      36.0       3               OCCUPATION              None         Handler           NaN   2.131522\n292      0      36.0       4               OCCUPATION              None         Handler           NaN  -2.509547\n293      0      36.0       5               OCCUPATION              None         Handler           NaN   1.595869\n294      0      36.0       6               OCCUPATION              None         Handler           NaN   2.184221\n295      0      36.0       7               OCCUPATION              None         Handler           NaN   3.672315\n296      0      37.0       0               OCCUPATION              None         House-s           NaN   0.766577\n297      0      37.0       1               OCCUPATION              None         House-s           NaN  -0.164899\n298      0      37.0       2               OCCUPATION              None         House-s           NaN  -0.448043\n299      0      37.0       3               OCCUPATION              None         House-s           NaN   0.504165\n300      0      37.0       4               OCCUPATION              None         House-s           NaN   0.215810\n301      0      37.0       5               OCCUPATION              None         House-s           NaN   0.683207\n302      0      37.0       6               OCCUPATION              None         House-s           NaN  -0.471185\n303      0      37.0       7               OCCUPATION              None         House-s           NaN  -0.368864\n304      0      38.0       0               OCCUPATION              None         Machine           NaN   2.277547\n305      0      38.0       1               OCCUPATION              None         Machine           NaN  -2.084400\n306      0      38.0       2               OCCUPATION              None         Machine           NaN   4.101360\n307      0      38.0       3               OCCUPATION              None         Machine           NaN   5.414098\n308      0      38.0       4               OCCUPATION              None         Machine           NaN  -2.907969\n309      0      38.0       5               OCCUPATION              None         Machine           NaN   0.273945\n310      0      38.0       6               OCCUPATION              None         Machine           NaN   3.162361\n311      0      38.0       7               OCCUPATION              None         Machine           NaN   0.973190\n312      0      39.0       0               OCCUPATION              None           Other           NaN   8.803913\n313      0      39.0       1               OCCUPATION              None           Other           NaN  -0.521883\n314      0      39.0       2               OCCUPATION              None           Other           NaN  -0.579962\n315      0      39.0       3               OCCUPATION              None           Other           NaN  -2.273106\n316      0      39.0       4               OCCUPATION              None           Other           NaN  -3.451223\n317      0      39.0       5               OCCUPATION              None           Other           NaN   0.346033\n318      0      39.0       6               OCCUPATION              None           Other           NaN   1.996006\n319      0      39.0       7               OCCUPATION              None           Other           NaN   2.682784\n320      0      40.0       0               OCCUPATION              None           Prof.           NaN  -3.113103\n321      0      40.0       1               OCCUPATION              None           Prof.           NaN   0.443587\n322      0      40.0       2               OCCUPATION              None           Prof.           NaN   6.191641\n323      0      40.0       3               OCCUPATION              None           Prof.           NaN  -0.700308\n324      0      40.0       4               OCCUPATION              None           Prof.           NaN   4.558704\n325      0      40.0       5               OCCUPATION              None           Prof.           NaN  -0.209167\n326      0      40.0       6               OCCUPATION              None           Prof.           NaN  -0.466015\n327      0      40.0       7               OCCUPATION              None           Prof.           NaN   0.053061\n328      0      41.0       0               OCCUPATION              None         Protec.           NaN  -2.464718\n329      0      41.0       1               OCCUPATION              None         Protec.           NaN   2.145325\n330      0      41.0       2               OCCUPATION              None         Protec.           NaN   0.710177\n331      0      41.0       3               OCCUPATION              None         Protec.           NaN  -4.680210\n332      0      41.0       4               OCCUPATION              None         Protec.           NaN   1.141968\n333      0      41.0       5               OCCUPATION              None         Protec.           NaN  -2.000889\n334      0      41.0       6               OCCUPATION              None         Protec.           NaN  -3.034615\n335      0      41.0       7               OCCUPATION              None         Protec.           NaN  -3.494889\n336      0      42.0       0               OCCUPATION              None           Sales           NaN  -6.097912\n337      0      42.0       1               OCCUPATION              None           Sales           NaN   1.953833\n338      0      42.0       2               OCCUPATION              None           Sales           NaN   2.549368\n339      0      42.0       3               OCCUPATION              None           Sales           NaN  -0.307911\n340      0      42.0       4               OCCUPATION              None           Sales           NaN   4.333625\n341      0      42.0       5               OCCUPATION              None           Sales           NaN  -0.043779\n342      0      42.0       6               OCCUPATION              None           Sales           NaN  -0.410939\n343      0      42.0       7               OCCUPATION              None           Sales           NaN  -4.879262\n344      0      43.0       0               OCCUPATION              None         TechSup           NaN   2.216369\n345      0      43.0       1               OCCUPATION              None         TechSup           NaN   4.388638\n346      0      43.0       2               OCCUPATION              None         TechSup           NaN   0.969149\n347      0      43.0       3               OCCUPATION              None         TechSup           NaN   0.267105\n348      0      43.0       4               OCCUPATION              None         TechSup           NaN  -1.122906\n349      0      43.0       5               OCCUPATION              None         TechSup           NaN   1.833922\n350      0      43.0       6               OCCUPATION              None         TechSup           NaN   1.360469\n351      0      43.0       7               OCCUPATION              None         TechSup           NaN  -3.369555\n352      0      44.0       0               OCCUPATION              None         Transp.           NaN  -1.580470\n353      0      44.0       1               OCCUPATION              None         Transp.           NaN  -1.629444\n354      0      44.0       2               OCCUPATION              None         Transp.           NaN  -1.848462\n355      0      44.0       3               OCCUPATION              None         Transp.           NaN  -1.274918\n356      0      44.0       4               OCCUPATION              None         Transp.           NaN  -1.983952\n357      0      44.0       5               OCCUPATION              None         Transp.           NaN   0.300409\n358      0      44.0       6               OCCUPATION              None         Transp.           NaN   0.869817\n359      0      44.0       7               OCCUPATION              None         Transp.           NaN   0.665342\n360      0       NaN       0                     None              None            None           NaN  -1.558044\n361      0       NaN       1                     None              None            None           NaN  -1.468525\n362      0       NaN       2                     None              None            None           NaN   0.995176\n363      0       NaN       3                     None              None            None           NaN  -2.804476\n364      0       NaN       4                     None              None            None           NaN  -1.658077\n365      0       NaN       5                     None              None            None           NaN  -1.965502\n366      0       NaN       6                     None              None            None           NaN   2.160845\n367      0       NaN       7                     None              None            None           NaN   0.754148\n368      1       0.0       0                     None              None            None           NaN  -0.663399\n369      1       0.0       1                     None              None            None           NaN  -1.758109\n370      1       0.0       2                     None              None            None           NaN  -1.624087\n371      1       0.0       3                     None              None            None           NaN  -1.413265\n372      1       0.0       4                     None              None            None           NaN  -0.377721\n373      1       0.0       5                     None              None            None           NaN  -3.661956\n374      1       0.0       6                     None              None            None           NaN   1.695092\n375      1       0.0       7                     None              None            None           NaN   1.367202\n376      1       1.0       0                     None              None            None           NaN   0.458008\n377      1       1.0       1                     None              None            None           NaN   2.532465\n378      1       1.0       2                     None              None            None           NaN  -1.191540\n379      1       1.0       3                     None              None            None           NaN   2.363947\n380      1       1.0       4                     None              None            None           NaN  -0.010308\n381      1       1.0       5                     None              None            None           NaN   0.016054\n382      1       1.0       6                     None              None            None           NaN   0.569328\n383      1       1.0       7                     None              None            None           NaN   0.338322\n384      1       2.0       0                     None              None            None           NaN   0.269925\n385      1       2.0       1                     None              None            None           NaN  -3.171036\n386      1       2.0       2                     None              None            None           NaN  -0.246926\n387      1       2.0       3                     None              None            None           NaN  -0.419957\n388      1       2.0       4                     None              None            None           NaN   0.029047\n389      1       2.0       5                     None              None            None           NaN  -3.658805\n390      1       2.0       6                     None              None            None           NaN  -2.902350\n391      1       2.0       7                     None              None            None           NaN   3.157396\n392      1       3.0       0                     None              None            None           NaN   0.768667\n393      1       3.0       1                     None              None            None           NaN  -0.529176\n394      1       3.0       2                     None              None            None           NaN  -0.467558\n395      1       3.0       3                     None              None            None           NaN  -0.747024\n396      1       3.0       4                     None              None            None           NaN   0.605948\n397      1       3.0       5                     None              None            None           NaN   3.145863\n398      1       3.0       6                     None              None            None           NaN   0.286752\n399      1       3.0       7                     None              None            None           NaN  -0.711415\n400      1       4.0       0                     None              None            None           NaN   0.682421\n401      1       4.0       1                     None              None            None           NaN  -0.081940\n402      1       4.0       2                     None              None            None           NaN  -0.229453\n403      1       4.0       3                     None              None            None           NaN   2.551194\n404      1       4.0       4                     None              None            None           NaN  -1.945702\n405      1       4.0       5                     None              None            None           NaN   6.284746\n406      1       4.0       6                     None              None            None           NaN  -0.159770\n407      1       4.0       7                     None              None            None           NaN  -0.680065\n408      1       5.0       0                     None              None            None           NaN  -0.320655\n409      1       5.0       1                     None              None            None           NaN   1.080205\n410      1       5.0       2                     None              None            None           NaN   1.981817\n411      1       5.0       3                     None              None            None           NaN  -0.554559\n412      1       5.0       4                     None              None            None           NaN  -2.974085\n413      1       5.0       5                     None              None            None           NaN  -0.833303\n414      1       5.0       6                     None              None            None           NaN   3.475878\n415      1       5.0       7                     None              None            None           NaN  -1.377325\n416      1       6.0       0                     None              None            None           NaN  -0.722386\n417      1       6.0       1                     None              None            None           NaN  -0.509972\n418      1       6.0       2                     None              None            None           NaN   1.088560\n419      1       6.0       3                     None              None            None           NaN  -0.809390\n420      1       6.0       4                     None              None            None           NaN  -0.160827\n421      1       6.0       5                     None              None            None           NaN  -3.321414\n422      1       6.0       6                     None              None            None           NaN  -0.339394\n423      1       6.0       7                     None              None            None           NaN   1.164097\n424      1       7.0       0                     None              None            None           NaN  -0.547512\n425      1       7.0       1                     None              None            None           NaN  -1.628488\n426      1       7.0       2                     None              None            None           NaN   0.035423\n427      1       7.0       3                     None              None            None           NaN  -1.746518\n428      1       7.0       4                     None              None            None           NaN   2.761440\n429      1       7.0       5                     None              None            None           NaN  -6.289808\n430      1       7.0       6                     None              None            None           NaN  -1.711682\n431      1       7.0       7                     None              None            None           NaN   0.408167\n432      1       NaN       0                     None              None            None           NaN  -0.883661\n433      1       NaN       1                     None              None            None           NaN  -0.020712\n434      1       NaN       2                     None              None            None           NaN   1.030562\n435      1       NaN       3                     None              None            None           NaN  -1.858612\n436      1       NaN       4                     None              None            None           NaN  -1.180546\n437      1       NaN       5                     None              None            None           NaN   0.515139\n438      1       NaN       6                     None              None            None           NaN   0.281073\n439      1       NaN       7                     None              None            None           NaN   0.397373\n440      2       0.0       0                     None              None            None           NaN  -0.094725\n441      2       0.0       1                     None              None            None           NaN  -0.439570\n442      2       1.0       0                     None              None            None           NaN  -0.389210\n443      2       1.0       1                     None              None            None           NaN  -0.333961\n444      2       2.0       0                     None              None            None           NaN  -0.378884\n445      2       2.0       1                     None              None            None           NaN  -1.369265\n446      2       3.0       0                     None              None            None           NaN  -1.762147\n447      2       3.0       1                     None              None            None           NaN   2.455322\n448      2       4.0       0                     None              None            None           NaN  -0.329539\n449      2       4.0       1                     None              None            None           NaN   2.081528\n450      2       5.0       0                     None              None            None           NaN   1.737203\n451      2       5.0       1                     None              None            None           NaN  -1.712433\n452      2       6.0       0                     None              None            None           NaN   1.602684\n453      2       6.0       1                     None              None            None           NaN  -1.264809\n454      2       7.0       0                     None              None            None           NaN  -0.384718\n455      2       7.0       1                     None              None            None           NaN   0.597912\n456      2       NaN       0                     None              None            None           NaN   2.249809\n457      2       NaN       1                     None              None            None           NaN   0.671454\n458      3       0.0       0                     None              None            None           0.0 -11.542191\n459      3       1.0       0                     None              None            None           0.0   4.572625\n460      3       NaN       0                     None              None            None           0.0  -2.598320\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%python","","nn_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1733787135968,"interpreter":"python.low","endTime":1733787136533,"results":[{"message":"0.666479\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Use 3 hidden layers and activation functions with held-aside validation and early stopping","message":["%python","","try:","    oml.drop(model = 'NN_CLAS_MODEL') ","except:","    pass","    ","settings = {'NNET_HIDDEN_LAYERS':'3',","            'NNET_NODES_PER_LAYER':'8,8,2',","            'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\",","            'NNET_HELDASIDE_MAX_FAIL':'6',","            'NNET_HELDASIDE_RATIO':'0.25',","            'NNET_ITERATIONS':'200',","            'NNET_REGULARIZER':'NNET_REGULARIZER_HELDASIDE',","            'NNET_TOLERANCE':'0.000001',","            'ODMS_RANDOM_SEED':'0'","            }","","nn_mod = oml.nn(**settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'NN_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1733787136989,"interpreter":"python.low","endTime":1733787139167,"results":[{"message":"\nModel Name: NN_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                                      setting value\n0                      ALGO_NAME                                ALGO_NEURAL_NETWORK\n1          CLAS_WEIGHTS_BALANCED                                                OFF\n2       LBFGS_GRADIENT_TOLERANCE                                         .000000001\n3            LBFGS_HISTORY_DEPTH                                                 20\n4            LBFGS_SCALE_HESSIAN                         LBFGS_SCALE_HESSIAN_ENABLE\n5               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BI...\n6        NNET_HELDASIDE_MAX_FAIL                                                  6\n7           NNET_HELDASIDE_RATIO                                               0.25\n8             NNET_HIDDEN_LAYERS                                                  3\n9                NNET_ITERATIONS                                                200\n10          NNET_NODES_PER_LAYER                                              8,8,2\n11              NNET_REGULARIZER                         NNET_REGULARIZER_HELDASIDE\n12                NNET_TOLERANCE                                           0.000001\n13                  ODMS_DETAILS                                        ODMS_ENABLE\n14  ODMS_MISSING_VALUE_TREATMENT                            ODMS_MISSING_VALUE_AUTO\n15              ODMS_RANDOM_SEED                                                  0\n16                 ODMS_SAMPLING                              ODMS_SAMPLING_DISABLE\n17                     PREP_AUTO                                                 ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              28\n2     LOSS_VALUE        0.360231\n3       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0         8       NNET_ACTIVATIONS_ARCTAN\n1                1         8  NNET_ACTIVATIONS_BIPOLAR_SIG\n2                2         2         NNET_ACTIVATIONS_TANH\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE  TARGET_VALUE    WEIGHT\n0        0       0.0       0  BOOKKEEPING_APPLICATION              None            None           NaN  0.134045\n1        0       0.0       1  BOOKKEEPING_APPLICATION              None            None           NaN  0.253737\n2        0       0.0       2  BOOKKEEPING_APPLICATION              None            None           NaN -0.412109\n3        0       0.0       3  BOOKKEEPING_APPLICATION              None            None           NaN -0.813264\n4        0       0.0       4  BOOKKEEPING_APPLICATION              None            None           NaN  0.506567\n5        0       0.0       5  BOOKKEEPING_APPLICATION              None            None           NaN -0.596734\n6        0       0.0       6  BOOKKEEPING_APPLICATION              None            None           NaN  0.571495\n7        0       0.0       7  BOOKKEEPING_APPLICATION              None            None           NaN -0.059153\n8        0       1.0       0      BULK_PACK_DISKETTES              None            None           NaN -0.404528\n9        0       1.0       1      BULK_PACK_DISKETTES              None            None           NaN  0.536848\n10       0       1.0       2      BULK_PACK_DISKETTES              None            None           NaN  0.408651\n11       0       1.0       3      BULK_PACK_DISKETTES              None            None           NaN  0.857359\n12       0       1.0       4      BULK_PACK_DISKETTES              None            None           NaN  0.486393\n13       0       1.0       5      BULK_PACK_DISKETTES              None            None           NaN  0.403827\n14       0       1.0       6      BULK_PACK_DISKETTES              None            None           NaN  0.119502\n15       0       1.0       7      BULK_PACK_DISKETTES              None            None           NaN  0.068173\n16       0       2.0       0       FLAT_PANEL_MONITOR              None            None           NaN  0.010795\n17       0       2.0       1       FLAT_PANEL_MONITOR              None            None           NaN  0.351272\n18       0       2.0       2       FLAT_PANEL_MONITOR              None            None           NaN -0.219080\n19       0       2.0       3       FLAT_PANEL_MONITOR              None            None           NaN  0.252507\n20       0       2.0       4       FLAT_PANEL_MONITOR              None            None           NaN -0.130454\n21       0       2.0       5       FLAT_PANEL_MONITOR              None            None           NaN -0.173399\n22       0       2.0       6       FLAT_PANEL_MONITOR              None            None           NaN -0.539782\n23       0       2.0       7       FLAT_PANEL_MONITOR              None            None           NaN  0.023562\n24       0       3.0       0     HOME_THEATER_PACKAGE              None            None           NaN -0.295273\n25       0       3.0       1     HOME_THEATER_PACKAGE              None            None           NaN -0.610766\n26       0       3.0       2     HOME_THEATER_PACKAGE              None            None           NaN -0.209365\n27       0       3.0       3     HOME_THEATER_PACKAGE              None            None           NaN -0.132607\n28       0       3.0       4     HOME_THEATER_PACKAGE              None            None           NaN -0.091842\n29       0       3.0       5     HOME_THEATER_PACKAGE              None            None           NaN -0.096479\n30       0       3.0       6     HOME_THEATER_PACKAGE              None            None           NaN -0.029436\n31       0       3.0       7     HOME_THEATER_PACKAGE              None            None           NaN  0.399920\n32       0       4.0       0         OS_DOC_SET_KANJI              None            None           NaN  0.122639\n33       0       4.0       1         OS_DOC_SET_KANJI              None            None           NaN  0.337016\n34       0       4.0       2         OS_DOC_SET_KANJI              None            None           NaN -0.481010\n35       0       4.0       3         OS_DOC_SET_KANJI              None            None           NaN  0.508320\n36       0       4.0       4         OS_DOC_SET_KANJI              None            None           NaN -0.482217\n37       0       4.0       5         OS_DOC_SET_KANJI              None            None           NaN -0.152996\n38       0       4.0       6         OS_DOC_SET_KANJI              None            None           NaN  0.543852\n39       0       4.0       7         OS_DOC_SET_KANJI              None            None           NaN  0.051997\n40       0       5.0       0         PRINTER_SUPPLIES              None            None           NaN  0.241342\n41       0       5.0       1         PRINTER_SUPPLIES              None            None           NaN  0.177983\n42       0       5.0       2         PRINTER_SUPPLIES              None            None           NaN -0.593673\n43       0       5.0       3         PRINTER_SUPPLIES              None            None           NaN  0.297604\n44       0       5.0       4         PRINTER_SUPPLIES              None            None           NaN -0.093283\n45       0       5.0       5         PRINTER_SUPPLIES              None            None           NaN -0.019828\n46       0       5.0       6         PRINTER_SUPPLIES              None            None           NaN -0.445602\n47       0       5.0       7         PRINTER_SUPPLIES              None            None           NaN -0.523718\n48       0       6.0       0            YRS_RESIDENCE              None            None           NaN  0.289342\n49       0       6.0       1            YRS_RESIDENCE              None            None           NaN  0.063336\n50       0       6.0       2            YRS_RESIDENCE              None            None           NaN  0.782868\n51       0       6.0       3            YRS_RESIDENCE              None            None           NaN -0.105143\n52       0       6.0       4            YRS_RESIDENCE              None            None           NaN -0.445613\n53       0       6.0       5            YRS_RESIDENCE              None            None           NaN  0.221488\n54       0       6.0       6            YRS_RESIDENCE              None            None           NaN  0.035614\n55       0       6.0       7            YRS_RESIDENCE              None            None           NaN -0.448550\n56       0       7.0       0              Y_BOX_GAMES              None            None           NaN -0.000579\n57       0       7.0       1              Y_BOX_GAMES              None            None           NaN  0.071851\n58       0       7.0       2              Y_BOX_GAMES              None            None           NaN -0.073450\n59       0       7.0       3              Y_BOX_GAMES              None            None           NaN  0.411751\n60       0       7.0       4              Y_BOX_GAMES              None            None           NaN -0.328255\n61       0       7.0       5              Y_BOX_GAMES              None            None           NaN  0.549827\n62       0       7.0       6              Y_BOX_GAMES              None            None           NaN -0.524135\n63       0       7.0       7              Y_BOX_GAMES              None            None           NaN -0.264134\n64       0       8.0       0                EDUCATION              None            10th           NaN -0.540899\n65       0       8.0       1                EDUCATION              None            10th           NaN  0.340859\n66       0       8.0       2                EDUCATION              None            10th           NaN  0.058728\n67       0       8.0       3                EDUCATION              None            10th           NaN -0.294175\n68       0       8.0       4                EDUCATION              None            10th           NaN  0.011708\n69       0       8.0       5                EDUCATION              None            10th           NaN -0.223116\n70       0       8.0       6                EDUCATION              None            10th           NaN  0.322309\n71       0       8.0       7                EDUCATION              None            10th           NaN -0.029841\n72       0       9.0       0                EDUCATION              None            11th           NaN -0.081591\n73       0       9.0       1                EDUCATION              None            11th           NaN  0.077328\n74       0       9.0       2                EDUCATION              None            11th           NaN  0.238138\n75       0       9.0       3                EDUCATION              None            11th           NaN  0.078611\n76       0       9.0       4                EDUCATION              None            11th           NaN -0.323026\n77       0       9.0       5                EDUCATION              None            11th           NaN -0.308100\n78       0       9.0       6                EDUCATION              None            11th           NaN  0.247184\n79       0       9.0       7                EDUCATION              None            11th           NaN  0.118178\n80       0      10.0       0                EDUCATION              None            12th           NaN  0.009275\n81       0      10.0       1                EDUCATION              None            12th           NaN  0.280552\n82       0      10.0       2                EDUCATION              None            12th           NaN -0.176097\n83       0      10.0       3                EDUCATION              None            12th           NaN  0.177509\n84       0      10.0       4                EDUCATION              None            12th           NaN  0.292280\n85       0      10.0       5                EDUCATION              None            12th           NaN -0.362029\n86       0      10.0       6                EDUCATION              None            12th           NaN  0.145677\n87       0      10.0       7                EDUCATION              None            12th           NaN -0.221874\n88       0      11.0       0                EDUCATION              None         1st-4th           NaN -0.159856\n89       0      11.0       1                EDUCATION              None         1st-4th           NaN  0.189041\n90       0      11.0       2                EDUCATION              None         1st-4th           NaN -0.328929\n91       0      11.0       3                EDUCATION              None         1st-4th           NaN  0.055520\n92       0      11.0       4                EDUCATION              None         1st-4th           NaN  0.244256\n93       0      11.0       5                EDUCATION              None         1st-4th           NaN -0.094391\n94       0      11.0       6                EDUCATION              None         1st-4th           NaN -0.213105\n95       0      11.0       7                EDUCATION              None         1st-4th           NaN -0.351280\n96       0      12.0       0                EDUCATION              None         5th-6th           NaN  0.058011\n97       0      12.0       1                EDUCATION              None         5th-6th           NaN -0.148099\n98       0      12.0       2                EDUCATION              None         5th-6th           NaN  0.283761\n99       0      12.0       3                EDUCATION              None         5th-6th           NaN -0.148678\n100      0      12.0       4                EDUCATION              None         5th-6th           NaN -0.357728\n101      0      12.0       5                EDUCATION              None         5th-6th           NaN -0.124024\n102      0      12.0       6                EDUCATION              None         5th-6th           NaN  0.118326\n103      0      12.0       7                EDUCATION              None         5th-6th           NaN -0.290270\n104      0      13.0       0                EDUCATION              None         7th-8th           NaN  0.222654\n105      0      13.0       1                EDUCATION              None         7th-8th           NaN -0.002715\n106      0      13.0       2                EDUCATION              None         7th-8th           NaN -0.223324\n107      0      13.0       3                EDUCATION              None         7th-8th           NaN -0.242841\n108      0      13.0       4                EDUCATION              None         7th-8th           NaN -0.177349\n109      0      13.0       5                EDUCATION              None         7th-8th           NaN  0.201515\n110      0      13.0       6                EDUCATION              None         7th-8th           NaN -0.231769\n111      0      13.0       7                EDUCATION              None         7th-8th           NaN  0.308876\n112      0      14.0       0                EDUCATION              None             9th           NaN  0.275445\n113      0      14.0       1                EDUCATION              None             9th           NaN -0.119616\n114      0      14.0       2                EDUCATION              None             9th           NaN -0.188715\n115      0      14.0       3                EDUCATION              None             9th           NaN -0.168359\n116      0      14.0       4                EDUCATION              None             9th           NaN -0.178486\n117      0      14.0       5                EDUCATION              None             9th           NaN -0.074214\n118      0      14.0       6                EDUCATION              None             9th           NaN  0.007916\n119      0      14.0       7                EDUCATION              None             9th           NaN -0.278599\n120      0      15.0       0                EDUCATION              None         < Bach.           NaN  0.199564\n121      0      15.0       1                EDUCATION              None         < Bach.           NaN -0.261758\n122      0      15.0       2                EDUCATION              None         < Bach.           NaN -0.313200\n123      0      15.0       3                EDUCATION              None         < Bach.           NaN -0.120379\n124      0      15.0       4                EDUCATION              None         < Bach.           NaN  0.281896\n125      0      15.0       5                EDUCATION              None         < Bach.           NaN -0.269666\n126      0      15.0       6                EDUCATION              None         < Bach.           NaN  0.375963\n127      0      15.0       7                EDUCATION              None         < Bach.           NaN -0.042000\n128      0      16.0       0                EDUCATION              None         Assoc-A           NaN -0.041558\n129      0      16.0       1                EDUCATION              None         Assoc-A           NaN -0.289187\n130      0      16.0       2                EDUCATION              None         Assoc-A           NaN -0.027819\n131      0      16.0       3                EDUCATION              None         Assoc-A           NaN -0.541545\n132      0      16.0       4                EDUCATION              None         Assoc-A           NaN  0.417098\n133      0      16.0       5                EDUCATION              None         Assoc-A           NaN  0.084190\n134      0      16.0       6                EDUCATION              None         Assoc-A           NaN  0.191724\n135      0      16.0       7                EDUCATION              None         Assoc-A           NaN -0.162902\n136      0      17.0       0                EDUCATION              None         Assoc-V           NaN  0.242312\n137      0      17.0       1                EDUCATION              None         Assoc-V           NaN  0.108392\n138      0      17.0       2                EDUCATION              None         Assoc-V           NaN -0.137395\n139      0      17.0       3                EDUCATION              None         Assoc-V           NaN  0.405580\n140      0      17.0       4                EDUCATION              None         Assoc-V           NaN  0.098388\n141      0      17.0       5                EDUCATION              None         Assoc-V           NaN -0.036720\n142      0      17.0       6                EDUCATION              None         Assoc-V           NaN -0.289298\n143      0      17.0       7                EDUCATION              None         Assoc-V           NaN -0.031495\n144      0      18.0       0                EDUCATION              None           Bach.           NaN  0.532556\n145      0      18.0       1                EDUCATION              None           Bach.           NaN -0.066389\n146      0      18.0       2                EDUCATION              None           Bach.           NaN  0.169697\n147      0      18.0       3                EDUCATION              None           Bach.           NaN -0.235206\n148      0      18.0       4                EDUCATION              None           Bach.           NaN  0.056926\n149      0      18.0       5                EDUCATION              None           Bach.           NaN  0.144186\n150      0      18.0       6                EDUCATION              None           Bach.           NaN  0.413995\n151      0      18.0       7                EDUCATION              None           Bach.           NaN  0.240537\n152      0      19.0       0                EDUCATION              None         HS-grad           NaN  0.131796\n153      0      19.0       1                EDUCATION              None         HS-grad           NaN -0.275164\n154      0      19.0       2                EDUCATION              None         HS-grad           NaN -0.593996\n155      0      19.0       3                EDUCATION              None         HS-grad           NaN  0.209124\n156      0      19.0       4                EDUCATION              None         HS-grad           NaN  0.088509\n157      0      19.0       5                EDUCATION              None         HS-grad           NaN  0.374988\n158      0      19.0       6                EDUCATION              None         HS-grad           NaN -0.072383\n159      0      19.0       7                EDUCATION              None         HS-grad           NaN -0.196548\n160      0      20.0       0                EDUCATION              None         Masters           NaN -0.187821\n161      0      20.0       1                EDUCATION              None         Masters           NaN  0.408284\n162      0      20.0       2                EDUCATION              None         Masters           NaN  0.060016\n163      0      20.0       3                EDUCATION              None         Masters           NaN -0.231182\n164      0      20.0       4                EDUCATION              None         Masters           NaN  0.309544\n165      0      20.0       5                EDUCATION              None         Masters           NaN -0.299857\n166      0      20.0       6                EDUCATION              None         Masters           NaN -0.317170\n167      0      20.0       7                EDUCATION              None         Masters           NaN  0.080805\n168      0      21.0       0                EDUCATION              None             PhD           NaN -0.521391\n169      0      21.0       1                EDUCATION              None             PhD           NaN  0.138987\n170      0      21.0       2                EDUCATION              None             PhD           NaN  0.462783\n171      0      21.0       3                EDUCATION              None             PhD           NaN -0.526641\n172      0      21.0       4                EDUCATION              None             PhD           NaN -0.274905\n173      0      21.0       5                EDUCATION              None             PhD           NaN -0.022880\n174      0      21.0       6                EDUCATION              None             PhD           NaN -0.175127\n175      0      21.0       7                EDUCATION              None             PhD           NaN  0.198721\n176      0      22.0       0                EDUCATION              None         Presch.           NaN  0.218261\n177      0      22.0       1                EDUCATION              None         Presch.           NaN  0.155115\n178      0      22.0       2                EDUCATION              None         Presch.           NaN  0.236710\n179      0      22.0       3                EDUCATION              None         Presch.           NaN  0.034001\n180      0      22.0       4                EDUCATION              None         Presch.           NaN -0.026962\n181      0      22.0       5                EDUCATION              None         Presch.           NaN -0.035188\n182      0      22.0       6                EDUCATION              None         Presch.           NaN  0.050804\n183      0      22.0       7                EDUCATION              None         Presch.           NaN -0.061564\n184      0      23.0       0                EDUCATION              None          Profsc           NaN -0.200644\n185      0      23.0       1                EDUCATION              None          Profsc           NaN -0.060877\n186      0      23.0       2                EDUCATION              None          Profsc           NaN  0.256061\n187      0      23.0       3                EDUCATION              None          Profsc           NaN -0.238619\n188      0      23.0       4                EDUCATION              None          Profsc           NaN -0.269071\n189      0      23.0       5                EDUCATION              None          Profsc           NaN  0.143389\n190      0      23.0       6                EDUCATION              None          Profsc           NaN  0.249076\n191      0      23.0       7                EDUCATION              None          Profsc           NaN -0.357913\n192      0      24.0       0           HOUSEHOLD_SIZE              None               1           NaN  0.579052\n193      0      24.0       1           HOUSEHOLD_SIZE              None               1           NaN -0.209636\n194      0      24.0       2           HOUSEHOLD_SIZE              None               1           NaN -0.067309\n195      0      24.0       3           HOUSEHOLD_SIZE              None               1           NaN  0.363720\n196      0      24.0       4           HOUSEHOLD_SIZE              None               1           NaN -0.305416\n197      0      24.0       5           HOUSEHOLD_SIZE              None               1           NaN  0.221680\n198      0      24.0       6           HOUSEHOLD_SIZE              None               1           NaN -0.070464\n199      0      24.0       7           HOUSEHOLD_SIZE              None               1           NaN  0.219044\n200      0      25.0       0           HOUSEHOLD_SIZE              None               2           NaN  0.685887\n201      0      25.0       1           HOUSEHOLD_SIZE              None               2           NaN  0.152847\n202      0      25.0       2           HOUSEHOLD_SIZE              None               2           NaN -0.473100\n203      0      25.0       3           HOUSEHOLD_SIZE              None               2           NaN -0.055785\n204      0      25.0       4           HOUSEHOLD_SIZE              None               2           NaN -0.349157\n205      0      25.0       5           HOUSEHOLD_SIZE              None               2           NaN  0.191579\n206      0      25.0       6           HOUSEHOLD_SIZE              None               2           NaN -0.283766\n207      0      25.0       7           HOUSEHOLD_SIZE              None               2           NaN  0.180008\n208      0      26.0       0           HOUSEHOLD_SIZE              None               3           NaN -0.656286\n209      0      26.0       1           HOUSEHOLD_SIZE              None               3           NaN -0.019715\n210      0      26.0       2           HOUSEHOLD_SIZE              None               3           NaN  0.686298\n211      0      26.0       3           HOUSEHOLD_SIZE              None               3           NaN -0.417805\n212      0      26.0       4           HOUSEHOLD_SIZE              None               3           NaN  0.332490\n213      0      26.0       5           HOUSEHOLD_SIZE              None               3           NaN -0.796138\n214      0      26.0       6           HOUSEHOLD_SIZE              None               3           NaN -0.108118\n215      0      26.0       7           HOUSEHOLD_SIZE              None               3           NaN -0.113708\n216      0      27.0       0           HOUSEHOLD_SIZE              None             4-5           NaN -1.504996\n217      0      27.0       1           HOUSEHOLD_SIZE              None             4-5           NaN -0.113694\n218      0      27.0       2           HOUSEHOLD_SIZE              None             4-5           NaN  0.787141\n219      0      27.0       3           HOUSEHOLD_SIZE              None             4-5           NaN -0.893047\n220      0      27.0       4           HOUSEHOLD_SIZE              None             4-5           NaN  0.234643\n221      0      27.0       5           HOUSEHOLD_SIZE              None             4-5           NaN -0.643913\n222      0      27.0       6           HOUSEHOLD_SIZE              None             4-5           NaN -0.325077\n223      0      27.0       7           HOUSEHOLD_SIZE              None             4-5           NaN -0.244258\n224      0      28.0       0           HOUSEHOLD_SIZE              None             6-8           NaN  0.208282\n225      0      28.0       1           HOUSEHOLD_SIZE              None             6-8           NaN -0.318152\n226      0      28.0       2           HOUSEHOLD_SIZE              None             6-8           NaN -0.685068\n227      0      28.0       3           HOUSEHOLD_SIZE              None             6-8           NaN  0.320192\n228      0      28.0       4           HOUSEHOLD_SIZE              None             6-8           NaN  0.295612\n229      0      28.0       5           HOUSEHOLD_SIZE              None             6-8           NaN  0.588769\n230      0      28.0       6           HOUSEHOLD_SIZE              None             6-8           NaN -0.237783\n231      0      28.0       7           HOUSEHOLD_SIZE              None             6-8           NaN  0.188487\n232      0      29.0       0           HOUSEHOLD_SIZE              None              9+           NaN  0.693268\n233      0      29.0       1           HOUSEHOLD_SIZE              None              9+           NaN -0.323353\n234      0      29.0       2           HOUSEHOLD_SIZE              None              9+           NaN -0.390124\n235      0      29.0       3           HOUSEHOLD_SIZE              None              9+           NaN  0.535339\n236      0      29.0       4           HOUSEHOLD_SIZE              None              9+           NaN -0.273391\n237      0      29.0       5           HOUSEHOLD_SIZE              None              9+           NaN  0.311208\n238      0      29.0       6           HOUSEHOLD_SIZE              None              9+           NaN -0.018602\n239      0      29.0       7           HOUSEHOLD_SIZE              None              9+           NaN -0.061112\n240      0      30.0       0               OCCUPATION              None               ?           NaN -0.025669\n241      0      30.0       1               OCCUPATION              None               ?           NaN -0.332704\n242      0      30.0       2               OCCUPATION              None               ?           NaN -0.672641\n243      0      30.0       3               OCCUPATION              None               ?           NaN  0.252881\n244      0      30.0       4               OCCUPATION              None               ?           NaN  0.166719\n245      0      30.0       5               OCCUPATION              None               ?           NaN -0.222602\n246      0      30.0       6               OCCUPATION              None               ?           NaN  0.288352\n247      0      30.0       7               OCCUPATION              None               ?           NaN -0.040834\n248      0      31.0       0               OCCUPATION              None         Armed-F           NaN -0.336901\n249      0      31.0       1               OCCUPATION              None         Armed-F           NaN -0.309441\n250      0      31.0       2               OCCUPATION              None         Armed-F           NaN -0.062520\n251      0      31.0       3               OCCUPATION              None         Armed-F           NaN -0.005155\n252      0      31.0       4               OCCUPATION              None         Armed-F           NaN -0.312397\n253      0      31.0       5               OCCUPATION              None         Armed-F           NaN  0.024445\n254      0      31.0       6               OCCUPATION              None         Armed-F           NaN  0.327236\n255      0      31.0       7               OCCUPATION              None         Armed-F           NaN  0.002940\n256      0      32.0       0               OCCUPATION              None         Cleric.           NaN -0.025959\n257      0      32.0       1               OCCUPATION              None         Cleric.           NaN  0.163211\n258      0      32.0       2               OCCUPATION              None         Cleric.           NaN  0.002680\n259      0      32.0       3               OCCUPATION              None         Cleric.           NaN -0.013494\n260      0      32.0       4               OCCUPATION              None         Cleric.           NaN -0.003181\n261      0      32.0       5               OCCUPATION              None         Cleric.           NaN -0.481576\n262      0      32.0       6               OCCUPATION              None         Cleric.           NaN  0.064959\n263      0      32.0       7               OCCUPATION              None         Cleric.           NaN  0.190751\n264      0      33.0       0               OCCUPATION              None          Crafts           NaN  0.028621\n265      0      33.0       1               OCCUPATION              None          Crafts           NaN -0.239169\n266      0      33.0       2               OCCUPATION              None          Crafts           NaN -0.057612\n267      0      33.0       3               OCCUPATION              None          Crafts           NaN  0.431228\n268      0      33.0       4               OCCUPATION              None          Crafts           NaN -0.221002\n269      0      33.0       5               OCCUPATION              None          Crafts           NaN -0.041027\n270      0      33.0       6               OCCUPATION              None          Crafts           NaN  0.036826\n271      0      33.0       7               OCCUPATION              None          Crafts           NaN -0.063393\n272      0      34.0       0               OCCUPATION              None           Exec.           NaN -0.372697\n273      0      34.0       1               OCCUPATION              None           Exec.           NaN -0.145235\n274      0      34.0       2               OCCUPATION              None           Exec.           NaN  0.432421\n275      0      34.0       3               OCCUPATION              None           Exec.           NaN -0.006131\n276      0      34.0       4               OCCUPATION              None           Exec.           NaN -0.119901\n277      0      34.0       5               OCCUPATION              None           Exec.           NaN -0.461107\n278      0      34.0       6               OCCUPATION              None           Exec.           NaN -0.054574\n279      0      34.0       7               OCCUPATION              None           Exec.           NaN -0.024238\n280      0      35.0       0               OCCUPATION              None         Farming           NaN  0.508697\n281      0      35.0       1               OCCUPATION              None         Farming           NaN -0.041744\n282      0      35.0       2               OCCUPATION              None         Farming           NaN  0.250714\n283      0      35.0       3               OCCUPATION              None         Farming           NaN -0.179887\n284      0      35.0       4               OCCUPATION              None         Farming           NaN -0.011532\n285      0      35.0       5               OCCUPATION              None         Farming           NaN  0.340281\n286      0      35.0       6               OCCUPATION              None         Farming           NaN -0.319490\n287      0      35.0       7               OCCUPATION              None         Farming           NaN  0.117532\n288      0      36.0       0               OCCUPATION              None         Handler           NaN  0.027636\n289      0      36.0       1               OCCUPATION              None         Handler           NaN  0.024469\n290      0      36.0       2               OCCUPATION              None         Handler           NaN -0.423394\n291      0      36.0       3               OCCUPATION              None         Handler           NaN  0.062600\n292      0      36.0       4               OCCUPATION              None         Handler           NaN  0.232471\n293      0      36.0       5               OCCUPATION              None         Handler           NaN  0.177743\n294      0      36.0       6               OCCUPATION              None         Handler           NaN -0.062507\n295      0      36.0       7               OCCUPATION              None         Handler           NaN  0.070664\n296      0      37.0       0               OCCUPATION              None         House-s           NaN  0.200714\n297      0      37.0       1               OCCUPATION              None         House-s           NaN  0.060158\n298      0      37.0       2               OCCUPATION              None         House-s           NaN  0.205166\n299      0      37.0       3               OCCUPATION              None         House-s           NaN  0.166016\n300      0      37.0       4               OCCUPATION              None         House-s           NaN  0.046339\n301      0      37.0       5               OCCUPATION              None         House-s           NaN  0.128811\n302      0      37.0       6               OCCUPATION              None         House-s           NaN -0.269903\n303      0      37.0       7               OCCUPATION              None         House-s           NaN -0.135969\n304      0      38.0       0               OCCUPATION              None         Machine           NaN -0.214676\n305      0      38.0       1               OCCUPATION              None         Machine           NaN -0.370786\n306      0      38.0       2               OCCUPATION              None         Machine           NaN -0.070509\n307      0      38.0       3               OCCUPATION              None         Machine           NaN  0.784548\n308      0      38.0       4               OCCUPATION              None         Machine           NaN -0.284982\n309      0      38.0       5               OCCUPATION              None         Machine           NaN -0.043914\n310      0      38.0       6               OCCUPATION              None         Machine           NaN  0.202136\n311      0      38.0       7               OCCUPATION              None         Machine           NaN -0.229534\n312      0      39.0       0               OCCUPATION              None           Other           NaN  0.530347\n313      0      39.0       1               OCCUPATION              None           Other           NaN -0.413807\n314      0      39.0       2               OCCUPATION              None           Other           NaN -0.366624\n315      0      39.0       3               OCCUPATION              None           Other           NaN  0.381056\n316      0      39.0       4               OCCUPATION              None           Other           NaN -0.414786\n317      0      39.0       5               OCCUPATION              None           Other           NaN -0.136005\n318      0      39.0       6               OCCUPATION              None           Other           NaN  0.071374\n319      0      39.0       7               OCCUPATION              None           Other           NaN  0.301071\n320      0      40.0       0               OCCUPATION              None           Prof.           NaN -0.312425\n321      0      40.0       1               OCCUPATION              None           Prof.           NaN  0.164278\n322      0      40.0       2               OCCUPATION              None           Prof.           NaN  0.475954\n323      0      40.0       3               OCCUPATION              None           Prof.           NaN -0.471882\n324      0      40.0       4               OCCUPATION              None           Prof.           NaN  0.218804\n325      0      40.0       5               OCCUPATION              None           Prof.           NaN -0.596645\n326      0      40.0       6               OCCUPATION              None           Prof.           NaN  0.288405\n327      0      40.0       7               OCCUPATION              None           Prof.           NaN  0.102999\n328      0      41.0       0               OCCUPATION              None         Protec.           NaN -0.357956\n329      0      41.0       1               OCCUPATION              None         Protec.           NaN  0.088234\n330      0      41.0       2               OCCUPATION              None         Protec.           NaN -0.030945\n331      0      41.0       3               OCCUPATION              None         Protec.           NaN -0.030627\n332      0      41.0       4               OCCUPATION              None         Protec.           NaN -0.262627\n333      0      41.0       5               OCCUPATION              None         Protec.           NaN  0.031917\n334      0      41.0       6               OCCUPATION              None         Protec.           NaN -0.141638\n335      0      41.0       7               OCCUPATION              None         Protec.           NaN -0.115033\n336      0      42.0       0               OCCUPATION              None           Sales           NaN -0.324287\n337      0      42.0       1               OCCUPATION              None           Sales           NaN  0.426580\n338      0      42.0       2               OCCUPATION              None           Sales           NaN  0.187911\n339      0      42.0       3               OCCUPATION              None           Sales           NaN -0.244552\n340      0      42.0       4               OCCUPATION              None           Sales           NaN  0.225044\n341      0      42.0       5               OCCUPATION              None           Sales           NaN -0.243970\n342      0      42.0       6               OCCUPATION              None           Sales           NaN  0.143329\n343      0      42.0       7               OCCUPATION              None           Sales           NaN -0.421874\n344      0      43.0       0               OCCUPATION              None         TechSup           NaN  0.016919\n345      0      43.0       1               OCCUPATION              None         TechSup           NaN  0.164006\n346      0      43.0       2               OCCUPATION              None         TechSup           NaN  0.017165\n347      0      43.0       3               OCCUPATION              None         TechSup           NaN -0.311168\n348      0      43.0       4               OCCUPATION              None         TechSup           NaN -0.303675\n349      0      43.0       5               OCCUPATION              None         TechSup           NaN  0.142232\n350      0      43.0       6               OCCUPATION              None         TechSup           NaN -0.195434\n351      0      43.0       7               OCCUPATION              None         TechSup           NaN -0.248536\n352      0      44.0       0               OCCUPATION              None         Transp.           NaN -0.155687\n353      0      44.0       1               OCCUPATION              None         Transp.           NaN -0.179379\n354      0      44.0       2               OCCUPATION              None         Transp.           NaN -0.052131\n355      0      44.0       3               OCCUPATION              None         Transp.           NaN  0.079057\n356      0      44.0       4               OCCUPATION              None         Transp.           NaN -0.392626\n357      0      44.0       5               OCCUPATION              None         Transp.           NaN -0.138836\n358      0      44.0       6               OCCUPATION              None         Transp.           NaN -0.047728\n359      0      44.0       7               OCCUPATION              None         Transp.           NaN  0.359979\n360      0       NaN       0                     None              None            None           NaN -0.515994\n361      0       NaN       1                     None              None            None           NaN -0.169079\n362      0       NaN       2                     None              None            None           NaN  0.077554\n363      0       NaN       3                     None              None            None           NaN -0.792231\n364      0       NaN       4                     None              None            None           NaN -0.436251\n365      0       NaN       5                     None              None            None           NaN -0.312876\n366      0       NaN       6                     None              None            None           NaN -0.128717\n367      0       NaN       7                     None              None            None           NaN  0.133104\n368      1       0.0       0                     None              None            None           NaN -0.452836\n369      1       0.0       1                     None              None            None           NaN -0.411045\n370      1       0.0       2                     None              None            None           NaN -0.347404\n371      1       0.0       3                     None              None            None           NaN -0.671507\n372      1       0.0       4                     None              None            None           NaN -0.385057\n373      1       0.0       5                     None              None            None           NaN  0.232871\n374      1       0.0       6                     None              None            None           NaN  0.440338\n375      1       0.0       7                     None              None            None           NaN  0.015227\n376      1       1.0       0                     None              None            None           NaN  0.193353\n377      1       1.0       1                     None              None            None           NaN  0.594352\n378      1       1.0       2                     None              None            None           NaN  0.276057\n379      1       1.0       3                     None              None            None           NaN  0.186348\n380      1       1.0       4                     None              None            None           NaN -0.067788\n381      1       1.0       5                     None              None            None           NaN -0.036597\n382      1       1.0       6                     None              None            None           NaN -0.323654\n383      1       1.0       7                     None              None            None           NaN  0.170346\n384      1       2.0       0                     None              None            None           NaN -0.117782\n385      1       2.0       1                     None              None            None           NaN  0.285396\n386      1       2.0       2                     None              None            None           NaN -0.185228\n387      1       2.0       3                     None              None            None           NaN  0.309289\n388      1       2.0       4                     None              None            None           NaN  0.055411\n389      1       2.0       5                     None              None            None           NaN -0.561681\n390      1       2.0       6                     None              None            None           NaN  0.117325\n391      1       2.0       7                     None              None            None           NaN  0.576574\n392      1       3.0       0                     None              None            None           NaN -0.486310\n393      1       3.0       1                     None              None            None           NaN -0.200046\n394      1       3.0       2                     None              None            None           NaN -0.274732\n395      1       3.0       3                     None              None            None           NaN -0.525763\n396      1       3.0       4                     None              None            None           NaN -0.020708\n397      1       3.0       5                     None              None            None           NaN  0.711767\n398      1       3.0       6                     None              None            None           NaN -0.130137\n399      1       3.0       7                     None              None            None           NaN -0.352884\n400      1       4.0       0                     None              None            None           NaN  0.226907\n401      1       4.0       1                     None              None            None           NaN  0.273435\n402      1       4.0       2                     None              None            None           NaN -0.333423\n403      1       4.0       3                     None              None            None           NaN  0.186919\n404      1       4.0       4                     None              None            None           NaN -0.519082\n405      1       4.0       5                     None              None            None           NaN  0.514206\n406      1       4.0       6                     None              None            None           NaN  0.388154\n407      1       4.0       7                     None              None            None           NaN -0.210419\n408      1       5.0       0                     None              None            None           NaN  0.348569\n409      1       5.0       1                     None              None            None           NaN  0.128085\n410      1       5.0       2                     None              None            None           NaN  0.569258\n411      1       5.0       3                     None              None            None           NaN -0.640586\n412      1       5.0       4                     None              None            None           NaN -0.553718\n413      1       5.0       5                     None              None            None           NaN -0.203961\n414      1       5.0       6                     None              None            None           NaN  0.548338\n415      1       5.0       7                     None              None            None           NaN -0.365451\n416      1       6.0       0                     None              None            None           NaN -0.599537\n417      1       6.0       1                     None              None            None           NaN  0.297536\n418      1       6.0       2                     None              None            None           NaN  0.209398\n419      1       6.0       3                     None              None            None           NaN -0.536047\n420      1       6.0       4                     None              None            None           NaN -0.053772\n421      1       6.0       5                     None              None            None           NaN -0.497732\n422      1       6.0       6                     None              None            None           NaN -0.133856\n423      1       6.0       7                     None              None            None           NaN  0.297264\n424      1       7.0       0                     None              None            None           NaN  0.005554\n425      1       7.0       1                     None              None            None           NaN -0.153835\n426      1       7.0       2                     None              None            None           NaN  0.312460\n427      1       7.0       3                     None              None            None           NaN -0.289147\n428      1       7.0       4                     None              None            None           NaN  0.040466\n429      1       7.0       5                     None              None            None           NaN -0.425817\n430      1       7.0       6                     None              None            None           NaN -0.306783\n431      1       7.0       7                     None              None            None           NaN -0.132849\n432      1       NaN       0                     None              None            None           NaN -0.321298\n433      1       NaN       1                     None              None            None           NaN  0.102628\n434      1       NaN       2                     None              None            None           NaN  0.585014\n435      1       NaN       3                     None              None            None           NaN  0.108788\n436      1       NaN       4                     None              None            None           NaN -0.821400\n437      1       NaN       5                     None              None            None           NaN  0.386042\n438      1       NaN       6                     None              None            None           NaN  0.442229\n439      1       NaN       7                     None              None            None           NaN  0.331935\n440      2       0.0       0                     None              None            None           NaN -0.091090\n441      2       0.0       1                     None              None            None           NaN  0.401204\n442      2       1.0       0                     None              None            None           NaN -0.368437\n443      2       1.0       1                     None              None            None           NaN -0.189021\n444      2       2.0       0                     None              None            None           NaN -0.201709\n445      2       2.0       1                     None              None            None           NaN -0.287590\n446      2       3.0       0                     None              None            None           NaN -0.616783\n447      2       3.0       1                     None              None            None           NaN  0.809921\n448      2       4.0       0                     None              None            None           NaN -0.401024\n449      2       4.0       1                     None              None            None           NaN -0.036199\n450      2       5.0       0                     None              None            None           NaN  0.791118\n451      2       5.0       1                     None              None            None           NaN -0.352380\n452      2       6.0       0                     None              None            None           NaN  0.504217\n453      2       6.0       1                     None              None            None           NaN -0.283137\n454      2       7.0       0                     None              None            None           NaN -0.493603\n455      2       7.0       1                     None              None            None           NaN  0.209211\n456      2       NaN       0                     None              None            None           NaN  0.727842\n457      2       NaN       1                     None              None            None           NaN  0.392872\n458      3       0.0       0                     None              None            None           0.0 -3.212657\n459      3       1.0       0                     None              None            None           0.0  0.536110\n460      3       NaN       0                     None              None            None           0.0 -1.630212\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%python","nn_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1733787139654,"interpreter":"python.low","endTime":1733787140220,"results":[{"message":"0.831549\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Score test data adding supplemental columns","message":["%python","","# Set the case ID attribute","case_id = 'CUST_ID'","","# Gather the Predictions","RES_DF = nn_mod.predict(TEST_X, supplemental_cols = TEST_X)","","# Additionally collect the PROBABILITY_OF_0 and PROBABILITY_OF_1","RES_PROB = nn_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","","# Join the result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1733787140690,"interpreter":"python.low","endTime":1733787141342,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display confusion matrix, lift chart and ROC curve ","message":["%python","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions","     and the target column,","     The Settings name (for the charts), ","     The name of the model used (for the charts),","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," ","    # Extract Statistics from the Confusion Matrix","    cf_local = conf_matrix.pull()","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","    ","    # Estimated AUC via Triangle (not very precise) could be","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","    # Compute real AUC using roc_curve by loading the","    # data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","    ","    # Store all statistics to export","    statistics = {'Algorithm' : name,","                  'Algorithm_setting' : settings_name,","                  'TN' : TN,","                  'TP' : TP,","                  'FP' : FP,","                  'FN' : FN,","                  'TPR' : TPR,","                  'FPR' : FPR,","                  'TNR' : TNR,","                  'FNR' : FNR,","                  'Precision' : Precision,","                  'Accuracy' : Accuracy,","                  'NPV' : NPV,","                  'DetectionRate' : DetectionRate,","                  'BalancedAccuracy' : BalancedAccuracy,","                  'AUC' : AUC,","                  'F1Score' : F1Score,","                  'MathewsCorrCoef' : MathewsCorrCoef","                  }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = round((TN/TOTAL*100),2)","    FP_P = round((FP/TOTAL*100),2)","    FN_P = round((FN/TOTAL*100),2)","    TP_P = round((TP/TOTAL*100),2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+","          \"F1Score: \"+str(round(F1Score,4))","          )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT, color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","                 textcoords='data', ","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","                                 connectionstyle='arc3, rad=0.3'), ","                 horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),","                 xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), ","                 xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13) ","    ax4.legend(loc=\"lower right\")","    ","    return(statistics, pred_local)","_ = evaluate_model(pred_data=RES_DF, settings_name='3-Layer - 8,8,2', name='Neural Network', target='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1733787141800,"interpreter":"python.low","endTime":1733787144304,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1163 (65.52%)\tFalse Positive: 122 (6.87%)\nACTUAL 1\tFalse Negative: 177 (9.97%)\tTrue Positive: 313 (17.63%)\nAccuracy: 83.1549%\tAUC: 0.88\tF1Score: 0.6768\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img src=data:image/png;base64,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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Classification RF.dsnb b/machine-learning/notebooks-oml/python/OML4Py Classification RF.dsnb
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+++ b/machine-learning/notebooks-oml/python/OML4Py Classification RF.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification RF","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1734626667498,"interpreter":"md.low","endTime":1734626668025,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification Modeling to Predict Target Customers using Random Forest","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, ","hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification random forest model using the Sales History (SH) schema data. ","All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734626668476,"interpreter":"md.low","endTime":1734626668957,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-random-forest\">Classification Modeling to Predict Target Customers using Random Forest<\/h2>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card,\nhyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification random forest model using the Sales History (SH) schema data.\nAll processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1734626669421,"interpreter":"md.low","endTime":1734626670023,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>"],"enabled":true,"result":{"startTime":1734626670484,"interpreter":"md.low","endTime":1734626670942,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1734626671409,"interpreter":"python.low","endTime":1734626673884,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","DEMO = oml.sync(table = \"SUPPLEMENTARY_DEMOGRAPHICS\", schema = \"SH\")","z.show(DEMO.head())"],"enabled":true,"result":{"startTime":1734626674341,"interpreter":"python.low","endTime":1734626675005,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNone\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%python","","DEMO.shape"],"enabled":true,"result":{"startTime":1734626675457,"interpreter":"python.low","endTime":1734626675956,"results":[{"message":"(4500, 14)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab('AFFINITY_CARD'))"],"enabled":true,"result":{"startTime":1734626676417,"interpreter":"python.low","endTime":1734626677067,"results":[{"message":"AFFINITY_CARD\tcount\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%python","","z.show(DEMO.crosstab(['HOUSEHOLD_SIZE', 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\"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\"]]"],"enabled":true,"result":{"startTime":1734626678563,"interpreter":"python.low","endTime":1734626679022,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first few rows of 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train and test data (60/40) ","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X  = TEST","TEST_Y  = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1734626680463,"interpreter":"python.low","endTime":1734626681229,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Random Forest model for predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1734626681686,"interpreter":"md.low","endTime":1734626682128,"results":[{"message":"<h3 id=\"build-a-random-forest-model-for-predicting-affinity_card\">Build a Random Forest model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest Model using default settings","message":["%python","","try:","    oml.drop(model = 'RF_CLAS_MODEL') ","except:","    pass","","setting = dict()","rf_mod = oml.rf(**setting)","rf_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'RF_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1734626682587,"interpreter":"python.low","endTime":1734626684864,"results":[{"message":"\nModel Name: RF_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Random Forest\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_RANDOM_FOREST\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5               ODMS_RANDOM_SEED                        0\n6                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                      PREP_AUTO                       ON\n8                 RFOR_NUM_TREES                       20\n9            RFOR_SAMPLING_RATIO                       .5\n10          TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n11           TREE_TERM_MAX_DEPTH                       16\n12         TREE_TERM_MINPCT_NODE                      .05\n13        TREE_TERM_MINPCT_SPLIT                       .1\n14         TREE_TERM_MINREC_NODE                       10\n15        TREE_TERM_MINREC_SPLIT                       20\n\nComputed Settings: \n  setting name setting value\n0    RFOR_MTRY             5\n\nGlobal Statistics: \n  attribute name attribute value\n0      AVG_DEPTH            10.8\n1  AVG_NODECOUNT           107.2\n2      MAX_DEPTH              12\n3  MAX_NODECOUNT             125\n4      MIN_DEPTH              12\n5  MIN_NODECOUNT              91\n6       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nImportance: \n\n             ATTRIBUTE_NAME ATTRIBUTE_SUBNAME  ATTRIBUTE_IMPORTANCE\n0   BOOKKEEPING_APPLICATION              None              0.317441\n1       BULK_PACK_DISKETTES              None              0.326693\n2                 EDUCATION              None              0.862095\n3        FLAT_PANEL_MONITOR              None              0.302492\n4      HOME_THEATER_PACKAGE              None              0.296460\n5            HOUSEHOLD_SIZE              None              1.073072\n6                OCCUPATION              None              1.211376\n7          OS_DOC_SET_KANJI              None              0.000000\n8          PRINTER_SUPPLIES              None              0.000000\n9             YRS_RESIDENCE              None              1.458507\n10              Y_BOX_GAMES              None              0.395659\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","rf_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1734626685323,"interpreter":"python.low","endTime":1734626685932,"results":[{"message":"0.846197\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Random Forest ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117');\">Random Forest<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","   ","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'","   ","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.","> 'RFOR_NUM_TREES' = '20'","","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)","> 'RFOR_SAMPLING_RATIO' = '0.5'","","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.","> 'RFOR_MTRY' = '0'"],"enabled":true,"result":{"startTime":1734626686386,"interpreter":"md.low","endTime":1734626686839,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-random-forest\">Examples of possible setting overrides for Random Forest<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117');\">Random Forest<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_NUM_TREES' = '20'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_SAMPLING_RATIO' = '0.5'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_MTRY' = '0'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest Model using explicit settings","message":["%python","","try:","    oml.drop(model = 'RF_CLAS_MODEL') ","except:","    pass","","settings = {'RFOR_MTRY': '3', ","            'RFOR_NUM_TREES': '100', ","            'RFOR_SAMPLING_RATIO': '0.5'}","","rf_mod = oml.rf(**settings)","rf_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'RF_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1734626687302,"interpreter":"python.low","endTime":1734626689481,"results":[{"message":"\nModel Name: RF_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Random Forest\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_RANDOM_FOREST\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5               ODMS_RANDOM_SEED                        0\n6                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                      PREP_AUTO                       ON\n8                      RFOR_MTRY                        3\n9                 RFOR_NUM_TREES                      100\n10           RFOR_SAMPLING_RATIO                      0.5\n11          TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n12           TREE_TERM_MAX_DEPTH                       16\n13         TREE_TERM_MINPCT_NODE                      .05\n14        TREE_TERM_MINPCT_SPLIT                       .1\n15         TREE_TERM_MINREC_NODE                       10\n16        TREE_TERM_MINREC_SPLIT                       20\n\nComputed Settings: \n  setting name setting value\n0    RFOR_MTRY             3\n\nGlobal Statistics: \n  attribute name attribute value\n0      AVG_DEPTH           10.17\n1  AVG_NODECOUNT            76.8\n2      MAX_DEPTH              13\n3  MAX_NODECOUNT             113\n4      MIN_DEPTH              13\n5  MIN_NODECOUNT              31\n6       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nImportance: \n\n             ATTRIBUTE_NAME ATTRIBUTE_SUBNAME  ATTRIBUTE_IMPORTANCE\n0   BOOKKEEPING_APPLICATION              None              0.340063\n1       BULK_PACK_DISKETTES              None              0.311205\n2                 EDUCATION              None              0.673752\n3        FLAT_PANEL_MONITOR              None              0.338212\n4      HOME_THEATER_PACKAGE              None              0.449530\n5            HOUSEHOLD_SIZE              None              0.956955\n6                OCCUPATION              None              0.979998\n7          OS_DOC_SET_KANJI              None              0.000000\n8          PRINTER_SUPPLIES              None              0.000000\n9             YRS_RESIDENCE              None              1.100493\n10              Y_BOX_GAMES              None              0.458672\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%python","","rf_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1734626689946,"interpreter":"python.low","endTime":1734626690598,"results":[{"message":"0.84\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Score test data adding supplemental columns","message":["%python","","# Set the case ID column for use below","case_id = 'CUST_ID'","","# Gather the Predictions","RES_DF = rf_mod.predict(TEST_X, supplemental_cols = TEST_X)","","# Additionally collect the PROBABILITY_OF_0 and PROBABILITY_OF_1","RES_PROB = rf_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","","# Join the entire result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1734626691059,"interpreter":"python.low","endTime":1734626691666,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display confusion matrix, lift chart and ROC curve ","message":["%python","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions","     and the target column,","     The Settings name (for the charts), ","     The name of the model used (for the charts),","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," ","    # Extract Statistics from the Confusion Matrix","    cf_local = conf_matrix.pull()","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","    ","    # Estimated AUC via Triangle (not very precise) could be","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","    # Compute real AUC using roc_curve by loading the","    # data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","    ","    # Store all statistics to export","    statistics = {'Algorithm' : name,","                  'Algorithm_setting' : settings_name,","                  'TN' : TN,","                  'TP' : TP,","                  'FP' : FP,","                  'FN' : FN,","                  'TPR' : TPR,","                  'FPR' : FPR,","                  'TNR' : TNR,","                  'FNR' : FNR,","                  'Precision' : Precision,","                  'Accuracy' : Accuracy,","                  'NPV' : NPV,","                  'DetectionRate' : DetectionRate,","                  'BalancedAccuracy' : BalancedAccuracy,","                  'AUC' : AUC,","                  'F1Score' : F1Score,","                  'MathewsCorrCoef' : MathewsCorrCoef","                  }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = round((TN/TOTAL*100),2)","    FP_P = round((FP/TOTAL*100),2)","    FN_P = round((FN/TOTAL*100),2)","    TP_P = round((TP/TOTAL*100),2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+","          \"F1Score: \"+str(round(F1Score,4))","          )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT, color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","                 textcoords='data', ","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","                                 connectionstyle='arc3, rad=0.3'), ","                 horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),","                 xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), ","                 xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13) ","    ax4.legend(loc=\"lower right\")","    ","    return(statistics, pred_local)","_ = evaluate_model(pred_data=RES_DF, settings_name='Random Subset:3,Num Trees:100,Sampling Ratio:0.5', name='Random Forest', target='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1734626692118,"interpreter":"python.low","endTime":1734626696819,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1279 (72.06%)\tFalse Positive: 223 (12.56%)\nACTUAL 1\tFalse Negative: 61 (3.44%)\tTrue Positive: 212 (11.94%)\nAccuracy: 84.0%\tAUC: 0.885\tF1Score: 0.5989\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img src=data:image/png;base64,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RES_DF.columns].head())"],"enabled":true,"result":{"startTime":1734626697303,"interpreter":"python.low","endTime":1734626698586,"results":[{"message":"PREDICTION\tPROBABILITY_OF_1\tCUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY_OF_0\tPROBABILITY_OF_1\n1\t0.5193264104901691\t102368\t1\t1\t0\tBach.\t0\t1\t3\tTechSup\t0\t1\t4\t0\t1\t0.4806735895098309\t0.5193264104901691\n1\t0.7480005348538685\t101031\t1\t1\t1\tMasters\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.2519994651461312\t0.7480005348538685\n1\t0.7881632415944854\t101990\t0\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.21183675840551447\t0.7881632415944854\n1\t0.8126092811404476\t104351\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.18739071885955205\t0.8126092811404476\n1\t0.5968192144375672\t102898\t0\t1\t1\tHS-grad\t1\t1\t3\tExec.\t0\t1\t5\t0\t1\t0.4031807855624326\t0.5968192144375672\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the OML4Py interface to score data and display prediction details","message":["%python","","RES_DF = rf_mod.predict(TEST_X, supplemental_cols = TEST_X[['CUST_ID', 'AFFINITY_CARD']], topN_attrs = True)","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1734626699038,"interpreter":"python.low","endTime":1734626712532,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100300\t1\t1\tY_BOX_GAMES\t0\t2.402\tHOME_THEATER_PACKAGE\t1\t2.022\tYRS_RESIDENCE\t4\t.568\tBOOKKEEPING_APPLICATION\t1\t.475\tHOUSEHOLD_SIZE\t3\t.164\n101100\t0\t0\tHOME_THEATER_PACKAGE\t0\t3.238\tFLAT_PANEL_MONITOR\t1\t.247\tHOUSEHOLD_SIZE\t2\t.145\tOCCUPATION\tOther\t.054\tEDUCATION\tAssoc-V\t.018\n101300\t1\t0\tBULK_PACK_DISKETTES\t1\t.19\tEDUCATION\t< Bach.\t.057\tOCCUPATION\tTechSup\t-.026\tFLAT_PANEL_MONITOR\t1\t-.028\tHOUSEHOLD_SIZE\t3\t-.158\n101400\t1\t0\tBOOKKEEPING_APPLICATION\t0\t8.97\tOCCUPATION\tMachine\t.046\tEDUCATION\t9th\t.03\tHOUSEHOLD_SIZE\t3\t-.143\tYRS_RESIDENCE\t4\t-1.882\n101500\t0\t0\tY_BOX_GAMES\t1\t5.053\tYRS_RESIDENCE\t2\t5.014\tHOME_THEATER_PACKAGE\t0\t2.465\tHOUSEHOLD_SIZE\t1\t.084\tFLAT_PANEL_MONITOR\t1\t.018\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test data into a table","message":["%python","","try:","    oml.drop(table = 'RF_TEST_TABLE') ","except:","    pass","_ = TEST_X.materialize(table = 'RF_TEST_TABLE') "],"enabled":true,"result":{"startTime":1734626712993,"interpreter":"python.low","endTime":1734626713697,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_AFFINITY_CARD,3) PRED_AFFINITY_CARD,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(RF_CLAS_MODEL USING *) PREDICTION_AFFINITY_CARD,","             PREDICTION_DETAILS(RF_CLAS_MODEL USING *) PD","      FROM RF_TEST_TABLE","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","      XMLTABLE('/Details'","               PASSING OUT.PD","               COLUMNS ","               \"Attribute1\" XMLType PATH 'Attribute[1]',","               \"Attribute2\" XMLType PATH 'Attribute[2]') ","      OUTPRED","      "],"enabled":true,"result":{"startTime":1734626714155,"interpreter":"sql.low","endTime":1734626716037,"results":[{"message":"CUST_ID\tPRED_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".213\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\"-.012\" \n100003\t0\t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\"3.595\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".147\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".171\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".027\" \n100007\t0\t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".657\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".163\" \n100010\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\"8.317\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"5.327\" \n100011\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"5.21\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\"3.291\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1734626716494,"interpreter":"md.low","endTime":1734626716932,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734713278355,"interpreter":"md.medium","endTime":1734713278778,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Classification modeling to predict Target Customers using Support Vector Machine Model","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or ","affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification support vector machine model using the Sales History (SH) ","schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734713279204,"interpreter":"md.medium","endTime":1734713279614,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine-model\">Classification modeling to predict Target Customers using Support Vector Machine Model<\/h2>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or\naffinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification support vector machine model using the Sales History (SH)\nschema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1734713280047,"interpreter":"md.medium","endTime":1734713280451,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1734713280870,"interpreter":"md.medium","endTime":1734713281272,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","import warnings","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","","warnings.simplefilter(action='ignore', 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\"SH\")","z.show(DEMO.head())"],"enabled":true,"result":{"startTime":1734713282532,"interpreter":"python.medium","endTime":1734713283392,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNone\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%python","","DEMO.shape"],"enabled":true,"result":{"startTime":1734713284023,"interpreter":"python.medium","endTime":1734713284440,"results":[{"message":"(4500, 14)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show distribution of AFFINITY_CARD 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'AFFINITY_CARD']))"],"enabled":true,"result":{"startTime":1734713289148,"interpreter":"python.medium","endTime":1734713289864,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_CARD\tcount\n1\t0\t681\n3\t1\t814\n9+\t0\t476\n9+\t1\t29\n4-5\t0\t112\n4-5\t1\t107\n6-8\t0\t146\n1\t1\t11\n2\t0\t1040\n2\t1\t109\n3\t0\t973\n6-8\t1\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an OML Data Frame with desired columns for analysis","message":["%python","","DEMO_DF = DEMO[[\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", "," \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", "," \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\"]]"],"enabled":true,"result":{"startTime":1734713290290,"interpreter":"python.medium","endTime":1734713290710,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first few records of DEMO_DF","message":["%python","","z.show(DEMO_DF.head())"],"enabled":true,"result":{"startTime":1734713291129,"interpreter":"python.medium","endTime":1734713294771,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n102547\t0\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n101050\t0\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n100040\t0\t0\t1\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n102117\t0\t1\t0\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n101074\t0\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create train and test data (60/40 split) ","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X = TEST","TEST_Y = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1734713295197,"interpreter":"python.medium","endTime":1734713296362,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Support Vector Machine classification model for predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1734713296790,"interpreter":"md.medium","endTime":1734713297201,"results":[{"message":"<h3 id=\"build-a-support-vector-machine-classification-model-for-predicting-affinity_card\">Build a Support Vector Machine classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using default settings","message":["%python","","try:","    oml.drop(model = 'SVM_CLAS_MODEL') ","except:","    pass","","settings = {}","","svm_mod = oml.svm(**settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'SVM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1734713297619,"interpreter":"python.medium","endTime":1734713302771,"results":[{"message":"\nModel Name: SVM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1         CLAS_WEIGHTS_BALANCED                           OFF\n2                  ODMS_DETAILS                   ODMS_ENABLE\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                     PREP_AUTO                            ON\n6           SVMS_CONV_TOLERANCE                         .0001\n7          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR               10\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              17\n2       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n    TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE    COEF\n0              1  BOOKKEEPING_APPLICATION              None            None  0.0773\n1              1      BULK_PACK_DISKETTES              None            None  0.0000\n2              1                EDUCATION              None            10th -0.0035\n3              1                EDUCATION              None            11th -0.0035\n4              1                EDUCATION              None            12th -0.0035\n5              1                EDUCATION              None         1st-4th -0.0035\n6              1                EDUCATION              None         5th-6th -0.0035\n7              1                EDUCATION              None         7th-8th -0.0035\n8              1                EDUCATION              None             9th -0.0035\n9              1                EDUCATION              None         < Bach. -0.2469\n10             1                EDUCATION              None         Assoc-A -0.2469\n11             1                EDUCATION              None         Assoc-V -0.2469\n12             1                EDUCATION              None           Bach. -0.2469\n13             1                EDUCATION              None         HS-grad -0.2469\n14             1                EDUCATION              None         Masters -0.2469\n15             1                EDUCATION              None             PhD  1.7531\n16             1                EDUCATION              None         Presch.  0.0000\n17             1                EDUCATION              None          Profsc -0.2469\n18             1       FLAT_PANEL_MONITOR              None            None  0.0000\n19             1     HOME_THEATER_PACKAGE              None            None  0.0000\n20             1           HOUSEHOLD_SIZE              None               1 -0.6667\n21             1           HOUSEHOLD_SIZE              None               2 -0.6667\n22             1           HOUSEHOLD_SIZE              None               3  1.3333\n23             1           HOUSEHOLD_SIZE              None             4-5  1.3333\n24             1           HOUSEHOLD_SIZE              None             6-8 -0.6667\n25             1           HOUSEHOLD_SIZE              None              9+ -0.6667\n26             1               OCCUPATION              None               ? -0.9333\n27             1               OCCUPATION              None         Armed-F  1.0667\n28             1               OCCUPATION              None         Cleric.  1.0667\n29             1               OCCUPATION              None          Crafts -0.9333\n30             1               OCCUPATION              None           Exec.  1.0667\n31             1               OCCUPATION              None         Farming -0.9333\n32             1               OCCUPATION              None         Handler -0.9333\n33             1               OCCUPATION              None         House-s -0.9333\n34             1               OCCUPATION              None         Machine -0.9333\n35             1               OCCUPATION              None           Other -0.9333\n36             1               OCCUPATION              None           Prof.  1.0667\n37             1               OCCUPATION              None         Protec.  1.0667\n38             1               OCCUPATION              None           Sales  1.0667\n39             1               OCCUPATION              None         TechSup  1.0667\n40             1               OCCUPATION              None         Transp. -0.9333\n41             1         OS_DOC_SET_KANJI              None            None -0.1082\n42             1            YRS_RESIDENCE              None            None  0.0000\n43             1              Y_BOX_GAMES              None            None -0.9203\n44             1                     None              None            None -1.7951\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test Data","message":["%python","svm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1734713303202,"interpreter":"python.medium","endTime":1734713307342,"results":[{"message":"0.84169\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> 'SVMS_EPSILON' : '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> 'SVMS_BATCH_ROWS' : '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> 'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> 'SVMS_CONV_TOLERANCE': '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> 'SVMS_NUM_PIVOTS': '200' "],"enabled":true,"result":{"startTime":1734713307765,"interpreter":"md.medium","endTime":1734713308201,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' : '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE': '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS': '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVM Model using linear kernel","message":["%python","","","try:","    oml.drop(model = 'SVM_CLAS_MODEL') ","except:","    pass","    ","    ","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_LINEAR', ","            'SVMS_SOLVER': 'SVMS_SOLVER_SGD',","            'SVMS_CONV_TOLERANCE': '0.001', ","            'SVMS_BATCH_ROWS': '2000',","            'SVMS_REGULARIZER': 'SVMS_REGULARIZER_L1'}","","svm_mod = oml.svm('classification', **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'SVM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1734713308626,"interpreter":"python.medium","endTime":1734713314475,"results":[{"message":"\nModel Name: SVM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1          CLAS_WEIGHTS_BALANCED                           OFF\n2                   ODMS_DETAILS                   ODMS_ENABLE\n3   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                      PREP_AUTO                            ON\n6                SVMS_BATCH_ROWS                          2000\n7            SVMS_CONV_TOLERANCE                         0.001\n8           SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n9               SVMS_REGULARIZER           SVMS_REGULARIZER_L1\n10                   SVMS_SOLVER               SVMS_SOLVER_SGD\n\nComputed Settings: \n             setting name setting value\n0  SVMS_COMPLEXITY_FACTOR          1000\n1     SVMS_NUM_ITERATIONS          1000\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS            1000\n2       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n    TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE    COEF\n0              1  BOOKKEEPING_APPLICATION              None            None  0.3749\n1              1      BULK_PACK_DISKETTES              None            None  0.0424\n2              1                EDUCATION              None            10th  0.6785\n3              1                EDUCATION              None            11th  0.6668\n4              1                EDUCATION              None            12th  0.0156\n5              1                EDUCATION              None         1st-4th -0.6141\n6              1                EDUCATION              None         5th-6th -0.7327\n7              1                EDUCATION              None         7th-8th -0.4346\n8              1                EDUCATION              None             9th  0.1763\n9              1                EDUCATION              None         < Bach. -0.3874\n10             1                EDUCATION              None         Assoc-A -0.2820\n11             1                EDUCATION              None         Assoc-V -0.4068\n12             1                EDUCATION              None           Bach. -0.2558\n13             1                EDUCATION              None         HS-grad -0.5199\n14             1                EDUCATION              None         Masters -0.1865\n15             1                EDUCATION              None             PhD  1.2106\n16             1                EDUCATION              None          Profsc -0.1575\n17             1       FLAT_PANEL_MONITOR              None            None -0.0370\n18             1     HOME_THEATER_PACKAGE              None            None  0.0559\n19             1           HOUSEHOLD_SIZE              None               1 -0.7847\n20             1           HOUSEHOLD_SIZE              None               2 -0.8389\n21             1           HOUSEHOLD_SIZE              None               3  1.0715\n22             1           HOUSEHOLD_SIZE              None             4-5  1.1580\n23             1           HOUSEHOLD_SIZE              None             6-8 -1.0365\n24             1           HOUSEHOLD_SIZE              None              9+ -0.8550\n25             1               OCCUPATION              None               ? -0.9483\n26             1               OCCUPATION              None         Armed-F  0.5928\n27             1               OCCUPATION              None         Cleric.  0.6079\n28             1               OCCUPATION              None          Crafts -0.8653\n29             1               OCCUPATION              None           Exec.  0.8812\n30             1               OCCUPATION              None         Farming -0.9189\n31             1               OCCUPATION              None         Handler -0.9143\n32             1               OCCUPATION              None         House-s -0.3442\n33             1               OCCUPATION              None         Machine -0.7477\n34             1               OCCUPATION              None           Other -0.9168\n35             1               OCCUPATION              None           Prof.  0.8336\n36             1               OCCUPATION              None         Protec.  0.6415\n37             1               OCCUPATION              None           Sales  0.6862\n38             1               OCCUPATION              None         TechSup  0.7539\n39             1               OCCUPATION              None         Transp. -0.6546\n40             1         OS_DOC_SET_KANJI              None            None -0.4410\n41             1            YRS_RESIDENCE              None            None  0.1338\n42             1              Y_BOX_GAMES              None            None -0.7248\n43             1                     None              None            None -1.3097\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on test data","message":["%python","svm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1734713314901,"interpreter":"python.medium","endTime":1734713315524,"results":[{"message":"0.84338\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVM model using Gaussian kernel","message":["%python","","try:","    oml.drop(model = 'SVM_CLAS_MODEL') ","except:","    pass","    ","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_GAUSSIAN', ","            'SVMS_CONV_TOLERANCE': '0.001', ","            'SVMS_NUM_PIVOTS': '200'  }","","svm_mod = oml.svm('classification', **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'SVM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1734713315955,"interpreter":"python.medium","endTime":1734713324699,"results":[{"message":"\nModel Name: SVM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1         CLAS_WEIGHTS_BALANCED                           OFF\n2                  ODMS_DETAILS                   ODMS_ENABLE\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                     PREP_AUTO                            ON\n6           SVMS_CONV_TOLERANCE                         0.001\n7          SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n8               SVMS_NUM_PIVOTS                           200\n\nComputed Settings: \n             setting name       setting value\n0  SVMS_COMPLEXITY_FACTOR                  10\n1     SVMS_NUM_ITERATIONS                  30\n2             SVMS_SOLVER     SVMS_SOLVER_IPM\n3            SVMS_STD_DEV  2.3452078799117149\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              14\n2       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%python","","svm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1734713325125,"interpreter":"python.medium","endTime":1734713326119,"results":[{"message":"0.841127\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Score test data adding supplemental columns","message":["%python","","# Set the case ID attribute","case_id = 'CUST_ID'","# Gather the Predictions","RES_DF = svm_mod.predict(TEST_X, supplemental_cols = TEST_X)","# Additionally collect the PROBABILITY_OF_0 and PROBABILITY_OF_1","RES_PROB = svm_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","# Join the entire result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1734713326545,"interpreter":"python.medium","endTime":1734713327391,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display confusion matrix, lift chart and ROC curve ","message":["%python\r","\r","def evaluate_model(pred_data='', \r","    settings_name={''}, \r","    name='',\r","    target=''\r","    ):\r","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions\r","     and the target column,\r","     The Settings name (for the charts), \r","     The name of the model used (for the charts),\r","     Supply the target column name for evaluation\r","     for computing the confusion matrix with the test dataset\"\"\"\r","    import oml\r","    import numpy as np\r","    import matplotlib.pyplot as plt\r","    from sklearn.metrics import auc\r","    from sklearn.metrics import roc_curve\r","\r","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)\r"," \r","    # Extract Statistics from the Confusion Matrix\r","    cf_local = conf_matrix.pull()\r","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])\r","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])\r","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])\r","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])\r","    TPR = TP/(TP+FN)\r","    FPR = FP/(FP+TN)\r","    TNR = TN/(TN+FP)\r","    FNR = FN/(FN+TP)\r","    Precision = TP/(TP+FP)\r","    Accuracy = (TP+TN)/(TP+TN+FP+FN)\r","    NPV = TN/(FN+TN)\r","    DetectionRate = TN/(FN+TN)\r","    BalancedAccuracy = (TPR+TNR)/2\r","    \r","    # Estimated AUC via Triangle (not very precise) could be\r","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR\r","    # Compute real AUC using roc_curve by loading the\r","    # data locally and using the roc_curve() function\r","    pred_local = pred_data.pull()\r","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])\r","    AUC = auc(fpr, tpr)\r","    opt_index = np.argmax(tpr - fpr)\r","    FPR_OPT = fpr[opt_index]\r","    TPR_OPT = tpr[opt_index]\r","    F1Score = 2*Precision*TPR/(Precision+TPR)\r","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5\r","    \r","    # Store all statistics to export\r","    statistics = {'Algorithm' : name,\r","                  'Algorithm_setting' : settings_name,\r","                  'TN' : TN,\r","                  'TP' : TP,\r","                  'FP' : FP,\r","                  'FN' : FN,\r","                  'TPR' : TPR,\r","                  'FPR' : FPR,\r","                  'TNR' : TNR,\r","                  'FNR' : FNR,\r","                  'Precision' : Precision,\r","                  'Accuracy' : Accuracy,\r","                  'NPV' : NPV,\r","                  'DetectionRate' : DetectionRate,\r","                  'BalancedAccuracy' : BalancedAccuracy,\r","                  'AUC' : AUC,\r","                  'F1Score' : F1Score,\r","                  'MathewsCorrCoef' : MathewsCorrCoef\r","                  }\r","    # Nice round stats for printing to screen\r","    TOTAL = TP+TN+FP+FN\r","    TN_P = round((TN/TOTAL*100),2)\r","    FP_P = round((FP/TOTAL*100),2)\r","    FN_P = round((FN/TOTAL*100),2)\r","    TP_P = round((TP/TOTAL*100),2)\r","    # Print the output nicely on Zeppelin native Table\r","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+\r","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+\r","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+\r","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+\r","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+\r","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+\r","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+\r","          \"F1Score: \"+str(round(F1Score,4))\r","          )\r","    \r","    # Multiple Charts for Evaluation\r","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])\r","    ax1, ax2, ax3, ax4 = axes.flatten()\r","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)\r","    \r","    # Statistics\r","    ax1.axis('off')\r","    \r","    # Function to return rounded numbers if the string is float, return\r","    # integers otherwise and return characters if not a number\r","    def round_if_float(content):\r","        try:\r","            val = float(content)\r","        except ValueError:\r","            return(content)\r","        else:\r","            if val.is_integer():\r","                return(int(content))\r","            else:\r","                return(round(float(content),4))\r","\r","    for num, name in enumerate(statistics):\r","        ax1.text(0.01, \r","        (-num*0.06+0.94),\r","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),\r","        ha='left', \r","        va='bottom', \r","        fontsize=12)\r","    \r","    # Produce Lift Chart\r","    ax2.set_title('Lift Chart')\r","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)\r","    data['row_id'] = range(0,0+len(data))\r","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)\r","    lift = data.groupby('decile')[target].agg(['count','sum'])\r","    lift.columns = ['count', target]\r","    lift['decile'] = range(1,11)\r","    \r","    data_ideal = pred_local.sort_values(by=target, ascending=False)\r","    data_ideal['row_id'] = range(0,0+len(data))\r","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)\r","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])\r","    lift_ideal.columns = ['count', 'IDEAL']\r","    lift['IDEAL']=lift_ideal['IDEAL']\r","    \r","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],\r","    color='darkorange', label='Ideal')\r","    ax2.bar(lift['decile'],lift[target]/lift['count'],\r","    color='blue', alpha=0.6, label='Model')\r","    ax2.axhline((lift[target]/lift['count']).mean(), \r","    color='grey', linestyle='--', label='Avg TARGET')\r","    ax2.set_ylim(0,1.15)\r","    ax2.set_xlabel('Decile', size=13)\r","    ax2.set_ylabel('Percent of Actual Targets', size=13)\r","    # Print labels.\r","    for dec in lift['decile']:\r","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, \r","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",\r","        ha='center', va='bottom')\r","    ax2.legend(loc=\"upper right\")\r","    \r","    # Produce Gains Chart\r","    ax3.set_title('Distributions of Predictions')\r","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)\r","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)\r","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')\r","    ax3.set_xlim([0,1])\r","    ax3.set_xlabel('Probability of 1', size=13)\r","    ax3.set_ylabel('Density', size=13)\r","    ax3.legend(loc=\"upper right\")\r","    \r","    # ROC curve Chart\r","    ax4.set_title('ROC Curve')\r","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')\r","    ax4.plot(FPR_OPT, TPR_OPT, 'bo', color='orange', markersize=6)\r","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')\r","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),\r","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), \r","                 textcoords='data', \r","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,\r","                                 connectionstyle='arc3, rad=0.3'), \r","                 horizontalalignment='right', verticalalignment='top')\r","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), \r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), \r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), \r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),\r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), \r","                 xycoords='axes fraction', size=13)\r","    ax4.set_xlim([-0.02, 1.02])\r","    ax4.set_ylim([0.0, 1.02])\r","    ax4.set_xlabel('False Positive Rate', size=13)\r","    ax4.set_ylabel('True Positive Rate', size=13) \r","    ax4.legend(loc=\"lower right\")\r","    \r","    return(statistics, pred_local)\r","_ = evaluate_model(pred_data=RES_DF, settings_name='Gaussian Kernel,Conv.Tolerance:0.001,Num Pivots:200', name='Support Vector Machine', target='AFFINITY_CARD')\r"],"enabled":true,"result":{"startTime":1734713327811,"interpreter":"python.medium","endTime":1734713340176,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1234 (69.52%)\tFalse Positive: 176 (9.92%)\nACTUAL 1\tFalse Negative: 106 (5.97%)\tTrue Positive: 259 (14.59%)\nAccuracy: 84.1127%\tAUC: 0.8728\tF1Score: 0.6475\n<stdin>:160: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"bo\" (-> color='b'). The keyword argument will take precedence.\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img 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PREDICTION_DETAILS(SVM_CLAS_MODEL USING *) PD","    FROM SVM_TEST_TABLE","    WHERE CUST_ID < 100015","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') ","    OUTPRED"],"enabled":true,"result":{"startTime":1734713364633,"interpreter":"sql.medium","endTime":1734713365177,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".674\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".037\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".014\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".323\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".1\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".011\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".536\" 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Clustering EM.dsnb b/machine-learning/notebooks-oml/python/OML4Py Clustering EM.dsnb
new file mode 100644
index 00000000..b436e555
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Clustering EM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Clustering EM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733161937999,"interpreter":"md.low","endTime":1733161938072,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Identifying Customer Segments using Expectation Maximization Clustering","","Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the Expectation Maximization in-database algortihm. ","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733161938142,"interpreter":"md.low","endTime":1733161938218,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1733161938293,"interpreter":"md.low","endTime":1733161938364,"results":[{"message":"<p><img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/expectation-maximization.html\" target=\"_blank\">OML4Py Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1733161938441,"interpreter":"md.low","endTime":1733161938515,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/expectation-maximization.html\" target=\"_blank\">OML4Py Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python\r","\r","import pandas as pd\r","import numpy as np\r","import matplotlib.pyplot as plt\r","import oml\r","\r","pd.set_option('display.max_rows', 500)\r","pd.set_option('display.max_columns', 500)\r","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1733161938596,"interpreter":"python.low","endTime":1733161940692,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data by merging CUSTOMERS with SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","CUSTOMERS = oml.sync(query = \"\"\"SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT ","                                FROM SH.CUSTOMERS\"\"\")","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES","                              FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1733161940769,"interpreter":"python.low","endTime":1733161940915,"results":[{"message":"<stdin>:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 5 rows of CUST_DF data","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1733161940988,"interpreter":"python.low","endTime":1733161941196,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build an Expectation Maximization model using the CUST_DF proxy object"],"enabled":true,"result":{"startTime":1733161941282,"interpreter":"md.low","endTime":1733161941366,"results":[{"message":"<h3 id=\"build-an-expectation-maximization-model-using-the-cust_df-proxy-object\">Build an Expectation Maximization model using the CUST_DF proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build EM clustering model with 3 clusters with default settings","message":["%python","","try:","    oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:","    pass","","setting = {'emcs_num_iterations': 20, 'emcs_random_seed': 7}","em_mod = oml.em(n_clusters = 3, **setting).fit(CUST_DF, ","                                               model_name = \"CUST_CLUSTER_MODEL\", ","                                               case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1733161941468,"interpreter":"python.low","endTime":1733161946172,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build EM clustering model with 3 clusters with explicit settings","message":["%python","","try:","    oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:","    pass","","setting = {'EMCS_NUM_ITERATIONS': 20,","           'EMCS_RANDOM_SEED': 7,","           'EMCS_CLUSTER_COMPONENTS': 'EMCS_CLUSTER_COMP_ENABLE',","           'EMCS_CLUSTER_STATISTICS': 'EMCS_CLUS_STATS_ENABLE',","           'EMCS_CLUSTER_THRESH': 2,","           'EMCS_LINKAGE_FUNCTION': 'EMCS_LINKAGE_SINGLE',","           'EMCS_LOGLIKE_IMPROVEMENT': .001,","           'EMCS_MAX_NUM_ATTR_2D': 50,","           'EMCS_MIN_PCT_ATTR_SUPPORT': .1,","           'EMCS_MODEL_SEARCH': 'EMCS_MODEL_SEARCH_DISABLE',","           'EMCS_NUM_COMPONENTS': 20,","           'EMCS_NUM_DISTRIBUTION': 'EMCS_NUM_DISTR_SYSTEM',","           'EMCS_NUM_EQUIWIDTH_BINS': 11,","           'EMCS_NUM_PROJECTIONS': 50,","           'EMCS_REMOVE_COMPONENTS': 'EMCS_REMOVE_COMPS_ENABLE',","           'EMCS_ATTRIBUTE_FILTER': 'EMCS_ATTR_FILTER_ENABLE',","           'EMCS_CONVERGENCE_CRITERION': 'EMCS_CONV_CRIT_HELDASIDE',","           'EMCS_NUM_QUANTILE_BINS': 11,","           'EMCS_NUM_TOPN_BINS': 15,","           'ODMS_DETAILS': 'ODMS_ENABLE',","           'ODMS_SAMPLING': 'ODMS_SAMPLING_DISABLE',","           'PREP_AUTO': 'ON'}","em_mod = oml.em(n_clusters = 3, **setting).fit(CUST_DF, model_name = \"CUST_CLUSTER_MODEL\", case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1733161946246,"interpreter":"python.low","endTime":1733161950942,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display Expectation Maximization model details from model proxy object","message":["%python","","em_mod"],"enabled":true,"result":{"startTime":1733161951018,"interpreter":"python.low","endTime":1733161952318,"results":[{"message":"\nModel Name: CUST_CLUSTER_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Expectation Maximization\n\nMining Function: CLUSTERING\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_EXPECTATION_MAXIMIZATION\n1              CLUS_NUM_CLUSTERS                              3\n2          EMCS_ATTRIBUTE_FILTER        EMCS_ATTR_FILTER_ENABLE\n3        EMCS_CLUSTER_COMPONENTS       EMCS_CLUSTER_COMP_ENABLE\n4        EMCS_CLUSTER_STATISTICS         EMCS_CLUS_STATS_ENABLE\n5            EMCS_CLUSTER_THRESH                              2\n6     EMCS_CONVERGENCE_CRITERION       EMCS_CONV_CRIT_HELDASIDE\n7          EMCS_LINKAGE_FUNCTION            EMCS_LINKAGE_SINGLE\n8       EMCS_LOGLIKE_IMPROVEMENT                          0.001\n9           EMCS_MAX_NUM_ATTR_2D                             50\n10     EMCS_MIN_PCT_ATTR_SUPPORT                            0.1\n11             EMCS_MODEL_SEARCH      EMCS_MODEL_SEARCH_DISABLE\n12           EMCS_NUM_COMPONENTS                             20\n13         EMCS_NUM_DISTRIBUTION          EMCS_NUM_DISTR_SYSTEM\n14       EMCS_NUM_EQUIWIDTH_BINS                             11\n15           EMCS_NUM_ITERATIONS                             20\n16          EMCS_NUM_PROJECTIONS                             50\n17        EMCS_NUM_QUANTILE_BINS                             11\n18            EMCS_NUM_TOPN_BINS                             15\n19              EMCS_RANDOM_SEED                              7\n20        EMCS_REMOVE_COMPONENTS       EMCS_REMOVE_COMPS_ENABLE\n21                  ODMS_DETAILS                    ODMS_ENABLE\n22  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n23                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n24                     PREP_AUTO                             ON\n\nGlobal Statistics: \n       attribute name attribute value\n0           CONVERGED              NO\n1       LOGLIKELIHOOD        -13.5244\n2        NUM_CLUSTERS               3\n3      NUM_COMPONENTS              20\n4            NUM_ROWS            4500\n5         RANDOM_SEED               7\n6  REMOVED_COMPONENTS               0\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nClusters: \n\n   CLUSTER_ID  CLUSTER_NAME  RECORD_COUNT  PARENT  TREE_LEVEL  LEFT_CHILD_ID  RIGHT_CHILD_ID\n0           1             1          4500     NaN           1            2.0             3.0\n1           2             2          1164     1.0           2            NaN             NaN\n2           3             3          3336     1.0           2            4.0             5.0\n3           4             4          3155     3.0           3            NaN             NaN\n4           5             5           181     3.0           3            NaN             NaN\n\nTaxonomy: \n\n   PARENT_CLUSTER_ID  CHILD_CLUSTER_ID\n0                  1               2.0\n1                  1               3.0\n2                  2               NaN\n3                  3               4.0\n4                  3               5.0\n5                  4               NaN\n6                  5               NaN\n\nCentroids: \n\n    CLUSTER_ID       ATTRIBUTE_NAME         MEAN            MODE_VALUE      VARIANCE\n0            1        AFFINITY_CARD     0.238222                  None  1.815127e-01\n1            1    CUST_CREDIT_LIMIT  7924.222222                  None  1.591424e+07\n2            1          CUST_GENDER          NaN                     M           NaN\n3            1    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n4            1  CUST_MARITAL_STATUS          NaN               Married           NaN\n5            1   CUST_YEAR_OF_BIRTH  1964.624444                  None  1.871268e+02\n6            1            EDUCATION          NaN               HS-grad           NaN\n7            1       HOUSEHOLD_SIZE          NaN                     3           NaN\n8            1           OCCUPATION          NaN                Crafts           NaN\n9            1        YRS_RESIDENCE     4.022000                  None  3.617431e+00\n10           1          Y_BOX_GAMES     0.312444                  None  2.148707e-01\n11           2        AFFINITY_CARD     0.016323                  None  1.607039e-02\n12           2    CUST_CREDIT_LIMIT  8265.034364                  None  1.567110e+07\n13           2          CUST_GENDER          NaN                     M           NaN\n14           2    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n15           2  CUST_MARITAL_STATUS          NaN                NeverM           NaN\n16           2   CUST_YEAR_OF_BIRTH  1980.033505                  None  1.136517e+01\n17           2            EDUCATION          NaN               < Bach.           NaN\n18           2       HOUSEHOLD_SIZE          NaN                     1           NaN\n19           2           OCCUPATION          NaN                 Other           NaN\n20           2        YRS_RESIDENCE     2.134880                  None  8.579741e-01\n21           2          Y_BOX_GAMES     1.000000                  None  0.000000e+00\n22           3        AFFINITY_CARD     0.315647                  None  2.160789e-01\n23           3    CUST_CREDIT_LIMIT  7805.305755                  None  1.594911e+07\n24           3          CUST_GENDER          NaN                     M           NaN\n25           3    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n26           3  CUST_MARITAL_STATUS          NaN               Married           NaN\n27           3   CUST_YEAR_OF_BIRTH  1959.247902                  None  1.366873e+02\n28           3            EDUCATION          NaN               HS-grad           NaN\n29           3       HOUSEHOLD_SIZE          NaN                     3           NaN\n30           3           OCCUPATION          NaN                 Exec.           NaN\n31           3        YRS_RESIDENCE     4.680456                  None  2.904158e+00\n32           3          Y_BOX_GAMES     0.072542                  None  6.729980e-02\n33           4        AFFINITY_CARD     0.301743                  None  2.107611e-01\n34           4    CUST_CREDIT_LIMIT  7799.683043                  None  1.589185e+07\n35           4          CUST_GENDER          NaN                     M           NaN\n36           4    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n37           4  CUST_MARITAL_STATUS          NaN               Married           NaN\n38           4   CUST_YEAR_OF_BIRTH  1959.118225                  None  1.406021e+02\n39           4            EDUCATION          NaN               HS-grad           NaN\n40           4       HOUSEHOLD_SIZE          NaN                     3           NaN\n41           4           OCCUPATION          NaN                Crafts           NaN\n42           4        YRS_RESIDENCE     4.721078                  None  2.919641e+00\n43           4          Y_BOX_GAMES     0.076704                  None  7.084265e-02\n44           5        AFFINITY_CARD     0.558011                  None  2.480049e-01\n45           5    CUST_CREDIT_LIMIT  7903.314917                  None  1.703088e+07\n46           5          CUST_GENDER          NaN                     F           NaN\n47           5    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n48           5  CUST_MARITAL_STATUS          NaN               Married           NaN\n49           5   CUST_YEAR_OF_BIRTH  1961.508287                  None  6.341799e+01\n50           5            EDUCATION          NaN               HS-grad           NaN\n51           5       HOUSEHOLD_SIZE          NaN                   4-5           NaN\n52           5           OCCUPATION          NaN               Cleric.           NaN\n53           5        YRS_RESIDENCE     3.972376                  None  2.115899e+00\n54           5          Y_BOX_GAMES     0.000000                  None  0.000000e+00\n\nLeaf Cluster Counts: \n\n   CLUSTER_ID   CNT\n0           2  1164\n1           4  3155\n2           5   181\n\nAttribute Importance: \n\n         ATTRIBUTE_NAME  ATTRIBUTE_IMPORTANCE_VALUE  ATTRIBUTE_RANK\n0         AFFINITY_CARD                    0.125510               7\n1     CUST_CREDIT_LIMIT                    0.083163              10\n2           CUST_GENDER                    0.187811               6\n3     CUST_INCOME_LEVEL                    0.087468               8\n4   CUST_MARITAL_STATUS                    0.232739               4\n5    CUST_YEAR_OF_BIRTH                    0.368715               2\n6             EDUCATION                    0.057243              11\n7        HOUSEHOLD_SIZE                    0.263308               3\n8            OCCUPATION                    0.085454               9\n9         YRS_RESIDENCE                    0.232491               5\n10          Y_BOX_GAMES                    0.411961               1\n\nComponents: \n\n    COMPONENT_ID  CLUSTER_ID  PRIOR_PROBABILITY\n0              1           2           0.083151\n1              2           4           0.041165\n2              3           4           0.025857\n3              4           2           0.055326\n4              5           2           0.032430\n5              6           4           0.037727\n6              7           4           0.045825\n7              8           2           0.044490\n8              9           4           0.081876\n9             10           4           0.072249\n10            11           4           0.040540\n11            12           4           0.052175\n12            13           4           0.051327\n13            14           5           0.041011\n14            15           4           0.055620\n15            16           4           0.051606\n16            17           4           0.042597\n17            18           4           0.052004\n18            19           4           0.052609\n19            20           2           0.040414\n\nCluster Hists: \n\n     cluster.id                                variable  bin.id  lower.bound  upper.bound          label  count\n0             1                           AFFINITY_CARD       1          0.0          0.0            0:0   3428\n1             1                           AFFINITY_CARD       2          0.0          1.0            0:1   1072\n2             1                           AFFINITY_CARD       3          NaN          NaN              :      0\n3             1                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    580\n4             1                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    415\n5             1                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000    405\n6             1                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    556\n7             1                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    946\n8             1                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000    373\n9             1                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    748\n10            1                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    477\n11            1                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n12            1                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n13            1                           CUST_GENDER:F       3          NaN          NaN              :   1510\n14            1                           CUST_GENDER:M       2          NaN          NaN              :   2990\n15            1               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n16            1       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     88\n17            1    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :    222\n18            1    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :    171\n19            1    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :    133\n20            1   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :    310\n21            1  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :    369\n22            1  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :    339\n23            1  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :    383\n24            1  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :    484\n25            1  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    965\n26            1  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    393\n27            1  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    643\n28            1             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n29            1             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :    615\n30            1             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :     75\n31            1              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      3\n32            1             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :   2034\n33            1              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :   1503\n34            1              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :    134\n35            1             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :    136\n36            1                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      5\n37            1                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6     40\n38            1                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9     73\n39            1                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2    192\n40            1                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5    337\n41            1                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8    533\n42            1                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1    878\n43            1                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4    853\n44            1                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    792\n45            1                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986    797\n46            1                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n47            1                       EDUCATION:'Other'       1          NaN          NaN              :     20\n48            1                          EDUCATION:10th       8          NaN          NaN              :    122\n49            1                          EDUCATION:11th       9          NaN          NaN              :    121\n50            1                          EDUCATION:12th      13          NaN          NaN              :     52\n51            1                       EDUCATION:5th-6th      15          NaN          NaN              :     39\n52            1                       EDUCATION:7th-8th      11          NaN          NaN              :     87\n53            1                           EDUCATION:9th      12          NaN          NaN              :     76\n54            1                       EDUCATION:< Bach.       3          NaN          NaN              :   1041\n55            1                       EDUCATION:Assoc-A       7          NaN          NaN              :    172\n56            1                       EDUCATION:Assoc-V       5          NaN          NaN              :    196\n57            1                         EDUCATION:Bach.       4          NaN          NaN              :    779\n58            1                       EDUCATION:HS-grad       2          NaN          NaN              :   1462\n59            1                       EDUCATION:Masters       6          NaN          NaN              :    190\n60            1                           EDUCATION:PhD      14          NaN          NaN              :     49\n61            1                        EDUCATION:Profsc      10          NaN          NaN              :     94\n62            1                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n63            1                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    692\n64            1                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :   1149\n65            1                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :   1787\n66            1                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :    219\n67            1                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :    148\n68            1                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :    505\n69            1                      OCCUPATION:'Other'       1          NaN          NaN              :      2\n70            1                            OCCUPATION:?       9          NaN          NaN              :    236\n71            1                      OCCUPATION:Cleric.       6          NaN          NaN              :    520\n72            1                       OCCUPATION:Crafts       2          NaN          NaN              :    572\n73            1                        OCCUPATION:Exec.       4          NaN          NaN              :    559\n74            1                      OCCUPATION:Farming      13          NaN          NaN              :    122\n75            1                      OCCUPATION:Handler      12          NaN          NaN              :    168\n76            1                      OCCUPATION:House-s      15          NaN          NaN              :     19\n77            1                      OCCUPATION:Machine       8          NaN          NaN              :    276\n78            1                        OCCUPATION:Other       7          NaN          NaN              :    468\n79            1                        OCCUPATION:Prof.       5          NaN          NaN              :    545\n80            1                      OCCUPATION:Protec.      14          NaN          NaN              :     89\n81            1                        OCCUPATION:Sales       3          NaN          NaN              :    560\n82            1                      OCCUPATION:TechSup      11          NaN          NaN              :    177\n83            1                      OCCUPATION:Transp.      10          NaN          NaN              :    187\n84            1                           YRS_RESIDENCE       1          0.0          0.0            0:0     48\n85            1                           YRS_RESIDENCE       2          0.0          1.0            0:1    276\n86            1                           YRS_RESIDENCE       3          1.0          2.0            1:2    625\n87            1                           YRS_RESIDENCE       4          2.0          3.0            2:3    961\n88            1                           YRS_RESIDENCE       5          3.0          4.0            3:4    937\n89            1                           YRS_RESIDENCE       6          4.0          5.0            4:5    757\n90            1                           YRS_RESIDENCE       7          5.0          6.0            5:6    473\n91            1                           YRS_RESIDENCE       8          6.0          7.0            6:7    231\n92            1                           YRS_RESIDENCE       9          7.0          8.0            7:8    101\n93            1                           YRS_RESIDENCE      10          8.0         14.0           8:14     91\n94            1                           YRS_RESIDENCE      11          NaN          NaN              :      0\n95            1                             Y_BOX_GAMES       1          0.0          0.0            0:0   3094\n96            1                             Y_BOX_GAMES       2          0.0          1.0            0:1   1406\n97            1                             Y_BOX_GAMES       3          NaN          NaN              :      0\n98            2                           AFFINITY_CARD       1          0.0          0.0            0:0   1145\n99            2                           AFFINITY_CARD       2          0.0          1.0            0:1     19\n100           2                           AFFINITY_CARD       3          NaN          NaN              :      0\n101           2                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    129\n102           2                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    100\n103           2                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000     93\n104           2                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    139\n105           2                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    257\n106           2                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000     82\n107           2                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    226\n108           2                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    138\n109           2                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n110           2                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n111           2                           CUST_GENDER:F       3          NaN          NaN              :    553\n112           2                           CUST_GENDER:M       2          NaN          NaN              :    611\n113           2               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n114           2       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     16\n115           2    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :     58\n116           2    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :     43\n117           2    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :     25\n118           2   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :     52\n119           2  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :     95\n120           2  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :     95\n121           2  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :     75\n122           2  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :     96\n123           2  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    266\n124           2  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    126\n125           2  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    217\n126           2             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n127           2             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :     61\n128           2             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :      9\n129           2              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      0\n130           2             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :     25\n131           2              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :   1041\n132           2              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :     28\n133           2             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :      0\n134           2                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      0\n135           2                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6      0\n136           2                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9      0\n137           2                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2      0\n138           2                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5      0\n139           2                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8      0\n140           2                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1      0\n141           2                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4      0\n142           2                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    404\n143           2                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986    760\n144           2                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n145           2                       EDUCATION:'Other'       1          NaN          NaN              :      3\n146           2                          EDUCATION:10th       8          NaN          NaN              :     60\n147           2                          EDUCATION:11th       9          NaN          NaN              :     54\n148           2                          EDUCATION:12th      13          NaN          NaN              :     26\n149           2                       EDUCATION:5th-6th      15          NaN          NaN              :     10\n150           2                       EDUCATION:7th-8th      11          NaN          NaN              :      7\n151           2                           EDUCATION:9th      12          NaN          NaN              :      8\n152           2                       EDUCATION:< Bach.       3          NaN          NaN              :    380\n153           2                       EDUCATION:Assoc-A       7          NaN          NaN              :     37\n154           2                       EDUCATION:Assoc-V       5          NaN          NaN              :     42\n155           2                         EDUCATION:Bach.       4          NaN          NaN              :    162\n156           2                       EDUCATION:HS-grad       2          NaN          NaN              :    352\n157           2                       EDUCATION:Masters       6          NaN          NaN              :      6\n158           2                           EDUCATION:PhD      14          NaN          NaN              :      3\n159           2                        EDUCATION:Profsc      10          NaN          NaN              :     14\n160           2                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n161           2                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    580\n162           2                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :    384\n163           2                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :      0\n164           2                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :     13\n165           2                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :     88\n166           2                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :     99\n167           2                      OCCUPATION:'Other'       1          NaN          NaN              :      1\n168           2                            OCCUPATION:?       9          NaN          NaN              :    112\n169           2                      OCCUPATION:Cleric.       6          NaN          NaN              :    186\n170           2                       OCCUPATION:Crafts       2          NaN          NaN              :     88\n171           2                        OCCUPATION:Exec.       4          NaN          NaN              :     58\n172           2                      OCCUPATION:Farming      13          NaN          NaN              :     27\n173           2                      OCCUPATION:Handler      12          NaN          NaN              :     69\n174           2                      OCCUPATION:House-s      15          NaN          NaN              :      3\n175           2                      OCCUPATION:Machine       8          NaN          NaN              :     55\n176           2                        OCCUPATION:Other       7          NaN          NaN              :    218\n177           2                        OCCUPATION:Prof.       5          NaN          NaN              :     94\n178           2                      OCCUPATION:Protec.      14          NaN          NaN              :     12\n179           2                        OCCUPATION:Sales       3          NaN          NaN              :    165\n180           2                      OCCUPATION:TechSup      11          NaN          NaN              :     54\n181           2                      OCCUPATION:Transp.      10          NaN          NaN              :     22\n182           2                           YRS_RESIDENCE       1          0.0          0.0            0:0     33\n183           2                           YRS_RESIDENCE       2          0.0          1.0            0:1    250\n184           2                           YRS_RESIDENCE       3          1.0          2.0            1:2    477\n185           2                           YRS_RESIDENCE       4          2.0          3.0            2:3    347\n186           2                           YRS_RESIDENCE       5          3.0          4.0            3:4     46\n187           2                           YRS_RESIDENCE       6          4.0          5.0            4:5     10\n188           2                           YRS_RESIDENCE       7          5.0          6.0            5:6      1\n189           2                           YRS_RESIDENCE       8          6.0          7.0            6:7      0\n190           2                           YRS_RESIDENCE       9          7.0          8.0            7:8      0\n191           2                           YRS_RESIDENCE      10          8.0         14.0           8:14      0\n192           2                           YRS_RESIDENCE      11          NaN          NaN              :      0\n193           2                             Y_BOX_GAMES       1          0.0          0.0            0:0      0\n194           2                             Y_BOX_GAMES       2          0.0          1.0            0:1   1164\n195           2                             Y_BOX_GAMES       3          NaN          NaN              :      0\n196           3                           AFFINITY_CARD       1          0.0          0.0            0:0   2283\n197           3                           AFFINITY_CARD       2          0.0          1.0            0:1   1053\n198           3                           AFFINITY_CARD       3          NaN          NaN              :      0\n199           3                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    451\n200           3                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    315\n201           3                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000    312\n202           3                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    417\n203           3                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    689\n204           3                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000    291\n205           3                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    522\n206           3                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    339\n207           3                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n208           3                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n209           3                           CUST_GENDER:F       3          NaN          NaN              :    957\n210           3                           CUST_GENDER:M       2          NaN          NaN              :   2379\n211           3               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n212           3       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     72\n213           3    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :    164\n214           3    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :    128\n215           3    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :    108\n216           3   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :    258\n217           3  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :    274\n218           3  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :    244\n219           3  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :    308\n220           3  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :    388\n221           3  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    699\n222           3  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    267\n223           3  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    426\n224           3             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n225           3             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :    554\n226           3             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :     66\n227           3              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      3\n228           3             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :   2009\n229           3              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :    462\n230           3              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :    106\n231           3             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :    136\n232           3                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      5\n233           3                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6     40\n234           3                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9     73\n235           3                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2    192\n236           3                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5    337\n237           3                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8    533\n238           3                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1    878\n239           3                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4    853\n240           3                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    388\n241           3                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986     37\n242           3                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n243           3                       EDUCATION:'Other'       1          NaN          NaN              :     17\n244           3                          EDUCATION:10th       8          NaN          NaN              :     62\n245           3                          EDUCATION:11th       9          NaN          NaN              :     67\n246           3                          EDUCATION:12th      13          NaN          NaN              :     26\n247           3                       EDUCATION:5th-6th      15          NaN          NaN              :     29\n248           3                       EDUCATION:7th-8th      11          NaN          NaN              :     80\n249           3                           EDUCATION:9th      12          NaN          NaN              :     68\n250           3                       EDUCATION:< Bach.       3          NaN          NaN              :    661\n251           3                       EDUCATION:Assoc-A       7          NaN          NaN              :    135\n252           3                       EDUCATION:Assoc-V       5          NaN          NaN              :    154\n253           3                         EDUCATION:Bach.       4          NaN          NaN              :    617\n254           3                       EDUCATION:HS-grad       2          NaN          NaN              :   1110\n255           3                       EDUCATION:Masters       6          NaN          NaN              :    184\n256           3                           EDUCATION:PhD      14          NaN          NaN              :     46\n257           3                        EDUCATION:Profsc      10          NaN          NaN              :     80\n258           3                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n259           3                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    112\n260           3                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :    765\n261           3                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :   1787\n262           3                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :    206\n263           3                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :     60\n264           3                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :    406\n265           3                      OCCUPATION:'Other'       1          NaN          NaN              :      1\n266           3                            OCCUPATION:?       9          NaN          NaN              :    124\n267           3                      OCCUPATION:Cleric.       6          NaN          NaN              :    334\n268           3                       OCCUPATION:Crafts       2          NaN          NaN              :    484\n269           3                        OCCUPATION:Exec.       4          NaN          NaN              :    501\n270           3                      OCCUPATION:Farming      13          NaN          NaN              :     95\n271           3                      OCCUPATION:Handler      12          NaN          NaN              :     99\n272           3                      OCCUPATION:House-s      15          NaN          NaN              :     16\n273           3                      OCCUPATION:Machine       8          NaN          NaN              :    221\n274           3                        OCCUPATION:Other       7          NaN          NaN              :    250\n275           3                        OCCUPATION:Prof.       5          NaN          NaN              :    451\n276           3                      OCCUPATION:Protec.      14          NaN          NaN              :     77\n277           3                        OCCUPATION:Sales       3          NaN          NaN              :    395\n278           3                      OCCUPATION:TechSup      11          NaN          NaN              :    123\n279           3                      OCCUPATION:Transp.      10          NaN          NaN              :    165\n280           3                           YRS_RESIDENCE       1          0.0          0.0            0:0     15\n281           3                           YRS_RESIDENCE       2          0.0          1.0            0:1     26\n282           3                           YRS_RESIDENCE       3          1.0          2.0            1:2    148\n283           3                           YRS_RESIDENCE       4          2.0          3.0            2:3    614\n284           3                           YRS_RESIDENCE       5          3.0          4.0            3:4    891\n285           3                           YRS_RESIDENCE       6          4.0          5.0            4:5    747\n286           3                           YRS_RESIDENCE       7          5.0          6.0            5:6    472\n287           3                           YRS_RESIDENCE       8          6.0          7.0            6:7    231\n288           3                           YRS_RESIDENCE       9          7.0          8.0            7:8    101\n289           3                           YRS_RESIDENCE      10          8.0         14.0           8:14     91\n290           3                           YRS_RESIDENCE      11          NaN          NaN              :      0\n291           3                             Y_BOX_GAMES       1          0.0          0.0            0:0   3094\n292           3                             Y_BOX_GAMES       2          0.0          1.0            0:1    242\n293           3                             Y_BOX_GAMES       3          NaN          NaN              :      0\n294           4                           AFFINITY_CARD       1          0.0          0.0            0:0   2203\n295           4                           AFFINITY_CARD       2          0.0          1.0            0:1    952\n296           4                           AFFINITY_CARD       3          NaN          NaN              :      0\n297           4                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    426\n298           4                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    300\n299           4                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000    294\n300           4                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    391\n301           4                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    654\n302           4                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000    278\n303           4                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    496\n304           4                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    316\n305           4                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n306           4                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n307           4                           CUST_GENDER:F       3          NaN          NaN              :    776\n308           4                           CUST_GENDER:M       2          NaN          NaN              :   2379\n309           4               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n310           4       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     70\n311           4    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :    159\n312           4    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :    124\n313           4    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :    104\n314           4   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :    233\n315           4  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :    260\n316           4  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :    236\n317           4  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :    293\n318           4  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :    361\n319           4  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    658\n320           4  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    250\n321           4  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    407\n322           4             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n323           4             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :    554\n324           4             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :     66\n325           4              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      1\n326           4             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :   1830\n327           4              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :    462\n328           4              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :    106\n329           4             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :    136\n330           4                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      5\n331           4                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6     40\n332           4                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9     73\n333           4                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2    188\n334           4                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5    325\n335           4                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8    507\n336           4                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1    821\n337           4                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4    778\n338           4                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    381\n339           4                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986     37\n340           4                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n341           4                       EDUCATION:'Other'       1          NaN          NaN              :     16\n342           4                          EDUCATION:10th       8          NaN          NaN              :     59\n343           4                          EDUCATION:11th       9          NaN          NaN              :     63\n344           4                          EDUCATION:12th      13          NaN          NaN              :     25\n345           4                       EDUCATION:5th-6th      15          NaN          NaN              :     29\n346           4                       EDUCATION:7th-8th      11          NaN          NaN              :     78\n347           4                           EDUCATION:9th      12          NaN          NaN              :     63\n348           4                       EDUCATION:< Bach.       3          NaN          NaN              :    628\n349           4                       EDUCATION:Assoc-A       7          NaN          NaN              :    127\n350           4                       EDUCATION:Assoc-V       5          NaN          NaN              :    144\n351           4                         EDUCATION:Bach.       4          NaN          NaN              :    581\n352           4                       EDUCATION:HS-grad       2          NaN          NaN              :   1051\n353           4                       EDUCATION:Masters       6          NaN          NaN              :    171\n354           4                           EDUCATION:PhD      14          NaN          NaN              :     44\n355           4                        EDUCATION:Profsc      10          NaN          NaN              :     76\n356           4                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n357           4                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    110\n358           4                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :    765\n359           4                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :   1787\n360           4                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :     27\n361           4                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :     60\n362           4                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :    406\n363           4                      OCCUPATION:'Other'       1          NaN          NaN              :      1\n364           4                            OCCUPATION:?       9          NaN          NaN              :    111\n365           4                      OCCUPATION:Cleric.       6          NaN          NaN              :    291\n366           4                       OCCUPATION:Crafts       2          NaN          NaN              :    479\n367           4                        OCCUPATION:Exec.       4          NaN          NaN              :    466\n368           4                      OCCUPATION:Farming      13          NaN          NaN              :     92\n369           4                      OCCUPATION:Handler      12          NaN          NaN              :     96\n370           4                      OCCUPATION:House-s      15          NaN          NaN              :     16\n371           4                      OCCUPATION:Machine       8          NaN          NaN              :    211\n372           4                        OCCUPATION:Other       7          NaN          NaN              :    234\n373           4                        OCCUPATION:Prof.       5          NaN          NaN              :    421\n374           4                      OCCUPATION:Protec.      14          NaN          NaN              :     77\n375           4                        OCCUPATION:Sales       3          NaN          NaN              :    383\n376           4                      OCCUPATION:TechSup      11          NaN          NaN              :    116\n377           4                      OCCUPATION:Transp.      10          NaN          NaN              :    161\n378           4                           YRS_RESIDENCE       1          0.0          0.0            0:0     13\n379           4                           YRS_RESIDENCE       2          0.0          1.0            0:1     22\n380           4                           YRS_RESIDENCE       3          1.0          2.0            1:2    132\n381           4                           YRS_RESIDENCE       4          2.0          3.0            2:3    570\n382           4                           YRS_RESIDENCE       5          3.0          4.0            3:4    833\n383           4                           YRS_RESIDENCE       6          4.0          5.0            4:5    708\n384           4                           YRS_RESIDENCE       7          5.0          6.0            5:6    464\n385           4                           YRS_RESIDENCE       8          6.0          7.0            6:7    226\n386           4                           YRS_RESIDENCE       9          7.0          8.0            7:8     97\n387           4                           YRS_RESIDENCE      10          8.0         14.0           8:14     90\n388           4                           YRS_RESIDENCE      11          NaN          NaN              :      0\n389           4                             Y_BOX_GAMES       1          0.0          0.0            0:0   2913\n390           4                             Y_BOX_GAMES       2          0.0          1.0            0:1    242\n391           4                             Y_BOX_GAMES       3          NaN          NaN              :      0\n392           5                           AFFINITY_CARD       1          0.0          0.0            0:0     80\n393           5                           AFFINITY_CARD       2          0.0          1.0            0:1    101\n394           5                           AFFINITY_CARD       3          NaN          NaN              :      0\n395           5                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500     25\n396           5                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000     15\n397           5                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000     18\n398           5                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000     26\n399           5                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000     35\n400           5                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000     13\n401           5                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000     26\n402           5                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000     23\n403           5                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n404           5                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n405           5                           CUST_GENDER:F       3          NaN          NaN              :    181\n406           5                           CUST_GENDER:M       2          NaN          NaN              :      0\n407           5               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n408           5       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :      2\n409           5    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :      5\n410           5    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :      4\n411           5    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :      4\n412           5   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :     25\n413           5  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :     14\n414           5  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :      8\n415           5  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :     15\n416           5  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :     27\n417           5  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :     41\n418           5  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :     17\n419           5  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :     19\n420           5             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n421           5             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :      0\n422           5             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :      0\n423           5              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      2\n424           5             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :    179\n425           5              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :      0\n426           5              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :      0\n427           5             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :      0\n428           5                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      0\n429           5                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6      0\n430           5                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9      0\n431           5                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2      4\n432           5                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5     12\n433           5                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8     26\n434           5                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1     57\n435           5                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4     75\n436           5                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7      7\n437           5                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986      0\n438           5                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n439           5                       EDUCATION:'Other'       1          NaN          NaN              :      1\n440           5                          EDUCATION:10th       8          NaN          NaN              :      3\n441           5                          EDUCATION:11th       9          NaN          NaN              :      4\n442           5                          EDUCATION:12th      13          NaN          NaN              :      1\n443           5                       EDUCATION:5th-6th      15          NaN          NaN              :      0\n444           5                       EDUCATION:7th-8th      11          NaN          NaN              :      2\n445           5                           EDUCATION:9th      12          NaN          NaN              :      5\n446           5                       EDUCATION:< Bach.       3          NaN          NaN              :     33\n447           5                       EDUCATION:Assoc-A       7          NaN          NaN              :      8\n448           5                       EDUCATION:Assoc-V       5          NaN          NaN              :     10\n449           5                         EDUCATION:Bach.       4          NaN          NaN              :     36\n450           5                       EDUCATION:HS-grad       2          NaN          NaN              :     59\n451           5                       EDUCATION:Masters       6          NaN          NaN              :     13\n452           5                           EDUCATION:PhD      14          NaN          NaN              :      2\n453           5                        EDUCATION:Profsc      10          NaN          NaN              :      4\n454           5                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n455           5                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :      2\n456           5                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :      0\n457           5                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :      0\n458           5                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :    179\n459           5                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :      0\n460           5                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :      0\n461           5                      OCCUPATION:'Other'       1          NaN          NaN              :      0\n462           5                            OCCUPATION:?       9          NaN          NaN              :     13\n463           5                      OCCUPATION:Cleric.       6          NaN          NaN              :     43\n464           5                       OCCUPATION:Crafts       2          NaN          NaN              :      5\n465           5                        OCCUPATION:Exec.       4          NaN          NaN              :     35\n466           5                      OCCUPATION:Farming      13          NaN          NaN              :      3\n467           5                      OCCUPATION:Handler      12          NaN          NaN              :      3\n468           5                      OCCUPATION:House-s      15          NaN          NaN              :      0\n469           5                      OCCUPATION:Machine       8          NaN          NaN              :     10\n470           5                        OCCUPATION:Other       7          NaN          NaN              :     16\n471           5                        OCCUPATION:Prof.       5          NaN          NaN              :     30\n472           5                      OCCUPATION:Protec.      14          NaN          NaN              :      0\n473           5                        OCCUPATION:Sales       3          NaN          NaN              :     12\n474           5                      OCCUPATION:TechSup      11          NaN          NaN              :      7\n475           5                      OCCUPATION:Transp.      10          NaN          NaN              :      4\n476           5                           YRS_RESIDENCE       1          0.0          0.0            0:0      2\n477           5                           YRS_RESIDENCE       2          0.0          1.0            0:1      4\n478           5                           YRS_RESIDENCE       3          1.0          2.0            1:2     16\n479           5                           YRS_RESIDENCE       4          2.0          3.0            2:3     44\n480           5                           YRS_RESIDENCE       5          3.0          4.0            3:4     58\n481           5                           YRS_RESIDENCE       6          4.0          5.0            4:5     39\n482           5                           YRS_RESIDENCE       7          5.0          6.0            5:6      8\n483           5                           YRS_RESIDENCE       8          6.0          7.0            6:7      5\n484           5                           YRS_RESIDENCE       9          7.0          8.0            7:8      4\n485           5                           YRS_RESIDENCE      10          8.0         14.0           8:14      1\n486           5                           YRS_RESIDENCE      11          NaN          NaN              :      0\n487           5                             Y_BOX_GAMES       1          0.0          0.0            0:0    181\n488           5                             Y_BOX_GAMES       2          0.0          1.0            0:1      0\n489           5                             Y_BOX_GAMES       3          NaN          NaN              :      0\n\nRules: \n\n     cluster.id  rhs.support  rhs.conf  lhr.support  lhs.conf              lhs.var  lhs.var.support  lhs.var.conf                                  predicate\n0             1         4500  1.000000         3428  0.761778        AFFINITY_CARD             3428      0.500000                         AFFINITY_CARD <= 0\n1             1         4500  1.000000         3428  0.761778        AFFINITY_CARD             3428      0.500000                         AFFINITY_CARD >= 0\n2             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN < Bach.\n3             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN Assoc-A\n4             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN Assoc-V\n5             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                         EDUCATION IN Bach.\n6             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN HS-grad\n7             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN Masters\n8             1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n9             1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n10            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n11            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n12            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n13            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n14            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n15            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n16            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN L: 300,000 and above\n17            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                        HOUSEHOLD_SIZE IN 1\n18            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                        HOUSEHOLD_SIZE IN 2\n19            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                        HOUSEHOLD_SIZE IN 3\n20            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                       HOUSEHOLD_SIZE IN 9+\n21            1         4500  1.000000         4152  0.761778  CUST_MARITAL_STATUS             3428      0.571429             CUST_MARITAL_STATUS IN Divorc.\n22            1         4500  1.000000         4152  0.761778  CUST_MARITAL_STATUS             3428      0.571429             CUST_MARITAL_STATUS IN Married\n23            1         4500  1.000000         4152  0.761778  CUST_MARITAL_STATUS             3428      0.571429              CUST_MARITAL_STATUS IN NeverM\n24            1         4500  1.000000         4190  0.761778   CUST_YEAR_OF_BIRTH             3428      0.400000                 CUST_YEAR_OF_BIRTH <= 1986\n25            1         4500  1.000000         4190  0.761778   CUST_YEAR_OF_BIRTH             3428      0.400000                CUST_YEAR_OF_BIRTH > 1942.2\n26            1         4500  1.000000         4260  0.761778        YRS_RESIDENCE             3428      0.300000                         YRS_RESIDENCE <= 7\n27            1         4500  1.000000         4260  0.761778        YRS_RESIDENCE             3428      0.300000                          YRS_RESIDENCE > 0\n28            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                            OCCUPATION IN ?\n29            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Cleric.\n30            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                       OCCUPATION IN Crafts\n31            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Exec.\n32            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Handler\n33            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Machine\n34            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Other\n35            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Prof.\n36            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Sales\n37            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN TechSup\n38            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Transp.\n39            1         4500  1.000000         4500  0.761778    CUST_CREDIT_LIMIT             3428      0.000000                 CUST_CREDIT_LIMIT <= 15000\n40            1         4500  1.000000         4500  0.761778    CUST_CREDIT_LIMIT             3428      0.000000                  CUST_CREDIT_LIMIT >= 1500\n41            1         4500  1.000000         4500  0.761778          CUST_GENDER             3428      0.000000                           CUST_GENDER IN F\n42            1         4500  1.000000         4500  0.761778          CUST_GENDER             3428      0.000000                           CUST_GENDER IN M\n43            1         4500  1.000000         4500  0.761778          Y_BOX_GAMES             3428      0.000000                           Y_BOX_GAMES <= 1\n44            1         4500  1.000000         4500  0.761778          Y_BOX_GAMES             3428      0.000000                           Y_BOX_GAMES >= 0\n45            2         1164  0.258667         1041  0.894330  CUST_MARITAL_STATUS             1041      0.106960               CUST_MARITAL_STATUS = NeverM\n46            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                          EDUCATION IN 10th\n47            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                          EDUCATION IN 11th\n48            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                       EDUCATION IN < Bach.\n49            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                       EDUCATION IN Assoc-V\n50            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                         EDUCATION IN Bach.\n51            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                       EDUCATION IN HS-grad\n52            2         1164  0.258667         1063  0.894330       HOUSEHOLD_SIZE             1041      0.111346                        HOUSEHOLD_SIZE IN 1\n53            2         1164  0.258667         1063  0.894330       HOUSEHOLD_SIZE             1041      0.111346                        HOUSEHOLD_SIZE IN 2\n54            2         1164  0.258667         1063  0.894330       HOUSEHOLD_SIZE             1041      0.111346                       HOUSEHOLD_SIZE IN 9+\n55            2         1164  0.258667         1074  0.894330        YRS_RESIDENCE             1041      0.089257                         YRS_RESIDENCE <= 3\n56            2         1164  0.258667         1074  0.894330        YRS_RESIDENCE             1041      0.089257                          YRS_RESIDENCE > 0\n57            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n58            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n59            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n60            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n61            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n62            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n63            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n64            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n65            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN L: 300,000 and above\n66            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                            OCCUPATION IN ?\n67            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN Cleric.\n68            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                       OCCUPATION IN Crafts\n69            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Exec.\n70            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN Handler\n71            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN Machine\n72            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Other\n73            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Prof.\n74            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Sales\n75            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN TechSup\n76            2         1164  0.258667         1145  0.894330        AFFINITY_CARD             1041      0.093743                         AFFINITY_CARD <= 0\n77            2         1164  0.258667         1145  0.894330        AFFINITY_CARD             1041      0.093743                         AFFINITY_CARD >= 0\n78            2         1164  0.258667         1164  0.894330    CUST_CREDIT_LIMIT             1041      0.000769                 CUST_CREDIT_LIMIT <= 15000\n79            2         1164  0.258667         1164  0.894330    CUST_CREDIT_LIMIT             1041      0.000769                  CUST_CREDIT_LIMIT >= 1500\n80            2         1164  0.258667         1164  0.894330          CUST_GENDER             1041      0.014623                           CUST_GENDER IN F\n81            2         1164  0.258667         1164  0.894330          CUST_GENDER             1041      0.014623                           CUST_GENDER IN M\n82            2         1164  0.258667         1164  0.894330   CUST_YEAR_OF_BIRTH             1041      0.135051                 CUST_YEAR_OF_BIRTH <= 1986\n83            2         1164  0.258667         1164  0.894330   CUST_YEAR_OF_BIRTH             1041      0.135051                CUST_YEAR_OF_BIRTH > 1971.4\n84            2         1164  0.258667         1164  0.894330          Y_BOX_GAMES             1041      0.480401                           Y_BOX_GAMES <= 1\n85            2         1164  0.258667         1164  0.894330          Y_BOX_GAMES             1041      0.480401                            Y_BOX_GAMES > 0\n86            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN < Bach.\n87            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN Assoc-A\n88            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN Assoc-V\n89            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                         EDUCATION IN Bach.\n90            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN HS-grad\n91            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN Masters\n92            3         3336  0.741333         2955  0.857614        YRS_RESIDENCE             2861      0.013965                         YRS_RESIDENCE <= 7\n93            3         3336  0.741333         2955  0.857614        YRS_RESIDENCE             2861      0.013965                          YRS_RESIDENCE > 2\n94            3         3336  0.741333         2958  0.857614       HOUSEHOLD_SIZE             2861      0.015623                        HOUSEHOLD_SIZE IN 2\n95            3         3336  0.741333         2958  0.857614       HOUSEHOLD_SIZE             2861      0.015623                        HOUSEHOLD_SIZE IN 3\n96            3         3336  0.741333         2958  0.857614       HOUSEHOLD_SIZE             2861      0.015623                       HOUSEHOLD_SIZE IN 9+\n97            3         3336  0.741333         3025  0.857614  CUST_MARITAL_STATUS             2861      0.014089             CUST_MARITAL_STATUS IN Divorc.\n98            3         3336  0.741333         3025  0.857614  CUST_MARITAL_STATUS             2861      0.014089             CUST_MARITAL_STATUS IN Married\n99            3         3336  0.741333         3025  0.857614  CUST_MARITAL_STATUS             2861      0.014089              CUST_MARITAL_STATUS IN NeverM\n100           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n101           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n102           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n103           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n104           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n105           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n106           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n107           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n108           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN L: 300,000 and above\n109           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                            OCCUPATION IN ?\n110           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN Cleric.\n111           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                       OCCUPATION IN Crafts\n112           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Exec.\n113           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN Machine\n114           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Other\n115           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Prof.\n116           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Sales\n117           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN TechSup\n118           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN Transp.\n119           3         3336  0.741333         3094  0.857614          Y_BOX_GAMES             2861      0.071001                           Y_BOX_GAMES <= 0\n120           3         3336  0.741333         3094  0.857614          Y_BOX_GAMES             2861      0.071001                           Y_BOX_GAMES >= 0\n121           3         3336  0.741333         3181  0.857614   CUST_YEAR_OF_BIRTH             2861      0.024159               CUST_YEAR_OF_BIRTH <= 1978.7\n122           3         3336  0.741333         3181  0.857614   CUST_YEAR_OF_BIRTH             2861      0.024159                CUST_YEAR_OF_BIRTH > 1934.9\n123           3         3336  0.741333         3336  0.857614        AFFINITY_CARD             2861      0.005412                         AFFINITY_CARD <= 1\n124           3         3336  0.741333         3336  0.857614        AFFINITY_CARD             2861      0.005412                         AFFINITY_CARD >= 0\n125           3         3336  0.741333         3336  0.857614    CUST_CREDIT_LIMIT             2861      0.000094                 CUST_CREDIT_LIMIT <= 15000\n126           3         3336  0.741333         3336  0.857614    CUST_CREDIT_LIMIT             2861      0.000094                  CUST_CREDIT_LIMIT >= 1500\n127           3         3336  0.741333         3336  0.857614          CUST_GENDER             2861      0.001996                           CUST_GENDER IN F\n128           3         3336  0.741333         3336  0.857614          CUST_GENDER             2861      0.001996                           CUST_GENDER IN M\n129           4         3155  0.701111         2379  0.754041          CUST_GENDER             2379      0.007041                            CUST_GENDER = M\n130           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN < Bach.\n131           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN Assoc-A\n132           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN Assoc-V\n133           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                         EDUCATION IN Bach.\n134           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN HS-grad\n135           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN Masters\n136           4         3155  0.701111         2801  0.754041        YRS_RESIDENCE             2379      0.015000                         YRS_RESIDENCE <= 7\n137           4         3155  0.701111         2801  0.754041        YRS_RESIDENCE             2379      0.015000                          YRS_RESIDENCE > 2\n138           4         3155  0.701111         2846  0.754041  CUST_MARITAL_STATUS             2379      0.012734             CUST_MARITAL_STATUS IN Divorc.\n139           4         3155  0.701111         2846  0.754041  CUST_MARITAL_STATUS             2379      0.012734             CUST_MARITAL_STATUS IN Married\n140           4         3155  0.701111         2846  0.754041  CUST_MARITAL_STATUS             2379      0.012734              CUST_MARITAL_STATUS IN NeverM\n141           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n142           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n143           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n144           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n145           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n146           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n147           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n148           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n149           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN L: 300,000 and above\n150           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                            OCCUPATION IN ?\n151           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN Cleric.\n152           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                       OCCUPATION IN Crafts\n153           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Exec.\n154           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN Machine\n155           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Other\n156           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Prof.\n157           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Sales\n158           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN TechSup\n159           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN Transp.\n160           4         3155  0.701111         2913  0.754041          Y_BOX_GAMES             2379      0.067727                           Y_BOX_GAMES <= 0\n161           4         3155  0.701111         2913  0.754041          Y_BOX_GAMES             2379      0.067727                           Y_BOX_GAMES >= 0\n162           4         3155  0.701111         2958  0.754041       HOUSEHOLD_SIZE             2379      0.020523                        HOUSEHOLD_SIZE IN 2\n163           4         3155  0.701111         2958  0.754041       HOUSEHOLD_SIZE             2379      0.020523                        HOUSEHOLD_SIZE IN 3\n164           4         3155  0.701111         2958  0.754041       HOUSEHOLD_SIZE             2379      0.020523                       HOUSEHOLD_SIZE IN 9+\n165           4         3155  0.701111         3000  0.754041   CUST_YEAR_OF_BIRTH             2379      0.023531               CUST_YEAR_OF_BIRTH <= 1978.7\n166           4         3155  0.701111         3000  0.754041   CUST_YEAR_OF_BIRTH             2379      0.023531                CUST_YEAR_OF_BIRTH > 1934.9\n167           4         3155  0.701111         3155  0.754041        AFFINITY_CARD             2379      0.003698                         AFFINITY_CARD <= 1\n168           4         3155  0.701111         3155  0.754041        AFFINITY_CARD             2379      0.003698                         AFFINITY_CARD >= 0\n169           4         3155  0.701111         3155  0.754041    CUST_CREDIT_LIMIT             2379      0.000101                 CUST_CREDIT_LIMIT <= 15000\n170           4         3155  0.701111         3155  0.754041    CUST_CREDIT_LIMIT             2379      0.000101                  CUST_CREDIT_LIMIT >= 1500\n171           5          181  0.040222          157  0.867403        YRS_RESIDENCE              157      0.011292                         YRS_RESIDENCE <= 5\n172           5          181  0.040222          157  0.867403        YRS_RESIDENCE              157      0.011292                          YRS_RESIDENCE > 1\n173           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN < Bach.\n174           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN Assoc-A\n175           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN Assoc-V\n176           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                         EDUCATION IN Bach.\n177           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN HS-grad\n178           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN Masters\n179           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n180           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n181           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n182           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n183           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n184           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n185           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n186           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN L: 300,000 and above\n187           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                            OCCUPATION IN ?\n188           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                      OCCUPATION IN Cleric.\n189           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Exec.\n190           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                      OCCUPATION IN Machine\n191           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Other\n192           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Prof.\n193           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Sales\n194           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                      OCCUPATION IN TechSup\n195           5          181  0.040222          170  0.867403   CUST_YEAR_OF_BIRTH              157      0.054366               CUST_YEAR_OF_BIRTH <= 1971.4\n196           5          181  0.040222          170  0.867403   CUST_YEAR_OF_BIRTH              157      0.054366                CUST_YEAR_OF_BIRTH > 1942.2\n197           5          181  0.040222          179  0.867403  CUST_MARITAL_STATUS              157      0.125248              CUST_MARITAL_STATUS = Married\n198           5          181  0.040222          179  0.867403       HOUSEHOLD_SIZE              157      0.320732                       HOUSEHOLD_SIZE = 4-5\n199           5          181  0.040222          181  0.867403        AFFINITY_CARD              157      0.078669                         AFFINITY_CARD <= 1\n200           5          181  0.040222          181  0.867403        AFFINITY_CARD              157      0.078669                         AFFINITY_CARD >= 0\n201           5          181  0.040222          181  0.867403    CUST_CREDIT_LIMIT              157      0.000944                 CUST_CREDIT_LIMIT <= 15000\n202           5          181  0.040222          181  0.867403    CUST_CREDIT_LIMIT              157      0.000944                  CUST_CREDIT_LIMIT >= 1500\n203           5          181  0.040222          181  0.867403          CUST_GENDER              157      0.456930                            CUST_GENDER = F\n204           5          181  0.040222          181  0.867403          Y_BOX_GAMES              157      0.177207                           Y_BOX_GAMES <= 0\n205           5          181  0.040222          181  0.867403          Y_BOX_GAMES              157      0.177207                           Y_BOX_GAMES >= 0\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model settings","message":["%python","","em_mod.settings"],"enabled":true,"result":{"startTime":1733161952417,"interpreter":"python.low","endTime":1733161952492,"results":[{"message":"{'emcs_num_iterations': 20, 'emcs_random_seed': 7, 'emcs_cluster_components': 'EMCS_CLUSTER_COMP_ENABLE', 'emcs_cluster_statistics': 'EMCS_CLUS_STATS_ENABLE', 'emcs_cluster_thresh': 2, 'emcs_linkage_function': 'EMCS_LINKAGE_SINGLE', 'emcs_loglike_improvement': 0.001, 'emcs_max_num_attr_2d': 50, 'emcs_min_pct_attr_support': 0.1, 'emcs_model_search': 'EMCS_MODEL_SEARCH_DISABLE', 'emcs_num_components': 20, 'emcs_num_distribution': 'EMCS_NUM_DISTR_SYSTEM', 'emcs_num_equiwidth_bins': 11, 'emcs_num_projections': 50, 'emcs_remove_components': 'EMCS_REMOVE_COMPS_ENABLE', 'emcs_attribute_filter': 'EMCS_ATTR_FILTER_ENABLE', 'emcs_convergence_criterion': 'EMCS_CONV_CRIT_HELDASIDE', 'emcs_num_quantile_bins': 11, 'emcs_num_topn_bins': 15, 'odms_details': 'ODMS_ENABLE', 'odms_sampling': 'ODMS_SAMPLING_DISABLE', 'prep_auto': 'ON', 'clus_num_clusters': 3}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster details for all clusters in the hierarchy with row counts and dispersion","message":["%python","","z.show(em_mod.clusters)"],"enabled":true,"result":{"startTime":1733161952566,"interpreter":"python.low","endTime":1733161952660,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tRECORD_COUNT\tPARENT\tTREE_LEVEL\tLEFT_CHILD_ID\tRIGHT_CHILD_ID\n1.0\t1.0\t4500.0\tnan\t1.0\t2.0\t3.0\n2.0\t2.0\t1164.0\t1.0\t2.0\tnan\tnan\n3.0\t3.0\t3336.0\t1.0\t2.0\t4.0\t5.0\n4.0\t4.0\t3155.0\t3.0\t3.0\tnan\tnan\n5.0\t5.0\t181.0\t3.0\t3.0\tnan\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Apply model to assign rows to clusters","message":["%python","","pred = em_mod.predict(CUST_DF, supplemental_cols = CUST_DF)"],"enabled":true,"result":{"startTime":1733161952735,"interpreter":"python.low","endTime":1733161952882,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use matplotlib scatterplot to view some cluster results","message":["%python","","pred_df = pred[['CUST_ID', 'CLUSTER_ID', 'CUST_YEAR_OF_BIRTH', 'YRS_RESIDENCE', 'CUST_CREDIT_LIMIT']].pull()","","fig = plt.figure()","","plt.style.use('seaborn')","","ax=fig.add_axes([0.1,0.1,0.8,0.8])","","ids = pred_df['CLUSTER_ID']","clusters = ids.drop_duplicates().values","handles = []","labs = []","colors = ['r', 'b', 'g']","for i, c in enumerate(clusters):","    xc = pred_df[pred_df['CLUSTER_ID'] == c]['YRS_RESIDENCE'].values","    yc = pred_df[pred_df['CLUSTER_ID'] == c]['CUST_YEAR_OF_BIRTH'].values","    ","    h = ax.scatter(xc, yc, color= colors[i])","    handles.append(h)","    labs.append('CLUSTER' + str(c))","ax.legend(handles, labs)","plt.title('EM Clustering')","","plt.grid(True)","","plt.xlabel('YRS_RESIDENCE')","plt.ylabel('CUST_YEAR_OF_BIRTH')","plt.show()"],"enabled":true,"result":{"startTime":1733161952959,"interpreter":"python.low","endTime":1733161953225,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Compare clusters","","Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals. "],"enabled":true,"result":{"startTime":1733161953301,"interpreter":"md.low","endTime":1733161953359,"results":[{"message":"<h3 id=\"compare-clusters\">Compare clusters<\/h3>\n<p>Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 4","message":["%python","","feature = 'CUST_YEAR_OF_BIRTH' ","","DF = pred[pred['CLUSTER_ID'] == 4].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1733161953435,"interpreter":"python.low","endTime":1733161953752,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1913\t5\n1921\t1\n1922\t6\n1923\t4\n1924\t6\n1925\t8\n1926\t12\n1927\t3\n1928\t14\n1929\t7\n1930\t4\n1931\t7\n1932\t13\n1933\t10\n1934\t18\n1935\t18\n1936\t22\n1937\t12\n1938\t26\n1939\t23\n1940\t28\n1941\t31\n1942\t28\n1943\t41\n1944\t51\n1945\t47\n1946\t45\n1947\t41\n1948\t50\n1949\t50\n1950\t56\n1951\t49\n1952\t76\n1953\t53\n1954\t91\n1955\t91\n1956\t91\n1957\t109\n1958\t100\n1959\t110\n1960\t95\n1961\t109\n1962\t107\n1963\t94\n1964\t97\n1965\t88\n1966\t112\n1967\t114\n1968\t125\n1969\t121\n1970\t103\n1971\t115\n1972\t87\n1973\t89\n1974\t51\n1975\t61\n1976\t28\n1977\t44\n1978\t21\n1979\t19\n1980\t6\n1981\t5\n1982\t2\n1983\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 5","message":["%python","","DF = pred[pred['CLUSTER_ID'] == 5].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1733161953832,"interpreter":"python.low","endTime":1733161954099,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1939\t2\n1940\t2\n1943\t2\n1944\t1\n1945\t2\n1947\t4\n1948\t2\n1949\t1\n1950\t4\n1951\t2\n1952\t7\n1953\t3\n1954\t2\n1955\t3\n1956\t5\n1957\t10\n1958\t5\n1959\t9\n1960\t5\n1961\t6\n1962\t6\n1963\t8\n1964\t8\n1965\t15\n1966\t6\n1967\t9\n1968\t12\n1969\t14\n1970\t13\n1971\t6\n1972\t4\n1973\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check cluster assignment probabilities for top cluster assignments","message":["%python","","pred_details = em_mod.predict_proba(CUST_DF, supplemental_cols = CUST_DF['CUST_ID'], topN=3 )","","z.show(pred_details.round(8).head())"],"enabled":true,"result":{"startTime":1733161954175,"interpreter":"python.low","endTime":1733161954818,"results":[{"message":"CUST_ID\tTOP_1\tTOP_1_PROB\tTOP_2\tTOP_2_PROB\tTOP_3\tTOP_3_PROB\n100134.0\t4.0\t0.9999999\t5.0\t1e-07\t2.0\t0.0\n102828.0\t4.0\t0.99999317\t5.0\t6.83e-06\t2.0\t0.0\n101232.0\t2.0\t0.99999999\t4.0\t1e-08\t5.0\t0.0\n100696.0\t4.0\t0.99999998\t5.0\t2e-08\t2.0\t0.0\n103948.0\t4.0\t1.0\t5.0\t0.0\t2.0\t0.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Accessing the prediction details for each cluster prediction","message":["%python","","pred_test = em_mod.predict(CUST_DF, supplemental_cols = CUST_DF['CUST_ID'], topN_attrs=3 )","","z.show(pred_test.round(8).head())"],"enabled":true,"result":{"startTime":1733161954893,"interpreter":"python.low","endTime":1733161960891,"results":[{"message":"CUST_ID\tCLUSTER_ID\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\n100100\t5\tHOUSEHOLD_SIZE\t4-5\t.001\tCUST_GENDER\tF\t.001\tNone\tNone\tNone\n100200\t2\tCUST_YEAR_OF_BIRTH\t1983\t1\tNone\tNone\tNone\tNone\tNone\tNone\n100500\t2\tCUST_YEAR_OF_BIRTH\t1983\t1\tNone\tNone\tNone\tNone\tNone\tNone\n101200\t2\tCUST_YEAR_OF_BIRTH\t1984\t1\tNone\tNone\tNone\tNone\tNone\tNone\n101500\t2\tCUST_YEAR_OF_BIRTH\t1981\t1\tNone\tNone\tNone\tNone\tNone\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use SQL to access prediction details","One feature of OML is the ability to use models from SQL as well as Python. Prediction details are exposed through the Python API and are available through the SQL API, which is illustrated here.","","We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view. ","","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well. "],"enabled":true,"result":{"startTime":1733161960966,"interpreter":"md.low","endTime":1733161961023,"results":[{"message":"<h2 id=\"use-sql-to-access-prediction-details\">Use SQL to access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as Python. Prediction details are exposed through the Python API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view.<\/p>\n<p>Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%python","","try:","    oml.drop(table = 'EM_CUSTOMERS360')","except:","    pass","_ = CUST_DF.materialize(table = 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\t\"CUST_GENDER\" actualValue=\"F\" weight=\".02\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'102805'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'100676'\",\"argument\":null,\"paragraphId\":null,\"options\":[{\"value\":\"'100676'\",\"displayName\":null},{\"value\":\"'101219'\",\"displayName\":null},{\"value\":\"'102805'\",\"displayName\":null}],\"isHidden\":false,\"isNotebook\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 3 most relevant attributes in batch for customers with their likely cluster assignments","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,0) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(CUST_CLUSTER_MODEL USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(CUST_CLUSTER_MODEL, NULL, 0.2 USING *) PSET","            FROM EM_CUSTOMERS360 V) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED FETCH FIRST 10 ROWS 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Clustering KM.dsnb b/machine-learning/notebooks-oml/python/OML4Py Clustering KM.dsnb
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+++ b/machine-learning/notebooks-oml/python/OML4Py Clustering KM.dsnb	
@@ -0,0 +1,1046 @@
+[
+  {
+    "name" : "OML4Py Clustering KM",
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+        "message" : [
+          "%md",
+          "",
+          "## Identifying Customer Segments using K-means Clustering",
+          "",
+          "Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization. ",
+          "In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using ",
+          "the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.",
+          "",
+          "See the documentation link below for details on the K-means in-database algortihm.",
+          "",
+          "Copyright (c) 2025 Oracle Corporation ",
+          "###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>",
+          "---"
+        ],
+        "selectedVisualization" : "html",
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+            {
+              "message" : "<h2 id=\"identifying-customer-segments-using-k-means-clustering\">Identifying Customer Segments using K-means Clustering</h2>\n<p>Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization.\nIn this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using\nthe unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.</p>\n<p>See the documentation link below for details on the K-means in-database algortihm.</p>\n<p>Copyright (c) 2025 Oracle Corporation</p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n<hr />\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+        "message" : [
+          "%md",
+          "![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"
+        ],
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+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "For more information...",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation</a>",
+          "* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub</a>",
+          "* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page</a>",
+          "* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering</a>",
+          "* <a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means</a>"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
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+            {
+              "message" : "<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation</a></li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub</a></li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page</a></li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering</a></li>\n<li><a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means</a></li>\n</ul>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Import libraries and set display options",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "import pandas as pd",
+          "import numpy as np",
+          "import matplotlib.pyplot as plt",
+          "import oml",
+          "",
+          "pd.set_option('display.max_rows', 500)",
+          "pd.set_option('display.max_columns', 500)",
+          "pd.set_option('display.width', 1000)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094337637,
+          "endTime" : 1738094340086,
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+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Prepare data by merging CUSTOMERS with SUPPLEMENTARY_DEMOGRAPHICS",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "CUSTOMERS = oml.sync(query = \"\"\"SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT ",
+          "                                FROM SH.CUSTOMERS\"\"\")",
+          "DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES",
+          "                              FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")",
+          "CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
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+          "startTime" : 1738094340185,
+          "endTime" : 1738094340286,
+          "interpreter" : "python.low",
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+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
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+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display the first 5 rows of CUST_DF data",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "z.show(CUST_DF.head())"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+          "startTime" : 1738094340381,
+          "endTime" : 1738094340529,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
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+      },
+      {
+        "row" : 0,
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "### Build a K-means model using the CUST_DF proxy object"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "forms" : "[]",
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+          "endTime" : 1738094340729,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"build-a-k-means-model-using-the-cust_df-proxy-object\">Build a K-means model using the CUST_DF proxy object</h3>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Build K-means model with 3 clusters with default settings",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "try:",
+          "    oml.drop(model=\"CUST_CLUSTER_MODEL\")",
+          "except:",
+          "    pass",
+          "",
+          "setting = {'KMNS_ITERATIONS': 20, 'KMNS_RANDOM_SEED': 7}",
+          "km_mod = oml.km(n_clusters = 3, **setting).fit(CUST_DF, model_name = \"CUST_CLUSTER_MODEL\", case_id = 'CUST_ID')"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094340829,
+          "endTime" : 1738094342253,
+          "interpreter" : "python.low",
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+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Build K-means model with 3 clusters with explicit settings",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "try:",
+          "    oml.drop(model=\"CUST_CLUSTER_MODEL\")",
+          "except:",
+          "    pass",
+          "",
+          "setting = {'KMNS_ITERATIONS'          : 20, ",
+          "           'KMNS_CONV_TOLERANCE'      : .001,",
+          "           'KMNS_DISTANCE'            : 'KMNS_EUCLIDEAN',",
+          "           'KMNS_MIN_PCT_ATTR_SUPPORT': .1,",
+          "           'KMNS_NUM_BINS'            : 11,",
+          "           'KMNS_SPLIT_CRITERION'     : 'KMNS_VARIANCE',",
+          "           'KMNS_RANDOM_SEED'         : 7,",
+          "           'KMNS_DETAILS'             : 'KMNS_DETAILS_ALL',",
+          "           'ODMS_DETAILS'             : 'ODMS_ENABLE',",
+          "           'ODMS_SAMPLING'            : 'ODMS_SAMPLING_DISABLE',",
+          "           'PREP_AUTO'                : 'ON'}",
+          "",
+          "km_mod = oml.km(n_clusters = 3, **setting)",
+          "km_mod = km_mod.fit(CUST_DF, ",
+          "                model_name = \"CUST_CLUSTER_MODEL\", ",
+          "                case_id = 'CUST_ID')"
+        ],
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+        "title" : "Display model details",
+        "hasTitle" : true,
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+          "%python",
+          "",
+          "km_mod"
+        ],
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+          "status" : "SUCCESS",
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+            {
+              "message" : "\nModel Name: CUST_CLUSTER_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: K-Means\n\nMining Function: CLUSTERING\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME              ALGO_KMEANS\n1              CLUS_NUM_CLUSTERS                        3\n2            KMNS_CONV_TOLERANCE                    0.001\n3                   KMNS_DETAILS         KMNS_DETAILS_ALL\n4                  KMNS_DISTANCE           KMNS_EUCLIDEAN\n5                KMNS_ITERATIONS                       20\n6      KMNS_MIN_PCT_ATTR_SUPPORT                      0.1\n7                  KMNS_NUM_BINS                       11\n8               KMNS_RANDOM_SEED                        7\n9           KMNS_SPLIT_CRITERION            KMNS_VARIANCE\n10                KMNS_WINSORIZE   KMNS_WINSORIZE_DISABLE\n11                  ODMS_DETAILS              ODMS_ENABLE\n12  ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n13                 ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n14                     PREP_AUTO                       ON\n\nComputed Settings: \n              setting name setting value\n0  ODMS_EXPLOSION_MIN_SUPP             4\n\nGlobal Statistics: \n   attribute name attribute value\n0       CONVERGED             YES\n1  NUM_ITERATIONS               4\n2        NUM_ROWS            4500\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nClusters: \n\n   CLUSTER_ID  ROW_CNT  PARENT_CLUSTER_ID  TREE_LEVEL  DISPERSION\n0           1     4500                NaN           1    6.274996\n1           2     3123                1.0           2    6.584108\n2           3     1377                1.0           2    5.573937\n3           4     2085                2.0           3    6.936116\n4           5     1038                2.0           3    5.877040\n\nTaxonomy: \n\n   PARENT_CLUSTER_ID  CHILD_CLUSTER_ID\n0                  1               2.0\n1                  1               3.0\n2                  2               4.0\n3                  2               5.0\n4                  3               NaN\n5                  4               NaN\n6                  5               NaN\n\nCentroids: \n\n    CLUSTER_ID       ATTRIBUTE_NAME         MEAN            MODE_VALUE      VARIANCE\n0            1        AFFINITY_CARD     0.238222                  None  1.815127e-01\n1            1    CUST_CREDIT_LIMIT  7924.222222                  None  1.591424e+07\n2            1          CUST_GENDER          NaN                     M           NaN\n3            1    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n4            1  CUST_MARITAL_STATUS          NaN               Married           NaN\n5            1   CUST_YEAR_OF_BIRTH  1964.624444                  None  1.871268e+02\n6            1            EDUCATION          NaN               HS-grad           NaN\n7            1       HOUSEHOLD_SIZE          NaN                     3           NaN\n8            1           OCCUPATION          NaN                Crafts           NaN\n9            1        YRS_RESIDENCE     4.022000                  None  3.617431e+00\n10           1          Y_BOX_GAMES     0.312444                  None  2.148707e-01\n11           2        AFFINITY_CARD     0.332373                  None  2.219722e-01\n12           2    CUST_CREDIT_LIMIT  7758.245277                  None  1.591287e+07\n13           2          CUST_GENDER          NaN                     M           NaN\n14           2    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n15           2  CUST_MARITAL_STATUS          NaN               Married           NaN\n16           2   CUST_YEAR_OF_BIRTH  1958.073647                  None  1.240862e+02\n17           2            EDUCATION          NaN               HS-grad           NaN\n18           2       HOUSEHOLD_SIZE          NaN                     3           NaN\n19           2           OCCUPATION          NaN                 Exec.           NaN\n20           2        YRS_RESIDENCE     4.797951                  None  2.846735e+00\n21           2          Y_BOX_GAMES     0.009606                  None  9.516917e-03\n22           3        AFFINITY_CARD     0.024691                  None  2.409920e-02\n23           3    CUST_CREDIT_LIMIT  8300.653595                  None  1.572459e+07\n24           3          CUST_GENDER          NaN                     M           NaN\n25           3    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n26           3  CUST_MARITAL_STATUS          NaN                NeverM           NaN\n27           3   CUST_YEAR_OF_BIRTH  1979.481481                  None  1.200711e+01\n28           3            EDUCATION          NaN               < Bach.           NaN\n29           3       HOUSEHOLD_SIZE          NaN                     1           NaN\n30           3           OCCUPATION          NaN                 Other           NaN\n31           3        YRS_RESIDENCE     2.262164                  None  9.028770e-01\n32           3          Y_BOX_GAMES     0.999274                  None  7.262164e-04\n33           4        AFFINITY_CARD     0.000000                  None  0.000000e+00\n34           4    CUST_CREDIT_LIMIT  7828.057554                  None  1.567640e+07\n35           4          CUST_GENDER          NaN                     M           NaN\n36           4    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n37           4  CUST_MARITAL_STATUS          NaN               Married           NaN\n38           4   CUST_YEAR_OF_BIRTH  1958.042206                  None  1.355078e+02\n39           4            EDUCATION          NaN               HS-grad           NaN\n40           4       HOUSEHOLD_SIZE          NaN                     3           NaN\n41           4           OCCUPATION          NaN                Crafts           NaN\n42           4        YRS_RESIDENCE     4.603357                  None  2.747110e+00\n43           4          Y_BOX_GAMES     0.000000                  None  0.000000e+00\n44           5        AFFINITY_CARD     1.000000                  None  0.000000e+00\n45           5    CUST_CREDIT_LIMIT  7618.015414                  None  1.637396e+07\n46           5          CUST_GENDER          NaN                     M           NaN\n47           5    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n48           5  CUST_MARITAL_STATUS          NaN               Married           NaN\n49           5   CUST_YEAR_OF_BIRTH  1958.136802                  None  1.012465e+02\n50           5            EDUCATION          NaN                 Bach.           NaN\n51           5       HOUSEHOLD_SIZE          NaN                     3           NaN\n52           5           OCCUPATION          NaN                 Exec.           NaN\n53           5        YRS_RESIDENCE     5.188825                  None  2.820627e+00\n54           5          Y_BOX_GAMES     0.028902                  None  2.809349e-02\n\nLeaf Cluster Counts: \n\n   CLUSTER_ID   CNT\n0           3  1377\n1           4  2085\n2           5  1038\n\nCluster Hists: \n\n     cluster.id       variable  bin.id  lower.bound  upper.bound  label  count\n0             1  AFFINITY_CARD       1          0.0          0.1   0:.1   3428\n1             1  AFFINITY_CARD       2          0.1          0.2  .1:.2      0\n2             1  AFFINITY_CARD       3          0.2          0.3  .2:.3      0\n3             1  AFFINITY_CARD       4          0.3          0.4  .3:.4      0\n4             1  AFFINITY_CARD       5          0.4          0.5  .4:.5      0\n..          ...            ...     ...          ...          ...    ...    ...\n520           5    Y_BOX_GAMES       6          0.5          0.6  .5:.6      0\n521           5    Y_BOX_GAMES       7          0.6          0.7  .6:.7      0\n522           5    Y_BOX_GAMES       8          0.7          0.8  .7:.8      0\n523           5    Y_BOX_GAMES       9          0.8          0.9  .8:.9      0\n524           5    Y_BOX_GAMES      10          0.9          1.0   .9:1     30\n\n[525 rows x 7 columns]\n\nRules: \n\n     cluster.id  rhs.support  rhs.conf  lhr.support  lhs.conf              lhs.var  lhs.var.support  lhs.var.conf                                  predicate\n0             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.600000                       EDUCATION IN < Bach.\n1             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.600000                       EDUCATION IN Assoc-A\n2             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.600000                       EDUCATION IN Assoc-V\n3             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.600000                         EDUCATION IN Bach.\n4             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.600000                       EDUCATION IN HS-grad\n5             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.600000                       EDUCATION IN Masters\n6             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n7             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n8             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n9             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n10            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n11            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n12            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n13            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n14            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN L: 300,000 and above\n15            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                        HOUSEHOLD_SIZE IN 1\n16            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                        HOUSEHOLD_SIZE IN 2\n17            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                        HOUSEHOLD_SIZE IN 3\n18            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                       HOUSEHOLD_SIZE IN 9+\n19            1         4500  1.000000         4152  0.853333  CUST_MARITAL_STATUS             3840      0.500000             CUST_MARITAL_STATUS IN Divorc.\n20            1         4500  1.000000         4152  0.853333  CUST_MARITAL_STATUS             3840      0.500000             CUST_MARITAL_STATUS IN Married\n21            1         4500  1.000000         4152  0.853333  CUST_MARITAL_STATUS             3840      0.500000              CUST_MARITAL_STATUS IN NeverM\n22            1         4500  1.000000         4190  0.853333   CUST_YEAR_OF_BIRTH             3840      0.400000                 CUST_YEAR_OF_BIRTH <= 1986\n23            1         4500  1.000000         4190  0.853333   CUST_YEAR_OF_BIRTH             3840      0.400000                CUST_YEAR_OF_BIRTH > 1942.2\n24            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                            OCCUPATION IN ?\n25            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                      OCCUPATION IN Cleric.\n26            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                       OCCUPATION IN Crafts\n27            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                        OCCUPATION IN Exec.\n28            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                      OCCUPATION IN Handler\n29            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                      OCCUPATION IN Machine\n30            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                        OCCUPATION IN Other\n31            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                        OCCUPATION IN Prof.\n32            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                        OCCUPATION IN Sales\n33            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                      OCCUPATION IN TechSup\n34            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.214286                      OCCUPATION IN Transp.\n35            1         4500  1.000000         4409  0.853333        YRS_RESIDENCE             3840      0.400000                       YRS_RESIDENCE <= 8.4\n36            1         4500  1.000000         4409  0.853333        YRS_RESIDENCE             3840      0.400000                         YRS_RESIDENCE >= 0\n37            1         4500  1.000000         4500  0.853333        AFFINITY_CARD             3840      0.000000                         AFFINITY_CARD <= 1\n38            1         4500  1.000000         4500  0.853333        AFFINITY_CARD             3840      0.000000                         AFFINITY_CARD >= 0\n39            1         4500  1.000000         4500  0.853333    CUST_CREDIT_LIMIT             3840      0.000000                 CUST_CREDIT_LIMIT <= 15000\n40            1         4500  1.000000         4500  0.853333    CUST_CREDIT_LIMIT             3840      0.000000                  CUST_CREDIT_LIMIT >= 1500\n41            1         4500  1.000000         4500  0.853333          CUST_GENDER             3840      0.000000                           CUST_GENDER IN F\n42            1         4500  1.000000         4500  0.853333          CUST_GENDER             3840      0.000000                           CUST_GENDER IN M\n43            1         4500  1.000000         4500  0.853333          Y_BOX_GAMES             3840      0.000000                           Y_BOX_GAMES <= 1\n44            1         4500  1.000000         4500  0.853333          Y_BOX_GAMES             3840      0.000000                           Y_BOX_GAMES >= 0\n45            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001110                       EDUCATION IN < Bach.\n46            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001110                       EDUCATION IN Assoc-A\n47            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001110                       EDUCATION IN Assoc-V\n48            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001110                         EDUCATION IN Bach.\n49            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001110                       EDUCATION IN HS-grad\n50            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001110                       EDUCATION IN Masters\n51            2         3123  0.694000         2772  0.858790       HOUSEHOLD_SIZE             2682      0.014310                        HOUSEHOLD_SIZE IN 2\n52            2         3123  0.694000         2772  0.858790       HOUSEHOLD_SIZE             2682      0.014310                        HOUSEHOLD_SIZE IN 3\n53            2         3123  0.694000         2772  0.858790       HOUSEHOLD_SIZE             2682      0.014310                       HOUSEHOLD_SIZE IN 9+\n54            2         3123  0.694000         2812  0.858790  CUST_MARITAL_STATUS             2682      0.013605             CUST_MARITAL_STATUS IN Divorc.\n55            2         3123  0.694000         2812  0.858790  CUST_MARITAL_STATUS             2682      0.013605             CUST_MARITAL_STATUS IN Married\n56            2         3123  0.694000         2812  0.858790  CUST_MARITAL_STATUS             2682      0.013605              CUST_MARITAL_STATUS IN NeverM\n57            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n58            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n59            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n60            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n61            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n62            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n63            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n64            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n65            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN L: 300,000 and above\n66            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                            OCCUPATION IN ?\n67            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                      OCCUPATION IN Cleric.\n68            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                       OCCUPATION IN Crafts\n69            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                        OCCUPATION IN Exec.\n70            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                      OCCUPATION IN Machine\n71            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                        OCCUPATION IN Other\n72            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                        OCCUPATION IN Prof.\n73            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                        OCCUPATION IN Sales\n74            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                      OCCUPATION IN TechSup\n75            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001416                      OCCUPATION IN Transp.\n76            2         3123  0.694000         2882  0.858790        YRS_RESIDENCE             2682      0.015238                       YRS_RESIDENCE <= 8.4\n77            2         3123  0.694000         2882  0.858790        YRS_RESIDENCE             2682      0.015238                        YRS_RESIDENCE > 2.8\n78            2         3123  0.694000         3005  0.858790   CUST_YEAR_OF_BIRTH             2682      0.037672               CUST_YEAR_OF_BIRTH <= 1978.7\n79            2         3123  0.694000         3005  0.858790   CUST_YEAR_OF_BIRTH             2682      0.037672                CUST_YEAR_OF_BIRTH > 1934.9\n80            2         3123  0.694000         3093  0.858790          Y_BOX_GAMES             2682      0.045059                          Y_BOX_GAMES <= .1\n81            2         3123  0.694000         3093  0.858790          Y_BOX_GAMES             2682      0.045059                           Y_BOX_GAMES >= 0\n82            2         3123  0.694000         3123  0.858790        AFFINITY_CARD             2682      0.002368                         AFFINITY_CARD <= 1\n83            2         3123  0.694000         3123  0.858790        AFFINITY_CARD             2682      0.002368                         AFFINITY_CARD >= 0\n84            2         3123  0.694000         3123  0.858790    CUST_CREDIT_LIMIT             2682      0.000147                 CUST_CREDIT_LIMIT <= 15000\n85            2         3123  0.694000         3123  0.858790    CUST_CREDIT_LIMIT             2682      0.000147                  CUST_CREDIT_LIMIT >= 1500\n86            2         3123  0.694000         3123  0.858790          CUST_GENDER             2682      0.001089                           CUST_GENDER IN F\n87            2         3123  0.694000         3123  0.858790          CUST_GENDER             2682      0.001089                           CUST_GENDER IN M\n88            3         1377  0.306000         1153  0.837328       HOUSEHOLD_SIZE             1153      0.041464                        HOUSEHOLD_SIZE IN 1\n89            3         1377  0.306000         1153  0.837328       HOUSEHOLD_SIZE             1153      0.041464                        HOUSEHOLD_SIZE IN 2\n90            3         1377  0.306000         1153  0.837328       HOUSEHOLD_SIZE             1153      0.041464                        HOUSEHOLD_SIZE IN 3\n91            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006743                          EDUCATION IN 10th\n92            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006743                          EDUCATION IN 11th\n93            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006743                       EDUCATION IN < Bach.\n94            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006743                       EDUCATION IN Assoc-V\n95            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006743                         EDUCATION IN Bach.\n96            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006743                       EDUCATION IN HS-grad\n97            3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n98            3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n99            3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n100           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n101           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n102           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n103           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n104           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n105           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN L: 300,000 and above\n106           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                            OCCUPATION IN ?\n107           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                      OCCUPATION IN Cleric.\n108           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                       OCCUPATION IN Crafts\n109           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                        OCCUPATION IN Exec.\n110           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                      OCCUPATION IN Handler\n111           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                      OCCUPATION IN Machine\n112           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                        OCCUPATION IN Other\n113           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                        OCCUPATION IN Prof.\n114           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                        OCCUPATION IN Sales\n115           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007458                      OCCUPATION IN TechSup\n116           3         1377  0.306000         1279  0.837328  CUST_MARITAL_STATUS             1153      0.055271             CUST_MARITAL_STATUS IN Married\n117           3         1377  0.306000         1279  0.837328  CUST_MARITAL_STATUS             1153      0.055271              CUST_MARITAL_STATUS IN NeverM\n118           3         1377  0.306000         1343  0.837328        AFFINITY_CARD             1153      0.024646                        AFFINITY_CARD <= .1\n119           3         1377  0.306000         1343  0.837328        AFFINITY_CARD             1153      0.024646                         AFFINITY_CARD >= 0\n120           3         1377  0.306000         1359  0.837328        YRS_RESIDENCE             1153      0.065320                       YRS_RESIDENCE <= 4.2\n121           3         1377  0.306000         1359  0.837328        YRS_RESIDENCE             1153      0.065320                         YRS_RESIDENCE >= 0\n122           3         1377  0.306000         1376  0.837328          Y_BOX_GAMES             1153      0.143419                           Y_BOX_GAMES <= 1\n123           3         1377  0.306000         1376  0.837328          Y_BOX_GAMES             1153      0.143419                           Y_BOX_GAMES > .9\n124           3         1377  0.306000         1377  0.837328    CUST_CREDIT_LIMIT             1153      0.000766                 CUST_CREDIT_LIMIT <= 15000\n125           3         1377  0.306000         1377  0.837328    CUST_CREDIT_LIMIT             1153      0.000766                  CUST_CREDIT_LIMIT >= 1500\n126           3         1377  0.306000         1377  0.837328          CUST_GENDER             1153      0.005175                           CUST_GENDER IN F\n127           3         1377  0.306000         1377  0.837328          CUST_GENDER             1153      0.005175                           CUST_GENDER IN M\n128           3         1377  0.306000         1377  0.837328   CUST_YEAR_OF_BIRTH             1153      0.133039                 CUST_YEAR_OF_BIRTH <= 1986\n129           3         1377  0.306000         1377  0.837328   CUST_YEAR_OF_BIRTH             1153      0.133039                CUST_YEAR_OF_BIRTH > 1971.4\n130           4         2085  0.463333         1704  0.817266            EDUCATION             1704      0.002460                       EDUCATION IN < Bach.\n131           4         2085  0.463333         1704  0.817266            EDUCATION             1704      0.002460                       EDUCATION IN Assoc-A\n132           4         2085  0.463333         1704  0.817266            EDUCATION             1704      0.002460                       EDUCATION IN Assoc-V\n133           4         2085  0.463333         1704  0.817266            EDUCATION             1704      0.002460                         EDUCATION IN Bach.\n134           4         2085  0.463333         1704  0.817266            EDUCATION             1704      0.002460                       EDUCATION IN HS-grad\n135           4         2085  0.463333         1802  0.817266  CUST_MARITAL_STATUS             1704      0.011379             CUST_MARITAL_STATUS IN Divorc.\n136           4         2085  0.463333         1802  0.817266  CUST_MARITAL_STATUS             1704      0.011379             CUST_MARITAL_STATUS IN Married\n137           4         2085  0.463333         1802  0.817266  CUST_MARITAL_STATUS             1704      0.011379              CUST_MARITAL_STATUS IN NeverM\n138           4         2085  0.463333         1844  0.817266       HOUSEHOLD_SIZE             1704      0.011541                        HOUSEHOLD_SIZE IN 2\n139           4         2085  0.463333         1844  0.817266       HOUSEHOLD_SIZE             1704      0.011541                        HOUSEHOLD_SIZE IN 3\n140           4         2085  0.463333         1844  0.817266       HOUSEHOLD_SIZE             1704      0.011541                       HOUSEHOLD_SIZE IN 9+\n141           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                            OCCUPATION IN ?\n142           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                      OCCUPATION IN Cleric.\n143           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                       OCCUPATION IN Crafts\n144           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                        OCCUPATION IN Exec.\n145           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                      OCCUPATION IN Handler\n146           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                      OCCUPATION IN Machine\n147           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                        OCCUPATION IN Other\n148           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                        OCCUPATION IN Prof.\n149           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                        OCCUPATION IN Sales\n150           4         2085  0.463333         1886  0.817266           OCCUPATION             1704      0.001434                      OCCUPATION IN Transp.\n151           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n152           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n153           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n154           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n155           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n156           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n157           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n158           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n159           4         2085  0.463333         1887  0.817266    CUST_INCOME_LEVEL             1704      0.000278  CUST_INCOME_LEVEL IN L: 300,000 and above\n160           4         2085  0.463333         1909  0.817266        YRS_RESIDENCE             1704      0.011783                       YRS_RESIDENCE <= 8.4\n161           4         2085  0.463333         1909  0.817266        YRS_RESIDENCE             1704      0.011783                        YRS_RESIDENCE > 2.8\n162           4         2085  0.463333         1989  0.817266   CUST_YEAR_OF_BIRTH             1704      0.038027               CUST_YEAR_OF_BIRTH <= 1978.7\n163           4         2085  0.463333         1989  0.817266   CUST_YEAR_OF_BIRTH             1704      0.038027                CUST_YEAR_OF_BIRTH > 1934.9\n164           4         2085  0.463333         2085  0.817266        AFFINITY_CARD             1704      0.039364                        AFFINITY_CARD <= .1\n165           4         2085  0.463333         2085  0.817266        AFFINITY_CARD             1704      0.039364                         AFFINITY_CARD >= 0\n166           4         2085  0.463333         2085  0.817266    CUST_CREDIT_LIMIT             1704      0.000065                 CUST_CREDIT_LIMIT <= 15000\n167           4         2085  0.463333         2085  0.817266    CUST_CREDIT_LIMIT             1704      0.000065                  CUST_CREDIT_LIMIT >= 1500\n168           4         2085  0.463333         2085  0.817266          CUST_GENDER             1704      0.001412                           CUST_GENDER IN F\n169           4         2085  0.463333         2085  0.817266          CUST_GENDER             1704      0.001412                           CUST_GENDER IN M\n170           4         2085  0.463333         2085  0.817266          Y_BOX_GAMES             1704      0.053345                          Y_BOX_GAMES <= .1\n171           4         2085  0.463333         2085  0.817266          Y_BOX_GAMES             1704      0.053345                           Y_BOX_GAMES >= 0\n172           5         1038  0.230667          891  0.858382          CUST_GENDER              891      0.038201                            CUST_GENDER = M\n173           5         1038  0.230667          909  0.858382  CUST_MARITAL_STATUS              891      0.062682              CUST_MARITAL_STATUS = Married\n174           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                      OCCUPATION IN Cleric.\n175           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                       OCCUPATION IN Crafts\n176           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                        OCCUPATION IN Exec.\n177           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                      OCCUPATION IN Machine\n178           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                        OCCUPATION IN Prof.\n179           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                        OCCUPATION IN Sales\n180           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                      OCCUPATION IN TechSup\n181           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021833                      OCCUPATION IN Transp.\n182           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n183           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n184           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n185           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n186           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n187           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n188           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n189           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n190           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN L: 300,000 and above\n191           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                       EDUCATION IN < Bach.\n192           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                       EDUCATION IN Assoc-A\n193           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                       EDUCATION IN Assoc-V\n194           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                         EDUCATION IN Bach.\n195           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                       EDUCATION IN HS-grad\n196           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                       EDUCATION IN Masters\n197           5         1038  0.230667          969  0.858382            EDUCATION              891      0.015393                        EDUCATION IN Profsc\n198           5         1038  0.230667          973  0.858382        YRS_RESIDENCE              891      0.028267                       YRS_RESIDENCE <= 8.4\n199           5         1038  0.230667          973  0.858382        YRS_RESIDENCE              891      0.028267                        YRS_RESIDENCE > 2.8\n200           5         1038  0.230667         1003  0.858382       HOUSEHOLD_SIZE              891      0.066799                        HOUSEHOLD_SIZE IN 2\n201           5         1038  0.230667         1003  0.858382       HOUSEHOLD_SIZE              891      0.066799                        HOUSEHOLD_SIZE IN 3\n202           5         1038  0.230667         1003  0.858382       HOUSEHOLD_SIZE              891      0.066799                      HOUSEHOLD_SIZE IN 4-5\n203           5         1038  0.230667         1008  0.858382          Y_BOX_GAMES              891      0.035169                          Y_BOX_GAMES <= .1\n204           5         1038  0.230667         1008  0.858382          Y_BOX_GAMES              891      0.035169                           Y_BOX_GAMES >= 0\n205           5         1038  0.230667         1016  0.858382   CUST_YEAR_OF_BIRTH              891      0.039020               CUST_YEAR_OF_BIRTH <= 1978.7\n206           5         1038  0.230667         1016  0.858382   CUST_YEAR_OF_BIRTH              891      0.039020                CUST_YEAR_OF_BIRTH > 1934.9\n207           5         1038  0.230667         1038  0.858382        AFFINITY_CARD              891      0.169369                         AFFINITY_CARD <= 1\n208           5         1038  0.230667         1038  0.858382        AFFINITY_CARD              891      0.169369                         AFFINITY_CARD > .9\n209           5         1038  0.230667         1038  0.858382    CUST_CREDIT_LIMIT              891      0.000585                 CUST_CREDIT_LIMIT <= 15000\n210           5         1038  0.230667         1038  0.858382    CUST_CREDIT_LIMIT              891      0.000585                  CUST_CREDIT_LIMIT >= 1500\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Display model settings",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "km_mod.settings"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094344639,
+          "endTime" : 1738094344729,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "{'kmns_iterations': 20, 'kmns_conv_tolerance': 0.001, 'kmns_distance': 'KMNS_EUCLIDEAN', 'kmns_min_pct_attr_support': 0.1, 'kmns_num_bins': 11, 'kmns_split_criterion': 'KMNS_VARIANCE', 'kmns_random_seed': 7, 'kmns_details': 'KMNS_DETAILS_ALL', 'odms_details': 'ODMS_ENABLE', 'odms_sampling': 'ODMS_SAMPLING_DISABLE', 'prep_auto': 'ON', 'clus_num_clusters': 3}\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display cluster details for all clusters in the hierarchy with row counts and dispersion",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "z.show(km_mod.clusters)"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094344833,
+          "endTime" : 1738094344987,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tROW_CNT\tPARENT_CLUSTER_ID\tTREE_LEVEL\tDISPERSION\n1.0\t4500.0\tnan\t1.0\t6.274995822757452\n2.0\t3123.0\t1.0\t2.0\t6.584108195502669\n3.0\t1377.0\t1.0\t2.0\t5.573937042740521\n4.0\t2085.0\t2.0\t3.0\t6.936116126222261\n5.0\t1038.0\t2.0\t3.0\t5.877040242178631\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Define function to format the cluster centroid rule",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "def display_rule(model, cluster_id, dat):",
+          "  rules = model.rules",
+          "  cluster_rules = rules[rules['cluster.id'] == cluster_id]",
+          "  vars = cluster_rules['lhs.var'].drop_duplicates().pull()",
+          "  res = []",
+          "",
+          "  for var in vars:",
+          "    rule = []",
+          "    var_rules = cluster_rules[cluster_rules['lhs.var'] == var, 'predicate'].pull()",
+          "    if isinstance(dat[var], oml.String):",
+          "      for r in var_rules:",
+          "        v = r.split(\" IN \")[1]",
+          "        if len(v) > 0:",
+          "          rule.append(v)",
+          "      rule = var + \" IN {\" + ','.join(rule) + \"}\"",
+          "    else:",
+          "      rule = \" and \".join(var_rules)",
+          "    res.append(rule + \"\\n\")",
+          "",
+          "  print(\"if \" + \" and \".join(res) + \"then cluster \" + str(cluster_id))"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094345083,
+          "endTime" : 1738094345168,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Display formatted rule for cluster 3",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "display_rule(model      = km_mod, ",
+          "             cluster_id = 3, ",
+          "             dat        = CUST_DF)"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094345264,
+          "endTime" : 1738094345665,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "if AFFINITY_CARD <= .1 and AFFINITY_CARD >= 0\n and CUST_CREDIT_LIMIT <= 15000 and CUST_CREDIT_LIMIT >= 1500\n and CUST_GENDER IN {F,M}\n and CUST_INCOME_LEVEL IN {B: 30,000 - 49,999,E: 90,000 - 109,999,F: 110,000 - 129,999,G: 130,000 - 149,999,H: 150,000 - 169,999,I: 170,000 - 189,999,J: 190,000 - 249,999,K: 250,000 - 299,999,L: 300,000 and above}\n and CUST_MARITAL_STATUS IN {Married,NeverM}\n and CUST_YEAR_OF_BIRTH <= 1986 and CUST_YEAR_OF_BIRTH > 1971.4\n and EDUCATION IN {10th,11th,< Bach.,Assoc-V,Bach.,HS-grad}\n and HOUSEHOLD_SIZE IN {1,2,3}\n and OCCUPATION IN {?,Cleric.,Crafts,Exec.,Handler,Machine,Other,Prof.,Sales,TechSup}\n and YRS_RESIDENCE <= 4.2 and YRS_RESIDENCE >= 0\n and Y_BOX_GAMES <= 1 and Y_BOX_GAMES > .9\nthen cluster 3\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 5,
+        "title" : "Apply clustering model to assign rows to clusters",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "pred = km_mod.predict(CUST_DF, supplemental_cols = CUST_DF)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094345761,
+          "endTime" : 1738094345884,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 7,
+        "title" : "Use matplotlib to view cluster results using birth year and years residence",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "pred_df = pred[['CUST_ID', 'CLUSTER_ID', 'CUST_YEAR_OF_BIRTH', 'YRS_RESIDENCE', 'CUST_CREDIT_LIMIT']].pull()",
+          "",
+          "fig = plt.figure()",
+          "ax=fig.add_axes([0.1,0.1,0.8,0.8])",
+          "",
+          "ids = pred_df['CLUSTER_ID']",
+          "clusters = ids.drop_duplicates().values",
+          "handles = []",
+          "labs = []",
+          "colors = ['r', 'b', 'g']",
+          "for i, c in enumerate(clusters):",
+          "    xc = pred_df[pred_df['CLUSTER_ID'] == c]['YRS_RESIDENCE'].values",
+          "    yc = pred_df[pred_df['CLUSTER_ID'] == c]['CUST_YEAR_OF_BIRTH'].values",
+          "    ",
+          "    h = ax.scatter(xc, yc, color= colors[i])",
+          "    handles.append(h)",
+          "    labs.append('CLUSTER' + str(c))",
+          "ax.legend(handles, labs)",
+          "plt.title('K-Means Clustering')",
+          "",
+          "plt.grid(True)",
+          "",
+          "plt.xlabel('YRS_RESIDENCE')",
+          "plt.ylabel('CUST_YEAR_OF_BIRTH')",
+          "plt.show()"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094345979,
+          "endTime" : 1738094346276,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<div style='width:auto;height:auto'><img 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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "### Compare clusters",
+          "",
+          "Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094346374,
+          "endTime" : 1738094346451,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"compare-clusters\">Compare clusters</h3>\n<p>Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
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+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Distribution of the year of birth for cluster 4",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "feature = 'CUST_YEAR_OF_BIRTH' ",
+          "",
+          "DF = pred[pred['CLUSTER_ID'] == 4].crosstab([feature])",
+          "z.show(DF.sort_values(feature))"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : "[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"y\":{\"max\":100}},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]",
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+        "result" : {
+          "startTime" : 1738094346543,
+          "endTime" : 1738094346754,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CUST_YEAR_OF_BIRTH\tcount\n1913\t5\n1921\t1\n1922\t6\n1923\t4\n1924\t2\n1925\t6\n1926\t10\n1927\t3\n1928\t14\n1929\t6\n1930\t4\n1931\t6\n1932\t9\n1933\t8\n1934\t12\n1935\t13\n1936\t12\n1937\t10\n1938\t17\n1939\t22\n1940\t22\n1941\t24\n1942\t16\n1943\t32\n1944\t31\n1945\t32\n1946\t33\n1947\t35\n1948\t34\n1949\t34\n1950\t38\n1951\t25\n1952\t49\n1953\t20\n1954\t61\n1955\t50\n1956\t63\n1957\t71\n1958\t66\n1959\t72\n1960\t50\n1961\t75\n1962\t81\n1963\t70\n1964\t83\n1965\t59\n1966\t86\n1967\t92\n1968\t93\n1969\t95\n1970\t89\n1971\t93\n1972\t78\n1973\t63\n",
+              "type" : "TABLE"
+            }
+          ],
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+        },
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+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Distribution of the year of birth for cluster 5",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "feature = 'CUST_YEAR_OF_BIRTH' ",
+          "",
+          "DF = pred[pred['CLUSTER_ID'] == 5].crosstab([feature])",
+          "z.show(DF.sort_values(feature))"
+        ],
+        "selectedVisualization" : "bar",
+        "visualizationConfig" : "[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"y\":{\"max\":100}},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094346859,
+          "endTime" : 1738094347117,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CUST_YEAR_OF_BIRTH\tcount\n1924\t4\n1925\t2\n1926\t2\n1929\t1\n1931\t1\n1932\t4\n1933\t2\n1934\t6\n1935\t5\n1936\t10\n1937\t2\n1938\t9\n1939\t3\n1940\t8\n1941\t7\n1942\t12\n1943\t11\n1944\t21\n1945\t17\n1946\t12\n1947\t10\n1948\t18\n1949\t17\n1950\t22\n1951\t26\n1952\t34\n1953\t36\n1954\t32\n1955\t44\n1956\t33\n1957\t48\n1958\t39\n1959\t47\n1960\t50\n1961\t40\n1962\t32\n1963\t32\n1964\t22\n1965\t44\n1966\t32\n1967\t31\n1968\t44\n1969\t40\n1970\t27\n1971\t28\n1972\t12\n1973\t29\n1974\t10\n1975\t13\n1976\t2\n1977\t4\n1978\t1\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "### Using SQL access prediction details",
+          "One feature of OML is the ability to use models from SQL as well as Python. While OML4Py v1.0 does not expose prediction details through a Python API, such details are available through the SQL API, which is illustrated here.",
+          "",
+          "We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view. ",
+          "",
+          "Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094347222,
+          "endTime" : 1738094347301,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"using-sql-access-prediction-details\">Using SQL access prediction details</h3>\n<p>One feature of OML is the ability to use models from SQL as well as Python. While OML4Py v1.0 does not expose prediction details through a Python API, such details are available through the SQL API, which is illustrated here.</p>\n<p>We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view.</p>\n<p>Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Create table for use in SQL query",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "try:",
+          "    oml.drop(table = 'CUSTOMERS360')",
+          "except:",
+          "    pass",
+          "_ = CUST_DF.materialize(table = 'CUSTOMERS360')"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094347402,
+          "endTime" : 1738094347616,
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "List 3 most relevant attributes for specific customers and likely cluster assignments",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CUST_ID,",
+          "       CLUSTER_ID,",
+          "       ROUND(PROB*100,0) PROB_PCT,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE",
+          "FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ",
+          "             CLUSTER_DETAILS(CUST_CLUSTER_MODEL USING T.*) DETAIL",
+          "      FROM (SELECT V.*, CLUSTER_SET(CUST_CLUSTER_MODEL, NULL, 0.2 USING *) PSET",
+          "            FROM CUSTOMERS360 V",
+          "            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,",
+          "                  TABLE(T.PSET) S",
+          "            ORDER BY 2 DESC) OUT,",
+          "            XMLTABLE('/Details'",
+          "                      PASSING OUT.DETAIL",
+          "                      COLUMNS ",
+          "                      \"Attribute1\" XMLType PATH 'Attribute[1]',",
+          "                      \"Attribute2\" XMLType PATH 'Attribute[2]',",
+          "                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"paragraphId\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isNotebook\":false,\"isProgrammatic\":false}]",
+        "result" : {
+          "startTime" : 1738094347721,
+          "endTime" : 1738094349605,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100456\t5\t29\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".156\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".122\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".074\" \n100456\t4\t50\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".156\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".122\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".074\" \n100456\t3\t21\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".156\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".122\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".074\" \n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{\"CUST_ID\":\"'100456'\"}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## End of Script"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094349708,
+          "endTime" : 1738094349786,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of Script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : true,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094349890,
+          "endTime" : 1738094349966,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Duplicates Removal.dsnb b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Duplicates Removal.dsnb
new file mode 100644
index 00000000..268ae34d
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Duplicates Removal.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Cleaning Duplicates Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736197371447,"interpreter":"md.low","endTime":1736197371884,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Cleaning: Duplicates Removal","In this notebook, we demonstrate how to remove duplicate records using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance. ","","The dataset `CUSTOMER_INSURANCE_LTV_PY` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736197372351,"interpreter":"md.low","endTime":1736197372789,"results":[{"message":"<h1 id=\"oml4py-data-cleaning-duplicates-removal\">OML4Py Data Cleaning: Duplicates Removal<\/h1>\n<p>In this notebook, we demonstrate how to remove duplicate records using OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_PY<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/clean-data.html\" target=\"_blank\">OML4Py Data Cleaning<\/a>","","","---"],"enabled":true,"result":{"startTime":1736197373248,"interpreter":"md.low","endTime":1736197373684,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/clean-data.html\" target=\"_blank\">OML4Py Data Cleaning<\/a><\/li>\n<\/ul>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries ","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1736197374133,"interpreter":"python.low","endTime":1736197374596,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":{"startTime":1736197375048,"interpreter":"python.low","endTime":1736197375508,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of unique customer IDs","message":["%python","","CUST_DF['CUSTOMER_ID'].nunique()"],"enabled":true,"result":{"startTime":1736197375956,"interpreter":"python.low","endTime":1736197376408,"results":[{"message":"13880\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show table dimensions - note there are more rows than customer IDs","message":["%python","","CUST_DF.shape"],"enabled":true,"result":{"startTime":1736197376861,"interpreter":"python.low","endTime":1736197377401,"results":[{"message":"(14880, 31)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Remove duplicate rows","message":["%python","","CUST_DF = CUST_DF.drop_duplicates()"],"enabled":true,"result":{"startTime":1736197377856,"interpreter":"python.low","endTime":1736197378393,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check number of unique customer IDs","message":["%python","","CUST_DF['CUSTOMER_ID'].nunique()"],"enabled":true,"result":{"startTime":1736197378843,"interpreter":"python.low","endTime":1736197379318,"results":[{"message":"13880\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check dimension of the OML dataframe - duplicated rows removed","message":["%python","","CUST_DF.shape"],"enabled":true,"result":{"startTime":1736197379765,"interpreter":"python.low","endTime":1736197380261,"results":[{"message":"(13880, 31)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736197380710,"interpreter":"md.low","endTime":1736197381147,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736197381615,"interpreter":"md.low","endTime":1736197382079,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Missing Data.dsnb b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Missing Data.dsnb
new file mode 100644
index 00000000..2f891803
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Missing Data.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Cleaning Missing Data ","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%python"," "],"enabled":true,"result":{"startTime":1736197788001,"interpreter":"python.low","endTime":1736197789471,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Cleaning: fill in missing data","In this notebook, we demonstrate how to fill in missing values using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_PY` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may *not* be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see [Data Preparation for GLM](https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12)","","Copyright (c) 2025 Oracle Corporation ","###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)","---"],"enabled":true,"result":{"startTime":1736197789946,"interpreter":"md.low","endTime":1736197790385,"results":[{"message":"<h1 id=\"oml4py-data-cleaning-fill-in-missing-data\">OML4Py Data Cleaning: fill in missing data<\/h1>\n<p>In this notebook, we demonstrate how to fill in missing values using OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_PY<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<hr />\n<p><strong>Note<\/strong>: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may <em>not<\/em> be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/prepare-and-explore-data1.html\" target=\"_blank\">OML4Py Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1736197790839,"interpreter":"md.low","endTime":1736197791281,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/prepare-and-explore-data1.html\" target=\"_blank\">OML4Py Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries ","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":{"startTime":1736197792743,"interpreter":"python.low","endTime":1736197793203,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":{"startTime":1736197793663,"interpreter":"python.low","endTime":1736197795128,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data Overview","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1736197796376,"interpreter":"python.low","endTime":1736197796912,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146.0\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076.0\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877.0\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Columns MARITAL_STATUS,  CREDIT_BALANCE,  BANK_FUNDS, INCOME have missing values","message":["%python","","z.show(CUST_DF[CUST_DF['CUSTOMER_ID'] < 'CU2'][['CUSTOMER_ID','MARITAL_STATUS', 'CREDIT_BALANCE', 'BANK_FUNDS', 'INCOME']].head())"],"enabled":true,"result":{"startTime":1736197797359,"interpreter":"python.low","endTime":1736197797927,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS\tCREDIT_BALANCE\tBANK_FUNDS\tINCOME\nCU11437                   \tNone\tNone\tNone\tNone\nCU11440                   \tNone\tNone\tNone\tNone\nCU11441                   \tNone\tNone\tNone\tNone\nCU15595                   \tNone\tNone\tNone\tNone\nCU15596                   \tNone\tNone\tNone\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find mode for the categorical column MARITAL_STATUS","message":["%python","","def col_mode(DF, col):","    SUMMARY_DF = CUST_DF.dropna().crosstab([col]).sort_values('count').tail(1)","    return SUMMARY_DF.pull()[col].values[0]","","mode = col_mode(CUST_DF, 'MARITAL_STATUS')","","print(mode)","     "],"enabled":true,"result":{"startTime":1736197798383,"interpreter":"python.low","endTime":1736197798953,"results":[{"message":"SINGLE\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Replace None with 'SINGLE' and then check the counts","message":["%python","","CUST_CAT_DF = CUST_DF.replace(old = [None], new = [mode], columns = ['MARITAL_STATUS'])    ","","z.show(CUST_CAT_DF.crosstab('MARITAL_STATUS'))"],"enabled":true,"result":{"startTime":1736197799401,"interpreter":"python.low","endTime":1736197800288,"results":[{"message":"MARITAL_STATUS\tcount\nDIV\t337\nDIVORCED\t2063\nM\t359\nMARRIED\t2393\nOTHER\t96\nSINGLE\t9157\nWIDOWED\t475\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Replace the missing numerical columns using the mean value","message":["%python","","num_cols = ['CREDIT_BALANCE', 'BANK_FUNDS', 'INCOME']","","mean_vals = [float(CUST_DF[col].mean()) for col in num_cols]","CUST_NUM_DF = CUST_DF","","print(mean_vals)","for i, col in enumerate(num_cols):","    CUST_NUM_DF = CUST_NUM_DF.replace(old = [None], new = [mean_vals[i]], columns = [col])    "],"enabled":true,"result":{"startTime":1736197800736,"interpreter":"python.low","endTime":1736197801841,"results":[{"message":"[3944.6798739852175, 3442.6848418756817, 65001.82382164062]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the OML proxy object after the replacement","message":["%python","","z.show(CUST_NUM_DF[CUST_NUM_DF['CUSTOMER_ID'] <'CU2'][ ['CUSTOMER_ID'] + num_cols].head())"],"enabled":true,"result":{"startTime":1736197802299,"interpreter":"python.low","endTime":1736197802828,"results":[{"message":"CUSTOMER_ID\tCREDIT_BALANCE\tBANK_FUNDS\tINCOME\nCU11437                   \t3944.6798739852175\t3442.6848418756817\t65001.82382164062\nCU11440                   \t3944.6798739852175\t3442.6848418756817\t65001.82382164062\nCU11441                   \t3944.6798739852175\t3442.6848418756817\t65001.82382164062\nCU15595                   \t3944.6798739852175\t3442.6848418756817\t65001.82382164062\nCU15596                   \t3944.6798739852175\t3442.6848418756817\t65001.82382164062\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736197803281,"interpreter":"md.low","endTime":1736197803724,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736197804177,"interpreter":"md.low","endTime":1736197804620,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Outlier Removal.dsnb b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Outlier Removal.dsnb
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+++ b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Outlier Removal.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4Py Data Cleaning Outlier Removal","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1738089255237,"interpreter":"md.low","endTime":1738089255814,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py  Data Cleaning: Outlier Removal","In this notebook, we demonstrate how to exclude records with outliers using OML4Py. ","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV, we focus on numerical values and remove the records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1738089256259,"interpreter":"md.low","endTime":1738089256744,"results":[{"message":"<h1 id=\"oml4py--data-cleaning-outlier-removal\">OML4Py  Data Cleaning: Outlier Removal<\/h1>\n<p>In this notebook, we demonstrate how to exclude records with outliers using OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>In the dataset CUSTOMER_INSURANCE_LTV, we focus on numerical values and remove the records with values in the top and bottom 5%.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a>"],"enabled":true,"result":{"startTime":1738089257184,"interpreter":"md.low","endTime":1738089257664,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1738089258100,"interpreter":"python.low","endTime":1738089262287,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')"],"enabled":true,"result":{"startTime":1738089262717,"interpreter":"python.low","endTime":1738089263346,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows in the table","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1738089263792,"interpreter":"python.low","endTime":1738089264341,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the percentiles","message":["%python","","CUST_DF['INCOME'].describe(percentiles = [0.05,0.95])"],"enabled":true,"result":{"startTime":1738089264812,"interpreter":"python.low","endTime":1738089265351,"results":[{"message":"count     13880.000000\nmean      65106.179683\nstd        6663.564844\nmin       37107.000000\n5%        57017.000000\n50%       64426.000000\n95%       75761.550000\nmax      111605.000000\nName: INCOME, dtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the 5 and 95 percentile","message":["%python","","df = CUST_DF['INCOME'].describe(percentiles = [0.05,0.95])","df = pd.DataFrame(df)","df = df.reset_index()","","lower_bound = df[df['index'] == '5%']['INCOME'].values[0]","upper_bound = df[df['index'] == '95%']['INCOME'].values[0]","","print(\"Lower: \", lower_bound)","print(\"Upper: \", upper_bound)"],"enabled":true,"result":{"startTime":1738089265808,"interpreter":"python.low","endTime":1738089266287,"results":[{"message":"Lower:  57017.0\nUpper:  75761.54999999999\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Remove data with INCOME out of bounds","message":["%python","","CLEAN_DF = CUST_DF[ (CUST_DF['INCOME'] < upper_bound) & (CUST_DF['INCOME'] > lower_bound)]"],"enabled":true,"result":{"startTime":1738089266784,"interpreter":"python.low","endTime":1738089267304,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(CUST_DF.head(10))"],"enabled":true,"result":{"startTime":1738089267738,"interpreter":"python.low","endTime":1738089268235,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663.0\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529.0\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the distribution after removal","message":["%python","","CLEAN_DF['INCOME'].describe()"],"enabled":true,"result":{"startTime":1738089268684,"interpreter":"python.low","endTime":1738089269261,"results":[{"message":"count    12490.000000\nmean     64733.429864\nstd       4428.169882\nmin      57018.000000\n25%      61100.000000\n50%      64427.000000\n75%      67937.500000\nmax      75761.000000\nName: INCOME, dtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot the distribution before outlier removal using OML  graphics","message":["%python\r","\r","import matplotlib.pyplot as plt\r","plt.style.use('seaborn-v0_8')\r","\r","plt.figure(figsize=[20,6])\r","oml.graphics.hist(CUST_DF['INCOME'], bins=10, color='C0',\r","                 linestyle='solid', edgecolor='black')\r","\r","plt.title('Histogram of INCOME before outlier removeal')\r","plt.xlabel('INCOME')\r","plt.ylabel('Customer Counts')\r","_ = plt.show()\r"],"enabled":true,"result":{"startTime":1738089269690,"interpreter":"python.low","endTime":1738089270482,"results":[{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Recode Synonymous Values.dsnb b/machine-learning/notebooks-oml/python/OML4Py Data Cleaning Recode Synonymous Values.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4Py Data Cleaning Recode Synonymous Values","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1738089360107,"interpreter":"md.low","endTime":1738089360693,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Cleaning: Recode Synonymous Values","In this notebook, we demonstrate how to recode synonymous value using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_PY` has special values generated by the `\"OML Run-me-first\"` notebook artificially, which `MUST` be run before this notebook.  Specifically, MARITAL_STATUS has divorced status coded as DIV, married coded as 'M'. ","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1738089361183,"interpreter":"md.low","endTime":1738089361712,"results":[{"message":"<h1 id=\"oml4py-data-cleaning-recode-synonymous-values\">OML4Py Data Cleaning: Recode Synonymous Values<\/h1>\n<p>In this notebook, we demonstrate how to recode synonymous value using OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_PY<\/code> has special values generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook artificially, which <code>MUST<\/code> be run before this notebook.  Specifically, MARITAL_STATUS has divorced status coded as DIV, married coded as 'M'.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4Py Data 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values","message":["%python","","z.show(CUST_DF.crosstab(['MARITAL_STATUS']))"],"enabled":true,"result":{"startTime":1738089367465,"interpreter":"python.low","endTime":1738089368217,"results":[{"message":"MARITAL_STATUS\tcount\nDIV\t337\nDIVORCED\t2063\nM\t359\nMARRIED\t2393\nOTHER\t96\nSINGLE\t2530\nWIDOWED\t475\nNone\t6627\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Recode the synonymous values 'M' and 'DIV'","message":["%python","","CUST_DF = CUST_DF.replace(old = ['M', 'DIV', None], new = ['MARRIED', 'DIVORCED', 'UNKNOWN'],  columns = 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Data Transformation Binning.dsnb b/machine-learning/notebooks-oml/python/OML4Py Data Transformation Binning.dsnb
new file mode 100644
index 00000000..8b937965
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Data Transformation Binning.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Binning","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733781753479,"interpreter":"md.low","endTime":1733781753933,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Transformation: Binning","In this notebook, we demonstrate how to bin numerical columns using OML4Py. Equi-width and quantile binning are shown. ","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to bin a numerical column and visualize the distribution.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","","**Note**: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. ","Supervised binning uses decision trees to create optimal bin boundaries. ","More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html');\">Naive Bayes<\/a>  ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733781754387,"interpreter":"md.low","endTime":1733781754833,"results":[{"message":"<h1 id=\"oml4py-data-transformation-binning\">OML4Py Data Transformation: Binning<\/h1>\n<p>In this notebook, we demonstrate how to bin numerical columns using OML4Py. Equi-width and quantile binning are shown.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to bin a numerical column and visualize the distribution.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning.\nSupervised binning uses decision trees to create optimal bin boundaries.\nMore details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html');\">Naive Bayes<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a>"],"enabled":true,"result":{"startTime":1733781755294,"interpreter":"md.low","endTime":1733781755743,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":{"startTime":1733781756193,"interpreter":"python.low","endTime":1733781756670,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to CUSTOMER_INSURANCE_LTV and view data","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1733781757121,"interpreter":"python.low","endTime":1733781757663,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA\t0\t3\t0\t0\t3\t0\t65871\tCU15154\tM\tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY\t0\t4\t0\t290\t4\t0\t68747\tCU15155\tM\tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI\t0\t3\t1000\t550\t3\t0\t68684\tCU15157\tM\tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT\t0\t5\t1200\t1000\t5\t0\t59354\tCU15286\tF\tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT\t0\t4\t1800\t0\t3\t0\t84801\tCU15287\tF\tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Bin INCOME column into 10 bins","message":["%python","","CUST_BIN_DF = CUST_DF.concat(CUST_DF['INCOME'].cut(bins = 10))"],"enabled":true,"result":{"startTime":1733781758115,"interpreter":"python.low","endTime":1733781758615,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Count\",\"series\":{\"availableSeriesElements\":[{\"id\":\"INCOME\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"INCOME\",\"Bin(INCOME)\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"View binned INCOME column values","message":["%python","","z.show(CUST_BIN_DF[['INCOME', 'Bin(INCOME)']].head())"],"enabled":true,"result":{"startTime":1733781759064,"interpreter":"python.low","endTime":1733781759599,"results":[{"message":"INCOME\tBin(INCOME)\n65871\t(59456.4, 66906.2]\n68747\t(66906.2, 74356.0]\n68684\t(66906.2, 74356.0]\n59354\t(52006.6, 59456.4]\n84801\t(81805.8, 89255.6]\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count_l\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Bin\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"Bin(INCOME)\",\"count_l\",\"mean(INCOME)_r\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot the distribution of binned INCOME","message":["%python","","INCOME_MEAN = CUST_BIN_DF.crosstab('Bin(INCOME)', values = 'INCOME', aggfunc = oml.DataFrame.mean).sort_values('mean(INCOME)')","","OUTPUT = CUST_BIN_DF.crosstab('Bin(INCOME)').merge(INCOME_MEAN, on = 'Bin(INCOME)').sort_values('mean(INCOME)_r')","","z.show(OUTPUT)"],"enabled":true,"result":{"startTime":1733781760063,"interpreter":"python.low","endTime":1733781760944,"results":[{"message":"Bin(INCOME)\tcount_l\tmean(INCOME)_r\n(37032.502, 44556.8]\t23\t41487.217391304344\n(44556.8, 52006.6]\t134\t48909.5\n(52006.6, 59456.4]\t2070\t57577.33381642512\n(59456.4, 66906.2]\t7053\t63166.39472564866\n(66906.2, 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float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Bin\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"Bin(INCOME)\",\"count\"],\"version\":1},\"area\":{\"version\":1},\"tree\":{},\"pyramid\":{\"version\":1},\"boxplot\":{}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Use quantiles for bin boundaries","message":["%python","","pct = CUST_DF['INCOME'].describe(percentiles = [0.25, 0.5, 0.75])[-5:]","","pct_list = list(pct)","pct_list.sort()","CUST_SET_DF = CUST_DF.concat(CUST_DF['INCOME'].cut(bins = pct_list))","","OUTPUT = CUST_SET_DF.crosstab('Bin(INCOME)')","z.show(OUTPUT[OUTPUT['Bin(INCOME)'].isnull() == False])"],"enabled":true,"result":{"startTime":1733781762330,"interpreter":"python.low","endTime":1733781763091,"results":[{"message":"Bin(INCOME)\tcount\n(37107.0, 60719.5]\t3469\n(60719.5, 64426.0]\t3470\n(64426.0, 68482.0]\t3472\n(68482.0, 111605.0]\t3468\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1733781763554,"interpreter":"md.low","endTime":1733781764002,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1733781764466,"interpreter":"md.low","endTime":1733781764951,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Data Transformation Categorical.dsnb b/machine-learning/notebooks-oml/python/OML4Py Data Transformation Categorical.dsnb
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--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Data Transformation Categorical.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Categorical","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736200664481,"interpreter":"md.low","endTime":1736200664939,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Transformation: convert categorical variables to numeric variables","","In this notebook, we demonstrate how to convert categorical variables to numeric variables using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, ","and whether or not the customer bought insurance.","","We show how to convert a categorical variable with each distinct level/value coded to an integer data type. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736200665389,"interpreter":"md.low","endTime":1736200665829,"results":[{"message":"<h1 id=\"oml4py-data-transformation-convert-categorical-variables-to-numeric-variables\">OML4Py Data Transformation: convert categorical variables to numeric variables<\/h1>\n<p>In this notebook, we demonstrate how to convert categorical variables to numeric variables using OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value,\nand whether or not the customer bought insurance.<\/p>\n<p>We show how to convert a categorical variable with each distinct level/value coded to an integer data type.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-prerequisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a prerequisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/prepare-and-explore-data1.html\" target=\"_blank\">OML4Py Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1736200666283,"interpreter":"md.low","endTime":1736200666722,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/prepare-and-explore-data1.html\" target=\"_blank\">OML4Py Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":{"startTime":1736200667175,"interpreter":"python.low","endTime":1736200667623,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1736200668072,"interpreter":"python.low","endTime":1736200668635,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the counts by state","message":["%python","","z.show(CUST_DF.crosstab(['STATE']).sort_values('count'))"],"enabled":true,"result":{"startTime":1736200669092,"interpreter":"python.low","endTime":1736200669797,"results":[{"message":"STATE\tcount\nAZ                        \t11\nAK                        \t18\nLA                        \t40\nAL                        \t46\nOK                        \t48\nNM                        \t49\nMO                        \t51\nTX                        \t85\nCO                        \t108\nOH                        \t132\nWI                        \t148\nWA                        \t154\nMS                        \t165\nOR                        \t233\nIL                        \t237\nNC                        \t238\nUT                        \t304\nDC                        \t413\nMN                        \t453\nNV                        \t488\nFL                        \t558\nMI                        \t2402\nCA                        \t2964\nNY                        \t4535\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Replace the state names with integers","message":["%python","","state_names = CUST_DF['STATE'].drop_duplicates().pull()","","state_ids = [0]*len(state_names)","","for i in range(len(state_names)):","    state_ids[i] = float(i)","    ","CUST_DF = CUST_DF.replace(old = state_names, new = state_ids, default = 0.0, columns = ['STATE'])"],"enabled":true,"result":{"startTime":1736200670253,"interpreter":"python.low","endTime":1736200670953,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique states, now encoded to integers","message":["%python","","z.show(CUST_DF.crosstab(['STATE']).sort_values('count'))"],"enabled":true,"result":{"startTime":1736200671400,"interpreter":"python.low","endTime":1736200672053,"results":[{"message":"STATE\tcount\n8\t11\n20\t18\n6\t40\n15\t46\n5\t48\n11\t49\n16\t51\n12\t85\n18\t108\n3\t132\n7\t148\n14\t154\n17\t165\n13\t233\n4\t237\n23\t238\n22\t304\n2\t413\n9\t453\n19\t488\n1\t558\n21\t2402\n0\t2964\n10\t4535\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736200821110,"interpreter":"md.low","endTime":1736200821565,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736200673397,"interpreter":"md.low","endTime":1736200673832,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Date Datatypes","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737152009666,"interpreter":"md.low","endTime":1737152010221,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Date and Time Classes","","Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","OML4Py provides three classes for manipulating dates and times: `oml.Datetime`, `oml.Timezone`, and `oml.Timedelta`.","","- `oml.Datetime` represents a table column of dates to support DATE or TIMESTAMP in Oracle Database. ","- `oml.Timezone` represents a table column of time zone data to support TIMESTAMP WITH TIMEZONE.","- `oml.Timedelta` represents a single column of differences between two dates or times to support INTERVAL DAY TO SECOND in Oracle Database.   ","","These classes are based on the <a href=\"https://docs.python.org/3.12/library/datetime.html\" target=\"_blank\">Python datetime library.<\/a>","","In this notebook, we highlight features of OML4Py's date and time functionality.","","---","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737152010703,"interpreter":"md.low","endTime":1737152011186,"results":[{"message":"<h1 id=\"oml4py-date-and-time-classes\">OML4Py Date and Time Classes<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>OML4Py provides three classes for manipulating dates and times: <code>oml.Datetime<\/code>, <code>oml.Timezone<\/code>, and <code>oml.Timedelta<\/code>.<\/p>\n<ul>\n<li><code>oml.Datetime<\/code> represents a table column of dates to support DATE or TIMESTAMP in Oracle Database.<\/li>\n<li><code>oml.Timezone<\/code> represents a table column of time zone data to support TIMESTAMP WITH TIMEZONE.<\/li>\n<li><code>oml.Timedelta<\/code> represents a single column of differences between two dates or times to support INTERVAL DAY TO SECOND in Oracle Database.<\/li>\n<\/ul>\n<p>These classes are based on the <a href=\"https://docs.python.org/3.12/library/datetime.html\" target=\"_blank\">Python datetime library.<\/a><\/p>\n<p>In this notebook, we highlight features of OML4Py's date and time functionality.<\/p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%python","","import oml","","import pandas as pd","import numpy as np","","import datetime","from datetime import datetime, timezone, timedelta"],"enabled":true,"result":{"startTime":1737152011680,"interpreter":"python.low","endTime":1737152014206,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## OML Datetime","","To create a date in Python, we use the `datetime` class of the `datetime` module, which is included in Python. The `datetime` class requires three parameters: *year*, *month*, and *day*. It also contains optional parameters for time and timezone, including *hour*, *minute*, *second*, *microsecond*, and *tzone*.","","Create a datetime object with the year, month, and day, then create a temporary proxy object using `oml.push`. The `oml.push `function requires a data frame or list as input, so we first convert the date object to a list. The resulting object is an object of class `oml.Datetime`.","","*Note: oml.Datetime is an alias to oml.core.datetime.Datetime. They are interchangeable functionally, as are aliases for the other `oml` classes, e.g., `oml.DateFrame`, `oml.Float`, etc.*"],"enabled":true,"result":{"startTime":1737152014669,"interpreter":"md.low","endTime":1737152015143,"results":[{"message":"<h2 id=\"oml-datetime\">OML Datetime<\/h2>\n<p>To create a date in Python, we use the <code>datetime<\/code> class of the <code>datetime<\/code> module, which is included in Python. The <code>datetime<\/code> class requires three parameters: <em>year<\/em>, <em>month<\/em>, and <em>day<\/em>. It also contains optional parameters for time and timezone, including <em>hour<\/em>, <em>minute<\/em>, <em>second<\/em>, <em>microsecond<\/em>, and <em>tzone<\/em>.<\/p>\n<p>Create a datetime object with the year, month, and day, then create a temporary proxy object using <code>oml.push<\/code>. The <code>oml.push <\/code>function requires a data frame or list as input, so we first convert the date object to a list. The resulting object is an object of class <code>oml.Datetime<\/code>.<\/p>\n<p><em>Note: oml.Datetime is an alias to oml.core.datetime.Datetime. They are interchangeable functionally, as are aliases for the other <code>oml<\/code> classes, e.g., <code>oml.DateFrame<\/code>, <code>oml.Float<\/code>, etc.<\/em><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"datetime ","message":["%python","","d1 = datetime(year=2004, month=7, day=24)","","print('d1:', d1)","print('type:', type(d1))","print('year:', d1.year)","print('month:', d1.month)","print('day:', d1.day)"],"enabled":true,"result":{"startTime":1737152015603,"interpreter":"python.low","endTime":1737152016146,"results":[{"message":"d1: 2004-07-24 00:00:00\ntype: <class 'datetime.datetime'>\nyear: 2004\nmonth: 7\nday: 24\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"oml.Datetime","message":["%python","","d1_lst = [d1] ","D1 = oml.push(d1_lst)","","print('D1:', D1)","print('type:', type(D1))","print('year:', D1.year)","print('month:', D1.month)","print('day:', D1.day)"],"enabled":true,"result":{"startTime":1737152016612,"interpreter":"python.low","endTime":1737152017279,"results":[{"message":"D1: [datetime.datetime(2004, 7, 24, 0, 0)]\ntype: <class 'oml.core.datetime.Datetime'>\nyear: [2004]\nmonth: [7]\nday: [24]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Datetime method `fromisoformat`","The datetime method `fromisoformat` constructs a date or datetime object from a string containing date in ISO format, e.g., yyyy-mm-dd. Create a datetime object with the year, month, day, and time, then create a temporary proxy object using `oml.push`. The resulting object is an object of class `oml.Datetime`.","","---"],"enabled":true,"result":{"startTime":1737152017744,"interpreter":"md.low","endTime":1737152018204,"results":[{"message":"<h2 id=\"datetime-method-fromisoformat\">Datetime method <code>fromisoformat<\/code><\/h2>\n<p>The datetime method <code>fromisoformat<\/code> constructs a date or datetime object from a string containing date in ISO format, e.g., yyyy-mm-dd. Create a datetime object with the year, month, day, and time, then create a temporary proxy object using <code>oml.push<\/code>. The resulting object is an object of class <code>oml.Datetime<\/code>.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","d2 = datetime.fromisoformat('2004-07-24 00:05:23+04:00')","d2_lst=[d2]","","D2 = oml.push(d2_lst)","","print('D2', D2)","print('type', type(D2))"],"enabled":true,"result":{"startTime":1737152018680,"interpreter":"python.low","endTime":1737152019267,"results":[{"message":"D2 [datetime.datetime(2004, 7, 24, 0, 5, 23, tzinfo=datetime.timezone(datetime.timedelta(seconds=14400)))]\ntype <class 'oml.core.datetime.Datetime'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","datetime.now()"],"enabled":true,"result":{"startTime":1737152019739,"interpreter":"python.low","endTime":1737152020207,"results":[{"message":"datetime.datetime(2025, 1, 17, 22, 13, 40, 193504)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","## Date format codes","","The `strftime` method converts a datetime object to a string, and `strptime` converts a string to a datetime object. ","","Dates have a default representation of: **YYYY-MM-DD:HH:MM:SS.mmmmmm** but you may want to print them in a specific format. ","","In that case, you can get a custom string representation using the available <a href=\"https://docs.python.org/3/library/datetime.html\" target=\"_blank\">format codes.<\/a>","","The format codes for the default date representation are shown in the table below.","","<table>","  <tr>","    <th>Code<\/th>","    <th>Example<\/th>","    <th>Description<\/th>","  <\/tr>","  <tr>","    <td>%a<\/td>","    <td>Sun<\/td>","    <td>Day abbreviated<\/td>","  <\/tr>","  <tr>","    <td>%b<\/td>","    <td>Sep<\/td>","    <td>Month abbreviated<\/td>","  <\/tr>","  <tr>","    <td>%d<\/td>","    <td>08<\/td>","    <td>Decimal Month<\/td>","  <\/tr>","  <tr>","    <td>%H<\/td>","    <td>07<\/td>","    <td>Decimal Hour<\/td>","  <\/tr>","  <tr>","    <td>%M<\/td>","    <td>06<\/td>","    <td>Decimal Minute<\/td>","  <\/tr>","  <tr>","    <td>%S<\/td>","    <td>05<\/td>","    <td>Decimal Second<\/td>","  <\/tr>","  <tr>","    <td>%Y<\/td>","    <td>2015<\/td>","    <td>Decimal Year<\/td>","  <\/tr>","<\/table>","<br>","","---"],"enabled":true,"result":{"startTime":1737152020692,"interpreter":"md.low","endTime":1737152021188,"results":[{"message":"<h2 id=\"date-format-codes\">Date format codes<\/h2>\n<p>The <code>strftime<\/code> method converts a datetime object to a string, and <code>strptime<\/code> converts a string to a datetime object.<\/p>\n<p>Dates have a default representation of: <strong>YYYY-MM-DD:HH:MM:SS.mmmmmm<\/strong> but you may want to print them in a specific format.<\/p>\n<p>In that case, you can get a custom string representation using the available <a href=\"https://docs.python.org/3/library/datetime.html\" target=\"_blank\">format codes.<\/a><\/p>\n<p>The format codes for the default date representation are shown in the table below.<\/p>\n<table>\n  <tr>\n    <th>Code<\/th>\n    <th>Example<\/th>\n    <th>Description<\/th>\n  <\/tr>\n  <tr>\n    <td>%a<\/td>\n    <td>Sun<\/td>\n    <td>Day abbreviated<\/td>\n  <\/tr>\n  <tr>\n    <td>%b<\/td>\n    <td>Sep<\/td>\n    <td>Month abbreviated<\/td>\n  <\/tr>\n  <tr>\n    <td>%d<\/td>\n    <td>08<\/td>\n    <td>Decimal Month<\/td>\n  <\/tr>\n  <tr>\n    <td>%H<\/td>\n    <td>07<\/td>\n    <td>Decimal Hour<\/td>\n  <\/tr>\n  <tr>\n    <td>%M<\/td>\n    <td>06<\/td>\n    <td>Decimal Minute<\/td>\n  <\/tr>\n  <tr>\n    <td>%S<\/td>\n    <td>05<\/td>\n    <td>Decimal Second<\/td>\n  <\/tr>\n  <tr>\n    <td>%Y<\/td>\n    <td>2015<\/td>\n    <td>Decimal Year<\/td>\n  <\/tr>\n<\/table>\n<br>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Convert date to string","message":["#%python","","D3 = D2.strftime()","","print('D3', D3)","print('type', type(D3))"],"enabled":true,"result":{"startTime":1737152021668,"interpreter":"md.low","endTime":1737152022132,"results":[{"message":"<p>#%python<\/p>\n<p>D3 = D2.strftime()<\/p>\n<p>print('D3', D3)\nprint('type', type(D3))<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Convert string to date","message":["%python","","d4 = \"14-Jul-05 20:01:01\"","d4_lst = [d4]","","# create a temporary proxy object","D4 = oml.push(d4_lst)","","# convert the string to an oml.Datetime object with the provided format","D5 = oml.Datetime.strptime(D4, format=\"DD-Mon-RR HH24:MI:SS\")","","print('D5', D5)","print('type', type(D5))"],"enabled":true,"result":{"startTime":1737152022613,"interpreter":"python.low","endTime":1737152023160,"results":[{"message":"D5 [datetime.datetime(2005, 7, 14, 20, 1, 1)]\ntype <class 'oml.core.datetime.Datetime'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## OML Timedelta","","`oml.Timedelta` objects represent a column of differences between two dates or times and can be used to perform simple arithmetic operations on `oml.Datetime` objects. `oml.Timedelta` objects can be multiplied by an integer value or a floating point value. Subtracting dates produces an `oml.Timedelta` object that can be added or subtracted by an `oml.Timedate` object to produce another date.","","Create a time-based object using the current date and time, and create an `oml.Timedelta` object named DELT1 to determine a past and future dates.","","---"],"enabled":true,"result":{"startTime":1737152023655,"interpreter":"md.low","endTime":1737152024144,"results":[{"message":"<h2 id=\"oml-timedelta\">OML Timedelta<\/h2>\n<p><code>oml.Timedelta<\/code> objects represent a column of differences between two dates or times and can be used to perform simple arithmetic operations on <code>oml.Datetime<\/code> objects. <code>oml.Timedelta<\/code> objects can be multiplied by an integer value or a floating point value. Subtracting dates produces an <code>oml.Timedelta<\/code> object that can be added or subtracted by an <code>oml.Timedate<\/code> object to produce another date.<\/p>\n<p>Create a time-based object using the current date and time, and create an <code>oml.Timedelta<\/code> object named DELT1 to determine a past and future dates.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"timedelta","message":["%python","","today = datetime.now()","print('today:', today)","","delt1 = timedelta(days=1, hours=2, seconds=5)","print('delt1:', delt1)","print('type:', type(delt1))"],"enabled":true,"result":{"startTime":1737152024618,"interpreter":"python.low","endTime":1737152025089,"results":[{"message":"today: 2025-01-17 22:13:45.073527\ndelt1: 1 day, 2:00:05\ntype: <class 'datetime.timedelta'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"oml.Timedelta","message":["%python","","dat = pd.DataFrame({'datetime': [today], 'timedelta': [delt1]})","","DAT = oml.push(dat, dbtypes = ['TIMESTAMP', 'INTERVAL DAY TO SECOND'])","print(DAT)","","TODAY = DAT['datetime']","DELT1 = DAT['timedelta']","","print('TODAY:', today)","print('DELT1:', DELT1)","print('type:', type(DELT1))"],"enabled":true,"result":{"startTime":1737152025569,"interpreter":"python.low","endTime":1737152026155,"results":[{"message":"                    datetime       timedelta\n0 2025-01-17 22:13:45.073527 1 days 02:00:05\nTODAY: 2025-01-17 22:13:45.073527\nDELT1: [datetime.timedelta(days=1, seconds=7205)]\ntype: <class 'oml.core.timedelta.Timedelta'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"timedelta operations","message":["%python","","past_date1 = today - delt1","print('past date 1:', past_date1)","","past_date2 = today - (delt1 *3)","print('past date 2:', past_date2)","","future_date1 = today + delt1","print('future date 1:', future_date1)","","future_date2 = today + (delt1 *3)","print('future date 2:', future_date2)"],"enabled":true,"result":{"startTime":1737152026613,"interpreter":"python.low","endTime":1737152027084,"results":[{"message":"past date 1: 2025-01-16 20:13:40.073527\npast date 2: 2025-01-14 16:13:30.073527\nfuture date 1: 2025-01-19 00:13:50.073527\nfuture date 2: 2025-01-21 04:14:00.073527\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"oml.Timedelta operations","message":["%python","","past_date3 = TODAY - DELT1","print('past date 3:', past_date1)","","past_date4 = TODAY - (DELT1 *3)","print('past date 4:', past_date2)","","future_date3 = TODAY + DELT1","print('future date 3:', future_date1)","","future_date4 = TODAY + (DELT1 *3)","print('future date 4:', future_date2)"],"enabled":true,"result":{"startTime":1737152027564,"interpreter":"python.low","endTime":1737152028025,"results":[{"message":"past date 3: 2025-01-16 20:13:40.073527\npast date 4: 2025-01-14 16:13:30.073527\nfuture date 3: 2025-01-19 00:13:50.073527\nfuture date 4: 2025-01-21 04:14:00.073527\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Value Comparison","","Dates and datetime objects can be compared using standard arithmetic comparison operators (like <, >, <=, >=, != etc.). ","","The examples below compare `oml.Datetime` columns in a proxy object."],"enabled":true,"result":{"startTime":1737152028505,"interpreter":"md.low","endTime":1737152028952,"results":[{"message":"<h2 id=\"value-comparison\">Value Comparison<\/h2>\n<p>Dates and datetime objects can be compared using standard arithmetic comparison operators (like &lt;, &gt;, &lt;=, &gt;=, != etc.).<\/p>\n<p>The examples below compare <code>oml.Datetime<\/code> columns in a proxy object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create Proxy data frame with two oml.Datetime columns","message":["%python","","d1 = datetime(2005, 7, 14, 5, 10, 30)","d2 = datetime(2004, 6, 30, 1, 22, 46)","d3 = datetime(2003, 12, 10, 12, 50, 25)","d4 = datetime(2002, 3, 20, 20, 42, 59)","","d3 = pd.DataFrame({'X': [d1, d2], 'Y': [d3, d4]})                                                              ","D3 = oml.push(d3, dbtypes = ['TIMESTAMP', 'TIMESTAMP'])","","print(D3)"],"enabled":true,"result":{"startTime":1737152029436,"interpreter":"python.low","endTime":1737152029980,"results":[{"message":"                    X                   Y\n0 2005-07-14 05:10:30 2003-12-10 12:50:25\n1 2004-06-30 01:22:46 2002-03-20 20:42:59\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["","%python","","D4 = D3['X']","D5 = D3['Y']","","print(\"D4:\", D4)","print(\"D4 type:\", type(D4))","","print(\"D5:\", D5)","print(\"D5 type:\", type(D5))"],"enabled":true,"result":{"startTime":1737152030465,"interpreter":"python.low","endTime":1737152031048,"results":[{"message":"D4: [datetime.datetime(2005, 7, 14, 5, 10, 30), datetime.datetime(2004, 6, 30, 1, 22, 46)]\nD4 type: <class 'oml.core.datetime.Datetime'>\nD5: [datetime.datetime(2003, 12, 10, 12, 50, 25), datetime.datetime(2002, 3, 20, 20, 42, 59)]\nD5 type: <class 'oml.core.datetime.Datetime'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"equal to","message":["%python","","# equal to","print(D4 == D5)","","# not equal to","print(D4 != D5) "],"enabled":true,"result":{"startTime":1737152031519,"interpreter":"python.low","endTime":1737152032018,"results":[{"message":"[False, False]\n[True, True]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"greater than","message":["%python","","# greater than","print(D4 > D5) ","","# greater than or equal to","print(D4 >= D5) ## "],"enabled":true,"result":{"startTime":1737152032487,"interpreter":"python.low","endTime":1737152032991,"results":[{"message":"[True, True]\n[True, True]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"less than","message":["%python","","# less than","print(D4 < D5)","","# less than or equal to","print(D4 <= D5)"],"enabled":true,"result":{"startTime":1737152033473,"interpreter":"python.low","endTime":1737152033964,"results":[{"message":"[False, False]\n[False, False]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"max/min","message":["%python","","print(\"max:\", D3['X'].max())","","print(\"min:\", D3['Y'].min())"],"enabled":true,"result":{"startTime":1737152034441,"interpreter":"python.low","endTime":1737152034957,"results":[{"message":"max: 2005-07-14 05:10:30\nmin: 2002-03-20 20:42:59\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Value Replacement","","Update specific or all elements of an `oml.Datetime` object, such as year, month, and day."],"enabled":true,"result":{"startTime":1737152035426,"interpreter":"md.low","endTime":1737152035867,"results":[{"message":"<h2 id=\"value-replacement\">Value Replacement<\/h2>\n<p>Update specific or all elements of an <code>oml.Datetime<\/code> object, such as year, month, and day.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","D4 = D3['X'].replace(year=2000)","print(\"D4:\", D4)","","D5 = D3['X'].replace(month=11)","print(\"D5:\", D5)","","D6 = D3['X'].replace(day=6)","print(\"D6:\", D6)"],"enabled":true,"result":{"startTime":1737152036353,"interpreter":"python.low","endTime":1737152036852,"results":[{"message":"D4: [datetime.datetime(2000, 7, 14, 5, 10, 30), datetime.datetime(2000, 6, 30, 1, 22, 46)]\nD5: [datetime.datetime(2005, 11, 14, 5, 10, 30), datetime.datetime(2004, 11, 30, 1, 22, 46)]\nD6: [datetime.datetime(2005, 7, 6, 5, 10, 30), datetime.datetime(2004, 6, 6, 1, 22, 46)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date Exploration","","Create an OML proxy object containing a datetime column and explore the object using the transparency layer functions `describe`, `nunique`, `min` and `max`.","","---"],"enabled":true,"result":{"startTime":1737152037315,"interpreter":"md.low","endTime":1737152037762,"results":[{"message":"<h2 id=\"date-exploration\">Date Exploration<\/h2>\n<p>Create an OML proxy object containing a datetime column and explore the object using the transparency layer functions <code>describe<\/code>, <code>nunique<\/code>, <code>min<\/code> and <code>max<\/code>.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","date_today = datetime.now()","ndays = 10","","df = pd.DataFrame({'Date': [date_today + timedelta(days=x) for x in range(ndays)], ","                   'Val': pd.Series(np.random.randn(ndays))})","","DF = oml.push(df)","DF.head()"],"enabled":true,"result":{"startTime":1737152038231,"interpreter":"python.low","endTime":1737152038851,"results":[{"message":"                        Date       Val\n0 2025-01-17 22:13:58.682495 -0.204708\n1 2025-01-18 22:13:58.682495  0.478943\n2 2025-01-19 22:13:58.682495 -0.519439\n3 2025-01-20 22:13:58.682495 -0.555730\n4 2025-01-21 22:13:58.682495  1.965781\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"describe date column","message":["%python","","DF['Date'].describe()"],"enabled":true,"result":{"startTime":1737152039340,"interpreter":"python.low","endTime":1737152039818,"results":[{"message":"count                             10\nunique                            10\ntop       2025-01-26 22:13:58.682495\nfreq                               1\nName: Date, dtype: object\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"number of unique dates","message":["%python","","DF['Date'].nunique()"],"enabled":true,"result":{"startTime":1737152040320,"interpreter":"python.low","endTime":1737152040787,"results":[{"message":"10\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"earliest date","message":["%python","","DF['Date'].min()"],"enabled":true,"result":{"startTime":1737152041258,"interpreter":"python.low","endTime":1737152041783,"results":[{"message":"Timestamp('2025-01-17 22:13:58.682495')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"latest date","message":["%python","","DF['Date'].max()"],"enabled":true,"result":{"startTime":1737152042263,"interpreter":"python.low","endTime":1737152042741,"results":[{"message":"Timestamp('2025-01-26 22:13:58.682495')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date sorting","","Explore the `sort_values`, `head`, and  `tail` functions by sorting values by Date in ascending and descending order.","","---"],"enabled":true,"result":{"startTime":1737152043221,"interpreter":"md.low","endTime":1737152043691,"results":[{"message":"<h2 id=\"date-sorting\">Date sorting<\/h2>\n<p>Explore the <code>sort_values<\/code>, <code>head<\/code>, and  <code>tail<\/code> functions by sorting values by Date in ascending and descending order.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"ascending order - first five rows","message":["%python","","DF.sort_values(by = ['Date'], ascending=False).head(5)"],"enabled":true,"result":{"startTime":1737152044176,"interpreter":"python.low","endTime":1737152045096,"results":[{"message":"                        Date       Val\n0 2025-01-26 22:13:58.682495  1.246435\n1 2025-01-25 22:13:58.682495  0.769023\n2 2025-01-24 22:13:58.682495  0.281746\n3 2025-01-23 22:13:58.682495  0.092908\n4 2025-01-22 22:13:58.682495  1.393406\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"descending order - last 5 rows","message":["%python","","DF.sort_values(by = ['Date'], ascending=False).tail(5)"],"enabled":true,"result":{"startTime":1737152045592,"interpreter":"python.low","endTime":1737152046484,"results":[{"message":"                        Date       Val\n0 2025-01-21 22:13:58.682495  1.965781\n1 2025-01-20 22:13:58.682495 -0.555730\n2 2025-01-19 22:13:58.682495 -0.519439\n3 2025-01-18 22:13:58.682495  0.478943\n4 2025-01-17 22:13:58.682495 -0.204708\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1737152046963,"interpreter":"md.low","endTime":1737152047416,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737152047887,"interpreter":"md.low","endTime":1737152048345,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Normalization and Scaling","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734627921148,"interpreter":"md.low","endTime":1734627921642,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","%md","","# OML4Py Data Transformation: Normalization and Scaling","","In this notebook, we demonstrate how to normalize and scale data using OML4Py.","","Using the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","","**Note** : when building or applying a model using in-database Oracle Machine Learning algorithms, ","automatic data preparatio will normalize data automatically as needed by specific algorithms. ","For instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734627922068,"interpreter":"md.low","endTime":1734627922508,"results":[{"message":"<h1 id=\"oml4py-data-transformation-normalization-and-scaling\">OML4Py Data Transformation: Normalization and Scaling<\/h1>\n<p>In this notebook, we demonstrate how to normalize and scale data using OML4Py.<\/p>\n<p>Using the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong> : when building or applying a model using in-database Oracle Machine Learning algorithms,\nautomatic data preparatio will normalize data automatically as needed by specific algorithms.\nFor instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1734627922924,"interpreter":"md.low","endTime":1734627923350,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl","","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1734627923765,"interpreter":"python.low","endTime":1734627926107,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for customer lifetime value table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')"],"enabled":true,"result":{"startTime":1734627926517,"interpreter":"python.low","endTime":1734627926979,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1734627927399,"interpreter":"python.low","endTime":1734627927930,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA\t0\t3\t0\t0\t3\t0\t65871\tCU15154\tM\tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY\t0\t4\t0\t290\t4\t0\t68747\tCU15155\tM\tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI\t0\t3\t1000\t550\t3\t0\t68684\tCU15157\tM\tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT\t0\t5\t1200\t1000\t5\t0\t59354\tCU15286\tF\tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT\t0\t4\t1800\t0\t3\t0\t84801\tCU15287\tF\tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalization by z-score - using mean and standard deviation","message":["%python","","income_mean = CUST_DF['INCOME'].mean()","income_std  = CUST_DF['INCOME'].std()","","CUST_DF = CUST_DF.concat({'INCOME_NORMALIZED':(CUST_DF['INCOME'] - income_mean)/income_std})","","z.show(CUST_DF[['INCOME', 'INCOME_NORMALIZED']].head().round(4))"],"enabled":true,"result":{"startTime":1734627928350,"interpreter":"python.low","endTime":1734627928965,"results":[{"message":"INCOME\tINCOME_NORMALIZED\n65871.0\t0.1148\n68747.0\t0.5464\n68684.0\t0.5369\n59354.0\t-0.8632\n84801.0\t2.9556\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalize INCOME using min-max scaling","message":["%python","","income_min = CUST_DF['INCOME'].min()","income_max = CUST_DF['INCOME'].max()","","CUST_DF = CUST_DF.concat({'INCOME_MINMAX_SCALED': (CUST_DF['INCOME'] - int(income_min))/ int(income_max - income_min)})","","z.show(CUST_DF[['INCOME', 'INCOME_MINMAX_SCALED']].head().round(4))"],"enabled":true,"result":{"startTime":1734627929385,"interpreter":"python.low","endTime":1734627930099,"results":[{"message":"INCOME\tINCOME_MINMAX_SCALED\n65871.0\t0.3861\n68747.0\t0.4247\n68684.0\t0.4239\n59354.0\t0.2986\n84801.0\t0.6402\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Filter zero values from column TOTAL_AUTOM_PAYMENTS, then transform using base 10 logarithm","message":["%python","","CUST_DF_FILTERED = CUST_DF[CUST_DF['TOTAL_AUTOM_PAYMENTS'] > 0]","CUST_DF_FILTERED = CUST_DF_FILTERED.concat({'LOG_TOTAL_AUTOM_PAYMENTS': CUST_DF_FILTERED['TOTAL_AUTOM_PAYMENTS'].log(10)})","","z.show(CUST_DF_FILTERED[['TOTAL_AUTOM_PAYMENTS', 'LOG_TOTAL_AUTOM_PAYMENTS']].head().round(4))"],"enabled":true,"result":{"startTime":1734627930511,"interpreter":"python.low","endTime":1734627931044,"results":[{"message":"TOTAL_AUTOM_PAYMENTS\tLOG_TOTAL_AUTOM_PAYMENTS\n287.0\t2.4579\n132.0\t2.1206\n628.0\t2.798\n2143.0\t3.331\n3707.0\t3.569\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Histogram on the original column","message":["%python","","import matplotlib.pyplot as plt","plt.style.use('seaborn-v0_8')","","plt.figure(figsize=[20,6])","oml.graphics.hist(CUST_DF['TOTAL_AUTOM_PAYMENTS'], bins=10, color='C0',","                 linestyle='solid', edgecolor='black')","","plt.title('Histogram of original TOTAL_AUTOM_PAYMENTS')","plt.xlabel('TOTAL_AUTOM_PAYMENTS')","plt.ylabel('Customer Counts')","_ = plt.show()"],"enabled":true,"result":{"startTime":1734627984897,"interpreter":"python.low","endTime":1734627985661,"results":[{"message":"<div style='width:auto;height:auto'><img 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plt.show()"],"enabled":true,"result":{"startTime":1734627986131,"interpreter":"python.low","endTime":1734627988609,"results":[{"message":"<div style='width:auto;height:auto'><img 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation One Hot Encoding","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736201892396,"interpreter":"md.low","endTime":1736201892811,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Transformation: One Hot Encoding","In this notebook, we demonstrate how to do one hot encoding using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","**Note**: If the user plans to use the in-database algorithms, one hot encoding is automatically applied for those algorithms requiring it. The in-database algorithms automatically explode the categorical columns and fit the model on the prepared data internally. For more details, see [Data Preparation for GLM](https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12)","","Copyright (c) 2025 Oracle Corporation ","###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1736201893228,"interpreter":"md.low","endTime":1736201893634,"results":[{"message":"<h1 id=\"oml4py-data-transformation-one-hot-encoding\">OML4Py Data Transformation: One Hot Encoding<\/h1>\n<p>In this notebook, we demonstrate how to do one hot encoding using OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p><strong>Note<\/strong>: If the user plans to use the in-database algorithms, one hot encoding is automatically applied for those algorithms requiring it. The in-database algorithms automatically explode the categorical columns and fit the model on the prepared data internally. For more details, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/cross-tabulate-data.html\" target=\"_blank\">OML4Py Cross-Tabulation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML Folder on Oracle Github<\/a>"],"enabled":true,"result":{"startTime":1736201894059,"interpreter":"md.low","endTime":1736201894479,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/cross-tabulate-data.html\" target=\"_blank\">OML4Py Cross-Tabulation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML Folder on Oracle Github<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries ","message":["%python","","import warnings","warnings.filterwarnings(\"ignore\")","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1736201894898,"interpreter":"python.low","endTime":1736201897339,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to CUSTOMER_INSURANCE_LTV and subset of columns","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')","cat_cols = ['MARITAL_STATUS', 'STATE', 'GENDER', 'REGION']","CUST_CAT_DF = CUST_DF[['CUSTOMER_ID'] + cat_cols]","z.show(CUST_CAT_DF.head())"],"enabled":true,"result":{"startTime":1736201897758,"interpreter":"python.low","endTime":1736201898387,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS\tSTATE\tGENDER\tREGION\nCU15154                   \tSINGLE\tCA                        \tM                         \tWest\nCU15155                   \tSINGLE\tNY                        \tM                         \tNorthEast\nCU15157                   \tMARRIED\tMI                        \tM                         \tMidwest\nCU15286                   \tMARRIED\tUT                        \tF                         \tSouthwest\nCU15287                   \tMARRIED\tUT                        \tF                         \tSouthwest\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create function for one hot encoding","message":["%python","","def encode(DF, ids, col):","    assert isinstance(ids, list)","    CNT_DF = DF.crosstab(ids, col)","    PIVOT_DF = CNT_DF.pivot_table(ids, col, 'count', aggfunc = oml.DataFrame.count)","    cols = PIVOT_DF.columns[len(ids):]","    new_columns = [ col + '_' + c.split('_')[1][1:-1] for c in cols]","    new_columns = ids + new_columns","    PIVOT_DF.rename(columns = new_columns)","    return PIVOT_DF","    "],"enabled":true,"result":{"startTime":1736201898839,"interpreter":"python.low","endTime":1736201899263,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one hot encoding to MARITAL_STATUS column and check the transformed data","message":["%python","","ONEHOT_DF = encode(CUST_CAT_DF, ['CUSTOMER_ID'], 'MARITAL_STATUS')    ","z.show(ONEHOT_DF.head())","    "],"enabled":true,"result":{"startTime":1736201899713,"interpreter":"python.low","endTime":1736201900785,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS_DIVORCED\tMARITAL_STATUS_MARRIED\tMARITAL_STATUS_OTHER\tMARITAL_STATUS_SINGLE\tMARITAL_STATUS_WIDOWED\nCU1                       \tnan\tnan\t1.0\tNone\tNone\nCU10                      \t1.0\tnan\tnan\tNone\tNone\nCU1000                    \tnan\t1.0\tnan\tNone\tNone\nCU10000                   \tnan\t1.0\tnan\tNone\tNone\nCU10001                   \t1.0\tnan\tnan\tNone\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one hot encoding to all selected categorical columns","message":["%python","","RES_DF = CUST_CAT_DF[['CUSTOMER_ID']]","for i, col in enumerate(cat_cols):","    if col == 'CUSTOMER_ID':","        continue","    ONEHOT_DF = encode(CUST_CAT_DF, ['CUSTOMER_ID'], col)","    RES_DF = RES_DF.merge(ONEHOT_DF, on ='CUSTOMER_ID', how = 'inner', suffixes = ['', ''])","    ","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1736201901201,"interpreter":"python.low","endTime":1736201903942,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS_DIVORCED\tMARITAL_STATUS_MARRIED\tMARITAL_STATUS_OTHER\tMARITAL_STATUS_SINGLE\tMARITAL_STATUS_WIDOWED\tSTATE_AK                        \tSTATE_AL                        \tSTATE_AZ                        \tSTATE_CA                        \tSTATE_CO                        \tSTATE_DC                        \tSTATE_FL                        \tSTATE_IL                        \tSTATE_LA                        \tSTATE_MI                        \tSTATE_MN                        \tSTATE_MO                        \tSTATE_MS                        \tSTATE_NC                        \tSTATE_NM                        \tSTATE_NV                        \tSTATE_NY                        \tSTATE_OH                        \tSTATE_OK                        \tSTATE_OR                        \tSTATE_TX                        \tSTATE_UT                        \tSTATE_WA                        \tSTATE_WI                        \tGENDER_F                         \tGENDER_M                         \tREGION_Midwest\tREGION_NorthEast\tREGION_South\tREGION_Southwest\tREGION_West\nCU1                       \tnan\tnan\t1.0\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tnan\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\t1.0\tNone\t1.0\tnan\tNone\tnan\tnan\tNone\t1.0\nCU10                      \t1.0\tnan\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tnan\tNone\tNone\tNone\t1.0\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\t1.0\tnan\tNone\tnan\tnan\tNone\t1.0\nCU1000                    \tnan\t1.0\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tnan\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\t1.0\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tnan\t1.0\tNone\t1.0\tnan\tNone\tnan\nCU10000                   \tnan\t1.0\tnan\tNone\tNone\tNone\tNone\tNone\t1.0\tNone\tNone\tnan\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tnan\t1.0\tNone\tnan\tnan\tNone\t1.0\nCU10001                   \t1.0\tnan\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\t1.0\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tNone\tNone\tNone\tNone\tnan\tNone\tnan\t1.0\tNone\tnan\t1.0\tNone\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Replace None and nan values with 0","message":["%python","","cols = RES_DF.columns[1:]","for col in cols:","    RES_DF = RES_DF.replace(old = [None], new = [0.0], default = 1.0, columns = [col])"],"enabled":true,"result":{"startTime":1736201904372,"interpreter":"python.low","endTime":1736201915732,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the result","message":["%python","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1736201916147,"interpreter":"python.low","endTime":1736201916990,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS_DIVORCED\tMARITAL_STATUS_MARRIED\tMARITAL_STATUS_OTHER\tMARITAL_STATUS_SINGLE\tMARITAL_STATUS_WIDOWED\tSTATE_AK                        \tSTATE_AL                        \tSTATE_AZ                        \tSTATE_CA                        \tSTATE_CO                        \tSTATE_DC                        \tSTATE_FL                        \tSTATE_IL                        \tSTATE_LA                        \tSTATE_MI                        \tSTATE_MN                        \tSTATE_MO                        \tSTATE_MS                        \tSTATE_NC                        \tSTATE_NM                        \tSTATE_NV                        \tSTATE_NY                        \tSTATE_OH                        \tSTATE_OK                        \tSTATE_OR                        \tSTATE_TX                        \tSTATE_UT                        \tSTATE_WA                        \tSTATE_WI                        \tGENDER_F                         \tGENDER_M                         \tREGION_Midwest\tREGION_NorthEast\tREGION_South\tREGION_Southwest\tREGION_West\nCU1                       \t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\t0\t0\t1\nCU10                      \t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t1\nCU1000                    \t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\nCU10000                   \t0\t1\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t1\nCU10001                   \t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t1\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736201917407,"interpreter":"md.low","endTime":1736201917806,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736201918227,"interpreter":"md.low","endTime":1736201918629,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Dataset Creation.dsnb b/machine-learning/notebooks-oml/python/OML4Py Dataset Creation.dsnb
new file mode 100644
index 00000000..2254d2d8
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Dataset Creation.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Dataset Creation ","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736202765776,"interpreter":"md.low","endTime":1736202766193,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Demonstration Dataset Creation","","This script loads the sample datasets `BREASTCANCER` and `IRIS` provided with the SciKit-Learn Python package and imports into them into the Oracle Database by using the oml.create() function. ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736202766605,"interpreter":"md.low","endTime":1736202767035,"results":[{"message":"<h1 id=\"oml4py-demonstration-dataset-creation\">OML4Py Demonstration Dataset Creation<\/h1>\n<p>This script loads the sample datasets <code>BREASTCANCER<\/code> and <code>IRIS<\/code> provided with the SciKit-Learn Python package and imports into them into the Oracle Database by using the oml.create() function.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","","from sklearn import datasets","import pandas as pd"],"enabled":true,"result":{"startTime":1736202767449,"interpreter":"python.low","endTime":1736202769995,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load breast cancer data and create table","message":["%python","","bc = datasets.load_breast_cancer()","bc_data = bc.data.astype(float)","X = pd.DataFrame(bc_data, columns = bc.feature_names)","y = pd.DataFrame(bc.target, columns = ['TARGET'])","","try:","    oml.drop(table='BREASTCANCER')","except:","    pass","BREASTCANCER = oml.create(pd.concat([X, y], axis=1), table = 'BREASTCANCER')","print(\"Shape:\",BREASTCANCER.shape)"],"enabled":true,"result":{"startTime":1736202770434,"interpreter":"python.low","endTime":1736202771507,"results":[{"message":"Shape: (569, 31)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load IRIS dataset and create table","message":["%python","","iris = datasets.load_iris()","X = pd.DataFrame(iris.data, ","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","","try:","    oml.drop(table='IRIS')","except:","    pass","IRIS = oml.create(pd.concat([X, y], axis=1), table = 'IRIS')","print(\"Shape:\",IRIS.shape)"],"enabled":true,"result":{"startTime":1736202771921,"interpreter":"python.low","endTime":1736202772492,"results":[{"message":"Shape: (150, 5)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736202772903,"interpreter":"md.low","endTime":1736202773326,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736202773739,"interpreter":"md.low","endTime":1736202774144,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Feature Engineering Aggregation.dsnb b/machine-learning/notebooks-oml/python/OML4Py Feature Engineering Aggregation.dsnb
new file mode 100644
index 00000000..897e4cd0
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Feature Engineering Aggregation.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Engineering Aggregation ","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736203770140,"interpreter":"md.low","endTime":1736203770608,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Feature Engineering: Aggregation","In this notebook, we demonstrate how to perform aggregation for min, max, mean, and count using OML4Py.","","We use the SH schema SALES table, which contains transaction records for each customer and products purchased. We create features by aggregating the amount sold for each customer and product pair.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736203771059,"interpreter":"md.low","endTime":1736203771527,"results":[{"message":"<h1 id=\"oml4py-feature-engineering-aggregation\">OML4Py Feature Engineering: Aggregation<\/h1>\n<p>In this notebook, we demonstrate how to perform aggregation for min, max, mean, and count using OML4Py.<\/p>\n<p>We use the SH schema SALES table, which contains transaction records for each customer and products purchased. We create features by aggregating the amount sold for each customer and product pair.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/cross-tabulate-data.html\" target=\"_blank\">OML4Py Cross-Tabulation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/combine-data.html\" target=\"_blank\">OML4Py Combining Data<\/a>"],"enabled":true,"result":{"startTime":1736203771989,"interpreter":"md.low","endTime":1736203772445,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a 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'PROD_ID'], values = 'AMOUNT_SOLD', aggfunc = oml.DataFrame.mean)","COUNT_DF = SALES_DF.crosstab(['CUST_ID', 'PROD_ID'], values = 'AMOUNT_SOLD', aggfunc = oml.DataFrame.count)"],"enabled":true,"result":{"startTime":1736203778343,"interpreter":"python.low","endTime":1736203779546,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Join all results","message":["%python","","AGG_DF = MAX_DF.merge(MIN_DF,   on = ['CUST_ID', 'PROD_ID'], how = 'inner', suffixes =['', '']) ","AGG_DF = AGG_DF.merge(MEAN_DF,  on = ['CUST_ID', 'PROD_ID'], how = 'inner', suffixes =['', ''])","AGG_DF = AGG_DF.merge(COUNT_DF, on = ['CUST_ID', 'PROD_ID'], how = 'inner', suffixes =['', 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Extraction ESA Wiki Model","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734645687839,"interpreter":"md.high","endTime":1734645688332,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for Python function *oml.esa* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we'll use the Wikipedia Model as an example:",""," - Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model "," - Examples of using the Wiki ESA model"," - Basic ESA Wiki model features for words"," - Basic ESA Wiki model features for phrases"," - ESA Wiki Model document similarity on phrases"," - ESA Wiki Model document similarity on long phrases","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734645688775,"interpreter":"md.high","endTime":1734645689213,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <code>semantic relatedness<\/code> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>The Oracle Machine Learning for Python function <em>oml.esa<\/em> extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>In this notebook, we'll use the Wikipedia Model as an example:<\/p>\n<ul>\n<li>Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model<\/li>\n<li>Examples of using the Wiki ESA model<\/li>\n<li>Basic ESA Wiki model features for words<\/li>\n<li>Basic ESA Wiki model features for phrases<\/li>\n<li>ESA Wiki Model document similarity on phrases<\/li>\n<li>ESA Wiki Model document similarity on long phrases<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a>"],"enabled":true,"result":{"startTime":1734645689639,"interpreter":"md.high","endTime":1734645690077,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Using the Wikipedia model as an example","","The model was pre-loaded to the Oracle Autonomous Database because it is a large 800 MB model object.","","Follow the Setup from the template notebook titled `OML Import Wiki ESA Model`.","","<p>Related Oracle blogs:<\/p>","<ul>","    <li><a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" target=\"_blank\">New Wikipedia-Based Cognitive Model Available for Text Processing<\/a><\/li>","    <li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" target=\"_blank\">Text Analytics using a pre-built Wikipedia-based Topic Model<\/a><\/li>","    <li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" target=\"_blank\">Explicit Semantic Analysis (ESA) for Text Analytics<\/a><\/li>","<\/ul>","","We need to create the proxy for the existing model into OML4Py"," - If the model is available on the same user, you can use the code:","    In Python: \"esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname') \""," - If the model is available on a different schema, you need to:","    1. Run as SYSDBA a \"grant execute on model to <user> or public\" in sql; then","    2. In Python \"esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname', model_owner = 'model owner schema name') \""],"enabled":true,"result":{"startTime":1734645690501,"interpreter":"md.high","endTime":1734645690923,"results":[{"message":"<h2 id=\"using-the-wikipedia-model-as-an-example\">Using the Wikipedia model as an example<\/h2>\n<p>The model was pre-loaded to the Oracle Autonomous Database because it is a large 800 MB model object.<\/p>\n<p>Follow the Setup from the template notebook titled <code>OML Import Wiki ESA Model<\/code>.<\/p>\n<p>Related Oracle blogs:<\/p>\n<ul>\n    <li><a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" target=\"_blank\">New Wikipedia-Based Cognitive Model Available for Text Processing<\/a><\/li>\n    <li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" target=\"_blank\">Text Analytics using a pre-built Wikipedia-based Topic Model<\/a><\/li>\n    <li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" target=\"_blank\">Explicit Semantic Analysis (ESA) for Text Analytics<\/a><\/li>\n<\/ul>\n<p>We need to create the proxy for the existing model into OML4Py<\/p>\n<ul>\n<li>If the model is available on the same user, you can use the code:\nIn Python: &quot;esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname') &quot;<\/li>\n<li>If the model is available on a different schema, you need to:\n<ol>\n<li>Run as SYSDBA a &quot;grant execute on model to <user> or public&quot; in sql; then<\/li>\n<li>In Python &quot;esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname', model_owner = 'model owner schema name') 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=> 'wiki_txtpol', lexer => 'wiki_lexer'); END;\")","cur.close()"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and suppress warnings","message":["%python","","import oml","import pandas as pd","","import warnings","warnings.filterwarnings('ignore')"],"enabled":true,"result":{"startTime":1734645691355,"interpreter":"python.high","endTime":1734645693804,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check to see if the WIKI_MODEL 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al-Dawla\tTEXT.CONSUMMATE\tNone\t0.0082\n'Adud al-Dawla\tTEXT.DAWLA\tNone\t0.7239\n'Adud al-Dawla\tTEXT.DAYLAMITE\tNone\t0.0502\n'Adud al-Dawla\tTEXT.DAYLAMITES\tNone\t0.0143\n'Adud al-Dawla\tTEXT.DESPOT\tNone\t0.0094\n'Adud al-Dawla\tTEXT.DINAR\tNone\t0.0096\n'Adud al-Dawla\tTEXT.DIYAR\tNone\t0.0191\n'Adud al-Dawla\tTEXT.EMBELLISH\tNone\t0.0079\n'Adud al-Dawla\tTEXT.EMIR\tNone\t0.0315\n'Adud al-Dawla\tTEXT.ENRICHMENT\tNone\t0.0081\n'Adud al-Dawla\tTEXT.EUPHRATES\tNone\t0.0087\n'Adud al-Dawla\tTEXT.FADL\tNone\t0.0227\n'Adud al-Dawla\tTEXT.FAKHR\tNone\t0.0276\n'Adud al-Dawla\tTEXT.FARAJ\tNone\t0.0117\n'Adud al-Dawla\tTEXT.GOR\tNone\t0.0113\n'Adud al-Dawla\tTEXT.GORGAN\tNone\t0.0122\n'Adud al-Dawla\tTEXT.GRAMMARIAN\tNone\t0.0093\n'Adud al-Dawla\tTEXT.HAMADAN\tNone\t0.017\n'Adud al-Dawla\tTEXT.HAMDANID\tNone\t0.0188\n'Adud al-Dawla\tTEXT.HAMDANIDS\tNone\t0.0259\n'Adud al-Dawla\tTEXT.HARUN\tNone\t0.0101\n'Adud al-Dawla\tTEXT.HASAN\tNone\t0.0151\n'Adud al-Dawla\tTEXT.HORMUZ\tNone\t0.0109\n'Adud al-Dawla\tTEXT.HUSAIN\tNone\t0.0149\n'Adud al-Dawla\tTEXT.HUSAYN\tNone\t0.0096\n'Adud al-Dawla\tTEXT.IBN\tNone\t0.0843\n'Adud al-Dawla\tTEXT.ILYAS\tNone\t0.017\n'Adud al-Dawla\tTEXT.IMAD\tNone\t0.0333\n'Adud al-Dawla\tTEXT.IMAM\tNone\t0.0117\n'Adud al-Dawla\tTEXT.IMPALE\tNone\t0.009\n'Adud al-Dawla\tTEXT.IMRAN\tNone\t0.0095\n'Adud al-Dawla\tTEXT.IRRIGATE\tNone\t0.0081\n'Adud al-Dawla\tTEXT.ISFAHAN\tNone\t0.0144\n'Adud al-Dawla\tTEXT.ISFANDIYAR\tNone\t0.0147\n'Adud al-Dawla\tTEXT.ISHAQ\tNone\t0.01\n'Adud al-Dawla\tTEXT.IZZ\tNone\t0.1291\n'Adud al-Dawla\tTEXT.KAKI\tNone\t0.0116\n'Adud al-Dawla\tTEXT.KARBALA\tNone\t0.011\n'Adud al-Dawla\tTEXT.KARD\tNone\t0.0138\n'Adud al-Dawla\tTEXT.KARUN\tNone\t0.0115\n'Adud al-Dawla\tTEXT.KERMAN\tNone\t0.0322\n'Adud al-Dawla\tTEXT.KERMANSHAH\tNone\t0.0117\n'Adud al-Dawla\tTEXT.KHALAF\tNone\t0.0117\n'Adud al-Dawla\tTEXT.KHORRAMSHAHR\tNone\t0.0135\n'Adud al-Dawla\tTEXT.KHUSRAW\tNone\t0.0776\n'Adud al-Dawla\tTEXT.KHUZESTAN\tNone\t0.011\n'Adud al-Dawla\tTEXT.KURD\tNone\t0.013\n'Adud al-Dawla\tTEXT.LAQAB\tNone\t0.0138\n'Adud al-Dawla\tTEXT.MAKAN\tNone\t0.0229\n'Adud al-Dawla\tTEXT.MAKRAN\tNone\t0.0178\n'Adud al-Dawla\tTEXT.MANSUR\tNone\t0.009\n'Adud al-Dawla\tTEXT.MARSHY\tNone\t0.0085\n'Adud al-Dawla\tTEXT.MARZUBAN\tNone\t0.0221\n'Adud al-Dawla\tTEXT.MOBAD\tNone\t0.0146\n'Adud al-Dawla\tTEXT.MOSUL\tNone\t0.0191\n'Adud al-Dawla\tTEXT.MU\tNone\t0.0489\n'Adud al-Dawla\tTEXT.MUDAR\tNone\t0.0131\n'Adud al-Dawla\tTEXT.MUFID\tNone\t0.0136\n'Adud al-Dawla\tTEXT.MUQADDASI\tNone\t0.012\n'Adud al-Dawla\tTEXT.NAJAF\tNone\t0.0108\n'Adud al-Dawla\tTEXT.NASIR\tNone\t0.0088\n'Adud al-Dawla\tTEXT.NISHAPUR\tNone\t0.0115\n'Adud al-Dawla\tTEXT.NOMAD\tNone\t0.0077\n'Adud al-Dawla\tTEXT.NOWRUZ\tNone\t0.0115\n'Adud al-Dawla\tTEXT.OMAN\tNone\t0.0116\n'Adud al-Dawla\tTEXT.PATRONIZE\tNone\t0.0116\n'Adud al-Dawla\tTEXT.PERSEPOLIS\tNone\t0.0103\n'Adud al-Dawla\tTEXT.POLEMIC\tNone\t0.0085\n'Adud al-Dawla\tTEXT.QABUS\tNone\t0.0516\n'Adud al-Dawla\tTEXT.QAZVIN\tNone\t0.0116\n'Adud al-Dawla\tTEXT.RAHBA\tNone\t0.0145\n'Adud al-Dawla\tTEXT.RECONQUER\tNone\t0.0082\n'Adud al-Dawla\tTEXT.RUKN\tNone\t0.0786\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{},\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Basic settings of the ESA Wiki model and the 21.7 mi rows of features","message":["%python","","esa_wiki_mod.set_params"],"enabled":true,"result":{"startTime":1734645750892,"interpreter":"python.high","endTime":1734645850239,"results":[{"message":"<bound method odmModel.set_params of \nModel Name: WIKI_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Explicit Semantic Analysis\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_EXPLICIT_SEMANTIC_ANALYS\n1                 ESAS_MIN_ITEMS                             25\n2             ESAS_TOPN_FEATURES                           1000\n3           ESAS_VALUE_THRESHOLD                      .00000001\n4                   ODMS_DETAILS                    ODMS_ENABLE\n5   ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n6                  ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n7         ODMS_TEXT_MAX_FEATURES                         200000\n8        ODMS_TEXT_MIN_DOCUMENTS                              5\n9          ODMS_TEXT_POLICY_NAME                    wiki_txtpol\n10                     PREP_AUTO                             ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS           161616\n\nAttributes: \nTEXT\n\nPartition: NO\n\nFeatures: \n\n                       FEATURE_ID ATTRIBUTE_NAME ATTRIBUTE_VALUE  COEFFICIENT\n0                  'Adud al-Dawla     TEXT.ABBAD            None     0.019127\n1                  'Adud al-Dawla   TEXT.ABBASID            None     0.024915\n2                  'Adud al-Dawla       TEXT.ABU            None     0.059714\n3                  'Adud al-Dawla      TEXT.ADUD            None     0.565945\n4                  'Adud al-Dawla   TEXT.ALABAMA            None     0.228319\n...                           ...            ...             ...          ...\n24359888  Zyuden Sentai Kyoryuger    TEXT.YAMATO            None     0.062645\n24359889  Zyuden Sentai Kyoryuger    TEXT.YUSUKE            None     0.056347\n24359890  Zyuden Sentai Kyoryuger    TEXT.ZYUDEN            None     0.296543\n24359891  Zyuden Sentai Kyoryuger  TEXT.ZYUOHGER            None     0.046699\n24359892  Zyuden Sentai Kyoryuger    TEXT.??MORI            None     0.068806\n\n[24359893 rows x 4 columns]\n\n>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Examples using the Wiki ESA model"],"enabled":true,"result":{"startTime":1734645850678,"interpreter":"md.high","endTime":1734645851085,"results":[{"message":"<h3 id=\"examples-using-the-wiki-esa-model\">Examples using the Wiki ESA model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Basic example of ESA features for the word 'Bank' ","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['Bank']}))","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1734645851516,"interpreter":"python.high","endTime":1734645862976,"results":[{"message":"FEATURE_ID\tVALUE\nBanking in India\t0.12073074740513993\nBanking in Australia\t0.1186153932769802\nBank of America\t0.11307155411695473\nBank\t0.10994163646234326\nCentral bank\t0.10780307790446561\nNational Bank Act\t0.10647042777456947\nStandard Bank\t0.10502497981253221\nBankBoston\t0.10474436983267525\nOnline banking\t0.10250315951850819\nBank of England\t0.09610674490262502\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for the words 'NFL games' ","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['NFL Games']}))","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1734645863391,"interpreter":"python.high","endTime":1734645875346,"results":[{"message":"FEATURE_ID\tVALUE\nNational Football League\t0.1448362549796241\nNFL International Series\t0.13968414188865144\nThursday Night Football\t0.11436240954509867\nHistory of the National Football League championship\t0.11272129823985683\nChicago College All-Star Game\t0.11121297109987173\nNFL Network\t0.11007694516545648\nNFL Films\t0.10627056953318934\nThe NFL Today\t0.10292445567064071\nNational Football League Christmas games\t0.10104092801135917\nPlayoff Bowl\t0.09258723455526119\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract features on text about Astronomy","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['A group of European-led astronomers has made a photograph of what appears to be a planet orbiting another star. If so, it would be the first confirmed picture of a world beyond our solar system.']}))","","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1734645875775,"interpreter":"python.high","endTime":1734645887326,"results":[{"message":"FEATURE_ID\tVALUE\nSolar System\t0.1783977405440812\nPlanets beyond Neptune\t0.16384415493902063\nPlanet Nine\t0.1556042629299215\nPlanet\t0.14360577141174127\nExoplanet\t0.14146374523098973\nFormation and evolution of the Solar System\t0.1380857932488385\nTerrestrial planet\t0.12349262676588602\nAstronomer\t0.11968229035028131\nSolar analog\t0.11919958268012823\nPlanetary habitability\t0.11168273107745952\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract features on text about diet and health","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['Nearly 70 percent of Americans say they are careful about what they eat, and even more say diet is essential to good health, according to a new nationwide health poll in which obesity ranked second among the biggest health concerns.']}))","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1734645887751,"interpreter":"python.high","endTime":1734645900194,"results":[{"message":"FEATURE_ID\tVALUE\nObesity\t0.2381281204952319\nChildhood obesity\t0.21566128131005566\nRobert Wood Johnson Foundation\t0.1790547081969936\nHealth\t0.1723830168425817\nHealthy diet\t0.16671289147333318\nDieting\t0.142162115212373\nPaleolithic diet\t0.1386597097885653\nHealth education\t0.12498303889516335\nHealth equity\t0.1149008443204024\nPlant-based diet\t0.11425470702731559\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract features on text about stocks and the Fed","message":["%python","","temp_pd = oml.push(pd.DataFrame(","    {'TEXT':['The more things change... Yes, I''m inclined to agree, especially with regards to the historical relationship between stock prices and bond yields. The two have generally traded together, rising during periods of economic growth and falling during periods of contraction. Consider the period from 1998 through 2010, during which the U.S. economy experienced two expansions as well as two recessions: Then central banks came to the rescue. Fed Chairman Ben Bernanke led from Washington with the help of the bank''s current $3.6T balance sheet. He''s accompanied by Mario Draghi at the European Central Bank and an equally forthright Shinzo Abe in Japan. Their coordinated monetary expansion has provided all the sugar needed for an equities moonshot, while they vowed to hold global borrowing costs at record lows']}))","","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1734645900626,"interpreter":"python.high","endTime":1734645915273,"results":[{"message":"FEATURE_ID\tVALUE\nBen Bernanke\t0.17031334068743392\nRecession\t0.15858335723440023\nMario Draghi\t0.14881649785122952\nCOVID-19 recession\t0.14502647137906452\nGreat Recession\t0.12883462217838215\nLong Depression\t0.12180667662946132\nGreat Depression in the United States\t0.1149944972327347\nMonetary policy\t0.10323437884992531\nGreat Recession in the United States\t0.10173350481024938\nGroup of Thirty\t0.09893093930605422\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Comparing Strings for Similarity of Text using the Wikipedia Model"],"enabled":true,"result":{"startTime":1734645915719,"interpreter":"md.high","endTime":1734645916125,"results":[{"message":"<h3 id=\"comparing-strings-for-similarity-of-text-using-the-wikipedia-model\">Comparing Strings for Similarity of Text using the Wikipedia Model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare 'street' vs. 'avenue' ","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['street','avenue']}))","","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))"],"enabled":true,"result":{"startTime":1734645916542,"interpreter":"python.high","endTime":1734645927793,"results":[{"message":"SIMILARITY\n0.1936898590428866\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare 'street' vs. 'farm'","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['street','farm']}))","","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))"],"enabled":true,"result":{"startTime":1734645928214,"interpreter":"python.high","endTime":1734645938171,"results":[{"message":"SIMILARITY\n0.007127425352303707\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare short texts about Golf","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['There are several PGA tour golfers from South Africa',","                                         'Nick Price won the 2002 Mastercard Colonial Open']}))","                                         ","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))","                                    "],"enabled":true,"result":{"startTime":1734645938587,"interpreter":"python.high","endTime":1734645949848,"results":[{"message":"SIMILARITY\n0.10969292803597186\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare texts about Golf and Football","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['There are several PGA tour golfers from South Africa',","                                         'John Elway played quarterback for the Denver Broncos']}))","                                         ","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))"],"enabled":true,"result":{"startTime":1734645950265,"interpreter":"python.high","endTime":1734645961214,"results":[{"message":"SIMILARITY\n0.003917419745909201\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and SpaceX Moon Gateway","message":["%python","","temp_pd = oml.push(pd.DataFrame(","    {'TEXT':['NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.',","             'NASA has selected SpaceX to deliver the first two segments of the moon-orbiting Gateway space station for its upcoming Artemis program, which aims to put astronauts back on the moon. The elements will launch atop a Falcon Heavy rocket, sometime in 2024. The flight, which is the second to be awarded to SpaceX this week will carry the Gateway''s power and habitation modules.  Once deposited in lunar orbit, the Gateway will serve as an outpost for astronauts and equipment heading to the moon as part of NASA Artemis program. Roughly one-sixth the size of the International Space Station, the Gateway will support research investigations, crew, and expeditions to the lunar surface.']}))","z.show(esa_wiki_mod.feature_compare(temp_pd,","                                    compare_cols = ['TEXT']))","                                    "],"enabled":true,"result":{"startTime":1734645961634,"interpreter":"python.high","endTime":1734645971883,"results":[{"message":"SIMILARITY\n0.48847744577133123\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and Tennis Naomi Osaka","message":["%python","","temp_pd = oml.push(pd.DataFrame(","    {'TEXT':['NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.',","             'Naomi Osaka, who hasnt lost an official match since last February, put together a dominating performance in the Australian Open semifinals, beating Williams 6-3, 6-4 and ending hopes that the 39-year old American would tie Margaret Court all-time Slam record at this tournament. Osaka, seeking her fourth major title and second Australian Open championship, will play the winner of the second semifinal between American Jennifer Brady, the No. 22 seed, and No. 25 seed Karolina Muchova.']}))","","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))","                                    "],"enabled":true,"result":{"startTime":1734645972309,"interpreter":"python.high","endTime":1734645982872,"results":[{"message":"SIMILARITY\n0.026660806390262026\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734645983293,"interpreter":"md.high","endTime":1734645983694,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734645984111,"interpreter":"md.high","endTime":1734645984519,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Extraction ESA","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1718663082306,"interpreter":"md.low","endTime":1718663082456,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for Python function *oml.esa* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we cover the basic usage of ESA, where we extract topics from the comments provided. ",""," - Create a small sample dataset"," - Use the ESA Model to predict a feature vector on test data"," - Identify top features"," - Document similarity"," - Change model settings","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1718663082522,"interpreter":"md.low","endTime":1718663082610,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <code>semantic relatedness<\/code> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>The Oracle Machine Learning for Python function <em>oml.esa<\/em> extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>In this notebook, we cover the basic usage of ESA, where we extract topics from the comments provided.<\/p>\n<ul>\n<li>Create a small sample dataset<\/li>\n<li>Use the ESA Model to predict a feature vector on test data<\/li>\n<li>Identify top features<\/li>\n<li>Document similarity<\/li>\n<li>Change model settings<\/li>\n<\/ul>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis"],"enabled":true,"result":{"startTime":1718663082681,"interpreter":"md.low","endTime":1718663082749,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","import pandas as pd"],"enabled":true,"result":{"startTime":1718663082819,"interpreter":"python.low","endTime":1718663084813,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create test and training data","message":["%python","","dat = oml.push(pd.DataFrame(","  {'COMMENTS':['Aids in Africa: Planning for a long war',","               'Mars rover maneuvers for rim shot',","               'Mars express confirms presence of water at Mars south pole',","               'NASA announces major Mars rover finding',","               'Drug access, Asia threat in focus at AIDS summit',","               'NASA Mars Odyssey THEMIS image: typical crater',","               'Road blocks for Aids'],","    'ID':[1,2,3,4,5,6,7]})).split(ratio=(0.7,0.3), seed = 1234)","","train_dat = dat[0]","test_dat = dat[1]"],"enabled":true,"result":{"startTime":1718663084879,"interpreter":"python.low","endTime":1718663085447,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the test data","message":["%python","","z.show(test_dat)"],"enabled":true,"result":{"startTime":1718663085512,"interpreter":"python.low","endTime":1718663085601,"results":[{"message":"COMMENTS\tID\nAids in Africa: Planning for a long war\t1\nNASA announces major Mars rover finding\t4\nRoad blocks for Aids\t7\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the training data","message":["%python","","z.show(train_dat)"],"enabled":true,"result":{"startTime":1718663085665,"interpreter":"python.low","endTime":1718663085755,"results":[{"message":"COMMENTS\tID\nMars rover maneuvers for rim shot\t2\nMars express confirms presence of water at Mars south pole\t3\nDrug access, Asia threat in focus at AIDS summit\t5\nNASA Mars Odyssey THEMIS image: typical crater\t6\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the help file for ESA","message":["%python","","help(oml.esa)"],"enabled":true,"result":{"startTime":1718663085843,"interpreter":"python.low","endTime":1718663086109,"results":[{"message":"Help on class esa in module oml.algo.esa:\n\nclass esa(oml.algo.model.odmModel)\n |  esa(model_name=None, model_owner=None, **params)\n |\n |  In-database `Explicit Semantic Analysis <https://www.oracle.com/machine-learning/explicit-semantic-analysis>`_ Model\n |\n |  Builds an Explicit Semantic Analysis (ESA) Model to be used for feature extraction.\n |  This function exposes the corresponding Oracle Machine Learning in-database algorithm.\n |  ESA uses concepts of an existing knowledge base as features rather than latent\n |  features derived by latent semantic analysis methods such as Singular\n |  Value Decomposition and Latent Dirichlet Allocation. Each row, for example,\n |  a document in the training data maps to a feature, that is, a concept.\n |  ESA works best with concepts represented by text documents.\n |  It has multiple applications in the area of text processing, most\n |  notably semantic relatedness (similarity) and explicit topic modeling.\n |  Text similarity use cases might involve, for example, resume matching, searching\n |  for similar blog postings, and so on.\n |\n |  :Attributes:\n |\n |      **features** : oml.DataFrame\n |\n |        Description of each feature extracted.\n |        It includes the following components:\n |\n |            - feature_id: The unique identifier of a feature as it appears in the training data\n |            - attribute_name: The attribute name\n |            - attribute_value: The attribute value\n |            - coefficient: The coefficient (weight) associated with the attribute in a particular feature.\n |\n |  Method resolution order:\n |      esa\n |      oml.algo.model.odmModel\n |      builtins.object\n |\n |  Methods defined here:\n |\n |  __init__(self, model_name=None, model_owner=None, **params)\n |      Initializes an instance of esa object.\n |\n |      Parameters\n |      ----------\n |      model_name : string or None (default)\n |          The name of an existing database Explicit Semantic Analysis model to create an oml.esa object from.\n |          The specified database model is not dropped when the oml.esa object is deleted.\n |      model_owner: string or None (default)\n |          The owner name of the existing Explicit Semantic Analysis model\n |          The current database user by default\n |      params : key-value pairs or dict\n |          Oracle Machine Learning parameter settings. Each list element's name and\n |          value refer to the parameter setting name and value, respectively.\n |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n |          for applicable parameters and valid values. Global and Automatic Data\n |          Preparation Settings in Table 5-5 apply generally to the model.\n |          Mining Function Settings for Feature Extraction and `Algorithm-specific\n |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-91FC543B-24E4-4D93-9D79-E95B1846F3B7>`_\n |          are applicable to Explicit Semantic Analysis model.\n |\n |  __repr__(self)\n |\n |  feature_compare(self, x, compare_cols=None, supplemental_cols=None)\n |      Compares features of data and generates relatedness.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |          The data used to measure relatedness.\n |      compare_cols : str, a list of str or None (default)\n |          The column(s) used to measure data relatedness.\n |          If None, all the columns of ``x`` are compared to measure relatedness.\n |      supplemental_cols : a list of str or None (default)\n |          A list of columns to display along with the resulting 'SIMILARITY' column.\n |\n |      Returns\n |      -------\n |      pred : oml.DataFrame\n |          Contains a 'SIMILARITY' column that measures relatedness and supplementary columns if specified.\n |\n |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n |      Fits an ESA Model according to the training data and parameter settings.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |          Attribute values for building the model.\n |      model_name : string or None (default)\n |          User-specified model name.\n |          The user-specified database model is not dropped when oml.esa object is deleted.\n |          If None, a system-generated model name will be used.\n |          The system-generated model is dropped when oml.esa object is deleted\n |          unless oml.esa object is saved into a datastore.\n |      case_id : string or None (default)\n |          The column name used as case id for building the model.\n |      ctx_settings : dict or None (default)\n |          A list to specify Oracle Text attribute-specific settings.\n |          This argument is applicable to building models in Oracle Database 12.2 or later.\n |          The name of each list element refers to the text column while the list value\n |          is a scalar string specifying the attribute-specific text transformation.\n |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n |          MAX_FEATURES.\n |\n |  predict(self, x, supplemental_cols=None, topN_attrs=False)\n |      Makes predictions on new data.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |        Attribute values used by the model to generate scores.\n |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n |          Data set presented with the prediction result.\n |          It must be concatenatable with ``x``.\n |      topN_attrs : boolean, positive integer, False (default)\n |          Returns the top N most important features with the corresponding weights.\n |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n |\n |      Returns\n |      -------\n |      pred : oml.DataFrame\n |        Contains the features specified by ``supplemental_cols`` and the\n |        results. The results include the most likely feature and its probability.\n |\n |  transform(self, x, supplemental_cols=None, topN=None)\n |      Make predictions and return relevancy for each feature on new data.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |        Attribute values used by the model to generate scores.\n |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n |          Data set presented with the prediction result.\n |          It must be concatenatable with ``x``.\n |      topN : positive integer or None (default)\n |          A positive integer that restricts the returned values to\n |          the specified number of features that have the highest topN values.\n |\n |      Returns\n |      -------\n |      pred : oml.DataFrame\n |        Contains the relevancy for each feature on new data and the specified ``supplemental_cols``.\n |\n |  ----------------------------------------------------------------------\n |  Methods inherited from oml.algo.model.odmModel:\n |\n |  export_sermodel(self, table=None, partition=None)\n |      Export model.\n |\n |      Parameters\n |      ----------\n |      table : string or None (default)\n |          A name for the new table where the serialized model is saved.\n |          If None, the serialized model will be saved to a temporary table.\n |      partition : string or None (default)\n |          Name of the partition that needs to be exported.\n |          If partition is None, all partitions are exported\n |\n |      Returns\n |      -------\n |      oml_bytes : an oml.Bytes object\n |        Contains the BLOB content from the model export\n |\n |  get_params(self, params=None, deep=False)\n |      Fetches parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : iterable of strings, None (default)\n |          Names of parameters to fetch. If ``params`` is None,\n |          fetches all settings.\n |      deep : boolean, False (default)\n |          Includes the computed and default parameters or not.\n |\n |      Returns\n |      -------\n |      settings : dict mapping str to str\n |\n |  set_params(self, **params)\n |      Changes parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : dict object mapping str to str\n |          The key should be the name of the setting, and the value should be\n |          the new setting.\n |\n |      Returns\n |      -------\n |      model : the model itself.\n |\n |  ----------------------------------------------------------------------\n |  Readonly properties inherited from oml.algo.model.odmModel:\n |\n |  model_owner\n |      The owner name of database mining model\n |\n |  ----------------------------------------------------------------------\n |  Data descriptors inherited from oml.algo.model.odmModel:\n |\n |  __dict__\n |      dictionary for instance variables\n |\n |  __weakref__\n |      list of weak references to the object\n |\n |  model_name\n |      The given name of database mining model\n |\n |  pivot_limit\n |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a policy for text indexing","message":["%script","","-- Drop the policy if it exists","BEGIN ctx_ddl.drop_policy('ESA_TXT_POLICY'); ","   exception when others then NULL;","END;","","BEGIN ctx_ddl.create_policy('ESA_TXT_POLICY'); ","   exception when others then NULL;","END;"],"enabled":true,"result":{"startTime":1718663086185,"interpreter":"script.low","endTime":1718663090879,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify model settings","message":["%python","","odm_settings = {'odms_text_policy_name': 'ESA_TXT_POLICY',","                '\"ODMS_TEXT_MIN_DOCUMENTS\"': 1,","                '\"ESAS_MIN_ITEMS\"': 1}","","ctx_settings = {'COMMENTS':","                'TEXT(POLICY_NAME:ESA_TXT_POLICY)(TOKEN_TYPE:STEM)'}"],"enabled":true,"result":{"startTime":1718663090964,"interpreter":"python.low","endTime":1718663091033,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create an ESA model object ","message":["%python","","# Delete if ESA model exists","try:","    oml.drop(model = 'ESA_MOD')","except:","    print('No such model')","    ","esa_mod = oml.esa(**odm_settings)"],"enabled":true,"result":{"startTime":1718663091100,"interpreter":"python.low","endTime":1718663091167,"results":[{"message":"No such model\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View the model settings","message":["%python","","esa_mod.settings"],"enabled":true,"result":{"startTime":1718663091233,"interpreter":"python.low","endTime":1718663091300,"results":[{"message":"{'odms_text_policy_name': 'ESA_TXT_POLICY', '\"odms_text_min_documents\"': 1, '\"esas_min_items\"': 1}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" Fit the model according to the training data and parameter settings","message":["%python","","esa_mod = esa_mod.fit(train_dat, case_id='ID', ctx_settings = ctx_settings)"],"enabled":true,"result":{"startTime":1718663091368,"interpreter":"python.low","endTime":1718663092457,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%python","","esa_mod"],"enabled":true,"result":{"startTime":1718663092523,"interpreter":"python.low","endTime":1718663092667,"results":[{"message":"\nAlgorithm Name: Explicit Semantic Analysis\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_EXPLICIT_SEMANTIC_ANALYS\n1                 ESAS_MIN_ITEMS                              1\n2             ESAS_TOPN_FEATURES                           1000\n3           ESAS_VALUE_THRESHOLD                      .00000001\n4                   ODMS_DETAILS                    ODMS_ENABLE\n5   ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n6                  ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n7         ODMS_TEXT_MAX_FEATURES                         300000\n8        ODMS_TEXT_MIN_DOCUMENTS                              1\n9          ODMS_TEXT_POLICY_NAME                 ESA_TXT_POLICY\n10                     PREP_AUTO                             ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS                4\n\nAttributes: \nCOMMENTS\n\nPartition: NO\n\nFeatures: \n\n    FEATURE_ID      ATTRIBUTE_NAME ATTRIBUTE_VALUE  COEFFICIENT\n0            2  COMMENTS.MANEUVERS            None     0.497330\n1            2       COMMENTS.MARS            None     0.103205\n2            2        COMMENTS.RIM            None     0.497330\n3            2      COMMENTS.ROVER            None     0.497330\n4            2       COMMENTS.SHOT            None     0.497330\n5            3   COMMENTS.CONFIRMS            None     0.404991\n6            3    COMMENTS.EXPRESS            None     0.404991\n7            3       COMMENTS.MARS            None     0.126065\n8            3       COMMENTS.POLE            None     0.404991\n9            3   COMMENTS.PRESENCE            None     0.404991\n10           3      COMMENTS.SOUTH            None     0.404991\n11           3      COMMENTS.WATER            None     0.404991\n12           5     COMMENTS.ACCESS            None     0.377964\n13           5       COMMENTS.AIDS            None     0.377964\n14           5       COMMENTS.ASIA            None     0.377964\n15           5       COMMENTS.DRUG            None     0.377964\n16           5      COMMENTS.FOCUS            None     0.377964\n17           5     COMMENTS.SUMMIT            None     0.377964\n18           5     COMMENTS.THREAT            None     0.377964\n19           6     COMMENTS.CRATER            None     0.406791\n20           6      COMMENTS.IMAGE            None     0.406791\n21           6       COMMENTS.MARS            None     0.084417\n22           6       COMMENTS.NASA            None     0.406791\n23           6    COMMENTS.ODYSSEY            None     0.406791\n24           6     COMMENTS.THEMIS            None     0.406791\n25           6    COMMENTS.TYPICAL            None     0.406791\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details for each 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top 2 features","message":["%python","","z.show(esa_mod.transform(test_dat, ","                         supplemental_cols = test_dat[:, ['ID', 'COMMENTS']], ","                         topN = 2).sort_values(by = ['ID']).round(4))"],"enabled":true,"result":{"startTime":1718663092887,"interpreter":"python.low","endTime":1718663093543,"results":[{"message":"ID\tCOMMENTS\tTOP_1\tTOP_1_VAL\tTOP_2\tTOP_2_VAL\n1\tAids in Africa: Planning for a long war\t5\t1.0\tnan\tnan\n4\tNASA announces major Mars rover finding\t2\t0.7738\t6.0\t0.6329\n7\tRoad blocks for Aids\t5\t1.0\tnan\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model name internal to the database","message":["%sql","","SELECT * from all_mining_models WHERE ALGORITHM='EXPLICIT_SEMANTIC_ANALYS'"],"enabled":true,"result":{"startTime":1718663093616,"interpreter":"sql.low","endTime":1718663093796,"results":[{"message":"OWNER\tMODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\tBUILD_DURATION\tMODEL_SIZE\tPARTITIONED\tCOMMENTS\nOMLUSER\tOPE$DM2189_146_00072\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2022-07-19 00:47:40\t\t0\tNO\t\nOMLUSER\tOPE$DM11105_146_00082\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2024-06-17 22:24:51\t\t0\tNO\t\nOMLUSER\tWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2023-11-08 19:36:42\t0\t0\tNO\t\nOMLUSER\tOPE$DM1202_146_00073\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2022-03-15 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ESA_MOD\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Explicit Semantic Analysis\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_EXPLICIT_SEMANTIC_ANALYS\n1                 ESAS_MIN_ITEMS                              1\n2             ESAS_TOPN_FEATURES                           1000\n3           ESAS_VALUE_THRESHOLD                      .00000001\n4                   ODMS_DETAILS                    ODMS_ENABLE\n5   ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n6                  ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n7         ODMS_TEXT_MAX_FEATURES                         300000\n8        ODMS_TEXT_MIN_DOCUMENTS                              1\n9          ODMS_TEXT_POLICY_NAME                 ESA_TXT_POLICY\n10                     PREP_AUTO                             ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS                4\n\nAttributes: \nCOMMENTS\n\nPartition: NO\n\nFeatures: \n\n    FEATURE_ID      ATTRIBUTE_NAME ATTRIBUTE_VALUE  COEFFICIENT\n0            2  COMMENTS.MANEUVERS            None     0.497330\n1            2       COMMENTS.MARS            None     0.103205\n2            2        COMMENTS.RIM            None     0.497330\n3            2      COMMENTS.ROVER            None     0.497330\n4            2       COMMENTS.SHOT            None     0.497330\n5            3   COMMENTS.CONFIRMS            None     0.404991\n6            3    COMMENTS.EXPRESS            None     0.404991\n7            3       COMMENTS.MARS            None     0.126065\n8            3       COMMENTS.POLE            None     0.404991\n9            3   COMMENTS.PRESENCE            None     0.404991\n10           3      COMMENTS.SOUTH            None     0.404991\n11           3      COMMENTS.WATER            None     0.404991\n12           5     COMMENTS.ACCESS            None     0.377964\n13           5       COMMENTS.AIDS            None     0.377964\n14           5       COMMENTS.ASIA            None     0.377964\n15           5       COMMENTS.DRUG            None     0.377964\n16           5      COMMENTS.FOCUS            None     0.377964\n17           5     COMMENTS.SUMMIT            None     0.377964\n18           5     COMMENTS.THREAT            None     0.377964\n19           6     COMMENTS.CRATER            None     0.406791\n20           6      COMMENTS.IMAGE            None     0.406791\n21           6       COMMENTS.MARS            None     0.084417\n22           6       COMMENTS.NASA            None     0.406791\n23           6    COMMENTS.ODYSSEY            None     0.406791\n24           6     COMMENTS.THEMIS            None     0.406791\n25           6    COMMENTS.TYPICAL            None     0.406791\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions on the test data","message":["%python","","esa_pred = esa_mod.predict(test_dat, supplemental_cols = test_dat[:, ['ID', 'COMMENTS']])","z.show(esa_pred)"],"enabled":true,"result":{"startTime":1718663094682,"interpreter":"python.low","endTime":1718663094797,"results":[{"message":"ID\tCOMMENTS\tFEATURE_ID\n1\tAids in Africa: Planning for a long war\t5\n4\tNASA announces major Mars rover finding\t2\n7\tRoad blocks for Aids\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Change the settings and refit the model","message":["%python","","new_setting = {'ESAS_VALUE_THRESHOLD': '0.01',","               'ODMS_TEXT_MAX_FEATURES': '2',","               'FEAT_NUM_FEATURES': '2',","               'ESAS_TOPN_FEATURES': '2'}","               "],"enabled":true,"result":{"startTime":1718663094864,"interpreter":"python.low","endTime":1718663094918,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Refit the model with new settings - 2 features maximum ","message":["%python","","# Delete if ESA model exists","try:","    oml.drop(model = 'ESA_MOD_2')","except:","    print('No such model')","    ","esa_mod.set_params(**new_setting).fit(train_dat, case_id = 'ID', ","                                      ctx_settings = ctx_settings,","                                      model_name='ESA_MOD_2')","","z.show(esa_mod.feature_compare(test_dat,","                        compare_cols = ['COMMENTS'],","                        supplemental_cols = 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view_name;"],"enabled":true,"result":{"startTime":1718663096155,"interpreter":"sql.low","endTime":1718663096247,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAESA_MOD_2\tExplicit Semantic Analysis Matrix\nDM$VFESA_MOD_2\tExplicit Semantic Analysis Features\nDM$VGESA_MOD_2\tGlobal Name-Value Pairs\nDM$VNESA_MOD_2\tNormalization and Missing Value Handling\nDM$VSESA_MOD_2\tComputed Settings\nDM$VWESA_MOD_2\tModel Build Alerts\nDM$VXESA_MOD_2\tText Features\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"The VA view shows associations among features (topics) and words","message":["%md","","*ATTRIBUTE_SUBNAME* refers to words, or tokens. The strength of the associations is measured by *COEFFICIENT*. Higher coefficient values indicate a stronger association."],"enabled":true,"result":{"startTime":1718663096314,"interpreter":"md.low","endTime":1718663096371,"results":[{"message":"<p><em>ATTRIBUTE_SUBNAME<\/em> refers to words, or tokens. The strength of the associations is measured by <em>COEFFICIENT<\/em>. Higher coefficient values indicate a stronger association.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * from dm$vaesa_mod_2 FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1718663096437,"interpreter":"sql.low","endTime":1718663096507,"results":[{"message":"PARTITION_NAME\tFEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n\t5\tCOMMENTS\tACCESS\t\t0.3779644730092272\n\t5\tCOMMENTS\tAIDS\t\t0.3779644730092272\n\t5\tCOMMENTS\tASIA\t\t0.3779644730092272\n\t3\tCOMMENTS\tCONFIRMS\t\t0.4049912817146355\n\t6\tCOMMENTS\tCRATER\t\t0.4067910617987843\n\t5\tCOMMENTS\tDRUG\t\t0.3779644730092272\n\t3\tCOMMENTS\tEXPRESS\t\t0.4049912817146355\n\t5\tCOMMENTS\tFOCUS\t\t0.3779644730092272\n\t6\tCOMMENTS\tIMAGE\t\t0.4067910617987843\n\t2\tCOMMENTS\tMANEUVERS\t\t0.4973300370260457\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the associations for selected words","message":["%sql","","SELECT ATTRIBUTE_SUBNAME, count(ATTRIBUTE_SUBNAME) FROM dm$vaesa_mod_2","WHERE ATTRIBUTE_SUBNAME='MARS'","GROUP BY ATTRIBUTE_SUBNAME"],"enabled":true,"result":{"startTime":1718663096572,"interpreter":"sql.low","endTime":1718663096684,"results":[{"message":"ATTRIBUTE_SUBNAME\tCOUNT(ATTRIBUTE_SUBNAME)\nMARS\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Clean-up","message":["%python","","try:","    oml.drop(model = 'ESA_MOD')","except:","    print('No such model')","","try:","    oml.drop(model = 'ESA_MOD_2')","except:","    print('No such model')","    "],"enabled":true,"result":{"startTime":1718663096755,"interpreter":"python.low","endTime":1718663097651,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1718663097719,"interpreter":"md.low","endTime":1718663097770,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1718663097839,"interpreter":"md.low","endTime":1718663097891,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/python/OML4Py Feature Extraction NMF.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Extraction NMF","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737152689953,"interpreter":"md.low","endTime":1737152690426,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Feature Extraction: Non-Negative Matrix Factorization (NMF)","","In this notebook, we demonstrate how to perform dimensionality reduction using the in-database non-negative matrix factorization algorithm. In this example, we illustrate the use of NMF to extract features from the Iris dataset.","","Non-Negative Matrix Factorization is a state-of-the-art feature extraction algorithm. NMF is useful when there are many attributes and the individual attributes have weak predictability. NMF derives new features as linear combinations of the original attributes, which can be used as input to other algorithms or to produce meaningful patterns, topics, or themes. ","","Each feature has a set of coefficients that are a measure of the weight each attribute conributes to the feature. There is a separate coefficient for each numerical attribute and for each distinct value of each categorical attribute. The coefficients are all non-negative, which makes it easier to interpret.","","### Matrix Factorization","","NMF uses techniques from multivariate analysis and linear algebra. It decomposes the data as a matrix *M* into the product of two lower ranking matrices *W* and *H*. The sub-matrix *W* contains the NMF basis; the sub-matrix *H* contains the associated coefficients (weights).","","The algorithm iteratively modifies of the values of *W* and *H* so that their product approaches *M*. The technique preserves much of the structure of the original data and guarantees that both basis and weights are non-negative. The algorithm terminates when the approximation error converges, or a specified number of iterations is reached.","","The NMF algorithm must be initialized with a seed to indicate the starting point for the iterations. Because of the high dimensionality of the processing space and the fact that there is no global minimization algorithm, the appropriate initialization can be critical in obtaining meaningful results. OML4Py uses a random seed that initializes the values of *W* and *H* based on a uniform distribution. This approach works well in most cases.","","#### Scoring with NMF","NMF can be used as a pre-processing step for dimensionality reduction in classification, regression, clustering, and other machine learning tasks. Scoring using an NMF model produces data projections in the new feature space. The magnitude of a projection indicates how strongly a record maps to a feature.","","#### Text Analysis with NMF","NMF is well-suited for analyzing text. In a text document, the same word can occur in different places with different meanings. For example, \"hike\" can be applied to the outdoors or to interest rates. By combining attributes, NMF introduces context, which is essential for explanatory power:","* \"hike\" + \"mountain\" -> \"outdoor sports\"","* \"hike\" + \"interest\" -> \"interest rates\"","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737152690914,"interpreter":"md.low","endTime":1737152691375,"results":[{"message":"<h1 id=\"oml4py-feature-extraction-non-negative-matrix-factorization-nmf\">OML4Py Feature Extraction: Non-Negative Matrix Factorization (NMF)<\/h1>\n<p>In this notebook, we demonstrate how to perform dimensionality reduction using the in-database non-negative matrix factorization algorithm. In this example, we illustrate the use of NMF to extract features from the Iris dataset.<\/p>\n<p>Non-Negative Matrix Factorization is a state-of-the-art feature extraction algorithm. NMF is useful when there are many attributes and the individual attributes have weak predictability. NMF derives new features as linear combinations of the original attributes, which can be used as input to other algorithms or to produce meaningful patterns, topics, or themes.<\/p>\n<p>Each feature has a set of coefficients that are a measure of the weight each attribute conributes to the feature. There is a separate coefficient for each numerical attribute and for each distinct value of each categorical attribute. The coefficients are all non-negative, which makes it easier to interpret.<\/p>\n<h3 id=\"matrix-factorization\">Matrix Factorization<\/h3>\n<p>NMF uses techniques from multivariate analysis and linear algebra. It decomposes the data as a matrix <em>M<\/em> into the product of two lower ranking matrices <em>W<\/em> and <em>H<\/em>. The sub-matrix <em>W<\/em> contains the NMF basis; the sub-matrix <em>H<\/em> contains the associated coefficients (weights).<\/p>\n<p>The algorithm iteratively modifies of the values of <em>W<\/em> and <em>H<\/em> so that their product approaches <em>M<\/em>. The technique preserves much of the structure of the original data and guarantees that both basis and weights are non-negative. The algorithm terminates when the approximation error converges, or a specified number of iterations is reached.<\/p>\n<p>The NMF algorithm must be initialized with a seed to indicate the starting point for the iterations. Because of the high dimensionality of the processing space and the fact that there is no global minimization algorithm, the appropriate initialization can be critical in obtaining meaningful results. OML4Py uses a random seed that initializes the values of <em>W<\/em> and <em>H<\/em> based on a uniform distribution. This approach works well in most cases.<\/p>\n<h4 id=\"scoring-with-nmf\">Scoring with NMF<\/h4>\n<p>NMF can be used as a pre-processing step for dimensionality reduction in classification, regression, clustering, and other machine learning tasks. Scoring using an NMF model produces data projections in the new feature space. The magnitude of a projection indicates how strongly a record maps to a feature.<\/p>\n<h4 id=\"text-analysis-with-nmf\">Text Analysis with NMF<\/h4>\n<p>NMF is well-suited for analyzing text. In a text document, the same word can occur in different places with different meanings. For example, &quot;hike&quot; can be applied to the outdoors or to interest rates. By combining attributes, NMF introduces context, which is essential for explanatory power:<\/p>\n<ul>\n<li>&quot;hike&quot; + &quot;mountain&quot; -&gt; &quot;outdoor sports&quot;<\/li>\n<li>&quot;hike&quot; + &quot;interest&quot; -&gt; &quot;interest rates&quot;<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/non-negative-matrix-factorization.html\" target=\"_blank\">OML NMF Documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/non-negative-matrix-factorization-model.html\" target=\"_blank\">OML4Py NMF Documentation<\/a>"],"enabled":true,"result":{"startTime":1737152691859,"interpreter":"md.low","endTime":1737152692317,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/non-negative-matrix-factorization.html\" target=\"_blank\">OML NMF Documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/non-negative-matrix-factorization-model.html\" target=\"_blank\">OML4Py NMF Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","import pandas as pd","import numpy as np","from sklearn import datasets ","import warnings","","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737152692800,"interpreter":"python.low","endTime":1737152693271,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table containing dataset iris from sklearn and generate a unique identifier column","message":["%python","","iris = datasets.load_iris()","x = pd.DataFrame(iris.data, columns = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])","x.insert(0, \"ID\", range(1, len(x) + 1))","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), columns = ['Species'])","","try: ","  oml.drop(table='IRIS_NMF')","except: ","  print(\"Table IRIS_NMF not found\")","","IRIS = oml.create(pd.concat([x, y], axis=1), table = 'IRIS_NMF')"],"enabled":true,"result":{"startTime":1737152693737,"interpreter":"python.low","endTime":1737152694274,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create training data and test data","message":["%python","","IRIS_SPLIT = IRIS.split()","IRIS_TRAIN = IRIS_SPLIT[0]","IRIS_TEST  = IRIS_SPLIT[1]"],"enabled":true,"result":{"startTime":1737152694736,"interpreter":"python.low","endTime":1737152695636,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"ODMS_DETAILS","message":["%md","","### Reducing the size of the resulting model with `ODMS_DETAILS`","","By disabling the `ODMS_DETAILS` setting, you can reduce the space consumed by the model, especially a partitioned model. The default value is ODMS_ENABLE.","","When the setting is ODMS_ENABLE, it creates model tables and views associated with the model. You can query the tables with SQL.","","Compare the content of the model objects with and without model details below. "],"enabled":true,"result":{"startTime":1737152696093,"interpreter":"md.low","endTime":1737152696535,"results":[{"message":"<h3 id=\"reducing-the-size-of-the-resulting-model-with-odms_details\">Reducing the size of the resulting model with <code>ODMS_DETAILS<\/code><\/h3>\n<p>By disabling the <code>ODMS_DETAILS<\/code> setting, you can reduce the space consumed by the model, especially a partitioned model. The default value is ODMS_ENABLE.<\/p>\n<p>When the setting is ODMS_ENABLE, it creates model tables and views associated with the model. You can query the tables with SQL.<\/p>\n<p>Compare the content of the model objects with and without model details below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create NMF model with model details on the training data","message":["%python","","nmf_mod_details = oml.nmf(ODMS_DETAILS = 'ODMS_ENABLE')","nmf_mod_details = nmf_mod_details.fit(IRIS_TRAIN)"],"enabled":true,"result":{"startTime":1737152697020,"interpreter":"python.low","endTime":1737152698915,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create NMF model without model details on the training data","message":["%python","","nmf_mod_no_details = oml.nmf(ODMS_DETAILS = 'ODMS_DISABLE')","nmf_mod_no_details = nmf_mod_no_details.fit(IRIS_TRAIN)"],"enabled":true,"result":{"startTime":1737152699376,"interpreter":"python.low","endTime":1737152700347,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show model with model details enabled","message":["%python","","nmf_mod_details"],"enabled":true,"result":{"startTime":1737152700844,"interpreter":"python.low","endTime":1737152701429,"results":[{"message":"\nAlgorithm Name: Non-Negative Matrix Factorizationx\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                   setting name                   setting value\n0                     ALGO_NAME  ALGO_NONNEGATIVE_MATRIX_FACTOR\n1           NMFS_CONV_TOLERANCE                             .05\n2      NMFS_NONNEGATIVE_SCORING      NMFS_NONNEG_SCORING_ENABLE\n3           NMFS_NUM_ITERATIONS                              50\n4              NMFS_RANDOM_SEED                              -1\n5                  ODMS_DETAILS                     ODMS_ENABLE\n6  ODMS_MISSING_VALUE_TREATMENT         ODMS_MISSING_VALUE_AUTO\n7                 ODMS_SAMPLING           ODMS_SAMPLING_DISABLE\n8                     PREP_AUTO                              ON\n\nComputed Settings: \n          setting name setting value\n0    FEAT_NUM_FEATURES             2\n1  NMFS_NUM_ITERATIONS             2\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     CONV_ERROR       0.0444448\n2     ITERATIONS               2\n3       NUM_ROWS             111\n4    SAMPLE_SIZE             111\n\nAttributes: \nID\nPetal_Length\nPetal_Width\nSepal_Length\nSepal_Width\nSpecies\n\nPartition: NO\n\nH: \n\n    FEATURE_ID  FEATURE_NAME ATTRIBUTE_NAME ATTRIBUTE_VALUE  COEFFICIENT\n0            1             1             ID            None     0.581551\n1            1             1   Petal_Length            None     0.355323\n2            1             1    Petal_Width            None     0.158492\n3            1             1   Sepal_Length            None     0.656558\n4            1             1    Sepal_Width            None     0.424101\n5            1             1        Species          setosa     0.089560\n6            1             1        Species      versicolor     0.534806\n7            1             1        Species       virginica     0.539590\n8            2             2             ID            None     0.344647\n9            2             2   Petal_Length            None     0.506623\n10           2             2    Petal_Width            None     0.650077\n11           2             2   Sepal_Length            None     0.170237\n12           2             2    Sepal_Width            None     0.248640\n13           2             2        Species          setosa     0.249221\n14           2             2        Species      versicolor     0.042316\n15           2             2        Species       virginica     0.093861\n\nW: \n\n    FEATURE_ID  FEATURE_NAME ATTRIBUTE_NAME ATTRIBUTE_VALUE  COEFFICIENT\n0            1             1             ID            None     0.288559\n1            1             1   Petal_Length            None    -0.062579\n2            1             1    Petal_Width            None    -0.370128\n3            1             1   Sepal_Length            None     0.502382\n4            1             1    Sepal_Width            None     0.212611\n5            1             1        Species      versicolor     0.486970\n6            1             1        Species          setosa    -0.113835\n7            1             1        Species       virginica     0.450038\n8            2             2             ID            None     0.119462\n9            2             2   Petal_Length            None     0.578697\n10           2             2    Petal_Width            None     0.982575\n11           2             2   Sepal_Length            None    -0.238993\n12           2             2    Sepal_Width            None     0.082511\n13           2             2        Species          setosa     0.353453\n14           2             2        Species      versicolor    -0.359264\n15           2             2        Species       virginica    -0.275074\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show model with model details disabled","message":["%python","","nmf_mod_no_details"],"enabled":true,"result":{"startTime":1737152701900,"interpreter":"python.low","endTime":1737152702398,"results":[{"message":"\nAlgorithm Name: Non-Negative Matrix Factorizationx\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                   setting name                   setting value\n0                     ALGO_NAME  ALGO_NONNEGATIVE_MATRIX_FACTOR\n1           NMFS_CONV_TOLERANCE                             .05\n2      NMFS_NONNEGATIVE_SCORING      NMFS_NONNEG_SCORING_ENABLE\n3           NMFS_NUM_ITERATIONS                              50\n4              NMFS_RANDOM_SEED                              -1\n5                  ODMS_DETAILS                    ODMS_DISABLE\n6  ODMS_MISSING_VALUE_TREATMENT         ODMS_MISSING_VALUE_AUTO\n7                 ODMS_SAMPLING           ODMS_SAMPLING_DISABLE\n8                     PREP_AUTO                              ON\n\nAttributes: \nID\nPetal_Length\nPetal_Width\nSepal_Length\nSepal_Width\nSpecies\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md\r","\r","#### Making Predictions\r","\r","Use `predict(x, supplemental_cols=None)` to make predictions on new data.\r","\r","**Parameters**\r","* `x` [an OML object] Attribute values used by the models to generate scores.\r","* `supplemental_cols` [oml.DataFrame, oml.Float, oml.String, or None (default)] Data set presented with the prediction result. It must be concatenatable with `x`.\r","\r","**Returns**\r","* `pred` [oml.DataFrame] Contains the predicted feature index on the new data and the specified `supplemental_cols`.\r"],"enabled":true,"result":{"startTime":1737152702868,"interpreter":"md.low","endTime":1737152703326,"results":[{"message":"<h4 id=\"making-predictions\">Making Predictions<\/h4>\n<p>Use <code>predict(x, supplemental_cols=None)<\/code> to make predictions on new data.<\/p>\n<p><strong>Parameters<\/strong><\/p>\n<ul>\n<li><code>x<\/code> [an OML object] Attribute values used by the models to generate scores.<\/li>\n<li><code>supplemental_cols<\/code> [oml.DataFrame, oml.Float, oml.String, or None (default)] Data set presented with the prediction result. It must be concatenatable with <code>x<\/code>.<\/li>\n<\/ul>\n<p><strong>Returns<\/strong><\/p>\n<ul>\n<li><code>pred<\/code> [oml.DataFrame] Contains the predicted feature index on the new data and the specified <code>supplemental_cols<\/code>.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Use the model to make predictions on test data","message":["%python","","z.show(nmf_mod_details.predict(IRIS_TEST, ","                               supplemental_cols = IRIS_TEST[:, ","                                                             ['Sepal_Length', ","                                                             'Sepal_Width', ","                                                             'Species']]))"],"enabled":true,"result":{"startTime":1737152703809,"interpreter":"python.low","endTime":1737152704368,"results":[{"message":"Sepal_Length\tSepal_Width\tSpecies\tFEATURE_ID\n5.0\t3.6\tsetosa\t2\n5.0\t3.4\tsetosa\t2\n4.4\t2.9\tsetosa\t2\n4.9\t3.1\tsetosa\t2\n4.8\t3.4\tsetosa\t2\n5.4\t3.9\tsetosa\t2\n5.2\t3.5\tsetosa\t2\n5.2\t3.4\tsetosa\t2\n4.4\t3.0\tsetosa\t2\n5.1\t3.4\tsetosa\t2\n4.8\t3.0\tsetosa\t2\n6.5\t2.8\tversicolor\t1\n6.6\t2.9\tversicolor\t1\n5.6\t3.0\tversicolor\t1\n6.7\t3.0\tversicolor\t1\n6.0\t2.9\tversicolor\t1\n5.4\t3.0\tversicolor\t1\n5.5\t2.5\tversicolor\t1\n5.5\t2.6\tversicolor\t1\n6.1\t3.0\tversicolor\t1\n5.6\t2.7\tversicolor\t1\n5.7\t2.9\tversicolor\t1\n6.3\t3.3\tvirginica\t2\n6.5\t3.0\tvirginica\t2\n7.6\t3.0\tvirginica\t2\n6.7\t2.5\tvirginica\t2\n5.7\t2.5\tvirginica\t2\n6.9\t3.2\tvirginica\t2\n6.7\t3.3\tvirginica\t2\n7.2\t3.2\tvirginica\t1\n6.2\t2.8\tvirginica\t2\n6.1\t3.0\tvirginica\t2\n7.2\t3.0\tvirginica\t1\n7.9\t3.8\tvirginica\t1\n6.0\t3.0\tvirginica\t2\n6.9\t3.1\tvirginica\t2\n5.8\t2.7\tvirginica\t2\n6.2\t3.4\tvirginica\t2\n5.9\t3.0\tvirginica\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Use the model to extract the features (in this case there are two)","message":["%python","","z.show(nmf_mod_details.transform(IRIS_TEST, ","                                 supplemental_cols = IRIS_TEST[:, ","                                                               ['Sepal_Length']], ","                                                               topN = 2).sort_values(by = ['Sepal_Length', ","                                                                                           'TOP_1', ","                                                                                           'TOP_1_VAL']))"],"enabled":true,"result":{"startTime":1737152704851,"interpreter":"python.low","endTime":1737152705836,"results":[{"message":"Sepal_Length\tTOP_1\tTOP_1_VAL\tTOP_2\tTOP_2_VAL\n4.4\t2.0\t0.4640412598647486\t1.0\t0.0\n4.4\t2.0\t0.48205070749975476\t1.0\t0.04551789626526008\n4.8\t2.0\t0.4751689824687339\t1.0\t0.08387423104029225\n4.8\t2.0\t0.5103715990146052\t1.0\t0.10187994337557373\n4.9\t2.0\t0.4054515638134286\t1.0\t0.08463053258080423\n5.0\t2.0\t0.44268094272018427\t1.0\t0.11952411722275179\n5.0\t2.0\t0.448051484516526\t1.0\t0.10663485080969141\n5.1\t2.0\t0.46702754297091664\t1.0\t0.1842263710849203\n5.2\t2.0\t0.4412505995275515\t1.0\t0.17847013228958625\n5.2\t2.0\t0.45368430229013673\t1.0\t0.18430527012309156\n5.4\t1.0\t0.6499526498070691\t2.0\t0.5825217627358026\n5.4\t2.0\t0.506702757504397\t1.0\t0.19897675269807696\n5.5\t1.0\t0.6663969384059616\t2.0\t0.4314427691076418\n5.5\t1.0\t0.6883980842021493\t2.0\t0.4339864468719251\n5.6\t1.0\t0.6441707999471009\t2.0\t0.5538353891251782\n5.6\t1.0\t0.7065840726377054\t2.0\t0.45496962012396946\n5.7\t1.0\t0.7430035656071692\t2.0\t0.4564416223827207\n5.7\t2.0\t0.9057475835340096\t1.0\t0.5875678025287024\n5.8\t2.0\t0.898029318555704\t1.0\t0.6913492833143242\n5.9\t2.0\t0.8660619462488786\t1.0\t0.7618645042396146\n6.0\t1.0\t0.7179716673379009\t2.0\t0.5320325991132174\n6.0\t2.0\t0.8206681476035247\t1.0\t0.7582295835872603\n6.1\t1.0\t0.7814865812553904\t2.0\t0.5078737621583218\n6.1\t2.0\t0.8145080593621481\t1.0\t0.7503520266489467\n6.2\t2.0\t0.7899818817438358\t1.0\t0.7465080857909812\n6.2\t2.0\t1.0920418890298853\t1.0\t0.7593724727062061\n6.3\t2.0\t1.1832986856044607\t1.0\t0.63385831322487\n6.5\t1.0\t0.7348203160798916\t2.0\t0.4837531078966465\n6.5\t2.0\t1.0197383466441023\t1.0\t0.6930731704138097\n6.6\t1.0\t0.797151023093405\t2.0\t0.40153122583564926\n6.7\t1.0\t0.7921518174053821\t2.0\t0.616376842986629\n6.7\t2.0\t0.8279783466871486\t1.0\t0.7478711549532522\n6.7\t2.0\t0.981497979338616\t1.0\t0.8049438277858421\n6.9\t2.0\t0.9433283726762365\t1.0\t0.8494049418513676\n6.9\t2.0\t1.042632242983969\t1.0\t0.7869409806297116\n7.2\t1.0\t0.9150118568162859\t2.0\t0.8503300457722371\n7.2\t1.0\t0.9381115785667398\t2.0\t0.7452066956074388\n7.6\t2.0\t0.9807565276082126\t1.0\t0.8645083826943603\n7.9\t1.0\t1.0482874881012707\t2.0\t0.9477442120749615\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md\r","\r","#### Feature Compare\r","\r","Use `feature_compare(x, compare_cols=None, supplemental_cols=None)` to compare features of data and generate relatedness.\r","\r","**Parameters**\r","* `x` [an OML object] The data used to measure relatedness.\r","* `compare_cols` [str, a list of str or None (default)] The column(s) used to measure data relatedness. If None, all the columns of `x` are compared to measure relatedness.\r","* `supplemental_cols` [a list of str or None (default)] A list of columns to display along with the resulting \u2018SIMILARITY\u2019 column.\r","\r","**Returns**\r","* `pred` Contains a \u2018SIMILARITY\u2019 column that measures relatedness and supplementary columns if specified.\r"],"enabled":true,"result":{"startTime":1737152706307,"interpreter":"md.low","endTime":1737152706749,"results":[{"message":"<h4 id=\"feature-compare\">Feature Compare<\/h4>\n<p>Use <code>feature_compare(x, compare_cols=None, supplemental_cols=None)<\/code> to compare features of data and generate relatedness.<\/p>\n<p><strong>Parameters<\/strong><\/p>\n<ul>\n<li><code>x<\/code> [an OML object] The data used to measure relatedness.<\/li>\n<li><code>compare_cols<\/code> [str, a list of str or None (default)] The column(s) used to measure data relatedness. If None, all the columns of <code>x<\/code> are compared to measure relatedness.<\/li>\n<li><code>supplemental_cols<\/code> [a list of str or None (default)] A list of columns to display along with the resulting \u2018SIMILARITY\u2019 column.<\/li>\n<\/ul>\n<p><strong>Returns<\/strong><\/p>\n<ul>\n<li><code>pred<\/code> Contains a \u2018SIMILARITY\u2019 column that measures relatedness and supplementary columns if specified.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare features","message":["%python","","z.show(nmf_mod_details.feature_compare(IRIS_TEST, compare_cols = \"Sepal_Length\",","    supplemental_cols = [\"Species\"]))"],"enabled":true,"result":{"startTime":1737152710776,"interpreter":"python.low","endTime":1737152711359,"results":[{"message":"Species_A\tSpecies_B\tSIMILARITY\nsetosa\tsetosa\t1.0\nsetosa\tsetosa\t0.9466506042923339\nsetosa\tsetosa\t0.9836372821804514\nsetosa\tsetosa\t0.9672745643609025\nsetosa\tsetosa\t0.9345491287218048\nsetosa\tsetosa\t0.9672745643609024\nsetosa\tsetosa\t0.9672745643609024\nsetosa\tsetosa\t0.9466506042923339\nsetosa\tsetosa\t0.9836372821804511\nsetosa\tsetosa\t0.9672745643609025\nsetosa\tversicolor\t0.7545592327067687\nsetosa\tversicolor\t0.7381965148872198\nsetosa\tversicolor\t0.9018236930827074\nsetosa\tversicolor\t0.7218337970676711\nsetosa\tversicolor\t0.8363728218045123\nsetosa\tversicolor\t0.9345491287218048\nsetosa\tversicolor\t0.9181864109022562\nsetosa\tversicolor\t0.9181864109022562\nsetosa\tversicolor\t0.8200101039849635\nsetosa\tversicolor\t0.9018236930827074\nsetosa\tversicolor\t0.8854609752631587\nsetosa\tvirginica\t0.7872846683458663\nsetosa\tvirginica\t0.7545592327067687\nsetosa\tvirginica\t0.5745693366917324\nsetosa\tvirginica\t0.7218337970676711\nsetosa\tvirginica\t0.8854609752631587\nsetosa\tvirginica\t0.6891083614285737\nsetosa\tvirginica\t0.7218337970676711\nsetosa\tvirginica\t0.6400202079699274\nsetosa\tvirginica\t0.803647386165415\nsetosa\tvirginica\t0.8200101039849635\nsetosa\tvirginica\t0.6400202079699274\nsetosa\tvirginica\t0.5254811832330861\nsetosa\tvirginica\t0.8363728218045123\nsetosa\tvirginica\t0.6891083614285737\nsetosa\tvirginica\t0.8690982574436099\nsetosa\tvirginica\t0.803647386165415\nsetosa\tvirginica\t0.852735539624061\nsetosa\tsetosa\t0.9466506042923339\nsetosa\tsetosa\t0.9836372821804514\nsetosa\tsetosa\t0.9672745643609025\nsetosa\tsetosa\t0.9345491287218048\nsetosa\tsetosa\t0.9672745643609024\nsetosa\tsetosa\t0.9672745643609024\nsetosa\tsetosa\t0.9466506042923339\nsetosa\tsetosa\t0.9836372821804511\nsetosa\tsetosa\t0.9672745643609025\nsetosa\tversicolor\t0.7545592327067687\nsetosa\tversicolor\t0.7381965148872198\nsetosa\tversicolor\t0.9018236930827074\nsetosa\tversicolor\t0.7218337970676711\nsetosa\tversicolor\t0.8363728218045123\nsetosa\tversicolor\t0.9345491287218048\nsetosa\tversicolor\t0.9181864109022562\nsetosa\tversicolor\t0.9181864109022562\nsetosa\tversicolor\t0.8200101039849635\nsetosa\tversicolor\t0.9018236930827074\nsetosa\tversicolor\t0.8854609752631587\nsetosa\tvirginica\t0.7872846683458663\nsetosa\tvirginica\t0.7545592327067687\nsetosa\tvirginica\t0.5745693366917324\nsetosa\tvirginica\t0.7218337970676711\nsetosa\tvirginica\t0.8854609752631587\nsetosa\tvirginica\t0.6891083614285737\nsetosa\tvirginica\t0.7218337970676711\nsetosa\tvirginica\t0.6400202079699274\nsetosa\tvirginica\t0.803647386165415\nsetosa\tvirginica\t0.8200101039849635\nsetosa\tvirginica\t0.6400202079699274\nsetosa\tvirginica\t0.5254811832330861\nsetosa\tvirginica\t0.8363728218045123\nsetosa\tvirginica\t0.6891083614285737\nsetosa\tvirginica\t0.8690982574436099\nsetosa\tvirginica\t0.803647386165415\nsetosa\tvirginica\t0.852735539624061\nsetosa\tsetosa\t0.960560382997515\nsetosa\tsetosa\t0.9717106764818138\nsetosa\tsetosa\t0.884398844108294\nsetosa\tsetosa\t0.9161314212880228\nsetosa\tsetosa\t0.9161314212880228\nsetosa\tsetosa\t1.0\nsetosa\tsetosa\t0.931645704419342\nsetosa\tsetosa\t0.9717106764818138\nsetosa\tversicolor\t0.705953191167541\nsetosa\tversicolor\t0.6896424102430023\nsetosa\tversicolor\t0.852232533122587\nsetosa\tversicolor\t0.6733264346111284\nsetosa\tversicolor\t0.7873873401188978\nsetosa\tversicolor\t0.884398844108294\nsetosa\tversicolor\t0.8683501339879499\nsetosa\tversicolor\t0.8683501339879499\nsetosa\tversicolor\t0.7711221602415042\nsetosa\tversicolor\t0.852232533122587\nsetosa\tversicolor\t0.8360663598201141\nsetosa\tvirginica\t0.7385552801818989\nsetosa\tvirginica\t0.705953191167541\nsetosa\tvirginica\t0.5263375150377496\nsetosa\tvirginica\t0.6733264346111284\nsetosa\tvirginica\t0.8360663598201141\nsetosa\tvirginica\t0.6406817300237304\nsetosa\tvirginica\t0.6733264346111284\nsetosa\tvirginica\t0.5916904380526152\nsetosa\tvirginica\t0.754843998661672\nsetosa\tvirginica\t0.7711221602415042\nsetosa\tvirginica\t0.5916904380526152\nsetosa\tvirginica\t0.4773067368464452\nsetosa\tvirginica\t0.7873873401188978\nsetosa\tvirginica\t0.6406817300237304\nsetosa\tvirginica\t0.8198646909493489\nsetosa\tvirginica\t0.754843998661672\nsetosa\tvirginica\t0.8036363123696153\nsetosa\tsetosa\t0.9836372821804511\nsetosa\tsetosa\t0.9181864109022563\nsetosa\tsetosa\t0.9509118465413539\nsetosa\tsetosa\t0.9509118465413539\nsetosa\tsetosa\t0.960560382997515\nsetosa\tsetosa\t0.9672745643609025\nsetosa\tsetosa\t0.9836372821804511\nsetosa\tversicolor\t0.73819651488722\nsetosa\tversicolor\t0.7218337970676714\nsetosa\tversicolor\t0.8854609752631587\nsetosa\tversicolor\t0.7054710792481225\nsetosa\tversicolor\t0.8200101039849638\nsetosa\tversicolor\t0.9181864109022563\nsetosa\tversicolor\t0.9018236930827076\nsetosa\tversicolor\t0.9018236930827076\nsetosa\tversicolor\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Example using Digits dataset","","Use NMF to transform the predictors and then build two neural network models: one using the transformed data and one using the original data to compare confusion matrices. ","","This illustrates the process and you'll notice that the results of pipelining NMF as a preprocessing step before building a neural network does not provide better results. However, it did reduce the number of predictors fed to the neural network from 64 to 16, whcih may have other benefits, including performance, especially at larger scales. "],"enabled":true,"result":{"startTime":1737152713009,"interpreter":"md.low","endTime":1737152713457,"results":[{"message":"<h2 id=\"example-using-digits-dataset\">Example using Digits dataset<\/h2>\n<p>Use NMF to transform the predictors and then build two neural network models: one using the transformed data and one using the original data to compare confusion matrices.<\/p>\n<p>This illustrates the process and you'll notice that the results of pipelining NMF as a preprocessing step before building a neural network does not provide better results. However, it did reduce the number of predictors fed to the neural network from 64 to 16, whcih may have other benefits, including performance, especially at larger scales.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table containing dataset digits from sklearn and generate a unique identifier column","message":["%python","","import oml","import pandas as pd","from sklearn import datasets ","import numpy as np","","digits = datasets.load_digits()","a = []","for i in range(0,digits.data.shape[1]):","    a.append(\"A\"+str(i))","","x = pd.DataFrame(digits.data.astype(np.float64), columns=a)","x.insert(0, 'ID', range(1, len(x) + 1))","y = pd.DataFrame(digits.target.astype(np.float64), columns=['Digit'])","","try: ","  oml.drop(table='DIGITS_NMF')","except: ","  print(\"Table DIGITS_NMF not found\")","  ","DIGITS = oml.create(pd.concat([x, y], axis=1), table = 'DIGITS_NMF')"],"enabled":true,"result":{"startTime":1737152713975,"interpreter":"python.low","endTime":1737152715051,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View a few rows of the data","message":["%python","","z.show(DIGITS.head())"],"enabled":true,"result":{"startTime":1737152715512,"interpreter":"python.low","endTime":1737152716123,"results":[{"message":"ID\tA0\tA1\tA2\tA3\tA4\tA5\tA6\tA7\tA8\tA9\tA10\tA11\tA12\tA13\tA14\tA15\tA16\tA17\tA18\tA19\tA20\tA21\tA22\tA23\tA24\tA25\tA26\tA27\tA28\tA29\tA30\tA31\tA32\tA33\tA34\tA35\tA36\tA37\tA38\tA39\tA40\tA41\tA42\tA43\tA44\tA45\tA46\tA47\tA48\tA49\tA50\tA51\tA52\tA53\tA54\tA55\tA56\tA57\tA58\tA59\tA60\tA61\tA62\tA63\tDigit\n1\t0\t0\t5\t13\t9\t1\t0\t0\t0\t0\t13\t15\t10\t15\t5\t0\t0\t3\t15\t2\t0\t11\t8\t0\t0\t4\t12\t0\t0\t8\t8\t0\t0\t5\t8\t0\t0\t9\t8\t0\t0\t4\t11\t0\t1\t12\t7\t0\t0\t2\t14\t5\t10\t12\t0\t0\t0\t0\t6\t13\t10\t0\t0\t0\t0\n2\t0\t0\t0\t12\t13\t5\t0\t0\t0\t0\t0\t11\t16\t9\t0\t0\t0\t0\t3\t15\t16\t6\t0\t0\t0\t7\t15\t16\t16\t2\t0\t0\t0\t0\t1\t16\t16\t3\t0\t0\t0\t0\t1\t16\t16\t6\t0\t0\t0\t0\t1\t16\t16\t6\t0\t0\t0\t0\t0\t11\t16\t10\t0\t0\t1\n3\t0\t0\t0\t4\t15\t12\t0\t0\t0\t0\t3\t16\t15\t14\t0\t0\t0\t0\t8\t13\t8\t16\t0\t0\t0\t0\t1\t6\t15\t11\t0\t0\t0\t1\t8\t13\t15\t1\t0\t0\t0\t9\t16\t16\t5\t0\t0\t0\t0\t3\t13\t16\t16\t11\t5\t0\t0\t0\t0\t3\t11\t16\t9\t0\t2\n4\t0\t0\t7\t15\t13\t1\t0\t0\t0\t8\t13\t6\t15\t4\t0\t0\t0\t2\t1\t13\t13\t0\t0\t0\t0\t0\t2\t15\t11\t1\t0\t0\t0\t0\t0\t1\t12\t12\t1\t0\t0\t0\t0\t0\t1\t10\t8\t0\t0\t0\t8\t4\t5\t14\t9\t0\t0\t0\t7\t13\t13\t9\t0\t0\t3\n5\t0\t0\t0\t1\t11\t0\t0\t0\t0\t0\t0\t7\t8\t0\t0\t0\t0\t0\t1\t13\t6\t2\t2\t0\t0\t0\t7\t15\t0\t9\t8\t0\t0\t5\t16\t10\t0\t16\t6\t0\t0\t4\t15\t16\t13\t16\t1\t0\t0\t0\t0\t3\t15\t10\t0\t0\t0\t0\t0\t2\t16\t4\t0\t0\t4\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build an NMF model to produce 16 features","message":["%python","","settings = {'FEAT_NUM_FEATURES':16}","nmf_mod_digits = oml.nmf(**settings)","nmf_mod_digits = nmf_mod_digits.fit(DIGITS.drop('Digit'), case_id = 'ID')"],"enabled":true,"result":{"startTime":1737152716600,"interpreter":"python.low","endTime":1737152718486,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use the 'transform' function to produce the reduced feature space to use for modeling with the neural network algorithm","message":["%python","","DIGITS_TRANSFORMED = nmf_mod_digits.transform(DIGITS, supplemental_cols = DIGITS[:, ['ID','Digit']])","","z.show(DIGITS_TRANSFORMED.head(10))"],"enabled":true,"result":{"startTime":1737152718941,"interpreter":"python.low","endTime":1737152720140,"results":[{"message":"ID\tDigit\t'1'\t'2'\t'3'\t'4'\t'5'\t'6'\t'7'\t'8'\t'9'\t'10'\t'11'\t'12'\t'13'\t'14'\t'15'\t'16'\n1.0\t0.0\t0.0\t1.6071504178640184\t1.002599166254152\t1.6250586968971266\tnan\t0.6383334262939638\tnan\tnan\t0.4489665355514332\tnan\t3.8499678436437206\t1.9336331127355935\tnan\t2.2143628851526653\t1.5452149105539648\tnan\n2.0\t1.0\t1.5721304072406956\tnan\t0.47091145820610525\tnan\t0.3205784621943756\t1.320089706218631\t2.766609568935462\t3.0424340682087294\tnan\t2.2904106005997615\tnan\t1.0318897086132979\t2.1326345762323156\tnan\tnan\t0.9160036831823531\n3.0\t2.0\t0.5725843315651238\tnan\t1.225549348721752\t1.9011651955360889\tnan\tnan\t1.7694869170415073\t0.6167196913804647\t0.9914307121462165\t1.4902295100805643\t0.7296931460656757\tnan\t1.3912501600688703\t1.2414607870931396\tnan\tnan\n4.0\t3.0\t0.0\t0.0\tnan\tnan\t0.2521646586899621\tnan\t2.3232424893643118\t2.3789403834776115\tnan\t0.7315338821318872\t0.9405320997877227\t3.4764132163802617\tnan\t2.4939113192694657\tnan\t2.8254895691552497\n5.0\t4.0\t0.2723255065871001\t0.0\t4.371911041493471\t2.380459979440643\tnan\t1.0108417577550652\t1.160166864695847\t3.182334042986028\tnan\t3.8986837453813825\tnan\t1.3756796568582768\tnan\tnan\t0.5939766660870788\tnan\n6.0\t5.0\t1.494530101935104\tnan\tnan\t1.761737438558485\t1.7898300516326007\tnan\t2.1569907285842245\t0.9355171207172428\tnan\tnan\t3.648034167263212\t0.745274498114143\tnan\t2.198762923393277\t1.313588716556601\t0.20652339255341312\n7.0\t6.0\t0.8139881990267508\tnan\t1.4953534411192644\t0.3759157749071318\tnan\t0.4259014673596216\t0.8134758713575312\t2.2999734694390344\tnan\t2.3958535476013805\t2.332573371072175\t3.9273284181493535\tnan\tnan\tnan\t0.17825925453999997\n8.0\t7.0\tnan\t2.6086635289324285\t0.8437846760069846\tnan\t2.5602977787743653\t5.443138649445814\t1.2456694913756754\tnan\t1.1364935411888273\t0.2128601552403819\tnan\tnan\t4.992551751082081\tnan\t1.5849903888814028\t1.0247699788716127\n9.0\t8.0\tnan\tnan\tnan\t1.2188235207234637\t4.168366986465387\tnan\t0.5232456117170995\t0.8125652294908741\t1.6746763345681743\t0.8162256731700024\tnan\t0.5219773403153822\tnan\t1.655567094305008\t1.4564045216852468\t0.09236467826669013\n10.0\t9.0\t0.0\t0.0\t0.22816075076310138\t3.5243255692897306\t2.8187556831795044\tnan\t1.4821820076086663\tnan\t0.5849091822379927\tnan\t1.728146036507437\tnan\tnan\t2.3186566584133876\t2.8988704023113474\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split and prepare the transformed data for modeling","message":["%python","","DIGITS_TRANSFORMED_SPLIT = DIGITS_TRANSFORMED.split(ratio = (0.6,0.4))","DIGITS_TRANSFORMED_TRAIN = DIGITS_TRANSFORMED_SPLIT[0]","DIGITS_TRANSFORMED_TEST  = DIGITS_TRANSFORMED_SPLIT[1]","","DIGITS_TRANSFORMED_TRAIN_X = DIGITS_TRANSFORMED_TRAIN.drop('Digit')","DIGITS_TRANSFORMED_TRAIN_Y = DIGITS_TRANSFORMED_TRAIN['Digit']"],"enabled":true,"result":{"startTime":1737152720622,"interpreter":"python.low","endTime":1737152722009,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split and prepare the original data for modeling","message":["%python","","DIGITS_SPLIT = DIGITS.split(ratio = (0.6,0.4))","DIGITS_TRAIN = DIGITS_SPLIT[0]","DIGITS_TEST  = DIGITS_SPLIT[1]","","DIGITS_TRAIN_X = DIGITS_TRAIN.drop('Digit')","DIGITS_TRAIN_Y = DIGITS_TRAIN['Digit']"],"enabled":true,"result":{"startTime":1737152722517,"interpreter":"python.low","endTime":1737152723280,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a 2-layer neural network using the transformed data","message":["%python","","try:","    oml.drop(model = 'NN_DIGITS_TRANSFORMED_MODEL') ","except:","    pass","","settings = {'NNET_HIDDEN_LAYERS':'2',","            'NNET_NODES_PER_LAYER':'8,4',","            'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\"}","","nn_mod_digits_transformed = oml.nn('classification', **settings)","nn_mod_digits_transformed.fit(DIGITS_TRANSFORMED_TRAIN_X, DIGITS_TRANSFORMED_TRAIN_Y, case_id = 'ID', model_name = 'NN_DIGITS_TRANSFORMED_MODEL')"],"enabled":true,"result":{"startTime":1737152723753,"interpreter":"python.low","endTime":1737152727007,"results":[{"message":"\nModel Name: NN_DIGITS_TRANSFORMED_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: CLASSIFICATION\n\nTarget: Digit\n\nSettings: \n                    setting name                                      setting value\n0                      ALGO_NAME                                ALGO_NEURAL_NETWORK\n1          CLAS_WEIGHTS_BALANCED                                                OFF\n2       LBFGS_GRADIENT_TOLERANCE                                         .000000001\n3            LBFGS_HISTORY_DEPTH                                                 20\n4            LBFGS_SCALE_HESSIAN                         LBFGS_SCALE_HESSIAN_ENABLE\n5               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIO...\n6             NNET_HIDDEN_LAYERS                                                  2\n7                NNET_ITERATIONS                                                200\n8           NNET_NODES_PER_LAYER                                                8,4\n9                 NNET_TOLERANCE                                            .000001\n10                  ODMS_DETAILS                                        ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT                            ODMS_MISSING_VALUE_AUTO\n12              ODMS_RANDOM_SEED                                                  0\n13                 ODMS_SAMPLING                              ODMS_SAMPLING_DISABLE\n14                     PREP_AUTO                                                 ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE         1.07929\n3       NUM_ROWS            1075\n\nAttributes: \n'1'\n'10'\n'11'\n'12'\n'13'\n'14'\n'15'\n'16'\n'2'\n'3'\n'4'\n'5'\n'6'\n'7'\n'8'\n'9'\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0         8  NNET_ACTIVATIONS_BIPOLAR_SIG\n1                1         4         NNET_ACTIVATIONS_TANH\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0        0       0.0       0  ...            None          NaN  0.212613\n1        0       0.0       1  ...            None          NaN  0.561249\n2        0       0.0       2  ...            None          NaN  0.265906\n3        0       0.0       3  ...            None          NaN  1.727418\n4        0       0.0       4  ...            None          NaN -2.343735\n..     ...       ...     ...  ...             ...          ...       ...\n217      2       NaN       5  ...            None          5.0 -3.622288\n218      2       NaN       6  ...            None          6.0 -5.042901\n219      2       NaN       7  ...            None          7.0  1.626482\n220      2       NaN       8  ...            None          8.0  1.711936\n221      2       NaN       9  ...            None          9.0  2.149850\n\n[222 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a 2-layer neural network using the original data","message":["%python","","try:","    oml.drop(model = 'NN_DIGITS_MODEL') ","except:","    pass","","settings = {'NNET_HIDDEN_LAYERS':'2',","            'NNET_NODES_PER_LAYER':'8,4',","            'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\"}","","nn_mod_digits = oml.nn('classification', **settings)","nn_mod_digits.fit(DIGITS_TRAIN_X, DIGITS_TRAIN_Y, case_id = 'ID', model_name = 'NN_DIGITS_MODEL')"],"enabled":true,"result":{"startTime":1737152727480,"interpreter":"python.low","endTime":1737152732076,"results":[{"message":"\nModel Name: NN_DIGITS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: CLASSIFICATION\n\nTarget: Digit\n\nSettings: \n                    setting name                                      setting value\n0                      ALGO_NAME                                ALGO_NEURAL_NETWORK\n1          CLAS_WEIGHTS_BALANCED                                                OFF\n2       LBFGS_GRADIENT_TOLERANCE                                         .000000001\n3            LBFGS_HISTORY_DEPTH                                                 20\n4            LBFGS_SCALE_HESSIAN                         LBFGS_SCALE_HESSIAN_ENABLE\n5               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIO...\n6             NNET_HIDDEN_LAYERS                                                  2\n7                NNET_ITERATIONS                                                200\n8           NNET_NODES_PER_LAYER                                                8,4\n9                 NNET_TOLERANCE                                            .000001\n10                  ODMS_DETAILS                                        ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT                            ODMS_MISSING_VALUE_AUTO\n12              ODMS_RANDOM_SEED                                                  0\n13                 ODMS_SAMPLING                              ODMS_SAMPLING_DISABLE\n14                     PREP_AUTO                                                 ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE         0.11884\n3       NUM_ROWS            1055\n\nAttributes: \nA0\nA1\nA10\nA11\nA12\nA13\nA14\nA15\nA16\nA17\nA18\nA19\nA2\nA20\nA21\nA22\nA23\nA24\nA25\nA26\nA27\nA28\nA29\nA3\nA30\nA31\nA32\nA33\nA34\nA35\nA36\nA37\nA38\nA39\nA4\nA40\nA41\nA42\nA43\nA44\nA45\nA46\nA47\nA48\nA49\nA5\nA50\nA51\nA52\nA53\nA54\nA55\nA56\nA57\nA58\nA59\nA6\nA60\nA61\nA62\nA63\nA7\nA8\nA9\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0         8  NNET_ACTIVATIONS_BIPOLAR_SIG\n1                1         4         NNET_ACTIVATIONS_TANH\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0        0       0.0       0  ...            None          NaN -0.179083\n1        0       0.0       1  ...            None          NaN  0.036176\n2        0       0.0       2  ...            None          NaN  0.081140\n3        0       0.0       3  ...            None          NaN  0.073680\n4        0       0.0       4  ...            None          NaN -0.283180\n..     ...       ...     ...  ...             ...          ...       ...\n601      2       NaN       5  ...            None          5.0 -1.210018\n602      2       NaN       6  ...            None          6.0 -2.678064\n603      2       NaN       7  ...            None          7.0 -4.357364\n604      2       NaN       8  ...            None          8.0  9.371522\n605      2       NaN       9  ...            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DIGITS_TEST[['ID','Digit']])"],"enabled":true,"result":{"startTime":1737152732591,"interpreter":"python.low","endTime":1737152734182,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Review each dataset"],"enabled":true,"result":{"startTime":1737152734646,"interpreter":"md.low","endTime":1737152735095,"results":[{"message":"<h3 id=\"review-each-dataset\">Review each dataset<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(PRED_DIGITS_TRANSFORMED.head())"],"enabled":true,"result":{"startTime":1737152735566,"interpreter":"python.low","endTime":1737152736657,"results":[{"message":"ID\tDigit\tPREDICTION\n149\t8\t3\n473\t7\t1\n559\t1\t1\n700\t8\t3\n760\t2\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(PRED_DIGITS.head())"],"enabled":true,"result":{"startTime":1737152737139,"interpreter":"python.low","endTime":1737152737701,"results":[{"message":"ID\tDigit\tPREDICTION\n1\t0\t0\n5\t4\t4\n8\t7\t7\n9\t8\t8\n13\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute the mean accuracy of the model using transformed data","message":["%python","","nn_mod_digits_transformed.score(DIGITS_TRANSFORMED_TEST.drop('Digit'), DIGITS_TRANSFORMED_TEST['Digit'])"],"enabled":true,"result":{"startTime":1737152738187,"interpreter":"python.low","endTime":1737152739797,"results":[{"message":"0.429363\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute the mean accuracy of the model using original data","message":["%python","","nn_mod_digits.score(DIGITS_TEST.drop('Digit'), DIGITS_TEST['Digit'])"],"enabled":true,"result":{"startTime":1737152740296,"interpreter":"python.low","endTime":1737152740934,"results":[{"message":"0.842318\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display the confusion matrix for the scored transformed data","message":["%python","","cm_data_transformed = PRED_DIGITS_TRANSFORMED.crosstab('Digit','PREDICTION').pull()","","df = pd.DataFrame(cm_data_transformed)","","cm = np.zeros((10, 10), dtype=int)","for _, row in df.iterrows():","    digit = row['Digit']","    prediction = row['PREDICTION']","    count = row['count']","    cm[digit][prediction] = count","","labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']","","print('Confusion Matrix:')","print('\\t' + '\\t'.join(labels))","","for i, row in enumerate(cm):","    print(labels[i], end='\\t')","    for count in row:","        print(count, end='\\t')","    print()"],"enabled":true,"result":{"startTime":1737152741406,"interpreter":"python.low","endTime":1737152743498,"results":[{"message":"Confusion Matrix:\n\t0\t1\t2\t3\t4\t5\t6\t7\t8\t9\n0\t52\t4\t0\t6\t0\t0\t0\t8\t1\t7\t\n1\t6\t47\t0\t12\t2\t0\t0\t5\t1\t2\t\n2\t0\t8\t23\t25\t0\t0\t0\t4\t3\t0\t\n3\t1\t18\t5\t35\t1\t1\t0\t8\t1\t5\t\n4\t0\t31\t0\t3\t28\t0\t0\t10\t3\t0\t\n5\t1\t5\t2\t28\t1\t9\t0\t19\t12\t4\t\n6\t0\t14\t0\t6\t12\t2\t30\t0\t3\t14\t\n7\t0\t7\t1\t12\t1\t0\t0\t40\t1\t0\t\n8\t0\t10\t0\t17\t2\t2\t0\t7\t22\t6\t\n9\t2\t13\t0\t17\t0\t0\t0\t1\t9\t24\t\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display the confusion matrix for the scored original data","message":["%python","","cm_data = PRED_DIGITS.crosstab('Digit','PREDICTION').pull()","","df = pd.DataFrame(cm_data)","","cm = np.zeros((10, 10), dtype=int)","for _, row in df.iterrows():","    digit = row['Digit']","    prediction = row['PREDICTION']","    count = row['count']","    cm[digit][prediction] = count","","labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']","","print('Confusion Matrix:')","print('\\t' + '\\t'.join(labels))","","for i, row in enumerate(cm):","    print(labels[i], end='\\t')","    for count in row:","        print(count, end='\\t')","    print()"],"enabled":true,"result":{"startTime":1737152743976,"interpreter":"python.low","endTime":1737152744648,"results":[{"message":"Confusion 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up","message":["%python","","oml.drop('IRIS_NMF')","oml.drop('DIGITS_NMF')"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# End of Script"],"enabled":true,"result":{"startTime":1737152745137,"interpreter":"md.low","endTime":1737152745592,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737152746087,"interpreter":"md.low","endTime":1737152746564,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Extraction SVD","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737153132894,"interpreter":"md.low","endTime":1737153133369,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Feature Extraction: Singular Value Decomposition","","The Oracle Machine Learning for Python function *oml.svd* creates a model that uses the Singular Value Decomposition (SVD) algorithm for feature extraction. ","","In this notebook, we demonstrate how to use the in-database SVD decomposion for feature extraction using the Breast Cancer dataset from the University of Wisconsin. ","We show that Principal Component Analysis (PCA) can be achieved using SVD results. All processing occurs inside Oracle Autonomous Database.","","#### Breast Cancer from the University of Wisconsin dataset (1992)"," - Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. "," - The original dataset contains 31 attributes, including the TARGET attribute that is '1' for malignant tumors, and '0' for benign tumors.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737153133843,"interpreter":"md.low","endTime":1737153134306,"results":[{"message":"<h1 id=\"oml4py-feature-extraction-singular-value-decomposition\">OML4Py Feature Extraction: Singular Value Decomposition<\/h1>\n<p>The Oracle Machine Learning for Python function <em>oml.svd<\/em> creates a model that uses the Singular Value Decomposition (SVD) algorithm for feature extraction.<\/p>\n<p>In this notebook, we demonstrate how to use the in-database SVD decomposion for feature extraction using the Breast Cancer dataset from the University of Wisconsin.\nWe show that Principal Component Analysis (PCA) can be achieved using SVD results. All processing occurs inside Oracle Autonomous Database.<\/p>\n<h4 id=\"breast-cancer-from-the-university-of-wisconsin-dataset-1992\">Breast Cancer from the University of Wisconsin dataset (1992)<\/h4>\n<ul>\n<li>Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.<\/li>\n<li>The original dataset contains 31 attributes, including the TARGET attribute that is '1' for malignant tumors, and '0' for benign tumors.<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-singular-value-decomposition\" target=\"_blank\">OML Singular Value Decomposition<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/singular-value-decomposition.html\" target=\"_blank\">OML4Py Singular Value Decomposition<\/a>"],"enabled":true,"result":{"startTime":1737153134785,"interpreter":"md.low","endTime":1737153135234,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-singular-value-decomposition\" target=\"_blank\">OML Singular Value Decomposition<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/singular-value-decomposition.html\" target=\"_blank\">OML4Py Singular Value Decomposition<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Initialize the environment","message":["%python","","import oml","import warnings","import pandas as pd","import matplotlib.pyplot as plt","plt.style.use('seaborn-v0_8')","","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737153135706,"interpreter":"python.low","endTime":1737153136177,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load data from Scikit-Learn","message":["%python","","from sklearn import datasets","bc = datasets.load_breast_cancer()","","bc_data = bc.data.astype(float)","X = pd.DataFrame(bc_data, columns = bc.feature_names)","y = pd.DataFrame(bc.target, columns = ['TARGET'])"],"enabled":true,"result":{"startTime":1737153136655,"interpreter":"python.low","endTime":1737153137115,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Push the dataset to Oracle Database","message":["%python","","try:","    oml.drop(table='BREASTCANCER')","except:","    pass","","BREASTCANCER = oml.create(pd.concat([X, y], axis=1), table = 'BREASTCANCER')","print(\"Shape:\",BREASTCANCER.shape)"],"enabled":true,"result":{"startTime":1737153137580,"interpreter":"python.low","endTime":1737153138354,"results":[{"message":"Shape: (569, 31)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build an SVD model","message":["%python","","try:","    oml.drop(model='SVD_TEST_MODEL')","except:","    pass","","# Create an oml SVD model object","svd_mod = oml.svd(ODMS_DETAILS = 'ODMS_ENABLE',","                  PREP_AUTO = 'ON',","                  SVDS_SCORING_MODE = 'SVDS_SCORING_PCA')","                  ","# Fit the model according to all data","svd_mod = svd_mod.fit(BREASTCANCER, model_name='SVD_TEST_MODEL')","","            "],"enabled":true,"result":{"startTime":1737153138818,"interpreter":"python.low","endTime":1737153140818,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%python","","svd_mod"],"enabled":true,"result":{"startTime":1737153141272,"interpreter":"python.low","endTime":1737153141882,"results":[{"message":"\nModel Name: SVD_TEST_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Singular Value Decomposition\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                   setting name               setting value\n0                     ALGO_NAME  ALGO_SINGULAR_VALUE_DECOMP\n1                  ODMS_DETAILS                 ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                          ON\n5             SVDS_SCORING_MODE            SVDS_SCORING_PCA\n6          SVDS_U_MATRIX_OUTPUT       SVDS_U_MATRIX_DISABLE\n\nComputed Settings: \n        setting name                   setting value\n0  FEAT_NUM_FEATURES                              25\n1        SVDS_SOLVER             SVDS_SOLVER_TSEIGEN\n2     SVDS_TOLERANCE  .00000000000012634338020234281\n\nGlobal Statistics: \n     attribute name  attribute value\n0    NUM_COMPONENTS               25\n1          NUM_ROWS              569\n2  SUGGESTED_CUTOFF                1\n\nAttributes: \nTARGET\narea error\ncompactness error\nconcave points error\nconcavity error\nfractal dimension error\nmean area\nmean compactness\nmean concave points\nmean concavity\nmean fractal dimension\nmean perimeter\nmean radius\nmean smoothness\nmean symmetry\nmean texture\nperimeter error\nradius error\nsmoothness error\nsymmetry error\ntexture error\nworst area\nworst compactness\nworst concave points\nworst concavity\nworst fractal dimension\nworst perimeter\nworst radius\nworst smoothness\nworst symmetry\nworst texture\n\nPartition: NO\n\nFeatures: \n\n     FEATURE_ID        ATTRIBUTE_NAME ATTRIBUTE_VALUE     VALUE\n0             1                TARGET            None -0.000532\n1             1            area error            None  0.055727\n2             1     compactness error            None  0.000006\n3             1  concave points error            None  0.000003\n4             1       concavity error            None  0.000009\n..          ...                   ...             ...       ...\n770          25       worst perimeter            None -0.000075\n771          25          worst radius            None  0.003581\n772          25      worst smoothness            None -0.340700\n773          25        worst symmetry            None  0.004821\n774          25         worst texture            None  0.000326\n\n[775 rows x 4 columns]\n\nD: \n\n    FEATURE_ID         VALUE\n0            1  15876.668139\n1            2   2037.679326\n2            3    632.280632\n3            4    176.227927\n4            5    150.525852\n5            6     41.311758\n6            7     32.116840\n7            8     14.526059\n8            9      9.462479\n9           10      7.128195\n10          11      6.586300\n11          12      3.703427\n12          13      2.062009\n13          14      1.340075\n14          15      1.095587\n15          16      0.866801\n16          17      0.589453\n17          18      0.456002\n18          19      0.364963\n19          20      0.323594\n20          21      0.304512\n21          22      0.208897\n22          23      0.179483\n23          24      0.139897\n24          25      0.126977\n\nV: \n\n             '1'           '2'       '3'  ...      '23'      '24'      '25'\n0  -5.324912e-04  2.202054e-04  0.001755  ...  0.004085  0.002958 -0.000435\n1  -6.509838e-05  3.483700e-04  0.006234  ...  0.001633 -0.004329 -0.001968\n2  -2.621551e-06 -1.611973e-05  0.000051  ...  0.195198 -0.009426  0.013318\n3  -1.241018e-06  1.226473e-05  0.000114  ... -0.128836  0.217987 -0.007298\n4  -8.056458e-07  1.494378e-06  0.000043  ... -0.018033  0.113949 -0.117526\n5  -8.545293e-08  2.896802e-07  0.000024  ...  0.108817  0.079694 -0.000952\n6   1.613564e-06 -5.530724e-05 -0.000041  ...  0.881396  0.075560  0.027621\n7   3.279150e-06  9.360061e-06  0.000048  ... -0.024674  0.031871  0.024283\n8   4.236946e-06 -1.481948e-05  0.000073  ... -0.119796 -0.052958  0.685787\n9   5.519183e-06  1.273576e-05  0.000128  ... -0.078333  0.798889 -0.100266\n10  6.420058e-06 -7.685666e-05 -0.000042  ...  0.029425  0.047782 -0.340700\n11  7.078046e-06 -2.524312e-05  0.000141  ...  0.025302  0.027729 -0.029181\n12  8.870945e-06  2.869205e-05  0.000207  ...  0.010554 -0.321016  0.236705\n13  1.789863e-05 -1.570431e-04 -0.000350  ...  0.022375 -0.016634  0.004821\n14  4.052601e-05 -2.688789e-06  0.000102  ... -0.347789 -0.094321 -0.243709\n15  4.778077e-05  4.635001e-05  0.000036  ... -0.014607  0.378582  0.457087\n16  7.366582e-05 -3.050540e-05 -0.000334  ...  0.045303 -0.084215 -0.103491\n17  8.193995e-05  7.514174e-05  0.000266  ...  0.084089 -0.091895 -0.221197\n18  1.012759e-04 -2.561048e-04 -0.000765  ... -0.028972 -0.082432  0.047812\n19  1.689286e-04 -1.754723e-04 -0.000847  ... -0.038092  0.064095  0.011275\n20  3.137425e-04 -5.386928e-05  0.006062  ...  0.016565 -0.014729 -0.008113\n21  2.196570e-03 -2.881617e-03 -0.006356  ...  0.000425 -0.000510 -0.000076\n22  2.236341e-03  8.196407e-04  0.043856  ...  0.000465 -0.001177 -0.000416\n23  3.067367e-03 -1.321528e-02 -0.031546  ... -0.000367  0.000574  0.000326\n24  5.086231e-03  9.287055e-03 -0.012343  ... -0.011535  0.003228 -0.028207\n25  7.154732e-03 -5.686763e-04 -0.015566  ... 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data - top 3 attributes","message":["%python","","# Bring the data locally for plotting","","BC_PCA_PROJECTIONS = oml.sync(table = \"BC_PCA_PROJECTIONS\")","bc_proj_local = BC_PCA_PROJECTIONS.pull()","","from pandas.plotting import scatter_matrix","","scattermat = scatter_matrix(","    bc_proj_local[['worst area','mean area', 'area error']],","    figsize  = [30, 30],","    marker   = \".\",","    s        = 400,","    diagonal = \"kde\",","    color='red'",")","","for ax in scattermat.ravel():","    ax.set_xlabel(ax.get_xlabel(), fontsize = 24, rotation = 90)","    ax.set_ylabel(ax.get_ylabel(), fontsize = 24, rotation = 90)","    ","_=plt.show()"],"enabled":true,"result":{"startTime":1737153151221,"interpreter":"python.low","endTime":1737153152607,"results":[{"message":"<div style='width:auto;height:auto'><img 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np.meshgrid(x,y)","Z = [0] * (X + Y)","","","# Plot","fig = plt.figure(figsize=[15,13])","ax1 = fig.add_subplot(111, projection='3d')","","ax1.scatter(bc_proj_local['worst area'], ","            bc_proj_local['mean area'], ","            bc_proj_local['area error'],","            marker='o',","            alpha=0.5,","            color='red',","            s=50)","","# Standard Wireframe at Z=0 for reference","ax1.plot_wireframe(X, Y, Z, rstride=10, cstride=10,","                   alpha=0.3,","                   color='gray')","                   ","ax1.set_title(\"Worst Area vs \"+","              \"Mean Area vs \"+","              \"Area Error \\n Breastcancer Dataset\", fontsize=16)","","ax1.set_xlabel(\"Worst Area\")","ax1.set_ylabel(\"Mean Area\")","ax1.set_zlabel(\"Area Error\")","","# Viewing Angle","ax1.view_init(10,120)"],"enabled":true,"result":{"startTime":1737153154774,"interpreter":"python.low","endTime":1737153155404,"results":[{"message":"<div 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np.meshgrid(x,y)","Z = [0] * (X + Y)","","","# Plot","fig = plt.figure(figsize=[15,13])","ax1 = fig.add_subplot(111, projection='3d')","","ax1.scatter(bc_proj_local['PCA_PROJ1'], ","            bc_proj_local['PCA_PROJ2'], ","            bc_proj_local['PCA_PROJ3'],","            marker='o',","            alpha=0.5,","            color='red',","            s=50)","","# Standard Wireframe at Z=0 for reference","ax1.plot_wireframe(X, Y, Z, rstride=10, cstride=10,","                   alpha=0.3,","                   color='gray')","                   ","ax1.set_title(\"First PCA vs \"+","              \"Second PCA vs \"+","              \"Third PCA \\n Breastcancer Dataset\", fontsize=16)","","ax1.set_xlabel(\"First PCA\")","ax1.set_ylabel(\"Second PCA\")","ax1.set_zlabel(\"Third PCA\")","","# Viewing Angle","ax1.view_init(10,120)"],"enabled":true,"result":{"startTime":1737153155906,"interpreter":"python.low","endTime":1737153156536,"results":[{"message":"<div 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Set new parameters and refit the model to produce U matrix output"],"enabled":true,"result":{"startTime":1737153157018,"interpreter":"md.low","endTime":1737153157477,"results":[{"message":"<h3 id=\"set-new-parameters-and-refit-the-model-to-produce-u-matrix-output\">Set new parameters and refit the model to produce U matrix output<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop the table if it exists","message":["%python","","try:","    oml.drop(table='BREASTCANCER_IND')","except:","    pass"],"enabled":true,"result":{"startTime":1737153157955,"interpreter":"python.low","endTime":1737153171886,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table with index variable","message":["%sql","","CREATE TABLE BREASTCANCER_IND as select rownum case_id, b.* from BREASTCANCER b"],"enabled":true,"result":{"startTime":1737153172357,"interpreter":"sql.low","endTime":1737153175142,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the new table with index variable","message":["%python","","BREASTCANCER_IND = oml.sync(table = \"BREASTCANCER_IND\")","z.show(BREASTCANCER_IND.head(10))"],"enabled":true,"result":{"startTime":1737153175612,"interpreter":"python.low","endTime":1737153176179,"results":[{"message":"CASE_ID\tmean radius\tmean texture\tmean perimeter\tmean area\tmean smoothness\tmean compactness\tmean concavity\tmean concave points\tmean symmetry\tmean fractal dimension\tradius error\ttexture error\tperimeter error\tarea error\tsmoothness error\tcompactness error\tconcavity error\tconcave points error\tsymmetry error\tfractal dimension error\tworst radius\tworst texture\tworst perimeter\tworst area\tworst smoothness\tworst compactness\tworst concavity\tworst concave points\tworst symmetry\tworst fractal dimension\tTARGET\n1.0\t17.99\t10.38\t122.8\t1001.0\t0.1184\t0.2776\t0.3001\t0.1471\t0.2419\t0.07871\t1.095\t0.9053\t8.589\t153.4\t0.006399\t0.04904\t0.05373\t0.01587\t0.03003\t0.006193\t25.38\t17.33\t184.6\t2019.0\t0.1622\t0.6656\t0.7119\t0.2654\t0.4601\t0.1189\t0.0\n2.0\t20.57\t17.77\t132.9\t1326.0\t0.08474\t0.07864\t0.0869\t0.07017\t0.1812\t0.05667\t0.5435\t0.7339\t3.398\t74.08\t0.005225\t0.01308\t0.0186\t0.0134\t0.01389\t0.003532\t24.99\t23.41\t158.8\t1956.0\t0.1238\t0.1866\t0.2416\t0.186\t0.275\t0.08902\t0.0\n3.0\t19.69\t21.25\t130.0\t1203.0\t0.1096\t0.1599\t0.1974\t0.1279\t0.2069\t0.05999\t0.7456\t0.7869\t4.585\t94.03\t0.00615\t0.04006\t0.03832\t0.02058\t0.0225\t0.004571\t23.57\t25.53\t152.5\t1709.0\t0.1444\t0.4245\t0.4504\t0.243\t0.3613\t0.08758\t0.0\n4.0\t11.42\t20.38\t77.58\t386.1\t0.1425\t0.2839\t0.2414\t0.1052\t0.2597\t0.09744\t0.4956\t1.156\t3.445\t27.23\t0.00911\t0.07458\t0.05661\t0.01867\t0.05963\t0.009208\t14.91\t26.5\t98.87\t567.7\t0.2098\t0.8663\t0.6869\t0.2575\t0.6638\t0.173\t0.0\n5.0\t20.29\t14.34\t135.1\t1297.0\t0.1003\t0.1328\t0.198\t0.1043\t0.1809\t0.05883\t0.7572\t0.7813\t5.438\t94.44\t0.01149\t0.02461\t0.05688\t0.01885\t0.01756\t0.005115\t22.54\t16.67\t152.2\t1575.0\t0.1374\t0.205\t0.4\t0.1625\t0.2364\t0.07678\t0.0\n6.0\t12.45\t15.7\t82.57\t477.1\t0.1278\t0.17\t0.1578\t0.08089\t0.2087\t0.07613\t0.3345\t0.8902\t2.217\t27.19\t0.00751\t0.03345\t0.03672\t0.01137\t0.02165\t0.005082\t15.47\t23.75\t103.4\t741.6\t0.1791\t0.5249\t0.5355\t0.1741\t0.3985\t0.1244\t0.0\n7.0\t18.25\t19.98\t119.6\t1040.0\t0.09463\t0.109\t0.1127\t0.074\t0.1794\t0.05742\t0.4467\t0.7732\t3.18\t53.91\t0.004314\t0.01382\t0.02254\t0.01039\t0.01369\t0.002179\t22.88\t27.66\t153.2\t1606.0\t0.1442\t0.2576\t0.3784\t0.1932\t0.3063\t0.08368\t0.0\n8.0\t13.71\t20.83\t90.2\t577.9\t0.1189\t0.1645\t0.09366\t0.05985\t0.2196\t0.07451\t0.5835\t1.377\t3.856\t50.96\t0.008805\t0.03029\t0.02488\t0.01448\t0.01486\t0.005412\t17.06\t28.14\t110.6\t897.0\t0.1654\t0.3682\t0.2678\t0.1556\t0.3196\t0.1151\t0.0\n9.0\t13.0\t21.82\t87.5\t519.8\t0.1273\t0.1932\t0.1859\t0.09353\t0.235\t0.07389\t0.3063\t1.002\t2.406\t24.32\t0.005731\t0.03502\t0.03553\t0.01226\t0.02143\t0.003749\t15.49\t30.73\t106.2\t739.3\t0.1703\t0.5401\t0.539\t0.206\t0.4378\t0.1072\t0.0\n10.0\t12.46\t24.04\t83.97\t475.9\t0.1186\t0.2396\t0.2273\t0.08543\t0.203\t0.08243\t0.2976\t1.599\t2.039\t23.94\t0.007149\t0.07217\t0.07743\t0.01432\t0.01789\t0.01008\t15.09\t40.68\t97.65\t711.4\t0.1853\t1.058\t1.105\t0.221\t0.4366\t0.2075\t0.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Set new parameters and refit the model ","message":["%python","","new_setting = {'SVDS_U_MATRIX_OUTPUT':'SVDS_U_MATRIX_ENABLE'}","","svd_mod2 = svd_mod.set_params(**new_setting).fit(BREASTCANCER_IND, case_id = \"CASE_ID\")","svd_mod2"],"enabled":true,"result":{"startTime":1737153176646,"interpreter":"python.low","endTime":1737153188248,"results":[{"message":"\nAlgorithm Name: Singular Value Decomposition\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                   setting name               setting value\n0                     ALGO_NAME  ALGO_SINGULAR_VALUE_DECOMP\n1                  ODMS_DETAILS                 ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                          ON\n5             SVDS_SCORING_MODE            SVDS_SCORING_PCA\n6          SVDS_U_MATRIX_OUTPUT        SVDS_U_MATRIX_ENABLE\n\nComputed Settings: \n        setting name                   setting value\n0  FEAT_NUM_FEATURES                              25\n1        SVDS_SOLVER             SVDS_SOLVER_TSEIGEN\n2     SVDS_TOLERANCE  .00000000000012634338020234281\n\nGlobal Statistics: \n     attribute name  attribute value\n0    NUM_COMPONENTS               25\n1          NUM_ROWS              569\n2  SUGGESTED_CUTOFF                1\n\nAttributes: \nTARGET\narea error\ncompactness error\nconcave points error\nconcavity error\nfractal dimension error\nmean area\nmean compactness\nmean concave points\nmean concavity\nmean fractal dimension\nmean perimeter\nmean radius\nmean smoothness\nmean symmetry\nmean texture\nperimeter error\nradius error\nsmoothness error\nsymmetry error\ntexture error\nworst area\nworst compactness\nworst concave points\nworst concavity\nworst fractal dimension\nworst perimeter\nworst radius\nworst smoothness\nworst symmetry\nworst texture\n\nPartition: NO\n\nFeatures: \n\n     FEATURE_ID        ATTRIBUTE_NAME ATTRIBUTE_VALUE     VALUE\n0             1                TARGET            None -0.000532\n1             1            area error            None  0.055727\n2             1     compactness error            None  0.000006\n3             1  concave points error            None  0.000003\n4             1       concavity error            None  0.000009\n..          ...                   ...             ...       ...\n770          25       worst perimeter            None -0.000075\n771          25          worst radius            None  0.003581\n772          25      worst smoothness            None -0.340700\n773          25        worst symmetry            None  0.004821\n774          25         worst texture            None  0.000326\n\n[775 rows x 4 columns]\n\nD: \n\n    FEATURE_ID         VALUE\n0            1  15876.668139\n1            2   2037.679326\n2            3    632.280632\n3            4    176.227927\n4            5    150.525852\n5            6     41.311758\n6            7     32.116840\n7            8     14.526059\n8            9      9.462479\n9           10      7.128195\n10          11      6.586300\n11          12      3.703427\n12          13      2.062009\n13          14      1.340075\n14          15      1.095587\n15          16      0.866801\n16          17      0.589453\n17          18      0.456002\n18          19      0.364963\n19          20      0.323594\n20          21      0.304512\n21          22      0.208897\n22          23      0.179483\n23          24      0.139897\n24          25      0.126977\n\nV: \n\n             '1'           '2'       '3'  ...      '23'      '24'      '25'\n0  -5.324912e-04  2.202054e-04  0.001755  ...  0.004085  0.002958 -0.000435\n1  -6.509838e-05  3.483700e-04  0.006234  ...  0.001633 -0.004329 -0.001968\n2  -2.621551e-06 -1.611973e-05  0.000051  ...  0.195198 -0.009426  0.013318\n3  -1.241018e-06  1.226473e-05  0.000114  ... -0.128836  0.217987 -0.007298\n4  -8.056458e-07  1.494378e-06  0.000043  ... -0.018033  0.113949 -0.117526\n5  -8.545293e-08  2.896802e-07  0.000024  ...  0.108817  0.079694 -0.000952\n6   1.613564e-06 -5.530724e-05 -0.000041  ...  0.881396  0.075560  0.027621\n7   3.279150e-06  9.360061e-06  0.000048  ... -0.024674  0.031871  0.024283\n8   4.236946e-06 -1.481948e-05  0.000073  ... -0.119796 -0.052958  0.685787\n9   5.519183e-06  1.273576e-05  0.000128  ... -0.078333  0.798889 -0.100266\n10  6.420058e-06 -7.685666e-05 -0.000042  ...  0.029425  0.047782 -0.340700\n11  7.078046e-06 -2.524312e-05  0.000141  ...  0.025302  0.027729 -0.029181\n12  8.870945e-06  2.869205e-05  0.000207  ...  0.010554 -0.321016  0.236705\n13  1.789863e-05 -1.570431e-04 -0.000350  ...  0.022375 -0.016634  0.004821\n14  4.052601e-05 -2.688789e-06  0.000102  ... -0.347789 -0.094321 -0.243709\n15  4.778077e-05  4.635001e-05  0.000036  ... -0.014607  0.378582  0.457087\n16  7.366582e-05 -3.050540e-05 -0.000334  ...  0.045303 -0.084215 -0.103491\n17  8.193995e-05  7.514174e-05  0.000266  ...  0.084089 -0.091895 -0.221197\n18  1.012759e-04 -2.561048e-04 -0.000765  ... -0.028972 -0.082432  0.047812\n19  1.689286e-04 -1.754723e-04 -0.000847  ... -0.038092  0.064095  0.011275\n20  3.137425e-04 -5.386928e-05  0.006062  ...  0.016565 -0.014729 -0.008113\n21  2.196570e-03 -2.881617e-03 -0.006356  ...  0.000425 -0.000510 -0.000076\n22  2.236341e-03  8.196407e-04  0.043856  ...  0.000465 -0.001177 -0.000416\n23  3.067367e-03 -1.321528e-02 -0.031546  ... -0.000367  0.000574  0.000326\n24  5.086231e-03  9.287055e-03 -0.012343  ... -0.011535  0.003228 -0.028207\n25  7.154732e-03 -5.686763e-04 -0.015566  ... -0.005972  0.002353  0.003581\n26  3.507633e-02  6.274807e-02 -0.071671  ...  0.003636 -0.001010  0.003573\n27  4.945764e-02 -1.859872e-04 -0.092316  ...  0.000200  0.000316 -0.000075\n28  5.572716e-02  7.511168e-03  0.990244  ... -0.000069  0.000126  0.000037\n29  5.168264e-01  8.518238e-01 -0.027894  ... -0.000088  0.000007  0.000027\n30  8.520633e-01 -5.197422e-01 -0.039317  ...  0.000026 -0.000024 -0.000015\n\n[31 rows x 25 columns]\n\nU: \n\n     CASE_ID          '1'         '2'  ...      '23'      '24'      '25'\n0          1  1160.142744 -293.917535  ... -0.000174 -0.002600 -0.011546\n1          2  1269.122597   15.630194  ...  0.019741 -0.001881 -0.006898\n2          3   995.794082   39.156723  ... -0.001416  0.004668  0.003891\n3          4  -407.180411  -67.380505  ...  0.007511 -0.000941 -0.007080\n4          5   930.341382  189.340710  ...  0.009554 -0.002682 -0.002662\n..       ...          ...         ...  ...       ...       ...       ...\n564      565  1414.126817  110.222522  ...  0.009525  0.003453  0.002208\n565      566  1045.019040   77.057573  ... -0.002502  0.002890  0.007925\n566      567   314.502046   47.553423  ... -0.003892  0.000005  0.004101\n567      568  1124.858290   34.129218  ... -0.007958 -0.004372 -0.001289\n568      569  -771.527711  -88.643113  ... -0.010417 -0.001148 -0.011425\n\n[569 rows x 26 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the oml.svd help file","message":["%python","","help(oml.svd)"],"enabled":true,"result":{"startTime":1737153188712,"interpreter":"python.low","endTime":1737153189234,"results":[{"message":"Help on class svd in module oml.algo.svd:\n\nclass svd(oml.algo.model.odmModel)\n |  svd(model_name=None, model_owner=None, **params)\n |\n |  In-database `Singular Value Decomposition <https://www.oracle.com/machine-learning/singular-value-decomposition>`_ Model\n |\n |  Builds a Singular Value Decomposition (SVD) Model that can be used for feature\n |  extraction. SVD provides orthogonal linear transformations that capture the\n |  underlying variance of the data by decomposing a rectangular matrix into three\n |  matrixes: U, D, and V. Matrix D is a diagonal matrix and its singular values\n |  reflect the amount of data variance captured by the bases. Columns of matrix V\n |  contain the right singular vectors and columns of matrix U contain the left singular\n |  vectors.\n |\n |  :Attributes:\n |\n |      **features** : oml.DataFrame\n |\n |        Features extracted by the fitted model including feature id and associated coefficient.\n |        It includes the following components:\n |\n |            - feature_id: The ID of a feature in the model\n |            - attribute_name: The attribute name\n |            - attribute_value: The attribute value\n |            - value: The matrix entry value\n |\n |      **u** : oml.DataFrame\n |\n |        A dataframe whose columns contain the left singular vectors.\n |        The column name is the corresponding feature id.\n |\n |      **v** : oml.DataFrame\n |\n |        A dataframe whose columns contain the right singular vectors.\n |        The column name is the corresponding feature id.\n |\n |      **d** : oml.DataFrame\n |\n |        A dataframe containing the singular values of the input data.\n |        It includes the following components:\n |\n |            - feature_id: The ID of a feature in the model\n |            - value: The singular values of the input data\n |\n |  Method resolution order:\n |      svd\n |      oml.algo.model.odmModel\n |      builtins.object\n |\n |  Methods defined here:\n |\n |  __init__(self, model_name=None, model_owner=None, **params)\n |      Initializes an instance of svd object.\n |\n |      Parameters\n |      ----------\n |      model_name : string or None (default)\n |          The name of an existing database Singular Value Decomposition model to create an oml.svd object from.\n |          The specified database model is not dropped when the oml.svd object is deleted.\n |      model_owner: string or None (default)\n |          The owner name of the existing Singular Value Decomposition model\n |          The current database user by default\n |      params : key-value pairs or dict\n |          Oracle Machine Learning parameter settings. Each list element's name and\n |          value refer to the parameter setting name and value, respectively.\n |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n |          Model Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/dmprg/specifying-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n |          for applicable parameters and valid values. Global and Automatic Data\n |          Preparation Settings in Table 5-5 apply generally to the model.\n |          Mining Function Settings for Feature Extraction and `Algorithm-specific\n |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117>`_\n |          are applicable to Singular Value Decomposition model.\n |\n |  __repr__(self)\n |\n |  feature_compare(self, x, compare_cols=None, supplemental_cols=None)\n |      Compares features of data and generates relatedness.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |          The data used to measure relatedness.\n |      compare_cols : str, a list of str or None (default)\n |          The column(s) used to measure data relatedness.\n |          If None, all the columns of ``x`` are compared to measure relatedness.\n |      supplemental_cols : a list of str or None (default)\n |          A list of columns to display along with the resulting 'SIMILARITY' column.\n |\n |      Returns\n |      -------\n |      pred : oml.DataFrame\n |          Contains a 'SIMILARITY' column that measures relatedness and supplementary columns if specified.\n |\n |  fit(self, x, model_name=None, case_id=None, ctx_settings=None)\n |      Fits an SVD Model according to the training data and parameter settings.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |          Attribute values for building the model.\n |      model_name : string or None (default)\n |          User-specified model name.\n |          The user-specified database model is not dropped when oml.svd object is deleted.\n |          If None, a system-generated model name will be used.\n |          The system-generated model is dropped when oml.svd object is deleted\n |          unless oml.svd object is saved into a datastore.\n |      case_id : string or None (default)\n |          The column name used as case id for building the model.\n |          ``case_id`` and SVDS_U_MATRIX_OUTPUT in ``odm_settings``\n |          must be specified in order to produce matrix U.\n |      ctx_settings : dict or None (default)\n |          A list to specify Oracle Text attribute-specific settings.\n |          This argument is applicable to building models in Oracle Database 12.2 or later.\n |          The name of each list element refers to the text column while the list value\n |          is a scalar string specifying the attribute-specific text transformation.\n |          The valid entries in the string include TEXT, POLICY_NAME, TOKEN_TYPE, and\n |          MAX_FEATURES.\n |\n |  predict(self, x, supplemental_cols=None, topN_attrs=False)\n |      Makes predictions on new data.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |        Attribute values used by the model to generate scores.\n |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n |          Data set presented with the prediction result.\n |          It must be concatenatable with ``x``.\n |      topN_attrs : boolean, positive integer, False (default)\n |          Returns the top N most important features with the corresponding weights.\n |          N is equal to the specified positive integer or 5 if topN_attrs is True.\n |\n |      Returns\n |      -------\n |      pred : oml.DataFrame\n |        Contains the predicted feature index on the new data and the specified ``supplemental_cols``.\n |\n |  transform(self, x, supplemental_cols=None, topN=None)\n |      Performs dimensionality reduction and returns value for each feature on new data.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |        Attribute values used by the model to generate scores.\n |      supplemental_cols : oml.DataFrame, oml.Float, oml.String, or None (default)\n |          Data set presented with the prediction result.\n |          It must be concatenatable with ``x``.\n |      topN : positive integer or None (default)\n |          A positive integer that restricts the returned values to\n |          the specified number of features that have the highest topN values.\n |          If None, all features will be returned.\n |\n |      Returns\n |      -------\n |      pred : oml.DataFrame\n |        Contains the values of new data after the SVD transform and the specified ``supplemental_cols``.\n |\n |  ----------------------------------------------------------------------\n |  Methods inherited from oml.algo.model.odmModel:\n |\n |  export_sermodel(self, table=None, partition=None)\n |      Export model.\n |\n |      Parameters\n |      ----------\n |      table : string or None (default)\n |          A name for the new table where the serialized model is saved.\n |          If None, the serialized model will be saved to a temporary table.\n |      partition : string or None (default)\n |          Name of the partition that needs to be exported.\n |          If partition is None, all partitions are exported\n |\n |      Returns\n |      -------\n |      oml_bytes : an oml.Bytes object\n |        Contains the BLOB content from the model export\n |\n |  get_params(self, params=None, deep=False)\n |      Fetches parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : iterable of strings, None (default)\n |          Names of parameters to fetch. If ``params`` is None,\n |          fetches all settings.\n |      deep : boolean, False (default)\n |          Includes the computed and default parameters or not.\n |\n |      Returns\n |      -------\n |      settings : dict mapping str to str\n |\n |  set_params(self, **params)\n |      Changes parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : dict object mapping str to str\n |          The key should be the name of the setting, and the value should be\n |          the new setting.\n |\n |      Returns\n |      -------\n |      model : the model itself.\n |\n |  ----------------------------------------------------------------------\n |  Readonly properties inherited from oml.algo.model.odmModel:\n |\n |  model_owner\n |      The owner name of database mining model\n |\n |  ----------------------------------------------------------------------\n |  Data descriptors inherited from oml.algo.model.odmModel:\n |\n |  __dict__\n |      dictionary for instance variables\n |\n |  __weakref__\n |      list of weak references to the object\n |\n |  model_name\n |      The given name of database mining model\n |\n |  pivot_limit\n |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1737153189705,"interpreter":"md.low","endTime":1737153190152,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737153190628,"interpreter":"md.low","endTime":1737153191075,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Selection Algorithm-based ","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736204154766,"interpreter":"md.low","endTime":1736204155207,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Feature Selection: Supervised Algorithm","","In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms via OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We build a random forest model to predict whether the customer by insurance or not, then use feature importance to perform feature selection. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a prerequisite to the current notebook.","","---","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736204155660,"interpreter":"md.low","endTime":1736204156096,"results":[{"message":"<h1 id=\"oml4py-feature-selection-supervised-algorithm\">OML4Py Feature Selection: Supervised Algorithm<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms via OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We build a random forest model to predict whether the customer by insurance or not, then use feature importance to perform feature selection.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-prerequisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a prerequisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/classification.html#GUID-9F922514-0F8D-42F5-BEB1-F59A09FA1CD2\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/random-forest.html\" target=\"_blank\">OML4Py Random Forest Documentation<\/a>"],"enabled":true,"result":{"startTime":1736204156548,"interpreter":"md.low","endTime":1736204156990,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/classification.html#GUID-9F922514-0F8D-42F5-BEB1-F59A09FA1CD2\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/random-forest.html\" target=\"_blank\">OML4Py Random Forest Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and suppress warnings","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1736204157436,"interpreter":"python.low","endTime":1736204157894,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMER_INSURANCE_LTV table","message":["%python","","CUST_INSUR_LTV_DF = oml.sync(table ='CUSTOMER_INSURANCE_LTV')","z.show(CUST_INSUR_LTV_DF.head(10))"],"enabled":true,"result":{"startTime":1736204158340,"interpreter":"python.low","endTime":1736204158869,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         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\tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529.0\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create Train and Test (60/40 split) datasets","message":["%python","","TRAIN, TEST = CUST_INSUR_LTV_DF.split(ratio = (0.6,0.4))","TRAIN_X     = TRAIN.drop('BUY_INSURANCE')","TRAIN_Y     = TRAIN['BUY_INSURANCE']","TEST_X      = TEST","TEST_Y      = TEST['BUY_INSURANCE']"],"enabled":true,"result":{"startTime":1736204159322,"interpreter":"python.low","endTime":1736204160079,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply Attribute Importance algorithm to obtain the attribute importance","message":["%python","","setting = {'ODMS_SAMPLING':'ODMS_SAMPLING_DISABLE'}","ai_mod = oml.ai(**setting)","ai_mod = ai_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUSTOMER_ID')","z.show(ai_mod.importance.head(10).round(4))"],"enabled":true,"result":{"startTime":1736204160525,"interpreter":"python.low","endTime":1736204162102,"results":[{"message":"variable\timportance\trank\nBANK_FUNDS\t0.2086\t1\nMONEY_MONTLY_OVERDRAWN\t0.1498\t2\nNUM_TRANS_ATM\t0.1473\t3\nNUM_TRANS_TELLER\t0.1154\t4\nTOTAL_AUTOM_PAYMENTS\t0.1072\t5\nMONTHLY_CHECKS\t0.0879\t6\nNUM_DEPENDENTS\t0.0301\t7\nCHECKING_BALANCE\t0.0268\t8\nCUSTOMER_TENURE\t0.024\t9\nPROFESSION\t0.015\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Plot the top 10 most important attributes","message":["%python","","z.show(ai_mod.importance.head(10).round(4))"],"enabled":true,"result":{"startTime":1736204162560,"interpreter":"python.low","endTime":1736204163047,"results":[{"message":"variable\timportance\trank\nBANK_FUNDS\t0.2086\t1\nMONEY_MONTLY_OVERDRAWN\t0.1498\t2\nNUM_TRANS_ATM\t0.1473\t3\nNUM_TRANS_TELLER\t0.1154\t4\nTOTAL_AUTOM_PAYMENTS\t0.1072\t5\nMONTHLY_CHECKS\t0.0879\t6\nNUM_DEPENDENTS\t0.0301\t7\nCHECKING_BALANCE\t0.0268\t8\nCUSTOMER_TENURE\t0.024\t9\nPROFESSION\t0.015\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest Model to predict who will purchase insurance","message":["%python","","settings = dict()","rf_mod = oml.rf(**settings)","rf_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUSTOMER_ID')"],"enabled":true,"result":{"startTime":1736204163500,"interpreter":"python.low","endTime":1736204166277,"results":[{"message":"\nAlgorithm Name: Random Forest\n\nMining Function: CLASSIFICATION\n\nTarget: BUY_INSURANCE\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_RANDOM_FOREST\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5               ODMS_RANDOM_SEED                        0\n6                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                      PREP_AUTO                       ON\n8                 RFOR_NUM_TREES                       20\n9            RFOR_SAMPLING_RATIO                       .5\n10          TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n11           TREE_TERM_MAX_DEPTH                       16\n12         TREE_TERM_MINPCT_NODE                      .05\n13        TREE_TERM_MINPCT_SPLIT                       .1\n14         TREE_TERM_MINREC_NODE                       10\n15        TREE_TERM_MINREC_SPLIT                       20\n\nComputed Settings: \n  setting name setting value\n0    RFOR_MTRY            14\n\nGlobal Statistics: \n  attribute name attribute value\n0      AVG_DEPTH           15.85\n1  AVG_NODECOUNT           355.1\n2      MAX_DEPTH              16\n3  MAX_NODECOUNT             375\n4      MIN_DEPTH              16\n5  MIN_NODECOUNT             331\n6       NUM_ROWS            8233\n\nAttributes: \nAGE\nBANK_FUNDS\nCAR_OWNERSHIP\nCHECKING_BALANCE\nCREDIT_BALANCE\nCREDIT_CARD_LIMITS\nCUSTOMER_TENURE\nGENDER\nHAS_CHILDREN\nHOME_OWNERSHIP\nINCOME\nLTV\nMARITAL_STATUS\nMONEY_MONTLY_OVERDRAWN\nMONTHLY_CHECKS\nMORTGAGE_AMOUNT\nNUM_DEPENDENTS\nNUM_MORTGAGES\nNUM_ONLINE_TRANS\nNUM_TRANS_ATM\nNUM_TRANS_KIOSK\nNUM_TRANS_TELLER\nPROFESSION\nTOTAL_AUTOM_PAYMENTS\n\nPartition: NO\n\nImportance: \n\n            ATTRIBUTE_NAME ATTRIBUTE_SUBNAME  ATTRIBUTE_IMPORTANCE\n0                      AGE              None              0.290137\n1               BANK_FUNDS              None              1.181696\n2            CAR_OWNERSHIP              None              0.004010\n3         CHECKING_BALANCE              None              0.805717\n4           CREDIT_BALANCE              None              0.297548\n5       CREDIT_CARD_LIMITS              None              0.235504\n6          CUSTOMER_TENURE              None              0.122713\n7               FIRST_NAME              None              0.000000\n8                   GENDER              None              0.229346\n9             HAS_CHILDREN              None              0.065185\n10          HOME_OWNERSHIP              None              0.019699\n11                  INCOME              None              0.174568\n12               LAST_NAME              None              0.000000\n13                     LTV              None              0.139544\n14                 LTV_BIN              None              0.000000\n15          MARITAL_STATUS              None              0.038575\n16  MONEY_MONTLY_OVERDRAWN              None              1.092332\n17          MONTHLY_CHECKS              None              0.384617\n18         MORTGAGE_AMOUNT              None              0.226217\n19          NUM_DEPENDENTS              None              0.085717\n20           NUM_MORTGAGES              None              0.011806\n21        NUM_ONLINE_TRANS              None              0.260157\n22           NUM_TRANS_ATM              None              0.522327\n23         NUM_TRANS_KIOSK              None              0.233655\n24        NUM_TRANS_TELLER              None              0.318589\n25              PROFESSION              None              0.109945\n26                  REGION              None              0.000000\n27                   STATE              None              0.000000\n28    TOTAL_AUTOM_PAYMENTS              None              0.865898\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the attribute importance from the trained random forest model","message":["%python","","z.show(rf_mod.importance.sort_values('ATTRIBUTE_IMPORTANCE', ascending = 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End of Script"],"enabled":true,"result":{"startTime":1736204168693,"interpreter":"md.low","endTime":1736204169147,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736204169605,"interpreter":"md.low","endTime":1736204170044,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Feature Selection Using Summary Statistics.dsnb b/machine-learning/notebooks-oml/python/OML4Py Feature Selection Using Summary Statistics.dsnb
new file mode 100644
index 00000000..f73ca23d
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Feature Selection Using Summary Statistics.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Selection Using Summary Statistics","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737153737291,"interpreter":"md.low","endTime":1737153737734,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Feature Selection: Summary Statistics","","In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to use OML4Py to select features based on number of distinct values, null values, proportion of constant values. ","","The dataset `CUSTOMER_INSURANCE_LTV_PY` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu:","##### Templates => Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737153738196,"interpreter":"md.low","endTime":1737153738633,"results":[{"message":"<h1 id=\"oml4py-feature-selection-summary-statistics\">OML4Py Feature Selection: Summary Statistics<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to use OML4Py to select features based on number of distinct values, null values, proportion of constant values.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_PY<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu:<\/h5>\n<h5 id=\"templates--examples-and-is-a-pre-requisite-to-the-current-notebook\">Templates =&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/prepare-data.html\" target=\"_blank\">OML4Py Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1737153739139,"interpreter":"md.low","endTime":1737153739598,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/prepare-data.html\" target=\"_blank\">OML4Py Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1737153740115,"interpreter":"python.low","endTime":1737153742686,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":{"startTime":1737153743119,"interpreter":"python.low","endTime":1737153744069,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from the table","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1737153744509,"interpreter":"python.low","endTime":1737153745071,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146.0\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076.0\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877.0\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Identify categorical columns","message":["%python","","cat_cols = CUST_DF.select_types([oml.core.string.String]).columns","print(cat_cols)"],"enabled":true,"result":{"startTime":1737153745527,"interpreter":"python.low","endTime":1737153745995,"results":[{"message":"['MARITAL_STATUS', 'STATE', 'CUSTOMER_ID', 'GENDER', 'PROFESSION', 'REGION', 'BUY_INSURANCE', 'LTV_BIN', 'FIRST_NAME', 'LAST_NAME']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Identify numeric columns","message":["%python","","num_cols = CUST_DF.select_types([oml.core.string.Float]).columns","print(num_cols)"],"enabled":true,"result":{"startTime":1737153746450,"interpreter":"python.low","endTime":1737153746912,"results":[{"message":"['MONEY_MONTLY_OVERDRAWN', 'LTV']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique values in each column","message":["%python","","unique_counts = CUST_DF.nunique()","unique_df = pd.DataFrame(unique_counts)","unique_df = unique_df.reset_index()","unique_df.columns = ['FEATURE', 'CNT']","z.show(unique_df.head(10))"],"enabled":true,"result":{"startTime":1737153747354,"interpreter":"python.low","endTime":1737153748038,"results":[{"message":"FEATURE\tCNT\nMARITAL_STATUS\t7\nSTATE\t24\nCREDIT_BALANCE\t963\nCUSTOMER_TENURE\t5\nMORTGAGE_AMOUNT\t1763\nBANK_FUNDS\t1178\nNUM_DEPENDENTS\t7\nHAS_CHILDREN\t2\nINCOME\t6374\nCUSTOMER_ID\t13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter categorical 'identifier' columns - filter out columns with > 95% unique values","message":["%python","","selected_cat_cols = []","total = CUST_DF.shape[0]","ratio = 0.95","for col in cat_cols:","    cnt = unique_df[unique_df['FEATURE'] == col]['CNT'].values[0]","    if cnt > 1 and cnt < ratio*total:","        selected_cat_cols.append(col)","print(selected_cat_cols)","print(len(selected_cat_cols))"],"enabled":true,"result":{"startTime":1737153748494,"interpreter":"python.low","endTime":1737153748969,"results":[{"message":"['MARITAL_STATUS', 'STATE', 'CUSTOMER_ID', 'GENDER', 'PROFESSION', 'REGION', 'BUY_INSURANCE', 'LTV_BIN', 'FIRST_NAME', 'LAST_NAME']\n10\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"To confirm selected column is not an 'identifier',  use crosstab function to display unique values","message":["%python","","col = 'MARITAL_STATUS'","CUST_DF.crosstab([col])"],"enabled":true,"result":{"startTime":1737153749417,"interpreter":"python.low","endTime":1737153750108,"results":[{"message":"  MARITAL_STATUS  count\n0        WIDOWED    475\n1              M    359\n2          OTHER     96\n3            DIV    337\n4       DIVORCED   2063\n5           None   6627\n6        MARRIED   2393\n7         SINGLE   2530\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create an OML dataframe with selected features only","message":["%python","","SELECTED_CAT_DF = CUST_DF[selected_cat_cols]","z.show(SELECTED_CAT_DF.head())"],"enabled":true,"result":{"startTime":1737153750558,"interpreter":"python.low","endTime":1737153751064,"results":[{"message":"MARITAL_STATUS\tSTATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nSINGLE\tCA                        \tCU8720                    \tM                         \tWaiter/Waitress\tWest\tYes\tMEDIUM\tKENNETH\tNADEAU\nDIVORCED\tWA                        \tCU8721                    \tM                         \tSoftware Engineer\tWest\tYes\tMEDIUM\tJARVIS\tCLARKE\nDIVORCED\tNY                        \tCU8722                    \tF                         \tIT Staff\tNorthEast\tNo\tVERY HIGH\tSTACEY\tHENSON\nMARRIED\tMI                        \tCU8665                    \tF                         \tDentist\tMidwest\tYes\tHIGH\tMARGRET\tRYAN\nMARRIED\tMI                        \tCU8666                    \tM                         \tIT Staff\tMidwest\tNo\tHIGH\tJOAN\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For all columns, identify columns with > 40% missing values","message":["%python","","many_missing_cols = []","ratio = 0.4","total = CUST_DF.shape[0]","threshold = total*ratio","for col in CUST_DF.columns:","    grp = CUST_DF.crosstab([col])","    if grp[ grp[col].isnull()]:","        if grp[ grp[col].isnull()].pull()['count'][0] > threshold:","            many_missing_cols.append(col)","print(many_missing_cols)    "],"enabled":true,"result":{"startTime":1737153751515,"interpreter":"python.low","endTime":1737153757397,"results":[{"message":"['MARITAL_STATUS', 'CREDIT_BALANCE', 'BANK_FUNDS', 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13859\n['CAR_OWNERSHIP']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create an OML dataframe with 'constant' columns filtered out","message":["%python","","selected_cols = [ col for col in CUST_DF.columns if col not in major_constant_cols ]","SELECTED_DF = CUST_DF[selected_cat_cols]","z.show(SELECTED_DF.head())"],"enabled":true,"result":{"startTime":1737153788791,"interpreter":"python.low","endTime":1737153789299,"results":[{"message":"MARITAL_STATUS\tSTATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nSINGLE\tCA                        \tCU8720                    \tM                         \tWaiter/Waitress\tWest\tYes\tMEDIUM\tKENNETH\tNADEAU\nDIVORCED\tWA                        \tCU8721                    \tM                         \tSoftware Engineer\tWest\tYes\tMEDIUM\tJARVIS\tCLARKE\nDIVORCED\tNY                        \tCU8722                    \tF                         \tIT Staff\tNorthEast\tNo\tVERY HIGH\tSTACEY\tHENSON\nMARRIED\tMI                        \tCU8665                    \tF                         \tDentist\tMidwest\tYes\tHIGH\tMARGRET\tRYAN\nMARRIED\tMI                        \tCU8666                    \tM                         \tIT Staff\tMidwest\tNo\tHIGH\tJOAN\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737153789864,"interpreter":"md.low","endTime":1737153790289,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737153790863,"interpreter":"md.low","endTime":1737153791361,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Importing Wide Datasets.dsnb b/machine-learning/notebooks-oml/python/OML4Py Importing Wide Datasets.dsnb
new file mode 100644
index 00000000..19bdcd75
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Importing Wide Datasets.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Importing Wide Datasets","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736204675171,"interpreter":"md.medium","endTime":1736204675645,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Importing Wide Datasets using OML4Py","","Oracle Database has a limit of 1,000 columns in a table or view. Machine learning projects may involve datasets, perhaps stored as CSV files, with a higher number of columns. OML4SQL in-database algorithms can handle such wide datasets by converting the columns to a special format known as \"nested columns\". This allows an efficient handling of high dimensional and sparse features, as well as accomodating more than 1000 predictor columns, when building OML machine learning models. However, importing a dataset with the more than 1,000 columns from a data file, such as a CSV file in Object Storage, involves some preprocessing. ","","In this notebook, we demonstrate the process of importing a wide dataset with 2,002 columns from CSV format into an Oracle Database table with nested columns. ","","The approach is to read the file by chunks, convert each chunk to a transactional format into a database table, and then pivot the data into the nested column format using SQL. Note that the nested column format can be used only through OML4SQL, not OML4Py. ","","To improve the efficiency of the CSV loading, the \"index apply\" functionality of OML4Py is also used to speed up the process and make it parallel.","","Copyright (c) 2025 Oracle Corporation                             ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736204676192,"interpreter":"md.medium","endTime":1736204676685,"results":[{"message":"<h1 id=\"importing-wide-datasets-using-oml4py\">Importing Wide Datasets using OML4Py<\/h1>\n<p>Oracle Database has a limit of 1,000 columns in a table or view. Machine learning projects may involve datasets, perhaps stored as CSV files, with a higher number of columns. OML4SQL in-database algorithms can handle such wide datasets by converting the columns to a special format known as &quot;nested columns&quot;. This allows an efficient handling of high dimensional and sparse features, as well as accomodating more than 1000 predictor columns, when building OML machine learning models. However, importing a dataset with the more than 1,000 columns from a data file, such as a CSV file in Object Storage, involves some preprocessing.<\/p>\n<p>In this notebook, we demonstrate the process of importing a wide dataset with 2,002 columns from CSV format into an Oracle Database table with nested columns.<\/p>\n<p>The approach is to read the file by chunks, convert each chunk to a transactional format into a database table, and then pivot the data into the nested column format using SQL. Note that the nested column format can be used only through OML4SQL, not OML4Py.<\/p>\n<p>To improve the efficiency of the CSV loading, the &quot;index apply&quot; functionality of OML4Py is also used to speed up the process and make it parallel.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle nested columns table documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/use-nested-data.html\" target=\"_blank\">OML using nested data documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/embedded-python-execution1.html\" target=\"_blank\">OML4Py embedded Python execution documentation<\/a>"],"enabled":true,"result":{"startTime":1736204677158,"interpreter":"md.medium","endTime":1736204677616,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle nested columns table documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/use-nested-data.html\" target=\"_blank\">OML using nested data documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/embedded-python-execution1.html\" target=\"_blank\">OML4Py embedded Python execution documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import Python libraries","message":["%python","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1736204678082,"interpreter":"python.medium","endTime":1736204680680,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table to hold the data in transactional format","message":["%script ","","BEGIN","     EXECUTE IMMEDIATE 'DROP TABLE TXN_TBL';","     EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","CREATE TABLE TXN_TBL( ID NUMBER, ","                      NAME VARCHAR2(32),","                      VALUE NUMBER","                    );"],"enabled":true,"result":{"startTime":1736204681139,"interpreter":"script.medium","endTime":1736204683520,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable TXN_TBL created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table that holds the label","message":["%script","","BEGIN","     EXECUTE IMMEDIATE 'DROP TABLE WIDE_LABEL_TBL';","     EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","CREATE TABLE WIDE_LABEL_TBL( ID NUMBER, ","                             CLASS INTEGER","                            );","/"],"enabled":true,"result":{"startTime":1736204683976,"interpreter":"script.medium","endTime":1736204684921,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable WIDE_LABEL_TBL created.\n\n\n---------------------------\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create supporting user-defined Python functions","* Function to insert multiple records into a database table","* Function to read the data in partitions and pivot the data into transactions","* Function for generating the partitions for the data","","---"],"enabled":true,"result":{"startTime":1736204685582,"interpreter":"md.medium","endTime":1736204686016,"results":[{"message":"<h3 id=\"create-supporting-user-defined-python-functions\">Create supporting user-defined Python functions<\/h3>\n<ul>\n<li>Function to insert multiple records into a database table<\/li>\n<li>Function to read the data in partitions and pivot the data into transactions<\/li>\n<li>Function for generating the partitions for the data<\/li>\n<\/ul>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Functions to insert multiple records into the database table","message":["%python","","import oracledb","","# Insert records using the provided cursor, the statement, and the records","","def insert_records(cr, records, statement):","    try:        ","        cr.executemany(statement, records)","        cr.connection.commit()","    except cx_Oracle.Error as error:","        print('Insert error:')","        print(error)","","# Append the rows in a partition and call the insert_records function","","def insertDF(df, statement):","    cr = oml.cursor()","","    records = []","    for row in df.itertuples(index=False):","        records.append(row)","    insert_records(cr, records, statement)","    cr.close()    "],"enabled":true,"result":{"startTime":1736204686467,"interpreter":"python.medium","endTime":1736204686920,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Function that writes each partition into transactional format","message":["%python","","import gc ","","def import_helper(idx, url, columns, ncol, partitions, chunk_size):","    # Verifies the partition to read","    read_rows = partitions[idx-1][0]","    # Check the largest index of that partition","    total = partitions[idx-1][1] + 1","    ","    while read_rows < total:","        offset = read_rows + 1","        # Reads the data from the defined chunks","        df = pd.read_csv(url, skiprows = offset, nrows = chunk_size, header = None)","        read_rows += df.shape[0]","        df.columns = columns","        # Pivots the data","        txn_df = pd.melt(df, id_vars = ['ID'], value_vars = columns[:ncol])","        txn_df.columns = ['ID', 'NAME', 'VALUE']","        print('Rows '+str(min(txn_df['ID']))+' to '+str(max(txn_df['ID']))+","              ': data pivoted')","        # Write the pivotted data to a database table ","        statement = \"INSERT INTO TXN_TBL (ID, NAME, VALUE) VALUES(:ID, :NAME, :VALUE)\"","        insertDF(txn_df, statement)","        statement = \"INSERT INTO WIDE_LABEL_TBL (ID, CLASS) VALUES(:ID, :CLASS)\"","        label_df = df[['ID','CLASS']]","        insertDF(label_df, statement)","        print('Rows '+str(min(txn_df['ID']))+' to '+str(max(txn_df['ID']))+","              ': inserted into TXN_TBL '+str(txn_df.shape[0])+' rows and '+str(txn_df.shape[1])+' columns')","        # Cleanup temporary elements used","        del df","        del txn_df","        del label_df","        gc.collect()"],"enabled":true,"result":{"startTime":1736204687373,"interpreter":"python.medium","endTime":1736204687822,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Function for generating the partitions for the data","message":["%python","","def generate_part(rows_to_process, num_partitions):","    segment_size = int(rows_to_process/num_partitions)","    result = []","    for i in range(num_partitions):","        result.append([i*segment_size, (i+1)*segment_size-1])","    result[len(result)-1][1] = rows_to_process-1","    return result"],"enabled":true,"result":{"startTime":1736204688271,"interpreter":"python.medium","endTime":1736204688722,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Non-parallel processing","","In the following example, we process the data in chunks sequentially. This takes a significant amount of time. Later, we show doing this in parallel using embedded Python execution. ","","---"],"enabled":true,"result":{"startTime":1736204689172,"interpreter":"md.medium","endTime":1736204689613,"results":[{"message":"<h3 id=\"non-parallel-processing\">Non-parallel processing<\/h3>\n<p>In the following example, we process the data in chunks sequentially. This takes a significant amount of time. Later, we show doing this in parallel using embedded Python execution.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check that the table is empty before inserting","message":["%sql","","SELECT COUNT(1) FROM TXN_TBL "],"enabled":true,"result":{"startTime":1736204690060,"interpreter":"sql.medium","endTime":1736204690707,"results":[{"message":"COUNT(1)\n0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create separate partition slices to take advantage of parallelism ","message":["%python","","# Define the total number or rows to process from the dataset","# to a fixed number for this example","rows_to_process = 2000","","# Specify the number of partitions to create","num_partitions = 5","","partitions = generate_part(rows_to_process, num_partitions)","print(\"Generated partitions:\\n\")","print(partitions)"],"enabled":true,"result":{"startTime":1736204691158,"interpreter":"python.medium","endTime":1736204691629,"results":[{"message":"Generated partitions:\n\n[[0, 399], [400, 799], [800, 1199], [1200, 1599], [1600, 1999]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Sequential run processing the first 2000 rows","message":["%python","","import ssl","ssl._create_default_https_context = ssl._create_unverified_context","","","url = 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/wide.csv'","","df = pd.read_csv(url, nrows = 0, skiprows = 0)","columns = list(df.columns)","","# Number of numerical columns in the dataset to be processed","ncol = 2000","# Set the number of records to process at a time (per chunk)","chunk_size = 100","","for i in range(1,6):","    import_helper(i, url, columns, ncol, partitions, chunk_size)"],"enabled":true,"result":{"startTime":1736204692142,"interpreter":"python.medium","endTime":1736204906268,"results":[{"message":"Rows 0 to 99: data pivoted\nRows 0 to 99: inserted into TXN_TBL 200000 rows and 3 columns\nRows 100 to 199: data pivoted\nRows 100 to 199: inserted into TXN_TBL 200000 rows and 3 columns\nRows 200 to 299: data pivoted\nRows 200 to 299: inserted into TXN_TBL 200000 rows and 3 columns\nRows 300 to 399: data pivoted\nRows 300 to 399: inserted into TXN_TBL 200000 rows and 3 columns\nRows 400 to 499: data pivoted\nRows 400 to 499: inserted into TXN_TBL 200000 rows and 3 columns\nRows 500 to 599: data pivoted\nRows 500 to 599: inserted into TXN_TBL 200000 rows and 3 columns\nRows 600 to 699: data pivoted\nRows 600 to 699: inserted into TXN_TBL 200000 rows and 3 columns\nRows 700 to 799: data pivoted\nRows 700 to 799: inserted into TXN_TBL 200000 rows and 3 columns\nRows 800 to 899: data pivoted\nRows 800 to 899: inserted into TXN_TBL 200000 rows and 3 columns\nRows 900 to 999: data pivoted\nRows 900 to 999: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1000 to 1099: data pivoted\nRows 1000 to 1099: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1100 to 1199: data pivoted\nRows 1100 to 1199: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1200 to 1299: data pivoted\nRows 1200 to 1299: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1300 to 1399: data pivoted\nRows 1300 to 1399: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1400 to 1499: data pivoted\nRows 1400 to 1499: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1500 to 1599: data pivoted\nRows 1500 to 1599: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1600 to 1699: data pivoted\nRows 1600 to 1699: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1700 to 1799: data pivoted\nRows 1700 to 1799: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1800 to 1899: data pivoted\nRows 1800 to 1899: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1900 to 1999: data pivoted\nRows 1900 to 1999: inserted into TXN_TBL 200000 rows and 3 columns\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show a sample of the resulting transactions","message":["%sql","","select * from TXN_TBL FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1736204906718,"interpreter":"sql.medium","endTime":1736204907372,"results":[{"message":"ID\tNAME\tVALUE\n1431\tCOL1099\t0.5326441388306492\n1432\tCOL1099\t-0.6320951504897115\n1433\tCOL1099\t-2.3234567893419107\n1434\tCOL1099\t-1.9078389732306136\n1435\tCOL1099\t-1.102783064724585\n1436\tCOL1099\t1.3058725061970131\n1437\tCOL1099\t-0.1992226912738213\n1438\tCOL1099\t-0.8792071994810811\n1439\tCOL1099\t-1.0186926489579629\n1440\tCOL1099\t2.2797658364020417\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Parallel processing using Embedded Python Execution","","The performance improvement is relative to the degree of parallelism enabled. Depending on your Autonomous Database instances CPU allocation, you can explicitly choose the number of Python engines to spawn in parallel. ","","---"],"enabled":true,"result":{"startTime":1736204907829,"interpreter":"md.medium","endTime":1736204908272,"results":[{"message":"<h3 id=\"parallel-processing-using-embedded-python-execution\">Parallel processing using Embedded Python Execution<\/h3>\n<p>The performance improvement is relative to the degree of parallelism enabled. Depending on your Autonomous Database instances CPU allocation, you can explicitly choose the number of Python engines to spawn in parallel.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Remove all data before parallel run","message":["%script","","DELETE FROM WIDE_LABEL_TBL;","DELETE FROM TXN_TBL;"],"enabled":true,"result":{"startTime":1736204908723,"interpreter":"script.medium","endTime":1736204922220,"results":[{"message":"\n2,000 rows deleted.\n\n\n---------------------------\n\n4,000,000 rows deleted.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Run in parallel using oml.index_apply","message":["%python","","url = 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/wide.csv'","","df = pd.read_csv(url, nrows = 0, skiprows = 0)","columns = list(df.columns)","","ncol = 2000","chunk_size = 100","_ = oml.index_apply(times = 5, func = import_helper, url = url, columns = columns, ","                    ncol = ncol, partitions = partitions, chunk_size = 100, ","                    graphics = False, parallel = True)"],"enabled":true,"result":{"startTime":1736204922689,"interpreter":"python.medium","endTime":1736205022279,"results":[{"message":"Rows 400 to 499: data pivoted\nRows 400 to 499: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1200 to 1299: data pivoted\nRows 800 to 899: data pivoted\nRows 0 to 99: data pivoted\nRows 800 to 899: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1200 to 1299: inserted into TXN_TBL 200000 rows and 3 columns\nRows 0 to 99: inserted into TXN_TBL 200000 rows and 3 columns\nRows 500 to 599: data pivoted\nRows 500 to 599: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1300 to 1399: data pivoted\nRows 900 to 999: data pivoted\nRows 1300 to 1399: inserted into TXN_TBL 200000 rows and 3 columns\nRows 100 to 199: data pivoted\nRows 900 to 999: inserted into TXN_TBL 200000 rows and 3 columns\nRows 100 to 199: inserted into TXN_TBL 200000 rows and 3 columns\nRows 600 to 699: data pivoted\nRows 600 to 699: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1400 to 1499: data pivoted\nRows 1400 to 1499: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1000 to 1099: data pivoted\nRows 1000 to 1099: inserted into TXN_TBL 200000 rows and 3 columns\nRows 200 to 299: data pivoted\nRows 200 to 299: inserted into TXN_TBL 200000 rows and 3 columns\nRows 700 to 799: data pivoted\nRows 1500 to 1599: data pivoted\nRows 700 to 799: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1500 to 1599: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1100 to 1199: data pivoted\nRows 1100 to 1199: inserted into TXN_TBL 200000 rows and 3 columns\nRows 300 to 399: data pivoted\nRows 300 to 399: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1600 to 1699: data pivoted\nRows 1600 to 1699: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1700 to 1799: data pivoted\nRows 1700 to 1799: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1800 to 1899: data pivoted\nRows 1800 to 1899: inserted into TXN_TBL 200000 rows and 3 columns\nRows 1900 to 1999: data pivoted\nRows 1900 to 1999: inserted into TXN_TBL 200000 rows and 3 columns\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the total data loaded","message":["%sql","","select count(1)","from txn_tbl"],"enabled":true,"result":{"startTime":1736205022742,"interpreter":"sql.medium","endTime":1736205023505,"results":[{"message":"COUNT(1)\n4000000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the table with id = 0","message":["%sql","","SELECT *","FROM txn_tbl","WHERE id = 0","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1736205023959,"interpreter":"sql.medium","endTime":1736205024600,"results":[{"message":"ID\tNAME\tVALUE\n0\tCOL1682\t0.6243604363152515\n0\tCOL1683\t-1.4923725443812914\n0\tCOL1684\t0.5193684643702374\n0\tCOL1685\t1.3536412340869812\n0\tCOL1686\t-0.4162404373568376\n0\tCOL1687\t-1.1446508438535214\n0\tCOL1688\t1.0735717054221396\n0\tCOL1689\t0.772978247549586\n0\tCOL1690\t0.0938844634754019\n0\tCOL1691\t-0.7042062638547963\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check if there are any duplicates","message":["%sql","","SELECT count(distinct name)","FROM TXN_TBL","where id = 0"],"enabled":true,"result":{"startTime":1736205025068,"interpreter":"sql.medium","endTime":1736205025886,"results":[{"message":"COUNT(DISTINCTNAME)\n2000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the label/class and the id","message":["%sql","","SELECT * FROM WIDE_LABEL_TBL FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1736205026339,"interpreter":"sql.medium","endTime":1736205026814,"results":[{"message":"ID\tCLASS\n400\t0\n401\t1\n402\t1\n403\t1\n404\t0\n405\t1\n406\t0\n407\t1\n408\t1\n409\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create the nested column from the transactional data"],"enabled":true,"result":{"startTime":1736205027269,"interpreter":"md.medium","endTime":1736205027713,"results":[{"message":"<h3 id=\"create-the-nested-column-from-the-transactional-data\">Create the nested column from the transactional data<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the nested column table and join the labels","message":["%script","","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE TXN_NESTED_COL_NUM';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE  TXN_NESTED_COL_NUM","  NESTED TABLE txn_nested store AS txn_nested_tab AS ","SELECT m.id, m.CLASS, txn_nested","  FROM WIDE_LABEL_TBL m,","  ( SELECT features.id,","           cast(COLLECT(dm_nested_numerical(features.name,features.value)) ","                        as dm_nested_numericals)  txn_nested","    FROM TXN_TBL features","    group by features.id) n","  WHERE m.id = n.id  ","/"],"enabled":true,"result":{"startTime":1736205028172,"interpreter":"script.medium","endTime":1736205054289,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable TXN_NESTED_COL_NUM created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check that the table contains our nested column","message":["%script","","SELECT * FROM TXN_NESTED_COL_NUM FETCH FIRST 5 ROWS ONLY"],"enabled":true,"result":{"startTime":1736205054750,"interpreter":"script.medium","endTime":1736205056431,"results":[{"message":"ID   CLASS   TXN_NESTED                  \n   1       1 oracle.sql.ARRAY@22a7a3f4   \n   0       1 oracle.sql.ARRAY@63d272e4   \n   2       0 oracle.sql.ARRAY@5b95d195   \n   3       0 oracle.sql.ARRAY@4cfd14c0   \n   9       0 oracle.sql.ARRAY@535aa6a8   \n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Building a Machine Learning model with a Nested Column as input"],"enabled":true,"result":{"startTime":1736205056893,"interpreter":"md.medium","endTime":1736205057332,"results":[{"message":"<h3 id=\"building-a-machine-learning-model-with-a-nested-column-as-input\">Building a Machine Learning model with a Nested Column as input<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a model using the nested column via OML4SQL","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLAS_USING_NESTED_COLUMN');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","","BEGIN","   ","    v_setlst(dbms_data_mining.algo_name) := dbms_data_mining.algo_generalized_linear_model;","    v_setlst(dbms_data_mining.prep_auto) :=  dbms_data_mining.prep_auto_on;","   ","   ","    DBMS_DATA_MINING.CREATE_MODEL2(","        model_name          => 'GLM_CLAS_USING_NESTED_COLUMN',","        mining_function     => dbms_data_mining.classification,","        data_query          => 'select * from TXN_NESTED_COL_NUM',","        set_list            => v_setlst,","        case_id_column_name => 'ID',","        target_column_name  => 'CLASS');","END;","/ "],"enabled":true,"result":{"startTime":1736205057792,"interpreter":"script.medium","endTime":1736205090108,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the attributes used in building the model","message":["%sql","","SELECT attribute_name, attribute_type","  FROM user_mining_model_attributes"," WHERE model_name = 'GLM_CLAS_USING_NESTED_COLUMN'","ORDER BY attribute_name;"],"enabled":true,"result":{"startTime":1736205090572,"interpreter":"sql.medium","endTime":1736205091050,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nCLASS\tCATEGORICAL\nTXN_NESTED\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List of available model views with various model details","message":["%sql","","SELECT *","  FROM user_mining_model_views"," WHERE model_name = 'GLM_CLAS_USING_NESTED_COLUMN'"],"enabled":true,"result":{"startTime":1736205091511,"interpreter":"sql.medium","endTime":1736205092013,"results":[{"message":"MODEL_NAME\tVIEW_NAME\tVIEW_TYPE\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VAGLM_CLAS_USING_NESTED_COLUMN\tGLM Classification Row Diagnostics\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VDGLM_CLAS_USING_NESTED_COLUMN\tGLM Classification Attribute Diagnostics\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VCGLM_CLAS_USING_NESTED_COLUMN\tScoring Cost Matrix\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VTGLM_CLAS_USING_NESTED_COLUMN\tClassification Targets\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VGGLM_CLAS_USING_NESTED_COLUMN\tGlobal Name-Value Pairs\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VSGLM_CLAS_USING_NESTED_COLUMN\tComputed Settings\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VWGLM_CLAS_USING_NESTED_COLUMN\tModel Build Alerts\nGLM_CLAS_USING_NESTED_COLUMN\tDM$VNGLM_CLAS_USING_NESTED_COLUMN\tNormalization and Missing Value Handling\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736205092470,"interpreter":"md.medium","endTime":1736205092914,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736205093370,"interpreter":"md.medium","endTime":1736205093820,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Partitioned Model SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734706431215,"interpreter":"md.low","endTime":1734706431682,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Partitioned Model","","Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. ","Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The applicable sub-model is ","chosen by the system based on the individual row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models. ","","In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. ","The model is then used to predict the target only, then predicting the target with prediction details. ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734706432163,"interpreter":"md.low","endTime":1734706432638,"results":[{"message":"<h1 id=\"oml4py-partitioned-model\">OML4Py Partitioned Model<\/h1>\n<p>Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition.\nSub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The applicable sub-model is\nchosen by the system based on the individual row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models.<\/p>\n<p>In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender.\nThe model is then used to predict the target only, then predicting the target with prediction details.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html\" target=\"_blank\">OML4Py Partitioned Models Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a>"],"enabled":true,"result":{"startTime":1734706433098,"interpreter":"md.low","endTime":1734706433556,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html\" target=\"_blank\">OML4Py Partitioned Models Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import oml","import pandas as pd","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1734706434015,"interpreter":"python.low","endTime":1734706436698,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare CUSTOMER_DEMO dataset using data from the SH schema","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","SUPPLEMENTARY_DEMOGRAPHICS = oml.sync(query = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS')","CUSTOMERS_DEMO = CUSTOMERS.merge(SUPPLEMENTARY_DEMOGRAPHICS, how = \"inner\", on = 'CUST_ID', suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1734706437148,"interpreter":"python.low","endTime":1734706437618,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%python","","CUSTOMERS_DEMO.shape"],"enabled":true,"result":{"startTime":1734706438074,"interpreter":"python.low","endTime":1734706438606,"results":[{"message":"(4500, 12)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUSTOMERS_DEMO data","message":["%python","","z.show(CUSTOMERS_DEMO.head())"],"enabled":true,"result":{"startTime":1734706439068,"interpreter":"python.low","endTime":1734706439576,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create data for training and testing datasets and drop missing values","message":["%python","","dat = CUSTOMERS_DEMO.split(ratio=(0.8, 0.2), seed = 6218945)","","dat[0] = dat[0].dropna()","","train_x = dat[0].drop('YRS_RESIDENCE')","train_y = dat[0]['YRS_RESIDENCE']","test_dat = dat[1]"],"enabled":true,"result":{"startTime":1734706440034,"interpreter":"python.low","endTime":1734706440599,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Partitioning (Shared Settings)","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Requests the building of a partitioned model. ","    The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.  ","> 'odms_partition_columns' : '<Comma separated list of attributes>'","     ","* Controls the parallel building of partitioned models.     ","    ODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all slaves.","    ODMS_PARTITION_BUILD_INTER builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.","    ODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.","> 'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'"],"enabled":true,"result":{"startTime":1734706441060,"interpreter":"md.low","endTime":1734706441534,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-partitioning-shared-settings\">Examples of possible setting overrides for Partitioning (Shared Settings)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Requests the building of a partitioned model.\nThe setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_columns' : '<Comma separated list of attributes>'<\/p>\n<\/blockquote>\n<ul>\n<li>Controls the parallel building of partitioned models.<br />\nODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all slaves.\nODMS_PARTITION_BUILD_INTER builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.\nODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build Partitioned Model using the SVM algorithm to predict YRS_RESIDENCE, partitioned by CUST_GENDER","message":["%python","","try:","    oml.drop(model = 'SVM_PARTITIONED_REGRESSION_MODEL') ","except:","    pass","","setting = {'svms_kernel_function'  :'dbms_data_mining.svms_linear',","           'odms_partition_columns':'CUST_GENDER'}","           ","svm_mod = oml.svm(\"regression\", **setting)"," ","svm_mod.fit(train_x, train_y , model_name = 'SVM_PARTITIONED_REGRESSION_MODEL')"],"enabled":true,"result":{"startTime":1734706441999,"interpreter":"python.low","endTime":1734706444188,"results":[{"message":"\nModel Name: SVM_PARTITIONED_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2           ODMS_MAX_PARTITIONS                          1000\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4        ODMS_PARTITION_COLUMNS                   CUST_GENDER\n5                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n6                     PREP_AUTO                            ON\n7           SVMS_CONV_TOLERANCE                         .0001\n8                  SVMS_EPSILON                            .1\n9          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n  partition name            setting name    setting value\n0              F  SVMS_COMPLEXITY_FACTOR                3\n1              F     SVMS_NUM_ITERATIONS               30\n2              F             SVMS_SOLVER  SVMS_SOLVER_IPM\n3              M  SVMS_COMPLEXITY_FACTOR                3\n4              M     SVMS_NUM_ITERATIONS               30\n5              M             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  partition name attribute name attribute value\n0              F      CONVERGED             YES\n1              F     ITERATIONS              15\n2              F       NUM_ROWS            1213\n3              M      CONVERGED             YES\n4              M     ITERATIONS              16\n5              M       NUM_ROWS            2381\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_ID\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: YES\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict on the test set and display prediction result - note the use of the top level model only","message":["%python","","y_pred_part = svm_mod.predict(test_dat, test_dat)","","z.show(y_pred_part[['PARTITION_NAME', 'PREDICTION'] + y_pred_part.columns].round(4).head())"],"enabled":true,"result":{"startTime":1734706444654,"interpreter":"python.low","endTime":1734706445312,"results":[{"message":"PARTITION_NAME\tPREDICTION\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPARTITION_NAME\nF\t4.3611\t100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\t4.3611\tF\nF\t3.4255\t102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\t3.4255\tF\nM\t3.1581\t103401\tM\tDivorc.\t1975\tI: 170,000 - 189,999\t10000\tHS-grad\t0\t2\tCrafts\t4\t1\t3.1581\tM\nM\t3.6503\t104077\tM\tNeverM\t1975\tL: 300,000 and above\t9000\tProfsc\t0\t2\tProf.\t3\t1\t3.6503\tM\nM\t1.9144\t101940\tM\tNeverM\t1981\tJ: 190,000 - 249,999\t7000\t< Bach.\t0\t1\t?\t2\t1\t1.9144\tM\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show model global statistics for each partitioned sub-model","message":["%python","z.show(svm_mod.global_stats)"],"enabled":true,"result":{"startTime":1734706445771,"interpreter":"python.low","endTime":1734706446221,"results":[{"message":"partition name\tattribute name\tattribute value\nF\tCONVERGED\tYES\nF\tITERATIONS\t15.0F\tNUM_ROWS\t1213.0\nM\tCONVERGED\tYES\nM\tITERATIONS\t16.0\nM\tNUM_ROWS\t2381.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test dataset","message":["%python","","try:","    oml.drop(table = 'TEST_DATA')","except:","    pass","_ = test_dat.materialize(table = 'TEST_DATA')"],"enabled":true,"result":{"startTime":1734706446682,"interpreter":"python.low","endTime":1734706447451,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details","message":["%python","","y_pred_part = svm_mod.predict(test_dat, ","                              supplemental_cols = test_dat[['CUST_ID', 'YRS_RESIDENCE']], ","                              topN_attrs = True)","                              ","z.show(y_pred_part.head())"],"enabled":true,"result":{"startTime":1734706447906,"interpreter":"python.low","endTime":1734706454597,"results":[{"message":"PARTITION_NAME\tCUST_ID\tYRS_RESIDENCE\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\nF\t103200\t3\t3.524589039100317\tCUST_MARITAL_STATUS\tDivorc.\t.108\tEDUCATION\tAssoc-A\t.101\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t.095\tY_BOX_GAMES\t0\t.011\tCUST_CREDIT_LIMIT\t5000\t-.006\nF\t103600\t1\t2.34926350944997\tCUST_CREDIT_LIMIT\t9000\t.002\tY_BOX_GAMES\t1\t-.013\tAFFINITY_CARD\t0\t-.02\tOCCUPATION\tSales\t-.075\tHOUSEHOLD_SIZE\t6-8\t-.114\nF\t100007\t5\t4.092473114061859\tCUST_YEAR_OF_BIRTH\t1963\t.319\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.128\tCUST_MARITAL_STATUS\tDivorc.\t.126\tY_BOX_GAMES\t0\t.013\tCUST_CREDIT_LIMIT\t9000\t.004\nF\t100041\t2\t1.765930950259441\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.049\tCUST_CREDIT_LIMIT\t15000\t.007\tY_BOX_GAMES\t1\t-.009\tAFFINITY_CARD\t0\t-.013\tOCCUPATION\tHandler\t-.141\nF\t100077\t4\t4.1380599983588\tCUST_YEAR_OF_BIRTH\t1959\t.397\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t.086\tCUST_MARITAL_STATUS\tMarried\t.062\tCUST_CREDIT_LIMIT\t15000\t.01\tY_BOX_GAMES\t0\t.007\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface so score data and display prediction details","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","      PREDICTION(SVM_PARTITIONED_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","      PREDICTION_DETAILS(SVM_PARTITIONED_REGRESSION_MODEL USING *) PD","      FROM TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","      XMLTABLE('/Details'","               PASSING OUT.PD","               COLUMNS ","               \"Attribute1\" XMLType PATH 'Attribute[1]',","               \"Attribute2\" XMLType PATH 'Attribute[2]') ","      OUTPRED"],"enabled":true,"result":{"startTime":1734706455066,"interpreter":"sql.low","endTime":1734706456944,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100007\t4.092\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1963\" weight=\".319\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".128\" \n100010\t2.978\t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".004\" \t\"CUST_ID\" actualValue=\"100010\" weight=\"-.001\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1734706457410,"interpreter":"md.low","endTime":1734706457862,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1734706458326,"interpreter":"md.low","endTime":1734706458770,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4Py REST API","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1738090722658,"interpreter":"md.low","endTime":1738090722933,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py REST API for invoking embedded Python execution","","OML4Py contains a REST API to run user-defined Python functions saved in the script repository. The REST API is used when separation between the client and the Database server is beneficial. For example, REST APIs can be called from a web development application, improving the portability of the interface to other types of platforms, increasing project scalability, and allowing components to evolve independently. Use the OML4Py REST API to build, train, deploy, and manage scripts.","","To run a script, it must reside in the OML4Py script repository. An Oracle Machine Learning cloud service account ***username*** and ***password*** must be provided for authentication.","","### **REST API Workflow**","","In this workflow, we highlight a few of the features of the REST API:","","- Authenticating the Database user and obtaining a token","- Loading a database table into the Database","- Saving scripts in the Database Python script repository","- Running the saved Python scripts from the REST API using both synchronous and asynchronous modes","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1738090723100,"interpreter":"md.low","endTime":1738090723310,"results":[{"message":"<h1 id=\"oml4py-rest-api-for-invoking-embedded-python-execution\">OML4Py REST API for invoking embedded Python execution<\/h1>\n<p>OML4Py contains a REST API to run user-defined Python functions saved in the script repository. The REST API is used when separation between the client and the Database server is beneficial. For example, REST APIs can be called from a web development application, improving the portability of the interface to other types of platforms, increasing project scalability, and allowing components to evolve independently. Use the OML4Py REST API to build, train, deploy, and manage scripts.<\/p>\n<p>To run a script, it must reside in the OML4Py script repository. An Oracle Machine Learning cloud service account <em><strong>username<\/strong><\/em> and <em><strong>password<\/strong><\/em> must be provided for authentication.<\/p>\n<h3 id=\"rest-api-workflow\"><strong>REST API Workflow<\/strong><\/h3>\n<p>In this workflow, we highlight a few of the features of the REST API:<\/p>\n<ul>\n<li>Authenticating the Database user and obtaining a token<\/li>\n<li>Loading a database table into the Database<\/li>\n<li>Saving scripts in the Database Python script repository<\/li>\n<li>Running the saved Python scripts from the REST API using both synchronous and asynchronous modes<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"REST API Authentication","message":["%md","","To authenticate and obtain a token, use ***cURL*** with the ***-d*** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' \\","                   -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"' <yourpassword>'\"}' ","                   \"<OML Service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance","","For example, Oracle Machine Learning user ***OMLUSER*** with password ***OMLPASSWD*** and cloud service url ***https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com*** authenticates and obtain a REST token using the following approach:"],"enabled":true,"result":{"startTime":1738090723802,"interpreter":"md.low","endTime":1738090723942,"results":[{"message":"<p>To authenticate and obtain a token, use <em><strong>cURL<\/strong><\/em> with the <em><strong>-d<\/strong><\/em> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' \\\n               -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, &quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' \n               &quot;&lt;OML Service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>oml-cloud-service-url<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n<p>For example, Oracle Machine Learning user <em><strong>OMLUSER<\/strong><\/em> with password <em><strong>OMLPASSWD<\/strong><\/em> and cloud service url <em><strong><a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a><\/strong><\/em> authenticates and obtain a REST token using the following approach:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export token to a variable","message":["%md ","","Copy the \"accessToken\" field from the response and assign it to variable token, surrounded by single quotes.","A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1738090724100,"interpreter":"md.low","endTime":1738090724235,"results":[{"message":"<p>Copy the &quot;accessToken&quot; field from the response and assign it to variable token, surrounded by single quotes.\nA token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ export token='eyJhbGciOiJ..."],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an OML Dataframe and load it to an Oracle Database table","message":["%md","","In this example, we load the **iris** data in Python and load the Dataframe into Oracle Database using the ***create*** function. "],"enabled":true,"result":{"startTime":1738090724346,"interpreter":"md.low","endTime":1738090724446,"results":[{"message":"<p>In this example, we load the <strong>iris<\/strong> data in Python and load the Dataframe into Oracle Database using the <em><strong>create<\/strong><\/em> function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","import oml","import pandas as pd","","from sklearn import datasets","from sklearn.datasets import load_iris","","iris = datasets.load_iris()","x = pd.DataFrame(iris.data, columns = ['SEPAL_LENGTH','SEPAL_WIDTH','PETAL_LENGTH','PETAL_WIDTH'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), columns = ['SPECIES'])","","try:","    oml.drop(table=\"IRIS\")","except:","    pass","    ","oml_iris = oml.create(pd.concat([x, y], axis=1), table = 'IRIS')","index = oml.DataFrame(oml_iris['SPECIES'])","z.show(oml_iris.head(10))"],"enabled":true,"result":{"startTime":1738090724551,"interpreter":"python.low","endTime":1738090728297,"results":[{"message":"SEPAL_LENGTH\tSEPAL_WIDTH\tPETAL_LENGTH\tPETAL_WIDTH\tSPECIES\n5.1\t3.5\t1.4\t0.2\tsetosa\n4.9\t3.0\t1.4\t0.2\tsetosa\n4.7\t3.2\t1.3\t0.2\tsetosa\n4.6\t3.1\t1.5\t0.2\tsetosa\n5.0\t3.6\t1.4\t0.2\tsetosa\n5.4\t3.9\t1.7\t0.4\tsetosa\n4.6\t3.4\t1.4\t0.3\tsetosa\n5.0\t3.4\t1.5\t0.2\tsetosa\n4.4\t2.9\t1.4\t0.2\tsetosa\n4.9\t3.1\t1.5\t0.1\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a Python script and save it to the OML4Py Script Repository","message":["%md","","In this script, we return the counts for each category in the **SPECIES** column and return the results sorted by the **SEPAL_LENGTH** column using ***oml.group_apply***.","Notice the value for ***group_count*** is a string, not a Python function object, however, the results are the same. The script is invoked from the Python interface, followed by the REST API."],"enabled":true,"result":{"startTime":1738090728424,"interpreter":"md.low","endTime":1738090728524,"results":[{"message":"<p>In this script, we return the counts for each category in the <strong>SPECIES<\/strong> column and return the results sorted by the <strong>SEPAL_LENGTH<\/strong> column using <em><strong>oml.group_apply<\/strong><\/em>.\nNotice the value for <em><strong>group_count<\/strong><\/em> is a string, not a Python function object, however, the results are the same. The script is invoked from the Python interface, followed by the REST API.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke script from Python","message":["%python","","group_count = \"\"\"def group_count(dat):","  import oml","  import pandas as pd","  return pd.DataFrame([(dat[\"SPECIES\"][0], dat[\"SEPAL_LENGTH\"][0], dat.shape[0])], \\","  columns = [\"SPECIES\",\"SEPAL_LENGTH\", \"COUNT\"])\"\"\"","","try:","    oml.script.drop(\"group_count\")","except: ","    pass","oml.script.create(\"group_count\", func=group_count, overwrite=True)"],"enabled":true,"result":{"startTime":1738090728654,"interpreter":"python.low","endTime":1738090728830,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Invoke script from the Python API for embedded Python execution","message":["%python","","res = oml.group_apply(oml_iris, index, func=group_count, oml_input_type=\"pandas.DataFrame\",\\","         func_value=pd.DataFrame([('a', 1, 1)], columns=[\"SPECIES\", \"SEPAL_LENGTH\",\"COUNT\"]),\\","             orderby=oml_iris[\"SEPAL_LENGTH\"])","","z.show(res.head(10))"],"enabled":true,"result":{"startTime":1738090728945,"interpreter":"python.low","endTime":1738090730506,"results":[{"message":"SPECIES\tSEPAL_LENGTH\tCOUNT\nsetosa\t4\t16\nvirginica\t5\t1\nsetosa\t5\t1\nversicolor\t5\t1\nsetosa\t5\t11\nversicolor\t5\t2\nsetosa\t5\t3\nversicolor\t5\t1\nsetosa\t5\t6\nversicolor\t5\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the Python script in the OML4Py Script Repository","message":["%md","","We can check for the existence of the saved ***group_count*** script from the Python and REST interfaces.  From Python, run the OML4Py function ***oml.script.dir***."],"enabled":true,"result":{"startTime":1738090730641,"interpreter":"md.low","endTime":1738090730778,"results":[{"message":"<p>We can check for the existence of the saved <em><strong>group_count<\/strong><\/em> script from the Python and REST interfaces.  From Python, run the OML4Py function <em><strong>oml.script.dir<\/strong><\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.script.dir(sctype='all')[['name', 'owner']]"],"enabled":true,"result":{"startTime":1738090730938,"interpreter":"python.low","endTime":1738090731321,"results":[{"message":"          name    owner\n0  group_count  OMLUSER\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Synchronous REST API calls","Now verify the existence of the ***group_count*** function in the script repository from the REST API with the ***cURL*** command. The default REST API execution mode is *synchronous*, so code execution will wait for the REST API call to return before additional calls may be initiated. ","","The Linux command line JSON parser ***jq*** is used to return the script name and owner.  To install jq on your Linux REST client, run the command ***sudo yum install jq***."],"enabled":true,"result":{"startTime":1738090731447,"interpreter":"md.low","endTime":1738090731576,"results":[{"message":"<h2 id=\"synchronous-rest-api-calls\">Synchronous REST API calls<\/h2>\n<p>Now verify the existence of the <em><strong>group_count<\/strong><\/em> function in the script repository from the REST API with the <em><strong>cURL<\/strong><\/em> command. The default REST API execution mode is <em>synchronous<\/em>, so code execution will wait for the REST API call to return before additional calls may be initiated.<\/p>\n<p>The Linux command line JSON parser <em><strong>jq<\/strong><\/em> is used to return the script name and owner.  To install jq on your Linux REST client, run the command <em><strong>sudo yum install jq<\/strong><\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","$ curl -X GET --header \"Authorization: Bearer <token>\" \\","              --header 'Accept: application/json' ","              \"<OML Service URL>/oml/api/py-scripts/v1/scripts\" | jq '.result[] | .name + \" \" + .owner'","","  HTTP/1.1 200 OK","  Date: Wed, 09 Mar 2022 21:44:39 GMT","  Content-Type: application/json","  Transfer-Encoding: chunked","  Connection: keep-alive","  Cache-Control: no-cache, no-store, private","  X-Frame-Options: SAMEORIGIN","  X-XSS-Protection: 1;mode=block","  Strict-Transport-Security: max-age=31536000; includeSubDomains","  X-Content-Type-Options: nosniff","","  \"group_count OWNER\"","  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"TextBox\",\"name\":\"omlserver\",\"displayName\":null,\"defaultValue\":\"\",\"argument\":null,\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Invoke the ***group_count*** function in the script repository from the REST API. Set ***parallelFlag*** to true to leverage Database parallelism."],"enabled":true,"result":{"startTime":1738090731797,"interpreter":"md.low","endTime":1738090731930,"results":[{"message":"<p>Invoke the <em><strong>group_count<\/strong><\/em> function in the script repository from the REST API. Set <em><strong>parallelFlag<\/strong><\/em> to true to leverage Database parallelism.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X POST --header \"Authorization: Bearer <token>\" \\","                  --header 'Content-Type: application/json' \\","                  --header 'Accept: application/json' \\","                  -d '{\"input\":\"select * from IRIS\", \"parameters\":\"{\\\"oml_input_type\\\":\\\"pandas.DataFrame\\\"}\", \"groupBy\":\"SPECIES\", \"orderBy\":\"SEPAL_LENGTH\", \"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/group-apply/group_count\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The result of the REST execution is a JSON representation of the value returned from the Python script. Dataframes, lists, and dictionaries and ","PNG images are supported. The first portion of the returned value is:"],"enabled":true,"result":{"startTime":1738090732048,"interpreter":"md.low","endTime":1738090732154,"results":[{"message":"<p>The result of the REST execution is a JSON representation of the value returned from the Python script. Dataframes, lists, and dictionaries and\nPNG images are supported. The first portion of the returned value is:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","{\"result\":{\"versicolor_8\":{\"SEPAL_LENGTH\":{\"0\":5.7},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":5}},\"versicolor_7\":{\"SEPAL_LENGTH\":{\"0\":5.6},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":2}},\"virginica_20\":{\"SEPAL_LENGTH\":{\"0\":6.7},\"SPECIES\":{\"0\":\"virginica\"},....."],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### **Asynchronous REST API calls**","","*Asynchronous* calls do not wait for the REST API call to return from the server. If the Python script is performing long running computations, results will not be immediate and the user may continue development on the client. The Asynchronous call's returned results are included in the response header. Display the response header by passing the ***-i*** flag to the ***cURL*** command. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes, and can be extended up to 12 hours. Set the ***asyncFlag*** to ***true*** to run asynchronous calls from the REST API.","","  "],"enabled":true,"result":{"startTime":1738090732287,"interpreter":"md.low","endTime":1738090732411,"results":[{"message":"<h3 id=\"asynchronous-rest-api-calls\"><strong>Asynchronous REST API calls<\/strong><\/h3>\n<p><em>Asynchronous<\/em> calls do not wait for the REST API call to return from the server. If the Python script is performing long running computations, results will not be immediate and the user may continue development on the client. The Asynchronous call's returned results are included in the response header. Display the response header by passing the <em><strong>-i<\/strong><\/em> flag to the <em><strong>cURL<\/strong><\/em> command. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes, and can be extended up to 12 hours. Set the <em><strong>asyncFlag<\/strong><\/em> to <em><strong>true<\/strong><\/em> to run asynchronous calls from the REST API.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"asyncFlag\":true, \"input\":\"select * from IRIS\", \"parameter\":\"{\\\"oml_input_type\\\":\\\u201cpandas.DataFrame\\\"}\", \"groupBy\":\"SPECIES\", \"orderBy\":\"SEPAL_LENGTH\", \"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/group-apply/group_count\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Setting the timeout flag for asychronous jobs","message":["%md","","The ***timeout*** flag is used to change the default timeout setting. ","","Below is the equivalent asynchronous REST call with a timeout of 300 seconds, or 5 minutes. The output includes the HTTP 201 status code indicating the job was created and a job id location that can be polled for status and results."],"enabled":true,"result":{"startTime":1738090732564,"interpreter":"md.low","endTime":1738090732672,"results":[{"message":"<p>The <em><strong>timeout<\/strong><\/em> flag is used to change the default timeout setting.<\/p>\n<p>Below is the equivalent asynchronous REST call with a timeout of 300 seconds, or 5 minutes. The output includes the HTTP 201 status code indicating the job was created and a job id location that can be polled for status and results.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"asyncFlag\":true,\"timeout\":300, \"input\":\"select * from IRIS\", \"parameter\":\"{\\\"oml_input_type\\\":\\\"pandas.DataFrame\\\"}\", \"groupBy\":\"SPECIES\", \"orderBy\":\"SEPAL_LENGTH\", \"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/group-apply/group_count\"","","  HTTP/1.1 201 Created","  Location: <OML Service URL>/oml/tenants/MYTENANT/databases/MYADW/api/py-scripts/v1/<job id location>","  Cache-Control: no-cache, no-store, private","  Content-Length: 0","  X-Frame-Options: SAMEORIGIN","  Proxy-Connection: Keep-Alive","  X-XSS-Protection: 1;mode=block","  X-Content-Type-Options: nosniff"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Poll the job status","message":["%md","","To poll the job status, pass the job location to ***cURL*** using the GET method. The HTTP response status 302 indicates the currently executing job is found ","and includes a job id location for future results."],"enabled":true,"result":{"startTime":1738090732791,"interpreter":"md.low","endTime":1738090732951,"results":[{"message":"<p>To poll the job status, pass the job location to <em><strong>cURL<\/strong><\/em> using the GET method. The HTTP response status 302 indicates the currently executing job is found\nand includes a job id location for future results.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -i -X GET --header \"Authorization: Bearer <token>\" \\","                 --header 'Accept: application/json' \\","                 \"<OML Service URL>/oml/api/py-scripts/v1/jobs/<job id location>\"","","  HTTP/1.1 302 Found","  Date: Wed, 09 Mar 2022 13:04:04 GMT","  Server: nginx/1.18.0","  Cache-Control: no-cache, no-store, private","  Content-Length: 0","  X-Frame-Options: SAMEORIGIN","  Content-Location: http://<oml-cloud-service-instance-url>/oml/tenants/TENANT1/databases/ADWP/api/py-scripts/v1/<job id location>/result","  Proxy-Connection: Keep-Alive","  X-XSS-Protection: 1;mode=block","  X-Content-Type-Options: nosniff","  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Poll the job at the results location","message":["  ","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/py-scripts/v1/jobs/<job id location>/result\"","","","  HTTP/1.1 200 OK","  Date: Wed, 09 Mar 2022 13:06:05 GMT","  Server: nginx/1.18.0","  Content-Type: application/json","  Cache-Control: no-cache, no-store, private","  Content-Length: 1038","  X-Frame-Options: SAMEORIGIN","  Proxy-Connection: Keep-Alive","  X-XSS-Protection: 1;mode=block","  X-Content-Type-Options: nosniff","  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"A portion of the returned value","message":["","","{\"result\":{\"versicolor_8\":{\"SEPAL_LENGTH\":{\"0\":5.7},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":5}},\"versicolor_7\":{\"SEPAL_LENGTH\":{\"0\":5.6},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":2}},\"virginica_20\":{\"SEPAL_LENGTH\":{\"0\":6.7},\"SPECIES\":{\"0\":\"virginica\"},....."],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### **Returning Model Details from the REST API**","","Let's create a linear model script and run it from the REST API. First, we'll define our function, ***build_lm_rest***, that returns a dict mapping of the model attributes. Note that the model itself cannot be returned directly from REST because LinearRegression models are not JSON-serializable. "],"enabled":true,"result":{"startTime":1738090733135,"interpreter":"md.low","endTime":1738090733234,"results":[{"message":"<h3 id=\"returning-model-details-from-the-rest-api\"><strong>Returning Model Details from the REST API<\/strong><\/h3>\n<p>Let's create a linear model script and run it from the REST API. First, we'll define our function, <em><strong>build_lm_rest<\/strong><\/em>, that returns a dict mapping of the model attributes. Note that the model itself cannot be returned directly from REST because LinearRegression models are not JSON-serializable.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","build_lm_rest = \"\"\"def build_rest(dat):","            from sklearn import linear_model","            import oml","            regr = linear_model.LinearRegression()","            import pandas as pd","            dat = pd.get_dummies(dat, drop_first=True)","            X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \"SPECIES_versicolor\", \"SPECIES_virginica\"]]","            y = dat[[\"SEPAL_LENGTH\"]]","            regr.fit(X, y)","            data = {}","            data[\"init_params\"] = regr.get_params()","            data[\"model_params\"] = mp = {}","            for p in (\"coef_\", \"intercept_\", \"_residues\", \"singular_\"):","                mp[p] = getattr(regr, p).tolist()","            return data\"\"\""],"enabled":true,"result":{"startTime":1738090733430,"interpreter":"python.low","endTime":1738090733581,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save the buld_lm_rest script to the Python script repository","message":["%python","","try:","    oml.script.drop(\"build_lm_rest\")","except: ","    pass","oml.script.create(\"build_lm_rest\", func=build_lm_rest, overwrite=True) "],"enabled":true,"result":{"startTime":1738090733708,"interpreter":"python.low","endTime":1738090733855,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Invoke the build_lm_rest function in the script repository from the REST API in asynchronous mode.","message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" \\","                  --header 'Content-Type: application/json' \\","                  --header 'Accept: application/json' \\","                  -d '{\"input\":\"select * from IRIS\", \"parameters\":\"{\\\"oml_input_type\\\":\\\"pandas.DataFrame\\\"}\"}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/table-apply/build_lm_rest\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"The returned value is a dict containing model attributes","message":["","{\"result\":{\"init_params\":{\"fit_intercept\":true,\"n_jobs\":null,\"normalize\":false,\"copy_X\":true},\"model_params\":{\"intercept_\":[2.1712662921550736],\"_residues\":[13.556485081974792],\"coef_\":[[0.4958889383885511,0.8292439122348054,-0.315155173326473,-0.7235619577807288,-1.0234978144908309]],\"singular_\":[23.873627650888057,7.279716799977891,4.192696791126727,2.194192042042924,0.7289542732694926]}}}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1738090734034,"interpreter":"md.low","endTime":1738090734183,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1738090734386,"interpreter":"md.low","endTime":1738090734483,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Regression GLM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737171205129,"interpreter":"md.low","endTime":1737171205595,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Regression Modeling to Predict Numerical Values using Generalized Linear Model","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737171206088,"interpreter":"md.low","endTime":1737171206543,"results":[{"message":"<h2 id=\"regression-modeling-to-predict-numerical-values-using-generalized-linear-model\">Regression Modeling to Predict Numerical Values using Generalized Linear Model<\/h2>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737171207012,"interpreter":"md.low","endTime":1737171207455,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">OML4Py Generalized Linear Model (GLM)<\/a>"],"enabled":true,"result":{"startTime":1737171207925,"interpreter":"md.low","endTime":1737171208375,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">OML4Py Generalized Linear Model (GLM)<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1737171208875,"interpreter":"python.low","endTime":1737171209348,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES"," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1737171209825,"interpreter":"python.low","endTime":1737171210341,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1737171210805,"interpreter":"python.low","endTime":1737171211306,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%python","","TRAIN, TEST = CUST_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('YRS_RESIDENCE')","TRAIN_Y = TRAIN['YRS_RESIDENCE']","TEST_X = TEST.drop('YRS_RESIDENCE')","TEST_Y = TEST['YRS_RESIDENCE']"],"enabled":true,"result":{"startTime":1737171211782,"interpreter":"python.low","endTime":1737171212323,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a GLM regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1737171212789,"interpreter":"md.low","endTime":1737171213254,"results":[{"message":"<h3 id=\"build-a-glm-regression-model-for-predicting-yrs_residence\">Build a GLM regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%python","","settings = {}","","try:","    oml.drop(model = 'GLM_REGRESSION_MODEL')","except:","    print('No such model')"," ","glm_mod = oml.glm(\"regression\", **settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'GLM_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171213722,"interpreter":"python.low","endTime":1737171216228,"results":[{"message":"\nModel Name: GLM_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                  setting value\n0                     ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1               GLMS_CONF_LEVEL                            .95\n2           GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n3            GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n4                  ODMS_DETAILS                    ODMS_ENABLE\n5  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n6                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n7                     PREP_AUTO                             ON\n\nComputed Settings: \n            setting name            setting value\n0    GLMS_CONV_TOLERANCE  .0000050000000000000004\n1    GLMS_NUM_ITERATIONS                       30\n2  GLMS_RIDGE_REGRESSION   GLMS_RIDGE_REG_DISABLE\n3            GLMS_SOLVER         GLMS_SOLVER_CHOL\n\nGlobal Statistics: \n             attribute name attribute value\n0         ADJUSTED_R_SQUARE        0.577297\n1                       AIC         1133.61\n2                 COEFF_VAR         30.4233\n3                 CONVERGED             YES\n4        CORRECTED_TOTAL_DF            2708\n5          CORRECTED_TOT_SS         9534.73\n6            DEPENDENT_MEAN         4.00997\n7                  ERROR_DF            2652\n8         ERROR_MEAN_SQUARE         1.48832\n9         ERROR_SUM_SQUARES         3947.01\n10                  F_VALUE         67.0427\n11                    GMSEP         1.52032\n12               HOCKING_SP     0.000561417\n13                      J_P         1.51963\n14                 MODEL_DF              56\n15          MODEL_F_P_VALUE               0\n16        MODEL_MEAN_SQUARE         99.7807\n17        MODEL_SUM_SQUARES         5587.72\n18               NUM_PARAMS              57\n19                 NUM_ROWS            2709\n20          RANK_DEFICIENCY               0\n21             ROOT_MEAN_SQ         1.21997\n22                     R_SQ        0.586038\n23                     SBIC         1470.16\n24  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n         attribute name       attribute value  coefficient  std error    t value       p value significance code\n0           (Intercept)                  None   153.324901   5.433089  28.220575  0.000000e+00               ***\n1         AFFINITY_CARD                  None     0.340645   0.068606   4.965264  7.295958e-07               ***\n2     CUST_CREDIT_LIMIT                  None     0.000016   0.000008   2.034881  4.196216e-02                 *\n3           CUST_GENDER                     F    -0.113475   0.071621  -1.584376  1.132274e-01                  \n4     CUST_INCOME_LEVEL       A: Below 30,000     0.640696   0.198163   3.233176  1.239233e-03                **\n5     CUST_INCOME_LEVEL    B: 30,000 - 49,999     0.144060   0.131455   1.095885  2.732286e-01                  \n6     CUST_INCOME_LEVEL    C: 50,000 - 69,999     0.217899   0.142770   1.526218  1.270749e-01                  \n7     CUST_INCOME_LEVEL    D: 70,000 - 89,999    -0.011105   0.153972  -0.072125  9.425082e-01                  \n8     CUST_INCOME_LEVEL   E: 90,000 - 109,999     0.205234   0.113004   1.816173  6.945669e-02                 .\n9     CUST_INCOME_LEVEL  F: 110,000 - 129,999    -0.081483   0.106074  -0.768174  4.424523e-01                  \n10    CUST_INCOME_LEVEL  G: 130,000 - 149,999     0.164336   0.103530   1.587333  1.125565e-01                  \n11    CUST_INCOME_LEVEL  H: 150,000 - 169,999     0.289081   0.102576   2.818204  4.865106e-03                **\n12    CUST_INCOME_LEVEL  I: 170,000 - 189,999    -0.066614   0.094168  -0.707398  4.793812e-01                  \n13    CUST_INCOME_LEVEL  K: 250,000 - 299,999     0.012108   0.093756   0.129141  8.972561e-01                  \n14    CUST_INCOME_LEVEL  L: 300,000 and above    -0.003457   0.080803  -0.042782  9.658787e-01                  \n15  CUST_MARITAL_STATUS               Divorc.     0.255132   0.324671   0.785816  4.320453e-01                  \n16  CUST_MARITAL_STATUS               Mabsent    -0.231022   0.368937  -0.626183  5.312489e-01                  \n17  CUST_MARITAL_STATUS                Mar-AF    -0.975312   0.720500  -1.353660  1.759602e-01                  \n18  CUST_MARITAL_STATUS                NeverM    -0.012757   0.314184  -0.040604  9.676143e-01                  \n19  CUST_MARITAL_STATUS                Separ.    -0.040635   0.344544  -0.117939  9.061248e-01                  \n20  CUST_MARITAL_STATUS               Widowed    -0.945589   0.351749  -2.688250  7.227677e-03                **\n21   CUST_YEAR_OF_BIRTH                  None    -0.075924   0.002775 -27.358344  0.000000e+00               ***\n22            EDUCATION                  10th    -0.055915   0.154137  -0.362762  7.168118e-01                  \n23            EDUCATION                  11th    -0.281752   0.141721  -1.988072  4.690639e-02                 *\n24            EDUCATION                  12th    -0.191792   0.204292  -0.938815  3.479110e-01                  \n25            EDUCATION               1st-4th     0.187000   0.416084   0.449428  6.531600e-01                  \n26            EDUCATION               5th-6th     0.312164   0.246440   1.266694  2.053761e-01                  \n27            EDUCATION               7th-8th    -0.528300   0.173677  -3.041848  2.374333e-03                **\n28            EDUCATION                   9th    -0.480972   0.176666  -2.722501  6.521376e-03                **\n29            EDUCATION               < Bach.    -0.205311   0.067205  -3.055003  2.272976e-03                **\n30            EDUCATION               Assoc-A     0.213468   0.127929   1.668638  9.530715e-02                 .\n31            EDUCATION               Assoc-V     0.039609   0.124333   0.318572  7.500761e-01                  \n32            EDUCATION                 Bach.     0.099102   0.079059   1.253519  2.101272e-01                  \n33            EDUCATION               Masters     0.335463   0.135236   2.480566  1.317900e-02                 *\n34            EDUCATION                   PhD     0.784167   0.263927   2.971147  2.993555e-03                **\n35            EDUCATION               Presch.    -1.130164   0.712683  -1.585789  1.129065e-01                  \n36            EDUCATION                Profsc     0.610080   0.176570   3.455173  5.585775e-04               ***\n37       HOUSEHOLD_SIZE                     1    -0.438462   0.321774  -1.362637  1.731127e-01                  \n38       HOUSEHOLD_SIZE                     2    -0.115546   0.321970  -0.358872  7.197194e-01                  \n39       HOUSEHOLD_SIZE                   4-5    -0.696323   0.134620  -5.172492  2.482309e-07               ***\n40       HOUSEHOLD_SIZE                   6-8    -0.351825   0.326693  -1.076929  2.816100e-01                  \n41       HOUSEHOLD_SIZE                    9+    -0.289468   0.332312  -0.871073  3.837929e-01                  \n42           OCCUPATION                     ?    -0.432258   0.127862  -3.380671  7.335587e-04               ***\n43           OCCUPATION               Armed-F    -0.524535   1.229877  -0.426494  6.697826e-01                  \n44           OCCUPATION               Cleric.     0.061129   0.096605   0.632768  5.269398e-01                  \n45           OCCUPATION                Crafts    -0.043418   0.096684  -0.449077  6.534131e-01                  \n46           OCCUPATION                 Exec.     0.302740   0.096055   3.151724  1.641210e-03                **\n47           OCCUPATION               Farming     0.217480   0.162922   1.334878  1.820308e-01                  \n48           OCCUPATION               Handler    -0.327132   0.149622  -2.186389  2.887428e-02                 *\n49           OCCUPATION               House-s    -1.237814   0.331445  -3.734594  1.919768e-04               ***\n50           OCCUPATION               Machine    -0.056752   0.118432  -0.479196  6.318385e-01                  \n51           OCCUPATION                 Other    -0.257805   0.101369  -2.543220  1.103994e-02                 *\n52           OCCUPATION                 Prof.    -0.140482   0.105139  -1.336155  1.816134e-01                  \n53           OCCUPATION               Protec.     0.028069   0.181098   0.154996  8.768363e-01                  \n54           OCCUPATION               TechSup    -0.338445   0.132818  -2.548177  1.088468e-02                 *\n55           OCCUPATION               Transp.     0.296478   0.136628   2.169960  3.009840e-02                 *\n56          Y_BOX_GAMES                  None    -0.463632   0.083046  -5.582842  2.606260e-08               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0         ADJUSTED_R_SQUARE     0.577297\n1                       AIC  1133.612459\n2                 COEFF_VAR    30.423332\n3        CORRECTED_TOTAL_DF  2708.000000\n4          CORRECTED_TOT_SS  9534.730897\n5            DEPENDENT_MEAN     4.009967\n6                  ERROR_DF  2652.000000\n7         ERROR_MEAN_SQUARE     1.488316\n8         ERROR_SUM_SQUARES  3947.013467\n9                   F_VALUE    67.042673\n10                    GMSEP     1.520316\n11               HOCKING_SP     0.000561\n12                      J_P     1.519631\n13          MODEL_CONVERGED     1.000000\n14                 MODEL_DF    56.000000\n15          MODEL_F_P_VALUE     0.000000\n16        MODEL_MEAN_SQUARE    99.780668\n17        MODEL_SUM_SQUARES  5587.717430\n18               NUM_PARAMS    57.000000\n19                 NUM_ROWS  2709.000000\n20          RANK_DEFICIENCY     0.000000\n21             ROOT_MEAN_SQ     1.219965\n22                     R_SQ     0.586038\n23                     SBIC  1470.159545\n24  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n57\n\nDeviance: \n\n3947.013467\n\nAIC: \n\n1134\n\nNull Deviance: \n\n9534.730897\n\nDF Residual: \n\n2652.0\n\nDF Null: \n\n2708.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> 'GLMS_CONF_LEVEL' : '0.95'","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> 'GLMS_RIDGE_REGRESSION' : 'GLMS_RIDGE_REG_DISABLE'","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> 'GLMS_RIDGE_VALUE' : '2'","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> 'GLMS_FTR_SELECTION' : 'GLMS_FTR_SELECTION_ENABLE'","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> 'GLMS_FTR_GENERATION' : 'GLMS_FTR_GENERATION_ENABLE'","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`","> 'GLMS_FTR_GEN_METHOD' : 'GLMS_FTR_GEN_QUADRATIC'","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> 'GLMS_FTR_SEL_CRIT' : 'GLMS_FTR_SEL_ALPHA_INV'","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> 'GLMS_MAX_FEATURES' : '30'","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> 'GLMS_PRUNE_MODEL' : 'GLMS_PRUNE_MODEL_ENABLE'","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> 'GLMS_REFERENCE_CLASS_NAME' : '<target_value>'","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> 'GLMS_ROW_DIAGNOSTICS' : 'GLMS_ROW_DIAG_ENABLE'","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> 'GLMS_CONV_TOLERANCE' : '0.0001'","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> 'GLMS_NUM_ITERATIONS' : '200'","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> 'GLMS_BATCH_ROWS' : '2000'","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> 'GLMS_SOLVER' : 'GLMS_SOLVER_SGD'","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.  ","> 'GLMS_SPARSE_SOLVER' : 'GLMS_SPARSE_SOLVER_ENABLE'","","","<strong> For Oracle release 23ai and newer. <\/strong>","- Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function. ","   For classification, the options are `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK` or `GLMS_CAUCHIT_LINK`.  For regression, the default is `GLMS_IDENTITY_LINK (default)`.","> 'GLMS_LINK_FUNCTION' : 'GLMS_LOGIT_LINK'"],"enabled":true,"result":{"startTime":1737171216734,"interpreter":"md.low","endTime":1737171217194,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/generalized-linear-model-23c.html\" target=\"_blank\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_CONF_LEVEL' : '0.95'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_RIDGE_REGRESSION' : 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_RIDGE_VALUE' : '2'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_SELECTION' : 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_GENERATION' : 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_GEN_METHOD' : 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_SEL_CRIT' : 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_MAX_FEATURES' : '30'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_PRUNE_MODEL' : 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_REFERENCE_CLASS_NAME' : '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_ROW_DIAGNOSTICS' : 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_CONV_TOLERANCE' : '0.0001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_NUM_ITERATIONS' : '200'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_BATCH_ROWS' : '2000'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_SOLVER' : 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_SPARSE_SOLVER' : 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> For Oracle release 23ai and newer. <\/strong><\/p>\n<ul>\n<li>Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function.\nFor classification, the options are <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code> or <code>GLMS_CAUCHIT_LINK<\/code>.  For regression, the default is <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_LINK_FUNCTION' : 'GLMS_LOGIT_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Feature Selection and Generation","message":["%python","","settings = {'GLMS_RIDGE_REGRESSION':'GLMS_RIDGE_REG_DISABLE',","            'GLMS_FTR_SELECTION':'GLMS_FTR_SELECTION_ENABLE',","            'GLMS_FTR_GEN_METHOD':'GLMS_FTR_GEN_QUADRATIC',","            'GLMS_FTR_GENERATION': 'GLMS_FTR_GENERATION_ENABLE', ","            'GLMS_FTR_SEL_CRIT':'GLMS_FTR_SEL_AIC',","            'GLMS_NUM_ITERATIONS':'50',","            'GLMS_MAX_FEATURES':'30',","            'GLMS_PRUNE_MODEL':'GLMS_PRUNE_MODEL_ENABLE',","            'GLMS_ROW_DIAGNOSTICS':'GLMS_ROW_DIAG_DISABLE',","            'GLMS_CONV_TOLERANCE':'0.0001',","            'GLMS_BATCH_ROWS':'2000'}","","try:","    oml.drop(model = 'GLM_REGRESSION_MODEL')","except:","    print('No such model')"," ","glm_mod = oml.glm(\"regression\", **settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'GLM_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171217662,"interpreter":"python.low","endTime":1737171220069,"results":[{"message":"\nModel Name: GLM_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1                GLMS_BATCH_ROWS                           2000\n2                GLMS_CONF_LEVEL                            .95\n3            GLMS_CONV_TOLERANCE                         0.0001\n4            GLMS_FTR_GENERATION     GLMS_FTR_GENERATION_ENABLE\n5            GLMS_FTR_GEN_METHOD         GLMS_FTR_GEN_QUADRATIC\n6             GLMS_FTR_SELECTION      GLMS_FTR_SELECTION_ENABLE\n7              GLMS_FTR_SEL_CRIT               GLMS_FTR_SEL_AIC\n8              GLMS_MAX_FEATURES                             30\n9            GLMS_NUM_ITERATIONS                             50\n10              GLMS_PRUNE_MODEL        GLMS_PRUNE_MODEL_ENABLE\n11         GLMS_RIDGE_REGRESSION         GLMS_RIDGE_REG_DISABLE\n12          GLMS_ROW_DIAGNOSTICS          GLMS_ROW_DIAG_DISABLE\n13             GLMS_SELECT_BLOCK      GLMS_SELECT_BLOCK_DISABLE\n14                  ODMS_DETAILS                    ODMS_ENABLE\n15  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n16                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n17                     PREP_AUTO                             ON\n\nGlobal Statistics: \n             attribute name attribute value\n0         ADJUSTED_R_SQUARE        0.570989\n1                       AIC         1141.24\n2                 COEFF_VAR         30.6495\n3                 CONVERGED             YES\n4        CORRECTED_TOTAL_DF            2708\n5          CORRECTED_TOT_SS         9534.73\n6            DEPENDENT_MEAN         4.00997\n7                  ERROR_DF            2685\n8         ERROR_MEAN_SQUARE         1.51053\n9         ERROR_SUM_SQUARES         4055.76\n10                  F_VALUE         157.704\n11                    GMSEP         1.52403\n12               HOCKING_SP     0.000562789\n13                      J_P         1.52391\n14                 MODEL_DF              23\n15          MODEL_F_P_VALUE               0\n16        MODEL_MEAN_SQUARE         238.216\n17        MODEL_SUM_SQUARES         5478.97\n18               NUM_PARAMS              24\n19                 NUM_ROWS            2709\n20             ROOT_MEAN_SQ         1.22903\n21                     R_SQ        0.574633\n22                     SBIC         1282.95\n23  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nAFFINITY_CARD\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n         attribute name       attribute value  coefficient  std error    t value       p value significance code\n0           (Intercept)                  None   151.619049   5.325376  28.471050  0.000000e+00               ***\n1         AFFINITY_CARD                  None     0.405005   0.065140   6.217413  5.842160e-10               ***\n2           CUST_GENDER                     F    -0.156305   0.064863  -2.409772  1.602937e-02                 *\n3     CUST_INCOME_LEVEL       A: Below 30,000     0.480111   0.178824   2.684823  7.301432e-03                **\n4     CUST_INCOME_LEVEL  F: 110,000 - 129,999    -0.185843   0.088004  -2.111773  3.479808e-02                 *\n5     CUST_INCOME_LEVEL  H: 150,000 - 169,999     0.210351   0.086174   2.440988  1.471132e-02                 *\n6   CUST_MARITAL_STATUS               Divorc.     0.224238   0.079465   2.821836  4.810077e-03                **\n7   CUST_MARITAL_STATUS               Widowed    -1.072453   0.153037  -7.007801  3.049532e-12               ***\n8    CUST_YEAR_OF_BIRTH                  None    -0.075006   0.002719 -27.585654  0.000000e+00               ***\n9             EDUCATION               < Bach.    -0.167355   0.058745  -2.848861  4.421013e-03                **\n10            EDUCATION               Masters     0.240118   0.121931   1.969301  4.902121e-02                 *\n11            EDUCATION                   PhD     0.724599   0.254657   2.845393  4.469288e-03                **\n12            EDUCATION                Profsc     0.507417   0.160317   3.165075  1.567788e-03                **\n13       HOUSEHOLD_SIZE                     1    -0.380655   0.080496  -4.728864  2.374468e-06               ***\n14       HOUSEHOLD_SIZE                   4-5    -0.671540   0.126990  -5.288148  1.335068e-07               ***\n15       HOUSEHOLD_SIZE                   6-8    -0.292035   0.136270  -2.143066  3.219746e-02                 *\n16       HOUSEHOLD_SIZE                    9+    -0.210189   0.088008  -2.388277  1.699627e-02                 *\n17           OCCUPATION                     ?    -0.433972   0.114666  -3.784672  1.572781e-04               ***\n18           OCCUPATION                 Exec.     0.372300   0.075989   4.899416  1.018327e-06               ***\n19           OCCUPATION               Handler    -0.331908   0.136608  -2.429631  1.517937e-02                 *\n20           OCCUPATION                 Other    -0.274482   0.081512  -3.367383  7.695453e-04               ***\n21           OCCUPATION               TechSup    -0.268835   0.119675  -2.246382  2.476053e-02                 *\n22           OCCUPATION               Transp.     0.302057   0.121823   2.479479  1.321838e-02                 *\n23          Y_BOX_GAMES                  None    -0.483562   0.081466  -5.935759  3.301191e-09               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0         ADJUSTED_R_SQUARE     0.570989\n1                       AIC  1141.242506\n2                 COEFF_VAR    30.649500\n3        CORRECTED_TOTAL_DF  2708.000000\n4          CORRECTED_TOT_SS  9534.730897\n5            DEPENDENT_MEAN     4.009967\n6                  ERROR_DF  2685.000000\n7         ERROR_MEAN_SQUARE     1.510527\n8         ERROR_SUM_SQUARES  4055.763666\n9                   F_VALUE   157.703930\n10                    GMSEP     1.524033\n11               HOCKING_SP     0.000563\n12                      J_P     1.523909\n13          MODEL_CONVERGED     1.000000\n14                 MODEL_DF    23.000000\n15          MODEL_F_P_VALUE     0.000000\n16        MODEL_MEAN_SQUARE   238.215967\n17        MODEL_SUM_SQUARES  5478.967231\n18               NUM_PARAMS    24.000000\n19                 NUM_ROWS  2709.000000\n20             ROOT_MEAN_SQ     1.229035\n21                     R_SQ     0.574633\n22                     SBIC  1282.946542\n23  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n24\n\nDeviance: \n\n4055.763666\n\nAIC: \n\n1141\n\nNull Deviance: \n\n9534.730897\n\nDF Residual: \n\n2685.0\n\nDF Null: \n\n2708.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Ridge Regression","message":["%python","","settings = {'GLMS_RIDGE_REGRESSION':'GLMS_RIDGE_REG_ENABLE',","            'GLMS_FTR_SELECTION':'GLMS_FTR_SELECTION_DISABLE',","            'GLMS_ROW_DIAGNOSTICS':'GLMS_ROW_DIAG_DISABLE',","            'GLMS_CONV_TOLERANCE':'0.0001',","            'GLMS_NUM_ITERATIONS':'50',","            'GLMS_RIDGE_VALUE':'0.1',","            'GLMS_BATCH_ROWS':'2000'}","","try:","    oml.drop(model = 'GLM_REGRESSION_MODEL')","except:","    print('No such model')"," ","glm_mod = oml.glm(\"regression\", **settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'GLM_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171220537,"interpreter":"python.low","endTime":1737171222923,"results":[{"message":"\nModel Name: GLM_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1                GLMS_BATCH_ROWS                           2000\n2                GLMS_CONF_LEVEL                            .95\n3            GLMS_CONV_TOLERANCE                         0.0001\n4            GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n5             GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n6            GLMS_NUM_ITERATIONS                             50\n7          GLMS_RIDGE_REGRESSION          GLMS_RIDGE_REG_ENABLE\n8               GLMS_RIDGE_VALUE                            0.1\n9           GLMS_ROW_DIAGNOSTICS          GLMS_ROW_DIAG_DISABLE\n10            GLMS_VIF_FOR_RIDGE         GLMS_VIF_RIDGE_DISABLE\n11                  ODMS_DETAILS                    ODMS_ENABLE\n12  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n13                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n14                     PREP_AUTO                             ON\n\nComputed Settings: \n  setting name     setting value\n0  GLMS_SOLVER  GLMS_SOLVER_CHOL\n\nGlobal Statistics: \n             attribute name attribute value\n0         ADJUSTED_R_SQUARE        0.577186\n1                       AIC         1134.32\n2                 COEFF_VAR         30.4273\n3                 CONVERGED             YES\n4        CORRECTED_TOTAL_DF            2708\n5          CORRECTED_TOT_SS         9534.73\n6            DEPENDENT_MEAN         4.00997\n7                  ERROR_DF            2652\n8         ERROR_MEAN_SQUARE         1.48871\n9         ERROR_SUM_SQUARES         3948.05\n10                  F_VALUE         67.0127\n11                    GMSEP         1.52071\n12               HOCKING_SP     0.000561564\n13                      J_P         1.52003\n14                 MODEL_DF              56\n15          MODEL_F_P_VALUE               0\n16        MODEL_MEAN_SQUARE         99.7622\n17        MODEL_SUM_SQUARES         5586.68\n18               NUM_PARAMS              57\n19                 NUM_ROWS            2709\n20          RANK_DEFICIENCY               0\n21             ROOT_MEAN_SQ         1.22013\n22                     R_SQ         0.58593\n23                     SBIC         1470.87\n24  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n         attribute name       attribute value  coefficient  std error    t value       p value significance code\n0           (Intercept)                  None   153.286770   5.432447  28.216893  0.000000e+00               ***\n1         AFFINITY_CARD                  None     0.341061   0.068599   4.971812  7.055932e-07               ***\n2     CUST_CREDIT_LIMIT                  None     0.000016   0.000008   2.031948  4.225836e-02                 *\n3           CUST_GENDER                     F    -0.113944   0.071593  -1.591559  1.116029e-01                  \n4     CUST_INCOME_LEVEL       A: Below 30,000     0.638712   0.197875   3.227863  1.262389e-03                **\n5     CUST_INCOME_LEVEL    B: 30,000 - 49,999     0.143608   0.131329   1.093496  2.742753e-01                  \n6     CUST_INCOME_LEVEL    C: 50,000 - 69,999     0.217494   0.142641   1.524762  1.274377e-01                  \n7     CUST_INCOME_LEVEL    D: 70,000 - 89,999    -0.011218   0.153834  -0.072924  9.418718e-01                  \n8     CUST_INCOME_LEVEL   E: 90,000 - 109,999     0.205015   0.112914   1.815669  6.953406e-02                 .\n9     CUST_INCOME_LEVEL  F: 110,000 - 129,999    -0.081756   0.105989  -0.771362  4.405610e-01                  \n10    CUST_INCOME_LEVEL  G: 130,000 - 149,999     0.164024   0.103451   1.585523  1.129668e-01                  \n11    CUST_INCOME_LEVEL  H: 150,000 - 169,999     0.288714   0.102497   2.816809  4.886220e-03                **\n12    CUST_INCOME_LEVEL  I: 170,000 - 189,999    -0.066784   0.094095  -0.709746  4.779238e-01                  \n13    CUST_INCOME_LEVEL  K: 250,000 - 299,999     0.012075   0.093706   0.128866  8.974734e-01                  \n14    CUST_INCOME_LEVEL  L: 300,000 and above    -0.003902   0.080750  -0.048323  9.614627e-01                  \n15  CUST_MARITAL_STATUS               Divorc.     0.253653   0.315765   0.803296  4.218758e-01                  \n16  CUST_MARITAL_STATUS               Mabsent    -0.232063   0.360465  -0.643788  5.197688e-01                  \n17  CUST_MARITAL_STATUS                Mar-AF    -0.942407   0.708334  -1.330455  1.834827e-01                  \n18  CUST_MARITAL_STATUS                NeverM    -0.014300   0.305534  -0.046804  9.626733e-01                  \n19  CUST_MARITAL_STATUS                Separ.    -0.041920   0.335911  -0.124795  9.006955e-01                  \n20  CUST_MARITAL_STATUS               Widowed    -0.946343   0.343085  -2.758334  5.849528e-03                **\n21   CUST_YEAR_OF_BIRTH                  None    -0.075905   0.002775 -27.354516  0.000000e+00               ***\n22            EDUCATION                  10th    -0.055951   0.154018  -0.363277  7.164271e-01                  \n23            EDUCATION                  11th    -0.281551   0.141632  -1.987909  4.692445e-02                 *\n24            EDUCATION                  12th    -0.191125   0.204022  -0.936783  3.489552e-01                  \n25            EDUCATION               1st-4th     0.185111   0.413668   0.447488  6.545592e-01                  \n26            EDUCATION               5th-6th     0.311115   0.245960   1.264899  2.060185e-01                  \n27            EDUCATION               7th-8th    -0.527250   0.173509  -3.038746  2.398826e-03                **\n28            EDUCATION                   9th    -0.480464   0.176480  -2.722481  6.521764e-03                **\n29            EDUCATION               < Bach.    -0.205203   0.067175  -3.054761  2.274804e-03                **\n30            EDUCATION               Assoc-A     0.212537   0.127838   1.662556  9.651949e-02                 .\n31            EDUCATION               Assoc-V     0.039585   0.124268   0.318543  7.500980e-01                  \n32            EDUCATION                 Bach.     0.098861   0.079016   1.251158  2.109872e-01                  \n33            EDUCATION               Masters     0.334172   0.135078   2.473920  1.342612e-02                 *\n34            EDUCATION                   PhD     0.780157   0.263323   2.962736  3.076294e-03                **\n35            EDUCATION               Presch.    -1.093416   0.700902  -1.560013  1.188762e-01                  \n36            EDUCATION                Profsc     0.608335   0.176374   3.449124  5.711853e-04               ***\n37       HOUSEHOLD_SIZE                     1    -0.436662   0.313081  -1.394725  1.632155e-01                  \n38       HOUSEHOLD_SIZE                     2    -0.113825   0.312996  -0.363662  7.161393e-01                  \n39       HOUSEHOLD_SIZE                   4-5    -0.696452   0.134493  -5.178353  2.406347e-07               ***\n40       HOUSEHOLD_SIZE                   6-8    -0.349792   0.318628  -1.097805  2.723895e-01                  \n41       HOUSEHOLD_SIZE                    9+    -0.287699   0.323291  -0.889908  3.735959e-01                  \n42           OCCUPATION                     ?    -0.431388   0.127740  -3.377076  7.431725e-04               ***\n43           OCCUPATION               Armed-F    -0.475247   1.171912  -0.405531  6.851201e-01                  \n44           OCCUPATION               Cleric.     0.061619   0.096504   0.638513  5.231951e-01                  \n45           OCCUPATION                Crafts    -0.043042   0.096574  -0.445692  6.558559e-01                  \n46           OCCUPATION                 Exec.     0.303436   0.095954   3.162299  1.582974e-03                **\n47           OCCUPATION               Farming     0.217302   0.162729   1.335361  1.818730e-01                  \n48           OCCUPATION               Handler    -0.326269   0.149467  -2.182882  2.913193e-02                 *\n49           OCCUPATION               House-s    -1.228783   0.330235  -3.720932  2.026102e-04               ***\n50           OCCUPATION               Machine    -0.056461   0.118308  -0.477237  6.332329e-01                  \n51           OCCUPATION                 Other    -0.257109   0.101257  -2.539175  1.116810e-02                 *\n52           OCCUPATION                 Prof.    -0.139071   0.105009  -1.324372  1.854937e-01                  \n53           OCCUPATION               Protec.     0.028677   0.180879   0.158544  8.740406e-01                  \n54           OCCUPATION               TechSup    -0.337437   0.132700  -2.542856  1.105141e-02                 *\n55           OCCUPATION               Transp.     0.296709   0.136495   2.173768  2.981078e-02                 *\n56          Y_BOX_GAMES                  None    -0.463980   0.083037  -5.587607  2.536575e-08               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0         ADJUSTED_R_SQUARE     0.577186\n1                       AIC  1134.321375\n2                 COEFF_VAR    30.427312\n3        CORRECTED_TOTAL_DF  2708.000000\n4          CORRECTED_TOT_SS  9534.730897\n5            DEPENDENT_MEAN     4.009967\n6                  ERROR_DF  2652.000000\n7         ERROR_MEAN_SQUARE     1.488705\n8         ERROR_SUM_SQUARES  3948.046492\n9                   F_VALUE    67.012740\n10                    GMSEP     1.520714\n11               HOCKING_SP     0.000562\n12                      J_P     1.520029\n13          MODEL_CONVERGED     1.000000\n14                 MODEL_DF    56.000000\n15          MODEL_F_P_VALUE     0.000000\n16        MODEL_MEAN_SQUARE    99.762222\n17        MODEL_SUM_SQUARES  5586.684405\n18               NUM_PARAMS    57.000000\n19                 NUM_ROWS  2709.000000\n20          RANK_DEFICIENCY     0.000000\n21             ROOT_MEAN_SQ     1.220125\n22                     R_SQ     0.585930\n23                     SBIC  1470.868461\n24  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n57\n\nDeviance: \n\n3948.046492\n\nAIC: \n\n1134\n\nNull Deviance: \n\n9534.730897\n\nDF Residual: \n\n2652.0\n\nDF Null: \n\n2708.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model fit details","message":["%python","","z.show(glm_mod.fit_details.round(6))"],"enabled":true,"result":{"startTime":1737171223386,"interpreter":"python.low","endTime":1737171223864,"results":[{"message":"name\tvalue\nADJUSTED_R_SQUARE\t0.577186\nAIC\t1134.321375\nCOEFF_VAR\t30.427312\nCORRECTED_TOTAL_DF\t2708.0\nCORRECTED_TOT_SS\t9534.730897\nDEPENDENT_MEAN\t4.009967\nERROR_DF\t2652.0\nERROR_MEAN_SQUARE\t1.488705\nERROR_SUM_SQUARES\t3948.046492\nF_VALUE\t67.01274\nGMSEP\t1.520714\nHOCKING_SP\t0.000562\nJ_P\t1.520029\nMODEL_CONVERGED\t1.0\nMODEL_DF\t56.0\nMODEL_F_P_VALUE\t0.0\nMODEL_MEAN_SQUARE\t99.762222\nMODEL_SUM_SQUARES\t5586.684405\nNUM_PARAMS\t57.0\nNUM_ROWS\t2709.0\nRANK_DEFICIENCY\t0.0\nROOT_MEAN_SQ\t1.220125\nR_SQ\t0.58593\nSBIC\t1470.868461\nVALID_COVARIANCE_MATRIX\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the coefficients","message":["%python","","z.show(glm_mod.coef.head(10).round(3))"],"enabled":true,"result":{"startTime":1737171224326,"interpreter":"python.low","endTime":1737171224803,"results":[{"message":"attribute name\tattribute value\tcoefficient\tstd error\tt value\tp value\n(Intercept)\tNone\t153.287\t5.432\t28.217\t0.0\nAFFINITY_CARD\tNone\t0.341\t0.069\t4.972\t0.0\nCUST_CREDIT_LIMIT\tNone\t0.0\t0.0\t2.032\t0.042\nCUST_YEAR_OF_BIRTH\tNone\t-0.076\t0.003\t-27.355\t0.0\nY_BOX_GAMES\tNone\t-0.464\t0.083\t-5.588\t0.0\nCUST_GENDER\tF\t-0.114\t0.072\t-1.592\t0.112\nCUST_INCOME_LEVEL\tA: Below 30,000\t0.639\t0.198\t3.228\t0.001\nCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t0.144\t0.131\t1.093\t0.274\nCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t0.217\t0.143\t1.525\t0.127\nCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t-0.011\t0.154\t-0.073\t0.942\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%python","","RES_DF = glm_mod.predict(TEST_X, supplemental_cols = TEST)","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE'] + RES_DF.columns].head(10).round(2))"],"enabled":true,"result":{"startTime":1737171225276,"interpreter":"python.low","endTime":1737171225929,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\n4.57\t2\t100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\t4.57\n1.84\t2\t101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\t1.84\n5.31\t5\t103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\t5.31\n2.23\t2\t102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\t2.23\n4.71\t4\t100558\tM\tMarried\t1964\tJ: 190,000 - 249,999\t11000\tAssoc-V\t1\t3\tMachine\t4\t0\t4.71\n3.1\t4\t103401\tM\tDivorc.\t1975\tI: 170,000 - 189,999\t10000\tHS-grad\t0\t2\tCrafts\t4\t1\t3.1\n6.83\t0\t102740\tF\tDivorc.\t1929\tK: 250,000 - 299,999\t15000\t10th\t0\t2\tOther\t0\t0\t6.83\n3.75\t3\t103829\tM\tDivorc.\t1973\tE: 90,000 - 109,999\t7000\tHS-grad\t0\t9+\tMachine\t3\t0\t3.75\n3.39\t3\t104077\tM\tNeverM\t1975\tL: 300,000 and above\t9000\tProfsc\t0\t2\tProf.\t3\t1\t3.39\n5.56\t5\t101798\tM\tMarried\t1960\tG: 130,000 - 149,999\t15000\tHS-grad\t1\t3\tExec.\t5\t0\t5.56\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%python","","import numpy as np","","# The coefficient of determination R?2 is part of the \"score\" method for OML models","CoefficientOfDeterminationR2 = glm_mod.score(TEST_X, TEST_Y)","    ","# Compute other statistics by hand - no need to pull data locally to Python","MeanAbsoluteError = (abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION']).sum())/RES_DF['YRS_RESIDENCE'].count()","MeanSquaredError =  ((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])**2).sum()/RES_DF['YRS_RESIDENCE'].count()","RootMeanSquaredError =  np.sqrt(MeanSquaredError)","","print('R^2:  ', CoefficientOfDeterminationR2.round(4))","print('MAE:  ', MeanAbsoluteError.round(4))","print('MSE:  ', MeanSquaredError.round(4))","print('RMSE: ', RootMeanSquaredError.round(4))"],"enabled":true,"result":{"startTime":1737171226389,"interpreter":"python.low","endTime":1737171227091,"results":[{"message":"R^2:   0.5569\nMAE:   0.8651\nMSE:   1.6673\nRMSE:  1.2912\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"groupByColumns\":[],\"showSeriesX\":[\"YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"showLabel\":\"no\",\"showDescription\":\"no\",\"viewport\":{\"xAxis\":{\"viewportMin\":0,\"viewportMax\":12},\"yAxis\":{\"viewportMin\":0,\"viewportMax\":8}},\"lastColumns\":[\"PREDICTION\",\"YRS_RESIDENCE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot the predicted and actual years of residence ","message":["%python","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE']].head(100))"],"enabled":true,"result":{"startTime":1737171227552,"interpreter":"python.low","endTime":1737171228097,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\n4.5719144390197695\t2.0\n1.8358546077171343\t2.0\n5.312181183257448\t5.0\n2.2323988564511934\t2.0\n4.7074403346226426\t4.0\n3.0985366454326506\t4.0\n6.827973805136371\t0.0\n3.7514697241400907\t3.0\n3.389984135786425\t3.0\n5.558543569472233\t5.0\n4.931785043571125\t5.0\n3.8213465043350157\t1.0\n4.671141595251757\t4.0\n4.637060248997734\t5.0\n3.9280077331279113\t4.0\n4.685315294174716\t5.0\n4.272350467119807\t5.0\n3.856041579641216\t3.0\n4.763705072196829\t5.0\n4.393157118465297\t4.0\n2.5768994429535015\t4.0\n4.201836156665166\t3.0\n1.2322666658095558\t1.0\n4.304581897574282\t3.0\n3.857562426946486\t3.0\n5.460466693888625\t5.0\n4.615890688182992\t5.0\n1.7520292001659499\t2.0\n5.5236154320363\t5.0\n3.1286523072376937\t3.0\n4.017237980750582\t5.0\n3.6495346652955702\t2.0\n4.209938366084485\t6.0\n4.183053991337577\t4.0\n4.720103459218868\t3.0\n1.7869371148460518\t2.0\n5.42860453450725\t6.0\n3.3738426542529765\t5.0\n4.053424503428202\t5.0\n5.1798039549652\t10.0\n1.3760803288660022\t1.0\n4.965887241808673\t7.0\n7.015572559176475\t7.0\n2.7634712122500327\t2.0\n5.727870296291155\t7.0\n2.913572577855731\t4.0\n4.7876479094512465\t5.0\n3.26951429413437\t3.0\n5.251748264297419\t7.0\n4.337527610211246\t4.0\n4.403361748758352\t5.0\n5.530763155593116\t4.0\n2.8202897431845524\t3.0\n2.7430449283528993\t3.0\n4.351015663708919\t4.0\n3.0686750672400445\t3.0\n2.380779069089642\t2.0\n7.15636719158629\t6.0\n4.440458514903426\t9.0\n1.6489176427547187\t1.0\n6.250639807930832\t6.0\n4.367295798221844\t4.0\n5.4246743553609615\t6.0\n5.940354153728628\t6.0\n2.6442704547251314\t2.0\n3.2130959589246526\t2.0\n4.397472901476675\t4.0\n5.736500253879456\t6.0\n1.444706462196018\t0.0\n3.922643064613897\t3.0\n4.183053991337577\t4.0\n4.865308075564015\t7.0\n4.286962609718216\t4.0\n4.993338829966666\t6.0\n3.9027181635342316\t3.0\n3.367056963161634\t4.0\n6.0895922922974455\t6.0\n2.452388436010973\t1.0\n3.7872751375271454\t3.0\n2.5385708497902058\t2.0\n2.5111421778298824\t3.0\n3.7327007295266026\t4.0\n3.389333337707298\t4.0\n2.5793226011902375\t3.0\n5.76123607273916\t5.0\n1.043852315260375\t2.0\n1.8154284561597063\t0.0\n1.9777145389965054\t2.0\n4.073812374671009\t3.0\n3.614646132188567\t3.0\n1.6360232192990276\t2.0\n5.457614570091117\t5.0\n3.9034462503274185\t5.0\n2.5393831245366214\t3.0\n4.727773483841454\t4.0\n5.607211323465547\t6.0\n5.950950046715688\t4.0\n1.497247642155167\t1.0\n5.9580709545216415\t5.0\n1.634772051570308\t3.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using matplotlib, plot the predicted and actual years of residence","message":["%python","","import numpy as np","import matplotlib.pyplot as plt","plt.style.use('seaborn-v0_8')","plt.figure(figsize=[9,7])","plt.margins(0.05)","","x = RES_DF[['PREDICTION']].pull()","y = RES_DF[['YRS_RESIDENCE']].pull()","n = len(x)","","plt.plot(x, y, '.', c='black', alpha=0.8,  ms=7)","","plt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],","          c='red', alpha=0.8,  ms=3,","          label='Perfect prediction reference line')","","plt.legend(frameon=True, facecolor='lightgray')         ","plt.xlabel('PREDICTION')","plt.ylabel('YRS_RESIDENCE')","plt.title('The prediction vs. actual value for years of residence',","          fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1737171228562,"interpreter":"python.low","endTime":1737171229188,"results":[{"message":"<div style='width:auto;height:auto'><img 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","plt.xlabel('PREDICTION')","plt.ylabel('RESIDUAL')","plt.title('Prediction of Years of Residence vs. Residuals', fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1737171229650,"interpreter":"python.low","endTime":1737171230227,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details in python (using topN_attrs)","message":["%python","","","RES_DF = glm_mod.predict(TEST, supplemental_cols = TEST[['CUST_ID']], proba = True, topN_attrs = True)","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1737171230688,"interpreter":"python.low","endTime":1737171244786,"results":[{"message":"CUST_ID\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100100\t4.472758165280599\tCUST_YEAR_OF_BIRTH\t1959\t.201\tEDUCATION\tMasters\t.155\tAFFINITY_CARD\t1\t.12\tY_BOX_GAMES\t0\t.067\tCUST_CREDIT_LIMIT\t10000\t.015\n100200\t1.5283330601173863\tCUST_CREDIT_LIMIT\t9000\t.006\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-.001\tCUST_MARITAL_STATUS\tNeverM\t-.005\tAFFINITY_CARD\t0\t-.03\tEDUCATION\t< Bach.\t-.075\n100300\t5.06005724048609\tCUST_YEAR_OF_BIRTH\t1961\t.25\tAFFINITY_CARD\t1\t.231\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t.147\tY_BOX_GAMES\t0\t.129\tEDUCATION\tBach.\t.089\n100400\t6.096717138233148\tCUST_YEAR_OF_BIRTH\t1944\t.503\tOCCUPATION\tTransp.\t.095\tCUST_MARITAL_STATUS\tDivorc.\t.081\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t.07\tY_BOX_GAMES\t0\t.046\n100900\t4.834481255590289\tCUST_YEAR_OF_BIRTH\t1959\t.376\tOCCUPATION\tExec.\t.265\tY_BOX_GAMES\t0\t.126\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-.072\tAFFINITY_CARD\t0\t-.072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize GLM_TEST_DATA for use in query below","message":["%python","","try:","    oml.drop(table = 'GLM_TEST_DATA')","except:","    pass","_ = TEST.materialize(table = 'GLM_TEST_DATA')"],"enabled":true,"result":{"startTime":1737171245255,"interpreter":"python.low","endTime":1737171245964,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL","message":["%sql","","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","       round(PRED_LOWER_LIMIT,1) LOWER_BOUND,","       round(PRED_UPPER_LIMIT,1) UPPER_BOUND,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(GLM_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_BOUNDS(GLM_REGRESSION_MODEL USING *).LOWER PRED_LOWER_LIMIT,","             PREDICTION_BOUNDS(GLM_REGRESSION_MODEL USING *).UPPER PRED_UPPER_LIMIT,","             PREDICTION_DETAILS(GLM_REGRESSION_MODEL USING *) PD","      FROM GLM_TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details'","              PASSING OUT.PD","              COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]',","               \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1737171246442,"interpreter":"sql.low","endTime":1737171247057,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tLOWER_BOUND\tUPPER_BOUND\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t4.186\t3.9\t4.4\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".219\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".155\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".107\" \n100003\t3.619\t3.4\t3.9\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".157\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".013\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\"-.015\" \n100005\t5.419\t5.1\t5.8\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".392\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".174\" \t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".143\" \n100009\t3.499\t3.2\t3.8\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".146\" \t\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".093\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".056\" \n100010\t3.006\t2.8\t3.2\t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".013\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\"-.003\" \t\"OCCUPATION\" actualValue=\"Crafts\" weight=\"-.034\" \n100012\t5.414\t4.9\t5.9\t\"EDUCATION\" actualValue=\"PhD\" weight=\".395\" \t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".146\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".131\" \n100014\t5.247\t5\t5.5\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".449\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".141\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".08\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# End of Script"],"enabled":true,"result":{"startTime":1737171247528,"interpreter":"md.low","endTime":1737171247981,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737171248481,"interpreter":"md.low","endTime":1737171249080,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Regression NN","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733787471788,"interpreter":"md.medium","endTime":1733787472211,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Neural Networks","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733787472663,"interpreter":"md.medium","endTime":1733787473074,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-neural-networks\">Regression Modeling to Predict Numerical Values using Neural Networks<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1733787473500,"interpreter":"md.medium","endTime":1733787473914,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1733787474340,"interpreter":"md.medium","endTime":1733787474743,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1733787475169,"interpreter":"python.medium","endTime":1733787475593,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer with demographics data","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","DEMO_DF = oml.sync(query = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS')","","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1733787476029,"interpreter":"python.medium","endTime":1733787476472,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1733787476892,"interpreter":"python.medium","endTime":1733787477471,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n103383\tM\tNeverM\t1976\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t1\tProtec.\t3\t1\n100924\tM\tNeverM\t1963\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t2\tCrafts\t4\t0\n102237\tM\tNeverM\t1975\tJ: 190,000 - 249,999\t11000\tProfsc\t0\t2\tProf.\t4\t1\n102078\tF\tNeverM\t1975\tJ: 190,000 - 249,999\t9000\tProfsc\t0\t2\tProf.\t6\t1\n104047\tM\tNeverM\t1983\tL: 300,000 and above\t7000\tHS-grad\t0\t1\tOther\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%python","","TRAIN, TEST = CUST_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('YRS_RESIDENCE')","TRAIN_Y = TRAIN['YRS_RESIDENCE']","TEST_X = TEST.drop('YRS_RESIDENCE')","TEST_Y = TEST['YRS_RESIDENCE']"],"enabled":true,"result":{"startTime":1733787477891,"interpreter":"python.medium","endTime":1733787478510,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1733787478934,"interpreter":"md.medium","endTime":1733787479340,"results":[{"message":"<h3 id=\"build-a-neural-network-regression-model-for-predicting-yrs_residence\">Build a Neural Network regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons and default activation function","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","setting = {'NNET_HIDDEN_LAYERS'  :'1',","           'NNET_NODES_PER_LAYER':'4'}","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1733787479763,"interpreter":"python.medium","endTime":1733787487723,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                  .000000001\n2            LBFGS_HISTORY_DEPTH                          20\n3            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n5             NNET_HIDDEN_LAYERS                           1\n6                NNET_ITERATIONS                         200\n7                 NNET_TOLERANCE                     .000001\n8                   ODMS_DETAILS                 ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n10              ODMS_RANDOM_SEED                           0\n11                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n12                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.105377\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0         0       0.0       0  ...            None         None -0.367653\n1         0       0.0       1  ...            None         None -0.000152\n2         0       0.0       2  ...            None         None  0.323073\n3         0       0.0       3  ...            None         None  0.445828\n4         0       0.0       4  ...            None         None -0.602031\n...     ...       ...     ...  ...             ...          ...       ...\n3196      1      46.0       0  ...            None         None  0.273744\n3197      1      47.0       0  ...            None         None -0.241160\n3198      1      48.0       0  ...            None         None -1.050772\n3199      1      49.0       0  ...            None         None -1.147731\n3200      1       NaN       0  ...            None         None -0.602011\n\n[3201 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NN ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html');\"> Neural Network <\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html');\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>' ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`,`NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and starting from Oracle Database 23ai `NNET_ACTIVATIONS_RELU`.","> 'NNET_HIDDEN_LAYERS' = '3'","","> 'NNET_NODES_PER_LAYER' = '8,8,2'","","> 'NNET_ACTIVATIONS' = \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> 'NNET_REGULARIZER' = 'NNET_REGULARIZER_HELDASIDE'","","> 'NNET_HELDASIDE_RATIO' = '0.25'","","> 'NNET_HELDASIDE_MAX_FAIL' = '6'  ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> 'NNET_REG_LAMBDA' = '1'","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> 'NNET_ITERATIONS' = '200'","","> 'NNET_TOLERANCE' = '0.000001'","","> 'ODMS_RANDOM_SEED' = '0'","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> 'LBFGS_HISTORY_DEPTH' = '10'","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> 'LBFGS_GRADIENT_TOLERANCE' = '0.0000001'","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> 'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_ENABLE'","","> 'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_DISABLE'"],"enabled":true,"result":{"startTime":1733787488186,"interpreter":"md.medium","endTime":1733787488623,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nn\">Examples of possible setting overrides for NN<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html');\"> Neural Network <\/a><\/li>\n<\/ul>\n<ul>\n<li>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html');\">All algorithms<\/a><\/li>\n<\/ul>\n<ul>\n<li>Specify a row weight column<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>,<code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and starting from Oracle Database 23ai <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_HIDDEN_LAYERS' = '3'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_NODES_PER_LAYER' = '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_ACTIVATIONS' = &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_REGULARIZER' = 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_HELDASIDE_RATIO' = '0.25'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_HELDASIDE_MAX_FAIL' = '6'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_REG_LAMBDA' = '1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_ITERATIONS' = '200'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_TOLERANCE' = '0.000001'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'ODMS_RANDOM_SEED' = '0'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_HISTORY_DEPTH' = '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_GRADIENT_TOLERANCE' = '0.0000001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons and specific activation function","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","setting = {'NNET_HIDDEN_LAYERS'  :'1',","           'NNET_NODES_PER_LAYER':'4',","           'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_BIPOLAR_SIG'\"","}","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1733787489048,"interpreter":"python.medium","endTime":1733787499303,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                  .000000001\n2            LBFGS_HISTORY_DEPTH                          20\n3            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n5             NNET_HIDDEN_LAYERS                           1\n6                NNET_ITERATIONS                         200\n7                 NNET_TOLERANCE                     .000001\n8                   ODMS_DETAILS                 ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n10              ODMS_RANDOM_SEED                           0\n11                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n12                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.105377\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0         0       0.0       0  ...            None         None -0.367653\n1         0       0.0       1  ...            None         None -0.000152\n2         0       0.0       2  ...            None         None  0.323073\n3         0       0.0       3  ...            None         None  0.445828\n4         0       0.0       4  ...            None         None -0.602031\n...     ...       ...     ...  ...             ...          ...       ...\n3196      1      46.0       0  ...            None         None  0.273744\n3197      1      47.0       0  ...            None         None -0.241160\n3198      1      48.0       0  ...            None         None -1.050772\n3199      1      49.0       0  ...            None         None -1.147731\n3200      1       NaN       0  ...            None         None -0.602011\n\n[3201 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 8,8,2 neurons and specific activation functions","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","setting = {'NNET_HIDDEN_LAYERS':'3',","           'NNET_NODES_PER_LAYER':'8,8,2',","           'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\"","           }","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1733787499732,"interpreter":"python.medium","endTime":1733787506080,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                  .000000001\n2            LBFGS_HISTORY_DEPTH                          20\n3            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n5             NNET_HIDDEN_LAYERS                           1\n6                NNET_ITERATIONS                         200\n7                 NNET_TOLERANCE                     .000001\n8                   ODMS_DETAILS                 ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n10              ODMS_RANDOM_SEED                           0\n11                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n12                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.105377\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0         0       0.0       0  ...            None         None -0.367653\n1         0       0.0       1  ...            None         None -0.000152\n2         0       0.0       2  ...            None         None  0.323073\n3         0       0.0       3  ...            None         None  0.445828\n4         0       0.0       4  ...            None         None -0.602031\n...     ...       ...     ...  ...             ...          ...       ...\n3196      1      46.0       0  ...            None         None  0.273744\n3197      1      47.0       0  ...            None         None -0.241160\n3198      1      48.0       0  ...            None         None -1.050772\n3199      1      49.0       0  ...            None         None -1.147731\n3200      1       NaN       0  ...            None         None -0.602011\n\n[3201 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 8,8,2 Neurons and multiple default options","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","settings = {'NNET_HIDDEN_LAYERS':'3',","            'NNET_NODES_PER_LAYER':'8,8,2',","            'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\",","            'NNET_HELDASIDE_MAX_FAIL':'6',","            'NNET_HELDASIDE_RATIO':'0.25',","            'NNET_ITERATIONS':'200',","            'NNET_REGULARIZER':'NNET_REGULARIZER_HELDASIDE',","            'NNET_TOLERANCE':'0.000001',","            'ODMS_RANDOM_SEED':'0'","            }","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1733787506508,"interpreter":"python.medium","endTime":1733787509363,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                                      setting value\n0                      ALGO_NAME                                ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                                         .000000001\n2            LBFGS_HISTORY_DEPTH                                                 20\n3            LBFGS_SCALE_HESSIAN                         LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BI...\n5        NNET_HELDASIDE_MAX_FAIL                                                  6\n6           NNET_HELDASIDE_RATIO                                               0.25\n7             NNET_HIDDEN_LAYERS                                                  3\n8                NNET_ITERATIONS                                                200\n9           NNET_NODES_PER_LAYER                                              8,8,2\n10              NNET_REGULARIZER                         NNET_REGULARIZER_HELDASIDE\n11                NNET_TOLERANCE                                           0.000001\n12                  ODMS_DETAILS                                        ODMS_ENABLE\n13  ODMS_MISSING_VALUE_TREATMENT                            ODMS_MISSING_VALUE_AUTO\n14              ODMS_RANDOM_SEED                                                  0\n15                 ODMS_SAMPLING                              ODMS_SAMPLING_DISABLE\n16                     PREP_AUTO                                                 ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              22\n2     LOSS_VALUE        0.248765\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0         8       NNET_ACTIVATIONS_ARCTAN\n1                1         8  NNET_ACTIVATIONS_BIPOLAR_SIG\n2                2         2         NNET_ACTIVATIONS_TANH\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0        0       0.0       0  ...            None         None -0.377668\n1        0       0.0       1  ...            None         None -0.042808\n2        0       0.0       2  ...            None         None  0.475154\n3        0       0.0       3  ...            None         None  0.253198\n4        0       0.0       4  ...            None         None -0.476047\n..     ...       ...     ...  ...             ...          ...       ...\n592      2       NaN       0  ...            None         None  0.286667\n593      2       NaN       1  ...            None         None  0.355751\n594      3       0.0       0  ...            None         None  0.721844\n595      3       1.0       0  ...            None         None  1.160136\n596      3       NaN       0  ...            None         None -0.452993\n\n[597 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model fit statistics","message":["%python","","z.show(nn_mod.global_stats.round(6))"],"enabled":true,"result":{"startTime":1733787514638,"interpreter":"python.medium","endTime":1733787515056,"results":[{"message":"attribute name\tattribute value\nCONVERGED\tYESITERATIONS\t22.0\nLOSS_VALUE\t0.24876522611293028\nNUM_ROWS\t2709.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%python","","RES_DF = nn_mod.predict(TEST_X, supplemental_cols = TEST)","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE'] + RES_DF.columns].round(2).head(20))"],"enabled":true,"result":{"startTime":1733787515482,"interpreter":"python.medium","endTime":1733787516326,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\n3.66\t4\t100924\tM\tNeverM\t1963\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t2\tCrafts\t4\t0\t3.66\n1.47\t2\t104047\tM\tNeverM\t1983\tL: 300,000 and above\t7000\tHS-grad\t0\t1\tOther\t2\t1\t1.47\n5.01\t5\t102510\tM\tMarried\t1956\tL: 300,000 and above\t15000\tAssoc-A\t1\t3\tProtec.\t5\t0\t5.01\n2.08\t3\t100799\tM\tNeverM\t1977\tG: 130,000 - 149,999\t7000\tHS-grad\t0\t1\tCrafts\t3\t1\t2.08\n1.76\t2\t102045\tM\tNeverM\t1981\tG: 130,000 - 149,999\t5000\tBach.\t0\t1\tSales\t2\t1\t1.76\n2.08\t3\t103363\tF\tDivorc.\t1974\tA: Below 30,000\t1500\tBach.\t0\t9+\tOther\t3\t1\t2.08\n3.77\t4\t100061\tM\tDivorc.\t1959\tD: 70,000 - 89,999\t5000\tAssoc-V\t0\t9+\tTechSup\t4\t0\t3.77\n5.39\t7\t102523\tF\tDivorc.\t1945\tJ: 190,000 - 249,999\t11000\t12th\t0\t2\tSales\t7\t0\t5.39\n2.03\t2\t103477\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\tAssoc-A\t0\t1\tExec.\t2\t1\t2.03\n2.81\t3\t102425\tF\tMarried\t1977\tE: 90,000 - 109,999\t7000\tBach.\t1\t4-5\tExec.\t3\t1\t2.81\n2.07\t3\t102305\tF\tDivorc.\t1976\tK: 250,000 - 299,999\t11000\t10th\t0\t2\tOther\t3\t1\t2.07\n1.77\t1\t103994\tM\tNeverM\t1982\tE: 90,000 - 109,999\t10000\t< Bach.\t0\t1\tExec.\t1\t1\t1.77\n4.87\t3\t100699\tF\tWidowed\t1941\tA: Below 30,000\t1500\tHS-grad\t0\t2\tSales\t3\t0\t4.87\n4.79\t4\t102685\tM\tMarried\t1960\tI: 170,000 - 189,999\t10000\tHS-grad\t1\t3\tMachine\t4\t0\t4.79\n2.57\t3\t103443\tF\tMarried\t1970\tF: 110,000 - 129,999\t5000\tBach.\t1\t4-5\tExec.\t3\t0\t2.57\n1.45\t2\t104472\tM\tNeverM\t1983\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t1\tOther\t2\t1\t1.45\n3.09\t5\t102217\tM\tDivorc.\t1968\tD: 70,000 - 89,999\t10000\tHS-grad\t0\t1\tFarming\t5\t0\t3.09\n4.27\t4\t102357\tF\tDivorc.\t1959\tJ: 190,000 - 249,999\t11000\t10th\t0\t9+\tCleric.\t4\t0\t4.27\n4.88\t5\t103949\tM\tMarried\t1957\tF: 110,000 - 129,999\t3000\tMasters\t1\t3\tProf.\t5\t0\t4.88\n5.67\t4\t101286\tM\tMarried\t1928\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t3\tFarming\t4\t0\t5.67\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST dataset","message":["%python","","import numpy as np","","# The coefficient of determination R?2 is part of the \"score\" method for OML models","CoefficientOfDeterminationR2 = nn_mod.score(TEST_X, TEST_Y)","    ","# Compute other statistics by hand - no need to pull data locally to Python","# RES_DF[['PREDICTION', 'YRS_RESIDENCE']","MeanAbsoluteError = (abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION']).sum())/RES_DF['YRS_RESIDENCE'].count()","MeanSquaredError =  ((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])**2).sum()/RES_DF['YRS_RESIDENCE'].count()","RootMeanSquaredError =  np.sqrt(MeanSquaredError)","","print('R^2: ', CoefficientOfDeterminationR2.round(4))","print('MAE: ', MeanAbsoluteError.round(4))","print('MSE: ', MeanSquaredError.round(4))","print('RMSE: ', RootMeanSquaredError.round(4))"],"enabled":true,"result":{"startTime":1733787516764,"interpreter":"python.medium","endTime":1733787517616,"results":[{"message":"R^2:  0.5019\nMAE:  0.9438\nMSE:  1.8741\nRMSE:  1.369\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using matplotlib, plot the predicted and actual years of residence","message":["%python","","import numpy as np","import matplotlib.pyplot as plt","plt.style.use('seaborn-v0_8')","plt.figure(figsize=[9,7])","plt.margins(0.05)","","x = RES_DF[['PREDICTION']].pull()","y = RES_DF[['YRS_RESIDENCE']].pull()","n = len(x)","","plt.plot(x, y, '.', c='black', alpha=0.8,  ms=7)","","plt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],","          c='red', alpha=0.8,  ms=3,","          label='Perfect prediction reference line')","","plt.legend(frameon=True, facecolor='lightgray')         ","plt.xlabel('PREDICTION')","plt.ylabel('YRS_RESIDENCE')","plt.title('The prediction vs. actual value for years of residence',","          fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1733787518042,"interpreter":"python.medium","endTime":1733787518681,"results":[{"message":"<div style='width:auto;height:auto'><img 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","plt.xlabel('PREDICTION')","plt.ylabel('RESIDUAL')","plt.title('Prediction of Years of Residence vs. Residuals', fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1733787519122,"interpreter":"python.medium","endTime":1733787519851,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details using Python","message":["%python","","","RES_DF = nn_mod.predict(TEST, supplemental_cols = TEST[['CUST_ID']], proba = True, topN_attrs = True)","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1733787520307,"interpreter":"python.medium","endTime":1733787535862,"results":[{"message":"CUST_ID\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100100\t3.948428784910212\tCUST_YEAR_OF_BIRTH\t1959\t.341\tY_BOX_GAMES\t0\t.028\tAFFINITY_CARD\t1\t-.007\tEDUCATION\tMasters\t-.024\tCUST_CREDIT_LIMIT\t10000\t-.027\n100200\t1.402756273647758\tCUST_MARITAL_STATUS\tNeverM\t.031\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.026\tCUST_CREDIT_LIMIT\t9000\t.004\tAFFINITY_CARD\t0\t-.007\tEDUCATION\t< Bach.\t-.032\n100300\t4.775847626897838\tCUST_YEAR_OF_BIRTH\t1961\t.424\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t.239\tY_BOX_GAMES\t0\t.059\tOCCUPATION\tProf.\t.053\tAFFINITY_CARD\t1\t.044\n100400\t5.304142246762019\tCUST_YEAR_OF_BIRTH\t1944\t.701\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t.054\tEDUCATION\t12th\t.023\tCUST_CREDIT_LIMIT\t9000\t.023\tCUST_MARITAL_STATUS\tDivorc.\t-.009\n100900\t4.289002481627409\tCUST_YEAR_OF_BIRTH\t1959\t.327\tOCCUPATION\tExec.\t.033\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-.048\tAFFINITY_CARD\t0\t-.113\tY_BOX_GAMES\t0\t-.205\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test data into a table for use in query below","message":["%python","","try:","    oml.drop(table = 'NN_TEST_DATA')","except:","    print(\"Table not exist\")","_ = TEST.materialize(table = 'NN_TEST_DATA')"],"enabled":true,"result":{"startTime":1733787536290,"interpreter":"python.medium","endTime":1733787537016,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details using SQL","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(NN_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","              PREDICTION_DETAILS(NN_REGRESSION_MODEL USING *) PD","       FROM NN_TEST_DATA","       WHERE CUST_ID < 100015","       ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","                PASSING OUT.PD","                COLUMNS ","                \"Attribute1\" XMLType PATH 'Attribute[1]',","                \"Attribute2\" XMLType PATH 'Attribute[2]',","                \"Attribute3\" XMLType PATH 'Attribute[3]') ","                OUTPRED"],"enabled":true,"result":{"startTime":1733787537447,"interpreter":"sql.medium","endTime":1733787538013,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t3.984\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".204\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".099\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".01\" \n100003\t3.293\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".125\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".085\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"7000\" weight=\".021\" \n100005\t5.032\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".435\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".248\" \t\"CUST_INCOME_LEVEL\" actualValue=\"B: 30,000 - 49,999\" weight=\".114\" \n100009\t3.573\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".247\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".173\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".04\" \n100010\t2.662\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".054\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".024\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".003\" \n100012\t3.871\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".241\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".028\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.022\" \n100014\t4.499\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".456\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\"-.009\" \t\"CUST_INCOME_LEVEL\" actualValue=\"B: 30,000 - 49,999\" weight=\"-.016\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1733787538435,"interpreter":"md.medium","endTime":1733787538845,"results":[{"message":"<h2 id=\"end-of-script\">End of 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Regression SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737171851159,"interpreter":"md.low","endTime":1737171851616,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Support Vector Machine","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737171852059,"interpreter":"md.low","endTime":1737171852484,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-support-vector-machine\">Regression Modeling to Predict Numerical Values using Support Vector Machine<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737171852926,"interpreter":"md.low","endTime":1737171853383,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/support-vector-machine.html\" target=\"_blank\">OML4Py Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1737171853854,"interpreter":"md.low","endTime":1737171854292,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/support-vector-machine.html\" target=\"_blank\">OML4Py Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1737171854726,"interpreter":"python.low","endTime":1737171855172,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES"," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1737171855629,"interpreter":"python.low","endTime":1737171856070,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%python","","z.show(CUST_DF.head(10))"],"enabled":true,"result":{"startTime":1737171856524,"interpreter":"python.low","endTime":1737171856994,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\n100804\tF\tDivorc.\t1943\tA: Below 30,000\t1500\t< Bach.\t0\t2\tProf.\t6\t0\n101610\tM\tNeverM\t1985\tI: 170,000 - 189,999\t3000\t11th\t0\t1\tHandler\t0\t1\n102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\n100593\tM\tMarried\t1963\tG: 130,000 - 149,999\t1500\tHS-grad\t1\t3\tProf.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%python","","TRAIN, TEST = CUST_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('YRS_RESIDENCE')","TRAIN_Y = TRAIN['YRS_RESIDENCE']","TEST_X = TEST.drop('YRS_RESIDENCE')","TEST_Y = TEST['YRS_RESIDENCE']"],"enabled":true,"result":{"startTime":1737171857457,"interpreter":"python.low","endTime":1737171857952,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Build a SVM Linear regression model for predicting YRS_RESIDENCE","---"],"enabled":true,"result":{"startTime":1737171858399,"interpreter":"md.low","endTime":1737171858846,"results":[{"message":"<h3 id=\"build-a-svm-linear-regression-model-for-predicting-yrs_residence\">Build a SVM Linear regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using default settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_LINEAR'}","","try:","    oml.drop(model = 'SVML_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVML_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171859278,"interpreter":"python.low","endTime":1737171861076,"results":[{"message":"\nModel Name: SVML_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                            ON\n5           SVMS_CONV_TOLERANCE                         .0001\n6                  SVMS_EPSILON                            .1\n7          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR                3\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              16\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/support-vector-machine.html\" target=\"_blank\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> 'SVMS_EPSILON' : '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> 'SVMS_BATCH_ROWS' : '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> 'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> 'SVMS_CONV_TOLERANCE': '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> 'SVMS_NUM_PIVOTS': '200' "],"enabled":true,"result":{"startTime":1737171861592,"interpreter":"md.low","endTime":1737171862026,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/support-vector-machine.html\" target=\"_blank\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' : '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE': '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS': '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using specific settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_LINEAR', ","            'SVMS_EPSILON':'0.1',","            'SVMS_CONV_TOLERANCE':'0.0001',","            'SVMS_COMPLEXITY_FACTOR':'0.1',","            'SVMS_BATCH_ROWS':'20000',","            'SVMS_REGULARIZER':'SVMS_REGULARIZER_L1',","            'SVMS_SOLVER':'SVMS_SOLVER_SGD'}","","try:","    oml.drop(model = 'SVML_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVML_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171862468,"interpreter":"python.low","endTime":1737171864530,"results":[{"message":"\nModel Name: SVML_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5                SVMS_BATCH_ROWS                         20000\n6         SVMS_COMPLEXITY_FACTOR                           0.1\n7            SVMS_CONV_TOLERANCE                        0.0001\n8                   SVMS_EPSILON                           0.1\n9           SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n10              SVMS_REGULARIZER           SVMS_REGULARIZER_L1\n11                   SVMS_SOLVER               SVMS_SOLVER_SGD\n\nComputed Settings: \n          setting name setting value\n0  SVMS_NUM_ITERATIONS          1000\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS            1000\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Build a SVM Gaussian regression model for predicting YRS_RESIDENCE","---"],"enabled":true,"result":{"startTime":1737171864966,"interpreter":"md.low","endTime":1737171865409,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-regression-model-for-predicting-yrs_residence\">Build a SVM Gaussian regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using default settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_GAUSSIAN'}","","try:","    oml.drop(model = 'SVMG_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVMG_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171865888,"interpreter":"python.low","endTime":1737171869142,"results":[{"message":"\nModel Name: SVMG_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                            ON\n5           SVMS_CONV_TOLERANCE                         .0001\n6                  SVMS_EPSILON                            .1\n7          SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n8               SVMS_NUM_PIVOTS                           200\n\nComputed Settings: \n             setting name       setting value\n0  SVMS_COMPLEXITY_FACTOR                   3\n1     SVMS_NUM_ITERATIONS                  30\n2             SVMS_SOLVER     SVMS_SOLVER_IPM\n3            SVMS_STD_DEV  2.2360679774997898\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              16\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using specific settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_GAUSSIAN', ","            'SVMS_EPSILON':'0.1',","            'SVMS_CONV_TOLERANCE':'0.0001',","            'SVMS_COMPLEXITY_FACTOR':'0.1',","            'SVMS_NUM_PIVOTS':'200',","            'SVMS_SOLVER':'SVMS_SOLVER_IPM',","            'ODMS_DETAILS':'ODMS_ENABLE'}","","try:","    oml.drop(model = 'SVMG_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVMG_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1737171869571,"interpreter":"python.low","endTime":1737171872799,"results":[{"message":"\nModel Name: SVMG_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5         SVMS_COMPLEXITY_FACTOR                           0.1\n6            SVMS_CONV_TOLERANCE                        0.0001\n7                   SVMS_EPSILON                           0.1\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                   SVMS_SOLVER               SVMS_SOLVER_IPM\n\nComputed Settings: \n          setting name       setting value\n0  SVMS_NUM_ITERATIONS                  30\n1         SVMS_STD_DEV  2.2360679774997898\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              13\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model fit details","message":["%python","","z.show(svm_mod.global_stats)"],"enabled":true,"result":{"startTime":1737171873223,"interpreter":"python.low","endTime":1737171873676,"results":[{"message":"attribute name\tattribute value\nCONVERGED\tYESITERATIONS\t13.0\nNUM_ROWS\t2709.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display computed settings","message":["%python","","svm_mod.settings"],"enabled":true,"result":{"startTime":1737171874115,"interpreter":"python.low","endTime":1737171874572,"results":[{"message":"{'svms_kernel_function': 'SVMS_GAUSSIAN', 'svms_epsilon': '0.1', 'svms_conv_tolerance': '0.0001', 'svms_complexity_factor': '0.1', 'svms_num_pivots': '200', 'svms_solver': 'SVMS_SOLVER_IPM', 'odms_details': 'ODMS_ENABLE'}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model attributes","message":["%python","","svm_mod.attributes"],"enabled":true,"result":{"startTime":1737171875015,"interpreter":"python.low","endTime":1737171875437,"results":[{"message":"['AFFINITY_CARD', 'CUST_CREDIT_LIMIT', 'CUST_GENDER', 'CUST_INCOME_LEVEL', 'CUST_MARITAL_STATUS', 'CUST_YEAR_OF_BIRTH', 'EDUCATION', 'HOUSEHOLD_SIZE', 'OCCUPATION', 'Y_BOX_GAMES']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%python","","RES_DF = svm_mod.predict(TEST.drop('YRS_RESIDENCE'), supplemental_cols = TEST)","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE'] + RES_DF.columns].head(10))"],"enabled":true,"result":{"startTime":1737171875893,"interpreter":"python.low","endTime":1737171876513,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\n4.2653551729119075\t2\t100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\t4.2653551729119075\n1.936767815982226\t2\t101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\t1.936767815982226\n5.39452953698137\t5\t103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\t5.39452953698137\n2.55804041173009\t2\t102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\t2.55804041173009\n4.3884216181451325\t4\t100558\tM\tMarried\t1964\tJ: 190,000 - 249,999\t11000\tAssoc-V\t1\t3\tMachine\t4\t0\t4.3884216181451325\n3.0708687121690614\t4\t103401\tM\tDivorc.\t1975\tI: 170,000 - 189,999\t10000\tHS-grad\t0\t2\tCrafts\t4\t1\t3.0708687121690614\n5.564799956637695\t0\t102740\tF\tDivorc.\t1929\tK: 250,000 - 299,999\t15000\t10th\t0\t2\tOther\t0\t0\t5.564799956637695\n3.3055610595204308\t3\t103829\tM\tDivorc.\t1973\tE: 90,000 - 109,999\t7000\tHS-grad\t0\t9+\tMachine\t3\t0\t3.3055610595204308\n3.0983529087758415\t3\t104077\tM\tNeverM\t1975\tL: 300,000 and above\t9000\tProfsc\t0\t2\tProf.\t3\t1\t3.0983529087758415\n4.905090463233349\t5\t101798\tM\tMarried\t1960\tG: 130,000 - 149,999\t15000\tHS-grad\t1\t3\tExec.\t5\t0\t4.905090463233349\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%python","","import numpy as np","","# The coefficient of determination R?2 is part of the \"score\" method for OML models","CoefficientOfDeterminationR2 = svm_mod.score(TEST_X, TEST_Y)","    ","# Compute other statistics by hand - no need to pull data locally to Python","# RES_DF[['PREDICTION', 'YRS_RESIDENCE']","MeanAbsoluteError = (abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION']).sum())/RES_DF['YRS_RESIDENCE'].count()","MeanSquaredError =  ((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])**2).sum()/RES_DF['YRS_RESIDENCE'].count()","RootMeanSquaredError =  np.sqrt(MeanSquaredError)","","print('R^2: ', CoefficientOfDeterminationR2.round(4))","print('MAE: ', MeanAbsoluteError.round(4))","print('MSE: ', MeanSquaredError.round(4))","print('RMSE: ', RootMeanSquaredError.round(4))"],"enabled":true,"result":{"startTime":1737171877052,"interpreter":"python.low","endTime":1737171878243,"results":[{"message":"R^2:  0.5754\nMAE:  0.8357\nMSE:  1.5975\nRMSE:  1.2639\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"scatterplot\":{\"groupByColumns\":[],\"showSeriesY\":[\"YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"viewport\":{\"xAxis\":{\"viewportMin\":6.830003665689149,\"viewportMax\":6.93937866568915},\"yAxis\":{\"viewportMin\":7.10402097902098,\"viewportMax\":7.291520979020979}},\"lastColumns\":[\"PREDICTION\",\"YRS_RESIDENCE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot the predicted and actual years of residence ","message":["%python","","z.show(RES_DF[['PREDICTION', 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matplotlib, plot the predicted and actual years of residence","message":["%python","","import numpy as np","import matplotlib.pyplot as plt","plt.style.use('seaborn-v0_8')","plt.figure(figsize=[9,7])","plt.margins(0.05)","","x = RES_DF[['PREDICTION']].pull()","y = RES_DF[['YRS_RESIDENCE']].pull()","n = len(x)","","plt.plot(x, y, '.', c='black', alpha=0.8,  ms=7)","","plt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],","          c='red', alpha=0.8,  ms=3,","          label='Perfect prediction reference line')","","plt.legend(frameon=True, facecolor='lightgray')         ","plt.xlabel('PREDICTION')","plt.ylabel('YRS_RESIDENCE')","plt.title('The prediction vs. actual value for years of residence',","          fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1737171879943,"interpreter":"python.low","endTime":1737171880931,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details in python (using topN_attrs)","message":["%python","","","RES_DF = svm_mod.predict(TEST, supplemental_cols = TEST[['CUST_ID']], proba = True, topN_attrs = True)","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1737171882394,"interpreter":"python.low","endTime":1737171899885,"results":[{"message":"CUST_ID\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100100\t4.516021016670691\tCUST_YEAR_OF_BIRTH\t1959\t.347\tY_BOX_GAMES\t0\t.066\tEDUCATION\tMasters\t.05\tAFFINITY_CARD\t1\t.013\tCUST_CREDIT_LIMIT\t10000\t-.023\n100200\t1.6824188766968171\tCUST_CREDIT_LIMIT\t9000\t.014\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-.013\tEDUCATION\t< Bach.\t-.019\tAFFINITY_CARD\t0\t-.023\tOCCUPATION\tOther\t-.04\n100300\t4.835697714769111\tCUST_YEAR_OF_BIRTH\t1961\t.428\tY_BOX_GAMES\t0\t.109\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t.096\tAFFINITY_CARD\t1\t.04\tEDUCATION\tBach.\t-.017\n100400\t5.85730022775374\tCUST_YEAR_OF_BIRTH\t1944\t.736\tY_BOX_GAMES\t0\t.05\tCUST_MARITAL_STATUS\tDivorc.\t.01\tCUST_CREDIT_LIMIT\t9000\t-.002\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t-.01\n100900\t4.688725760525475\tCUST_YEAR_OF_BIRTH\t1959\t.425\tOCCUPATION\tExec.\t.074\tY_BOX_GAMES\t0\t.068\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-.085\tAFFINITY_CARD\t0\t-.129\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize SVM_TEST_DATA for use in query below","message":["%python","","try:","    oml.drop(table = 'SVM_TEST_DATA')","except:","    pass","_ = TEST.materialize(table = 'SVM_TEST_DATA')"],"enabled":true,"result":{"startTime":1737171900323,"interpreter":"python.low","endTime":1737171901027,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(SVMG_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","              PREDICTION_DETAILS(SVMG_REGRESSION_MODEL USING *) PD","              FROM SVM_TEST_DATA","              WHERE CUST_ID < 100015","              ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","       PASSING OUT.PD","       COLUMNS ","       \"Attribute1\" XMLType PATH 'Attribute[1]',","       \"Attribute2\" XMLType PATH 'Attribute[2]',","       \"Attribute3\" XMLType PATH 'Attribute[3]') ","       OUTPRED"],"enabled":true,"result":{"startTime":1737171901477,"interpreter":"sql.low","endTime":1737171901973,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t4.473\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".273\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".105\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".103\" \n100003\t3.617\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".158\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".048\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".007\" \n100005\t4.904\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".444\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".072\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".067\" \n100009\t3.418\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".179\" \t\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".039\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".034\" \n100010\t3.06\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".032\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".012\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\"-.01\" \n100012\t4.064\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".142\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".096\" \t\"EDUCATION\" actualValue=\"PhD\" weight=\".012\" \n100014\t5.122\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".537\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".072\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".057\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1737171902418,"interpreter":"md.low","endTime":1737171902875,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737171903315,"interpreter":"md.low","endTime":1737171903747,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":"zeppelin","template":null,"templateConfig":null,"name":"OML4Py SQL API for Embedded Python Execution","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736459742397,"interpreter":"md.low","endTime":1736459742904,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py SQL API for invoking embedded Python execution","","Oracle Machine Learning for Python (OML4Py) embedded Python execution  allows users to run user-defined Python functions (UDFs) on Python engines spawned and managed by the database environment. Through system-provided data-parallel and task-parallel functionality, embedded Python execution also enables automated and simplified runtime support for greater scalability and performance. OML4Py provides three interfaces for embedded Python execution: Python, SQL, and REST, and this notebook provides an overview of the OML4Py SQL interface for embedded Python execution on Oracle Autonomous Database.","","On Autonomous Database, OML4Py provides SQL interfaces for setting authorization tokens, managing access control list (ACL) privileges, and synchronously and asynchronously running UDFs, also referred to as scripts. Supporting OML4Py embedded Python execution is the ability to store UDFs in the database script repository and Python objects in the database datastore \u2013 there are no separate flat files to manage, back up, or secure. The datastore also enables loading saved objects for use in UDFs. Since it\u2019s under database control and invoked through SQL, scheduling Python functions to run is easily achieved using DBMS_SCHEDULER or other scheduling interfaces that invoke SQL.","","To use the SQL API, a UDF must reside in the OML4Py script repository, and an Oracle Machine Learning (OML) cloud account `username` and `password` must be provided for authentication.","","### **SQL API Workflow**","","In this notebook, we provide a typical workflow for using the OML4Py SQL API for embedded Python execution with Oracle Autonomous Database:","","- Connect to Autonomous Database as the ADMIN user and add the OML user to cloud host ACL list","- Confirm the host ACL list contains an entry for the OML user","- Obtain an authorization token to access the SQL API","- Confirm the token is set","- Create a UDF and store it in the script repository","- Run the UDF using embedded Python execution from the OML4Py Python and SQL APIs","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736459743413,"interpreter":"md.low","endTime":1736459743888,"results":[{"message":"<h1 id=\"oml4py-sql-api-for-invoking-embedded-python-execution\">OML4Py SQL API for invoking embedded Python execution<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) embedded Python execution  allows users to run user-defined Python functions (UDFs) on Python engines spawned and managed by the database environment. Through system-provided data-parallel and task-parallel functionality, embedded Python execution also enables automated and simplified runtime support for greater scalability and performance. OML4Py provides three interfaces for embedded Python execution: Python, SQL, and REST, and this notebook provides an overview of the OML4Py SQL interface for embedded Python execution on Oracle Autonomous Database.<\/p>\n<p>On Autonomous Database, OML4Py provides SQL interfaces for setting authorization tokens, managing access control list (ACL) privileges, and synchronously and asynchronously running UDFs, also referred to as scripts. Supporting OML4Py embedded Python execution is the ability to store UDFs in the database script repository and Python objects in the database datastore \u2013 there are no separate flat files to manage, back up, or secure. The datastore also enables loading saved objects for use in UDFs. Since it\u2019s under database control and invoked through SQL, scheduling Python functions to run is easily achieved using DBMS_SCHEDULER or other scheduling interfaces that invoke SQL.<\/p>\n<p>To use the SQL API, a UDF must reside in the OML4Py script repository, and an Oracle Machine Learning (OML) cloud account <code>username<\/code> and <code>password<\/code> must be provided for authentication.<\/p>\n<h3 id=\"sql-api-workflow\"><strong>SQL API Workflow<\/strong><\/h3>\n<p>In this notebook, we provide a typical workflow for using the OML4Py SQL API for embedded Python execution with Oracle Autonomous Database:<\/p>\n<ul>\n<li>Connect to Autonomous Database as the ADMIN user and add the OML user to cloud host ACL list<\/li>\n<li>Confirm the host ACL list contains an entry for the OML user<\/li>\n<li>Obtain an authorization token to access the SQL API<\/li>\n<li>Confirm the token is set<\/li>\n<li>Create a UDF and store it in the script repository<\/li>\n<li>Run the UDF using embedded Python execution from the OML4Py Python and SQL APIs<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","Refer to the OML4Py User's Guide","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/\" target=\"_blank\">Oracle Machine Learning for Python User's Guide<\/a>"],"enabled":true,"result":{"startTime":1736459744388,"interpreter":"md.low","endTime":1736459744835,"results":[{"message":"<p>Refer to the OML4Py User's Guide\n<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/\" target=\"_blank\">Oracle Machine Learning for Python User's Guide<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Add the OML user to the cloud host ACL list - must be ADMIN user","message":["%md","","You can control and restrict access to your Autonomous Database by specifying network access control lists (ACLs). The ACL provides additional protection to your Autonomous Database by allowing only authorized IP addresses to connect to the database. ","","OML4Py provides the `pyqAppendHostAce` function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where the Autonomous Database resides, e.g. *adb.us-region-1.oraclecloudapps.com*. Run the `pyqAppendHostAce` function as **ADMIN** user in a SQL Developer or SQL Developer Web instance."],"enabled":true,"result":{"startTime":1736459745292,"interpreter":"md.low","endTime":1736459745737,"results":[{"message":"<p>You can control and restrict access to your Autonomous Database by specifying network access control lists (ACLs). The ACL provides additional protection to your Autonomous Database by allowing only authorized IP addresses to connect to the database.<\/p>\n<p>OML4Py provides the <code>pyqAppendHostAce<\/code> function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where the Autonomous Database resides, e.g. <em>adb.us-region-1.oraclecloudapps.com<\/em>. Run the <code>pyqAppendHostAce<\/code> function as <strong>ADMIN<\/strong> user in a SQL Developer or SQL Developer Web instance.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","SQL> exec pyqAppendHostAce('OMLUSER','adb.us-sanjose-1.oraclecloudapps.com');","","  PL/SQL procedure successfully completed.","  Elapsed: 00:00:00.375"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Confirm the host ACL list contains an entry for the OML user","message":["%md","","The `pyqGetHostACE` function gets the existing host access control entry (ACE) for the specified user. If the user has access to the Autonomous Database cloud host, the ADMIN user can run the following to check the user's privileges. In this example, the hostname is returned if the host ACL entry exists for the specified user, and if not, an exception is raised. "],"enabled":true,"result":{"startTime":1736459746198,"interpreter":"md.low","endTime":1736459746645,"results":[{"message":"<p>The <code>pyqGetHostACE<\/code> function gets the existing host access control entry (ACE) for the specified user. If the user has access to the Autonomous Database cloud host, the ADMIN user can run the following to check the user's privileges. In this example, the hostname is returned if the host ACL entry exists for the specified user, and if not, an exception is raised.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["SQL> set serveroutput on","","DECLARE ","    hostname VARCHAR2(4000);","BEGIN","    hostname := pyqGetHostACE('OMLUSER'); ","    DBMS_OUTPUT.put_line ('hostname: ' || hostname);","END;","/","","SQL> hostname: https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com","PL/SQL procedure successfully completed."],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Obtain an authorization token","message":["%md","","The OML4Py SQL API uses tokens to authenticate an OML user. To authenticate and obtain an access token, use `cURL` or another REST client, such as Postman, to exchange the OML user credentials for an authorization token. Send a POST request to the token URL endpoint */oauth2/v1/token* with your OML username and password. The token URL can be obtained from the OML service console in Autonomous Database:","","- Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.","- Click the hamburger menu and select the provisioned Autonomous Database instance. ","- Click **Service Console** and then click **Development**.","- Scroll down to **Oracle Machine Learning RESTful Services** tile and click Copy to obtain the following URLs for:","- Obtaining the REST authentication token for REST APIs provided by OML:","","    *oml-cloud-service-location-url*/omlusers/","","The URL *oml-cloud-service-location-url* includes the tenancy ID, location, and database name. For example, https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com.","","For example,","","- qtraya2braestch is the tenancy ID","- omldb is the database name","- us-sanjose-1 is the datacenter region","- oraclecloudapps.com is the root domain"],"enabled":true,"result":{"startTime":1736459747108,"interpreter":"md.low","endTime":1736459747551,"results":[{"message":"<p>The OML4Py SQL API uses tokens to authenticate an OML user. To authenticate and obtain an access token, use <code>cURL<\/code> or another REST client, such as Postman, to exchange the OML user credentials for an authorization token. Send a POST request to the token URL endpoint <em>/oauth2/v1/token<\/em> with your OML username and password. The token URL can be obtained from the OML service console in Autonomous Database:<\/p>\n<ul>\n<li>\n<p>Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.<\/p>\n<\/li>\n<li>\n<p>Click the hamburger menu and select the provisioned Autonomous Database instance.<\/p>\n<\/li>\n<li>\n<p>Click <strong>Service Console<\/strong> and then click <strong>Development<\/strong>.<\/p>\n<\/li>\n<li>\n<p>Scroll down to <strong>Oracle Machine Learning RESTful Services<\/strong> tile and click Copy to obtain the following URLs for:<\/p>\n<\/li>\n<li>\n<p>Obtaining the REST authentication token for REST APIs provided by OML:<\/p>\n<p><em>oml-cloud-service-location-url<\/em>/omlusers/<\/p>\n<\/li>\n<\/ul>\n<p>The URL <em>oml-cloud-service-location-url<\/em> includes the tenancy ID, location, and database name. For example, <a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a>.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>qtraya2braestch is the tenancy ID<\/li>\n<li>omldb is the database name<\/li>\n<li>us-sanjose-1 is the datacenter region<\/li>\n<li>oraclecloudapps.com is the root domain<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define get_token2 function that obtains access token","message":["%script","","create or replace function get_token2 (","  p_url       varchar2,","  p_username  varchar2,","  p_password  varchar2,","  p_verbose   number default 0) ","  return varchar2","  authid definer as pragma autonomous_transaction;","  l_buffer            varchar2(4000);","  l_request_content   clob;","  l_response_content  clob;","  l_request           utl_http.req;","  l_response          utl_http.resp;","  l_url               varchar2(1000);","  l_smt               varchar2(1000);","  l_access_token      varchar2(4000);","begin","  l_url := p_url;","  l_request_content := q'[{\"grant_type\":\"password\", \"username\":\"]' || p_username || q'[\", \"password\":\"]' || p_password ||","  q'[\"}]';","  begin","    utl_http.set_wallet('');","    l_request := utl_http.begin_request(l_url,'POST',' HTTP/1.1');","    utl_http.set_header(l_request,'Content-Type','application/json');","    utl_http.set_header(l_request,'Accept','application/json');","    utl_http.set_header(l_request,'Content-Length',length(l_request_content));","    utl_http.write_text(l_request,l_request_content);","    l_response := utl_http.get_response(l_request);","    if (l_response.status_code != utl_http.HTTP_OK) THEN","      RAISE_APPLICATION_ERROR(-20999, 'Token request failed with status code: '||l_response.status_code||', reason: '||l_response.reason_phrase);","    end if;","    if p_verbose > 0 then","      dbms_output.put_line('HTTP response status code: ' || l_response.status_code);","      dbms_output.put_line('HTTP response reason phrase: ' || l_response.reason_phrase);","    end if;","    begin","      loop","        utl_http.read_line(l_response,l_buffer);","        l_access_token := regexp_substr(l_buffer,'\"accessToken\":\"([^\"]+)\"',1,1,'i',1);","        if p_verbose > 0 then","          dbms_output.put_line('l_access_token: [' || l_access_token || ']');","        end if;","      end loop;","      utl_http.end_response(l_response);","    exception","      when utl_http.end_of_body then utl_http.end_response(l_response);","    end;","  end;","  return(l_access_token);","  dbms_output.put_line('Access token is: ' || l_access_token);","end get_token2;","/"],"enabled":true,"result":{"startTime":1736459748006,"interpreter":"script.low","endTime":1736459748506,"results":[{"message":"\nFunction GET_TOKEN2 compiled\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE ACCESS_DETAILS';","EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","CREATE TABLE ACCESS_DETAILS (","  NAME  VARCHAR2(20),","  VALUE VARCHAR2(1000)","  );","/","--Enable this paragraph and replace URL, USERNAME, and PASSWORD values with your OML URL, username, and password","INSERT INTO ACCESS_DETAILS VALUES ('URL', '< YOUR OML URL>/omlusers/api/oauth2/v1/token');","INSERT INTO ACCESS_DETAILS VALUES ('USERNAME', 'YOUR OML USER');","INSERT INTO ACCESS_DETAILS VALUES ('PASSWORD', 'YOUR OML USER PASSWORD');"],"enabled":false,"result":{"startTime":1736459748964,"interpreter":"script.low","endTime":1736459749570,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable ACCESS_DETAILS created.\n\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Obtain and set the authorization token","message":["%md ","","OML4Py provides APIs to persist the authorization token for use with the upcoming SQL calls:","","- The `pyqSetAuthToken` procedure sets the access token in the token store.","- The `pyqIsTokenSet` function confirms if the authorization token status. ","","The following example shows how to set the token in the token store and confirm it's status. A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1736459750028,"interpreter":"md.low","endTime":1736459750475,"results":[{"message":"<p>OML4Py provides APIs to persist the authorization token for use with the upcoming SQL calls:<\/p>\n<ul>\n<li>The <code>pyqSetAuthToken<\/code> procedure sets the access token in the token store.<\/li>\n<li>The <code>pyqIsTokenSet<\/code> function confirms if the authorization token status.<\/li>\n<\/ul>\n<p>The following example shows how to set the token in the token store and confirm it's status. A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","   access_token VARCHAR2(2000);","   is_set BOOLEAN;","BEGIN","   with url_string  as (select VALUE from ACCESS_DETAILS where NAME = 'URL'), ","        username    as (select VALUE from ACCESS_DETAILS where NAME = 'USERNAME'),","        pswd        as (select VALUE from ACCESS_DETAILS where NAME = 'PASSWORD')","   SELECT get_token2(url_string.VALUE, username.VALUE, pswd.VALUE) INTO access_token ","   FROM url_string, username, pswd ","   WHERE localtimestamp=localtimestamp;","","   pyqSetAuthToken(access_token);","     is_set := pyqIsTokenSet();","     IF (is_set) THEN","        DBMS_OUTPUT.put_line ('token is set');","       --enable to return token","       --DBMS_OUTPUT.put_line (''|| access_token);","    END IF;","END;"],"enabled":true,"result":{"startTime":1736459750939,"interpreter":"script.low","endTime":1736459752827,"results":[{"message":"token is set\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load the IRIS data to a database table and obtain an OML Dataframe proxy object for use with OML4Py","message":["%md","","In this example, we load the `iris` dataset in Python and create a database table using the `oml.create` function, which returns an OML DataFrame proxy object. "],"enabled":true,"result":{"startTime":1736459753311,"interpreter":"md.low","endTime":1736459753753,"results":[{"message":"<p>In this example, we load the <code>iris<\/code> dataset in Python and create a database table using the <code>oml.create<\/code> function, which returns an OML DataFrame proxy object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"lastColumns\":[\"SEPAL_LENGTH\",\"SEPAL_WIDTH\",\"PETAL_LENGTH\",\"PETAL_WIDTH\",\"SPECIES\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"IRIS table data","message":["%python","","import oml","","import pandas as pd","from sklearn import datasets ","import warnings","warnings.simplefilter(action='ignore', category=FutureWarning)","","iris = datasets.load_iris()","iris_data = iris.data.astype(float)","","X = pd.DataFrame(iris_data,","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)),","                 columns = ['SPECIES'])","","try:","    oml.drop(table='IRIS')","except:","    pass","","iris = pd.concat([X, y], axis=1)","IRIS = oml.create(iris, table = 'IRIS')","","z.show(IRIS)"],"enabled":true,"result":{"startTime":1736459754234,"interpreter":"python.low","endTime":1736459757116,"results":[{"message":"SEPAL_LENGTH\tSEPAL_WIDTH\tPETAL_LENGTH\tPETAL_WIDTH\tSPECIES\n4.6\t3.6\t1.0\t0.2\tsetosa\n5.1\t2.5\t3.0\t1.1\tversicolor\n6.0\t2.2\t4.0\t1.0\tversicolor\n5.8\t2.6\t4.0\t1.2\tversicolor\n5.5\t2.3\t4.0\t1.3\tversicolor\n5.5\t2.5\t4.0\t1.3\tversicolor\n6.1\t2.8\t4.0\t1.3\tversicolor\n5.7\t2.5\t5.0\t2.0\tvirginica\n6.0\t2.2\t5.0\t1.5\tvirginica\n6.3\t2.5\t5.0\t1.9\tvirginica\n6.7\t3.0\t5.0\t1.7\tversicolor\n7.2\t3.2\t6.0\t1.8\tvirginica\n6.3\t3.3\t6.0\t2.5\tvirginica\n4.3\t3.0\t1.1\t0.1\tsetosa\n5.8\t4.0\t1.2\t0.2\tsetosa\n5.0\t3.2\t1.2\t0.2\tsetosa\n4.4\t3.0\t1.3\t0.2\tsetosa\n4.4\t3.2\t1.3\t0.2\tsetosa\n4.7\t3.2\t1.3\t0.2\tsetosa\n5.5\t3.5\t1.3\t0.2\tsetosa\n4.5\t2.3\t1.3\t0.3\tsetosa\n5.0\t3.5\t1.3\t0.3\tsetosa\n5.4\t3.9\t1.3\t0.4\tsetosa\n4.8\t3.0\t1.4\t0.1\tsetosa\n4.9\t3.6\t1.4\t0.1\tsetosa\n4.9\t3.0\t1.4\t0.2\tsetosa\n4.4\t2.9\t1.4\t0.2\tsetosa\n4.6\t3.2\t1.4\t0.2\tsetosa\n5.0\t3.3\t1.4\t0.2\tsetosa\n5.2\t3.4\t1.4\t0.2\tsetosa\n5.1\t3.5\t1.4\t0.2\tsetosa\n5.0\t3.6\t1.4\t0.2\tsetosa\n5.5\t4.2\t1.4\t0.2\tsetosa\n4.8\t3.0\t1.4\t0.3\tsetosa\n4.6\t3.4\t1.4\t0.3\tsetosa\n5.1\t3.5\t1.4\t0.3\tsetosa\n4.9\t3.1\t1.5\t0.1\tsetosa\n5.2\t4.1\t1.5\t0.1\tsetosa\n4.6\t3.1\t1.5\t0.2\tsetosa\n4.9\t3.1\t1.5\t0.2\tsetosa\n5.0\t3.4\t1.5\t0.2\tsetosa\n5.1\t3.4\t1.5\t0.2\tsetosa\n5.2\t3.5\t1.5\t0.2\tsetosa\n5.3\t3.7\t1.5\t0.2\tsetosa\n5.4\t3.7\t1.5\t0.2\tsetosa\n5.1\t3.8\t1.5\t0.3\tsetosa\n5.4\t3.4\t1.5\t0.4\tsetosa\n5.1\t3.7\t1.5\t0.4\tsetosa\n5.7\t4.4\t1.5\t0.4\tsetosa\n5.0\t3.0\t1.6\t0.2\tsetosa\n4.8\t3.1\t1.6\t0.2\tsetosa\n4.7\t3.2\t1.6\t0.2\tsetosa\n4.8\t3.4\t1.6\t0.2\tsetosa\n5.1\t3.8\t1.6\t0.2\tsetosa\n5.0\t3.4\t1.6\t0.4\tsetosa\n5.0\t3.5\t1.6\t0.6\tsetosa\n5.4\t3.4\t1.7\t0.2\tsetosa\n5.7\t3.8\t1.7\t0.3\tsetosa\n5.4\t3.9\t1.7\t0.4\tsetosa\n5.1\t3.3\t1.7\t0.5\tsetosa\n4.8\t3.4\t1.9\t0.2\tsetosa\n5.1\t3.8\t1.9\t0.4\tsetosa\n5.0\t2.3\t3.3\t1.0\tversicolor\n4.9\t2.4\t3.3\t1.0\tversicolor\n5.0\t2.0\t3.5\t1.0\tversicolor\n5.7\t2.6\t3.5\t1.0\tversicolor\n5.6\t2.9\t3.6\t1.3\tversicolor\n5.5\t2.4\t3.7\t1.0\tversicolor\n5.5\t2.4\t3.8\t1.1\tversicolor\n5.6\t2.5\t3.9\t1.1\tversicolor\n5.8\t2.7\t3.9\t1.2\tversicolor\n5.2\t2.7\t3.9\t1.4\tversicolor\n5.8\t2.7\t4.1\t1.0\tversicolor\n5.6\t3.0\t4.1\t1.3\tversicolor\n5.7\t2.8\t4.1\t1.3\tversicolor\n5.7\t3.0\t4.2\t1.2\tversicolor\n5.6\t2.7\t4.2\t1.3\tversicolor\n5.7\t2.9\t4.2\t1.3\tversicolor\n5.9\t3.0\t4.2\t1.5\tversicolor\n6.2\t2.9\t4.3\t1.3\tversicolor\n6.4\t2.9\t4.3\t1.3\tversicolor\n5.5\t2.6\t4.4\t1.2\tversicolor\n6.3\t2.3\t4.4\t1.3\tversicolor\n6.6\t3.0\t4.4\t1.4\tversicolor\n6.7\t3.1\t4.4\t1.4\tversicolor\n4.9\t2.5\t4.5\t1.7\tvirginica\n5.7\t2.8\t4.5\t1.3\tversicolor\n5.4\t3.0\t4.5\t1.5\tversicolor\n5.6\t3.0\t4.5\t1.5\tversicolor\n6.2\t2.2\t4.5\t1.5\tversicolor\n6.0\t2.9\t4.5\t1.5\tversicolor\n6.4\t3.2\t4.5\t1.5\tversicolor\n6.0\t3.4\t4.5\t1.6\tversicolor\n6.6\t2.9\t4.6\t1.3\tversicolor\n6.1\t3.0\t4.6\t1.4\tversicolor\n6.5\t2.8\t4.6\t1.5\tversicolor\n6.1\t2.8\t4.7\t1.2\tversicolor\n6.1\t2.9\t4.7\t1.4\tversicolor\n7.0\t3.2\t4.7\t1.4\tversicolor\n6.7\t3.1\t4.7\t1.5\tversicolor\n6.3\t3.3\t4.7\t1.6\tversicolor\n6.0\t3.0\t4.8\t1.8\tvirginica\n6.2\t2.8\t4.8\t1.8\tvirginica\n6.8\t2.8\t4.8\t1.4\tversicolor\n5.9\t3.2\t4.8\t1.8\tversicolor\n5.6\t2.8\t4.9\t2.0\tvirginica\n6.1\t3.0\t4.9\t1.8\tvirginica\n6.3\t2.7\t4.9\t1.8\tvirginica\n6.3\t2.5\t4.9\t1.5\tversicolor\n6.9\t3.1\t4.9\t1.5\tversicolor\n6.5\t3.2\t5.1\t2.0\tvirginica\n6.3\t2.8\t5.1\t1.5\tvirginica\n5.9\t3.0\t5.1\t1.8\tvirginica\n5.8\t2.7\t5.1\t1.9\tvirginica\n5.8\t2.7\t5.1\t1.9\tvirginica\n6.9\t3.1\t5.1\t2.3\tvirginica\n5.8\t2.8\t5.1\t2.4\tvirginica\n6.0\t2.7\t5.1\t1.6\tversicolor\n6.5\t3.0\t5.2\t2.0\tvirginica\n6.7\t3.0\t5.2\t2.3\tvirginica\n6.4\t2.7\t5.3\t1.9\tvirginica\n6.4\t3.2\t5.3\t2.3\tvirginica\n6.9\t3.1\t5.4\t2.1\tvirginica\n6.2\t3.4\t5.4\t2.3\tvirginica\n6.5\t3.0\t5.5\t1.8\tvirginica\n6.4\t3.1\t5.5\t1.8\tvirginica\n6.8\t3.0\t5.5\t2.1\tvirginica\n6.1\t2.6\t5.6\t1.4\tvirginica\n6.3\t2.9\t5.6\t1.8\tvirginica\n6.4\t2.8\t5.6\t2.1\tvirginica\n6.4\t2.8\t5.6\t2.2\tvirginica\n6.7\t3.1\t5.6\t2.4\tvirginica\n6.3\t3.4\t5.6\t2.4\tvirginica\n6.7\t3.3\t5.7\t2.1\tvirginica\n6.9\t3.2\t5.7\t2.3\tvirginica\n6.7\t3.3\t5.7\t2.5\tvirginica\n7.2\t3.0\t5.8\t1.6\tvirginica\n6.7\t2.5\t5.8\t1.8\tvirginica\n6.5\t3.0\t5.8\t2.2\tvirginica\n7.1\t3.0\t5.9\t2.1\tvirginica\n6.8\t3.2\t5.9\t2.3\tvirginica\n7.4\t2.8\t6.1\t1.9\tvirginica\n7.7\t3.0\t6.1\t2.3\tvirginica\n7.2\t3.6\t6.1\t2.5\tvirginica\n7.3\t2.9\t6.3\t1.8\tvirginica\n7.9\t3.8\t6.4\t2.0\tvirginica\n7.6\t3.0\t6.6\t2.1\tvirginica\n7.7\t2.8\t6.7\t2.0\tvirginica\n7.7\t3.8\t6.7\t2.2\tvirginica\n7.7\t2.6\t6.9\t2.3\tvirginica\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a simple UDF and save it to the Python script repository in the database","message":["%md","","In this UDF, we return the counts for each category in the `SPECIES` column  using `oml.group_apply`. The function will be invoked from the embedded Python execution Python and SQL interfaces. "],"enabled":true,"result":{"startTime":1736459758274,"interpreter":"md.low","endTime":1736459758712,"results":[{"message":"<p>In this UDF, we return the counts for each category in the <code>SPECIES<\/code> column  using <code>oml.group_apply<\/code>. The function will be invoked from the embedded Python execution Python and SQL interfaces.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","def group_count(dat):","  import oml","  import pandas as pd","  return pd.DataFrame([(dat[\"SPECIES\"][0], dat.shape[0])], ","  columns = [\"SPECIES\", \"COUNT\"])"],"enabled":true,"result":{"startTime":1736459759179,"interpreter":"python.low","endTime":1736459759636,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Run function locally in Python interpreter on explicit subset of data","message":["%python","","group_count(iris[iris[\"SPECIES\"] == 'setosa'])"],"enabled":true,"result":{"startTime":1736459760092,"interpreter":"python.low","endTime":1736459760591,"results":[{"message":"  SPECIES  COUNT\n0  setosa     50\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use group_apply to illustrate automated data-parallel processing","message":["%md","","Group the data by the `SPECIES` column and run the user-defined function for each category in `SPECIES`: *versicolor*, *virginica*, and *setosa*.","","Notice that we're providing the actual function object in the Python API for argument `func`."],"enabled":true,"result":{"startTime":1736459761067,"interpreter":"md.low","endTime":1736459761510,"results":[{"message":"<p>Group the data by the <code>SPECIES<\/code> column and run the user-defined function for each category in <code>SPECIES<\/code>: <em>versicolor<\/em>, <em>virginica<\/em>, and <em>setosa<\/em>.<\/p>\n<p>Notice that we're providing the actual function object in the Python API for argument <code>func<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","index = IRIS['SPECIES']","","res = oml.group_apply(data       = IRIS, ","                      index      = index, ","                      func       = group_count, ","                      func_value = pd.DataFrame([('a', 1)],columns=[\"SPECIES\", \"CNT\"]))","","res"],"enabled":true,"result":{"startTime":1736459761987,"interpreter":"python.low","endTime":1736459763567,"results":[{"message":"      SPECIES  CNT\n0   virginica   50\n1      setosa   50\n2  versicolor   50\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Save this UDF in the Python script repository","Save this UDF in the Python script repository using `oml.script.create` with the name \"group_count\", and overwrite any existing function."],"enabled":true,"result":{"startTime":1736459764024,"interpreter":"md.low","endTime":1736459764462,"results":[{"message":"<h3 id=\"save-this-udf-in-the-python-script-repository\">Save this UDF in the Python script repository<\/h3>\n<p>Save this UDF in the Python script repository using <code>oml.script.create<\/code> with the name &quot;group_count&quot;, and overwrite any existing function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","group_count_str = \"\"\"def group_count(dat):","  import oml","  import pandas as pd","  return pd.DataFrame([(dat[\"SPECIES\"][0], dat.shape[0])], columns = [\"SPECIES\", \"COUNT\"])\"\"\"","","oml.script.create(\"group_count\", func=group_count_str, overwrite=True) "],"enabled":true,"result":{"startTime":1736459764928,"interpreter":"python.low","endTime":1736459765458,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the UDF in the Python script repository","message":["%md","","Check for the existence of the `group_count` script in the script repository. Run the OML4Py function `oml.script.dir` and pass the script name to the `name` parameter."],"enabled":true,"result":{"startTime":1736459765915,"interpreter":"md.low","endTime":1736459766350,"results":[{"message":"<p>Check for the existence of the <code>group_count<\/code> script in the script repository. Run the OML4Py function <code>oml.script.dir<\/code> and pass the script name to the <code>name<\/code> parameter.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.script.dir(name = \"group_count\")"],"enabled":true,"result":{"startTime":1736459766811,"interpreter":"python.low","endTime":1736459767280,"results":[{"message":"          name  ...                date\n0  group_count  ... 2025-01-09 21:56:05\n\n[1 rows x 4 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the same function using the SQL API table function pyqGroupEval","message":["%md","","Invoke the `group_count` function that's stored in the script repository from the SQL API. As in we did in the Python API, group data by the `SPECIES` column and set the `oml_parallel_flag` to true to leverage multiple Python engines in parallel. Specify the Autonomous Database service level to use with the optional `oml_service_level` argument. The service names are `LOW`, `MEDIUM`, and `HIGH`. The service names provide different levels of performance and concurrency for an Autonomous Database. In this case, we use service level `MEDIUM` to enable moderate parallelism.","","The `OUT_FMT` parameter controls the format of output returned by the embedded Python execution functions.","","The supported output formats are:","","- JSON","- Relational","- XML","- PNG","- Asynchronous Mode Output","","The Python function's supported output formats are:","","- boolean, ","- dict, ","- float","- int","- list","- str","- tuple","- pandas.DataFrame object","","The default SQL API execution mode is *synchronous*, so code execution will wait for the SQL invocation to return results before additional calls may be initiated. "],"enabled":true,"result":{"startTime":1736459767733,"interpreter":"md.low","endTime":1736459768182,"results":[{"message":"<p>Invoke the <code>group_count<\/code> function that's stored in the script repository from the SQL API. As in we did in the Python API, group data by the <code>SPECIES<\/code> column and set the <code>oml_parallel_flag<\/code> to true to leverage multiple Python engines in parallel. Specify the Autonomous Database service level to use with the optional <code>oml_service_level<\/code> argument. The service names are <code>LOW<\/code>, <code>MEDIUM<\/code>, and <code>HIGH<\/code>. The service names provide different levels of performance and concurrency for an Autonomous Database. In this case, we use service level <code>MEDIUM<\/code> to enable moderate parallelism.<\/p>\n<p>The <code>OUT_FMT<\/code> parameter controls the format of output returned by the embedded Python execution functions.<\/p>\n<p>The supported output formats are:<\/p>\n<ul>\n<li>JSON<\/li>\n<li>Relational<\/li>\n<li>XML<\/li>\n<li>PNG<\/li>\n<li>Asynchronous Mode Output<\/li>\n<\/ul>\n<p>The Python function's supported output formats are:<\/p>\n<ul>\n<li>boolean,<\/li>\n<li>dict,<\/li>\n<li>float<\/li>\n<li>int<\/li>\n<li>list<\/li>\n<li>str<\/li>\n<li>tuple<\/li>\n<li>pandas.DataFrame object<\/li>\n<\/ul>\n<p>The default SQL API execution mode is <em>synchronous<\/em>, so code execution will wait for the SQL invocation to return results before additional calls may be initiated.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * ","FROM   table(pyqGroupEval(","            inp_nam  => 'IRIS',","            par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", \"oml_parallel_flag\":true, \"oml_service_level\":\"MEDIUM\"}',","            out_fmt  => '{\"SPECIES\":\"varchar2(10)\", \"COUNT\":\"number\"}',","            grp_col  => 'SPECIES',","            ord_col  =>  NULL,","            scr_name => 'group_count'));"],"enabled":true,"result":{"startTime":1736459768636,"interpreter":"sql.low","endTime":1736459780822,"results":[{"message":"SPECIES\tCOUNT\nvirginica\t50\nsetosa\t50\nversicolor\t50\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Asynchronous SQL API jobs","message":["%md","","Asynchronous jobs do not wait for the SQL API call to return from the server with the result. If the UDF is performing long running computations, results may take a while to compute. With asynchroous invocation, users may continue working in the notebook or from their client interface.","","When a function is run asynchronously, it's run as a job which can be tracked by using the `pyqJobStatus` and `pyqJobResult` functions. In asynchronous mode, the SQL API returns a URL directly after the asynchronous job is submitted to the server. The URL contains a job ID, which can be used to fetch the job status and job result in subsequent SQL calls.","","When *submitting* asynchronous jobs, set `OUT_FMT` to `NULL` for JSON, PNG, and relational outputs. ","","When *fetching* the job result, specify the `OUT_FMT` in the `pyqJobResult` call. ","","Note that if XML output is expected from the asynchronous job, you must set `OUT_FMT` to XML when submitting the job and fetching the job result.","","To run a function in asynchronous mode, set the control argument `oml_async_flag` to `true` - the default value is `false` for synchronous mode. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes."],"enabled":true,"result":{"startTime":1736459781292,"interpreter":"md.low","endTime":1736459781734,"results":[{"message":"<p>Asynchronous jobs do not wait for the SQL API call to return from the server with the result. If the UDF is performing long running computations, results may take a while to compute. With asynchroous invocation, users may continue working in the notebook or from their client interface.<\/p>\n<p>When a function is run asynchronously, it's run as a job which can be tracked by using the <code>pyqJobStatus<\/code> and <code>pyqJobResult<\/code> functions. In asynchronous mode, the SQL API returns a URL directly after the asynchronous job is submitted to the server. The URL contains a job ID, which can be used to fetch the job status and job result in subsequent SQL calls.<\/p>\n<p>When <em>submitting<\/em> asynchronous jobs, set <code>OUT_FMT<\/code> to <code>NULL<\/code> for JSON, PNG, and relational outputs.<\/p>\n<p>When <em>fetching<\/em> the job result, specify the <code>OUT_FMT<\/code> in the <code>pyqJobResult<\/code> call.<\/p>\n<p>Note that if XML output is expected from the asynchronous job, you must set <code>OUT_FMT<\/code> to XML when submitting the job and fetching the job result.<\/p>\n<p>To run a function in asynchronous mode, set the control argument <code>oml_async_flag<\/code> to <code>true<\/code> - the default value is <code>false<\/code> for synchronous mode. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Invoke pyqGroupEval asynchronously and store the JOB_ID in a table for future result retrieval","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE JOB_TABLE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE JOB_TABLE (JOB_ID) AS","SELECT SUBSTR(VALUE, INSTR(VALUE, '/', -1 ) + 1, 36) ","FROM   table(pyqGroupEval(","            inp_nam  => 'IRIS',","            par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", ","                          \"oml_parallel_flag\":true, ","                          \"oml_service_level\":\"MEDIUM\", ","                          \"oml_async_flag\":true}',   -- setting async flag to true","            out_fmt  => NULL,","            grp_col  => 'SPECIES',","            ord_col  =>  NULL,","            scr_name => 'group_count'));","            ","     "],"enabled":true,"result":{"startTime":1736459782198,"interpreter":"script.low","endTime":1736459783776,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable JOB_TABLE created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the JOB_ID","message":["%sql","","SELECT * FROM JOB_TABLE;"],"enabled":true,"result":{"startTime":1736459784242,"interpreter":"sql.low","endTime":1736459784715,"results":[{"message":"JOB_ID\n1c6a9ce9-b78e-4686-8d01-e1e6dbb1dad8\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Give the asynchronous job a chance to complete before viewing result","message":["%script","","-- wait for job to complete","BEGIN sys.dbms_session.sleep(5); END;"],"enabled":true,"result":{"startTime":1736459785176,"interpreter":"script.low","endTime":1736459790854,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Check job status","message":["%md","","To poll the job status, pass the job ID to the `pyqJobStatus` function. The response indicates the job is found, completed, and includes the job ID and location containing the results. If the job is pending, the response will ","be: *job is still running*."],"enabled":true,"result":{"startTime":1736459791318,"interpreter":"md.low","endTime":1736459791758,"results":[{"message":"<p>To poll the job status, pass the job ID to the <code>pyqJobStatus<\/code> function. The response indicates the job is found, completed, and includes the job ID and location containing the results. If the job is pending, the response will\nbe: <em>job is still running<\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","   job_id varchar2(1000);","   job_status varchar2(1000);","BEGIN","    SELECT JOB_ID INTO job_id FROM JOB_TABLE;","    -- DBMS_OUTPUT.PUT_LINE('Job ID is: '||job_id);","    SELECT VALUE INTO job_status FROM pyqJobStatus(job_id => job_id);","    DBMS_OUTPUT.PUT_LINE('Job status URL is: '|| job_status);","END;","/"],"enabled":true,"result":{"startTime":1736459792225,"interpreter":"script.low","endTime":1736459797803,"results":[{"message":"Job status URL is:\nhttps://qtraya2braestch-omlla.adb.us-sanjose-1.oraclecloudapps.com/oml/api/py-sc\nripts/v1/jobs/1c6a9ce9-b78e-4686-8d01-e1e6dbb1dad8/result\n\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Retrieve result from the completed job","message":["%md","","Run a SELECT statement that invokes the `pyqJobResult` function and returns the result. The `OUT_FMT` argument specifies a JSON string that contains the column names and data types of the table returned by ","`pyqGroupEval`."],"enabled":true,"result":{"startTime":1736459798264,"interpreter":"md.low","endTime":1736459798704,"results":[{"message":"<p>Run a SELECT statement that invokes the <code>pyqJobResult<\/code> function and returns the result. The <code>OUT_FMT<\/code> argument specifies a JSON string that contains the column names and data types of the table returned by\n<code>pyqGroupEval<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script\r","\r","SET LINESIZE 1000;\r","\r","DECLARE\r","  job_id varchar2(100);\r","  job_result varchar2(4000);\r","BEGIN\r","  SELECT JOB_ID INTO job_id FROM JOB_TABLE;\r","  -- enable to print Job Id\r","  -- DBMS_OUTPUT.PUT_LINE('Job Id is: '|| job_id);\r","    \r","  SELECT VALUE INTO job_result from pyqJobResult(job_id => job_id);\r","  DBMS_OUTPUT.PUT_LINE('Job result is: '|| job_result);\r","END;\r"],"enabled":true,"result":{"startTime":1736459799161,"interpreter":"script.low","endTime":1736459799761,"results":[{"message":"\n---------------------------\nJob result is: {\"virginica\":[{\"SPECIES\":\"virginica\",\"COUNT\":50}],\"setosa\":[{\"SPECIES\":\"setosa\",\"COUNT\":50}],\"versicolor\":[{\"SPECIES\":\"versicolor\",\"COUNT\":50}]}\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build a scikit-learn Python model using embedded Python execution","message":["%md","","Let's create a UDF that builds a linear model using `scikit-learn` and runs it from the Python and SQL APIs. First, we'll define our function, `build_lm`, and store it in the script repository. ","","This UDF builds a model and stores it in the datastore, and returns the predictor coefficients in a DataFrame. "],"enabled":true,"result":{"startTime":1736459800219,"interpreter":"md.low","endTime":1736459800658,"results":[{"message":"<p>Let's create a UDF that builds a linear model using <code>scikit-learn<\/code> and runs it from the Python and SQL APIs. First, we'll define our function, <code>build_lm<\/code>, and store it in the script repository.<\/p>\n<p>This UDF builds a model and stores it in the datastore, and returns the predictor coefficients in a DataFrame.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","build_lm = \"\"\"def build_lm(dat, dsname):","            from sklearn import linear_model","            import oml","            regr = linear_model.LinearRegression()","            import pandas as pd","            X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"]]","            y = dat[[\"SEPAL_LENGTH\"]]","            res = regr.fit(X, y)","            res_coef = res.coef_","            res.df = pd.DataFrame(res_coef, columns = ['Coef_SEPAL_WIDTH','Coef_PETAL_LENGTH','Coef_PETAL_WIDTH'])","            oml.ds.save(objs={'regr':regr}, name=dsname, overwrite=True)","            return res.df\"\"\"","            ","oml.script.create(\"build_lm\", func=build_lm, overwrite=True) "],"enabled":true,"result":{"startTime":1736459801142,"interpreter":"python.low","endTime":1736459801596,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use table_apply to test the UDF from Python","message":["%md","","Invoke the `build_lm` function in the script repository on the IRIS data from the Python API for embedded Python execution using `oml.table_apply`, and specify the datastore name `ds1`. ","","The returned value is a DataFrame containing the coefficients of the predictors."],"enabled":true,"result":{"startTime":1736459802057,"interpreter":"md.low","endTime":1736459802504,"results":[{"message":"<p>Invoke the <code>build_lm<\/code> function in the script repository on the IRIS data from the Python API for embedded Python execution using <code>oml.table_apply<\/code>, and specify the datastore name <code>ds1<\/code>.<\/p>\n<p>The returned value is a DataFrame containing the coefficients of the predictors.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.table_apply(data   = IRIS, ","                func   = \"build_lm\", ","                dsname = \"ds1\")","                "],"enabled":true,"result":{"startTime":1736459802993,"interpreter":"python.low","endTime":1736459804667,"results":[{"message":"   Coef_SEPAL_WIDTH  Coef_PETAL_LENGTH  Coef_PETAL_WIDTH\n0          0.650837           0.709132         -0.556483\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View datastore contents from Python and SQL","message":["%md","","Model `regr` is now stored in datastore `ds1`. We can view datastore contents either from Python or SQL. ","First, display the model object name and class by passing the datastore name to the `oml.ds.describe` command.","Then, display the same information by qyering the `USER_PYQ_DATASTORES` view."],"enabled":true,"result":{"startTime":1736459805124,"interpreter":"md.low","endTime":1736459805567,"results":[{"message":"<p>Model <code>regr<\/code> is now stored in datastore <code>ds1<\/code>. We can view datastore contents either from Python or SQL.\nFirst, display the model object name and class by passing the datastore name to the <code>oml.ds.describe<\/code> command.\nThen, display the same information by qyering the <code>USER_PYQ_DATASTORES<\/code> view.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.ds.describe(name=\"ds1\")[['object_name', 'class']]"],"enabled":true,"result":{"startTime":1736459806028,"interpreter":"python.low","endTime":1736459807055,"results":[{"message":"  object_name             class\n0        regr  LinearRegression\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT OBJNAME, CLASS ","FROM   ALL_PYQ_DATASTORE_CONTENTS ","WHERE  DSNAME = 'ds1';"],"enabled":true,"result":{"startTime":1736459807546,"interpreter":"sql.low","endTime":1736459808497,"results":[{"message":"OBJNAME\tCLASS\nregr\tLinearRegression\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the same function using the SQL API table function pyqTableEval","message":["%md","","Invoke the `build_lm` function by running a SELECT statement that uses `pyqTableEval`. The `inp_nam` parameter of `pyqTableEval` specifies the IRIS table as the data to pass to the UDF. The `PAR_LST` parameter specifies the names of the model and datastore to pass to the UDF. The `OUT_FMT` parameter specifies returning the value as a relational table and the `SCR_NAME` parameter specifies the `build_lm` UDF in the script repository to invoke. For this invocation, we persist the model to a new datastore, `ds2`."],"enabled":true,"result":{"startTime":1736459808955,"interpreter":"md.low","endTime":1736459809393,"results":[{"message":"<p>Invoke the <code>build_lm<\/code> function by running a SELECT statement that uses <code>pyqTableEval<\/code>. The <code>inp_nam<\/code> parameter of <code>pyqTableEval<\/code> specifies the IRIS table as the data to pass to the UDF. The <code>PAR_LST<\/code> parameter specifies the names of the model and datastore to pass to the UDF. The <code>OUT_FMT<\/code> parameter specifies returning the value as a relational table and the <code>SCR_NAME<\/code> parameter specifies the <code>build_lm<\/code> UDF in the script repository to invoke. For this invocation, we persist the model to a new datastore, <code>ds2<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Return output as a structured table","message":["%sql\r","\r","SELECT * \r","FROM   table(pyqTableEval( \r","              inp_nam  => 'IRIS', \r","              par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", \"dsname\":\"ds2\"}',\r","              out_fmt  => '{\"Coef_SEPAL_WIDTH\":\"number\", \"Coef_PETAL_LENGTH\":\"number\", \"Coef_PETAL_WIDTH\":\"number\"}',\r","              scr_name => 'build_lm'));\r","            "],"enabled":true,"result":{"startTime":1736459809844,"interpreter":"sql.low","endTime":1736459821432,"results":[{"message":"Coef_SEPAL_WIDTH\tCoef_PETAL_LENGTH\tCoef_PETAL_WIDTH\n0.6508371593\t0.7091319591\t-0.5564826602\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md",""," Display the `regr` model object name and class residing in datastore `ds2`;"],"enabled":true,"result":{"startTime":1736459821889,"interpreter":"md.low","endTime":1736459822328,"results":[{"message":"<p>Display the <code>regr<\/code> model object name and class residing in datastore <code>ds2<\/code>;<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT OBJNAME, CLASS","FROM   ALL_PYQ_DATASTORE_CONTENTS ","WHERE   DSNAME = 'ds2';"],"enabled":true,"result":{"startTime":1736459822811,"interpreter":"sql.low","endTime":1736459823790,"results":[{"message":"OBJNAME\tCLASS\nregr\tLinearRegression\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Return output as a JSON string","message":["%md","","Run the function again, this time returning the output as a JSON string, and store the model in datastore `ds3`."],"enabled":true,"result":{"startTime":1736459824248,"interpreter":"md.low","endTime":1736459824684,"results":[{"message":"<p>Run the function again, this time returning the output as a JSON string, and store the model in datastore <code>ds3<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"JSON output","message":["%sql\r","\r","SELECT value \r","FROM   table(pyqTableEval( \r","               inp_nam  => 'IRIS', \r","               par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", \"dsname\":\"ds3\"}',\r","               out_fmt  => 'JSON', \r","               scr_name => 'build_lm'));\r","    "],"enabled":true,"result":{"startTime":1736459825147,"interpreter":"sql.low","endTime":1736459836633,"results":[{"message":"VALUE\n[{\"Coef_PETAL_LENGTH\":0.7091319591,\"Coef_PETAL_WIDTH\":-0.5564826602,\"Coef_SEPAL_WIDTH\":0.6508371593}]\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Return output as an XML string","message":["%md","","In the SQL query, change the `out_fmt` parameter to `XML` to return an XML string, and persist the model to datastore `ds4`."],"enabled":true,"result":{"startTime":1736459837088,"interpreter":"md.low","endTime":1736459837523,"results":[{"message":"<p>In the SQL query, change the <code>out_fmt<\/code> parameter to <code>XML<\/code> to return an XML string, and persist the model to datastore <code>ds4<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"XML output","message":["%sql","","SELECT value ","FROM   table(pyqTableEval( ","               inp_nam  => 'IRIS', ","               par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", \"dsname\":\"ds4\"}',","               out_fmt  => 'XML', ","               scr_name => 'build_lm'));","               "],"enabled":true,"result":{"startTime":1736459837980,"interpreter":"sql.low","endTime":1736459849473,"results":[{"message":"VALUE\n<root><pandas_dataFrame><ROW-pandas_dataFrame><Coef_SEPAL_WIDTH>0.650837159313218<\/Coef_SEPAL_WIDTH><Coef_PETAL_LENGTH>0.709131959136729<\/Coef_PETAL_LENGTH><Coef_PETAL_WIDTH>-0.556482660167024<\/Coef_PETAL_WIDTH><\/ROW-pandas_dataFrame><\/pandas_dataFrame><\/root>\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a UDF to score using system-supported data-parallelism","message":["%md","","Define a UDF to score data using our third-party model created above. We name this UDF `score_mod` and save it to the script repository. The `score_mod` function loads the datastore containing the model and scores the daa using that model."],"enabled":true,"result":{"startTime":1736459849926,"interpreter":"md.low","endTime":1736459850382,"results":[{"message":"<p>Define a UDF to score data using our third-party model created above. We name this UDF <code>score_mod<\/code> and save it to the script repository. The <code>score_mod<\/code> function loads the datastore containing the model and scores the daa using that model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","score_mod = \"\"\"def score_mod(dat, dsname):","            import oml","            import pandas as pd","            import matplotlib.pyplot as plt","            obj_dict = oml.ds.load(name=dsname, to_globals=False)  ","            regr = obj_dict[\"regr\"]                                      ","            pred = regr.predict(dat[['SEPAL_WIDTH','PETAL_LENGTH','PETAL_WIDTH']])   ","            return pd.concat([dat[['SPECIES', 'SEPAL_LENGTH']], pd.DataFrame(pred, columns=['Pred_SEPAL_LENGTH'])], axis=1)\"\"\" ","","oml.script.create(\"score_mod\", func=score_mod, overwrite=True) "],"enabled":true,"result":{"startTime":1736459850840,"interpreter":"python.low","endTime":1736459851299,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use row_apply to test the UDF from Python using data parallelism","message":["%md","","To test our UDF working with embedded Python execution, we use `oml.row_apply` to score the IRIS data using the model in the datastore. We'll next invoke the equivalent 'pyqRowEval' table function from the SQL API. ","","We run the script `score_mod` from the SQL API synchronously, which is the default. ","","Scoring occurs in parallel with 50 rows processed per UDF invocation across the multiple engines. This example results in three function invocations that are run in parallel if the service level is set to `MEDIUM` or `HIGH`. ","","In this example, we are loading the model stored in datastore `ds1`."],"enabled":true,"result":{"startTime":1736459851755,"interpreter":"md.low","endTime":1736459852197,"results":[{"message":"<p>To test our UDF working with embedded Python execution, we use <code>oml.row_apply<\/code> to score the IRIS data using the model in the datastore. We'll next invoke the equivalent 'pyqRowEval' table function from the SQL API.<\/p>\n<p>We run the script <code>score_mod<\/code> from the SQL API synchronously, which is the default.<\/p>\n<p>Scoring occurs in parallel with 50 rows processed per UDF invocation across the multiple engines. This example results in three function invocations that are run in parallel if the service level is set to <code>MEDIUM<\/code> or <code>HIGH<\/code>.<\/p>\n<p>In this example, we are loading the model stored in datastore <code>ds1<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","res = oml.row_apply(data     = IRIS, ","                    func     = score_mod, ","                    dsname   = \"ds1\", ","                    rows     = 50, ","                    parallel = True) ","","z.show(res)"],"enabled":true,"result":{"startTime":1736459852651,"interpreter":"python.low","endTime":1736459855130,"results":[{"message":"SPECIES\tSEPAL_LENGTH\tPred_SEPAL_LENGTH\nsetosa\t4.6\t4.796846693548454\nversicolor\t5.1\t4.9983553424270495\nversicolor\t6.0\t5.567884419786516\nversicolor\t5.8\t5.716922751478398\nversicolor\t5.5\t5.46602333766773\nversicolor\t5.5\t5.596190769530374\nversicolor\t6.1\t5.79144191732434\nvirginica\t5.7\t5.915784866550187\nvirginica\t6.0\t5.998775048839733\nvirginica\t6.3\t5.971433132566889\nversicolor\t6.7\t6.408148244256902\nvirginica\t7.2\t7.191799369239573\nvirginica\t6.3\t6.867345223053978\nsetosa\t4.3\t4.532905859890898\nsetosa\t5.8\t5.1990079491010865\nsetosa\t5.0\t4.678338221650511\nsetosa\t4.4\t4.6190839857015416\nsetosa\t4.4\t4.749251417564185\nsetosa\t4.7\t4.749251417564185\nsetosa\t5.5\t4.94450256535815\nsetosa\t4.5\t4.107849708165586\nsetosa\t5.0\t4.888854299341448\nsetosa\t5.4\t5.093540897050033\nsetosa\t4.8\t4.745645447631916\nsetosa\t4.9\t5.136147743219848\nsetosa\t4.9\t4.6899971816152135\nsetosa\t4.4\t4.624913465683893\nsetosa\t4.6\t4.820164613477857\nsetosa\t5.0\t4.88524832940918\nsetosa\t5.2\t4.950332045340501\nsetosa\t5.1\t5.015415761271823\nsetosa\t5.0\t5.080499477203145\nsetosa\t5.5\t5.471001772791077\nsetosa\t4.8\t4.634348915598512\nsetosa\t4.6\t4.894683779323799\nsetosa\t5.1\t4.95976749525512\nsetosa\t4.9\t4.881642359476911\nsetosa\t5.2\t5.5324795187901294\nsetosa\t4.6\t4.825994093460208\nsetosa\t4.9\t4.825994093460208\nsetosa\t5.0\t5.021245241254174\nsetosa\t5.1\t5.021245241254174\nsetosa\t5.2\t5.086328957185495\nsetosa\t5.3\t5.216496389048141\nsetosa\t5.4\t5.216496389048141\nsetosa\t5.1\t5.225931838962758\nsetosa\t5.4\t4.909948709220769\nsetosa\t5.1\t5.105199857014735\nsetosa\t5.7\t5.560785868533988\nsetosa\t5.0\t4.83182357344256\nsetosa\t4.8\t4.896907289373882\nsetosa\t4.7\t4.961991005305204\nsetosa\t4.8\t5.092158437167847\nsetosa\t5.1\t5.352493300893134\nsetosa\t5.0\t4.980861905134443\nsetosa\t5.0\t4.934649089032359\nsetosa\t5.4\t5.16307163308152\nsetosa\t5.7\t5.367758230790105\nsetosa\t5.4\t5.377193680704725\nsetosa\t5.1\t4.9310431191000905\nsetosa\t4.8\t5.304898024908866\nsetosa\t5.1\t5.453936356600748\nversicolor\t5.0\t5.136575764322127\nversicolor\t4.9\t5.201659480253449\nversicolor\t5.0\t5.083151008355507\nversicolor\t5.7\t5.473653303943439\nversicolor\t5.6\t5.572872849600969\nversicolor\t5.5\t5.485312263908141\nversicolor\t5.5\t5.5005771938051105\nversicolor\t5.6\t5.636574105650106\nversicolor\t5.8\t5.711093271496047\nversicolor\t5.2\t5.599796739462643\nversicolor\t5.8\t5.964216195356798\nversicolor\t5.6\t5.992522545100656\nversicolor\t5.7\t5.862355113238013\nversicolor\t5.7\t6.119084007031032\nversicolor\t5.6\t5.868184593220365\nversicolor\t5.7\t5.998352025083008\nversicolor\t5.9\t5.952139208980925\nversicolor\t6.2\t6.06926522099668\nversicolor\t6.4\t6.06926522099668\nversicolor\t5.5\t6.00057553513309\nversicolor\t6.3\t5.7496761213224215\nversicolor\t6.6\t6.149613866824973\nversicolor\t6.7\t6.214697582756295\nvirginica\t4.9\t5.72816368503193\nversicolor\t5.7\t6.146007896892704\nversicolor\t5.4\t6.164878796721943\nversicolor\t5.6\t6.164878796721943\nversicolor\t6.2\t5.644209069271368\nversicolor\t6.0\t6.099795080790622\nversicolor\t6.4\t6.295046228584587\nversicolor\t6.0\t6.369565394430528\nversicolor\t6.6\t6.282004808737699\nversicolor\t6.1\t6.291440258652319\nversicolor\t6.5\t6.105624560772972\nversicolor\t6.1\t6.343482554736752\nversicolor\t6.1\t6.29726973863467\nversicolor\t7.0\t6.492520886428636\nversicolor\t6.7\t6.371788904480611\nversicolor\t6.3\t6.446308070326552\nvirginica\t6.0\t6.210673586412855\nvirginica\t6.2\t6.0805061545502115\nversicolor\t6.8\t6.303099218617021\nversicolor\t5.9\t6.340841018275499\nvirginica\t5.6\t6.040122818430479\nvirginica\t6.1\t6.281586782326529\nvirginica\t6.3\t6.0863356345325625\nversicolor\t6.3\t6.123113000720026\nversicolor\t6.9\t6.5136152963079565\nvirginica\t6.5\t6.442284073983112\nvirginica\t6.3\t6.460190540341336\nvirginica\t5.9\t6.423413174153873\nvirginica\t5.8\t6.172513760343206\nvirginica\t5.8\t6.172513760343206\nvirginica\t6.9\t6.210255560001683\nvirginica\t5.8\t5.9593561461910145\nversicolor\t6.0\t6.3394585583933125\nvirginica\t6.5\t6.383029838034141\nvirginica\t6.7\t6.216085039984034\nvirginica\t6.4\t6.314340152170551\nvirginica\t6.4\t6.417165667760352\nvirginica\t6.9\t6.534291679776107\nvirginica\t6.2\t6.618246295536668\nvirginica\t6.5\t6.707065957808566\nvirginica\t6.4\t6.772149673739887\nvirginica\t6.8\t6.540121159758458\nvirginica\t6.1\t6.74023735406376\nvirginica\t6.3\t6.712895437790915\nvirginica\t6.4\t6.4808669238094865\nvirginica\t6.4\t6.425218657792784\nvirginica\t6.7\t6.509173273553346\nvirginica\t6.3\t6.70442442134731\nvirginica\t6.7\t6.877198699379769\nvirginica\t6.9\t6.700818451415043\nvirginica\t6.7\t6.65460563531296\nvirginica\t7.2\t7.031102077582989\nvirginica\t6.7\t6.5943869658929755\nvirginica\t6.5\t6.697212481482774\nvirginica\t7.1\t6.8237739434131495\nvirginica\t6.8\t6.842644843242389\nvirginica\t7.4\t6.946729435411257\nvirginica\t7.7\t6.854303803207091\nvirginica\t7.2\t7.133509566761616\nvirginica\t7.3\t7.209287809186626\nvirginica\t7.9\t7.754657916448791\nvirginica\t7.6\t7.320166314808859\nvirginica\t7.7\t7.316560344876591\nvirginica\t7.7\t7.856100972156405\nvirginica\t7.7\t7.161274506791187\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Run the same function using the SQL API table function pyqRowEval with structured table output","message":["%sql","","SELECT *  ","FROM   table(pyqRowEval(","               inp_nam  => 'IRIS',","               par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", \"dsname\":\"ds1\", \"oml_parallel_flag\":true, \"oml_service_level\":\"MEDIUM\"}',","               out_fmt  => '{\"SPECIES\":\"varchar2(10)\", \"SEPAL_LENGTH\":\"number\", \"Pred_SEPAL_LENGTH\":\"number\"}',","               row_num  => 50,","               scr_name => 'score_mod'));"],"enabled":true,"result":{"startTime":1736459855616,"interpreter":"sql.low","endTime":1736459867203,"results":[{"message":"SPECIES\tSEPAL_LENGTH\tPred_SEPAL_LENGTH\nsetosa\t4.6\t4.7968466935\nversicolor\t5.1\t4.9983553424\nversicolor\t6\t5.5678844198\nversicolor\t5.8\t5.7169227515\nversicolor\t5.5\t5.4660233377\nversicolor\t5.5\t5.5961907695\nversicolor\t6.1\t5.7914419173\nvirginica\t5.7\t5.9157848666\nvirginica\t6\t5.9987750488\nvirginica\t6.3\t5.9714331326\nversicolor\t6.7\t6.4081482443\nvirginica\t7.2\t7.1917993692\nvirginica\t6.3\t6.8673452231\nsetosa\t4.3\t4.5329058599\nsetosa\t5.8\t5.1990079491\nsetosa\t5\t4.6783382217\nsetosa\t4.4\t4.6190839857\nsetosa\t4.4\t4.7492514176\nsetosa\t4.7\t4.7492514176\nsetosa\t5.5\t4.9445025654\nsetosa\t4.5\t4.1078497082\nsetosa\t5\t4.8888542993\nsetosa\t5.4\t5.0935408971\nsetosa\t4.8\t4.7456454476\nsetosa\t4.9\t5.1361477432\nsetosa\t4.9\t4.6899971816\nsetosa\t4.4\t4.6249134657\nsetosa\t4.6\t4.8201646135\nsetosa\t5\t4.8852483294\nsetosa\t5.2\t4.9503320453\nsetosa\t5.1\t5.0154157613\nsetosa\t5\t5.0804994772\nsetosa\t5.5\t5.4710017728\nsetosa\t4.8\t4.6343489156\nsetosa\t4.6\t4.8946837793\nsetosa\t5.1\t4.9597674953\nsetosa\t4.9\t4.8816423595\nsetosa\t5.2\t5.5324795188\nsetosa\t4.6\t4.8259940935\nsetosa\t4.9\t4.8259940935\nsetosa\t5\t5.0212452413\nsetosa\t5.1\t5.0212452413\nsetosa\t5.2\t5.0863289572\nsetosa\t5.3\t5.216496389\nsetosa\t5.4\t5.216496389\nsetosa\t5.1\t5.225931839\nsetosa\t5.4\t4.9099487092\nsetosa\t5.1\t5.105199857\nsetosa\t5.7\t5.5607858685\nsetosa\t5\t4.8318235734\nsetosa\t4.8\t4.8969072894\nsetosa\t4.7\t4.9619910053\nsetosa\t4.8\t5.0921584372\nsetosa\t5.1\t5.3524933009\nsetosa\t5\t4.9808619051\nsetosa\t5\t4.934649089\nsetosa\t5.4\t5.1630716331\nsetosa\t5.7\t5.3677582308\nsetosa\t5.4\t5.3771936807\nsetosa\t5.1\t4.9310431191\nsetosa\t4.8\t5.3048980249\nsetosa\t5.1\t5.4539363566\nversicolor\t5\t5.1365757643\nversicolor\t4.9\t5.2016594803\nversicolor\t5\t5.0831510084\nversicolor\t5.7\t5.4736533039\nversicolor\t5.6\t5.5728728496\nversicolor\t5.5\t5.4853122639\nversicolor\t5.5\t5.5005771938\nversicolor\t5.6\t5.6365741057\nversicolor\t5.8\t5.7110932715\nversicolor\t5.2\t5.5997967395\nversicolor\t5.8\t5.9642161954\nversicolor\t5.6\t5.9925225451\nversicolor\t5.7\t5.8623551132\nversicolor\t5.7\t6.119084007\nversicolor\t5.6\t5.8681845932\nversicolor\t5.7\t5.9983520251\nversicolor\t5.9\t5.952139209\nversicolor\t6.2\t6.069265221\nversicolor\t6.4\t6.069265221\nversicolor\t5.5\t6.0005755351\nversicolor\t6.3\t5.7496761213\nversicolor\t6.6\t6.1496138668\nversicolor\t6.7\t6.2146975828\nvirginica\t4.9\t5.728163685\nversicolor\t5.7\t6.1460078969\nversicolor\t5.4\t6.1648787967\nversicolor\t5.6\t6.1648787967\nversicolor\t6.2\t5.6442090693\nversicolor\t6\t6.0997950808\nversicolor\t6.4\t6.2950462286\nversicolor\t6\t6.3695653944\nversicolor\t6.6\t6.2820048087\nversicolor\t6.1\t6.2914402587\nversicolor\t6.5\t6.1056245608\nversicolor\t6.1\t6.3434825547\nversicolor\t6.1\t6.2972697386\nversicolor\t7\t6.4925208864\nversicolor\t6.7\t6.3717889045\nversicolor\t6.3\t6.4463080703\nvirginica\t6\t6.2106735864\nvirginica\t6.2\t6.0805061546\nversicolor\t6.8\t6.3030992186\nversicolor\t5.9\t6.3408410183\nvirginica\t5.6\t6.0401228184\nvirginica\t6.1\t6.2815867823\nvirginica\t6.3\t6.0863356345\nversicolor\t6.3\t6.1231130007\nversicolor\t6.9\t6.5136152963\nvirginica\t6.5\t6.442284074\nvirginica\t6.3\t6.4601905403\nvirginica\t5.9\t6.4234131742\nvirginica\t5.8\t6.1725137603\nvirginica\t5.8\t6.1725137603\nvirginica\t6.9\t6.21025556\nvirginica\t5.8\t5.9593561462\nversicolor\t6\t6.3394585584\nvirginica\t6.5\t6.383029838\nvirginica\t6.7\t6.21608504\nvirginica\t6.4\t6.3143401522\nvirginica\t6.4\t6.4171656678\nvirginica\t6.9\t6.5342916798\nvirginica\t6.2\t6.6182462955\nvirginica\t6.5\t6.7070659578\nvirginica\t6.4\t6.7721496737\nvirginica\t6.8\t6.5401211598\nvirginica\t6.1\t6.7402373541\nvirginica\t6.3\t6.7128954378\nvirginica\t6.4\t6.4808669238\nvirginica\t6.4\t6.4252186578\nvirginica\t6.7\t6.5091732736\nvirginica\t6.3\t6.7044244213\nvirginica\t6.7\t6.8771986994\nvirginica\t6.9\t6.7008184514\nvirginica\t6.7\t6.6546056353\nvirginica\t7.2\t7.0311020776\nvirginica\t6.7\t6.5943869659\nvirginica\t6.5\t6.6972124815\nvirginica\t7.1\t6.8237739434\nvirginica\t6.8\t6.8426448432\nvirginica\t7.4\t6.9467294354\nvirginica\t7.7\t6.8543038032\nvirginica\t7.2\t7.1335095668\nvirginica\t7.3\t7.2092878092\nvirginica\t7.9\t7.7546579164\nvirginica\t7.6\t7.3201663148\nvirginica\t7.7\t7.3165603449\nvirginica\t7.7\t7.8561009722\nvirginica\t7.7\t7.1612745068\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"PNG output","message":["%md","","In the Python API, we can return images by setting the `graphics` argument to `True`. This returns the PNG image along with the structured content in OML Notebooks. ","","For the SQL API, when `OUT_FMT` is specified with `PNG`, the embedded Python execution functions return the response in a table with fixed columns, including an image bytes column. When calling the OML4Py SQL API, you must set the special control argument `oml_graphics_flag` to `true` to capture images rendered in the script. This is not currently supported for OML Notebooks, but the example is provided here for use in other SQL IDEs like SQL Developer. ","","The PNG output consists of four columns. The *NAME* column contains the name of the row. The *NAME* column value is *NULL* for `pyqEval` and `pyqTableEval` function output. For `pyqRowEval`, `pyqGroupEval`, `pyqIndexEval`, the *NAME* column value is the chunk/group/index name. The *ID* column indicates the ID of the image. The *VALUE* column contains the value returned from the UDF. The *TITLE* column contains the titles of the rendered PNG image(s). The *IMAGE* column is a BLOB column containing the bytes of the PNG image(s) rendered by the UDF.","","The following example shows the OML4Py Python API returning the density plot (with output), and then the corresponding `pyqTableEval` function call."],"enabled":true,"result":{"startTime":1736459867713,"interpreter":"md.low","endTime":1736459868152,"results":[{"message":"<p>In the Python API, we can return images by setting the <code>graphics<\/code> argument to <code>True<\/code>. This returns the PNG image along with the structured content in OML Notebooks.<\/p>\n<p>For the SQL API, when <code>OUT_FMT<\/code> is specified with <code>PNG<\/code>, the embedded Python execution functions return the response in a table with fixed columns, including an image bytes column. When calling the OML4Py SQL API, you must set the special control argument <code>oml_graphics_flag<\/code> to <code>true<\/code> to capture images rendered in the script. This is not currently supported for OML Notebooks, but the example is provided here for use in other SQL IDEs like SQL Developer.<\/p>\n<p>The PNG output consists of four columns. The <em>NAME<\/em> column contains the name of the row. The <em>NAME<\/em> column value is <em>NULL<\/em> for <code>pyqEval<\/code> and <code>pyqTableEval<\/code> function output. For <code>pyqRowEval<\/code>, <code>pyqGroupEval<\/code>, <code>pyqIndexEval<\/code>, the <em>NAME<\/em> column value is the chunk/group/index name. The <em>ID<\/em> column indicates the ID of the image. The <em>VALUE<\/em> column contains the value returned from the UDF. The <em>TITLE<\/em> column contains the titles of the rendered PNG image(s). The <em>IMAGE<\/em> column is a BLOB column containing the bytes of the PNG image(s) rendered by the UDF.<\/p>\n<p>The following example shows the OML4Py Python API returning the density plot (with output), and then the corresponding <code>pyqTableEval<\/code> function call.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create the density plot UDF in the script repository","message":["%python","","density_plt = \"\"\"def density_plt(dat):","    dat.plot(kind='density')","    return(True)\"\"\"","","oml.script.create(\"density_plt\", func=density_plt, overwrite=True) "],"enabled":true,"result":{"startTime":1736459868609,"interpreter":"python.low","endTime":1736459869063,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke UDF from Python and return PNG image","message":["%python","","res = oml.table_apply(data = IRIS, ","                func=\"density_plt\", ","                graphics=True)","","res"],"enabled":true,"result":{"startTime":1736459869526,"interpreter":"python.low","endTime":1736459870184,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\nTrue\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Corresponding SQL invocation to return PNG images","message":["%sql","","SELECT * ","FROM table(pyqTableEval( ","             inp_nam  => 'IRIS', ","             par_lst  => '{\"oml_input_type\":\"pandas.DataFrame\", \"oml_graphics_flag\":true}',","             out_fmt  => 'PNG', ","             scr_name => 'density_plt'));","    "],"enabled":true,"result":{"startTime":1736459870647,"interpreter":"sql.low","endTime":1736459882244,"results":[{"message":"<html><table><tr><th>NAME<\/th><th>ID<\/th><th>VALUE<\/th><th>TITLE<\/th><th>IMAGE<\/th><\/tr><tr><td><\/td><td>1<\/td><td>true<\/td><td><\/td><td><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Agglomerative Clustering and Regionalization.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Agglomerative Clustering and Regionalization.dsnb
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Agglomerative clustering is a type of hierarchical clustering that merges clusters based on similarity until stopping criteria are met. Regionalization is a technique that includes a spatial proximity constraint on the merging of clusters. This technique is useful for creating regions such as trade areas and sales territories.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737650321143,"interpreter":"md.low","endTime":1737650321748,"results":[{"message":"<h1 id=\"agglomerative-clustering-and-regionalization---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Agglomerative Clustering and Regionalization - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook you explore agglomerative clustering in a spatial context using regionalization. Agglomerative clustering is a type of hierarchical clustering that merges clusters based on similarity until stopping criteria are met. Regionalization is a technique that includes a spatial proximity constraint on the merging of clusters. This technique is useful for creating regions such as trade areas and sales territories.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks.  "],"enabled":true,"result":{"startTime":1737650322306,"interpreter":"md.low","endTime":1737650322892,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python  ","","import geopandas as gpd","import matplotlib.pyplot as plt","import numpy as np","from sklearn.preprocessing import RobustScaler, StandardScaler","from libpysal import weights","from esda.adbscan import get_cluster_boundary","import pandas as pd","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.clustering import AgglomerativeClustering","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition, KernelBasedWeightsDefinition","from oraclesai.vis import plot_clusters","from oraclesai import enable_geodataframes","enable_geodataframes(z)","","# Needed for reproducibility.","import random","random_state = 456","random.seed(random_state)"," "],"enabled":true,"result":{"startTime":1737650323444,"interpreter":"python.low","endTime":1737650327587,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_block_groups'))","z.show(block_groups_sdf.head())"," "],"enabled":true,"result":{"startTime":1737650328096,"interpreter":"python.low","endTime":1737650328878,"results":[{"message":"GEOID\tC_LON\tC_LAT\tMEAN_INCOME\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tPER_MALE\tPER_FEMALE\tLAN_EN\tLAN_ES\tLAN_IE\tLAN_AP\tLAN_OL\tCOMPUTER\tINTERNET\tHOUSE_VALUE\tIND_AGGR\tIND_CONSTR\tIND_MANU\tIND_RETAIL\tIND_TRANSP\tIND_INFO\tIND_FINAN\tIND_EDU\tIND_HEALTH\tIND_ENTER\tIND_PADMIN\tgeometry\n060376700031\t-118.32\t33.8\t84552.289\t75577\t9.746\t37.306\t0.537\t0.463\t0.796\t0.296\t0.198\t0.0\t0\t1.0\t0.904\t627700\t0.0\t0.189\t0.192\t0.124\t0.025\t0.099\t0.073\t0.025\t0.082\t0.079\t0.023\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t-118.32\t33.794\t93991.301\t74609\t11.504\t39.161\t0.445\t0.555\t0.989\t0.118\t0.0\t0.145\t0\t0.982\t0.959\t599300\t0.015\t0.037\t0.126\t0.172\t0.045\t0.028\t0.033\t0.045\t0.08\t0.063\t0.047\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t-118.326\t33.792\t78313.714\t55592\t10.976\t47.854\t0.509\t0.491\t0.888\t0.144\t0.032\t0.094\t0\t0.966\t0.829\t609500\t0.012\t0.055\t0.187\t0.089\t0.058\t0.008\t0.046\t0.121\t0.115\t0.007\t0.04\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t-118.325\t33.799\t77076.074\t73558\t10.115\t39.283\t0.51\t0.49\t0.917\t0.235\t0.036\t0.014\t0\t0.941\t0.859\t471000\t0.0\t0.054\t0.147\t0.115\t0.084\t0.032\t0.055\t0.08\t0.075\t0.077\t0.012\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t-118.319\t33.787\t62402.628\t54738\t10.716\t37.998\t0.511\t0.489\t0.648\t0.301\t0.042\t0.216\t0\t0.967\t0.882\t389900\t0.021\t0.132\t0.335\t0.014\t0.046\t0.06\t0.039\t0.107\t0.043\t0.021\t0.057\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","In this notebook you detect agglomerative clusters based only on median_income, mean_age, mean_education_level, house_value, and spatial context. Next you create training training data in projected coordinates as required by spatial clustering algorithms."],"enabled":true,"result":{"startTime":1737650329399,"interpreter":"md.low","endTime":1737650329917,"results":[{"message":"<p>In this notebook you detect agglomerative clusters based only on median_income, mean_age, mean_education_level, house_value, and spatial context. Next you create training training data in projected coordinates as required by spatial clustering algorithms.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create training data","message":["%python","","X = block_groups_sdf[['MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'geometry']]","# Convert to projected coordinates as required for spatial weights","X = X.to_crs('epsg:3857')","z.show(X.head())",""," "],"enabled":true,"result":{"startTime":1737650330506,"interpreter":"python.low","endTime":1737650331382,"results":[{"message":"MEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tgeometry\n75577\t37.306\t9.746\t627700\tPOLYGON ((-13171614.921 4002458.952, -13171006.56 4002139.446, -13171013.684 4001739.038, -13171618.149 4001743.458, -13171614.921 4002458.952))\n74609\t39.161\t11.504\t599300\tPOLYGON ((-13171618.149 4001743.458, -13171013.684 4001739.038, -13171015.02 4000576.34, -13171318.477 4000575.938, -13171317.364 4000963.044, -13171619.596 4000964.384, -13171618.149 4001743.458))\n55592\t47.854\t10.976\t609500\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n73558\t39.283\t10.115\t471000\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 4002094.032))\n54738\t37.998\t10.716\t389900\tPOLYGON ((-13171620.932 4000577.813, -13171318.477 4000575.938, -13171015.02 4000576.34, -13170712.342 4000574.063, -13170717.463 4000012.581, -13171091.051 4000012.313, -13171186.007 3999904.092, -13171571.395 4000085.443, -13171620.932 3999984.052, -13171620.932 4000577.813))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","With **agglomerative clustering**, samples are combined into clusters based on the the similarity of feature values. ","A stopping condition is defined based on either the number of clusters to form, or linkage distance (measure of cluster similarity). ","Linkage refers to the relationship between clusters, and linkage_distance is a measure of the similarity of clusters and is inversly proportional to similarity: small linkage distance means high similarity and vice versa. It does not represent a physical distance on the ground. Variables are scaled prior to agglomerative clustering to avoid bias. Selecting linkage_distance is generally based on experimenting with values and fine-tuning based on results.","","* Agglomerative clustering (non-spatial) defines clusters based on feature values and stopping condition. ","* Agglomerative clustering with regionalization (spatial) adds spatial context by combining clusters based on their proximity, and is invoked by including a spatial_weights definition as a parameter."],"enabled":true,"result":{"startTime":1737650331922,"interpreter":"md.low","endTime":1737650332433,"results":[{"message":"<p>With <strong>agglomerative clustering<\/strong>, samples are combined into clusters based on the the similarity of feature values.\nA stopping condition is defined based on either the number of clusters to form, or linkage distance (measure of cluster similarity).\nLinkage refers to the relationship between clusters, and linkage_distance is a measure of the similarity of clusters and is inversly proportional to similarity: small linkage distance means high similarity and vice versa. It does not represent a physical distance on the ground. Variables are scaled prior to agglomerative clustering to avoid bias. Selecting linkage_distance is generally based on experimenting with values and fine-tuning based on results.<\/p>\n<ul>\n<li>Agglomerative clustering (non-spatial) defines clusters based on feature values and stopping condition.<\/li>\n<li>Agglomerative clustering with regionalization (spatial) adds spatial context by combining clusters based on their proximity, and is invoked by including a spatial_weights definition as a parameter.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","###### Agglomerative Clustering based on number of clusters",""," "],"enabled":true,"result":{"startTime":1737650332973,"interpreter":"md.low","endTime":1737650333466,"results":[{"message":"<h6 id=\"agglomerative-clustering-based-on-number-of-clusters\">Agglomerative Clustering based on number of clusters<\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and train a non-spatial model and review labels","message":["%python"," ","n_pipeline = SpatialPipeline([('scale',   StandardScaler()), \\","                            ('cluster', AgglomerativeClustering(n_clusters=8))])","n_pipeline.fit(X)","np.unique(n_pipeline.named_steps['cluster'].labels_)","  "],"enabled":true,"result":{"startTime":1737650333984,"interpreter":"python.low","endTime":1737650343803,"results":[{"message":"array([0, 1, 2, 3, 4, 5, 6, 7])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and train a spatial model based on KNN weights and review labels","message":["%python"," ","n_knn_pipeline = SpatialPipeline([('scale', StandardScaler()), \\","                                   ('cluster', AgglomerativeClustering(n_clusters=8, \\","                                               spatial_weights_definition=KNNWeightsDefinition(k=5)))])","n_knn_pipeline.fit(X)","np.unique(n_knn_pipeline.named_steps['cluster'].labels_)"],"enabled":true,"result":{"startTime":1737650344412,"interpreter":"python.low","endTime":1737650345645,"results":[{"message":"array([0, 1, 2, 3, 4, 5, 6, 7])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you plot results on maps to compare results of clustering based on number of clusters as stopping condition. Each color represents a cluster label. Observe that the spatial model generally results in clusters that form contiguous regions, versus the non-spatial model which assigns block groups to clusters regardless of their location."," "],"enabled":true,"result":{"startTime":1737650346166,"interpreter":"md.low","endTime":1737650346654,"results":[{"message":"<p>Next you plot results on maps to compare results of clustering based on number of clusters as stopping condition. Each color represents a cluster label. Observe that the spatial model generally results in clusters that form contiguous regions, versus the non-spatial model which assigns block groups to clusters regardless of their location.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps of clustering results","message":["%python","","","_, axs = plt.subplots(1, 2, figsize=(15, 10))","","   ","plot_clusters(X, n_pipeline.named_steps['cluster'].labels_, title='Without regionalization (non-spatial)', ax=axs[0], with_noise=False, with_bounds=False, cmap=\"Dark2\", with_legend=True, with_basemap=True) ","plot_clusters(X, n_knn_pipeline.named_steps['cluster'].labels_, title='With regionalization (KNN weights)', ax=axs[1], with_noise=False, with_bounds=False, cmap=\"Dark2\", with_legend=True, with_basemap=True) "],"enabled":true,"result":{"startTime":1737650347320,"interpreter":"python.low","endTime":1737650352764,"results":[{"message":"<div style='width:auto;height:auto'><img 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distance_threshold<\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and train a non-spatial model and review labels","message":["%python","","d_pipeline = SpatialPipeline([('scale',   StandardScaler()), \\","                            ('cluster', AgglomerativeClustering(n_clusters=None, distance_threshold=16))])","d_pipeline.fit(X)","np.unique(d_pipeline.named_steps['cluster'].labels_)"," "],"enabled":true,"result":{"startTime":1737650354612,"interpreter":"python.low","endTime":1737650355799,"results":[{"message":"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and train a spatial model based on KNN weights and review labels","message":["%python","","d_knn_pipeline = SpatialPipeline([('scale', StandardScaler()), \\","                                ('cluster', AgglomerativeClustering(n_clusters=None, distance_threshold=16, \\","                                            spatial_weights_definition=KNNWeightsDefinition(k=5)))])","d_knn_pipeline.fit(X)","np.unique(d_knn_pipeline.named_steps['cluster'].labels_)"," "],"enabled":true,"result":{"startTime":1737650356409,"interpreter":"python.low","endTime":1737650357578,"results":[{"message":"array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","Next you plot results on maps to compare results of clustering based on distance_threshold (cluster similarity) as stopping condition. Each color represents a cluster label. Observe that the spatial model generally results in clusters that form contiguous regions, versus the non-spatial model which assigns block groups to clusters regardless of their location."," "],"enabled":true,"result":{"startTime":1737650358140,"interpreter":"md.low","endTime":1737650358694,"results":[{"message":"<p>Next you plot results on maps to compare results of clustering based on distance_threshold (cluster similarity) as stopping condition. Each color represents a cluster label. Observe that the spatial model generally results in clusters that form contiguous regions, versus the non-spatial model which assigns block groups to clusters regardless of their location.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps of clustering results","message":["%python","","","_, axs = plt.subplots(1, 2, figsize=(15, 10))","    ","plot_clusters(X, d_pipeline.named_steps['cluster'].labels_, title='Without regionalization (non-spatial)', ax=axs[0], with_noise=False, with_bounds=False, cmap=\"Dark2\", with_legend=True, with_basemap=True) ","plot_clusters(X, d_knn_pipeline.named_steps['cluster'].labels_, title='With regionalization (knn weights)', ax=axs[1], with_noise=False, with_bounds=False, cmap=\"Dark2\", with_legend=True, with_basemap=True) "],"enabled":true,"result":{"startTime":1737650359235,"interpreter":"python.low","endTime":1737650362069,"results":[{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Categorical Lag Transformer.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Categorical Lag Transformer.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Categorical Lag Transformer","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Categorical Lag Transformer - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you use the Categorical Lag Transformer to determine the most common value of a categorical feature in the neighborhood of each observation. This result is itself a new feature providing geographic context used in subsequent model training.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/) "],"enabled":true,"result":{"startTime":1737650713966,"interpreter":"md.low","endTime":1737650714627,"results":[{"message":"<h1 id=\"categorical-lag-transformer---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Categorical Lag Transformer - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you use the Categorical Lag Transformer to determine the most common value of a categorical feature in the neighborhood of each observation. This result is itself a new feature providing geographic context used in subsequent model training.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737650715166,"interpreter":"md.low","endTime":1737650715686,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","You will use US Census data for income in the City of Los Angeles. The dataset contains MEDIAN_INCOME for regions called Block Groups. As the dataset does not include an categorical variable, you will generate one for income and then use the CategoricalLagTransformer to generate a new feature with the most common income level in the neighborhood of each observation."],"enabled":true,"result":{"startTime":1737650716258,"interpreter":"md.low","endTime":1737650716747,"results":[{"message":"<p>You will use US Census data for income in the City of Los Angeles. The dataset contains MEDIAN_INCOME for regions called Block Groups. As the dataset does not include an categorical variable, you will generate one for income and then use the CategoricalLagTransformer to generate a new feature with the most common income level in the neighborhood of each observation.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","from libpysal import weights","import matplotlib.pyplot as plt","from sklearn.pipeline import Pipeline","from sklearn.impute import SimpleImputer","from sklearn.preprocessing import StandardScaler, OneHotEncoder","import scipy.sparse as sparse","import pandas as pd","import numpy as np","","","import random","random_state = 456","random.seed(random_state)","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.regression import OLSRegressor","from oraclesai.pipeline import SpatialPipeline, SpatialFeatureUnion, SpatialColumnTransformer","from oraclesai.preprocessing import SpatialLagTransformer, CategoricalLagTransformer","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai import enable_geodataframes","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737650717298,"interpreter":"python.low","endTime":1737650721016,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID', 'MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'geometry']]","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737650721537,"interpreter":"python.low","endTime":1737650722198,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tgeometry\n060376700031\t75577\t37.306\t9.746\t627700\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t39.161\t11.504\t599300\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t47.854\t10.976\t609500\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t39.283\t10.115\t471000\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t37.998\t10.716\t389900\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The CategoricalLagTransformer only works with categorical variables. ","Since you don't have a categorical feature in your dataset, you next create one based on MEDIAN_INCOME. ","The categorical variable will be called INCOME_CLASS with values Low, Medium-Low, Medium-High, High based on quantiles. "],"enabled":true,"result":{"startTime":1737650722712,"interpreter":"md.low","endTime":1737650723231,"results":[{"message":"<p>The CategoricalLagTransformer only works with categorical variables.\nSince you don't have a categorical feature in your dataset, you next create one based on MEDIAN_INCOME.\nThe categorical variable will be called INCOME_CLASS with values Low, Medium-Low, Medium-High, High based on quantiles.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Generate a categorical variable","message":["%python","","labels=['4-Low', '3-Medium-Low', '2-Medium-High', '1-High']","block_groups_sdf = block_groups_sdf.add_column(\"INCOME_CLASS\", pd.qcut(block_groups_sdf[\"MEDIAN_INCOME\"].values, [0, 0.25, 0.5, 0.75, 1], labels=labels).tolist())","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737650723772,"interpreter":"python.low","endTime":1737650740627,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tINCOME_CLASS\tgeometry\n060376700031\t75577\t37.306\t9.746\t627700\t2-Medium-High\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t39.161\t11.504\t599300\t2-Medium-High\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t47.854\t10.976\t609500\t3-Medium-Low\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t39.283\t10.115\t471000\t2-Medium-High\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t37.998\t10.716\t389900\t3-Medium-Low\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you create an instance of CategoricalLagTransformer using knn5 as the spatial weights definition. The spatial weights define the neighborhood for each observation","over which the most common categorical value will be determined. "],"enabled":true,"result":{"startTime":1737650741150,"interpreter":"md.low","endTime":1737650741680,"results":[{"message":"<p>Next you create an instance of CategoricalLagTransformer using knn5 as the spatial weights definition. The spatial weights define the neighborhood for each observation\nover which the most common categorical value will be determined.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create instance of CategoricalLagTransformer","message":["%python","","categorical_lag_transformer = CategoricalLagTransformer(KNNWeightsDefinition(k=5))"],"enabled":true,"result":{"startTime":1737650742176,"interpreter":"python.low","endTime":1737650742653,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Run the CategoricalLagTransformer for INCOME_CLASS","message":["%python","","X_categorical_lag = categorical_lag_transformer.fit_transform(block_groups_sdf[['INCOME_CLASS']], geometries='geometry')","X_categorical_lag"],"enabled":true,"result":{"startTime":1737650743158,"interpreter":"python.low","endTime":1737650744092,"results":[{"message":"array([['2-Medium-High'],\n       ['2-Medium-High'],\n       ['2-Medium-High'],\n       ...,\n       ['1-High'],\n       ['1-High'],\n       ['2-Medium-High']], dtype=object)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," ","Next you add INCOME_CLASS_LAG to your data as a new feature. Note that CategoricalLagTransformer can also be used for feature engineering in the pipeline context with with SpatialFeatureUnion and SpatialColumnTransformer."],"enabled":true,"result":{"startTime":1737650744648,"interpreter":"md.low","endTime":1737650745136,"results":[{"message":"<p>Next you add INCOME_CLASS_LAG to your data as a new feature. Note that CategoricalLagTransformer can also be used for feature engineering in the pipeline context with with SpatialFeatureUnion and SpatialColumnTransformer.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Add INCOME_CLASS_LAG as new feature","message":["%python","","block_groups_sdf = block_groups_sdf.add_column('INCOME_CLASS_LAG', X_categorical_lag.flatten())","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737650745639,"interpreter":"python.low","endTime":1737650746142,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tINCOME_CLASS\tINCOME_CLASS_LAG\tgeometry\n060376700031\t75577\t37.306\t9.746\t627700\t2-Medium-High\t2-Medium-High\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t39.161\t11.504\t599300\t2-Medium-High\t2-Medium-High\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t47.854\t10.976\t609500\t3-Medium-Low\t2-Medium-High\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t39.283\t10.115\t471000\t2-Medium-High\t2-Medium-High\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t37.998\t10.716\t389900\t3-Medium-Low\t2-Medium-High\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","","","","Finally, you plot maps of block groups color coded by INCOME_CLASS and INCOME_CLASS_LAG. The first map is color coded the category of the median income for each block group. The second map is color coded by the most common category of the median income for neighbors of each block group. "],"enabled":true,"result":{"startTime":1737650746760,"interpreter":"md.low","endTime":1737650747302,"results":[{"message":"<p>Finally, you plot maps of block groups color coded by INCOME_CLASS and INCOME_CLASS_LAG. The first map is color coded the category of the median income for each block group. The second map is color coded by the most common category of the median income for neighbors of each block group.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps of INCOME_CLASS and INCOME_CLASS_LAG","message":["%python","","# Setup figure and ax","fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,10))","","# Plot unique values choropleth including ","# a legend and with no boundary lines","","block_groups_sdf.plot(column='INCOME_CLASS', cmap=\"Spectral\", categorical=True, legend=True, with_basemap=True, linewidth=0, ax=ax1)","block_groups_sdf.plot(column='INCOME_CLASS_LAG',cmap=\"Spectral\", categorical=True, legend=True, with_basemap=True, linewidth=0, ax=ax2)","","ax1.set_title('INCOME_CLASS')","ax2.set_title('INCOME_CLASS_LAG');","ax1.set_axis_off()","ax2.set_axis_off()","","# Display the map","plt.show()"],"enabled":true,"result":{"startTime":1737650747827,"interpreter":"python.low","endTime":1737650752584,"results":[{"message":"<div style='width:auto;height:auto'><img 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Geographically closer things tend to be more related, and incorporating this information into models can lead to better results."," Observe that INCOME_CLASS_LAG forms larger and more contiguous regions. This can leveraged to define clusters have more significant spatial dependence then the original INCOME_CLASS feature. Note that CategoricalLagTransformer is compatible with pipelines, so categorical lag variables may also be added using SpatialFeatureUnion  and SpatialColumnTransformer."],"enabled":true,"result":{"startTime":1737650753155,"interpreter":"md.low","endTime":1737650753618,"results":[{"message":"<p>By identifying the most common value from the neighbors of each observation, you have added new geographic context to your data. Geographically closer things tend to be more related, and incorporating this information into models can lead to better results.\nObserve that INCOME_CLASS_LAG forms larger and more contiguous regions. This can leveraged to define clusters have more significant spatial dependence then the original INCOME_CLASS feature. 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI DBSCAN Accidents","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial AI in Oracle Machine Learning for Python (OML4Py) - DBScan Accidents","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you detect clusters of car accidents based on location and severity using the DBSCAN algorithm. ","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737650824736,"interpreter":"md.low","endTime":1737650825415,"results":[{"message":"<h1 id=\"spatial-ai-in-oracle-machine-learning-for-python-oml4py---dbscan-accidents\">Spatial AI in Oracle Machine Learning for Python (OML4Py) - DBScan Accidents<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you detect clusters of car accidents based on location and severity using the DBSCAN algorithm.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md"," ","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737650825897,"interpreter":"md.low","endTime":1737650826361,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os ","import pandas as pd","import geopandas as gpd","import matplotlib.pyplot as plt","import numpy as np","from sklearn.preprocessing import StandardScaler, RobustScaler","from libpysal import weights","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, FileSpatialDataset, GeoDataFrameDataset, DBSpatialDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.preprocessing import SCoordTransformer","from oraclesai.clustering import DBScanClustering, KMeansClustering","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai.vis import plot_clusters, plot_geometries","from oraclesai import enable_geodataframes","enable_geodataframes(z)","","# Needed for reproducibility.","import random","random_state = 456","random.seed(random_state)"," "],"enabled":true,"result":{"startTime":1737650826849,"interpreter":"python.low","endTime":1737650830441,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","###### Prepare the crash data"," "],"enabled":true,"result":{"startTime":1737650830926,"interpreter":"md.low","endTime":1737650831387,"results":[{"message":"<h6 id=\"prepare-the-crash-data\">Prepare the crash data<\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create spatial data frame for accident data","message":["%python","","accidents_sdf =SpatialDataFrame.create(DBSpatialDataset( table='chicago_accidents'))","z.show(accidents_sdf.head(100))"," "],"enabled":true,"result":{"startTime":1737650831946,"interpreter":"python.low","endTime":1737650832564,"results":[{"message":"OGR_FID\tX\tY\tREPORT_TYP\tCRASH_TYPE\tPRIM_CONTR\tDAMAGE\tNUM_UNITS\tDAMAGE_RATING\tINJURY_RATING\tYYYY\tMM\tDD\tgeometry\n229\t-9754525.426\t5147648.59\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tIMPROPER OVERTAKING/PASSING\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.626 41.911)\n231\t-9764077.355\t5149433.999\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tUNABLE TO DETERMINE\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.712 41.923)\n233\t-9758522.788\t5145147.118\tON SCENE\tINJURY AND / OR TOW DUE TO CRASH\tFAILING TO YIELD RIGHT-OF-WAY\tOVER $1,500\t2\t3\t2\t2022\t11\t19\tPOINT (-87.662 41.894)\n339\t-9761738.196\t5128400.563\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tUNABLE TO DETERMINE\t$501 - 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MM  DD                    geometry\n0      233 -9.758523e+06  5.145147e+06  ...  11  19   POINT (-87.6623 41.89422)\n1      341 -9.751662e+06  5.115099e+06  ...  11  19  POINT (-87.60067 41.69297)\n2      275 -9.756649e+06  5.131320e+06  ...  11  19  POINT (-87.64547 41.80169)\n3      287 -9.747024e+06  5.111095e+06  ...  11  19    POINT (-87.559 41.66611)\n4      293 -9.763469e+06  5.159505e+06  ...  11  19  POINT (-87.70673 41.99015)\n\n[5 rows x 14 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," ","###### Detect clusters","","","[DBSCAN](https://en.wikipedia.org/wiki/DBSCAN) is an algorithm for detecting density-based clusters from large amounts of data. The algorithm uses the following parameters.","","* **min_samples** - minimum number of points required for a region to be considered a cluster.","* **eps** - distance threshold for searching points in the neighborhood of a point.","","The algorithm starts at a point, and if at least `min_samples` points are within a radius of `eps,` then all the points in the neighborhood are considered part of the same cluster. The process is repeated for all the points in the neighborhood, allowing clusters of different shapes.  ","","* A **core point** at least has a `min_samples` number of points in its neighborhood within the radius `eps.`","* A **border point** is reachable from a core point, but there are fewer than the `min_samples` number of points within its neighborhood.","* A **noise point** or outlier is neither a core point nor a border point, and it is a point that is not reachable from any core points; these are represented with black color. "],"enabled":true,"result":{"startTime":1737650834290,"interpreter":"md.low","endTime":1737650834777,"results":[{"message":"<h6 id=\"detect-clusters\">Detect clusters<\/h6>\n<p><a href=\"https://en.wikipedia.org/wiki/DBSCAN\">DBSCAN<\/a> is an algorithm for detecting density-based clusters from large amounts of data. The algorithm uses the following parameters.<\/p>\n<ul>\n<li><strong>min_samples<\/strong> - minimum number of points required for a region to be considered a cluster.<\/li>\n<li><strong>eps<\/strong> - distance threshold for searching points in the neighborhood of a point.<\/li>\n<\/ul>\n<p>The algorithm starts at a point, and if at least <code>min_samples<\/code> points are within a radius of <code>eps,<\/code> then all the points in the neighborhood are considered part of the same cluster. The process is repeated for all the points in the neighborhood, allowing clusters of different shapes.<\/p>\n<ul>\n<li>A <strong>core point<\/strong> at least has a <code>min_samples<\/code> number of points in its neighborhood within the radius <code>eps.<\/code><\/li>\n<li>A <strong>border point<\/strong> is reachable from a core point, but there are fewer than the <code>min_samples<\/code> number of points within its neighborhood.<\/li>\n<li>A <strong>noise point<\/strong> or outlier is neither a core point nor a border point, and it is a point that is not reachable from any core points; these are represented with black color.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Convert to projected coordinates for DBSCAN","message":["%python","","","X = accidents_injury_sdf[['geometry']].to_crs(\"EPSG:3857\")","X.head()"],"enabled":true,"result":{"startTime":1737650835300,"interpreter":"python.low","endTime":1737650835812,"results":[{"message":"                           geometry\n0  POINT (-9758522.788 5145147.118)\n1  POINT (-9751662.368 5115098.589)\n2  POINT (-9756648.885 5131320.116)\n3  POINT (-9747023.637 5111095.265)\n4  POINT (-9763468.772 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create boundary shape","message":["%python","from shapely.geometry import LineString","bounds=X.total_bounds","min_x,min_y,max_x,max_y = bounds[0],bounds[1],bounds[2],bounds[3]","","boundary =  {'geometry': gpd.GeoSeries(                 \\","                           LineString([ (min_x, min_y), \\","                                        (min_x, max_y), \\","                                        (max_x, max_y), \\","                                        (max_x, min_y), \\","                                        (min_x, min_y)  \\","                                        ]))             \\","             }","boundary_gdf = gpd.GeoDataFrame(boundary, crs=\"EPSG:3857\")"],"enabled":true,"result":{"startTime":1737650846033,"interpreter":"python.low","endTime":1737650848524,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Compare clusters based on various min_samples","message":["%python","","min_samples_arr = [25, 20, 15, 10]","_, axs = plt.subplots(2, 2, figsize=(15, 15))","    ","for i, min_samples in enumerate(min_samples_arr):","    dbscan = DBScanClustering(eps=500, min_samples=min_samples)","    labels = dbscan.fit_predict(geom_as_features) ","    ax_row, ax_col = i // 2, i % 2","    plot_clusters(X, labels, title=f'min_samples={min_samples}', ax=axs[ax_row][ax_col], with_noise=False, with_bounds=False, cmap=\"gist_ncar\", with_legend=False, with_basemap=True, background_data=boundary_gdf)","     "," "],"enabled":true,"result":{"startTime":1737650849010,"interpreter":"python.low","endTime":1737650855600,"results":[{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Embedded Execution for SQL.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Embedded Execution for SQL.dsnb
new file mode 100644
index 00000000..4ffdd40e
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Embedded Execution for SQL.dsnb	
@@ -0,0 +1,832 @@
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+    "tags" : null,
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+          "",
+          "# Spatial AI Embedded Execution for SQL - Spatial AI in Oracle Machine Learning for Python (OML4Py)",
+          "",
+          "Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ",
+          "",
+          "OML4Py embedded execution provides SQL and REST interfaces to invoke models. In this notebook you explore using Spatial AI with embedded execution for SQL. ",
+          "Spatial AI exposes some of its functionality as User Defined Functions (UDF). OML4Py provides APIs for executing UDFs from REST or SQL. ",
+          "You will register Spatial AI UDFs, get and set an authorization token to be able to execute OML calls to UDFs, and run some of the available Spatial AI UDFs from SQL.",
+          "",
+          "**Copyright (c) 2025 Oracle Corporation**",
+          "[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)",
+          " ",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
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+            {
+              "message" : "<h1 id=\"spatial-ai-embedded-execution-for-sql---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial AI Embedded Execution for SQL - Spatial AI in Oracle Machine Learning for Python (OML4Py)</h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.</p>\n<p>OML4Py embedded execution provides SQL and REST interfaces to invoke models. In this notebook you explore using Spatial AI with embedded execution for SQL.\nSpatial AI exposes some of its functionality as User Defined Functions (UDF). OML4Py provides APIs for executing UDFs from REST or SQL.\nYou will register Spatial AI UDFs, get and set an authorization token to be able to execute OML calls to UDFs, and run some of the available Spatial AI UDFs from SQL.</p>\n<p><strong>Copyright (c) 2025 Oracle Corporation</strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0</a></p>\n",
+              "type" : "HTML"
+            }
+          ],
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Before you begin",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
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+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first</strong> which loads data used in all sample notebooks.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Import required libraries",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "from oraclesai.oml import register_sai_scripts",
+          "import oml",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Register Spatial AI UDFs for the current user",
+        "hasTitle" : true,
+        "message" : [
+          "%python ",
+          "",
+          "register_sai_scripts(is_global=False)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
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+          "interpreter" : "python.low",
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+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
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+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "List the registered built-in UDFs",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "import oml",
+          "z.show(oml.script.dir(sctype='all')[['name', 'description']])"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
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+        "hideGutter" : true,
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "name\tdescription\nclustering\tPeforms clustering on the given spatial table, selecting the given columns or all the columns of the table if no columns parameter was provided. Available clustering methods are: DBSCAN, Agglomerative, KMeans.\ncompute_global_spatial_autocorrelation\tComputes the Moran I index for the given spatial table and column.  Returns the following statistics: I, expected I, p value, z value\ncompute_local_spatial_autocorrelation\tComputes the statistics for the Local Spatial Autocorrelation of all the rows from the given spatial tables using Local Moran. Returns a tabular result containing the statistics for row from the input table.  Returned statistics include: I, p value, z value, quadrant.\ncompute_spatial_weights\tComputes the spatial weights for the given spatial table.  Stores the spatial weights object in the data store specified by the save_weights_as parameter.\ncreate_spatial_lag\tCreates a spatial lag for the given column of the provided spatial table.  Returns a tabular result which includes the calculated spatial lag for each row from the input table.\nmypyenv_test\tNone\npyqFun2\tNone\npyqFun3\tNone\npyqFun4\tNone\nsai_test\tNone\nsai_test2\tNone\nsai_test3\tNone\nsai_test_3\tNone\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Add OML user to Access Control List",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "To use the SQL API for Embedded Python Execution with Autonomous Database (ADB), an OML user need to be added to the Access Control List (ACL) of the OML host. ",
+          "",
+          "* Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.",
+          "* Click the hamburger menu and select Autonomous Database instance that is provisioned.",
+          "",
+          "Perform the following steps:",
+          "",
+          "* From your ADB console, select Database Actions > SQL",
+          "* Log into as ADMIN or another admin user",
+          "* Run the following function:",
+          "",
+          "`exec pyqAppendHostAce('<OML username'>,'adb.<Region ID>.oraclecloudapps.com');`",
+          "",
+          "where Region ID  is the region of your ADB.",
+          "",
+          "For example, if your username is OMLUSER and your ADB resides in the Ashburn region,you would run the following:",
+          "",
+          "`exec pyqAppendHostAce('OMLUSER','adb.us-region-1.oraclecloudapps.com');`",
+          "",
+          "This procedure only needs to be performed once per user.",
+          "",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
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+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>To use the SQL API for Embedded Python Execution with Autonomous Database (ADB), an OML user need to be added to the Access Control List (ACL) of the OML host.</p>\n<ul>\n<li>Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.</li>\n<li>Click the hamburger menu and select Autonomous Database instance that is provisioned.</li>\n</ul>\n<p>Perform the following steps:</p>\n<ul>\n<li>From your ADB console, select Database Actions &gt; SQL</li>\n<li>Log into as ADMIN or another admin user</li>\n<li>Run the following function:</li>\n</ul>\n<p><code>exec pyqAppendHostAce('&lt;OML username'&gt;,'adb.&lt;Region ID&gt;.oraclecloudapps.com');</code></p>\n<p>where Region ID  is the region of your ADB.</p>\n<p>For example, if your username is OMLUSER and your ADB resides in the Ashburn region,you would run the following:</p>\n<p><code>exec pyqAppendHostAce('OMLUSER','adb.us-region-1.oraclecloudapps.com');</code></p>\n<p>This procedure only needs to be performed once per user.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Get OML Cloud Service URL",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Next you obtain your OML REST URL which is needed to obtain a token. This can be obtained from your ADB console as follows:",
+          "",
+          "* On your ADB console page, click Tool Configuration",
+          "* Scroll down to \"Oracle Machine Learning user interface\"",
+          "* Under Access URL click Copy and paste to an editor",
+          "* Remove \"/oml\" from the pasted URL. ",
+          "",
+          "For example, for a pasted URL https://abcxyz123-omldb.adb.us-ashburn-1.oraclecloudapps.com/oml/, your will use the following in the next step: https://abcxyz123-omldb.adb.us-ashburn-1.oraclecloudapps.com"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Next you obtain your OML REST URL which is needed to obtain a token. This can be obtained from your ADB console as follows:</p>\n<ul>\n<li>On your ADB console page, click Tool Configuration</li>\n<li>Scroll down to &quot;Oracle Machine Learning user interface&quot;</li>\n<li>Under Access URL click Copy and paste to an editor</li>\n<li>Remove &quot;/oml&quot; from the pasted URL.</li>\n</ul>\n<p>For example, for a pasted URL <a href=\"https://abcxyz123-omldb.adb.us-ashburn-1.oraclecloudapps.com/oml/\">https://abcxyz123-omldb.adb.us-ashburn-1.oraclecloudapps.com/oml/</a>, your will use the following in the next step: <a href=\"https://abcxyz123-omldb.adb.us-ashburn-1.oraclecloudapps.com\">https://abcxyz123-omldb.adb.us-ashburn-1.oraclecloudapps.com</a></p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Obtain an access token ",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "You are now ready to obtain an access token. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ",
+          "",
+          "    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"<yourusername>\", ",
+          "    \"password\":\"<yourpassword>\"}' \"<oml-cloud-service-url>/omlusers/api/oauth2/v1/token\"",
+          "",
+          "where   ",
+          "",
+          "* _-X POST_ specifies to use a POST request when communicating with the HTTP server",
+          "* _--header_ defines the headers required for the request (application/json)",
+          "* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server",
+          "* _Content-Type_ and _Accept_ define the response format (JSON)",
+          "* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role",
+          "* _yourpassword_ is the password for the user name",
+          "* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance",
+          "",
+          "For example, Oracle Machine Learning user **OMLUSER** with password **OMLPASSWD** and cloud service url **https://abcxyz123-omldb.adb.us-sanjose-1.oraclecloudapps.com** authenticates and obtain a REST token using the following approach:",
+          "",
+          "$ export omlserver=https://abcxyz123-omldb.adb.us-sanjose-1.oraclecloudapps.com",
+          "$ export username=OMLUSER",
+          "$ export password=OMLPASSWD",
+          "",
+          "curl -i -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'$\\{username\\}'\", \"password\":\"'$\\{password\\}'\"}' \"$\\{omlserver\\}/omlusers/api/oauth2/v1/token\"",
+          "",
+          "  HTTP/1.1 200 OK",
+          "  Date: Wed, 09 Mar 2022 21:59:16 GMT",
+          "  Content-Type: application/json",
+          "  Content-Length: 1027",
+          "  Connection: keep-alive",
+          "  Cache-Control: no-cache, no-store, private",
+          "  X-Frame-Options: SAMEORIGIN",
+          "  X-XSS-Protection: 1;mode=block",
+          "  Strict-Transport-Security: max-age=31536000; includeSubDomains",
+          "  X-Content-Type-Options: nosniff",
+          "",
+          "  {\"accessToken\":\"eyJhbGciOiJ...\",\"expiresIn\":3600,\"tokenType\":\"Bearer\"}"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>You are now ready to obtain an access token. To authenticate and obtain a token, use <strong>cURL</strong> with the <strong>-d</strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token</em>.</p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;&lt;yourusername&gt;&quot;, \n&quot;password&quot;:&quot;&lt;yourpassword&gt;&quot;}' &quot;&lt;oml-cloud-service-url&gt;/omlusers/api/oauth2/v1/token&quot;\n</code></pre>\n<p>where</p>\n<ul>\n<li><em>-X POST</em> specifies to use a POST request when communicating with the HTTP server</li>\n<li><em>--header</em> defines the headers required for the request (application/json)</li>\n<li><em>-d <data></em> sends the username and password authentication credentials as data in a POST request to the HTTP server</li>\n<li><em>Content-Type</em> and <em>Accept</em> define the response format (JSON)</li>\n<li><em>yourusername</em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role</li>\n<li><em>yourpassword</em> is the password for the user name</li>\n<li><em>oml-cloud-service-url</em> is the REST server portion of the Oracle Machine Learning user management cloud service instance</li>\n</ul>\n<p>For example, Oracle Machine Learning user <strong>OMLUSER</strong> with password <strong>OMLPASSWD</strong> and cloud service url <strong><a href=\"https://abcxyz123-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://abcxyz123-omldb.adb.us-sanjose-1.oraclecloudapps.com</a></strong> authenticates and obtain a REST token using the following approach:</p>\n<p>$ export omlserver=<a href=\"https://abcxyz123-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://abcxyz123-omldb.adb.us-sanjose-1.oraclecloudapps.com</a>\n$ export username=OMLUSER\n$ export password=OMLPASSWD</p>\n<p>curl -i -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'${username}'&quot;, &quot;password&quot;:&quot;'${password}'&quot;}' &quot;${omlserver}/omlusers/api/oauth2/v1/token&quot;</p>\n<p>HTTP/1.1 200 OK\nDate: Wed, 09 Mar 2022 21:59:16 GMT\nContent-Type: application/json\nContent-Length: 1027\nConnection: keep-alive\nCache-Control: no-cache, no-store, private\nX-Frame-Options: SAMEORIGIN\nX-XSS-Protection: 1;mode=block\nStrict-Transport-Security: max-age=31536000; includeSubDomains\nX-Content-Type-Options: nosniff</p>\n<p>{&quot;accessToken&quot;:&quot;eyJhbGciOiJ...&quot;,&quot;expiresIn&quot;:3600,&quot;tokenType&quot;:&quot;Bearer&quot;}</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Set access token",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "-- Paste the access token from the previous step into this command",
+          "exec pyqSetAuthToken('<your access token>')"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "Now that access has been established with an auth token, you next create UDFs using Spatial AI features and invoke them using the OML4Py embedded execution API for SQL.",
+          "",
+          "The following important considerations apply to all Spatial AI calls from SQL:",
+          "* Functions are called using the OML4Py function `pyqEval` using the parameter `\"oml_connect\": true`.",
+          "* Functions can compute spatial weights on the fly by passing the `weights_def parameter`. The computed weights can be stored in an OML data store by providing the `save_weights_as` parameter.",
+          "* Functions can reuse spatial weights that are stored in an OML data storeusing the `weights` parameter. The exception to this rule is the `compute_spatial_weights` function.",
+          "* Output formats are  controlled using the different types allowed by OML which are listed [here](https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/output-formats-autonomous-database.html). Note that only the clustering function can generate image outputs."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Now that access has been established with an auth token, you next create UDFs using Spatial AI features and invoke them using the OML4Py embedded execution API for SQL.</p>\n<p>The following important considerations apply to all Spatial AI calls from SQL:</p>\n<ul>\n<li>Functions are called using the OML4Py function <code>pyqEval</code> using the parameter <code>&quot;oml_connect&quot;: true</code>.</li>\n<li>Functions can compute spatial weights on the fly by passing the <code>weights_def parameter</code>. The computed weights can be stored in an OML data store by providing the <code>save_weights_as</code> parameter.</li>\n<li>Functions can reuse spatial weights that are stored in an OML data storeusing the <code>weights</code> parameter. The exception to this rule is the <code>compute_spatial_weights</code> function.</li>\n<li>Output formats are  controlled using the different types allowed by OML which are listed <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/output-formats-autonomous-database.html\">here</a>. Note that only the clustering function can generate image outputs.</li>\n</ul>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Compute spatial weights",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select *",
+          "    from table( ",
+          "        pyqEval(",
+          "            par_lst => '{  ",
+          "                \"oml_connect\": true, ",
+          "                \"table\": \"la_block_groups\", ",
+          "                \"weights_def\": {\"type\": \"KNN\", \"k\": 4},",
+          "                \"save_weights_as\": {\"ds_name\": \"spatial\", \"obj_name\": \"la_bg_knn4\", \"append\": true, \"overwrite_obj\": true}",
+          "            }',",
+          "            out_fmt => 'XML',",
+          "            scr_name =>  'compute_spatial_weights'",
+          "        )",
+          "    );  "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "NAME\tVALUE\n\t<root><bool>TRUE</bool></root>\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Compute global spatial autocorrelation using existing spatial weights",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select *",
+          "    from table( ",
+          "        pyqEval(",
+          "            par_lst => '{  ",
+          "                \"oml_connect\": true, ",
+          "                \"table\": \"la_block_groups\", ",
+          "                \"column\": \"median_income\",",
+          "                \"weights\": {\"ds_name\":\"spatial\", \"obj_name\": \"la_bg_knn4\"}",
+          "            }',",
+          "            out_fmt => '{ \"I\": \"NUMBER\", \"expected_I\": \"NUMBER\",  \"p_value\": \"NUMBER\", \"z_value\": \"NUMBER\" }',",
+          "            scr_name => 'compute_global_spatial_autocorrelation'",
+          "        )",
+          "    )  "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "I\texpected_I\tp_value\tz_value\n0.6658882028\t-0.0002910361\t0.001\t57.9475435417\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Compute global spatial autocorrelation with new spatial weights",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select *",
+          "    from table( ",
+          "        pyqEval(",
+          "            '{  ",
+          "                \"oml_connect\": true, ",
+          "                \"table\": \"la_block_groups\", ",
+          "                \"column\": \"median_income\",",
+          "                \"weights_def\": {\"type\":\"Queen\"},",
+          "                \"save_weights_as\": {\"ds_name\":\"spatial\", \"obj_name\": \"la_bg_queen\", \"append\": true, \"overwrite_obj\": true}",
+          "            }',",
+          "            '{ \"I\": \"NUMBER\", \"expected_I\": \"NUMBER\",  \"p_value\": \"NUMBER\", \"z_value\": \"NUMBER\" }',",
+          "            'compute_global_spatial_autocorrelation'",
+          "        )",
+          "    ); "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "I\texpected_I\tp_value\tz_value\n0.6765793161\t-0.0002910361\t0.001\t64.848199503\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Get local spatial autocorrelation statistics",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select *",
+          "    from table( ",
+          "        pyqEval(",
+          "            '{  ",
+          "                \"oml_connect\": true, ",
+          "                \"table\": \"la_block_groups\", ",
+          "                \"key_column\": \"geoid\",",
+          "                \"column\": \"median_income\",",
+          "                \"weights\": {\"ds_name\":\"spatial\", \"obj_name\": \"la_bg_knn4\"}",
+          "            }',",
+          "            '{ \"geoid\": \"VARCHAR2(50)\", \"I\": \"NUMBER\", \"p_value\": \"NUMBER\",  \"z_value\": \"NUMBER\", \"quadrant\": \"NUMBER\" }',",
+          "            'compute_local_spatial_autocorrelation'",
+          "        )",
+          "    ); "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : 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+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create a spatial lag for a column",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select *",
+          "    from table( ",
+          "        pyqEval(",
+          "            '{  ",
+          "                \"oml_connect\": true, ",
+          "                \"table\": \"la_block_groups\", ",
+          "                \"key_column\": \"geoid\",",
+          "                \"column\": \"median_income\",",
+          "                \"weights\": {\"ds_name\":\"spatial\", \"obj_name\": \"la_bg_knn4\"}",
+          "            }',",
+          "            '{ \"geoid\": \"VARCHAR2(50)\", \"MEDIAN_INCOME_SLAG\": \"NUMBER\" }',",
+          "            'create_spatial_lag'",
+          "        )",
+          "    );"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : 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+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Perform DBSCAN clustering with regionalization",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select *",
+          "    from table( ",
+          "        pyqEval(",
+          "            '{  ",
+          "                \"oml_connect\": true, ",
+          "                \"table\": \"la_block_groups\",",
+          "                \"columns\": [\"median_income\"],",
+          "                \"method\": \"DBSCAN\",",
+          "                \"eps\": 0.8, \"min_samples\": 4,",
+          "                \"key_column\": \"geoid\",",
+          "                \"weights_def\": {\"type\": \"Queen\"}",
+          "            }',",
+          "            '{ \"geoid\": \"VARCHAR2(50)\", \"label\": \"NUMBER\" }',",
+          "            'clustering'",
+          "        )",
+          "    );  "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : 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+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Perform K-Means clustering and plot the results",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select IMAGE",
+          "    from table( ",
+          "        pyqEval(",
+          "            '{  ",
+          "                \"oml_connect\": true, \"oml_graphics_flag\": true,",
+          "                \"table\": \"la_block_groups\",",
+          "                \"columns\": [\"median_income\"],",
+          "                \"key_column\": \"geoid\",",
+          "                \"method\": \"KMEANS\",",
+          "                \"n_clusters\": 6,",
+          "                \"geometry_as_feature\": true, ",
+          "                \"plot\": {\"with_basemap\": true, \"title\": \"KMeans Clusters\"}",
+          "            }',",
+          "            'PNG',",
+          "            'clustering'",
+          "        )",
+          "    ); "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<html><table><tr><th>IMAGE</th></tr><tr><td><img width=100% height=100% 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Exploratory Analysis.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Exploratory Analysis.dsnb
new file mode 100644
index 00000000..92901cf8
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Exploratory Analysis.dsnb	
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Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you perform exploratory analysis to identifty spatial effects in your data. Spatial effects include the predictive dependence of a value on nearby values (&quot;spatial dependence&quot;), and the regional variation of predictors of a value (&quot;spatial heterogeneity&quot;). These are foundational concepts and are part of virtually all Spatial AI worksflows.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks. "," "],"enabled":true,"result":{"startTime":1737651899291,"interpreter":"md.low","endTime":1737651899798,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","import matplotlib.pyplot as plt","from matplotlib.colors import Normalize, LinearSegmentedColormap ","import numpy as np","import sklearn.metrics as metrics","from sklearn.preprocessing import RobustScaler","from libpysal import weights","import warnings","import pandas as pd","import oml","","# Libraries and constants for reproducibility.","import random","random_state = 456","random.seed(random_state)","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.weights import SpatialWeights, QueenWeightsDefinition, DistanceBandWeightsDefinition, KNNWeightsDefinition, KernelBasedWeightsDefinition","from oraclesai.analysis import MoranITest","from oraclesai.clustering import LISAHotspotClustering","from oraclesai.vis import plot_geometries","from oraclesai import enable_geodataframes","","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737651900317,"interpreter":"python.low","endTime":1737651904229,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You will perform exploratory analysis on census data for the City of Los Angeles. The data includes median income, house value, and many demographic features for areas called \"block groups\". "],"enabled":true,"result":{"startTime":1737651904713,"interpreter":"md.low","endTime":1737651905225,"results":[{"message":"<p>You will perform exploratory analysis on census data for the City of Los Angeles. The data includes median income, house value, and many demographic features for areas called &quot;block groups&quot;.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python ","","sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_block_groups'))","sdf = sdf.to_crs('epsg:3857')","z.show(sdf.head())"],"enabled":true,"result":{"startTime":1737651905778,"interpreter":"python.low","endTime":1737651906450,"results":[{"message":"GEOID\tC_LON\tC_LAT\tMEAN_INCOME\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tPER_MALE\tPER_FEMALE\tLAN_EN\tLAN_ES\tLAN_IE\tLAN_AP\tLAN_OL\tCOMPUTER\tINTERNET\tHOUSE_VALUE\tIND_AGGR\tIND_CONSTR\tIND_MANU\tIND_RETAIL\tIND_TRANSP\tIND_INFO\tIND_FINAN\tIND_EDU\tIND_HEALTH\tIND_ENTER\tIND_PADMIN\tgeometry\n060376700031\t-118.32\t33.8\t84552.289\t75577\t9.746\t37.306\t0.537\t0.463\t0.796\t0.296\t0.198\t0.0\t0\t1.0\t0.904\t627700\t0.0\t0.189\t0.192\t0.124\t0.025\t0.099\t0.073\t0.025\t0.082\t0.079\t0.023\tPOLYGON ((-13171614.921 4002458.952, -13171006.56 4002139.446, -13171013.684 4001739.038, -13171618.149 4001743.458, -13171614.921 4002458.952))\n060376700032\t-118.32\t33.794\t93991.301\t74609\t11.504\t39.161\t0.445\t0.555\t0.989\t0.118\t0.0\t0.145\t0\t0.982\t0.959\t599300\t0.015\t0.037\t0.126\t0.172\t0.045\t0.028\t0.033\t0.045\t0.08\t0.063\t0.047\tPOLYGON ((-13171618.149 4001743.458, -13171013.684 4001739.038, -13171015.02 4000576.34, -13171318.477 4000575.938, -13171317.364 4000963.044, -13171619.596 4000964.384, -13171618.149 4001743.458))\n060376700033\t-118.326\t33.792\t78313.714\t55592\t10.976\t47.854\t0.509\t0.491\t0.888\t0.144\t0.032\t0.094\t0\t0.966\t0.829\t609500\t0.012\t0.055\t0.187\t0.089\t0.058\t0.008\t0.046\t0.121\t0.115\t0.007\t0.04\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n060376700034\t-118.325\t33.799\t77076.074\t73558\t10.115\t39.283\t0.51\t0.49\t0.917\t0.235\t0.036\t0.014\t0\t0.941\t0.859\t471000\t0.0\t0.054\t0.147\t0.115\t0.084\t0.032\t0.055\t0.08\t0.075\t0.077\t0.012\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 4002094.032))\n060376701001\t-118.319\t33.787\t62402.628\t54738\t10.716\t37.998\t0.511\t0.489\t0.648\t0.301\t0.042\t0.216\t0\t0.967\t0.882\t389900\t0.021\t0.132\t0.335\t0.014\t0.046\t0.06\t0.039\t0.107\t0.043\t0.021\t0.057\tPOLYGON ((-13171620.932 4000577.813, -13171318.477 4000575.938, -13171015.02 4000576.34, -13170712.342 4000574.063, -13170717.463 4000012.581, -13171091.051 4000012.313, -13171186.007 3999904.092, -13171571.395 4000085.443, -13171620.932 3999984.052, -13171620.932 4000577.813))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","###### Spatial Weights","","Spatial Weights capture the spatial relationship between each observation and nearby observations. The meaning of \"nearby\" is defined by the type of spatial weights. Many operations in Spatial AI, including exploratory analysis, rely on spatial weights. ","","A `SpatialWeights` object is created by providing a `SpatialDataFrame` and a `SpatialWeightsDefinition`. Spatial AI provides the following `SpatialWeightsDefinition` classes:","","* **`QueenWeightsDefinition`** establishes that two observations are neighbors if they share a common vertex. ","* **`DistanceBandWeightsDefinition`** defines a zone (in this case of 2 kilometers) around each sample. So every sample within that zone will be considered a neighbor.","* **`KNNWeightsDefinition`**  establishes a K-nearest neighbors strategy to identify the neighbors of a location.","* **`KernelBasedWeightsDefinition`** uses a kernel function around the location to define the neighbors. It is a decay function, so closer observations have a greater weight, while more distant locations have a smaller weight.","","","The following example shows how to create Queen spatial weights. "],"enabled":true,"result":{"startTime":1737651906948,"interpreter":"md.low","endTime":1737651907464,"results":[{"message":"<h6 id=\"spatial-weights\">Spatial Weights<\/h6>\n<p>Spatial Weights capture the spatial relationship between each observation and nearby observations. The meaning of &quot;nearby&quot; is defined by the type of spatial weights. Many operations in Spatial AI, including exploratory analysis, rely on spatial weights.<\/p>\n<p>A <code>SpatialWeights<\/code> object is created by providing a <code>SpatialDataFrame<\/code> and a <code>SpatialWeightsDefinition<\/code>. Spatial AI provides the following <code>SpatialWeightsDefinition<\/code> classes:<\/p>\n<ul>\n<li><strong><code>QueenWeightsDefinition<\/code><\/strong> establishes that two observations are neighbors if they share a common vertex.<\/li>\n<li><strong><code>DistanceBandWeightsDefinition<\/code><\/strong> defines a zone (in this case of 2 kilometers) around each sample. So every sample within that zone will be considered a neighbor.<\/li>\n<li><strong><code>KNNWeightsDefinition<\/code><\/strong>  establishes a K-nearest neighbors strategy to identify the neighbors of a location.<\/li>\n<li><strong><code>KernelBasedWeightsDefinition<\/code><\/strong> uses a kernel function around the location to define the neighbors. It is a decay function, so closer observations have a greater weight, while more distant locations have a smaller weight.<\/li>\n<\/ul>\n<p>The following example shows how to create Queen spatial weights.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define spatial weights","message":["%python","","queen_def = QueenWeightsDefinition()","queen_weights = SpatialWeights.create(sdf, queen_def)"],"enabled":true,"result":{"startTime":1737651907965,"interpreter":"python.low","endTime":1737651916458,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review the neighbor indexes for the first block group","message":["%python","","queen_weights.neighbors[0]"," "],"enabled":true,"result":{"startTime":1737651916920,"interpreter":"python.low","endTime":1737651917514,"results":[{"message":"[1, 67, 3, 68]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review the GEOIDs of the first block group and neighbors","message":["%python","","gdf = sdf.as_geodataframe()","print(f'Block group with index 0: {gdf.iloc[0][\"GEOID\"]}')","print(f'Neighbors: {gdf.iloc[queen_weights.neighbors[0]][\"GEOID\"].tolist()}')"],"enabled":true,"result":{"startTime":1737651918014,"interpreter":"python.low","endTime":1737651918509,"results":[{"message":"Block group with index 0: 060376700031\nNeighbors: ['060376700032', '060376700012', '060376700034', '060376700022']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot the first block group and its neighbors","message":["%python","","_, ax = plt.subplots()","plot_geometries(gdf.iloc[0].geometry ,ax=ax, color='red')","gdf.iloc[queen_weights.neighbors[0]].plot(ax=ax)"," "],"enabled":true,"result":{"startTime":1737651918996,"interpreter":"python.low","endTime":1737651919660,"results":[{"message":"<Axes: >\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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Spatial dependence and spatial heterogeneity are examples of systematic variation. The most popular metric to measure global spatial autocorrelation is the [Moran's I test](https://en.wikipedia.org/wiki/Moran%27s_I)."],"enabled":true,"result":{"startTime":1737651920170,"interpreter":"md.low","endTime":1737651920700,"results":[{"message":"<h6 id=\"global-spatial-autocorrelation\">Global Spatial Autocorrelation<\/h6>\n<p>Global spatial autocorrelation determines whether a dataset exhibits systematic spatial variation of observed values. Spatial dependence and spatial heterogeneity are examples of systematic variation. The most popular metric to measure global spatial autocorrelation is the <a href=\"https://en.wikipedia.org/wiki/Moran%27s_I\">Moran's I test<\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot a map of block groups colored by median income","message":["%python ","","_, ax = plt.subplots(1, 1, figsize=(10, 10))","ax.set_title('Median Income')","ax.set_axis_off()","sdf.plot(column='MEDIAN_INCOME', ax=ax, legend=True, with_basemap=True)"],"enabled":true,"result":{"startTime":1737651921249,"interpreter":"python.low","endTime":1737651923485,"results":[{"message":"<div style='width:auto;height:auto'><img 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aqga7QWuOWXGLxBJZpkc0tbFgqsRXL/+lZbb9aazzPJwwCbMcmDEJKJZe2tnaCIGB+PkMsvvExuZ6L5916aYFjOzU10LoS8C7bra78v44yyH6wWEphKAPHjuq1gzCoZo0rTMMkHotV9+/5Hp7vo5QiEYtjGEZNDXRFEIQEfkDMcUgk4quWe9wuld+dHwSU3NIdeQzx0nA3a6AfvC/OE5/ruWuP99ibGzcs4RAvXZKBFkKsqrKYr5DP09DQiGPH8D0Py75NGV+t0WEASqGUwWp1GJWSDh/I5wu4JY+mpiZM0yTUIYaxuSDRtmxM0ypneKlmej3Pq6lP3ogf+IRhWM0KVwLyreQhQh1SXCfotO3Ff5ZLbmnTi/EMQ4FpUHJdQh0Si0ULHtc6MalclKFQbD7YrtzP9T3C4IWT7RMCNIGW96S4OySAFkKsqVLKkS/kqa+rZ3Z2BtO6TQsKlQJloHU5iN7gGChGQWdl1XsYhJsOfpVSmKscs+8H5QWKmxOG4R39ilixGMyGevPPD8qlL6aBMhS+H+D7eeLxGLZtowClolrxyu/O8z38IMAyLRx74y4rS4Puyn2FEOJeJQG0EGJNSiksy6RYLBGGIclkikIhRyweuy37RinQm1vmFoTRwkHLssjlsuTzeZzYzbfXe2FWr5U7doTBytrpTagEx5ZtokNNoVCkUCiiAMdxSCYTNR09okWAAaE2UVpVdlIOuFV12zAM8XyfMAxW1m0L8QIR1UDLe1PcHRJACyHWVQnKwjDEcRxyuezt3Hm5dGPjEFqhSCQSBEFALp8nkbj1IP6FRsNtqylWhsKyTXzXrwa9vu8T6qgEpVIaEoQhxVIJhUKjoxptx8bAqMk2S9AshBCLJIAWQmzIMk1KpRKpVAqAMNRRze1toDbRKUJrje04JBIJstks9u2qw36JCvyAMFwMeJ2YjWEoSssWHlYsrecOdIDnKRzbpuS5BFKqIV5EXkhdOMRLmwTQQogNmZZJIV8glUrhODGCwMcwbn064VYWrlmWVc6iulsqIYmGlYTRNMOl1wP6xbTgSEdlLKEOCTdYWKg1GEqhjCjDr2HLiyU1WoJnIYRYgwTQQogNRS3RIJfL4cQcstkS1hodHu4ErTUKRRAEN/WYvu/j+f4dOLK7R6NXDFlZi1JRWzuCcpm5aWBa5pZeOwmexYuNRhNIqZG4S2QSoRBiQ0opnJhNPp/Htmxsy45GNt+lD6swXBwmclMDuO+hD1WlFKZlYpoGhhl15gjDe+f5CyHE3SAZaCHEppimidYh09PTNDY2kstlCfxgy5nNrdJao8MQrRW2bd9Ud4p7kWlFteVaa8LAJwxCDDPKmUj9uHipki4c4m6RDLQQYlOUUiSTCYIwIJOZI5lM4QchwV3o0BD1NY6m8RnKuKcyyjejMoWwcjEtk8APolHf3FsZeSGEuBMkgBZCbJphGtTVJdFak8vlqK+rJ/A1nustllhofdsDtMpCwEKhgOM40QAUsSmVANp2bMJQ43s+gR9KJl8IIW6BlHAIITatktFMJOMUCiVKpRINDQ1kc1mKxRKmaWKZRrkNhIEybt85umEoSqUiyWSKUqm44fZ3MqB/MVJGtBA0DEKU0ii1WOax6X3AqiPX5fUVLwQaCKSEQ9wlEkALIbbMMAySiTjFUhHXLVFXV099XT25XI5iMY9pKizLjlqp3YZ6W8u2cF0X3/er7ey0Xn+keKg1JbckAfQSlm2hzei18L1oEWilhr0ySGU9tu1gmbVBt9aaYvl1FkKIe4UE0EKIm6IMRTweIwgCFhbmsW0Hx3FoaGgil8tRKJRwYnZNu7ubDaZ1GGIoA1S0D9t2CPwAy17nnzCtN+yXfK9RSqHMaES35Vj4XkDo+lE22jSjRYYaULVZ5crvzVAKY9m3CuGLqZe2eMmTRYTibpEAWghx05RSWJaFaZoEQUChmEOHEI8nSCQSFItFioUSKHAcB9M0thxEa63xPJ9YPEYYRosWTdPcsA5aPkbXVinFsR1VroeOaqPxFYZSmLZJGISEocayzMVJ66v96uSFFkLcgySAFkLcskogbVkWYRhScougIZFIUldXRz6fJ5vNkkwlsNfLGi8TTR6MBqDYtkWp5C4G4BvG4RLZbUQphWWb5VaBBkEQTTkMSyHRC6zR2kCtG0EL8cKgQQapiLtGAmghxG1lGAZOueNDoZCjUDBIp9NYlkU2u4C1xb7RxWIJJ+aglCqvTTQIAn/jfcjn6Kap8thvwzTQocYv9/f2PT+aaEjUgWX5K17JXC8tlZEe00KIe4EE0EKI204phWkqDMMhDEMymTnS6QYSiSSuW8JxnE0FWtEQkLCa2bZMs1zS4RFPxDY+BsMAFKEOZZHbRiqJfSPKSke15lY0Bt3zMUMD3wqimmdN+YRGUygWo3IaBZYV1VEv/90aqnKdJpRFneIOkop8cbdIAC2EuGOiQNoktEKy2SwNDQ3kC4pisYBlmosdINYIpqv3DwPCUJNMpHBdd9UgbTnDMIjH4gCUXBc/8G/783upqi76NKLpj0EYEngBuWyussawOtUw8IMoc62jzierNS60y+U9AK7n4fne3XkiQghxh0gALYS44yzLwi2VyMzNUVdfj23ZuK6LW3IJdYBCYZSDtUpgVr2vbeG5HoZp4jgOuVwO09y4v3QlCJRs5y1SYJoGhlIEQYgqd0IJgrDcUzoa1OK7PoaxRl9ptbS0Q34f4s7QaOkDLe4amUQohLgrbMdGGVE5R6lUZK7ocW06SzxZT1NTM8lkHa7n4y6ZahgtTjTxPB/LtDEMA9dzMc2tDQARt65S2mGWyzQs24zaFNoW5QiZwA/QoV6z97bWG3WaFkKIFwfJQAsh7rgo+2hg2Q6WpfGDkIsDw/zMJ76IoRS9rQ08snc7733tgyQIKRZL2HbUHs80ozKPeDxOEASrDcITzwOlVBQ3E9VC245F4IfRgBaiyZGGaazoGy0RtLhjNATy/hJ3iWSghRB3RaXWWRkGtm2xt7uNx/ZtI9SaG5Nz/MU3z/CW//xxPv6N88TiKcCgWHQpldxqX+kgCAg26P+8+oPf9qfzgvJ8d76o/G5Ny8CO2dh2NDwn8AICL8pKL5IIRwjx4icZaCHEXRGGmmsTM3hBSDoRYzZb4Ofe8kquT8zyrz/+DwC4fsD/+OLT/O0zF/k3b30Nrz28Mxo3bS62vsvl8liWSSy+fheOimhM9epM08Qot8dbtPSHygTF2p+jzOuS29fY//qhYtTjOiw/uGVZGMqoHaetIQiDdScq2pbN09fHmMsXeWBnO41Jp/ZY1zsCrauPf7MMpTDNlR8loQ7xfR/fjcpybNsGKJffKFnUKYR4UZMAWghxx12fmOUnPvL3zOWKNdebhuKRPdtWbD+eyfIzf/p3vOWhQ/zC93w7sXIHB9u2OTU+y8lnL/GTr3+U+KaHsqweTNqWhbnWwrfV9nIbM71aa4IwhCDKqFumibUsENVaU/LWH0muFHz+7A3+4fQ1PvKj30V7OrnOo9YefxAGuDeT0V/CMExijlNzndYa1/NAg2lbBGGIW3JxYg62ZWMYwaYD6Eq7PCE2opE2duLukQBaCHHHFT1/RfAMEISab14ZWvN+//vpC1wYnuC33/NmdrY387enLvFLn/0agdY8PTjOr7zlNRzobNnw8dcLe5/v8oeKW4kR04kY3/fyo+zrbEajb+o5RV2ab8bqCwZ9z8dzfQwjKu/wgwBnlXuvx1CKmBPD8z384NYCfSGEuJ0kgBZC3HF7O5pJOhZ5d+tf218Znebdv/sX/ODr7ud3vvDN6vV903O872N/y4+9+gF+4NGj5aEpa1gST1qmFQ1kQWOo53cZiG1FxwKsf/zLGIbBQtHn4sgUCccm73p84Nsf4FbC4JsVhCEl1wWi8ozK81FKEU/EsCwTrZc9vw0O0VBGdWKlYRjIylGxOYrgpb7gQbxgSAAthLjjHMvk0X09/NO5vpu6/462Bj7zzHls08ALFr+k9cOQ3/vS03z92iC/9OZX091Yv8YeFj9UDcOoDvV4PimlVpRsbMUXz/Xxu597qvrz9z52mOaUc9fX6GmtF8sxFDUnBGEYYhkWylh7WM5qDMPAtuzF8g2p4BBCvMBIFw4hxF3xzx/Yv+VEoqEUx3a2c3J0nIHpDPdt71p1u5OD4/zAH3+Gz565umo5QbUDyJIDWKtX8YvFqYHxmp/H57I83+1GqqNStK5OJywWV5bubNbi7+fF+3sSd48GQn33LuLe9vynYYQQLwlBGK5bhvDYvh5+8jse4b8//uSm9mebBnu2NXNqdKJ63cjM/Jrb512Pf/9/vsJnz16hpylNwrZJOTbJmE3CtnBME8NQWKaBbRgoosoAUxk4VlR6oADL05hGNHmv0sfYNBRKRwF9pauHMmr/a0Qj+jDKOzbU0sCdxf+y+PNaEnG7+metFSEqOp5KuzjD4PTARM19xjM5INr/zbgd8YAfBIS6ROAHeG60iNCyLYIw2FS23TRMLMusltZorXF9L1psud59ylnvxedQ7mWil2XIhRDiNpEAWghxy8YzWf6fj32O97z6OK89vIu4s/KfFs8P+NqlwU3tLxmz6Wyt4/z4VM31o3ML7Ots4crE9Jr3fapvlKf6Rrf2BMoOtDUzfHL1fZ/Y0cG5C2M3td/ldm9v5vrgzKq3vf2Nx/iRdz4KREH2R79ymo9/9fSG+xybywJRELnZEPp2J9G0jlrzAdF4bz/A830cP8A0zA3LOCqlGxVhGFb3t959LMtas1uH1powDG65XZ94cZAaaHG3SAAthLglfZNzfPBjn2NiPsev/q+v8lv/55u88uAO/CBkeGaB7uZ6MvkSA1MZphbyG+6vMRknVW9zdWp21dvr41vt5bB5KW/tDPps4eZLEZabyRRWvb61KcUP/V8P3tQ+xzPZ8p+2EkLfftUg2aA8nTDAdV0c26k5LIVi+WDv1eLrzS6LfDGX4wghXnwkgBZC3JKnr48wMZ+r/px3ff7h9PXqz5dG184WL9fRkCKwNINzC2tuc3ZwnK6GekYza29zswpZb83bXP/2dZjNLBQwDUWwrJCyvSVFbFn2/s0P7OOvn7xAvrT2sUGlhOOFk32rBNKmZRIGIaEOMYlKLQzDIBaLsTw0Xt4VxTAM4rH4qvvX5f9XS+7jBz5BGC55FaI/SWx9b9BIBlrcPbKIUAhxS1Ixe+ONNmF7S5q88pnIrp+l9oKQmGnSkFg9sLoV+Zy75m3ZYgnLvD3/ZB7d37kieAYYmZjnk//nJM+eG2Yyk8MPQmzT3NRrPDaXvWPx8632yi5Xi9fsrzI4ZunFWFZDr5TCNM1VL5X7m4ZRPb4giEo+vOrFw/O9FZluIYS4VZKBFkLckkf2bKOrsY7RuezGG69hb2czg9l5Ct7mFnv1T83R1VBHPJ1ifEn2+5ZoyK0TQC8UXO7f28nZS1F9tWUa+MHWs9LdHWnOXxlf9ba5+SIf/eun2f9gF09PjG/pcaazBTw/xCrHoCtLGqLFizXXbzKuvB3lERpNqMPyIsragFzryqK/5SUdG7e/k9INsVSoJQMt7g7JQAshbklLfZIPv/dN9DSnb+r+h3pauZ6Z3XTwXDGayeK6AYe72zd/Jw33tbbzYLqdo/Fm9qk0270kLVmb5Ixi17YmnHU6iVybmmFPcxonF6DmXOo82NfRyIGdbZt6+Ia6OImETWNDYs1tDu/rQPmahBWVO2wlSF9aSqMB1w8oeT6eH1CJlsNQU/J8Sp5/UycAN8MPAgqFAsViiWCViYJaa0qlEoVSseZSct1qgFxpO7g8YPYDv7r9avsWQog7QQJoIcQt62qq59fe+dot3+94bzsXJqfwb7Kp6ly+yPmhCe7f3kXCXv8LtYRlcV9dK5cuTnDu+gRXh2YYnJxnMpMnV/QItSaW0+z3YiTWGLQyX3TpDwvs3BmNDy8VffquTnH9zCiP7uogZptrPn4qYZOsc7g6MI2vNZ1tK4e+HNjVxsWrE1w6NUZPNk5LfPUylZ0tDRzpbF1x/diybwFCrauXCr3k+rtR2hBWfrcKQh2u+ZhahzVBcnRZDPBDraNyjNWy1CjCcO19CyHE7SYBtBDittjb0Uw6EdvSfXK+x63mQBVwqn+U//bON7KvrWXVbRzTYJdRz6X+qVVvB9jRmubGVwcYvD5Nb9agfpVWfABF3+et33uCV7xsd/W6g4c6Kc3m2KlNEqvUKzu2QWtrHSMTUR/rufkC84US27sbq9vs2dHCtcHparBbKLhs9+Pc19XGfurY09bIifoW9rc301A0GXxynENmmoYlr/ng9PyGw2pqq5HvLK01OgwxzcU65dUqLpRSqFUy/1pHrezCMCQMAlzPIwiDxevCEEMZ2LZ1y3Xa4sWvsojwbl3EvU1qoIUQt4VhKO7r7eCrFwc2fZ+RyQWOd7SBqbgxPcuCu36nibXcv72DA61p/sd73sqvf/arPH7+SvU2EzgUb141eD7e3YoGrk9nqB/zyJWDu9HBOdo76rHabWaLtcf0wXe+gm9/eC/f9sBuUsmv0Dc4w7n+CTSK460N9MwWGEk65PJRPbVpKLZva+LqQG03klzeJfBDdu9oxvNChscz+OVOH8m4TToV4/r1aSg3NNEDMLns+IdvzNLSUUemHEM/e2OU735oX/kOrBqtGobC2SBb7wfBqoscb4YONYZdGxxXsshBGFb/HLMdXLyaoSehDimWSrCkdrvkuqsGy1ILLYS4mySAFkLcssoUwtb65JbuV3B9zg5GYWF93OF4VzunRyc2uNdKbziyG8ex8dwSH/rnr8UowpmpcdrjKean8jXBc33CIZ2IMTyzgHcjy8SVaRKGYmxZwJiZzbOrqwOn3mW83L/6X7zlId78yoMAmKbBy16xh3/47avocjZqIFdgX9xGL5SYbIiTyRXZs7OFS32rZ76LblSLnMkUKZaiwNGxTdpbU/QN1vbBXivfVciWoBxAv/LA9g1fK6UU5gbZ2mjy360FpFprAj+AFQsBF/frei5hGKKUIh6Lrx4Yo1ccigTLYjUaRSBfrIu7RAJoIcQt+9tnLvP4qauMzd18R4yFosvZvgmO7WzjzOjyXOva0vEY3354dzSRzjbxijl+/I2P8oO//kkmvZWjv3c1pRn+h352NCWYzETTAPWy4NkwDbr3tnL58gT16Tg921K87KFdvPP1x6rbXLgyzq/99y9U67frHIu26RIXB8bYdbADZl0OPtzLt073r3nsrY1JcjmPbCVbbSp6uxu5skbAvZpYzOb9rz3My/f3sKe9oVpzrNGU227UBKZRbXH058rVt7v8QWsd9X4OQ6xl2e4w1OV6ZWoWCIZhIIGxEOJFQ07VhBC3rC2d4vTARE0XiJt1dWiGnsaVC+zW8pOve4j6RCzqLWxZmJaiPR3jX37no3Q0pFZs75Si/+ZmCysCZwAU7DrSwfW+qORiYb5IVyLJQ4e7CMNocVvf0Ay/+Nufo+RGWeOYZbC9oBkbiLLGNy6O07u9ifd89wNs72pc9bjTdXG62tLMzUdTCZWCE0e3bSl4BtjWlub9r72fA10t+OWuG5XOG45pErdtbGtxcePSLhxRd447IwxDTNNc0YrOD3yKpRLFUrEmYC65bk35hhA3I9Tqrl3EvU0CaCHELQnCkD/4wtO3bX8lPyAohiTXWMS31IGOZr7rvn0119m2TbFY5LtfeZjtT0zQ+41R7stoHkzWcaglTTC1dpCvgf33bePy1cUM+N5DHXxrYYp/9/Ev8a0LAwyPzvHz//nvyeaiSNxUsF9bDF1ZvM+hB3fwxNev8we/9vf82x9+Fft6aztmJOM2qfoY5wYn2L8ruu3AkU6+NjDMgf2ba8tnGIoD+9opVDriKYVpGOXBIlH5s1IKw1AYS4JYTZSdvlMdKyrZZ1AYSxYP1mwj3TKEEC9yEkALIW5JyQsYmllZKnErxjM59rY0b7jdW0/sXxGgKaWwHYtCKc+P/9rbKc4UGPvqDYY+dZqZj51k5O/O0emVONgWp72pth/zwfu3ceHy4pCTnXtbeS7IEAJFz+cX//zLfPivvsnMXFQTrdAciyfpOze2uI+Hd3DxyjgoxfXLE/zP//qPlB4/x77uJgBs26StvY7hqXn8QHNpdJoHH9zOcyMToBRnZqbZs2tli7qlDh/qJN6T4uTMdHXhpQJsy8S2jPJI7ChbHi67bKVMYulrq7bQdSAMQwxj4yEoQtxO0oVD3E0SQAshbkkyZvOaQztv+35Df/1AL2aZvOHonlWDNMMwME3F7uPdvO57Hl5x+9TwHFef6qdwZZSe9mjh44H7ujl/aTF47tnRxAUrj78k4PSDkG8Nj7PnaDtozf3peq49N1S9fc/xbZyayNB53zZiCZumlhSzF4eZG5lj8pNPcbSnhd6eZm6Mzi0+Tw1PnRvm8PZoGEug4UouQ29P06rPu6e7kWfGJ5jORaUflrH4/IMwJAh0dbKf6/uUXK/m4m1h2EhtsL25wFuHUY21bdtYprSXuxdorckVXabn88zlinhBsKL/uBAvNRJACyFu2Xee2Hvb93lxeIq2urW7enzb/l5SMWfV25RS2LaN65V4z7/9LuoaV5/8V8iWmDo5wNFDbZy/Utv9I99mUSwHmzHTYG+6AYgGepyemub4iS6uPLnYsq/3YAcX53OEGq73T9NwoI1kwsZLJGlsryf0Q+yRLMniys7XWmuuX57kYE+UeS4FIYNhga6OldMdjbQNSuFYJkpBR1Nd+bhCSp6HG/gE5QEkepXLZq0MfDcOhLXWBEGIZZvEYjFijkPMiRFzYji2s6Ustnhx0FpTdH28IKSxLk7MMplZKDCXLTA1n7/LR6MItHHXLuLeJu8AIcQte3B3F23prbWw20ioNT31ay8m/K7je9fNbiqlsC0LM6b5sX//tjW3892Ai587x/4djTXXB9eybKtLYSo46tcx99wUx5sWy0q+OT7Jtu/ejwa27W6hz/dqJioODs9RaIxT1KCbGjj0phNcvTJF/zf6Oda6sjwl1LomvMyVPGYdn907W9h5qI2GnfXQ4XBpMuoc0lQf5y8/9E7+w3u/nSAIFif+3YSoC0ZIEIbRfvQqreI2Efsqoppr27KjgS3L6rIlfn7pCbWm4PqkYjaGUtiGoiHuUB93iFtrT+YU4sVOAmghxC0zDYPvOL7ntu/30vA0x7vaub+7Y0U2eldLw4b1vIZpYJiKB163j+96zyvX3jDU9D1+joPbG6pXZeYKmFdzvLKujeuXJwn8kOEnx3iwcXHa4TNzs+x+20FGTSi6K0sjRsfn0T1pth/r4fz5qDwkDDUj3xrgcMuyEg0NMWXgWCZtDdFznc2XOLcwx4WJGcYW8hTKXTM6mlK89w0P0NaQIgxDim6Jklta97XYSBCGlDyfoufhh+GKWHdTtdMKLNMi5jg1JzdBEFByXWlT9xKgtWY+X2KhUCIMNUGgMQyFZRoEQYhCYZkmhlIkNhjYc9uPDQgx7tpF3NvkHSCEuC2+8/59G2+0RUXP52zfBKdujFNY8DjYvhi8fuPqAEGw/iBwpRSmaaJ1yA/89Bs49NDOdbf1HIv9h9qrpQ4LCyWuXBintzfKGIeh5voTIzzSGJVatMfjjJ+fYUd344qA0yjXJrd1N3CpWKB1e2P1Nt8PmXxqmP3NDTX3uXBxnP0NDRjzAU118TWPtSWd5E2PLL7eywNT04gC8crFMrf+T/1NhbrV/tKygPClpuh6zCxENc5+EOD6AQXXI+96xMqZZh2EKENhWgamaWLZkoEWL10SQAshbov2dArTuHNBU971SNp29edPPXuJIPDLg0HWDveirhw2XujxM7/7bpraV9YVa2DPW+/j8uAsF65Nse9Qe7XcoFDwGBmbY+/etur2V54Y5pWN7TQNB8zNFrh8cYK925sxzehORw500NVaz9H7tnHm2jiz8wVm6kw6di+Wbriuz9yzY+xuWjyeY3s6uHJjirlMgUZtkVijlV/NJEGlMA0TwzAwVNSyzjSiLGDlYhp39p/6qEwjCpZM86UbNIVa14wfv1dorVkouNTFY8Qdi4ZUnLq4Q77kYZkG8fL71DCNaPqkBtTtH9AjxAuJBNBCiNtieHaeOx1XVDpIdKVTLBRcfunvv8KnnztPGG6ciXYcGyep+H//fz9M+7IOF/v/+X2cKQ9OAbh0bYpd+1pR5RMCzwu5dmOSgwc7AEgkbGb655icWKje5/rVKXZ2NHLkQAcXzo8xOpphfHCOlqaoHGMhVyK9r4UdSwLxUtEjf3qC3sZoIeDEXI5kPDpJGBmdZ1c6Xc1kL2UuySibhkE8FiMRixN3bGK2dccD5qWUUuVFgjZKq5pje7GrPLe4E8O2LObzJeayReYLi+Uypmni2HbNxbbsF3XwGIQhfhBW2yBmiy4x28K2DBKOjWkYxGyL1nSSVMyufuMQlUwZ5T7gz9OxSxs7cZe8dP6lE0Jsyp3Knu3rbOEHX3ls4w03oBQc2d7Gzo5GjvW201penHh4Rys53+NAazNjMzm0r/nC9UH+45ef4B8v39hwv4ahoimDR7v4k2/8Ej/4b94EwN7vOMKpwZkV21/tm2H77mYsO/pnUmu4eGmcI8e6aNjXSGpXw4pFcX03plmYKZBIREHw9GSWZjvqFHJkfwfPXRzlSlhk77Gu6n0KORf//Azb0nWMTWdpb62rllxcuz7Fsa6VPaGXZvorwYsqr9J7vsonfM/HtEwsK8pGLn2fvVjytbqcYa4Ej0qVM/mWhVHOsNcnHIquT9H1KJQ8ckUPpUxsy8axnfLlxR1Az+dLzOUK5Ioe8/mo1jkVr31OlZHwgR9Wx7ZD1EIy1OHqUz6FeAm5uxX+Qojnn16cA3e7P+Tf/uhh/vwbZ/G3mIGKJyy270pjNIGjLE5/c7GlnGkokkmbiyPTBEuCsu7WekZnol7I/+OJU3z7gd3rZl4rWehiqYDWmnf95HeQaEvy4b94qrrN8aPdTE5lGR2LBsPcGJhlR08Dk8MZXDdEmYpMEq4NZWAaDr9sGyNPjeF7QeVba4aH5ujoTGNbBso0mHFdDu5t59zVcVDghZqxBOy7fxtXnhsGoFjwaB0t0Nwa48boHId6W7l4PZpsePHCOCeOdXGyf7FH9VYzzDdzzjQ1n+fC0DSvOrx94/0TZSrtcolNUF6EaFSP84UZTFUyrEXXx/ND/DDEKo8fR2ss00BjYBkGlXMWP9RYhoHnR+9xxzDJ5AokYw7J+OptFV9sGpJxXD8gWyiRSjjEbWtl8BxqAj9AGUb0OvoaZYBpRa9fEASY6u6W82itpL2cuGskgBbiHhOGPqAwjCg4WHXU8rKIa71Ae+m2rfUJXn90F4+furapY+nsTuHsMbmYm2JS5yAf1fc+eH83oae5fG6aINTkSt6K+874xeqfr89meMsff4rfesvrOdix9hS/ShBdKkX3fdPbHsVsSvDf/+Cr7N3Txi/+mzdSKHp86D/8HX0DM6A1acumobeFGyNzdBxt49zAVHV/5wen2HV/G/WeohhTTJ2awC34jI/N09KaItGdAsPgUt9ENYTcuaOZsfkcY0WXowfbmRzK0LCjkZn5AkdSzTzlzdQs+osnrBXh58lrY7zzP3wK0zB45MA2Pvi2l1Vvi0Z4b+rlX9c/ne3nI/94mn883c0j+7r4zgfX7vXtuT46jLoxuJ5HqEMcy15z+xcCrTWuH5AresRsi5aGeoIwJO5E7dgCrSm5Hn4QkiuWKHk+vh9QF3fw/IC4Y2FbZrnjiMFkJothKGJ3ufPEnRA9DxPHTlbbES4X+AGGaVZLdgxTE/ohvhdUA+gXcxZeiI28+P+mCyFuQtT3VxnAsiBaaw26/LWsDqPFaYZZjcqUUkuCZh1ltMs/zxVdjuxp5+9PXVu3QjCVtNl+f5pn8qOE2drwMNCaJ4NhMKA1lqRYXH1y3kyxtm3beDbP/3z2HL/6ptes+8yVUjixKIjWOsbrX3OUdF2cfXvbicdt4nGb3/ilt/DL/+nvcXzNhVNRlvjQoz1cnllYsb8bo7MA2KbixNEu+s6NU8i7TE/l2NNZx4RbpFKivX9vO1dGp/HL2cvpeoPOg+1cvBZlm585N8yBI12cvRZl4Dva68g7cGpJ9hnA9QPGZrIAZHpaqKUZHJ/n2tBiWUpzOsneHatPNlzyylT/9MXTfXz0i2fQwBNXRjjdP8Gj+7fRUp9Ydg+FDjShConFHPwgwPOjk51SGBKPxV+QQZTWmnzJo+j5pJMxHMsiZltLMuZRfaOdMMtZ6hi5QhGIMuvJmE2u6JJORp1STMOgqS7JQr5IybZIxOwli1vVbTmhuduUqh17s/ykWkNNb2+FwrRMDK0Jg+jfDXUHFxWvZWUDRiHuDAmghbjHVAIarTWEIZiVn6PbA9/Hdd1qTWNle8Mwyl/RKzzfxzQUwwtZvjYwxDMj41ycnGEiF00eO9rbzNX+xQDOtg16dqZJtFhk7BLjbpan8iPrHmfKsgmCtb/6j1smGbf2urRjEYZhTSC01mvgxGxKxSL19WkeeXAvuXyWsJxFrauL8YM//Agf+92vAnDoxDZOnxunsTlBa1OCqUyhdodac7i5hTNnR+jubMCazpOqj3E2kSeVckgWHHp7mjnXN159nXekU+hLGSZNaGxIMFfe55Vzoxx/YBuz+AwXc+TmV2bfl6pmPJcEON84NcCf/M1z1Z9fdf8OfvaHV/bBNtTi61TyAj7yxVM8e32MG+NzNVnvguvzS5/8Cr/7L95Qvc40DHQYvS/i8Vh0pa9xLJtARzWxYRiW2wi+sEo4glBT8nwaU/FqOUylnGM1Sinijo3rRW84y4yGwxRcj3gseu4x28JJp8iXXOYW8viBD2gSjo1lrv5Nz4uN7/mgQa3190tVhueYmC+sX7kQt50E0ELco6qZ5DAEwyQMQkqlElqHWJaJbVsYhipPqdN4XhRYa63JZPP835//Cv2Z+RX7Pd7Twpwzz8HGFkhq5swiN/IZTutxKEFDGCN1xmZbexux7RZnChO4YW2W+eFkN5efmsFbp5Y6tkq7tM+cu8p3H97Hno7WDRfTVdrbZbMLNDQ0EuokxUKBWDwaAvLEtRGeS+V5zat28cyzg6AUczMFGknQ1ZpidCZX3df9PR1cOBWdEIyMZdi/v50r8TwLnsfCvMeOvfVcuTZVjXEPNjcw+8wYxUIUHPe0pCjGbYrF6GfX97lRXMDzV8++V3zqF95Ba3noigb+8gtn+esvnKNQ8mu2e/LsMO/75c+gNbz6gZ382NsfAmBoap65fImJTI4//NyzjM3llj9E1bHe9uhxwijDaMUsTDN6DY1yHWylBloFAW7o4gc+Gr1hv+67LV9yq6UaUBlHXdp0qZJSikTMZj5fYj5XIBV3qplY04C6uI0XGPhBQL7kkYo7WOaLM4CuZNKjRYEK0zZRlDvBrPeUnoenq4FAeiOIu0QCaCHuMZXuAhVhGBCU6ztNU2FZdk3wGf1ZY5oxgiAkmy/wiYtXVg2eAa6MZ2jYqdEdIU9Pjq24/aBu5WJ2ilzWg+uwZ0cT09sLTJQWg7en8iPc92gHc2eKTC8Ua+6fdCzyrr9qAF0KAn7581/nxx47wav27dzwtTCMaPR0LpclnW5AocgXctiOzTcuDuJrzT8tjPPwsS4unY2ey9xMgZ11cezWegamFlBoblxaXPTY0pLiUiLHrLuYHh+YXeDIgWYGzk5xrLmRwW8MVYPK5u56ZuMGHS2NzIzO07Wjmb6xWZq2JZjI5Nc9/sqYbAC0ZmYuz+yy1wui7PLkbLSvQsljeiFPqOFX/vJrXBqeXrH96o+lQIPvB1iWSSzmrAg4NVGw5Zf7c/tBgB+sfxJwtwVhiBeEpOLOitKlrWTKTUORitu4nk8QrnwvmobCUCYayJVc6mIOhvH8DpipZNnDUG8qK14Z8a7D6N8M0zJWbasoxL1IAmgh7jGlklttNRbVOWtMyyIWs1HLhh9UAgqFIgjh0sQkv/H1p7kwtbLtW0XB82mbrOekN8RDbT0rgmhdrM1GjgwsUDdr8+h923DNgEvFafKBh51QTB/IcF+xh/MXpvGCkIM7WtBtPhMXi1hrZJouTc/yG196gpft3o5trf9PnFIKy7bwXI98Pk8hgE98/SJ/+/RFJuejgDMExqzaeuu+gVl29TSyu62e65MLtO9pYrZ/np11MUZ2OMwuZGq2N4Gmy0Xa7Douf3Wgen3nvhamLFiYzTE1m8OyDGZvRMH49lyKQsJhobAYiHe31NNSn8D1o0lwlcWGJdfD93wW8huP857M5HnXb30Gd4Ps9nIlN/r6Ph6PYdsWodb4XpQxr3Su8HzvBTlopPKeLnk+uaJLYkn2+Vb3qVl5Urp0m5gVlbBk8kX8MArSG5JxYrZ5V4NprTUF148y4jF73emUS39/YaCxLBNlqBfc73Ul6cIh7h4JoIW4x9i2jef5BEGAbdvE4/FyOUeIDlcPqoJQ89zEMD977ovsSrTR01CPbRrM5AtkirWFyEe7m5lIzkEIz2WGeFXbTvIzIQvxIpdzM9Wa66WyCx4XvxZ1t0i9wsbXIcXQo6h8nkj00fZAHYcW2jhdP0wRDw5Be64O5lZ/jhO5PP/z6bP80CPHN1UPbVpm1JnDivH4yavV4LmiM4iRWXa/G0NzHNrRxP62NNcm5zhqKq594SIdr+jF3m5Uy0/iyuSBcZvrZ6MSjwPHurh0ZpTe411cz+dxc4uveWVxYUdjknAoR0+jw416i6IXlWR8xwO7ec93PLAi8PovH/sqX35m417YAJMz2S0HzwBPXR3j5KEpXn4wamvneh5B+f1iVLLTL7Bsc4VlWmSLLn6gqUvEsG4xE1zpgVxwfYIgxPPDqG3hGkF03LaI2xauHzCfL1Fw/aiDh2XelROOSseRouvRVBfHWKe8qaZko9zOLwxDTHV3A34hXujkVE2Ie4xlWcQTcVKpJM7Sr+FX/RCPltnPZLP8v+f/iTm/yHPmIANNE1xLjxF0lTja3VKTzdNxn/Ew6lahFDy50E8u66Imon9uLjnTJBJrnLsrzYxbwA0DLs3OYpf7yE6S5cn6vih4Lh+WGVv/ef7Rkyf5zKkLmwpOKsMf6hyL3/ihN5F0aluw3SBHz/aVXSzsQDP1mQs8mqpj8Ot9AIx/vZ/75ywU0GTaHL2u6Tu7mIXXCo48toNLmSyuu3rA2R6LMzWZZeLKDPuDOKahuH9vN29+7CCu69UOKdGa5y6uvyAT4NChDg4d6iDRsMELt4bxTI5ssUSxFF2CJSdbrufheusvdny+aA1zuQKWadKaridm3Z5AMF/ysE2DdDJGtugSrDMNc3HhLiRiNvWJGH4Iju3c8amRQRAyny+RL3nUJWIbBs9hEBKUT+TQGmUYay8afIHRQIhx1y7i3ibvACHuMcowMQwTw4ymq638MI2Cs0qQFoSavxq6wLS7rPMEMK+LnHaGiO8MOLqviaPdLWSmgpq5GYHW5DpyjE/meSjVRcYrseNQw6rHpjXY5YlvXhiyM9m85vMIzfWznQdbWvj82RtcHpvaMIhWSmHbFrlclsM72tnX3FyTKJ8oFBlt9tnVW3s8mb5pDKW4fiND74O91etHHr/Cy/0k204VGLkRlbtYlsGeI51cPj/OxedG2NmeXvN4CoauTjMcPjXOsXiai4OTdLW1YhombsmtPqfx6SzzufVLN5SCsVyOyzOznBybXHfb9Xh+GNU536XBKIYyiDkxYuVR2lthWxYxJwpULdMknYyjjNs30iURs0jG7CiTbJvVwSrr8YIAyzAwzaiTTSZfwLLsO9ozu+j5GErRkIxjr1H3vBg4B+gwKtkwTCOaLGmb1SmeQohFEkALIYAosFbKQOtK8Bxln0tFl5hp0R5L0uwkqLccYkbtoqmsLnHaG+G0M0RXj4Na9mk7Wlpg3/11nDs5w8N2N2H9Gu3CUHTEUtWfHWwI4VjYRRO1PYjNDWodrUBx+tIEf/z1k2QLhQ2DaMMwCMIABUzMZLmvu6s22lIKd7rI0f0d7Olt4b49bcyPZdj2in3kci79Cy7dhxdHdC88M0bn3mioS7LOobO3iWuXooEqex/qYXI8S0dL3Yrj2NmWJnNxkq7WFGY5ih98YoTDdWm+fPo6DQ2NmKaN57qEYVBtN7gerWF2IMvepsYNt11KoXko3UpTuU3dVidM3iqlFLZlYVsW5iqLRtcT9S838IOQ+uTt7UcdtWpbDEYtc3OL6/ygXOoB1MWjvyfzuSJ3aup1pYOOUw6C1wqeAz+IBqMYRtRl43kaBy/Ei4nUQAshqi3tlGGitBHVQitFoeBiWibvO/Qw7z34cDVIKHkev3P+G3xi6NyKfY2SpS2erOmqAfBcfoRj+7Zx9vw0h7taqqOvl+vQdfSXK45PzUzwSOMOzn9pCgODY9t6mNqWYZwssYxBe12SiWxtvfKe5kYG5uYxzCiMf/LMEL+XfJp/9e2PkXDsNQODaHCEouh6TM3nmMzkeHBfF88MjoKC6WyB+nzI5NPRsR3Y307rrnZKXpQJ97yACcumbWcLTn2cCcdi9PI4xx7dwdTQHEP9s6Bg/yO9nLkcDUaJZS0a6mNkFqIM8oHdbUw/N0JuoUR2ocj+o91cvDwBSnHjSwP8bV2cubkCH/+bJ9Fa09aU3FJGdaq4skPHeh5oaefi6TGaG5Ps3daG/TyXOCvUlrLffhBiGAq7EnzfoUDVsdYP7rXWlMoT+irlToZS1CUcSq7HzEKOhGNWF/bdruBVA34YkjTXz3CHoY4yzWqD1nQvAoF+kT8B8aIhGWghBMBi1ql8cV2fUIckEvEoM1X++lcpRcy2+Zd7H2ZnqrF6/wYzTlxZDOTnmSwW6IqlOVjXxvG6juo2N1KTHD/ezPnRaXbvaSLurAw8pi7maj7DB4pRuzwNXB2epbG/npbTDQR5RZdK1tz3WHsbw5fmiE1pHDf6580PNF87M8jvfvWpdZ9/Jes+NJmploOfujLK0Y42nHJgE0s7AHR0ppnsn2aof5b8yBxNTdFx9G5rIK8M5uvjZLNRUDwym6OlvR7DNNj10HbOXV6cKjg3V6DOsInHTA4f6ORS3yStB9urk+sunx3h8KHO6PcDcGacL/7VE0zN5ZjO5LnYN8WlvsXR4pXXaUdPIwcPdNDUEK9e79gmo5mVkxSrt1sGXQ117G5poCuZ5ERLC/2Xon3PzOW5cG4MXdAkYnHisTiWaW24QPN2Mg2TeCxGIh7HsZ1N3kvflWzqWo+htSYIQ/IlD9f3o6xzebtQR33XISTumGSLLpnyAsPwNqWkPb82aF+LYZRrWyT2FGLTJAMthKihAK0MSq5LMplcM/hwbJvXtu3kI7mTJA2b4qxJT7qRa4wRahgpZBkpZAHN/vpmruRmyPoup9QwjfUNXJqa5YFd7Zy5NFHzuT09WaBuj8OCH3X32GE0cIHFILFvPMoAaw2zmUJNGiA3GWVY/UCTzS12B8kXPEbns2s+59lcgQ8//iRT8wUanHjNbRf7J9nR3oBraWaeHaO1NUV+LEO+nDVemC2wZ0czHU1Jrj4zBEBr2iUes2huSfGLv/CdpOvj/Pff/RLffKpvxWOPjs1z/OU7OHV6GK3hwtgs9z3Sy5Un+qPHPzXEoeM9qHyBy1+9gmWb7Pln+7iWX5lNrks51LcnuTqRgWjCOA8f7oJCiLINnp4eXzM4O9rexpnLYzSnE3SYMa5emVixjTJUdbKgNk1UGHXfCPWdL+1QSlXLODYXYJbbrj2Pndc0Ud9twzCoS8RWxKea6Phs06AhFScIomC76PrYlkEq7tx0u70gDCm6tUH7ekdaaccHty8DfrdplAxSEXeNvNOEELUqHQPC1XvbRpsoDMPgoaao5vdArIO873F9dpaeWAM7Yo3sTrQQVxagWBpe+Tqkd29U+/vs2AS79zZzYGdU0tHZUsfObY3V4NlA4cyuzFLv72jGALI5NxrwQZR9HplYzLDmS4tdIQxD0V6fWr4bAAanM/zYH/0fHj95jaevj1RHai81MJHBXfDoPdHF9FSO7t2tNbdfOzWMsaSjxtRghu66GL/0C2+ks7OBVF2cf/Ejr6S5uTZjbloGux/u5smro+w+2FaN9U6NTLP/wR3Ra21EZQvefHRcvhdQ+OoNuuK1Wdj21hSq3qJ/YrHh3vGuNi6cGuHshVHOnB6mcVrxsm1d3L+tgx3N0ULOuG1yvLuNodHofl0tKQaG5lZ9rZbyfR/P96rBs1rjf3dCpV/58kuF1ppCyWU+V8DeoLziTlJAMuaQcKx1u19UssS2ZZJOxkgnY/hBeFMTHKOpih5zuSIx26r+/Vj3OA1F4If4no8OtzZQRoh7lQTQQtzDwrD8obniAzP60PXXaU1mmgaH61ppshPcmI4CV19r+kcL3BjNc2V4gbpCHb1WI+Ol2uyv5yzu99LkDGdmp+nd04TVYHE1N8f2yUb2zLWwc64J5SqSjo1jmRhK0ZiMc71vBh1AvujhTGh6/CRjN2onI2aWZGgNpWhKJlYEMGcGxvmxP/o/DM8sBt65NTpazCwUyDdbhMC1gVl69rXV3H71wig7lgTWQxcn+fM/+Gq17Vxbax2/9PPfWe2ukUjYHL1vGxf6o0zvhYFpDh5ZLHc5OT7N/vu2sfNQJxevTDJQ1HSV919YKJE4OUZjuTPFrt4WpgOX6YVy8K81D3a1c+XsGEEQ/W4d26S7t4mnL41w9tIYYdanvT5Ju5HgwuUJZsv3TZp2tR/1+hbfM3EnRjweX/WSiC1e4k7slgeYQLmcw4nV7DsRi2MYJguFEjMLBUqeTzqVoC6x2LbvboeF0Ynm1kpIlFJYZjRhcivHq7XGW9JnOp2MbWpYS+Vk2Cq39wv8YLEX9JLLi0Wojbt2Efc2eQcIcQ/zfZ98vrCiBbRS4MQcPH+14LqyjSKVSPBIopfp4sqsLcB4Pk8uF5L1a4etFLW/YturU7Ncn54j1DA1V2BkMsvoZA6fEDfjES4EGHnNwnQUGFeOS2uYms2vmMJXdBcfI1/y6ZuZqwkI/unsDf7VRx4ns/R+GoanVh9RDnB5dJqd93fj+yGhY9LatdiKbvuBdiZzJdp7Flv0DV2b5hO//+VqEL1rVys/92/eQGtrioamBCefG+REV1u1B/fZ/ikOHe0GoD4VY6Hgce16NGq75AUktrfS1F4PwOzYPJ3XZzm2vY2ZQpFC+fkawIMd7Zw/O1o9jvpUjK72NHpJMnZ8JktxpsTodG1dtF9ce6Xgam8FRRSAmWtdTLN6MUyz+g3HrZjJFPiNP/5ytdOGUopCyWM2m0cBrQ0pWtJ1xFcsGn3xBIIQlarkiu6qA2oqHTaWDmJZKLjEbIvGVBzL2HhUd4VSKirPKbevq3Tl8F2foLxI9sUWSAtxp0kNtBD3mKVjhw2jXM+qQ6KB05GoL7Jd7Te81oQ10zR5oLWDz/ZdX/PxOhsSTLtzNdfNByU0elNf8S//Grt6j030dq7ES45l8FhXB9lsjjAM+ZtnrvBH/3RqRTjVmoyTmVy/U8VcPeza3cLY0ByJhI2Tsmnd3sD1/jkAzMYEqYYYKoSg6PPZTzxLuiXBm773IZyYw7buBrSKap8BLpwZ5cR93Zwcjvozj08tcKynhZmheYan5qqP29tdz9DpYRqbkyTyLvlsieSRbs5cHKerM03Wsgh1yL5kmvMXFge3tDWnQEH/yBzOlEHv9gb6y3XkhdLKE5n1fiN/+4VzXOuboqujnraWFO2tKbra60nE4+vcq3bf6y3qW/5eW227p88N8Z//+EvMLRT5Vz/48uqUPY2mIZVAobEta8nwkiW/5ajQd1PH+nyzTYOFQqn6HrVMk3BJEJsveXh+SBCGpJNRll2jb3pEeOU+pmViVLKrGnw/wPf86sTOFzINd7kG+oU5eVPcHRJAC3GPqQ2gDVCKIAhX9Nk1zWgCmVtyicVjawTRENuoE4MVQG0CmlLob7o+1lpjgEaiLk5nZzpa9Kg10RIiUIaBocA2DG6MzAFwKN3I1/7hOt/4/A2aOlJcLsyzt7OJxmSc+VKJwckMeTfgvt3dXA8nGJpee8Fh3+QcrZ0tFK9MUix4NLUmGB9fbKXX1JIgXyziegGjA9FKvq/8w0UW3CJ7jnbw8f95mqnp2hZ/F0+NcPBIB7PzOcwrcwwUfRo66qu3d7c4jJweQYeaidF52ralaTnUwZly3fLo2DyH9rSBZXDh6mKXjx1djczMF8jmo1+A64Zkxwu0NSaZnKtt/1ehg7UDzKHRTLVWeqnGdJzujgbaWuqqPZGVikpnVLmEIaoBrgTQVGuClbHkzyqqVzfKpQ/xuE1oRO9FQymGxjL87y+dr8bAf/a3z5FKRtP8DBW9o2zHJOd7BKHG80M8P2BnZwPf/uDu6GRxzWf3whJ3LCwr6mPtegGhjrLRRdcnZptoDelkrDoe3LZMUrHNLBjcWHUfCizbJAhCQj94wQfQQtxNEkALcY8JgxDDMMqBdBREl0qlaqu6CqUUdXVJ8vkCfi4guayGuDqkwVj/Q3XcXwxGbWVy1GjHuKbRKZsJt0DMsrAtA8s0sC0T01DElUWY89EhmJmAo92tUfATQqg1oYZ4SdFNgsCC6wsZ5pb1OK6M41Zoxq5myBWiuuvdiXauTEfTAY/3tnNxbIZtjXV4cznabE2xObluAA3wXGaGo8e7GDo9yuxUkbbuOibHcvTua6FvKEN73CY7lotayh3poP/qDKN/dpa2+0YZGc2tus8gUyI1VWB6PiopKWYKJBImva11XH1msLqdE7OwTAN/ZnE/7a11xIKAwc9d5PCxbpymJGbSZj5bwluWZV7IlmiNW6TiNrlibY17UzLGteu1bfE2Y26+yNx8Ea6Mb7zxFnX3NDI4uTJoB/j0F2r7kHe21pMzfGayte+Ff/bQbl5zYsdtP7Y7Samof7VpGHh+yGy2gELhWNHUw3QqVq6VVsRsc91Fird6HKZpRIsLQ40y1ZrfSj3fNEr6QIu7RgJoIe4xnudV+9O6JZdSySUIAoqFIk7MiWpKiQJk07KIxWLksjnmPR8n5pQD52jxYRCGsEp9ZkVzMs6UO0eTk+CQbmfqi/MMj88BsOdEB1Pzq9dOH25t5vqV6XWfh63hcn+0TdyxeGRfF09OjFRrbIuez65tTTQYNteGF0sapkpLgqtyKnN4LsvhbW3888eO8Of/8MxGLyFBqLmecOnYlmZ2eJ7J0SyHH9vO6edGUcBsCA1dddQ1Jum/GgXru4510T85T0NDnEymNsBra0syNjaPqRQt7XVMT2TJzpdoaI4zuKQcI56yaUzHGL06BVfhvu86ypwBo6UCQ1N5jj+8naGv3KjZd51j8ugr99B4fw9PnRlidHKeqakcvb3NBE3QPz5HUG4L15tOc2H09gfBt8K2N5/1NOssZiZWnvxsNOjkhcxQivqEg9arl48rpTDvQp9r04pqo8MgxDANMF687e6EuB0kgBbiHhMEGt93UUqRTKZIpxvK1wcUyiOvPd8nDEN0yQMVtbSDxYmFWkMsFnVeOByLwVNfr3kMQykaY3EeaO9kLlvP+fOzGEoxN74YONrrZIo288G8dJui63P23AgP7+/kmblxQqJM9bWJWY43ttTcr8F2GF7cSfX6fL7I5MQ0HenakeFrWSiUqN9TT3yhSOOeZp65PsmxB7Zx6dlh/CAk0ZBg8HpUwpFKx7gyPIsfaNrq4hQcE7fc9q6np4GRkQxhCB7gNSZIZG0KeY/MTJGO3mYmL03Q2JLEzxcZ65+pHkMpm6dPBXjlOvEL2qWrs47s2GIQ6bkBTQ0JfuhtJ3j3W48znfF4+uwI33zuBlf6Jql3LHbubCEf+Li5gGTCRqOXjHRf/LbB9+9+AUS+6G68EdDT2cDMwuonZM4WgvAXokp5y/N9DJZtVeuiDa2iQJoXViAdSm8EcZdIAC3EPaaxsaGczSp3TzDN6kS5VCoVBc5LFlpVvq4Nw3K/3/L9AAanZ/nSmSv8yP59vKJnHw0xh7QTI2XbGChKrsubP/5pgkAzFKsNbtZb0b+ZQRmr3f/c5TEe2NvByYVJ/PLtcV0bPCWNxX/2lu7jFQd20NbSyKOpJB//2tqLIpeamMvy6Kt38dxT0dCTM9fGOXy8k7lCCTu3mJnftq+NczeibPnkeJbeXa0MjM7Su6OJvv5pwiXrJAOlePdPvpqP/PaX8LyA8ZEF9j+0naHnBiguGQ6z/9X7KTTG8WaiIN0yDY46CQYmRthztAuv5DM/W8B2TN7x46+K2pRZFl1tNm/+tr1817ftQWtYyLmMT2eZmMoykysyO18gV/CYzxXJZIvM50rMZwvM50qkmxO0Nqai8hitKeRcpmfyZOYL1Yp2DWzrSpNujOMGIZZp0HdtCtddvzVeY0OcUEOx6FJyQxTQkI7T1poCE8YmVy+rUQqOHOjk1OA4a71tXswZ6BeK6qJMNKZllr+FCqKyL2PldkK81EkALcQ9JpfLRlHHkgVVqvwJqMtDMTSaMAixbRvHiWFZFmdHJxmZncfzfUbn5vnKlX7Oj0VB4asfcVDJNvY0HV4cVRyGlNzFFnHTXoGluV2toDdZVz2GIAyjBY0mJCyT9sYUYajRQYhRrn2uNFHQOsRUirqkU82IV7KkV/unuG9HK2dyU7ihJiws6zRRWgxs9ZKI6/5d3bQ01pH2PHZ2NFQnHq6lIenQHI/zrcuDHNvbweXy4r3z/VEN8YSh2L6tgcxsnuujta3x+m9M8cjLdvPEMzdqmkJs72nil3/+O2lrrSMVc/hvv/r37Optou/kCHuPbOPikzfQwMHXHeLcpah/9H2v6eXK5By/9KPfjjuYYf/PfidNbXWEYRiNMlnSh3h5DbvWmljMprU5xZF9HUSD8XT5JKrSySR63Z84M8iv/n//xNxCge62NA8c6mF4Yp7pYpG4FaO9qY5EzGZ8ZoGBuQWYW2yP17utgbhpYViKUsFnejJHLu9SXx+je1sDs/kiI5Pl7U0w60wSMQtsk9NXx6mPrz26W2u4UW5/uJYXewb6haSSDVfKQBsQeH70d1hrDNPAtG6uC4gQLzYSQAtxj4nFyy2vlmWZAZSK/kkIgpBSWMIwDTyvRKlU4MnL1/n9r51csb+4bTHXfJk/HL7Iz8Z+lD2p8gS9cqZ6tQ/TmGNy7emxFdcD7NrZwrUzi7fdt62Fy6dGVmynHk5Vu0ssd+HaBAd7W7lUmqUYUxQbFztAlBxI5RSGgv4r0zQqE9M0aEskcBwLpeD/fefL+LH//jgHnAT1I3lCL6BueyMTrU5UyhAEDE3MM5ApgVKMu3niMYtiZcGe1hzraOHSt/o5+OB2ztxYXJinDMWhY9t48tQAvTta6CvXce/qbmRbXYKmxgSe53PwWBuvfd1evvTpM0DUqWPfAzvw6xPV4Blg8JtD/Kf/9nYO7emAQ9uq1xsbdEdZr53cal75wC5+4UcUfhDwihO9GETfFIRaMzOfZ3RygfHpPGMzWfpGZjl1aaTai7t/bJXOHe0JMtkSs/2TK24LgpCFvAu4dLbWM16eMHm4q4W4YTJVLDIwlamW4GxPp5nLTqzZYzoMYXrepTkdXzYXU2xk/d7PUdAcnbxSHsoUdU1B3f1stNYQyIATcZdIAC3EPUhHKdtyxlmhlgS6YRjiui6O40St7KyoV/TOloZV92UoMK/ch7Ozn78c+yw/t+cD1duUWhznvPTDNO7YZHMrexBDlAGttVbP4PWf49X+KfbvaMEPo9VXley154fLJu2F5eehMA0DV0OxWOJhM8HA568zW05t7tzdwOnsNMfqW+gfrg0I041x7I56bjw9EgXPLQ1c+lY/pmUwMLWYia2rj9HclebMxeiEYHRyno6Oehodm6Gzo4x4IX/4m1/g+37kUXxXc+qriwsCk3Uxio6zsrwl1OzfWTsV8XapZPUrv5NXPrAzmpBX7r28UCxE9d5Jh2MHujmqy60E0RSLPv/0TB//87MnmVulNnluYf1+2wAnetowch5eKs72eIrsVJEbw3MAHL+/i9PD0YnJxWsTnDjYycmB1RdAfu6Jq/z5P5wG4D1vuo+3f9uhm3g17j2VbykCPyqp0URtBpUR/Zb98pAVVW4haNk2hlFeJxHqF0vLbSFuipyqCXEv0hqtg3I9RG1GLvrqn+poX1hsqbWavOvz3LUMsWIzl3N9XM7dIO95nJuY5LPXblSDr6V9n01znczUJj90LWtxH5ZpsKOjgc7mOprTCeoSDqahuDYwTVMiRn0yts6eKvtQuOWx5pe/1k//F67VlHhMxKJAfMjP0daUql5/aG8bZxdmeHZ6goMP9nAs3cjV56IAef+xHjIL0TCMHTubsetsbvQvdhcpFn06mlK4QYDjRPmMr/7jRabGs7R3NfNzv/O9xJMOTW11JHY0cWNojtHhDN1Lph22ttXXtB/cqsoI6OXT7rTWzOeLTM4vMLuQZ2Yhz/jsPPmSy2w2z1w2TzLm0NmUrk79S8Qc4jGHWCxGOp3ku161nz/8+bfyqgd239SxGSUfv+gTdywuXxhjpBw8w8rTqouXx9nV3rjqfpYO45mdj9rBbbYP+b2ocuIUhprAj+qcTdus9oEOysNVDNPAdiws28RyLEzLqJZxmJaJdddrzxXhXbyIe5tkoIW4B2kdVmtblVoefK38YKgE1evus/zfL059k7/+smIyHw3qqKfSj3nRegHf8gz0Wo/regE7uxpJp+JMzecYmKjNCrc1pshki5y6MUZLOklDKs7QZGbN0oaGhgbS6TigGL46V3Ob5ZhcC6LnM1ss4cZsdvc0E0vZPDsxXi0d+Ob0KK9NNwHQ3FaP1ZzgQLqbkfEMVoODzoak/Tjz80WUgqM727j09T46utM0727G7JuhPZ2AICqv2HWwkx/6N6/nf378STKTUd9n1w3IZEq0ttUxNZmlfcnAlc0qeT4L+SKhDil5fjWrHLPtaMGdijqbKDRNqSSmET2/bKHEQq5A3LFIJuOY5cWllXKQv3vqMo8/czXqTexYPLinm1ce3sEHv/9l5Aouz14Y2tTxGUqRStj4Cy5ag21bJFJ29C0C5W8fDHAcE8rlA0EQkJspkk7EmC/UjnVvaUxScn0Krk/BDYiXJycur9PfqolMgblsqdqD2SiXLVSGxhhR3VD1+o7GJEq98NOyuhw4KyOaNlqpo1cKtIrGp2v0umVASqn1x1oK8SInAbQQ9yKlql+vLv8ANIyo3CGaThj1fi2VShR9l4M7W8r9YEP6hucoeit7QF/MXafOOcDkskF3Sx+lMRVjhtW7KiwvUQjX+B441NA3NrfOk9T0djZwcWKW8bko+OztaKSlIUm4q6W8YI7qV82jo/MUsx6GofBDTXtPU1S+UF5Q92iskYt2FCwZSjGlXEzfp6elAUMp0okYjmkxWfTYfX8PI4OzDI7NMTqT5cixLs6UFxn2dDUQBiHb0ymuPDkAwPjIPHuaEjTVx7hxYYz//MG/5D989D2MTWb5k498i2KhduBJLlvCcUzSDXF27Kpt07eR+XyRVNyhLhEjV3SJ2Yp0Mo5lGgRhSNH10aGmLu5QKnl84eQ1ckUPxzKxLaP8XxPHMjnbN87Q9AJHdrQxPL3AZ564VPNY37o4xO/93ZMc7Gnl0f1dzOeKXB3YeFBLR3OKwpcGGQB2PNJD31QG6uHIoQ6enItqpofDGShXrtzv1jFycgJvpMTeR3t4dlkAfXVkhqM72zl3fYJTV8f4k//zDNva0pRcn+lMlne9/uiWXsOKv3viCn/11Qub3v5Pf/a7aanf+NuQu6VSagGUyzKifw9CrVGGgWmtbFNXCYxfiBl8jdRAi7tHAmgh7kEKVc3zViYSVm9TCt8PCMNilGkKozrpj1y+wKnsYvDjNBnsb2jG8hS5nEtI1Gli3s+SSgYwF23XEI+RdCwSysRpM9BAOhXjwJ42OltTLBQ9cgWXMNQYhsJKmByoby9nGjUx02L3g93lum1ARx07EkmHg/Gm8iKnStAfYBAtYIrZJji1XyG7VkBOuZzNz6z4+P+V3/xbFmaiWt2OXU0Mt9UGOs7gHHTYhICvISDEK4+LRrHYWxp4uKeN/KVxnLkiR450VINngLlMgYO7Wrnw5cVWee09DYzO5EkmHGJJh4W5Ah/5nS/y7MWJmvKDpWZn8nR0pbnvgW1LFoGuH9REAbJLfSJG3LGJl6c1VlimiWNZBH7AfDbHf/z0t3ji8vAae4PDPa1cGJziS2f7133ci0NTXByaoiEeoymdYHaNAToVS5/FWvnalroE6UQMwwCjL6qn13rtpGdlPxOzOT7x+dPV6191381PKKxk5jdr/QV5d8fSYwhDTeAF0d//Sha9PHXQMO7MZEMhXiokgBbiXqRU1IZKrwzOKgF1LBar1jB+9UYfp6ZqM4duGHJ2drGed0dsMfPT0+lzttw4Yy50yXnlDOoOMJXi93/o22hNJSnkC/z+p5/mm9+6suahHt/VwdnJ2RXXH5m2mHx6WUmAgvze1uqPbccWs7P7DrfyTGGKS/kFDuxqpq9vpmaRU03me5W4wcLEvRCl1Q1g16NdnBuaIm4Z7NjWwOXxxWNUOc2e/Z1cuzxGrM8iZpmU/IDW5hROSXPq1DCHH9rOlScH6N3fyvBUjlLJZ36+yI5jXaQ1PHV2dM2uEhXjo/P8r0+dZN/BTlKpePRVO6sHPlprcsUSydj6GVCtNdlCccPguT7hMDK9sObtq8kUS7S0NrCQK+GvcmJgGIrOpjrScZvKFxhLn0nMttjd1oAGmhsSPDkYdWt5WSmJ7ZjEYnb5GwJWtLUz1gitcwWfLzx9g9c/tGtLz2Xl0W3s+Y6fK1NEwyCMFg5H12LZ0YlUEAQEfvSt0kZdXF6oAlnaJe4SeacJcQ9SgDJMDMNaUQMdhhrTMKIOHOWslB9upvXXYjDh1M2vudVj27fRVldehLepVmqrRx16WYSkTEXP8cU2bkrB6EKu+rORXMxGX5qeiepTa3aoV/9zZX/Lso2V8MPzQwZH5tneFNUimwquXRknb2qUoRgfmWdXLM72zjThbInJ8SjoPH91giMv66VvdJ5duxaD/oHBOUoJG73J7N+Fs6P8fx/+KoVCiSAICYIg6uO87HUreT6eH5CM2Wu+5lprfD/gw599et3g2VCKjsY6Mvmt1w9fn8lw8GDnqrc1pOLkvzTA2OPXVr19V1sjH/vAW/j4B95Cd/OS2m+l2L+vndxcgatfG6DhQp6uPo/dk4qXJ5vZm26gNFdif3MTJ7Z31Ozz2csj/Onjp7b8POBmMtDPTwu9SjcNgMAPq1nmMIxKNVDR+9u0zGhBoG1K/bIQG5AMtBD3onLbqdUznCsXByWdtQdZrEa7KWD1NnWH2hazwoZhYGwYhKx+e0NPI6nGFKpc2RE6BgOTixnR1tY6bvhRgFefjpFfdjzLv05fWmvd0BAnlqw85+h62zS5NrVYerD0tMP1AgoLHo2JOI2Wzaw/Q//ILA892Mvpp/rovzbN/iOdjM0Xq0Nsjh/o4PTpKE1/5coEu3a3cuP6VPnnSQ4cbufK+Qk2Y9v2Jkwj+p0a5YBouZLnk4rH1g2ewzDkqctDfO7k+pMYj/W2c/rG6i3jNuOZoVEePNDN2UuL/b3bGhK0u4rlYfvS34sfBHhuACoaSb/k4Fc8RrHoUSx6NLakGMgvvi/SdSsz8M5Gwz/0yuaKwMqTsA1sYsDmbVMJmhXRQs+lJ5xRj/aom4a5ZMjMi71kQ6MI9Yv7OYgXDwmghbjHbFSHaSiDMNR4nodd/mp3o6/9lxudsFgrgI6V+0pHj6U2EUCvfrxTOZeBwZWlHRWNzUnIRQF0S1uS09PTNbcvD2aWBhhT2QKj07WLHNOp2tdg+SKq2WyRndsbafBMKkc1lMmy71AXVy6McvncGIePdXGxb4qDXY1c/FY/++7bxpWrkwRByOhohs6uNGPlqYXZgsfuYx1cP7N2oGoYig/8q9fy+jceJggCCoUiMcfBXlbbDFCfjC+O215WM10Jtmbnc/zO3z215uNVjM+tvgB0NbtbGzBDhW2bWJZBruSSdT3m8Th6qBtXh/i+FwV7jkV3cjvaD6v17nUtKU44aQA6m+vwgwCForM+ye6WBhZKLsFIiOkotm+PauIr9fMAiYQNSxa0rhYk2nGTvz55pXq/yn8rb4mmRIw3Htq5skPMVgPoLUbQ1f0v+zu70V601gRBVKpRYZpR9wwTooyzqaonWy/2wFmI54ME0ELcYwrl9mVQXnG/pA2ZDqOyA9/3KZVKODEHheLyxMaZ0MqHtaUsTvYvDslYHnSYYUgul0ehcD2vWnO5lrUW0a11fYWKGZBb/Rii65YlapfWQ2+iWDVc1jf5wI4Wzk9Mc39dU3V3za1Jrlyfoqe3haH+afouT3DfoQ7OPRF13xg6P8b2Pa0MDs5SLHgU4zYNDXFa2uu5Pj1PuJBn574Whq5ML3944nGLn/n5N/HAw71AlHm2LAvfD7Bsa0VQpCrPK5rRjaFUNXAOwxDP9fidv3uK0dmNg+N0Isb4bG7D7drTSQZHMqsGjlPkaG1KsmAFZItLJkoawJIvPF65vZ3//APfvuL+73/Ng7z/NQ+iteZ//dk3+avf+gJhsPJxkse3QWJZF4ll6lsT/N6Xn1nzeexrb+KNh3euiFw3Pvm7Nbe06FDrch1zpVRDrShDeimWaUgNtLhbJIAW4h5jmAaqEnsqRalYwnGcqC5S6Sgr7NgUS9EHcKVjx3fs3EkQBBS1S1HXTpELtSa0owVdnbqLI4nWcn/eEBW3aoYnF+Y8zlwa58ThbnA1TZbNfc3N1W4aallUm8TiSO/ipD1FFFgkDIP6pkTUj7Z8S6jD6pAMq9HmaFM7OtQ4lsHDTmv5JKG8HydqX2dYJrrOpFF7eEWPMNRYSYPWeGyxUwlgGQaXh/LVnzP+Ymu5uG0xmo0CygXlYxiKPXtbOXc5yh5PxXx6d7dR0gFnL0+wY18bg1cmae9tJo+mtbWOqaksc7N57jvUyfnJTNTdAxgvlujqaWB0aLHPdbohQUN9rCYzqZTCcWxKxVIURFsmV86O0NJeT3N7VC9c6Utc/b2FISXXZWa+wH//7NN889Im+zRvcoFZWzzBdLh2oD01m2ffrlbOF1eeICw+r/UzpEop/q8feIyhsyN847PnVtxeeV8t3d9y4QYVSmsljrcaP9+1Lhw6+kZFmVEf57s9zkSIe4EE0ELcY+LxxVKEMNR4rkcs7lTLNSrXB0FIPB7DMAy+99hRvvfYUXK5PF+Ze4q/nP5azT4VVLs6l75az+znFitZM6+pI+8ulnMkci59qUkee2AXvh9QzPpcvrh2hvvQ0U4u9E+uuH5bdwODy4anLGUHMbKlKMg93NTE4NlJunc0ctmvzbC2N6bom8+x/Rp4xeg4d1k2Y5drH7O+o469r92B1mAqzbMDi7fv39nCc4NRsDxSLHDicCdnL41Vb4/FLSxlMD6eJQg0Y/MFDjyynQuDs3heQFtLimTSZndnA+e/2cfOgx1c9HxiMZuf/Ylvp6M5wS/92//N7Eye9o40QcllqG+a3/qVz/Kr//V72L2vHShPjHRsstk8/+t/fIvH/+JZAOoa4vTua+cV/+wwr3/biSULygJOf7Of//W/n+Pp1M0PFFlNd2OKi5vo+XzlxhT3H+6svn7LKaXIlIp8buB6VLtL1GWjLm+j82F1cMmRtx5nbjZPIe9FmVYd1Zo7dQ4H0zGUodB+gJ1waMYh0Dq6sPHI6bUC361moDfzzcatiso3AlCqOrBISjSEuP0kgBbinqaj8oZln+tRX9it761TtXP+cwvL7rz4ZwMYG5snvTsK4qPA5OaCivWyeds60lwvLQbK1SNYJZCo1gUvTTOu1oXDUpwcjAL9VGzxn87ulnpOD0XBX1MqTucUFI3FDP32rjTZaxmuZ0v07munvzRHoeAxPLZAIm7heQGT0zl6Oxu4fmYUgL6L4zz4sp384L98NXt3tRGGIR/82dfx8T9+grHBGXLZqOShWPR46hvXawLowNf88a9/gSe+eLl6DNlMkXNPD3Du6QF8P+DTH/0mCqhrSDBwNToRuP+Ne3kyWLt7ylbVWw6bXWZ49sIYB/a1cWl0ZcBtKMVEIc+vPlV70vaahW4uLKkPb29MYV+coq6jnsmx1dvr7dvdhGe5TFyrfZzm3j3rHl+wRgp6q4sI71oCWkPl79W9FDxrIJRBKuIukQBaiHvdyviZSqnsik03iADMk62wYsKgru70sUQHF73R6lXGzUbqrB92NzUlYWzxOOxKqz5Lc2RPx5J6YAiUhkyu5rk1dadpakrVPIhKWVzzoox3ruhjWwaeH+KkLIIcdDfVE7+cZ2IiKlk4dLwTzw8YOz2BX57Y2H9lghOv3sszT/UxN1ego7OeouPjugH9YxkOndjGjacG6NjWSN/UAk891c/eXW0opZgu+pSSRs1Uwvf82Kv4599zf/Xn7HyR//Izf82F5wbXfG3+5L98vvrnmcnya6Rg6PPXOPG9Bzk5uXY5xWbtaK7j6tDM5u+gYWIoQ2tjkqmF/Go3r3qfpRRQzLuoiQV697TQf23l89CBXvVTz9ZRFnutDHFlVHft4+qtB9DoFYtPV+/vcWtMy8T3/PJwonsriBbibpEAWoh7zPIgOFpAuPz6xemES683lLFyIVLlNgyufn5l8APRV+kvj7Vz8eRoee96yX/XDyDWvnXtW3w0poJ0PBZ1lxguZyQtxVNzq+dFl2agJ2cLjA/Wloc0dtdDY/TnZNwiX/SrCwf3tjVReGaazIJLe3sdccfCCDQJj2rwbFoGvfd38/XrIzx4/3bOPTdIseCzv6eJ833ThKHmwuA0j7x2H+euTZCfL/KJv3ia9tY6Mp7H//fJb6E1HDnRxfDpcf7vf/PPeNXrDiwe82iG//j/fIrhG1sLgJP1Mdp7W3BLPur0NIlei4K7egeVTdEaO9x61W0273J0R8uKAPr6+Cx/941LPGp1UVI+MW1hhQYJx2T/9hYcxyJhGJTOTzEMFHIugxfHOfxAD54bVMssDODCVy9z33ccZdfuFgpFn+x8kexCkfFPXOWBo5149SZ9nT6ZYm05S6g1f/21S1F/dENhltsvXh7e+CRh34k2lBUt0H16bpJhCiQtm5Rtk7Asknb056Rl4RiLr1uoQ1zXrS52Xfxv5afVRcFyNEnQ9/xocam9QZu+lwxF8FJcGSlekCSAFuIeUygU0ToalqLReL6PWXLLAziihYNaa3zPp1RyF7O1gOu6a3bNaA/bOD9X2xlDA+1WjE4/zcXTizXBYRBSLC92CzfoprF8YEpFsErHhQo/DOhN1zNxPmooVwkp1+sitnTGxWpdI5bGHw2pOPlilnzgc6i1iemvjxN4IXt2tTDx7CAtD/VwcmQG04AD+9oYH86Q3t/EmdEo4Hqmf4zHHuql//wYl06Osv94Jxf7ptl/oJ1vTUxwdH8bF8rlHB/+gy+zbW9r9RuBc+NzfNvr9/LgY73lqZGKvkvj/PoH/5LZqc23lwPo3NFEKdDcuByVpsTiNg8c38PXJzfXf3o1e9ob6Rucu6n7DgzNYBi1meCBqQwDUytr3fd2NDE4sBjAxhImB952iODqHPaeRuYUxM9NMXp1nNx8VFKjgMzEAv1XF0s4bNNg/2M7OTswgz+kadrZFgXQWvMKo5VvMYNtGPyPv392y88nmbR5MhxHl5uMPHFq/aIWyzCqwfX7jx7nu3ZEHVaWLmbdTMmTUtFQFMM08F2fMIwWEwohbh8JoIW4x9i2FXWfMFQ1kDYtE8uyqgGZ1hrfMrHKAyaqi86CADVVT/fIEdAQT9rk/WjyXVCw2fGa5OIDaUhmNWrER2uf+zqaqze1p1JYloWhXOK+pk2ZNJ5o5OyuAgZg6CgrrjRcUjnsQ8lo8RhQyWjXj8Ux6q1oOxaz6IZSOHU2RVvTfaINrTWNjkOuGC0ue1A1lzuLaFJ1DtmSh21bOHtLGBiEOiChTRpb66JHKyf8zIRJQ1xVW8B1dSZwfPBni0x4IYf3tNL3zRt0vayXkzNRxjsIYTauaD/RyfklC+q272nmKTXHrm11ZGbyXDs9xvFHenhqbAqU4uTwBEf2t3P18gRBoJnsn2NbVz3DU9F+v3R6gOZPPsEP/+DLuPjcML/1bz9NIbekFdwm7D7SxeDALG5pMdtcKnrc+N+X2f6d2xmc3dqY7uh3rvGLNz9tbz5b4sTRbi6MTFLw1s+CW8s6gZS8gNMj05AERqNTpl0HGunubWTu6X4mq0F9bYYyCEJK8wX88glZwow+FutNm8GvDWO/po7JKxnu29XBwGSG2WxtB5r1dO1pYIjNn4z4Yci8W6pebkU147yk1/dLPQstNdDibpIAWoh7zNJuG1prDNPENC0sy6q53rYCLNOqKdnwPJ/CpM2Tn4k+3I8cbuLZoUqA4LF0YsWDXe2cPzNEvVYU8ot1uwDGYbAsE8M0QSvmZ/IkVJoxc7USkNUlsgZja2RcHd+pduAAONbWsurX7Ue2t3Lj6iQdTXXMDC0uoOtsTDExUbvvjvZ6JkcWt1EKEkkb1w041NvE8NMDND26nfMzi4Fnd0eaQs5j3M3SVB9ndqHI7kOtnJ+fww9DrjVotu1oYGYgw4UnhzjyUBfnJudIOTbGXJHt2xoZHJ6jUPSIz7o01sWZy0bTDD/z1cuEBY8vf/zZDXtiL7fnWBfXrkytmt0P/ZDt2mbtKmrQq4x2NxUc7m7j/PWVHVO2wsiFhJMeO9vraWxOYlgKHWgCX2NYCkxF1nNJbKI5242JDDcA52ATDx7qYPSZIcJVjn3w8iSJ7jSFUlDdb4Pp4Pshnutja4PT18exTYOdHY30TcxGXT420mDATa7LvF2hrlJRGZFpasyNJi4KITZNAmgh7mFaa3QYbro/bWXwxkaOb2vjyteHNhEELNZaqy0mjrTWNDclsR2TfM4lm3PRWtPWmGS4VFi27Vr7iP7bkIwzsyTSWe05Lj+8bd2NDI3MARA4CuN4B9fnFnse79nRzMjoPMVyhrezrY7W4+08N7mYiS55Ps6xFpJZl/xMgaHnxjl6fweFGxn6Rsepa0zQ2p5iajrH7Fye7mQDRSeqUT58oIu/PzdE964mpq9uvu55z9G1g+cKf6qw6qdDezpJOhGjzrY40BGNDzdV1Ct8cDxzy8EzQN/EHAoYHV9gdPwmsuCrcEPNwq4GBtwSg2hSj3aRciwSpkG9MlBjOexUjAsj80x9aZyXPdyOXQi5SpSRrq+zmZ0r4AUhCtj3UAuFVg+zz6DvwtyKhYEVw/7WSmqWMm5TCG3ZVvQNkR+iwjDq7f4SDqKlBlrcLRJAC3FPU7d9GtneziaGnhjd1G4VanEoxxYD6Pi2BOMz8+TdQvS1fRK2NdTT4sUZnq4NoDeKF+LWJupDl+2koSHB0MgcTSmHzJlxjH2LJSqH93bQPzhTDZ4V0JROUJr2MFRUi13v2PSQROUCzEdbsP9pDK/oM352inQy+pYgO1egNWlTl4qRzZUYGcmwZ28bqsHmzJWopnymI0H9fB0LExsHaxrIF/11g2eA6QtTND/WwkwuKlcwlOJIdwvnr04yw8YTCG9FJlfk2K4Ozl3fbBO8jcUdi8HRqI5ao8j6Plk/+t2k62J4TSa7u1LsTNnEtOLKPwxU75tQJsmkw+xc9J6qO+7glgLO5iegHR5t7+HSl1eewGzf38DZwtJR8ytmX66r0t866ki32ernlfuAaPCNNiEMoq4cQohbJwG0EPeYFd02WNmFo5JpXq1HdO3OFv+oFNzf3c71bw6vCNA6OtN0djVU44ftvc3luy924dBbzIrN5Ivk3drSkJIfYNpbjxCWdxZZLWO9tGVZ744mzl+MFvltSzpcnVhgR2uKERuOHejkzMUx9u1q5fqNaUxTsbO7kYvlhXoPHW6njzyJBUX/zCzxmMV8o+aRt+7l2l9eiso1Ehb1DXEWMkWmRubZvr+NYsmLFtjFDC5eW6yrnVsoEj/cQqzgUlpYvw5aAdmpLE0tKWan1w6Ei3Mluq6XKG63onrvuhQXrk7etdzetfFp2hpTTM7dnmB9//ZWzl4eW/W27vY0Fwen8C2DC3aW+2KNGKaqjgWPKZN4bPEEK2u6nFSjiztY5dyrrtFhsCvLzlg9aSPOTK6IEdfETQu/BFem5zY8ZtMwiMfi1Z8rIbTn+fhBFPxbplVeHBhdH+rVS3kqwbi/xu0vFVorqYEWd40E0ELcY0qlxSBLhyGu52EUFb4Z9Y1VhiIMQjzPq8bHlbpRt+ThL+nCUbk+4Vjs1DGufX2VUdAaXvGaPbzjBx7Btq1qVqyyqMmMmzS31+E4Jm1WMhrtojUhoAnRGsLytLiwHOiHOmrmZRvG4m3loSxj2VUysWvUcFRqh5d/Bb9aFw5dDj7icYv5bLG6S13ex8D5MU5850FOXowCtSs3pjiyt42FTJGrS1rLZTMl9ranOdkfbVcs+ZzYs42vjg3x4Nt2MfxX15mdLdDd3UCp6OOWfAYvT3LokR1MmiEXh1dmO2czRY4e6uL6M4MbdjVZmCvQlnRIJB0K+bUD7unL0xw+uo/BTJ4bQ7Nrbncn5Es+pmFwoLeNS6tMobydPC/qPnPhxiQHd7XxzOQkux5uQ13OUip6jIcux445jIwrCnnNjb+f5eVv2MG3vEECvfIEs7UzSe5QwEyxwIy75JuQJW/LB7d38czg+s/LUGrVkem+Wvz7ZxoGdnnxrx8EsHqDHGDxJLEy4lsIcWskgBbiHuM4tSO7rZKL7djlLhxUs9FKKRzHLnfhiLbXmpp2WMPDGXYmUnQn48yfHGOHYUT7qOxfh2ApkmbU0WM5yzTJdtqc3w2H6m0y/d6KbVaKhq/EJnySk7WdGvbsT3F+eJqkWuxvrZTCblw9K1UJ5q2sS1chqsPWGuzeBNTZKKJuJa1d9YzPZWlpaKanLsns9TnSrfWgIJaw6Tm6LerOOx21P0MpWpqSFN2AxuYkQ6NRffXuPS30T83TP7PA4T1tnL8xye772vjmaJTRfGpqnJd9924GPnOdkZEMu3e30H95gpa2OgZLRcazhVWfx9FkHZefGWTvsW6undy49nxyJMP2fW0MD/sE/uoBtwYms0WmM5tf2Hk7LRRcLgxNsm9HCwnHZmRinpn5mzuW8dksbc0pmhuSmKbixuAMhXJ5TWHJYtOLNybZ2dvE9ek56notDne0cS0/SdA8yaHvyzD/zW1cPV/Cm4HexlbmzRxq2Vs2lrK4XJwDYEeqgUY7zvnMZE32d6NSjGPbGqmrW+s9u7LDxmao8mhv3w+wDfWSrYMOJAMt7hIJoIW4x9RmtaK+z4Zh1ATGWkeZ6MptFcs/dOezJRZyJeypGTKjay/4CrxgRTZNKYVhGrdtvnF7a4qLwzPVCYNLw5S1Fz6Wxx2HmsySGuJYT4rpzGKwWt+eZHg6en5N2mSkfzEjW7+nlYElPx94uJucpbAtk9Figcz0LMf2tmLYJuf7p6o9jq8OTLN7ZwuzQ1ka2h0yxSgb/M25cV7+xp30P97H9evTHH1oO+ezWRbWCJ4PtzVw+etRz4yrFyfYf38PV59b5ZuAZQavTLLnWDdXL63eZq39cCsXZm5+EdxtoeDK8BQHWpvojSXY1hyjlDC4vEoWfj3jM1lsy2C+6FIoeezf1sLAyCyWZZIr1EbAjfFozHzW93lieJL797cRhIPMMIvxSIZX7T3E2Chcnc5Q7zg4KXu1hwSgwY5x9sYM9bEEftolblpMlwqodTp4NMRtLofDaOPgqrfblo1VbrW39O+jYztgRWVXrutWv6VxnFi1/Ch0QvK51d9HQoitkVM1Ie5pUTnE8gBTa9YsBVj+0b+jvWHd4Hl9ajHM3WJCbHk5Z0tLilBrWluSNDcnaW5K0NiYoCEdX/Mr62qpxrKAZkUFx5KfF0bXDyqTbsjMbJ7R8XnSCQeU4sL0LAueVw2eYzGTroMt3CguEHdsOrMOsSUZ+m8WJ+l9zXZ2H+zgubkMC6XVM/PpuMPc2dpSgMsXJth9vHvdY6y4dmaEA0e7Vr0t1pna1D7upD3NDRyx0oycmuTymTGun59g6JkxjrY0k4o7W9qX54fVbLPtmOzpbcElpKezoWa7a31TNCajIFoBM5MFJp6pJ63qCAkZbD5H15HohGnBdfnS7DD7X96OVX6PKbX4ZqmUBi2UXA7Ut5KbDNkWNOKX1j5p7G6MeqnbxuoLW5VaPLGtBNBKRdMRTdMsn6guZqiXXh/4IaZpVgNwIcTNkwBaiHuM7/sEQVAt0zAMhVLLs8NURyDXXF/9v0UNjk0saRNPOsSS0Z+dRPkSt3Di9ppJZqXArAQBW4ygly9UNAyDlpYkA1aBIbvAkFNkJFZkNFHCXSsWrD72sn0vP6EoR9Bxx2JquLaxr+GYNLXX09RRT3NnHZfPT7CrPU3JDXBnXWxD4WsYz+bpaEmRTsdI76rn0ug0BdcnYwcU510OGOlqplADfpdDsT2Gu1ZNs9bsCm0WMrWDPXbsbmE+V6L7WOcaT7rWpWcH2X9klW3XKO24o7QGrYlbJvfXNTFxeoqB/tr+3Qq4cW6c1pJBe1PdTT3M+b4JskUP1wu4Ojxd814vlHx669M1218573H909vYFm4HIFS1pUNX3QypzgRH9nfQWp8ioVcGqBcHZnGDkNH5HBPrZIHrE9F97a32daxS1UWDhmHUlHmEYdSGLwxCfC/YdPvKFwsNhKi7dhH3NjkNFeIeE4YhaPC0ByjCch/o5ZPKwlCXM7SLt4d6Zc9opaFUWH9qnFvyoimG1QWEALo8PjxcPK4tPY/a4/CDrXeAXW0gSHR8yzPy0c9dTXU1/aIBAj9gdqa2W8TkuUkaupOk25OMj0VDZ3JFj23ddah4jBuTc9VtuxNJgoLLWF+GEwebeXZ+msfq2xj+3/10P7yNHd1NDIzULuIzFJxIpbny3HDN9fsOddJ3cRTfC2jb1UzLriamb6y/AFABV04Ns/NgJ31XF7PZ9t0a/aw1u5rSNGMzNpChUPBw7JCr+fUn+E2NLpBacNizv5lrIyuH5GzwkPSPzQFUa6GXOn9tgt07m7g+NVd9zy5kQr728Rjf9tajuHW1AbABTC7kmVzIs7++gf1jKQoPGUyWcnQ1pBjN5CguWXzbmUgyml/tmwxN3oz2vTQD/T+uP8lEcYGYYeEYJk75v2nHAVUiRJMwHZrsFA1Wghanno54esVJaTwWww8CtA4puS4qAMN8afeFFuJOkQBaiHuM4zjVgDAMQ4pFhed5BIGPaZjVD1TXdfF9H5TC9/xqEO0vG7Ec+gGxBofVazCix8l6JYbHpzFUlBHzAx9TKTJ5l+tTlf68i0Hrtvo6GuKxchu9aFFedMzRCO5Qa5qLiqA+CTq6pxUzabQT7LZswnL7Pa01gdZYhqK9qy7aVlcSnRozYdDSXodhGTQ2xtBh1M0jtE3CpFM+aYgC7bhjUacMFiyjOoAm1IrAX9n6IJcpsuNYK6fHFoPXXR0NzJ+apr67jli9SckLeLC9nUtnRtEa9vS2cOPcNC/rbKD/8agP8dhTI3Q90k1nW5qxyShwtw2DQ0ZsRfB86Hg3F58bqJabTA/O0fWde6jLe2THN6hlDjWj1yfp7GlkbGiO/Q/vYHq+dMs9whWajroUrfE4cQyCQkCp4OG5Aa7rUyr5FIsBU8MzTC25n+et005iiVzWpXh6kuMPdnN2YKJaInM7pMqLbWtfAsXZr5ik0mnaW1wmwijYXR5/Tk7lMD+vOPDKDr5VP4ZjmjXfJIRaE++M7nu8bhtPXo1OAB7d3crJXPS7X9o28Uvj17i4sPKE4uGWLq4Wr6+43kDxByfex65U2+KRK4UyFY5p4AcBpmVGaxPMl9IX0UoWEYq7RgJoIe5BSwcsmIaB4zhYlkkQBARBSBD4hGFILO6gUARBQDKRIAgCbKe29jRojtH3QP26j9c3Ocyf/d5iwNfTUs/gbG3d9NJFjO31KZ4bWX+QRs+cTXZJ3+PeRosrY6tnIpsOxrmSWNlT+DoFaIEOK878jcXsayqTQ80Xoy4cwOTXIAXU3bcNfSMKZHr3tjJwaRyrTsGlvvKiS4UqXyZGx2j/v44zkSlwqKeF8W+N4rkB+Usljjzchdka4/zpxX7CI+PzPLC3izNful4N2tq70lzPZIjHbJoakpTyJXYUDG4sGzJy+Hg3558dqN7PTtqkX9fL6ZEZuh9oxf5KCS+3focTt+iTm82x56Eezl+eINHgYHdaeEG4brcHU0FrKkHacUiZNpaG0A3Jz5eYnFggN5QhR2bdx74VgR9y7YkhtrWnaNneSGiW3986+t3VpeJs397E8NQCg+OzTM3ll3yjoimUvFVLjDx/9W9VNJq+4SypaZsjB5s456+e4Q8CjTkScn+qlasNOaaDxVIbpaLWjEvtaa3nTHH1xZ+eXnlC0Z2oJxOs/n4P0fxJ/5f51cPfs+rtoDENg0AFK755EkJsjgTQQtxjSqUSlbya1hrf96Oez0uiiEqmWIdQLBUxLTMaxBDqaj/kisDdeq3sah/XSz/DN/N5vjzb6K9bArLBDpcFUKtN6nNiFgMXlwau0T6dhMO2Q10E9bGa7fNj86Suz9D8YA+587P0vrwnKh34+iBWYGAUFo+3qSFBLGHz5IVhTjy6nctPDLJjTwuX7BJFN2DB9TjY0UzzoMXl64tBlmUb/3/2/jtOsvwu78Xf3xPqVK7qqs45TE47s7OZRVqhsEgEkbEtDBhfZGNs5Gts8LUNNrYvxuAEvmCbJBEMRhh+CLAkVkhahY3aMDt5pqdz7uqunE/4/v441RW6qmdmV6sFNPW8XrvTfeqkOqe6zvP9fJ/P8zA5HedaE3n2Rb1ojw5zs1b9Xk8VePCd0yx/YhbbPPgaqbpK/OgAN2bdWnAxUyUkBbbt4PVqeDzuf5qu4DiSasWiXDYplUyK0uStMLtTFMED58eZme7F59Xx+z1kMiWuXN3g2o0tZl92B2mqqnDi2AD3nxvj8cemiUZ8dWMWWfNZFO6vZHIlLs5tcXl+hwuzG6zVKv3ygM9MeNCP7tPY2M6RuWHyyMwAS7IxGGwhowJm53eJhX30HY5yvWZv1wmhqGS10Lg/P3ftaX557nk0obJabN8u4rXZrB68v+dTt/j9tRd4IDrFVKC/9cVaKKIQrgRJ6F8dtnYScG7jcNJFF28mugS6iy7uMVRqdmmuLMLBsmxE1cSx3ZqYbNIk27aN1+fF5/UiFIFl2/gcwYnROFu5IruZEsurKdQI9GoG/n3d/Y7jIITSRohDaCy3LkIVCvePuM1suUrlzm9kHxesdpBS7EFzBKd6e+vaa/dB61Ygy7aNH4NDp0cQAizLxhFKo8mqFtLSNxBmezlJGJdg+wIeYoNhLClIZKokTva16LLPve0wO4kcAUuy26uxtOJWrh9+YpKLL7q2c6fPDLOTKVI0TdYTLgm7sLjNw2+b4tmtLSynob12buVZzJc5+sAEmDaZ3QJej8rclUYVOzQYpHwqxmqiUfE9OdLLrWfXmbx/jJUvLXewGHEx+eA412cTHDncx625HRxH0hP1sZ3IUyyaFIt349F9e4TDXs6cGkHTFDRNRdfdf1VVQToSy3ZqMyAOlu3w4ouLFIpVImEvT777BE++5zi98WAL2ZNS8h3f5obiLK8kKZctxsd6CARqThq3IYZSShRV8OiZUR45NQLcx066xNWVJC/MrbOWzaPt04JvZQs4qiQcMEg6Fa7M7XB0KMauDgmzQutozD12MlviZDVCsH+Ql7ZaExGXq0mOz/jw7PNJ36rk2aocLL3RxcH2eXv45cXP8qs8zTcMnuX7xr+WiO5veV3TNEzT/dtX7ybOvosuuqijS6C76OIeQyjccC6QUpKjgN/nRdMbXweO405xB4OBukbSDSYRyLzJ6mcWABjtD9Iz5cfj11GTVa6/tHZX5ePIeBTGW5fZOLyydvvGsWY0V6AlkDzAJxlAy0pWb3VOfps+3sehB0f4mx94J7pHJ5PJ8cPf9mtUy61T+P54iK3dxjF8Po3kZpbekShW2WY0FmJ5p9FgaEvJ+m6eUNnEEQ22/6WlTY4c7mVpdodCsUIk7mftZqsk48Z6EtVwSfx0f4TSSzuUEBRNiytzjXWPjw0yGRzHr6vYtsN6v8bmdrr++rnRfuY+71an5xeSHH5ogoXnFzvWVndmE5yY6mX22QWOPjbFtdkEAd+dSdrrwehIlB/70Xff9fr/9Zee5tTJYR5/bAZdP9jWDUDTVKan+jqucyfs6YMBBuJBBntDvP3MKLbt8PJCgo+9NEsuW2FxPQkC0qUKJ0d7SS67A71MoYLveoVTpyJU7GYC3foZfWVtixN9vXi1xt9aslokWS1yPNyHgmiTdhwETdwd4XWQ/Mnmq3w2cZVfu/8HiXn2O5d8dVVs7b9E5mIrKyt87/d+L1tbWwgh+OAHP8iHPvQhkskk3/3d383i4iKTk5N89KMfpaenByklH/rQh/j4xz+O3+/nIx/5CPfffz8Av/Ebv8G//bf/FoB/8S/+Bd/3fd8HwMsvv8z3f//3UyqVeN/73sfP//zPI4Q48BhdvHn4y/NJ66KLLt4SSCnrBNlxZK2xbu93p+bK4f6757rRWN76cM9u51l6YQVtPsfNz84RRHJoLMLxI32MDIfp4ITnnkMHktApPvu276Np/UjIS6l6sBOIs980uhlCogiBaVqUyxUEYFbbq9lty/Zdi5jWWo/YG0fkChUmesP19acGo2ytZjh8apD5TI7rqztMjrY+2NK5Mr1BH6eHe8k+u0WlYBLvd63VBgYbFmvXrm+iBT28dmubyws77L6wyX3RGGN9Ec719zL/+eUWejQ7v8vMo5MdKVomkWf2xUVmHhjn5rwbVBLwvrkE+m6bA/fwD/7eE7zj7UcOJM9vBlRFRdd0dL2WuonrNqOqCobhoSfooaraXC3vogxqZCqulvlGolV/XCxW2XwxwZDpqcdmCyk4PTnQst71xC7JTJkj2gBh4a0vv5bdJqDcnbe1KgRJK/263qcQgqgecP/WcbXjZtV0LSu/qhoJ//JA0zT+43/8j1y9epXnn3+eX/zFX+Tq1av8zM/8DO985zuZnZ3lne98Jz/zMz8DwCc+8QlmZ2eZnZ3ll3/5l/mhH/ohAJLJJD/1Uz/FCy+8wIsvvshP/dRPkUq5Eq0f+qEf4ld+5Vfq233yk58EOPAYXbx56Fagu+jiHkO1UsV2HBQhsB0H07RQVXeZbdl1cahl21QrJkJxGwpVVcWsmlgdpBJ2TfZRzFWYv7LJyIlBdJ/GxKE4jhR4PO5XjaugkGg+jbMRHUTNaEsIlDQ85Ol1o8SFcCNW9tw3avHFkloDlpQsOY00univj4HeCI4jW/2ra5rXYFHl7GBv4xxoDCS80oO5XuTZpSuoqkKlUmXqSH99B44Ex5ZEol7sCZfoSkeia25a496gwry5A2GXiPi8GiKqEQob5LIVbi7ucnK6F1UK5i5uc+S+IV5bbFSSU+UKA71BtnbcKfvxkShhr8H8p5fqBDgQ8nL4xADXbyU4dXqYa5fWAbh2bZMjR/u5eWPb9Uh+dR3do6KfGCLaEyCdam2evDG3y4nHppl9ttW9QQJHH5vmymxjFsApVjlzbph87fMCrjvE3jnV/5WQ2y2xunJ7yzzzNhrsvwgIIdA0rf4g3JMtQW2GQ9pomsrFpS0mYiF6Qj7mkilyVbOuoQZaVBu5YoX+o1FK6yVs2+bq9V2G4gH0ppmZ+d0M7AJoPBY9ROb5FOFJL86Ih4XoFruiveG1GScj/ayYi6/rvd4XGUcAtm1TrVRxbBtV0zr6vf9VhUT8pdJADw0NMTTkBhWFQiGOHz/O2toaH/vYx3j66acB+L7v+z6eeOIJ/v2///d87GMf43u/93sRQvDII4+QTqfZ2Njg6aef5t3vfjexWAyAd7/73Xzyk5/kiSeeIJvN8sgjjwDwvd/7vfzRH/0R733vew88RhdvHroEuosu7jEY3kaz21512fAaaFqrhMPVPxstEdy2bbe4Zexhf0x3NWywuu7qcEM2lPLVlteHRqPoepDV3RyFA1L27oieRnVU7zV4eftg147HZJy51zYPfF0fz/Pnf3YFcJvpLL8PgNGpOMtbrjb5hF9n/UrDSWTs5AjxyT40j3s9Nl9Z49RfO0MlrLFSzvPc8jr9Q360osnIYIQNpUJvVePQfYMt5Dng96BPeUlZVcJlg3yhSkzRmP3MUkv12NEF12/ugBBcvrHJqZNDXKvpn+fmd5iYjLG06FZFq1WbxG6OrGNy7rFpVucSpHbyeHQNw9DYSZU4/TUz7K6k2N7IYDsOM49MceVmgzwHggaGR2P+2WXM++LsZO7QJigl0yd7Cdkqcze3O84odBp8/WXHXnPqajLHajJHPOgjGvGylW+QXFVV2PsUCyFYTGQIhjwYIR0BJHYLxCcaTjU+TSPk8bgpoBU3uTL8kA8cmEoOsBtvt6ZrhlBefxz3fZEJoNYDoYDmaY8D7+Irh8XFRV599VUefvhhtra26sR6cHCQrS33+2BtbY2xsbH6NqOjo6ytrd12+ejoaNty4MBjdPHmoUugu+jiHkNrA1ZDidC8XIiGyKJ1ubgrh4w7Q7J42SW040NheuIBTAU20gVS+fIdtnWxp4GWwFrpDj7Hd/QHbn6PTcdoIoHVfeclgJ2tPPlcBd2jYlZtsp9fYOWJPopVC0NTGfAH8A14ueSkKecdrICP3iYeGo/6KY8q3CykAThyLMqJZT+zL7TbmbVIVITgyq0tjhzp59bNbWxbUrUc+gaD7GwXOHp6iKs3tjh8pJ8Xbq1yn+EjvZLChDrR26sVByM+Dp+f4dpaQ5IQiwdQNZWd3SLlbIXezQqZkIJ5u3RCIeqe3vHJEDPhEJcvtHpVv14Jx1uNTh/t/W4vu/kS3orGcDxISTHZyZfqsghBIxU+X65SUiQnp/u5Mr+Nk7fAcGdVTgRjrH9iAxVYxP3s2gMO7EhyaRM77A7Kejw+ypSoNtnYjfoiLJU3X/ff4VbZvTd7NoudnGa+GuC8hcrURCLBAw88UP/9gx/8IB/84Afb1svn83z7t387/+W//BfC4daUy73ekq8k3opj3IvoEuguuriHIYRbPev45VoLHWnfpsO6tUXBmJ/hU8Ospu/e1GxrI8vWRqP5bigeoHcghNQFiVyJrVShY2PinnRiYCjEXO72BPqOkcXNryuC4+dG68dcTbj7DgUNTjw8tZfrQr7gVtXLRZPJI4N4a5HlA0aEz1d3Oe2NMv+0K7MYO9/LbD6DKR1Kl3cYOBJB92qsRSukyo1qYq+lsXBhvz+JC6+n9etaIphfSzE21uM2URYrqKrC0ftGuHJtgxMnazIRIQ5MeQxGvAR6/Lz6hVniw1H0WADD0CgUKySTRbyGe8yd2V1OPDbGa/lcx/3shz/g4cJmgiNnBlm9nqBa04/H4wdlqv/VQtm0iOoGvVEfvUE/mVKZoQcHKMxm0HUdardUCMGV+S0mh6NsrmV5IB7lpUi64z7X9Cwjso9g0FMfvCarJR6ID3Eh2xhQ9Rge7mDp3REf23iZ98XP0OcJoaoKtvOXezDzVwF9fX289NJLt13HNE2+/du/nQ984AN827d9GwADAwNsbGwwNDTExsYG/f2uZGxkZISVlZX6tqurq4yMjDAyMlKXY+wtf+KJJxgZGWF1dbVt/dsdo4s3D93OgS66uMehKJ0JtDhgeSfsqWLD/SGuLO+SvtN0/22Q3C1w8+oms69tkJ5PEzcVjvdGODPWy2hvqP6ltcd5e4b8B+6rjtdRbFMVhSs3trlyfYsrTb7P6/M7XH15mWuvLHP15WVkk9Zy8WaC6xfXuXFpnZ1n3aqr9DW+Xvf0r1JCKVvBs1Zku9ckZTbs+oIenZU/azw896M52nkPpuWwky+zkS6Qy1dIZ0ok00WOn2qQZ/e47RdA0xV0v4etNbcyubueRk0XCfo8ZLPueZUrFsGw2+i2/OwK9w3E7nD1oDfip1w2MS2HK+s7hKcijI/H+Df/6pv4d//m/Xfc/iuFg6LoW3HwQG0/Lq0kSJbKZBMl4pqX2a0UhUGdYG9jkLA3cFlcTzM22YOjgi6Utn1Ghr1sVnM4tkTxtR5nPpdmxOdhOujheMTgwf4Nzkb3O2ncGaa0+e21ZzAMD4qioKlay3/7ZVhdfPmQUvK3//bf5vjx4/yjf/SP6su/+Zu/md/4jd8AXHeN97///fXlv/mbv4mUkueff55IJMLQ0BBPPvkkTz31FKlUilQqxVNPPcWTTz7J0NAQ4XCY559/Hiklv/mbv9myr07H6OLNQ7cC3UUX9xj2HDfq3YJNDhx7sEwL27KpVquoSuOBb1t2x2rm3rK161toQN/DE6xtHJw+d8eKcBPy+Qr5mw0LuqChMTAU4opTBCGocudK2uuZrD5ozODZZ+nmNE2rS0DzKIBAEeDVVXKOic+rIwToCMJeD35NxR8ysPMmI0oP2zSqz5PeIBn74GtWypXpifrrjii5mq48X2zoy30+HU9I5+Lidssb6TQQskwHx269MuntPJlEnsnJXsK9QYj40BVBPl2kUqiSuZjAGPVQ6SDFiAYNxvujXJvfxrId/trX30d/LICU4FFVTp8celOnkaWUOMj6v46UdQeNveV2rVHUqboVV8eRNUmGqPuAO02phJZlY0vHjW+vLVvaPbjqvrSbYSwWZnHVFcRkihXS2TwjgQBrhUJLv4DpOFyrZDgz1NcW/x6/P0DFkVyf3+XwuVZHlmS1xLsmV5Gq+zeQcmAicB8X0q//mn02eY0rL6+hCoXHY0f5wal3NM7PsqhU78J//S8xpAT7L1ET4TPPPMNv/dZvcfr0ac6ePQvAT//0T/NP/+k/5bu+67v4tV/7NSYmJvjoRz8KwPve9z4+/vGPc+jQIfx+Px/+8IcBiMVi/MRP/AQPPvggAD/5kz9Zbyj8pV/6pbqN3Xvf+17e+973Ahx4jC7ePHQJdBdd3GPI5ws4tuOSGSEwq1WqVbPuKCFrDYS27aCUBYqiULEsMuUKlYpJwTKJjYZRFMWNrlYVNL/GyEzcrWYrAiPkweuJARJdita4YCHQDYXgQKjFAcBxJCKmU/ALpICgo1DOm9hSEnJUKvlqTbPtkqJ//te/lkS6yEAsgBZQeW1jm7VMgcemhukP+VEVBU1VURUVWTbh/Q5erxdVqekBaymM4XCEj3/kWbb7w9z32DTv+u5z/OyP/0n9WLImZQlFfXh9OlJKAj6NrRsbaDU7wN7vvo8ru1nOhXqYvb5N8OUSGbWMz+chn68Q2Xbwv7yLA+yJTcKytaIcyonbBl5vr6b4Xx/7ETKZFIVihb/1Q/+rbZ2R4QhCU5gYiLC01djbQcRVdHBgkBLWFnZQQx5mrzR00cdODLJTKXOoJ8pKoUCxXOHwSJxEsoCiCNK5EpdmG42aj5+b5OjknX2Zf/DVX6FoV9vIbzMptpvJce3nux0UqUIhcm2c5eXX33h3N1hJZjk908f16wkEIIo25Vd3eeBYP4rdOoiRwJXFBDO9USJxH5ndEkKBxYE0oxt9bDoJikoHEitaB61RPcfPHV3FQUFKwVY1wn9eDLVvtw8S2Kq4cqkvpee5LznOucgEmqJ+lblB/+XA448/fmCx4NOf/nTbMiEEv/iLv9hx/R/4gR/gB37gB9qWP/DAA1y+fLlteTwe73iMLt48dAl0F13cY/B6vbU4bld4IQDdU5vCla5Hs5RQLBQJBP1omsbVhTV+5PefAuBBI8aqcEA6YAM2BAZDLF9rVInHbFhbdG3mxOl+CsVWF47hgRBkTcplk1ym0Zw38fZRXk24UdL3jQ1wed2t7J0ywqzM77bsI6DovOM9Z91Ko+3w4Mwouq7XK7TQcBmxahVTr8+Lqir16epyuYLX8OFYDumdApdfXOIH/tmTjM3EufJyayOfokUo196HoQqsSmtTH4Dqb3yl2rZkYjzGlWsbCK19ejycU3iM3vrvPgtmzo/Uf5cAjoPl3QuyUVhc26UnqKBqCmdODVOqWph77xWJz6tx+eIGhqFxZDrOVjpPJl85kG42E+tj97nd/JZtk9otsLXPHUVRBZblMHd1i77BEMNDca4vdA6ngbufZUhUsxTt6p1XfIOwpUPoTAGW9z7tbz4urSc4d3SAKze23FRFCfPXtjmiD9XX0bXGDMbcThr/oM7R0V6cMZOXqhuMOO5nwRadrlsrgS45OcLKF+q/R3zHOOR7EFNKMpYkbd35fS4UE/zza7/Poz0z/Ktj33ZXAUjNUITSuJxNCZ+vZ3bpK4G/TDZ2XXx1o0ugu+jiHoPelDgopUSpVl0vXK2Z/Dk1j2a3WtvqFdv+gHJep3xSCMF60SXOp8+PsrueRTpurPLd4qlPXeOB+ydQVbVGiDWklKiq2ma1V7RLmJaJZZkUClVUVUHT9DrR3vt3dzPL9mqGt733OEsLSfechFsRF03XJxj1oemNNy1q2xec1jCXvYHDymaGQNigkG1UF+2SzY0ll4CGAgbFRLHtynoNjZzeICTX5rZ47L4hfIbOv/mJb+A/fuQLfOq52frrx4bcad1KxWLx2jbDQxEKavVA2tg0KcC//pUPIKWkUCiiqiqf+fQNfv3Dz2PVnTcae0ls5oj33l6Hq911OMdXnvAsVROcf+wQLz97d02QbwQXt7aZHokiSo337XEEJ/whlmX7IKZYNXm1muIBI05c82HV/Kc7D3ZaCbR/X+CKwnX+yeR1AHbMJ/jnt9zBUL8RIqZ7uZ7vPND5zqGHOBoaQuL6rrd4W98Buu5qp5th2zblv+IykC66uFt0CXQXXdzj6NQsKAR1Ocbd4NrSDjNHelm7uXNX6zc/pC81xVhP39XWLi5e2mhzCTlIqqCoCqpU8Xq9eL1eV+tqWZjVKiIAjt0gKL/6/36C3d0Cu/tcL5Smimw+VSC72/ABHk27pGE+meH4/cNopiSzU6xX41RDZez8CFc/2/D3bT734f4wc4k7N15eubXJ4+dGMC0TvRPT2bdsfSPDiRMDyK07E0fbtkG6gwld13nPkycZGAzx0d97hXKmQjlZ5GhfxB1MKYKdK9vcf2aQV5bb/bWDPg+xaKO5s1K1+D+fvcbsQoIf/7tf17KuLt6a5rWV8DJT909QXZKs3UbX/EZhO5Jdp4IjtLqtoXQkK5e3iPYH0Ec6J2WqyyrqnIb/lIpXVzEdu0N7fyuB9t1moNlcAd6u5JgJCh4x/CAF17IOGashZXlb7xGOhtxZD2fPXuYuK8h7g+uWZX/BVmlukEq3GbKLtwZdAt1FF/c49jS+rctcIiUdCSq0VgnbH7BSQtnXVPW9w3NUSjh8qA/RtKLjOPhUjdO1xECtiW/4B31MRIcaRxe11DjLQtM6xzzvEQlRSzNs/l3XNXRdq005g4z5GP/aQ3iQZBN5cqkiDLT6tSYyJeJhL4VsB5/q2r4rtsOFrVq1T4FDfTE8doDNYoW4ZTE90UOlamGaDlrT4MTT5IAQChpMjMfq1+S11cag5NK1VT73qQCmaWIYHnQHTk3310/BDxydiiME+AIGwbDXfe+qYPqhib2TbTquSmwwjJTw2U9eBgS2tJF+nb0m0wceGud//coLHa9x+nMLnH1iigtLDRI93BfGLln4PHr9PvyXD3+Bzz53C11T+JGy21y5h0dih/mz7Ysd9/9mouhUGR0t8ULG5Gy4jyu3kZ+8USQLJWL9PXh9ekv0e2a7QPqpAifeP8bV3dYIcMdxMMsO11/aJhz0MGAF2PEVKNiNAZvcp4H2iYMbZytVD0MihsfrpkaWbZ2icK+vV50hU/u78igaNwqXiGoqvZ44ilDwNMlMWiQdNZ9r6Wq83Jc7uXYI4fYcIFv+5rro4qsRXQLdRRf3OBSlPRxF1KKzO/ovH7Cf6utImRMCbs63E5iTSj8bF1zv5EOPNBK2tgyTa5VW4oHSueLV/NDea15Uag/29vNwyXW5YjG30NBYe/pCzPh0/D1+Co7DWrJAperg8ekdCfRBNS8bSa7gVqdL2TI7rzY8nqMzDbeFXLpRFRwcinD5uktIvYZWG8C4WN5M81v//YuUatKQow+MMbvYINiHJ2KsPLMAwDd/4EG+54efwDQt/tsvfIYbK+3hLP1Bg91adXr2Z92Go56hEGta4xpG/EbbdnsQAOt5zk0N8erCBkfG46wspahW7XoIzR/+2SU++9wtAD70t95WJ89LmQwbhTxjygiCi6/LKeWN4mZ5leMj0zy/sslDJwZZnku/8STMA3BrO8WDD/eTe2oZVVcwvDqVshv97XwpRfy0j3yxwv0yymtqFlVpPIYL+Srzn9zFh8LkTC/2Ocn16i5COjRLe0t2mFfK34ItVWypoko47v8sQU+C60tjBLbiqCeeAaB5XmMokGQmOMTlTJ6sVeaziU2ez/wWRZngB4a+h9OhkwhFvWMlee9vTNaSmGRN/qEIBa/hfl5My6JqfuW07QfB7rZDdvEWoUugu+jiHodtOcgOeoDm1MHm56k44AHVHGzmDxmM1CqhpiWoOEpLlTtSdli/w3l17KVqW6l9kdtUaIOgTpol7YlytZUBt+GvGbYtWZ3fBWqNkIpgYiRC7NQw0aECum0hpSSXLLbs53bYLlfoHQyT3My2LPfoKmtraQCOHx3k2uxtIneFYGgixvw1l2A7ZYt6skv7ynX9+kGEaGgyRk9/GMuyMU2batXGY6jQFO6SKZQJ1SQJnVAqVln77Dz3PTFFKlOsh6Z8+9/7DeJRP6mMu6+psRhPPDwDwJ8tzPNjT3+mTpqHIqMcG9e4Xl08+L2/STBDCby6jxfLm0TGPZy0+7jSYTD35eBLO9vcf/8Apg2emQixosPGQpLMep5I0eLMYyNcemaF8d4A+qH27QWC7bk8YgHu/+ZBJFdaXr+eD/Lx9caw7bBvgJ+/Oc53Hi2hWJLVRJWJ/TsFSjJFSaY4ETnN87tlruTcz9r56Ak+svE7/H31B5kOTndMJ205v71ZHcepNSRLEEpj4M1boWzvoou/WHQJdBdd3ONwdc4dglSafh4IBfhr509QNS3EtskC7SSvOfb6RlM1NxA0KORbG4uMJk/l8aEI0YAPx3HwNDWeOdkqD0V6kdLBU9Lpa3KskNQkJgfwVkc6IAVqTSva3gjZ2M/+cwfYL8uVjmRzJU1oKMLViqsVjp8YZSrowWfZlIIeyLdrXLdSeYanegh6PSzP77Iw7GPmeC/pZ5bq64wNRljKJAiHvCyutlbZpZScGO/FdiQ72SK72RKeiIGqKRw55foq339owLV5k+CVNqEj7nVKL+/w+7/w564rSa7IyeODtVYxWb+36ytpdncKLceM9Qa4b6pVXpNczbC9j/g3rqG75vKzy8w8Os7aZsM+b7cpkfLrHj2EVnMj+fj8XEvFOVWosi3THff/ZmPXzPHokV4+ezVHxq7yPG41+taNXUy7c2LjG8FNo8SDRNheyrOrCiYfGMLaKrG9ksbZrnDyxBDXrm2STBa5/+Qgt5IZsqXWvxPpwPIfpTjybWfRjlyiIt3BiO3s0x4jKJgWH7msc3+0n63sDie3HiDf9woore8pps6QKrd+3jfKJUI+k19e+wjviX8dD0ceIqDfITVSSsCpjd1qVpVv5EK9iXAHyn/RZ9HFvYIuge6ii3scB+kUmxePxyL8o3c9QrFY4tNfvMXnmO2w/t1PwksJJ6b7ERKuX9tkDdeu7viRRtzs3LXt+s+Th3pZWNxt24/4kGg7rhACVdXqD/S9aeZO57lXYNsfDuMSzXbYpWp9w91Cld1anPcJn8HYfJH4sT5kj4cd02Q9ladYNlkqpwHojwfo9YXRdBVdb+gyAobrqDA21sOV660NeZYtmb3uXgdFEZw+2k+uaKHrKlcvb7Sd38xwiMVn2u/NoUdnmF1od5num4zh8agt71XTVG68stay3vRML9vtvYIuatfz8GScG5+a5cTbZ7i62BhgBf0e/vEPPsGZYw1Lt7lUqmUXZUvSL/pI1l2yv7K4WV1kOj7G/K5L8F8sbzJ9JIJYl2xnCnfY+u6Qr1RZ7jURQmDbkrnVFB5d5diDYwhHcunaBuOjPUhHcvXSBh6Pyv3HBphLpskUG0RaIPj0H6qc/qb3MHZ4kbJ+mer+z2uHSvFnXizzTY8+gGd0npVyQ+aTtOaJG0d5xNNP1RYYqkVK3kQCBadIVI/iVQ+W7exB1lIdG8eWtWq00q1Cd3FPoEugu+jiHoNlWUhH1oMdHNvBsizADShRFIF03NAKy6ppWaVEKALb7pxECDXruw7oNAssheTG1XZGJp0DyPwBx6xWqji2XfeCVlSBR/egaEpdllGtVilXqti19cB1GFEVhUqlis8baK9Ai85a74POz3EkxVSZ4nONKO6RqJf4WAgxGCCtSFZTBbbTLjmbUBuSllLe1VTn8+32X83X1HEk+UyZzUKJ8AHuKJLO1+mg8zYdSWWffGUr0U5imwl/2znu3RsBOJLVL8wz9fAYC+tuNT0YMHjkXKugwOlwdbM5Be7M294UOEj6hkrM77qphADzlQyhPp1j4V6ur9ydm8ydMLeT5oFHhph9zhUsVU2biysJzh0bBmB5NQUSjh7uJ5UscPXiOrqucv7YIPPpNKmC+9lQNcEFJ8OnLhrMRB7kSK8XmlIsm/VTzQNEW1bwq/scR4Rk13Et71CgsO9W9Ou9qIr2xhw1pGxhzbquH7zuVwRdF44u3jp0CXQXXdxjsC0b23FqkgaBIx1s2229sR0HVVHciGPbXS4dh431DE//+Q0s06KkwfHjg6hlC+ReBVi6yYShgMs8hSsVkLbj2pTVUv32jGYdAX1TMcp18iVwbAe/qnLqUB9Sghr3ktddOUbIUTkerLErn4rtuAEpv/nLz6Cqan0g8LZ3HebEqXEEAsexkYqCdCR/9L9fo1q1EELBth1s20HgDgi++wOPtV2jZvIQ7Q3SPxFHIvEaKqcNvaWy5jgOdNAHl9JlMpkS+S+4pLnXqxI/NYQ2EiJ+yktRlQgBmUyJQzN93FroTNo0TcGyHOKxAEmzis+rMzAaRfFqLZU+Cfilw5Hzk40FtX+NWJAj0SB77GbPrcxTrTKguXZzTk/QDdZRQJZb5SiG52ACHY362N7M1SvRjuUglzPoXhXTsutR8M3XtJMePVey8Pk8lJy3pvFsuZzg8cMzfHG2UZnPOSZf0rZ47PAQl2a3b7P13eOV7W2OH46xOtuQ55iK5PipIa5d3gABN25tIwQcO9rP3PwOVy6toWsK9x8fYjWfI3I+zBcqbhPoXMZhLlPkSM8I3mCFpfIOoklz1ExapZRUnTKngmOAxJEqFUfBkTZL5TWcDgOuW6UFRnyjdybQQiD2pYw2b6MoSrcE3cVXNboEuosu7jF4jNYQBuk4eAxPPZ0P3AdvuVzB49FRVZV8rsqf/OFrAEw9NMJrOyniCYtqseFgEAga5MvtjgYRw0Mu1+5ccez+ES6ttZLG+wZ6uHXBlQ8c/poJXtt0ScfxSITlG7V1ozpmLdxjcX25pVR8+Fg/J8+4T21N02uNkII//djFtkbBPbz3G8/i87VWypq5gz9ocL1WLZ+ZiVPcdSu0xUKVXM2R49DJITrB6zfcqrjPw85Kkq2XVvHfMPgPz/44jiN56eIK/+9/+BTJ1MEx03qNQGcyJQ4f6WN2NkFkZoiXXlpsW/fwUIili2tty2e+1s9sB4/uPix2NzKuzGDQtc6Lxfyk9zXVHX90hvMPT9YGVe5MhWM72I6DbTk8/DWTFFNF+gZDSAl2yeT+o6Nc3dzF79MoFMp4dA1VU+tJfQARr8FwKMjN3SSzW0V6cr1MHSqzvt9x5U3ACSYRjjuYE1K4KZqWipAS2doly7PWBo8eG+Ly9S+fRDtSshuw6o13AFLAhY0E/VNhRoMBVhZT5HJlrt3c5tBUnLn5XUzL4eqlNRRFkH1CgX3KkpupLKQk54cm8QuFPb8NRTT/HUPRyVF0rgIQ1w6xUHY/ywPGAAWrRNZunXH4g+2P0ePp4UzoVMu+9kMIxXUGkXszO+6AfI9EK51s7rro4qsIXQLdRRf3GJqrRHvSh7YKodPQDe+R0Lb97Pv9oLjoA43v7iSZ7iA78Ps9ZG5jl1epVU6FECjcnS5bCAVtv0RBCA6fG0NVFFAEbNVIhu2wPe8S0YmjA4yNxzAty21a7ADNUElrflIVCwX3mqm1Rkld11DuouHJ69UolS0s2+HaNVdbvFvtXKXVAwbf+Lcea72XtsPKzu11vS3XqcM1s/Jlfuq//fUDt7Vtm+c/d53//nOfoVxy70Hqz28AoE05fPBbf7nuzjH4A9PsGHk8PhiNBrmcSnCor4fdTBnbsdk1Ozcrfrl45WOdBynqSRVrfyVWwHPVDR4+McTVq18+id7MFjh3po/519x97VXgt7NFtrNFNK9gaqyPnXyJoFfj+GQcIQQ3l3aZOtSHuasxmlb54uD+PgDByxu7qCg8MjjM5d0dLLsxe9CcLrr3vvawVd3Cp/gIKD4KTuu1+dXVj3B/+CzfPfTtBNSDmwn3Gghd4qy0jDz/okJVnG7Zu4u3CF0C3UUX9zhuZ3O2h1bSfcBKr5Mnd1xeWxgMeZmrtifzWZbN8YkYC7tZytV214tcrtw2GACYjvlxbFmL+G4c2XEkGoJCrsrUTG/deUIIyY3L7U4jzXtdutF4/eRjk0wd7m8bVXhCXlILbjVVKAIcV0u+dwrbu3dumpvo8TMU9JKqWGzu5JES5hZ3iEZ9ZNOtxMcIe/nAj76nTtLBvWa/+Z+eItvvb7su2a32xkKpKIx87XTrPfd72tbbgxACTdN44GsO0fPbL1MtVOve0gArC/uCQzbLVMdcwrqWz9Hn9XMrk6LX6+P8cIQrdntz5JcL5TakShUC64AP6QvlDc6fGGDu2i72lxkKUokIQkGDXL7S0I3XYDmS2a0Ucb/B9Vc36tp3TVNYuLAOFyB+XxQGO+/bxiFTNZGOQEHWJTqWZdE8t7K/lyCm97BS7twd+kr2AvPFBd4ee5ygGuTRnodbXheAFEqLfARaB2P7ewu66OKrCV0C3UUX9xj2B424lnASIZyWgAR3ytmNeLad5qpvozK9b8cdj3cwOT/44dp/up+X06m25dWqza3ZBKOH4ixsptuih5/57Czf/B3n8PqM+ik5jsPqbALLPKBKrKgUClUW5hoSB6+3c/PTQWdcLpos3Gz3Eo6NxynWQk8URbhNmqqCxNVw+313brKq7BRY+qIbAX7s7BhO0IPeE8Dr9XDhleWWdW3btRVzLf727qFDKVti8Wo7Me3t7VBdFDC7007syxUTw3Nwc5miKKSSRZAwfriP5dnO3spmogJj7s8jvhD+skaCIjvlEl9cMjkzNsaKXOm47RvF7QaIdxIavFze4szxPtZuZl5XWNB+FE0L5TE/yp9XsWyHoVgQy5ZUTIty1aRq2oypBnN2Y1Bj1aRKA+8ZoDClQOXgAZeDpGCavLizzch4kB7bg20nWwi0orbOtBiK97bnnLYyfGz7/6Ci8kj0odZBFe73huO4n7m9mazGCm89eZYS7K6NXRdvEboEuosu7jHksu5DeC8opVKpUi6VWzSaILAsy22QE4JCrlENdmrVsf2SDfsgB40DHqS22U5G9oh6xWmtLjv7HD58e/Hd+3a9sZbhs09d4bG3H6pPJzu2c1sC5ThOW0XwIKp8kAPJQZW25mY5UXPOEIogn3MlFQHvnXWizcesFKps1mQcw5O9beuapk02k8OREtuy6zHntt2Z+LWcdy2QpaFnbcXaeorpyb4Dz1NRXH1xqWSyvJzi8Okhbl7aaNuT/UgYqjVy7UiuXUzwwMlBLjjblG0LTQ1xxDPBzaLrlT1jDGFisVx5fWEnp8wZVl+ziD1awZsO8gq5jutpKLiC6INxsZzg+JEYybkixTeYXJgvVlkI5/i684MoGYfUVr5OkAHGowHmr3Z+j0tTFVZvQ56h9V6u5fOsAbIQ5nhxEvyLtVfeGKkdNPpr/uGtd9OyLBzbRlFVBKDpDUohEPXPfBddfDWiS6C76OIeg+E1kI6DUPYqzE7dyULXtTqRKpXK+P0+FEUh0BNg8oERbNsh5Dc4jYKimYQiPpLJIiDxGCoVrZUQOo6DD4W4LVFVgVqzSLMdBzYznG7iLVI6eL2C2DeOkvcpjGlx17FDOpRsSd9oPxLoTYCZtzgUC6GFgm3ykhs3d3nyG8826TElY9NxzGrNEYLmY0p+/ec+iccjmI7569soCtghXysBkBAMeRg9OeTa/Dk1/a9lY3hU+oPt1WRvzI9S01drg0FsG/x+jZ/4B3/oLtNVJmyJY1MnKL6Ah2jUy8ZcAiklehMJbw6DWV/cYfi+MdZ3GsRwz27OMDwoXkG1WsWy7QMHEKGpPkRfBIBETSetKgr/9We+tX4sn1cnGPDg9R4s49i7lrL+M9y8keDw6WEWrm+1DJaaM0D2Bgc3ruxwfDJG+AhczC8CcD42Rd7K8NpL4DgaDzw4ys1qexz5fniExsTWJC89516Xzf8t4QDyDKAc5Fm4D9fKSaanI+hLSotP890iVSiBhOdiCQ7f8jA5HOXWck3eIiUhW5CSEs2n4O/3kl1qDFrj0mD1Dh7ZnZxNriSyXEkM8dj4UcYmX8Q0XfI/YJ3gDz+jkhuNMjBu4Gg5Cp71jpaTUS3C3x3/wY4NhUK4pLlZa/0XpX3eQ9fGrou3Cl0C3UUX9xiMJhcOKSWObWN4jZo+2IXjOFQqVTRNQ1UVFE3jRs1m7dh0L0uvunKAE/eNsFwjAcGQQdppr+RFfQbZbBnD0BCv3X5qfnLQxyu9FlRw/+uA00md1KLbaKYK0Vb99fs9aHqr1GB1fpdquV0zDaCoOwhHtngue/0eSh0Iw8zRPlbm2wNdIkEPiVoc9+SpYTw1zfB2rsJObdCg41bdo3E/6aamvumJWF1ecfSBcS4vJTltqOzWmhWjpxoOH/vJSTRotBBoj0cjHAnVHRC8Pi/VapVgLMD0mWGSG1nSTT7PuXyVnVqYyB57EkIwPtrT8nm4E6SUHavzsze2mTzchx72IHwerEKFSknjkAhjIQmbOjGPh2S1yuJimqGqn74TQRJmnpeT65zxjVGpZpEInn/O5u0PzXDJnjvwPKJaAN+lfi7dbCbMtyd0yusgfPOVDKNjQXrXfezkDnZO6YQTh/p51lmnhI3v/hArn99lZCDM2laWid4wyzd3UTyC2PcPsGLkmVocIPXFNKWdCv6iesen9e0aZp9dzvJrhwRVX5WVqgdR29nF1Qys6kCMd5weozz4Qst2XsXg74z+bUJKqC4Jsm0bhECpWU9Co1lx/+fz9YQrddHFXzV0h2pddHGPw3ac1/egO2DVN6Nh6M143h7sBtIZA8PRA0Ng2vZ90K6bC9VhP1c3C1zdLLT64R0kZVEbVXFH7UDmbkPw9H2v7ScwboS5SqFsM7eYIVWRRCZ6OXR+gkPnxvD7tTdNq2pZdsfPkQlIj8puqcLl7TSzn9qk/FQO66k8q3+yjZwtcy4cB0AtqnheCDNuuJZ6F0srnDveAxLujw7yzFM5jpuHAAioBnGGmfC5spIxvZfK0z0s3GxvPr0dbtdg2AmrZp7ysER9HfKE4xO9PCvX65+TF9RthmbCpFJFwgEd85BD6ocNcj/k50U2WK/keGZoi/IHXI9uc/3OZH2FNEeGo/j3O2/UUC3leXfok/zr/lcZMQttAwdFab93X9/7HiJ2D1vbbj+ClJLVjR12dtOAW/WuVKtUKlUsy6qHFTX3UryVkAgc+db918W9jW4Fuosu7nGoqvqGp12byeqdHpWVikXkoSnADd9Yp9UbWkoJfWGOP7fd+F3W8kDe18f1TPquzmm/ThPAG/IhPA3t6uSxQeZvbmPbDoHeEEaq2LKVx6tRKrRrXYUAY3+VDYm4i+ayg7jE0vwOMyeHWLiy8bq5rOY4GHtOHALyS7v8zcd+DoBv+MBD/I1/8I69k6wjnSqRrvlO9wV1QpbJwHgMze8hWzCpSkmpVMaj6yAEmqbelafv3v3aD0URFKoWqVtJjp0a4Pqiq/PVVEHY58F2JFs3kjw2GmEuUyGZL+P9pMKxrx/EY3l58XKKB2ODvLawBQhe+HyOBx85TDJU4no2hSev8LXRI7zyx1mq1devT1beQB1pwyzwyOQgV+bvrMsOenV2PPtsBAU8M57ibf4+MgsFcoM2aat92sVrKZSA1NMpHvsbwzyrHWypV5E21ws7HA3EmM20WwHu6eF7PWn+3pHf5O09h/nPr3wjX1x1/w7LFXfg8aD+EL3bo3jGbEaz42yLFKGgn91UlmQqS2I3zehwH9l8kfWNBB5doycaQgjB5tYumqYxPNSL3+c9UHvfRRdfDegS6C66uMfQaap9/xS8S4ZkbblsqSS1cKTmXxyHs2Ox2uJaKp3tsJJuEOVMLXjE69dJi3ZJhQUUd9qrbYFmensHrr9XAWu4UEjKJZNKU8iLbUuqFau+u0rpbomXqG/Xcsymk2qtujVZet2GHReqNjRLcVuF2p1+BKCcr7Q0WDqOxKz5LVuW7VbWa5r2A4+dLTN/eb3+e+9QhB/9m7/N4RODHDoxwNTRPqYO99cDeFxCrbbuUrpx4Z3eoVAVFlfTBFXB/CvrnD4/wqWFbcbjETYvNJxBSqaCPeY+ksolh/mPlTl2PNxEnhvIVxWu11xaqo7DZ3bXeOjwJJevvP4I7jfa57ZhFBnuCbKTK93WnaOvx8dF2h1lEPBsb4LTywGith/0dtIr8+5+hQPbv73OQ2+P8dpMgUoHqdS41+QPHnuGRCHM9zx9nnS1VYJjma2f8ZN9s/zqk/+Zp5ce5aefux+hmngdH494HiWhZfClDVJmkVgkxOpGgmKpwszEMFuJJKZpcX12if54lI3tXSqmRb5QIhT0UyiVmVtY4/DUqKu57rmbq/nmoesD3cVbhS6B7qKLewzpTB5VVdBVBSEE5XIFx3FQFKUW6S1AuB32lUoFRQgq5UZlTDpOncQ2N6jpqsqN5xbajuc71l+f7m709Uk03W1ibOFhjlNvhNt7QQhQJfg0DQl4dBWv1/3qUoRA7pOOCAV2E64VmKq678nr06hLmoVACIkv4BJCVVfxB/d04bX3YqhuWl0tD7v+k4BAyGg5nsRtOgxEvCDdimuwFjvu8aj1n4XlxqUbhkYwZLRY8OVzZQ6dHUHTVcIRLxgKvqGA+7KhEezxgwDdqxLs8bnHrB09EPc3SLsm6udXrVRZX3EbEU3TJf26R8VosuhzrPaBg2XZmFWTKxdWuPilJUpFE4+hceT0IA9/4DTHh+NE/F5UVUXVVDweN/FRIjtO2auqO4hRNRWBxc2X1zh2YoDFZIYTD02wcnWTQr5CJOLHG/LyUjFR+yzA1SvbnDo+0LK/B08O8KzaqoOWAl7qW+Kh6Uku3kVVuBmvV8Kxh6VKlhOH4uymqvRoBseUGIvzKQpls2UgsbiRZeiQlw3RnsZpCdDv85N9KQuPtx9D25eXk/xcksODMS772sl2wVbo8SwTN6p86VtfZTZ5jI+uPMhHrvsA0R6qUsMTE8/xtvHneDr9Dha3HiAQ81PwltE0jXhPBK+hs5PMoKkKQwNx8oViXa4z2B/DdhyKpQq7qQymaSGlpCcaQtUUypW7k0Z10cVfRXQJdBdd3GOQqkLRtPArHry6hq47eGtNhHv8Zy9dzus1UBSFc2cm+NP/+YOUSmU+/8U5fqkWq615NJRa9bZUNvH5PZSKrU99z0oaPdmqS/VUbfJnvMz091DeLrObqr2uK5Q97VPqHzpxhocfma4XU6WEcrnM+/+f322zzwurFj/03R9heCjMxkoaAC+SSskkFPWR0jWuLaXqbF7vC1K42FrR86qCX/mTv8sv/7cv8plPX68v79cUCoX2FECp6RQq7nkUXm40SgYm4pT2mvZ0BYlA0xQKHXyWPYd6+cf/+psJBA0+fvkWn/z4F9xjrhrkak2TgZ4gybyJz6fzq7/+ARTdzzf9w1+r7yPq0ykk3HVXrm/xo9/+6wAcOjfq/nt8kGsXGk4WvlC7s4YQgnztfk0e6WdxbpdqxeLyS6s8HSxiIpnsi3BqtI+jgz0cH+5hMBpyLQg7lKD3pKKVpibOhatbDIxGWMzlmDo+wLUvuX7W2y9voY6r9XsqAE8OHgj38Wpuh7N9fSz+8Tpnv2eIC1arr/X51DC7N7KcmOjl2sbOXcth1C+jYmk77kmm7ArP2RswBlNVP7vLlfrxFSEIqAY47QQa4EU1wX2mlwkRYkm2uoVU/O1vordiEA7qZO3Wwc+AYbGcm2EyfA0hJEfi1/gX8Wv86JlJPnrzND7t4Cq5IuAd0S/ymc3vQlUV8qUyAeF1nViEwOczKJUqrG0k2ElmGOyPo6oKaxsJNrZ36YlG6ItFiUZCrpuOYWBZ7vfHWwkJXW1yF28ZugS6iy7uMcTDQSzbplgxyZUqOKaJVAS6rtMoEEvsWsiDoiioilKv1HXSGIObvD0y3cutJjnAnTC3neLsaH+DQB8ApRYn3pANyJa0vWY01vnyHqRCiPZdvN5dvo71q1Wbj/3+K3zgBx7jsZnRetW7eSd6k02gogj8hopHV6l28NTuePAv02Ksz2OwXi2zmMiwmMjwp7XlA5EA9w3GmXp0FFmx6/7GQgiq0k3GM0275Yy2VjMEIwYJvwFTYYh5CZcrhPqDXNl0nV1ODse59prrJX36SC9bX9rGMSUbH07y8N8Y5QXVHQw8aI6w+Gc1SzgVDp3sY2MhQ77cOfK85ZJ8GZ+TG6ldzg8M8nJis365FzxFwod0pq0QZk7i9EiuO+nbnQD5UxrHntUZD3n4wqmGy8uikWMiqGLVpBzBkMH3nD3B3+n18+9mX+JiplFtv5o3ePLZx7j+5A2EaFR+fcoif/PoOnD70B6HCMODo0TDQQb7Yvi8BtFI0J2tGh0ABNs7KQb74sRjkdrsVdkl2F4Pk2MDrG7sIIQgHgsjHQeP5/bWh1108VcZXQLdRRf3IDRVJeRTkF4PuXwB23awZVNFS0os26ZcNRFCULFsdFXFuINgNJkrM3FsgJ31DKrqinplh8jt5vLgcirHQCwIjkS7i2a1PRzk+vGmOHk4ksXZBBqC0aFwfb8+j8rx+4ZbSNfrdjEBhqfjLfuQSFAEf/oHr/LkN56mtz/EI1MjPLewhiekMzLtemKHQwajY1E0TeHWbAK/r8ixqT5SWVc37rUlQ4f7EAj0sIeRqThSSjwelZGJGIahMToZr2vU/bqKEXLT6KRloygCj08nudBBswv0CL2t+RNgK1PgIpDcdivrYa+HkXAAjxQUUmV8Xh0z2/45yGcqlAq7jJ8Z5NU5N1J6NOdWLY8Ox1m4vFO/Sos3d5gejZFO5Mnkyiz9zg7vfO8UO31FFv/3LgKBN6hTOuphNplgZChIb1JjJ3Pw4EwCPlV1xfdvEC9vbXKmr5+b6V3KNW1yFpPXtORd63/nPHmmqmHWPpMgeNxDXnVPqCodht41xMofudX2//sfv5v7H5gA4MPjI/zg5/+UV3YaUdymFJQZwUerXaRUD6M6V25/EkKnJxLE6/VwZGas5SW/z/2MxHvcvwXHcdjY2iWTLWDoOv29PQQDfo4dGkdKiWVamPLgQe5XEl0f6C7eKnQJdBdd3GMoFFxCoQiBx/C4xLiDD3TOcQgG/G4EtZQUyhXS2UJLal8zbVQUiEX9pFZTFFONRsD+h0YJJ4qsNUc7N1VCk4USpR13T/19PqY+2OumbbjzsUgbnFiHNyIlZ4b79vUxSvy2ZL9T89hMH7bt4PFqxAPemgxEIhRBs2K1/9wA6VspMB3++Q/9Xtshg0f6WHj2VtvyE48fYvrkEPurvplCBzNrIVjdLrQvry37nQ8/x4/8+Hv4F+/9Wr7hl/4XqZjObNomHvFza9ZtpvMaGv/yx/8YgMG3jXCr7Mo2bgGccK3PvkbxsLbsEmF/j4+15RRryymmp+MsXHVJV2y6l50d9/MgfToSQcx/sP9z0Dn4teYmyWy5Srap+hvv9zIR8BEOGGQyJdY3svUqtW05bnPh/cNcXkqwupjm9LFeVm8mWwYmqqqgZ028OQtfX4ieiI+r/2eDwzN9bKsKUkoCD0a5nnQr0WvFPKdHevHpGis77ZphgJMTfaTXK/h6VUryjTtGXExsMxYKUXZsEuXXZ6O3B2tMh9fg0Uu9ZEdtbkaypPQKqQn3vN7/bffVyTOAR1V5sG+4hUADvJw5zePRBoG2xDk059U7n4Dw3XVyoKIoHJ4aIZuNEAj43QCmGmTN2k4/QHPdRRdfLeh+wrvo4h7DXtNfuVyhUjXRatpnx3GwTMulk7WAFcuyUBS3Ccyna1iGTtU0iUS9mJaD6diE+gLEDvVglCzmPr/YdrxU2SStOBz/mkm2X12nXKy2ECPXj9b93dRtXgstt+3jmwOnMGsWZXsNa6ZlMXttG2ufh/NUPIj7FhrHmJ1zNduBiJek0UpeJ2f6MMIeKtkqt8YE2b4gY4EAfGyN40f7yCVyWKaDbdnoZme3jmrVZv76VtvymQfG8Ya8IGBlMVnj17evVn/uz6/z5DeeZPpwPz//re/kY09ddY9hdi6TBkXnqfkW55SmQ97ODWT/uvvL+bp58Lb7fZFPDsfZEwWpiuDm1TVOPzSK4fMxPehD86iIVIVCwWJjM8vNV9Y5dnKAG5sp5q7vMDXaw2oiS9V07+/MUJTlF9fcRsySiX/GfXzNziWYGOsh4EBpW6J6BLYjGQwGSF3NUipUOXd8gEtr20TCsJty30PAq7OSzJIpVjgT6OUFY+vLUv2s5HKc7R94XQTaJ1RCqkHGrpAMuPd39YVtwqO95BR3AHLVTPDkfSN8x3fd37Z9uto+G7C//ipltZZxefC9c8QQwngEzdRwHOeuQnREzeJQ01pDi6QjkY5ErzWXdtHFVyu6BLqLLu4x+HxehBB4vQaFQpFKpYIQAtOyMKtmzRcaqlUToZTdxLFa9Ldj26iKIF2zo3MMlfUQrG+lOB0MdD5grfPv2m6e8JE446akkCpyuDeKpgg0R+FWwiW40pRM630uj9l7+ErJ4nYWKz2Pu1gBJKZlM3AyWiOKok6sdV2jLziI7tXom/GzZyYtpUQzNAyPSw4UoaCoCt6Al1j/GM52kd2ab22iVOL+Q3E0zZU0eHzubvwhg7NvO1Q7rXrHJWrAYPJIX5vEWEgHTXMXTk71uKRDV4kMhDtkrMj6sj/9k4vc98AYtpQ4FZto0MvR8Ri+QbBNB5C89rlFAPRKZ2LUbEsYjft54PFpV5OMw3jNqSOb6FAJRzJ1agiEwO/3MK4oVEomyUQeWTDhgNu8P+pZk4KrN13f4p6wKwEoqXBhPVlf5/7+GGtfWARFMD4Zw1u2OD/dz0vzWyysphgb7iGVK1EoVnn8kRm0U6P8zw+/gFdTMa9tc/TUIDeWdgnqGjdfXYOr8ODXDnMlXCS4DjtZdwbgyuVN3vutGs7h5whkTvOZp/wMRnq4uOSe3/XFHc7e18eFyutz8NiPy4ltYj4vyUrnhsH9OKP1c/XyNiFdxT/opSwg9B1xXog3/J5tx+F933euoxziH55+GK/H4g9uLZCvDe5+a7GfB8/2o+PuQ+cKtnIaxbmMLb2UxSBl/JSljUAhIGwU+TCG54cJeX1kshk8HtlCog8iwnvfFfuh1Bx+3nJ0A066eAvRJdBddHGPYc8fWVEU/H4/ju3Up1z90XDNjcMhk87h9/tq9lcuSSsVy2h6o+KpKs1NbQc19TUeaNmySVZKjp/sp5JxWEvkWlw0HEvh8ivtU+mXuNr5zXgPeJN7zmeR/S+0SyoCioZelZSCthubB6AqLF/dbFv3/FiMH/vZb4WmFr9yqcKHf/oTvPLU9bb144cH2EkU3Knxmn90pC9IJ7fiI0f7uXnDJT2eiJ9f+KUvAjB5pp/D4zH+5f/1RP1aZrIlPlgj0IsXNwic8FKwWyvUzQToyMkh3vtd5zG8Bj/50x/nei2WfbTjPRPcWkoDMDXdy9KCK4iJ9QZQb1PF3E9bmtMd9857f7BGQamt40g25nehFpN+8rEJVitVvvbBQzxyboJkOs2D900gpcTv1/mTX3+exHqG5EaG808e47VX1+rHN3crTGp+VraaddwS3+GbZKRJJvwK7/zOEX73VxrvxdBVZs3Ouu/XA0tKpkJRkpX2z04nKDWlU8W0ubqyw3TIi/cli8dKIaoli3LJpKcvQOT/CXRM2Pxk4kW2WOH0qMnVNYM//dovMeJ5sWUdiZeSVNi2w1Sdddj36XN9Py5ipBYJ+h7F63uIcqkf0yyhCIHhNdB1rSMhdhynxWa8VK5QrZoYuobjSArFIqFg2x9hF118VaBLoLvo4l5D03NQUQSG4UHTNRRVrcf72rb72t5/exsJRbRM1aeaKpiqphCKN8qTQria5LYCqRDsli3mrTLUm/Rdbzr7L6Bpv2BZvHtwhM+vrN7RpUJAzQ2k9ZrcqdimaQpWpbGPzjs/OCzGNB2EaPbRbrxWKla5Xx/iC/bB1VNNUymXK6ha69S8tydAeI+UZzrERTedR3KngBSgvz+IJhRURaAg6v/6MRjRelzHFCAvBCPn+1AQ+BSFwmKO4laJ0309GAGdguIQdAT7z3rkcJyKJnnswUm+870niMfjZLNRTLNKqVjhyudmSSyl6tfh6ievc/qJI1y6vokiwBcwKGVapTbHHu1FiNX6dUvLNb71/aP8/z6mAoKZiRjPOR1Ir+Q2N6wzbiSTeBW13lB4OySb3GckoHhVVm80FPw+v4415mOwJ1j7O2w6Tm6DP1i9yLA/wJazyeGRXkxabeMqzLBszmPz7B3OxKLifIJK4RMoooepvmcwTT+5XJ58Lo+maXg8Ooqq1Jt3peNgVk0cr43jgGlaXLw6RzZX4P7ThxHlCpeuzTMyNHzH6/BmQdINUunirUOXQHfRxT2GPfeHPQmCbTuoGjSzBSE6T8O6+sZGZTGXL7P3zHakQnJ/E5Iq8Gt32RUvBPm8yaP9g6zJAsul3J23eZNwuZLk/nAvr+T2yMtBsojOy9/13ecp5U1Gpnt593c9gO5xv1r/2d/5nwBoulo3epDAseODLQMRIQS6oXL8vpH6+s0olqr8yccuu+mQCKpVi+NnhhGAZTlcf26Jx942ybO5pqjniIfDj08hpWQ7WeDP/+A1VFUl4tE4fdgt0SeX0vVYb1FzTWmBhJPHB1xi4rjU5LVCmUoHiazHF2WjFmCzH8MRd2C1tZSGJThyapDLpRRn+t3uUG/UYPhYPyuazcVSGQp5Ln/xBuGgwfe97yEMwyC1meK3//X/4fLTNxmZHmRtxT2WbqjseCTTD42iS4HtEZQ+vcSZd05zcXWHsZMxnpVJnnl6lO9/okDecSUk/r4dTh8/w6Xr26T9VdjneDflixBVvbyab9e23w55s+pa2+20E3KB5NhAFIBErkzfgJfeAa8bB6+AksyT2y7QNxgiOeVhpVzm4eEQPX4dROPmVGyTH7v8e0z4Y8yVFhACUnKH/7Qe4+cmxzBqLhyWNLBp9xy/HRyZYnP39+iNfID+/n5s26ZUKpHP5zC8nn3aekmxWAIJhVKZUrmCYXjYTWWJ90SoVF5/tHoXXfxVQZdAd9HFPYgGebZxpONKOvZpWKUj2yzhpJQ4hsLY8V78uo4iIblWqyG+Die3g2zf1nfyrO/kOXyqj2XeOgK9YZc45gkzaPjYrHSoxN4GUkr6RqP8o//8XW0Djj1Zi9ZU+RWO5Mal1hCQ0bEe/EEPlzfdyurpI4Mtr1dNm9/6yAv1371ejXLFrXAOj7hT5LOfX+Sht0/wYta9H4WQypVV9xrK6xZzzy0BMHV2lLkFl0TGR6KN86r5Xre8Aym59qWllnMZvG+cJbVdCqPc5v7vvy6OdAdhedui/5sPcWM3Q2Q4wOb8dst6v/HJi4QDXr7rXQ/wZ7/6PM/80QUAKleWmb5/ivm5JL1PTDO7ngbg/NEhbv3ZTQSw9NQcD37rMT4bdhtNAx6BThhIElGG+IM/HsS2Uoz0hpg3W4m/V1GRKYknfudmuk5YzeWaRD4NnBmOMWvXHDJC8Eqo1S/mMX2QWG+AtXGVbNnVUX/bYyexLBOjKZQkUc2RtytcyW1wIjzESsX9PF0r2fytW2f5bzNhIuLKbRsH26Hg099BwPM9SPM02VyGSrVCKBgiHA4jpYNlW3i9HoQQOI6bPGl43Z6Krd0MXq+B1/CQ2M0wPNSPYbz1U0pdDXQXbxW6BLqLLu4xSNwueaEoTY/Xu3vQKoqCqqkYIYOyaWNaNhNDEWxboqsqI2NRl4Q5e2EsDlrES6AWm+1yNIER0BgNGchac5/jSNdP2ZYoCAyhMBQKuHKJ2naKEChCsJLOYb8ZZs/78EW5zdlEgFFvBM2rMvp1YXSPhqIoKKor0wgNBvnjT11GUQQioLJJCdO06c2Dt87BGuc2OBolEvOjqgrWWBTHlpTyJXZSrQS0kCnhDzS05c0DDGd/3jmtd6v5UiibZQZ6/WwViy1NhC2uJy02hM36c3e5Y3e2KdxDvKiyLtxwHU1RUIUr4/D1COJfb6IgcCy3Wq0IBY+qEXFsNt1gRXw+HS2gQRnmdxv2cplSB8s/4Bf/8Eukbu3yiV/6dH2ZWbGYf/4Wx7/jPC/WyPP4YJjZz8zVL1V0NMzVTJqzwV5eULcoVCUf/vwQ3/9oD5/+TJBiWXIkGOLWapLjeg/bvSU2qkWGjADhooeFbMb1J38D2CoWuH9gkFeaAlYAXltP8fDkOJdKrtPMkCfMSLoHx3avtKrYhGJ+bpqutKM/EuDseC+6p6FBvpxd5RfmPlXfZ6rqoKDg4N63hCn5wI0p/r/pIH2eAmlrEikEGmVCauvATRBAUsDveT8R7w+jqaNIKSlZZXxeAykl6UyagD9AKBQmm81QrZq1+HZqvRQC27bZSiTxeQ2EEBRLZYrFu2uk7KKLv6roEuguurjHIBB1v1dVUVAV9a475iUSx3G4cqt9Wnu16eeeuTx21X2gBycipGtuCMdPDXHl+hax0SDzWntK3H19vSw/s8naYoGVb24nVDHVh51688kzgIlEHvay8JkNVEVw9N3HXMlD2Mv7v+EUtmXzzEuL/KdffwaAI2f6eUa4FcSHqyG2/3ixbZ+9g2F2tlor6ZG4n5mvG0dbL9flNIuzCRxap8b3EFJVfED89BAipFGtSSnmX3ATH3PZMsdODSGRGD6d/qzJTO8gelVwZsj1yfZmqxx7ZArbspHS4ciRPoQi0Pw6vb0BQCBrUhshoH8kihDg92r4HpsGqM9U5NCxZQnbtqk2NQWOlA0GnQi2dMiVihhenYpjsuqkSCkFjs70oXs8LCwnKef3WfJJiSZgpC/MWqLVs/nQQJT//dI8R95zgrWnrjZtIrny+y9x+lvPsa2pxEsOScs9n4Bfx39/H6mtXVI3y9w/3svlYBIhFD79+QhrOznO9vdz5ZZb8V7bzlKo2PiFRlZU2LXdWYgN+frkD814ZWuTyXCEoOHh8m4CBJwTvfiu6JwPTZIbdUny9acazYuPRONc9efrcpKvP38ERGMmQ0rJryw+zVyhUanfKGc4G5li29wkZ7s9CRUJPzjXi6AXySQAHqHxY+OXGTGSBI3vx9DPoyuHcGQSVeltOXdFcbXOuq6hKA7FYoFyuYTfH6BarVCpVPB49LooP18o4fHonDkx436XSEkyk8Uwbp9++GajG+XdxVuJLoHuoot7DM1k2bHd5rRODhpCaddA1+rBdzxGbYa+5indvP2dNtyrxCk8XBiuaX7hxaBbOWv2jP5K4KVqgvPDETbWM/zpn10DYHI8xnd+6/1YitViJaYoSv1UlAMGIL6gQbi6RzJlfdlLmzucG+lj8c8W6tekWULTfD9yOyVWVtIA9J3sZXkrg9fQ2BMX5PMVrl1z9bZTh3pZ3kzD8i5Hj/ZzbdUl+GeDIVKbOVJbreQ0Pt5DIuGSRCfowZEQjfrIJF1yNzkZJ2joXLu4Vt9mqm+E/qgPVRFULZuCZWLhINIKN7+411QqAAuvqnJqdIR81qRo22zNufKShbkdQhNebAVifp1CyWZpPcOR0abEHCkZj4dYWklh25JrKpx82wwrn5+rryKA7c/dxHN4lJuJPKMTMQq5EiHLYut3L3Lsu09yPZFmdnmXQ/1hfCMGl/YkR5qoBerAWH+EK+Vk44OLG1QSVQ2W30Aj4R4WsxmEhIlwBKGCnlO4dmmrPmsQ7GmVOGzHBbubjYHl/TNDLZ+t31l9nsvZNfbjQmaNY8GBOoHeuzrNfylVafEbW4/wH49/Ax7tUH25KhrkudmacQ+KouAxdGzbJp/PEY32oCgqxWIBy7KQhkHA7+P08Wk0TcVxHCbHBtE9OmPDA3TRxVcrupmXXXRxD0MoAtt2Wqbt9yCbIqqldPWOnay0OqHvSC+eoSA9fYGO1W3bkvQGfRweiHFmrB+vrmJoKkbNes3v05l/dpf555IsPJdkzAhjOCp+5ytc0RIC40j7tP2e88ZBSW0HXZetUS9LY0btPy9LY162xlzvvVc3Eky/Z6plL51QKnduxDp0qI9I0Nv0n4HeRLwX5ncZ6AnsvS2SiTyDYz0d93UgBFy5tsHxMyONRVmL4nyJ3K0ik0kffZ8uM/TpKrGbtYGClPQaOkdDIbSUw9KFHLvzZdCbmiYVQSxgYJZt1ndKZAouaVzcTBP0akz3hujzeFhfz2HX5A2HB6PcrCr0f8103Y3EEzAIn5ogWRsErC4lMasW1XIVM1ch85ELnO113/PKdpZwTmM0GgLgwvoWhw714tGVFvnMHqq2zc35JGfD/a/vmjWhR/fSV/TjWA66pZDwVzn0YH+9gTSfap2FKTQF9eiaypHhHjTNnSH6bOIav770+Y7HUYWCUNpndPZj1DfUQp73Q0pJuVRGKEqrbl8IVFVFURVSqRQej4dwOIKqaViWhaapdfmGEK6zj89rEPAf5DP5lYNT84J+K/7r4t5GtwLdRRf3MPYeeLZjg7VH4VztspQSs2pi23adSCvi7gISNjazOLZkeDrOTqo9mS2zWeDoyBDPpt0K6WA0QCpfpmq6JGw3VeTwkV5ma16+M8/5kfNFQgOS2oz0VwwJpbMW14Xo+LMnZDD9NVNta685DanC8ek+dysBiVVXH/ryZoLzj4+z8MX29MU9WNUmO7RmC0JVkN1nPdc7EKr/XDVtvDZoTaQ/PhjG3xuseTRLnvyG04SjPhCCj/33p10NuibY76Vx5doGx08NcuPyJpWcCfHa+26qmju2w+mBOLlkic3NPPl9ntsFxcYX0inlTBxbYlpOiwd4b9hLUFfZ3CqwnG6tlB8e6WHh6g5CCLZVDyMPz5Ce3aTnzBQrS6mWdTWPzvv+xgP89s98Eqtq8y3TY9x33wSv3Frn1cvr+H0aRw7HuLmT5OrWDlOjUbJOK/k8NzbAVraA7Ugu5zu5dt8dho0gt8ophrJBNgsFAobOpgcK0wr3BXq58VpDCiWBdKGhGz4zOYCuNAZtOetgTbEtnTtaKQK8s/fsbV93HMetOHdIERRCuJHd0qRUKhEOh+mN95JOp3Bsp80isYsuvtrRJdBddHEPYq+ibFm2myaGipSuPEIIBcOjEwyE6o1oiqK4FShFIRxK3n7n7MlEJLfmd+kL+1Artsv9mivdTbxwM12onVdjWcDT+HraW/xWhJulnQrN9cg9Cze5X4/S9HPFdJi71e7DPPnQMIZfByTXl3ZwpKQn1OrVeyGf4viZgQOVKYGAQTrTSp6EI1m8dWdit76R5fixAUTa3d6yHWZvNvSzR04OMj7pTuH/wt//HcyKRWQwDAHf/rdYv3W5VAlq7hRqEwFWDI2b11uvgaYrDByOkDMkS+kcxw8HKb3iEt6Qo7KrKgz3BihXLLbSJSLxMJbV2KdPUYhHfSxf30UAqhD0VyVry3kGj461kWfD0PjQTzzJifvGGZnq5cqL8zz89Sd4wu9jIOjnxuw2pZLF5pUkZ870cXErwXIyg/A0PliqEFzZ2qFi2Rwf7MXSHNbKb0wLfT2/S5/Pz0bW3b5atEnVmutezm/z0GnXbcWREq+u8aWb6/Vtzx8aqevOAaYDfQceZybQh7NvEBCwgpSVErbi/qH16EHORQ6uPtdxhz8yRVWxLJN8IY/f58fvD5Av5P5SEGhJtzLcxVuHLoHuoot7ELZtY5k2Ho9BIOzKLEzTpFqtUqm4TUKIUpP+smFxlq/k6R0Lo9X0wAqgqoJ+x0M+WUJRBHoAVFVxiXdIR1tKs72URDaVUNMbOR4Z7KmTNCEERkljrxZrWw2yXXeUeAuejUmrTLNy07Ftcrk8jiMpNzlFWJbNnhDZdtolMACJdIGtfGuVeL+BiO1I5o0KZzxB2Gzfn9fX+Jpu9qE2q+1BHU6H87h+fYuzw/Hae2l9PZ8rUCoGQAhUVcFknwNIs2NH7edsuoQmQlhSYjXdD6NJH943EsQ/4mc2k+FqKQu1S5DQbCTQ1xdAj3moDlbRI16WXnKJt8evg5RMhQPECjbLr60TOz/KtiNBSo5HAsxddEnmznoW3au1XIeRyRjxgSCFfJHTj8xgWzbLsxuMHRrkfY8eoTca4F9/+DOUqxa3Xt3mgbODlBSbSzuNwch4LMJ8Og0Crm3toCkKD48N81px667CUQD8qsaYN0xENXhtd7vjOjbwXLFRgX5QtBLkB4+MNoX2HIyjwQE2qstI0frBimzMEJFQGVpnV09wJjyNKr581aaiCFAVSqUi0nHw+fw1y0v5FxPf3UUXf0HoEuguurjHUC5XUBWVaLQH27YpFAoUiwWEEK7eUhHomlaP+26OrQYQmspaprUiF39CJ3pBsnK9vSrqmwqTdMoce3yCgGa4HsYlk+2tPNtbrfs5dW6MyHgYR0pmd9L15brm1oSziTInhB8h3AapxSmLovnmhjXYAg5PxClhIxSFUMjgn33kc0igx+thZNrV1IYUnQc3gjhS4vd1Lh+HJoM4SmvF2YuCL220JDwCrEnJ4GSUzcU0G4ksoxM9SMAzFiTar4IES8BgpAfVlkR2LfaPKAIKDM7UGvGkq81WFAWfrTE2ECK/kWEs5Km/jiPRdA1N0/hXv/n92JbDTjLPb//mSwjAKVmMDLo+04VchaHRqPu+DIO5coFruRQjIS+5XJnN9QyTp+JkDMmtZAYSrQMHTVHoD/rwvdPPc4UdEFWw4EYug1dXME2Hm2tJ7nN0lp9eZK+2bG3nQcDZoR5uvNDwpLaqNqcemOLSK4v1ZfM3tvn5f/0U//TffQNXX5rjv/zI7+ExNP7uz34Lh86M8uDRYf7TP3gf/+x/PEU6X+bahU0OPTpAZMzLsC+Iain4hMZcOlV3SLEchwtLW8QDPgb6A7ya2TpwIDdoBNisFBgygmxtFFjc5y99EPyqytxKo5ruN3QOD8UwzcaALaAarkXgvqmK1VKKAV8PCbMxM9RjxrlwM4UtJWftYca8Q+wmgjB9+/OQkpagpE4QQrhWloqgVHKdORRFca0x1S6B7uLeQZdAd9HFPQa/z20sy2QylCtlQOL3+9A09+ugcxWpsWx/pPDey6KpAjk0EiGxlcOyGsG615cSGMNxqvkqA30Bktkyg/0hRNnGrwjW5hJU7DK7VNzD+QVq7StKMW1CqKiOgO0qhlenfzBMMZRm8c6KktcFRQg2VlNka9+OQ4Nh5pKuxOTYUIyFbZcURXWjLiE4Oh3vuK/dYpGVaqsGPO41yCc766x9Ho2ZY70sX98hVZNteEcDLO5L+PMpCoUOEo5Jv8ZKvtC2vM/rZ22hfX1FCEzTQtd1pk+MIKXEu7LDxmq64/ntYWZwmDnAljB8Mk5FSG7tpjF8Kos7reEg/SEfQ74Aq6sZ5m7scngi1iITKFRNjh/v4+bFbWxHIsZCcMF9TQJ23KDfo2HJVmJ36Mww16+sceTECDevNpwp3vmNJwmFgqzdukqlVKVcqLC7mmHm9AiZbI5sski4IPBHg6yn8xQNm+1Mke1i4z5FBwxOKHG2S8W6Hnu3UGJ3ocTDk8O8kFunDRJGlRAToQi2cCj0mfSV/Cynsu3r7sOxQA/Xzcb9OTczjG1ZqGpDFjEV6OP7Jx7n15e+0LJtwa6SqmgMiH5YHKBcga10GVu6n58Lc3t/ICn+1oMpZvoObiTd83a+W9h1G0OBZduo1DzdbRvbsg9suv1Kohvl3cVbhS6B7qKLewzFYgHbcbXPoWCAcqWM0sGy7nWjafNKUEWofg6FAyxlG1VmRd3zs3UjqDVFsLKS5tBElGrZwnEcKmaHaXIdSqVGpTmbK7OdyPGNf/MS8ysP8NSlN8/a7nCkB6u6BXcbQQ5oAZ3+oTDbG3cmS+I2D/hS1eK6muPYsT6Wa3piu2wduH4bDrBcO0hious61UoVj0evD6DsDo4s+7G7kObkI3EKJYsvbTR0z1bRvXeqIjjS24NTcAgIlSs3G+vMraTomTJINVVXU1rj3q5YFXy9fuKPjLCKxeVskdMjMS5/aYMTD4+z9KUVBsZ7WFxKYZo2t25ucuz0CNcvrfHNf/1+nnjyBI50ePu3nccIeli9uc0T3/EAqqqysZXk3//XT1OuWPgLOocneki+VOD8UBw5rPJK1pVbGKrGhZubSCAe8HLaiFDGwfEJfGWNKSfIiihgNcsmhPu+Lyy6soyz4wMknPbBTBskZHZaNe4PHh7Fsky8vtbZi782+gif27nR4gMNULSryJtxVjfStz3Uxy7c4B+9+5E7n9NdYq8/wLZtpGXjaCqO7VCtVJFStgwAuujiqw1dAt1FF/cYNF3FUN04XinlbQnd68GFkwnOegfZeSXLejKHRHCp1NrYtF+jWyftuoYMGugHGGAcRI+rssDo9FP8nYFH+bXPBLCcL/+9xIoKnVWrB5+HaTksV0p4B30Yhoahq3h0lVSnfGtFMFoLKqnvV0piPT7W/G4VdFM6GKd6UQWU0ZlZjbWIp4UtCZ1zyYnjQNDvIZ8rY/T4mEoU3CZRR+LU/q18TYjKkSk8qupKM2p6VSfsQdM0SsUyobBr3xeN+PiFX/0A//Lv/57rw13bz+hknOVbO9iOQ24tTWwnxEqhVYKTy5c5P9jH8kqGhWtuJXpyKNqyjuNIjughvtREoOdSaQ4NBvGjEo/4ePVUmeVsYzAyv5tG0xWubmY4/MAIxe0iZm2g5TiSG1c3eM+3nON7/87XUSwWQLjX9OzbDvPAO45TLBQxvAaRcJDjRwd45eIaxZJJNF0lVSqRSpeY6W8o34e9QW5QQgCZQhlZDrL4mutFPjYSRcylmVQE8ff284K3UXG/tdWQYVxYbg8b6oRzkTg3brZW7R+4jf7Zku0DzOGdMS5t3LnR8ROX5/jhdzyAoXV+9EvZWUffeWV3A8dxHUC8Pl8t4ttBKAqG4XnrNdGyG6TSxVuHLoHuoot7DNq+h6cQAsuy77oK3WkdIQQpvcJnj60SOuThvq0hll9JUyy16pOdGunZ44LN+5LOwVXkO52VGXiOv/dNk3zsi0dY2v3yqtGVuXzrAQ/aXYfl5apFudqoGAf6Q+0rAYtN+u49+EM6c9n25THDi7PROhDxqyq+9UbC4WB/mK2NLJ6ATmKzvQpuINnOt9sJOgp4PDr5fJFq1UTTNFRdoX8wRHa3tXpqmxalQoP0xgp71oaCw0NRpCWZW0sxHAmSyzfWW9xIM9gbZDPVIHg7mwUISRCCsOHh66bHeem1VVazBeJU3fjyJhTKJqeP9jJ/eRsl7AMBgZJJIeceZ/JwP5/8zFVC/SF+4G+/HdM0SaVS6LqOpqo4jk02W+A//dqzXLqxxbHD/dyaSxAJeZmWgkiPj4pQiXoM0tUK6j5LZcfb+EDsJgu1yqskuCz59nOT9E33uPZvZYWPX7vF0m1kG0LAWDxMplhhTAswO9tKnqMBL+PxMKlSmuvpLc5Gx9muZHkts8LvrDzHcql1/WPKKC9duTuXkGy5whdvrfDOY+2Wi+A2/lomd9cQKKg78yBoIvyi9j7v3ADZRRd/ldEl0F10cQ9DSlezqGt3/6DbzbYTsWbktCpfHFkiMKgzU47XuahqC+Z/pzaVL6D3UASnXCPUb0K6YJkNvuVxjbmtk8xv6tiOxLZBOqrrFOII1Kr7gFeEK6WwkCymUxRr7gpRr5fEtTS6R63nuDmOg0DWq3N7pMB27PrPUjot2vD6T45TTwyUwrUIBAetA7FwbMd1NtkXtpgvV4ioAqdprkARoGkNOQxCouhKi4NGM6TsXFXMZvKkVQ3p2GSzOQzDQ6ViUsyV8fr0WjVR1M/fE9T33gzJxRSnv3aY1USOVLbMbs3ve2kzjaqIFo/n/miglUCnijxxaJjHT47zdYcnUBH8vZs77GTL7BbKnBzr5eZyq2a76JFMjPdwY3UXx5H0jQaJ7+oYXp2lRBZHwu/97gvs7hT40X/yPvr6+kilkiiqis/v45d++wUu3XCrwtfnE0yN91CtWqxvZFjfyMBVePRwP5s5QU+vzSP+XparedbNEjnRGBQVSybhoEEhX2Hh0jZ//eQo33n6XN3GLZMo0e/4KAmbbbtIybLINc3EHJ/u5cLuNifiMViwcST0BL3kihUsR/LgkTHWSrv83dc+TNGuEvcE2a0eTJALmxpwO+/yVlxIbPHI9AgBj6ftNTcsSMG27TsSYMdx0HXdHXg3jzilbNJGv7XoRnl38VaiS6C76KKL11UpmttIEgv5eMdjU7y0uMHSjVTH9QqqycXAZv33qGUQqpEqoQguh8tMDQaZ0PtwCq9D59t65gCo6FjlR/jltQKwBrUwwcPaGC9tuBW7B/2DXNtX7QM4t+6hciLMBSfFsOMjHcxjA3ucKbGRIwAMPNLPte0kRCAy4ueLpSQ85H6FTlZsQi+3T9lH7VGstEueNp/wYCOx/ArarXZC64soBDpoRx7wx1kLlFkvNAYuqi6o1Aj00EiEV/tLMO0jrnhg1l0nNhgiOhBCIinrnbWov/ZLz5C4meIf//MnOXK8F4/HQyFb4Ue+68MAjM70srLo3l/bq7N8zq2onxsf4PL1TVhsaJsPj/Rway1FvmRydDzO9ZXGtV5aT6MIMHSNmYEoyUwZX17hG04exqpWqZgW3/zAEX7+/7yIA8gOT6a5zRTTnkDdyi+RKzMyFCC1WaTaZGX3/LM3+Q//osiHfvJbUBSVUrHEH33qOp97YbFlf4urKYaOxwn1eMmlXA1ypWSRXsyRXsxxaqYf8UqCk/EAfYfknsMgfp+HgXiQ7S138GLbDvlsCVXTQEquLm1zfd19757THtKywqnxXii461/Y3cajKHg/l6PvUBwx1MOtjRSaJpjsi+I9ZPKjl3+Hou1+bg4iz8N6D+F8lNfm7kJnjfuXcmZ6gN+6doWitPiJtz3evo5wHXgqlarrD3+b7wXLtNA0D4qioKhuhLequi4+2l8CT+guuvhKo0ugu+jiHsZeuphlWnfd8PNj3/k4tiP571de4eLuDkefiKOqcFx1/aQV0ejmFwACCrJEOpejnp5SK04uFPMs9EBf3MPh/mEQSt0z+G4RtE/zRy8O0heJg3Gr5bXmqrBygAdutWLifDbBwDuCt62Dd6oa3xGvY5MDq/BCEDd8LQT6gDwXHJ/C1NsmEULgNTRevrgKwMShwdseW9VUdF2jXNqXdic7/kinN6Y2NV3ubwSNBA3G/CFuraS4WnMDSaQLbCTSBAydm9cSXP3kHOfSXl6Olrm+uUtvyEsqV0ZXFR47OcY3PXaUudkEH/noywAYuopWclrkIpqu0HumnwuvLvHvf/yj/PjPfjuf/NxVfvePL7Sd79BAmLntNLFeLwP+EFtrOdLJBhnVPSojkzGqFYuN2R3+8YfewemTI8RjAf7hj/1vsmWX4F59doGP/fzn6tslnxysn9+2VQIBW5Ui24UCe0X5BwtRSokkLwa2cWqX07IlC9tpNJkmUW3Icw5C9lKAm4m7k27M9PcgA/Bixh0G/MH1G/zIQw8S8Rpt6yqqgqIq9VTCg9D8umM7OI6sBQ7JFr/ytxrdCnQXbxW6BLqLLu5h7Hm6WtbdT7kKIdBUwZ+vLGBLydXcwYl4fkXjpL8PJ+uhn36qoQSVXLWNKiacKglPlfv7++FW+36sA6aEr11/G8+uuLHUfZG7fgstUFSVdCbHsWthdk4fvN7tNNqqrhCO+bFMm1Khett1AQbDgdZ9S4mhqgz0BNpXNsEvW4lMC9lu4guOonBpya0Knz7UaIrzWIIxXwAJlGyL3WqldtzGtl6fl2wmh9n8WWh6XcHV7q4lc9hIhgfCACSSeUzT4cbqLtGgQTpfYXEjzUhfkEjASzpfYXWrXRPsN3T+4BOXufH5FbabdNvjj/exXC4yMhom7g2BofAT3/F2FFVjvDfM7//JRfriATxFm8W5phkFAf3nBri8ukN0KsTCVpKP/vbzfORTF9qvKRCMGrBTIJkvU9Q1Tp0fYu6Fhj1dvmSyvN04r56Qj1iPH4BTJ4bpjQdxHIm3WGX6+CAIgR7Q+WzNPq4/GmBbuCS73/CzKQvEDS9HNww2P7XO4UdGmSu3ejAqCqxV22dJmqEJhfH0BBcPIM9TvVHC4RoxlpCSFa7mkrDvFry2tcXbJsbbthdCoCoKtu2gqp210FK6jaVKTe5hmiaO4/ZRuN8nb3RGqYsu/uqgS6C76KIL4PUlic2mk8xlO0s3mnFc7eO1+cY0/wm/RiVXbY/jq+Eg39iO3tPAK8s0COQdTv0gSrsX/rB8Y5vTQzO8LDqfnqepMr6/Wlzxa6yNuOT3DDoLl9p9gme0EDctt7KYvdUesNHb42e90D4dPxTyklxI442rlDsMJOQB1eiWc9+1Kd5wj6koggcfGORLuQS+fj99qsZv/eEr/O4fX6An4uUD33aGcI0oerxNjwgHdm6m8Y94udjkMHHfVD9Xb24hJQz3hkjXKsIRv5dri/sGV1JyaCiGIVRuLe7w0tVNipvZlls3LHXkkSjPpRJQK7pf29rhULwHv1fn1//TdxPwG3zy41f4tcUvYNvum558eITLay75TOfLhMZCPPXRl6Cn88zKxmqW/riX7UKZsmnx0nqC6LEQUz4/2eUsxWJrNf7K5RUmJiNoqspf+477cGy32vp7v/Y8Lz49B8Dg2cagJdgU2W6oKo/RS/L3N9gupRHAnOzQ1OlI/KpB1uo8DRNSvfiXBri40Jk8nxzt45XiNtxFfsurm50JNLgzElal2vE1KSWWZbe4dWiaimG4zZ2OI+84gPxKoRvl3cVbiS6B7qKLexx7jXGvRwf9e7NX72q9pNpKBKpFC9WjogV1JPZdKxw6TSX3D4VYp0Fy7tS4dNCxmkl7YnaXnr4QvT3++voer0ZVSJyixamRfi6vbdPv+Bjyu4RZSge92ji/TNTDyNvdyLdcseFC4pE1IncAtzioAdBxJOlMielAhEwU1gqFfaS5M4NuXtw8pe44kvJqESKQL5jEQl4kbjT55naeKze32a0NKnxelYHHR90NgwYsFhjz+PGEwyia2/CoKwqGqnAkFkWmLURtALKyna03E4Z8Hqb7oiQSeRbnG1XXnXSRw0firDfZuK2+lmA15mu5Br//2nX+ydc+iM/vq7vFvPcbTjE+0cOv/o8vovV7ubDW2EfQp+NdyrOTyHH6yDQXttrTdnL5CjFdpSfoIVVr8ksXypR7gqxI9/fgoRBBvwefrjG7m+X/+63n0XUVVREIAZqqUMw0PuPC0/gcKIYg7vXy3YeO822TR/jnP/wHWKXGZ3TU42O7sk8yg2BIjZG1GsEwY3ocezmEWZUkU1U2cq3EWxWCY6O9aJrCQiVz17Kh37h4ib9+6gT9gfZZj73vAjdkR6tbXgJUK1XK5Up9ueM4ri3i3t+RdGeE/qKCVLro4q1Cl0B30cU9jtdTeQYoWRZ/vHjzrtZdKKc5GouzkHRLYvMPqzhSYZEyE9EwEcOod/ALwOhRORLt4eaL+6rbAg4/MEQpDL4s4MCzsdSeohpVEUi9fdq4mVTvt0bbg9IUP5zayZOROokmx4j+oRALtWrhiWwP50NxHDRKlk2uXGEzW+D+Jg/hpXSjijzo89Z/DhcEJ70hjKrKRofz8KBwtN9NNGy+HXbSrfStrWdgHU6Px/BHfdgzJRwp0X0qh3vd4wQcjema77KBZHrUTZ3zeFQmp2II108EX8jD8bif2JrDtWuNRk+AZ15Yrv9cLluU/bCVK3Gk5sCxuNBORsf9fuYuuR2Qh872M7ueJF+qcm56gGrJYm4pyZXd1iZLIQTnTw5zfDDKHzYR6Gre5KR3iJfzu6hCcH54gIdHh/D5XVJt23Zdb3v4cD8/+x+/nXzR5MLsOhdurHNoLMajp0a49Pwyv/Jzf05yNoESUelUFE2migxoQcq6Sqmm2zaaBmv5UpV8jVz7vDo3mgJh9nB2IEbfdJxY1A9hnaNhL5oieN+5w/z8kRl0oXJrdp3kjvu5GJuK4Q8a6GWFE+EoV/dZFxpFL6ju38MxZ5JXni5g7wtkifq9jMRDlCoWoZCHF9Ot9/Bu4EjJxa1t3jXd2dJO17WataFaC0txqFarOLaN1+dFURQsy02xtC0L0zQRQlCtVKlWTRzJX0gzoexWoLt4i9Al0F10cY9DCHEguewEXVFaLMruhJDfAzXO1XycpXS7LvbMSJhZZRtN8SIdt6dwcDRAcdjDC9s1AhZt3WasJ0xP0EvBk4R95haiiQwpSGJ+L8l9U/PNz9tKxWKyr5f57cYcuGiylROOZP4Vl/4en+wleXGT4YBO9HGFydEYi6ut5FJVGgRi6Xn3/GMddM79/SGChhdpV11P3aYyohFWCZ4bAdmQjigSXi3XzrECpN0fAwNRdp5xGwebqZ7y5ASXqk3Xe9f9L+KLceJQvyuRqbH2aqnK1FSMhYUkO4k8x8ZG2MqVDhxkzQzHCCkqmxuuPEUvO5wZ6ye9lqOwUWR+t/U+xyI+3v3YId779qMM9kUoFit86g8vkcs2mgHFpRz/7G89yuNTowR1DQQumVNd2zSPruHxeJA1yzSfz8+74xHe9fCxejPs0PsH6B+M8bP/zx9y/sg4X9rcIRQwmBiNMjIQJhL2Mruww6tXNjh6pJ8LyV10VSGV7GDTKCU+j0FP2IeqKKiqgqoIFEVgaDpbm1m2mnTcgYDBd/7jv4amaSiKwtNPfb7+mqMqXL3pDjZ0XcU46qHSlP5YSkDPaADfci9f6uCwEfV7EYOCL1U2eaB/iIL9xvXGF7a2OhJoIYTrqIGJZVoUS26VXVM1AsFAfUaoXK4SCATQdQ+FfBGP4cFjGAhFwes1uj7QXXxVo0ugu+jiHocQCo7txvHuD1nphJJl4de0Fuuw26Fq3l2y2YmZKBeLLvk7+94weVFhpZph1slzNjAK2zDRF2SpqXlqJBIiIyss7mQYDbYTibDf4b2HDEzLi5VR2NTK+HpUBv0BQqqOJgWG18a/U6Sw5SbPxbxe5ptFpAeMFdQaiagUTHJbORY3U4wP9xAOGYA7xa8qgtCgH09Ir+mXBR5FoacaRSKpOg5ruTzzlTJKqMzL5faGzPOeXq7t80T2aGrHmXpd1zueq663e/4ClAICX0Hh6qsrLcsHBkKotcq8UyN3woZBv6uNFvX/wfZ6lkrIqKeIL1xvnKsE+sfDJFIFzhwZ4F2PTPO2hw9jGDqWZVOtmggkb/u6Y/yfP3qNYMjLj/yTJzl93xiRaKBFOgBucIeUkkKhQDbr3qM9jibB1Y4IBcPjQdN0zjw4xS/87gfJZsr805EYfTHX3zCXy1EqF7Edyf/4n8/zmWfneeiBUeaWdtmoNTz2BXyMBgKIbJWtpRSm3KU6135/+k63O5w40iGbzSCEYGeryOc/cYnjh/qxBaxnGqTYNG2OhaK8lnYHXgFVpWdZ59Z6mJVCK5HXVYWxeJhsqMpyJQcCXkq7g7n7hvtZ2smSq3bWLR+EC5sHZW66UDUN27bxer3outYStiSlRBHujEAwGKBUKrqWdkKA7AapdPHVjy6B7qKLexhSSqrVKpqmtlRLb4efeOFp0tW7D25IFu7Ol84bFPU8iAuV1ia85WqSU1MxzEC5Xlr1qAo5USVV05EaQgfp/nu6p4eAniHtvIQlJBjgKzwCQMm2Wcg1VUU9wMMqESPGsBHAUvxMxvtJr+dIbZVaquYHNiLWeMLyeqv0JDYZZjOZZzwa43qt6TLuMdCzkKpUOu4j5vPy9slGc1dy9e6sygCkYXPm57z1M5VI9LKP4qLNKaMfVVcwVcm1G27TnyMlnpifnt4gqZ3Gcba2ckw8Ns7V5W1eTbjkTtM0dpKdPYdLZZOx0SiVgkks5ENVFSoVk0LF5OShEf6v73mcnrBBoVBAOg7lUgXDMPAaPlRV5Vu+82Fu3UzwoR9/kmhUx5Zl0umqW3GXoGk6mqahaRrFYgGhCDyGXidozUTbrUpbmKZbzfeFVDw+Lx7VQkqJqqqEw2G8Xi+WZfFDf/MJPv/CIpmNAiOREId6oiS3cyQvbDNHgzDHejs4pODGqvtqum/pSN79Taf5vh9+O4oiqFSqfPgXP02lbHLjsqtrHjw2QCbTNAuyWWEoYjBZ0dl4YZ3MYUFu3HIlHKO9lP02m2aB3WqJjNjtmJnyWmqbuN+HYr6+2aTrOzuULQtvh4HznsUluJ7PjirZ34rg1GcAfITDYYqlErbt1O8xQCj4Bu1x3iCc1+Md2UUXXwa6BLqLLu5hOI6D4zh4vMZdNR8VLZNPry7e9f6Dqt4xQrodki1xsHVA0iySFiUm6KkvOzncx0s7De1n3IrT4/TiUW1M7RnSeyXRGmLDed4Vt1FMH8W8xtyqyUay0eSXqVTJVKqoAzo3KrsQB/+QFztocEwPYpcdPIXmZrxGZd0QgpMjMZfQNUkwpCJQ4gK9yWNbQRDcNPGNBjq6boxHwvzkOxohF//y9z/bfrWk7Hi7TGmy6FlsWTamT/CFCy4RPH60j1cTCcKaQtV0kIkK61QYmI4xOBNHWo5LWoVARTB9qg9wyWlJOpx5ZIxy0UTBrTJKAXpM4VYwjUQj++EE2T09CW7Iy5DPg65UMS2FUChUD+iQUlKpVEim0nj9Kj/509+Aoir1Smfze3UcB8uuUq2W3bjxDtraZjK9v+lUeiTVqkk6kyYYCKKqKh6PB8MwMAyDx06PsbaZYW02QaVqowjQcR1LDK+OrqsEe7wcOjeCUkuL3GucU02bSrkx+yGlG4ntOA6XX1nmwovLLecSC3lZ2ahVz4GgLSm+mGKh1ozYPxbi5BGdK+VtXna26k4knW54SPMwE+pBFyqO7XAhffuK8n5YUnIlkeD80FDH1/dItJSSSrmCGvC1VKArlSqKKOH3+4lEovh8ftfK7g4hLF108dWALoHuoot7GLJGMk3Lch98ioJQBILO069L2bvwx2rCuDfCrSZCdRBmRiPMldut35rhIElWixwZ6cGv+ngp0do49eKKSxLfdSTIfm8DgKqosmituUQkBKeOHmXjufb1mt91sWpyK9l6/ocf6UVHwXZUpk72k9kuUC2azM119u8deGyozWEjkynhrZicOhrjSibl2m/VdOWOdMmeqOkkvtxQipata/e01AMCBWfdZmsrT6wnwGs32q//yKk41/bd87jXSyrfuMLHxuPcMtIkdR8xN6kcgL/3f7+DRx+folR0w0Rs2ySXryCla5no2A6lqonpSOK+IJ6aq8N+7OlxVVV1We0bgBACj8eVjWSzaaSEYDCIz+fHtm2yuTKLS0lOHB3g6o0tNyr8TD/Jp+eoAlVAnY7W9NUSHAk1v2zdo3Hk1HD9cxOoeTBXqyaf/pPLHD0xiGFoVCtuBbwv6uVdbz/C9twOWytJ5l5ulc94vBpSkxwKx1ktZUm2OXW4p3A+Psh8Is2lXKJl+YQnRD9evmS2Nzx2wlImeyCBbr52juNgWTYejxsZb5oWRm25rHlCG4ZRn9WybRuJJBgI3tV5vBlwZ1W6xL2LtwZdAt1FF/coXCLj1B+AbuXUbbwzdE/HZMKFfY4Bd0JQdtbe7kekRwPzzutlrDJ57xaxQvzAdTp14YfUHrJW69x32Ujynq+JIqVgJ6Hx6mwBEHjFwVKWsMfDck0jOweu/GMUzsf89Ic1ejSNasEknciTTBQJRrykixWGIiHO9vYCAl3CIlnKZYvl17aZ7g+iD/uwK5IH/H0EKzq/+7mLVLLuBXEKFucG+3G9uvfeo0R3bXdRVEHJ774Q8jkEto4gNQchVapS4BS9TPRqLO1k6952pt2qS798dZ2ZqV7mlncYGQwTifprZFbWdMWNa7qf5IpawmPSLHH8a3oZyPt4z/tOcv7BcYrFIj6/r95QtifRdmUWDrmqSSwSxPC8QWb8OrBXTdV1DcdxKBQKaJrblKjXos6XVlIYhkqlYrfpdcqpElvbeTdpU1EQimuvGJiMUShUAMHqUpKJQ31IKbl5ZY2XvjAPwOh4D6uL7gBrfWGX//K738+f/e9LXH+1tToNsOAvcX2rkUQ4GgwzGAkwl09hqCo9Hi9WRXJhrbXafNoTo3StSDqXoxh3YOzurstu8c4zRG5Vv1UmUyyWUBWFSCTaZHtnkkolKRSK7qBHU95SAt1FF28lugS6iy7uQexVAU3bwp2NlzWLMzdx7qDp1y9stD/wb4dK+e4aDbe3KgT6PBTsOzdB2dIh3OuwkXHPej82UgqxaOsyy4mwVm6tyG2YCSDhstB+eHg4itweZUJbJ+yzmV3XKVehYkGp7CARjAVD3NpqrzQndgokkgUY6SHU48UxLVRVEukP4fHqiLLD1Q23Qh4zWuOTE9t52M4z8zVDvJLYwbQllazFzYtbbceRgEdXMDwalS23MtkbD3B1wv35pBkm/cfuNTx6upeXk7tAgZMz/UCD7O6h+T7nMiUCfg8Bv8Gl+QY5O34sxrV0uuM2jbNy0f/1/fyr818HQKVSRQiBz+tt2UZKSdW0SOaL/P/Ze+/wyNL6yv9zU+WoUg6t0FJLncP09CQGGDIYY+wHlmAbG1hjsFl7vfaa3QW82Ou4u/w2GKfdxTYYw2CSMZhoYGYIk6dzq1s5p1KVKlfd+P7+uJJK1ZI6zEAb03WeR0+3bt1766ZSnff7nu85fq8Hr3brv4ZkWUbVVAqFPLFYHG1dElIsGQzta+bK6AqNET+Jo53u8yGgEvSSpuCeru2sp9I73DnUwn98672YhsPPvPrP3Cqo4/D4erjK1SjkKizNr/HjP32CwSOd/O/f+QK5NZfEtpzoQk/K3BNKkGl1GM6vsVgosFio6tOTO2TdxxQPpUtFsushNuWKxf1yW7U6vEVatD5WBqB3MMH+xsbrXq+NeG5Jcs9Nr+h4PV7C4TB+f1XWUSwWEcJBVmRC4SCKsnsU+A8KdRu7Om4V6gS6jjpuM2zE8Nq2tRmishWapu1IoLOGztdnJ2/4fSQB85n8ddfrbQ8TiWjkbkIzOV5KcaK3i6cnt5NZewfFgy2u7wSSsjLs7fTQHf4a3cDeIwMkTVe+4Emd4iuP+Mit1pIXWZbwaypqt49+PYhcgbVMhbSuM9DTyOXLrsykr69aMS+YJoePteKULDKrJdLrtmm6ZdOUCBHzepnL5An3BbEcgWU7mLaNaTuYtkMZgSrbbLS0WVb13KwtZPZmrAYBVtNFWlsieD21swZm2nRv5obG+KrtpC2E5RtLk7zXtvGsSy6EcGPYNdltbnMcQVk3MG2HRDiIpv7zaWUVRUY3DSzLIhyqDmpWU0VkWeLyIxM163cd2VnmsFFNV1SJD3zwp2hIBNErBk9+e2cCDTA+vEx3fzMDhxr53w/+Al/9zBkunJvlifnVzSZZAdzz/BZWG2wM4VBxLMqWRdE0sdcrwRoSTYqfrjU/4wX3s9DVFiXU4GP4mZ3cxmvxO296EfGw/7rrCeEGo8iyjGnYBAIhfL6qF/SGzMZxHLc6jztI2SkAqY46flRQJ9B11HGbwbIsDMvc1VJiN/upR+Zn0K+T9rcVh0LNXEluD924GnKrwdnSItykne2alOOOngSKKvCrKo9cXkYg0duWZ6NeJwS0egYZL91Yc1WvvzqdXd5SDQ9Fc5y4s4FFkeJovoHRJ8v09YaZ7h8nJkKEPRoTsxbzazqEgA6BT9HpNhooZnX8Ee9mM5jhODydXb8uUQg2BmjtjpCzLeYyeebIu3Z6QR/xkI9Im0rFpzN/voKZ2z4QsKzqPdk6UNha/ctkyxzuaIKy4N6uDvKWCY5DoywTiwawTAfLtrEtB2E7xKN+SmUDy3ZYXM5zx33NrEUNFEVFAmLCbToVAmaUzOb75Eyd767M8EBbL4oi4/FqZPNFFE2jbBh4NJWAx0Mo4HOrov+MkCQJWZEwDIOhfa3808NuONBqqsixgWaMaHCzYi+EwJcI0BVUN/sGhO0QUFW8ftVtsFMVBvY3gYD5mRR9g804AwLLsMFxAAchywgB4xOrtEwuo6oqwnEYuq+TTzx0ofb4gNlHqrMQ2vpPGOh4dRseS2HuYppCqUQqIth/qIUZpchZI0enaiPL19bPh/weokHvrq9vnLdt26wk11hZzRAKhfB6PHR1+BFCsLy8jGHoBIMhEgl3kFip6Ni2Q7FQRAhBJBy7uRvznFCP8q7j1qFOValWEAABAABJREFUoOuo4zaDbu4uk9i5xugi4rkxPTOAgkQ5c31GfHQoztnS7HXX2wmz5SyzZEGHoViEewfjjC2UkbwyMbmXNXvS9WKWJCrO9aUhHkklKJ/e/L3kVAgpcbxSM6aS4pzqnk+yYQ3/S7yMyAtIEqyQYcXOMNDXxh4jylraYTmtM1LOMdIFdEGDD1hyfaEbEirptMnGWKRoWkzn8xSNqghcVWRSxQqpYoX9bWEuSPP4jmkM6k1Mni7hWFViZJg2bYqfZnwEWgW5gSCZ0SK6UiXT86t55lfd2YD+oSYuplw5yVApyOLyVbMEVwWfSEB2vMSZU+kbcmr5x9kRXtjaQ8WwyJV1VEnC7/MQDvg2pRI/LNiIoj52uLtmea5iMj9a6/nc1+BlbDGzbR97D7bgD/g3U/ds26a9K8Fv/sFrNwei733b3+AN+BidcKvEOhKfWapWiLubo3QdaGHk6bnrHzOw8MVF9r+om7bBBhZFkYlKngm7wkY0Z1BRd2yk3QpVkVlO5WhJRLa7lqw7n5iGhapqFMtusqAiwdzCIslUin19nWTzBZDA67WxLIvVdAYQeDQFASRTGbq69uz09nXU8S8edQJdRx23ISQ2PF6rjMi0TLeJcJdtOkLhG97/HcF2zkxu1/BuRUuDn2F5YVt64M1ClWTmyimQUnibVb63bk13f3MvGWcS3bmxN9gbTCDJrq2cXzmOwzxe2ihZHoToBHJsXJ2yom+7TkXHYcKYghDc19zGV067qY0vPKQi5DJKj0k2OkPc20DWyuORvIRFFLEcZ3nOQ0nXsB2JnhaHe+76KJViL5l0P5f0IB1SiIjm4YKzSPQeL13ZBmyfhbdVRcQFlYDFspJi1Sqx7xUtNB8PsHLxxvy3t8LvU4mGfOuyXwnDtDAth+xahSOFBOfCOzuNbMXDS1PMZzKoQkaVJMJBP95dHDb+ubEhQejtbiIS9pLLuxriidk1WlvDrC1VBxeOtfNz5NiuJthU3JCR5EKa//rOBxHrmmFZkVEjAdSQj1jEiyzLCN0kpPgorKcICgTPLC5z8p49DD86gwTseVEbWa9NtCCzfCaFnq/tsk35dc5k1+/HVdd2TRg4bRp9iRgUbSZ2cIjJFCr85v/+Gn/4yy+lMR5ct55jM7ZbkmTC4QiapiHEIkLYhEI+AkUfpmmRWsuyuJJCkmQy2RLpXJHZuUVUTaUpHqFQ1snnb9zD/PuFuga6jluFOoGuo47bELIsoam1zge27U777kZ02oNhGn1+7mxpx6eoOEIwX8wzV8iR1XX6Y3EiHh+r+RIXpq5voZWIe1l2blwSshss4dAdaGKyvELZqZKM1XKIrvA+pstr19i6isB68p5PTnChOAdIrOpevruaX+f4u38xt3niTJRckhJUPKx6z/DAnW3oyhpZeb0WGAAFBa+i4HN8lOwyOZGHpjleNuQlqNR66gXCYwTCY0ytvInFpSJdYR93+xKkyzYX7AU8dygYG5INwaYERmg2j6trNJz0cSAdZ/xsFmuL68ZOk/oDPQne+KqDHNvfTqFQ5l+/+zPb1glO+vj3r7+bu9u7afQGKNsmOUNneG2VC2vLXM6tslgu4FMU5kt5TrR0oJcrqP+MOufrwbZtvB43zGVooIUnnqk2yTb3NhKO+FFUBcex8XpVBjriaF6VcMQNq1FkmbAk8e1/PI+w3art7ESKaGt0PXrd5balioVkWOTHq4PKowP7KcXdz6Dm0Zghx1PTixy8oxWjXOHxyiobZWTtqMy+mTDpmSqh96/hdvzugpJhcmExiSxJHNnfzMxkmkqldlZoMZXnq09M8fbXnMQw3OAaRdHwelQ0TcM0TUrlEpVKmWTafY48msrA3i5MwyTg85EvlskVyoRCIcq6QYPfi6oqhIN+18Kwjjp+RFEn0HXUcZtBkWU869HOW0MRvB7PNYlOQNX47KteT9zr2/aaEKLaiW+YvOuzX+bi8vbY463whiWuO898g/BJ2xuhhvOrDOcF9zY3kLOub9WlSi619MrtwDIgcT5bvqECeVAJAi65afeHkWUoytubuGxsFvRF9vr7kGhgvDSPKUymKzEO7Bx0R4PP5EAiwJqVJ6aGMJQsp5obWNLXaPc0czqVQt8yEJmqpPHKHnKWQeHQHE39FomxLkYvFNENG3ujIi8Eba1h3vxjh+lsDfOZb4+QKRsc7d3ZlSF1pcBrO/cRDru2ZHH8tAdhMNbIa3uHdtzGkH84ifMGJCQsy/VnPnGst4ZAj0wnwRbo+jrpXHIbSo8f7eTXf+H5yAgUVeXzf/kd/upPvwGAN+DBUVTMq2LuOweakK+6FmKxyMyMK5dpPdi0+W18cTFFe1OY47FGzufSWI6D6TiYpVrya+ZN2CXkb+sgyRGCM6tJCEObx0c2V/uh+/hXnuHk/k5OHtyz7s9tYxg6xWIBWZFRFBnNo9Lf08Heno7N7Z46cxlZkgj6vS6BDvjY293B7PwSuVyBimHS3HBrUwgFdR/oOm4d6gS6jjpuM6iqm/S2lSzv1jh4NXYizxvbbyDo0VCv032vqhLn9PkbPOLrY/f4Ygn5WmW6LTDFxnou+fHLITLm9XXcEcXPcKHapNjqd1i9DuseK03gkz30BbrQJMGlwixDgQZkaXvTpSybzFVccr5q5LGFQ8Z0BwQ5a5qmkI82X5yyoXAh45LpgXiUC6k1Ls36uaMlTvLgDOFBi8GpHvRlh0P+GIW8wYF9zTwzucwff+FpTr51idNC5fOf7KT3cDPTl5I4WyxNDg414/dtv/+7PTcbg6of1uozgKqp6LpBoVDgwL7WmtcqusVAb4KxMXcg2BfQWBlL48TXGJucY19fJ/a6kF1RZRrbY7R2N3D28Rk3jGhdviGBGzqi1fYQWLbF3oPNrutNzANrVWK7kMxzMOSnzwgiBWDKLFJYqyW+et7YlUA7u8iWojF/DYHWFJm+/Y38p8e/zo/le/mZwaN4VBVFkfH5vZv3LhIKbvrCbyxrSsRYXE7hlTXCIT+GYbKcTCNJEq3NDRSKFUrlHXLH66jjRwR1Al1HHbcZVOUH+7GfTGc4u3ht1wvLEsRUDxnz2ZegNUnmfxxYYKrSzNdTLtHzy4KyUyVsv9Zt8nTlxjTQlXXHAku4ko+ykyemdVyXRLd5G1nR3WpzwhNk1R65brOdJIEuDNbMNEdCJoFoFxkrQoP2zW3rBuTqNdrJjq9gVxgtLjIQrNqs+dbNFQzH5tHFVcJajAMdDhP9l6Hfdc8YuDzIZXGJ4JDJXSfzLAr3HDpebXL2E02ofSH2RELMXlylUjHh7ij/7pFvslop8/vPeyE90VtbXfxBYCNlzzAN+vc2o2kKplmtHq+uFWlN+PH7vYTKFouWg2nYmJbFcnKNdCZP66EG3vPnb+TiM1NcemqWzoEGvEF3RsQwTSzLIejXkIHOoSaEI8gly5imw/SIS86bjzVtO7bldIFMscLRcAzz8RIt7RH8QQ2QsC2HkObDf1nQ0B5hMpXHsmxaeiKMVHIkwl7aWwLkKwbN4QCL6QLt8SiZ+aoEpO14A8PGGstihVODHr5YeZR7rQ6OBrc3/bW1bA8u2tPZQjwWxuvRsG2HcChMKOhFUSQs00LVVLLZW6+BrqOOW4U6ga6jjtsMP+iK4CfPXrqBtZ5bPDXAQDjIS2JfB+BY+GX84uUY/2MwyzuHY4BLpl/f8BBfmXrghvbnWy9Al+xlZJpxcOgP+XhqbXcS0KrFuJCv6lr3R/2kbrBpESBtrVFyWmj3fB5b7OxW0OJ7Aq98J7pzbSI/VlzkcCKBTwoxms0g4SDWq+950+DMjMKpjiFWpAkMyWD6wCj7wwlkzWZer0oXks4Sd70+wdQ3w1wYX8XXpnHgYCefXRjdXGcis0Z3JHLNZ0kIsZl8+MMMSZIQwkFTFfp7EwyPVAd/a5kKvTgsPTSKdKCF9t4owZDKY18bxzJ0UheWcWSJmeHVzYq7N6BR8pfdUCLJTe9rDGiszmVp2ROjsSvKvjs7MZCZ/tYUAIXpPJ2DIeZyVc3warbEkb4WTk8t09cQYGE2U3Pcg4fbWJrNEk4ESWfcGYlmJAq6SUF3ewFCmkZDUYOUiVex0bcMBi/qaYTktg0/+aRJ+3GJj858lw8e3v4c7uTnLEkSPo+GoGqZKEvg9Xo2K/Mezy2mGP8yHrk6fkRQJ9B11FHH9w2n55f4zPkr113PoylIz9F+40CoWik8Gfwanz5yHz3aE8TVnyRjwV8dWOKJ0mEyN6B/Bogq7nSzwOFYqIVnCgs076xYcdcTAB5s4RLssOol44zc9HkYQkaSHFRpasfXVWmNbn+YkeK1myEFMFtJASn8Po2hhhaG01Xyrzs2355N87zuTubFBKZwOJdzmz2PRgfIORM0KM2srLTy4KSOFV0hcdJHByG+bNdaDY6n09zX2oYsKyhKVQ60lVDbtr3u5vDDK+GAjZhq143jwGBbDYHubQ5jl10LxMVL7kDJ6E1w/uwyja0BUk/PcPLHD/COP3wty6trtMYa+P23/BWhUgnHcfB4NDfBz6+y72QHr37XKRRFwTBNLj6ztPk+pbUKCTlKOOihVDGxbUF7IszofApHQENriHTy6oGce10LpslOaAr4ieQVplfd52YunWcxZjDY20DQVrkgZ8mtW1o6AqJKkLPZGVb0HM3eyA1dO0e4Vnea5kHXdSq6jm3bmKYJSFj2TZq711HHvyDUY4LqqOM2gxACsR7x+/1ExbJ471cevoYeuQrDdOgR26etbwYHQ7XWXL3ad5Ew+dtDE3zu6Cz7vI9wwHsJFRn1Bv7UhZSpzf9nrbMcDXXhVbK7rt/ha2ChUn19MBzDZmcycy3E1eI1X1817mfFuDmpS9kxaYrsTFy/M12gST9Ik9xGh9jPXmmQnDNBTElQyLTwyLjOhmNbyqnwtLO6TZJyZi3FfMUA3GQ6vWJg2/bmMyWEwLLsH2oHjo04e3BdaUzT4NCBjpp1fF4PC0tFgi2hzWUb57PhaW0alttopyqE4wHaj3STsxQKjka6AhlDwtMWQmv2I5BYSheQJRk57iVwPEHDvc00//geFr0GKdVAalA5eqgN07YpGy4B1WJe9h1pY09vAk1z39dYf6241VlDAoTgcGMj3pRgabUq2ciky4S8Hi6l13gymyS8xdddWp8REsCqfv300A0oioLP70NWJMrlEsGAn2AoiKqqeLyeH7hcbCc4SLfsp47bG/UKdB113G4QAuHYSJKMuKqZ8LlgrVRhuXBtMlgDzbnp9MGtGPRP77i8VXlq8/+NWpK/3fsIQTnPsH6Ej60e4mxpeyU3ogaQpTM1ywrWM7R6TxBUPBR3SGBc0NPEvQFaPU1Yjth08bhRdHobSZsFAvIT11wvpl3mVS37+MSc96aEL8vWKj41QMXafuxPLG/YDKbYG/NwMtpFuRjlK8PXD5wBeGR2mkdmpwlqGkeaW/mFoyc4GokhSc5mNddxHPx+3w8tgQYwDGOzqdY0TA4OtW++FvBpmxSp6XAXsjGNrMr4wz7IF/F4XAu6gN9LPBLC59HwqAr7jnVSMWxkRcZetw/MdQS4sJTl2//nu7S3hckWTTobwyyvlehuSWBrMumKe+1LhknJtog1BFjJlpCEYCKXJxL0EG4I0drkQ67YePwa/ftbcXwyy9naz93M5VSNdaFHkQl6NLw+P9mKTtCjYirVwd69xyMMG250eYc/fpNXUaAoMoqqICuy6zYiSev/3uSu6qjjXxDqBLqOOm43SO4XmyMc5GtGp9wcyrtMJe+GinTz1dqtOFfcy4nQk9ddL6S4FbUDvnP8fuc5vpe/h99bjNWs0xcIX51F4R6jfYYXNL6QLy1XSWi/vxmPrHKluEjWKpG1SgRlLwsVDwdiQ6TF5Rs6/qiiMBSwkKVrOxVocpIGT5o3dDyfC/kEF3LXj0cHyNplDnb4kYwohiUwbIeK5ZAqG+RNi7agiqZI5A0HHB+e4Apwc+SpaJo8Oj/LuZUl/vbHfop2j4bX66FQrnCptMLXJkaxhcMbuo5zON52/R3eYlimRblccR0mBDQ0JOjqiDE7n8FaqzCScjXJc8ki/+Pv34Esq7zvHQ8SDHnxBzx0DjTT0pnA6/Ogqgpen5dESxSP363ujlxwGzMbu4KbMzMdB4LMPrTMms8l4D5VRfLUerJfXEjSEXftEDtjEZZms6xmqlIkSYI2T4TCch5dt2mJB4hEfLRk4f6Um4ooAjJa0EdqOc/SbBbTLrEnHOBKEIqGRU/Yz75BmSdO60iqDYbrYR5Rt1tC7gZ3sGQjy2BbNrIkua4ikuT+ZbnFgyfBrQ1SqY8Pbm/UCXQdddyWkHH7grZ/BWydht9cdvX/tpRCN/775OxCzX66omFms3kSAfcLObUlVCEa9DCl3xgR3EBA0SjZVdJ9NheHlu3rGaINU4Txy/PIbK+Iq9L2imyTuvO0tRAOLd4xokoXWVtwMNjOpeKC2yCGhIyEQNDpi1O2TAzDR0RtJ8sC4UoXpXQrmurgbZ1ASAZxtYmMVSRvlylbfvAUEUJCuk71WpZsmn3f4oR8kgu5G9OnAswbaSDt/qVXAS9EQjInvW3IgfNYwp0CWHKgxdnHqw+ofPHSzU8LFE2TX/vWV/mHn3g9pXKJTyyc4a9nn958PTNm8keveiVB343FwV8qDBNTY7T7frCkOxAIYFomtmVTqejYtsXBoTZm59bYu6+Z9GqBBo+KX4LHv36Fl73+JH/w4TfgD/hxbAdFVTB0wyWK65Z9XlVhaTxJ9FALg3ftISks4vdDd0SQdYosrevOV9aKyLKEVbGR/dsjzufX8shhicaWEKsL+dowHOE27Om6+yyn10qk10qEggEmzlc/h4P39DI/U51xWbycQr1DxUJw8UqeQ3KYAz0q33kyz4l9e1EbCzdMeje04whrvRFTIK9b3SHEeohMnWLW8aOLOoGuo47bDMK217/wt8s3hHDT1EzLrAZuwBbevDvR+/zFagNd1Ovhb17/SkZW14j5vZxZTPKRZy6ymC9yZH8c4bM4W7o+gVYkiec1hTnV+BSmHeJ/XWl3u/6ReHPLMAAV0cOM1YCCxJJdYMFaAko0K20MeSKoko0ibJYcD7YQjFa6gNrGQp8ygkaQgNxN2UmSMyMUzVZmKw08k1ljINhInxbgdG5qcxtny7W4UlrCI6loqoLHbkCxI3zztGAjXOU+MYjefJakWfW+vlSe51I5REC+l15fhBZPgZh2CUXeKS5bBhyi2lN0+17LdCV33Wu3G2zhMFyZ54FwDwvW2ObyZUbIp45yPN7E6XTypstrIUvlK7PT7Iv4+fjcmc3lx9VOLnwnxcMdU7zqrn07bmtYNh5VwXAM/m7pUzyRfZI3tb3xB0qgpfWZGE3T0DQNRVEwTYvDBzr5yjeGGZ5NcWRvM9NfOo9p2CRHl3nRa4/j8/soFkqoqgKS+9GwLZtzI0sc3teOZdo0DTTxZLZ6j5TpMDNDrmWd4bOBGLYjaGsIkl4tEvbJHIkkUAMK86UiHd4gQoNzS0nsxQqe2QpNDX6Ce6MksyXaG8OIks2GEMfjURg60EzhzAoen4qxrou+8tgkQ/f0sZYrEwx68fhUghqcMTMATE+ViZ/Kce+pBkpk6fHvMCK97oV0r6Xm0dxrgut/rcgy3w+3nZs9mFsZpFJvIru9USfQddRxu+Ea3y9CCHTDwNnBb/h6eNvJw7znK4/wpiNDvOX4ASRJYqg5gYREeyTCq/cPICSJ6VKGy/kVBnIJhnMrjOZXMRybE/F27okZFK0cYdUkrOhEArOY0kX3DTT4N/vu5qn0APMlG8OzyHcrg+uEebuuecVOslLeHimeowmoVvzavDFU+QIGLayaUwi7g0/PNa+/6pL8K8WkK3dxC407whAW4yXXwaFZiyDh2aQPxYqEI3au7JYcnYulJBdLkFCP8MKG79XIOoanXs1nzwcwLOiOC151pMyirmCI5xaD/miqwJGGKFm72gi5t6PMZx632BMO4whoDgbImTpjucw1n5uBQIzRqTSfFRdo6K49trUz7nl/5Gun6WtvYKhre9LhMyML3H2gi2+lH+KJrCvLafK4TaaOcJClHxxV2RhEqpqKoRscOdTpviAESlHfTBVMLeV44uvD3P2KAwjhUKlYOOvJfbZt89/++tuUdYvB1jhrau29GX8sR9fhBLNmirJiEAv7yOQrREM+rkytEmgNMDOSpmtPDGdV50qpQFd3HFmSWBlOIQG5dJm2ngQzCyUmFmoHgM0tYc5YWTL7JBiI0qR5cQTsnwdFN1nIFCHjzsb0ao20tAfo1sIkhzPkT3uZfccKRVtnf7Q2TOZmrqG8JTRHCIFl2yj/DE2EddRxq1B/uuuo43aDEKBsnzJ2v/SsZ0WeAe7qbOUtxw/w1jsOIUsSqqqiqSoS1S9WSZIYjDYxGG3iJzgIgG4aLBSzdIUbCJi/h2Z9YXOfNs18qxxnzXYJcpP/MV7Z8RiWE2TBuomGxS0IqzNA7+bv+0MQkQeZMSfdY1TmGQgNMVqorZAHVS9lu3LNKvwGfLkEgqrtmEe7sdTylJVnLvNj9EWexlKmUewBPnG6mvG9t9Gio+Ef+Enf88mbIb60emNNfzuh4jgE5YYaAp1Rpwh49jGXd499oeD+2+jzsycaYb6UZ7lSJW9tviDNpp8r0+61Oj29RFM6QLDPSxEdv6yxPFtEQmJ5rciv/PE/8rX/+nMAWLbDn3z+cU6PLjLY1ciRwQTfTFWDZGJqlD+e/hNe3fxj9Pp7nvV53ihkWcZxHFqbG2hsCGKXTca+VWvJ+KW/eYyD9+3B7/ehV3Q8mgYeDd20yRXdQc+FuZ0i7CWCo1HocWcXEo0ugfb4VAYGmhhfy7o2hDOZzS0CUQ8HRQML+bnNZelsacdxzNxshh65gTNeAySJpOU+F9klncbe2hCU5eksvpTCeMmdHQkGPLTbQS4yy4Xcs08HddZdTVw5h1uVb2p6bk47zwZ1H+g6bhXqMxB11HGbQVqvCu0k37CsZ2+LIUkSbz95GHldG+nRNNeua93pYzc9pCLJNHkCmKaJob0LR6o6ISis8ECgRIPSULONKj878gzgURZJKC4p3RdsIarMbpLnDRyMuoTQJ2v0BGIA+BWNA5HOG3qP4fkqyYz7vcg3OKvcprXylw9r/Kd/uJtPPfp6vnDmyOZrXsVhMecOfNoDjzAY/RLtntBuu7ohbB0rNdsDpKYPYFjbGUi6UuHM8gpt/mDN8sVyEcsviGzRNifzJfqyXQgBHWoUaQvlMyybpXQB3bD4D//3a3zuO8NMLWeYWFzjG6vfouxUhxkfW/hbRkujJLTtKXg/CDiOS/4UReG+u/eyVjbYe6pn83V/yIs5FOWp+VX8fh+BYAB93fd4aXX3sB0JQVMsiDOmciw3xL6JA4TDfvb2N/LM8goXVlOUDYtIyFuznT5Xxj+pEwi611ZWZOLR3Rv8Ag212ysIEp1xSsChfa3ENQ2v4WCslulrjTF4qIXEHQ3494cpfheOrvQRvxzHMG9yZmP9cVEUZTNARbrGTE0ddfyooF6BrqOO2ww7Edlq9fn7U77RVK2m8rwbhBDYjr35f0dOYKkvwmN+bHMdRazwYj8scyePFG4+qORqSBLcGTG5rDfR4X+cjLO9iqupp3ldVzdI85TMu5kqwYqeZ0XPcyTaycUtVcGdEA96KOkuGTzWpzC5YNET6yXtnbzmdrmlRhzhNlueX5LZWuNoCUu89b5HMLbcouc3rPD3KxEq10kp3A1pQ8Wn+jCFyT+d9ZKtXNsZZTKTo90XZKGyPoCR4GIhxeFIglyleh3PTa1w7GgvimGxQO31fff//iIvPNbLUyPVZrexhRR//1kPwRdW15soTxJQAoSV5zZIuFEI4aCqKpIk8fIXHeLzXzrHtGWjeBTa793DpXaJK5LB1HfPce9AF9FgACEE5VKF2aU1IgEvjdEAQa+GgoRh2ORyZVbTRdYW8qyRJ7joITgQJW2VCHg0DrU3IQkBjsT8fHUmoK8xir1UZixZYHCwGcmvMr2Y4cpkkhOHOigUdUYmayvdfhROhRtZtMvMFgsgSVyZTLHvWAfDz8zVVK6zMzlO7zXBkugLRFi9WGThIgT8Gp6f2T47dU1smV0yDAMhXHcTVVUxDAOf7xppRD8A3EoXjjpub9QJdB111IEQAvM5VJ83IEkSHk1DVW48QENV1M3ABQkJRzkJWwj0BhqZwC13PfcvyISvwl5lDofdJRCS7PpMZw0/bHHzSOnXTzYMb3FVMCpeGvsv4mT3w3UIdKBlEa5E2ekcNQUMsVizrC34Pd7SuYePzx+iYO9+Lgoy9g7Jj+dyWeJaI4d8UbKVaw+eJAmOFWOYSYuFxtoZgIBf27b+hfNrnGhsAmrDaFZzJT79yMWaZUKAL7D9PX+67c039BxtOMY8F9cHWZaplMtUKhX2D7bT0RaloOsE33yQZ1ZztDdEWFvLsJwr8ucPn+E3Xn4XXq+Xz3z8KcbHknTHwgyPrVzzPYp5g+Izri4/A8iKhBVUsB33+FsbQvSEgpx7vJr8aAvB5dFqXPzE+QWKOZ19Q80UDYtgyIvmU8mslZidXMO2He453MoTdgqfT2Xs/MK2p2llIcfAUBOjVpGZUp6hYwnUFfum5Q/Sum0d4FrnCc3l05KErMgYxrOXGNVRxw876gS6jjpuc2xUn78fyYSa6pLhm7HCUq7SYzty947rKqTo9Rxm0ph5TscohExZlHDI3MDKTVzKXUUWVQ/X4N0A+DzVyvHYsk5LooIZPXPdt/ObjeyWLvOTR1Ls5GoQ1GZ4bUsrH1vYbm93ONRKVqwQkkNczueJqn4iqo+o5sGWKsiOj/lKnpyQOb5H4/SM7tryRcN4VYWSYbGYL9AcCuANKRjDFrIiEcVDdstFeLy0xJ6uMHHTx5UlV+drOQ6mI9iTiDKT2j3RcQOtvRZbWz5f2PACDocPXXe77ycc4WBZFrIs89KX7OejT12gUNJxhMBYM/GqCrpl8/mzIxzubKTL9vKlz53Hshze+LY78XpVzlystXOMhH0M9jXx5NnZ7e9nC/Z3N1M2LAKSxNilZTJttZ8dzVP9mu5sjbJyeZ2AJwusltx70NIeZqZURosqdDTFUYMqd9CEt9OdzSiVDeZnMwAkGoM0t4TB1BiVilgIYhUJzetB+K5dfXbTGx2kLU2diuL+X5IkvF4PQghUxUCRZRznuTW61lHHDzPqGug66rjN8Wy0z/I68b2aKEvP0S1Bchbx6B/c9fXD3hIKNznFfBW82p2sWQvXXxEoW/2smeWaZV755uoOy4USDXo/Qr5+c2ZJ2+4aIuNwqNWhOX521+2a/E/wQENV6uCRVI5GGpmzJ8g7BRatJaK+Emir5KQ5Zq0JFswF5uwJHDXJtDWJp3kRnyZxYk8LM3aWUT3NvMgRb/SiBCWmC1lOd6YxdYfI0w7PLzQToVp5nqnk4SqJrrBsjKxO1H/9afzMkkpCdt1PEnIz+viNW6pdS2N/M/vQVBXLcgeTL7h/iExJZ09zFICVTJHD0aqDyEcePsdHP/EE1nru+Vf//iK/9q/v4+C+6nE3JUL89/e/hpce7qK3YWcpyszYCiFLYuT8Eo4tmJxb48CdHfjCrva5XDRQ10X08XD1Oja1Rjf/H0sE8XtVTMthajHDmUuLXL60xOiVZUavLFMs6AwdaCHRGCSVLDB8YZHJJxfxrNemddlBGDbHD3ZsG0gLIRi5srzxC7Zlold09Irh/ugGhq5jGDqGYWAY5vqMlo3Pd+OhLN8PCOFKOG7VTx23N+oV6DrquI2xId24We2zqqhomoYjHGzbxrJtHMfBti3UHRw+buxgSvjK/wZZ7BzRDeAV47wifIDvlAo17hE3CokgM5Xl66+4DpntFTTlBgYJ9lVcOZeKQsf1389baQVqkwlPdArecOo7GOLa8oAmj6u53utvRFKKzJhXVep3CWuRJGhQopw/10DFdJhPF9BkGXPdBzxVqQ4gypLNSq9Bd3uCyWdS7An7MAb8KJJMwFEIFav3/nBDgrWRPNlshYHuJrLlnX1IvH6ZfS8OEfJB8uEu2ofacUyJ6coiZ3NniagWqlRCCBuBwCOHafOdvOa1uBkYjsViJcNSJUNWL3GHt4tQKERvexMHuho5O7nMkd4Wzk8uc358maG+BhbyeeRVm3HFpqkrQnGlxNt/6QV4NIX3/PID/OcPfg3bFvzOb7yCx75yia88+CSRxhAHEmEureY2dcNej8LepgaScxmCfg/FskHfqTa+k0/RcTKK76ksU5MpNE2hrTGEtuVxNE2L/r5G5hezUBE4yxV6OmOEE34qto1fSIzPuVr9TLpEOOwjEQ/QmAhuDjb6TY2KaSEKJrJX4zufv0BqLM0LXjzIoaOdWJbNn33oIV728v2UyzrCcXXioVAQj8eD4zjouo7j2DiOQJYlVFUjFnUTLTeaCuuo40cRdQJdRx23GYTj1MR5W/bNVZ8lXL0ogCIryJKMqmquH67jbFpZ3dxBCbz6H16TPG8gIC7xEn+UzxRuXg8tq8fRjRuL2gawhQZc3Vh3/cHG1U6A06ky7e3XdiboUDv4+mPbY72fmpN5w6nrH2tR0ugNSqSdGXbg/deEv9xCQdfpjkcIBVUqJZM1Y2fCO0mOBo8X07JJpovwuLs8DXS3Rkk0+kiVKqhFQXbN3cfodJLOrhhz6e0BMIMvCHNameSgaGfstMHl0+7y572myIfn/4p7o1EUntpcP64NPCsCbVg2C6kcE2KR4dI8c+U0c+U0K3quJhTn1ztfwctjcVRV5WUnBrg0u8q5qWX2dzVyeXaVYqpCs+NhIeM6b8R6o7z1zXdy1/17cRyHUrHMB3795Qhb8LE/fojTj04iyRLJ8RThqI/jvQnGKhWaEmHK6RJXhl1de3d3A8GuOKmpAnKTxMxajr5TcZxHUxgli8XFLCsrOYbu62Z6NY8Z9RD2aHT6FSplCyFgcTbD4rpUo6cvQfRwIx0BP1bOwLEFoyMr4FTPdXComYkLSwDsP9HJwlwG07R5+y89n0rF5IP/9Ws8+cQUb/+FB4jHQu5g2bI2f2Cj+i8jya60w7ZtSqUihmHiCJvmpmcRzvIccCuDVOq4vVEn0HXUcZvB1TAqIMCy7JvWPiuKUiPfkCTXqExWVZRnqaOWndOo1pdvfH2yNKl7SVo7ee7ujhX9xqvPAEXLx9WphTdyhnrDMqf8rTwx4hLGVLlCv4hRljK7b0MZ29kudXjpPgtDXF9yUrBlMs7NV+UBJMll/PGwl6fTS7uvKGCvHKWIharImFbtSCEa9JEcL3JysImsbKJ5ZEzDXac1FNxGoBtavIwGFsCGi9YCkWACveiuf+VJQVOPawNXewjXl8IIIRgrrvCd1TGWxwxOP51kea2IIwR9L4d5b201v8mKEy8lAMHffHGCg69tozlh8so79/PHX3gM2xHoloUsSUQ1DxNzri95Q9iPSJv83wefpGtPAwN7m/F4PSAZTE2keeLhMUIRLz/77hfwt3/2bdZWi5ijSQ7c3csz52drHqbp6arv+L6hdi6upJhIZbj7FV04GZOVM0kKaZ2ZdIFUtkQqW0KSJTwRjVLFpGFfhN7GKGRMHEfgCXm4PLlIc8DPyIh7vt6ARmtbBL9XZeTC0qbFoNerMjXpvv+//qUXYJk2v/df/pHhS0u0tccIBDQKhTymaSIr0hapljuIlQAkCRuBcASFQhGvz0sgcGslHHXUcStRJ9B11HGbQVqvHj+b6jOw6Zix476ftQ41eP1VrkKbFrtpAm3fZHpfUK2tCHf64kwWr0/CM1YRJbDMVlGwP99D+RqNhAG2NwECvHDftZ07ABwRIqQIJKQbCnq5GiVfksPtexnO7RQjDkcizcxVsrQbQVYf3p2kp3JlJAvGnl5GAKpSlbssJ/M16wbCKtrzyySN6jWONnlZKbqSkcMvdHCp/NWa3J0JtC0czmVneXh1lO+mxljR3ffz+lRUM7EpU/KZfqi1TMaSbE4/5pL7gKLwPz/6GO943UmOHOznzoEOHrsyx8TiGofbGhlet49rigXRMharqRIBv8Yf/d5Xee/7X0FPTyOGAR29EX75fS9jz95G2joS7NvfxZ996BtMLGcpVPRdR2KDQy0ousrRHrdy+/jMIo4QeAYUjsT3oC9VWF1zG1tVRaJiWEgSaKqC5QiurDt29B1w9eSiXP2M67rF9JRLlIcOtlApuE2IvQdauXRxkVP39tHdm+A//YfPMTOdRtMU/v17Xk65XERVZXx+7w1ZU8rrA+znqkt/NqgHqdRxq1An0HXUcdvB/VLbaJa6GciSjKzI3/cvRke+AYHwVehXZ5hSIuTs7bKAXd/nJrUNilRrtxFUveTt69vYAUSVEFu1FJWKCtHd1y+shYDahsVfuDsP2tPXfS9ZcuUEN0OeZaRN6cKanUPSSpSsnX2g87aOOixo6QgQ6JSYmcvsuN58MoemyhzY28Kl8WXsLWLwpdUckUY/ubJLmDuHAgwb1UpwoxYiEQ5TvFOl+GR+kwhJV1Wct1aghRBMlJb55uoFvpMaJqxEOZ+tHeDojsXeQwEuPuo+J/6yn4O5QYyUW9vWNJnZXBlEgWPRBMnJHBcLK/yvjz7G/3hfK//up57Pz33w79jbENkkz60NIVg1WFtz71dfZwNXnpnnd3/nK/z27/wYTc0hDN3krucPEgqFuHB2lj/7y4cZn1hF0xRSqaqzS2t7lIZEENNyKBR0FpMFig0Gi7O1z5lh2TyVXAYF7jjVyuqVLJGIj8VcgRbhZflKho791ZHBcqbIyUQcZcsjH4/6CIV8BGUJdJvp8RSKpjA7u4bHq3LHqT38+r/9FPm8K715+7++j/aOEJp647aUQghsywZtu7VhHXX8KKFOoOuo4zaEIwTWs2jwcaXTP4CqkhRCEEO6EWu5dSgiyYD3Tp4u3TiB3q16uRscobJVAz1SWKLZG6Jg76wP7ve1MVZxNa1e2w9b47w9Dtei3h6tlvxqsmCg/THMG+TE0+WdZxMUSeZopJWAkmWm5GOmkuJopA2vleSZkX4CfplswUY4nh23B/DJKinTQWTsTdK4G0zL4eLkMomon3S2dt3OhgiX5l2nEbNUe2JNRhMT3iI5y8CHoJzSkJolrvYMFNjoTo6vr1zmi8tnmC67+2vVWhgrbK+ghxUvyxPVKre1oHJ+tErcB7saaHW8tKAwcb7qgjIxm+bP/va7vPfdP8av/vh9/PePPQRAR2MEY7FEKW9wqK0B4VfIJ11CnFkr8V9++8u85z++iL17O1FkD//zd7/IN758nv5Trj1ja0uYfMWgozNOPl/B69e4MOzW2lVFpqUnRoPjZXGXp2V/ZyPnymuEBzT2mAEiXg9XTi8gAwEkuhNBQqqKWrYZeWISRVNo6U3gVWVE0WBxeJmuvga8IR/BsJfO3gTDl5c5fLyDP/vTR3AEaJrCK151gHvv70GRZbeqLLsNomI9snsn720hBI6z/vqzmAn5fqDujlHHrUKdQNdRx20IyzKfle/zc7WpuxYcuRPFydzUNo3KtZPztr3HTVagbVH7J7I30ETJ2Z1AKrqfXl8Lk5VlCvnaa6Wo15bLeIIltjqLHmp1MMWNSVSEgLRZpsfTjSQ0EKDJEonQeRxRwGaKioAmH3QF9lCxn+bxcy9jLFWVVbSEJRRN4g6lmYLfouSYJPUyRcskXHariZWyRb64vdHxahztb+XcyHYtdVyrVkhjoqr3jql+nr6QwRbgR6ZHDnDxU3me93OH4fDnanpF89YCn198M/+4/DJWjOrxL1eKlHeQJHVn2ji/7K4X0FQ0G463NGKWLVZTRWbOJPH7NEq6ua0n9SuPXKEpEmBk1ZWt7GmKUJgpoJctDrTEGH9ihtauGPP5yuamq6sF/vxPH+Xn3no/n/r4Yww/PokEzJydp3FPjGDQw8xilqxcwXEEmS3Xs/9AC+cnV2AZDh9r4ny2OiDQFJn9PU1uFVqCIiY94RD+iRJHOxsQmTKX/uE8AEmgo6/RtZ0zbfyWzfxYEiGgpSNCcjFPpZxGAHpzGEmC86fn6elr4u7n93H3vXsIh33Ylk0uV0RVFVRVxnEEFd0g4PO6A/AtPu5rmTym6V5/TZUJBOv65zp+tFEn0HXUcZvB1T7ffPVZU7XN1LEbwUYVyrEdJFlClq4j/ZBuPvJXvsmKsiJpWOLG09E2mus2EFQ9TBd2t5Mre3KcP6sALSSt2sq4pN1cKtup7huTigBI4gRTM608XbbYGsTyS3cLCqK6H0kCXcyQmnlFDXkGWCmUORFt4vJwlbRFghoDB+JMnnOb5krl6w9YOpsjO5JnACOnc1ewEScgcf7xZUJDXgq2TqMWYXb9vqiSwtKye+2Spw32HLn6Hjvodj+6UzsA7A5EuZSvVpAloFWJEl7ROOmNsSKbLKQKlIMmAVRsGRpbQzQnQpimjepRsB2HXEFnNV1EUxU6YkE+8amnCQQ0jt3Rxvy5JHrFYrApwviTbjBKvCXCXL52RmJ8PMlvve+zyBKEgh5KRQNTt2iVZRCCgb5GRidqB0dD+1s5P7G8adWiz1dg3Tq6JRpECas8tbpcQ/KfyiU5tWQzPZVm38G2mv0tTqfYf7Kb4QtLzE2kGDjUiuQI5mYzVMomkizRfrgFW5I4fmcP556ZYe++BK989QH8fh/CcZAlCdMycRyHS6MzNDVEWU6ucXCoB7/Hi6JqyLJMNl/k0ug0TYk4umFQLlWIx6M1Ep5bAUHdn7mOW4c6ga6jjtsM5rPQPkuShKoqyDcRq2zZrtWV7ThuSpnm2TF8ZfM9nGv7HG97HzSerBSuv+IWtGjtzOijN7y+T8nBluAW3bHoCTQxVdoeeAIwo6/SEOpkKVNbpZUliYq6c4NeFbXXpbdp+obr5apcIlU2t+0DqwPk2mN1ckf5+uXadEVwq9glHHo6okzOZ5CQKBZNJp6sukPkCjtLV7aiWN59oFApVJgZXkUA/cdakHIWVtgmnPXygkoE1QTNkbi4Ll8oFXd+VhbLgxhO7QBDkwRDwQRBPGTPqCyNlEkWHTrjgrFLK9DjZoULReLcuKuTjgS92GWbsl4ddPg8Kl1tUfyyzMSYe89KJZNKtoK3K8CQ1cDFb1afocXU7s+gI6C1L8HEeVfWM3tlhdi+BLlchbbWCItLObr3NOCPerk8s1rjc+jxKfiaPEiSxKJRwspv/8xakqB0fxxlNsPIxUW6B1uYubJM975mKhWLuZEkPp9GU0uI7FKO5EKOoRNdDF9covtoOyPrbiIT81kkj8S+Y41Mz6/Q09nMwtIqPV2tLK9mUBWZuYUkmqKQzuQZHplBUWQODu11K9BCEA4FOXJgL8VSme88dpZ8sczU3DL3nIzven3qqONfMupJhHXUcZvh2YcbuF/uQghs28bc4gdr2Tb2+o9l21R0Hd0wsNfDOIQQGKaxO3EXAuk6QSFXY40jrFrXI6W1kJ0zhNXG66+4DumqUJORwjKadO26Q3+nd9uyI20xCurOpHsDKbHIeuAcmixAGbuhY/RIbSwVumjaoYAvSbX3Wgj4+ummXfc1kllj2MrQM9hAJFirifbIMqXK9SvQa7kyXs/2a9QQ9jF7xb1fEuAxJUY+k2Pir4vwmMrM1+eYeGiO5fPVe1ooCEynkdXK8/le8qeYyP8YZ9Ov5Z+WTXS79lgm9WWiRgDjisbs+SKVdTu8DVLqUdcHQuuPoCxLhCM+Bo+04A9XZ1YqhkVTY4hXPbCfO090oWnudnLeZjqX59ulJP0v7MXr0+jZ10yhbBCO+GhsCtHZGaO9PYasVInwyGSKnn1Nm2+dzVbQdZu2hiB79jVSlGzOTyxjbqnWqooMHRo5wyCr61jrn5s9sQhtYdexRpEl7gk2UfhmkoHjne4yn4fBO7tZWcqxPJehkC0T1CRUCZILblV/4uIijS1BksXawZBAYmigC8O0WFxOsbKawXEcMtk8jiMIB/1EwwE8Ho2+7lbKFYNCqSpnSqbWeOLMMGcujBGNBFFkmWDg5meVnivELfyp4/ZGvQJdRx23GZ6N9tltGnLWI2wFuqHfdHqhIwSO42yGsNSigMT1q5tbMWFcX4t7NYRUpll2yN+ge5/pxLi6iW2lkscveyg7O1daS54sV1eCw0GH6zk0F50yJ/b6eWqszGCTwObazXobsJ0E//Xp2LblXgVkNYfPuAOPWqEormBLFqZz/ft2OZPmSFcjuctVMms5Dof7mxkeu85AR5LobIkyPls7uMmWdFoSATLrDXeFXPX87C3PkqpWn49ixcOfjh1e/82tlvpklVa/wqrl1FzldjXBuc+YxIM+WttiqIqMKst4hcregy0EAhp52UNYUhkH9pxq4gknCeTouidM4HGJUta9p/ce24My6OcX772f7z09w5e/cxmPUInoHnIlg0crazSeasATjFJ5LEdFt8hvUcTE4wGaWsKMDLuSjOVchWDIC4pExrBRVZmJyytE+2JuBXtL5TkU9NDWGsFadXiev4lSUPBMxZV7BCSVkVSOfc1xmvMKs383BcDoVJrDp3q4cmkBo2Kx72ALo0+5EpP0cp723ka6j7SjRf1kSwaRgEZqBycVn9eLR1Ndf3hckmg7AlmSkGUZRVEIBHyEgn40TcXZ8izFImH293cjSVApVyhXdFZWt79HHXX8qKBega6jjjpuCLphYJiuHvLZVl+cLd37WyE5Nxdw4tDApDFz/RV3gCVGaPfuvaF1zR2cKdJmkYgapN2389T0rL5KR7y28qb5bozst7a4g4jMTYwlrg4a2cAdLQn+4nQz/++Cwp+eCfKFyyeZSd5Hb2eQwA1o2adLObbW2Rzg6ewK3e2x6267ktkuEbFtQTQR2Py9XNwyAJGrMhnTtDfNfIWzXUOrIJOyc9tSHT1Co6ybLKTzTK9kGV9c48p8iqJhMTybopLRmT23QjHlXtwFqSoBmS3mce4O4AtpyLKE2SXx7578Kh848zD7e5pZMnSeyqQwHcGhnmYEsGrofHdthZ4XdSPJtQeztlZidGSFQ0fbkWTIFw2aexsIxXzs729mqK+ZQkEn6FS/ggXQt6cBn19jZHKViYkUly8uEzTcdRq9PgzLnVHIl3TyEfBG3dkOw7Qp2TZGxR0ZToysEm0MsvdAG217GigYFqPJApfGksSCPsYeGadVYZtp8sjELGvZAs1NCVRVZXRinmyuiKzIaJpCMp1FU90QJk1VkOVqmFIw4CMcChDwe5ElCcO4ealYHXX8S0K9Al1HHXXcEIQQmJa5Ldj6ZmDZFuoOOmj5JuUbafqAG9cyXw2fmEZGua4rhyRtJwAR1U9cC6LKMkHFh4RgpFjbNNfX6WF+rcqCLe3GEgJT8iLQQmoX7e/OB7nzYt1WsbY0VKV1h0cWHGCJ9rYwoVSAleLujYpZ3WCwLcrM4pZmSEkiFN4uUbkauZJOQ8TP2pYq8/OPd/PSk/2oyCiKhG3ZVIRBYzzCU4/PcPfedvb2txIK+xCywDAMynoJqVPCp2p4ZAVFkjF0A69Xxet1j8PV5Utc+NYE1tmHQQhaD3Uwv5ADAfedbOU9b38hkpD40P/3DbIeG19PAHvVplPyIyHRkPCTkUx894c5sODnmbw7oHsyuUDPqRh//u5X85sf/hqzqznOT69wqLuJi9OuJOf0/DJD97eTeTKJXqoOCoQjKBUN+odaGL20zOhUmqOHWjh3bv1ZEQK5YnGsMYYS86B7Fc6vx3pvRXImT1dfkL5KiG+vH5ciSfiGy2Sy1YHZyGiSnp44+UyZtuYQss/DpcfcEJ49De7A5fDeFi4/NIIELI2k6OltINAZw/aqjI4laYiF6ehoIBwMcOTAXgrFMj172vBqCsH9fZimjderoSgKg/3deDzuQCwUDLB/oJtKpYJe0XEch1gkgHyr+/lE3caujluHOoGuo446bhkcx8G27R1Sym7uS2/OfC40HmyWaPAcZ9WYveZ68g6x0TmrjCJJnMu522qSQqs3ypJeJckFLcPWCb7r6Z8317OLtMU8LGZMNKkZ84YGFjfpRIJEhxTjzC6NkFvhj2pwFafb6uzXGAvQFAsiBEjrGl4FeNn9+xjqaUKWFYRwqJTL9He3onm0Gh/hQr6IJEmYxQqf/cwTADS1hOnb28Tdz9vHy151hEwmg6opqOsa5lKxjCRL+H21Vf4Jx0NqPuMeV38L6RW3uc8ryzQnQiiKgsergrBJlSs1mTXpbJmT+9sZX07jOxzfjDM3HJt/9a1P8YWXvpn/9c5X8eY/+jQV0yJbqp1RuLyUoutYHN9Ykez6+7Z3RJlbzNKYqKZszi8VkCRoiPqJKwqqI6gUTGjwMrKSpqM9yvxC7WBrNV0kWPEwOlAdzCmSTDlZW+X3elWKc2kq+QqXJ1Y4fN++zddkRWbvC7tZO7dU80mLRgNc/vYEv/P/3kxHXyMej4LH512vKPsJBvwIx8GxLdQttnSypODzaZufYVmWsG0LQzfw+Xz4/X4URSYcDlFHHT+qqBPoOuqo45bCsu1tbhxCit3UPrzXaeS7Eag3sA+vkmZrHPcGth67KWwUVBRJxl631ZvTU3Q0dDGfdqvQEbONtGfqho6rp1VlMWNimZ2gXp9A7ybh2G1I0qiFEA70xKNMpDPX3Pd8pYhA1ITn5M1qlbUpGuDKeijJoPCyMO3qlDtecYTmeABFcZ1biiXVbTq17c0mVCHcZ0GSaqN5kst5kst5Hv/eBJl0ide9+RTJZBJVU1AUBV3XXX2uECjrenoBGHqV1DpbGvJM01q3U1sftAm3grv1ItmOIJUqkc5WGEuuMenJsCcQJWWUmCu64ubGSIDXPe8AH/vWOUAwONDI6MoavY0xxsZTzKaydO2LElclKNvkSwa6bmFv0QmvrhY5NtTM/KUVfD0JLkys0t0WJeu4A8KCsHYk0W37G3iiWH0WMrpOyKwdOEVDGuVlg0rZpOsF/Ux3+nBae12P57JJeC2LNFw7aNIrJp37W/jYpTF+52gnYhd9vOQmKIEASZbhKktK27YxdINQOIjXe/247x8o6qqROm4R6gS6jjrquKXYKQ3QkdsRaEg3KBCJKsr1V7rmMUDOSl93PUkeQ5OOYYpaqYd+VWDHbCXN0Ugn5/NuVbrH20zWWz3GuZk4/r1T23S7OyEWdd/r/NweDvU8U/OaKh3gm8t9dPhM9ka+unE219/pFiSNApGIh2YtzEQ6Q2PAT1skxGQ6iy0cyuthGLIkocgSdxoRNI9KY1uI17ziEK2xMF/8/Hm+/rVL+A3XDaK1JKGEFD70kbdgWTaKbCNLVXK7obX9q/d+hitPTLkVaKdKpJvv6sVzpAGBO0sRC/v5g3e/DAkJTdMIBAI4wsbj0bBtd99//btf5jtfOEukIUg2V6H3YDtSwIsQMPL0zCbpH728yE/+/F/VXIMI0NwSZnl13YLuYHBTX+xVFdqVKJWyQ1QOkFOqMpc3PP8Qn/3eMELA8FIK2xFcXkqRCHrJFXUiMT+loxqd83D+4ry70VX3fHa5gCLD5Xl3sFGeW8NKKPi9KnJJsJCr9Q8fPNCC4YGQrlKw3HuT1w2c++PwqeLm7V9JVwh1N9N4RxOnl1KQrCXhA4ko2YiPctotvQfCXhZn15h9YQJ7ZIbkUg5VU0k0ha/zBF09ewS27SAE17SprKOOHzXUmwjrqKOOWwpZ3uFLVopiqa+84X0E5ZsLJbkaHqWDknN9XbKEZ8dI4unSKq3eaM2ykm2gSgpRNcAzpx1GFqtT7GOpHMWx48Szx/E4wat3V4v1t/vEaR+yfbT2PawGHll1OJtVt2+w825qcKIxgSMJgoqHVMmtjnfFI6StCp2JMAOJBpqDATojYYaaEszbRVYXCoyeX0YrCgY7moiHA6iSRCFTQao4HOpsJJssUcjptLRG6OiMEwq7U/herwdVVZFl9/9Lkynmx5ZZGF9hcTLJykya5GyaSqFCOldmLVcmW9CZXsyg+TT8QRXbtolGoyiyimVZSEhIsoTjOFimjV4xsS2XwAnHPXFpnT5LwODeZt7z7hfi9+3eONkSD9IUce+L6RUsFAuslEssFAvsDSY214sEvLzu3iEkiZrKcr7sVsXztsGVlVWKEYmmJle+4Ni1dyKVKRMdaMSyHNqaQpQKOknDIJkpYfpgaKiZof0tNDYG6etv5PzyKp68oPWsxSFvbHM/V9bW6HxNz+bvkiwRvrOZy0s7WzueX0rR8vxu+l/Uz6H7++geaCb+oj0YwuHewS6+98guPQWSVHUJuRY3lvihIM+uU9Ct+anj9ka9Al1HHXXcUijKzuN2U3sTmvUPN7QP9Tm1MoIktwOT111Ptw5gie2NhmXHRLMMDoe7WDVzLFayjBZXCCs+omqAO/dFeOxKbdLfRDrHRBp64gM0DAxjSTs7c6yubVSuJb56cR8vPXJ287Wi5TbOWc7WL+/tVFlTJPY0r9IYjZAuaYxk8jR4fUQjC7DqRUJisVSgMeDnqdQSSHA03kyurKP5FaaLOVjPZVHWPY0t297ULFuWe/1F0WTqTBIJ16Iwm8mhKAqGaWJZNnK5ggAs06SQL1Iu7GzNJ2wHhIwsSS5fk2WWV9I0xgNkMhmamppQVZV0Oocj3IQ81SMTjgfw+jVUvwevTyWWCNYQOUkCRZU4NNTMf/71B/i/f/sUhaKBLMvE4oFNS8WRlTRBr4e21hAXS7UEVHVqZzuO9rbyrfNTm7+HvBp60eTwvlaeWXC10yMiT4PhPjcrK3k698SYn8lsbjM6usqRfY2MPTqFZTrsnTdY3OMjmSuTiAfx+lTm7DKk3OtVmipQzBmIJzLsORlkxnQHZ4+XVxlqC5JfLLL3Bd08uXBtyY8S8vD4Uoojjo/5Z2YZ2DPEr778Ln7yziH+47s/ST5n4A9oOLZYt50U+Pwa/rCPl73iwK6fXcdxtgxZ6qjj9kCdQNdRRx0/FBBy3w3LOPLiOlXc672XncInh6k4+Wuut1SOA6s7vpazypzLzXIo0sFixa1m5+0K+XIFr5alLdbCYmY7SZ5ay9NX7mc1cHHH/eZLVYlLLVGGrOlWUa0ae7BaSYwqwWsO2cyb46CCEoH9YQkNPxUsoA3JlihZJk3hAKu5MjaCs2su+QobGveJFnJhC48uEWuxaWyNEI758fl9yLKE1+va+7V2xvgvf/RafvGB/0lhIcOVp+c58YJ9OI7Dr/3ipyjkdUBwx117+LfveRnv+uu38ZY//wJDUpjFybWqi5osiHzhPAA//7s/yUt/9l63MVGScGwHwzAIBAKUyiUqlQoeTeWt7/sx3vq+VyPEure4cG0Sq0VQV7RbKet4PBr9fa389r9/KY7jEAz48ayfgyRJPP93P4ItCzJNBnqldsA0lcviCNcL2bEdhqdXaiqtQa+G4oOJzNrmsnSlQsuhOMpjuqu/ll2levXQJKYWcnh8GpapszC+SmQtwNBL9vLQYjXOewPesHuspZJBw0WN+H4Pa7bBnaEEC4vTBGI+LqSvLUnSVJlkSQdJ4pyi0/0TA/zSz7+Ajq4G5mfTTI4lmRzb3li670AbwxNJXvSSoc1Gzg1s6NnL5Qqax20qdNaTR/+5qtF157w6bhXqEo466qjjlsLZNcijeMMa6FXruVWgTTFBq7pAgHuQrlE1U2/gL2Te2m7arDsWvZ271ycya9sbEzewVqgSYuOqwJe04RKYpC7QJDfdbqvVXn/cy2uOmMybU7UbSgJTKmFLBnc0tDKXLmAKh7HcGvZGBVvAfjVGfFYlk9ZZeiiNNm5zeT7N5bkURctCVRVUVUXT3HOTkFDV6nkqquuWISsKtu2s/whyJYP/89AZ/u6pKzgSSIqMQNqUB0hbwnUkScLj8eDzefF4NDxejVw+x2f+38MkGhIE/AFkWcHj8eD1evD5fHg8Hjxedxuvd+PHg8fjwTId8hmd9HKRpek1ZkdTTI4scmFynm9fmuC7lyfpaYzQPBgkWdlu67dWqXAxlUQIQTZf4p++MEyLt3r/Ah4PA31NaLJMyFuViQwX1+i7uwOEQFNl+gdqEzBzJZOmoVYAPD6VQEOQS58f5kRSZjBQ615RjspoXvc6p1NF+mc1TgYaWPise59b7myjfPXDchW6GyJk8tUZgONDHXR0NQBw+vFp+gaaa0JsNiCvL9tIZNyAEALDMMlkcpiGiaEbZNYypNMZ8vkipmnWfaDr+JFGvQJdRx11/JAgsO73cP0vXU167mN/gU6pIlB8GjY7a6pb/Ctcr84wXUrRG0gwVa6d+rfU3cNTLi3l2N+uYUvVgYBHUlEkhWJF4Fuv9J1fVHidFMcUa0hojOTdqmrehgdn7uINe77NVgmHX5NZMK9tzafIguSWCOZGvPQSIbtaYX4thypLNIZcgrg1bdI0bfSKjqIoBIMa7e1RwmEP5XKZxvYopVwFSQJDN3Bsm3DEh6LISBJYCB56ZhJJkmjzBghZEk1+DQcQXhWfZdHQHluXb4BpWCTnMjS2R/H4NDyaxD989LuMnJvlV37v9Xz0v32BYr6EZTiUHNDLJtF4gJmRZSplk+e94iA//SsPAPCZDz/KP33mbM018AU8/NLvv4oxvcAnn7lCrqzDqqA9HESWJeIRP2m/jiMgVS6zmCuwP5bg7798gVS6hLqswTpXDvu9DC8kKeomAY/KgbYElxbdZ2FNLnNnT4KCJTG5tMbegSbmZtfQ1wNPRuczHLyjE0NIoMj4UgXmxlZhbJX9L+lhuOTOkIwvr9F5JEpoVmdtqUA5b+B8JUV7U5B4U5TkpVX69kaxEUwna5sQNxCUFea2RLGfOujGfwsh+KcvnmduOo3Xq9I72IKiyMxMpSiVDCQJTtyxp0a+IYTA0A3y+SKaphIOB3FsgUCg6zqmaWIYBrFo5JrP4vcbrjtLXUZSx61BnUDXUUcdtxTyblO7koLrj3D95r4m5ftT2eoMFZizdm9IVOSLBJQ7KNnXbloMq9sryhPGEgOtexhd2l7VrFg2UWMPae/45rKfavTz+shnobd23WnnOE+U1pgrvpCxYrU6PVVS+PzYK8lWqpXHZNEiWFvo3IRsaXRNHGStoPDChlYeXVvhjkyM1ONpRrpT7G2Jc6o9xNTFJLLhEq3aWyXh9bmk+OWvPMzLX3l485X/9ql31LyXYVo88LIhPv4R19s5Xg7hXK5KHCSvTPn0NAB/8shv8M2/e4rhrwyvv6iwNJ3iN378Q/zWR97Gobv7kBS3cfB7X7vIzNgyc8ML7jkpEk7ADQnpG2phftIlrquLWb71udM4joPjCPYd66yRFwhN4U//6CF+7bdezI+//dX8/dlxHnz8Atm8O+hp3RNhMeVKIt64fz8v6e4hlc7xha9dcs+vbIEGDSE/c6kcRd29Xv2tCeZWM67cQwgu5fP4UwWC/gSdiTAz0ylsW9C5J47frzE+lmQsXSKoqWTWynR0NrA6uYpl2oQXKxCF9niImM+LIwSzDRU6/XE6VY1zIysUcxVSKwVaDrWSNS0aV+1dv9U1w+FwWwPnFlPIksThvS3u8zWxyty0e666bjF2ydVxR2J+evub0Pwa//bXX7q5HyEEum5QyBfxB1zPZ2ldd+6sx35rHo1ioUQ+XyAeT2w/mDrq+BFAnUDXUUcdtwzS+nT9bvpIIUWQxPUJtMFzs7HbfD/n2tUqSbI4FInwxNrOOugNrBoFJAFbi18C8LTkkJYUV65w9b4tP2wJ9dutctYtn2ZFeRm/O13757rf38rDk2m2Nm4tFw36kHF2CFdpnRgify7A+LkFonuDHIv4WTi9jKrKRP0BxpfW8IfilEsmsZBv/ZggFPCgqjIej0ylUkGWZWRZRgj3rCRJQtXUTc0yVINStl6LWmx5TbAZCQ0w/NQ0j339MuD6C2/1dAaYm1yt2XYDW9dLLqzxZ+8/416ne/cyMlp7/xpbI2gehb//5Dl+9hfu4mfvOsDrTu7nbx49x7cnZjmTdvXgPeEI+nCJM7E5SnndjRkH0ktF4q0hQpqHmZT7vMaDPoZnV9jX2Uhqoaolzl9YIJ0cI/7ACToaQixmSszNuoOJ/UOtLC/n8PlUpEyZ+aUch452kk8VKMoSsiThmyyzuJikcU+Uoc4IZctg6pkZ+rpirKyV6elKUPErxBaLlEsWrf0hTNsmlatt2BQFA/vMCi2S69jxZ3/4NX7x11/Ck98d5+iJLlaWcyzOZTZHTblMmWhDgL6BZiKRjedBoFd0yuUKwaCria/9LAss28YrewmFg+RzhW13/gcKQe2HsI46foCoE+g66qjjlkGRlc3wi50gpCiIa0sQANLWtSO4rwch4DujP8Pomo8XHz9/zXW7gtM8sXbtpsWFSoYj0U7O5+Zqli+ZGfqa9zC+sr0K/Z1LOvce7WFtPWDlWnmCqbLK1Q4HhlivEgs4GW9HADmrQuN0E2bZuqqhDvQZH45l09UeZXY8W1Pnz65b2sleiY72CNGwj0hbgDe/5CD79jQiSRJ6Rcda94hWNZVcusjMlWUcx3Eb5GQJVZFh3WIuk6o2aAqn9uz8ER8d+5rRNAXd0AknAuy7Y4/7PiWdQjpPZ28DkxdmKBdLOJJMojVMIORBOA6SLLs6bNPCVmRkWSIQ0ujd71ZVvT6N3lM9CE2moS3CXa3RzQo0QEW3GR1f5fy5Bf7qzx/lLe+4i47OBL/yklPMf6vI6GSWO+ItBFMS3zk9zdnhJf7qt36K//37P8VHP/kkT56ZpSsa4dx81fWiuzHK2allTLv6bEZ8HvLjLnnXz45Q7O+mJeYnKbmWf5Nn5+nsTTC5mGHvUDNjwysMj63Q3hVjcSlPc1cD+nweCUjNZEnNZNmzrwlTlpgaXqahOczi9CrNooHkiFtFFnM5Wl/Zu41Ayz6VmfEkQ3f3cWlkhUcfGmXk4iKqppBZyGLoJnv6GpmZz7okWgiCER+BgIZhmNiWhW4YCEcQCgVRNXXHgfCG/7csy4gfAlu7Our4QaFOoOuoo45bBtu2sGwFdZfABSFFd9hqO1as61epr4WS3sM/jltEPCYeKYwhdnfjkJURjkVfypnstavQpV0aG1uaJcZX4HnHgphllcevuMduOg6ebIz/cjxFQC6SkJd23feVUmDbsrxucGe0HdN2eHpheXP5kqox6I2RnSyRSlZJVIPfZp8TIOip9UP2ehTCfg+9zTEApsdSPK8nwXvf/gC6ruPxeFzrOtMmGAqgqiqOIxgem+X/+5VP7XrM/S+oRklffa9LssZsCd767vtoaIryvNcc48X/6i6EEPzf//BpTn/FHdT87bkZ9p7sY2Iqy8FT3YRao1iWzdnzS8iKTEIRrC641dy+Qx1MTlSdKPa8ZJArUymOv2SI1732BKqqUC5XUFWV3/w3n6JYWNeoqzK/9d4v83t/+GpMX4HptSwNPh/jT6zQqbrX/XUvPkQsEiAWCfC+f/dSvn16mvd+4eHN94r4PVyeW8WnykysZjaXq1sGi+mpFfY0RVkqmzQ2hyks5LAMm6krKwye6GB0MkVnawh5OUOwUEY4gvCCSexkGxPfmkY4gtbWMF5HsLji2tg1tcXIrBW5ssU9Y8/Jds4sbPeCNgLu171VrsqRUskC+/a3srJe1Z+dWOXAiS4c22FlIQuOQ7zBR7FQRFFVNFXD6/NcMzBFWp9NEEIwv5Siq6Nzx/XqqONfOuoEuo466rhlEIBpmW5z2Q6yBls5iWp/97r72UmicDNYyBwBIGfoaDRgsDOBdlsaBR2BEmeuw9kzZnHH5aPODHfv72bYmKLRFwY8bFSTVWFxwPvMjtttxdP57X+q/cUYTy8tb1tesEyetpLQDHv3RChdqlApW+vnUyt76O2OY0dk5o0iZ2dXONbWtHneAB/82CNMLhaIhnz86uvvIBINYZoWEzMLlByXnPf/2GGSJQNZkvCrCqZlIxxBVt99lmDDf1mSZGRFQTiuFd307BK2ffV27rG86wM/TlNHjGKhxFtf+ec4jiDe17hJoJFg/8luSkVXF7xh9iIhVXNA1mPDN7ytwxEfY+Or6LrFt745yoimMz+SpqMzSlNbhMtXVjg00EJTc9UVQ5ZlzGSeO6Uwac1mXC/S1xzn7NQy+2JR7KyNp93HRDrDvqzD9JYzmXlylM4TfZQrPjx+jVDERyFXwSyZmKZDwNCZGFvB41PpuXMvU7M5hBDsPdKKajhcubJCw3qD6cGTPVw+M0uiL4GTEUTbwviONRJQNOT54ja3G3n9xhczV0k7rprNGX5mlmDER2dPnF95z0tpaI6grCd/XteaToBlWQjhwTQt0pmdGxp/kKgbf9Rxq1An0HXUUcctheO49maSst0r1tJeh2b+DbK4tqetT/Je8/XrYTzdBKwTXju6q9HGcub59MUXQB0FOq65z5RRIKh6KdpV942hYJTLxSyj0hQAJafCfcdjpFcUhuerJO9aEHi4kK9xEQYB46nrV+HHKzle1NZGuWSgCLBTDrZl4/UqtDYEmb6UJHJXHHuddRi65eqR4wpfuDDK6ZFl8iWL2eUsf/KZJ9i3v4VQwM98YRVLFbS98yCW8GLPWSxNZQn5NEpFt8LZf6yVzs4AEhKK4dDV34C2YXPnk2mNtFKSXauztWyB8kqaxeUU9lVyj2KmwODhtk1CKNalKULAyGiSg/cMcOnRURxHcOnCAv0v7Cdp2iSn3Sqs7ThYlg1CcsNdtlSF+4aauXzZHYQMDy9zJlhGQiK9VMTUXVnLQr5IY7Qq4XEch4WxVRb+yaXG9+xvphhzZx/CjsqVuTXiS0V6H73M9PT2SvDcMxMEE0niLz2JEAJFlVA1GQmB5HP9no2KxfKjY+x/3gBrFiSnM2TWia+jWwwe7eLy2Vn6jnVwZcStPkc6Iyi2zOWvjtEW9dJ6uIWUx2YqmWWgOYY16g400is58HlAkmjvijMxXJ35CIW8tDYHsCSZK+eX+MxHnqChKcQrX3eCSGz7LMjVELgR7QCr6SzF4s7BOXXU8aOAOoGuo446bjlM03RT4q6O9ZZ82OqLkM1PX3N7r7R7LPP1UKp08+hc1btZN8M1zXyb7+Gc4NNj0BPp5NX7Jkl49pEydq4yg1td7ws0cT7v6qB/rCnC+zs+zIeWfxbLgc8lTX65s8J/m56FKJwK9uFcx7sXwBZ+ttVyJRhsjvD0/M6xzVsxOZMiX3IJ3lAohCYs/EJibiztUvKN6y8EsgShoMoXpyb44tQErch4NAXHcVjJlnnsO64LReOBAONWAWLQsqgz2BWFqeymxhhgxCmSrbiVaWXDPWRdPdDiCbBolOgtZJlfWmVxOUVHWxP5wnat+NLYMktjy+C8dv06C7oHmzF0m1JR5/LcGgfu24e+rvkVV41Kzj8xw9//r0cA6Lmzi0vJHL2RIK3tUZ654qYw9u9vwS5bDMSjXFpd40BTgotXlmmKB1nIFnns0ix72+JEg24z3fJCdfBSLprMP7bMC0528PTwEgFZouHpcZZ2IM8bKKbyjH/yIQ698QWM5yqMXFom3BKisuXQbctGzxTJoqApMvv7W5Ach9L8GmurRTr3t3JlJIksSwwebmdmdI1C3n2uy1mdye/M0PfSXkrRIKGLGeYuuM4letkk2BiiVDKJxXwEhlowDRPVq7F8bo7x+TT77+4D4Jvr4Tb3vWT/DRHo9RhIHEewnExzYF/PDWzzfUa9Al3HLUI9SKWOOurYFbIso6nPnqzuBkc4VHQd09oetmArz7vu9l755o5JlfybsgS/Z4GmQJW4FirhHbdZLrrT9lM5B68cIap5rvkebd4Qr28aQZPc6e6XxEYBeHfL3/Bv2/6Gvxl8jNfEvsBb2l0SlvGv1FSgTeElJbpr9mkLmYv2Qe6Mbz/fruY8ypbBR8Lj4Z5Om5cdT/HCozM8cGyalx/Sa0htRbZZmsmRX6sOIAKGTCQFgaRgYjiFtcXNwpQEFWFjSAJpa5DGVocNITibT1M44UdsKck4zu4ymw0rQ69HY2ZuGUmC1qY4TYnYrttVyhUq5QqWaTE9vsr8TJq1VBHLdLgwvkqgNYqibnd4cbY6fmy54ErEiyPAEWBaDlNTKZRlncagj5Fxt6rb2hxmf2uCTz50njf9/qf46NfPUNZN8tnq9Yu0huntbeD0FbeSu7+iszS6u55963U7/4mH6FQsAkEvsbAfdYsbSe+9/aS9Xva2N1BaLjB6do658STZdAl/c4jJKXeWJtEcxvHIVMrmNvKoZUzMJ5LMZSt4A+7zm+iOk+htpK0rzqUzC4xdSTI9mWH8cnLbtff4NIZOdqPr1x/orZ8VQrj72NPZSlvL7W1h97a3vY3m5mYOHTq0uewDH/gAHR0dHDt2jGPHjvGlL31p87U/+IM/oL+/n8HBQb761a9uLv/KV77C4OAg/f39/OEf/uHm8snJSe666y76+/t5wxvegGG4I1Rd13nDG95Af38/d911F1NTUz/4k70NUa9A11FHHTtCUzU01e20d4Szgzb1ucOybFSltpvfVu5A4EVi9yCSiHxjNnYyHhKeAUr2KD65i7ydwavEeMPBNB98zP1yH01GORpVcK6q88Z9OuCSXYUw+wJeJnYoQAcVDy9JePnV1r8jqBQZa/sZPrpg81dLg9yz99ub6+3xTgHwS80f4xvpn2ehUuBYR4hR/QBZJ8GkVcFgkv3++2iRM0xbUeaNeQxxmYORdp5cq5Lon2jz8ePNH6TB+kX+8ryHkx0xQq1PgGZsRsIIQCePLB8HoLczSvobi0hXV+hkaIj6URQZ4TgofpnFdXnLVi661UljK0WVJAnbEdiOjawogEu2NiQXV8tmE61+lLDM/kQT42tZcrJNpWxw6cwaq6ksxp4Q3a8/jN0SZEDx0yC7X1PffWYW44kZLMtm376mTX06koSQQAibtqgPf9niSEecg3d34/EqLE6m0E50ISHhaDIHuxvRDBtHluhoDjO/nMPRLXw+DdmS6FT9rAYlLOFgFEymple5o7OJkkfw4S8/zWe+fYm+ovts7j3VzehKFiei4giB16Ow8r3J7Q/JNTDyjbMk9rbS0HsULPfaDbzoAFIogDye5PJ81dmlpTlEJV1iMVl9EJNLObSmAHsGm5i4sMS+g23u7I6qcPGMW3VWVQVv0AMSWPEQkxOr9Pc3bTsWactsBLJEx74WLl1Y5JtfucTewZbrnoskSes2hxKNDTfWEPz9hfRDFaTy8z//87z73e/mLW95S83yX/u1X+M3fuM3apZdunSJBx98kIsXL7KwsMBLXvISRkZGAPjlX/5lvv71r9PZ2cmdd97Ja17zGg4cOMB73vMefu3Xfo03vvGNvPOd7+TDH/4w73rXu/jwhz9MPB5nbGyMBx98kPe85z188pOfvGXnfbugTqDrqOM2hiuhcBPnNqpPsizjUbXNTnshBIos/0AItCOcWr9gIQAvFc8H8JgfRhFjO27XIU/hk3xUxPYY7a1o9AyQs84AULTH8UpxfLKXrtgs4BLoK8kyd+0doCRdrtk2oBXZINAfPd/L4SadA4EeLpWmNtf50qGHSWjJmu1+qflj/Hisl1mzq2Z5xWnnt8ZewOvbVrg/6vBgBb6RWeIbmQMA/GSbRmdgnOHyMG6kyOLmtiGlBLiE5Ge6ZB5o+AskCV7e+xd4/P+GYek7V7vcbcKjKXR0+hgu5uhr9FNKlok0+vDHvGiqSjJvsFYwUVQJ2xJEEj5Yz4VxCbH7XGytTkY1Px2AIktUtqQ4bvVzVlUFTHtbcE68PcKTpRUOagpnFrKwmMKrKkQfdz2D7ZfEWeyW6AzIvOb+Q9yztwufz8Mv/vLHWVx0m9KCi3ns9Uq5WI+a7u6OMz++wvy4ay333t/+KUIxHx/+828xPDZacwx72sIUcxXy2TJ33tXLM2fn6OhvZGpujX4zSjQoU5RgdGqVQz3NXB5eQpUlTgWCoGkUSnn23rmHi3NpmptDrFgGB3uaUSoG4xtNjTeB1PgSLV1NjI0mOfLKYxRtGH1mZtt6XlWhfNUAaOBwO7PFMmKljKYpTC1lKVdMDgxVCW9PW5jR7yXpe8EAI+u2eslkAUWVsa3qfW3tSbi+3kEf0dYo4yMr3PP8ft7w83ff5BlJ9Rhv4PnPf/4NV38///nP88Y3vhGv10tvby/9/f088YQbRNTf309fnyureeMb38jnP/959u/fzze/+U0+/vGPA/BzP/dzfOADH+Bd73oXn//85/nABz4AwOte9zre/e53r/cO/PAMLn4UUCfQddRxm0JVVbzrsgSBwLIsTNNE20KeYaOqpCBL1ma0syy7LhqOcFBkBSGcbc1fNwrLtmoau3TDwHbuRVUOE7N/AVksbttGIclLQ/v4Qv7aBPrqOW1TrGHaa6hK++ayZKnEXGqIxsYxHKpT1dlKNYZ4qSxYmrGRSTPYEWbVyOOTtW3keQNdnkm6PNVKpBAa/2XiBfzDiso/rLTzc50WHd4wtpAxhU3KLCBdw1kkqFXwywne2p3lZLSqD5ckONnyNS4n23fcLpDqINbq5+KKq9kNn2ok7JN4VCRBMgGTo6kw4bRCvDPEzNnVGsKrKgrg6qf9fi+Yrs7YW1TJzl27QWwz+vmq7+yNX31BHxupk1u/1zfeXwjB3//NaR4JXeE/v+9VO7q2bGwrBHhDXn7zT9+EEGCZNrIiCAQC+P3bUyIlSaalNcpaqsjiSo69A81cmU/T39XA9OUVNE0hMhgnGvLizZtEwz56QkFGH3MbBxONARaK7rMXjfpwShLTsykij49ue68bxfLUMo0nBjhzfmHnc3QE5VylhpgOHu/kwlyKfR0NTExl2XewjeEplyCbuo0kSwhHYAID9+1leJ08ax6FjvYotETWw43c5MTZsSTFggHr9oeDB9v4lf/0cjwelypcTYqlLfcK3FkH27Kx1i0dLdMmsrNC6geHfwG8/UMf+hAf/ehHOXnyJB/84AeJx+PMz89z993VgUpnZyfz8/MAdHV11Sx//PHHSaVSxGIxVFXdtv78/PzmNqqqEo1GSaVSNDbuElNax7NCnUDXUcdtCAnQtkonBCiKiu04rsXcVZUKRZbxer2bUb2SLLlT6OtVDcu2sI1rx13vBtNyCbRwxDpZl7BsgSlCVNRfImC+f8ftZHb2Xd6KtDlJSGmj4lxFwkWtnvnLwyYv2P9iuhu/hY2BInk4n/LBVbIOB2hSG1g18kTUa2uiN6A7rfzHsRfxpZWq7OQjcwr9IZXZkonu2LT4GggrF3bdR4fX4PcO/BMxdXrbaxH5CgP+44yWay3twlMHeeZrGt626nU6m3Z1s729ESZ117pP3ucjmTNYlQp4XxgkAzTJQSQJ4ij4ZQUhQVssxO/8wks2Pbwl2SV1hmni93ndqXtb8MTEPL/7xe/SossEbAkViaXjkstrhMBoAEpQFjemq83lK5w9P4/iCFR3J+w53omsyCAESlHHsQUtLRGO3b/PlaIIgWGYZLNZXv3a45imzZe/cIZKxUSW3WN3HIehE11cGFmhfa8GQmCuR3nH4wGM5TKDnWGufGsC1augHfESCHooFQ3ivXEWZjMAlAyL5dUCTfEA2emdB1RXQ/Gq2FfpilNTK3QfG9y2bmdnjFBQY3Uhh2lYBCM+GrwaTT1xzk0mGdjbxOQzLunO6dV7bTgOgXiAjvYItmExOlV1tjENm0vD23XaYa1KCbp6EvzH33vNJnl2HAfLsrYkPkruPUC4ch0hsB1X6mVZNqqq4vF+//snfpiQTCY5efLk5u/veMc7eMc73nGNLeBd73oX73//+5Ekife///38+q//On/5l3/5gz7UOn4AqBPoOuq4jbFBgCVJQsbVPe9U6ZMkCUVSkCWx+ftO/z5b6OvkW0LaDGIAMKTD+PEjsb3aKW33prgKMpYoU3GCqFIES1Q9aUdXD9WsmTN0ppaaSev3c6rzGSpijbu7LvPtxYFte82W3GsQ167951PgYdG8lzed7mTZ2H59xgrV6nmyUsGv7pzA2KE1c7/vNFN2A2u7nPLRwCyj5fXZBAGB06d47NEyYOPdYSrdX1RAASQwhUNW31lvrhV85NYbDrubIzRFgtXK8i5QPQoV08KyVQzTQQgwN8qCWy7DlVyKWKcPIcCjyMBVsc/rA7XRyyu8/z9/gdZEcJO8jcxlNqueodUCRsXEqJj83PP+OwhBV38zf/Cxt6LrBv6Ah7e/8wW88Wfv4eFvDHPizh7++Pe+yJVLC4RbIhw62MboUoZ9PQkmL7qDEF/QQ3mtxJWxNH339jD79BzDT87g9akMHm5hcd2qr6c7zvhSBoCmlghGIoie2t2pZQOtr7yLqCoY/dJTmKXqwNMj197ggX3NjJ2Zwx/00NAcYm4+B5SINgY4N7UKkoRh2Pi7o3S0Rrl8rlq9np52CfPkpEHQ78Gyrj1DJCE25Rwde+IkJYv3/f4/8t7feBmNDUEqZR1ZkdE8mqt7t2z3Hkis6+fXZ6cUBVmWsSwLy7KIx657Ob5/ENxSDXRTUxNPPfXUTW3T0lKV1vzCL/wCr371qwHo6Ohgdrb6N2Bubo6ODtc6c6fliUSCTCaDZVmoqlqz/sa+Ojs7sSyLbDZLInF7N3T+IFB34aijjtsQAjBM041hFlVSvFtC4AY2yPaNLr/54xI1WlvDjqNr79xxXU3MsM/bs+NrXrmHgn0/ABVnFVlqZuufu4enI9u2Ob+8QrnUyofPHMVjnyCZ79tx35dSWd7WFuKP9z6x4+um6Kcg/ydyni8xJ/36juT5ajgI1iqHti1PqHGe71tGpkibuv2YN9CkfJdWrQHF9CK+cR9PPXoNeYUQJNNFDnpj7q/XnPOuvuY4DuVyhVKp7P4U3X+LxVJ1WamMXjHW32bn/W6QNFtASq+QNtyfrce3sb2zTR/vYuujpmny5vGZuoVp2CzPrVEsljfJw0pyhUJhjaN3NJHKpPDGfLT0NpJcyXPp7AJqziSkang8Kt09DUxPp+nsSWAYNpfHV9F64vTe1YUkyxi6jZE32N/XiO64hHegr4kLY8s0H9pzjWtZRalsMjyTJ/7AHWj+6kyGU6y18du4VuWiQSBc9Vr0+zwMxYIc6Wlkem6NTLbMWm7nez7Q4GMtvd0ecNt6B9po2Zvg2PN6WXRMsgWd8clVTp+dc23uNBWv1+POFgmBEGDbDqVimXyuQC5foFgsYRom+VyBUrGEad6oe8ftg8XF6mzY5z73uU2Hjte85jU8+OCD6LrO5OQko6OjnDp1ijvvvJPR0VEmJycxDIMHH3yQ17zmNUiSxAMPPMCnP+1Kuj7ykY/wEz/xE5v7+shHPgLApz/9aV70ohfV9c8/ANQr0HXUcZvCdmx0Q+D1uF+Kz+UP7G761OcKIQRl6TV4+T9IbK/sHfCsMbKleCqhgXQMUzh8ckHwzu5DmM4FctY0TZ795K2LAOxLmJxZ3u7kcXkxTSAW4H+e3p1UDoQsntf8ZuTgO8npDxFy/hCZzObrFe3dSN57USWJISXMSW8nT+lzu+5vA5pi4QgFWdqoQgpeHDCQhduU5hcX6fOeYkLfTtwlCU5ay3zs8/czv1yttJ9oa2bi4godsiuNkSUJya+SyZYpTJoc7wzhVxVOBJvchlHcwJYXH+rGq6oUFyt8e8I99mg84IagyLV1lz/68qPk1knze15x92aFurUxTGM0gCRJLFH1RN7bnmN/dwXD9GCaHiqGTDyc5zd+6gkQMrYqUbKbWcyaUIRcuplPfmYPkiMItsdwHMHPvP0uAn4fD393jOGzczTldbw+ldZ9LQwcaEXPV/jPP/9RhIAHXnuEl77+DgC+871R/teffodIyEtPJIimyfT3JrAtgVEwCAW9JBrD5AoG7/zF+/inb47w5a9cIpMp06woVMomnqYAnpUiI8PLxGJ+9nc2kCq55NW358Y0pm1xH5m1MslkgYM/eR8XPv4tAEqLa6BVRcPFLdVpe6vnoRBMPzXNnuPVmGzH3l5hbo16ya/kaW2LbkqzJMl9YOT15wZJwuNRuDK6gkCCaejojDGrZxFILK9ksWwbRQhMBIqiYBoWPp+fdDqF5tEIePzrcg7QdQOv1/N9G1TfNH6INNBvetObeOihh1hdXaWzs5Pf/u3f5qGHHuLMmTNIkkRPTw9/8Rd/AcDBgwf5V//qX3HgwAFUVeVP/uRPNhMgP/ShD/Hyl78c27Z529vexsGDBwH4oz/6I974xjfyvve9j+PHj/P2t78dgLe//e387M/+LP39/TQ0NPDggw/+81yAH3HUCXQdddzGcISDaVl4PTem5/3ngOUoGMrL8Nqf2/aaJmZ4SegEDxdmMDERmPzjcoTZiqsF/dOpOK9sfjGdvm8gqJ7j3niWDYeNrSiZJntEI0lR3Nb89oJGgxc2F3hN28NoobcjyQqO7wGy5Sai4leRKeCIGI5yDHWdOCiKzJ75BswOm7P69mbIrfiLkXZa/f28pf/vkCXBXk8XmjhXs05cnKHfe4qxq0j05NR9/J9P3kWuWDvIEMLZ5uHrWZfhmKbD8mSOFkswKtVWKD/4Uy8hGvBxIbzEn3/vDAABn4qmqZtNSxt4fGqBVMElkO9R7kPTXN3rUjrPWraCqsjQUF3flm1CLd+u2YeERDxYbcCzpGPsCbrvu1Y8zH//b8Ga9YeGWolEQzz06DjTqwWacxWisQCLc1l6BpoxyiazY27DXCFb2Zbv3N0Q4sqTs0RifnAEE5eXcBxBMOzFzOn8zE/fQ19fBz/TGuNVrzzA5/7uab75yfXjKRnkFAevXyWTKZPJlGlqDrGnMYx5g3O6lx58hKE3PcCV8VUuja/SeXIvc0+NkxxZQD48tGkBmN0Su711kLohx5B8Khsp9Ja1Xd8TyhVRG8PMLF47UntgsNklz+uYn8swdKCFRHOIV798CNMwsWRpU+vs8Xjw+/34fD78Ad8mUXYc17FHuc5M1u2CT3ziE9uWbZDcnfDe976X9773vduWv+pVr+JVr3rVtuV9fX2bTh1b4fP5+NSnPnWTR1vHzaJOoOuo4zbHVuuxZwMhXAeP76dtlaooCMCxLUJ8FI/999jyEWTn3LZad5xneHW4nacqTcybSX6y5SzfTB3jQsHARkKTBVGtj7R5joR2kLx1kY7Y04Q9ryBvbGc8l1dXub+ni1/e/206tAmezgzhVR3ujX8JadOlIwv4XW247xAF849BVLDlg2hqLTF/5al9/MZfjNP74w1MVnaPKDeFw2ypwFrlLhL+xxj07vznOSrOsM97JyP6kwB4nFfwtYfvI1fc3sCm2A6DrdFNzYMQgon8VTIB3eZoR4KKsFmr6KyWyuhlHUNVauQ0You0Arbo37fs60asslRlO9ETuJKAzU0lNiuJsuTQ1RnbfCPHEVWP6fWFkixvru84ouaYHOFgGCaKouCVJfa2RdHXg1BymTK5TJmDh1oxdJPR8RRzU6vccaoTIQSxWJxwOIKpnKGzv5G5sVUkoLUpgjchUdEEIUVl8uIy3X4PpaUM++7Zx8ijI9e8BpIsYWxGk0O0v4O5p8axKiZNCT+ppHuPbMvi0FATxVQWv1WhJ6YgHIEoFWg72kaqaNId8hMLqKQX8/izOWzLwXEcbNNmJlNm7yuOwA6zN1uhqNs/B51tUX72p0+gqiqBgA9FcZ8H23YwDVf+tb3KXJXdCFG9T3XU8aOIOoGuo47bGIqsIMuKO3V/1XfdjVaQhBCY9vdP6+jRNDRVcw/HvIjX+CwSAsU5hy0fR3FOb9tGFQvs9yQ4U7yfJm+Wu+IlLhRUPBLs8Z0hb7nVyLQ5RkwdoGiP8qt3rfC7327dtq/uYITMmTwDJy7z4YkXczJxhZOxR6466SzQuu4aAnjdKVV3Wrz2ugVVlb7mONaYw2HaWd2bZ3HdAWMnfHyynbf3vZRY+Iu7rhMWZ9krH2fRvAfVei0/ec8KbYkIiixhyDZjsltxzDkSXBAEVS+q7lAqV9izJ4Zt2+ve3wK9YjI6XCXfIUUm5Pfx3aeneObKPIeaEuRtE1WSKJd1VEVGkmXX5/la0p1dXvL5sjsut1FQsREoyM6WMBIB82O1g4Nq86r7uyxL+Pwan3jk36Eo8jqJFIDAMAx8fi+KoiA7MnNnFjlwqJWB/S2MXVlBEoLk2DKri1na9jRw8AV7qeg6a2tp/P4ASwsFHj4zTdk22HeqExPB5GyK/ftaGR9fRpbg8OEWRh6bZW9jkPEZ6HzBMdbOjlDM7Kw9HnrFHVyerMpabMdBC3ppGmwnHtXw4iekCObPzXLhsusHffC+frfvU5WZuLCA6tFYyLgzLYH2KKvTaaRcfttAtnyd5sGt13MDp05189NvOoE/4Mfn826u40oKJITGNinPBhx7ncDbNo79z0Gg65XvOm4N6gS6jjpuY7h2XwaCDQs5GUWWUZUb+9MghMD8PlefbXtLOqF2gqLzm4St/wCA7JzFlnpQxNS27XTh4VNLJhDAI0l0e2UWDQdBB+ASaIFFzp4nrHTTFHmIkPYGCmb1C1eRJLyzML9U5nc/9wqyfWE+PHGcv3/ePB3+8c31HCfLujyxhny4zVWiZkr7T/7mewjTZnp9Gr29HGJxYHcCnTF1PjkV5a2NZdRduIDtBCjqb8CuHKViGciS4KdfdpCWxgaupJO84Zv/4K4owz7HS2m2SELzkM2UkQwDw6xWgUN+FY+mbC6LRfwEgz40TeWfHqmec9egF1VRkNen8sulCgJR4xttmtZm1bqtKUxLQwQQdJerFoUesT0gBMBBA2xsaT+q2Grpt/3ZEk5tAI+06QLhLlNUGYX153OL2+FGRVQvm0xeXKS9N8H+o+188xNuNT/aEuGrX7/CQ4+M8cCpHk7e38fn//pxwpNrDN3Tx+XlNXo6GxDJPPmSzlBvE9lMmflhN7zFE3TJ5uJKif0vOo5dNkiNzLAyXmsxmPcGYEvS5uRCAev/Z++/4yQ562t//F2pc+6e6cl5ZmdzkrTKEQmRTRBgwDYGbMAEB2yucbz2Nb6OcI1NMMZwwQQRhBFRsgLKWmlzmpmdnGc6TOdYXeH7R/Uk7Ur27/W7yDb0eb1Gq+muqq56qnb7PJ/nfM7ZPcQK4M5XWXnakrPYnQreoJt8uoharTF9pu7GIFuVaEzTsuWr34LmzhCGYZBdK6CWrQtfS7+wX/dzR/jQoQ7e8No9/Pn77+F/fOwNONu3r6gYho4iK1QqFcS6ZeC2Y5nmhkba4b1UJtVAAz8taBDoBhr4GYZhbolnfo4Lx3bXg8szOcM00f4fVp/BqsZV1SqKomAYJjXjahDehdv8KiIlBHRMxEuCR9YX9QFU0+Tv+0d4z+ROvroU4c1tCkbdN1o3q4h4MalxRavOI/Ob/wzu8ARZuZAB4NELdg6FvFT0In8y8gZ+c+ghHk4e4aEVD+/eFebO5xgurEtZxLoGFGBpNcv4jFU9PbC/ldNzMZanCuzZFeV8bTup2orFapl/Xbmdu9ruv+S9khrlman30hTdz+TsEj1drQiiwOJSAkWWWUtmLjkva1zrjhEtfiolFUQBwwBV05A8IjW3jKGZhDwufvXT36Mn4mfn3hb0mk5J1bC7FGx224bPsmnaAZOvvus1nF9OcGYhhmDoVMqWPGIpVSCVuzToZueRy1dEdVMCAUyjsK2IeDkZQKWqIuWL6JpWr4yKIJiUSmUEQUStaui6YcWPCyaGaVWpa3U2vT4mV90wwNW3DDH69DQ2u8xSvWmvry3AQ98+w/iZJdLxPB29ERYem8Qji3Tu7SIRcmO3KczNpRhsDzKRtarJxta/JqLIxIUVRMnG7ldcycQj5/CEPXQcHuTc0va0QlXdImupS6p23riDuXyNNpfMxSfHUbZoz52dzSzEy4jr1nH1v5+xbI3Wdh9G3JqsuUJusIt0tkSwe2yo5RrzFy/jVb0+xKbJnbcM8Pcf+QHdg81k14pE2wObm5kmum7gdNgoFPIbPs+mrmMKUKvpOBxOfD4fpmmSzWWJhF/k8I6GaqSBFwkNAt1AAw0AbASjrOtFRUHAwESR5cu6bPwktM/r0A0DfYs3cUl4GzUOEjR/A9FcQBMPIj9HytEsnubWQBcPZzR2uWTcTPAPAx4eL9eoGttDV9YTB3c2l3hkftMezqNtD3448XiSPbc083QiztOJ9ZSwImdPT6Hft8DLf+UmACZOzjFwsItyuYLLtZl8N7u0qXkePbPC7n1RLswnKJ/WEXYDJjTVPMgVKLk0MlJlgzw+mOzgrucEDK6m+7mw8n5KFQfuUgWPy0HA62Y1lsTr8zC/uEq68Jzqdv14cp3U55JlYrHtnssun8JKn6XL3e12cHE6ia4ZTM8l6W8PMqJnt1UTN9wcEPC4nRwZ6OTIgJV8FvF7uXqggw9+/IdopoT/5S6m1BS3NPXwu8M3ICgakrDu+iLw5divUzULvPwrv0SqAD9+32sRREsqoGs6v/trX6WrSWK+oPL2d1/HdTf28YHb/o6O4TamplN8+H+9gsPX9/PH7/u65SgjifzZR75HJlEgPR7j1W8/wl3vuRFBEDh4VTcf/cwb+eG9Z6lWNW5/w34++8ffZ2V2jTvfchUHbt/J3V99ltS4RTL1mk77YJTRkVW8fidNXQFs5Sof+8NXcGYswaNPT5GbzyKKAm3tfppbfazX7Nf9nQ3d5OK5FXqu2YVqCsT05+80lCRrXIdu3MGFmFU51gPWuG+VTOjac/TFAmCa9A2FiU/G0OqEPLgjyphoEMvmIQttYS+hZg+p+HM9t60/drX5+NsP3Wvdx5ZLbRN13ao+G4ZhhSnVibuma2iagdvtwel0Ua1WyefzKLZLnW4aaOCnBQ0C3UADDQBWhVKvf/Eadd2kJEoIz9ERrMsUfhLV5xdCTdhNjt/Ab/41snHqEhItoPPbbcd5JneAhapOztyJXzhFj7Kfi8/JCZEEe/3P7dXQckrF57Lz0bffxr1Pj/HgqWnyJ4qE9jpJqeX6PgJrcym+8KdPcNXL9pFdK/BXv/RZ/u7pP8AwjG1EZ2Els+34smC9l1gssyMUIZ8HpyCzmCgAMh7RS8TnxO4QcLVuH9tzyWv53Nnb8Ho0SqUkDjWPmdU4lsmTXMvQtlRkaXUNh0Phlkg7eaNmVQvtVboPtRENuFlcSpCtaqTTJVR189pLuRr9jhCjpTxm/d4760ExdocMpXUji8tPlrauUCiKjKrWqKkG5YqGRzAoGxqmCD6va9t+uqlRNS0yp2OiGSDZXBsV1QvHZ1iZS9PeF6lXkSUUmwxbGg4lSSaXqSAIAudPLnLssUnuePkeRAE+/Xv3bmh3TdNkZT6N0+kgX9VZKNf44heOMj8Rx+5UeO2v3EAg4mHfwU6OPTrOI98/x8xIjOU1S8fcPtjMuekEa1WNt9llbjjSw7VXdCJJCg67k2q1RGoty/knZvD6Hch2GbtToVquYZomDkXCZZOoSZAU4LmF9ZY2P+/50C089MNzPP7g5MYFLqcreAIuRKed0EArhmEiO2xUyyq6bmLoBjYbdLS4mD4+u+2YUosXEgW8DoXWgIf4SIK+A20Eoh4Si1nydXmHaZgMt3qZOmFZFv7O37yOK28a2nYs0zTRajp+v5dCIW81+hqWBEwUJQKBAKZpkk6lMDAwTB1R/E9IImxUoBt4kdAg0A000MDz4nKNQoZpkiylCYrzSCYYBDGI1H2Ef7KoCK/AxgWc5g/rJHo/knFmoz5uZ4HXRA7x1bjBOyYG+ObQOLvkKTJ6CzFtUzKxfqap0nb7Prdb4QM/fxX7elvY2dnEfDzL+NIar/V6GJVC3NHRx20dvYzeP8rHeILRZ6Y4dv858ukikixR02rbHNNec/seTpxf5MJ4/bPryW1dwyEml9J0rsqoahVXu0RJ19EMk9V649ncKjw+eAvXdz/KPbOv4i/PdWMTC8QScdZLhu1lF8nJBQTAvbhpk7dnR5SpevV7YE1mcG+Yu15/iIWlGO/710dI7pHok30EiiL55RLpdIX8hRxKv7S52rDO8KRNSc9/fLXBvOR/L7dnoZbFIfgBk3U1kVm349B1g0y6hD/swu5QCIiWd7Fpgj/ixuZQCDd5KBUq3PvlZ/D6HKQSBcbOLfHS1x3E6VJo6Qoh191MJs8t8T/f8RUAem4ewjBMTp9d5i+/8Isc+/EYgmLWI6glDt8wyL6re3nmx+Pc/fmjqOUa2YlV9rb4KUsic1Nxdu3rRlFkamqNSqWI1+tDlhU6+8JMj8VYLtVwuFwcOtJKcTnJ6BNj1j3b0YIh2LelwVx/yyC/8sFbKFcrnDq2uO29QlmjbXcXkstOOmFNNjwhgUK2giSL9O9sYm0mgcNtJ9TqJ7WS3Rjv1fqEeCDgZeyUlVI4+sw8joEAnjbPBoGWVI2JE5te5f6Qe/Ne1KHrBrIsWz7QmoaANaFxu73Iskw+n0fTVBRFwqbYqFZUNK0RpNLATy8aBLqBBhq4LARBqDstbIcoCHxp5WHeF/kiTYpFDA086MJOdHEnVWMI1RzCoMkiAtv8yf7/hyTkN9iYbJxBF3cCGpJhNV5dLIuAQVoDkBApcI0jw49LfrJGFofYRFG3FtoTpc0KWYvTxcpigVI9PU2RJf74rTeTjn2Uq3t+jOb8MNnaADabzJFXHmDfTcNMn5nn5AMXiHZHkBUZobrZ0KVqGm6njcOHu1gzVOSKycxYgr1Xt/DsSgwECDd5mJ5aY6+3lWcy8Uuu9e8fPchjB9p5JN6G4SmyohbxhG2U1ura1+e1INzSGFmPVhYEgdZoGIciYwCTWhHscKDPj7EgEg26aY/IpGuW9KBSrv9ZX2WoZqr83p9+fzOJbv0D6ql0B/d38ObXHdqwNtsgX89z76uaxndOLvLZR6+67PvJRIF/+LtHAQEnJoXRZcxCGVEUyJcNEqeXAHjo26eYHFmlb2eUW16xm9tfsx+A2EqWpaKKWp8EDu5r558f/w3cbhd//3c/ZnwqyWtfdwCv38W/fvkkx5+a483vuY6+YcuZxel0cMsr9nH1LTu4+9OP8ei3TxObTyOIAl8uVejua+aN77mRSKufWq1GJpMhGAzy9g++lD/5wFfYd2U7sbEFUjMrJOc3HTfEliDE68ErDoVfft8N3HanlUZ34sQshdylserLyRKhwKYXtqyIBJs8KCKMbxBfy6rOGfbR1OanbSDCWrMbVTO2h7AAPT4Pom5Q8zuJRr3ER1c33hNEgaZWf70ZsFbf15KM+H3W67Ik43a7sdlslMtlMtkMmlarN5mKgIAkS1SrKi8qTOBFjPJu4GcbDQLdQAMNXBamaaLWaiiybEk5NqzDBAxEPp14GX/U9n8BECkgGsdQjGMb8SQ6EQr8MgIaopmlLL19g2y9cHz088MuraHUntz2mmSMWp8n7kU0LnC+cGnVSzKT3OAa5PuFLIapImB9scdLFrlqdbvp072cy8SZT27arHU0+RlwZ8EAqfwJnM7rKash7HYbv/LXb+J3bvtLqmWV5u4woihwejrOFx54kIqqEXQ5EAQoV2vMxbPs6WrCjOV5SlplX1cTI/NJUl7rXBcm1nC0SFT0zWayqN+JsS9BTGknYebIqdY5l7Qa6/90/0dHcYNom9CtuHDYwXCKmDKYMYNUtkwqW6a/18fq3hkC+2UqFY39AwH0WZG9TWHsNVhayZDJVS5rFNYUcZFJZ5FkCUw2/YDrrhy6blAuletZh/Db9/6YY/OrlxynXKnisNm2eVCvwzAsycJWUm5s8VM2dIOqXuX/fuYJpsetCYmu6VQqFQQEtJpGuVTmjjuHKZVUXvmqvXz9n56kXFJZmkuRWStTrar1yaMVHCNJIkcfneSaV+7BJsJD3zqNBJx6YoqOvgg3v2of3qCLWq1CuVxmaE8HXb0Rzj0zjdfvoLnJi92hsLaYIrGYpjS5wuBgK7rHxfv/xx109VopkJquc3Td9cQ08XrtBHxOnHYZCRNlyzVHOwIsjq2SvkyTZrlQZX48zl2/fhN7rurDNE2+9sVnmTizuUIxdnIRmyLR0uzGSBXJbon6PnRdP76gk2pFxeFw4nQ6N1Yf1kNygsEgAPl8DrWmYrcr2O0K5VKZSkVFsVmJhevNtA008NOIBoFuoIEGnhd6ndBJz2kGcko2Hkik+JWmTlqVhcvuK5HEb/41Bg4EqpjiTSAPb+inDcPAMC2nhHWyJSBskGtRFJElCV3X0etkymF8/xL3jQ2YVWpEqW5jlZsSFLs5QbdtF3PqAhFlFzntHD0+lUQhQtldIlbnrnOJzLbDVh1/grP0VkxEbDxFlVejaRotPRHe+edv4JO//hXGj89SSBfZ29vML95xgCfPzXF6fIVU3qo07u5pYmw+yfChMDNCnEWlgCRASHJQlPP4fU7i5mbYRX+bh+qBabJSFZcs4pkKsqu3iROsUMNgX7cV7edftdPqtCqT9roaRQB8yByxWdukjcIGgRYEAfVUlvx8mvbr2jiTT7HH7rfulyIgDVuEXpU0Uu40tjMeli9sptg1h9woGAR8DrwRJyVVQxIEFK/EjFxmOVukPeihWq1RricgqqY1sNOFNGrdBUMAvILCvibLocHERDBAN02OnlrEXtWpVmq0dwYAcDtk2voipFJ5zp+Zp2e4Ca1mBXmEol4Uh0S0PUAuX7D006aBw6Ww73AHTa0etJrl1iGIArWaRiTs4L3vuxZBFHjDO65lLZ7nvR+5E7tToVKpIksSlapKtVLloXvPYxgGv/DBW3G67Lz056/kR187xpHbh7njjYew2WyWdSECDoeDxHKO2YvWxCCfrVAMO5k+NgumScfOVmw2mezkKn967/uIRENbGvEMMhNxWhSRbLJAKZFnq4v07it6ABjY28rEifnLRnevI9oVZH5yjX1XDaI4JLxeO739kW3+2Q5Np1pSESWR3kOd6PUkw6Jp8M+ffJRQyEc+V8Vml3n3B2/feH7WVxYKhUKdPNs2XrfZ7ZTLZWql8v/rhaf/MH4CPc0NNHBZNAh0Aw008ILQdR1N17dZ3DklO37ZSVS+PHneChGrSuZQP05F/kdLUmCabBgpY9mMUfdPXm9MXPeCliSJSrVqRQgbP3yB8ywwpfZse82yu9vEIWWZktFEUbdsxHY1FXkk6ySnV+kyA0CZc/NxqjUNe72JzhRbKDv+Fp1W1FoQr9tNLp9DkiRuftMRTj00wlPfPcV3P/Uwr/3Nl3Ddrg5GpmOk8la1NdrkwvQKBIZ8lP0iaNYq83XNbUw+u0pzs5dcwEQrWd/8+4a9rPSPgmj9vqKt0dYZ5vxIgp27w5w1E5zNWNKZ7oSfZKGMTZIQE5vV64PNEabOWyQuFHBhGAa1Wg1N0zdioNcrvCYQ6bcjvbzCiL55P72Sk0Jy+xJ8c8hDIlnABM6vJEEQaIt4mc5l8DrtDHW1ICKwklhhZ08TNUNnRS7Rp4RosXv56tQE2WoVQzV4+vjmZ/VGAyyPphCARyd1Lh6dw+WxoZpQ22LxtvDdEfjuCJ/8l7cSjgQsgmZupg3abAqSJPErH7zNujbTpFgsI4kigmj5nK+n6Rm6QSFfQJZlXv/LV2NzKJTLFWRZQrEpyIqMpukkVrPUqhparQbYaOuK8PYP3UE6kyWbyWG321AUBb8/gGEYuAMy7/2DV/Hx37/HGud1oisILI7HEQT4jX98K063Y+OcBUGgWqmyNJmgUry87CG9VmD4QBdjJ2Yv+/5WOH1OvvmpJ3jgG6f4hd+4FV0zmJ28jH3d8+DixQQ79nWwlCugOGV+PpfD4XBgq68MZLNZdEPbIM/W5VmSL6/XU08hNF58CUcDDbyIeH4/nQYaaKCBOjRN2yYXcEo2slqZBwt3ktHC5PTAv3sMhdNI2oMAGzpZ68cKb1nX6iqylUS4rtsV65ZnJlCV3vC8x48ZPp5Wpxl0bTYGZs3+bduIZLjOnsUn2BGNHr4w5yWnW5pTZ8panl7NFPjkfce27WfKBxDlKKIAaq2G1+ujWlXRdYN3/sUbCbcF+NE/P0ZiKUU8kWZmbJWrZA9XdoSZFcscTySYz+RYiOfZ5QmjrulUHCZCn51RW4GlUhFJFLjqoJeVwZEN8ryOdP84fZ0OKssaVxRaN37Wt3pupU/fIgWp1XRqqko+V6BYLBFtcjPYH8YjiOwNBfEj0tTmxDvtpSvdjEe0RDhpvYj4czmCnda4OOwyk3NWIE0k6tn40FDEDYLA9UNdKJJkVefDbv78fS/lrtv3kf5RmYXZMk+MrfKZ06f42ugI8/Pb0wjduoQAhN12Zk4todgkugebGeoM0tfsYbDVx3BXkJ3dIYY6AoiIKIqMLMvIisw//MH3+fQf/YB8urLxHG38CAKitP67sPmnJGF32BEEAV/QTjabQ62qVCoq1apalyzIvOu37+BP//EtrCXyFAsly3VCkvB5PRvJfD6fVcVfXV0lnU5z/Z07ee3brwNgLVWhdaBp41qvfc1+Dtw4jMNpp1KuoNUrv4os4Q06eT6szCaZu7jyvO+vo7U3zGzdhi+TLKJp+rawmxeCIMCu3e34nQqqArF0EQEB3aiRyWbqE5IixnPI8+b+wkYgkxUB/vzX8xOD+SL+NPAzjUYFuoEGGvh3oRs6hqEj1LXQLtEiqX81b8fgOlrsXj7aeYxe+/gLHseufoKyfAOCuPnFWtJj2AQfcv21y4W2rFcODSH0vMdOmUEgzXUBlYn62vfJQju3ec9tbGPgQDWDePVmfvNMO6mt/VpbPvbbR0e5brCDI8Od2z5DURQqlTJutxuvx0epVEJxyrzjL17P1/7mAb7xuWeYvJggvWbJMfqv6YIQ+F0OuqN+KqbO/GqWglpjRs2zViojAC6bTHPYzchygUiXg6KxXdvqqrmwqU5mUmmW05tSj3DdFk43zG3/mEtbmj91w8TtceEP+NA0jZXxJMl4AadLoWCarAKEHdQ0g+6eEKt2AaczSKTJhi8oYbsCmgQPoihwcdpqhpvPbnpNz+UtMnx1d5R8rrAh06ipKo+dnkHTdRRE1C3Sm3J8c+Bbgh4mxhMIQJdgY7Kq0THYRL5QJShL2G2SpXPWdBCETU21ZqBWa5iGyZknJ0GAtVgOxSbjdNsQRZHMWgHD1AlGfFbEt25g6JatnCxa6YWmaWK326hUqtY4KjJq1SLRdruCzWaju78ZwzDQaho//tF5Dl3Ti8frwO1x4/P6+PLdz7B3V5hI2IGiKKyuxnj5Ww/gDTj5zheeQCttTmgGDnWiqtbka32cZFlClmW8QSeJxctHnZsmtPc2M3nuhVd9FKcNBAGn24Y34OTIS4a49xunXnAfgP6hKOVMidFTcwDYV+rdDIL13Gs1HdM066E1zx+u1EADPytoEOgGGmjgPwRdN5BEi5g5JctH2aiXYVareX5/4Uq+OvDCBFoihlL7Mpr9V6z9TZ0fJ97JkdCf45U6kQQ7sui6ZD9ZkjF0A9EsXfLeOpL1gIoD7gTvaI6wqkrEKzKqdx8JXSJvxMkYC8AYopxCoH3b/hv2bSYcbIvyv7/yOJ//8GsJeTfJviBalXDTNHE4HDgcDkqlEnuuG+KqmRz/+i/PbjumZoOeziAOUULN11hazjA80MTxZIzlXIGd7QEqGRUxZ+BGZjKn073cSbFlYttxSkoJ944Eu6UWzi8nGe6IoNgkJFGgrdmLaZg4nSY2BMQ1FSVvcO3hbnbvb+ep0/PMLqT4zGefwDBNanUJh2KToVoPmKmTIX/ERRsGy/kCC/MVFuZhwOkhcz5B/44mwkEnLqeNiXpYS3ern5oAfredp88v8dCjU8iSyFtu34kgiDw9skB3ix+5pmAKJogCEibVTHnDnSXqcpEhR9TrZOrMEu3dQRZmLQs+T3eQ8TNLG+PQv7uV6YuWjd+Zp6f5i1//Bv29AcrxDAB/97vfIRHL8+GPv4GugSbe/5rP8Mq3XsFb3n8LYDVTyoqMKFnPim4YSKKIqtYsD29JoqZqIIDdZuPkQ6PsvLoXt9eFIiucPb7IP/3tIzicT3LtbYO8/m3X8ejj5/jy3UfxeGz8+nuuo6c7iCRJyIrEtXf20xR28qkPfW3jGnp2dWC32y2JQ0WrpyMKiJKA5wUq0ACK44V9lfv3tLO4mOHg9QP86h++vN7cZ0NWbM+7T2t7EJfbxtR4DI99c+KlPkdKIskSpVIJWZb/Q9VXK0a9YWPXwE8vGgS6gQYa+P8ZTmn7F3Kr3cs7o5fasF0OttoXSQuH0QU3AjJuuY2nU78NgIjCdeGPEVB2bGwvCAKSKOKWz2JXv3zZY07X9jKnTgPgEDXeGvn+tveTZidlY27jd4M4f7p/ivc9O7i5UV1Pe7AtyrlJS2P8l199nD9/120bftiGYWDoOuVyCY/Hu2W5WuKN77qOMyfmmB6JIckiR24apO3GDtS1GCfOLm8UuGdmUoSaHaTKFfwOhXQsT0XVaG72AnDmfIVro53MCNsrja5cmLF4DrdNwWaXOLW06WstAa45i9UMYCc+m2XPvjauv6aLr99/FrVUZWLUWtYPrVsAytIGgV4/t8lUlrViibDTRsjnxOVUcOYMMoBR0XEVDNwtdihZBNrrcXBm3rrvQdnO6Jh1ToWyyptftpdssUpXS4AzFzefjYMtTcSfjHHNnhYqzTbKSWtS1IrElAnKFgmOIUnsurYfrVS1GiDrjYnmlgh6b1uIwYAH0zBIryfsbSV49YvbKhsaO77ArsM9uNwOKuUqum5gs9vqTh46kiRx7qkJPv6rXyLSHuAV77mJa169n7s/ZznAVMo1Hv7+CKW8jqPDSuwrFFTGJ5N0dwUAk2pVxTBM+g618Of3/jr/8JtfY/r8IjsPDFCtlREFkWg0SiaToVwuo+kG1eL2xMzn4vltCy38yh+8hr7dHRimydLiGuWySjZbQ5QkWrqCCKKIUK8e2502/F4Hp5+dwaw7mYSjXnxtXiRFtvSdqc3EQlmWqFQr+O1+jLorx+Wq0GbdaadWUzeO+6KiYWPXwIuEBoFuoIEG/kOQpM2WiYP+Pvyyi6xmkZ9hl8Kt3ge2bW+Y8CfLb2efO8Me5yRxrYlrXI8gC1XGC59nWb20Wm1Qo2pcuoQtAM7qBy/rwBHXe3i8koX6eynDaTHKLXCKfsr69tf8ypOE7f2sVa3rWgnmONQe5ezEJjF9ZmyRux88zRtv3Q+Y5LIF62QKBSRJxul0Wg1gNZ2v/fgss302uoxm3vOhlyD5ZP72288wu5ol4nWyVrAcOUqVGl2ak16Xi4XvLTF4TRvnZhIkkwWoF9/PPCXSeYOLrLFZcZ9rOU9rbA82U9lwJQEIyDa6ay6msRojPS1e4rNZPB4HXp8Xr8eBuMVOLLgjRHPFAFUnWapsuCX0dAfRHBKtIQ/xfJH51Qw2Rca7apFWlyKzMJ8lMZ/lyjt7yWo1kgXr/ERBYHEhs/EZI9MJvve4FRyy1RYQQF+yJCiyILLw6ALDO6KEm0PMnFgg2ORmdiq5se3F6STNrT6CQSeiIOIQKwztacHUTVo6g7zhV6/H4bVTqdRQ1RpTx+dp74tw5vQ8Z5+cYPeeKA7ZmhmZpsn7b/gLCtkynbvb+ewffo/f/Ngb2XllN6Vi2apACyKKzYqqvudj1vOcXMrwxT+8lxOPzzA7md4c95CbD/7+KxFlnZ4uDx1tPqLNHsCy1tOrKooioxsGilfg97/yKzx893GcHhc+2btBPmVZplar8eR9Z7m4JczkchDcDnqvHsRpl6mVqqxMxiiki0g2if3XD/HYUxN8+tM/ZmY6sY28Du1vI2Xq5LNbVnDSsKsrjN/vxOWxk14r4O8McGwxgV6qcrA9DKnC5soMliWhJEmIgngJgV5359B1nWpVRa5rzBto4KcVDQLdQAMN/Ieg6zqSKKGbBrFqloP+Ph5ZOw/Aj9Mp7grvZdhu6Y1NE76WvovHMylqZoiTxSt4OpPi7h0tNMsr5PTc835OWr1A1H4VhllDWP8nyqxeQp7Lhps5fQenqkV0M7Pxeu0yFagWMUfqklc17uoq8JkJq4I4R4YWzclQyI9XkClnq7hCDr50fIQ9/c10hbxIsojX60EQBArFPLVaDa/Xyx9+9RFOTVuuF/vu6CdhavzvzzxMvmQtg/eGAqxRAgRkQaB5RWBqdIW+1zdRbq/ADCTSJfa2RjiXXaNYNgis9pJrHtmw9XNqTpYTJUrVGvv6NpvSKobO3OLm9a9XKV1um+WK4HFQSVrk3eVSuLBmjX1fWwBTddEWcOPw2pDtEiMLa1Rr1kwj5LbR1xTg4swyoiiwMm+RR0GA8dUM+VKVYMDJ3o4I86k8XreDcIcXe13erFd0DrQ1gyTQ74eSpqGaBsv/toAgCiwspvF47UyPrFKr6bR3B/FH3KQy2xvlfGEXo/WmOEcij17TqVSrOJw2bnjlzo0Gymy6yHc/+Sh9V/Vw5tQCq8dnCYedtPUEKBRKCAIU8xXKhSq1mka1XMPb5KRUKmOz27DZFKsxVNMRBZE3/487+fpf3c/MWSsZcDW/WR0WBHjHh+5AlHVyuRyHD7SxEk+RSGWJRoIsLWTo6glvSBgEUaCqVghe28r1f/Rpon4P0YCXZq+LZp+LzoCfZ+85jdNtp1xS2XGwi1KuhOiQKKXLJJasSYgki6zGc3R2h5hdTKMKEk0728llyxREke/dc/KSpxzAJsuIZQ1JEtD1dctISJdV4oaGmNfoafLy9OxmYma2/hxss7Gp29WtU/N10mwYhjVp0DQ0TUexKRvuIi82hEZzXwMvEhoEuoEGGvh3YX0RWl+GBa3Chy788yXbTFa6Nwi0IIAsWIT3aHaTulYMN1XTTUG/NEBjHYvlh8lrC2S1Ca73fwatqhGU/mLjfdV08mBlH7HaAiZLl+zfLV9KlX3M45IGKOmT217vkxMcKA/izivkEypqVSM/lWdZ1egfauJ4ZQ2Pw87nHzjLb7/qME2R4IY7iMNhp1qpousu8uVNvej9J6dYjOeo6jrRDg/uoA3NL7BDb2L5QoaejML0QgJ/l4tnogsMSptkODWfxxWU6G0JcHQsxbW+IWYcFwEoyWV6Oh2YFT9nEpvJdhVDZ+8dYWJShqqgcdZcQTwg8yN5ih8+OkX7YCvznipKj4+gzUXqhFXhddoUKjWd6UQOpaJQUa145kOdEdSagSqZ6HVJQUdPkHOBMr3+Dto0O6v5MoVylXSmTHvIx5c/9HP88Ng4x8eXOTNu3dtATaRcqoFp0p6skkkWGD7cTcaE7r4ws7Nr7NrZymjCkqpkkgVkVaO7M8BqvEC1LtdILGc3GgDXCZmiyPzv99+zQerXIcmWo8v6Wsm6xEZRZCRR5M++/V5ME779ucewt9jxBCwJh2FY1VS73YZpM6lUqgwc6uLPvvMBnvreaZ54cIzTF6xxU2wSHdd180fffJg3JXfxmuuHqKg1RibmcTpsjJ1O8I0vneC1d+3j1lfswjRMRAnu+/yT2HY1UdF05tayzK1tVuZ3G06yp61xcIU8zE4nEStVVJcNTdXpP9DB2kIaRIFiocrExRgDfWEQRSZGVukZbGLs+Bw79reTLlSJr+RAEBjo8LOYKKDXNLxeB9nMZgPqrh0tjIxak5WdPU1MnF7C2+8jX6kx0BlmdmrzGQPQNB27zV7XbFvjZRhWIyeCtQohyTK2ujtHo8mwgZ92NAh0Aw00gCRJGIaxUVFaX6I161+UdpttI43QKzsRETYaCAFkQWLYOcGXUm/kfNHFXeFx5qtuILOxTa/TT5syw6qxA1ghlr+OJs+TPFfWWTZilKtWJexY9g+JSHZ8zG5Uwp5Vr2C1dvlmRbfURptw9rLvRUwHJ4sDTKcPMZZq40JSJqfq9CYFRqc3ZQN7doaRqiYnSRNwObBrEqenY9z39DTvfP3V24iBKAlUKmV2d0eZXNkk7rOJDOZumVHNqvYqOYE9RhB3TUZ2WY1g5TUVmyhRNFVrnBHQNIO+gB9ZljFMkyeeKXLTzX1MmtP4JRc1j8qFlU0HjHWsOQrMk77kdVQIahop3SL4baKbvvYgi/HsZVPiTGBpLEkhbbmAdOgVorky2rVNqAWdi2tpQlKI5KkEzX477TsivPymXfi9bt58ywEWFnL4bTLNio3F+nn2hLwkxiwyVqpbtjndNuwOmdnxTblMV0eAiyfmAei7shsVcLntmJJAeqQ+4aoP/Xf+6WmCYTdevxPBBEMSME2YOzNv2dTVjykIAqlYga/89UMAyC47NVUjlSiyOpti7Pg8h27YgW7oFPJFNF1nOZ6iUCzj9TjpaImw79ZhbF1Omo8tMXl+jYRP4mTKup67Hx5hYinNyw+1EfR7WF3Kcf83LmCTRR776nHOPDiK2y1SLatMP7tI+6EO2L0Zyb0Od05jnU6XilYJv6srRFUyWV3IMzWZRJQEkmnredI1A1OSmHx2Fm+zi1ymiABMnFliYKCJpjYfWrnC9FPjOD127ANhqhWNwY4gdhMkE6rqZoOfZhjsGWpmWTGo6jqZ5PZmXV3TMQ0Th9tBJpPBxApDUqsqCJbHts2mbIz5fxoa9nINvIhoEOgGGvgZwzpJ3miAk2TkOpkyDIOapmGrR/aqtVp9u80QFbFOotf1z2AFWbx7YhCTClBhudzHci2z8b4AvCs0xb9efA2TqXb69t3NidVB9jXZibhHyJQHSJRbWC642dc0Q6vvMQAqRoyUOU1C2kOXBKv6AOPV7Q4V63AIO/nERJTmXh+3+B9FM0RGsns5trab48kmTiVNEuV1+7Ra/QfGgjq7h0PMjVkE+HxsjYPDrQSyDmw1kXi2yJUdUb77jXPs62njwP4Onjw/x80H+hBFkVpNo9cb2HYuxWqNnoSP5oAdd1ZiYT7Lkp5hd1+UxdU0nVcGWbmugKrqLKtZ9t/Qjj4nsyiVOZ9KIWUEesJ+ZjNZnnisxp0v7cYwNe6bqLB9Td1CNGQnoHVR56eYQMmokC2XWctbZFgEqpNl4gkrmTCbK7N7uAW1qpEolaioGtGQh8KiNZmQZZH4yAq6qiPLm6xEzVpkvJCtsr+riSuGmigVS4iSzFPn5hmyu5h5fJaOVi/uDj9yRSMBuLx25ubTCAIsLqTo6QoxfnYZAEkWWJ6yZBr+sJupmTUM07oOf9RqrrTZpI20vOOPTlLMWrIUWZGoydZXmf0y3C2bKnLsYWvC5WsNksuUue2ufaSSGT7xG/dw42v284pfupaW3hCPPHUKp91GtCnE7GLMknOIMjabg+COds6Or1ArbpcSnbi4wuRCkl+8ZZgnfriAXtPpCtiYn82jmxozWyZnVrDIpQRaXbnUXcbucZBb2tw3uiOC4nHi8hQpFWrMziSRFJGAx058Jkn/zlY0UWLyvNWw2t4VZMehbhYmVqlligimgJqrsjiVxDRMBq/ossZeFHBIEnOjS1R2BBloCzE+vhm6otZ05pdSDA92USwWiSVTXJxa4PC+Ifw+N9Pzq6TSWa46uAtJEuvV/EvvQwMN/LShQaAbaOBnDLqmI8kSAgI2WdmoRG4NQdhscJKoaZpFurd8K/oU1zYCbZrQbe9CiHtZmK9xNluiK9yObpiUqxr5isq7NMs27mAgQCH/ChxNJT5zLoSBFTghCwZHmtMEpE0rL58UQjenURDQTYknKx7g0gqsQ4zy+dlmVNPg92dauUH8Le6brlLYsNGqXLLPOqq6zqQvTyTkIJuq0BcNMjEaZ6A3wqlkjF0dEc4tJWlr9fKdRy/wN99/mmS2RCxd5JVXDfHpTz3CU0/PMHhVlIlshmaPk6jbxcJKjsK0RTYjfhcup8yZmVX2DrdQbaqyrFrVRIchY1QURgpphrsjpOOWH7Fa1Qh7FK64psK0ZklPbrp6D48evfRaLpQWCeutjGa2L7tf4+3gRNEiYf02H/F6TLlhmCysZFlYqdc9WxS8nR58fjeRZjdmtoaYUVk4Pk5oVwsXF3J0hTw0hV3kj1kTDUkSmF1I8aWvnsDrsWM6BAolFbVgTUySK3mSK3kGd0UJtnhJr+ax2TTCrT6cfieSobNjVzOaZmCzicSm1hBlkWhvmNSM9RkdPUEWlq1x6mj3szC7fn2bhL57OMrkZP11wfIsN+s6eBPzOU4QdT25W+G6t+8hrHr5wRee5ndf92mO3LmLliNhrn35NRi6jmGYzC3G6Oho43P3HmPsefyZAbKlGj/4wRi5TIWwR2B+ZBWXz45RLG/bbj2yfhtMExFwh+yYNZNKsYZhmEyOxWjp8OAMCOQrNRZSZRy5Ko5YktbuCDaPi4VkgcxKhmpRZfr4HPtv3cmqXUatakS6Q4w+MoqAiV0UKRydJg90XT3A3MwaatmS17SmyzijKpWySrZcJaBut55bjeX4zg8v8OEPdFCrVUGw4tBTmRw+n4d4IoVuGJTKFZZjSQzDpDUaxtANkqksVxwIPO+4/b+H0HDhaOBFQ4NAN9DAzxgM3UBW5I3Y461Lrs/VLkqihC7odYeCzfda7EEWypvVsR5HHyfSK9TOaKxXSGcSlzYK2iWJ5GqBakLhy3c8yluffSknUwW+fevjOMQLgIoodHOyYlnaGaal/60aIke1a8jql1afZcHJj1Z2k62T5Zqp87i+Qruvi4trmecdB7/NTla1KtI5VeWK/nY0d43ZuRRVVePCxVUO7Gzh9KxlwaZFXRxfiaPVAzAefHaCB795hqDPiWmCOFumd2cAdIPRmc2x2dXbzPRqiqqp0dcf4tRiDBZhz61tFMoqpVGZCzlLfrEYyxFyWRZ3TW01nIOLzOubE4Z04CId0UEWY5uaawGTqqFR1Z9jMwKUbRX235ImaIawjSqU7TKV6naCJIoCVcMgmSuRzG1Oig6ErEqpb28boLGaKhDyOvnU372J4yfmOXF6gR8/timl6TvcileRWZ6uN/w5FSLNVgBLpNVPMORCEkUUt8KFM8uXXWrv6IuwPBajI+jC5lQ4dEUXbztghdnIgPnmw9RqGt/9/FGq5RrBoJP0aobOiB2bU+GNv3ErdrfDmuy993q+d/dJqmWVlh0tNDW5SSykcTW7EDFBEJk+OcPiiSmGrh1i8uwiZx44S3WswM+9/yWEw2EuTK/ysW8eYyH+/OQZoMtpozJZoLXTS2tEwkiV+eDHXsf5iWlG/nWCi0dnrQ0vJy8QBJKyQFWUwQ7YZHrCdkqzKaqpAk6XDBEPRlanTdChycP801M4fQ4OvmQXg9d08OQ9Z5GQGTm5gMOpsPNQJ2fPr+LqjtLZ4kEtVevPymaTqSCCz2VjbTyOzWnD4bSRMaGUK7Mz4iFngt0m4VIUhtuayBfylr4ZiIT8ZHNFcvmilQhpmkzOLuFxO/G6HYxNzON2OahpDR/oBn560SDQDTTwMwa73Y7dvqlpfiGsV6Q1TUPa4oV8yNvHsYxFZvscPRxPWc1IVpf/8x9vdzDExYU4P3dbhUy5hU/te4B/WT6MQ9xMSiuZbdiFIgFJQTOtyus5zUGidjnphsBY9hpmS2zTZdfQcLXk6NcDTGUyl+x1oKmJ89kEeyIRzieTXEuUC49vNiQKAgx1RSgVagiCVWFfSObZ3xvl3MwqB7qamXpqGdMwSafLtLT4WF3NsbPdxxO1BPt6w8wuZenrCHFhNo7dLmF4JMZjFlEWAdcpG1lBIJnb9NotVFQGAgEOXVdlwTaC+pyxVM0aQ1ekWfqBCxOB3nCAmbUMA9ke9CC0uby0uV0YJqiGgSIKpLU8afL0ZUWcsSo79kSpyQIrqQLpXJmWJg9TepHnwum00XJ9P2qzG+oNb9fu7MRuk7jqyk6OHp/ZuOfBoBMw2d0cQOutkV8r4u9uZvLJSToGm1ncImMAaHnTLuSaQfZUjPzK5vX7vA6WsqsU6vKMl951iKuu3oxjN02TUrHMP//pfWRTJdz72li4aOmom9oD7L6yb0PPL4oCd//zM5iGwepiFr/PweqcVdk2dAPTlHjJe24k0Bng3z7zGOHOEIl0mYe+cpTbf/lGHjo9zjceHadSd6Nw2CQciojPZWM+sTleEZeCvFjlTz7+GgwTsoU856+cwxN24IzZt5tYPI+Ps6vFSTWtbjx8WlUlvZIBwN8TYi2r43NI5KZj5GLWvVArNS6eXGTmQpwDd/Sz/8ZeBnu6+O7/PcrFpyfw57I0d3QwOpFk1+42mvqjNO9pI16oEN4Zxe1zoJycpwZoNZ1SocrOoovSSpqcJJCsc19REvjI/3gFYG74obucdnKFIgtLccJBH8lUlkqlSk9nCy6Hnem5ZQzDIBTwXvZ6f6JoaKAbeJHQINANNPAzBpdrMzLbfM6S8uUJtYCJuSHl0DWd7CrYdQcuu50zaYscNStelnSTy2l015FLWPKDQ31p7j3WQabYw4de9lVm9UFqpkWQy4YTr7iAZm5WXkuX8YYGKKvX8nRKwqUHabUrzKmLGJh0OEJMrZpoZpZev5+Z7Ob+OwIh5gs5aqbJ+UyC64JtzD6w2cwmigI7OkOMzVrXtX93K6fr9l5z8TTdYR8XZpN0tnmJL+bQNANTEpAVEUOGveEmRuJJhloDXJiLgwBVVacn4CVdrBBxOAhXFC6OrNHS4UMWRbS6r3NnkwfbvmUErwRbY8a3YFlb4Y6b9rBb2s+bDu/i7hMjPFOc5JQxhWE3mNhS9LvC10y6ak0A1EkH3cM+1KyK2yajjqzRFnHT4rLj9bpIqSqLqRyGYeKyKxxLZtC6NsmzJArsaQ/w6MMjTE8kKaVKtEY8xOIFwq1+zk0m2GezMXd8noFr+qlUdfr2d2CoGsMHOhg7bXkcN+1s5vyqRWSFbjtX90cwUlXmxuMYxnZ9cU3TqNVTAgVRxNB1qtXqBkcS7QoD11phOL6gk+X5JImVPGuJAvlMhfhKlt7BCDe/ag+mZkVRV0s1ZFnC53ExNbdEaG+E1/zvV3D0n4/Td6iTK950kPtPjPK1H49v42J+p0wsWyVf1vjwm6/iU/eeQhQExIkC6WKNB34wysteu4eIzU+0OYAkyQS9HmbPLW8cY2vE+lbYIg5g8xmVlM2vZl9PhHiqSrBUwmz1YfcooOq4Qm4SKzlsfgePfPk0o4/N84b33sieQ23c/5mHiPZEuPjjEZq7wtgMjcRylky6iFrWCB/pIV+qUcpX6N7ZitPrJLFWppgtkUkWQRQgZHlaX3n1AG6vvBnfXZ9UB7xulmNrtLcMsJbO4fW4mVuMYbcpuF3O/zQbuwYaeLHQINANNPAzhvUABNM00WoaVVXFNE0kUUKxyQgICKKwUW2SJQlJFNF0HV3XGVlJ8tlHx9hzQ4T5cgXd1NhpayM+bvJCWuNmt4vliRwC0Nu0wl9/p425hMi7bxukxzbJrD5AzZxEAcwtOmdRGKKoJy45nmLu4WsrbkTVx3QxB3m4oqkTTSwzvqyTVy0tblzK0e3zMZfLsTMYZnE2S5PXjeY2yGoqTxSWufaqKNNPxZFEgYFwgPJqBadNpqxqjI2u0tHpI1usEhQUsoWqlRoXlpFjklW9q9Zo3tfEyfkYu/a34XPYSRlVFNlyMxkabObMYpw9gRAr0xkWq5ZUYnUxx74DUU4nExzc62Ola4SUaCCXm7EJblTh0sowwIJjhDf1XYUgCPz8FbvpSjg4MXJphV6o+1EEiwFmZ6zq9+6hKKmYNb5+rwOyKjNnUgwebGOtAu0tAdxuGxgCuq4jAgcGW/i5m3bx5MOTfP4zjwMwvL+NWCyPy2NjOpHFpkgsP2ulPeqKzNxEkt2tLmbOznL4pXu54qZBTj05hbSvCVYtzXJH0MvFBxYRALvThuh1MnRdP4mVPAhw/OlZvv81y9u4a6iZuYmEFfzitOHvsINNZvKctfrRuzPKh9/+1UvG4MjNA/zSB27h659+nEd/MALAta/Yw03X7Mc0TRRF4et/dR8TJxZw+Ry4a3a+f355G3ke7gozNm+dc1vYw/7+KB9950188+5TnC9Y4/rDe0co1wx+7vV7ObR3CNMw2bu7nz/+xrv5+1+/m/79Hbiu7uD46NQl56g7N4m1ALiddoYPdVspnAYc7okQPzlFzLQRDvhQ7QayqtHcFmTm/DItPWGcToVPfuQ77Lq6n659nTg8dmodTdjtErpNxuZQsNtleodaSAqbLQ2iXWGsTvIFoe5fsmViHWny1s/KmtxEm4IEvG5Ap60ljNfjYudgF26Xi8RaBk3X6e9uQ9ONfzc58SeCRgW6gRcJ4r+/SQMNNPDTBMMw0HWdXK5ALJ5kaSXO0mqC5ViC1dUkq7EEyWSaUrGMVtOsL3FJwqYoyJLEdy5M4rErKLqN3loU72QzZ58ss5p4fvIM0O7ysN7a5ZBLzCWsf36end6BJFTpllLIDKNvsb4DyJuXLgPbhTa+vdRJtexhvrgpAciWQSwHN8gzQEGvkZKKXBVtZX42g6obLGXyNJfceCTLbeQpKcbgngj9QT+z40lWVnO0yA4cioRpmkRlOz2Sk1iySLPH0gYvr+XpOdRC785mSj6RRKFM144wp2dXaXK7Wc2W6OwOEG7zMr68xmExQHmycEnQQ2GxwNVXOljqOY8hGrQJA5xdsqPmB7lczxmAgcE/zH0V1bCuc9DX/IJjH14Nbfx/paCSXLX06W/55Wt4zRsPAaDJoNYMZhbSnB+LMTGTQKgYXDwT446rhgj6PZw5tRkvni9WCfqdtA2EKJZVeiJeaqUasl1mbi6N121j8tkpKsUqOw528Jt/9TpadzZzIb5p9xcubq5XVMs1DBMMSaSp3U8qUaBa0Ums5Eis5EjFC0SiPgQBMqkymbUShr45QIpy+equYVjR0pXSpm7cNE0cDjsOh53Z0WXu+79WRHcpV2Hq9AJ/9f6Xc+O+TgSgO+rj4oJFniVR4N2vPogoijQFnLzrl6/iymu7cThl2vtDPPTAOH/7l4+QShYBE1mSiHaH+dN7fo0jr9iDPeq59ARNE0HV6QnZ2eER8S/GGP+384wenWTk6QlWx1a48Og4psdDb2+YZKpMa9jB8nSSmdEYN7zuEN6gi5kz8+y5spuRM0ssxMrURAVJkZiJF6kIIlUDciWNi6Mx3E4Fh03C1xZkfkvyo3gZRqDYZERRRK9PoG2KjMOhoMgyPq8LQQCPy4kgWOS6vSWCosi4nHacjSTCBn6K0ahAN9DAzxiy2TyappMtFKlU1S3OADWKFcsmTZZECqUyDrsNn8eNsy77qNU0REEgUSiTeBRCLoHUFmLyQiinLE3C7i6DbCW48fpgi1VdloUq3YrJTHmzQmeakNYsb2ARBUk4wDMZELQ24nmBeGWz6W2Ht4npbIaqkeJwcysn4qus07Nul5/zF+L1CGzrtYV0jm7Rz4ItR03XqeU0gg7bxvGWlrPs2htlqVJBNUxmxpLsPtDKmdk4+3a2MDIfp+YEQxeJ+rzMFwvEV61q5MTSGocH2zkxt0S300VbQmRqzbrOHTuaOb+8hsep0NnsZ3w6geO4D9srnDiqfTyasAjNqeQat7l3khLHiIh9lMwkpfoyv092caWvHZtoTQCabB5ckg2PbCevVSjrNZyiglK23FY000P3zSHkhEpYUPjLj72HqfE4bR1+vvfDs5jAwtpm02dXs49qpsrExQQdLX7aWwKoVY3zZxZxu2109YUZHV3FNMGfL9OnabhqMLCvDdHtYGwuS1tQYaKqIQgwfHU3Tz90AWMggJHKABDyOFh8fHsQjq7rXBxPEIlYkxRji6B+ZtTygu7b3UJiJbexvSBaYdOqqiHJonV3BQGhLjnQahqFQgFd13B57FTLVuy3qtZQFJlv/d3DhLrCOO0ycyPL7LlmkN6OKL/9lht41bUrfPORMeZi1ue99fa9DHWGN0JdZEXi7e+9li/801GePWo9p7Mza3z0T+/ng795E7v3dqHrOqqh0n+gk1MnxvHLNsI2B15BwlEVSC8WiK2l8JyZsZwxnoNIb5TkaIzEcpbEcpaB3a0sTcbYcUU3C+Mx7nr/rTS1B3nm/rN8/6snNvabnbCet53723HKIkOHOjYkFWZZw2FAS2cAqI8VAooi0tTmxzBNRheta7bZ5Dp5NsCsN+rWNEzDRNtINdQxzHWNtImhm0iyhCw3KEYDP71oPN0NNPAzhtVECkEQUC/TIW9xaZOaplPTyhQrFUrlCl63VWk6sRjngYuzdPk9vGFXH1d3tRIrlHhibplnFuPMZi61mAMIOu3MT2UQgJv3lGj3HePXXtbLp37kJerbrGoKzynPimIYs7507JQ6+MaqRcI7ZYV4ZbPy/PbOGmP5GlXDIlwn0ytcEW3leGyV/b4WJs6n6WkJkHOomGsmazmrSW1uLUt/0IdvRWB2Ns2cabJnRxPlUo2aU+DkilV5jLidDO9uYfT0CoN7m0llSkRafJydT+B12BBlcVsaIcC56RVuam3n/FNz6Fus1C5ejHP4YDujMwku1r2P52ZytC0PcNJM0ur0sFK2ri1X8pDQ+rhQKXJjW5CSmaXfGeYVwfuwi0kSZWhy/jKCIPA3e1/HB898BRGBQU8ETDj9fQ1d7+G8UKBS0xi0+VhMZ/jkV57imoNdtGIwV0/z8xWhKxrClAUuXkxsVIav3NeJruucPjXLFVd18/q3XMFvvf8bmCZEwm48skAulmP2xDymbiLKIvbdHcxpBo6rBmmJevlf7/gqwT1trGYrtNSfs75WhTm3Aib09ISZHY8jSSKyTSaVKYPXjqFIVhPnxgMKUxdW8Uk6+USeiUfT6y9TdoE+d6nUx8y0IQgiP//+m3jjr92AKEiUSiWqFZWxY3OcfWIznbL/YA8dQy2IoogkSXQ0+/jdt13PsyNLHL+4zMuO9Fo+x6KAoRvY7XYEAV75mj0bBBogky7zlx99kF9973VceaQHm91meUAvVpHPFMlS3KJ4ttA82Mzi2e0TinBrgIsXYwTCbjJrlpxHwqSUr3LxzBI3vWYf/iZrslHOVpl8fIzOI0MszGzaGRbTJUTd4OLs9qCdnp4Q8+eXt72280AHY6fr5xC2quWKImKz2TDRkWXZsgWsN9ba7TYuB8Mw0DWdcqlEwB+87DY/MTQkHA28SGgQ6AYa+BlD7YVsMp4DwzApVqpUVCtQ5eTCKr96aAcHomFEQSBbKGIHbutu5SU9rSwXynxrdJZzse1+xD0eH+N1ffSBnjUEavzCtT/km0/exRPjh7l95ywARZqAzYY+QewDphGRgc0l+gVtnDu7h7lvLserWzQ+0vevlDSJXzr7Dk6nLRJ/IrXCre4eVmZK9LUGmTDSREUXwT0qwoiNZEbFJom0CG4uzC4hAKYgUPOIKD4nEzMWGdvX1czo6Cp2m0wk4iaoisREncSaRXKL1RpvvHoPX3v03LZr1nSDVLm6jTyvI5MqX2Int/ZAjoGbQkxmc1zR1YYpG5wpWJMLn2JHF6pIpsidwUewi1aVerX0SVzSjbht/XS5QnWyaTJbShA1wvR1BFlNFEjmSnSEfayM5zA0g/seH+e+x8fxeRz0+7wIgMduI50qE08Xt7WB7h6KMHpuHr2q8Ru/+1JOHJurkyiBliYvoyetc5TbIrhLJZr7m4khkE5ZqwPdgoAv4mFqabPC7XIqzI7FUasabo+d6bFVtJqBaUKttvl8ypJ0iYxFAJTLNOOJ4uUlHG6Xm0g4gqZp2Gw2K+Wxnrr5T7/3fXZc0YdoWnpdAWhqtQif1+ulWq0gSiLX7Ovi0FAUTdMx6wzNZrPhcrtQKypf/PyxSz63WtWYmUlz5ZEeSsWyZRdYeX5bNyV0qbxDkiUiTT6SsRyD+9qplKpMnbMIrs0uc+bYAp//ywd56V37+frH7ifS10Sxsr2KbVesZMbdO7bIfEywOxXm2Q51675173fFJqMoCoViAdOgbn0poesalXLVuiH10Bur+g+iaGn/VbVhY9fATy8aBLqBBhr4d6HX3RFeOdi57fVtERUmtLqdvP/wMKOpHN+8MM1SvkiT28nc2Cah7g4vYWLj5Py1aAb86FSA23da7yW17eS+ZEgogoeV6kE67CXW0wMBlvUx3tw/yP/s+AoiZTwyfGHfl3jb6V/kQjbPYWcrZ+5fweuyIxy00SK60UNJ5tQyzTu9REc8eDUHR5dWOHhlCxPHVtk12MSF+SQCsKuvGUmD0bp0oKpqtEWDjBxfoHdnMwnJJBr08AdvuYm9PVGqtRrffmps2/lfSCbob/KSSGyvzM/NpYh2+hAVEbfPhuSUqcgGehV0w+TEbILBUJBmt5e4lidXq/LkEvxcRz8u8Qeb41No5kOfPMPHP9yFqQocOL+DdE+GOU+MgBbk2WIcXBANu7A7bOgjBgLgddnpjPhxKDJ2HQ4f7uH4+ALadhMMHHaZsadmeeDeC/zV596KIAicPT5PU9BNc6uPXHKzyTEccRM/lcA9IJJbztI32ITLZWPp2WladrXC4iaB7m0PcvGMRQQ7uzb/X1wP8FFEos1+vB4Hu/e1YxoGB24YpK27CUkWefjeU7TVI69FATTdRJYl/O2W1ntodxvNLQEM3UC02/jypx9BkgTspkluLY+qmzzxwCj5jEXyhwZDjDx0HtM0CbcFKBaLOJ1OotEWUqkUxVIRUbDiqnXdQNc1yxNZgK9+5ThTE5dWvoMhFz//lqtQbGI9gVCgWn1+Qlm7zKQgvrDGjj5LsjJxbpmBgx0MXt3H8ugKHf3NjJ5d5tHvneOp+0fYcaCHkl1maj7Djuv7WTy7RCjiZnZklf59bUyd3V5t7tkR3fZ7tCOA5JAZurIbwzS5OG39nbXZZGw2G81NFgE3TXObW4qu61Z8ev1H13Vq9QRTu83xvNf7E4FJI0ilgRcNDQLdQAMN/D+FIAjsCvv5/ev3c3Q5yenROFPVTa3yfWd385pDRd73TxYZ/7krraASE4ml2tzmgUyZlDZPRb+Ch5Il7oo62UqgAUaqEzxQuJ47PRap9MkZvrj/y/z+qffy7PfXEBDIF1V6016yUZWsae2fq1YIG2FmktZC+qlYjNuHWnhqdhUEARNQ0ipsITVdYS9zI1Z1fGY0zqteMcy733kDbodCuVThjVcPcnJyhdktoRsOu0JnRxOlUpVw0IXHbUfTDXKdImerKXTALlUISjYS+TJmyWTXYJiR7BoTqTTOnMy+9k7OVBcQBNjvOw1AIdPNj77xBjwBB9OLKX77b3/AvoEWLhxLsscTZs4dw7viZEdAZDyTJlYu0aVYTZy7uppYnUszGVvB5VSopMpWBdppo70rhOJWWKtU8bvspC/EuO+ec7i9dqLtfmqqxunjc6wlCuiaQTa+OTGIhj14D/cwsWRd/+Jkkr6wC72qMRXblNs4HAr5tRJNUR92u4zH42DnwU4EQaBa04iGXCTjeVZmEvhlk/Gjlib+Vb90FXuOdKLrBp/7yN2sLW+O89DNu1hdzbGWtD7nprsOcd1tOzCB+751im/VmwSDQQeZi8sMHukjnynR3OankK+wdG6e/pt24Q940E2NWCyGz+cjGAwSiUQQUwKFQrHekGhJNwzD4LFHLnLfDy5c9u/BW3/xSgTJpFKp4nI7MQ2DcuVSjfM6Ks9j/7i6nAEg3OZjciyGaVrVZ8Fpw+m2US6q+CMezp+P4XTZ2Lm3ldGpBLLfib89wMLoKqIJYZ8N0zQxTRP/rg5ESaA57MDmUNA1A2fAyWhd5iFJIm0dIfw+J163tV+1WqVUKmIYhqWBxpq0CGzaYTqdLrxez4b2uaGBbuCnGY2nu4EGGviJQBZFru9o5spohIfPLvLwmQU03eBj9wZo9u2n2W8Qz4rs77I0qCYuqmaRDV8G6QokPcGPYipeSSL+3FSROj4XqxGUb+eI4wEAfjD5co6dy9HbGmR2JUNXe4AxV5pCqsaQ2U7ZHadnsYuRVN0NwjS5vuhl5LFprjrSybOZFAcCAeYfmwNRYMcN3VQrGrmpNOXSJgE69sAEb3ntIQyvjKbphAJe/vitt/Crf/9daprBzo4wS+kCj8cW2dUTYO5CfGPfbiOEHrWWyKu6jttnI54vIyCwtlzE7VcoajXKmsaJuTV2NXUy0Jaj1/0YAPff8wZOnioyvNcHwMRcEqNmVQWzj+e5NjvMyaOW73Jv0E50TwhHRmBvW4SLF1Y3zqMp4mYxZenBy2WVQqGE4baTFmrE0iW6h4JE+8Oo6Qo/vOec5XRhCnR2hYg2+8gF3ciKhCgK2E0DU5HZ3x+hapjopQq1ssrAwW6WEiUqhkqlpKJnKiSUIgNdAUxTo1AoM7blnLZiq4+wKEnY7DY0Td+0RxMsy8Xn+g2LgogoiZYt25a3NpreBAFJEtFWEqjJHM09TUyPxujZIVCr1TBNg2w2S6VSIRgM4vcHUBQb2WwWQRBxOB0k4nk+/9mnL3veTpfCjuEmtJqGqqr4nF5EUUJVjctuD5C5zPPt9rvIpssIgkDTQBP5cytWE2RV48LpRdweO8O7WtFrBol4AUEUmD67TMBlo30wwomJBNFrelF8DuRimdiYNQFU+usWj4sZazxEgf29UYb7FLKxLMnFDLFYlhjw0jt2o6oqsVgMpe5Pra2vFK3rawTQdYNypUK1WkUURex2Ow7Hi1yBhktcbhpo4CeFBoFuoIEGfqKwKyIvO9zF1Tui/PD4HMcnYvzx3U1UayJNPgOffRQAkTxOIUzZzGKaIFGmpB+kbObpIcJs8fJhKn2uDnoDb0Or1fiXs1389RNuQGNWKbB/qJULK3F2d4Z5prbKcr5Ix2ITZ1YS9EeDzGYzXJXxMHPKInDjzyxw89U9nH100uIGusnyE/P07W8nVtpePXzLL12B0y0iyzJutwsQaA+6+cXrdnPiR2NIFZNs2ZIZjNVy7BhuYn6s3jQ4neK6jnae1CxSPZXOcqCzmXMLcbJFlb1tLRzLbi65jyTWcB/zcq/vt1hZEZibt6QT8bxV2fe57cwupOls8bN2Jo6/ZdP6r5iuwnyN5VQBdJOhoQgTE2uYponHadmMRVu90OJgLJ+n1+6kmtdAEpjPF6z4Z6fA0mceRitp2OwydqcCFZVMooCiSISjPpbrDZcm1sRANk38XheJbAVBApfXhtNjQ9QNIgEnY09ZE6edN+2gtdlTz5kWEGRpk/PWpQJ9u1ux2SXKpTI1TSfSE8YZcOFuCVEqqlRUjVCTG0/QiShAYjnNsQdHrOqoqnHwml4EAWRZYCSRwcSkf7iJsftO8cYPv5zxiwmW12awOWwIgoDD4UCWZcrlMqurqzgcDsLhMG1tbRuV2M9+6jHKl3HNACiXavyfv3mED/7WjXi9TiTJWsl4oQp0QTUIB10U05urNdHuCDMrRaJ9Yc5NJHFHPPQ3e5g5s4ShGRQLVbLpEuV8heF97QiywOiZJaqVGu2KhM/nIJYoICLgbQvT2RLGNE1WY3kCQctZp3dPB/6Il9NPXOoj3toT5tknJ7jpVXtRFBnFpqDVNAzDwO6wYVMUS++MSblUoarWqEoV7A47lUqJcrlEU9MLWyw20MB/VzQIdAMNNPCiIOix89abh7h+VyvfOTrNbCxHSYUT83dwuOshBFSCUpSylrWa4Yw5Viq76RCbmfxnO527fSjXTVAzNRyijYqhMuTu5kO9v4RTcvDVH72FB+fmAKuy3BH0cW46hlozGDud4Kq9TWRSVZbrcoLp1TRH2qPMPzGzcY7Dw9FN8lyHoRnMnF2mf1cz1arO4kLG2n9ijdvv3IPDaUdVaxTyJR769lke+PIx1IqGcEKg93UDzKxlUTWdSVuRHUPNiGUdl01BicNN0Rb0+odJaYFr7E2IgghLJm/aswvTJXIhHudCMk5mocDRwnYN7frSeUeTj9FsgoAisyYIpHJlZFnENGHXQJTzYysbgvVsoUp/V5BiSUV3gGd/mIvFIhQ0EAQCZZHkYgWvQ0E/s4BpQs/OFmZL1mf3dAdIraRZmkvT5hJYubiCp28ns12bTXCyJPLJ33s13a0h/vmfnuCHPzgPWCmP8kKaWsiN2+/A1E3mjs9QylsNpgOHupnc4gzh39cGwEc+/Va8AZd1bE1nLa0SX8zDgiUhaemLMHdxs8IvVmt8qx41ftubruDDf/FzSJLV2Jb/ndv55mce4dg9x7nyZXu567dfRk3V+OFXjnLuqZlN33O7DU3TmLkwTz5VZM91/YTDYXw+Hw8/OMb5eoDL82FqMsknPvYYH/ytG3E47Njtdiov0EQIEGgPbCPQTp8LVoq4OsIwEaNYqHKxUCXUHSLisTN1Zgm7IrGSLJJJFol2BugdagZZ5MTFGDabRH9/E7IJRkHFabe+8ocGmhAME2VHOzPTa/TKl4+ECEb9jJ1boFQq4vG4QQBVreFyOXG6rOryRiiTTae6xRZTsdn4T7HEaFSgG3iR0CDQDTTQwIuK7mYvH3jVPs7OrPG9Z2f4tc92s6vzF/mTN43yraODXHlDjPnqjSxWvcTyCgv/YqNaMpg8VmWnbZjuW3Te1fV6vh9/lNdGb8Uh2fnGd07xpa8fQ5ZF9h2JoLrAo8qo9a44v9NGYUqltdnDKkUEE3a1RXhmMcbhwy0sHl1mcDDCxdNLlz1nvaYzc2aFSJt/47XHH5lEViR++Vev5dyxOe7+5OOs1C3hPFEPLbf3k7OZHPQ4KVVrxDMF0qKKOp5CqztN9AxEmMjkNmPhtsAry/zWW67Dtldmrljgm6kTxFfyZHNlMtmS1bTp9eBy2BmdtCrbqfkcmCaZiTQH93fyll+6lk998dENUtES9eL124kXytibbRxbl7EIAkMhP6qmMz9pvdYVcDJd388TcLM+MVkeX6HzUC+1lRwrY4u4fA7KdgXUTRu/N9+5j942q6HvcsrebKpIS2eQtlYvJ39sNV7KisTqcm7bdorbTtvONv78N+6pX4Jp6ZCf42xyaWT05u/r+wmCJfXwBT0EPHbUqk7nng6K+SJOl5OXvfUId9x1xEontCn1CHGTb/zNg8yPxdhxuIvXfuBmnH433/z0w+webmFyJvmCjYFTk0k+/fdP8YHfupFgUKDyAhVoAG9LELZMIAxBoH0oit2pWJPK+mWnUiVSqRI7r+6hskWHHlvIgCgwdGUPByI+ZhJZ1jIlWrz2Dcs6URTo29FMKVNmZSH1PONnoVypoWsGkiQiiELd/9mqPj93H62m4XQ6sNttlCtVanWi3UADP61oEOgGGmjgRYcoCBzoi7CrK8iTo6v824k57vqbvbz2NTXujt1OTM3jMqssTQbpu8pH6cE4mDD6ZAlXtgP7B2z8fNvLME2Tb993li993bIR0zQDb8LkjJBF1Q12d4RZTuRxorCaLrCaLrC3L4wuwsiSZQVnygLDw9HnJc92r42mXU0IYTtJXaXLCFOczLIWz3P6xCKf+OP7OP34BN5WL90v30HODW6fnYymIRoCZ+fiG8QnS5XrjnQy9sQsALOTSXYeaqE4aKPoNVj7bgJMGNzfwuMzy7Q/foF3vewKBm0BQh4Hx+as/UQRgiE3GCZuUeJAfwuIAk02B12tAYZ3tXPlVb3E43E+9N7r+Od7TjIbyzGfKkDJkn+0OOv//JsmVzY3M3Zqu0uDVLDkJ4IoMHNqls6OAMGWAGf/7RzpvEprxE0WaB1oZuroEldd2cV5tUjQ7+JNd+zBNCxiaT5PSXB1Ic1aLMuuVx9kqe7LDQI7gq6NoJSaLLM0n7lk355X9NImtiII4Mro2AwJT6sPKVey3B8cCi6vw5KSKOJ2smda51TIlvnXv3+YJ+89zWs/cCuH7tiF3+dDEASMmpXW+cx955mv64Yvnpjnr9/1ZSJ7ukgs50gsZwlEvXQONDM5Gb/kHNcxNrrKpz7xOL/2gespV1+YQGvP8VUuJrK4Bts5d2aBltYAgaCLsZFl1tl0pVBjbjVP/+Eu7KLloW13KJx4bJyevjCtLhspEfQtiY12h8LUme3Pumle/h4V8xV8ASdSvZlW1zRkRa4Hpmzf3zBN3C5L+iLLMqVymUKhSCAQutyhG2jgvz0aBLqBBhr4T4NNlrhlbztXDUb58dg8T3mPo6oagiFSWOxkrZJnjQr7X95E6sE1tKrBifOL/NmnHuIPfu02vvSjU3ztgTMcGI5ycSzGwFATI1Ietd5Qt5Qu0Ce7OJtMgyAgCaAnVdyBzcrY8BVt5J+MXXJugijgu7OL6WSWJaMIiSKCALbWMIGrovRlo6SSWTIRGcfrBphcy0ExjUtT6BQFxuqR1ft7o5yd3jz+0VycPd0h4ok8HQejLFcqxKQqiXyZ61/Sgi0jcmrB2v4bT4xy4+4udnS38As/fxVPPztLLlfBMGBtrUgw4GRmdm2DIL3tTYd51ct3o+sGq7FVllNZPvHoGc7kExzsitDqElmLFVBrBi6nDQpFuvzeS8gzQGopA0DXQDNzZ+cpZkp4nAqhNj9uQUcBnB47mYrl3zz+7DyRoJOenT5mJ5JIkkDfYBNOh0JXPfEOTBSbtGFXNz+VQDMhlth06Qh67MRjdWlGd5DWW/u2nZdhmkwFDZby1kTgSCTE/ONLhIIuCnXHjr69HZSQwQS9Hte+eWNBsdUTHDuCxOdT/OPvfItbT1/Fr/3FW9A0jXK5iigIfPfTj2/bdceRPi6Mr22sGGRiedKxPDuv6GYxUSCfv3yc/YVzK3zmH56k8jya6XUkKtvDeNLxLN1HBmAWVlcyrK5k6OoOIysSsigyed4iwlMTFoGXZBGfTQbTpFqosjqyCqJA53WbY7jRgFlHS08YT7MXLlwqScllS1z3kuGNCYhuGBt67q3QdR1D1zeq/IIk4HI5qZQvPx4NNPDTgAaBbqCBBv7T4XbIvGS4A2++wBPuEVjsZSK7uTR9Jpeg/1Y/9qMViukqz5xZ4H999iGeHFsEBM4kk1xxpJ3HyjEGwiHmltL4nXYcSzWmMmsc2NHEhUyKVsXO3FIWlrLs2xFh784obzoyjHFkGIBjj09vfKZpmLiXVXo6g+zrjbK7O8Jgi5/ZpTR3332aiytZeq5u5dxyctu19DT7SS3lcSoy5ZrGmZkYuzojjC5Y2zntCje+YT+PfPMUF9eylFWNwUSERLDMMSNBT8G9cSxNN/j4vc/wt++6HafDxi+86RCf/KenMIHBnVEWs3l27GphdnKNt7/tKl5yyzDFYpFKpcLTF1f4/PELJEoWiTmVrJ9nFJq8HlSHyK5QM8EStHQLloevYaDrJjXdwKzUECQBm32z2ri2nKapNcDYk+MADNw8zORUZuP9XLpMfDHD7//6PYTCbv7+S2+lmC6yNG5JTEQBuLiZPCk1hTDM7c4UW+UZ5ZrGZF1msBWOdktKY5cl0hct2YyxpYq6taKq6waVShVJEhFFCVmWeMnbjjB8uIf2gRYWJ1b5yv/+AS958zU4nU7W1tb45scfZObcMmsrm42rNofCwlyG4Z0t5EsqS3W5jgCsXlzBq9Vo39vN2OSlntAAIxdW0GziZeU664gXa7htElrdkSMY9fKB37yRmekUd3/lOJPjCebn1sA0OdgVpKstwPzy5vgPDjUzfmKBgR3Nm77PhkkxVUS0yximSVnT6dzXzlw9rXApWcTuv1RqIdskykWVXYcsi0Fd19E1HYfdvq2ib5omum4gydK2yrQoijj/EyQcDReOBl4sNAh0Aw008F8ChmnQkg3yOuUW/qE4fcn7U4UszYeduI7J9Ef9dfJsoaMvwKOVGAZwcS3FgbYwhbE86YxV0ZuZWuN1dwzznR+PbuxTy1VRE0VWEyk0w+TIK3owRIPZi0m8fifdg2Ey6SLtRZPDbS7sLoGPPv40c7kCrrr12/LpBM2DHuJZqxoa8joopSqksmV2D7VwMmZVBmcTWRRJYEdLmIVYlt2DUcRbdvPk6Axnkmkmp5IcOdLMM0YcsVuBNBzob0GRJK4aaqVcqiCY8JJbd3L6guUmMjJnEeIL5SR//nt3MtzXRCGfJ1+s8vDZWb74yAUCHicHWpo4Hd9O6hL5Eom6g0evzU1lxHq/ozNAraJTyFcoVk2klhAxU6Ltpl04bCJORUIzDAZffhDTNFiYupTcrqNYqPK5TzxGLvfCVUhHpcYen30jxk7MFtm1uwXdMHFJAvsilqPIetKdaZroRYUOKYC9CopPA58b0TDgQAeCKOD0ONhhk8A08TgsUmfoBpVyFVGyfh881I0oigwe6uJD//wLNDU1U61WqVYrvPlDd/Dk986QiedJ1zXG/Qe7GD1vrQysLWbYMRAmnq6QThYIKQLzE0kSM0kGrx0kgUQmU9p+nQ4bVfOFU0BNINQRJD5t3VtPwIkoChw42Mm+/R08c3SGr3/lOHZN5/zjlmvGoTv2UFR1ZubWWJ5MIAhsC7gBa1Ki69ZExe6yMTu9PSnUuIyEwxtwkcpV2Lm/3RrzunXd+vhthVpVEUXxEl3082mrG2jgpwENAt1AAw38l4DdZqM1GiKXL/F2XytfL8aJV7eTr3i1zNte2sevXnc1v/V/vsdsvMCB/givv3Mfv/e9x6hoOkFRJj5boL3NR3qtjCKJvPW1uzm8t5NCsciDz87T0exmdjXPXCyP3aZw/YFu+ns62PXBXj72p98jsZRnLZEjn6liAqWgnUWlxmnNIoxNV/vIPZpBrWh0lEVSssBwZ4Tx1RSGXaCpycPCaoaB1iCTq2kG2oOsVSpgE8mXVf7iq4/w6d98LU8+OkZfq5/pWJbEuSzBvXbmzTx/9As3cMfuPtSqSmwlw+f+7jHe/I6ruXgxyfx4giWzittp4/1vu5ajp+f4wreP85F3Xsf0YoZPfeUZAlE3hgmpfJlUvswVgy0cj61etqOvWd+MdF53GAGQHBJOWSK1ViK1ZpHBXXvbGDlnVTa7e0NULyNJEASBof1tVAoqJ5+dp60zsPGeaZr07GknNpOkUrRioPWiyuSJ7aHS9sEWikWVvTuaSJ5ZQpC2ejsLeMoh1LxKdZ34mSYOu8zKSes4/XvbmTxphfK09YbqEd4SdocdtapSLlcolco4nQ5KpQp2uwObzUY8HrckCgIcedkuWgabWTof4/5/eYq5mTTNHQHGzy4xuLOZ6bNLBJo8HDrUwfjRyY1zn31qAofXwZ5rh6gqCtlChWjAhVBROZncnkh5OTibvVAn0P4mL4IgoqoasixxzbV9XHlVN0/eN8I3Z5KsreYopPJMTq7R2hnE7XEgtQuMn9w+ngLQ2x8CBCRFQjPrj4JpYprgdMp0726tE14TQzdw2O24IzVsDglVrVGpVJEVecN1Y50cr4ezOGz2f/faGmjgpwkNAt1AAw38l0ChWKZYrhBtDuEtOfmlpMQ9UoLp0qY+9teG93KNL4zTJvPWW4YYWSpwZX8Inwjv3NPHd8YWUJdqZKsVsoUKg0NBXnWkn2iTk4XlGHde08v8apaLC9bSvGnCtx+dwOWwcf3BLmyKwMvfsod/+/ooY6dXcLe5EfdGeHTOqjzu3OVntJTlolbkysPNLJ6IE5vNcOj6do7OW17SlVoZW8BDXjRwGjq7+5o4tWRVopP2MgGfk/l4li/df4wP/PZL+eiffRe7X6JQUjmUa+HDv3wjEbuTfK7IA9+/wL/efZJqVSNX1hidTCAAh6/q5t3vuZGgz05Pi5snT81zYSrJX/3TY/i9Diaz290szk2ssr8rgmAXWauUWcrlMeoDYMaql70fumYg2rbrh1V103HCrlze+sw0TU6nMtYvHoiEbLTsaMbrsoFuUFxKo7rLtPa3sLKYxe6U2XWwE90wuXjGWlUw6jIOQ5TIaoAGYNR/QEqWSNVDQNYRCLu2/PbcSuh6eIqEw+lAURTK5Qr5fBFd1wlFQ2iahq5ryLKMpmmMX1jh479/Hwev7uXIz13B9790lLa+Joq5CnOjqwzu72D0+Bw2m4xmCuy+aYhiqsjsuSUq+QoX7z+78flpoHm4FTz/AUmDZzN8xBdyY3fYEUURTbPisWVZ5OZX7mNwdxuf/YN7qdSjq1cW0kiyQF9/M73DLcyMbYbTCLrB7FOzAPRdN8D0fJZdw1HGn55GsctUhe33srM3zNhkgkjUw8JMio7eEGq1CqaNYrGELMso9WZCTdPRdeOyFej/FDSivBt4kdAg0A000MB/CbicdtbSOWbnrUppeyjA+0J+vrg4w9lSljc0dXBbcyu6aSKIAmPzOR58do4mt0QPJm0uB6/t7eDLc1YjmcMm89qbhtjT30KlqlIpV/jXR6c3yDOA0y7zwTddiV0RcbksCy6328Fr33GIr/5whNPJDHrC2t5mE9G26HUnA2WG+0PUUirzT60QGnaRKljSjni2wN7OZiZXU/h9FmkyMena5cPwGqR/UOJbj41w7e5Obr91N0fH5xEVibGpJBfOLROSZL702adYmLUq3j0H2piuVujbFWVmJMapY3Msv3wNqdNLOOjG6XVwcXmNlqiXldU8QcFJZ1uIscSmxGJsflOrHXAo7OgOI1R0jFiegcFmBNGqPtoUCcOw/lRV3dLtYoIpoABD+1oxTZOmkIuudj/Vco34YsZqDBRAtss0ySaJuoRBN0wW1xPvBAFdEbAPRxGavewcbsUoVhk9MUfnwGbgxrqkQHbb6DnSs83JwzRBnb9UOrJVhbA6v8bem3eSWM3h8LnZSqgFQUBWZNySi2wmj8ftwW63k8lY52gYBpqm8d0vnwJgYmQFNZ1n5/52lhaztHX6WZpQmbu4ysDedibPLNDWF2H0+DyGbjJ8zQDTp+dRy9sbAkXbpc13l0NpC5n1hT2IooiiyMiyRE2toao6kmTygy88xcXjs7i8Dnbs7WBqLsvAFjeZzqFmJFFkdmx12/WLzxMZvn0srcFcixfo6mvG4ZTRdR2n01GP9bb+PgmiiGkY9fActlWmG2jgpx0NAt1AAw38l4AsS/R0RtF1YyNqGeCDnt3ETJ3rurvQ1BpOp4OLs0m+85i1bH73Q5P8wduv4+or+hBFkbaWCP/wnZP80dtvZt9QG4auk8sV+fJ9Izx5ZrOBzeNU+INfuYWDOzvrjVA6lUoV7G7+/jvHmI1nrA0F2LG7mUkty0Q5v/4SO2QvyxPL6HXHj46yn4xQYWdriHy6wszEGs6gwrnpGH3tQVSnxjFpCUpw5Eg7+ViFzy2d4i/veilPfWCcubk1dl7dwf0/OI8aK7M4l8LtsbPn1gEePjW7cd4Ol0KlVOOf/ulJ/uIvXoMoi3zhh6epqBouh8L+ve1MTMaZG01weLiFkUwKzTTpbQ3gdMiYosmZ5QTPLK6y2+UnOZveOLbNJqGWahsUq7s/wvjK5WUHr9rbwjvfeg2CAKcev8jn/ucPSNfdM7recHCDQD/X8gygWtWYXkgz3B3m/OQafq+DTDLPjv0dxJbS5NbnKZKA5LK+psz6fyxyZyLWJzOCICAAQk1D9jgRAFWH0VML6JpBtVLbkBlsRa2mYZomPp+PWq1GqVREVWsIosDZZ+aYGrVWHTq7Q4w8O0tbdwi/R0ESoGdnK6Mn5siuFRAkgUKuglF3Qhk7tUDXjhYMw2Dx3KZOX/gPEuhEabPK7/Bs2toJgoDNbsM0TVKJLE//wKpwl/IVxp+aJNDkZXl2c2KxMGVNmDoGmgm1BnAGHGCC4nNwsMWHLIkcuG0HZlnFECRGzy+j1j2t11cAuvubcDoVapqGTVE23Etsdhu6bmCaBvlcAUPXyecKSJKEJEkoNgVZll58Mm3SCFJp4EVDg0A30EAD/yWwboH1XMJlk2U6kUll84R8HqpVlc6Iize8ZA/fevA8zUE3vV1RXE4npVKJff1R/uGDLyUU9GLoBoVCiY9/9SjPnN8kMz63HXvQzu987gFefe0w737lYbK5El97+ALPji6TqVqyhpZ2L9UWEdUJmUXrtWaXi655kcWHF7ed5+L5BPtubmd6NkWlajVcdToCrBXKVIoqruAmGTrrjVF16Yxk1/ji5Ane9r6bOHN0kkcfHKeQtz7n6hsHeOd7bkQ1TZ68sEBV1ckVqgwNhpg/E2N5Kcs3v3WKsVKeSl1aUarUKJkaklNmuD3AyNgKu/a0UXGZlNE5sRLf4BeKIBDIwVYPkVDIRay0WaGX5ecnfbqmY5omsixz6MaddA4c2yDQXKYp7XIw66TT9so+fGmVwkyOfLJAWBIJdgQpl6qcv0y1uQsrIXIrNE3fcK/YCsMwqJQriJKIKAiYG69VCYVCKIpCJpPG7rDcJUqlMj/4+mkAghE34yfnEUSB5bkUA8NNjB2bw+13cuimAU49OkHXrjYMScbpcRCbW0MQBQyHncWFNDtu201mPsnqRAzFZYfSCycRAlR0k5YWH5nVHLWqTrVSQdctucQ6QQ03B/ibH/0G9/zDQzx6z0l0zUCQBPxRD/6o1yKuAhua5rKukXXIIJgIWg20LechmgjlAgO7mhDLOhfqvtc7d7fR2RtidnKFQMRtVcBrNUTRcjOxkh1FRMnSljscViKnpukUiyXE9eAab+A/8ig00MB/OzQIdAMNNPDfAoViCV3XCft91DSNN9wyhMsm8sqbdhPwOSkVS1TKVbxeD5Ikks8V0AyTT9z9DM+cX9pIcvP7HMgehZU1i+zd++QYgmHy5Nl51rKWBGNnfxNas8SJfAItZ0AO9rREcFYE0vfHWC1e3s935elVeq5sZmzWcjmYmEtyaF8r+UANLaNzuLmNi7UkBc1a3g8JDk4ej/Pw8hyvvXYPL3Uo3PO1kwwdaOPxMwsMH5vl1S/fy8+/8gD/99snaA65qWSqdHUHMQW4f2yetS1eu6IgUMha+u/Dt1bZ9cY408+GmVutkamo24pzNdNkwlmh+4Z2smjE1TKZTif9Cx5mnl62qpCddozDPhRJpHlOZOXCJpnV6lVKa+JjcvubriSXKhJfzJB7dpY+j51QW5DMuRW6FZFrXrqLfdf28rv/8webJ2ECgoCmSDzhr9F9XTNrO1z45wySVQO/atCpWBOPppCT1FIGAXC0+REiXkQB7PEsRk1HtkkEvNa2voCLfNLSgWeTef76A9+4xHrtpW+9gptf3ophGNRqtY0KtSiJvOd37+Cbn3+afKpIZiFN+1AUURKoFK2qeqWkMj0aI9QWQHE7mByJIUoCO68fRDRMzter12Mj1p/uoXbcQQ+UMv/+gw74Wv1kVnOU8mV0w0DQDGqGhiiJSJKILMuEW/y8+6OvR1Zk/u3LR3E47czGipc9Xo8is7TF57l3qJmZSWvq5HBIVEZmN957xXtuQ7W7ePCe41w8OceD95ziTe+5lpfedYhqRbW8oEURQbTOxTQMbDabZVtXl3iYppUYWau9sO/1TwSNCnQDLxIaBLqBBhr4b4NypUpOKRHyeTl3Yo79bWH8XoelyayqeLxu7HYbtZqGVjP42peP8ezEIi1hH66sjrvTw2K5SCxtNSa6HApvu20vX3/gPPnSpmZ1ZCpBV2szmmFVOUMOO+6JKivHkpc9r3XUqjrOqoAii9TqFdLZqRTxI1UKTTX6zSC7C03E/UVa8l6mR9KMqEl6mvx8/v5jfOb9r+b4M3OIpgCCwOe/9DTdHX5uvrKTCyNLnD+xTEYz8HptqK12UoUKggBd7QHmFjPsH2ghFssRCIk4dp8iZRZY09vxuYOs5EuXnG+8ViVesyreYb+DsUqWsSboenWYoSUbT7hTVErWdXSGm7btq2vGhqWxKIocuWM3h28ZppgvM3Funq9//GHmTy5SLlrj2vHL19LVFcRhl6nUpQIbsoot5FYTBeJ11w/FZSNeTyUMuBTidZeQHd0hVAPmFtJ4VlKoRRVPwEkhZRHInuEo8yOWW4jD72Ji5NKgnBtfsw9FUajVatQ0rR4GYqAoMv3DId76nhv5xB98l11XdnHh3Cb5HLx+CKdgcu6ZWSRFJNwVRpJEdN2gXKpRSORoa/ezvLRZyS/mq2jPqZi/EASP5WhRylcolUpIooTdbgPBqp6bhoku6UiSxC1vuIJ/+/JRDMOAy/d1IogiP/cLR+rDLLA8t7ZBoBEEdt6407oXAgTa/dz17tu5+c69fOFj9zE1ukLPjhYk2ToHwzAxDCupsVKuohsGlUoFm6IgyRKCICKKVrPjC61gNNDAf3c0CHQDDTTw3wr5fBFMk6/94zMU8xV++6Mvp6MnhNfjRrEp1NQa6VSWz33sMU4dneOqK7qYmc0Qy6tIyzkiR6IksiWu39PJ+197Nc0hL7t7W/idv78Pre6VKwDJU2kiw05y1SrRJ1VWkrkXPC9REum/qo3Tc3F29UW4MJtEEgWaD3iZwiLsk8U0vmgLv992I7/zLw+g16u4s4ksezsi/N29R3nP+27kjz98L50DIRZWc3z+S0exGRah1uskLJ9XaQ44yEsiPbtCPOlIsLszyNRImmKhymveBHmzQNDoY2TSxO++fGVyK8LNLhbzVgV+nhLz7dsJ94SjyOCtrSw9FadW0S9LCGVFwhd0c+iGYYYOdPGV//MwP77nDIpNYvhQJ4Zh4HAoGwT6ctVC+xbS9XzFxPRUAl9nmF1DzdiaPYw8NQkmDBzoRBAFXE7lefbcDkEQqFQqmKZpyTsEsNeDQv7t26dRqxojJxfZcbiDdKZMfD7DwuwaSj2Su3OgiYvPzBCK+mjuibA8ESOXKtLmsjF8oINMskA2XaSr35qMKYqEJAl1OYaIJAoIooAkCoiiUHeyAEUyiQ40o8kyDzwxz9JKlgO7ouzd2YzLYUeXDDK5CrIkEO0N8rf/9utkMlX++CPfu+x1xhMF3vKeGzaq8LWaRqmgWnKLXIUPveULG9vuQyKXz7L3ym7+7hu/xqM/OsuV1w1jmBqlchG73YaiWC4cuq5jExRsNhtGPWjFMLWNsX1u6uGLgUaQSgMvFhoEuoEGGvhvBRPIF8u840M38bcf+SF/+4f38ZG/ejV9Qx7L47ek8vn/8ySnjlo+wGPH5xnY08JYroquGdQmc7z39Ye56879gPVF39UW5JrrennssamNBjrTNBkKBXl6aZngFWGK910ad22dj0nnq1pIdemc+54VSDI6m6Qt4mU5mUeV9A0ThD3BJj5x7cvw2+y87cZ9fPGRMxvHGU+m8QkKU8UygZAToaiysy/C7GQSta7tbWn3s1oP9ogv5Nh3fRv3O6zPvKCl2X1FmNzDZR75N5Ejv2RjdqydsMdBsvQfiFQ2Xph5JMolEpTwXW9nt+pDLV8+FGRdy+71uXnnR17G0P52EksZ/GE35XIFv2Zi1uo2dc9xqhAEAdvzVC2dThudfRHApFJSmTg6gwB09kfq+8Jk3aO6f3cru27aiQkUC89z7SYbnsaGYdRzXKxzHz+/zPzMGrGlDEP72inqJqtrZQYOduJQq4wenQXAZldoag8gyyKJ2SS5egXc3exnrJ4EqNgkLp5foXdfCxXRsB5gXYcXyFRZBGgPMj2VhHoz4BPHZnE6FLo7AiytZMkXVW68qpt3/fyV/OuPx9CrGt0DYUp5lUK+Qrm0KZ8oFqokYnmaolYojSxL+AJOBEHA5d7u3yyKIpIobsgvdl3RQqmcx+Vy4/P6KBZLmFi+1GpVxeV2oSgy5pb7ZsliNKrPiSZvoIGfJjQIdAMNNPDfDqZp4mty8I7fvJl//KuH+Ovf+wF/8LevpbU9zKf/4j5OPL09yXDy/CpDe6LoQQcz8Rzfuvcs1x/qpS3q5+mpRf7se4+zVihzxf4WRs+s0jEYZLm5whNri9wYaWflO0uXPQ9/nxv1TgePV1bxVxwcvqqV808tYxogCQKCAIUzFdyHFV7S1cfvHrgOT13T+ws37eP+E5Os5i3S5RFkhgQXn7r3aa7uiZDOlxjfIj3weu3IHU7EZAHDMAlH3Jzy5dgXiHA6Y5Ho6VKWVr+D1FoVW3wf50Zk2oMekqUKAiY7wmHipSKpUuWSSOm1tQo4+HeRq1Z5mgS3t7Vz+tQiBw52oSiXkl5BELDZFG559UGKhRKrSwmcbjuZ/4+9/46TLT/Le9Hvb8XKqburcw57904zs/dEaTQzyggRbWQZC0kgX8PFx8fYvjgdY7hcwBjsg419sAEHgg0GbIwkBEooh8mzc+reoXMOlatW/N0/VnVV9969Z0YgDTBaX31GqrBqpSpNP+tdz/u8OzXqTbtMfLRj7wvd+2YxVJVWbVtKjh/NI4H12TU25wNved90H0dOD1Lerh5a5ZRCcPXc0l2v70dR1OYmJI7tEImYaIqC67r8z197mqXZdZBQsxwW1oM7CI26w+LlNSbPDIGU6LrC5lI7xWRkuodIMoblS4QqkJ7E2Wts9P7spdF6wzkwKvzps4tkM1GefnGRYnnfhUJCwcxEScVMEqZOPhvHsi2shtFqLtyrRnvewcbGPSuH53koSmDD0A2dWr2KIhSSySSu61Kv11u/oTvj6/aaHU3T4DUnrECHvEaEAjokJOQvJbV6g6Nnenjnd5/ik39wgX/xDz9Md1+aqxfuFrudvUk2ujViGY36oouwXH7yFz7FsbcM8T+ev9xa7tzmJo++cYA/qS8iAzcDXy0v83BHnJVqobWcUKHvr/XwfHwDq17hUX+QmzMFXrDXGB/IsL1dIdIX4UjaZLNaY4gcjZLfEs8rywX+8y99gaEKbCYlpxOdrJ/b5HqtyH1PDlNyJHMXDqZP5CeyvLhd4OH7erl2dgVxOsamX2B302Esk6bo2gxtRlkuBILu8x9Nsas6KCIQ6Ke68sxcCwa6dEUN8h0JTFPD83w8z6dRtimlDEr2q6saFm+W+fcf/RyO7zM83EF/b4q3PD7BsdNDB8SUoijMXlzn3/7zP+SvfOghbMsJGjp9Sb0RVDm9ptfc8ySO57Enxz3X59rnr921bUUTXJkPjlNVQJ/qQ4npcH6huZ6D9hJFVYglTHRTRVEEQg1iEsu7VbYWdtnc3kHVNJCgmSqeZZFMmCQTBpm+NBUfkJJkVMPtSjBzNRCxU0c76RvpQNUUSrs15q6uMfn4FDevrpFIRsjmYiw1x2bLV2+BftU4js9HPnX3+QGwXJ/NUp1N6oxNdZFMmTiui2kawbTFJmZE8LO//n7WlnZYXy4yPNGJ67jomoFE4rpeS3R7vsvO7jbJZIpcLpjwWCwWMAz90Mi6MBM65PVMKKBDQkL+0rJbLPHO955g8fY2V84ts7NVuWuZ0elu7IzJJasIG1VOnskz9+IGiiq4fHuD0/09OK7HZr1GqqFy+YVVRk6nuG2VUAQ8fDRDubeE/msqju3ReTLD5pt8vtxYBR+6lBgXz7ergsWYizR1zq9ucmqgk1IPrBZ2uF7Y4dOzt9j8ygYf/p8v4jbzo584M8DFry4Ti+kce2SAZ2+ugKLw8P0DrSqqYapcrwfH9sLOFg8+0c8nlQ0E4Po+5ZpNz7LK8nK7GprJxElqko16AwUobrQ9zZW6TWXp7ng4deDVjWOejKeQz1VpXF1DKAIR0dksNvjJ33iWyZP9fNcPPMaDT07ie5IP/7dn+YPfeAar7vBbv/RVpBn82RHA/NV1VEXQcb3G0M0dOrodCobGyFQHq8/NEY0I5H0DQbxvs0otAVsKIo4bDEVJR7AsF88RqLnAopAZ6OAnfv2DzeqyjREx0DSNX//ZT/DZ/30OAMd2+NRvfoH/9E9+m4FHj7YylH/8v76fy1+YBeCBJyb40Z/5Tm5cWeYnfuC3KADjJ/rY2Gx6yn3Jyo3gouToQyMohsbs9eCuQaXcIGLuszV43wAF/Sr59necJB6P4boujYaFlBLDMDAMHU3TmJjuZWK6FwjO815EofQlqqaiazoSiZQKqupTq1VxbIdEIkHDalCtVFtjvoVQWhnufy6EFeiQ14hQQIeEhPylxfclu8US7/3hR/h/fuLTbK4dHPox/fgw52Y3YbfCow/28Uxxk4sb25x8KM+NrV3qiyWQkvv6urEX63ipGJ0PpSkkbR7rTLIti1yy5iAOD3/HKBXd4IK3TXTfH+kB0szQFq7rpSpxQ+fEUBfnlzeZGMqySiC4/vUXnib30VJLPAPcvLzG1JEu5ut1Xrq5wZGhHLPbBa5UCmSzMQq7NYaOdPFCMwLNB+ayNqcyXcxs7WB5HjuNBp2jGcRqUOmUgB5XiRZ8KGuMDqa5fvXuJIo78V5lfnNmUba8ytKX3Dy/3HKEzF5c5l/9g//FI289CprCM5+daX1OAGZUD1Ik/KZQ8yWqIvA9ifSgWrXx0xGqpQapzgSNQ5oVfWhZI6Tt4dgenqm1zqtEkEjGmlPzVHRdD2wFB3y6si1q9xVK986AEPDdP/gmNE1F0xTG7x9AKIJUVGNgIMXSYvFA3LUZMxmY7KbLlZSrNg3LYWut3Fq19zWkcHy9+d2PnuXdbz3Gfcf7MU0D23awbRvLsjBNE8PQW/nropmVLYTSzma/QxBLKanV6vgln1w2R61WpWE1MPSgau37/r6hNyEhr0/+HC8TQ0JCQv7sSAmOtHnv334Yo1nd1DSFvqcG+IJfZPyhPgCuv7DCfZnAc3txbZuRfIa0YfCm/n6uzqwzeF8HFyeLnGWT2+UipYrOeiMQ5BPKIGeNBk831qk6Lt0iBUBeiWMtHxRGJ/qD6W0Xl4Oq9I2FXY6ncwCsiQbdT/a0ljUjGv3jHVy/sclwZ7DOWwu7nOjvpGI7pMYzKIpgzq+3PjN9tIvrdoELG5uM57Kt12dKBYYe6WRkOMvodCfLO2Vu3d5hbaPMztrdlfnD2Ivte0V8cN2D4mi/VtIMld2qxepmGaEGEjIS0znyhiGsQg27VMerNOjJJ1rnIZuLBcJrH0IIsskIuVSEXCpKLhWlozka/U6ElOT703QPZDBNjaUbG9SrFm6zmrq3vtb+0hbQ4o5R3wCPvGOawcmu5rKCGzObzF7boF6osvTiTdjYQjTq+9YHV19cYOb5OTJRjZ2FXbIJg+7eoCruOS/TNfgN5ukX5/mxn/84f+sf/i7/+48v0LB9kskEsVhQlS6XKlSrNWzbwfM8PC+IyFMU5a4pjr7vU683sBoWjUbgu04mU5hmBMd1m6PHg6mFe5MLQ0Jej4QV6JCQkNcFmXyUv/I3z/CH//08+kOdnGtaHnZ1lyNn+thZLKOogsHOFItbJaQLo7EEF84uM5COsaPU8ffd/72wucWjg0M0Shovrm8f2Nal1W2e6h/h6ovbzFtB3m9c1zmay3Ht+ibJodiB5SPrknhCp+o6fCG9w6NDaUxNoe5LZua2QQjmrm3S2R1lq9zAc30Gu5Jc3tjmiTdP8Ln5YAR5ZybGS+oWSDjR1cmlja2W9JtIZyg2HBJZjRvz25zK5LjlNxgbyjGztcurwX+V97/LCxXGenPEkuOBpxhwXJ96w6FcsajVbC41m+vGzgxirRaxu+LM7NbQDbVVPV5b2OXEmQEuf2GWoRMDlCoW7GsKFEDt5vZd2493JVqPu3pSuI6HYagsNH3R25+8yPMfO0c8FeHd738Eo9nkWC3VMWNmkKXcHPZxFwJUTeE7/uYb+O//6k9QhMD1fY5MdLTE9dHHg9zkaEJn8qEYQgjWl4vBOPqpPEZEQwDFnRq9mUDwu44Hf86xyGsbZf7r7zzHf/tfL/DGh0Z599uOMT3ZjZQS23awGhZVzyMRTxCNRvE870AlOci6ruO4LvFEDMdx2draIhaLEYvGKDYFuKY1bTqvsQdayDDGLuS1IxTQISEhrxsmTnVx//uP8QdngxSORERnsVpm1vGgAyg26EnHecNoHy9cDJoN33Cmn8vnV4m/aBJ7UKMmXJBwOtfDpdu71ByHx/QeFE/wFblKRyxCdzbGxfoWe1PHRzJpZMVDOhLX9UlaKutCYAiFM0qW2S+ucerNeZ7WtzE0FXEizu3PrtORi9HTk2RtrYxlu/QZBtnJBFcWt4jHTHpycZaqFTLxCLvVOmJIpeH6xDSNUsPa7zzAb8D8bom4rpNLRbiwu82ZUz0I+9UrirRh0nDvHriynw4zQm2jxJJVwHVcqlUbw1DRdAXfk3ieRFEEvh+kVKhxjcWIwCsG633gzACFhQIbqyXGJru4/IXZpgD30HQVvkav8MZaGds+fER2tdTg3JdmmHn2NgDTb5jAqgeNi1IKjM4sfU/ejyMlXZO9eK5HLGHw0//jB8h2JfnM770IQGdvmq3Nu7O0J0/1MXt5DYCjDwzgS58bF1c4enqwtcxeUd11fIj8xbjp67g+n3/6Jp9/+iajQzn+zw+9iaMT3YG9w7IR+5I6gtHnEul7lMsVVE0jnU6iKAqu4yGExHEsPM8lHo9TLpcQQhxoVAwJeT0iZGhSCgn5puJLzz7/570L31CkD//lS9e5MB9YKI4NdXJhPcjS7U3H0X2VqFCIo/HAkV7+xtvvY+bGKj/7q1+kazjF5dESnXqCa9vbDGgJ8psmt5d3OX4yT23A52pxk1oz+utMoofkgs6lG+utauZEb5aby7s8NNXL2rktdncD4SiBvnfnaTxborzUFmODgxkWlgt0D2dIDSQ5d2u9FQ9magqW4zE93kOjW1KLutwsFTma62C5UiGuacyVSgzEk6xutm0ap9MdzN7eQdNUjF0f61XaB7ofy3K19PLV6pOxLMX/vc7ERBc3ZzfIdcWovq9AMVpjfGuSsy8EthchJaauEb1qHfi8JgRHhcCr280LgKCCHckmMCIauw2HdCpKbWGbaDpKVdeCZZrVUIDOfALfBQQUanYrSUTxZTBiulxDNmwkkmQ6SnmnihCCTD5NqRKkjLztex7A81Q+97GzSN9H1VR83+cDP/oUKze2KWxXefGz19lYLKDpKh39GRShIJGtCxcjomLVXISASGeCqhWo5agicZo5zPmRHKmOFD4+v/v07Kv6Hl5rhBB8+9unef/3PEwsqtOoW+RyHezu7uL5Lq7j4jgOuqGTSMSb1g4olcpEoxEMQ8d1XIRQiEajlMtldENDVVWSifRrdhyRgUEG/u4/eM22l/m93+KFF154zbYX8heLsAIdEhLyukIo8H2PjfNvSjVWd6tcWdji5HAndhwQgupSjfX1Crbj8ebTY0RMnRPTA/z0338bv/zxF6lWXAreDm9Uepm9uMltr0Y6bvJSbIPqTns4xYSepbLo4Ne9lng+ms+h1iS6qnD+xjq9SvCv2ETcoK8nTfG5GtXVdoU3EtGJxA1OPTLE8wvreDdqTI/ludoU/xPZNLLksrC8Q8dIB5d3dshFIlzY2EBKGNrMMJjvoW5IVmkLaDlvcb9IY2c0rm+0E0JeCWNLwitE98bKUAR2d2pMHe9i6a1rFKM1TM/g+oW2J1jTVE6mcizGN0nmojgNj0jCID4QQ9yus/w/n8W128I+P9zB5moZRVVYjAUWmL6pPAslm0hEw7290Vr2A7/+fUw/MIqiKLzviV/AtlySmSjlQrD9vv4Uq81Iu6kzw9y+FjRQTici3DgX2GEef/cxNGFy48W5A8f3q//sj1jal7U8fLSbSrHBzAvzd52Lyfv6mW0mpRx5+3GuNsdjT+ZMbl8KBqk8/NZJvuW9j+A47l9YAS2l5KOfusIzL83zQ+97hEdOj1Ov1xGKRJEC1/PQdR2BwGpYGKaJEDRHdqtBGoquNfOhGyQSSSqVMsIMY+xCXr/8xbifFBISEvJ1JGJo/L+eOkbUCASsogoamsv54iad/YnWyO5f/v1nWd+pcHuzwM/8yfN8ZXODY06WU4UsVy6u4zSX6z6WpEognsfNLI/KflavVljcLlExPLJxk/u7Opmb2eLm/DYnezqxfR+9x+QNDw/xnu+6n5mbm6xvVhif7gZgYqILJWtwZXmHa7e3SCeDKSbL60USUYOUqbM5u8vCrS2mZITodYecabLTaOADD4tOFpYKXD+7RmZVMJoOKn298Rhrl3e5eWWT031dvPPRSaZHuohFXrmha2G2wFA8+bLLeLtB9V0qPktvXWUnHlScM16a2j4rxamhbq75JZZH4Vq6zs1um8vxCovVMteubTJwov+ONTfr0a9Cc3m+j+u4uO4+68a+e6mVSjvL+uBN1oNxG9FEENuXyyfpHemgb7QDTVfpbf4vBDnW+f7Mofuxf826EIwMZhkZzB5YRlEUEKAb2qseMf7nxcZWlZ/6xc/yOx89T6lUDJoFLZtEIk4qnSSRDKrPjUaDWrWO6wYXj3vnWNM0fOnhuA7xeALbdl5hi98A5Gv4T8g3NWEFOiQk5HVJV9Lk+5+c5jO3V3lxZ5NoVaUvG+eZ4jpvPJLn6tV13nBqmM98/ArPlbe4thFUD68vbvO4l6RTRjirWvR0JXlWrALQbcTZWGugCqX193O5UObxgV4uPdse4HLt+jrHJzvxV+ucu7jIB7/3MZ59YYEr19eYndvm6PFurlxdZ+RYN8WKRa1qM9QZpyAaSCn5wLee5EufvMGtapVjk3muX1pDnVV48JEBCv1wQ5ZYuxQ0Lw7lU1z7yhISOHOygxgGNwneSysGH/obj7TSFLYKNX7n0xf48BevHHrOBJCr6Cy8jIj1+jV4Abz31NiJt6veUTcKtIVrsd6gIx3DNDXWC23LyqQTZ5Zd4vk0kw+NBl5ZAVpEp2OoM1DQpglI9ITJQmkL1/WZenwiaGgDLpxd5ubMDggYP94TfBcCFq4HVWrH9YglI/i+j2Zo9I3nkRLUfRMTfSmJNQV0LG6wNBt8NtWXpX8wy/byLulMhO2VXRKZKLHBzsCGM5Bm7nMX8WwP9glEr2az1BwlPjbaFtGapjRHhkM8ZlCr/zmIyq+R//77z3H9xio//P4HMXS1mbYh0XUdM2JiEuRoO45DvVZHUZVmprSOrmvYloUe19H1P4dJhCEhrxGhgA4JCXndMt2bZtGuc2Fni7rjkaj7JEydZ+wN3jzVy/ILq5xfn0EogqNv78XarZO/XKXhFihPJcnqEeLDGrIpKHu8FJfdLfyMQGxD1owwpCS4MLNOT1eCjc0KsZjOwFiOrdldhrvSrNo7/Oovf5Ef+tBj/IP/6yOMDGZZWg4arXRPYhgqruPTqDo89eAob394hHNXN1BTJl0dMW5dD+wEnucz+9UFhp4YIrnhU2tO8UsZBmsE4nfl4jZHxjpbx3/j1lbrsSM9sqkoz11ZfNlzNn9lh/yZKI4niaoqa43agXSOJdHg6LE0M+m51muarxLdzBLRt2k4QVXYl3C96ad+YLybK7c36FBMNi8HqRq7BYfVhbvj9TRdxW1mJvc1Y+QOICVf+ZMZNjcCUa4LmDzex5Xz7QuYsck8c80qtGearMwF73UNZjn60AhSBlF7sURzdvm+C4a9h67jUdxubsPQqDavDWzLI9WdobC6i6IKjj40jCIUdEMlkwsSN8xMjPxED77nc+SBkaaAlsRjBpvbdzcj/kXCMDS+/z2P8o4nxtB0BVUNBHStVkfKGpGIiWmaaLpGJGJiGAaO42JZNrbtoGsaQlGo1WqkUqnXfP/DFI6Q14pQQIeEhLyuecdID4vFCs8urLFZrjPd00EMldL1Elvrgf1A+pL6V7bIlixW1ssMvWOc2/Uy1GHzmRqPfXsHjbrCxblAkM4Vijwx0s/MuXWuW8FrSk5jItvBfKnKxcVNjk90cuXsOr19ac6fX+LZr95gcijH7ExQ6fz2dx/jO779JJbjETFUdksNVlZLXLq8wZ989QbFSp2Hc1lu7jZwmk2AAihc2oR8YAUQAlbnCweOVyhtZ95WscLnNme4XFpjtVHkTeWjrGy1h80kEiZdoyk0RQmSMHyfuuNS2GpQqlnUgeO3Jan7c+x0+MxUi+xaDRoPt1WK5qv0XJ3i+bl1erIJREKwvFtG2efFOLu6zmO5LjY/vUrBckFKChsHh9609v+Q6remq8x8+UbreceRnv2f4Or5ZTKZCIVCkEss73F/3XF9rl1oepPffoRo4pAKabNavJ/AohDs2MLtbVBMuoc78X3JzIvBBcn0/f0UZoN157oSbKwEdwHMqMHePf9E7C9uRVYRgne++RgffM9jGFoQR2c1XFRVRdcNotEovudTq9WoVmvBYBpFJRKNENFMpDRwXQ/HcfAsGwQYDQNd/4ttWwkJ+dMSCuiQkJDXNxLef2KM2ztFeswEa7dL3K5ZdGSjJLIRKruB6Bof6eRtj4/zn//FJ3n8yCDVnQ2uLmxx9LEs5/15hCG4b3SA2cUqNdfj2d1VBqMR6pZLX0cCKeDyelBxTcVMdAdcT5LojWGMxHl6ZQMrK+gZz5LrT7KmuPz6r32V219ZYGerwvhTE1xoCvSpyS6GTZPLzy6Q7YjTOZhhYbEAQHmnzpGpTs6ubTEykCGiK3REMtQVHzwfud6OgbOkx49f+aPWeVj84sFEjM7RFJfXtng5Un1JVv44aJSbSOn4jk/2W3Ms9LTF87W54LjXdit0pWKoQqDsy3M2VAVbSsYnukAIFGDmE4fbSA6Ug+9BMm4EzWsINtaCaZKu65OJBWJtc/7u7Oi9c7CH5/to5iFRayL4J5aIUCk0E1RkIOwVVUFRBKqqYGjKXRP6Dt+mj/SDiYvxVyGg+3tSFEoNqjX7FZfdYzIdpcswqDsSEdGoWxYblsN21XpVpvLHTg/ygfc8yvTUII7jUK44xOJRPM9v2TRUVcUwdeKJODEZo15v0Kg3KJVKRCPRYHCKrqHrGr7n07CsV/NVfv0JK9AhrxGhgA4JCXndYygKf/v0cX7+wy+SVBQe0uJoNyooj/az/JUV3v+BR3nr26ZRFMEDj4yR6YjzplKN/+P/+Rhr+V1wQQpJSa8Qt3QmsmkuFneI9hscz8S5PN9ObTg60EFlrszthQ0m7stjqYJSw2ZxrQTA6Xwn19d2Kd60MHWVif4Ecr3Mjc/McvzRYWYKVUxFMHc2qGZWKxZqqcyxE4NcmQm2M/vMIg9+5xQ7KZuvbmyAR/AP8MbRLvqHe4irknK8bRfodtOsbh2s+lreK8fbuQMRaCZ12aXANnLrI9sM9+WJO308N7d2YPnNUo3jI3kMQ6VLRtls1LE9n3gVLl8Jlo1GdEYfGUYBVBF4oIUAxfWgbrVEkBHVSWaDQSV0NCcQSolEEonqCAG50UxQMVaCyrFAoGoKXrOhMRLVmXpoFAiSQVTXwfd8nv3EFT7xP86CprG0UGjF6PX2JhCuy8TJPmpN34aiKuxeWMUnGCPuAkvAVD7L8In+wLadjjHywDAAjzw+wl/7/kdRNQWrXGPdthAC4vdoIvz5f/ZuJsa68X0fq1HnH/70x78mAW0isMo2MxdXGZnsYvGFIAe9K2HSNZQj0hHHi5tUDZVSw2anUMNtJse88aEhPvS9D5GIGtTr9aBi3GoKVNE0FdNs2zR8z0fXNTRVI5FMoKoK9XqDSrWKaRpEIhFUVSUSiWA1GiQTL9+UGhLyl5VQQIeEhHxT0JeK8O0PjvG5l+YofWWB4naVM8koP/6r30d0X2Uw0xEHoCMV401PjPGJG7c4MpDkqlwmtp5mqb5LoW7x0EAXzo7L5flNElGdsZ4stbrDzOIW0wMdmBMxvKLP7etbxGI6wyNp5jeKLFp1MskoxaqF5XhcNyXHHh1g+Zklbj8zz8BUF9uFOr2ne1k/t8b4SJZrn7/KzuIO009Nc3WxSLInxpfsVeIlncmOLLPb7ezmcszn4to20/dFWGYLIeHM4gS+BUUOVpu9VzG6+8rODgNpk0axXb32XJ/tX2oQe7PNsaPd3Lq1RaMZSdfTmSCS0Hju2gr3TXSzuW/c9R71hsO1FYeRwQyLM+2Lj+NTXVz/atuqMTCRZ3G9Bs3q795wj0wuyvZqcEEScS0aNZtkNka5FuzD8JFu5i8FVfPJh0aZvRZsY/r+ftXhL+gAAQAASURBVDx375gFnusTiwffvVCCQTBrNzcpbJSZfGCI2WZTYLY7STRhoGkaqqag6iqqpqBEdeaa0XUsFNoH+Mkr/Pb/7w8BGDvew+yzNwGY+IEnDj3Hhq4SMTV838exv/ayrRS0LzoyUSLvPkY6YhBRBGrDZeYPLwLQfaSbXUWlIxWlS4HUUJq/+d6HUDSNnUoNz5f05POoqobreuh6M8VGUTBNI8h7dl1q1ToSSCRiaJqGrut4nkej0aBUKqOqgeh+zYephJMIQ15DQgEdEhLyTcObp3u5trKL+iaDE6s2f+vHv+2AeN7PWrnCb52/zLF8J+fPr3O0Z4RC2aIvESeVMHlhY5Mz6S50TaG7L8nF2SBrOBLXqB+Dl5xNjk12otwW1GoO/u0SI2MZ5tYLDI71oqoKpqGxvFHE60nRawyy+sVFUh0xXiwEleLe6RyLZ5da+zTz+auMvP8Mcz0eVrmC5Xrs1BoMZ1IozQrsYiFozEtWI+Se6yKRMLi0fLhNw30VAtrxfLKjaVbPbRx43fcly19Yxn5bDr3TZNiMoZkq59c2oBJUlW8u7dJnBJnOuneIMJR3vnZQ/Ujfh60dIqkolaHu9hta+3OvaKO4p4VBgpTU1nYOvJrtTt21JwKoN/aV+ZukupMk09G7rAoHRVz7Tdk4PIHDb1Z8XSeYgvk1j8AW7T2ORDTyMZWq67FcbpCMtH/fkVQUKjYDyQjXn77N8YTBxmKJEw+OUKs32CmWEZtbdOaylCulpl2lLYKFEGiaRiRqUi5VcV2v5RcXgmCkdwxs28aybFzXJZftJCTk9UgooENCQr5pUAR84E1T/MwfvMijf/0kuXw7JeD6/Cb/8fef5Q2nhnnjqWH+40tnMVWVG02BdXu9yAMiR3mzzpX8Dv2pBLOXNzk6lefc6gZTvWn0MZ0r5jZLdmBVuFjf5Ph7Oqn+rwKNugs3i/QOJXCEZGYrqBrn4hHOL22iCnjsreOcXy8AgRxKD6aodY0jfvscUsLIX72PBd/G0A6K/oVC6cDz6ViGxS9XqdZtdop3V4D3EK/SpKroh4tUz/Op+i6FqsNWtT0gpmwF9oOKZVNpPnYjsUN2QJLvTjYry2BoCr2jnQghKG6VX5V/V9WCfbuX6LzXGoR4tUfPXU2FrdeFoFS9WxR3xSPtJ377w37V4rDxCwK4cXWVy+eXWF/ZpcdTyKYSCEXg6CqKoqBHNM7Pb9z12db++T7RmEFjs8LCvuEwHaeHEFO9JFIROvIp7ku7rM5sMDSV59KLS1w++7/4jvc9xHt/6HFyqSS3FlfxfZ+uzhyVSnAhpyhK6/wKIdD1wO8spUTK4CLM8yTSd1HU4H3TNPG/xrHsISF/mQgFdEhIyDcVSVPj+998jF/+5HnuH+/l/rFennvmJj/1P79CreFw8eY6v/IHz9E7mOL+0S6+vLTMtJnGvW0xs7tB78OdeI0qHXUDPR3jUjM/uprzuKnttCLY9rhc3+L493SS/QrMz+3QK3Xw2qJqp9rgSDqFslDjysoax+7v4/LGLhPTeS40BdMD7zvNruPx0nYgaEayEQ5DAR70MpRvWTjuqxnfLelIRGk4LlXr3vnEinH4rfje4SwL8m6vbjZiUOKgcPcO8VtL32fnubZlY3+dfPxkH/ViU5TfoWD3D0fZE3b7kze8fcLt4OP2PghFOVSg763F31edl/dQ0NL3iTR9zfuXkQJ0M2im82R7PXaxBqnEgXXoukoyrvOR33qRz3zs8l3v1Zvr7+yK09eRYKdcb9ll9nB9v2V9qcYPCnQtorO9ucX2ZoV4wuTa88GExoHRLCMTHewWqnzuYxe5cXmVv/Gjb2TmxgLJRHCxk0omaTRqgee5OXHQ9Tw812vaWVR0TQsaMjWB12wedB2vZbl5zQktHCGvEaGADgkJ+abjWE+KJ48P8rO/+yW+LdvDR//78xz/ziOUdclmtYaUkobjUhJlTk9kqH3VZnu3xsREJy86BU71dlFdrtF9LEHMinBjdofllSr3dXbxIusHtjUay8ASXBRFjvVnSPoqc19Y4sFH+5grVRhzDW48E1SsVVXBSwjGB/JcvBaI53wmzsp6jZ6+NBAIaLvu09+ZYrnYrjz3GDF611RuLQUJFBOTeYyYjmwmKQghAmUnQFUFl5a26M3F+cruGr4OmaxJ3oigWYKbzSr4Hk4uENCJlEmmI4YZ0dF0hWMP9dO7WebcC8tIKfF9SX9PirXPrfHEGwZYj7qtBIvUap2juo8Qglh3lkrFQtRsxh4eQ/qSWFynultH1VWsRAQL6OjNoEZNpITOuNEapBIzBN/xN95G33AH0nV56Zk5Lp1dJK0GIi8RNxiUEgGYaZO+U/0IBcyYzsBUHt+X1MsNunuSJOMa8WyUwlYNicRUJEZEp7xbo2u0CwiOqyMeWGSkL1vT91Tfw14N7lBMPTjM9SvNCnF/Hqc7yLCer8GR9z7BjdlNHDNO141t5L7pff3TPfzsP/tj3vrOo/yLX34vju2wtV7h3//Mpw6I/63NKmzC0LEubiy3Pe8Aa7bHxOkhthZ2KBdqB97bf0HnNNqTG8trRRYutDPBq3GNF796m86hOFHT4OylGWLRCLFIBE0VZDJJ0qk4MzeXcByHkcFuZq/c5NjUCOubO3Tk0iyvbiKlxDB0BvvyzN5a5KnO/J3/9wsJeV0QCuiQkJBvOnwp+c7TQ/g3Snz0j57nvodHqHdGuHg1qM5lOg1iDwque5vUPQfxJEx6XZQMSW3b5gVvFXoAq0hM0+nuiLG5XePahR1OPdzFhfomCU3nuMxz9asbbMsgG83LKMx8LtjGrc/Mc+RkL9evBuK5sztJ/ViUr1i7sANnprqo7No0VqqUyha1is3pY924DQ/hSPLbCt35Ll7a2eRUZ57eNZUXl9p+6Ruzh9/uj8UNlIzOxFAH1+0SPoCAgm1RsC16ozGOxzKoWhCNIQQoPgxOdbIwv0NpOcg3fstbj/K+9zzKr/3v5zC7Nimv1vAcia6qeJ7P7ladje126kfGcll5MTj2qSfjzF0+mN4xfrSbuesbpLIxdsygeh2f7GTxdtujrJkaE6eH+et/+3GOnhoKxmQDF69ssrjWThwZHu1gsXn84/kUC82Gw8xUF9FElNkLwXnq6EmxvVZiaLqHtZVgmWxCZXc1OEaRiN3TvgGQ70+3Hqv78rcVcbAKrCgK0pdsbVSIeD72vmmEjarNSqmKqutMHOmlWqkRi0YZGu1ASsnt7eqBSrl+SGOe7fks7thY60WEEKSyMfKjnXiRCLqpceyBAep1h4ipMHG0i0bDpbZVPLgSU+Wjv36O7/6h+5FHfDqyKep1i82dAiP9eeYWVsl3ZRFIXM9jbWOXcrnK0uomtuMgdgWGoTM23Me5S7Pcml9B0/8cJEZYgQ55jQgFdEhIyDclioCn3jTKzBfmOP2GUR7/lmMUf/NPaHg2nJRcWi+Tj+RYNteRCswomyDhSCZH12KS7bjNdVGg5jr4gzH0ooLj+iydLfHoyV5WXyyhpiVd8TiblaAqeK1W4g339XLz/CpmVMfpiZBcjpAfz/BirErdak/me7GwxRtTPVy9HghIx/GYm90iretsrLaF6akjGTa+ssQGBIJ87nDhvMfIWAcX5jcYnMxRbBTuej+l6czf2Lnr9ZPx1AExOTzSgaIobE/6nKfCmQtptq6X0JpNff6dyvPA80NUTvP9/eO29y899eAIm9tVrl5aRdNVvvTJq/z+f/kqAL1jnXTlA2uEEIJY5PC4OM/xmLu2yvBUNzsbpZbQ3b9rX5Pp4B5iTVHAjGh4nsSX8oCVoWMwy+pM+zvyfZ9EMhhdLvbys6VsjQXXh7M4TruKfJgrwttX0ZZSUtqp0jHUya3mXYyjDwxw69Y2I0MZFs4GVefJ+/uIpqMUN0vUdus0XA/H9vjIfz7P5q0CnuuyvVShuF3lp/7Lh5hbXOP2/Aqjgz14nsf80hr5jgwLS2sM9uVxXTfYueZ+2I5LPHa41Sgk5PVAKKBDQkK+aenoi/OW7z7Gf/8PX2TyVC/T/TmeWVzh3NIumqKQNCKcMAawXJ9qwyFfjrD9QpnZeuDWHYoY9A4kcToFvdMxLl3cxLEl7jVJpehwvRgsd6QvixZRmNnawRqK0Ofl2UjBiysbdE8lAvF8iGf5qigy/rYBbvzJEgKo1mxEHDrzcbaao6w3rxdaom/99g7ZTJTdezQOCiFY2i3zwFQvX761TCyic3K0mxe227YTea9mvMRBUTo0lDvwvPKoir6gthRetOoxlYjjxjVcBZjdpu/EIOlcjPf/w3dgNSxUXWVlZoMvffgs61WH4++YZnmrSq5plUAI+ie7EVGDmett0fnJ3z+PpqmsLgZWhkxPks21tp0lmjA48s4TIMFQJB21OlKCars4tsf8zDrjJ3spN4fonH7TON/y1x9CSolrWZi6jkTyn372U4eei9a5utfrtoddCL6DgfsGuHZltfVePBM/uLArqa8XoOEipcTzfRQBx+7vx3V93KhG1fYp1ixKFftQX7Hn+diWy9RbT6D4Pp7tEEvFyNR9CtuVVlKJ2vzfeCrC7JUg2i+aNHEjgKKg6Qp63eILv3PxwPoXZ7bo7EiztVticKAHc2ObnUKZ0ZE+KrUGHdkUhqFz7cYCxWIFIaC7K8vCystfzH0jCGPsQl4rQgEdEhLyTc3Dbx1lrd7gn3z4ixTqDfJqhEf7eqloLhc2ApEhgPePH+HaS2tU991+rzdcbt3YhRvBMpNdCRIpEyOqo6sKTtPDemslEHqqIrhQaoqK5h309XKNE93dvLB70Dvd35mipDt8pbLGQ2/pZvGzwfuVqk1Hf5quHh2GI+i2wFu3aCzX6MylkKZ6TwHtSUnUEVheUC2sWy6Xrq3TORxlqxZ8xuXw5ISK6x54ns0dTNW4Xtvh8ce7KV4KROlyU9yOPtCHqsDK7cCb7SMYPxZYFRRVobJWwirX2Ny0yQxm2NwMqvAdnQk6upNce2mRO/niJ65y9FR/67nmS3rTkWAgC4KooXKpmc88PZyhty+FEAJ137EJREtQjh3r4aGnppHSp1qtkUjE8TzvFQW0AMyojlV3UHWF4cnA97xf495lAdHaFfaRE/1EUyZr61V21ko88/GLNBo2TsPnytNBdnQ8FaFaCs7pifsGKL24wCOTeRoRA8v3KVs22+U6vi+5dqX9G5p+IMFuzUZNRBCWxUhMkGqmqaRyMaorwV2MXFeS5bJFxDTJ5RS2bhz8HQK88KXrHHuqn7GhXgxDJ51O8oaHTxKNRnj80fvQtSApJJ1OYlk2EVNHSkk2k7prXSEhrxdCAR0SEvJNjVAET719gmc+cQ7L8dAyOje/uEFHT4zogIahKPzTU2d4tKcXa+oE//53nuGFKyt3rUcCq5sVaArAyaEsVzYLB5bx/MPLY5durDM5nmG2ECw/OdjJXKPYEuvP17Z58Ik8S1/cYOChTm4nbTZqNajWONbdgeg1iZ+IMfOxVfzDwjcETDzax2ylQCqd4uLK5oG3zQI82NNNDZfojqSRD269S9lMt5CwQAN5KsnwcJqz+U3ec+7DiHNg79vgbp9H5dNFMgmDWuUVJuntxcgF4wMBuH1hhfxYBw8/Msr6WpHNraavuaVCJUJRSCZNIkJy9GggWDUhSWejSASqItAVODreiQQMx2L+6grRpEnvkV6mTg+BADNukMgmyXQlmb24wvpiEc/3cQUoiorn+0yc7MN1PGoVC9t28T2J5/i4rofnePieh1WzyXQlcB2P+ZvBeR0f72h/53ekj1SqwUCaXHcS39DY3gkuXG5cWOCP/uNnAMgPZlvL7x+J7vuStdtb6IbK/E77/CrA8HgniYiG50salktHJsp7PvAIm+slnLVdNoo1Mr0ZACJxk72G1FjcBKBWc0kmTA5LDL9+boE3fvcxuruy+L6P7/vE9AiKEJhG+86EoWsY+3zPun64lSYk5PVAKKBDQkK+6UlHDd7/6BT/4fOXuL1Z4IGj3Vy6usZbx4f4f7/1DGmhIgHpWHzo247xPW+ZxG+mMdQbDtcXtvlfn509sM7ZhV2Ojua4trZ7+EbvoCuVQEsbYML5jbtvfb9g7zD+tiwvVArQDFrIRiNccbZw7KCy2vfuOIPLJksv7jB0JMdyxMaXEtvzeH4zqCw6ndC4Q9RtF+tsF+vkYhFKNUnW0YIqajP1Ys+rIKUksutz5LKOgmj6bnUk0BOLs7tdxZ1KUifQxIPJOLufmQdfksjGMaOBNcL3fBzLwVUEtuUiCKq2T7xzmvf+rTfyr372k1y7usYP/u0neds7pvkPP/1xAMbHOrj17E3K63D+RmCLEHpQ7ezsS7O1azE20UFju8TcraDiPX6yj6qvUC06dJoRrjV9wVNH8jTKdZZmN6jWLBZmA+mYGe5gpynclVrjQI7znYhmQ19hs0LvaFf79abojSZMNtcOjk/f2qjQNZCmphhEXb8lkMW+JkTfl3T3pUAI4sf76ZYS2XAxpE+6M7FvmmKbpfkdZKl956E3n+S9P/BY6/leSkqt3OCjv/scMr1IxNQwDY2JBwaooRKJHi4J5q+tMz40QMOu4boeSPnKA2z4UwyECQn5S0QooENCQkKAY70Z3jI9yGevLnJhZYP7p7o5ne9lMJPBdT1q1RqJeGBbiEajLd9xtd5gfady6Drn5nc5MpTl+vq9RbQQMH4mz+d3l+65zB43KwfF2HBXio1yO31ipVFlq6vOkbd1EhFRtm8fTFpIRgxmFg9Wn+/E9X3y8QQSgeN5WLZHw3Ko1h2qdRun7tKYr97VbJcbMtnZrLGbaA956YhFaJQauM3cYl9KastbfO/0P6d/vJPFs7cBeODtJ/j5f/W9jEzm2Vgvce3qXqyfOChgdY3JJ4/ieR6aGvitr3959uC+CHGgE3C/htM0lVQmCp4Hvr8vOeNPJ/Q83+fI6WEURaBHdKame9BUBdNQmL6vH11XKNVddFNlc34bRVVQBHQN5Ngo2MEUzGZ1ff9YFykl600LTLK/g+3t4DseH+5gVzeJdqTpSHuYUQ2kh+oHyR5K2qBWtikX6uS6Ezi20zw0gWXZLK5uMj7cR6nU4NZs+3fQeTTPylYFSvDg955BAU4f6SGmKKws7nDj0jIbKwWSOQ2hKKiaiuDufOy/EII59ECHvEaEAjokJCSkybedGGCbBnZKcPYL65y7vYHrS97zpmmEENQbFg3bQWnGuwHNW9qH/9X2fMnt+R2OjXZwZfXuZItUxsQYi/DS7tfebBU3dC5V259TmlrT9n1WG1WuN3Y5fbSX+YUdSjWLiK4xmktzaf7lBbTjesxcXzv0PUMIpGeTzkQoFRoH3kskTDzPZ7Iny0a5xoQeZ/7T80yfGeLa04FQRkoMUwtGRGsKsWQERVNId8ZJZ2PsblXYWi+RyURBiEDg7q+W6xrX5g6ex6MPDAYjpg2Nrd2VpiAVHDk9hJSgGwrpjjhCwLFj3fyjn/tuPnDyxyE1gVDvHuPe05OkO58MpuzVG6iqiu/7zFxcvWtZ1/WYuxScqyNnhpg9vwzA4ESOxavrjB3NU6m7aKZKYV+TYyShszWzzRbQP9HBP/+17+FLv3uOWTOY7ldZLzA01sXKWpX9irBaD8657/tsNa1CQ4Mpll846BNXDZUXn7/N3D/YJt+XxogpJLImZlSgS8H66sELq619nvlqxWKnVOeJN03yxBsmqFs2u4Uy/f2dFIu7aKpAUXSk7+27OhEIRbnnwJmQkNcjoYAOCQkJaWJoKu86OsCPz7zEmYfybF2v8EJtjW9zJxGKgqKIVkzbnngQQtwd2bYPKWH21jYnxju5tLLden14IsOcXqNcLN7zs/ciomlM9mZ5vhSIujMDaebFAhk9xvycQSZuUmzYvLi2Sjyhc/9gH2rZ4+z8wQYxISV90RhxRcdAYJoaF9wGx0Zy2I5Ho9KgVHWwm5YBKSX1uku+J9US0MeO9bC+VmJlJUgDyWz5lC/uMGcFQv3KpVUmTvZx6+IKAkljLbBKzO3zYa/NbfOD3/UrAPR2JajOB+dJ1G2ErtLdl2Z3OxCTXfkkUvqtDOjZ68ExdfYkmyccEHD9anBxMXmkk+J6IF4956B1Zc8+kcpEmL6vHyklG3Pb7Gw07yh4HviSdOcdyRmH4O1LUXGbjxuWjRAqyh2VWWdfI6ptu8TjEXZ3S61KvXRcrIaFYeoovodhKFi236pS7x+w4hwy4VEA64sl1hdLXHkpuLPR2RVn+9Ymv8MX6J3sor8zRjwRQY/p+KZOzXYpWQ5zn7+B7/rkPvCG1rpMI7jQECKwp6itankQnyf25V5L32v62pUDtpTXBBmmcIS8doQCOiQkJGQfA5E47x+c5LcWbzBwIsmntufgWcHPveFJIJjqFxQ5X52A3uP6zS2Oj3ZwdWOHkfs7OVfcglfos9tPOmLw5JBg00/yzO5WSzwDRGIefl2y41SR0iCyb/R21XZ4bn2Fk2aOR5QcpZ0ahqmiqILN9Qq1WjB0O5kwYToDwK1mlXdAVaksFMjEDJKZKNGEgWaqmNIj2R3HkuDZLttNz/DEWCcJBI7VFnWRqM7WcgF4mZHY93gmJTiFKuvPzTDy5HGwPba/eOnA0qKn6T0W+z69b4UH46flgX3YE7al1V0WmraR7HjPK+3gvtXt29d9C2nNpA0p92U776O03bbdqIpCdz6Habar4UIRaIpKrVSDFxZIdydYvK8TLRWjZ6dCZyZKVzqKZdtYDZuJM0N4UoIrqRRqeNJnq+gcMKYkkxH2Lt9KW1VUVWX1avAbSuVTlHZqxJIRfF+iagpLt7Y4cWYY35founZgtPmh56HVTCqRyEPzqkNCXk+EAjokJCTkDp7KdHOpUmDDbqAKiKgaqqah6TqOV8H32mLJ87x7WjjuZHu3yugjHZzdOCzr4HDu707wvukZ3jn4cUzV5v3P/P0DyRcAPu3nvpTULffO1XDR2oGc5OGePM61KmsLhdZ78ZhBwtApnN/m5ESGBbGFlJDry6ARNLgpAmTDxWk4VAsVNpqxdJFkhCOPT2DvNph/fp7xE30HttuoO/QOZqhXLOKpCOZwJ4oiEIpAUYP4My1mAi69Q1niCZ3+mI5EsLVTwq/ZDJwaRLUspJT0nxpsjQdHgpKMAxIzqmNGDSJJA+lLhhKxIG0jpjJ8rBehCGpVi+c/c4nRE32YhoIZNxif7MRQfMZOBrF4ejpORz4VZDJrAlVVUVXIVW1qdYtkIooiJbW6hRnTGJjMBTujSHpGM2iKIBLTGT3Riy9dtHQC2/PoOdmD6ga/EzNiUJEuhqYhTMGnPvoi5WodVVPwXB8hwLLbVWrpSRzXp1i3aMwXsKsOha3qgfM8eXqQmZtBNb57JItOg2hcI5WOogjo6c8gpEOtaLG+VKJ/JEdpvYhmaJR2gq7UbGeCoZ4UkvZFgOu6aKqG73svI4plK0xl70JCIGjULVLJe30mJOQvN6GADgkJCbkDIQQf6p/gn11/kfu7e3h+fZWa6xCLRMilU1RrjVa90fP9lx33vMf48RzWCY9NqwSvwvI8mc2il+EduRW+Y+Qjrdd7Y21xnI3oZKI6O27pwGfXirXW45P9nZR8q/W8oUuueXUeHx1k9tkldE0lm4iw0hzRvXB+k+MTXcF46N0qUaGwtVYm35Ni+3Lg8e0bygT7MtyBETNY+cIMycF2dNt+dF1FGU6y0SmpqirGrb2LBwkEx5IaDeLOdnyfhX1TFuMvLDHT9BWz6zA+3cPSdtsGIxSB2Aye5/vTrG3fnX89OdnB/K2goh6NR/j4f/lS672pB4a5efmgtzk31MHOVnD+/I4EUkrSmSjlleD8GEMGi2fnARg50cvSTHA8Yyf7KG/WqK5sM/bgCLdng232nk6xuFRicCjD8rngWISAncc6cSXkkxF++98EExVJJunoTRDRBW/+9mOsr5So7jZYWy9zG3CbP7T9Fo499pslzKiObVWwLZfiTmC1eeLtx/jgjzzJxuIOP/HB3yLSmSCu6fR3J5j50g0AYqlIK3f7PR96IwCO6xIxI9yr0fKuxkEJQihB+ov9Ndxi+XoRWjhCXiNCAR0SEhJyCHFF42+PHuNnrp3lSCbHL770PP/04TegaRqWbbeqzo7j8nJWz3TSRHvA4FJil15hsugXONbdx9X1w73PA8kkWVvnxuWdQLJMHXy/J1LlVG8ONV7nVn2LHUC3FU6k+tm0SkSicTaboqkvHcdWPG4V2tvqjsVwpeSzyhpHnuxgaMvk6osHE0C2FgoUi3WGjvWwTpWu7iSO3RbuqhYcsKIqzF8LrA85x8GIaAdGcQ8fz7OYkDxXCbZfcV0mvvMoiZslFi7tz9IW+/57/8vijqcv7ws4enqQ1dvbrfHZQgTDa+7YzD3Xf9jmX+7iaH+Um2nqaM1jV/f9IPa82rrW/nMrJXQZJquWddAaIQSO7VOreHzHB55svfxjP/9xTvselufhf+cRTB96Ny2E6+O7PlbNxrVdFFXge5JIxLxrX7t7UuSyKa43BbIWMSgVC3Sl2+O2tX3fXb4/jZSyOQxIIIQIcsFl+9zciZQSgUAIBcdxULW7x7KHhLxeCAV0SEhIyD2YiiT5npFJPrkyz8ZCjW8ZGeeBfDdD/T0tr+3a5tY9hd3YkSznB3bpy6joeNyoBVXJjXgRXVFw9oknRQgezORZPLvJTcNvaT1FSNZrfTy7/jDPrg6yrkSZ1+ZhX7HVkT7niyscUYfQdJVNGgxmk8zZRbgj/KPXTLAlG4Ckt0PlueUtHnhiiBtfWmhV7+zNCmNHu7l1eY3j9/ezdHGFvmM99HTH8QClEmx8+dYmYyf6uXV5hWxXkqMn+/mTP7rK0EQndm+El8olaBe/8YDr9RpPDWcZjuhBeoaqEu2KM9KbRMQM8lUbUXNRFUE0ZTJ6orfZXCeJd8aYlDl8z0PVNIQQzJ5rin8huHrl7qSMbjVO71AWRVEwTY2BI71Nr4EknkkwNNWNlBJVVZBANBOlozuF70ukGWzDcz16JjuJxw0U3yf+wABI0GL7BoXoGk4qxuBU9wEhuvfbqFQtpk8PBFEpQuGrdnAODUO/ywq/550GqDcczp87eIFzaizPtasHb2NMTXaiqgqd+TgI6OlLs7tTxWq4CAHZzhiNhsVms5K+d11g7LsI2HtNKILOnsDGogjQdQ3DMKhUBYqiIuVh03paB4wErIZNJBq593LfKMIKdMhrRCigQ0JCQl6G7+oc5GJxG09K/r9Pf5Hfe/d3YqqB+POkj207d91ST8QMEmeifNXc4IHODi7X1vH3/WXfosqpvgEuLrXzoU9rHcw+vRakfOxLV1usdPHk//jO5jMXRZQZfDBJya2T1ePEFZN1p0TZbWB7koxtcCbSgRpTAwENRFA4qmRYpd6snEu+600u1mYRx/N5bnONqSd6cK9VKKxXqFctbj4/z8h0D5c/P8vow8Ocu9lOzYgaCpl8gg/843chJTRqNg9/yxE+/yfX6BjMMr9UxMjfu/pYlQo3N9o2k7G+JBc2dqEA93dkuH02EIyjT45yVjSgWVCdjsLas7dan1NUAYaJqikkUhG6k5Fm5bkdM2joKrNng8+knjrG0lLb7hLLZViYOZhMkhvMtVI4FE3F9wJPcjIbo7MrgVOzWTgXxPJNPDbOkUdGEYqg7gcm4MVb23Brm6nTQ8xc32pVvTfWy6DCzkqRRCaKeyoN3K33hDj42sZmmTsRfstw3KJetekeyLKyVmJ9u31uH3/LFO/54KP0DWSC4T+ez9BEF2vNGDzX9xmY7KK8W6NWa05JzCfwfQ/XlcTMCPFYDEVR0DUd1/M4bIaKbMYHCkXFtoIBPqoaVqBDXr+EAjokJCTkZfBclx+dfoAfevoz3NfZza9ePM/fOfUAKCqFUoVKtc7N5bZFYnQ8y9WRIjfEFmPJFJdqa4cWxW4Y6ySMOBXbwVAV5q8FXt47Ez0imsNET4Yba4Xm+5ArdDNbWGcdCTToS6XpzunUKx7M1VmbCUTikXSUjq4Y25s1VopBKVp2KnznuyJsGC/S1zVFOh2hWPSZ2dgh2W8yGM9QXi8zeLSbSDJC/5lBru4T+gB122fiiSOcemKMeDzG0sIW/+LHP87s9bbIvjO67dXytcaQ/dfP/D1iCQPbstE0DW3fKGnf9/mdX/g4l1srf+V92r/I3uNkJkZptUBptcDgRL69flXl1kbQzOdLSEY1XFPHdlxKO8Hrcl+DaWvd+9M77mhA7epJUS5Z/N8/+r/I92XQuhJ37WPVcfFzUXLZKIm4SdTQ2LiyRmmnxpHTQ1zb5+sub1X49G8/SzwZ4cbFZeo1m6WbW4wc66F3OIvQFMgliEZN/vaPvwtdUyjuVPE8H9/z0TW9ZUOJRKLs7GyjayqIIAZQUZTgggVQVBUpwbJsDEO/K33kG40gjLELee0IBXRISEjIK5DyBN935Di/ce0SjuvzV0cmKFSq+DWbszMbvHB1g6ip0fFgkqcjGyDAUBQc3eZed7trOJzsjXFpvsh0uoMFJ7gl7/vN+dlNtZUxauwqKrlYhBEtiS4FZ29tkeoxKTWbtFZKddKWAViMOCqaLnAdyW6xzm7xYGNdfCzOJ85v8a1vHWTFmGHi21S6Kkf5xEcEg4k4xT+eJZWKcq1Uh9LdTXl7XLy6zhe+MMv99/Xx8z/9KVaXDzYyvpyAdpMq4w/0BBFvqoqeMVuNlco+AZTUdM70tkdkx1SFzIPD+J4fTMMT8KkPn0dKieu6HLt/gJNnRlrLSwlC18gM5wGB6jkk/AZ+Uxwq9SqRiEbPZJ6VhSJSSoqFBmYsuAUwfHowGBDi+fR2RhCKQiRmEHtjYEzXogbOlaBafvyhYS4/OwcI9HiUeC7BfWN5Ggimx3IIIdhqNlCOTOXJNDOUTUvCsR6susPudpWNuW3KOzU2bgQnZPzNd5jgIRixLiWbW1U2t6pomkJ3zGS0O42CZHisk/nmtqyKxcd/62rro2ZMR/o+ty6u3LXaX/qxPySWiJBIRYinIjz+rceYONnfspQ4jh1UlYUCIkjocN22AUVRVTQtiLwzTeMvxmTCkJBvEKGADgkJCXkFLNvhe0eO8MmFORaqRf7elz7DplXnW2t9fPqZeYaG0tyerHJbtH2pJ7tynC/f7cndzxV1jYf7Bpn93Dr7O9wUIdgzhWw0kqzXa0hDEt8Q7GzVue9oHivm8YLdth8ULQddUbjQXYZuyJomOSNCUuhE6wo3v7TB+OkuXqgEwqq61QG5RTw8tpYMwOHK5g5jCYOWN0BKBjrjJD1JxBDYtkdDEew0PHarNh/58AW+/Cezd4lnAPVe4klKzu/uwL6J28fSbZEcTZo8/o5pOrtTXL+5ybVr7amIU+NdzJ1vCz9FU7g8007l+Ksf5ICAbm6OYjN3ua8vQXmnHf/mux6Nmo2mCMamugDJ/M1t6rUgQm721jau45OI6dQvzAEweKSHxfmgIj9+/0B7XRKmHhjEcz2kJ5E++J7k+vNzrWWyzYEsdsVi6dOzAHSNdLDnZjEiGp3dcco7bQuGrx/0SwghuLUQnL9MLkoyGSETN7n+xdtsrAbfw8hkJwNDOZYWdnjzd5zire8+zsZSgY2VAhvLu9iWy9y1gx7qdGeCxZsH4xWNqEbPeI4u36der2NZDaLRpjAWAulrTeuGRCKwLZdKpYppmgeGq7ymhBXokNeIUECHhISEvAo8y+EHhqf4F1dfomBbgGSuUWH4kRzPJtYPJDyMpdJceAXxDODh82xkntHvSGN9RtKoBEkXqiJaKR9XC0l8GYi+rpEEO1t1rlzbYCrdedf6DEXFbWZC71oWu1bgac2aJj3TKS65heaSEr1rkb346GSm7eGOZKN4BYvJ3iQUGiy+tMwWMHW0k5vP3W4tl4kblC7ZZN55iunjQWOeEEHahKIKbmv7ugf3cSyWYW7+YGdjtt/g773/cQQwPtjB1EggqH/xF/4Erh0+VvzVIARohkYsaaIoCoqmEk9HcR2vOTkwaHibPbvYrJhLIokIPfk4kUQELRdHUZRgut6Dw6iqihHRiecD/7Jmtj2+jZrN7evN6YfHupl9cZ7ph0db7yfSUdKZCIYC5d2DGc57xBMRFucLZPsymFEVTVfQpc90b4aa47Fbs1A0hWLT47xbbLBbbOD1p0gc6SAZNYhpKioQUSDvpLj/sVE686nWNnzfZ3e3iKpo1AoW68sF1pd2Ke1WWby9w8ZygfWlArWKRbYrQTIWxfM8qrUqpqGxN32QQDfjuC4CMAwTwxRYlornutSqNWLx6J/6uwsJ+YtOKKBDQkJCXgEpJTuFEtNmkjPpTjbdOr7m8yXzbnEX0zQczULePcsEAFUKHpnpZ2wqy/jJDqqOTdFu8Pybl5n/wxKC5uS6poCO+SqPxALfbdxv/yu7jzg/+dDjxPVmhnKjzmqpzMWFrZZnVQIOHtVli8XtClZTc3elVPRqLwNyAMfx2dxVAI+ortE9kWV9rsjt2R2mj3az561oFZSVoMutUQ1u3f/Tn3gnmVwqaKZ0PeKJGEII/ta/+gM4aJ2+86Qy2ZvGRGE4Heddjx+565Z/Ohvl2LHe1vO4pnDkRA+e56OpKiiCqzfbYnx1pchv/8oXKS9utxoJSztlqmvBjojJTjqPBFXjnfUSJGKQSTE8lGahOZUvkU+ztlKE9SrjQuIjQBW46RSeIlCjBvZWCSSoZnCej9w/yPVDLBH7GZjsYvXaKsWtCp196f2nAc1QcW2P3a0Khq6w22zwA0jaknKhTjYbJ+n5VMsNHjozSM12qTQcdqoNNE1hq9Rgq9Q4sM1MTOen/v7vEUGhoydFz0CWzp4UHf1xjj8wTO9IB6l8nJpZJ6clGH2sh9GhHjqyaSqlBtVqnUgkQqFYYmF5DUVRGOzrJGIaOK7L7K1lKtUg0aWrM0tXNo1hGpimQa3WoFqpkk5lX/a8fF0JR3mHvIaEAjokJCTkVSClRNNU/tb4Mf7+tS9Rb9ytkFUBw5kYlbrDGW+Qsm4x4x+8VZ7zo8xe3cYp+fz9b3lja90nk3k+tzTHF84uoAoFmiYOv+CyeK4pYhVBZjBKodqgS4vxnaMTB9a9sF7g9//w+qH7r6sCq3l/e7Pk85Evwelojqvzm0QNyZlsBxtPr3G53G6IdN32Mfq6DtlA+E1MdbB8ZQVVV6lVGwgFXNdDURQi0WActHeP0c8pG/qkhud6LF4MjqunK4Hreq3pd3sUdmpc2RdNd6Q/ze0XF1rPhaaQPRZEynm+z5e/fJNjR/Jc/+SF1jITJ9sC3JUwvxWIzKmxrn25bfvOUyqCaXlIXzJ/aRnXconn4pRTwecGh3MsN+0Po8fyoChcv7B8oPswaqpM39eP6rmcfGiIQsViZanQuijy3Pa52V7aJXZyBBCkM1HipmB7ZRfpCAq7VaolC9+H7a0KUV3FarhUtsrcbsbY9UxkWZorkEuapLMRbBVMXcO1XayyTTWmsXNti9vX2nafroEUP/1r70NKie0EKTJ+WXD5/AIXokuceXAC3RQ42MSSMaqVCrvFMgLozWfBhLnFdSzb4eT0KK7jcv3mEvFoBF9CaW2LdDJB1NwX8xcS8jojFNAhISEhrxLX9UgpKh8YPcGv3Dh34D0FwRuyWeav+WyWPTbZoSsRRR9WcaVHn5tkoJxi7fkyNRxUAbbttMZSP5jrY+xdKTZ3a1wrFVrrlft0qPQlw+kElYaN9D2q1b2KYyDMLOvek990Nagy33k8Y9kUzjPbLJYOGeyiazCaw3F8iLczfW/MbINmggTTNNB0DVVV+fJnr/Obv/QVPE8Sf0PPofuxsVygsC9mDQJBWS6XW5VzgWiKu4MXKf4dolwA29sH7RCed/AY94ea+PviBhVFaTV4rm/UiPUElVLL8mjUAw+02dTE+4uaB/ZBikOTPerlOrNNu8vQw2MsLATnNqUEn3Udr7XeySenMdJxLp5folSsk+9Nsr1cQQDZfJLt5r7oEY1GJfh+rUZ7zHckEcWvlCkWG8STBgvNc9ufjlC4VQAg05VARVArBr+XzaUSpd0aqXQwZ7tQrHDuszNceC6YlHjpmWWWZjf5e//6Wzh/eZbuzvRdM2jWN3eZHO1HUxW2tivEYxEQgo3NHRKJGFdn5zk6MXTXufmGE1agQ14jQgEdEhIS8jXg+T5PRDr5ZDzNXDUQRgqChwdiLPm3sd12Y9lmpc7wWheGr9C9q+G7kqGeQIzEYwa/+bGzSAlqw29WQQUD6Th2yzoadNqNn2o32XlIjnZnWdup8l8+ez6wojZVout5nMzn2oXVpriTBNXxlviTcN/xXqK24I/+4wt4jcOrxRA0DgI0Ggc9zaqq0NmbwrIczKhOo+bzsd+7gOcFW+9WDdLxIM1C+hJfBt5ZMQA9A4EnN+24lNYqrN7Y5P/+p3/UynBWVQVVU1DjOpOj+0aESxh+8gh6w8ZzfIQQ+HoQnabpCq7jEVUEY6cGkM2zEE1FGHpoHCEEYw8M8z1/bwqJ5NkPn6WwUmIkoxHtiLUa9/xalRNHOtjZqiKz3UDQTPejP/at6IbO+sI2v/eLn0P6kup2mY7hLAKBYSg4S5sgBMXVQmuXY4qkOxrEvHVM9LOxG/jnu470MP/l6xTKNuszW/SP5IilokjfpxhRcRsesXSkJaATCYNCU0DXq20BvX8Yj2G2/6RHlHY1v7Bdp6s3jS8ljVLwPc5cWKF3IPD0JBMxDKEyMJDCx8dzXDRDQag+mlBxHAeaFzV7aKqK47hUaw12CmXWtwqYERMpYbdQpt6wKNXuneISEvKXnVBAh4SEhHyN+J7PD/Qe4SduPIeC4KH+GEssgALjkwovNh0EmqKQcw1WL+5gpGOs7FZbzYEAz11bxdBUxPbByvHQsQ4u1g4f9d1i/e6XsnGTYvHw5r18KoZ1vr3Of/Qdb+JLn776MuIZavW2p9bTFXpHc7iuh2t7FLZrrC0X2dwssrtb5j/9wpfZXGuncWysldmq3LsiDjBqqKwv3vs4x+/r5+at7btezylQ2qqi6SpO8yIh1xFj98bdJ2Xy9CCLi8F+3feEyf2PjOF5Hl/8b1+htF1i7tIio2dGWL7RttokO1KszrW91clMlLHJLiLRCNJy2VgqAKAbKrYZHGMyaVBeCD4Tz8Rbn7VqDhvN19NDXWw0z9HGGnzb33yca9c2WAeWm9uLIXEbwUXL0o1Nxh8aBKHiF6okR9KgCFTHQ+uKUSg2qDvtiru3r/wauWOIyeZ6mWQuRkJXqWzXuHFxjcffeRwIrDoLN7bYXG1/f9l8jI5sBtvxiER0ytWDdw0G+7u4fnOJvnyOdDLBTrFKsVSlYdv0dHWwtrlzYKR5SMjrjVBAh4SEhPwpGNMTvKVzkHmryDLtdIr51C360+M4jmSMBNeeX6e/N8XKaonjU91cnd/C3WclGO/Lcmv7oPBbuLLNfWfynN++Yw73K+Afcvv61EgeTVVY36nsn6qNEIL523eL0/3sF0CelGyvllBUgaZrRBI6taLNR//b86xeWUONGqRzUQRBlnU0blCyvVYF+zAa9xD77Z28x+uHHOerSRze7z2WUrY+c2fzojhkA4GtxEfut3Ds+5y2T7C6dtt6kupIMPnAML7vE02YjE3lW5Xct/31h4l//jo3bjzbWn7wWA92zaZarFPereP5Asd2WL0UeMGTmSilhfbvpbFTZDifonMsj+JDdKIbx/fRK3d79MtFi0w2xre88xjxuIHjuEQjJr3dHZhaELNXLtRxXZ98X4ZiuUp/b56oqRCN6EjANHSklGRTCQa6O6g1LGzX4+jkMPFYhOu3lmjYNsenRujIpu7ah284oYUj5DUiFNAhISEhf0re0zHKj958muPRCVaMG/ToWeJ2llKnxsrTJS55dYbGsixslejtTHB5Zp03nh7GjOh85vmbSAlO6WCVVlEE73zzES5fX+VYb5Yrmy8XZXGQ/bfYFSF4/1tO8YG33o/refzC736ZZ17aPbCsrh8+ajnVk6BYrDO/WkQI6DA1tLqL4/rgBj7h5kZYubVNYSUYN909lmN9KXhcenOSW8dA81WSUiOnR4m4CnbDBgnpWARxyyE1kUFTVURTrAdWj2D/nJ4o+qTeErh706u9zxwSbScERx8cDpb1fTzbQXo+mtoWufs90HvbAFA7UiSmFCQC6fuITJJYPqi4SkCP6vzwO38JCfSOd6GNdiORqLqKubAJCGqbNl1H+kilIkiCiwjP9SnVPW7fap73uYPVdk1RmZw+6BW/fqUtjntGc9y+ucXYWNvGEgjo9vK1coNauYEbi7E7s01xKPA1H8kmD/1uC7s1vvLsHD/0I0+xNLfF8HgPx6ZGOHV6mI//jxeQQCYXZ/pEL5Njg+Syaeq1CulUu6reaNjYtsPIUA+apiGEQt3xaNgOp6bHMHX91Qx9DAn5S00ooENCQkL+lCQUjXd0DvEHazd5PD/BV6+UgKCp7f7xHPPXC8xvBKIp1x1na7tCJhnl777vjbz37Sf58OeucPaZeYQIBF1/b5q/94NPMDnaied7PP/SHH9w+RbP3Q4EY0RXyadidKdjdKViRA2dQrXBdqXB8k6Zuu2SS0Q4OdzFu85M8Mj0MIoi0BSFtxwd4pk/nG3vvDzomd1PpWIhJei6QiymI+vuPQt7xr7kjP0C3mkmeLgK7OKy65dBAWLB+53SQ1s/GLt2J2ZPhMVC+a7XjzQ36fs+Uyf7cR0fVVe41hxaMjLWwe3nbwDw5HvP8L4f/T40XWX2+Vv86Nt+Ht/z2V4t0D0UeIAdH0rl9oWML6G6z36iCEGtElTLbdulsdfEJ2HizAg0vdv4Pk7NprpVRnoeju2R3td8eSdS+vQP51rf/13H36xqm/sudGIJk6nHxvEdD6tmU92tkc4nub1WJTeYYU+il7cPz5qGIN3k537ijxkbSLFwYYVMZ4JCc3kBFHeqxKJGMJ67ORBFCAUhFCzLwrYcEok4mqYipaTcsPF8n2wyhrJ3Lv6cCGPsQl4rQgEdEhIS8mfgLakePrp+i1pNZ7+RYDNj8cjRXr714UlWtiucnuyl9g6b5Y0SlmXT0xHnh/7qg/jf9QCO47GxXaEjG8MwNKrVGr7vc2K6l+NHulnZrRA3NeKmHgwEUfZESqC8fCnZKFT52Nmb9OfSvPfJk9SbObyqqiIUQb12sNLdqDfQ1MM9qnsCKN8VZ/3qJgAdmbsHt+w1Q+4RMduPFeXlRVREqNwjKnsfL6+GfE8yey4Ypd3RfXjF1YjoDE92omk6M0/fYPH6WnMQiKS4HeQtHyZeD3APQaiqgpnZzQPWkOH+FDtzGziWi2aoDIzmmJzqRAqJMA18RUEgSKcjCBV0U+WBhwap11wc26NSsSjs1mjUHeZvbNE7mkXuW79u6lx9funAfnQdScJag3hHDGp1hIDObJRM3KBatSmVGjRqzl3HoUX0oOlvX+70HqmOGKqmBqO7EQhFRfqSRsMmEouiGwYSKFfreL5PJhELPc8h31SEAjokJCTkz0BGNXk824clffKxGFXH4bsnp3jP5BRxD3Rd48HJPqSUuGmXicFsYBMQAkVV0VQVw4SRWAQEqKqKLyWO7QSpFIpgMhFjr0wp2dNBgYgWBOkbo/EY36OpJJIxVFUlFo/ium5LaN9/coD/+G/fg6oqCAGpVIT3/cAjfNdfuw/D0NgT/6qq8U9//A+p1W28ffYHoalEOhJ0THaythFUhaWUiK440dQWEycG+JF/9T0oqsB1Pf7Wpz4Bh0XjNVmSNSafyBCXGhlPo7phU7EcBnJJZi+uIwRECj5988HyIxGdtZmg0e9H/81fYXisp3UxIaVk4eY6v/VvP4cQ4kDFdu7aOu9/7F+3N9zZtkPseJKRY33EcBkfSIAEP6IHXuUHBkBKFE1BERJ6EiiqQiQVJdIXRN5FkTR2q1y/2faSx00Vx2pW322P0lqRWsVic7nA5BunuDEbLPsjP/GtdPc1mzItj2svLQIwdV8/q9sV4h1xevpSxKIafqnB0TND2A0H3VDRTA3Xal9+7KX9mekI1Opk01FuXDzoq49GddKZKJG4gZGP43o+unNvf3quK04iHkfXdYRQ8H0f3/ORUmIYQb5zrWG9rHiWr3hl8g0grECHvEaEAjokJCTkz8h7u8aY6u3F1DW0qElcDyqx9WaMVyQa3Mb3fb+pfmW76rknitV25JyiCHRDRzZDoIUI8oZ9Tx7olhNCoKkqmqbh+z7JmIGpqfhNgW4YzRg5KYlEddLpOGrTciGlRFMUEgkD3dCCSDjfx/OgXLFoWA6G0RainiepVW2yvqRW2293kNQrNo7tksoG/gzHcYPcvJdDCGbdIPXhVLKDpUsFAHozCeymsBOewKoFdgkPgd30i8cTJrFEpFXlllJiRnWunQ0qs6NT+dZmOntSfOuHnmRoqptaxaJWtqiU6nz8d57n5qVV7JrN7T8+11o+/8gURirGzEK7gTOVMKg3xfvQkW5mm0N0jg9muXluiZOPj7NVttE0FeG3I+Yg+LoOK2AXdqrMXFpB+j7RRLtyrxkqCEG1amE7PqurOzQWdlpf++h9AzjRKKneKOlMjI5MBNf1mX5gEDVu0pGNkY6b3OkSb9SdVr61LnzKFYt+FFRdxTtESOe6kkQjEYQQxOMJSs0BO6qqIoTA8Twatks2eW/x7DivfI8hJOQvK6GADgkJCfkzklR0avUGphbHoC0mzIhJpVxFqHaQSywDcawoSiCUhQJCIH2vNVBFVXUUIQAP3w+qxUIEn2Fftu9+n2mj3qBeb+C6HmChairRaNt763kenuch7rBVeJ53wMYhhMDzXPbKePWGQ2Myg6Yp2Pk43NxG0xRi+2wbe2u8s9ooXlUuRvOz9ygb7n9d1YPR3fiyGQUoW1v/yK8/zRf/6GJr2d3tKtNvPoEvJbnhTiZO9qIqGpGoTq4zmHrY2Zvk5qVVUl1pSttlInETANeTCF/Sk4sG+dmqiqEJlu+17xKsmh1MGgTuH8+iGcEkwO/7J+/ihU9eotYce75fSX/l09f4jV/4LACjR/NMnehF01X8fcvE4gYL8za9w1k254NGxHjcYHA4x+5ulcXbWyhDWRZmNjj62BgXPxf4vkeP95Ca6MStOcQyUex0BN/2qNcsGoU6yxULXVPZWiwxOtXFwuWDctswNcyEged56LqOaZrIIlSrVUDi+z6256MpClbDQtc0VE098Jv0ff/AJMvXhL2fRUjIa0AooENCQkK+DlRrdVLxGI16A11PtESvpqnYlo1uBMkEihI0YwkpUYTAlxJVVVGE0h58ImWzuqwgkYjmf+7UpEIIXMelVquj6zqe52GYBrp+cISylBxYPwQCR0qJUNQD21VVtS30JLiej+v52G5QpfQbLo3CvgEZmXs3yb16xOEP9+3v9WIdRrLomoJ/h3e7VqyhSJ+h0XZDXq3SwPd9bl5c4e/+13931xYnzgwDoEUMeu4b4cZCMwO5YNFdrrH9zA20dILJUwMs39zav1N37er+iwUHFTca47F3jdMz3IFmaK0F+4dz9E31oCiCVDrC+FRX8HtQ4OO/8xIAQ6f6W+tSmseZyqdaAnrm3CJ2swIeieqUdoLmP2dfFdmxXPAlc9fXGZzs4vpWie58kuVGne7+BCyXkFLSP9HZnFB5kM7eFDXbplSuYJomlUoFRQEzYqAoKnXbxjQ0hAwG7DSwgt+6qqI37R2NeuO1F9AhIa8hoYAOCQkJ+Trguh61eoNkLBpUdrXgX6+6oeM1fAxdb9VM926D+1KiSD+Qx830gj3xvCfK9lIQ9iNbUW8+vmzaNUwDy7YDMa6IfSOnRWs53/NbYs7zfAIricTzZFN4yubrAYmEScmx7tr2HS+0Hu5t82v3vsrDHx6yHscNspg9z2udK+n70HwcNFgGxw0CXVc5enqorXuFaE5G9JiazmNEdBTf4+h0NxKB73lEIwf/NPpS0juZR0qIZKLQtEK09u9A9EPw+IG3HOGBp47wyd/4SuuYHnvrEe5/0yS+7yGadx8AatUGT//JdXa3qqxdX6evK0EiHUU0v4uZ29ukx7vo6kpgCIGQEqtuUy82WGtaTfbbamoVi3QmGGdpJiNQrJJMRqBaIxU12Ca4MNoUPn7Da+cDNunoSYGiIJB4nkejUccwg6ZBFAUdQTIebf1mpJSBl9t1sRoWnuc3q9evvcQIUzhCXitCAR0SEhLydaJu2WRSCayGhRpXm6JYCZrdVPWuuIfAH3uwMnyvCLA9keLYDp7v4bnB3Dnf8/E8l2qlhue6VCpVatX6gaQOicT3PGzbCbSSDGwQUvrBrffW3gT0ZuLYtku+N4XYLiINlZim0N2TIKIp9HTGEH5gsdAFdA/nMGMG55+70aoA5wuAFW0Js72121m1LdaaL8YswWA2EVhYfOjvSSGRJOI6I0NZhBA03HaUnuO6lEplKjs1tldLlLbLOHU7iJNrrnfvYiSa0CkVqihCIAVN4bpXbdeIqArVaoNbtwut45+cDjzUQZ6zD5KWRUOLG0xNBaPVTbn33bS/p71EDsdxqNXqzUp/8JoRbSe1yOYdCISCYRp09KWpNhwyuTixZATN1DAEHJnsolp32C3UqNUcbs61GxY7OhOt6TnCcjh9ZpBGw+X2tTUy6UBAqxENiu3IQtNo/9kvVyw6oiZT9/cjpEBRFWYvLJHLJ4MIO0UEjahKcNHhui62FKSb4nnvog+aVhdDx/cllmWh61rrIjIk5PVI+OsOCQkJ+TphWTa246JrKq7rNgWEDMSu7zcjwdp8rXm51WoN3/MwDINIxAQh8DwP11VxHDfwUUuIxiJBUsKB9bc9wyBxbBfbtonFY00hFAz/cGyHwrlF6lWLPak28cAQs8/eAmBzfpfJI13cOBekRuRO9bK+UmZ9pcyF59oTPnrGOmGxQCxhUqtYwRS7qI4s7bN/NIk+NMC8HVRQ1yiRiBoMdKU4fWaId715GiEU/tEv/DGXm+O2jYhBIpHgpU9e51f/ye/ftb7pNx3h2vnAtRxLR1lp2h+OnBni+vmVA8vGj+aJRhSOHAli+nzfJx4N/jTuicj949eFpjB7PdiPY5PNZsV953mvsVE3dOLNrOR4MsImRRLNqrBolnx930dRgwusgdEcN66ssb5ShFaac2DjyA5k+Te/8h6EUPmp/+N/EklEMKIauq6RjeqUijXW5ndI5+JcPR80UtrNlI5Wkkpzv9Q7fnOV3QaGJlhq2lQmT/SS7U7uLY7jOCiKgm3b1G2PRDyKpir3/O3aloXv+80BK38OedBhBTrkNSIU0CEhISFfJzzfZ6dYoqczR6NhkUhozRxmBcdxm/7nP72oUBUF13XxfA+n3k57UBQF0zDwPZ9oLEI8EXtV29lrEtsTfY7jEolGGZ7q5trZthi+c1XK/tSFVxAs+z97rz3ae/3kcB7X9bh9e5sb2xs89fBoM0YtqOTvoSoKuq4TiUeYfmQMCMR/EPsXCPXDtioOscN4rodVr3Nr3wTAIw+NHPi83D8j/RAPtKkpHB/PATA81snk0W66B7IoiuDBtx3j+c9fZzIe4dKFVW7dLqCqAk1KPMtGKCpKOoKePtxLnutOsrFeZnO9QjIeYeWO8etTDw2zsbCDiBnIiM7QdC/RiI6R1JkwB1HiJomEieU200yctkVHUQSV3QqiWULv6k2j6RrZrgSGHmSOO46DlD4Nx0XVVaIR456/reCuBgcaWENCXq+EAjokJCTk60i9YWHZDpoaxMmpqkokYmJZFoah/5kEdDQWRWv6SjVNbQnyPbuCZdvBWOxXsY32KOt2ZVpTVeoNix/4h+/io7/5FRp1K8iNNjUmFSWwKgiIJU0m7htA+j6RpMn4qf6mdaJtT6hWrHts+W6EhKNDnZRLDeJxg4nxLpDB2Okvf24maJZ0201yl56+yfK5Fa4+e4trL8zftb7px6daj9WIxvAj40GudDLKyHQPUkIspuI0PBJRDUuHow+NBDnQmiCSiHLkkTEwDcyYyeCRbqQiUJFohsL0WAdCU4lpKhOn+tla3GW9afF46h3TPPHtJ6nXg4r61GMT/Of/8FUAbv/XZ7AaLtmOOH1dca598ToAY+8+Sck+GH8XSxj0DndixnQ2dupcu7TG0GAaI6JjNychmjEDobWj6Cpli/m5wBOtnshR8C2G6xrlho1dq5EfTLG0XGhvI2rQPZZj5dwKAvClz9WXFnj7953G0DU0TceyLSzLxvF8culkMyHmbqSU2LaNbmh33WkJCXk9EgrokJCQkK8jvi/ZKZbpzKZwHAdVVdE1DduycRz3Ty2ihRBomoqmHS5Ogul6r76BLxjkcof/utmAN3VqgH/0C99LsVggEjX5t//8D5m92LY+TN0/wExTqE0c72Fm7e5x24Mp89UfG3DjWmCL0BIath2IZcX1+MPffAGArje00yn+4N99nsrqvYe07D+7ni+5PRfYE6aP9zI3E0xWHB9JcuO520yeHuHGpXZIXX6yh5lLQTVaS0Zx91VsJ8ZzXP3MpWB/jg9zYzNIwMik2rF+uqk2G0WDc1vYrr3i8SuOy06xfsBkkx/IcmN2g+n7guO+dnEVBUlDESR7Umi6yu52lV3bxYpoZHtTVJuiXQgo1YMLmErVImJqFGsWSLCr7QubStUiO5JFeccIXfEoGx++CkAiG0VTBJqmYdsWlu0Si5r3nFwJQROt73mo+6ZR/nkQNhGGvFaEAjokJCTk60yt3qBqGigEw0yEEOiGFmTm6t84b2jQk/bqxinf2Th4cD3NqvafyVB697r9rzGd415L/39++X309WRxHY9//L5fAySdPWn+8S++B11T+cRvfJnVq4uohoai3eN8NG0Z8o7v4u48jQBVVXBqh1fV91+06M0mvb3vWNMPueC54zw4xTqVqo05niWbiTGQTVBeCWL1DE2htyfJ1kqR//GFWZJxk/7+DNeuBtnNlWalf3e3RqK5vkQqQonA9lwqW+Q7ExTdOqmowRY14nGD/p405UoDM6axvVoKbDzNc5LKxdHUoAnQdj0cXxJ9me/O931s28ZoimfX9VqRjSEhr1dCAR0SEhLyDaBcrRM1TWzbwWxmM9u2g+u6rQmBh3GvCvKdontvORk8CdIevh47LuG3f+dZVlcLCOGTycTQYjrTpwfRFQXXcYmmI0w/MID0IZuPcuTUALfPLgKSsdNDSCG4/vRNEukoiiro7E0G2cUSUgkTq9GOgVNUBV1XOX6ku5lKIVrTGY2YypE3DIIDiqGTGsrjex7JjjjZfAq74VApBk2JiXSUVC4WeKYl7KwUAOgYy3NsJB1cxDg208fy6KrAtiyOPTlN3FCZOtEbDJmRoHam6OjPArB0cxNfEWQ6E8QjCttLO+T6s3T2Z1nftVsVY800MOIeSPj3P/7HSPnH/J8/9W4efdsxdF2hbyADSIquxGq4NGNCWoNxFEMHLCzbY3WjTC5usrwUND5W14psXFkFoHM4x+Zahd3Ncuu7L5UaAOi6Sq3QQADxZARwSCciVCq1YPBNqU7c0NgCujuTzNwMKvHTXcH0yErdZvyvHkdc28Y0NdKpNPVGg2q9QSoZw3ccfF+iqnf/Dh3HhWaG+PUbC6ysb2OaOrqm8bYnHvt6/CpfPWEFOuQ1IhTQISEhId8AHNfBkzIYoqLrqKqCaRpYDbvVGHfX9L59HmJfBvFprSmEh9DOej7IXj7yK3O42vj9//3CgVzh48MdzF5Y5tiJPq6+uMDkQ8NcvRU0s715aIrv/eCjfOCXPgfAP/y5v0IkbvBjz9yi3LQL9Pem2doKLBe5vgylamBriHckKJUbpH3BjUurd+3HyMO93FJrcPmgRURrCs/9Fe29LG0AIxFh+L4hpB+Mxi7Wg3OZkrB0bQUhID/WxY1zSxyd7GRtcacl3lUXtrYDUdoXE/hIVKvB7FyV6YdHuHpljZ3lwLrRO9bJ7moR23Jbw03aOxTsy8hEnu3bG9gNh+RYN8eP91K3Dy6bMHVOZJPcqtWpWS6Xb28x+dAAK+tFiqbO0JPjJH2J4kl6hnJ4ns/9bxxjbnadbTcYsa4bKoVCsN+RuAGuQyJiUKGGGdGgBBFd5/jRnoP7aQW/obrlcm19lwc0hcGebqSU7BSLGKZOPGJiN7PF1X02jr3ccNdxiTTHftcaFsMDPQz251ldP9jw+M3Ghz70IT72sY+Rz+e5dCmw/uzs7PDe976Xubk5RkZG+L3f+z2y2SxSSn7kR36EP/7jPyYWi/Hrv/7rnD59GoDf+I3f4Kd/+qcB+LEf+zE++MEPAvDiiy/y/d///dTrdb71W7+VX/zFX0QIcc9thHx9Ce+vhISEhHwDkBKK5Qq+7+M1J7JpmhY899oNcXsTAffj+T7+3j/ycJG8J6yF0sx5lsF6PN87dPm79o+7q9339E/fMQLc9/bvk8DZ1wC315z4Nc9SuYNsLobQBLtWg97e9IH3ttdLrC7usravIQ5BKy3DajgszGyweGMDVygsLJVYWC7hxaKUjChFPQrNpIjYYJ5CKsNOMs1OIoXY5+HdWi6wPr/N5sIOR47lqVbtA8e1urhL11CWeNKkI5+gsztJV0+S3oEMF756k//803/Eb/7rTzHRnC7YqDtcPbvI8swGihCkOuIArC8XuXlxhY5E2zderlpUqjYLO1WubpWx6h4Xn5nn+/7OG/nn/+6v8f1/5ynqa0Vs6VNo2GxX6iTHsvSd6iE1lOLEsR7y+SRTU3li2QjdHQk0BJevrXH5Wnt0t2N5dOeC/TjemaGrK4PneewWC/hIUrFoO+/5kC/VdVx0Q2tNTRQEDa6moZPvzHxtX/qfFfka//MKfP/3fz+f+MQnDrz2L//lv+Stb30rs7OzvPWtb+Vf/st/CcDHP/5xZmdnmZ2d5Vd/9Vf54R/+YSAQ3D/5kz/Js88+y3PPPcdP/uRPsrsb3J344R/+Yf7Tf/pPrc/tbete2wj5+hJWoENCQkK+QTQaFg3HwfAM9GZV2DANLMtuTSM8LLEg0CpBJNsrVpJlEM8mfQ9FKAci14Lb6w5Sgq5rByrZexPkXn21mgPTDfeIxWKkUqnW83q9gaLzNbG3xyOjHaiGipRQrweWAYSAwQjsaxr8tz/+R5S2qq3hIBBEsomm0NeSMfa9cfhG9w5BgWjMaE0wPEwXNZrV+L2Ei1jcYGikg1qhzsatLVTfpVY+6I82dcFcU6gOTuQPvOc4HpfPLaEbKsefmOTS2SC7Od70TyuqYHOrcuAzuyvB8V87t0z/SDeGoWNXbaJeGSNqYDseO7s1dnZrRHNRXtpsjx8/OtLNjXqVm7JCz8MpjiwKhCtRIhqugIFknJ5IlMX/fZn73vco1VoVx5ek4jEUJbhT4rlea0w30HpNItE149A7KvuTU74ZeeKJJ5ibmzvw2kc+8hE+//nPA/DBD36Qp556ip/7uZ/jIx/5CB/4wAcQQvDoo49SKBRYXV3l85//PG9/+9vJ5YKYxLe//e184hOf4KmnnqJUKvHoo48C8IEPfIAPf/jDvOtd77rnNkK+voQCOiQkJOQbhC8ljuviOi6+6aM084urlSqO46Lr2uHV2r00DRkkYwSDPA5GzgW3zz18L/A+R0yTnp4UjUadet1qTWeORoLhHfV6rRWBJwjyj/fGhbuuh6IowWTCQwT9ZqXByIk+HNsl3REnGTM5PtaJX21AucHORjtporOzg2jcOHir/xXOkxTQ1ZdmbqlwwJYx0h/s+5XSLtO9KdZWg8a6vWrnAcHWFMRSSq5cWKHvaC/Slyj7xknv34+9Y/eLNazb7Qzo2FR36/Hw8T4gGLl+a7XC2FSeVNJkZ6PM7UsrrWElb3rHFFsrRXY2yjzw+ASRuM7l59vxeos3Nugb7SCmeOhZE7tuoSVjXFuq0bA9BkY60A0V0bz46cknWd5s21aSpsb2WiCIZ86v8KZ3ncAwdDzXZ/vmJuNvHOfqVrW1vKccPOMV126eZ8GqVaO3CNvngmMefmoMsVpl8Qs3g+NP6xRqDTLJOIbWHDnv+ziuixlpV8h936dhWUHm877rLwnsFEpoqsLiygb9vX28VgjunTX+F4X19XV6e3sB6OnpYX09+B6Wl5cZHBxsLTcwMMDy8vLLvj4wMHDX6y+3jZCvL6GADgkJCfkGUq7WiRgGfqkSiFMBrutSKVdQNQ3LsvF82fQ9y5a1wpU+0pNIP4hikxqAbAq/4Ha6YUbpyGXoSibQmmLHNM1WZRloCmOJpqmtiXpSSnTdpF6vYzWcoMHRsptDMKJ3iZCNzQqR3gwZTaVetzn31RsAmMIHTfBD7/klRh8aI5WOsbVaZvRozwFxu9+G4jht/29rGSlZ3zhYcQUOWF2U/c1ryh2fJ7BvlEplFKFQLtZY3g1EY9TeZ5fZt769ovudPvL90vPWWh3P9dF0hVjCZHVuB328g2jCJJIwWgL6vX/3KTq6Uq31Sin5lZ/5BIqht4S6YShc+uhz5HozrN/eQDNU4sko+lCcG/PBsb/pW45y+slJLlxbY7NY49vedowvP3cb3ZMUmvt07ewylm1j2gblYnDhcvsrNzn69iN4EQMpQPUgaeiUm9aa9XJbXAN4+76DouWSdttHnelMoAowVKV1fn1fNu9etKvMjuOg7NmI9t3BGOjtYnO7QKlSoyt30HrzemNzc5MHH3yw9fwHf/AH+cEf/MFX/fn9o9C/UbwW2/hmJRTQISEhId9AbNuhUq0Tjxit8caKovKp6/P85jOX2a7ePdoaIBUxcOcDW4AQUB4V7Jd6p7N5ipUGC9sldFUhn4oz0pHmZ9/zlqY3WsHQDXzfa1a7dQzDoFKptIR2Lpc7tOL84//8u/nNT79AqdIAEYjMiONz/WMzB5ZTFCUQ977k1swG4/f183/9499jbCJPJh1neCgDSIQPI+MdWI92UcZnqiJo3CihagqQRNoeR9MxhCIwRlMUzKCSaMZVTnZ2AZAogZ4M/Mlvf2CIbDyKJGisA9AjKqoa2D8ef8cRfDtIhojFTYYmu5BI6rZH/0SwvrqvMHhmjKoq6X9guOVFl6qgdyyC43gI20cogmhUxypWUQyNymYJoQiSMZ1UPEj3cD0P27L3DZKB7c0q1y+3K38d+Ti+hEbVYvLBEepli6Xrq/hW2z++vV7h7/xfD/KdjuTF83OcPtnH97z7BLZtUa+6lHaqVMoNPv/hC6wtFBk+FlR3VV1h5comcqKD9a0KqWSEaiq4WMimI6xYjfaXJiU0z1l+NIchVKIRlanHJtA1hZWVMveLYGy8qijB9+/5zTsDtdagHstyiEYjB3PEhaC7K0d3Vw7P8ygW7s4H/4bzGqZwdHV18cILL3xNn+nu7mZ1dZXe3l5WV1fJ5wN7T39/P4uLi63llpaW6O/vp7+/v2XH2Hv9qaeeor+/n6WlpbuWf7lthHx9CQV0SEhIyDeYhm2TiEWJJ+JBAgdQtt2WeM7E9m6Ni1b1N2bo7BIIaNm0Au8XB6WqxcJ2YGlwPJ/l3TI1y6ZhNYhGI/ieR9WxmxPm9mLuJOVymWQqTrlcwnU9YrEoum6wtF3Gdn00TSORjzFbLLBdqHLUjAV50PJuL7GiHBzVrQjB7k6VF5+7zWguxuKVdrPawLsmOLsbWDQyRprFW+2EBj2mkT/Shaqr+FGNC6vB56aHO7nUtC4c09IsLgYe5B/9wScZGewEaDZl+tiWHUTV6Qaj4338tyufCT6XS7W8y3eSzkQpbd9d+d5Dc3081yedi1FavHeixKc/dxuvaZpJC5XKTpVUOsH0/QOBPad5R6DzsUmkL/Fdj+6xLIquogroG8phRHT0qMnP/tQfkUxFSaYiVLYbvOVtx0kkPKyERVdPBqTkqx+/yjOfbl/MjBztprRTo2sriqoqDOaTDBYb1HMGfl+Ele22gH7DToq58iaJI3lqElZmt+iIG+z+/9n77yjJ8vM8E3x+14b36W1511XtDTwIS4BOC3pKFClSM9JIIocrUbuQNKSk1R6RKzMaaUhqpBVEUqKWbmWGXg2CcITpRnejTfmqrMys9C4yvLnuN3/csBmR1dVEo7q7+j7nAJVx4/rI0/neL97v/fb8KnW53uD7fuxJdF1HSg/HcWk0GoRME8dxqFt2y9ssCYfNoR56KSWNRnN4BvbbnO/8zu/kV3/1V/nkJz/Jr/7qr/Jd3/VdneW/8Au/wA/8wA/wzDPPkEwmmZiY4KMf/Sh/9+/+3U7j4NNPP83P/dzPkclkSCQSfPWrX+XJJ5/kP/yH/8BP/MRP3PEYAa8vgYAOCAgI+CZj2Q5N28a27c4kwl7R4YUE+Uajb5uE6LcWCA4o6CGYLRuHpvX/p91xHFzHo1qpoKoKqqriaZJm00Li0bQa/P1ffZob6/1CM66q7P7RLQCMuSExWOLwl/FshNOPzuF5Eum5WKNhvH3/gaGWUJn47mNoNqz8zgJ2zWE9AvlyhZNW6I7X6B9HYlk2rusi8H3liUSylXIi2e5N56g3mY+p3S2l9AfOoJAaiVEKG6iagqaraCGdaqnGynoB2/Y6t3tYjKBQFTKjCZKZKJeeW+Lyhn/Mc+kYty5ucPRIlsUrm33bmE6TZk88IEDZVtjLKlCqw3Zp4Di/8qkv8v3f+xjf+h0Poen+uOxTD82webuIUAWmqbOy6Iv7nVt7zJ8eI+R4XL+0iWO5hKMG33JhjJ2cYEnWuX1jFykhmQyzvpgnl4uSX+3er1gihKppLQ+7AtK//lDIRIgQ0pPUanUc16VSqREyTUzT6Ey1bDeuWk2LWDz6qp/l682baRLhD/7gD/K5z32O3d1dpqen+Yf/8B/yyU9+ku/7vu/jU5/6FHNzc/zWb/0WAB//+Mf5gz/4A44fP04kEuGXf/mXAchkMvzMz/wMjz/+OAA/+7M/22ko/KVf+qVOjN3HPvYxPvaxjwEceoyA15dAQAcEBAR8k5FS4riuP+7Y8xv1Pn7hBJ+5ssitnQIz8fiAgH6tPDwxRlRXh4o9P/HDQ6JgCL0nkswffqEoSl/TX/e8e372JCMTCaSEsakkGwtb5DcHBV+bfKHGxtJ+5/X0XKzz88VdX6ifzqQASE0l2WklNvjNk37Jvff4bo+PWjcMdM0gGjF8G4nrsrO5z6/8s//OK88sUil276VtW0RSEfIbBSr7NeqVblpGNBNj6Xq/yM1FBKwXyGSiaJM5QBCJm4ixo/4KQpCKh9h+aYW93Sp7u1Xmc12h2E5BGWY7VdTBhaW9KomZEUo1e3ADQMHjN//1Z/n//3+/wLd853k+8cPvIjoexrI91m8OVsWrW2WW96skpxNESk1KuzUWvuJbA06fybHWsm9EQv6f/7GRWJ+ATqb95I02juN0HviEEEghURSBLjRc18W2ber1OrphEImEURRBrVbHMI2BB7m3G7/+678+dPlnPvOZgWVCCH7xF39x6Po/9mM/xo/92I8NLH/sscc6+dK9ZLPZoccIeH15e/92BwQEBNwj6k2LdELQaDSJRMJMpGJEW5nDqnz1Jp+4ZuBIr1O5TqEjjVinSnrp+hbTmfjQInU7Ls91Xbx2B15PFfywKLuDp7XTSsFI56LkNw4Xz60D9L1a/+IK7/2O01QMD9UFVUKoLpl8YAoxEWVxaxuAG8t7zI8m0VUFQ3T/RKlK1w4QCfvpHNVqxa9CK4LNtR2+8vSVwWuQcO2V9c5rLR7BKfvNd6ox+CfQj2fzKGyXQTOQHiS9GLtu98ZubZWYyUSpFuqtK+2+Z8S6dpyBOyKGR+qNJUOHCuh0KkK1ZlMpNvij33yBL/7BJc6/d4ZSsTp0/fhElN29BsVCk1BYIz0dZ3/V9yJbrQ9UCMHGqh+LV9jpt7BkstG+MxdK/yAfIQThSBgp/W8B/IZUjVq9gW3ZqJoKEj+dIyDgPiYQ0AEBAQH3gGbTotZsEpHGwEjkhvXqebnytt03wOQKu8PWOnwYCt0hJ208z8OyrIEhGZoiSIdCWL0DUnr24/acR1+VuDfR4oB+9ByJeqPAxotrA+c1lzjS93p92xd3x+NZYiGDsK5hFbvn8uLLi6TiYRRVRVUVhIDN7QqjJ0Zwmg5Ow8aq+/8byCa2XbREhONnxvsi8+5Edb/O0WM5zJjJlZZdovf6FAmqAghB0XVQtOFCeeL4KK7dvkeSaq2BoetEzcP/FJu6xlaxwehUEi1lUN4o89Xfv8nksQyjM1kWLq712RaaPd8kNOoOjqYwfXoUQ1WoqRBPhMikI6zc3GV0JMbGyn7f8RKpSN9rRVFQe2Lx2g9afiOq0Xn4MkMm1UqNarVGOBx6TfnirytvIgtHwP1NIKADAgIC7hHFUoVQNo3jOKiqwT/8zvfyE//896guVpmjW7E7kkqwdjGPoDu4oqmqeIrsCITwIzHWau3qYWsCX1jpDBMZxkHv9We/eItf/+2vd16PSJBILMslltXZmRFopl/5VTQFtSX0lm/toaZ8S8bEXIa/86++F01V+dT/+icsXfJHciuOh+K1h7X4+786RDxDf1xdL3oVxI06DaDX4PJvPvUVKpX+wSWTEwnWhISQ6v8v5d9PNxOBiF/pP/XgNFdfXMUGtgtNJkd1jp/KocVDNCIGEjDyFXIzWRShgKGCFEgNCmeTeIrgzOQksuz7mI+eTOG5HhsLZUZCBhvS5tZeGaZjMMSusX5rj3q1ObDcVWA2GyFmaOTLVTYrXRuIovqdmhK40bAhZTIyk8QqNFhf3CY7k8HUBesLeyQyYTZXy4xOxAnHDLa3K9TrDvsNm9pODc+VoAjmjqQ5cX6ckBTsHhDQ8WSoz3/SdtQMo/f3qR2XGI1G8KSkWChhmiaJeGr4xgEBb3ECAR0QEBBwj2haNtV6A13XMAydXDzCR544zjOXV1hqNaEBzESjlEq+ZDxzdpztRoOtfAmE4BPvP8tzV9cpCpeC2y/GdEf1Ww0PUTxCCJSeCiISmk1n6LqJTJg1qjitiqn0ZOfnAzslFgt3vNSu01ofOrnTf1YOexQQQ8Tp4dXOfi91WxzG4iEufuEqAJMfOsW1Hb/qPVOyKaz4P4tGAykhM5Hgxfkekd/TG6erCiMVCyoWqbkEhUpL6g95kDmYOd2mslOlcsm3mSiqIJeJoigK1WKd5BG/YUx6MBoPsV1usFNpci4ZYXelzN6Wb8+YOjHG6HSK515cZXfDf7ASiuDY8RxhU+PKpm/5UIXg0m6BmuVwrDGYkpE+YOFQFIEc4o/vRUq/sdC2HVLpBIqiYFs2tfo35uv/MxFUoAPuEYGADggICLhHSCmp1OqETYNwOISha/yV73qCUrXJylYRtz3opHcjAWv7ZZ56YIbvePcpjk3n+B++63H+4u/+7sD+m55L07JQVAXP86PrPK87AVH0jWqTqJpCOKxTrw/6b/WIPyUxmvG/0tciOqlMBEX1PbGK6otx01RxXUkxX0bTFJKpcGcYzN1ymPY9bA/jIzGyiXDfsmjUQKgC6YHTum7X9VB7hWzv4EJl6OL+47ffuIMVofcyJ9KxroAeQvvh4iCVQp3MRJL8RhHPlRR7fMmVtTwn55KULUnWMNlu1+JVhYm5DMlkmNsLO5SKddYKVY6dHKVes1lf3Ud6ktWlPGaPh3n6eIZLVp2pTIzmQvHgqZBMRwaWvZoRw3U9Go0m0VikM6LeMI2+0d8BAfcbgYAOCAgIuIc0LZtao0m4aXUarX7yE0/woUfm+Fu/9Gnipk5ICOYmUxi6imZJHspm+cnvfZJMMuoPtXCcTiW5F8t1cWwXW3P8aqcEVVU60+Kk9HqqwoIPvu8EZ09P8rf+zn8d2JenARbstoaX6AmT4pXtgfUmphP8vR//NZau978Xixnf2I26A/n1EsXdWt+y6dk0K/nBwR3jo/HOz7JHKvdWg3sL5R1B3KOM72Tl7d2nieCsorO/XWbTqhJJmPiH8YfNmJZLU/qVbCOsI+fG/W8ABIxlw1gORBIhthe6A1iqhRpb65touko8qjGfCLNUqiM0hfXb+6yzj6opnH98hmLVYvn6Do7jcersBPm9Cpl0hOsXNzr7c2M65OuMhEIs7G8yPZchGjO5eXUL1/VIpSN91Xwp+ydJHsTzPGq1GrqmtaLuut9w3HMPtHxzxdgF3N8EAjogICDgHiKlpGHZNBrNTm6u57qYpp/ecCyV5PILGwPbJaIRPxas9V9tdUhcXdN10Q3t0AQE1xXYttUa6+21/jdccbhC8mAozXX8pInD3BilQoOl6zt3vOYf/9sfIhI1+e1/9yU2eyLT2hxarH4NVexDK95ieAW6bwx47+pD6tF3EoK9h60U6kQjBqV8jVgixAOPz3e2dV2XVz79SicHulmzOPlEjMs3/GZQR1WpFOtUinVUQ8VtNZYWdsogJY7tcuOZRc684xghC0I9H4jreFQ0eKlcJDIX5XgmSXW3xt5uhfnZLJlcjPxuBd1QWSxWUBXB2ou+ZWR1OU8qE+Fjf+4R3vH+E4Qj/Q8+nufRaFgYhjF0YIplWTi2QyIRJyDg7UQgoAMCAgLuMc2mheO6/tfe0QhayCRcsZkbS5FOR5mbSeNJ6X91LvwRKn5aRrcSOJuIY7lu39frUkpq9YYvilvKTkq/Rtpez22PnQbqlk1JrfGBj57glRf9PGRFCNAUGjsNarbLxHQSgGhII34k4wvSnh3GkiYnzo/7g156Zr0ohoJopVGcfmiMRDrCS1+bQAu3/uy09yEhZKgcicU7O22HgoSFYH4i2VlXIPCQxAoWyZEo7SsTQFiFuWSIvpMATAGTc2kQYIYN5o+P4boO8aTBsQtTICXh8TiqGwMk6XyDbDLiP+gU/Sq3mTCZNyL4kla0ggAlAoEQ4GRdEIJ9x+aT/69vx9BVPM8lGgvjSYmiKFiWxU99uj+zd+fiCiIURkrYKXWtH6lcnL31gv+7UreJZKLUSr7fvdFwWb2xw4mowfHTo2xtlFA1lYuXN5kaiTMynmCjVGHLqfGuR6b5+jO3QMLZ81MYUYNL9TLZeIitG7c7x5uYzjAxk+HkA5PUG/2VfQB5iHfbnzjof5Oitob4vOEEFeiAe0QgoAMCAgLuMY7rUms0yaVDaJrffHdkIs0v/73vBuhJrjhcDfyj972v+6KVnlGpVEFCJBo+ULzt2jbak/gcx+XXb1/j/7jxMiSA93ZXHftTDRzQG91K7Zgepb426JmdPJ7lb/2TT7Ti5BRAYtsO/+Cn/ysrW76XNxKLEotH+eGf+iA/+BP/aWAfZ+sOxT9eZubMGMZoBE8V2AJo2my9vE0yGWbP6TY7plfyNIr1O93iDslHZlm/XQBgfbnQPeYjU/yDX/txpOfRbFpEohFc16VaqRFPxPBcyQ98/78DYDQcovpMYXDnQCikdWIIG02HWtNjdCxFo97EDJlIz+t80zDxrqMs/NHlzrb7K3mOfuQCC7f22N2vk8lEKeWrxFLhjoAGmJjLIqXAjJjs7vkC17Ndbr2ygQTOfOgkm7d22NmtsLNb4ez5SfKlGjdeWMVr+a6vvLTCT/3Mx/mOWITLl9b50o0iWxtFVE2hXKwzPpnCspoDg3jamvhghb+dA+25HkYrz3zYOgEB9yuBgA4ICAh4A6jWGsQi4Y5vtLd657+GV2/f6kdRVBQh0PVXb96S0mK1VhlYbohuMkOvv7ev0k1LWAlBrWbze7/1oh+31sJ1/KmLbTTNTx1x3OGCqr1vbTLGM9XugJYLyRQAuqGC05sW8hqE2SGmWFVVW6O/XRRHaT3ICDRdQ9NU3J7thvnNO/tRFKB7rTdu7lDZqlAuVFFV1fedOx6NpsVNy+KBH3qciu0Rbno4lkcoanDkySmEBOP2PqVnqxgHbBToGgsL+yAlZiuBpG29EcBWtb9xMV+tczKTJKbHWF3JUy42CIV1jp/JkEzHeeCRSd7/kbP80//lv7G+kmd1eY+JqTSGYdCo13Ecx4/xE60caE31rUaeR7vCL6WkXq+jaSq25XS/NugI7jvft28WgQc64F4RCOiAgICANwDbcSiUKoRMk0jE9yx/41+By7uWlkIIEAJTUXkoPIanSL5W3sBoVZGHre8mBxMaKi785q98dWD52UemOHVuAiEEz3xtEd3QD817bhcqTUPlaCrTuRZRGr7+xGwW13KA7gAYr1Xp7SmZIqVE0Yf/mTvsVrcXH1Y9TWcijI0nOg8RioDLl7pNf5evbvLlF9e4+uLqwLaV81meW9/Hbbgcj0RZXfZHmscfzLFWrvHkSIzJozl0s/8BSBWSZDpMJGaSy8W48sLtzgNKPBMhX+wX0DtLBXKjSa6+uIpQBPPHcmRnk3zl+iYXTihM5+L8xn/6EiXX4dTDs1jVJplcjHgshq7pNBoNHNej/XugqRqO4yGE7NxXIQTxWIJoNNqpWrdFtuxZJyDgfiUQ0AEBAQFvELVGE8u2URt+7JeqDubyvnbuXrQkhcH4ZoTL+R3OnM8BYKICfrX3RCiC9Hz7QcRRGDa8Wygwf268x3os8PAolBqstqwTq8UqhUKdUEjDSwgkkpnRJOu3i32nXMfjaqV7lCed4Y1pm9sV6sW7yxg+MZY45J0736c+Ad2zam40zqVb3cSReGd0t8+zzy3zM//zB/n5n/wtmgfiAXt3GU+FOWOOYG0WiHoKa8C+obLVlOj02yjshkNpvcDUo7Nc/OotshMJ4klfUI9Ppajc2ObM+UlEOszN23tIT+LVbeZPjHB7YZflGztUxg2e/q/PAhALG2SvVBg/luPqrS1GUlH+x2/7F/zs//7DnH/8CKFQfxNqu+G0LYzbPyuKgud5NJtNbNvqWaf74BMKTdzxPr/uBBXogHtEIKADAgIC3iA8z6NUqREJh3ActxM392dGcsfvsKWUOC0rhOs4iBLk874QLZkW2GCg4rUE9MpyoaMdx0ZjzOSiHZ0sW/+nKXD51t7AsWYmk0POQOC1/u1Tpa1TlrZHVO8+RLRHlx+8IuVVBnv0X/Pw5X7RtOU1H5LO0TeVvC/Jo3+HB20K1ZrF0396gwcen6W83/Vpe57HC9LqVL5dHG794UsgBCfiJmOZbq517cBwm2rB9z3bti9M9zZK7G2UUHUVXVfxHI/1K5s4URNTVTlzfoL8epGV1QJnL0xy++YuN6rVzi1PRkwqhR1uPr/C3INTxBWVy8U6/+gn/yOf+Evv5Nt+4CnUVgOo53nYttVqYm07NXz/s5Rgmkbr97Y9nl4gRCAtAu5/gt/ygICAgDeQar2OJyV4HlJq35iAvotN2/pPIqkWrc5y2/arpUorO9p2DyQvCFi/tTuwv/mQStxyQdJKnPBPImuqpI9kfE2fCDGTi/tf/7cSQGJC50OfeNQf+7y4i1mx0csujzla51KMiCB8JINQBLmWwJRSEoqbMJ3qBIIgfbEtFIGiCNR4GFsIpCfRNTh9YRKEQI8YNFvXlZxKDVRU/Y9BdpafOz+FRKKpgnQijFD8fRzkzEQSRSg0Fbi5us+ffPEmP/CRs/yfv/j5vvUuvO8ouq7z9fo+FQ+Of/AB1taKXLtV4Exymu3FEsdPjCBdj+Q7j6FFwjiepN6wYWeVUMQgmghRbSV2uLbLXmvoytyJUa4u74PtQcNh5fY+CEExX2PsVJbbdNM1MqbJun/b2NquoGr+Q0u5UOdX/8Vn2Frf58//xPv8+DzHpdm0iMfDfQ2G7QhEw1Bpf/0QWDYC3k4EAjogICDgDcS2HYqlColYBKtpYfYMo3itSE/eUUQLITBa0+GklH1G4LRuckqLozsmdUNi1y2OPTpBw7LxPA8NybFz44DfWGbbjt8Y6NkYhuKbgVtf60tgf7XAxpLv8Y1ko1TLFoapYRf8kdLj82k+/InTpJNxXirX+ezFTcbPjrC+0rVwnJhOcOuZxdd8H068+wRXl/YBOHksw82X/VztU+88wktbBQD08RhCKChC9gyacVFag2cQLi9f96P9Jkfj7Cz413L0fL8lQVEEC5d9D/Sxh6Y6y//gKwv88N/+MM8+fYVrL/l+6LWv3gZVwNE0S5sFckWX3FicuYkYyn6J/ZdX2G9tP//kUa5f6Xqrw2Gdy6+s4zkeI3NZ0ukIO+v7bK4XQAik1q3cC8UvFY+OxthY3ufo8SSznsJeo0HVdjBs/ylq+lgOVJXbL/f7ta2Gg2H4DwqK6uA47kCTa7uR0H8Nr7Xh9ZtF0EQYcK8IBHRAQEDAG0y5WiOVjGFbNqgKpq6/ZhHt5z3LgRiywxAC0mGTB6dHEQjWvAKLdomzdhql5Wl4aTffWT8d0RFXBgemTMwmKO0MZgebo11LguiM0+4dXiKZmxnn2s3b7Bf9KurAub/uYkgM/Cjptl66rscffvoqjgOW1WOjuEMcW++ndPLUGLmpFPn9GqVyg5fWCrzvOy9ghDRsy2Xh8gZIyMZCzMSjKNOC3e0Stz99CYDR4+PsbPrTFHW1//N3Xc+3lQjBzkaJnY0S8ydy5MsWB/Esl1MnR4mGDXQhWXhlB8d20YDJqIGuO5x4eBpNVbn9/O2BYTqu43V+/1RVbT1gqJ0mQnoq9QEBb1cCAR0QEBDwBlNvNPnThRV+d+EWVdvm333i217zPqSUOLbjTyu8KwSFSpNXlvyGuOyo3wwXj0cxcyEStTpre7WOhlVVhX/663+B1c0dvvyHN3jm6VsA6CGdqQfGUFQF23GIRMI4jsPcXIxH4ybzMxN87g8uUihUUVUF/XgOz/MIRXX+4//+RfKFcsdw7NZsZqYShEIGihDIUvWurz89kSQ3P4IiBKF4GDq13DvetZav1xeL0vP4vT+6TPFAg6IcCPHr0rTczkyYj3z4LLPzWQAuXdngkz/7Ozz1+Bw/82/+Aptr+/zCz/4eekjl4ksb3GCH40/MYvXE/WWykY6Alj3LkRLHcjHTJs16V9hvb3ZHl3uNbsPitRdXsFvZ1DPvmaNYKJGIRpmPRVl4YYMrVzaZn0yzf2OVenVQgDuO6yemtPLFEf4kSqU1p1G27BvdaLs2vUNs3oCK9AE/e0DAN5NAQAcEBAS8wViuy8/96XNMpuNc2t3j2vYep0azr3k/vitD3GVl8MDQi5beEYrgesvrPDYao2nZaKpKxBCEwwaJeARNU0imQ9i2gx4xWOgZUEJr9Pc7P3qM4ydHEEKwsrBDtdjEDOvUDP/Pzuh4jKsXbwAwe9SPrtu6laeXY/PDGhGHE46ZXL/kWy5OPTQ9dJ0+G8KQ29Cda9jPne5ntW4xM5Nkd6WI61hUylUkkv19X9z+h1/7Kr//b/+U1ZadJZ2Ldjd2Pep2VygvvHgbPRrBtlzqPYNilFZDn2nqfQK6UbX8sxYCq2mj6gqKEERNlXrV8v3oRmuUuOchdIVE3GQmGmJzaQ9VU4klw4Ck0vPQ0Kg3WV3eBunhSQhHdCzLBikRioL0vE4koW/v8O+oUHzfObTXk2QyBATclwQCOiAgIOANRlcUvmV6gme395gKR/nPL1/hb777cRzbaTXJyW5xrzXSuiPqhO99lkgc16FRb+A6rYxkwJOtJAsp/aoutL6C9/pymYUiOBpJUbG6Fcnt7e6gFTMb5dP/5WVUVVDda+JYLp4r8VxJLGYSCmn+4A0hkBLSiRizkyOUyrXWsBE6DYZAZxmAdkiqhnfICOlh9Ipcp69627NSX5iGR71Wx/Mktm1TE4ovEoeUMKXnoWkK8UQIXRXMTSbRFAVNEb5gVQTJdJjf/YNLWJZLpdKkUGgwmYkS0lUMqXD0zJj/2Rkq8SMZBKDFdYxGiOgjRwGYmE6xslNFB2q6QiJfZ/LkKKu3CzjZKLWmRfZkjr39BmO5KNu39mA+Qd3yuOE4EPX/pKcsSbPREtp1l+OTaVZf2eHaqh8luLJVotZjUVG11o1RBBPHRymULP72Jz7Vef/nf/MvMZ1LIYTSSeGo1RpIzyMS9centwevdH4t+yeq3zuCCnTAPSIQ0AEBAQFvAt47NcbvLd7G8SSfubnMk+MjnJ1KsOxe4zPLda7kmx1x4rXi1yaNKHuLdSQSgSAe0mkkXb/619qv742mGxXXcvxKKTF69Gl4XcMwdVJmmF36K8H+fuDlF1fRpODqxc2eN0A4LvmN/tHaf/K1Zf7PP72Jqik0PjGFCggpmHylVTWVMHF2HAkoIyHMU0cGfN/S1sjMpP3mxBs7FFcLAIyeGBuwJUeTIdj2z0HRBFMnRvi+73+Yi6+sUm0J9L1m1+ZQ3a3xc3/7d5CexHU936bgeqSbLumWBz0zGmN3o0h1pYgsNSjt12g2bKIxk/X17ljzmWNZFjfLXFsYvG/xuElj1V939niOa6WuX/yBXJS9/TqRmoPreIygdu0jUhKqWuBBtdxE18C2PBRFxXE8mraL50nSsTD1fL/VxegZxOLkG5SLzc7rRsMhEwtRK3Urzq7tkZnOIKVke6PE/LFc3/5UVUFVNdSeBx1VtfAE6LrfbBgkcAS83QgEdEBAQMCbgKRp8K3zM8yP5fjwkRnqFZvnbt3kudRvkIg/yEs3/f9cp02D43EDIUAVgmvbXa/vI9MeXxvs8zuUh9WuTWR12a82ZyOHDCgRgleKVR7KJDlxYRJc/6t6I6xRHdLItlussbRR5MiRLNdLfrJGXNGJr5QH1j2aHGWp0F3+aGYEq2azeHufetUXvXPZBM2yXzXd3KzgWP1TCqd6htAIoXJ7ZZ9zF6a5cnWT2ysFADKzXUuI53hcv7hx2K3xzzdustnats3YSJzF5TyhkNap8u5slDh6LIciFFzPw3U8XNfzR4OrKqstAW0fyHduV81DIZ1qpemPxO5eBMlMpJOFHY4Y2M06mu6L2Gbr+qPG4J9xzejeC7vmUOwZgy6EIL9VPLCFIL/b/bbhYBG3bcvoX0jLPfLmEc6CIIUj4N4RCOiAgICANwmfOD5HNp3m537x8yzczvP+H61CCjzXZDoaQQjBEzmPf/bAvwDg00sf54vMATCT9Pilb/89Hvxv337XxxumNWqWjSqEn03dQ1sovZgvcnQmw/pzawAcOzkydN+dFAdNAc8jGw4TU3RsBgX0Qdvx7YvbWE0XVVWYGE/41o+epI/jD075HgHhCyYpQdcV1pbyTM5nUFoC8qC26xN7dyH8CvuD6SLRkP9nc2wyxdZ6AcPQ0A2V0m6VfL67fjwRolxqIBTB6YenEYAHnNUVbufLVOoW7fCLmZOjXH1hhWajf3JhNBnGdXwBbbSEstqywTRa6zrbVc7GI3ieJDUSY3evSjJiIGfSSE+iaQpTmh+NmMxE2Kg1SIVD7G2XaPRMSjx6ZgxFETi2N+D5FkIMfjsggxSOgLc3gYAOCAgIeJMgpaTeqJFIm5ycySDjfrVRN2qs1vwq4plUtGd9//2o4fEbP/h5otraazreMAlZdxwenMtxY+nuStmeKkhP9I/c7o1FW18t8PDRHNcX99BCLr/8y3/RP3Yr/cLzPJ5+8QbPfvZF3hEbYf1mHikk4ZBK5niKV/Z9W8SZngmF1xd2cGyPRDJEOe/bNqbn/G41J27yymJ34EsoonP85CieJ9mtd60MdxP2t71RRNfVzgRAALfp/7x826/8NxwbajZHT+b6BHR7QqH0JDeW8337OPPgJJ6EkO7/Ca7Y/mdbPZCIoYd13FalWQ+1BbR/5rbloimCzdvdbyAiYYO1q9uET4+xtuhPhzRCGo2W+FZUQTITZmV5i3BY58SJ0daWksut/YznoiS9/t8Mr2Vz6RXMbWtQr4/+TUGg6QPuEYGADggICHgTcbW0zeceuIGHJF1ReV/sYRR9F8gBos/7KxFIPH77z79MUr/6mo91mNbYVZucnx1lq8fn29jtepyFKzl7egyJL+yMkOlXg1tNZFJ6FBIqcSVKqdFkf6016lsIstlo37GklJih1nAXT1Lq8eamX4MaEgJyZ8fY3uv3A5eaNlfXfRGuKoJTR0dxbZvti9uvuk/pweh0krXlrrd5f3dIBZ1uZbhNKKRRbN2+cFjrE9BXX14HBLPzaTRF4DgupqnhOh5mSENpVXzVkIZ0PBKpMOGIQSIVwtAVEgm/ohyKm9iW22nObItZTVfITSSwbAtFVQhFDD9JJWIQipisAslUmHrDZnenSq1mMX96BEVKwrqKbnvMnxvvJJK8+NwS1XqTmfls56HHcfwG13K56lsnxIHGVilRhEIqGcRwBNyfBAI6ICAg4E3ElBonp0fYtqvsWy5f3LL56IzKD18Q/MeXfRvAdnOCleYMN5uTPPnOFLOxr3S27ws/6Mnx7b7ffeELHjkQ37ZWriDiMD4W5+aNrtBsC2TPsrn6gl/tPnJ6lMWVXk+tIJoNseq2bRSik7KhCEGpXGvlB/tjvyXQbFoYrag2vfUvB2wkQul6exXNj0gTgNoaOCIFbO1VQfpDEQHw/FHU6USY6ZE4K0v+qOuFW3tk1Ve3cADEk+G+19sbJc4/OUul1KRabuLYru95rlhEas3OzS8tNDA9ifQkUUOh1vIYy1yUE0dHuX55k5WrOyiADIX4lf/6PyKl5GufucK/+un/4p/+g1PcWir4+9vzty89v0JuPMFOodFnhlEUQf7ipi9mEexulJg4mmZjpcRENMTG0j559pk7P0E4olPfq7CzX2fmgXGWFi1Wru4wP5tka61IodCkl5uf+hrv+dbj/OhfeR8Ny2I3X8RzXcZG0pSqdcZyaQqlCpqqUqpUqdWbuK7L9MQo9xoR2EoC7hGBgA4ICAh4nbEsm0bTJh4LI4Sg2bRwXI9I+M5jupuWjRCCD8aP8pnKIt83eYGHcyF+a/eXQF/i244/we/frPIj9b/AtluiaDd5LN1fcTXrKk6PhcIYh4rre11HvAh7ja44EjGV2mT7lb+NrigYJwqsUGAF4N3+uzOFJJX/7Fc4F3crHJlOsbVaAEVgh/v/lBTqDjEjRKnmJ26cSiS5fSuPg80P/dX/xJGZNImYyZcKvtXi7ESGyHWLq/Sme0i8UNdoYcUMqqkQALHdGtJyEZqCV/WvRzgeNPtj737sh36VMw9PUt6qcnnTF6BzIT9hot2M92rInip4ZibJ2OlRLn3u1sB6ruvRbDhDbTG6rnWWh8MGNxZ2mD+WY2nBv/56zcIwdKSUxHssOs3aYHMmgHWgGRF8m4VhqJ0BKgCO07aGdD/z5Vf8xsn2nq1m9xiapuA6wy0ZilBYWF7DsmyOzk2xvrHN5vY+tUaTbDrB7l6RUMhgdWOH2alRSuUau/kiczMzQ/cXEPBWJxDQAQEBAa8zxXKV26vbnD05RyhksLGVp2nZHD86hZB+BF37a3cpJZ6UqIpCsVhBURTemZ7ih84/RjIcpmQXUdFwcYhGFngodZ6LjZ2OrJsKeyw1j4JQcD15p6nTd9X05UmJN8Q64bj94jSaCkMrVm7IgcjFwi0B3TvKG8Ihnd0ru9SjBkz6y4cNL4GDg096Q5y7Sw8SierE4yFUTUFRBbbj9l13ez9qj6f6TpSaDsqDozQthy3ArTZIzyXQDR1VVVA0BUVVQIFJdaSz/3qjieu4mKpg+YXug4FpqFSqFls7FXIjMXZ3KlSrTVzXY3+3SKnQfSCqFOuMTCW7iR6a6lf0dZW4qkJrdLtoVetNBayWR3tyPkM4paGHQ+iaQmYkQTFfZX+7wshMEik8dm+XaRSbHDsxwsKNHWzH7TQtHsQwdBp1C8PQyGUS1OsNNrf3Os2Y7QcN09DJZvy0E9seFPoBAfcLgYAOCAgIeJ2RUmIaOuVqDUVVaFo2qqJQqdTZL5RxXY9E3E/VKJZ920E0GgIp2dsvU67UaFSbJOZdPlP4b9ieTmH5UUpWlCtijzniCASRmMJitcaP3jx/6Ln0Cc8wnDLj7Lu+qKzVByPrDtXYdYu5fQfXcwmZBpF4lOxIFENXyeWifkOZ4+K2x3IPjHj2OTqd5ubaCqGQ3lnuei6j2ShSFUhd0DWiDD8ZeWC5oghCYZVjp0a4dWuP6k43ku34mX4bQftMNP3u/vzl14qUxvzK95nTY3x9ewfScG4yiSxZ2FWbmxX/eMcnUiwuHRghLiVnz4+heP7DSaElcGt1i2TKRAhwbI8ffuc/xbFdMmPdhsziTpX9MxkOStpjIzH2Pr8ycK5jmSjbLd/6X/7khyhaNX7r/3iu8/6RE1n2tys0LZdywf/sC9tV4iMJ/55oKvWajVDVwd8DCZGwSdO2ub22xcbmHplUnHK1zur6Dnv5IlMTI76gRwxN7vimE4zyDriHBAI6ICAg4JtALBqmWmugqSqmqfvTAqUkFgvTbFiUK3V0TSUaCZGIRdjYzhM2/VHZ2UyCxZ1l/njvC+wun+PrK35W25F5QWgjwrLtWzIunHHYM+4++HnXqpMUGlt1Py0iqw8ZlX2I5lE92O1JfNho2Q+SMZ3d4qDVIN7TNNdbifSc7gjodhaG53psb1eYP5HlRUqddU8Ko7tdXwKE/69lOZx8YIKNtSI3L25x+p3zfQkgwy6nvZ/O9L1XoVm1yKbS7BXqlHvGXV9a3+WRkQylz92CM36etm4O+ZMqBJcb3ftzJBqGfX8/Gxtljp3McevaLrqp4tgujR7bhpSSlKqQP1D9VwrDs7r13vznpsvx4+Od15qm4NRdhCIoFxodD/rMyRGWWmPG2wN3EskQxQPH0DWV2ekxVFVhc2uPTDJGJhUnmYiS3y8zMzlKLBombOp4rkvENJCmQUDA/UogoAMCAgK+CRiGRtNy2C+USSXjlMpVCqUKausr9/ZEQF3TOl/DCyHQdQ1FEeh2lMUXH+disSso6/tQtXs9ri4c0CgHRygflIm6rkH9kDfvhPfaSnua1hVzvSO5qzVfmPmpFHrrfX/frutCj7PCaEjmXQNd09jb9lvmVOGP1gZo1GyuX97qrC88OHYsh6ooKAp+Q6HW73Vu2zmEdnceaIBMLMReoc7WTgUlQaciXMKjuFshaYxRtBzqTYtUwgQhcF0PQ1VQpZ+yoagKjusSj5nYuQiu4yGR1BsOE1NxzEYIq2b5VoxEFtPwUzk8VUU3dVTFT7kImQYh2yM3n8RDElU0HM+3eCSjYUJRA+lJtlbyOK7D8VN+TnfI1FEcyD4a58UXbwNw5Mw4Qlc4cSKHAGIhjZMnRhAKjGcinft14Z1HOPPQFIamoiiCibEsjm3jOC6RsEk0HOp8aRAydRShEAoZb0g1+F4OUgmK3W9vAgEdEBAQ8DqjqioCgWkYFEsVwmGTRqOJJ6VvbZCgKAqapqKqCkKAoWtomoqm+l+BG7rGB3IjzISj5KJh0oqKkwjziy+9BIAmBFE0Umq0Ncrb58R8DQQ0GgoNawo1Idmv1/A8zx+mIVUeSKZxXQ+1Ljv7moiatFXQHr44SHkhGopDAwdMhckTI9i2g6H7/l/HcdESJuPZKA3LIhQxAbBsGyybuUwM1/MIOYLRTMh/OGjt23U9zsTi1BtNzFbzX71iMZ820VQV13FRq5L8iu8JHk0YRHXJ7nKRkckYpm4iFJCm3vHhenWb5RdX+z6Lo4/1N7HJVjVXfQ0C2pT+/m3HZSyZYKPoWzY2SlXCwPGqi5zPsldssF/pNuxNZCKUX9gmORIh38rxrk0q5Ne6D0Ul/OsbSYXY264iAW8szvREgo0beWY8lfJSN2+jCHQfGWAqa7K56p/Pbaocn02z/MIqi5e7vuvj5ye49HU/NeXouYnO8oXr/VF+Z8+McfPl9YHr/+GfeC8nH5hGVdTOQ1e1WsOrNYhGo30jvgMC3i4EAjogICDgdSaViAG+rksmfIFhjGUQCCzbRlXVVtZvd8rbeOv99rLJ8SzTiuAJw2B2ahxVVbBsm8+sLJLNeFQTC1TVGimj2almAzz14C2EsIgp03wo/eMIAZbt4LqeP/5bVdFUFdu2+Sdfeoar5TLpSIj92a5wihOm5DaZKeao55rcdHdQJ0IsW3VAA0sCfiV8Ph1lIV/A1FVqdneQyOyuS6llDeg6kn0E4LmS7a/6Ii9xJAXA7nYNejRdaibFdAiUtTyNcoiNsn/MrStbeK1mt/RjR9lpVadPHh3MHJYH7A/tQrpQDhd9EjBMf8KgZqiIus3YaAxNV0lkI0TiOkJVSAJ8x0lWd2z2r/gnPn88w9Jawd9Pexqj6B5LHzJ6G2h5wv17Y5gqSuv8wsadbRDxdJjN1W6MoD2kCVDpuVblDukjhzVztkd29zaDtv3N7d/XNw1BWTjgHhEI6ICAgIDXmd6KXFu7aKrvTQir5tBt2u93XrcsEK7n0Wg0WLEKLDjP8aEnLvKFPDiyLZQOeDZauK7LjcU1suk4hq4TCplYlo3rOmAIytU6divmzDkQXeZ5ktPFMfZuWhhpf52DjXttdK0l/ITonIbAT2O4W3St/9qTyRCx+QiRS9vc+vxNADIPH6Et2vt0Xo832owZnHh8tvW+P9nFUCVnpxJIT+I5LvHWtRpJk9FzYziOh227nX/bP3dc265HdbXIwlOarxbtCp253ADjcLqkQWsstmmonD3lj8WOoHCZrb5ouVBouCDuFdbhkI7a+n3wXI/jp0aRrcSNRs1idTlPNGKSjocJa/1/xhuWy+mHprsLhKDeE2O3u1lk9MNzCKH0TTNXEIi94bF5/oriwKI3kWgOCHgDCAR0QEBAwJsYz/P4J0uf4SuVZR5MhplL3eb9uTP88U6JO5mYHc/lZbnE3kKRVCKGEAKr0cDDIxoJ47kuO2W/cikljIoUYXwP9lqjjBty2duwmUTleDWHmRfUog7H59IU96romupbOAz/CcH1PNLXuhFskWOhu75GRVHQdZWZk2n2RyUXZQFEiQ/2eKd1TaOdXnxYpbRcd7jVGmHd5th4jJWvLnZeTx8fBU3BcjzW1ooHdzGURsMho0TJS//4mqLwSG4UJe8gmx56zmO/4BvLVQmvLPnV6FOtOLdG3YZXsYzEMlHGj4/i2C5qxMR1/QeDWtXikuyv4avTBtorZTYpkzc1FKVbWW9YNleuHT5lsV61uLWxP/S9p8z40OUAbxW9HHigA+4VgYAOCAgIeJNjeL74MpUxJo0TrFtXeHfmQf40v3eomHSlx2+Uv+j/V77lrMipBhVR6zQRnsqd5e+dfYqdcpFfqT/jL7Qgo5vcMHeZOTpK/Wsu+xs24yETKaFhO+wUuxXN6F6ZxyJxXmpUSIxEKe74o53vVl1MzqbQJkJsnWmwRMFf2MkW7nJYvF5vxvPBSjowkMpRyldhNP6a+icBTulxqkkFDYX9pTJip8byVT+J5OQ7pjHmIlSaNvVwVyjbrQcAq+mgGCaed1gd319no5WrPT0a64z+LuXraDGtbziOi5+4YVsuVtNh/Eia9Q3fV12r3zl7OTebZEuRA/flTjiW00rt6C6TUiKUNyCqLiDgTUIgoAMCAgLe5DxmTLEqy/zzC9/N1eozrO/coCmvMmLM4gibjFD4gUgFTVN5RbocpmAPip2JqMHHjh/l0voqv7LUXd7OcBbHm+w/7S9rC9WDDWOqJbn50goX3j3P816BI2en2Gk0sWuHV1wjUYPJYxm2HZsbxRrHlBj1lj3jofkxXtrf8i9B6V6D2miitjKz+wa99FxTKmIQn8uA9EdoS09i6Arqw7MIVfFTLRyJWbXQq4fYFQ6httlg8Xq3EjyeSnd+3qhUKdStvvsEYLdsNr6vWaNet/E8DzOkoRsauqai6Sq6puC53e1ipo4SNUmf0XE9j4mohiv9S1WEAgJ2QltMT/pee9UQJE+O+v52XcGZz7Z8y63KtJSoqopl27zzwyc4Wq3xtT9dRgjBXCbGxpVtpJToRxUiY3GkJ3FdiSc9/98hItnz5OGe6TeSoCwccI8IBHRAQEDAm5x5LU2l0uDi/hqbdoysPocgwvm4wccTeXTZZF7/GgC/fvWHQTdRXA/oz4jWVa1jI9Zdk3w1xq+9cAXLtnhsZc63eVg27hkXoQni+1FiRxx/UElTEmrohBSd6IkcDdtFUxQiLXG1c2mLcx+ex5WS/b0iU2pk4Dqm59OomRDXd4vs7HXtE1EheHBuBCEUik6jU7nVz45xLJ0AIajcyHcaB0+/5yQIgaoK3LDB6GgcKSU7S3vsrvfbMsaOZrnd8iefOppj9auLUHdITMRe02fQ9Pqb8xbqFdTjUSzbZSPfFdZKzeGMpeO5EqdYI5WNYlsudtVCtVwWXvSbNa2qfeixrj53u+91bjzBzlZ5YD3v9ha1kh8LOHt2gsU6PPzEHI6q4Loe1y5tDGzzib/8BCMyw9O/dxWAyViYwp5vvZmaSVMpNwe28SvN/csURQw0EN7NpMuAgPuFQEAHBAQEvMkRQvCYOc1ff/k3SRsa7x3PsmlfJ6KEOa5+oU/cfKkgKTpNpsJxCPvLpqyjfO0Vm3Xgzz/4bv7KYw/xb7/4PP/pxavAKsmQyVQ1xN5WmUbdQc4p7Fo13tGIcnXH98se1SPUXvZTMyZOjbDSEqonZlKAn8m8sNiNTmsTi5tMzqXZLtZZLdap2j0DOqTk4UwSZ7nI85ODFeF8JsS1fV/cne/xD1y9to3nSjK5GHvlemf56UcmiR/N+FFwSFwp/ea8q/6DxPJagejpcapfX0W+xlzrstV/fvXmcAHs2R7rB6cRfoOEI8MbMnu/DShslyCeoFCoc+vmDidPjQ6urykcmx9le7cr+Hszuj0p0U3NF8aKn7rhNF1cx/VjEBFIWpVtTwL+Axe0LB30WG+k9Evvh9uqX3/kvfVAB7y9CQR0QEBAwFuAx80pnt/b4owbRlZKZKKjWLLBy+UnSUpfsHlS0nUC9ESOIXCl5PsfOMOT01MYitr3frHRpKg2eXA2w9K1/ia8Np4Opx+aIr9VHtq7eLD6GE+EmT87xuJqnstL/j5VVTB5q4rbSrtwHY9b7DD3xBRnrWxnt9GUgXBAiK64649Q6z92MhVmbCxOyfO4sVvqe28s1a2EN5oOluX4s2deg4COxU3WrTt7i9t8M/SbYahDlyfPzjCpCTxPopsq+wv7PRXhwQ9pcirN7PQUlt1tMlQUf9/pXIyv7xUh158Sc2F+EtM08VyQnusPAZISXdMJh8KYpomi+CkdqtqN3/M8zx+MExBwnxII6ICAgIC3ADk1SvhGkotqk/FkiIWKzrg5wj+7NM4LV7sVT3Gh5o/k66GtqX7g/Fnm00kcx+kbjX0QBVCkQDMVjkylkJ4kpqhc+tIaDz40zW6twczxnF91TJuk3j+N60mSistMLgEO3H5lh3JlsKoceXQaXVewohK9dZpeUmdxJ99Zx8irWK7Lg8ls9xqGnGe5VGcsF2N7vUhpq8K5C+Ocz9fwHA/XcnAsFy1eZd/sJoK0hb50717qpnNR1qm9+op8cwT0YX16e8Umyy0Lx8mzY/3n0brO7EiMick0IDh7fppms0ml0r2WdgXacz2G3WVV1Ugmk2QyGf/zbgnotlD3PI9KpYLjOIDsCHJ/cA+EQnefxhIQ8FYiENABAQEBbxGemBvh00u3cVWHk5FJrlQ2mEv1e3m7DXZdKdfWylJKpJTYtkO4phA54LjQc/6/8a+FaZYki+F9ZieTvLizw2xrOEzTdVle6Qr2mXgWkTJRpaS8vs+lyjamofLoQ+N4N0u+2FKF//W+Ai+vF9BUhaOnRrislSk6FudCWR4Kj/SdtxCC7a2e+DbR+6OffW1bLts9nudGzWHp6/3+4UgmCscmB2/ma/DrhpMhKN6dgP5mpFIIMbwhc2ouzepinmqlSbPpV8jbglhKCIV1RkeSXPz6CgAf/Ngp9vZ22d7eIWxqhEwNoUqyYzFCUYORhNbJ0BayNVVduq1t9G7yRmv/ruu2fp/sjp1ESj/DXAiB8kZMKAwsHAH3iEBABwQEBLxFeHRmhN/++i1O1zLc1jf50dn38LGxC/y2uM4vf8Uf8d0W0J7n0ZYv7a/SK5UqRUXgOi7DpI3bmtrXFmHlukU+7wvHdgBb79fy2dEooajO11f7bR9Ny+WmW8VYy1Pf73qeVV2FkTCO63H98hbGBV+Uu7bHjaXh1pE2fbFrh2jU5hCbhVUbnrYh5N2LOz1mkpYRwoZG2NQJmTohQyNs6IRMDVVRKNealKoN4lLFna2gmhqKoaIaKoqhoOgKQlcRmiBU9pvwWl14yJZx15O+DUcV/jcAnuPiNBxCYYMjJ8eoGpAv17EdF8t2eXm7gBJTGE8myRcaxNMRVpbz5EbixONhVEXlykV/hLemKcyeymKYBoamYecb2IAzm2IxIUjGVAq1rp/8wmiGWy9ssMc+n/iuCxim1hq+0lt5tohFIwgl3BmkI2ldW0DAfU4goAMCAgLeIkQMjfOTWTJ7Kf7xt38X1ZqN6in81fc8wuWNXZ5ZWmP29hiaqqAqgtxIioLdRCXMCeHyz3//WSZiUQobVRwpOTWXoWZ3RWfY0JkSSTxFEs/qZDMRdip1joQTpC2VxkM59oRk9EwGAWzv10hKODqe6o52xheBtq4w8m3HKO3XOoJcERC3WrMTBbgqZCMh4prO9MlUZ6Zi+9+0arBXbWC7HmvjEZBjgOSnn3gQQ9c7cX2KUADJy4ubFM/FUIVf8RZSokg4XtBQNMV/BHAlcnYURYFQrUGj+ere5u/5yDnOnJkgmUzSbDawLJu//V//mOfX1nA8D8fzmEjE+EvfcoGUafC3muuA0/rfIJObYDfuzlN9KG0hKyGka0QMHd3QaEZMVm/n2TuQRnL83BjJVAzTNBjJJQ7uZsDD3jvq3BfFSmc6Yns7XVNRNbVvVPgbiSBoIgy4dwQCOiAgIOAtxBNzI/z2i4sYza/yh1+6xk//0Hv46BMn+B/e/TDPLK2xVuhOA9SVLFf3ixhKk5wdYrVcglG4ver7jePjYfZq3QpxaCTD0l63CS+RCrPceq2pUTZ3hkSpAbc2CgPLZ46keXZpjaPpNEvbvpgzNLVvrHWbk7MZbtYG9/FANts5fi8PPnGEWCTct0xKyUKzxq2v3xhYv3yt6U8DPEAkG6YxmNo2gMDF81yKxQKKIlA1le1yjarV3edqocxvPn+FJ2bH7rAnn1BM/8YFdA972yUatf7rU1Sl5Wv2eccHjhENmQghyGR6Igbb9p47FI0PTSwRwSCVgLcvb47HxoCAgICAu+LceBrLcXl2ZR3Xk/zWZ17hv3/1OmFP4fxIrrNe2FSIT1mcmY1jeZLRUd8u0a40njs51ieeX53DRgEeslj6po+ya/HYySnOz41yejo3fOVDeK3izNyxmb7WZPpakyfdbn6afkiKxd3uX1UVzJCBGTLQDR1VVXlkdmJgvevbeX7tuSuvfp7h1692NX0iS+MdGaz35ch95xy1MwnKJ2OMv3e6s87csSxPvfsEuubfB8PUSCRazX3tW3Dgc+y9M8PynYUQqG+SynMfUt67/wW8rQkq0AEBAQFvITRV4ZHpHF9Y2OCxE2NcvLHF737pGlcWtzl7ulv9tByP9fgq6VQMbiu8tLvDkWwCTVHQVIV61eJMItWnA0LnBBMXQh0tJda7b+qh4X8uVOChyW7mcNG2WNwpdHyw26UqEjAcwdZeZeg+FA9ipkGleXfTAf+nT/7nvsl9f+PH3sNTj8zRqNudSnOv6DPM4ed+twJatGLaetf/6Y88RanR5OnLt+5qH71ooeGC/s/C/laFnZRf1bddtzNF8vZembGZFHsrBT72fRdIxiOd81dVlUwmQqnUfYA6mMqiaSrxyThSgndIbKHreX7cc1CEDngbEgjogICAgLcYT86P8oWFDV7e3+WxU5PsF/xGv+3NMsR8RaMqggk1y01rnUcfOMbzF8tE0zpaA8Zm41gHJusJBKaQeKLrQr4+ukMzXedEaIS9sgeLg+fieZJrN7Y6r8+cGQMp+yqYO6UqihCcOTEKtuTa8k5fxVN6ENvwGAmHUbVWnrAqUPpPscN+sd4noC3bF5CO092gNwHi8Ar08P133lcEpqmxWi5jr27hAo7r4rgeluuxvFe88w4OQTVev8pttdRkYj7FRqFCrekwPxpjY6vCdCbO5su3OXFujHMPTPH7//FZQhGDRt3GtV2irXsS9RQemcihCD8rXKgCTxEU9+scPzbC1GSq3/LRQgj/c3qziefAAx1wrwgEdEBAQMBbjPlMjLF4mK1yHRuPpZYHebdQ411PZimM7LDp7XPT8gVlMbHF8bEJbrllCqLJ8ZkUt5cHxd+5pQyrK11T8Ll3pZgKZXluY5vxcPSw8AsfKRECrl3dIoyfIqEfiDG7vrGLEIKxyTgzqRRLq3l2i1UURaFWsagdyI0+khwhYmj0+gwkEAqBgoKqKgihUKtbbO2WqdUsonGTyRNZ9qIgypKZcJRqadBH7dN/RbmxGCMn03y9XqDuujjSAyH4pecvsvX5u4uxuxvU17ECDTAajbBR8Kv78VSIja0KrPnn+7HvucCXf/8Kv/VLX+jb5tgTswC4JYvrL693lo/PpFlp7ev7/28P89QT81jNBgfvlZSyb4phQMDbjUBABwQEBLzFEELw5Pwov/PKMpuNrrDTdQXhKniOgSO6ecDx+hh2QqOweeeOOSEEpuGnLcSiBnsNlxulPd47HeNdRz/Pv//04zw8NcKNL3ezlkNmg9jt6uDOzqWx3eECK2LqfHVxBSScnM+SjkWJhg2q9X4BvfjyDhdOjvPSra2+5TUHPK/bhPcv/8OXAHgom6RabnLjBV8QZoEaQ86tfb2tf1PpMLELaZ6TeR4yXMoVp31DAFBe7zKr/vp6h9VmTzUeycMjWRa+tMy5R6Y4dWqcT//Gixx7ZAZDU7ny7BJwp0pt943f/52X+d9+7r/zyZ/9Vh5/4ujAmp7r9Q1VecORBDnQAfeMQEAHBAQEvAV5bGaE3315idFQmIJa4eSjYzy3v03RU3hho8GTyRPIUBOravLM1j4nUikimkbNsUnFIHkiQrOp92gOgWVI8q34temxJM8XtlrvdO0OdeH11SIPiqeZEyNUKg3kHURVZxsB17f3uL69h54QnD4yhtqQ3FzawfXa2ch3r4g857Wpp1BUZ/5Cmq9RwJb7gMCzJAnD8EdRI3E9+brbFIT++u5wf72MP58cVgsV8l/eQwgYO5rmc390lauX/Yk5iiIYPTFKLGaiRnUAStUmJx8Yx7JcGk0H01SJFwAJC1f9z196cmjPXNsbHhDwdiQQ0AEBAQFvQXKxECcnMkjL4+jZLIWQje15lKsWhKBmw8X1GrRGUG/VakxnY8zP1LlmXQJAFOe4VewOz3hADEawZUMmCbMbkXZ7t0Q8YVIr+dVs15WMjMURQiAUQalYZ79QJ03q0HMfJrlsT/LKzg4A8QmTs+k0TsXpH6DSYnwk5keo0cqNbo2X1hWd8ZlWJnU739iT5LcrWD15z+GIwfijozwbKtJgv2/f9YqFvF1H4P+B1ABlZNAD/I3gvs7hFbubFZKnoxRrTRqWw7EjWcKmxtOfvk4mHeHEIzMsvLSG53psb5coFjVmk2Ok4iaVYp1qu5lQEUTCBrGoCUAs3gmJZrC0K1CEQIjhKR1vFCJwlQTcIwIBHRAQEPAW5YmzY/ybq35sWq7u5yLfzpfQpxQ81Z9oN5OOkY0bYHhcXi8wg97ZXjswMW5YtfdDR+pMjj4N+KO2aw2bxLEEqbU6he0KC4vDJwgmIwYPzI+2qrcHJPNhucItypbF81t+9XMsHuXC2Sl2tkts7Po51DfDNZwh53oVYHZwf48bCVYX/OzrbC7GrUc1rnuFoccels/8eleg3de5000AxyIJGgmPmNAJ16qsFn3rSn6/Rn6/BkmTM7kot75yiybAWIza1e2+/Zx811Eu3twe2L/jeX1+ZynBc11s20G1rNZodV9iu67rC2vFn0xIfGB3AQH3BYGADggICHiL8lA6ha4oOJ5HyFDJRsLs1eqMalG2rRqhiSZrssaaBViQMTNAd5CJrgw2hrVxBhrEuu9t5iukRkKMhFPsLBeGntu+Z3ElP/y9aSN619e4Va6yVa6ClIzPxhiNRknfLPUNABGtajRCtBa33hQCJ6ahmh7jE0kycZON69u4XvLQ48l7UMG0ef0PcvOFDU6eH2fjK4vMzmfI93yz0Ka3mj+0aizgyAn/QQnD/71KChXpuji2g6IofXVoKSWKovTZeESrKt0e7R0QcL8SCOiAgICAtyhhVeOJsTFuiDzL1j4PpybZq9Vx8Nhp1slFNCy3K5ijpgo94q0tN0eJkCFEQhiciKcQikB4cCKewis2qbkPU3MSzM2l/QEpEjzPQxlRSQmHnB5G01UURXT+p6XjJFORnlHRrWMKSGAwl0n4+/L8JjRV60+mEIDred0x0Z5E13Us22Lhcwt9U/ZejZOPzLF9cZ2d1uusMkLVGxSYABuTLvpMGFURqEJBFYJUxEQ3NFRF8fOtHYnbdDv60JWSlWoJ91UEo6GqmJr6TWm6UwTUL+3i1h0Km2UEEnmg8t87nltRFdKjcVC6glcxNG61KtB62qTedEhWPMS3ncMwjb6R3Z7n0Wi0IgffTANVAtEecI8IBHRAQEDAW5h3j41RzttsWBWMsC+Y7FYZdUxNUnK7X8mbigK2yjE5z8aShh6OADWOeCmuvbDLJn582eSpFJeqvuVBNiJslyZbeyj0HTulmaw87vA3HjrBj194sONFdhyXRqNBNBr1BadsB9D5edMIX4A1mxaNepNEIoaqqq112s5maNQbmKEQorW+63jU63X+2r987jXdo1ql0Scl9TsM4W04Lg2nf9z4T77rQT7wwEm0lsj/0kvL/Oy//eO+ddKGxmg2hmkolEoNpAuO7WI3XRzbxbI9BB5g46Vf3xg7gDOTGRaeW+HIqRGWLm3xwJOzWLbX8Z9ICdGkycwHz/pDUBTYW+qPMsz2NGG2Rb5lOXidgSlBw2BAQJtAQAcEBAS8hTkdj/PpHYWj8RRV22/2s1oRb5rn/yf+WChLuBJiYaHCLbstDh0ePBpHkwrqgVFzEaX7pyGdCLFd6s9AjhgasbCJqQmgzi+8+AI7jTqffPIdqKqK50kU0c5pHi66FEXBcVwUVUHVBquYUkoUVe3sQ1EUNA1s2x66vztx8Bwmnq+w87BOiUG/8zD2G1afDzqXGmwqbFgOtzcKjKQi7G/6/uNzp8e5tLqNo3rMphOsb/t51NGoyR7l13wdhyGQlNdKqKrC3pp/DKtqoRpaq9HPfyip1h1uLvme9TPzmSF7GhTQQghqVQvPdZE9n6c/SOVNVHluEQxSCbhXBAI6ICAg4C2MgmAmFONmfY8Np4oiFGa0BGtWkcS2wWlviuubRWAwA9opeYwvRrjc2OlbblpdYbSlNNAUhWPjaRLxEKuFEmv7Zcp1h7FEV0j+5tUr7Nbr/Nx73ofK3VUrhRCHZixLCa7jdkZFt6vbf5bEB6GAZqh4jofneqxe2+ICY7z0iE5ZvrqI3qvV8TwPVVX53Ss3eHljm5l3jVBxbRKqwfKXulX+g6fnuP4FRJImp9NjVJsWN7fy7dS514UTExluv7DK9JEMxe0K8WSYnZUCAv9BRdUUhIDRM+NkMhEUIdB0hdxovDNyXSgCTe9Wxtv2eCHgmS8t8p73n0Q3ug2oUvoJJwEBb1cCAR0QEBDwFkZKyblUimVZ4vniBk+lJlGWJfnbCtfkHt6EyiOTY1ze2R2wJlQbNpWGNbDPxdV9jFEFS3psNGo8dWKM55e3eDgyxur+4ZXTzywv8Vcbdf7xk+8ibnxjElEIX9Qd1NevJRe6ze0rm52fzz42z+WX1li9tsVDyjivPKJTcO9c1d6vN3Fsh+v5An//M1/sLNeAx2JjnJkdQW0JfQVJJhdGVVW0qM4D8Qlf9AvffR4ixLHsGFvPbGNZ7qHHfC2EWnbw1cX8wHtzx3L880/9MAA//wt/zEi+yq3Lm4yGxtnd6p/QmBzviczoqTSvrRZw3f5zFW/GCGjJ4BNMQMA3iUBABwQEBLzF8SyHyKrCu0qTXF3a7XtvNhLjxdtbZKJhpjImCz3JGOGIzjBqTZtHj43QCLlIJDWngXJbsFIo8dDsmN9EKCXgsSgrfdu+sLXFX/3sH/MLH/gwsW/wuoZVsb/RqYCVSrcSX9ouM/9Skv2Hoyzbh08s3K01cFyX/3zpmn9eUvJYeJTSQoUb+Q0AIusWAkiMRbg51VK0eXhodoyXF7cG9jkX0l8fAS0l27f7s6ynZtLEojqu7RJLhfnf/vXnQErK5a4X3KrUmdJskvNjbBctdncqgCSZ9OMQrZYQPfHABM1KE096nW8D2qjK4RadgID7nUBABwQEBLzFkWWPy6/scGwiNfBeVPr/mc9X61SbFqdHs1zd2eP4SJpb1RKpqEmxOmjvyNs1rrhda8eR5CjrhQq7lW56xWgqDOHB81kql/ixT/8h//oj38qJ9DCv7V1e18HYtfbLb0Cz2XZXtFoNm/JWmfAXmjz03lFetPeHbrNbreO6HuOxCI+EcljLdW5t94tiRRHDLQ2HnKthvD6NhOOZGIXLXQtJOGwQi5tcf3EVgJHJBMViFQRMTCRQW4J3ebnIqVyYy//tGQBmHj2KGVbZs/1vJI5P5/hLHzrL+546BtKlWq21qrvdC3oz1noDD3TAvSIQ0AEBAQFvYa6s7vObf3oDgIX1AtMjCVZ3ul/Nl4tN2qETTcdlYTPPU8em+NrWBpbrcTSRGiqgVU+hN6zC0AcbxuQdhOx2rcaP/sHv8S8/8CEem5g8fMU7YB+wDdiOPWAl+EZoNhwIG9RrFo2n13jPt8zyRXYH1tut1nn+8hov/MkSyyuDNgnwBbTrybsWlfrrJKBHo6FONooQ0CjWuP5iT9OnJ7Ff9i0sWiLS95ktFh3S40n2N4usPH8LcyzBh993ko9/8Cwnj4121qvXW/f8wOftj/iWQRU64G1JIKADAgIC3sL8ycurFKotH7MAU+sKXSEgGTEJ2zXqLeF5ZjLHV9fXOuNURnSTeCjZGcXdGYJxA96jTHVHNZs6mcRIK13YTxlWXcmkZSIUv0mtLc62oxYL9RJly+KvPv1H/KN3vZcPzc13GtaQfsaz4zh4rVzpg82BUkp/HddFU32xqWkamj7cdnK39Io9z/XQNAXH9fOoFz+zzAfeMcvnw3u4PZpwv1bnd754jYVDxPPB/fZfyPDFr5eAptmfhz17agxVShav+1XpSMzg5LlxhCIIqyoNxeXM2XFUq86tz15mv1QnN5Mh8/A82tFRfuLH34MQom/yoOd5eJ7EsuzO74fn+p9fs2n5EYStBk//mwLpTyJEEP9GfTyvlaACHXCPCAR0QEBAwFuYiUyUGxvdPN+FjQIzIwnW9sqcnslx6fYO8yfSXCnuc2FmlBf2tvuqkBaSW0Oazw4Smo2SrzX6jx2NULlWHFh38sNjLLT0oeV5fPKLn+OdC1NMx7pqyvMkruuS2VCwKx6u6+FJcFzP/5/jYgqBpigYmoqmKghFfMMVaHGgjKrrKo7bTeK4/pXbvOPMGC9NNyi34gA9CU89PM35Y2Os7ZTZ3KuwX/KtLFLCWCpCpVFEKIJo0mQ82T2KdDzGEhG81kOCJyUSiWlrhEOa3/fm+e9JZCfdwu+He3U16HkemqF2pjFurBc5cjRNOG6iaQpmWOf611YAOPrQFAut8d3HJ8OMnhzHnslys2qz0vQY263w85/6E1a2yiD8B7CIqfPRp45y/miOUrHcGc0upcTzPGzb8e0rEnRda90Tf0Lhm67JMCDgdSQQ0AEBAQFvYZ46OcYXLq13Fwjf+jCSinD5tu9hDrkKF2ZGeX5ve0DUlNS7zVW++9KeV7Shx/osgS+trw1d98JKjN1bg8keCjBWFxQLgxMDQ4dUe489MkNJVREtmdxezfM8X2BKiKSjsLzX2cbQVeqN/ii7pStbPGDleG7exW5d94Zs8Ne//XE0XcN1vZZ1AXwx6XUq0NKTLfGp0Jog0zqRdgSfwHX9ym04FOqMx+7uz9/McVz+wZ//dyxf3uROyKeOYx24H1du7QP+xERvv9EZT6MIQSRmMjmbgqjOld0y1JzOjapULT73wu2+fQkBUyMxHjoxSiIZR23ldXtS0qg3CIVDrQxviar2VtUHE1S+2QgCD3TAvSMQ0AEBAQFvAFJKGk0LtzWSWtNUTEM/1ArgeR6242LoWt86k5kocyNxlne6InRzvz9RYm+vxoJbZ9gAvjW7yt2YItrFUF0RHE2mQEp0RVAZtvLdT9k+tEh5JJdg45Xtoe8dfWACRfgXUys3uH3DX0/RNda2hp5RhzOZeN9rfYi3G2BlYZd3Jaf4XGofhODzi2v82JMXSJrGAaH42pBSUqs6mKaJGTKHft5SSnY2tl9VPAMYyQhsH54gUio2mDySwWgNrGnWLW5e2uToI1N960UjBqO5GMVCkXYv5NRInP/pex7j9FwOy7bRda1z7Z4ncR0X4w6/swEB9zOBgA4ICAh4A/A8ycraDoauoaoKtXqT8dE0iXi05SWl4xn2xbbN1vY+s9OjHV9y+/1vfXiGX/6Tq1jOcOW6U6xzaiLJteag3SLvNTmZNklNxTFQaVZthCJQNYXttSKViu+vdlqqSlEUFld9y0cmZnJ8LIGma6AqeJo/ttuLfOP+Xl07fMrdwqUN3FYE3KmHZzrLFUPj+Hy287pjh1Bhcck/54Pp1IZ2+LnefGGNb3n/HJ9Vd9koVXl5dYt3RMPo34APW0qJ47qEjdAdH5ae++PLd7W/unN4yVULqYx9YAJNSFZ+/SasFDrv9R771PFRVndKLKzliWbD1BoW56ay/C9/44MkE1GaTWvIBEiJ63UH3QQEvN0IBHRAQEDAG8hILoVhaKxv7lGvW4RMk+29AkhJIh7FNHR280Vsx6VpWdi2Q75QwnU9YtEwhqERV11+8MkZ/uDlTXbKg4kaRyYS6DXJY1aCsGaAI3HDsFmuIqVAHVO5sj0Y4XZ6JEGlYjExHmOh2fb8dgWbJyWrrYSH+VMj3Fr30z9GYzkYnHb9mnDuMOVOEaLTBKmYGkcuTKGpKkbUwPZAtGwGQvjnqEU0TiQnEALqKweGh0zHiBxPcfPLq0OPdeNzyzz50TmecXa5vJ3nyeOz3FXJ/rDrclyk591xDLbruLz4hRuvui8JbB4YhgIQn4kRfVeWi16RG1aemUgME9/vPXt6DBE1kDGNiGMjkVxZasUVCsFsNk55t8bVy9tIqXRGrIvOSPAuSqt59E1Dq4ExIOBeEAjogICAgDcIx3HZ2NzzkyY0lbFcms2dPJ4nCZk6Wzv7mIZOKGQQi4bZ2tknXyghhCCbSbKxtUckbGLZDqdmR9CFx9duV3jpwGCNxUqZ8USU4qVuBfrI6SzbRb8pcDoxJMwZWHXr6PNhWGswWXHQNBWhumy0Vvc82R3MYfX6iL9xEeN6r+4DOfrUPJdWSngtsX1yLM4rG/0NkdPTKa7v+zYMpGR2ob8Kv6c5vOKWOZEMUSr2N0m22f7sKqc+MMa5mUm8byC6TUqJ57poutYRpsPWKZdqXH9+5VX3l53JsF3zvyGQwPgTo9RPm7xc2kP2jGdfqVX4yAfmWV4ucylfgTycPD1Kpd6dQqnrKsfms1SKdTa3fRvMynqBTDraOa/ehychRMcPHRDwdiQQ0AEBAQFvEJqmMj6WwbIc9vJFFEXQbNqoioLnSiLhEE3L8kV0y3vrOC6xaISQqSOgJbYNQiEDU9f46PlxptMhPn1pq2PpiOk6zYOjmHvcx+GwAa1mvaPhMOqeg0Si2Sq267K/WiV7KkkoYuIhYd8XWH1jNUT/3g8jaZpMRWPQikrLbkMtpJGNRri9162mGsrh1gqhCqb+3FkuFqqcfmySW8+uDVxTG91UoekvnwyHqVf7fdWaB6jgPpREfLE5dBiKbblEny1y/C/kWhFvElV9dQFda9qs50s0LLc1uVHSqDXQdQ3DrKK0qrptLd6esnj5T67g2EPSRvxnAIyoQWIqzciJMco7FVLvzHIr0uCrjQqUhnvAi5M61WtdG0av/p+bTlGybC7d3uGBY6Mcn4/T2K7z//mFz/Dv/8UPHnp9w8aqy9Z1dpsn7y1BE2HAvSIQ0AEBAQFvEKqqomkqkXCIeqPJ3n6ZTDpOqVwD4QuqVCJGfr+Mrqu+bSMVo1Aq02g2URQF09RxHF9sqYogGglxJBviL7/vKE9f30SoBlvlCqW61ef/db2uQNu3GsxOxdjdaaA2PfIHqrQC2JYu9f3+tAxPyk5fYm91Ut5BxUxF41zd6KZgZG/qzI8k8AoWx0aTOJ4kpKnsXhscaAKQTIXQHh/l4rZ/jlZI4chjU7hNF2EMVkSF0hVx46rJwbpuW0DfalSYVhVsb3hMXr3a5F/8/B/w8//ke2k2a5gh49AqcpsXFzf5f/7qp++4zjDeGUox/4lHWlVu/15KCVXHZef3X8H5wHEWyg04Guba6RquzMPw4nmHYqK/oi8lhE2NU2Mpnt/Ygda1bIUdFrf8ez99Ms7f++3PEtFV/ucPPnRgezl88iIS6foj4IUIKtQB9y+BgA4ICAh4A1AUwfREDl3XUBTB2Gga13HRdQ3T0HEcj1gshKqqmKaO50lGchqG7lejLdthJBdGQMdSMDUxgq5r6LqGbTtkchVeWN2l2fQroCFV4Lm+6NH07n/+aw2X20YTNQc1DWaeGOOWXkJpVTwFMLIcQm1NpJvKxlipVahKiXJsFEVRWECiiDAKApEN8dCoQqXisbhXxu3RWTXbohcpJYvPrzOMsfE48VSInc0KIxNxqnWbtfUiJ6sSIxVnsVThykpXaD+eHRvYR28RNNwaOjJ2JIttueTXC+h19648zUKB69c2+de/+Fn+2k+8n0ajTuiQFI1vBEWCqHp+yoiEdjVfSo+YlITfc4yXy75aFhkdt3x3Jdcb1SKzUQ276oCUGAiiFY8r6+vMnx9haW/QS726X2Z1v0w21rb4HLjWoZcufA+6fA1RLK8nQQU64B4RCOiAgICANwAhBKbZVW6aqnYm7sVj/R14sWi/RzkSCQ3t0WvvLxI2IWwylYyxUWqw3CiCBONslKwaImILQmmTjAyzl69RqzukRk0KjSZV20EL65Sb/UI3p0cp5v0GxRktyX5jsFmxTSZr8kp9E1SITRjENRMXPzsZpUHvtOzik1GKp30l1tZjQsBRESP/lV028n5E2/4tq/Pm2pUdmg2HpKFy8rEZqlLiaqBIydFjWV/UtqwRdbVHyHlghHXCkylWF3YwZzOgGeRiKkIRpHMqruN13AeKEK1MZ4GqC2obZT799EVm5rOceWQc1yuh6arvWJCe38PWFtQSdkuHx8sdxvFQlJufH2xoPHU8y43PXWH8hx6Fdd/jbut3rxZdKRm5kKbySomxZIQrL3YfWsJavxR4cGYMz/ao1iwKpTpexWGYWvY8byCFQwiBRAmqzwH3PYGADggICLhPURXBzd0CuqYwl05Qs2wulXzx9cDkCIsjVcxxlchVyVg0SqEtiofoslC0K4jK1cHhJr1oUuG8Pe5Xo21/xrfS2qeQgomYjvR8AVa0m5RktwHxUT1D1FK4/ewGtuUO9dG2bRm25eI1XG62cqBPnhljfc2/PikheyrFzd2uHUUAqbPj3FjwG+yaTYeVhTwVW8NDUEz6642PJ1jfK/fdiJimoW/4P//7f/cFct8yxbWtrhVlGKemsnd8fxhpwyT1xLQfUdj3Obgc+c7zvLRZ6CwpetbBze9IOG5QrFjc2q31Le+9xTFTp1pusrzRPU4iYg6sdyfeyFzowAMdcK8IBHRAQEDAfUo7Cs72PBb3ikynuqO0q2ULTGi6LuOJCCnD7LynSMG51AggMBQFBYGuCbI5l916g4Xq4OTAXryaZOOVKoqi+I1yqkBRBKrSappTmiiKH4OmxPorlfrtJgs37yxMlUMEmhBgWW7/gh6GeXabTYeJaIK1noeCVx2h7Ukqz24z/mCSzcLhg1tes5aTkrWtMvsH0kCS8dDQiv9W/e4q3OPhKHP1CJee3+LMZILbtw6Mbu850cZek+X9fjuHlP7Y9Vqt0RL2/qhx27KpiVpHMCuK0len9qQkHkve1TkGBLzVCAR0QEBAwH3KwXY4vWeCnml0f1Z1BcfxeGhmjErdQiK5nN9F9qihByOjbNXuXHluU7Rr7My6Q85gkBn67Sn1elcoeoCm+gJcurLj31Z6GgN74+68AwI5JBQm4lGk9FAVFaUEZkQnNRr1hZ7jgRSkdZNm3NfbAkFMUxmLR1BcieL5y3UhKPTsu1G2GFmxKeV0IqZGNHRgRIuAWFjnyFjKf93yqStK217SHTfuR8IJDBe2rhdJpyMgBIoAz4PxkRj7i/3pIaGIxk7Tv1fzsQQFu0mh2S+yVQSPJEe5sryLmvMvcEt1mT+RQ2qgJAwqnsuuZTM26k9o/JEPXWBqPOlbMWSrmiw9NE1FaT0AtSMB/WvovZb+HJR7buKQwB3ywwMCXk8CAR0QEBBwn9J0+gWs1ZMwYYR1aIAmFPK7NeJRk1fKvv3hYT1HaMUgrGtMZ+OEYipyWKzaIYRrgvOXBSoCx/HwPEllQgeh4HkeiqJg2y4SyEYUZEHB86Q/mEU4yCkTT8KxUzku7vjV6EezWZY/78fVRRMmmu77lrWeOLmDdemll/tFpxhNUUwrrDb8a8nuWViWS3o0SqHWtTXkW6OxH4olWbi0BYAXGozV21kucCY3xW7IZa1Yxnb7G+eW9gYnP96JB0cyFKvNziTK9vWMj0QH1q3XHKK6QdW28ZqSuWiCQrOb/YyEh2KjvHzLvwcLpQJCEeTrTfI0OXMkxytLg0knEyMpTs2O9i3zPI9yuUooZHaSRzzPw3VdwpHQq6aRBATcjwQCOiAgIOA+xeoR0JqisNFjN2hXlz3pceR0lq3bVcj5y9pfyddthxubvqhWhCBhGpSad+G7dSTV5a69YOKRUTYblaFD4qQHTWu4OO8f1NGVx7WGQ6nk2xxyUynmZtIIQNXuwnvbZ+vwf67k6xDqLs1FQ4R0Da/ZPeHeqncvS8+vceT9s5i5JNe2Bqc5vhZiukEjLgBBztMpt6ZK7hQHrRoCmI0nuZLfpdK02ChXGc1E2G49CDyRGuelW1ud9Qv1JheOZ1i8fmd7zKEM+fCG5W6/4QQF6IB7RCCgAwICAu5Tmrbnx6IJmIiFceuSWMTEMFTUKjyZGMdTPZp4HBlLUNdKFJvW0AEZnpTMpeK88iqNc8DAmOqK6SEPdX8crnj60h0OWef6pY3OzycfnLjjebVtBt0j+8fe3Soz+8gorvDHgI/VFSr5BjXX8jeSreWzMVTVr557UuJ5HrqmUbi+hzcX5kg6xnq5RtN57RFuk+k4T5yc55kb/mj0ySNpbmzk/fjBiAH52sA2ac9EQVBqWkhgOhRnu1rjsfQYLy1sDawvo382wSulb5WRnuz4MoQQqJr6hjYMBgS8kQQCOiAgIOA+pem4frSaBFfCTqHGTsEXYkfGU+iWRs2yWNovMXkqiedIHs7mkFJybjaLY3tYjku96VCqW9Rt51WO6BMOmbTbDKPZMLFUhNPpcJ93tt2oJ6seMCgO/yy8WvSwxLceDCyXYFiwuOef9UbPe3FV4Dq+1F6uD5lW4vj3ZK6qgKERQmU6G2MxXx76IHIY7zo5zVMX5vnMSzepWTaVpsWRTIJqxWJjq7+pT9cV4tEQWkmSDoUotM7rlfUd3jc5RalscW4811lfaf0OxKXKyYkUqqISCmscO5b1M5t7uLqxy26jji4U3Jbn3PMkp8ZiHCQUMgeWBQS8XQgEdEBAQMB9Sq+Fw3Y9HjgygudB03KoNx0s2yWV8EVQU7oUbYurpX3KRhMJzOfirEnfJ3s+Ok7tpsOJLZNw3ECNqKimgqeBa0BdutRcl7Jl+Z1vLXRT5eU7VK3PJVKHvjd80h1MTSaYmky2LAStdaQ/4vtOSCkHGg3bxA7JLW43Lrqv0py2vFHkNCkiN2ook3BSM/CEJBIJ88hjM0RiGpVKjWPz0xRLFWzHIZvLUKo3KdWafOShExybyDFiRMjXqjiuh+06qJ5gJBkjFg8RihrslqusbhepN+psX6wzN5mhYPoC2pOSiuOycLXf23xGibOyut/3YHDkyXEuFg+kcQCv/GHr854Y4cqtrqf6P/7fv6NvquObtfIcxNgF3CsCAR0QEBBwn5IyDR7Ljbaa0iSW4Xuhi5bFZsP31SazvoBuV0urtsNUKs5qrcxoPMRaq/ipKAoSSWGrRmGrv2J8/GSOpY0CqoD5Pcnu3h65XIx0NooaUqllJOv5ylCzxp2EWGG7xiO5HHm7idfToLd5ZYvSfr8n5MSjMxhhlRMXxnoSJFqi2fHQdI2QVMkoNslE2B9kk/Gn8iFBDSscj2X865SStsPXSdRwLAeJJBLRCBk6TcuvOksp0TQV6UkMQ0OTkmzShKqLoQu/2l2pciStc/rENJvb+8xOpSinTBzHZWwkDVLiOA664lAulzkynuLZry32Xdt0KAwueKpAkZKeCd8DvLi1xcnJJGvr3QbGUGRw1KLuCR4YyXYjAVWBo9KxrCjugQjA1u9QQECATyCgAwICAu5TVvbKbCyVcFyPkKGxn2ilT8RC5GJhdiv1ztf0vXFwYc0XXJbtEdUMqo7fOHho1kJLax1LJdi65k+429upsLdTYWIuzXLcIZLQmE7GCSkq1ZrF2l65k1N9GNv5Ktv5Kg+cGOXlxW3mHhshJXTC61VGe/sOpeTGlS2OPTbNpSHJEm3On5zg4rXh70dCOmXPHsiO1hr+OSZjJu6zWwwOvL4zf+mn3sOFc0dIJRPkcmlURSWVSgASRVFwXY9iseT/7DmEzcG7vLNZgvXuvTrz7ikurvlVfU0bXF8d0dE2/ZQ+8GMKDyKbHjdXu/dibjLFFbsruh/KjvSv/1YRz2+V8wx4yxMI6ICAgID7lKbjoasKjuvRtB1SkRCFWpO9SoORWJhsLNyJIOvNfFZbIvLFrT1SpsmJeAbTCVHlkMEdrW2HVZPj2QjnTZVQVOe5na3OqGs9ojCTiJOoKRxNxlgpV7F7BHUuFcFxPQrlBtst3/bydoll4GQZattVPAmu6/kTCwHvkDSPu6HWsDGiKtYhDYCKEHeRat3P4+8+wge/7QK262+pd0Zm9zQyep5fDQ8ZqKpKqCdPOpMMM5OOo1Rt6g2bxWXfctEbG6dqykA1+srOLu+bGKFWdbi8n8doSk6OppBSIgBFVQY/q1edHfPaGyMDAu5nAgEdEBAQcJ9iOS6apoLlICV4TY9zE1kubeyxU6kjgLlcorO+2Rq00qutCk2LYtMi7TYZlTq6pmAfEJl3ijNrhBQWVvK+naLb14bteizulxjxEuy8uE1MUxifTmKmQ5TxcKIKi5tFpsbirO2W+2I4nFKN+na3Fjw5nyU1EsfVVRjS53e36NqggG71PR4eA3IIyXSYH/kb78U0Ddx6A096qAdq+FL62ddKa7AKwMRo9/OYySa4/uVlAE49MD70OM2GDUN6+a6EK4S+WOTCVJor19cG3j+Snexf8CrX1x6qMqwS/WbyQwce6IB7RSCgAwICAu5TGrbD8Zk0ru1RrTaxVIdLK7s8OD/GK2s7eFJ2hn9EYxrrDT+FIqLrzMYS1Bybsm1huS6FRoMxzyCt6sTGItxau7vM49VWLrFpaFTFYA3X033x5Tgeq0v7sOQvbz6ZYuZYmoiqMqFI1ncqXY13QLBtLO2xsbTHsSfm0DTB+FjSj6iTICwPz/EQAkxNZWwkhiIEEjrrSOmnc2hxjZBroCqCiKpjaAq6q6AooErJEv2DWe7Ej/zEexidSCOEQNc1bNtBUwdj3xzbRtO0zvL56Uznvf3bhc7PiiLIjkYRmoKua4xlYiAltuUykQwjFX9yoUCgtIzMs+8Zp3qjythcCiF8u4jXqoYf9ONIKVGAlGHScJ1OxdnQVLKJMNGQjtW0+hoJ/fNS0YI4u4C3IYGADggICLhPadouL6x384Cn8yrnQhH2C0Vm03FS0TBuS0BXHbuz3gvbm3370YWC0/qOP5+vkc/XOH0ix1qxSrnaHazihlUSj491bKghTWWp6ItyU9cYOtr7EN1VtW1K24XO66mZOEldZ2l5f0DEdXalCFKJCLeKXS/vKSvMRquhLpeMsV4+xIYCGBMh9qwm4/Eo25f7HxDGwncf2fauD53kqfed6ohKTdOoNyxMKTv2GPBTRppNi1g81ll3JBslGjFIxkLs9ExS9ICy7ZI3XZaub/QdbyyVxPqNWwPnoZ0bZ1G3+297SzgfbThkXK3j3CitVEg2XDxZwwD2IgV++//9vWQSYZqWhef6jZgHv2047LN4Q5AEg1QC7hmBgA4ICAi4D5FS9sXY5WJhKosVpLA58fFF9Pwsq0sm1xZcQKAcIjx0ReHBxBihpsCpO2jvmaBxq8LNG7uEwzpn5rPYLcWsqILbpeEC1dAHR2EDNHIq0e+e8n29sjvsZOdmf/Td2n6FbU3BTOvE0wmsikVt58CxxGCzmzOmk6iYlErNVxVXbSlouYN+X+UuK6zZ0Rh/8a+9B13vVpUVITB0lXq90ao2t+wQLf+zpnXvjRCC+ek0wpEcrPE3Gw5n50aIjUQo1BqoqsKVxZ1D3Re3L2+SfmIcCzieS+IpAkPTuLi6jaZrVGqHT5Ws1ixMTUG2zgkhUBTfOx1UmwMCAgEdEBAQcF9iOZKTo2n2G01cTzKnR1iQZR7787BOBTKXiWXgqUd0rMV3UGioJCMmDVwWygVCqsb5xAjRvMbCM3tU6l2xlR4JoW4p1Os2N65sc+qhcY5PZ161+KcKf6BLL64uuLZb6FsmgEdOj+HhC2JXSlzPI19rkFYM1rea7J9KcfJds1gXt8m3xPawaujCfomz6QilUvNVhV/71Cr1Bo+FEriOh92KrEuEw5QnEmxv3DmH48d/6v2ks/GBY4VME8u2WznUfsRdrVpDGZI/PT+dYXlpj9n5LB//rgsAXF3Y5NLiLktXtjkWn+L6yh4PHBsDwEMy+a5p2o8AUno09hrsX9vjWDzCtuJx5Wr3m4hkPMRoPMYiOwPHBhjJxjg6k8EwTFRFw1U8DN2k2bAwTQOUN5fvuY0ARJDCEXCPCAR0QEBAwH3I9l6N1Ze7VVxv1I+mu7E0QjY6TiPk2zQ8T2Pfhpe29ogbOnPJOGdCGXKNMGvPlbhZHpzBPROPcdvpCknX8rixtU8sPJg33GZnv8pUKkJuKs6LO9vcKdNBEYKvrw2OogYIZ1QEEsf1uLxVgBGD4w+cwVgp4XguksFKt9IS1q8mrSajUWZSSSKOwuKXlvr34Xhsb5UYm0qytVYcuv2HvuMcDz91ZKi4FEJgGt2EDcdxkZ5EC2nUG00i4VDXBz2TYWuzxN//558gFg8hpWT71yuHnnfT9fharL978vxcBq7tUdmrkntglM2FQue9YrnB3vbhDwKPnJvh7/z1jwKgqt37Wa/XqFarKKqCqiqdinRAwNuRQEAHBAQE3IesbJb7XlutiLf9ssPtL8zy+FOj7O2neGGlTMMpAFC2bGwJ3zt2gk/94UvUrOGju9XGcPlbqdtDl7fZLdTYLdSYzcRIT0SpOw4JqfFYYoTSZs3PpBZ+pVqbDgNd0StbP0lPYslm335vbhXAgIlMmJGEyc5mszM50FCUjoAWAo7Np9mNO37yBX7jHQAeNJbq7G5ViQ95EFBbUw4zuehQAT0+neT7//I7+hoC74Rt24AkHDapNy0/Yq6nkfDli2vE4qHO+nfapztkPPnNcpnU985x2/UYZdCq4R+/y9xUir/3Nz6Cruk4dp3tnW0EYBgmyWQSRVEIhyMYhkmtVsO2LWzPQTe0N5eQDtL2Au4RgYAOCAgIuA9Z2+5WLCVgjJgcOTnJ1XqBimXxuYsGZavQWedIOokeU7hU2ea9iWn+1x/9IP/4v3yZ27vlgX171kGV8tq+Nt/OV9jO++d3ejrLzcX+4SaaplCt1IZtCsBcNDp0+Ua+yrrZxBw1mYvEWN8skduRrLeaCiWgaApLzeHV3CO2L5ybtkskpNFodB8gOvpwiE5UFMFf/pvvJ5GM3ZWQlFJiWVarkquiKAqe53XynafGEnj7TX7r154lkQwjpaRYa3D08QmEohAPG1yYGyUkFR6eGqUcl0xk4nQtHP7UQEVR0BTBTCbBQx/K0Gz4zYBCEXjSY3nLr0KfOzHKhdPjxMPgSYuQoeO6LtLzsG0bx3FA+DnWphkiEokgRJRCoUCtWkdRBGrrwaHtQRdCoKnDfe8BAfcDgYAOCAgIuA+53TPK+eiFHM819nk8PkFMMUnGw4znItQSTahoVIXFpcoOVAEBv3ztJb7nW8/wL3/8I/zbp1/gj19e7sTdAdiNA2ka31D18bV7ViOhw60idduh7rgU6k10A2bmk7gLEivfFuR3ONfWJWaTEWy7wenjI1gNB0UVRDTBYJqyz8e/90HOPTSHogzPSe6lLTI918M0/GQPVYiWN9pHUxRe+PIiL3y5O9L72OOTvNCaFPhIJsvCc90kjuiFFLe94Q8cJ8cz/NS3PoGu61QqVVzHJRqL8uzXb/HZZ/yM6Y99yxm+5R3HW+kp7dDr7vnU6w0818MwNGr1KrVaFdMMkUgkcN0oOzs7NJsWqqqiaiqKUHBdh5rrkUplhp1WQMBbnkBABwQEBNxn1Oo22y3BmMyE2dYtTlkJrj2zTvRcguV8keV8kXMPZChESyS1EKNemO2673duuC4/+eWn+Wfv+CA//d3v4Yfee57/9tWr/OHXb1GzHOz6AWvHN9C3ZZuw/Y7+ZSEhMF85fKdmePifrrCpUevJmrYlvGAVUWYkj56dwkWQ363BiBwq+ts2kZipU1Ytllb2qbWSKk5OJwHYXC/26cupuTTf+t3nqdfrCASiV0T3XIKiKr69A99y4boumu5fh6qqWI6NLu/O/jHI4dt0CudCdM5HVfsbF4VQ+qYbHqT9nqqqHU+041js5WuEzBCjo6NUq1UcxxfRCIFh6m/IVO2giTDgXhEI6ICAgID7jLVWvJsEcmeSrF7bZ6XiC8HwrkQzFRzPY2upzuZUAU9IjodH2K7XGTWizOspLpW2+b5P/xd+6vyTfMfcCf76dz7FD7z3HL/zzDW+9t8X+473jRSgBYO2Vc/zeGx+YmDdcsOi2rTYKdU6ddJeQubwrGkPwbPlXcITJvOpOLGaTUUO+runxxOIrH/fUkcSKJ4/HtyVEs1QGXtwHA/IzCb8ZBDX4yMff4BYPNKKpfMry647JO/agWajiQTcVrxgpVIlHA6h6RrekOi8dC5KPBf2hegd8paFIjlzYbTzQUjpIT3fwmFoCv/y689zJJ3mo+PTnW3uJJiH0tKlvdnWUkoazTqOYxONxpAy1Jqu6GFbNt4Qb3ZAwP1CIKADAgIC7jPWWg2Exx4cwS47VCvdJrLNtRJnHh/l8t4u8TmV9VauXLhukC1HKdsOX/e2iRk6kRGVf/TCF/lXF5/le46e4fuPneVHP/Io3/uuc3z28zf43f9+id3N6qvaFu6EWvH4YCGF0p7w0fLuXrm+OrDu/GPj3NgtM51LkMqFMHV/tLimKsRNk93dCtAc2A4gmwiz0azzUnP4+wBruxXK+eGzwE/NZVna6/dOq6rg0YdniUYjHWHZvhe9XuDWZUErjq9crhCNhvFcD9t2Wj5jlxpgmAayNQUwfSzBp+d928aHyzFYH37eQghuNfsbG9tNibIhuVrd53v104de992UiuUhXzMYho6iKFSqZRShous6pmkSCUdedZ+vO8EglYB7SCCgAwICAu4zVlsNhEZE49rXN/sqtfFEiGv5PVxP0qRbha02bUrNrtCuWDbHRZptUaNoNfnU1Rf55Wsv8YHJef4fD72D7/z2B/m8u4O7X0MWFAamftwlUoBqCZaub+PYvnBUNYULZ8c76xRqTW5vFjvaSFEE+XID6Be7T8yPM5bX+IosDJTFR3NxNgqDkXy9KOrhVd5hDwlnT46RSvU3NLYF80Erhv9S4HouihDouo4wBKEQeNKjXKnRaFjUG03qNT8ho7nXhDnfblKId6u5B6vVbtPD+9rgg4Hs+Tc7omJZNlJ6VCrV7khvoNm0qNXqnWtUhPCTPVpl/nY1udr5TfLPyXVdBAJVU/3qu7Sp1qr+1EJNQ9M0xsfHCQi4HwkEdEBAQMB9g0cu+jX+5ve8TMMxeK5S5uFHvT4xpxhlmlLieQqaXqepWWiKh+au4Gsqge3qrJRG2Wq4PJoYQWt5Zr+ysc4fry1yaXeH2UaSxb2CL7oVOP1omubtJvmdw9MzhiFVhYtbe6QnYxyNhbl5ZRvX8bj8crfceubBydbKrWs4xDPiOC5LX17l8aNZQmORVkVS+n7jxqv/uVPvIKA//K7jPPAj7yEWC2PoKooCjtW869g68EW4Yzuoqtqd6CdARUXXNSJhEyEUVKXGT33yw2zv1nFvLmDFFNSGwwPJFFrNQ1RcTp0cRVH8a/LignkjQr1i+UpdytYEQTANFadocfHpW+x8ZYX9G1tICfGpFOeycfSowUvPLHHrpbXWOfr3WSgCO6nTtFxURfDD3/mAf94dK4nf+CiQCE/0Vd6FInAcm2ZzeDX/m4e8q2p6QMDrQSCgAwICAt7iqEqTsdgXmUz8Aaa6xZJ7ni9VQ5Qi1w/dRsH3HrfzLNyeYIvt/fex7qq8WO23UTw1OcOzm1tsNCskwybVnizhq8UCkZzGfDTB2tKdp/X1cnu7SHY6zt5qmf1qg5MPjaFVHG5d3x1Yt2uLGL4vy/Yr6qu39uBW/yjwY6kQhIZt1WXYJMM2o5kY05NJDMPoxtAJfaAh79VwHBdVG4x386REUVRUVSEaC/PebznLl55bZPH3nuusYxoqTtm/54moSbnsV53HTqQJNdv3RSIlrRg7gaIKlq/699KajLH4pVsAnHzqKMsL+5y4MMmzS8O9Ie5cjKblEIsY/Mife5B4PIbSc73Nhn98M2R2ltVrDRRVwTB0bHt4jnhAwP1AIKADAgIC3qKE9RqTyc8wEvlDFIr8fv1bWLfHqHslNGGhoOHx2kSMgs7Ta4Jj0cEGsGcKKzw1NsuL2ztcbezx2NFJLt7sjoOu2Q43zCpnTqZYvl64q+M1bZet/Urnj1GxbrFZqDB5Mk3SU1i8uYdsRbytvrRNSlEQY4ec+5Cx2G3cu/hrdycB7Ytml0ajQSik4XkSozVZ8E4ecD+Tuf2vh+M4hHqmDna27+mKlJ5E07SBYSd959pjzKlaNrdLw+0pM2aiZ5veExvcz50Qwt9Br9dbVVVq9TqqqnYSRYQifOGuKP7Y73uMCArQAfeIQEAHBAQEvMVQhc3xkd8mE/pjFNH1vp7TtzmqhRlTbTLKEp+tv59Xmkuvad+aMIjrChdLOxyLpFmo9Zubv1q8zTtGZ7m6V+S5+jpPzE3y8nJXRNuuRzOlcHoyw9X1/Kse7/hkit2NKtXWtDyn5e9dL1RRcjHS8wmk5lsSPFfiuS7JV/LMrRcxwjrxTJS9mQjLjfodrRTqpgXTh74N3FlA//9++RlWlvx78TN//2McP5FDN3S8ViSd1bSpVy3qFYt6tUmj6VCpNKnVLGoVi1q1Sb1m8b0/8nhnMmIbr8d73HfOlssx3Z/I6HkemqJwi0FR3RbwZ6Nx7LqN17KuiP+rvf+Ok+su7/7/1+nTZ3Z2tq92V73bkiUXMMZ0gxMMjkMvJuSmh5AQuG+H743DDSkmvyQQuMkNCQlxCAachGobN4gBgwvuRb1re51ezpzy++PMzu5qV7LXGNmWr+fjoYekmTNnzsyurfd89vpcl6oQq566PH1mokC6Jcz0zMnrwxUFdMNAmfcBRVEUdF0jHDKpVqvorkEoZAUjvk/xQUaIM4UEaCGEeA5R8NnY8S8krZ8tum+l8fiCv28P7ePRmsVyWhPYfol3bhjlsw+2M1Ersyme4VglT9GZ22B4V+4YF7WupFhOUnHrnDWQwQF0VaNWdUgWNA4dGmPD6jR7hk4dom27Tqk0d+66M7di7vs+E1NlJqbKtLdFcGyXmVwNx3YoZ6uUs1X0dS0crZ56cyDAwYeG2NDXzR5v8WRFHYhpBjFDw2yNYWoahqqgqQoaCooCmj33Hn792ntQNZU2Q+Pgw0NUijXq9sLWdevPH2D3rtEFt63b1Ek4El7UQm62Y4aiKDiOi2GY2LaNAgwfn4u/ZkiH8OJwOrsAPj2cp3RCELZbo6xb346qKITq80Z6N7L6xEiezZu7aIlZHDyeXXRdswcbhr64dGZ22qAFtVodzzTwPR9ffQaXgaUGWpwmEqCFEOI5ZCB9w5LheSkt6jBvT67AVCrkvHburmYYso894eMcjgLt5JwauXwNBeiPpWg1w80hIr/KHWdjuJNdBxeub/bFE9RqsG5jOwf2TrBh4NQr0ZoH/VYYPWNxcChLNGQQCZk4jksiGmKMIPCOT5ZJRyx62mJ4g3PPqU1XOC8TD/5crLN2TQZNV/HrLsHHjWCstREyMWZCpNvC1HI1ivkaNdulUq1TdzyghhI3mDycbZ5bUSAcNgmHDdpa59qyHTkSvJ5YX5r89NKbJpday95+QT+maSxaKZ/fVcNxHMKxCMVS8cn1avb95gr2UmamSsxMBX3B1/bGlzzGsR2OPTLIlgtW8dihqSWLzJ2T1DMrqoYGqGrwAcLzfZTGKHEhzmQSoIUQ4jnDozP2nQW3OKSp+wnCypGFt/squuLRqh4HIK5N8rtROBbawpDbxqjjc+wk5R11v8z8olwfOFLMcoTsguMeKA5yzqpeHjg0t+Gv3YjwcHmUoxRo3xIme7zCxu5Wdg8v3NQ395J8xo9naU9mgGCKYrbRtzq2RA3t8EiB3nmBc+zRCSAoIYmloxRzVVZu6uTQvokFj1u1rZfddxxk9ctXs+f40kUNmj4XWDes62DP3jHKJZtyyUZfIhGfakyI5yy+d9t5fUuGYsd10TQVz/PQVD1oD6fAuRes4tr//B8AfOuXj/If9+xu9ss2fpUjmqviOh7pmIVW8bF9HzMVoj1qMt1oZdjWmWDy+AwKoOoaVk8GRVE4Olgg3RolH7PwNRXfhz13HWLdlm4GS3XKlblSEUWhOYFwPkVR8AFV0wmFg2mGnhfUeT8j8dkHRWa3iNNEArQQQjxnqFScfqLGfgBumnwdf/RQirtf+gBhDaquyc1jl/LNw6tYGa9zzVlfXHSGPu0x+jSwNY1rZ96OEjmGr9TwqOJSwSBBtbaesKZxQWcEVV0cnBYq8NJQgv/eFXTeuG96lJUDSRKeye5jE6TVKPsPTrJpTYZdQ4tDdKgRkg0jeB5N16FRDz0/0MJciAv1pglrBpqqNMdQK4qPkqtghvQFnSKa71yjRd2xXx4jtj5OsWwvOsYwNdrbYpimzok5t1pzWX1WJy5Q9zzsugvKyd8b94QA3dPXQveK9KLVZ7+xWmvoOp7nYRgGjuugqiqGoWGY4WZruHLdBVwipk5pam7l29BUqvPKYFq7k8QtHU1V0XSV48WgTt53oTpvDLuma5RVBcJzLVgOPTZMuiNBqiPJsXkfEU5WHz77ejRNa5aiBCO/pQ5anNkkQAshxHPIYOks9rKGcyKH+fPdLbjAFw6dzWTlYh4eNRgqlYAiD06qrIm/g3f2fxNTDUKT50OpnqLoxPiL+9/IrYcqwOIJdamIyYs3hTnGQ6deZp0VghdvWMvP9gSrlodzwVS87hUxrJUx1h+NsPfxcbasyfDY4OSCEgFTD/4ZsswgxGnzgtqJm9GaD1M1CsXFm+nilkbV0PGWahPXmLhYr9RZn25hNFIhbZmoPrh1F6/u4dd9RnNlNvRn2LV7bMHjq1WHx08oRUm1Jpd8O1RVQTmhHcS28/owjMX/5DaHw2gqdbtOOGRQrdYWD2LxIWoZKEBbNLJghEz9hMEq2dE8U2MFFEWhrTtBJGahKGCYKpGYidIobQk1grOmq0RiQSu6SMyitSsJmkqoK8Fjx6aCleYnWZKhAKqmoesSL8SZTb7DhRDiOeSnM+fwXfsXtFUvZqwxOfDrRw3WRyPUvblVSMfz+MuHwnz78B/wybMe5sWZ/+Z7R6/gqp+2N444+ca7bNk+ebPlk5iI7OdlWzbyk8fmzjtcLDJMke2r21k30MvhO0bZ2Jlm3/gM7mwbtcZSr47Cyp4WIrpOWNU4Opk/+SUscbuiKnP7x1SFNeesoFCoousqoZDOzLxa5WN3HyPzwhUcunthn+vVG4P3puZ5rN/UgeP41F2XWs2hXLHRPAd3fr3yCaGye2Urk6N57JpDubhwhfusc1egKipuYwKg7/vYtTqe7zWHn+CDYZjUarXgdrTmsQe+/gjJx0YAyKzOMP/Kq7WFGxjtmSKVsWzwvraF+ccbPoBlmdz2nfvZc+8tzeM0z4NEmLqhU7aD11WerjA53fgaPgLbLl7NsFd/0sNiXM/DMI0nPvA3RWqvxWkiAVoIIZ4jBr1JdrnHyDir+cVEccF9mqIyXlm8oe1grsy7fr6Wl/duox0VeDKTAn38cIElOqad0oi5m5dt3sRPHl/4HA9OjjMQS1AtOahtsKo1yVC+RNl2UBprsPvuOd48PhazIHryLBRLhVhzVieGrjcrtSu2Q/74DI7r4mtw/Og0mqYGwdrzcF0fXVdxHA+7XCfqq7R2xZkamevKMZsRDx9vrDT7PmFLJ2zqRAyNZCYadJ7QVVQ1uPL+123Am6lSPJajZjvYdZdwzERVobMrgWlohKIGfStbKZaC90UhCMWu6+F6wTjsum2TiCfRNI1QKEw+n2uu4h4/OMHBRnhuXNYC1drCDX6vfMcFhE0V0zIxwxq+7+F5frOf9tyJFvy2pEM/PUjfls5m7fZsX2vwF4ZqPxhJHpRxqLKJUJzxJEALIcSzmO/77PeGuNvbzYxXpJBrZ19uLjxbqs5KPcPITOmU5/nxYIlNVitbMhHqeOydPHlnjFeeG+ZY/chTut4Raxcv2bSJO3bNhWhL01hRC1Pp8th7NHjetnSUlliIvF0nkgxRzs0VJcxGL/eE0oTZbhOFYo3Dhxdfv5EJUw4pZItlaDXp7khw9UdeSiYda650g8/uR0f4wX8+QlssTGYgaL3meR5hFHrjYeyaS6Vss7I7xb6HhykTfOwInd/B+MziDyC9bQlGYhobHBN/rEi57BAq2s2NfC969QYSydiiEo5aLVjp1xSFcqWKYQQrt4qiUK87OE4dUCiXy6xYmwE/aKmXbIsRTkUJRyy6VqQhaTI2liWfq+K4Hut29tLeFmX44BR23WH3Q8dQNZXsTIGO3lRjwdvHCBmYLREMxaezJyhH8VWl8UnCx1eCMSuaD5VKFaceXI/rusHmwcYo8tnR4b4XjE13HRdFDdryJeKpU36/PO2eZbl9YGCAeDweDJvRde677z6mp6d505vexJEjRxgYGOD666+npaUF3/f5yEc+wk033UQkEuFf//VfOeeccwC49tpr+fM//3MA/vf//t9ceeWVANx///28613volKpcOmll/L3f//3T/qnBeLXIwFaCCGehRzf5VH3MPe4e5j0c6TUGIfG0szUFpZeOJ6Hb7isakmSKoc4nMsumSEUYHCqQLUe/Lh/RSJONGyyZ3Lhxr4XbopwTH98iTM8eeOh3Vy8cSM/3V1mZTSOe7jOI7lxNswLUxPTQeDf3pGmtSfF0WzQMznRGSeXq5LOwXR+ioQCXRd0sa+QZ8bzUTuihCYrZMayzVXRIC8oTLfMde1oS0e5+iMvp7cnvajzxZaze5kar/Dlz9++4Pa1WzoZHZ4bQ35i54nQEjXMAGErCL5maK50ob0r2QzQ51y4CsPQF1yH63m4vkfECqOqCl5jsp+iBFP8NE1F1zVQYGBdF1f/01sbE/80PNfHskIkEgkcx2FoZIREPIrnBSvATt3BdV3+fx/8Fi1dKYYngu+Z1RvaGTscdCdZe84K9j82BkzRsqmD0SPB90H7mjbGGqvyRipMreaQUoNrMU0TFAXP84JBLY0APbvY7Ps+dbsetOpTlSc95fBM99///d9kMpnm36+55hpe/vKXc9VVV3HNNddwzTXX8NnPfpYf/ehH7N+/n/3793PPPffwgQ98gHvuuYfp6Wn+z//5P9x3330oisKOHTu47LLLaGlp4QMf+AD/9E//xPnnn8+ll17KzTffzGte85pn8NU+f0iAFkKIZ5nj3jj/Vb+Toj8Xlu1SOzO1xavMLh57SxOsjbQSNk4+Ork3lmBs3ir1UL6An4cdvR3cPxJsmFvbZZFP7f21V/F8fCbCu3h5zzk88LMZvMZCspZcXBtbHi0xdGRuNdl1PKrVE2pHFMhX5iYuehWPwgn9l/s2dDKtqVD3SMYtPvmRly4ZniHoPqEbi8PdiWOgT3wbjBM7S/g+lq4RNYN/SrXG7119Lex5eAgIhp9sPa9/0ejuer2OaRjNshFVUXCcIPgWi0UMU18wKtzzPHRdx7JMfN+nWq1h2zb5YpGZYomWVByzUXtsqzZ1x0FBWfC8C/8891rmv0cL3hVl9liwLGvJHtbzeZ6HoiiEQtYztgqqPAdKR77//e9zxx13AHDllVfykpe8hM9+9rN8//vf553vfCeKonDBBReQzWYZGRnhjjvu4JWvfCXpdBqAV77yldx888285CUvIZ/Pc8EFFwDwzne+k+9973sSoE8TCdBCCPEsMuMV+M/6zyn5cyUN/foKbps8dYlGlBCKrrCxtZUDM1lsb+HGsowZZoyF51AAd17gGFgBx/ylB2Ysh4ZKy8wafr4vy7a1bezdG6x67hmepC0ZJper4AOd/QnCqsXKkE65UCM7WcJfIr37QF9LolkuUG1RCbVb+IpCb1knP11mOF/Db0wN/JP3XMSqgY6TDiJRVRXDXHyf4nhs7W3B9zy8ukdEgYENHTQWuInlHKwpB7tcp1qqUSnWcB0PtbUlOK8RnDM0ry3clh0rME7oCuJ6XrBhUNebHS58fBzHoVqtUndsQiFr4bWxMAhrmorjONQdB8/3+cX3HuA7f39b8H75sP68gcV1yAsy7dx9Zshg7Vk9eL6PGbNIpGP4CpixELFEiFAkaLH3xGTN+USKovCqV70KRVF43/vex3vf+17Gxsbo6uoCoLOzk7Gx4APs0NAQK1asaD62t7eXoaGhU97e29u76HZxekiAFkKIZ4mK5/AjezeleU3KDEXn/uEnXlV7qDz3D6cV19kR6WyuLANo3uLAeHZPBw+Ozh2z7zD0d24iFHFwtTJuJUE+cpiS98SjsgHiapSU28rIsTB3HQ26UDxUmGTbujb27pvA9XyqKYWob6KeFcVVNY7vrzBVK4EJnRe0M70Cuv02KvdN09LfgodPueIwOpRrPk9kIM1RJ1ilbk1HKI0VqdouCjDQ28LWTT2nXAENVkkXr4a7NYcDD8xtZly3fQWHj86tjm9Y3crw/olFj1Mam/O0pMXaLV3sn7fhb/sLBxaET9/3G+O69eY11usOumYSi8WoVqvN22f7Kp8oWJEOSj6i4TAR08AMGSTb4kEzD89j+MB4M5ivO7sHVVUwdAVVVWhpi2GYGr0rWxg8PAOKwr5943Ove1sv+3aNsmFrDx/7m98JuoKcuAHx2eo0rkBPTEywc+fO5t/f+9738t73vnfBMXfeeSc9PT2Mj4/zyle+kg0bFraNnB3hLp57JEALIcSzgOt7/EvuMVxdhXnZrlvt4fFa9eQPXELNc3iwMETcjFCwgyCbLSw+h3PCqmKMOD97dJKwrtPb1cqu6hS/vamTkn140WMVwFQMan6dldoA+/eYPDBiN+6Za+Hm+T4PFyfZujbD/v2TZEtVNpzTzv1T45yb7mBoZqbZSUM1FY7m8xylgLFR5ay0wuG7JxZ1j0hGLDb2BDWlVd+n5YJOOiZtDk8WGJssBuOknyCUhCMG4YhB94o0vufhuD6GecJKcX3hKv6i6SoNvucRbgyCqZXrrN7UiW5o6KbG9gtXNdt7+L6P02hjN3913PM8ImELTdOo1arNGmZVDYbEBKHbaa4oB78rWJaFZVlUKmXaXp5ixys340OzFOS26+6jULK54b8eAWD1+jY812NqNM/UaJ5YMhy8h7WFJTNz77ePbddxHLdZdjJ3EM3NhrPz3X0fHMdFsetBl5LnQDnFr6OtrY377rvvlMf09PQA0N7ezuWXX869995LR0cHIyMjdHV1MTIyQnt7e/PY48fnPsANDg7S09NDT09Ps+Rj9vaXvOQl9PT0MDg4uOh4cXpIgBZCiGeB7xQPcKA6Q2862/zpuoLC4+NPLYRsjLfx+EgwstpUNQan84uOKVVtZiNQRyxK1XbQFIVVrXEeqgYrkvlSaEGgNxSdFVY/D2anaTPDdJsOd+WmGFjZRrJgkltiwInr+zxanmLrmgyDx7JMOkGY/9X0GKwFSzdoDYdRTAsaexodz0MtLW691pmO8uCekSXLtLvaYpgVj5/fc4hXvnjDKUN0KGxSKdcZHpyhUg6uuaszzorVGayQgaZrRJMhNieDSYBe3SWsKazoT9HSkyZbsKnbQbeO43cdQ6s67Hp0ko6BNMcaq9Qvf+0W4okIjus027/VajaGrgVBk6D1W92uUzfruK5DtVZD1zVcx6VuO3iei6ZpzQ2Cnu/he8EmQqWxoe+H//wLPNdD1dTGEBdwPR/T0HEKJbbv7KVzZQbLUlmzOt04n4tu6viKxvRYHs3L4Ps+WthENVXWb+okqiv81z/9HDOk84ortgUfi+bycqP39ryvhE8QspVgCE7Qy/o08nlyg39Ok1KphOd5xONxSqUSt956K1dffTWXXXYZ1157LVdddRXXXnstr3vd6wC47LLL+L//9//y5je/mXvuuYdkMklXVxeXXHIJn/jEJ5iZCf57vvXWW/mrv/or0uk0iUSCu+++m/PPP59/+7d/48Mf/vAz+ZKfVyRACyHEM2x3dYqbs4d444BBX2IKiDFajTBcSHJLeW7zXEjTqDZWMHd2ZFB8nWDLno+Hj+t7uHg4nkura7FabWn0G/YZ8YqLnvfITI5tvR08ODJGZyzKo0PjrEjG8U2f2SqSR8YqrOgNJtetCa/k8VyRXxaDcH2sUmCoUd1xpDZBZkucrrFW9hwucOK0E9fzeawyzYUrO7m7tHDKX81xGS4EQ1dmtYSDmm7DVKnbc6koFQ8xkl26HnyiUOZP33oBTtWlUqkSDocAlgzSs3XKbR0Jjh0OUnu17jJ+cLJ5zMYdfeyZV9KxdnMnx/eMEEpGOHpg8VhyAHNevfPFr9mA4zrU7TqVcgXX84LR6P7shjtwGqvchmFQKOaJRsLB9VrBV9a263iuh+M4GIaBbmi4jofrus3SkJv+9S5qlbkPLtFkmFK+SiIdJXd4hPMu3crvfeRNlEplfM8nGotQLJQwLRPLMvn8n/wH+351FIDOjd2MHM+iWzpavc6Dd+wn1Rrlte+4oNlm72Q8z4MyRGZfw/Pc2NgYl19+ORD8VOCtb30rr371qzn33HN54xvfyD//8z/T39/P9ddfD8Cll17KTTfdxJo1a4hEInzta18DIJ1O88lPfpJzzz0XgKuvvrq5ofAf/uEfmm3sXvOa18gGwtNIArQQQjyDXN/jW7k99EdiJCK3kG+M6IsYUHIu4YXdGX45PME57RmGvCE2KN08NDFNxFS4d/rYSc/rWHWOT3ksObZvnv1jU2xqa2W2oKBk1zlazTbvz1YdzjM7CGlR7pwcX/T4+UUOk/UCpAtkpruZzNmLjnU8jzsLIws2Lp7MdKXKvVRZGQuRnZ6rwa47wTMqCrQmInS1RunKxOlujbNxIMNZa7spFEo4dYeqUsM0DFRNbawAzz1vIR+cMxoNNW8rl4Jr9oFwxERVFTp7kxiWjm5omKEgHE8NTtHaEWdqbG4Iyyy9UQZywUvXsm5zsMGr6JXQdB08j0h47vl838dzfRKJJDW7hqapqJq6oAY6FLKaremqtRq+D6lUCtM0G/2i609YKjFbZ9ssuGi0n1uy/tb30QwNt1RDa2yK9JkrLX6i2vL5v043Bf9Z1YVj1apVPPzww4tub21t5cc//vGi2xVF4Utf+tKS53r3u9/Nu9/97kW379y5k8cee+zXv1ixbBKghRDiGfTz0iCDdoG39NZR5/VRq9dX8nhpGphmXWecw3YQlmPR4P4nygnjdpELUwNMTdYIRXRUS8XRfKq+S8GxGS9XcH2fct1hz+hU8yfzHZkQI/WFwTBCG3dOHuZUYTysGry0TeH2cZdS9eSdPJ5MeJ6vrT/BzHQFBYhHTfSYTvvqBH9/5atIxSKNOuHgumaDm2na6LqGqirUbBtFUXAdt1FaEBx7cNcQm9a0EQvp9PelqNselXIVtzNJqVij4vk4rsfosZnmtfStCequp4eyrO7vWDJAa7pGNG7x9g9chKqquK6H5wcryaGQ1SzFcN2gFCMeTzT6J9ewTmj/NvtnRVEwLRPd0LHtOqVyCd/3sSyr0at67jGXXvkCju4axq7VMU2djq4YiY4UhUKVUrHG3bfsYuxYlk3nruC8l20EoKUrybpzB1AUBStqkUxF2PvQIKs3d+H7YIX0J705z2/UQssCtDjTSYAWQohnSNmr893cfnpDEWKhWxfcN1hcA2QBmLDngprf6KahqKcONBsLnVQmXIYGF9c+A6xdEWcoUmtuMmwNh8i0WTxUm1iUk6ers/H65C5ojRMJ/Zjf7lzN9x9vp/I0FaP6no+mKKxdl+HRwhSjuaB04liuRHs6ufh4P6ibNgwDTdOCrhS+h6/raPPKK9ozCfb+Yj8bd65slnAAxMJGs+56/kY/VVNBnXsPtBN7Qs/ersL7/9cryHQkyBdLZHMFwqaJaZp4nk+w+KsSDoUwDAPP8ygWCxhP0GMZaA5ZyReLVCpVOtrb0XV9QVjd9qI1/Oy/7qeYLZNIR8nPlPE0jSvf8FUA1rZFOPzoMLFkqBmgZybL7Hs06BzS1Z9mpNF55MDjw9RrLqnM/EmOp6YqioRn8bwgAVoIIZ4hN+QPUvVdXtozuSB0eL7Cg/nFdb4dVoLBfFCcXPVqmGhsdPvxgUe0QwuOdctwaGiaZIuBZ6uEDI3OdJx9xyep1V0sV2dDyKLQ4QAKu+tTjNiFJXPyTNEHbfHtsyxVpzX8AACqeZDLXlLijrvO41j2ybW/O5mzW1opH67QvTbFA7nJBff96tAwO1d2L3pMsLrrNlZBZ8Octuj6B9YHfXjLje4kqwbSWCooik+9HKJu1zE0hVRbnHKphl11KObm6tFPlie3v2Al21+4irHJGaazOTzXo6W3h7ZMG54X1DI7joNt21QqZXzfRz9hSuGJvEYHjFy+SDZfxKk7rF+9El3XcV2Xzpesp/zoMBOD08FC8YJuHSxoQTd3m4dds/FcF8eZ+4mBP78ziz/3h2q1ilPX5m5ufKZS5h02O4nQm7daHY8t/pDzG/UsKuEQZzYJ0EII8QyYdqrcUTzOG1eWsKxdC+5TFZ+UYTF6Qnsx23NoTypEjBSmqtKfX8F9o5NoqkLXuiRj3lyvZM0CK6Qw8Pop8ne1U8rV2T82wboVbdRsh2m7SiRv8HB48YrziR4bz/Ki1SvYXT6+5P2v6DBBmSt10MKjvOzFP+bB+1/Jw0OnHgCzlBWpGMmUyczhMqNqBU5YRH/JuhW0xSNLPjYYMqI1Nt2d/J+4dEeCK973Ym77j/tZOdCK7jjsfXiQroE0I40VaStqkZ2au/7qvI16yhJ9QF7zpnN4+eVbOT40RqkcfHiIx6KYjZ7PhUIB13PQVBVFVTFM/aT1wrPTB6s1m4npLJVKDbse/LQgZFnNIOy6LvumCihdMdZs6uK2e4+S3thNqlDBMHXiloXaEyMdCZ5j2vd52+9sYc2qDNVaDaUGr3jjWbzo0o2oqsZt//kgZsgEfFRVwXV9IjETz/NwZ6/T95slPyhzs7y9xjW77gnt/4Q4A0mAFkKIZ0BcM+kPx3C9Cj89dgkX9exC148AYNtr6DISjNYWds6YqZeZqQcjrKdKGkohWEV1PZ9UvpWx2FyAVk3YcYnJIWcKzp1CBbYrfRy5tcLgTCOR5mBrT4ZH3YWruyfyUbjzYI6d/W0cthcOEtmRyhAyblv0GEUvsf3cH9ASu5Q79tYW3X+i/pY48ZTBcXIcdKfAg0TRWvLYy7etYcfa/iXvm60XDlahT94P2vM8rnj/xfz4Px/g6ANzmzFNc+6fxRP7QJdLNVo3raBarrNn11wnEVVTePP7X8QrLj+L4fFJKtWgFV2mJUUqGUNX9cbqc51Q+NRjrmeHpGTzBaazOWq1Oq7nYRg6iViMZCKGpqlMzczg+T7hUAjf83E8n92TeXwfhsdKuKNZ4i0Rcn6F3s4IhyfmPgisOWsFXS1hDMNAVVX6VrcRT8QwDIP//MdfcuxA8DU2LR275tDSFiMSiZzyA0nwnvqUy2Wi0cgz14XjdK5AS6nK85oEaCGEeAYYisqLw/0cnkrRryrceGQtr12VY7RwPj8aKqMxQ28iznh98UY1CDZrTZXmSiQeG55iw4YODrhjbFC6sSIKh+J7FjxGtS0SpknMMig2Vrer++p0rY0y4p16pXhNS4yh+sL2c9uSGQYSd5y05lVRPVZuvoFk/CXcvSfOdNmm3igRiJkGybBFImKQD1fZ70wubOkBS6zxBjT11HW2hq5Tq9VOGaAdx6Ellaa9p4Xs1NwHlfnDVOwTN0P6UMrXqJTnOozEEiE+9GevYfsLVjGTK1Ct2eiaRk9nO6lELFiRdbzmivETheeaXWdyOstMNh8EZ12nvbWFVDKO7/vkC0VmcgXqdQfTMIhGIs3MaJk60ZjFitWtKK1hzJBJ7uD0oucc2jeJ2Zeib21n0HPadRu11Cdc2+xf/bnSjlN34fAbNdAyXU+c+SRACyHEM+QFkW5eEAnqeIcmCnx971nUGv2aXUB3I8DJAvRihRGNXq2Xx6bzrG6NoadPuD88ReTlOXZUk/z0O8Ftk7kyqd0hNm9q5XF36d7GmbCJHZnE9uYS7vnpDL2x21GUJ17xS/fdwaV9wZ89X2Fy+mK+e7jG9Gzf55M07ZiduDdfyNBZ2dZyyoCmaiqqouA4Lqa5dG2xqihUq1UmRrILbtcMjURLhEgiRGtnDD1scmTvXPu+SMykUrbRNJU3f+BFvPAV62htT+G6LjO5PJqq0tPVTjIebW76s91gBX6pd8r3fVzPw7brjU2HRaq14HhD1+ntaseyDLL5Irl8kZpt43lBUNV1vfGe+gz0ppnZPUE+UmV4vATHZ4inwvPex0BHMsJXr/o+l737Ajr7Wzl6YIy/+/9uaLa2+8NPXkpLJoaiKHz6kz/Eth2i8dBzY6rg6R6kcop9AeLMJwFaCCGeBV6XXMs1Y3cvuG1fMcv21g72V8YWHb9UoBnKza2kDmYrDPgKzAu4407QXSFt1YC5dJ0tVcnfV+PCs3r4hTq06EfTq1ssDnpzq64vzrTSFrntKXVb0BWVO4afXBjbsqoDx/bYMzhXYrKhI00kdOoyCKDZI9kwFq+szr53j91zmFRbjD/47GW0diQJR03CEavZi1lRFI7sG+cT77i2+dh0WwzH8fiTv3wdG7b1Ns4Hk9NZyuUqpmkSjYSamwJng6nv+8GGu2btske1VqNQLFMqV6jaNo5zwhK8ArlCkfJ4lZod9HtWVQXT0EnGY4TDYfKFKpl0lGQszGj1hJHcJ2wmBBjLlelOWLiuS7VSZWRwGitkMD6Sw6l7/PSn++npa6G7N8X0dIla1cF1/eb3xKlW9ZuvV4jnAQnQQgjxDHu8OskP8weXvE8/yTKXd9ICh0DNdUnU28ib4+iKRo+6mlJNZaLk8UCuRjphUsjPhWLP99n18Dg7ejJMdFQ55gdhfFNCY0f3HrqrfdyfLfKC1gip0O1PKTyH1BTT+S3M1J44QIc1nYeHx9nY0brg9k1dafQnmIgHoOl6MFZ6CV5jIuB9d+zlzX/yEsIZi0xXCqOxoju77q0oCt19rXT1tTDS6Aed6Yjzh5/5bTp7gg8gvu+TKxSZyubwCTb1VWs2RqMkYjY4z26uyxdKFEpBaK7Z9eY0waXU6w7T2aBePRoOkYhHMQ0D13XJtLYSDoX5rx/cz9R4kanxIkYmzLFcET+koKxpZcYPgu/RkSJxZa4eo7Cihcenyvz4qh/y++86j2u+9nZc12N8KMvQSJ7bb3wc23bZuLmLfbvHUFWFet0BH0zLPGVI9rxntg/0s2mQijizSYAWQohn2A35g+yvzSx5n3/KEolT92c2qy1gjtOlrOGmfQtrnDf2xSg8Nr3oMYeGcjAEA5EYhq7yyt8eoqbvpyO2n9dEDRRl6VD6RJzKC/l/u3XsJ/kj9pCmU228vg0DGVRFZdfhcbb0ZNBUddEK/FKh7mRBz3VcQqEwL718G37EIVcoMjo+RSIWpVKt4bguyXiUeCxKJGbx+e+8j0N7Rhk8MslZL+jHNIzm81eqNUbHJ3Hd4IU5rsvkVJaQFfR9rlSqOI5DIpGiWK4wNjW9YJ+boigYuoah61iWie/7zOTmynZmV5sTsRjRSARd1zEMA8MwGiUicx+w6q43r4587knCIY1QyCCXrzZb2nm+T65QY9/BSVB1VvZnWLGqnbauFJ//i1u54KLVvOySTXz8k13ksuXmtbqu11yFXqrWOVi9P/nXVYgzhQRoIYR4hr0yNsC+kwTools96eMy8RCThZN3uFBcjQFjDTfvXbxBUG05dQFnrtyow7UcZp/hqYbnKFv5u8eX989NEKDr7B6bK99oDZvce3iUR0em5wVohXjIYmSmSK5cJVeuNX+v1p3m5wvlhA8ab37hVt52wXrqjku+WGY6m2cmV2ieV9c14rNjH4F4W4hWNcLQ6ASRcIi+3k5su87Q6AQ1e357OyiWywyOjFOr2dh1h0SjK0UsGmN8agYfH8PQiUXCxKIRopEQmqahqSqO61Kr2bieRyoepzXdQiwaDa5LAc/1qNVq2LZNNBrl5Fst5/T0pXi4mEWJqKRCIRKWSUWHjVs7+MFNu4HdQDAcZuOqDJqu8qGPvRxNU9E0lVQqeJ5gsqKL6wK+j2EajUmIc69efabTs6xAi9NEArQQQjzDtoXbadPDTDgnDh7xmTxJFw4FGEtm6eiI02MkOHC4QOmEGtjRks6ePUtPIpxUyk/q2lT9qYXm+RR1irDWQmUZ7YF1ZfHmv77WBN976MCi2/tbkhybzC26/WQMTeV3zt2IpoIZNYmEQxRL5WZ41jSVWCS88DUowVRE1/Ow63VqNZuxielmv+f5B3qeT74w96HFrtepVCpEI2FSyTjhkEU0EsayzGbXCgjKPHzXo6u9jWgkQigUwrZt8vnGa1PmVtVd10PXdVTtiQOrbsyWpijMVGvMVIOPRNsSqQXHua5He3eCN/3u2dRqNRzHaX7s0DQNK2Sh61pz86LUO4vnMwnQQgjxDFMVhbe3bObzE/ctWE9sNcNUvRxpI0raiGEpFq6rUK1CcjSKU/ch73NvfJjMigix8QhjuSAYp0PWSctCAI6Wc7RaFnZt6VTb1h5lYqIImr3k/ctR9IZ5/7Y2Pnd/gifbPLd+Qm2wqWl47tKri6mIxbEl71na68/dRMLSULVg6EoqEaNUrjQDdMg0CYWCHtT1ukOxXCGZiKGqKsNjEziuy8j4JMXS4kmLvu+jqSqWZaDrwSpz2LJQFNB1nfZ0CquxCXJ2WIrruHi+j4JCOBwlHIFCvsjoeDDkpj2TplKtYtcdkvGgD7TruhQKBbZs6Gme62Rcb4mvse+TnaigaQpnb+1m/bo0xZLN6lVpQgmVsamZZoVQyDTRNZW666IQdAexQhamYaCo88s4/CexHv6b5MsKtDhtJEALIcSzwJZQhssTa/lleZikHsbQDWaqFXJ5n4Ouy0GK0Gj79oLxXh7eN9eZ46y+Dkb7CuTTJbZ1t+LZPo7jM14pnuTZwPV9egYSHN47F7IjEYPO1TEmrSr7K3k2rklS16aeTJXAEyorD/OODS/i63ueXICueQt72/W1xHl8cIrOVJRixW72sYYn3lA53/lrVvD+l+/A8x0MLdgQF49FCZlZKrXgw0IkEkbXNBzHZXBkjFK5ysq+bhKxKJPTWSrVGsVFPy0AyzRIxKMk43HCIbNZIzw7KjtYuZ1bba7XHRRFJRSKoDbquiuVMp7nUq5VmMxmMXSdRDzK4Mg4juthGjrxWLQxbdGlrzfF777uHP7je/ef4lUvfs9VVaFnfYZLelvZsiWNM296YLW28EPT7N91XUPXNCzDQCuWsUwDw9CDDwyNDwXBqHL9hNIOIc48EqCFEOJZ4tLEKg6Wi9yXmzjlceOjC2uaDx6bRh9S2byyDTUEbtTlcWXpns7zGbG5kLNyfQt7Q1mGnUloZMPduRyX1AdAP/lK9nLEYnfxjo0v4LYjGqOVxaG3P5yiTYvxaHmUqusw22j3rK52HNvBB0ZzJTKxMLV5Ld+ml1gJXsqF6/u4+vUvwnftZuADMHQNy7Ko1GxUNSjf8H2fiakZ8oVSc8MeyuJa6uDxOuGwRUcmTSQcWryxTlWp1+uYlhUE1ZqNgkooHCYaiTaDc6lcwjQNDFVH0TSms3mqNZtSuYLrecEI7cbbFmw+1CmXy/ze2y/kZ7/cx9j40uU+2Ykyiunjz7t2z4dfHBlioDdBza6jaUv3y57PcVwcx50L1JqGaeiYho5VraE3+m/bNRtN0/B9n+QJZSJCnCme+L8YIYQQp4WiKLynYzNdZvSUx6WSoUW3Oa7HngOT7HpskkLNI+lH6AsnTv5cvoLi+HR0RVl5bpoH9UnKzuKJJg8MplGepn8qPFwi0Tt5/eaf8bHzhuiNaGyItZHQLVZF0kxP17l/eIyWeozV4bk+1Y7noahz4W+yWKHuec1fuWqNmHXq1nYv2bSSq193IRo+odBcn+agDV2pWcusKgqGoZPNF5nO5lFUhY62NOGQFYzjnrdSG7JM2jMtDKzooq+nc8nw7Ps+nuvhOA66ppOIJ0gmUqRSKQzdoFarUSgUKJfLWJaJpmnBOHJDJ9oI8tPZfLN7xnyqpuL5Lgoen/urN7FhXfuSrz2aNBeE5/n+8559DE2fegrlyTiuS7laI1soMZnNMZktkC9XcZtdOp7SaZ86n6CE43T9Es9rsgIthBDPImFV5+JEN9+a3H/SY0LRk/+vuyMd5TF3GirQ5oeJaDpl1yGmGawOtTBYKlKuO1Rch3sYD+apnGI/4c+O+jwy9hJetspnoGMXJW/xUJfl8vHx6lHW6e389+AkEd1gjEozwI+VSuRrc91FdjU6caxpT3J4fPFmwWKtzsb2VvYMn3zVvVStoeARjkSaIdf3faq1GmMTU9Qbz21ZJrquUZgq4bgukXCIVCIGQLlSa5ZjRCMhejrbCYWsZs/o+Vw3WK31XA/TtEil4mhasAGvVCo2z4MSrFBbocX9lWf/PtvlQ1PVRfebpkGpVCSViPL317yF67/7K6795t04zlwNuesvXefekYrwlovXUq45Tzgg5Ym4rofr2tRsm2KpTChkEY+Gn/iBQjxHSYAWQohnmbMjGb7FyQN0tXSS2ddAsi0EStB5Y6JaYWNLC6amsT+b5eHS5EkfdyrZKnxnl4K2exNv2rqBttaHqHpPvuvFUn6yq4PHRqYxTZWys7jTR937zI9sAAA0EElEQVR1aY2H6O6KUq97HDg8Q7RfQ5uC1XqK+glT+9y8w0A4TrlSJ24HvYh9HwqtMF4oc+nZqwmHg5X7et3B9YJV4dGJqQU1v4auo2tac4V6dtU5O51jciaL5/sYuk5ne4bwvDKQuQmDLk7dRVVVwqEIpmk26p2DbhpBCzujMZCkce2eR7VmU64ELQtTyTiqohBqDC2ZP1EwXyxRqdbwfZ9wyCIej2JaBuVykVAowjvefCEb17Xxyb+8iUql8b4u0anj7S/bxLaVaTT16V8mdj2PUrlCqVxh7arVT/v5T+l0jvIWz2sSoIUQ4lmm24jQpoeYcBb3gNZ8haHRpVvTAUyF7QUhYvfM01O/DOD6Ctc9ohExdvD+CyYpqY885XPZDiQtC0/zsO3Fvawd3ye8Gh6sDIMJ685p5Z7SMNu3dDD2yyJVe+5DRG8mQdoI8eBIsDpuugb5fPDere7v5oL1/ZzTm2EmV8C26+SLJVzXCzpgeF4Qgv2gg0TNDno363pQf+37Prl8ifHJaVzPQ1UU2lpbiEXCC9rPua6L63iYpkksFkHTVCqVKuVyCR8fTVMxLaO5sdD3g5Z41WqNyeksxVIFx3VRVaW5cTASDqFrWnN13PN9JqezzddtmQarLBPLMoMa7mqFSCTC1s39nHN2D7+4+0jwGk7IyCs7EuxYvXDCoxBieaQGWgghnmUUReGsaGbJ+/pJUqsv/SP5aMjggJv9DV5ZoFyHBwaXrrd9slZmPGaqVVRXJawtvZZztDz3QWFfKSjPGDTyC1q2nd3dxuT+GXwFUtFghTkUmlcPXXLZubKL6VwwKMXzfZLxGKaho6gKkXCIns42Usk4EEwWPD48RrkcBPC64zIxNdMM2umWJK0tiWYIrtt1bNtBVXQSiSSWFcK2a+RyOVy3jhUyCYdDmGawmuz5PpVGaD46OMKR4yNk88VmbbXnBZMIXdfF933UJVaIFQXCIYtMOtXs8YwS9K/OZrN4nscnPvrbfPnv3szFF65cFKBfsLFrGV+p5xbF90/bL/H8JivQQgjxLLQtkuHHucFFtx9X8qxMpZjILi5cTsYsjisnn1z4dLr7mMumPhOXp9YneiBTAmLMVKvs6OrgvsnRRccs1VV4ol6hiygRy2Bta4o9j4/SaJDBynQSNx7j2OG5WuipwTw7VnaheXXCkWBFF8CuJ3BdD8MISjaikTDlSpWaXV8wHMXzgpVqRVFobUnS2ZZGVVXsmo3r+VimRSwW1PpWq1Wq1Qq6oS9abZ7tJ50vliiXK9Qd56T70ArFEseH/WCSob2wvEXXNVqSCTItSUzTaK6CK43Nj67rUioX0TWD9et6+cSftHPdTXcz84s9TOTKxEIG21Yt/eFsuUxDx66fvJxIiDOZBGghhHgW2hBuwVBU6v7Cos46HrGNJhN3LQ7Qw5NFLki38Xh4hoL/mw02VVchwioK7Fn2YxVUwuEpINicV7AX10BrioJ7kvLcte0pJoeK7N89hqmrqKrC0T0TqKqCqiqszrqMHJlGVRUi0Qr/53e/zJs++jK2v3gd0xN5Du+fYMNZ3SQSQQs5p+6gqQqpRIzxqeyioSSz4bmjLR1sPKzWiIQjmGbQmSOfz1Gt1nBdh0Qyjq7rwe2FEr7vUyxXKJbK2HYd70msXDquSza/uCVdLBqmI9NKNBJaUD4yG9QVRUHXgx7Mds1mZmaGSCTCG165g0sv3MA9e4c5PjqF8SRa1j0Zpxre8ox5Nl6TOCNJgBZCiGchS9XYFG7h4fLizhKnCmH79k2TChkkNlgMeU+tPdmTVay2wOKOeqcUV/r5/q/WMZKfew0xc3ELOl1VOa/ax+N7g2CsqQqWoRNbo3HgsXG2rO/kbz73tiWf46q3/QuOHZRE1GtFssUyn/vwt7nwd7bzy18N4joeA2va+LPP/Q7hkIVhBJMCMy0akVCYSrVKqVyhUrPxfZ90S5JMSxKn7mJZFvFYstlNo1wpY5oG1HzK5SqaphGNRfF8j5HxSWo1+2mZzqcoCqlEnFijs0W97jA5naVm1+nuzGCZ5oJjTcvEdV2KxQLhcARN09i5rpOOKIs2YD5VT9d5hHgukgAthBDPUmdHMgsDtA8DJPHwml0mllKq1klhLn3n0yRpKUQjxyguo+uBrpjccP86Dk4tvPDHxyfZ0duGEXFx9TrYBo8fy+PVfVzfx3V96i5U6y7eXoOXn7+KrRu7F53/yN4x/vVvbmP46HTzNt8H3dJxag6jIwVcJxhIUslVePBnx3jV75xDoRi0lTN0nVQySTqdhsZKc7laCSbvqRrJRLwxTRBqtRqVSgXLsnBdl2y+SKFUplqvU63ZpFIJIuHQoql+T5Xv+8266GKpzNjENKVKFU1TsW2nGaB938euO7iuSzhkoaoq1VoFVVWYmsmd2aHXJ5gQI8RpIAFaCCGepc6OZghPHGAdbYRKBoMjeSbKNSaosa4ryZHhxa3kLEOjc2OCB72n1rLuyTA1+B8v3EXRG3nSjzGUEJX8hXhqHE0t4M4LOjXXRUXhkdpRqIGhaGhqEqe2OJ0nIxYfescFRKMR9oxP8fNDx7j32DBxy2K1beGrCgObOnHrQTu5RCrGwz9+FEVRODKYp7+vBQoVju8e4puf+xHbXtTHZHaaUrmKqiromo6mqcQiETLpFtKpVHMsteM4lMtBBw/XczBMHVVVKBQrZGfLNSpVPN9H0zUS8RjZfGHJIShPheO6jE1MM5XN4ZwkCHu+z9jENJVqlYHerkaHDhO/0X5PCPH0kP+ahBDiGeb5PpVyFdfzUZSgF7FlGbQZYTaPt7N7dHErOj2sLbqtNR7CXq38RsMzwKvX+MsKz5YS40uPnAVUQatyzpoe7t83veCYh45PsWl1N/vdYeq+S3c0TDKrsj6caAyY88l4Bi2RCHsOTXB0pshNRw9zpJQnVw3a4M2QoLB3fMF5t8TC4PkomkLdcTl6bIbWdFAGMTWa4+Ff7OPcl21kcibH5HS2uUJrmgY+LuVKEIzxQVEVNFVF1RR0Y65Hc75YXlAPPDt+u6M9Qsiymv2dfx2KEpRtlMqVk4ZnAHwfx3WoO86CUh9FUYhGwkzN5J6dtctPC5kQKE4fCdBCCPEMcxyXI8fHSCaiKIpCvlimv7edcMjiRQMdSwboo9kCmZYIlWqdTCqMntZ4TJmh4p0QrvzGhrynpRIXorqB3diDpqBikqTGE/WaVhq/AhNucdERMdPAqofwlSAsdsd0jt5wdMExBSCxrYcbK/v45YPHAGh/cWszQC81SG9o13EgyFVXfvAiNE1DVcApVinmKkyPFbAsszmG2/eDns2pRAxd159wOp/ruosCsuf7lKs16nWXRDz6tARo3w8mIZqWievV8Dyvebtdr1Nv9K72fRo50sdxHHx/rpQnErIwdE06ZwjxNJAALYQQzwK6ppFuSaBpKtVqMMyjXndJOXUSpk7eXhh6SnadEnWSMYMH2xYGWN1T2Ki34GZdxnMVbNelJRoiFQthhjUOeDPMqIs7XzwZPWGL28am+HBmA0Oldu4d9/mt/odx9bmezV7tPCx1lLoRhNxadd2CcwxWcoS0KFV33rhpx2WfcrQZgveVhonOe8xs/Fc15eTD5hqrjxs2daIS9FQuHhvH1FMoisLWs7uxGtP9UGBmosCBx4ZwXY9cvthcmY2Gw0TmDUo5FaUxMbBSXTgMJlgtLhOPRZnSc81hKE94PoLR3qqmNga0zL1au17H8zzCISvo+tFosTc8NsHkdJZIY9JiuVrFdT2GRieIR4MNhOmWBIZhYFnmmR2gZQVanCYSoIUQ4lmgUqsxODxBtWoTi4WJRcIUimXisQg7O1P85NjSZRlVZy5gJVyNtUoLx0cKHLYX1kdPFatMFYOVUE1R2NASoxz3OK6UFw3aOOV16sGmuH/c3Uqtsdr9oyNbeUV3FjN8CAWNf9yn47KCF2V62Zg6wn8eXdiqw4dgQMi8xfKy69JbjzJuBkHcQKelK4yqKrSsTHGsGJRSVNIGXnUu/KdrBue3tBOa8nDGbbrO7ye3b4yxg8H7FTYVKsUamqZy+7fuQ9XU5gqt53mc89J1za4bEAwjSbck0dQn1+otCKdJcoVSc1UYglXoYqkSbGLUNRQlCPQ+fvD7CUFP01RClomhG3iN0d5L1U47rotXrQWDYBwXx3VxXY+KW1sU4qs1m2rNRtNUotEwRkRvfl8JIX49EqCFEOJZIByy6Otpp1SuMpMtNLotBKHurLYEE1WHqUqNqXKN2rxVyf7eOK26Syivcngsx95TlFNkoiFev3UlerHI/orDzw6P0WWF6cpE2a9myaunXpmMGwbDBP2J5+/vO1aBfzmYQle2syau4TbWiO+cVLhzcmXzuC1aN8VShUQiwsP1uRpoS9NY0aMypAcdR0xXw/uJxdFQcD2Hhyaax07lK5y9oo1EzMJxPA7eM0LYMlCngsDpuR4rlrp4Ba78xGswjLmWea7rUqvZ5Evl5iTARCxGPBZ5UqvPs3zfx/cXr4vX6w4zuTyO62EaOoahYZkGQUm1T7lcaY4ND5kmPlAql5+wU4bnedTsenMozBOJhsOEGl06IuEwamMi4lMVDlnUavavdQ4hnuskQAshxDNMIdg4qOsaLak4pXKFmVwR1/NQFQUDeO2qdkzTIBwyqbg+dx8c5uYj4+w7kn3C86dDJi9d2cGL1/WiqyqHj9a4bGU3L13Xy3cfO8L9xydojZjY7T5VZS68xTHockN4KswoNm2REPuVxfXLsxxfYU/+5H3tSlaFkKIxPr2wP/WKWIghfW5TootHMmkyMbl07XC15FDMBff1r2gB4Fh5hjV9GY49Nrbg2NmVXkVR+Mbf3s6P/+N+apV6s4zj7R9/Besv6gOCVfF0Ko66jPAMYJnByrFdX1gW4/seIdOk4FSb7ezKlaDbh2kYzNaFO65Lcd70wyfD9/0ly0J0TZtfbo5pGHS0pxur4EG5iWkav1Z7Pdf1nqaK+t8ACfXiNJEALYQQzzBd1+jtzqA1JsR1d2ZwPY+WVAzbDlYoFYIuEKqiElPgNVtXUtU07jh48m4YAy1xLh5o56zuNGFrrmtEb08bhqHTair89sYV3H98gqmyTXLEYOuKVlRFJT9d5fhUgWHmftxvJz3WrGrlgLp4uMuTcbjcWB2Pwba2Lh46mAUg3rJwkIqr+aTXhk8aoMvluVIFQ1Wb79us9lVttLQn8HwfJVdAVVVA4aZ/u7t5zGxnDc3UqDXC5Owkv9nV52BlOfilqupJV6VNQyceizA1s7BsxnZcTMNH17Rm2PUbfa0r7rxyi6ch8+maRlumhUQsOj8/4/k+qqI0X6PXaGf36wToEz8oCPF8JAFaCCGeYYqiBNPsGnRdQyf48fzJeveapsobz15FS8Tie48eWZDBNraneNX6Xta3JReFPkVRsOY9V8IyiRg65bpDqVZn94EgHK9qTbC1K43r+RydKVCyHcZzZdSHFNbsyHDA//Va5aloqIrCzlVpHvEOsyiahhenykjSJJ62CBVUetWgvV25bOP70NEaI2LqdGZijEwWGR8Oaqkjo5PUijV0Syfd0xqcqHFq3/dJNFraQVCj7Dhuc2hJqVJlJlfAcz16utoXvG8nSsSiTGfzqIqCZZnoukaxGJRjGLr2pDcRPlWGoZNOJRZ8vziOy7Hh0cVdQrxlTL95LpFBKuI0kgAthBDPUYqi8Kp1vQy0pvjKLx9jc0eaV6ztpq8l9qTPETF1/vq3z+fITIFdYzPsGp2hXHf4yIs2Y+pBiLddl4eGp7l17yBDuRK1vT6xdRZFak9w9qVtD/XywMEgqLvK0vW+qq+zflWGvYcmMQyV1mQE7dwQDxTHSLg6Pf93YYAf2NjFwV8dACDT39K8ffYn+qqqMJ1fuOra0Zti83krGZ6YwvW8xhQ/m3JFYXR8ilKliucFUx9ncnk6MuklV6EVRSEctsikU0QjYaLhEK7ncbg2RN1x0HWrufr/m1Kt2YxNTBOPRhoXFaxK1+sO/qJQqfC0LHsL8TwmAVoIIZ7j1rXGuObS8zC0J9c54kSaqrC6NcHq1gSv3dTf/LH/LFPTOG9FG1s7W/jyXbvZN5Fj3VgbuzqGqZ+8qdwi28O9KMADByeZLdS9/0CWDW0DjMYHqRGs0qbdKEcfKZIv14inLQoVm8FSiUxjVV4BYl0xavkavuc3NuXNUQHF90BR5m5fKvgSDEyxTJN6o7VbvlCiUCxTKM2Vrvg+VCpBazhdX3rjnqHrdLSlse06UzM5CqUydt0JSkA8H01RcH6DAdr3fSans0xn59oJdmTS9HZ14J7QG9zzvMa0wqf2AejZy4clNnMK8ZsgAVoIIc4ATzU8L+Vkm+jChs6HLtzEV+/Zy6OD06yutWP1qjyujOIoTxxcFBUePDCFf0LBxp6JadamUgy6warytFai+0UG649k2LtvcamIb6js3xgC5trj9UwodPWmiMQsALq39vDIzw5Qt8JghSGk09/XgjJv9TWZjqKpKol4hFK5gu/7zeCsaSq6plGzg3rfQqnCVDZHe2vLgtVkH3DqDsVyhVy+SLlabYbxWXa9HrTPOw2lE/PLM1zPJRK2ljxmOps/AwO0EKePBGghhBBPmqlpvO+CDfzb/fu599gETEBEi7CiLUEsZXAsNs2IX1jysQ+UBtmyoodHjy0c431WVyv73cMLbnNVn8eyU/R1xBgZCzp/zK7fRjWDygnlI1Z7mKN7xpsdKDZu6Fhwv6IoHH1saMFtXf1pUCAejTKp5xqDShrDVCIhopEQ45PBxkfP85rDSwBsu065UqVYKlOp1rDr9UUNIDRVxbKMoMOKppHNF5uPPx3yhRK+z6L6cs/3qVTO0PAsXTjEaSIBWgghxLJoqsqVO9eRrznsGZuh7nocGs3CKIRNnXM29bJPm1iyRto05uJcOmIxXa5hhU84xtfpzGUY9SYpxz1MXcV2PPxGhP7Hl/4W73jgOwseYzsO6VQ4qNRQFAxDI92ZAIL6Z91QqVmNVfpGH+ZYMoLrepSqNXRdW9BdwjT0RbXDlZrN8OgENbtOrWY3e0fPp2salmUSi4SJRSOEQyaapmHbdarVGqXTuOo7O0hFCPH0kwAthBBi2VRF4ZL1vewZWzi4pWI77H5oGtAYSLbR3h/mUWOECkE4fbAyyPb13ai+Ap7C9IFqo6xizqqxFTx0PCjdGCuU2bK2lQO7G91BEil6EnHeetFWoNFqDjhyw35mJud6VLd3xJmemPt7OGZSaAwTmWWFLep1h1y+uKicIV8oNQP7rGq1RnWJAKzrGiHLIh6NEI2Egi4cmrZgw6Fh6IRC1mkN0M870oVDnEYSoIUQQjwl6zMJ+lpiHJtZerjKWK7E2CMlIlaEczdk2BsdY6xW5KHCMBkrQrqWYEeyk1BBY5u2mtkijcFsMFTE1DU2drRSd1xW9iUpHXC4cF0vpq7xwVefu+C5/uyGg6Aq4AebChXLoG9HH/g+iqqiqlA4sLCeWtc1fN+jvbWlsfGv1KwAcD0PrfG4pUZqq4pCNBImHos2Q7N2il7RiqIQjUaYmrfJTwjx3CUBWgghxFOiKAqvWd/LP92z95Rjncu1Or96eITt6zvp7kzg11WGJsuUHY9jg+MnbahmOy6HRrMUqzZnrWhn8nCZ6dEyP33kSLBmrYCuqOD4mAMt9EaNoAbW9ymg4BkqvuejKCqq4mMUq6AE171pWy8f/fPLQPVQFIUV3R1MzWQZm5xpbhBsy7RQrzuLBqQApJJxujvb0FS1+V4sZfZcrusFLfGQBnK/UVIDLU4TCdBCCCGesm09GT73uhYGc2UOTRc4Ml3k0FSOmfLiUoUH944SOWzgE0z322DFiU/UiSQtrJiBn9TYn88SsQxqdZdspUpPOs7e4SkeOT7Ojt423LLD1df+pHnO/vYkI4dmFj3X1jUd7DowN9Y7FjbR3WAD344LV/PHf/5aDFPHrtlUqjWqdh1d1zEMHbvReSNfKJFuSaBkF/ZwVlWFZCJ2yhVn3/dxPY9azaZQLJMvlqjWahKehThDSIAWQgjxazE1jVXpOKvScSAIjzMVmweHp/jx/mFmynOT8MqNcNqbirP/vlEUIDsx13PZ0hQG1saoRMGwYM/wVLNC2rU9Hr97kHXb2tg3EnTyUFDoTkbxG8/rKwr4oKsntPWbl3Pf94lX4bgO2ckCrutiaBrZYgkfCFlmM0DXbBtD0zFNozkKG8A0DCKh0KLwHEww9KjWapQrVQrFoEPHUpsNhRDPbRKghRBCPK0URSEdsXj5mm5esrqLB4ZnuGnXEUbzc0G53TZZqnLac33275kAIN0SYd1AO3vtHFN2DaUcjNnO7suyfVM7oBBSVfbsGV10HjMcYWtPJiibUBU04CjBcYcPjhBJGdh1B1VTaW9JYRo6+VKFSDjU7PPseR6e7xENhxYEaE1TUdWF4dnzPGZyBbK5goTmZ5KUcIjTRAK0EEKI3xhNUTi3J82O7jSPjWe5adcxXNfj4P3ji/oTn2h6psz0TBnT0PjtF/fxy1sPoQDlks3+Xw0D0N+VQlEW5ya/6nLsriPNv3f1t3D5ledz8aWb8XS3WdfsOS6FcpmQaYLv47gulmlQrdn4Pkxn82jawumDtl3HrjuE590+O5ykVK481bdKCPEcIgFaCCHEb5yqwFkdKba2J9k/mONnk8c5eHT6CR+XSUd575vP5bxtfTx81lpu+sVexuwyPj6KrxA1dVa5mcbfG5v2FAXD0lixuYOQqVOpu0zPlLni916AaRrkCiVyhSKOE6wSF8tVXMfFNHRqNRvLNJv9kwvF8qJrclyPSrVGyDKbZRyappFJJ6nWariujJN+ZviyAi1OGwnQQgghThtFUVi3IsXaNyUZHC3x83sHeXTP2KLjVEXht162nrf/zk7isWBk93nb+5kI1bn61p81j7NUjXWJMOMHgxXlttYona1xxrJVjLCOZ+lMHS40NwEqikI8GiZkmRSdYLXY932q9ToR06RadxYMVFmK7/uUShVakvEFrysRj5EqVZbs2iGEOLNIgBZCCHHaKYrCiq4Yb3v9BsYm+7njrmM8tGsU3wddU/n//uCl7Dy7D+2EgSQntsurOS72eoONA21YnsbDt4wwNRJUV69ameah8Rk2tEWpledCsaqqmIYBzJVbeF7QL1pTVWpPEKABKrUajuNiGHP/jGqqSjqVWLC6LU4jHziNo9LF85sEaCGEEM8Y34f21jBv/O31vPQFK/jZvcdZt7KNszf3LArPwfFBgF7dliSd0Zi0JhhSDjAEnPur1SfUVQd/21MuEQkZC+6LhEPM5AoL2tOFQha+7z+pAF2zbaq1YAT4/GsMWSbRcJhcYenhMkKIM4MEaCGEEM8Kba0RrnjNeiDYvNfZ1sqJ3egqoSK922qMKYeYX/gRdgyOP/bEpROe5zM5nWVyOrsgPGuqSjQSbraweyKu6zE0OkEoZGHoOqZhYJk6mqYRjYTIF0sLzi9OE3nPxWkiAVoIIcSzzmzATSVilKs1NFUlHouwqqWF1aU092cXrvBuOdzHntLEovMM9LYAYBoaKFCt1ZicnsGuOwuOcz2PUqW6rPZz1Zrd3Gw4S1XVRlcQCXJCnMkkQAshhHjW8X2fqZkcM7l8s6tFJBxibSbD32x5A0fLU3x35CFuHXscHHjF2g1oxzQe3z3XEzqXr3LMCiYihi0DAMd1cU9SJzs5tXii4XJ5UoP7zJIPLuI0kQAthBDiWSmY7DcXiMqVKoMj4yTjMZLhEO/vvYj/0X8h+/MT7Mj0c/+9xxc8fmqqRLQ/RKnuAD6+D5FQiEQsykyusPj5ftMvSAhxxpAALYQQ4jnDcYIhKNPZPLquEYtEWBVrYXwizz33HV1wrKIo2LM9mT2fx+47xo4L1xCLRsjmi1JmccbxwZOvqTg91Cc+RAghhHh28X2fet1hJpfn2PAY2UKWz/zvV3HlW3cQCgVrQ21tUWxvLkB/9uPfYddDx6XMQgjxa5MALYQQ4jnN933KlSq+YrN2bYL3vXsnsahJLGSwZshhzbBD34iD78PB/SOMT87I6rMQ4tciJRxCCCHOKG1tYf74Dy7khz/Yzchg0NrOaqxKe6pL3XFO9XDxXOWD78tPF8TpISvQQgghzjjtbTEuuWjNgtte+Op19K1PP0NXJIQ4k8gKtBBCiDNOqVLB110u+q0NROIm0ZYQG87pWjTZUJxhZBOhOE0kQAshhDjjOI5Loi3Eiy/f8ExfihDiDCQBWgghhBBnBtkcKk4TqYEWQgghhBBiGWQFWgghhBDPfb4P0uNbnCayAi2EEEIIIcQyyAq0EEIIIc4MUgMtThNZgRZCCCGEEGIZZAVaCCGEEGcEX2qgxWkiK9BCCCGEEEIsgwRoIYQQQgghlkFKOIQQQghxBvBlE6E4bWQFWgghhBBCiGWQFWghhBBCPPf5gCcr0OL0kBVoIYQQQgghlkFWoIUQz3qqquJ5HoqiAOBLneNz3uzXdPZ3IZ4WvnwvidNDArQQz1OmabKqbwVtrWlc12VwZJTjwyO/Vjhdt3ol0XCEfKHAwaPHWNm3glQiQaVaYc+BQ0/4+DUD/bRlWgFwHAdd19E1jUqlQigUolqrgQ+P7NmDbdeXdW0hy2LtygFSyQT1ep19h44wnc0uOq6jLUNfTzchyyKbz3P42CDFUmlZzzWfoij0dXfR19sDQLFUYmxikpBlUa5UGZ2YWPY5DcOgt7MDu+4wPjnJutUrMQ2TsYkJRscnWL9mFSErxHQ2y9HBoad87b8OTdPo7erE8zwGR0YXfF9t3bgeU9eJRCKUy2XK1Rq79x846blClkV3RzulSoWxicnTcflCCHFKEqCFeB4yDJ1ztmzi05/+NAcOHMC2bd75zndy0cUXc3x4hEg4jKoo2PU6UzNZfN8nHo0Si0Wp1Wyms1kS8RiRUAhFVXHqDqVKhUqxyGte9Squv/56OtoyeHWbF1/0Ir71rW/RkkxiGgaqplIoFCmWy6SSCcKhEApQdxyi4RBvfctb8H2fwcFBVqxYQWdnJ77v85d/+Zd89atfZdOmTfT0DxAyLaLRCNVqjZlcDsPQSadSaKpKqVwhVyg0X28iFmPdqgGuvfZaAOr1Oq9+9atZs7KfA4ePNo/r6+lmemKcd171v9B1nXg8zjXXXMPw+ASapmHoOr7vk80XqFSraJpGOpVE0zSyuTyO69KSSKAbwf9ac/kCHW0ZHn3oQf7wDz5EJBKhpaWFL33pS3z961/nt37rt1BUhXq9jqHrKIpCqVxBURXCoRDlxutQFIWWZALLsiiWSnR3dHDXL+5k27ZtbN24njt//nM+/elP85Of/ASAB++/n6uuuor777+fiakpEvE4+JDN57Hr9eBcpglAoVSiUCwRDoVIJeIA5ItFSuXK3PeLrtPSeJ35QoFypUo6FXw988US5UqFlmQC0zRRFYVytUoyHufgvr1kMhlWD/SRzeXRG68xlUjwe7/3e3zta1/j8ssv55ZbbqGrvY1svkDdqZNKJCiWyriuSzKRYHV/H9/85nW8/vWvBx+msjOkEkl0XSOXL1Cz7eD7yzRQUChXK1imueT3gjhz+YAvNdDiNJEaaCGeh1Z0dfH3f//3RKNRvvnNb3LdddfxxS9+kbGREdpSSQoz0/zktlsZHTzOOVs3s2blAPGwxe03/4jc1AQXnX8ubakkpXyOn/74do4eOsA5WzdTq9UwTZNrr72WNQP9XHvttYRCISqVCgO93dx71y+49aYbaU3GOWvTBkKaSjE7w60/uolidoZEIsH111/Pt7/9bTzP4/rrr+cLX/gCb3rTmwiFQs3rX7dqFWFD4/abf0Q5n+WCHdvZtmkj9/7yF9z0wx8Q0lXaGyvZAGdt2sDnP/95NmzYwMTEBOFwmNtuuw3fcYhGIkAQEltTSd7znvfwpS99ieuuu44rrriCD33oQ2xev46R48eYHBvlx7fewsreblZ0d3HBOdvY/egj/PTHt7NuZT8v2LGdkcFj5KenuPWmG1nR2U5vVyd/9md/xre+9S2uu+46/vZv/5ZCocC//Mu/cN111zEzMY5bq1Iu5Ln5xhtYt2qAernEbT+6CUPxWb96FTvP2kpuapLbfnQTiXCIlmSCr33ta/z7v/87D9x/P9lsFs/zuOGGG1g90M+///u/4zgOAD3tbdxx+2387L9/zLqV/Zy1cQOlXJZCdoZbbrqRRDjEts0bWdnbzU9uu5Xbb7mZFZ0dzfclEYtxztbNPPire7ntRzfR3ZbhReftZHx4iNtvuZm2VIJzt52FW6tSKxW55aYb8WpV+nq6+cY3vsG//du/8ehDDxEPhyhmZ7jj9ts4fvw4V155ZfPrc9ddd/GT225l1YoeXrDjHMaGBtm2eRMb165hbOg41WqFr371q1x33XXkZ6a44JztHN6/lztuv43+7k7OPXsrhZkpyvkct918E2sH+k/6vSCEEE8HCdBCPA/FYzG+973v8f73v597HniI4yOjvOMd7+Cmm27ikUce4d3vfjdbt27lG9/4Bnf8939TLRX56Ec/yo4dO/jGN77B7bffziOPPMIHPvAB1q9fz9e+9jV+9atfAbB582Z2796N7/s89NBDbNu2DYBsNktPTw/nnHMOv//7v08kFOLHP/4xH//4x9m5cyef+cxneOSRR3h0997mdeaLRSampvnCF75ApTK3Inrw4AH+9E//lJ07d/KVr3yFX917L3/9139NvV7nxS9+MbVaDUMPVoEj4RAHDhxg586d/MM//AOf+MQn+L3f+z3e+9738sMf/pCOtgwA0UiEe++9lwsvvBDXh5/f8yte/opX8Nhjj+H7PldffTU//elPWbNmDW95y1vo7+3hox/9KPl8ntWrV/Oud70LgKuvvpqf//znrF69mve85z0ArF69mo9//OPceOON+L6PYRjE43H6+/vJZDJ897vf5c///M/Zvn07P/nJT/jyl7/Meeedxyc+8QkqpSLf//73+M53vsPOnTv58Ic/zMzMDC0tLfT09NDZ2QnA61//er773e8yOTmJZVnEYjEAxsfH2bp1KytWrOA973kP8ViUf/7nf+YrX/kKO3bs4F3vehee6/KhD32IDRs2cM4551Aul9F1DYANa1fz1re+Fd/32blzJ7lcjm9/+9t8+9vfZuvWrbzlLW+hWqnwH//xH/zt3/4t27dv54Mf/CCVSoV0Ok1XVxe9vb088MADvP/972fdunVEo1Guueaa4Gucz/PLX/6SlStXNp/nmmuuwTB0wuFQ48/B+zUwMEAmk+Hzn/88d911F2vWrOGKK65AURT+8R//ka9+9ats3779pN8L4gzn+0EN9On6JZ7XJEAL8TykqirVahXLsnBdF8dxsSyrGVKvuOIKNm/Zwp/8yZ9w44038oMf/ADXdfnWt75FPp/ntttuA4LQdt755/O6172OXbt2Nc//spe9jE9/+tNcdNFFzdtCoRA/+MEPuO666xgbG2N4eBiAd77znazfuJH3v//93HLLLcRj0Se8/u9+97t4nsc3v/lNSqUSt99+O5s3b+b666/nzjvvpK+vL6iXbrzWYrFIJBIhHA6jaRqVahW9UY4xG65UVWnWWjuuCwSlHqqq4rouiqLw0Y9+lPPOv4DW1lbGx8e58cYbeeSRR7jhhht47LHHKBaLGIbBH/7hH3LJJZdQrVYB+Pd//3de97rXceedd3LhhRfi+z7t7e1s27aNdGuwOvqe97yHF77whXznO9/Btm2uu+46KpUKP//5z5uv81vf+halUokHHniA7u5utmzZQjoTfACIRCIkEgk+97nP8da3vrX5Xpmmyde//vXmNc764Ac/yMbNm3nNa17Dvffey+bNm/nyl7/M/v376e7uplazg9fuOBSLRV73+tdjReOsX7+eH/zgB/zpn/4pW7Zu5ZJLLuGee+4B4Pd///fZsXMn27dvb5bgrFu3ju6eXgDe8IY3sHbDRtLpdPM6NE3jj//4j3nBC19ILBZjZmZm0dc7HA7T1tbG9u3bGRgY4MYbb+STn/wk5+zYyebNm9m7N/jQ9eEPf5gtW7ee9HtBCCGeLvKxXIjnoVKlzIUXXsgtt9zCS176MmLRCJ/9i8/wzne+k1qtRjabJd4IM7FYjHg8zmWXXcbv//7vA8HGuFtvvRXTNKlWa2iatqCTwpvf/GZ27tzJ3Xffzac+9SkA/uEf/oHXvva1vOxlL+Pyyy+nXg82AWazWaKRSPO53CfRkSEej/O7v/u7vPnNb25ej6qqvPrVr+aGG27gQx/6EJ//wheYmslSLlc4/5xt/N3f/R0XXXQRn/rUp+jt7aWvr49zzz2XiakpAMqVKjt27OAv/uIv+Mu//EtMw2B4aIh0Oo2u67iuS6FQIJlMks/nCYfDdHZ28ld/9VdomsZf//Vfo6oqhmGgKAqO4zS7hiiKwosuuohXvOIV7Nu3j6NHjza7TyQTCSAIicVSmXg8zhve8AbOP//85mNvueUW/viP/5jVq1cDwYeCX/ziF3ieR2tLS/N9ecc73sGHP/xhPvOZzzRv+8QnPsE//dM/kU6nOfvss5u3Z7NZNoRCzff9k5/8JFNTU3zxi1/kC1/4Ale88U0MjoximibFYpFwKERrS6r5/mezWTq7upqPBxZ9P8y+xlRy7jWWyuUFX0unEdDj8TiFQoFQKNR8/3RNY6rx9ZnfrSMSiZDP50mlUguePxwOMzYxyRVXXLHk94I480kNtDhdJEAL8Tw0NDLK1Vdfzbve9S5uu+02xsfHWbt2LS996Uu5+eabufPOO7nqqqu47777+H//7//R09PD2972Ng4dOoRt21x66aWnPH86nebAgQOo6twPubZv384XvvAFbrjhBvbv39+8/Xvf+x6jo6PcfffdXH/99RweHGblit5Tnv/KK6/k7W9/O48//jjlcpkrrriChx56iLGxMSYnJznvvPOaXTo83+fY8AiXX345P/jBD+jp6aFarWLbNtu2beOX9z0AQLVWQzNMPvjBD3LppZeyadMmHnnkEb74xS8CQTj8X//rf2FZFuvXryeRSPCHf/iHvOUtb+Gss85iamqKL3zhC0te79ve9jY6OzupVCo4jsOaNWu4+OKL+ehHP8ob3vCG5nGT09P80R/9ER/4wAfYuXMnk5OTfOQjH+Gqq67ij/7ojzjvvPMYGRnhU5/6FBdeeCGf+cxnuOyyy0g0QvgLXvAC7rnnngXv+86dO7nqqqvQNK35oQXgS1/6EjfeeCP79u3j/PPP53/+z/9JNBrl8ccf533vex92vY7v++QKBd7whjfw9re/nYGBAS655BI+9KEP8bGPfYyzzjqL48ePc8EFF/CjH/1o0es+77zz+OQnP8n+/ftZu3btku+NZVl87GMfwzAMzj77bCKRCK997Wt5xzveQTqdbtZyX3zxxXzkIx/hjW98Ix/72Me48sorWbNmDalUioGBgeb5WpLJoJ5/ie8FIYR4uii+NFQV4nnl5/cEtcotyQRbNqxnamqKSCRC3XUZG5/k4L497N69m7e97W0kk0lGxicolkpsXr+O8fFxTNOkpaUFz/Ow63WODg7R39ONpmnNkHZkcIjxySnaWtOsXNGL7/vYjkO9VkNvtC/TdZ2vfOUrdHR0cMEFF9DW1sahY8cZm5hky/p1RCNhRicmsW2bvp5uDMPAtm18YHB4hNUD/YyOjhIOh0mlUvi+z9DQENFoFCsU4pFde6g3whdARybDutUrcV0XTdOYnJ5m74FDePP+F6ipKqsH+mltSTE1NUV7ezsHjx6jq72N37n8cn74wx8yMzNDa2srj+zew4ruLhKxGGNjY3R3d6PrOrZtky+WqNk2bekWTNPE931GRkYwDIPW1lZ27z9Ib3cn8Wi0WR5SdxweenwXmXSa1f19DA8Pk0qlUDWNialpers6GR4eJpPJ4Hoetl0nlUzgui6qquK4Lrv3HaBQKtHf20NnWwbHddE1nenpKZLJoIuGaZq8//3v5+Mf/zjhcJiuri5mcjkSsRhDQ0NkMhkqtRp7DhxqlrhsXLcGFSgWi3R1dTE2MUk6lWR6epr29naOD4/QkcngekE7xIHeHuqOQ6lcoa01Tb1eR9M0HMdh1/6DDPT2EAmHsCwr+Jr6PtlslnQ6zWN797N+9UqKhQLxeBxN0yiUyhiGTjQcbqxCK7iuQ6FQIJ1OMzI2HjyP43BsaJiNa9ec8ntBnD4XnX/uaXuuhJLmfPWVp+35ZrYf5L777jttzyeeXSRAC/E8MxugZxm6jut5wY/aEwnqlRLHjx9n+85zOT48sqA0wzQMPN9vrgouRVGUBT1/5//d0HV8fFzXoyPTysF9e0kmk7R1djE6MXnSx514zoXX4+E4Qc2yaZp4nnfK63syFEXB0HXsxortts0b+du/+Rs+9alPcc8DDzVvh6C0YPZY3/dPet2maeB7/qIgd7LXZpkmjuviNuqxZ2+rO86Cr8nJHr/guQ0D13XxfJ9VfSv43nf+iyuuuIKRySkKxVLzeNMwcFwH111cRhN8QFKbq7knvkenek1LXePsbYqioCgKuqYtOJdhGHiNa17qXKqqomkq9bqz5HM8Xd8L4tdz2gO08vLT9nwz5xyWAP08JgFaiOeZEwP0fIqisKq/D01VOXx8cMGP/J9uhq4z0NeL7/kcOnb8WT2NLpVM0NXeznQ2+5wf5BEJh+nr6aZSqXB0aPiZvhxxhpMALc5UEqCFEEII8Zz36le/msnJ0/cBN5PJcPPNN5+25xPPLhKghRBCCCGEWAbpAy2EEEIIIcQySIAWQgghhBBiGSRACyGEEEIIsQwSoIUQQgghhFgGCdBCCCGEEEIsgwRoIYQQQgghlkECtBBCCCGEEMsgAVoIIYQQQohlkAAthBBCCCHEMkiAFkIIIYQQYhkkQAshhBBCCLEMEqCFEEIIIYRYBgnQQgghhBBCLIMEaCGEEEIIIZZBArQQQgghhBDLIAFaCCGEEEKIZZAALYQQQgghxDJIgBZCCCGEEGIZJEALIYQQQgixDBKghRBCCCGEWAYJ0EIIIYQQQiyDBGghhBBCCCGWQQK0EEIIIYQQyyABWgghhBBCiGWQAC2EEEIIIcQySIAWQgghhBBiGSRACyGEEEIIsQwSoIUQQgghhFgGCdBCCCGEEEIsgwRoIYQQQgghlkECtBBCCCGEEMsgAVoIIYQQQohlkAAthBBCCCHEMkiAFkIIIYQQYhkkQAshhBBCCLEMEqCFEEIIIYRYBgnQQgghhBBCLIMEaCGEEEIIIZZBArQQQgghhBDLIAFaCCGEEEKIZZAALYQQQgghxDJIgBZCCCGEEGIZJEALIYQQQgixDBKghRBCCCGEWAYJ0EIIIYQQQiyDBGghhBBCCCGWQQK0EEIIIYQQyyABWgghhBBCiGWQAC2EEEIIIcQySIAWQgghhBBiGSRACyGEEEIIsQwSoIUQQgghhFgGCdBCCCGEEEIsgwRoIYQQQgghlkECtBBCCCGEEMsgAVoIIYQQQohlkAAthBBCCCHEMkiAFkIIIYQQYhkkQAshhBBCCLEMEqCFEEIIIYRYBgnQQgghhBBCLIMEaCGEEEIIIZZBArQQQgghhBDLIAFaCCGEEEKIZZAALYQQQgghxDJIgB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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","In the preceding map, notice that high-income regions tend to be close to other high-income areas. So median income is expected to have a positive spatial autocorrelation index. However, it is important to note that visual inspection is subjective and insufficient to identify the presense of spatial autocorrelation. Spatial autocorrelation is quantified by `I` value returned by the `MoranITest`. It is a statistic that indicates whether a variable is positively spatially correlated (`I` value closer to 1) or negatively spatially correlated (`I` value closer to -1). `MoranITest` requires spatial data and a description of the spatial relationship through spatial weights or a spatial weights definition. ","","Next you will create spatial weights based on several spatial weights definitions and then apply the `MoranITest`. By using multiple weights definitions you will identify the one that best describes the underlyng spatial relationshp for the given variable (the one with the most positive or negative spatial autocorrelation index)."],"enabled":true,"result":{"startTime":1737651924049,"interpreter":"md.low","endTime":1737651924533,"results":[{"message":"<p>In the preceding map, notice that high-income regions tend to be close to other high-income areas. So median income is expected to have a positive spatial autocorrelation index. However, it is important to note that visual inspection is subjective and insufficient to identify the presense of spatial autocorrelation. Spatial autocorrelation is quantified by <code>I<\/code> value returned by the <code>MoranITest<\/code>. It is a statistic that indicates whether a variable is positively spatially correlated (<code>I<\/code> value closer to 1) or negatively spatially correlated (<code>I<\/code> value closer to -1). <code>MoranITest<\/code> requires spatial data and a description of the spatial relationship through spatial weights or a spatial weights definition.<\/p>\n<p>Next you will create spatial weights based on several spatial weights definitions and then apply the <code>MoranITest<\/code>. By using multiple weights definitions you will identify the one that best describes the underlyng spatial relationshp for the given variable (the one with the most positive or negative spatial autocorrelation index).<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a series of spatial weights definitions","message":["%python ","","db_weights_def = DistanceBandWeightsDefinition(threshold=1000.0)","knn_weights_def = KNNWeightsDefinition(k=2)","kernel_weights_def = KernelBasedWeightsDefinition(k=10, fixed=False, function='gaussian')","queen_weights_def = QueenWeightsDefinition()","weights_specs = [","    db_weights_def,","    knn_weights_def,","    kernel_weights_def,","    queen_weights_def","]"],"enabled":true,"result":{"startTime":1737651925036,"interpreter":"python.low","endTime":1737651925503,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Calculate Moran I for each spatial weight definition","message":["%python","","for i, weights_spec in enumerate(weights_specs):","    mi_result = MoranITest.create(sdf, weights_spec, column_name='MEDIAN_INCOME')","    print(f\"Weights definition: {mi_result.spatial_weights.definition}\\nMoran I's results: I:{mi_result.i}, p value:{mi_result.p_value}\\n\")"],"enabled":true,"result":{"startTime":1737651925967,"interpreter":"python.low","endTime":1737651927956,"results":[{"message":"Weights definition: DistanceBandWeightsDefinition(alpha=-1.0,binary=True,threshold=1000.0,p=2)\nMoran I's results: I:0.5719605389665813, p value:0.001\n\nWeights definition: KNNWeightsDefinition(k=2)\nMoran I's results: I:0.6945150221963798, p value:0.001\n\nWeights definition: KernelBasedWeightsDefinition(fixed=False,function=gaussian,bandwidth=None,k=11)\nMoran I's results: I:0.663147087878812, p value:0.001\n\nWeights definition: QueenWeightsDefinition()\nMoran I's results: I:0.6765793161449112, p value:0.001\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Calculate Moral I with KNN and k=4","message":["%python","","knn4_weights = SpatialWeights.create(sdf, KNNWeightsDefinition(k=4))","mi_result = MoranITest.create(sdf, knn4_weights, column_name='MEDIAN_INCOME')","print(f\"Weights definition: {mi_result.spatial_weights.definition}\\nMoran I's results: I:{mi_result.i}, p value:{mi_result.p_value}\\n\")",""," "],"enabled":true,"result":{"startTime":1737651928484,"interpreter":"python.low","endTime":1737651929144,"results":[{"message":"Weights definition: KNNWeightsDefinition(k=4)\nMoran I's results: I:0.6668108892262943, p value:0.001\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","In the preceding results, observe the significantly positive `I` values (spatial autocorrelation index) for all weights definitions, especially with `KNNWeightsDefinition(k=2)`. Also observe the `p` values  which represent the probability that there is no spatial autocorrelation. In this case the probability of no spatial autocorrelartion is 0.1%. In general, you may assume spatial autocorrelation exists when the `p` value is below 0.05 (5%). You may proceed having established that the data exhibit positive spatial autocorrelation. "],"enabled":true,"result":{"startTime":1737651929661,"interpreter":"md.low","endTime":1737651930126,"results":[{"message":"<p>In the preceding results, observe the significantly positive <code>I<\/code> values (spatial autocorrelation index) for all weights definitions, especially with <code>KNNWeightsDefinition(k=2)<\/code>. Also observe the <code>p<\/code> values  which represent the probability that there is no spatial autocorrelation. In this case the probability of no spatial autocorrelartion is 0.1%. In general, you may assume spatial autocorrelation exists when the <code>p<\/code> value is below 0.05 (5%). You may proceed having established that the data exhibit positive spatial autocorrelation.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","######  Local Indicator of Spatial Association (LISA)","","Local Indicator of Spatial Association (LISA) analysis measures the behavior of observed values in a specific region. While the Moran's I test can indicate the presence of positive spatial autocorrelation (i.e., observed values are simlar to nearby values), it does not pinpoint where similar values are clustered. Once the presence of positive spatial autocorrelation is established, LISA is used to identify hotspots (i.e., clustering of relatively high values) and coldspots (i.e., clustering of relatively low values) in the observed values.","","For LISA analysis, we can use a Local Moran's I test, which measures the relationship between an observation and neighboring observations. With LISA, each observation is labelled with one of 4 possible values, referred to as quadrants 1-4:","","","1. HH (High-High). A High value surrounded by high values (Hotspot).","","1. LH (Low-High). A low value is surrounded by high values (Doughnut).","","1. LL (Low-Low). A low value is surrounded by low values (Coldspot).","","1. HL (High-Low). A high value around low values (Diamond). "],"enabled":true,"result":{"startTime":1737651930597,"interpreter":"md.low","endTime":1737651931095,"results":[{"message":"<h6 id=\"local-indicator-of-spatial-association-lisa\">Local Indicator of Spatial Association (LISA)<\/h6>\n<p>Local Indicator of Spatial Association (LISA) analysis measures the behavior of observed values in a specific region. While the Moran's I test can indicate the presence of positive spatial autocorrelation (i.e., observed values are simlar to nearby values), it does not pinpoint where similar values are clustered. Once the presence of positive spatial autocorrelation is established, LISA is used to identify hotspots (i.e., clustering of relatively high values) and coldspots (i.e., clustering of relatively low values) in the observed values.<\/p>\n<p>For LISA analysis, we can use a Local Moran's I test, which measures the relationship between an observation and neighboring observations. With LISA, each observation is labelled with one of 4 possible values, referred to as quadrants 1-4:<\/p>\n<ol>\n<li>\n<p>HH (High-High). A High value surrounded by high values (Hotspot).<\/p>\n<\/li>\n<li>\n<p>LH (Low-High). A low value is surrounded by high values (Doughnut).<\/p>\n<\/li>\n<li>\n<p>LL (Low-Low). A low value is surrounded by low values (Coldspot).<\/p>\n<\/li>\n<li>\n<p>HL (High-Low). A high value around low values (Diamond).<\/p>\n<\/li>\n<\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Perform LISA analysis","message":["%python ","","X = sdf['MEDIAN_INCOME']","lisa = LISAHotspotClustering()","lisa.fit(X, spatial_weights=queen_weights)"],"enabled":true,"result":{"startTime":1737651931622,"interpreter":"python.low","endTime":1737651939912,"results":[{"message":"LISAHotspotClustering()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Print LISA results","message":["%python","","print(f'Quadrants: {lisa.labels_}')","print(f'Local I: {lisa.Is}')","print(f'p-values: {lisa.ps}')"," "],"enabled":true,"result":{"startTime":1737651940449,"interpreter":"python.low","endTime":1737651940905,"results":[{"message":"Quadrants: [4 4 3 ... 1 1 1]\nLocal I: [-0.00569983 -0.01300506  0.01646344 ...  2.35295367  1.61011919\n  0.03805387]\np-values: [0.474 0.45  0.496 ... 0.004 0.001 0.409]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Add columns with LISA quadrant and p-value","message":["%python"," ","sdf_labelled = sdf[['GEOID','MEDIAN_INCOME']]","sdf_labelled = sdf_labelled.add_column(\"quadrant\", series=lisa.labels_) ","sdf_labelled = sdf_labelled.add_column(\"p\", series=lisa.ps) ","z.show(sdf_labelled.head())"],"enabled":true,"result":{"startTime":1737651941429,"interpreter":"python.low","endTime":1737651949471,"results":[{"message":"GEOID\tMEDIAN_INCOME\tquadrant\tp\tgeometry\n060376700031\t75577\t4\t0.474\tPOLYGON ((-13171614.921 4002458.952, -13171006.56 4002139.446, -13171013.684 4001739.038, -13171618.149 4001743.458, -13171614.921 4002458.952))\n060376700032\t74609\t4\t0.45\tPOLYGON ((-13171618.149 4001743.458, -13171013.684 4001739.038, -13171015.02 4000576.34, -13171318.477 4000575.938, -13171317.364 4000963.044, -13171619.596 4000964.384, -13171618.149 4001743.458))\n060376700033\t55592\t3\t0.496\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n060376700034\t73558\t4\t0.455\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 4002094.032))\n060376701001\t54738\t2\t0.307\tPOLYGON ((-13171620.932 4000577.813, -13171318.477 4000575.938, -13171015.02 4000576.34, -13170712.342 4000574.063, -13170717.463 4000012.581, -13171091.051 4000012.313, -13171186.007 3999904.092, -13171571.395 4000085.443, -13171620.932 3999984.052, -13171620.932 4000577.813))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review block groups in quadrant 1 (hotspots)","message":["%python ","","sdf_labelled[sdf_labelled[\"quadrant\"]==1].head()"],"enabled":true,"result":{"startTime":1737651949978,"interpreter":"python.low","endTime":1737651950452,"results":[{"message":"           GEOID  MEDIAN_INCOME  ... quadrant      p\n6   060377009021          94861  ...        1  0.156\n8   060377009023          87561  ...        1  0.089\n9   060377009024          84019  ...        1  0.037\n10  060377010002         130579  ...        1  0.001\n11  060377013022          83750  ...        1  0.230\n\n[5 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review block groups in quadrant 3 (coldspots)","message":["%python ","","sdf_labelled[sdf_labelled[\"quadrant\"]==3].head()"],"enabled":true,"result":{"startTime":1737651950929,"interpreter":"python.low","endTime":1737651951425,"results":[{"message":"           GEOID  MEDIAN_INCOME  ... quadrant      p\n2   060376700033          55592  ...        3  0.496\n29  060373118021          64211  ...        3  0.160\n30  060373118022          40542  ...        3  0.076\n37  060373201003          44663  ...        3  0.251\n38  060373201004          68611  ...        3  0.276\n\n[5 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create SpatialDataFrame with hotspots and coldspots","message":["%python"," ","sdf_labelled = sdf_labelled[(sdf_labelled['quadrant']==1) | (sdf_labelled['quadrant']==3)]"],"enabled":true,"result":{"startTime":1737651951923,"interpreter":"python.low","endTime":1737651952392,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Add text labels for hotspots and coldspots labels","message":["%python","","get_label = lambda x: 'HOTSPOT'  if x==1 else ( \\","                      'COLDSPOT' if x==3 else ( \\","                       None ))","vf = np.vectorize(get_label)","sdf_labelled = sdf_labelled.add_column('label',vf(sdf_labelled['quadrant'].values))","z.show(sdf_labelled[[\"GEOID\",\"label\"]].head(50))"],"enabled":true,"result":{"startTime":1737651952871,"interpreter":"python.low","endTime":1737651953360,"results":[{"message":"GEOID\tlabel\tgeometry\n060376700033\tCOLDSPOT\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n060377009021\tHOTSPOT\tPOLYGON ((-13179541.425 4037808.895, -13179078.002 4037808.76, -13179080.674 4037158.774, -13179541.871 4037158.371, -13179541.425 4037808.895))\n060377009023\tHOTSPOT\tPOLYGON ((-13180132.309 4036899.704, -13180124.628 4037159.177, -13179541.871 4037158.371, -13179544.431 4036470.262, -13179857.573 4036470.665, -13180060.731 4036470.934, -13180125.296 4036463.007, -13180125.296 4036471.203, -13180132.309 4036899.704))\n060377009024\tHOTSPOT\tPOLYGON ((-13180124.962 4037809.566, -13179558.457 4037808.626, -13179541.425 4037808.895, -13179541.871 4037158.371, -13180124.628 4037159.177, -13180124.962 4037809.566))\n060377010002\tHOTSPOT\tPOLYGON ((-13181824.477 4037442.441, -13181378.531 4037811.985, -13180608.645 4037809.432, -13180611.873 4037162.268, -13180973.996 4037160.521, -13180973.217 4037532.072, -13181337.231 4037611.221, -13181721.172 4037291.94, -13181748.779 4037318.277, -13181824.477 4037442.441))\n060377013022\tHOTSPOT\tPOLYGON ((-13191557.808 4032405.612, -13191242.662 4032714.413, -13190892.34 4032353.497, -13191101.175 4032147.053, -13191420.662 4031840.819, -13191767.311 4032199.435, -13191557.808 4032405.612))\n060377015011\tHOTSPOT\tPOLYGON ((-13190052.212 4033178.235, -13189838.923 4033383.892, -13189483.035 4033018.79, -13189907.83 4032604.538, -13190261.715 4032972.851, -13190052.212 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cmap=classes_cmap, with_basemap=True, legend=True)"],"enabled":true,"result":{"startTime":1737651953937,"interpreter":"python.low","endTime":1737651955753,"results":[{"message":"<div style='width:auto;height:auto'><img 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","","Training a GWR model creates a local OLS regression model for every observation in the training dataset by incorporating the target and explanatory variables from the observations within their neighborhood, where neighborhood is defined by the spatial weights. For prediction, GWR creates a regression model for each observation of the prediction set, using the neighbors from the training set. Since numerous local models are trained behind the scenes, training and prediction takes significantly longer than a non-spatial global model.","","In this notebook, you will use demographic data from the US Census for the City of Los Angeles for areas called block groups. You will use GWR to predict median income for block groups.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1737662007125,"interpreter":"md.low","endTime":1737662007734,"results":[{"message":"<h1 id=\"geographically-weighted-regression-gwr---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Geographically Weighted Regression (GWR) - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>Training a GWR model creates a local OLS regression model for every observation in the training dataset by incorporating the target and explanatory variables from the observations within their neighborhood, where neighborhood is defined by the spatial weights. For prediction, GWR creates a regression model for each observation of the prediction set, using the neighbors from the training set. Since numerous local models are trained behind the scenes, training and prediction takes significantly longer than a non-spatial global model.<\/p>\n<p>In this notebook, you will use demographic data from the US Census for the City of Los Angeles for areas called block groups. You will use GWR to predict median income for block groups.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737662008249,"interpreter":"md.low","endTime":1737662008729,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample 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oraclesai.preprocessing import spatial_train_test_split","from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset","from oraclesai.regression import GWRRegressor, OLSRegressor","from oraclesai.pipeline import SpatialPipeline","from oraclesai.weights import SpatialWeights, DistanceBandWeightsDefinition, KNNWeightsDefinition","from oraclesai import enable_geodataframes","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737662009223,"interpreter":"python.low","endTime":1737662012864,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you train a pipeline that scales independent variables and trains a GWR model."," "],"enabled":true,"result":{"startTime":1737662037602,"interpreter":"md.low","endTime":1737662038077,"results":[{"message":"<p>Next you train a pipeline that scales independent variables and trains a GWR model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a GWR pipeline ","message":["%python","","gwr_model = GWRRegressor(spatial_weights_definition=DistanceBandWeightsDefinition(threshold=5000))","gwr_pipe = SpatialPipeline([('scale', RobustScaler()), ('gwr_regression', gwr_model)])","gwr_pipe.fit(X_train, \"MEDIAN_INCOME\")"],"enabled":true,"result":{"startTime":1737662038634,"interpreter":"python.low","endTime":1737662041665,"results":[{"message":"SpatialPipeline(steps=[('scale', RobustScaler()),\n                       ('gwr_regression',\n                        GWRRegressor(bandwidth=5000,\n                                     spatial_weights_definition=<oraclesai.weights.weights_definition.DistanceBandWeightsDefinition object at 0x7f0e77e02ff0>))])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you view a summary of the model, which includes the results of the global OLS model and the GWR model. The summary also displays the averages of the GWR parameter values.   "," "],"enabled":true,"result":{"startTime":1737662042173,"interpreter":"md.low","endTime":1737662042639,"results":[{"message":"<p>Next you view a summary of the model, which includes the results of the global OLS model and the GWR model. The summary also displays the averages of the GWR parameter values.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View a summary of the GWR model","message":["%python","","print(gwr_pipe.named_steps['gwr_regression'].summary)"],"enabled":true,"result":{"startTime":1737662043194,"interpreter":"python.low","endTime":1737662043808,"results":[{"message":"===========================================================================\nModel type                                                         Gaussian\nNumber of observations:                                                2737\nNumber of covariates:                                                     5\n\nGlobal Regression Results\n---------------------------------------------------------------------------\nResidual sum of squares:                                       1579080941895.333\nLog-likelihood:                                                  -31490.722\nAIC:                                                              62991.445\nAICc:                                                             62993.476\nBIC:                                                           1579080920272.597\nR2:                                                                   0.633\nAdj. R2:                                                              0.632\n\nVariable                              Est.         SE  t(Est/SE)    p-value\n------------------------------- ---------- ---------- ---------- ----------\nX0                               64594.857    528.607    122.198      0.000\nX1                                3183.002    815.739      3.902      0.000\nX2                               11638.567   1558.831      7.466      0.000\nX3                               17579.332    595.832     29.504      0.000\nX4                               13626.739   1000.415     13.621      0.000\n\nGeographically Weighted Regression (GWR) Results\n---------------------------------------------------------------------------\nSpatial kernel:                                          Fixed bisquare\nBandwidth used:                                                    5000.000\n\nDiagnostic information\n---------------------------------------------------------------------------\nResidual sum of squares:                                       828781843334.029\nEffective number of parameters (trace(S)):                          393.837\nDegree of freedom (n - trace(S)):                                  2343.163\nSigma estimate:                                                   18806.970\nLog-likelihood:                                                  -30608.531\nAIC:                                                              62006.735\nAICc:                                                             62140.250\nBIC:                                                              64342.042\nR2:                                                                   0.807\nAdjusted R2:                                                          0.775\nAdj. alpha (95%):                                                     0.001\nAdj. critical t value (95%):                                          3.420\n\nSummary Statistics For GWR Parameter Estimates\n---------------------------------------------------------------------------\nVariable                   Mean        STD        Min     Median        Max\n-------------------- ---------- ---------- ---------- ---------- ----------\nX0                    39968.911 800793.882 -41809538.943  58163.397 117369.069\nX1                     3183.093   9107.340 -186798.330   3942.278  52491.751\nX2                    19454.900 323891.109 -16845931.757  19786.248 598194.021\nX3                    21178.681 266469.208 -22497.223  14798.045 13941178.198\nX4                    11232.534  87713.067 -4532594.216  11041.422 131644.997\n===========================================================================\n\nNone\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You now evaluate model performance using the predict method of the pipeline with the test set. "],"enabled":true,"result":{"startTime":1737662044289,"interpreter":"md.low","endTime":1737662044745,"results":[{"message":"<p>You now evaluate model performance using the predict method of the pipeline with the test set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply predict method to test data","message":["%python","","gwr_predictions_test = gwr_pipe.predict(X_test.drop([\"MEDIAN_INCOME\"])).flatten()","gwr_predictions_test[0:9]"],"enabled":true,"result":{"startTime":1737662045238,"interpreter":"python.low","endTime":1737662046730,"results":[{"message":"array([ 82901.84027028, 112123.17759644,  73075.38231379, 141739.26673521,\n       136057.50026255,  66468.16244586,  71026.4994907 ,  83939.300287  ,\n        93084.32378569])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps comparing predictions to target","message":["%python","","fig, ax = plt.subplots(1, 2, figsize=(30,15))","","# Set plot's labels and titles ","ax[0].set_title('Test data', fontsize=25);","ax[1].set_title('GWR prediction',  fontsize=25);","","# Plot the choropleth map","X_test.plot(ax=ax[0], column=X_test[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True  )","X_test.plot(ax=ax[1], column=gwr_predictions_test, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True  )"],"enabled":true,"result":{"startTime":1737662047256,"interpreter":"python.low","endTime":1737662049252,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The following metrics are useful for evaluating model performance:","* **r2** measures how well the model explains variability in the dependent variable. ","* **mae** mean absolute error, is the average of the absolute value of the difference between the predictions and the actual values."],"enabled":true,"result":{"startTime":1737662049775,"interpreter":"md.low","endTime":1737662050240,"results":[{"message":"<p>The following metrics are useful for evaluating model performance:<\/p>\n<ul>\n<li><strong>r2<\/strong> measures how well the model explains variability in the dependent variable.<\/li>\n<li><strong>mae<\/strong> mean absolute error, is the average of the absolute value of the difference between the predictions and the actual values.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review test set metrics","message":["%python","","print(\"GWR test set metrics\")","print(\"r2: \",metrics.r2_score(X_test['MEDIAN_INCOME'].get_values(), gwr_predictions_test))","print(\"mae: \",metrics.mean_absolute_error(X_test['MEDIAN_INCOME'].get_values(), gwr_predictions_test))"],"enabled":true,"result":{"startTime":1737662050744,"interpreter":"python.low","endTime":1737662051612,"results":[{"message":"GWR test set metrics\nr2:  0.7323021551838308\nmae:  15008.591678918341\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create OLS metrics for comparison","message":["%python","","ols_pipe = SpatialPipeline([('scale', RobustScaler()),('regression', OLSRegressor())])","ols_pipe.fit(X_train, \"MEDIAN_INCOME\")","ols_predictions_test = ols_pipe.predict(X_test.drop([\"MEDIAN_INCOME\"])).flatten()","print(\"OLS test set metrics\")","print(\"r2: \",metrics.r2_score(X_test['MEDIAN_INCOME'].get_values(), ols_predictions_test))","print(\"mae: \",metrics.mean_absolute_error(X_test['MEDIAN_INCOME'].get_values(), ols_predictions_test))","",""," "],"enabled":true,"result":{"startTime":1737662052119,"interpreter":"python.low","endTime":1737662053313,"results":[{"message":"OLS test set metrics\nr2:  0.6507811702546026\nmae:  17902.6877887209\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Observe the GWR model metrics indicate more reliable predictioons than a global OLS model.","","In summary, GWR is well suited for scenarios involving spatial heterogeniety and generally provides more reliabale predictions than a OLS model. However training a GWR model is more time consuming than training an OLS model since multiple regression equations are estimated. When using GWR, if the parameter `spatial_weights_definition` is not defined, the GWR model estimates an optimal bandwidth value. However this operation can be extremely time-consuming so it is generally better to do tests by providing a variety of spatial weights definitions."],"enabled":true,"result":{"startTime":1737662053842,"interpreter":"md.low","endTime":1737662054329,"results":[{"message":"<p>Observe the GWR model metrics indicate more reliable predictioons than a global OLS model.<\/p>\n<p>In summary, GWR is well suited for scenarios involving spatial heterogeniety and generally provides more reliabale predictions than a OLS model. However training a GWR model is more time consuming than training an OLS model since multiple regression equations are estimated. When using GWR, if the parameter <code>spatial_weights_definition<\/code> is not defined, the GWR model estimates an optimal bandwidth value. However this operation can be extremely time-consuming so it is generally better to do tests by providing a variety of spatial weights definitions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1737662054844,"interpreter":"md.low","endTime":1737662055322,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Geographical Classifier","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Geographical Classifier - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you create models using geographical classification algorithms to predict a categorical target variable incorporating spatial effects.  You will use will use the Los Angeles Housing dataset which contains housing information from the US Census for regions called block groups. ","","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737652334482,"interpreter":"md.low","endTime":1737652334990,"results":[{"message":"<h1 id=\"geographical-classifier---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Geographical Classifier - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you create models using geographical classification algorithms to predict a categorical target variable incorporating spatial effects.  You will use will use the Los Angeles Housing dataset which contains housing information from the US Census for regions called block groups.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737652335539,"interpreter":"md.low","endTime":1737652336010,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import sys","import shutil","import math","import random","import matplotlib.pyplot as plt","import matplotlib.image as mpimg","from matplotlib import cm","import numpy as np","import pandas as pd","import warnings","import geopandas as gpd","","from shapely.geometry import Point, Polygon, LineString, box","from sklearn.preprocessing import RobustScaler","from sklearn.metrics import f1_score, accuracy_score","from sklearn.ensemble import RandomForestClassifier","from sklearn.svm import SVC","","import oml","","# Oracle Spatial AI API","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition","from oraclesai.analysis import MoranITest","from oraclesai.pipeline import SpatialPipeline","from oraclesai.classification import GeographicalClassifier","from oraclesai import enable_geodataframes","from oraclesai.preprocessing import spatial_train_test_split","enable_geodataframes(z)","","pd.options.mode.chained_assignment = None","pd.options.display.max_columns = None","random_state = 456","random.seed(random_state)"],"enabled":true,"result":{"startTime":1737652336539,"interpreter":"python.low","endTime":1737652337086,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a SpatialDataFrame for LA_MEDIAN_HOUSE_VALUES table","message":["%python","","house_values_sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_median_house_values'))","","# Convert to projected coordinate system as required for spatial weights","house_values_sdf = house_values_sdf.to_crs('epsg:3857')","","z.show(house_values_sdf.head())"],"enabled":true,"result":{"startTime":1737652337657,"interpreter":"python.low","endTime":1737652338747,"results":[{"message":"GEOID\tHOUSE_VALUE_MEDIAN\tBEDROOMS_MEDIAN\tBEDROOMS_TOTAL\tCOMPLETE_KITCHEN_PERC\tCOMPLETE_PLUMBING_PERC\tEDU_LEVEL_SCORE_MEDIAN\tHOUSE_AGE_MEDIAN\tPOPULATION_DENSITY\tPOPULATION_TOTAL\tRENTED_PERC\tROOMS_MEDIAN\tROOMS_TOTAL\tUNITS_MEAN\tUNITS_MEDIAN\tUNITS_TOTAL\tYEARS_BUILT_MEDIAN\tgeometry\n060372149021\t771700\t1\t1158\t0.967\t1.0\t20\t39\t0.607\t1010\t0.863\t3.2\t2675\t29.654\t34.5\t24850.0\t1975.0\tPOLYGON ((-13178420.883 4038686.844, -13178415.429 4039069.331, -13178055.087 4039104.005, -13177934.64 4039064.089, -13177867.959 4039063.955, -13177757.308 4039017.454, -13177621.609 4039016.513, -13177610.589 4038659.697, -13177602.685 4038402.205, -13177599.345 4038344.822, -13177730.925 4038465.368, -13178420.327 4038490.365, -13178420.883 4038686.844))\n060372149022\t888800\t2\t1880\t1.0\t1.0\t20\t35\t0.303\t1568\t0.676\t3.4\t4098\t18.473\t14.5\t20745.0\t1979.0\tPOLYGON ((-13178420.327 4038490.365, -13177730.925 4038465.368, -13177599.345 4038344.822, -13177528.769 4038218.229, -13177576.302 4038115.961, -13178298.432 4038163.265, -13178419.325 4038173.747, -13178420.327 4038490.365))\n060372170011\t1554100\t3\t1149\t0.966\t0.939\t20\t75\t0.251\t1371\t0.327\t6.1\t2470\t2.666\t1.0\t1090.5\tnan\tPOLYGON ((-13179224.61 4035337.076, -13179065.98 4035459.741, -13179056.629 4035846.15, -13178831.541 4035846.956, -13178717.216 4035847.225, -13178455.615 4035815.113, -13178482.109 4035598.798, -13178613.8 4035364.619, -13178677.029 4035085.168, -13179116.407 4035185.393, -13179183.088 4035245.045, -13179224.61 4035337.076))\n060372187012\t860300\t2\t1208\t1.0\t1.0\t17\t75\t0.543\t1200\t0.562\t5.2\t2728\t6.915\t1.0\t3333.0\tnan\tPOLYGON ((-13173305.975 4034080.68, -13173256.215 4034153.626, -13173139.107 4034154.029, -13172552.565 4034156.447, -13172843.777 4033523.73, -13172856.022 4033497.132, -13173558.448 4033457.102, -13173305.975 4034080.68))\n060372197002\t654500\t2\t665\t0.947\t1.0\t15\t75\t0.4\t1055\t0.757\t3.3\t1514\t7.782\t2.0\t3369.5\tnan\tPOLYGON ((-13174667.079 4033393.429, -13174153.117 4033422.982, -13173992.037 4033432.251, -13173558.448 4033457.102, -13173737.004 4033174.071, -13173926.693 4033173.265, -13174762.368 4033169.37, -13174667.079 4033393.429))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The GeographicalClassifier needs a categorical target variable. Your dataset does not incude a categorical target variable, so next you generate one for HOUSE_VALUE_MEDIAN as follows:","","* House prices below the mean minus 0.5 standard deviation are classified as Low","* House prices above the mean plus 0.5 standard deviation are classified as High","* House prices between low and high are classified as Medium","","The first step is to create an array of categorical target values."," "],"enabled":true,"result":{"startTime":1737652339340,"interpreter":"md.low","endTime":1737652339902,"results":[{"message":"<p>The GeographicalClassifier needs a categorical target variable. Your dataset does not incude a categorical target variable, so next you generate one for HOUSE_VALUE_MEDIAN as follows:<\/p>\n<ul>\n<li>House prices below the mean minus 0.5 standard deviation are classified as Low<\/li>\n<li>House prices above the mean plus 0.5 standard deviation are classified as High<\/li>\n<li>House prices between low and high are classified as Medium<\/li>\n<\/ul>\n<p>The first step is to create an array of categorical target values.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Classify HOUSE_VALUE_MEDIAN values","message":["%python","","y = house_values_sdf[\"HOUSE_VALUE_MEDIAN\"].get_values()","y_mean = y.mean()","y_std = y.std()","","def classify(val):","    if val < y_mean - y_std * 0.5:","        return 'Low'","    if val > y_mean + y_std * 0.5:","        return 'High'","    return 'Medium'","","y_class = [classify(val) for val in y]","","# Plot a histogram of categorical target values","plt.hist(y_class)"," "],"enabled":true,"result":{"startTime":1737652340469,"interpreter":"python.low","endTime":1737652353219,"results":[{"message":"(array([2362.,    0.,    0.,    0.,    0., 1242.,    0.,    0.,    0.,\n       2098.]), array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]), <BarContainer object of 10 artists>)\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you create a SpatialDataFrame with the block group id (GEIOD), the newly created categorical target variable, and features to use for prediction."],"enabled":true,"result":{"startTime":1737652353753,"interpreter":"md.low","endTime":1737652354470,"results":[{"message":"<p>Next you create a SpatialDataFrame with the block group id (GEIOD), the newly created categorical target variable, and features to use for prediction.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame with categorical target variable and features","message":["%python","","house_values_sdf = house_values_sdf.add_column('HOUSE_VALUE_CLASS',y_class)","target = ['HOUSE_VALUE_CLASS']","features = ['BEDROOMS_TOTAL',","            'EDU_LEVEL_SCORE_MEDIAN',","            'POPULATION_DENSITY',","            'ROOMS_TOTAL',","            'COMPLETE_PLUMBING_PERC',","            'COMPLETE_KITCHEN_PERC',","            'HOUSE_AGE_MEDIAN',","            'RENTED_PERC',","            'UNITS_TOTAL']","","house_values_sdf = house_values_sdf[[\"GEOID\"] + target + features]","house_values_sdf = house_values_sdf.dropna()","z.show(house_values_sdf.head(50))"],"enabled":true,"result":{"startTime":1737652354985,"interpreter":"python.low","endTime":1737652367761,"results":[{"message":"GEOID\tHOUSE_VALUE_CLASS\tBEDROOMS_TOTAL\tEDU_LEVEL_SCORE_MEDIAN\tPOPULATION_DENSITY\tROOMS_TOTAL\tCOMPLETE_PLUMBING_PERC\tCOMPLETE_KITCHEN_PERC\tHOUSE_AGE_MEDIAN\tRENTED_PERC\tUNITS_TOTAL\tgeometry\n060372149021\tMedium\t1158\t20.0\t0.607\t2675\t1.0\t0.967\t39.0\t0.863\t24850.0\tPOLYGON ((-13178420.883 4038686.844, -13178415.429 4039069.331, -13178055.087 4039104.005, -13177934.64 4039064.089, -13177867.959 4039063.955, -13177757.308 4039017.454, -13177621.609 4039016.513, -13177610.589 4038659.697, -13177602.685 4038402.205, -13177599.345 4038344.822, -13177730.925 4038465.368, -13178420.327 4038490.365, -13178420.883 4038686.844))\n060372149022\tHigh\t1880\t20.0\t0.303\t4098\t1.0\t1.0\t35.0\t0.676\t20745.0\tPOLYGON ((-13178420.327 4038490.365, -13177730.925 4038465.368, -13177599.345 4038344.822, 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((-13138918.494 4019143.51, -13138432.918 4018734.994, -13138413.994 4018718.895, -13138285.531 4018619.217, -13138366.795 4018596.679, -13138933.3 4018438.108, -13138918.494 4019143.51))\n060375313022\tLow\t978\t15.0\t0.982\t1926\t1.0\t1.0\t60.0\t0.563\t1132.0\tPOLYGON ((-13157102.644 4031529.623, -13155986.332 4031509.88, -13155743.433 4031445.816, -13155881.247 4031339.58, -13155881.469 4030905.647, -13156422.816 4030903.632, -13156426.49 4031339.311, -13157101.308 4031335.685, -13157102.644 4031529.623))\n060375330012\tLow\t1213\t15.0\t0.817\t2558\t1.0\t0.995\t57.0\t0.687\t731.0\tPOLYGON ((-13163272.416 4026119.381, -13162976.194 4026116.831, -13162868.326 4026174.287, -13162789.289 4026172.676, -13162786.283 4025411.132, -13163269.521 4025413.28, -13163272.416 4026119.381))\n060375403003\tLow\t323\t15.0\t1.823\t641\t1.0\t1.0\t60.0\t0.333\t200.0\tPOLYGON ((-13160867.803 4019760.94, -13160527.054 4019582.631, -13160340.149 4019482.14, -13160248.756 4019760.806, -13160185.971 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4020439.991, -13160073.539 4020399.47, -13159675.683 4020351.032, -13159809.044 4019721.897, -13159781.102 4019576.862, -13160185.971 4019619.795, -13160248.756 4019760.806, -13160340.149 4019482.14, -13160527.054 4019582.631, -13160426.867 4020124.411))\n060375405011\tLow\t556\t15.0\t0.845\t1073\t1.0\t1.0\t57.0\t0.293\t628.5\tPOLYGON ((-13159973.796 4018959.307, -13159848.228 4019160.146, -13159059.975 4018744.519, -13159025.577 4018234.598, -13159050.513 4018375.056, -13159615.348 4018538.053, -13159577.944 4018663.086, -13159979.14 4018794.157, -13159973.796 4018959.307))\n060375403004\tLow\t926\t15.0\t0.801\t2094\t0.951\t0.951\t52.0\t0.689\t2103.0\tPOLYGON ((-13159809.044 4019721.897, -13159675.683 4020351.032, -13159543.101 4020335.199, -13159463.063 4020325.27, -13159436.123 4020249.729, -13159365.213 4019976.284, -13159256.454 4019558.883, -13159341.279 4019536.075, -13159781.102 4019576.862, -13159809.044 4019721.897))\n060375404001\tLow\t640\t14.0\t0.629\t1171\t0.968\t0.944\t35.0\t0.487\t280.5\tPOLYGON ((-13161215.788 4020390.211, -13160944.057 4020358.68, -13160867.803 4019760.94, -13160858.452 4019697.747, -13161326.996 4019941.534, -13161215.788 4020390.211))\n060375404002\tLow\t594\t14.0\t0.851\t1094\t1.0\t0.987\t43.0\t0.605\t442.0\tPOLYGON ((-13161378.315 4019734.643, -13161326.996 4019941.534, -13160858.452 4019697.747, -13160814.593 4019359.781, -13160811.476 4019324.63, -13160930.365 4019289.748, -13161265.771 4019270.965, -13161339.464 4019272.575, -13161403.473 4019633.48, -13161378.315 4019734.643))\n060373012031\tMedium\t1649\t20.0\t0.543\t3710\t1.0\t1.0\t51.0\t0.77\t17361.0\tPOLYGON ((-13164744.059 4051514.543, -13164292.77 4051446.06, -13164291.1 4051213.842, -13164103.416 4051213.303, -13164103.638 4050799.6, -13164655.004 4050802.829, -13164657.007 4050904.403, -13164744.059 4051514.543))\n060373012032\tMedium\t2255\t18.0\t0.845\t5372\t1.0\t0.994\t42.0\t0.425\t31768.5\tPOLYGON ((-13165335.722 4051599.979, -13164758.976 4051516.83, -13164744.059 4051514.543, -13164657.007 4050904.403, -13164655.004 4050802.829, -13164811.63 4050797.716, -13165189.449 4050719.686, -13165218.726 4050880.052, -13165335.722 4051599.979))\n060375331051\tLow\t1124\t15.0\t0.585\t2645\t1.0\t0.976\t55.0\t0.864\t4028.0\tPOLYGON ((-13160099.921 4026769.946, -13159490.781 4026695.705, -13159483.545 4026231.61, -13159979.251 4026299.807, -13160092.908 4026333.905, -13160099.588 4026756.924, -13160099.921 4026769.946))\n060375332012\tLow\t937\t15.0\t0.915\t2049\t0.974\t0.974\t69.0\t0.653\t942.0\tPOLYGON ((-13159136.674 4026621.331, -13158412.095 4026532.189, -13158251.127 4026461.709, -13158247.009 4026121.932, -13158223.965 4026071.322, -13158404.414 4026095.486, -13159127.546 4026186.101, -13159136.674 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test = house_values_sdf.split(ratio=(0.8, 0.2), seed=random_state)","","X_train = train.drop('GEOID').drop(target)","y_train = train[target]","","X_test = test.drop('GEOID').drop(target)","y_test = test[target]"],"enabled":true,"result":{"startTime":1737652368294,"interpreter":"python.low","endTime":1737652368854,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","Next you train classification models and make predictions, first using a non-spatial scikit-learn algorithm and then using spatial algorithms that combine localized and global scikit-learn algorithms. For each algorithm, you test and evaluate model performance. You calculate the model's accuracy_score, which represents the percent of test predictions that are correct."," "],"enabled":true,"result":{"startTime":1737652369453,"interpreter":"md.low","endTime":1737652369904,"results":[{"message":"<p>Next you train classification models and make predictions, first using a non-spatial scikit-learn algorithm and then using spatial algorithms that combine localized and global scikit-learn algorithms. For each algorithm, you test and evaluate model performance. You calculate the model's accuracy_score, which represents the percent of test predictions that are correct.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a non-spatial RandomForestClassifier model","message":["%python","","rfc_model = SpatialPipeline([('model', RandomForestClassifier(n_estimators=10, random_state=random_state))]) ","rfc_model.fit(X_train, y_train)"],"enabled":true,"result":{"startTime":1737652370475,"interpreter":"python.low","endTime":1737652371200,"results":[{"message":"/usr/local/lib/python3.12/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n  return fit_method(estimator, *args, **kwargs)\nSpatialPipeline(steps=[('model',\n                        RandomForestClassifier(n_estimators=10,\n                                               random_state=456))])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Test and score the non-spatial RandomForestClassifier model","message":["%python","","predictions_test_rfc = rfc_model.predict(X_test)","","test_df = pd.DataFrame(y_test.get_values().flatten(), columns=['Target'])","test_df.insert(1, 'Prediction', predictions_test_rfc)","print('\\nExample results: ')","print(test_df.head(10))","","accuracy_test_rfc = round(accuracy_score(y_test.get_values(), predictions_test_rfc), 5)","print('\\nAccuracy score:', accuracy_test_rfc)",""," "],"enabled":true,"result":{"startTime":1737652371714,"interpreter":"python.low","endTime":1737652372274,"results":[{"message":"\nExample results: \n   Target Prediction\n0    High       High\n1  Medium     Medium\n2  Medium        Low\n3     Low        Low\n4     Low        Low\n5     Low        Low\n6  Medium     Medium\n7     Low        Low\n8     Low        Low\n9     Low        Low\n\nAccuracy score: 0.67584\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Before creating spatial models, you create spatial weights which describe the spatial relationships of block groups, in this case nearest neighbors. The weights are used in spatial model training and prediction."," "],"enabled":true,"result":{"startTime":1737652373018,"interpreter":"md.low","endTime":1737652373486,"results":[{"message":"<p>Before creating spatial models, you create spatial weights which describe the spatial relationships of block groups, in this case nearest neighbors. The weights are used in spatial model training and prediction.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create spatial weights for split data","message":["%python","","knn_w_def = KNNWeightsDefinition(k=25)","train_weights = SpatialWeights.create(X_train, knn_w_def)","test_weights = SpatialWeights.create(X_test, knn_w_def)"],"enabled":true,"result":{"startTime":1737652374009,"interpreter":"python.low","endTime":1737652374728,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","GeographicalClassifier uses a scikit-learn classification algorithm to create global and local models, where \"local\" is defined by spatial weights. Predictions are made using a combination of the global and local models. Because the algorithm requires the training of numerous local models, it is expected that the time to train is significantly greater than a non-spatial global model. By setting the training parameter n_jobs=-1, the workload is spread across available cores and will improve performance. ","","Next you create a model with GeographicalClassifier using RandomForestClassifier. You set local_weight=0.8 which means that 80% of the prediction result is affected by local models rather than the global model."],"enabled":true,"result":{"startTime":1737652375362,"interpreter":"md.low","endTime":1737652375853,"results":[{"message":"<p>GeographicalClassifier uses a scikit-learn classification algorithm to create global and local models, where &quot;local&quot; is defined by spatial weights. Predictions are made using a combination of the global and local models. Because the algorithm requires the training of numerous local models, it is expected that the time to train is significantly greater than a non-spatial global model. By setting the training parameter n_jobs=-1, the workload is spread across available cores and will improve performance.<\/p>\n<p>Next you create a model with GeographicalClassifier using RandomForestClassifier. You set local_weight=0.8 which means that 80% of the prediction result is affected by local models rather than the global model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a GeographicalClassifier model using RandomForestClassifier","message":["%python","","grfc_model = GeographicalClassifier(model_cls=RandomForestClassifier, n_estimators=10, local_weight=0.80, random_state=random_state)","grfc_model.fit(X_train, y_train, n_jobs=-1, spatial_weights=train_weights)"],"enabled":true,"result":{"startTime":1737652376426,"interpreter":"python.low","endTime":1737652411084,"results":[{"message":"GeographicalClassifier(global_model=RandomForestClassifier(n_estimators=10,\n                                                           random_state=456),\n                       local_weight=0.8,\n                       model_cls=<class 'sklearn.ensemble._forest.RandomForestClassifier'>)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Test and score GeographicalClassifier model using RandomForestClassifier","message":["%python","","predictions_test_grfc = grfc_model.predict(X_test)","","test_df = pd.DataFrame(test[['GEOID']].get_values().flatten(), columns=['GEOID'])","test_df.insert(1, 'Target', y_test.get_values().flatten())","test_df.insert(2, 'Prediction', predictions_test_grfc)","","print('\\nExample results: ')","print(test_df.head(10))","","accuracy_test_grfc = round(accuracy_score(y_test.get_values(), predictions_test_grfc), 5)","print('\\nAccuracy score:', accuracy_test_grfc)"," "],"enabled":true,"result":{"startTime":1737652411635,"interpreter":"python.low","endTime":1737652412950,"results":[{"message":"\nExample results: \n          GEOID  Target Prediction\n0  060372187012    High     Medium\n1  060372197002  Medium     Medium\n2  060372318001  Medium        Low\n3  060372374013     Low        Low\n4  060372392013     Low        Low\n5  060375330021     Low        Low\n6  060375031062  Medium        Low\n7  060375405011     Low        Low\n8  060375331051     Low        Low\n9  060375407002     Low        Low\n\nAccuracy score: 0.72202\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you create a model with GeographicalClassifier using SupportVectorClassifier (SVC)."],"enabled":true,"result":{"startTime":1737652413474,"interpreter":"md.low","endTime":1737652414073,"results":[{"message":"<p>Next you create a model with GeographicalClassifier using SupportVectorClassifier (SVC).<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a GeographicalClassifier model using SupportVectorClassifier (SVC)","message":["%python","","gsvc_model = GeographicalClassifier(model_cls=SVC, probability=True, local_weight=0.8, random_state=random_state) ","gsvc_model.fit(X_train, y_train, n_jobs=-1, spatial_weights=train_weights)"],"enabled":true,"result":{"startTime":1737652414586,"interpreter":"python.low","endTime":1737652424410,"results":[{"message":"GeographicalClassifier(global_model=SVC(probability=True, random_state=456),\n                       local_weight=0.8,\n                       model_cls=<class 'sklearn.svm._classes.SVC'>)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Test and score GeographicalClassifier model using SupportVectorClassifier (SVC)","message":["%python","","predictions_test_gsvc = gsvc_model.predict(X_test)","","test_df = pd.DataFrame(test[['GEOID']].get_values().flatten(), columns=['GEOID'])","test_df.insert(1, 'Target', y_test.get_values().flatten())","test_df.insert(2, 'Prediction', predictions_test_gsvc)","","print('\\nExample results: ')","print(test_df.head(10))","","accuracy_test_gsvc = round(accuracy_score(y_test.get_values(), predictions_test_gsvc), 5)","print('\\nAccuracy score:', accuracy_test_gsvc)"," "],"enabled":true,"result":{"startTime":1737652424897,"interpreter":"python.low","endTime":1737652425870,"results":[{"message":"\nExample results: \n          GEOID  Target Prediction\n0  060372187012    High     Medium\n1  060372197002  Medium     Medium\n2  060372318001  Medium        Low\n3  060372374013     Low        Low\n4  060372392013     Low        Low\n5  060375330021     Low        Low\n6  060375031062  Medium     Medium\n7  060375405011     Low        Low\n8  060375331051     Low        Low\n9  060375407002     Low        Low\n\nAccuracy score: 0.70604\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Finally you compare the accurace scores of the non-spatial model and 2 spatial models. "],"enabled":true,"result":{"startTime":1737652426352,"interpreter":"md.low","endTime":1737652426813,"results":[{"message":"<p>Finally you compare the accurace scores of the non-spatial model and 2 spatial models.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare accuracy scores","message":["%python","","print('Accuracy scores:') ","print('Non-spatial RandomForestClassifier\\t\\t\\t', accuracy_test_rfc)","print('GeographicalClassifier using RandomForestClassifier\\t', accuracy_test_grfc)","print('GeographicalClassifier using SupportVectorClassifier\\t', accuracy_test_gsvc)"],"enabled":true,"result":{"startTime":1737652427294,"interpreter":"python.low","endTime":1737652427770,"results":[{"message":"Accuracy scores:\nNon-spatial RandomForestClassifier\t\t\t 0.67584\nGeographicalClassifier using RandomForestClassifier\t 0.72202\nGeographicalClassifier using SupportVectorClassifier\t 0.70604\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","","","Observe that the spatial models improve the models prediction, as they capture regional variation in the behavior of the data. The underlying spatial relationships of block groups specified by spatial weights are key to spatial model behavior. To best define spatial weights, experimenting with various weights definitions and parameters is generally required. If spatial weights are not supplied for model training then a weights definition will be auto-seleted based on training and testing with numerous definitions. However, since many local models need to be trained, the training time is dramatically longer than non-spatial models."],"enabled":true,"result":{"startTime":1737652428334,"interpreter":"md.low","endTime":1737652428796,"results":[{"message":"<p>Observe that the spatial models improve the models prediction, as they capture regional variation in the behavior of the data. The underlying spatial relationships of block groups specified by spatial weights are key to spatial model behavior. To best define spatial weights, experimenting with various weights definitions and parameters is generally required. If spatial weights are not supplied for model training then a weights definition will be auto-seleted based on training and testing with numerous definitions. However, since many local models need to be trained, the training time is dramatically longer than non-spatial models.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"," "],"enabled":true,"result":{"startTime":1737652429375,"interpreter":"md.low","endTime":1737652429873,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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Because numerous local models are trained, the time to train a Geographical Regressor is significantly longer than training solely a global model. A geographical model may improve the quality of the predictions when there is spatial variance in the data.","","In this notebook, you train and compare non-spatial and GeographicalRegressor models based on sklearn RandomForestRegressor.  ","","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737652557611,"interpreter":"md.low","endTime":1737652558219,"results":[{"message":"<h1 id=\"geographical-regressor---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Geographical Regressor - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>The GeographicalRegressor uses a sklearn regressor to create a global model containing all training observations and multiple local models. Predictions are based on a weighted combination global and the local model. Because numerous local models are trained, the time to train a Geographical Regressor is significantly longer than training solely a global model. A geographical model may improve the quality of the predictions when there is spatial variance in the data.<\/p>\n<p>In this notebook, you train and compare non-spatial and GeographicalRegressor models based on sklearn RandomForestRegressor.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737652558842,"interpreter":"md.low","endTime":1737652559462,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load required libraries","message":["%python","","import os","import sys","import shutil","import math","import random","import matplotlib.pyplot as plt","import matplotlib.image as mpimg","from matplotlib import cm","import numpy as np","import pandas as pd","import warnings","import geopandas as gpd","","import oml","","","from shapely.geometry import Point, Polygon, LineString, box","from sklearn.preprocessing import RobustScaler","from sklearn.metrics import r2_score","from sklearn.ensemble import RandomForestRegressor","from sklearn.svm import SVR","","import oml","","# Oracle Spatial AI API","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition","from oraclesai.analysis import MoranITest","from oraclesai.pipeline import SpatialPipeline","from oraclesai.regression import GeographicalRegressor","from oraclesai.classification import GeographicalClassifier","from oraclesai import enable_geodataframes","enable_geodataframes(z)","","random_state = 456","random.seed(random_state)"],"enabled":true,"result":{"startTime":1737652559958,"interpreter":"python.low","endTime":1737652563831,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You will use housing data from the US Census for the City of Los Angeles to predict home values. The dataset includes house value and numerous features for regions called block groups."],"enabled":true,"result":{"startTime":1737652564358,"interpreter":"md.low","endTime":1737652564809,"results":[{"message":"<p>You will use housing data from the US Census for the City of Los Angeles to predict home values. The dataset includes house value and numerous features for regions called block groups.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_MEDIAN_HOUSE_VALES table","message":["%python","","houses_full = SpatialDataFrame.create(DBSpatialDataset( table='la_median_house_values'))","# convert to projected coordinate system as required fo spatial weights","houses_full = 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'COMPLETE_PLUMBING_PERC',","    'COMPLETE_KITCHEN_PERC',","    'HOUSE_AGE_MEDIAN',","    'RENTED_PERC',","    'UNITS_TOTAL'","]","","tgt_col = 'HOUSE_VALUE_MEDIAN'","","# select a subset of columns","houses = houses_full[[tgt_col] + feat_cols]","","# remove rows with null values","houses = houses.dropna()","","# show first rows","z.show(houses.head())"],"enabled":true,"result":{"startTime":1737652566517,"interpreter":"python.low","endTime":1737652567032,"results":[{"message":"HOUSE_VALUE_MEDIAN\tBEDROOMS_TOTAL\tEDU_LEVEL_SCORE_MEDIAN\tPOPULATION_DENSITY\tROOMS_TOTAL\tCOMPLETE_PLUMBING_PERC\tCOMPLETE_KITCHEN_PERC\tHOUSE_AGE_MEDIAN\tRENTED_PERC\tUNITS_TOTAL\tgeometry\n771700\t1158\t20\t0.607\t2675\t1.0\t0.967\t39\t0.863\t24850.0\tPOLYGON ((-13178420.883 4038686.844, -13178415.429 4039069.331, -13178055.087 4039104.005, -13177934.64 4039064.089, -13177867.959 4039063.955, -13177757.308 4039017.454, -13177621.609 4039016.513, -13177610.589 4038659.697, -13177602.685 4038402.205, 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test[tgt_col].values","","X_valid = valid.drop(tgt_col)","y_valid = valid[tgt_col].values"],"enabled":true,"result":{"startTime":1737652567585,"interpreter":"python.low","endTime":1737652576797,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create spatial weights","message":["%python","","knn_w_def = KNNWeightsDefinition(k=3)","train_weights = SpatialWeights.create(X_train, knn_w_def)","valid_weights = SpatialWeights.create(X_valid, knn_w_def)","test_weights = SpatialWeights.create(X_test, knn_w_def)"],"enabled":true,"result":{"startTime":1737652577287,"interpreter":"python.low","endTime":1737652586076,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a non-spatial RandomForestRegressor","message":["%python","","rf_model = SpatialPipeline([('model', RandomForestRegressor(n_estimators=10, random_state=random_state))])","rf_model.fit(X_train, y_train)"],"enabled":true,"result":{"startTime":1737652586578,"interpreter":"python.low","endTime":1737652587521,"results":[{"message":"SpatialPipeline(steps=[('model',\n                        RandomForestRegressor(n_estimators=10,\n                                              random_state=456))])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","Next you train a GeographicalRegressor using RandomForestRegressor. Training takes considerably longer as it is training multiple local models based on the spatial weights. The fit() parameter n_jobs is used to specify the number of concurrent tasks that can be performed to train local models. The value -1 means that it will use all available CPUs. "],"enabled":true,"result":{"startTime":1737652588035,"interpreter":"md.low","endTime":1737652588478,"results":[{"message":"<p>Next you train a GeographicalRegressor using RandomForestRegressor. Training takes considerably longer as it is training multiple local models based on the spatial weights. The fit() parameter n_jobs is used to specify the number of concurrent tasks that can be performed to train local models. The value -1 means that it will use all available CPUs.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a GeographicalRegressor using RandomForestRegressor","message":["%python","","grf_model = GeographicalRegressor(model_cls=RandomForestRegressor, local_weight=0.8, n_estimators=10, random_state=random_state)","grf_model.fit(X_train, y=y_train, spatial_weights=train_weights, n_jobs=-1)"],"enabled":true,"result":{"startTime":1737652588962,"interpreter":"python.low","endTime":1737652612776,"results":[{"message":"GeographicalRegressor(global_model=RandomForestRegressor(n_estimators=10,\n                                                         random_state=456),\n                      local_weight=0.8,\n                      model_cls=<class 'sklearn.ensemble._forest.RandomForestRegressor'>)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review some predictions","message":["%python","","print(\"Test target variable\")","print(y_test[0:9]) ","print(\"\\nRandomForestRegressor\")","print(rf_model.predict(X_test)[0:9]) ","print(\"\\nGeographicalRegressor using RandomForestRegressor\")","print(grf_model.predict(X_test)[0:9])"],"enabled":true,"result":{"startTime":1737652613270,"interpreter":"python.low","endTime":1737652614977,"results":[{"message":"Test target variable\n[771700 888800 860300 477800 464600 480600 420200 384300 323300]\n\nRandomForestRegressor\n[734250. 927950. 675210. 697030. 423920. 483990. 435880. 420180. 361610.]\n\nGeographicalRegressor using RandomForestRegressor\n[ 970346. 1178294.  657074.  852374.  387832.  433982.  415832.  370196.\n  361642.]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Finally you compare model statistics:","* R-squared measures how well the model explains variation of the dependent variable from independent variables. Larger values indicate more reliable predictions.","* Spatial autocorrrelation in residuals measures the presence of a spatial pattern in prediction errors. Smaller values indicate more reliable predictions."],"enabled":true,"result":{"startTime":1737652615478,"interpreter":"md.low","endTime":1737652615952,"results":[{"message":"<p>Finally you compare model statistics:<\/p>\n<ul>\n<li>R-squared measures how well the model explains variation of the dependent variable from independent variables. Larger values indicate more reliable predictions.<\/li>\n<li>Spatial autocorrrelation in residuals measures the presence of a spatial pattern in prediction errors. Smaller values indicate more reliable predictions.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare model statistics","message":["%python","","print('*** Non-spatial model ***')","","# validation","preds = rf_model.predict(X_valid)","r2 = r2_score(y_valid, preds)","print('Validation')","print('\\tR-squared:', r2)","","# test","print('Test results:')","preds = rf_model.predict(X_test)","r2 = r2_score(y_test, preds)","print('\\tR-squared:', r2)","","# check spatial autocorrelation in the residuals","residuals = y_test - preds","moran_i_result = MoranITest.create(residuals, test_weights)","print('\\tSpatial autocorrrelation in residuals:')","print('\\t\\tObserved I:', moran_i_result.i, '\\n\\t\\tExpected I:', moran_i_result.expected_i, '\\n\\t\\tp-value:', moran_i_result.p_value)","","print('\\n*** GeographicalRegressor model ***')","","# validation","preds = grf_model.predict(X_valid)","r2 = r2_score(y_valid, preds)","print('Validation')","print('\\tR-squared:', r2)","","# test","preds = grf_model.predict(X_test)","r2 = r2_score(y_test, preds)","print('Test')","print('\\tR-squared:', r2)","","# check spatial autocorrelation in the residuals","residuals = y_test - preds","moran_i_result = MoranITest.create(residuals, test_weights)","print('\\tSpatial autocorrrelation in residuals:')","print('\\t\\tObserved I:', moran_i_result.i, '\\n\\t\\tExpected I:', moran_i_result.expected_i, '\\n\\t\\tp-value:', moran_i_result.p_value)"," "],"enabled":true,"result":{"startTime":1737652616580,"interpreter":"python.low","endTime":1737652618820,"results":[{"message":"*** Non-spatial model ***\nValidation\n\tR-squared: 0.6340287277654035\nTest results:\n\tR-squared: 0.575448690511084\n\tSpatial autocorrrelation in residuals:\n\t\tObserved I: 0.1800356494006625 \n\t\tExpected I: -0.0005861664712778429 \n\t\tp-value: 0.001\n\n*** GeographicalRegressor model ***\nValidation\n\tR-squared: 0.7127030866785334\nTest\n\tR-squared: 0.7056500827057428\n\tSpatial autocorrrelation in residuals:\n\t\tObserved I: 0.1489903896453732 \n\t\tExpected I: -0.0005861664712778429 \n\t\tp-value: 0.001\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","Observe that compared to a non-spatial RandomForestRegressor, GeographicalRegressor using RandomForestRegressor has a higher R-squared and lower spatial autocorrrelation in residuals, indicating that predictions will be more reliable.","","In summary, GeographicalRegressor models can improve predictions for situations where data behaves differently in different areas. One important parameter of a geographical is the description of the underlying spatial relationship which can be specified as spatial weights definitions or as spatial weights objects. For that, users need to have a prior knowledge of such spatial relationship, otherwise, it needs to be searched by trying different values. The Spatial AI API provide an automatic way to look for the spatial weights definition that better fits a model. Since GeographicalRegressor trains multiple local models, training time can be dramatically longer than regular non-spatial models."],"enabled":true,"result":{"startTime":1737652619312,"interpreter":"md.low","endTime":1737652619873,"results":[{"message":"<p>Observe that compared to a non-spatial RandomForestRegressor, GeographicalRegressor using RandomForestRegressor has a higher R-squared and lower spatial autocorrrelation in residuals, indicating that predictions will be more reliable.<\/p>\n<p>In summary, GeographicalRegressor models can improve predictions for situations where data behaves differently in different areas. One important parameter of a geographical is the description of the underlying spatial relationship which can be specified as spatial weights definitions or as spatial weights objects. For that, users need to have a prior knowledge of such spatial relationship, otherwise, it needs to be searched by trying different values. The Spatial AI API provide an automatic way to look for the spatial weights definition that better fits a model. Since GeographicalRegressor trains multiple local models, training time can be dramatically longer than regular non-spatial models.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737652620381,"interpreter":"md.low","endTime":1737652620871,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Hotspot Clustering","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Hotspot Clustering - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you detect median income hotspots and coldspots in Census data for the City of Los Angeles. ","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "," "],"enabled":true,"result":{"startTime":1737724235407,"interpreter":"md.low","endTime":1737724235955,"results":[{"message":"<h1 id=\"spatial-hotspot-clustering---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Hotspot Clustering - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you detect median income hotspots and coldspots in Census data for the City of Los Angeles.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737724236466,"interpreter":"md.low","endTime":1737724236936,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","######  Local Indicator of Spatial Association (LISA)","","Local Indicator of Spatial Association (LISA) analysis measures the behavior of observed values in a specific region. While the Moran's I test can indicate the presence of positive spatial autocorrelation (i.e., observed values are simlar to nearby values), it does not pinpoint where similar values are clustered. Once the presence of positive spatial autocorrelation is established, LISA is used to identify hotspots (i.e., clustering of relatively high values) and coldspots (i.e., clustering of relatively low values) in the observed values.","","For LISA analysis, we can use a Local Moran's I test, which measures the relationship between an observation and neighboring observations. With LISA, each observastion is labelled with one of 4 possible values, referred to as quadrants 1-4:","","","1. HH (High-High). A High value surrounded by high values (Hotspot).","","1. LH (Low-High). A low value is surrounded by high values (Doughnut).","","1. LL (Low-Low). A low value is surrounded by low values (Coldspot).","","1. HL (High-Low). A high value around low values (Diamond). "," "],"enabled":true,"result":{"startTime":1737724237475,"interpreter":"md.low","endTime":1737724237932,"results":[{"message":"<h6 id=\"local-indicator-of-spatial-association-lisa\">Local Indicator of Spatial Association (LISA)<\/h6>\n<p>Local Indicator of Spatial Association (LISA) analysis measures the behavior of observed values in a specific region. While the Moran's I test can indicate the presence of positive spatial autocorrelation (i.e., observed values are simlar to nearby values), it does not pinpoint where similar values are clustered. Once the presence of positive spatial autocorrelation is established, LISA is used to identify hotspots (i.e., clustering of relatively high values) and coldspots (i.e., clustering of relatively low values) in the observed values.<\/p>\n<p>For LISA analysis, we can use a Local Moran's I test, which measures the relationship between an observation and neighboring observations. With LISA, each observastion is labelled with one of 4 possible values, referred to as quadrants 1-4:<\/p>\n<ol>\n<li>\n<p>HH (High-High). A High value surrounded by high values (Hotspot).<\/p>\n<\/li>\n<li>\n<p>LH (Low-High). A low value is surrounded by high values (Doughnut).<\/p>\n<\/li>\n<li>\n<p>LL (Low-Low). A low value is surrounded by low values (Coldspot).<\/p>\n<\/li>\n<li>\n<p>HL (High-Low). A high value around low values (Diamond).<\/p>\n<\/li>\n<\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas as gpd","import matplotlib.pyplot as plt","from matplotlib.colors import LinearSegmentedColormap","import numpy as np","from sklearn.preprocessing import RobustScaler","from libpysal import weights","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.clustering import LISAHotspotClustering","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai.vis import plot_clusters","from oraclesai import enable_geodataframes","enable_geodataframes(z)","","# Needed for reproducibility.","import random","random_state = 456","random.seed(random_state)"," "],"enabled":true,"result":{"startTime":1737724238418,"interpreter":"python.low","endTime":1737724241990,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a SpatialDataFrame for LA_BLOCK_GROUPS table with median income","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID','MEDIAN_INCOME','geometry']]","z.show(block_groups_sdf.head())"," "],"enabled":true,"result":{"startTime":1737724242528,"interpreter":"python.low","endTime":1737724243106,"results":[{"message":"GEOID\tMEDIAN_INCOME\tgeometry\n060376700031\t75577\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create training data","message":["%python","","X = block_groups_sdf[['MEDIAN_INCOME','geometry']]","z.show(X.head())"],"enabled":true,"result":{"startTime":1737724243628,"interpreter":"python.low","endTime":1737724244119,"results":[{"message":"MEDIAN_INCOME\tgeometry\n75577\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n74609\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n55592\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n73558\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n54738\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Clustering involves distance calulations which require projected coordinates. The following step converts from geodetic (units of longitude, latitude) to projected (units of meters)."],"enabled":true,"result":{"startTime":1737724244592,"interpreter":"md.low","endTime":1737724245024,"results":[{"message":"<p>Clustering involves distance calulations which require projected coordinates. 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You specify `max_p_value` to only label block groups that have <= 5% chance of being spatially random, and the `spatial_weights_definition` parameter, which defines the interaction between an observation and its neighbors. If you were only interested in labelling specific only LISA quadrants, you could also include the parameter `supported_quadrants`."," "," "],"enabled":true,"result":{"startTime":1737724246575,"interpreter":"md.low","endTime":1737724247015,"results":[{"message":"<h6 id=\"detect-hotspots-and-coldspots\">Detect hotspots and coldspots<\/h6>\n<p>You next create an instance of <code>LISAHotspotClustering<\/code> for the column median_income. You specify <code>max_p_value<\/code> to only label block groups that have &lt;= 5% chance of being spatially random, and the <code>spatial_weights_definition<\/code> parameter, which defines the interaction between an observation and its neighbors. If you were only interested in labelling specific only LISA quadrants, you could also include the parameter <code>supported_quadrants<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Peform LISA analysis and review labels","message":["%python","","lisa = LISAHotspotClustering(max_p_value=0.05, spatial_weights_definition=KNNWeightsDefinition(k=5))","lisa.fit(X) ","lisa.labels_"],"enabled":true,"result":{"startTime":1737724247503,"interpreter":"python.low","endTime":1737724257002,"results":[{"message":"array([-1, -1, -1, ...,  3, -1, 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34.067, -118.403 34.062, -118.407 34.062, -118.407 34.065, -118.41 34.066, -118.413 34.063, -118.414 34.063, -118.414 34.064))\n060377015011\t96429\t1\tHOTSPOT\tPOLYGON ((-118.488 34.033, -118.486 34.034, -118.483 34.031, -118.487 34.028, -118.49 34.031, -118.488 34.033))\n060376704071\t87946\t1\tHOTSPOT\tPOLYGON ((-118.391 33.779, -118.39 33.779, -118.381 33.778, -118.379 33.777, -118.381 33.775, -118.381 33.775, -118.385 33.777, -118.39 33.777, -118.391 33.779))\n060376704072\t88516\t1\tHOTSPOT\tPOLYGON ((-118.393 33.777, -118.391 33.779, -118.39 33.777, -118.385 33.777, -118.381 33.775, -118.384 33.775, -118.383 33.773, -118.382 33.772, -118.385 33.771, -118.384 33.774, -118.388 33.775, -118.39 33.776, -118.39 33.774, -118.392 33.773, -118.393 33.777))\n060376704073\t181250\t1\tHOTSPOT\tPOLYGON ((-118.392 33.773, -118.39 33.774, -118.39 33.776, -118.388 33.775, -118.384 33.774, -118.385 33.771, -118.382 33.772, -118.381 33.769, -118.387 33.769, -118.389 33.766, -118.391 33.765, -118.391 33.767, -118.392 33.773))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot a map of hotspots and coldspots","message":["%python","","classes_colors = ['blue', 'red']","classes_cmap = LinearSegmentedColormap.from_list('seg_classes', classes_colors, N=len(classes_colors))","_, axs = plt.subplots(1, 1, figsize=(10, 10))","plot_clusters(block_groups_labelled, block_groups_labelled['LABEL'].values, with_noise=False, with_bounds=False, ax=axs, cmap=classes_cmap, with_basemap=True)"],"enabled":true,"result":{"startTime":1737724267791,"interpreter":"python.low","endTime":1737724271083,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","###### Detect outliers ","","LISA can be also used to identify outliers. Outliers are regions with high-income surrounded by low-income areas (High-Low) and low-income observations with high-income neighbors (Low-High). These are labelled by LISA with quadrants 2 and 4. Next you add these to your result."," "],"enabled":true,"result":{"startTime":1737724271670,"interpreter":"md.low","endTime":1737724272109,"results":[{"message":"<h6 id=\"detect-outliers\">Detect outliers<\/h6>\n<p>LISA can be also used to identify outliers. Outliers are regions with high-income surrounded by low-income areas (High-Low) and low-income observations with high-income neighbors (Low-High). These are labelled by LISA with quadrants 2 and 4. Next you add these to your result.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create text labels for hotspots, coldspots,and outliers","message":["%python","","get_label = lambda x: 'HOTSPOT'  if x==1 else ( \\","                      'OUTLIER'  if x==2 else ( \\","                      'COLDSPOT' if x==3 else ( \\","                      'OUTLIER'  if x==4 else ( \\","                       None ))))","vf = np.vectorize(get_label)","block_groups_labelled = block_groups_sdf.add_column('QUADRANT',lisa.labels_)","block_groups_labelled = block_groups_labelled.add_column('LABEL',vf(block_groups_labelled['QUADRANT'].values))","z.show(block_groups_labelled.head()) "],"enabled":true,"result":{"startTime":1737724272621,"interpreter":"python.low","endTime":1737724273092,"results":[{"message":"GEOID\tMEDIAN_INCOME\tQUADRANT\tLABEL\tgeometry\n060376700031\t75577\t-1\tNone\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t-1\tNone\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t-1\tNone\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t-1\tNone\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t-1\tNone\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Reduce results to only hotspots, coldspots, and outliers","message":["%python","","block_groups_labelled = block_groups_labelled[block_groups_labelled[\"QUADRANT\"] != -1] ","np.unique(block_groups_labelled[\"QUADRANT\"].values)"],"enabled":true,"result":{"startTime":1737724273651,"interpreter":"python.low","endTime":1737724274230,"results":[{"message":"array([1, 2, 3, 4])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot map of hotspots, coldspots, and outliers","message":["%python","","classes_colors = ['blue','red','green']","classes_cmap = LinearSegmentedColormap.from_list('seg_classes', classes_colors, N=len(classes_colors))","_, axs = plt.subplots(1, 1, figsize=(10, 10))","plot_clusters(block_groups_labelled, block_groups_labelled['LABEL'].values, with_noise=False, with_bounds=False, ax=axs, cmap=classes_cmap, with_basemap=True)"," "],"enabled":true,"result":{"startTime":1737724274715,"interpreter":"python.low","endTime":1737724275847,"results":[{"message":"<div style='width:auto;height:auto'><img 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The `regions_` property is a dictionary with quadrant labels as keys and a list of contiguous areas of the same quadrant as the value, each element of the list contains the indexes of the observations that form that region.",""," "],"enabled":true,"result":{"startTime":1737724276392,"interpreter":"md.low","endTime":1737724276859,"results":[{"message":"<h6 id=\"detect-contiguous-regions\">Detect contiguous Regions<\/h6>\n<p>The <code>regions_<\/code> property of a <code>LISAHotspotClustering<\/code> allows us to identify contiguous areas of the same label, for example, adjacents hotspots.  The <code>regions_<\/code> property is a dictionary with quadrant labels as keys and a list of contiguous areas of the same quadrant as the value, each element of the list contains the indexes of the observations that form that region.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review the number of contiguous regions per LISA label","message":["%python","","print ('Number of contiguous regions:')","print(f\"Label -1: {len(lisa.regions_[-1])}\")","print(f\"Label 1: {len(lisa.regions_[1])}\")","print(f\"Label 2: {len(lisa.regions_[2])}\")","print(f\"Label 3: {len(lisa.regions_[3])}\")","print(f\"Label 4: {len(lisa.regions_[4])}\")",""," "," "],"enabled":true,"result":{"startTime":1737724277356,"interpreter":"python.low","endTime":1737724277855,"results":[{"message":"Number of contiguous regions:\nLabel -1: 66\nLabel 1: 34\nLabel 2: 25\nLabel 3: 54\nLabel 4: 21\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review the block group indexes comprising the first contiguous hotspots","message":["%python","","lisa.regions_[1][0] "],"enabled":true,"result":{"startTime":1737724278340,"interpreter":"python.low","endTime":1737724278861,"results":[{"message":"[10, 75, 74, 72, 70, 679, 570, 568, 2414, 1814, 1813, 2154, 454, 1816, 696, 2707, 2679, 2706, 237, 2010, 1867, 1866, 1865, 2200, 2198, 2155, 1074, 1872, 1869, 3240, 1870, 1871, 2622, 340, 2413, 73, 69, 681, 35, 246, 34]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Verify that all items in contiguous hotspot are labelled quadrant 1","message":["%python","","lisa.labels_[lisa.regions_[1][0]]"," "],"enabled":true,"result":{"startTime":1737724279363,"interpreter":"python.low","endTime":1737724279897,"results":[{"message":"array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI KMeans Clustering.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI KMeans Clustering.dsnb
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K-means is not a spatial algorithm in that it does not incorporate spatial effects in the data. However coordinates can be used as features in K-means in order to incorporate location similarity in creating clusters. ","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737724150146,"interpreter":"md.low","endTime":1737724150739,"results":[{"message":"<h1 id=\"kmeans-clustering---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">KMeans Clustering - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you work with the K-means clustering algorithm, which groups samples having similar feature values, where similarity is based on the distance between vectors of feature values. K-means is not a spatial algorithm in that it does not incorporate spatial effects in the data. However coordinates can be used as features in K-means in order to incorporate location similarity in creating clusters.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md"," ","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks. "],"enabled":true,"result":{"startTime":1737724151300,"interpreter":"md.low","endTime":1737724151765,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import geopandas as gpd","import matplotlib.pyplot as plt","import numpy as np","from sklearn.preprocessing import RobustScaler","from sklearn.compose import ColumnTransformer","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset","from oraclesai.pipeline import SpatialPipeline, SpatialFeatureUnion","from oraclesai.preprocessing import SCoordTransformer","from oraclesai.clustering import KMeansClustering","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai.vis import plot_clusters","from oraclesai import enable_geodataframes","enable_geodataframes(z)","","# For reproducibility.","import random","random_state = 456","random.seed(random_state)"," "],"enabled":true,"result":{"startTime":1737724152282,"interpreter":"python.low","endTime":1737724157092,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","In this example you work with demographic data from the US Census Bureau for areas called [block groups](https://en.wikipedia.org/wiki/Census_block_group). You will identify clusters of block groups based on a series of demographic features and block group centroid coordinates."],"enabled":true,"result":{"startTime":1737724157598,"interpreter":"md.low","endTime":1737724158047,"results":[{"message":"<p>In this example you work with demographic data from the US Census Bureau for areas called <a href=\"https://en.wikipedia.org/wiki/Census_block_group\">block groups<\/a>. You will identify clusters of block groups based on a series of demographic features and block group centroid coordinates.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review data","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID','MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'INTERNET', 'geometry']]","z.show(block_groups_sdf.head())"," "],"enabled":true,"result":{"startTime":1737724158554,"interpreter":"python.low","endTime":1737724159800,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tINTERNET\tgeometry\n060376700031\t75577\t37.306\t9.746\t627700\t0.904\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t39.161\t11.504\t599300\t0.959\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t47.854\t10.976\t609500\t0.829\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t39.283\t10.115\t471000\t0.859\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t37.998\t10.716\t389900\t0.882\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","You will work with training data with features `median_income`, `mean_age`, `house_value`, and `mean_education_level`. The observations assigned to the same cluster will share common characteristics involving these features. The `geometry` column is not considered part of the training set, but you will generate features from block group centroid coordinates."," "],"enabled":true,"result":{"startTime":1737724160302,"interpreter":"md.low","endTime":1737724160772,"results":[{"message":"<p>You will work with training data with features <code>median_income<\/code>, <code>mean_age<\/code>, <code>house_value<\/code>, and <code>mean_education_level<\/code>. The observations assigned to the same cluster will share common characteristics involving these features. The <code>geometry<\/code> column is not considered part of the training set, but you will generate features from block group centroid coordinates.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Define training data","message":["%python","","X = block_groups_sdf[['MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'geometry']]","# Convert to projected coordinates ","X = X.to_crs('epsg:3857')","z.show(X.head())"," "],"enabled":true,"result":{"startTime":1737724161274,"interpreter":"python.low","endTime":1737724162047,"results":[{"message":"MEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tgeometry\n75577\t37.306\t9.746\t627700\tPOLYGON ((-13171614.921 4002458.952, -13171006.56 4002139.446, -13171013.684 4001739.038, -13171618.149 4001743.458, -13171614.921 4002458.952))\n74609\t39.161\t11.504\t599300\tPOLYGON ((-13171618.149 4001743.458, -13171013.684 4001739.038, -13171015.02 4000576.34, -13171318.477 4000575.938, -13171317.364 4000963.044, -13171619.596 4000964.384, -13171618.149 4001743.458))\n55592\t47.854\t10.976\t609500\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n73558\t39.283\t10.115\t471000\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 4002094.032))\n54738\t37.998\t10.716\t389900\tPOLYGON ((-13171620.932 4000577.813, -13171318.477 4000575.938, -13171015.02 4000576.34, -13170712.342 4000574.063, -13170717.463 4000012.581, -13171091.051 4000012.313, -13171186.007 3999904.092, -13171571.395 4000085.443, -13171620.932 3999984.052, -13171620.932 4000577.813))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","The K-Means algorithm requires the number of clusters to form. You will first use `KMeansClustering` by providing a number of clusters, and then using an initialization method to estimate the number of clusters inherent in the data. In each case you use a spatial pipeline that transforms geometries to centroid coordinate features, scales the data, and finally performs K-Means clustering."," "],"enabled":true,"result":{"startTime":1737724162559,"interpreter":"md.low","endTime":1737724163028,"results":[{"message":"<p>The K-Means algorithm requires the number of clusters to form. You will first use <code>KMeansClustering<\/code> by providing a number of clusters, and then using an initialization method to estimate the number of clusters inherent in the data. In each case you use a spatial pipeline that transforms geometries to centroid coordinate features, scales the data, and finally performs K-Means clustering.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create K-Means model","message":["%python","","kmeans = KMeansClustering(random_state=23, n_clusters=5)","","# get the indexes of non spatial columnes","non_spatial_cols = [idx for idx, col in enumerate(X.columns) if col != 'geometry']","# convert the geometries to points and add the x and y ordinates as features","selector_pipe = SpatialFeatureUnion(","    [","        ('select_centroids', SCoordTransformer()),","        ('select_ns_cols', ColumnTransformer([(\"selector\", \"passthrough\", non_spatial_cols)], remainder=\"drop\"))","        ","    ]",")","kmeans_pipe = SpatialPipeline([('select', selector_pipe), ('scale', RobustScaler()), ('clustering', kmeans)])"," "],"enabled":true,"result":{"startTime":1737724163515,"interpreter":"python.low","endTime":1737724163991,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply the model","message":["%python","","kmeans_pipe.fit(X)","labels_ = kmeans_pipe.named_steps['clustering'].labels_","np.unique(labels_)"," "],"enabled":true,"result":{"startTime":1737724164608,"interpreter":"python.low","endTime":1737724171325,"results":[{"message":"array([0, 1, 2, 3, 4], dtype=int32)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Observations assigned to the same cluster share common characteristics according to the features of the training data."," "],"enabled":true,"result":{"startTime":1737724171823,"interpreter":"md.low","endTime":1737724172330,"results":[{"message":"<p>Observations assigned to the same cluster share common characteristics according to the features of the training 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For those occasions, it is desirable to use an estimation method that suggests the number of clusters. With `KMeansClustering` there are 2 methods for estimating the number of clusters:  ","* **Elbow** method (default when `init_method` is not specified) calculates the number of clusters based on the sum of the square distance between points in a cluster and the cluster centroid.","* **Silhouette** method calulates number of clusters based on measuring the proximity of point in one cluster to points in the neighboring clusters."," "],"enabled":true,"result":{"startTime":1737724177589,"interpreter":"md.low","endTime":1737724178078,"results":[{"message":"<p>Without prior knowledge of data distribution, defining a number of clusters is complicated. For those occasions, it is desirable to use an estimation method that suggests the number of clusters. With <code>KMeansClustering<\/code> there are 2 methods for estimating the number of clusters:<\/p>\n<ul>\n<li><strong>Elbow<\/strong> method (default when <code>init_method<\/code> is not specified) calculates the number of clusters based on the sum of the square distance between points in a cluster and the cluster centroid.<\/li>\n<li><strong>Silhouette<\/strong> method calulates number of clusters based on measuring the proximity of point in one cluster to points in the neighboring clusters.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create K-Means model with Elbow method","message":["%python","","kmeans = KMeansClustering(random_state=23)","","# get the indexes of non spatial columnes","non_spatial_cols = [idx for idx, col in enumerate(X.columns) if col != 'geometry']","","# convert the geometries to points and add the x and y ordinates as features","selector_pipe = SpatialFeatureUnion(","    [","        ('select_centroids', SCoordTransformer()),","        ('select_ns_cols', ColumnTransformer([(\"selector\", \"passthrough\", non_spatial_cols)], remainder=\"drop\"))","        ","    ]",")","kmeans_pipe = SpatialPipeline([('select', selector_pipe), ('scale', RobustScaler()), ('clustering', kmeans)])"," "],"enabled":true,"result":{"startTime":1737724178589,"interpreter":"python.low","endTime":1737724179069,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply the 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"],"enabled":true,"result":{"startTime":1737724181199,"interpreter":"python.low","endTime":1737724182712,"results":[{"message":"<div style='width:auto;height:auto'><img 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]",")","kmeans_silhouette_pipe = SpatialPipeline([('select', selector_pipe), ('scale', RobustScaler()), ('clustering', kmeans_silhouette)])"," "],"enabled":true,"result":{"startTime":1737724183223,"interpreter":"python.low","endTime":1737724183698,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply the model","message":["%python","","kmeans_silhouette_pipe.fit(X)","labels_ = kmeans_silhouette_pipe.named_steps['clustering'].labels_","np.unique(labels_)"," "],"enabled":true,"result":{"startTime":1737724184209,"interpreter":"python.low","endTime":1737724186751,"results":[{"message":"array([0, 1], dtype=int32)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot a map colored by cluster label","message":["%python","","_, axs = plt.subplots(figsize=(12, 12))","plot_clusters(X.as_geodataframe(), labels_, title='K-Means clusters with Silhouette Initialization', with_noise=True, with_bounds=False, ax=axs, with_basemap=True)"," "],"enabled":true,"result":{"startTime":1737724187256,"interpreter":"python.low","endTime":1737724188648,"results":[{"message":"<div style='width:auto;height:auto'><img 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI LOF Accidents","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial AI LOF Accidents - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you detect traffic accidents that are geographic outliers using the Local Outlier Factor (LOF) alogorithm. LOF measures the local deviation in density of a given observation. Observations are scored based on their isolation from surrounding observations. Using the k-nearest neighbors, it compares the local density of an observation to the local densities of its neighbors, and those observations with a significantly lower density than their neighbors are considered outliers. In a spatial context, the LOF score helps to identify geographically isolated observations. ","","In this notebook, you will identify car accidents that are geographic outliers from a dataset for the City of Chicago.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/) "],"enabled":true,"result":{"startTime":1737674604384,"interpreter":"md.low","endTime":1737674605007,"results":[{"message":"<h1 id=\"spatial-ai-lof-accidents---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial AI LOF Accidents - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you detect traffic accidents that are geographic outliers using the Local Outlier Factor (LOF) alogorithm. LOF measures the local deviation in density of a given observation. Observations are scored based on their isolation from surrounding observations. Using the k-nearest neighbors, it compares the local density of an observation to the local densities of its neighbors, and those observations with a significantly lower density than their neighbors are considered outliers. In a spatial context, the LOF score helps to identify geographically isolated observations.<\/p>\n<p>In this notebook, you will identify car accidents that are geographic outliers from a dataset for the City of Chicago.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737674605548,"interpreter":"md.low","endTime":1737674606057,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import pandas as pd","import geopandas as gpd","import shapely","import matplotlib.pyplot as plt","from matplotlib.lines import Line2D","import matplotlib.colors as colors","import numpy as np","from sklearn.preprocessing import RobustScaler, StandardScaler","from libpysal import weights","from esda.adbscan import get_cluster_boundary","import pandas as pd","","# Libraries and constants for reproducibility.","import random","random_state = 456","random.seed(random_state)","","import oml","","# Spatial AI imports","","from oraclesai import SpatialDataFrame, FileSpatialDataset, GeoDataFrameDataset, DBSpatialDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.outliers import LocalOutlierFactor","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai.vis import add_basemap","from oraclesai import enable_geodataframes","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737674606589,"interpreter":"python.low","endTime":1737674610342,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for CHICAGO_ACCIDENTS table","message":["%python","","accidents_sdf =SpatialDataFrame.create(DBSpatialDataset( table='chicago_accidents'))","z.show(accidents_sdf.head())"],"enabled":true,"result":{"startTime":1737674610877,"interpreter":"python.low","endTime":1737674611545,"results":[{"message":"OGR_FID\tX\tY\tREPORT_TYP\tCRASH_TYPE\tPRIM_CONTR\tDAMAGE\tNUM_UNITS\tDAMAGE_RATING\tINJURY_RATING\tYYYY\tMM\tDD\tgeometry\n229\t-9754525.426\t5147648.59\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tIMPROPER OVERTAKING/PASSING\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.626 41.911)\n231\t-9764077.355\t5149433.999\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tUNABLE TO DETERMINE\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.712 41.923)\n233\t-9758522.788\t5145147.118\tON SCENE\tINJURY AND / OR TOW DUE TO CRASH\tFAILING TO YIELD RIGHT-OF-WAY\tOVER $1,500\t2\t3\t2\t2022\t11\t19\tPOINT (-87.662 41.894)\n339\t-9761738.196\t5128400.563\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tUNABLE TO DETERMINE\t$501 - $1,500\t2\t2\t0\t2022\t11\t19\tPOINT (-87.691 41.782)\n341\t-9751662.368\t5115098.589\tON SCENE\tINJURY AND / OR TOW DUE TO CRASH\tIMPROPER LANE USAGE\tOVER $1,500\t2\t3\t2\t2022\t11\t19\tPOINT (-87.601 41.693)\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","For this example, you will detect geographic outliers for accidents involving serious injury. Next you review the data to determine criteria for those accidents."],"enabled":true,"result":{"startTime":1737674612064,"interpreter":"md.low","endTime":1737674612598,"results":[{"message":"<p>For this example, you will detect geographic outliers for accidents involving serious injury. Next you review the data to determine criteria for those accidents.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review distinct values of CRASH_TYPE and INJURY_RATING","message":["%python","","accidents_sdf[['CRASH_TYPE','INJURY_RATING']].as_geodataframe() \\","                                             .drop('geometry', axis=1) \\","                                             .drop_duplicates() \\","                                             .sort_values(by=['INJURY_RATING','CRASH_TYPE']) "," "],"enabled":true,"result":{"startTime":1737674613183,"interpreter":"python.low","endTime":1737674619143,"results":[{"message":"                             CRASH_TYPE  INJURY_RATING\n7      INJURY AND / OR TOW DUE TO CRASH              0\n0                NO INJURY / DRIVE AWAY              0\n26     INJURY AND / OR TOW DUE TO CRASH              1\n10998            NO INJURY / DRIVE AWAY              1\n2      INJURY AND / OR TOW DUE TO CRASH              2\n17754            NO INJURY / DRIVE AWAY              2\n29     INJURY AND / OR TOW DUE TO CRASH              3\n867    INJURY AND / OR TOW DUE TO CRASH              4\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You will use detect outliers for accidents with INJURY_RATING 3-4 which all involve injury, and indicate serious injury.","","Next you create training data containing point geometries for INJURY_RATING>=3 in a projected coordinate system as required by the LOF algorithm."],"enabled":true,"result":{"startTime":1737674619674,"interpreter":"md.low","endTime":1737674620191,"results":[{"message":"<p>You will use detect outliers for accidents with INJURY_RATING 3-4 which all involve injury, and indicate serious injury.<\/p>\n<p>Next you create training data containing point geometries for INJURY_RATING&gt;=3 in a projected coordinate system as required by the LOF algorithm.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create training data","message":["%python","","X = accidents_sdf[accidents_sdf['INJURY_RATING']>=3]","# Convert to projected coordinate system as required by the LOF algorithm","X = X['geometry'].to_crs(\"EPSG:3857\")","X.head()"],"enabled":true,"result":{"startTime":1737674620774,"interpreter":"python.low","endTime":1737674621447,"results":[{"message":"                           geometry\n0  POINT (-9747023.637 5111095.265)\n1  POINT (-9763468.772 5159504.631)\n2   POINT (-9764055.48 5148319.632)\n3    POINT (-9754405.05 5145246.81)\n4  POINT (-9750568.775 5126008.878)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you create a instance of LOF, supplying a spatial weights definition which establishes the neighbors to be included in the model. You will use K-Nearest Neighbors weights with k=20, meaning the 20 nearest accidents will be used to identify outliers."],"enabled":true,"result":{"startTime":1737674621983,"interpreter":"md.low","endTime":1737674622505,"results":[{"message":"<p>Next you create a instance of LOF, supplying a spatial weights definition which establishes the neighbors to be included in the model. You will use K-Nearest Neighbors weights with k=20, meaning the 20 nearest accidents will be used to identify outliers.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a Spatial LOF instance ","message":["%python","","slof_model = LocalOutlierFactor(spatial_weights_definition=KNNWeightsDefinition(k=20))"],"enabled":true,"result":{"startTime":1737674623020,"interpreter":"python.low","endTime":1737674623513,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You next call the `fit` method to label each observation with an Outlier Factor which is exposed as the `outlier_factor_` property."],"enabled":true,"result":{"startTime":1737674624007,"interpreter":"md.low","endTime":1737674624519,"results":[{"message":"<p>You next call the <code>fit<\/code> method to label each observation with an Outlier Factor which is exposed as the <code>outlier_factor_<\/code> property.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Fit the model","message":["%python","","slof_model.fit(X)"],"enabled":true,"result":{"startTime":1737674625065,"interpreter":"python.low","endTime":1737674632419,"results":[{"message":"<oraclesai.outliers.local_outlier_factor.LocalOutlierFactor object at 0x7ff0ca503ec0>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The fit model can now provide the Outlier Factor for each accident. Values imply amount of isolation. Next you review the distribution of those values."," "],"enabled":true,"result":{"startTime":1737674632935,"interpreter":"md.low","endTime":1737674633471,"results":[{"message":"<p>The fit model can now provide the Outlier Factor for each accident. Values imply amount of isolation. Next you review the distribution of those values.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" Review the distribution of Outlier Factor values","message":["%python","","pd.DataFrame({'Outlier Factor':slof_model.outlier_factor_}).hist()","",""," "],"enabled":true,"result":{"startTime":1737674634029,"interpreter":"python.low","endTime":1737674637397,"results":[{"message":"array([[<Axes: title={'center': 'Outlier Factor'}>]], dtype=object)\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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Next you label the accidents for plotting."],"enabled":true,"result":{"startTime":1737674637989,"interpreter":"md.low","endTime":1737674638483,"results":[{"message":"<p>For this example, accidents with Outlier Factor &gt;= -1.5 will be considered geographic outliers. Next you label the accidents for plotting.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Add a outlier label column for plotting","message":["%python","","accidents_lof_sdf = X.add_column('OUTLIER_FACTOR', slof_model.outlier_factor_)","get_label = lambda x: 'Outlier' if x>=1.5 else 'Normal' ","vf = np.vectorize(get_label)","accidents_lof_sdf = accidents_lof_sdf.add_column('LABEL',vf(accidents_lof_sdf['OUTLIER_FACTOR'].values))"],"enabled":true,"result":{"startTime":1737674639052,"interpreter":"python.low","endTime":1737674639574,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review the number of outliers","message":["%python","","pd.DataFrame({'Labels':accidents_lof_sdf['LABEL'].values}).value_counts()"],"enabled":true,"result":{"startTime":1737674640122,"interpreter":"python.low","endTime":1737674640904,"results":[{"message":"Labels \nNormal     912\nOutlier     30\nName: count, dtype: int64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot a map of of accidents highlighting outliers","message":["%python","","_, axs = plt.subplots(1, 1, figsize=(15, 15))","accidents_lof_sdf.plot(column='LABEL', ax=axs, categorical=True, cmap=colors.ListedColormap(['#666666','#FF0000']), markersize=15, legend=True, with_basemap=True)"],"enabled":true,"result":{"startTime":1737674641441,"interpreter":"python.low","endTime":1737674645972,"results":[{"message":"<div style='width:auto;height:auto'><img 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results","message":["%python","","fig, ax = plt.subplots(1, 1, figsize=(15, 15))","","ax.set_title('Training and Test data');","","accidents_lof_sdf.plot(ax=ax, color='grey', markersize=15, with_basemap=True)","novelty_sdf.plot(ax=ax, markersize=18, column='LABEL', categorical=True, cmap=colors.ListedColormap(['#FF0000','#00FF00']), with_basemap=True)","","grey_dot = Line2D([],[],marker='o',color='white',label='Training data',markerfacecolor='grey', markersize=10)","red_dot = Line2D([],[],marker='o',color='w',label='Test data (novelty)',markerfacecolor='#FF0000', markersize=10)","green_dot = Line2D([],[],marker='o',color='w',label='Test data (normal)',markerfacecolor='#00FF00', markersize=10)","","plt.legend(handles=[grey_dot, red_dot, green_dot])","","plt.show()",""," "],"enabled":true,"result":{"startTime":1737674660353,"interpreter":"python.low","endTime":1737674661758,"results":[{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI OLS Regressor.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI OLS Regressor.dsnb
new file mode 100644
index 00000000..c63422a9
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Spatial AI OLS Regressor.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI OLS Regressor","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OLS Regressor with Spatial Statistics - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you work with the OLS Regressor which performs ordinary least squares regression without spatial context and returns spatial statistics useful for determining whether a spatial regression would provide more reliable predictions. Some experience with spatial statistics is needed to evaluate the statistics. For an automated evaluation and determination, please see the sample notebook on Spatial Adaptive Regressor.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737673944200,"interpreter":"md.low","endTime":1737673945387,"results":[{"message":"<h1 id=\"ols-regressor-with-spatial-statistics---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">OLS Regressor with Spatial Statistics - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you work with the OLS Regressor which performs ordinary least squares regression without spatial context and returns spatial statistics useful for determining whether a spatial regression would provide more reliable predictions. Some experience with spatial statistics is needed to evaluate the statistics. For an automated evaluation and determination, please see the sample notebook on Spatial Adaptive Regressor.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737673946543,"interpreter":"md.low","endTime":1737673947312,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The two main methods of incorporating spatial dependence into OLS regression are as follows: ","","*   The Spatial Lag algorithm introduces the spatial lag of the dependent variable into the linear regression equation. This is suitable when dependent variable values are influenced by neighboring dependent variable values.","*   The Spatial Error algorithm introduces the spatial lag in the error term of the linear regression equation. This is suitable when the residuals of dependent variable values are influenced by neighboring dependent variable residual values."],"enabled":true,"result":{"startTime":1737673948077,"interpreter":"md.low","endTime":1737673948811,"results":[{"message":"<p>The two main methods of incorporating spatial dependence into OLS regression are as follows:<\/p>\n<ul>\n<li>The Spatial Lag algorithm introduces the spatial lag of the dependent variable into the linear regression equation. This is suitable when dependent variable values are influenced by neighboring dependent variable values.<\/li>\n<li>The Spatial Error algorithm introduces the spatial lag in the error term of the linear regression equation. This is suitable when the residuals of dependent variable values are influenced by neighboring dependent variable residual values.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","import matplotlib.pyplot as plt","import numpy as np","import sklearn.metrics as metrics","from sklearn.preprocessing import RobustScaler","from libpysal import weights","import warnings","warnings.filterwarnings('ignore')","","# Import pandas libraries and set parameters","import pandas as pd","pd.options.mode.chained_assignment = None","pd.options.display.max_columns = None","","# Libraries and constants for reproducibility.","import random","random_state = 456","random.seed(random_state)","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.regression import OLSRegressor","from oraclesai.metrics import moran_res, lm_lag, lm_error, rlm_lag, rlm_error, koenker_bassett, log_likelihood, aic, vif, jarque_bera","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai import enable_geodataframes","enable_geodataframes(z)"," "],"enabled":true,"result":{"startTime":1737673949511,"interpreter":"python.low","endTime":1737674177999,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You will use Census data for the City of Los Angeles to create a non-spatial OLS model for predicting median income, and also generate spatial statistics that determine whether a spatial regression would provide more reliable predictions."," "],"enabled":true,"result":{"startTime":1737674178575,"interpreter":"md.low","endTime":1737674179157,"results":[{"message":"<p>You will use Census data for the City of Los Angeles to create a non-spatial OLS model for predicting median income, and also generate spatial statistics that determine whether a spatial regression would provide more reliable predictions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review data","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID','MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'INTERNET', 'geometry' ]]","","z.show(block_groups_sdf.head())"," "],"enabled":true,"result":{"startTime":1737674179733,"interpreter":"python.low","endTime":1737674180373,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tINTERNET\tgeometry\n060376700031\t75577\t37.306\t9.746\t627700\t0.904\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t39.161\t11.504\t599300\t0.959\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t47.854\t10.976\t609500\t0.829\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t39.283\t10.115\t471000\t0.859\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t37.998\t10.716\t389900\t0.882\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you define training, validation, and test data containing the target and explanatory variables, as well as the `geometry` column which is needed to generate spatial statistics. "],"enabled":true,"result":{"startTime":1737674181076,"interpreter":"md.low","endTime":1737674181623,"results":[{"message":"<p>Next you define training, validation, and test data containing the target and explanatory variables, as well as the <code>geometry<\/code> column which is needed to generate spatial statistics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create train and test sets","message":["%python","","X = block_groups_sdf[['MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'INTERNET', 'geometry']]","# Convert to projected coordinates as required for spatial weights","X = X.to_crs(\"EPSG:3857\")","X_train, X_test, X_valid = X.split(ratio=(0.7, 0.2, 0.1), seed=random_state)"," "],"enabled":true,"result":{"startTime":1737674182138,"interpreter":"python.low","endTime":1737674182762,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps colored by median income","message":["%python","","fig, ax = plt.subplots(2, 2, figsize=(15,15))","fig.suptitle('Median Income Choropleth Maps', fontsize=18)","","# Set titles ","ax[0][0].set_title('Full Dataset');","ax[0][1].set_title('Training');","ax[1][0].set_title('Validation ');","ax[1][1].set_title('Test');","","# Plot the choropleth maps","X.plot(ax=ax[0][0], column=X[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True, alpha=0.7)","X_train.plot(ax=ax[0][1], column=X_train[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True, alpha=0.7)","X_valid.plot(ax=ax[1][0], column=X_valid[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black',linewidth=0.1, with_basemap=True, alpha=0.7)","X_test.plot(ax=ax[1][1], column=X_test[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True, alpha=0.7)"],"enabled":true,"result":{"startTime":1737674183278,"interpreter":"python.low","endTime":1737674205783,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","Next you train an OLS model to predict MEDIAN_INCOME and supply spatial weights. The OLSRegressor does not account for spatial context in its predictions. However by including spatial weights as a parameter to OLSRegressor, a set of spatial statistics is generated which indicates whether a spatial regression model would result in more reliable preditions. The choice of a spatial weights definition impacts the results, and experiments are genenerally needed to arrive at an optimal choice.",""," "],"enabled":true,"result":{"startTime":1737674206390,"interpreter":"md.low","endTime":1737674206929,"results":[{"message":"<p>Next you train an OLS model to predict MEDIAN_INCOME and supply spatial weights. The OLSRegressor does not account for spatial context in its predictions. However by including spatial weights as a parameter to OLSRegressor, a set of spatial statistics is generated which indicates whether a spatial regression model would result in more reliable preditions. The choice of a spatial weights definition impacts the results, and experiments are genenerally needed to arrive at an optimal choice.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train model","message":["%python","","ols_pipeline = SpatialPipeline([('scale', RobustScaler()), ","                                ('regression', OLSRegressor(KNNWeightsDefinition(k=10)))])","ols_pipeline.fit(X_train, \"MEDIAN_INCOME\")"],"enabled":true,"result":{"startTime":1737674207447,"interpreter":"python.low","endTime":1737674208186,"results":[{"message":"SpatialPipeline(steps=[('scale', RobustScaler()),\n                       ('regression',\n                        OLSRegressor(spatial_weights_definition=<oraclesai.weights.weights_definition.KNNWeightsDefinition object at 0x7f6644dc9c70>))])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The two main methods of incorporating spatial dependence into OLS regression are as follows: ","","*   The Spatial Lag algorithm introduces the spatial lag of the dependent variable into the linear regression equation. This is suitable when dependent variable values are influenced by neighboring dependent variable values.","*   The Spatial Error algorithm introduces the spatial lag in the error term of the linear regression equation. This is suitable when the residuals of dependent variable values are influenced by neighboring dependent variable residual values.","","","Since your model was trained with spatial weights, spatial statistics are included in the result to determine whether a spatial algorithm would be preferred. Review the model summary and observe spatial statistics in the last section. "],"enabled":true,"result":{"startTime":1737674208774,"interpreter":"md.low","endTime":1737674209293,"results":[{"message":"<p>The two main methods of incorporating spatial dependence into OLS regression are as follows:<\/p>\n<ul>\n<li>The Spatial Lag algorithm introduces the spatial lag of the dependent variable into the linear regression equation. This is suitable when dependent variable values are influenced by neighboring dependent variable values.<\/li>\n<li>The Spatial Error algorithm introduces the spatial lag in the error term of the linear regression equation. This is suitable when the residuals of dependent variable values are influenced by neighboring dependent variable residual values.<\/li>\n<\/ul>\n<p>Since your model was trained with spatial weights, spatial statistics are included in the result to determine whether a spatial algorithm would be preferred. Review the model summary and observe spatial statistics in the last section.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Review model statistics","message":["%python","","print(ols_pipeline.named_steps['regression'].summary)"," "],"enabled":true,"result":{"startTime":1737674209805,"interpreter":"python.low","endTime":1737674210381,"results":[{"message":"REGRESSION RESULTS\n------------------\n\nSUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n-----------------------------------------\nData set            :     unknown\nWeights matrix      :     unknown\nDependent Variable  :     dep_var                Number of Observations:        2406\nMean dependent var  :  69250.4165                Number of Variables   :           5\nS.D. dependent var  :  39115.9968                Degrees of Freedom    :        2401\nR-squared           :      0.6379\nAdjusted R-squared  :      0.6373\nSum squared residual: 1.33227e+12                F-statistic           :   1057.6663\nSigma-square        :554882830.234                Prob(F-statistic)     :           0\nS.E. of regression  :   23555.951                Log likelihood        :  -27632.987\nSigma-square ML     :553729707.145                Akaike info criterion :   55275.975\nS.E of regression ML:  23531.4621                Schwarz criterion     :   55304.903\n\n------------------------------------------------------------------------------------\n            Variable     Coefficient       Std.Error     t-Statistic     Probability\n------------------------------------------------------------------------------------\n            CONSTANT     64328.79953       552.90083       116.34781         0.00000\n            MEAN_AGE      3582.04149       844.14682         4.24339         0.00002\nMEAN_EDUCATION_LEVEL     11108.49615      1642.02925         6.76510         0.00000\n         HOUSE_VALUE     17199.00909       618.79755        27.79424         0.00000\n            INTERNET     13715.26545      1043.19970        13.14731         0.00000\n------------------------------------------------------------------------------------\n\nREGRESSION DIAGNOSTICS\nMULTICOLLINEARITY CONDITION NUMBER            3.845\n\nTEST ON NORMALITY OF ERRORS\nTEST                             DF        VALUE           PROB\nJarque-Bera                       2         819.445           0.0000\n\nDIAGNOSTICS FOR HETEROSKEDASTICITY\nRANDOM COEFFICIENTS\nTEST                             DF        VALUE           PROB\nBreusch-Pagan test                4         803.635           0.0000\nKoenker-Bassett test              4         355.377           0.0000\n\nDIAGNOSTICS FOR SPATIAL DEPENDENCE\nTEST                           MI/DF       VALUE           PROB\nMoran's I (error)              0.2567        29.669           0.0000\nLagrange Multiplier (lag)         1         338.397           0.0000\nRobust LM (lag)                   1           9.583           0.0020\nLagrange Multiplier (error)       1         861.779           0.0000\nRobust LM (error)                 1         532.965           0.0000\nLagrange Multiplier (SARMA)       2         871.362           0.0000\n\n================================ END OF REPORT =====================================\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","The spatial statistics above includes Moran's I value, which detects the presence of non-random spatial patterns in the data. Positive Moran's I indicates spatial depedence (observed values are influenced by nearby values), and negative  Moran's I indicates spatial heterogeniety (observed values are influenced by other regional factors).  Here we see positive Moran's I indivaring spatial dependence.","","In the presence of spatial dependence, Lagrange Multiplier (LM) tests are used to compare an OLS model to models including Spatial Lag or Spatial Error. LM tests return p-value (probability that the observed spatial pattern is random) and z-score (deviation of observed value from the mean in units of standard deviation). A \"significant p-value\" is one that is sufficiently small to allow rejection of randomness. A common threshold is 0.05, meaning 95% probability that the pattern is not random.","","To determine whether a spatial algotithm is prefered you may use the following logic:","","* If neither LM error or lag p-value is significant then the non-spatial OLS model is preferred.","* If only one LM test has significant p-value then the associated spatial alogithm is preferred. ","* If both LM tests have significant p-values then evaluate Robust LM tests: ","* If only one Robust LM  model has a significant robust p-value then the associated spatial alogithm is preferred. ","* If both Robust LM error and lag test p-values are significant then the model with a larger z-score is preferred."," "," "," In the spatial statistics results above, observe that:",""," * Positive Moran's I indicates spatial dependence. "," * Both LM error and lag p-values are significant, so review the Robust LM tests."," * Both Robust LM error and lag p-values are significant, so review Robust LM z-scores."," * The Robust LM error z-score is larger than the Robust LM lag z-score."," * Therefore, a Spatial Error regression model would be expected to provide the most reliable predictions.","","These and other spatial statistics are available for evaluation by experienced users using functions in the `oraclesai.metrics` module. For those without experience in evaluating these statistics, the `SpatialAdaptiveRegressor` automatically selects the preferred alogrithm."],"enabled":true,"result":{"startTime":1737674210927,"interpreter":"md.low","endTime":1737674211460,"results":[{"message":"<p>The spatial statistics above includes Moran's I value, which detects the presence of non-random spatial patterns in the data. Positive Moran's I indicates spatial depedence (observed values are influenced by nearby values), and negative  Moran's I indicates spatial heterogeniety (observed values are influenced by other regional factors).  Here we see positive Moran's I indivaring spatial dependence.<\/p>\n<p>In the presence of spatial dependence, Lagrange Multiplier (LM) tests are used to compare an OLS model to models including Spatial Lag or Spatial Error. LM tests return p-value (probability that the observed spatial pattern is random) and z-score (deviation of observed value from the mean in units of standard deviation). A &quot;significant p-value&quot; is one that is sufficiently small to allow rejection of randomness. A common threshold is 0.05, meaning 95% probability that the pattern is not random.<\/p>\n<p>To determine whether a spatial algotithm is prefered you may use the following logic:<\/p>\n<ul>\n<li>If neither LM error or lag p-value is significant then the non-spatial OLS model is preferred.<\/li>\n<li>If only one LM test has significant p-value then the associated spatial alogithm is preferred.<\/li>\n<li>If both LM tests have significant p-values then evaluate Robust LM tests:<\/li>\n<li>If only one Robust LM  model has a significant robust p-value then the associated spatial alogithm is preferred.<\/li>\n<li>If both Robust LM error and lag test p-values are significant then the model with a larger z-score is preferred.<\/li>\n<\/ul>\n<p>In the spatial statistics results above, observe that:<\/p>\n<ul>\n<li>Positive Moran's I indicates spatial dependence.<\/li>\n<li>Both LM error and lag p-values are significant, so review the Robust LM tests.<\/li>\n<li>Both Robust LM error and lag p-values are significant, so review Robust LM z-scores.<\/li>\n<li>The Robust LM error z-score is larger than the Robust LM lag z-score.<\/li>\n<li>Therefore, a Spatial Error regression model would be expected to provide the most reliable predictions.<\/li>\n<\/ul>\n<p>These and other spatial statistics are available for evaluation by experienced users using functions in the <code>oraclesai.metrics<\/code> module. For those without experience in evaluating these statistics, the <code>SpatialAdaptiveRegressor<\/code> automatically selects the preferred alogrithm.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["## End of Script"],"enabled":true,"result":{"startTime":1737674211989,"interpreter":"md.low","endTime":1737674212551,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI PAR Object Store.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI PAR Object Store.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI PAR Object Store","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# PAR Object Store - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you read spatial data from OCI Object Storage using a Preauthenticated Request (PAR). A PAR provides a user with access to an OCI bucket or object without requiring the user to supply credentials, as explained [here](https://docs.oracle.com/en-us/iaas/Content/Object/Tasks/usingpreauthenticatedrequests.htm).","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1737673854014,"interpreter":"md.low","endTime":1737673854551,"results":[{"message":"<h1 id=\"par-object-store---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">PAR Object Store - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you read spatial data from OCI Object Storage using a Preauthenticated Request (PAR). A PAR provides a user with access to an OCI bucket or object without requiring the user to supply credentials, as explained <a href=\"https://docs.oracle.com/en-us/iaas/Content/Object/Tasks/usingpreauthenticatedrequests.htm\">here<\/a>.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libaries","message":["%python","","from oraclesai import SpatialDataFrame, PARObjStoreSpatialDataset, DBSpatialDataset, read, write","from oraclesai import SpatialError","from oraclesai import enable_geodataframes","import fiona","","enable_geodataframes(z)"," "],"enabled":true,"result":{"startTime":1737673855047,"interpreter":"python.low","endTime":1737673858642,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","For this example notebook you use a PAR providing read-only access to sample data. "],"enabled":true,"result":{"startTime":1737673859192,"interpreter":"md.low","endTime":1737673859767,"results":[{"message":"<p>For this example notebook you use a PAR providing read-only access to sample data.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Assign the PAR to a variable","message":["%python","","par_url = 'https://objectstorage.us-ashburn-1.oraclecloud.com/p/ko8wrcjVJJ78L9WbTKqYR67GoGfeUGVKPpg_lN1vXL5uBAZZaQBBilWuEMRqnQJh/n/ogcs/b/spatial_ai/o/'"],"enabled":true,"result":{"startTime":1737673860405,"interpreter":"python.low","endTime":1737673861061,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you print the URL to list the sample data files. You may run the URL in another browser tab."],"enabled":true,"result":{"startTime":1737673861615,"interpreter":"md.low","endTime":1737673862101,"results":[{"message":"<p>Next you print the URL to list the sample data files. You may run the URL in another browser tab.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"URL to list the sample data files","message":["%python","","print(f'{par_url}?prefix=example_notebooks')"," "],"enabled":true,"result":{"startTime":1737673862737,"interpreter":"python.low","endTime":1737673863472,"results":[{"message":"https://objectstorage.us-ashburn-1.oraclecloud.com/p/ko8wrcjVJJ78L9WbTKqYR67GoGfeUGVKPpg_lN1vXL5uBAZZaQBBilWuEMRqnQJh/n/ogcs/b/spatial_ai/o/?prefix=example_notebooks\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You can list the formats supported by the `PARObjStoreSpatialDataset`. The format name is used as the `driver` parameter. If the `driver` parameter is not specified then then format is inferreded from the suffix."," "],"enabled":true,"result":{"startTime":1737673864077,"interpreter":"md.low","endTime":1737673864773,"results":[{"message":"<p>You can list the formats supported by the <code>PARObjStoreSpatialDataset<\/code>. The format name is used as the <code>driver<\/code> parameter. If the <code>driver<\/code> parameter is not specified then then format is inferreded from the suffix.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"List the formats supported by ","message":["%python","","list(fiona.supported_drivers.keys())"," "],"enabled":true,"result":{"startTime":1737673865576,"interpreter":"python.low","endTime":1737673866554,"results":[{"message":"['DXF', 'CSV', 'OpenFileGDB', 'ESRIJSON', 'ESRI Shapefile', 'FlatGeobuf', 'GeoJSON', 'GeoJSONSeq', 'GML', 'OGR_GMT', 'MapInfo File', 'DGN', 'S57', 'TopoJSON']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","Next you load spatial data accessed from the PAR to a GeoDataFrame using the `read()` function. In the first example the data format is ShapeFile, which is composed of several files with a common name and different suffixes. You specify the name and the read operation loads from the collection of files. You specify the format driver, however if the driver parameter is not specified, the read() operation will infer the driver from the file suffix."],"enabled":true,"result":{"startTime":1737673867302,"interpreter":"md.low","endTime":1737673868697,"results":[{"message":"<p>Next you load spatial data accessed from the PAR to a GeoDataFrame using the <code>read()<\/code> function. In the first example the data format is ShapeFile, which is composed of several files with a common name and different suffixes. You specify the name and the read operation loads from the collection of files. You specify the format driver, however if the driver parameter is not specified, the read() operation will infer the driver from the file suffix.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for accidents from PAR","message":["%python","","accidents_sdf = SpatialDataFrame.create(PARObjStoreSpatialDataset(par_url, object_name='example_notebooks/chicago_accidents_shp', driver='ESRI Shapefile'))","z.show(accidents_sdf.head())"],"enabled":true,"result":{"startTime":1737673869412,"interpreter":"python.low","endTime":1737673876717,"results":[{"message":"OGR_FID\tX\tY\tREPORT_TYP\tCRASH_TYPE\tPRIM_CONTR\tDAMAGE\tNUM_UNITS\tDAMAGE_RAT\tINJURY_RAT\tYYYY\tMM\tDD\tgeometry\n833\t-9758257.0\t5156891.5\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tUNABLE TO DETERMINE\tOVER $1,500\t2\t3\t0\t2022\t11\t17\tPOINT (-9758257.182 5156891.291)\n835\t-9758290.0\t5159921.5\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tIMPROPER BACKING\tOVER $1,500\t2\t3\t0\t2022\t11\t17\tPOINT (-9758289.976 5159921.575)\n837\t-9766271.0\t5142421.0\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tUNABLE TO DETERMINE\tOVER $1,500\t2\t3\t0\t2022\t11\t17\tPOINT (-9766271.119 5142421.113)\n839\t-9758104.0\t5150784.5\tON SCENE\tNO INJURY / DRIVE AWAY\tIMPROPER OVERTAKING/PASSING\t$500 OR LESS\t2\t1\t0\t2022\t11\t17\tPOINT (-9758104.073 5150784.475)\n841\t-9756532.0\t5125047.5\tON SCENE\tNO INJURY / DRIVE AWAY\tDISREGARDING TRAFFIC SIGNALS\t$501 - $1,500\t2\t2\t0\t2022\t11\t17\tPOINT (-9756532.232 5125047.566)\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for schools from PAR","message":["%python","","schools_sdf = SpatialDataFrame.create(PARObjStoreSpatialDataset(par_url, object_name='example_notebooks/OSM_LA_SCHOOLS.geojson', driver='GeoJSON'))","z.show(schools_sdf.head()) "],"enabled":true,"result":{"startTime":1737673877251,"interpreter":"python.low","endTime":1737673878192,"results":[{"message":"NAME\tID\tgeometry\nPalm Crest Elementary School\t358790275\tPOINT (-118.218 34.222)\nPanorama Baptist School\t358790361\tPOINT (-118.436 34.229)\nParadise Canyon Elementary School\t358790368\tPOINT (-118.189 34.209)\nFrank D Parent Elementary School\t358790387\tPOINT (-118.375 33.983)\nPark Western Elementary School\t358790402\tPOINT (-118.304 33.753)\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Now that the data are loaded to SpatialDataFrames, you could continue a workflow, for example writing them to Oracle Spatial tables and appyling spatial algorithms.","","`PARObjStoreSpatialDataset` also supports `write()` so that you can save results to Object Storage using a PAR with write prividge."],"enabled":true,"result":{"startTime":1737673878732,"interpreter":"md.low","endTime":1737673879279,"results":[{"message":"<p>Now that the data are loaded to SpatialDataFrames, you could continue a workflow, for example writing them to Oracle Spatial tables and appyling spatial algorithms.<\/p>\n<p><code>PARObjStoreSpatialDataset<\/code> also supports <code>write()<\/code> so that you can save results to Object Storage using a PAR with write prividge.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"," "],"enabled":true,"result":{"startTime":1737673879771,"interpreter":"md.low","endTime":1737673880477,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Run Me First.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Run Me First.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Run Me First","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Run-me-first: Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you load data for use in other sample notebooks. Data are loaded and prepared from files stored in OCI Object Storage. ","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1737662327170,"interpreter":"md.low","endTime":1737662327750,"results":[{"message":"<h1 id=\"run-me-first-spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Run-me-first: Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you load data for use in other sample notebooks. Data are loaded and prepared from files stored in OCI Object Storage.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import pandas as pd","from oraclesai import PARObjStoreSpatialDataset, DBSpatialDataset, read, write","from oraclesai import enable_geodataframes","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737662328307,"interpreter":"python.low","endTime":1737662331828,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Assign object storage PAR to a variable","message":["%python","","par_url = 'https://objectstorage.us-ashburn-1.oraclecloud.com/p/ko8wrcjVJJ78L9WbTKqYR67GoGfeUGVKPpg_lN1vXL5uBAZZaQBBilWuEMRqnQJh/n/ogcs/b/spatial_ai/o/'"," "],"enabled":true,"result":{"startTime":1737662332314,"interpreter":"python.low","endTime":1737662332814,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Load traffic accident data","","The table will contain data about traffic crashes on city streets within the City of Chicago. The file is loaded from OCI Object Storage and was originally sourced from the [Chicago Data Portal](https://data.cityofchicago.org)."],"enabled":true,"result":{"startTime":1737662333314,"interpreter":"md.low","endTime":1737662333868,"results":[{"message":"<h5 id=\"load-traffic-accident-data\">Load traffic accident data<\/h5>\n<p>The table will contain data about traffic crashes on city streets within the City of Chicago. The file is loaded from OCI Object Storage and was originally sourced from the <a href=\"https://data.cityofchicago.org\">Chicago Data Portal<\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create GeoDataFrame for traffic accident data","message":["%python","","gdf = read(PARObjStoreSpatialDataset(par_url, object_name='example_notebooks/chicago_accidents.zip')).to_crs('epsg:4326')","z.show(gdf.head())"],"enabled":true,"result":{"startTime":1737662334361,"interpreter":"python.low","endTime":1737662340389,"results":[{"message":"OGR_FID\tX\tY\tREPORT_TYP\tCRASH_TYPE\tPRIM_CONTR\tDAMAGE\tNUM_UNITS\tDAMAGE_RATING\tINJURY_RATING\tYYYY\tMM\tDD\tgeometry\n205\t-9764007.805\t5159947.68\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tFAILING TO YIELD RIGHT-OF-WAY\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.712 41.993)\n207\t-9762842.411\t5143294.991\tON SCENE\tNO INJURY / DRIVE AWAY\tDRIVING ON WRONG SIDE/WRONG WAY\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.701 41.882)\n235\t-9768549.625\t5153978.595\tON SCENE\tINJURY AND / OR TOW DUE TO CRASH\tFAILING TO REDUCE SPEED TO AVOID CRASH\tOVER $1,500\t3\t3\t2\t2022\t11\t19\tPOINT (-87.752 41.953)\n237\t-9767337.518\t5128923.74\tON SCENE\tINJURY AND / OR TOW DUE TO CRASH\tFAILING TO YIELD RIGHT-OF-WAY\tOVER $1,500\t2\t3\t0\t2022\t11\t19\tPOINT (-87.741 41.786)\n239\t-9767253.997\t5147445.025\tNOT ON SCENE (DESK REPORT)\tNO INJURY / DRIVE AWAY\tFOLLOWING TOO CLOSELY\t$500 OR LESS\t2\t1\t0\t2022\t11\t19\tPOINT (-87.741 41.91)\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Write GeoDataFrame to table","message":["%python"," ","write(gdf, DBSpatialDataset(table='chicago_accidents'), if_exists='replace', create_spatial_index=True) ","del gdf "],"enabled":true,"result":{"startTime":1737662340921,"interpreter":"python.low","endTime":1737662356548,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count table rows","message":["%sql","","select count(*) from chicago_accidents;"," "],"enabled":true,"result":{"startTime":1737662357072,"interpreter":"sql.low","endTime":1737662359088,"results":[{"message":"COUNT(*)\n47798\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Load demographics data","","Create a GeoDataFrame from a zipped GeoJSON file containing data about demographics within the City of Los Angeles. The file is loaded from OCI Object Storage and was originally sourced from the [US Census Bureau](https://www.census.gov/data.html)."],"enabled":true,"result":{"startTime":1737662359641,"interpreter":"md.low","endTime":1737662360132,"results":[{"message":"<h5 id=\"load-demographics-data\">Load demographics data<\/h5>\n<p>Create a GeoDataFrame from a zipped GeoJSON file containing data about demographics within the City of Los Angeles. The file is loaded from OCI Object Storage and was originally sourced from the <a href=\"https://www.census.gov/data.html\">US Census Bureau<\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create GeoDataFrame for demographics data","message":["%python","","gdf = read(PARObjStoreSpatialDataset(par_url, object_name='example_notebooks/income_census_la.zip')).to_crs('epsg:4326')","z.show(gdf.head())"],"enabled":true,"result":{"startTime":1737662360742,"interpreter":"python.low","endTime":1737662363731,"results":[{"message":"GEOID\tc_lon\tc_lat\tmean_income\tmedian_income\tmean_education_level\tmean_age\tper_male\tper_female\tlan_en\tlan_es\tlan_ie\tlan_ap\tlan_ol\tcomputer\tinternet\thouse_value\tind_aggr\tind_constr\tind_manu\tind_retail\tind_transp\tind_info\tind_finan\tind_edu\tind_health\tind_enter\tind_padmin\tgeometry\n060373104003\t-118.34\t34.204\t66311.004\t45434.0\t10.977\t42.421\t0.466\t0.534\t0.848\t0.116\t0.184\t0.05\t0.085\t0.901\t0.874\t597800.0\t0.0\t0.047\t0.103\t0.102\t0.035\t0.128\t0.083\t0.058\t0.039\t0.076\t0.034\tPOLYGON ((-118.35 34.207, -118.343 34.207, -118.341 34.207, -118.34 34.206, -118.339 34.206, -118.334 34.206, -118.334 34.203, -118.335 34.203, -118.335 34.199, -118.338 34.2, -118.339 34.199, -118.349 34.206, -118.35 34.207, -118.35 34.207))\n060376510024\t-118.324\t33.808\t121099.764\t133672.0\t10.813\t38.939\t0.561\t0.439\t0.81\t0.211\t0.007\t0.273\t0.0\t0.976\t0.949\t756100.0\t0.008\t0.055\t0.212\t0.075\t0.037\t0.02\t0.033\t0.06\t0.125\t0.092\t0.028\tPOLYGON ((-118.328 33.81, -118.32 33.81, -118.32 33.808, -118.32 33.808, -118.32 33.807, -118.328 33.807, -118.328 33.81))\n060376511011\t-118.333\t33.818\t118194.268\t103688.0\t11.557\t43.086\t0.482\t0.518\t0.887\t0.042\t0.056\t0.189\t0.022\t0.958\t0.941\t845400.0\t0.006\t0.023\t0.168\t0.144\t0.05\t0.059\t0.086\t0.124\t0.097\t0.025\t0.026\tPOLYGON ((-118.34 33.824, -118.328 33.822, -118.328 33.82, -118.328 33.817, -118.328 33.813, -118.331 33.813, -118.337 33.815, -118.337 33.822, -118.34 33.822, -118.34 33.824))\n060376511012\t-118.338\t33.808\t94055.787\t81891.0\t11.729\t39.578\t0.467\t0.533\t0.805\t0.093\t0.043\t0.322\t0.012\t0.835\t0.828\t799900.0\t0.0\t0.025\t0.15\t0.122\t0.071\t0.038\t0.088\t0.113\t0.061\t0.054\t0.029\tPOLYGON ((-118.351 33.816, -118.348 33.815, -118.348 33.818, -118.343 33.819, -118.341 33.818, -118.341 33.819, -118.34 33.822, -118.337 33.822, -118.337 33.815, -118.331 33.813, -118.328 33.813, -118.328 33.81, -118.328 33.807, -118.328 33.806, -118.328 33.803, -118.326 33.803, -118.326 33.801, -118.328 33.801, -118.328 33.798, -118.328 33.79, -118.326 33.79, -118.328 33.79, -118.329 33.79, -118.329 33.79, -118.33 33.79, -118.333 33.792, -118.337 33.795, -118.344 33.801, -118.348 33.804, -118.351 33.805, -118.351 33.816))\n060376512011\t-118.361\t33.822\t144310.509\t130221.0\t11.893\t46.619\t0.499\t0.501\t0.992\t0.013\t0.053\t0.061\t0.043\t0.965\t0.965\t837900.0\t0.0\t0.114\t0.149\t0.109\t0.057\t0.059\t0.062\t0.069\t0.161\t0.052\t0.028\tPOLYGON ((-118.372 33.816, -118.369 33.814, -118.362 33.817, -118.362 33.819, -118.363 33.82, -118.362 33.822, -118.362 33.826, -118.36 33.826, -118.36 33.826, -118.36 33.82, -118.36 33.817, -118.361 33.817, -118.37 33.813, -118.371 33.814, -118.373 33.815, -118.372 33.816))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Write GeoDataFrame to table","message":["%python","","write(gdf, DBSpatialDataset(table='la_block_groups'), if_exists='replace', create_spatial_index=True) ","del gdf"],"enabled":true,"result":{"startTime":1737662364264,"interpreter":"python.low","endTime":1737662367506,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count table rows","message":["%sql","","select count(*) from la_block_groups;"],"enabled":true,"result":{"startTime":1737662368116,"interpreter":"sql.low","endTime":1737662368675,"results":[{"message":"COUNT(*)\n3437\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Load house value data","","Create a GeoDataFrame from a zipped GeoJSON file containing data about home values within the City of Los Angeles. The file is loaded from OCI Object Storage and was originally sourced from the [US Census Bureau](https://www.census.gov/data.html)"," "],"enabled":true,"result":{"startTime":1737662369204,"interpreter":"md.low","endTime":1737662369748,"results":[{"message":"<h5 id=\"load-house-value-data\">Load house value data<\/h5>\n<p>Create a GeoDataFrame from a zipped GeoJSON file containing data about home values within the City of Los Angeles. The file is loaded from OCI Object Storage and was originally sourced from the <a href=\"https://www.census.gov/data.html\">US Census Bureau<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create GeoDataFrame for house value data","message":["%python","","gdf = read(PARObjStoreSpatialDataset(par_url, object_name='example_notebooks/median_house_values_la.zip')).to_crs('epsg:4326')","z.show(gdf.head())"],"enabled":true,"result":{"startTime":1737662370270,"interpreter":"python.low","endTime":1737662374636,"results":[{"message":"geoid\thouse_value_median\tbedrooms_median\tbedrooms_total\tcomplete_kitchen_perc\tcomplete_plumbing_perc\tedu_level_score_median\thouse_age_median\tpopulation_density\tpopulation_total\trented_perc\trooms_median\trooms_total\tunits_mean\tunits_median\tunits_total\tyears_built_median\tgeometry\n060371044041\t453400.0\t2.0\t918\t1.0\t1.0\t15.0\t60.0\t0.883\t1781.0\t0.499\t4.8\t1891\t1.906\t1.0\t756.5\t1954.0\tPOLYGON ((-118.43 34.265, -118.425 34.269, -118.422 34.266, -118.426 34.264, -118.427 34.262, -118.43 34.265))\n060371249032\t590300.0\t2.0\t1041\t0.986\t1.0\t18.0\t46.0\t0.337\t1166.0\t0.731\t4.0\t2457\t13.645\t14.5\t7955.0\t1968.0\tPOLYGON ((-118.4 34.168, -118.397 34.168, -118.396 34.165, -118.4 34.165, -118.4 34.168))\n060371047032\t447700.0\t2.0\t582\t0.881\t0.895\t15.0\t58.0\t0.961\t1226.0\t0.39\t4.5\t1150\t2.546\t1.0\t748.5\t1956.0\tPOLYGON ((-118.405 34.257, -118.404 34.258, -118.401 34.255, -118.406 34.251, -118.403 34.249, -118.403 34.248, -118.409 34.254, -118.405 34.257))\n060371047041\t423100.0\t3.0\t1497\t0.953\t1.0\t17.0\t37.0\t2.354\t1640.0\t0.234\t5.9\t2928\t6.749\t1.0\t3455.5\t1977.0\tPOLYGON ((-118.413 34.262, -118.411 34.267, -118.409 34.267, -118.407 34.266, -118.405 34.268, -118.402 34.265, -118.403 34.259, -118.404 34.259, -118.404 34.258, -118.405 34.257, -118.409 34.261, -118.412 34.261, -118.413 34.262))\n060371048212\t440500.0\t3.0\t531\t0.94\t0.94\t15.0\t57.0\t0.588\t871.0\t0.527\t4.8\t948\t7.658\t1.0\t1531.5\t1957.0\tPOLYGON ((-118.423 34.25, -118.419 34.252, -118.416 34.249, -118.42 34.247, -118.423 34.25))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Write GeoDataFrame to table","message":["%python","","write(gdf, DBSpatialDataset(table='la_median_house_values'), if_exists='replace', create_spatial_index=True) ","del gdf"],"enabled":true,"result":{"startTime":1737662375126,"interpreter":"python.low","endTime":1737662379449,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count table rows","message":["%sql","","select count(*) from la_median_house_values;"," "],"enabled":true,"result":{"startTime":1737662379946,"interpreter":"sql.low","endTime":1737662380498,"results":[{"message":"COUNT(*)\n5702\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Load schools data","","Create a GeoDataFrame from a zipped GeoJSON file containing data about schools within the City of Los Angeles. The file is loaded from OCI Object Storage and was originally sourced from [OpenStreetMap](https://www.openstreetmap.org)."],"enabled":true,"result":{"startTime":1737662381017,"interpreter":"md.low","endTime":1737662381503,"results":[{"message":"<h5 id=\"load-schools-data\">Load schools data<\/h5>\n<p>Create a GeoDataFrame from a zipped GeoJSON file containing data about schools within the City of Los Angeles. The file is loaded from OCI Object Storage and was originally sourced from <a href=\"https://www.openstreetmap.org\">OpenStreetMap<\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create GeoDataFrame for schools data","message":["%python","","gdf = read(PARObjStoreSpatialDataset(par_url, object_name='example_notebooks/osm_edu_nodes_la.json')).to_crs('epsg:4326')","z.show(gdf.head())"],"enabled":true,"result":{"startTime":1737662586720,"interpreter":"python.low","endTime":1737662587598,"results":[{"message":"id\ttags\tgeometry\n358779437\t{'amenity': 'school', 'ele': '30', 'gnis:county_id': '037', 'gnis:created': '06/14/2000', 'gnis:feature_id': '238414', 'gnis:state_id': '06', 'name': 'John Adams Elementary School'}\tPOINT (-118.318 33.809)\n358779446\t{'amenity': 'school', 'ele': '19', 'gnis:county_id': '037', 'gnis:created': '06/14/2000', 'gnis:feature_id': '238417', 'gnis:state_id': '06', 'name': 'Jane Addams Elementary School'}\tPOINT (-118.356 33.893)\n358779518\t{'amenity': 'school', 'ele': '167', 'gnis:county_id': '037', 'gnis:created': '06/14/2000', 'gnis:feature_id': '238488', 'gnis:state_id': '06', 'name': 'Aldama Elementary School', 'operator': 'Los Angeles Unified School District'}\tPOINT (-118.206 34.11)\n358779550\t{'amenity': 'school', 'ele': '158', 'gnis:county_id': '037', 'gnis:created': '01/19/1981', 'gnis:feature_id': '238526', 'gnis:state_id': '06', 'name': 'All Saints School'}\tPOINT (-118.172 34.086)\n358779618\t{'amenity': 'school', 'ele': '52', 'gnis:county_id': '037', 'gnis:created': '06/14/2000', 'gnis:feature_id': '238557', 'gnis:state_id': '06', 'name': 'Alta Loma Elementary School'}\tPOINT (-118.338 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Create the model"," ","Start by creating a SpatialPipeline with steps to scale the data and run SLXClassifier."],"enabled":true,"result":{"startTime":1737673510607,"interpreter":"md.low","endTime":1737673511413,"results":[{"message":"<h5 id=\"create-the-model\">Create the model<\/h5>\n<p>Start by creating a SpatialPipeline with steps to scale the data and run SLXClassifier.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create model","message":["%python","","weights_def = KNNWeightsDefinition(k=15)","slx_classifier = SLXClassifier(spatial_weights_definition=weights_def, random_state=random_state, balance_method=\"smote\")","classifier_pipeline = SpatialPipeline([('scale', RobustScaler()), ('slxclassifier', slx_classifier)])"," "],"enabled":true,"result":{"startTime":1737673512352,"interpreter":"python.low","endTime":1737673512967,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","","Next you train the model. The SLXClassifier fit method has the parameter `column_ids` to specify the columns for adding spatial lag as features for training. In a pipeline, you pass parameters to a specific step using the format `stepname__parameter`, so in this case `slxclassifier__column_ids`. You will train the model incorporating spatial lag for all independent variables."," "],"enabled":true,"result":{"startTime":1737673513927,"interpreter":"md.low","endTime":1737673514447,"results":[{"message":"<p>Next you train the model. The SLXClassifier fit method has the parameter <code>column_ids<\/code> to specify the columns for adding spatial lag as features for training. In a pipeline, you pass parameters to a specific step using the format <code>stepname__parameter<\/code>, so in this case <code>slxclassifier__column_ids<\/code>. You will train the model incorporating spatial lag for all independent variables.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train model","message":["%python","","classifier_pipeline.fit(X_train.drop([\"GEOID\"]), \"INCOME_CATEGORY\", slxclassifier__column_ids=[\"MEAN_AGE\",\"MEAN_EDUCATION_LEVEL\",\"HOUSE_VALUE\",\"INTERNET\"])"," "],"enabled":true,"result":{"startTime":1737673514983,"interpreter":"python.low","endTime":1737673515707,"results":[{"message":"SpatialPipeline(steps=[('scale', RobustScaler()),\n                       ('slxclassifier',\n                        SLXClassifier(balance_method=('smote',),\n                                      random_state=(456,),\n                                      spatial_weights_definition=<oraclesai.weights.weights_definition.KNNWeightsDefinition object at 0x7f2ae00f4d10>))])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You next review model metrics:","","* `score` is model accuracy with the training data.","* `summary` shows parameter values among other statistics. Notice the features added to your model, one for each column specified in the `fit` method."," "],"enabled":true,"result":{"startTime":1737673516229,"interpreter":"md.low","endTime":1737673516747,"results":[{"message":"<p>You next review model metrics:<\/p>\n<ul>\n<li><code>score<\/code> is model accuracy with the training data.<\/li>\n<li><code>summary<\/code> shows parameter values among other statistics. Notice the features added to your model, one for each column specified in the <code>fit<\/code> method.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Print model statistics","message":["%python","","print('Score: ', classifier_pipeline.score(X_train.drop([\"GEOID\"]), \"INCOME_CATEGORY\"))","print('\\nSummary:', classifier_pipeline.named_steps['slxclassifier'].summary)",""," "],"enabled":true,"result":{"startTime":1737673517241,"interpreter":"python.low","endTime":1737673517934,"results":[{"message":"Score:  0.5894545454545455\n\nSummary: Multi-Class Logistic Model Results\n---------------------------------------------------------------------------\n     label    deviance          llf         aic           bic       D2   adj_D2\n     0-Low 1878.089675  -939.044838 1896.089675 -19828.865644 0.397218 0.395458\n    3-High 1526.421803  -763.210901 1544.421803 -20180.533516 0.506291 0.504850\n 1-Mid_low 2859.974270 -1429.987135 2877.974270 -18846.981049 0.074964 0.072264\n2-Mid_high 2787.515958 -1393.757979 2805.515958 -18919.439361 0.092545 0.089896\n\nParameters (Average Results)\nVariable                        Est.       STD        Min        Median     Max       \n------------------------------- ---------- ---------- ---------- ---------- ----------\nconstant                            -1.635      0.789     -2.533     -1.592     -0.825\nMEAN_AGE                             0.053      0.402     -0.546      0.102      0.554\nMEAN_EDUCATION_LEVEL                 0.324      1.883     -1.891      0.002      3.186\nHOUSE_VALUE                         -0.258      0.716     -0.688     -0.664      0.983\nINTERNET                             0.261      1.168     -1.546      0.491      1.611\nSLX-MEAN_AGE                         0.023      0.020      0.000      0.021      0.049\nSLX-MEAN_EDUCATION_LEVEL            -0.034      0.098     -0.192     -0.001      0.058\nSLX-HOUSE_VALUE                     -0.003      0.044     -0.050     -0.015      0.069\nSLX-INTERNET                         0.037      0.097     -0.094      0.033      0.177\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Evaluate the Model","","To evaluate the model, you classify the validation and test data using the `predict` method."," "],"enabled":true,"result":{"startTime":1737673518477,"interpreter":"md.low","endTime":1737673518949,"results":[{"message":"<h5 id=\"evaluate-the-model\">Evaluate the Model<\/h5>\n<p>To evaluate the model, you classify the validation and test data using the <code>predict<\/code> method.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get test predictions","message":["%python","","predictions_test = classifier_pipeline.predict(X_test.drop([\"GEOID\",\"INCOME_CATEGORY\"]))","predictions_test[0:9]"," "],"enabled":true,"result":{"startTime":1737673519487,"interpreter":"python.low","endTime":1737673520047,"results":[{"message":"array(['3-High', '3-High', '2-Mid_high', '3-High', '3-High', '3-High',\n       '3-High', '3-High', '3-High'], dtype='<U10')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps colored by actual and predicted categorical values","message":["%python","","fig, ax = plt.subplots(1, 2, figsize=(15,15))","","# Set plot's labels and titles ","ax[0].set_title('Test Data');","ax[1].set_title('Prediction - SLX Classifier');","","# Plot the choropleth map","X_test.plot(ax=ax[0], column=X_test[\"INCOME_CATEGORY\"].values, cmap=plt.get_cmap(\"Paired\"), legend=True, edgecolor='black', alpha=0.75, linewidth=0.1, with_basemap=True )","X_test.plot(ax=ax[1], column=predictions_test, cmap=plt.get_cmap(\"Paired\"), legend=True, edgecolor='black', linewidth=0.1, alpha=0.75, with_basemap=True )"," 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","A valuable metric measure model performance is `accuracy` which is the percent of predictions that the model got right."," "],"enabled":true,"result":{"startTime":1737673523132,"interpreter":"md.low","endTime":1737673523774,"results":[{"message":"<p>A valuable metric measure model performance is <code>accuracy<\/code> which is the percent of predictions that the model got right.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Print the model's accuracy score","message":["%python","","print(\"Test accuracy score: \",metrics.accuracy_score(X_test['INCOME_CATEGORY'].values, predictions_test))",""," "],"enabled":true,"result":{"startTime":1737673524455,"interpreter":"python.low","endTime":1737673525134,"results":[{"message":"Test accuracy score:  0.5502183406113537\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The underlying algorithm for SLXClassifier is sklearn LogisticRegression. So to compare the results of your spatial and non-spatial model predictions, you review the test prediction accuracy using non-spatial LogisticRegression."," "],"enabled":true,"result":{"startTime":1737673525724,"interpreter":"md.low","endTime":1737673526225,"results":[{"message":"<p>The underlying algorithm for SLXClassifier is sklearn LogisticRegression. So to compare the results of your spatial and non-spatial model predictions, you review the test prediction accuracy using non-spatial LogisticRegression.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Print accuracy score of non-spatial model","message":["%python","","logreg_classifier = LogisticRegression(random_state=random_state)","log_reg_classifier_pipeline = SpatialPipeline([('scale', RobustScaler()), ('logregclassifier', logreg_classifier)])","log_reg_classifier_pipeline.fit(X_train.drop(\"GEOID\"), \"INCOME_CATEGORY\")","log_reg_predictions_test = log_reg_classifier_pipeline.predict(X_test.drop([\"GEOID\",\"INCOME_CATEGORY\"]))","","print(\"Non-spatial accuracy score: \",metrics.accuracy_score(X_test['INCOME_CATEGORY'].values, log_reg_predictions_test))"],"enabled":true,"result":{"startTime":1737673526927,"interpreter":"python.low","endTime":1737673527569,"results":[{"message":"Non-spatial accuracy score:  0.5458515283842795\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","Observe the accuracy improvement associated with SLXClassifier versus non-spatial LogisticRegression. SLXClassifier incorporates the spatial lag into the training set to provide a spatial context to your data, and in this case helps to obtain more reliable predictions."," "],"enabled":true,"result":{"startTime":1737673528174,"interpreter":"md.low","endTime":1737673528764,"results":[{"message":"<p>Observe the accuracy improvement associated with SLXClassifier versus non-spatial LogisticRegression. SLXClassifier incorporates the spatial lag into the training set to provide a spatial context to your data, and in this case helps to obtain more reliable predictions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script "],"enabled":true,"result":{"startTime":1737673529319,"interpreter":"md.low","endTime":1737673529844,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Save Load Run.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Save Load Run.dsnb
new file mode 100644
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--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Save Load Run.dsnb	
@@ -0,0 +1,862 @@
+[
+  {
+    "name" : "OML4Py Spatial AI Save Load Run",
+    "description" : null,
+    "tags" : null,
+    "version" : "7",
+    "layout" : null,
+    "type" : "low",
+    "snapshot" : false,
+    "isEditable" : true,
+    "isRunnable" : true,
+    "template" : null,
+    "templateConfig" : null,
+    "paragraphs" : [
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "# Save Load Run - Spatial AI in Oracle Machine Learning for Python (OML4Py)",
+          "",
+          "Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ",
+          "",
+          "This notebook provides basic examples of:",
+          "* Saving, loading, and running a spatial model",
+          "* Saving predictions from a spatial model to a database table",
+          "* Saving and invoking spatial model with embedded execution",
+          "",
+          "The model used is a spatial lag regression. For a thorough example of working with spatial lag regression see the sample notebook OML4Py Spatial AI Spatial Lag Regressor.",
+          "",
+          "",
+          "",
+          "**Copyright (c) 2025 Oracle Corporation**",
+          "[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h1 id=\"save-load-run---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Save Load Run - Spatial AI in Oracle Machine Learning for Python (OML4Py)</h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.</p>\n<p>This notebook provides basic examples of:</p>\n<ul>\n<li>Saving, loading, and running a spatial model</li>\n<li>Saving predictions from a spatial model to a database table</li>\n<li>Saving and invoking spatial model with embedded execution</li>\n</ul>\n<p>The model used is a spatial lag regression. For a thorough example of working with spatial lag regression see the sample notebook OML4Py Spatial AI Spatial Lag Regressor.</p>\n<p><strong>Copyright (c) 2025 Oracle Corporation</strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0</a></p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Before you begin",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          " ",
+          "Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first</strong> which loads data used in all sample notebooks.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Import required libraries",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "import os",
+          "import oml",
+          "import geopandas",
+          "import matplotlib.pyplot as plt",
+          "import numpy as np",
+          "import sklearn.metrics as metrics",
+          "from sklearn.preprocessing import RobustScaler",
+          "from libpysal import weights",
+          "import pandas as pd",
+          "",
+          "# Libraries and constants for reproducibility.",
+          "import random",
+          "random_state = 456",
+          "random.seed(random_state)",
+          "",
+          "import oml",
+          "",
+          "# Spatial AI imports",
+          "from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset",
+          "from oraclesai.pipeline import SpatialPipeline",
+          "from oraclesai.metrics import aic",
+          "from oraclesai.regression import SpatialLagRegressor",
+          "from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition",
+          "from oraclesai.vis import elocation",
+          "from oraclesai import enable_geodataframes",
+          "enable_geodataframes(z)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "##### Create a model",
+          "",
+          "Begin by creating a spatial regression model to predict income. You use for the city of Los Angeles from the US Census Bureau. The data includes median income and a variety of other demographic features for areas called block groups.",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h5 id=\"create-a-model\">Create a model</h5>\n<p>Begin by creating a spatial regression model to predict income. You use for the city of Los Angeles from the US Census Bureau. The data includes median income and a variety of other demographic features for areas called block groups.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create a SpatialDataFrame and split to train, test, validation sets",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "block_groups = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))",
+          "X = block_groups[['GEOID','MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'INTERNET', 'geometry']]",
+          "X_train, X_test, X_valid = X.split(ratio=(0.7, 0.2, 0.1), seed=random_state)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Train a spatial regression model",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "",
+          "slr_pipeline = SpatialPipeline([(\"scaler\", RobustScaler()), \\",
+          "                                        (\"spatial_lag_regressor\", SpatialLagRegressor(KNNWeightsDefinition(k=5)))])",
+          "slr_pipeline.fit(X_train.drop(\"GEOID\"), \"MEDIAN_INCOME\")",
+          " "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "SpatialPipeline(steps=[('scaler', RobustScaler()),\n                       ('spatial_lag_regressor',\n                        SpatialLagRegressor(spatial_weights_definition=<oraclesai.weights.weights_definition.KNNWeightsDefinition object at 0x7f40030a9fd0>))])\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "View the model summary",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "print(slr_pipeline.named_steps[\"spatial_lag_regressor\"].summary)",
+          " "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "REGRESSION RESULTS\n------------------\n\nSUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)\n-----------------------------------------------------------------\nData set            :     unknown\nWeights matrix      :     unknown\nDependent Variable  :     dep_var                Number of Observations:        2438\nMean dependent var  :  69108.4746                Number of Variables   :           6\nS.D. dependent var  :  39883.6240                Degrees of Freedom    :        2432\nPseudo R-squared    :      0.6911\nSpatial Pseudo R-squared:  0.6203\nLog likelihood      : -27892.5395\nSigma-square ML     :491418429.4577                Akaike info criterion :   55797.079\nS.E of regression   :  22167.9595                Schwarz criterion     :   55831.873\n\n------------------------------------------------------------------------------------\n            Variable     Coefficient       Std.Error     z-Statistic     Probability\n------------------------------------------------------------------------------------\n            CONSTANT     38034.61195      1473.22756        25.81720         0.00000\n            MEAN_AGE      2668.98082       778.85538         3.42680         0.00061\nMEAN_EDUCATION_LEVEL      3227.76860      1556.48501         2.07376         0.03810\n         HOUSE_VALUE     12447.64626       650.05460        19.14862         0.00000\n            INTERNET     11396.13170       988.90748        11.52396         0.00000\n           W_dep_var         0.40284         0.02153        18.70871         0.00000\n------------------------------------------------------------------------------------\n\nSPATIAL LAG MODEL IMPACTS\nImpacts computed using the 'simple' method.\n            Variable         Direct        Indirect          Total\n            MEAN_AGE      2668.9808       1800.4503       4469.4311\nMEAN_EDUCATION_LEVEL      3227.7686       2177.3993       5405.1679\n         HOUSE_VALUE     12447.6463       8396.9762      20844.6225\n            INTERNET     11396.1317       7687.6419      19083.7736\n================================ END OF REPORT =====================================\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Perform test predictions",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "",
+          "test_predictions = slr_pipeline.predict(X_test.drop(\"MEDIAN_INCOME\").drop(\"GEOID\")).flatten()",
+          "test_predictions_sdf = X_test[[\"GEOID\"]].add_column('PREDICTION',test_predictions)",
+          "z.show(test_predictions_sdf.head())",
+          " "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "GEOID\tPREDICTION\tgeometry\n060373201002\t57052.555\tPOLYGON ((-118.436 34.297, -118.429 34.302, -118.426 34.299, -118.433 34.294, -118.434 34.295, -118.436 34.297))\n060376214002\t108972.653\tPOLYGON ((-118.386 33.825, -118.382 33.824, -118.379 33.821, -118.377 33.821, -118.377 33.818, -118.38 33.818, -118.384 33.821, -118.385 33.821, -118.386 33.825))\n060376500012\t79280.613\tPOLYGON ((-118.326 33.883, -118.318 33.886, -118.318 33.882, -118.322 33.882, -118.322 33.88, -118.326 33.88, -118.326 33.883))\n060376503002\t84953.868\tPOLYGON ((-118.352 33.873, -118.344 33.873, -118.344 33.867, -118.347 33.867, -118.347 33.869, -118.352 33.869, -118.352 33.873))\n060377010002\t133139.797\tPOLYGON ((-118.414 34.064, -118.41 34.067, -118.403 34.067, -118.403 34.062, -118.407 34.062, -118.407 34.065, -118.41 34.066, -118.413 34.063, -118.414 34.063, -118.414 34.064))\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          " ",
+          "##### Save predictions to a table "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h5 id=\"save-predictions-to-a-table\">Save predictions to a table</h5>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Save test prediction results to a database table",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "test_predictions_sdf.write(DBSpatialDataset(table='test_predictions'), if_exists='replace')",
+          "",
+          "",
+          " "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Data out of bounds. Dimension Element: [ name:X, lower bounds: -180.0, upper bounds:180.0 ],  Data: [ lower bounds: -118.558813, upper bounds: -118.167357\nData out of bounds. Dimension Element: [ name:Y, lower bounds: -90.0, upper bounds:90.0 ],  Data: [ lower bounds: 33.706979, upper bounds: 34.33389\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Verify row count in predictions table",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "select count(*) from test_predictions;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "COUNT(*)\n667\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "View content in predictions table",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "z.show(SpatialDataFrame.create(DBSpatialDataset( table='test_predictions')).head())",
+          "",
+          " "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "GEOID\tPREDICTION\tgeometry\n060371066451\t83009.742\tPOLYGON ((-118.49 34.289, -118.489 34.291, -118.484 34.286, -118.485 34.282, -118.484 34.28, -118.485 34.28, -118.485 34.279, -118.486 34.278, -118.486 34.277, -118.489 34.277, -118.489 34.279, -118.489 34.282, -118.49 34.287, -118.49 34.289))\n060371863011\t76971.291\tPOLYGON ((-118.253 34.124, -118.239 34.126, -118.237 34.125, -118.232 34.125, -118.231 34.123, -118.23 34.121, -118.231 34.12, -118.235 34.118, -118.236 34.119, -118.24 34.121, -118.241 34.122, -118.242 34.123, -118.244 34.121, -118.247 34.119, -118.25 34.122, -118.253 34.124))\n060371871022\t75497.57\tPOLYGON ((-118.256 34.109, -118.254 34.113, -118.252 34.117, -118.247 34.113, -118.247 34.112, -118.244 34.113, -118.244 34.111, -118.238 34.105, -118.238 34.104, -118.238 34.103, -118.237 34.102, -118.235 34.1, -118.234 34.098, -118.23 34.094, -118.227 34.091, -118.226 34.089, -118.226 34.086, -118.226 34.083, -118.228 34.083, -118.228 34.083, -118.229 34.088, -118.229 34.09, -118.231 34.092, -118.234 34.094, -118.239 34.095, -118.24 34.097, -118.242 34.1, -118.242 34.104, -118.243 34.105, -118.247 34.108, -118.25 34.108, -118.255 34.108, -118.257 34.108, -118.256 34.109))\n060377018022\t98762.11\tPOLYGON ((-118.482 34.016, -118.477 34.02, -118.475 34.02, -118.473 34.018, -118.474 34.017, -118.479 34.014, -118.482 34.016))\n060377022021\t109413.639\tPOLYGON ((-118.475 34.008, -118.474 34.007, -118.472 34.007, -118.469 34.003, -118.469 34.003, -118.47 34.002, -118.47 34.002, -118.472 34.004, -118.474 34.005, -118.476 34.007, -118.475 34.008))\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          " ",
+          "##### Save model to Datastore "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h5 id=\"save-model-to-datastore\">Save model to Datastore</h5>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Save your model to an OML Datastore",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "oml.ds.save({'slr_pipeline': slr_pipeline}, 'slr_ds', description=' ', overwrite=True)",
+          "print(oml.ds.describe('slr_ds'))",
+          " "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "    object_name            class     size  length  row_count  col_count\n0  slr_pipeline  SpatialPipeline  1149751       1          1          1\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Load and score your model from your OML Datastore",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "objs = oml.ds.load('slr_ds', objs=['slr_pipeline'], to_globals=False)",
+          "load_slr_pipeline = objs['slr_pipeline']",
+          "print('score: ', load_slr_pipeline.score(X_test.drop(\"GEOID\"), y=\"MEDIAN_INCOME\"))"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "score:  0.6180254554626836\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "##### Store model for embedded execution",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h5 id=\"store-model-for-embedded-execution\">Store model for embedded execution</h5>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Store your model as a script for embedded execution",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "slr_model_fn = \"\"\"def slr_pipeline_predict_(X):",
+          "                  import oml",
+          "                  objs = oml.ds.load('slr_ds', objs=['slr_pipeline'], to_globals=False)",
+          "                  return objs['slr_pipeline'].predict(X)\"\"\"",
+          " ",
+          "oml.script.create(\"slrModelPredict\", slr_model_fn, is_global=True, overwrite=True)",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Invoke your model with embedded execution",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "slr_prediction = oml.do_eval(func='slrModelPredict', X=X_test.drop(\"MEDIAN_INCOME\").drop(\"GEOID\"))",
+          "slr_prediction.flatten()[:10]",
+          "",
+          "",
+          " "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "array([ 57052.5554592 , 108972.65263617,  79280.61322168,  84953.86779151,\n       133139.79708008, 110716.01911665, 148665.70680954, 141737.78796968,\n        94883.8143976 ,  75982.94208938])\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "##### Cleanup",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h5 id=\"cleanup\">Cleanup</h5>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Drop predictions table",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "drop table test_predictions;",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Remove your datastore and script",
+        "hasTitle" : true,
+        "message" : [
+          "%python",
+          "",
+          "",
+          "oml.ds.delete('slr_ds')",
+          "oml.script.drop(\"slrModelPredict\", is_global=True)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "python.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## End of Script",
+          " "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of Script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Scoord Transformer.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Scoord Transformer.dsnb
new file mode 100644
index 00000000..88215f7d
--- /dev/null
+++ b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Scoord Transformer.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Scoord Transformer","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Coordinates Transformer - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook you work with the `ScoordTransformer`, which returns the coordinates from the geometry of a dataset. If the geometry consists of polygons, it returns the centroids of the polygons  `ScoordTransformer` is useful when you want to add coordinates as new features in a dataset. Note that in this context \"Transformer\" means scikit-learn transform and not geospatial coordinate system transform. ","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1737673636479,"interpreter":"md.low","endTime":1737673637163,"results":[{"message":"<h1 id=\"spatial-coordinates-transformer---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Coordinates Transformer - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook you work with the <code>ScoordTransformer<\/code>, which returns the coordinates from the geometry of a dataset. If the geometry consists of polygons, it returns the centroids of the polygons  <code>ScoordTransformer<\/code> is useful when you want to add coordinates as new features in a dataset. Note that in this context &quot;Transformer&quot; means scikit-learn transform and not geospatial coordinate system transform.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737673637737,"interpreter":"md.low","endTime":1737673638335,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas as gpd","from sklearn.preprocessing import RobustScaler","from sklearn.impute import SimpleImputer","from libpysal import weights","import matplotlib.pyplot as plt","import pandas as pd","import numpy as np","","import random","random_state = 456","random.seed(random_state)","","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.pipeline import SpatialPipeline, SpatialFeatureUnion, SpatialColumnTransformer","from oraclesai.preprocessing import SCoordTransformer","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai import enable_geodataframes","from oraclesai import vis","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737673638866,"interpreter":"python.low","endTime":1737673642788,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","You will use demographic data from the US Census for regions in the City of Los Angeles. The regions are called block groups and have a unique identifier called GEOID. "],"enabled":true,"result":{"startTime":1737673643467,"interpreter":"md.low","endTime":1737673644125,"results":[{"message":"<p>You will use demographic data from the US Census for regions in the City of Los Angeles. The regions are called block groups and have a unique identifier called GEOID.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a SpatialDataFrame for the LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID','MEAN_INCOME', 'MEAN_EDUCATION_LEVEL', 'MEAN_AGE','geometry']]","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737673644856,"interpreter":"python.low","endTime":1737673645684,"results":[{"message":"GEOID\tMEAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tgeometry\n060376700031\t84552.289\t9.746\t37.306\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t93991.301\t11.504\t39.161\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t78313.714\t10.976\t47.854\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t77076.074\t10.115\t39.283\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t62402.628\t10.716\t37.998\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot the block group polyons","message":["%python","","fig, ax = plt.subplots(figsize=(16,12))","block_groups_sdf.plot(ax=ax, edgecolor='black', linewidth=0.5, alpha=0.4, with_basemap=True)"],"enabled":true,"result":{"startTime":1737673646663,"interpreter":"python.low","endTime":1737673656309,"results":[{"message":"<div style='width:auto;height:auto'><img 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Since the input is polygons, the result will be an array or centroid coordinates."],"enabled":true,"result":{"startTime":1737673656855,"interpreter":"md.low","endTime":1737673657362,"results":[{"message":"<p>Applying the <code>ScoordTransfomer<\/code> will return a numpy array of the input geometry's coordinates. Since the input is polygons, the result will be an array or centroid coordinates.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get coordinates with SCoordTransformer","message":["%python","","coordinates = SCoordTransformer().fit_transform(block_groups_sdf)","coordinates"],"enabled":true,"result":{"startTime":1737673657878,"interpreter":"python.low","endTime":1737673658743,"results":[{"message":"<stdin>:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n\narray([[-118.32017558,   33.80037246],\n       [-118.31968157,   33.79445738],\n       [-118.32510512,   33.79278376],\n       ...,\n       [-118.32077   ,   34.0380282 ],\n       [-118.30661238,   34.02740168],\n       [-118.44668825,   34.30208492]])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot the points from SCoordTransformer","message":["%python","","fig, ax = plt.subplots(figsize=(16,12))","","coords_df = pd.DataFrame({'x': coordinates[:, 0], 'y': coordinates[:, 1]})","coords_gdf = gpd.GeoDataFrame(coords_df, geometry=gpd.points_from_xy(coords_df['x'], coords_df['y'])).set_crs(\"epsg:4326\")","vis.plot_geometries(coords_gdf, ax=ax, markersize=4, with_basemap=True)"],"enabled":true,"result":{"startTime":1737673659281,"interpreter":"python.low","endTime":1737673660371,"results":[{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Spatial Fixed Effects Regressor.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Spatial Fixed Effects Regressor.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Spatial Fixed Effects Regressor","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Fixed Effects Regressor - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook you work with spatial fixed effects regression, which incorporates a categorical variable representing \"regime\". In the spatial context, regime represents fixed regions such as county or gridded areas. The algorithm calculates regression equations for each regime thereby accounting for spatial heterogeneity. Spatial AI includes SpatialRegimesRegressor with performs linear regression with terms that vary depending on the regime. Spatial AI also includes SpatialFixedEffectsRegressor which is a simplified version of SpatialRegimesRegressor, computing only an intercept or constant parameter for each regime while the other model parameters remain constant. In this notebook you work with SpatialFixedEffectsRegressor.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737673341438,"interpreter":"md.low","endTime":1737673342008,"results":[{"message":"<h1 id=\"spatial-fixed-effects-regressor---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Fixed Effects Regressor - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook you work with spatial fixed effects regression, which incorporates a categorical variable representing &quot;regime&quot;. In the spatial context, regime represents fixed regions such as county or gridded areas. The algorithm calculates regression equations for each regime thereby accounting for spatial heterogeneity. Spatial AI includes SpatialRegimesRegressor with performs linear regression with terms that vary depending on the regime. Spatial AI also includes SpatialFixedEffectsRegressor which is a simplified version of SpatialRegimesRegressor, computing only an intercept or constant parameter for each regime while the other model parameters remain constant. In this notebook you work with SpatialFixedEffectsRegressor.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737673342525,"interpreter":"md.low","endTime":1737673343035,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","import matplotlib.pyplot as plt","import numpy as np","import sklearn.metrics as metrics","from sklearn.preprocessing import RobustScaler","from libpysal import weights","import warnings","warnings.filterwarnings('ignore')","","# Import pandas libraries and set parameters","import pandas as pd","pd.options.mode.chained_assignment = None","pd.options.display.max_columns = None","","# Libraries and constants for reproducibility.","import random","random_state = 456","random.seed(random_state)","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset, GeoDataFrameDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.regression import SpatialFixedEffectsRegressor, OLSRegressor","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition ","from oraclesai import enable_geodataframes","enable_geodataframes(z)"," "],"enabled":true,"result":{"startTime":1737673343542,"interpreter":"python.low","endTime":1737673347260,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md",""," ","##### Prepare data ","","In this example you work with demographic data from the US Census Bureau for areas called [block groups](https://en.wikipedia.org/wiki/Census_block_group) in the City of Los Angeles and create a model to predict income. 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"],"enabled":true,"result":{"startTime":1737673377097,"interpreter":"md.low","endTime":1737673377688,"results":[{"message":"<p>Plotting block groups colored by GRID_CELL_ID helps to visualize the regimes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot a map of block groups colored by GRID_CELL_ID","message":["%python","","# Setup figure and ax","f, ax = plt.subplots(1, figsize=(15, 15))","# Plot unique values in a choropleth map","block_groups_grid.plot(column=\"GRID_CELL_ID\", categorical=True, legend=True, linewidth=0, ax=ax, alpha=0.75, with_basemap=True)","# Remove axis","ax.set_axis_off()","# Set the titles","ax.set_title('GRID_CELL_ID');","# Display the map","plt.show()"," "],"enabled":true,"result":{"startTime":1737673378323,"interpreter":"python.low","endTime":1737673380596,"results":[{"message":"<div style='width:auto;height:auto'><img 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You then split the data into training and test sets."],"enabled":true,"result":{"startTime":1737673381307,"interpreter":"md.low","endTime":1737673381972,"results":[{"message":"<p>To prepare for model training and testing, you convert coordinates from geodetic (longitude, latitude) to projected (meters) which is preferred for spatial weights calculation. 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seed=random_state)"],"enabled":true,"result":{"startTime":1737673382859,"interpreter":"python.low","endTime":1737673383576,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps of split data colored by MEDIAN_INCOME","message":["%python","","fig, ax = plt.subplots(1, 3, figsize=(25,10))","","# Set plot's labels and titles ","ax[0].set_title('Full Dataset - Median Income');","ax[1].set_title('Training - Median Income');","ax[2].set_title('Test - Median Income');","","# Plot the choropleth map","X.plot(ax=ax[0], column=X[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True, alpha=0.75 )","X_train.plot(ax=ax[1], column=X_train[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True, alpha=0.75 )","X_test.plot(ax=ax[2], column=X_test[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, with_basemap=True, alpha=0.75 )"," "],"enabled":true,"result":{"startTime":1737673384971,"interpreter":"python.low","endTime":1737673389469,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Nex, extract the regimes (`GRID_CELL_ID` column) for the training and test sets as Numpy lists and store them in separate variables. You will then remove the column from the training and test sets."," "],"enabled":true,"result":{"startTime":1737673390648,"interpreter":"md.low","endTime":1737673391969,"results":[{"message":"<p>Nex, extract the regimes (<code>GRID_CELL_ID<\/code> column) for the training and test sets as Numpy lists and store them in separate variables. You will then remove the column from the training and test sets.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Move GRID_CELL_ID from column to list","message":["%python","","regimes_train = X_train[\"GRID_CELL_ID\"].values.tolist()","regimes_test = X_test[\"GRID_CELL_ID\"].values.tolist()","","X_train = X_train.drop(\"GRID_CELL_ID\")","X_test = X_test.drop(\"GRID_CELL_ID\")"],"enabled":true,"result":{"startTime":1737673393363,"interpreter":"python.low","endTime":1737673394628,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The SpatialFixedEffectsRegressor uses spatial weights which defines neighborhoods for each sample."],"enabled":true,"result":{"startTime":1737673396327,"interpreter":"md.low","endTime":1737673396975,"results":[{"message":"<p>The SpatialFixedEffectsRegressor uses spatial weights which defines neighborhoods for each sample.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Calculate spatial weights for training data","message":["%python","","weights_definition = KNNWeightsDefinition(k=10)","spatial_weights = SpatialWeights.create(X_train[\"geometry\"].values, weights_definition)"," "],"enabled":true,"result":{"startTime":1737673397539,"interpreter":"python.low","endTime":1737673398366,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Train model","","For this model, you will compute an intercept or constant parameter for each regime. Other parameters will remain constant for all the regimes. With SpatialFixedEffectsRegressor you have only one linear equation and the intercept or constant parameter varies by regime. In this case, you have four regimes, so you will get four different constant values to use in the linear equation.","","Begin by creating spatial pipeline with a scaling process and SpatialFixedEffectsRegressor. The spatial weights are used to obtain spatial diagnostics. "," "],"enabled":true,"result":{"startTime":1737673398922,"interpreter":"md.low","endTime":1737673399486,"results":[{"message":"<h5 id=\"train-model\">Train model<\/h5>\n<p>For this model, you will compute an intercept or constant parameter for each regime. Other parameters will remain constant for all the regimes. With SpatialFixedEffectsRegressor you have only one linear equation and the intercept or constant parameter varies by regime. In this case, you have four regimes, so you will get four different constant values to use in the linear equation.<\/p>\n<p>Begin by creating spatial pipeline with a scaling process and SpatialFixedEffectsRegressor. The spatial weights are used to obtain spatial diagnostics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create spatial pipeline with scaler and SpatialFixedEffectsRegressor","message":["%python","","fe_model = SpatialFixedEffectsRegressor(spatial_weights_definition=weights_definition)","fe_pipe = SpatialPipeline([('scale', RobustScaler()), ('fe', fe_model)])"," "],"enabled":true,"result":{"startTime":1737673400007,"interpreter":"python.low","endTime":1737673400598,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Train the model by specifying the regimes and using the `MEDIAN_INCOME as the target variable."," "],"enabled":true,"result":{"startTime":1737673401126,"interpreter":"md.low","endTime":1737673401665,"results":[{"message":"<p>Train the model by specifying the regimes and using the `MEDIAN_INCOME as the target variable.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train the model","message":["%python","","fe_pipe.fit(X_train.drop(\"GEOID\"), \"MEDIAN_INCOME\", fe__regimes=regimes_train)"," "],"enabled":true,"result":{"startTime":1737673402289,"interpreter":"python.low","endTime":1737673403065,"results":[{"message":"SpatialPipeline(steps=[('scale', RobustScaler()),\n                       ('fe',\n                        SpatialFixedEffectsRegressor(spatial_weights_definition=<oraclesai.weights.weights_definition.KNNWeightsDefinition object at 0x7f8e5c97d550>))])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Print a model summary","message":["%python","","print(fe_pipe.named_steps['fe'].summary)"," "],"enabled":true,"result":{"startTime":1737673403609,"interpreter":"python.low","endTime":1737673404192,"results":[{"message":"REGRESSION RESULTS\n------------------\n\nSUMMARY OF OUTPUT: ORDINARY LEAST SQUARES - REGIMES\n---------------------------------------------------\nData set            :     unknown\nWeights matrix      :     unknown\nDependent Variable  :     dep_var                Number of Observations:        2750\nMean dependent var  :  69927.3713                Number of Variables   :          12\nS.D. dependent var  :  40344.0309                Degrees of Freedom    :        2738\nR-squared           :      0.6630\nAdjusted R-squared  :      0.6616\nSum squared residual: 1.50792e+12                F-statistic           :    489.6675\nSigma-square        :550738091.231                Prob(F-statistic)     :           0\nS.E. of regression  :   23467.810                Log likelihood        :  -31570.376\nSigma-square ML     :548334870.469                Akaike info criterion :   63164.753\nS.E of regression ML:  23416.5512                Schwarz criterion     :   63235.785\n\n------------------------------------------------------------------------------------\n            Variable     Coefficient       Std.Error     t-Statistic     Probability\n------------------------------------------------------------------------------------\n          1_CONSTANT     69210.46850      1425.31710        48.55794         0.00000\n          2_CONSTANT     71882.59448      1346.76308        53.37434         0.00000\n          3_CONSTANT     52959.54402      2090.07242        25.33862         0.00000\n          4_CONSTANT     54020.94151      1082.63612        49.89760         0.00000\n          5_CONSTANT     63908.35804      1121.61635        56.97880         0.00000\n          6_CONSTANT     68784.40193      1302.06100        52.82733         0.00000\n          7_CONSTANT     65709.39014      1451.61966        45.26626         0.00000\n          8_CONSTANT     67564.46143      2004.47932        33.70674         0.00000\n    _Global_MEAN_AGE      3404.37475       789.37240         4.31276         0.00002\n_Global_MEAN_EDUCATION_LEVEL     12609.73384      1575.02570         8.00605         0.00000\n _Global_HOUSE_VALUE     19577.39225       612.83212        31.94577         0.00000\n    _Global_INTERNET     12769.17635       989.79572        12.90082         0.00000\n------------------------------------------------------------------------------------\nRegimes variable: unknown\n\nREGIMES DIAGNOSTICS - CHOW TEST\n                 VARIABLE        DF        VALUE           PROB\n                 CONSTANT         7         180.965           0.0000\n              Global test         7         180.965           0.0000\n\nREGRESSION DIAGNOSTICS\nMULTICOLLINEARITY CONDITION NUMBER            4.222\n\nTEST ON NORMALITY OF ERRORS\nTEST                             DF        VALUE           PROB\nJarque-Bera                       2        1012.077           0.0000\n\nDIAGNOSTICS FOR HETEROSKEDASTICITY\nRANDOM COEFFICIENTS\nTEST                             DF        VALUE           PROB\nBreusch-Pagan test               11        1202.291           0.0000\nKoenker-Bassett test             11         514.970           0.0000\n\nDIAGNOSTICS FOR SPATIAL DEPENDENCE\nTEST                           MI/DF       VALUE           PROB\nMoran's I (error)              0.2350        29.549           0.0000\nLagrange Multiplier (lag)         1         392.591           0.0000\nRobust LM (lag)                   1           0.005           0.9441\nLagrange Multiplier (error)       1         821.486           0.0000\nRobust LM (error)                 1         428.900           0.0000\nLagrange Multiplier (SARMA)       2         821.491           0.0000\n\n================================ END OF REPORT =====================================\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","In the summary above, note the 8 \"CONSTANT\" parameters, one for each regime. The other parameters are shared by all regimes. To predict new values, the model will use the constant parameter for the corresponding regime in the regression equation along with the other shared parameters. "," "," "],"enabled":true,"result":{"startTime":1737673404760,"interpreter":"md.low","endTime":1737673405442,"results":[{"message":"<p>In the summary above, note the 8 &quot;CONSTANT&quot; parameters, one for each regime. The other parameters are shared by all regimes. To predict new values, the model will use the constant parameter for the corresponding regime in the regression equation along with the other shared parameters.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Evaluation with test set","","You next test the model and obtain metrics to measure the model's performance."," "],"enabled":true,"result":{"startTime":1737673405991,"interpreter":"md.low","endTime":1737673406590,"results":[{"message":"<h5 id=\"evaluation-with-test-set\">Evaluation with test set<\/h5>\n<p>You next test the model and obtain metrics to measure the model's performance.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Make prediction with test set","message":["%python","","fe_predictions_test = fe_pipe.predict(X_test.drop([\"GEOID\",\"MEDIAN_INCOME\"]), fe__regimes=regimes_test).flatten()"," "],"enabled":true,"result":{"startTime":1737673407215,"interpreter":"python.low","endTime":1737673407773,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps of test data and predictions","message":["%python","","fig, ax = plt.subplots(1, 2, figsize=(16,12))","","# Set plot's labels and titles ","ax[0].set_title('Test Data');","ax[1].set_title('Prediction');","","# Plot the choropleth map","X_test.plot(ax=ax[0], column=X_test[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, alpha=0.75, with_basemap=True )","X_test.plot(ax=ax[1], column=fe_predictions_test, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, alpha=0.75, with_basemap=True )"," "],"enabled":true,"result":{"startTime":1737673408313,"interpreter":"python.low","endTime":1737673410033,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Print test set metrics","message":["%python","","print(\"Test set metrics\")","print(\"r2:\", metrics.r2_score(X_test['MEDIAN_INCOME'].values, fe_predictions_test))","print(\"mae:\", metrics.mean_absolute_error(X_test['MEDIAN_INCOME'].values, fe_predictions_test))"," "],"enabled":true,"result":{"startTime":1737673410570,"interpreter":"python.low","endTime":1737673411305,"results":[{"message":"Test set metrics\nr2: 0.6478517616592452\nmae: 17168.952207750295\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare to non-spatial OLS regression","message":["%python","","ols_pipeline = SpatialPipeline([('scale', RobustScaler()), ","                                ('regression', OLSRegressor())])","ols_pipeline.fit(X_train.drop(\"GEOID\"), \"MEDIAN_INCOME\")","ols_predictions_test = ols_pipeline.predict(X_test.drop([\"GEOID\",\"MEDIAN_INCOME\"])).flatten()","","print(\"Test set metrics\\n\")","print(\"r2:\",metrics.r2_score(X_test['MEDIAN_INCOME'].values, ols_predictions_test))","print(\"mae:\",metrics.mean_absolute_error(X_test['MEDIAN_INCOME'].values, ols_predictions_test))"," "],"enabled":true,"result":{"startTime":1737673411836,"interpreter":"python.low","endTime":1737673412403,"results":[{"message":"Test set metrics\n\nr2: 0.6210814795451667\nmae: 17956.52526455691\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","By using different linear regression equations for each regime, the model can better explain the variability of the data. In this example, the SpatialFixedEffectsRegressor model changes only the intercept parameter according to regime, making it less expensive to train than a SpatialRegimesRegressor model which would calculate all parameters for each regime."," "],"enabled":true,"result":{"startTime":1737673413054,"interpreter":"md.low","endTime":1737673413957,"results":[{"message":"<p>By using different linear regression equations for each regime, the model can better explain the variability of the data. In this example, the SpatialFixedEffectsRegressor model changes only the intercept parameter according to regime, making it less expensive to train than a SpatialRegimesRegressor model which would calculate all parameters for each regime.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"," "],"enabled":true,"result":{"startTime":1737673414556,"interpreter":"md.low","endTime":1737673415288,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Spatial Imputer","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Imputer - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you work with the Spatial Imputer. Imputers are preprocessors that fill missing values with an aggregate value, such as the median of all other non-null values in the column. Once missing values are filled, the data can be used for model training and pattern detection. The Spatial Imputer adds spatial context, filling missing values based on an aggregate of neighboring values.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737673172792,"interpreter":"md.low","endTime":1737673173438,"results":[{"message":"<h1 id=\"spatial-imputer---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Imputer - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you work with the Spatial Imputer. Imputers are preprocessors that fill missing values with an aggregate value, such as the median of all other non-null values in the column. Once missing values are filled, the data can be used for model training and pattern detection. The Spatial Imputer adds spatial context, filling missing values based on an aggregate of neighboring values.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737673173994,"interpreter":"md.low","endTime":1737673174483,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","from sklearn.preprocessing import RobustScaler","from sklearn.impute import SimpleImputer","from libpysal import weights","import matplotlib.pyplot as plt","import pandas as pd","import numpy as np","import warnings","import math","","import random","random_state = 456","random.seed(random_state)","","from oraclesai import SpatialDataFrame, DBSpatialDataset, GeoDataFrameDataset","from oraclesai.preprocessing import SpatialImputer","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai import enable_geodataframes","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737673174970,"interpreter":"python.low","endTime":1737673179659,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","In this example, you will use Census data for block groups in the City of Los Angeles and fill missing values for INTERNET (% of household with internet access per block group)."],"enabled":true,"result":{"startTime":1737673180155,"interpreter":"md.low","endTime":1737673180636,"results":[{"message":"<p>In this example, you will use Census data for block groups in the City of Los Angeles and fill missing values for INTERNET (% of household with internet access per block group).<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset( table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['MEDIAN_INCOME', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'INTERNET', 'HOUSE_VALUE', 'geometry']]","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737673181139,"interpreter":"python.low","endTime":1737673182159,"results":[{"message":"MEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tINTERNET\tHOUSE_VALUE\tgeometry\n75577\t37.306\t9.746\t0.904\t627700\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n74609\t39.161\t11.504\t0.959\t599300\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n55592\t47.854\t10.976\t0.829\t609500\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n73558\t39.283\t10.115\t0.859\t471000\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n54738\t37.998\t10.716\t0.882\t389900\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Convert to projected coordinates as required by spatial imputer","message":["%python","","block_groups_sdf = block_groups_sdf.to_crs(\"EPSG:3857\")","z.show(block_groups_sdf.head())"," "],"enabled":true,"result":{"startTime":1737673182713,"interpreter":"python.low","endTime":1737673183220,"results":[{"message":"MEDIAN_INCOME\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tINTERNET\tHOUSE_VALUE\tgeometry\n75577\t37.306\t9.746\t0.904\t627700\tPOLYGON ((-13171614.921 4002458.952, -13171006.56 4002139.446, -13171013.684 4001739.038, -13171618.149 4001743.458, -13171614.921 4002458.952))\n74609\t39.161\t11.504\t0.959\t599300\tPOLYGON ((-13171618.149 4001743.458, -13171013.684 4001739.038, -13171015.02 4000576.34, -13171318.477 4000575.938, -13171317.364 4000963.044, -13171619.596 4000964.384, -13171618.149 4001743.458))\n55592\t47.854\t10.976\t0.829\t609500\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n73558\t39.283\t10.115\t0.859\t471000\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 4002094.032))\n54738\t37.998\t10.716\t0.882\t389900\tPOLYGON ((-13171620.932 4000577.813, -13171318.477 4000575.938, -13171015.02 4000576.34, -13170712.342 4000574.063, -13170717.463 4000012.581, -13171091.051 4000012.313, -13171186.007 3999904.092, -13171571.395 4000085.443, -13171620.932 3999984.052, -13171620.932 4000577.813))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","There are no missing values in the source data. So generate a random set of row indexes for removing the INTERNET value to create missing values."," "],"enabled":true,"result":{"startTime":1737673183683,"interpreter":"md.low","endTime":1737673184120,"results":[{"message":"<p>There are no missing values in the source data. So generate a random set of row indexes for removing the INTERNET value to create missing values.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generate random indexes to create missing values","message":["%python","","all_indexes = range(0, block_groups_sdf.shape[0] - 1)","set_to_missing = sorted(random.sample(all_indexes, math.floor(block_groups_sdf.shape[0] * 0.10)))","set_to_missing"],"enabled":true,"result":{"startTime":1737673184590,"interpreter":"python.low","endTime":1737673185183,"results":[{"message":"[0, 5, 38, 52, 54, 88, 92, 97, 102, 112, 123, 137, 147, 150, 164, 165, 171, 172, 173, 179, 183, 192, 200, 204, 231, 250, 260, 264, 271, 278, 293, 301, 303, 305, 313, 314, 334, 336, 345, 354, 357, 363, 365, 409, 431, 460, 470, 472, 479, 485, 489, 496, 499, 508, 515, 518, 523, 528, 529, 546, 551, 552, 553, 569, 576, 595, 601, 620, 621, 622, 658, 660, 673, 675, 685, 688, 696, 704, 710, 723, 733, 750, 758, 763, 774, 778, 800, 818, 821, 829, 837, 864, 882, 931, 934, 940, 955, 959, 990, 993, 1008, 1013, 1017, 1018, 1036, 1039, 1043, 1064, 1077, 1101, 1103, 1105, 1106, 1111, 1113, 1115, 1123, 1161, 1178, 1222, 1229, 1246, 1251, 1265, 1273, 1284, 1296, 1299, 1302, 1312, 1336, 1337, 1388, 1391, 1417, 1421, 1451, 1457, 1481, 1486, 1513, 1518, 1526, 1528, 1534, 1537, 1555, 1562, 1576, 1578, 1580, 1592, 1593, 1599, 1621, 1629, 1651, 1654, 1660, 1673, 1699, 1703, 1705, 1714, 1763, 1782, 1794, 1796, 1805, 1816, 1822, 1828, 1833, 1840, 1855, 1858, 1860, 1877, 1893, 1899, 1909, 1914, 1929, 1943, 1954, 1956, 1960, 1970, 1976, 1977, 1978, 1985, 1990, 1994, 1997, 2000, 2002, 2005, 2013, 2014, 2017, 2034, 2041, 2054, 2059, 2109, 2113, 2125, 2133, 2134, 2136, 2158, 2183, 2187, 2198, 2200, 2207, 2211, 2235, 2248, 2251, 2256, 2258, 2261, 2262, 2287, 2291, 2298, 2310, 2312, 2318, 2328, 2342, 2351, 2355, 2366, 2368, 2370, 2374, 2376, 2382, 2389, 2408, 2422, 2464, 2485, 2507, 2521, 2525, 2526, 2528, 2538, 2558, 2574, 2593, 2608, 2611, 2634, 2638, 2639, 2640, 2648, 2649, 2654, 2657, 2660, 2672, 2677, 2686, 2705, 2712, 2716, 2718, 2732, 2736, 2758, 2765, 2777, 2782, 2787, 2796, 2799, 2816, 2822, 2837, 2845, 2849, 2850, 2860, 2872, 2877, 2910, 2948, 2952, 2955, 2968, 2992, 2993, 2996, 3005, 3008, 3009, 3024, 3041, 3044, 3064, 3074, 3100, 3105, 3123, 3125, 3128, 3132, 3146, 3171, 3188, 3191, 3201, 3202, 3210, 3213, 3216, 3217, 3231, 3234, 3253, 3268, 3291, 3309, 3311, 3314, 3330, 3352, 3368, 3371, 3373, 3380, 3388, 3401, 3403, 3405, 3431, 3432]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","To clear the value of INTERNET for the random set of rows, you will use the `loc()` method supported by GeoPandas GeoDataFrames. "],"enabled":true,"result":{"startTime":1737673185702,"interpreter":"md.low","endTime":1737673186255,"results":[{"message":"<p>To clear the value of INTERNET for the random set of rows, you will use the <code>loc()<\/code> method supported by GeoPandas GeoDataFrames.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create missing values for column INTERNET","message":["%python","","X = block_groups_sdf.as_geodataframe()","for row in set_to_missing:","    X.loc[row, \"INTERNET\"] = np.nan","","print(f\"Total rows: {X.shape[0]}\")","print(f\"Rows with INTERNET missing: {len(X[X['INTERNET'].isnull()==True])}\")","","","",""," "],"enabled":true,"result":{"startTime":1737673186717,"interpreter":"python.low","endTime":1737673195124,"results":[{"message":"Total rows: 3437\nRows with INTERNET missing: 343\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an instance of SpatialImputer","message":["%python","","spatial_imputer = SpatialImputer(missing_values=np.nan, spatial_weights_definition=KNNWeightsDefinition(k=10))",""," "," "],"enabled":true,"result":{"startTime":1737673195823,"interpreter":"python.low","endTime":1737673196460,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Fill missing values","message":["%python","","X_imputed = spatial_imputer.fit_transform(X, y=\"MEDIAN_INCOME\")","X_imputed_df = pd.DataFrame(X_imputed, columns = ['MEAN_EDUCATION_LEVEL', 'MEAN_AGE', 'INTERNET', 'HOUSE_VALUE'])","X_imputed_df"," "],"enabled":true,"result":{"startTime":1737673197116,"interpreter":"python.low","endTime":1737673199357,"results":[{"message":"      MEAN_EDUCATION_LEVEL   MEAN_AGE  INTERNET  HOUSE_VALUE\n0                37.305699   9.746392  0.859448     627700.0\n1                39.160548  11.504026  0.958746     599300.0\n2                47.853779  10.975709  0.828851     609500.0\n3                39.282889  10.115431  0.858836     471000.0\n4                37.997861  10.715607  0.881992     389900.0\n...                    ...        ...       ...          ...\n3432             42.924373  11.626251  0.941973     626300.0\n3433             47.819277  11.832298  0.964809     599100.0\n3434             40.681424   9.075908  0.711982     638300.0\n3435             38.765759   9.176699  0.852071     473200.0\n3436             34.191356   8.001604  0.811554     364500.0\n\n[3437 rows x 4 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confirm no missing values in INTERNET column","message":["%python","","len(X_imputed_df[X_imputed_df['INTERNET'].isnull()==True])"," "],"enabled":true,"result":{"startTime":1737673200025,"interpreter":"python.low","endTime":1737673201930,"results":[{"message":"0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Plot a map of before and after filling of missing values","message":["%python","","fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,14))","","# Set plot's labels and titles ","ax1.set_title('INTERNET before imputer (missing data in red)');","ax2.set_title('INTERNET after spatial imputer');","","# Plot the choropleth maps","","","X.plot(ax=ax1, column=\"INTERNET\", cmap=plt.get_cmap(\"Greens\"), edgecolor='black', linewidth=0.1, legend=True, missing_kwds={","        \"color\": \"red\",","        \"edgecolor\": \"white\",","       # \"hatch\": \"///\",","        \"label\": \"Missing values\",","    })","X.plot(ax=ax2, column=X_imputed_df[\"INTERNET\"].values, cmap=plt.get_cmap(\"Greens\"), edgecolor='black',  linewidth=0.1)",""," "],"enabled":true,"result":{"startTime":1737673202745,"interpreter":"python.low","endTime":1737673205218,"results":[{"message":"<Axes: title={'center': 'INTERNET after spatial imputer'}>\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI Spatial Lag Transformer.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI Spatial Lag Transformer.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Spatial Lag Transformer","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Lag Transformer - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","The spatial lag of a feature reflects the aggregate value of that feature in the neighborhood around each observation, and is used across Spatial AI in prediction and pattern detection models. ","","In this notebook you work with demographic data for [US Census Block Groups](https://en.wikipedia.org/wiki/Census_block_group) in the City of Los Angeles. You will generate spatial lag for age and house value from neighboring areas. ","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737668441469,"interpreter":"md.low","endTime":1737668442159,"results":[{"message":"<h1 id=\"spatial-lag-transformer---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Lag Transformer - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>The spatial lag of a feature reflects the aggregate value of that feature in the neighborhood around each observation, and is used across Spatial AI in prediction and pattern detection models.<\/p>\n<p>In this notebook you work with demographic data for <a href=\"https://en.wikipedia.org/wiki/Census_block_group\">US Census Block Groups<\/a> in the City of Los Angeles. You will generate spatial lag for age and house value from neighboring areas.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."," "],"enabled":true,"result":{"startTime":1737668442779,"interpreter":"md.low","endTime":1737668443317,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","from sklearn.preprocessing import RobustScaler","from sklearn.impute import SimpleImputer","from libpysal import weights","import matplotlib.pyplot as plt","import pandas as pd","import numpy as np","","import random","random_state = 456","random.seed(random_state)","","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.pipeline import SpatialPipeline, SpatialFeatureUnion, SpatialColumnTransformer","from oraclesai.preprocessing import SpatialLagTransformer","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai import enable_geodataframes","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737668443826,"interpreter":"python.low","endTime":1737668447399,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID','MEDIAN_INCOME', 'MEAN_AGE', 'HOUSE_VALUE', 'geometry']]","# convert to projected coordinate system as required by spatial weights","block_groups_sdf = block_groups_sdf.to_crs(\"epsg:3857\")","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737668447933,"interpreter":"python.low","endTime":1737668448593,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_AGE\tHOUSE_VALUE\tgeometry\n060376700031\t75577\t37.306\t627700\tPOLYGON ((-13171614.921 4002458.952, -13171006.56 4002139.446, -13171013.684 4001739.038, -13171618.149 4001743.458, -13171614.921 4002458.952))\n060376700032\t74609\t39.161\t599300\tPOLYGON ((-13171618.149 4001743.458, -13171013.684 4001739.038, -13171015.02 4000576.34, -13171318.477 4000575.938, -13171317.364 4000963.044, -13171619.596 4000964.384, -13171618.149 4001743.458))\n060376700033\t55592\t47.854\t609500\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n060376700034\t73558\t39.283\t471000\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 4002094.032))\n060376701001\t54738\t37.998\t389900\tPOLYGON ((-13171620.932 4000577.813, -13171318.477 4000575.938, -13171015.02 4000576.34, -13170712.342 4000574.063, -13170717.463 4000012.581, -13171091.051 4000012.313, -13171186.007 3999904.092, -13171571.395 4000085.443, -13171620.932 3999984.052, -13171620.932 4000577.813))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you create a SpatialLagTransformer. The neighbors to use for calculating spatial lag is based on a spatial weights definition. In this case you use the 5 nearest neighbors. "],"enabled":true,"result":{"startTime":1737668449219,"interpreter":"md.low","endTime":1737668449751,"results":[{"message":"<p>Next you create a SpatialLagTransformer. The neighbors to use for calculating spatial lag is based on a spatial weights definition. In this case you use the 5 nearest neighbors.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a SpatialLagTransformer","message":["%python","","spatial_lag_transformer = SpatialLagTransformer(spatial_weights_definition= KNNWeightsDefinition(k=5)) "],"enabled":true,"result":{"startTime":1737668450280,"interpreter":"python.low","endTime":1737668450783,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You will use the SpatialLagTransformer to transform the original data and incorporate the spatial lag of `mean_age` and `house_value` as new features. "],"enabled":true,"result":{"startTime":1737668451314,"interpreter":"md.low","endTime":1737668451809,"results":[{"message":"<p>You will use the SpatialLagTransformer to transform the original data and incorporate the spatial lag of <code>mean_age<\/code> and <code>house_value<\/code> as new features.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply the SpatialLagTransformer","message":["%python","","block_groups_lag = spatial_lag_transformer.fit_transform(block_groups_sdf.drop(\"GEOID\"), y=\"MEDIAN_INCOME\")","df = pd.DataFrame(block_groups_lag, columns=['mean_age_lag', 'house_value_lag'])","df.insert(0, 'GEOID', block_groups_sdf[[\"GEOID\"]].get_values().flatten())","z.show(df.head())"],"enabled":true,"result":{"startTime":1737668452373,"interpreter":"python.low","endTime":1737668467627,"results":[{"message":"GEOID\tmean_age_lag\thouse_value_lag\n060376700031\t39.959478064337624\t552980.0\n060376700032\t40.11939191015061\t536360.0\n060376700033\t42.53606992859642\t452720.0\n060376700034\t41.12949758172174\t616220.0\n060376701001\t41.989081664093014\t602700.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","SpatialFeatureUnion is a type of spatial pipeline that returns results for each step. Next you use SpatialLagTransformer with SpatialFeatureUnion to generate lag features for different spatial weights definitions. "],"enabled":true,"result":{"startTime":1737668468136,"interpreter":"md.low","endTime":1737668468639,"results":[{"message":"<p>SpatialFeatureUnion is a type of spatial pipeline that returns results for each step. Next you use SpatialLagTransformer with SpatialFeatureUnion to generate lag features for different spatial weights definitions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use SpatialLagTransformer with SpatialFeatureUnion","message":["%python","","spatial_feature_union = SpatialFeatureUnion([(\"spatial_lag_knn5\", SpatialLagTransformer(spatial_weights_definition= KNNWeightsDefinition(k=5))) , ","                                           (\"spatial_lag_knn10\",SpatialLagTransformer(spatial_weights_definition= KNNWeightsDefinition(k=10)))])","lag_feature_union = spatial_feature_union.fit_transform(block_groups_sdf.drop(\"GEOID\"), y=\"MEDIAN_INCOME\")","df = pd.DataFrame(lag_feature_union, columns=['age_lag_knn5', 'houseval_lag_knn5','age_lag_knn10', 'houseval_lag_knn10' ])","df.insert(0, 'GEOID', block_groups_sdf[[\"GEOID\"]].get_values().flatten())","z.show(df.head())","","                                                            ","","                                "],"enabled":true,"result":{"startTime":1737668469148,"interpreter":"python.low","endTime":1737668484903,"results":[{"message":"GEOID\tage_lag_knn5\thouseval_lag_knn5\tage_lag_knn10\thouseval_lag_knn10\n060376700031\t39.959478064337624\t552980.0\t41.31516929216667\t593620.0\n060376700032\t40.11939191015061\t536360.0\t40.55946442843291\t541160.0\n060376700033\t42.53606992859642\t452720.0\t41.058474969394794\t544580.0\n060376700034\t41.12949758172174\t616220.0\t41.017264960537766\t606690.0\n060376701001\t41.989081664093014\t602700.0\t40.8813156088885\t547440.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","Note that you can also use `SpatialLagTransformer` with `SpatialColumnTransformer`. As with `SpatialFeatureUnion`, the `SpatialColumnTransformer` appends the resulting data of each step in the outcome, with the difference that it does it using specific columns for each step instead of using the whole dataset.","","In summary, adding spatial lags to a dataset provides geographic information. Closer elements tend to be more related than distant elements. Having this geographic information can be used for training spatial models using spatial lag and spatial error algorithms that include spatial dependence in the regression equation.","","Adding the spatial lags as features can be done in different ways, either by using the `SpatialLagTransfomer` directly or by using methods that append these features like the `SpatialFeatureUnion` and the `SpatialColumnTransformer`."],"enabled":true,"result":{"startTime":1737668485441,"interpreter":"md.low","endTime":1737668485978,"results":[{"message":"<p>Note that you can also use <code>SpatialLagTransformer<\/code> with <code>SpatialColumnTransformer<\/code>. As with <code>SpatialFeatureUnion<\/code>, the <code>SpatialColumnTransformer<\/code> appends the resulting data of each step in the outcome, with the difference that it does it using specific columns for each step instead of using the whole dataset.<\/p>\n<p>In summary, adding spatial lags to a dataset provides geographic information. Closer elements tend to be more related than distant elements. Having this geographic information can be used for training spatial models using spatial lag and spatial error algorithms that include spatial dependence in the regression equation.<\/p>\n<p>Adding the spatial lags as features can be done in different ways, either by using the <code>SpatialLagTransfomer<\/code> directly or by using methods that append these features like the <code>SpatialFeatureUnion<\/code> and the <code>SpatialColumnTransformer<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script "],"enabled":true,"result":{"startTime":1737668486548,"interpreter":"md.low","endTime":1737668487079,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Spatial Lag and Error Regressors","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Spatial AI Spatial Lag and Error Regressors - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook you explore two types of spatial regression algorithms, SpatialLagRegressor and SpatialErrorRegressor.","* Spatial lag regression models are useful when the value of a target variable in one location is influenced directly by values in neighboring locations, suggesting that nearby observations share a relationship that affects the outcome.","* Spatial error regression models are beneficial when there is spatial autocorrelation present in the model\u2019s residuals, indicating that the observed error in one location is related to errors in neighboring locations.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737668543057,"interpreter":"md.low","endTime":1737668543641,"results":[{"message":"<h1 id=\"oml4py-spatial-ai-spatial-lag-and-error-regressors---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">OML4Py Spatial AI Spatial Lag and Error Regressors - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook you explore two types of spatial regression algorithms, SpatialLagRegressor and SpatialErrorRegressor.<\/p>\n<ul>\n<li>Spatial lag regression models are useful when the value of a target variable in one location is influenced directly by values in neighboring locations, suggesting that nearby observations share a relationship that affects the outcome.<\/li>\n<li>Spatial error regression models are beneficial when there is spatial autocorrelation present in the model\u2019s residuals, indicating that the observed error in one location is related to errors in neighboring locations.<\/li>\n<\/ul>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md"," ","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737668544120,"interpreter":"md.low","endTime":1737668544598,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","import matplotlib.pyplot as plt","import numpy as np","import sklearn.metrics as metrics","from sklearn.preprocessing import RobustScaler","from libpysal import weights","","# Libraries and constants for reproducibility.","import random","random_state = 0","random.seed(random_state)","","import oml","","# Spatial AI imports","from oraclesai import SpatialDataFrame, DBSpatialDataset, FileSpatialDataset","from oraclesai.pipeline import SpatialPipeline","from oraclesai.metrics import aic","from oraclesai.regression import SpatialLagRegressor, SpatialErrorRegressor, OLSRegressor","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai.vis import elocation","from oraclesai import enable_geodataframes","from oraclesai.analysis import MoranITest","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737668545058,"interpreter":"python.low","endTime":1737668548661,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," ","In this example you work with demographic data from the US Census Bureau for areas called [block groups](https://en.wikipedia.org/wiki/Census_block_group) in the City of Los Angeles and create a model to predict income."],"enabled":true,"result":{"startTime":1737668549183,"interpreter":"md.low","endTime":1737668549729,"results":[{"message":"<p>In this example you work with demographic data from the US Census Bureau for areas called <a href=\"https://en.wikipedia.org/wiki/Census_block_group\">block groups<\/a> in the City of Los Angeles and create a model to predict income.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID', 'MEDIAN_INCOME', 'MEAN_EDUCATION_LEVEL', 'MEAN_AGE', 'LAN_EN', 'INTERNET', 'HOUSE_VALUE', 'geometry']]","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737668550258,"interpreter":"python.low","endTime":1737668550915,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tLAN_EN\tINTERNET\tHOUSE_VALUE\tgeometry\n060376700031\t75577\t9.746\t37.306\t0.796\t0.904\t627700\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t11.504\t39.161\t0.989\t0.959\t599300\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t10.976\t47.854\t0.888\t0.829\t609500\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t10.115\t39.283\t0.917\t0.859\t471000\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t10.716\t37.998\t0.648\t0.882\t389900\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Explore the data","","Before creating any spatial model it is important to explore the data and check for spatial effects. In this case we are interested identifying spatial dependence for the target variable.","","For that we need to model the spatial relationship as spatial weights and later use them to run Moran's I and quantify the spatial depencene of the target variable.","","You will start by creating spatial weights. Typically, this process would performed with multiple spatial weights definitions and comparing the different resulting Moran's I values. ","For spatial weights calculation it is preferred that coordinates be in a projected coordiate system, so convert your SpatialDataFrame from longitude/latitude to projected coordinates."],"enabled":true,"result":{"startTime":1737668551457,"interpreter":"md.low","endTime":1737668551927,"results":[{"message":"<h5 id=\"explore-the-data\">Explore the data<\/h5>\n<p>Before creating any spatial model it is important to explore the data and check for spatial effects. In this case we are interested identifying spatial dependence for the target variable.<\/p>\n<p>For that we need to model the spatial relationship as spatial weights and later use them to run Moran's I and quantify the spatial depencene of the target variable.<\/p>\n<p>You will start by creating spatial weights. Typically, this process would performed with multiple spatial weights definitions and comparing the different resulting Moran's I values.\nFor spatial weights calculation it is preferred that coordinates be in a projected coordiate system, so convert your SpatialDataFrame from longitude/latitude to projected coordinates.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Convert to projected coordinate system","message":["%python","","X = block_groups_sdf.to_crs(\"epsg:3857\")"," "],"enabled":true,"result":{"startTime":1737668552478,"interpreter":"python.low","endTime":1737668552960,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Computing spatial weights","message":["%python","","spatial_weights_definition =  KNNWeightsDefinition(k=3)","","spatial_weights = SpatialWeights.create(X, spatial_weights_definition)","","print(\"Neighbors of the 1st observation:\", spatial_weights.neighbors[0])"," "],"enabled":true,"result":{"startTime":1737668553470,"interpreter":"python.low","endTime":1737668562095,"results":[{"message":"Neighbors of the 1st observation: [67, 3, 1]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Measure spatial autocorrelation of the target variable","message":["%python","","mi = MoranITest.create(X, weights_or_def=spatial_weights, column_name='MEDIAN_INCOME')","","print(\"Moran's I value (actual):\",mi.i)","print(\"Expected Moran's I value (if no spatial dependence):\",mi.expected_i)","print(\"p-value (probability of spatial randomness):\",mi.p_value)"," "],"enabled":true,"result":{"startTime":1737668562602,"interpreter":"python.low","endTime":1737668563185,"results":[{"message":"Moran's I value (actual): 0.6766626877358299\nExpected Moran's I value (if no spatial dependence): -0.0002910360884749709\np-value (probability of spatial randomness): 0.001\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","From the previous result observe that the target variable is highly spatially autocorrelated (Moran's I value close to 1 and very low p-value).","","So you can continue using the current spatial weights definition for your models."],"enabled":true,"result":{"startTime":1737668563738,"interpreter":"md.low","endTime":1737668564222,"results":[{"message":"<p>From the previous result observe that the target variable is highly spatially autocorrelated (Moran's I value close to 1 and very low p-value).<\/p>\n<p>So you can continue using the current spatial weights definition for your models.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","##### Create Models","","Next you create models to analyze the relationships of the independent variables to the target variable and measure the impact of spatial dependence."],"enabled":true,"result":{"startTime":1737668564769,"interpreter":"md.low","endTime":1737668565244,"results":[{"message":"<h5 id=\"create-models\">Create Models<\/h5>\n<p>Next you create models to analyze the relationships of the independent variables to the target variable and measure the impact of spatial dependence.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create train and test sets","message":["%python","","X_train, X_test = X.split(ratio=(0.7, 0.3), seed=random_state)"],"enabled":true,"result":{"startTime":1737668565784,"interpreter":"python.low","endTime":1737668566330,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot maps of block groups color-coded by median income","message":["%python","","fig, ax = plt.subplots(1, 3, figsize=(25,10))","","# Set plot's labels and titles ","ax[0].set_title('Full Dataset - Median Income');","ax[1].set_title('Training - Median Income');","ax[2].set_title('Test - Median Income');","","# Plot the choropleth map","X.plot(ax=ax[0], column=X[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, alpha=0.75, with_basemap=True )","X_train.plot(ax=ax[1], column=X_train[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, alpha=0.75, with_basemap=True )","X_test.plot(ax=ax[2], column=X_test[\"MEDIAN_INCOME\"].values, cmap=plt.get_cmap(\"jet\"), legend=True, edgecolor='black', linewidth=0.1, alpha=0.75, with_basemap=True )"],"enabled":true,"result":{"startTime":1737668566818,"interpreter":"python.low","endTime":1737668585522,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Begin by training an non-spatial OLS model using `OLSRegressor` which provides spatial metrics."],"enabled":true,"result":{"startTime":1737668586115,"interpreter":"md.low","endTime":1737668586581,"results":[{"message":"<p>Begin by training an non-spatial OLS model using <code>OLSRegressor<\/code> which provides spatial metrics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a model with OLSRegressor","message":["%python","","ols_pipeline = SpatialPipeline([","    ('scaler', RobustScaler()),","    ('model', OLSRegressor(spatial_weights_definition=spatial_weights_definition))","])","","ols_pipeline.fit(X_train.drop('GEOID'), y='MEDIAN_INCOME')","","print(ols_pipeline.named_steps['model'].summary)"],"enabled":true,"result":{"startTime":1737668587155,"interpreter":"python.low","endTime":1737668588351,"results":[{"message":"REGRESSION RESULTS\n------------------\n\nSUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n-----------------------------------------\nData set            :     unknown\nWeights matrix      :     unknown\nDependent Variable  :     dep_var                Number of Observations:        2442\nMean dependent var  :  69508.5713                Number of Variables   :           6\nS.D. dependent var  :  39756.2324                Degrees of Freedom    :        2436\nR-squared           :      0.6371\nAdjusted R-squared  :      0.6363\nSum squared residual: 1.40024e+12                F-statistic           :    855.1997\nSigma-square        :574812723.889                Prob(F-statistic)     :           0\nS.E. of regression  :   23975.252                Log likelihood        :  -28089.071\nSigma-square ML     :573400407.614                Akaike info criterion :   56190.141\nS.E of regression ML:  23945.7806                Schwarz criterion     :   56224.945\n\n------------------------------------------------------------------------------------\n            Variable     Coefficient       Std.Error     t-Statistic     Probability\n------------------------------------------------------------------------------------\n            CONSTANT     64584.35630       562.13167       114.89187         0.00000\nMEAN_EDUCATION_LEVEL      1590.15186      2054.48792         0.77399         0.43901\n            MEAN_AGE      2185.22858       850.85580         2.56827         0.01028\n              LAN_EN     11204.41979      1457.24394         7.68877         0.00000\n            INTERNET     13805.83241      1049.86474        13.15011         0.00000\n         HOUSE_VALUE     17924.40166       623.59438        28.74369         0.00000\n------------------------------------------------------------------------------------\n\nREGRESSION DIAGNOSTICS\nMULTICOLLINEARITY CONDITION NUMBER            5.254\n\nTEST ON NORMALITY OF ERRORS\nTEST                             DF        VALUE           PROB\nJarque-Bera                       2        1017.736           0.0000\n\nDIAGNOSTICS FOR HETEROSKEDASTICITY\nRANDOM COEFFICIENTS\nTEST                             DF        VALUE           PROB\nBreusch-Pagan test                5        1002.391           0.0000\nKoenker-Bassett test              5         419.880           0.0000\n\nDIAGNOSTICS FOR SPATIAL DEPENDENCE\nTEST                           MI/DF       VALUE           PROB\nMoran's I (error)              0.3183        20.621           0.0000\nLagrange Multiplier (lag)         1         361.545           0.0000\nRobust LM (lag)                   1           8.225           0.0041\nLagrange Multiplier (error)       1         420.084           0.0000\nRobust LM (error)                 1          66.764           0.0000\nLagrange Multiplier (SARMA)       2         428.309           0.0000\n\n================================ END OF REPORT =====================================\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Observe that the r2 metric indicates that OLS does a decent job of explaining variance in the target variable. Also observe that the Moran's I value indicates significant Moran's I (error) (spatial autocorrelation of error). This violates the OLS assumption that errors are independent. Also observe the significant values for the heteroskedasticity metrics, which indicates the variance of errors is not consistent across samples. ","","Given the significant Robust LM (lag) and Robust LM (error) metrics, it is appropriate to train and test spatial lag and spatial error models."],"enabled":true,"result":{"startTime":1737668588843,"interpreter":"md.low","endTime":1737668589296,"results":[{"message":"<p>Observe that the r2 metric indicates that OLS does a decent job of explaining variance in the target variable. Also observe that the Moran's I value indicates significant Moran's I (error) (spatial autocorrelation of error). This violates the OLS assumption that errors are independent. Also observe the significant values for the heteroskedasticity metrics, which indicates the variance of errors is not consistent across samples.<\/p>\n<p>Given the significant Robust LM (lag) and Robust LM (error) metrics, it is appropriate to train and test spatial lag and spatial error models.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a SpatialErrorRegressor model","message":["%python","","error_model_pipeline = SpatialPipeline([","    ('scaler', RobustScaler()),","    ('model', SpatialErrorRegressor(spatial_weights_definition=spatial_weights_definition))","])","","error_model_pipeline.fit(X_train.drop('GEOID'), y='MEDIAN_INCOME')","","print(error_model_pipeline.named_steps['model'].summary)"," "],"enabled":true,"result":{"startTime":1737668589812,"interpreter":"python.low","endTime":1737668605542,"results":[{"message":"/usr/local/lib/python3.12/site-packages/spreg/ml_error.py:184: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.\n  res = minimize_scalar(\nREGRESSION RESULTS\n------------------\n\nSUMMARY OF OUTPUT: ML SPATIAL ERROR (METHOD = full)\n---------------------------------------------------\nData set            :     unknown\nWeights matrix      :     unknown\nDependent Variable  :     dep_var                Number of Observations:        2442\nMean dependent var  :  69508.5713                Number of Variables   :           6\nS.D. dependent var  :  39756.2324                Degrees of Freedom    :        2436\nPseudo R-squared    :      0.6331\nLog likelihood      : -27898.3952\nSigma-square ML     :463214091.5331                Akaike info criterion :   55808.790\nS.E of regression   :  21522.4091                Schwarz criterion     :   55843.594\n\n------------------------------------------------------------------------------------\n            Variable     Coefficient       Std.Error     z-Statistic     Probability\n------------------------------------------------------------------------------------\n            CONSTANT     65392.98539       842.03924        77.66026         0.00000\nMEAN_EDUCATION_LEVEL     11573.09624      2083.68877         5.55414         0.00000\n            MEAN_AGE      1847.35529       801.93527         2.30362         0.02124\n              LAN_EN      8211.11663      1487.89245         5.51862         0.00000\n            INTERNET     10685.29204       993.62979        10.75380         0.00000\n         HOUSE_VALUE     16293.51171       715.24454        22.78034         0.00000\n              lambda         0.43989         0.02020        21.77425         0.00000\n------------------------------------------------------------------------------------\n================================ END OF REPORT =====================================\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Observe that the spatial error model has lower AIC (Akaike info criterion) and higher Log likelihood than non-spatial OLS, indicating that the spatial error model has a better model fit."," Also, spatial error model's significant lambda value indicates that correcting for spatial autocorrelation of errors is important and justifies use of the SpatialErrorRegressor algorithm.","","Next you train and test a spatial lag regression model, which is appropriate in the case of spatial dependence where a target variable value is influenced by neighboring values. You previously calculated Moran's I and identified spatial dependence of the target variable. The spatial metrics in the OLSRegressor summary also indicated significant Robust LM (lag). Therefore evaluation of a SpatialLagRegressor model is justified."],"enabled":true,"result":{"startTime":1737668606013,"interpreter":"md.low","endTime":1737668606471,"results":[{"message":"<p>Observe that the spatial error model has lower AIC (Akaike info criterion) and higher Log likelihood than non-spatial OLS, indicating that the spatial error model has a better model fit.\nAlso, spatial error model's significant lambda value indicates that correcting for spatial autocorrelation of errors is important and justifies use of the SpatialErrorRegressor algorithm.<\/p>\n<p>Next you train and test a spatial lag regression model, which is appropriate in the case of spatial dependence where a target variable value is influenced by neighboring values. You previously calculated Moran's I and identified spatial dependence of the target variable. The spatial metrics in the OLSRegressor summary also indicated significant Robust LM (lag). Therefore evaluation of a SpatialLagRegressor model is justified.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train a SpatialLagRegressor Model","message":["%python","lag_model_pipeline = SpatialPipeline([","    ('scaler', RobustScaler()),","    ('model', SpatialLagRegressor(spatial_weights_definition=spatial_weights_definition))","])","","lag_model_pipeline.fit(X_train.drop('GEOID'), y='MEDIAN_INCOME')","","print(lag_model_pipeline.named_steps['model'].summary)"," "],"enabled":true,"result":{"startTime":1737668606953,"interpreter":"python.low","endTime":1737668624292,"results":[{"message":"REGRESSION RESULTS\n------------------\n\nSUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)\n-----------------------------------------------------------------\nData set            :     unknown\nWeights matrix      :     unknown\nDependent Variable  :     dep_var                Number of Observations:        2442\nMean dependent var  :  69508.5713                Number of Variables   :           7\nS.D. dependent var  :  39756.2324                Degrees of Freedom    :        2435\nPseudo R-squared    :      0.6941\nSpatial Pseudo R-squared:  0.6327\nLog likelihood      : -27925.6984\nSigma-square ML     :483655717.2630                Akaike info criterion :   55865.397\nS.E of regression   :  21992.1740                Schwarz criterion     :   55906.001\n\n------------------------------------------------------------------------------------\n            Variable     Coefficient       Std.Error     z-Statistic     Probability\n------------------------------------------------------------------------------------\n            CONSTANT     41720.57178      1345.49209        31.00767         0.00000\nMEAN_EDUCATION_LEVEL     -2138.08467      1921.69029        -1.11261         0.26588\n            MEAN_AGE      1552.01318       780.65065         1.98810         0.04680\n              LAN_EN      9154.97252      1339.09560         6.83668         0.00000\n            INTERNET     11112.94684       968.55251        11.47377         0.00000\n         HOUSE_VALUE     12798.79332       633.36689        20.20755         0.00000\n           W_dep_var         0.35756         0.01913        18.68643         0.00000\n------------------------------------------------------------------------------------\n================================ END OF REPORT =====================================\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Observe that the spatial lag model has lower AIC (Akaike info criterion) and higher Log likelihood than non-spatial OLS, indicating that the spatial lag model has a better model fit.","Also, the spatial lag model's significant W_dep_var (lag of target variable) value indicates that the model is addressing the inherent spatial structure fo the target variable. This justifies evaluation of a SpatialLagRegressor model.","","In conclusion, OLS provides a baseline but is limited in that spatial effects are not incorporated. Spatial error models are suitable for scenarios where OLS errors are spatially autocorrelated. Finally, spatial lag regression is suitable when the value of a target variable is influenced by neighboring target variable values.","",""," "],"enabled":true,"result":{"startTime":1737668624794,"interpreter":"md.low","endTime":1737668625251,"results":[{"message":"<p>Observe that the spatial lag model has lower AIC (Akaike info criterion) and higher Log likelihood than non-spatial OLS, indicating that the spatial lag model has a better model fit.\nAlso, the spatial lag model's significant W_dep_var (lag of target variable) value indicates that the model is addressing the inherent spatial structure fo the target variable. This justifies evaluation of a SpatialLagRegressor model.<\/p>\n<p>In conclusion, OLS provides a baseline but is limited in that spatial effects are not incorporated. Spatial error models are suitable for scenarios where OLS errors are spatially autocorrelated. Finally, spatial lag regression is suitable when the value of a target variable is influenced by neighboring target variable values.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"," "],"enabled":true,"result":{"startTime":1737668625725,"interpreter":"md.low","endTime":1737668626175,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Spatial Operations","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Operations - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","Spatial AI provides a `SpatialDataFrame` which can be created from data in Oracle Spatial, OCI Object Storage, or the local file system. `SpatialDataFrame` allows you to work with your spatial data as if it was a GeoPandas dataframe and execute operations such as merge, groupby, and aggregate. A set of spatial operations is supported for a `SpatialDataFrame`, such as proximity analysis and measurements such as area and distance. When a `SpatialDataFrame` references data in Oracle Spatial, these operations are performed natively by the database. ","","In this notebook you create `SpatialDataFrame` objects referencing data in Oracle Spatial. You then run several spatial operations which are performed by Oracle Spatial.","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737664887549,"interpreter":"md.low","endTime":1737664888050,"results":[{"message":"<h1 id=\"spatial-operations---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Operations - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>Spatial AI provides a <code>SpatialDataFrame<\/code> which can be created from data in Oracle Spatial, OCI Object Storage, or the local file system. <code>SpatialDataFrame<\/code> allows you to work with your spatial data as if it was a GeoPandas dataframe and execute operations such as merge, groupby, and aggregate. A set of spatial operations is supported for a <code>SpatialDataFrame<\/code>, such as proximity analysis and measurements such as area and distance. When a <code>SpatialDataFrame<\/code> references data in Oracle Spatial, these operations are performed natively by the database.<\/p>\n<p>In this notebook you create <code>SpatialDataFrame<\/code> objects referencing data in Oracle Spatial. You then run several spatial operations which are performed by Oracle Spatial.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md"," ","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737664888568,"interpreter":"md.low","endTime":1737664889073,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import required libraries","message":["%python","","import os ","from shapely.geometry import Point,Polygon","import pandas as pd","import geopandas as gpd","from oraclesai import SpatialDataFrame, DBSpatialDataset, read, write, enable_geodataframes, SpatialFilterMask","enable_geodataframes(z)"],"enabled":true,"result":{"startTime":1737664889609,"interpreter":"python.low","endTime":1737664890124,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","###### Create SpatialDataFrames referencing tables in Oracle Spatial"],"enabled":true,"result":{"startTime":1737664890625,"interpreter":"md.low","endTime":1737664891233,"results":[{"message":"<h6 id=\"create-spatialdataframes-referencing-tables-in-oracle-spatial\">Create SpatialDataFrames referencing tables in Oracle Spatial<\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_block_groups')) [['GEOID', 'MEDIAN_INCOME', 'MEAN_EDUCATION_LEVEL', 'MEAN_AGE']]","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737664891750,"interpreter":"python.low","endTime":1737664892274,"results":[{"message":"GEOID\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tgeometry\n060376700031\t75577\t9.746\t37.306\tPOLYGON ((-118.323 33.804, -118.317 33.801, -118.317 33.798, -118.323 33.798, -118.323 33.804))\n060376700032\t74609\t11.504\t39.161\tPOLYGON ((-118.323 33.798, -118.317 33.798, -118.317 33.79, -118.32 33.79, -118.32 33.792, -118.323 33.792, -118.323 33.798))\n060376700033\t55592\t10.976\t47.854\tPOLYGON ((-118.328 33.798, -118.325 33.798, -118.325 33.792, -118.323 33.792, -118.32 33.792, -118.32 33.79, -118.323 33.79, -118.326 33.79, -118.326 33.79, -118.328 33.79, -118.328 33.798))\n060376700034\t73558\t10.115\t39.283\tPOLYGON ((-118.328 33.801, -118.326 33.801, -118.326 33.803, -118.328 33.803, -118.328 33.806, -118.323 33.804, -118.323 33.798, -118.323 33.792, -118.325 33.792, -118.325 33.798, -118.328 33.798, -118.328 33.801))\n060376701001\t54738\t10.716\t37.998\tPOLYGON ((-118.323 33.79, -118.32 33.79, -118.317 33.79, -118.315 33.79, -118.315 33.785, -118.318 33.785, -118.319 33.785, -118.322 33.786, -118.323 33.785, -118.323 33.79))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for SCHOOLS table","message":["%python ","","schools_sdf = SpatialDataFrame.create(DBSpatialDataset(table='schools'))","z.show(schools_sdf.head())"],"enabled":true,"result":{"startTime":1737664892811,"interpreter":"python.low","endTime":1737664893435,"results":[{"message":"ID\tNAME\tgeometry\n358789722\tEdward J Richardson Middle School\tPOINT (-118.368 33.811)\n358789751\tNinety-Fifth Street Elementary School\tPOINT (-118.294 33.949)\n358789753\tNinety-Seventh Street Elementary School\tPOINT (-118.281 33.948)\n358789762\tNobel Middle School\tPOINT (-118.551 34.251)\n358789785\tNormandie Elementary School\tPOINT (-118.299 34.003)\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","As the SpatialDataFrames reference data in Oracle Spatial, the following operations are performed natively by the database."],"enabled":true,"result":{"startTime":1737664893989,"interpreter":"md.low","endTime":1737664894465,"results":[{"message":"<p>As the SpatialDataFrames reference data in Oracle Spatial, the following operations are performed natively by the 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contained by each block group","message":["%python"," ","z.show(block_groups_sdf.relate(schools_sdf, mask=SpatialFilterMask.CONTAINS)[\"GEOID\"] \\","                       .groupby('GEOID') \\","                       .aggregate(count={'GEOID': 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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI Spatial Pipeline","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Spatial Pipeline - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you experiment with the Spatial AI Pipeline classes, which extend sklearn Pipeline, ColumnTransformer and FeatureUnion to handle spatial transformers and estimators. ","To see the benefits, you will perform the same operations without and then with pipelines.","","The key concepts for pipelines are:","- Transformer which provides fit() and transform() methods to transform data as needed for model input. ","- Estimator which provides fit( ) and predict( ) methods to infer properties based on training. ","- ColumnTransformer and FeatureUnion which combine transformers into a new transformer combining their results.","- Pipeline which combines transformers and, if needed, a final estimator.","","The pipeline operations in Spatial AI are `SpatialColumnTransformer` and `SpatialFeatureUnion`, and `SpatialPipeline`.","","","**Copyright (c) 2025 Oracle Corporation**","[The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)"," "],"enabled":true,"result":{"startTime":1737665362766,"interpreter":"md.low","endTime":1737665363326,"results":[{"message":"<h1 id=\"spatial-pipeline---spatial-ai-in-oracle-machine-learning-for-python-oml4py\">Spatial Pipeline - Spatial AI in Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data.<\/p>\n<p>In this notebook, you experiment with the Spatial AI Pipeline classes, which extend sklearn Pipeline, ColumnTransformer and FeatureUnion to handle spatial transformers and estimators.\nTo see the benefits, you will perform the same operations without and then with pipelines.<\/p>\n<p>The key concepts for pipelines are:<\/p>\n<ul>\n<li>Transformer which provides fit() and transform() methods to transform data as needed for model input.<\/li>\n<li>Estimator which provides fit( ) and predict( ) methods to infer properties based on training.<\/li>\n<li>ColumnTransformer and FeatureUnion which combine transformers into a new transformer combining their results.<\/li>\n<li>Pipeline which combines transformers and, if needed, a final estimator.<\/li>\n<\/ul>\n<p>The pipeline operations in Spatial AI are <code>SpatialColumnTransformer<\/code> and <code>SpatialFeatureUnion<\/code>, and <code>SpatialPipeline<\/code>.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737665363825,"interpreter":"md.low","endTime":1737665364296,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample notebooks.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import required libraries","message":["%python","","import os","import geopandas","from sklearn.preprocessing import RobustScaler","from sklearn.impute import SimpleImputer","from libpysal import weights","import matplotlib.pyplot as plt","import pandas as pd","import numpy as np","","import random","random_state = 456","random.seed(random_state)","","from oraclesai import SpatialDataFrame, DBSpatialDataset","from oraclesai.pipeline import SpatialPipeline, SpatialFeatureUnion, SpatialColumnTransformer","from oraclesai.preprocessing import SpatialLagTransformer, SpatialImputer","from oraclesai.weights import SpatialWeights, KNNWeightsDefinition, DistanceBandWeightsDefinition, QueenWeightsDefinition, RookWeightsDefinition","from oraclesai import enable_geodataframes","from oraclesai.regression import GWRRegressor","enable_geodataframes(z)"," "],"enabled":true,"result":{"startTime":1737665364798,"interpreter":"python.low","endTime":1737665368489,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"series\":{\"availableSeriesElements\":[{\"id\":\"GEOID\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"GEOID\",\"MEAN_AGE\",\"MEAN_EDUCATION_LEVEL\",\"HOUSE_VALUE\",\"MEDIAN_INCOME\",\"geometry\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Create SpatialDataFrame for LA_BLOCK_GROUPS table","message":["%python","","block_groups_sdf = SpatialDataFrame.create(DBSpatialDataset(table='la_block_groups'))","block_groups_sdf = block_groups_sdf[['GEOID', 'MEAN_AGE', 'MEAN_EDUCATION_LEVEL', 'HOUSE_VALUE', 'MEDIAN_INCOME', 'geometry']]","# Convert to projected coordinated as required for spatial weights","block_groups_sdf = block_groups_sdf.to_crs('epsg:3857')","z.show(block_groups_sdf.head())"],"enabled":true,"result":{"startTime":1737665368964,"interpreter":"python.low","endTime":1737665369632,"results":[{"message":"GEOID\tMEAN_AGE\tMEAN_EDUCATION_LEVEL\tHOUSE_VALUE\tMEDIAN_INCOME\tgeometry\n060376700031\t37.306\t9.746\t627700\t75577\tPOLYGON ((-13171614.921 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First without using a pipeline. "],"enabled":true,"result":{"startTime":1737665370159,"interpreter":"md.low","endTime":1737665370604,"results":[{"message":"<h5 id=\"assemble-the-results-of-multiple-spatial-transformers\">Assemble the results of multiple spatial transformers<\/h5>\n<p>Assemble the spatial lags based on mean and median for mean_age and house_value. First without using a pipeline.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define variables for transformer","message":["%python","","X = block_groups_sdf.drop(\"GEOID\").drop(\"MEDIAN_INCOME\")","spatial_weights = SpatialWeights.create(block_groups_sdf, KNNWeightsDefinition(k=20))","lag_columns = ['MEAN_AGE_LAG','MEAN_EDUCATION_LEVEL_LAG','MEAN_HOUSE_VALUE_LAG','MEDIAN_AGE_LAG','MEDIAN_EDUCATION_LEVEL_LAG','MEDIAN_HOUSE_VALUE_LAG']"," "],"enabled":true,"result":{"startTime":1737665371092,"interpreter":"python.low","endTime":1737665383214,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Assemble results of transformers","message":["%python","","mean_result = SpatialLagTransformer(strategy=\"mean\").fit_transform(X, spatial_weights=spatial_weights)","median_result = SpatialLagTransformer(strategy=\"median\").fit_transform(X, spatial_weights=spatial_weights)","","z.show(pd.DataFrame(np.concatenate((mean_result, median_result), axis=1), columns=lag_columns).head())"],"enabled":true,"result":{"startTime":1737665390840,"interpreter":"python.low","endTime":1737665402959,"results":[{"message":"MEAN_AGE_LAG\tMEAN_EDUCATION_LEVEL_LAG\tMEAN_HOUSE_VALUE_LAG\tMEDIAN_AGE_LAG\tMEDIAN_EDUCATION_LEVEL_LAG\tMEDIAN_HOUSE_VALUE_LAG\n40.66353729678744\t10.669541125981235\t562280.0\t40.36510197710949\t10.791361723145643\t582700.0\n39.63003284820595\t10.120157374848905\t532040.0\t39.13960639418132\t10.41018323071129\t519400.0\n40.78304255956752\t10.930462872647484\t634740.0\t41.22209163368304\t11.007437687509348\t617700.0\n40.68773015468646\t10.864451673511873\t603925.0\t40.36510197710949\t10.89919367262554\t611250.0\n39.53882629201207\t10.206520825997432\t563465.0\t39.22171852224933\t10.270578381544205\t550800.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next you repeat using pipeline. Instead of running transforms individually and concatenating, you may use a single SpatialFeatureUnion pipeline to perform a series of transforms and return the combined result of a single Numpy array with the results of all transforms. This is convenient when numerous transforms are involved."],"enabled":true,"result":{"startTime":1737665403498,"interpreter":"md.low","endTime":1737665403963,"results":[{"message":"<p>Next you repeat using pipeline. Instead of running transforms individually and concatenating, you may use a single SpatialFeatureUnion pipeline to perform a series of transforms and return the combined result of a single Numpy array with the results of all transforms. This is convenient when numerous transforms are involved.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Assemble spatial transformer results with SpatialFeatureUnion","message":["%python","","lag_results = SpatialFeatureUnion([(\"spatial_lag_mean\", SpatialLagTransformer(strategy=\"mean\")), \\","                                   (\"spatial_lag_median\", SpatialLagTransformer(strategy=\"median\"))]) \\","                                   .fit_transform(X, spatial_weights=spatial_weights)","","z.show(pd.DataFrame(lag_results, columns=lag_columns).head())"],"enabled":true,"result":{"startTime":1737665404448,"interpreter":"python.low","endTime":1737665405027,"results":[{"message":"MEAN_AGE_LAG\tMEAN_EDUCATION_LEVEL_LAG\tMEAN_HOUSE_VALUE_LAG\tMEDIAN_AGE_LAG\tMEDIAN_EDUCATION_LEVEL_LAG\tMEDIAN_HOUSE_VALUE_LAG\n40.66353729678744\t10.669541125981235\t562280.0\t40.36510197710949\t10.791361723145643\t582700.0\n39.63003284820595\t10.120157374848905\t532040.0\t39.13960639418132\t10.41018323071129\t519400.0\n40.78304255956752\t10.930462872647484\t634740.0\t41.22209163368304\t11.007437687509348\t617700.0\n40.68773015468646\t10.864451673511873\t603925.0\t40.36510197710949\t10.89919367262554\t611250.0\n39.53882629201207\t10.206520825997432\t563465.0\t39.22171852224933\t10.270578381544205\t550800.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","SpatialColumnTransformer is also provided, which has the same capability as SpatialFeatureUnion with the added ability to specify specific columns for transform. "],"enabled":true,"result":{"startTime":1737665405501,"interpreter":"md.low","endTime":1737665405958,"results":[{"message":"<p>SpatialColumnTransformer is also provided, which has the same capability as SpatialFeatureUnion with the added ability to specify specific columns for transform.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," ","##### Spatial regression","","Next you scale features and train a model, first without and then with a spatial pipeline. "],"enabled":true,"result":{"startTime":1737665406453,"interpreter":"md.low","endTime":1737665406907,"results":[{"message":"<h5 id=\"spatial-regression\">Spatial regression<\/h5>\n<p>Next you scale features and train a model, first without and then with a spatial pipeline.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data for training, testing, validation","message":["%python"," ","X_train, X_test, X_valid = block_groups_sdf.split(ratio=(0.7, 0.2, 0.1), seed=random_state)"],"enabled":true,"result":{"startTime":1737665407393,"interpreter":"python.low","endTime":1737665407949,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Create instance of RobustScaler to fit_transform training data, and then to transform test data. Note, here we have to use .get_values() but later you will see that pipeline handles this automatically "," "],"enabled":true,"result":{"startTime":1737665408462,"interpreter":"md.low","endTime":1737665408903,"results":[{"message":"<p>Create instance of RobustScaler to fit_transform training data, and then to transform test data. Note, here we have to use .get_values() but later you will see that pipeline handles this automatically<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Scale features for model training","message":["%python","","scaler = RobustScaler().fit(X_train.drop(['GEOID', 'MEDIAN_INCOME']).get_values())","scaled_df = pd.DataFrame(scaler.transform(X_train.drop(['GEOID','MEDIAN_INCOME']).get_values()))","X_train_scaled = X_train.add_column('AGE_SCALED',scaled_df[0]) \\","                               .add_column('EDUCATION_LEVEL_SCALED',scaled_df[1]) \\","                               .add_column('HOUSE_VALUE_SCALED',scaled_df[2])","X_train_scaled = X_train_scaled[['MEDIAN_INCOME','AGE_SCALED', 'EDUCATION_LEVEL_SCALED', 'HOUSE_VALUE_SCALED','geometry' ]]","z.show(X_train_scaled.head())","",""," "],"enabled":true,"result":{"startTime":1737665409787,"interpreter":"python.low","endTime":1737665412305,"results":[{"message":"MEDIAN_INCOME\tAGE_SCALED\tEDUCATION_LEVEL_SCALED\tHOUSE_VALUE_SCALED\tgeometry\n55592\t1.344\t0.295\t0.399\tPOLYGON ((-13172221.723 4001742.521, -13171917.599 4001742.655, -13171921.495 4000965.188, -13171619.596 4000964.384, -13171317.364 4000963.044, -13171318.477 4000575.938, -13171620.932 4000577.813, -13172015.56 4000578.081, -13172039.716 4000577.947, -13172233.857 4000679.478, -13172221.723 4001742.521))\n73558\t0.227\t0.057\t0.012\tPOLYGON ((-13172222.837 4002094.032, -13171984.724 4002093.229, -13171983.722 4002423.986, -13172204.246 4002425.058, -13172206.918 4002769.356, -13171614.921 4002458.952, -13171618.149 4001743.458, -13171619.596 4000964.384, -13171921.495 4000965.188, -13171917.599 4001742.655, -13172221.723 4001742.521, -13172222.837 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4037158.371))\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Train GWR model","message":["%python","","gwr_model = GWRRegressor(spatial_weights_definition=KNNWeightsDefinition(k=20))","gwr_model.fit(X_train_scaled.drop(['MEDIAN_INCOME']), X_train_scaled['MEDIAN_INCOME'].get_values())"],"enabled":true,"result":{"startTime":1737665412784,"interpreter":"python.low","endTime":1737665415591,"results":[{"message":"GWRRegressor(bandwidth=20, fixed=False,\n             spatial_weights_definition=<oraclesai.weights.weights_definition.KNNWeightsDefinition object at 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diff --git a/machine-learning/notebooks-oml/python/OML4Py Spatial AI SpatialDataFrame to OML DataFrame.dsnb b/machine-learning/notebooks-oml/python/OML4Py Spatial AI SpatialDataFrame to OML DataFrame.dsnb
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@@ -0,0 +1 @@
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methods","message":["%python","","z.show(df.describe().reset_index())"],"enabled":true,"result":{"startTime":1737665273150,"interpreter":"python.low","endTime":1737665274071,"results":[{"message":"index\tMEAN_INCOME\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tPER_MALE\tPER_FEMALE\tCOMPUTER\tINTERNET\tHOUSE_VALUE\tIND_AGGR\tIND_CONSTR\tIND_MANU\tIND_RETAIL\tIND_TRANSP\tIND_INFO\tIND_FINAN\tIND_EDU\tIND_HEALTH\tIND_ENTER\tIND_PADMIN\ncount\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\nmean\t80003.10223816388\t69962.4084957812\t9.817747524326473\t37.68904761865219\t0.4909985316922785\t0.5090014683077215\t0.8806816465426919\t0.7855957012842326\t603320.3375036368\t0.002364321671095843\t0.052106847532813125\t0.157386900943463\t0.10176306245169582\t0.053789837337874805\t0.05410122296116851\t0.06231341588395844\t0.07968731777643581\t0.09840743552842247\t0.05803486474113851\t0.030407205589473112\nstd\t33112.091563003625\t40246.952648018276\t2.0537298197977107\t5.596124829897268\t0.054549332016207576\t0.05454933201620758\t0.09465504953890733\t0.14271324196534974\t379778.4960574246\t0.00580577830149433\t0.03389222312643915\t0.08843238294690901\t0.041018932892306466\t0.03687621487266088\t0.04914906964371605\t0.03826513250833895\t0.04313747159192474\t0.04393084104657485\t0.039477158667807606\t0.030153414589800715\nmin\t21608.260245901638\t11938.0\t4.963302752293578\t21.93948655256724\t0.17682721455035366\t0.2642663043478261\t0.42735042735042733\t0.17647058823529413\t10900.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\n25%\t53664.32584269663\t41336.0\t8.048780487804878\t33.67862654320987\t0.4579097193981293\t0.47625602202339984\t0.824438202247191\t0.6883720930232559\t357100.0\t0.0\t0.03162650602409638\t0.10158311345646438\t0.08045977011494253\t0.030379746835443037\t0.023880597014925373\t0.03807106598984772\t0.053742802303262956\t0.07520891364902507\t0.034482758620689655\t0.014311270125223614\n50%\t72705.22538293217\t58185.0\t9.894398530762167\t37.51750972762646\t0.4915176524530032\t0.5084823475469968\t0.9\t0.807909604519774\t464300.0\t0.0\t0.05282357485308457\t0.15441601544247543\t0.10356442639470376\t0.05227545506182165\t0.050957566809393805\t0.06552378518340898\t0.08325333153713312\t0.09861487399025333\t0.058107559228302665\t0.032422912037214285\n75%\t99809.18734793187\t86786.0\t11.641509433962264\t41.2542270531401\t0.5237439779766001\t0.5420902806018707\t0.9567430025445293\t0.9013157894736842\t711700.0\t0.0026998498954085115\t0.05949367088607595\t0.1839080459770115\t0.11359026369168357\t0.06331877729257641\t0.05818181818181818\t0.07152317880794702\t0.08919667590027701\t0.11296534017971759\t0.0664819944598338\t0.0327613104524181\nmax\t196494.70309278351\t250000.0\t13.948453608247423\t63.67406273909717\t0.735733695652174\t0.8231727854496463\t1.0\t1.0\t2000000.0\t0.08417508417508418\t0.3157894736842105\t1.0\t0.6956521739130435\t0.26262626262626265\t0.6666666666666666\t0.5625\t0.487146529562982\t0.3917808219178082\t0.3877159309021113\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","As an example of an operation on the OML Dataframe, you next calculate feature correlation using `oml.DataFrame.corr`, which performs correlation analysis across columns"],"enabled":true,"result":{"startTime":1737665274547,"interpreter":"md.low","endTime":1737665274998,"results":[{"message":"<p>As an example of an operation on the OML Dataframe, you next calculate feature correlation using <code>oml.DataFrame.corr<\/code>, which performs correlation analysis across columns<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply an OML DataFrame method","message":["%python","","z.show(df.corr().reset_index())"],"enabled":true,"result":{"startTime":1737665275486,"interpreter":"python.low","endTime":1737665276988,"results":[{"message":"index\tMEAN_INCOME\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tPER_MALE\tPER_FEMALE\tCOMPUTER\tINTERNET\tHOUSE_VALUE\tIND_AGGR\tIND_CONSTR\tIND_MANU\tIND_RETAIL\tIND_TRANSP\tIND_INFO\tIND_FINAN\tIND_EDU\tIND_HEALTH\tIND_ENTER\tIND_PADMIN\nMEAN_INCOME\t1.0\t0.9520036367651651\t0.7856424923121363\t0.5409290934552723\t0.019860998377949406\t-0.01986099837794936\t0.5925466556276282\t0.7009688674418161\t0.7491306840949725\t-0.035463443030321125\t-0.25352063252685436\t-0.3969879305629417\t-0.14483874938594501\t-0.1802218747268549\t0.3980205931378391\t0.39433074946526064\t0.21388120831710059\t0.11585208440858275\t-0.2922325488236088\t0.05478259722714204\nMEDIAN_INCOME\t0.9520036367651651\t1.0\t0.6945178987782278\t0.46993415358162477\t0.013211376353693356\t-0.013211376353693315\t0.5318192177381785\t0.623556994021767\t0.7360860795342679\t-0.030049043104869434\t-0.22313052094172167\t-0.3376137963176389\t-0.15275284130451017\t-0.17266310820886308\t0.34071574320901105\t0.36705608984656773\t0.18309029529102955\t0.1132143754639592\t-0.28473672272814654\t0.040201898892484815\nMEAN_EDUCATION_LEVEL\t0.7856424923121363\t0.6945178987782278\t1.0\t0.6250295625617058\t-0.053934080379895444\t0.05393408037989549\t0.6256924268855376\t0.7268057698560363\t0.6889038622870629\t-0.06569056529442194\t-0.31114004983502996\t-0.5779144318055983\t-0.11862730843127135\t-0.1794786792133113\t0.48378127058233206\t0.4372974833345171\t0.3073265615369018\t0.1636735686588157\t-0.24402267783270157\t0.12411588289262925\nMEAN_AGE\t0.5409290934552723\t0.46993415358162477\t0.6250295625617058\t1.0\t-0.07079462259400594\t0.07079462259400598\t0.27467661599669774\t0.4040483036986475\t0.4527967497021918\t-0.06201968860490018\t-0.23359440556630906\t-0.418086713405628\t-0.0780999565549909\t-0.09640203049174352\t0.3030979081400221\t0.33823799691674833\t0.230082288525231\t0.17141640602389452\t-0.2117830072211085\t0.15156710407634394\nPER_MALE\t0.019860998377949406\t0.013211376353693356\t-0.053934080379895444\t-0.07079462259400594\t1.0\t-1.0\t0.01891824133857573\t0.03281998788161601\t-0.008517130465083636\t0.006017635893458174\t0.043613868555168696\t0.06539285114693255\t0.008120715508370593\t-0.06305830799713799\t0.0025619050065386812\t-0.059360073134774226\t-0.10631780263567753\t-0.05994486968723651\t0.09096291524517205\t-0.07607629573416619\nPER_FEMALE\t-0.01986099837794936\t-0.013211376353693315\t0.05393408037989549\t0.07079462259400598\t-1.0\t1.0\t-0.018918241338575698\t-0.03281998788161597\t0.008517130465083664\t-0.006017635893458193\t-0.043613868555168724\t-0.06539285114693258\t-0.008120715508370624\t0.06305830799713799\t-0.0025619050065386574\t0.05936007313477423\t0.10631780263567754\t0.05994486968723654\t-0.09096291524517204\t0.07607629573416623\nCOMPUTER\t0.5925466556276282\t0.5318192177381785\t0.6256924268855376\t0.27467661599669774\t0.01891824133857573\t-0.018918241338575698\t1.0\t0.8428239675466156\t0.4448434857993781\t-0.026268397264199165\t-0.172519128703843\t-0.34774068631687843\t-0.062266971053521275\t-0.13440022039488633\t0.31399156210005763\t0.2734195190851723\t0.17218247269707918\t0.06354795579392776\t-0.13706527592645226\t0.042749084481171214\nINTERNET\t0.7009688674418161\t0.623556994021767\t0.7268057698560363\t0.4040483036986475\t0.03281998788161601\t-0.03281998788161597\t0.8428239675466156\t1.0\t0.5167736915122524\t-0.022197484580212236\t-0.19499394597885578\t-0.4031441099788719\t-0.07046045652820022\t-0.15755037797488095\t0.372785411567845\t0.33675762650831154\t0.19491373401886006\t0.08566890477224255\t-0.19121403017972655\t0.051061508136982806\nHOUSE_VALUE\t0.7491306840949725\t0.7360860795342679\t0.6889038622870629\t0.4527967497021918\t-0.008517130465083636\t0.008517130465083664\t0.4448434857993781\t0.5167736915122524\t1.0\t-0.059105689542857213\t-0.2340642068121792\t-0.3755670736398413\t-0.15523584222589043\t-0.2686128403892686\t0.39563859164872633\t0.3322466497373544\t0.15056980969690928\t0.05983280169136577\t-0.1743735843844158\t-0.03133392046152342\nIND_AGGR\t-0.035463443030321125\t-0.030049043104869434\t-0.06569056529442194\t-0.06201968860490018\t0.006017635893458174\t-0.006017635893458193\t-0.026268397264199165\t-0.022197484580212236\t-0.059105689542857213\t1.0\t0.0378075922998076\t0.07154794243098266\t-0.01546545363504018\t0.020562702227719245\t-0.05881341784700666\t-0.08013365769959108\t-0.0486991462484294\t-0.055137613465325606\t0.007209991540197723\t-0.02207018744911222\nIND_CONSTR\t-0.25352063252685436\t-0.22313052094172167\t-0.31114004983502996\t-0.23359440556630906\t0.043613868555168696\t-0.043613868555168724\t-0.172519128703843\t-0.19499394597885578\t-0.2340642068121792\t0.0378075922998076\t1.0\t0.1927923868356311\t-0.005239797497474294\t-0.025143911916218828\t-0.23278634030423134\t-0.21015092964180257\t-0.22345970803865545\t-0.1536155402068982\t0.11962825843856885\t-0.13401094948966644\nIND_MANU\t-0.3969879305629417\t-0.3376137963176389\t-0.5779144318055983\t-0.418086713405628\t0.06539285114693255\t-0.06539285114693258\t-0.34774068631687843\t-0.4031441099788719\t-0.3755670736398413\t0.07154794243098266\t0.1927923868356311\t1.0\t0.00313350021373656\t0.0566913436960838\t-0.4975374931409926\t-0.44274921528232025\t-0.34595213741583924\t-0.294850023945911\t0.05950458100451086\t-0.18047951821906152\nIND_RETAIL\t-0.14483874938594501\t-0.15275284130451017\t-0.11862730843127135\t-0.0780999565549909\t0.008120715508370593\t-0.008120715508370624\t-0.062266971053521275\t-0.07046045652820022\t-0.15523584222589043\t-0.01546545363504018\t-0.005239797497474294\t0.00313350021373656\t1.0\t-0.04321845036533889\t-0.1667505044650445\t-0.1426995120670633\t-0.15611689721685737\t-0.09118002237672591\t0.05299389834818355\t-0.1287431646609338\nIND_TRANSP\t-0.1802218747268549\t-0.17266310820886308\t-0.1794786792133113\t-0.09640203049174352\t-0.06305830799713799\t0.06305830799713799\t-0.13440022039488633\t-0.15755037797488095\t-0.2686128403892686\t0.020562702227719245\t-0.025143911916218828\t0.0566913436960838\t-0.04321845036533889\t1.0\t-0.2775928802112495\t-0.153976882685548\t-0.01820323773941207\t0.05365217062247045\t-0.03224230374755789\t0.14940717833705094\nIND_INFO\t0.3980205931378391\t0.34071574320901105\t0.48378127058233206\t0.3030979081400221\t0.0025619050065386812\t-0.0025619050065386574\t0.31399156210005763\t0.372785411567845\t0.39563859164872633\t-0.05881341784700666\t-0.23278634030423134\t-0.4975374931409926\t-0.1667505044650445\t-0.2775928802112495\t1.0\t0.24076503605549668\t0.09452826290188618\t-0.058731047528153536\t-0.19598590370331082\t-0.037167947544243914\nIND_FINAN\t0.39433074946526064\t0.36705608984656773\t0.4372974833345171\t0.33823799691674833\t-0.059360073134774226\t0.05936007313477423\t0.2734195190851723\t0.33675762650831154\t0.3322466497373544\t-0.08013365769959108\t-0.21015092964180257\t-0.44274921528232025\t-0.1426995120670633\t-0.153976882685548\t0.24076503605549668\t1.0\t0.1362441915333901\t0.07080023598801419\t-0.2337398192053145\t0.038677070363223445\nIND_EDU\t0.21388120831710059\t0.18309029529102955\t0.3073265615369018\t0.230082288525231\t-0.10631780263567753\t0.10631780263567754\t0.17218247269707918\t0.19491373401886006\t0.15056980969690928\t-0.0486991462484294\t-0.22345970803865545\t-0.34595213741583924\t-0.15611689721685737\t-0.01820323773941207\t0.09452826290188618\t0.1362441915333901\t1.0\t0.12162793820860274\t-0.2665723484258818\t0.15348617016884994\nIND_HEALTH\t0.11585208440858275\t0.1132143754639592\t0.1636735686588157\t0.17141640602389452\t-0.05994486968723651\t0.05994486968723654\t0.06354795579392776\t0.08566890477224255\t0.05983280169136577\t-0.055137613465325606\t-0.1536155402068982\t-0.294850023945911\t-0.09118002237672591\t0.05365217062247045\t-0.058731047528153536\t0.07080023598801419\t0.12162793820860274\t1.0\t-0.16942257562881843\t0.09318579690750398\nIND_ENTER\t-0.2922325488236088\t-0.28473672272814654\t-0.24402267783270157\t-0.2117830072211085\t0.09096291524517205\t-0.09096291524517204\t-0.13706527592645226\t-0.19121403017972655\t-0.1743735843844158\t0.007209991540197723\t0.11962825843856885\t0.05950458100451086\t0.05299389834818355\t-0.03224230374755789\t-0.19598590370331082\t-0.2337398192053145\t-0.2665723484258818\t-0.16942257562881843\t1.0\t-0.13056751028278551\nIND_PADMIN\t0.05478259722714204\t0.040201898892484815\t0.12411588289262925\t0.15156710407634394\t-0.07607629573416619\t0.07607629573416623\t0.042749084481171214\t0.051061508136982806\t-0.03133392046152342\t-0.02207018744911222\t-0.13401094948966644\t-0.18047951821906152\t-0.1287431646609338\t0.14940717833705094\t-0.037167947544243914\t0.038677070363223445\t0.15348617016884994\t0.09318579690750398\t-0.13056751028278551\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","To remove correlation effects, remove the column MEAN_INCOME."],"enabled":true,"result":{"startTime":1737665277493,"interpreter":"md.low","endTime":1737665277940,"results":[{"message":"<p>To remove correlation effects, remove the column MEAN_INCOME.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop a column","message":["%python","","df = df.drop(\"MEAN_INCOME\")"],"enabled":true,"result":{"startTime":1737665278423,"interpreter":"python.low","endTime":1737665278878,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","Finally merge the DataFrame back to a SpatialDataFrame where you would continue your spatial workflow. You transform to SpatialDataframe using `merge_oml_dataframe()` which reinstates the geometry from the original SpatialDataframe."],"enabled":true,"result":{"startTime":1737665279415,"interpreter":"md.low","endTime":1737665279904,"results":[{"message":"<p>Finally merge the DataFrame back to a SpatialDataFrame where you would continue your spatial workflow. You transform to SpatialDataframe using <code>merge_oml_dataframe()<\/code> which reinstates the geometry from the original SpatialDataframe.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Merge SpatialDataFrame with OML DataDrame","message":["%python","","","sdf_from_df =sdf['GEOID'].merge_oml_dataframe(df, 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Statistical Functions","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737172231417,"interpreter":"md.low","endTime":1737172231879,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Statistical Functions","","In this notebook, various statistical functions are highlighted using data from the SH schema through the OML4Py transparency layer. ","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737172232322,"interpreter":"md.low","endTime":1737172232768,"results":[{"message":"<h1 id=\"oml4py-statistical-functions\">OML4Py Statistical Functions<\/h1>\n<p>In this notebook, various statistical functions are highlighted using data from the SH schema through the OML4Py transparency layer.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/statistics-5663173.jpg\" alt=\"OML Notebooks\" width=\"270\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737172233222,"interpreter":"md.low","endTime":1737172233636,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/statistics-5663173.jpg\" alt=\"OML Notebooks\" width=\"270\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/exploratory-data-analysis-methods.html\" target=\"_blank\">OML4Py Statistical Functions<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/sql-statistical-functions.html\" target=\"_blank\">OML4SQL Statistical Functions<\/a>"],"enabled":true,"result":{"startTime":1737172234069,"interpreter":"md.low","endTime":1737172234511,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/exploratory-data-analysis-methods.html\" target=\"_blank\">OML4Py Statistical Functions<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/sql-statistical-functions.html\" target=\"_blank\">OML4SQL Statistical Functions<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import oml","import pandas as pd","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","","import warnings","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737172234948,"interpreter":"python.low","endTime":1737172235422,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the dataset","message":["%python","","CUST_DF = oml.sync(query = 'SELECT CUST_ID, CUST_FIRST_NAME, CUST_LAST_NAME, CUST_MARITAL_STATUS, CUST_CREDIT_LIMIT, CUST_INCOME_LEVEL, CUST_GENDER FROM 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149,999\tM\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display median CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS","message":["%python","","res = CUST_DF.crosstab(index = ['CUST_MARITAL_STATUS'], ","                       values = 'CUST_CREDIT_LIMIT', ","                       aggfunc = oml.DataFrame.median)","z.show(res)"],"enabled":true,"result":{"startTime":1737172236858,"interpreter":"python.low","endTime":1737172237625,"results":[{"message":"CUST_MARITAL_STATUS\tmedian(CUST_CREDIT_LIMIT)\nDivorc.\t9000\nMabsent\t9000\nMar-AF\t11000\nMarried\t9000\nNeverM\t9000\nSepar.\t9000\nWidowed\t7000\ndivorced\t7000\nmarried\t5000\nsingle\t7000\nwidow\t7000\nNone\t7000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display median CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS in descending order","message":["%python","","res = CUST_DF.crosstab(index = ['CUST_MARITAL_STATUS'], ","                                values = 'CUST_CREDIT_LIMIT', ","                                aggfunc = oml.DataFrame.median).sort_values('median(CUST_CREDIT_LIMIT)', ascending = False)","z.show(res)"],"enabled":true,"result":{"startTime":1737172238059,"interpreter":"python.low","endTime":1737172238798,"results":[{"message":"CUST_MARITAL_STATUS\tmedian(CUST_CREDIT_LIMIT)\nMar-AF\t11000\nDivorc.\t9000\nMabsent\t9000\nMarried\t9000\nNeverM\t9000\nSepar.\t9000\nWidowed\t7000\nNone\t7000\nwidow\t7000\nsingle\t7000\ndivorced\t7000\nmarried\t5000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Median value of 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\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/python/OML4Py Text Mining SVM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Text Mining SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736279744218,"interpreter":"md.low","endTime":1736279744725,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Building Model using Text Mining Capability in Oracle Machine Learning ","","Oracle Machine Learning handles not only structured data, but also unstructured text data by enabling model building used automatically extracted features. By leveraging Oracle Text, OML in-database algorithms predictive features can automatically be extracted from the text column. ","","In this notebook, we build an SVM model to predict customers most likely to be positive responders to an Affinity Card loyalty program. The data comes with a text column that contains user generated comments. With a few additional specifications, the algorithm automatically uses the text column and builds the model on both the structured data and unstructured text. ","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736279745145,"interpreter":"md.low","endTime":1736279745596,"results":[{"message":"<h2 id=\"building-model-using-text-mining-capability-in-oracle-machine-learning\">Building Model using Text Mining Capability in Oracle Machine Learning<\/h2>\n<p>Oracle Machine Learning handles not only structured data, but also unstructured text data by enabling model building used automatically extracted features. By leveraging Oracle Text, OML in-database algorithms predictive features can automatically be extracted from the text column.<\/p>\n<p>In this notebook, we build an SVM model to predict customers most likely to be positive responders to an Affinity Card loyalty program. The data comes with a text column that contains user generated comments. With a few additional specifications, the algorithm automatically uses the text column and builds the model on both the structured data and unstructured text.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/ml-operations-unstructured-text.html\" target=\"_blank\">OML Text Mining<\/a>"],"enabled":true,"result":{"startTime":1736279746030,"interpreter":"md.low","endTime":1736279746650,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/ml-operations-unstructured-text.html\" target=\"_blank\">OML Text Mining<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import oml","","import pandas as pd","import numpy as np","import matplotlib.pyplot as plt","import warnings","","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1736279747080,"interpreter":"python.low","endTime":1736279749525,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy objects to data using oml.sync query argument","message":["%python","","CUSTOMERS = oml.sync( query = 'SELECT CUST_ID, COUNTRY_ID, CUST_GENDER, 2003-CUST_YEAR_OF_BIRTH AGE, CUST_MARITAL_STATUS, CUST_INCOME_LEVEL FROM sh.customers WHERE cust_id between 100001 and 104500')","SUPP_DEMO = oml.sync(query = '''SELECT CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE, AFFINITY_CARD,"," BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES, Y_BOX_GAMES,os_doc_set_kanji, comments FROM sh.supplementary_demographics''')"," ","COUNTRIES = oml.sync(query = 'SELECT COUNTRY_NAME, COUNTRY_ID FROM SH.COUNTRIES')"],"enabled":true,"result":{"startTime":1736279749949,"interpreter":"python.low","endTime":1736279750392,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data by joining tables of customer details","message":["%python","","MINING_DATA = CUSTOMERS.merge(SUPP_DEMO, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])","MINING_DATA = MINING_DATA.merge(COUNTRIES, how = \"inner\", on = 'COUNTRY_ID', suffixes = [\"\", \"\"])","MINING_DATA = MINING_DATA[[ 'CUST_ID', 'CUST_GENDER', 'AGE', 'CUST_MARITAL_STATUS', 'COUNTRY_NAME', 'CUST_INCOME_LEVEL', "," 'EDUCATION', 'OCCUPATION', 'HOUSEHOLD_SIZE', 'YRS_RESIDENCE', 'AFFINITY_CARD', "," 'BULK_PACK_DISKETTES', 'FLAT_PANEL_MONITOR', 'HOME_THEATER_PACKAGE',"," 'BOOKKEEPING_APPLICATION', 'PRINTER_SUPPLIES', 'Y_BOX_GAMES', 'OS_DOC_SET_KANJI', 'COMMENTS']]"],"enabled":true,"result":{"startTime":1736279750811,"interpreter":"python.low","endTime":1736279751392,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the dataset","message":["%python","","z.show(MINING_DATA.head(10))"],"enabled":true,"result":{"startTime":1736279751815,"interpreter":"python.low","endTime":1736279752540,"results":[{"message":"CUST_ID\tCUST_GENDER\tAGE\tCUST_MARITAL_STATUS\tCOUNTRY_NAME\tCUST_INCOME_LEVEL\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\tF\t17\tNeverM\tUnited States of America\tI: 170,000 - 189,999\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n101050\tM\t17\tNeverM\tUnited States of America\tL: 300,000 and above\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n100040\tF\t17\tNeverM\tUnited States of America\tJ: 190,000 - 249,999\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n102117\tM\t19\tNeverM\tUnited States of America\tB: 30,000 - 49,999\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNone\n101074\tM\t17\tNeverM\tUnited States of America\tJ: 190,000 - 249,999\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n104179\tM\t17\tNeverM\tUnited States of America\tJ: 190,000 - 249,999\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNone\n100417\tM\t18\tNeverM\tUnited States of America\tB: 30,000 - 49,999\t11th\tHandler\t1\t1\t0\t0\t0\t0\t0\t1\t1\t0\tNone\n101146\tF\t22\tNeverM\tUnited States of America\tJ: 190,000 - 249,999\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\tNone\n103420\tM\t20\tNeverM\tUnited States of America\tJ: 190,000 - 249,999\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\tNone\n101987\tF\t19\tNeverM\tUnited States of America\tJ: 190,000 - 249,999\t< Bach.\tOther\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Notice the text column 'COMMENTS'","message":["%python","","z.show(MINING_DATA[MINING_DATA['COMMENTS'].isnull() == False][['CUST_ID', 'COMMENTS']].head(10))"],"enabled":true,"result":{"startTime":1736279752961,"interpreter":"python.low","endTime":1736279753494,"results":[{"message":"CUST_ID\tCOMMENTS\n103476\tI am unhappy with the service at your store. Do not consider me a loyal customer just because I use your Affinity Card\n102624\tCould you send an Affinity Card to my mother in France?  Let me know and I'll send you here address.\n103693\tThanks a lot for my new affinity card. I love the discounts and have since started shopping at your store for everything.\n101639\tThe more times that I shop at your store, the more times I am impressed.  Don't change anything\n102847\tAffinity card makese sense only for bulk purchases. For all others, driving so far is not worth the discount. Either offer free shipping or build a store close by.\n100945\tDon't send me any more promotions.  I get too much lousy junk mail already\n100281\tShopping at your store is a hassle. I rarely shop there and usually forget to bring your new loyalty card and hence never get the items at the sale price.  Can a store manager look up my account on-line?\n104290\tCan I use my Affinity card to buy bulk purchases and ship them to my mother in Arizona?\n102105\tI am unhappy with the service at your store. Do not consider me a loyal customer just because I use your Affinity Card\n100681\tGreat program.  Love the discount. Signed happy customer.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Filter rows without comments for the demo","message":["%python","","MINING_TEXT = MINING_DATA[MINING_DATA['COMMENTS'].isnull() == False]","try:","    oml.drop(table = 'MINING_TEXT')","except:","    print(\"No such table\")","MINING_TEXT.materialize(table = 'MINING_TEXT')","MINING_TEXT = oml.sync(table = 'MINING_TEXT')"],"enabled":true,"result":{"startTime":1736279753926,"interpreter":"python.low","endTime":1736279755169,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare train and test datasets","message":["%python","","dat = MINING_TEXT.split(ratio=(0.8, 0.2), seed = 6218945)","train_x = dat[0].drop('AFFINITY_CARD')","train_y = dat[0]['AFFINITY_CARD']","test_dat = dat[1]"],"enabled":true,"result":{"startTime":1736279755587,"interpreter":"python.low","endTime":1736279756369,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create Oracle Text policy object","message":["%python","","from oml import cursor","cur = cursor()","cur.execute(\"BEGIN ctx_ddl.drop_policy('DM_SVM_POLICY'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","cur.execute(\"BEGIN ctx_ddl.create_policy('DM_SVM_POLICY'); END;\")","cur.close()"],"enabled":true,"result":{"startTime":1736279756788,"interpreter":"python.low","endTime":1736279757452,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Alternatively, drop and create Oracle Text policy in PL/SQL","message":["%script","","BEGIN ","    ctx_ddl.drop_policy('DM_SVM_POLICY'); ","EXCEPTION WHEN OTHERS THEN NULL; ","END;","BEGIN ","    ctx_ddl.create_policy('DM_SVM_POLICY'); ","END;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build the model combining structured data with unstructured text column","message":["%python","","","odm_settings = {'ODMS_TEXT_POLICY_NAME':   'DM_SVM_POLICY', ","                'ODMS_TEXT_MIN_DOCUMENTS': 1,","                'ODMS_TEXT_MAX_FEATURES':  3000}","","ctx_settings = {'COMMENTS': 'TEXT(POLICY_NAME:DM_SVM_POLICY)(TOKEN_TYPE:STEM)'}","","svm_mod = oml.svm(\"classification\", **odm_settings)","svm_mod.fit(x = train_x, y = train_y, case_id = 'CUST_ID', ctx_settings = ctx_settings)"],"enabled":true,"result":{"startTime":1736279757871,"interpreter":"python.low","endTime":1736279761913,"results":[{"message":"\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1          CLAS_WEIGHTS_BALANCED                           OFF\n2                   ODMS_DETAILS                   ODMS_ENABLE\n3   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5         ODMS_TEXT_MAX_FEATURES                          3000\n6        ODMS_TEXT_MIN_DOCUMENTS                             1\n7          ODMS_TEXT_POLICY_NAME                 DM_SVM_POLICY\n8                      PREP_AUTO                            ON\n9            SVMS_CONV_TOLERANCE                         .0001\n10          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR               10\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              18\n2       NUM_ROWS            3437\n\nAttributes: \nAGE\nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nCOMMENTS\nCOUNTRY_NAME\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n     TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME           ATTRIBUTE_VALUE    COEF\n0               1                      AGE              None                      None -0.1460\n1               1  BOOKKEEPING_APPLICATION              None                      None  0.2734\n2               1      BULK_PACK_DISKETTES              None                      None -0.0126\n3               1                 COMMENTS                10                      None  1.7737\n4               1                 COMMENTS             ABOUT                      None  1.3772\n5               1                 COMMENTS           ACCOUNT                      None -0.2607\n6               1                 COMMENTS           ADDRESS                      None  1.5673\n7               1                 COMMENTS        ADVERTISED                      None -0.9846\n8               1                 COMMENTS          AFFINITY                      None  0.5514\n9               1                 COMMENTS             AFTER                      None -0.7498\n10              1                 COMMENTS             AGAIN                      None  1.2237\n11              1                 COMMENTS           ALREADY                      None -1.2259\n12              1                 COMMENTS                AM                      None  1.1270\n13              1                 COMMENTS           AMAZING                      None -1.2203\n14              1                 COMMENTS          ANYTHING                      None -0.7776\n15              1                 COMMENTS             APPLY                      None  1.8730\n16              1                 COMMENTS           ARIZONA                      None -0.0093\n17              1                 COMMENTS               ASK                      None -0.5609\n18              1                 COMMENTS           ATTRACT                      None -0.7498\n19              1                 COMMENTS         AUSTRALIA                      None  1.0206\n20              1                 COMMENTS              AWAY                      None -1.4821\n21              1                 COMMENTS            BEFORE                      None -0.0131\n22              1                 COMMENTS            BETTER                      None -0.2705\n23              1                 COMMENTS               BIT                      None -0.5609\n24              1                 COMMENTS             BRING                      None -0.5781\n25              1                 COMMENTS           BROTHER                      None -0.2529\n26              1                 COMMENTS             BUILD                      None -0.7813\n27              1                 COMMENTS              BULK                      None -0.1135\n28              1                 COMMENTS               BUY                      None -1.1282\n29              1                 COMMENTS              CARD                      None  0.3741\n30              1                 COMMENTS             CARDS                      None -0.5629\n31              1                 COMMENTS            CHANCE                      None  0.8088\n32              1                 COMMENTS            CHANGE                      None -0.7776\n33              1                 COMMENTS          CHILDREN                      None -0.5609\n34              1                 COMMENTS             CLOSE                      None -0.7813\n35              1                 COMMENTS        COMPETITOR                      None  0.8666\n36              1                 COMMENTS          COMPLAIN                      None -0.5609\n37              1                 COMMENTS          COMPUTER                      None -2.2205\n38              1                 COMMENTS          CONSIDER                      None -1.2218\n39              1                 COMMENTS        CONSIDERED                      None  1.0206\n40              1                 COMMENTS       CONVENIENCE                      None  0.8088\n41              1                 COMMENTS              COST                      None -0.9846\n42              1                 COMMENTS          CUSTOMER                      None -3.9367\n43              1                 COMMENTS              DEAL                      None -0.8351\n44              1                 COMMENTS              DEAR                      None -0.5629\n45              1                 COMMENTS         DELIGHTED                      None  1.4059\n46              1                 COMMENTS              DIDN                      None -1.3821\n47              1                 COMMENTS          DIRECTLY                      None -0.0849\n48              1                 COMMENTS          DISCOUNT                      None -0.6005\n49              1                 COMMENTS         DISCOUNTS                      None -0.4308\n50              1                 COMMENTS     DOCUMENTATION                      None -1.3332\n51              1                 COMMENTS               DON                      None -0.8389\n52              1                 COMMENTS           DRIVING                      None -0.7813\n53              1                 COMMENTS        ELECTRONIC                      None -0.8351\n54              1                 COMMENTS          ELIGIBLE                      None  0.8088\n55              1                 COMMENTS              ELSE                      None -0.0131\n56              1                 COMMENTS         ELSEWHERE                      None -1.4821\n57              1                 COMMENTS               END                      None -1.0305\n58              1                 COMMENTS            ENOUGH                      None -0.7498\n59              1                 COMMENTS               ETC                      None -0.5609\n60              1                 COMMENTS              EVEN                      None  0.7237\n61              1                 COMMENTS              EVER                      None  1.0206\n62              1                 COMMENTS             EVERY                      None  0.7270\n63              1                 COMMENTS          EVERYONE                      None -0.0131\n64              1                 COMMENTS        EVERYTHING                      None  0.0360\n65              1                 COMMENTS         EXCELLENT                      None  1.8730\n66              1                 COMMENTS         EXPENSIVE                      None  0.0181\n67              1                 COMMENTS               FAR                      None -0.7813\n68              1                 COMMENTS              FEEL                      None -0.5629\n69              1                 COMMENTS               FEW                      None  1.0206\n70              1                 COMMENTS           FILLING                      None -0.0983\n71              1                 COMMENTS              FLAT                      None -0.1792\n72              1                 COMMENTS            FORGET                      None  2.5360\n73              1                 COMMENTS              FORM                      None -0.0983\n74              1                 COMMENTS            FRANCE                      None  1.5673\n75              1                 COMMENTS              FREE                      None -0.0095\n76              1                 COMMENTS               GET                      None  1.6563\n77              1                 COMMENTS           GETTING                      None  0.0873\n78              1                 COMMENTS             GIFTS                      None  1.0206\n79              1                 COMMENTS          GIMMICKS                      None  1.7737\n80              1                 COMMENTS              GIVE                      None -0.3487\n81              1                 COMMENTS            GIVING                      None  0.0936\n82              1                 COMMENTS             GOING                      None -0.0983\n83              1                 COMMENTS              GOOD                      None -0.0115\n84              1                 COMMENTS             GREAT                      None -0.7743\n85              1                 COMMENTS             HAPPY                      None  0.4941\n86              1                 COMMENTS            HASSLE                      None -0.5781\n87              1                 COMMENTS             HENCE                      None -0.2607\n88              1                 COMMENTS              IDEA                      None  1.0730\n89              1                 COMMENTS       IMMEDIATELY                      None  1.4059\n90              1                 COMMENTS           IMPOSES                      None -0.5629\n91              1                 COMMENTS         IMPRESSED                      None  0.6668\n92              1                 COMMENTS          INCLUDED                      None  0.9186\n93              1                 COMMENTS       INFORMATION                      None  0.0936\n94              1                 COMMENTS          INVASION                      None -0.5629\n95              1                 COMMENTS             ITEMS                      None -0.3482\n96              1                 COMMENTS              JUNK                      None -0.0012\n97              1                 COMMENTS              KEEP                      None  1.1660\n98              1                 COMMENTS              KNOW                      None -0.0454\n99              1                 COMMENTS             LARGE                      None  0.8088\n100             1                 COMMENTS            LARGER                      None  0.8088\n101             1                 COMMENTS              LAST                      None -0.1949\n102             1                 COMMENTS              LATE                      None  1.6133\n103             1                 COMMENTS            LATEST                      None -0.9846\n104             1                 COMMENTS               LET                      None  2.1793\n105             1                 COMMENTS              LIKE                      None  1.8466\n106             1                 COMMENTS              LINE                      None -1.2537\n107             1                 COMMENTS              LIST                      None -1.2284\n108             1                 COMMENTS            LITTLE                      None  1.6133\n109             1                 COMMENTS              LIVE                      None  1.0206\n110             1                 COMMENTS              LOOK                      None -0.2607\n111             1                 COMMENTS               LOT                      None -0.7348\n112             1                 COMMENTS             LOUSY                      None -2.1280\n113             1                 COMMENTS              LOVE                      None -2.7158\n114             1                 COMMENTS             LOYAL                      None -1.2218\n115             1                 COMMENTS           LOYALTY                      None -0.2607\n116             1                 COMMENTS                 M                      None  0.0936\n117             1                 COMMENTS              MADE                      None  1.8730\n118             1                 COMMENTS              MAIL                      None -0.0012\n119             1                 COMMENTS            MAKESE                      None -0.7813\n120             1                 COMMENTS           MANAGER                      None -0.9364\n121             1                 COMMENTS           MANUALS                      None -0.0849\n122             1                 COMMENTS              MANY                      None -0.8890\n123             1                 COMMENTS        MEMBERSHIP                      None  0.0873\n124             1                 COMMENTS             MODEL                      None -0.9846\n125             1                 COMMENTS           MONITOR                      None -0.1792\n126             1                 COMMENTS             MONTH                      None -0.7395\n127             1                 COMMENTS              MORE                      None -2.3602\n128             1                 COMMENTS            MOSTLY                      None -1.0305\n129             1                 COMMENTS            MOTHER                      None  1.0480\n130             1                 COMMENTS             MOUSE                      None -0.0444\n131             1                 COMMENTS              MOVE                      None -0.7498\n132             1                 COMMENTS             MOVED                      None  1.0206\n133             1                 COMMENTS              MUCH                      None -1.7869\n134             1                 COMMENTS              NAME                      None -1.2284\n135             1                 COMMENTS              NEED                      None -1.2865\n136             1                 COMMENTS             NEVER                      None  2.2015\n137             1                 COMMENTS               NEW                      None -0.5416\n138             1                 COMMENTS               NOW                      None  0.5726\n139             1                 COMMENTS             OFFER                      None -1.9714\n140             1                 COMMENTS          OFFERING                      None  0.7309\n141             1                 COMMENTS             OFTEN                      None -0.5629\n142             1                 COMMENTS              ONCE                      None  1.4059\n143             1                 COMMENTS             OTHER                      None  3.5844\n144             1                 COMMENTS            OTHERS                      None -0.7813\n145             1                 COMMENTS              PADS                      None -0.0444\n146             1                 COMMENTS              PAGE                      None -0.0983\n147             1                 COMMENTS              PAIN                      None -0.8351\n148             1                 COMMENTS             PANEL                      None -0.1792\n149             1                 COMMENTS              PAST                      None  0.0873\n150             1                 COMMENTS          PERSONAL                      None -0.3487\n151             1                 COMMENTS            PLEASE                      None -1.4484\n152             1                 COMMENTS           PLEASED                      None  1.4059\n153             1                 COMMENTS             PRICE                      None -0.2607\n154             1                 COMMENTS            PRICES                      None  0.9670\n155             1                 COMMENTS           PRIVACY                      None -0.3538\n156             1                 COMMENTS          PRODUCTS                      None -0.5602\n157             1                 COMMENTS           PROGRAM                      None -0.9562\n158             1                 COMMENTS        PROMOTIONS                      None -1.2259\n159             1                 COMMENTS          PURCHASE                      None  1.3183\n160             1                 COMMENTS         PURCHASED                      None  0.1025\n161             1                 COMMENTS         PURCHASES                      None  0.0224\n162             1                 COMMENTS          QUANTITY                      None  0.8088\n163             1                 COMMENTS         QUESTIONS                      None -0.5609\n164             1                 COMMENTS            RARELY                      None -0.2607\n165             1                 COMMENTS             READY                      None -0.7498\n166             1                 COMMENTS          RECEIVED                      None  0.0873\n167             1                 COMMENTS          RECENTLY                      None  0.5143\n168             1                 COMMENTS            RECORD                      None -1.5181\n169             1                 COMMENTS          REMEMBER                      None -0.4717\n170             1                 COMMENTS            REMOVE                      None -1.2284\n171             1                 COMMENTS           RESPECT                      None  0.0936\n172             1                 COMMENTS            RETURN                      None -0.8351\n173             1                 COMMENTS               RUN                      None  0.8088\n174             1                 COMMENTS              SALE                      None -1.3791\n175             1                 COMMENTS         SATISFIED                      None  0.9602\n176             1                 COMMENTS               SAY                      None -1.5181\n177             1                 COMMENTS              SEND                      None  0.1220\n178             1                 COMMENTS           SENDING                      None  2.7815\n179             1                 COMMENTS             SENSE                      None -0.7813\n180             1                 COMMENTS              SENT                      None  1.4059\n181             1                 COMMENTS           SERVICE                      None -1.2218\n182             1                 COMMENTS           SEVERAL                      None  0.9602\n183             1                 COMMENTS              SHIP                      None -0.0736\n184             1                 COMMENTS          SHIPPING                      None -0.0095\n185             1                 COMMENTS              SHOP                      None -0.4012\n186             1                 COMMENTS           SHOPPED                      None  1.4059\n187             1                 COMMENTS          SHOPPERS                      None -0.7498\n188             1                 COMMENTS          SHOPPING                      None -0.6983\n189             1                 COMMENTS             SHOPS                      None  3.5844\n190             1                 COMMENTS            SIGNED                      None -0.6687\n191             1                 COMMENTS            SISTER                      None  1.2445\n192             1                 COMMENTS             SMALL                      None  0.8088\n193             1                 COMMENTS             SORRY                      None -0.5602\n194             1                 COMMENTS             SPEAK                      None  0.0873\n195             1                 COMMENTS          SPECIALS                      None -0.9240\n196             1                 COMMENTS             START                      None -0.0131\n197             1                 COMMENTS           STARTED                      None  0.0360\n198             1                 COMMENTS           STOPPED                      None -0.8351\n199             1                 COMMENTS             STORE                      None -1.8061\n200             1                 COMMENTS            STORES                      None  1.6133\n201             1                 COMMENTS             TEXAS                      None  5.2929\n202             1                 COMMENTS             THANK                      None  0.7704\n203             1                 COMMENTS            THANKS                      None  0.5745\n204             1                 COMMENTS            THINGS                      None -1.0305\n205             1                 COMMENTS             THINK                      None  0.3873\n206             1                 COMMENTS             THREE                      None -0.0983\n207             1                 COMMENTS             THREW                      None -1.4821\n208             1                 COMMENTS              TIME                      None  0.5125\n209             1                 COMMENTS             TIMES                      None -1.2325\n210             1                 COMMENTS             TIRED                      None  0.5423\n211             1                 COMMENTS              TOLD                      None  1.9473\n212             1                 COMMENTS             TRIED                      None -0.8351\n213             1                 COMMENTS           UNHAPPY                      None  0.7908\n214             1                 COMMENTS                UP                      None  0.2474\n215             1                 COMMENTS          UPBUYING                      None -1.0305\n216             1                 COMMENTS           UPGRADE                      None -0.9846\n217             1                 COMMENTS                US                      None -0.5609\n218             1                 COMMENTS               USE                      None  0.1590\n219             1                 COMMENTS              USED                      None -1.6557\n220             1                 COMMENTS              USES                      None -0.2705\n221             1                 COMMENTS           USUALLY                      None -0.2607\n222             1                 COMMENTS           VARIETY                      None -0.7498\n223             1                 COMMENTS              WANT                      None -0.6921\n224             1                 COMMENTS            WANTED                      None  0.9602\n225             1                 COMMENTS             WASTE                      None -0.0983\n226             1                 COMMENTS               WAY                      None -0.7498\n227             1                 COMMENTS              WEEK                      None -0.9846\n228             1                 COMMENTS            WEEKLY                      None -0.9240\n229             1                 COMMENTS             WEREN                      None -0.0849\n230             1                 COMMENTS              WISH                      None  0.0936\n231             1                 COMMENTS           WITHOUT                      None -0.2705\n232             1                 COMMENTS              WORK                      None  1.7099\n233             1                 COMMENTS             WORTH                      None -0.7813\n234             1                 COMMENTS             WRITE                      None  0.9602\n235             1                 COMMENTS             YEARS                      None -0.0131\n236             1             COUNTRY_NAME              None                 Argentina -0.3938\n237             1             COUNTRY_NAME              None                 Australia -0.4916\n238             1             COUNTRY_NAME              None                    Brazil -0.1988\n239             1             COUNTRY_NAME              None                    Canada  1.5125\n240             1             COUNTRY_NAME              None                     China  0.0000\n241             1             COUNTRY_NAME              None                   Denmark  0.0000\n242             1             COUNTRY_NAME              None                    France  0.0000\n243             1             COUNTRY_NAME              None                   Germany  0.0895\n244             1             COUNTRY_NAME              None                     Italy -0.3943\n245             1             COUNTRY_NAME              None                     Japan  0.0000\n246             1             COUNTRY_NAME              None               New Zealand  0.0000\n247             1             COUNTRY_NAME              None                    Poland -1.1204\n248             1             COUNTRY_NAME              None              Saudi Arabia  1.3478\n249             1             COUNTRY_NAME              None                 Singapore -0.5296\n250             1             COUNTRY_NAME              None              South Africa  0.0000\n251             1             COUNTRY_NAME              None                     Spain  0.0000\n252             1             COUNTRY_NAME              None                    Turkey -0.1481\n253             1             COUNTRY_NAME              None            United Kingdom  0.4888\n254             1             COUNTRY_NAME              None  United States of America -0.1620\n255             1              CUST_GENDER              None                         F -0.2174\n256             1              CUST_GENDER              None                         M  0.2174\n257             1        CUST_INCOME_LEVEL              None           A: Below 30,000 -0.0821\n258             1        CUST_INCOME_LEVEL              None        B: 30,000 - 49,999 -0.1808\n259             1        CUST_INCOME_LEVEL              None        C: 50,000 - 69,999  0.0073\n260             1        CUST_INCOME_LEVEL              None        D: 70,000 - 89,999  0.0688\n261             1        CUST_INCOME_LEVEL              None       E: 90,000 - 109,999  0.1965\n262             1        CUST_INCOME_LEVEL              None      F: 110,000 - 129,999  0.0137\n263             1        CUST_INCOME_LEVEL              None      G: 130,000 - 149,999 -0.0173\n264             1        CUST_INCOME_LEVEL              None      H: 150,000 - 169,999  0.2057\n265             1        CUST_INCOME_LEVEL              None      I: 170,000 - 189,999  0.1104\n266             1        CUST_INCOME_LEVEL              None      J: 190,000 - 249,999  0.1593\n267             1        CUST_INCOME_LEVEL              None      K: 250,000 - 299,999 -0.2608\n268             1        CUST_INCOME_LEVEL              None      L: 300,000 and above -0.2206\n269             1      CUST_MARITAL_STATUS              None                   Divorc. -0.0064\n270             1      CUST_MARITAL_STATUS              None                   Mabsent  0.0984\n271             1      CUST_MARITAL_STATUS              None                    Mar-AF -1.0121\n272             1      CUST_MARITAL_STATUS              None                   Married  0.7690\n273             1      CUST_MARITAL_STATUS              None                    NeverM  0.0973\n274             1      CUST_MARITAL_STATUS              None                    Separ.  0.4189\n275             1      CUST_MARITAL_STATUS              None                   Widowed -0.3652\n276             1                EDUCATION              None                      10th -0.0207\n277             1                EDUCATION              None                      11th -0.2618\n278             1                EDUCATION              None                      12th -0.2562\n279             1                EDUCATION              None                   1st-4th  0.0000\n280             1                EDUCATION              None                   5th-6th  0.0000\n281             1                EDUCATION              None                   7th-8th  0.1083\n282             1                EDUCATION              None                       9th  0.3439\n283             1                EDUCATION              None                   < Bach. -0.4278\n284             1                EDUCATION              None                   Assoc-A  0.1106\n285             1                EDUCATION              None                   Assoc-V -0.5395\n286             1                EDUCATION              None                     Bach. -0.0281\n287             1                EDUCATION              None                   HS-grad -0.4022\n288             1                EDUCATION              None                   Masters  0.0464\n289             1                EDUCATION              None                       PhD  0.9167\n290             1                EDUCATION              None                   Presch.  0.0000\n291             1                EDUCATION              None                    Profsc  0.4104\n292             1       FLAT_PANEL_MONITOR              None                      None  0.0248\n293             1     HOME_THEATER_PACKAGE              None                      None  0.1701\n294             1           HOUSEHOLD_SIZE              None                         1 -0.2043\n295             1           HOUSEHOLD_SIZE              None                         2 -0.2494\n296             1           HOUSEHOLD_SIZE              None                         3  0.5076\n297             1           HOUSEHOLD_SIZE              None                       4-5  1.2811\n298             1           HOUSEHOLD_SIZE              None                       6-8 -1.2076\n299             1           HOUSEHOLD_SIZE              None                        9+ -0.1275\n300             1               OCCUPATION              None                         ? -0.2533\n301             1               OCCUPATION              None                   Armed-F  0.6799\n302             1               OCCUPATION              None                   Cleric.  0.5313\n303             1               OCCUPATION              None                    Crafts -0.7328\n304             1               OCCUPATION              None                     Exec.  0.7023\n305             1               OCCUPATION              None                   Farming -0.7966\n306             1               OCCUPATION              None                   Handler -0.6069\n307             1               OCCUPATION              None                   House-s  0.0000\n308             1               OCCUPATION              None                   Machine  0.1041\n309             1               OCCUPATION              None                     Other -0.7376\n310             1               OCCUPATION              None                     Prof.  0.6200\n311             1               OCCUPATION              None                   Protec.  0.3349\n312             1               OCCUPATION              None                     Sales  0.5465\n313             1               OCCUPATION              None                   TechSup  0.3110\n314             1               OCCUPATION              None                   Transp. -0.7027\n315             1         OS_DOC_SET_KANJI              None                      None  0.0000\n316             1            YRS_RESIDENCE              None                      None  0.2141\n317             1              Y_BOX_GAMES              None                      None -0.3805\n318             1                     None              None                      None -1.4056\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions on test dataset","message":["%python","","case_id = 'CUST_ID'","target = 'AFFINITY_CARD'","SVM_RES_DF = svm_mod.predict(test_dat, supplemental_cols = test_dat[[case_id, target]])","SVM_RES_PROB = svm_mod.predict_proba(test_dat, supplemental_cols = test_dat)","SVM_RES_DF = SVM_RES_DF.merge(SVM_RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"]) ","SVM_RES_DF = SVM_RES_DF[['CUST_ID', 'PREDICTION', 'PROBABILITY_OF_1', target]]","z.show(SVM_RES_DF.head(10).round(12))"],"enabled":true,"result":{"startTime":1736279762347,"interpreter":"python.low","endTime":1736279764089,"results":[{"message":"CUST_ID\tPREDICTION\tPROBABILITY_OF_1\tAFFINITY_CARD\n101977.0\t0.0\t2.2265e-08\t0.0\n100908.0\t0.0\t1.4470488e-05\t0.0\n101259.0\t0.0\t2.1669e-08\t0.0\n101709.0\t0.0\t4.27295e-07\t0.0\n100008.0\t0.0\t2.7708e-07\t0.0\n102701.0\t0.0\t7.20536e-07\t0.0\n103783.0\t0.0\t5.19115e-07\t0.0\n103956.0\t0.0\t1.37e-10\t0.0\n101699.0\t1.0\t0.999995577731\t1.0\n102531.0\t0.0\t0.000283073348\t0.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display confusion matrix and ROC, lift chart","message":["%python","","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions","     and the target column,","     The Settings name (for the charts), ","     The name of the model used (for the charts),","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," ","    # Extract Statistics from the Confusion Matrix","    cf_local = conf_matrix.pull()","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","    ","    # Estimated AUC via Triangle (not very precise) could be","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","    # Compute real AUC using roc_curve by loading the","    # data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","    ","    # Store all statistics to export","    statistics = {'Algorithm' : name,","                  'Algorithm_setting' : settings_name,","                  'TN' : TN,","                  'TP' : TP,","                  'FP' : FP,","                  'FN' : FN,","                  'TPR' : TPR,","                  'FPR' : FPR,","                  'TNR' : TNR,","                  'FNR' : FNR,","                  'Precision' : Precision,","                  'Accuracy' : Accuracy,","                  'NPV' : NPV,","                  'DetectionRate' : DetectionRate,","                  'BalancedAccuracy' : BalancedAccuracy,","                  'AUC' : AUC,","                  'F1Score' : F1Score,","                  'MathewsCorrCoef' : MathewsCorrCoef","                  }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = round((TN/TOTAL*100),2)","    FP_P = round((FP/TOTAL*100),2)","    FN_P = round((FN/TOTAL*100),2)","    TP_P = round((TP/TOTAL*100),2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+","          \"F1Score: \"+str(round(F1Score,4))","          )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT, color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","                 textcoords='data', ","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","                                 connectionstyle='arc3, rad=0.3'), ","                 horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), ","                 xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),","                 xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), ","                 xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13) ","    ax4.legend(loc=\"lower right\")"," ","    return(statistics, pred_local)"," ","","out_dt, pred_l = evaluate_model(pred_data=SVM_RES_DF, settings_name='Support Vector Machine including Unstructured Data', name='SVM', target='AFFINITY_CARD' )"],"enabled":true,"result":{"startTime":1736279764510,"interpreter":"python.low","endTime":1736279772862,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 623 (72.61%)\tFalse Positive: 42 (4.9%)\nACTUAL 1\tFalse Negative: 30 (3.5%)\tTrue Positive: 163 (19.0%)\nAccuracy: 91.6084%\tAUC: 0.9594\tF1Score: 0.8191\n","type":"TABLE"},{"message":"<div style='width:auto;height:auto'><img 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\"COEF\"]].head(10))"],"enabled":true,"result":{"startTime":1736279773292,"interpreter":"python.low","endTime":1736279773716,"results":[{"message":"TARGET_VALUE\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tCOEF\n1\tCOMMENTS\tTEXAS\tNone\t5.2929\n1\tCOMMENTS\tCUSTOMER\tNone\t-3.9367\n1\tCOMMENTS\tOTHER\tNone\t3.5844\n1\tCOMMENTS\tSHOPS\tNone\t3.5844\n1\tCOMMENTS\tSENDING\tNone\t2.7815\n1\tCOMMENTS\tLOVE\tNone\t-2.7158\n1\tCOMMENTS\tFORGET\tNone\t2.536\n1\tCOMMENTS\tMORE\tNone\t-2.3602\n1\tCOMMENTS\tCOMPUTER\tNone\t-2.2205\n1\tCOMMENTS\tNEVER\tNone\t2.2015\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model global 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This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734449432027,"interpreter":"md.low","endTime":1734449432496,"results":[{"message":"<h1 id=\"forecasting-sales-using-exponential-smoothing-algorithm-for-time-series-data\">Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data<\/h1>\n<p>Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" 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Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>"],"enabled":true,"result":{"startTime":1734449434780,"interpreter":"md.low","endTime":1734449435244,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time 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column with datetime type.  For example, if you want to predict for quarterly sales, the setting is `EXSM_INTERVAL_QTR`.","> 'EXSM_INTERVAL' : 'EXSM_INTERVAL_QTR';","* Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future","> 'EXSM_PREDICTION_STEP' : '4';","* Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.","> 'EXSM_MODEL' : 'EXSM_WINTERS';","* Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.","> 'EXSM_SEASONALITY' : '4'","* Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.     ","> 'EXSM_SETMISSING' : 'EXSM_MISS_AUTO';"],"enabled":true,"result":{"startTime":1734449440700,"interpreter":"md.low","endTime":1734449441177,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-esm\">Examples of possible setting overrides for ESM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/exponential-smoothing-method.html\" target=\"_blank\">OML Time Series<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is <code>EXSM_INTERVAL_QTR<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_INTERVAL' : 'EXSM_INTERVAL_QTR';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_PREDICTION_STEP' : '4';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_MODEL' : 'EXSM_WINTERS';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_SEASONALITY' : '4'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_SETMISSING' : 'EXSM_MISS_AUTO';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","help(oml.esm)"],"enabled":true,"result":{"startTime":1734449441632,"interpreter":"python.low","endTime":1734449442202,"results":[{"message":"Help on class esm in module oml.algo.esm:\n\nclass esm(oml.algo.model.odmModel)\n |  esm(model_name=None, model_owner=None, **params)\n |\n |  In-database `Exponential Smoothing <https://www.oracle.com/machine-learning/exponential-smoothing>`_ Model\n |\n |  Exponential Smoothing methods have been widely used in forecasting for over half\n |  a century. It has applications at the strategic, tactical, and operation level.\n |  For example, at a strategic level, forecasting is used for projecting return on\n |  investment, growth and the effect of innovations. At a tactical level, forecasting\n |  is used for projecting costs, inventory requirements, and customer satisfaction.\n |  At an operational level, forecasting is used for setting targets and predicting\n |  quality and conformance with standards.\n |\n |  In its simplest form, Exponential Smoothing is a moving average method with a\n |  single parameter which models an exponentially decreasing effect of past levels\n |  on future values. With a variety of extensions, Exponential Smoothing covers a\n |  broader class of models than competitors, such as the Box-Jenkins auto-regressive\n |  integrated moving average (ARIMA) approach. Oracle Data Mining implements\n |  Exponential Smoothing using a state of the art state space method that\n |  incorporates a single source of error (SSOE) assumption which provides theoretical\n |  and performance advantages.\n |\n |  :Attributes:\n |\n |      **prediction** : oml.DataFrame\n |\n |        The prediction information.\n |        It includes the following components:\n |\n |        - time_seq: The time sequence used as case id\n |        - value: The real value in the training data\n |        - prediction: The predicted value by the model\n |        - lower: The lower bound of the predicted value\n |        - upper: The upper bound of the predicted value\n |\n |  Method resolution order:\n |      esm\n |      oml.algo.model.odmModel\n |      builtins.object\n |\n |  Methods defined here:\n |\n |  __init__(self, model_name=None, model_owner=None, **params)\n |      Initializes an instance of esm object.\n |\n |      Parameters\n |      ----------\n |      model_name : string or None (default)\n |          The name of an existing database Decision Tree model to create an oml.esm object from.\n |          The specified database model is not dropped when the oml.esm object is deleted.\n |      model_owner: string or None (default)\n |          The owner name of the existing Decision Tree model\n |          The current database user by default\n |      params : key-value pairs or dict\n |          Oracle Machine Learning parameter settings. Each dict element's name and\n |          value refer to the parameter setting name and value, respectively.\n |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n |          Model Settings <https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmprg/specify-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n |          for applicable parameters and valid values. Global and Automatic Data\n |          Preparation Settings in Table 4-10 apply generally to the model.\n |          Mining Function Settings for Classification and `Algorithm-specific\n |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566>`_\n |          are applicable to Decision Tree model.\n |\n |  __repr__(self)\n |\n |  fit(self, x, y, time_seq, model_name=None)\n |      Fits a exponential smoothing model according to the training data and parameter settings.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |          Attribute values for building the model.\n |      y : a single column OML object, or string\n |          Target values.\n |          If y is a single column OML object, target values specified by y must be combinable with x.\n |          If y is a string, y is the name of the column in x that specifies the target values.\n |      time_seq: string\n |         The name of the column in x used as time sequence to build the model.\n |         The column must be oml.Integer, oml.Float (Number based), or oml.Datetime\n |      model_name : string or None (default)\n |          User-specified model name.\n |          The user-specified database model is not dropped when oml.esm object is deleted.\n |          If None, a system-generated model name will be used.\n |          The system-generated model is dropped when oml.esm object is deleted\n |          unless oml.esm object is saved into a datastore.\n |\n |  ----------------------------------------------------------------------\n |  Methods inherited from oml.algo.model.odmModel:\n |\n |  export_sermodel(self, table=None, partition=None)\n |      Export model.\n |\n |      Parameters\n |      ----------\n |      table : string or None (default)\n |          A name for the new table where the serialized model is saved.\n |          If None, the serialized model will be saved to a temporary table.\n |      partition : string or None (default)\n |          Name of the partition that needs to be exported.\n |          If partition is None, all partitions are exported\n |\n |      Returns\n |      -------\n |      oml_bytes : an oml.Bytes object\n |        Contains the BLOB content from the model export\n |\n |  get_params(self, params=None, deep=False)\n |      Fetches parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : iterable of strings, None (default)\n |          Names of parameters to fetch. If ``params`` is None,\n |          fetches all settings.\n |      deep : boolean, False (default)\n |          Includes the computed and default parameters or not.\n |\n |      Returns\n |      -------\n |      settings : dict mapping str to str\n |\n |  set_params(self, **params)\n |      Changes parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : dict object mapping str to str\n |          The key should be the name of the setting, and the value should be\n |          the new setting.\n |\n |      Returns\n |      -------\n |      model : the model itself.\n |\n |  ----------------------------------------------------------------------\n |  Readonly properties inherited from oml.algo.model.odmModel:\n |\n |  model_owner\n |      The owner name of database mining model\n |\n |  ----------------------------------------------------------------------\n |  Data descriptors inherited from oml.algo.model.odmModel:\n |\n |  __dict__\n |      dictionary for instance variables\n |\n |  __weakref__\n |      list of weak references to the object\n |\n |  model_name\n |      The given name of database mining model\n |\n |  pivot_limit\n |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a Holt-Winters Model using quarterly setting","message":["%python","","try:","    oml.drop(model = 'ESM_SALES_FORECAST_1') ","except:","    print(\"No such model\")","    ","","","settings = {'EXSM_INTERVAL'       : 'EXSM_INTERVAL_QTR', # accumulation int'l = quarter","            'EXSM_PREDICTION_STEP': 4,                   # prediction step = 4 quarters","            'EXSM_MODEL'          : 'EXSM_WINTERS',      # ESM model = Holt-Winters","            'EXSM_SEASONALITY'    : '4'}                 # seasonal cycle = 4 quarters ","","train_x=ESM_SH_DATA[:,0]","train_y=ESM_SH_DATA[:,1]","","ESM_MOD = oml.esm(**settings).fit(train_x, train_y, time_seq = 'A',model_name='ESM_SALES_FORECAST_1',)"],"enabled":true,"result":{"startTime":1734449442664,"interpreter":"python.low","endTime":1734449445648,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Review the forecast model settings","message":["%python","","z.show(ESM_MOD)"],"enabled":true,"result":{"startTime":1734449446101,"interpreter":"python.low","endTime":1734449446671,"results":[{"message":"\nModel Name: ESM_SALES_FORECAST_1\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Exponential Smoothing\n\nMining Function: TIME_SERIES\n\nTarget: B\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME  ALGO_EXPONENTIAL_SMOOTHING\n1                EXSM_ACCUMULATE             EXSM_ACCU_TOTAL\n2          EXSM_CONFIDENCE_LEVEL                         .95\n3                  EXSM_INTERVAL           EXSM_INTERVAL_QTR\n4                     EXSM_MODEL                EXSM_WINTERS\n5                      EXSM_NMSE                           3\n6         EXSM_OPTIMIZATION_CRIT           EXSM_OPT_CRIT_LIK\n7           EXSM_PREDICTION_STEP                           4\n8               EXSM_SEASONALITY                           4\n9                EXSM_SETMISSING              EXSM_MISS_AUTO\n10                  ODMS_DETAILS                 ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n12                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n13                     PREP_AUTO                          ON\n\nGlobal Statistics: \n       attribute name attribute value\n0   -2 LOG-LIKELIHOOD         -227.41\n1                 AIC          472.82\n2                AICC          502.82\n3               ALPHA        0.746948\n4                AMSE     2.54708e+11\n5                BETA       0.0559241\n6                 BIC         479.773\n7           CONVERGED             YES\n8               GAMMA     0.000100143\n9       INITIAL LEVEL     6.11021e+06\n10   INITIAL SEASON 1        0.997969\n11   INITIAL SEASON 2         1.02306\n12   INITIAL SEASON 3        0.938255\n13   INITIAL SEASON 4         1.04071\n14      INITIAL TREND         42050.1\n15                MAE          273859\n16                MSE     1.30577e+11\n17           NUM_ROWS          918843\n18              SIGMA       0.0871202\n19                STD       0.0871202\n\nAttributes: \n\nPartition: NO\n\nPrediction: \n\n     TIME_SEQ         VALUE    PREDICTION         LOWER         UPPER\n0  1998-01-01  6.480684e+06  6.402751e+06           NaN           NaN\n1  1998-04-01  5.593994e+06  5.868258e+06           NaN           NaN\n2  1998-07-01  6.071823e+06  6.205873e+06           NaN           NaN\n3  1998-10-01  5.937414e+06  5.978511e+06           NaN           NaN\n4  1999-01-01  6.093747e+06  6.223665e+06           NaN           NaN\n5  1999-04-01  4.925472e+06  5.535877e+06           NaN           NaN\n6  1999-07-01  5.827050e+06  5.515472e+06           NaN           NaN\n7  1999-10-01  5.373679e+06  5.601171e+06           NaN           NaN\n8  2000-01-01  5.984889e+06  5.644292e+06           NaN           NaN\n9  2000-04-01  5.371731e+06  5.317402e+06           NaN           NaN\n10 2000-07-01  6.121239e+06  5.845169e+06           NaN           NaN\n11 2000-10-01  6.287647e+06  5.920688e+06           NaN           NaN\n12 2001-01-01  6.547097e+06  6.500126e+06           NaN           NaN\n13 2001-04-01  6.922468e+06  5.930069e+06           NaN           NaN\n14 2001-07-01  7.195999e+06  7.376867e+06           NaN           NaN\n15 2001-10-01  7.470898e+06  7.154088e+06           NaN           NaN\n16 2002-01-01           NaN  7.819637e+06  6.484414e+06  9.154860e+06\n17 2002-04-01           NaN  7.151013e+06  5.591487e+06  8.710540e+06\n18 2002-07-01           NaN  7.907806e+06  5.843738e+06  9.971875e+06\n19 2002-10-01           NaN  7.821557e+06  5.462865e+06  1.018025e+07\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review global diagnostics and model quality","message":["%python","","z.show(ESM_MOD.global_stats)"],"enabled":true,"result":{"startTime":1734449447134,"interpreter":"python.low","endTime":1734449447586,"results":[{"message":"attribute name\tattribute value\n-2 LOG-LIKELIHOOD\t-227.40992629778518\nAIC\t472.81985259557035\nAICC\t502.81985259557035\nALPHA\t0.746947813267497\nAMSE\t254707632297.6781\nBETA\t0.05592410989615662\nBIC\t479.7731510957284CONVERGED\tYES\nGAMMA\t0.00010014302300732534\nINITIAL LEVEL\t6110213.924121352\nINITIAL SEASON 1\t0.9979691764644829\nINITIAL SEASON 2\t1.0230610589110443\nINITIAL SEASON 3\t0.9382551830027352\nINITIAL SEASON 4\t1.0407145816217378\nINITIAL TREND\t42050.12261203614\nMAE\t273858.85874766804\nMSE\t130577271283.2728\nNUM_ROWS\t918843.0\nSIGMA\t0.08712023294116777\nSTD\t0.08712023294116777\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Forecast AMOUNT_SOLD","message":["%python","","# Sort results by descending time sequence so latest points are shown first.","# The model predicts 4 values into the future with LOWER and UPPER condifence bounds.","","z.show(ESM_MOD.prediction.sort_values(by='TIME_SEQ', ascending=False))"],"enabled":true,"result":{"startTime":1734449448041,"interpreter":"python.low","endTime":1734449448574,"results":[{"message":"TIME_SEQ\tVALUE\tPREDICTION\tLOWER\tUPPER\n2002-10-01 00:00:00\tnan\t7821557.483721262\t5462864.876822842\t10180250.090619681\n2002-07-01 00:00:00\tnan\t7907806.499484639\t5843737.671509017\t9971875.32746026\n2002-04-01 00:00:00\tnan\t7151013.397867097\t5591487.09771536\t8710539.698018832\n2002-01-01 00:00:00\tnan\t7819637.041280045\t6484414.345140811\t9154859.73741928\n2001-10-01 00:00:00\t7470897.51999978\t7154087.563070033\tnan\tnan\n2001-07-01 00:00:00\t7195998.630000379\t7376866.595966368\tnan\tnan\n2001-04-01 00:00:00\t6922468.390000418\t5930069.144510286\tnan\tnan\n2001-01-01 00:00:00\t6547097.440000162\t6500126.226157493\tnan\tnan\n2000-10-01 00:00:00\t6287646.919999797\t5920687.514083858\tnan\tnan\n2000-07-01 00:00:00\t6121239.28999967\t5845169.12679054\tnan\tnan\n2000-04-01 00:00:00\t5371730.920000249\t5317402.430733974\tnan\tnan\n2000-01-01 00:00:00\t5984889.489999551\t5644291.534300694\tnan\tnan\n1999-10-01 00:00:00\t5373678.6700003\t5601170.870404041\tnan\tnan\n1999-07-01 00:00:00\t5827050.150000022\t5515472.102882127\tnan\tnan\n1999-04-01 00:00:00\t4925471.629999909\t5535877.141282769\tnan\tnan\n1999-01-01 00:00:00\t6093747.209999715\t6223664.94716229\tnan\tnan\n1998-10-01 00:00:00\t5937413.710001296\t5978510.77964177\tnan\tnan\n1998-07-01 00:00:00\t6071823.100001069\t6205873.360363123\tnan\tnan\n1998-04-01 00:00:00\t5593994.140000758\t5868257.561096561\tnan\tnan\n1998-01-01 00:00:00\t6480684.000001145\t6402750.903422597\tnan\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View forecast values with confidence intervals using SQL query using model detail view","message":["%sql","","SELECT TO_CHAR(CASE_ID,'YYYY-MON') DATE_ID, VALUE ACTUAL_SOLD, ","       round(PREDICTION,2) FORECAST_SOLD, ","       round(LOWER,2) LOWER_BOUND, round(UPPER,2) UPPER_BOUND","FROM   DM$VPESM_SALES_FORECAST_1","ORDER BY CASE_ID DESC;"],"enabled":true,"result":{"startTime":1734449449030,"interpreter":"sql.low","endTime":1734449450907,"results":[{"message":"DATE_ID\tACTUAL_SOLD\tFORECAST_SOLD\tLOWER_BOUND\tUPPER_BOUND\n2002-OCT\t\t7821557.48\t5462864.88\t10180250.09\n2002-JUL\t\t7907806.5\t5843737.67\t9971875.33\n2002-APR\t\t7151013.4\t5591487.1\t8710539.7\n2002-JAN\t\t7819637.04\t6484414.35\t9154859.74\n2001-OCT\t7470897.5199997798\t7154087.56\t\t\n2001-JUL\t7195998.6300003789\t7376866.6\t\t\n2001-APR\t6922468.3900004178\t5930069.14\t\t\n2001-JAN\t6547097.4400001625\t6500126.23\t\t\n2000-OCT\t6287646.9199997969\t5920687.51\t\t\n2000-JUL\t6121239.2899996703\t5845169.13\t\t\n2000-APR\t5371730.9200002486\t5317402.43\t\t\n2000-JAN\t5984889.4899995513\t5644291.53\t\t\n1999-OCT\t5373678.6700002998\t5601170.87\t\t\n1999-JUL\t5827050.1500000218\t5515472.1\t\t\n1999-APR\t4925471.6299999086\t5535877.14\t\t\n1999-JAN\t6093747.209999715\t6223664.95\t\t\n1998-OCT\t5937413.7100012964\t5978510.78\t\t\n1998-JUL\t6071823.1000010688\t6205873.36\t\t\n1998-APR\t5593994.1400007578\t5868257.56\t\t\n1998-JAN\t6480684.0000011446\t6402750.9\t\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"table\":{\"version\":1},\"line\":{\"showSeries\":[\"VALUE\",\"PREDICTION\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"VALUE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"PREDICTION\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"TIME_SEQ\",\"VALUE\",\"PREDICTION\",\"LOWER\",\"UPPER\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"line","title":"Chart forecasted AMOUNT_SOLD values with confidence intervals","message":["%python","","z.show(ESM_MOD.prediction.sort_values(by='TIME_SEQ'))"],"enabled":true,"result":{"startTime":1734449451362,"interpreter":"python.low","endTime":1734449451889,"results":[{"message":"TIME_SEQ\tVALUE\tPREDICTION\tLOWER\tUPPER\n1998-01-01 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00:00:00\t7195998.630000379\t7376866.595966368\tnan\tnan\n2001-10-01 00:00:00\t7470897.51999978\t7154087.563070033\tnan\tnan\n2002-01-01 00:00:00\tnan\t7819637.041280045\t6484414.345140811\t9154859.73741928\n2002-04-01 00:00:00\tnan\t7151013.397867097\t5591487.09771536\t8710539.698018832\n2002-07-01 00:00:00\tnan\t7907806.499484639\t5843737.671509017\t9971875.32746026\n2002-10-01 00:00:00\tnan\t7821557.483721262\t5462864.876822842\t10180250.090619681\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734449452351,"interpreter":"md.low","endTime":1734449452787,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1734449453250,"interpreter":"md.low","endTime":1734449453688,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/README.md b/machine-learning/notebooks-oml/python/README.md
similarity index 97%
rename from machine-learning/notebooks/python/README.md
rename to machine-learning/notebooks-oml/python/README.md
index ddc75d23..7c71c716 100644
--- a/machine-learning/notebooks/python/README.md
+++ b/machine-learning/notebooks-oml/python/README.md
@@ -57,6 +57,6 @@ The specific denomination "OfficeHours_" in the name of the file indicates that
 
 [Oracle Machine Learning Notebooks](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/)
 
-#### Copyright (c) 2024 Oracle Corporation and/or its affilitiates.
+#### Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
 
 ###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)
diff --git a/machine-learning/notebooks-oml/r/OML Run-me-first.dsnb b/machine-learning/notebooks-oml/r/OML Run-me-first.dsnb
new file mode 100644
index 00000000..1b4dbbab
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML Run-me-first.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Run-me-first","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1737138360559,"interpreter":"md.low","endTime":1737138361104,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Run-me-first: Data preparation and creation for examples","","In this notebook, we will load data used in other notebooks that require the `CUSTOMER_INSURANCE_LTV` table, and will also prepare the dataset used by the `Data Preparation` notebooks, in particular those for data cleaning and feature selection.","","The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.","","We will take the original table and will tweak the data, with some original values being replaced by `NULL`, and adding `duplicated rows` artificially to generate a resulting table we will call `CUSTOMER_INSURANCE_LTV_NOISE`. ","","The source of the data is a CSV file stored in <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\"> Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the `DBMS_CLOUD.CREATE_EXTERNAL_TABLE` function among others.","","###### `IMPORTANT` Please run this notebook before running any of the Data Preparation notebooks.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737138361570,"interpreter":"md.low","endTime":1737138362088,"results":[{"message":"<h1 id=\"oml-run-me-first-data-preparation-and-creation-for-examples\">OML Run-me-first: Data preparation and creation for examples<\/h1>\n<p>In this notebook, we will load data used in other notebooks that require the <code>CUSTOMER_INSURANCE_LTV<\/code> table, and will also prepare the dataset used by the <code>Data Preparation<\/code> notebooks, in particular those for data cleaning and feature selection.<\/p>\n<p>The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We will take the original table and will tweak the data, with some original values being replaced by <code>NULL<\/code>, and adding <code>duplicated rows<\/code> artificially to generate a resulting table we will call <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code>.<\/p>\n<p>The source of the data is a CSV file stored in <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\"> Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the <code>DBMS_CLOUD.CREATE_EXTERNAL_TABLE<\/code> function among others.<\/p>\n<h6 id=\"important-please-run-this-notebook-before-running-any-of-the-data-preparation-notebooks\"><code>IMPORTANT<\/code> Please run this notebook before running any of the Data Preparation notebooks.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Cleanup if tables exist","message":["%script","","-- DELETE PREVIOUS DATA IF IT EXISTS","BEGIN ","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE EXT_CUSTOMER_INSURANCE_LTV';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_NOISE';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","   BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_SQL';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_PY';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","END;  "],"enabled":true,"result":{"startTime":1737138362545,"interpreter":"script.low","endTime":1737138365251,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV` table ","","This table will be used in other notebooks and allow us to build the `CUSTOMER_INSURANCE_LTV_NOISE` that will also be used.","","We will pull the data from OML dataset storage in GitHub using the `DBMS_CLOUD` capabilities.","","More details on `DBMS_CLOUD` can be found <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">in the Documentation<\/a>"],"enabled":true,"result":{"startTime":1737138365721,"interpreter":"md.low","endTime":1737138366172,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv-table\">Create the <code>CUSTOMER_INSURANCE_LTV<\/code> table<\/h4>\n<p>This table will be used in other notebooks and allow us to build the <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code> that will also be used.<\/p>\n<p>We will pull the data from OML dataset storage in GitHub using the <code>DBMS_CLOUD<\/code> capabilities.<\/p>\n<p>More details on <code>DBMS_CLOUD<\/code> can be found <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">in the Documentation<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the data from Object Storage","message":["%script","","-- LOADS THE DATA FROM GITHUB BY CREATING AN EXTERNAL TABLE AND THEN CREATING A TABLE WITH THAT","DECLARE","     uri_data        varchar2(1000) := 'https://raw.githubusercontent.com/oracle/oracle-db-examples/master/machine-learning/datasets/CUST_INSUR_LTV_APPLY.csv';","     csv_format      varchar2(1000) := '{\"dateformat\":\"YYYY-MM-DD\", \"skipheaders\":\"1\", \"delimiter\":\",\", \"ignoreblanklines\":\"true\", \"removequotes\":\"true\", \"blankasnull\":\"true\", \"trimspaces\":\"lrtrim\", \"truncatecol\":\"true\", \"ignoremissingcolumns\":\"true\"}';","BEGIN","DBMS_CLOUD.CREATE_EXTERNAL_TABLE(","            TABLE_NAME => 'EXT_CUSTOMER_INSURANCE_LTV',","            FILE_URI_LIST => uri_data,","            FORMAT => csv_format,","            COLUMN_LIST => 'MARITAL_STATUS          VARCHAR2(26),","                            STATE                   CHAR(26),","                            CREDIT_BALANCE          NUMBER(8,0),","                            CUSTOMER_TENURE         NUMBER(3,0),","                            MORTGAGE_AMOUNT         NUMBER(7,0),","                            BANK_FUNDS              NUMBER(7,0),","                            NUM_DEPENDENTS          NUMBER(3,0),","                            HAS_CHILDREN            NUMBER(3,0),","                            INCOME                  NUMBER(7,0),","                            CUSTOMER_ID             CHAR(26),","                            GENDER                  CHAR(26),","                            PROFESSION              VARCHAR2(35),","                            CREDIT_CARD_LIMITS      NUMBER(6,0),","                            REGION                  VARCHAR2(26),","                            HOME_OWNERSHIP          NUMBER(3,0),","                            NUM_ONLINE_TRANS        NUMBER(6,0),","                            BUY_INSURANCE           VARCHAR2(26),","                            MONTHLY_CHECKS          NUMBER(4,0),","                            NUM_TRANS_KIOSK         NUMBER(3,0),","                            AGE                     NUMBER(4,0),","                            MONEY_MONTLY_OVERDRAWN  NUMBER(6,2),","                            LTV                     NUMBER(9,2),","                            TOTAL_AUTOM_PAYMENTS    NUMBER(8,0),","                            NUM_TRANS_TELLER        NUMBER(3,0),","                            CHECKING_BALANCE        NUMBER(7,0),","                            NUM_TRANS_ATM           NUMBER(3,0),","                            LTV_BIN                 VARCHAR2(26),","                            FIRST_NAME              VARCHAR2(26),","                            NUM_MORTGAGES           NUMBER(3,0),","                            CAR_OWNERSHIP           NUMBER(3,0),","                            LAST_NAME               VARCHAR2(26)');","                            ","-- WRITE A TABLE INTO THE CURRENT USER WITH THE NAME \"CUSTOMER_INSURANCE_LTV\"                             ","  EXECUTE IMMEDIATE 'create table CUSTOMER_INSURANCE_LTV as select * from EXT_CUSTOMER_INSURANCE_LTV';","  EXCEPTION WHEN OTHERS THEN NULL;","","END;"],"enabled":true,"result":{"startTime":1737138366645,"interpreter":"script.low","endTime":1737138369552,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the number of records","message":["%sql","","SELECT count(*) FROM CUSTOMER_INSURANCE_LTV;"],"enabled":true,"result":{"startTime":1737138370067,"interpreter":"sql.low","endTime":1737138370613,"results":[{"message":"COUNT(*)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the contents of the CUSTOMER_INSURANCE_LTV table","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737138371093,"interpreter":"sql.low","endTime":1737138371596,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV_NOISE` table","","---"],"enabled":true,"result":{"startTime":1737138372065,"interpreter":"md.low","endTime":1737138372531,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv_noise-table\">Create the <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code> table<\/h4>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table with noise from the customer insurance LTV table","message":["%script","","CREATE TABLE CUSTOMER_INSURANCE_LTV_NOISE AS","SELECT * FROM CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1737138372995,"interpreter":"script.low","endTime":1737138374786,"results":[{"message":"\nTable CUSTOMER_INSURANCE_LTV_NOISE created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Insert duplicated rows into the table with noise","message":["%script","","BEGIN ","    INSERT INTO CUSTOMER_INSURANCE_LTV_NOISE","    SELECT * FROM CUSTOMER_INSURANCE_LTV ","    WHERE ORA_HASH(CUSTOMER_ID, 13, 10) = 0","    FETCH FIRST 1000 ROWS ONLY;","    COMMIT;","END;"],"enabled":true,"result":{"startTime":1737138375250,"interpreter":"script.low","endTime":1737138376373,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For customer id  < 'CU2', change INCOME, BANK_FUNDS, CREDIT_BALANCE, MARITAL_STATUS to null ","message":["%sql","","UPDATE  CUSTOMER_INSURANCE_LTV_NOISE","SET INCOME = NULL, BANK_FUNDS = NULL, CREDIT_BALANCE = NULL, MARITAL_STATUS = NULL","WHERE CUSTOMER_ID < 'CU2';"],"enabled":true,"result":{"startTime":1737138376837,"interpreter":"sql.low","endTime":1737138377592,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For customer id < CU8, change the LAST_NAME to 'THANOS'","message":["%sql","","UPDATE  CUSTOMER_INSURANCE_LTV_NOISE","SET LAST_NAME = 'THANOS'","WHERE CUSTOMER_ID < 'CU8';"],"enabled":true,"result":{"startTime":1737138378180,"interpreter":"sql.low","endTime":1737138379016,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For customer id >= 'CU3' and  < 'CU4', use synonyms","message":["%script","","BEGIN","    UPDATE CUSTOMER_INSURANCE_LTV_NOISE","    SET MARITAL_STATUS = 'DIV'","    WHERE CUSTOMER_ID >= 'CU3' and CUSTOMER_ID < 'CU4'","          AND MARITAL_STATUS = 'DIVORCED';","          ","    COMMIT;","    ","    UPDATE CUSTOMER_INSURANCE_LTV_NOISE","    SET MARITAL_STATUS = 'M'","    WHERE CUSTOMER_ID >= 'CU3' and CUSTOMER_ID < 'CU4'","          AND MARITAL_STATUS = 'MARRIED';","          ","    COMMIT;","END;"],"enabled":true,"result":{"startTime":1737138379481,"interpreter":"script.low","endTime":1737138380033,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV_SQL` and the `CUSTOMER_INSURANCE_LTV_PY` tables ","","---"],"enabled":true,"result":{"startTime":1737138380501,"interpreter":"md.low","endTime":1737138380947,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv_sql-and-the-customer_insurance_ltv_py-tables\">Create the <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> and the <code>CUSTOMER_INSURANCE_LTV_PY<\/code> tables<\/h4>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create noise tables for SQL and Python","message":["%script","","CREATE TABLE CUSTOMER_INSURANCE_LTV_SQL AS SELECT * FROM CUSTOMER_INSURANCE_LTV_NOISE;","CREATE TABLE CUSTOMER_INSURANCE_LTV_PY AS SELECT * FROM CUSTOMER_INSURANCE_LTV_NOISE;"],"enabled":true,"result":{"startTime":1737138381425,"interpreter":"script.low","endTime":1737138384411,"results":[{"message":"\nTable CUSTOMER_INSURANCE_LTV_SQL created.\n\n\n---------------------------\n\nTable CUSTOMER_INSURANCE_LTV_PY created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737138384885,"interpreter":"md.low","endTime":1737138385400,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%sql"],"enabled":true,"result":{"startTime":1737138385871,"interpreter":"sql.low","endTime":1737138386333,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML Third-Party Packages - Environment Creation.dsnb b/machine-learning/notebooks-oml/r/OML Third-Party Packages - Environment Creation.dsnb
new file mode 100644
index 00000000..03576052
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML Third-Party Packages - Environment Creation.dsnb	
@@ -0,0 +1,2512 @@
+[
+  {
+    "name" : "OML Third-Party Packages - Environment Creation",
+    "description" : null,
+    "tags" : null,
+    "version" : "7",
+    "layout" : "jupyter",
+    "type" : "medium",
+    "snapshot" : false,
+    "isEditable" : true,
+    "isRunnable" : true,
+    "template" : null,
+    "templateConfig" : null,
+    "paragraphs" : [
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          " "
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : true,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988358548,
+          "endTime" : 1739988359568,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "# OML Third-Party Packages - Environment Creation",
+          "",
+          "Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. ",
+          "",
+          "Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the OML4Py Python, SQL, and REST APIs, and the OML4R R, SQL, and REST APIs.",
+          "",
+          "In this notebook, we demonstrate a typical workflow for third-party environment creation and package installation in OML notebooks. Section 1 contains common commands used by ADMIN while creating and testing conda environments. In section 2, the ADMIN user creates a conda environment, installs packages, and uploads the conda environment to an Object Storage bucket associated with the Autonomous Database. ",
+          "",
+          "In the template notebooks titled, *OML Third-Party Packages - R Environment Usage* and *OML Third-Party Packages - Python Environment Usage*, the OML user downloads and activates the environment, and uses the packages in their notebook session.",
+          "",
+          "---",
+          "Copyright (c) 2025 Oracle Corporation ",
+          "###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>"
+        ],
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+              "message" : "<h1 id=\"oml-third-party-packages---environment-creation\">OML Third-Party Packages - Environment Creation</h1>\n<p>Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages.</p>\n<p>Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the OML4Py Python, SQL, and REST APIs, and the OML4R R, SQL, and REST APIs.</p>\n<p>In this notebook, we demonstrate a typical workflow for third-party environment creation and package installation in OML notebooks. Section 1 contains common commands used by ADMIN while creating and testing conda environments. In section 2, the ADMIN user creates a conda environment, installs packages, and uploads the conda environment to an Object Storage bucket associated with the Autonomous Database.</p>\n<p>In the template notebooks titled, <em>OML Third-Party Packages - R Environment Usage</em> and <em>OML Third-Party Packages - Python Environment Usage</em>, the OML user downloads and activates the environment, and uses the packages in their notebook session.</p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation</p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n",
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+        "row" : 0,
+        "col" : 0,
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+        "title" : "Supported conda commands",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Refer to [Using Oracle Machine Learning on Autonomous Database](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-classic.html#GUID-5A206265-9EB0-4E49-A882-BFBF2DB5DB71) for a table of supported conda commands.",
+          "",
+          "This notebook reviews the following conda commands:",
+          "",
+          "-  `--help`",
+          "-  `install`",
+          "-  `info`",
+          "-  `search`",
+          "-  `env list`",
+          "-  `create`",
+          "-  `activate`",
+          "-  `list`",
+          "-  `install`",
+          "-  `uninstall`",
+          "-  `remove`",
+          "-  `list-saved-envs`",
+          "-  `upload`",
+          "-  `delete`"
+        ],
+        "selectedVisualization" : "html",
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+            {
+              "message" : "<p>Refer to <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-classic.html#GUID-5A206265-9EB0-4E49-A882-BFBF2DB5DB71\">Using Oracle Machine Learning on Autonomous Database</a> for a table of supported conda commands.</p>\n<p>This notebook reviews the following conda commands:</p>\n<ul>\n<li><code>--help</code></li>\n<li><code>install</code></li>\n<li><code>info</code></li>\n<li><code>search</code></li>\n<li><code>env list</code></li>\n<li><code>create</code></li>\n<li><code>activate</code></li>\n<li><code>list</code></li>\n<li><code>install</code></li>\n<li><code>uninstall</code></li>\n<li><code>remove</code></li>\n<li><code>list-saved-envs</code></li>\n<li><code>upload</code></li>\n<li><code>delete</code></li>\n</ul>\n",
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+        "message" : [
+          "%md",
+          "",
+          "### Section 1: Commands for Creating and Managing Environments"
+        ],
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+              "message" : "<h3 id=\"section-1-commands-for-creating-and-managing-environments\">Section 1: Commands for Creating and Managing Environments</h3>\n",
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+        "message" : [
+          "%md",
+          "---",
+          "### Conda Help",
+          "---",
+          "",
+          "To get help for conda commands, run the command name followed by the `--help` flag. The `conda` command is not run explicitly because the %conda interpreter provides the conda context."
+        ],
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+              "message" : "<p>To get help for conda commands, run the command name followed by the <code>--help</code> flag. The <code>conda</code> command is not run explicitly because the %conda interpreter provides the conda context.</p>\n",
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+        "width" : 12,
+        "title" : "Get help for all conda commands",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "--help"
+        ],
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+              "message" : "usage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n",
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+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Get help for a specific conda command",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "install --help"
+        ],
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+            {
+              "message" : "usage: conda install [-h] [--revision REVISION] [-n ENVIRONMENT | -p PATH]\n                     [-c CHANNEL] [--use-local] [--override-channels]\n                     [--repodata-fn REPODATA_FNS] [--experimental {jlap,lock}]\n                     [--no-lock] [--repodata-use-zst | --no-repodata-use-zst]\n                     [--strict-channel-priority] [--no-channel-priority]\n                     [--no-deps | --only-deps] [--no-pin] [--copy]\n                     [--no-shortcuts] [--shortcuts-only SHORTCUTS_ONLY] [-C]\n                     [-k] [--offline] [--json] [-v] [-q] [-d] [-y]\n                     [--download-only] [--show-channel-urls] [--file FILE]\n                     [--solver {classic,libmamba}] [--force-reinstall]\n                     [--freeze-installed | --update-deps | -S | --update-all | --update-specs]\n                     [-m] [--clobber] [--dev]\n                     [package_spec ...]\n\nInstall a list of packages into a specified conda environment.\n\nThis command accepts a list of package specifications (e.g, bitarray=0.8)\nand installs a set of packages consistent with those specifications and\ncompatible with the underlying environment. If full compatibility cannot\nbe assured, an error is reported and the environment is not changed.\n\nConda attempts to install the newest versions of the requested packages. To\naccomplish this, it may update some packages that are already installed, or\ninstall additional packages. To prevent existing packages from updating,\nuse the --freeze-installed option. This may force conda to install older\nversions of the requested packages, and it does not prevent additional\ndependency packages from being installed.\n\nIf you wish to skip dependency checking altogether, use the '--no-deps'\noption. This may result in an environment with incompatible packages, so\nthis option must be used with great caution.\n\nconda can also be called with a list of explicit conda package filenames\n(e.g. ./lxml-3.2.0-py27_0.tar.bz2). Using conda in this mode implies the\n--no-deps option, and should likewise be used with great caution. Explicit\nfilenames and package specifications cannot be mixed in a single command.\n\npositional arguments:\n  package_spec          List of packages to install or update in the conda\n                        environment.\n\noptions:\n  -h, --help            Show this help message and exit.\n  --revision REVISION   Revert to the specified REVISION.\n  --file FILE           Read package versions from the given file. Repeated\n                        file specifications can be passed (e.g. --file=file1\n                        --file=file2).\n  --dev                 Use `sys.executable -m conda` in wrapper scripts\n                        instead of CONDA_EXE. This is mainly for use during\n                        tests where we test new conda sources against old\n                        Python versions.\n\nTarget Environment Specification:\n  -n ENVIRONMENT, --name ENVIRONMENT\n                        Name of environment.\n  -p PATH, --prefix PATH\n                        Full path to environment location (i.e. prefix).\n\nChannel Customization:\n  -c CHANNEL, --channel CHANNEL\n                        Additional channel to search for packages. These are\n                        URLs searched in the order they are given (including\n                        local directories using the 'file://' syntax or simply\n                        a path like '/home/conda/mychan' or '../mychan').\n                        Then, the defaults or channels from .condarc are\n                        searched (unless --override-channels is given). You\n                        can use 'defaults' to get the default packages for\n                        conda. You can also use any name and the .condarc\n                        channel_alias value will be prepended. The default\n                        channel_alias is https://conda.anaconda.org/.\n  --use-local           Use locally built packages. Identical to '-c local'.\n  --override-channels   Do not search default or .condarc channels. Requires\n                        --channel.\n  --repodata-fn REPODATA_FNS\n                        Specify file name of repodata on the remote server\n                        where your channels are configured or within local\n                        backups. Conda will try whatever you specify, but will\n                        ultimately fall back to repodata.json if your specs\n                        are not satisfiable with what you specify here. This\n                        is used to employ repodata that is smaller and reduced\n                        in time scope. You may pass this flag more than once.\n                        Leftmost entries are tried first, and the fallback to\n                        repodata.json is added for you automatically. For more\n                        information, see conda config --describe repodata_fns.\n  --experimental {jlap,lock}\n                        jlap: Download incremental package index data from\n                        repodata.jlap; implies 'lock'. lock: use locking when\n                        reading, updating index (repodata.json) cache. Now\n                        enabled.\n  --no-lock             Disable locking when reading, updating index\n                        (repodata.json) cache.\n  --repodata-use-zst, --no-repodata-use-zst\n                        Check for/do not check for repodata.json.zst. Enabled\n                        by default.\n\nSolver Mode Modifiers:\n  --strict-channel-priority\n                        Packages in lower priority channels are not considered\n                        if a package with the same name appears in a higher\n                        priority channel.\n  --no-channel-priority\n                        Package version takes precedence over channel\n                        priority. Overrides the value given by `conda config\n                        --show channel_priority`.\n  --no-deps             Do not install, update, remove, or change\n                        dependencies. This WILL lead to broken environments\n                        and inconsistent behavior. Use at your own risk.\n  --only-deps           Only install dependencies.\n  --no-pin              Ignore pinned file.\n  --solver {classic,libmamba}\n                        Choose which solver backend to use.\n  --force-reinstall     Ensure that any user-requested package for the current\n                        operation is uninstalled and reinstalled, even if that\n                        package already exists in the environment.\n  --freeze-installed, --no-update-deps\n                        Do not update or change already-installed\n                        dependencies.\n  --update-deps         Update dependencies that have available updates.\n  -S, --satisfied-skip-solve\n                        Exit early and do not run the solver if the requested\n                        specs are satisfied. Also skips aggressive updates as\n                        configured by the 'aggressive_update_packages' config\n                        setting. Use 'conda info --describe\n                        aggressive_update_packages' to view your setting.\n                        --satisfied-skip-solve is similar to the default\n                        behavior of 'pip install'.\n  --update-all, --all   Update all installed packages in the environment.\n  --update-specs        Update based on provided specifications.\n\nPackage Linking and Install-time Options:\n  --copy                Install all packages using copies instead of hard- or\n                        soft-linking.\n  --no-shortcuts        Don't install start menu shortcuts\n  --shortcuts-only SHORTCUTS_ONLY\n                        Install shortcuts only for this package name. Can be\n                        used several times.\n  -m, --mkdir           Create the environment directory, if necessary.\n  --clobber             Allow clobbering (i.e. overwriting) of overlapping\n                        file paths within packages and suppress related\n                        warnings.\n\nNetworking Options:\n  -C, --use-index-cache\n                        Use cache of channel index files, even if it has\n                        expired. This is useful if you don't want conda to\n                        check whether a new version of the repodata file\n                        exists, which will save bandwidth.\n  -k, --insecure        Allow conda to perform \"insecure\" SSL connections and\n                        transfers. Equivalent to setting 'ssl_verify' to\n                        'false'.\n  --offline             Offline mode. Don't connect to the Internet.\n\nOutput, Prompt, and Flow Control Options:\n  --json                Report all output as json. Suitable for using conda\n                        programmatically.\n  -v, --verbose         Can be used multiple times. Once for detailed output,\n                        twice for INFO logging, thrice for DEBUG logging, four\n                        times for TRACE logging.\n  -q, --quiet           Do not display progress bar.\n  -d, --dry-run         Only display what would have been done.\n  -y, --yes             Sets any confirmation values to 'yes' automatically.\n                        Users will not be asked to confirm any adding,\n                        deleting, backups, etc.\n  --download-only       Solve an environment and ensure package caches are\n                        populated, but exit prior to unlinking and linking\n                        packages into the prefix.\n  --show-channel-urls   Show channel urls. Overrides the value given by `conda\n                        config --show show_channel_urls`.\n\nExamples:\n\nInstall the package 'scipy' into the currently-active environment::\n\n    conda install scipy\n\nInstall a list of packages into an environment, myenv::\n\n    conda install -n myenv scipy curl wheel\n\nInstall a specific version of 'python' into an environment, myenv::\n\n    conda install -p path/to/myenv python=3.11\n\n\n",
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+          "%md",
+          "---",
+          "### Conda Info",
+          "---",
+          "The `info` command provides information about the conda installation, including the conda version and available channels."
+        ],
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+          "%conda ",
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+              "message" : "\n     active environment : base\n    active env location : /opt/conda\n            shell level : 1\n       user config file : /u01/.condarc\n populated config files : /u01/.condarc\n          conda version : 24.1.2\n    conda-build version : not installed\n         python version : 3.12.1.final.0\n                 solver : libmamba (default)\n       virtual packages : __archspec=1=zen3\n                          __conda=24.1.2=0\n                          __glibc=2.17=0\n                          __linux=5.4.17=0\n                          __unix=0=0\n       base environment : /opt/conda  (read only)\n      conda av data dir : /opt/conda/etc/conda\n  conda av metadata url : None\n           channel URLs : https://repo.anaconda.com/pkgs/main/linux-64\n                          https://repo.anaconda.com/pkgs/main/noarch\n                          https://repo.anaconda.com/pkgs/r/linux-64\n                          https://repo.anaconda.com/pkgs/r/noarch\n          package cache : /u01/.conda/pkgs\n       envs directories : /u01/.conda/envs\n                          /opt/conda/envs\n               platform : linux-64\n             user-agent : conda/24.1.2 requests/2.32.3 CPython/3.12.1 Linux/5.4.17-2136.338.4.2.el7uek.x86_64 oracle/7.9 glibc/2.17 solver/libmamba conda-libmamba-solver/23.12.0 libmambapy/1.5.3\n                UID:GID : 1001:1001\n             netrc file : None\n           offline mode : False\n\n\n\n\n",
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+          "%md",
+          "",
+          "---",
+          "### Conda Search",
+          "---",
+          "The `search` command allows the user to search for packages and display associated information, including the package version and the channel where it resides."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988385886,
+          "endTime" : 1739988386329,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>The <code>search</code> command allows the user to search for packages and display associated information, including the package version and the channel where it resides.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Search for a specific package",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "search scikit-learn"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988386829,
+          "endTime" : 1739988390371,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Loading channels: ...working... done\n# Name                       Version           Build  Channel             \nscikit-learn                  0.19.0 py27_nomklh0ffebdf_2  pkgs/main           \nscikit-learn                  0.19.0  py27hd893acb_2  pkgs/main           \nscikit-learn                  0.19.0 py35_nomklh375dd1d_2  pkgs/main           \nscikit-learn                  0.19.0  py35h25e8076_2  pkgs/main           \nscikit-learn                  0.19.0 py36_nomklh41feb14_2  pkgs/main           \nscikit-learn                  0.19.0  py36h97ac459_2  pkgs/main           \nscikit-learn                  0.19.1 py27_nomklh6479e79_0  pkgs/main           \nscikit-learn                  0.19.1 py27_nomklh6cfcb94_0  pkgs/main           \nscikit-learn                  0.19.1  py27h445a80a_0  pkgs/main           \nscikit-learn                  0.19.1  py27hedc7406_0  pkgs/main           \nscikit-learn                  0.19.1 py35_nomklh26d41a3_0  pkgs/main           \nscikit-learn                  0.19.1  py35hbf1f462_0  pkgs/main           \nscikit-learn                  0.19.1 py36_nomklh27f7947_0  pkgs/main           \nscikit-learn                  0.19.1 py36_nomklh6cfcb94_0  pkgs/main           \nscikit-learn                  0.19.1  py36h7aa7ec6_0  pkgs/main           \nscikit-learn                  0.19.1  py36hedc7406_0  pkgs/main           \nscikit-learn                  0.19.1 py37_nomklh6cfcb94_0  pkgs/main           \nscikit-learn                  0.19.1  py37hedc7406_0  pkgs/main           \nscikit-learn                  0.19.2  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py27h4989274_0  pkgs/main           \nscikit-learn                  0.19.2  py35h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py35h4989274_0  pkgs/main           \nscikit-learn                  0.19.2  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py36h4989274_0  pkgs/main           \nscikit-learn                  0.19.2  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.19.2  py37h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py27h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py27h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py27h4989274_1  pkgs/main           \nscikit-learn                  0.20.0  py35h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py35h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py35h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py35h4989274_1  pkgs/main           \nscikit-learn                  0.20.0  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py36h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py36h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py36h4989274_1  pkgs/main           \nscikit-learn                  0.20.0  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.0  py37h22eb022_1  pkgs/main           \nscikit-learn                  0.20.0  py37h4989274_0  pkgs/main           \nscikit-learn                  0.20.0  py37h4989274_1  pkgs/main           \nscikit-learn                  0.20.1  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.1  py27h4989274_0  pkgs/main           \nscikit-learn                  0.20.1  py27hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.1  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.1  py36h4989274_0  pkgs/main           \nscikit-learn                  0.20.1  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.1  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.1  py37h4989274_0  pkgs/main           \nscikit-learn                  0.20.1  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.2  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.2  py27hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.2  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.2  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.2  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.2  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.3  py27h22eb022_0  pkgs/main           \nscikit-learn                  0.20.3  py27hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.3  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.20.3  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.20.3  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.20.3  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.1  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.21.1  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.1  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.21.1  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.1  py38h22eb022_0  pkgs/main           \nscikit-learn                  0.21.1  py38hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.2  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.21.2  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.2  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.21.2  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.3  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.21.3  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.21.3  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.21.3  py37hd81dba3_0  pkgs/main           \nscikit-learn                    0.22  py36h22eb022_0  pkgs/main           \nscikit-learn                    0.22  py36hd81dba3_0  pkgs/main           \nscikit-learn                    0.22  py37h22eb022_0  pkgs/main           \nscikit-learn                    0.22  py37hd81dba3_0  pkgs/main           \nscikit-learn                    0.22  py38h22eb022_0  pkgs/main           \nscikit-learn                    0.22  py38hd81dba3_0  pkgs/main           \nscikit-learn                  0.22.1  py36h22eb022_0  pkgs/main           \nscikit-learn                  0.22.1  py36hd81dba3_0  pkgs/main           \nscikit-learn                  0.22.1  py37h22eb022_0  pkgs/main           \nscikit-learn                  0.22.1  py37hd81dba3_0  pkgs/main           \nscikit-learn                  0.22.1  py38h22eb022_0  pkgs/main           \nscikit-learn                  0.22.1  py38hd81dba3_0  pkgs/main           \nscikit-learn                  0.23.1  py36h423224d_0  pkgs/main           \nscikit-learn                  0.23.1  py36h7ea95a0_0  pkgs/main           \nscikit-learn                  0.23.1  py37h423224d_0  pkgs/main           \nscikit-learn                  0.23.1  py37h7ea95a0_0  pkgs/main           \nscikit-learn                  0.23.1  py38h423224d_0  pkgs/main           \nscikit-learn                  0.23.1  py38h7ea95a0_0  pkgs/main           \nscikit-learn                  0.23.2  py36h0573a6f_0  pkgs/main           \nscikit-learn                  0.23.2  py37h0573a6f_0  pkgs/main           \nscikit-learn                  0.23.2  py38h0573a6f_0  pkgs/main           \nscikit-learn                  0.23.2  py39ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.1  py36ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.1  py37ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.1  py38ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.1  py39ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.2  py36ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.2  py37ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.2  py38ha9443f7_0  pkgs/main           \nscikit-learn                  0.24.2  py39ha9443f7_0  pkgs/main           \nscikit-learn                   1.0.1 py310h00e6091_0  pkgs/main           \nscikit-learn                   1.0.1  py37h51133e4_0  pkgs/main           \nscikit-learn                   1.0.1  py38h51133e4_0  pkgs/main           \nscikit-learn                   1.0.1  py39h51133e4_0  pkgs/main           \nscikit-learn                   1.0.2  py37h51133e4_0  pkgs/main           \nscikit-learn                   1.0.2  py37h51133e4_1  pkgs/main           \nscikit-learn                   1.0.2  py38h51133e4_0  pkgs/main           \nscikit-learn                   1.0.2  py38h51133e4_1  pkgs/main           \nscikit-learn                   1.0.2  py39h51133e4_0  pkgs/main           \nscikit-learn                   1.0.2  py39h51133e4_1  pkgs/main           \nscikit-learn                   1.1.1 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.1  py38h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.1  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.2 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.2  py38h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.2  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.3 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.3 py310h6a678d5_1  pkgs/main           \nscikit-learn                   1.1.3 py311h6a678d5_1  pkgs/main           \nscikit-learn                   1.1.3  py38h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.3  py38h6a678d5_1  pkgs/main           \nscikit-learn                   1.1.3  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.1.3  py39h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.0 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.0 py310h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.0  py38h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.0  py38h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.0  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.0  py39h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.1 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.1 py311h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.1  py38h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.1  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.2 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.2 py310h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.2 py311h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.2 py311h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.2  py38h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.2  py38h6a678d5_1  pkgs/main           \nscikit-learn                   1.2.2  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.2.2  py39h6a678d5_1  pkgs/main           \nscikit-learn                   1.3.0 py310h1128e8f_0  pkgs/main           \nscikit-learn                   1.3.0 py310h1128e8f_1  pkgs/main           \nscikit-learn                   1.3.0 py311ha02d727_0  pkgs/main           \nscikit-learn                   1.3.0 py311ha02d727_1  pkgs/main           \nscikit-learn                   1.3.0 py312h526ad5a_2  pkgs/main           \nscikit-learn                   1.3.0  py38h1128e8f_0  pkgs/main           \nscikit-learn                   1.3.0  py38h1128e8f_1  pkgs/main           \nscikit-learn                   1.3.0  py39h1128e8f_0  pkgs/main           \nscikit-learn                   1.3.0  py39h1128e8f_1  pkgs/main           \nscikit-learn                   1.4.2 py310h1128e8f_1  pkgs/main           \nscikit-learn                   1.4.2 py311ha02d727_1  pkgs/main           \nscikit-learn                   1.4.2 py312h526ad5a_1  pkgs/main           \nscikit-learn                   1.4.2  py39h1128e8f_1  pkgs/main           \nscikit-learn                   1.5.1 py310h1128e8f_0  pkgs/main           \nscikit-learn                   1.5.1 py311ha02d727_0  pkgs/main           \nscikit-learn                   1.5.1 py312h526ad5a_0  pkgs/main           \nscikit-learn                   1.5.1  py39h1128e8f_0  pkgs/main           \nscikit-learn                   1.5.2 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.5.2 py311h6a678d5_0  pkgs/main           \nscikit-learn                   1.5.2 py312h6a678d5_0  pkgs/main           \nscikit-learn                   1.5.2 py313h6a678d5_0  pkgs/main           \nscikit-learn                   1.5.2  py39h6a678d5_0  pkgs/main           \nscikit-learn                   1.6.1 py310h6a678d5_0  pkgs/main           \nscikit-learn                   1.6.1 py311h6a678d5_0  pkgs/main           \nscikit-learn                   1.6.1 py312h6a678d5_0  pkgs/main           \nscikit-learn                   1.6.1 py313h6a678d5_0  pkgs/main           \nscikit-learn                   1.6.1  py39h6a678d5_0  pkgs/main           \n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Search for packages containing 'scikit' in the package name",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "search '*scikit*'"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988390852,
+          "endTime" : 1739988397641,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Loading channels: ...working... done\n# Name                       Version           Build  Channel             \nscikit-bio                     0.5.2  py35h3010b51_0  pkgs/main           \nscikit-bio                     0.5.2  py36h3010b51_0  pkgs/main           \nscikit-bio                     0.5.4  py35hdd07704_0  pkgs/main           \nscikit-bio                     0.5.4  py36hdd07704_0  pkgs/main           \nscikit-bio                     0.5.6  py36h6323ea4_0  pkgs/main           \nscikit-bio                     0.5.6  py37h6323ea4_0  pkgs/main           \nscikit-bio                     0.5.6  py38h6323ea4_0  pkgs/main           \nscikit-bio                     0.5.6  py39h6323ea4_0  pkgs/main           \nscikit-build                  0.11.1 py310h295c915_2  pkgs/main           \nscikit-build                  0.11.1  py36h2531618_2  pkgs/main           \nscikit-build                  0.11.1  py37h2531618_2  pkgs/main           \nscikit-build                  0.11.1  py38h2531618_2  pkgs/main           \nscikit-build                  0.11.1  py39h2531618_2  pkgs/main           \nscikit-build                  0.15.0 py310h6a678d5_0  pkgs/main           \nscikit-build                  0.15.0 py311h6a678d5_0  pkgs/main           \nscikit-build                  0.15.0 py312h6a678d5_0  pkgs/main           \nscikit-build                  0.15.0  py37h6a678d5_0  pkgs/main           \nscikit-build                  0.15.0  py38h6a678d5_0  pkgs/main           \nscikit-build                  0.15.0  py39h6a678d5_0  pkgs/main           \nscikit-build                  0.18.1 py310h6a678d5_0  pkgs/main           \nscikit-build                  0.18.1 py311h6a678d5_0  pkgs/main           \nscikit-build                  0.18.1 py312h6a678d5_0  pkgs/main           \nscikit-build                  0.18.1 py313h6a678d5_0  pkgs/main           \nscikit-build                  0.18.1  py39h6a678d5_0  pkgs/main           \nscikit-build-core              0.6.1 py310h1128e8f_0  pkgs/main           \nscikit-build-core              0.6.1 py310h1128e8f_1  pkgs/main           \nscikit-build-core              0.6.1 py311ha02d727_0  pkgs/main           \nscikit-build-core              0.6.1 py311ha02d727_1  pkgs/main           \nscikit-build-core              0.6.1 py312h526ad5a_1  pkgs/main           \nscikit-build-core              0.6.1  py38h1128e8f_0  pkgs/main           \nscikit-build-core              0.6.1  py38h1128e8f_1  pkgs/main           \nscikit-build-core              0.6.1  py39h1128e8f_0  pkgs/main           \nscikit-build-core              0.6.1  py39h1128e8f_1  pkgs/main           \nscikit-build-core             0.10.1 py310h1128e8f_0  pkgs/main           \nscikit-build-core             0.10.1 py311ha02d727_0  pkgs/main           \nscikit-build-core             0.10.1 py312h526ad5a_0  pkgs/main           \nscikit-build-core             0.10.1 py313h06d7b56_0  pkgs/main           \nscikit-build-core             0.10.1  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+          "%md",
+          "",
+          "### Enhanced Conda Commands",
+          "",
+          "A set of enhanced conda commands in the conda environment lifecycle management package `env-lcm` supports the management of environments saved to Object Storage, including uploading, downloading, listing, and deleting available environments."
+        ],
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+              "message" : "<h3 id=\"enhanced-conda-commands\">Enhanced Conda Commands</h3>\n<p>A set of enhanced conda commands in the conda environment lifecycle management package <code>env-lcm</code> supports the management of environments saved to Object Storage, including uploading, downloading, listing, and deleting available environments.</p>\n",
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+        "title" : "Help for conda lifecycle environment commands",
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+          "%conda",
+          "",
+          "env-lcm --help "
+        ],
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+            {
+              "message" : "Usage: conda-env-lcm [OPTIONS] COMMAND [ARGS]...\n\n  ADB-S Command Line Interface (CLI) to manage persistence of conda\n  environments\n\nOptions:\n  -v, --version  Show the version and exit.\n  --help         Show this message and exit.\n\nCommands:\n  delete           Delete a saved conda environment\n  download         Download a saved conda environment\n  import           Create or update a conda environment from saved metadata\n  list-local-envs  List locally available environments for use\n  list-saved-envs  List saved conda environments\n  upload           Save conda environment for later use\n\n\n",
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+            }
+          ],
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+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "### Creating Conda Environments",
+          "",
+          "The ADMIN user has the permissions to create conda environments and install packages.",
+          "",
+          "Start by listing the environments available by default. Conda contains default environments with some core system libraries and conda dependencies. The active environment is marked with an asterisk (*)."
+        ],
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+            {
+              "message" : "<h3 id=\"creating-conda-environments\">Creating Conda Environments</h3>\n<p>The ADMIN user has the permissions to create conda environments and install packages.</p>\n<p>Start by listing the environments available by default. Conda contains default environments with some core system libraries and conda dependencies. The active environment is marked with an asterisk (*).</p>\n",
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+          "%conda",
+          "",
+          "env list"
+        ],
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "# conda environments:\n#\nbase                  *  /opt/conda\n\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
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+        "width" : 12,
+        "title" : "Installing and removing packges from conda environments",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "---",
+          "### Create a Conda Environment",
+          "---",
+          "",
+          "This section demonstrates creating and installing packages to a conda environment, then removes the environment. We illustrate commonly used options available for environment creation and testing. The environment exists for the duration of the notebook session and does not persist between sessions unless it is saved to Object Storage. For instructions that include both creating and persisting an environment for OML users, refer to the section 2, *Create a Conda Environment and Upload to Object Storage*.",
+          "",
+          "As ADMIN user:",
+          "",
+          "- Use the `create` command to create an environment and install the Python *keras* package. ",
+          "- Verify that the new environment is created, and activate the environment.",
+          "- Install, then uninstall an additional Python package into the environment, *pytorch*.",
+          "- Deactivate and remove the environment.",
+          "",
+          "Notes:",
+          "",
+          "- When conda installs a package into an environment it also installs any required dependencies. While we are demonstrating that it's possible to install packages to an existing environment, it is a best practice to install all the packages that you want in a specific environment at the same time to avoid package dependency conflicts.",
+          "",
+          "- The ADMIN user can access the conda environment from Python and R, but does not have the capability to run embedded Python and R execution commands.",
+          "",
+          "For help with the conda `create` command, enter `create --help` in a %conda paragraph."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
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+          "interpreter" : "md.medium",
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>This section demonstrates creating and installing packages to a conda environment, then removes the environment. We illustrate commonly used options available for environment creation and testing. The environment exists for the duration of the notebook session and does not persist between sessions unless it is saved to Object Storage. For instructions that include both creating and persisting an environment for OML users, refer to the section 2, <em>Create a Conda Environment and Upload to Object Storage</em>.</p>\n<p>As ADMIN user:</p>\n<ul>\n<li>Use the <code>create</code> command to create an environment and install the Python <em>keras</em> package.</li>\n<li>Verify that the new environment is created, and activate the environment.</li>\n<li>Install, then uninstall an additional Python package into the environment, <em>pytorch</em>.</li>\n<li>Deactivate and remove the environment.</li>\n</ul>\n<p>Notes:</p>\n<ul>\n<li>\n<p>When conda installs a package into an environment it also installs any required dependencies. While we are demonstrating that it's possible to install packages to an existing environment, it is a best practice to install all the packages that you want in a specific environment at the same time to avoid package dependency conflicts.</p>\n</li>\n<li>\n<p>The ADMIN user can access the conda environment from Python and R, but does not have the capability to run embedded Python and R execution commands.</p>\n</li>\n</ul>\n<p>For help with the conda <code>create</code> command, enter <code>create --help</code> in a %conda paragraph.</p>\n",
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+        "title" : "Create conda environment",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Create conda environment called *myenv* with Python 3.12 for OML4Py compatibility and install the *keras* package."
+        ],
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+            {
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+          ],
+          "forms" : "[]"
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+          "%conda ",
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+          "create -n myenv python=3.12.6 keras "
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+              "message" : "Channels:\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/myenv\n\n  added / updated specs:\n    - keras\n    - python=3.12.6\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _libgcc_mutex-0.1          |             main           3 KB\n    _openmp_mutex-5.1          |            1_gnu          21 KB\n    absl-py-2.1.0              |  py312h06a4308_0         239 KB\n    blas-1.0                   |              mkl           6 KB\n    bzip2-1.0.8                |       h5eee18b_6         262 KB\n    c-ares-1.19.1              |       h5eee18b_0         118 KB\n    ca-certificates-2024.12.31 |       h06a4308_0         128 KB\n    expat-2.6.4                |       h6a678d5_0         180 KB\n    h5py-3.12.1                |  py312h5842655_1         1.4 MB\n    hdf5-1.14.5                |       h2b7332f_2         5.9 MB\n    intel-openmp-2023.1.0      |   hdb19cb5_46306        17.2 MB\n    keras-3.6.0                |  py312h06a4308_0         2.9 MB\n    krb5-1.20.1                |       h143b758_1         1.3 MB\n    ld_impl_linux-64-2.40      |       h12ee557_0         710 KB\n    libcurl-8.12.0             |       hc9e6f67_0         468 KB\n    libedit-3.1.20230828       |       h5eee18b_0         179 KB\n    libev-4.33                 |       h7f8727e_1         111 KB\n    libffi-3.4.4               |       h6a678d5_1         141 KB\n    libgcc-ng-11.2.0           |       h1234567_1         5.3 MB\n    libgfortran-ng-11.2.0      |       h00389a5_1          20 KB\n    libgfortran5-11.2.0        |       h1234567_1         2.0 MB\n    libgomp-11.2.0             |       h1234567_1         474 KB\n    libnghttp2-1.57.0          |       h2d74bed_0         674 KB\n    libssh2-1.11.1             |       h251f7ec_0         308 KB\n    libstdcxx-ng-11.2.0        |       h1234567_1         4.7 MB\n    libuuid-1.41.5             |       h5eee18b_0          27 KB\n    lz4-c-1.9.4                |       h6a678d5_1         156 KB\n    markdown-it-py-2.2.0       |  py312h06a4308_1         134 KB\n    mdurl-0.1.0                |  py312h06a4308_0          22 KB\n    mkl-2023.1.0               |   h213fc3f_46344       171.5 MB\n    mkl-service-2.4.0          |  py312h5eee18b_2          67 KB\n    mkl_fft-1.3.11             |  py312h5eee18b_0         205 KB\n    mkl_random-1.2.8           |  py312h526ad5a_0         324 KB\n    ml_dtypes-0.5.0            |  py312h6a678d5_0         302 KB\n    namex-0.0.7                |  py312h06a4308_0          15 KB\n    ncurses-6.4                |       h6a678d5_0         914 KB\n    numpy-2.0.1                |  py312hc5e2394_1          11 KB\n    numpy-base-2.0.1           |  py312h0da6c21_1         8.5 MB\n    openssl-3.0.15             |       h5eee18b_0         5.2 MB\n    optree-0.12.1              |  py312hdb19cb5_0         326 KB\n    packaging-24.2             |  py312h06a4308_0         195 KB\n    pip-25.0                   |  py312h06a4308_0         2.8 MB\n    pygments-2.15.1            |  py312h06a4308_1         1.7 MB\n    python-3.12.6              |       h5148396_1        34.6 MB\n    readline-8.2               |       h5eee18b_0         357 KB\n    rich-13.9.4                |  py312h06a4308_0         615 KB\n    setuptools-75.8.0          |  py312h06a4308_0         2.2 MB\n    sqlite-3.45.3              |       h5eee18b_0         1.2 MB\n    tbb-2021.8.0               |       hdb19cb5_0         1.6 MB\n    tk-8.6.14                  |       h39e8969_0         3.4 MB\n    typing-extensions-4.12.2   |  py312h06a4308_0           9 KB\n    typing_extensions-4.12.2   |  py312h06a4308_0          79 KB\n    tzdata-2025a               |       h04d1e81_0         117 KB\n    wheel-0.45.1               |  py312h06a4308_0         147 KB\n    xz-5.6.4                   |       h5eee18b_1         567 KB\n    zlib-1.2.13                |       h5eee18b_1         111 KB\n    zstd-1.5.6                 |       hc292b87_0         664 KB\n    ------------------------------------------------------------\n                                           Total:       282.8 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main \n  _openmp_mutex      pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu \n  absl-py            pkgs/main/linux-64::absl-py-2.1.0-py312h06a4308_0 \n  blas               pkgs/main/linux-64::blas-1.0-mkl \n  bzip2              pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6 \n  c-ares             pkgs/main/linux-64::c-ares-1.19.1-h5eee18b_0 \n  ca-certificates    pkgs/main/linux-64::ca-certificates-2024.12.31-h06a4308_0 \n  expat              pkgs/main/linux-64::expat-2.6.4-h6a678d5_0 \n  h5py               pkgs/main/linux-64::h5py-3.12.1-py312h5842655_1 \n  hdf5               pkgs/main/linux-64::hdf5-1.14.5-h2b7332f_2 \n  intel-openmp       pkgs/main/linux-64::intel-openmp-2023.1.0-hdb19cb5_46306 \n  keras              pkgs/main/linux-64::keras-3.6.0-py312h06a4308_0 \n  krb5               pkgs/main/linux-64::krb5-1.20.1-h143b758_1 \n  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0 \n  libcurl            pkgs/main/linux-64::libcurl-8.12.0-hc9e6f67_0 \n  libedit            pkgs/main/linux-64::libedit-3.1.20230828-h5eee18b_0 \n  libev              pkgs/main/linux-64::libev-4.33-h7f8727e_1 \n  libffi             pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1 \n  libgcc-ng          pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 \n  libgfortran-ng     pkgs/main/linux-64::libgfortran-ng-11.2.0-h00389a5_1 \n  libgfortran5       pkgs/main/linux-64::libgfortran5-11.2.0-h1234567_1 \n  libgomp            pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 \n  libnghttp2         pkgs/main/linux-64::libnghttp2-1.57.0-h2d74bed_0 \n  libssh2            pkgs/main/linux-64::libssh2-1.11.1-h251f7ec_0 \n  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 \n  libuuid            pkgs/main/linux-64::libuuid-1.41.5-h5eee18b_0 \n  lz4-c              pkgs/main/linux-64::lz4-c-1.9.4-h6a678d5_1 \n  markdown-it-py     pkgs/main/linux-64::markdown-it-py-2.2.0-py312h06a4308_1 \n  mdurl              pkgs/main/linux-64::mdurl-0.1.0-py312h06a4308_0 \n  mkl                pkgs/main/linux-64::mkl-2023.1.0-h213fc3f_46344 \n  mkl-service        pkgs/main/linux-64::mkl-service-2.4.0-py312h5eee18b_2 \n  mkl_fft            pkgs/main/linux-64::mkl_fft-1.3.11-py312h5eee18b_0 \n  mkl_random         pkgs/main/linux-64::mkl_random-1.2.8-py312h526ad5a_0 \n  ml_dtypes          pkgs/main/linux-64::ml_dtypes-0.5.0-py312h6a678d5_0 \n  namex              pkgs/main/linux-64::namex-0.0.7-py312h06a4308_0 \n  ncurses            pkgs/main/linux-64::ncurses-6.4-h6a678d5_0 \n  numpy              pkgs/main/linux-64::numpy-2.0.1-py312hc5e2394_1 \n  numpy-base         pkgs/main/linux-64::numpy-base-2.0.1-py312h0da6c21_1 \n  openssl            pkgs/main/linux-64::openssl-3.0.15-h5eee18b_0 \n  optree             pkgs/main/linux-64::optree-0.12.1-py312hdb19cb5_0 \n  packaging          pkgs/main/linux-64::packaging-24.2-py312h06a4308_0 \n  pip                pkgs/main/linux-64::pip-25.0-py312h06a4308_0 \n  pygments           pkgs/main/linux-64::pygments-2.15.1-py312h06a4308_1 \n  python             pkgs/main/linux-64::python-3.12.6-h5148396_1 \n  readline           pkgs/main/linux-64::readline-8.2-h5eee18b_0 \n  rich               pkgs/main/linux-64::rich-13.9.4-py312h06a4308_0 \n  setuptools         pkgs/main/linux-64::setuptools-75.8.0-py312h06a4308_0 \n  sqlite             pkgs/main/linux-64::sqlite-3.45.3-h5eee18b_0 \n  tbb                pkgs/main/linux-64::tbb-2021.8.0-hdb19cb5_0 \n  tk                 pkgs/main/linux-64::tk-8.6.14-h39e8969_0 \n  typing-extensions  pkgs/main/linux-64::typing-extensions-4.12.2-py312h06a4308_0 \n  typing_extensions  pkgs/main/linux-64::typing_extensions-4.12.2-py312h06a4308_0 \n  tzdata             pkgs/main/noarch::tzdata-2025a-h04d1e81_0 \n  wheel              pkgs/main/linux-64::wheel-0.45.1-py312h06a4308_0 \n  xz                 pkgs/main/linux-64::xz-5.6.4-h5eee18b_1 \n  zlib               pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1 \n  zstd               pkgs/main/linux-64::zstd-1.5.6-hc292b87_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate myenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\n",
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+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Verify the *myenv* environment is in the list of environments. The asterisk (*) indicates active environments. The new environment is created but not activated."
+        ],
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+        "visualizationConfig" : null,
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+              "message" : "<p>Verify the <em>myenv</em> environment is in the list of environments. The asterisk (*) indicates active environments. The new environment is created but not activated.</p>\n",
+              "type" : "HTML"
+            }
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+          "forms" : "[]"
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+          "results" : [
+            {
+              "message" : "# conda environments:\n#\nbase                  *  /opt/conda\nmyenv                    /u01/.conda/envs/myenv\n\n\n\n",
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+        "width" : 12,
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+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Activate the *myenv* environment and list the environments to verify the activation. The asterisk (*) next to the environment name confirms the activation."
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+          "results" : [
+            {
+              "message" : "<p>Activate the <em>myenv</em> environment and list the environments to verify the activation. The asterisk (*) next to the environment name confirms the activation.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+          "%conda",
+          "",
+          "activate myenv"
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nConda environment 'myenv' activated\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Show the activated environment in the environment listing",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "env list"
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+        "selectedVisualization" : "raw",
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+          "interpreter" : "conda.conda_medium",
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+          "results" : [
+            {
+              "message" : "# conda environments:\n#\nbase                     /opt/conda\nmyenv                 *  /u01/.conda/envs/myenv\n\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
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+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "### Installing and Uninstalling Libraries",
+          "",
+          "The ADMIN user can install and uninstall libraries into an environment using the `install` and `uninstall` commands.",
+          "",
+          "For help with the conda `install` and `uninstall` commmands, type `install --help` and `uninstall --help` in a %conda paragraph."
+        ],
+        "selectedVisualization" : "html",
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+        "title" : "Install additional packages",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Install the *pytorch* package into the activated *myenv* environment. Note, to avoid dependency conflicts, install all packages when the environment is built."
+        ],
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+          "install pytorch -c pytorch"
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+          "status" : "SUCCESS",
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+            {
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cuda-version-12.4          |       hbda6634_3          19 KB\n    filelock-3.13.1            |  py312h06a4308_0          24 KB\n    gmp-6.3.0                  |       h6a678d5_0         608 KB\n    gmpy2-2.2.1                |  py312h5eee18b_0         258 KB\n    jinja2-3.1.5               |  py312h06a4308_0         352 KB\n    libcublas-12.4.5.8         |       h99ab3db_1       247.6 MB\n    libcufft-11.2.1.3          |       h99ab3db_1       174.5 MB\n    libcufile-1.9.1.3          |       h99ab3db_1         995 KB\n    libcurand-10.3.5.147       |       h99ab3db_1        42.4 MB\n    libcusolver-11.6.1.9       |       h99ab3db_1        83.5 MB\n    libcusparse-12.3.1.170     |       h99ab3db_1       120.0 MB\n    libnpp-12.2.5.30           |       h99ab3db_1       100.5 MB\n    libnvfatbin-12.4.127       |       h7934f7d_2         857 KB\n    libnvjitlink-12.4.127      |       h99ab3db_1        17.4 MB\n    libnvjpeg-12.3.1.117       |       h6a678d5_1         2.6 MB\n    llvm-openmp-14.0.6         |       h9e868ea_0         4.4 MB\n    markupsafe-3.0.2           |  py312h5eee18b_0          26 KB\n    mpc-1.3.1                  |       h5eee18b_0         129 KB\n    mpfr-4.2.1                 |       h5eee18b_0         821 KB\n    mpmath-1.3.0               |  py312h06a4308_0         988 KB\n    networkx-3.4.2             |  py312h06a4308_0         3.1 MB\n    ocl-icd-2.3.2              |       h5eee18b_1         136 KB\n    pytorch-2.5.1              |py3.12_cuda12.4_cudnn9.1.0_0        1.46 GB  pytorch\n    pytorch-cuda-12.4          |       hc786d27_7           7 KB  pytorch\n    pytorch-mutex-1.0          |             cuda           3 KB  pytorch\n    pyyaml-6.0.2               |  py312h5eee18b_0         217 KB\n    sympy-1.13.3               |  py312h06a4308_1        15.0 MB\n    torchtriton-3.1.0          |            py312       233.6 MB  pytorch\n    yaml-0.2.5                 |       h7b6447c_0          75 KB\n    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           h251f7ec_0  \nlibstdcxx-ng              11.2.0               h1234567_1  \nlibuuid                   1.41.5               h5eee18b_0  \nllvm-openmp               14.0.6               h9e868ea_0  \nlz4-c                     1.9.4                h6a678d5_1  \nmarkdown-it-py            2.2.0           py312h06a4308_1  \nmarkupsafe                3.0.2           py312h5eee18b_0  \nmdurl                     0.1.0           py312h06a4308_0  \nmkl                       2023.1.0         h213fc3f_46344  \nmkl-service               2.4.0           py312h5eee18b_2  \nmkl_fft                   1.3.11          py312h5eee18b_0  \nmkl_random                1.2.8           py312h526ad5a_0  \nml_dtypes                 0.5.0           py312h6a678d5_0  \nmpc                       1.3.1                h5eee18b_0  \nmpfr                      4.2.1                h5eee18b_0  \nmpmath                    1.3.0           py312h06a4308_0  \nnamex                     0.0.7           py312h06a4308_0  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+              "message" : "# packages in environment at /u01/.conda/envs/myenv:\n#\n# Name                    Version                   Build  Channel\n_libgcc_mutex             0.1                        main  \n_openmp_mutex             5.1                       1_gnu  \nabsl-py                   2.1.0           py312h06a4308_0  \nblas                      1.0                         mkl  \nbzip2                     1.0.8                h5eee18b_6  \nc-ares                    1.19.1               h5eee18b_0  \nca-certificates           2024.12.31           h06a4308_0  \nexpat                     2.6.4                h6a678d5_0  \nh5py                      3.12.1          py312h5842655_1  \nhdf5                      1.14.5               h2b7332f_2  \nintel-openmp              2023.1.0         hdb19cb5_46306  \nkeras                     3.6.0           py312h06a4308_0  \nkrb5                      1.20.1               h143b758_1  \nld_impl_linux-64          2.40                 h12ee557_0  \nlibcurl                   8.12.0               hc9e6f67_0  \nlibedit                   3.1.20230828         h5eee18b_0  \nlibev                     4.33                 h7f8727e_1  \nlibffi                    3.4.4                h6a678d5_1  \nlibgcc-ng                 11.2.0               h1234567_1  \nlibgfortran-ng            11.2.0               h00389a5_1  \nlibgfortran5              11.2.0               h1234567_1  \nlibgomp                   11.2.0               h1234567_1  \nlibnghttp2                1.57.0               h2d74bed_0  \nlibssh2                   1.11.1               h251f7ec_0  \nlibstdcxx-ng              11.2.0               h1234567_1  \nlibuuid                   1.41.5               h5eee18b_0  \nlz4-c                     1.9.4                h6a678d5_1  \nmarkdown-it-py            2.2.0           py312h06a4308_1  \nmdurl                     0.1.0           py312h06a4308_0  \nmkl                       2023.1.0         h213fc3f_46344  \nmkl-service               2.4.0           py312h5eee18b_2  \nmkl_fft                   1.3.11          py312h5eee18b_0  \nmkl_random                1.2.8           py312h526ad5a_0  \nml_dtypes                 0.5.0           py312h6a678d5_0  \nnamex                     0.0.7           py312h06a4308_0  \nncurses                   6.4                  h6a678d5_0  \nnumpy                     2.0.1           py312hc5e2394_1  \nnumpy-base                2.0.1           py312h0da6c21_1  \nopenssl                   3.0.15               h5eee18b_0  \noptree                    0.12.1          py312hdb19cb5_0  \npackaging                 24.2            py312h06a4308_0  \npip                       25.0            py312h06a4308_0  \npygments                  2.15.1          py312h06a4308_1  \npython                    3.12.6               h5148396_1  \nreadline                  8.2                  h5eee18b_0  \nrich                      13.9.4          py312h06a4308_0  \nsetuptools                75.8.0          py312h06a4308_0  \nsqlite                    3.45.3               h5eee18b_0  \ntbb                       2021.8.0             hdb19cb5_0  \ntk                        8.6.14               h39e8969_0  \ntyping-extensions         4.12.2          py312h06a4308_0  \ntyping_extensions         4.12.2          py312h06a4308_0  \ntzdata                    2025a                h04d1e81_0  \nwheel                     0.45.1          py312h06a4308_0  \nxz                        5.6.4                h5eee18b_1  \nzlib                      1.2.13               h5eee18b_1  \nzstd                      1.5.6                hc292b87_0  \n\n\n",
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+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "### Removing Environments",
+          "---",
+          "If you don't intend to upload the environment to Object Storage for the OML users in the database, you can simply exit the notebook session and it will go out of scope.  Alternatively, it can be explicitly removed using the `env remove` command. Remove the *myenv* environment and verify it was removed. A best practice is to deactivate the environment prior to removal.",
+          "",
+          "For help on the `env remove` command, type `env remove --help` in the %conda interpreter."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>If you don't intend to upload the environment to Object Storage for the OML users in the database, you can simply exit the notebook session and it will go out of scope.  Alternatively, it can be explicitly removed using the <code>env remove</code> command. Remove the <em>myenv</em> environment and verify it was removed. A best practice is to deactivate the environment prior to removal.</p>\n<p>For help on the <code>env remove</code> command, type <code>env remove --help</code> in the %conda interpreter.</p>\n",
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+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Deactivate the environment ",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "deactivate"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nConda environment 'conda' deactivated\n\n",
+              "type" : "TEXT"
+            }
+          ],
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+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Remove the environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "env remove -n myenv",
+          "",
+          "env list"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nRemove all packages in environment /u01/.conda/envs/myenv:\n\n\n\nusage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n# conda environments:\n#\nbase                  *  /opt/conda\n\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "",
+          "### Specify Packages for Installation",
+          "",
+          "",
+          "",
+          "#### Install Packages from the `conda-forge` Channel",
+          "* **Conda channels** are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The `conda` command searches a set of channels. By default, packages are automatically downloaded and updated from the [`default` channel] (https://repo.anaconda.com/pkgs/).",
+          "* The **conda-forge** channel is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. We use the [`conda-forge` channel] (https://conda-forge.org/), a community channel made up of thousands of contributors, in the following examples.",
+          "",
+          "",
+          "#### Install a Specific Version of a Package",
+          "* To install a specific version of a package, use `<package_name>=<version>`"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "hideInIFrame" : false,
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+          "startTime" : 1739988672882,
+          "endTime" : 1739988673332,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"specify-packages-for-installation\">Specify Packages for Installation</h3>\n<h4 id=\"install-packages-from-the-conda-forge-channel\">Install Packages from the <code>conda-forge</code> Channel</h4>\n<ul>\n<li><strong>Conda channels</strong> are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The <code>conda</code> command searches a set of channels. By default, packages are automatically downloaded and updated from the [<code>default</code> channel] (<a href=\"https://repo.anaconda.com/pkgs/\">https://repo.anaconda.com/pkgs/</a>).</li>\n<li>The <strong>conda-forge</strong> channel is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. We use the [<code>conda-forge</code> channel] (<a href=\"https://conda-forge.org/\">https://conda-forge.org/</a>), a community channel made up of thousands of contributors, in the following examples.</li>\n</ul>\n<h4 id=\"install-a-specific-version-of-a-package\">Install a Specific Version of a Package</h4>\n<ul>\n<li>To install a specific version of a package, use <code>&lt;package_name&gt;=&lt;version&gt;</code></li>\n</ul>\n",
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+            }
+          ],
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create an environment using conda-forge ",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "create -n mychannelenv -c conda-forge python=3.12.6",
+          "",
+          "activate mychannelenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+          "endTime" : 1739988701037,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Channels:\n - conda-forge\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/mychannelenv\n\n  added / updated specs:\n    - python=3.12.6\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n    bzip2-1.0.8                |       h4bc722e_7         247 KB  conda-forge\n    ca-certificates-2025.1.31  |       hbcca054_0         154 KB  conda-forge\n    ld_impl_linux-64-2.43      |       h712a8e2_2         654 KB  conda-forge\n    libexpat-2.6.4             |       h5888daf_0          72 KB  conda-forge\n    libffi-3.4.6               |       h2dba641_0          52 KB  conda-forge\n    libgcc-14.2.0              |       h77fa898_1         829 KB  conda-forge\n    libgcc-ng-14.2.0           |       h69a702a_1          53 KB  conda-forge\n    libgomp-14.2.0             |       h77fa898_1         450 KB  conda-forge\n    liblzma-5.6.4              |       hb9d3cd8_0         109 KB  conda-forge\n    liblzma-devel-5.6.4        |       hb9d3cd8_0         370 KB  conda-forge\n    libnsl-2.0.1               |       hd590300_0          33 KB  conda-forge\n    libsqlite-3.49.1           |       hee588c1_1         894 KB  conda-forge\n    libuuid-2.38.1             |       h0b41bf4_0          33 KB  conda-forge\n    libxcrypt-4.4.36           |       hd590300_1          98 KB  conda-forge\n    libzlib-1.3.1              |       hb9d3cd8_2          60 KB  conda-forge\n    ncurses-6.5                |       h2d0b736_3         871 KB  conda-forge\n    openssl-3.4.1              |       h7b32b05_0         2.8 MB  conda-forge\n    pip-25.0.1                 |     pyh8b19718_0         1.2 MB  conda-forge\n    python-3.12.6              |hc5c86c4_2_cpython        30.1 MB  conda-forge\n    readline-8.2               |       h8228510_1         275 KB  conda-forge\n    setuptools-75.8.0          |     pyhff2d567_0         757 KB  conda-forge\n    tk-8.6.13                  |noxft_h4845f30_101         3.2 MB  conda-forge\n    tzdata-2025a               |       h78e105d_0         120 KB  conda-forge\n    wheel-0.45.1               |     pyhd8ed1ab_1          61 KB  conda-forge\n    xz-5.6.4                   |       hbcc6ac9_0          23 KB  conda-forge\n    xz-gpl-tools-5.6.4         |       hbcc6ac9_0          33 KB  conda-forge\n    xz-tools-5.6.4             |       hb9d3cd8_0          88 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:        43.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_gnu \n  bzip2              conda-forge/linux-64::bzip2-1.0.8-h4bc722e_7 \n  ca-certificates    conda-forge/linux-64::ca-certificates-2025.1.31-hbcca054_0 \n  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.43-h712a8e2_2 \n  libexpat           conda-forge/linux-64::libexpat-2.6.4-h5888daf_0 \n  libffi             conda-forge/linux-64::libffi-3.4.6-h2dba641_0 \n  libgcc             conda-forge/linux-64::libgcc-14.2.0-h77fa898_1 \n  libgcc-ng          conda-forge/linux-64::libgcc-ng-14.2.0-h69a702a_1 \n  libgomp            conda-forge/linux-64::libgomp-14.2.0-h77fa898_1 \n  liblzma            conda-forge/linux-64::liblzma-5.6.4-hb9d3cd8_0 \n  liblzma-devel      conda-forge/linux-64::liblzma-devel-5.6.4-hb9d3cd8_0 \n  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0 \n  libsqlite          conda-forge/linux-64::libsqlite-3.49.1-hee588c1_1 \n  libuuid            conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0 \n  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 \n  libzlib            conda-forge/linux-64::libzlib-1.3.1-hb9d3cd8_2 \n  ncurses            conda-forge/linux-64::ncurses-6.5-h2d0b736_3 \n  openssl            conda-forge/linux-64::openssl-3.4.1-h7b32b05_0 \n  pip                conda-forge/noarch::pip-25.0.1-pyh8b19718_0 \n  python             conda-forge/linux-64::python-3.12.6-hc5c86c4_2_cpython \n  readline           conda-forge/linux-64::readline-8.2-h8228510_1 \n  setuptools         conda-forge/noarch::setuptools-75.8.0-pyhff2d567_0 \n  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101 \n  tzdata             conda-forge/noarch::tzdata-2025a-h78e105d_0 \n  wheel              conda-forge/noarch::wheel-0.45.1-pyhd8ed1ab_1 \n  xz                 conda-forge/linux-64::xz-5.6.4-hbcc6ac9_0 \n  xz-gpl-tools       conda-forge/linux-64::xz-gpl-tools-5.6.4-hbcc6ac9_0 \n  xz-tools           conda-forge/linux-64::xz-tools-5.6.4-hb9d3cd8_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate mychannelenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\nusage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n\nConda environment 'mychannelenv' activated\n\n",
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+        "title" : "Install a package from conda-forge by specifying the channel",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "install seaborn --channel conda-forge"
+        ],
+        "selectedVisualization" : "raw",
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+        "hideCode" : false,
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70 KB  conda-forge\n    lcms2-2.17                 |       h717163a_0         242 KB  conda-forge\n    lerc-4.0.0                 |       h27087fc_0         275 KB  conda-forge\n    libblas-3.9.0              |28_h59b9bed_openblas          16 KB  conda-forge\n    libbrotlicommon-1.1.0      |       hb9d3cd8_2          67 KB  conda-forge\n    libbrotlidec-1.1.0         |       hb9d3cd8_2          32 KB  conda-forge\n    libbrotlienc-1.1.0         |       hb9d3cd8_2         275 KB  conda-forge\n    libcblas-3.9.0             |28_he106b2a_openblas          16 KB  conda-forge\n    libdeflate-1.23            |       h4ddbbb0_0          71 KB  conda-forge\n    libgfortran-14.2.0         |       h69a702a_1          53 KB  conda-forge\n    libgfortran5-14.2.0        |       hd5240d6_1         1.4 MB  conda-forge\n    libjpeg-turbo-3.0.0        |       hd590300_1         604 KB  conda-forge\n    liblapack-3.9.0            |28_h7ac8fdf_openblas          16 KB  conda-forge\n    libopenblas-0.3.28         |pthreads_h94d23a6_1         5.3 MB  conda-forge\n    libpng-1.6.47              |       h943b412_0         282 KB  conda-forge\n    libstdcxx-14.2.0           |       hc0a3c3a_1         3.7 MB  conda-forge\n    libstdcxx-ng-14.2.0        |       h4852527_1          53 KB  conda-forge\n    libtiff-4.7.0              |       hd9ff511_3         418 KB  conda-forge\n    libwebp-base-1.5.0         |       h851e524_0         420 KB  conda-forge\n    libxcb-1.17.0              |       h8a09558_0         387 KB  conda-forge\n    matplotlib-base-3.10.0     |  py312hd3ec401_0         7.8 MB  conda-forge\n    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n    numpy-2.2.3                |  py312h72c5963_0         8.1 MB  conda-forge\n    openjpeg-2.5.3             |       h5fbd93e_0         335 KB  conda-forge\n    packaging-24.2             |     pyhd8ed1ab_2          59 KB  conda-forge\n    pandas-2.2.3               |  py312hf9745cd_1        14.7 MB  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pyhd8ed1ab_0          16 KB  conda-forge\n    statsmodels-0.14.4         |  py312hc0a28a1_0        11.5 MB  conda-forge\n    unicodedata2-16.0.0        |  py312h66e93f0_0         395 KB  conda-forge\n    xorg-libxau-1.0.12         |       hb9d3cd8_0          14 KB  conda-forge\n    xorg-libxdmcp-1.1.5        |       hb9d3cd8_0          19 KB  conda-forge\n    zstd-1.5.6                 |       ha6fb4c9_0         542 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:       119.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n  brotli             conda-forge/linux-64::brotli-1.1.0-hb9d3cd8_2 \n  brotli-bin         conda-forge/linux-64::brotli-bin-1.1.0-hb9d3cd8_2 \n  contourpy          conda-forge/linux-64::contourpy-1.3.1-py312h68727a3_0 \n  cycler             conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_1 \n  fonttools          conda-forge/linux-64::fonttools-4.56.0-py312h178313f_0 \n  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.8-py312h84d6215_0 \n  lcms2              conda-forge/linux-64::lcms2-2.17-h717163a_0 \n  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n  libblas            conda-forge/linux-64::libblas-3.9.0-28_h59b9bed_openblas \n  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.1.0-hb9d3cd8_2 \n  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.1.0-hb9d3cd8_2 \n  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.1.0-hb9d3cd8_2 \n  libcblas           conda-forge/linux-64::libcblas-3.9.0-28_he106b2a_openblas \n  libdeflate         conda-forge/linux-64::libdeflate-1.23-h4ddbbb0_0 \n  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_1 \n  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hd5240d6_1 \n  libjpeg-turbo      conda-forge/linux-64::libjpeg-turbo-3.0.0-hd590300_1 \n  liblapack          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statsmodels        conda-forge/linux-64::statsmodels-0.14.4-py312hc0a28a1_0 \n  unicodedata2       conda-forge/linux-64::unicodedata2-16.0.0-py312h66e93f0_0 \n  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.12-hb9d3cd8_0 \n  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 \n  zstd               conda-forge/linux-64::zstd-1.5.6-ha6fb4c9_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n\n\n",
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+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "install seaborn=0.13.2"
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+            {
+              "message" : "Channels:\n - defaults\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/mychannelenv\n\n  added / updated specs:\n    - seaborn=0.13.2\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    blas-2.128                 |         openblas          16 KB  conda-forge\n    blas-devel-3.9.0           |28_h1ea3ea9_openblas          16 KB  conda-forge\n    liblapacke-3.9.0           |28_he2f377e_openblas          16 KB  conda-forge\n    numpy-1.26.4               |  py312h2809609_0          10 KB\n    numpy-base-1.26.4          |  py312he1a6c75_0         7.7 MB\n    openblas-0.3.28            |pthreads_h6ec200e_1         5.5 MB  conda-forge\n    seaborn-0.13.2             |  py312h06a4308_0         723 KB\n    seaborn-base-0.13.2        |     pyhd8ed1ab_0         229 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:        14.1 MB\n\nThe following NEW packages will be INSTALLED:\n\n  blas               conda-forge/linux-64::blas-2.128-openblas \n  blas-devel         conda-forge/linux-64::blas-devel-3.9.0-28_h1ea3ea9_openblas \n  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-28_he2f377e_openblas \n  numpy-base         pkgs/main/linux-64::numpy-base-1.26.4-py312he1a6c75_0 \n  openblas           conda-forge/linux-64::openblas-0.3.28-pthreads_h6ec200e_1 \n\nThe following packages will be SUPERSEDED by a higher-priority channel:\n\n  numpy              conda-forge::numpy-2.2.3-py312h72c596~ --> pkgs/main::numpy-1.26.4-py312h2809609_0 \n  seaborn            conda-forge/noarch::seaborn-0.13.2-hd~ --> pkgs/main/linux-64::seaborn-0.13.2-py312h06a4308_0 \n\nThe following packages will be DOWNGRADED:\n\n  seaborn-base                          0.13.2-pyhd8ed1ab_3 --> 0.13.2-pyhd8ed1ab_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n\n\n",
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+        "message" : [
+          "%md",
+          "",
+          "",
+          "## Section 2: Create a Conda Environment and Upload to Object Storage",
+          "",
+          "",
+          "This section demonstrates creating two conda environments and uploading them to Object Storage. An environment saved in Object Storage can be used by all of the Autonomous Database users in the database, and will remain in Object Storage until ADMIN deletes it.",
+          "",
+          "As ADMIN user:",
+          "",
+          "- Create one environment for Python packages named *mypyenv*, and one for R packages named *myrenv*.",
+          "- Install the *tensorflow* and *seaborn* packages into the Python environment and the *forecast* and *ggplot2* packages into the R environment.",
+          "- Upload the environments to Object Storage. ",
+          "- Delete the environments from Object Storage. "
+        ],
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+              "message" : "<h2 id=\"section-2-create-a-conda-environment-and-upload-to-object-storage\">Section 2: Create a Conda Environment and Upload to Object Storage</h2>\n<p>This section demonstrates creating two conda environments and uploading them to Object Storage. An environment saved in Object Storage can be used by all of the Autonomous Database users in the database, and will remain in Object Storage until ADMIN deletes it.</p>\n<p>As ADMIN user:</p>\n<ul>\n<li>Create one environment for Python packages named <em>mypyenv</em>, and one for R packages named <em>myrenv</em>.</li>\n<li>Install the <em>tensorflow</em> and <em>seaborn</em> packages into the Python environment and the <em>forecast</em> and <em>ggplot2</em> packages into the R environment.</li>\n<li>Upload the environments to Object Storage.</li>\n<li>Delete the environments from Object Storage.</li>\n</ul>\n",
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+        "message" : [
+          "%md",
+          "",
+          "Create a conda environment named *mypyenv* with Python 3.12 for OML4Py compatibility and install the `tensorflow` and `seaborn` packages."
+        ],
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Create a conda environment named <em>mypyenv</em> with Python 3.12 for OML4Py compatibility and install the <code>tensorflow</code> and <code>seaborn</code> packages.</p>\n",
+              "type" : "HTML"
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create Python conda environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "create -n mypyenv -c conda-forge --strict-channel-priority python=3.12.6 tensorflow seaborn"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":500,\"lastColumns\":[],\"version\":1}}]",
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+              "message" : "Channels:\n - conda-forge\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/mypyenv\n\n  added / updated specs:\n    - python=3.12.6\n    - seaborn\n    - tensorflow\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge\n    absl-py-2.1.0              |     pyhd8ed1ab_1         105 KB  conda-forge\n    astunparse-1.6.3           |     pyhd8ed1ab_3          18 KB  conda-forge\n    brotli-python-1.1.0        |  py312h2ec8cdc_2         342 KB  conda-forge\n    c-ares-1.34.4              |       hb9d3cd8_0         201 KB  conda-forge\n    cached-property-1.5.2      |       hd8ed1ab_1           4 KB  conda-forge\n    cached_property-1.5.2      |     pyha770c72_1          11 KB  conda-forge\n    certifi-2025.1.31          |     pyhd8ed1ab_0         159 KB  conda-forge\n    cffi-1.17.1                |  py312h06ac9bb_0         288 KB  conda-forge\n    charset-normalizer-3.4.1   |     pyhd8ed1ab_0          46 KB  conda-forge\n    flatbuffers-24.12.23       |       h8f4948b_0         1.5 MB  conda-forge\n    gast-0.6.0                 |     pyhd8ed1ab_0          24 KB  conda-forge\n    giflib-5.2.2               |       hd590300_0          75 KB  conda-forge\n    google-pasta-0.2.0         |     pyhd8ed1ab_2          48 KB  conda-forge\n    grpcio-1.67.1              |  py312hacea422_1         879 KB  conda-forge\n    h2-4.2.0                   |     pyhd8ed1ab_0          53 KB  conda-forge\n    h5py-3.13.0                |nompi_py312hedeef09_100         1.3 MB  conda-forge\n    hdf5-1.14.3                |nompi_h2d575fe_109         3.7 MB  conda-forge\n    hpack-4.1.0                |     pyhd8ed1ab_0          30 KB  conda-forge\n    hyperframe-6.1.0           |     pyhd8ed1ab_0          17 KB  conda-forge\n    icu-75.1                   |       he02047a_0        11.6 MB  conda-forge\n    idna-3.10                  |     pyhd8ed1ab_1          49 KB  conda-forge\n    importlib-metadata-8.6.1   |     pyha770c72_0          28 KB  conda-forge\n    keras-3.8.0                |     pyh753f3f9_0         716 KB  conda-forge\n    keyutils-1.6.1             |       h166bdaf_0         115 KB  conda-forge\n    krb5-1.21.3                |       h659f571_0         1.3 MB  conda-forge\n    libabseil-20240722.0       | cxx17_hbbce691_4         1.3 MB  conda-forge\n    libaec-1.1.3               |       h59595ed_0          35 KB  conda-forge\n    libblas-3.9.0              |30_h59b9bed_openblas          17 KB  conda-forge\n    libcblas-3.9.0             |30_he106b2a_openblas          16 KB  conda-forge\n    libcurl-8.12.1             |       h332b0f4_0         417 KB  conda-forge\n    libedit-3.1.20250104       | pl5321h7949ede_0         132 KB  conda-forge\n    libev-4.33                 |       hd590300_2         110 KB  conda-forge\n    libgrpc-1.67.1             |       h25350d4_1         7.4 MB  conda-forge\n    libhwloc-2.11.2            |default_h0d58e46_1001         2.3 MB  conda-forge\n    libiconv-1.18              |       h4ce23a2_0         697 KB  conda-forge\n    liblapack-3.9.0            |30_h7ac8fdf_openblas          16 KB  conda-forge\n    libnghttp2-1.64.0          |       h161d5f1_0         632 KB  conda-forge\n    libopenblas-0.3.29         |pthreads_h94d23a6_0         5.6 MB  conda-forge\n    libprotobuf-5.28.3         |       h6128344_1         2.8 MB  conda-forge\n    libre2-11-2024.07.02       |       hbbce691_2         205 KB  conda-forge\n    libssh2-1.11.1             |       hf672d98_0         297 KB  conda-forge\n    libxml2-2.13.6             |       h8d12d68_0         674 KB  conda-forge\n    llvm-openmp-19.1.7         |       h024ca30_0         3.0 MB  conda-forge\n    markdown-3.6               |     pyhd8ed1ab_0          76 KB  conda-forge\n    markdown-it-py-3.0.0       |     pyhd8ed1ab_1          63 KB  conda-forge\n    markupsafe-3.0.2           |  py312h178313f_1          24 KB  conda-forge\n    mdurl-0.1.2                |     pyhd8ed1ab_1          14 KB  conda-forge\n    mkl-2024.2.2               |      ha957f24_16       118.9 MB  conda-forge\n    ml_dtypes-0.4.0            |  py312hf9745cd_2         163 KB  conda-forge\n    namex-0.0.8                |     pyhd8ed1ab_1          11 KB  conda-forge\n    opt_einsum-3.4.0           |     pyhd8ed1ab_1          61 KB  conda-forge\n    optree-0.14.0              |  py312h68727a3_1         362 KB  conda-forge\n    protobuf-5.28.3            |  py312h2ec8cdc_0         454 KB  conda-forge\n    pycparser-2.22             |     pyh29332c3_1         108 KB  conda-forge\n    pygments-2.19.1            |     pyhd8ed1ab_0         868 KB  conda-forge\n    pysocks-1.7.1              |     pyha55dd90_7          21 KB  conda-forge\n    python-flatbuffers-25.2.10 |     pyhbc23db3_0          34 KB  conda-forge\n    re2-2024.07.02             |       h9925aae_2          26 KB  conda-forge\n    requests-2.32.3            |     pyhd8ed1ab_1          57 KB  conda-forge\n    rich-13.9.4                |     pyhd8ed1ab_1         181 KB  conda-forge\n    snappy-1.2.1               |       h8bd8927_1          42 KB  conda-forge\n    tbb-2021.13.0              |       hceb3a55_1         172 KB  conda-forge\n    tensorboard-2.18.0         |     pyhd8ed1ab_1         4.9 MB  conda-forge\n    tensorboard-data-server-0.7.0|  py312hda17c39_2         3.4 MB  conda-forge\n    tensorflow-2.18.0          |cpu_py312h69ecde4_0          44 KB  conda-forge\n    tensorflow-base-2.18.0     |cpu_py312h099d1c6_0       150.7 MB  conda-forge\n    tensorflow-estimator-2.18.0|cpu_py312hc0a35a6_0         683 KB  conda-forge\n    termcolor-2.5.0            |     pyhd8ed1ab_1          12 KB  conda-forge\n    typing-extensions-4.12.2   |       hd8ed1ab_1          10 KB  conda-forge\n    typing_extensions-4.12.2   |     pyha770c72_1          39 KB  conda-forge\n    urllib3-2.3.0              |     pyhd8ed1ab_0          98 KB  conda-forge\n    werkzeug-3.1.3             |     pyhd8ed1ab_1         238 KB  conda-forge\n    wrapt-1.17.2               |  py312h66e93f0_0          62 KB  conda-forge\n    zipp-3.21.0                |     pyhd8ed1ab_1          21 KB  conda-forge\n    zstandard-0.23.0           |  py312hef9b889_1         410 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:       330.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_kmp_llvm \n  absl-py            conda-forge/noarch::absl-py-2.1.0-pyhd8ed1ab_1 \n  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\n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate mypyenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\n",
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+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "Create a conda environment named *myrenv* with R-4.0.5 for OML4R compatibility and install the `forecast` and `ggplot2` packages."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
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+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Create a conda environment named <em>myrenv</em> with R-4.0.5 for OML4R compatibility and install the <code>forecast</code> and <code>ggplot2</code> packages.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create R conda environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "create -n myrenv -c conda-forge --strict-channel-priority r-base=4.0.5 r-forecast r-ggplot2"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Channels:\n - conda-forge\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): ...working... done\nSolving environment: ...working... done\n\n## Package Plan ##\n\n  environment location: /u01/.conda/envs/myrenv\n\n  added / updated specs:\n    - r-base=4.0.5\n    - r-forecast\n    - r-ggplot2\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    _r-mutex-1.0.1             |      anacondar_1           3 KB  conda-forge\n    binutils_impl_linux-64-2.43|       h4bf12b8_2         5.4 MB  conda-forge\n    bwidget-1.10.1             |       ha770c72_0         126 KB  conda-forge\n    cairo-1.16.0               |    ha61ee94_1014         1.5 MB  conda-forge\n    curl-7.86.0                |       h2283fc2_1          91 KB  conda-forge\n    expat-2.6.4                |       h5888daf_0         135 KB  conda-forge\n    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n    font-ttf-ubuntu-0.83       |       h77eed37_3         1.5 MB  conda-forge\n    fontconfig-2.14.2          |       h14ed4e7_0         266 KB  conda-forge\n    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge\n    fonts-conda-forge-1        |                0           4 KB  conda-forge\n    fribidi-1.0.10             |       h36c2ea0_0         112 KB  conda-forge\n    gcc_impl_linux-64-14.2.0   |       h6b349bd_1        69.1 MB  conda-forge\n    gettext-0.23.1             |       h5888daf_0         473 KB  conda-forge\n    gettext-tools-0.23.1       |       h5888daf_0         2.8 MB  conda-forge\n    gfortran_impl_linux-64-14.2.0|       hc73f493_1        16.4 MB  conda-forge\n    graphite2-1.3.13           |    h59595ed_1003          95 KB  conda-forge\n    gsl-2.7                    |       he838d99_0         3.2 MB  conda-forge\n    gxx_impl_linux-64-14.2.0   |       h2c03514_1        13.7 MB  conda-forge\n    harfbuzz-6.0.0             |       h8e241bc_0         1.2 MB  conda-forge\n    icu-70.1                   |       h27087fc_0        13.5 MB  conda-forge\n    jpeg-9e                    |       h0b41bf4_3         235 KB  conda-forge\n    kernel-headers_linux-64-3.10.0|      he073ed8_18         921 KB  conda-forge\n    krb5-1.19.3                |       h08a2579_0         1.4 MB  conda-forge\n    libasprintf-0.23.1         |       h8e693c7_0          42 KB  conda-forge\n    libasprintf-devel-0.23.1   |       h8e693c7_0          33 KB  conda-forge\n    libcurl-7.86.0             |       h2283fc2_1         351 KB  conda-forge\n    libdeflate-1.14            |       h166bdaf_0          81 KB  conda-forge\n    libgcc-devel_linux-64-14.2.0|     h41c2201_101         2.6 MB  conda-forge\n    libgettextpo-0.23.1        |       h5888daf_0         163 KB  conda-forge\n    libgettextpo-devel-0.23.1  |       h5888daf_0          36 KB  conda-forge\n    libgfortran-ng-14.2.0      |       h69a702a_1          53 KB  conda-forge\n    libglib-2.78.1             |       hebfc3b9_0         2.6 MB  conda-forge\n    libhwloc-2.9.1             |       hd6dc26d_0         2.5 MB  conda-forge\n    libnghttp2-1.58.0          |       h47da74e_1         617 KB  conda-forge\n    libpng-1.6.43              |       h2797004_0         281 KB  conda-forge\n    libsanitizer-14.2.0        |       h2a3dede_1         4.3 MB  conda-forge\n    libssh2-1.11.0             |       h0841786_0         265 KB  conda-forge\n    libstdcxx-devel_linux-64-14.2.0|     h41c2201_101        12.9 MB  conda-forge\n    libtiff-4.4.0              |       h82bc61c_5         473 KB  conda-forge\n    libxcb-1.13                |    h7f98852_1004         391 KB  conda-forge\n    libxml2-2.10.3             |       hca2bb57_4         697 KB  conda-forge\n    libzlib-1.2.13             |       h4ab18f5_6          60 KB  conda-forge\n    make-4.4.1                 |       hb9d3cd8_2         501 KB  conda-forge\n    pango-1.50.14              |       hd33c08f_0         427 KB  conda-forge\n    pcre2-10.40                |       hc3806b6_0         2.3 MB  conda-forge\n    pixman-0.44.2              |       h29eaf8c_0         372 KB  conda-forge\n    r-backports-1.4.1          |    r40hcfec24a_0         110 KB  conda-forge\n    r-base-4.0.5               |       hb87df5d_8        23.8 MB  conda-forge\n    r-brio-1.1.3               |    r40hcfec24a_0          40 KB  conda-forge\n    r-callr-3.7.2              |    r40hc72bb7e_0         427 KB  conda-forge\n    r-cli-3.4.1                |    r40h7525677_0         1.2 MB  conda-forge\n    r-colorspace-2.0_3         |    r40h06615bd_0         2.5 MB  conda-forge\n    r-crayon-1.5.1             |    r40hc72bb7e_0         164 KB  conda-forge\n    r-curl-4.3.2               |    r40hcfec24a_0         777 KB  conda-forge\n    r-desc-1.4.2               |    r40hc72bb7e_0         332 KB  conda-forge\n    r-diffobj-0.3.5            |    r40hcfec24a_0         1.0 MB  conda-forge\n    r-digest-0.6.29            |    r40h03ef668_0         208 KB  conda-forge\n    r-ellipsis-0.3.2           |    r40hcfec24a_0          42 KB  conda-forge\n    r-evaluate-0.16            |    r40hc72bb7e_0          85 KB  conda-forge\n    r-fansi-1.0.3              |    r40h06615bd_0         323 KB  conda-forge\n    r-farver-2.1.1             |    r40h7525677_0         1.4 MB  conda-forge\n    r-forecast-8.17.0          |    r40h37cf8d7_0         1.5 MB  conda-forge\n    r-fracdiff-1.5_1           |    r40hb699f27_1         116 KB  conda-forge\n    r-fs-1.5.2                 |    r40h7525677_1         554 KB  conda-forge\n    r-generics-0.1.3           |    r40hc72bb7e_0          93 KB  conda-forge\n    r-ggplot2-3.3.6            |    r40hc72bb7e_0         4.0 MB  conda-forge\n    r-glue-1.6.2               |    r40h06615bd_0         154 KB  conda-forge\n    r-gtable-0.3.1             |    r40hc72bb7e_0         174 KB  conda-forge\n    r-isoband-0.2.5            |    r40h03ef668_0         1.8 MB  conda-forge\n    r-jsonlite-1.8.0           |    r40h06615bd_0         960 KB  conda-forge\n    r-labeling-0.4.2           |    r40hc72bb7e_1          67 KB  conda-forge\n    r-lattice-0.20_45          |    r40hcfec24a_0         1.1 MB  conda-forge\n    r-lifecycle-1.0.2          |    r40hc72bb7e_0         110 KB  conda-forge\n    r-lmtest-0.9_40            |    r40h8da6f51_0         413 KB  conda-forge\n    r-magrittr-2.0.3           |    r40h06615bd_0         215 KB  conda-forge\n    r-mass-7.3_58.1            |    r40h06615bd_0         1.1 MB  conda-forge\n    r-matrix-1.4_1             |    r40h0154571_0         4.8 MB  conda-forge\n    r-mgcv-1.8_40              |    r40h0154571_0         3.0 MB  conda-forge\n    r-munsell-0.5.0            | r40hc72bb7e_1004         247 KB  conda-forge\n    r-nlme-3.1_159             |    r40h8da6f51_0         2.3 MB  conda-forge\n    r-nnet-7.3_17              |    r40hcfec24a_0         132 KB  conda-forge\n    r-pillar-1.8.1             |    r40hc72bb7e_0         672 KB  conda-forge\n    r-pkgconfig-2.0.3          |    r40hc72bb7e_1          25 KB  conda-forge\n    r-pkgload-1.3.0            |    r40hc72bb7e_0         191 KB  conda-forge\n    r-praise-1.0.0             | r40hc72bb7e_1005          24 KB  conda-forge\n    r-processx-3.7.0           |    r40h06615bd_0         330 KB  conda-forge\n    r-ps-1.7.1                 |    r40h06615bd_0         323 KB  conda-forge\n    r-quadprog-1.5_8           |    r40h742201e_3          47 KB  conda-forge\n    r-quantmod-0.4.20          |    r40hc72bb7e_0         1.0 MB  conda-forge\n    r-r6-2.5.1                 |    r40hc72bb7e_0          89 KB  conda-forge\n    r-rcolorbrewer-1.1_3       |    r40h785f33e_0          65 KB  conda-forge\n    r-rcpp-1.0.9               |    r40h7525677_1         2.0 MB  conda-forge\n    r-rcpparmadillo-0.11.2.3.1 |    r40h9f5de39_0         950 KB  conda-forge\n    r-rematch2-2.1.2           |    r40hc72bb7e_1          52 KB  conda-forge\n    r-rlang-1.0.6              |    r40h7525677_0         1.5 MB  conda-forge\n    r-rprojroot-2.0.3          |    r40hc72bb7e_0         115 KB  conda-forge\n    r-scales-1.2.1             |    r40hc72bb7e_0         611 KB  conda-forge\n    r-testthat-3.1.4           |    r40h7525677_0         1.6 MB  conda-forge\n    r-tibble-3.1.8             |    r40h06615bd_0         695 KB  conda-forge\n    r-timedate-4021.104        |    r40hc72bb7e_0         1.6 MB  conda-forge\n    r-tseries-0.10_51          |    r40h1463581_0         391 KB  conda-forge\n    r-ttr-0.24.3               |    r40h06615bd_0         531 KB  conda-forge\n    r-urca-1.3_0               | r40h8da6f51_1006         1.1 MB  conda-forge\n    r-utf8-1.2.2               |    r40hcfec24a_0         162 KB  conda-forge\n    r-vctrs-0.4.1              |    r40h7525677_0         1.2 MB  conda-forge\n    r-viridislite-0.4.1        |    r40hc72bb7e_0         1.3 MB  conda-forge\n    r-waldo-0.4.0              |    r40hc72bb7e_0         109 KB  conda-forge\n    r-withr-2.5.0              |    r40hc72bb7e_0         240 KB  conda-forge\n    r-xts-0.12.1               |    r40h06615bd_0         906 KB  conda-forge\n    r-zoo-1.8_11               |    r40h06615bd_0        1015 KB  conda-forge\n    sed-4.8                    |       he412f7d_0         264 KB  conda-forge\n    sysroot_linux-64-2.17      |      h0157908_18        14.5 MB  conda-forge\n    tbb-2021.9.0               |       hf52228f_0         1.5 MB  conda-forge\n    tktable-2.10               |       h8bc8fbc_6          89 KB  conda-forge\n    xorg-kbproto-1.0.7         |    hb9d3cd8_1003          30 KB  conda-forge\n    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n    xorg-libx11-1.8.4          |       h0b41bf4_0         810 KB  conda-forge\n    xorg-libxext-1.3.4         |       h0b41bf4_2          49 KB  conda-forge\n    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n    xorg-libxt-1.3.0           |       hd590300_0         371 KB  conda-forge\n    xorg-renderproto-0.11.1    |    hb9d3cd8_1003          12 KB  conda-forge\n    xorg-xextproto-7.3.0       |    hb9d3cd8_1004          30 KB  conda-forge\n    xorg-xproto-7.0.31         |    hb9d3cd8_1008          72 KB  conda-forge\n    zlib-1.2.13                |       h4ab18f5_6          91 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:       256.9 MB\n\nThe following NEW packages will be INSTALLED:\n\n  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_kmp_llvm \n  _r-mutex           conda-forge/noarch::_r-mutex-1.0.1-anacondar_1 \n  binutils_impl_lin~ conda-forge/linux-64::binutils_impl_linux-64-2.43-h4bf12b8_2 \n  bwidget            conda-forge/linux-64::bwidget-1.10.1-ha770c72_0 \n  bzip2              conda-forge/linux-64::bzip2-1.0.8-h4bc722e_7 \n  c-ares             conda-forge/linux-64::c-ares-1.34.4-hb9d3cd8_0 \n  ca-certificates    conda-forge/linux-64::ca-certificates-2025.1.31-hbcca054_0 \n  cairo              conda-forge/linux-64::cairo-1.16.0-ha61ee94_1014 \n  curl               conda-forge/linux-64::curl-7.86.0-h2283fc2_1 \n  expat              conda-forge/linux-64::expat-2.6.4-h5888daf_0 \n  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 \n  fontconfig         conda-forge/linux-64::fontconfig-2.14.2-h14ed4e7_0 \n  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 \n  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0 \n  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n  fribidi            conda-forge/linux-64::fribidi-1.0.10-h36c2ea0_0 \n  gcc_impl_linux-64  conda-forge/linux-64::gcc_impl_linux-64-14.2.0-h6b349bd_1 \n  gettext            conda-forge/linux-64::gettext-0.23.1-h5888daf_0 \n  gettext-tools      conda-forge/linux-64::gettext-tools-0.23.1-h5888daf_0 \n  gfortran_impl_lin~ conda-forge/linux-64::gfortran_impl_linux-64-14.2.0-hc73f493_1 \n  graphite2          conda-forge/linux-64::graphite2-1.3.13-h59595ed_1003 \n  gsl                conda-forge/linux-64::gsl-2.7-he838d99_0 \n  gxx_impl_linux-64  conda-forge/linux-64::gxx_impl_linux-64-14.2.0-h2c03514_1 \n  harfbuzz           conda-forge/linux-64::harfbuzz-6.0.0-h8e241bc_0 \n  icu                conda-forge/linux-64::icu-70.1-h27087fc_0 \n  jpeg               conda-forge/linux-64::jpeg-9e-h0b41bf4_3 \n  kernel-headers_li~ conda-forge/noarch::kernel-headers_linux-64-3.10.0-he073ed8_18 \n  keyutils           conda-forge/linux-64::keyutils-1.6.1-h166bdaf_0 \n  krb5               conda-forge/linux-64::krb5-1.19.3-h08a2579_0 \n  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.43-h712a8e2_2 \n  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n  libasprintf        conda-forge/linux-64::libasprintf-0.23.1-h8e693c7_0 \n  libasprintf-devel  conda-forge/linux-64::libasprintf-devel-0.23.1-h8e693c7_0 \n  libblas            conda-forge/linux-64::libblas-3.9.0-30_h59b9bed_openblas \n  libcblas           conda-forge/linux-64::libcblas-3.9.0-30_he106b2a_openblas \n  libcurl            conda-forge/linux-64::libcurl-7.86.0-h2283fc2_1 \n  libdeflate         conda-forge/linux-64::libdeflate-1.14-h166bdaf_0 \n  libedit            conda-forge/linux-64::libedit-3.1.20250104-pl5321h7949ede_0 \n  libev              conda-forge/linux-64::libev-4.33-hd590300_2 \n  libexpat           conda-forge/linux-64::libexpat-2.6.4-h5888daf_0 \n  libffi             conda-forge/linux-64::libffi-3.4.6-h2dba641_0 \n  libgcc             conda-forge/linux-64::libgcc-14.2.0-h77fa898_1 \n  libgcc-devel_linu~ conda-forge/noarch::libgcc-devel_linux-64-14.2.0-h41c2201_101 \n  libgcc-ng          conda-forge/linux-64::libgcc-ng-14.2.0-h69a702a_1 \n  libgettextpo       conda-forge/linux-64::libgettextpo-0.23.1-h5888daf_0 \n  libgettextpo-devel conda-forge/linux-64::libgettextpo-devel-0.23.1-h5888daf_0 \n  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_1 \n  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-14.2.0-h69a702a_1 \n  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hd5240d6_1 \n  libglib            conda-forge/linux-64::libglib-2.78.1-hebfc3b9_0 \n  libgomp            conda-forge/linux-64::libgomp-14.2.0-h77fa898_1 \n  libhwloc           conda-forge/linux-64::libhwloc-2.9.1-hd6dc26d_0 \n  libiconv           conda-forge/linux-64::libiconv-1.18-h4ce23a2_0 \n  liblapack          conda-forge/linux-64::liblapack-3.9.0-30_h7ac8fdf_openblas \n  liblzma            conda-forge/linux-64::liblzma-5.6.4-hb9d3cd8_0 \n  liblzma-devel      conda-forge/linux-64::liblzma-devel-5.6.4-hb9d3cd8_0 \n  libnghttp2         conda-forge/linux-64::libnghttp2-1.58.0-h47da74e_1 \n  libopenblas        conda-forge/linux-64::libopenblas-0.3.29-pthreads_h94d23a6_0 \n  libpng             conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n  libsanitizer       conda-forge/linux-64::libsanitizer-14.2.0-h2a3dede_1 \n  libssh2            conda-forge/linux-64::libssh2-1.11.0-h0841786_0 \n  libstdcxx          conda-forge/linux-64::libstdcxx-14.2.0-hc0a3c3a_1 \n  libstdcxx-devel_l~ conda-forge/noarch::libstdcxx-devel_linux-64-14.2.0-h41c2201_101 \n  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-14.2.0-h4852527_1 \n  libtiff            conda-forge/linux-64::libtiff-4.4.0-h82bc61c_5 \n  libuuid            conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0 \n  libwebp-base       conda-forge/linux-64::libwebp-base-1.5.0-h851e524_0 \n  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1004 \n  libxml2            conda-forge/linux-64::libxml2-2.10.3-hca2bb57_4 \n  libzlib            conda-forge/linux-64::libzlib-1.2.13-h4ab18f5_6 \n  llvm-openmp        conda-forge/linux-64::llvm-openmp-19.1.7-h024ca30_0 \n  make               conda-forge/linux-64::make-4.4.1-hb9d3cd8_2 \n  mkl                conda-forge/linux-64::mkl-2024.2.2-ha957f24_16 \n  ncurses            conda-forge/linux-64::ncurses-6.5-h2d0b736_3 \n  openssl            conda-forge/linux-64::openssl-3.4.1-h7b32b05_0 \n  pango              conda-forge/linux-64::pango-1.50.14-hd33c08f_0 \n  pcre2              conda-forge/linux-64::pcre2-10.40-hc3806b6_0 \n  pixman             conda-forge/linux-64::pixman-0.44.2-h29eaf8c_0 \n  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 \n  r-backports        conda-forge/linux-64::r-backports-1.4.1-r40hcfec24a_0 \n  r-base             conda-forge/linux-64::r-base-4.0.5-hb87df5d_8 \n  r-brio             conda-forge/linux-64::r-brio-1.1.3-r40hcfec24a_0 \n  r-callr            conda-forge/noarch::r-callr-3.7.2-r40hc72bb7e_0 \n  r-cli              conda-forge/linux-64::r-cli-3.4.1-r40h7525677_0 \n  r-colorspace       conda-forge/linux-64::r-colorspace-2.0_3-r40h06615bd_0 \n  r-crayon           conda-forge/noarch::r-crayon-1.5.1-r40hc72bb7e_0 \n  r-curl             conda-forge/linux-64::r-curl-4.3.2-r40hcfec24a_0 \n  r-desc             conda-forge/noarch::r-desc-1.4.2-r40hc72bb7e_0 \n  r-diffobj          conda-forge/linux-64::r-diffobj-0.3.5-r40hcfec24a_0 \n  r-digest           conda-forge/linux-64::r-digest-0.6.29-r40h03ef668_0 \n  r-ellipsis         conda-forge/linux-64::r-ellipsis-0.3.2-r40hcfec24a_0 \n  r-evaluate         conda-forge/noarch::r-evaluate-0.16-r40hc72bb7e_0 \n  r-fansi            conda-forge/linux-64::r-fansi-1.0.3-r40h06615bd_0 \n  r-farver           conda-forge/linux-64::r-farver-2.1.1-r40h7525677_0 \n  r-forecast         conda-forge/linux-64::r-forecast-8.17.0-r40h37cf8d7_0 \n  r-fracdiff         conda-forge/linux-64::r-fracdiff-1.5_1-r40hb699f27_1 \n  r-fs               conda-forge/linux-64::r-fs-1.5.2-r40h7525677_1 \n  r-generics         conda-forge/noarch::r-generics-0.1.3-r40hc72bb7e_0 \n  r-ggplot2          conda-forge/noarch::r-ggplot2-3.3.6-r40hc72bb7e_0 \n  r-glue             conda-forge/linux-64::r-glue-1.6.2-r40h06615bd_0 \n  r-gtable           conda-forge/noarch::r-gtable-0.3.1-r40hc72bb7e_0 \n  r-isoband          conda-forge/linux-64::r-isoband-0.2.5-r40h03ef668_0 \n  r-jsonlite         conda-forge/linux-64::r-jsonlite-1.8.0-r40h06615bd_0 \n  r-labeling         conda-forge/noarch::r-labeling-0.4.2-r40hc72bb7e_1 \n  r-lattice          conda-forge/linux-64::r-lattice-0.20_45-r40hcfec24a_0 \n  r-lifecycle        conda-forge/noarch::r-lifecycle-1.0.2-r40hc72bb7e_0 \n  r-lmtest           conda-forge/linux-64::r-lmtest-0.9_40-r40h8da6f51_0 \n  r-magrittr         conda-forge/linux-64::r-magrittr-2.0.3-r40h06615bd_0 \n  r-mass             conda-forge/linux-64::r-mass-7.3_58.1-r40h06615bd_0 \n  r-matrix           conda-forge/linux-64::r-matrix-1.4_1-r40h0154571_0 \n  r-mgcv             conda-forge/linux-64::r-mgcv-1.8_40-r40h0154571_0 \n  r-munsell          conda-forge/noarch::r-munsell-0.5.0-r40hc72bb7e_1004 \n  r-nlme             conda-forge/linux-64::r-nlme-3.1_159-r40h8da6f51_0 \n  r-nnet             conda-forge/linux-64::r-nnet-7.3_17-r40hcfec24a_0 \n  r-pillar           conda-forge/noarch::r-pillar-1.8.1-r40hc72bb7e_0 \n  r-pkgconfig        conda-forge/noarch::r-pkgconfig-2.0.3-r40hc72bb7e_1 \n  r-pkgload          conda-forge/noarch::r-pkgload-1.3.0-r40hc72bb7e_0 \n  r-praise           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conda-forge/linux-64::readline-8.2-h8228510_1 \n  sed                conda-forge/linux-64::sed-4.8-he412f7d_0 \n  sysroot_linux-64   conda-forge/noarch::sysroot_linux-64-2.17-h0157908_18 \n  tbb                conda-forge/linux-64::tbb-2021.9.0-hf52228f_0 \n  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101 \n  tktable            conda-forge/linux-64::tktable-2.10-h8bc8fbc_6 \n  tzdata             conda-forge/noarch::tzdata-2025a-h78e105d_0 \n  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-hb9d3cd8_1003 \n  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0 \n  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000 \n  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.8.4-h0b41bf4_0 \n  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.12-hb9d3cd8_0 \n  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 \n  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h0b41bf4_2 \n  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003 \n  xorg-libxt         conda-forge/linux-64::xorg-libxt-1.3.0-hd590300_0 \n  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-hb9d3cd8_1003 \n  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-hb9d3cd8_1004 \n  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-hb9d3cd8_1008 \n  xz                 conda-forge/linux-64::xz-5.6.4-hbcc6ac9_0 \n  xz-gpl-tools       conda-forge/linux-64::xz-gpl-tools-5.6.4-hbcc6ac9_0 \n  xz-tools           conda-forge/linux-64::xz-tools-5.6.4-hb9d3cd8_0 \n  zlib               conda-forge/linux-64::zlib-1.2.13-h4ab18f5_6 \n  zstd               conda-forge/linux-64::zstd-1.5.6-ha6fb4c9_0 \n\n\n\nDownloading and Extracting Packages: ...working... done\nPreparing transaction: ...working... done\nVerifying transaction: ...working... done\nExecuting transaction: ...working... done\n#\n# To activate this environment, use\n#\n#     $ conda activate myrenv\n#\n# To deactivate an active environment, use\n#\n#     $ conda deactivate\n\n\n\n",
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+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Upload environment to Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Upload the environments to the Object Storage bucket associated with the Autonomous Database instance using the  `upload` command. We provide environment descriptions and two tags, one for the user name and one for the application name, and we overwrite any environment with the same name if it exists.",
+          "",
+          "The application tag is required for use with embedded execution. For example, OML4Py embedded Python execution works with conda environments containing the OML4PY tag, and OML4R embedded R execution works with conda environments containing the OML4R tag.",
+          "",
+          "There is one Object Storage bucket for each data center region. The conda environments are saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and only available to users through OML notebooks. There is a 8G maximum size for a single conda environment, and no size limit on  Object Storage.",
+          "",
+          "To get help for the `upload` command, type `upload --help` in the %conda interpreter."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
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+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Upload the environments to the Object Storage bucket associated with the Autonomous Database instance using the  <code>upload</code> command. We provide environment descriptions and two tags, one for the user name and one for the application name, and we overwrite any environment with the same name if it exists.</p>\n<p>The application tag is required for use with embedded execution. For example, OML4Py embedded Python execution works with conda environments containing the OML4PY tag, and OML4R embedded R execution works with conda environments containing the OML4R tag.</p>\n<p>There is one Object Storage bucket for each data center region. The conda environments are saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and only available to users through OML notebooks. There is a 8G maximum size for a single conda environment, and no size limit on  Object Storage.</p>\n<p>To get help for the <code>upload</code> command, type <code>upload --help</code> in the %conda interpreter.</p>\n",
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+            }
+          ],
+          "forms" : "[]"
+        },
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+      {
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+        "width" : 12,
+        "title" : "Save Python environment to Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "upload mypyenv --overwrite --description 'Install Python seaborn and tensorflow packages' -t user 'OMLUSER' -t application OML4PY"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
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+          "endTime" : 1739988900143,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\nUploading conda environment mypyenv\nUpload successful for conda environment mypyenv\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Save R environment to Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "upload myrenv --overwrite --description 'Install R forecast and ggplot2 packages' -t user 'OMLUSER' -t application 'OML4R'"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988900630,
+          "endTime" : 1739988941791,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\nUploading conda environment myrenv\nUpload successful for conda environment myrenv\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
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+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "The environments are now available for the OML user to download and will remain in Object Storage until it is deleted. ",
+          "",
+          "Verify the environments are saved to Object storage using the `list-saved-envs`,  passing the environment name to the `-e` flag."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
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+          "endTime" : 1739988942795,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>The environments are now available for the OML user to download and will remain in Object Storage until it is deleted.</p>\n<p>Verify the environments are saved to Object storage using the <code>list-saved-envs</code>,  passing the environment name to the <code>-e</code> flag.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Search for the saved environments",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs -e mypyenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "enabled" : true,
+        "forms" : "[]",
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+          "startTime" : 1739988943282,
+          "endTime" : 1739988945466,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\n{\n  \"name\": \"mypyenv\",\n  \"size\": \"2.2 GiB\",\n  \"description\": \"Install Python seaborn and tensorflow packages\",\n  \"tags\": {\n    \"application\": \"OML4PY\",\n    \"user\": \"OMLUSER\"\n  },\n  \"number_of_installed_packages\": 149\n}\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
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+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs -e myrenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988945956,
+          "endTime" : 1739988948142,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\n{\n  \"name\": \"myrenv\",\n  \"size\": \"1.5 GiB\",\n  \"description\": \"Install R forecast and ggplot2 packages\",\n  \"tags\": {\n    \"application\": \"OML4R\",\n    \"user\": \"OMLUSER\"\n  },\n  \"number_of_installed_packages\": 171\n}\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "### Delete Environment from Object Storage",
+          "---",
+          "",
+          "If an environment is no longer in use, it can be deleted from Object Storage using the `delete` command. ",
+          "",
+          "For help on the conda `delete` command, type `delete --help` in a %conda paragraph.",
+          "",
+          "Note, do not run the paragraphs containing the `delete` commands if you want to use them in an OML user session."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988948625,
+          "endTime" : 1739988949077,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>If an environment is no longer in use, it can be deleted from Object Storage using the <code>delete</code> command.</p>\n<p>For help on the conda <code>delete</code> command, type <code>delete --help</code> in a %conda paragraph.</p>\n<p>Note, do not run the paragraphs containing the <code>delete</code> commands if you want to use them in an OML user session.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Delete R environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "delete myrenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1718053975458,
+          "endTime" : 1718053977055,
+          "interpreter" : "conda.conda_medium",
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+          "status" : null,
+          "results" : null,
+          "forms" : null
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Delete Python environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "delete mypyenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
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+          "forms" : null
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Confirm environment deletion",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1718053978796,
+          "endTime" : 1718053980390,
+          "interpreter" : "conda.conda_medium",
+          "taskStatus" : null,
+          "status" : null,
+          "results" : null,
+          "forms" : null
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Access Control List",
+        "hasTitle" : true,
+        "message" : [
+          "%md\r",
+          "---\r",
+          "## Optional: Add OML user to the Access Control List\r",
+          "---\r",
+          "\r",
+          "A requirement of the SQL API for embedded R and Python execution on Autonomous Database is adding the OML user to the Access Control List (ACL). The ACL provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database. \r",
+          "\r",
+          "As ADMIN, run the network access control list `rqAppendHostAce` function (OML4R) or `pyqAppendHostAce` (OML4Py) to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides.  \r",
+          "\r",
+          "For example, if your username is *OMLUSER* and your Autonomous Database resides in the Ashburn region, the root domain is *adb.us-ashburn-1.oraclecloudapps.com*. \r"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988949566,
+          "endTime" : 1739988950024,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>A requirement of the SQL API for embedded R and Python execution on Autonomous Database is adding the OML user to the Access Control List (ACL). The ACL provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database.</p>\n<p>As ADMIN, run the network access control list <code>rqAppendHostAce</code> function (OML4R) or <code>pyqAppendHostAce</code> (OML4Py) to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides.</p>\n<p>For example, if your username is <em>OMLUSER</em> and your Autonomous Database resides in the Ashburn region, the root domain is <em>adb.us-ashburn-1.oraclecloudapps.com</em>.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "OML4R",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "-- replace username and root domain with your database values",
+          "",
+          "exec rqAppendHostAce('OMLUSER','adb.us-ashburn-1.oraclecloudapps.com')"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739989149503,
+          "endTime" : 1739989150161,
+          "interpreter" : "script.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "OML4Py",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "-- replace username and root domain with your database values",
+          "",
+          "exec pyqAppendHostAce('OMLUSER','adb.us-ashburn-1.oraclecloudapps.com')"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739985092571,
+          "endTime" : 1739985094835,
+          "interpreter" : "script.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "## Use Conda Environments",
+          "---",
+          "",
+          "Refer to the template notebooks, *OML Third-Party Packages - Python Environment Usage* and *OML Third-Party Packages - R Environment Usage* for the steps to download and use the saved environments, *mypyenv* and *myrenv*. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988950510,
+          "endTime" : 1739988950959,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Refer to the template notebooks, <em>OML Third-Party Packages - Python Environment Usage</em> and <em>OML Third-Party Packages - R Environment Usage</em> for the steps to download and use the saved environments, <em>mypyenv</em> and <em>myrenv</em>.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## End of Script"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739988951458,
+          "endTime" : 1739988951907,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of Script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML Third-Party Packages - R Environment Usage.dsnb b/machine-learning/notebooks-oml/r/OML Third-Party Packages - R Environment Usage.dsnb
new file mode 100644
index 00000000..4f38cdf7
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML Third-Party Packages - R Environment Usage.dsnb	
@@ -0,0 +1,1456 @@
+[
+  {
+    "name" : "OML Third-Party Packages - R Environment Usage",
+    "description" : null,
+    "tags" : null,
+    "version" : "7",
+    "layout" : null,
+    "type" : "low",
+    "snapshot" : false,
+    "isEditable" : true,
+    "isRunnable" : true,
+    "template" : null,
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+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 0,
+          "endTime" : 1739992580357,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "ERROR",
+          "results" : [
+            {
+              "message" : "Interpreter creation failed. Caused by: Container already exist OML-INTRP-a1a63433-5b92-4f0f-b83c-81069191ae52-1847393936-LOW-n_dsPgKPm3",
+              "type" : "TEXT"
+            },
+            {
+              "message" : "OLDS-844666e91bde96d963f6707efee39a8f",
+              "type" : "TEXT"
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+          ],
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+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "# OML Third-Party Packages - R Environment Usage",
+          "",
+          "Oracle Machine Learning Notebooks provides a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, as well as switch between environments to use project-specific packages. ",
+          "",
+          "Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session and used in embedded execution using the native language, SQL, and REST APIs. ",
+          "",
+          "In the template notebook titled *OML Third-Party Packages - Environment Creation*, the ADMIN user creates a conda environment, installs packages, and uploads the conda environments to Object Storage.",
+          "",
+          "In this notebook, we demonstrate a typical workflow for conda environment usage in OML Notebooks using R and OML4R. The OML user downloads and uses the packages in conda environments that were previously created and saved to an Object Storage bucket folder associated with the Autonomous Database.",
+          "",
+          "---",
+          "Copyright (c) 2025 Oracle Corporation ",
+          "###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992596699,
+          "endTime" : 1739992597265,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h1 id=\"oml-third-party-packages---r-environment-usage\">OML Third-Party Packages - R Environment Usage</h1>\n<p>Oracle Machine Learning Notebooks provides a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, as well as switch between environments to use project-specific packages.</p>\n<p>Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session and used in embedded execution using the native language, SQL, and REST APIs.</p>\n<p>In the template notebook titled <em>OML Third-Party Packages - Environment Creation</em>, the ADMIN user creates a conda environment, installs packages, and uploads the conda environments to Object Storage.</p>\n<p>In this notebook, we demonstrate a typical workflow for conda environment usage in OML Notebooks using R and OML4R. The OML user downloads and uses the packages in conda environments that were previously created and saved to an Object Storage bucket folder associated with the Autonomous Database.</p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation</p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "List a named environment in Object Storage",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Use `list-saved-envs` to list and return the details for a named conda environment in Object Storage.  The environment name, size, and number of packages is returned along with the description and tags provided by the ADMIN when uploading the environment.",
+          "",
+          "The application tag indicates how the environment can be used.  For example, the environment `myrenv` was created with the application tag *OML4R* for use with R and OML4R."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992623878,
+          "endTime" : 1739992624343,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Use <code>list-saved-envs</code> to list and return the details for a named conda environment in Object Storage.  The environment name, size, and number of packages is returned along with the description and tags provided by the ADMIN when uploading the environment.</p>\n<p>The application tag indicates how the environment can be used.  For example, the environment <code>myrenv</code> was created with the application tag <em>OML4R</em> for use with R and OML4R.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%conda",
+          "",
+          "list-saved-envs -e myrenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992636769,
+          "endTime" : 1739992640560,
+          "interpreter" : "conda.conda_low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\n{\n  \"name\": \"myrenv\",\n  \"size\": \"1.5 GiB\",\n  \"description\": \"Install R forecast and ggplot2 packages\",\n  \"tags\": {\n    \"application\": \"OML4R\",\n    \"user\": \"OMLUSER\"\n  },\n  \"number_of_installed_packages\": 171\n}\n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "---",
+          "## Using an R environment",
+          "---",
+          "",
+          "In this example, we predict the number of international passengers traveling by airplane using time series data. The R packages used are *forecast* and *ggplot2* with the conda environment *myrenv*. We first run the code and create a UDF using R, followed by invoking the UDF in OML4R embedded R execution from the R, SQL and REST APIs."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992641039,
+          "endTime" : 1739992641496,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>In this example, we predict the number of international passengers traveling by airplane using time series data. The R packages used are <em>forecast</em> and <em>ggplot2</em> with the conda environment <em>myrenv</em>. We first run the code and create a UDF using R, followed by invoking the UDF in OML4R embedded R execution from the R, SQL and REST APIs.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Download and activate the environment",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Conda environments are available after they are downloaded and activated using the `download` and `activate` functions in a %conda paragraph. An activated environment is available until it is deactivated."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992641975,
+          "endTime" : 1739992642433,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Conda environments are available after they are downloaded and activated using the <code>download</code> and <code>activate</code> functions in a %conda paragraph. An activated environment is available until it is deactivated.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Download and activate the 'myrenv' environment",
+        "hasTitle" : true,
+        "message" : [
+          "%conda",
+          "",
+          "download myrenv ",
+          "",
+          "activate myrenv"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : "[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992642944,
+          "endTime" : 1739992668876,
+          "interpreter" : "conda.conda_low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "/opt/conda/lib/python3.12/site-packages/charset_normalizer/api.py:105: UserWarning: Trying to detect encoding from a tiny portion of (15) byte(s).\n  warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))\nDownloading conda environment myrenv\nDownload successful for conda environment myrenv\n\n\nusage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...\n\nconda is a tool for managing and deploying applications, environments and packages.\n\noptions:\n  -h, --help          Show this help message and exit.\n  -v, --verbose       Can be used multiple times. Once for detailed output,\n                      twice for INFO logging, thrice for DEBUG logging, four\n                      times for TRACE logging.\n  --no-plugins        Disable all plugins that are not built into conda.\n  -V, --version       Show the conda version number and exit.\n\ncommands:\n  The following built-in and plugins subcommands are available.\n\n  COMMAND\n    activate          Activate a conda environment.\n    clean             Remove unused packages and caches.\n    compare           Compare packages between conda environments.\n    config            Modify configuration values in .condarc.\n    content-trust     Signing and verification tools for Conda\n    create            Create a new conda environment from a list of specified\n                      packages.\n    deactivate        Deactivate the current active conda environment.\n    doctor            Display a health report for your environment.\n    env-lcm           See `conda env-lcm --help`.\n    info              Display information about current conda install.\n    init              Initialize conda for shell interaction.\n    install           Install a list of packages into a specified conda\n                      environment.\n    list              List installed packages in a conda environment.\n    notices           Retrieve latest channel notifications.\n    pack              See `conda pack --help`.\n    package           Create low-level conda packages. (EXPERIMENTAL)\n    remove (uninstall)\n                      Remove a list of packages from a specified conda\n                      environment.\n    rename            Rename an existing environment.\n    repoquery         Advanced search for repodata.\n    run               Run an executable in a conda environment.\n    search            Search for packages and display associated information\n                      using the MatchSpec format.\n    update (upgrade)  Update conda packages to the latest compatible version.\n\n\n\nConda environment 'myrenv' activated\n\n",
+              "type" : "TEXT"
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              hab24e00_0    conda-forge\nfont-ttf-inconsolata      3.000                h77eed37_0    conda-forge\nfont-ttf-source-code-pro  2.038                h77eed37_0    conda-forge\nfont-ttf-ubuntu           0.83                 h77eed37_3    conda-forge\nfontconfig                2.14.2               h14ed4e7_0    conda-forge\nfonts-conda-ecosystem     1                             0    conda-forge\nfonts-conda-forge         1                             0    conda-forge\nfreetype                  2.12.1               h267a509_2    conda-forge\nfribidi                   1.0.10               h36c2ea0_0    conda-forge\ngcc_impl_linux-64         14.2.0               h6b349bd_1    conda-forge\ngettext                   0.23.1               h5888daf_0    conda-forge\ngettext-tools             0.23.1               h5888daf_0    conda-forge\ngfortran_impl_linux-64    14.2.0               hc73f493_1    conda-forge\ngraphite2                 1.3.13            h59595ed_1003    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+              "message" : "Registered S3 method overwritten by 'quantmod':\n  method            from\n  as.zoo.data.frame zoo \n\n",
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+          "%r",
+          "",
+          "data(\"AirPassengers\")",
+          "",
+          "# Through the end of 1956, no months in 1957",
+          "air.train <- window(AirPassengers, end = 1956+11/12)",
+          "",
+          "# everything in 1957 and beyond",
+          "air.test  <- window(AirPassengers, start = 1957)",
+          "",
+          "n.test <- length(air.test)"
+        ],
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+        "title" : "Fit an arima model to the data and compute performance",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "air.model <- auto.arima(air.train)"
+        ],
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+        "width" : 6,
+        "title" : "Create multi-step forecasts for each day",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "accuracy(air.model)",
+          "",
+          "air.multi.forecast <- air.model %>% forecast(h = n.test)",
+          "",
+          "air.multi.forecast %>% autoplot()"
+        ],
+        "selectedVisualization" : "table",
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+              "message" : "<table class=\"dataframe\">\n<caption>A matrix: 1 x 7 of type dbl</caption>\n<thead>\n\t<tr><th></th><th scope=col>ME</th><th scope=col>RMSE</th><th scope=col>MAE</th><th scope=col>MPE</th><th scope=col>MAPE</th><th scope=col>MASE</th><th scope=col>ACF1</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>Training set</th><td>0.442465</td><td>8.834477</td><td>6.351386</td><td>0.1376787</td><td>2.870884</td><td>0.2174955</td><td>0.003856404</td></tr>\n</tbody>\n</table>\n\n",
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R7BDgAATAimWVLsKbgxthJ2zhLsAADAhFB0rxNb/txmmppSBDsAAIBx4drrRCnp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EABR2fKOD4O3YSZJhLcVCw/rCxBPX+Qku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\n3/eEIwAAAABJRU5ErkJggg==",
+              "type" : "IMG"
+            }
+          ],
+          "forms" : "[]"
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+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Compare performance to the test data",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "accuracy(air.multi.forecast, x = air.test)",
+          "",
+          "air.multi.forecast %>%",
+          "  autoplot() +",
+          "  geom_line(",
+          "    aes(",
+          "      x = as.numeric(time(air.test)),",
+          "      y = as.numeric(air.test)",
+          "    ),",
+          "    col = \"red\"",
+          "  )"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992684245,
+          "endTime" : 1739992685008,
+          "interpreter" : "r.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<table class=\"dataframe\">\n<caption>A matrix: 2 x 8 of type dbl</caption>\n<thead>\n\t<tr><th></th><th scope=col>ME</th><th scope=col>RMSE</th><th scope=col>MAE</th><th scope=col>MPE</th><th scope=col>MAPE</th><th scope=col>MASE</th><th scope=col>ACF1</th><th scope=col>Theil's U</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>Training set</th><td>0.442465</td><td> 8.834477</td><td> 6.351386</td><td>0.1376787</td><td>2.870884</td><td>0.2174955</td><td>0.003856404</td><td>       NA</td></tr>\n\t<tr><th scope=row>Test set</th><td>6.849037</td><td>26.471642</td><td>19.501241</td><td>0.8396142</td><td>4.405293</td><td>0.6677963</td><td>0.675272574</td><td>0.5053222</td></tr>\n</tbody>\n</table>\n\n",
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s6aYgMEBjsiIiKaEDQNlbSOLjoa2wjz8BjsiIiIaELQtIpiT9GFsY2wcpbBjoiI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\nSrEEQSxsEonEG2+8AXmEnRsGY4eGhnBwwYJjl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EGYIxaLPfDAAwBw8cUXBwKB3BtwRKAYiSfo2FmbeDUIOlu2DMaisBMEARfaFhWl0rpz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\nSIgoKSmJ9LYmrud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+              "type" : "IMG"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create R UDF",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "multi.forecast <- function() {",
+          "\tlibrary(forecast)",
+          "\tlibrary(ggplot2)",
+          "\tdata(\"AirPassengers\")",
+          "    air.train <- window(AirPassengers, end = 1956+11/12)",
+          "    air.test  <- window(AirPassengers, start = 1957)",
+          "    n.test <- length(air.test)",
+          "\tair.model <- auto.arima(air.train)",
+          "\taccuracy(air.model)",
+          "\tair.multi.forecast <- air.model %>% forecast(h = n.test)",
+          "\tair.multi.forecast %>% autoplot()",
+          "\tres <- accuracy(air.multi.forecast, x = air.test)",
+          "\tprint(air.multi.forecast %>%",
+          "\t\tautoplot() + geom_line(",
+          "\t\taes(x = as.numeric(time(air.test)), y = as.numeric(air.test)), col = \"red\"))",
+          "\treturn(res)}",
+          "\t"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992685512,
+          "endTime" : 1739992686020,
+          "interpreter" : "r.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Run the UDF in R",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "multi.forecast()"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992686505,
+          "endTime" : 1739992688786,
+          "interpreter" : "r.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<table class=\"dataframe\">\n<caption>A matrix: 2 x 8 of type dbl</caption>\n<thead>\n\t<tr><th></th><th scope=col>ME</th><th scope=col>RMSE</th><th scope=col>MAE</th><th scope=col>MPE</th><th scope=col>MAPE</th><th scope=col>MASE</th><th scope=col>ACF1</th><th scope=col>Theil's U</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>Training set</th><td>0.442465</td><td> 8.834477</td><td> 6.351386</td><td>0.1376787</td><td>2.870884</td><td>0.2174955</td><td>0.003856404</td><td>       NA</td></tr>\n\t<tr><th scope=row>Test set</th><td>6.849037</td><td>26.471642</td><td>19.501241</td><td>0.8396142</td><td>4.405293</td><td>0.6677963</td><td>0.675272574</td><td>0.5053222</td></tr>\n</tbody>\n</table>\n\n",
+              "type" : "HTML"
+            },
+            {
+              "message" : 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s6aYgMEBjsiIiKaEDQNlbSOLjoa2wjz8BjsiIiIaELQtIpiT9GFsY2wcpbBjoiI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\nSrEEQSxsEonEG2+8AXmEnRsGY4eGhnBwwYJjl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EGYIxaLPfDAAwBw8cUXBwKB3BtwRKAYiSfo2FmbeDUIOlu2DMaisBMEARfaFhWl0rpz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\nSIgoKSmJ9LYmrud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+            }
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+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Run the UDF using the R API for embedded R execution",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "ore.doEval(FUN=multi.forecast, ore.graphics=TRUE)"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992689283,
+          "endTime" : 1739992692570,
+          "interpreter" : "r.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "                   ME      RMSE       MAE       MPE     MAPE      MASE\nTraining set 0.442465  8.834477  6.351386 0.1376787 2.870884 0.2174955\nTest set     6.849037 26.471642 19.501241 0.8396142 4.405293 0.6677963\n                    ACF1 Theil's U\nTraining set 0.003856404        NA\nTest set     0.675272574 0.5053222\n",
+              "type" : "TEXT"
+            },
+            {
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XLmVu+Zdy8nJiDc9ebr3QdbGmVi/5LvVIYQetBeCnYbb3eiFyCfUdPZREnWJHtF\nULbZ1i7QHIBdNtlDQbM4q2RisUKppN51MRbNZqvXI0v3bgabzxixxUQuPbsylz7942GeO8NQ\nUh6Rg/LlBqjZbHKVGKplqGHNpNREKlY0qRWOUKXVbn/IpKbyiF4plZvvH+u98P5HFwemIxPL\nqWxeKSiqQXVV0ZW8zhWL1WlQFQBWBmudt3LkcCf3y9G+Mz+ay7Q3T7U01lbX1ddV1+vgc8gu\ns8A97Gq26Yezj3UfifW7rpAzEvF0kc+xNqfX4nQJzO3LAwNgEQSHz2uxLqn5aCyfzyt3lTA5\nQbQ6nSbT2qJGAiCwLC9KxGw12ZxOURBWLzQsgMhxnChSQ1eUUskwNpp/ZGTmSotXx+eUiaSr\nxy157HomnVRZqhVsTo/Z69GXJkbsvkBcCVlM/D31y0IEFiNXFy8Nnr69UY2hqaz3gNjwzRc6\nur55ojLAky1c+4gEfLXbN3+wk04XIjcuDHeZbvj0hYj3FWdNQ6udT4nwYLB7jMYTIIQBAEoN\nSssH69M1QAkEbu8TRMsNvCfR0HLmA4YhhNx97w2gAAalFAxCCAGGuZ2Mym/WKAe928//9CCE\nMAwBUn5j973VcsQFFhiGAcLc+z0wKAVCCSHMl5RYKQAYal5JL85e+fTaZ2c+GZfEfe2vfOul\nQ03u6g0/KyGEviR7Kdg9Er09qf1h9Sv6wOfh2/9BGJZlmQd2cgNKKNhtwa72b3yzuTm833H7\nHgOrF0oAzg6iv7G1Mgggw+3P2/QR1xQDIJpc7r/09s97xzODwuHw894Xq5yywIpskSVzoxev\nffH90zJQQy+/Gw7A4W08evgPLbaJpYOzsVypmMuOzZ67PHzGkherup599sipo00O+pBDYZMP\nZwLy+o/f2PpdB1C+YdTDC4m0/ODbT3Ln2R79gltuIgAApGbnZnsvza3Mjsxq8f8vdf0d+88E\nYAlQXcnN9a+sZOcK11z+4LX4M5wIzXd3hSUMtftfPNr6xrN1gl6imeXUzLXB0eS5wU8n+z0f\ntLe8VGNyeDY/WZ8AsHaPt6mny30mle/9+Vlx0OMxO9tqaxuqLTyb34bGEwCJZWWrRRAFms9l\nlVKJPl1BB4ATiWQ1iZyJKRaK+VKxROnaHs+g60YhXyjqOjWbBVG0rE2oBQBNhWy2pJAcsfOy\nYLGwHGsyBJPdzrMlLZ/JKKr6lL1ZAryJFc2yyHKGls/nlJJy93lbU7VcLuC9VsQAACAASURB\nVFcCB7FYJJ6T794QsVSi2VxRFUqsVbZwsvm+3LctDAA1OXZh9urHP/9k5Nqcr/KllzsPH36l\nwVazwRJchNCXCYPdXXiBEQSR1Rhdyec0RTFWq3IGgKZppVxO0ykjmEzcvWu9GIYwzAOfiDme\niCaBk2w2V3D/Cz2nDr5Vy1keNoFHLwAvMqIocXpWL+VzqqLoD9mz1gBtJRMdu3GufyjrzZ/o\nqTva9rWeKpdFtAlpTr3yhZ4c/8nZ0p3KAwsgWysarRXVnvqZtrmJ8enZqanJ6dzM8sRU3+SN\ngmjXwx2OBj4IBAgQUi4CrG17vJmHdzoapID8OEsJH951ksBSnpZKBbVU1Cktl10ogGbopXxB\nVTTCiRLPcey9l+Fy/eXBFyLlPZQf44pNVYBidG5+9OpYmrEQn4WklzP55cydnzCpguhMztLY\nxPWZbFCWmuW7KhWSC7xdbcee/+7v9Mh6AZJzsdGKsx+dSY+ezUwPnb4wXieFw06PiYGHFk3v\nfR8AjMnpsXUc9F29aJm5OsoqS0xFa02ovs5r4tj7j5jHaTwHwPG8yeWyWRZ1JRHP5/M5AOnu\nePTEMTLhXTazZJGS0Uwyk05rVDKAYYACaJpWTCTSmZJOnC6zLDvumsJolEp6PF4oMVnRJ1sl\nl5XwnMBIstdrFxeMQjyeL5UKANIj94v+6hBgLZxod1h5MVNaikVzeYcOMgNAwADQi6V8NJoq\ngJt3eyyidPdCHL2QV+OJnC6WTC67jbfL23t3ZgpgqPlEKTU3dv3ilU/PXF/wxBwdz588daK7\nsdMvmTZ3QCOEth0Gu1WEAYuDd9rcYjZVTCzM5ZMBGwTKI4QaQDKbS87OZouCaA95zSbTo+dF\nWeyML+C2aDZ1ZWE6Oj+f0cN24B9yvSAM2F2C2+Uz5dKF+PRUKu63QGi9IhjVIR8tpaILSVl3\n1HYc72pp81c6JZ5lWEWBlaVcPDqvGgK9szgOAMojj7K7kje7reGO5p50Mb80+MXHX/z9301E\nxz+7PN/l8IV4hmWAkPsLk5t4+FyXI7C/Yvv2iJAtjMtrl1Wrno8vZ6MxxQiKwJWDXaakxJeW\nsqms3VLhMVss4pddX6EZoHNzcwvX+hXzwe+81PH8G8182L72y6cA+fjU4LUf/fU4O9d3fbHD\nIkHAud4TEcJKvC3kaX25K0v5pYF3pse/eOfd7qo3wvWeOgGsm48SFg9T3VPpG+wyLV5RWrN8\na2ONvSHErXNwPU7jHQBEFMVgKOCZSsDQYiqVWgLwAdi31nFfKgfDVnm9Fo9LuR6ZjSwtFXWP\nDsAABcgVi/HZ2dhKRuArqxxOZ8Vdmz+ruXxhbjalMBlXrcNprbSCxBJisVRXhx3LRJ2ai+f2\nrQBUPHK/6K8OA+ARxUAwIJnV7ML0RDzmUsHLlbeohHQmEx+fiKX0FlN12G2xuO/K30oqlZlf\nSBKH4qqscIgVZnjYiu3HYgCoucXBhfM/+eiTiV9c5oIvvvj8iVOvd4WbfMKOvA00QrsFBrtV\nhAWxwuwIN9fJqWhs6OqlUfu+CV+NSzIEPZGL9E2MjF+dl419Ffs6Kt1O86OvwoKbtTfW1bin\nZ8evjVy95RSvVbTXVli9VpHRS0Yxm0utJIrsiuaq9FmrvQJIAbO7prXemlqO9V8+N2ztGHDX\ne62ClTdoMZVOFSNR3e53uQIWiSFAgIBuaFqxoOuaQRhDLyayy5MLV24ODkwuKJpnLZupBcgt\nRpKl8Shjddg8HpvZYbe6bEbRiLkk2cjppWK2SFWdsBJnMkk8Y5RS8Vwul9GoxACvFzf38G39\nXfAu1lpfG7bPzM4MjfWNeq1jFQ2VDtbGKblI/8zQzZvTNMF4W9qqAv4vfXK2UUgYib65+eTN\nqNcXbu84efRwPVdjW7twGQDFuFeQh9yF8dz568NzNeZ4j3OdbSVub1BsZgVTsKFFeO7gjZ/P\nXzp3dnS47UpDnbtatmx6g37CWwlfX9d57OW3VL/SpIf2dde4qizMg5dto5DQHqvxnEl21NR5\nb416k1PFeGw8DSYJ7I8V3Y3EdCk6dXVSj+qW8P7mgNfu5baWMKiu0vh4dGnp2qQOzorw/iaf\nbLJynsq6qrqZOX761vKI98qEvy1kDZmNUmxobuTm9clixPDXdTSEgm7z7c9gBoCeyyTnx2cy\n0GBqaK1wOQI8EQAYm8Pd1OLtTzlXxpKJ9GwW7BKYtn5qXK+dgnuLz0NLaSM2Pj2fujapuRtr\nw211Hs5iMvvqW6uH80k60Tszar08VVHj4JxMphQbmBwd650kxVpfS1dDhdtqul1r1AH0VCw+\nO7lQMtda3PVBm+zhNrtG55FtBDC0fCK3NHSr99zZj/uHU162pXvfoUMnupuaKgS36akpdyK0\nJ2GwW8MCW2VzNvcc9C9cWPzlp+9ctZRqqp2dHs1VnJw99+71S8OfzfnbT9U+d7zGX8E++p5A\nbAUvsx0dtcml8Qu9718vJO1e6VRn+HCtUyjGS9GpuVuX+lekS/kDrx+prfaGgKuyuVoO9YQW\nz0y99+kPL/Iph9dypNZWa1P1lZHR4ei5y4XmU/sPBtvdko232lxyZi4zN3xlrM3BJgNeLj65\nfOvKx/985otLwwsl9U69QUnB4qXxgej3r4h1bY3dPY1hj9nJ57SVydjS4kyCKlWyy2WXRInl\neJvNKjL5wuJsItGwUjK8IsNv9uHbuqUrU8Gb2LbWUGxx/MzNT6/pWlOdoycsNpozc9c+6/vi\n3JkJgWmvfe5oXV3NurWx7WSkoupw7+x8ob/U8GaFq6GWl+8p5hAASTY5G+orry9GoxevznX7\nZmlz1UajvowcCFc+93pr/w/bPvtkqn+M+jq7HcGgZfNjVyYAsenZ79Uc/sbXKQ8sL8kiT9YZ\nPXzsxjtls9zY7LsZa8if12OR4QhU+qDy8YLdwpVU78/+5kfFy8Wab/6bP3nOZnewWywd6SV9\n4rOJ06f//Y+KdN/xt/7n8PGgtZO317U3x1dm3VduLN1gftHbXFJ91jCN9X0xfP7zM2NQqgv3\nHG2trfGudosBUEylEsPDMzmuw7d/f8Dl8gEwAMTp5No7A5+er0kOp1ZSEzGo84HzMU6ND7Tz\nWHDrwS63og/9/NL7Q//Lj4rdf/C9N6vrjpjFsM3b2tMxsnzZ+slno9fs7zQ2vN5ibhaXYlff\n77t8/bMZe1V37bHjzf4KcfXN6gCFyMrKrbF5w+OtaGkJmc2uTS++3gStlJ5fuvjT8x/3/edP\ncpUvv/bcd3/r1RZnV0gUtnW4FyH0GDDYrSIAIEiOUNWxr/dwfbP6rDr92cd/098rUF7PF+JL\naVrd/GbLM0cPHKvgK0zw6GBHOFaw+w68sJ9Y3pJGJ5OLkbM/fv+66YIs8izhGAIAvLuisd7n\n81pWX72y8tg3jvA3Y+x4JnXt6tszE2bJIvI8C0TyQaBRcjqtwPJswO5te+alQ2Q4emHs571J\n68JpE2eUtFI6JfqJv1A7P+Zeu8hzEjhrHB691T6VGl/4dPwzAAJUpWoqGVdzTW+29vS0dNvD\nbkEk9kDH0abcrY7FvrkvFv9i7mOBWFwOe2OHWxOdDdap/IYP39bfBcfyNv+BUx1UepmdXche\nvfyT+Vsm1syqxfhSJu/yHDvW0H342bClxrKJhP34DAAtFY3MXrk5n3UVw21+t7NOJvI9AYoA\nEE6WHY313pG8L34zObfv6pRu8hLvQ94bADC8k7Hva2658sLhq7+MjkxeOj/Q+aLNJVc96maz\nqxgARjBZBNMGK3kNACUVjUQeq/Eu2UxsbRWh2WOtn0wpkStXZvcfchk2MwEg2XmI9p+5Nv3F\njVkNllbmJ6/NxaJkgP7yP/zkpuVWUOKqDlXWtby0zxtySQBQyKYSkfmE6snzNpvA2TlgCEBi\npLA0+FHvbN/EnAZLU323rudLxaHT/I//t78+y54Oebiqnn1NtS/t88oiSw0jl4om4tG44bHx\nFrdIzBwhrChVHqruZt6cPzu4FI+c+8H7fcIVE1WSC9mc2dL9akfXkReb7A3O1bis5aE4Fl1e\nvjzuYrorjzxTFfDevpsyEdzE2VUTHj3SeGUxttg/sHhEdhkmkQCQdds5/MWm2wmgKxAbWJga\n+ejSzGx0QYOlgc8HhpQCvfxPkcxE8VPGF6rlqnpOtAVOtrgBQFOVdHwpkc7EGTcjmHwikVjC\nyi65/rm2I/JvLp+d1ccnf/WffnqGszBFJbGQLAWrXz1+5FjPczVS0LKaqpQEFMfmFzI3Zvyu\njsrwgYDDKrKgbHRMbaGddoCVbHL85oXBm5dHFqNG4dqnGiSiTu6Du0YyzNU9rsYjJxqtzRWb\nPawRQttiTwQ7huE4yWqyqE5VkgX2ITdAJABEsPl8Pa93iS4+/4tPLg+cfnc2WmCKrN3qb2o6\n0nH0tW+caPV239mZmLC8JFicZrPZZuKE+6sQDMObnK3PtnmqLOb3L53v/eD8p1eXMgsplbP6\nrP7acMuRnuqqEwcqq92mu179jR5bwMW/+/H5vk/PnL6cgSzYrIGmliOeo19vcgc8TsIS4me9\nxuFXTxrymZlfnB8aSH+W0lhr0Bmuaj7e5hGdtoUUZ5PMPHAMgGAGb4svqx6rGf/8fN+VK7em\nV/KxIsNafb76g3UH3zzwzL6XDljtBATFWdF5siVNj/a9d23gyk8+zuZVr6ep+5XKf3HA7z5c\nPX7p0kYPf0jXs4xgFmXdYTPJInfXxhOP7jpX+8lWu0cr/eLcuSsffX56Pq0nVcHib6pqP3Tk\n5ddPHmw6UiGu7mtCCMsLsk22WG0yJ96pfJHy3s2S1WE2m20Sc9cLEQCWE2WTzWkxS2aBWe/W\nRxRAzWYykxORLF9la99X5XVUCw9slAPAmcyW2iZ/1VKTMAjpxOi83iGzFRscdawNWFtdUyv/\nbNOV96IDE/1TiSPhEgREYHhJsDitqhnMAs/cbhPDS4LZYTVkYuYfemeL1Z8ULU4bMbNmjme1\nXPwxGw9WGeSGisrh4z38cibWe2k0Ut1SqjELBNjcMsyfufzB2b/44fXinb1/F+DcX38C8Klo\nkXpKPc85OsO2kEsEoLlcPprIGLYWu7O2ym7yCcAAQGqyNP7hJ29f+snn48XynrqSGZb7Ybl/\nDoD1NEg93LdftZ5odskiYxh6Op1N5VXWV+kKhGrMrJMHQnjefyDAO9/UFM+Zcz//5MNry7nF\nLEieen9zd8+rb5zsaX2u2mQWmNUtObJGbGBlKXJjubrBXnO0219hX/2N8A7g26vr/M8cMP4x\nsThyczrRbFLcokgAUlPrtXMAlgc2004XD6BpEBuM3Hz/7b+/eGlkpVT+IGIxwfjp5fHTQwB8\n7Qmpxy5K8skWFwDVVDWRSOcMVgw3en3eahNYCDCcQ6g63mSYHWz+/c+uvvvF+dNJNanLkqeu\nvrun59W3TnYGTvjLRwYFAKMY0yNX5pYLA/HGY6HwwXaPzQJkNdhRoLqSL+VSqaSQshmCSeJZ\nhjM2304bQDxfWBofSy0u5kQJkpMXr01evHbvceU5/kd1LzVWOoVysDPUkqbkFS2VSGTziqpu\n8ebCCKHN2xPBzuSuqnvtX791xGgvVTbWVZg3eNuEA9bhDHft+5rHdTh5IpEraaAxAm+y271+\nX6XH71jbcI0BED1NR4/8l/+2mqtKWUMdlQ9OrCIAgmAJBfa/cixwsPpEMp1X86pBeEkwWc0O\nr8frDboEq7h2qWYBTJaK1tpnLV9vebnrzXReBQ0EXrbZvX5flavCfvseAKzkMIePdrxUZ216\nPZlTciolvEk0W+wVNq6YN069ToJtYg3UOgCABcZk9jWGD1terjne8XIqW9RKOiG8SbK6rZ5g\nZcBpJyAAACeDram62/Yt14Fn04VUQdUMSbJ7Qu2NLpnaAhZ7/YYPXw9vCzgO/87zdarnZWdd\nnd95e33iZrqOAcYiu5uaTn3X3vr8/lgmp1DFYHjZbnZ4vJVhv9u0mtIIgGD1NXX+F/99RVE8\nLjc3BNdm+bMAZl/LiWN/+m8bhZqsw98eXBuJFAHc9ce/Y/Z3HXN3Cl5bwPbgQUEABHfDka7f\n/3O34nmDrTzQ5F3/9hScDew9racq/8z5PBtqstazNTbGZH7EUWeqPhKSPH/UwiSovbq1IiAD\nT8DX+Uq3o8qmVKpysNVvLd/e6vYX1bBqDjZWWDdYnVqx/9UeZ9ihhTVzsLHCabIfEX7/zz1b\nb/zt316wuumV12rfz/Zf/nDuGVtfa7DZDHZbGBreeul3jwaOx/UH66UMx3qbPP6qsNsEoAAU\nVlay4xOar7G5Yt+harfNQYABAHe7qdP6G3/2ypHvZPQHrvFEtLHexpqQ3yyyAEVdT83PZ1eS\nQuORg0097SFJWC1UsrzZ52578bC7w38oni5oeQ1YyWqyeTyV1QGPWbqzuaJeTMXnL/TOLuXz\nh18JtLYctsNdm8wwALy3sbXlhRfD70Yn+s8Nn/RbKhzNMgjudlOn5ddqJytA4Jmqk5V/VvGN\n76WKD05GZSwVrLexPewqr2UpFFKTk7kScXa/drKtrc5Hyss4CABrctf6ur75QuB44/PJnGIo\nlGNFq9Xj91T6g07T6icHCqCnFucXPvtiSQ3pJ9+oq63qsN6zNMdQCvFr78wPDf7vH1ceOHHk\n0Hdeb/OYG6XNt5MFqPSEn3/9v63sWknH6Po1c9HXaAvd+YtLTF2bPfP9s6OzX4ytjPfOl8S6\n1vUehRD69RG6wWZhCKG9rDQP2YFf/WTgo/fnGn7vey2njuy3gmsLu54UAbI3/+mdG+9/nDr5\nx/auUy9WckHLVid65UrZyOUf/GBkYiV18o8bWlteqOLkrW2lQQG07MKtyff/8krUctry1uuH\nwt/u8TD3Ve7zo7lI/8/+duDWIt/we99ra6/db13nJshfZjsNgGx0YujKD34wywZSJ//4SK3z\nRHCrn70NAC3af3ryg788B8/0O7/2e8d9J5psBIBAASB1451/vPCTfzizDLdSJoBAy6nnn/uX\nv30oYO3YaH/ubbB888Oxd//jr/riH4waAKKzet/+7/7XJ7rbXgqD/BTto4PQboDBDiH0EEYR\ntOzKcjYeLcnBCrPLYWW3dF9gHUDPRCKZaExzhjiryyURacvbm+mGpqSWFvN5RXOGZLPZJZEt\n3uaEAlCtmCvE5tMKl2B9HrtUYRfgvmCn53Qlu7yQzZUYOVhhschWdksbv21LO3Uln00vLhWJ\nqDlDdhPvlLbaXRSAKtlEITqfBHuW9wYcgtPMAQABHUDLRBaTS0sJBfIaAyCanS5XZcAmctYv\nefCmlInmV6ajWTWWBwCGkyxWf9hps7glWG8WBELo8WGwQwghhBDaJfCzEkIIIYTQLoHBDiGE\nEEJol8BghxBCCCG0S2CwQwghhBDaJTDYIYQQQgjtEhjsEEIIIYR2CQx2CCGEEEK7BAY7hBBC\nCKFdAoMdQgghhNAu8SXfR+ZJw/tqIIQQQmj3IWT9Ww5ixQ4hhBBCaJfY5RU7ADAMwzAMwOod\nAACQVYZhYIdsl/LHJoZhCCGUUjzetlG5b/Gg3XbkLmsnSbQtyr0KqycBPGi3CyGEZVkAKJ9m\n93LHlrviYRW73R/sFEVRVRVPW2tEUSSEKIqCfbKNWJbleV4QBE3Tyn27l08624sQwvM8wzB4\n0G4jhmF4nmdZttyxiqI86RbtHizLchxnrMJTwXZhWVaSJEKIqqqapum6/qRb9MQQQkwmkyAI\n6353lwe7cq7XNA2vB2s4jmNZVtf1vfxXsb3Khbq1j5Ll4w3P5ttl7WM6/iFvI4Zhyqlu7ST5\npFu0q5TPCeXTLJ4KtlG5Mw3D0HV9Lx+05UL7w76Lc+wQQgghhHYJDHYIIYQQQrsEBjuEEEII\noV0Cgx1CCCGE0C6BwQ4hhBBCaJfAYIcQQgghtEtgsEMIIYQQ2iUw2CGEEEII7RIY7BBCCCGE\ndgkMdgghhBBCuwQGO4QQQgihXQKDHUIIIYTQLoHBDiGEEEJol8BghxBCCCG0S2CwQwghhBDa\nJTDYIYQQQgjtEhjsEEIIIYR2CQx2CCGEEEK7BAY7hBBCCKFdAoMdQgghhNAugcEOIYQQQmiX\nwGCHEEIIIbRLYLBDCCGEENolMNghhBBCCO0SGOwQQgghhHYJDHYIIYQQQrsEBjuEEEIIoV0C\ngx1CCCGE0C6BwQ4hhBBCaJfAYIcQQgghtEtwT7oBCCGEEEJPDKVAKRBy+3/X/mOHwoodQggh\nhPYuTSP5PKsouyQRYcUOIYQQQntXOdgJgkEpcBxlGErIDq7bYbBDCCGE0N6l66RQYItFJp83\nLBbdZDJYlmKwQwghhBDaSSgFADAMoqrEMAjLEkIAgMiyzjD0CTfucWGwQwghhNDeVV48YRhg\nGCSb5XTdEASD4zDYIYQQQgjtKIZBDKNcuiMAoOug67D6lR05026XrAFBCCGEENqScqGO0nvi\nW7l0ZxhPqlG/Lgx2CCGEENqj1stwpJztnkh7fn0Y7BBCCCG0F61OrXvwi4Tu1Cl2GOwQQggh\ntFc9OBRL6e1pdjsUBjuEEEII7UXl4tzaUok1hkF2btEOgx1CCCGE9iZC6ToVO0rJfV/cQTDY\nIYQQQmgveth0ugcn3u0gGOwQQgghtEeVdye+7yu6jtudIIQQQgjtKKtz7O4bdcXtThBCCCGE\ndqCHbXfy4IqKnQKDHUIIIYT2orU5dg+sir1/RcUOgsEOIYQQQnvUg6OuaxW7HQqDHUIIIYT2\notV7xT74ddzuBCGEEEJohyEPLp5Yu8/Yg0O0OwL3pBuAEEIIIfSVKie2cnR7IL2VV07gnScQ\nQgghhHaO1ZtMrP+tHXpXMQx2CCGEENpz7kp1BOD+GXUY7BBCCCGEdoyNN6ujdJ3pdzsCBjuE\nEEII7TnlmtyDtbq17+r6jlw8gcEOIYQQQnvO2tLXdeFQLEIIIYTQjrHxZnWUgq7jUCxCCCGE\n0FPv7s3qNvgBrNghhBBCCO0A5ej28O8SXcehWIQQQgihnaC86HXjih0OxSKEEEII7QCPqtjh\n4gmEEEIIoR2ivHhi433sNlhd8dTCYIcQQgihPae8eGLDfezIBiW9pxYGO4QQQgjtORsPtuKq\nWIQQQgihnaGc6jaeY6frt8dqd1a8w2CHEEIIoT3nkcsjVsdqdxgMdgghhBDacx410koAyMZ3\np3g6YbBDCCGE0J6zuu5145/ZeTPtMNghhBBCaA+htJzqHp3YVmfafSXN2iYY7BBCCCG0t5Sz\n3SMTm2EQXYedNRqLwQ4hhBBCe8hdyybIw/axW/tJrNghhBBCCD29Nt7o5G44xw4hhBBC6Km2\n+fvAbmaBxdMGgx1CCCGE9pDVOtyjZ84ZBuy4O8ZisEMIIYTQHrL5mXM4xw4hhBBC6Km2yb1O\ntvSTTw8MdgghhBDaQ8pz7Dbzk4aBFTuEEEIIoafY5ufYrS6ewDl2CCGEEEJPpa2sigVd30mp\nDjDYIYQQQmhP2fzMOcO4/W8HwWCHEEIIoT2kfD+xTf5kOQKW/+0IGOwQQgghtIdsfigW4PYt\nZXdKqgMMdgghhBDaU1az2maKdqScAnfQaCwGO4QQQgjtIZvf7uSun98xRTsMdgghhBDaQ7Ya\n1AyDbCkIPlkY7BBCCCG0h2w12JXHbbFihxBCCCH0FNrCnsPlcVtd3zFDsdyTbgBCCCGE0Feh\nHM62WoEzjK3NyXuysGKHEEIIoT2kPBS7eeVVsTvlxmIY7BBCCCG0VzzGpnRbXUX7ZGGwQwgh\nhNCe8Hgbl5RXxe6UOXYY7BBCCCG0VxjGFlZOwGqFD/exQwghhBB6ujzeXV9xHzuEEEIIoafO\nlu4Su8YwALc7eYoQQhiGIWTHZO0vW7k3GAYz/bZZO8bKWJYlhNCdcg546q11b7ljn3RzdgmG\nYdYOWoZhWJZ90i3aPcp9u3YGwFPBdrn7iH3sg5ZS0DRmq1UtwwBKy6/55H+b5SNqg5PhLg92\nhBCO4/BisKZ8dQQAURTxdLNd1s41AMCybLlvsXu3y9pBi3F5G5V7tXyl5HkeT5LbaC3YrXnS\nLdol1sIcx3EMwxhb2rBklaqCrsNWMyGlhBAiCKIkPcZrbjPDMAzD2KA6s8uDHQDgn9bd1oof\nlNLH+6tAD1qr1QFAuWPxkNtGa6EZD9pttJY8YPU68aRbtKusnWPxVLC9yp1Z7tvHO2h1HXSd\nGAYAbP7DDKEUDIOW/z3Gi26vRx5XuzzYUUo1TVMUBU9bZYQQURQZhimVSrquP+nm7BLl5CEI\nAsdxuq6XSqXyH96TbtcuUT5oCSHljn3Szdkl1o5YhmFUVS2VSk+6RbsHx3Ecx5WTh67reCrY\nLizLchwHAJqmqaqqadpjPImikGKR0TR2q/lH12mxqBKiEwJPtsBNKS2fFR/2A7s82JXhZ6Y1\na12BfbK97u5MLBJvu/KJDHt1G93Xk9ix2+i+0yz27Ta6+1TweB1bXgbxGJ8QKaWaRnWdPiXz\nUTd4+ziDHiGEEEJ7AqXlTey2XHOjlOyUTU8w2CGEEEJor3jsrYYN43FKfV89DHYIIYQQ2hMe\nb4NiuLMB3g64sRgGO4QQQgjtFY9911es2CGEEEIIPUVWK3aPMcdux9xYDIMdQgghhPaEx7ul\nGNwJdl9Cm7bbntjuBCGEEEIIHn/xBDGMjUp9a8/5xG/jghU7hBBCCO0J5e1OHm8otrwB3gah\n8ClZV4HBDiGEEEJ7xWMPp5a3snv4dx9/Wcb2wmCHEEIIoT1h3fhFKS3ff3WrD7z3BwgGO4QQ\nQgihrw6l6+xaYhigqvDI26dvvCq2fLMyDHYIIYQQQl+6ta2JH8xepRLE40Y2SzWNGsb60ayc\nCDfY33htB+PtbviWYbBDCCGE0B6xzo1i83m6sGAkElRVN5qBV45uD/vu01Oxw+1OEEIIIbT7\nlQdhKYW1bFfOYZpGCwWaSBgA4HYTi4UQ8uCuJaRcqzMMwjD0wT1Nu4JC0wAAIABJREFUnp45\ndhjsEEIIIbTLrY2l3v9lCoYBmkZTKVoqgSAwkkQ5Dsh6+9E95EnufOtpgEOxCCGEENr9KH1w\nPSxo2u0hVF2HUsmIx41o1FCUhz0D6Pr6E+koJQ/71lcMK3YIIYQQ2v0evEtsOc/p+u26napC\nKkUZxrDZbke0+8p25fHWdRdY4D52CCGEEEJfkYfsYAe6TteGUCmFUokWClTTHjrequv/P3t3\nuuRIkiSIWVXNzIG48qjMrMqqrqunr+mea0kOV3ZlhRQ+Ad9g35AvwF/8N+TMrHA409PbPX1U\n1dRdecYFwN3MVJU/DO6BwBUekYgMRKZ+UtLShfQAHKjISE01PZZHbyIrf+k1s8DOGGOMMW++\nxYwdQMnYnfvX9nBWARYr8lZOs1O9YJnsa2OBnTHGGGPefEtr7EpgN/t4SeMtnVfcFtIt+SXL\n2BljjDHGvCbdzom5GC5nYNa5K7uOisUnYZ6fZtdNLV7VMPuaWWBnjDHGmDcermiemE/OtQ8u\n75BYmpZrY7ubP4cFC+yMMcYY8zZY3AbWZuDm0nia8/KhdCKY83yNXbdMbNW2sdfMAjtjjDHG\nvOFWdcXmPJ+cW1p4N/NLOBfzremouBEW2BljjDHmzac6P0C4i+EufHD2lxYDuy3ZEltYYGeM\nMcaYN9zS5oZVXbGLabyZJykzis++pJ1afH33fjkW2BljjDHmzbe4zlVVV2Xg1u6ExblYcEv6\nYQsL7Iwxxhjz5uvmksxaWmOXs+a89DkQAOdG1q1ZIHsjLLAzxhhjzBuujCNZWmPXZ7jd+a9C\n5rNuiW5C3pawwM4YY4wxb741NXZLH1x1urqQscMy7uQabvkqLLAzxhhjzBtubtxJtytiRY2d\nrsnAiSAzLRzFXs99X54FdsYYY4x5481vnijnpyK6GJOtGndSqELOZym6pRPybpAFdsYYY4x5\n853vilURXbsQVldtkihdsd1TLV0ge4MssDPGGGPMG25xjh0zMC9J1/Wpscv5XI2dHcUaY4wx\nxrwO3ZST2XPYEr3lvGZYXam0W/LLIjg736TbFXs9t39pFtgZY4wx5k02E9UhwDQCY9alGbui\n9FWsOIqdboztQkbbPGGMMcYY85qo4mLBnEjJ2C2P7FQ1JV21MbY0XpQvLU++PfxN34Axxhhj\n3ihdmwIRAADe9CllG9XNHcUuXwhblLBPRGe/qoUi0ywdIi7dQnuDLGNnjDHGmE0Sgbp2MdKW\nhDuLW2IB1tXYQbt/Yu00O8gZt6oftrCMnTHGGGM2SQSbhsrk3qqSEBRuNG9XEmyLR7EXtb6u\nm2bXtlBs0TKxwgI7Y4wxxmySCNQ1pkSTCR0ccAj5pu8HlwV2unQ6cfurkLOuysapgggxq+rK\nZ7gpFtgZY4wxZpPKaLeUMGd0DhBhOOSbytupQkrTDOLsg69+FMuMqiuDv5tigZ0xxhhjNqNt\nFD3boDoeEzMQifd6I1GdKsRIMeJcBLZ+CrFqyditTMeVuND7Jf22N8sCO2OMMcZsTDfarYQ7\nOSMilVUNrz+wKy0OKWFKNJd+W5+xa7ti1zwzMiOiNU8YY4wx5s01M/4DoT3TZEYRRHzdSTtm\njBFTQuZzL9zuDVtTY6el6XWpko/MuQR2m77pV2PjTowxxhizMarzh56rHnwNcsbJxDMviXaY\ndX2NHfPFNXazu8W2hGXsjDHGGLMx7WwRPP8gvuYpvuW1UsLJhHKe/6W57RGLRCAlvegoFojW\nXXMjLLAzxhhjzMYsNhO0UdTrzmyJQIzYNEsziNPAbpXSPLF+NQUzEk0Hqdz4do2OBXbGGGOM\n2ZilQ+Ne/94tZmya0jMxH3OJaDlmXXM/XfNEuWYhbkNo6/C2qiUWLLAzxhhjzKZ0p5xz2mgP\nAa49Duq6ceva5UyLy17b4cMXPEkX/K3KxqlCzluTqWtZ84QxxhhjNmZp7dprXr1VQq7JhFJa\nEniVwO7ClRHlntfMuttOlrEzxhhjzMYsbYBdej57DS9dXgtypqahpqGlGbULZ9TNXsmsiEC0\nNDM3++C2RH8W2BljjDFmY0q6bm5/1/pOhc2+ekp0cuLGY7eqAK5/Hq6EgM5t/DavkQV2xhhj\njNmYNXPsrjVjV9pUY6TJhMZjFyN1Q5IXrtTeGTvNWatq6wrp1rDAzhhjjDEb09bY4cKD1xse\nlaju+Hhdrq67mZQuLLGbXtkzBNwe1jxhjDHGmM3oFsXOPd4OKL7G2C5GPD11pa6uzdUtf7ly\nFNuzxi7nXiHg9rCMnTHGGGM2ZumSia4rdsVYuA2IkUajkqu74Nn7h2tl7ZiIrooRt5Bl7Iwx\nxhizMapLMnPtnrFrfd2+ZXyXbZ64XUexlrEzxhhjzMYsWzKBs49vPF1XXqscAfcL1/o3T0DO\nt2yOnWXsjDHGGLMxZWTd4uOqWP65ptftPwO5bJXocxZb2ixuV8bOAjtjjDHGbEA5hJ0bYjer\nlN9dh3ZfWc+Lgbl/V6wFdsYYY4x5+7SHrcsDrGVHtJt83f7hV/87sZVixhhjjHlLzVS5rZoz\nUk5pNxwo9a+ua2+jb8aOWVO6Zc0TlrEzxhhjzAaoLhl0MvOr3ezi1/q6i/q3RLRX3qaUnQV2\nxhhjjNmA9ZkzVWBe3lexodft+8yleaJnV2zPK7eHHcUaY4wxZgPavtSVAdY11dj1f+aSexPR\n3tdPa+yub67yxlnGzhhjjDEboIoidOFR7DW87iVq7NqlZ/2fWZmB+VVu8LWywM4YY4wxG3BR\nD+l1zbFrQ8aLn/kKwWX7JXpbKu0ssDPGGGPMBqyvdSu/eh2LxVSRudfT9q+um1U2xt6SuM4C\nO2OMMcZswoXdqdc0oLj/XLquZu5ST84MOd+WhJ0FdsYYY4zZhNL3urYr9roGFPfst71qxu42\njSm2wM4YY4wxG3DhmLqelXCX1b95ol0RdrkYjbnv3LttYIGdMcYYYzZAdV3arK2xO5seskGX\nOYq93O7XkuSzjJ0xxhhj3i7rd8XCdNQIrhl0d9XX7duTcbXjYGa9RTV2NqDYGGOMMRtw0ZEo\ndtcQ6QaH/V4YUM5cqbPjTojjsDkZ1keD5vh0792TOx8s/aqLxrhsFwvsjDHGGLMBfbpT+3ew\nXvJ1+2bsZqvlQqrvH/77w2e/f/Ts91988l+WBnbl9Jb51mTs7CjWGGOMMa+klM31ORJtZ85t\nJl8387p9a+xmM3aem7tHXz/+4V8/+fLvHj77Q9UcE8fFr7pdGTsL7IwxxhjzqtpVXReU0G18\nsVgX2/Up3Wtr7KYxms/Nwcl3j5794YPv/vmdF5/tjl/6vCSwa5snbkdkZ4GdMcYYY15JG131\nunKDyyfaCXZ9rxc53xWr4jiSJADdGz979PwPw+Zo8auYISXL2BljjDHmrVHWhV14WZsz21jr\nxKVm482NO0FVz9FxQoW98fOHz38/rI+X3rDV2BljjDHmbXGppV4bXCxWDmH7h1yleeIssANx\nHImnGbuHz1Zl7NRq7IwxxhjztrhMX2rfmXM9XTZjNxuAoorj6CQBwO74+YMXfxrWRyjzQVw5\nwFUFVd3+xJ0FdsYYY4x5JarYfwfrZrtiS5jYM9ya2xWLqi5HxwlUdyYv33nx+XBySJLnAs8S\nDl52ZcVNscDOGGOMMa+kDbB61dhtdo7dpXpsS41dl3VDFcdJEeude0yhSqOD0+/vnHznuFn2\nhZts5r0+FtgZY4wx5lX1DHraPtZNZeyw586J9nqdz9hxFHTj3Yep2iXOd05/uHv0dcj1sttW\n5m0/hwUL7IwxxhjzitqWiD4Zu77DhHu/9CVaMea6YkHFc2Q/OLz30cn+41jt7Y2e3j/8wqfJ\n4hfmfDvGFNtKMWOMMca8kv4tERufY9e/Gxfac9uz5glQJ1HIne49AhUE2Zm8fOflFyFPAHR6\nSfvlJbDbfhbYGWOMMeZV9Rw7svFitZ6ZwtlXn+uKRZXsBif7j5Pf3R/9cP/lF9WSjJ2W/RPb\nz45ijTHGGPNKLjvHrkSBG8nb9e+KLem68+NO1OWEKuyqk/3Hzx78FFUPTn/YmbwIcTz7pF2N\n3fYfxVpgZ4wxxphXcqle13a162Zet+ezLd1RW7piUSS76mT/vecPfsoUdiYv90+f7NSHqDL7\n5dYVa4wxxpi3hSrMVqSt3h6LALi5SjvsfRSrIipyLuWGqk5iydiN9t99/s5P6uFdx/He4Zd3\nTr4jOTt5bTN2r37D1+611tipCgjnnHNmURUgIO+9GwSHCKCgnIQ5ZRZFRXLeO+89ASEAgAor\n55QlsygSkvPeO0cOAXFjW+eMMcYYcykLq72mnadEoLrkj+iSP3NuA697mSPg2X5YhS5jB5p9\nNdp9+PL+J5Od+6hy7+jrg+Pvfnj3Vzxzec63Y2Psaw3spDnkk2/+9Icv//DHr5+fNie6y/d+\n/tOffvK//Icf7Q+IuBl/+7sfvv78N589fzZ2Mdz/4Oe/+OTnP/vowL0zRACJx9+Nvv3dv33+\n5PdfHUV/f/jgg09+9cuPHr/z0YHzFtcZY4wxN6RsnuiCnpTg6Eicwzt3cGn0JgLMSLSBKKnn\ngOK5LbEAQMokiSQDALsB+4pdNd59px7eHTZHO/XLxaNYy9jNUAGI9fHTl3/675//8dvfffb8\ntIm1uyvp8TvvNSyqUks8fPrV55//7t8++278vEahH8Z+Z1w93P304M4gOBmNXnz/1W9/9/lX\nL//0tBZ6Pjgaj4fvKFWPdu/sEL5y3G+MMcaYK5rdPJEzHB2pcxoCDYcQwnz2pQSC3mM7VeTS\nShC5kClcd/1c5wRJdpJQWZGyHzAFJl8P7sRqP8TxoDmdywTauJM5EeT5828+/+f/69c/3P10\n8B/+09883vvg/p4f3LlzsHtQOZd/iKPP//DZyedPH7z/t//lV3vN/dM//PaHw9/+/b99uPez\nx+/s7zRfPX3y3T/8Olef/uXf/udP7p3+oXl58k+fffkF089+tOtCtfO63okxxhhjZs0FWMw6\nHpeKNLl/n+7dmw/sREog+EoZu8tuJzvf/aDEyXFEVSHHfiDkAUBcYBdIM0nGmaBTBHJWG1A8\ngyOMvj8+PPz982r//fd/8uc//9l7+x/eHXhs6+dOj/jld09jeDl89MuPf/Lpw+buqHn28uTz\nb74ajT44TMEf/zA6HX8lj37x8NM//8XP7p7moy+//P0fXuTn+y/4J0OFHTuNNcYYY25CF2N1\nibSUYDJREfUed3bUe3AOZ69nftXmiZlo8tJdsQjgpAR2IuSyK4Edlryd40iS5pKJzLB+pdiW\nxHyvqys2NfDy29NR/GLnF3uPP/nbn9x7fLcKOPOf4vREnz8d795PH/z48f27Hzx4cPDRL97d\nG35U/5DT5HmT0vNnMcnxBz/dffT4x3d2Hj7+6O67H34I4/14+DzL6W3oQDbGGGPeVLM9sKWH\ntK718FCPj2U00pznLsbZo9urESnLxHo9yfyyV1Xi7HJEFUWXS8YOQZxX8iRMkmePibtxJ6ui\nt02N5Xt1ryljp8w6HnEa5QE8+fd//c3/+btRrqS6e/D+px88fvDjR7sup1zX4IMb7nnnvCOA\n/SrQbpiI5LqRWNcijnb2fDUIREBD74c7VcwYJ0kTAzgAgJOTk88+++zrr7/+/vvvmTmE8Jd/\n+Zcff/zxzs6O97ZmAwDAe4+Iw+HwFvT23B6IWD5Y7719thtXPlvEVz24MR1EdM4RESJWVUVk\no682hoiISFVVVW7F3LNXUwKaydmmBhWZbnco7/7kRJ3jx49dVQHAdIoFIhHRYBB2d/u+EBE5\n5wAghOCcC4GbBmK8xFiMuRo7J8lzRFVFYj8Q57XL2El2nGeft2ueUFXVJS9a0ochDPq/o6th\nZlVd83v29TVPaIzKY/Dy5Mvv//XfvvuyHo73Pnj0S/mbv3KP7g93mXNOSOSCRyIABQjOaQhR\nRWLSnLIquhBo2mDjkVzwTJRn6xlPT09/85vf/MM//MO//Mu/xBj39vb+63/9rx988EFVVYPB\n4DW92dvAwtxr4pxzr97Bb5axb9prQkQhhJu+C3NblQAuBAZI5QERYD4bLHJ6qinp3bt4cOBm\nohEioqpyOzuX/ktFiWlEVFW9F0Tu04FRMnYi3ZVdjd30KJbRA4CQF/Ik2UmeS8HNtl8sBpMl\ndxhC2Nm53j8CUko55y0I7JAoBNCQT+j+J3/zy5/9b3+RjybHo6+++PfxF/TFTx9+AH4vVMqc\nm6giAALQ5AxNM6jIDSoMVYWEKTacS3Y0iaQmeha/G8C172NnZ+fTTz9l5nfffTfnPBgMPvro\nI+99nssCv8Wcc4jIt2Iaz+1R8h/OOWa2z3azELEkluyD3aDyqXYfLN+KZr9bony2APD2ZOyY\nIcazKIgZcp47lp22lCJOQyJmjZGbRkoarw9ELH8DERERYZa6hqbRnp/xfI2dlhq7hCqKlN1A\nyAFgF9iRpMUnEYGcIQRYDKuYgVmaJjXN9cYbzCxzc5bPe02BHToHu3sunA4aufvwow/+h796\nV76sv/zs+F9/Wz/bexr5vh/c2dt3qdZ4OG7uj5sc8vNRw0d6cNeFvYGr9vcc1e74ZRrfPYl3\nAh/HyclxrjLs7AUctPHxcDj88MMPd3d3P/74YxFxzj1+/JiIcs5vw2+wPqqqQsSUkn0gm1JO\nCQHAOSciKaVyCnPT9/WGKD/N7Zt2s4ioZECJiJljjDd9R2+O8ne8EtWt/wP4zVAm0qWEAFRW\nSrRHltMLSkSVM+R8NpFYRGPUlCTGvp8PkSNyqpgS58w5c11jjGXtRK/T2LmuWMfZcURQmamx\n42lgN19j172XuWF4M5+D5qwx5v7v6GrKj8E1PwxfV8bOB7j/cOdefC+8PDgX5+J0bcT+PvHD\nnX96GQ7T9y8fDNzkwfG/PRnFrwbv/ZnfeTCsqgcPqq++u/PtH8cf7Xx+cv/B8VdHT775Gvb2\nqvsPHe23/01DCI8ePbp3716Xoiu/qeyvpEVXWJNztg9kU7p0XflRXv46ZSHIRpSgueSW7Jt2\ng7oCu5KxS2lJcsJcTflDp/w0yDm/BYEd5ow5O9Xpn+5l9dbs+26jPe26Jcr0kJQ4pb6/qYkU\nMaSE4/H0x2xdY9NQv31i5b/IbI3d9CgWVJRcdpWQV0BxXsiRZDpfY9fetuaszEtekRlS0pwv\n8Y6ubH1Z4esK7NwAdh/fe6f55YcvdfTln/7f8bdyFI9HxwcP7zx892Hl9of3HXz0o/u/a46/\n++73/3T4RdqffPeM37/zsx/fvXtw4Cq39/7de+Ofv/N9fP6HX//D0cHkmzTJ8ujHdz547553\nw/Y9Oud2dnZ2dqZT7URkMpk0TWN/yhZdJslSSps1+/fyt+Sv6a+HqpaeifLHpH2qm9J9mNq6\n6Tt6c3QfafmQ3/jPVkSZUYRmHoHFgJZZZ89nyxy78gn1fCFmmUykrunkRJlRtQSUvdZOwExb\na8dJcjmiqiDlbo7d6ho7OMvY6WKOUARSAubX9GPq5o9iAQcQfvTw4fhv/+KP//jrf/67//uH\nE9nFOx+8+8v/9P6f/eTHO/5O9Q5R9bMff4vjz//uH3/7zamL/sGn/9Mv//xvf/H43eEuEQ4+\nfvju+H/+yz/+/X//9X/7by9jeLD7+Cc//88ff/qzH90NztoijDHGmBshgnMVdaV79Pw15zJ2\nV5hjxwwx6umpHh2hCJUE4WWfoUv6oypx6YoVxTKguKuxc2tr7HRpjrBk7LYhifTaAjsECIM7\n7z38yf/4FzufHHx0XMMAhnfvfvDj99+7tx/IEYHfu/vhn/+4uqvvHR3VlPzBo48+ee/9nfs7\nRIgA1fDe+4/+/D/+9d2f3fuzUfIH1Z2Hj3/86PH9KlDvXmdjjDHGbFSZ9NH96+JO1WWFdyUT\nP43t+vwpLqKTidZ1Oee99B/7qjCXIHSSHaeyeaIcxZKyUBAKqEKSUec7YMvg5aUlISKaUt/0\n4bV6beMDEADD/qOw/+hXH8Ovll7iBruPf7H7+Bcf/vXyZ6gO3qsO3vurP4O/us4bNcYYY0xP\n3fqH2WPWxdVbc62y5bLyVRdGdeWrRKBpNMYrHnUu7IpVkuQkAagisauEHLKU5glUIWFSRlCd\nOXVtM3ZL7oC5/NJV7m2zbCilMcYYY65udowITMvpFmvslkR7Itiz9UEEcsZV2bKezh3FAjjO\nxEnRMXlFKmVz4jy7AICoTJxQzr2eCMS4/B7ao9ibL6m0wM4YY4wxV4aqeL54bj45B9Mei/lo\nr9swe6HSezvX/dBTyQvON0+oOklOkpATFxSpZA6ZArugiKTiONF8YKerj2JhS45iLbAzxhhj\nzBV1YVOnrGRdNu4E5ibDld7YCwM7VUgJU8JXCZvaGruzR0gSSRbyTGF63Ioo5NkFQEQRxxF1\nMWOnqzN2W7Eu1lb0GGOMMebq5rJuq2vs5qM9kb6xWs4YI105bOoK7M4PKE4kmcmLC6WQTgFK\nnKdIqOy5ITm3RqKctzLPrRpTmKYkLWNnjDHGmNtsblXXzCzic5eVuGfhKBZFsE+La84lY3fF\nGRiLaUVUKMPqxAWm0M6lQ2njPFLxHOcCu1Jjt7ijtHwCW7L10AI7Y4wxxlyd6rmlXos1dqsL\n7+DCWK103ZbA7qphky4LK9VxIk4lkju7eefZVYqEKi7HuRq77kx5NjlXWmXn0ng3yAI7Y4wx\nxlxdaZ7oUmJLG2C7B2czZz2bJ0pLbM5XDuxKBDn/xWc1di5MZ64gCoVMlSKisuNIOl9P166+\nPRuJVzbhbsMhbGGBnTHGGGOuaPYotu08XTLOrZxUzj3eZ9wJM8aIS9ez9reYsUNQx8lJZvJM\nvvxKqbETFwCpNE/MZezaW9IYdfb0+RXnsGyWBXbGGGOMubouCae6pJCu6DoqVLXrjT0/MXi5\nnCFGEimnvVcJ78qC1/nRegrEmTiJC+LOauyYPM/U2KEs1NMtbA/bnkEnhQV2xhhjjLmi2ePU\nNi23PFJTPZe0K1944cZYZoqRXjFjlxI0zdxprLpz406mz992xU6PYt3yjB3EOFdjZxk7Y4wx\nxrwJzgYUi0BKKztDS+ZsNgDqdsWuwVwydle8OVUVgfFYR6P5+XNl3EnbBlveCpbNE6V5wnNE\nXZqx09klE+VdW8bOGGOMMW+CLj4rra+rQhzVaffo7FbZCwcUlxq7Vxl0wqwnJ3J4KCmdPY5l\nVyzncvYK7Ry7bvPE2hq7c0V1qrpVzRM2oNgYY4wxl9bW1Z1NLRGR9YHd7LZWmM6xAwCc20jR\nXa8KzMh89Z0TdQ0nJzqZLAxbUXCSSZKQZ/Jt8V7ZPFEBEC0bd1Iww2zzxLaNO7HAzhhjjDFX\nNDu+ZP2Q3rlRxgDYZezKlyAuv/5q6bpyH+Oxvnihk8niUWnXFRuYppsnoHTFtpsn3MLmiYJZ\nY0RrnjDGGGPMG2VuncP6iW6qmtK5zNniQog5PfdSrLq3nGE81sNDiXH+NXA6xy6pc7MDioWc\nkBdyAOCXzbGDs65YnXkhq7EzxhhjzC1X1kLMZuzmp4qcv5h5/shyTZldWSZ75aHEOcNkoqOR\njkY6W13XPbvj5CQJeSHfDSgGJCEn6KDtrlh85lJjV3qB23nFsD1HsRbYGWOMMeYqSljTuXAH\nw+wmrhauOmxtxx1f8d6aRp8/l9FIVj2Dk0ScGf3sUSwAKKK4oEiOIy6rsROBGPV888QWZeys\nxs4YY4wxVzFbIQcAIpozrErZdc0Tc2tkRVAEnFu8Hi91FFumH5fO3JT0+FhfvtTJZHkyEEGI\nMwmLCzxzFAsAAMgu+IyO09LmiTJsmRmYlWjrVopZYGeMMcaYqyhhWbcQ4sKj2KUB0KrlE1fI\n2IlAXet4rKencnysR0ealxylAoCiCklGFSYvFOBcxo6YgqJznJbW2MHZsTIgTufYbc+AYgvs\njDHGGHMV3Wji4qLmiWkwNPdgm5nTZV2xl6ixOzmRw0OdTHQy0Ri1aXR+xEmLhD1HVFZEdhW7\nMJsWVMQyys5JXFpjB22QWkr3Vm1RuykW2BljjDHmKubaWueWqC5eXJoM5sKgVeetlz2KPTnR\nb7/lyWRZq8R5JNnlhoQBkF0lzp/L2EEJ7IhW1NgVZZodwPQdbU9sZ4GdMcYYYy6t6wnttDV2\nK6+fG1AMS4bbzV1/iYxdzjo7N3gNEva5IRVFYl8xna+xQyqj7FbV2LUvBzEqIm7PIWxhgZ0x\nxhhjrqLLVJX/LV0FFx7Flq8qB68lelvVFdtzOnHJAfYfFEySfW7ao9jALswORy7ns4rOrZhj\nB+3suhiRaIuq6woL7IwxxhhzFaXGbna32JqCs/Yodu5h3EjGrsv89bmeJHtuUFiR2FUyP+6E\n2AVB8ivm2BWl99Y5XLNs40ZYYGeMMcaYq+gyaqraJ65SBREtl53P2C35wlJj1ycD1/XP9gyw\nSNhNj2KnzRPnng2Qp0exy3fFwlnGDpxbDFVvmAV2xhhjjLmKkicrR6t9RpN0ZXmzW8WYkXnV\nUWyv5ol290Pf2ybJPkcUVqBMFXebJ4quK3b1uBNo9094r2smvNwI2zxhjDHGbLuu/3SrYoj2\nHBbLLLdufeoaZdbd7JXM84V03TvtOUmk3dba96MhzT43pAzduJPZZ0Pis80TK8edpKRNoz2r\n+l4nC+yMMcaYW0BkWnO2PeHdbPNEz+0Lc7Puus0Tc2+qze1dImPXP8AiYccNikzL6eYHFJ+t\nFLuoKxZS2rqjWAvsjDHGmG2nCpOJG40oxl5lZ69HN0O4/2Ho4mbVNrbDucCuTz9sd/GltrWS\nZJ9rUlbE7Bdr7IipUqQ1A4qhTT3GuG7Cy42wGjtjjDHmFogRm4ZEQFUGAwUA7Bv5XIuyNGIm\nY9crtGpDQG2TZKX3ogSI2j3z3Bbai55TL5mxyz6f74qdH3dywUqx7o3EuHVHsRbYGWOMMduu\nNBnESCkhM3vPzs3v4Hr9Sldsl7Hr2cG6eKUq5ozOoXPaPvPnti5OAAAgAElEQVTy4Xb9n3ON\nMu6EdBrYsTt3FHuueWL1USwAiEDT6OxYvm1ggZ0xxhhzC4hAziiCiBCCDodSVTect5upsdNV\nXbEojMoIoIBCTsQtBmFzk437jybuvjxnZe7dPCHscgRVRRIKQudiIQXqxp2sap7o7nPbFsWC\nBXbGGGPMraCKpXmiaejoyKvmqrrhuv0yoBhmJsktmk4DVlGkFHZVXUq62Aab87nawbmSu4tu\n4yoZOwQVdEJe8Vy/wVnzRF7XPNG99LaxwM4YY4zZarOzTmbOZClndu6GM3aloVUEmHXpPLd3\nn/7ug+//PwAY7T786sP/KPvvLt0Y2y4WUzjL2PWvsbts8wQ7juUcVpHmP8HpUWzZFbsuY7ed\nLLAzxhhjtt3s7I+S38oZmZHoZlJG3XLYEk4tz9iposp7T37z17/+P4T8k0e/ePbgZyN9d3Hm\nnEhJRmL3dasWyK66GWa43Bw7Tm1gN/8qXcBHpcauvNXtqaG7iI07McYYY7ba0tkfXevoTVHF\nrudAFRZXpvo82Rs9vXf41TsvPtsdPQtpgsIi8+NO4Cxjd+6R/hm4djxy3+vLHDtBYl/NncPC\ntBYwCDlSJmVUKXnE28IydsYYY8xWK1tT58ImVWQu0dWNJe3K2glY0TwxaE4fvPz83tGX+6dP\nhLzPDaos3RJRumK74LX/aOL2er3CSjFFl92ywG66ecKhCgmTZCHf+15unmXsjDHGmK22NNDp\nRo3ciLkauKVHsfujpx999ff3D79EECfJSUTQpYNRFr98LoF34c1cYfNEG8AthGyITIFdAATU\n7DmibtmourUssDPGGGO22tJOgnJ8eVOB3dyrt4M/un8X4rQ/evKj7/7pzsm3ig6FHac2Y7c0\nsMMy7rj7p0/Y2l55ha7YmeaJuedEZFdmoGBps8DVY4q3kAV2xhhjzFZbGuWoYv++0Y1baHdQ\n5rMqN1Tx3Bycfv/+9/+yf/qEnSctEZIuXT7WbYydef5LvLVS4XepwM7lqIhMQWGheBG6qcVl\n4l1D27ZcYi2rsTPGGGO22tIYbuZ89gaCu5yxrol5GhXN5cyqNH7w/I/vPP9s0JyMd+6P9h7t\njp93Gbty6iqiiIB4tlisBItEWmoK+3XFahmecqkAlyRPj2L9YEXGzgs5BUTL2BljjDFms1Z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h3TS7pbex+K6vDFV8blyOoJrAj2EYlQCg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kOeCkHczbUnURmLEmoOhVFAbFLc7PWYeeu1N1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\nEhbZbrqm0nFk2m873U7bYzyMF2icPfxtk6ZpVVWnpqZIkoSIBFj4c7q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IYOWrIAiwFWNqakoUxaipTwQCgQDgwwk67YIA7xd2YONd1sH6\nQcIOcSXxPM913SAIMAxjWfaUCiMMELRaLc/zVFU9XdhhGCbLcjabtSyrXC7btn3e0JMwDF3X\nXV9ff/LkSRAEExMTi4uLqVQK/pZhmImJiZWVlV/84hcMw6TT6Vu3bs3MzAwzftsPmF6yLIdh\nWCgUPM+DOi9spx3++UcOQRAsy8J7DiMmQ3qWCAQCcdH0LRbD+28kyTAK2g4JO8SVpFgsQieZ\n7/vvvvvu4uLi8+6padqTJ0+q1SpJkqlU6oWToYqiTE5Orq2tDebYQXfdkydPtre35+bmbty4\ncbxjbHJy8uOPP4auMlVVaZoelZcGS7rS6bSiKOVyud1ul0qldDqdz+eHHMu4UBYWFv7t3/5N\nluVcLtcrWyMQCEQ0AXPuyC/8BBGSZCTWyCJhh7iSVKvV+/fv7+/ve543MzMzPz9PEMSRbzPf\n9y3LOjg4ePDggaZpExMTExMTiUTi9FVUsixPTk4+ffq00WgYhuG67rlWVzWbzc3Nza2trU6n\n8+677964cYPjuCO6TVVVVVXP9XrPCEEQsGtVVVUIbWm1WizL5nK5KAu7ycnJf/iHf2AYhuM4\ntCgMgUBEnBP3TxAEFhFhF7meGwTiLOi6vr29vba2trq6Wq1WLcuCsmw/oOq+++67P//5z57n\nvfnmm7lcjmXZ0+0x8I1omjYMA9YhHH/mU9ja2vrzn/9crVbj8fhrr722vLw85kFUgiDS6XQy\nmWw2m9Vq1XVdSZIiW4oFGIaJx+OiKDIMgyYnEAhExDlRw+E4RlGRiLhDwg5xxQjDMAxD0zRL\npVKlUqnX68ViEfrh+u9j23a1Wr13797Dhw87nc709PQHH3yQy+VIkjy9YU6W5Xw+z3Fct9ut\nVqvNZvOMws51XV3XNzY2/va3v1EUtbS0NDc3l0wmx6xUSJKcmJjIZrNBEFiWhR12DUY515ck\nSZZlodEQTU4gEIiIA1Oxxxy7qJRikbBDXD0gKK5er+u67nnewcHB9vZ2t9vtv0O3293Z2fnv\n//7vhw8fJpPJN95445e//GUul3vhkyuKAv6W4zjlcrler59R2Nm2Xa/X19bWHj16lE6nX3/9\n9VQqNf6gXXDswJuEPWPRF3YIBAJxhcBxcOx+ZM6BjReF7AHUY4e4Yvi+b5qmruvdbtf3fZIk\nC4XC9vb20tIS3CEIgm63+9e//vXzzz8vFouZTObDDz+8c+fOGXM0WJaVJEmWZdhM32g0zijs\nSqXSgwcPtra2aJpeWlq6c+cOxLMN9WrPD0EQyWQSlJwgCLIsQzIfcsIQCARiJMBU7PEbKQqL\nQikWCTvEsISHBEEQ9qX6QFkNGOHlPM9rt9vtdttxHHCkSqXS1tZWt9uFq7uu2263v/jiiz/9\n6U+2bb/55pv/9E//lM1mz1gSpWk6FovBZvpGo3EWxy4MQ9jx8Mc//vHg4CCZTC4vL6+srFzK\nHABBEJDqIstyPB7P5XLxeBw1riEQCMSoONwY+6MbozM8gYQdYlig7tnpdOr1eq/RDQqCiqJw\nHDfa6QHXdZvNZrvdDoIgm83mcrlOp7Ozs6PrOhh4BwcHjx8/Xltb833/o48+eu+995LJ5POy\n606EIIhEIhGPx5vNZr1e933/9PtbllWtVp88efLgwYNEIvHqq6/m8/kRhpicF5IkJUlaWlqK\nx+OZTCaTyVzKMRAIBOInyaGGO1KKjUqPHRJ2iGEJgqBSqezs7GxsbHQ6HbiRpun5+fm5ubnp\n6WlRFEd4Oc/zGo1Gu93GMCyXy925c+cvf/kLNMPpui6K4t7e3v3796vVqiRJd+/evX37tiiK\n5xKXUM1UVXVnZwc2robh0cgioDel8fDhw8ePH1er1Rs3brzzzjtnNwhHDlikiqK8+uqrnU4n\nkUhMTk5eykkQCATiJwmOhz/O1woPF8he4qH+DhJ2iGFxXffbb7/97LPPHj161Gw24UaappeX\nl998883f/va3oxV2ruuCsCNJ8tq1a2+//fbTp0/L5fLu7m4ul5uZmVlfX//iiy9isdiNGzfm\n5+fT6fR5NRY4dolE4vHjx81m07Zt3/dPlIZBENTr9UePHv3+97/f2dnJ5XK3b9++e/fupa9P\nUFX1o48+8jyPpunRvv8IBALxknM4PPGj/4zCzgkACTvEsPi+v7u7u7a2puu6JElQG202m9vb\n2xzH/fznP89kMjRNj6rTDoRdt9sVRTGXyy0sLKRSqVKptLu7q6qqZVmbm5ulUun9999/7bXX\nMpkMx3HnvQQ4dqlUKggCeC2CIBzXapZlaZr29ddf37t3b3t7W5bl99577/XXX+9thr1EWJZF\nRh0CgUBcBGDO4TiGYbBz8gedF4U6LIaEHWJ4fN+v1+udTmd+fv7u3bsff/zxxsbG119//fnn\nn29ubjYaDcuyKIoaobADFy2RSGSz2cnJyXQ6TdP03t4eLEjd3Nx0XXdlZeWtt95SFGWAS4Bj\nNzExQVGUYRilUklRlOPCrtPp7O7u/uEPf7h3754oim+88ca///u/JxIJkiSPzJEgEAgE4qcE\nQfy9x44gQorCojOihoQdYigcxzEMo9lsWpaVyWRmZ2dzuRyEt33//felUmltbW1iYmJxcXFU\niW7g2Nm2nUwmJUliGCaXy2UyGdigJQhCt9tdWVmZm5tLp9ODXRQGSycmJiCm+ODgYHJysj8D\nD0Tb48eP//jHP+7u7iaTyQ8++ODDDz+cnJwkSdJ13ZG8UgQCgUBEELApYGNsEOAnxtpdItHo\n9ENcWWzb1jRN0zTXdScmJiYnJ2Ox2NTU1O3bt9PptG3bq6urm5ubnucN72BBqAoIO8dxUqmU\nIAgkSebz+VwuV6vVHj9+fP/+/SAIXn/99ZmZGUVRBhvIJQhCUZRUKsXzvOM4hUJB07QjJ/E8\n79GjR//1X//VarUWFhZ++9vfvvfee+ed0kAgEAjEFYUgQtgYe9hjd9kHOiQyB0FcTTRN29/f\nNwyDYZhsNptMJgmCoGlakqTZ2dlMJrOzswPCbiSXA2HXbDYdx0kkEj1hNzs7SxCEZVmO48zM\nzPziF7/IZrPDXIgkSY7jUqkUTdPFYhGGcHtYlgXbzMAdfP/99zOZDAoBRiAQiJcHcOxgHpai\nIjQ8gYQdYihA2FmWxfN8Op1WVZUgCIqieJ6fm5vL5/PVanV3d1fTNMdxhr+c67qmaXY6nSAI\nUqmUKIoEQWSz2ZmZGY7jGIZRVfXatWs3b95UVXXgq+A4ThAEy7LpdJpl2VKpdETYwabaRqPh\ned78/Pzt27dVVUVeHQKBQLwk4PgPG2MxDMPxkCSjknWCIWGHGBJN0wqFgud5oiiqqgpKC8Mw\niqKuXbu2tLTkeR5shugloQxMGIaGYWiaZts2TdPpdBocu1QqlcvlEonE9PT0m2++ubCwoCjK\n8FsfGIbJZDI8zxeLxSOlWMMw9vb2NE0jSTKZTIJdN+TlEAgEAnGF6Dl2UeuxQx4DYig0TTs4\nOKAoCjreenIKKqTXrl0TRbHdbn///feKogxZHg2CoN1uQx02Ho+n02lIaOM4LpfL/eIXvzAM\nY3Z2dmFhYSQyi2XZbDYrSVKz2Ww2m7qusywLL9AwjN3dXcuy4vF4PB6XJAnZdQgEAvFSQRBQ\nfg1xPCTJIDqlWPTTCDEUrVbr4OCA47hsNtu/tosgiImJibm5uYmJiXK5/PDhw9nZ2Tt37gxz\nrSAINE1rNBqu63IcB44d/NXk5OS//Mu/hGHI8/y5toedAsuyuVxOURRd1xuNRqPRSCQSPWG3\nt7fneR6sZEXddQgEAvGyAd11ERyeQMIOMSAQ1aZpWrlcXlpaymQyLMv26xuKomRZXlpasixr\nbW3t4OCg2+3SND2wuRWGYbvdbrVaYRjGYrF4PM5xHFyRoihoqiNJclQai+O4qampbDZLEESj\n0djc3OxtcTAMY39/H8OwqakpQRCQqkMgEIiXDXDscPzvf4gIkVGYiKtGEASO42iaBulxk5OT\n/QVQ2FgqiuLKyko6nd7e3j44OOh0OsNkvPVKsTiOx2IxSZJ6WyVgFJemaeLHC/yGARy7bDbL\n83yz2Xz27JmmaWEYBkGg6/r+/j5BEDMzM2hhFwKBQLyEgGMHXt3hIopIgIQdYkC63W6pVNI0\nLQiCRCIBjt2R+8DC1tnZWQzDqtXqxsbGkfHScwEGYafTkSRJUZTzboA9LziOsywbj8enp6c9\nz1tfX2+3257ngWuoaRrHcTMzM71yMAKBQCBeEnqqLlJbYgEk7BAD0u12YVwUx3HY7nV8ZKFX\nzWQYRtO0nZ0dwzAGviL02HU6HVEUZVkeYdX1RCC3RVGUubm5IAg2NjZgIBf67UzTFARhenoa\nOXYIBALxEkIQIRJ2iJ8U0GfWbrcpikomkyfu7yJJUhRFRVFkWXYcp1QqmaY58BWhFAvCTlGU\n8XS2ybK8sLBAUdTOzk6z2ex0OgcHB9Vq1fd9RVGmp6eRY4dAIBAvIQSBkWS0xiaAiB0HcXUA\nYWfbtqqqiqJApNyR+0DrmyAIqVQqCIJSqdTtdge7HDS3tdttXddlWZZlmRjLN5MkSQsLC6Io\nappWrVYLhcLu7m6z2eR5XlXVZDI5qiFcBAKBQFwhoBRLUSFJRmhyAkPCDjEwIOyCIMhkMrAj\n9XkWGs/zmUwGw7ByuTyMsIP+NsMwoMduPMIOHLtkMum6bqlU2tjY2Nra6nQ6iqLE43FRFIdP\nQkYgEAjEleOwFBuhnRNAxI6DuDqYpnlwcID1RX68UNiBYxeGYRieuyOh2+22Wi2o5IJHOB5h\nB4dPJpOiKNZqtWfPnm1tbRmGMTk5GY/HRziEi0AgEIirBWyJJUnUY4e44kBV1DCMQqGA4zgI\nu1Puz/N8NpvFcbxSqZimGQTBABc1TbNWq3W7XZIkE4nE2IQdy7KJRALW4DYajdXV1a2tLcuy\n8vm8oihjOAACgUAgogbkEhNESNMhRaFSLOKK4/t+p9PRNA0iP84o7DiOM00TmuQ8zzvvRWF3\nmWmakEUsy/I4rbJkMjk/P6/r+traWrFY9H1/amoqHo+P7QAIBAKBiBo4jpFkEDXHDm2eiCiu\n6zqOYxiGZVm9GzmO43me47jL7etyXbfVajWbTdM0Y7FYPp+PxWKn3B+qmbFYzLbtdrutaRrD\nMGd/CZ7nOY6zt7f3/fffd7tdRVESiYQkSeNx7IBUKjU/P18sFovFom3b169fR8IOgUAgEBcc\nqDoISNhFFF3Xq9Xqo0ePDg4Oek1ps7Oz8/PzU1NTiUTiEs/mOE65XK7X60EQKIpyFscuk8lI\nkhSGYavVqtVq8Xic5/kzXs4wjEql8tVXX/3v//4vRVHXr19PpVJjXuSVTCYXFha+/vpry7KC\nIIAEOyTsEAgEAhE1kLCLKMVi8eHDh3/729/29vZIkgzD0Pf9vb29Uqn04Ycfwl7Uy2rbdxyn\nWq1qmgbbYFOpVG+114mwLAvDBwRBwAqymZmZs1zI8zzLsjY3N+/fv//48WPTNN9888133nkn\nlUoNvHB2MBKJxLVr1yBjhaIoSZImJiZQNDECgUC8zESqta4HEnYRZXNz849//OOzZ8+q1Sr4\nYZZlbW1tPXv2bH5+fnl5+RLP5rpurVZrt9scx8my/MI5BoZhVFWVJImm6U6nU6vVzrgx1nXd\ner3+zTff/Md//Ifrurlc7uOPP/7oo49kWR7RSzkrqqrOzc3F43Gog8uynEwmTy9AIxAIBAIx\nfpCwixy+77uuWygU1tbW4vH4jRs3FhcXMQyrVCpPnjzZ39/f398vlUqJROKyonHBset2u6lU\nSpKkF+5sxXGcJElJkpLJZLfbrdVqjuOc/pAgCEzT3Nvb+/Of//zVV19ZlnXr1q133333lVde\nGcOW2ONQFBWLxaampm7cuEFRVC6XYxgGBZ0gEAgEImogYRc5XNftdDqlUun/b+/OuuM47/SA\nv7VXd1f1vu+NjQAIEKLARZRlyR5ZziyxM54zM05ycnIzl7nMB8nc5SbJlc+cOIuyOLFHc7zJ\n2kiLkrgBIHagu9EAet+7q2vLRVkIhysIgADVfH4XOpRUIF7U+aPq6XfN5/Pnz59/991333rr\nLZqmNzY2fvKTnywtLeVyuVwuZ7fbTz/YWVP9rGDX7/eDwaAsy8/8KisASZIUCAQURXlmj521\nF3GlUllcXPz5z39eKBS8Xu/Vq1d/9KMf2Wy2M1k4QtM0z/OZTKbZbNI0nUqlnrIhMwAAwFlB\nsHvp1Ov15eXlvb09iqKSyeTExITdbmdZNpPJxGIxp9O5s7OztLR0hvuoqapaKpUURYnH44cf\nFbXmpRWLxWf22PX7/Vqt9stf/vKTTz6p1Wrnzp177733Ll68aLPZTnlq3YN4nr98+fLo6ChF\nUWfYXQoAAPAUCHYvnVqttrCwUK1WZVmOxWLxeFwURZZlRVGMxWKRSKRcLi8vL1+9elXX9RM8\n+cAwDPVr1r4qj71M07Rut1ur1XRdD4fDh+mxs1jBbmdnp1qtKory2MYbhqHr+u7u7v37969f\nv37//v1gMHjx4sXvfve7fr//bLMUy7IjIyMjIyNn2AYAAICnwwbFL51arXb37t1+v59Op62l\nlwdTyqLR6MzMTLfbXVtbs+LREc7mehJVVWu1WjabXVhYKBaLj73GNE1rk+FOp0PTtLWJySH/\nflmWQ6GQaZqNRqPT6Ty2005V1U6nc/369Z/85Cerq6t+v/+HP/zhe++9d+apDgAA4BsBwe4l\nouu6tbZgfX2dYZipqSmfz8dxnNWzRVFUNBq9cOGCIAilUimbzRaLxaMdz/VYnU5naWnpk08+\n+Yd/+Ievvvpqb2/POpj1QYZhNBqNSqXS7/cFQQiHw4cfipUkKRQKcRxnnfrabrcfbXypVLpx\n48bNmzfX1tZCodC1a9fm5+czmYwoiqe/YAIAAOAbB8HuJaJpWqPRKBaL1tqI2dlZa7+6A5FI\nZG5uzuv1ttvtlZWV7e3tI5zN9SSNRuPGjRs/+9nPfvrTn/7mN79ZWVlpNpsPXWMYRq1WK5VK\ng8HAOijseXvsRFEcDAaVSqVerz8a7La3t99///1bt26ZpvnWW2/96Ec/GhkZOeW9iAEAAL65\nMMfuJdLr9TY2NnK5nK7rwWBwamrqobMNbDab3++Px+O5XG5jYyORSMzPzx//+1q7H7fbbeu7\n93q9xcVFURQNw7Db7dbSjYMra7VapVKhKEqSJI/Hc/gDJBwORzAYtNvtuq6Xy+WHgp3Vhnq9\nvra2Jori1NTU5ORkJBKx2WyneXQYAADANxpemS8F69Cwbre7vLycy+VYlg2Hw+Pj4w8NdAqC\n4HQ6k8lkMBjM5XKbm5uKohx/NNY0zcFg0Gg0dnZ2Go2GLMu5XO6DDz64e/fu/v6+NZPPmsxn\n9dhVKhVrXzqXy3X4qW8Oh8Pn8zkcDsMwKpVKrVZ7sOXW0o1ms1koFDwez5tvvjkyMuJyuc5w\nGSwAAMA3DoLdS6TT6aysrNRqtdHR0UgkwvP8oxPLrLWZ4+PjiqLs7u5ms9lGo3HM72sYxt7e\n3vb2drvdTiaTP/7xjy9dukTT9I0bN37xi1/s7u4eDPhawa7RaLhcLo/H81x9aRRFcRzncrkk\nSWo0Gg/12KmqWq1W6/W6pmmSJIXD4cP3BQIAAIAFwe5loShKrVbb3t7u9/sTExPhcNhaNvHQ\nZSzLJpPJ0dFRmqbL5fLm5ma1Wj3mt7Z2GMlms4PBIB6P/9Ef/dEbb7yRyWR2dnY+++yzlZWV\ncrlshTDDMOr1eqPRcDqdHo+HYZjDz36zTll1uVyyLDcajYd67Kxg12g0DMOwBm2ffv4sAAAA\nPArB7qVgjU7u7OwUi0WO42ZmZiKRyGOvpGk6Eomk02mPx9Pv95eXl0ul0jG/u67rOzs7uVyO\n5/lQKJROp99+++2//Mu/DAaD2Wz2o48+un37trU7iWEYVr/aEXrsCCEURXk8HpfL1Ww2H+2x\nO1hRIUlSMBhEjx0AAMDzQrB7gawFAYPBoNVqdbvdp0yGs/rMtra2er2eLMvj4+N+v/+xV1IU\nZbfbvV5vOBxmGGZjY6NcLh+zkZqmFQqF3d1dj8djHW6RTqcvXrw4MTEhSdLy8vLi4mKr1er1\netYmdv1+3+PxHCHY0TTtdrtdLle73bZGXQ/uiRXs2u22KIqyLMuyfCZHhwEAAHyjIdi9WAdb\n0z12d48HL8tmsxsbG4Zh+Hy+TCbj9XqfdLGV7RKJhMPh2NraqlQqx2mhYRiDwcDqLIxEIrFY\njGEYSZJisdhrr702MTGRz+dXVlasEdhms9lqtXRd93q9Rw52bre70+k0Gg1VVR8MduVyudvt\nSpIkSRI2rgMAADgCLDl8gTRNW11dXVlZWVpaEgRhfHx8fHw8nU5zHPfgYk+rzyyfzxcKBWs3\nE7vd/qRYY81pE0UxkUhks9mVlZVqtdrpdHieP1oXV7PZzOfzlUrFMIxkMhmPxxmGoWlaFMWJ\niYlyuXznzp1isfjll19Go1FCSLfbZVnW7/cfLdj5fL5AIEDTdKvV2tvbEwTB2qvPmmPX7/f9\nfr8kSdjiBAAA4AgQ7F4U0zRVVV1aWvrlL3/56aefchw3Ozv77rvv2u12t9vtcDhomrbii2EY\n/X4/n8/v7++fP38+kUjwPP/0ZGMFO4/HU6/Xralpbrf7aMGuWq1ubW3VajWWZVOpVDQatUIn\nwzAjIyONRsPr9TYajd///vdjY2Ner7fX6/E8HwgEjhbsvF6vdf5Ep9PJ5/Nut/sg2FUqFUVR\nAoGAJElH+EEAAAAAwe4F0jQtm81ubW1Zm/2urq52Op27d+9eu3btwoULkUjEbrcTQur1ei6X\ns1aeptPpRCLxzFFIK9hZ0+yq1er6+vrExITD4ThCI8vl8srKymAwcLvdkUjE5/NZcY2iKJ7n\n/X7/zMzMwsLC3bt3e71eMpns9/tWNpUk6XkPhKBp2uPxBINBh8PR7/ez2Ww8Hrf+10GwS6VS\nCHYAAABHg2D3oqiq2m63C4VCpVKJRqPWGal7e3vZbNaaWGaz2QRBoGm6Wq1ubGzU63WO41Kp\nVCwWe+auvDzPB4NBKyE1Go2NjY1IJBIOh5+rhdaew6VSaXV11TTNYDBoDYNacc3ads7j8czO\nzlYqlcXFRWu+4GAw8Hq91jS4570n1nkVXq/X7XbXarVsNjs1NWUtMbEOkNU0LRAIHP6YMgAA\nAHgQgt2L0mw2d3Z2SqUSTdOXLl2amJhwuVw3btz49NNP79y50+l04vG42+0WRbFYLC4tLfV6\nPbfbHY/Hg8HgM4c4aZp2OBwejycUCg0Gg/X19dnZ2aO1s1gsrq6uBoPBTCZjt9sf6oSTJGlu\nbq5QKPz2t7/N5XKVSkWWZb/fz/P80b6dNXsvHA5bo8+1Ws06cqPZbHY6HY7jgsEgeuwAAACO\nBlPUX5Ryuby2ttZoNOx2+8TExPz8/KVLl65du3b16lVCyOrq6r1797a3tzVN29/fX15eZlk2\nFot5vV6bzfbMIU5rs1/reDHDMDY2NprN5sHBX4c0GAxqtVqpVCqXy16vd2RkxAp2D353QRAi\nkcjBoopKpWKz2QKBAM/zD115GNaXiKIYjUbtdnuhUKjX67quN5vNWq2mKIrVE4lgBwAAcDQI\ndi9KsVhcXFzsdrtW/IrH4x6PZ35+/s///M+TyWS9Xr9+/fqdO3cURSkWiysrK5IkjY6OPtfE\nNVmWR0dHKYra3NxsNpvPe2hst9u1djnpd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+              "type" : "IMG"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Save the UDF to the script repository",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "ore.scriptCreate(\"multi.forecast\", multi.forecast, overwrite=TRUE)"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992693064,
+          "endTime" : 1739992693576,
+          "interpreter" : "r.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
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+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Run the UDF by referencing it as a named script",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "ore.doEval(FUN.NAME=\"multi.forecast\", ore.graphics=TRUE)"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
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+        "enabled" : true,
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+          "endTime" : 1739992696131,
+          "interpreter" : "r.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "                   ME      RMSE       MAE       MPE     MAPE      MASE\nTraining set 0.442465  8.834477  6.351386 0.1376787 2.870884 0.2174955\nTest set     6.849037 26.471642 19.501241 0.8396142 4.405293 0.6677963\n                    ACF1 Theil's U\nTraining set 0.003856404        NA\nTest set     0.675272574 0.5053222\n",
+              "type" : "TEXT"
+            },
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XLmVu+Zdy8nJiDc9ebr3QdbGmVi/5LvVIYQetBeCnYbb3eiFyCfUdPZREnWJHtF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h3TS7pbex+K6vDFV8blyOoJrAj2EYlQCg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kOeCkHczbUnURmLEmoOhVFAbFLc7PWYeeu1N1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\nEhbZbrqm0nFk2m873U7bYzyMF2icPfxtk6ZpVVWnpqZIkoSIBFj4c7q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IYOWrIAiwFWNqakoUxaipTwQCgQDgwwk67YIA7xd2YONd1sH6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\nNVmWR0dHKYra3NxsNpvPe2hst9u1djnpd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+              "type" : "IMG"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Authorization Token",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "---",
+          "### Running UDFs in the SQL and REST APIs for embedded R execution",
+          "---",
+          "To use the SQL and REST APIs for embedded R execution, the UDF function must be stored in the script repository, and an Oracle Machine Learning (OML) cloud account USERNAME, PASSWORD, and URL must be provided to obtain an authentication token. To use a conda environment when calling OML4R script execution endpoints, specify the conda environment in the `env_name` field when using SQL, and the `envName` field when using REST.",
+          "",
+          "Obtain the token URL from the OML service console in Autonomous Database by first signing into your Oracle Cloud Infrastructure account with your OCI user name and password:",
+          "",
+          "- Click the “three line menu” and select the desired Autonomous Database instance.",
+          "- Click Database Actions and scroll down to the Oracle Machine Learning RESTful Services tile.",
+          "- Click “Copy” to get the REST authentication token for REST APIs URL of the form: /omlusers/api/oauth2/v1/token",
+          "",
+          "The includes the location, tenancy ID, and database name separated by dashes. For example, https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992696624,
+          "endTime" : 1739992697069,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>To use the SQL and REST APIs for embedded R execution, the UDF function must be stored in the script repository, and an Oracle Machine Learning (OML) cloud account USERNAME, PASSWORD, and URL must be provided to obtain an authentication token. To use a conda environment when calling OML4R script execution endpoints, specify the conda environment in the <code>env_name</code> field when using SQL, and the <code>envName</code> field when using REST.</p>\n<p>Obtain the token URL from the OML service console in Autonomous Database by first signing into your Oracle Cloud Infrastructure account with your OCI user name and password:</p>\n<ul>\n<li>Click the “three line menu” and select the desired Autonomous Database instance.</li>\n<li>Click Database Actions and scroll down to the Oracle Machine Learning RESTful Services tile.</li>\n<li>Click “Copy” to get the REST authentication token for REST APIs URL of the form: /omlusers/api/oauth2/v1/token</li>\n</ul>\n<p>The includes the location, tenancy ID, and database name separated by dashes. For example, <a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com</a>.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Define function to get authorization token",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "create or replace function get_token2 (",
+          "  p_url       varchar2,",
+          "  p_username  varchar2,",
+          "  p_password  varchar2,",
+          "  p_verbose   number default 0) ",
+          "  return varchar2",
+          "  authid definer as pragma autonomous_transaction;",
+          "  l_buffer            varchar2(4000);",
+          "  l_request_content   clob;",
+          "  l_response_content  clob;",
+          "  l_request           utl_http.req;",
+          "  l_response          utl_http.resp;",
+          "  l_url               varchar2(1000);",
+          "  l_smt               varchar2(1000);",
+          "  l_access_token      varchar2(4000);",
+          "begin",
+          "  l_url := p_url;",
+          "  l_request_content := q'[{\"grant_type\":\"password\", \"username\":\"]' || p_username || q'[\", \"password\":\"]' || p_password ||",
+          "  q'[\"}]';",
+          "  begin",
+          "    utl_http.set_wallet('');",
+          "    l_request := utl_http.begin_request(l_url,'POST',' HTTP/1.1');",
+          "    utl_http.set_header(l_request,'Content-Type','application/json');",
+          "    utl_http.set_header(l_request,'Accept','application/json');",
+          "    utl_http.set_header(l_request,'Content-Length',length(l_request_content));",
+          "    utl_http.write_text(l_request,l_request_content);",
+          "    l_response := utl_http.get_response(l_request);",
+          "    if (l_response.status_code != utl_http.HTTP_OK) THEN",
+          "      RAISE_APPLICATION_ERROR(-20999, 'Token request failed with status code: '||l_response.status_code||', reason: '||l_response.reason_phrase);",
+          "    end if;",
+          "    if p_verbose > 0 then",
+          "      dbms_output.put_line('HTTP response status code: ' || l_response.status_code);",
+          "      dbms_output.put_line('HTTP response reason phrase: ' || l_response.reason_phrase);",
+          "    end if;",
+          "    begin",
+          "      loop",
+          "        utl_http.read_line(l_response,l_buffer);",
+          "        l_access_token := regexp_substr(l_buffer,'\"accessToken\":\"([^\"]+)\"',1,1,'i',1);",
+          "        if p_verbose > 0 then",
+          "          dbms_output.put_line('l_access_token: [' || l_access_token || ']');",
+          "        end if;",
+          "      end loop;",
+          "      utl_http.end_response(l_response);",
+          "    exception",
+          "      when utl_http.end_of_body then utl_http.end_response(l_response);",
+          "    end;",
+          "  end;",
+          "  return(l_access_token);",
+          "  dbms_output.put_line('Access token is: ' || l_access_token);",
+          "end get_token2;",
+          "/"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739992697578,
+          "endTime" : 1739992699660,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nFunction GET_TOKEN2 compiled\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create access details table to support token acquisition",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "BEGIN EXECUTE IMMEDIATE 'DROP TABLE ACCESS_DETAILS';",
+          "EXCEPTION WHEN OTHERS THEN NULL; ",
+          "END;",
+          "/",
+          "",
+          "CREATE TABLE ACCESS_DETAILS (",
+          "  NAME  VARCHAR2(20),",
+          "  VALUE VARCHAR2(1000)",
+          "  );",
+          "/",
+          "",
+          "-- Important! Replace the OML URL, username and password with your database-specific values",
+          "",
+          "INSERT INTO ACCESS_DETAILS VALUES ('URL', '<YOUR OML URL>/omlusers/api/oauth2/v1/token');",
+          "INSERT INTO ACCESS_DETAILS VALUES ('USERNAME', 'OMLUSER');",
+          "INSERT INTO ACCESS_DETAILS VALUES ('PASSWORD', 'PASSWORD');"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993241192,
+          "endTime" : 1739993241862,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable ACCESS_DETAILS created.\n\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Obtain and set token ",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "DECLARE",
+          "   access_token VARCHAR2(2000);",
+          "   is_set BOOLEAN;",
+          "BEGIN",
+          "   with url_string  as (select VALUE from ACCESS_DETAILS where NAME = 'URL'), ",
+          "        username    as (select VALUE from ACCESS_DETAILS where NAME = 'USERNAME'),",
+          "        pswd        as (select VALUE from ACCESS_DETAILS where NAME = 'PASSWORD')",
+          "   SELECT get_token2(url_string.VALUE, username.VALUE, pswd.VALUE) INTO access_token ",
+          "   FROM url_string, username, pswd ",
+          "   WHERE localtimestamp=localtimestamp;",
+          "",
+          "   rqSetAuthToken(access_token);",
+          "     is_set := rqIsTokenSet();",
+          "     IF (is_set) THEN",
+          "        DBMS_OUTPUT.put_line ('token is set');",
+          "       --enable to return token",
+          "       --DBMS_OUTPUT.put_line (''|| access_token);",
+          "    END IF;",
+          "END;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993248366,
+          "endTime" : 1739993249961,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "token is set\n\n\nPL/SQL procedure successfully completed.\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Access Control List",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "The Access Control List (ACL) provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database. ",
+          "",
+          "As ADMIN user in SQL Developer, use the network access control lists `rqAppendHostAce` function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides, e.g. *adb.us-region-1.oraclecloudapps.com*. ",
+          "",
+          "Then run the token store function `rqSetAuthToken` to persist the authorization token issued by a cloud host for use with the upcoming SQL calls."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993257223,
+          "endTime" : 1739993257761,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>The Access Control List (ACL) provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database.</p>\n<p>As ADMIN user in SQL Developer, use the network access control lists <code>rqAppendHostAce</code> function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides, e.g. <em>adb.us-region-1.oraclecloudapps.com</em>.</p>\n<p>Then run the token store function <code>rqSetAuthToken</code> to persist the authorization token issued by a cloud host for use with the upcoming SQL calls.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Add user to Access control list",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "SQL> exec rqAppendHostAce('OMLUSER','adb.us-sanjose-1.oraclecloudapps.com');",
+          "",
+          "  PL/SQL procedure successfully completed.",
+          "  Elapsed: 00:00:00.370"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Show the list of available OML4R conda environments",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "set long 1000",
+          "",
+          "select * from table(rqListEnvs())"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993258257,
+          "endTime" : 1739993270274,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nname     size      description                               number_of_installed_packages   \nmyrenv   1.5 GiB   Install R forecast and ggplot2 packages                              171 \n\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the R UDF using the SQL API for embedded R execution - synchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "SELECT name, id, value, dbms_lob.substr(image,100,1) image FROM table(rqEval2( ",
+          "    par_lst => '{\"ore_graphics_flag\":true}', ",
+          "    out_fmt => 'PNG', ",
+          "    scr_owner=> NULL,",
+          "    scr_name => 'multi.forecast',",
+          "    env_name => 'myrenv'));"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993270810,
+          "endTime" : 1739993354143,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "NAME   ID   VALUE                                                                              IMAGE                                                                                                                                                                                                      \n          1 [[0.4425,8.8345,6.3514,0.1377,2.8709,0.2175,0.0039,\"NA\"],[6.849,26.4716,19.5012,   89504E470D0A1A0A0000000D49484452000001E0000001E008060000007DD4BE950000200049444154789CECDD7798646599F7F1EFA99C3B4D6402306406040902CA0A282A410517D41103088BA8BB882BAEAE2B665D71511731A02F8318D015C54436EF   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the R UDF using the SQL API for embedded R execution - asynchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "set long 1000",
+          "",
+          "SELECT value FROM table(rqEval2( ",
+          "    par_lst => '{\"ore_graphics_flag\":true, \"ore_async_flag\":true}', ",
+          "    out_fmt => 'PNG', ",
+          "    scr_owner=> NULL,",
+          "    scr_name => 'multi.forecast',",
+          "    env_name => 'myrenv'));   "
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993354660,
+          "endTime" : 1739993367163,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nVALUE                                                                                                                                           \nhttps://hmnsuoc10zlzdol-moviestreamworkshop.adb.ap-tokyo-1.oraclecloudapps.com/oml/api/r-scripts/v1/jobs/1704f396-a55e-4688-9264-96cb800addb4   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Get the job status",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "Poll the job status sing the `pyqJobStatus` function. If the job is still running, the return value will be job is still running. When the job completes, a job ID and result location are returned.",
+          "",
+          "Note, replace your job ID with the one shown here."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993367737,
+          "endTime" : 1739993368217,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<p>Poll the job status sing the <code>pyqJobStatus</code> function. If the job is still running, the return value will be job is still running. When the job completes, a job ID and result location are returned.</p>\n<p>Note, replace your job ID with the one shown here.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%script",
+          "",
+          "set long 1000",
+          "",
+          "SELECT value from rqJobStatus(job_id => '1704f396-a55e-4688-9264-96cb800addb4');"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993472159,
+          "endTime" : 1739993477768,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nVALUE                                                                                                                                                  \nhttps://hmnsuoc10zlzdol-moviestreamworkshop.adb.ap-tokyo-1.oraclecloudapps.com/oml/api/r-scripts/v1/jobs/1704f396-a55e-4688-9264-96cb800addb4/result   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Retrieve the result",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "set long 500",
+          "",
+          "SELECT NAME, ID, VALUE, dbms_lob.substr(image,100,1) image FROM rqJobResult(job_id => '1704f396-a55e-4688-9264-96cb800addb4', out_fmt=>'PNG');"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993485049,
+          "endTime" : 1739993485805,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\n---------------------------\nNAME   ID   VALUE                                                                                                                  IMAGE                                                                                                                                                                                                      \n          1 [[0.4425,8.8345,6.3514,0.1377,2.8709,0.2175,0.0039,\"NA\"],[6.849,26.4716,19.5012,0.8396,4.4053,0.6678,0.6753,0.5053]]   89504E470D0A1A0A0000000D49484452000001E0000001E008060000007DD4BE950000200049444154789CECDD7798646599F7F1EFA99C3B4D6402306406040902CA0A282A410517D41103088BA8BB882BAEAE2B665D71511731A02F8318D015C54436EF   \n\n\n\n---------------------------\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the R UDF using the REST API for embedded R execution -  synchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "$ curl -i -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' ",
+          "       --header 'Accept: application/json' -d '{\"input\":\"DF\", \"envName\":\"myrenv\", \"graphicsFlag\":true}' ",
+          "       \"<omlserver>/oml/api/r-scripts/v1/do-eval/multi.forecast\"",
+          "    ",
+          "Returns the result:",
+          "",
+          "[[0.4425,8.8345,6.3514,0.1377,2.8709,0.2175,0.0039,\"NA\"]...   89504E470D0A1A0A0000000D4948445..."
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Run the R UDF using the REST API for embedded R execution -  asynchronous mode",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "$ curl -i -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' ",
+          "       --header 'Accept: application/json' -d '{\"input\":\"DF\", \"envName\":\"myrenv\", \"graphicsFlag\":true, \"asyncMode\":true}' ",
+          "       \"<omlserver>/oml/api/r-scripts/v1/do-eval/multi.forecast\"",
+          "    ",
+          "Returns the job ID:",
+          "",
+          "Location: <YOUR OML URL>/oml/api/r-scripts/v1/jobs/3887d8a2-9b91-448c-940e-542b39205049"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 0,
+        "title" : "Retrieve the result",
+        "hasTitle" : true,
+        "message" : [
+          "",
+          "$ curl -i -X GET --header \"Authorization: Bearer <token>\" \\",
+          "       --header 'Accept: application/json' \\",
+          "       \"<omlserver>/oml/api/r-scripts/v1/jobs/3887d8a2-9b91-448c-940e-542b39205049/result\"",
+          "",
+          "Returns:",
+          "",
+          "[[0.4425,8.8345,6.3514,0.1377,2.8709,0.2175,0.0039,\"NA\"]...   89504E470D0A1A0A0000000D4948445..."
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : false,
+        "forms" : null,
+        "result" : null,
+        "relations" : [ ],
+        "dynamicFormParams" : null
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## End of script"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1739993514879,
+          "endTime" : 1739993515343,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R -0- Tour.dsnb b/machine-learning/notebooks-oml/r/OML4R -0- Tour.dsnb
new file mode 100644
index 00000000..6391077e
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML4R -0- Tour.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R -0- Tour","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734632260860,"interpreter":"md.low","endTime":1734632261317,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning for R (OML4R)","","***Oracle Machine Learning for R*** (OML4R) makes the open-source R statistical programming language and environment ready for the enterprise and big data. ","Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database and Oracle Database, allowing users to run scripts ","for statistical analysis, data exploration, machine learning, and visualization analysis using R syntax.","","OML4R extends R's capabilities through three primary areas: transparent access and manipulation of database data from R, in-database machine learning algorithms, ","and ease of deployment using embedded R execution. This allows you to benefit from the simplicity of R and the power of Oracle Database without the need to deal ","with complexities of sourcing, moving, and securing data. In this notebook, we highlight the range of OML4R features.","","* Creating database tables","* Transparency layer functionality","* Embedded R execution","* REST API invocation of embedded R execution","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734632261775,"interpreter":"md.low","endTime":1734632262232,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-r-oml4r\">Oracle Machine Learning for R (OML4R)<\/h1>\n<p><em><strong>Oracle Machine Learning for R<\/strong><\/em> (OML4R) makes the open-source R statistical programming language and environment ready for the enterprise and big data.\nDesigned for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database and Oracle Database, allowing users to run scripts\nfor statistical analysis, data exploration, machine learning, and visualization analysis using R syntax.<\/p>\n<p>OML4R extends R's capabilities through three primary areas: transparent access and manipulation of database data from R, in-database machine learning algorithms,\nand ease of deployment using embedded R execution. This allows you to benefit from the simplicity of R and the power of Oracle Database without the need to deal\nwith complexities of sourcing, moving, and securing data. In this notebook, we highlight the range of OML4R features.<\/p>\n<ul>\n<li>Creating database tables<\/li>\n<li>Transparency layer functionality<\/li>\n<li>Embedded R execution<\/li>\n<li>REST API invocation of embedded R execution<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Import libraries supporting OML4R","","","To use OML4R, first import the library ORE.","","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1734632262707,"interpreter":"md.low","endTime":1734632263163,"results":[{"message":"<h2 id=\"import-libraries-supporting-oml4r\">Import libraries supporting OML4R<\/h2>\n<p>To use OML4R, first import the library ORE.<\/p>\n<p>Users can get online help using R's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load ORE library ","message":["%r","","library(ORE)","options(warn=-1)","options(ore.warn.order=FALSE)","options(width = 200)"],"enabled":true,"result":{"startTime":1734632263642,"interpreter":"r.low","endTime":1734632264143,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create a database table and get proxy object","","Here, we illustrate how to get proxy objects to database tables and views. Using OML proxy objects, we'll explore the OML4Py transparency layer functionality. "],"enabled":true,"result":{"startTime":1734632264603,"interpreter":"md.low","endTime":1734632265052,"results":[{"message":"<h2 id=\"create-a-database-table-and-get-proxy-object\">Create a database table and get proxy object<\/h2>\n<p>Here, we illustrate how to get proxy objects to database tables and views. Using OML proxy objects, we'll explore the OML4Py transparency layer functionality.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create database table","message":["%r","","tryCatch({ore.drop(table=\"IRIS\")})","","rownames(iris) <- seq(nrow(iris))","","ore.create(iris, table = \"IRIS\")","","cat(\"Class: \", class(IRIS))","cat(\"\\nShape: \", dim(IRIS))"],"enabled":true,"result":{"startTime":1734632265526,"interpreter":"r.low","endTime":1734632266174,"results":[{"message":"The following object is masked _by_ .GlobalEnv:\n\n    IRIS\n\n\nClass:  ore.frame\nShape:  150 5","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"List available data.frame proxy objects","message":["%r","","ore.ls()"],"enabled":true,"result":{"startTime":1734632266646,"interpreter":"r.low","endTime":1734632267147,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'IRIS'<\/li><li>'iris_descr'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"List column names","message":["%r","","colnames(IRIS)"],"enabled":true,"result":{"startTime":1734632267734,"interpreter":"r.low","endTime":1734632268239,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'Sepal.Length'<\/li><li>'Sepal.Width'<\/li><li>'Petal.Length'<\/li><li>'Petal.Width'<\/li><li>'Species'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get proxy object for named database table and split data","message":["%r","","set.seed(123)","sampleSize <- .6 * nrow(IRIS)","index <- sample(1:nrow(IRIS), sampleSize)","rownames(IRIS) <- IRIS$Species","","#Create training data set","IRIS.train <- IRIS[index, ]","","#Create test data set","IRIS.test <- IRIS[setdiff(1:nrow(IRIS), index), ]","","cat(\"\\nTraining data: \")","dim(IRIS.train)","cat(\"\\nTest data: \")","dim(IRIS.test)"],"enabled":true,"result":{"startTime":1734632268710,"interpreter":"r.low","endTime":1734632269606,"results":[{"message":"\nTraining data: \n","type":"TEXT"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>90<\/li><li>5<\/li><\/ol>\n\nTest data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>60<\/li><li>5<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View first 5 rows of the table IRIS ","message":["%r","","z.show(head(IRIS,10))"],"enabled":true,"result":{"startTime":1734632270075,"interpreter":"r.low","endTime":1734632270576,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n4.6\t3.6\t1\t0.2\tsetosa\n4.3\t3\t1.1\t0.1\tsetosa\n5\t3.2\t1.2\t0.2\tsetosa\n5.8\t4\t1.2\t0.2\tsetosa\n5\t3.5\t1.3\t0.3\tsetosa\n4.4\t3.2\t1.3\t0.2\tsetosa\n4.7\t3.2\t1.3\t0.2\tsetosa\n5.5\t3.5\t1.3\t0.2\tsetosa\n4.5\t2.3\t1.3\t0.3\tsetosa\n4.4\t3\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"boxplot\":{\"showSeries\":[\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\"],\"lastColumns\":[\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\",\"Species\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"boxplot","title":"View boxplot of IRIS data","message":["%r","","z.show(IRIS)"],"enabled":true,"result":{"startTime":1734632271104,"interpreter":"r.low","endTime":1734632271670,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n4.6\t3.6\t1\t0.2\tsetosa\n4.3\t3\t1.1\t0.1\tsetosa\n5\t3.2\t1.2\t0.2\tsetosa\n5.8\t4\t1.2\t0.2\tsetosa\n4.4\t3\t1.3\t0.2\tsetosa\n4.4\t3.2\t1.3\t0.2\tsetosa\n4.7\t3.2\t1.3\t0.2\tsetosa\n5.5\t3.5\t1.3\t0.2\tsetosa\n4.5\t2.3\t1.3\t0.3\tsetosa\n5\t3.5\t1.3\t0.3\tsetosa\n5.4\t3.9\t1.3\t0.4\tsetosa\n4.8\t3\t1.4\t0.1\tsetosa\n4.9\t3.6\t1.4\t0.1\tsetosa\n4.4\t2.9\t1.4\t0.2\tsetosa\n4.9\t3\t1.4\t0.2\tsetosa\n4.6\t3.2\t1.4\t0.2\tsetosa\n5\t3.3\t1.4\t0.2\tsetosa\n5.2\t3.4\t1.4\t0.2\tsetosa\n5.1\t3.5\t1.4\t0.2\tsetosa\n5\t3.6\t1.4\t0.2\tsetosa\n5.5\t4.2\t1.4\t0.2\tsetosa\n4.8\t3\t1.4\t0.3\tsetosa\n4.6\t3.4\t1.4\t0.3\tsetosa\n5.1\t3.5\t1.4\t0.3\tsetosa\n4.9\t3.1\t1.5\t0.1\tsetosa\n5.2\t4.1\t1.5\t0.1\tsetosa\n4.6\t3.1\t1.5\t0.2\tsetosa\n4.9\t3.1\t1.5\t0.2\tsetosa\n5\t3.4\t1.5\t0.2\tsetosa\n5.1\t3.4\t1.5\t0.2\tsetosa\n5.2\t3.5\t1.5\t0.2\tsetosa\n5.3\t3.7\t1.5\t0.2\tsetosa\n5.4\t3.7\t1.5\t0.2\tsetosa\n5.1\t3.8\t1.5\t0.3\tsetosa\n5.4\t3.4\t1.5\t0.4\tsetosa\n5.1\t3.7\t1.5\t0.4\tsetosa\n5.7\t4.4\t1.5\t0.4\tsetosa\n5\t3\t1.6\t0.2\tsetosa\n4.8\t3.1\t1.6\t0.2\tsetosa\n4.7\t3.2\t1.6\t0.2\tsetosa\n4.8\t3.4\t1.6\t0.2\tsetosa\n5.1\t3.8\t1.6\t0.2\tsetosa\n5\t3.4\t1.6\t0.4\tsetosa\n5\t3.5\t1.6\t0.6\tsetosa\n5.4\t3.4\t1.7\t0.2\tsetosa\n5.7\t3.8\t1.7\t0.3\tsetosa\n5.4\t3.9\t1.7\t0.4\tsetosa\n5.1\t3.3\t1.7\t0.5\tsetosa\n4.8\t3.4\t1.9\t0.2\tsetosa\n5.1\t3.8\t1.9\t0.4\tsetosa\n5.1\t2.5\t3\t1.1\tversicolor\n6\t2.7\t5.1\t1.6\tversicolor\n6.7\t3\t5\t1.7\tversicolor\n6.9\t3.1\t4.9\t1.5\tversicolor\n6.3\t2.5\t4.9\t1.5\tversicolor\n5.9\t3.2\t4.8\t1.8\tversicolor\n6.8\t2.8\t4.8\t1.4\tversicolor\n6.3\t3.3\t4.7\t1.6\tversicolor\n6.7\t3.1\t4.7\t1.5\tversicolor\n7\t3.2\t4.7\t1.4\tversicolor\n6.1\t2.9\t4.7\t1.4\tversicolor\n6.1\t2.8\t4.7\t1.2\tversicolor\n6.5\t2.8\t4.6\t1.5\tversicolor\n6.1\t3\t4.6\t1.4\tversicolor\n6.6\t2.9\t4.6\t1.3\tversicolor\n6\t3.4\t4.5\t1.6\tversicolor\n6.4\t3.2\t4.5\t1.5\tversicolor\n5.6\t3\t4.5\t1.5\tversicolor\n5.4\t3\t4.5\t1.5\tversicolor\n6\t2.9\t4.5\t1.5\tversicolor\n6.2\t2.2\t4.5\t1.5\tversicolor\n5.7\t2.8\t4.5\t1.3\tversicolor\n5\t2.3\t3.3\t1\tversicolor\n4.9\t2.4\t3.3\t1\tversicolor\n5\t2\t3.5\t1\tversicolor\n5.7\t2.6\t3.5\t1\tversicolor\n5.6\t2.9\t3.6\t1.3\tversicolor\n5.5\t2.4\t3.7\t1\tversicolor\n5.5\t2.4\t3.8\t1.1\tversicolor\n5.6\t2.5\t3.9\t1.1\tversicolor\n5.8\t2.7\t3.9\t1.2\tversicolor\n5.2\t2.7\t3.9\t1.4\tversicolor\n6\t2.2\t4\t1\tversicolor\n5.8\t2.6\t4\t1.2\tversicolor\n5.5\t2.3\t4\t1.3\tversicolor\n5.5\t2.5\t4\t1.3\tversicolor\n6.1\t2.8\t4\t1.3\tversicolor\n5.8\t2.7\t4.1\t1\tversicolor\n5.7\t2.8\t4.1\t1.3\tversicolor\n5.6\t3\t4.1\t1.3\tversicolor\n5.7\t3\t4.2\t1.2\tversicolor\n5.6\t2.7\t4.2\t1.3\tversicolor\n5.7\t2.9\t4.2\t1.3\tversicolor\n5.9\t3\t4.2\t1.5\tversicolor\n6.2\t2.9\t4.3\t1.3\tversicolor\n6.4\t2.9\t4.3\t1.3\tversicolor\n5.5\t2.6\t4.4\t1.2\tversicolor\n6.3\t2.3\t4.4\t1.3\tversicolor\n6.6\t3\t4.4\t1.4\tversicolor\n6.7\t3.1\t4.4\t1.4\tversicolor\n4.9\t2.5\t4.5\t1.7\tvirginica\n6.2\t2.8\t4.8\t1.8\tvirginica\n6\t3\t4.8\t1.8\tvirginica\n6.3\t2.7\t4.9\t1.8\tvirginica\n6.1\t3\t4.9\t1.8\tvirginica\n5.6\t2.8\t4.9\t2\tvirginica\n6\t2.2\t5\t1.5\tvirginica\n6.3\t2.5\t5\t1.9\tvirginica\n5.7\t2.5\t5\t2\tvirginica\n6.3\t2.8\t5.1\t1.5\tvirginica\n5.9\t3\t5.1\t1.8\tvirginica\n5.8\t2.7\t5.1\t1.9\tvirginica\n5.8\t2.7\t5.1\t1.9\tvirginica\n6.5\t3.2\t5.1\t2\tvirginica\n6.9\t3.1\t5.1\t2.3\tvirginica\n5.8\t2.8\t5.1\t2.4\tvirginica\n6.5\t3\t5.2\t2\tvirginica\n6.7\t3\t5.2\t2.3\tvirginica\n6.4\t2.7\t5.3\t1.9\tvirginica\n6.4\t3.2\t5.3\t2.3\tvirginica\n6.9\t3.1\t5.4\t2.1\tvirginica\n6.2\t3.4\t5.4\t2.3\tvirginica\n6.5\t3\t5.5\t1.8\tvirginica\n6.4\t3.1\t5.5\t1.8\tvirginica\n6.8\t3\t5.5\t2.1\tvirginica\n6.1\t2.6\t5.6\t1.4\tvirginica\n6.3\t2.9\t5.6\t1.8\tvirginica\n6.4\t2.8\t5.6\t2.1\tvirginica\n6.4\t2.8\t5.6\t2.2\tvirginica\n6.7\t3.1\t5.6\t2.4\tvirginica\n6.3\t3.4\t5.6\t2.4\tvirginica\n6.7\t3.3\t5.7\t2.1\tvirginica\n6.9\t3.2\t5.7\t2.3\tvirginica\n6.7\t3.3\t5.7\t2.5\tvirginica\n7.2\t3\t5.8\t1.6\tvirginica\n6.7\t2.5\t5.8\t1.8\tvirginica\n6.5\t3\t5.8\t2.2\tvirginica\n7.1\t3\t5.9\t2.1\tvirginica\n6.8\t3.2\t5.9\t2.3\tvirginica\n7.2\t3.2\t6\t1.8\tvirginica\n6.3\t3.3\t6\t2.5\tvirginica\n7.4\t2.8\t6.1\t1.9\tvirginica\n7.7\t3\t6.1\t2.3\tvirginica\n7.2\t3.6\t6.1\t2.5\tvirginica\n7.3\t2.9\t6.3\t1.8\tvirginica\n7.9\t3.8\t6.4\t2\tvirginica\n7.6\t3\t6.6\t2.1\tvirginica\n7.7\t2.8\t6.7\t2\tvirginica\n7.7\t3.8\t6.7\t2.2\tvirginica\n7.7\t2.6\t6.9\t2.3\tvirginica\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute summary statistics using summary( )","message":["%r","","summary(IRIS)"],"enabled":true,"result":{"startTime":1734632272153,"interpreter":"r.low","endTime":1734632273633,"results":[{"message":"  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width          Species  \n Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50  \n 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300   versicolor:50  \n Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50  \n Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199                  \n 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800                  \n Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500                  ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## In-database machine learning algorithms","","Oracle Machine Learning provides over 30 machine learning parallelized algorithms in the Oracle Database kernel. These algorithms are exposed through three commonly used languages for data science: Python, R, and SQL. ","","In this section, we show examples illustrating using the in-database machine learning algorithms directly from R."],"enabled":true,"result":{"startTime":1734632274101,"interpreter":"md.low","endTime":1734632274544,"results":[{"message":"<h2 id=\"in-database-machine-learning-algorithms\">In-database machine learning algorithms<\/h2>\n<p>Oracle Machine Learning provides over 30 machine learning parallelized algorithms in the Oracle Database kernel. These algorithms are exposed through three commonly used languages for data science: Python, R, and SQL.<\/p>\n<p>In this section, we show examples illustrating using the in-database machine learning algorithms directly from R.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build an attribute importance model","","First, build an attribute importance model using the ***ore.odmAI*** function, where a model proxy object is stored in variable ***ai***. ","This produces a ranking of attributes with their associated importance values.","","Notice that petal width is most predictive of the target species. The importance value produced by this algorithm provides a relative ranking, not an absolute importance value."],"enabled":true,"result":{"startTime":1734632275007,"interpreter":"md.low","endTime":1734632275455,"results":[{"message":"<h3 id=\"build-an-attribute-importance-model\">Build an attribute importance model<\/h3>\n<p>First, build an attribute importance model using the <em><strong>ore.odmAI<\/strong><\/em> function, where a model proxy object is stored in variable <em><strong>ai<\/strong><\/em>.\nThis produces a ranking of attributes with their associated importance values.<\/p>\n<p>Notice that petal width is most predictive of the target species. The importance value produced by this algorithm provides a relative ranking, not an absolute importance value.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ai <- ore.odmAI(Species ~ ., IRIS, auto.data.prep = TRUE)","ai"],"enabled":true,"result":{"startTime":1734632275921,"interpreter":"r.low","endTime":1734632276882,"results":[{"message":"\nCall:\nore.odmAI(formula = Species ~ ., data = IRIS, auto.data.prep = TRUE)\n\nImportance: \n             importance rank\nPetal.Width   1.0509353    1\nPetal.Length  1.0306334    2\nSepal.Length  0.4548240    3\nSepal.Width   0.1915138    4\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","res <- ai$importance","res$attributes <- rownames(res)","res"],"enabled":true,"result":{"startTime":1734632277352,"interpreter":"r.low","endTime":1734632277889,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 4 x 3<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>importance<\/th><th scope=col>rank<\/th><th scope=col>attributes<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>Petal.Width<\/th><td>1.0509353<\/td><td>1<\/td><td>Petal.Width <\/td><\/tr>\n\t<tr><th scope=row>Petal.Length<\/th><td>1.0306334<\/td><td>2<\/td><td>Petal.Length<\/td><\/tr>\n\t<tr><th scope=row>Sepal.Length<\/th><td>0.4548240<\/td><td>3<\/td><td>Sepal.Length<\/td><\/tr>\n\t<tr><th scope=row>Sepal.Width<\/th><td>0.1915138<\/td><td>4<\/td><td>Sepal.Width <\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build an in-database Random Forest model to predict Species"],"enabled":true,"result":{"startTime":1734632278353,"interpreter":"md.low","endTime":1734632278797,"results":[{"message":"<h3 id=\"build-an-in-database-random-forest-model-to-predict-species\">Build an in-database Random Forest model to predict Species<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","MOD.RF <- ore.odmRF(Species~., IRIS)   ","","summary(MOD.RF)"],"enabled":true,"result":{"startTime":1734632279259,"interpreter":"r.low","endTime":1734632280372,"results":[{"message":"\nCall:\nore.odmRF(formula = Species ~ ., data = IRIS)\n\nSettings: \n                                               value\nclas.max.sup.bins                                 32\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.random.seed                                   0\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nrfor.num.trees                                    20\nrfor.sampling.ratio                               .5\nimpurity.metric                        impurity.gini\nterm.max.depth                                    16\nterm.minpct.node                                 .05\nterm.minpct.split                                 .1\nterm.minrec.node                                  10\nterm.minrec.split                                 20\n\nImportance: \n  ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_IMPORTANCE\n1   Petal.Length              <NA>            0.7110469\n2    Petal.Width              <NA>            0.5898557\n3   Sepal.Length              <NA>            0.1702209\n4    Sepal.Width              <NA>            0.1319053","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Use in-database model to make predictions using model proxy object"],"enabled":true,"result":{"startTime":1734632280849,"interpreter":"md.low","endTime":1734632281296,"results":[{"message":"<h3 id=\"use-in-database-model-to-make-predictions-using-model-proxy-object\">Use in-database model to make predictions using model proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","PRED <- predict(MOD.RF, IRIS, type=c(\"class\",\"raw\"), supplemental.cols=\"Species\") ","","head(PRED)"],"enabled":true,"result":{"startTime":1734632281772,"interpreter":"r.low","endTime":1734632283065,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 5<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>'setosa'<\/th><th scope=col>'versicolor'<\/th><th scope=col>'virginica'<\/th><th scope=col>Species<\/th><th scope=col>PREDICTION<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>setosa<\/th><td>0.9961538<\/td><td>0.003846154<\/td><td>0<\/td><td>setosa<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.1<\/th><td>0.9961538<\/td><td>0.003846154<\/td><td>0<\/td><td>setosa<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.2<\/th><td>0.9961538<\/td><td>0.003846154<\/td><td>0<\/td><td>setosa<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.3<\/th><td>0.9961538<\/td><td>0.003846154<\/td><td>0<\/td><td>setosa<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.4<\/th><td>0.9961538<\/td><td>0.003846154<\/td><td>0<\/td><td>setosa<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.5<\/th><td>0.9961538<\/td><td>0.003846154<\/td><td>0<\/td><td>setosa<\/td><td>setosa<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute a Confusion Matrix for the predicted results","message":["%r","","with(PRED, table(Species, PREDICTION))"],"enabled":true,"result":{"startTime":1734632283528,"interpreter":"r.low","endTime":1734632284182,"results":[{"message":"            PREDICTION\nSpecies      setosa versicolor virginica\n  setosa         50          0         0\n  versicolor      0         47         3\n  virginica       0          2        48","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Transparency Layer functionality","","OML4R has overloaded open source R methods and functions that you can use to operate directly on data in your database instance.","","The methods and functions are in packages that implement a transparency layer that translates R functions into SQL.","","The Oracle Machine Learning for R transparency layer is implemented by the OREbase, OREgraphics, and OREstats packages.","","These OML4R packages contain overloaded methods of functions in the open source R base, stats, and graphics packages, respectively. ","The OML4R packages also contain OML4R versions of some of the open source R functions.","","With the methods and functions in these packages, you can create R objects that specify data in an Oracle Database instance. ","When you run an R expression that uses such an object, the method or function transparently generates a SQL query and sends it to the database. ","The database then runs the query and returns the results of the operation as an R object."],"enabled":true,"result":{"startTime":1734632284650,"interpreter":"md.low","endTime":1734632285087,"results":[{"message":"<h2 id=\"transparency-layer-functionality\">Transparency Layer functionality<\/h2>\n<p>OML4R has overloaded open source R methods and functions that you can use to operate directly on data in your database instance.<\/p>\n<p>The methods and functions are in packages that implement a transparency layer that translates R functions into SQL.<\/p>\n<p>The Oracle Machine Learning for R transparency layer is implemented by the OREbase, OREgraphics, and OREstats packages.<\/p>\n<p>These OML4R packages contain overloaded methods of functions in the open source R base, stats, and graphics packages, respectively.\nThe OML4R packages also contain OML4R versions of some of the open source R functions.<\/p>\n<p>With the methods and functions in these packages, you can create R objects that specify data in an Oracle Database instance.\nWhen you run an R expression that uses such an object, the method or function transparently generates a SQL query and sends it to the database.\nThe database then runs the query and returns the results of the operation as an R object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"List the OREbase package functions in the transparency layer ","message":["%r","","cat(\"Overloaded R base package functions:\\n\", format(ls(\"package:OREbase\"), width=30))"],"enabled":true,"result":{"startTime":1734632285553,"interpreter":"r.low","endTime":1734632286061,"results":[{"message":"Overloaded R base package functions:\n %in%                           Arith                          Compare                        I                              Logic                          Math                           NCOL                           NROW                           Summary                        as.data.frame                  as.env                         as.factor                      as.list                        as.matrix                      as.ore                         as.ore.character               as.ore.date                    as.ore.datetime                as.ore.difftime                as.ore.factor                  as.ore.frame                   as.ore.integer                 as.ore.logical                 as.ore.matrix                  as.ore.numeric                 as.ore.vector                  atan2                          attach                         backsolve                      besselI                        besselJ                        besselK                        besselY                        by                             casefold                       cbind                          chartr                         coerce                         colMeans                       colSums                        colnames                       colnames<-                     crossprod                      cut                            data.frame                     diff                           eval                           factorial                      forwardsolve                   grepl                          gsub                           head                           ifelse                         interaction                    is.factor                      is.ore                         is.ore.character               is.ore.date                    is.ore.datetime                is.ore.difftime                is.ore.factor                  is.ore.frame                   is.ore.integer                 is.ore.logical                 is.ore.matrix                  is.ore.numeric                 is.ore.vector                  is.vector                      levels                         lfactorial                     logb                           max.col                        mean                           merge                          nchar                          ncol                           nlevels                        nrow                           order                          ore.attach                     ore.connect                    ore.const                      ore.create                     ore.detach                     ore.disconnect                 ore.drop                       ore.exec                       ore.exists                     ore.frame                      ore.get                        ore.grant                      ore.hash                       ore.hiveOptions                ore.hour                       ore.impalaOptions              ore.is.connected               ore.ls                         ore.make.names                 ore.mday                       ore.minute                     ore.month                      ore.pull                       ore.push                       ore.recode                     ore.revoke                     ore.rm                         ore.second                     ore.showHiveOptions            ore.showImpalaOptions          ore.sync                       ore.year                       paste                          pmax                           pmin                           rank                           rbind                          row.names                      row.names<-                    rowMeans                       rowSums                        rownames                       rownames<-                     scale                          show                           solve                          sort                           split                          sub                            subset                         substr                         substring                      summary                        t                              table                          tabulate                       tail                           tapply                         tcrossprod                     tolower                        toupper                        transform                      unique                         with                           within                         xor                            zapsmall                      ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"List the OREstats package functions in the transparency layer ","message":["%r","","cat(\"Overloaded R stats package functions:\\n\\n\", format(ls(\"package:OREstats\"), width=30))"],"enabled":true,"result":{"startTime":1734632286530,"interpreter":"r.low","endTime":1734632287035,"results":[{"message":"Overloaded R stats package functions:\n\n IQR                            aggregate                      ave                            binom.test                     chisq.test                     complete.cases                 cor                            cov                            dbeta                          dbinom                         dcauchy                        dchisq                         dexp                           df                             dgamma                         dgeom                          dlnorm                         dlogis                         dnbinom                        dnorm                          dpois                          dsignrank                      dt                             dunif                          dweibull                       factanal                       fitdistr                       fivenum                        get_all_vars                   ks.test                        mad                            median                         model.frame                    model.matrix                   na.omit                        ore.getXlevels                 ore.getXnlevels                ore.rollmax                    ore.rollmean                   ore.rollmin                    ore.rollsd                     ore.rollsum                    ore.rollvar                    pbeta                          pbinom                         pcauchy                        pchisq                         pexp                           pf                             pgamma                         pgeom                          plnorm                         plogis                         pnbinom                        pnorm                          ppois                          prcomp                         princomp                       prop.test                      psignrank                      pt                             punif                          pweibull                       qbeta                          qbinom                         qcauchy                        qchisq                         qexp                           qf                             qgamma                         qgeom                          qlnorm                         qlogis                         qnbinom                        qnorm                          qpois                          qsignrank                      qt                             quantile                       qunif                          qweibull                       reorder                        sd                             svd                            t.test                         terms                          var                            var.test                       wilcox.test                   ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"List the OREgraphics package functions in the transparency layer ","message":["%r","","cat(\"Overloaded graphics package functions:\\n\\n\", format(ls(\"package:OREgraphics\"), width=30))"],"enabled":true,"result":{"startTime":1734632287505,"interpreter":"r.low","endTime":1734632288002,"results":[{"message":"Overloaded graphics package functions:\n\n arrows                         boxplot.stats                  co.intervals                   coplot                         matlines                       matplot                        matpoints                      polygon                        polypath                       rug                            segments                       smoothScatter                  symbols                        xspline                        xy.coords                     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Pull the IRIS proxy object to the local R session","","OML4R data.frame proxy objects enable data in the database as tables and views to be manipulated - without data being pulled to the client - while allowing the use of familiar R functions and syntax.","","However, to use local R functionality, for example, from third-party packages, the data needs to be pulled to the client. This can be done using ***ore.pull***."],"enabled":true,"result":{"startTime":1734632288476,"interpreter":"md.low","endTime":1734632288920,"results":[{"message":"<h4 id=\"pull-the-iris-proxy-object-to-the-local-r-session\">Pull the IRIS proxy object to the local R session<\/h4>\n<p>OML4R data.frame proxy objects enable data in the database as tables and views to be manipulated - without data being pulled to the client - while allowing the use of familiar R functions and syntax.<\/p>\n<p>However, to use local R functionality, for example, from third-party packages, the data needs to be pulled to the client. This can be done using <em><strong>ore.pull<\/strong><\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","local_iris <- ore.pull(IRIS)","","cat(\"Type IRIS: \", class(IRIS))","cat(\"\\nType local_iris: \", class(local_iris))"],"enabled":true,"result":{"startTime":1734632289389,"interpreter":"r.low","endTime":1734632289893,"results":[{"message":"Type IRIS:  ore.frame\nType local_iris:  data.frame","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Overloaded aggregate functions","","Perform a few aggregate operations on the *IRIS* database table *Sepal.Length* column. ","","A key observation is that these statistics are computed in-database, leveraging the parallelism and scalability of Oracle Database for the computation. No data moves to the client except the final statistic(s). "],"enabled":true,"result":{"startTime":1734632290365,"interpreter":"md.low","endTime":1734632290807,"results":[{"message":"<h2 id=\"overloaded-aggregate-functions\">Overloaded aggregate functions<\/h2>\n<p>Perform a few aggregate operations on the <em>IRIS<\/em> database table <em>Sepal.Length<\/em> column.<\/p>\n<p>A key observation is that these statistics are computed in-database, leveraging the parallelism and scalability of Oracle Database for the computation. No data moves to the client except the final statistic(s).<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","cat(  \"Min:   \",    min(IRIS$Sepal.Length))","cat(\"\\nMax:   \",    max(IRIS$Sepal.Length))","cat(\"\\nMean:  \",   mean(IRIS$Sepal.Length))","cat(\"\\nMedian:\", median(IRIS$Sepal.Length))"],"enabled":true,"result":{"startTime":1734632291269,"interpreter":"r.low","endTime":1734632291834,"results":[{"message":"Min:    4.3\nMax:    7.9\nMean:   5.843333\nMedian: 5.8","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Date and time handling","","Support for key data types in Oracle Database and R enables transparent access and manipulation of database data. As such, date and time handling is essential for time series data. In the example below, we create a data frame to illustrate using overloaded functionality for date and time data. "],"enabled":true,"result":{"startTime":1734632292311,"interpreter":"md.low","endTime":1734632292754,"results":[{"message":"<h3 id=\"date-and-time-handling\">Date and time handling<\/h3>\n<p>Support for key data types in Oracle Database and R enables transparent access and manipulation of database data. As such, date and time handling is essential for time series data. In the example below, we create a data frame to illustrate using overloaded functionality for date and time data.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Aggregating date & time data","message":["%r","","N <- 500","mydata <- data.frame(datetime = seq(as.POSIXct(\"2001/01/01\"), ","                                    as.POSIXct(\"2001/12/31\"), length.out = N),","                     difftime = as.difftime(runif(N), units = \"mins\"),","                     x = rnorm(N))","","MYDATA <- ore.push(mydata)","class(MYDATA)","class(MYDATA$datetime)","head(MYDATA, 3)"],"enabled":true,"result":{"startTime":1734632293219,"interpreter":"r.low","endTime":1734632293781,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"'ore.datetime'\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 3 x 3<\/caption>\n<thead>\n\t<tr><th scope=col>datetime<\/th><th scope=col>difftime<\/th><th scope=col>x<\/th><\/tr>\n\t<tr><th scope=col>&lt;dttm&gt;<\/th><th scope=col>&lt;drtn&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>2001-01-01 00:00:00<\/td><td>39.59031 secs<\/td><td>-0.7722500<\/td><\/tr>\n\t<tr><td>2001-01-01 17:30:25<\/td><td>49.30833 secs<\/td><td> 0.6809156<\/td><\/tr>\n\t<tr><td>2001-01-02 11:00:50<\/td><td>47.17689 secs<\/td><td>-0.2075835<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Filter data","","Select rows using three different criteria. "],"enabled":true,"result":{"startTime":1734632294248,"interpreter":"md.low","endTime":1734632294688,"results":[{"message":"<h3 id=\"filter-data\">Filter data<\/h3>\n<p>Select rows using three different criteria.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","IRIS1 <- IRIS[IRIS$Sepal.Length > 5,]","head(IRIS1)","","IRIS2 <- IRIS[IRIS$Sepal.Length > 5, c(1,3)]","head(IRIS2, 3)","","IRIS3 <- IRIS[IRIS$Sepal.Length > 5 | IRIS$Species == \"Setosa\", 1:3]","head(IRIS3, 6)"],"enabled":true,"result":{"startTime":1734632295157,"interpreter":"r.low","endTime":1734632295767,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 5<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Sepal.Length<\/th><th scope=col>Sepal.Width<\/th><th scope=col>Petal.Length<\/th><th scope=col>Petal.Width<\/th><th scope=col>Species<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;fct&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>setosa<\/th><td>5.8<\/td><td>4.0<\/td><td>1.2<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.1<\/th><td>5.5<\/td><td>3.5<\/td><td>1.3<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.2<\/th><td>5.4<\/td><td>3.9<\/td><td>1.3<\/td><td>0.4<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.3<\/th><td>5.2<\/td><td>3.4<\/td><td>1.4<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.4<\/th><td>5.1<\/td><td>3.5<\/td><td>1.4<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>setosa.5<\/th><td>5.5<\/td><td>4.2<\/td><td>1.4<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 3 x 2<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Sepal.Length<\/th><th scope=col>Petal.Length<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>setosa<\/th><td>5.8<\/td><td>1.2<\/td><\/tr>\n\t<tr><th scope=row>setosa.1<\/th><td>5.5<\/td><td>1.3<\/td><\/tr>\n\t<tr><th scope=row>setosa.2<\/th><td>5.4<\/td><td>1.3<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 3<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Sepal.Length<\/th><th scope=col>Sepal.Width<\/th><th scope=col>Petal.Length<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>setosa<\/th><td>5.8<\/td><td>4.0<\/td><td>1.2<\/td><\/tr>\n\t<tr><th scope=row>setosa.1<\/th><td>5.5<\/td><td>3.5<\/td><td>1.3<\/td><\/tr>\n\t<tr><th scope=row>setosa.2<\/th><td>5.4<\/td><td>3.9<\/td><td>1.3<\/td><\/tr>\n\t<tr><th scope=row>setosa.3<\/th><td>5.2<\/td><td>3.4<\/td><td>1.4<\/td><\/tr>\n\t<tr><th scope=row>setosa.4<\/th><td>5.1<\/td><td>3.5<\/td><td>1.4<\/td><\/tr>\n\t<tr><th scope=row>setosa.5<\/th><td>5.5<\/td><td>4.2<\/td><td>1.4<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Join two tables","","Data tables and views can be joined in the database using the overloaded ***merge*** function. ","This avoids the need to pull  data to the client, while benefitting from Oracle Database scalability and performance.","","Here, we create a temporary table with textual descriptions of each species that we can join with the IRIS data set."],"enabled":true,"result":{"startTime":1734632296250,"interpreter":"md.low","endTime":1734632296696,"results":[{"message":"<h3 id=\"join-two-tables\">Join two tables<\/h3>\n<p>Data tables and views can be joined in the database using the overloaded <em><strong>merge<\/strong><\/em> function.\nThis avoids the need to pull  data to the client, while benefitting from Oracle Database scalability and performance.<\/p>\n<p>Here, we create a temporary table with textual descriptions of each species that we can join with the IRIS data set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}},{\"table\":{\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","library(OREdplyr)  # OREdplyr must be explicitly loaded to use","","df1 <- data.frame(Species = c(\"setosa\", \"versicolor\",\"virginica\"), ","     description = c(\"Iris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\",  ","    \"Iris versicolor is commonly known as, e.g., the blue flag, harlequin blueflag, larger blue flag, ","       and in Britain and Ireland as purple iris.\", ","     \"Iris virginica is a perennial species of flowering plant native to eastern North America.\" ))","","#creating the tables","ore.drop(\"iris_descr\")","ore.create(df1, table=\"iris_descr\")","","IRIS_2 <- merge(IRIS, iris_descr, by.x=\"Species\")","","z.show(head(IRIS_2))"],"enabled":true,"result":{"startTime":1734632297168,"interpreter":"r.low","endTime":1734632297871,"results":[{"message":"Species\tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tdescription\nsetosa\t4.6\t3.6\t1\t0.2\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\nsetosa\t4.3\t3\t1.1\t0.1\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\nsetosa\t5\t3.2\t1.2\t0.2\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\nsetosa\t5.8\t4\t1.2\t0.2\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\nsetosa\t4.4\t3\t1.3\t0.2\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\nsetosa\t4.4\t3.2\t1.3\t0.2\tIris setosa is also known as bristle-pointed iris. There are also plants with pink and white flowers.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compute pairwise correlation","","Using only numeric columns, we can compute a correlation matrix on the proxy object for the database table IRIS using the overloaded ***cor*** function. Here we see that petal length and petal width are highly correlated."],"enabled":true,"result":{"startTime":1734632298341,"interpreter":"md.low","endTime":1734632298779,"results":[{"message":"<h2 id=\"compute-pairwise-correlation\">Compute pairwise correlation<\/h2>\n<p>Using only numeric columns, we can compute a correlation matrix on the proxy object for the database table IRIS using the overloaded <em><strong>cor<\/strong><\/em> function. Here we see that petal length and petal width are highly correlated.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","IRIS_NUMERIC <- IRIS[, c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")]","class(IRIS_NUMERIC)","","res <- as.data.frame(cor(IRIS_NUMERIC, use = \"all.obs\"))","class(res)","res"],"enabled":true,"result":{"startTime":1734632299235,"interpreter":"r.low","endTime":1734632300120,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"'data.frame'\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 4 x 4<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Sepal.Length<\/th><th scope=col>Sepal.Width<\/th><th scope=col>Petal.Length<\/th><th scope=col>Petal.Width<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>Sepal.Length<\/th><td> 1.0000000<\/td><td>-0.1175698<\/td><td> 0.8717538<\/td><td> 0.8179411<\/td><\/tr>\n\t<tr><th scope=row>Sepal.Width<\/th><td>-0.1175698<\/td><td> 1.0000000<\/td><td>-0.4284401<\/td><td>-0.3661259<\/td><\/tr>\n\t<tr><th scope=row>Petal.Length<\/th><td> 0.8717538<\/td><td>-0.4284401<\/td><td> 1.0000000<\/td><td> 0.9628654<\/td><\/tr>\n\t<tr><th scope=row>Petal.Width<\/th><td> 0.8179411<\/td><td>-0.3661259<\/td><td> 0.9628654<\/td><td> 1.0000000<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Overloaded data visualization functions","","OML4R overloads select graphics functions. ","### boxplot","Here, we use the overloaded ***boxplot*** function to show the distribution of the ***IRIS*** table 'length' column. The statistical computations take place in the database - avoiding data movement. The function returns only the summary statistics needed to produce the plot, which enables scalability. No overhead is incurred for moving the data to the client for processing, whether 150 rows (as in IRIS) or 150 million rows.","","### histogram","Similarly, we use the overloaded ***hist*** function, where the statistics are computed in the database. Only the summary statistics needed for the plot are returned to the client."],"enabled":true,"result":{"startTime":1734632300586,"interpreter":"md.low","endTime":1734632301027,"results":[{"message":"<h2 id=\"overloaded-data-visualization-functions\">Overloaded data visualization functions<\/h2>\n<p>OML4R overloads select graphics functions.<\/p>\n<h3 id=\"boxplot\">boxplot<\/h3>\n<p>Here, we use the overloaded <em><strong>boxplot<\/strong><\/em> function to show the distribution of the <em><strong>IRIS<\/strong><\/em> table 'length' column. The statistical computations take place in the database - avoiding data movement. The function returns only the summary statistics needed to produce the plot, which enables scalability. No overhead is incurred for moving the data to the client for processing, whether 150 rows (as in IRIS) or 150 million rows.<\/p>\n<h3 id=\"histogram\">histogram<\/h3>\n<p>Similarly, we use the overloaded <em><strong>hist<\/strong><\/em> function, where the statistics are computed in the database. Only the summary statistics needed for the plot are returned to the client.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Overloaded boxplot() with in-database statistics computation","message":["%r","","boxplot(IRIS$Sepal.Length, horizontal=TRUE, ","        notch=TRUE, ","        col=\"darkgreen\", ","        showmeans=TRUE, xlab=\"cm\", ","        main=\"Distribution of IRIS$Sepal.Length\")","        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Pv/TiS8nuSfx/QAxgPcIOaAjbhvCd8Oc//3nO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\nLl26hBCGDh1aw7qRnRX1ORTVar5nRRWXXHJJCGHYsGG1u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JYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQ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message":["%md","## Return data and multiple images using embedded R execution","","Here, we create a function ***RandomRedDots*** that creates a simple data.frame and generates two plots of random red dots. ","It also allows for two arguments, namely the number of dots to be generated in each plot. ","","We then create this function in the R script repository, also named \"RandomRedDots\", and run the native R function."],"enabled":true,"result":{"startTime":1734632303688,"interpreter":"md.low","endTime":1734632304130,"results":[{"message":"<h2 id=\"return-data-and-multiple-images-using-embedded-r-execution\">Return data and multiple images using embedded R execution<\/h2>\n<p>Here, we create a function <em><strong>RandomRedDots<\/strong><\/em> that creates a simple data.frame and generates two plots of random red dots.\nIt also allows for two arguments, namely the number of dots to be generated in each plot.<\/p>\n<p>We then create this function in the R script repository, also named &quot;RandomRedDots&quot;, and run the native R function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define an R function to plot N random red dots and return a two-column data.frame","message":["%r","","RandomRedDots <- function(numDots=100){","  id <- 1:10","  plot( 1:numDots, rnorm(numDots), pch = 21, bg = \"red\", cex = 2)","  data.frame(id=id, val=id / 100)","}"],"enabled":true,"result":{"startTime":1734632304606,"interpreter":"r.low","endTime":1734632305118,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{}},{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":"Run the UDF directly in R engine","message":["%r","","RandomRedDots(200)"],"enabled":true,"result":{"startTime":1734632305585,"interpreter":"r.low","endTime":1734632306156,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 10 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>id<\/th><th scope=col>val<\/th><\/tr>\n\t<tr><th scope=col>&lt;int&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td> 1<\/td><td>0.01<\/td><\/tr>\n\t<tr><td> 2<\/td><td>0.02<\/td><\/tr>\n\t<tr><td> 3<\/td><td>0.03<\/td><\/tr>\n\t<tr><td> 4<\/td><td>0.04<\/td><\/tr>\n\t<tr><td> 5<\/td><td>0.05<\/td><\/tr>\n\t<tr><td> 6<\/td><td>0.06<\/td><\/tr>\n\t<tr><td> 7<\/td><td>0.07<\/td><\/tr>\n\t<tr><td> 8<\/td><td>0.08<\/td><\/tr>\n\t<tr><td> 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cOuINOPYsWMA5s+fb+pC3oDBLntGCna6Y46H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rKwsc9dLREQWhsGOKJ+kpqa+27r13r17PwYuKEo/wPG/DTyAscAVWW4mxJw5c4YO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\nAAAAAElFTkSuQmCC","type":"IMG"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Use doEval from embedded R execution","","We can invoke this function with the embedded R execution function ***ore.doEval***. Note, however, that ***ore.doEval*** does not automatically pass data to the user-defined function but does allow arguments to be passed.","","Here, we see the two images and data.frame that are returned, using the default arguments to ***RandomRedDots***."],"enabled":true,"result":{"startTime":1734632306631,"interpreter":"md.low","endTime":1734632307077,"results":[{"message":"<h3 id=\"use-doeval-from-embedded-r-execution\">Use doEval from embedded R execution<\/h3>\n<p>We can invoke this function with the embedded R execution function <em><strong>ore.doEval<\/strong><\/em>. Note, however, that <em><strong>ore.doEval<\/strong><\/em> does not automatically pass data to the user-defined function but does allow arguments to be passed.<\/p>\n<p>Here, we see the two images and data.frame that are returned, using the default arguments to <em><strong>RandomRedDots<\/strong><\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Store user-defined R function in database R script repository","message":["%r","","ore.scriptCreate(\"RandomRedDots\", RandomRedDots, overwrite=TRUE) ","","ore.scriptList(\"RandomRedDots\")"],"enabled":true,"result":{"startTime":1734632307545,"interpreter":"r.low","endTime":1734632308053,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>RandomRedDots<\/td><td><span style=white-space:pre-wrap>function (numDots = 100) \n{\n    id &lt;- 1:10\n    plot(1:numDots, rnorm(numDots), pch = 21, bg = \"red\", cex = 2)\n    data.frame(id = id, val = id/100)\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{}},{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","res <- NULL","res <- ore.doEval(FUN.NAME=\"RandomRedDots\", numDots=100)","res"],"enabled":true,"result":{"startTime":1734632308530,"interpreter":"r.low","endTime":1734632310008,"results":[{"message":"   id  val\n1   1 0.01\n2   2 0.02\n3   3 0.03\n4   4 0.04\n5   5 0.05\n6   6 0.06\n7   7 0.07\n8   8 0.08\n9   9 0.09\n10 10 0.10\n","type":"TEXT"},{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nxFohauZ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dV1H2ObSgMykYPIJYoq59VK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\n33ijq7dX+FaCncEToSlhNz4+vrCw8OMf/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zqdRo6FwaNQFKVWq925devmtWvz8/Op7W2kaaD3saVpxmRqa23t7+8/dvp0a1vbg/dSqVRa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\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38Yj3Oadg4h/wt7klSAOwxz02RCgUCo\nvb2rp6eR9yOnKIplWY5lWYbRKaoMYHrhnutfoQMUAFRBsDgcgiTRNC1JkmixlHm+RNOYomA3\nvW6YokoAZZYVZVmSJDJN58BqamqSJElX1XGe/83ly9fi8RHD6ESoDWMZgAOoAmwDRCnqPsNM\nmkyuzs6eEycufP/7nb29vPDi6jqvA8a4XC6Xy+VKpcIwjNlslmXZbDbvdbteLh6PTz14YE2n\n/Rh7MBZ3PJvCZxhnML4Vjd67efP42NirbeX+tP+CXSgUGhoayufz09PTHo/nyd6RNE3b7fa2\ntrYTJ04AQDwe13dTE1xV1YmJibW1NUVRXlAzYn5+fmJiIpvNer07X4RHEC/H8zzP8z0DA8ff\neSd948bt2VmmWu3W9TBC0p9Wtqv3H8QpKsqydyRpKxzuOXNm6OhRl9vdyPXZ6zVFJUnyeTx6\nqbSaz4cBdr6ksAYQY5iizeZvbbU5HDRNO51Ou8eT4/kUQBVjacenMwWgAJADqHKczeWy2Wwk\n2B1YZlnmBWFodJThOMpiKS8sbMTj5Wx2JZfjDYNGSGeYiiTlLZay3+8Oh/uGhgYPH+7s7XV7\nPHvS11vfRq9QKCS2t5Pb28lEolQs1splQ1UpmmYEwWyxWK1Wr9/v9fk8j6ehN2C3dDaVWp2f\nl7PZZsOQd7ZXb50d4BBC9zKZlWi0WCw+2Xns1TV139l/wS4SiVSr1WvXrmUymcOHD/v9/qf/\n1mazHTt2jOf5crlcKBQKhcIOX7Zarf7iF7/45S9/mc1mFUV53sN0Xdc0TdM0MtuaeBUOj456\nvN4fK8rdRCKWyRypVN5DKPynwa7ehXCJZb8UhILDEezpufAXfzE4MiI0QP/BS1lkOdLaqufz\nU/m8eTfBrkLTCzxfc7tHDh1yeb00Tft8Pm84nGHZTcMoAlh2fDorAyQxzmCscJzb63W5XCTY\nHWQsy3b39ISbmoaPHJl99Gj86tXJ6en1xcVypaLruigIHr8/FIkMHT9++MSJpqYmj8dT3/xg\nT1pbn6u6sbFx4+rVm1ev3rt9W1cU2jBkmtYBiobBCoLVZjt5/vyJM2fGxsbqM5f2pKkvVshm\nt6PR3nw+aBi7OnPJAAIAV61uZ7OVWq1ezvZVtXJ/2n/Bzu129/f3i6KIEPp6DSGapkVRbG1t\nvXDhQjgc3tra6uvr28nLCoJw6tQpm81WrVZf0NW3vr6+tram63pXV9eTzkKC+Pbq1wlZlkNN\nTW9/+KHP641PT8fn53+yvGxRVRljAYCiKAXjMkUVabrS0uLr7Bzp6+s4fDjS3W2xWvfF2c3r\n9588d26lXL4Ui/kNI4QQ97IZ0wZAAmCZojY5zuXx9PT1+Xw+iqJEUXS63ZHubj2ZvLy+flRV\nB142Glv/6wWaviYIQldX/9CQ3elkyeYBB1t9X8r67DSWYSx2e//Ro9lUStE0AyGOZS0Wi8Pl\nCjc3N7W0WK3Weh/Y64cx1jRte2vrzs2biw8ebE5N2RYXvxeLmQ1DwligaYRxDaEyy5azWfX6\n9ZuZzObiYt/IyMixY0/q9jcOXdOUSoXWdXH3Jdl5ABohA6EnkwtJj93TGuub3gmHw+FwODo7\nO7/y50+vpfD7/X6//8SJE5qm7XAttCiK77///vvvv//ih127du3KlSvlctnr9ZJgR3znOI6z\n2+0XLl481N9/9fLl61euXFZVtVxmdd1MUTRACWOdYTDHjQ4Pnzp79uTp013d3cx+KE1c5/X7\nT54/v7aycnt8/Gil0qUoTgDh+TUmDIAqwDLADMflrdbmYLCru9vldtcvxi6nc2hoaDaVupTJ\nmBGKaBr/OCZ+5dXqZwcNQAGYZNkvLZbeoaHhY8fsdvt++eiIV4plWYvFYrFYOr52cWkET0Zg\nF+fmfv2Tn8Tv3nWkUmcV5W1d9wHYn3rklq4v12qfPHjwYHHx4fj49vZ2qKnJHww22gULIWQg\nRH1tn+gdoiiKfvyx7IvFIq/T/gt2L6CqKkKI5/n611yvJk++cmLfYRjG6XIdP3Gita3tnffe\nK+Zy5WIRKQrGmBYEk9ksW61en88XCHh9vv21a4LZbG5qajo0OLg+NvZgZqa0sfGOrrdiLD3n\n5J4EWAH4mGVXgsFj779/9O23zWbzk/drdzhOnTtHVau/XVy8ZRhQLo8YRtuz5uvUS8su0vRN\nhnlgt9NNTb1Hjx45doyUqyD2C0VRLn/xxfhnn7Gzs2PZ7JiitBiGD+Ar63KtABGAP9P17krl\nRiKRGh//mSSdeeedU+fO7U27n8NkNtvcbmVtLbvLvXpVgDKAxjAiz/M8T/YD/Lo3J9jpur64\nuFipVLq6uuona4ZhSKoj9p36sIIsy3JHR3skAgCFQiGXy9VqNYSQyWSyWCz1fqb9eDoTRVEU\nxd7+/mw6/Yim7xuGPZUq1mo+AAljAYB+XAm2BlCh6SWKmmXZlWAQRkZG33lnaHhYeGqXCLMs\n9xw6lE2n78/MZKembqyu4nK5rKo2jE0Yc0+Vq6hQVA5gShRvWq2os7NzcLD38OH2SIR01xH7\nQq1WSyUSD2/fnrl8eWBr61i5fNYwTM+6HZIATAAehHyKUtP1h/Pz12s1t98/ODIiiiLHcXvQ\n+mex2Gy+pqbywkKcphWE6n3qOzmjlQFiAJokuZ1OyWQiV/mve3OCXaVS+clPfrK2tvYv//Iv\nAwMDe90cgvjOSJLE8zxCCGNcv13Z73Gkq7vbZrPJZvN9k+nnt27Ztrf7MG4xjCBCAgAGKADE\nGGae49Y5LmO1Hnv//SPvvjs0POzxeJ4+j1MUxXHcoYGB//2f/un673536+OP/y0Wk9PpPsNo\nQsiDEAWgAyQYZo2mp2i65nbbOjtPfO97J959NxQO7/ePkTg4EonE9NRUIhqVksnT1erhly04\noABcGL9jGJDP319dXVtaWlpaampqcrlcr6nFL+Px+zv6+29OThYWF8+r6s736s1Q1AOK0jye\n7u5u0uP+TG9OsDMMY3l5eX5+nmw4Qbwx6n1yHMc1zn32d8Jqs5lMptLx46wosh5PJRpNp9Nq\nJrNZKLCGgQFUni/abCWPx+HxBEOhoxcu1FPdV+q51Hs3nS6XbLEo1arO8+srK+VYLJVK1QqF\njXKZwhjRdFmWSzab6HL5W1s7u7qGx8Z6Dh3aX0PYxAEX39iYHB9n19fby+VWXfcAwPP7t/64\nxSpACOM2RWnK5arLy1MPH8pmcwMFO4+ns7d33O9PSNKKYdgR8r4skdQ78tco6ibDmFtbj504\n4SF1x57lzQl2BEHsIxzP9w8MtLW3nzhzZmlubvL+/fX5+cmVFaVWoyjKYrcH2tr6+vr6Bgd7\nDh0yy7Iois8bc2EYRhTFoydO9A0Nra+tRRcX5x492lxZWYzHMUIMx7lDoVAkcmFoqLWtLRAM\nmhpvhSBBvNjm2trEjRudm5v9qrrzTioawIfQCVXdjEbv3brV0dnZOEtDXC4XTVH2QGDFan2o\nKJKuOzF+8WGpAWQBFinqCsO839V1/t13A429185eIWc3giBet3pXGS8INMNwPC/wvM3pzAwO\nFjKZerEh0WSyOZ1uvz8QCLjcbpqmXzBsWu+3q28HXp+e6Pb5cplMuVCoD17LNpvd5QoEAg6n\nU5ZlMgJL7Dv5fD4ei42USk0AO9ykof4YK0A7xolsdml1tVwqvdpW7gbDMLIsHz99mq5Ulj77\nTFlbw7regVD4WWvkDYAcQJSibtD0Ujg81N/fNzoaDIWk/bDHxuv3RgU7QRBMJhM5axPEfsGy\nrCzLsiy3tLZ++1ejabpeDinS0fHtX40gGkepVEptb0u1mv/xFtI7JAGEAZhCYTser1Uqr6p9\nu0dRFC8IY6dP86L4f0aj8UIBCgVN08yGwQGwj4vb1TfFVigqCnCXZX8jy5aenve+//3h0dGv\nF7Il6t6cYCcIwunTp7u6ujwez163hSAIgiC+Mwgh3TAohJhdVn2jATgAQEjXdbSb/ZRfA5qm\nZVnu7un5u7//+5mOjuVLl36+vf15Pt9sGD6EZIQoABUgwbJxjovTdM3nGzx9uvfkyWOnTgUC\ngb1ufuN6c4Idx3EjIyPlctnhcOx1WwiCIAjiO0PTNMOyiKb1x9W2dwjVq8TRNNN49d7qMygC\nweC5t96yWix5hDai0djWVr5U2q5UzIpCAWgMkzSbExZLzWx2t7WNvvfe0MhIJBJ5w9aTfbfe\nnGDHsmx7e7thGGYy6E4QBEG8QUyiaLfZarVaplaz/+nm0S9WA0gCIJPJ6XA05nbSLMtardaR\nI0eaW1o2NzY219aSKyvZzc1sLgcYc5IUCgQGwuFQa2ugqcnr99usVlK77sXenGBH03SjbZlC\nEARBEN+ew+UKRyIFRYmWywGEdn6py1PUHE1rXm9rZ6fFan2FTfymaJrmed7ldrvcbq/fH25p\n2YxEUolEpVgEAE4UnW631+sNBoMOh+MNqOL5Grw5wY4gCIIg3kieQKBzaCiTTE5vbw9omnfH\ns+UyNH2f5+3hcO/hw7aGKWL3PDabTZblpqYmZBj1GYEURdE0/WYUZn9tSLAjCIIgiIbW1NQ0\nOjb2u6mp2ZmZJV2XMXYBvHg8sgqQAFhgmFWeD7a0DI+ONk514uepFzYi8+e+JRLsCIIgCKKh\nBUMhjuN+/9lnMYtl1jBsqioBiM+6hNe78nSAHMA0RS0IQt7pdLa1DQ4OkrrcBwT5mgmCIAii\nobEsa7XZzl+4YEbo9qefbq2vFw2jC+OmZz1YA1gEeEjTX7Is7u39yw8+OHL8ONt4q2KJV4QE\nO4IgCIJoaAzDSJJ0eHQUKGotk1lmGEs8XlTVvGGYMBYwrs8+MwCqFFWg6SmWnZHl7UCgc2zs\nrQ8+aG1tJStJDw4S7AiCIAii0TEM4/P5RkZHeZ5/0Nx8+ze/GU+l5EqlTdf9hiEhZFBUiabX\nGGaV4zSbzdHZ+b3vf39wbKwjEjHL8l43n3h9SLAjCIIgiEZHUZTJZPL5/SzHcRyn8Xx6c7Oa\nShVyOaNYZFUV07QmCDWbTXY4HD5fa2fn8bNnW9vbrTYb6a47UEiwIwiCIIj9ged5r9frOHPm\nyNGjsfX11Wh0fWUlsblZKRQYlpVsts7W1pZIpKmpyeP1CqLIsiypEnLQkGBHEARBEPsDRVEs\nyzIMI5pMFE3LVmsgHC4WCmqtRtM0L4oOp9PpdNrtdkmS6o/f6yYTrxsJdkQDwRhjjA3DMAwD\nGQZCCGNMURRFUQzD0E/VqCRnK4IgDqz6CdBut9vt9ra2tr1uDtFYSLAjGoiu67VaLRaLra2u\npra2Ctmspqosx0kWiz8YDDY1hUIhh8NBUh1BEARBPBMJdkRD0DStWCikksl4PL4+Px9bWMiu\nrpYSCb1WYwTBZLdvtrdvdHQkOzrCLS0er9csyzzPk4RHEARBEE8jwY5oCMVC4eH9+zdv3Lj6\nhz/ImYyvXPYWi021GouQRtMVjtuYnr5vtzNOZ1Nv74c//GFvX5/b7SZLvQiCIAjiaSTYEXtM\n1/WteHxxdvbGpUvrN244xsebFKUdwK/rDoQ4AA2gBODMZGybm1uCkNnevsqyhXz+2IkTNpvN\nZDLt9TsgCIIgiEZBgh2xx3RNm3j48NoXX4x//nl7NPqP5XILQvX9rWkACgADYABd16uG8UBR\nbs7Pf7a1tZJIePz+SCRCgh1BEARBPEHK2xB7qVAorK+vPxgfX71x48jGxtvlcidCPgALQH2L\nawH+//bu9CmOM7EfeN89PfcNzMBwI44BYS6dto6stXbsjeVNyl5vjtrKm7zIy/wD+yZv8z5J\n1WYrv61U2bvxOmsra0sWQicIARISiPtmYIBh7runu38vnphSSWsbWZZHjL6fVyrU0zzdzDzP\nd56rKR1FSRRloii7pjWr6pFc7kgkIjx48L8ffTR5/z5ZOVvs6wAAAHghoMcOiikSDs/PzT0c\nGYndvXtWlrtV1fT13zY4ivJRlFHTeFm+Oj//+e6uy+M5dvIkz/OYbAcAAEChxw6Ka2l+fvDS\nJdfq6vFCwaOqEkV96zJXA0U1U1RjoWBMpyOBwNTUVDQa/SHKCgAA8MJDsINiWltdvTc05AgG\nuxXFSVHCPoKdSFEeiqoqFMozmXQgMDM1FUOwAwAAoCgKwQ6KKxQOLy4u2hKJJooyPM0Lbara\nns9zGxuzDx8i2AEAABAIdlAcqqoWCoV0Oh2Pxw35POmu2z+DplUqChuNbgUCmVTqeZUSAADg\nQMHiCSgOVVVzuVxBlilFETRtP7PrHiVQlJ2imHQ6Fg7nc7nnVUoAAIADBT12UBw0TXMcRzGM\nQlHfYbcSjaJkiqJYlud5hsHbGAAAgKIQ7KBYGIb5v21KGKZA03mKUp/m5TJFpShKE0W90cjx\n/PMqJQAAwIGCYAdFQ9O0xWAoczgSorhOUU81nppgmAWeV1yuqvp6g8n0vIoIAABwoCDYQXHQ\nNE3TtMPhqKmvj1gsszSd+urpYd9Mpag8RYVpeo7jtLKy+kOHzFbrD1FiAACAFx6CHRRTZU1N\n58mTm5WVtwUhur+pcgWKilHUOkU9pCjW4+k4fNhutz/vcgIAABwICHZQTJU+X9fRo0x19ZLR\nOMFxyzRd+PpOO7JgYoeihlh2weFw+f3exsaKigpJkn7QQgMAALyosN0JFFNlZaWk0w3fuLEw\nPn4tn1cKBbemfd2bUqOoLEWtMMwnHJeqrj5x7lyb3280Gmn6qXZKAQAAKFkIdlBMHMdZrNYT\np0/rKGrpypVby8uxZLJBlmtU1U5RBoqiKUr5qqNug6IesuyC2azV1zefOHHyzJma2lrsdQIA\nALAHwQ6KiWEYnU534tQpm8Px/6LRKUWZDgZ7k8lj6XS1prk0jaEomaLSFDXLsg847rpeH66q\nOnr0aM+pU33Hjun1+mJfAQAAwAsEwQ6KjKZpURRr6up+9rd/O9/VtTg5GZuY+Hh6upBIqNks\nR9MaTcsUJbpcksfzSleX5/Dhlra26tpaQXiqh5ABAACUPgQ7KDKapnmed7lcDoejsrraWVX1\nwOWadDh2IpFEOs1SFM0wNMNUVlRU1NZ2Hz/e1tFRXl6OvjoAAIAnIdjBi4JhGLfb3dfX19LS\nkozHc/l8XpYLssywrCAIkk6nlySz1Wo0mURRLHZhAQAAXkQIdvBCICtbJUmSJMnldmuapmma\noiiFQoFhGEEQsPQVAADgWyHYwQuKpmmO4zgOb1EAAID9QqsJLyL0zwEAAHwH2AMMAAAAoEQg\n2AEAAACUCAQ7AAAAgBKBYAcAAABQIhDsAAAAAEoEgh0AAABAicB2J0+HYRiax3G4/AAAIABJ\nREFUppeXlx88eLC5uWm1Wvf5QrLXrqZpz7V4AAAA8CSGYXief8a9tBYWFmZnZ2tqanieZ5gX\ntGsMwe7piKKo1+t3dnbu379/9+7d/f9dU6nU7u6uIAg6ne65lhD+pGw2m8vlJEkSBKHYZXlJ\nKYqSTqcZhtHr9din8IeXz+czmYwoiqiCigJVUBFpmpbJZCiKcjgcz/hEykKhkMvlFEWRJOmF\n3T//BS3WC8vn8wmC4PP5QqEQTdP7b5+Ghob+67/+6/Tp02+//fZzLSH8SZ999ll/f//7779/\n5MiRYpflJbW8vPzhhx86nc7333/faDQWuzgvneHh4Q8//PD06dM/+clPil2Wl9GFCxcuX76M\nKqgoUqnUhx9+uLOz8/Of/7y+vv5ZTkWeddnc3Ozz+QwGw/dVwu8Xgt3TcTgcNputrq6uUCg8\n1QtVVf3d737n9/s/+OCD51Q2+AbLy8s3btw4duzY+++/X+yyvKRGR0e//PLL6urqn/70pw6H\no9jFeelwHPfJJ5+0tbWhCiqK1dXV69evowoqikgkcvv27UKhcObMmd7e3mc8G03TgiA8Y8/f\nc4Vg99QYhhFF8Wm708lj7Hme1+v1z6lg8A3I1ApBEHD/i0Wn0zEMw3GcJEn4K/zwUAUVF6qg\nIspmsxzHsSyr0+m+l/v/ws6uIxDsng4Ze2VZ9mlfSF5CGrbvv1jwbcjnkGVZ3P9iYVmWzF7g\nOA5/hR8eqqDiQhVURBzH7bXdL8P9f6FTJwAAAADsH4IdAAAAQIlAsAMAAAAoEQh2AAAAACUC\nwQ4AAACgRCDYAQAAAJQIBLsfCMdxBoMBD5MpFkEQDAbDy7DQ/YXFMIwkSS/yrp6lDVVQcaEK\nKi5RFCVJesH3n/u+vBQX+SIQBMFms0mSVOyCvKQkSbLZbGjViojjOLPZbDQaX5K69UWDKqi4\nUAUVEcMwRqPRYrG8JMGa/eUvf1nsMrwsHA5HT09PVVVVsQvykqqsrOzt7XW5XMUuyMvLaDT6\n/f7Gxkae54tdlpcRqqDi8nq9qIKKheO4hoaG9vZ2k8lU7LI8d7SmacUuAwAAAAB8DzAmAgAA\nAFAiEOwAAAAASgSCHQAAAECJQLADAAAAKBEIdgAAAAAlAsEOAAAAoEQg2AEAAACUCAQ7AAAA\ngBKBYAcAAABQIhDsAAAAAEoEgh0AAABAiUCwAwAAACgRCHYAAAAAJQLBDgAAAKBEINgBAAAA\nlAgEOwAAAIASgWAHAAAAUCK4YheglGmaRlFUKpUKBALb29u7u7uyLFMUZTabnU5ndXW12Wzm\neZ6m6WKX9MCTZTmRSIRCoWAwmEgk0uk0wzA6nc5qtZaVlXm9XkEQWJalvvqjRCKR9fX1UCgU\niURUVeU4zmw2l5eX+3w+nU7H83yxL+igIrc3Ho/PzMxsb28nk8m6urqmpiZJkkRR3DsgGAwG\nAoHd3d1EIqGqqk6nM5vNVVVVXq+X53nyl4KnoqpqMpkMh8OBQCASiaTTaZ7n9Xp9VVVVWVmZ\nyWQSBIGiKE3TCoVCIBAIBoOhUCibzaqqajAYbDabz+dzuVw8zzMMvvA/BXJL4/H42tra7u5u\nPB5XFIWiKEmSbDZbZWWl3W7X6/UMw6BFeEapVGp1dXVzc3NnZ4fcZJqmHQ5HV1eX0+l89Mj9\n1/Ml2SIg2D1fmqaFw+Fr167dvHlzbGwsmUxSFNXY2Njb23v+/PmmpiaO4/AxfnbZbHZ9fX1k\nZOTq1asLCwsbGxuCIDidzpaWlpMnT77xxhtWq3UvLqiqura29vnnnw8PDz948ECWZb1e39jY\neOrUqfPnz7vd7gP3MX7RBIPBjz766ObNm8vLy3/913/993//9x6PhwQ7iqJUVZ2amrp06dLY\n2Nji4qIsy06ns7Gx8a233nrjjTeMRiOC3XegKMrW1tb4+PjFixcnJycDgYDJZPJ4PG+//fbJ\nkyfr6+v3gl0ulyOflNHR0e3t7Xw+X11d3dbWdv78+SNHjhiNRgS7p6JpWjabXV1dvXDhwsjI\nyPz8fDabpSiqoqLC7/f/+Z//eXt7e2VlJbmraBGeRTgc7u/vv3LlytDQUDabpWmapumurq5/\n/ud/fizYUY/U83fu3Ll///431PP7P/KgYH/5y18Wuwwlq1AozM3NjYyMXL9+PZ1Ot7W1+f3+\nlpYWTdOi0Wg8Htc0jXRRFLukB14oFLpx48b6+rrVaq2tre3o6GhqanI4HDs7O/F4PJvNiqLo\ndrspikokEtPT04ODg9evX9fr9Z2dnX6/3+fzZTKZVCq1u7srSZLX6y32BR1UiqIsLS2Nj4+P\nj48vLy+vr6+3tbX19PSYTCZJkiiK2tnZefDgwfXr10dHRz0eT0dHR3t7u8PhiEajmUwmEolY\nrVaHw1Hs6zhgYrHY+vr6xYsXh4aGGIapqqrq6ury+/11dXUsyyqKUlFRYTQaKYpaXV29e/fu\n9evXV1ZW6urq2tvbOzo6JEna3d3NZDKZTMblcpEjYZ/y+fzo6OitW7cmJyc5juvu7j58+HBb\nW5tOp0un04FAQFGUhoYGQRDQIjwjTdNYlnW5XA0NDbW1tQ6HIxwOcxz3+uuvV1ZWPnrkt9bz\nOp2O1PP7P/IAQY/dcyTL8sTExLVr1yYnJ1tbW3/2s5+Vl5dTFPXb3/725s2bly5dyufz3d3d\noijiK/IzSqfTMzMzFEWdOnWqrq6uvLw8Go1OTEz853/+5+Tk5M7OjsFg8Pv9FEXF4/Hh4eHB\nwcGZmZl33333Zz/7mdFoDAaDv/3tb6empqampoxGY1dXF8Mw+N78tFRVzWazk5OT4+PjiqJI\nkvTkPdzc3BwYGBgZGdnY2HjzzTf/7M/+zGg03rt3j9z/mZkZj8dTX1+P+/9Udnd3Hz58eOnS\npZWVlXfffffEiRPt7e2qqsZisRs3bmxubuZyOXLk4uLi5cuX7927x/P8uXPn2tvbjUbj559/\n/umnnw4NDW1tbR06dMjtdpO+kOJe1EGRz+fHxsYGBgbS6XRfX9/f/M3fOJ1OTdM+//zzK1eu\n9Pf35/P5c+fO6fV6tAjPyGq1njhx4sSJExRFzc7Ojo6ORqPRdDr95JGP1vPvvPPOz3/+88fq\neYPB0N3dzTDM/o88QJ8IBLvnqFAozM7OLi0tHTp0qLe31+fzWSwWVVWPHTumKMonn3yyvLwc\nDAZ5njeZTMUu7MHmdrv/8i//kqKo8vJys9ksSRLLstXV1V1dXePj46urq+FwmByZSCTu378f\ni8WOHTt2+PBhj8fD8zzHcWfPnlVV9ZNPPllbW9vZ2bFYLKSHCfYvlUoFg8F79+4tLy93d3eb\nTKbp6enHjtnZ2RkeHuZ5/vTp0y0tLeXl5TzPk3HYzz77bGhoiEwRM5vNZOgQ9mNubq6/v1+S\npGPHjh0/frylpcVsNmuaJknSq6++qijKXifo2tra2NiY2+1ua2urr68nw0yHDx+WZfmTTz5Z\nXFzc2NiorKy0WCwHqBkrLlVVyaTGurq6Q4cOOZ1Ok8mkKEpra2soFBoZGUkkEvF43GQyoUX4\nwXxrPb+6ukrq+f0feYBaBAS750VRlFwut7a2trW1deTIkebmZqfTqdPpFEVpaWmJRCL//d//\nvb29vbW1ZbPZ8DF+RhaLpbe399GfCIJgs9lcLpder0+n07Isa5pGJpgvLCyoqtrR0VFfX2+z\n2SiK4nm+s7Nzfn4+Ho9vb29vb28LgnCAPsZFt7ck4sGDB1tbWxzHkQEmjuMePUZRlEgkMj09\n3dXV1d3d7fP5rFYrRVFer1eSpMHBwZ2dna2tLTIgjmC3H+TOr66uDg8P19fXV1VVsSy7s7Oz\nubnJcZwoiqR60el0qqqqqhoMBufm5lpaWtrb2ysqKiwWC0VRdXV1DMN8/vnn6+vrW1tbsVjM\nZDKh02ifSAJmWdZgMOj1+r3VPwaDwWQyiaJI07SiKIVCAS3CD+DJer6hoeHr6nmWZfd55MFq\nERDsnhdZllOpVCKRIHPDnU4nqSgZhrFYLA6HQ5IkWZY3Nze9Xm9FRUWxy1uCcrnc+vp6IpEg\nqwLJtPFkMhmPx41Go9frJa0aRVE8z9vtdpvNJopiKpXa3Ny02+12u7245T9YVFUdHx//wx/+\nUF1dffToUY/HMz8//2ivj6Io2Ww2kUjEYjFRFCsrKw0GA/kvnU7ncrnMZjPLsrFYbGdnx+12\n7/0vfAPSjO3u7q6srPh8vmg0+pvf/CYSicRiMYvFUl5e/tprr3V1dVVWVvI8T+5/MpkkK/72\nlrMYDAa32200GlVVDYVC4XDY4/EU97oOEDI4sLa2tr6+Pjs729XVRf4oZGqByWTyer02m41l\n2X22CB6PBy3Cd7bPej6dTm9sbEiStJ8jD1yLgGD3vBQKhUwmk8vlNE0zGo0Gg4F8jGmaJtlf\np9NpmpZIJMgSKvgeKYqyvb09Ozs7Pz+vKEpPT4/P56MoKp/PZ7PZXC5nNpstFotOpyPHk71R\nCLJzAdmGAPYpFottbGwsLS2Fw+GTJ092dnZaLJbHZqWoqprL5bLZbD6fFwTBYrHs9clxHMdx\nHNkShVTKZC8D+Faqqubz+XQ6HY1GNzc3JUniOM5qtRqNxnw+HwqFbt++nUgkzp4963K5yJu/\nUCjodDqTybS3+pjneVIjsSybTqczmYyqqsW9rgOE47hDhw7FYrFbt25NTU39/ve/J99JHj58\nGAqFWltbOzo6DAaDqqpoEX4Yj9Xzez1tj9XzsVgslUrt58gD1yIg2D0vpONdURSapkVRfGyh\nE3nfyLJM6tliFbJUybJMFjo9fPiwsbHx3LlzTU1NmqbJspzP51VVZVlWp9M9OlBIURTHcTqd\njqZp8ocrVuEPomAwePXq1Y2NDbJgraGhIZVKPXaMpmn5fJ6MiZNb/di2JmTokOQ/BIt9Iv2g\n5I09Nzen0+k++OADstD4/v37g4ODg4ODk5OT1dXVpIkitY0gCE/O0BcEgazczOfzRbqaA4nn\neb/fT9P09PT08PDwF198oSgKwzB6vb6lpeXtt9/u6+sjQwFoEX4A+6/ns9ks+Z5Zei0Cgt3z\nwjAM2eqTvM8e+6ySRk7TNGzH+r0LBAILCwsDAwNzc3O9vb3d3d21tbVms5mmadIzxDAM6ed4\n7LOqKApp0rBH6/7Jskwmqdy6dcvr9Z47d666uprc5MeO3Lv/FEUpiiLL8mPpjfyQfHAwc3+f\nyF1lGIZlWY/H09zc3NTUVFtbazAYSPUyMzOzsbGxtrbmdrudTiepbQqFAknYj56KxD6WZR9r\n3uCb5fP5O3fu3LlzJ51Ot7S0vPrqq4Ig0DQdCoVkWR4aGpJlmfSPokX4Aeyzntc0TRAEsk7i\nW488cC0CPsDPC8/z5BuApml7PfAURWmaRj7YuVyODD9h16LvC0kGc3Nz169fv337NkVRP/3p\nT/v6+srLyzmOU1WV53lRFMnOXmRFBXkhmRMjyzIZBNHr9Wjb9kmW5d3d3fn5+Tt37lRVVfX0\n9Oj1+kgkQp7/Qbrf4vG41Wol1Sh5BAiZgbrXtqmqShq2bDZLvjofrGq0iFiWJTdWFEWyg2NN\nTQ3ZrLWurs5ut3/22Wdks/7d3d2KigoSmnO5XCaT2WvGVFUli71kWSadeQjW+0R2J75y5crV\nq1c9Hs/x48d/8pOfkCVBw8PD165d+/TTT0OhUGdnZ1lZ2T5bBCwbekaP1fN7PdCP1vM0Tev1\nejL9Yz9HHqwW4SCV9WDhed5gMJBdG0KhUCgUqq6u5nle07RYLLa7u5tOp51OJ9meo9iFLRGb\nm5sTExPXr1+/e/due3v74cOH29vb7Xb73lwWURSNRiO54YFAgEy8oyhKluVYLBaJRHK5nMFg\nqKioOEALoIorn89vb28Hg8FoNHrhwoW5uTmydb4syysrK+l0+ubNm9FotKenp6urq7Oz02Qy\nWSwWsq6lrKyMnCSbzcZiMfIsJovF4nK50LbtE03TZHcMl8u1N2d0DwkNe4fp9XqTyWQ0GuPx\n+NbW1t6erqlUamtrK5lM0jTtdDptNhuC9T7l8/lEIhEMBlOpVGtrK6lwSO3h9/uTyeSVK1fi\n8fjq6qrBYCCVz7e2CFgS+yyerOerqqrIfz1az+v1eo/HY7PZ4vH4tx554FoEfHqfF/Ldy+12\nW63WQCCwvLycTCbJoP7KysrS0pKqqmTZGvZ5f3YkXkxNTV27dm1xcZFhmM7OzjNnzlRXV+89\nIom0bWT1E8/zs7OzgUAgnU7ncrlEIjE/P7+5ucnzvM1mKysrO1gf4+KiadpmszU3N4uiuLKy\nsrCwMDc3t7CwsL29rShKNBpdXV0NBoOxWIyiKIvF4vP58vn81NTU9vY26brY3d2dmpoKh8MG\ng4EsGESw2ycyCOt0OmtqaiiKCoVC5FErZMZ3MBgkz00mqUIURYfD4fV6o9Ho3NxcJBLJZDL5\nfH5zc3N6ejqdTptMJrfbjWC3f+RBseRtrNfrjUYjGUtlWdZoNJpMJpqmyeoWVVX32SIg2D2L\nfdbzdrud3Or9HHngWgT02D1HHMc1NTVtbGzMzMyIotjS0uJ2uzVNGxoaGhoaMhgM1dXV5eXl\n2Nbh2UWj0Rs3bgwODg4NDfX19Z09e7a5udnhcDw5zG0ymTo6OkZHRwcHB8lMf71ev7Oz09/f\nPz097fV6q6qqXC7Xwep4LyKDwUD2GT5z5sze0F6hUEilUpcvX/7Xf/3XEydOvPfee2SLNbvd\n7nK5+vr6ZmdnBwYGqqqq3G63Xq+fm5u7cOHC9vZ2fX291+u12+2Yn/BUyCD45OTk6Oio3+83\nGo12u312dnZ4eHhjY0MUxaqqqoqKCpZlydPGVlZWbt26RR4gq9fr79+//8UXX+Tz+dra2oqK\nCrPZjGC3TyRDWK1WmqYnJiYcDofH4yG7Q09OTo6NjUWj0b2RGbQIP5hH63nyKOqvq+f3f2Sx\nr+kpHKSyHjg8z7e2tqZSqWQyGQwGL168SDrnJiYmcrlcd3c32Z0fbdiz2NvHa3Bw8NatW4uL\ni06ns6qqKhgMku9Y5OFIfr+fPFLMZDJ1d3fncrmdnZ35+flPP/1UEIRkMjk9PS2K4pkzZ1pa\nWp4c0oKvw/O81Wolj+jd+yEZn1pYWOA4rqyszO/3k7hGUVRZWdnJkycVRRkdHb137140GhVF\ncXNzc2lpye12HzlypKamZm9/Ndin6urq48ePp1KpQCBw+/ZtMvC3t2aivLy8traWbEBTU1Pz\n6quvFgqFYDB48+bNubk5URRnZ2e3t7cbGxvb29vdbje6S/ePZVlJkjo6OkhH6b1793K5nCRJ\nmqatra0FAoG6urqOjg6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Use the OML4R REST API for invoking embedded R execution","","Another option is to use the OML4R Embedded R Execution REST API to run user-defined R functions (R UDFs) saved in the script repository. The REST API is typically used in the development of applications. For example, REST APIs can be called from a web development application, improving the portability of the interface to other platforms, increasing project scalability, and enabling components to evolve independently. Use the OML4R REST API to deploy UDFs. ","","To run a UDF from the REST API, it must be stored in the OMLR script repository. An Oracle Machine Learning cloud service account ***username*** and ***password*** must be provided for authentication.","","To authenticate and obtain a token, use ***cURL*** with the ***-d*** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", ","    \"password\":\"' <yourpassword>'\"}' \"<oml-cloud-service-url>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _- -header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance ","","For example, Oracle Machine Learning user ***OMLUSER*** with password ***OMLPASSWD*** and cloud service URL ***https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com*** authenticates and obtain a REST token using the following approach:"],"enabled":true,"result":{"startTime":1734632310487,"interpreter":"md.low","endTime":1734632310948,"results":[{"message":"<h3 id=\"use-the-oml4r-rest-api-for-invoking-embedded-r-execution\">Use the OML4R REST API for invoking embedded R execution<\/h3>\n<p>Another option is to use the OML4R Embedded R Execution REST API to run user-defined R functions (R UDFs) saved in the script repository. The REST API is typically used in the development of applications. For example, REST APIs can be called from a web development application, improving the portability of the interface to other platforms, increasing project scalability, and enabling components to evolve independently. Use the OML4R REST API to deploy UDFs.<\/p>\n<p>To run a UDF from the REST API, it must be stored in the OMLR script repository. An Oracle Machine Learning cloud service account <em><strong>username<\/strong><\/em> and <em><strong>password<\/strong><\/em> must be provided for authentication.<\/p>\n<p>To authenticate and obtain a token, use <em><strong>cURL<\/strong><\/em> with the <em><strong>-d<\/strong><\/em> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, \n&quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' &quot;&lt;oml-cloud-service-url&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>- -header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>oml-cloud-service-url<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n<p>For example, Oracle Machine Learning user <em><strong>OMLUSER<\/strong><\/em> with password <em><strong>OMLPASSWD<\/strong><\/em> and cloud service URL <em><strong><a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a><\/strong><\/em> authenticates and obtain a REST token using the following approach:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Export the OML cloud service URL to environment variable *omlserver* and exchange database credentials for an access token."],"enabled":true,"result":{"startTime":1734632311413,"interpreter":"md.low","endTime":1734632311850,"results":[{"message":"<p>Export the OML cloud service URL to environment variable <em>omlserver<\/em> and exchange database credentials for an access token.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["$ export omlserver=https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com","$ export username=OMLUSER","$ export password=OMLPASSWD","","$ curl -i -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'${username}'\", ","        \"password\":\"'${password}'\"}' \"${omlserver}/omlusers/api/oauth2/v1/token\"","","","    HTTP/1.1 200 OK","    Date: Wed, 09 Mar 2022 21:59:16 GMT","    Content-Type: application/json","    Content-Length: 1027","    Connection: keep-alive","    Cache-Control: no-cache, no-store, private","    X-Frame-Options: SAMEORIGIN","    X-XSS-Protection: 1;mode=block","    Strict-Transport-Security: max-age=31536000; includeSubDomains","    X-Content-Type-Options: nosniff"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Copy the *accessToken* field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and ","it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1734632312320,"interpreter":"md.low","endTime":1734632312763,"results":[{"message":"<p>Copy the <em>accessToken<\/em> field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and\nit can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["$ export token='eyJhbGciOiJ...'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Now invoke the ***RandomRedDots*** function that is stored in the script repository from the REST API with the ***cURL*** command. Set ***graphicsFlag*** to ***true*** to return the ","PNG image bits from the function in JSON format. The default REST API execution mode is *synchronous*, so code client will wait for the REST API ","call to return before additional calls may be run."],"enabled":true,"result":{"startTime":1734632313233,"interpreter":"md.low","endTime":1734632313679,"results":[{"message":"<p>Now invoke the <em><strong>RandomRedDots<\/strong><\/em> function that is stored in the script repository from the REST API with the <em><strong>cURL<\/strong><\/em> command. Set <em><strong>graphicsFlag<\/strong><\/em> to <em><strong>true<\/strong><\/em> to return the\nPNG image bits from the function in JSON format. The default REST API execution mode is <em>synchronous<\/em>, so code client will wait for the REST API\ncall to return before additional calls may be run.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -X POST --header \"Authorization: Bearer ${token}\" -header 'Content-Type: application/json' \u2013header 'Accept: application/json' -d '{\"graphicsFlag\":true}' ","    \"${omlserver}/oml/api/r-scripts/v1/do-eval/RandomRedDots\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","As a result of invoking the REST endpoint, we get back a JSON representation of the value returned from the UDF. Dataframes, vectors, matrices, lists, and images are supported. Image bytes are returned in PNG format. A truncated portion of the returned value is shown here."],"enabled":true,"result":{"startTime":1734632314138,"interpreter":"md.low","endTime":1734632314579,"results":[{"message":"<p>As a result of invoking the REST endpoint, we get back a JSON representation of the value returned from the UDF. Dataframes, vectors, matrices, lists, and images are supported. Image bytes are returned in PNG format. 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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4R -1- Introduction","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734710992877,"interpreter":"md.low","endTime":1734710993297,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning for R (OML4R)","","***Oracle Machine Learning for R*** (OML4R) makes the open source R programming language and environment ready for the enterprise and big data. ","Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R ","commands for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions ","are overloaded that translate R behavior for running in-database, as well as new automatic machine learning capabilities. ","","In this notebook, we highlight a few of the OML4R features:","* Load ORE Library","* Create database tables","* Use the transparency layer","* Rank attributes for predicitve value using the in-database attribute importance algorithm","* Build predicitve models","* Score data using these models","","Copyright (c) 2025 Oracle Corporation","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1734710993724,"interpreter":"md.low","endTime":1734710994128,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Import libraries supporting OML4R","","To use OML4R, first import the library **ORE**.","","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1734710994562,"interpreter":"md.low","endTime":1734710994970,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","library(ORE)","library(lattice)","","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1734710995399,"interpreter":"r.low","endTime":1734710995876,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the first few rows from the iris data.frame","message":["%r","","z.show(head(iris, 10))"],"enabled":true,"result":{"startTime":1734710996302,"interpreter":"r.low","endTime":1734710996770,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## OML4R Transparency Layer functionality","### Using an R data.frame, create a database table and obtain a proxy object","We have a couple of options for loading data into the database from R. In this first example, we load the **iris** dataset This R data.frame can then be loaded into the database using the ***push*** function, which creates a temporary table and returns a proxy object that we assign to variable IRIS_TMP. Such temporary tables will be automatically deleted when the database connection is terminated unless the proxy object is saved in a datastore (the topics of 'datastore' is covered in another notebook).","","We then invoke standard functions like ***shape*** and ***columns***, as well as ***head***, to begin to explore the data."],"enabled":true,"result":{"startTime":1734710997206,"interpreter":"md.low","endTime":1734710997622,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Look up help on OML4R and other functions","message":["%r","","help(ore.push)"],"enabled":true,"result":{"startTime":1734710998066,"interpreter":"r.low","endTime":1734710998736,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","IRIS_TMP <- ore.push(iris)","","dim(IRIS_TMP)","colnames(IRIS_TMP)","z.show(head(IRIS_TMP,10))"],"enabled":true,"result":{"startTime":1734710999187,"interpreter":"r.low","endTime":1734710999765,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","We can also create a persistent table using the ***create*** function and specify an R data.frame and a tablename. ","For emample, here we sepecify **IRIS2**. This table is now accessible both within OML4R and directly from SQL.","Use the function ***z.show*** to automatically pull the desired data to R  for display in the notebook. ","By doing this, you can also use the built-in notebook visualization options such as a bar, pie, area, line, and scatter plots."],"enabled":true,"result":{"startTime":1734711000194,"interpreter":"md.low","endTime":1734711000597,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","ore.attach()","","tryCatch(ore.drop(table=\"IRIS2\"))","","ore.create(iris, table=\"IRIS2\")","","z.show(head(IRIS2, 10))"],"enabled":true,"result":{"startTime":1734711001038,"interpreter":"r.low","endTime":1734711001638,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Transparency layer functions available on an ORE DataFrame","message":["%md","### Transparency layer functions available on an ORE data.frame, or `ore.frame`.","List the functions defined for an ORE DataFrame proxy object, in this case \"IRIS.\""],"enabled":true,"result":{"startTime":1734711002079,"interpreter":"md.low","endTime":1734711002485,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","class(IRIS_TMP)"],"enabled":true,"result":{"startTime":1734711002919,"interpreter":"r.low","endTime":1734711003426,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","class <- methods(class=class(IRIS_TMP))","class"],"enabled":true,"result":{"startTime":1734711003848,"interpreter":"r.low","endTime":1734711004428,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Using standard R syntax on database data","Perform a few operations on the SEPAL_LENGTH column of the IRIS database table using the transparency layer. Note that standard R syntax is used"],"enabled":true,"result":{"startTime":1734711004879,"interpreter":"md.low","endTime":1734711005299,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","cat(\"Add 5 to each SEPAL_LENGTH: \\n\")","IRIS_TMP[1:20,1]+5","","cat(\"\\n\\nMin SEPAL_LENGTH:  \", min(IRIS_TMP[,1]))","cat(\"\\n\\nMin SEPAL_LENGTH:  \", min(IRIS_TMP$Sepal.Length))","","cat(\"\\n\\nMean SEPAL_LENGTH:  \", mean(IRIS_TMP[,1]))","cat(\"\\n\\nMean SEPAL_LENGTH:  \", mean(IRIS_TMP$Sepal.Length))"],"enabled":true,"result":{"startTime":1734711005723,"interpreter":"r.low","endTime":1734711006299,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","Explore masking using the IRIS Petal.Length column. Note that standard R syntax is used. "],"enabled":true,"result":{"startTime":1734711006731,"interpreter":"md.low","endTime":1734711007145,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","base <- IRIS_TMP$Petal.Length","","MASK1 <- IRIS_TMP[,3] > 5.1","mask1 <- IRIS_TMP$Petal.Length[MASK1]","","MASK2 <- IRIS_TMP[,3] < 1.4 | IRIS_TMP[,3] > 5.6","mask2 <- IRIS_TMP$Petal.Length[MASK2]"],"enabled":true,"result":{"startTime":1734711007568,"interpreter":"r.low","endTime":1734711008035,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","cat(\"Petal Length:\\n\")","base","","cat(\"\\n\\nPetal Length > 5.1:\\n\")","mask1","","cat(\"\\n\\nPetal Length  < 1.4 | > 5.6:\\n\")","mask2"],"enabled":true,"result":{"startTime":1734711008459,"interpreter":"r.low","endTime":1734711009020,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Explore data by computing pairwise correlation","Using only numeric columns, compute a correlation matrix using the **IRIS_TMP** proxy object for the temporary database table using the overloaded **cor** function. Here we see that petal length and petal width are highly correlated."],"enabled":true,"result":{"startTime":1734711009443,"interpreter":"md.low","endTime":1734711009850,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","IRIS_NUMERIC <- IRIS_TMP[, c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")]","class(IRIS_NUMERIC)","","RES <- as.data.frame(cor(IRIS_NUMERIC, use = \"all.obs\"))","class(RES)","","RES"],"enabled":true,"result":{"startTime":1734711010287,"interpreter":"r.low","endTime":1734711011147,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Explore column distributions","","OML4R overloads select graphics functions as well. Here, we illustrate the use of ***hist*** and ***boxplot*** to show the distribution of petal length. Here, the statistical computations take place in the database - avoiding data movement - and return only the summary statistics needed to produce the plot, which enables scalability. No overhead is incurred for moving the data to the client for processing, whether 150 rows (as in IRIS) or 150 million rows."],"enabled":true,"result":{"startTime":1734711011577,"interpreter":"md.low","endTime":1734711011994,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","hist(IRIS_TMP$Sepal.Length, col=\"darkred\")"],"enabled":true,"result":{"startTime":1734711012436,"interpreter":"r.low","endTime":1734711013102,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","boxplot(IRIS_TMP$Sepal.Length, horizontal=TRUE, notch=TRUE, col=\"darkgreen\", ","        showmeans=TRUE, xlab=\"cm\", main=\"Distribution of IRIS Attributes\")"],"enabled":true,"result":{"startTime":1734711013528,"interpreter":"r.low","endTime":1734711014065,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Use in-database ML algorithms","### Compute attribute importance variable ranking","","Using the **IRIS** table, determine which variables (a.k.a., attributes or predictors) are most important in predicting target Species.","","Notice that petal width is most predictive of the target Species. The importance value produced by this algorithm provides a relative ranking, not an absolute number, to be used to distinguish importance among variables."],"enabled":true,"result":{"startTime":1734711014503,"interpreter":"md.low","endTime":1734711014904,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","help(ore.odmAI)"],"enabled":true,"result":{"startTime":1734711015333,"interpreter":"r.low","endTime":1734711015870,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","AI <- ore.odmAI(Species ~ ., IRIS_TMP, auto.data.prep = TRUE)","AI"],"enabled":true,"result":{"startTime":1734711016308,"interpreter":"r.low","endTime":1734711017852,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","res <- AI$importance","res$attributes <- rownames(res)","","z.show(res)"],"enabled":true,"result":{"startTime":1734711018275,"interpreter":"r.low","endTime":1734711018737,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build predictive models","In this example, we set up the data and a ***demo*** cost matrix to build a Decision Tree model to predict Species from the **IRIS** table.","Rebuild the model and supply a test cost matrix."],"enabled":true,"result":{"startTime":1734711019165,"interpreter":"md.low","endTime":1734711019583,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","help(ore.odmDT)"],"enabled":true,"result":{"startTime":1734711020017,"interpreter":"r.low","endTime":1734711020715,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","Build a Decision Tree model specifying a maximum tree depth of 4 and display model details."],"enabled":true,"result":{"startTime":1734711021145,"interpreter":"md.low","endTime":1734711021550,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","options(ore.parallel=4)","","MOD1 <- ore.odmDT(Species~., IRIS_TMP, max.depth=4)","MOD1"],"enabled":true,"result":{"startTime":1734711021979,"interpreter":"r.low","endTime":1734711023624,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define a cost matrix","message":["%r","","value <- c(0, 0.4, 0.5, 0.8, 0, 0.5, 0.2, 0.6, 0)","cost.matrix <- matrix(value,ncol=3,byrow=TRUE)","colnames(cost.matrix) <- c('setosa','versicolor', 'virginica')","rownames(cost.matrix) <- c('setosa','versicolor', 'virginica')","names(dimnames(cost.matrix)) <- c(\"ACTUAL_TARGET_VALUE\",\"PREDICTED_TARGET_VALUE\")","cost.matrix <- as.table(cost.matrix)","cost.matrix"],"enabled":true,"result":{"startTime":1734711024058,"interpreter":"r.low","endTime":1734711024525,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Rebuild model and supply cost matrix","message":["%r","","MOD2 <- ore.odmDT(Species~., IRIS_TMP, max.depth=4, cost.matrix=cost.matrix)","MOD2"],"enabled":true,"result":{"startTime":1734711024956,"interpreter":"r.low","endTime":1734711026611,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Use the Decision Tree model to predict Species","","When predicting values, users can include supplemental columns from the original data. ","This can be particularly useful to compare actual target values against predicted values, ","or to include unique keys for subsequent row identification. This is necessary since row order ","is not implicitly maintained on results from the database. This is consistent with how SQL and ","relational databases provide ordering only if requested by the user via a sort."],"enabled":true,"result":{"startTime":1734711027050,"interpreter":"md.low","endTime":1734711027450,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","PREDICTION <- predict(MOD1, IRIS_TMP, type=\"class\", supplemental.cols = ","                      c(\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\",\"Species\"))","","z.show(head(PREDICTION, 10))"],"enabled":true,"result":{"startTime":1734711027878,"interpreter":"r.low","endTime":1734711028543,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Generate and display a Confusion Matrix for the predicted results","Using the overloaded **crosstab** function, compute the confusion matrix between the actual values in SPECIES and the predictions in the PREDICTION column."],"enabled":true,"result":{"startTime":1734711028977,"interpreter":"md.low","endTime":1734711029390,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","with(PREDICTION, table(Species, PREDICTION))"],"enabled":true,"result":{"startTime":1734711029811,"interpreter":"r.low","endTime":1734711030479,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build an Anomaly Detection Model with SVM","","The Support Vector Machine algorithm supports a one-class SVM option for anomaly detection when now target variable is supplied. ","Predictions with value 0 are considered anomalies. Users can specify various settings to affect model results, e.g., ","the outlier rate or the type of SVM kernel functions."],"enabled":true,"result":{"startTime":1734711030911,"interpreter":"md.low","endTime":1734711031318,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","SVM.MOD <- ore.odmSVM(~ ., IRIS_TMP, \"anomaly.detection\", odm.settings=list(SVMS_OUTLIER_RATE = 0.1))","SVM.MOD","","summary(SVM.MOD)"],"enabled":true,"result":{"startTime":1734711031754,"interpreter":"r.low","endTime":1734711034000,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","We can predict which cases are anomalies using the ***predict*** function. if ***proba*** is set to True, we will get the probability associated with the prediction."],"enabled":true,"result":{"startTime":1734711034432,"interpreter":"md.low","endTime":1734711034835,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","SVM.PRED  <- predict(SVM.MOD, IRIS_TMP, proba = TRUE)","z.show(head(SVM.PRED, 10))"],"enabled":true,"result":{"startTime":1734711035265,"interpreter":"r.low","endTime":1734711036046,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","Let's assess how many anomalies were identified. Here we see that roughly 5% are reported as indicated from our setting above."],"enabled":true,"result":{"startTime":1734711036479,"interpreter":"md.low","endTime":1734711036894,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","with(SVM.PRED, table(PREDICTION))"],"enabled":true,"result":{"startTime":1734711037328,"interpreter":"r.low","endTime":1734711037990,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1734711038420,"interpreter":"md.low","endTime":1734711038839,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734711039276,"interpreter":"md.low","endTime":1734711039709,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R -2- Data Selection and Manipulation.dsnb b/machine-learning/notebooks-oml/r/OML4R -2- Data Selection and Manipulation.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R -2- Data Selection and Manipulation","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736205739896,"interpreter":"md.medium","endTime":1736205740363,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","# Oracle Machine Learning for R - Data Selection and Manipulation","","***Oracle Machine Learning for R*** (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight features of the transparency layer involving data selection and manipulation.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736205740787,"interpreter":"md.medium","endTime":1736205741224,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-r---data-selection-and-manipulation\">Oracle Machine Learning for R - Data Selection and Manipulation<\/h1>\n<p><em><strong>Oracle Machine Learning for R<\/strong><\/em> (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight features of the transparency layer involving data selection and manipulation.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Import libraries supporting OML4R and create data table"],"enabled":true,"result":{"startTime":1736205741658,"interpreter":"md.medium","endTime":1736205742073,"results":[{"message":"<h3 id=\"import-libraries-supporting-oml4r-and-create-data-table\">Import libraries supporting OML4R and create data table<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736205742498,"interpreter":"r.medium","endTime":1736205747741,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"raw\":{\"height\":300,\"lastColumns\":[\"'data.frame':\",\"150 obs. of  5 variables:\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Obtain proxy object for IRIS_R table and view proxy object structure","message":["%r","","ore.sync(table=\"IRIS_R\")","","IRIS <- IRIS_R","","str(IRIS)"],"enabled":true,"result":{"startTime":1736205748166,"interpreter":"r.medium","endTime":1736205751208,"results":[{"message":"'data.frame':\t150 obs. of  5 variables:\nFormal class 'ore.frame' [package \"OREbase\"] with 14 slots\n  ..@ .Data    : list()\n  ..@ dataQry  : Named chr \"( select /*+ no_merge(t) */  \\\"Sepal.Length\\\" VAL001,\\\"Sepal.Width\\\" VAL002,\\\"Petal.Length\\\" VAL003,\\\"Petal.Wid\"| __truncated__\n  .. ..- attr(*, \"names\")= chr \"8699_1\"\n  ..@ dataObj  : chr \"8699_1\"\n  ..@ desc     :'data.frame':\t5 obs. of  2 variables:\n  .. ..$ name  : chr  \"Sepal.Length\" \"Sepal.Width\" \"Petal.Length\" \"Petal.Width\" ...\n  .. ..$ Sclass: chr  \"numeric\" \"numeric\" \"numeric\" \"numeric\" ...\n  ..@ sqlName  : chr \n  ..@ sqlValue : chr  \"\\\"Sepal.Length\\\"\" \"\\\"Sepal.Width\\\"\" \"\\\"Petal.Length\\\"\" \"\\\"Petal.Width\\\"\" ...\n  ..@ sqlTable : chr \"\\\"OMLUSER\\\".\\\"IRIS_R\\\"\"\n  ..@ sqlExtra :List of 2\n  .. ..$ rIdTab: chr \"\"\n  .. ..$ rIdSeq: NULL\n  ..@ sqlPred  : chr \"\"\n  ..@ sqlGrpby : chr \"\"\n  ..@ extRef   : list()\n  ..@ names    : chr \n  ..@ row.names: int \n  ..@ .S3Class : chr \"data.frame\"\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"boxplot\":{\"showSeries\":[\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\"],\"title\":{\"text\":\"Boxplot of IRIS species flower dimensions\",\"color\":\"rgb(1, 154, 4)\"},\"lastColumns\":[\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\",\"Species\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"boxplot","title":"Compare flower dimension distribution across iris 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"%md","### Select table columns using proxy object IRIS"],"enabled":true,"result":{"startTime":1736205752628,"interpreter":"md.medium","endTime":1736205753030,"results":[{"message":"<h3 id=\"select-table-columns-using-proxy-object-iris\">Select table columns using proxy object IRIS<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select columns by name","message":["%r","","IRIS_projected1 <- IRIS[, c(\"Sepal.Length\", \"Petal.Length\")]","z.show(head(IRIS_projected1))"],"enabled":true,"result":{"startTime":1736205753462,"interpreter":"r.medium","endTime":1736205753991,"results":[{"message":"Sepal.Length\tPetal.Length\n4.6\t1\n4.3\t1.1\n5\t1.2\n5.8\t1.2\n4.4\t1.3\n4.4\t1.3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select columns by index range","message":["%r","","IRIS_projected2 <- IRIS[1:3]","z.show(head(IRIS_projected2))"],"enabled":true,"result":{"startTime":1736205754432,"interpreter":"r.medium","endTime":1736205755189,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\n4.6\t3.6\t1\n4.3\t3\t1.1\n5\t3.2\t1.2\n5.8\t4\t1.2\n4.4\t3\t1.3\n4.4\t3.2\t1.3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select columns by data type","message":["%r","","IRIS_projected3 <- IRIS[,sapply(as.list(IRIS), is.numeric)]","z.show(head(IRIS_projected3))"],"enabled":true,"result":{"startTime":1736205755615,"interpreter":"r.medium","endTime":1736205756086,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\n4.6\t3.6\t1\t0.2\n4.3\t3\t1.1\t0.1\n5\t3.2\t1.2\t0.2\n5.8\t4\t1.2\t0.2\n4.4\t3\t1.3\t0.2\n4.4\t3.2\t1.3\t0.2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Select table rows using proxy object IRIS","","* Select sepal length and petal length where petal length is less than 1.5","* Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering","* Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0"],"enabled":true,"result":{"startTime":1736205756511,"interpreter":"md.medium","endTime":1736205756916,"results":[{"message":"<h3 id=\"select-table-rows-using-proxy-object-iris\">Select table rows using proxy object IRIS<\/h3>\n<ul>\n<li>Select sepal length and petal length where petal length is less than 1.5<\/li>\n<li>Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering<\/li>\n<li>Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select rows based on simple filter","message":["%r","","IRIS_projected4 <- IRIS_projected1[IRIS_projected1$Petal.Length <1.5 ,]","z.show(head(IRIS_projected4))"],"enabled":true,"result":{"startTime":1736205757352,"interpreter":"r.medium","endTime":1736205757815,"results":[{"message":"Sepal.Length\tPetal.Length\n4.6\t1\n4.3\t1.1\n5\t1.2\n5.8\t1.2\n4.4\t1.3\n4.4\t1.3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compound row selection with 'or'","message":["%r","","IRIS_projected5 <- IRIS[IRIS$Petal.Length <1.5 | IRIS$Sepal.Length == 5 ,]","z.show(head(IRIS_projected5))"],"enabled":true,"result":{"startTime":1736205758246,"interpreter":"r.medium","endTime":1736205758711,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n4.6\t3.6\t1\t0.2\tsetosa\n4.3\t3\t1.1\t0.1\tsetosa\n5\t3.2\t1.2\t0.2\tsetosa\n5.8\t4\t1.2\t0.2\tsetosa\n4.4\t3\t1.3\t0.2\tsetosa\n4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compound row selection with 'and'","message":["%r","","IRIS_projected6 <- IRIS[IRIS$Petal.Length <1.5 & IRIS$Sepal.Length > 5 ,]","z.show(head(IRIS_projected6))"],"enabled":true,"result":{"startTime":1736205759133,"interpreter":"r.medium","endTime":1736205759608,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n5.8\t4\t1.2\t0.2\tsetosa\n5.5\t3.5\t1.3\t0.2\tsetosa\n5.4\t3.9\t1.3\t0.4\tsetosa\n5.2\t3.4\t1.4\t0.2\tsetosa\n5.1\t3.5\t1.4\t0.2\tsetosa\n5.5\t4.2\t1.4\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Use DataFrame objects","","Let's explore operations involving DataFrames."],"enabled":true,"result":{"startTime":1736205760025,"interpreter":"md.medium","endTime":1736205760431,"results":[{"message":"<h3 id=\"use-dataframe-objects\">Use DataFrame objects<\/h3>\n<p>Let's explore operations involving DataFrames.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create demo data in temporary table","message":["%r","","my_df <- data.frame(id  = c(1,2,3,4,5),","                    val = c(\"a\", \"b\", \"c\", \"d\", \"e\"),","                    ch  = c(\"p\", \"q\", \"r\", \"a\", \"b\"),","                    num = c(4,3,6.7,7.2,5))","","MY_DF <- ore.push(my_df)","","NUM1 <- MY_DF$id","NUM2 <- MY_DF$num","","NUM_APPENDED <- cbind(NUM1,NUM2)","z.show(NUM_APPENDED)"],"enabled":true,"result":{"startTime":1736205760851,"interpreter":"r.medium","endTime":1736205761955,"results":[{"message":"NUM1\tNUM2\n1\t4\n2\t3\n3\t6.7\n4\t7.2\n5\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using concat()","message":["%md","","Next, try a few examples using the ***concat*** function.","","* Create two ore.frame proxy objects and combine the objects column-wise","* Create an derived column with the rounded exponential of two times the values in the num column of the ore.frame proxy object, then concatenate it with another ore.frame proxy object using a new column name","* Concatenate an ore.frame proxy object with multiple objects "],"enabled":true,"result":{"startTime":1736205762379,"interpreter":"md.medium","endTime":1736205762793,"results":[{"message":"<p>Next, try a few examples using the <em><strong>concat<\/strong><\/em> function.<\/p>\n<ul>\n<li>Create two ore.frame proxy objects and combine the objects column-wise<\/li>\n<li>Create an derived column with the rounded exponential of two times the values in the num column of the ore.frame proxy object, then concatenate it with another ore.frame proxy object using a new column name<\/li>\n<li>Concatenate an ore.frame proxy object with multiple objects<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Column-wise concatenation","message":["%r","","X <- MY_DF[, c(\"id\", \"val\")]","Y <- MY_DF[, c(\"num\", \"ch\")]","","XY_APPEND <- cbind(X,Y)","z.show(XY_APPEND)"],"enabled":true,"result":{"startTime":1736205763213,"interpreter":"r.medium","endTime":1736205763756,"results":[{"message":"id\tval\tnum\tch\n1\ta\t4\tp\n2\tb\t3\tq\n3\tc\t6.7\tr\n4\td\t7.2\ta\n5\te\t5\tb\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Concatenate new derived column","message":["%r","","W <- round((exp(MY_DF$num*2)),2)","","YW_APPEND <- cbind(Y,W)","z.show(YW_APPEND)"],"enabled":true,"result":{"startTime":1736205764175,"interpreter":"r.medium","endTime":1736205764711,"results":[{"message":"num\tch\tW\n4\tp\t2980.96\n3\tq\t403.43\n6.7\tr\t660003.22\n7.2\ta\t1794074.77\n5\tb\t22026.47\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Concatenate with renaming","message":["%r","","colnames(Y) <- c(\"New_Num\", \"New_ch\")","","XWY_APPEND <- 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ore.push(my_df4)"],"enabled":true,"result":{"startTime":1736205814548,"interpreter":"r.medium","endTime":1736205815081,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the categories that the most entries belonged to","message":["%r","","CROSSTAB <- table(MY_DF4$GENDER, MY_DF4$HAND) ","CROSSTAB"],"enabled":true,"result":{"startTime":1736205815521,"interpreter":"r.medium","endTime":1736205816053,"results":[{"message":"   \n    L R\n  F 1 5\n  M 2 2","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For each gender and across entries, find ratio of entries with different hand values","message":["%r","","round(CROSSTAB/nrow(MY_DF4),3)"],"enabled":true,"result":{"startTime":1736205816476,"interpreter":"r.medium","endTime":1736205817051,"results":[{"message":"   \n        L     R\n  F 0.083 0.417\n  M 0.167 0.167","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find the mean speed across all gender and hand combinations","message":["%r","","group <- MY_DF4 %>% group_by(GENDER, HAND)","","z.show(group %>% summarise(meanSPEED = mean(SPEED)))"],"enabled":true,"result":{"startTime":1736205817468,"interpreter":"r.medium","endTime":1736205818084,"results":[{"message":"GENDER\tHAND\tmeanSPEED\nF\tR\t40.78\nM\tNA\t29.3\nM\tL\t45.85\nNA\tR\t46.4\nF\tL\t34.1\nM\tR\t27.85\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find the median vlaues for accuracy and speed by gender and hand","message":["%r","","group2 <- MY_DF4 %>% group_by(GENDER, HAND)","","z.show(group2 %>% summarise(mSPEED = median(SPEED), mACCURACY = median(ACCURACY)))"],"enabled":true,"result":{"startTime":1736205818507,"interpreter":"r.medium","endTime":1736205819289,"results":[{"message":"GENDER\tHAND\tmSPEED\tmACCURACY\nF\tL\t34.1\t0.96\nM\tR\t27.85\t0.925\nNA\tR\t46.4\t0.89\nF\tR\t39.6\t0.87\nM\tL\t45.85\t0.91\nM\tNA\t29.3\t0.97\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find the max and min speeds for every gender and hand combination and across all combinations","message":["%r","","group3 <- MY_DF4 %>% group_by(GENDER, HAND)","","z.show(group3 %>% summarise(minSPEED = min(SPEED), maxSPEED = max(SPEED)))"],"enabled":true,"result":{"startTime":1736205819707,"interpreter":"r.medium","endTime":1736205820283,"results":[{"message":"GENDER\tHAND\tminSPEED\tmaxSPEED\nNA\tR\t46.4\t46.4\nF\tL\t34.1\t34.1\nM\tR\t25.3\t30.4\nF\tR\t12\t60.8\nM\tNA\t29.3\t29.3\nM\tL\t40.5\t51.2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Create new columns","","Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client engine."],"enabled":true,"result":{"startTime":1736205820701,"interpreter":"md.medium","endTime":1736205821103,"results":[{"message":"<h3 id=\"create-new-columns\">Create new columns<\/h3>\n<p>Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client engine.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%r","","shopping_cart <- data.frame(","  ItemName = c('paper_towel', 'ground_pork', 'tofu', 'eggs',","                'pork_loin', 'whole_milk', 'egg_custard'),","  ItemType = c('grocery', 'meat', 'grocery', 'dairy', 'meat',","                'dairy', 'bakery'),","  Quantity = c(1, 2.6, 4, 1, 1.9, 1, 1),","  UnitPrice = c(1.19, 2.79, 0.99, 2.49, 3.19, 2.5, 3.99)",")","","SHOPPING_CART <- ore.push(shopping_cart)"],"enabled":true,"result":{"startTime":1736205821521,"interpreter":"r.medium","endTime":1736205821988,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a column Total = Quantity * Unit_price","message":["%r","","SHOPPING_CART$Total <- with(SHOPPING_CART, Quantity * UnitPrice)","z.show(SHOPPING_CART)"],"enabled":true,"result":{"startTime":1736205822400,"interpreter":"r.medium","endTime":1736205822926,"results":[{"message":"ItemName\tItemType\tQuantity\tUnitPrice\tTotal\npaper_towel\tgrocery\t1\t1.19\t1.19\nground_pork\tmeat\t2.6\t2.79\t7.254\ntofu\tgrocery\t4\t0.99\t3.96\neggs\tdairy\t1\t2.49\t2.49\npork_loin\tmeat\t1.9\t3.19\t6.061\nwhole_milk\tdairy\t1\t2.5\t2.5\negg_custard\tbakery\t1\t3.99\t3.99\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Count the pattern 'egg' in the Item_name column","message":["%r","","char <- as.character(shopping_cart$\"ItemName\") # MH: there should be no need to use \" around the col name","EggPattern <- lengths(regmatches(char, gregexpr(\"egg\", char)))","LCHAR_FRAME <- ore.frame(EggPattern)","EGG_PATTERN <- cbind(SHOPPING_CART,LCHAR_FRAME)","","z.show(EGG_PATTERN)"],"enabled":true,"result":{"startTime":1736205823341,"interpreter":"r.medium","endTime":1736205824260,"results":[{"message":"ItemName\tItemType\tQuantity\tUnitPrice\tTotal\tEggPattern\npaper_towel\tgrocery\t1\t1.19\t1.19\t0\nground_pork\tmeat\t2.6\t2.79\t7.254\t0\ntofu\tgrocery\t4\t0.99\t3.96\t0\neggs\tdairy\t1\t2.49\t2.49\t1\npork_loin\tmeat\t1.9\t3.19\t6.061\t0\nwhole_milk\tdairy\t1\t2.5\t2.5\t0\negg_custard\tbakery\t1\t3.99\t3.99\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check whether items are of grocery category","message":["%r","","SHOPPING_CART$IsGrocery <- ifelse(SHOPPING_CART$ItemType == \"grocery\",\"YES\") ","z.show(SHOPPING_CART)"],"enabled":true,"result":{"startTime":1736205824677,"interpreter":"r.medium","endTime":1736205825398,"results":[{"message":"ItemName\tItemType\tQuantity\tUnitPrice\tTotal\tIsGrocery\npaper_towel\tgrocery\t1\t1.19\t1.19\tYES\nground_pork\tmeat\t2.6\t2.79\t7.254\tNA\ntofu\tgrocery\t4\t0.99\t3.96\tYES\neggs\tdairy\t1\t2.49\t2.49\tNA\npork_loin\tmeat\t1.9\t3.19\t6.061\tNA\nwhole_milk\tdairy\t1\t2.5\t2.5\tNA\negg_custard\tbakery\t1\t3.99\t3.99\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Calculate the length of item names","message":["%r","","SHOPPING_CART$NameLength <- nchar(as.character(SHOPPING_CART$ItemName))","z.show(SHOPPING_CART)"],"enabled":true,"result":{"startTime":1736205825859,"interpreter":"r.medium","endTime":1736205826328,"results":[{"message":"ItemName\tItemType\tQuantity\tUnitPrice\tTotal\tIsGrocery\tNameLength\npaper_towel\tgrocery\t1\t1.19\t1.19\tYES\t11\nground_pork\tmeat\t2.6\t2.79\t7.254\tNA\t11\ntofu\tgrocery\t4\t0.99\t3.96\tYES\t4\neggs\tdairy\t1\t2.49\t2.49\tNA\t4\npork_loin\tmeat\t1.9\t3.19\t6.061\tNA\t9\nwhole_milk\tdairy\t1\t2.5\t2.5\tNA\t10\negg_custard\tbakery\t1\t3.99\t3.99\tNA\t11\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Calculate ceiling of UnitPrice","message":["%r","","SHOPPING_CART$UnitPriceCeiling <- ceiling(SHOPPING_CART$UnitPrice)","z.show(SHOPPING_CART)"],"enabled":true,"result":{"startTime":1736205826757,"interpreter":"r.medium","endTime":1736205827391,"results":[{"message":"ItemName\tItemType\tQuantity\tUnitPrice\tTotal\tIsGrocery\tNameLength\tUnitPriceCeiling\npaper_towel\tgrocery\t1\t1.19\t1.19\tYES\t11\t2\nground_pork\tmeat\t2.6\t2.79\t7.254\tNA\t11\t3\ntofu\tgrocery\t4\t0.99\t3.96\tYES\t4\t1\neggs\tdairy\t1\t2.49\t2.49\tNA\t4\t3\npork_loin\tmeat\t1.9\t3.19\t6.061\tNA\t9\t4\nwhole_milk\tdairy\t1\t2.5\t2.5\tNA\t10\t3\negg_custard\tbakery\t1\t3.99\t3.99\tNA\t11\t4\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use a few other functions to modify the Price column","message":["%r","","SHOPPING_CART$UnitPriceFloor <- floor(SHOPPING_CART$UnitPrice)","SHOPPING_CART$UnitPriceExp   <- exp(SHOPPING_CART$UnitPrice)","SHOPPING_CART$UnitPriceLog   <- log(SHOPPING_CART$UnitPrice)","SHOPPING_CART$UnitPriceSqrt  <- sqrt(SHOPPING_CART$UnitPrice)","z.show(SHOPPING_CART)"],"enabled":true,"result":{"startTime":1736205827805,"interpreter":"r.medium","endTime":1736205828333,"results":[{"message":"ItemName\tItemType\tQuantity\tUnitPrice\tTotal\tIsGrocery\tNameLength\tUnitPriceCeiling\tUnitPriceFloor\tUnitPriceExp\tUnitPriceLog\tUnitPriceSqrt\npaper_towel\tgrocery\t1\t1.19\t1.19\tYES\t11\t2\t1\t3.28708120738312\t0.173953307123438\t1.09087121146357\nground_pork\tmeat\t2.6\t2.79\t7.254\tNA\t11\t3\t2\t16.2810198017884\t1.02604159583327\t1.67032930884901\ntofu\tgrocery\t4\t0.99\t3.96\tYES\t4\t1\t0\t2.69123447234926\t-0.0100503358535015\t0.99498743710662\neggs\tdairy\t1\t2.49\t2.49\tNA\t4\t3\t2\t12.0612761204447\t0.912282710476616\t1.57797338380595\npork_loin\tmeat\t1.9\t3.19\t6.061\tNA\t9\t4\t3\t24.2884274430946\t1.16002091679675\t1.78605710994918\nwhole_milk\tdairy\t1\t2.5\t2.5\tNA\t10\t3\t2\t12.1824939607035\t0.916290731874155\t1.58113883008419\negg_custard\tbakery\t1\t3.99\t3.99\tNA\t11\t4\t3\t54.0548893633266\t1.38379123090177\t1.99749843554382\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, using a modified version of the *iris* data set, explore ***sort_values*** and ***tail*** functions.","","We modify the dataset by replacing a few entries with NaNs to test how the ***na_position*** parameter works in the ***sort_values*** method."],"enabled":true,"result":{"startTime":1736205828748,"interpreter":"md.medium","endTime":1736205829152,"results":[{"message":"<p>Next, using a modified version of the <em>iris<\/em> data set, explore <em><strong>sort_values<\/strong><\/em> and <em><strong>tail<\/strong><\/em> functions.<\/p>\n<p>We modify the dataset by replacing a few entries with NaNs to test how the <em><strong>na_position<\/strong><\/em> parameter works in the <em><strong>sort_values<\/strong><\/em> method.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Sort data set by Sepal.Length then by Sepal.Width in descending order","message":["%r","","res <- ore.sort(data = IRIS, by = c(\"Sepal.Length\", \"Sepal.Width\"), reverse = TRUE)","z.show(head(res))"],"enabled":true,"result":{"startTime":1736205829566,"interpreter":"r.medium","endTime":1736205830181,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n7.9\t3.8\t6.4\t2\tvirginica\n7.7\t3.8\t6.7\t2.2\tvirginica\n7.7\t3\t6.1\t2.3\tvirginica\n7.7\t2.8\t6.7\t2\tvirginica\n7.7\t2.6\t6.9\t2.3\tvirginica\n7.6\t3\t6.6\t2.1\tvirginica\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display last 5 rows of the data set","message":["%r","","z.show(tail(IRIS))"],"enabled":true,"result":{"startTime":1736205830601,"interpreter":"r.medium","endTime":1736205831220,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n7.3\t2.9\t6.3\t1.8\tvirginica\n7.9\t3.8\t6.4\t2\tvirginica\n7.6\t3\t6.6\t2.1\tvirginica\n7.7\t2.8\t6.7\t2\tvirginica\n7.7\t3.8\t6.7\t2.2\tvirginica\n7.7\t2.6\t6.9\t2.3\tvirginica\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics."],"enabled":true,"result":{"startTime":1736205831633,"interpreter":"md.medium","endTime":1736205832032,"results":[{"message":"<p>Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%r","","my_df5 <- data.frame(","  numeric_col = c(1, 1.4, -4, 3.145, 5, NA),","  string_col = c(NA, NA, 'a', 'a', 'a', 'b'),","  byte_col = c('ba', 'bb', 'bc', 'bc', 'bd', 'be'))","","MY_DF5 <- ore.push(my_df5)"],"enabled":true,"result":{"startTime":1736205832462,"interpreter":"r.medium","endTime":1736205832925,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Combine a Boolean column with MY_DF4","message":["%r","","boolean_col <- MY_DF5$numeric_col > 3","MY_NDF5 <- cbind(MY_DF5,boolean_col)","","z.show(MY_NDF5)"],"enabled":true,"result":{"startTime":1736205833345,"interpreter":"r.medium","endTime":1736205833877,"results":[{"message":"numeric_col\tstring_col\tbyte_col\tboolean_col\n1\tNA\tba\tFALSE\n1.4\tNA\tbb\tFALSE\n-4\ta\tbc\tFALSE\n3.145\ta\tbc\tTRUE\n5\ta\tbd\tTRUE\nNA\tb\tbe\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Use the `summary` function to compute a variety of statistics on all columns. Then, exclude the Float columns."],"enabled":true,"result":{"startTime":1736205834309,"interpreter":"md.medium","endTime":1736205834713,"results":[{"message":"<p>Use the <code>summary<\/code> function to compute a variety of statistics on all columns. Then, exclude the Float columns.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get summary statistics on all columns","message":["%r","","summary(MY_NDF5)"],"enabled":true,"result":{"startTime":1736205835139,"interpreter":"r.medium","endTime":1736205836878,"results":[{"message":"  numeric_col      string_col              byte_col              boolean_col \n Min.   :-4.000   Length:6               Length:6               Min.   :0.0  \n 1st Qu.: 1.000   Class :ore.character   Class :ore.character   1st Qu.:0.0  \n Median : 1.400   Mode  :character       Mode  :character       Median :0.0  \n Mean   : 1.309                                                 Mean   :0.4  \n 3rd Qu.: 3.145                                                 3rd Qu.:1.0  \n Max.   : 5.000                                                 Max.   :1.0  \n NA's   :1                                                      NA's   :1    ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Compute the sum of values in each Float or Boolean column","message":["%r","","sum(MY_NDF5$numeric_col, na.rm = TRUE)","sum(MY_NDF5$boolean_col, na.rm = TRUE)"],"enabled":true,"result":{"startTime":1736205837311,"interpreter":"r.medium","endTime":1736205837848,"results":[{"message":"6.545\n","type":"HTML"},{"message":"2","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","Find the cumulative sum of values in each Float or Boolean column after MY_DF5 is sorted by the bytes column in descending order. Then, compute skew, median, and kurtosis for float columns."],"enabled":true,"result":{"startTime":1736205838282,"interpreter":"md.medium","endTime":1736205838682,"results":[{"message":"<p>Find the cumulative sum of values in each Float or Boolean column after MY_DF5 is sorted by the bytes column in descending order. 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of proanthocyanins and histogram of proline","message":["%r","","ore.sync(table=\"WINE\")","","par(mfrow=c(1,2))","boxplot(WINE$proanthocyanins,main = \"Proanthocyanins\\nDistribution\", col=\"darkred\")","hist(WINE$proline, breaks = 10, col = 'darkgreen', ","     xlab = 'proline content', ","     ylab = '# of wine instances', main = 'Proline content' 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nEyZM+PPPP+URDw+PDz/88IUXXlCwKgAAHAGXYqEm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\n/KVRo0aJyPnz59s+euH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diff --git a/machine-learning/notebooks-oml/r/OML4R -3- Datastore and Script Repository.dsnb b/machine-learning/notebooks-oml/r/OML4R -3- Datastore and Script Repository.dsnb
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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4R -3- Datastore and Script Repository","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1715778947108,"interpreter":"md.low","endTime":1715778947259,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for R - Datastore and Script Repository","","***Oracle Machine Learning for R*** (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we highlight the datastore and script repository features of OML4R.","","With a datastore, you can store R objects in a named datastore entry. This named datastore can then be used in subsequent R sessions and even be made available to other users or programs by granting/revoking read permissions.","","R objects, including OML4R proxy objects, exist for the duration of the current R session unless you explicitly save them. You can save one or more R objects, including OML4R proxy objects, to a named datastore and then load those objects in a later R session. This is also useful when using embedded R execution. Datastores exist in the user\u2019s Oracle Database schema. A datastore, and the objects it contains, persist in the database until explicitly deleted.","","Using a datastore, you can:","","* Save OML4R and other R objects in one R session and load them in another R session","* Pass non-scalar arguments to R functions for use in embedded R execution from both the R, and more importantly, the SQL API where only scalar values can be passed to functions","* List available datastores and explore the contents of a datastore","","With the script repository, users can:","","* Create and store user-defined R functions as scripts in Oracle Database","* Grant or revoke the read/execute privilege to a script","* List available scripts","* Load a script function into the R environment","* Drop a script from the script repository","","You can make scripts either private or global. A private script is available only to the owner. A global script is available to any user. For private scripts, the owner of the script may grant the read privilege to other users or revoke that privilege.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1738091043617,"interpreter":"md.low","endTime":1738091043832,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-r---datastore-and-script-repository\">Oracle Machine Learning for R - Datastore and Script Repository<\/h1>\n<p><em><strong>Oracle Machine Learning for R<\/strong><\/em> (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we highlight the datastore and script repository features of OML4R.<\/p>\n<p>With a datastore, you can store R objects in a named datastore entry. This named datastore can then be used in subsequent R sessions and even be made available to other users or programs by granting/revoking read permissions.<\/p>\n<p>R objects, including OML4R proxy objects, exist for the duration of the current R session unless you explicitly save them. You can save one or more R objects, including OML4R proxy objects, to a named datastore and then load those objects in a later R session. This is also useful when using embedded R execution. Datastores exist in the user\u2019s Oracle Database schema. A datastore, and the objects it contains, persist in the database until explicitly deleted.<\/p>\n<p>Using a datastore, you can:<\/p>\n<ul>\n<li>Save OML4R and other R objects in one R session and load them in another R session<\/li>\n<li>Pass non-scalar arguments to R functions for use in embedded R execution from both the R, and more importantly, the SQL API where only scalar values can be passed to functions<\/li>\n<li>List available datastores and explore the contents of a datastore<\/li>\n<\/ul>\n<p>With the script repository, users can:<\/p>\n<ul>\n<li>Create and store user-defined R functions as scripts in Oracle Database<\/li>\n<li>Grant or revoke the read/execute privilege to a script<\/li>\n<li>List available scripts<\/li>\n<li>Load a script function into the R environment<\/li>\n<li>Drop a script from the script repository<\/li>\n<\/ul>\n<p>You can make scripts either private or global. A private script is available only to the owner. A global script is available to any user. For private scripts, the owner of the script may grant the read privilege to other users or revoke that privilege.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries and create a data table","message":["","%md","","To use OML4R, first import the library ORE.","","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1715778947530,"interpreter":"md.low","endTime":1715778947605,"results":[{"message":"<p>To use OML4R, first import the library ORE.<\/p>\n<p>Users can get online help using R's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(MASS)  # for Boston data set"],"enabled":true,"result":{"startTime":1715778947682,"interpreter":"r.low","endTime":1715778953382,"results":[{"message":"\nAttaching package: \u2018MASS\u2019\n\n\nThe following object is masked from \u2018package:OREstats\u2019:\n\n    fitdistr\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the help file for the ore.move function","message":["%r","","?ore.move"],"enabled":true,"result":{"startTime":1715778953480,"interpreter":"r.low","endTime":1715778953801,"results":[{"message":"ore.move                 package:OREds                 R Documentation\n\nOracle R Enterprise Datastore Moving Function\n\nDescription:\n\n     Renames a datastore in the user's Oracle Database schema, moves or\n     renames R objects within a datastore, or both moves and renames R\n     objects within a datastore.\n\nUsage:\n\n     ore.move(name = stop(\"parameter 'name' must be specified\"),\n              newname = NULL,\n              object.names = NULL,\n              object.newnames = NULL,\n              overwrite = FALSE)\n     \nArguments:\n\n    name: A character string specifying the datastore to be renamed or\n          modified.\n\n newname: A character string specifying the newname of the datastore or\n          the name of the datastore that is the destination for objects\n          moved from the source datastore. Can be left unspecified if\n          objects are only being renamed and not moved out of the\n          datastore specified by \u2018name\u2019.  Cannot be an empty string.\n\nobject.names: A character vector specifying the R objects to be moved\n          or renamed, or both.\n\nobject.newnames: A character vector specifying the newnames of the R\n          objects specified by \u2018object.names\u2019.  If not \u2018NULL\u2019, must be\n          the same length as \u2018object.names\u2019.  Cannot contain an empty\n          string.\n\noverwrite: If \u2018TRUE\u2019 and renaming a datastore, then if the datastore\n          specified by \u2018newname\u2019 already exists, the datastore\n          specified by \u2018newname\u2019 and its contents are overwritten by\n          the contents of the datastore specified by \u2018name\u2019.  If \u2018TRUE\u2019\n          and moving or renaming datastore R objects, or both, then if\n          the destination datastore already has objects whose names are\n          in \u2018object.newnames\u2019, those objects are overwritten.  If\n          \u2018FALSE\u2019, then an attempt to rename a datastore or object to\n          an existing datastore or object, or an attempt to move an R\n          object to a datastore where an object of the same name\n          already exists will cause an error.  Default value is\n          \u2018FALSE\u2019.\n\nDetails:\n\n     This function performs one of three actions depending on the\n     input. If \u2018object.names\u2019 or \u2018object.newnames\u2019 is a character\n     vector of length zero, the function treats it as \u2018NULL\u2019.\n\n1) Rename a datastore in the user's Oracle Database schema. Neither\n          \u2018name\u2019 nor \u2018newname\u2019 may be \u2018NULL\u2019, and \u2018newname\u2019 may not be\n          an empty string. \u2018old.obj.name\u2019 and \u2018object.newnames\u2019 must\n          both be \u2018NULL\u2019. The datastore specified by \u2018name\u2019 will be\n          renamed to the value specified by \u2018newname\u2019.\n\n2) Rename objects within a datastore. \u2018name\u2019 must not be \u2018NULL\u2019.\n          \u2018newname\u2019 must be either \u2018NULL\u2019 or the same as \u2018name\u2019.\n          \u2018object.names\u2019 and \u2018object.newnames\u2019 must have distinct\n          values, and neither can be \u2018NULL\u2019.\n\n3) Move objects from one datastore to another. \u2018name\u2019 and \u2018newname\u2019\n          must have distinct values, and neither can be \u2018NULL\u2019.\n          \u2018object.names\u2019 cannot be \u2018NULL\u2019. If \u2018object.newnames\u2019 is not\n          \u2018NULL\u2019 and if the names in it are distinct from\n          \u2018object.names\u2019, the objects are renamed in the datastore\n          specified by \u2018newname\u2019.\n     An error occurs if there is not a one-to-one mapping of names in\n     \u2018object.names\u2019 to \u2018object.newnames\u2019.\n\nAuthor(s):\n\n     Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\nSee Also:\n\n     \u2018ore.save\u2019, \u2018ore.load\u2019, \u2018ore.delete\u2019, \u2018ore.datastore\u2019\n     \u2018ore.datastoreSummary\u2019\n\nExamples:\n\n     if (any(sapply(c(\"a1\", \"a2\", \"b1\", \"b2\", \"w1\", \"w2\", \"x1\", \"x2\", \"z1\", \"z2\"), exists)))\n       stop(\"object a1, a2, b1, b2, w1, w2, x1, x2, z1, or z2 exists\")\n     \n     if (nrow(ore.datastore(pattern=\"^rq\\\\$ds_[123]$\")) > 0)\n       stop(\"datastore 'rq$ds_1', 'rq$ds_2', or 'rq$ds_3' exists\")\n     \n     a1 <- data.frame(a=c(\"foo\", \"bar\"), b=c(\"xx\", \"yy\"))\n     a2 <- data.frame(a=1:10, b=11:20)\n     b1 <- ore.push(a1)\n     b2 <- ore.push(a2)\n     w1 <- stats::runif(20)\n     w2 <- stats::runif(50)\n     x1 <- list(a = 1, b = TRUE, c = \"oops\")\n     x2 <- list(a = w1, b = w2, c = \"data\", d=200)\n     z1 <- ore.push(x1)\n     z2 <- ore.push(w1)\n     \n     ore.save(a1, a2, w1, w2, x1, x2, name=\"rq$ds_1\")\n     ore.save(b1, b2, name=\"rq$ds_2\")\n     ore.save(z1, z2, name=\"rq$ds_3\")\n     \n     #rename a datastore\n     ore.move(name=\"rq$ds_1\", newname=\"rq$ds_1_renamed\")\n     ds <- ore.datastore()\n     ## Not run:\n     \n     ds\n     ## End(Not run)\n     \n     \n     \n     #rename objects within a datastore\n     ore.move(name=\"rq$ds_1_renamed\", object.names=c(\"x1\", \"x2\"),\n              object.newnames=c(\"y1\", \"y2\"))\n     ore.datastoreSummary(name=\"rq$ds_1_renamed\")\n     \n     #move objects from one datastore to another\n     ore.move(name=\"rq$ds_1_renamed\", newname=\"rq$ds_2\", object.names=c(\"y1\", \"y2\"))\n     ore.datastoreSummary(name=\"rq$ds_1_renamed\")\n     ore.datastoreSummary(name=\"rq$ds_2\")\n     \n     #move objects from one datastore to another, and renaming them\n     ore.move(name=\"rq$ds_2\", newname=\"rq$ds_1_renamed\", object.names=c(\"y1\", \"y2\"),\n              object.newnames=c(\"z1\", \"z2\"))\n     ore.datastoreSummary(name=\"rq$ds_1_renamed\")\n     ore.datastoreSummary(name=\"rq$ds_2\")\n     \n     #rename a datastore, overwriting the existing datastore\n     ore.move(name=\"rq$ds_1_renamed\", newname=\"rq$ds_2\", overwrite = TRUE)\n     ore.datastoreSummary(name=\"rq$ds_2\")\n     \n     #rename objects within a datastore, overwriting existing objects of the same name\n     ore.move(name=\"rq$ds_2\", object.names=c(\"w1\", \"w2\"), object.newnames=c(\"z1\", \"z2\"),\n              overwrite = TRUE)\n     ore.datastoreSummary(name=\"rq$ds_2\")\n     \n     #transfering objects to another datastore, overwriting existing objects of the same name\n     ore.move(name=\"rq$ds_2\", newname=\"rq$ds_3\", object.names=c(\"z1\", \"z2\"),\n              overwrite=TRUE)\n     ore.datastoreSummary(name=\"rq$ds_2\")\n     ore.datastoreSummary(name=\"rq$ds_3\")\n     \n     #rename objects while transfering them to another datastore, overwriting existing objects of the same name\n     ore.move(name=\"rq$ds_3\", newname=\"rq$ds_2\", object.names=c(\"z1\", \"z2\"),\n              object.newnames=c(\"a1\", \"a2\"), overwrite=TRUE)\n     ore.datastoreSummary(name=\"rq$ds_2\")\n     ore.datastoreSummary(name=\"rq$ds_3\")\n     \n     ore.delete(name=\"rq$ds_2\")\n     ore.delete(name=\"rq$ds_3\")\n     rm(a1, a2, b1, b2, w1, w2, x1, x2, z1, z2)\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete datastore myDataStore1 if it exists","message":["%r","","try(ore.delete(\"myDatastore1\"), silent=TRUE)"],"enabled":true,"result":{"startTime":1715778953891,"interpreter":"r.low","endTime":1715778954114,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete datastore mynewDataStore1 if it exists","message":["%r","","try(ore.delete(\"myNewDatastore1\"), silent=TRUE)"],"enabled":true,"result":{"startTime":1715778954189,"interpreter":"r.low","endTime":1715778954308,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create datastore myDatastore1","message":["%r","","# create simple example R objects","x <- stats::runif(20)","y <- list(a = 1, b = TRUE, c = \"value\")","z <- ore.push(x)  # create a temporary object for x in the database and return proxy object","","# save all objects in current R environment to datastore 'myDatastore' in user's schema","ore.save(x, y, z, name='myDatastore1',","         description = 'my first datastore')","","# list information about datastores in user's schema","ore.datastore('myDatastore1')","","# show detailed object information about datastore 'myDatastore'","ore.datastoreSummary(name='myDatastore1')"],"enabled":true,"result":{"startTime":1715778954386,"interpreter":"r.low","endTime":1715778958188,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 5<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>object.count<\/th><th scope=col>size<\/th><th scope=col>creation.date<\/th><th scope=col>description<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dttm&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>myDatastore1<\/td><td>3<\/td><td>1296<\/td><td>2024-05-15 13:15:54<\/td><td>my first datastore<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 3 x 6<\/caption>\n<thead>\n\t<tr><th scope=col>object.name<\/th><th scope=col>class<\/th><th scope=col>size<\/th><th scope=col>length<\/th><th scope=col>row.count<\/th><th scope=col>col.count<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>x<\/td><td>numeric    <\/td><td>182<\/td><td>20<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n\t<tr><td>y<\/td><td>list       <\/td><td>131<\/td><td> 3<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n\t<tr><td>z<\/td><td>ore.numeric<\/td><td>983<\/td><td>20<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Rename the datastore and object within the datastore","message":["%r","","#rename a datastore","ore.move(name='myDatastore1', newname='myNewDatastore1')","","#rename objects within a datastore, overwriting existing objects of the same name","ore.move(name='myNewDatastore1', ","         object.names=c(\"x\", \"y\"), ","         object.newnames=c(\"x.new\", \"y.new\"),","         overwrite = TRUE)","         ","ore.datastoreSummary(name='myNewDatastore1')"],"enabled":true,"result":{"startTime":1715778958270,"interpreter":"r.low","endTime":1715778959465,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 3 x 6<\/caption>\n<thead>\n\t<tr><th scope=col>object.name<\/th><th scope=col>class<\/th><th scope=col>size<\/th><th scope=col>length<\/th><th scope=col>row.count<\/th><th scope=col>col.count<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>x.new<\/td><td>numeric    <\/td><td>182<\/td><td>20<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n\t<tr><td>y.new<\/td><td>list       <\/td><td>131<\/td><td> 3<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n\t<tr><td>z    <\/td><td>ore.numeric<\/td><td>983<\/td><td>20<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create dataframes and load them into Autonomous Database","","We load three data sets, then invoke create and display the columns for each. These will be used in exploring datastore functionality."],"enabled":true,"result":{"startTime":1715778959548,"interpreter":"md.low","endTime":1715778959607,"results":[{"message":"<h3 id=\"create-dataframes-and-load-them-into-autonomous-database\">Create dataframes and load them into Autonomous Database<\/h3>\n<p>We load three data sets, then invoke create and display the columns for each. These will be used in exploring datastore functionality.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create IRIS temporary database table and get proxy object","message":["%r","","IRIS_TMP <- ore.push(iris)","","z.show(colnames(IRIS_TMP))"],"enabled":true,"result":{"startTime":1715778959683,"interpreter":"r.low","endTime":1715778959874,"results":[{"message":"Sepal.Length Sepal.Width Petal.Length Petal.Width Species","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create MTCARS temporary data table","message":["%r","","MTCARS_TMP <- ore.push(mtcars)  ","","z.show(colnames(MTCARS_TMP))"],"enabled":true,"result":{"startTime":1715778959957,"interpreter":"r.low","endTime":1715778960143,"results":[{"message":"mpg cyl disp hp drat wt qsec vs am gear carb","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create AIR temporary data table","message":["%r","","AIR_TMP <- ore.push(airquality)","","z.show(colnames(AIR_TMP))"],"enabled":true,"result":{"startTime":1715778960223,"interpreter":"r.low","endTime":1715778960412,"results":[{"message":"Ozone Solar.R Wind Temp Month Day","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Save R objects to datastore","","Save the original *iris* data set and the temporary AIR proxy object to a datastore named \u201cds_rdata\u201d, overwriting if the named datastore already exists. Note that you can store actual data objects in a datastore, but large data objects should remain as database tables for performance and scalability.","","By storing the AIR_TMP object, the temporary table will not be deleted when the session terminates."],"enabled":true,"result":{"startTime":1715778960499,"interpreter":"md.low","endTime":1715778960562,"results":[{"message":"<h3 id=\"save-r-objects-to-datastore\">Save R objects to datastore<\/h3>\n<p>Save the original <em>iris<\/em> data set and the temporary AIR proxy object to a datastore named \u201cds_rdata\u201d, overwriting if the named datastore already exists. Note that you can store actual data objects in a datastore, but large data objects should remain as database tables for performance and scalability.<\/p>\n<p>By storing the AIR_TMP object, the temporary table will not be deleted when the session terminates.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.save(iris, AIR_TMP, name=\"ds_rdata\", description=\"r datasets\", overwrite=TRUE)"],"enabled":true,"result":{"startTime":1715778960645,"interpreter":"r.low","endTime":1715778961547,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Add a third object, the temporary MTCARS proxy object, to that same datastore","message":["%r","","ore.save(MTCARS_TMP, name=\"ds_rdata\", append=TRUE)"],"enabled":true,"result":{"startTime":1715778961625,"interpreter":"r.low","endTime":1715778962351,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save the IRIS_TMP data frame to a new datastore and list datastores","message":["%md","","Save the *IRIS_TMP* data frame to a new datastore, and then list the datastores. Notice we see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. Our two datastores *ds_iris_proxy* and *ds_rdata* are present, with the latter containing three objects."],"enabled":true,"result":{"startTime":1715778962431,"interpreter":"md.low","endTime":1715778962496,"results":[{"message":"<p>Save the <em>IRIS_TMP<\/em> data frame to a new datastore, and then list the datastores. Notice we see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. Our two datastores <em>ds_iris_proxy<\/em> and <em>ds_rdata<\/em> are present, with the latter containing three objects.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.save(IRIS_TMP, name=\"ds_iris_proxy\", description=\"iris data.frame proxy object\", overwrite=TRUE)","","ore.datastore()"],"enabled":true,"result":{"startTime":1715778962575,"interpreter":"r.low","endTime":1715778963571,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 14 x 5<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>object.count<\/th><th scope=col>size<\/th><th scope=col>creation.date<\/th><th scope=col>description<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dttm&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>ChurnModel                  <\/td><td>1<\/td><td>207578866<\/td><td>2024-05-12 15:28:43<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>NaiveBayesModel-iris-species<\/td><td>1<\/td><td>     1624<\/td><td>2022-12-04 17:06:57<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds-1                        <\/td><td>1<\/td><td>    17492<\/td><td>2024-05-02 17:24:02<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds-3                        <\/td><td>1<\/td><td>    47908<\/td><td>2024-05-02 17:24:04<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds-test                     <\/td><td>1<\/td><td>    41177<\/td><td>2023-08-07 19:39:11<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds1                         <\/td><td>1<\/td><td>    83898<\/td><td>2024-05-02 19:14:48<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds2                         <\/td><td>1<\/td><td>    47906<\/td><td>2024-05-02 19:49:20<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds_iris_proxy               <\/td><td>1<\/td><td>     1583<\/td><td>2024-05-15 13:16:03<\/td><td>iris data.frame proxy object<\/td><\/tr>\n\t<tr><td>ds_r_1                      <\/td><td>1<\/td><td>    61786<\/td><td>2024-01-16 21:43:39<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds_r_2                      <\/td><td>1<\/td><td>    33535<\/td><td>2024-01-16 21:46:55<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds_rdata                    <\/td><td>3<\/td><td>     9230<\/td><td>2024-05-15 13:16:00<\/td><td>r datasets                  <\/td><\/tr>\n\t<tr><td>myNewDatastore              <\/td><td>3<\/td><td>     1301<\/td><td>2023-12-05 16:50:20<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>myNewDatastore1             <\/td><td>3<\/td><td>     1296<\/td><td>2024-05-15 13:15:54<\/td><td>my first datastore          <\/td><\/tr>\n\t<tr><td>rmodel                      <\/td><td>1<\/td><td>    47912<\/td><td>2022-11-21 23:27:01<\/td><td>NA                          <\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Store model objects in a datastore","","To illustrate storing other types of objects in datastores, we'll create regression models using base R and OML4R."],"enabled":true,"result":{"startTime":1715778963656,"interpreter":"md.low","endTime":1715778963716,"results":[{"message":"<h3 id=\"store-model-objects-in-a-datastore\">Store model objects in a datastore<\/h3>\n<p>To illustrate storing other types of objects in datastores, we'll create regression models using base R and OML4R.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","regr1 <- lm(medv ~ ., data = Boston)","","regr2 <- ore.odmGLM(formula = Ozone ~ Wind+Temp, data = AIR_TMP)"],"enabled":true,"result":{"startTime":1715778963799,"interpreter":"r.low","endTime":1715778965699,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save objects to datastore and grant read privilege","message":["%md","","Save objects to a datastore and allow the read privilege to be granted to them. 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The result is empty.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.revoke(name=\"ds_rmodel\", type=\"datastore\", user=NULL)","","ore.datastore(type=\"grant\")"],"enabled":true,"result":{"startTime":1715778974392,"interpreter":"r.low","endTime":1715778975487,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 0 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>grantee<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Grant read privilege to user OMLUSER2","message":["%r","","ore.grant(name=\"ds_rmodel\", type=\"datastore\", user=\"OMLUSER2\")","","ore.datastore(type=\"grant\")"],"enabled":true,"result":{"startTime":1715778975564,"interpreter":"r.low","endTime":1715778976661,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>grantee<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>ds_rmodel<\/td><td>OMLUSER2<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Delete datastore content","","Delete some objects from the datastore. Delete a datastore. Show the existing datastores again."],"enabled":true,"result":{"startTime":1715778976743,"interpreter":"md.low","endTime":1715778976802,"results":[{"message":"<h3 id=\"delete-datastore-content\">Delete datastore content<\/h3>\n<p>Delete some objects from the datastore. Delete a datastore. Show the existing datastores again.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","ore.delete(name=\"ds_rdata\", list=c(\"iris\", \"AIR_TMP\"))","","ore.delete(name=\"ds_rdata\")","","ore.delete(name=\"ds_rmodel\") ","","ore.datastore()"],"enabled":true,"result":{"startTime":1715778976880,"interpreter":"r.low","endTime":1715778978478,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'iris'<\/li><li>'AIR_TMP'<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ds_rdata'\n","type":"HTML"},{"message":"'ds_rmodel'\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 13 x 5<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>object.count<\/th><th scope=col>size<\/th><th scope=col>creation.date<\/th><th scope=col>description<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dttm&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>ChurnModel                  <\/td><td>1<\/td><td>207578866<\/td><td>2024-05-12 15:28:43<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>NaiveBayesModel-iris-species<\/td><td>1<\/td><td>     1624<\/td><td>2022-12-04 17:06:57<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds-1                        <\/td><td>1<\/td><td>    17492<\/td><td>2024-05-02 17:24:02<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds-3                        <\/td><td>1<\/td><td>    47908<\/td><td>2024-05-02 17:24:04<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds-test                     <\/td><td>1<\/td><td>    41177<\/td><td>2023-08-07 19:39:11<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds1                         <\/td><td>1<\/td><td>    83898<\/td><td>2024-05-02 19:14:48<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds2                         <\/td><td>1<\/td><td>    47906<\/td><td>2024-05-02 19:49:20<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds_iris_proxy               <\/td><td>1<\/td><td>     1583<\/td><td>2024-05-15 13:16:03<\/td><td>iris data.frame proxy object<\/td><\/tr>\n\t<tr><td>ds_r_1                      <\/td><td>1<\/td><td>    61786<\/td><td>2024-01-16 21:43:39<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>ds_r_2                      <\/td><td>1<\/td><td>    33535<\/td><td>2024-01-16 21:46:55<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>myNewDatastore              <\/td><td>3<\/td><td>     1301<\/td><td>2023-12-05 16:50:20<\/td><td>NA                          <\/td><\/tr>\n\t<tr><td>myNewDatastore1             <\/td><td>3<\/td><td>     1296<\/td><td>2024-05-15 13:15:54<\/td><td>my first datastore          <\/td><\/tr>\n\t<tr><td>rmodel                      <\/td><td>1<\/td><td>    47912<\/td><td>2022-11-21 23:27:01<\/td><td>NA                          <\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### R Script Repository","","To illustrate using the R Script Repository, we first define a function ***build_lm1*** that will fit a regression model. With this function, we create a script named \u201cMyLM_function1\u201d. OML4R stores named user-defined functions -- the scripts -- in the repository."],"enabled":true,"result":{"startTime":1715778978564,"interpreter":"md.low","endTime":1715778978626,"results":[{"message":"<h3 id=\"r-script-repository\">R Script Repository<\/h3>\n<p>To illustrate using the R Script Repository, we first define a function <em><strong>build_lm1<\/strong><\/em> that will fit a regression model. With this function, we create a script named \u201cMyLM_function1\u201d. OML4R stores named user-defined functions -- the scripts -- in the repository.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define function object","message":["%r","","build_lm1 <- function(dat){","  ","  regr <- lm(Sepal.Length  ~ ., data=dat)","  ","  return(regr)","}"],"enabled":true,"result":{"startTime":1715778978707,"interpreter":"r.low","endTime":1715778978828,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View function","message":["%r","","build_lm1"],"enabled":true,"result":{"startTime":1715778978905,"interpreter":"r.low","endTime":1715778979024,"results":[{"message":"<pre class=language-r><code>function (dat) \n{\n<span style=white-space:pre-wrap>    regr &lt;- lm(Sepal.Length ~ ., data = dat)<\/span>\n<span style=white-space:pre-wrap>    return(regr)<\/span>\n}<\/code><\/pre>","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create scripts in repository","","Create ***MyLM_function1*** in the script repository"],"enabled":true,"result":{"startTime":1715778979102,"interpreter":"md.low","endTime":1715778979165,"results":[{"message":"<h3 id=\"create-scripts-in-repository\">Create scripts in repository<\/h3>\n<p>Create <em><strong>MyLM_function1<\/strong><\/em> in the script repository<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptDrop(\"MyLM_function1\", silent=TRUE)","","ore.scriptCreate(\"MyLM_function1\", FUN=build_lm1, overwrite=TRUE)"],"enabled":true,"result":{"startTime":1715778979247,"interpreter":"r.low","endTime":1715778979368,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List scripts available only to the current user","message":["%r","","ore.scriptList(type=\"user\")"],"enabled":true,"result":{"startTime":1715778979447,"interpreter":"r.low","endTime":1715778979632,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 16 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td><span style=white-space:pre-wrap>MyLM_function1         <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    regr &lt;- lm(Sepal.Length ~ ., data = dat)\n    return(regr)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots          <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    print(xyplot(y ~ x, main = \"Random Red Dots\", pch = 19, col = \"red\"))\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    print(xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\"))\n}                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots.2        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    fig1 &lt;- xyplot(y ~ x, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    fig2 &lt;- xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig2)\n}                                                                                                                                                                                                                                    <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots.3        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    fig1 &lt;- xyplot(sample(num_dots_1) ~ sample(num_dots_1), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    fig2 &lt;- xyplot(sample(num_dots_2) ~ sample(num_dots_2), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    print(fig2)\n}                                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots2         <\/span><\/td><td><span style=white-space:pre-wrap>function(num_dots_1=100, num_dots_2=10){\n            id &lt;- 1:10\n            plot( 1:num_dots_1, rnorm(num_dots_1), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            plot( 1:num_dots_2, rnorm(num_dots_2), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            data.frame(id=id, val=id / 100)\n            }                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>build.lmRestR          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- lm(Petal.Length ~ ., dat)\n    res &lt;- summary(mod)\n    res.list &lt;- list(Coef = res$coefficients, R2 = res$r.squared)\n    res.list\n}                                                                                                                                                                                                                                                                                                                                                                                                               <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    regr &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    x &lt;- dat[[\"Petal.Width\"]]\n    y &lt;- dat[[\"Petal.Length\"]]\n    xyplot(y ~ x, xlab = \"Petal Width\", ylab = \"Petal Length\", panel = function(x, y) {\n        panel.dotplot(x, y)\n        panel.abline(lm(y ~ x))\n    }, xlim = c(0, 2.7), ylim = c(0, 7.5), main = \"Prediction of Petal Length\")\n    return(regr)\n}                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildLM.2              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    ore.save(mod, name = dsname, overwrite = TRUE)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                       <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildLM.group          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- naiveBayes(Species ~ ., dat)\n    ore.save(mod, name = dsname)\n    TRUE\n}                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>group.countR           <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    x &lt;- aggregate(df[, \"Species\"], by = list(df$Species), FUN = length)\n    names(x) &lt;- c(\"SPECIES\", \"COUNT\")\n    return(x)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>multi.forecast         <\/span><\/td><td><span style=white-space:pre-wrap>function () \n{\n    library(forecast)\n    library(ggplot2)\n    data(\"AirPassengers\")\n    air.train &lt;- window(AirPassengers, end = 1956 + 11/12)\n    air.test &lt;- window(AirPassengers, start = 1957)\n    n.test &lt;- length(air.test)\n    air.model &lt;- auto.arima(air.train)\n    accuracy(air.model)\n    air.multi.forecast &lt;- air.model %&gt;% forecast(h = n.test)\n    air.multi.forecast %&gt;% autoplot()\n    res &lt;- accuracy(air.multi.forecast, x = air.test)\n    print(air.multi.forecast %&gt;% autoplot() + geom_line(aes(x = as.numeric(time(air.test)), y = as.numeric(air.test)), col = \"red\"))\n    return(res)\n}<\/span><\/td><\/tr>\n\t<tr><td>myLinearRegressionModel<\/td><td><span style=white-space:pre-wrap>function(dat, dsname){\n       mod &lt;- lm(Petal.Length~Petal.Width, dat)\n       ore.save(mod, name=dsname, overwrite = TRUE)\n       return(TRUE)\n      }                                                                                                                                                                                                                                                                                                                                                                                                                                                                     <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>scoreData              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    ore.load(dsname)\n    res &lt;- data.frame(IS_CHURNER = dat$IS_CHURNER)\n    res$PREDICTION &lt;- predict(rpart.mod.r, dat)\n    res\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>scoreLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    ore.load(dsname)\n    dat$Petal.Length_prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Petal.Length_prediction\", \"Petal.Length\", \"Species\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>scoreNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    ore.load(dsname)\n    dat$Prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Species\", \"Prediction\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Grant read privilege to the MyLM_function1 script to user OMLUSER2","message":["%r","","ore.grant(name=\"MyLM_function1\", type=\"rqscript\", \"OMLUSER2\")"],"enabled":true,"result":{"startTime":1715778979727,"interpreter":"r.low","endTime":1715778979846,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"List the scripts which read privilege has been granted","message":["%r","","ore.scriptList(type=\"grant\")"],"enabled":true,"result":{"startTime":1715778979923,"interpreter":"r.low","endTime":1715778980107,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>GRANTEE<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>MyLM_function1<\/td><td>OMLUSER2<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Revoke the read privilege to the MyLM_function1 script from the user OMLUSER2","message":["%r","","ore.revoke(name=\"MyLM_function1\", type=\"rqscript\", user=\"OMLUSER2\")"],"enabled":true,"result":{"startTime":1715778980187,"interpreter":"r.low","endTime":1715778980309,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Invoke function using embedded R execution","","We'll use embedded R execution to invoke this function. First we get a proxy object to the IRIS table. then we use ***tableApply***, providing the proxy object and function name. We'll view the result and then pull the coefficients."],"enabled":true,"result":{"startTime":1715778980384,"interpreter":"md.low","endTime":1715778980443,"results":[{"message":"<h3 id=\"invoke-function-using-embedded-r-execution\">Invoke function using embedded R execution<\/h3>\n<p>We'll use embedded R execution to invoke this function. First we get a proxy object to the IRIS table. then we use <em><strong>tableApply<\/strong><\/em>, providing the proxy object and function name. We'll view the result and then pull the coefficients.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get IRIS proxy object","message":["%r","","ore.sync(table=\"IRIS_R\")","","IRIS <- IRIS_R"],"enabled":true,"result":{"startTime":1715778980526,"interpreter":"r.low","endTime":1715778983228,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use tableApply to provide full IRIS data set to function","message":["%r","","res <- ore.tableApply(IRIS, FUN.NAME=\"MyLM_function1\")"],"enabled":true,"result":{"startTime":1715778983306,"interpreter":"r.low","endTime":1715778983627,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show model coefficients","message":["%r","","res_df <- data.frame(attribute = names(res$coefficients), coefficients = res$coefficients)","","z.show(res_df)"],"enabled":true,"result":{"startTime":1715778983700,"interpreter":"r.low","endTime":1715778983819,"results":[{"message":"attribute\tcoefficients\n(Intercept)\t2.17126629215508\nSepal.Width\t0.495888938388551\nPetal.Length\t0.829243912234805\nPetal.Width\t-0.315155173326472\nSpeciesversicolor\t-0.723561957780729\nSpeciesvirginica\t-1.02349781449083\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Storing functions as 'global'","","Let's define and save another function ***build_lm2***, but this time as global. We'll then invoke that function to build another model."],"enabled":true,"result":{"startTime":1715778983897,"interpreter":"md.low","endTime":1715778983957,"results":[{"message":"<h3 id=\"storing-functions-as-global\">Storing functions as 'global'<\/h3>\n<p>Let's define and save another function <em><strong>build_lm2<\/strong><\/em>, but this time as global. We'll then invoke that function to build another model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","build_lm2 <- function(dat){","  ","  regr <- lm(Petal.Width  ~ Petal.Length, data=dat)","  ","  return(regr)","}"],"enabled":true,"result":{"startTime":1715778984037,"interpreter":"r.low","endTime":1715778984157,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Store function as a global script","message":["%r","","ore.scriptCreate(\"MyGlobalLM_function\", FUN=build_lm2, overwrite=TRUE, global=TRUE)"],"enabled":true,"result":{"startTime":1715778984231,"interpreter":"r.low","endTime":1715778984372,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Invoke tableApply on the global script","message":["%r","","res <- ore.tableApply(IRIS, FUN.NAME=\"MyGlobalLM_function\")","","res"],"enabled":true,"result":{"startTime":1715778984448,"interpreter":"r.low","endTime":1715778984569,"results":[{"message":"\nCall:\nlm(formula = Petal.Width ~ Petal.Length, data = dat)\n\nCoefficients:\n (Intercept)  Petal.Length  \n     -0.3631        0.4158  \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List scripts available to the current user","message":["%r","","ore.scriptList()"],"enabled":true,"result":{"startTime":1715778984646,"interpreter":"r.low","endTime":1715778984831,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 16 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td><span style=white-space:pre-wrap>MyLM_function1         <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    regr &lt;- lm(Sepal.Length ~ ., data = dat)\n    return(regr)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots          <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    print(xyplot(y ~ x, main = \"Random Red Dots\", pch = 19, col = \"red\"))\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    print(xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\"))\n}                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots.2        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    fig1 &lt;- xyplot(y ~ x, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    fig2 &lt;- xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig2)\n}                                                                                                                                                                                                                                    <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots.3        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    fig1 &lt;- xyplot(sample(num_dots_1) ~ sample(num_dots_1), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    fig2 &lt;- xyplot(sample(num_dots_2) ~ sample(num_dots_2), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    print(fig2)\n}                                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RandomRedDots2         <\/span><\/td><td><span style=white-space:pre-wrap>function(num_dots_1=100, num_dots_2=10){\n            id &lt;- 1:10\n            plot( 1:num_dots_1, rnorm(num_dots_1), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            plot( 1:num_dots_2, rnorm(num_dots_2), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            data.frame(id=id, val=id / 100)\n            }                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>build.lmRestR          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- lm(Petal.Length ~ ., dat)\n    res &lt;- summary(mod)\n    res.list &lt;- list(Coef = res$coefficients, R2 = res$r.squared)\n    res.list\n}                                                                                                                                                                                                                                                                                                                                                                                                               <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    regr &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    x &lt;- dat[[\"Petal.Width\"]]\n    y &lt;- dat[[\"Petal.Length\"]]\n    xyplot(y ~ x, xlab = \"Petal Width\", ylab = \"Petal Length\", panel = function(x, y) {\n        panel.dotplot(x, y)\n        panel.abline(lm(y ~ x))\n    }, xlim = c(0, 2.7), ylim = c(0, 7.5), main = \"Prediction of Petal Length\")\n    return(regr)\n}                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildLM.2              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    ore.save(mod, name = dsname, overwrite = TRUE)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                       <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildLM.group          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>buildNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- naiveBayes(Species ~ ., dat)\n    ore.save(mod, name = dsname)\n    TRUE\n}                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>group.countR           <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    x &lt;- aggregate(df[, \"Species\"], by = list(df$Species), FUN = length)\n    names(x) &lt;- c(\"SPECIES\", \"COUNT\")\n    return(x)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>multi.forecast         <\/span><\/td><td><span style=white-space:pre-wrap>function () \n{\n    library(forecast)\n    library(ggplot2)\n    data(\"AirPassengers\")\n    air.train &lt;- window(AirPassengers, end = 1956 + 11/12)\n    air.test &lt;- window(AirPassengers, start = 1957)\n    n.test &lt;- length(air.test)\n    air.model &lt;- auto.arima(air.train)\n    accuracy(air.model)\n    air.multi.forecast &lt;- air.model %&gt;% forecast(h = n.test)\n    air.multi.forecast %&gt;% autoplot()\n    res &lt;- accuracy(air.multi.forecast, x = air.test)\n    print(air.multi.forecast %&gt;% autoplot() + geom_line(aes(x = as.numeric(time(air.test)), y = as.numeric(air.test)), col = \"red\"))\n    return(res)\n}<\/span><\/td><\/tr>\n\t<tr><td>myLinearRegressionModel<\/td><td><span style=white-space:pre-wrap>function(dat, dsname){\n       mod &lt;- lm(Petal.Length~Petal.Width, dat)\n       ore.save(mod, name=dsname, overwrite = TRUE)\n       return(TRUE)\n      }                                                                                                                                                                                                                                                                                                                                                                                                                                                                     <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>scoreData              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    ore.load(dsname)\n    res &lt;- data.frame(IS_CHURNER = dat$IS_CHURNER)\n    res$PREDICTION &lt;- predict(rpart.mod.r, dat)\n    res\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>scoreLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    ore.load(dsname)\n    dat$Petal.Length_prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Petal.Length_prediction\", \"Petal.Length\", \"Species\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>scoreNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    ore.load(dsname)\n    dat$Prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Species\", \"Prediction\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"OWNER\",\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List all scripts available to the current user","message":["%r","","ore.scriptList(type='all')"],"enabled":true,"result":{"startTime":1715778984914,"interpreter":"r.low","endTime":1715778985099,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 33 x 3<\/caption>\n<thead>\n\t<tr><th scope=col>OWNER<\/th><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>MyLM_function1         <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    regr &lt;- lm(Sepal.Length ~ ., data = dat)\n    return(regr)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots          <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    print(xyplot(y ~ x, main = \"Random Red Dots\", pch = 19, col = \"red\"))\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    print(xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\"))\n}                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots.2        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    fig1 &lt;- xyplot(y ~ x, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    fig2 &lt;- xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig2)\n}                                                                                                                                                                                                                                    <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots.3        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    fig1 &lt;- xyplot(sample(num_dots_1) ~ sample(num_dots_1), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    fig2 &lt;- xyplot(sample(num_dots_2) ~ sample(num_dots_2), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    print(fig2)\n}                                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots2         <\/span><\/td><td><span style=white-space:pre-wrap>function(num_dots_1=100, num_dots_2=10){\n            id &lt;- 1:10\n            plot( 1:num_dots_1, rnorm(num_dots_1), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            plot( 1:num_dots_2, rnorm(num_dots_2), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            data.frame(id=id, val=id / 100)\n            }                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>build.lmRestR          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- lm(Petal.Length ~ ., dat)\n    res &lt;- summary(mod)\n    res.list &lt;- list(Coef = res$coefficients, R2 = res$r.squared)\n    res.list\n}                                                                                                                                                                                                                                                                                                                                                                                                               <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    regr &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    x &lt;- dat[[\"Petal.Width\"]]\n    y &lt;- dat[[\"Petal.Length\"]]\n    xyplot(y ~ x, xlab = \"Petal Width\", ylab = \"Petal Length\", panel = function(x, y) {\n        panel.dotplot(x, y)\n        panel.abline(lm(y ~ x))\n    }, xlim = c(0, 2.7), ylim = c(0, 7.5), main = \"Prediction of Petal Length\")\n    return(regr)\n}                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildLM.2              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    ore.save(mod, name = dsname, overwrite = TRUE)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                       <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildLM.group          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- naiveBayes(Species ~ ., dat)\n    ore.save(mod, name = dsname)\n    TRUE\n}                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>group.countR           <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    x &lt;- aggregate(df[, \"Species\"], by = list(df$Species), FUN = length)\n    names(x) &lt;- c(\"SPECIES\", \"COUNT\")\n    return(x)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>multi.forecast         <\/span><\/td><td><span style=white-space:pre-wrap>function () \n{\n    library(forecast)\n    library(ggplot2)\n    data(\"AirPassengers\")\n    air.train &lt;- window(AirPassengers, end = 1956 + 11/12)\n    air.test &lt;- window(AirPassengers, start = 1957)\n    n.test &lt;- length(air.test)\n    air.model &lt;- auto.arima(air.train)\n    accuracy(air.model)\n    air.multi.forecast &lt;- air.model %&gt;% forecast(h = n.test)\n    air.multi.forecast %&gt;% autoplot()\n    res &lt;- accuracy(air.multi.forecast, x = air.test)\n    print(air.multi.forecast %&gt;% autoplot() + geom_line(aes(x = as.numeric(time(air.test)), y = as.numeric(air.test)), col = \"red\"))\n    return(res)\n}<\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td>myLinearRegressionModel<\/td><td><span style=white-space:pre-wrap>function(dat, dsname){\n       mod &lt;- lm(Petal.Length~Petal.Width, dat)\n       ore.save(mod, name=dsname, overwrite = TRUE)\n       return(TRUE)\n      }                                                                                                                                                                                                                                                                                                                                                                                                                                                                     <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>scoreData              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    ore.load(dsname)\n    res &lt;- data.frame(IS_CHURNER = dat$IS_CHURNER)\n    res$PREDICTION &lt;- predict(rpart.mod.r, dat)\n    res\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>scoreLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    ore.load(dsname)\n    dat$Petal.Length_prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Petal.Length_prediction\", \"Petal.Length\", \"Species\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>scoreNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    ore.load(dsname)\n    dat$Prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Species\", \"Prediction\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>MyGlobalLM_function    <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    regr &lt;- lm(Petal.Width ~ Petal.Length, data = dat)\n    return(regr)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQ$R.Version           <\/span><\/td><td><span style=white-space:pre-wrap>function()\n{\n  v &lt;- as.character(R.Version())\n  v[v == \"\"] &lt;- NA_character_\n  data.frame(name=names(R.Version()), value=unname(v),\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                <\/span><\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQ$getRversion         <\/td><td>function()\n{\n  data.frame(Version=as.character(getRversion()),\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      <\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQ$installed.packages  <\/td><td>function()\n{\n  data.frame(installed.packages()[,c(1L,3L,2L),drop=FALSE],\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            <\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQ$packageVersion      <\/td><td>function(pkg)\n{\n  data.frame(Version=as.character(packageVersion(pkg=pkg)),\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQG$boxplot            <\/td><td>function(x, ...)\n{\n  boxplot(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$cdplot             <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$cdplot requires 2 columns to produce graphic\")\n  x[[2L]] &lt;- as.factor(x[[2L]])\n  cdplot(x[[1L]], x[[2L]], ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                          <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$hist               <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (is.data.frame(x))\n    x &lt;- x[[1L]]\n  hist(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <\/span><\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQG$matplot            <\/td><td>function(x, ...)\n{\n  matplot(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$pairs              <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$pairs requires at least 2 columns to produce graphic\")\n  pairs(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$plot1d             <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (is.data.frame(x))\n    x &lt;- x[[1L]]\n  if (is.character(x))\n    x &lt;- as.factor(x)\n  plot(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                            <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$plot2d             <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$plot2d requires 2 columns to produce graphic\")\n  x &lt;- x[1:2]\n  if (is.character(x[[1L]]))\n    x[[1L]] &lt;- as.factor(x[[1L]])\n  if (is.character(x[[2L]]))\n    x[[2L]] &lt;- as.factor(x[[2L]])\n  plot(x[1:2], ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                      <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$smoothScatter      <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$smoothScatter requires 2 columns to produce graphic\")\n  x &lt;- x[1:2]\n  if (is.character(x[[1L]]))\n    x[[1L]] &lt;- as.factor(x[[1L]])\n  if (is.character(x[[2L]]))\n    x[[2L]] &lt;- as.factor(x[[2L]])\n  smoothScatter(x[1:2], ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                      <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>build.glm.mod          <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    glm.mod &lt;- ore.odmGLM(Petal.Length ~ ., IRIS_R)\n    summary(glm.mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>build.lm.mod.2         <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    lm.mod &lt;- lm(Petal.Length ~ Petal.Width, df)\n    lm.mod\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>build.lm.mod.3         <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    lm.mod &lt;- lm(Petal.Length ~ Petal.Width, df)\n    lm.mod\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>group.count            <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    x &lt;- data.frame(table(dat$species))\n    names(x) &lt;- c(\"species\", \"count\")\n    x\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List scripts available to all users","message":["%r","","ore.scriptList(type=\"global\")"],"enabled":true,"result":{"startTime":1715778985181,"interpreter":"r.low","endTime":1715778985305,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 5 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>MyGlobalLM_function<\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    regr &lt;- lm(Petal.Width ~ Petal.Length, data = dat)\n    return(regr)\n}             <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>build.glm.mod      <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    glm.mod &lt;- ore.odmGLM(Petal.Length ~ ., IRIS_R)\n    summary(glm.mod)\n}             <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>build.lm.mod.2     <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    lm.mod &lt;- lm(Petal.Length ~ Petal.Width, df)\n    lm.mod\n}                          <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>build.lm.mod.3     <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    lm.mod &lt;- lm(Petal.Length ~ Petal.Width, df)\n    lm.mod\n}                          <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>group.count        <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    x &lt;- data.frame(table(dat$species))\n    names(x) &lt;- c(\"species\", \"count\")\n    x\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Loading functions from the script repository","","Load the MyLM_function1 and MyGlobalLM_function scripts, and pull the models to the local R session. Build the models on the IRIS data set and pull the coefficients."],"enabled":true,"result":{"startTime":1715778985397,"interpreter":"md.low","endTime":1715778985456,"results":[{"message":"<h3 id=\"loading-functions-from-the-script-repository\">Loading functions from the script repository<\/h3>\n<p>Load the MyLM_function1 and MyGlobalLM_function scripts, and pull the models to the local R session. Build the models on the IRIS data set and pull the coefficients.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ore.scriptLoad(name=\"MyLM_function1\", newname = \"MYLM\")","ore.scriptLoad(name=\"MyGlobalLM_function\", newname = \"GlobalMYLM\")","","cat(\"MYLM: \\n\")","MYLM(iris)","cat(\"\\n\\nGlobalMYLM: \\n\")","GlobalMYLM(iris)"],"enabled":true,"result":{"startTime":1715778985535,"interpreter":"r.low","endTime":1715778985660,"results":[{"message":"MYLM: \n\nCall:\nlm(formula = Sepal.Length ~ ., data = dat)\n\nCoefficients:\n      (Intercept)        Sepal.Width       Petal.Length        Petal.Width  \n           2.1713             0.4959             0.8292            -0.3152  \nSpeciesversicolor   Speciesvirginica  \n          -0.7236            -1.0235  \n\n\nGlobalMYLM: \n\nCall:\nlm(formula = Petal.Width ~ Petal.Length, data = dat)\n\nCoefficients:\n (Intercept)  Petal.Length  \n     -0.3631        0.4158  \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Drop scripts from repository","","Drop one of the private scripts.","","Drop the global script.","","List the available scripts again."],"enabled":true,"result":{"startTime":1715778985736,"interpreter":"md.low","endTime":1715778985794,"results":[{"message":"<h3 id=\"drop-scripts-from-repository\">Drop scripts from repository<\/h3>\n<p>Drop one of the private scripts.<\/p>\n<p>Drop the global script.<\/p>\n<p>List the available scripts again.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"OWNER\",\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptDrop(\"MyLM_function1\")","ore.scriptDrop(\"MyGlobalLM_function\", global=TRUE)","ore.scriptList(type=\"all\")"],"enabled":true,"result":{"startTime":1715778985875,"interpreter":"r.low","endTime":1715778986059,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 31 x 3<\/caption>\n<thead>\n\t<tr><th scope=col>OWNER<\/th><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots          <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    print(xyplot(y ~ x, main = \"Random Red Dots\", pch = 19, col = \"red\"))\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    print(xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\"))\n}                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots.2        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    x &lt;- sample(num_dots_1)\n    y &lt;- sample(num_dots_1)\n    fig1 &lt;- xyplot(y ~ x, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    x1 &lt;- sample(num_dots_2)\n    y1 &lt;- sample(num_dots_2)\n    fig2 &lt;- xyplot(y1 ~ x1, pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig2)\n}                                                                                                                                                                                                                                    <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots.3        <\/span><\/td><td><span style=white-space:pre-wrap>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n    library(lattice)\n    fig1 &lt;- xyplot(sample(num_dots_1) ~ sample(num_dots_1), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    fig2 &lt;- xyplot(sample(num_dots_2) ~ sample(num_dots_2), pch = 19, col = \"red\", main = \"Random Red Dots\")\n    print(fig1)\n    print(fig2)\n}                                                                                                                                                                                                                                                                                        <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>RandomRedDots2         <\/span><\/td><td><span style=white-space:pre-wrap>function(num_dots_1=100, num_dots_2=10){\n            id &lt;- 1:10\n            plot( 1:num_dots_1, rnorm(num_dots_1), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            plot( 1:num_dots_2, rnorm(num_dots_2), pch = 21, \n                  bg = \"red\", cex = 2, main=\"Random Red Dots\" )\n            data.frame(id=id, val=id / 100)\n            }                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>build.lmRestR          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- lm(Petal.Length ~ ., dat)\n    res &lt;- summary(mod)\n    res.list &lt;- list(Coef = res$coefficients, R2 = res$r.squared)\n    res.list\n}                                                                                                                                                                                                                                                                                                                                                                                                               <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    regr &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    x &lt;- dat[[\"Petal.Width\"]]\n    y &lt;- dat[[\"Petal.Length\"]]\n    xyplot(y ~ x, xlab = \"Petal Width\", ylab = \"Petal Length\", panel = function(x, y) {\n        panel.dotplot(x, y)\n        panel.abline(lm(y ~ x))\n    }, xlim = c(0, 2.7), ylim = c(0, 7.5), main = \"Prediction of Petal Length\")\n    return(regr)\n}                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildLM.2              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    ore.save(mod, name = dsname, overwrite = TRUE)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                       <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildLM.group          <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    return(mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>buildNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    dat$Species &lt;- as.factor(dat$Species)\n    mod &lt;- naiveBayes(Species ~ ., dat)\n    ore.save(mod, name = dsname)\n    TRUE\n}                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>group.countR           <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    x &lt;- aggregate(df[, \"Species\"], by = list(df$Species), FUN = length)\n    names(x) &lt;- c(\"SPECIES\", \"COUNT\")\n    return(x)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>multi.forecast         <\/span><\/td><td><span style=white-space:pre-wrap>function () \n{\n    library(forecast)\n    library(ggplot2)\n    data(\"AirPassengers\")\n    air.train &lt;- window(AirPassengers, end = 1956 + 11/12)\n    air.test &lt;- window(AirPassengers, start = 1957)\n    n.test &lt;- length(air.test)\n    air.model &lt;- auto.arima(air.train)\n    accuracy(air.model)\n    air.multi.forecast &lt;- air.model %&gt;% forecast(h = n.test)\n    air.multi.forecast %&gt;% autoplot()\n    res &lt;- accuracy(air.multi.forecast, x = air.test)\n    print(air.multi.forecast %&gt;% autoplot() + geom_line(aes(x = as.numeric(time(air.test)), y = as.numeric(air.test)), col = \"red\"))\n    return(res)\n}<\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td>myLinearRegressionModel<\/td><td><span style=white-space:pre-wrap>function(dat, dsname){\n       mod &lt;- lm(Petal.Length~Petal.Width, dat)\n       ore.save(mod, name=dsname, overwrite = TRUE)\n       return(TRUE)\n      }                                                                                                                                                                                                                                                                                                                                                                                                                                                                     <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>scoreData              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    ore.load(dsname)\n    res &lt;- data.frame(IS_CHURNER = dat$IS_CHURNER)\n    res$PREDICTION &lt;- predict(rpart.mod.r, dat)\n    res\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>scoreLM.1              <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    ore.load(dsname)\n    dat$Petal.Length_prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Petal.Length_prediction\", \"Petal.Length\", \"Species\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                  <\/span><\/td><\/tr>\n\t<tr><td>OMLUSER<\/td><td><span style=white-space:pre-wrap>scoreNBmodel           <\/span><\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(e1071)\n    ore.load(dsname)\n    dat$Prediction &lt;- predict(mod, newdata = dat)\n    dat[, c(\"Species\", \"Prediction\")]\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                              <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQ$R.Version           <\/span><\/td><td><span style=white-space:pre-wrap>function()\n{\n  v &lt;- as.character(R.Version())\n  v[v == \"\"] &lt;- NA_character_\n  data.frame(name=names(R.Version()), value=unname(v),\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                <\/span><\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQ$getRversion         <\/td><td>function()\n{\n  data.frame(Version=as.character(getRversion()),\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      <\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQ$installed.packages  <\/td><td>function()\n{\n  data.frame(installed.packages()[,c(1L,3L,2L),drop=FALSE],\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            <\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQ$packageVersion      <\/td><td>function(pkg)\n{\n  data.frame(Version=as.character(packageVersion(pkg=pkg)),\n             stringsAsFactors=FALSE)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQG$boxplot            <\/td><td>function(x, ...)\n{\n  boxplot(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$cdplot             <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$cdplot requires 2 columns to produce graphic\")\n  x[[2L]] &lt;- as.factor(x[[2L]])\n  cdplot(x[[1L]], x[[2L]], ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                          <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$hist               <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (is.data.frame(x))\n    x &lt;- x[[1L]]\n  hist(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <\/span><\/td><\/tr>\n\t<tr><td>RQSYS  <\/td><td>RQG$matplot            <\/td><td>function(x, ...)\n{\n  matplot(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$pairs              <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$pairs requires at least 2 columns to produce graphic\")\n  pairs(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$plot1d             <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (is.data.frame(x))\n    x &lt;- x[[1L]]\n  if (is.character(x))\n    x &lt;- as.factor(x)\n  plot(x, ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                            <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$plot2d             <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$plot2d requires 2 columns to produce graphic\")\n  x &lt;- x[1:2]\n  if (is.character(x[[1L]]))\n    x[[1L]] &lt;- as.factor(x[[1L]])\n  if (is.character(x[[2L]]))\n    x[[2L]] &lt;- as.factor(x[[2L]])\n  plot(x[1:2], ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                                      <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>RQG$smoothScatter      <\/span><\/td><td><span style=white-space:pre-wrap>function(x, ...)\n{\n  if (NCOL(x) &lt; 2L)\n    stop(\"script RQG$smoothScatter requires 2 columns to produce graphic\")\n  x &lt;- x[1:2]\n  if (is.character(x[[1L]]))\n    x[[1L]] &lt;- as.factor(x[[1L]])\n  if (is.character(x[[2L]]))\n    x[[2L]] &lt;- as.factor(x[[2L]])\n  smoothScatter(x[1:2], ...)\n  invisible(NULL)\n}                                                                                                                                                                                                                                                                                                      <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>build.glm.mod          <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    glm.mod &lt;- ore.odmGLM(Petal.Length ~ ., IRIS_R)\n    summary(glm.mod)\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>build.lm.mod.2         <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    lm.mod &lt;- lm(Petal.Length ~ Petal.Width, df)\n    lm.mod\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>build.lm.mod.3         <\/span><\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    lm.mod &lt;- lm(Petal.Length ~ Petal.Width, df)\n    lm.mod\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <\/span><\/td><\/tr>\n\t<tr><td><span style=white-space:pre-wrap>RQSYS  <\/span><\/td><td><span style=white-space:pre-wrap>group.count            <\/span><\/td><td><span style=white-space:pre-wrap>function (dat) \n{\n    x &lt;- data.frame(table(dat$species))\n    names(x) &lt;- c(\"species\", \"count\")\n    x\n}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715778986138,"interpreter":"md.low","endTime":1715778986196,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715778986285,"interpreter":"md.low","endTime":1715778986344,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":false,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R -4- Embedded R Execution","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737478963084,"interpreter":"md.medium","endTime":1737478963596,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for R - Embedded R Execution","","***Oracle Machine Learning for R*** (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight using OML4R embedded R execution. We first build a linear model in R directly, then create user-define R function to build the linear model, save the function to the script repository and then score data in parallel using R engines spawned by the Autonomous Database environment. We then show the invokation of these scripts using the SQL interface with embedded R execution. ","","To use the SQL API for embedded R execution, a user-defined R function must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account `USERNAME`, `PASSWORD`, and `URL` must be provided to obtain an authentication token.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737478964038,"interpreter":"md.medium","endTime":1737478964500,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-r---embedded-r-execution\">Oracle Machine Learning for R - Embedded R Execution<\/h1>\n<p><em><strong>Oracle Machine Learning for R<\/strong><\/em> (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight using OML4R embedded R execution. We first build a linear model in R directly, then create user-define R function to build the linear model, save the function to the script repository and then score data in parallel using R engines spawned by the Autonomous Database environment. We then show the invokation of these scripts using the SQL interface with embedded R execution.<\/p>\n<p>To use the SQL API for embedded R execution, a user-defined R function must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account <code>USERNAME<\/code>, <code>PASSWORD<\/code>, and <code>URL<\/code> must be provided to obtain an authentication token.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Import libraries supporting OML4R and create a data table","","To use OML4R, first load the ***ORE*** library.","","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1737478964958,"interpreter":"md.medium","endTime":1737478965415,"results":[{"message":"<h2 id=\"import-libraries-supporting-oml4r-and-create-a-data-table\">Import libraries supporting OML4R and create a data table<\/h2>\n<p>To use OML4R, first load the <em><strong>ORE<\/strong><\/em> library.<\/p>\n<p>Users can get online help using R's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries","message":["%r","","library(ORE)","library(lattice)","","options(warn=-1)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737478965976,"interpreter":"r.medium","endTime":1737478971030,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object for the IRIS_R table","message":["%r","","ore.sync(table = 'IRIS_R')","IRIS <- IRIS_R","z.show(head(IRIS))"],"enabled":true,"result":{"startTime":1737478971479,"interpreter":"r.medium","endTime":1737478974427,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n4.6\t3.6\t1\t0.2\tsetosa\n4.3\t3\t1.1\t0.1\tsetosa\n5\t3.2\t1.2\t0.2\tsetosa\n5.8\t4\t1.2\t0.2\tsetosa\n4.4\t3\t1.3\t0.2\tsetosa\n4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build and score a linear model directly in R","","A best practice with embedded R execution is to validate your R script, and then your user-defined function before invoking the embedded R APIs. The next few paragraphs exercise the functionality we want.","","We will create a linear model and pull the data to our local environment to invoke the base R linear model function. "],"enabled":true,"result":{"startTime":1737478974873,"interpreter":"md.medium","endTime":1737478975304,"results":[{"message":"<h2 id=\"build-and-score-a-linear-model-directly-in-r\">Build and score a linear model directly in R<\/h2>\n<p>A best practice with embedded R execution is to validate your R script, and then your user-defined function before invoking the embedded R APIs. The next few paragraphs exercise the functionality we want.<\/p>\n<p>We will create a linear model and pull the data to our local environment to invoke the base R linear model function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","iris_df <- ore.pull(IRIS)","","lm_mod <- lm(Petal.Length~Petal.Width, iris_df)"],"enabled":true,"result":{"startTime":1737478975740,"interpreter":"r.medium","endTime":1737478976285,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","prediction <- predict(lm_mod,iris_df)","RES_DF <- cbind(prediction,iris_df)","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1737478976755,"interpreter":"r.medium","endTime":1737478977262,"results":[{"message":"prediction\tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n1.52954613187489\t4.6\t3.6\t1\t0.2\tsetosa\n1.3065520823627\t4.3\t3\t1.1\t0.1\tsetosa\n1.52954613187489\t5\t3.2\t1.2\t0.2\tsetosa\n1.52954613187489\t5.8\t4\t1.2\t0.2\tsetosa\n1.52954613187489\t4.4\t3\t1.3\t0.2\tsetosa\n1.52954613187489\t4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Model quality assessment","","Assess model quality using mean squared error and R^2, and generate a scatterplot of the data along with a plot of the regression line."],"enabled":true,"result":{"startTime":1737478977700,"interpreter":"md.medium","endTime":1737478978176,"results":[{"message":"<h2 id=\"model-quality-assessment\">Model quality assessment<\/h2>\n<p>Assess model quality using mean squared error and R^2, and generate a scatterplot of the data along with a plot of the regression line.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","summary(lm_mod)"],"enabled":true,"result":{"startTime":1737478978648,"interpreter":"r.medium","endTime":1737478979154,"results":[{"message":"\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = iris_df)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-1.33542 -0.30347 -0.02955  0.25776  1.39453 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept)  1.08356    0.07297   14.85   <2e-16 ***\nPetal.Width  2.22994    0.05140   43.39   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 0.4782 on 148 degrees of freedom\nMultiple R-squared:  0.9271,\tAdjusted R-squared:  0.9266 \nF-statistic:  1882 on 1 and 148 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","","","x <- ore.pull(RES_DF[['Petal.Width']])","y <- ore.pull(RES_DF[['Petal.Length']])","","xyplot(y~x, xlab = \"Petal Width\", ylab = \"Petal Length\", ","  panel = function(x,y) {","  panel.dotplot(x,y)","  panel.abline(lm(y ~ x))","  }, ","  xlim=c(0,2.7), ","  ylim=c(0,7.5),","  main=\"Prediction of Petal Length\")"," "],"enabled":true,"result":{"startTime":1737478979637,"interpreter":"r.medium","endTime":1737478980272,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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BtaoUUPsikQgtUiXiRdPEBFRaTe5JVxsBKZ/0xlM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Build this same model using embedded R execution","","Using embedded R execution, let's build the same linear model, but with spawned R engines under control of the Autonomous Database environment. First, we'll define our function, ***buildLM.1***, that returns the model as the result. We refer to this as a \"user-defined function.\" As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded R execution can return images as well as structured content in the R API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.","","We run this function to ensure it returns what we expect. In this case, both an image and a model."],"enabled":true,"result":{"startTime":1737478980729,"interpreter":"md.medium","endTime":1737478981140,"results":[{"message":"<h2 id=\"build-this-same-model-using-embedded-r-execution\">Build this same model using embedded R execution<\/h2>\n<p>Using embedded R execution, let's build the same linear model, but with spawned R engines under control of the Autonomous Database environment. First, we'll define our function, <em><strong>buildLM.1<\/strong><\/em>, that returns the model as the result. We refer to this as a &quot;user-defined function.&quot; As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded R execution can return images as well as structured content in the R API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.<\/p>\n<p>We run this function to ensure it returns what we expect. In this case, both an image and a model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.1 <- function(dat){","  library(lattice)","  ","  regr <- lm(Petal.Length~Petal.Width, dat)","","  x <- dat[['Petal.Width']]","  y <- dat[['Petal.Length']]","","  print(xyplot(y~x, xlab = \"Petal Width\", ylab = \"Petal Length\", ","    panel = function(x,y) {","    panel.dotplot(x,y)","    panel.abline(lm(y ~ x))","    }, ","    xlim=c(0,2.7), ","    ylim=c(0,7.5),","    main=\"Prediction of Petal Length\"))","    ","    ","    return(regr)","}"],"enabled":true,"result":{"startTime":1737478981590,"interpreter":"r.medium","endTime":1737478982077,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"image\":{\"height\":700,\"lastColumns\":[]}},{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","buildLM.1(iris_df)"],"enabled":true,"result":{"startTime":1737478982515,"interpreter":"r.medium","endTime":1737478983073,"results":[{"message":"\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n      1.084        2.230  \n\n","type":"TEXT"},{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nLiSFq0NSu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xKoPVfeHXRLyaJEmv1y9d\nurRbt27Jycnbtm1btWqVhYWF2EXRW+R/KJaIiCQiIhYH7uBeHKrYo0sNtKggdkHZNCiP0NG4\nH4+bsXC1QUMX5H3eXSn14sULPz+/AwcONGjQICgoqEGDBmJXRAIY7IiIyBT+dxxzjkOX7TqE\n8Z74sZe0jnVWd0B1B7GLkKTQ0FAfH58HDx74+fmtXLnS2tpa7IpIGA/FEhFRsfs9ArOOvZXq\nACwPxU/nRSqICizz8Gu7du2ePXu2Zs2agIAApjopk/+IHRsUExGJ7scQC8GhhJ/O6Uc3STN9\nPVRAiYmJ48aN27FjR506dTZv3tyoUSPuUrNIrYNdJvkHOzYoJiIS3fUYV8Hpd14qol/EqXj0\nSJKuXbvm7+//4MGDAQMGLFiwwMbGhvvT7NigWBxsUExEJDpLc+FT6cyVcHZyVErqPDsCAGze\nvHnixIk6nW7hwoUTJkwQuxwpYoNicbBBMRGR6NpURtANgelelWBdhl/R0pKcnOzv779169aq\nVatu3769ZcuWYlckUVKLdJk4/E1ERMXuy/cFuocoFZjVUYRiKB83b95s3br11q1bP/zww7Cw\nMKa6EofBjoiIil0zN+wdispl30wpb42tH6Mb71kgJZs2bfLw8Lh58+b8+fN37drFE5lKIvkf\niiUiIinoVgN3J+Pqc9yNQ1V7NHZFGe6CJEOtVk+bNm3ZsmXvvffe9u3b27RpI3ZFZCD+VRER\nkYmolGheAc2ldMMJAnDnzh1vb+/Lly936dJl8+bNrq7ClzBTicBDsURERKXXrl27vLy8rl27\nNmvWrMOHDzPVlXQcsSMiIiqNMjIyvv766wULFri4uBw4cKBbt25iV0RGwGBHRERU6jx8+HDQ\noEEhISEdOnTYunVrhQo8QC4TPBRLRERUuvzxxx/NmjU7d+7cpEmTjhw5wlQnJwx2REREpYVG\no5k9e3a/fv0UCsW+ffuWLl1qbs5jd7LC1UlERFQqREVFDR48+NSpU56enoGBgdWqVRO7IjI+\njtgRERHJ39GjRz08PE6fPj1p0qRTp04x1ckVR+yIiKikOvMIF54gJQNNXNG1Bixy3bWMAOj1\n+u+//37GjBk2Njbbt2/39vbOnP73SxyPRFQi6jqjS3U4W4tbZk5RSTh6H/fiUM0Bnaq/ddsS\nygeDHRERlTxxagwMxF/330yp6Ygdg9CEXdjeFhMT4+fnd+jQoWbNmgUFBdWsWROAHpgajMVn\nodO/fpqNCqv6wrexmKVmt+wcvghGuvb1Q5US33TG1Lai1lRC8FAsERGVPKQPJIkAACAASURB\nVH473kp1AO7GofcWJKeLVJAknTx5smnTpocOHfLz8zt9+nRmqgOw7BwWnXmT6gC8ysDwnQh5\nLE6dOey9jckH36Q6ABk6TDuC7dfFq6nkkP+IXUZGhlqtFrsKIiIymtsvFfvuWOae/jgRv4Zn\nfNpEm3tWaaPX65cvX/7VV19ZWlpu3LjRx8cHQObeUA/83xlLQJHjJVo9Fp/RBnyYIUK5b1t4\nykJw4GnRaV2/mhJK7hkZ4v+ucpN/sEtOTo6LixO7CiIiMpqz98sAAsEOQOjD9H6VE01cj9Qk\nJiZ+/vnn+/fvr1Wr1po1a+rWrZt9PxifpnyUKHzEOuypXgp7zMvPhcu78lzxMi4uZyAVT3Jy\nMqQX7+Qf7GxtbR0dHcWugoiIjMbONs+rJKzLWJby7/ywsLChQ4c+ePBg6NChy5Yts7GxyfEE\nZVqe0cjC3EwKvz0zpXCFZkqFkwTKy2JrawtApVKJXchb5B/sVCqVlZWV2FUQEZHRtKqS5yyv\nyuZWVvLfteVl9erVEydOVCqVS5YsmTx5suBzrKxQ0xF3hQbmWlRUSGGP6VERwfcEpjevACmU\nl0VqkS4TL54gIqISpoYjBjUUmF7LCd4NTF6NNCQlJQ0ePHjMmDHVqlULCQnJK9Vl+vJ9gYkW\nZpjSprjKK5Rp7ZB7zE6RR9mUg7SC3ZUrV7y9vV1cXOzs7Lp27XrixAmxKyIiIila1w8f139r\nSlM3HPBF6RytCw8Pb968+fbt2z/66KNz5865u7vn//yRzfBdF1hmO6DtbI3ffeAujWYxXapj\nQz/YWbyZktmNpXdt8WoqOST0F3Do0KH+/fv/+9//Xrx4saura2ho6KJFi9q3by92XUREZBwv\nU3EiEvfiUMUe7arAzdbwRdmo8JsPImJx6SlepaOJK7wqCQzzlAYBAQHjxo3TarX5HH7N7ct2\n+KwZzjxCVCLqlEPbKrCR0nHFT9zRrirWX8KNGNRzxqdNUaec2DWVEFIJdvHx8cOGDRs9evR3\n332XOaVt27Zt27IXIRGRTARcxvj9b/rMWZrhuy74vHWRllnfGfWdi15aSZWamjpp0qS1a9dW\nqVJl+/btrVq1KtTLXW3wUb1iKq2o1l7C5INI+ed60x9CsLAbJniJWlMJIZVDsZs2bYqNjZ00\naZLYhRARkfEde4ARu9/qHpymxZTDbDlruFu3brVs2XLt2rV9+/YNCwsrbKqTskN34b/3TaoD\noNZg4gHsuileTSWHVILd8ePHHRwcLly40KxZM0tLS2dn5yFDhkRGRopdFxERGcHCt+9zkGXB\nKZOXIgubN2/28PCIiIiYNWvWrl27nJycxK7ImL4/DaGNBd+fNnUlJZFUDsU+fvw4KSlp7Nix\n69at6969+7Vr1z755JM2bdqEh4eXL18+r1dptdr9+/fndWOJs2fPAkhLS+OdJ4iIxHXxicCt\nDgBcfo7kFLW5VAYZSoC0tLQZM2YsX768YsWKu3fvbtOmTXq6hG7GYBR5bS2XniJVrZbOiZRp\naWkAQkJC8nqClZVVr169zMzybLtYHKQS7HQ6nVar/fLLLz/++GMArVu3Xr16defOnZcvXz57\n9uy8XnX06NEPP/ww/yWnpKRIoY82EVGppncV3FUrgPj4ODPp7Kul7fHjx2PGjAkLC2vbtu3y\n5ctdXFzkuYPLY2sB9PFS+rwpKSkADhw4sHjx4ryeExwc3LVrVxMWJZlg5+zsDKBDhw5ZU9q1\na2dmZhYaGprPqzp16rRnz558Rux++OEHa2trKfTRJiIqzZq64ZBQy9mG5XXOTvyKLpC9e/f6\n+/snJCRMmTJlzpw5Jh4HMqWmbjj+UGC6u6teUjt0a2trAD179pwzZ47gE6ysrDp16mTaoiQT\n7Nzd3Q8cOJB7ukKR3//jzMzM+vbtm88TfvjhB0tLS0k1qiYiKoWmtkPwfYHT7Ka1U/Ir+p00\nGs1///vf77//3tnZ+cCBAz169BC7ouI17X2c2Cxwmp3UthZLS0sArVq18vb2FruWN6RyXoOP\njw+A7B2JT58+rdVqW7cu2qXwREQkAZ2rY+2HsM7WKc3CDAu6Ymhj8WoqIR49etShQ4cFCxa8\n//774eHhsk91AHrWworeKJNt6MnSDEs+wID6eb+G/iGVEbtmzZplNrGrXbt2t27drl+/PmbM\nmOrVq48fP17s0oiIyAhGNEXfOjj24HWD4veropKd2DUVs8eJOB+FF6mo54zW78GAa0T+/PPP\noUOHxsTETJo0adGiRca9OWlMCs49RlQSajmhTeW3gpToxnpgQH0cj8T9OFR1QPuqqFCEdtal\nioRW488//1yjRo1p06YNGjTI0dGxd+/e3333nYODg9h1ERGRcThbY2DpuJerVo//HMaP56D9\n54BiTUdsGwiPigVeglY7d+7cuXPn2tnZ/fbbbwMGDDBuhQvPYOZRqDWvH7raYEN/9Kxl3Dcp\nEheb0nvn36KQULAzMzObPn369OnTxS6EiIioSOYcw5K3m2DcjUOPX3FjPFxt3v3y6OhoX1/f\nI0eOeHh4BAYGVq9e3bjl/RKOqcFvTXn+Ch9tQ6g/GrsY963I1KRyjh0REZE8pGqw+KzA9Jep\nWHnh3S8/duxY06ZNjxw54u/vf/r0aaOnOgDfnhSYmKbFojNGfysyNQmN2BWTjIwMNigmIiKT\nCX+ufJVhITjr/GOdWp1nP2G9Xr948eJZs2ZZW1tv2rRp4MCBOp3O6Luw+DTF3y8tBWeFRunV\n6jTjvp2MZWRkvPtJJif/YJecnCzP/o1ERCRJCQkqwFlwVlp6Rl67pJcvX06cOPHo0aMNGzZc\ns2ZNtWrVimnnFZ+mBFwFZ2VotNxjFlxycjKkF+/kH+xsbW0l1c+QiIjkrYUNLM2QphWY1ayi\nmeAu6eLFi76+vpGRkUOHDv3xxx8zO98WEwfgPTv94ySBNrHubgruMQvO1tYWgHEvVS46+Qc7\nlUolqX6GREQkb1ZWGN0CP53POd1ahfEtza2s3trz6vX6ZcuWTZ061czMbM2aNaNGjTJBhf9u\njSmHc05UKvB5GzMrK9ne0MLopBbpMsk/2BEREZnYou6IfoXA62+mlCuDLR+j2tstvBITE0eO\nHPnbb7/VrVs3KCiocWMT9Wv+d2s8SsTSkDd3d7BW4efeaFPZNO9PxYjBjohIPpLTcfEpHiWg\ndjk0c4OFxAZf1BqEPcPdl6hijxYVYSPF8Q7jsDTD9oGY2hYhjxGbgvrO+KAWyr59xcKlS5d8\nfHzu3r3r6+u7cuXKzON6+bsWjWvRsLdCMze4FaFhrwL4oQfGtMCJSEQlobYTutUsUB8Wkj4G\nOyIimVgXhimHkPDPRY2V7LChP7rVELWmbPbdwag9eJb8+qGjFZb2hF8TUWsqZi0qoEUF4VkB\nAQFjx47V6XRLliyZPHnyOxd1Nw5Df8f5qNcPzZX4lyf+r7shd7PIUs8Z9YSv8aASjH3siIjk\nYOdNjN7zJtUBiErCh1tx5bl4NWVz4QkGbH+T6gDEqTF8Jw78LV5NIklOTh46dOjw4cNdXFxO\nnDhRkFT3KgPdAt6kOgAaHZadw/QjxVgnlVAMdkREcvC/42/Ol8qi1uD70yIUk9u8U0jPdZWo\nHph7XIxqxBMREdGqVautW7f269cvPDzcy8urIK/adBn34wWmLzuHOPZppbfJ/1AsGxQTkeyl\naXH5mfDl/yGPJdFyNuSRJSDQXyP0CVJS1UqBOTK0efPmyZMnp6WlffPNN59//rlCoSjg7unM\nQxUgcL5khg4hkemdquqMXSkViNQ62GWSf7Bjg2Iikr0UjUIPN8FZaRk6KXwHZmhdBYOdTo/Y\nl3EquR89SktL+/rrr3/99deKFSuuXLnSw8MjPl5oCC4Pr1IdgDKCs+ISkuPixA/upRMbFIuD\nDYqJSPYcgWr2+gcJAsmpsaskWs42csHRSIHpdZz0LuXEL69Y3blzx9fX9+rVqz179ly7dq2T\nk1Nhl9CskvkOoZMRFUDL6jaOdsXYzZjywQbF4mCDYiIqDSa1wueHBKZPbq2Uwnfg5NbCwW5S\nK4UUyis+O3fuHDFiRHJy8qxZs2bOnKlUGjI4OcoDC0OQmGtg7qP6qFle+K6vZAJSi3SZ5D78\nTURUOkxuifGeb00xV2LJB+hRU6SC3tavLuZ3hVm2IUUF8HlrjPUQr6ZilpaWNnny5AEDBlha\nWh48eHD27NmGpToAbrbYOQjl3j4Y274q1n5ohDpJZuQ/YkdEVBooFfipF0a3wF/3Xzco/qAW\nqju8+4UmM60tPq6Pw3fx90tUdUDn6mjsInZNxebhw4eDBg0KCQlp1rrj5O+3VG2QRzu7Autc\nHX9Pwp5buBYNByt4VkLXGkInLRZGqgbXohGViNrlUN8ZUruERaPDtWg8iEcVezR2hexPxDQW\nBjsiIvlwd4W7q9hF5K2WE2oV+gSzkmfv3r3Dhw+Pj49XtZ8W1vHbT/80w5/wqoRNH6FOOcMX\n62CFT9yNVuTGy5hyCC9SXz+sUw4b+6PVe0ZbfhEduovRe/Ao8fXDinb4uTc+rCtqTSUEAzAR\nEZFxaDSa2bNn9+/fP11vpvfdn9F5PpSv25Scj0KXAMRLo/vWzpsYsetNqgNw+wW6b8Jd8a+f\nBoDQJ/hw65tUB+BJEj4OxKmH4tVUcjDYERERGcHjx487duw4Z86c5s1bmI0JRa0Pcj4hEasu\nilJaTjOPCrSzTkqXSjvrb04ItLPW6DCnlLWzNoz8D8WyQTERERW3Y8eODR8+PCYmZvz48UM/\nn992k63g005H6tQt0k1cWw6JabgWLXwl8pmHkmhnffqhcDvrs48gqR261DrYZZJ/sGODYiIi\nKj56vX7FihXz5s2zsbFZtWpVnz59wqLVgHCwS1ZniL5LeqlWAsLBLiVdK3p5ANK1wt2207V4\nGRcnnWs82KBYHGxQTERExSQ2NnbEiBFHjhxp2rTp5s2ba9SoAaC5tUKlRIbQjb4au5mJvkty\nAFys9dEpAgGpoYsk2lnXK6cPfSpQXj1nvZMEysvCBsXiYINiIiIqDidOnBgyZMiTJ0/8/PxW\nrVpVpszrRnNuVhjSGAGXcz5fpcRYL3MrK/H3vP/ywuxjAtMntDSzshK4Ka2JTWiJ4bsEpo/3\nklY7a6lFuky8eIKIiKhw9Hr90qVLu3btmpSUtHXr1oCAgKxUl+mnXuhU7a2XlDHHpgGo72zC\nKvM2430MafTWFDMFFnVHd2m0s/7EHVPb5jzJblJL+LcQp56SRfz/NxARlSDpWkTEIjYFdcvh\nvbJiV1PS6PS4H4+7L1HVATUdYV4yxxZevHjxySef7N+/v379+kFBQQ0bNsz9HDsLHPkEe2/j\nj9uIfgXPShjRFJXsivrWz1/hejTsrVDfGdZFGC0yV2LLxxjrgRORiEpCTUf0rYu6Reixl+l+\nAvbfhoUZetVCpaL9dSzoiiGNcOgu7sehmgO61oBHxaKWV0ow2BERFdTKC5jxF17+0/3r/SpY\n369UdNw1ijOPMHIPbsa+fljVHit6o1dtUWsqvAsXLvj4+Ny/f9/Pz+/nn3+2sbHJ65nLQzHz\n6OvGdXtu4a97WN8P1Qy9F8jTZIzeg313Xj+0tcD0dvjq/SLdfKJ9VbSvWoTXZ/M0Ge+vf6sN\nnrsbToxAWQvDl9nUDU2FL6Kg/JTM/y4REZncujCM2/cm1QE4+RCdNiJOQu0XpOtGDLpvepPq\nAEQmoP82nCxRLWdXr17dtm3bp0+frlq1KiAgIP9UN+nAW+2Ijz5A541IMqjVSboW3Te9SXUA\nktPx37/wzQlDllYcmvycs7nx5WdosUqkako3BjsionfT6fH1XwLTHydiRajJqymBvj2JV7ma\nQmToMOuoGNUUXmJi4qBBg8aMGVOtWrVz5875+/vn8+S8Ptf9eKw2qEHxlqu4Fi0wfd5JA5Oi\ncf14HrEpAtP/fomDd01eTakn/0OxbFBMREV356XiabKl4KwTD3RTPCWwd5W2k5HCLWdPP0Kq\nWi2dzmSCLl++7Ovre/fu3Q8//HD16tX29vb571auRitepApvLcfva8c3K3Tbs2P3VYDAxaqp\nGpx9kN6+ilBjFRPaFWGR1zjR1svajpWk1ebNiKTWwS6T/IMdGxQTUdHFxKkA4V11UqqGXzLv\nlJLuKhjsMrSIeRGnkvDRo6CgoOnTp2u12v/973+jRo3S6XTvXN1G31oSUxyAMoKzYuKT4+xE\nvldEclo5QPhkuriU9Li4eBPXYzJsUCwONigmoqJrbAMLM4H7VwJo6Cp+y1npq+eM048Fpld3\n0LuUk+hvLzU19fPPP//ll18qV67866+/enl5FfCFTcsozBTQ5r4bK9DIzdyAraWRm/nOv4Vn\ntahq42hvXdgFGpe7q9n5p8KzWldRyfivgw2KxcEGxURUdFZWGNIIG3O1nFUqMMZTEj1dJW6s\np3CwG+sprZazWW7evOnt7X3t2rW+fftu3LixUOmkohUGNsD26zmnmyngb9DWMtIDC0Og1uSc\n3q0G6rkKDw2a0pwuWHcZulxB1sIMk9uYW5nLNmlILdJlkvDwNxGRlCzrifervDVFpcSK3myv\nVSDDmmBKa4GJn+eaKAW//vqrh4fHzZs3Z82atWvXLgPGnH7ug1bvvTXFwgxrPkRjF0PqqWqP\nzQNyNq5r7IKNHxmyNKOrYIsVvaF8+0i7mQKBPrCWbaiTLv7KiYgKpKwljn6K3Tdx5hGiX6F+\neXxcH3WK3NO19FjUHd4NsSEcYc9Q3xnD3XPem0EK1Gr1tGnTli1b9t57723btq1t27aGLcfR\nCidH4PcIHL6Lp8nwqgg/d9QswjHJAfXhWQnbr+F6DMpawqsSvBvAQjIjxWNaoHMNzDiCq9FQ\nKtCiIhZ0RQVbscsqlRjsiIgKykyBAfUxoL7YdZRMW65i9F6kZADA+cfYegXzu+HfrcQuK5s7\nd+74+PiEh4d37tx5y5Ytrq6uRVnaxsv46k9EvwKAg3dw8SlW9inSzScql8V/2hSlouJV2xGB\n3mIXQTwUS0REJhB8H8N2vE51mdJ1+PwQ1oeLV9Pbdu3a5eXldfXq1VmzZgUHBxcx1a0Lw6g9\nr1MdAD3wx2103ijQzI/IuBjsiIio2I37A0IXiWLqYVNXkptGo5k+ffqAAQNUKtX+/ftnz56t\nVBZp56jR4as/BabffoG1l4qyYKJ3k/+hWDYoJiISXWSc8KWvL1KRolaLOMbw+PHjYcOGnTt3\nrl27dgEBARUqVCj6LuNajCL6lfDFqn/d1Y5x56idTEitg10m+Qc7NigmIhKdTl8hr1lPYxKs\nzcW5d8KRI0cmTZqUkJAwcuTImTNnqlQqo+wvnr/Ms0FxIttZywgbFIuDDYqJiERnYyF8V1Nz\nJSqVtzd5OdBqtfPmzZs3b56Dg8OOHTs++OADIy68qZVCqRDo6wagvqshDYpJmtigWBxsUExE\nJLqBDbBB6DqJjtVg+q/o6OhoX1/fI0eOeHh4BAYGVq9e3bjLr2SFfnWx82bO6UoFRrVgO2v5\nkFqky8SLJ4iIqNit7YcGzjknVrTD7iGmruTYsWPu7u5Hjhzx9/c/ffq00VNdplV90dTtrSnm\nSvzYE83zPCJNZBzyH7EjIiLRKYHr4/F/Z7H9Oh4nwtUGfepgTieTji7o9frvv/9+xowZNjY2\n27dv9/HxKb73Km+NkFHYehV/3sfzZHhUwtBGaGTQbSeK1YtURCWiphNspDj2RIZgsCMiIhOZ\n0lrgxmKmERsb6+fnd/DgwWbNmgUGBtaqVau433HXTcw5jgfxAHD0Ae7HYVlPlLcu7rctqL/u\nY/JBXIsGAAXQrSZW9C7SvTFIIngoloiIZO78+fMeHh4HDx708/M7deqUCVLdlqsY8tvrVAdA\no8O2a+iyEWpNcb9zgZx8iJ6bX6c6AHrg8F2034Dnr/J9GZUEDHZERCRber1+6dKl7dq1i46O\nXrduXUBAgLV1sQ+aafX4IligIfPVaKwPK+43L5BpwUjX5pz4JAkLT4tRDRmV/A/FskExEVHp\nlJiYOGbMmF27dtWtW3fz5s0NGzY0ze7gRqziSZJwH7vgv7WfNRa57VlKBkIeC1+JfOSuTq0W\naktDQqTWwS6T/IMdGxQTEZVCV69e9ff3j4yM/PjjjxcsWGBtbW2yfcGT2DwbFMeliN+gODZV\nqYdwsEtQ60QvrwRhg2JxsEExEVFps27duilTpigUioULF06YMMHE797EMs8GxXXLi9+guKwD\n7C31CWmK3LNql1OKXl4JwgbF4mCDYiKi0iMpKcnf33/btm21a9cOCgpyd3c3fQ1VrNCzFvbd\nyTldAXwmjQbFn7jjx/MC0z9rruQes+CkFuky8eIJIiKSiYiIiFatWm3btq1///7nz58XJdVl\nWt0X9d5uyKxUYEE3tKwkUkFv+64L2lXJOfFfnhjUSIxqyKjkP2JHREQy9iIVyemoao+AgIBx\n48alp6fPnz9/6tSpCoXAoUaTqWiHC/7YEIazj/EyFfWcMbQxPCuKWNFbbC1wdDh+vYK/7uN+\nPBqWx8cN0K2G2GWRMTDYERFRibT2Er45gcgEQKO2ODwp/fya9ypXDty+vXVrkZogv81GhQle\nmOAldh15CH2ClRcQ+gQ6Pc5HIfoV6jmjclmxy6IiY7AjIqKSZ9EZfBEMAIi9hSCf9OdXUKdP\nk6kbW7d2ErmykiDkMTpn65acrsXOm7j0FBf84SyZe2OQYXiOHRERlTBxasw8CgCI2IG1LRFz\nAx1nYcju/Y+d/rwvcm0lwtRggXtgRCbg/86KUQ0ZlfxH7NigmIhIZo7+rUxV6xE8FeeWwcYF\nw35HjS6Zsw7e1rStII37dklVqganHwlf+nrojm5WWzYoLiipdbDLJP9gxwbFREQyc+PmC/wy\nCo/PoVpHDNwKW7esWTGJ6XFxCSLWJn2xqUqdXjjYxaWyQXEhsEGxONigmIhITv7444/vR/kj\nMR7tpqHzt1C+1RaurqsFv/PzV9YBdhZIEhqYq+nEBsWFwAbF4mCDYiIiedBoNP/973+///77\ncuXKlR13IMqlR44nWJphSBNzKyv579qKyLcJVl4QmD68GRsUF4LUIl0mXjxBREQlwOPHjzt2\n7LhgwYJ27dqFh4fvmtGjXJm3nmCuxMo+qOYgUn0lyrwu8MjVVO/TphjWRIxqyKj43xoiIpK6\nv/76a+jQodHR0ZMmTVq0aJFKpaoEXP0XVl5AaBRSNWjsgpHN4e5qhPd6loxy1lDJetzDwQqn\nPsO6Szh6Hw8S0LA8+tXDR/XELouMgcGOiIikS6vVzp07d+7cuXZ2dkFBQR9//HHWrAq2mNPR\naG8Up8a0YATdQLwaKiVavocFXdGmstGWLzU3YrAjAicfIl2LW7HQ6tGmMlxtxC6LiozBjoiI\nJComJsbX1zc4OLhFixaBgYE1ahTXTa9SNej4C648f/0wQ4dTD9FpI4L90L5qMb2nmC49RfsN\nePXP1ZxJ6fj1CkIeI3Q0HHiKXQkn67FmIiIqsY4fP+7u7h4cHOzv73/mzJniS3UAVl54k+qy\npGsx+WDxvaeYvgh+k+qy/P2SDYrlQP4jdmxQTERUsuj1+uXLl3/11VdWVlYBAQHe3t46na5Y\nv8n337YQHOkIf4aHL9NcrPXF99aml6rB8QfC43IHbutmtGaD4oKSWge7TPIPdmxQTERUgsTF\nxU2cOPGvv/6qXbv2mjVr6tSpY4Lv8Nhk57wOYUU+T1Q5yOpWFjGpSm0eDYpfpOi5xyw4NigW\nBxsUExGVFBcvXhw2bNiDBw+GDh36448/Wlub6I701Z2U4TEC082VqFfJzs7CNFWYiJ09bC2Q\nLDQwV81RwT1mwbFBsTjYoJiIqERYvXr1xIkTlUrlmjVrRo0aZcq39muKnbcEpvetg/JlZbgH\n8WmI9WEC04e5s0FxIUgt0mXixRNERCSyxMREHx+fMWPGVK9e/fz58yZOdQA+qofRzXNOrOmI\nH3uZuBATWdAVjVxyTvRpiE+bilENGZX8R+yIiCQuVQOVEubG+492cjpsJXz0MEd5YWFh3t7e\nd+/eHTBgwPr16+3t7Qu1NLUGao0RmnSs7osPaiHwOiJiUcEW71fFBC/YWxZ1sdLkbI1zo7A8\nFMce4FEC6jqjX134NoGiyEtO00KpkHl7Z4ljsCMiEodGh8VnseYS7sVBpUTzCpjZAR/UMnyB\nd15iWjCOPkC8Gq426FsX87rA2URnqb3b81eYfgT7biMmBY5W6FoDC7rh5J6AsWPH6nS6JUuW\nTJ48uVAL/PoolpxFcgYAWJihZ20EesPC0EiRnI5TD3H6ER4n4n4cAPSqjWZuBi5N+iITcOoh\nTj9EQhpiUuBsjd514GhoPtYDqy9i2TncfgEF0MgFU9ticCOjVkwFw2BHRCSOT3Zi67XXP6dp\ncfYxem3GpgHwbWzI0m69QOu1iPunJcjzV1h7CSciETLK8L21EcWkoPVa3I9//TBOjaDw5D3z\nxqSFbalater27dtbtmxZqAV+uhsbw988TNdi9000XoFbEwwpL02LzhsR+uT1w6R0HLqLE5H4\nazhavWfIAiXuWjTarkdi2uuHz5Kx8sLrrcWwK0W+OPxWD7ywZxjyOx4n4j9tjFAtFQpHS4mI\nRPDX/TepLosemHQAaoN6a0wLfpPqstx+gYWnDVma0c07+SbVAUDsTaxtnRa2pZLHh2FhYYVN\ndc+TERAuMP32C6y5ZEh5ay6+SXVZUjWYdMCQpUnffw6/SXVZbsTgB4MaFEfE4ocQgelfH8Xz\nV4YskIpC/iN2bFBMRBK0J8Jc8Bv4ZSpO3EtvX0VXqKVp9Tj4t/C43B+39TPb5tqHm9wfty2R\ndQbX5U3YNw6aNHSdn9hpapkyaYX9ll5x3lyfx/5rwyWdX4NCt9jde0u4QXHoEzx6mVZeXg2K\n1Rr8eV94a9l7Sze1ZaF/e3sizHV6gdWh1uDgrYxBDbSFLrGEkFoHO2afxgAAIABJREFUu0zy\nD3ZsUExEEvQs0SGvb+CHsa/i7AoXdBLTFWla4dPBYpJ1UvgOjH3lCiigUSN4Gs4tQ9n3MHAb\nqrRNysDzF/EWysIlp8iX9nn99l6kGPJ5o5PybFD84HmiuewaFGt0wsEu9pUhDYqfxNkBtoKz\nHr9MjYuT7agdGxSLgw2KiUiCqpfL8+u3bgUbR8cyhVqaA1DWUuDgGoAqDpJoOVvZXhEXcQdB\n3nh2GTW6YMBm2LoCKG+tdy3nUNilNa5ghuvCs96zVxrweas4CDcoNlOgTkU7mV0ba2cPaxVS\nhNKIYVtLzfJmec2q7VrG0VHCV2gXDRsUi4MNiolIgoY0wfdnocs1UFXTEW2qWZgVvu2ET0Os\nFTq9bEhjSbScbRK/68qaEUhPQsdZ6DATitfDY4MbKQwob1xLTPsTWqFhvi/aGfJ5h7pjzx2B\n6R/Ugqu9+L89oxtQH79eEZhu2NbycSNM/wupuYY1y5VBz7oqKwtp5R4jklqky8SLJ4iIRNDE\nFXM65pxoa4GNH8GAVAdgXhfUd845sVsN/MvTkKUZkUajmT59+q9ffaRSqeB7AB1nZ6W6Jq6Y\n08mQZVqbY0E3gel9auODmoYs0Kchhua6GPm9svhJpg2KF3ZDbaecE3vXxqhcXZoLopIdlvWE\n8u3t1sIM6/oZeI0tFYX8R+yIiKTpv+3hWQlrL+F6NGws4FkRn7dGrVy72wJytsa50fjhLI4+\nwKME1HTCh3UxpoUR+h7r9FAoDGxd++jRIx8fn5CQkA4dOgRs3rrzcYV9t3EvDlUd0Lk6/l8r\n2Bg65DGlNRq64PNDiIyHVg8XG0xuiSmtDVyaAvh1AHrUROB13H4BFxu0q4L/tJFQF0DjcrNF\nqD8Wn8XxB4hKQi0n9K+HUc0N/E8FgFHN0aA8lofiynOYK9HUDf9uhSauRi2aCobBjohIHDo9\n7rzAnRe4GwdrFewtX+9iDWZngZkdMLOD0Srcfh0/nMXVaCiAJq74TxsMqF+Il//xxx/Dhw+P\ni4ubNGnSokWLVCrV5EqYXLjGJvlRKeFmg6hEaDVwLgNbC+hh+L0T1BrceYk7L3EvDglpcLVF\n9CvZBjsA9pYCY8ZF0aYy2lQ25gLJMDwUS0QkjtF7MfEALj9Huhbxavx5H5034vcIscv6x7cn\nMfg3nItCSgZeZeDsY3wciMUF63Om0Whmz57dr18/hUKxb9++pUuXGv1spM1X0S0ARx8gMQ1p\nWoQ9w9g/MHG/gUvL0KHHJnxzArdfQKtH9CvsiEDLtbj01KhFExU/BjsiIhGcfIj1YTkn6vT4\n1z6kSaDt14N4zDkmMP2rP/Ek6R2vjYqK6tSp05w5c1q0aHHhwoWePXsavbyUDEzcj9zXTiwP\nFegzXBDrLuHkw5wTk9MxUaYNiknG5H8olg2KiUiCdlwXblAc/Qon76W3q1y4BsVGtyfCLEMn\nMMaWpsW+mxl+jfPMnsePHx8+fHh0dPT48eO/++47CwuL4vgG/vO+Mk4tfFr+zuuaxk6Fbju3\n+6Zwg+KzjxAVl1aujKwaFJOxSK2DXSb5Bzs2KCYiCXqakGeD4gcxrxraivzf0UcvbAHhg6cP\nXwi3nNXr9StWrJg/f761tfXKlSv79u376tWrV6+KpTltZEwZQDjYRcWnx8UlFHaBzxKFGxTr\ngbvPEpXyalBMxsIGxeJgg2IikqAqjnl+/dZ2K3SDYqOrkXfL2ZouAi1nY2NjP/vss+DgYHd3\n9y1bttSoUaNYy6vlmud5RNXKWRjwnV/ZwUywQbFSgboVy9pbcsSOBLBBsTjYoJiIJGhwEywK\nEThLrKo92lazKHqPkiLq3xBTjgi0nLW1QN/6Kiurt/ZkJ0+eHDx48JMnT/z8/FatWlWmTLGn\n0k61UMEWT5NzTlcq4NPY3Mqq0Ls2n0bYK9SguGsNuMrsvhNkPFKLdJnE/vIgIiqVmlfA9HY5\nJ1qZY30/I3SeK7oKtljaM2frEDMFlvdCuWyxTa/XL126tEuXLomJiVu2bAkICDBBqgNgaYZ1\n/WCZa1Rxdkc0LG/IAoc2Rv96OSe62Mi2QTHJmPxH7IiIpOm7LvCshDUXcS0athbwrISpbQ3M\nJcVhdHPUKYdl5xD+DAqgWQX8v1Zom61RWUJCwsiRI3///fd69eoFBQU1atTIlOX1rIVzo7Ho\nDEKjoNagkQvGeKBvHQOXplTgNx+svoig64iIRQVbtK2CL9uhop1RiyYqfgx2RETi0AH7buPU\nIySnQalAnBodq0ko2AHoUBUdqgrPunjxoo+Pz7179/z8/H7++WcbG5t3Lu1pMvpvxZVoqDWw\nUKKuMwJ9UK+c4eW5u2LTR4a/PAetDglqJKQhMQ3WKiSooeYlE1QCMdgREYmj2Upcef76Z60e\nz5Lx2W5EJmC28W4dUUxWr149ceJEpVK5ZMmSyZMnF+QlUYmotQzqf9qkpOtwNRqNlyN0DJpK\n4MZTGh36bEHwvdcP78XhXhz23sbJEWjkImplRIUkgVM5iIhKn7WX3qS67OYeR4qEB4qSkpIG\nDx48ZsyYqlWrhoSEFDDVAfD57U2qy6LRwyfQyBUaZuPlN6kuS7waEwy9lQWRWOQ/YscGxUQk\nQWsuCHfE1emx9nyGf3MJ3H0il4iICF9f34iIiL59+65Zs8be3r7g364Xnwh3J/j7JaTwFb3j\nhvDqOBGJp/FpjlZsd0ICpNbBLpP8gx0bFBORBMWk5HmE78az9Li4RFMWUxBBQUHTp0/XaDQz\nZswYP368Tqcr1FdruraC4HQ98CgmwdZc5DttPEnIs0HxnaeJtdmgmISwQbE42KCYiCSovLXi\nfh73R2hY0ZAWu8UnNTV1ypQpGzZsqFy58q+//url5WXAQizMhO+Bq1Cgcnn7opZYZBXthRsU\nK4DaFcpyxI4EsUGxONigmIgkaIwXzu8WmG6mwEgPlZW5VHYVt27d8vb2vnr1ap8+fTZu3Ojk\n5GTYcjwr4dRDgem1nSCFr+iBDbH/b4HpHauhggMbFJMwqUW6TLx4gohIBJ81RVM3gemzOsJa\nMv/j3rx5s4eHR0RExKxZs3bv3m1wqgMQ6I0yuXaC5koEeRepQmP5xB0f1Mo50akMGxRTycNg\nR0QkjrAxGOMBeysoAHMFKtph0wB83V7ssgAAaWlpkydPHjZsmL29/dGjR2fPnq1UFml/UcEW\n9yeh9Xsoo4ICsDSDuxtuTEATCfQ6AWCmwJ4hWNgNnhVR1hK1nfBZM1z0RwMptRUkKgjJ/MeQ\niEjyIhPw7QmceYTYFNR1xuBGGNMCSsW7X5iXlb2xsrfx6jOSyMhIHx+f8+fPd+rUacuWLW5u\nQkOLhacFniUjTQM9kKFDdDLUUjrpXKXEf9rgP23EroOoaBjsiIgK5Fo02m9A3D+tOZ6/wolI\n/HUfgd4576laou3evXvEiBHx8fHTpk379ttvzcxy3ZDVIPfiUPtH6P65CEGnx9NkuP+MU6PQ\nppJR3oGIAB6KJSIqoHH73qS6LL/dQNB1MaopBhqNZvr06R999JG5ufmBAwfmz59vrFQHoPPG\nN6kuix7ovdlY70BEQGkYsWOD4v/P3p0H1Jj2/wN/t6+kUCKJ7GuSLNnFWMY6imnBjBFjZuI7\nnplm8JuYDGEe1Ix5yjaEtmMNZbINE1myjS07iVC00t75/XGQ5VTO6Zxzn+r9+su5zum6P2bm\n3PPuvu7rcxNR5T1+rhGXJH13pOhS8UhbdVpTlMuDBw88PDxOnDjh5OS0adMmS0tLxZ45k7Kk\nb33NyFWLBsVEclC3DnYS1T/YsUExEVXe9ac6gPRgdz+juKqfZOLi4r766qu0tLQpU6b89NNP\nOjo6Cv8bicXSGxQDuPU4y0xXHZ+0QVQ+NigWBhsUE1Hl2eqUeR+dZW2tqnuSKS4uXrx48eLF\ni2vVqhUWFjZq1CglHUgDKKvJr61FbSUdlEip2KBYGGxQTESV10wfXSxxJkXKW2PaaunrK+xe\nNFV68uSJu7v7gQMHunTpIhKJmjZtqrxjWRrjYY6U8Vp6atGgmEgO6hbpJLh5gojog/wxHEbv\nncYH28K9oxDVVNqRI0fs7OwOHDjg5eV1/PhxpaY6APs8pe8dDvtEqYclqnEY7IiIPohjI5yc\nik/awNIYOppob46FA7D7U2hVtWYnYrF4yZIlAwcOzMnJCQ8PDw4O1tXVVfZBO5jjwpeob/gy\n3mkAJnrYPxHDWyj7yEQ1S/VfiiWiKuT6U/x6HAkPkV2ADuaY2gVD33vQk4AKS3A3E1kFAPA4\nB/ezoF25NdjoG1h7FhefoJYuHBriPz3Rsq78s2Xl49fj+Psu7mehuRk+bokZXaHz9u/vaWlp\nEydOjImJsbOzE4lEzZur7p9vB3M8+U5lRyOqoRjsiEhdHL2HYVvw/NUOs5vPsCMRC/rhp76C\nlvXKvlsYvqW0GVvqCwQn4MAt3PSWc8Kfj8D379KX5x4h9CJiPNDbWp7ZUl+g93pce/ry5d0M\nHLiN3dcQ7Q7dV+nz9OnTrq6ud+/e9fT0DAoKMjQ0lLN0IlJXXIolIrVQLMbknaWp7rUFR3Dh\nsRAFvcdtq5QWu7fS8eNBeWa78BgLjrw7+LwQk3eiuKzto+X68UBpqnvt4B38fgoAxGJxQEBA\nr169Hj9+vGbNmpCQEKY6omqp+l+xY4Nioirh1EPNOxlS7vQqESP836JWvYtUX9KbEp9qpudJ\nvxEt9F+xr1O+rBOGXdAuEUs5A99Ox7E7BY4NS2SaTQyIrkjfWxp+scTDNm369Ok7duxo2bLl\nli1b2rdvz7MiUeWpWwc7ieof7NigmKhKuJaiD0hPTneeFqanZ6i4nnfE3zEoq7z0XMhxkrn7\nrE5ZZ+Drj563MJAteGUVaGTlN5D61u0rZ7stdrl79+7YsWOXLFliZGTEUyKRQrBBsTDYoJio\nSmhSv8w7QyxNdAT/FrdvVGZ5xrqQo7wGtcs8/VrXMzI1NZBpttpi6Gkh//3HN1wISd8zPUez\nZNmyZV9//bWsRRJROdigWBhsUExUJfRphroGeJor5a1RbbT19QU+WfVuCn1t5ElbEB7cQkOO\nk8zotgg4LWW8rgH6NNOV4687tAV2Jr7xuiAHu71wMayWRZPYqEhHR0eZZySicqlbpJPg5gki\nUgv62vhtmJQeth4d0c9G9eVI8d/BUgZN9BA0XJ7Z+tnAvcO7gxrAb8MgX4hd4gyz15f5Uq9i\nbXdcDDPuOOr8+XNMdUQ1B4MdEamLT9tjnwd6WcNAG1oaaFUXvw7GhtFCl/XKjK7YNBb1DF6m\nT21NdLfC7Vly5jAAG8fg18FoVRdaGjDQRi9r7PPAp+3lnK1lXZyainFtYXQlBGu6Iu1at0n+\nd+J32DTgvShENUj1X4oloipksC0G26JEjMIS6Cni+avbryL8Eq6mwdwIPRtjVnfUle3utbcM\nbY5rXXH8Ph5mo4M5JrSHWSVu9NDSgENDnH8EfZ2XDYpb15N/NgCNDPIaxvk8jwy0smocERHe\ns2fPSk1HRFUQgx0RqR1NDcWkuhl78b+E0peH7mDDeRz9DE3ryDPbnQz0+RPJWS9fJqZBdAVf\nOuAPuZZiASw7Dp/9eN20Li4Jf57DX57o1kie2a5fv+7i4vLvv/8OHDgwNDTU3NxczrKIqCrj\nUiwRVU97b7yV6iSSs/DlHjknnL6nNNW99r8E7L0hz2yXU/HjAbzTijgzHxN3SGmDXKEdO3Y4\nOjpevnzZ19c3NjaWqY6oxqr+V+zYoJioZtp8XgeQct1v/208SM+vayBbekrL1ThwW6+MAxUP\nbCxzI6st57WLpTUovv4U8fcKujT40AbF+fn5c+fOXbVqVf369aOiogYMGFBQUCBrMUQkB3Xr\nYCdR/YMdGxQT1Ux3ntWVGuxKxLj6ILtdXdnOyFef6pSIpQe7O8+K5TjJ3H5aZoPiqw+fN9P7\noF9HHzx4MH369DNnzvTs2XPVqlUWFhY83RGpDBsUC4MNiolqpvq1tPFI+ltNzGuZ1pbtil0T\nrfc7sbxkXktbjpOMea0yT7+NP6xB8d69e7/44ovMzMzZs2cvWLBAS0sRtyUS0Qdjg2JhsEEx\nUc00vCX2SLv7rW19tDCXfu2tHC310bY+rqRKO1ArTTlOMiPa4Lf3bgEEUEe/4gbFRUVFCxcu\n9PPzMzMzi46OHjJkiKxHJ6LKU7dIJ8HNE0RUPX3eGY7vbS/V0cRvQ+WcMHAodN47ZTo2wmd2\n8sw2qBk+aSNlfPlHMCg31T148KBfv34LFizo0qXL6dOnmeqI6E0MdkRUPelq4cBEfOOI+oYv\nX/a2xqFJGNBUzgkHNsWhSehtDV0tAKhviG8ccWDiy5dyCBuH+f1gVRsAtDXRuQF2jK8gJh4+\nfNjBweH48ePe3t5xcXE2NjZyHpuIqqnqvxRLRDVWLV0Mb4nsAlx+AgsjOFmjo0WlJuxljaOf\noagEmfmVanQsoaOJsylIyQaAohJcSUPaizI/LBaLly5dOmfOHGNj48jIyHHjxr3/mfxirD2L\nw3eQnIVmphjZCuPbS3lK24dLTENQAv59DA0NdLLAjK5oblaJ6YhI+RjsiKja+k8s/htf+nLP\nDaw5iyOTX14kk5u2pgJSHQCzpUjPLX2ZX4Spu3HoLkLHvvvJ1NRUDw+P2NhYe3v7yMhIW1vb\n92fLysegTTj14OXLkw8QdgnbryJsHMre+FGeXdcwYSvyil6+PHQHwWewzRVDmsszGxGpBpdi\niah6ir31VqqTuJ2OL/cKUc17Zv31Vqp7Lewi3umdcPToUTs7u9jYWE9Pz7i4OKmpDsD/O1ya\n6l4TXcHas/KUl56HyTtLU53Ei0JM3IFstskjUmPV/4odGxQT1Uwbz0lvUBx9AykZ+ab6sj/e\nQaGCE/RQxjLp+IiS0FEFAMRi8apVq+bMmaOnp7dx40ZXV1cAUk9oYmDTBekThpwvmdRO5iy2\n47JWRp6UHX+pL7DnauGYVsWyTkhU/ahbBzuJ6h/s2KCYqGa69bTMBsWXk2VuUKxwBcWWZb11\nLkWcnp6enp7u7e198ODBFi1arF69ulWrVuWcyrILNNLzGkh96066WI5zYOIjY0B6K4crKbn9\nzJ/LOiFR9cMGxcJgg2KimqmecdkdgOvXMjUR+IqdJlDWU8PMjTXv3Lnj7u5+9+5dNze3wMBA\nIyOj8merVQIdTRRKm9HUQFOOc6CFSZl7fS1NDUxNdWWdkKj6YYNiYbBBMVHNNKQF9t6UMt6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CPayEKEjJgkei9W8oEb87/mkHvJnqjh49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\nTWrduvWePXs0pLq4JXdSXaGdZ2Xg1xpLarvwTqqzuJwt0R9buS7QHofT5Mk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Use the `tableApply` function","","________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***tableApply*** function takes the proxy object *IRIS* as input data and loads that data to the user-defined function as a data frame. The user-defined function is passed as a string. We see that the model comes back as an OML object and see that it's the linear model. "],"enabled":true,"result":{"startTime":1737478983535,"interpreter":"md.medium","endTime":1737478983968,"results":[{"message":"<h2 id=\"use-the-tableapply-function\">Use the <code>tableApply<\/code> function<\/h2>\n<hr />\n<p>The <em><strong>tableApply<\/strong><\/em> function takes the proxy object <em>IRIS<\/em> as input data and loads that data to the user-defined function as a data frame. The user-defined function is passed as a string. We see that the model comes back as an OML object and see that it's the linear model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.1 <- function(dat,dsname){","  library(lattice)","  ","  regr <- lm(Petal.Length~Petal.Width, dat)","","  x <- dat[['Petal.Width']]","  y <- dat[['Petal.Length']]","","  xyplot(y~x, xlab = \"Petal Width\", ylab = \"Petal Length\", ","    panel = function(x,y) {","    panel.dotplot(x,y)","    panel.abline(lm(y ~ x))","    }, ","    xlim=c(0,2.7), ","    ylim=c(0,7.5),","    main=\"Prediction of Petal Length\")","    ","  return(regr)","}"],"enabled":true,"result":{"startTime":1737478984429,"interpreter":"r.medium","endTime":1737478984931,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","mod <- ore.tableApply(IRIS, ","                      buildLM.1, ","                      dsname = 'ds-00')","","cat(\"Type :\",class(mod))","cat(\"\\n\")","cat(\"Coefficient :\",(mod$coefficient))"],"enabled":true,"result":{"startTime":1737478985360,"interpreter":"r.medium","endTime":1737478986287,"results":[{"message":"Type : lm\nCoefficient : 1.083558 2.22994","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Store the function in the script repository","","We can now save this user-defined function in the script repository and invoke this function by providing the function name."],"enabled":true,"result":{"startTime":1737478986710,"interpreter":"md.medium","endTime":1737478987121,"results":[{"message":"<h2 id=\"store-the-function-in-the-script-repository\">Store the function in the script repository<\/h2>\n<p>We can now save this user-defined function in the script repository and invoke this function by providing the function name.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","ore.scriptCreate(name = 'buildLM.1', ","                 FUN  = buildLM.1,     ","                 overwrite = TRUE)","                 ","ore.scriptList(name = \"buildLM.1\")"],"enabled":true,"result":{"startTime":1737478987559,"interpreter":"r.medium","endTime":1737478988031,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>buildLM.1<\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    regr &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    x &lt;- dat[[\"Petal.Width\"]]\n    y &lt;- dat[[\"Petal.Length\"]]\n    xyplot(y ~ x, xlab = \"Petal Width\", ylab = \"Petal Length\", panel = function(x, y) {\n        panel.dotplot(x, y)\n        panel.abline(lm(y ~ x))\n    }, xlim = c(0, 2.7), ylim = c(0, 7.5), main = \"Prediction of Petal Length\")\n    return(regr)\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Store the output of the function to the data store","","Notice the value to ***func*** is now a string, not a R function object, however, the results are the same. That's because we are now referencing the script in the script repository. We will save the model outputted by the tableApply to the datastore ds-0."],"enabled":true,"result":{"startTime":1737478988461,"interpreter":"md.medium","endTime":1737478988879,"results":[{"message":"<h2 id=\"store-the-output-of-the-function-to-the-data-store\">Store the output of the function to the data store<\/h2>\n<p>Notice the value to <em><strong>func<\/strong><\/em> is now a string, not a R function object, however, the results are the same. That's because we are now referencing the script in the script repository. We will save the model outputted by the tableApply to the datastore ds-0.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","mod <- ore.tableApply(IRIS, ","                      FUN.NAME = 'buildLM.1', ","                      dsname = 'ds-0')","","cat(\"Type :\",class(mod))","cat(\"\\n\")","cat(\"Coefficient :\",(mod$coefficient))","","ore.save(mod, name=\"ds1\", overwrite = TRUE)"],"enabled":true,"result":{"startTime":1737478989309,"interpreter":"r.medium","endTime":1737478992368,"results":[{"message":"Type : lm\nCoefficient : 1.083558 2.22994","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Apply a function to a chunck of rows with `rowApply` and `groupApply`","______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***ore.rowApply*** function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native R models. In this example, the user-defined function ***scoreLM.1*** takes a data frame and a model name. The ***ore.rowApply*** invocation takes as arguments the proxy object *IRIS*, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.","","For illustrative purposes, you can also use the ***ore.groupApply*** function to partition the data according to one or more columns and score as well, passing the model as an argument, and invoke the model on each partition."],"enabled":true,"result":{"startTime":1737478992795,"interpreter":"md.medium","endTime":1737478993212,"results":[{"message":"<h2 id=\"apply-a-function-to-a-chunck-of-rows-with-rowapply-and-groupapply\">Apply a function to a chunck of rows with <code>rowApply<\/code> and <code>groupApply<\/code><\/h2>\n<hr />\n<p>The <em><strong>ore.rowApply<\/strong><\/em> function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native R models. In this example, the user-defined function <em><strong>scoreLM.1<\/strong><\/em> takes a data frame and a model name. The <em><strong>ore.rowApply<\/strong><\/em> invocation takes as arguments the proxy object <em>IRIS<\/em>, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.<\/p>\n<p>For illustrative purposes, you can also use the <em><strong>ore.groupApply<\/strong><\/em> function to partition the data according to one or more columns and score as well, passing the model as an argument, and invoke the model on each partition.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","scoreLM.1 <- function(dat, dsname){","  library(lattice)","  ore.load(dsname)","  dat$Petal.Length_prediction <- predict(mod, newdata = dat)","  dat[,c(\"Petal.Length_prediction\",\"Petal.Length\",\"Species\")]","}","","ore.scriptCreate(name = 'scoreLM.1', ","                 FUN  = scoreLM.1,     ","                 overwrite = TRUE)"],"enabled":true,"result":{"startTime":1737478993636,"interpreter":"r.medium","endTime":1737478994165,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","res <- ore.rowApply(IRIS, ","                    scoreLM.1,","                    dsname = \"ds1\", ","                    rows = 75, ","                    parallel = 2)","","z.show(head(res))"],"enabled":true,"result":{"startTime":1737478994607,"interpreter":"r.medium","endTime":1737479031719,"results":[{"message":"Petal.Length_prediction\tPetal.Length\tSpecies\n1.52954613187489\t1\tsetosa\n1.3065520823627\t1.1\tsetosa\n1.52954613187489\t1.2\tsetosa\n1.52954613187489\t1.2\tsetosa\n1.52954613187489\t1.3\tsetosa\n1.52954613187489\t1.3\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","res <- ore.groupApply(IRIS, ","                      INDEX = IRIS$Species, ","                      scoreLM.1,","                      dsname = \"ds1\", ","                      parallel = 2)","","z.show(head(res))"],"enabled":true,"result":{"startTime":1737479032172,"interpreter":"r.medium","endTime":1737479082698,"results":[{"message":"Petal.Length_prediction\tPetal.Length\tSpecies\n1.52954613187489\t1\tsetosa\n1.3065520823627\t1.1\tsetosa\n1.52954613187489\t1.2\tsetosa\n1.52954613187489\t1.2\tsetosa\n1.52954613187489\t1.3\tsetosa\n1.52954613187489\t1.3\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build one model per SPECIES using Group Apply","","A better use of ***groupApply*** - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function can be invoked on each group. The function ***groupApply*** makes this easy, as well as specifying the desired number of parallel R engines using the ***parallel*** argument.","","Here, we build three models, one specific to each species, and return them."],"enabled":true,"result":{"startTime":1737479083138,"interpreter":"md.medium","endTime":1737479083550,"results":[{"message":"<h2 id=\"build-one-model-per-species-using-group-apply\">Build one model per SPECIES using Group Apply<\/h2>\n<p>A better use of <em><strong>groupApply<\/strong><\/em> - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function can be invoked on each group. The function <em><strong>groupApply<\/strong><\/em> makes this easy, as well as specifying the desired number of parallel R engines using the <em><strong>parallel<\/strong><\/em> argument.<\/p>\n<p>Here, we build three models, one specific to each species, and return them.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.group <- function(dat){","  library(lattice)","  mod <- lm(Petal.Length~Petal.Width, dat)","  return(mod)","}","","res <- ore.groupApply(IRIS[,c(\"Petal.Length\",\"Petal.Width\",\"Species\")], ","                      INDEX = IRIS$Species, ","                      buildLM.group,","                      parallel = 2)","                      ","                                           ","","ore.scriptCreate(name = 'buildLM.group', ","                 FUN  =  buildLM.group,     ","                 overwrite = TRUE)"],"enabled":true,"result":{"startTime":1737479083996,"interpreter":"r.medium","endTime":1737479117813,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Save each model in a datastore","_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","Next, change the user-defined function to save each model in a datastore. The datastore allows storing R objects in the database under the provided name. The object assumes the name it is assigned in R, here mod and the corresponding linear model."],"enabled":true,"result":{"startTime":1737479118295,"interpreter":"md.medium","endTime":1737479118717,"results":[{"message":"<h2 id=\"save-each-model-in-a-datastore\">Save each model in a datastore<\/h2>\n<hr />\n<p>Next, change the user-defined function to save each model in a datastore. The datastore allows storing R objects in the database under the provided name. The object assumes the name it is assigned in R, here mod and the corresponding linear model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.2 <- function(dat,dsname){","  library(lattice)","  mod <- lm(Petal.Length~Petal.Width, dat)","  ore.save(mod, name=dsname, overwrite=TRUE)","  return(mod)","}"],"enabled":true,"result":{"startTime":1737479119159,"interpreter":"r.medium","endTime":1737479119641,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `groupApply` to build one model per `SPECIES`","_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***groupApply*** function takes the data, the ***index*** parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the R engine. ","","We then return the outcome, which contains three elements each assigned the model object."],"enabled":true,"result":{"startTime":1737479120075,"interpreter":"md.medium","endTime":1737479120494,"results":[{"message":"<h2 id=\"use-groupapply-to-build-one-model-per-species\">Use <code>groupApply<\/code> to build one model per <code>SPECIES<\/code><\/h2>\n<hr />\n<p>The <em><strong>groupApply<\/strong><\/em> function takes the data, the <em><strong>index<\/strong><\/em> parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the R engine.<\/p>\n<p>We then return the outcome, which contains three elements each assigned the model object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ore.groupApply(IRIS[,c(\"Petal.Length\",\"Petal.Width\",\"Species\")], ","                      INDEX = IRIS$Species,","                      dsname = 'ds-1',","                      buildLM.2,","                      parallel = 1)","                      "],"enabled":true,"result":{"startTime":1737479716311,"interpreter":"r.medium","endTime":1737479717525,"results":[{"message":"$setosa\n\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n     1.3276       0.5465  \n\n\n$versicolor\n\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n      1.781        1.869  \n\n\n$virginica\n\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n     4.2407       0.6473  \n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Load the models from the datastore","","When we view the datastore, we get the models loaded into the client R engine, assigned to their respective variables."],"enabled":true,"result":{"startTime":1737479170041,"interpreter":"md.medium","endTime":1737479170479,"results":[{"message":"<h2 id=\"load-the-models-from-the-datastore\">Load the models from the datastore<\/h2>\n<p>When we view the datastore, we get the models loaded into the client R engine, assigned to their respective variables.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","ore.datastore(\"ds-1\")","ore.datastoreSummary(\"ds-1\")"],"enabled":true,"result":{"startTime":1737479171050,"interpreter":"r.medium","endTime":1737479172146,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 5<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>object.count<\/th><th scope=col>size<\/th><th scope=col>creation.date<\/th><th scope=col>description<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dttm&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>ds-1<\/td><td>1<\/td><td>17492<\/td><td>2025-01-21 17:06:09<\/td><td>NA<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 6<\/caption>\n<thead>\n\t<tr><th scope=col>object.name<\/th><th scope=col>class<\/th><th scope=col>size<\/th><th scope=col>length<\/th><th scope=col>row.count<\/th><th scope=col>col.count<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>mod<\/td><td>lm<\/td><td>17492<\/td><td>12<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Create a script that stores the model in the datastore and run `tableApply` ","","Again, we create this script in the R script repository and then invoke it by name using  ***tableApply***. We then pull the model to the client and view its type."],"enabled":true,"result":{"startTime":1737479172618,"interpreter":"md.medium","endTime":1737479173094,"results":[{"message":"<h2 id=\"create-a-script-that-stores-the-model-in-the-datastore-and-run-tableapply\">Create a script that stores the model in the datastore and run <code>tableApply<\/code><\/h2>\n<p>Again, we create this script in the R script repository and then invoke it by name using  <em><strong>tableApply<\/strong><\/em>. We then pull the model to the client and view its type.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","buildLM.2 <- function(dat,dsname){","  library(lattice)","  mod <- lm(Petal.Length~Petal.Width, dat)","  ore.save(mod, name=dsname, overwrite = TRUE)","  return(mod)","}","","","ore.scriptCreate(name = 'buildLM.2', ","                 FUN  =  buildLM.2,     ","                 overwrite = TRUE)","","ore.scriptList(name = 'buildLM.2')"],"enabled":true,"result":{"startTime":1737479173575,"interpreter":"r.medium","endTime":1737479174140,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>buildLM.2<\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    ore.save(mod, name = dsname, overwrite = TRUE)\n    return(mod)\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":[" %r","","mod <- ore.tableApply(IRIS, ","                      FUN.NAME = 'buildLM.2', ","                      dsname = 'ds-3')","","cat(\"Type :\",class(mod))","cat(\"\\n\")","cat(\"Coefficient :\",(mod$coefficient))","","mod"],"enabled":true,"result":{"startTime":1737479174622,"interpreter":"r.medium","endTime":1737479175284,"results":[{"message":"Type : lm\nCoefficient : 1.083558 2.22994\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n      1.084        2.230  \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the loaded model normally to make predictions","","The model can now be used normally, e.g., to predict outcomes."],"enabled":true,"result":{"startTime":1737479175739,"interpreter":"md.medium","endTime":1737479176207,"results":[{"message":"<h2 id=\"use-the-loaded-model-normally-to-make-predictions\">Use the loaded model normally to make predictions<\/h2>\n<p>The model can now be used normally, e.g., to predict outcomes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","prediction <- predict(mod,iris_df)","RES_DF <- cbind(prediction,iris_df)","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1737479176650,"interpreter":"r.medium","endTime":1737479177128,"results":[{"message":"prediction\tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n1.52954613187489\t4.6\t3.6\t1\t0.2\tsetosa\n1.3065520823627\t4.3\t3\t1.1\t0.1\tsetosa\n1.52954613187489\t5\t3.2\t1.2\t0.2\tsetosa\n1.52954613187489\t5.8\t4\t1.2\t0.2\tsetosa\n1.52954613187489\t4.4\t3\t1.3\t0.2\tsetosa\n1.52954613187489\t4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Model quality assessment","","Let's assess model quality using summary statistics computed from the model."],"enabled":true,"result":{"startTime":1737479177630,"interpreter":"md.medium","endTime":1737479178037,"results":[{"message":"<h2 id=\"model-quality-assessment\">Model quality assessment<\/h2>\n<p>Let's assess model quality using summary statistics computed from the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","summary(mod)"],"enabled":true,"result":{"startTime":1737479178469,"interpreter":"r.medium","endTime":1737479178934,"results":[{"message":"\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-1.33542 -0.30347 -0.02955  0.25776  1.39453 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept)  1.08356    0.07297   14.85   <2e-16 ***\nPetal.Width  2.22994    0.05140   43.39   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 0.4782 on 148 degrees of freedom\nMultiple R-squared:  0.9271,\tAdjusted R-squared:  0.9266 \nF-statistic:  1882 on 1 and 148 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `groupApply` to count `SPECIES` separately","","The ***group_apply*** function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set."],"enabled":true,"result":{"startTime":1737479179420,"interpreter":"md.medium","endTime":1737479179830,"results":[{"message":"<h2 id=\"use-groupapply-to-count-species-separately\">Use <code>groupApply<\/code> to count <code>SPECIES<\/code> separately<\/h2>\n<p>The <em><strong>group_apply<\/strong><\/em> function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","groupCount <- function(dat){","  table(dat$Species)","}","","ore.groupApply(IRIS, ","               INDEX = IRIS$Species, ","               groupCount, ","               parallel = 2)","                    "],"enabled":true,"result":{"startTime":1737479180267,"interpreter":"r.medium","endTime":1737479216571,"results":[{"message":"$setosa\n\nsetosa \n    50 \n\n$versicolor\n\nversicolor \n        50 \n\n$virginica\n\nvirginica \n       50 \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `indexApply` to invoke a function a number of times","_________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***ore.indexApply*** function allows the same function to invoked a specified number of times. The first argument to the user-defined function is an index number for the function execution. For example, if the \"times\" argument is 12, each function invocation will receive a distinct value between 1 and 10. This can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis."],"enabled":true,"result":{"startTime":1737479217005,"interpreter":"md.medium","endTime":1737479217417,"results":[{"message":"<h2 id=\"use-indexapply-to-invoke-a-function-a-number-of-times\">Use <code>indexApply<\/code> to invoke a function a number of times<\/h2>\n<hr />\n<p>The <em><strong>ore.indexApply<\/strong><\/em> function allows the same function to invoked a specified number of times. The first argument to the user-defined function is an index number for the function execution. For example, if the &quot;times&quot; argument is 12, each function invocation will receive a distinct value between 1 and 10. This can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","computeMean <- function(index){","    set.seed(index)","    x <- round(runif(100,2,10),4)","    return(mean(x))","}","","ore.indexApply(12, computeMean)"],"enabled":true,"result":{"startTime":1737479217864,"interpreter":"r.medium","endTime":1737479218349,"results":[{"message":"<dl>\n\t<dt>$`1`<\/dt>\n\t\t<dd>6.142776<\/dd>\n\t<dt>$`2`<\/dt>\n\t\t<dd>5.932833<\/dd>\n\t<dt>$`3`<\/dt>\n\t\t<dd>5.872673<\/dd>\n\t<dt>$`4`<\/dt>\n\t\t<dd>6.383635<\/dd>\n\t<dt>$`5`<\/dt>\n\t\t<dd>6.147493<\/dd>\n\t<dt>$`6`<\/dt>\n\t\t<dd>6.251832<\/dd>\n\t<dt>$`7`<\/dt>\n\t\t<dd>6.07391<\/dd>\n\t<dt>$`8`<\/dt>\n\t\t<dd>5.981312<\/dd>\n\t<dt>$`9`<\/dt>\n\t\t<dd>5.927451<\/dd>\n\t<dt>$`10`<\/dt>\n\t\t<dd>5.562602<\/dd>\n\t<dt>$`11`<\/dt>\n\t\t<dd>5.320832<\/dd>\n\t<dt>$`12`<\/dt>\n\t\t<dd>5.837725<\/dd>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Use the SQL API for embedded R execution","","On Autonomous Database, OML4R provides a SQL interfaces for setting authorization tokens, and running UDFs to database tables directly from SQL. Supporting OML4R embedded R execution is the ability to load and store UDFs in the database script repository and R objects in the database datastore as displayed previously in the notebook. The datastore also enables loading saved objects for use in UDFs. Since it\u2019s under database control and invoked through SQL, deploying R-based solution in production systems is greatly simplified. Further, scheduling R functions to run is easily achieved using DBMS_SCHEDULER or other scheduling interfaces that invoke SQL.","","The service levels  `LOW`, `MEDIUM`, and `HIGH` provide different levels of performance and concurrency for an Autonomous Database instance. In this case, we use service level `MEDIUM` to enable moderate parallelism.","","The `OUT_FMT` parameter controls the format of output returned by the embedded R execution functions.","","The supported output formats are:","","- JSON","- Relational","- XML","- PNG","- Asynchronous Mode Output","","To use the SQL API for embedded R execution, a UDF must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account `USERNAME` `PASSWORD` and `URL` must be provided for an authentication token.","","## SQL API Workflow","","In the coming steps, we will provide a typical workflow for using the OML4R SQL API for embedded R execution with Oracle Autonomous Database:","","- Connect to Autonomous Database as the ADMIN user and add the OML user to cloud host ACL list","- Confirm the host ACL list contains an entry for the OML user","- Obtain an authorization token to access the SQL API","- Confirm the token is set","- Invoke UDFs using embedded R execution from the OML4R R and SQL APIs"],"enabled":true,"result":{"startTime":1737479218803,"interpreter":"md.medium","endTime":1737479219220,"results":[{"message":"<h1 id=\"use-the-sql-api-for-embedded-r-execution\">Use the SQL API for embedded R execution<\/h1>\n<p>On Autonomous Database, OML4R provides a SQL interfaces for setting authorization tokens, and running UDFs to database tables directly from SQL. Supporting OML4R embedded R execution is the ability to load and store UDFs in the database script repository and R objects in the database datastore as displayed previously in the notebook. The datastore also enables loading saved objects for use in UDFs. Since it\u2019s under database control and invoked through SQL, deploying R-based solution in production systems is greatly simplified. Further, scheduling R functions to run is easily achieved using DBMS_SCHEDULER or other scheduling interfaces that invoke SQL.<\/p>\n<p>The service levels  <code>LOW<\/code>, <code>MEDIUM<\/code>, and <code>HIGH<\/code> provide different levels of performance and concurrency for an Autonomous Database instance. In this case, we use service level <code>MEDIUM<\/code> to enable moderate parallelism.<\/p>\n<p>The <code>OUT_FMT<\/code> parameter controls the format of output returned by the embedded R execution functions.<\/p>\n<p>The supported output formats are:<\/p>\n<ul>\n<li>JSON<\/li>\n<li>Relational<\/li>\n<li>XML<\/li>\n<li>PNG<\/li>\n<li>Asynchronous Mode Output<\/li>\n<\/ul>\n<p>To use the SQL API for embedded R execution, a UDF must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account <code>USERNAME<\/code> <code>PASSWORD<\/code> and <code>URL<\/code> must be provided for an authentication token.<\/p>\n<h2 id=\"sql-api-workflow\">SQL API Workflow<\/h2>\n<p>In the coming steps, we will provide a typical workflow for using the OML4R SQL API for embedded R execution with Oracle Autonomous Database:<\/p>\n<ul>\n<li>Connect to Autonomous Database as the ADMIN user and add the OML user to cloud host ACL list<\/li>\n<li>Confirm the host ACL list contains an entry for the OML user<\/li>\n<li>Obtain an authorization token to access the SQL API<\/li>\n<li>Confirm the token is set<\/li>\n<li>Invoke UDFs using embedded R execution from the OML4R R and SQL APIs<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Add the OML user to the cloud host ACL list - must be ADMIN user","message":["%md","","You can control and restrict access to your Autonomous Database by specifying network access control lists (ACLs). The ACL provides additional protection to your Autonomous Database by allowing only authorized IP addresses to connect to the database. ","","OML4R provides the `rqAppendHostAce` function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where the Autonomous Database resides, e.g. *adb.us-region-1.oraclecloudapps.com*. Run the `rqAppendHostAce` function as **ADMIN** user in a SQL Developer or SQL Developer Web instance."],"enabled":true,"result":{"startTime":1737479219695,"interpreter":"md.medium","endTime":1737479220106,"results":[{"message":"<p>You can control and restrict access to your Autonomous Database by specifying network access control lists (ACLs). The ACL provides additional protection to your Autonomous Database by allowing only authorized IP addresses to connect to the database.<\/p>\n<p>OML4R provides the <code>rqAppendHostAce<\/code> function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where the Autonomous Database resides, e.g. <em>adb.us-region-1.oraclecloudapps.com<\/em>. Run the <code>rqAppendHostAce<\/code> function as <strong>ADMIN<\/strong> user in a SQL Developer or SQL Developer Web instance.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","SQL> exec rqAppendHostAce('OMLUSER','adb.us-sanjose-1.oraclecloudapps.com');","","  PL/SQL procedure successfully completed.","  Elapsed: 00:00:00.375"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Confirm the host ACL list contains an entry for the OML user","message":["%md","","The `rqGetHostACE` function gets the existing host access control entry (ACE) for the specified user. If the user has access to the Autonomous Database cloud host, the ADMIN user can run the following to check the user's privileges. In this example, the hostname is returned if the host ACL entry exists for the specified user, and if not, an exception is raised. "],"enabled":true,"result":{"startTime":1737479220563,"interpreter":"md.medium","endTime":1737479220986,"results":[{"message":"<p>The <code>rqGetHostACE<\/code> function gets the existing host access control entry (ACE) for the specified user. If the user has access to the Autonomous Database cloud host, the ADMIN user can run the following to check the user's privileges. In this example, the hostname is returned if the host ACL entry exists for the specified user, and if not, an exception is raised.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["SQL> set serveroutput on","","DECLARE ","    hostname VARCHAR2(4000);","BEGIN","    hostname := rqGetHostACE('OMLUSER'); ","    DBMS_OUTPUT.put_line ('hostname: ' || hostname);","END;","/","","SQL> hostname: https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com","PL/SQL procedure successfully completed."," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Obtain an authorization token","message":["%md","","The OML4R SQL API uses tokens to authenticate an OML user. To authenticate and obtain an access token, use `cURL` or another REST client, such as Postman, to exchange the OML user credentials for an authorization token. Send a POST request to the token URL endpoint */oauth2/v1/token* with your OML username and password. The token URL can be obtained from the OML service console in Autonomous Database:","","- Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.","- Click the hamburger menu and select the provisioned Autonomous Database instance. ","- Click **Service Console** and then click **Development**.","- Scroll down to **Oracle Machine Learning RESTful Services** tile and click Copy to obtain the following URLs for:","- Obtaining the REST authentication token for REST APIs provided by OML:","","    *oml-cloud-service-location-url*/omlusers/","","The URL *oml-cloud-service-location-url* includes the tenancy ID, location, and database name. For example, https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com.","","For example,","","- qtraya2braestch is the tenancy ID","- omldb is the database name","- us-sanjose-1 is the datacenter region","- oraclecloudapps.com is the root domain"],"enabled":true,"result":{"startTime":1737479221427,"interpreter":"md.medium","endTime":1737479221844,"results":[{"message":"<p>The OML4R SQL API uses tokens to authenticate an OML user. To authenticate and obtain an access token, use <code>cURL<\/code> or another REST client, such as Postman, to exchange the OML user credentials for an authorization token. Send a POST request to the token URL endpoint <em>/oauth2/v1/token<\/em> with your OML username and password. The token URL can be obtained from the OML service console in Autonomous Database:<\/p>\n<ul>\n<li>\n<p>Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.<\/p>\n<\/li>\n<li>\n<p>Click the hamburger menu and select the provisioned Autonomous Database instance.<\/p>\n<\/li>\n<li>\n<p>Click <strong>Service Console<\/strong> and then click <strong>Development<\/strong>.<\/p>\n<\/li>\n<li>\n<p>Scroll down to <strong>Oracle Machine Learning RESTful Services<\/strong> tile and click Copy to obtain the following URLs for:<\/p>\n<\/li>\n<li>\n<p>Obtaining the REST authentication token for REST APIs provided by OML:<\/p>\n<p><em>oml-cloud-service-location-url<\/em>/omlusers/<\/p>\n<\/li>\n<\/ul>\n<p>The URL <em>oml-cloud-service-location-url<\/em> includes the tenancy ID, location, and database name. For example, <a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a>.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>qtraya2braestch is the tenancy ID<\/li>\n<li>omldb is the database name<\/li>\n<li>us-sanjose-1 is the datacenter region<\/li>\n<li>oraclecloudapps.com is the root domain<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define get_token2 function that obtains access token","message":["%script","","create or replace function get_token2 (","  p_url       varchar2,","  p_username  varchar2,","  p_password  varchar2,","  p_verbose   number default 0) ","  return varchar2","  authid definer as pragma autonomous_transaction;","  l_buffer            varchar2(4000);","  l_request_content   clob;","  l_response_content  clob;","  l_request           utl_http.req;","  l_response          utl_http.resp;","  l_url               varchar2(1000);","  l_smt               varchar2(1000);","  l_access_token      varchar2(4000);","begin","  l_url := p_url;","  l_request_content := q'[{\"grant_type\":\"password\", \"username\":\"]' || p_username || q'[\", \"password\":\"]' || p_password ||","  q'[\"}]';","  begin","    utl_http.set_wallet('');","    l_request := utl_http.begin_request(l_url,'POST',' HTTP/1.1');","    utl_http.set_header(l_request,'Content-Type','application/json');","    utl_http.set_header(l_request,'Accept','application/json');","    utl_http.set_header(l_request,'Content-Length',length(l_request_content));","    utl_http.write_text(l_request,l_request_content);","    l_response := utl_http.get_response(l_request);","    if (l_response.status_code != utl_http.HTTP_OK) THEN","      RAISE_APPLICATION_ERROR(-20999, 'Token request failed with status code: '||l_response.status_code||', reason: '||l_response.reason_phrase);","    end if;","    if p_verbose > 0 then","      dbms_output.put_line('HTTP response status code: ' || l_response.status_code);","      dbms_output.put_line('HTTP response reason phrase: ' || l_response.reason_phrase);","    end if;","    begin","      loop","        utl_http.read_line(l_response,l_buffer);","        l_access_token := regexp_substr(l_buffer,'\"accessToken\":\"([^\"]+)\"',1,1,'i',1);","        if p_verbose > 0 then","          dbms_output.put_line('l_access_token: [' || l_access_token || ']');","        end if;","      end loop;","      utl_http.end_response(l_response);","    exception","      when utl_http.end_of_body then utl_http.end_response(l_response);","    end;","  end;","  return(l_access_token);","  dbms_output.put_line('Access token is: ' || l_access_token);","end get_token2;","/"],"enabled":true,"result":{"startTime":1737479222321,"interpreter":"script.medium","endTime":1737479224384,"results":[{"message":"\nFunction GET_TOKEN2 compiled\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Create access details table to support token acquisition","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE ACCESS_DETAILS';","EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","CREATE TABLE ACCESS_DETAILS (","  NAME  VARCHAR2(20),","  VALUE VARCHAR2(1000)","  );","/","","-- Replace the OML token URL, username, and password with your database-specific values.","-- For example, the OML token URL is the OML access URL for ADB broker token authentication:","-- https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com/omlusers/api/oauth2/v1/token","","INSERT INTO ACCESS_DETAILS VALUES ('URL', '<YOUR OML TOKEN URL>');","INSERT INTO ACCESS_DETAILS VALUES ('USERNAME', '<YOUR OMLUSER>');","INSERT INTO ACCESS_DETAILS VALUES ('PASSWORD', '<YOUR PASSWORD>');"],"enabled":true,"result":{"startTime":1737479224833,"interpreter":"script.medium","endTime":1737479225434,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable ACCESS_DETAILS created.\n\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","## OML4R APIs to persist the authorization token","","OML4R provides APIs to persist the authorization token for use with the upcoming SQL calls:","","- The `rqSetAuthToken` procedure sets the access token in the token store.","- The `rqIsTokenSet` function confirms if the authorization token status. ","","The following example shows how to set the token in the token store and confirm it's status. A token has a lifecycle of 3,600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1737479225909,"interpreter":"md.medium","endTime":1737479226355,"results":[{"message":"<h2 id=\"oml4r-apis-to-persist-the-authorization-token\">OML4R APIs to persist the authorization token<\/h2>\n<p>OML4R provides APIs to persist the authorization token for use with the upcoming SQL calls:<\/p>\n<ul>\n<li>The <code>rqSetAuthToken<\/code> procedure sets the access token in the token store.<\/li>\n<li>The <code>rqIsTokenSet<\/code> function confirms if the authorization token status.<\/li>\n<\/ul>\n<p>The following example shows how to set the token in the token store and confirm it's status. A token has a lifecycle of 3,600 seconds, or 1 hour, and it can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","   access_token VARCHAR2(2000);","   is_set BOOLEAN;","BEGIN","   with url_string  as (select VALUE from ACCESS_DETAILS where NAME = 'URL'), ","        username    as (select VALUE from ACCESS_DETAILS where NAME = 'USERNAME'),","        pswd        as (select VALUE from ACCESS_DETAILS where NAME = 'PASSWORD')","   SELECT get_token2(url_string.VALUE, username.VALUE, pswd.VALUE) INTO access_token ","   FROM url_string, username, pswd ","   WHERE localtimestamp=localtimestamp;","","   rqSetAuthToken(access_token);","     is_set := rqIsTokenSet();","     IF (is_set) THEN","        DBMS_OUTPUT.put_line ('token is set');","       --enable to return token","       --DBMS_OUTPUT.put_line (''|| access_token);","    END IF;","END;"],"enabled":true,"result":{"startTime":1737479226786,"interpreter":"script.medium","endTime":1737479228950,"results":[{"message":"token is set\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the same function using the SQL API table function rqTableEval","message":["%md","","## Use `rqTableEval2` to run the `buildLM.2` UDF","","Invoke the `buildLM.2` UDF that was created in R with the SQL API by running a SELECT statement that uses `rqTableEval2`. ","","The `rqTableEval2` function runs a user-defined R function on data from an Oracle Database table.","","The `inp_nam` parameter of `rqTableEval2` specifies the IRIS table as the data to pass to the UDF. The `par_lst` parameter specifies the names of the model and datastore to pass to the UDF. The `out_fmt` parameter specifies returning the value as an XML string and the `scr_name` parameter specifies the script name `buildLM.2` UDF in the script repository to invoke. For this invocation, we persist the model to a new datastore, `ds2`."],"enabled":true,"result":{"startTime":1737479229402,"interpreter":"md.medium","endTime":1737479229839,"results":[{"message":"<h2 id=\"use-rqtableeval2-to-run-the-buildlm2-udf\">Use <code>rqTableEval2<\/code> to run the <code>buildLM.2<\/code> UDF<\/h2>\n<p>Invoke the <code>buildLM.2<\/code> UDF that was created in R with the SQL API by running a SELECT statement that uses <code>rqTableEval2<\/code>.<\/p>\n<p>The <code>rqTableEval2<\/code> function runs a user-defined R function on data from an Oracle Database table.<\/p>\n<p>The <code>inp_nam<\/code> parameter of <code>rqTableEval2<\/code> specifies the IRIS table as the data to pass to the UDF. The <code>par_lst<\/code> parameter specifies the names of the model and datastore to pass to the UDF. The <code>out_fmt<\/code> parameter specifies returning the value as an XML string and the <code>scr_name<\/code> parameter specifies the script name <code>buildLM.2<\/code> UDF in the script repository to invoke. For this invocation, we persist the model to a new datastore, <code>ds2<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","  ","SELECT *  FROM table(rqTableEval2(","        inp_nam  => 'IRIS_R',","        par_lst  => '{\"dsname\":\"ds2\", \"ore_service_level\":\"MEDIUM\"}',","--         par_lst  => '{\"dsname\":\"ds2\"}',","        out_fmt  => 'XML',","        scr_name => 'buildLM.2'));","    "],"enabled":true,"result":{"startTime":1737479230406,"interpreter":"script.medium","endTime":1737479267058,"results":[{"message":"NAME   VALUE                                                                              \n       <root><lm_obj><coefficients><ROW-coefficients><value>1.08355803285051<\/value><\/R   \n\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `rqRowEval2` to process chunks of data in parallel","","* The `rqRowEval2` function chunks data into sets of rows and then runs a user-defined R function on each chunk.","* The `rRowEval2` function passes the data specified by the `inp_nam` parameter to the R function specified by the `scr_name` parameter. You can pass arguments to the R function with the `par_lst` parameter.","* The `row_num` parameter specifies the maximum number of rows to pass to each invocation of the R function. The last set of rows may have fewer rows than the number specified."],"enabled":true,"result":{"startTime":1737479267551,"interpreter":"md.medium","endTime":1737479268071,"results":[{"message":"<h2 id=\"use-rqroweval2-to-process-chunks-of-data-in-parallel\">Use <code>rqRowEval2<\/code> to process chunks of data in parallel<\/h2>\n<ul>\n<li>The <code>rqRowEval2<\/code> function chunks data into sets of rows and then runs a user-defined R function on each chunk.<\/li>\n<li>The <code>rRowEval2<\/code> function passes the data specified by the <code>inp_nam<\/code> parameter to the R function specified by the <code>scr_name<\/code> parameter. You can pass arguments to the R function with the <code>par_lst<\/code> parameter.<\/li>\n<li>The <code>row_num<\/code> parameter specifies the maximum number of rows to pass to each invocation of the R function. The last set of rows may have fewer rows than the number specified.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Run the same function using the SQL API table function rqRowEval with structured table output","message":["%sql","","SELECT *  FROM table(rqRowEval2(","        inp_nam  => 'IRIS_R',","        par_lst  => '{\"dsname\":\"ds2\", \"ore_parallel_flag\":true, \"ore_service_level\":\"MEDIUM\"}',","        out_fmt  => '{\"Petal.Length_prediction\":\"number\", \"Petal.Length\":\"number\", \"Species\":\"varchar2(20)\"}',","        row_num  => 50,","        scr_name => 'scoreLM.1'))","        FETCH FIRST 10 ROWS ONLY;","        "],"enabled":true,"result":{"startTime":1737479268592,"interpreter":"sql.medium","endTime":1737479315409,"results":[{"message":"Petal.Length_prediction\tPetal.Length\tSpecies\n1.5295\t1\tsetosa\n1.3066\t1.1\tsetosa\n1.5295\t1.2\tsetosa\n1.5295\t1.2\tsetosa\n1.5295\t1.3\tsetosa\n1.5295\t1.3\tsetosa\n1.5295\t1.3\tsetosa\n1.5295\t1.3\tsetosa\n1.7525\t1.3\tsetosa\n1.7525\t1.3\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `rqGroupEval2` to evaluate groups of rows in parallel","","Invoke the `buildLM.group` function that's stored in the script repository from the SQL API. ","* As in we did in the R API, group data by the `Species` column and set the `ore_parallel_flag` to true to leverage multiple R engines in parallel. ","* Specify the Autonomous Database service level to use with the optional `ore_service_level` argument. The service names are `LOW`, `MEDIUM`, and `HIGH`. ","* The service names provide different levels of performance and concurrency for an Autonomous Database. In this case, we use service level `MEDIUM` to enable moderate parallelism."],"enabled":true,"result":{"startTime":1737479315876,"interpreter":"md.medium","endTime":1737479316306,"results":[{"message":"<h2 id=\"use-rqgroupeval2-to-evaluate-groups-of-rows-in-parallel\">Use <code>rqGroupEval2<\/code> to evaluate groups of rows in parallel<\/h2>\n<p>Invoke the <code>buildLM.group<\/code> function that's stored in the script repository from the SQL API.<\/p>\n<ul>\n<li>As in we did in the R API, group data by the <code>Species<\/code> column and set the <code>ore_parallel_flag<\/code> to true to leverage multiple R engines in parallel.<\/li>\n<li>Specify the Autonomous Database service level to use with the optional <code>ore_service_level<\/code> argument. The service names are <code>LOW<\/code>, <code>MEDIUM<\/code>, and <code>HIGH<\/code>.<\/li>\n<li>The service names provide different levels of performance and concurrency for an Autonomous Database. In this case, we use service level <code>MEDIUM<\/code> to enable moderate parallelism.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":100,\"lastColumns\":[\"NAME\",\"VALUE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%sql","","SELECT * FROM table(rqGroupEval2(","            inp_nam  => 'IRIS_R',","            par_lst  => '{\"ore_parallel_flag\":true, \"ore_service_level\":\"MEDIUM\"}',","            out_fmt  => 'XML',","            grp_col  => 'Species',","            scr_name => 'buildLM.group'));","    ","            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Multiple images returned from embedded R execution","","Here, we create a function ***RandomRedDots*** that creates a simple data frame and generates two plots of random red dots. ","We create a function in the R Script Repository named \"RandomRedDots\", and then run the native R function."],"enabled":true,"result":{"startTime":1737479363876,"interpreter":"md.medium","endTime":1737479364303,"results":[{"message":"<h2 id=\"multiple-images-returned-from-embedded-r-execution\">Multiple images returned from embedded R execution<\/h2>\n<p>Here, we create a function <em><strong>RandomRedDots<\/strong><\/em> that creates a simple data frame and generates two plots of random red dots.\nWe create a function in the R Script Repository named &quot;RandomRedDots&quot;, and then run the native R function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","RandomRedDots <- function(num_dots_1 = 100, num_dots_2=10){","","    library(lattice)","    x <- sample(num_dots_1)","    y <- sample(num_dots_1)","","    print(xyplot(y~x,","      main=\"Random Red Dots\",","      pch = 19,","      col = \"red\"))","    ","    x1 <- sample(num_dots_2)","    y1 <- sample(num_dots_2) ","      ","    print(xyplot(y1~x1,","      pch = 19,","      col = \"red\",","      main=\"Random Red Dots\"))","      ","}","","","ore.scriptCreate(name = 'RandomRedDots', ","                 FUN  =  RandomRedDots,     ","                 overwrite = TRUE)","                 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VLERG5ERMAgJxCsYN1paRkckCm41sLFdKdd+ZUHACwuJQUHTwo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\nh7tZhCvpo49UtqxVuQAvKHYA8p5vvtH58x7nrc+Zo4QEcwPBX73+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CWEHABAJ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\n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Using the `graphicsFlag` option for the REST API ","","If we simply invoke the named function, we see that the result is an ORE object. 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LxdbW1gKBQDKZvH379q9//evh4eFgMGg0Gr/6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\nV4ypE4b8frq6qK4+kAWoqW5mhp/+lK++YnaWaJRkkkSCTIZMhq0td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kgkyGQe2HvP59nY4OOP+frrI3fYQgghxHfT2spf/RWjow9sdKl7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\nthYdHQgEuMGOaJMx2NG2oWlYXsbICD7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iUiqVurq6Wltb9+7d29XV1djY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Call the `RandomRedDots` function from a REST API","","Invoke the *RandomRedDots* function from the REST API with *graphicsFlag* to *true*:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/r-scripts/v1/do-eval/RandomRedDots\"%md"],"enabled":true,"result":{"startTime":1737479370267,"interpreter":"md.medium","endTime":1737479370699,"results":[{"message":"<h2 id=\"call-the-randomreddots-function-from-a-rest-api\">Call the <code>RandomRedDots<\/code> function from a REST API<\/h2>\n<p>Invoke the <em>RandomRedDots<\/em> function from the REST API with <em>graphicsFlag<\/em> to <em>true<\/em>:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/r-scripts/v1/do-eval/RandomRedDots&quot;%md\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A second option creating separate figure objects: `RandomRedDots2`","","Here, we create separate figure objects and then create the scatter plot. ","* We'll store this in the script repository as \"RandomRedDots2\" and invoke the function to see the results. ","* Then, when we invoke \"RandomRedDots2\" using embedded R execution, we get both plots shown in the result."],"enabled":true,"result":{"startTime":1737479371139,"interpreter":"md.medium","endTime":1737479371572,"results":[{"message":"<h2 id=\"a-second-option-creating-separate-figure-objects-randomreddots2\">A second option creating separate figure objects: <code>RandomRedDots2<\/code><\/h2>\n<p>Here, we create separate figure objects and then create the scatter plot.<\/p>\n<ul>\n<li>We'll store this in the script repository as &quot;RandomRedDots2&quot; and invoke the function to see the results.<\/li>\n<li>Then, when we invoke &quot;RandomRedDots2&quot; using embedded R execution, we get both plots shown in the result.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","RandomRedDots.2 <- function(num_dots_1 = 100, num_dots_2=10){","","    library(lattice)","    x <- sample(num_dots_1)","    y <- sample(num_dots_1)","","    fig1 <- xyplot(y~x, ","      pch = 19,","      col = \"red\", ","      main=\"Random Red Dots\")","      ","    print(fig1)","    ","    x1 <- sample(num_dots_2)","    y1 <- sample(num_dots_2) ","      ","    fig2 <- xyplot(y1~x1,","      pch = 19,","      col = \"red\",","      main=\"Random Red Dots\")","      ","    print(fig2)","","}","","ore.scriptCreate(name = 'RandomRedDots.2', ","                 FUN  =  RandomRedDots.2,     ","                 overwrite = TRUE)","                 "],"enabled":true,"result":{"startTime":1737479372029,"interpreter":"r.medium","endTime":1737479372516,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","res <- ore.doEval(FUN.NAME = 'RandomRedDots.2', num_dots_1=600, num_dots_2 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zv30udxlydP4+VKwGIlCBHjmDDBtMnko8Ux7cYuMDJCV9+iXz5jJuI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\nevVC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q1Kp3Ke5\nwISQQ3RhJeS7iZXeXVtb+/TTT1OpVC6Xm5yc1Gg0Fy9etFgsOp3u3Llz+Xz+zp07MzMzc3Nz\nXq93cHCQ1YxgYd/q6upe9fqIxeL+/n65XM7n8wcHB6enpx89erS8vGyxWFguWnNz8+Li4raD\nv62trTqdzmw2P3r0aHx83OPx/PM//zMbkmP19o4fP97X13fx4sXGxkZW02RP2rxXRCJRR0dH\nJBIZGBhwOp2ZTGZpaUkgEJw6daq7u1sikVit1rfffttoNNbV1T158mRtbe327dv3799no66s\n3ofFYjlx4kRHR4darRaJRKVSacv+HBoaWl1dfZH9+eLu3bu3tLQklUpZRRsAxWKxo6PD4XD0\n9fV1dHTU1NSwfl+2mcePH2dNHR4eHh8fn5qamp2d5fP5xWIxnU5rtdru7u6zZ8+eO3eurq6O\nPTno9fqenp6lpaWVlZWlpaVwOPzxxx/fvHlTLpe3t7f39fVduXKFpSrOzMyMj4/7/f5gMJhK\npVKpVCaTEYlEbW1tJ0+ePH/+fHt7+y6zZAgh3xIFdoTsu5qamnPnzlVVVbE6Zz09PVVVVeVR\nLbFYbLFY+vv7f/jDH7IZBmKxuLGxka2VLhaLm5ubWYGx9vZ21tEikUhqa2utVqvD4fD7/W63\nO5vNsriEvScLFmUy2fHjx8VisdFoLNeYZan0x48f//GPfwyA3bDtdjsbzOXz+WazmYViVqu1\ns7OThYwsYquvr6+vr9fpdOl0WiwW9/X1sYrB7J21Wq1CoSgWi3q9vr6+fmNjg21OqVRiQQ8r\nh8sGdlmQoVarT5w4oVAo3G43K/NWbqdGo2ltbU0kEmazGUBtbW1dXR2biCCVSs1m8xtvvMGm\nrNbX13d1dRkMhuceCKPReOLECZVKxXZUTU1NQ0NDufeIz+cbjcaOjo4/+qM/amtry+Vy5Tod\n7AUKhYJtoFqtrq2tXVlZCQaDrAaKTCZTqVRms9lmszU3N7MePjaA/sr7cydCobCpqeny5ct2\nuz0ajZZ/LhAI2AmjUCjY9BRWErm6ulokEm2uCMgqvAgEgqqqqoaGhkAgEIlEisUi65mrqqqq\nq6vr6upqb28vj6Kyo9PX15fL5dbX1wOBANvwUqlks9kMBoNcLmeHjFVvZjN+WKkUNsRvNptZ\nF+D+le4jhADg7d9ke0IIk8lk2ALz5fq9MplMIBCUk+7Za8qVbNlr5HI5G6xkWerZbDaXy5W/\nsKwuHUuWZ+X+Weo6Cwvy+Twra1coFNh8BVb6H1/n+LPRNAAsC6pcIo69P1uFIpfLsbCMvYzF\nDYVCoTzjgf2VSCTaUu6Y/WGhUGA3fvbn7B3YZ5VLbOzSTvY+rBn4ev0MFmewzP1kMsl+xaIZ\nkUhULhG3k1wul8lk2Mex95TJZCzoKW97Pp8vvzNreblJ5TaztpVnD7CdzzaQ7SXWzm+/P7fF\nhn3ZGiTPXsDZvi2/f/ndnq38t+VIlf+WbQVrSfkP2Qex2iXsTN58KpaXLyu/bfnob9457Bix\nk2H3I0UIeWUU2BFCCCGEVAh6bCKEEEIIqRAU2BFCCCGEVAgK7AghhBBCKgQFdoQQQgghFYIC\nO0IIIYSQCkGBHSGEEEJIhaDAjhBCCCGkQlBgRwghhBBSISiwI4QQQgipEBTYEUIIIYRUCArs\nCCGEEEIqBAV2hBBCCCEVggI7QgghhJAKQYEdIYQQQkiFoMCOEEIIIaRCUGBHCCGEEFIhKLAj\nhBBCCKkQFNgRQgghhFQICuwIIYQQQioEBXaEEEIIIRWCAjtCCCGEkApBgR0hhBBCSIWgwI4Q\nQgghpEJQYEcIIYQQUiEosCOEEEIIqRAU2BFCCCGEVAgK7AghhBBCKgQFdoQQQgghFYICO0II\nIYSQCkGBHSGEEEJIhaDAjhBCCCGkQlBgRwghhBBSISiwI4QQQgipEBTYEUIIIYRUCArsCCGE\nEEIqBAV2hBBCCCEVggI7QgghhJAKITzoBpAd5fN5p9OZyWRsNptCoTjo5hwBoVDI6XQajUaL\nxXLQbTkC6AR7WXSCvRQ6wV5cMpl0Op1isdhmswmFdF9+Prfb7ff7bTabTqc76LYcOtRjd3il\nUqkPPvjg5z//+fr6+kG35WgYGxv7u7/7u6+++uqgG3I00An2stgJ9uWXXx50Q44GOsFenNPp\n/PnPf/6b3/wmmUwedFuOhoGBgb/927998uTJQTfkMKLA7vDK5/MzMzOjo6OxWOyg23I0+Hy+\nBw8erK6uHnRDjgY6wV4WnWAvhU6wFxeLxUZHR2dmZnK53EG35WhYXV29f/++1+s96IYcRhTY\nHV6lUimRSEQikXw+f9BtORqy2WwoFEqlUgfdkKOBTrCXxU4w6lN5QXSCvbh8Ph+NRuPxeKlU\nOui2HA2pVCoUCmWz2YNuyGFEgd2hViwWi8XiQbfiyCiVSoVCga6ML45OsJdCJ9jLohPsxdG+\nein0ZdwFBXaEEEIIIRWCAjtCCCGEkApBgR0hhBBCSIWgwI4QQgghpEJQYEcIIYQQUiEosCOE\nEEIIqRA8mi38XC6X6/PPP19bW8vn869zd6VSqRs3bgQCgWvXrtXU1Ly2zz26xsbGbty4cerU\nqcuXLx90W44AOsFeFjvBTp48eeXKlYNuyxFAJ9iLczqdN27c0Ol0165dk8vlB92cI+DmzZv3\n79+/du1aT0/P6/90oVCoVCrfeuutzs7O1//pz0WB3fMNDQ39zd/8zeDgIJ9PHZyEEELId1qp\nVNJoND/72c9+9KMfHXRbtkGLDT9fsVjMZDI9PT1/8Rd/oVQqD7o5hBBCCDkwH3744cDAQKFQ\nOOiGbI8Cu+crlUq5XK6hoeHP/uzPjEbjQTeHEEIIIQdmcXHx888/P7QrhTgdACUAACAASURB\nVNDYIiGEEEJIhaDAjhBCCCGkQlBgRwghhBBSISiwI4QQQgipEBTYEUIIIYRUCArsCCGEEEIq\nBAV2hBBCCCEVggI7QgghhJAKQYEdIYQQQkiFoMCOEEIIIaRCUGBHCCGEEFIhKLAjhBBCCKkQ\nFNgRQgghhFQICuwIIYQQQioEBXaEEEIIIRWCAjtCCCGEkApBgR0hhBBCSIWgwI4QQgghpEJQ\nYEcIIYQQUiEosCOEEEIIqRAU2BFCCCGEVAgK7AghhBBCKgQFdoQQQgghFYICO0IIIYSQCkGB\nHSGEEEJIhaDAjhBCCCGkQlBgRwghhBBSISiwI4QQQgipEBTYEUIIIYRUCArsCCGEEEIqBAV2\nhBBCCCEVQnjQDdiqUCik0+nV1dWJiQmv1xsOh/P5vF6vP3nypMPhMBqNkUjE4/FMTk6ura3l\ncjmNRlNTU9Pa2trQ0AAgk8n4/f7FxcXJyclkMlksFmtqaurr61tbWzUaDQAej3fQm/jdUCoB\nQC6HWAypFHI5yOVQKiEWQyTa908vFhGNwuPBzAy8XsTjEIuhVKK5GbW1MBggle57GwghhJDX\n7tAFdtls1ufzjYyMfPjhhwsLCxsbG5lMpr6+XiwWq1QqnU7ndrsfP378ySefjIyMpNNpi8XS\n3d3NAjihUJhMJufn5wcGBj755JNQKFQsFru7u8+ePavX6xUKhVB46La3MpVKKBaRSiESwcYG\nwmEkkzAYYDZDp4NKBZEI/H3rLS4Wkc3C5cLICG7cwNQUvF4oFKiqwltv4fx5iEQQCiEQgKL8\n7yz24FEsIpdDPo9iETweeDyIRBAIvnFulErci0sl7jV02hBCDrFDF+j4fL7f//73s7OzUqn0\n2rVrDodDIBBotdqWlhaz2VwsFh8/fvzb3/62rq7u/fffV6lUbrd7dHR0amrKZrNZrdZAIPDx\nxx8Hg8Fr165pNBqRSDQ6OjozMzMxMSEQCOx2u+g1dBeRdBqhED7/HEND8PuRTKJYhEgEpRK9\nvejtRU8P9Pr9+vRYDG43fvtbfPUVVlYQDCKVQjSKcBjpNFZX8e676OmB1QoK9L/Lsln4/Rgf\nx+oqvF6IxVCr0d6OhgYYjU/7dEslpFLIZFAsQiKBQkGBHSHkMDtEN7ZSqZTL5Xw+37179+Lx\neF9f38WLF0+fPi2RSFg0lk6nE4nE8vLy5OTkmTNn3n77baPRODQ0NDk56fV6p6am+Hx+KBSa\nmJjQarXnz5+vq6sTi8WBQGB6enphYUGv11utVgrsXgevF5OT+PRT3LyJWAz5PHg8FIsQCuFy\nIZHgBkOl0n3pt/P5MD6OW7cwMIBcDsUi9/NIBIEAwmEYjZDLYTZTYPcdxfp0FxcxNYX79zE7\ni/V1SCTQ67GxgVAI3d0wGiEUIhSCz4dIhHs4kcmg1cJohMHwmpIKyB5ina/U57qTYhHFIpJJ\nxOPI5VAqQSSCTAalEkLhPo6xkL12iG5sxWIxGo16vV6Px2OxWK5du1ZfXy+VSvlfn0/JZNLp\ndObzebPZ7HA4HA6HWCy22+2nT5+ORCLT09MymaxYLCoUipqamqamJqPRmM/nOzs7C4VCMBh0\nuVyFQuFgt/G7YmQE/+//YWQEwSCy2acjWXw+xsbA56O+HnI5amv35WIxP4/PP8fq6jeiOiaf\nh9eLmzeh1aK/nzLtvqNyOYRC+OADfPopvF5EIkgkwOdDLMbCAp48wXvvoaMDOh2++gqffMLl\nEgBcj93Vq3jnHVgs0GoPekvIyygUUCxyQ+3kWYUCMhnMzWFyEn4/cjkYjWhoQHc3lEpIJAfd\nPvKiDlFgVyqV4vF4NBotlUrpdNrpdHo8nnQ6rVAoDAaDw+HIZDLBYLBUKmm1Wo1Go1KpAKjV\narvdnkgknE6n3++XSCRSqVSj0Wg0GrlcnslkTCaT0WicnZ2NRCLFLbf5r01PT09PT2ez2Xw+\n/+xvFxYWAoEAm5xBnoNl162t4dEjeL1Ip78RWhWL8PuxvIypKVitsNn2pc/D58P0NEKhrVEd\na14yiaUlOJ2gKP87y+/H9DQeP8boKNJp5PPcycDjcQP3Wi1WV6HR4O5d3L6NRAKZDHg8CIUQ\niwGgVMKVK1AoIBAc7Z4M9oVliQrsGYz10Gi14POPds8W27R4HGtr8HoRCDzdQL0edXUwGqFU\nHu3Dt1cKBeRyWFnByAimpjA3h1AIuRy3oxYW0NODri4uO5kAALLZ7N27d/nbnT98Pl8ikdTW\n1nZ1dUkOIiA+RAepVColEolUKqVSqXw+33/8x384nU6n01lTU9PX1/fnf/7nGo0mHo8DUCqV\n5RFVoVCoVCoBJBKJeDzO4/GkUqlUKmWzX8v/zGaz6XS6xLqOnnHz5s1/+Id/CIVCiUTi2d/m\n8/lEInHy5Mn92vJKUiyiUEAwiLU1ZLPbhFaFAiIRzM6iuRknTuxLG2IxeDxIpbb/bT6PSATx\nOHY4GUjlW1nB3btYX0cqxc2cYEol5PPwePDZZ5DLIRQiEIDfj2KRO1uyWaRSuH0bKyswmdDQ\nAJnsaEcGxSLyeayvY3oa4TCKRW67FIpK6KFhR/PTT3HnDh4/RjoNHg86Hdrb8cMfor//yB++\nvVIoIB7Hgwf4v/8XbjeSSe7qLRZDJoNcjv/9v2G3Q6OhwK4smUz+27/9269+9atnfyWRSLRa\n7Z/8yZ80NDR81wM7ACKRiM/nx2IxiURy7Nixvr6+ZDLp8/kKhcLjx48tFotarQaQSqXKXWuF\nQiGVSgGQSqUymUwkEmWz2Ww2y2K4UqnE/ikUCkUi0U61To4dO/aXf/mXqVQqm80++1un03nj\nxo392uYKUyqhUEA+j3x++8iJvSCVQjq9X6EVS4QKBLb/LZ8PuRxS6T72RhSLSKfh92NyEk4n\ngkEAEIlgs6G2Fk1NldAdcqQFg1hcRDj8jaiujA3UxuMQCpFKfaNnlyVppVIIh+FyYWMDNttR\nvdWxcbfpady6hZUVbGwgHkexCI0GFgvq6nDqFE6cOMJdktkshodx/z4GBjAxAZcL7K4RjSKT\nAYBQCO++C4MBMtnBtnQPsOtqPI5QiIvJNBqoVJDJXuj8jERw/z7u38faGiIR7ntRKnFPMtEo\nHj/G55/jzBk0Ne3/xhwNEonkrbfe6unpefZXAoFALpd3dHRIDyjb5xBdkng8nkwmk0gkiUSi\nurr6/PnzjY2NCoXixo0bQ0ND4+PjsVjs3LlzAGKxWDqdzufzPB4vnU6zsiYajUatVvP5/HQ6\nnUwmc7lcPp8vFArxeDwej0ulUrlcvlNgd+bMmTNnzuzUsIcPH46Nje3XZh8G7HbFeuNZGgoA\nPp/reOfzX+7KzipH8Pnb3DI3f+L+Uaths8HtBo+3zQeJxTAYoNHs1+2KDQAFg5icxH//N4aH\nsbSEYhFyOfr7ce4cJBJugvBRCezY6cGOZrnex1Fp/LbYvGkWxzyrUEA6jUIBYjG2y80AgHwe\nPh9cLhiNUCj2tbH7JZdDMIgHD/AP/wCfD5nM03x5pRI6HTIZtLYe1a47FpQ8fIiPPsLEBPz+\np4cyHsfiIpxOlEro7YVEcrQDO/bdZBVD3W4sLyMeRz4Pux3V1TCZoFRCJHrOFzYcxp07GBxE\nLPY0KxpfZyUWCpiagkIBu50CuzKZTPanf/qnP/3pTw+6Ids4RIEdn8/XarVVVVWs5tzmoWse\nj8fj8RQKhc1mEwgEHo9neXl5aWlJq9Wur6/fv3/fYDC0trbW19fH4/FEIrG+vj43N8cq201M\nTMzOzjY2NlqtVgHlzG6rVEIigY0NjIxgZQWBAFf6oasLzc2oroZc/qJvxedDIoFKBYPhacr5\nZkIh1Go0NqKmZr+6Opqa8Oab8Hqxvv40Ti1/usmECxdw/DiXLLXn2Fjz73+PL77A7CzcbsRi\nKJWQTuPJE+6R+sIFXLlyZG4n6TSSSSQSKBSgUu17f+drIJdz/TTbhv7ssWSXuJ/dStPprSmk\nR0swiM8+w61bCAaRTD7tvCz/75Mn+OILnDiB+vqDbemrYIHdygpmZhCNbk2oZc+xPh9GRyGX\nw2Q6oFbuhWIR4TDm5vDpp5ifRyTCTRqTy2GxoK+PKy+1e3SeyWBjAz7fNiMt7GwPhbCygnh8\nXzeF7JVDFNjxeDy5XG42m1taWvL5/NraWiqVEovFbre7UCgYDIbq6mqTyWQymTQazfr6+oMH\nD3Q63fz8vMvlqqmpaW9vr6mp8fv9RqMxk8kMDw+7XC6xWLy4uJhMJmtqampqaio2sGPXqUzm\n6TIPAgEXnMnlzxlMYf0Tc3MYHcXAABeLSCTQ6bCxgUgEfX2wWiGXv1AXF7sjWq3o7sbUFFwu\nrr+HVRng86FWo7oabW2w2/drbprNhlOnMD0Nvx8+Hzd1XyCASASTCV1duHABHR37VasikYDb\njQcP8OWX3EezO2Umg9VVRCKIRqFQ4PRpiMWHd3Yeu7jHYtw+9PsRi6FQgEYDgwEmEwwGbk7o\nUYzw9HrU12NqCnz+1ls+jweJBBoNeLwd0zTZ+cznH+0a15EIBgfx5AnXSbM5QmUTSubmcP8+\namuPZGCXzyOVgs8Hj2f7YKVQQDiM+Xk0Nx9QE/dIoYCFBdy+jQ8/xPw8CoWnl1yDAR4PikXY\n7dDpdovtCgXEYtzD27PK6QfbpSqRQ+gQBXYA+Hy+yWR6++23Hz9+/N///d+RSCSTychksrq6\nuosXL3Z3dysUit7e3mQyOT4+Pjg4yIqbWCyWtra2jo4OuVwuFAqvXbs2PDx848aNdDoNwGg0\ntre3d3Z22u32il15gn3xXC4MD2N5GX4/1GpUVeH4cTQ3P2fmVzoNrxe/+Q1X+oGFhnw+Njbg\n9WJ+HvE4Tp1Ca+tLdHH19SGfxy9/iWCQq43ExmfFYtTUoKMDx47B4divHju1Gg0N+OM/RlUV\nbt7EwgKCQW4q3IULOHcOp07BZtuvoMrrxdQUN7a1pb+QXT0XF7G8zIV3hzawA1AoYH4eN25g\nYgLLy9ytkceDzYbOTrz5Ji5cOKphTU0NTp7E8DAmJp6OMjNCIbRadHcjlcLExI4dcnw+TCZU\nV+9Xv+9rkMnA6YTHs01VIDa0x1bki0QOqH3fTjbLhSksmfXZflmW6RsIbDOqcLRkMvjyS/z2\nt1heRiLxdEtLJQQCePQIGg26utDYCItlxzfh8yGTQSLZ8RstEnHTichRcIiOE0uAUyqVXV1d\nADKZTCAQSKfTJpOpsbGxo6ODlRd2OBx8Pp+tMJbJZIxGY2NjY3t7u06n4/F4Go2mr6+PzZOI\nxWLFYrGpqam9vd1ms7HJs0cV+7pGItyMzlwOQiE34snjIZ3G8DBGRjA+jvV1hEJQKmE0wuVC\nTw/6+2GxQCLZPrzzeDA2xv0ty7ErXxricWSz0Gohl6Oh4SXuYVYrTp9GIMANyCYSyGYhl0Or\nRXMzF9Xt3+FgPWGdnVAooFZz6cBSKTQa9PWhvR01NfuYFxWNYn0d0eg290s26TIeRySCUAha\n7eEtpJdMYm0Ng4NcJ67Lxd0gCwWsrT19crBYoNEcdFtfnsGA1lacPo1YDAsLCIW4aiYiEaxW\ntLbi0iXEYhAIuMo45VmxrNeZFfeqrYXJdIQDu2IRmQy3osazSiVkMkgkdswyPOQCAczNcfN8\nt83oZWMaR708Wy6HeBxLS5ibQzS69WCl0/B4sLyM2VlotbsFdlIp6upgs3HlfjbvMXbOm81o\naTmSX/bvpEMU2DFSqdRqtZpMpnPnzhWLxVKpxOfzhUKhVCoVCAQ8Hs9kMul0ura2tnw+XyqV\nBAKBUCgUi8UsLpRKpQ6Hw2azXb58mU2MZfNhxUf3+ltWKsHtxuQkVlcRjUKpREMDenshECAc\nxq9+hU8+QTLJJUGzkcd797jsCrkcRuP2b7u8jFu3sL7ODcds/kqzcr5ffgmjEd///ksEQ2w2\n1k9+gjffxMwMPB7E47BYYLejvp6LMvcV61DR6dDZ+XQlUFaBli0Gun+SSQQCSCS2CewArhYa\ny1uvqoJOt48t+TbCYdy7h5s38eQJNwTDHuVLJfh8SCRgNEKnw/nzR/Jar1SithbXr0Onwwcf\nYHISwSAEAqhU6O7GG2/g7bcRj0OjwRdfwOfjHnjwdTRQV4fTp9HUBKPxqM4YBcDjPadULyva\nd0Q3cH0djx7B7UYut81v+Xyua7au7mhXmc5kEIkgFkMyuc0oKnuSDIUwM/Oc8XSVCj092NjA\nwgJyuW/sNHbZdDhw+vTRTkb8Ljl0gR2PxxMKhUKhULZDarlAIBAIBDvVhuHxeHw+XyQSyV88\n3/+QY9kSi4u4fx/T01hchN+PVApSKYxG3L7N5QONj8PnQ6HAdbnxeMhkkE5jcRE3b0Ikwhtv\nbJ+tHwpxI4PbPtqyECQc5sKUF7zKsxm1LKtDLud6/lQqaDTQal/HLMLyTev1P47LZNDpuCoD\nbJrhZuxSKxRCo9nfyRPZLMJhLC1hfByhEJJJrou3thYNDWhq2rEHl0kkMDHBJZ6zxJryhpQ7\nCYaG0NqKlpZ93Ip9wu5VdXUQCKDTcYW72LwfhwP19bDZuFhWo0F9PVwuhEIolbgM0a4u9PRw\nf350yWSor8fq6jb9dix9kG3poX322EkggLU13LmDgQFsbGzzAtY1q1Khpga9vbBaX3sT9w5L\nJGD3iJ2Uyx3sQqXCsWOIx7np/MvLALghbLMZTU04fx5nz8Js3tvmv6Jkkhu8isUgFkOhgMnE\nZbYc0eSQvXboAjuyFct3mZrCv/4r5ufh9XLJWyxw4fNhscBoxMrKN7qI2Fc9l4Pfj1u3YDDg\n7NntI4lEAj7f1u73Mja1gs3+e6kaJezqqdF8qx6d8kx+tmms4411RgqFh/RrrFKhuhpqNUQi\nLs7ejLVfJuPWq90P7NAnElhawpdf4je/4WY6SySorsbp03jzTRgMXPS5k1QKi4tYWdlm4ic7\nEE4nxscRCHAP96xD9AiVQREIYDLBZEJf345LiJrNsNvR24uJCW6GtcWCtjY0NqK29oDavXcU\nCrS3w+WC280Ny+Lre7lQCLkc9fXo64PBcNANfUl+Px4+xJ07ePBg+2R/gYCbMdrYiJ4e6PWv\nvYl750W+d+wxe/eHEHYyFIuIxcDjYXOh/uZmXLyICxfQ17dnzX5lbI6gz4flZayvw+3morq2\nNlRVQa2mNW0ZCuwOvUQC8/MYH8faGpcMxPrkWGI+jwe/H4kEYrHtV9DKZOD1cpMYtqVUoqoK\na2vbpxizbgyp9GDqlGYyCAQwO4uhIfj9iMe5K3JvLxobD2ltWLMZHR2wWqFScfu/HNux0Z/m\nZrS1QaXar2m5pRLicczO4j/+Aw8eYHmZOzeyWfh8GBrigvVz53a7UrN4epen/HweySRcLszN\noViEQgGDAVLpUc052+m+yHrsdDouLZ3VvlapXm/j9odWi4sXUSjA78fiIrzep2mUrJPm9Gmc\nPn1YOmlenM+HBw+wtLT9yjc8HqRSNDTgRz/CpUtQqY52HMBmcLPUl2erbbPED70ezc07puJs\nZrXirbfQ1YUf/hCpFIpFSKUwGLjlHw8Dvx+3b2N0FNPTiESQTHJrY+h06O/HtWtHNet3rx2+\n+yLZIpnE3BxmZ7nKHeX02HL3ezyORGLHuC2fRyKxY4ccAIMBTU2Yn+dKCm9Jm5XJuCqXQuFr\n7YxhZaj8foyO4t49fPUVV3uFDSYGAojHIRZDqz101eDUatTWorMTa2tYW0MggFgMADfqx1JV\nenq4MjT7oVjkVsu9fRtPnjzttMjnEYtxlSzSaZhM6O0Fdohp+HwuStv2tzwe8nlEo5ic5EIB\nlQoWC6xWWCxHr6TtLie2XA65/OgFNy9CoUBbG5JJuN0wm7G29rQyeU0Nurtx4sSRrEbLViz0\neHZ8LGHFLC9exPHjr/uytudEIigUcDi4a3g0+o1hWVaM3WiESoVIhPu2isVQqaBQbLOcmk4H\nnQ4dHdwiFoUCFAouz/LA91KxyM3oGhjAnTuYm+OubOWqk14v9HqcOEGBHSiwOwLSaayuwunc\nurpR2e4jpAIB9x3e6ZtZV4dz5/DkCSYnt+aEiUQwGnHmDLq7X3dPDCuJOTGBX/4Sjx/D4+Ei\nkkQC0Si8XrjdEArR0QGH47U27Ln4fCgUuH4ddXW4dQvj41haAgC5HD09OH4c586hvn4fQ598\nHouLmJhAOLxNXdZcDm430mlcurRb0qRUipoaVFcjGt26+BsbTWY9wZ9+ivv3uaJuIhEuXcKb\nb6K9/WjnLX1HsOPY2or/9b/g9cLn43p2tVoYjTCbj94gLMOuErtUXGOhAMvlqAASCS5fBo+H\n//xPLCxwmRIsvNPruSTC5WU8fgyfDxIJzGb09KCtDU1N23/92YmhVKJUOkQLyhUKWF3F6Cie\nPMHyMpLJp30ZbCR6agq//CUEAnR0HGhDDwUK7A49djNmSWbbxnDstGZfvy03cj4fKhUaG3er\nBmwwoK0NZ89yaVVsujsbga2pQVcX3ngDnZ37NW64ExadDA5idBQLC09LjOZyXB3mcpraawvs\n2CNjLMbVvsrnoVRCrYbBwJVuYaEzSy50OLiOq/p6OJ0AIJOhuRnNzfteNaBQwMYGVle/ce0r\nYyUP83lEIshmuWzFZ6nV6O9HIMCVmC6vHVl+dmd1W9ipgk1J3IUCV936MJdfJvj6uqHVQquF\nzcZ1/LMa1HL5Nt05RwUrzM4WR3n2gslClkpazF4oRFMTd3ViRU/YGLRMBqkUIhE8Hm5dY7eb\n68NjiZXJJOz2bXqjN99QDo9CAevrmJmBy7VNYRcAgQDGxuB0ckUhDlv7X69KObkrGFu0fpfr\n7OayBVuqrYpEMBhw4sRuCy0oFKipwbvvoroaH36IiQl4vdyc1nPncOkSLlyA1fq6A7tcDmNj\nuHePW+Rx8wWaRborK/jkEzQ04PLl19QktgLswgIeP4bLhWQStbVoaUFv7zbLcojFsFig13Np\nTPg64BMK931Pss5O1se5y5NALodEAkrl9nc4nQ5XriCRwKNHyOeRyXB3C7GY2/8s0XPz5bVU\nwugofD5u1q1OR4HdkSGVQiKBXv80dj/wobdXplCgthY+H3y+bXJL2DisxQKhEMViJZyifD50\nOvT1oaEBLheWlriMHZsN4TD+8z8xPo5wGKkUV6xxaQlTU5ifRyiEy5ePTJpBoQCXC4uL3Jpp\nz8pkEApxIwxSKQV25HCTy9HYiLo6SKXfyMTH13dogwFmM8xmlEqYmkIohHye63Jrb8eJEzh/\nHk1N28QTbBo8G9ZkC+8cO4aODsjlEIu5osQOx8FUYS0W4fdjY2PH6CSZ5MrjvR5uN1ZXce8e\nRke5lcGyWUxPY2wMExPo6cGZM9wlsnxTFAoPpleA7Tq3m5vnuC1W74PVZNmWSAS9HqdO4f/8\nHywtweVCPI5MBmIxvF7MzXF9llsOTSaDaBROJ5xOrpA9ORIq6S5os+Htt5FKYXkZ+fzTMpbs\nv2IRXi8ePUIuB7sdej06OtDZCZnsCM/7YXWdDAZuLkU2yy28sb6OYBDBIBfqsWf+fB65HGZn\nUSrBasWpU89ZGfnw2L2qS3nQ4Og+k+wdCuwOPbkczc1cFwirBV/GCn/YbOjqQmsrN5NxZQXZ\nLFe07PRpXLmCU6dQVfWN92TfgWQSoRAmJzE1hUgEPB7sdjQ1oaMDKhW3KMJBfUlYwY5tu9wZ\nNhS4S+yyhy0BsL6O27fxwQcYHHw6204kgkyG4WEsLnI3iQMf32G9aMEgvN4d04zYbUAm262g\nIKvJ0tODjg5MT2N6GsEgEgnweJiYgNOJTGb7Q1MowOuFywW7fW+2CF9f0Nk4b3nR4aPesUT2\nSXU1rl7FygoGB5FMcmtnsy8sO4U2NrCxgfv3YTDAbsd770GjeToocUTPKPaNYLMfAGQyePQI\nGxvc13bzpYBdIlwu+Hy4cAG5HLfhLOBjadblpZDFYq6AyIHvFraIM6vPsO0gOwtwD20ZrNfr\noO9D5LnYoF5fH957D3fu4PFj7qwtFLhlnS5fxltvwWyGQIDjxxGNIpOBSASpFDYbV1Nti1yO\nWwL8o4+4pzq23rlaDYsFdXX43ve4hNyD+obw+dx68z7f9i8Qi/e9xm9ZqYTxcXzwAVeWvXxN\nYXVD/H6srmJyEhoNamtf95j1Fuk0olHuUr7TRGmtFk1NLzQEw4au7HaoVNxDRSAAv3+3+JU9\nM2xemO7bK5W4kqSsiGO5BJ1EcvCRNDlsWI7d22/DZML0NLd2ttf7tPu/fGZGo1hZwaefIhDA\nn/8513dVGcr5GDstCsfitlQKfj90OhQKmJnB5CTGx7nuPXY1O3kSDQ0wmQ5+zFoggN2OlhZu\nFsiW0VgeDwoFjEZotRCJKuc4viq6LB56IhG0WrS2co9Wm8vesidOlgbHilP09XEPXrvnciWT\nmJ3FrVv44ANEIigUuGE1kQhqNYxGqNVob+dWiT0QfD5sNjQ0YH19mxJ9bEmA5ubXMXGvUEA2\ni5UVDA8jm/3GUDgrDheNYmMD8/OorYXNdsCBHZtZwmYQ7xRaKZVwOF6oLiu7Pur13ItZ4bqJ\niR2vm+X+QqcTLhd3Or3CA3SxyA2isXqkySTW17n3TKchEMBqhd2OmhqYTJBKKbwjT7EUiJ4e\n1NdzBc9WVjA+jmBw6wIbqRTSaYyOIhRCfz+6unZLTjhaWLko1q2+0wMeK1SUTiMYRCCAu3e5\nqs5snWtW/S4eRzqN48ehUBzwlU0ggM2G1lbU1SEUgt/PjbDj6+dPiwU9PbDbK+QIfju0C44I\ngwH9/airww9+gHAYmQxkMqjV0GphMn0jeZ/dR3e/lfr9+OQT3LyJSOTpUyz7j41+Tkzg7l30\n9z9nhcH9IxKhp4cbKWbPZ+UwpTzt9No1tLXte0tYTm4yueMuZYPC6+vwep+zbs9rwGrU7VLH\nhBVoNRheZWE3kYhbgXenNy8UkEphfJyrg3XyJE6cgFz+0pdaNmk3AAUHxQAAIABJREFUGuWC\n5tVV3L2L2VlkMtwgESth+N57uHgRdXXcrGRCythDBSv2MT+PbBajo9u8jAVAsRi8Xni9qK6u\nkLCAz4dSyZX5ZGsbbsGendh6XNPTGBrC//wPZma4yWqsFmYqBa8Xfj+MRtjtB7y4HFtjqasL\nFy6gUMDDh8jlwOdzC6UoFDh2DH/xF+juPshGHhoVcRJ/F0ilkEqh16OhAbEYslnuJ8+u+MlS\nDXZSrmk8NYWFBaTT23RBFQpYWcHYGBoaDiywY89nfX24fBkqFZaXnybvszGCc+dw7hxqal5T\ne3bJ22W/YuWjDhxL+9vp5sTODbZK5rMD9M/F1mKy23HqFEZGsLgIbMpZxtcTljc2kM8jlUIo\nhEgEXV1obn6h92enH1vKbGMDoRASCXg8cDoxNob1de5UZxG2RAKlEjwelEpuzvh3PrGGPMWi\nf4MBfD78fi5jeFssNTkSQTj8QsszvILyAo/5PDc1fr+zXPh8VFXBbudWUN3Sf89qber1MBgg\nFmNmBrdvY2oKXu/TcVs2ET4Wg8WC0VGuTsIBYtUhrFacOwe5nCuxybLJZTIYDDh+HP39R7X4\n4l6jwO5IYZ1VWi33//FKqb7sIdXnQyi0TQ8TC1DcbszMIBL51i1+VXw+tFp0dkIqRX09btzA\n2hqCQWi1aGzEW2/h1Clu6GS/scdBNgK+bWzHUoyVSsjlBx9bsJUTd7qNsTELrfZFh2K3/fOm\nJvzoRxAIuPp87HQq75xiEdEokklsbGBuDiMjeP/9lwjsEgl8/jk+/RQLC4hGASCd5vLf2S2n\nvIezWXz1FdJpHDuG6mrKqiHbKxSQSHA1HXd5NkuluLUW9gObtJFIIJGAWs2tOrOv1wqhkCvG\nVM6F2DziIRBw5UutVggEmJ7GgwcIh7dm47Fg1OXC3bswmdDevo8NfkEaDc6eRWcnrl2D2w2f\nDwoFdDrYbFziEF0HAFBgd8Swa8G3zGMtX1B2SW8vry19UNjVh62Ww+fDbOZKMbFhxOZmWK37\nuCrXZuyJ0GiEzQa/H7HY1v3GHn/r6w/F2rWsU+3kSXg8GB7mMlFYg9n+rK5GaytaW1/l0Zad\nD3o9jh1DPg+7HWtrWF7G7Ow3Vrkod054PMhmMTGB6WlYLNwDyS4WFjAygocPMTUFnw/pNJcG\ntPl2u3nmSjwOpxOPH0OpRHPzjgugke8y1tWk0ex4brDuNJUKev3ep5FFo5ibw/w8FhaQTCKb\n5bII6uvR1IS2tv06adlkgoYGvPceTCaMjHDXLjZEW1uL3l5cuIDOThSLiMe5vLpnsYWnnc6D\nfMjfjFVsZVc5jQY1NVzZJrWaq1ZDVwAAFNh955RDQ7EYItGOKfZSKdTqA86WBSAWw2iE0Yjj\nxw+sDew6YrGgtRUAMpmn1RPKJdqlUhiNkMkQjXI7ViTigs7XfKFhKXSnTyOX44KtcneaVAqT\nCZ2d6OpCQ8Orr2SvVnOzp8+exb17uHkTHs821312S4jHMTmJkRGcOvX8wG5uDv/zPxgZ4crH\nP3dou1BAKIRHj7j1P3Q6rnIeXdxJmVgMsxlGI1cp41nlQdu9LdjJJhL5fLhzB198gYEBrj5w\noQCDAefO4Z13YLVCo3n+h5aTPcr94mxlhV2evVlxEFbMxWRCqYSFBW4ZRpMJx4/j8mW88w5U\nKi6ky2Z3fM7PZrmH6sODXWAps3ZnFNh9J7Gix04nlpe3zhRjqyja7ejro3yFp3p7AeCTT/Dw\nIbduLxuXZP/5fPiXf8FHH3FdRx0d3PysA4kwBALU1uLSJWi1mJt7uq6i1QqHA+3taG7eLeXo\nBbGlxLVaqNVcCvNOZmfx6afcHOfdeTwYH+fmyrxIwmKhgGCQG0WamcH3voeLFymqI9/AFryu\nr0d3NwQCrK09PbvYAscOB3p64HBArd7LHvd8HvPzGBzEwAAmJpBMolDgKiRHIhgdhVwOtRrH\njnFPjLtgj2cbG3C5uHI/7HGXlbjafdsNBpw5A4sFsRiSyadPoRYLVCou4U8uh1K5fWEU1sNX\nU7O/qyCSvUaB3XeSUoneXoRCiMUQCnFPYywPQ6nk0i96el4lDeu1YSVawmFujjC7aMpk3Pqt\nmxdv3RP19dDpkE5DLMbyMsJhLuGa5YGFQhgYAL6ezOtycSt/lPuQXid23VepUF2N9nbMzSGR\nQLEIux11dXs2h5TN0tBouCSeXQI7lwvDw3j33ee/ZziMtbVtqtvshHUKzs8jEMDSEvR6dHVB\nodiDsHUnLAOVVfbH16WeX0MuPHllrFR7fT3On+dWQyn3P8nl0GrR1/f/2Xvzp7bSLM/7A0KA\nhASSEEKIfbUxYDDe8Za205mVlZlVNVVd3RVTE909MzF/0kRMTFTMRHdE93TN9Nvd2VlZWZWL\n02k7va8YzL6vQgItICEBEu8P50mxWGCM2a1vEB3VaSGuru69z3nO+S6cOUNJyRZbOy0s0NXF\ngwc8f87Q0AqL4GCQgQE1TBQrq7UgJZ3LxeAgvb309zM7i0ZDXh7FxZSXY7Ott/2Wzy5x4eu8\nRt6tv381z0Q2+RYLhw/vm+SxBIBEYfeOIjubq1fRaHC76e5mYkJ19SMR8vM5fZozZ6iu3iH7\n380hHMbn484dbt9mbEzNQGXzLZZUW7vWClPngw84eZLpadxuRkf505/45pslxzUgEqGrSwWd\nhcOcO7c682PHICKb9HQKCpZGsZILvlVITlZNu5SU9Qo78UyOy+BZBbFgeCNxsfD5/H6iUTo6\naG7m8GHy8t7gHd4IovYVawzhVublYTQmwtP2OkpK+Iu/4PBhnj2js5PxcZKT1c7n6FFqa7d+\nEyuF3bNnTE2tvvhj4vGHD6mvX+9N5uaYmODmTX7/e1wugkHm59UONj+fw4f58EPef/+tjlOr\n5eRJvF6++EKplARS1aWmUlDAhQu7Zo+QwKaQKOzeSaSnU1TEiRNMT9Pfz/i4qkukNqqvp6bm\n9Yyo3YJwTQYGuHePO3d49IiJCWZmVC/N6WR2lkiE4uKtHCUL006SPCIR+vrw+0lOZm5uRS2y\nuKg6iA8fkpFBXd2uFXbSMpTsyO37E2YzNptyG1lLUSge9xspsvV6srNZWFiP8fMqFhcVnWB4\nmM5O8vK2pbALh5maYmCAzk4mJvB41FQrJ4fSUg4dwmJJkH72LgwGDAZSUzGbKSpSfjopKYRC\nTEzQ10cwqIzZt4pmF40yOYnTGT/tWiITR0dVy/9VCK/O6+XuXa5f5/59ZmYU0w5ITsbpxOUi\nL4/DhzGbN2NLKdBqOXSIUIhAQJkKyfPTaCQ7m+JiLl6ksnJPT28SeAWJwu6dRFISqanU1FBe\njsejZrKLi+TkYDLt9Q5ENKr84v/7f2doSDkqyyhWcq89HrxePv54WziCQq3z+dRDcLlzsiAS\nIRikrQ2Tac2n9sFAcjJWKw4HGRmkpKxZ2KWlbVSIk51NWRmhkJrGbry2k/Goy0V//3bpbAIB\n2tv5+mv+9V/VEhuTzjQ18dvfUlOTKOz2Oux2srOpqKCnh+vXaW3l2TP0emw2LlzgxAmlrd4S\niA2QFElxsbCgdLLrvMP4OJ99xu3b+HyrhW6Tk8zO8uIFR44oBsLmIJRcEQXfv8/33zMxwews\nhYXU1nLtGtXV2Gy7L6RL4E2QKOzeSUj7JDVV/WRlKU9/g2GJM7RnEQzS00NnJ06nCsaNueNK\nlFZHB8nJNDZSV7f1pi3ybqEQTmcc6xNBNEowyMyMomkfYAKWiOzee49IhMePVzcnpAguKeHU\nKXJyXv9uhw7x8cfMzeHxKDFvJKIafmlpJCWpUVHccy5/KzV16x1wZCMxNMTXX3PzJsPDzM2t\n+EKfPkWr5Re/wOFQiem7iHAYrxenk+FhlUFitZKbS0EBGRkrLkUxVxPWoEZzwFdu+dTiV3fr\nFnfu0N6uYrI1GgYH8XoZGODiRaqrt8a3KDkZi4XcXGZm4nSgxY4kbpA3qG/wyRPu3uXZMyYn\n49gXzM8rI5L2dhWvtzmIN2pWFlVV6PUUF6sHl8lEbq6KbdzjK0ICryBR2L3zkASLfYRgkK4u\n+vqYnV39vBPyytCQmrCIz/t2PJLm55meJhxe8wWi7ZCFc9fzs7cJcmKzs7lyhXCYvj4mJ5fq\nbFCBRYcOcf78hkbSVVVkZdHdzfAwgQChkEo9FhlKUhJjY0xPx6ntpHMmXPgtdKwQiERjYIAb\nN3jyJM6X3tPD6CglJVy9qiKbdwXir+H309tLczOPHqk2T1UVtbUqfFn0AdGoyhUQ756FBcW/\nTEtTAeo7v4rH7Dxi/S0hP2ztwch+7I9/5LPP1NUV+3N9fXR3q7vVZtuCwk4kDiUljI8r6+Pl\nFsHJyWRlUV6+eqogE1ip6v7hH7h1i2AwjliVH3U8brcSPbwl0tJwOHA4OHXqbd8qgT2ARGGX\nwH7D/DxuNx6PEsO+CnF4mp1lenorSTPLYTBQXMzgIAMDcYaGsh03GklNPfhO6Ho9VVWK1n3/\nPs+fq+9Fq6W4mIYGrl7l9OkNqerS0rBa+fWvqaujv19ZqhoMWCyUlhIO88c/0tLC+Pjq2k7s\nrB0Oamu3PvgoGmVigpGR9ep4wO9nZIS8vF0r7ISt//w5N27Q0cH4uGoUDQ7S2sqTJzQ18ZOf\noNHg8/HDDzx/zvg4waBq1VutXLhAXR3Z2buw04tGcToZH2d8nHBYSTXFZWMLJVxDQ9y/r77K\nVTKdhQUmJvjuO7KyOHVqC/6oVsvx40oA6/MtkXGlYE1Jobycn/+cmpoVvyVZz/fu8U//RHv7\nepNcgXSpD/xDJoE3RKKwS2C/QToTr5VPSs9s4zytN4LJRHU13d2kpqp9//LtuFarbNsyMt7g\nmTs/r+ZoHo+a4SYno9NhNJKZqfyE9+BAJC0Nu52jR5VlqF6vLLtSU6mq4swZTp6komJDbyXu\nDKdOcfiwEmv7fGRmYrVSVkYgoOTG4r0suRRAUhJZWdjtVFVRWbmZDNxIRJ38uTklBkxPV0NV\nMR7z+/F4WFhQo8y4F5Vk4+4Wx1wYXa2tfPst168zPLx08Y+O0ttLVxdJSTQ2Eg7T38/XX3P7\nNkNDBIMqbzcnR41uGxuxWrc982o5vF5cLtrb6e5mYIDZWWUvV1mpMmYk0+/tMTZGczMTE6vv\nWX6Mwnv5ktpapcV5y2opJUURRltb0WpV8HEohE6nUrBOn+bcOez2Fb8VDjM5SVsbP/ygeopr\nQS7FjAzlvZxAAsuQKOwS2G9ITSUnR/UV4jbtZKwj/p/blPGVm8uFCyo8OylpRViqRoPBQFMT\n77//ZuoNUcndvMmtW0xNMTtLWhqlpRw7xqlTHD26p/fl2dk0NFBczCefqEZRSgoGA1lZbyzL\n1WgwGjl0iJISFhZUT0KnQ6/n448xGAiF6O5mbEzR75KTOXSIDz+kqYnc3M1UAKEQHg9jYzid\npKaSmUlxMSaTIvbx4xW1/iZhI6/ZPog0+OFDrl/H6VyxpZmfJxDA5aKvjxcv6Ori3j3a21VV\nJ7fP9DRzc3zxBW43ZvNOBx+3tnL9Ok+f0tPD9DSRiIq/s1r56CMuXKC6emtE+n4/w8PqT7z6\nTUWjqr4Xqu5bdl4lA6aykv/232hv58ULxsaYmsJqpbCQo0eprCQvb3VNNj1NXx9TU69/f3FX\ndjioqdn6LnUC+xyJwm4/QwgZkpIuNHOdjtTUA851TU+ntJSiItUcWr6aCnnFZqOiAquVtLTt\nKoYyMigo4PRpJiYYGmJ8HJ+PxUXVviop4dIljh7dqEwyElHFys2b3L7No0fKM0WrZXBQUac1\nGgoK9q7pgFirZGcv1Teba/nIrwibexWkBSJ9tYEBnE7VVklP5/BhmpooK3vjCVowyOgofX10\ndTE+zuQkWq3ydq6o4MgRzGaVsWE2K7u+tUq39HRVC+4KxNZxbEyNMl8lns7MMD5OezstLTx5\nwuQkMzNLPW/hgw4OYjLR14fDQXr6TmwkwmEVOnfjBt3djI+rklR6pWK7KKQ3o3EL+HaxCOy1\nvkT571uidhI1T1YWR45gsWCzKWpBVpYSJVgscYJiRTkk8/H1s7wtFoqKKCnBYKCzk9ZW1W9O\nT1eOPxkZe9rcIIHtRKKw28+QRVQkVKGQIqaYTDs6Rtl5ZGRw+DAdHRgM+P0rRq5i3lZRwcWL\nOBzbuDJpNOh0XL5MdTU3b/L4sSLbmUycOcPZs5SWvj7wJ4b5eTweHj3id79jdFStuLIe9/fj\nchEKEQrxwQd7t7Bbjm067bJo1dZSWUkgQCCg8gMsFpU2sYnurNfLDz9w4wa3bql8DukCajSc\nP8/f/A3V1Tgc5OaSn7/mMhlLkY+pE3YewSBu92q5bgxyLU1NqVmn262mrqteE40SCNDXR0kJ\ndvt2dbuXY2aGoSFevuTp0xXeH9JHDAS4c4dolLNnKSzcgusqI2PJdvFVIoeUfVrtJq+luBBj\nqfx8bDYld5ALTEQqryISUYqWdao6eYf8fK5cobKS2VnV7/R4VLj25ctcvUpBQaKwe2eRKOz2\nJxYWCATo6uL+fZxORQCSXL+SEmprKS3dvAB+j0OjQa+nro6//VueP6etDbebQACtltxcyso4\ne5azZ8nP38bqVvb0Ys/W1ERxMR4Pi4vodBQVUVS0Uec2QSDAy5e0tSmn5dhwWeZrkQjt7aSk\nUF9PXd3Sb8UkwMPDqrJPSiI3V6nbTCb168EgqanKxWYH9Lnbfc6lNSgCWGEg6XRqGX6jPy07\nop4e7t7lyRPGxtS7xbo1z56h0ykTE9HKnD/P4iItLavrp+Jijh+nvh69fieKobgQXmDcekUg\niiLBWt0gsQP0+VY087YVgQCDg7hcSuEe93gk6MXnw2J52ws4P5/jx3n5kq6u1SRdjUbJVMXK\nbqsuY3kfSZTeCHQ65YGyVmEngRkXLnDkCIWFjIzw/fe0tTE0pNLGRkcJhRgZ4YMPaGzcLvVY\nAnsbicJufyIcZnyc27f53e+YmFCLuuy5a2r4yU+4ehW7fcXT/MAgOZnUVKqrKSjgxg2MRsVK\n0emoqqKpiRMnqKvbiZ6lVotWS339a3KBXotAgLY2urvjiOCkehsYIBTC5VoaFQGhED6fMrZo\nacHrJTmZmhqOHeP4cYCFBaanmZpSdMOMDDWpl0tiX/d0ZaV8G+liJILbTVcXjx/T0RFH8SqT\nwbIyPvwQnY6CAmXXJ0PM5QbFdXX86lfU1+9mgyQtTWW6xx0jimpYXiP1X9xvP0YyW79j9FrI\n78bsfuRciRR01Z+WUbjPF5+eKKdXSnC/X80i3gYOB42N3LzJixfKXk6MzZOS0OvJy+PUKY4c\n2U1Lv4wMCgvJziYtTc0ilkPG06Wl/OY3FBUxPs733/P736sPIpC0wxcvyMwkN5e0tERh9w4i\nUdjtT7hcfPYZ168zPk4gsKLHMzDAn/+sCODZ2QfWDV+4UCdOkJ+vLOVkz22zkZOzz6oWGZP5\nfGtaG4gQWLLnY858jx9z4wYtLfT04HIRDpOUxOQknZ08eEBWFh6PcvSNTahrajhzBpvtAJb7\nb4polPFxBgfVBPZVyDmXdlFWFkYjR49iNFJdrdh4Ug1Ik/jIkW2JOdk4UlLIyMDhoKBAPROW\nf6gY9/TkSdXdj1u3SS9c4hnepoSKhf719DA5SSiEwUBBARUVZGauKMeF0iCbjVfv2VhnUXzU\n3/6m1ulwOPjkE0wm7t6lvx+PR33q2lqOH+fyZY4c2c1KSELqamu5cIGWFoaHl54JQkU4dYr3\n3qO8nMlJvvpKUetWXcCRCIEAT55gtWI2K0F9Au8SEoXdfoPsX7u6+P57njzB611dDcjYoqaG\n/n5SUw9sYSc9m7Iyysp2+1DeGrKASUbFWpB/inHv5uZoaeGLLxgawuVaarG4XPT2qtGtkK40\nGsXsOXKEiQnMZjSat125DwDE3mJqarXUIAb5RoTdr9eTlUVhIVYrhw8rjYIwHwoLMZsVbWsX\nkZKCXs+hQxw7xvPnjI0tjVOlcJFQ0dpaenux2xWRf7nqKCZELSrafOkvJ21qisFBWlp48YLR\nUWZn1ZTT7aaykpISJfAC0tMVRTJuYSf1qNhcp6dvQWEnhITTp8nKIj2dzk5cLiXfPn6cEyc4\nfnyXt4VSwlZXc+WKsjqamVE0G70ei4X33uPCBRwOBgcVtffV3aBkpXR14XDw3nu78CkS2G0k\nCrv9hvl5mpu5dYu+PjXCWAXZLjudtLeTm7stgegJbC1ES2uzkZoaZ/8NauwoU5XkZKanGR+n\nv5/+fvz+FYMzEdi6XMqERapAICmJvj6SkzEamZvjwoWt9H3dpxCiwvqreExHKUhNxWRCp1MM\nTo2G9PTtSjd5U6SlceGCGrY+ekRPD/PzynSjqIiPPuK99ygooKGBDz/khx9ob1+6cqRzJlrg\nykrs9k3W/TLMffaMv/s7pfsJBJifJy2NBw/48kt+/nN+9StsNmU3aDRSWYnVGv8EysFnZpKd\nTWbm1pTOycnKmqewUMk1RL6amal+9sJXWVGB2azcHPv7CQRUomtFBRUV5OdjNBIOMzVFMBj/\nHWT/Pzm5nhNeAgcXicJuX2FqipER7t7l3j0mJtYMkBZ1m+Q7JbD3Ib2Wnh7u31d2HqsMXAoL\nqa9fktnOzjI6isul7MdWeVssz2VaDp+PgQEePsRu58yZd72wS07GZCInZ8ms7tUXSD1tMCyR\nrkQwKwYor0Jcgv1+hoaYnFQtsZQULBZycpTR7vYFdokhTiCgzEG02iV+28ICHo9iBxYUcO2a\nGvk5ncrKzmzGZqO6muPHKSjYvLZ3dpb+fp4+5f59xf0VjYLU0BoNFguZmZw4QWWlIn3m5lJT\nw9mzdHYyPq6614uLqlF37JgiD2zVeFSKxfT0XR6dr4+sLAwGDAby85XuVTQT+flkZalvR+rR\ndepvITXuhTo1gR1HorDbVxga4u5dvv+eBw8IBOK/RqYhG8lmSGCPwGikvp7+foxGxY5abuCS\nmkpdHZ9+SmGhev3sLE4nfv+bMdwXFvB6aW3lyJH46ZPvFCSCrKSEjIz4i5/oTKVdtMFe0eIi\nwSC9vfzhDzx5Ql8f0Sg6HQ0NnD7N++9TVLS9E9vU1CVthFarHgILC4yN8fnnBAIUFFBeztWr\nFBbS0MDTpzidhMOUlSnZzSa8AJfD5+PhQ2W9IfxOgTh9RCI8fIjbzcKCmmJnZKjZqEbD73+v\n3IukHjUacTj4+GM++GB1PMO7AOksmkyUly91VZcXahKg7HTi88X5dYljsVh2UwiSwO4hUdjt\nK/T3c/s2fX0rBBOrIJ0Gq5Xi4gNLsDtgkCithgb+63+lpYXOTiYnmZ1VXqMVFZw/z4kT5OQs\nvV6v34z9ciTCzIyyP33HkZyM2UxFBefOAbx8qQaX/Eg4q6ri/Hnq6+M0RUIhZmbo7GRwcMmY\nuqAAm42nT9XP4CBuN4uLqsCaniYQ4NQpTp7crgCoaJSxMTo6lJ431i0TdWooREsL//zP/Oxn\nXLumKtr8fEXhslgUbeMtnxihEP39DA/HDzldXMTno7eXL79kcJCCAmprOXWK3FxOnVLOQTFK\nmQihjh/H4Vizq3qAIRfh8mtPgu9EzCtamYsXiUaVgfnyO1oIeXV1nDz5xrkvCRwIJAq7fYWh\nIR48UJFBa0Hu+by8TeZm7kHIXl/Wp5h1gph8ss9tOwSiWq2poaSE27e5eZP+fhWTWlPDxYtU\nVVFcvPT61FRFOX9TJ2rp5sb6uLHFYLmLygE4nxuBlA6lpcrEROz+WeYM0tjIb35DefmKEyJT\nQp+PwUG+/Zb795Vu0WDg1Clqavj8cx48UCPyWGUTDDIxwfg4kQh1ddtVpiwsMDRESwtdXYyN\nLdEwYl+3aFRLS/ngA7KzsVqprt7iY5ibw+nE5YpPFQXCYVwurl/n4UNsNj76SJkhV1dz6BCz\ns4oWJpkNEiP2jlyQ60DIFYEAfr+yuhQzlGPHGB7m5UsCAXXCY9YtNhuNjZw6lSjs3k0kCrt9\nhbk5ZmZew4fNzqa6mqNHKS8/IIWdaAV6eujrY35eObmXllJZeaC28lotGRk0NGC3q325VovZ\njN2++ulsNFJSgs1Gejrz86v36+skJolKw2pd4VW7sKBIPCLtPDDncyOQRqnFwrFjuFx4vWpf\nZLNRVhbnDpqfJxTiu+/47DP6+xkbWyLSzczw/LnyiZ2fX1HWRCJMT9Pfz8AAHg9padtCcJSO\n3cAAfn/8jZ+4msuPaD62HCKt1elecxVJ5wmUsvvcORoblft0drYqUBLuazFMTdHXx4MHPHqk\nnJxj+ShpafziF7x8yeAgMzOqIK6v59Qpmpqw2xOn8d1EorDbb1gnx1CkbaWlXLhAXR25uTt7\nZNsA8Z3v66O5meZm2toIh0lPp6yMujrCYQoL1cc8AOWIPKmLi1VzToqzUIhgEL9fxZiKf01q\nKjYb5eXU1qrU1NjkS4jh4jErzbkYV0+iMqqrKS0lJQW/H59PWb8GAiQnk5FBVpbqlMQI2gcb\nOh3Fxdjt1NbicuF2o9ViMKih5KsN0UCA0VHu3+eLL5ibU11kweSker3oV5bX1mKbMjnJxAQe\nj9LVbjmiUeVHLWklcV8QDqtrY5vW+9RU7HZyctQAeq0NhhxJJEJPDzdv4nBw/PhSolcCMYjK\neHiYW7f4+mtu3VIXpEz/MzI4cYLGRtLSKCrC70erxWLh9GmamsjPVy3PBN49JAq7fYXMTPLy\nmJuLo3IXU/LiYk6f5pe/XCLa72tMT/PkCbducf06Y2PK3iUlhe5uWlq4d49f/pJPPz2w9Uck\ngstFWxvj43g8ZGWRl8eRI1itpKXR1ITRyJdfcu8ew8MEgyQlYbdjt+Ny4fEQCjE3x/y8ol1r\nNOTl8eGHNDWRlkZrK/fvc/8+3d2qvZSczNGjnDjBuXNUVu72h99BSDGXmkpOjuqFpKXF1xtO\nTPDDD/T0qLpkef20PDshboiCRsPCAlNT5OZiNm/9pxDBhBzyDJ6FAAAgAElEQVR53K6tzJGl\neNqmW8ZgoK6OoSGePlWF71qQVvHkJO3tTExsy8EcAMzPMzlJczP/8i/09KiEIX4MpAmFaG1l\nfp5f/YrGRtXJ02rV3mybqJwJ7AckCrt9heJimpqYm8PrXSJLSQ9PXGdPn+bsWcrLycjY7WN9\nO8gi5PFw9y63btHcrHwZgKQkvF68XpxODh3i+HHM5n3/eZcj5vL64gXt7XR2MjGB10tmJjYb\nXV0cOUJ9PTk5nD5NJILDoZp2khVrteJ243IxNsbUFH6/ypLKyaGmhlOnsFgYHubRI775hmfP\nGBxcWi2kgSdhALm5O2eJIl+308nYGC6XUkeKLrWggIICDIZtXKjEjENG4evD56Ojg/Hx+JLz\ndSQp8gFlwrhN1tDJyVitOBxqpL6qaSeDzsxM5d6yTceg11NZSUMDnZ10dDA0FD8rTCBZxl4v\ns7PbcjAHAOEwo6P09Ch+5PJLTmQxExNqsn/o0DZ+rQnsNyQKu32F6mqSkhgcpLtb7YaFj5KS\nglZLXh4ffMDZs7uZWbmFkFzOW7d49IhgcAXlf36e6WkWFujpoa2NI0cOVGEHzM/T38///t88\neoTPx+ws4bBypdfpuHaN7GyKirDblR9ErDWi0ZCcTDjMxATPntHTw8gImZnY7TQ2UlWFxcL4\nOE+ecPMmN24o/9gYenvx+TAaVUdwJwu7SISODq5f5949uroIh9Hrycnho4/46U8pKdkTHQjJ\nNpVt1RtBGlTSFNwmrbp42IpkSqOJU9iJL5rFso3PBzFDPnWK2Vm0WiYm4nBAE9g4xBdwZCR+\neu/iIrOzapc7PU1WVqKwS0CQKOz2Fcxmqqr4xS/Iy1MJodPTGAzKuKG+nqNHMZsPQhLo4qIi\nDAWDq6noAqH8u90MDq5QjB4AzM/T1sbdu0reKL4VkYjKw/X7aWnh8895/33OniUlJY5yIhol\nPZ2UFEpL8ftJT1eLenY2Wi2Tk9y/T1eXaowtRyjE1BQtLdhsO0rTHBvj5Uu+/56bN+nrY2JC\nlUFeL6mpeDx88gknTqDX7zIHK5Y88UbQaDAalYecybRd/DaNRpkMnz9PcjJtbcrARbQdGRnU\n1XH1KkePbiN1QXqfeXmcO4fVSmMjL17Q2cnwMDMzcZh/Mm0IhfB4lINPAssRiRAMKgbtOo3P\nuTnC4U26lkajBIMEAszMKPdmozERL7vfkSjs9hUkM/HTT6mt5eZNurpwuVTz5sIFamuV4/wB\ngIRniAevEP9XPddkEuH343IdtFHO/Dytrdy7x9gYgcBSN06G7xIO9oc/kJ/P2bPwisRB5vKS\njxQXk5PKaO1VeXU0yuws3d1KmbsDkK91dJSvvuKHH3j6lPl5VQGEQoRCqsB1OCguxuHY5ctb\nr8fhwGQiOXkpon59iKmY3c6xY1RXYzRuV2GXnIzFQlUVly8rTxahXUqKg9XKqVP86lc4HNvy\n15fDYsFspraWn/yEf/93vvqKUEhVHnHbTl4vQ0Pk5yvy30GlzG4OsSiOtZCUpAyhNtgWlZeJ\nK97CAnNzuN243TidJCVhMuFwkJe3pJtOfB37EInCbr9B1IsymTp9mlCI9HQyMrDblbHZwYBo\nQWRoFXc9EB9mSWc/YJL+aJTxceVfENflVbSZcR3nNwKxvVjHNCccZnZ2R2NLXC4ePKC/P053\nNhJhdpbOTlpaMBo3n3a1JcjJ4cwZent58GCpAF0HIstobOTkSc6c4ciRbW9KGY0cO4bVysmT\neDxMTysldU6O0v/uwEQ7EmFuTjWBqqqYn2dqSlncreImhsP4fHz3HT4fv/41Z8+qTnMCAjG+\nzssjLW0FHSUGkRJnZWE2b3TPI7SHkRFaWxWFV9yz5+aUoabNRlER165RV5fg7e1TJG6h/YZY\n1qHVutuHsp2Qws5oxGAgLS3Oei+FnclEXt4uL/ZbjmiUmRm83tU5sDHMzeHzEQpt8v1FKLDx\nlEnpnAWDhMOq/aPXo9NtGQMvElGBBC5XnFJJrDGGhujtpaFha/7ipmE2c+QIdXU0N+N04vGs\n6S0iEF+3hgbef5+jR8nO3vb+h0jj8/M5doypKbxeNYg3m1VNuU0HIBequLpMTTE1xfQ0waCa\nnttsZGczNbX6XC0sEAzS2srEBIcPU16O3Z4o7JYg4qGiInJzmZ9fkSIo32N2NuXl2GwYDK//\nZqXzFwgwPMyLF9y5w+PHNDcTDrOwoOi5kqFcUKCktRJwnMB+Q+IWSmBPQupXecT09zM3t9rL\nXraqublUVx80uyYRMIq2MS4kQHbT619GBkVFyrZtVTdUhDhWK3b7Um9pcpKBATo6GBsjGsVm\no6KC8vKtoTZGo0tf7lqzJKn8pqZeY829AxAH3ffew2Dg2295/Jjx8ddEtKWkcOgQDQ0YjTs3\n1UpOJj2dnBw1NU5J2YlqaXERt5uvv+bRI16+VNsA2aHZ7Vgs8fcSwqkIBunqorUVg+Gg7dPe\nBlLYVVdz7BhJSXR1qZGrsFOSkmhs5Gc/W52Psg4knuSf/okHD+jtZXJSuQ0IMVfexOMhGuXG\nDZKT+fjjRGG3H5Eo7BLYk4gZuDQ1KecLpxOvl8VFNBrS03E4qKykvh67fefEm+sjljclOtZo\nlNRUMjMxm0lJeQNFixjOlZQwOsrMTJxWZVYWRUWb90Kz2Th7Fo+HgQEly4j52Gk06PXU1NDY\nSGYmHg8jI7x4wYsX9PbidBKNYrXS0UFdHbW1lJRsQec4ZqP/qkkHP14J0qXedVWQHGdpqWL6\nWyzcvs3goMqfeBVSKJtMZGfv6HHKiZLztjOIRBgdpbmZ27d5+JCeHiIRRf8yGtVwf63WpgiD\nRkcZHKS+focOeF9AOr7l5Vy7Rm4uRUWqVZ+SorqwFy/S1ERu7nqFnTjeDQ2pVJLubq5fp7MT\nn2+F2DbG5BMPmtZWsrO5eHEnPmYCW41EYZfAHobVyiefYLEQifD0qVo+ZQx98iS//rXSi+wR\neq+QV0ZHaWtjYoJwGIuF8nLq6tDr36AoSUmhooKjR2lvZ3JyKfSTH01obTaOHSM/f5PHWVDA\nRx8xOMjdu6pbFkvgTU3FaOTsWa5cwWKht5evvuL773nwQI1iQY1ia2s5eZJf/vJtCzuh9Ujd\nlpISpycX4/1IGPxeQFYWBgMFBRw6xMwMgQDBYHwzXplt7ZHrc1sxP09LC9ev8+ABfX3MzqqL\nSrY6L14ol6K4EF2n283EBHNzO3vc+wFCjjx+XMUqTk2Rnk5hIdXVKiFw/XZsKITbzTff8G//\nxugoLpei2K5FohXyw/Cw+h4T2IdIFHYJ7A3MzuJy0ddHezvT0yo6zGymtBSLhV/+kqYmJiYI\nBpX9R0EBWq2iae/MpGkdyDips5Pbt+npYXhY7YaNRmw2Cgo4e5bTp0lL2xDBOSWFkhJOnGB4\nGI2Gzk4WFlTzQ6ejoIBTp/jJTzYfDpGejs3G1aukptLfr4zZNBrVCKyo4OxZsrOVl97Nm7S2\nMjm51NiTBkBXF0BDA2Vlb6X0lKInP59r17h7l5aWJdszmbaLu3J1NXV1e8WFIWZoLEyA0VHc\n7iUDbYEUyjYbVVXvRBD7wgLd3TQ3MzFBKKS+wZgAMxBYMsF+FbJdEVJXgmD3KrRatFry89Hr\nKSggGFRtYJsNvX69W0+2ml1dfPkld+/S3o7f/xrmQOwX5+fX5PgmsOeRuIsS2G3I08fvp72d\nGzf44gvGxpiZITOT8nI+/JDz57l2TbVzxscZG2N4WG1DMzJIS8NkIiNDLbe7Akl5f/qU//k/\n1fxUCEYS8ZSaSjBIaanK0HwtNBoKCtBoFNl8elpFCUWjZGVx9ChNTVy5svkqR9aJixdpaOD+\nfVpbGRtDq1UmxseOKZKTx8PgII8eKZvZGMR1ZXyc+Xn6+nC5SE9/W2GyNBFDIUZGllQaKSlq\nlu1wUFNDTc3rO3axeiLWjZDqkO0RDWRkUF3N0BAdHcpYkR+tBOXgCwuVteSBRySiiJg+3+qR\nq9zg64dcS/yxyD8TiAvJcS4peYNfkd5bRwf/5/8wNLQmYeBVyF2zA49TOR7ZGMtPbHogrvvb\nd/MeaCQKuwR2G5EI4+O8eMG//RuPH6vYU5GA9fbyxz/i86l5lsfDvXt0dirlf3IyBgPZ2bz/\nPsePY7fvGu3a6+XePR48wOVSVZ2sbTLvWFjg6VM++4xr16it3eh7ZmVx5gyFhVy5gt/P7Cw6\nHZmZ5OZSULCetGKD0GoxGqmvp6iI2VmSk9HpyM5GryclhWBQJdaz9lM1GmVyEqcTm+1tGdZm\nM0ePsrCA3U5rKwMDzM5iMGC3U1dHQwMNDaSmbuj5Hokow1vpN8hyKNy4LYfRSEMDfr+qaYaH\nlzwXDQasVk6f5pNPKCra+j+9ByGmaGuVDpLAK0Xe8jmgaDsyMqiqorZ2u2I53k2Ew4yNMT7O\n7Owb5H9IA9Vup6BgJzia0SgeD21tdHXR16dcVwoLFYklJyfRxN0EEqcsgd1GJEJ/Pw8fcvs2\nXV1LzaHZWRWEkJSE1crCAk4nt26pIWCsAaDXqwaJeHCwG9u76WmePaOlRVGbYx0LGV/KNCQ9\nXQkONgi9nvJySkpUO3NmRrm4bVXYqEaDTkdRUfyyQ5yKw+HXGMaGQgQCr7dzey0MBpVhmpdH\nfj5dXUxPq8CGQ4coK2NhgfFxMjOVb2rcQwqHCQYZG8PpxOVSdjAWCzk55OVhsWy9R1p6OsXF\nNDQwOorZTHe30vaKV7CM4I8dOyCe4TFIQzT2STUa9XVIk3Kt/YZsG+QFk5NMTxONotUqYkBZ\nGUeOUFy8V4RQBwPCXJyaeo0pzyrsZJ0tvf+uLm7f5ulTXr4kFEKjUSTj+XmOHKGwcIX7UgIb\nQKKwS2C3IbTre/dwu1cz0IXqMTDAn//M7CwejxIlCORWn5/nu++Ym6O0FKt1d+w0QyEGBxkd\njfMAlVVwcpK+Pqan3/idpQOUmancp0U6ugOQvIT09DWfpzKs0enQ67fsnFss6HTk5TE9zfQ0\nfX28eMHjx9y9S3Y2xcUcP05x8ZrGGVNT9Pby2Wc8erT0RUgI2Kefcu4cRUVbv1AlJVFWxl/9\nFZcuMT6unHgzM1U1mZu7NE46MFhcJBxW5meitpGLQeikweBqZyJAo8FioaaGggLMZh49orub\nUAijEYeDc+e4eJHqanS6hB3uVkLYjTLf2Ei7TnZxOh1WK5cu8cEH2+6WGg5z6xbXr9PczPCw\nSmFOSlIe7N3dXLvGb3+7V8i1+weJwi6B3UYkwtgYAwMEAvETh3w+ursJh5U9+qo1IxpldFQF\njAYCuyOSjUaVi2/cbbGUp2v96/qQR+3OR2totZjNmEyqy7VcmSsQ9YDFgs22ZYcnfERREHd3\n09VFRwfj43g8ZGWRl8fICI2NnDiB2bxi+Ctt0bY2vvmG77+ntXXJu0EWqrQ0ZmaorcViATAa\nycoiM/Nt+0NypUm2Zm4uZWVKNGMwkJl50ELZFxYIhxkYoL8fr1d1atPSyMykuFh9/BMnVL6z\n2OhIS0/IUoWFXLxISQkWC3l5DA0RDpORQU4ODQ3U15ORkRi6bTGkK//acarcy3o9Fgt2O3l5\nlJZy9ixlZdvbQJ2bw+ulpYX79xUFMLaxD4eZmcHvJy+PsTGSkhK13RshcSMlsNtYXFT3cFzD\nCH4MWlhnxyktBLcbr1epKHYYUnutNSLkxynVPlrmU1PJyVGO9iL+WHX+Ra9qt5Ofv5VEnMVF\nFVb7v/4Xjx8TCKjOUHIyra3cucPlyxiNHD68orATXt2DB/z93+P1EgyueM9wmG+/paOD06fJ\nyVE9tkOHqKzcynVLVtDs7CX/2APWqAuH8Xi4fp3/7/9jfBy/X132Oh0/+xm/+AXV1aSm8uKF\nci2Zn2d+XumHdDoqK/noIxwO9HpOnVJlX8xk5+D1NfcCRJJisbzm3IpbXn6+EmbV1an7a52G\n/ZYgGMTtpq+P3l7ldRCD3NFTU4yM0NNDenqisHsjJAq7gwtZiUMhXC58PgIB1Q6x2cjK2kP2\nWrIby8rC643TGQJSUtDpmJ+P/68xCO9nV6DXU1XF4KAyZHnV9iIvj5qa/ZSQIQTqykp+8xt+\n+IFHj5SAA0hLIyODY8dU+OnbyziWY36etjbu3qWvj8lJxbKXK3lujtlZXr7kiy+AFTZ+wSDj\n47hcTE/H4e+LsnhoiMVFVQ6+eKGWsbo6amq2RrUa8wQ+eJDzKaaGt27R3s7MjFJqi3rx++8J\nhfjoI0pL+e1vOXGCoSHV1TOZyMmhqEg5LxqNaLUJIt0OQatVHIYjR1RyTMy0SKyztVpSUlhY\nUHm+ou92OHaI9REI4HKpB+arj+5olPl5ZmZwOrcm5OZdQqKwO6CQUZSYw7W1MTyMy6WUekeO\nKLKRMFJ3vbzTaLBacThwOuNPYyUcyedbr7CTNWa3llWDgSNHGB6Ob3uh01FSwrFjOx0/8DaQ\nC6O8nN/8Bp2O6WlGR5VI1mgkJ4crV/jFL7Dbt2YOK9VbNEowSHOz0hevGrsvLrKwwMAAX35J\nRQVXry79UzDI6OiKJM1VWFjA68XrVf9vaioZGXR24nZjs5GZuYf2OXsTkQg9Pfz+9/T2MjW1\ndJ5F6vT4MUNDVFZSV8df/iVjYzx7xvg4Ph82GyUl1NcreeP6J1muAXFQk0tCGp9SfyQY9G8K\nIVSUlHD8uOqBCQOSH+NehKUwPa2UamILmptLRQUOh7q1t++ch0J4vczPx7/7ZAkThXvCJ/kN\nkSjsDigiEWZm+PJL7t1jZASPR3XsMjKw2ThyhI8/pqxs25vtG4FWS2MjXi8jI/h8K2T5sq0s\nLubqVZ494+7d+O8gY0GrVSVj7jwMBurrmZlRvi29vUoqGIlgsVBczLlzXLu2+ayI3YKkZl25\nQnm5ykmT5A+jkZKSrQxzE2WM34/TSVsbg4OK8f0qpAMdCKz4j+Ips3EKo4STdndjMnH6NBYL\nZvPBbLZtCcR7aHJSib7jVs8LC4yOMjBAZSV2O6dOKdOi9HQMBkymDZ1eIVR0dPDoEW4309Ok\np5ObS10dVVWUlh40ffHOoKSEX/6SykqeP1fGk8DMDG434TA+H3Nz6t7x++nr409/Ynycv/xL\nTp9ekjxvB/R6srOVCc6rF1VMnmW1JuKD3xSJwu6AwudjYIAbN/jqK7xexWCQuUl6On19OBzK\n7WLXqS2SoBUI0N4O4HYTCil2TkYG2dmcPMl775GUxPAwExP4/bBsH5mSgsNBeblyU1v+GBLT\nrEBALUjhsDLTysxU3ihb9dkl4ae+nqkpjEbMZvWoSkrCbqeqitOnqavb/VP9phAr45oajhxR\nT/9olJQU0tK24HEfowpMTCjTaXHFe/mSsbE1u7ORCLOzq8Op5GvdOPVejFudTvr7GRykpOSg\nCR22FjLLlll83FgwudEmJ5mYoKyM7Ow3Zh2IwMjt5tkzfviB775jbExRZvPzGR8nEECnw2LZ\nQ5n0cgHLHJMfJQh70FDXalU/YiQ0OsrcnBLpxzSzAsn1mZ3F7aaxkZoaDIZtfGrp9VitSsMk\np3F5eSfkS1FNZWZu1zEcUCQKuwOK7m5u3KCjg6kpVdXJw1fcvYeG+OorkpOx23d/E5ycjMlE\nTQ1/+7fU1HD7NmNj+HxYLJSU0NREQwPV1Wqy+d13tLcvPWuSkjAYeP99fvIT7PbVz6BIhGCQ\nzk7u3aO/H6eTjAzy8mhs5PBhSku3+JlVVMSnn3LmDG73UqSYxYLVSk7Ovh/2yThMqtWtQjTK\n2Bj//u88fkxfH6EQc3OKErpWrqiYbK2a/2ZkUFBAZuZGbR0EorkRcl5R0e7fCHsWUkzLCC/u\nty8d09lZZmc3yXMVl9qXL/nHf+TJE5xO9dSSOsPnY2oKoKGBQ4fe4pNsKWTz5vcraVdaGmYz\n6el7dP9mt6toPq8Xp5PvvuPhwziJsTIKF9Lq6CjFxdt4X8j4qKAAh4Ph4RWqWKnqxI+9rEyJ\n2RPYMBKF3QHFyAhPnjA6qqipsdVO/rfHw4sXVFSsuXzuJERbZ7VSX4/BgNms8huysnA4qK+n\nsBCzmZoaNBr0eioq1IdKTiYjA6uV99+nvp7MzKVVR6pYl4tHj3jyRAVauN3odNhsOJ1KuOdw\nbM0jQ/6uTkdurkpZnZ4mEsFgUG5wO/asn5tTPTCPR3Uo9XpycpTD36abUtvBxYxEGBnh+XPu\n3OHJE8bH1TxoYWFNHYzYoVVUYLOt+O9paWRnK6v6/n41bIrFi61V6skneqMZ7ruJGBlrHSQl\noddjMGzyGhOX8qdPaW6mt3cFH0PqjLQ0ZSKzFwq7+XkCAUZG6O5WE+rFRVJTMZspLKSigqys\nPdRZFOh0ijhhMhGJoNOteWsI087jYWoKh2MbDyklBaOR48eZmeHpUwYGcLnUg91sxuGgupqT\nJ7FadyIA42AhUdgdULjddHYyNRXHQ0Qo6kNDOJ17aEkTU7TMTGpq1Lou9F5xCZGQGZuN2lqc\nToaGCIVUMHZhoRqtLi+eFhfVuOHv/o6HD5XwYn6epCSGhmhvp7+f+XnOnt3KvaAwt2TYLccj\n9dBONuqEgnb3Li0tqpDNz+fUKU6c2HPur2JMfeuWCqtdlRz/KsQRTfxsVyVmSjexvp5PPuEP\nf1ALrfxEIvEvcmHwSGDJlkyWDzC0WqxWsrJWhPAuR3Ky8sfZdNjr/DwvX/LgAZOTq8Ov5F4e\nHOSbb6iu5qOPNvkpthBzc4yNcf06//APTE4q2oAwfc+d4z/9J6qq9lxhF4PQr181MFr1mkBg\nRQttm5CWxqVLFBRgtXLvHs3NhEKkpFBVxYkT/PSnVFevJtgksAEkCruDi3V6FbFlb+8gJmtd\na2FISVEvyMjAYlHeZmslgUYiKrdgcJDJSUKhpSGdRFW2tysvgIaGtzrsSETZBDQ3MzZGIKCM\nQmw2ioo4epScnB2yF56dZXqaO3e4dYveXsbGmJlRsrjubh4/5vJlampIS9sr5Z00aV6+VEtj\n3IphOZPSZKKykqYmrlyhtHT1y5KSKCnh8mUyMzl+HLdbST36++nvV6zNV988PR2HA5ttr5yT\nvYnkZBWedvkyT57Q2bnkTyG7r9xcysuVlHJzhZ2ks4yPrynOEIH/zMyS7/RWYXoanw+XS6mq\nRfRtMsXndYk0e3SUL77gu+/o6SEQWDIQSU7m0SOSk/n0U6UC3oPXlVaLyaRs4eKeaqFbZGZi\nMm07P0G8rkpKuHKFigrGxtSDPSeHggKqqpTsJlHYvSEShd0BhTiJr0Un12hIT99oqvregazE\n6emvb7MtLNDTw4sXcXIS5dEsk9kzZ1SCzebOg/BRhob44Qf+7/9V/l7RKBoNlZWcOaMGWGtF\nYK2ClNoSoiCQVuUGD29mhv5+/vxn/v7vFZMy1vV8+JCcHAwG8vM3ejA7gEhERYbI2PpVSK2f\nnq5OY0EBFy5w6RJNTfHVuA6HcmqQMCKfj2iUu3cJhXC78fuXint5ZyHjSyd4b/KidgtyKYrD\nsFxIgMnEuXOqJSyF8uKies6Ul9PYSFUVdvvm/+L6LuXCl91a6ojs8ZxO+vpob2dsjGgUu13F\nE4tyaNUjVNqHIyN8+SX378fpe7W3MzhIXh4XLqgYwL0GrZacHCyWNU2hZXednb2VoTJrQXwP\nRNtx9uz2/q13CYnC7oDC4aChgZkZZRS0Sm2UmUlVFUVFBzbDJ9YAWGUXHIMM6SSmTHLlNwHp\nIvzpT/z5z3R3q8mFvPPICI8eodEwNcVHH21Iri9U8YkJJiZYWECrJTcXs3kFd3AdDA7y2Wc0\nNy95gIFakufmmJ7m8WNycrhwYfOr73ZgnbaxWAD+9Kd89BFaLQYDdvtrnPOSkzEaKSwkK0uR\n/QsKqKnhm2949mypbhBl9JUrXL5McfH+2+FsNxYWmJvj+XNlUCfGddKRcjj4m79hbAyPh4UF\nTCYKC6mupqZmdRv1jZCUpOLppO396lWh1WI0bjHXanycly+5f5+HD5maYmYGUMlajY2cPMnR\noxQVrfiVaBS3e73OosDrZXCQwsJN9i+3FSkpZGYqy+jWVgYGliQUIuwVIX9JyU4UdglsDw7o\nup5Afj4nTzI6ysSEqu1EVSoy0rw8jh3j0KEDW9jJxjomB17rNULA2nRehd/PwACPHvHw4eph\nosej8mEtFi5deo1+IlbSDQ4yOMjICHNzpKWRn09BAYWFZGeTlfWa1t3EBD/8oIjnyyFtRXGT\nycujoWGvFHZCt5coArk4V/2rTkdODmfO8B//I8nJG2qqyW/pdEte0HY7paVEo+j1S3QosdH5\n4AOamrDZdloPK40ir5fpaUUSEBmQwbBUu+xWoSnfgtdLfz+3b/PNN3R14XSyuKi6m/LcKCpS\n945I10tL3zYbQKMhP5+yMnp68HpXb8aSkjCZKCtTiXBvD+mLj4xw4wa3bvHw4VJjUpiXMvY1\nm7HbSUlZuvZEBjs1pbYNcSHdx4kJrNYtONQthwiqioq4cEElQUtGCKDTYTJx+DDHjlFWljAZ\n2b84oOt6AsXFpKertID2dvx+UlLU2DEvj7o6PviAhoa9uKHcEgj9Ttwvk5PXLN1kkLTpccnE\nBC9fMjUVx2UjGmV2ltFR1YEwGtc71aEQAwPcusVnnyl7Gjng1FRKSmho4NIlzp59Dddkdhan\nk+npOP8kY7WJCUZHXxPLtpPQaCgooKJCxQotn7LJqNRspqpKzUk3vZzr9RQW8hd/weXL+P2q\n/hbLXKFS7fwtIKGrt2/z9KmSAaWmUl1NfT319RQV7XL7cHGRri7+8R95/pyuLqan1TUjV6bf\nz9AQv/kNZ84obVNMbvk20GqprcXr5elTVUfG7lkhrZaU8NOfcvjw2/4hgYgDurv5+mv6+lQM\nXUy7E43S3c38vDJGzspaalzJrbS8Kf4q5DUS5LBnUVzMf/gPlJVRWcnLl4yOqjH04cM0NtLQ\nsFp4nsC+QqKwO6AwGNBqOXMGrZaqKqammJ9X5ImiIoLc6BIAACAASURBVGU5K+ZqBxJSNJSX\n8/BhnMJOo8Fkwm7HZnurnCK/X9VtcZ/g4v4VCDAzozpwr0KWE4+HH37g22+5f1+F58TULePj\nuN3KJv7wYRwO1buSocnyVpMsSGuVsNKefCObt+1GSgqHDjExwfAwwSDT00spUqIsLi3l0iXK\nyt7qKpWzVFpKYaHq4EajSra885AvqKODBw948ICWFmXFrNUyOqoi9YJBlfi3K5CLdniYR49U\nXG/smhGXQRnbBQJKw7RVZbFGg91OXR1XrpCZSX+/yrkSM7OiIuVgNzPD998rfr3RSHa2Gs2/\n6d5sfp7JScbGGB1VXMwYYu508q8TE+h0S4XdcleXtZQH0ShpaSvKwT0Ig0GZD5tMlJYyMcHi\nItnZlJRQXk5xcYKcsK+RKOwOLkR7L3EIbjeTk8olTqK30tP3IrF3q5CSQnm5IsClpKxw8ou5\nZpw5Q0nJWz2/wmH8frU1j/uU12iUuUA4rGRoryIaxenkD3/gzh1F8GcZ88zpZGqKjg6++Yb/\n8l+4fJm0NMXpliHm8o+s15OaGj9XMbYg7Z3hu1ZLXR1JSarrKYIPKezS0pRn9c9+tmU5bDKB\n4kfR364gGmVujlu3+B//Q0WoSe8nKQmfj95eZc788ce7VthJGPz4OOPj+P2rdyxy/DMzjIww\nNKScYrYEoo48fJi//muqqvjTn5SrWVYWpaVcvUpxMTodzc08fUoggFZLWRnHj3Pp0mZSQ+bm\n1J0F8bcNSUlLdDq7fenrSE7GalW+7nGNBaSwy8razep84xBJuCTJgrIXEGpEAvsZe+Ypn8BG\nMDvLzAzDw0qYJl7nDgd5eYraHIPcmSIL0OnUy9LS1NzkoE5gYxAqYXk5P/85ubk0NzM1RSBA\nSgpZWZSVqZiyysq3eoQZjeTlqWopLplPbEtNpjVnVdIbcLvxepVr/6o3ESaTWIZ+/jltbaSm\nqi6jDBMbGykvV6mLjY1EIrS3rz4YiVysqqKubg8tNsKHkyDLmhoGB1UcrV6PyUR+PnV15OVt\nWWtthw0F48LnY3CQvj6cTmZnV/AyReXT3ExmJk1NOBy74/KwsIDfz8zMmnsVoWz6fIpFulWQ\nb0eno6CAM2cwm1W8tajg09Lo76e9nZ4e+vuZmyMlheFh5Ul58SJNTUpCvvE/J0XMWmdYHqev\nvkbYkAUFnDpFOExbG+HwktRaIgQPHaK6eivbmdsB+VApKXtop5fA1iHxpe4TCNtXLNMePqSz\nE58Po5HiYo4fV0nb8hxZ9agS47eMDHJyduXAtxfLTWhjj1fp/czNYTBw8SJpaczMkJaGz0d6\nOvn5NDVx4QIXL76tiajZTEkJFgvp6czOrlYtiFWHwYDFsmZhF40yPa0EhusMSefn8Xj46iu+\n/VZJOKUrkJ/PX/81ej0FBeTkcPYsPp+abMa4dLJeZmdTW0t9/ZqNw52HLIS5uXz6KU1NDAzg\ndOL1YjarHCGjcU9UY1sIr5eXLxkZUXYhyyeAQsrs6cFiUTXTrjTUxb08FFrzBXK7zc6uqTd/\nG4jFmslEba36W4DTyc2bPHvG558vZc0lJTE4SFsbN2+SnEx9vWpXbxCy1RFJ01oXmBjirEqU\nlrspL4/z5wmHmZrC71+RFVtVxdWr1NaSlbXZs5BAAm+LRGG3TzA9jdPJt9/y7bc4nSowSiZW\nT59SWcknn3Ds2Dvn5RiJKOWBtEAkor6/n95efD6lOlxYIC1NmZjr9WRmYrNht5Oe/rbnymaj\npoayMjo6cLsJBFRtJ/2A3FzOnuXMmdf8oQ3qPVlm0SIaUuEG/eEPTE/zV3+FzaYWm/l5Xrxg\neJjFRdV1OHqU06dpaqKsbHe4Za9FRgZFRdhs6qqWBfXgXcliAufzMTcXvyoS5pZcupugjr09\nxLVbQibi7jSEASmMjp3hkHk83LjB/fuqjygQN0opQPv6ePJEMVA3iNRUcnOxWtf0cpNbOCcH\nhyOOx4qYYGdlcegQAwOMjQFkZJCfT2UlNTVbxh9IIIFNIVHY7RO43Tx6xLff8sUXKt1ycVHx\nITo7aWujuJiiIszmnej/yxN/fp5wWLHRU1PRaklN3Tk1hnhGjI4yMEBnJ0NDyuc2JYWODtra\n8HgIBgFyc6msVFPLoqIlI4y3h5B76utxu+nrU+b1svJJTMLVq0p6vDzEVpoiXi8zMwQCuN0M\nDCin09cqG6S2E8gs78kTtFree08Ztcvil5VFby+RiNItnjnDhQvU1OxdpZtM3A48hKMWc9Z4\nFVKy72J8rST7Wa2YzczMrD5UiaCQaPadMTlbWFBtzp6e1YJuOZmLiwwP09pKbu4bFHbSGiwo\n4PBholFGRla0/JOTsdmU02d2dhw3HKEQSDxxfz9DQwAGA6Wl5OWRk3OQ6csJ7AckCrt9gv5+\n/vmfef58hTJfHkYzM7hcvHhBQQHHj+8QsSMaxedjZERZSOTkkJNDdvbOLc/hMF4vf/wj//qv\n+P0EAksuISJEja2OU1O0tgK43fz611tZ2AE6HR9+SGUlz5/T2UlfHxoNRiO1tdTUUF1Nbu4K\nFotQlHp6uH2btjZ6ewmF1JnchJ3e4iKhENPTuN34fFitqoS9fJmZGUIhNXIym5dChBLYRaSn\nk5NDZiapqczPx6neIhGV5iRj6J2HXL2FhTQ0kJREV9fScWo0pKRgsVBUREkJ+fnb/qhZXFS6\n8rWICnI3TU0xMKCc2DYIsVCpruY//2c+/5wvv1wKnZMN6rlzfPopR44ov6S40OnIzSUri6oq\n+NFPe53XJ5DATiFR2O15SI/H46GtDadz9fIfjRIOMz1NXx/d3VRXb/vBzM0xNUV7O/39jIwo\nLw/hRZWUKLfSt/EQ2SCcTh4+5PZtHjxQHDt5+r+6AIhNQ0cHkQjnzyvm9ZY8fGVek59PZiaZ\nmZSUMDKiSI3l5cpYOFbpyoH5/Srs/OZNuroYGlIWGOJF8qpP72sRuwAkGlXCc0HpLURhevBm\nmvsURiPl5eTmkpamvvfY1x2LGC4sVO56u/KtCTGgqIhr1zCZyMpifByfj8VFDAZycjh0iPp6\nKip2KOReOHDrx14TT9kqjU+XC5dryRvSaMRsJi9PJZDm5HDyJHNzZGSoO0juF4OBs2c5ffo1\nvTdRHmwtt0GeY+GwMsFJTd1D4c4J7B8kCrs9D3lCzc6q3tirkKV9dJTBwfVYz1t1MLOzdHby\nu9/x7Jmafop/hFCeP/hACUW3G/39/L//x7Nn8cWkq45ZdCf9/UxOEgqh12/lrlqcGmpqOHRI\n9TbEUUUkdavgcvHZZ9y+TVeXskFhmWBT/u8b1Xay7MlKsEoJmyjp9hrMZmprKSwkPV11lGOX\nrrixVFVx9CgWyy5rFYuLyc1VeRJPntDXRySCw0FtLU1NnD69Q1WdZEPL3boW4U+jwWDAal09\nK5AtqGyinj7F5SISoayM2louX6amRhkApafz6adcvcrkpIoUE3++jIzVsomdwcICwSBuNx4P\nmZlkZe2hcOcE9g8Shd1+QEzJv86SL3u77X4Szc3x4AHXr9PSwvCwqkvkqCTh0Wrl0CEqKrYx\ntyrW+urtZXJyo2wkKe9kSiuOIVuCWEGWmvoaytHiIsEgTifd3QwMqNzSmNm9LFHiTqfX4/Uq\ngqBgLedh6eaK33Jm5mr5XqKq22tIScFg4MwZAgGam+nqYnJSCWALC6mo4Nw5Tp7EYtnl706O\ns6KC9HQqKvB4iEYVta64GJNpy64uacXNzSkHb3FQW2UvYjBQWcn4OMPDyvZv+b+mpJCfr6rh\n5Whv5+lT5QI9OKjsr8Wiz+XixAkVmiz9dZ0OvV49zVJTVSm5uQ8o3N+hIQYG8HqZm1MT29JS\nMjPXK4h9Ptrb6epaSvtITVWGStXVHD+OTpewJklgg0hcKHsesuRL3SZeu69CoyEzE5Npe+98\neQTfv8+f/8zgIDMzKx6yCwv09Smal16/7YGk6yRoxYVwoufmVIdv57G4yPQ0LhdjY0xOrs6a\nlApPhnF5eQwPq6QyOfK5OaVTWfXti8OCXq/SsRKV3B6HNHFPnaKkhC++ICODvj6Vr9DQwJkz\nnDvHoUO7T9KSCykvj7w8Tp7clj8h9Di5sGdmCAYxGJTjcWyPJEVeZiZ1dbjdTE/j86mhhNzO\n6ekYjZSUUFeHyaTeWYrF1lb++Z9pbWVoaKkc9HgYGKC7m5ERysqwWhVpRNp+bzlnkFt4dhaP\nh2fPuHNHPSSlTXv+PKWlSum86vuNTY1v3eL6de7eVbV+JIJOR1ERH35IQcEKn+SNHMxyjgcs\n2fLt+tWVwPYjUdjtE5jNHD2q3JtWeYeK31JFBTU12z4DlQfQ6Gic8kie1C4Xra2KTbytkOfU\nxksZecwZjVgsOx36HjsAWZkk9P1VOp30FHNyOHGC995jcZGJCebm0GpxOhkeZnAQt3upzyff\n++nTXL6srPAThd2+gEgoxO1MyAzJyWRnY7ORm/uufIlCNm1u5tkzJY+QdODDh6mvV96cAouF\ny5dVX621lZ6epRcfOcK5c1y8iMm01C+fmyMYZGCAlhbc7hV+geIW5PUyNkZvLzYbDseWbYaF\nEvPkCf/yL3R3MzyskkXS0ujt5fFj3n+fDz7AZlttcbewQH8/jx5x5w7t7QSDS3TbSISRER4/\nxmTi0iVOn36Dg/F4mJhgcFAliNhs5Odjty9VwAkcXCQKuz0PedDn5tLUpIaJEmMVGwJarZSU\nUFNDRcW2u5RFo/j9ylD3VUQiTE8rqez2QT54WhoWC36/Ynavj+RkMjPJy8NqVTmPb4lQiEAA\nr1c9NMV6JiMDkwmDYcXARY4tHFbOJq/26pZD4hqrq6msxGhkZGQpSLSvjxcv6OtjelplZQq1\n6NIlzp1TvYcE9gUkvramhpqa3T6U3YDY/QwNcfcuN25w+7ZqwkWjmEwcO0YwSE4OdrsqgDIy\nqKlREV7Z2eTkMD9PSgpWK42NXLtGcfGKO25uDo8Hp5ORkdV+gdIbCwaZmlL5PatE62+DcJjx\ncZ4+5fPPmZggHF5SRA0O8uIFGg3Z2Zw6tbqwi0To6+PpU2Vevfz5EIkwOUl7O0B+/kYLu5kZ\nvF46O9WPPK4lO7u6mtJS5amewMFFYjHYJygq4mc/Q6tldpaODsbHVVNdo+HYMa5e5cQJ8vJ2\nyOtknabCjvUbsrKorlYFVmzcEBfCXTtyhEuXKCraGg/nyUlevuTOHR48IBAgGkWvp7qa8+ep\nq4vTsJycZGiI3l5GR9e0b5DpktGI3Y7dTk4OeXlqYRDda0eHclQJBlVWZmUlVVUUFiasTBLY\nN4hE6O/nwQO++YbmZnX/8mO83osXKgWxqYnGRviRi1JcTGYmFy6o201YrZmZZGevjnURF+hg\nUAX9xfWUCYVUjt+bitDXgd/Pw4c8e6ZUbssZtPKAev5cOeSVla34RSGxvHwZJ6UtJvx6+RKn\nc6NHMjTE48d8++3SwYio2WJRE//3309YKB9sJAq7fQKDgbIyzpwhKYnaWiYmVMpCejrHjnHy\nJMXFOxEqIK0vkwmfL45EV6gqYv+x3cjL4733iETw+/H7mZ1V0rnFRRWhxo/capMJu52zZzl/\nHofjbSkmMsppaeGbb7h/n+ZmNZVOT8fpVOYvej1mM3o9i4uMjdHZSU+Pisfo78fvj7OciIg1\nMxO7naIiLBaMxhXl2sICRiP5+VRVEQ6TkoLDQWEhZvMOSRQTSGBLsLBATw9PntDVhcu1opQJ\nh3G56OxUj5FYYSfRzxvkmUghuM5tLm8YV7T+NggG6e6mv1+lCy6/x+W55HTS2qqUKNGo2pSK\nEenYGG73amF77HdDIaamlIc5626ehVTX2clXX/HwIT09ap6wuEhKiqLQJCVx+DBZWUty43BY\nRRFKoHbMecdmIydH9fbeEYbAQUGisNsnEH+pxkZqagiF1ENBq+X/Z+/Mnts60/T+I7ESIBaC\nAEES3MB9007tsi3ZbbU99kxn3JWpLJ3pSWVyMVf5H+Y+d0klValKptJdM9OdpMfj6ZHd3m3J\nsrUvFPd93wESIEAQBMBcvJ8BigIpUiIpUsJTrq62CR4cAud85/3e91lsNozGnZR5bg6tloIC\nSkqIRteHXQqR3+WiuXnXlRNAWRk/+xmRCCMjDA8rcaiMWuQMJZZD2IctLTQ2Ul29A59SOExP\nD1ev8rvfMT2tlmYgFKKri7Ex+NHVIieHeJyODv7H/6Cnh6kppf6T5Xstx07CKnJzKSigrIzK\nyjTnqdGoNl5TU0pFm6FCZ3DgEIvR08P9+0oRvBarq6ysMDnJrVucPfuMxzcYlH/kOi5yErKJ\nEtbEDtYr0Sjj44oXm/Z9ZQQciSiPybk5RkeVldXcHMvLG44d1mogNoc0Ix894soVQqHHPl6Z\nQff1YTbT14fbTUkJ2dmK+9vby5UrPHjA8DDZ2dhsnD2rzPwyrkkHEPuusJuYmPj8889HRkZW\nVlZWV1cBrVbr8XguX77s8XgAn883NTXV3t4ur7HZbCUlJXV1dZWVlcDy8vLs7Gx/f397e3s4\nHE4kEiUlJV6vt66uzmazAVkH9BpNEsv0esxmtTSILmzPHu0y/jh9mlCIjz9mYOAxu5OcHOU7\ncOLENrJ9nhl6PXl5tLQQjzM6yvy8EhOYzbhcOJ2qFJbsy9LSNE5Xz4ZgkDt3uH9fjXuSK7hs\nrGMx2tvVCVit9PRw+zYdHUxMEAgo07LktltmxEYjNhtFRcr6taUlvW2KFH8ZIl0GBx1JFVFa\nni6oBtLS0jMe32BQLsQeD7OzigUrkJvOaCQvj/JyZXeyU5CUl03KIJFMzcxw+zZ37jAyoqRU\nKyt0dyunPam0nvxFjYb79/lf/0tpICoqlL3RuvdaWmJ6mvl5NQteBwlhCwQYG2N2lqIiNf7+\n9FOuXlUB036/eqysrOD3Ewxy5Ah1dXuUC5zBDmHfPScmJiZ+/etf3759G1hdXc3KytLr9S0t\nLUeOHCkuLgYmJyfv3bv3ySef3L9/PxKJFBYWHjp0SAo4rVYbDod7e3u//fbbTz75xO/3JxKJ\nQ4cOnT171uFwmM1m7UvwXEy2al7InabXc/Ikej2Dg0SjKYNiIC+PQ4c4fpwjR3ZLnJusohIJ\n5c9eUoLZzPQ0wSAaDU6n8tnaPXVwMMj9+7S3p9EFS79hYAC9ngsXKCzk7l3u3lVqknXrrIyJ\nc3IoLqa8nMZGzpzh4kWs1sz+OIOXHLHY+n7/Wkjf/Zk9icQtRXJgu7tZWVFNMlD7qPx8iovx\nenG7Nyvsko4hcjKrq6rASubWrLtPZQ9pt2+40xaz5fFxvv2Wv/s7BgZSP0rGfyf5JEkI9Vaj\n4d49JiZobOTYMTQaKirUKrf2NIQ7GA5v6MMng1dJ4xAZ79QUf/gD//iPKlRN3nphAb+f8XEW\nF1ldxevNiO4PFvZjoZOdnX3y5Mk//dM/NZvN8q9Op7OsrCyRSMTj8Xv37n344Yfl5eV//ud/\nbrFYJicnHzx40NHR4fF4iouL5+bmrly54vP53n33XZvNptPpHjx40NXV1dbWptFoSktLdS/E\n6uKlgdi119byl3/J4KC689dGinm9u663koKyvZ1r19TWU7anWVk0NHDxIg0Nu1jYxWIEg8qS\nIC0iEWVzEIkwMMDICEtLaRjc8qgwGHj/fd58E7sdlwuzOeMyn8FLDhEQlJTg96fynZPIysJs\npqjoeXm6Uv1cvcqdOwwMqH65201ZGefPc+YMHs9T7jVxDJmcpLeXuTmWlnA68XhUKNyT1ZvF\nwuHDjI7y8OH6IGDZxZWVUVNDRwd+P2Njj7Ukk46VkmkmGYkCKXNXV1XFtrDA8DBdXbz2Gn/0\nR+v5tbJXFAVxWsgQI9lZHB/n7l2mptQJr/2tWAyfj9u3KSvjvfcyRN6Dhf1Y2GVlZVmt1pqa\nGrvdrtFoLBaL1Wq1Wq0rKyuhUGhwcLC9vf3MmTOXL192Op137txpb2+fnp7u6OjIzs72+/1t\nbW12u/38+fPl5eV6vX5ubq6zs7Ovr8/hcBQXF2cKu+eCTGMLCykowOdTvajlZQoKcDofi0bd\nQcgWVjaRwaDyW7l9mytXGBtjfl5taoGpKfR6jEbl67b1Bm2SzhwOKzqLwYDRiNGY5iBr4782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hQwM0NjIoUPk53PmDNnZtLenNtCyURN5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\nVhge5osvuHmTzk5FJC0p4dQpfvELWlvJynrtgJmNoNGQ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1mjIzqaoiLNnaWjYMTH1rkLa1eEwHg/Dw0xOsrhIJIJWi81GQQG1tRQVodOR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AOT3U1LCmTNr/bPYz0pJt413w9ycMn2N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lqtR4/fjwW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ELrl7t/H5CQKBeGy+8QJXLiA2loqcEXI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bwoFsllp6rvrNklILvFh3HVW8YVpauLll7l5\nk/FxgFxu++fr9VRX88ornDqF0bifT6oocUQqd0UFJ08SicgGEpsNr5f6etVAUsYoYVfuiNvq\n5iaPHnH5MpcvMzPD2hpaLXV19PYSi0nPDre72Gs9RIRLp7j/6fXodKXuunIkKBRIpxkflylG\nYm5AkM+TSpHPs7DAjRv4fGUo7MSJp8EA7JlBIoy4993QzuvlxAmGhvD7WVkhHCYeR6vFYKCy\nku5uLlxgYECuTXFMEAFxtbWMjhKPk0xKn2pxuVOUL0rYHQNyOSYm+NnPGB9nfp5IhFQKjYZE\ngrU11teZm+NP/5TR0WIv9BARQWHCuNXppKJCjf3vA1vDsKHQE2b3AtEMsLnJ1BRjY8VY3wEj\nzr+qq9Hr90wWBlwumpufI6PzWRAC7k//lLY2Pv6YyUkeP8ZoxOVibIyxMUZHqa9Xwu74YjJh\nMMh5Z0W5o4RduZPJEAwyM8P16ywsEA5v99hlMsRispQyOkpvLyZTORfnxUGYmNZcWiIQIBYj\nncZmw+Ohvp76eurqVBPSt2XrOPLriINIYY1WfggTYJ+PgQGCQaann/g9aDS43TQ00N5OXR1W\n634+tU6HyURHBxUV2Gx0d7O6il6Pw0FfH52d1Ndjs+3nMyqOFqrZ5jih7mHlTjLJwgLz82xs\nEI/vPB3L51ldRaPB7ycUwuMp8w9/ocDKCr/5DVeucOcO2aycIqyu5rXXeP11vF4l7F4ckROv\n08lc1F21nTC/LddmL4OB2lq++13yeZaXiUbldkL4wTY0cO4cg4NUV+9/4USjwWKRESliZkV0\nFxiNstlAoVAcD9Q9rNzJZNjYIBiU3qRfJ58nmyUUYnMTu70MZxW3yGaZmODaNT7/nPFxAgHy\neak/4nEMBsxmqqtpa8PnO4z1iN/88jIbG8RiaLVYLFRXU1V1hE9M9Hrq6mhtZWmJaPSJEQGN\nBp0Or5eBAWpqirfEA0Mc5dvt9PcDVFaytMTGhowU83rp7GRoiK6uA9k8CBkn+uoUCsUxRgm7\nciebJRIhGt0la0gg7gfx+LZLfrmSTnPrFu+/z82b+P1PmHaGQnz5pew1NpkOXNiJP0QiQSjE\nxATT06yuotPh8XDiBDqddKY4cvMcYnqgrY2+PpJJ0mlSqe13nXhdDQ0MD5ezZYzVSkcHNTWM\njDA9zfQ0uRw2G+3tNDZSU6NKwgqF4kBRl5hyx2DA7cbp3HM4oFAgn8dux+0u871+LsfsLOPj\nBIM7Jazo+g8EuH6dtjZGRg58JZkMv/89v/sdfj9ra0SjMt6xqorGRn70I0ZHj6TrmNHISy/J\ng7/bt1lcJJ9Hq6VQoLKSsTEuXKC3l8rKYi/0gDGZpHl1XZ2chHU4sNmOaiFWoVAcHY7abUPx\nvBiNVFVJV3pRk/tq3U6jwWTC4cDjORbCbnmZxUXi8V1cxHI5gkEePmR9/cBXEovh9/PJJ/zy\nl8RiMuhJo8FgkIJAHMg2Ne3z7OQhoNfT0SE9UW02ZmbkS9PpqK3l/HnGxqivL+cTf4HBILsJ\na2uLvRSFYr8RN5FYjEiEWIxkkkIBgwGLBYcDh0N22SqKhBJ25Y7ZTGMjzc14vUSjpNPb2k60\n4whHsbo6nM6jVx96XsTA5l7esNks8fgTR7QHxOIi77/P7duEQqRSZLOy209Y62k0fPIJVit/\n8idHT9iBLFP95CecO8fmphw9djpxu/F6ZfFYoVAcXcRI0Owst27J5MBslspKWlsZG2NkRI7s\nKIqE+tWXO6KxqaODt97i9m0ePJD3Wo0Gh4PqaoaGGB2lpaVsBxW3EG4UdrvMVduBmF2orNxn\nH4pd2djg5k3m5kgktt1ntmLf4nGmpqit5fXXD3wl+47oC7TZsNmoryeVks12FRVYLMoZ9WiQ\ny8leBTHmrI6PFTtYX+fRI65e5epVpqbw+8nlcLuZnmZzk2CQgQEaG49el3C5oITdMUCrpacH\nn4/f/57332dykkAAnY6mJoaHuXCBs2ePZGXoedFq8XqpqSEef6Kpnz8ObLrdtLcfRvtXOCwH\nJr4+rSK0XSDAwgLJ5IGv5EDR6bBYMJspFNTRzFEim5V7P9EboISdYgdzc/ziF1y9yp078nIK\nrK2xsMD0NDdu8N//Ow0N6iNfLJSwK3fER8tiQa/n1CmcTlZXiUSkXWpdHW1tuFzH4tptNDI6\nKguWyaQ8AM3n0ekwGjGbaWnhwgXa2w98JU/fyH49xP2IovbrR4h8nmCQxUVu38bvJxIB0Omo\nrKSxkaEhfD7M5mNxoVA8hVyOdJrHj7l+nYcPCYW2g4mFf5MYjZ+ZYXGR6moslmKv+DiihN2R\nYivhdMvy9BlDToVt7IkTnDhxKAstSQwGTp1Co+H+fTmykE6TyWCxYLPhdHLiBOfPU1V14CvR\n66mo2HOAQKvFbMZiUTdRxSEh6sSPH/Ppp/yf/8P4ONEouRw6HQ0NjI6i02EwUFOjxPpxJ5cj\nGsXvZ3yclZUnzhxErkwsJmMq5+dxOJSwKwpK2B0pQiHW11lbIx7HbKayUg4Yqm70Z0GrpaKC\n3l7+23/j/n0ePpTVO6+Xujo6OzlxArf7MHp+KysZHiYcZmVl5zCHOL5sa6OnR2VAKQ6J/8/e\neX7Hed35/TO9N8wMZtBBFKKQIEGwU5RIyd2WJXuzcTaJvSc5e5KcnOTV/iX5B7zJiU8cx3bW\n9tpRWdmmJFIWKbGLIEiCANHbYIDpveTFvQJECKQIFswMcD9HL0QOCNwZPM99fvdXvt9cjnCY\nd97hnXcYHSUel2ZoxSJLS9y4gVZLOMy/+Be7omdD8RiE3XM8/kgR9VKJXI7VVcLh7RhEU2yG\nCuxqhHiccJiJCSYmWFggkcBiIRiko0OaCKkppK9Eo8FspqkJn4+eHkZGWF4mmcTvp7mZ3l48\nnm3q7vf5OHKEhQUmJ4lESKXWDbhsNunNcPAgDscLX4lCASQSzM1x5QqXLpHJPDRaFIuRy5FI\n4PPxrW9hNqtj5K6mXJbSCo/K3Qph1HyeXG6HtJTUICoUqBEmJnj7bTnWKgQyhAWWzcYbb/DG\nG/j9Kg54IrRaTCaamnC7pc2aaLCz2bbPT7O+npMnCYdZWmJ4mPl5dDqZIAkGZUX45Enc7m1a\nj2KXEw4zOko0Sqm08WEsqrSxGCsrhEJYrbhcFVqlogrQ63G7sdtlX9CX0WoxGnG5cLtVrqFS\nqM+96slmiUQYGeH8eakYJBrsRI+dRoPLhcPB6dP09FR6rbWAGIAVehyVwmzGbObQIfJ5OjuZ\nn5cqvno9ra10d3PgAMGg6rFTbBPJJKHQ5sLdfO7LkkoRjZLJqMCu2hG9bktLhMPSJVyjwe3G\n68XrlcX0p65L6PU4HHi91NeTThOJPDTsJSaprVaCQRobd74OebWiAruqJ51mdJQbN7h5k1BI\nDpbz+Z2p0fDJJ0SjNDSowK7G2L+f7m4iEdmPotfj8Uj/NzV+qNhORLE1m90kYwfyDCm65tf2\nH0XVUiqRzXL7NleuMDxMNCon5w4dYmgIm+2Zuk10OhwOAgG6ukilHnIhF2dmkwmHg9ZW2tsx\nm5/Xe1JsCRXYVT3pNBMTTE7KZpc11lRto1Hm5ohGyeXQ61VAUAOIjdVkwmjEaMTpxO+XCslm\ncw0fc4XwgUgkq/HJGsJuJxCQVrZr7Z5riCYBgwGvVw30VDXiF3frFp9+yvXr3LnD3BypFFot\noRBTU8zMcOgQAwPU1T3ljxD39Z49/PCHNDTwySfMzLCyQrmM3S77gw8fpq8Ps3n7mlsUD6MC\nu6onk2Fmhrm5jU3NX/yCaFR6YT1qUklRnYh5DrO5htvp1nqls1kpH6PToddLs1SjUUkT1wAu\nF62teDyYTOuyZGsI70GRp1FTsdWMCMGvXeN//k8mJgiFyOXkb3Nmhlu3mJxkZYWGBtzuZzp6\ntbYSDOL1YjJx/TqTkxSL+P3S4uib38TpVA12FUR99FWPaL16/E0ilP3VE1Sx/WSzhMPcusVH\nH0ntayEr09LCgQMcP47TufPd6mod4bmydy9jY0xNEYtJfTJRXPP7OXyYo0dxONTTuqoRvZIT\nE0xNEY0+JKVUKJBOMz6O38/8PA0Nz1STFRMS+/bh8fDaayQSlMuYzTidNDXhdKrR6cqi7tKq\nx2DA7Za3ygYjrLUvsFoxGJQRp2JbEemBSIQbNzh3jrfeYmGBWAzA6aSri3AYq5W9e2lqqvRa\nFY/FZiMYZHCQSASnk/l5YjHKZfR6XC46OnjtNQYHN5kcLxRkmrZQkCLGJpP8GrUXbT/JpCzv\nLC+TzT5U4REyJcL1a2mJWOyZJNBFHqGpSd3a1YkK7Koei4XOTtrbsVhIpze6i2q1uFw0NMjI\nT22mim2jXJY5gF/+kosXmZ1df5bE49y/T7HIygp/9Vdq968BTCZeeYX2dm7e5N49xsYArFZ6\ne+nro6+PhoZN0jCpFKEQy8tEIjgc1NXR3IzVqjaiypBOs7hIPL75EAyg0VAsyt/XNljsKCqE\nCuyqHouF9nb6+ti3j9FR5ufXeyOEyWlfH6dP09SkuusU20qpRCjE2BgjI0xOPpROzueJRnnw\ngEyGoSGyWfR61Uld1eh0BIPY7djttLbS1wdIA+XmZvz+9QlH8VuOxRgbk3NdKyvE49hseDw0\nN9PVRU+P9KdWbCdrQ0ub9s895iXFzkLdeFWPyURLC4ODzM1RLhMOo9Oh1VIuYzJht3PqFP/m\n39DQUOmFKnYZxSIzM4yNkUhskiEoFonHyeVYWiKZ3Fb9Z8VTY7XS3c2ePTLzKhp8hdP0FymX\nWVrit7/lL39hbEzG9MLj2OPhG9/gP/0nAgEV2G03RiMeD1br5m05QnBOp8PpxG5XiYAdjLrx\nqh7RzdDczGuvEQxy5AjJJLkcBgMul+xrbmxUXss7nFKJSITlZdkWncvhcuHz0dqKz1eZ9spS\niXicSGRjN88a4kGSThOPYzTWsIzL7kHsNo/vfC8UmJiQghqjo4RC6799vZ5YjGvXeOstTp1i\ncHAblqxYR2RbRXq1UFhXmOPzGN3hwOejvh6XSwV2OxgV2NUIgQCBAAcPEo0yP08iIU1Fm5pU\na93Op1Agm2VmhuFhLl5keppkkuZm9u7l1CkpRSEextt8JeRypNMPDd99ESFsWyiQTCq7gp1D\nocCdO1y8yJ07LCw8JK4pHELv3JFWByqw22ZsNlpbZelco5E6ROLeNBikvbhQKnE61VNjB6MC\nu5rCZMLtxmikUECvVwqQu4X5eUZH+eMfuXyZ+XnicQoFpqe5d4/hYYaG+P73aW/f7iO4Vktd\nHcEgNht6Pfn8xi8olSgWsVqpr1ca9DuHYlFee9HoxlmutUHp+/dZXq7Q+nYxIi134gTAhx9y\n/ToLC6TTaDQ0NtLRwZkznDpFMKiiup2NCuxqCiH6qsTfq5xSSdYfV1dJpykW0WqlcoTTuZ5d\ne3ImJjh3jj/+kZs314/gCwsYjYyNsbIiBaVcrm0N9LVa6utpbaWuDqt1Y6edRiOTyn6/Uivd\nUZRKhMMsLpJOb5KpLZdJpVhcJJGoxOJ2N+Jo19NDXZ1M5M/MkEyi0dDaSm8vr73GwAAWiwrs\ndjZqt1UonjcipXHzJh98wIMHxGKYTLhcnDjB0aMcPLhlP5/hYX7zG+bnyeXWI6dSiVyOeJz5\neW7exONhYGBbAzudjuZmentpaWFuTtaL14RtDQba2zl9mr4+9Hr1INlRqN9mNWM2Ewzyne/w\n0kvrfkVmMzYbXi8Wi6rz7HhUYKdQbB0RxGQyZLNyLGBN3CGRIBzm4kU+/JBLl+SJWZyek0mS\nSXQ6enrkxMOT/KB8nqUlJiY2ylOLslcmw+oq4+N0ddHf/6Le76ZoNLJZ+9VXcThkH30kgk4n\nBW8PHuTMGTo7VZv2jmKtBD83RzK5MWmn0WC14vcr87GKIZyKWloqvQ5FxVCBnUKxdbJZVldZ\nWmJlBZcLr5dgUAZ2KyuMjPDWW/z5zyQSMscmupgvXWJlRdqnPmHZNJ8nFpM1r00VRwsFEglm\nZlha2nw09UUTDPKjH3HwIJcuMTLCgweYzTQ0cOQIAwP09GC1VmBViheHTkdrK3v3Mj5OJPLQ\n8ISwIHO76erC56vcEhWKXY0K7BSKJ0PEVZEIn33G6Cjj48RiZDKYTDidNDTQ38+RI4yN8dFH\njI8TjVIsyn8lsmuJBLOzXLyIz8fBg080T6DTyRGZTcdO+bxd2mrFbN7uApn4cXo9TicdHRgM\ndHezsiLTky0t1Ndjt6u6z05Dr6enh1hMClAvLa1fnOJS7O3l9delxLFCodh2ai+wS6fTmUym\n/HD2wmKxWCwWoFQqlUqlfD6fy+XK5XK5XNbr9QaDwWAwaLVaQKO6QxRPTT7P/DzvvccHH3Dz\nppyKEErR9fV861s0NXH3Lh9/zPz8xoFBkBZbV6/S2bnJDOmm6PVYLJjN6PWbJ+Q0GkwmvF7c\n7spUPIWQvd+vHIp2C3o9nZ0Ad++SSgHk85TLaDRSoPjQIV5/nfr6yi5TUe2USnJwvlikVJIa\nimsWNepJ/QzUXmD3pz/96fe//30ul8vn84BOp9Pr9W+88cabb74JZDKZubm54eHhTz75JB6P\nl0qlrq6uvr6+w4cPe73eSq9dUcsUCoyO8sknXL3KgweyPCp2H/Fgu3GD3/6W+/dZXSWb3fyb\nFIskk6TTm9dVv4xIyDU0MDDAxATLy+tZQIHQke/ro71dTZ4qtgmNhkCAN9/kwAGmph6yFGtq\noqtLTmUqFI8hlZLKOLOzpFJSF6mjg/Z25Xv2jNTek+D27du//e1vvV6v3W4HdDqdwWBYWVkR\n+blYLHbz5s3Lly/fvHkznU4D4XA4Ho8HAgGz2Wy1WlXGTvGUFAqMj3P1KvfusbT0UGuREHm/\nfx+d7qHB1WdHbHDt7bz0EgYDxSKxmPzRwik4GKSvj54eGhtV0VOxHYgt1OlkaIjubkIhlpdZ\nXcXppK6OpiasVvVgVjyOfJ50mgcPuHuXmzd58IB4XPZvCAfCpiY8HnUVPTW1F9hpNBq73f63\nf/u3Z8+eFX/UaDRNTU1AsVhcWFh46623stnsd77znfr6epPJ9N577927d294eNhkMnV1dWnV\ngJ7i6SgWmZjgzh0ikY2FVNFCt7LCnTs0N9PURDjMysomEZ5eT13dlv189u/H4ZB2q8PDrK4C\n2Gz4/XzrW7z6Kj09OBxq+FSx3QgzA6+XfB69HoMBk0k9jBVfQSzG1BR/+AN/+AORCIkE+TxG\nIw4Hn3xCZyf/9t/y0kuVcUrcEdReYAeUSqVwODwzM6PVal0uV319vdFoLJfL8Xh8eXl5aWnJ\n5/MNDQ01NzfrdLqxsbHbt2/PzMzU19e3t7cbVIGg+ikUyOWYmWFhQfqiAg4HHg/NzXi96PUV\nCGKENerq6uY5uXKZbJZIhIMHOXqUpSXm52XvyBparfRZ6unZWqFKqE/F47hc7NtHNEqphM1G\nXR2nTjEwgM+H0fgc3qNCsSWEsoZCsSXm5zl3jgsXuH5dbpLlMlotoZD87+BBaX2mZuqfitq7\nJ8vlcjKZ/MUvfvGb3/zGaDTu3bv3pZdeeuWVV9xudzgcDofDVqu1qampq6vL4/Hkcrn+/v5C\noRAKhebm5ooV0YNQbJV8nkiEDz/k/HlGRohGATo62L+f736Xw4exWiuTnRKtvo+qtApZk+5u\nvvlNbt1ieFiGpCK2E97qgQCvvcaxY1uLw3Q67HZOneLoUfJ5KW5nMEgJPaGfolAoFDXBxAS/\n+hX37z/U0LKmuK7TcesWLS3Y7SqwezpqL7A7derU3//934vR13Q6HYvFrl696na7fT7f8vJy\nJpMxm802m81oNOr1+mKx6HA47Hb79PR0KpUqP+KRfOnSpYsXL6bT6dwXr7PPmZ2dnZ2d7ejo\neMHv7GHKZWnOI0zWDQbsdgyGHX4+Fu/6xg3On+fyZYaHpdchkMnI7N3CAmfO0NAA2zs5pdUS\nCNDayuIiqdTGGVWh1ltfT1MTbW28/jpeL/fuyaSj2YzTSXc3g4McPbrlfjjRayLGY/m88qvV\nqqY6hUJRS4izcSLB0tLmpnOFAqkUMzNMTDA4uO3r2wKZTOY3v/nN+Pj4l1/S6XQWi6W/v//M\nmTNCr2Obqb0o4fTp0y+99BKQTqcXFxf/+Z//+ac//WlDQ0NbW5sYlTUajUajUQxJaDQa8cc1\nAZRNv+e1a9d++tOfrq6uJpPJL79aLBY3/fsXhQhuslnSaUIhVlfXJ4ZcLmw2DIYd+0QXt/2V\nK/zsZywsEIlQKMgMmdDgnZtjaYnubvz+7Y5x9XpaW+npYWyMSIRMZj11J2Isj4eeHlpaCAR4\n/XX6+/nzn7l9m7k5KXT36qscOkQwyFPf6iKQ1WhUik6hUNQe5TL5PJkMqdTmkk8ibxcKsbDw\nSG2B6iCTybz77rvnzp378ksmk8nj8bz55pvHjx9Xgd3WMBqNPp+vvb19//79FotlYWHB7XYb\njcZMJrMmdFcul8UfjUajyWR61EjsmTNnAoFANpstfFl7DMbGxn72s5+92DfzRcTE0Ecfcf48\noZBsqNLpMJk4eJChIXp7aWzcvvVsJ6kU4TDz8ywvk0qtR3V8viPEYiwtMTtLczN+/7YGuHo9\nfX2kUty7RyLB8jKFgpRf0usxGunr46//moEBNBpsNtrb+fa3OXGCVAqDAauVhga8XiUDoVAo\ndinCRdpgeNzRVLi+gA87AAAgAElEQVTSORxVXp6yWq0/+clPRJppA1qt1mQytbW1WStUSq7q\nD+7xaDQag8FgsVhsNptWq83n8xaLJZ/PZzKZaDQai8UMBkOxWAyFQuFw2GazuVyuR43E9vX1\n9T1aJ/2TTz55++23X9j7+BKRCOPjvP8+v/sdKyskk/IeKBaZmiIWw2zG7cZk2oF5u0yGxUXC\n4XUzrjVE/TGdJhplYYGVFbze7fa8b2oil+PECQwGpqZIpcjlMBqxWnG5OHmSl18mEJCiwSaT\nclWqeUTfpOiIEH0awlxBdHmqvKlCsSWE6ZzFgsdDPE4isbFlWUR+Ph+BQJUPhBmNxpdeeunH\nP/5xpReyCTUc2OXz+eXl5YmJiVu3bh0+fLixsbG5uXlhYSGZTM7MzIyOjuZyOaPRePv27Xv3\n7g0ODjY2NupqIhJ68ID//b/55BPm58lmKRRkAa5cZmSEVIpAgPp6Ght3YGNpNsvKimyqexRi\ntCIef6TL1otDq6WxkR/9iBMnGB9ncZFIBKdT6mq2thIIYDJt96oUL45ymVyOsTEePGBpCcDl\norub7m7M5ip/8CgU1YhGg8fD/v0Ui4yNbZQOEGFfVxf9/djtlVtlbVNLgV0mk0kkEvfv3xft\nioVCIZlMTk1N2e32xsbG9vZ2v99fKpV6e3tXV1c/+uij4eFhg8EwPT1tsVg6OjqE+kml38Rj\nEemBpSUuX2Z8nGRyY+yyskKxyN27dHfjdu/AwE6rlZlI0Wi4KeJIt/1ZehFeW610dOD10ti4\nUXDfZkOrVcJLOwQhwTA6yvAwd+9K2w/A4eDOHbq7OXiQ9nbp9qZQKJ4EsT02NvLaa+j1ZLOy\nPiPmwzQaWlvp7+fAAVpbn8hNW7EZtbQlJRKJycnJn//85z//+c9FHdbhcHR1dR06dOjQoUNC\nfFij0XznO985f/7822+/HYlEyuVyf3//iRMn9u3b19LSoq/yLbhcplAgGmVqitXVTTJSxSKZ\nDBMTjI7S21uJJb5gLBb8fmy2x0mKGAyVtEYVOBxYrbS1rVscKi3NHYbo6fzwQ/7hH1heJhol\nkwEwGDCb8fn4z/8Zp7MCQzwKRa3T2soPfoDBQDTKrVuk0+h06HTo9QwN8eabHD5MILADe422\ni1rakiwWS319/WuvveZ2uwG9Xm82m4XscEdHh1AettlsPT09er0+EAgIfZOmpqY9e/bU19fX\ngDSxCOwKhY3Z6S9SLJJKkUhsbglf65jNBAI0N9PcvD44IoI8oQPn88lX6+oqedur/qpKkcuR\nSjE2xuioDLYMBurraW+nuZlAAJ6TCE4oxL173LzJxATJJJmMvOO0WvnH8+exWvn612lqeg4/\nTqHYPYiyzJEjmM3MzbG6KiW9LBZ6e+nvp75enZeehVr67Gw2m81ma2lp+cEPfvCorzGbzS0t\nLS0tLcJwrMYQpVihwf2Yh1OptB7u7DAsFiwW9uyhv5979+S0hHigmkw4HLS3s3cvLS14PJVe\nq2J7EdX5RILZWT74gPfeY2qKaBSrlf5+Xn6ZkydxuZ5bmX5xkQsXGBmRmjtr56hiUZ67Pv5Y\nJhjEiLrK1yoUT4hWi9HIwAD790ufoWQSvR6nU95HWq18wKnb6qmopcBu56PVYjZjs+FwyMfJ\nBnQ6rFZaWmhvf3ottOpnaAiLhUuXuHGD8XHicYCWFjo7OX6coSHq6yu9RMW2UyqRTnPtGr/8\nJSMjTE4Sj5PNEo9TKLCyQjhMLMb+/TQ3P4cfF48zNsbSkhS1+fJiVlcJhapcakuhqHZ0Oumd\nI6biymUpF6Ua7J4BFdhVE6LA5/PR308+z4MH8HmiQjSW2mx4vXR00NGxkwO79nYCARwO/H7u\n3iUapVymo4PeXo4coaND9V7sRrJZlpa4eZN33pER1dqETTLJ/LxUt/Z6ZW30Gc/6oq07mdy8\n50FooOzUjgiFYtvI50kmCYVYWWF1lWJRjs3W1eH3Y7djMqm83VZRgV310dHBv/7XGAzMzcni\nrPCP0uupr2fvXgYG6OnBZqv0Ql8YInM5MEB7O8mkzFxaLNhsuFxqTGGXkkjw2WfcvSs167+Y\nRRMGvnfukM1y6tTz+XFCr+4xjbmi11u1WioUT43IfI+M8E//xL17sqlaq8Vmo6uL73+f/fsJ\nBlW/3VZRn1f14fVy+DCRCBoNq6vEYuRymEw4nXR20ttLby8uV6VX+SLRaNDrqaujrq7SS1FU\nDZkM09PMza3PMXyRUololLk5aShsMDxr9O9w0NXF9DRjYxtbWoXgjtCSfF7phGSSeJxkklwO\nnQ67Hbcbo1E90hQ7llKJTIZr13j/fT74gIkJ0mlZnjIamZ2Veihnz2K3qyrNllC7RvVhs2Gx\n8OabnDzJ8DCTkySTOJ20tNDdTWenksBV7EbyeUIhwmHy+UdODolHRSqF3f6suTRxvhof5+pV\nstmHQkmdDrOZjo7nmThfWWFiQo6DmM20tNDfj9utAjvFjqVYJJHgvff4v/+X5WXpvi0Cu2yW\nsTFWVsjnGRzcmTZLLxK1a1QfotPO5cJkwmikvZ1cDrMZlwuvF4ej0utTKCqBXo/LhdOJXr/R\nbm4NcdY3mZ5DhdTjob+fY8dYXOTuXebn5aReqYTPR1sbZ8/y8svPYTp7aoobN7hzh/v3CYdJ\npTCZ8HgIBDh2jKNH8Xh2ct+FYteSSDA/L2Wtcrn15gpxa+fzxOOEQkxPYzarWYotoQK7asVo\nxGjE6az0OhSPRihf5POyx0ujkWJ7ovVKNQI+X4xGgkH8fozGzauxBoN0cX0uc0VOJ04nq6vS\n4E70dAvD4s5OBgd59VVOnHimjJponx0b49e/5sYN7t+XvYN6vbyW/vqvcTrp61OBnWIHEo8z\nPy8nkDY9pwl5o6kp/H4pUal4MlRgp1A8LeEws7PcusXEBKkUZjNerxRnUuP6zx27nYEBHjzg\n3DlSqYf63kQ83dPD0aNSVe55sWcPJhP79rG4KI2PzGb5mBHT2c8SvqfTTE9z6xa3b0tjaPGm\n1hS8rlxBq+Xf//vn/KYUimognyed3kTVSyBuBPE1+fz2rqzmUYGdQrF1sllSKe7c4epVLl7k\nzh0SCaxWgkGpxNHTQ2MjOp2amnxuWCy0tbFvHwcOMDZGKEQmQz4vRw28Xo4c4dVXaWh4XLAl\nnhYifnqS347fL+WHMhk5z2S3Y7E8nz7XdFraAwoLwbUn3FoycnycbJZvflO+TXUtKXYSQupB\nJMI3RZRB1Oz51lGBnUKxdVZXuXePt97inXdYXiYWo1BAr2dhgdlZPv2Uv/s7XnvtObTwK9bQ\narFaOXSI//Jf+PBDLlxgfp54HLOZri6OHuXUKY4exe1+3DcplSgUyOcpFLBaMRqf6EfrdFgs\nUvpEmFo+FzIZpqaYnX1k3qJQIJ0mkSCRUNeSYqfhctHUhNn8uFKszUZLy1fc1IovoQI7hWLr\nLCzwwQdcvMjIyEPOBMkksRgrK1y/Tmsre/fucGGa7UQMFQUCUl7O62V5mWQSk0nOkHZ309Cw\nyT8UFszRKBMTLC5KY8pSScp979mD34/D8ciwSWiDi+zC86VUIpuVI7ebPthEE96jXlUoahqr\nFb+fzk46O5meJpFY70MQUnaNjezZQ1OTGhncKiqwUyi2zswMb73F/fvkcg/9fakkhdQ/+4zG\nRgIBFdg9Z0wmDAZOn+bYMVlR1WjQ6TAYHikmXC6TyTA5ya9+xSefcP++VPy22di7lx/9iGPH\nsForkA/TauXkuxi1eVTSQq9/DrJ8CkW1YTLJDoqVFf70J7JZCgV5F4iT29AQg4M0NKjhoa2i\nAjvFMyOaljIZQiESCbJZbDacTtxurNZKL+55I86U6bR8s5t+QT7P0hKzs8pI9PkjwjiLZQuj\nr7kcFy9y/jyXLjE6Sigk1e0NBin9nUjgcFBXt93zLmYzbW00N2MykU5vnPMVFoIimyiCP4Vi\nJyHEifbvl6ZK4+MsLJDPo9USDLJnD4OD9PdjNquLf6uowE7xbJRKlEokEoRC3L3L4iKxGH6/\nzKIHAuuiYjsm5SBCN2ESsOmrhQKRiFTXVFQWka776CN+9zumpojHZfwkdGqmplhcRKPhyBFM\npu0O7CwW9uyhvR23m0yGZBKQqTutFp0On4+ODurqlCa5Ymcihtmbm+nt5f59RkbIZtHp6O2l\nq4v2duz2Si+xJlGBneLZSKcJh3nrLT79lHBYZuwsFlwuOjsZGJCDijsJkewRrfeZzCZfoNPh\ndlNX9zinUcX2kMuRSLC4yPw86fRDDrN8nmxeWWF4WLbcbScmE4EAg4O8/joff8yNG/IIVC5j\nMuFwcPo03/senZ3buiqFYpsREpVWK62tUjDS7cbpVOeZp0YFdopnY3GRzz7j3Xf58EM5bFgq\nSTPNtjZCIdrasNlwOB6ZsRMprkyGaJRMhlwOvV6KM9tsGAzVlYcX78Jup7WVdJrFxY2xggj7\nGhpoa3s+SrmKZyGfJ5EgGiUW29yLrFgkFmNykq6u7V6b8NLo7uYb38BoxGCgWJSXk92Oz8fZ\ns5w5ozrHFTscgwGXC5eLlpZKL2WHoAI7xbNx4wa//CUjIyQSsiwrKBSYnWVkhE8/xWxmYOCR\nxy8hLz49zaVLPHhAOIzdTn09x4/T24vHU43ntmCQs2cpl+WI5QYjUauVffs4dEg9kitPLifV\njHW6TSrjImOXzRKLVawh0u/n2DHa2vjWtwiHSacxmXC7qa8nEMDtVi6ZCoViS6jATvG0iOTH\n7CxXrrC0tDEdUipJx5jbt2lpoa9v8/gsmyUe59NPuXKFGzeYnmZ1VRbFQiFmZzl2jMZGLJbq\nytsFApw+TS5HLsfMDMvLUsfOYqG1ld5eDh1SGbuqQK9/nIO40DERdc9KnR+ED6bbTXs7kQiZ\nDCYTNhsulzKmq1XW5rXFr0/9EhXbiwrsFM9AqUQkwuzs5qbsQjxsdJT+/k2cPQXJJNPT/OpX\nvPMOqZSceNfp0Ou5cYNr12RLe0NDdQV29fV4PBgM2Gy8/z63bpFKYbFQX883vsG3v83evdTV\nVdeadyc2Gz4fRuPGirlA9Ay4XLS14fFs++K+gF6PTofZvB4QqIundhEChMIsREV1im1HBXaK\nZ0PYom/61OTzUtejXgXGxvjgA+7eJRyW30e03OVyZLNMTPDBB1Lu6LnLwz4Lwn6gsxODgdZW\nFhbI5TAasdvp6aGnB5dLVdCqAq0Wi4VDh5iZ4eZNeZkJhGyK0Cju76e+vpLrVMFcrZPLEYsx\nPs5nn7G6SjIpvVKCQTo76e/Haq2uTUyxc1HX2a5kLd5asxtfszPf0vlyTRv2UdGbTiclWB/F\n6CjvvcfU1MZKrgjvQiEuXMDv5+WXq0sST3xKTU00NXHyZKVXo3g0Gg0mE8eOkc2SSFAsrivG\nWSzU1dHeTm8vvb04nZVeq6I2EdqWqRQTE7z/Pr/8JdPTrKzI6c6BAb72NdxuGhqUXLlie1CB\n3a4knycalVprRiNWK4EATueWqwYaDV4vnZ3MzrK6urHeKob+entpb3/kUTUWY26OZHJz2f1c\njuVlIpHH5fwUisej19PRgV6P283ICPfvy0Gf5mb27GH/fvr6VDek4ukRiuXj4/yf/8PHHzM1\nJY8QGg2JBPfuodORSvGNb3DmTKXXqtgVqMBul1EsEg6zuMjMDAsLhEKyc7y1leZmad6ylr17\nPOJrWlo4doxPPyWdJpeT25lQWPV4aG1l3z7a2h4Z2OXzpFKbO6ADpRLptPJvUDwTOh319Tgc\n0pjy9m0pU9zWRmcnvb243et97grFVimXiUQYH+ejj7h+nUxGHlOFOLZwU4hEaGnhpZek5bFC\n8SJRgd0uI5fjo4+4cIHhYcJhCgXpbu7xsG8f/+pf0duL3b6Fh9yBAxiNZLOsrBCNSkMYoS7R\n3c3Roxw5wp49jwzsxDxgNLr5qzoddjsWi3roKp4Vo5H6eux2urqkJaXFgtWKzaauLsUzUSgw\nPb2eCf4iogU5GuXBAxYXSaUwmx/XmqJQPA9UYLebWF1ldpbLlzl/nvFxEgmZqNBoMJuJRKRF\nxMGDW5B+8PsxGPj613G7WVoimaRQkGMEfX0cPEhb2+Pk3IJBBgZIJolENlZytVocDvbupa1N\nDSJsE9ks6TRLS0Qi0ttH2JX6fHJepHZZc5jdZnsJxY5H6FkuLcn2zQ1dJWsOhIkEiYRUX1co\nXiQqsNtNzM1x+TLXrnH3riyAruUqcjnGx/nDH9Bq6e3dQmBnNOLz8cMf8uqrzM4SDhOP4/US\nDOLzfbUtTEcHZ88yO8vkpBzpWMNgwOfj+HEGBtRWuE2kUiwu8pe/cOsWKytYLDQ3c/w4R49i\nsdR2YKdQvCBKJVIp4nGy2c11ncSQmRibtVqraw5MsRNRgd1uYnGRW7eYnyeVkhvQ2uGyWCSR\nYHycmRk5oPqE9Smh0WCzYTRiNhMMks1KD7EnKToIr8y5OXQ6bt8mFCKblfpwvb0cOcKpU7Lz\nXfFCSaWIRvnLX7hwgfFxOdFiMuHx8OAB16/z6qvs24fBoMI7heIhtFqcTurq5OHnyx3D4sgq\nDEXUGVXx4lHPyx2E6OcQInCiIiCiLr0egwGDgVCIe/dka92X/63o8xUmCk8e2PH5FIVI3fl8\nW1uzyOrlcthsAKOjxOOyXvbKK5w5w5Ej+P1b+56KpyAeZ2yMd97hf/0vCgWpKSgunitX8Ptx\nOGhuVg5XCsVGtFp8PpqacDgwGjepxup0GI04HNTVqdtHsQ2owG4Hkc0SCnH7NpcusbREPI7R\niNdLby/9/QwMbKx1Vgk6HXv2YLPR0yOd2oUNVFMTwaCyW90mJif5zW+4cUOaiIjrRKgJZjLE\nYly7RjDIyZMVFvJVKKoNnY6WFnp7aW5mYYFyWR6tAY0GvZ72doaG6O+XXhQKxQtGBXY7AtGf\nGw5z/ToffMB77zE3RySCyUQgwNAQsRh2O9ksbjcmk1Qk2YBwrzebt3vr0enw+/H72bdvW3+u\n4ossLfGXvzA+/lCTkFBeFa3fd+7Q1MT+/SqwU+wixC1QKJDPS0V3oxGD4SGvMK2Wujo6Oxka\nolBgYoJIhEQCrRazGY+HwUG+/nW6u1W6TrE9qMBuR1AqEYtx5w6/+AWffsrCApkMxSKZDIuL\nXLokReZMJvbtY3yc6Wm5T60hnJcaGvD70euVAMSuQwzDJhKbvCRiu6Ul5uaUpqBidyH6W+Jx\nlpbIZimVCATweDCZHorSNBqCQf7mbzhwgMuXuX+fqSnMZvx+Dh7kwAEGBqirq9zbUOwuVGC3\nIygWmZ1leJhbt5iYIJ9f//tUilxODh+88gqHDnH3LvPzckxBIKzQAwFOnpS6dCqw24WsWcx9\nmQ0edArFjkeoo4dC3L3L7CzLy+RylErU1dHQQFcXDQ14vety7lYrnZ3YbDid7N3LwgJGI3V1\ndHfT0kIwqCbAFNuGutR2BIUC9+9L8+kNgxEi3SIqsydPcvgwIyPMzZHLEYmsW49bLLS38/rr\nHD++Ba0TxY5ByLwZDORym7yq0WC14nCoh5Nit1AqsbLC9ev8j//B7dtks+unmpYWfvADTp/G\n5Xoob6fVEgjg9crxI7G1ikFyVYRVbCNqm94RrClkfnH3+SKFAuk0+TwmEydO4HYzNiYHLPR6\n6RXb1cXAAB6Pau99SsQnHwqxuMjyMqkUBgMeD42NuFzY7ZVe32PxehkaArh/X45FryHi/r17\n2b+/2t+FQvFcKJfJZrlwgXffZXiY+fn1WVcxG/Hee2g0NDbi90tdOpG30+vV4UdRcdQluCMo\nlUgmiccfaboqWoDFIfLwYQYHWVxkfp65OWnq1dFBff1DHcGKJ0fs+GKAdHycmzcZHWV5GYuF\nPXs4dIiODgwG9PrqPbjX13PqFIkECwukUg+V6YX6zP79HDyohpQVuwKhOXzhAn/4A/H4xjR2\nKMT589jtnDwpjekUimpCBXY7Ap0On4/GRkZHiUQ20TQRQkpmMyYTej1aLV4vFovs/BAaSyqk\ne0bu3eNPf2J4mPv3CYdJpdDruXaNCxc4fZqXX5bRc3XS0MDZs7KENDzMzAzlMlotRiOHDnHy\nJCdO0NqK2VzphSoUL55UilCIeHzjkJlAnJOjUe7fx+Op3ptasVtRgd2OQKejqYmODq5dIxTa\nuBlpNDidNDXh80ndc2HDqrIvzwsxpHLvHm+/LWdT8nmZH9Xp0OtJpwGsVim2XIUxtMuF0yld\nzN1uHjygWESvx2zm1CleeYW+PuWyqtgOxJhOLEYkIme/AKMRiwW3G5cLrfaFt4sI7cZMhkJh\nc+3PcplUioUF4vEXuxKFYuuowG5HoNfT00MkwocfMjsrhZfEfiQmXvfs4dvfpr+/0gvdoaTT\nPHjA3buMjxMOy+k5PtdKKJW4eZNkkp4e9u2r3hZGjYaeHgIBvvY1EglyOXQ66Srm8UhrEIXi\nRSN0mq5f56OPGBtjcRHA76ejg1OnOHHiibwKn5FSad2AZ9NjmJgTF+c3haLKUIHdjkAoZPb0\n8LWv4XYzOcnqKvE4BoPM1R09ypkztLdXeqE7lEyGmRmmp1lZIZV66Igvqjarq0xOEgqRTGK1\nVmN7tXh6uVy4XIB8aIlTQRXmFxU7EtGrOj/PzZv85S98/DGTk4TDlMt4vUxMkEqRTjM4SGsr\nPCLzXSySz5PNks/LEFA0n2wJkwmH43EXv0aD2YzXqw48iiqk+h4wiqfDaKS9nX/37xgc5M9/\nZnSUuTkcDtraePllBgfp7cViqfQqdyi5HPPzLC5KXehHEYuxsiKnKKocjUa5lSsqQLnMvXv8\nwz9w5w4zMzI+A7JZwmEWFxkZ4b/+V5qbHxmr5fPE4/Jk6/PhdmO1bjmws9mor3/khil0TFwu\nOjtVf4KiCqn6B4ziSRDHSqMRv5+BASwWlpeJRtcnXoNBrNbqHcmsdUQYZDRKtdIvK86IvJ2Y\nU6mJBFhNLFKxw8jniUaZmeHBAxYXZfJb3E2i6BkKYTIxPU0oJO0fBOL+Wlnh5k3Gxpiels15\nZjN1dbS309NDf/8W7j6dDpuN48cJh/n0U0Kh9R+k0eBw0NnJgQO0tckMt0JRTajAbgchZDDb\n21XJdbvR63E6Ze1Gq92k21p06pjNWCw7Obxek30RnYXiXYvxEVSwqPgqcjmWlqQvTiKx0ba4\nWCSZJByW2XGr9SEpddEO8dZbXLjAyAjFIjodhQJuN4cP8+1v09yM243B8EQrEdqNL72ERsPq\n6nqzbLmMTkdDAydOcPQoLS1K60RRhajATqF4ZiwWOjpoadk8JSCqNk1N1NXt8Lyp0G6dmGB2\nlkgErRa7ndZWWltrowCtqCyFAtEoicTjnOvEwOzqKi0t63+Zy3HjBh99xNWrTE2RTsvUWqlE\nNMrt27jdBIMcOEBX15MuRqejsZGXXsLl4sED5ufJZCiXCQRoaaGnh7Y21a6gqE7UVqtQPDMm\nE42NdHWxdy8aDYuLcmgOMBhk2Dc0REPDjrVrE+93fp7paW7fZmyMUAitFrebnh7CYdra8Puf\npo1dsXsQibGvnDMVX/PF4K9QYGSEjz+WwuBrZtlAKsX0NLduEQjgdm8hsNNq8XhwOGhqYnGR\niQkZLzY3S5dYpemoqFZUYKdQPDOiFHvgAD/5CW+/zZ/+RC4nFWdEru7rX+d736Ojo9ILfWEU\ni+RynDvHr3/N8jKrqyQS0rXC6aShgR//mLNncblUkkPxSAwG6upwuR5XtddocLmoq3uoqFoo\nMDHByAjR6OZm2UtLXLvGgQNbXpJWi80m0+2iFCtk3p+wpKtQVAIV2CkUz4xwaGhs5ORJNBq8\nXtm7rdHgdtPQwMmT7Nu3k4/4q6s8eMDly1y6RDpNJiPVUkSD3dQU7e1SlAeIx6WQit8v/xOf\njGrC2+UYDPh8BIP4/aTTRKPrwxNiENVmw+ejoYH6+odOCMIHIhze3Cy7XCaZZHHxacSEhRiy\nXq9kTRQ1hArsFIrnhNOJzUZLC9//PrEY2ay0cbNa5Sl/Bwcuc3PSTi0WI5+XWRNRny0WiUb5\n8EMmJ/F6icd58EB6n5w4wfHjHD26wz8cxRNiNOLz0dzMnj2kUqRS6wrAWq3M57W20tJCIPBQ\nTV9IsgtJ4U0plaSNhEKxC1CBneJhxM6YyZDNyqOqybST+/2fI+JwbzBgs2Gzybk8vR69fudH\nLbGY9FJbc91YQ2gdz8wQi2GzkcmwvCxnhLNZlpeJRDh4kN5eVaXd7YgUb2cn//JfsmcP164x\nO8vqKoDbTVMTBw9y+DBdXRt3JGF+HQjIQ8WG8E6nw+mkrQ2PZ/vei0JROVRgp/gc0bmcz5PJ\nEIkQjWIwYDbjdEplgW2waNwZiN6yXUUiwdQUKyubd74XiywvEw7L62fta5aXmZ4mEqFUoqND\nuVwoANraaG6muRmvl5s3mZ4GaGpiYICXX+bkyU1OSmtm2ZEI6fRDoxXidOr10tdHILDd70Wh\nqAQqsFN8TibDwgI3b3L+vLTG0mgwmfD5OHCAM2fw+VSjiWJz9Hoslq/uKN+QzCsUCIe5epW2\nNr773R0u8qd4QkT0392Nw8HJkySTlMvYbHg8BIOb57+NRo4dI5NhaYlkUo4uFYuy4GC10t3N\nt7/N3r3b/24Uiu1HBXaKz3N1q6tcv84f/8jvf080Sj5PqYTRSF0ds7NYLAwOSrEAlVZRbMBi\noaGBqSlmZzfXZxbeABsolUgmmZxkZoZEAqdTBXYKWZANBgkGn/Sf6PV0d1MoMDqK2UwkQipF\nNovNhtOJz8exYxw+LGd3aoJyWfbDiN4GIZhssaiyieJJUIGdAspl0mkePOAf/5GPPyYUIp+X\nj+FikaUlPvmESISf/ITOThXVKTbB5+PwYRYWuH9/veFdIOYZ18YbNyCe4oUCq6u43TtW50/x\nQhEtm93d/Mf/yNgYd+6wvEw8jt9PUxM9PbS34/HUkkS22Hjn5lhYIJ/HZKK9nT17MJtVK6ri\nK6mdC13x4jr518YAACAASURBVCiVCIeZnOTePWZnyWQeeqlYZGGBTIbJSRIJtbMoNsHrZWiI\n2VlmZmTDuzgAlEpyKDiVIpPZJJkn0GiUdrHi6RHXj9NJXx9+Pw0NMmnnduP309qK3Y5WWzOH\n0qkpxsYYHWVykqUlikVMJpqaaG9n/37a27HZlJCe4jGowE4BxSLz8+vS6hsQCp/xOCsrLC/j\n96vATrGRujqcTilNfOECySRarRShcLvxellYkKM5G2I78TU6HW73rps4UTxfRHhXX78uJrym\nQlcrIZ3g+nV+/WtGRpielnKYej0GA243f/d3fPvbtLSowE7xGFRgp/i8FJtMrldgNyWXI5HA\n7d7GlSlqBJ0OnY6+PnQ6enulsSZgNOJwoNfz1ltcuyYft2vXmE6Hw0FrK52dO9xFV7ENiOhN\nXIo1SjTK0hK3bnHjBvPzcmAcZLoxHufPf0an4803sdsrvdZtIRZjaYnJSaanyefR6fB4aGmh\nqwurVaUYHoUK7BQAFAqyNeoxxTJhG/WVTo6KXUtHB3v2cPYsqRQrK2i1uFzodMTjzM0xOUk0\nSjotrTw1GqxWGho4coR9+9RIrKImEaeUNXuMZ0wNRiIMD3P7Nvfvy9legdh183k++ohymZde\norVV/sSditC/XFzk+nX+8hcuXSKZxGiko4PjxzEapc8bO/pDeFpUYKcArRa/n8ZGrNbN+5zE\n2KzdTkMDVuu2r09RUwiJZlH/MhjQatHp+P73qa/n4kXGxlhclFJ/fX0MDnL6NP396vCtqEmE\nvYqwMjObn3U+Ix6XN8ijjtDZLIkEiQSZzA73a4nHGRnh0iXOnWNigvl52bMRiUhZrtdf53vf\nw2CopZmY7UJ9IgrQavH5aGmhvp7ZWSKRhxQ+NRpsNurrCQY3em8rFBsQU6463UPzrXo9R4/i\n92O309TE7Ky8qAYHOXiQoSG8XjU5oagZxN6YSLC6SixGPE4mI8X2XC48HhyOpzwAC1+WePyh\nHfiLPzeflxoohcKOHSEXbzwa5dNPOXeODz8kHpfJS42G1VXm57l9m2CQw4fx+XA4KrveKkQF\ndgrQ6fD76exk/34iEe7elfUyUVnQ69mzh9deY/9+jEb1AFZsGVF17ejA7ebrXyebhc+NnhwO\nHA51USlqjGKRiQnOnePGDe7elYMaRiP9/bz6KgMDdHc/zbcVJyJhwbJpu7P4y5ruI3xCIhE+\n+ojLl0kk1pOXonaUywFMTHD5MkeOqMDuy6jATvG5ClRjIy+/LNuelpaIRNDpsFjWnSc6O3f+\nbqJ4EYiarMGgtmDF4xBlzVSKWEwOUFut8r+qKrcJVe2LFzl3juFhHjyQYo1aLaEQ2SzFIm43\nDgdm89a+s81GczMej9xpN3Q8a7VSclnUfHdwHVboMMzPS1HVL7IW2y0uMjqq3EQ2pZruFkVl\n8Xr51rfYt4+REe7eZWICoxGfj/376eqirU2pUSgUiheIKDWGw4yOytiusZGmJhoaqiuwW13l\nww85d45Ll1hdXR9x0GgYHWVhAZOJtjapJ7wl3G76+/n0U/T6jVGdOB15vQSD2O07uSVmTaVh\n7YPdQKlEqcTKCjMzJJPbu7jaoJruFkVl0elkuk6vJxjkwAF0Omw22Vpns6l6mUKheCGUSiQS\nzMzw8ceMjbG0JLtB3G4CAbq76e9nYKBausoSCT77jNu3iURkWVBQLst03dgYV6/icOD3b+07\n2+20tTE0xOQkIyPMz8tdV0xmeDycOcNrr+Hz7eR0nUaD0ShHQx4lvyU81txuNXS1KSqwUzyM\n6Hnq6Kj0OhSKKkY8b0olCgXpNy8etAaD7H9Sp6AtUSoRCnHlCv/9v3P7NsXiuvSSx8PAAN/9\nLnv2oNdXuBtE/N5TKe7eZXxczkxs+IJ8nulprl5l//4tf39Rdz5yhHQaoFCQ11W5jNtNczPf\n/CZf+xou17O/larGZMJmw2zGYKBQ2Pghi0F7t1sqOSi+hArsFAqFYuuUSqyucucOExPMzgJY\nLHR309FBczM2W6XXV1Nks3z4If/v/zE1RSr1kJB1NMq9ezQ1MTDA3r00NVV4qaLHSwT0j8on\nZTJEow8l87ZEWxsmEx0dzM1JjWLRFRMMsncvDsfO73XWaHA4OHiQSIQ7d8hkHhJ/0WoxGGht\nlVOxii+hAjuFQlFpxFP82fVdtwdhgzY7y+gon3zC8DDj4wA2GwcPMjhILkdzM07nzn8APxeE\nDtxnn3HxIuGwHJpeI5Vibo7RUW7dwuWqfGAnEHnZDX39grVBiqe+mH0+vF46O0kkWF6mVJJ1\nWI9Hfv+djXiDLhdHj5JOk82ysEAkIrcIkwmPh2CQ/n66u3eLA8cWUYGdQqGoNGuOsTURCYnm\n7nfe4b33mJhgeZloFECvZ3qa27eZneXUKY4fr423U3GKRTIZIhEikU1CpbWJipGRp9QQeb6I\nDjAx9JrLbTK4qtPh89HR8awz4EYjLpccWRM5ql2F283LL+NwYLFw+TK3bpHLSS/gw4f5+tcZ\nGlJnp0ehAjuFQrHtiO60mRkWFgiFSKWk4KrdTmsrDQ04HNXbFh2PMz3NjRtcucLKCun0+vhe\nNEo8LrVqDxxQuo9PhFA5yeXIZje3NBQpvVhsYzJv+xHJJKeToSFWVrh8mXR6fc0i/DKbaW9/\n1irhmtB31d4FLxqTiWCQgQE0Ghob6e8nn0evp66Ovj5OnKC+fvd+OF+FCuwUCsW2I+TKhLL8\nJ5+wtEShgNNJMMgPf8jZs3R2Vu+uvbTE8DBTU4TDZDIPiTIUCiwv8/HHNDXxV3+F3a4Cu69G\nlOAfX2EUgU6VVCG9Xr75TbJZ7twhn6dQoFSSciQWCzYbPT288oqqEj4rWi2Njfj9nD69nsoV\n0bPJpHJ1j0EFdoonplgkn2d1lWSSYlFOm5vN1fsAVlQtY2PcuCGjuokJ4nFKJWIxYjHefZd4\nnNdfp6urSgOjaJSZGaJRqW2xAWFenkwSDmO343ZXYok1hfBs8Hjw+Vhd3ZiWE+Y3dXXs3btl\n9ZAXhMVCWxtnzlAuc/8+09OkUgA+H83NdHZy+rSqEj4rIojX69HrlYTqVlGBneIJEIKQiQSR\nCJOThELkcrjdtLbi8+HxVF6GQFEriEHCO3f4p3/iyhXGx8nn5V+m06TTvP8+4TAdHdTVYbVW\nY2CXSrG8LI83X56LFLklcQSqr1eB3VcjfLQaGmhpkS6oa8XNcnldX3P/fgKBii70c4xGAgHs\ndnp6uHpVVuQ1GtraGBjg2LEdLiCsqHpUYKd4AhIJQiE+/JDz54nHZVuJwYDVytAQx4+zdy/B\nYKVXqagdZma4coXFxU00IwoFIhFu3SIYJBCoLssBgcWCx4PFgl6/HpWuIeQw9Hpcri0bD+xa\nTCZeeYVymd/9jvv31/XhSiVcLvbt49gx+vurJWMnMBqpq2NwkOZmqWxit+PxYLWqU66islTf\npqmoKsT2Ggrx6ae8+y5vvy0lpvi85XlhgUxGTuMbDNWYX3lyxDtKJEgkZJVNNM2IRv4qjDBq\nEfEhh8M8ePBQbmYN8SsYH2d2dpNCZzXgcNDYKOXEvhyYCodlh0MFdlvAYGDfPrRalpdxu4lE\npECxmII8epSjR2lurq6SnLA/ttloa6v0UhSKh1DPKsVXUSwyOsrPfsbw8EPGfOJ5NjxMKkVz\nMy0t1NVVi+fP0yG8pe/d4+ZN5udJpXC56OxkcBC/f+ervW8PYsJRxEOb6ruWy+RyrK4Si20+\nI1lx6uvp68PrlRm7Ly5SuPC1tNDUhNOp2k+fFI0Gm42+Pv7Df2BujtlZEgnyefx+gkEaG/H5\n1IepUDwhKrBTPJZCgWSShQXu3mVpaZMMyuoqGg0TE8zPY7PVamBXKlEsMjXF9et89hkjIywt\nkUrhcHDvHhMTDA1x4AB2u8rBPAe0WvR6DAbZu/nlV0UixGKplinIDdhsNDVx9CgrK4yOSrkW\n0Q3m89HeztGjHDqkSnJbQIyUulw4nTQ20t4uvWK9XlwubDaVL1conhx1tygeSz7PyooUDn1U\n+qRQYH6eqSmZpahFRJbo2jX+239jZkYqZgnJ3CtXsFj4m7/B5aKtTQV2z4rwUTWbMZulL9OX\nv8Bup7mZ+voqDYxEc9V3v0sgwK9/zfXrzM9Le4CODk6d4gc/oLe3Vg85lUV4SVmtMpv7jBYO\nCsWuRAV2tU82SzLJ8DAPHrC8TDaLRoPXSzBIXx/NzdKY/KkRhtyPQdgGfLmLvIaIx7l9m+vX\nmZ5meZlcbn3gUXy8V67gcvHGG8qa8FkRimV79/Ktb3H5MuPj6x+1UO03m/H5OHCArq4qHS0U\nAhzBIENDGAy88op0njAYaGigrU26DtR0v2lFEAGcsORSKBRPiwrsahnxOIzFmJ7mn/+Z999n\ndJRYDK2Wri4OHODNN7FY8HrlRvl0B98n1A6tHvnQpyAe5+pVPvtsE3EyUS68dYtUisFBBgdh\nF9g1vmj6+3njDVIp6dwg8sEmE2Yzbjft7QwO0tlZvQU4rRaXC5eL3l45DqLCkcfzxYmrtTj+\nSaSJFQrFFqnWfVPxJAhhhY8/5ve/Z3iYiQliMXI5NBpmZ8nlyOeZneWNN55+bstgkEp1j4nb\nRPaipaWGy5SZDHNzLC4+0tRIhCBChUE9h56dQIDDhymV6O7m9m3Z0ej309TEgQMcOEBbW/VG\ndV9GhXRPSDxONEomg0aD243NhtmsbiiF4vlSO1un4ssIgftbt3j3XSIRksn1oGRlhXicRIJi\nkWPHCAafsuNHr8duJxCgq4t8nvl5+fdrVVe3m5YW2tpobKzhpqJikUTikZKzQD4v/UwVzwWX\nC7sdi4XmZpqbmZsjkSAQoK2Nw4fp6MBorNIGuy+j4pLHI86f0SiLiywsyCBetIsEAgSDuN1q\n5FyheI6owK6WSSSYmmJxkUSCXO6hVJPwNRKb6fw8bW1P34qu09HdzY9/zO9+x7vvyr8UWhWl\nknRF7O/H663SjqgnQa/H4cBme1xW0mRCr1dP8eeGVovXi9VKa6ssfxuNWCxSJUTlwHYMxSKp\nFFev8o//KA12xdlJp6Ojg2PHOHWKI0cqvUqFYuegArtaJp1maYlolHx+c8/KXI5UinCYWAyf\n72kCOxHH1Ndz/DiZDFYr8TipFKUSRiNWK4cOcfRozY+LWix0dHDvHrdvb5wCEb1Tfj9dXTid\nKrB7bmg0cjbW46n0UhQvknicmzc5f54LF1hYIJGQN5GQN4/HsVppasLlwmqtzApLJSIRolFi\nMQoF2UApKsW1W4VQ7GJUYFfLCB3XdHrz6qHoSi4WicVIJJ5J61UIEHi9nD3L+DgLC+Tz1NXR\n1kYggM9Xw7k6gd3OgQNMTnLhApkM+fz6S0JWrb2dY8eqy9FIoagJlpd5+23ef5+pKVKp9SNo\nuczcHLEYwSBdXfT0VCywKxaZneXOHUZHSSQwGunpYd8+mptVYKeoRVRgV8uYTNTVPdJmR1RL\nxenzGcUXhJqUy4XFgtlMSwulknTMFO3PtY54U4cPMzvLtWvcubPudiUkZ195hddeo7Gx0gtV\nKGoHceBMpRgfZ2qKdHpjD2s+TyzG5CSffUZ9PQ0NFVjknTvcvMlnnzE6ysIC2Sw6Hbducfky\nR48yMEBbG3Z7BRamUDwtKrCrZSwWAgFcLmlttAFRQxSqYG73c2haWvNGrEWEt0Q+v660LNTI\nDAb0eoxGAgGGhmQvnRAozufRaOjo4MQJzp7lzBnV+KVQbI1SiUyG+XlCoU1mj4SW0Pw8IyMc\nPlyBtZVKfPYZv/gFw8NMTclOZbEzOJ0sLpLLyZosakpGUTOowK6WEa6U7e20tbGwwOrquiW5\nTofJRHs7fX3SsHyX70qxGKEQn37K3busrkrVtJ4e+vvp6iIQAKir48ABPB7OniWToVhEr8ft\npr6e5malgK9QbJkvCtc9CuHR/HgV9BdBMkkoxN27DA8TCq0f+cQYbyLB5cuYTOzbp5xqFbWF\nCuxqGZNJRicnT3LnDpOTcpACcDrx+Rgc5MgRgsFHlmt3A8UihQITE1y7xp//zNWrhEIUi1gs\nDA3J/xcHdJtNxsrlshS0E+OZFdFQFZV0Ybolkq96fc0ogCgUa2i1mEwYDJtrCYlXbbYK9Okm\nk8zMMDnJzMzGyLJUIptlYoK6OqnPYjCoc52iVlCBXe1z+DD19Vy8KJvDhPNEdzf793P8OP39\nu90FK5slGuXcOX7+cxYXWV0ll6Nc5v+3d2excd3n2cCf2fedw+GQHC7iLnGRTe2bl9jxkgSF\ns7QI0KToRdECQS9604u0F73qRVH0tg3QIgjSFEYBt1/q5JO/ylJs2VpsiZIo0SQl7vtwmeHs\n+8z5Lv4n1GJSsiWRMzx8fvCFTRKewzk85zzzX943mcT161haQiqFbBYvvvjQGyUeM2Usiy9J\nyOcRj2N9HVotTCZ5jSPRLrLRI87lwsrKo7OxGo3ch62rqwyl7ESwi0Q2H1MUn6aKRaytYX0d\nVitXYtBuwWC3+3m9sNuh0aC6Gp2dSCSgVqOhAfv2oa0NNTV7/YNmJIKREdy+jaGhR+vCrKwg\nmYTNBpMJLS33g51oklYu4lmytISpKayuIh6XJ9a9XtTWoqUFLhfHD2gXEH+iDgcOH0YshitX\nkE7LKUp0cLFY4HajrQ29vXC7d/rwxGcnsajuMYrFZyopQLTjGOx2P1EP7MABtLffX2On1cr/\n8PG/uoqrVzE5KQ/UPUispBkchNmMt98u0/F9SaGAsTF88gl+8xvMz9/f3Sy2d/zoR+ju5pml\nXaOqCm+/jVIJo6Py8jUxJ6vRwO1GSwt6evDii2WYitVq5Up1my6fFfcKlQomEwwGXm60izDY\n7X7ijqPXc3nv5pJJLCzIEy5fViohmZS3wRaL5d8hEYtheRmfforz5zExgfV1qFTy8EYqBQBu\nN5JJnD7NaVnaHQwG1Nbi1CkAmJ/HygoyGQBwuVBfj5YWvPBCecrFWa1oaoLHs/lVLxb/2e3w\neGC3cx72a5Ak5HJIJLC0JN9aLRY4HHINB3B/8bZjsCOly+UQDsupaFOFglwGRQS78opEMDGB\ny5dx6dKjbeLEQWYy0Olw+DC7p9PuoNPB6cSRIzh4EOPjuHdPXl1QX4+GBjQ2lq28uc2Gffvg\n98NqRal0f7oDv1/853ajpgZeL6sKfA3inVxfx9wcBgexsIBYDNXVaGhAdzc0GphM8kIXvqXb\nhsGOlM5sRm0tJiagVm8yaKdSQa+H0VgpXeeXl/HFF1hbe+gxI5RKSKextCSPeYjNvES7gloN\nvR719bDZ5AqRFgus1nJedGIq9vhxpFL43e/wxRfysD0Aux0+H775Tbz8Mpcpfz3xOJaW8Nvf\n4vPPsbaGeByZjLyYsrERfX148025ehRtGz4YSOnsdrS34+5daLX3+0kIohKpz4dAABZLRQS7\nSASzs4jHN9+pl88jGkU0Kj+BGOxotxDda9zur7FJQizIS6cRiSCZRDYrX7A2G2w2WCzy3/9T\npy6NBhoN9u+XVzXYbEgm5X27Hg8CAbz2Go4cgcPBYPc1LC7i+nX8v/+HS5fk0TtxpzIYUFWF\nlRW0tMBmg8NREfdbheKDgZSuuhonTmB4GBcvAngo22k0sFhw+DBOn66UVvT5PFIp5PNb1nQV\nS+6yWRQKbGRJSiZqEs3O4pNPMDqKxUVoNLDb8eKL6O3F/v1wOp/Dqzid6OyEx4PvfAfRqJxC\nrFY4HPB65b4+9NXdvo1338W9e8hm5Y1foh6nJCEUwvQ0BgZgteKFFxjstg//ZEnprFY0NuLI\nESwtYXoawSDicUiSXECruRlnzqC/H3Z7uQ8UAGA2w+2G2QyNZpOCrmo1dDp5owxvi6RghQLS\naVy/js8/x+efY3ISy8tQq2G1IhTC2hqyWbS1we9/1uAlyrw7nSgUkEqhVJKrC+3MXjQxMC8y\nkGhvWMbamc9IZLilJdy+jfX1hwpLiWyXTmN1FSMjaGpCTw8/l24fBjtSOnG7fPVV7NuHs2fx\n2WeYm0OxCKsVJ07gzBkcPIimpkr5XO7xoLUVAwPQ6TaZjRXNfy0W2Gy8LZKS5fMIh/HrX+N/\n/gexGNJpuafO2hpWVzE1hXgc2Syqqp7blavRwGqV/33HopWopReLIZ+XK5BXyI3oKYj0lkhg\nbW2T3uUACgXE45iexsJCGTrI7SW79m+I6CsSn4BdLmi1eP11dHQgEoEkQa9HUxP27UN1dQVV\nivH50NuLGzcwPY3VVaRSKBblIvgqFaqrcfIkjh7lllhSuGAQd+5gehpra8jl7u8lKhYRj2N+\nHp9+Co8Hx48/t2thh4fKlpcxPo6JCUxPI5ORu1dXV6OxER0daG2VD2l3EVsiHt8dWK3mbMN2\nY7CjvcFkgsmE6uqdft2Np9HGvKq4r21VMM/rhcmEAwcwOwuNBqEQMhloNNBqodOhtRVvv40T\nJzhcRwq3tISbNxEMPpTqhGIRoRDicbS1yZOYuysAiS0FMzM4dw4ffYSrV+WgUyyiuRlHj+I7\n30FdHfT6XTZ6Jxr26HQwGCBJm9cNFesXzeZddsp2m131d0O065RKyOexuIjFRWQyUKngdqO6\nGj7flndtoxGvvopAAHfvYm4OwSB0OlgsaGpCayv6+lBdzY+8pHCxGBYXkUhsUvcHvx9dE82U\nbTaYzeU4xKeVSGB8HJcu4aOPMDEh7xsFUCphZQXXr8PhgNmMnh7U15f7WL++6mrs34+ZGayu\nPjrfqtXC6cSBA9i3b5dl1t2Gby7R9hCRbm0Ns7OYmMDUFJJJuTNYYyNaW1FTA48HeHjCRaWC\nTof9+9HQgJYWzM1hcRE6HWw2tLWhvh4WS9kKuhLtmGJR3vr9GGJR165r5JpIYGgI169jaEje\nh7shGkUqBZcLdjuqq3dZsBP3sYYGnDgBlQqZDJJJeSWJaIro9cod5BobGey2Fd9cou2RzyMU\nwief4N13sbyMZFJuVms2w+/Hvn341rfwxhtbTkkYjWhsRE0NMhl59lb0teRYHe0FVqvcE2LT\n7eFipk/UtDMay3SITyuRwJ07GB2Vc8+DxG86M4Pr1+UmbLvOgQMwGlEsIpnE/DzSaWg0KJWg\n1+OFF/DKK+jrQ20t72PbisGOaHtEo7h2DR9/jKtX5YLDG73Pl5awuIi6OnR0yA2LvmyjECs9\nUTKJSASrq3LHDo0GNhu8Xvj9jMK7VXU1urpw/Tq0WrlS8Qa1GhYL/H7U19/vT7WL5HJYXkYo\ntHm5ylJJnoZ+TBfESubxwGDAyy/DbsfCAhIJFIvyYpL+fjnV7a6p812IwY5oe6yt4f/+X3z8\nMdbXH7qDi0aKmQyGhtDaikOHmN6eVSSC4WFcvYrr15FKwWBASwuOHMGrr8Ll2n0PfgJQV4di\nEefOQa9HsfjQRkutFh4PDh/GgQMVtJ/9qyuVkMkgm93yB/J5pNO7tSCIVgu7HW+8gVOnEIkg\nHkciAYcDHg8sFphMu/KU7TYMdkTPm5gnSqexsICVlU1Wf4tSqPPzGB6W6xrQ0xFlsW7exKVL\nuHsXMzPI5aDTYWUFy8uYnMRLL+H4cXkb8nMkTrH4R62W68rSc2Q0oqYG3/wmjEbcuYOFBUQi\nUKlgNMrrtM6cQV8f9Prd987r9fD5UFWF9fVNKoOoVLDZUFMjNzrbdcTeWLMZBgMsFmSzyGZh\nNMpF1/kpa0cw2BFtg1IJ2SwiEcRimyzuFoFgZQWTk4jFynF8u594HMZi+PxznDuH8+cRjcpl\nUVUqLC7i3j18+im0WvT2wmx+PuMEorZ+LodsFrkc8nkUCtBqYTLJvQpER1R6dno9XC688QYa\nG/H++xgcxNwcNBo4HDh1CidO4OhRVFfvvlSH3y+fra+XK6U/uEFEVKz0etHaWim9cJ6aiHec\ndS0HBjuibbDxgH/Mg0erZWewZyJJWF/H5csYGJCX8mx8XeypVKkwPY3BQbS3w+9/Dq8oOvle\nu4arV7G8jEgEAIxGOBw4cgTHjsHjud+9gJ6RWg2bDe3t+MEP8I1vIJmESiUPd1VXw+HYlakO\ngMuF06cRj2N4WP5D3Vh9K+rA9fbiD/4ADQ3lPlDarRjsiLaBSnW/FGciscmgnXhoeb3PYU+f\nGLvK55HJyDWxdDrodDAaFZ4aCwVEIrh3DzMzj7YwErVmACwuYnQUPt+zBjvxJq+vY2wMH32E\ns2cxP4/1dahUMJlgtyMchlqNF15AUxM0mt2aOSqKeG9NJtTUlPtQniuLBfv3Y3UVX3yB+XmE\nw3KDGZMJNhucThw7hmPHdv2IHZUPgx3RNlCpYDajoQHT0/LCrweznfhcXl+Pvj643c/h5SQJ\nsRjm5hCJIJmEy4WqKrnonVJJEpJJebvxprOfYhQkHMb8PJLJ5/OiY2P4xS9w6xYmJ+8vb08m\nkc3io4+wuIi/+Av4fLu74ydtN40GJhMOHYLXiy++wPAwVlbkvrf79qG7Gy0tcDr5J0RPjX86\nRM+bGK1xOHDsGFIppNNYX0c6LX+9VILLhbo6dHdj/344nU//QmLOcX0do6OYnMTsLKJRpNOw\n2+H1ymWQ29thMChz4ZdYkARsUjNigygB+OzjZ2In4+Iibt7E1BRisfsvKvrFLS2hUMDkJJaX\nUVvLpzJtSQzne71wueByobZW3jjvcKC+Hi0tz21JKO1VvPsQbQ+PB2+8AY0Gs7PyJkqRQopF\n1Nbi1CkcPoyOjmedLc3nMT2NX/wCAwMIh5HLoViEVisX+nrjDfzZn0GnU2CwE2OiVitUqs17\nD4jNqs9rvrtQQDSKtTWsrSGZ3CRKShLyeSwsYGYGLhfXjNMTqNXQ69HcjEBA3hsrPoTodJzH\np2fEYEe0PXQ6uN3o70ephIUFrK7Kq77MZgQC6OhAe/uzfi7P5TA4iE8+wfAw5uaQTMpPCJUK\n8TjSafm7fX3KLKoiKma1t2N5GfPzm89319biwAG4XM/6WqKSfiq1ZQMrsaovHkcs9oRGWIon\nNn0vG3hLaQAAIABJREFULGB2Vs7BOh08HgQC8Hqfz9oDBRDpTSyHJXquGOxIiR4cUCnXx1+x\nkqa7G93dWFvD6ioSCahU8PngcMBqfQ6jaPk8btzA736H6WlEow/liXwe+TxGR3H+PKxWBQY7\n0QbebkdvL8JhuflEJiPnWvHm2+1obkZ393PYqSrq1zyyReORHxA7HLPZ3de99HkR110uh3gc\nX3yBy5dx9y5WV2Eyoa0NR4+iuxtmM3Q6hW/rISorBjtSKFFyrEKmIK1WaLXI5+UKq8+rqqpY\n2jU19VCxD0HMDIZCGBnB8ePP4bUqk9uNV16B1QqLBV98gYkJubBcVRUOHMCJEzh9GlbrcxgU\n0elQVQWHQ55V/zIxs+b1wufb0wukSiUMD+M3v8HwMCYnEQohlYJWi4UF3L2L48dx6hS6ulBX\nV+4DJVIsBjtSkHQa8Tii0fvTYaLmiMslVx4pF6NxW1qVl0qIRBAKySNVX/5uIoGlJSXXQBaV\nI0Rl4KoqVFcjn4dOB58P/f147TXU1T2fd16rhcMBrxc1NfJumAfjnVoNsxluN2pr5Qa1e1Oh\ngEQCo6P47W8xOyvvCRBv1NISxsaQSiGfh9OJ2lqgfKPpRIrGYEcKsrKCmzdx9Spu3JCzjnjw\nnz6Nnh5lTkeq1fKk5FY/8JjvKoCYdQ0EYLfj+HF5W4NKBYMBdrvcj/y5EANyjY147TV88gmu\nX5dLBuL3i/lEt4C2NtTXb0uI3xVSKYyN4d49rK4iHr+f6vD76jMjI8hk0N+PgwflP10iet4Y\n7EgRMhmsrWFwEB9+iOvXMTSEXA6SBKMRKytIJpHPw2KBw1Hp2xVFi6FEQu4RLvotbrUmSa2G\n2w2fTy5N/MgUoUYDmw2BAByOnTjyshCx1WqF1bq9s3siQdbV4ZVXYDDAasXKCqJRFIuwWlFV\nhfZ29PaitXVPd57IZjE/j4UFxGKbrDUslRCNYmEBoRASCVgslbJSgkhZGOxIERIJDA3ho4/w\n299idRWplDw1mUxidBTz83IHxs7OSg92+TySSczNIRyGJMHtRiAAq3XzYKfRoL4era1ynbxH\nJgdFrazubqUV7i8jnw9ut1yDUBS0y+Xg86GrCwcPord3747VCbkcVlawtiZ/0vgy0WovHsf6\nOgwGbggl2g4MdqQI8Thu3MDgINbWkE7fX3AmSfIzZnQUly/D5Xo+PUO3QzyO1VXcuoXBQUQi\nSKUAwGSC04neXvT1yf0xH6TToa8PyaS8mCkavZ/tDAY51b38Mpqbd/p3USqtFlot6upgMKC6\nGuEwCgVYrXLLMput3MdXbmKtoWi8oVZvsstEbGnS62EycbiOaJsw2JEixOMYHMTIyKMDV/j9\n/tDJSVy5gsOHy3R8jyViaDiM27fxX/+F//mfR7/7rW+hVMKhQ7DZHlozJ/qF6/WYmEAmg4WF\n+8MkNhtaWtDfj1OnlDwVWxZOJ5zO+0s2xekrFJDJ3K8jKCLgRm+MPUKng8sFh0Ouif3lYCfe\nEItly0FoInpmDHakCMUiUqnNN4cKmQyiUeRyO3tYX02phFwOo6N4913cuoVM5qHvShJu3ECh\nALMZ1dUwGh9qVyVmY3/4Qxw/Lq9tymRgs8HjQX099u3jSqZtVywin8fEBO7dQyiEXA42Gxoa\n0NkJu73Sp/6fL7MZra1oaoLRCI3modlYsUixuhrt7aiqUmybO6IKwGBHSvH4HXaVvP+uWEQs\nhulpXLmC5eVNquDOzCCdxksv4eBBVFXdD3biYel249gx7N+PlRV5DtfphNuNqqq9vuRru4le\nvaurmJ7GrVu4dQvBINJpuN1oa8P6Otrb0dwMvX6vtI41GFBbi7Y27N+PsTEsL8s97gC5XnRn\nJ44cgd/P1XVE22dv3G5I8fR6eDxwuRCPbzIBpFbD4UBdHSyWchzck2SzWF7G2tqWHQtEO9TV\nVSwuwmbbPK6ZzaithdeLYlHuU8Rn53YrFpFOY2AAP/85ZmflYryFAgwG3LqFDz/EO+/ge9+D\n1wu7vdzHuiPENGtvL/7sz3DuHM6flzfAqtWorkZnJ15/Ha+9hvr6ch8okZIx2JEi2Gw4eBCr\nq1hdlVumCmJNj0aDpiYcPQqvt6xHuYVSSZ5HfgxJQjqNZPLRDhMbxKKurRSLCIWwsoLFRSQS\nchVfsxn19aipgd2+p5slPDVRtm1gANeuyZOwolqbWo2VFWg0qKlBTQ2OH99DwU6tRk0NjhyB\nSgWnUx5CFnvSm5vR34/29r0yfklUJrzASBFcLrzyCuJxDAwgm71f1E1U/dDrceAA3nqrQgt/\niK2CW60O3CB6qz/xxzZVKGBiAleu4MMP5RZkdjtqavCtb+H0abS3M9g9jUgEV67gxg25bNtG\n5hZnqljE4CAAeXZy7zCbYTTirbfw6qvylSguQ3El8i+NaJsx2NGOEyuTMhnE4ygWoVLBZoPJ\nBI3m6ddTGwyoq8OJE0inMToqZ5dSCQ6HXOntlVdQU1OhK9n1elRVweXaciGg2GgpahE/xXMx\nGMT0ND78EJcuYWQEoRCyWUQiiESg1SISwZtvor0dLhc3Kn49mQxmZ7GwsEkxXgCShPV1TE8j\nkSjHwZWPGLfTamEyyZ9DlN3+hKjCMNjRDhIN1HM5JJMIhRAMIpeT526qqmCzQa+HRvM0zwCx\nxu7IEbS04PJlXLmC1VUUCvD78cILeOkleL2VW2ZMr4fPB68XZjO0WrnnxIN0Olgs8PlQU/M0\nPbLm53H5Ms6dw7VryOXkCJJIIJHA2hqWl+HzwWaD3c5g9/WIeryhEPL5zUdSk0m5Wu/exDxH\nVA4MdrSDxA6Ae/fwu9/dH1QDYDajoQFnzqCrC3V1T78Ex2CQ411Tk1xUzGyGx1Pp+0PVahiN\n6OjA97+Pjz7C9evA77fxiinanh6cPo3Ozqes/jUxgd/9DnNzD/XuFAoFhEL4/HN4PNi3j9Nk\nX49WC6sVZjPU6s3XPup0crVeIqKdwjsO7RQxVnf3Lj7+GP/n/2BqSl5pDkClQn29POzhcsFi\necqhI7EV1GpFS8vzPfbttdFg/vXX5XlqsUwQkJclnTmD115Dc/NTxtOlJdy+jVBok/BRKiEW\nw+goOjs37wFFj6HXw+9HdTWmplAoPDpot7EXuzIXABBlMkinkckgn4dKBb0eZjMMBn7A2+0Y\n7GinFIuIx3H+PH79a8zOIpl86EG4sID334ckobNTHufYa1wu9PTA5cLx45ifl3vFulyoq0Nj\nI+rrn767fDaLWGyT8nhCoYD1dcRiWxZboa2IvdiLixgaQj7/UDLWaKDVorm5cvdiE62tYWoK\n09MIhaDVwu9HeztqavgXu9sx2NFOSaWwuirfR75ctiOZxNwcpqawsCC3m9xrDAYYDDCbUVeH\n5WVEo5Ak2O3w+WC1PtMbotXCYNhypZeYCNbruRzqa7NY0N6OYBBTUxgdxdzc/d3NTidqa3Ho\nEE6frtC92LSXra1hehrDwxgextwc1teh1cLrxcgIurvR3Y3q6r1So0eJGOxop8RiWFh4qMjc\nlyUSmJ5GVRV8vh08skpiMECng812f5L66XaTPEgsNMxmN8l2KpXcz97jYYunr81oRFMTcjkU\nCtDrsbqKYlH+8xbl6159FS+/zIktqjjT03jvPVy/jqEhpNPI5eSpWKMRr7yCWGwPFV9UIgY7\n2imFwkO1vrb6mXR6y0nDvUAkuee7O7WtDd/8Ji5cQCJxf12jeC2tVt5u0t3N/PG1iboefj+O\nHYPTiUOH5L9wnQ7V1WhuRns7F9g9RNQ5Gh/H9DSCQaRSkCR4PPD50NGB2lrodPyAsb1yOUSj\nGB/H9esYG5OX3opPI5kMkkncugWVCn4/Ghp4OnYpBjvaQY8vrvsV6/TS19XRAUnC0hIWF5FK\nyXtjxSIwsR/5xAl0d7MF2VNyu+F2o69P7iBSKMhla1js40HiuhbrMS5fxsWLuHMH4TBKJbS0\noKcHb78NoxEul1zQh2/dNsnlEAxiYgLDw1hdfehTtCisPT6OaBQvv4zDh+WPLrTbMNjRThE7\nBE2mLdsnSBKsVjQ2wunc8YNTNLcbnZ343vfQ0IBbt7C4iHgcdjuqq3HwIA4dwv79sNl4B39W\nYl77qWsx7gU3buDcOdy6hbExrK7KawMmJxGPIxrF5CT+4A/Q2Mh3bxtlswgG5Ui36X1YfCCJ\nRLC6ypbTu5TSgl2hUMhkMrFYLBwOFwoFSZKsVqvNZnO5XHq9HoCKt4xyESV26+pQU4PlZaTT\nwANjeAYDvF7U1aG+Hg5HGQ9TgSwWGI04eVLe7zY9jUgELhdqa3HiBPbvh8PxNHWP6REqFZ+C\nWyoWkc9jZAQffID5eXkGUFz+2SzW1xEOI5tFfz+qq2EyMdttl2IRiYQ8Cb4pEezEtOzjV85Q\npVJasMtkMjMzM9euXbtw4UIkEimVSgcOHOjv7z99+nQN96aVl04HhwMnTyKZxLlzWFiQvy76\nZVVV4eRJHD1auY2/djW1Gm43TCbU1yOdRqEAnQ4GA5xOWCyMI7TtMhmEw1hYwNyc3EtwI1iU\nSsjnEY9jZQWzs6ivRyDA8ePtolbDZILR+LgWjpIEg0Fu80i7kHKCnSRJpVIpHA5fvnx5dHQU\ngMPhUKlUS0tLt27dqq+v1+v1TqdTw7/UchFlNQ4cQKkEnQ7T04hG5V6xdjsCARw7hr4+2Gys\n1P/8qVQwGuU1TEQ7L5vF2hoiEXmV5yPDRaJ6eSKB5WWEw6it5U1gu+j18Hrh8Wz5Dosmvw4H\nHA6ehV1KUaetWCwGg8GzZ89qNJrvfve7DQ0NRqPxV7/61d27d5uamsxms81mY7ArJ60W+/bB\n50N3N6anMTaGbBYaDVpa0NiIujrY7Rw9IlKgTAahENLpLfeUiGwnamVzB9X2MRrR0AC/X97f\n8whR9MRmg8cDj4cjdruUcoJdqVRaX18PhUKSJFVVVXV1dfn9fpVK1dHRUSgUVlZWFhYWOjo6\nyn2Ye5tYhGS1oq4OFguqq1EoQK2GxwOnE1YrUx2RMun18lLOrXa+i+I7NhssFi6w20YaDaxW\n7N+P738fV6/i9m3k8/LMuMEAqxWHDuHUKbS2cg/Q7qW0YBcOh81ms9/vb2pqcjqd2Wy2o6Mj\nk8nMzs4Gg8HiFktBM5lMJpMpbVE4NxaLFdhG8zkSt2+bDYFAuQ+FiHaE0QivV958/eWlXaJ8\no8mEqio4nVxgt43E+9zVBbMZRiPW15FKIZuFJMFmg8+HV1/F977HiuVPJElSIpEIh8Obflel\nUun1epPJpC7H26icYCdJUiaTyeVyBoPBaDRu7H41Go1GozGbzWYyGWmLEf6PPvro7NmzyWQy\ns1nbpVAoNDMz097evo1HT0SkYEYjqqoQCKClBYuLCIdRKMhDd2o19Hq4XPD70dSE6mrOAG47\niwUNDXjnHRw8iFgMmQw0GlgscDjQ3AyPh9vknyiVSv3yl7/89NNPv/wtrVZrtVqPHj36zjvv\nWJ+6x/czUE6wA1AqlUqlklqtVqvVItipVCrxn+JbWwW7SCQyNTUVjUbTogbHw5LJ5KZfJyKi\nr0RURGttxfHjGBrC1BSiUeRycvVKpxOtrTh4EIEA7HbOAG47sZWqqgovvIB4HNms3FHaZOJA\n3Vck1vTnN+uTpNfr7XZ7c3PzVtOA2005wU6lUhmNRoPB8ODgnBjGy2Qyer3eYDBsVcTupZde\nam9vLxQKm56G4eHhf/qnf9reoyciUryDB+F245NPcO0aRkawvg5JQmMjOjtx5gxefBE1NUx1\nO0qlgtksFw5Uq/nmf3UWi+VHP/rRm2+++eVvqVQqrVbr8XjMZSrdpahgZ7fbbTZbKpVaW1tb\nXl6WJEmSpLm5uYWFBbvd7vF4ttoS6/f7/X7/Vv9ntVpdltFUIiJFqaqC1QpJQlUVOjvlDbBi\nBravD42N0OmYLXYUq2o/LY1G09raeuzYsXIfyCaUE+w0Go3H4/F4PMlkcn5+fmxsrFgsGo3G\n4eHhsbGxM2fO1NbWstYJEVHZiOq4fX3Yv19eYydJ0Gqh0UCvZ9U0oudCUReSTqfz+XynT5+e\nn5//8MMPjUajVqsNBoN1dXWdnZ2BQIDBjoiobEQRO4OBa/OJto9ygp2Y1a6urn799dcvXrz4\n/vvvh0KhYrHY399/+PDhzs7O2tpaNoolIiIiBVNOsBNMJlNzc7PJZOrq6spms5IkeTwen8/n\n8XiY6oiIiEjZlBbs9Hq91+v1er09PT3lPhYiIiKiHcWKNUREREQKwWBHREREpBAMdkREREQK\nwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAM\ndkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBH\nREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkRE\nREQKwWBHREREpBAMdkREREQKoS33AewOGo1mfX39xo0bTqez3MdCREREZbO4uKjValUqVbkP\nZHMMdk+mVqsNBsOdO3f+5m/+RqPRlPtwiIiIqGxWV1eNRmPF5gEGuyfzeDyvvfZaY2NjLpeT\nJGlnXvTOnTv37t3r7+9vamramVek7TAzMzMwMNDW1tbT01PuY6GnFwqFBgYGrFZrf3+/wWAo\n9+HQU8rlcgMDA7FY7NChQx6Pp9yHQ09vaGjo7t275XpEdnR0OJ3Oin06M9g9WSAQ+MlPfpLP\n53fyRf/hH/5hfn7+Rz/60Q9+8IOdfF16vt57773R0dG33nrrr//6r8t9LPT0bty48dOf/rSx\nsfHv//7vGQh2r/X19Z/+9KcTExN/9Vd/dejQoXIfDj29f/zHf5ybmyvjI1KtVlsslrK89BMx\n2D2ZVqt1OBw7/KJms1mlUtlstqqqqh1+aXqObDabSqUym808j7ua0+nUarUGg8Hj8fBU7l5i\nXY1Wq3U6nTyPuxofkY/BXbFERERECsFgR0RERKQQDHZERERECsFgR0RERKQQDHZERERECsFg\nR0RERKQQDHYVymg0OhwOvV5f7gOhZ6LT6RwOh9FoLPeB0DPRaDQ2m01UWCj3sdDTE7WHbDZb\nxfYMoK+Ij8jHYB27CuV0OhsaGiq2/iF9RVarNRAIsMXwbmcwGPx+v9frVav5YXgXU6vVXq83\nlUqxfchu53A4+Ijciubv/u7vyn0MtAmLxbJ///7e3t6dr41Mz5HRaGxrazt48KDX6y33sdDT\n0+l0dXV1fX199fX1Wi0/D+9WarXa7XZ3d3e3tbWZTKZyHw49PavVykfkVlQ71vyUiIiIiLYV\npxWIiIiIFILBjoiIiEghGOyIiIiIFILBjoiIiEghGOyIiIiIFILBjoiIiEghWJCp4uRyuUwm\ns76+HolESqWSRqOxWq12u93lcqnVaha+r1jhcHh+fj6Xy5VKJfEVUTQrEAjodLpSqZTJZBKJ\nRDgczmQypVLJbDZbrVaXy2U2mwHwzJbLgydOr9dbrdba2lpxUgA88cTl83lesJUgmUzOz88n\nEolisajVao1Go9/vd7lcGz+wtLS0uLhYKpVEkS+VSqVSqfx+f11dHYBCoZDJZGKxWDgcLhQK\nkiRZrVabzeZyuUR7A57KHSBJUqlUisVisVgsHo/n83lJkiwWi81mczgcohzx4x+RkiTxTstg\nV3FisdjMzMzFixcvXbqUyWTMZnN3d/fRo0dPnz7NipqVbHBw8J//+Z9XVlYymQwAtVqt0+le\nf/31v/zLv3Q4HKVSaXFxcWho6MKFC7Ozs5lMpq2traen58yZMx0dHeU+9j3t9u3b//Iv/7K8\nvJzL5TweT19f349//OO2tjbx3S+fuNbW1t7e3o0TJy7YTz755NNPP+UFW0bz8/M/+9nPbt++\nnUqlHA5HIBD48Y9/fOrUqY0fuHDhwi9+8YtMJpPL5QBoNBqdTvcnf/Inf/qnfwogk8nMzMxc\nu3btwoULIjEcOHCgv7//9OnTNTU1Zfut9hhJkrLZ7O3btwcGBm7evBkKhQC0t7cfPHjwxIkT\n7e3teNIV98QLdi9gsKsg4sPKwsLC+fPn5+fnDQaDyWRSqVTj4+Nms7mxsdHv97PKdsUKh8OD\ng4Mul2vfvn0A1Gq1VqsVTajEqM/g4OC1a9eSyaT4fBmNRgcGBsSggtPpZI+jcrFYLIFAQKPR\nRCKRlZWVsbGxVColvrXpiYvFYuLEOZ1Oh8PBC7ZCGAyGmpqaUCgUjUZDodDQ0ND6+vqDP7C0\ntDQ0NNTR0SGuUI1Go9VqnU6nuPGGw+HLly+Pjo4CsNvtarV6aWnp1q1b9fX1er3e6XSyvewO\nSKfT6+vrS0tLoVDIYrGo1episbi6uvrpp5/6fL6qqiqz2fyYK87n8+l0uq0u2L1zp2WwqyCS\nJBUKhfHx8f/8z/88cuTID3/4Q5/Pt7a29qtf/eru3btNTU0ajYbPiYqlUqnUavVbb731k5/8\nZOOLBoPBYrEUi8VEInHx4sXbt2+/8847PT09Pp/v/fff/+CDD4aHh91u9/79+/fC7aYy9fb2\ntra2hkKhubm5//iP/4jH4xvf2vTE/eY3vzl79uzw8LDT6WxvbxcX7OHDh798warVal6wO6a+\nvv7P//zPI5HI2traf//3f1+4cOGRH1CpVFar9cc//vF3vvOdjS+K6blisRgMBs+ePavRaL77\n3e82NDQYjcaN82g2m202G4PdDojH44uLi4lEorq6+q233nI4HJlM5r333jt79mxXV1ddXV1d\nXd1jrrhSqeR2u7e6YPfOnZbBroLkcrm1tbVoNKrRaGpra7u7u61Wq8Vi6erqWl9fn5ycrK2t\n3ZghokojSZIkSTdu3Pi3f/s3rVZrs9kaGxubm5ubm5vj8XgwGMzn83a7vbW1taOjw2aztba2\ndnZ2plKp6enp5ubmBxcD0U4yGAwGg0GSpHg8LlZTbXj8iRsbGzMajeKC9fv9X75g/X6/mDyi\nHaDVah0Ohxjj2WoSPJPJfPzxx6urqxqNxuv1iiu0rq5ufX09FApJklRVVdXV1eX3+1UqVUdH\nR6FQWFlZWVhY2DuzeOVlsVj8fr84iY2NjSaTqVAoVFVV6XQ6rVZbLBY3HpGbXnFGozGXy/FO\ny2BXQXK53OrqajKZdLlcdXV1TU1NACRJ6unpGRkZmZmZ6erqKvcx0pb0er3dbr9z587g4KBO\np6uuru7v73/ppZdqamoikUgwGNRoNDU1NY2NjbW1tQCam5v7+vpmZ2fFQpByHz5tQgS7rU7c\n+Pi4w+F4zAXb2dlZ5l+AHiAS/Mcff/zxxx/rdLrm5uZDhw594xvf8Hq9oVAoHA6bzWa/39/U\n1OR0OrPZbEdHRyaTmZ2dDQaDxWKx3Ie/J9hsNpvNFggExH8WCgWR0lwulxg0DYVCj7niTCaT\nVqvlnZbBroKUSqV0Ol0sFi0Wy8bIgVqtNpvNOp0ulUqJNb9Umfr6+v72b/82k8kUi8VYLBYM\nBoeHhwcGBhoaGvL5fCqV0mq14qYjfl5swCwWi+l0emMjLVWUXC73+BOXTCbFBbsxv8MLtmK9\n+uqrPp+vVCrlcrl4PD4zM3P79m2Px1NdXR0KhXK5nMFgMBqNG7smjUaj0WjMZrOZTEZspKUd\nFgwGb9++HQ6HW1paAoGAy+WKRCKPueKSySTvtGCwqyhiDa8kSRqNRq2WSwyqVCqNRqNSqYrF\nIm8ulayhoaGhoUGSJDFfcOvWrZGRkaWlpampKbPZXCqVVL8nfl6tVms0GvHzPLOVSVySjzlx\n4tyJK1R898ELdo88RXaL/fv3i0mPTCYTCoXOnz//+eefLywsTE9PixOtVqs3KtSIJbNi59NG\nhRTaAeKtzuVy6+vr4+Pjd+7cUalUL774YiAQsFqtoqDJVlecuOh4p2WwqyAajcZoNGo0mmQy\nmc1mxReLxWIqlcrn8+JDSXmPkL4KjUbjcrkaGhr2798fj8dXV1d9Pp9YLJLP5zfmdHK5XCKR\n0Gg0JpNpI8dTRdHpdF8+cdlsVpw4i8VisVji8XgymdwYnHvwgn1kxR5VCL1eX1VV1dTU1N3d\nbTabg8Gg1+s1GAwPDs6JcmiZTEav1xsMhr1Q/KyihMPhixcvTk9PJxKJgwcPHjlyxO12F4vF\njUfkplecxWJ5zAW7d+60DHYVRKvVut1usRx7bW1teXnZZDJFo9HJyclIJFJTU8MddruC2N0s\nZvHy+bxOp7Pb7Q6Ho1AohEKhlZWVmpoak8m0tLQ0Pj6u1+t9Pt9e2Ki1G1kslqqqqkdOXDAY\nFCfO7/eLBZTRaHR1dZUX7G4hSVI+n89ms8lk0mazGQwGp9MJIJVKiRuv2Ag1Nze3sLBgt9s9\nHg+3xO6MUqmUz+enpqbGx8dnZmbEFpb29nax6i6VSm08Ije94mprax9zwe6dO+2eSK+7hcFg\n8Pl8FoslFArNzs5OTEysrq6urKzcvHkzGAw2NTVVVVWV+xjpyUqlUiQSmZ2dHRkZWVtb83q9\n9fX1fr+/WCwuLi5OTEzMzc1Fo9Hx8fHr16/rdLqmpiZWsq1MDofjyyduYmJCnLj29vampqbH\nXLBer7fcvwFtIpfLhUKh6enpoaGhVCpVV1cXCAQ8Ho/oXTE2Nra0tBSJRIaHh8fGxjweT21t\nLYPdziiVSqlU6uOPPz579mwqlWptbX3zzTc3tiQ/8RHZ0tLymAt279xpOWJXQVQqlcFgaG5u\nfuONN/L5/HvvvWc0GvP5fC6Xa2trO3DgQHV1dbmPkTaRzWbX19dHR0c/++yzbDYrRuySyaTD\n4ejs7Gxtba2urtZqtYcOHVKpVLdv3753757RaFxZWfH7/a2trS0tLXvkdlOZJicnP/vss5mZ\nmZmZmYGBgXQ6/bOf/eyFF17o7OxsaGhwOBxbnbi2tjan08kLtkKsrq5+9tln9+7dW1hYGByk\nwUkVAAAHrklEQVQcnJ+ff/fddycnJzs7O8Xo6Z07d27evAmgVCoVCoVoNNrS0tLV1bVv3z6n\n01ksFk+fPj0/P//hhx8ajUatVhsMBuvq6jo7O0X96nL/fntCNBoVm1quXLlit9vHxsZu3ryp\n0+n0en1PT097e7vP53vMFVdXV2c0GnmnZbCrIGKt7r59+955552zZ8+eO3culUrZbLb+/v6u\nrq6uri7RKY8qTT6fX1tbu3r16s9+9rNYLCbK0jY2Np44ceLQoUOtra0mkymfzx8/flyr1b7/\n/vvj4+OpVKq7u/vIkSNdXV1NTU1cwVNGMzMzv/71r4eHh+fn58VX3n333ZGRkW9/+9tGo7G2\ntvaRE3fgwIGjR492dXU1NzerVCpxwX7wwQf/+7//ywu2jMLh8Pnz5y9dujQzM5PP5wGcPXv2\nzp073/72t3t7e+vr68+dO/fv//7vKpVKr9c7HI6urq6TJ08ePHhQVM1QqVSvv/76xYsX33//\n/VAoVCwW+/v7Dx8+3NnZWVtbyyt0Z4gCxYuLi9PT0wBu374tvm6xWP7oj/7IYDC43e7HX3GF\nQmGrC3bv3GlVe2STyC6SSqWi0ej8/PzCwkKxWNTpdB6Px+/3BwIBrVa7R/4ud5d8Pp9IJGZn\nZ0dHR/P5vHhyWK3W6upqn89XU1Oj1WpLpZJYFzI7OxuPxwuFgsvlqqqqCgQCbrcbe6M1dWVa\nWloaHR2NRCLJZHLji263OxAI1NTUeDyeR06c0+n0er0bJy6dTvOCrQTRaHRkZGRlZSWRSGzs\nRxb94txut9lsnpqampiYAKBWq0WXMJ/P5/P5xMBqLpeLRqPBYHB2dlaMu3s8Hp/PFwgEREDn\nqdwB8Xh8fX393r17wWDwwa9rtdrm5ub6+nqPxyPupVtdcZIkPf6C3QvnkcGOiIiISCG4eYKI\niIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiI\niBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhI\nIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBRCW+4DICLaZSRJyuVy+Xw+n89LkiS+qFKpzGazwWAo\n77ER0R7HYEdE9PXkcrnPP//85s2bd+/ejUajAFQqldVq/eM//uOTJ0+W++iIaE9jsCMi+npK\npVIsFltaWpqcnAwGg+l0OhQKATh16hSDHRGVF4MdEdHXo9frjxw50tbWFo/HFxcXJycnP/jg\ng+vXr5f7uIiIGOyIiIB8Pp9KpUZGRiKRSH19vc/nc7vdGo2mUChMT0+vrKxks1mv19vW1qbX\n6zUajdfr9Xq9AGpqavR6PVMdEVUIBjsiImSz2eXl5V/96lcjIyNvv/32yZMn7Xa7RqPJZrNX\nrly5dOlSOBw+cuRIXV2dTqdTqVTlPl4ios0x2BERQafTORyOnp6eTCZz8+ZNSZJ8Pl+pVFpe\nXr5161YwGDx48GB3d7fBYGCqI6JKxmBHRASDweB2u0+fPq1Wq3/+859nMpm+vr5kMjk5OXnv\n3j0AZ86c6e/vNxqNDHZEVMkY7IiIAECj0fj9/p6enpMnTy4uLr777ruRSCQej7e2th48eLCx\nsdFkMjHVEVGFY+cJIiIAUKvVTqezqanp6NGjTqfz008/vXLlysTERFNT08mTJ30+n16vZ7Aj\nogrHYEdEdJ/NZuvt7W1qakomkzqdLhAI7Nu3r6GhwWg0lvvQiIiejMGOiAgAJEmSJCmTySws\nLESjUYfDYTKZisViLBaLRCKFQqHcB0hE9GQMdkREMkmS1tbWPvjgg6GhoebmZqvVGgwGv/ji\ni6GhoUQiUe6jIyJ6Mm6eICICgEKhMD4+PjAwMDk5abVaT58+HQqF5ubmVlZWLly44Ha7dTqd\nzWZTq/l5mIgqF4MdERGKxWI6nR4cHLxy5Uo4HD58+PC3v/3tdDo9Njb2r//6r5cuXTpw4IDL\n5TKZTDqdDkChUMjn87lcTuyczWazkiQlEolwOAxAp9OZzWa1Ws3NFkS0wxjsiIgQDoenp6ev\nXr06PT195syZY8eO2e12q9WqUqmOHz9+8+bNixcvZjKZ73//+w6HQ5KksbGxoaGhq1evzszM\nRCKRqampRCLxy1/+8pNPPgFw+PDhP/zDP3S73QaDody/GRHtLQx2RETI5/PpdFqj0fh8vuPH\nj/f29ppMJo1GYzAYDh06pFarR0ZGkslksVgUeyzS6XQo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Call the `RandomRedDots2` function from a REST API","","Here is the equivalent REST API call:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/r-scripts/v1/do-eval/RandomRedDots2\""],"enabled":true,"result":{"startTime":1737479375489,"interpreter":"md.medium","endTime":1737479375919,"results":[{"message":"<h2 id=\"call-the-randomreddots2-function-from-a-rest-api\">Call the <code>RandomRedDots2<\/code> function from a REST API<\/h2>\n<p>Here is the equivalent REST API call:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/r-scripts/v1/do-eval/RandomRedDots2&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A third option creating subplots: `RandomRedDots3`","","Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example."],"enabled":true,"result":{"startTime":1737479376362,"interpreter":"md.medium","endTime":1737479376774,"results":[{"message":"<h2 id=\"a-third-option-creating-subplots-randomreddots3\">A third option creating subplots: <code>RandomRedDots3<\/code><\/h2>\n<p>Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}},{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}},{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","RandomRedDots.3 <- function(num_dots_1 = 100, num_dots_2=10){","","    library(lattice)","    fig1 <- xyplot(sample(num_dots_1)~sample(num_dots_1), ","      pch = 19,","      col = \"red\", ","      main=\"Random Red Dots\")","      ","    fig2 <- xyplot(sample(num_dots_2)~sample(num_dots_2),","      pch = 19,","      col = \"red\", ","      main=\"Random Red Dots\")","      ","      print(fig1)","      print(fig2)","}","","ore.scriptCreate(name = 'RandomRedDots.3', ","                 FUN  =  RandomRedDots.3,     ","                 overwrite = TRUE)","                 ","ore.doEval(FUN.NAME = 'RandomRedDots.3')"],"enabled":true,"result":{"startTime":1737479377224,"interpreter":"r.medium","endTime":1737479378026,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\ndgAAAIog7GA3WVlZ27Zty8rKcvUgyAGvjtvipXFnvDrujF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iLFy9u27bthRdeyLHqRKRq1aoDBw7ctm1bUlLSqFGjVq1a5ZhRARRtmZnS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s9ns8vKy3+8XB14oFOrr66uqqvR6vXjJRPVmseJHlEoRQ/w1NTWiC3Dn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oCAZw9i9On0dqKJ5pc7gDtdt3aGmR5/XVARUE2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\nn9VqpSgKTblms9loNErTtNPpZBhme/V9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lrHI/Hp6am5G5cs9lcW1vb2tra1dUl+zC73W5ZUjKZ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ADSCYAcAAKARBDsAAACNINgBAABoBMEO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\n1tP4+Pi77767sLAgInv27Ont7eUH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Call the RandomRedDots3 function from a REST API","","Here is the equivalent REST API call:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/r-scripts/v1/do-eval/RandomRedDots3\""],"enabled":true,"result":{"startTime":1737479378481,"interpreter":"md.medium","endTime":1737479378962,"results":[{"message":"<h2 id=\"call-the-randomreddots3-function-from-a-rest-api\">Call the RandomRedDots3 function from a REST API<\/h2>\n<p>Here is the equivalent REST API call:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/r-scripts/v1/do-eval/RandomRedDots3&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Load a function from the R script repository","","Finally, note that a function stored in the R Script Repository can be loaded back in the R client engine, we can view its source, and finally invoke the function."],"enabled":true,"result":{"startTime":1737479379401,"interpreter":"md.medium","endTime":1737479379824,"results":[{"message":"<h2 id=\"load-a-function-from-the-r-script-repository\">Load a function from the R script repository<\/h2>\n<p>Finally, note that a function stored in the R Script Repository can be loaded back in the R client engine, we can view its source, and finally invoke the function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{},\"image\":{\"height\":700,\"lastColumns\":[]}},{\"image\":{\"height\":700,\"lastColumns\":[]}},{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","ore.scriptLoad(name = 'RandomRedDots.3')","RandomRedDots.3","RandomRedDots.3()"],"enabled":true,"result":{"startTime":1737479380269,"interpreter":"r.medium","endTime":1737479380856,"results":[{"message":"<pre class=language-r><code>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n<span style=white-space:pre-wrap>    library(lattice)<\/span>\n<span style=white-space:pre-wrap>    fig1 &lt;- xyplot(sample(num_dots_1) ~ sample(num_dots_1), pch = 19, <\/span>\n<span style=white-space:pre-wrap>        col = \"red\", main = \"Random Red Dots\")<\/span>\n<span style=white-space:pre-wrap>    fig2 &lt;- xyplot(sample(num_dots_2) ~ sample(num_dots_2), pch = 19, <\/span>\n<span style=white-space:pre-wrap>        col = \"red\", main = \"Random Red Dots\")<\/span>\n<span style=white-space:pre-wrap>    print(fig1)<\/span>\n<span style=white-space:pre-wrap>    print(fig2)<\/span>\n}<\/code><\/pre>\n","type":"HTML"},{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nsbArLP788882bdr8+eefcgexITznEuMJlxhPuMR4wiXGE24Q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r15YtUr/lmurVhZZaJjItvECOBER5U58vOFZSzROnDD12YgIdOyIkyeR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\nzkWS0HNYrSDAw4O364iIyOpZzx27vXv37t69e/bs2Y0aNXJycurRo8ew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ARhB0AAIAi\nCDsAAABFEHYAAACKIOwAAAAUQdgBgENlZsrp03L+vBgMjh4FQLFH2AGAgyQkSL9+4ukpdetK\njRpStqyMGiVpaY4eC0AxRtgBgCP8+qs0ayZffy137vy95OZNWbhQWrWSP/906GQAijHCDgAc\nYfBgSUnJZfmpUzJunN2nAaAIwg4A7O7nnyU6Os+D6lavlps37TsQAEUQdgBgdz/9ZGntnTty\n+rS9RgGgFMIOAOzu7t3CbgAAuSHsAMDuate2tNbFRWrVstcoAJRC2AGA3bVqJdWri0tu/wfW\n6SQ4WCpWtPtMAFRA2AGA3bm6ymefiU6Xve10OilTRubNc9BYAIo9wg4AHKFzZ9m6VapWvW/h\n44/Lf/8r9es7aCYAxV4JRw8AAA+qTp3k7Fk5flzi4sTdXRo2FH9/R88EoHgj7ADAcUqUkObN\npXlzR88BQBF8FAsAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrAD\nAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARh\nBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAI\nwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABA\nEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAA\ngCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsA\nAABFEHYAAACKKE5hd/To0T59+vj5+ZUqVapmzZojR45MSkpy9FC4X0qKGAyOHgIAgAdUsQm7\nuLi4J5988vLlyzt37kxJSVm1atW2bduefPLJmzdvOno0iBw+LCEh4u0tFSpImTLSoYPs3Ono\nmQAAeOAUm7D7v//7v/T09A8//LB+/fqlSpVq3bp1eHh4YmLi999/7+jRHnhr1siTT0pUlKSl\niYj89Zfs2SNdusgHHzh6MgAAHizFJuy8vLw0LYedJCTIkCFiMNz3Cazx9rhxEhvrqLkAAHgA\nFZuwGzJkSN26dceOHXvy5Mnbt2/v379/5syZ3bt3b9u2raNHe7AtWybp6ZKVlX25wSBZWbJw\noSNmAgDgAVXC0QMUVLly5bZu3dq7d+8GDRoYl/To0WP16tU6nc7CozIzM6OiotLT03NdGx0d\nLSJZOaMEBRcTIzpd7idM6HRy5IjdBwIAwB6M/WBsiVx5eHh069bN1dXVjkMVn7A7efJk+/bt\nGzZs+PPPP9eoUePo0aNDhw5t3br1/v37y5Qpk9ejdu/eHRoaavmZ4+LiinrYB8mtW3mGncEg\nt27ZfSAAAOzB2A96vV6v1+e1zc6dO4ODg+04VPEJu6lTp6akpKxbt65ChQoi0rp1688//7xN\nmzYLFiyYMGFCXo8KCgqKiIiwsMdOr9c3bNjQVkM/CB59VHbvzn2VTic1ath3GgAA7MTYD2Fh\nYYGBgblu4OHhERQUZN+hik/YnTp1qnLlysaqM/L39zcut/AoV1fXHj16WNhAr9e7uBSbAw2d\nUa9esnx57qsMBunVy77TAABgJ8Z+CAwM7Nu3r6Nn+UexaZrq1asnJyenpKSYlpw4ccK43HFD\nQSQkRLp3z31Vs2by8sv2nQYAgAdasQm7sWPHiki/fv1++eWXW7du7d+/f9iwYRUqVBg2bJij\nR3vg/d//yaBBku0slu7dZft2cXNz0EwAADyIik3YtW/fPjo62tPTs0uXLhUqVBg0aNCTTz4Z\nGxvr5+fn6NEeeJ6esnKlnDolK1bI22/LkiVy/Lhs2SIVKzp6MgAAHizF5hg7EQkICNi0aZOj\np0Ae6tSROnUcPQQAAA+0YrPHDgAAAJYRdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAI\nwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABA\nEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAA\ngCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsA\nAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2\nAAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog\n7AAAABRRQtPWN27c+Pnnn69cuZKamurt7V2pUqVGjRqVLl3aRsMBAACg4AoUdklJScuWLduy\nZcuPP/6YmZlpvsrV1bVZs2YhISFDhgzx8/OzzZAAAADIXz5hFxcXN3ny5IiICGPP+fr61q1b\nt3z58mXLlk1LS7t27dqpU6eOHDly5MiRadOmhYaGvvvuuw0aNLDL5AAAALiPpbAbPnz4smXL\nRKRDhw4DBgwICgqqVq1azs3i4+N379791VdfRUREREREDBky5LPPPrPVvAAA4AG3Z498/LEc\nPSqXL0v9+tKxo7z5plSo4OixnIKlkydWrVo1atSohISEHTt2vPjii7lWnYhUr179pZde2rFj\nR3x8/KhRo1atWmWbUQEAwANv+nTp0EE2bpTff5e//pLYWHnvPfH3l5MnHT2ZU7AUdufOnZs/\nf/4jjzxSwOeqUqXK/Pnzz507VxSDAQAA3G/rVnn7bRERg+HvJcYbf/whvXvL3bsOG8xpWAq7\nypUrW/GM1j0KAAAgH++/LzrdP1VnYjDIyZMSFeWImZwL17EDAADFxP79uVSdyYEDdhzFSRU0\n7C5evPj7778b8ng1d+3atWvXrqKbCgAA4H4ZGXLnjqUNrl+31yjOK/+w+/HHHx9//PEqVarU\nrFmzatWqS5cuzblNp06dOnXqZIPxAAAARETEzS2fU1+5nm6+YXfhwoXg4ODjx4+LSOnSpS9c\nuDB06NCBAwfeu3fPLuMBAAD8T2iopbXdu9trDueVT9jNmTPn2rVrjz766I8//njjxo1ff/01\nODh49erV/fv3z/YVFAAAALb19ttStqy45FYvgwbJ44/bfSCnk0/Ybdu2TUT0en3Tpk1FpE6d\nOtu2bRsxYsQ333wzaNCgrKwse8wIAAAgIo8+Kjt2SM4Lsb3wgnz6qSMGcjr5fKVYfHy8iLRr\n1860xNXVdfHixVlZWUuWLHF3d1+6dKlOp7PtjAAAAEYtW8qZM7J5sxw7JpcvS7160rGjNGvm\n6LGcRT5h5+7ufufOHZcc+zwXL16cmpq6fPlyDw+PhQsX2mw8AACA+3l4SL9+0q+fo+dwRvmE\nXfXq1U+cOBEfH9+4cWPz5S4uLl9++WVqauqiRYs8PT1tOSEAAAAKJJ9j7AIDA0Xk0KFDOVe5\nublt3LixZcuW77//vk1GAwAAgBb5hF2vXr1EZP369bmu9fT0jIqKatiwYdHPBQAAAI3y+Si2\nQ4cOq1atcnNzy2uD8uXL79ix44svvijqwQAAAKBNPmFXsmTJgQMHWt7mkUcemTBhQtGNBAAA\nAGsU9LtiNQkJCQkJCbHFMwMAACAv+eyxs05kZKQtnhYAAAAW2GSPHQAAAOyPsAMAAFAEYQcA\nAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAowibfPHHkyBFbPC0AAAAs\n0LDH7t69e1u2bNmxY4dpyaJFi/z9/Rs0aLBy5UrzLQMCAgICAopsRgAAABSAhrD7+uuve/To\nsWrVKuPd7du3v/rqq3FxcSdPnhw8ePD+/fttMyEAAAAKREPYLVu2TESGDh1qvPvpp5+2aNHi\n0qVLo0aNMhgMCxYssMmAAAAAKBgNYXfq1CkRqV+/vogYDIY9e/YMGzbMx8dnzJgxInL48GEb\njQgAAICC0BB2V69eFREvLy8RiY+Pv3btWsuWLUWkatWqInLx4kXbTAgAAFARqDQAACAASURB\nVIAC0RB2Pj4+IpKQkCAiBw4c8PDwqFevnoikpqaKSJkyZWwzIQAAAApEQ9g1b95cRObOnZuY\nmLhgwYK2bdu6ubmJyPnz50WkVq1atpkQAAAABaIh7MaMGaPT6ZYsWVKtWrUDBw6EhYUZl0dG\nRopI9+7dbTIgAAAACkbDBYrbtm0bERGxePFiERk0aNDTTz9tXH7w4MFatWo9//zzNhkQAAAA\nBaPtmydCQkJCQkKyLTS/ZDEAAAAcRcNHscHBwcHBwVpXAQAAwD407LH77rvvrFgFAAAA+9Cw\nxy4vGRkZIlKihLZPdQEAAFC0iiDsDhw4ICK+vr6FfyoAAABYLf/dbMarEOd1Nz09PSkpSUS6\ndu1atJMBAABAk/zD7tdff7VwV0TKlSvXo0ePOXPmFOVcAAAA0Cj/sLtx44bxhvFLw0x3jUqW\nLOnu7m6LyQAAAKBJ/mFXunRp4w29Xm9+FwAAAE5Fw6msY8aMsd0cAAAAKCTN1yhJSUlZs2ZN\nTExMamqqt7d3ixYt+vfvX65cOVsMBwAAgILTFnabNm168cUXr1+/blqyYsWK8PDwVatW9ejR\no6hnAwAAgAYawu7YsWP9+vXLyMho0KDBkCFDqlatmpiYuHz58ri4uL59+x45cqRRo0a2GxQA\nAACWaQi79957LyMjo3PnzpGRkabvmXj99de7d+++Y8eOWbNmffXVV7YZEgAAAPnT8M0Te/bs\nEZFZs2aZf3tYiRIlZs6cKSJ79+4t6tkAAACggYawu3r1qojUrVs323Ljd1FcuXKlCMcCAACA\nVhrCzsvLS0QSExOzLTcuMa4FAACAo2gIu1atWonI+++/n225cUlgYGARjgUAAACtNJw8ERYW\nFhkZuXTp0pSUlKFDhxrPiv388883btyo0+nCwsJsNyUAAADypSHsOnbs+MEHH7z55psbN27c\nuHGjabmrq+sHH3wQFBRkg/EAAABQUNouUDx27NhOnTotXbo0JiYmLS3Ny8srICBg6NCh/v7+\nNpoPAAAABaT5K8UaNWo0b948W4wCAACAwtBw8gQAAACcmaU9dp988omm53rttdcKNwwAAACs\nZyns/v3vf2t6LsIOAADAgSyF3axZs7ItOX78+Nq1aytUqPDMM8/4+fklJSVt2rQpJSXlueee\na9KkiS3nBAAAQD4shd2ECRPM7x47dmzatGktWrTYvn17uXLljAvnzp3bqVOnzZs3T5w40YZj\nAgAAID8aTp546623bt++rdfrTVUnIuXLl583b97t27cnTZpkg/Gy++mnn/r27fvwww+XKVMm\nODh43759dvihAAAAxYKGsDtw4ICI5PzItWnTpiKyf//+IhwrV9u3b2/ZsuVjjz0WGxv7559/\nvvPOO3q93tY/FACAYu/iRZk0STp3lnr1pHt3mTlTrl1z9EywCQ3XsUtPTxeRP/74o3Tp0ubL\nL126JCK3b98u2smySU1NHThw4LBhw2bOnGlc0rp169atW9v0hwIAUOzt2iW9e8v16+LiIllZ\ncuaMREXJxx/L1q3y+OOOHg5FTMMeu0aNGonIokWLsi1fsGCBaa3trFq16urVq6NHj7bpTwEA\nQClJSdKzp9y8KSKSlfXPP//4Q7p1+3s5FKIh7MaOHSsiH3744eDBg/fs2XPmzJk9e/a89NJL\n8+fPF5GwsDBbzSgiInv37vX29o6JiWnatKm7u3vFihX79+8fHx9v0x8KAEDxNn++/PXX3zFn\nzmCQ5GRZvtwRM8GGNHwU+9xzz505c2bq1KkrVqxYsWKFabmLi8u0adOee+65op/OTFJS0o0b\nN1555ZWlS5d27tz5559/HjRo0JNPPnns2LFKlSrl9ajMzMyoqCjjh8g5RUdHi0hWzn/dAQBQ\nw549otOJwZDLKp1O9uwRjdeshYmxH4wtkSsPD49u3bq5urracSiN3xU7efLk0NDQZcuWxcbG\npqament7N2/efMiQIXa4iF1WVlZmZubEiRN79+4tIoGBgZ999lmHDh0WLFgwderUvB61e/fu\n0NBQy88cFxdXtKMCAOAs/vwz96oTEZ1OUlLsO41SjP2g1+stnMq5c+fO4OBgOw6lMexEpEmT\nJsbPXu2sYsWKItKuXTvTkjZt2ri6uh45csTCo4KCgiIiIizssdPr9Q0bNizaUQEAcBY+PnL+\nfO5tZzCIj4/dB1KHsR/CwsICAwNz3cDDwyMoKMi+Q2kPu4IICQkRkS1bthThczZp0mTr1q05\nl+t0OguPcnV17dGjh4UN9Hq9i4uGAw0BAChOOneWgwdzX2UwSKdO9p1GKcZ+CAwM7Nu3r6Nn\n+YdNmiYyMjIyMrJon/PZZ58VEfMrEu/fvz8zMzOvTAYAADJ6tFSsKDl3guh0UreuDBzoiJlg\nQ8VmZ1XTpk2NF7HbuHHjzZs3Dx06NGLEiBo1aowaNcrRowEA4KwqVJDt2+WRR/6+a/qQqn59\n2bpV3N0dNRdsxCYfxdrIokWLatasOX78+H79+pUrV6579+4zZ8709vZ29FwAADixZs3k1Cn5\n8ks5ckQSEqRWLXnySenXj6pTUnEKO1dX1wkTJkyYMMHRgwAAUKyULi2vvCKvvOLoOWBzxeaj\nWAAAAFhG2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAibHK5E8vf3woAAABb0BZ2WVlZ\n69evj4mJSUxMzMjIyLb2m2++Md4ICAgomukAAABQYBrCLjExsWvXrnFxcbabBgAAAFbTEHZh\nYWFxcXFVq1YNDw9v0KCBm5ub7cYCAACAVhrCbseOHSKyevXqp556ymbzAAAAwEoazoo1GAzC\n8XMAAADOSkPYNWnSREQuXrxos2EAAABgPQ1hN27cOBFZtGiRzYYBAACA9TQcYxcaGrpo0aIx\nY8ZcvXr1mWeeqVSpUrYN2rRpU6SzAQAAQANt17G7e/duqVKlVq5cuXLlypxrjQfhAQAAwCE0\nhN3ChQtHjx4tIvXq1atfvz6XOwEAAHAqGsJu/vz5IjJp0qTp06fbbB4AAABYScPJE/Hx8SLy\n+uuv22wYAAAAWE9D2Pn4+IiIq6urzYYBAACA9TSEXZ8+fURk7969NhsGAAAA1tMQdtOmTWvV\nqlVYWNjRo0dtNxAAAACso+HkiXbt2mVkZJw/f75Zs2bVqlXLeR27mJiYIp0NAAAAGmgIu9jY\nWNPthISEhIQEG8wDAAAAK2kIu+TkZNvNAQAAgELSEHa+vr62mwMAAACFpOHkCQAAADgzDXvs\nFi9ebHmDV155pXDDAAAAwHoawm7kyJGWNyDsAAAAHEhD2OX8itgrV65ERUWdPXt2wIAB9erV\nK9LBAAAAoI2GsJs0aVLOhbNnz+7WrVtUVNS0adOKbioAAABoVtiTJ0qVKjVjxoxr166Fh4cX\nyUAAAACwThGcFdu4cWMR2bVrV+GfCgAAAFYrgrBLS0sTkZs3bxb+qQAAAGC1woZdVlbWe++9\nJyJ16tQpinkAAABgJQ0nT/Tp0yfbkoyMjLi4uHPnzolIWFhYUc4FAAAAjTSE3fr163Nd7uvr\nO3ny5MGDBxfRSAAAALCGhrCLjo7OtsTV1bVChQo1atTQ6XRFOhUAAAA00xB2rVq1st0cAAAA\nKKQiOCsWAAAAzkDDHjuj6OjogwcPXr16NTMzM9uq2bNnF9FUAAAA0ExD2N24caNnz57ff/99\nXhsQdgAAAA6kIezCw8O///57Dw+P4cOHN27c2N3d3XZjAQAAQCsNYbdhwwYRWbp06fPPP2+z\neQAAAGAlDSdPXLlyRUS6d+9us2EAAABgPQ1h5+fnJyIZGRk2GwYAAADW0xB2AwYMEBELJ08A\nAADAgTSE3VtvvdW2bds33ngjJibGdgMBAADAOhpOnmjTpk1GRkZSUlKLFi2qVatWqVKlbBsQ\nfAAAAA6kIexiY2NNtxMSEhISEmwwDwAAAKykIeySk5NtNwcAAAAKSUPY+fr62m4OAAAAFJKG\nkycAAADgzCyF3XfffWfFM1r3KAAAABSSpbALDg5u167dtm3bsrKy8n2izMzMbdu2tW3bNjg4\nuOjGA4D7nTolU6ZInz4SEiLjx8vBg44eCACciKVj7BYvXjxlypSuXbtWrlz5ueeeCwoKeuKJ\nJ3x8fMy3uXTp0uHDh3fv3r127dpLly75+vouXrzYxjMDeFDNnSsTJ0pmpuh0otNJZKTMmSMj\nR8onn4gLB5YAgMWwGzFixPPPPz9v3rxPP/1Ur9fr9XoR8fb2Ll++fJkyZa5fv56SkpKWlmbc\nuGrVqjNmzBg9enTp0qXtMTiAB82aNTJunOh0IiIGgxgMfy9ftEgqVZJ33nHgaADgJPI5K7ZM\nmTKTJ08ODw/fsmVLZGTkvn37fv3119TUVONanU5Xv379du3ahYSEdO3a1YW/MQOwnbfeEhcX\nyXlkiE4n770nY8eKl5cjxgIAJ1Kgy524uro+88wzzzzzjIjcuXPnypUraWlp3t7elSpVcnNz\ns/GEACBy5oz8/nvuqwwGuXNH/vtfCQmx70wA4HQ0XMfOyN3d3c/Pz8/PzxbTAEDuLl8u7AYA\n8ADgw1MAxUG5cvlsUL68XeYAAKemeY/dli1b1q5de+7cuYyMjJiYGBH5+uuvMzIy+vXrV7Jk\nSRtMCAAi9eqJj49cuZLLMXYi4uoqbdrYfSYAcDrawm748OFLlizJtnDNmjWbNm3y8vLq0aNH\n0Q0GAGZcXGTSJPn3v3NfO3KkVKxo34EAwBlp+Ch21apVS5YsefTRR3/44Qfz5QMHDhSRNWvW\nFPFoAGBu1CgZPfrv2y4uf1/KTkRCQ+X99x04FwA4Dw177D799FMR+fDDD1u3bm2+3N/fX0QO\nHDhQtJMBwH10Opk/X/71L1m1Sk6ckIwMadhQ/vUv6d3778IDgAeehrA7fvy4iLRt2zbb8qpV\nq4pIcnJyEY4FALlr317at3f0EADgpDR8FJuRkSEixi+W0Jn9/fjmzZsiwpkTAAAAjqUh7CpX\nriwiSUlJ2Zb/8ssvIvLoo48W3VQAAADQTEPYBQcHi8iGDRvEbI+dwWD48MMPRaRLly42GA8A\nAAAFpSHs3nzzzVKlSk2ZMuWzzz4zLvnuu+969+797bffli1bdsyYMbaZEAAAAAWiIezq1q27\ndu1aFxeXESNGZGVliUhwcPDGjRs9PT2//vpr4ykUAAAAcBRtFygODQ399ddfFy9e/MMPP1y9\netXb27t169avvfYaXx0LAADgcJq/UqxKlSrTp0+3xSgAAAAoDA0fxQIAAMCZEXYAAACKsPRR\nbO3atTU919mzZws3DAAAAKxnKex+++03u80BAACAQrIUdnfv3rXbHAAAACgkS2FXooTmc2YB\nAADgKFam24ULF1JTU729vatUqVK0AwEAAMA62s6KTU9PnzJlSuXKlf38/Pz9/f38/CpXrjxl\nypT09HQbzQcAAIAC0rDH7q+//urYseOhQ4dEpHz58n5+fklJSZcuXZo2bdr27du/++47T09P\nm80JAACAfGjYYzdjxoxDhw6VL19+06ZNf/755/Hjx//888+NGzeWK1fu0KFDM2fOtN2UAAAA\nyJeGsFuzZo2IzJs375lnnjEt7Nmzp16vN60FAACAo2gIuwsXLojI008/nW15165dTWsBAADg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diff --git a/machine-learning/notebooks-oml/r/OML4R Anomaly Detection SVM.dsnb b/machine-learning/notebooks-oml/r/OML4R Anomaly Detection SVM.dsnb
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In this notebook, you will build a One-Class SVM model and then use it to flag unusual or suspicious records. The data used in this example is from the SH schema. The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).","","Copyright (c) 2025 Oracle Corporation","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737608669487,"interpreter":"md.low","endTime":1737608669926,"results":[{"message":"<h1 id=\"detect-unusual-or-rare-occurrences-using-svm-anomaly-detection\">Detect Unusual or Rare Occurrences using SVM Anomaly Detection<\/h1>\n<p>Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.) in your data using the semi-supervised learning algorithm One-Class Support Vector Machine. In this notebook, you will build a One-Class SVM model and then use it to flag unusual or suspicious records. The data used in this example is from the SH schema. The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html#OREUG-GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">OML4R Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1737608670405,"interpreter":"md.low","endTime":1737608670856,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html#OREUG-GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">OML4R Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737608671329,"interpreter":"r.low","endTime":1737608671852,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create OML Data Frames for tables CUSTOMERS and SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","ore.sync(query= c(\"CUSTOMERS\" =  'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')) ","ore.sync(query= c(\"SUPP_DEM\" = \"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()"],"enabled":true,"result":{"startTime":1737608672329,"interpreter":"r.low","endTime":1737608672849,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMERS","message":["%r","","z.show(head(CUSTOMERS, 10))"],"enabled":true,"result":{"startTime":1737608673319,"interpreter":"r.low","endTime":1737608673827,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n49671\tM\tmarried\t1976\tG: 130,000 - 149,999\t1500\n3228\tM\tNA\t1964\tG: 130,000 - 149,999\t7000\n6783\tM\tsingle\t1942\tG: 130,000 - 149,999\t11000\n10338\tM\tmarried\t1977\tG: 130,000 - 149,999\t1500\n13894\tM\tNA\t1949\tG: 130,000 - 149,999\t9000\n17449\tM\tsingle\t1950\tG: 130,000 - 149,999\t9000\n21005\tM\tmarried\t1946\tG: 130,000 - 149,999\t3000\n24561\tM\tNA\t1978\tG: 130,000 - 149,999\t7000\n28116\tM\tsingle\t1949\tG: 130,000 - 149,999\t11000\n31671\tM\tmarried\t1951\tG: 130,000 - 149,999\t1500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPP_DEM","message":["%r","","z.show(head(SUPP_DEM, 10))"],"enabled":true,"result":{"startTime":1737608674310,"interpreter":"r.low","endTime":1737608674834,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n102547\t10th\t0\t1\tOther\t0\t1\n101050\t10th\t0\t1\tOther\t0\t1\n100040\t11th\t0\t1\tSales\t0\t1\n102117\tHS-grad\t0\t1\tFarming\t0\t1\n101074\t10th\t0\t1\tHandler\t1\t1\n104179\t10th\t0\t1\tHandler\t1\t1\n100417\t11th\t0\t1\tHandler\t1\t1\n101146\t< Bach.\t0\t1\t?\t1\t1\n103420\t< Bach.\t0\t1\t?\t1\t1\n101987\t< Bach.\t0\t1\tOther\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Merge CUSTOMERS and SUPP_DEM on CUST_ID column using proxy objects ","message":["%r","","CUST_DF <- merge(CUSTOMERS, SUPP_DEM, by = \"CUST_ID\")  "],"enabled":true,"result":{"startTime":1737608675300,"interpreter":"r.low","endTime":1737608676269,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUST_DF","message":["%r","","z.show(head(CUST_DF, 10))"],"enabled":true,"result":{"startTime":1737608676736,"interpreter":"r.low","endTime":1737608677319,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100100\tF\tMarried\t1959\tJ: 190,000 - 249,999\t10000\tMasters\t1\t4-5\tProf.\t4\t0\n100200\tM\tNeverM\t1983\tL: 300,000 and above\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100300\tM\tMarried\t1961\tG: 130,000 - 149,999\t10000\tBach.\t1\t3\tProf.\t4\t0\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\n100500\tM\tNeverM\t1983\tH: 150,000 - 169,999\t15000\tHS-grad\t0\t1\tCrafts\t2\t1\n100600\tM\tMarried\t1978\tK: 250,000 - 299,999\t9000\t11th\t0\t3\tTransp.\t3\t1\n100700\tM\tMarried\t1947\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t3\tCrafts\t6\t0\n100800\tM\tMarried\t1935\tK: 250,000 - 299,999\t11000\tBach.\t0\t3\tSales\t5\t0\n100900\tM\tMarried\t1959\tF: 110,000 - 129,999\t1500\tHS-grad\t0\t3\tExec.\t4\t0\n101000\tM\tMarried\t1936\tG: 130,000 - 149,999\t7000\t7th-8th\t0\t3\tCrafts\t7\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"showSeriesX\":[\"sum.YRS_RESIDENCE.\"],\"extraDescription\":[],\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL.STATUS\",\"sum.YRS_RESIDENCE.\"],\"version\":1},\"bar\":{\"groupByColumns\":[\"CUST_MARITAL.STATUS\",\"CUST_YEAR_OF_BIRTH\"],\"showSeries\":[\"AVG.YRS_RESIDENCE.\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"AVG.YRS_RESIDENCE.\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL.STATUS\",\"AVG.YRS_RESIDENCE.\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Visualize the distribution of birth date, marital status, and years of residence","message":["%r","","DF <- aggregate(CUST_DF$YRS_RESIDENCE, by = list(CUST_YEAR_OF_BIRTH = CUST_DF$CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS = CUST_DF$CUST_MARITAL_STATUS), FUN = mean)","colnames(DF) <- c('CUST_YEAR_OF_BIRTH','CUST_MARITAL STATUS', 'AVG(YRS_RESIDENCE)')","z.show(head(DF, 60))"],"enabled":true,"result":{"startTime":1737608677793,"interpreter":"r.low","endTime":1737608678453,"results":[{"message":"CUST_YEAR_OF_BIRTH\tCUST_MARITAL.STATUS\tAVG.YRS_RESIDENCE.\n1913\tMarried\t9.66666666666667\n1913\tNeverM\t11.5\n1921\tMarried\t8\n1922\tDivorc.\t4\n1922\tWidowed\t1\n1923\tMarried\t3.33333333333333\n1923\tNeverM\t4\n1924\tMabsent\t2\n1924\tMarried\t5\n1925\tMarried\t5.71428571428571\n1925\tWidowed\t10\n1926\tMabsent\t4\n1926\tMarried\t4.85714285714286\n1926\tNeverM\t4\n1926\tWidowed\t4\n1927\tWidowed\t4.66666666666667\n1928\tDivorc.\t4\n1928\tMabsent\t6\n1928\tMar-AF\t2\n1928\tMarried\t4\n1928\tNeverM\t8\n1928\tWidowed\t4.8\n1929\tDivorc.\t0\n1929\tMarried\t6.33333333333333\n1929\tWidowed\t2.66666666666667\n1930\tMarried\t3.5\n1930\tWidowed\t7\n1931\tDivorc.\t2\n1931\tMarried\t4.5\n1931\tWidowed\t5.5\n1932\tMarried\t5.55555555555556\n1932\tNeverM\t4\n1932\tSepar.\t0\n1932\tWidowed\t2\n1933\tMarried\t7.57142857142857\n1933\tWidowed\t3\n1934\tDivorc.\t7\n1934\tMarried\t6.85714285714286\n1934\tWidowed\t2\n1935\tMabsent\t5\n1935\tMarried\t6.9\n1935\tNeverM\t5\n1935\tWidowed\t4\n1936\tDivorc.\t3.5\n1936\tMarried\t5.71428571428571\n1936\tNeverM\t8\n1936\tWidowed\t3\n1937\tMarried\t7.28571428571429\n1937\tWidowed\t5.4\n1938\tDivorc.\t7.66666666666667\n1938\tMarried\t7.66666666666667\n1938\tSepar.\t3\n1938\tWidowed\t4.77777777777778\n1939\tDivorc.\t8.66666666666667\n1939\tMarried\t4.625\n1939\tNeverM\t5.5\n1939\tSepar.\t6\n1939\tWidowed\t4.66666666666667\n1940\tDivorc.\t6\n1940\tMarried\t5.65\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build an Anomaly Detection Model","message":["%md","### Build an Anomaly Detection model","","Build a one-class SVM anomaly detection model using ore.odmSVM.  Specify the outlier rate, which is the proportion of rows that should be flagged as outliers. ","","Note that settings can be specified as explicit arguments - as indicated in the R documentation ?ore.odmSVM - or through a list of name-value pairs specified in the odm.settings argument. "],"enabled":true,"result":{"startTime":1737608678915,"interpreter":"md.low","endTime":1737608679379,"results":[{"message":"<h3 id=\"build-an-anomaly-detection-model\">Build an Anomaly Detection model<\/h3>\n<p>Build a one-class SVM anomaly detection model using ore.odmSVM.  Specify the outlier rate, which is the proportion of rows that should be flagged as outliers.<\/p>\n<p>Note that settings can be specified as explicit arguments - as indicated in the R documentation ?ore.odmSVM - or through a list of name-value pairs specified in the odm.settings argument.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build the One-Class SVM model","message":["%r","","SVM.MOD <- ore.odmSVM(~., data = CUST_DF, type = \"anomaly.detection\", outlier.rate = 0.01)"],"enabled":true,"result":{"startTime":1737608679841,"interpreter":"r.low","endTime":1737608683138,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Examples of possible setting overrides for SVM using the name-value settings specification with odm.settings","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html#OREUG-GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">OML4R Support Vector Machine<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a>","","* Specify a row weight column","","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' = '<row_weight_column_name>'","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is ODMS_MISSING_VALUE_AUTO. The option ODMS_MISSING_VALUE_MEAN_MODE replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option ODMS_MISSING_VALUE_AUTO performs different strategies for different algorithms. When ODMS_MISSING_VALUE_TREATMENT is set to ODMS_MISSING_VALUE_DELETE_ROW, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","","> 'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_AUTO'","","* Switch between Kernel Types - Linear or Gaussian","By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' = 'SVMS_LINEAR'","","> 'SVMS_KERNEL_FUNCTION' = 'SVMS_GAUSSIAN'","* Value of complexity factor for SVM algorithm (both classification and regression).","Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1'","*  Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.","> 'SVMS_REGULARIZER' = 'SVMS_REGULARIZER_L1'","","> 'SVMS_REGULARIZER' = 'SVMS_REGULARIZER_L2'","*  Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0. The default is 0.1.","> 'SVMS_EPSILON' = '0.1'  ","*  Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20000.","> 'SVMS_BATCH_ROWS' = '20000'","*  Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.","> 'SVMS_SOLVER' = 'SVMS_SOLVER_SGD'","> 'SVMS_SOLVER' = 'SVMS_SOLVER_IPM'","*  Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.","> 'SVMS_CONV_TOLERANCE' = '0.001'","*  Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.","> 'SVMS_NUM_PIVOTS' = '200'"],"enabled":true,"result":{"startTime":1737608683605,"interpreter":"md.low","endTime":1737608684066,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm-using-the-name-value-settings-specification-with-odmsettings\">Examples of possible setting overrides for SVM using the name-value settings specification with odm.settings<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html#OREUG-GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">OML4R Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is ODMS_MISSING_VALUE_AUTO. The option ODMS_MISSING_VALUE_MEAN_MODE replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option ODMS_MISSING_VALUE_AUTO performs different strategies for different algorithms. When ODMS_MISSING_VALUE_TREATMENT is set to ODMS_MISSING_VALUE_DELETE_ROW, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian\nBy default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' = 'SVMS_LINEAR'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' = 'SVMS_GAUSSIAN'<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression).\nDefault value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' = 'SVMS_REGULARIZER_L1'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'SVMS_REGULARIZER' = 'SVMS_REGULARIZER_L2'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0. The default is 0.1.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' = '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' = '20000'<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' = 'SVMS_SOLVER_SGD'\n'SVMS_SOLVER' = 'SVMS_SOLVER_IPM'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE' = '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS' = '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build the SVM model using the odm.settings","message":["%r","","settings <- list(svms_outlier_rate = '0.01', svms_regularizer = 'SVMS_REGULARIZER_L1', svms_kernel_function = 'SVMS_LINEAR', svms_conv_tolerance = 0.001 )","","SVM.MOD <- ore.odmSVM(~., data = CUST_DF, ","                      type = \"anomaly.detection\",","                      odm.settings = settings)","summary(SVM.MOD)"],"enabled":true,"result":{"startTime":1737608684554,"interpreter":"r.low","endTime":1737608686260,"results":[{"message":"\nCall:\nore.odmSVM(formula = ~., data = CUST_DF, type = \"anomaly.detection\", \n    odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 0.001\nkernel.cache.size                              5e+07\nkernel.function                               linear\noutlier.rate                                    0.01\nregularizer                           regularizer.l1\n\nCoefficients: \n              variable                value     estimate\n1          (Intercept)                      -1.000000491\n2        AFFINITY_CARD                       0.533187088\n3    CUST_CREDIT_LIMIT                       0.904174856\n4          CUST_GENDER                    F  0.944724238\n5          CUST_GENDER                    M  1.086436436\n6              CUST_ID                       0.974924793\n7    CUST_INCOME_LEVEL      A: Below 30,000  0.069078694\n8    CUST_INCOME_LEVEL   B: 30,000 - 49,999  0.145265468\n9    CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.098754992\n10   CUST_INCOME_LEVEL   D: 70,000 - 89,999  0.080186591\n11   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.169994663\n12   CUST_INCOME_LEVEL F: 110,000 - 129,999  0.200750695\n13   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.175458584\n14   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.200093355\n15   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.226143730\n16   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.289046239\n17   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.158834634\n18   CUST_INCOME_LEVEL L: 300,000 and above  0.217553029\n19 CUST_MARITAL_STATUS              Divorc.  0.434445683\n20 CUST_MARITAL_STATUS              Mabsent  0.059256134\n21 CUST_MARITAL_STATUS               Mar-AF  0.002444605\n22 CUST_MARITAL_STATUS              Married  0.716268228\n23 CUST_MARITAL_STATUS               NeverM  0.600822589\n24 CUST_MARITAL_STATUS               Separ.  0.105091063\n25 CUST_MARITAL_STATUS              Widowed  0.112832373\n26  CUST_YEAR_OF_BIRTH                       1.411096227\n27           EDUCATION                 10th  0.080537332\n28           EDUCATION                 11th  0.083574205\n29           EDUCATION                 12th  0.035647395\n30           EDUCATION              1st-4th  0.012169450\n31           EDUCATION              5th-6th  0.025580999\n32           EDUCATION              7th-8th  0.063932059\n33           EDUCATION                  9th  0.057288066\n34           EDUCATION              < Bach.  0.418922975\n35           EDUCATION              Assoc-A  0.108468443\n36           EDUCATION              Assoc-V  0.125578850\n37           EDUCATION                Bach.  0.312883893\n38           EDUCATION              HS-grad  0.518107344\n39           EDUCATION              Masters  0.111040748\n40           EDUCATION                  PhD  0.025214822\n41           EDUCATION              Presch.  0.001690826\n42           EDUCATION               Profsc  0.050523266\n43      HOUSEHOLD_SIZE                    1  0.313540719\n44      HOUSEHOLD_SIZE                    2  0.547847331\n45      HOUSEHOLD_SIZE                    3  0.552514933\n46      HOUSEHOLD_SIZE                  4-5  0.151945929\n47      HOUSEHOLD_SIZE                  6-8  0.095757336\n48      HOUSEHOLD_SIZE                   9+  0.369554425\n49          OCCUPATION                    ?  0.140320370\n50          OCCUPATION              Armed-F  0.001552633\n51          OCCUPATION              Cleric.  0.243564911\n52          OCCUPATION               Crafts  0.204346175\n53          OCCUPATION                Exec.  0.220996584\n54          OCCUPATION              Farming  0.070156757\n55          OCCUPATION              Handler  0.082790910\n56          OCCUPATION              House-s  0.014856271\n57          OCCUPATION              Machine  0.130661379\n58          OCCUPATION                Other  0.230752921\n59          OCCUPATION                Prof.  0.253894918\n60          OCCUPATION              Protec.  0.038816702\n61          OCCUPATION                Sales  0.218183276\n62          OCCUPATION              TechSup  0.097716939\n63          OCCUPATION              Transp.  0.082549927\n64       YRS_RESIDENCE                       0.747704907\n65         Y_BOX_GAMES                       0.595857354\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke predict() to score data for anomalies","message":["%r","","RES_DF <- predict(SVM.MOD, CUST_DF, supplemental.cols = colnames(CUST_DF), proba = TRUE, type = \"class\")"],"enabled":true,"result":{"startTime":1737608686732,"interpreter":"r.low","endTime":1737608687349,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Anomalies are indicated by PREDICTION = 0, display a few rows flagged as anomalies","message":["%r","","z.show(head(RES_DF[RES_DF$PREDICTION == 0,], 10))"],"enabled":true,"result":{"startTime":1737608687835,"interpreter":"r.low","endTime":1737608688359,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\t0\t0.562704326544703\n100001\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\t< Bach.\t0\t2\tExec.\t3\t0\t0\t0.557876401433934\n100017\tF\tMabsent\t1935\tI: 170,000 - 189,999\t10000\t< Bach.\t0\t9+\tFarming\t5\t0\t0\t0.542615745076834\n100031\tF\tDivorc.\t1967\tH: 150,000 - 169,999\t1500\tBach.\t0\t6-8\tSales\t4\t0\t0\t0.500269901330298\n100072\tM\tMabsent\t1971\tJ: 190,000 - 249,999\t9000\t5th-6th\t0\t6-8\tCrafts\t3\t0\t0\t0.524271250281955\n100132\tF\tWidowed\t1926\tI: 170,000 - 189,999\t3000\t< Bach.\t0\t2\tHouse-s\t4\t0\t0\t0.637477532369609\n100148\tF\tWidowed\t1936\tI: 170,000 - 189,999\t9000\t7th-8th\t0\t2\tOther\t3\t0\t0\t0.553179536335528\n100170\tF\tWidowed\t1943\tF: 110,000 - 129,999\t3000\t7th-8th\t0\t9+\t?\t3\t0\t0\t0.701078977491907\n100199\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.803307844886286\n100289\tM\tSepar.\t1949\tB: 30,000 - 49,999\t1500\t9th\t0\t9+\tCrafts\t5\t0\t0\t0.629913409360123\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"CUST_MARITAL_STATUS\",\"Bin_PROBABILITY\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_MARITAL_STATUS\",\"Bin_PROBABILITY\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot the probability to be an anomaly against bin frequency vs. marital status","message":["%r","","rownames(RES_DF) <- RES_DF['CUST_ID']","RES_DF <- mutate(RES_DF,\"Bin_PROBABILITY\" = cut(RES_DF$PROBABILITY, breaks = 5, dig.lab=3)) ","temp <- ore.crosstab(CUST_MARITAL_STATUS ~ Bin_PROBABILITY, RES_DF)","","z.show(temp)"],"enabled":true,"result":{"startTime":1737608688825,"interpreter":"r.low","endTime":1737608689643,"results":[{"message":"CUST_MARITAL_STATUS\tBin_PROBABILITY\tORE.FREQ\tORE.STRATA\tORE.GROUP\nDivorc.\t(0.5,0.59]\t22\t1\t1\nDivorc.\t(0.59,0.68]\t55\t1\t1\nDivorc.\t(0.68,0.769]\t164\t1\t1\nDivorc.\t(0.769,0.859]\t293\t1\t1\nDivorc.\t(0.859,0.949]\t81\t1\t1\nMabsent\t(0.5,0.59]\t13\t1\t1\nMabsent\t(0.59,0.68]\t20\t1\t1\nMabsent\t(0.68,0.769]\t26\t1\t1\nMabsent\t(0.769,0.859]\t14\t1\t1\nMabsent\t(0.859,0.949]\t2\t1\t1\nMar-AF\t(0.5,0.59]\t2\t1\t1\nMar-AF\t(0.59,0.68]\t1\t1\t1\nMarried\t(0.5,0.59]\t6\t1\t1\nMarried\t(0.59,0.68]\t30\t1\t1\nMarried\t(0.68,0.769]\t141\t1\t1\nMarried\t(0.769,0.859]\t724\t1\t1\nMarried\t(0.859,0.949]\t1133\t1\t1\nNeverM\t(0.5,0.59]\t4\t1\t1\nNeverM\t(0.59,0.68]\t21\t1\t1\nNeverM\t(0.68,0.769]\t148\t1\t1\nNeverM\t(0.769,0.859]\t603\t1\t1\nNeverM\t(0.859,0.949]\t727\t1\t1\nSepar.\t(0.5,0.59]\t21\t1\t1\nSepar.\t(0.59,0.68]\t23\t1\t1\nSepar.\t(0.68,0.769]\t50\t1\t1\nSepar.\t(0.769,0.859]\t39\t1\t1\nSepar.\t(0.859,0.949]\t1\t1\t1\nWidowed\t(0.5,0.59]\t36\t1\t1\nWidowed\t(0.59,0.68]\t49\t1\t1\nWidowed\t(0.68,0.769]\t38\t1\t1\nWidowed\t(0.769,0.859]\t13\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the top 5 most anomalous cases","message":["%r","","z.show(head(ore.sort(RES_DF[RES_DF$PREDICTION == 0,], by = c('PROBABILITY'), reverse =TRUE),5)  )"],"enabled":true,"result":{"startTime":1737608690112,"interpreter":"r.low","endTime":1737608691061,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY\tBin_PROBABILITY\n100199\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.803307844886286\t(0.769,0.859]\n100170\tF\tWidowed\t1943\tF: 110,000 - 129,999\t3000\t7th-8th\t0\t9+\t?\t3\t0\t0\t0.701078977491907\t(0.68,0.769]\n103154\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.684590840278712\t(0.68,0.769]\n101137\tF\tWidowed\t1935\tH: 150,000 - 169,999\t1500\t10th\t0\t9+\tOther\t3\t0\t0\t0.67996173775069\t(0.68,0.769]\n100646\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\tBach.\t0\t9+\tTechSup\t2\t0\t0\t0.667046911688017\t(0.59,0.68]\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Assign explicit model names","We can assign a model name explicitly at the time we build the model. This is facilitated through the use of `odm.settings`. We'll rebuild the model with the assigned name 'SVM_ANOMALY_DETECTION_MODEL' for use in the query below."],"enabled":true,"result":{"startTime":1737608691564,"interpreter":"md.low","endTime":1737608692024,"results":[{"message":"<h3 id=\"assign-explicit-model-names\">Assign explicit model names<\/h3>\n<p>We can assign a model name explicitly at the time we build the model. This is facilitated through the use of <code>odm.settings<\/code>. We'll rebuild the model with the assigned name 'SVM_ANOMALY_DETECTION_MODEL' for use in the query below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ore.drop(model = 'SVM_ANOMALY_DETECTION_MODEL')","","MOD.SVM2 <- ore.odmSVM(formula = ~., data = CUST_DF, type = 'anomaly.detection', odm.settings = list(model_name = \"SVM_ANOMALY_DETECTION_MODEL\"))"],"enabled":true,"result":{"startTime":1737608692486,"interpreter":"r.low","endTime":1737608695684,"results":[{"message":"Warning message:\n\u201cModel SVM_ANOMALY_DETECTION_MODEL does not exist on database.\u201d\nWarning message:\n\u201cThe created mining model SVM_ANOMALY_DETECTION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize CUST_DF data frame into table CUSTOMER360_TAB for use in the PREDICTION_DETAILS query","message":["%r","","ore.drop(\"CUSTOMERS360_TAB\")","ore.create(CUST_DF, \"CUSTOMERS360_TAB\")"],"enabled":true,"result":{"startTime":1737608696141,"interpreter":"r.low","endTime":1737608697925,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View PREDICTION_DETAILS in SQL of top 3 attributes that explain factors leading to prediction","message":["%sql","","SELECT CUST_ID, "," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE"," FROM (SELECT CUST_ID, PREDICTION_DETAILS(SVM_ANOMALY_DETECTION_MODEL, '0' USING *) PREDICTION_DETAILS FROM CUSTOMERS360_TAB","       WHERE PREDICTION_PROBABILITY(SVM_ANOMALY_DETECTION_MODEL, '0' USING *) > 0.50","       AND OCCUPATION = 'TechSup'","       ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","                PASSING OUT.PREDICTION_DETAILS","                COLUMNS ","                \"Attribute1\" XMLType PATH 'Attribute[1]',","                \"Attribute2\" XMLType PATH 'Attribute[2]') ","       OUTPRED","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737608698378,"interpreter":"sql.low","endTime":1737608698959,"results":[{"message":"CUST_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100061\t\"EDUCATION\" actualValue=\"Assoc-V\" weight=\".039\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".039\" \n100489\t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".043\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".037\" \n100646\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".066\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".022\" \n100792\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".055\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".033\" \n100941\t\"EDUCATION\" actualValue=\"9th\" weight=\".051\" \t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".033\" \n101706\t\"CUST_MARITAL_STATUS\" actualValue=\"Mabsent\" weight=\".081\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".039\" \n102254\t\"CUST_MARITAL_STATUS\" actualValue=\"Separ.\" weight=\".079\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".053\" \n102862\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".055\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".043\" \n102922\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".064\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".023\" \n103341\t\"EDUCATION\" actualValue=\"Assoc-V\" weight=\".039\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".039\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View prediction details in transactional format","message":["%sql","","WITH d AS"," (SELECT CUST_ID, PREDICTION_DETAILS(SVM_ANOMALY_DETECTION_MODEL, '0' using *) anomalyDetails FROM CUSTOMERS360_TAB)","  SELECT d.CUST_ID, x.name, x.weight","         from d,","         XMLTable('/Details'","                  PASSING d.anomalyDetails","                  COLUMNS","                  class varchar2(20) PATH '@class') c,","         XMLTable('/Details/Attribute'","                  PASSING d.anomalyDetails","                  COLUMNS","                  name varchar2(20) PATH '@name',","                  weight varchar2(30) PATH '@weight') x","    WHERE c.class='0'","ORDER BY CUST_ID, name","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737608699423,"interpreter":"sql.low","endTime":1737608710321,"results":[{"message":"CUST_ID\tNAME\tWEIGHT\n100001\tCUST_CREDIT_LIMIT\t.023\n100001\tCUST_ID\t.028\n100001\tCUST_INCOME_LEVEL\t.018\n100001\tCUST_MARITAL_STATUS\t.064\n100001\tY_BOX_GAMES\t.015\n100002\tCUST_GENDER\t.012\n100002\tCUST_ID\t.028\n100002\tCUST_INCOME_LEVEL\t.01\n100002\tEDUCATION\t.021\n100002\tY_BOX_GAMES\t.017\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Final 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In his notebook, you will build association rules models using the A Priori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1738093048934,"interpreter":"md.low","endTime":1738093049034,"results":[{"message":"<h1 id=\"association-rules-market-basket-analysis\">Association Rules (Market Basket Analysis)<\/h1>\n<p>The Association Rules machine learning technique, also known as Market Basket Analysis, can be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing. In his notebook, you will build association rules models using the A Priori algorithm with data from the SH schema (SH.SALES). 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sAAAiIewAACIh7AAAIiHsAAAiIewAACIh7AAAIiHsAAAiIewAACIh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\nHQBAJP4/8P/isAoaM10AAAAASUVORK5CYII=","type":"IMG"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Examples of possible setting overrides for Association Rules (A Priori)","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-3BAEC55B-BC9F-4D73-8809-B2D219F5CF89\" target=\"_blank\">Algorithm settings<\/a>","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared settings<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`. The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms. When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","* Specify the name of a column that contains the items in a transaction.","When you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns: (1) Case ID, either categorical or numeric, and (2) Item ID, either categorical or numeric.","> 'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'    ","* Specify the Minimum support for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_SUPPORT' : '0.1'","* Specify the Minimum confidence for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_CONFIDENCE' : '0.1'","* Specify the Maximum rule length for association rules. The default value is 4.  It requires a number between 2 and 20 (inclusive).","> 'ASSO_MAX_RULE_LENGTH': '4'"],"enabled":true,"result":{"startTime":1738093057718,"interpreter":"md.low","endTime":1738093057818,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-association-rules-a-priori\">Examples of possible setting overrides for Association Rules (A Priori)<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-3BAEC55B-BC9F-4D73-8809-B2D219F5CF89\" target=\"_blank\">Algorithm settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>. The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms. When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the name of a column that contains the items in a transaction.\nWhen you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns: (1) Case ID, either categorical or numeric, and (2) Item ID, either categorical or numeric.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum support for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_SUPPORT' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum confidence for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_CONFIDENCE' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Maximum rule length for association rules. The default value is 4.  It requires a number between 2 and 20 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MAX_RULE_LENGTH': '4'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify settings and create Association Rules model","message":["%r","","AR.MOD <- ore.odmAssocRules(~., SALES_TRANS_CUST, ","                            case.id.column = \"CUST_ID\", ","                            item.id.column = \"PROD_NAME\", ","                            min.support = 0.04, ","                            min.confidence = 0.1, ","                            max.rule.length = 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           0.1\nasso.min.rev.confidence                            0\nasso.min.support                                0.04\nasso.min.support.int                               1\nodms.details                             odms.enable\nodms.item.id.column.name                   PROD_NAME\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"pie","title":"Display pie chart of top 10 most frequent purchased items","message":["%r","","itemsets <- AR.MOD$itemsets","purchased_items = itemsets[order(itemsets$NUMBER_OF_ITEMS, itemsets$SUPPORT, decreasing = 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4R Attribute Importance MDL","description":null,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738093615751,"interpreter":"md.medium","endTime":1738093616000,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Identify Key Factors","message":["%md","# Identify Key Factors","","Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we compute Attribute Importance, which uses the Minimum Description Length algorithm, on the SH schema data. All functionality executes inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1738093616112,"interpreter":"md.medium","endTime":1738093616221,"results":[{"message":"<h1 id=\"identify-key-factors\">Identify Key Factors<\/h1>\n<p>Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we compute Attribute Importance, which uses the Minimum Description Length algorithm, on the SH schema data. All functionality executes inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/attributeimportance-5663163.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1738093616359,"interpreter":"md.medium","endTime":1738093616455,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/attributeimportance-5663163.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a>"],"enabled":true,"result":{"startTime":1738093616582,"interpreter":"md.medium","endTime":1738093616699,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1738093616806,"interpreter":"r.medium","endTime":1738093622842,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Obtain proxy object for SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","ore.sync(schema=\"SH\",table=\"SUPPLEMENTARY_DEMOGRAPHICS\")","ore.attach(\"SH\")","","SUPP_DF <- SUPPLEMENTARY_DEMOGRAPHICS"],"enabled":true,"result":{"startTime":1738093622941,"interpreter":"r.medium","endTime":1738093624273,"results":[{"message":"Warning message in ore.sync(schema = \"SH\", table = \"SUPPLEMENTARY_DEMOGRAPHICS\"):\n\u201cThe specified schema 'SH' is not in search path. 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I love the discounts and have since started shopping at your store for everything.\n100002\tBach.\tProf.\t2\t4\t0\t1\t1\t1\t1\t1\t0\t0\tThe more times that I shop at your store, the more times I am impressed.  Don't change anything\n100003\t< Bach.\tSales\t2\t6\t0\t1\t1\t0\t1\t1\t0\t0\tIt is a good way to attract new shoppers. After shopping at your store for more than a month, I am ready to move on though. Not enough variety\n100004\t< Bach.\tSales\t2\t5\t0\t1\t1\t1\t1\t1\t0\t0\tThanks but even with your discounts, your products are too expensive. Sorry.\n100005\tAssoc-A\tCrafts\t3\t5\t1\t0\t0\t1\t1\t1\t0\t0\tAffinity card is a great idea. But your store is still too expensive. I am tired of your lousy junk mail.\n100006\t< Bach.\tProf.\t9+\t2\t0\t0\t0\t0\t1\t1\t1\t0\tI am not going to waste my time filling up this three page form. Lousy idea.\n100007\tHS-grad\tOther\t2\t5\t0\t1\t1\t1\t1\t1\t0\t0\tThanks but even with your discounts, your products are too expensive. Sorry.\n100008\t< Bach.\tCrafts\t2\t5\t0\t1\t1\t1\t1\t1\t0\t0\tI shop your store a lot.  I love your weekly specials.\n100009\tBach.\tProf.\t3\t3\t1\t0\t0\t0\t1\t1\t1\t0\tThank you! But I am very unhappy with all the junk mail you keep sending.\n100010\tHS-grad\tCrafts\t3\t3\t0\t1\t1\t0\t1\t1\t1\t0\tI am unhappy with the service at your store. Do not consider me a loyal customer just because I use your Affinity Card\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"pie","title":"Display a pie chart for education levels","message":["%r","","z.show(head(ore.crosstab(~EDUCATION, SUPP_DF), 10))"],"enabled":true,"result":{"startTime":1738093624714,"interpreter":"r.medium","endTime":1738093624937,"results":[{"message":"EDUCATION\tORE.FREQ\tORE.STRATA\tORE.GROUP\n10th\t122\t1\t1\n11th\t121\t1\t1\n12th\t52\t1\t1\n1st-4th\t17\t1\t1\n5th-6th\t39\t1\t1\n7th-8th\t87\t1\t1\n9th\t76\t1\t1\n< Bach.\t1041\t1\t1\nAssoc-A\t172\t1\t1\nAssoc-V\t196\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display the number of rows and columns","message":["%r","","dim(SUPP_DF)"],"enabled":true,"result":{"startTime":1738093625044,"interpreter":"r.medium","endTime":1738093625208,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>14<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select the columns to consider for attribute importance analysis","message":["%r ","","DATA_DF <- SUPP_DF[, c(\"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", ","                       \"CUST_ID\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", ","                       \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\",   ","                       \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","z.show(head(DATA_DF, 10))"],"enabled":true,"result":{"startTime":1738093625312,"interpreter":"r.medium","endTime":1738093625528,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t1\t0\t100001\t< Bach.\t0\t1\t2\tExec.\t0\t1\t3\t0\n0\t1\t1\t100002\tBach.\t1\t1\t2\tProf.\t0\t1\t4\t0\n0\t1\t1\t100003\t< Bach.\t1\t0\t2\tSales\t0\t1\t6\t0\n0\t1\t1\t100004\t< Bach.\t1\t1\t2\tSales\t0\t1\t5\t0\n1\t1\t0\t100005\tAssoc-A\t0\t1\t3\tCrafts\t0\t1\t5\t0\n0\t1\t0\t100006\t< Bach.\t0\t0\t9+\tProf.\t0\t1\t2\t1\n0\t1\t1\t100007\tHS-grad\t1\t1\t2\tOther\t0\t1\t5\t0\n0\t1\t1\t100008\t< Bach.\t1\t1\t2\tCrafts\t0\t1\t5\t0\n1\t1\t0\t100009\tBach.\t0\t0\t3\tProf.\t0\t1\t3\t1\n0\t1\t1\t100010\tHS-grad\t1\t0\t3\tCrafts\t0\t1\t3\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Examples of setting overrides for Attribute Importance","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of shared settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","* Specifies whether Oracle Data Mining should invoke automatic data preparation for the build","> auto.data.prep = FALSE","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. To handle missing values, either `na.pass` to allow missing values or `na.omit` to remove rows with missing values. The default value is `na.pass`","> na.action = na.pass or na.omit"],"enabled":true,"result":{"startTime":1738093625630,"interpreter":"md.medium","endTime":1738093625709,"results":[{"message":"<h2 id=\"examples-of-setting-overrides-for-attribute-importance\">Examples of setting overrides for Attribute Importance<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of shared settings can be found in the Documentation link:<\/p>\n<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/li>\n<li>Specifies whether Oracle Data Mining should invoke automatic data preparation for the build<\/li>\n<\/ul>\n<blockquote>\n<p>auto.data.prep = FALSE<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. To handle missing values, either <code>na.pass<\/code> to allow missing values or <code>na.omit<\/code> to remove rows with missing values. The default value is <code>na.pass<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>na.action = na.pass or na.omit<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Compute Attribute Importance to rank variables most predictive of AFFINITY_CARD","message":["%r","","AI_MOD <- ore.odmAI(AFFINITY_CARD ~., SUPP_DF)","","class(AI_MOD)"],"enabled":true,"result":{"startTime":1738093625812,"interpreter":"r.medium","endTime":1738093627045,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'ore.odmAI'<\/li><li>'ore.model'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model object","message":["%r","","AI_MOD"],"enabled":true,"result":{"startTime":1738093627146,"interpreter":"r.medium","endTime":1738093627300,"results":[{"message":"\nCall:\nore.odmAI(formula = AFFINITY_CARD ~ ., data = SUPP_DF)\n\nImportance: \n                        importance rank\nCOMMENTS                0.34022157    1\nHOUSEHOLD_SIZE          0.17020901    2\nYRS_RESIDENCE           0.09263171    3\nY_BOX_GAMES             0.08047519    4\nOCCUPATION              0.07326430    5\nHOME_THEATER_PACKAGE    0.05947446    6\nEDUCATION               0.05524484    7\nBOOKKEEPING_APPLICATION 0.01579748    8\nOS_DOC_SET_KANJI        0.00000000    9\nFLAT_PANEL_MONITOR      0.00000000    9\nCUST_ID                 0.00000000    9\nBULK_PACK_DISKETTES     0.00000000    9\nPRINTER_SUPPLIES        0.00000000    9\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display ranked attributes that most influence target","message":["%r","","AI_MOD$importance$attribute <- row.names(AI_MOD$importance)","z.show(AI_MOD$importance)"],"enabled":true,"result":{"startTime":1738093627466,"interpreter":"r.medium","endTime":1738093627625,"results":[{"message":"importance\trank\tattribute\n0.340221572217487\t1\tCOMMENTS\n0.170209014224814\t2\tHOUSEHOLD_SIZE\n0.0926317122260526\t3\tYRS_RESIDENCE\n0.0804751905273532\t4\tY_BOX_GAMES\n0.0732643019481289\t5\tOCCUPATION\n0.0594744616913272\t6\tHOME_THEATER_PACKAGE\n0.0552448356309264\t7\tEDUCATION\n0.0157974797190234\t8\tBOOKKEEPING_APPLICATION\n0\t9\tOS_DOC_SET_KANJI\n0\t9\tFLAT_PANEL_MONITOR\n0\t9\tCUST_ID\n0\t9\tBULK_PACK_DISKETTES\n0\t9\tPRINTER_SUPPLIES\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Last\",\"visualizations\":{\"orientationValue\":\"horizontal\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"importance\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"importance\",\"rank\",\"attribute\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph the attributes that most influence AFFINITY_CARD high responders","message":["%r","","z.show(AI_MOD$importance)"],"enabled":true,"result":{"startTime":1738093627728,"interpreter":"r.medium","endTime":1738093627887,"results":[{"message":"importance\trank\tattribute\n0.340221572217487\t1\tCOMMENTS\n0.170209014224814\t2\tHOUSEHOLD_SIZE\n0.0926317122260526\t3\tYRS_RESIDENCE\n0.0804751905273532\t4\tY_BOX_GAMES\n0.0732643019481289\t5\tOCCUPATION\n0.0594744616913272\t6\tHOME_THEATER_PACKAGE\n0.0552448356309264\t7\tEDUCATION\n0.0157974797190234\t8\tBOOKKEEPING_APPLICATION\n0\t9\tOS_DOC_SET_KANJI\n0\t9\tFLAT_PANEL_MONITOR\n0\t9\tCUST_ID\n0\t9\tBULK_PACK_DISKETTES\n0\t9\tPRINTER_SUPPLIES\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a data.frame proxy object with the top attributes","message":["%r","","selectedAttributes <- AI_MOD$importance[AI_MOD$importance$importance > 0.05, 'attribute']","","DF <- SUPP_DF[, c('CUST_ID',selectedAttributes)]","","z.show(head(DF, 10))"],"enabled":true,"result":{"startTime":1738093628005,"interpreter":"r.medium","endTime":1738093628156,"results":[{"message":"CUST_ID\tCOMMENTS\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tY_BOX_GAMES\tOCCUPATION\tHOME_THEATER_PACKAGE\tEDUCATION\n100001\tThanks a lot for my new affinity card. I love the discounts and have since started shopping at your store for everything.\t2\t3\t0\tExec.\t1\t< Bach.\n100002\tThe more times that I shop at your store, the more times I am impressed.  Don't change anything\t2\t4\t0\tProf.\t1\tBach.\n100003\tIt is a good way to attract new shoppers. After shopping at your store for more than a month, I am ready to move on though. Not enough variety\t2\t6\t0\tSales\t0\t< Bach.\n100004\tThanks but even with your discounts, your products are too expensive. Sorry.\t2\t5\t0\tSales\t1\t< Bach.\n100005\tAffinity card is a great idea. But your store is still too expensive. I am tired of your lousy junk mail.\t3\t5\t0\tCrafts\t1\tAssoc-A\n100006\tI am not going to waste my time filling up this three page form. Lousy idea.\t9+\t2\t1\tProf.\t0\t< Bach.\n100007\tThanks but even with your discounts, your products are too expensive. Sorry.\t2\t5\t0\tOther\t1\tHS-grad\n100008\tI shop your store a lot.  I love your weekly specials.\t2\t5\t0\tCrafts\t1\t< Bach.\n100009\tThank you! But I am very unhappy with all the junk mail you keep sending.\t3\t3\t1\tProf.\t0\tBach.\n100010\tI am unhappy with the service at your store. Do not consider me a loyal customer just because I use your Affinity Card\t3\t3\t1\tCrafts\t0\tHS-grad\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1738093628407,"interpreter":"md.medium","endTime":1738093628592,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738093628760,"interpreter":"md.medium","endTime":1738093628890,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification DT","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737673239577,"interpreter":"md.low","endTime":1737673240007,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification modeling to predict Target Customers using a Decision Tree Model","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737673240449,"interpreter":"md.low","endTime":1737673240876,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-a-decision-tree-model\">Classification modeling to predict Target Customers using a Decision Tree Model<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737673241322,"interpreter":"md.low","endTime":1737673241732,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-decision-tree-model.html\" target=\"_blank\">OML4R Decision Tree<\/a>"],"enabled":true,"result":{"startTime":1737673242201,"interpreter":"md.low","endTime":1737673242611,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-decision-tree-model.html\" target=\"_blank\">OML4R Decision Tree<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(\"SUP_DEM\" = \"select * from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()"],"enabled":true,"result":{"startTime":1737673243036,"interpreter":"r.low","endTime":1737673243528,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","z.show(head(SUP_DEM))"],"enabled":true,"result":{"startTime":1737673243950,"interpreter":"r.low","endTime":1737673244441,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%r","","z.show(dim(SUP_DEM))"],"enabled":true,"result":{"startTime":1737673244865,"interpreter":"r.low","endTime":1737673245414,"results":[{"message":"4500 14","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"ORE.FREQ\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%r","","z.show(ore.crosstab(~AFFINITY_CARD,SUP_DEM))"],"enabled":true,"result":{"startTime":1737673245876,"interpreter":"r.low","endTime":1737673246378,"results":[{"message":"AFFINITY_CARD\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t3428\t1\t1\n1\t1072\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"ORE.FREQ\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%r","","z.show(ore.crosstab(AFFINITY_CARD~HOUSEHOLD_SIZE,SUP_DEM))"],"enabled":true,"result":{"startTime":1737673246804,"interpreter":"r.low","endTime":1737673247282,"results":[{"message":"AFFINITY_CARD\tHOUSEHOLD_SIZE\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t1\t681\t1\t1\n0\t2\t1040\t1\t1\n0\t3\t973\t1\t1\n0\t4-5\t112\t1\t1\n0\t6-8\t146\t1\t1\n0\t9+\t476\t1\t1\n1\t1\t11\t1\t1\n1\t2\t109\t1\t1\n1\t3\t814\t1\t1\n1\t4-5\t107\t1\t1\n1\t6-8\t2\t1\t1\n1\t9+\t29\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Select a subset of the original columns","message":["%r","","DEMO_DF <- SUP_DEM[,c(\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                      \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","                      "],"enabled":true,"result":{"startTime":1737673247747,"interpreter":"r.low","endTime":1737673248216,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data (60/40 split)","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","# Create training data set","rownames(DEMO_DF) <- DEMO_DF$CUST_ID","DEMO_DF.train <- DEMO_DF[group==FALSE,]","","# Create test data set","DEMO_DF.test <- DEMO_DF[group==TRUE,]","","cat(\"\\n====== Records | Columns\\n\\nTrain Data: \",dim(DEMO_DF.train),\"\\nTest data: \",dim(DEMO_DF.test))"],"enabled":true,"result":{"startTime":1737673248655,"interpreter":"r.low","endTime":1737673249926,"results":[{"message":"\n====== Records | Columns\n\nTrain Data:  2700 13 \nTest data:  1800 13","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Build a Decision Tree Model for Predicting AFFINITY_CARD"],"enabled":true,"result":{"startTime":1737673250364,"interpreter":"md.low","endTime":1737673250792,"results":[{"message":"<h2 id=\"build-a-decision-tree-model-for-predicting-affinity_card\">Build a Decision Tree Model for Predicting AFFINITY_CARD<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Help on the ore.odmDT function","message":["%r","","?ore.odmDT"],"enabled":true,"result":{"startTime":1737673251233,"interpreter":"r.low","endTime":1737673252012,"results":[{"message":"ore.odmDT                package:OREdm                 R Documentation\n\nIn-Database Decision Tree Models\n\nDescription:\n\n     Fits a classification tree using Oracle Data Mining.\n\nUsage:\n\n     ore.odmDT(formula, \n            data, \n            auto.data.prep = TRUE,\n            cost.matrix = NULL,\n            impurity.metric = \"gini\",\n            max.depth = 7, \n            min.rec.split = 20,\n            min.pct.split = 0.1,\n            min.rec.node = 10,\n            min.pct.node = 0.05,\n            na.action = na.pass,\n            odm.settings = NULL)\n     \n     \n     ## S3 method for class 'ore.odmDT'\n     predict(object,\n       newdata,                \n       supplemental.cols = NULL,\n       type = c(\"class\",\"raw\"),\n       na.action = na.pass,\n       bestN = NULL,\n       topN.attrs = FALSE,...)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. The details of model specification are given under\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build.\n\ncost.matrix: An optional numerical square matrix that specifies the\n          costs for incorrectly predicting the target values.\n          Specifying the row and column names of the matrix is\n          required. The values of the names are possible target values:\n          the row names represent actual target values and the column\n          names represent predicted target values. The vectors of the\n          row and column names must be the same. In general, the\n          diagonal entries of the matrix are zeros. The default value\n          is \u2018NULL\u2019.\n\nimpurity.metric: Tree impurity metric \"gini\" or \"entropy\". The default\n          value is \"gini\".\n\nmax.depth: The maximum depth of the tree, from root to leaf inclusive.\n          A value in the range of 2 to 20. The default value is 7.\n\nmin.rec.split: The minimum number of cases required in a node for a\n          further split to be possible. The value must be positive. The\n          default value is 20.\n\nmin.pct.split: The minimum number of cases required in a node for a\n          further split to be possible. The value is expressed as a\n          percentage of all rows in the training data, and must be in\n          the range of 0 to 20. The default value is 0.1 (0.1 percent).\n\nmin.rec.node: The optional minimum number of cases required in a child\n          node. The value must be positive. The default value is 10.\n\nmin.pct.node: The optional minimum number of cases required in a child\n          node. The value is expressed as a percentage of the rows in\n          the training data, and must be in the range of 0 to 10. The\n          default value is 0.05 (0.05 percent).\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\n  object: An object of type \u2018ore.odmDT\u2019.\n\n newdata: The data used for scoring.\n\nsupplemental.cols: The columns from \u2018newdata\u2019 to include as the columns\n          in the \u2018ore.frame\u2019 prediction result.\n\n    type: If set to \u2018\"raw\"\u2019, provides probability for each class\n          returned. If set to \u2018\"class\"\u2019, the class with the maximum\n          probability is returned. The default value is\n          \u2018c(\"class\",\"raw\")\u2019.\n\n   bestN: A positive integer that restricts the returned target classes\n          to the specified number of those that have the highest\n          probability.\n\ntopN.attrs: boolean, positive integer, False (default).  Returns the\n          top N most influence attributes of the predicted target value\n          for regression if topN_attrs is not False.  Returns the top N\n          most influence attributes of the highest probability class\n          for classification if topN_attrs is not False.  N is equal to\n          the specified positive integer or 5 if topN_attrs is True.\n\n     ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n     The Decision Tree algorithm can be used for both binary and\n     multiclass classification problems.  The tree structure, created\n     in the model build, is used for a series of simple tests. Each\n     test is based on a single predictor. It is a membership test:\n     either IN or NOT IN a list of values (categorical predictor); or\n     LESS THAN or EQUAL TO some value (numeric predictor).\n\n     The \u2018formula\u2019 specification has the form \u2018response ~ terms\u2019 where\n     \u2018response\u2019 is the numeric or character response vector and \u2018terms\u2019\n     is a series of terms, for example, the column names, to include in\n     the model. Multiple terms are specified using \u2018+\u2019 between column\n     names. Use \u2018response ~ .\u2019 if all columns in \u2018data\u2019 should be used\n     for model building. Functions can be applied to \u2018response\u2019 and\n     \u2018terms\u2019 to realize transformations. To exclude columns, use \u2018-\u2019\n     before each column name to exclude.\n\n     The function \u2018predict\u2019 computes predictions based on the input\n     data and model. Results are specified in the section on Value.\n\nValue:\n\n     The function \u2018ore.odmDT\u2019 returns an object of class \u2018ore.odmDT\u2019,\n     which includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of attributes used to build the model. The\n          columns include: name, type (numerical or categorical), data\n          type, data length (size), precision and scale for numeric\n          data, and whether the variable is the target.\n\ndistributions: The target class distributions at each tree node.\n\n   nodes: The node summary information. See \u2018summary.ore.odmDT\u2019.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\n    call: The matched call.\n     The function \u2018predict\u2019 returns an \u2018ore.frame\u2019 with columns\n     according to the \u2018type\u2019 and \u2018supplemental.cols\u2019 parameters. If\n     \u2018type\u2019 is \u2018\"class\"\u2019, the result includes the most likely target\n     class and its probability. If \u2018type\u2019 is \u2018\"raw\"\u2019, the result\n     includes one column for each target class and the column values\n     reflect the probability for that class. Both can be specified\n     together. If \u2018supplemental.cols\u2019 are specified, the named columns\n     are included in the result.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmSVM\u2019, \u2018ore.odmGLM\u2019, \u2018ore.odmNB\u2019, \u2018partitions\u2019\n\nExamples:\n\n       m <- mtcars\n       m$gear <- as.factor(m$gear)\n       m$cyl  <- as.factor(m$cyl)\n       m$vs   <- as.factor(m$vs)\n       m$ID   <- 1:nrow(m)\n       MTCARS <- ore.push(m)\n       row.names(MTCARS) <- MTCARS\n     \n       dt.mod  <- ore.odmDT(gear ~ ., MTCARS)\n       summary(dt.mod)\n     \n       dt.res  <- predict (dt.mod, MTCARS,\"gear\")\n       with(dt.res, table(gear,PREDICTION))  # generate confusion matrix\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree Model using default settings","message":["%r","","ore.drop(model = 'DT_CLASSIFICATION_MODEL')","","MOD <- ore.odmDT(AFFINITY_CARD~., DEMO_DF.train, odm.settings=list(model_name=\"DT_CLASSIFICATION_MODEL\"))    ","","RES <- predict(MOD, DEMO_DF.test, ","               type= c(\"raw\",\"class\"), norm.votes=TRUE, cache.model=TRUE, ","               supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"EDUCATION\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"YRS_RESIDENCE\")) ","","colnames(RES) <- c( 'PROB_OF_0', 'PROB_OF_1', 'CUST_ID', 'AFFINITY_CARD', 'EDUCATION',","                    'HOUSEHOLD_SIZE', 'OCCUPATION', 'YRS_RESIDENCE', 'PREDICTION')"],"enabled":true,"result":{"startTime":1737673252443,"interpreter":"r.low","endTime":1737673254297,"results":[{"message":"Warning message:\n\u201cModel DT_CLASSIFICATION_MODEL does not exist on database.\u201d\nWarning message:\n\u201cThe created mining model DT_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix","message":["%r","","CMATRIX <- with(RES, table(AFFINITY_CARD,PREDICTION))   ","CMATRIX"],"enabled":true,"result":{"startTime":1737673254730,"interpreter":"r.low","endTime":1737673255545,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 1273   99\n            1  211  217","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%r","","ACCURACY <- CMATRIX/sum(CMATRIX)","round(sum(diag(ACCURACY)),3)"],"enabled":true,"result":{"startTime":1737673255968,"interpreter":"r.low","endTime":1737673256437,"results":[{"message":"0.828","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Decision Tree ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-decision-tree-model.html\" target=\"_blank\">Decision Tree<\/a> ","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' = '<row_weight_column_name>'","   ","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_DELETE_ROW'","","* Specify Tree impurity metric for Decision Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> 'TREE_IMPURITY_METRIC' = 'TREE_IMPURITY_GINI'","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> 'TREE_TERM_MAX_DEPTH' = 7","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> 'TREE_TERM_MINPCT_NODE' = 0.05","    ","* Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> 'TREE_TERM_MINPCT_SPLIT' = 0.1","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ","> 'TREE_TERM_MINREC_NODE' = 10","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ",">  'TREE_TERM_MINREC_SPLIT' = 20","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.",". 'CLAS_MAX_SUP_BINS' = 32"],"enabled":true,"result":{"startTime":1737673256873,"interpreter":"md.low","endTime":1737673257308,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-decision-tree\">Examples of possible setting overrides for Decision Tree<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-decision-tree-model.html\" target=\"_blank\">Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify Tree impurity metric for Decision Tree.\nTree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_IMPURITY_METRIC' = 'TREE_IMPURITY_GINI'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\nFor Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MAX_DEPTH' = 7<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\nIt requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINPCT_NODE' = 0.05<\/p>\n<\/blockquote>\n<ul>\n<li>Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\nIt requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINPCT_SPLIT' = 0.1<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node.\nIt requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINREC_NODE' = 10<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\nNo split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINREC_SPLIT' = 20<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute.\nFor Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.\n. 'CLAS_MAX_SUP_BINS' = 32<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction results","message":["%r","","z.show(ore.sort(RES[(RES$\"PROB_OF_1\" > 0.5),], by = 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as.data.frame(sensitivity2$PROB_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DENSITY CHART","dx <- density(df$PROB_OF_0)","dx2 <- density(df$PROB_OF_1)","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = 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6ahSxB1bu48diixVIseh/omsba/H2uH7j2rtb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\ncOj0Tv2ydfyfW0pKive9LIfDEc+slSh+05Bkqquro2q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Use SQL interface for prediction details"],"enabled":true,"result":{"startTime":1737673260473,"interpreter":"md.low","endTime":1737673260926,"results":[{"message":"<h3 id=\"use-sql-interface-for-prediction-details\">Use SQL interface for prediction details<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table = \"DT_TEST_TABLE\")","ore.create(DEMO_DF, table =\"DT_TEST_TABLE\")"],"enabled":true,"result":{"startTime":1737673261365,"interpreter":"r.low","endTime":1737673262321,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface so score data and display prediction details","message":["%sql","","SELECT CUST_ID,","    round(PRED_AFFINITY_CARD,3) PREDICTION_AFFINITY_CARD,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(DT_CLASSIFICATION_MODEL USING *) PRED_AFFINITY_CARD,","             PREDICTION_DETAILS(DT_CLASSIFICATION_MODEL USING *) PD","      FROM DT_TEST_TABLE","      WHERE rownum < 20","      ORDER BY CUST_ID) OUT,","               XMLTABLE('/Details'","                        PASSING OUT.PD","                        COLUMNS ","                        \"Attribute1\" XMLType PATH 'Attribute[1]',","                        \"Attribute2\" XMLType PATH 'Attribute[2]')  OUTPRED","                        "],"enabled":true,"result":{"startTime":1737673265060,"interpreter":"sql.low","endTime":1737673265587,"results":[{"message":"CUST_ID\tPREDICTION_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100100\t1\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".24\" \t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".224\" \n100200\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".18\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".03\" \n100300\t1\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".24\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".224\" \n100400\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"6-8\" weight=\".18\" \t\"EDUCATION\" actualValue=\"12th\" weight=\".013\" \n100500\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".18\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".03\" \n100600\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".141\" \t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".12\" \n100700\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".12\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".034\" \n100800\t1\t\"EDUCATION\" actualValue=\"Bach.\" weight=\".266\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".224\" \n100900\t1\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".24\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".224\" \n101000\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".12\" \t\"EDUCATION\" actualValue=\"7th-8th\" weight=\".034\" \n101100\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".18\" \t\"EDUCATION\" actualValue=\"Assoc-V\" weight=\".013\" \n101200\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".18\" \t\"YRS_RESIDENCE\" actualValue=\"1\" weight=\".03\" \n101300\t0\t\"OCCUPATION\" actualValue=\"TechSup\" weight=\".12\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".034\" \n101400\t0\t\"OCCUPATION\" actualValue=\"Machine\" weight=\".12\" \t\"EDUCATION\" actualValue=\"9th\" weight=\".034\" \n101500\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".18\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".03\" \n101600\t1\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".24\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".224\" \n101700\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".18\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".03\" \n101800\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".12\" \t\"EDUCATION\" actualValue=\"Assoc-V\" weight=\".034\" \n101900\t0\t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".12\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".034\" 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DEMO_DF[group==TRUE,]"],"enabled":true,"result":{"startTime":1737673275466,"interpreter":"r.low","endTime":1737673277071,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1737673277699,"interpreter":"md.low","endTime":1737673278121,"results":[{"message":"<h2 id=\"end-of-script\">End of 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification GLM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737738310779,"interpreter":"md.low","endTime":1737738311280,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Generalized Linear Model","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.","","This notebook builds and applies a classification generalized linear model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737738311744,"interpreter":"md.low","endTime":1737738312205,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-generalized-linear-model\">Classification Modeling to Predict Target Customers using Generalized Linear Model<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification generalized linear model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737738312683,"interpreter":"md.low","endTime":1737738313159,"results":[{"message":"<dl>\n<img 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Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-5E59530F-EBD9-414E-8C8B-63F8079772CE\" target=\"_blank\">OML4R Generalized Linear Model<\/a>"],"enabled":true,"result":{"startTime":1737738313653,"interpreter":"md.low","endTime":1737738314117,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-5E59530F-EBD9-414E-8C8B-63F8079772CE\" target=\"_blank\">OML4R Generalized Linear Model<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display 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Build a Logistic Regression model to predict AFFINITY_CARD","<hr>"],"enabled":true,"result":{"startTime":1737738321743,"interpreter":"md.low","endTime":1737738322225,"results":[{"message":"<h3 id=\"build-a-logistic-regression-model-to-predict-affinity_card\">Build a Logistic Regression model to predict AFFINITY_CARD<\/h3>\n<hr>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Logistic Regression Model using default settings","message":["%r","","GLM_MOD <- ore.odmGLM(AFFINITY_CARD ~., DEMO_DF,","                      type=\"logistic\")","summary(GLM_MOD)"],"enabled":true,"result":{"startTime":1737738322826,"interpreter":"r.low","endTime":1737738325447,"results":[{"message":"\nResponse:\nAFFINITY_CARD == \"1\"\n\nCall:\nore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF, type = \"logistic\")\n\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-2.31151  -0.54500  -0.21471  -0.00787   3.11538  \n\nCoefficients:\n                          Estimate Std. Error z value Pr(>|z|)\n(Intercept)             -7.874e+00         NA      NA       NA\nCUST_ID                 -2.411e-05         NA      NA       NA\nBOOKKEEPING_APPLICATION  8.477e+00         NA      NA       NA\nBULK_PACK_DISKETTES      4.414e-01         NA      NA       NA\nEDUCATION10th            8.409e+00         NA      NA       NA\nEDUCATION11th            8.477e+00         NA      NA       NA\nEDUCATION12th            7.992e+00         NA      NA       NA\nEDUCATION1st-4th        -3.118e-01         NA      NA       NA\nEDUCATION5th-6th         5.568e-01         NA      NA       NA\nEDUCATION7th-8th         7.311e+00         NA      NA       NA\nEDUCATION9th             7.779e+00         NA      NA       NA\nEDUCATION< Bach.         4.005e-01         NA      NA       NA\nEDUCATIONAssoc-A         7.060e-01         NA      NA       NA\nEDUCATIONAssoc-V         3.084e-01         NA      NA       NA\nEDUCATIONBach.           8.199e-01         NA      NA       NA\nEDUCATIONMasters         1.415e+00         NA      NA       NA\nEDUCATIONPhD             1.520e+00         NA      NA       NA\nEDUCATIONPresch.         0.000e+00         NA      NA       NA\nEDUCATIONProfsc          1.491e+00         NA      NA       NA\nFLAT_PANEL_MONITOR      -3.689e-01         NA      NA       NA\nHOME_THEATER_PACKAGE     2.287e-01         NA      NA       NA\nHOUSEHOLD_SIZE1         -2.746e+00         NA      NA       NA\nHOUSEHOLD_SIZE2         -2.090e+00         NA      NA       NA\nHOUSEHOLD_SIZE4-5        4.053e-01         NA      NA       NA\nHOUSEHOLD_SIZE6-8       -3.673e+00         NA      NA       NA\nHOUSEHOLD_SIZE9+        -2.596e+00         NA      NA       NA\nOCCUPATION?             -6.126e-01         NA      NA       NA\nOCCUPATIONArmed-F        2.319e+00         NA      NA       NA\nOCCUPATIONCleric.        3.910e-01         NA      NA       NA\nOCCUPATIONExec.          9.936e-01         NA      NA       NA\nOCCUPATIONFarming       -7.144e-01         NA      NA       NA\nOCCUPATIONHandler       -1.068e+00         NA      NA       NA\nOCCUPATIONHouse-s       -6.907e+00         NA      NA       NA\nOCCUPATIONMachine        2.258e-02         NA      NA       NA\nOCCUPATIONOther         -6.436e-01         NA      NA       NA\nOCCUPATIONProf.          9.399e-01         NA      NA       NA\nOCCUPATIONProtec.        6.187e-01         NA      NA       NA\nOCCUPATIONSales          5.465e-01         NA      NA       NA\nOCCUPATIONTechSup        6.473e-01         NA      NA       NA\nOCCUPATIONTransp.       -5.477e-03         NA      NA       NA\nOS_DOC_SET_KANJI        -7.595e+00         NA      NA       NA\nPRINTER_SUPPLIES         0.000e+00         NA      NA       NA\nYRS_RESIDENCE            1.642e-01         NA      NA       NA\nY_BOX_GAMES             -9.477e-01         NA      NA       NA\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 4941.2  on 4499  degrees of freedom\nResidual deviance: 3147.8  on 4458  degrees of freedom\nAIC: 3231.8\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Getting model predictions","message":["%r","","GLM_RES <- predict(GLM_MOD, DEMO_DF, ","                   supplemental.cols=c('AFFINITY_CARD', \"CUST_ID\"))","","colnames(GLM_RES) <- c(\"AFFINITY_CARD\",\"CUST_ID\", \"PROB_OF_1\")","","GLM_RES$PREDICTION <- 1*(GLM_RES$PROB_OF_1 >0.5)","","GLM_RES$PROB_OF_0 <- 1 - (GLM_RES$PROB_OF_1)","","z.show(head(GLM_RES))"],"enabled":true,"result":{"startTime":1737738325922,"interpreter":"r.low","endTime":1737738326544,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tPROB_OF_1\tPREDICTION\tPROB_OF_0\n0\t102547\t0.00202043617075988\t0\t0.99797956382924\n0\t101050\t0.00209452561304771\t0\t0.997905474386952\n0\t100040\t0.00751679962524646\t0\t0.992483200374754\n0\t102117\t0.00189381238680385\t0\t0.998106187613196\n0\t101074\t0.00161501291037138\t0\t0.998384987089629\n0\t104179\t0.00149871449412712\t0\t0.998501285505873\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(GLM_RES, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737738327017,"interpreter":"r.low","endTime":1737738327618,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 3160  268\n     1  456  616\n\nAccuracy:\n","type":"TEXT"},{"message":"0.8391","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-generaized-linear-model.html\" target=\"_blank\" >Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\" >All algorithms<\/a>","","- Specify a row weight column","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","- Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.","> GLMS_CONF_LEVEL = 0.95","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).","> GLMS_RIDGE_VALUE = 2","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.","> GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC","> GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.","> GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> GLMS_MAX_FEATURES = 30","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'","- Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> GLMS_REFERENCE_CLASS_NAME = '<target_value>'","- Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE","> GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> GLMS_CONV_TOLERANCE = 0.0001","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> GLMS_NUM_ITERATIONS = 200","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.","> GLMS_BATCH_ROWS = 2000","- Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)","> GLMS_SOLVER = 'GLMS_SOLVER_SGD'","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.","> GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'","","<strong> Available starting with Oracle Database 23ai <\/strong>","- Specify the `link` function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK`, `GLMS_CAUCHIT_LINK` .  For regression, only one option is available: `GLMS_IDENTITY_LINK (default)`.","> GLMS_LINK_FUNCTION = 'GLMS_LOGIT_LINK'"],"enabled":true,"result":{"startTime":1737738328108,"interpreter":"md.low","endTime":1737738328642,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-generaized-linear-model.html\" target=\"_blank\" >Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\" >All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONF_LEVEL = 0.95<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_VALUE = 2<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_MAX_FEATURES = 30<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_REFERENCE_CLASS_NAME = '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONV_TOLERANCE = 0.0001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_NUM_ITERATIONS = 200<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_BATCH_ROWS = 2000<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SOLVER = 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> Available starting with Oracle Database 23ai <\/strong><\/p>\n<ul>\n<li>Specify the <code>link<\/code> function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code>, <code>GLMS_CAUCHIT_LINK<\/code> .  For regression, only one option is available: <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_LINK_FUNCTION = 'GLMS_LOGIT_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Logistic Regression Model using ridge regression","message":["%r","","GLM_MOD2 <- ore.odmGLM(AFFINITY_CARD~.,DEMO_DF, type=\"logistic\", ","                       ridge = TRUE, ridge.value = 2)","summary(GLM_MOD2)"],"enabled":true,"result":{"startTime":1737738329124,"interpreter":"r.low","endTime":1737738332044,"results":[{"message":"\nResponse:\nAFFINITY_CARD == \"1\"\n\nCall:\nore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF, type = \"logistic\", \n    ridge = TRUE, ridge.value = 2)\n\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-2.29669  -0.56798  -0.24545  -0.07685   3.09395  \n\nCoefficients:\n                        Estimate\n(Intercept)               -0.201\nCUST_ID                    0.000\nBOOKKEEPING_APPLICATION    0.715\nBULK_PACK_DISKETTES        0.350\nEDUCATION10th              0.462\nEDUCATION11th              0.502\nEDUCATION12th              0.089\nEDUCATION1st-4th          -0.587\nEDUCATION5th-6th          -0.635\nEDUCATION7th-8th          -0.364\nEDUCATION9th              -0.053\nEDUCATION< Bach.           0.330\nEDUCATIONAssoc-A           0.604\nEDUCATIONAssoc-V           0.225\nEDUCATIONBach.             0.741\nEDUCATIONMasters           1.233\nEDUCATIONPhD               1.133\nEDUCATIONPresch.          -0.129\nEDUCATIONProfsc            1.236\nFLAT_PANEL_MONITOR        -0.288\nHOME_THEATER_PACKAGE       0.222\nHOUSEHOLD_SIZE1           -2.294\nHOUSEHOLD_SIZE2           -1.965\nHOUSEHOLD_SIZE4-5          0.440\nHOUSEHOLD_SIZE6-8         -2.245\nHOUSEHOLD_SIZE9+          -2.330\nOCCUPATION?               -0.540\nOCCUPATIONArmed-F          0.157\nOCCUPATIONCleric.          0.298\nOCCUPATIONExec.            0.949\nOCCUPATIONFarming         -0.634\nOCCUPATIONHandler         -0.854\nOCCUPATIONHouse-s         -0.305\nOCCUPATIONMachine         -0.028\nOCCUPATIONOther           -0.669\nOCCUPATIONProf.            0.922\nOCCUPATIONProtec.          0.530\nOCCUPATIONSales            0.497\nOCCUPATIONTechSup          0.547\nOCCUPATIONTransp.         -0.047\nOS_DOC_SET_KANJI          -0.321\nPRINTER_SUPPLIES           0.000\nYRS_RESIDENCE              0.170\nY_BOX_GAMES               -0.954\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 4941.2  on 4499  degrees of freedom\nResidual deviance: 3169.2  on 4457  degrees of freedom\nAIC: 3255.2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Getting model predictions","message":["","%r","","GLM_RES2 <- predict(GLM_MOD2, DEMO_DF, ","                    supplemental.cols=c('AFFINITY_CARD', \"CUST_ID\"))","","colnames(GLM_RES2) <- c(\"AFFINITY_CARD\",\"CUST_ID\", \"PROB_OF_1\")","","GLM_RES2$PREDICTION <- 1*(GLM_RES2$PROB_OF_1 >0.5)","","GLM_RES2$PROB_OF_0 <- 1 - (GLM_RES2$PROB_OF_1)","","z.show(head(GLM_RES2))"],"enabled":true,"result":{"startTime":1737738332518,"interpreter":"r.low","endTime":1737738333256,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tPROB_OF_1\tPREDICTION\tPROB_OF_0\n0\t102547\t0.0026107353964662\t0\t0.997389264603534\n0\t101050\t0.00270163591736562\t0\t0.997298364082634\n0\t100040\t0.00917958753102508\t0\t0.990820412468975\n0\t102117\t0.0033019816874611\t0\t0.996698018312539\n0\t101074\t0.00265825832053112\t0\t0.997341741679469\n0\t104179\t0.0024760781011046\t0\t0.997523921898895\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(GLM_RES2, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737738333724,"interpreter":"r.low","endTime":1737738334304,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 3184  244\n     1  475  597\n\nAccuracy:\n","type":"TEXT"},{"message":"0.8402","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Logistic Regression Model using specific settings","message":["%r","","ore.drop(model = 'GLM_CLASSIFICATION_MODEL')","","settings <-  list(GLMS_FTR_SELECTION  = 'GLMS_FTR_SELECTION_ENABLE', ","                  GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE',  ","                  GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_CUBIC', ","                  GLMS_FTR_SEL_CRIT   = 'GLMS_FTR_SEL_AIC', ","                  GLMS_MAX_FEATURES   = 100,","                  MODEL_NAME = 'GLM_CLASSIFICATION_MODEL')","                       ","GLM_MOD3 <- ore.odmGLM(AFFINITY_CARD~.,DEMO_DF, ","                       odm.settings = settings,","                       type=\"logistic\") ","summary(GLM_MOD3)"],"enabled":true,"result":{"startTime":1737738334780,"interpreter":"r.low","endTime":1737738338491,"results":[{"message":"Warning message:\n\u201cThe created mining model GLM_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nResponse:\nAFFINITY_CARD == \"1\"\n\nCall:\nore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF, type = \"logistic\", \n    odm.settings = settings)\n\nDeviance Residuals: \n    Min       1Q   Median       3Q      Max  \n-2.5097  -0.6444  -0.2608  -0.0780   3.2420  \n\nCoefficients:\n                                            Estimate Std. Error z value\n(Intercept)                               -1.273e+00  8.059e-02 -15.798\nY_BOX_GAMES                               -1.550e+00  1.729e-01  -8.961\nCUST_ID*EDUCATION_< Bach.                  4.870e-06  1.169e-06   4.166\nEDUCATION_Bach.                            5.448e-01  1.957e-01   2.784\nBOOKKEEPING_APPLICATION*OCCUPATION_Exec.   1.578e+00  1.362e-01  11.585\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_2  -1.364e+00  2.043e-01  -6.675\nOCCUPATION_Prof.                           1.381e+00  1.601e-01   8.625\nHOME_THEATER_PACKAGE*OCCUPATION_Sales      1.052e+00  1.679e-01   6.265\nHOUSEHOLD_SIZE_4-5                         8.719e-01  2.658e-01   3.280\nHOME_THEATER_PACKAGE*OCCUPATION_Cleric.    8.947e-01  1.973e-01   4.535\nEDUCATION_Bach.*BULK_PACK_DISKETTES        5.916e-01  2.052e-01   2.882\nHOME_THEATER_PACKAGE*HOUSEHOLD_SIZE_2     -7.275e-01  2.094e-01  -3.474\nHOME_THEATER_PACKAGE*EDUCATION_Masters     1.685e+00  2.223e-01   7.579\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_9+ -2.759e+00  2.239e-01 -12.322\nYRS_RESIDENCE*HOUSEHOLD_SIZE_6-8          -1.020e+00  2.375e-01  -4.293\nBULK_PACK_DISKETTES*EDUCATION_Assoc-A      1.173e+00  2.525e-01   4.646\nHOME_THEATER_PACKAGE*OCCUPATION_TechSup    1.021e+00  3.120e-01   3.271\nOCCUPATION_Protec.                         9.889e-01  2.773e-01   3.566\nEDUCATION_Bach.*OCCUPATION_Sales           6.811e-01  2.796e-01   2.436\nHOUSEHOLD_SIZE_2*EDUCATION_Bach.          -9.713e-01  3.010e-01  -3.227\nHOUSEHOLD_SIZE_1                          -2.972e+00  3.261e-01  -9.114\nBULK_PACK_DISKETTES*HOUSEHOLD_SIZE_4-5    -1.065e+00  3.255e-01  -3.273\nEDUCATION_Profsc                          -2.121e+00  9.389e-01  -2.259\nY_BOX_GAMES*OCCUPATION_Cleric.             1.287e+00  3.495e-01   3.683\nFLAT_PANEL_MONITOR*OCCUPATION_TechSup      7.587e-01  3.849e-01   1.971\nEDUCATION_PhD                              1.731e+00  3.876e-01   4.465\nOCCUPATION_Prof.*EDUCATION_Profsc          2.135e+00  7.263e-01   2.939\nHOME_THEATER_PACKAGE*EDUCATION_Profsc      2.984e+00  8.042e-01   3.711\n                                          Pr(>|z|)    \n(Intercept)                                < 2e-16 ***\nY_BOX_GAMES                                < 2e-16 ***\nCUST_ID*EDUCATION_< Bach.                 3.10e-05 ***\nEDUCATION_Bach.                           0.005364 ** \nBOOKKEEPING_APPLICATION*OCCUPATION_Exec.   < 2e-16 ***\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_2  2.47e-11 ***\nOCCUPATION_Prof.                           < 2e-16 ***\nHOME_THEATER_PACKAGE*OCCUPATION_Sales     3.72e-10 ***\nHOUSEHOLD_SIZE_4-5                        0.001037 ** \nHOME_THEATER_PACKAGE*OCCUPATION_Cleric.   5.77e-06 ***\nEDUCATION_Bach.*BULK_PACK_DISKETTES       0.003948 ** \nHOME_THEATER_PACKAGE*HOUSEHOLD_SIZE_2     0.000512 ***\nHOME_THEATER_PACKAGE*EDUCATION_Masters    3.48e-14 ***\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_9+  < 2e-16 ***\nYRS_RESIDENCE*HOUSEHOLD_SIZE_6-8          1.77e-05 ***\nBULK_PACK_DISKETTES*EDUCATION_Assoc-A     3.39e-06 ***\nHOME_THEATER_PACKAGE*OCCUPATION_TechSup   0.001071 ** \nOCCUPATION_Protec.                        0.000362 ***\nEDUCATION_Bach.*OCCUPATION_Sales          0.014854 *  \nHOUSEHOLD_SIZE_2*EDUCATION_Bach.          0.001253 ** \nHOUSEHOLD_SIZE_1                           < 2e-16 ***\nBULK_PACK_DISKETTES*HOUSEHOLD_SIZE_4-5    0.001066 ** \nEDUCATION_Profsc                          0.023883 *  \nY_BOX_GAMES*OCCUPATION_Cleric.            0.000231 ***\nFLAT_PANEL_MONITOR*OCCUPATION_TechSup     0.048705 *  \nEDUCATION_PhD                             7.99e-06 ***\nOCCUPATION_Prof.*EDUCATION_Profsc         0.003292 ** \nHOME_THEATER_PACKAGE*EDUCATION_Profsc     0.000207 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 4941.2  on 4499  degrees of freedom\nResidual deviance: 3183.3  on 4472  degrees of freedom\nAIC: 3239.3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Getting model predictions","message":["%r","","GLM_RES3 <- predict(GLM_MOD3, DEMO_DF, ","                    supplemental.cols=c('AFFINITY_CARD', \"CUST_ID\"))","","colnames(GLM_RES3) <- c(\"AFFINITY_CARD\",\"CUST_ID\", \"PROB_OF_1\")","","GLM_RES3$PREDICTION <- 1*(GLM_RES3$PROB_OF_1 >0.5)","","GLM_RES3$PROB_OF_0 <- 1 - (GLM_RES3$PROB_OF_1)","","z.show(head(GLM_RES3))"],"enabled":true,"result":{"startTime":1737738338961,"interpreter":"r.low","endTime":1737738339952,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tPROB_OF_1\tPREDICTION\tPROB_OF_0\n0\t102547\t0.00303388213718482\t0\t0.996966117862815\n0\t101050\t0.00303388213718482\t0\t0.996966117862815\n0\t100040\t0.00303388213718482\t0\t0.996966117862815\n0\t102117\t0.00303388213718482\t0\t0.996966117862815\n0\t101074\t0.00303388213718482\t0\t0.996966117862815\n0\t104179\t0.00303388213718482\t0\t0.996966117862815\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(GLM_RES3, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737738340450,"interpreter":"r.low","endTime":1737738341024,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 3195  233\n     1  468  604\n\nAccuracy:\n","type":"TEXT"},{"message":"0.8442","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data adding all columns as supplemental and sorting by highest probability","message":["%r","","pred <- predict(GLM_MOD3, DEMO_DF, supplemental.cols=colnames(DEMO_DF))","","pred$PROB_OF_1 <- pred$PREDICTION","pred$PROB_OF_0 <- 1 - pred$PREDICTION","","pred$PREDICTION <- 1*(pred$PROB_OF_1 >0.5)","","ordered_results <- ore.sort(pred, ","                              by = c('PROB_OF_1'), ","                              reverse = TRUE)","z.show(head(ordered_results,n=20))"],"enabled":true,"result":{"startTime":1737738341488,"interpreter":"r.low","endTime":1737738342457,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROB_OF_1\tPROB_OF_0\n100442\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.957118849501696\t0.0428811504983037\n100601\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n102037\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n102025\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n101990\t0\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n101935\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t6\t0\t1\t0.957118849501696\t0.0428811504983037\n101863\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t9\t0\t1\t0.957118849501696\t0.0428811504983037\n101697\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.957118849501696\t0.0428811504983037\n101581\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.957118849501696\t0.0428811504983037\n101570\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n101560\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.957118849501696\t0.0428811504983037\n101526\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.957118849501696\t0.0428811504983037\n101426\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n101189\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t6\t0\t1\t0.957118849501696\t0.0428811504983037\n101145\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.957118849501696\t0.0428811504983037\n101086\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.957118849501696\t0.0428811504983037\n101049\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\t1\t0.957118849501696\t0.0428811504983037\n100962\t1\t1\t1\tProfsc\t0\t1\t3\tProf.\t0\t1\t11\t0\t1\t0.957118849501696\t0.0428811504983037\n100607\t1\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.957118849501696\t0.0428811504983037\n102189\t1\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\t1\t0.957118849501696\t0.0428811504983037\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Graph ROC curve, lift, and probability densities","message":["%r","","# Pull Data locally","df <- ore.pull(pred)","","# ROC Curve","sensitivity <-df[order(df$PROB_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0) - cumsum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0))","spec <- cumsum(sensitivity$PROB_OF_1)/sum(sensitivity$PROB_OF_1)","","# LIFT CHART","sensitivity2 <- df[order(df$PROB_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROB_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROB_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DENSITY CHART","dx <- density(df$PROB_OF_0)","dx2 <- density(df$PROB_OF_1)","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE) "],"enabled":true,"result":{"startTime":1737738342951,"interpreter":"r.low","endTime":1737738343582,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAFoCAIAAAAXZAVmAAAABmJLR0QA/wD/AP+gvaeTAAAg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doakkLpc7evToiIiIOXPmBAYGamho0B0RQgg1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\nAAAAAFuqvn+nZmfjZfVhw4i9eGZxptctL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the test data into a table","message":["%r","","ore.drop(\"DEMO_DF_GLM\", silent=TRUE)","","ore.create(DEMO_DF, \"DEMO_DF_GLM\")"],"enabled":true,"result":{"startTime":1737738344062,"interpreter":"r.low","endTime":1737738344962,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use SQL interface to score data and display prediction details order by highest probabilities","message":["%sql","","SELECT CUST_ID,","       round(PROB_AFFINITY_CARD,3) PROBABILITY_AFFINITY_CARD,","       PRED_AFFINITY_CARD PREDICTION_AFFINITY_CARD,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","      PREDICTION(GLM_CLASSIFICATION_MODEL USING *) PRED_AFFINITY_CARD,","      PREDICTION_PROBABILITY(GLM_CLASSIFICATION_MODEL, 1 USING *) PROB_AFFINITY_CARD,","      PREDICTION_DETAILS(GLM_CLASSIFICATION_MODEL USING *) PD","      FROM DEMO_DF_GLM","      ORDER BY PROB_AFFINITY_CARD DESC) OUT,","XMLTABLE('/Details'","         PASSING OUT.PD","         COLUMNS","         \"Attribute1\" XMLType PATH 'Attribute[1]',","         \"Attribute2\" XMLType PATH 'Attribute[2]',","         \"Attribute3\" XMLType PATH 'Attribute[3]')","OUTPRED","FETCH FIRST 50 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737738345434,"interpreter":"sql.low","endTime":1737738346014,"results":[{"message":"CUST_ID\tPROBABILITY_AFFINITY_CARD\tPREDICTION_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100442\t0.957\t1\t\"OCCUPATION\" actualValue=\"Prof.\" 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diff --git a/machine-learning/notebooks-oml/r/OML4R Classification NB.dsnb b/machine-learning/notebooks-oml/r/OML4R Classification NB.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification NB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737672561305,"interpreter":"md.low","endTime":1737672561770,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Predicting Target Customers using Classification using Naive Bayes","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.","","This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","","[The Universal Permissive License (UPL), Version 1.0](https://gcc59e2cf7a6f5f-oml2.adb-compdev1.us-phoenix-1.oraclecloudapps.com/oml/zep/%20//oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1737672562272,"interpreter":"md.low","endTime":1737672562720,"results":[{"message":"<h1 id=\"predicting-target-customers-using-classification-using-naive-bayes\">Predicting Target Customers using Classification using Naive Bayes<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. 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target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-naive-bayes-model.html\" target=\"_blank\">OML4R Naive Bayes<\/a>"],"enabled":true,"result":{"startTime":1737672564135,"interpreter":"md.low","endTime":1737672564607,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-naive-bayes\" 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responders","message":["%r","","z.show(ore.crosstab(AFFINITY_CARD~HOUSEHOLD_SIZE,DEMO))"],"enabled":true,"result":{"startTime":1737672569132,"interpreter":"r.low","endTime":1737672569653,"results":[{"message":"AFFINITY_CARD\tHOUSEHOLD_SIZE\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t1\t681\t1\t1\n0\t2\t1040\t1\t1\n0\t3\t973\t1\t1\n0\t4-5\t112\t1\t1\n0\t6-8\t146\t1\t1\n0\t9+\t476\t1\t1\n1\t1\t11\t1\t1\n1\t2\t109\t1\t1\n1\t3\t814\t1\t1\n1\t4-5\t107\t1\t1\n1\t6-8\t2\t1\t1\n1\t9+\t29\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Select desired columns for model building","message":["%r","","DEMO_DF <- DEMO[,c(\"CUST_ID\", \"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                   \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","","rownames(DEMO_DF) <- (DEMO_DF$CUST_ID)"],"enabled":true,"result":{"startTime":1737672570132,"interpreter":"r.low","endTime":1737672570897,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first few records of DEMO_DF","message":["%r","","z.show(head(DEMO_DF))"],"enabled":true,"result":{"startTime":1737672571354,"interpreter":"r.low","endTime":1737672571991,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n100001\t0\t1\t0\t< Bach.\t0\t1\t2\tExec.\t0\t1\t3\t0\n100002\t0\t1\t1\tBach.\t1\t1\t2\tProf.\t0\t1\t4\t0\n100003\t0\t1\t1\t< Bach.\t1\t0\t2\tSales\t0\t1\t6\t0\n100004\t0\t1\t1\t< Bach.\t1\t1\t2\tSales\t0\t1\t5\t0\n100005\t1\t1\t0\tAssoc-A\t0\t1\t3\tCrafts\t0\t1\t5\t0\n100006\t0\t1\t0\t< Bach.\t0\t0\t9+\tProf.\t0\t1\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into train and test sets (60/40)","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","# Create training data set","DEMO_DF.train <- DEMO_DF[group==FALSE,]","","# Create test data set","DEMO_DF.test <- DEMO_DF[group==TRUE,]","","cat(\"\\n====== Records | Columns\\n\\nTrain Data: \",dim(DEMO_DF.train),\"\\nTest data: \",dim(DEMO_DF.test))"],"enabled":true,"result":{"startTime":1737672572462,"interpreter":"r.low","endTime":1737672573593,"results":[{"message":"\n====== Records | Columns\n\nTrain Data:  2700 13 \nTest data:  1800 13","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build a Naive Bayes model to predict AFFINITY_CARD","<hr>"],"enabled":true,"result":{"startTime":1737672574062,"interpreter":"md.low","endTime":1737672574512,"results":[{"message":"<h3 id=\"build-a-naive-bayes-model-to-predict-affinity_card\">Build a Naive Bayes model to predict AFFINITY_CARD<\/h3>\n<hr>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model using default settings","message":["%r","","nb_mod <- ore.odmNB(AFFINITY_CARD ~., DEMO_DF.train)","summary(nb_mod)"],"enabled":true,"result":{"startTime":1737672574984,"interpreter":"r.low","endTime":1737672576713,"results":[{"message":"\nCall:\nore.odmNB(formula = AFFINITY_CARD ~ ., data = DEMO_DF.train)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nApriori: \n        0         1 \n0.7614815 0.2385185 \n\nTables: \n$BOOKKEEPING_APPLICATION\n    ( ; .5]   (.5;  )\n0 0.1473735 0.8526265\n1 0.0326087 0.9673913\n\n$EDUCATION\n  '10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', 'Presch.'\n0                                                                0.14737354\n1                                                                0.03260870\n  '< Bach.', 'Assoc-A', 'Assoc-V', 'HS-grad'    'Bach.'\n0                                 0.67558366 0.13813230\n1                                 0.50155280 0.26708075\n  'Masters', 'PhD', 'Profsc'\n0                 0.03891051\n1                 0.19875776\n\n$HOME_THEATER_PACKAGE\n    ( ; .5]   (.5;  )\n0 0.5155642 0.4844358\n1 0.1832298 0.8167702\n\n$HOUSEHOLD_SIZE\n  '1', '6-8'  '2', '9+' '3', '4-5'\n0 0.24562257 0.43968872 0.31468872\n1 0.01397516 0.12267081 0.86335404\n\n$OCCUPATION\n  '?', 'Cleric.', 'Farming', 'Handler', 'House-s', 'Other'\n0                                                0.4061284\n1                                                0.1366460\n  'Crafts', 'Machine', 'Sales', 'TechSup', 'Transp.'\n0                                          0.4056420\n1                                          0.3571429\n  'Exec.', 'Prof.', 'Protec.'\n0                   0.1882296\n1                   0.5062112\n\n$YRS_RESIDENCE\n    ( ; 2.5] (2.5; 3.5] (3.5; 6.5]   (6.5;  )\n0 0.26702335 0.25000000 0.41926070 0.06371595\n1 0.02173913 0.11180124 0.68167702 0.18478261\n\n$Y_BOX_GAMES\n     ( ; .5]    (.5;  )\n0 0.60846304 0.39153696\n1 0.95031056 0.04968944\n\n\nLevels: \n[1] \"0\" \"1\"\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data using model proxy object returning predictions and class probabilities","message":["%r","","nb_res <- predict(nb_mod, DEMO_DF.test, c('AFFINITY_CARD', \"CUST_ID\"))","","z.show(head(nb_res, 10))"],"enabled":true,"result":{"startTime":1737672577211,"interpreter":"r.low","endTime":1737672578233,"results":[{"message":"X.0.\tX.1.\tAFFINITY_CARD\tCUST_ID\tPREDICTION\n0.810949132919523\t0.189050867080477\t0\t100001\t0\n0.311778159206504\t0.688221840793496\t0\t100002\t1\n0.782792530320276\t0.217207469679724\t0\t100004\t0\n0.907224899426563\t0.0927751005734371\t1\t100009\t0\n0.999642796119885\t0.00035720388011539\t0\t100011\t0\n0.904127874316971\t0.0958721256830293\t0\t100017\t0\n0.995111585869023\t0.00488841413097739\t0\t100018\t0\n0.996022921801772\t0.0039770781982284\t0\t100025\t0\n0.990888839545903\t0.0091111604540966\t0\t100027\t0\n0.871552932090672\t0.128447067909328\t0\t100031\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Show model accuracy on data","message":["%r","","","confusion_matrix <- with(nb_res, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737672578709,"interpreter":"r.low","endTime":1737672579454,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1095  277\n     1  103  325\n\nAccuracy:\n","type":"TEXT"},{"message":"0.7889","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for NB","message":["%md","### Examples of setting overrides for Naive Bayes","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-naive-bayes-model.html\" target=\"_blank\">Algorithm Settings<\/a>","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a>","","* Enable Sampling, this allows the use to request sampling of the build data","> ODMS_SAMPLING = 'ODMS_SAMPLING_DISABLE'","","* Specify the value of pairwise threshold for the Naive Bayes algorithm","The value required is between 0 and 1 (including the edges), and the default value is 0.","> NABS_PAIRWISE_THRESHOLD = 0","* Specify the value of a singleton threshold for the NB algorithm","The value required is between 0 and 1 (including the edges), and the default value is 0.","> NABS_SINGLETON_THRESHOLD = 0"],"enabled":true,"result":{"startTime":1737672579935,"interpreter":"md.low","endTime":1737672580376,"results":[{"message":"<h3 id=\"examples-of-setting-overrides-for-naive-bayes\">Examples of setting overrides for Naive Bayes<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-naive-bayes-model.html\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Enable Sampling, this allows the use to request sampling of the build data<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_SAMPLING = 'ODMS_SAMPLING_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of pairwise threshold for the Naive Bayes algorithm\nThe value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>NABS_PAIRWISE_THRESHOLD = 0<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of a singleton threshold for the NB algorithm\nThe value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>NABS_SINGLETON_THRESHOLD = 0<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes Model using explicit settings","message":["%r","","settings <- list(NABS_PAIRWISE_THRESHOLD = 0, NABS_SINGLETON_THRESHOLD = 0)","nb_mod2 <- ore.odmNB(AFFINITY_CARD ~., DEMO_DF.train, odm.settings = settings)","summary(nb_mod2)"],"enabled":true,"result":{"startTime":1737672580868,"interpreter":"r.low","endTime":1737672582370,"results":[{"message":"\nCall:\nore.odmNB(formula = AFFINITY_CARD ~ ., data = DEMO_DF.train, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nApriori: \n        0         1 \n0.7614815 0.2385185 \n\nTables: \n$BOOKKEEPING_APPLICATION\n    ( ; .5]   (.5;  )\n0 0.1473735 0.8526265\n1 0.0326087 0.9673913\n\n$EDUCATION\n  '10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', 'Presch.'\n0                                                                0.14737354\n1                                                                0.03260870\n  '< Bach.', 'Assoc-A', 'Assoc-V', 'HS-grad'    'Bach.'\n0                                 0.67558366 0.13813230\n1                                 0.50155280 0.26708075\n  'Masters', 'PhD', 'Profsc'\n0                 0.03891051\n1                 0.19875776\n\n$HOME_THEATER_PACKAGE\n    ( ; .5]   (.5;  )\n0 0.5155642 0.4844358\n1 0.1832298 0.8167702\n\n$HOUSEHOLD_SIZE\n  '1', '6-8'  '2', '9+' '3', '4-5'\n0 0.24562257 0.43968872 0.31468872\n1 0.01397516 0.12267081 0.86335404\n\n$OCCUPATION\n  '?', 'Cleric.', 'Farming', 'Handler', 'House-s', 'Other'\n0                                                0.4061284\n1                                                0.1366460\n  'Crafts', 'Machine', 'Sales', 'TechSup', 'Transp.'\n0                                          0.4056420\n1                                          0.3571429\n  'Exec.', 'Prof.', 'Protec.'\n0                   0.1882296\n1                   0.5062112\n\n$YRS_RESIDENCE\n    ( ; 2.5] (2.5; 3.5] (3.5; 6.5]   (6.5;  )\n0 0.26702335 0.25000000 0.41926070 0.06371595\n1 0.02173913 0.11180124 0.68167702 0.18478261\n\n$Y_BOX_GAMES\n     ( ; .5]    (.5;  )\n0 0.60846304 0.39153696\n1 0.95031056 0.04968944\n\n\nLevels: \n[1] \"0\" \"1\"\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","nb_res <- predict(nb_mod2, DEMO_DF.test, c('AFFINITY_CARD', \"CUST_ID\"))","confusion_matrix <- with(nb_res, table(AFFINITY_CARD, PREDICTION))","colnames(nb_res) <- c( 'PROB_OF_0', 'PROB_OF_1', 'AFFINITY_CARD', 'CUST_ID' ,'PREDICTION')","res <- ore.push(nb_res)","","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\", round(sum(diag(accuracy)),4))"],"enabled":true,"result":{"startTime":1737672582941,"interpreter":"r.low","endTime":1737672583984,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 1095  277\n            1  103  325\n\nAccuracy: 0.7889","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Display ROC Curve, Lift Chart and Probability Density","message":["%r","","df <- ore.pull(res)","","sensitivity <-df[order(df$PROB_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0) - cumsum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0))","spec <- cumsum(sensitivity$PROB_OF_1)/sum(sensitivity$PROB_OF_1)","","# LIFT CHART","sensitivity2 <- df[order(df$PROB_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROB_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROB_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DISTRIBUTION CHART","dx <- density(df$PROB_OF_0)","dx2 <- density(df$PROB_OF_1)","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE) 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EGiFk4uSEE2iYR6hQWaUfPp84WjiKOZNEjkDz+LxSTqmlodq/a9OwDjRSjXUo2ZmZL0+d\n6jJypGtgoDKvC+RHEMS6deu2bdvm5eUVHR0N2TMAAEiFwWAYGRkZGRmpbPaMEKqWsoQDiZRB\nwwi0QpEj0JaGloa6hpLchRyBRgjls/MVEpZy0TACrSLrUCb9/DMhEHhv3qycywGqEASxZs2a\nH3/8cciQIX/++acRfHsAAADyET+JX3GrYIkh5PNrSxqrbCVfYRYSaOUgR6AlHH5GCNmYfCiz\nzK/M72vfl/qwlIuGBFoV1qFs4HJfnjpl4e7eZfhwJVwOUEUgECxZsuTkyZPDhg2LiopisVh0\nRwQAAGpP/CR+Ra2CJVZNQQEhFOK25CUcZAJd9fYtIRQyJFgkF8hAhgTaWM+YpcPi8DiovSzE\nQUMCrQrrUKadO1dXUTHk++8lWRkHqAiBQLBw4cL//e9//v7+V65c0dPTozsiAABoD8RP4lfQ\nKljikTMIkVQj0O/3UhHweJy8PEn2LwQykCGBRgh1Nu6cUZyBoIRDZqqwDuWLU6eYLFbPoCDl\nXA7Ir76+fvbs2VeuXJkwYcKlS5d0dHTojggAADoERayC1SY8FxCToYQDIVSZlQUJtCIQBJFd\nlo3b0iXQJu8T6EpIoGVC+zqUlVlZ+Q8f9pwzR9tQosp3QDsej/fJJ5/88ccfn3zyyW+//cZk\nMumOSNUJBIIXL154eHgghFJSUk6ePOns7Lx06VK6JgyJqaEUvv+WFgCgBLm5uTExMcbGxmPG\njBEdPw4JCRE/Dq1k5AxCHRMTpsTj3Ia2tpra2oL6eoRQZXa23dChioqvAyuuLq7h1eC2hGvY\nYZ2NGz8IFbALxJ+pFmhIoGlfhzL13DlEEG6zZyvhWkB+XC538uTJ0dHRc+bM+fXXX7W0OvTa\nixLau3cvm8328PCora0dPXq0ra3tmTNnSkpKvv32W1riEVNDmZycrORgAOiwnjx5Mnr0aA6H\nIxAI7O3tL1++PGDAAHzTtm3bVCqBJkegWdKs8c/Q1DS0t2dnZiKYR6gwMqxhh5HzCGEEWnb0\nrkOZeu6cvpWVw+jRSrsikFlNTc3EiRNv3779+eef//zzzxowI0Qyv/zyS2xsLEIoNjbWwsLi\n0aNHiYmJ06dPpyuBFlND6e7uTktIAHRA33zzTWBg4I8//lhRUbF69epRo0Zdv359yJAhdMfV\ngprCQtyQvH4DM3ZyggRaocgEWlND095UiiIZGIGmDF6HUskXLXn2rCwlxXP5cg0YyFR5bDbb\n398/Pj5++fLlP/30kyqvV6pq8vPzcX3U7du3cfGim5tb4fs3JOUTU0MJH4oAUJrHjx+fPXtW\nR0fH2to6PDw8ODjY39//+vXrPj4+dIfW1IdtCKVNoLt2xQ1IoBWETKBtTWy1tbQlvyOZQBdV\nFQmEAk0NTeqDU6IO99aVeu4cQgjqN1RfRUWFn59ffHz8hg0bDh48CNmzVFxdXU+ePJmTkxMe\nHu7n54cQSk9Pd3V1pTsuAACdmEymQCAgf9y5c+eXX345bty4+/fv0xhVi8gSDqlHoGEpaAXL\nLs3GDanqN5BICQdfyC/llFIblfJ1rASaEArTLlwwdnS0+fhjumMB4hQVFQ0bNuzx48chISGh\noaF0h6N+duzYsX79egcHh379+uGxpUOHDn3xxRd0xwUAoNPw4cPj4uJEj2zbtu2rr77y9/en\nK6TWfCjhsLaW6o5kAs3Jy8OzCQG1yCU4ulp0leqO1kYf/ikLKtW+iqNjJdD5Dx9W5eT0mDkT\nln9WZQUFBSNHjnz58uXevXu3bt1KdzhqycnJqbi4ODMzMyIiAg/eL1y4cNCgQXTHBQCg0/r1\n60+fPt3k4NatWzdu3KhSa4MSAgH3/TaEUpdwvE+gCaGwOieH4siASAlHV/OuUt1RdDNCSKDV\nTPrlywgh18BAugMBrXr79q2Pj09qauqBAwe+/PJLusNRV25ubiwWy8nJiSx98fb27tWrF71R\nAQDo5eXlFRUV1fz4N99802SOL71qioqI96UmBtKswoGaLQVNZVgAISEhzClv/Fgi1Rp2CCET\nfRM9ZuMOaO1gHmHHSqBfR0QYOzpaeXjQHQhoWXp6uo+PT3Z29vHjx1etWkV3OO0Kh8MxMDCg\nOwoAAGjbf3ZRkbKEQ9/Kisli4TYk0JTLrcit5zcWxkhbA40QsjZu/NdsByPQHWglivK0tMo3\nb/qtXEl3IKBlaWlpo0aNKioqOnnyZPONKoGE1qxZ06SBEBIKhUlJSf369aMpKAAAkEKNyJJB\n0pZwIISMHRxKk5MRJNAKIPMi0Ji1sTXuobCKtlWhqNKBEuh39+4hhBx8fekOBLQgKSnJz8+v\nsrIyPDx82rRpdIejxl6/ft2kgRBiMpleXl6rV6+mKSgAAJACmUAz9fW1pV/u1tjRERJoBSGX\n4NDW0iaXpZNce1oKugMl0Hn37zM0NGwGD6Y7ENBUQkLCmDFjOBzOxYsXJ02aRHc46g0XOM6f\nP//XX3+lOxYAAJAFmUDrS1m/gcFKdopDjkB3Mesiw0LOZAINI9DqJO/+fXM3Nz1zc7oDAf9x\n//798ePH8/n8yMjI0bA9JEUgewYAqC+Z17DDPiTQ2dlUhQQwMoGWoX4DidRAF1aqfQLdUSYR\nVr97V5mdbauSG5Z2ZPfu3Rs3bpxAIIiKioLsmVoXLlwYPXq0s7Mz/vH7778vLi6mNyQAAJAE\nOYlQhgJohJDR+80IucXFDRwOVVEBhNCbkje4Ie0SHBi5FHQ7mETYURLovPv3EUKQQKuUGzdu\n+Pv7a2pqxsTEjBgxgu5w2pVjx46tWrXKx8fnzZvGFzszM7OdO3fSGxUAAEjiQwlHp04y3P0/\nK9nBIDSlPuyiIuUi0Bg5As2t51bXVVMUFD1aTaCnTp0aFRUluuenWsu9fx8hZOfjQ3cgoFFU\nVNTUqVN1dXVv3brl7e1Ndzjtzb59+y5fvvztt9+SR8aNGxceHk5jSAAAICGqSjgQlEFTisfn\n5bPzcVvGEg6RzQjVvQy61QSayWROnz7d3t5+48aN6enpyoxJEfLu3ze0tzdycKA7EIAQQuHh\n4VOnTjU2Nr53796AAQPoDqcdysrKwn9YciMVU1PT8vJyWoMCAACJkAm0bCUcOsbGumZmuA0J\nNIWyS7OFhBC3ZSzhMBZJoNW8DLrVBDo8PDw/P3/Tpk03b950dXX18fE5depUTU2NMoOjCo/N\nLn35Euo3VMS5c+fmzp1rYWFx+/bt3r170x1O+2Rra5ucnIxEEug///zTxcWF1qAAAKBt9dXV\nDe+TDdlW4UCwEIdiiC4C7WQhSwJtZWhFviu12wQaIWRmZrZy5cqnT58mJiZ6eHgsXbrU2tp6\n8eLFL1++VFp8lMj75x9CKLSDBFoF/PLLL0FBQba2tn///XfPnj3pDqfdWrZs2YIFC2JiYhgM\nRnJy8t69e5ctW7Z8+XK64wIAgDZwi4rItoFMNdAIIROnxvSO/X4eCJAfmUAb6hqas2RZ00xb\nS9vMoPHLgaKqIvEnq7i2l7HLz8+/efPmrVu3NDQ0Jk2alJOT07dv37CwsHXr1ikhPkrk/fMP\nghmEKuDnn39esWJFt27dYmJi7O3t6Q6nPVu3bl11dfWkSZMEAkGvXr309PTWr1//xRdf0B0X\nAAC0QXQbQtlqoBGMQCvGm9LGTyOyFUBj1kbWZZwy1I5roBsaGq5evRoQENClS5cLFy6sXLmy\noKDgzJkzt27dio6O3r59uzKjlFPe/fs6JiYWvXrRHUiHtmvXruXLl7u6ut65cweyZ0VjMBjb\ntm0rKytLTEx88uRJaWlpSEgI3UEBAEDbOO/XsEMMhmyrcCDRBBpGoKkj5xp2GFkGre4j0K0m\n0DY2NvPnz7e1tX348GFSUtKKFStMTEzwTSNHjmSxWDJfsqam5tq1a7jN4XAWLVpkYmKCy0V4\nPJ7M3bZGwOMVPHpkO3gwQ6OjrNmngnbt2rVx40YPD4+4uDgbGxu6w+kodHV1PT09vby89PX1\n6Y4FAAAkQpZw6JmZaWpry9aJ8fsSjoaaGtGaECAPOXdRwToZNX4oUvca6FZLOBYsWLB161YD\nAwPRg2lpaT169EAI5ebmynzJ4OBgNzc33N68eXN0dPSWLVsQQgcOHDAxMaF8bLvwyRMBjwf1\nG3QhCOLrr7/eu3evl5fXzZs3zWEnSKVYvHhx84M6OjpOTk6BgYHwDQAAQGXJuYYd1mQlO5lH\nsoEoMoGWbQYhRq5k125LOHbv3t0ke0YIkYmvPM6ePTtz5kzcvnLlypUrV9atW7du3brLly+f\nPXtW/v6byH/4ECFkO2gQ5T2DNhEEsWbNmr179w4ZMuT27duQPStNaWnpiRMnbt26VVRUVFxc\nfOvWrRMnTmRmZv7yyy89evRISEigO0AAAGhZzfsBY3myXuOuXRmamrgN8wgpUV5TzuaycZuS\nEg51H4GWoqqBw+E0T6llQBBEZWUlbpeUlLi7u+N2r1698vPz5e+/ifwHDzSYTGtYbFjphELh\n4sWLf/zxx2HDht24ccPIyIjuiDoQS0vLr776KisrKzIyMiIiIisr68svv+zSpUtaWtrSpUu/\n+uorugMEAICWcakYgdZgMg3t7HAbyqApkVmSSbblSaDJEo7i6mKCIOQNiz4tlHCsWbOmSQMh\nJBQKk5KS+vXrJ/8lx40bt3bt2nPnzunq6g4dOjQqKiowMBAhFBERgetDqFXw77+Wffpo6elR\n3jMQQyAQLFq06PTp0/7+/leuXNGDv79y/fnnny9fvtR8PwCjqakZHBzcp08fBoPx9ddfwwKC\nAKiyqVOnLly40N/fn3wKdygfSjhk2kWFZOLkVPX2LYIRaIqQMwg1GBqy7eONkSUc9fz68ppy\n2ZbDUwUtJNCvX79u0kAIMZlMLy+v1atXy3/J0NDQwYMHOzk5TZ48ecCAAUFBQWfOnCEIIiYm\n5sqVK/L3L4rHZnPy8x1Gj6a2WyBefX397Nmzr1y5EhAQcOnSJV1dXboj6nCqq6tzcnLM3u/F\nhRDKycmpqqpCCDEYDENDQ/pCAwC0Ae8EbGZmNm/evIULF3bv3l0RV8nJyTl27Njr169tbGyW\nLFni6uqqiKvI4EMCLV/hsrGzM7pzB8EINEXIEWhbU1tdpuxv6//ZjLCqsF0l0FFRUQih+fPn\n//rrr4q4pK2tbWJiYmho6LVr17KysgiCSExMHDx4cFxcHOW7Ole8fo0QMu3WjdpugRg8Hm/m\nzJnXrl375JNPfvvtNyaTSXdEHdG0adMCAwN37NjRv39/giASEhI2bdo0ffp0hNDFixenTp1K\nd4AAgFaFh4eXl5efPXv25MmTu3btGjJkyMKFCwMDA+WvojQxMWGz2QihZ8+eDR06lMVide/e\nPS4u7ujRo/Hx8b1UYbFXguAWF+OmnDP/Puylkpkp/kwgCXIEWp4lOJBICQdCqKiqyN3GXa6w\n6NNqDbSCsmfMzMwsLCwsPT29oaGBz+fn5uaGh4dTnj2j9587TZydKe8ZtIjL5U6YMOHatWtz\n5sw5c+YMZM90+fnnn/38/ObOnevk5OTs7Dx37tyxY8ceOnQIIeTu7r5z5055Ok9ISAgKCnJ2\ndmaxWAYGBs7OzkFBQTAxEQAKKWgnYHIC0saNG8ePH5+VlXXnzp2srKyJEydu3ryZisDlVVte\nLqivx215aqCRyEp21Xl5/NpaeSPr8MgRaGdLuXIqS0NLLY3G0Vu1nkfYdAR6xYoVCKGDBw/i\nRnMHDx5UeFDUwZ87yWcRUKiampqJEyfevn17yZIlhw8f1oCFt+mjr69/8ODBsLCwN2/eMBgM\nR0dHcinokSNHytNzRETEtGnTPD09Z8yYYWlpyWAwiouLY2Njvb29r169OmHCBCrCBwAgpMid\ngB8/fvz48WNtbW2EkLa29o4dO4aoxmKv/9nHW74E+sPYGUFUZmebU7GMWEdGVQKtwdCwMLTA\nqbNa76XSNIEmF3iWZ6Vn2eCNWvC3S80JBILr16/X1dWJHnz48KH4PvHUARNIoBWPzWaPGzfu\n4cOHy5cv/+mnnxgMBt0RdWgjRoy4c+eOvr4+5d/JBgcHh4WFrV27tsnxffv2bdq0CRJoAOTX\n0NAQGRl58uTJv/76q1evXitXrpw7dy5+i7x9+/bUqVPlT6C5XK6pqSn5o4WFRU1NjZx9UqJG\nJIHWt7KSpyvRL5/Zr19DAi0PHp+XV5GH2/IswYFZG1njBFqtl4JumkCTewSSDaURXfSjuTt3\n7kycOLHFm7hcbmv3qszK0jYy0rOwkDc4IFZFRcXYsWMfPXq0YcOG0NBQusMBKCEhgcPhyLNj\naGsyMjLmz5/f/PiCBQs2bdpE+eUA6IBsbGx4PN6sWbMePnzYpLhRzp2AEULe3t4IIYIgbt26\nhZfAQgglJibi47Sreb+PN0NTU9/SUp6udE1Ndc3M6srLEZRByy2rNEtICHHbxcpFzt7IMuh2\nNQJNmjRp0ty5cydMmKC0JRRCQkLE3DpixIiIiIjmI9D79+8Xs01xVXa2cdeuFAUIWlZUVOTn\n5/f8+XPInlWHv7//+fPnP/vsM8p77tKlS2Rk5Lx585ocj4yMdHBwoPxyAHRAitsJmNzrNyAg\nAC/Lgx08eBBvCUw7cgahnoUFQ+5V/ExdXAoePUKQQMsts1hkEWg5tiHE2sdeKq0m0AYGBvPn\nz2cymdOmTZs7d+6wYcPoLWnV1NRs8dvh/fv3t3YXQiCoyslx9PdXZFwdXWFhoa+vb0pKyt69\ne7/88ku6wwGNDA0NP//88ytXrvTs2ROXOWLyf8LZsmXLokWLfv/99xEjRlhaWiKESkpK7t69\nGxkZeeLECTk7BwAghHbv3h0WFtbkoJubm/y7TrQ2U/DSpUty9kwVsgZazgJozNjZGRJoSrwp\nbVyCw1TfVP6F59r5CPS5c+dqamp+//33s2fP+vn5WVtbz549e+7cub1795b/qgkJCQcOHHjw\n4EFRURFBENbW1oMGDVqzZo2Xl5f8nZNqCguFDQ3GMCqmMG/fvvX19c3MzDxw4MCqVavoDgd8\nkJ2dPXLkSD6f//z5c2p7njdvnpWV1Z49e4KDg3HRpIGBgbe3d2Rk5NixY6m9FgAAo2onYNnI\nNgdJNpz3JRyUJNCmLo3FBpBAy+l1cePGIPLXbyCRvVTaVQ20KAMDg7lz586dO7e4uDg8PPzI\nkSNhYWHyfwJW2iz+6nfvEEKG9vZUdQhEZWVljRo1Kicn5/jx4wsXLqQ7HPAfMTExiut87Nix\nY8eOJQiiuroaIWRoaAhzRgGghKJ3Am6N+En8ss1Bks2HEWj5FoHGTN4n0JVZWUI+X0NLXM4D\nxKA2gSZHoEuqSwRCgaaGWu642faDicvlxsbG3rx5Mz093e79zvLyUNos/urcXAQJtGKkpaX5\n+voWFhaePHmyeTks6AgYDIaRkRHdUQDQrih6J+DWiJ/EL9scJNmQz8QQtgAAIABJREFUq3DI\nuYsKRo5ACxsaqt6+hU0hZJZRlIEb1IxAv6+BFggFpZxS0a1V1EirCTSfz4+JiTl79uy1a9c0\nNTWnTZt28+bN4cOHy39Jpc3i5+TlIYRYtrYU9gkQQklJSX5+fpWVleHh4dOmTaM7HNAyPp+f\nkpKSk5PD5/PJg5MnT5a/Z+WUYAHQASl6J+DWiJ/EL8McJJnVUFrCQY5AI4TYr19DAi0bvpCf\nXZaN292sKNjauclmhO0tgba1tWWz2f7+/qdOnQoICKBwLQ6lzeKvKSxEFD0JASkhIWHMmDEc\nDufixYuTJk2iOxzQsoyMjMmTJ7969UogEDCZzIaGBoSQgYEBh8ORs2fYSAUARVNy9qw6CKGw\ntrQUtykp4dC3stIxNuZVViKEKjIyuo4ZI3+fHVB2aXaDoAG3qa2BRggVVhb2sesjf5/K12oC\nvX379hkzZogutE4Vpc3ix6vhUPIkBNg///wzbtw4Pp8fGRk5evRousMBrVqzZs2wYcMSExN1\ndXXr6uqSkpIWLVrU4jc/0pKtBEvMJCShUCh/VAC0A/TuBLx48eLjx48rrn9J1JaWCt9/Y0bV\n4JeJi0tRQgJCqCIjg5IOO6CM4g9/OkoSaDMDM20t7Xp+PVLneYStJtBLlixR0CWVNoufW1Ki\nqaOjDWWaFImLiwsICBAKhZGRkXJuBw0ULT4+/tSpUzo6OgghgUDQr1+/06dPT5kyRf4yStlK\nsMRMQkpOTpYzJADaBxp3AkYInThxgvYEWnQfb0pqoBFCpt26QQItJ7IA2lTf1NJQrt1tMAaD\nYWVolVuRi9R5JbumCbRyPgErZxZ/bVmZrpkZ5d12TH/99dfUqVN1dHRu3rz58ccf0x0OaEN5\nebmVlRVCyMLCorCw0N7e3tnZOS8vT/6eZSvBEjMJyd3dXf6oAGgHlLAT8JkzZxTUMyVw4SVG\n1Qi0affuuFGRnk5Jhx1QelHjn65bJwoKoDFrY2ucQKvvXipNE2hlfgJW9Cx+Hputq4ASlA4o\nKipqxowZenp6f/3110cffUR3OEAKH330UWho6Nq1a0+ePOniQsFXb7KVYImZhETvDk0AqCCB\nQPDixQsPDw+EUEpKysmTJ52dnZcuXSr/SFNQUBAVASoKmUBrMJl65vLu1oGRC3FUZmcL6us1\nRTaWAhIiE+junbpT1SdZBt1+RqDJD76hoaF411BRaWlpygiKIjw22wj28ZbbxYsX586da2Zm\nFh0dTck2OkAJPv/8c9wIDQ0NCAj4+eefTU1NL168KH/PsJEKAIq2d+9eNpvt4eFRW1s7evRo\nW1vbM2fOlJSUfPvtt3L27OHhcfz48RYXzFGF1dzJBNqgUydEUTzkCDQhEFS+eWPWLLEBbfow\nAk3FEhwYufJGO6yBbnHXUEq2ElWaeg5H29CQ7ijU27lz5z799FNLS8vo6Gj4ql2NHDlyBDd6\n9+6dnZ2dl5fXqVMnJpNJSeewkQoACvXLL7/ExsYihGJjYy0sLB49epSYmDh9+nT5E+hly5Yd\nOXLk2LFjVIRJPTz1HyGkb2VFVZ9mrq5ku/zVK0igpVXbUPuu/B1ud7embgT6/VLQ6lvCIcWX\np/RuJSo1gmiABFo+v/zyS1BQkK2t7d9//w3Zs7pYunRpkyMMBsPOzo6q7Fm0WyMjIyMjI8ie\nAaBWfn4+ro+6ffs2rn1yc3MrLKQgz5gzZ07fvn1bvCk6Olr+/uX0YQSauvVndUxMyPmI5a9e\nUdVtx5FRlCEkGtdKcu3kKv5kybXPEWi6thKlVkNtLSEUMtUo41cxP//884oVK7p27RobG+vo\n6Eh3OEBSR48eJYefFaGmpiY6OhpvyMLhcFavXn3lyhUNDY05c+bs2bMHr/sBAJCHq6vryZMn\nJ06cGB4efv78eYRQenq6qysFuYuBgUFrKwT4+vrK37+cFJFAI4TMXF3x+h4VkEBLL62wsXaX\nwWAooga6vKa8nl+vraV+tektJNB0bSVKLT6XixBiUr3LaAcRFha2YcOGHj16xMbG2tjY0B0O\nUCHBwcFubm64vXnz5ujo6C1btiCEDhw4YGJisn37dlqjA6A92LFjx/Tp01euXBkQEODj44MQ\nOnTo0BdffEF3XApHJtD6VCfQuXFxCKEytZrHpSJeFTZ+6rAztTPQoWxQkizhIAiiuLrYztSO\nqp6VpoUEmq6tRKnFr61FCGnp6dEdiPrZtWvXxo0bPTw8bt26hb9GBIB09uxZ8qP1lStXrl69\nOmDAAITQkCFDZs2aBQk0APIbN25ccXFxcXGxo6MjLpFauHAhfqK1b4obgcaNckigpUeOQLt1\ndqOwWzKBRggVVha2kwQaU+vsGUECLastW7Z8//33Xl5eN2/eNKdoFSGgZLq6uq3d1GQlZhkQ\nBFFZWWliYoIQKikpIYvje/XqlZ+fL2fnAACMxWKxWCzyR29vbxqDUQ5hQ0NdeTluU7uFsHnP\nnrhRV17OLSnRh4EhaZAj0D2sqZx/+Z/dvNWzDJqejVSUABJoaREEsXbt2h9++GHw4MHXr19X\n6BLdQKH27NmjuM7HjRu3du3ac+fO6erqDh06NCoqKjAwECEUERHRfOFLAIAM+Hz+6dOn//nn\nn/L3CSWmuA1WVEFNUREhbJysZtC5M4U9/2chjrQ0SKAlRxCEghJoQ11DAx2DGl4NUtuFOOjc\nSEWh+HV1CCGt1ofigCihULhkyZITJ04MGzYsMjLSEFYvUWetffqlRGho6ODBg52cnCZPnjxg\nwICgoKAzZ84QBBETE3PlyhXFXReAjmP16tVnz54dP368nZ36fa8tM9F9vKkdgTbq2lVLTw8P\nq5Wnptr5+FDYefv2ruIdh8fBbWoTaISQtZF1Zkkmajcj0ErYSlQ5BDweQkgT1gSQgEAgWLRo\n0enTp8eOHXv16lU9GLYHrbO1tU1MTAwNDb127VpWVhZBEImJiYMHD46Li+sINZoAKMHFixdj\nYmL69+9PdyBKxSkoINvU1kAzNDTMXF2Lk5IQQqUpKRT23O6l5H/4c1FbA40Q6mTUCSfQaroZ\nYas10IrbSlQ5cAINI9BtamhomD179uXLlwMCAi5duiSmfBYAzMzMLCwsLCwsjO5AAGifNDQ0\nOmBBFDkCzdTX16a6htC8Z0+cQJdBAi2NlILGP5eZgZnotD9KqPteKq1upLJ371688S/eSjQu\nLm7btm1qNMUel3DACLR49fX1gYGBly9fDgwMvHr1KmTP7QDeHRAAoL5Gjx599epVuqNQtpr3\nI9DUrmGHkfMIy5KTKe+8HSNHoCkffkYi8wgLKgvEn6maWh2BVtxWosoBJRxt4nK5U6ZMuXXr\n1uzZs0+fPq2l1eqDAagR0Zn7AAB1pKuru3Dhwj/++MPFxUX0W9/Q0FAao1I0soSDpYDNB8gE\nmpOfX1dermtmRvkl2qWXeS9xw92G+t2I1X0EutWcSXFbiSoHJNDi1dTUTJw48fbt20uWLDl8\n+LCGhhSbugMAAFCc7Ozs4cOHV1ZWJiQk0B2L8nxYBJrSGYSYhfuH/K8sNdV28GDKL9H+EASR\nWpiK2z1telLef2fjxrVW2skkQpLithJVDliFQ4zKykp/f/+HDx9+8cUXBw8eVJe6dgAA6Ahi\nYmLoDoEGHxJoSteww0ycncmFOEpfvoQEWhLvKt5V1Vbhdi+bXpT3T45A1/BqqmqrjPTUbPHc\nVscdd+zYsX79egcHh379+qnjVqLChgaEkAaTSXcgKqeiosLPz+/hw4cbNmw4dOgQZM8AAABo\nR9ZAU7sEB8bQ1DR3a6zihTJoCZH1GwihXrbUJ9DkCDRSzzLoVkeg1X0rUUF9PUJIU1ub7kBU\nS3Fx8ejRo58/f75hw4b2XU7XMbW5ArS6bIQEQAd34cKFEydOvHnzJjMzEyH0/fffL1myxMrK\niu64FEihI9AIIYtevYoSExFCJS9eKKL/9udFXuMfytLQspMR9XU1ogl0YVWhq7Xa1Dhg4uaN\nKW0r0ezs7K5du1LbJ4xAN1dYWOjr65ucnLx9+/bNmzfTHQ6gnrrvfwQAQAgdO3bsm2++WbFi\nBVnLYWZmtnPnzgMHDtAbmOLUVVTg+gqkmEmECCGL3r1xo+T5c0X03/48z238Q/Wx66OI/q2M\nrDQ1NAVCAWpnI9DK3ErU0dGRIAhq+xTy+QghDVhZ4r2cnJxRo0ZlZmYeOHBg9erVdIcDFELd\n9z8CACCE9u3bd/ny5aFDh27duhUfGTdu3Pbt29txAl0jskSBIko4EEIWvRqLEOrKyzl5eSxb\nW0VcpT15kds4At3btrci+tfS0LJgWeBdVArY7SiBVtxWotOnTxdz8PLly5RchRAIECTQ72Vl\nZY0aNert27fHjh1btGgR3eEAAABoVVZWFi6YJOeomJqaNhnMamdqFLYNIcmyz4dh1OJnzyCB\nFo/H56UVpuG2ghJohFBn486NCXR7GoFW3FaiV65c+eijj/AaeaS6ujpqr4JHoBmamtR2q47S\n0tJ8fX0LCwtPnjz56aef0h0OUBI+n5+SkpKTk8Pn88mDkydPpjEkAIAkbG1tk5OT+/fvTybQ\nf/75p4uLC71RKRSZQGtoaekrptSbZWOjb2nJLSlBCJU8f+40bpwirtJupOSnNAgacLuvfV8F\nXaWzceekd0monSXQittK9Icffti7d+/WrVvHvX/4MhiMqKgoaq8ihBFohBBCKSkpvr6+ZWVl\nFy5caHHsH7RLGRkZkydPfvXqlUAgYDKZDQ0NCCEDAwMOh0N3aACANixbtmzBggX79+9nMBjJ\nycl//fXXd99993//93+UdJ6QkHDgwIEHDx4UFRURBGFtbT1o0KA1a9Z4eXlR0r9sOPn5uKHf\nqRNDYfsSWHp4vI2ORgiVJCUp6BLtxtOcp7ihpaGliF1UsM4mjfMI21UCjbcSnTdvHuWXXLVq\nlY+Pz6xZs65fv7579249PT3KL4FgBBohhFBiYqKfn191dXV4eDgMPXYoa9asGTZsWGJioq6u\nbl1dXVJS0qJFi+bPn093XACAtq1bt666unrSpEkCgaBXr156enrr16+nZBnZiIiIadOmeXp6\nzpgxw9LSksFgFBcXx8bGent7X716FW+aRguyBpqlmCU4MKv3CXQxJNBtIWcQunV202UqaksN\nciGOdpVAK3QrUU9PzydPnixfvtzLy+vcuXPyd9hcYw10B06g//nnn/Hjxzc0NERGRvr5+dEd\nDlCq+Pj4U6dO6ejoIIQEAkG/fv1Onz49ZcoUmD8KgOpjMBjbtm3btGlTamqqUCh0c3PT19en\npOfg4OCwsLC1a9c2Ob5v375NmzbRmECTI9AKWsMOs/LwwI2KjIwGDocpss4YaCIxJxE3PLp4\nKO4qZAKdz85X3FUUpNUEWtFbibJYrNOnT//222++vr6K6B8n0B12BDouLi4gIEAoFEZGRo4c\nOZLucICylZeX4yVj/5+9O4+LaX0DAP6eaV+1i5SIhGwlQiiFSgjVvfwqkSXXEiE72fflWqNF\nJTtFqRtJJN2kIlulyJbSvk37zPn9cWruaBkzNXOmqef78cc7Z95z3qfMdJ55512UlJRyc3PV\n1dW1tLSys7P5HRcAgF3i4uIjRozg7jUzMjJa/CZqwYIFmzZt4m5bHPlvFxWeJtCNv0+cTs9L\nSYH9CFtDx+kp31OI8gh1Lr8ImfWUa1iysKSypKquSkKEJ0MSeKTVBJqcrUQdHByMjIxSUlK4\nfmV6F06gIyIiZs+eLSoqev/+/TFjxvA7HMBPo0aNOnDgwJo1a3x9fTv3JCQAOoeqqqqTJ08G\nBQVlZWVhGNanT585c+asXLlSXJwLX6NraGiEhoY2H5wZGhrau3fv9l+/zRg90DxaBJqgoK0t\nIiVVR6UihH4mJUEC3ZrMvEzGJt56vfV41xDzXio/Sn5oKWvxri2u4/8cuz59+vTp0+e31Wg0\nWnh4eJPFOv7999/W6uN0OuqSQzjCwsJsbGwkJCQiIiJGjRrF73AAfyxdupQoHDhwwMrK6uzZ\ns/Ly8jdu3OBvVAAA1urq6szMzJKSkiwsLExMTBBCaWlp27ZtCw0NffTokXC7p8Vv27bN2dk5\nODjYxMSEWAsrPz//8ePHoaGhPj4+XPgB2uq/BJqXPdCYkJDKiBHZsbEIoZ+8+Xa9c0j60vDL\noWAUcnqgEUI5pTmdJ4EmeStROTk5hFBJSUmLz0ZHR8+YMaPFpyorK5sfbBjCwbPJvB3TjRs3\n7O3t5eXlIyMjhw7lydZBQCB4enoShSFDhnz+/Dk7O7t79+4isDEnAB3b+fPns7OzU1NTmfuV\nPn78aGJi4u3t7eLi0s7rOzo6qqioHDlyZPPmzVQqFSEkJSVlaGgYGhpqbm7ezou3WW1ZGdEr\njBDi9fLM3fX1iQQ6NzGRpw0JtBefXxCF/t37y0rI8q4hVVlVCkah43QkgHuptJpAk7+V6OrV\nq1k8a2JiEhIS0rwH+vjx4y3OriB6oLvUEI4rV67Mnz9fSUnp4cOHgwfzatEZIHAwDOP6dkgA\nAF64devW/v37m3wrq6WltX//fh8fn/Yn0Aghc3Nzc3NzHMfLy8sRQjIyMszrBPAFo/sZ8XgI\nB0JItXF3i6K0tNryclEZGZ42J6ASPzd8uhjZm/ubgTATFRZVllEm9lL5USpg8whbTaDJ30rU\nw8ODxbNCQkItThA+fvx4i/W7Wg+0l5eXi4tLr169oqKiYKgrWLRoUYvHvb29SY4EAMC+d+/e\ntTjt29TUtPnSGe2BYZisLLs9i5wOoeQUqQm0gQFRwOn0n0lJ6sbGPG1OENHoNMYSHAZ9DHjd\nXE+5nkQCnV0sYNPcW02gBX0r0YYe6K6RQJ87d2758uWamppRUVHsDCgHnR7zhil0Oj0jI+PV\nq1d8XKMKAMCO4uLiJtv0ElRUVIqLi3nXLneHUHKKkUBTREQkWvrxuUhBW1tMTq6mpAQhlPP8\nOSTQzb378Y5a0zCixkCTjASa2LRF4FayazWB5ulWoiTshITjOOoaCfShQ4c2bNigo6Pz8OFD\nNR6PHgOC4tq1a02O7N69Oz8/ny/BAADYRKPRKC3dtigUSn19Pe/a5e4QSk5VNK6wKd2jB8/v\n2himamBAbKeS8/w5b9sSTM+zGn4tIkIiIzR4OIOQoCbXkLdkl3SWHmjebSVK0k5IXaMH+uDB\ngxs3bhw0aNDDhw978HLyMhB0K1euHDJkyMmTJ/kdCACAFWN+9Ilydwglp/7bRYXH4zcIPUaP\nhgSahfhP8URhmPowEhZmZizE0Xl6oHm3lSg5OyERPdCI33MjeGrbtm179uzR19e/f/++oqIi\nv8MBHVpubi7zuA4AQAc0f/781p7S1NQkMRBSlX//ThRkSJnu3LNxe4SKHz/KvnyR5esC2B1Q\nXGYcURirNZaE5hg90N+Lv5PQHBe1mkDzbitRknZC6tQJNI7jbm5uJ06cGDduXHh4OPtzQUAX\nceTIEeaHxcXFly9ftrKy4lc8LCYh0el0PgUFQIfj5+fHr6YXLVrEr0nGjG0IeboINENPQ0OE\nYUSSkP3sGSTQzAorCtN/phNlw76GJLSoJt+QQFfWVpZUlshJypHQKFf8ZlV2YitRGo1WVVXF\nrSbJ2QmpYQx0Z0ygcRxfuXLlmTNnJkyYcO/ePRlYhQc0ExgYyPxQXl7e0dHR3d2dX/GwmIT0\n7t07koMBADTn4+PDrwT6vzHQpEzjEVdQUNTRKUxNRQj9iIsbOG8eCY0KiriPcQ1f4JPeA40Q\nyi7JFuwEurS01M/Pz9XVlXh47ty5devWVVVVmZmZXb9+XV5evp1NkrQTUiftgabRaM7Ozv7+\n/ubm5kFBQRISgrRxPCDNtWvXdHR0mhxMS0trfpAcLCYhwZrlAJCmyUfrjgCn0ysYPdBkzYPv\nOW4ckUB/j40lp0VB8SzzGVHoJd+rtyIZffO/JNDF2YN7CswdoYUE+u+//2aUP3z4sGrVKnt7\n+3Hjxh07dmzv3r1NvhpuA3J2QuqUPdB1dXXz5s27deuWlZXVzZs3xcXF+R0R6KAGDhzI6EVg\nfZAcLCYhtbjmAACAFxwcHPgdQlOVP3/S6+qIMjljoBFCvYyM3nh7I4QK3rypKSkRkxOYXk9e\ne5rxlCiM7z+enBYVpBQkRCSq6qqQoA2DbiGBvnHjxu3bt4lycHBw3759fX19MQwbPHiwo6Nj\n+xNoRM5OSHxKFHintrb2jz/+uHPnjp2dXWBgIGzLDDhSUVEhJSXF7ygAAPw0fPhwb2/vFleM\n5Vd/E2MGISKxB7rXhAlEAafTvz99qgVr5COEEKqsrUz80rAHIWkJNIZhavJqmXmZSNBWsmsh\ngf706RNjLHJcXNyUKVOI99Xw4cOzs7n5s3G0E1Kb2+Dt9clSWVk5a9asBw8ezJs3z9/fX1j4\nN4PXQZfFWM+VeWFXOp3+6tUrPT09PgUFAOgQli1b5unp6eXlxe9A/sOXBLpbnz6yGhplX78i\nhL49fgwJNOFZ5rPa+lqiPFF7Imntqsk1JNDfir6R1mj7tZCHqaiovHv3Tl9fv66uLjY29tSp\nU8TxgoICJSUlcsNru840hINKpc6cOTMqKmrx4sWenp7wlTdgITMzs0kBISQiIqKvr8+Y2AAA\n6Jr+97//1dbWtvhUZGQkycEQGAm0hKKiCJcW+2JHr4kT31+6hBD69vgxaY12cNHp0URBRUZl\nYI+BpLWrrqBOFAR+CMeMGTMWLly4du3a6OjompqaqVOnEscTEhLGNK6eKAA6yyTC0tJSS0vL\nuLi4ZcuWnTlzpnN8JAC8c+/ePYSQk5MTH9fDAgB0TFJSUitWrGjxKTMzM5KDIVQ0JtDSZA2A\nJmiYmBAJdN6rV9VFReIKCmS23jFFpzUk0MYDjMlMNnrJN/zXC1YC3UJf5q5duzQ0NIiFMry8\nvBg7dPz9998rV64kN7yurri4eMqUKXFxce7u7mfPnoXsGbAJsmcAgEAo+9bwrb2sujqZ7WpM\nmkQUcDr9a3Q0mU13TKVVpYmfGwZAT9KZRGbTjAT6W7EgDeFoIYGWk5MLDQ2tqKgoLi6eO3cu\n43hkZKSRkRGJsbVLJxjCkZeXZ2xsnJCQsGHDhoMHD/I7HCBIYmNjly1bxnxk2bJlsbBgEwCg\ngylvTKBlyE2gZXv3luvXjyh/efiQzKY7psfpj+vp9UTZdKApmU0zhnCUVJaUV5eT2XR7tDqa\nVkxMrEn2KSYmxvt4QIPc3NxJkya9fv169+7dBw4c4Hc4QMC4ubk5OzszH1m4cOG6dev4FQ8A\nALSIXwk0Qkhz8mSi8PnBA5Kb7oAevGv4JWgqavZT6Udm0+ry//3XC9Aojs47HU2Ql7H7+vXr\n+PHj379/f+LEia1bt/I7HCB4Xr9+PWDAAOYjAwYMeP36Nb/iAQCA5nAajbENIR8S6ClTiELp\np0/FGRkkt97RPHjfkEBP1Z1KctOMHmiE0NeiryS33madN4EmCOAQjs+fP5uYmHz8+PHUqVOw\nbAJoG3V19ce/Ti2Pjo7u2bMnn8IBAIAWVPz4Qa9vGDZAfgKtYWoqJCpKlLP++Yfk1juUjLwM\nYiE5hNDUwWQn0IpSipKiDQuwQAIN2ig9Pd3IyOjLly8XL15cvnw5v8MBgsrFxcXZ2fnChQtp\naWmpqannz59ftGiRi4sLv+MCAID/ECsxE2Q1NEhuXVRGpufYsUS5iyfQ4W/CiYKIkIipDqkD\noBFCGIYxOqEFaCnozrsfhwBOInz//r2ZmVlBQcG1a9dsbGz4HQ4QYG5ubkVFRatXr66qqkII\nSUhIrFmzZu3atfyOCwAA/sMYAI1RKKTt482sj4UFsQ70t8eP66hUka66XWvY6zCiYNTfSFaC\nxzvctURDQSM9Nx0JVA90502gBU1ycvLUqVPLyspu3LhhbW3N73CAYMMwbO/evVu2bElNTcUw\nTEdHR5LEHQoAAIAdjB5oqR49KCIi7b9gSWXJg/cP3v94X0+vV5dXn6A9gfWGIFpWVjEbNiCE\n6qurPz940H/WrPbHIHBKq0qffHhClKcP/c2mjJV5eV8fPSpKT6fV1nbr3bu3mVm3vn3bH4OG\nQsP3D5BAA868ePHC3Ny8uro6NDR0SuO0BgDaSVJSUl9fnyjT6fSoqKjJjbPOAQCA78o+fyYK\nsr17t/NS1BrqztCdZ6LPVNZWMh/XVdNdOWnl/LHzxYRbWElMcdAgOS2tko8fEUKZd+8SCXRN\nfc3TjKevvr4qqChQlFYc2XvkBO0JQhShdkbId4Wpqd+io8u/fxcWF1cYOFDDxERCSQkhFP4m\nnLGDt9Uwq9ZO//LwYeKxY58fPMBptP+OYpjW9Okmx47JaWm1JzZGAv2l8Et7rkOmTptA44Kz\nE2FMTIyVlRWdTg8JCTE1JXvsEej0vn37dvHiRV9f3y9fvuCCvDoNAKCTKfvSkC21cwD0p/xP\n009Pf//jffOn3ma/XXpp6d6wvR4zPBzHODbPg/vNnJl47BhC6NO9e++/vT315MyVhCtlVWXM\ndfoq9907a++fBn+2J0g+yk9JiV6zpsl+MZiQUB9z8xErVtz9fIc4MqjnoP4q/Zufnv3s2dNN\nm74/fdrCpXH8Y0jIt+joaZcva03/Te81C70VGz5BfSv6RqPTBOLjCkwi5LOIiAhzc3MKhRIZ\nGQnZM+Ciurq627dvW1hYaGpqPnr0yNXVNSsri99BAQDAfxhDOLpparb5Ip8LP088PJE5e+6n\n0k9XTVdUWJRx5GvR14V+C4d4DLmacJWxXUhD5cYxk1WFhbNchno+8WySPSOEPuV/mnth7nzf\n+TX1NW2Ok19eX7gQOGpU890WcRrtU1jYbQsL9a239DOREB3NGtF0BEvO8+dBVlZXjYx+yZ4x\nTE5LS2nIEMaom9ry8ruzZ2feudPmIBkJdB2tLqc0p83XIVOn7YEWCGFhYTY2NhISEhEREaNG\njeJ3OKCTSE1N9fHxCQgIoFAoTk5OERERTZa0AwCAjqCUMYSItwETAAAgAElEQVSjrQl0RU2F\n1Ukrxu4bY7XGnrM/N7TXUIQQtYZ6O/n28cjjr769Ip5NzUmd5zXP/Zb7HwZ/jOs3rrts98KK\nwheFCSLSwhIV9QihIZ/wD42rfSpKK2oqamYVZBVRi4gjAf8GFFQUBP8VzJyad3CJx449Zpo+\nLiwuLj9gQG1ZWSlTf0r3Qvqfj9H0eKRdm55Wc12mV6/a8vK8V68ygoJyX7xgvpqkioreypW6\nCxZIq6khhCrz859t357i6YkQotfX35s3b+7Tp90bxw1ypI9SH0b5c+FnxubeHRkk0Hxz8+bN\n//3vf/Ly8pGRkUOHDuV3OKCTGDduXHx8vKmp6dmzZ2fOnCkiIgL7wAMAOqDK/Py6igqi3OYe\n6L8u//XuxzuibKNvc3nRZUZ2KyUm5TjG0cHQ4Xby7a13thKLPCCEvhd/P/rg6NEHRxkXmaWB\nxr5HCKEhn1HERIl5Y+0XjV9koGmAYRiNTruReMP1mmt+eT5CKPxN+JJLS/wW+LUtWpKlXb36\nuHEDWkxIaPTGjaPc3UVlZRFCZV+/vg8IeHXuXMWPH0QFqWqUHXgrO/BWi5cSk5MbtX69nqsr\n80IlksrKk8+dUx469OHy5QjH66uqQuzs5r96JSojw2moavJqIkIidbQ6hNDngs9G/Yw4vQL5\nYAgHf1y9enXevHmKioqPHj2C7BlwUVxcnL6+/urVq2fPni3CjVntAADAC4wZhKitPdAhKSGX\n/r1ElI36GTFnzwwYhtno27zd+dZvgV+LA3wRQimNvZ9S1SjW/OIFxwuj+owilsEVogjNHTU3\nYUtCX+WGtSb84/zPPj7bhmhJVvD27f1Fi4glfYXExKzv3DHas4fInhFCshoahlu3/i/93d1J\nYj/lWF1HrFs3w61bF3/8OHrz5haX+Ru+bNmYbduIcumnT8SqJpwSpggzep2zCgRjtGHnTaA7\n8GQpLy8ve3v7nj17Pn36dPDgwfwOB3QqSUlJI0eOnDdvnqam5s6dO799E5hF6QEAXQpjFAFG\nobRhFY7K2sqVV1YSZQUphatLrrIYWSFMEZ4/dn7anrRw13DHMY7Me0d3l+0+3HKWkJI88TDn\nTgs7qmgqaj50e6gio0I8XHtjLaPbu2Oi1dTcmzu3rrISIYQwzPLSJS2rFpbXCEuNiO1bc9QG\nnbNCvf5nI6+tjVEa0kIxObk+FhZTvbyWfvtmtHu3uIICi+bG7tihbmxMlFPOn89JSGhDzIxR\nHJBAdwAdcgmOc+fOubi4aGhoPH78uF+/fvwOB3Q2enp6Z8+ezcnJ2bt376NHj/r06YMQun79\nellZ02kxAADAR4wEWqpHD2FxcU5PP3z/MGPN4ON/HGdn1CwFo1joWvgv9P968GvF6YqvB7+W\nnSrLPZp7+6+g4Q5ORJ2MoKCGvPNXfZT6XF1ylVgdorqu2umiE41Oa16tg4jbubPg7VuiPHrD\nhgG2ti1Wuxx/GSGEIyQ2YuCfgTed09NdqVSX7OwVhYUri4vnhIcPWbSInfEYGIVi7usrIimJ\nEMLp9OjVq9vQiQkJNFu+f//u5+cXHBxc+evL1MPDgy/xkObw4cN//fWXtrZ2bGwskdkAwAsS\nEhIODg5PnjxJTU3dsGHD6tWrlZWVLSws+B0XAAA0KPn0iSjIcb4TR1553pH7R4iyUT8jB0MH\nTq8gJSalrqAuI96QHQ6ytycKteXlra0mMUln0vqp64ly4ufEv6P+5rRRchS8efPiSMMvR9XA\nYNyuXS1W+1n2M+JtBFG2N2z48YXFxaV79mTd39yibn36jN60iSj/+PffjOBgTq/ASKA/5X/i\n9Fy+4EMCnZiYOGTIkMWLF8+ZM2fgwIEvmOZ47ty5k/x4SHPw4EF3d/dBgwY9evRITU2N3+GA\nLqF///4HDhz49u3bjRs3hIVh0jAAoMHXr1+3bds2d+7ctWvXpqenkx9AaWMC3Y3z7qTD9w9X\n1FQghDAMO2p3FGv3F87d9fSUGkdUvvXza62axwwPxtaGHiEeP0p+tLNd7sPxh8uX0+vqEEJC\noqLmvr6tbfEYGB9IrOhHwSiMBLo9Rq5dK6PeMDbmmYcHp53QjFHm2SXZjI1dOjI+JNBbtmyx\ns7OrqKj48eOHoaGhqalpbGws+WGQbPv27Rs3btTT03vy5EmPHj34HQ7oWoSFhWfOnBkaGsrv\nQAAA/CQn1zBfLCUlZciQIb6+vrm5udeuXdPX13/b+I0/aRg90JzuBV1QUXDu8TmibD3celQf\n7iwCO9jJiSh8jYoqZZrgyExMWMzT3pPI18uryzcGbeRK01yUevUqY83mkWvXKunqtlbT95kv\nUTDRMWFsBNgewhISjNmEBW/eZN69y9HpjASaRqd9Lvzc/nh4jQ8J9IsXL/bu3SsmJqaqqnr9\n+vUVK1ZYWFg8bXGHm04Bx/E1a9bs3r3bwMAgMjJSSUmJ3xEBAADoikpLS4nCxo0bp02blpWV\nFR0dnZWVNWPGjK1bt5IZCb2urrxxFxVOd4E+9egUtYaKEMIwbPv07dwKaZC9PdFZi9Ppb319\nW6s2QXvCvFHziHJgfGBCVlsmzPFIXWVlzMaGnJ5YZ6O1ms8ynzG2nlk4biG3AtB1cmLMB31+\n4ABH52op//cy+Jj/kVsh8Q4fEmgREREa00bq+/btc3Nzs7S07JT90DiOr1q16sSJExMmTIiK\nilLgfFwRAB1NUlKSg4ODlpaWtLS0lJSUlpaWg4NDUlISv+MCALCL6MkSFRVFCImKiu7du/fF\nr1tm8FrZly/0+oYdATlKoKvqqs5GN6wiN23ItOHqw7kVkpSqKmMz6jc+PsQoiBYdtDkoLSaN\nEMJx3O2GG95hVv1KPHq0vHHlpQmHDhGz+lp0PuY8UVCUVpytN5tbAVBERAwaV57Oef685d2/\nW6EkrdRNohtR/pgHCXRLjI2NY2JimI/s3Llz3bp1nW+GE41GW7hw4enTp83NzSMiImQ4X1q8\nDV6+fIlh2Lhx45ocNzQ0XLRoEfORwMBADMOqq6uJhwUFBW5ubv369RMXF+/Vq9f06dN5sX1d\nQECAtra2mJiYrq5uayMKysrKXF1dNTQ0JCQkDA0Nnz171rzOyZMnMQyzsbFhHMnJyXF1ddXU\n1JSWltbT07t8+TLXgwcIoZCQEENDw/T0dFtb2507d+7evdvW1jYtLc3Q0BCGiAAgKCorK+Xl\n5RkPlZSUqFQqmQEUZ2YyynKcLEgVGB9YUFFAlNdNXcfdqIYtXUoUKn78YDECQU1Ozd3cnSg/\ny3x2O/k2d8NoG2pubsKhQ0RZbdw4HTu71moWVBTcTLxJlB3HOIqLcLwECgu6CxdKKCoS5aQT\nJzg6t3/3hoW6M/MyWdfsCPiQQLu7u/v7+zc5uGPHjo0bN4qJiXGrFb5/Iqyvr3dycvLz85s2\nbVpwcLCEhAQ57Xp5eRkYGMTHx6emprJ/VnZ2tr6+/qtXrwIDA0tKSl68eLF8+fLdu3czf1fQ\nfhEREc7Ozlu3bs3NzV24cOHs2bNb7LZ0cnKKjIwMCwvLz893cHCYOnXqx4+/fBh99erV0aNH\nm2x+fu7cuaFDhz579iwnJ2fZsmWOjo7379/nYvCAsHnz5kOHDiUkJBw4cGDt2rVubm4HDhx4\n8eLFwYMHNzVOwQYAdFiGhoaGhoY4jj948IBxMDk52dDQkMwwijMyiIKYnJyksjL7J56KOkUU\n9HvrT9SeyN2oepuZMbL55FOnWNRcO2UtY+G8jbc3doRJb8+2b2/Y2RHDjI8dY7GSr/dT7+q6\naoQQhmFLJyzlbhgikpJDGz+HZN69W/blC/vn9lNu+OVn5GVwNype4EMCra+vf+/evebHt2zZ\nwugN5Yr2T8tts9raWjs7u8DAQDs7u+DgYHHOV7hsm6qqqitXrnh4eJiamvr4+LB/4sqVKxFC\n4eHhhoaG4uLiPXr0MDc3j4qKEhIS4mJ4R44cmT59uqOjo7y8vJub2/Dhw080+3haWVl59+7d\nLVu2DBkyRFpaevny5QMHDjzF9IesoqLizz//PHfunPKvf3N37drl7OyspqYmIyOzePFiNTW1\n+Ph4LgYPCBkZGU6NU22YLViwICNDAP7kAdCV7d6928rKysrKasuWLcxrw58+fXpb4/QvcpQ0\n/rmQ56T7OeZDzJvsN0R5lekqrkeFUSgj/vqLKH+Picl7+bK1mpKiknus9xDlj/kfT0ef5now\nHCl4+/ZN47htHTu7HqNanVhZR6s7E32GKE8eNHmA6gCuBzPir78aRpPTaCnnz7N/IqMH+sPP\nD1yPiusEZiMVGo0WGhp681f//vsvv+NqQWVl5YwZM4KDg+fOnXv58mUyt1O+deuWrKysubn5\nkiVLAgIC6lofwsWsoqIiNDT0r7/+YifR37p1K9YK1kM+cByPj483btysCCFkamoaFxfXvBqO\n400+/DCPj1++fLmZmZmlpWVrDVGpVH9//6KiIquWNl4C7aShodHiUI3Q0NDenO8lBgAg01Ym\nzIP6bt682XzgH08VfWjIkOS1tdk/y/OJJ1FQklayG9nqEIX20F24kLF1SOLx4yxqOoxx0NPQ\nI8p77u0prCjkRTxsinZzw2k0hJCwuPj4/ftZ1LyReON78XeivGoS9z+EIISk1dT6W1sT5Tc+\nPrRadrvntbs3vBg+F3zuCJ36rHWgdWGJ5XVKSkpafDY6OnrGjBktPlXZ0qZB/EKlUmfOnBkV\nFbV48WJPT08KhdSPKN7e3s7OzhQKZebMmStWrAgJCZkzZ85vz/r06VN9fb02e3/F9uzZs2fP\nnjbEVl5eTqVSmbuNVVRUcnNzm1STkpKysLDYt2/f0KFD+/Tpc+nSpeTkZFVVVeLZwMDAFy9e\ntDZf7e3bt0OGDEEIiYuLe3t76+vrtyFOwNq2bducnZ2Dg4NNTEyI/838/PzHjx+HhoZy9KUH\nAKArK25ceZr9BDq/PD8oOYgoLxi3gLsjdxnEunXTXbAg+eRJhFDatWtGu3e3ts04BaMcsT0y\n6egkhFBxZfH2u9vP/O8ML0L6rY+hoV8iI4my3qpVLNbVxnH88P3DRHmA6gCLIbyaezZs2bL0\nmzcRQpV5eRnBwTp//MHOWYwe6Hp6/aeCTzqqOjwKjys6UAK9evVqFs+amJiEhIQ0GePx77//\nHj9+XLL1eaYkKy0ttbS0jIuLW7Zs2ZkzZ0geQ5KZmfns2bPAwECEkIiIyIIFC7y9vdlJoInx\n4uSPeGne00zw9/ffsGHD5MmTS0tLzc3NXVxciC7PrKys1atXR0ZGtjagXFdXF8fxkpKSGzdu\nLFiwQEZGprUPXaDNHB0dVVRUjhw5snnzZmLWkZSUFDGD0NzcnN/RAQDagnUHFo1GCw8Pb37/\nbXNz9VVVZY1r2CmwnUD7x/nX1NcghDAMWzJhSZtb/y39NWtenT1Lr6+n19W9OHLEtPXB0CY6\nJtYjrO+8vIMQOh9zfvGExVxcFYRN9dXV0WvWEGXJ7t0Nt2xhUTn8TXjKtxSivG7KOgrGqz4+\nDWNjhQEDitLTEUJvvL3ZTKAHdP9vPEl6bjok0OxivY+3kJDQ9Mb1ZZgdZ/kNC5mKi4stLCye\nP3/u7u5+8OBB8gPw9vam0WgaGv8th06hUL59+6auro4QkpGRKS8vZ65fXl4uJiYmKirat29f\nYWFhNnei2rp16969e1t8Kjo6mnmERhMyMjJSUlL5+fmMI/n5+d27d29eU0lJibkvc/bs2X37\n9kUIpaSkFBYW6unpMVfGMCwrK0tTU5NxRE5ObsmSJeHh4Z6enpBA84K5ubm5uTmO48QrSkZG\nho/zDQAA7ce6A4vr3wAXZ2TgdDpRVtBhN0libPwxSWdSPxUORk5zqpum5oA//ki9fBkh9Mbb\n23DzZqnWtz87Ynvknzf/1NTX0Oi0ZYHLnm18xrustEXP9+8vaZxnP37vXlFZWRaVd9/bTRR6\nyvV0GMPx/uccwLChixc/XrcOIfT10aPSrCx29puUk5TrLtv9Z9lPhFB6Lh92x+SIwIyB7uDy\n8vJMTEyeP3++YcMGvmTP9fX1/v7+165dw5kYGRldvHiRqDBgwIA3b94wn/L69ev+/ftTKBQZ\nGRkrK6uzZ8+yM4lzz549eCtYZM8IIQzDDA0No6OjGUcePXo0duxY1s0VFhY+ePDA2toaIWRt\nbc3c3LRp0+bMmYPjOHP2zFBXVwdZHU9hGCYrKysrKwu/ZwAEnYeHB4s+LOIb4Bu/WrNmDUKo\nbd8AF6WlEQWMQpEfwNYkttjM2NSchqWlFo9f3IZGOTJ60yaMQkEI1VdXx+/bx6KmlrIWY0m7\n+E/xpx+ROpuw4N27hMaUo8fo0boLFrCoHPY67HnWc6K8fup6MWGurXvWokGOjkKiogghnE5/\n0/rGNE0wdkpPy03jVWRcwp8EupNtxJCbm2tqapqSkrJr164DHG69wy1hYWFFRUVN1tK2trb2\n9fWl0+kIoZUrV378+HHr1q0/f/4sKyu7fv26v7//usYFz0+ePEmn0y0tLZ8/f15TU5ObmxsR\nEWFmZsbdZezWrVt37969gICA4uLi48ePv3z5ktHt4eHhwdhj1sfH5+rVq+Xl5enp6TY2Ntra\n2suWLfvtxW1tbWNjY8vLy/Py8o4fPx4REdHiYhGg/TrZ+xcAwBrxDbDtr8aMGdPmCxa+b9gD\nT1ZDg8VmH8wuPmvoDFKUVrQeYd3mptmkNHjwAFtbovzay6u1nb0Jmyw3MXrENwdvJm0FCVpt\n7T/z59NqahBCFGHhyZ6eWOvTrug4fdvdhoVWenTrwdMxMARJZWWtxi8u3vn74+ylE4xhG4zP\nSx0WHxLoTrYRw9evX8ePH//u3bvjx4+TvAwQMx8fn0mTJsn++t3NrFmzvnz58vDhQ4TQgAED\nYmJiEhIShg4d2qtXr8OHD1+4cGH+/PlETXV19aSkpKFDh86bN09WVlZfX//06dNbtmzh7jJ2\n5ubmPj4+u3fvVlVV9fHxuX37dovz/Kytre/fv6+urj5hwgQdHZ2oqCh2FghftWrV7t271dXV\nBw8efPfu3bt379o2/vkDXNTJ3r8AdDUd4QMwI4FWGDiQnfoVNRU3Xtwgyv8b/T9ed50Sxu7c\niQkJIYRoNTWxLPc5lxCR8HL0IkZuUGuocy/MJVZZ5rWnmzb9bPyPM1i3TmU4q+HXV55fefm1\nYVW+zZabJUXJmDw2ZGHDJuHl3759bpzmyBqjB/p9znu+b+jBGh/GQBMbMaxpHPPOcOzYsU2b\nNrU40LnD+vz5s6mpaVZW1qlTp5YvX87HSEJCQpof1NTUZH79jR49mnnl/CZUVFROnDjRfGFm\n7nJ0dHR0dGx+nPkLREVFRT8/v99eqslq4uPHj4edU0jQtvcvi0lI9MahkAAAXiOWZhoxYoSt\nra2ysjKGYXl5eVFRUYaGhkFBQaTdfwveviUKSoMHs1P/ZuLNipoKorxw3EJehfUrhQEDdJ2c\n3vj4IIRSr1zRW7GiR+t7zRgPMF5luurEwxMIoeSvyYv8F11yvsTT4W3vAgISjx0jysrDho1l\nOYuMWkPdFNSw0VUfpT4kdD8TNKdMkVZTq8jORgi9vXixDxsTzQf1HEQUyqrKvhZ97a3YcVdH\n5UMCzWIjBsHaySw9Pd3U1DQ3N9fX1xdGC4Auom3vXxaTkN69e8et2AAArHWEDixaTQ1jG0JF\n9hJoxvgNPQ29YerDeBVZM+N27Uq7fr2uogLh+MMVK+yfP8da/1Z2/+z9TzOeJn1JQghdfn5Z\nXkr+5J8nWefQ1UVFr6PD05OfFeb/qMPowkoKPYYNN5hgpaXan3VgGcHB9xuX8RaVkbG6elWI\n5fe0e8L2MNZ+PjDngKiwKOvrcwsmJDTY0fH5/v0Iocy7d6uLisQVFFifottTl1F+9+NdR06g\n+TCEo3NsxPD+/XsTE5OfP3/6+flB9gy6jra9f1lMQhrM3h0UANB+HWEn0cLUVHp9PVFWHjLk\nt/Uz8zJjMxv20nIa58S7wJqT7tnTcPNmovwzKYlYHLo14iLiQX8FqXZr2LXg9KPTNp42RdSi\n5jUryoove6zaPaDHKSXFGBuHn/s8671CsAv3aPsCvv/hdlVDe/kA6T2rZn7KfNP8XJxGSzh4\nMMTWll5XhxDCKBTLgABFliNhXn9/ffTBUaI8QXuCrT6pgxt1FywgNhWn1dSkXrny2/qq3VSV\nZRr2i2DsOtkx8aEHuhNsxJCcnDx16tSysrIbN27MmjWL3+EAQJ62vX9ZLENJ8mZDAHRlxAfg\n5uPoyOzAKmhcDwoTElIcNOi39f3i/IixiKLCovNGzeNtcM2MdHN7FxBALBsSu3VrH0tLhdaX\nDdFQ0AhbFTb52GQibw5KDnry4cmSCUtMdUzlJOXKqspSPid/8A1QCXsjXYm3vJ0BQuJ1qM8H\nKvoQcut0SF3/HqPn2PcZM166Z09aTU1uYuLrCxcKGN/aYZjpqVP9rFlNqaytr11wcUEdrQ4h\nJCIkcmYe2TtUyPfvrzZuXHZsLELorZ/fiBUrfnvKELUhj9IeIYRef3/N8/jagQ8JtKBvxPDi\nxQtzc/Pq6urQ0NApU6bwOxwASCXo718AurKO0IGV/7ohK1LQ1hZuZVcsBhqdFvBvAFGeMWyG\norQib4NrRkhMbMqFC9eNjXE6va6yMmzevHlxcSzGS+hp6D10e2h1yupHyQ+EUGFF4f7w/fvD\n9yOE+uSiObGob7PNaqpFUZUERZSGSVJpGNOsOQqOxD7kvNp/+BU63LwhiojI5LNnhzDtx96i\nLXe2JH9NJsobzDfoqumyrs8LugsWEAn0z6SkgjdvlH73tcPQXkMhgW6V4G7E8PTp02nTptHp\n9JCQEFNTU36HAwAfCO77F4AuriN8AM579YooKA/7/Wjmh6kPvxV9I8oLxrFa5Jh3eo0fr7dq\nVdKJEwihn8nJUatWTTl/nkX9ERojkrclL7209O6ru8QR0XpkmYDGvkfMfyjzu4sKWYwZaecw\nYfxMJWklhFB9VdXHuJhnt/y+hkdIfS1h8VdVQUfH4uJFFpMaCUHJQYzBG0N7Dd1qxWotEd4Z\nYGv7aNWqOioVIfTG19fkd/vfMXZzTMtJq66r5tGe7e3Hz50IiY0Y+BgApx4/fjx9+nQhIaGI\niIjf7gACQOcmcO9fAADi+wdgHM972bCYGutl1wi+sQ0bcKjJqU0dPJWHgbE0fv/+b0+eEJG/\nvnBBvn9/g8ZdFFrUXbb7neV3nmY8PRN95n1k2LQHFYpl/z1L1+w+YueWyY4rm5wlLCExwHTq\nANOpCKFXL594HV5DjXvZOw/JVDVUwISFexkZ6To5DZw3jyIiwjrm51nPHXwciNEvUmJSVxdf\nJWf5v+ZEZWS0bWze+fsjhFIvX55w8CCxwUprGAl0Pb3+9ffXo/qMIiNKzsHoQ3aFhYVZWFiI\niIjcv38fsmfQlX3//t3Pzy84OLjJLr4sdjIDAHQo/NpJtPTz56rCQqLcvaV9AJgVVhQyOnEd\nxzoKUbi5LwFHhMXFZ9y8yVhB4om7e/Lff//2LMOeekveq84PqmRkzyJSUibHjq3L+N48e25i\n+IiJZ64k2/8TGbi673YHdOAPtO9P5O5Yf94KE7c0Yid7tjhhUVlbiRDCMOyCwwXG8nB8wVgQ\nujI//2NLq+4yG9RzEKPXmVjVpGOCBJotN2/enDVrlqysbHR09OjRo/kdDgB8k5iYOGTIkMWL\nF8+ZM2fgwIEvXrxgPLVz504+BgYA6PhyGX8xMKy7nh7rypfiL9XU1yCEMAwjbfnn1shpac28\nfbth9DOOP1q9+tGqVcQugC3A8fSbNy8OGpT8999441L3vSZMcHr9Wn/NGoowu1/+mw00e+Px\nZpHFyiJZrFga0SkoOi16yI4hO0N3UmuorZ11+fnlSUcmFVcWEw+3WW2bN5rsyZdN9Bo/Xr5/\nw9p8r729WVcWERIZ1qtheE/il0TeRtYOkED/3tWrV+fNm6eoqBgVFTWMjTFbAHRiW7ZssbOz\nq6io+PHjh6GhoampaWxsLL+DAgAIhpyEBKIgp6X12yWBvZ56EYWJ2hMZe2Xzkbqx8YwbNxjD\nD5JPnQoYMeLDrVuMVfkQQnUVFe8vXQrQ1w+1syv7+pU4KCwhYXLs2B/R0d369uW0UUlRyZNz\nTz50e6ipqEkcqaqr8gjx0NqstTdsL2N1Z0Li58Tpp6bbe9sTfc8IIVdT150zOkDXBoYNcXYm\nil8iI1lvjY4QYgzbSMhK4Glc7cHPMdACwdvbe+nSpb169YqKiurXj/9vYAD468WLF5cvXxYT\nE1NVVb1+/frmzZstLCzCw8PHjx/P79AAAB1dzvPnREHVwIB1zWeZz97/aNjxe/H4xbwNi21a\nM2bMCgkJsbOrLStDCBWmpobY2orJyXUfMUJcQaHs69f8lBRabS3zKerGxlPOn5fX1m5Pu5N0\nJr32eL0leMuZ6DN0nI4Q+ln2c+udrdvubhvQfYB2d20aTnub/fZL4RfGKRiGbZ22tUNkzwgh\nhAbPnx+7bRu9rg6n0994exvt2cOisoFmw8vj/Y/3pVWl3SS6kRIjZ6AHmhVPT8+lS5dqaGhE\nR0dD9gwAQkhERIRGozEe7tu3z83NzdLSEvqhAQCs0WprfyY1DGnt+bsVJDyfeBIFRWnF2Xqz\neRsZJzSnTrVPSGAewF1TUvI1OvrD7du5L14wZ8+yGhqWly798ehRO7Nngoy4zMm5J59vfj6+\n/3+9FTiOp+WmhaSEhL0OY86elWWUg5YF7Zq5q+MskSSlqtqvcT/aNz4+xEYwrTHUanh50HF6\nh+2EhgS6VYcPH162bFn//v1jY2P7cv61C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prediction results","message":["%r","","predicted_results <- ore.sort(nb_res[(nb_res$PROB_OF_1 > 0.5),], ","                              by = c('PREDICTION','PROB_OF_1'), ","                              reverse = TRUE)","","z.show(predicted_results)"],"enabled":true,"result":{"startTime":1737672604630,"interpreter":"r.low","endTime":1737672605413,"results":[{"message":"PROB_OF_0\tPROB_OF_1\tAFFINITY_CARD\tCUST_ID\tPREDICTION\n0.00968170643844318\t0.990318293561557\t1\t100179\t1\n0.00968170643844318\t0.990318293561557\t1\t100780\t1\n0.00968170643844318\t0.990318293561557\t1\t101559\t1\n0.00968170643844318\t0.990318293561557\t1\t101560\t1\n0.00968170643844318\t0.990318293561557\t1\t101708\t1\n0.00968170643844318\t0.990318293561557\t1\t101831\t1\n0.00968170643844318\t0.990318293561557\t0\t101861\t1\n0.00968170643844318\t0.990318293561557\t0\t102687\t1\n0.00968170643844318\t0.990318293561557\t1\t102734\t1\n0.00968170643844318\t0.990318293561557\t1\t103543\t1\n0.00968170643844318\t0.990318293561557\t1\t103801\t1\n0.00968170643844318\t0.990318293561557\t1\t104245\t1\n0.00968170643844318\t0.990318293561557\t1\t104307\t1\n0.0171390831422237\t0.982860916857776\t1\t100052\t1\n0.0171390831422237\t0.982860916857776\t1\t100100\t1\n0.0171390831422237\t0.982860916857776\t1\t100245\t1\n0.0171390831422237\t0.982860916857776\t1\t100544\t1\n0.0171390831422237\t0.982860916857776\t1\t100601\t1\n0.0171390831422237\t0.982860916857776\t1\t100633\t1\n0.0171390831422237\t0.982860916857776\t1\t100668\t1\n0.0171390831422237\t0.982860916857776\t1\t100816\t1\n0.0171390831422237\t0.982860916857776\t1\t100889\t1\n0.0171390831422237\t0.982860916857776\t1\t100985\t1\n0.0171390831422237\t0.982860916857776\t1\t101010\t1\n0.0171390831422237\t0.982860916857776\t1\t101026\t1\n0.0171390831422237\t0.982860916857776\t1\t101044\t1\n0.0171390831422237\t0.982860916857776\t1\t101049\t1\n0.0171390831422237\t0.982860916857776\t1\t101189\t1\n0.0171390831422237\t0.982860916857776\t1\t101208\t1\n0.0171390831422237\t0.982860916857776\t1\t101426\t1\n0.0171390831422237\t0.982860916857776\t1\t101494\t1\n0.0171390831422237\t0.982860916857776\t0\t101733\t1\n0.0171390831422237\t0.982860916857776\t0\t101990\t1\n0.0171390831422237\t0.982860916857776\t1\t102025\t1\n0.0171390831422237\t0.982860916857776\t1\t102037\t1\n0.0171390831422237\t0.982860916857776\t1\t102083\t1\n0.0171390831422237\t0.982860916857776\t1\t102266\t1\n0.0171390831422237\t0.982860916857776\t0\t102330\t1\n0.0171390831422237\t0.982860916857776\t1\t102380\t1\n0.0171390831422237\t0.982860916857776\t0\t102585\t1\n0.0171390831422237\t0.982860916857776\t1\t102669\t1\n0.0171390831422237\t0.982860916857776\t1\t102881\t1\n0.0171390831422237\t0.982860916857776\t1\t103127\t1\n0.0171390831422237\t0.982860916857776\t1\t103149\t1\n0.0171390831422237\t0.982860916857776\t1\t103266\t1\n0.0171390831422237\t0.982860916857776\t1\t103294\t1\n0.0171390831422237\t0.982860916857776\t1\t103332\t1\n0.0171390831422237\t0.982860916857776\t1\t103353\t1\n0.0171390831422237\t0.982860916857776\t1\t103439\t1\n0.0171390831422237\t0.982860916857776\t1\t103463\t1\n0.0171390831422237\t0.982860916857776\t1\t103613\t1\n0.0171390831422237\t0.982860916857776\t1\t103873\t1\n0.0171390831422237\t0.982860916857776\t1\t103949\t1\n0.0171390831422237\t0.982860916857776\t1\t104136\t1\n0.0171390831422237\t0.982860916857776\t0\t104410\t1\n0.0171390831422237\t0.982860916857776\t1\t104419\t1\n0.0251774955533605\t0.974822504446639\t1\t100081\t1\n0.0251774955533605\t0.974822504446639\t1\t100089\t1\n0.0251774955533605\t0.974822504446639\t1\t100224\t1\n0.0251774955533605\t0.974822504446639\t1\t100256\t1\n0.0251774955533605\t0.974822504446639\t1\t100988\t1\n0.0251774955533605\t0.974822504446639\t1\t101442\t1\n0.0251774955533605\t0.974822504446639\t0\t101445\t1\n0.0251774955533605\t0.974822504446639\t1\t101499\t1\n0.0251774955533605\t0.974822504446639\t1\t101662\t1\n0.0251774955533605\t0.974822504446639\t0\t103165\t1\n0.0251774955533605\t0.974822504446639\t1\t103276\t1\n0.0251774955533605\t0.974822504446639\t1\t103361\t1\n0.0251774955533605\t0.974822504446639\t1\t104093\t1\n0.0251774955533605\t0.974822504446639\t0\t104223\t1\n0.0289963166924954\t0.971003683307505\t1\t100817\t1\n0.0289963166924954\t0.971003683307505\t1\t100953\t1\n0.0440397870012385\t0.955960212998762\t0\t100036\t1\n0.0440397870012385\t0.955960212998762\t0\t100344\t1\n0.0440397870012385\t0.955960212998762\t1\t100355\t1\n0.0440397870012385\t0.955960212998762\t1\t100406\t1\n0.0440397870012385\t0.955960212998762\t0\t100621\t1\n0.0440397870012385\t0.955960212998762\t1\t100778\t1\n0.0440397870012385\t0.955960212998762\t1\t101078\t1\n0.0440397870012385\t0.955960212998762\t1\t101139\t1\n0.0440397870012385\t0.955960212998762\t1\t101144\t1\n0.0440397870012385\t0.955960212998762\t0\t101191\t1\n0.0440397870012385\t0.955960212998762\t1\t101233\t1\n0.0440397870012385\t0.955960212998762\t1\t101260\t1\n0.0440397870012385\t0.955960212998762\t1\t101469\t1\n0.0440397870012385\t0.955960212998762\t1\t101605\t1\n0.0440397870012385\t0.955960212998762\t1\t101606\t1\n0.0440397870012385\t0.955960212998762\t1\t101621\t1\n0.0440397870012385\t0.955960212998762\t0\t101635\t1\n0.0440397870012385\t0.955960212998762\t1\t101793\t1\n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the proxy object into a table","message":["%r","","ore.drop(table=\"DEMO_DF_NB\")","ore.create(DEMO_DF, \"DEMO_DF_NB\")"],"enabled":true,"result":{"startTime":1737672626095,"interpreter":"r.low","endTime":1737672627012,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create named model to be used in SQL interface","message":["%r","","ore.drop(model = 'NB_CLASSIFICATION_MODEL')","","mod <- ore.odmNB(formula = AFFINITY_CARD ~ ., ","                 data = DEMO_DF.train, ","                 odm.settings = list(model_name = \"NB_CLASSIFICATION_MODEL\"))"],"enabled":true,"result":{"startTime":1737672627473,"interpreter":"r.low","endTime":1737672629254,"results":[{"message":"Warning message:\n\u201cThe created mining model NB_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use the SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,","       round(PREDICTION_AFFINITY_CARD,3) PREDICTION_AFFINITY_CARD,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(NB_CLASSIFICATION_MODEL USING *) PREDICTION_AFFINITY_CARD,","             PREDICTION_DETAILS(NB_CLASSIFICATION_MODEL USING *) PD","      FROM DEMO_DF_NB","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","          XMLTABLE('/Details'","                   PASSING OUT.PD","                   COLUMNS","                     \"Attribute1\" XMLType PATH 'Attribute[1]',","                     \"Attribute2\" XMLType PATH 'Attribute[2]')","OUTPRED"],"enabled":true,"result":{"startTime":1737672629713,"interpreter":"sql.low","endTime":1737672630292,"results":[{"message":"CUST_ID\tPREDICTION_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t0\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".488\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".271\" \n100002\t1\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\"1.555\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".938\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".271\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".24\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".271\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".219\" \n100005\t1\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".953\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".535\" \n100006\t0\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".488\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".355\" \n100007\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".271\" \t\"OCCUPATION\" actualValue=\"Other\" weight=\".248\" \n100008\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".271\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".219\" \n100009\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".501\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".488\" \n100010\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".501\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".336\" \n100011\t0\t\"EDUCATION\" actualValue=\"9th\" weight=\".293\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"0\" weight=\".293\" \n100012\t1\t\"EDUCATION\" actualValue=\"PhD\" weight=\"1.364\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".953\" \n100013\t1\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".953\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".535\" \n100014\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".271\" \t\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".248\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737672630761,"interpreter":"md.low","endTime":1737672631227,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737672631694,"interpreter":"md.low","endTime":1737672632162,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification NN","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737674211150,"interpreter":"md.medium","endTime":1737674211605,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Neural Network","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card ","loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, ","when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification neural network model using the ","Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737674212114,"interpreter":"md.medium","endTime":1737674212561,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-neural-network\">Classification Modeling to Predict Target Customers using Neural Network<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card\nloyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who,\nwhen given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification neural network model using the\nSales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737674213044,"interpreter":"md.medium","endTime":1737674213486,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">OML4R Neural Network<\/a>"],"enabled":true,"result":{"startTime":1737674213962,"interpreter":"md.medium","endTime":1737674214423,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">OML4R Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","library(lattice)","","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737674214890,"interpreter":"r.medium","endTime":1737674215396,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare dataset combining customer and demographics data","message":["%r","","ore.sync(query = c(\"QUERY_CUSTOMERS\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH AGE, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.sync(query = c(\"QUERY_DEMO_DF\" =  'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","CUST_DF <- merge(QUERY_CUSTOMERS,QUERY_DEMO_DF)"],"enabled":true,"result":{"startTime":1737674215876,"interpreter":"r.medium","endTime":1737674217072,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","head(CUST_DF)"],"enabled":true,"result":{"startTime":1737674217537,"interpreter":"r.medium","endTime":1737674218121,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 12<\/caption>\n<thead>\n\t<tr><th scope=col>CUST_ID<\/th><th scope=col>CUST_GENDER<\/th><th scope=col>CUST_MARITAL_STATUS<\/th><th scope=col>AGE<\/th><th scope=col>CUST_INCOME_LEVEL<\/th><th scope=col>CUST_CREDIT_LIMIT<\/th><th scope=col>EDUCATION<\/th><th scope=col>AFFINITY_CARD<\/th><th scope=col>HOUSEHOLD_SIZE<\/th><th scope=col>OCCUPATION<\/th><th scope=col>YRS_RESIDENCE<\/th><th scope=col>Y_BOX_GAMES<\/th><\/tr>\n\t<tr><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;int&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>104088<\/td><td>M<\/td><td>NeverM <\/td><td>1980<\/td><td>L: 300,000 and above<\/td><td>11000<\/td><td>Bach.  <\/td><td>0<\/td><td>1<\/td><td>Exec. <\/td><td>1<\/td><td>1<\/td><\/tr>\n\t<tr><td>104089<\/td><td>M<\/td><td>Married<\/td><td>1970<\/td><td>H: 150,000 - 169,999<\/td><td> 5000<\/td><td>&lt; Bach.<\/td><td>0<\/td><td>3<\/td><td>Crafts<\/td><td>3<\/td><td>0<\/td><\/tr>\n\t<tr><td>104090<\/td><td>M<\/td><td>Married<\/td><td>1953<\/td><td>G: 130,000 - 149,999<\/td><td> 7000<\/td><td>Masters<\/td><td>1<\/td><td>3<\/td><td>Prof. <\/td><td>6<\/td><td>0<\/td><\/tr>\n\t<tr><td>104091<\/td><td>M<\/td><td>Married<\/td><td>1964<\/td><td>J: 190,000 - 249,999<\/td><td>15000<\/td><td>HS-grad<\/td><td>0<\/td><td>3<\/td><td>Crafts<\/td><td>5<\/td><td>0<\/td><\/tr>\n\t<tr><td>104092<\/td><td>F<\/td><td>NeverM <\/td><td>1958<\/td><td>L: 300,000 and above<\/td><td>15000<\/td><td>Bach.  <\/td><td>0<\/td><td>2<\/td><td>Sales <\/td><td>5<\/td><td>0<\/td><\/tr>\n\t<tr><td>104093<\/td><td>M<\/td><td>Married<\/td><td>1956<\/td><td>F: 110,000 - 129,999<\/td><td> 1500<\/td><td>Bach.  <\/td><td>1<\/td><td>3<\/td><td>Prof. <\/td><td>7<\/td><td>0<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","group <- as.integer(1:nrow(CUST_DF) %in% index)","","# Create training data set","rownames(CUST_DF) <- CUST_DF$CUST_ID","CUST_DF.train <- CUST_DF[group==FALSE,]","","# Create test data set","CUST_DF.test <- CUST_DF[group==TRUE,]","","cat(\"\\n====== Records | Columns\\n\\nTrain Data: \",dim(CUST_DF.train),\"\\nTest data: \",dim(CUST_DF.test))"],"enabled":true,"result":{"startTime":1737674218630,"interpreter":"r.medium","endTime":1737674220928,"results":[{"message":"\n====== Records | Columns\n\nTrain Data:  2700 12 \nTest data:  1800 12","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a NN classification model to predict AFFINITY_CARD using default model settings","message":["%r","","ore.drop(model=\"NN_CLASSIFICATION_MODEL\", silent=TRUE)","","nn.mod1 <- ore.odmNN(AFFINITY_CARD~.-CUST_ID, ","                     CUST_DF.train, ","                     \"classification\", ","                     odm.settings=list(model_name=\"NN_CLASSIFICATION_MODEL\"))"],"enabled":true,"result":{"startTime":1737674221404,"interpreter":"r.medium","endTime":1737674227002,"results":[{"message":"Warning message:\n\u201cThe created mining model NN_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict outcomes using TEST dataset","message":["%r","","RES_DF <- predict(nn.mod1, CUST_DF.test, supplemental.cols='AFFINITY_CARD')","colnames(RES_DF) <- c( 'PROB_OF_0', 'PROB_OF_1', 'AFFINITY_CARD', 'PREDICTION')","","","z.show(head(RES_DF,20))"],"enabled":true,"result":{"startTime":1737674227475,"interpreter":"r.medium","endTime":1737674228470,"results":[{"message":"PROB_OF_0\tPROB_OF_1\tAFFINITY_CARD\tPREDICTION\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n9.21190219549395e-07\t0.99999907880978\t1\t1\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n0\t1\t1\t1\n0.999999999935559\t6.44406445277317e-11\t0\t0\n0\t1\t1\t1\n0\t1\t1\t1\n1\t0\t0\t0\n1\t0\t0\t0\n0.999423598647633\t0.000576401352367341\t0\t0\n0\t1\t1\t1\n1\t0\t0\t0\n1\t0\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NN ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>' ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO' ","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS.  The options for the activation functions are one or more of the following strings: NNET_ACTIVATIONS_ARCTAN, NNET_ACTIVATIONS_BIPOLAR_SIG, NNET_ACTIVATIONS_LINEAR, NNET_ACTIVATIONS_LOG_SIG, NNET_ACTIVATIONS_TANH and <strong>starting from Oracle Database 23ai<\/strong> NNET_ACTIVATIONS_RELU.","> NNET_HIDDEN_LAYERS = 3","","> NNET_NODES_PER_LAYER = '8,8,2'","","> NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'","","> NNET_HELDASIDE_RATIO = 0.25","","> NNET_HELDASIDE_MAX_FAIL = 6","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> NNET_REG_LAMBDA = 1","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> NNET_ITERATIONS = 200","","> NNET_TOLERANCE = 0.000001","","> ODMS_RANDOM_SEED = 0","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> LBFGS_HISTORY_DEPTH = 10","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> LBFGS_GRADIENT_TOLERANCE = 0.0000001","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'","","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'"],"enabled":true,"result":{"startTime":1737674228931,"interpreter":"md.medium","endTime":1737674229386,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nn\">Examples of possible setting overrides for NN<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS.  The options for the activation functions are one or more of the following strings: NNET_ACTIVATIONS_ARCTAN, NNET_ACTIVATIONS_BIPOLAR_SIG, NNET_ACTIVATIONS_LINEAR, NNET_ACTIVATIONS_LOG_SIG, NNET_ACTIVATIONS_TANH and <strong>starting from Oracle Database 23ai<\/strong> NNET_ACTIVATIONS_RELU.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_HIDDEN_LAYERS = 3<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_NODES_PER_LAYER = '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_ACTIVATIONS = &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_RATIO = 0.25<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_MAX_FAIL = 6<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REG_LAMBDA = 1<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_ITERATIONS = 200<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_TOLERANCE = 0.000001<\/p>\n<\/blockquote>\n<blockquote>\n<p>ODMS_RANDOM_SEED = 0<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_HISTORY_DEPTH = 10<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_GRADIENT_TOLERANCE = 0.0000001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use a single layer with 4 neurons and specific activation function","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS = 1,","                 NNET_NODES_PER_LAYER = '4',","                 NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH'\")","                 ","nn.mod2 <- ore.odmNN(AFFINITY_CARD~.-CUST_ID, ","                     CUST_DF.train, ","                     \"classification\", ","                     odm.settings = settings)"],"enabled":true,"result":{"startTime":1737674229847,"interpreter":"r.medium","endTime":1737674234063,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use 3 layers with 8,8,2 neurons and specific activation functions","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS   = 3,","                 NNET_NODES_PER_LAYER = c('8','8','4'),","                 NNET_ACTIVATIONS     = c(\"'NNET_ACTIVATIONS_TANH'\",","                                          \"'NNET_ACTIVATIONS_LOG_SIG'\", ","                                          \"'NNET_ACTIVATIONS_LINEAR'\")","                 )  ","nn.mod3 <- ore.odmNN(AFFINITY_CARD~.-CUST_ID, ","                     CUST_DF.train, ","                     'classification', ","                     odm.settings = settings) "],"enabled":true,"result":{"startTime":1737674234530,"interpreter":"r.medium","endTime":1737674237826,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model fit statistics","message":["%r","","summary(nn.mod2)"],"enabled":true,"result":{"startTime":1737674238287,"interpreter":"r.medium","endTime":1737674241376,"results":[{"message":"\nCall:\nore.odmNN(formula = AFFINITY_CARD ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"classification\", odm.settings = settings)\n\nSettings: \n                                                  value\nclas.weights.balanced                               OFF\nlbfgs.gradient.tolerance                     .000000001\nlbfgs.history.depth                                  20\nlbfgs.scale.hessian          LBFGS_SCALE_HESSIAN_ENABLE\nactivations                     'NNET_ACTIVATIONS_TANH'\nhidden.layers                                         1\niterations                                          200\nnodes.per.layer                                       4\ntolerance                                       .000001\nodms.details                                odms.enable\nodms.missing.value.treatment    odms.missing.value.auto\nodms.random.seed                                      0\nodms.sampling                     odms.sampling.disable\nprep.auto                                            ON\n\nNumber of Layers: \n[1] 2\n\nNodes per Layer: \n[1]  4 60\n\nWeight: \n    LAYER IDX_FROM IDX_TO      ATTRIBUTE_NAME      ATTRIBUTE_VALUE\n1       0        0      0                 AGE                 <NA>\n2       0        0      1                 AGE                 <NA>\n3       0        0      2                 AGE                 <NA>\n4       0        0      3                 AGE                 <NA>\n5       0        1      0   CUST_CREDIT_LIMIT                 <NA>\n6       0        1      1   CUST_CREDIT_LIMIT                 <NA>\n7       0        1      2   CUST_CREDIT_LIMIT                 <NA>\n8       0        1      3   CUST_CREDIT_LIMIT                 <NA>\n9       0        2      0       YRS_RESIDENCE                 <NA>\n10      0        2      1       YRS_RESIDENCE                 <NA>\n11      0        2      2       YRS_RESIDENCE                 <NA>\n12      0        2      3       YRS_RESIDENCE                 <NA>\n13      0        3      0         Y_BOX_GAMES                 <NA>\n14      0        3      1         Y_BOX_GAMES                 <NA>\n15      0        3      2         Y_BOX_GAMES                 <NA>\n16      0        3      3         Y_BOX_GAMES                 <NA>\n17      0        4      0         CUST_GENDER                    F\n18      0        4      1         CUST_GENDER                    F\n19      0        4      2         CUST_GENDER                    F\n20      0        4      3         CUST_GENDER                    F\n21      0        5      0         CUST_GENDER                    M\n22      0        5      1         CUST_GENDER                    M\n23      0        5      2         CUST_GENDER                    M\n24      0        5      3         CUST_GENDER                    M\n25      0        6      0   CUST_INCOME_LEVEL      A: Below 30,000\n26      0        6      1   CUST_INCOME_LEVEL      A: Below 30,000\n27      0        6      2   CUST_INCOME_LEVEL      A: Below 30,000\n28      0        6      3   CUST_INCOME_LEVEL      A: Below 30,000\n29      0        7      0   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n30      0        7      1   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n31      0        7      2   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n32      0        7      3   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n33      0        8      0   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n34      0        8      1   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n35      0        8      2   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n36      0        8      3   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n37      0        9      0   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n38      0        9      1   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n39      0        9      2   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n40      0        9      3   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n41      0       10      0   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n42      0       10      1   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n43      0       10      2   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n44      0       10      3   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n45      0       11      0   CUST_INCOME_LEVEL F: 110,000 - 129,999\n46      0       11      1   CUST_INCOME_LEVEL F: 110,000 - 129,999\n47      0       11      2   CUST_INCOME_LEVEL F: 110,000 - 129,999\n48      0       11      3   CUST_INCOME_LEVEL F: 110,000 - 129,999\n49      0       12      0   CUST_INCOME_LEVEL G: 130,000 - 149,999\n50      0       12      1   CUST_INCOME_LEVEL G: 130,000 - 149,999\n51      0       12      2   CUST_INCOME_LEVEL G: 130,000 - 149,999\n52      0       12      3   CUST_INCOME_LEVEL G: 130,000 - 149,999\n53      0       13      0   CUST_INCOME_LEVEL H: 150,000 - 169,999\n54      0       13      1   CUST_INCOME_LEVEL H: 150,000 - 169,999\n55      0       13      2   CUST_INCOME_LEVEL H: 150,000 - 169,999\n56      0       13      3   CUST_INCOME_LEVEL H: 150,000 - 169,999\n57      0       14      0   CUST_INCOME_LEVEL I: 170,000 - 189,999\n58      0       14      1   CUST_INCOME_LEVEL I: 170,000 - 189,999\n59      0       14      2   CUST_INCOME_LEVEL I: 170,000 - 189,999\n60      0       14      3   CUST_INCOME_LEVEL I: 170,000 - 189,999\n61      0       15      0   CUST_INCOME_LEVEL J: 190,000 - 249,999\n62      0       15      1   CUST_INCOME_LEVEL J: 190,000 - 249,999\n63      0       15      2   CUST_INCOME_LEVEL J: 190,000 - 249,999\n64      0       15      3   CUST_INCOME_LEVEL J: 190,000 - 249,999\n65      0       16      0   CUST_INCOME_LEVEL K: 250,000 - 299,999\n66      0       16      1   CUST_INCOME_LEVEL K: 250,000 - 299,999\n67      0       16      2   CUST_INCOME_LEVEL K: 250,000 - 299,999\n68      0       16      3   CUST_INCOME_LEVEL K: 250,000 - 299,999\n69      0       17      0   CUST_INCOME_LEVEL L: 300,000 and above\n70      0       17      1   CUST_INCOME_LEVEL L: 300,000 and above\n71      0       17      2   CUST_INCOME_LEVEL L: 300,000 and above\n72      0       17      3   CUST_INCOME_LEVEL L: 300,000 and above\n73      0       18      0 CUST_MARITAL_STATUS              Divorc.\n74      0       18      1 CUST_MARITAL_STATUS              Divorc.\n75      0       18      2 CUST_MARITAL_STATUS              Divorc.\n76      0       18      3 CUST_MARITAL_STATUS              Divorc.\n77      0       19      0 CUST_MARITAL_STATUS              Mabsent\n78      0       19      1 CUST_MARITAL_STATUS              Mabsent\n79      0       19      2 CUST_MARITAL_STATUS              Mabsent\n80      0       19      3 CUST_MARITAL_STATUS              Mabsent\n81      0       20      0 CUST_MARITAL_STATUS              Married\n82      0       20      1 CUST_MARITAL_STATUS              Married\n83      0       20      2 CUST_MARITAL_STATUS              Married\n84      0       20      3 CUST_MARITAL_STATUS              Married\n85      0       21      0 CUST_MARITAL_STATUS               NeverM\n86      0       21      1 CUST_MARITAL_STATUS               NeverM\n87      0       21      2 CUST_MARITAL_STATUS               NeverM\n88      0       21      3 CUST_MARITAL_STATUS               NeverM\n89      0       22      0 CUST_MARITAL_STATUS               Separ.\n90      0       22      1 CUST_MARITAL_STATUS               Separ.\n91      0       22      2 CUST_MARITAL_STATUS               Separ.\n92      0       22      3 CUST_MARITAL_STATUS               Separ.\n93      0       23      0 CUST_MARITAL_STATUS              Widowed\n94      0       23      1 CUST_MARITAL_STATUS              Widowed\n95      0       23      2 CUST_MARITAL_STATUS              Widowed\n96      0       23      3 CUST_MARITAL_STATUS              Widowed\n97      0       24      0           EDUCATION                 10th\n98      0       24      1           EDUCATION                 10th\n99      0       24      2           EDUCATION                 10th\n100     0       24      3           EDUCATION                 10th\n101     0       25      0           EDUCATION                 11th\n102     0       25      1           EDUCATION                 11th\n103     0       25      2           EDUCATION                 11th\n104     0       25      3           EDUCATION                 11th\n105     0       26      0           EDUCATION                 12th\n106     0       26      1           EDUCATION                 12th\n107     0       26      2           EDUCATION                 12th\n108     0       26      3           EDUCATION                 12th\n109     0       27      0           EDUCATION              1st-4th\n110     0       27      1           EDUCATION              1st-4th\n111     0       27      2           EDUCATION              1st-4th\n112     0       27      3           EDUCATION              1st-4th\n113     0       28      0           EDUCATION              5th-6th\n114     0       28      1           EDUCATION              5th-6th\n115     0       28      2           EDUCATION              5th-6th\n116     0       28      3           EDUCATION              5th-6th\n117     0       29      0           EDUCATION              7th-8th\n118     0       29      1           EDUCATION              7th-8th\n119     0       29      2           EDUCATION              7th-8th\n120     0       29      3           EDUCATION              7th-8th\n121     0       30      0           EDUCATION                  9th\n122     0       30      1           EDUCATION                  9th\n123     0       30      2           EDUCATION                  9th\n124     0       30      3           EDUCATION                  9th\n125     0       31      0           EDUCATION              < Bach.\n126     0       31      1           EDUCATION              < Bach.\n127     0       31      2           EDUCATION              < Bach.\n128     0       31      3           EDUCATION              < Bach.\n129     0       32      0           EDUCATION              Assoc-A\n130     0       32      1           EDUCATION              Assoc-A\n131     0       32      2           EDUCATION              Assoc-A\n132     0       32      3           EDUCATION              Assoc-A\n133     0       33      0           EDUCATION              Assoc-V\n134     0       33      1           EDUCATION              Assoc-V\n135     0       33      2           EDUCATION              Assoc-V\n136     0       33      3           EDUCATION              Assoc-V\n137     0       34      0           EDUCATION                Bach.\n138     0       34      1           EDUCATION                Bach.\n139     0       34      2           EDUCATION                Bach.\n140     0       34      3           EDUCATION                Bach.\n141     0       35      0           EDUCATION              HS-grad\n142     0       35      1           EDUCATION              HS-grad\n143     0       35      2           EDUCATION              HS-grad\n144     0       35      3           EDUCATION              HS-grad\n145     0       36      0           EDUCATION              Masters\n146     0       36      1           EDUCATION              Masters\n147     0       36      2           EDUCATION              Masters\n148     0       36      3           EDUCATION              Masters\n149     0       37      0           EDUCATION                  PhD\n150     0       37      1           EDUCATION                  PhD\n151     0       37      2           EDUCATION                  PhD\n152     0       37      3           EDUCATION                  PhD\n153     0       38      0           EDUCATION              Presch.\n154     0       38      1           EDUCATION              Presch.\n155     0       38      2           EDUCATION              Presch.\n156     0       38      3           EDUCATION              Presch.\n157     0       39      0           EDUCATION               Profsc\n158     0       39      1           EDUCATION               Profsc\n159     0       39      2           EDUCATION               Profsc\n160     0       39      3           EDUCATION               Profsc\n161     0       40      0      HOUSEHOLD_SIZE                    1\n162     0       40      1      HOUSEHOLD_SIZE                    1\n163     0       40      2      HOUSEHOLD_SIZE                    1\n164     0       40      3      HOUSEHOLD_SIZE                    1\n165     0       41      0      HOUSEHOLD_SIZE                    2\n166     0       41      1      HOUSEHOLD_SIZE                    2\n167     0       41      2      HOUSEHOLD_SIZE                    2\n168     0       41      3      HOUSEHOLD_SIZE                    2\n169     0       42      0      HOUSEHOLD_SIZE                    3\n170     0       42      1      HOUSEHOLD_SIZE                    3\n171     0       42      2      HOUSEHOLD_SIZE                    3\n172     0       42      3      HOUSEHOLD_SIZE                    3\n173     0       43      0      HOUSEHOLD_SIZE                  4-5\n174     0       43      1      HOUSEHOLD_SIZE                  4-5\n175     0       43      2      HOUSEHOLD_SIZE                  4-5\n176     0       43      3      HOUSEHOLD_SIZE                  4-5\n177     0       44      0      HOUSEHOLD_SIZE                  6-8\n178     0       44      1      HOUSEHOLD_SIZE                  6-8\n179     0       44      2      HOUSEHOLD_SIZE                  6-8\n180     0       44      3      HOUSEHOLD_SIZE                  6-8\n181     0       45      0      HOUSEHOLD_SIZE                   9+\n182     0       45      1      HOUSEHOLD_SIZE                   9+\n183     0       45      2      HOUSEHOLD_SIZE                   9+\n184     0       45      3      HOUSEHOLD_SIZE                   9+\n185     0       46      0          OCCUPATION                    ?\n186     0       46      1          OCCUPATION                    ?\n187     0       46      2          OCCUPATION                    ?\n188     0       46      3          OCCUPATION                    ?\n189     0       47      0          OCCUPATION              Cleric.\n190     0       47      1          OCCUPATION              Cleric.\n191     0       47      2          OCCUPATION              Cleric.\n192     0       47      3          OCCUPATION              Cleric.\n193     0       48      0          OCCUPATION               Crafts\n194     0       48      1          OCCUPATION               Crafts\n195     0       48      2          OCCUPATION               Crafts\n196     0       48      3          OCCUPATION               Crafts\n197     0       49      0          OCCUPATION                Exec.\n198     0       49      1          OCCUPATION                Exec.\n199     0       49      2          OCCUPATION                Exec.\n200     0       49      3          OCCUPATION                Exec.\n201     0       50      0          OCCUPATION              Farming\n202     0       50      1          OCCUPATION              Farming\n203     0       50      2          OCCUPATION              Farming\n204     0       50      3          OCCUPATION              Farming\n205     0       51      0          OCCUPATION              Handler\n206     0       51      1          OCCUPATION              Handler\n207     0       51      2          OCCUPATION              Handler\n208     0       51      3          OCCUPATION              Handler\n209     0       52      0          OCCUPATION              House-s\n210     0       52      1          OCCUPATION              House-s\n211     0       52      2          OCCUPATION              House-s\n212     0       52      3          OCCUPATION              House-s\n213     0       53      0          OCCUPATION              Machine\n214     0       53      1          OCCUPATION              Machine\n215     0       53      2          OCCUPATION              Machine\n216     0       53      3          OCCUPATION              Machine\n217     0       54      0          OCCUPATION                Other\n218     0       54      1          OCCUPATION                Other\n219     0       54      2          OCCUPATION                Other\n220     0       54      3          OCCUPATION                Other\n221     0       55      0          OCCUPATION                Prof.\n222     0       55      1          OCCUPATION                Prof.\n223     0       55      2          OCCUPATION                Prof.\n224     0       55      3          OCCUPATION                Prof.\n225     0       56      0          OCCUPATION              Protec.\n226     0       56      1          OCCUPATION              Protec.\n227     0       56      2          OCCUPATION              Protec.\n228     0       56      3          OCCUPATION              Protec.\n229     0       57      0          OCCUPATION                Sales\n230     0       57      1          OCCUPATION                Sales\n231     0       57      2          OCCUPATION                Sales\n232     0       57      3          OCCUPATION                Sales\n233     0       58      0          OCCUPATION              TechSup\n234     0       58      1          OCCUPATION              TechSup\n235     0       58      2          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-0.9003497","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using the Single Layer NN model and test data","message":["%r","","RES_DF <- predict(nn.mod2, CUST_DF.test, supplemental.cols='AFFINITY_CARD')","","colnames(RES_DF) <- c( 'PROB_OF_0', 'PROB_OF_1', 'AFFINITY_CARD', 'PREDICTION')","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1737674241845,"interpreter":"r.medium","endTime":1737674242981,"results":[{"message":"PROB_OF_0\tPROB_OF_1\tAFFINITY_CARD\tPREDICTION\n0.906833755557264\t0.0931662444427356\t0\t0\n0.998121672967662\t0.00187832703233768\t0\t0\n0.683045837017122\t0.316954162982878\t0\t0\n0.906833755557264\t0.0931662444427356\t1\t0\n0.998121672967662\t0.00187832703233768\t0\t0\n0.988374196702332\t0.0116258032976682\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST dataset","message":["%r","","C_MATRIX <- with(RES_DF, table(AFFINITY_CARD, PREDICTION))   ","C_MATRIX"],"enabled":true,"result":{"startTime":1737674243447,"interpreter":"r.medium","endTime":1737674247262,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 1207  165\n            1  173  255","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute model accuracy","message":["%r","","ACCURACY <- C_MATRIX/sum(C_MATRIX)","round(sum(diag(ACCURACY)),3)"],"enabled":true,"result":{"startTime":1737674247756,"interpreter":"r.medium","endTime":1737674248257,"results":[{"message":"0.812","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Generate statistics for ROC Curve, Lift Chart, and Probability Densities","message":["%r","","# Pull Data locally","df <- ore.pull(RES_DF)","","# ROC Curve","sensitivity <-df[order(df$PROB_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0) - cumsum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0))","spec <- cumsum(sensitivity$PROB_OF_1)/sum(sensitivity$PROB_OF_1)","","# LIFT CHART","sensitivity2 <- df[order(df$PROB_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROB_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROB_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DISTRIBUTION CHART","dx <- density(df$PROB_OF_0)","dx2 <- density(df$PROB_OF_1)","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE) "],"enabled":true,"result":{"startTime":1737674248733,"interpreter":"r.medium","endTime":1737674249500,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAFoCAIAAAAXZAVmAAAABmJLR0QA/wD/AP+gvaeTAAAg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fUMCbQxSieTihx/e3LHDxtV15NGjrUNC2I4Iqlq9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\nT/nrL7JU6yFtn3E+ZBJ5WdiyJloT63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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification RF","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737674817252,"interpreter":"md.low","endTime":1737674817709,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Random Forest","","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. ","Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, ","allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database ","tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we highlight the Random Forest algorithm for classification in OML4R and predict customers most likely to be ","positive responders to an Affinity Card loyalty program.","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, ","that is, increase purchases more than the Affinity Card program's offered discount.","","This notebook builds and applies a classification random forest model using the Sales History (SH) schema data. All processing occurs ","inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1737674818200,"interpreter":"md.low","endTime":1737674818665,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-random-forest\">Classification Modeling to Predict Target Customers using Random Forest<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data.\nDesigned for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database,\nallowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database\ntables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we highlight the Random Forest algorithm for classification in OML4R and predict customers most likely to be\npositive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond,\nthat is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification random forest model using the Sales History (SH) schema data. All processing occurs\ninside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737674819142,"interpreter":"md.low","endTime":1737674819606,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-random-forest-model-using-ore-odmrf.html\" target=\"_blank\">OML4R Random Forest<\/a>"],"enabled":true,"result":{"startTime":1737674820085,"interpreter":"md.low","endTime":1737674820533,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-random-forest-model-using-ore-odmrf.html\" target=\"_blank\">OML4R Random Forest<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(\"SUP_DEM\" = \"select * from 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na.action = na.pass,\n            odm.settings = NULL,\n            ctx.settings = NULL)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. The details of model specification are given under\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build. The default value is \u2018TRUE\u2019.\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmRF\u2019.\n\nctx.settings: Text mining settings.\n\nDetails:\n\n     Random Forest is a popular ensemble learning technique for\n     classification and regression. By combining the ideas of bagging\n     and random selection of variables, the algorithm produces a\n     collection of decision trees with controlled variance, while\n     avoiding overfitting - a common problem for decision trees.\n\n     The \u2018formula\u2019 specification has the form \u2018attribute ~ terms\u2019 where\n     \u2018attribute\u2019 is the attribute to be used for model build and\n     \u2018terms\u2019 is a series of terms, for example, the column names, to\n     include in the model. Multiple terms are specified using \u2018+\u2019\n     between column names. Use \u2018response ~ .\u2019 if all columns in \u2018data\u2019\n     should be used for model building. To exclude columns, use \u2018-\u2019\n     before each column name to exclude.\n\nValue:\n\n     The \u2018ore.odmRF\u2019 function returns an object of class \u2018ore.odmRF\u2019,\n     which includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of variables (attribiutes) used to build the\n          model. The columns include: name, type (numerical or\n          categorical), data type, data length (size), precision and\n          scale for numeric data, and whether the variable is the\n          target.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\nimportance: Attribute importance of the fitted model\n\n    call: The matched call.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmSVM\u2019, \u2018ore.odmGLM\u2019, \u2018ore.odmDT\u2019, \u2018partitions\u2019\n\nExamples:\n\n         IRIS <- ore.push(iris)\n         mod <- ore.odmRF(Species~., IRIS)\n         mod\n         summary(mod)\n         importance(mod)\n         ans <- predict(mod, IRIS)\n         head(ans,3)\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","z.show(head(SUP_DEM))"],"enabled":true,"result":{"startTime":1737674823267,"interpreter":"r.low","endTime":1737674823775,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display number of rows and columns","message":["%r","","dim(SUP_DEM)"],"enabled":true,"result":{"startTime":1737674824250,"interpreter":"r.low","endTime":1737674824827,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>14<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"ORE.FREQ\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 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responders","message":["%r","","z.show(ore.crosstab(~AFFINITY_CARD,SUP_DEM))"],"enabled":true,"result":{"startTime":1737674825309,"interpreter":"r.low","endTime":1737674825847,"results":[{"message":"AFFINITY_CARD\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t3428\t1\t1\n1\t1072\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"ORE.FREQ\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD ","message":["%r","","z.show(ore.crosstab(AFFINITY_CARD~HOUSEHOLD_SIZE,SUP_DEM))"],"enabled":true,"result":{"startTime":1737674826321,"interpreter":"r.low","endTime":1737674826830,"results":[{"message":"AFFINITY_CARD\tHOUSEHOLD_SIZE\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t1\t681\t1\t1\n0\t2\t1040\t1\t1\n0\t3\t973\t1\t1\n0\t4-5\t112\t1\t1\n0\t6-8\t146\t1\t1\n0\t9+\t476\t1\t1\n1\t1\t11\t1\t1\n1\t2\t109\t1\t1\n1\t3\t814\t1\t1\n1\t4-5\t107\t1\t1\n1\t6-8\t2\t1\t1\n1\t9+\t29\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Select desired columns for model building","message":["%r","","DEMO_DF <- SUP_DEM[,c(\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", ","                      \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", ","                      \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]"],"enabled":true,"result":{"startTime":1737674827299,"interpreter":"r.low","endTime":1737674827798,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into train and test sets (60/40)","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","# Create training data set","rownames(DEMO_DF) <- DEMO_DF$CUST_ID","DEMO_DF.train <- DEMO_DF[group==FALSE,]","","# Create test data set","DEMO_DF.test <- DEMO_DF[group==TRUE,]","","cat(\"\\n====== Records | Columns\\n\\nTrain Data: \",dim(DEMO_DF.train),\"\\nTest data: \",dim(DEMO_DF.test))"],"enabled":true,"result":{"startTime":1737674828270,"interpreter":"r.low","endTime":1737674829579,"results":[{"message":"\n====== Records | Columns\n\nTrain Data:  2700 13 \nTest data:  1800 13","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build a Random Forest model using Default settings","message":["%r","","MOD <- ore.odmRF(AFFINITY_CARD~., DEMO_DF)     ","# tree10 <- grabTree(MOD, k = 10, labelVar = TRUE) # optionally grab a specific tree from the model"],"enabled":true,"result":{"startTime":1737674830036,"interpreter":"r.low","endTime":1737674831630,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data using model proxy object returning predictions and class probabilities","message":["%r","","RES <- predict(MOD, DEMO_DF, type=c(\"class\",\"raw\"), supplemental.cols= \"AFFINITY_CARD\")","","colnames(RES) <- c( 'PROB_OF_0', 'PROB_OF_1', 'AFFINITY_CARD', 'PREDICTION')","","","z.show(head(RES, 10)) "],"enabled":true,"result":{"startTime":1737674832089,"interpreter":"r.low","endTime":1737674833141,"results":[{"message":"PROB_OF_0\tPROB_OF_1\tAFFINITY_CARD\tPREDICTION\n0.946509009009009\t0.053490990990991\t0\t0\n0.788620252296723\t0.211379747703277\t0\t0\n0.750908743643398\t0.249091256356602\t0\t0\n0.976557409944507\t0.0234425900554933\t0\t0\n0.371858775899024\t0.628141224100976\t1\t1\n0.988796992481203\t0.011203007518797\t0\t0\n0.992261904761905\t0.00773809523809524\t0\t0\n0.976557409944507\t0.0234425900554933\t0\t0\n0.55546987874097\t0.44453012125903\t1\t0\n0.964608636977058\t0.035391363022942\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(RES, table(AFFINITY_CARD, PREDICTION))   ","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737674833624,"interpreter":"r.low","endTime":1737674834381,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 3199  229\n     1  465  607\nAccuracy:\n","type":"TEXT"},{"message":"0.8458","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Random Forest ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-random-forest-model-using-ore-odmrf.html\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' = '<row_weight_column_name>'","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_DELETE_ROW'","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.","> 'RFOR_NUM_TREES' = 20","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)","> 'RFOR_SAMPLING_RATIO' = 0.5","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.","> 'RFOR_MTRY' = 0"],"enabled":true,"result":{"startTime":1737674834847,"interpreter":"md.low","endTime":1737674835314,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-random-forest\">Examples of possible setting overrides for Random Forest<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-random-forest-model-using-ore-odmrf.html\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_NUM_TREES' = 20<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_SAMPLING_RATIO' = 0.5<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_MTRY' = 0<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest model using explicit settings and score data","message":["%r","","ore.drop (model = 'RF_CLASSIFICATION_MODEL')","","settings = list(RFOR_MTRY = 3, ","                RFOR_NUM_TREES = 100, ","                RFOR_SAMPLING_RATIO =0.5,","                model_name=\"RF_CLASSIFICATION_MODEL\")","","MOD2 <- ore.odmRF(AFFINITY_CARD~., DEMO_DF.train, odm.settings= settings)","","RES2 <- predict(MOD2, DEMO_DF.test, type= c(\"class\",\"raw\"), norm.votes=TRUE, cache.model=TRUE, ","                supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", ","                                    \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", ","                                    \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\"))","","colnames(RES2) <- c( \"PROB_OF_0\", \"PROB_OF_1\", \"CUST_ID\", \"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", ","                     \"BULK_PACK_DISKETTES\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                     \"HOUSEHOLD_SIZE\",\"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", ","                     \"Y_BOX_GAMES\", 'PREDICTION')"],"enabled":true,"result":{"startTime":1737674835805,"interpreter":"r.low","endTime":1737674838091,"results":[{"message":"Warning message:\n\u201cThe created mining model RF_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix","message":["%r","","confusion_matrix <- with(RES2, table(AFFINITY_CARD, PREDICTION))   ","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737674838568,"interpreter":"r.low","endTime":1737674839528,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1291   81\n     1  239  189\n\nAccuracy:\n","type":"TEXT"},{"message":"0.8222","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Display ROC curve, lift chart, and probability densities","message":["%r","","# Pull Data locally","df <- ore.pull(RES2)","","# ROC Curve","sensitivity <-df[order(df$PROB_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0) - cumsum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0))","spec <- cumsum(sensitivity$PROB_OF_1)/sum(sensitivity$PROB_OF_1)","","# LIFT CHART","sensitivity2 <- df[order(df$PROB_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROB_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROB_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DENSITY CHART","dx <- density(df$PROB_OF_0)","dx2 <- density(df$PROB_OF_1)","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE) 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bzgAABJjCf/eTlcaMaX9cWEJCZdy4wlevEEL5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\nOTk5fmx4wWYyG6qq8H2D8MGDB9bW1lQq9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+++ b/machine-learning/notebooks-oml/r/OML4R Classification SVM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737745968059,"interpreter":"md.medium","endTime":1737745968510,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification modeling to predict Target Customers using Support Vector Machine Model","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready ","for the enterprise and big data. Designed for problems involving both large and small data volumes, ","OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, ","machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions ","are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or ","affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification support vector machine model using the Sales History (SH) schema data. ","All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737745968952,"interpreter":"md.medium","endTime":1737745969362,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine-model\">Classification modeling to predict Target Customers using Support Vector Machine Model<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready\nfor the enterprise and big data. Designed for problems involving both large and small data volumes,\nOML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical,\nmachine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions\nare overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or\naffinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification support vector machine model using the Sales History (SH) schema data.\nAll processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737745969857,"interpreter":"md.medium","endTime":1737745970278,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">OML4R Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1737745970737,"interpreter":"md.medium","endTime":1737745971142,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">OML4R Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and create proxy object for SUPPLEMENTARY_DEMOGRAPHICS table","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","","ore.sync(query = c(\"SUP_DEM\" = \"select * from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()"],"enabled":true,"result":{"startTime":1737745971594,"interpreter":"r.medium","endTime":1737745972078,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","z.show(head(SUP_DEM, 10))"],"enabled":true,"result":{"startTime":1737745972501,"interpreter":"r.medium","endTime":1737745973189,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100417\t11th\tHandler\t1\t1\t0\t0\t0\t0\t0\t1\t1\t0\tNA\n101146\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\tNA\n103420\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\tNA\n101987\t< Bach.\tOther\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%r","","z.show(dim(SUP_DEM))"],"enabled":true,"result":{"startTime":1737745973614,"interpreter":"r.medium","endTime":1737745976669,"results":[{"message":"4500 14","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"ORE.FREQ\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%r","","z.show(ore.crosstab(~AFFINITY_CARD,SUP_DEM))"],"enabled":true,"result":{"startTime":1737745977128,"interpreter":"r.medium","endTime":1737745977675,"results":[{"message":"AFFINITY_CARD\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t3428\t1\t1\n1\t1072\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"showSeries\":[\"ORE.FREQ\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%r","","z.show(head(ore.crosstab(AFFINITY_CARD~HOUSEHOLD_SIZE,SUP_DEM), 10))"],"enabled":true,"result":{"startTime":1737745978108,"interpreter":"r.medium","endTime":1737745978685,"results":[{"message":"AFFINITY_CARD\tHOUSEHOLD_SIZE\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t1\t681\t1\t1\n0\t2\t1040\t1\t1\n0\t3\t973\t1\t1\n0\t4-5\t112\t1\t1\n0\t6-8\t146\t1\t1\n0\t9+\t476\t1\t1\n1\t1\t11\t1\t1\n1\t2\t109\t1\t1\n1\t3\t814\t1\t1\n1\t4-5\t107\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Select columns for use in model building","message":["%r","","DEMO_DF <- SUP_DEM[,c(\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                      \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","                      "],"enabled":true,"result":{"startTime":1737745979128,"interpreter":"r.medium","endTime":1737745979592,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40 split)","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","# Create training data set","rownames(DEMO_DF) <- DEMO_DF$CUST_ID","DEMO_DF.train <- DEMO_DF[group==FALSE,]","","# Create test data set","DEMO_DF.test <- DEMO_DF[group==TRUE,]","cat(\"\\n======== Records | Columns\\n\\nTrain Data: \",","    dim(DEMO_DF.train),","    \"\\nTest data: \",","    dim(DEMO_DF.test))"],"enabled":true,"result":{"startTime":1737745980036,"interpreter":"r.medium","endTime":1737745982287,"results":[{"message":"\n======== Records | Columns\n\nTrain Data:  2700 13 \nTest data:  1800 13","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Build a Support Vector Machine model for predicting AFFINITY_CARD"],"enabled":true,"result":{"startTime":1737745982716,"interpreter":"md.medium","endTime":1737745983127,"results":[{"message":"<h2 id=\"build-a-support-vector-machine-model-for-predicting-affinity_card\">Build a Support Vector Machine model for predicting AFFINITY_CARD<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Classification model using default settings","message":["%r","","ore.drop(model = 'SVM_CLASSIFICATION_MODEL')","","MOD <- ore.odmSVM(formula=AFFINITY_CARD~., ","                  data=DEMO_DF.train, ","                  type=\"classification\", ","                  kernel.function = \"system.determined\",","                  odm.settings=list(model_name=\"SVM_CLASSIFICATION_MODEL\"))"],"enabled":true,"result":{"startTime":1737745983562,"interpreter":"r.medium","endTime":1737745985812,"results":[{"message":"Warning message:\n\u201cModel SVM_CLASSIFICATION_MODEL does not exist on database.\u201d\nWarning message:\n\u201cThe default kernel function is linear.\u201d\nWarning message:\n\u201cThe created mining model SVM_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Score the Test data"],"enabled":true,"result":{"startTime":1737745986281,"interpreter":"md.medium","endTime":1737745986702,"results":[{"message":"<h2 id=\"score-the-test-data\">Score the Test data<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r"," "," RES <- predict(MOD, DEMO_DF.test, ","               type= c(\"raw\",\"class\"), norm.votes=TRUE, cache.model=TRUE, ","               supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"EDUCATION\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"YRS_RESIDENCE\")) ","","colnames(RES) <- c(\"PROB_OF_0\",\"PROB_OF_1\",\"CUST_ID\", \"AFFINITY_CARD\", ","                   \"EDUCATION\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", ","                   \"YRS_RESIDENCE\",\"PREDICTION\")"," z.show(RES)"],"enabled":true,"result":{"startTime":1737745987130,"interpreter":"r.medium","endTime":1737745989685,"results":[{"message":"PROB_OF_0\tPROB_OF_1\tCUST_ID\tAFFINITY_CARD\tEDUCATION\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tPREDICTION\n0.970687769649018\t0.0293122303509821\t100001\t0\t< Bach.\t2\tExec.\t3\t0\n0.970687769080363\t0.0293122309196367\t100002\t0\tBach.\t2\tProf.\t4\t0\n0.970687771009223\t0.0293122289907766\t100004\t0\t< Bach.\t2\tSales\t5\t0\n0.970687766302041\t0.0293122336979587\t100009\t1\tBach.\t3\tProf.\t3\t0\n0.999972464314725\t2.75356852746175e-05\t100011\t0\t9th\t2\tFarming\t3\t0\n0.999972464308502\t2.7535691498103e-05\t100017\t0\t< Bach.\t9+\tFarming\t5\t0\n0.999999974890834\t2.51091659406387e-08\t100018\t0\t< 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Bach.\t3\tCrafts\t6\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix and Model Accuracy on test data","message":["%r","","confusion_matrix <- with(RES, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737745990141,"interpreter":"r.medium","endTime":1737745991066,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1238  134\n     1  169  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href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","   ","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","  ","* Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'","","> SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'","   ","* Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> SVMS_COMPLEXITY_FACTOR = 0.1","","* Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.    ","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'","","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'","      ","* Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> SVMS_BATCH_ROWS = 20000","   ","* Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","> SVMS_SOLVER = 'SVMS_SOLVER_SGD'","","> SVMS_SOLVER = 'SVMS_SOLVER_IPM'","","* Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> SVMS_CONV_TOLERANCE = 0.001","","* Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> SVMS_NUM_PIVOTS = 200"],"enabled":true,"result":{"startTime":1737745991507,"interpreter":"md.medium","endTime":1737745991916,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_COMPLEXITY_FACTOR = 0.1<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_BATCH_ROWS = 20000<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_IPM'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_CONV_TOLERANCE = 0.001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_NUM_PIVOTS = 200<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build and Score an SVM model using a Linear kernel","message":["%r","","MOD2 <- ore.odmSVM(formula=AFFINITY_CARD~., ","                   data=DEMO_DF.train,","                   type=\"classification\", ","                   kernel.function = \"linear\")   ","","RES2 <- predict(MOD2, ","                DEMO_DF.test, ","                type=\"class\", ","                supplemental.cols=\"AFFINITY_CARD\") "],"enabled":true,"result":{"startTime":1737745992351,"interpreter":"r.medium","endTime":1737745994295,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix and Model Accuracy on test data","message":["%r","","confusion_matrix <- with(RES2, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737745994723,"interpreter":"r.medium","endTime":1737745995294,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1238  134\n     1  169  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"],"enabled":true,"result":{"startTime":1737745995787,"interpreter":"r.medium","endTime":1737745999644,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix and Model Accuracy on test data","message":["%r","","confusion_matrix <- with(RES3, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1737746000071,"interpreter":"r.medium","endTime":1737746000800,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1266  106\n     1  191  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0.5),], by = c('PROB_OF_1'), reverse=TRUE), 10))"],"enabled":true,"result":{"startTime":1737746001228,"interpreter":"r.medium","endTime":1737746002687,"results":[{"message":"PROB_OF_0\tPROB_OF_1\tCUST_ID\tAFFINITY_CARD\tEDUCATION\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tPREDICTION\n0.00562316232739668\t0.994376837672603\t104307\t1\tProfsc\t3\tProf.\t11\t1\n0.00925519605015424\t0.990744803949846\t103332\t1\tMasters\t3\tExec.\t6\t1\n0.0143504157401412\t0.985649584259859\t103071\t1\tBach.\t3\tExec.\t6\t1\n0.0158756680300747\t0.984124331969925\t103127\t1\tPhD\t3\tProf.\t4\t1\n0.0186380031201121\t0.981361996879888\t103149\t1\tPhD\t3\tProf.\t4\t1\n0.0188090535484342\t0.981190946451566\t101208\t1\tMasters\t3\tProf.\t6\t1\n0.0189762237094578\t0.981023776290542\t103353\t1\tPhD\t3\tProf.\t4\t1\n0.0233423262444489\t0.976657673755551\t102585\t0\tPhD\t3\tProf.\t5\t1\n0.024576300002654\t0.975423699997346\t100344\t0\tBach.\t3\tProf.\t5\t1\n0.0248601200879531\t0.975139879912047\t103439\t1\tProfsc\t3\tExec.\t6\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict affinity card responders with prediction details","message":["%r","","RES4 <- predict(MOD3, ","                DEMO_DF.test,  ","                supplemental.cols=\"AFFINITY_CARD\", ","                topN.attrs = 3)","","z.show(head(RES4,10))"],"enabled":true,"result":{"startTime":1737746003123,"interpreter":"r.medium","endTime":1737746021903,"results":[{"message":"AFFINITY_CARD\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\n0\t0\tHOUSEHOLD_SIZE\t2\t.675\tBULK_PACK_DISKETTES\t0\t.069\tHOME_THEATER_PACKAGE\t1\t.018\n0\t0\tHOUSEHOLD_SIZE\t2\t.714\tFLAT_PANEL_MONITOR\t1\t.054\tHOME_THEATER_PACKAGE\t1\t.009\n0\t0\tHOUSEHOLD_SIZE\t2\t.514\tOCCUPATION\tSales\t.106\tFLAT_PANEL_MONITOR\t1\t.01\n1\t0\tY_BOX_GAMES\t1\t.48\tHOME_THEATER_PACKAGE\t0\t.11\tBULK_PACK_DISKETTES\t0\t.096\n0\t0\tOCCUPATION\tFarming\t.091\tHOUSEHOLD_SIZE\t2\t.037\tBULK_PACK_DISKETTES\t0\t.023\n0\t0\tHOUSEHOLD_SIZE\t9+\t.196\tOCCUPATION\tFarming\t.047\tFLAT_PANEL_MONITOR\t1\t.002\n0\t0\tOS_DOC_SET_KANJI\t1\t.053\tNA\tNA\tNA\tNA\tNA\tNA\n0\t0\tY_BOX_GAMES\t1\t.124\tHOUSEHOLD_SIZE\t2\t.117\tOCCUPATION\tProf.\t.05\n0\t0\tFLAT_PANEL_MONITOR\t1\t.088\tBOOKKEEPING_APPLICATION\t0\t.082\tYRS_RESIDENCE\t3\t.022\n0\t0\tHOUSEHOLD_SIZE\t6-8\t.691\tOCCUPATION\tSales\t.095\tFLAT_PANEL_MONITOR\t0\t.021\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Display ROC Curve, Lift Chart, and Distribution Chart"],"enabled":true,"result":{"startTime":1737746022349,"interpreter":"md.medium","endTime":1737746022777,"results":[{"message":"<h2 id=\"display-roc-curve-lift-chart-and-distribution-chart\">Display ROC Curve, Lift Chart, and Distribution Chart<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","# Pull Data locally","df <- ore.pull(RES3)","","# ROC Curve","sensitivity <-df[order(df$PROB_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0) - cumsum(sensitivity$PROB_OF_0)/sum(sensitivity$PROB_OF_0))","spec <- cumsum(sensitivity$PROB_OF_1)/sum(sensitivity$PROB_OF_1)","# LIFT CHART","sensitivity2 <- df[order(df$PROB_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROB_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROB_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","# DENSITY CHART","dx <- density(df$PROB_OF_0)","dx2 <- density(df$PROB_OF_1)","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE) "],"enabled":true,"result":{"startTime":1737746023207,"interpreter":"r.medium","endTime":1737746024242,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAFoCAIAAAAXZAVmAAAABmJLR0QA/wD/AP+gvaeTAAAg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WvMK7giZqKysrH379j19+rRdu3Zz5szp2rUrvfFU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\nti2uLAaA8mrTWIWjfgJNLfCszUrPmiEbtZSVNf7JQy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b1l7GztLW1tLGpqagAgMoWtOBBy0DVQKu7BAcojECLa8USqcTK3EqXkelakwm0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\n+ISHhw8fPlxPTw8hVFhYGB8fHxkZGf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PREDICTION_PROBABILITY(SVM_CLASSIFICATION_MODEL, 1 USING *) PROB_AFFINITY_CARD,","      PREDICTION_DETAILS(SVM_CLASSIFICATION_MODEL USING *) PREDICTION_DETAILS","      FROM SVM_TEST_TABLE","      ORDER BY PROB_AFFINITY_CARD DESC) OUT,","XMLTABLE('/Details'","         PASSING OUT.PREDICTION_DETAILS","         COLUMNS","         \"Attribute1\" XMLType PATH 'Attribute[1]',","         \"Attribute2\" XMLType PATH 'Attribute[2]',","         \"Attribute3\" XMLType PATH 'Attribute[3]')","OUTPRED","FETCH FIRST 50 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737746027880,"interpreter":"sql.medium","endTime":1737746028446,"results":[{"message":"CUST_ID\tPROBABILITY_AFFINITY_CARD\tPREDICTION_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100012\t0.971\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".18\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"1\" weight=\".005\" \t\n102734\t0.971\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".18\" \t\"BOOKKEEPING_APPLICATION\" 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R Clustering EM.dsnb b/machine-learning/notebooks-oml/r/OML4R Clustering EM.dsnb
new file mode 100644
index 00000000..6ec47870
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Clustering EM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733161750711,"interpreter":"md.low","endTime":1733161750773,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Identifying Customer Segments using Expectation Maximization Clustering","Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the Expectation Maximization in-database algortihm.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","","----"],"enabled":true,"result":{"startTime":1733161750849,"interpreter":"md.low","endTime":1733161750905,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1733161751011,"interpreter":"md.low","endTime":1733161751069,"results":[{"message":"<p><img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1733161751147,"interpreter":"md.low","endTime":1733161751210,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1733161751295,"interpreter":"r.low","endTime":1733161751418,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare customer demographic data using proxy objects","message":["%r","","ore.sync(query = c(\"SUPPLEMENTARY_DEMOGRAPHICS\" = \"select CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.sync(query = c(\"CUSTOMERS\" = \"select CUST_ID, CUST_GENDER, CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS from SH.CUSTOMERS\"))","","CUST_DF <- merge(SUPPLEMENTARY_DEMOGRAPHICS,CUSTOMERS, by=\"CUST_ID\")"],"enabled":true,"result":{"startTime":1733161751491,"interpreter":"r.low","endTime":1733161751988,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first 5 rows of CUST_DF data","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1733161752063,"interpreter":"r.low","endTime":1733161752182,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tCUST_GENDER\tCUST_YEAR_OF_BIRTH\tCUST_MARITAL_STATUS\n100100\tMasters\tProf.\t4-5\t4\tF\t1959\tMarried\n100200\t< Bach.\tOther\t1\t2\tM\t1983\tNeverM\n100300\tBach.\tProf.\t3\t4\tM\t1961\tMarried\n100400\t12th\tTransp.\t6-8\t6\tM\t1944\tDivorc.\n100500\tHS-grad\tCrafts\t1\t2\tM\t1983\tNeverM\n100600\t11th\tTransp.\t3\t3\tM\t1978\tMarried\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get dimensions of data set using the CUST_DF proxy object","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1733161752265,"interpreter":"r.low","endTime":1733161752452,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>8<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an Expectation Maximization model"],"enabled":true,"result":{"startTime":1733161752528,"interpreter":"md.low","endTime":1733161752610,"results":[{"message":"<h3 id=\"build-an-expectation-maximization-model\">Build an Expectation Maximization model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an EM clustering model with 3 clusters and default settings","message":["%r","","EM.MOD1 <- ore.odmEM(~.-CUST_ID, CUST_DF, num.centers = 3)","","EM.MOD1"],"enabled":true,"result":{"startTime":1733161752692,"interpreter":"r.low","endTime":1733161755792,"results":[{"message":"\nCall:\nore.odmEM(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\ncluster.components               cluster.comp.enable\ncluster.statistics                 clus.stats.enable\ncluster.thresh                                     2\nlinkage.function                      linkage.single\nloglike.improvement                             .001\nmax.num.attr.2d                                   50\nmin.pct.attr.support                              .1\nmodel.search                    model.search.disable\nnum.components                                    20\nnum.distribution                    num.distr.system\nnum.equiwidth.bins                                11\nnum.iterations                                   100\nnum.projections                                   50\nrandom.seed                                        0\nremove.components                remove.comps.enable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display EM model details","message":["%r","","summary(EM.MOD1)"],"enabled":true,"result":{"startTime":1733161755876,"interpreter":"r.low","endTime":1733161756025,"results":[{"message":"\nCall:\nore.odmEM(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\ncluster.components               cluster.comp.enable\ncluster.statistics                 clus.stats.enable\ncluster.thresh                                     2\nlinkage.function                      linkage.single\nloglike.improvement                             .001\nmax.num.attr.2d                                   50\nmin.pct.attr.support                              .1\nmodel.search                    model.search.disable\nnum.components                                    20\nnum.distribution                    num.distr.system\nnum.equiwidth.bins                                11\nnum.iterations                                   100\nnum.projections                                   50\nrandom.seed                                        0\nremove.components                remove.comps.enable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS MEAN.CUST_YEAR_OF_BIRTH\n3               NA                       NA                1963.555\n4               NA                       NA                1968.397\n5               NA                       NA                1959.571\n  MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.OCCUPATION MEAN.YRS_RESIDENCE\n3             NA                  NA              NA           3.784404\n4             NA                  NA              NA           3.430186\n5             NA                  NA              NA           4.863636\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster histogram data for clusters in the hierarchy","message":["%r","","z.show(head(clusterhists(EM.MOD1),10))"],"enabled":true,"result":{"startTime":1733161756103,"interpreter":"r.low","endTime":1733161756327,"results":[{"message":"cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_GENDER:F\t3\tNA\tNA\t:\t1510\n1\tCUST_GENDER:M\t2\tNA\tNA\t:\t2990\n1\tCUST_MARITAL_STATUS:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_MARITAL_STATUS:Divorc.\t4\tNA\tNA\t:\t615\n1\tCUST_MARITAL_STATUS:Mabsent\t7\tNA\tNA\t:\t75\n1\tCUST_MARITAL_STATUS:Mar-AF\t8\tNA\tNA\t:\t3\n1\tCUST_MARITAL_STATUS:Married\t2\tNA\tNA\t:\t2034\n1\tCUST_MARITAL_STATUS:NeverM\t3\tNA\tNA\t:\t1503\n1\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\t:\t134\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "],"enabled":true,"result":{"startTime":1733161756411,"interpreter":"md.low","endTime":1733161756471,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Expectation Maximization clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA');\"> EM clustering<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All Algorithms<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '<row_weight_column_name>';  ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') = '5';","* Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5. ","> v_setlst('OCLT_SENSITIVITY') = '0.5';","* Whether or not to include uncorrelated attributes in the model. When EMCS_ATTRIBUTE_FILTER is enabled, uncorrelated attributes are not included. Default is system-determined.","> v_setlist('EMCS_ATTRIBUTE_FILTER') = 'EMCS_ATTR_FILTER_ENABLE'","* Maximum number of correlated attributes to include in the model. Note: This setting applies only to attributes that are not nested (2D). Default is 50.","> v_setlist('EMCS_MAX_NUM_ATTR_2D') = 'TO_CHAR( numeric_expr >=1)';","* The distribution for modeling numeric attributes. Applies to the input table or view as a whole and does not allow per-attribute specifications. The options include Bernoulli, Gaussian, or system-determined distribution. When Bernoulli or Gaussian distribution is chosen, all numeric attributes are modeled using the same type of distribution. When the distribution is system-determined, individual attributes may use different distributions (either Bernoulli or Gaussian), depending on the data. Default is EMCS_NUM_DISTR_SYSTEM.","> v_setlist('EMCS_NUM_DISTRIBUTION') = 'EMCS_NUM_DISTR_SYSTEM';","* Number of equi-width bins that will be used for gathering cluster statistics for numeric columns. Default is 11.","> v_setlist('EMCS_NUM_EQUIWIDTH_BINS') = 'TO_CHAR( 1 <numeric_expr <=255)';","* Specifies the number of projections that will be used for each nested column. If a column has fewer distinct attributes than the specified number of projections, the data will not be projected. The setting applies to all nested columns. Default is 50.","> v_setlist('EMCS_NUM_PROJECTIONS') = 'TO_CHAR( numeric_expr >=1)'","* Specifies the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. Default is system-determined.","> v_setlist('EMCS_NUM_QUANTILE_BINS') = 'TO_CHAR( 1 <numeric_expr <=255)'","* Specifies the number of top-N bins that will be used for modeling categorical columns with multivalued Bernoulli distributions. Default is system-determined.","> v_setlist('EMCS_NUM_TOPN_BINS') = 'TO_CHAR( 1 <numeric_expr <=255)'"],"enabled":true,"result":{"startTime":1733161756554,"interpreter":"md.low","endTime":1733161756633,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation Maximization clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA');\"> EM clustering<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All Algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') = '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('OCLT_SENSITIVITY') = '0.5';<\/p>\n<\/blockquote>\n<ul>\n<li>Whether or not to include uncorrelated attributes in the model. When EMCS_ATTRIBUTE_FILTER is enabled, uncorrelated attributes are not included. Default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_ATTRIBUTE_FILTER') = 'EMCS_ATTR_FILTER_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Maximum number of correlated attributes to include in the model. Note: This setting applies only to attributes that are not nested (2D). Default is 50.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_MAX_NUM_ATTR_2D') = 'TO_CHAR( numeric_expr &gt;=1)';<\/p>\n<\/blockquote>\n<ul>\n<li>The distribution for modeling numeric attributes. Applies to the input table or view as a whole and does not allow per-attribute specifications. The options include Bernoulli, Gaussian, or system-determined distribution. When Bernoulli or Gaussian distribution is chosen, all numeric attributes are modeled using the same type of distribution. When the distribution is system-determined, individual attributes may use different distributions (either Bernoulli or Gaussian), depending on the data. Default is EMCS_NUM_DISTR_SYSTEM.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_DISTRIBUTION') = 'EMCS_NUM_DISTR_SYSTEM';<\/p>\n<\/blockquote>\n<ul>\n<li>Number of equi-width bins that will be used for gathering cluster statistics for numeric columns. Default is 11.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_EQUIWIDTH_BINS') = 'TO_CHAR( 1 &lt;numeric_expr &lt;=255)';<\/p>\n<\/blockquote>\n<ul>\n<li>Specifies the number of projections that will be used for each nested column. If a column has fewer distinct attributes than the specified number of projections, the data will not be projected. The setting applies to all nested columns. Default is 50.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_PROJECTIONS') = 'TO_CHAR( numeric_expr &gt;=1)'<\/p>\n<\/blockquote>\n<ul>\n<li>Specifies the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. Default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_QUANTILE_BINS') = 'TO_CHAR( 1 &lt;numeric_expr &lt;=255)'<\/p>\n<\/blockquote>\n<ul>\n<li>Specifies the number of top-N bins that will be used for modeling categorical columns with multivalued Bernoulli distributions. Default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_TOPN_BINS') = 'TO_CHAR( 1 &lt;numeric_expr &lt;=255)'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build EM clustering model with 3 clusters with explicit settings","message":["%r","","ore.drop(model = 'EM_CLUSTERING_MODEL')","","settings = list(","  EMCS_NUM_ITERATIONS= 20,","  EMCS_RANDOM_SEED= 7,","  EMCS_CLUSTER_COMPONENTS= 'EMCS_CLUSTER_COMP_ENABLE',","  EMCS_CLUSTER_STATISTICS= 'EMCS_CLUS_STATS_ENABLE',","  EMCS_CLUSTER_THRESH= 2,","  EMCS_LINKAGE_FUNCTION= 'EMCS_LINKAGE_SINGLE',","  EMCS_LOGLIKE_IMPROVEMENT= .001,","  EMCS_MAX_NUM_ATTR_2D= 50,","  EMCS_MIN_PCT_ATTR_SUPPORT= .1,","  EMCS_MODEL_SEARCH= 'EMCS_MODEL_SEARCH_DISABLE',","  EMCS_NUM_COMPONENTS= 20,","  EMCS_NUM_DISTRIBUTION= 'EMCS_NUM_DISTR_SYSTEM',","  EMCS_NUM_EQUIWIDTH_BINS= 11,","  EMCS_REMOVE_COMPONENTS= 'EMCS_REMOVE_COMPS_ENABLE',","  EMCS_ATTRIBUTE_FILTER= 'EMCS_ATTR_FILTER_ENABLE',","  EMCS_CONVERGENCE_CRITERION= 'EMCS_CONV_CRIT_HELDASIDE',","  EMCS_NUM_QUANTILE_BINS= 11,","  EMCS_NUM_TOPN_BINS= 15,","  ODMS_DETAILS= 'ODMS_ENABLE',","  ODMS_SAMPLING= 'ODMS_SAMPLING_DISABLE',","  PREP_AUTO= 'ON',","  model_name= \"EM_CLUSTERING_MODEL\")","","EM.MOD2 <- ore.odmEM(~.-CUST_ID, CUST_DF, num.centers = 3,odm.settings = settings)"],"enabled":true,"result":{"startTime":1733161756739,"interpreter":"r.low","endTime":1733161760043,"results":[{"message":"Warning message:\n\u201cThe created mining model EM_CLUSTERING_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details","message":["%r","","summary(EM.MOD2)"],"enabled":true,"result":{"startTime":1733161760127,"interpreter":"r.low","endTime":1733161760273,"results":[{"message":"\nCall:\nore.odmEM(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nattribute.filter                  attr.filter.enable\ncluster.components               cluster.comp.enable\ncluster.statistics                 clus.stats.enable\ncluster.thresh                                     2\nconvergence.criterion            conv.crit.heldaside\nlinkage.function                      linkage.single\nloglike.improvement                            0.001\nmax.num.attr.2d                                   50\nmin.pct.attr.support                             0.1\nmodel.search                    model.search.disable\nnum.components                                    20\nnum.distribution                    num.distr.system\nnum.equiwidth.bins                                11\nnum.iterations                                    20\nnum.projections                                   50\nnum.quantile.bins                                 11\nnum.topn.bins                                     15\nrandom.seed                                        7\nremove.components                remove.comps.enable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS MEAN.CUST_YEAR_OF_BIRTH\n3               NA                       NA                1959.533\n4               NA                       NA                1968.319\n5               NA                       NA                1964.435\n  MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.OCCUPATION MEAN.YRS_RESIDENCE\n3             NA                  NA              NA           4.867778\n4             NA                  NA              NA           3.434765\n5             NA                  NA              NA           3.736842\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display summary of clusters","message":["%r","","z.show(EM.MOD2$clusters)"],"enabled":true,"result":{"startTime":1733161760356,"interpreter":"r.low","endTime":1733161760472,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tRECORD_COUNT\tPARENT\tTREE_LEVEL\tLEFT_CHILD_ID\tRIGHT_CHILD_ID\n1\t1\t4500\tNA\t1\t2\t3\n2\t2\t2700\t1\t2\t4\t5\n3\t3\t1800\t1\t2\tNA\tNA\n4\t4\t2491\t2\t3\tNA\tNA\n5\t5\t209\t2\t3\tNA\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster histograms for clusters in the hierarchy with row counts","message":["%r","","z.show(head(clusterhists(EM.MOD2),10))"],"enabled":true,"result":{"startTime":1733161760550,"interpreter":"r.low","endTime":1733161760668,"results":[{"message":"cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_GENDER:F\t3\tNA\tNA\t:\t1510\n1\tCUST_GENDER:M\t2\tNA\tNA\t:\t2990\n1\tCUST_MARITAL_STATUS:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_MARITAL_STATUS:Divorc.\t4\tNA\tNA\t:\t615\n1\tCUST_MARITAL_STATUS:Mabsent\t7\tNA\tNA\t:\t75\n1\tCUST_MARITAL_STATUS:Mar-AF\t8\tNA\tNA\t:\t3\n1\tCUST_MARITAL_STATUS:Married\t2\tNA\tNA\t:\t2034\n1\tCUST_MARITAL_STATUS:NeverM\t3\tNA\tNA\t:\t1503\n1\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\t:\t134\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Apply model to assign rows to clusters","message":["%r","","EM.PRED <- predict(EM.MOD2, CUST_DF, supplemental.cols=c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\"))","class(EM.PRED)","","z.show(head(EM.PRED)) "],"enabled":true,"result":{"startTime":1733161760740,"interpreter":"r.low","endTime":1733161761060,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"YRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.3.\tX.4.\tX.5.\tCLUSTER_ID\n4\t1959\t4.00041843584483e-07\t2.86871551237016e-07\t0.999999313086605\t5\n2\t1983\t2.08246808532547e-10\t0.999999999791687\t6.66017104164389e-14\t4\n4\t1961\t0.999999927247648\t6.53106154009255e-08\t7.44173731283541e-09\t3\n6\t1944\t1.3925293493128e-05\t0.999986074706404\t1.02766131649824e-13\t4\n2\t1983\t9.50471234719966e-10\t0.99999999904944\t8.90591137473432e-14\t4\n3\t1978\t0.999999211550069\t7.87290929569139e-07\t1.15900106205538e-09\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compare Clusters","Compare the  distribution for years residence and year of birth across two clusters using the overloaded `hist` functions. "],"enabled":true,"result":{"startTime":1733161761139,"interpreter":"md.low","endTime":1733161761196,"results":[{"message":"<h2 id=\"compare-clusters\">Compare Clusters<\/h2>\n<p>Compare the  distribution for years residence and year of birth across two clusters using the overloaded <code>hist<\/code> functions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot histograms of select clusters and attributes","message":["%r","","dat.cluster3 <- EM.PRED[EM.PRED$CLUSTER_ID == 3,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","dat.cluster5 <- EM.PRED[EM.PRED$CLUSTER_ID == 5,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","","par(mfrow=c(2,2)) ","hist(dat.cluster3$YRS_RESIDENCE, main=\"Cluster 3 YRS_RESIDENCE\", col=\"darkred\", xlim=c(0,15), ylim=c(0,600))","hist(dat.cluster5$YRS_RESIDENCE, main=\"Cluster 5 YRS_RESIDENCE\", col=\"darkgreen\", xlim=c(0,15),ylim=c(0,600))","","hist(dat.cluster3$CUST_YEAR_OF_BIRTH, main=\"Cluster 3 CUST_YEAR_OF_BIRTH\", col=\"darkred\", xlim=c(1910,1990), ylim=c(0,250))","hist(dat.cluster5$CUST_YEAR_OF_BIRTH, main=\"Cluster 5 CUST_YEAR_OF_BIRTH\", col=\"darkgreen\", xlim=c(1910,1990), ylim=c(0,250))"],"enabled":true,"result":{"startTime":1733161761281,"interpreter":"r.low","endTime":1733161762273,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nnnnmmTNnznz99ddbtmz5888/r1+/7ubmFhAQ0KhRox49evTp08f45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KBu5DoAKmD6rtiRI0du2rTJDqEAgAOR6wCogOkjdhMmTHjvvfcu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\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gAmRK4DYA42V9hNnTpV/pyQkBAVFdWr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\n69fvl19+sUpfhCm6c/z48eHDhzdu3NjFxaVZs2aTJ0/OzMy0Vnd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Using SQL to access prediction details","One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.","","We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well."],"enabled":true,"result":{"startTime":1733161762350,"interpreter":"md.low","endTime":1733161762409,"results":[{"message":"<h2 id=\"using-sql-to-access-prediction-details\">Using SQL to access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.\nThen, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table=\"CUST_DF_EM\")","ore.create(CUST_DF, table='CUST_DF_EM')"],"enabled":true,"result":{"startTime":1733161762481,"interpreter":"r.low","endTime":1733161763046,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top 3 predictive attributes for specific customers with corresponding cluster assignments","message":["%sql\r","\r","SELECT CUST_ID,\r","       CLUSTER_ID,\r","       ROUND(PROB*100,0) PROB_PCT,\r","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,\r","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,\r","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE\r","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, \r","             CLUSTER_DETAILS(EM_CLUSTERING_MODEL USING T.*) DETAIL\r","      FROM (SELECT V.*, CLUSTER_SET(EM_CLUSTERING_MODEL, NULL, 0.2 USING *) PSET\r","            FROM CUST_DF_EM V\r","            WHERE cust_id = ${CUST_ID ='100676','100676'|'101219'|'102805'}) T,\r","                  TABLE(T.PSET) S\r","            ORDER BY 2 DESC) OUT,\r","            XMLTABLE('/Details'\r","                      PASSING OUT.DETAIL\r","                      COLUMNS \r","                      \"Attribute1\" XMLType PATH 'Attribute[1]',\r","                      \"Attribute2\" XMLType PATH 'Attribute[2]',\r","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1733161763123,"interpreter":"sql.low","endTime":1733161763347,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n102805\t5\t100\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".655\" \t\"HOUSEHOLD_SIZE\" 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diff --git a/machine-learning/notebooks-oml/r/OML4R Clustering KM.dsnb b/machine-learning/notebooks-oml/r/OML4R Clustering KM.dsnb
new file mode 100644
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+++ b/machine-learning/notebooks-oml/r/OML4R Clustering KM.dsnb	
@@ -0,0 +1,1145 @@
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+    "name" : "OML4R Clustering KM",
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+        "message" : [
+          "%md",
+          "# Identifying Customer Segments using K-Means Clustering",
+          "Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization. ",
+          "In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema ",
+          "using the unsupervised learning K-Means (KM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.",
+          "",
+          "See the documentation link below for details on the K-means in-database algortihm.",
+          "",
+          "---",
+          "",
+          "Copyright (c) 2025 Oracle Corporation",
+          "###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>---"
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+              "message" : "<h1 id=\"identifying-customer-segments-using-k-means-clustering\">Identifying Customer Segments using K-Means Clustering</h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: K-means, O-Cluster, and Expectation Maximization.\nIn this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema\nusing the unsupervised learning K-Means (KM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.</p>\n<p>See the documentation link below for details on the K-means in-database algortihm.</p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation</p>\n<h6 id=\"the-universal-permissive-license-upl-version-10---\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>---</h6>\n",
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+          "![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"
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+      {
+        "row" : 0,
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+        "width" : 12,
+        "title" : "For more information...",
+        "hasTitle" : true,
+        "message" : [
+          "%md",
+          "",
+          "* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation</a>",
+          "* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub</a>",
+          "* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page</a>",
+          "* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering</a>",
+          "* <a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means</a>"
+        ],
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+        "col" : 0,
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+        "width" : 12,
+        "title" : "Import libraries",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "library(ORE)",
+          "library(OREdplyr)",
+          "options(ore.warn.order=FALSE)"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
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+      {
+        "row" : 0,
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Prepare data set for model building from customer demographic data in proxy object CUST_DF",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "ore.sync(query = c(\"SUPPLEMENTARY_DEMOGRAPHICS\" = \"select CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))",
+          "ore.sync(query = c(\"CUSTOMERS\" = \"select CUST_ID, CUST_GENDER, CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS from SH.CUSTOMERS\"))",
+          "",
+          "CUST_DF <- merge(SUPPLEMENTARY_DEMOGRAPHICS,CUSTOMERS, by=\"CUST_ID\")"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
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+      {
+        "row" : 0,
+        "col" : 0,
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+        "width" : 12,
+        "title" : "Display first 5 rows of CUST_DF data",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "z.show(head(CUST_DF,5))"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : "[{\"table\":{\"version\":1},\"scatterplot\":{\"groupByColumns\":[\"CUST_MARITAL_STATUS\"],\"showSeriesX\":[\"CUST_YEAR_OF_BIRTH\"],\"showSeriesY\":[\"YRS_RESIDENCE\"],\"label\":[\"CUST_GENDER\"],\"extraDescription\":[],\"lastColumns\":[\"CUST_ID\",\"EDUCATION\",\"OCCUPATION\",\"HOUSEHOLD_SIZE\",\"YRS_RESIDENCE\",\"CUST_GENDER\",\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL_STATUS\"],\"version\":1}}]",
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+          "## Build a K-means model using the CUST_DF proxy object"
+        ],
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+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "KM.MOD1 <- ore.odmKMeans(~.-CUST_ID, CUST_DF, num.centers = 3)",
+          "",
+          "KM.MOD1"
+        ],
+        "selectedVisualization" : "raw",
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+              "message" : "\nCall:\nore.odmKMeans(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nblock.growth                                       2\nconv.tolerance                                  0.01\ndetails                                  details.all\ndistance                                   euclidean\niterations                                         3\nmin.pct.attr.support                             0.1\nnum.bins                                          10\nrandom.seed                                        0\nsplit.criterion                             variance\nwinsorize                          winsorize.disable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n",
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+              "message" : "\nCall:\nore.odmKMeans(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nblock.growth                                       2\nconv.tolerance                                  0.01\ndetails                                  details.all\ndistance                                   euclidean\niterations                                         3\nmin.pct.attr.support                             0.1\nnum.bins                                          10\nrandom.seed                                        0\nsplit.criterion                             variance\nwinsorize                          winsorize.disable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  CUST_YEAR_OF_BIRTH YRS_RESIDENCE CUST_GENDER CUST_MARITAL_STATUS EDUCATION\n2           1977.412      2.387486           M              NeverM   HS-grad\n4           1955.836      5.461489           M             Married   HS-grad\n5           1957.472      4.525833           F             Divorc.   HS-grad\n  HOUSEHOLD_SIZE OCCUPATION\n2              1      Other\n4              3      Exec.\n5              2    Cleric.\n",
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+        "row" : 0,
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display cluster histogram data for all clusters in the hierarchy",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "z.show(head(clusterhists(KM.MOD1),20))"
+        ],
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+            {
+              "message" : "cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:F\t1\tNA\tNA\t:\t1510\n1\tCUST_GENDER:M\t2\tNA\tNA\t:\t2990\n1\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\t:\t615\n1\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\t:\t75\n1\tCUST_MARITAL_STATUS:Married\t3\tNA\tNA\t:\t2034\n1\tCUST_MARITAL_STATUS:NeverM\t4\tNA\tNA\t:\t1503\n1\tCUST_MARITAL_STATUS:Separ.\t5\tNA\tNA\t:\t134\n1\tCUST_MARITAL_STATUS:Widowed\t6\tNA\tNA\t:\t136\n1\tCUST_YEAR_OF_BIRTH\t1\t1913\t1921.11111111111\t1913:1921.1111111\t6\n1\tCUST_YEAR_OF_BIRTH\t2\t1921.11111111111\t1929.22222222222\t1921.1111111:1929.2222222\t60\n1\tCUST_YEAR_OF_BIRTH\t3\t1929.22222222222\t1937.33333333333\t1929.2222222:1937.3333333\t104\n1\tCUST_YEAR_OF_BIRTH\t4\t1937.33333333333\t1945.44444444444\t1937.3333333:1945.4444444\t284\n1\tCUST_YEAR_OF_BIRTH\t5\t1945.44444444444\t1953.55555555556\t1945.4444444:1953.5555556\t443\n1\tCUST_YEAR_OF_BIRTH\t6\t1953.55555555556\t1961.66666666667\t1953.5555556:1961.6666667\t841\n1\tCUST_YEAR_OF_BIRTH\t7\t1961.66666666667\t1969.77777777778\t1961.6666667:1969.7777778\t936\n1\tCUST_YEAR_OF_BIRTH\t8\t1969.77777777778\t1977.88888888889\t1969.7777778:1977.8888889\t897\n1\tCUST_YEAR_OF_BIRTH\t9\t1977.88888888889\t1986\t1977.8888889:1986\t929\n1\tEDUCATION:10th\t1\tNA\tNA\t:\t122\n1\tEDUCATION:11th\t2\tNA\tNA\t:\t121\n1\tEDUCATION:12th\t3\tNA\tNA\t:\t52\n",
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+        "message" : [
+          "%md",
+          "",
+          "### Examples of possible setting overrides for K-means clustering",
+          "",
+          "If the user does not override the default settings, then relevant settings are determined by the algorithm",
+          "",
+          "A complete list of settings can be found in the Documentation link:",
+          "",
+          "-- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C');\"> KMeans clustering</a>",
+          "",
+          "-- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms</a>",
+          "",
+          "-- Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings </a>",
+          "",
+          "-- Specify a row weight column ",
+          "    v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '<row_weight_column_name>';",
+          "   ",
+          "-- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.",
+          "    v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';",
+          "    ",
+          "--  Specify the maximum number of leaf clusters generated by a clustering algorithm. ",
+          "  The algorithm may return fewer clusters, depending on the data.  Enhanced K-means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. ",
+          "  For K-means and O-Cluster, the default is 10. ",
+          "     v_setlst('CLUS_NUM_CLUSTERS') = '5';",
+          "",
+          "-- Minimum Convergence Tolerance for K-means. The algorithm iterates until the minimum Convergence Tolerance is satisfied or until the maximum number of iterations, specified in KMNS_ITERATIONS, is reached. Decreasing the Convergence Tolerance produces a more accurate solution but may result in longer run times. The default Convergence Tolerance is 0.001.",
+          "    v_setlst('KMNS_CONV_TOLERANCE') = 'TO_CHAR(0<numeric_expr<1)';",
+          "    ",
+          "-- Distance function for K-means. The default distance function is KMNS_EUCLIDEAN.",
+          "    v_setlist('KMNS_DISTANCE') = 'KMNS_EUCLIDEAN'",
+          "    ",
+          "-- Maximum number of iterations for K-means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in KMNS_CONV_TOLERANCE, is satisfied. The default number of iterations is 20.",
+          "    v_setlist('KMNS_ITERATIONS') = 'TO_CHAR(positive_numeric_expr)';",
+          "    ",
+          "-- Minimum percentage of attribute values that must be non-null in order for the attribute to be included in the rule description for the cluster. If the data is sparse or includes many missing values, a minimum support that is too high can cause very short rules or even empty rules. The default minimum support is 0.1.",
+          "    v_setlist('KMNS_MIN_PCT_ATTR_SUPPORT') = 'TO_CHAR(0<=numeric_expr<=1)';",
+          "    ",
+          "-- Number of bins in the attribute histogram produced by K-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. The default number of histogram bins is 11.",
+          "    v_setlist('KMNS_NUM_BINS') = 'TO_CHAR(numeric_expr>0)';",
+          "    ",
+          "-- Split criterion for K-means. The split criterion controls the initialization of new K-means clusters. The algorithm builds a binary tree and adds one new cluster at a time.",
+          "When the split criterion is based on size, the new cluster is placed in the area where the largest current cluster is located. When the split criterion is based on the variance, the new cluster is placed in the area of the most spread-out cluster. The default split criterion is the KMNS_VARIANCE.",
+          "    v_setlist('KMNS_SPLIT_CRITERION') = 'KMNS_VARIANCE'",
+          "    ",
+          "-- This setting controls the seed of the random generator used during the K-means initialization. It must be a non-negative integer value. The default is 0.",
+          "    v_setlist('KMNS_RANDOM_SEED') = 'Non-negative integer'",
+          "    ",
+          "-- This setting determines the level of cluster detail that are computed during the build. KMNS_DETAILS_NONE: No cluster details are computed. Only the scoring information is persisted.",
+          "KMNS_DETAILS_HIERARCHY: Cluster hierarchy and cluster record counts are computed. This is the default value. KMNS_DETAILS_ALL: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.",
+          "    v_setlist('KMNS_DETAILS') = 'KMNS_DETAILS_HIERARCHY'"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094984600,
+          "endTime" : 1738094984695,
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+          "results" : [
+            {
+              "message" : "<h3 id=\"examples-of-possible-setting-overrides-for-k-means-clustering\">Examples of possible setting overrides for K-means clustering</h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm</p>\n<p>A complete list of settings can be found in the Documentation link:</p>\n<p>-- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C');\"> KMeans clustering</a></p>\n<p>-- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms</a></p>\n<p>-- Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings </a></p>\n<p>-- Specify a row weight column\nv_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '&lt;row_weight_column_name&gt;';</p>\n<p>-- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO</code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE</code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO</code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT</code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW</code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.\nv_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';</p>\n<p>--  Specify the maximum number of leaf clusters generated by a clustering algorithm.\nThe algorithm may return fewer clusters, depending on the data.  Enhanced K-means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points.\nFor K-means and O-Cluster, the default is 10.\nv_setlst('CLUS_NUM_CLUSTERS') = '5';</p>\n<p>-- Minimum Convergence Tolerance for K-means. The algorithm iterates until the minimum Convergence Tolerance is satisfied or until the maximum number of iterations, specified in KMNS_ITERATIONS, is reached. Decreasing the Convergence Tolerance produces a more accurate solution but may result in longer run times. The default Convergence Tolerance is 0.001.\nv_setlst('KMNS_CONV_TOLERANCE') = 'TO_CHAR(0&lt;numeric_expr&lt;1)';</p>\n<p>-- Distance function for K-means. The default distance function is KMNS_EUCLIDEAN.\nv_setlist('KMNS_DISTANCE') = 'KMNS_EUCLIDEAN'</p>\n<p>-- Maximum number of iterations for K-means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in KMNS_CONV_TOLERANCE, is satisfied. The default number of iterations is 20.\nv_setlist('KMNS_ITERATIONS') = 'TO_CHAR(positive_numeric_expr)';</p>\n<p>-- Minimum percentage of attribute values that must be non-null in order for the attribute to be included in the rule description for the cluster. If the data is sparse or includes many missing values, a minimum support that is too high can cause very short rules or even empty rules. The default minimum support is 0.1.\nv_setlist('KMNS_MIN_PCT_ATTR_SUPPORT') = 'TO_CHAR(0&lt;=numeric_expr&lt;=1)';</p>\n<p>-- Number of bins in the attribute histogram produced by K-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. The default number of histogram bins is 11.\nv_setlist('KMNS_NUM_BINS') = 'TO_CHAR(numeric_expr&gt;0)';</p>\n<p>-- Split criterion for K-means. The split criterion controls the initialization of new K-means clusters. The algorithm builds a binary tree and adds one new cluster at a time.\nWhen the split criterion is based on size, the new cluster is placed in the area where the largest current cluster is located. When the split criterion is based on the variance, the new cluster is placed in the area of the most spread-out cluster. The default split criterion is the KMNS_VARIANCE.\nv_setlist('KMNS_SPLIT_CRITERION') = 'KMNS_VARIANCE'</p>\n<p>-- This setting controls the seed of the random generator used during the K-means initialization. It must be a non-negative integer value. The default is 0.\nv_setlist('KMNS_RANDOM_SEED') = 'Non-negative integer'</p>\n<p>-- This setting determines the level of cluster detail that are computed during the build. KMNS_DETAILS_NONE: No cluster details are computed. Only the scoring information is persisted.\nKMNS_DETAILS_HIERARCHY: Cluster hierarchy and cluster record counts are computed. This is the default value. KMNS_DETAILS_ALL: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.\nv_setlist('KMNS_DETAILS') = 'KMNS_DETAILS_HIERARCHY'</p>\n",
+              "type" : "HTML"
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+        "message" : [
+          "%md",
+          "## Alternative approach to creating clustering model using a settings table",
+          "",
+          "The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
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+          "startTime" : 1738094984798,
+          "endTime" : 1738094984877,
+          "interpreter" : "md.medium",
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table</h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Build K-means clustering model with 3 clusters with explicit settings",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "ore.drop(model='KM_CLUSTERING_MODEL')",
+          "",
+          "settings = list(KMNS_ITERATIONS           = 20,",
+          "                KMNS_CONV_TOLERANCE       = .001,",
+          "                KMNS_DISTANCE             = 'KMNS_EUCLIDEAN',",
+          "                KMNS_MIN_PCT_ATTR_SUPPORT = 0.05,",
+          "                KMNS_NUM_BINS             = 11,",
+          "                KMNS_SPLIT_CRITERION      = 'KMNS_VARIANCE',",
+          "                KMNS_RANDOM_SEED          = 7,",
+          "                KMNS_DETAILS              = 'KMNS_DETAILS_ALL',",
+          "                ODMS_DETAILS              = 'ODMS_ENABLE',",
+          "                ODMS_SAMPLING             = 'ODMS_SAMPLING_DISABLE',",
+          "                PREP_AUTO                 = 'ON',",
+          "                model_name                ='KM_CLUSTERING_MODEL')",
+          "",
+          "KM.MOD2 <- ore.odmKMeans(~.-CUST_ID, CUST_DF, num.centers = 3,odm.settings = settings)"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
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+          "interpreter" : "r.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "Warning message:\n“The created mining model KM_CLUSTERING_MODEL is explicitly named. ORE does not manage this mining model's life cycle.”\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
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+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display model details from model proxy object",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "summary(KM.MOD2)"
+        ],
+        "selectedVisualization" : "raw",
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+            {
+              "message" : "\nCall:\nore.odmKMeans(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nblock.growth                                       2\nconv.tolerance                                 0.001\ndetails                                  details.all\ndistance                                   euclidean\niterations                                        20\nmin.pct.attr.support                            0.05\nnum.bins                                          11\nrandom.seed                                        7\nsplit.criterion                             variance\nwinsorize                          winsorize.disable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  CUST_YEAR_OF_BIRTH YRS_RESIDENCE CUST_GENDER CUST_MARITAL_STATUS EDUCATION\n2           1977.012      2.408642           F              NeverM   HS-grad\n4           1946.907      6.660398           M             Married   HS-grad\n5           1962.574      4.137652           M             Married   HS-grad\n  HOUSEHOLD_SIZE OCCUPATION\n2              1      Other\n4              3      Exec.\n5              3     Crafts\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display summary of clusters",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "z.show(KM.MOD2$clusters)"
+        ],
+        "selectedVisualization" : "table",
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+        "hideCode" : false,
+        "hideResult" : false,
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+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tROW_CNT\tPARENT_CLUSTER_ID\tTREE_LEVEL\tDISPERSION\tIS_LEAF\n1\t4500\tNA\t1\t3.85800326273478\tFALSE\n2\t1620\t1\t2\t3.6862578766992\tTRUE\n3\t2880\t1\t2\t3.95461004237979\tFALSE\n4\t904\t3\t3\t4.37477755654011\tTRUE\n5\t1976\t3\t3\t3.76238765735908\tTRUE\n",
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Define function to output formatted rule",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "displayRule <- function (model, cluster.id, dat) {",
+          "  rules  <- rules(model)",
+          "  cluster.rules <- rules[rules$cluster.id == cluster.id, ]",
+          "  vars   <- unique(cluster.rules$lhs.var)",
+          "  res    <- c()",
+          "",
+          "  for (var in vars) {",
+          "     rule <- c()",
+          "     var.rules <- cluster.rules[cluster.rules$lhs.var == var, 'predicate']",
+          "     if (class(dat[[var]]) == \"ore.character\") {",
+          "       for (r in var.rules)",
+          "         rule <- c(rule, tail(strsplit(r, \" IN \")[[1]], 1))",
+          "       rule <- paste(var, \" IN {'\", paste(rule, collapse=\"','\"), \"'}\", sep=\"\")",
+          "     } else",
+          "       rule <- paste(var.rules, collapse = \" and \")",
+          "     res <- c(res, paste0(rule, \"\\n\"))",
+          "  }",
+          "",
+          "  cat(paste(\"if \", paste(res, collapse = \"  and \"), \"then cluster \", cluster.id, sep=\"\"))    ",
+          "}"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094987893,
+          "endTime" : 1738094988053,
+          "interpreter" : "r.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Generate a formatted rule for cluster 5",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "displayRule(model      = KM.MOD2, ",
+          "            cluster.id = 5, ",
+          "            dat        = CUST_DF)"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094988154,
+          "endTime" : 1738094988367,
+          "interpreter" : "r.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "if CUST_GENDER IN {'CUST_GENDER = M'}\n  and CUST_MARITAL_STATUS IN {'Divorc.','Married'}\n  and CUST_YEAR_OF_BIRTH <= 1978.7 and CUST_YEAR_OF_BIRTH > 1949.5\n  and EDUCATION IN {'< Bach.','Assoc-A','Assoc-V','Bach.','HS-grad','Masters'}\n  and HOUSEHOLD_SIZE IN {'2','3','9+'}\n  and OCCUPATION IN {'Cleric.','Crafts','Exec.','Machine','Other','Prof.','Sales','TechSup','Transp.'}\n  and YRS_RESIDENCE <= 7 and YRS_RESIDENCE > 2.8\nthen cluster 5",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Apply model to assign rows to clusters",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "KM.PRED <- predict(KM.MOD2, CUST_DF, supplemental.cols=c(\"CUST_ID\",\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\"))",
+          "class(KM.PRED)",
+          "",
+          "z.show(head(KM.PRED))"
+        ],
+        "selectedVisualization" : null,
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094988464,
+          "endTime" : 1738094988817,
+          "interpreter" : "r.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "'ore.frame'\n",
+              "type" : "HTML"
+            },
+            {
+              "message" : "CUST_ID\tYRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.2.\tX.4.\tX.5.\tCLUSTER_ID\n100134\t2\t1965\t0.464412708913132\t0.167099733375034\t0.368487557711834\t2\n102828\t4\t1967\t0.439988397351463\t0.208853463315369\t0.351158139333169\t2\n101232\t2\t1979\t0.589990184168435\t0.0985396194553096\t0.311470196376256\t2\n100696\t3\t1971\t0.332566047205089\t0.20240620111688\t0.465027751678031\t5\n103948\t4\t1966\t0.41087765171082\t0.222416714270255\t0.366705634018924\t2\n103791\t5\t1952\t0.210965218445713\t0.402310613289335\t0.386724168264952\t4\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "## Compare Clusters",
+          "Compare the  distribution for years residence and year of birth across two clusters using the overloaded `hist` functions. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094988913,
+          "endTime" : 1738094988997,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"compare-clusters\">Compare Clusters</h2>\n<p>Compare the  distribution for years residence and year of birth across two clusters using the overloaded <code>hist</code> functions.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 9,
+        "title" : "Plot histograms of select clusters and attributes",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "dat.cluster4 <- KM.PRED[KM.PRED$CLUSTER_ID == 4,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]",
+          "dat.cluster5 <- KM.PRED[KM.PRED$CLUSTER_ID == 5,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]",
+          "",
+          "par(mfrow=c(2,2)) ",
+          "hist(dat.cluster4$YRS_RESIDENCE, main=\"Cluster 4 YRS_RESIDENCE\", col=\"darkred\", xlim=c(0,15), ylim=c(0,600))",
+          "hist(dat.cluster5$YRS_RESIDENCE, main=\"Cluster 5 YRS_RESIDENCE\", col=\"darkgreen\", xlim=c(0,15),ylim=c(0,600))",
+          "",
+          "hist(dat.cluster4$CUST_YEAR_OF_BIRTH, main=\"Cluster 4 CUST_YEAR_OF_BIRTH\", col=\"darkred\", xlim=c(1910,1990), ylim=c(0,250))",
+          "hist(dat.cluster5$CUST_YEAR_OF_BIRTH, main=\"Cluster 5 CUST_YEAR_OF_BIRTH\", col=\"darkgreen\", xlim=c(1910,1990), ylim=c(0,250))"
+        ],
+        "selectedVisualization" : "image",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094989098,
+          "endTime" : 1738094989938,
+          "interpreter" : "r.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : 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GyDXAegTDNe2OXn5wcHB3fs2HH69OnKqYo5c+Zs377dwrEBgPWQ6wCogPHCLioq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\nT7+T4cOHyzdVdejQIT0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+S6InXq1MnZ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\nKirq2rVrmqUqlcrPz8/Ly0szR/3h3rp1q2bOW2+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+            }
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+      },
+      {
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Distribution of the year of birth for cluster 5",
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "## Using SQL to access prediction details",
+          "One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.",
+          "",
+          "We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.",
+          "Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094990044,
+          "endTime" : 1738094990123,
+          "interpreter" : "md.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"using-sql-to-access-prediction-details\">Using SQL to access prediction details</h2>\n<p>One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.</p>\n<p>We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.\nThen, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create table for use in SQL query",
+        "hasTitle" : true,
+        "message" : [
+          "%r",
+          "",
+          "ore.drop(table=\"CUST_DF_KM\")",
+          "ore.create(CUST_DF,table=\"CUST_DF_KM\")"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094990226,
+          "endTime" : 1738094990831,
+          "interpreter" : "r.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "List top 3 predictive attributes for specific customers and likely cluster assignments",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CUST_ID,",
+          "       CLUSTER_ID,",
+          "       ROUND(PROB*100,0) PROB_PCT,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE",
+          "FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ",
+          "             CLUSTER_DETAILS(KM_CLUSTERING_MODEL USING T.*) DETAIL",
+          "      FROM (SELECT V.*, CLUSTER_SET(KM_CLUSTERING_MODEL, NULL, 0.2 USING *) PSET",
+          "            FROM CUST_DF_KM V",
+          "            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,",
+          "                  TABLE(T.PSET) S",
+          "            ORDER BY 2 DESC) OUT,",
+          "            XMLTABLE('/Details'",
+          "                      PASSING OUT.DETAIL",
+          "                      COLUMNS ",
+          "                      \"Attribute1\" XMLType PATH 'Attribute[1]',",
+          "                      \"Attribute2\" XMLType PATH 'Attribute[2]',",
+          "                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"paragraphId\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isNotebook\":false,\"isProgrammatic\":false}]",
+        "result" : {
+          "startTime" : 1738094990946,
+          "endTime" : 1738094991163,
+          "interpreter" : "sql.medium",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
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+        "hideGutter" : true,
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diff --git a/machine-learning/notebooks/r/OML4R Clustering OC.dsnb b/machine-learning/notebooks-oml/r/OML4R Clustering OC.dsnb
old mode 100755
new mode 100644
similarity index 89%
rename from machine-learning/notebooks/r/OML4R Clustering OC.dsnb
rename to machine-learning/notebooks-oml/r/OML4R Clustering OC.dsnb
index ceb850bb..2d68b96b
--- a/machine-learning/notebooks/r/OML4R Clustering OC.dsnb	
+++ b/machine-learning/notebooks-oml/r/OML4R Clustering OC.dsnb	
@@ -1 +1 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Clustering OC","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715347416030,"interpreter":"md.low","endTime":1715347416184,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Identifying Customer Segments using Orthogonal Partitioning Clustering","","Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.","","See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715347416260,"interpreter":"md.low","endTime":1715347416356,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-orthogonal-partitioning-clustering\">Identifying Customer Segments using Orthogonal Partitioning Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1715347416440,"interpreter":"md.low","endTime":1715347416514,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F73B1E57-148D-40E5-A256-C60AD72BF210\" target=\"_blank\">Algorithm Settings<\/a>"],"enabled":true,"result":{"startTime":1715347439489,"interpreter":"md.low","endTime":1715347439548,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F73B1E57-148D-40E5-A256-C60AD72BF210\" target=\"_blank\">Algorithm Settings<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(dplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715347416742,"interpreter":"r.low","endTime":1715347421643,"results":[{"message":"\nAttaching package: \u2018dplyr\u2019\n\n\nThe following objects are masked from \u2018package:arules\u2019:\n\n    intersect, recode, setdiff, setequal, union\n\n\nThe following objects are masked from \u2018package:stats\u2019:\n\n    filter, lag\n\n\nThe following objects are masked from \u2018package:base\u2019:\n\n    intersect, setdiff, setequal, union\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data set for model building from customer demographic data in proxy object CUST_DF","message":["%r","","ore.sync(query = c(\"SUPPLEMENTARY_DEMOGRAPHICS\" = \"select CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.sync(query = c(\"CUSTOMERS\" = \"select CUST_ID, CUST_GENDER, CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS from SH.CUSTOMERS\"))","","CUST_DF <- merge(SUPPLEMENTARY_DEMOGRAPHICS,CUSTOMERS, by=\"CUST_ID\")","","rownames(CUST_DF) <- CUST_DF$CUST_ID"],"enabled":true,"result":{"startTime":1715347421719,"interpreter":"r.low","endTime":1715347422620,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUST_DF data","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1715347422693,"interpreter":"r.low","endTime":1715347422878,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tCUST_GENDER\tCUST_YEAR_OF_BIRTH\tCUST_MARITAL_STATUS\n100001\t< Bach.\tExec.\t2\t3\tF\t1941\tWidowed\n100002\tBach.\tProf.\t2\t4\tF\t1962\tNeverM\n100003\t< Bach.\tSales\t2\t6\tM\t1969\tNeverM\n100004\t< Bach.\tSales\t2\t5\tF\t1953\tDivorc.\n100005\tAssoc-A\tCrafts\t3\t5\tM\t1957\tMarried\n100006\t< Bach.\tProf.\t9+\t2\tF\t1983\tNeverM\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get dimensions of data set using the CUST_DF proxy object","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1715347422955,"interpreter":"r.low","endTime":1715347423139,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>8<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an O-Cluster model using the CUST_DF proxy object"],"enabled":true,"result":{"startTime":1715347423216,"interpreter":"md.low","endTime":1715347423276,"results":[{"message":"<h3 id=\"build-an-o-cluster-model-using-the-cust_df-proxy-object\">Build an O-Cluster model using the CUST_DF proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an OC Clustering model with 3 clusters and default settings","message":["%r","","OC.MOD1 <- ore.odmOC(~.-CUST_ID, CUST_DF, num.centers = 3)","","OC.MOD1"],"enabled":true,"result":{"startTime":1715347423353,"interpreter":"r.low","endTime":1715347426452,"results":[{"message":"\nCall:\nore.odmOC(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nmax.buffer                                     50000\nsensitivity                                      0.5\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display OC model details","message":["%r","","summary(OC.MOD1)"],"enabled":true,"result":{"startTime":1715347426528,"interpreter":"r.low","endTime":1715347426685,"results":[{"message":"\nCall:\nore.odmOC(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nmax.buffer                                     50000\nsensitivity                                      0.5\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nClusters: \n  CLUSTER_ID CLUSTER_NAME ROW_CNT PARENT_CLUSTER_ID TREE_LEVEL DISPERSION\n1          1            1    4500                NA          1         NA\n2          2            2     968                 1          2         NA\n3          3            3    3532                 1          2         NA\n4          4            4    2103                 3          3         NA\n5          5            5    1429                 3          3         NA\n  IS_LEAF\n1   FALSE\n2    TRUE\n3   FALSE\n4    TRUE\n5    TRUE\n\nCenters: \n  MEAN.CUST_YEAR_OF_BIRTH MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS\n2                1946.555               NA                       NA\n4                1963.449               NA                       NA\n5                1978.594               NA                       NA\n  MEAN.YRS_RESIDENCE MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.OCCUPATION\n2           5.810950             NA                  NA              NA\n4           4.378031             NA                  NA              NA\n5           2.286214             NA                  NA              NA\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster histogram data for all clusters in the hierarchy","message":["%r","","z.show(clusterhists(OC.MOD1))"],"enabled":true,"result":{"startTime":1715347426761,"interpreter":"r.low","endTime":1715347427083,"results":[{"message":"cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.335555555555555\n1\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.664444444444444\n1\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.136666666666667\n1\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0166666666666667\n1\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.000666666666666667\n1\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.452\n1\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.334\n1\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0297777777777778\n1\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.0302222222222222\n1\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0.00111111111111111\n1\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n1\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0.00155555555555556\n1\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0.00666666666666667\n1\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0.00533333333333333\n1\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0.00533333333333333\n1\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0.0151111111111111\n1\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0.014\n1\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0.0197777777777778\n1\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0.042\n1\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.0328888888888889\n1\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0431111111111111\n1\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.0753333333333333\n1\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.0762222222222222\n1\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.0728888888888889\n1\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.0951111111111111\n1\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.0877777777777778\n1\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.0728888888888889\n1\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.102\n1\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.0811111111111111\n1\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.0753333333333333\n1\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0.0744444444444444\n1\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0271111111111111\n1\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0268888888888889\n1\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.0115555555555556\n1\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00377777777777778\n1\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.00866666666666667\n1\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.0193333333333333\n1\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.0168888888888889\n1\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.231333333333333\n1\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0382222222222222\n1\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0435555555555556\n1\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.173111111111111\n1\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.324888888888889\n1\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0422222222222222\n1\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.0108888888888889\n1\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000666666666666667\n1\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.0208888888888889\n1\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.153777777777778\n1\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.255333333333333\n1\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.397111111111111\n1\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0486666666666667\n1\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0328888888888889\n1\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.112222222222222\n1\tOCCUPATION:?\t1\tNA\tNA\t?\t0.0524444444444444\n1\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000444444444444444\n1\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.115555555555556\n1\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.127111111111111\n1\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.124222222222222\n1\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0271111111111111\n1\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0373333333333333\n1\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.00422222222222222\n1\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0613333333333333\n1\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.104\n1\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.121111111111111\n1\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0197777777777778\n1\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.124444444444444\n1\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0393333333333333\n1\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0415555555555556\n1\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.0106666666666667\n1\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.0613333333333333\n1\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.138888888888889\n1\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.213555555555555\n1\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.208222222222222\n1\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.168222222222222\n1\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.105111111111111\n1\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.0513333333333333\n1\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.0224444444444444\n1\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0.0108888888888889\n1\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0.00555555555555555\n1\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0.002\n1\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0.000666666666666667\n1\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0.000444444444444444\n1\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0.000666666666666667\n2\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.302685950413223\n2\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.697314049586777\n2\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.152892561983471\n2\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0309917355371901\n2\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.00103305785123967\n2\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.586776859504132\n2\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.0609504132231405\n2\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0320247933884297\n2\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.135330578512397\n2\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0.00516528925619835\n2\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n2\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0.00723140495867769\n2\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0.0309917355371901\n2\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0.0247933884297521\n2\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0.0247933884297521\n2\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0.0702479338842975\n2\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0.0650826446280992\n2\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0.0919421487603306\n2\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0.195247933884298\n2\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.127066115702479\n2\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0857438016528926\n2\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.0857438016528926\n2\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.0557851239669422\n2\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.0485537190082645\n2\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.0630165289256198\n2\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.0134297520661157\n2\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.00206611570247934\n2\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.00103305785123967\n2\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.00206611570247934\n2\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0\n2\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0\n2\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0454545454545455\n2\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0402892561983471\n2\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.00619834710743802\n2\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00929752066115703\n2\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.00619834710743802\n2\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.0692148760330579\n2\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.0537190082644628\n2\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.169421487603306\n2\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0268595041322314\n2\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0320247933884297\n2\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.115702479338843\n2\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.304752066115702\n2\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0382231404958678\n2\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.0144628099173554\n2\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.00103305785123967\n2\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.0671487603305785\n2\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.00929752066115703\n2\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.269628099173554\n2\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.541322314049587\n2\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0454545454545455\n2\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0165289256198347\n2\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.117768595041322\n2\tOCCUPATION:?\t1\tNA\tNA\t?\t0.113636363636364\n2\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0\n2\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.0671487603305785\n2\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.107438016528926\n2\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.103305785123967\n2\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0816115702479339\n2\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0578512396694215\n2\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.012396694214876\n2\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0454545454545455\n2\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.0888429752066116\n2\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.114669421487603\n2\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0165289256198347\n2\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.12396694214876\n2\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0185950413223141\n2\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0485537190082645\n2\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.0113636363636364\n2\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.00413223140495868\n2\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.0485537190082645\n2\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.0733471074380165\n2\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.121900826446281\n2\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.133264462809917\n2\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.270661157024793\n2\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.153925619834711\n2\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.0888429752066116\n2\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0.0506198347107438\n2\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0.0258264462809917\n2\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0.00929752066115703\n2\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0.00309917355371901\n2\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0.00206611570247934\n2\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0.00309917355371901\n3\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.344563986409966\n3\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.655436013590034\n3\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.132219705549264\n3\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0127406568516421\n3\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.000566251415628539\n3\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.415062287655719\n3\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.408833522083805\n3\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0291619479048698\n3\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.00141562853907135\n3\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0\n3\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n3\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0\n3\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0\n3\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0\n3\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0\n3\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0\n3\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0\n3\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0\n3\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0\n3\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.00707814269535674\n3\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0314269535673839\n3\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.072480181200453\n3\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.0818233295583239\n3\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.0795583238958097\n3\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.103907134767837\n3\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.108154020385051\n3\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.0922989807474519\n3\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.129671574178935\n3\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.10277463193658\n3\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.0959796149490374\n3\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0.0948471121177803\n3\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.022083805209513\n3\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0232163080407701\n3\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.0130237825594564\n3\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00226500566251416\n3\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.0093431483578709\n3\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.00566251415628539\n3\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.00679501698754247\n3\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.248301245753114\n3\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0413363533408834\n3\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0467157417893545\n3\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.188844847112118\n3\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.330407701019253\n3\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0433182332955832\n3\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.00990939977349943\n3\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000566251415628539\n3\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.00821064552661382\n3\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.193374858437146\n3\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.251415628539071\n3\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.357587768969422\n3\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0495469988674972\n3\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0373725934314836\n3\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.110702151755379\n3\tOCCUPATION:?\t1\tNA\tNA\t?\t0.035673839184598\n3\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000566251415628539\n3\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.128822197055493\n3\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.132502831257078\n3\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.12995469988675\n3\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0121744054360136\n3\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0317100792751982\n3\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.00198187995469989\n3\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0656851642129105\n3\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.108154020385051\n3\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.122876557191393\n3\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0206681766704417\n3\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.124575311438279\n3\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0450169875424689\n3\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0396375990939977\n3\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.010475651189128\n3\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.0770101925254813\n3\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.163646659116648\n3\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.2519818799547\n3\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.231879954699887\n3\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.177802944507361\n3\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.0597395243488109\n3\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.0232163080407701\n3\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.00424688561721404\n3\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0\n3\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0\n3\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0\n3\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0\n3\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0\n3\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0\n4\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.281978126485972\n4\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.718021873514028\n4\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.21017593913457\n4\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0128388017118402\n4\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.000951022349025202\n4\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.604374702805516\n4\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.139324774132192\n4\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0309082263433191\n4\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.0014265335235378\n4\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0\n4\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n4\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0\n4\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0\n4\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0\n4\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0\n4\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0\n4\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0\n4\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0\n4\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0\n4\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.011887779362815\n4\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0527817403708987\n4\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.121730860675226\n4\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.136947218259629\n4\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.132667617689016\n4\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.163575844032335\n4\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.163575844032335\n4\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.115549215406562\n4\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.0884450784593438\n4\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.00998573466476462\n4\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.00285306704707561\n4\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0\n4\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0014265335235378\n4\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.000951022349025202\n4\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.00855920114122682\n4\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00285306704707561\n4\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.00713266761768902\n4\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.00332857822158821\n4\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.00523062291963861\n4\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.204945316214931\n4\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0470756062767475\n4\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0565858297669995\n4\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.220637184973847\n4\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.346647646219686\n4\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0718021873514028\n4\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.0152163575844032\n4\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000475511174512601\n4\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.00713266761768902\n4\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.0156918687589158\n4\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.227294341417023\n4\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.540180694246315\n4\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0608654303376129\n4\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0180694246314788\n4\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.137898240608654\n4\tOCCUPATION:?\t1\tNA\tNA\t?\t0.000951022349025202\n4\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000475511174512601\n4\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.108416547788873\n4\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.16642891107941\n4\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.181169757489301\n4\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.00665715644317641\n4\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0014265335235378\n4\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.0014265335235378\n4\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0741797432239658\n4\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.070375653827865\n4\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.156443176414646\n4\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0242510699001427\n4\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.112696148359486\n4\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0456490727532097\n4\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0494531621493105\n4\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.000951022349025202\n4\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.00951022349025202\n4\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.0228245363766049\n4\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.181169757489301\n4\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.348074179743224\n4\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.291012838801712\n4\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.100332857822159\n4\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.0389919163100333\n4\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.00713266761768902\n4\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0\n4\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0\n4\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0\n4\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0\n4\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0\n4\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0\n5\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.43666899930021\n5\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.56333100069979\n5\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.0174947515745276\n5\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0125962211336599\n5\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0\n5\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.136459062281316\n5\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.805458362491253\n5\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.026592022393282\n5\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.00139958012596221\n5\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0\n5\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n5\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0\n5\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0\n5\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0\n5\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0\n5\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0\n5\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0\n5\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0\n5\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0\n5\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0\n5\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0\n5\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0\n5\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.000699790062981106\n5\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.00139958012596221\n5\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.0160951714485654\n5\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.026592022393282\n5\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.0580825752274318\n5\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.190342897130861\n5\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.239328201539538\n5\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.233030090972708\n5\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0.23442967109867\n5\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0524842547235829\n5\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0559832050384885\n5\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.019594121763471\n5\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00139958012596221\n5\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.0125962211336599\n5\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.00909727081875437\n5\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.00909727081875437\n5\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.312106368089573\n5\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.032890132960112\n5\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0321903428971309\n5\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.142057382785164\n5\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.306508047585724\n5\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.00139958012596221\n5\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.00209937018894332\n5\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000699790062981106\n5\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.00979706088173548\n5\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.454863540937719\n5\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.286913925822253\n5\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.0888733379986004\n5\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.032890132960112\n5\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0657802659202239\n5\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.0706787963610917\n5\tOCCUPATION:?\t1\tNA\tNA\t?\t0.0867739678096571\n5\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000699790062981106\n5\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.158852344296711\n5\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.0825752274317705\n5\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.0545836249125262\n5\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0202939118264521\n5\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0762771168649405\n5\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.00279916025192442\n5\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.053184044786564\n5\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.163750874737579\n5\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.0734779566130161\n5\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0153953813855843\n5\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.142057382785164\n5\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0440867739678097\n5\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0251924422673198\n5\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.0244926522043387\n5\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.176347095871239\n5\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.370888733379986\n5\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.356193142057383\n5\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.0608817354793562\n5\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.0111966410076977\n5\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0\n5\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0\n5\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0\n5\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0\n5\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0\n5\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0\n5\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0\n5\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0\n5\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370');\"> O-Cluster clustering<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms<\/a>","","- Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '<row_weight_column_name>';","   ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';","    ","- Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') = '5';","","- Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5. ","> v_setlst('OCLT_SENSITIVITY') = '0.5';"],"enabled":true,"result":{"startTime":1715347427181,"interpreter":"md.low","endTime":1715347427252,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-orthogonal-partitioning-cluster-clustering\">Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370');\"> O-Cluster clustering<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') = '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('OCLT_SENSITIVITY') = '0.5';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an OC Clustering model with explicit settings","message":["%r","","ore.drop(model = 'OC_CLUSTERING_MODEL')","","settings = list(PREP_AUTO        = 'ON',","                OCLT_SENSITIVITY = 0.5,","                model_name       = 'OC_CLUSTERING_MODEL')","","OC.MOD2 <- ore.odmOC(~.-CUST_ID, CUST_DF, num.centers = 5, odm.settings = settings)"],"enabled":true,"result":{"startTime":1715347427339,"interpreter":"r.low","endTime":1715347431240,"results":[{"message":"Warning message:\n\u201cThe created mining model OC_CLUSTERING_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details","message":["%r","","options(width=200)","summary(OC.MOD2)"],"enabled":true,"result":{"startTime":1715347431397,"interpreter":"r.low","endTime":1715347431549,"results":[{"message":"\nCall:\nore.odmOC(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 5, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclus.num.clusters                                  5\nmax.buffer                                     50000\nsensitivity                                      0.5\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nClusters: \n  CLUSTER_ID CLUSTER_NAME ROW_CNT PARENT_CLUSTER_ID TREE_LEVEL DISPERSION IS_LEAF\n1          1            1    4500                NA          1         NA   FALSE\n2          2            2     860                 1          2         NA    TRUE\n3          3            3    3640                 1          2         NA   FALSE\n4          4            4    2322                 3          3         NA   FALSE\n5          5            5    1318                 3          3         NA   FALSE\n6          6            6    1125                 4          4         NA    TRUE\n7          7            7    1197                 4          4         NA    TRUE\n8          8            8     548                 5          4         NA    TRUE\n9          9            9     770                 5          4         NA    TRUE\n\nCenters: \n  MEAN.CUST_YEAR_OF_BIRTH MEAN.OCCUPATION MEAN.YRS_RESIDENCE MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS\n2                1944.652              NA           5.905814             NA                  NA               NA                       NA\n6                1963.431              NA           4.418667             NA                  NA               NA                       NA\n7                1964.416              NA           4.246449             NA                  NA               NA                       NA\n8                1978.126              NA           2.430657             NA                  NA               NA                       NA\n9                1979.390              NA           2.122078             NA                  NA               NA                       NA\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details for each cluster","message":["%r","","z.show(OC.MOD2$clusters)"],"enabled":true,"result":{"startTime":1715347431631,"interpreter":"r.low","endTime":1715347431753,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tROW_CNT\tPARENT_CLUSTER_ID\tTREE_LEVEL\tDISPERSION\tIS_LEAF\n1\t1\t4500\tNA\t1\tNA\tFALSE\n2\t2\t860\t1\t2\tNA\tTRUE\n3\t3\t3640\t1\t2\tNA\tFALSE\n4\t4\t2322\t3\t3\tNA\tFALSE\n5\t5\t1318\t3\t3\tNA\tFALSE\n6\t6\t1125\t4\t4\tNA\tTRUE\n7\t7\t1197\t4\t4\tNA\tTRUE\n8\t8\t548\t5\t4\tNA\tTRUE\n9\t9\t770\t5\t4\tNA\tTRUE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show leaf clusters IDs","message":["%r","","z.show(OC.MOD2$leaf.cluster.counts)"],"enabled":true,"result":{"startTime":1715347431828,"interpreter":"r.low","endTime":1715347431948,"results":[{"message":"CLUSTER_ID\tCNT\n2\t860\n6\t1125\n7\t1197\n8\t548\n9\t770\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data. The larger the number, the more relevant the attribute."],"enabled":true,"result":{"startTime":1715347432024,"interpreter":"md.low","endTime":1715347432085,"results":[{"message":"<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.\nFor an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.<\/p>\n<p>Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data. The larger the number, the more relevant the attribute.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%r","","z.show(OC.MOD2$rules)"],"enabled":true,"result":{"startTime":1715347432160,"interpreter":"r.low","endTime":1715347432347,"results":[{"message":"rhs.cluster.id\trhs.support\trhs.conf\tlhs.support\tlhs.conf\tlhs.var\tlhs.var.support\tlhs.var.conf\tpredicate\n1\t4500\t1\t3840\t0.853333333333333\tCUST_YEAR_OF_BIRTH\t4190\t0.380952380952381\tCUST_YEAR_OF_BIRTH>=1942.86\n1\t4500\t1\t3840\t0.853333333333333\tCUST_YEAR_OF_BIRTH\t4190\t0.380952380952381\tCUST_YEAR_OF_BIRTH<=1986\n1\t4500\t1\t3840\t0.853333333333333\tYRS_RESIDENCE\t4260\t0.533333333333333\tYRS_RESIDENCE>=.933333\n1\t4500\t1\t3840\t0.853333333333333\tYRS_RESIDENCE\t4260\t0.533333333333333\tYRS_RESIDENCE<=7.46667\n1\t4500\t1\t3840\t0.853333333333333\tCUST_GENDER\t4500\t0\tCUST_GENDERINF\n1\t4500\t1\t3840\t0.853333333333333\tCUST_GENDER\t4500\t0\tCUST_GENDERINM\n1\t4500\t1\t3840\t0.853333333333333\tCUST_MARITAL_STATUS\t4152\t0.571428571428571\tCUST_MARITAL_STATUSINDivorc.\n1\t4500\t1\t3840\t0.853333333333333\tCUST_MARITAL_STATUS\t4152\t0.571428571428571\tCUST_MARITAL_STATUSINMarried\n1\t4500\t1\t3840\t0.853333333333333\tCUST_MARITAL_STATUS\t4152\t0.571428571428571\tCUST_MARITAL_STATUSINNeverM\n1\t4500\t1\t3840\t0.853333333333333\tEDUCATION\t3840\t0.625\tEDUCATIONIN< 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Bach.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tEDUCATION\t1209\t0.0125992563160772\tEDUCATIONINAssoc-A\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tEDUCATION\t1209\t0.0125992563160772\tEDUCATIONINBach.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tEDUCATION\t1209\t0.0125992563160772\tEDUCATIONINHS-grad\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tHOUSEHOLD_SIZE\t1161\t0.0635887985951071\tHOUSEHOLD_SIZEIN1\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tHOUSEHOLD_SIZE\t1161\t0.0635887985951071\tHOUSEHOLD_SIZEIN2\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tHOUSEHOLD_SIZE\t1161\t0.0635887985951071\tHOUSEHOLD_SIZEIN3\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONIN?\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINCleric.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINCrafts\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINExec.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINHandler\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINMachine\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINOther\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINProf.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINSales\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINTechSup\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_YEAR_OF_BIRTH\t1104\t0.0370509079525408\tCUST_YEAR_OF_BIRTH>=1949.5\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_YEAR_OF_BIRTH\t1104\t0.0370509079525408\tCUST_YEAR_OF_BIRTH<=1979.36\n6\t1125\t0.25\t1066\t0.947555555555556\tYRS_RESIDENCE\t1066\t0.0218197512952169\tYRS_RESIDENCE>=2.8\n6\t1125\t0.25\t1066\t0.947555555555556\tYRS_RESIDENCE\t1066\t0.0218197512952169\tYRS_RESIDENCE<=7.46667\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_GENDER\t1125\t0.00197545916841683\tCUST_GENDERINF\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_GENDER\t1125\t0.00197545916841683\tCUST_GENDERINM\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_MARITAL_STATUS\t1081\t0.0197605042134215\tCUST_MARITAL_STATUSINDivorc.\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_MARITAL_STATUS\t1081\t0.0197605042134215\tCUST_MARITAL_STATUSINMarried\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_MARITAL_STATUS\t1081\t0.0197605042134215\tCUST_MARITAL_STATUSINNeverM\n6\t1125\t0.25\t1066\t0.947555555555556\tEDUCATION\t1072\t0.00902692842723922\tEDUCATIONIN< 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model to assign rows to clusters","message":["%r","","OC.PRED <- predict(OC.MOD2, CUST_DF, supplemental.cols=c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\"))","class(OC.PRED)","","z.show(head(OC.PRED))"],"enabled":true,"result":{"startTime":1715347432461,"interpreter":"r.low","endTime":1715347433280,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"YRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.2.\tX.6.\tX.7.\tX.8.\tX.9.\tCLUSTER_ID\n3\t1941\t0.999999770737136\t2.28804645351765e-07\t6.39983774067865e-13\t4.57575855508718e-10\t2.76168037463528e-15\t2\n4\t1962\t0.000102248959587262\t2.2841845156172e-06\t0.999892268193504\t6.88679485692155e-11\t3.1985935252414e-06\t7\n6\t1969\t3.17857885001162e-05\t6.29008686491956e-06\t0.999960125812194\t4.05590372323367e-10\t1.79790685056757e-06\t7\n5\t1953\t0.0516621238531008\t5.93526749916806e-06\t0.948331940879246\t5.64553433856911e-18\t1.54510627730725e-13\t7\n5\t1957\t0.00270105742056999\t0.997294480271078\t4.45878790818348e-06\t3.52044147243595e-09\t1.94982857452668e-15\t6\n2\t1983\t6.09406772350066e-05\t1.34274728398413e-06\t0.890926819208708\t0.0270289625529077\t0.0819819348138651\t7\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compare Clusters","Compare the  distribution for years residence and year of birth across two clusters using the overloaded `hist` functions. "],"enabled":true,"result":{"startTime":1715347433368,"interpreter":"md.low","endTime":1715347433429,"results":[{"message":"<h2 id=\"compare-clusters\">Compare Clusters<\/h2>\n<p>Compare the  distribution for years residence and year of birth across two clusters using the overloaded <code>hist<\/code> functions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":800,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot histograms of select clusters and attributes","message":["%r","","dat.cluster6 <- OC.PRED[OC.PRED$CLUSTER_ID == 6,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","dat.cluster9 <- OC.PRED[OC.PRED$CLUSTER_ID == 9,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","","par(mfrow=c(2,2)) ","hist(dat.cluster6$YRS_RESIDENCE, main=\"Cluster 6 YRS_RESIDENCE\", col=\"darkred\", xlim=c(0,15), ylim=c(0,600))","hist(dat.cluster9$YRS_RESIDENCE, main=\"Cluster 9 YRS_RESIDENCE\", col=\"darkgreen\", xlim=c(0,15),ylim=c(0,600))","","hist(dat.cluster6$CUST_YEAR_OF_BIRTH, main=\"Cluster 6 CUST_YEAR_OF_BIRTH\", col=\"darkred\", xlim=c(1910,1990), ylim=c(0,250))","hist(dat.cluster9$CUST_YEAR_OF_BIRTH, main=\"Cluster 9 CUST_YEAR_OF_BIRTH\", col=\"darkgreen\", xlim=c(1910,1990), ylim=c(0,250))"],"enabled":true,"result":{"startTime":1715347433505,"interpreter":"r.low","endTime":1715347435505,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nFHmIuwLavXu3kmL0GX1/4osvvqgzy8SJE/XvFCvuXKe9vf3WrVuVp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AsODr5w4cKKFStatmyp/abqMtC0adOkpKQFCxZ8+OGHZdkvgAqI\nXAdABYwXdomJib/88kvbtm3LIBp9lSpVioiIeOONN3TOjACAeZHrAKiA8cKuSpUqDRs2LINQ\nDKhTp06dOnWsGwMAdSPXAVAB44Xd+PHj4+LiRo8eXQbRwDbJlxclJSWVdEbeMItyhFwHQAWM\nF3ZvvfXWe++9d+3atX79+vn4+Gg/AsDLy8uSscFW3BBCCBEVFRUVFVXSeXnDLMoLch0AFTBe\n2Ckvt3nnnXd0JvG6mwpCfr5WSV8yyxtmUb6Q6wCogPHCbsmSJWUQB2wfL5mFupHrAKiA8cJu\n5syZZRAHAFgXuQ6ACpj6SrEjR44sXrx41qxZ8sfff/9do9FYLCoAsA5yHYByzXhhV1BQMHjw\n4E6dOs2ePVs5VbFgwYKdO3daODYAKDvkOgAqYLywi4iIOHHiRExMzIMHD5SRkydPXrNmjSUD\nA4AyRa4DoALGr7H75ptvNmzYEBAQoD3S39+/FE81AwCbRa4DoALGj9ilpqZ26NBBHlYe7OTm\n5pabm2vBuACgbJHrAKiA8cLOy8vr3LlzOiN///133nsDQE3IdQBUwPip2MDAwOnTp8fExHh5\necl7sRkZGWFhYbxOAICakOssqrCwMD4+Pj8/v8ipvH4QMBfjhV1ERET79u0bNWrUo0cPSZLG\njBmzd+9eR0fHHTt2lEF8AFA2yHUWlZCQMGDAAAMNeP0gYBbGT8X6+fkdPXo0MDBw//79kiR9\n//333bt3//XXX729vcsgPgAoG+Q6i/rP2wX9hXhR7z9/rQYAnozxI3ZCiHr16n377bdCiJyc\nHFdXV+13YwOAapDrLM5biPrWjgFQNZMKO4Wbm5uF4gAA20GuA1BOGS/sDh06VNykrl27mjUY\nALAach0AFTBe2D3//PPFTZIkyazBAIDVkOsAqIDxwi4uLk4ZliTpxo0bO3fudHd3HzVqlCUD\nKz3DN9XLD5Hnrd4AdJDrAKiA8cJO//7zSZMmhYeH37171zIhPanExETDN9ULIc6cOVM2wQAo\nL8h1AFSgZDdPKKZMmdKlS5fx48ebNxqzCAgIiI2NNbAXGxUV1aJFizKOCkB5RK4DUL6UsrB7\n+PDhjRs3zBuKuTg4OPTv399Ag6ioKHt74w/wAwByHYDypcS/+YcPH/7222/jxo3z9/e3REAA\nYAvIdQDKI+NH7JydnbU/FhQUCCFq1KjBa3YAqAm5DoAKGC/sJk2apP2xWrVqzzzzzJAhQ6pU\nqWKxqACgrJHrAKiA8cJu+fLlZRAHAFgXuQ6ACnBdLQAAgEoYP2L3008/mbKgHj16PGEoAGBF\n5DoAKmC8sAsICDBlQbxyB0C5Rq4DoALGC7uEhIS1a9cWFBQMHDiwZs2at2hq7sMAACAASURB\nVG/fjomJcXZ2Dg4OLoP4AKBskOsAqIDxwu706dOtW7eeN2+eMiY4OHju3Llnz5596623LBkb\nAJQdch0AFTBe2K1YseK3337TGTl16tR27dqR7ACoBrkOgAoYvyv27t27OTk5OiOzs7Nt9sXY\nAFAK5DoAKmC8sGvdunVoaKj8EHZZfn7+zJkzn332WUsGBgBlilwHQAWMn4pdvHhx7969n376\n6V69eskXFO/duzczM3P//v1lEB8AlA1yHQAVMF7Yde/e/Zdffpk3b962bdvy8/OdnZ0DAgLm\nz5/frl27MogPAMoGuQ6AChgv7IQQHTp0SEhIkCQpJyfHzc3Nzs7O0mEBQNkj1wEo70x9pdiR\nI0ciIyPnz58vZ7rff/9do9FYMjAAsAJyHYByzXhhV1BQMHjw4E6dOs2ePXvJkiXyyAULFuzc\nudPCsQFA2SHXAVAB44VdRETEiRMnYmJiHjx4oIycPHnymjVrLBkYAJQpch0AFTB+jd0333yz\nYcMGnbco+vv7JyUlWSwqkZqaGhsbe+rUqfT0dEmSvLy8/P39Bw4c6OPjY7lOAVRk5DoAKmC8\nsEtNTe3QoYM8rFxK7Obmlpuba4mAJEmaN2/eokWLHj16pDNp+vTpc+bMef/99y3RL4AKjlwH\nQAWMn4r18vI6d+6czsjff/+9Tp06lghoxYoVkZGR06ZNO3z4cHp6ukaj0Wg06enphw8fDgkJ\nWbRo0cqVKy3RL4AKjlwHQAWMH7ELDAycPn16TEyMl5eXvBebkZERFhYWFBRkiYCio6M3btw4\nfPhw7ZGenp6dO3fu3Llzx44dw8PDp02bZomuAVRk5DobVFhYGB8fn5+fX+RUZ2fnvn37Ojg4\nlHFUgC0zXthFRES0b9++UaNGPXr0kCRpzJgxe/fudXR03LFjhyUCunz5cv/+/Yub2r9//9Gj\nR1uiXwAVHLnOBiUkJAwYMMBAg7i4OAtV3kA5ZfxUrJ+f39GjRwMDA/fv3y9J0vfff9+9e/df\nf/3V29vbEgF5e3snJycXNzU5OdlC/QKo4Mh1NigvL08IIfyFeFHvP3+tBgD+y6Q3T9SrV+/b\nb78VQuTk5Li6ulr0aewjR44cPnx4ZGTkgAEDqlatqozPysqKjY0NDQ0dO3as5XoHUJGR62yU\ntxD1rR0DUE4YL+wcHR2jo6Nff/11IYSbm5ulA4qIiDhz5syYMWPs7e19fHw8PT0lScrMzLx5\n86ZGowkKCuJOMQCWQK4DoALGC7vq1asPHTq0DEKRubq6xsfH7969e/v27SkpKWlpaUIIPz+/\nfv36DR06tHfv3mUWCYAKhVwHQAWMF3YjR46MjY0dN25cGUSj6NOnT58+fcqyRwAVHLkOgAoY\nL+xmzZoVGhp68eLFAQMG+Pr6Ojo6KpO8vLwsGRsAlB1yHQAVMF7Y+fr6ygMLFizQmSRJkvkj\nEkLwmh0AZY5cB0AFjBd2S5YsKYM4FLxmB4BVkOsAqIDxwm7mzJllEIdCec3OkCFDmjVr5uHh\nIYTIzMw8e/bs9u3bFy1aVL16dZ7GDsDsyHUAVMCk59iVpSd/zY7hV9AkJSUJITQajRljBoCS\nItcBsIRiC7t27dqdOHFC+bh///6ePXuWQUBP/pqdxMREw6+gEUKcOXOmNMEBUB1yHQA1Kbaw\n03nXTa9evSx3+bA2+TU7Xbp0KS4qo6/ZCQgIiI2NNbAXGxUV1aJFiycNFIAqkOsAqInNnYp9\n8tfsODg4GNgPFkJERUXZ2xt/SS4AWA65DoAl2Fxhx2t2AFQE5DoAlmBzhR2v2QFQEZDrAFiC\nocJuy5YtBj4KIV555RXzRySE4DU7AMoQuQ6Aahgq7EaOHGngo7BksgOAMkOuA6AaxRZ227Zt\nK8s49D169Ej7XY2K69ev16lTp+zjsSmGn19VHPm5VgC0kesAqEmxhd2wYcPKMg5t69evnzt3\n7o0bN+rXrx8WFhYcHKw91c/Pr2weRmDLEhISjD6/CoApyHUA1MTmbp7Yv3//hAkTWrVq1b59\n+59//nnSpEl79+7dvHmzk5OTtUOzIXl5eUKIbkLUK8lcJ4Q4a6GAAJQQuQ6AJdhcYbdo0aKp\nU6euXLnSzs7uwYMHb7/99ueffz5o0KDt27c7OztbOzrbUk+IViVpf5XCDrAZ5DoAlmBzz65M\nSUmZM2eOnZ2dEMLd3f2zzz6LiopKSEgYNGhQSS8pAwCbRa4DYAk2V9jl5eW5ublpj5kxY8ba\ntWv37t07YMAA8h0AdSDXAbAEmyvsmjZtGhMTozNy4sSJX3755Y8//sgdAwDUgVwHwBJs7hq7\noUOHzp49OzAwUOcF2OPGjXNxcRk9erS1AgMAMyqnuc7os5acnZ379u3r4OBQllEBUNhcYffa\na68dP378yy+/DAsL05n08ssvu7q6Ll261CqBAYAZldNcZ8qzluLi4oKCgsomHgA6bK6wq1mz\n5nfffVfc1KCgIPIFABUop7lOftaS8BfCu6jJd4RI+W8bANZgc4UdAMDWeQtR39oxACiKzd08\nAQAAgNKhsAMAAFAJCjsAAACVoLADAABQCQo7AAAAlaCwAwAAUAkKOwAAAJWgsAMAAFAJCjsA\nAACVoLADAABQCV4pBkuRhBBCJCUllXRGZ2fnvn37Ojg4mD0kAADUjcIOlnJDCCFEVFRUVFRU\nSeeNi4uzzTegAwBgyyjsYCmFQgghuglRryRzXRXiZyHy8vIsEhMAAKpGYQfLqidEK2vHAABA\nBcHNEwAAACpBYQcAAKASFHYAAAAqQWEHAACgEiq8eaKwsDA+Pj4/P7/IqfJj1TQaTdkGBQBm\nRq4DoE+FhV1iYuKAAQMMtzlz5kzZBAMAFkKuA6BPhYVdQEBAbGysgb3YqKioFi1alHFUAGBe\n5DoA+lRY2Dk4OPTv399Ag6ioKHt7Li4EUL6R6wDo4zcPAACgEhR2AAAAKkFhBwAAoBIUdgAA\nACpBYQcAAKASFHYAAAAqQWEHAACgEhR2AAAAKkFhBwAAoBIUdgAAACpBYQcAAKASFHYAAAAq\nQWEHAACgEhR2AAAAKkFhBwAAoBIUdgAAACpBYQcAAKASFHYAAAAqUcnaAQD/jySEECIpKakU\n8zo7O/ft29fBwcG8IQEAUF5Q2FlZYWFhfHx8fn5+ieYqXd1TLtwQQggRFRUVFRVVitnj4uKC\ngoLMGxIAAOUFhZ2VJSQkDBgwwNpR2JBCIYQQ3YSoV8IZrwrxsxB5eXnmjwkAgHKCws7K5EKk\npHXMCSHOWigg21BPiFbWjgFAOWX0TAiXbUDFKOxsQknrmKtqL+wAWJrh6qdclz6mnAnhsg2o\nFYUdAFRERquf8lv6/OeSDH8hvIuafEeIFC7bgGpR2AFARWSo+lFH6eMtRH1rxwCUOQo7AKjA\nqH4AdeEBxQAAACpBYQcAAKASnIqFSpT6lRXl+u4/AAC02Whhl5qaGhsbe+rUqfT0dEmSvLy8\n/P39Bw4c6OPjY+3Qila6F0gIVb9Doow9ySsryu/dfyjvyl2uA2DjbK6wkyRp3rx5ixYtevTo\nkc6k6dOnz5kz5/3337dGXEbwAgmrK90rK3hfBaylnOY6NZCEKH6nmkP4KO9srrBbsWJFZGTk\ntGnThgwZ0qxZMw8PDyFEZmbm2bNnt2/fvmjRourVq0+bNs1yATzJy1tL8SIs1b9DoozxygqU\nF1bPdRVXmhAGj+5zCB/lms0VdtHR0Rs3bhw+fLj2SE9Pz86dO3fu3Lljx47h4eGGk53hykyu\nwDQaTXGzW+XY29UStr9bhnOVcXdlHGQpOgLMwuq57j/uFDXy9v+WUORii56r1DOW5VxCiAwh\nhBDeQrjpTcoR4g6H8FHOSTbG0dExLy+vuKl5eXlOTk6Gl7Bv3z6j33rBggXFzR4XF1cGqx02\nJS4urvSbLFAq5DqbRUJAuWZzR+y8vb2Tk5O7dOlS5NTk5GRv7yLfEfM/AQEBsbGxxe3FajSa\n8+fPz5o1q7jZAwMDDcxeHI1Gc/r06ZYtW9rbl+wJMqWbsSznUneQQghnZ+fAwMASzQI8OVvO\ndQZ+TYZ/aKWbsSwnGZ1KQkB5ZydJkrVj+H9CQ0O/+eabyMjIAQMGVK1aVRmflZUVGxsbGho6\nduzYxYsXWzFCAHhy5DoAlmBzhV1ubu6wYcMSEhLs7e19fHw8PT0lScrMzLx586ZGowkKCtq6\ndauLi4u1wwSAJ0KuA2AJNlfYyXbv3r19+/aUlJS0tDQhRI0aNVq1ajV06NDevXtbOzQAMBty\nHQDzstHCDgAAACXFu2IBAABUgsIOAABAJWzucSdW9/Dhw0WLFjVp0qSkT81QMY1Gk5KS4u/v\nzzqRsUL0yQ/XmD17tpOTk7VjgUnIdfr4aetgheiz/VxHYacrMjJy3rx51o4CKJfs7e3fe+89\na0cBk5DrgFKz5VxHYaerUaNGQoi33nqrU6dO1o7FViQlJUVFRbFOFKwQffI6kX8+KBfIdfr4\naetgheiz/VxHYadLPuDcqVMnnXc4VnBRUVGsE22sEH1RUVGcrylHyHVF4qetgxWiz8Zzne1G\nBgAAgBKhsAMAAFAJCjsAAACVoLADAABQCQo7AAAAlaCwAwAAUAkKOwAAAJWgsNPl4uKi/B8y\n1okOVog+1km5w59MH+tEBytEn+2vEztJkqwdg20pLCw8cODAiy++6ODgYO1YbAXrRAcrRB/r\npNzhT6aPdaKDFaLP9tcJhR0AAIBKcCoWAABAJSjsAAAAVILCDgAAQCUo7AAAAFSCwg4AAEAl\nKOwAAABUgsIOAABAJSjsAAAAVILCDgAAQCUo7AAAAFSCwg4AAEAlKOwAAABUgsIOAABAJSjs\n/icvLy8sLMzPz69y5cpNmzZduXKltSOystOnT9vpWbVqlbXjKlPHjx+fOXNmvXr17Ozs3nvv\nPf0GFW2zMbxC2GbKhYq20RrFdivIdXrKca6T8F9BQUG1atU6dOhQXl7e1q1bK1euPG/ePGsH\nZU0pKSlCiC+//NLagVjNlStX2rdvv2TJkj179ggh3n33Xf02FWqzMbpC2GbKhQq10ZqC7ZZc\np6Nc57pK1ispbcvevXt37ty5cePGLl26CCGGDx9+9OjRjz76KDg42MfHx9rRwTrq1at37Ngx\nIcS5c+eKbFDRNhujKwS2r6JttDAFuU5Huc51nIr9j++//97e3n7AgAHKmCFDhjx8+HDHjh1W\njAo2js0G5Q4bLUqBzaYcobD7j1OnTvn4+FStWlUZ06xZMyGEfLi1Ips5c2blypWrVq36/PPP\nf/fdd9YOx7aw2RSJbcaWsdEWh+3WADabItnmNkNh9x9paWkeHh7aY+SP9+7ds1JE1ufg4DB5\n8uS9e/emp6cfO3asfv36w4cPX7x4sbXjsiFsNjrYZmwfG60+tluj2Gx02PI2Q2H3P3Z2diaO\nrCCaNWu2evXqNm3auLm5NW3adMOGDS+88MLcuXPT0tKsHZoNYbPRxjZTLrDR6mC7NQWbjTZb\n3mYo7P6jRo0aGRkZ2mMyMzOFENWrV7dSRLZo4MCBDx8+PH78uLUDsRVsNkaxzdgaNlpTsN3q\nYLMxyna2GQq7//D3979582ZWVpYy5uzZs0KIVq1aWS8o2Do2G5Q7bLQoBTabcoTC7j+GDBmi\n0WhiY2OVMdu3b3d0dNS+CQixsbFOTk7t2rWzdiC2gs3GKLYZW8NGawq2Wx1sNkbZ0DZj7Qfp\n2ZDAwEAfHx/tpy+Gh4dbOyhrCgkJWbly5blz53Jzc8+dOzd+/HghxMKFC60dlxXI+6ZFPrSz\nYm42xa0QtplyoWJutAaw3SrIdTrKY66jsPufnJycmTNn+vr6Ojo6Nm7cOCoqSqPRWDsoa7p+\n/XpYWFjz5s0rV67s4eHxwgsvfP/999YOqqz5+vrq7w4lJiYqDSraZmN4hbDNlAsVbaM1iu1W\nItfpKb+5zk6SpNIf7gMAAIDN4Bo7AAAAlaCwAwAAUAkKOwAAAJWgsAMAAFAJCjsAAACVoLAD\nAABQCQo7AAAAlaCwAwAAUAkKOwAAAJWgsAMAAFAJCjsAAACVoLADAABQCQo7AAAAlaCwAwAA\nUAkKOwAAAJWgsAMAAFAJCjsAAACVoLADAABQCQo7AAAAlaCwAwAAUAkKOwAAAJWgsAMAAFAJ\nCjsAAACVoLADAABQCQo7AAAAlaCwAwAAUAkKO7PZvXu3nZ3dTz/9VAZ9ff7553Z2dleuXCmD\nvszu3r17PXr0sHYUAEqJXGcich2sgsLOOlatWmVnZ3f9+nVrB2LmSE6dOjV8+HBvb293d/ee\nPXv+/PPPJs44ZMgQNze3Cxcu6Izv06dPtWrVrly58t1339n9l4ODg7e398CBA5OTk3XaHz9+\nfOjQofXq1XNxcWncuPGYMWN++eUXUwIwZfnabbS9//77JgYgL2HLli36C3Rycnrqqac6d+48\nZ86cixcvlqhfuYG9vb1OwFOnTrWzs8vOztYeefny5WnTpjVt2tTNzc3Dw6NVq1ZvvvnmyZMn\nTf+OgOlUmetOnz49aNAgT09PFxeXtm3bbt682fR5yXXkOkujsIPZ7Nmzp2PHjo0aNUpOTk5L\nS4uIiIiKitJucOrUqcGDBzdv3vzgwYPPPPPMjBkzbty4IU9at26du7v76NGjHz9+rLRfvXr1\nnj17Vq5c+fTTT8tjNm/eLElSfn7+rl27Ll682K1bN+38uGvXrk6dOhUWFu7cuTM9PX379u2P\nHz/u1q3brVu3TPwKhpev3UabkghKF4C8wLy8vDNnzrzzzjtHjhxp3rz5mjVrTO9XJklSaGio\n4S8YGxvr7+9/+vTp6OjoW7duXb9+ffny5efPn+/SpUuJ+gIqrH/961+dOnXKyso6cuRIamrq\n6NGjR40atXbtWu025DoDnZLrLI3CDuaRmZk5evToN95448MPP/Tz83NycurSpcsPP/ygNLh4\n8WKXLl2qV6+emJjYrVu3+Pj4+vXrK5Wfl5fXF198cfz48QULFshjLly4EBoaOnTo0HHjxun0\n5ejo2L59+48//jg3N/eLL75Qxi9evNjNzW3r1q3+/v4uLi4tW7bcvHlzVFSUvX3JtvPilm/U\nkwQg7zoHBQX9+OOPgwcPfvPNNw8ePFiisAMDAxMTE+Pj44trcOHChVdeeaV79+779u0LCAhw\nd3evUqXKCy+8sH//fqNZEoBs/vz5jx8/3rx5c5MmTTw9Pd96663hw4fPmjVLOVxErjOMXGdp\nFHald+jQoc6dO7u4uNStW1fn0JQQYufOncqhXTc3t/bt23/99dfypJkzZ4aEhAgh/Pz85Ab7\n9+8vsotr16698cYbdevWdXZ2bt68+dKlSx8+fKjfbMaMGc7Ozvq9Hzp0SP547969yZMnK8fM\n33777bS0NMORXL9+fcKECbVr13Zycqpfv354eLjS9dKlS+3s7FJTU2fOnFmrVi1HR0chxMaN\nG+/duzdt2rTiVteWLVvy8/NXr15ds2ZNOzu7Jk2aTJ8+fenSpUqDfv36BQcHf/jhh0ePHn38\n+PGYMWOqVaumsx+srVGjRnKcypj09HQvLy8nJyedlePt7V3cQgzQX75RZgnAzs5OXsORkZGm\nzyWEmDhxYpMmTcLCwgoLC4tsEBkZmZeX9/HHHzs4OOj0OG/evBL1hQqFXKed644cOdKkSZOa\nNWsqMfTo0SMrKyshIUH+SK4zEbnOQijsSunEiRM9e/asVavWmTNnTpw4kZGRofOzDAoKko/r\najSaixcvDhs27O9//7t8BGvp0qWffPKJEOLatWtym549e+p3cfny5bZt2yYnJ3/77bd3797d\nvn37nTt3SrpnIxszZsxPP/0UExOTkZGxe/fup59++ssvvzQQydWrV9u1a3f27NnY2NiMjIwv\nvvjiq6++evXVV7WXGRoa2rJly7Nnz3766adCiIMHD3p4eJw4ceLZZ5+tXLmyl5fXyJEjr169\nqrTPyckpLCzMzMw0EOeyZcvq168/ZsyY995779ixY19++WWNGjWKa3z+/HkhRN26dZUxXbt2\nvXTp0meffSZJUinWktHlG2WuAOrUqSOfxCnRXJUqVfroo49Onz791VdfFdlg9+7dDRo0aNKk\nyZPEhoqGXKeT6zQajU6n8u9duXiLXGc6cp1FSCiVwMDAmjVr5uXlKWP69OkjhEhMTCxulp49\ne/bp00ce1kkxRRo2bFiVKlVu376tP+mzzz4TQly+fFn+OH369MqVK2s3iIuLE0L88ssv8kdn\nZ+c5c+YU2UuRkYwcOdLDw+Pu3bvKmJiYGCFEUlKSJElLliwRQoSHh2vP0rFjRwcHh2rVqn33\n3XdZWVm//vprw4YNa9eufefOHblBUlKSvb198+bNlyxZIl+cUWQ8R44ckfexpk6dqj1+27Zt\n4r/XQzx8+PDo0aPNmzf39PS8ePGi0iY9Pb1Xr15CiKeeemrQoEHz588/duxYkb3oM2X5cht9\nyh/daADaveh/1CZvTvfv3zelX7lBXFycJEldunSpXbt2Tk6OJElTpkwRQjx48ECSpMePH9vZ\n2fXo0cOU9WCgL1Q05DqdXDdo0CBnZ2ftaEeMGCGEmDhxovyRXCeR66yKI3alIUlSYmJi7969\ntU8KDBo0SLtNYWHh0qVLn332WTc3N+XI/19//WV6LwkJCT179izdoXUdf/vb39atW7d69ep/\n//vfprSPi4vr0aOHl5eXMubFF18UQmjvVw0YMEB7Fo1GU1hYOGfOnKFDh7q7u3fq1GndunWp\nqanyPq4Q4rnnntuxY4ejo2NoaGhSUlKNGjVGjx596dIlna47duzYv39/IcQHH3ygH9jIkSPl\nm6o6duyYnp5+6NChBg0aKFM9PT337t179uzZiIgIHx+fjRs3dujQoW/fvjq3ShlgePky/dyk\nPNHgyQNQSJIkhLCzszOlX21LlixJTU39+OOPi1ygiUzsC6pHrhN6ue7dd9/VaDSjRo36888/\nMzMzV6xYIR+eVM76ketKhFxndhR2pZGTk5Ofn699jYUQQudjWFjYO++8ExIScunSpcePH0uS\nNGjQoEePHpneRU5Ojq+vb+ki1Nmyt23b9tJLL82ePbtevXoNGjT4xz/+IV93UlzX2dnZO3bs\nqFSpkoODg4ODg729vbu7uxBCey6d2OTM2L17d2VM165dHRwcjh8/rowJCgr6448/zp4926hR\noxEjRnz//ffPP/98enq6TgCVK1dW/q9D/hHm5ubGxsbm5+fPmDFD+84yWdOmTSdPnrx69eoL\nFy4sXrw4ISHB9IsqTFm+UU8SgOL69evu7u7yai+RTp06DR06NDIy8u7du9rjK1WqVLt2bRP/\ntQNk5Dqhl+vatWt38OBBOzu7Nm3a1K5de/v27fKxH+1m5DrTkevMjsKuNNzc3ORD8dojdT5u\n2LBBvtakZs2a8p7c5cuXTe/C1dXV1dVVuUPesGrVqhUUFGhfa6wzo5+f3zfffJOenp6cnDx+\n/Pjo6OihQ4ca6NrFxWXUqFGPHz8uLCwsLCzUaDTyroz2Ja7ydcSKv/3tb0UuTXs/TObl5VW7\ndu01a9Z8/fXXqampiYmJpnxHbS4uLv3791+9evW+ffv0L+XWFhoa6uLiYuLjnUqxfKNKF8D1\n69fPnj2rXSWXyEcffZSfnx8REaEzvk+fPpcuXdJ/qAFQHHKd0Mt1Qojnnntu7969Dx48yM3N\nPXjwoBxPt27ddJqR64wi11kChV1pyOfv9+3bV1BQoIzcsWOHTjPt/bBTp06dOnVK+ejm5iaE\n0J5dv4t+/fodOHDgzp07RuN55plnhBBnzpxRxuzcuVO/WaVKldq0aRMeHv7qq68eOnRI3tPV\nj8TOzi4oKGjfvn0ZGRlGu1a8/PLLQgjtJxIfPny4sLCwU6dOhsN2cXExvRdtI0eO7Nix48KF\nC5X94Dlz5uicCEhLS8vPz/fw8DDL8o0ySwCSJM2cOVMIERYWZvpc2ho1ahQcHLx27do///xT\ne7yceWfOnKlz9bekikc3wRLIdab49NNP27Zt+/zzzxsOm1yng1xnIRR2pRQREZGenj569Ogr\nV67cvXs3IiJC53D6gAEDtm7dumfPnpycnF9++WXixInaOyUtW7YUQuzatUt71/OPP/6ws7Ob\nMWOG/HHx4sXOzs6BgYGHDh3Kzs4+f/78rFmz9u3bpx/MkCFDatSoMXPmzGvXrt2+fTs8PFz7\nsHZaWlrPnj137Nhx48aNgoKCo0eP7t+/v3v37vKxtCIjiYyMtLe379ev3y+//PLgwYPbt2/v\n379/6NChv//+e3Er5Nlnn5UfYvfDDz9kZ2cfPXo0ODi4fv368jWtQojw8PB33333t99+y8rK\nkiTpt99+mzZtWuPGjUu9ryaE+OCDD+7fv7948WL5Y1xcXMuWLb/66qt79+7l5OQcOXJkyJAh\n9vb2b7/9tlmWb9STBKDRaO7evbtz584XX3wxJiZmzZo1Xbt2LV3YQoi5c+e6urru3btXe2ST\nJk02b9584MCBl1566aeffsrOzs7Ozv7xxx979uyp/SwGQBu5Tse///3vcePGnTx5sqCg4Pz5\n86NGjfrXv/61ceNGpQG5zjByncUVfU8FTPDTTz917NixcuXKderUWbJkifwQI+WGmqysrODg\n4Jo1a7q5ub3wwgunTp0aMWJEvXr1lNnnzp1bu3Zt+XGO+/btkyRJTiXTp09X2ly9enX8+PE+\nPj6VK1du0aLF0qVLCwoKJL07xSRJ+uWXX9q1ayc/h+mzzz7TuVPsxx9/HDJkiK+vr4uLS8OG\nDUNDQzMyMgxEIknSzZs3p0yZ8vTTTzs6OtauXbtPnz4//PCDfHuXfKeY9n1kssePH3/00UeN\nGjVydHT09vYeP378zZs3lan37t1bvHhxx44dq1WrZm9v37hx4ylTpqSmpuqvWPkWM+278KTi\nb6oKCAhwcXG5ceOGJEmXL1+eP39+x44da9SoIV9mMWTIkMOHDxf3/TkpFQAAIABJREFUFyzp\n8ou7i2rEiBFyY6MBFHmnmMzR0bFGjRrPPffc7Nmz//rrL/3YDPSrfaeYYuHChXIz+U4xxcWL\nF6dOndq4cWNnZ+eqVav6+/tPnjz55MmTJvaFCohcp702NBrNt99+27ZtW2dnZ19f39dff13n\nTltynX4v5LqyZCeZ4yk4gOlu3br1yiuvlM0bxAHAWsh1sApOxaKslfSlNwBQHpHrYBVsdihr\n+vfJAoD6kOtgFRR2KGtPPfVU2Z+bOH36tF0xhg0bVsbBAKgIyHWwCq6xAwAAUAmO2AEAAKgE\nhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUd\nAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAA\ngEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBK\nUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDY\nAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEA\nAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACo\nBIUdAACASpSbwi43N3f16tVBQUF16tRxcXGpWrVqw4YN+/Tps2zZstTUVLlNTEyM3X+dPn3a\nugFbztWrV2fMmNGiRQt3d/dq1aq1bt367bffPnfunImzm7Imz507p6zJSZMmac++adMmZdKa\nNWu0Jx06dGjEiBF169atXLmyq6trrVq1WrZsOWjQoNmzZ8sNatWqZWcCpX2R8vPzGzduLLe0\nt7dPSkrSnipJUrdu3ZRFbd68Wfu76KtUqVKRvQwePFi72dmzZ3Ua6C/W3t6+SpUqzZo1e+ON\nN06dOmXCn0JXampqeHj4c8895+Xl5ejo6OXl1bFjx/fee0/5uxjo3ZQvpa+4b9G4ceNRo0Yd\nOHCguMbKVmH6eijRX9/AFiiEaNiwoTypVq1aJVrD5QK5Tvbrr7+OGDHC19fXycnJy8urd+/e\nW7duLdESyHXayHVFfgt15jqpPNizZ0/NmjWL+wrR0dFysx9++EEZmZKSYqFglC7mzZtnoS4M\n2LBhQ+XKlfVXgq+vrymzm7gmtX/bwcHB2kvYuHGjfntJkqKiouzs7IpcrIODg9zGQNfawsLC\nDH+LxMREpS9/f/9Hjx4pk7744gtlOYGBgTrfxUBs2tLT052cnLSbvfPOOzptDC/W0dExPj7e\nlL+IYu3atUX+ZYUQlStXXrt2rem9F/mlimR4OUKIZcuWFdlY2SpMXw8l+usb2AIlSXrmmWfk\nSTVr1izRSrZ95DpZZGRkkflk9OjRjx8/NmUJ5LriYtNGrlOoKdeVgyN2u3btCgwMvH37thCi\ndu3aGzZsuHv3bk5Ozrlz52JiYl577TUXFxdrx1hG9uzZM27cuIKCAjs7u8mTJ58+fVpeD+vX\nr2/btq3R2S23Ji9evBgaGipJkhAiJCTk6tWr+fn558+fX7hwYY0aNZRmt27dUra8jIwMZXy/\nfv20N8pFixYZ7q5Hjx4TJkyQh1NSUj7++GN5+N69e7NmzZKH3dzcoqOjdWbU/9k8fvxYf/lb\nt259+PCh9phvvvlG0vp3rsjFPnjwYOPGjXIWfvTo0fTp0w1/C23r1q0LDg4uKCgQQvTp0+fU\nqVP5+fmnTp0KDAwUQhQUFAQHB69bt85A70a/lFHycgoLC48cOVK9enV55LvvvpudnV2iJRS3\nHsz111cxcp3swIEDs2bNkn9xCxcuzMzM3L17d5UqVYQQmzZtWrZsmdElkOvIdQaoP9dJti09\nPd3Dw0MO1cPD49KlSwYaq3sv9vHjx08//bTc9ZQpU0o6e4nWZEn3YpV04+npqdFotNtnZmaO\nHTtWvwsDm7spMjIyfHx85NldXV0vX74sSdK4ceOUZS5fvtzodylO165d5fbau5U///yzdpvi\nFhsQEKCMv3v3rindXb9+XemodevWBQUFyqSCgoLWrVsrwdy4caPUX6pIxS1n8uTJyvhjx44Z\naFy69WD0r18Bj9iR6xRBQUFyv/Xr11fySXBwsLJy8vLyDMxOrjM9LZDrVJnrbP2IXXR0dGZm\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Glubm7lypXz8vIsHBUAmBa5DoA52Nyt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\n3769zi9Fll6RkZE0xzZFRkYq4D4LSilynY1TUnPIdRLeAgAAAIW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ten customers most likely in cluster 6","message":["%r","","z.show(ore.sort(OC.PRED, \"'6'\", reverse = TRUE)[1:10,c(1,2,4)])"],"enabled":true,"result":{"startTime":1715347435586,"interpreter":"r.low","endTime":1715347436597,"results":[{"message":"YRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.6.\n5\t1967\t0.99999999909654\n5\t1967\t0.99999999909654\n4\t1963\t0.999999998899651\n4\t1963\t0.999999998899651\n5\t1965\t0.999999998732417\n5\t1965\t0.999999998732417\n6\t1964\t0.999999997750633\n6\t1965\t0.999999997687977\n6\t1965\t0.999999997687977\n4\t1966\t0.999999997319572\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Using SQL access prediction details","One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.","","We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well."],"enabled":true,"result":{"startTime":1715347436680,"interpreter":"md.low","endTime":1715347436744,"results":[{"message":"<h2 id=\"using-sql-access-prediction-details\">Using SQL access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.\nThen, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table='CUST_DF_OC')","ore.create(CUST_DF, table='CUST_DF_OC')"],"enabled":true,"result":{"startTime":1715347436823,"interpreter":"r.low","endTime":1715347437394,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top 3 predictive attributes for specific customers and likely cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. These represent customers ","--    with > 20% likelihood of assignment to their cluster.","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,2) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(OC_CLUSTERING_MODEL USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(OC_CLUSTERING_MODEL, NULL, 0.2 USING *) PSET","            FROM CUST_DF_OC V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;"],"enabled":true,"result":{"startTime":1715347437470,"interpreter":"sql.low","endTime":1715347439175,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n101362\t8\t99.42\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" operator=\"between\" range=\"(1.976E+003:1.979E+003]\" weight=\".\t\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".02\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'101362'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715347439252,"interpreter":"md.low","endTime":1715347439312,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715347439390,"interpreter":"md.low","endTime":1715347439449,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Clustering OC","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733168715124,"interpreter":"md.low","endTime":1733168715205,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Identifying Customer Segments using Orthogonal Partitioning Clustering","","Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.","","See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1733168715300,"interpreter":"md.low","endTime":1733168715361,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-orthogonal-partitioning-clustering\">Identifying Customer Segments using Orthogonal Partitioning Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1733168715448,"interpreter":"md.low","endTime":1733168715515,"results":[{"message":"<p><img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F73B1E57-148D-40E5-A256-C60AD72BF210\" target=\"_blank\">Algorithm Settings<\/a>"],"enabled":true,"result":{"startTime":1733168715595,"interpreter":"md.low","endTime":1733168715652,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F73B1E57-148D-40E5-A256-C60AD72BF210\" target=\"_blank\">Algorithm Settings<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(dplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1733168715741,"interpreter":"r.low","endTime":1733168715859,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data set for model building from customer demographic data in proxy object CUST_DF","message":["%r","","ore.sync(query = c(\"SUPPLEMENTARY_DEMOGRAPHICS\" = \"select CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.sync(query = c(\"CUSTOMERS\" = \"select CUST_ID, CUST_GENDER, CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS from SH.CUSTOMERS\"))","","CUST_DF <- merge(SUPPLEMENTARY_DEMOGRAPHICS,CUSTOMERS, by=\"CUST_ID\")","","rownames(CUST_DF) <- CUST_DF$CUST_IK"],"enabled":true,"result":{"startTime":1733168715935,"interpreter":"r.low","endTime":1733168716369,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUST_DF data","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1733168716454,"interpreter":"r.low","endTime":1733168716572,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tCUST_GENDER\tCUST_YEAR_OF_BIRTH\tCUST_MARITAL_STATUS\n100100\tMasters\tProf.\t4-5\t4\tF\t1959\tMarried\n100200\t< Bach.\tOther\t1\t2\tM\t1983\tNeverM\n100300\tBach.\tProf.\t3\t4\tM\t1961\tMarried\n100400\t12th\tTransp.\t6-8\t6\tM\t1944\tDivorc.\n100500\tHS-grad\tCrafts\t1\t2\tM\t1983\tNeverM\n100600\t11th\tTransp.\t3\t3\tM\t1978\tMarried\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get dimensions of data set using the CUST_DF proxy object","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1733168716653,"interpreter":"r.low","endTime":1733168716844,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>8<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an O-Cluster model using the CUST_DF proxy object"],"enabled":true,"result":{"startTime":1733168716942,"interpreter":"md.low","endTime":1733168716999,"results":[{"message":"<h3 id=\"build-an-o-cluster-model-using-the-cust_df-proxy-object\">Build an O-Cluster model using the CUST_DF proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an OC Clustering model with 3 clusters and default settings","message":["%r","","rownames(CUST_DF) <- CUST_DF$CUST_ID","","OC.MOD1 <- ore.odmOC(~.-CUST_ID, CUST_DF, num.centers = 3)","","OC.MOD1"],"enabled":true,"result":{"startTime":1733168717080,"interpreter":"r.low","endTime":1733168719279,"results":[{"message":"\nCall:\nore.odmOC(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nmax.buffer                                     50000\nsensitivity                                      0.5\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display OC model details","message":["%r","","summary(OC.MOD1)"],"enabled":true,"result":{"startTime":1733168719356,"interpreter":"r.low","endTime":1733168719502,"results":[{"message":"\nCall:\nore.odmOC(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nmax.buffer                                     50000\nsensitivity                                      0.5\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nClusters: \n  CLUSTER_ID CLUSTER_NAME ROW_CNT PARENT_CLUSTER_ID TREE_LEVEL DISPERSION IS_LEAF\n1          1            1    4500                NA          1         NA   FALSE\n2          2            2     968                 1          2         NA    TRUE\n3          3            3    3532                 1          2         NA   FALSE\n4          4            4    2103                 3          3         NA    TRUE\n5          5            5    1429                 3          3         NA    TRUE\n\nCenters: \n  MEAN.CUST_YEAR_OF_BIRTH MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS MEAN.YRS_RESIDENCE MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.OCCUPATION\n2                1946.555               NA                       NA           5.810950             NA                  NA              NA\n4                1963.449               NA                       NA           4.378031             NA                  NA              NA\n5                1978.594               NA                       NA           2.286214             NA                  NA              NA\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster histogram data for all clusters in the hierarchy","message":["%r","","z.show(clusterhists(OC.MOD1))"],"enabled":true,"result":{"startTime":1733168719586,"interpreter":"r.low","endTime":1733168720081,"results":[{"message":"cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.335555555555555\n1\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.664444444444444\n1\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.136666666666667\n1\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0166666666666667\n1\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.000666666666666667\n1\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.452\n1\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.334\n1\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0297777777777778\n1\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.0302222222222222\n1\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0.00111111111111111\n1\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n1\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0.00155555555555556\n1\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0.00666666666666667\n1\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0.00533333333333333\n1\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0.00533333333333333\n1\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0.0151111111111111\n1\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0.014\n1\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0.0197777777777778\n1\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0.042\n1\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.0328888888888889\n1\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0431111111111111\n1\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.0753333333333333\n1\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.0762222222222222\n1\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.0728888888888889\n1\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.0951111111111111\n1\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.0877777777777778\n1\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.0728888888888889\n1\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.102\n1\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.0811111111111111\n1\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.0753333333333333\n1\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0.0744444444444444\n1\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0271111111111111\n1\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0268888888888889\n1\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.0115555555555556\n1\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00377777777777778\n1\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.00866666666666667\n1\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.0193333333333333\n1\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.0168888888888889\n1\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.231333333333333\n1\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0382222222222222\n1\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0435555555555556\n1\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.173111111111111\n1\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.324888888888889\n1\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0422222222222222\n1\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.0108888888888889\n1\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000666666666666667\n1\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.0208888888888889\n1\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.153777777777778\n1\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.255333333333333\n1\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.397111111111111\n1\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0486666666666667\n1\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0328888888888889\n1\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.112222222222222\n1\tOCCUPATION:?\t1\tNA\tNA\t?\t0.0524444444444444\n1\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000444444444444444\n1\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.115555555555556\n1\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.127111111111111\n1\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.124222222222222\n1\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0271111111111111\n1\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0373333333333333\n1\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.00422222222222222\n1\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0613333333333333\n1\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.104\n1\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.121111111111111\n1\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0197777777777778\n1\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.124444444444444\n1\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0393333333333333\n1\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0415555555555556\n1\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.0106666666666667\n1\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.0613333333333333\n1\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.138888888888889\n1\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.213555555555555\n1\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.208222222222222\n1\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.168222222222222\n1\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.105111111111111\n1\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.0513333333333333\n1\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.0224444444444444\n1\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0.0108888888888889\n1\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0.00555555555555555\n1\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0.002\n1\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0.000666666666666667\n1\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0.000444444444444444\n1\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0.000666666666666667\n2\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.302685950413223\n2\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.697314049586777\n2\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.152892561983471\n2\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0309917355371901\n2\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.00103305785123967\n2\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.586776859504132\n2\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.0609504132231405\n2\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0320247933884297\n2\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.135330578512397\n2\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0.00516528925619835\n2\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n2\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0.00723140495867769\n2\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0.0309917355371901\n2\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0.0247933884297521\n2\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0.0247933884297521\n2\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0.0702479338842975\n2\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0.0650826446280992\n2\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0.0919421487603306\n2\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0.195247933884298\n2\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.127066115702479\n2\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0857438016528926\n2\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.0857438016528926\n2\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.0557851239669422\n2\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.0485537190082645\n2\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.0630165289256198\n2\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.0134297520661157\n2\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.00206611570247934\n2\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.00103305785123967\n2\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.00206611570247934\n2\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0\n2\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0\n2\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0454545454545455\n2\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0402892561983471\n2\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.00619834710743802\n2\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00929752066115703\n2\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.00619834710743802\n2\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.0692148760330579\n2\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.0537190082644628\n2\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.169421487603306\n2\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0268595041322314\n2\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0320247933884297\n2\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.115702479338843\n2\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.304752066115702\n2\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0382231404958678\n2\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.0144628099173554\n2\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.00103305785123967\n2\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.0671487603305785\n2\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.00929752066115703\n2\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.269628099173554\n2\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.541322314049587\n2\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0454545454545455\n2\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0165289256198347\n2\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.117768595041322\n2\tOCCUPATION:?\t1\tNA\tNA\t?\t0.113636363636364\n2\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0\n2\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.0671487603305785\n2\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.107438016528926\n2\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.103305785123967\n2\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0816115702479339\n2\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0578512396694215\n2\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.012396694214876\n2\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0454545454545455\n2\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.0888429752066116\n2\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.114669421487603\n2\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0165289256198347\n2\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.12396694214876\n2\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0185950413223141\n2\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0485537190082645\n2\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.0113636363636364\n2\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.00413223140495868\n2\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.0485537190082645\n2\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.0733471074380165\n2\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.121900826446281\n2\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.133264462809917\n2\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.270661157024793\n2\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.153925619834711\n2\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.0888429752066116\n2\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0.0506198347107438\n2\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0.0258264462809917\n2\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0.00929752066115703\n2\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0.00309917355371901\n2\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0.00206611570247934\n2\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0.00309917355371901\n3\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.344563986409966\n3\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.655436013590034\n3\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.132219705549264\n3\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0127406568516421\n3\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.000566251415628539\n3\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.415062287655719\n3\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.408833522083805\n3\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0291619479048698\n3\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.00141562853907135\n3\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0\n3\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n3\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0\n3\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0\n3\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0\n3\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0\n3\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0\n3\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0\n3\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0\n3\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0\n3\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.00707814269535674\n3\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0314269535673839\n3\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.072480181200453\n3\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.0818233295583239\n3\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.0795583238958097\n3\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.103907134767837\n3\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.108154020385051\n3\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.0922989807474519\n3\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.129671574178935\n3\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.10277463193658\n3\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.0959796149490374\n3\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0.0948471121177803\n3\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.022083805209513\n3\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0232163080407701\n3\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.0130237825594564\n3\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00226500566251416\n3\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.0093431483578709\n3\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.00566251415628539\n3\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.00679501698754247\n3\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.248301245753114\n3\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0413363533408834\n3\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0467157417893545\n3\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.188844847112118\n3\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.330407701019253\n3\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0433182332955832\n3\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.00990939977349943\n3\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000566251415628539\n3\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.00821064552661382\n3\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.193374858437146\n3\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.251415628539071\n3\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.357587768969422\n3\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0495469988674972\n3\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0373725934314836\n3\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.110702151755379\n3\tOCCUPATION:?\t1\tNA\tNA\t?\t0.035673839184598\n3\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000566251415628539\n3\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.128822197055493\n3\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.132502831257078\n3\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.12995469988675\n3\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0121744054360136\n3\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0317100792751982\n3\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.00198187995469989\n3\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0656851642129105\n3\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.108154020385051\n3\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.122876557191393\n3\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0206681766704417\n3\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.124575311438279\n3\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0450169875424689\n3\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0396375990939977\n3\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.010475651189128\n3\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.0770101925254813\n3\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.163646659116648\n3\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.2519818799547\n3\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.231879954699887\n3\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.177802944507361\n3\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.0597395243488109\n3\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.0232163080407701\n3\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.00424688561721404\n3\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0\n3\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0\n3\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0\n3\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0\n3\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0\n3\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0\n4\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.281978126485972\n4\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.718021873514028\n4\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.21017593913457\n4\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0128388017118402\n4\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0.000951022349025202\n4\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.604374702805516\n4\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.139324774132192\n4\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.0309082263433191\n4\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.0014265335235378\n4\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0\n4\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n4\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0\n4\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0\n4\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0\n4\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0\n4\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0\n4\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0\n4\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0\n4\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0\n4\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0.011887779362815\n4\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0.0527817403708987\n4\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0.121730860675226\n4\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.136947218259629\n4\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.132667617689016\n4\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.163575844032335\n4\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.163575844032335\n4\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.115549215406562\n4\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.0884450784593438\n4\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.00998573466476462\n4\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.00285306704707561\n4\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0\n4\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0014265335235378\n4\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.000951022349025202\n4\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.00855920114122682\n4\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00285306704707561\n4\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.00713266761768902\n4\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.00332857822158821\n4\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.00523062291963861\n4\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.204945316214931\n4\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.0470756062767475\n4\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0565858297669995\n4\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.220637184973847\n4\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.346647646219686\n4\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.0718021873514028\n4\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.0152163575844032\n4\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000475511174512601\n4\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.00713266761768902\n4\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.0156918687589158\n4\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.227294341417023\n4\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.540180694246315\n4\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.0608654303376129\n4\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0180694246314788\n4\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.137898240608654\n4\tOCCUPATION:?\t1\tNA\tNA\t?\t0.000951022349025202\n4\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000475511174512601\n4\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.108416547788873\n4\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.16642891107941\n4\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.181169757489301\n4\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.00665715644317641\n4\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0014265335235378\n4\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.0014265335235378\n4\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.0741797432239658\n4\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.070375653827865\n4\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.156443176414646\n4\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0242510699001427\n4\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.112696148359486\n4\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0456490727532097\n4\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0494531621493105\n4\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.000951022349025202\n4\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.00951022349025202\n4\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.0228245363766049\n4\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.181169757489301\n4\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.348074179743224\n4\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.291012838801712\n4\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0.100332857822159\n4\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0.0389919163100333\n4\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0.00713266761768902\n4\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0\n4\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0\n4\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0\n4\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0\n4\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0\n4\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0\n5\tCUST_GENDER:F\t1\tNA\tNA\tF\t0.43666899930021\n5\tCUST_GENDER:M\t2\tNA\tNA\tM\t0.56333100069979\n5\tCUST_MARITAL_STATUS:Divorc.\t1\tNA\tNA\tDivorc.\t0.0174947515745276\n5\tCUST_MARITAL_STATUS:Mabsent\t2\tNA\tNA\tMabsent\t0.0125962211336599\n5\tCUST_MARITAL_STATUS:Mar-AF\t3\tNA\tNA\tMar-AF\t0\n5\tCUST_MARITAL_STATUS:Married\t4\tNA\tNA\tMarried\t0.136459062281316\n5\tCUST_MARITAL_STATUS:NeverM\t5\tNA\tNA\tNeverM\t0.805458362491253\n5\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\tSepar.\t0.026592022393282\n5\tCUST_MARITAL_STATUS:Widowed\t7\tNA\tNA\tWidowed\t0.00139958012596221\n5\tCUST_YEAR_OF_BIRTH\t1\t1913\t1916.31818181818\t[1913; 1916.32]\t0\n5\tCUST_YEAR_OF_BIRTH\t2\t1916.31818181818\t1919.63636363636\t(1916.32; 1919.64]\t0\n5\tCUST_YEAR_OF_BIRTH\t3\t1919.63636363636\t1922.95454545455\t(1919.64; 1922.95]\t0\n5\tCUST_YEAR_OF_BIRTH\t4\t1922.95454545455\t1926.27272727273\t(1922.95; 1926.27]\t0\n5\tCUST_YEAR_OF_BIRTH\t5\t1926.27272727273\t1929.59090909091\t(1926.27; 1929.59]\t0\n5\tCUST_YEAR_OF_BIRTH\t6\t1929.59090909091\t1932.90909090909\t(1929.59; 1932.91]\t0\n5\tCUST_YEAR_OF_BIRTH\t7\t1932.90909090909\t1936.22727272727\t(1932.91; 1936.23]\t0\n5\tCUST_YEAR_OF_BIRTH\t8\t1936.22727272727\t1939.54545454545\t(1936.23; 1939.55]\t0\n5\tCUST_YEAR_OF_BIRTH\t9\t1939.54545454545\t1942.86363636364\t(1939.55; 1942.86]\t0\n5\tCUST_YEAR_OF_BIRTH\t10\t1942.86363636364\t1946.18181818182\t(1942.86; 1946.18]\t0\n5\tCUST_YEAR_OF_BIRTH\t11\t1946.18181818182\t1949.5\t(1946.18; 1949.5]\t0\n5\tCUST_YEAR_OF_BIRTH\t12\t1949.5\t1952.81818181818\t(1949.5; 1952.82]\t0\n5\tCUST_YEAR_OF_BIRTH\t13\t1952.81818181818\t1956.13636363636\t(1952.82; 1956.14]\t0\n5\tCUST_YEAR_OF_BIRTH\t14\t1956.13636363636\t1959.45454545454\t(1956.14; 1959.45]\t0.000699790062981106\n5\tCUST_YEAR_OF_BIRTH\t15\t1959.45454545454\t1962.77272727273\t(1959.45; 1962.77]\t0.00139958012596221\n5\tCUST_YEAR_OF_BIRTH\t16\t1962.77272727273\t1966.09090909091\t(1962.77; 1966.09]\t0.0160951714485654\n5\tCUST_YEAR_OF_BIRTH\t17\t1966.09090909091\t1969.40909090909\t(1966.09; 1969.41]\t0.026592022393282\n5\tCUST_YEAR_OF_BIRTH\t18\t1969.40909090909\t1972.72727272727\t(1969.41; 1972.73]\t0.0580825752274318\n5\tCUST_YEAR_OF_BIRTH\t19\t1972.72727272727\t1976.04545454545\t(1972.73; 1976.05]\t0.190342897130861\n5\tCUST_YEAR_OF_BIRTH\t20\t1976.04545454545\t1979.36363636364\t(1976.05; 1979.36]\t0.239328201539538\n5\tCUST_YEAR_OF_BIRTH\t21\t1979.36363636364\t1982.68181818182\t(1979.36; 1982.68]\t0.233030090972708\n5\tCUST_YEAR_OF_BIRTH\t22\t1982.68181818182\t1986\t(1982.68; 1986]\t0.23442967109867\n5\tEDUCATION:10th\t1\tNA\tNA\t10th\t0.0524842547235829\n5\tEDUCATION:11th\t2\tNA\tNA\t11th\t0.0559832050384885\n5\tEDUCATION:12th\t3\tNA\tNA\t12th\t0.019594121763471\n5\tEDUCATION:1st-4th\t4\tNA\tNA\t1st-4th\t0.00139958012596221\n5\tEDUCATION:5th-6th\t5\tNA\tNA\t5th-6th\t0.0125962211336599\n5\tEDUCATION:7th-8th\t6\tNA\tNA\t7th-8th\t0.00909727081875437\n5\tEDUCATION:9th\t7\tNA\tNA\t9th\t0.00909727081875437\n5\tEDUCATION:< Bach.\t8\tNA\tNA\t< Bach.\t0.312106368089573\n5\tEDUCATION:Assoc-A\t9\tNA\tNA\tAssoc-A\t0.032890132960112\n5\tEDUCATION:Assoc-V\t10\tNA\tNA\tAssoc-V\t0.0321903428971309\n5\tEDUCATION:Bach.\t11\tNA\tNA\tBach.\t0.142057382785164\n5\tEDUCATION:HS-grad\t12\tNA\tNA\tHS-grad\t0.306508047585724\n5\tEDUCATION:Masters\t13\tNA\tNA\tMasters\t0.00139958012596221\n5\tEDUCATION:PhD\t14\tNA\tNA\tPhD\t0.00209937018894332\n5\tEDUCATION:Presch.\t15\tNA\tNA\tPresch.\t0.000699790062981106\n5\tEDUCATION:Profsc\t16\tNA\tNA\tProfsc\t0.00979706088173548\n5\tHOUSEHOLD_SIZE:1\t1\tNA\tNA\t1\t0.454863540937719\n5\tHOUSEHOLD_SIZE:2\t2\tNA\tNA\t2\t0.286913925822253\n5\tHOUSEHOLD_SIZE:3\t3\tNA\tNA\t3\t0.0888733379986004\n5\tHOUSEHOLD_SIZE:4-5\t4\tNA\tNA\t4-5\t0.032890132960112\n5\tHOUSEHOLD_SIZE:6-8\t5\tNA\tNA\t6-8\t0.0657802659202239\n5\tHOUSEHOLD_SIZE:9+\t6\tNA\tNA\t9+\t0.0706787963610917\n5\tOCCUPATION:?\t1\tNA\tNA\t?\t0.0867739678096571\n5\tOCCUPATION:Armed-F\t2\tNA\tNA\tArmed-F\t0.000699790062981106\n5\tOCCUPATION:Cleric.\t3\tNA\tNA\tCleric.\t0.158852344296711\n5\tOCCUPATION:Crafts\t4\tNA\tNA\tCrafts\t0.0825752274317705\n5\tOCCUPATION:Exec.\t5\tNA\tNA\tExec.\t0.0545836249125262\n5\tOCCUPATION:Farming\t6\tNA\tNA\tFarming\t0.0202939118264521\n5\tOCCUPATION:Handler\t7\tNA\tNA\tHandler\t0.0762771168649405\n5\tOCCUPATION:House-s\t8\tNA\tNA\tHouse-s\t0.00279916025192442\n5\tOCCUPATION:Machine\t9\tNA\tNA\tMachine\t0.053184044786564\n5\tOCCUPATION:Other\t10\tNA\tNA\tOther\t0.163750874737579\n5\tOCCUPATION:Prof.\t11\tNA\tNA\tProf.\t0.0734779566130161\n5\tOCCUPATION:Protec.\t12\tNA\tNA\tProtec.\t0.0153953813855843\n5\tOCCUPATION:Sales\t13\tNA\tNA\tSales\t0.142057382785164\n5\tOCCUPATION:TechSup\t14\tNA\tNA\tTechSup\t0.0440867739678097\n5\tOCCUPATION:Transp.\t15\tNA\tNA\tTransp.\t0.0251924422673198\n5\tYRS_RESIDENCE\t1\t0\t0.933333333333333\t[0; .933333]\t0.0244926522043387\n5\tYRS_RESIDENCE\t2\t0.933333333333333\t1.86666666666667\t(.933333; 1.86667]\t0.176347095871239\n5\tYRS_RESIDENCE\t3\t1.86666666666667\t2.8\t(1.86667; 2.8]\t0.370888733379986\n5\tYRS_RESIDENCE\t4\t2.8\t3.73333333333333\t(2.8; 3.73333]\t0.356193142057383\n5\tYRS_RESIDENCE\t5\t3.73333333333333\t4.66666666666667\t(3.73333; 4.66667]\t0.0608817354793562\n5\tYRS_RESIDENCE\t6\t4.66666666666667\t5.6\t(4.66667; 5.6]\t0.0111966410076977\n5\tYRS_RESIDENCE\t7\t5.6\t6.53333333333333\t(5.6; 6.53333]\t0\n5\tYRS_RESIDENCE\t8\t6.53333333333333\t7.46666666666667\t(6.53333; 7.46667]\t0\n5\tYRS_RESIDENCE\t9\t7.46666666666667\t8.4\t(7.46667; 8.4]\t0\n5\tYRS_RESIDENCE\t10\t8.4\t9.33333333333333\t(8.4; 9.33333]\t0\n5\tYRS_RESIDENCE\t11\t9.33333333333333\t10.2666666666667\t(9.33333; 10.2667]\t0\n5\tYRS_RESIDENCE\t12\t10.2666666666667\t11.2\t(10.2667; 11.2]\t0\n5\tYRS_RESIDENCE\t13\t11.2\t12.1333333333333\t(11.2; 12.1333]\t0\n5\tYRS_RESIDENCE\t14\t12.1333333333333\t13.0666666666667\t(12.1333; 13.0667]\t0\n5\tYRS_RESIDENCE\t15\t13.0666666666667\t14\t(13.0667; 14]\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370');\"> O-Cluster clustering<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms<\/a>","","- Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '<row_weight_column_name>';","   ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';","    ","- Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') = '5';","","- Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5. ","> v_setlst('OCLT_SENSITIVITY') = '0.5';"],"enabled":true,"result":{"startTime":1733168720190,"interpreter":"md.low","endTime":1733168720260,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-orthogonal-partitioning-cluster-clustering\">Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370');\"> O-Cluster clustering<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') = '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('OCLT_SENSITIVITY') = '0.5';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an OC Clustering model with explicit settings","message":["%r","","ore.drop(model = 'OC_CLUSTERING_MODEL')","","settings = list(PREP_AUTO        = 'ON',","                OCLT_SENSITIVITY = 0.5,","                model_name       = 'OC_CLUSTERING_MODEL')","","OC.MOD2 <- ore.odmOC(~.-CUST_ID, CUST_DF, num.centers = 5, odm.settings = settings)"],"enabled":true,"result":{"startTime":1733168720340,"interpreter":"r.low","endTime":1733168723038,"results":[{"message":"Warning message:\n\u201cThe created mining model OC_CLUSTERING_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details","message":["%r","","options(width=200)","summary(OC.MOD2)"],"enabled":true,"result":{"startTime":1733168723125,"interpreter":"r.low","endTime":1733168723242,"results":[{"message":"\nCall:\nore.odmOC(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 5, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclus.num.clusters                                  5\nmax.buffer                                     50000\nsensitivity                                      0.5\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nClusters: \n  CLUSTER_ID CLUSTER_NAME ROW_CNT PARENT_CLUSTER_ID TREE_LEVEL DISPERSION IS_LEAF\n1          1            1    4500                NA          1         NA   FALSE\n2          2            2     860                 1          2         NA    TRUE\n3          3            3    3640                 1          2         NA   FALSE\n4          4            4    2322                 3          3         NA   FALSE\n5          5            5    1318                 3          3         NA   FALSE\n6          6            6    1125                 4          4         NA    TRUE\n7          7            7    1197                 4          4         NA    TRUE\n8          8            8     548                 5          4         NA    TRUE\n9          9            9     770                 5          4         NA    TRUE\n\nCenters: \n  MEAN.CUST_YEAR_OF_BIRTH MEAN.OCCUPATION MEAN.YRS_RESIDENCE MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS\n2                1944.652              NA           5.905814             NA                  NA               NA                       NA\n6                1963.431              NA           4.418667             NA                  NA               NA                       NA\n7                1964.416              NA           4.246449             NA                  NA               NA                       NA\n8                1978.126              NA           2.430657             NA                  NA               NA                       NA\n9                1979.390              NA           2.122078             NA                  NA               NA                       NA\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details for each cluster","message":["%r","","z.show(OC.MOD2$clusters)"],"enabled":true,"result":{"startTime":1733168723334,"interpreter":"r.low","endTime":1733168723452,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tROW_CNT\tPARENT_CLUSTER_ID\tTREE_LEVEL\tDISPERSION\tIS_LEAF\n1\t1\t4500\tNA\t1\tNA\tFALSE\n2\t2\t860\t1\t2\tNA\tTRUE\n3\t3\t3640\t1\t2\tNA\tFALSE\n4\t4\t2322\t3\t3\tNA\tFALSE\n5\t5\t1318\t3\t3\tNA\tFALSE\n6\t6\t1125\t4\t4\tNA\tTRUE\n7\t7\t1197\t4\t4\tNA\tTRUE\n8\t8\t548\t5\t4\tNA\tTRUE\n9\t9\t770\t5\t4\tNA\tTRUE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show leaf clusters IDs","message":["%r","","z.show(OC.MOD2$leaf.cluster.counts)"],"enabled":true,"result":{"startTime":1733168723530,"interpreter":"r.low","endTime":1733168723648,"results":[{"message":"CLUSTER_ID\tCNT\n2\t860\n6\t1125\n7\t1197\n8\t548\n9\t770\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data. The larger the number, the more relevant the attribute."],"enabled":true,"result":{"startTime":1733168723723,"interpreter":"md.low","endTime":1733168723776,"results":[{"message":"<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.\nFor an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.<\/p>\n<p>Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data. The larger the number, the more relevant the attribute.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%r","","z.show(OC.MOD2$rules)"],"enabled":true,"result":{"startTime":1733168723855,"interpreter":"r.low","endTime":1733168724035,"results":[{"message":"rhs.cluster.id\trhs.support\trhs.conf\tlhs.support\tlhs.conf\tlhs.var\tlhs.var.support\tlhs.var.conf\tpredicate\n1\t4500\t1\t3840\t0.853333333333333\tCUST_YEAR_OF_BIRTH\t4190\t0.380952380952381\tCUST_YEAR_OF_BIRTH>=1942.86\n1\t4500\t1\t3840\t0.853333333333333\tCUST_YEAR_OF_BIRTH\t4190\t0.380952380952381\tCUST_YEAR_OF_BIRTH<=1986\n1\t4500\t1\t3840\t0.853333333333333\tYRS_RESIDENCE\t4260\t0.533333333333333\tYRS_RESIDENCE>=.933333\n1\t4500\t1\t3840\t0.853333333333333\tYRS_RESIDENCE\t4260\t0.533333333333333\tYRS_RESIDENCE<=7.46667\n1\t4500\t1\t3840\t0.853333333333333\tCUST_GENDER\t4500\t0\tCUST_GENDERINF\n1\t4500\t1\t3840\t0.853333333333333\tCUST_GENDER\t4500\t0\tCUST_GENDERINM\n1\t4500\t1\t3840\t0.853333333333333\tCUST_MARITAL_STATUS\t4152\t0.571428571428571\tCUST_MARITAL_STATUSINDivorc.\n1\t4500\t1\t3840\t0.853333333333333\tCUST_MARITAL_STATUS\t4152\t0.571428571428571\tCUST_MARITAL_STATUSINMarried\n1\t4500\t1\t3840\t0.853333333333333\tCUST_MARITAL_STATUS\t4152\t0.571428571428571\tCUST_MARITAL_STATUSINNeverM\n1\t4500\t1\t3840\t0.853333333333333\tEDUCATION\t3840\t0.625\tEDUCATIONIN< 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Bach.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tEDUCATION\t1209\t0.0125992563160772\tEDUCATIONINAssoc-A\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tEDUCATION\t1209\t0.0125992563160772\tEDUCATIONINBach.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tEDUCATION\t1209\t0.0125992563160772\tEDUCATIONINHS-grad\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tHOUSEHOLD_SIZE\t1161\t0.0635887985951071\tHOUSEHOLD_SIZEIN1\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tHOUSEHOLD_SIZE\t1161\t0.0635887985951071\tHOUSEHOLD_SIZEIN2\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tHOUSEHOLD_SIZE\t1161\t0.0635887985951071\tHOUSEHOLD_SIZEIN3\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONIN?\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINCleric.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINCrafts\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINExec.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINHandler\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINMachine\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINOther\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINProf.\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINSales\n5\t1318\t0.292888888888889\t1161\t0.880880121396055\tOCCUPATION\t1247\t0.0100687410487145\tOCCUPATIONINTechSup\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_YEAR_OF_BIRTH\t1104\t0.0370509079525408\tCUST_YEAR_OF_BIRTH>=1949.5\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_YEAR_OF_BIRTH\t1104\t0.0370509079525408\tCUST_YEAR_OF_BIRTH<=1979.36\n6\t1125\t0.25\t1066\t0.947555555555556\tYRS_RESIDENCE\t1066\t0.0218197512952169\tYRS_RESIDENCE>=2.8\n6\t1125\t0.25\t1066\t0.947555555555556\tYRS_RESIDENCE\t1066\t0.0218197512952169\tYRS_RESIDENCE<=7.46667\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_GENDER\t1125\t0.00197545916841683\tCUST_GENDERINF\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_GENDER\t1125\t0.00197545916841683\tCUST_GENDERINM\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_MARITAL_STATUS\t1081\t0.0197605042134215\tCUST_MARITAL_STATUSINDivorc.\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_MARITAL_STATUS\t1081\t0.0197605042134215\tCUST_MARITAL_STATUSINMarried\n6\t1125\t0.25\t1066\t0.947555555555556\tCUST_MARITAL_STATUS\t1081\t0.0197605042134215\tCUST_MARITAL_STATUSINNeverM\n6\t1125\t0.25\t1066\t0.947555555555556\tEDUCATION\t1072\t0.00902692842723922\tEDUCATIONIN< 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model to assign rows to clusters","message":["%r","","OC.PRED <- predict(OC.MOD2, CUST_DF, supplemental.cols=c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\"))","class(OC.PRED)","","z.show(head(OC.PRED))"],"enabled":true,"result":{"startTime":1733168724124,"interpreter":"r.low","endTime":1733168724692,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"YRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.2.\tX.6.\tX.7.\tX.8.\tX.9.\tCLUSTER_ID\n3\t1941\t0.999999770737136\t2.28804645351765e-07\t6.39983774067865e-13\t4.57575855508718e-10\t2.76168037463528e-15\t2\n4\t1962\t0.000102248959587262\t2.2841845156172e-06\t0.999892268193504\t6.88679485692155e-11\t3.1985935252414e-06\t7\n6\t1969\t3.17857885001162e-05\t6.29008686491956e-06\t0.999960125812194\t4.05590372323367e-10\t1.79790685056757e-06\t7\n5\t1953\t0.0516621238531008\t5.93526749916806e-06\t0.948331940879246\t5.64553433856911e-18\t1.54510627730725e-13\t7\n5\t1957\t0.00270105742056999\t0.997294480271078\t4.45878790818348e-06\t3.52044147243595e-09\t1.94982857452668e-15\t6\n2\t1983\t6.09406772350066e-05\t1.34274728398413e-06\t0.890926819208708\t0.0270289625529077\t0.0819819348138651\t7\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compare Clusters","Compare the  distribution for years residence and year of birth across two clusters using the overloaded `hist` functions. "],"enabled":true,"result":{"startTime":1733168724773,"interpreter":"md.low","endTime":1733168724834,"results":[{"message":"<h2 id=\"compare-clusters\">Compare Clusters<\/h2>\n<p>Compare the  distribution for years residence and year of birth across two clusters using the overloaded <code>hist<\/code> functions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":800,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot histograms of select clusters and attributes","message":["%r","","dat.cluster6 <- OC.PRED[OC.PRED$CLUSTER_ID == 6,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","dat.cluster9 <- OC.PRED[OC.PRED$CLUSTER_ID == 9,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","","par(mfrow=c(2,2)) ","hist(dat.cluster6$YRS_RESIDENCE, main=\"Cluster 6 YRS_RESIDENCE\", col=\"darkred\", xlim=c(0,15), ylim=c(0,600))","hist(dat.cluster9$YRS_RESIDENCE, main=\"Cluster 9 YRS_RESIDENCE\", col=\"darkgreen\", xlim=c(0,15),ylim=c(0,600))","","hist(dat.cluster6$CUST_YEAR_OF_BIRTH, main=\"Cluster 6 CUST_YEAR_OF_BIRTH\", col=\"darkred\", xlim=c(1910,1990), ylim=c(0,250))","hist(dat.cluster9$CUST_YEAR_OF_BIRTH, main=\"Cluster 9 CUST_YEAR_OF_BIRTH\", col=\"darkgreen\", xlim=c(1910,1990), ylim=c(0,250))"],"enabled":true,"result":{"startTime":1733168724913,"interpreter":"r.low","endTime":1733168726208,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nFHmIuwLavXu3kmL0GX1/4osvvqgzy8SJE/XvFCvuXKe9vf3WrVuVp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AsODr5w4cKKFStatmyp/abqMtC0adOkpKQFCxZ8+OGHZdkvgAqI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\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Glubm7lypXz8vIsHBUAmBa5DoA52Nyt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\n3769zi9Fll6RkZE0xzZFRkYq4D4LSilynY1TUnPIdRLeAgAAAIW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ten customers most likely in cluster 6","message":["%r","","z.show(ore.sort(OC.PRED, \"'6'\", reverse = TRUE)[1:10,c(1,2,4)])"],"enabled":true,"result":{"startTime":1733168726287,"interpreter":"r.low","endTime":1733168727096,"results":[{"message":"YRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.6.\n5\t1967\t0.99999999909654\n5\t1967\t0.99999999909654\n4\t1963\t0.999999998899651\n4\t1963\t0.999999998899651\n5\t1965\t0.999999998732417\n5\t1965\t0.999999998732417\n6\t1964\t0.999999997750633\n6\t1965\t0.999999997687977\n6\t1965\t0.999999997687977\n4\t1966\t0.999999997319572\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Using SQL access prediction details","One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.","","We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well."],"enabled":true,"result":{"startTime":1733168727170,"interpreter":"md.low","endTime":1733168727227,"results":[{"message":"<h2 id=\"using-sql-access-prediction-details\">Using SQL access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.\nThen, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table='CUST_DF_OC')","ore.create(CUST_DF, table='CUST_DF_OC')"],"enabled":true,"result":{"startTime":1733168727303,"interpreter":"r.low","endTime":1733168727732,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top 3 predictive attributes for specific customers and likely cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R Data Cleaning Duplicate Removal.dsnb b/machine-learning/notebooks-oml/r/OML4R Data Cleaning Duplicate Removal.dsnb
new file mode 100644
index 00000000..8cbb2551
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML4R Data Cleaning Duplicate Removal.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Duplicate Removal","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1717442933182,"interpreter":"md.medium","endTime":1717442933337,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning for R - Data Cleaning - Duplicates Removal","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we demonstrate how to remove duplicate records using OML4R.","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","The dataset CUSTOMER_INSURANCE_LTV is generated by the \"OML Run-me-first\" notebook, which MUST be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---","","Import R libraries and obtain a proxy object for the data table to perform duplicate removal in the database.","Note that users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1736207142926,"interpreter":"md.medium","endTime":1736207143413,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-r---data-cleaning---duplicates-removal\">Oracle Machine Learning for R - Data Cleaning - Duplicates Removal<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we demonstrate how to remove duplicate records using OML4R.\nWe use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.\nThe dataset CUSTOMER_INSURANCE_LTV is generated by the &quot;OML Run-me-first&quot; notebook, which MUST be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-prerequisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a prerequisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n<p>Import R libraries and obtain a proxy object for the data table to perform duplicate removal in the database.\nNote that users can get online help using R's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1717442933721,"interpreter":"r.medium","endTime":1717442938415,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV table","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV_R', use.keys= FALSE)","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV_R"],"enabled":true,"result":{"startTime":1717442938487,"interpreter":"r.medium","endTime":1717442943582,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the distinct rows from the dataset","message":["%r","","n_distinct(CUST_DF$CUSTOMER_ID)"],"enabled":true,"result":{"startTime":1717442943655,"interpreter":"r.medium","endTime":1717442943875,"results":[{"message":"13880","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show table dimensions - note there are more rows than customer IDs","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1717442943946,"interpreter":"r.medium","endTime":1717442944210,"results":[{"message":"<style>\n.list-inline {list-style: none; 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diff --git a/machine-learning/notebooks-oml/r/OML4R Data Cleaning Missing Data.dsnb b/machine-learning/notebooks-oml/r/OML4R Data Cleaning Missing Data.dsnb
new file mode 100644
index 00000000..d738aad3
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML4R Data Cleaning Missing Data.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Missing Data","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736208812724,"interpreter":"md.low","endTime":1736208813170,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4R Data Cleaning: Fill in Missing Data","","In this notebook, we demonstrate how to perform missing value replacement using OML4R.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance. ","","The dataset `CUSTOMER_INSURANCE_LTV_R` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may *not* be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see [Data Preparation for GLM](https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12)","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---","","Import R libraries and obtain a proxy object to the data table.","To use OML4R, first import the library ORE.","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1736209057045,"interpreter":"md.low","endTime":1736209057452,"results":[{"message":"<h1 id=\"oml4r-data-cleaning-fill-in-missing-data\">OML4R Data Cleaning: Fill in Missing Data<\/h1>\n<p>In this notebook, we demonstrate how to perform missing value replacement using OML4R.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_R<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<hr />\n<p><strong>Note<\/strong>: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may <em>not<\/em> be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n<p>Import R libraries and obtain a proxy object to the data table.\nTo use OML4R, first import the library ORE.\nUsers can get online help using R's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736208814514,"interpreter":"r.low","endTime":1736208819463,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_R table and view a few rows","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV_R', use.keys= FALSE)","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV_R","","z.show(head(CUST_DF, 10))"],"enabled":true,"result":{"startTime":1736208819892,"interpreter":"r.low","endTime":1736208820810,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         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\tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Assign row names to enable row indexing","message":["%r","","rownames(CUST_DF) <- 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        : 606   Mean   :  3945  \n M       : 359   NV                        : 521   3rd Qu.:     0  \n (Other) : 433   MN                        : 489   Max.   :608951  \n NA's    :6627   (Other)                   :2676   NA's   :6627    \n CUSTOMER_TENURE MORTGAGE_AMOUNT      BANK_FUNDS     NUM_DEPENDENTS \n Min.   :1.000   Min.   :    0.00   Min.   :     0   Min.   :0.000  \n 1st Qu.:1.000   1st Qu.:    1.75   1st Qu.:     0   1st Qu.:1.000  \n Median :3.000   Median : 1097.50   Median :  1000   Median :2.000  \n Mean   :2.457   Mean   : 2008.32   Mean   :  3443   Mean   :2.065  \n 3rd Qu.:3.000   3rd Qu.: 2713.25   3rd Qu.:  4350   3rd Qu.:3.000  \n Max.   :5.000   Max.   :90000.00   Max.   :105000   Max.   :6.000  \n                                    NA's   :6627                    \n  HAS_CHILDREN        INCOME                           CUSTOMER_ID   \n Min.   :0.0000   Min.   : 37107   CU10027                   :    2  \n 1st Qu.:0.0000   1st Qu.: 60838   CU1004                    :    2  \n Median :0.0000   Median : 64376   CU10090                   :    2  \n Mean   :0.4983   Mean   : 65002   CU10093                   :    2  \n 3rd Qu.:1.0000   3rd Qu.: 68196   CU1010                    :    2  \n Max.   :1.0000   Max.   :111605   CU1021                    :    2  \n                  NA's   :6627     (Other)                   :14868  \n                        GENDER                     PROFESSION  \n M                         :10177   Programmer/Developer:2397  \n F                         : 4703   IT Staff            :1153  \n                                    Clerical            : 599  \n                                    Nurse               : 598  \n                                    Not specified       : 459  \n                                    Author              : 458  \n                                    (Other)             :9216  \n CREDIT_CARD_LIMITS       REGION     HOME_OWNERSHIP   NUM_ONLINE_TRANS\n Min.   : 500       NorthEast:5280   Min.   :0.0000   Min.   :    0   \n 1st Qu.: 900       West     :4201   1st Qu.:1.0000   1st Qu.:  210   \n Median :1000       Midwest  :3264   Median :1.0000   Median :  800   \n Mean   :1256       South    :1123   Mean   :0.8046   Mean   : 1336   \n 3rd Qu.:1500       Southwest:1012   3rd Qu.:1.0000   3rd Qu.: 1625   \n Max.   :5000                        Max.   :2.0000   Max.   :45000   \n                                                                      \n BUY_INSURANCE MONTHLY_CHECKS   NUM_TRANS_KIOSK       AGE       \n No :10864     Min.   : 0.000   Min.   : 0.000   Min.   : 0.00  \n Yes: 4016     1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.:27.00  \n               Median : 3.000   Median : 1.000   Median :36.00  \n               Mean   : 4.538   Mean   : 1.923   Mean   :37.39  \n               3rd Qu.: 6.000   3rd Qu.: 3.000   3rd Qu.:46.00  \n               Max.   :18.000   Max.   :10.000   Max.   :84.00  \n                                                                \n MONEY_MONTLY_OVERDRAWN      LTV        TOTAL_AUTOM_PAYMENTS NUM_TRANS_TELLER\n Min.   :-77.34         Min.   :    0   Min.   :     0.0     Min.   :0.000   \n 1st Qu.: 53.06         1st Qu.:18586   1st Qu.:   151.0     1st Qu.:1.000   \n Median : 53.25         Median :23037   Median :   600.5     Median :1.000   \n Mean   : 53.63         Mean   :22248   Mean   :  5312.4     Mean   :1.799   \n 3rd Qu.: 53.80         3rd Qu.:26102   3rd Qu.:  2465.0     3rd Qu.:3.000   \n Max.   : 93.64         Max.   :47502   Max.   :744527.0     Max.   :9.000   \n                                                                             \n CHECKING_BALANCE  NUM_TRANS_ATM        LTV_BIN        FIRST_NAME   \n Min.   :   25.0   Min.   :0.000   HIGH     :7127   LOGAN   :   20  \n 1st Qu.:   25.0   1st Qu.:1.000   MEDIUM   :4910   JAYSON  :   19  \n Median :   25.0   Median :3.000   VERY HIGH:1425   LACY    :   19  \n Mean   : 1095.6   Mean   :2.864   LOW      :1418   JIM     :   18  \n 3rd Qu.:  271.2   3rd Qu.:4.000                    LAURENCE:   18  \n Max.   :24760.0   Max.   :8.000                    ROBERTO :   18  \n                                                    (Other) :14768  \n NUM_MORTGAGES    CAR_OWNERSHIP        LAST_NAME    \n Min.   :0.0000   Min.   :0.0000   THANOS   :12829  \n 1st Qu.:0.0000   1st Qu.:1.0000   HARE     :    6  \n Median :1.0000   Median :1.0000   RUSSO    :    6  \n Mean   :0.8044   Mean   :0.9314   BERNIER  :    5  \n 3rd Qu.:1.0000   3rd Qu.:1.0000   CALDWELL :    5  \n Max.   :2.0000   Max.   :1.0000   DELATORRE:    5  \n                                   (Other)  : 2024  ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Columns MARITAL_STATUS, CREDIT_BALANCE, BANK_FUNDS, and INCOME have missing values","message":["%r","","MISS_VAL <- CUST_DF[ , c(\"CUSTOMER_ID\", \"MARITAL_STATUS\", \"CREDIT_BALANCE\", \"BANK_FUNDS\", \"INCOME\")]","z.show(head(is.na(MISS_VAL)))"],"enabled":true,"result":{"startTime":1736208828132,"interpreter":"r.low","endTime":1736208828595,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS\tCREDIT_BALANCE\tBANK_FUNDS\tINCOME\nFALSE\tTRUE\tTRUE\tTRUE\tTRUE\nFALSE\tTRUE\tTRUE\tTRUE\tTRUE\nFALSE\tTRUE\tTRUE\tTRUE\tTRUE\nFALSE\tTRUE\tTRUE\tTRUE\tTRUE\nFALSE\tTRUE\tTRUE\tTRUE\tTRUE\nFALSE\tTRUE\tTRUE\tTRUE\tTRUE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Determine number missing in each 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round(mean(MISS_VAL$CREDIT_BALANCE, na.rm = TRUE),2)","MISS_VAL$BANK_FUNDS[is.na(MISS_VAL$BANK_FUNDS)] <- round(mean(MISS_VAL$BANK_FUNDS, na.rm = TRUE),2)","MISS_VAL$INCOME[is.na(MISS_VAL$INCOME)] <- round(mean(MISS_VAL$INCOME, na.rm = TRUE),2)"],"enabled":true,"result":{"startTime":1736208829898,"interpreter":"r.low","endTime":1736208830426,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check for missing data","message":["%r","","z.show(ore.summary(MISS_VAL, c(\"MARITAL_STATUS\", \"CREDIT_BALANCE\", \"BANK_FUNDS\", \"INCOME\"), stats = c(\"nmiss\")))"],"enabled":true,"result":{"startTime":1736208830844,"interpreter":"r.low","endTime":1736208831309,"results":[{"message":"FREQ\tNMISS.MARITAL_STATUS.\tNMISS.CREDIT_BALANCE.\tNMISS.BANK_FUNDS.\tNMISS.INCOME.\n14880\t0\t0\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the OML data frame after the replacement","message":["%r","","z.show(MISS_VAL[2053:2059, c('CUSTOMER_ID', 'MARITAL_STATUS', 'BANK_FUNDS', 'CREDIT_BALANCE', 'INCOME')])"],"enabled":true,"result":{"startTime":1736208831729,"interpreter":"r.low","endTime":1736208832263,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS\tBANK_FUNDS\tCREDIT_BALANCE\tINCOME\nCU11989                   \tSINGLE\t3442.68\t3944.68\t65001.82\nCU1199                    \tSINGLE\t3442.68\t3944.68\t65001.82\nCU11990                   \tSINGLE\t3442.68\t3944.68\t65001.82\nCU11992                   \tSINGLE\t3442.68\t3944.68\t65001.82\nCU11993                   \tSINGLE\t3442.68\t3944.68\t65001.82\nCU11994                   \tSINGLE\t3442.68\t3944.68\t65001.82\nCU11996                   \tSINGLE\t3442.68\t3944.68\t65001.82\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":{"startTime":1736208832698,"interpreter":"md.low","endTime":1736208833101,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Outlier Removal","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736213908065,"interpreter":"md.high","endTime":1736213908480,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R  Data Cleaning: Outlier Removal","In this notebook, we demonstrate how to exclude records with outliers using OML4R. ","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV, we focus on numerical values and remove the records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736213908892,"interpreter":"md.high","endTime":1736213909296,"results":[{"message":"<h1 id=\"oml4r--data-cleaning-outlier-removal\">OML4R  Data Cleaning: Outlier Removal<\/h1>\n<p>In this notebook, we demonstrate how to exclude records with outliers using OML4R.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>In the dataset CUSTOMER_INSURANCE_LTV, we focus on numerical values and remove the records with values in the top and bottom 5%.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736213909707,"interpreter":"r.high","endTime":1736213910173,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_R table","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV', use.keys= FALSE)","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1736213910589,"interpreter":"r.high","endTime":1736213911058,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from the table","message":["%r","z.show(head(CUST_DF, 10))"],"enabled":true,"result":{"startTime":1736213911476,"interpreter":"r.high","endTime":1736213911936,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nMARRIED\tNY                        \t0\t4\t7000\t17500\t3\t1\t59847\tCU1193                    \tF                         \tAdministrator\t900\tNorthEast\t1\t2500\tNo\t17\t0\t41\t53.73\t20061.75\t15104\t5\t11421\t4\tMEDIUM\tVIVIANA\t1\t1\tGERBER\nSINGLE\tCA                        \t47571\t2\t0\t2358\t0\t1\t57729\tCU1194                    \tM                         \tAuthor\t900\tWest\t0\t508\tNo\t3\t0\t65\t56.9\t16932.25\t58004\t3\t25\t6\tMEDIUM\tCLETUS\t0\t1\tFRANCE\nDIVORCED\tNY                        \t18584\t4\t2000\t2200\t4\t1\t60442\tCU1195                    \tF                         \tPublisher\t1000\tNorthEast\t1\t400\tNo\t1\t5\t36\t53.47\t17710.5\t23447\t3\t605\t4\tMEDIUM\tGERTIE\t1\t1\tWOOD\nSINGLE\tNC                        \t0\t2\t0\t0\t0\t1\t68218\tCU1196                    \tM                         \tAdministrative Assistant\t700\tSouth\t0\t0\tNo\t2\t0\t55\t53.19\t18554.5\t1321\t1\t674\t3\tMEDIUM\tANDREW\t0\t1\tKERN\nDIVORCED\tCA                        \t0\t1\t2200\t3501\t1\t1\t58893\tCU1198                    \tF                         \tPROF-30\t1200\tWest\t1\t1\tNo\t13\t0\t54\t53.89\t23623.25\t7832\t1\t7252\t4\tHIGH\tREGENA\t1\t1\tMCWILLIAMS\nMARRIED\tCA                        \t0\t3\t1079\t0\t3\t1\t60578\tCU1199                    \tM                         \tIT Staff\t1000\tWest\t1\t1079\tNo\t4\t0\t26\t53.13\t18244.5\t794\t0\t287\t2\tMEDIUM\tANGELO\t1\t1\tSPENCE\nWIDOWED\tCA                        \t0\t1\t12000\t12600\t1\t1\t64433\tCU1200                    \tF                         \tMedical Doctor\t800\tWest\t1\t5000\tYes\t3\t0\t55\t54.12\t25108.25\t1970\t4\t25\t5\tHIGH\tCORDIE\t1\t1\tBULLOCK\nMARRIED\tCA                        \t0\t3\t800\t0\t3\t1\t57072\tCU1201                    \tM                         \tAuthor\t1600\tWest\t1\t800\tNo\t3\t0\t76\t53.2\t22368\t1679\t1\t25\t3\tHIGH\tELWOOD\t1\t1\tMABRY\nMARRIED\tNY                        \t0\t4\t1089\t3060\t0\t1\t55182\tCU1202                    \tM                         \tVeterinarian\t1400\tNorthEast\t1\t960\tNo\t13\t0\t38\t53.7\t24595.5\t3746\t2\t2400\t4\tHIGH\tROLLAND\t1\t1\tSHEA\nDIVORCED\tNY                        \t0\t1\t2994\t0\t1\t1\t61258\tCU1203                    \tF                         \tPublisher\t1500\tNorthEast\t1\t2994\tNo\t1\t0\t39\t53.08\t22714.5\t773\t1\t25\t1\tHIGH\tOLLIE\t1\t1\tDODGE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the percentiles","message":["%r","","summary(CUST_DF$INCOME)"],"enabled":true,"result":{"startTime":1736213912369,"interpreter":"r.high","endTime":1736213913215,"results":[{"message":"   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n  37110   60720   64430   65110   68480  111600 ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get the 5 and 95 percentile along with the interquartile range","message":["%r","","Q1 <- quantile(CUST_DF$INCOME, .05, na.rm=TRUE)","Q3 <- quantile(CUST_DF$INCOME, .95, na.rm=TRUE)","IQR <- IQR(CUST_DF$INCOME, na.rm=TRUE)"],"enabled":true,"result":{"startTime":1736213913634,"interpreter":"r.high","endTime":1736213915482,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":450,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Remove data with INCOME out of bounds and create plot","message":["%r","","par(mfrow=c(2,2))","NO_OUTLIERS <- subset(CUST_DF$INCOME, CUST_DF$INCOME > (Q1 - 1.5*IQR) & CUST_DF$INCOME < (Q3 + 1.5*IQR))","hist(NO_OUTLIERS, col=\"red\")","hist(CUST_DF$INCOME, col=\"red\")"],"enabled":true,"result":{"startTime":1736213915892,"interpreter":"r.high","endTime":1736213916885,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\ntroRbFxZDAaD6UJTasjK4uLiRYsWqZ0/deqUcj7p2LFjTXe13KEvKx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Ky3LncUgyAbcyfY9evX79PP/201MT9+/cXFxc7pksA4AQa1zpu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\nugQEBJw+fTo/P//QoUM//vhjZGTkjRs3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diff --git a/machine-learning/notebooks-oml/r/OML4R Data Cleaning Recode Synonymous Values.dsnb b/machine-learning/notebooks-oml/r/OML4R Data Cleaning Recode Synonymous Values.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Recode Synonymous Values","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736376303364,"interpreter":"md.low","endTime":1736376303809,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Cleaning: Recode Synonymous Values","In this notebook, we demonstrate how to recode synonymous value using OML4R.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_R` has special values generated by the `\"OML Run-me-first\"` notebook artificially, which `MUST` be run before this notebook.  Specifically, MARITAL_STATUS has divorced status coded as DIV, married coded as 'M'. ","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736376304261,"interpreter":"md.low","endTime":1736376304711,"results":[{"message":"<h1 id=\"oml4r-data-cleaning-recode-synonymous-values\">OML4R Data Cleaning: Recode Synonymous Values<\/h1>\n<p>In this notebook, we demonstrate how to recode synonymous value using OML4R.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_R<\/code> has special values generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook artificially, which <code>MUST<\/code> be run before this notebook.  Specifically, MARITAL_STATUS has divorced status coded as DIV, married coded as 'M'.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4R Data Preparation"],"enabled":true,"result":{"startTime":1736376305176,"interpreter":"md.low","endTime":1736376305622,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4R Data 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table","message":["%r","","ore.sync(table=\"CUSTOMER_INSURANCE_LTV_R\")","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV_R","z.show(head(CUST_DF, 10))"],"enabled":true,"result":{"startTime":1736376307039,"interpreter":"r.low","endTime":1736376307651,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         \tPROF-15\t800\tSouth\t1\t1600\tYes\t1\t1\t66\t54.97\t16933\t1923\t4\t25\t5\tMEDIUM\tMARGART\t1\t1\tCALDWELL\nMARRIED\tTX                        \t0\t3\t845\t450\t3\t1\t62061\tCU9113                    \tM                         \tProgrammer/Developer\t1500\tSouthwest\t1\t845\tYes\t8\t6\t26\t53.01\t18615.25\t226\t2\t25\t2\tMEDIUM\tNICKY\t1\t1\tBARON\nSINGLE\tNV                        \t0\t2\t0\t0\t0\t0\t74394\tCU9114                    \tM                         \tSales Representative\t1500\tSouthwest\t0\t0\tNo\t0\t1\t0\t53.06\t0\t0\t0\t25\t0\tLOW\tHUNG\t0\t0\tFINNEY\nSINGLE\tMI                        \t0\t3\t0\t0\t3\t0\t64255\tCU8462                    \tM                         \tPROF-31\t1500\tMidwest\t0\t0\tNo\t1\t1\t28\t53.14\t14363.75\t0\t0\t25\t2\tMEDIUM\tFREDDIE\t0\t0\tMAYBERRY\nDIVORCED\tMI                        \t0\t1\t2513\t1150\t1\t0\t70749\tCU8506                    \tM                         \tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the distinct values","message":["%r","","crosstab <- ore.crosstab(~MARITAL_STATUS, CUST_DF)","row.names(crosstab) <- 1:nrow(crosstab)","z.show(crosstab[, c(\"MARITAL_STATUS\", \"ORE$FREQ\")])"],"enabled":true,"result":{"startTime":1736376308110,"interpreter":"r.low","endTime":1736376308813,"results":[{"message":"MARITAL_STATUS\tORE.FREQ\nDIV\t337\nDIVORCED\t2063\nM\t359\nMARRIED\t2393\nOTHER\t96\nSINGLE\t2530\nWIDOWED\t475\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Recode the synonymous values 'M' and 'DIV'","message":["%r","","CUST_DF$MARITAL_STATUS[CUST_DF$MARITAL_STATUS == 'M'] <- \"MARRIED\"","CUST_DF$MARITAL_STATUS[CUST_DF$MARITAL_STATUS == 'DIV'] <- 'DIVORCED'","CUST_DF$MARITAL_STATUS[is.na(CUST_DF$MARITAL_STATUS) == TRUE] <- 'UNKNOWN'"],"enabled":true,"result":{"startTime":1736376309283,"interpreter":"r.low","endTime":1736376309781,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the recoded values","message":["%r","","crosstab2 <- ore.crosstab(~MARITAL_STATUS, CUST_DF)","row.names(crosstab2) <- 1:nrow(crosstab2)","z.show(crosstab2[, c(\"MARITAL_STATUS\", \"ORE$FREQ\")])"],"enabled":true,"result":{"startTime":1736376310238,"interpreter":"r.low","endTime":1736376311187,"results":[{"message":"MARITAL_STATUS\tORE.FREQ\nDIVORCED\t2400\nMARRIED\t2752\nOTHER\t96\nSINGLE\t2530\nUNKNOWN\t6627\nWIDOWED\t475\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736376311640,"interpreter":"md.low","endTime":1736376312087,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736376312551,"interpreter":"md.low","endTime":1736376313005,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Binning","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733781473654,"interpreter":"md.low","endTime":1733781474104,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4R Data Transformation: Binning","In this notebook, we demonstrate how to bin numerical columns using OML4R. Equi-width and quantile binning are shown.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to bin a numerical column and visualize the distribution.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which MUST be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. Supervised binning uses decision trees to create optimal bin boundaries. More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0');\">Naive Bayes<\/a>  ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733781474560,"interpreter":"md.low","endTime":1733781475002,"results":[{"message":"<h1 id=\"oml4r-data-transformation-binning\">OML4R Data Transformation: Binning<\/h1>\n<p>In this notebook, we demonstrate how to bin numerical columns using OML4R. Equi-width and quantile binning are shown.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to bin a numerical column and visualize the distribution.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which MUST be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. Supervised binning uses decision trees to create optimal bin boundaries. More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0');\">Naive Bayes<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1733781475476,"interpreter":"md.low","endTime":1733781475924,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R Libraries and set options","message":["%r","","library(ORE)","library(OREdplyr)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1733781476379,"interpreter":"r.low","endTime":1733781476881,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object for CUSTOMER_INSURANCE_LTV and view data","message":["%r","","ore.sync(table = \"CUSTOMER_INSURANCE_LTV\")","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1733781477336,"interpreter":"r.low","endTime":1733781477902,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA\t0\t3\t0\t0\t3\t0\t65871\tCU15154\tM\tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY\t0\t4\t0\t290\t4\t0\t68747\tCU15155\tM\tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI\t0\t3\t1000\t550\t3\t0\t68684\tCU15157\tM\tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT\t0\t5\t1200\t1000\t5\t0\t59354\tCU15286\tF\tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT\t0\t4\t1800\t0\t3\t0\t84801\tCU15287\tF\tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT\t0\t1\t1400\t0\t1\t0\t73987\tCU15289\tM\tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Bin INCOME column into 10 bins","message":["%r","","rownames(CUST_DF) <- CUST_DF$CUSTOMER_ID # this enables row indexing","","CUST_BIN_DF <- mutate(CUST_DF, ","                      \"INCOME_BIN\" = cut(CUST_DF$INCOME, breaks=10, dig.lab=7))"],"enabled":true,"result":{"startTime":1733781478364,"interpreter":"r.low","endTime":1733781479560,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View binned INCOME column values","message":["%r","","# Update datatype of binned column for proper output format","CUST_BIN_DF['INCOME_BIN'] <- as.ore.character(CUST_BIN_DF$INCOME_BIN)","","z.show(head(CUST_BIN_DF[c(\"INCOME_BIN\", \"INCOME\")],10))"],"enabled":true,"result":{"startTime":1733781502559,"interpreter":"r.low","endTime":1733781503213,"results":[{"message":"INCOME_BIN\tINCOME\n(66906.2,74356]\t70508\n(66906.2,74356]\t68361\n(59456.4,66906.2]\t60185\n(66906.2,74356]\t71740\n(66906.2,74356]\t68482\n(66906.2,74356]\t71016\n(52006.6,59456.4]\t58641\n(66906.2,74356]\t70738\n(59456.4,66906.2]\t60554\n(59456.4,66906.2]\t66340\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot the distribution of binned income","message":["%r","","OUTPUT <- count(CUST_BIN_DF, INCOME_BIN)","rownames(OUTPUT) <- OUTPUT$INCOME_BIN # ordering rows of OUTPUT","","output <- ore.pull(OUTPUT) # Pull output ore.frame to data.frame for plotting","","output$Bin_INCOME <- factor(output$INCOME_BIN, ","                            levels=c(\"(37032.5,44556.8]\", \"(44556.8,52006.6]\", ","                                     \"(52006.6,59456.4]\", \"(59456.4,66906.2]\", ","                                     \"(66906.2,74356]\", \"(74356,81805.8]\", ","                                     \"(81805.8,89255.6]\", \"(89255.6,96705.4]\", ","                                     \"(96705.4,104155.2]\", \"(104155.2,111679.5]\"))  # Order the output correctly","","barchart(n ~ Bin_INCOME, data = output, horiz = FALSE,","         ylab=\"Count\", scales = list(x = list(rot = 45, relation=\"free\")), col = \"blue\")"],"enabled":true,"result":{"startTime":1733781481067,"interpreter":"r.low","endTime":1733781481892,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nE68Rr433nHiNnDp1asWKFcuWLTt48GCll5z1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quantiles for INCOME column ","message":["%r","","z.show(data.frame(quantile(CUST_DF$INCOME)))"],"enabled":true,"result":{"startTime":1733781482377,"interpreter":"r.low","endTime":1733781483197,"results":[{"message":"quantile.CUST_DF.INCOME.\n37107\n60719.5\n64426\n68482\n111605\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Use quantiles for bin boundaries","message":["%r","","income_quantiles <- quantile(CUST_DF$INCOME)","","CUST_SET_DF <- transmute(CUST_DF, ","                         \"INCOME_BIN\" = cut(CUST_DF$INCOME, ","                                            breaks=income_quantiles, ","                                            dig.lab=10))","","CUST_SET_DF <- count(CUST_SET_DF, INCOME_BIN)","rownames(CUST_SET_DF) <- CUST_SET_DF$INCOME_BIN","","","cust_set_df <- ore.pull(CUST_SET_DF) # Pull data to client for plotting","","barchart(n ~ INCOME_BIN, data = cust_set_df, ","         horiz = FALSE, ylab = \"Count\", col =\" Blue\")"],"enabled":true,"result":{"startTime":1733781483658,"interpreter":"r.low","endTime":1733781485041,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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diff --git a/machine-learning/notebooks-oml/r/OML4R Data Transformation Categorical Recode.dsnb b/machine-learning/notebooks-oml/r/OML4R Data Transformation Categorical Recode.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Categorical Recode","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736276033321,"interpreter":"md.low","endTime":1736276033774,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation: recode categorical variables","","In this notebook, we demonstrate how to recode a categorical string variable to a numeric variable and string-to-string recoding using OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, ","and whether or not the customer bought insurance, we show how to convert a categorical variable with each distinct level/value coded to an integer data type. Note that ***ore.recode***, which supports this transformation, can also be used to recode string values as illustrated in the last example. ","","The dataset CUSTOMER_INSURANCE_LTV is generated by the \"OML Run-me-first\" notebook, which MUST be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736276034225,"interpreter":"md.low","endTime":1736276034669,"results":[{"message":"<h1 id=\"oml4r-data-transformation-recode-categorical-variables\">OML4R Data Transformation: recode categorical variables<\/h1>\n<p>In this notebook, we demonstrate how to recode a categorical string variable to a numeric variable and string-to-string recoding using OML4R.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value,\nand whether or not the customer bought insurance, we show how to convert a categorical variable with each distinct level/value coded to an integer data type. Note that <em><strong>ore.recode<\/strong><\/em>, which supports this transformation, can also be used to recode string values as illustrated in the last example.<\/p>\n<p>The dataset CUSTOMER_INSURANCE_LTV is generated by the &quot;OML Run-me-first&quot; notebook, which MUST be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-prerequisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a prerequisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1736276035117,"interpreter":"md.low","endTime":1736276035553,"results":[{"message":"<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736276036011,"interpreter":"r.low","endTime":1736276036517,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%r","","ore.sync(query = c(\"CUST_DF\" = \"select * from CUSTOMER_INSURANCE_LTV\"))","ore.attach()","z.show(head(CUST_DF, 5))"],"enabled":true,"result":{"startTime":1736276036971,"interpreter":"r.low","endTime":1736276037498,"results":[{"message":"The following object is masked _by_ .GlobalEnv:\n\n    CUST_DF\n\n\nThe following object is masked _by_ .GlobalEnv:\n\n    CUST_DF\n\n\n\n","type":"TEXT"},{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\t4\t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         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HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique states by count","message":["%r","","output <- count(CUST_DF, STATE)","colnames(output)[2] <- \"count\"","z.show(arrange(output, count))"],"enabled":true,"result":{"startTime":1736276037960,"interpreter":"r.low","endTime":1736276038528,"results":[{"message":"STATE\tcount\n3\t11\n1\t18\n9\t40\n2\t46\n19\t48\n15\t49\n12\t51\n21\t85\n5\t108\n18\t132\n24\t148\n23\t154\n13\t165\n20\t233\n8\t237\n14\t238\n22\t304\n6\t413\n11\t453\n16\t488\n7\t558\n10\t2402\n4\t2964\n17\t4535\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Replace the state names with integers","message":["%r","","# Get vector of unique values in STATE column and create vector of integers to replace with.","state_names <- ore.pull(c(unique(CUST_DF$STATE)))","state_nums <- c(1:length(state_names))","","# Set STATE column to ore.character datatype before replacement.","CUST_DF$STATE <- as.ore.character(CUST_DF$STATE)","","# Replace the state names. ","CUST_DF$STATE <- ore.recode(CUST_DF$STATE, state_names, state_nums)"],"enabled":true,"result":{"startTime":1736276038978,"interpreter":"r.low","endTime":1736276039481,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the unique states encoded as integers","message":["%r","","output <- arrange(count(CUST_DF, STATE), n)","colnames(output)[2] <- \"count\"","","class(output$STATE)","z.show(output)"],"enabled":true,"result":{"startTime":1736276039939,"interpreter":"r.low","endTime":1736276040505,"results":[{"message":"'ore.numeric'\n","type":"HTML"},{"message":"STATE\tcount\n3\t11\n1\t18\n9\t40\n2\t46\n19\t48\n15\t49\n12\t51\n21\t85\n5\t108\n18\t132\n24\t148\n23\t154\n13\t165\n20\t233\n8\t237\n14\t238\n22\t304\n6\t413\n11\t453\n16\t488\n7\t558\n10\t2402\n4\t2964\n17\t4535\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Simple example recoding string values on a vector","message":["%r","","vec <- ore.push(c(\"a\", \"b\", NA, \"a\", \"c\", NA, \"e\"))","","cat(class(vec), \"\\n\")","","ore.recode(vec, c(\"a\", \"b\", \"c\"), c(\"able\", \"baker\", \"charlie\"))"],"enabled":true,"result":{"startTime":1736276040965,"interpreter":"r.low","endTime":1736276041540,"results":[{"message":"ore.character \n[1] \"able\"    \"baker\"   NA        \"able\"    \"charlie\" NA        NA       ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Recode to multiple columns with different values","message":["%r","","ore.recode(vec, c(\"a\", \"b\", \"c\"),","            cbind(lower = c(\"able\", \"baker\", \"charlie\"),","                  upper = c(\"ABLE\", \"BAKER\", \"CHARLIE\")))"],"enabled":true,"result":{"startTime":1736276042001,"interpreter":"r.low","endTime":1736276042568,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 7 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>lower<\/th><th scope=col>upper<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>able   <\/td><td>ABLE   <\/td><\/tr>\n\t<tr><td>baker  <\/td><td>BAKER  <\/td><\/tr>\n\t<tr><td>NA     <\/td><td>NA     <\/td><\/tr>\n\t<tr><td>able   <\/td><td>ABLE   <\/td><\/tr>\n\t<tr><td>charlie<\/td><td>CHARLIE<\/td><\/tr>\n\t<tr><td>NA     <\/td><td>NA     <\/td><\/tr>\n\t<tr><td>NA     <\/td><td>NA     <\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736276043024,"interpreter":"md.low","endTime":1736276043465,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736276043944,"interpreter":"md.low","endTime":1736276044404,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Date Datatypes","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737732345243,"interpreter":"md.medium","endTime":1737732345743,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation Date Datatypes","","In this notebook, we demonstrate how to perform various operations on date and time data using database table proxy objects using OML4R.","","Using a sequence of date and time data spanning from 2024/01/01 to 2024/12/31, we show how to do operations on date and time data. These operations include arithmetic, aggregation, ","comparisons, accessing details of data, coercion, as well as an example of ARIMA with a rolling mean window function.","","","For more information the Documentation can be found at <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-A1BC12C1-B431-4F71-9DC5-4ECCA0C693AC\" target=\"_blank\">OML4R documentation on preparing time series data<\/a>","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1737732346224,"interpreter":"md.medium","endTime":1737732346685,"results":[{"message":"<h1 id=\"oml4r-data-transformation-date-datatypes\">OML4R Data Transformation Date Datatypes<\/h1>\n<p>In this notebook, we demonstrate how to perform various operations on date and time data using database table proxy objects using OML4R.<\/p>\n<p>Using a sequence of date and time data spanning from 2024/01/01 to 2024/12/31, we show how to do operations on date and time data. These operations include arithmetic, aggregation,\ncomparisons, accessing details of data, coercion, as well as an example of ARIMA with a rolling mean window function.<\/p>\n<p>For more information the Documentation can be found at <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-A1BC12C1-B431-4F71-9DC5-4ECCA0C693AC\" target=\"_blank\">OML4R documentation on preparing time series data<\/a><\/p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries and set seed","message":["%r","","library(ORE)","set.seed(100)"],"enabled":true,"result":{"startTime":1737732347167,"interpreter":"r.medium","endTime":1737732347730,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","## Create Datetime dataset","","Create datetime dataset as a database table with ore.push"],"enabled":true,"result":{"startTime":1737732348208,"interpreter":"md.medium","endTime":1737732348679,"results":[{"message":"<h2 id=\"create-datetime-dataset\">Create Datetime dataset<\/h2>\n<p>Create datetime dataset as a database table with ore.push<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r ","","n <- 500","","mydata <- data.frame(datetime = seq(as.POSIXct(\"2024/01/01\"), as.POSIXct(\"2024/12/31\"), length.out = n), ","                     difftime = as.difftime(runif(n), units = \"mins\"), ","                     x = rnorm(n))","","MYDATA <- ore.push(mydata) # create temporary table and return proxy object","","class(MYDATA)","class(MYDATA$datetime)","","z.show(head(MYDATA, 3))"],"enabled":true,"result":{"startTime":1737732349137,"interpreter":"r.medium","endTime":1737732349755,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"'ore.datetime'\n","type":"HTML"},{"message":"datetime\tdifftime\tx\n2024-01-01 00:00:00\t18.465967\t-0.771408001996856\n2024-01-01 17:33:18\t15.46035\t-0.507342353723463\n2024-01-02 11:06:36\t33.139346\t-0.270022583786385\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time arithmetic"],"enabled":true,"result":{"startTime":1737732350249,"interpreter":"md.medium","endTime":1737732350707,"results":[{"message":"<h2 id=\"date-and-time-arithmetic\">Date and time arithmetic<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create day1Shift dataset by adding 1 day to each date in MYDATA$datetime","message":["%r  ","","options(\"width\"=200)","","day1Shift <- MYDATA$datetime + as.difftime(1, units = \"days\")","","cat(class(day1Shift), \"\\n\")","head(MYDATA$datetime, 3)","cat(\"\\n\")","head(day1Shift, 3)"],"enabled":true,"result":{"startTime":1737732351188,"interpreter":"r.medium","endTime":1737732351850,"results":[{"message":"ore.datetime \n[1] \"2024-01-01 00:00:00 UTC\" \"2024-01-01 17:33:18 UTC\" \"2024-01-02 11:06:36 UTC\"\n[1] \"2024-01-02 00:00:00 UTC\" \"2024-01-02 17:33:18 UTC\" \"2024-01-03 11:06:36 UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use diff function to get the differences in time between each value of MYDATA$datetime.","message":["%r","","lag1Diff <- diff(MYDATA$datetime)","cat(class(lag1Diff), \"\\n\")","head(lag1Diff, 3)"],"enabled":true,"result":{"startTime":1737732352324,"interpreter":"r.medium","endTime":1737732355649,"results":[{"message":"ore.difftime \nTime differences in secs\n[1] 63198.4 63198.4 63198.4","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time aggregates","","Obtain statistical aggregates of your date and time data."],"enabled":true,"result":{"startTime":1737732356113,"interpreter":"md.medium","endTime":1737732356577,"results":[{"message":"<h2 id=\"date-and-time-aggregates\">Date and time aggregates<\/h2>\n<p>Obtain statistical aggregates of your date and time data.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","min(MYDATA$datetime)"],"enabled":true,"result":{"startTime":1737732357061,"interpreter":"r.medium","endTime":1737732357634,"results":[{"message":"[1] \"2024-01-01 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UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","range(MYDATA$datetime)"],"enabled":true,"result":{"startTime":1737732362154,"interpreter":"r.medium","endTime":1737732362655,"results":[{"message":"[1] \"2024-01-01 UTC\" \"2024-12-31 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probs = c(0, 0.05, 0.10))"],"enabled":true,"result":{"startTime":1737732364102,"interpreter":"r.medium","endTime":1737732368388,"results":[{"message":"                       0%                        5%                       10% \n\"2024-01-01 00:00:00 UTC\" \"2024-01-19 06:00:00 UTC\" \"2024-02-06 11:59:59 UTC\" ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time comparisons"],"enabled":true,"result":{"startTime":1737732368849,"interpreter":"md.medium","endTime":1737732369311,"results":[{"message":"<h2 id=\"date-and-time-comparisons\">Date and time comparisons<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use comparison to identify all data that is in Q1","message":["%r ","","isQ1 <- MYDATA$datetime < as.Date(\"2024/04/01\")","cat(class(isQ1), \"\\n\")","head(isQ1, 3)"],"enabled":true,"result":{"startTime":1737732369786,"interpreter":"r.medium","endTime":1737732370409,"results":[{"message":"ore.logical \n[1] TRUE TRUE TRUE","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use comparison and identified Q1 data to identify subset of data that is in March","message":["%r","","isMarch <- isQ1 & MYDATA$datetime > as.Date(\"2024/03/01\")","cat(class(isMarch), \"\\n\")","cat(sum(isMarch), \"\\n\")","head(isMarch[70:85,], 100)"],"enabled":true,"result":{"startTime":1737732370873,"interpreter":"r.medium","endTime":1737732373788,"results":[{"message":"ore.logical \n42 \n [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Use comparison to get subset of data that is at the end of the year","message":["%r   ","","eoySubset <- MYDATA[MYDATA$datetime > as.Date(\"2024/12/27\"), ]","class(eoySubset)","z.show(head(eoySubset))"],"enabled":true,"result":{"startTime":1737732374258,"interpreter":"r.medium","endTime":1737732374872,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"datetime\tdifftime\tx\n2024-12-27 08:13:28\t6.263731\t-1.33717666886641\n2024-12-28 01:46:46\t34.794086\t2.31131788765912\n2024-12-28 19:20:04\t19.985368\t-0.350491028513415\n2024-12-29 12:53:23\t0.226655\t-0.321103323805441\n2024-12-30 06:26:41\t59.675767\t-0.428961592336069\n2024-12-31 00:00:00\t22.154092\t-0.229611260023931\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time accessors","","Extract various features from your date and time data.","","Create a function to extract all details from data given a dataframe DF and column name dates.","After running this function, an attribute importance algorithm can then tell you how impactful these new columns are."],"enabled":true,"result":{"startTime":1737732375374,"interpreter":"md.medium","endTime":1737732375830,"results":[{"message":"<h2 id=\"date-and-time-accessors\">Date and time accessors<\/h2>\n<p>Extract various features from your date and time data.<\/p>\n<p>Create a function to extract all details from data given a dataframe DF and column name dates.\nAfter running this function, an attribute importance algorithm can then tell you how impactful these new columns are.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","dateFeatureExtraction <- function(dat, dateCol){","  ","  datesIndex <- which(colnames(dat) == dateCol)","  colnames(dat)[datesIndex] <- \"datesCol\"","  ","  dat <- cbind(dat, year = ore.year(dat$datesCol),  month = ore.month(dat$datesCol),","               dayOfMonth = ore.mday(dat$datesCol), hour = ore.hour(dat$datesCol),","               minute = 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inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>0.38066831972221<\/li><li>0.541023680700716<\/li><li>0.663727122738579<\/li><\/ol>\n<\/dd>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of script"],"enabled":true,"result":{"startTime":1737732400367,"interpreter":"md.medium","endTime":1737732400814,"results":[{"message":"<h1 id=\"end-of-script\">End of 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Normalization and Scaling","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734627865592,"interpreter":"md.low","endTime":1734627866035,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation: Normalization and Scaling","","In this notebook, we demonstrate how to normalize and scale data using OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data using using z-score (mean and standard deviation), min-max scaling, and log scaling.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note** : when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms. 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data using using z-score (mean and standard deviation), min-max scaling, and log scaling.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong> : when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms.\nFor instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1734627867402,"interpreter":"md.low","endTime":1734627867841,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R libraries and set display options","message":["%r","","library(ORE)","library(OREdplyr)","options(scipen = 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CUST_DF\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%r","","z.show(head(CUST_DF, 5))"],"enabled":true,"result":{"startTime":1734627870337,"interpreter":"r.low","endTime":1734627870833,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\tINCOME_NORMALIZED\tINCOME_MINMAX_SCALED\nSINGLE\tCA\t0\t3\t0\t0\t3\t0\t65871\tCU15154\tM\tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\t0.114776449977208\t0.386104324948321\nSINGLE\tNY\t0\t4\t0\t290\t4\t0\t68747\tCU15155\tM\tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\t0.546377262345792\t0.424709388171495\nMARRIED\tMI\t0\t3\t1000\t550\t3\t0\t68684\tCU15157\tM\tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\t0.536922863465674\t0.423863727885312\nMARRIED\tUT\t0\t5\t1200\t1000\t5\t0\t59354\tCU15286\tF\tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\t-0.863228589732828\t0.298625466455475\nMARRIED\tUT\t0\t4\t1800\t0\t3\t0\t84801\tCU15287\tF\tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\t2.95559820871758\t0.640205106177347\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalization by z-score - using mean and standard deviation","message":["%r","","income_mean <- summarise(CUST_DF, income_mean = mean(INCOME, na.rm=TRUE))","income_std  <- summarise(CUST_DF, income_std = sd(INCOME, na.rm=TRUE))","","CUST_DF <- mutate(CUST_DF, INCOME_NORMALIZED = (INCOME - income_mean) / income_std)","","z.show(head(round(CUST_DF[, c(\"INCOME\", \"INCOME_NORMALIZED\")], digits = 4), 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IwW7QoEGHDx+uq6sbNmxYR23q6uqGDx8+aNCg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\nl6u2trapqSk0NNRiscTGxqampmZkZJhMJrV7BAAAUIHGgp3T6ZwzZ47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on the Log Transformed column","message":["%r","","hist(CUST_DF_FILTERED$LOG_TOTAL_AUTOM_PAYMENTS, breaks = 10, col = \"blue\", ","     xlab = \"LOG_TOTAL_AUTOM_PAYMENTS\", ylab = \"Customer Count\", ","     main = paste(\"Histogram of LOG_TOTAL_AUTOM_PAYMENTS\"))","     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h4YGBgdXV1SaTaffu3atWrTp27FhsbGxxcXFU\nVJSn8wIAALiVLxW71NRUi8WSkJCQnZ2t0Wjkq/r06TN69OiUlJSkpKS8vLz58+dv2LDBUzkB\nAAA8wpcmTxQUFAghMjIybFqdlUajSU9PF0Js377dncEAAAC8gS8Vu/LyciFE165d7YyR1koj\nAQAAriq+VOwiIiKEEDt37rQzZteuXUKIyMhIN2UCAADwGr5U7KZMmSKE0Ol0hYWFjQ4oLCzU\n6XRCiMTERLcmAwAA8AK+NHlCr9cbDIaSkpIRI0ZER0cPHTo0PDxco9FYLBaTybRnzx6j0SiE\nGDBggF6v93RYAAAAd/OlYqfVanfs2DF37twVK1YYjUapxskFBgYmJycvWLAgJCTEIwkBAAA8\nyJeKnRAiNDR06dKlaWlp+fn5RqOxrKysqqoqKCgoLCwsOjo6JiZGq9V6OiMAAIBn+Fixk2i1\n2vj4+Pj4eE8HAQAA8CK+NHkCAAAAdvjeFrvKysqNGzcaDAaj0Wg2mysrK4ODg8PCwqKiouLi\n4hITE4OCgjydEQAAwAN8rNjl5+fPnDnTZDLJF1ZUVJjN5tLS0pycHL1en5WVFRMT46mEAAAA\nnuJLxc5oNMbGxloslp49e+p0umHDhoWHhwcGBlZXV5tMpt27d69aterYsWOxsbHFxcVRUVGe\nzgsAAOBWvlTsUlNTLRZLQkJCdna2zeVi+/TpM3r06JSUlKSkpLy8vPnz52/YsMFTOQEAADzC\nlyZPFBQUCCEyMjJsWp2VRqNJT08XQmzfvt2dwQAAALyBLxW78vJyIUTXrl3tjJHWSiMBAACu\nKr5U7CIiIoQQO3futDNm165dQojIyEg3ZQIAAPAavlTspkyZIoTQ6XSFhYWNDigsLNTpdEKI\nxMREtyYDAADwAr40eUKv1xsMhpKSkhEjRkRHRw8dOjQ8PFyj0VgsFpPJtGfPHunqsQMGDNDr\n9Z4OCwAA4G6+VOy0Wu2OHTvmzp27YsUKo9Eo1Ti5wMDA5OTkBQsWhISEeCQhAACAB/lSsRNC\nhIaGLl26NC0tLT8/32g0lpWVVVVVBQUFhYWFRUdHx8TEaLXaZj1hbW2twWCorq62M6auru7w\n4cNz5sxp06ZNy+IDAAC4kI8VO4lWq42Pj4+Pj2/5U+Xn50+cOFHJyFatWs2bN6/lrwgAAOAi\nPlnsnCgmJubTTz+1v8WuqKgoMzOzV69ebksFAADgAN8rdpWVlRs3bjQYDEaj0Ww2V1ZWBgcH\nh4WFRUVFxcXFJSYmBgUFKX82f39/JVv+MjMzW7XypRnEAADgKuRjxS4/P3/mzJkmk0m+sKKi\nwmw2l5aW5uTk6PX6rKysmJgYTyUEAADwFF8qdkajMTY21mKx9OzZU6fTDVgIaoUAACAASURB\nVBs2LDw8PDAwsLq62mQy7d69e9WqVceOHYuNjS0uLo6KivJ0XgAAALfypWKXmppqsVgSEhKy\ns7NtLhfbp0+f0aNHp6SkJCUl5eXlzZ8/f8OGDZ7KCQAA4BG+dNxYQUGBECIjI8Om1VlpNJr0\n9HQhxPbt290ZDAAAwBv4UrErLy8XQnTt2tXOGGmtNBIAAOCq4kvFLiIiQgixc+dOO2N27dol\nhIiMjHRTJgAAAK/hS8VuypQpQgidTldYWNjogMLCQp1OJ4RITEx0azIAAAAv4EuTJ/R6vcFg\nKCkpGTFiRHR09NChQ8PDwzUajcViMZlMe/bska4eO2DAAL1e7+mwAAAA7uZLxU6r1e7YsWPu\n3LkrVqwwGo1SjZMLDAxMTk5esGBBSEiIRxICAAB4kC8VOyFEaGjo0qVL09LS8vPzjUZjWVlZ\nVVVVUFBQWFhYdHR0TEyMVqv1dEYAAADP8LFiJ9FqtfHx8UouBQYAAHD18KXJEwAAALDD97bY\nnT59ev/+/V26dLn55pulJWVlZcuXLz969Gjnzp2TkpIGDx7s2YQAAAAe4WPFLj09Xa/XX758\nWQgxevToLVu2nDx58pZbbjlz5ow0YMmSJRkZGSkpKR6NCQAA4AG+VOz27t37wgsvCCGuv/56\nf3//r7/++rXXXtu/f/+ZM2emTp16++2379y5Mzs7+/nnn4+JiRk4cKCn8wIAALiVLxW7JUuW\nCCGmT5/+wQcf+Pn5PfjggytWrDh//vzDDz/8r3/9SwjxxBNPCCGys7OXLVsmLQEAALh6+NLk\niX379gkhXnjhBT8/PyFESkrKmTNnamtrpT4nefzxx8UfFxYDAAC4qvhSsTt16pQQomfPntLd\nhjeEEH379hVCnDhxwu3pAAAAPMyXil1oaKgQoqamRrobFBQk3ZCflLhdu3ZCiNraWrenAwAA\n8DBfOsauR48ev/32208//dShQwdpyahRo2zGmEwmIURkZKS7wwEAAHiaL22xu/vuu4UQX3/9\ntXVJQUFBQUGBfMyRI0eEEDfddJN7owEAAHieLxW7qVOntmvX7tNPP7Uz5oMPPhBCTJs2zV2h\nAAAAvIUv7Yrt3bv3uXPn7I8ZO3bsyJEj4+Li3BMJAADAe/hSsVNi1qxZno4AAADgGb60KxYA\nAAB2UOwAAABUgmIHAACgEhQ7AAAAlaDYAQAAqATFDgAAQCUodgAAACpBsQMAAFAJih0AAIBK\nUOwAAABUgmIHAACgEhQ7AAAAlaDYAQAAqATFDgAAQCUodgAAACpBsQMAAFAJih0AAIBKUOwA\nAABUgmIHAACgEhQ7AAAAlaDYAQAAqATFDgAAQCUodgAAACpBsQMAAFAJih0AAIBKUOwAAABU\ngmIHAACgEhQ7AAAAlaDYAQAAqATFDgAAQCUodgAAACpBsQMAAFAJih0AAIBKUOwAAABUgmIH\nAACgEhQ7AAAAlaDYAQAAqATFDgAAQCUodgAAACpBsQMAAFAJih0AAIBKUOwAAABUgmIHAACg\nEhQ7AAAAlaDYAQAAqERrTwcA1KROCFFUVOTpGO4TGBgYFxfn7+/v6SAAACEodoBT7RdCZGZm\nZmZmejqJ+2zevHnChAmeTgEAEIJiBzhVjRBCiBQhhns4iJt8I8SSqqoqT8cAAPwvih3gdMOF\nSPR0BgDA1YjJEwAAACpBsQMAAFAJih0AAIBKUOwAAABUgmIHAACgEhQ7AAAAlaDYAQAAqATF\nDgAAQCUodgAAACpBsQMAAFAJih0AAIBKUOwAAABUQmmxGzNmzJgxY5q7CgAAAG7TWuG4r776\nyoFVAAAAcJuW7oqtqakRQrRurbQgAgAAwEVaWuwKCwuFEF26dHFGGAAAADiuiS1tffv2tXO3\nurr61KlTQojY2FinJwMAAECzNFHsDh8+bOeuEKJDhw7x8fGLFy92ci4AAAA0UxPF7sKFC9KN\ntm3byu9KAgICNBqNi5IBAACgWZoodlqtVrqRmZkpvwsAAABvo3Q26zPPPOPSHAAAAGih5p2m\npKio6Jtvvvn9999ra2ttVi1cuNB5qQAAANBsSovdhQsXEhISvv766ysNoNgBAAB4ltJip9fr\nv/7668DAwEceeaR///7MmQAAAPA2Sotdbm6uEGLlypXTp093ZR4AAAA4SOmVJ8xmsxBi/Pjx\nrgwDAAAAxyktdl27dhV/XBkWAAAAXkhpsZsxY4YQws7kCQAAAHiW0mI3d+7c22+//bnnnisu\nLnZpIAAAADhG6eSJkSNH1tTUnDp1asiQIZGRkZ06dbIZQOEDAADwLKXFbt++fdbbJ06cOHHi\nhGvyAAAAwEFKi93p06ddmgMAAAAtpLTYdenSxaU5AAAA0EJKJ08AAADAyyndYrd8+XL7Ax57\n7LEWhwEAAIDjlBa7xx9/3P4Aih0AAIBnKS12r7/+us0Ss9lsMBiOHTs2Y8aMvn37OjsYAAAA\nmkdpsZs3b17DhQsXLoyLizMYDKmpqU5NBQAAgGZr0eSJoKCgBQsWnD17Vq/XOysQAAAAHNPS\nWbH9+/cXQnz55ZfOCAMAAADHtbTYnT9/Xghx8eJFZ4QBAACA41pU7Orq6hYtWiSE6N27t5Py\nAAAAwEFKJ09MnjzZZklNTU1paemPP/4ohJg9e7aTcwEAAKCZlBa7jz/+uNHlXbp0efnllx98\n8EHnRQIAAIAjlBa7oqIimyX+/v4dO3a87rrr/Pz8nJ0KAAAAzaa02A0fPtylOQAAANBCLZ0V\nCwAAAC+hdIud5MyZM+vXry8uLj537lz79u2HDBkybdq0Dh06uCgcAAAAlGtGscvLy5s1a1Z5\nebl1yZo1a/R6fVZWVnx8vAuyAQAAoBmUFrvvvvtu6tSpNTU1N954o06ni4iIOHny5OrVq0tL\nSxMTE/fu3RsdHe3SoAAAALBPabFbtGhRTU3NuHHjPvvss9at//dRTz/99Pjx47dt25aWlrZu\n3TqXhQQAAEDTlE6eKCgoEEKkpaVZW50QonXr1m+++aYQYvv27S7IBgAAgGZQWux+//13IUSf\nPn1slvft21cIYTabnRsLAAAAzaW02LVr104IcfLkSZvl0hJprXtUVlauXbs2KSmpX79+YWFh\nWq02LCwsKioqKSlp7dq1VVVVbksCAADgVZQWO+kExRkZGTbLpSW33HKLc2NdSX5+fu/evWfN\nmpWTk3Pw4EGz2VxRUWE2m0tLS3NycmbNmtWrV6/8/Hz3hAEAAPAqSidPzJ49+7PPPlu5cuWZ\nM2eSk5OlWbErVqzYtGmTn5/f7NmzXZpSYjQaY2NjLRZLz549dTrdsGHDwsPDAwMDq6urTSbT\n7t27V61adezYsdjY2OLi4qioKDdEAgAA8B5Ki92dd9751ltvPf/885s2bdq0aZN1ub+//1tv\nvRUTE+OaeP9HamqqxWJJSEjIzs7WaDTyVX369Bk9enRKSkpSUlJeXt78+fM3bNjghkgAAADe\noxknKE5JSRk7duzKlSuLi4vPnz/frl27wYMHJycnu23bmDQzNyMjw6bVWWk0mvT09Ly8PGbp\nAgCAq1DzLikWHR399ttvuyhKk6SLXnTt2tXOGGmt/PIYAAAAVwmlkye8QUREhBBi586ddsbs\n2rVLCBEZGemmTAAAAF7DXrGrqKiYM2fOggUL7Ix544035syZU1FR4exgjZgyZYoQQqfTFRYW\nNjqgsLBQp9MJIRITE92QBwAAwKvY2xW7ZcuWRYsWJSUl2Rlz4MCBnJycQYMGuaFL6fV6g8FQ\nUlIyYsSI6OjooUOHhoeHazQai8ViMpn27NljNBqFEAMGDNDr9a4OAwAA4G3sFbvc3FwhxOTJ\nk+2MmTx5ck5OTm5urhuKnVar3bFjx9y5c1esWGE0GqUaJxcYGJicnLxgwYKQkBBXhwEAAPA2\n9ordwYMHxR8XDbsSaa000g1CQ0OXLl2alpaWn59vNBrLysqqqqqCgoLCwsKio6NjYmK0Wq17\nkgAAAHgbe8Xu119/FUJce+21dsZIa0+fPu3cWPZptdr4+Pj4+Hh3vigAAICXszd54vz580KI\ntm3b2hkjrZVGAgAAwIPsFbsOHTqIP7bbXYm0VhrpHpWVlWvXrk1KSurXr19YWJhWqw0LC4uK\nikpKSlq7dm1VVZXbkgAAAHgVe8XuxhtvFH9c7+FK8vPzhRD9+vVzaip7L9e7d+9Zs2bl5OQc\nPHjQbDZXVFSYzebS0tKcnJxZs2b16tVLigQAAHC1sVfsxo8fL4TIyMioqalpdIDFYklPTxdC\nxMXFuSKcDaPRGBsbazKZevbs+eabb3711VeHDh36+eefDx069NVXX7355ps9e/Y0mUyxsbEH\nDhxwQx4AAACvYq/YPfLIIx07dvz++++nTJly7tw5m7Vnz56dPHlyaWlpp06dHnnkEVeG/F+p\nqakWiyUhIeHAgQMvvfTS6NGj+/Tp061btz59+owePfqll146cOBAQkKCxWKZP3++G/IAAAB4\nFXuzYkNDQz/88MMJEyZ88skn3bp1mzRp0sCBA0NDQ8vLy/fv35+bm1teXh4QELB+/Xr7Eyyc\nRdopnJGRodFoGh2g0WjS09Pz8vK2b9/uhjwAAABexV6xE0LcddddX3755axZs44fP75mzZo1\na9bI13bv3j0rK2vkyJEuDChTXl4uhOjataudMdJaaSQAAMBVpYliJ4QYNWrU0aNHc3Nzv/ji\ni6NHj5aXl4eGhvbq1Wvs2LGTJk0KCAhwQ0pJRETEf//73507d44ZM+ZKY3bt2iWEiIyMdFsq\nAAAAL9F0sRNCBAQETJ06derUqa5OY9+UKVPS0tJ0Ol12dvatt97acEBhYaFOpxNCuOH6ZgAA\nAN5GUbHzEnq93mAwlJSUjBgxIjo6eujQoeHh4RqNxmKxmEymPXv2SFePHTBggF6v93RYAAAA\nd/OlYqfVanfs2DF37twVK1YYjUapxskFBgYmJycvWLAgJCTEIwkBAAA8yJeKnRAiNDR06dKl\naWlp+fn5RqOxrKysqqoqKCgoLCwsOjo6JiZGq9U26wlra2sNBkN1dbWdMUVFRUKIurq6FkUH\nAABwMR8rdhKtVhsfHx8fH9/yp8rPz584caKSkaWlpS1/OQAAANfxyWLnRDExMZ9++mmTW+wy\nMzPddtk0AAAAx/hesausrNy4caPBYDAajWazubKyMjg4OCwsLCoqKi4uLjExMSgoSPmz+fv7\nK9nyl5mZ2aqVvat0AAAAeJyPFbv8/PyZM2eaTCb5woqKCrPZXFpampOTo9frs7KyYmJiPJUQ\nAADAU5QWuwceeKCurm7t2rUuTWOf0WiMjY21WCw9e/bU6XTDhg0LDw8PDAysrq42mUy7d+9e\ntWrVsWPHYmNji4uLo6KiPBgVAADA/ZQWu3Xr1l26dMmzxS41NdVisSQkJGRnZ9tcLrZPnz6j\nR49OSUlJSkrKy8ubP3/+hg0bPJUTAADAI5QeNyZdg/XXX391ZZgmFBQUCCEyMjJsWp2VRqNJ\nT08XQmzfvt2dwQAAALyB0mI3adIkIcQXX3zhyjBNKC8vF39UzCuR1kojAQAAripKi91rr702\ndOjQZ5999j//+Y9LA9kREREhhNi5c6edMbt27RJCREZGuikTAACA11B6jN0dd9xhsVjMZvPd\nd999zTXXdOvWzeb0H8XFxS6I939MmTIlLS1Np9NlZ2ffeuutDQcUFhbqdDohRGJioqvDAAAA\neBulxW7fvn3W22fOnDlz5oxr8tij1+sNBkNJScmIESOio6OHDh0aHh6u0WgsFovJZNqzZ490\n9dgBAwbo9Xr3xwMAAPAspcXu9OnTLs2hhFar3bFjx9y5c1esWGE0GqUaJxcYGJicnLxgwYKQ\nkBCPJAQAAPAgpcWuS5cuLs2hUGho6NKlS9PS0vLz841GY1lZWVVVVVBQUFhYWHR0dExMjFar\n9XRGAAAAz/CxK09ItFptfHy8kkuBAQAAXD2aV+y2bNmSnZ39448/1tTUSLMlPvroo5qamqlT\npwYEBLgmIQAAABRpRrF75JFH3n//fZuF69evz8vLa9eunTu3n/3yyy8Wi6V79+5+fn4N1375\n5ZdCiDFjxrgtDwAAgDdQeh67rKys999/v3v37tKJ4qzuu+8+IcT69eudH60x33777YABA8LD\nw6+//vqIiIiVK1c2HDN27NixY8e6Jw8AAID3UFrs3nvvPSHEkiVLRowYIV8eFRUlhCgsLHR6\nsoZMJtOYMWNKSkqEEFqt1mQyJScn33fffZcvX3bDqwMAAHg5pcVOqlO33367zXLpahDuORnK\n4sWLz549271792+//fbChQuHDx8eM2bMhx9+OG3atNraWjcEAAAA8GZKi11NTY0QQjqZiPzI\ntosXLwoh3DNzYuvWrUKIzMzMgQMHCiF69+69devWRx99dOPGjffff39dXZ0bMgAAAHgtpZMn\nrr322uPHj586dapHjx7y5QcPHhRCdO/e3enJGjp+/LgQYtSoUdYl/v7+y5cvr6ure//99zUa\nzcqVKxudTgEAAHA1ULrFTppkmpubK2Rb7Orr65csWSKEuOuuu1wT7//QaDRCCJtr1Aohli9f\nnpiYuHr16ieffNINMQAAALyT0i12zz///Lp161599dV27dpJS7766qt33nln8+bNoaGhzzzz\njMsS/n/dunUzGo3Hjx/v37+/fHmrVq0++OCDc+fOLVu2jIuJAQCAq5bSLXZ9+vTJzs5u1arV\no48+Kh3NNmbMmE2bNoWEhHz00UfSFApXu+WWW4QQu3fvbriqTZs2mzZtGjZsWEZGhhuSAAAA\neCGlxU4IMXHixMOHD8+bN++OO+6IiooaOXLkiy++eOjQIffshxVCTJo0SQjx8ccfN7o2JCTE\nYDD069fPPWEAAAC8TfMuKRYeHv7666+7KEqTRo8enZWV1aZNmysNuOaaa7Zt27Z27Vp3pgIA\nAPASzSt2nhUQECBd6MKOP//5z3PmzHFPHgAAAK/SjGJXV1f38ccfFxcXnzx5UjqtndzGjRud\nGgwAAADNo7TYnTx5MjY2trS01KVpAAAA4DClxW727NmlpaURERF6vf7GG2+0c6AbAAAAPEJp\nsdu2bZsQ4sMPP7zttttcmQcAAAAOUnq6k/r6eiHE4MGDXRkGAAAAjlNa7G666SYhxC+//OLK\nMAAAAHCc0mL3wgsvCCGWLVvmyjAAAABwnNJj7CZOnLhs2bJnnnnm999/v+eeezp16mQzYOTI\nkc7OBgAAgGZoxnnsLl26FBQU9O9///vf//53w7XSQXgAAADwFKXF7t133/3b3/4mhOjbt+8N\nN9zA6U4AAAC8jdJi9/e//10IMW/ePA9eKxYAAAB2KJ08cfz4cSHE008/7cowAAAAcJzSYte5\nc2chhL+/vyvDAAAAwHFKi93kyZOFENu3b3dlGAAAADhOabFLTU0dPnz47Nmz9+/f79JAAAAA\ncIzSyROjRo2qqan5+eefb7755sjIyIbnsSsuLnZ2NgAAADSD0mK3b98+6+0TJ06cOHHCNXkA\nAADgIKXF7vTp0y7NAQAAgBZSWuy6dOni0hwAAABoIaWTJwAAAODlmnGtWCHEmTNn1q9fX1xc\nfO7cufbt2w8ZMmTatGkdOnRwUTgAAAAo14xil5eXN2vWrPLycuuSNWvW6PX6rKys+Ph4F2QD\nAABAMygtdt99993UqVNrampuvPFGnU4XERFx8uTJ1atXl5aWJiYm7t27Nzo62qVBAQAAYJ/S\nYrdo0aKamppx48Z99tlnrVv/76Oefvrp8ePHb9u2LS0tbd26dS4LCQAAgKYpnTxRUFAghEhL\nS7O2OiFE69at33zzTcGlxgAAALyA0mL3+++/CyH69Oljs7xv375CCLPZ7NxYAAAAaC6lxa5d\nu3ZCiJMnT9osl5ZIawEAAOBBSovd8OHDhRAZGRk2y6Ult9xyi3NjAQAAoLmUTp6YPXv2Z599\ntnLlyjNnziQnJ0uzYlesWLFp0yY/P7/Zs2e7NCUAAACapLTY3XnnnW+99dbzzz+/adOmTZs2\nWZf7+/u/9dZbMTExrokHAAAApZpxguKUlJSxY8euXLmyuLj4/Pnz7dq1Gzx4cHJyclRUlOvy\nAQAAQKHmXVIsOjr67bffdlEUAAAAtITSyRNjxowZM2ZMc1cBAADAbZRusfvqq68cWAUAAAC3\nad6u2IZqamqEEPLLUcCO2tpag8FQXV3t6SBuUlRU5OkIAABcRVpayAoLC4UQXbp0cUYY9fv8\n888nTpzo6RQAAECdmih20hXDrnS3urr61KlTQojY2FinJ1OlqqoqIYQQKUIM93AUN1krxBZP\nZwAA4GrRRLE7fPiwnbtCiA4dOsTHxy9evNjJuVRuuBCJns7gHuyKBQDAfZoodhcuXJButG3b\nVn5XEhAQoNFoXJQMAAAAzdJEsdNqtdKNzMxM+V0AAAB4G6WTJ5555hmX5gDgg+rEVTb3OTAw\nMC4uzt/f39NBAKBxSovd5cuXt27d2qZNm3HjxklLli1b9s4779TV1b344ouzZs1yWUIAXmu/\nECIzM1Paon+V2Lx584QJEzydAgAap7TYffTRRzNmzLjvvvukYvef//zniSeekFY9+OCDPXv2\nHDFihKsyAvBSNUKIq2mW9zdCLPljbjsAeCOlxW7VqlVCiOTkZOnue++9N2TIkM2bN7/++uvv\nvPPOO++8Q7EDrlZXzyxvAPB2Sq8Ve+jQISHEDTfcIISor68vKCh4+OGHO3fuLB17t2fPHtdF\nBAAAgBJKi93vv/8uhGjXrp0Q4vjx42fPnh02bJgQIiIiQgjxyy+/uCwhAAAAFFFa7Dp37iyE\nOHHihBCisLAwMDBQugrFuXPnxB9nuQMAAIAHKS12gwYNEkKkp6efPHnynXfeuf3229u0aSOE\n+Pnnn4UQPXr0cFlCAAAAKKK02D3zzDN+fn7vv/9+ZGRkYWHh7NmzpeWfffaZEGL8+PGuCggA\nAABllM6Kvf322z/99NPly5cLIe6///67775bWv7NN9/06NFj+vTprgoIAAAAZZQWOyHEhAkT\nGp6Wc9u2bU7NAwAAAAcp3RULAAAAL6d0i520E9aOxx57rMVhAAAA4Dilxe7xxx+3P4BiBwAA\n4FlKi93rr79us8RsNhsMhmPHjs2YMUM6px0AAAA8SGmxmzdvXsOFCxcujIuLMxgMqampTk0F\nAACAZmvR5ImgoKAFCxacPXtWr9c7KxAAAAAc09JZsf379xdCfPnll84IAwAAAMe1tNidP39e\nCHHx4kVnhAEAAIDjWlTs6urqFi1aJITo3bu3k/IAAADAQUonT0yePNlmSU1NTWlp6Y8//iiE\nsF46FgAAAJ6itNh9/PHHjS7v0qXLyy+//OCDDzovEgAAAByhtNgVFRXZLPH39+/YseN1113n\n5+fn7FQAAABoNqXFbvjw4S7NAQAAgBZq6axYAAAAeAl7xa6iomLOnDkLFiywM+aNN96YM2dO\nRUWFs4MBAACgeewVuy1btixatOjAgQN2xhw4cGDRokUGg8HZwQAAANA89opdbm6uaOxEJ3LS\nWmkkAAAAPMhesTt48KAQom/fvnbGSGulkQAAAPAge8Xu119/FUJce+21dsZIa0+fPu3cWAAA\nAGgue8VOug5s27Zt7YyR1kojAQAA4EH2il2HDh3EH9vtrkRaK40EAACAB9krdjfeeKMQoqCg\nwM6Y/Px8IUS/fv2cmgoAAADNZq/YjR8/XgiRkZFRU1PT6ACLxZKeni6EiIuLc0U4AAAAKGev\n2D3yyCMdO3b8/vvvp0yZcu7cOZu1Z8+enTx5cmlpaadOnR555BFXhgQAAEDT7F0rNjQ09MMP\nP5wwYcInn3zSrVu3SZMmDRw4MDQ0tLy8fP/+/bm5ueXl5QEBAevXr7c/wQIAAABuYK/YCSHu\nuuuuL7/8ctasWcePH1+zZs2aNWvka7t3756VlTVy5EgXBgQAAIAyTRQ7IcSoUaOOHj2am5v7\nxRdfHD16tLy8PDQ0tFevXmPHjp00aVJAQIAbUgIAAKBJTRc7IURAQMDUqVOnTp3q6jQAAABw\nmL3JEwAAAPAhFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSC\nYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcA\nAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAAUAmKHQAAgEq09nSAZqusrNy4caPB\nYDAajWazubKyMjg4OCwsLCoqKi4uLjExMSgoyNMZAQAAPMDHil1+fv7MmTNNJpN8YUVFhdls\nLi0tzcnJ0ev1WVlZMTExnkoIAADgKb5U7IxGY2xsrMVi6dmzp06nGzZsWHh4eGBgYHV1tclk\n2r1796pVq44dOxYbG1tcXBwVFeXpvAAAAG7lS8UuNTXVYrEkJCRkZ2drNBr5qj59+owePTol\nJSUpKSkvL2/+/PkbNmzwVE4AAACP8KXJEwUFBUKIjIwMm1ZnpdFo0tPThRDbt293ZzAAAABv\n4EvFrry8XAjRtWtXO2OktdJIAACAq4ovFbuIiAghxM6dO+2M2bVrlxAiMjLSTZkAAAC8hi8V\nuylTpgghdDpdYWFhowMKCwt1Op0QIjEx0a3JAAAAvIAvTZ7Q6/UGg6GkpGTEiBHR0dFDhw4N\nDw/XaDQWi8VkMu3Zs8doNAohBgwYoNfrPR0WAADA3Xyp2Gm12h07dsydO3fFihVGo1GqcXKB\ngYHJyckLFiwICQnxSEIAAAAP8qViJ4QIDQ1dunRpWlpafn6+0WgsKyurqqoKCgoKCwuLjo6O\niYnRarWezggAAOAZPlbsJFqtNj4+Pj4+3tNBAAAAvIgvTZ4AAACAHb63xa6ysnLjxo0Gg8Fo\nNJrN5srKyuDg4LCwsKioqLi4uMTExKCgIE9nBAAA8AAfK3b5+fkzkhluyAAAIABJREFUZ840\nmUzyhRUVFWazubS0NCcnR6/XZ2VlxcTEeCohAACAp/hSsTMajbGxsRaLpWfPnjqdbtiwYeHh\n4YGBgdXV1SaTaffu3atWrTp27FhsbGxxcXFUVJSn8wIAALiVLxW71NRUi8WSkJCQnZ1tc7nY\nPn36jB49OiUlJSkpKS8vb/78+Rs2bPBUTgAAAI/wpckTBQUFQoiMjAybVmel0WjS09OFENu3\nb3dnMAAAAG/gS8WuvLxcCNG1a1c7Y6S10kgAAICrii8Vu4iICCHEzp077YzZtWuXECIyMtJN\nmQAAALyGLxW7KVOmCCF0Ol1hYWGjAwoLC3U6nRAiMTHRrckAAAC8gC9NntDr9QaDoaSkZMSI\nEdHR0UOHDg0PD9doNBaLxWQy7dmzR7p67IABA/R6vafDAgAAuJsvFTutVrtjx465c+euWLHC\naDRKNU4uMDAwOTl5wYIFISEhHkkIAADgQb5U7IQQoaGhS5cuTUtLy8/PNxqNZWVlVVVVQUFB\nYWFh0dHRMTExWq22WU9YW1trMBiqq6vtjCkqKhJC1NXVtSg6AACAi/lYsZNotdr4+Pj4+PiW\nP1V+fv7EiROVjCwtLW35ywEAALiOTxY7J4qJifn000+b3GKXmZnZr18/t6UCAABwgO8Vu8rK\nyo0bNxoMBqPRaDabKysrg4ODw8LCoqKi4uLiEhMTg4KClD+bv7+/ki1/mZmZrVr50gxiAABw\nFfKxYpefnz9z5kyTySRfWFFRYTabS0tLc3Jy9Hp9VlZWTEyMpxICAAB4ii8VO6PRGBsba7FY\nevbsqdPphg0bFh4eHhgYWF1dbTKZdu/evWrVqmPHjsXGxhYXF0dFRXk6LwAAgFv5UrFLTU21\nWCwJCQnZ2dk2l4vt06fP6NGjU1JSkpKS8vLy5s+fv2HDBk/lBAAA8AhfOm6soKBACJGRkWHT\n6qw0Gk16eroQYvv27e4MBgAA4A18qdiVl5cLIbp27WpnjLRWGgkAAHBV8aViFxERIYTYuXOn\nnTG7du0SQkRGRropEwAAgNfwpWI3ZcoUIYROpyssLGx0QGFhoU6nE0IkJia6NRkAAIAX8KXJ\nE3q93mAwlJSUjBgxIjo6eujQoeHh4RqNxmKxmEymPXv2SFePHTBggF6v93RYAAAAd/OlYqfV\nanfs2DF37twVK1YYjUapxskFBgYmJycvWLAgJCTEIwkBqFqd+OPi0VeJwMDAuLg4f39/TwcB\noJQvFTshRGho6NKlS9PS0vLz841GY1lZWVVVVVBQUFhYWHR0dExMjFar9XRGAGq1XwiRmZmZ\nmZnp6STus3nz5gkTJng6BQClfKzYSbRabXx8vJJLgQGA89QIIYRIEWK4h4O4yTdCLKmqqvJ0\nDADN4JPFDgA8Z7gQTM8C4KV8aVasQv/85z//+c9/ejoFAACAu6lwi91f//pXIcRTTz3l6SAA\nAABupcItdgAAAFcnX9pid/HiRU9HAAAA8F6+VOzatm3r6QgAAADei12xAAAAKuFLxa579+5C\niCNHjlyyy9MxAQAAPMOXit3YsWOFEF9//XXr/9fenYfndOf/H3/fN7JIYo8ogtY2sVWIFhc6\nITWkaq/ddIKim9ZWS2kRxlLzzQzVqjWqTDotQlqprbFdtrHEBKHxFUVKhcRyRzbu+/fHuSbf\n/LK5k3s5ycfz8YeLc859zutzDrlfzn3OuYukd0wAAAB9lKVi1717dxHZvXu33kEAAABKo7J0\nfisoKKhPnz6VKlUqerHVq1c7Jw8AAECpUpaKXZUqVSIjI5+62JgxY5wQBgAAoLQpSx/FAgAA\noAgUOwAAAEVQ7AAAABRBsQMAAFAExQ4AAEARFDsAAABFUOwAAAAUQbEDAABQBMUOAABAERQ7\nAAAARVDsAAAAFEGxAwAAUATFDgAAQBEUOwAAAEVQ7AAAABRBsQMAAFAExQ4AAEARFDsAAABF\nUOwAAAAUQbEDAABQBMUOAABAERQ7AAAARVDsAAAAFEGxAwAAUATFDgAAQBEUOwAAAEVQ7AAA\nABRBsQMAAFAExQ4AAEARFDsAAABFUOwAAAAUQbEDAABQBMUOAABAERQ7AAAARVDsAAAAFEGx\nAwAAUATFDgAAQBEUOwAAAEVQ7AAAABRBsQMAAFAExQ4AAEARFDsAAABFUOwAAAAUQbEDAABQ\nBMUOAABAERQ7AAAARVDsAAAAFEGxAwAAUATFDgAAQBEUOwAAAEVQ7AAAABRBsQMAAFAExQ4A\nAEARFDsAAABFU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diff --git a/machine-learning/notebooks-oml/r/OML4R Data Transformation One-Hot Encoding.dsnb b/machine-learning/notebooks-oml/r/OML4R Data Transformation One-Hot Encoding.dsnb
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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4R Data Transformation One-Hot Encoding","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736276279471,"interpreter":"md.low","endTime":1736276280001,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation: One-Hot Encoding","In this notebook, we demonstrate how to perform one-hot encoding using OML4R using the customer insurance lifetime value dataset, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","","**Note**: If the user plans to use the in-database algorithms, one hot encoding is automatically applied for those algorithms requiring it. The in-databae algorithms automatically explode the categorical columns and fit the model on the prepared data internally. For more details, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a>","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1738092570660,"interpreter":"md.low","endTime":1738092570875,"results":[{"message":"<h1 id=\"oml4r-data-transformation-one-hot-encoding\">OML4R Data Transformation: One-Hot Encoding<\/h1>\n<p>In this notebook, we demonstrate how to perform one-hot encoding using OML4R using the customer insurance lifetime value dataset, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p><strong>Note<\/strong>: If the user plans to use the in-database algorithms, one hot encoding is automatically applied for those algorithms requiring it. The in-databae algorithms automatically explode the categorical columns and fit the model on the prepared data internally. For more details, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1736276281390,"interpreter":"md.low","endTime":1736276281850,"results":[{"message":"<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736276282312,"interpreter":"r.low","endTime":1736276287751,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to CUSTOMER_INSURANCE_LTV and subset of columns","message":["%r","","ore.sync(query = c(\"CUST_DF\" = \"SELECT * FROM CUSTOMER_INSURANCE_LTV\"))","ore.attach()","","CUST_CAT_DF <- CUST_DF[, c(\"CUSTOMER_ID\", \"MARITAL_STATUS\", \"STATE\", \"GENDER\", \"REGION\")]","","z.show(head(CUST_CAT_DF, 5))"],"enabled":true,"result":{"startTime":1736276288201,"interpreter":"r.low","endTime":1736276288814,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS\tSTATE\tGENDER\tREGION\nCU15154                   \tSINGLE\tCA                        \tM                         \tWest\nCU15155                   \tSINGLE\tNY                        \tM                         \tNorthEast\nCU15157                   \tMARRIED\tMI                        \tM                         \tMidwest\nCU15286                   \tMARRIED\tUT                        \tF                         \tSouthwest\nCU15287                   \tMARRIED\tUT                        \tF                         \tSouthwest\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create function for one hot encoding","message":["%r ","","encode <- function(DF, ids, col) {","  uniqueValues <- ore.pull(unique(DF[col])) # Get the unique values of column col","  RESULT <- cbind(ids, DF[col]) # RESULT begins as ids and col to be encoded","","  # Rename columns","  col_index <- which(colnames(RESULT) == col)","  colnames(RESULT)[col_index] <- \"target_col\""," ","  # Add One Hot Encoded columns to RESULT","  for (x in 1:nrow(uniqueValues)){","    RESULT <- cbind(RESULT, new_col = ifelse(RESULT$target_col == uniqueValues[x,], 1, 0)) # Add encoded column","    # Rename encoded column","    index <- which(colnames(RESULT) == \"new_col\")","    colnames(RESULT)[index] <- paste(col, gsub(\" \", \"\", uniqueValues[x,1]), sep='.')","  }"," ","  # Now that One Hot Encoded columns have been added, remove original column","  RESULT <- RESULT[-c(col_index)]"," ","  return(RESULT)","}"],"enabled":true,"result":{"startTime":1736276289266,"interpreter":"r.low","endTime":1736276289766,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one hot encoding to MARITAL_STATUS column and check the transformed data","message":["%r","","rownames(CUST_CAT_DF) <- CUST_CAT_DF$CUSTOMER_ID # Ordering rows of CUST_CAT_DF before applying function","","ONEHOT_DF <- encode(CUST_CAT_DF, CUST_CAT_DF['CUSTOMER_ID'], \"MARITAL_STATUS\")","","z.show(head(ONEHOT_DF))"],"enabled":true,"result":{"startTime":1736276290224,"interpreter":"r.low","endTime":1736276291342,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS.OTHER\tMARITAL_STATUS.DIVORCED\tMARITAL_STATUS.MARRIED\tMARITAL_STATUS.SINGLE\tMARITAL_STATUS.WIDOWED\nCU1                       \t1\t0\t0\t0\t0\nCU10                      \t0\t1\t0\t0\t0\nCU1000                    \t0\t0\t1\t0\t0\nCU10000                   \t0\t0\t1\t0\t0\nCU10001                   \t0\t1\t0\t0\t0\nCU10003                   \t0\t1\t0\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one-hot encoding to all selected categorical columns","message":["%r","","RES_DF <- CUST_CAT_DF[\"CUSTOMER_ID\"]","","for (col in colnames(CUST_CAT_DF)[-c(1)]){","  ","  ONEHOT_DF <- encode(CUST_CAT_DF, CUST_CAT_DF[\"CUSTOMER_ID\"], col)","  ","  RES_DF <- full_join(RES_DF, ONEHOT_DF)","}","","rownames(RES_DF) <- 1:nrow(RES_DF) # Renaming rows of RES_DF","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1736276291791,"interpreter":"r.low","endTime":1736276295365,"results":[{"message":"CUSTOMER_ID\tMARITAL_STATUS.OTHER\tMARITAL_STATUS.DIVORCED\tMARITAL_STATUS.MARRIED\tMARITAL_STATUS.SINGLE\tMARITAL_STATUS.WIDOWED\tSTATE.WA\tSTATE.MN\tSTATE.NY\tSTATE.CA\tSTATE.FL\tSTATE.MI\tSTATE.NV\tSTATE.NM\tSTATE.DC\tSTATE.WI\tSTATE.MS\tSTATE.IL\tSTATE.AL\tSTATE.CO\tSTATE.OH\tSTATE.MO\tSTATE.OR\tSTATE.TX\tSTATE.LA\tSTATE.NC\tSTATE.AZ\tSTATE.UT\tSTATE.OK\tSTATE.AK\tGENDER.F\tGENDER.M\tREGION.West\tREGION.NorthEast\tREGION.South\tREGION.Midwest\tREGION.Southwest\nCU1                       \t1\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\t0\nCU10                      \t0\t1\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\t0\nCU1000                    \t0\t0\t1\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\nCU10000                   \t0\t0\t1\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t1\t0\t0\t0\t0\nCU10001                   \t0\t1\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t1\t0\t0\nCU10003                   \t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t1\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736276295817,"interpreter":"md.low","endTime":1736276296263,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736276296720,"interpreter":"md.low","endTime":1736276297157,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks-oml/r/OML4R Dataset Creation.dsnb b/machine-learning/notebooks-oml/r/OML4R Dataset Creation.dsnb
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+++ b/machine-learning/notebooks-oml/r/OML4R Dataset Creation.dsnb	
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Dataset Creation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736279346859,"interpreter":"md.low","endTime":1736279358067,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Dataset Creation","","This script loads the sample datasets `MTCARS` and `IRIS` and imports them into your Oracle Autonomous Database instnace using the ore.create() function.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736279358502,"interpreter":"md.low","endTime":1736279358935,"results":[{"message":"<h1 id=\"oml4r-dataset-creation\">OML4R Dataset Creation<\/h1>\n<p>This script loads the sample datasets <code>MTCARS<\/code> and <code>IRIS<\/code> and imports them into your Oracle Autonomous Database instnace using the ore.create() function.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)"],"enabled":true,"result":{"startTime":1736279359362,"interpreter":"r.low","endTime":1736279359850,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use mtcars dataset to create table","message":["%r","","options(warn=-1)","ore.sync(use.keys=FALSE)","ore.attach()","","if (ore.exists(\"MTCARS\")) ore.drop(table=\"MTCARS\")","","ore.create(mtcars, table=\"MTCARS\")","","ore.exec(\"ANALYZE TABLE MTCARS COMPUTE STATISTICS\")","","z.show(cat(\"Shape:\", dim(MTCARS)))"],"enabled":true,"result":{"startTime":1736279360278,"interpreter":"r.low","endTime":1736279364342,"results":[{"message":"Shape: 32 11","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use iris dataset to create table.","message":["%r","","if (ore.exists(\"IRIS_R\")) ore.drop(table=\"IRIS_R\")","","ore.create(iris, table = \"IRIS_R\")","","ore.exec(\"ANALYZE TABLE IRIS_R COMPUTE STATISTICS\")","","z.show(cat(\"Shape:\", dim(IRIS_R)))"],"enabled":true,"result":{"startTime":1736279364771,"interpreter":"r.low","endTime":1736279365394,"results":[{"message":"Shape: 150 5","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1736279365820,"interpreter":"md.low","endTime":1736279366227,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736279366655,"interpreter":"md.low","endTime":1736279367102,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks-oml/r/OML4R Feature Engineering Aggregation.dsnb b/machine-learning/notebooks-oml/r/OML4R Feature Engineering Aggregation.dsnb
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+++ b/machine-learning/notebooks-oml/r/OML4R Feature Engineering Aggregation.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Engineering Aggregation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736280010207,"interpreter":"md.low","endTime":1736280010648,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Feature Engineering: Aggregation","In this notebook, we demonstrate how to perform aggregation for min, max, mean, and count using OML4R.","","Using the SH schema SALES table, which contains transaction records for each customer and products purchased, we create features by aggregating the amount sold for each customer and product pair.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736280011076,"interpreter":"md.low","endTime":1736280011496,"results":[{"message":"<h1 id=\"oml4r-feature-engineering-aggregation\">OML4R Feature Engineering: Aggregation<\/h1>\n<p>In this notebook, we demonstrate how to perform aggregation for min, max, mean, and count using OML4R.<\/p>\n<p>Using the SH schema SALES table, which contains transaction records for each customer and products purchased, we create features by aggregating the amount sold for each customer and product pair.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4R Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1736280269328,"interpreter":"md.low","endTime":1736280269739,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4R Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736280273974,"interpreter":"r.low","endTime":1736280274438,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to the SALES data and view a few records.","message":["%r","","ore.sync(query = c(\"SALES_DF\" = \"select cust_id, prod_id, amount_sold from sh.sales\"))","ore.attach()","","z.show(head(SALES_DF, 10))"],"enabled":true,"result":{"startTime":1736280018254,"interpreter":"r.low","endTime":1736280018832,"results":[{"message":"CUST_ID\tPROD_ID\tAMOUNT_SOLD\n524\t13\t1205.99\n2128\t13\t1250.25\n3212\t13\t1250.25\n3375\t13\t1250.25\n5204\t13\t1250.25\n7082\t13\t1250.25\n9316\t13\t1250.25\n12782\t13\t1250.25\n13869\t13\t1250.25\n1949\t13\t1210.21\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View multiple records for each customer and product pair with count","message":["%r","","OUTPUT <- rename(count(SALES_DF, CUST_ID, PROD_ID), 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COUNT_DF)"],"enabled":true,"result":{"startTime":1736280026332,"interpreter":"r.low","endTime":1736280031579,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View aggregated data","message":["%r","","z.show(head(round(AGG_DF, 2), 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Extraction ESA Wiki Model","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734646598511,"interpreter":"md.high","endTime":1734646599012,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for R function *ore.odmESA* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we'll use the Wikipedia Model as an example:",""," - Create an OML4R proxy ESA model object for the pre-loaded Wikipedia model "," - Examples of using the Wiki ESA model"," - Basic ESA Wiki model features for words"," - Basic ESA Wiki model features for phrases"," - ESA Wiki Model document similarity on phrases"," - ESA Wiki Model document similarity on long phrases","","*Note: The pre-built Wikipedia model must be installed in your Autonomous Database instance to run this notebook. See instructions below.*","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734646599445,"interpreter":"md.high","endTime":1734646599884,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <code>semantic relatedness<\/code> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>The Oracle Machine Learning for R function <em>ore.odmESA<\/em> extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>In this notebook, we'll use the Wikipedia Model as an example:<\/p>\n<ul>\n<li>Create an OML4R proxy ESA model object for the pre-loaded Wikipedia model<\/li>\n<li>Examples of using the Wiki ESA model<\/li>\n<li>Basic ESA Wiki model features for words<\/li>\n<li>Basic ESA Wiki model features for phrases<\/li>\n<li>ESA Wiki Model document similarity on phrases<\/li>\n<li>ESA Wiki Model document similarity on long phrases<\/li>\n<\/ul>\n<p><em>Note: The pre-built Wikipedia model must be installed in your Autonomous Database instance to run this notebook. See instructions below.<\/em><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a>"],"enabled":true,"result":{"startTime":1734646600321,"interpreter":"md.high","endTime":1734646600742,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using the Wikipedia model as an example","","The pre-built Wikipedia model must be loaded to the Oracle Autonomous Database. It is a relatively large model object at 800 MB.","","Follow the setup in template notebook `OML Import ESA Wiki Model`.","","<p>Related Oracle blogs:<\/p>","<ul>","    <li><a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" target=\"_blank\">New Wikipedia-Based Cognitive Model Available for Text Processing<\/a><\/li>","    <li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" target=\"_blank\">Text Analytics using a pre-built Wikipedia-based Topic Model<\/a><\/li>","    <li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" target=\"_blank\">Explicit Semantic Analysis (ESA) for Text Analytics<\/a><\/li>","<\/ul>","","We will also need to create a model proxy object for the Wiki model into OML4R"," - Use the included function ore.createESA.wiki_model() to create our proxy object"," - This function retrieves information about the Wikipedia model and uses that information to create our ESA model using the ore.odmESA function"],"enabled":true,"result":{"startTime":1734646838525,"interpreter":"md.high","endTime":1734646838981,"results":[{"message":"<h2 id=\"using-the-wikipedia-model-as-an-example\">Using the Wikipedia model as an example<\/h2>\n<p>The pre-built Wikipedia model must be loaded to the Oracle Autonomous Database. It is a relatively large model object at 800 MB.<\/p>\n<p>Follow the setup in template notebook <code>OML Import ESA Wiki Model<\/code>.<\/p>\n<p>Related Oracle blogs:<\/p>\n<ul>\n    <li><a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" target=\"_blank\">New Wikipedia-Based Cognitive Model Available for Text Processing<\/a><\/li>\n    <li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" target=\"_blank\">Text Analytics using a pre-built Wikipedia-based Topic Model<\/a><\/li>\n    <li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" target=\"_blank\">Explicit Semantic Analysis (ESA) for Text Analytics<\/a><\/li>\n<\/ul>\n<p>We will also need to create a model proxy object for the Wiki model into OML4R<\/p>\n<ul>\n<li>Use the included function ore.createESA.wiki_model() to create our proxy object<\/li>\n<li>This function retrieves information about the Wikipedia model and uses that information to create our ESA model using the ore.odmESA function<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete existing policies and create new policies for the Wiki Lexer with settings","message":["%r ","","# Should be executed in a separate session, otherwise it will not work","","ore.exec(\"BEGIN ctx_ddl.drop_policy('wiki_txtpol'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","ore.exec(\"BEGIN ctx_ddl.drop_preference( 'wiki_lexer'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","ore.exec(\"BEGIN ctx_ddl.create_preference('wiki_lexer', 'BASIC_LEXER'); END;\")","ore.exec(\"BEGIN ctx_ddl.set_attribute('wiki_lexer', 'INDEX_STEMS', 'ENGLISH'); END;\")","ore.exec(\"BEGIN ctx_ddl.create_policy(policy_name => 'wiki_txtpol', lexer => 'wiki_lexer'); END;\")"],"enabled":true,"result":{"startTime":1734646602024,"interpreter":"r.high","endTime":1734646607280,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1734646607707,"interpreter":"r.high","endTime":1734646608181,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" Check to see if the WIKI_MODEL is available","message":["%sql","","select MODEL_NAME, MINING_FUNCTION, ALGORITHM, ALGORITHM_TYPE, CREATION_DATE from USER_MINING_MODELS where MODEL_NAME='WIKI_MODEL'"],"enabled":true,"result":{"startTime":1734646608600,"interpreter":"sql.high","endTime":1734646610444,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\nWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2024-12-19 21:17:32\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create function for getting a proxy model object for the pre-loaded Wikipedia Model","message":["%r"," ","ore.createESA.wiki_model <- function () {","  model.name <- \"WIKI_MODEL\"","  attr(model.name, \"owner\") <- OREbase:::.ore.schema()","  formula <- \"~ TEXT\"","  formula <- as.formula(formula, env = parent.frame())","  env.lst <- list()","  model.settings   <- OREdm:::.ore.getModelSettings(model.name,\"esas_\")","  model.attributes <- OREdm:::.ore.getModelAttributes(model.name)","  query <- paste(","    \"SELECT \",","    \"FEATURE_ID, \",","    \"nvl2(attribute_subname, \", \"attribute_name\",\"||\",\"'.'\", \"||\",","    \"attribute_subname,  attribute_name) attribute_name,\",","    \"COEFFICIENT \",","    \"FROM \", \"DM$VAWIKI_MODEL\",","    \" where ROWNUM <= 10 order by 1,2,3\",","    sep=\"\");","  features.all <- OREdm:::.ore.frame4query(query, extRef = env.lst)","  esa.mod <- OREdm:::.create.ore.odmESA(model.name,","                                        model.settings,","                                        model.attributes,","                                        formula,","                                        env.lst,","                                        match.call(),","                                        features=features.all)","}"],"enabled":true,"result":{"startTime":1734646610866,"interpreter":"r.high","endTime":1734646611374,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Call the function to obtain proxy object","message":["%r ","","esa_wiki_mod <- ore.createESA.wiki_model()"],"enabled":true,"result":{"startTime":1734646611790,"interpreter":"r.high","endTime":1734646612360,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the first 100 rows of the 21.7 mi rows of features","message":["%r"," ","z.show(head(features(esa_wiki_mod), 100))"],"enabled":true,"result":{"startTime":1734646612775,"interpreter":"r.high","endTime":1734646685695,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n'Adud al-Dawla\tTEXT.ABBAD\tNA\t0.0191274174186567\n'Adud al-Dawla\tTEXT.ABBASID\tNA\t0.0249151625558249\n'Adud al-Dawla\tTEXT.ABU\tNA\t0.059714023220152\n'Adud al-Dawla\tTEXT.ADUD\tNA\t0.565944673491387\n'Adud al-Dawla\tTEXT.ALABAMA\tNA\t0.22831900492864\n'Adud al-Dawla\tTEXT.ARDASHIR\tNA\t0.0111940903957132\n'Adud al-Dawla\tTEXT.AYYAD\tNA\t0.0249760282115664\n'Adud al-Dawla\tTEXT.BADR\tNA\t0.00986964445080499\n'Adud al-Dawla\tTEXT.BAGHDAD\tNA\t0.0459066966195087\n'Adud al-Dawla\tTEXT.BAHA\tNA\t0.0104589006276045\n'Adud al-Dawla\tTEXT.BAHRAM\tNA\t0.0161236096703584\n'Adud al-Dawla\tTEXT.BAKR\tNA\t0.0134365270355992\n'Adud al-Dawla\tTEXT.BALOCH\tNA\t0.0104772299872558\n'Adud al-Dawla\tTEXT.BAM\tNA\t0.00887829440770621\n'Adud al-Dawla\tTEXT.BANI\tNA\t0.00975646268058504\n'Adud al-Dawla\tTEXT.BAQILLANI\tNA\t0.0144646112351293\n'Adud al-Dawla\tTEXT.BARDAS\tNA\t0.0128361499358996\n'Adud al-Dawla\tTEXT.BEDOUIN\tNA\t0.00872220567501009\n'Adud al-Dawla\tTEXT.BUYID\tNA\t0.0846594574850417\n'Adud al-Dawla\tTEXT.BUYIDS\tNA\t0.0194587149365686\n'Adud al-Dawla\tTEXT.BYZANTINES\tNA\t0.0202176515677629\n'Adud al-Dawla\tTEXT.BYZANTIUM\tNA\t0.00820039029135046\n'Adud al-Dawla\tTEXT.CALIPH\tNA\t0.0232450776566311\n'Adud al-Dawla\tTEXT.CARAVANSERAI\tNA\t0.0114335306364663\n'Adud al-Dawla\tTEXT.CHILDLESS\tNA\t0.00801411366862151\n'Adud al-Dawla\tTEXT.CHOSROES\tNA\t0.0134748733114124\n'Adud al-Dawla\tTEXT.CONCUBINE\tNA\t0.00802143665804979\n'Adud al-Dawla\tTEXT.CONSUMMATE\tNA\t0.00815164021102195\n'Adud al-Dawla\tTEXT.DAWLA\tNA\t0.723942878242064\n'Adud al-Dawla\tTEXT.DAYLAMITE\tNA\t0.0501999484162784\n'Adud al-Dawla\tTEXT.DAYLAMITES\tNA\t0.0143428417394205\n'Adud al-Dawla\tTEXT.DESPOT\tNA\t0.00935953765459191\n'Adud al-Dawla\tTEXT.DINAR\tNA\t0.00961407929094202\n'Adud al-Dawla\tTEXT.DIYAR\tNA\t0.0191274174186567\n'Adud al-Dawla\tTEXT.EMBELLISH\tNA\t0.00786795749121632\n'Adud al-Dawla\tTEXT.EMIR\tNA\t0.0314917677186082\n'Adud al-Dawla\tTEXT.ENRICHMENT\tNA\t0.00811412374362538\n'Adud al-Dawla\tTEXT.EUPHRATES\tNA\t0.00874861239537755\n'Adud al-Dawla\tTEXT.FADL\tNA\t0.0226663780212495\n'Adud al-Dawla\tTEXT.FAKHR\tNA\t0.0275993124557871\n'Adud al-Dawla\tTEXT.FARAJ\tNA\t0.0116971284535617\n'Adud al-Dawla\tTEXT.GOR\tNA\t0.0112543478978115\n'Adud al-Dawla\tTEXT.GORGAN\tNA\t0.0122048966987822\n'Adud al-Dawla\tTEXT.GRAMMARIAN\tNA\t0.00925693943414468\n'Adud al-Dawla\tTEXT.HAMADAN\tNA\t0.0169517398437838\n'Adud al-Dawla\tTEXT.HAMDANID\tNA\t0.0188381765657075\n'Adud al-Dawla\tTEXT.HAMDANIDS\tNA\t0.0259449538696399\n'Adud al-Dawla\tTEXT.HARUN\tNA\t0.0101103503589411\n'Adud al-Dawla\tTEXT.HASAN\tNA\t0.0151329041024834\n'Adud al-Dawla\tTEXT.HORMUZ\tNA\t0.0109490678074295\n'Adud al-Dawla\tTEXT.HUSAIN\tNA\t0.0149480328455242\n'Adud al-Dawla\tTEXT.HUSAYN\tNA\t0.00956774133608969\n'Adud al-Dawla\tTEXT.IBN\tNA\t0.0842530506638853\n'Adud al-Dawla\tTEXT.ILYAS\tNA\t0.0170241892897465\n'Adud al-Dawla\tTEXT.IMAD\tNA\t0.0332821869454185\n'Adud al-Dawla\tTEXT.IMAM\tNA\t0.0116784256335568\n'Adud al-Dawla\tTEXT.IMPALE\tNA\t0.00899550414807461\n'Adud al-Dawla\tTEXT.IMRAN\tNA\t0.0095032072007147\n'Adud al-Dawla\tTEXT.IRRIGATE\tNA\t0.00806235431550003\n'Adud al-Dawla\tTEXT.ISFAHAN\tNA\t0.0144289732453719\n'Adud al-Dawla\tTEXT.ISFANDIYAR\tNA\t0.0147419758625679\n'Adud al-Dawla\tTEXT.ISHAQ\tNA\t0.00995878319735963\n'Adud al-Dawla\tTEXT.IZZ\tNA\t0.129121646709541\n'Adud al-Dawla\tTEXT.KAKI\tNA\t0.0116170421624621\n'Adud al-Dawla\tTEXT.KARBALA\tNA\t0.0109616928162925\n'Adud al-Dawla\tTEXT.KARD\tNA\t0.0137655251180668\n'Adud al-Dawla\tTEXT.KARUN\tNA\t0.0115225213027311\n'Adud al-Dawla\tTEXT.KERMAN\tNA\t0.0322142654226283\n'Adud al-Dawla\tTEXT.KERMANSHAH\tNA\t0.0116971284535617\n'Adud al-Dawla\tTEXT.KHALAF\tNA\t0.0117388107268482\n'Adud al-Dawla\tTEXT.KHORRAMSHAHR\tNA\t0.0134748733114124\n'Adud al-Dawla\tTEXT.KHUSRAW\tNA\t0.0776226895619185\n'Adud al-Dawla\tTEXT.KHUZESTAN\tNA\t0.0110264383618927\n'Adud al-Dawla\tTEXT.KURD\tNA\t0.0129519058817242\n'Adud al-Dawla\tTEXT.LAQAB\tNA\t0.0137655251180668\n'Adud al-Dawla\tTEXT.MAKAN\tNA\t0.0228670612729326\n'Adud al-Dawla\tTEXT.MAKRAN\tNA\t0.017806772201188\n'Adud al-Dawla\tTEXT.MANSUR\tNA\t0.00904863982642141\n'Adud al-Dawla\tTEXT.MARSHY\tNA\t0.00847075416697272\n'Adud al-Dawla\tTEXT.MARZUBAN\tNA\t0.0221129637938519\n'Adud al-Dawla\tTEXT.MOBAD\tNA\t0.0145969810444171\n'Adud al-Dawla\tTEXT.MOSUL\tNA\t0.0190851912529848\n'Adud al-Dawla\tTEXT.MU\tNA\t0.0488914103923194\n'Adud al-Dawla\tTEXT.MUDAR\tNA\t0.0131222336053104\n'Adud al-Dawla\tTEXT.MUFID\tNA\t0.0136132687777778\n'Adud al-Dawla\tTEXT.MUQADDASI\tNA\t0.0119662927857039\n'Adud al-Dawla\tTEXT.NAJAF\tNA\t0.0108399242519064\n'Adud al-Dawla\tTEXT.NASIR\tNA\t0.00877548570835643\n'Adud al-Dawla\tTEXT.NISHAPUR\tNA\t0.0114509174976689\n'Adud al-Dawla\tTEXT.NOMAD\tNA\t0.00768822108128912\n'Adud al-Dawla\tTEXT.NOWRUZ\tNA\t0.0114509174976689\n'Adud al-Dawla\tTEXT.OMAN\tNA\t0.0116225388283156\n'Adud al-Dawla\tTEXT.PATRONIZE\tNA\t0.0116340695331498\n'Adud al-Dawla\tTEXT.PERSEPOLIS\tNA\t0.0103280488741183\n'Adud al-Dawla\tTEXT.POLEMIC\tNA\t0.00847075416697272\n'Adud al-Dawla\tTEXT.QABUS\tNA\t0.0515969173816329\n'Adud al-Dawla\tTEXT.QAZVIN\tNA\t0.0116170421624621\n'Adud al-Dawla\tTEXT.RAHBA\tNA\t0.0144646112351293\n'Adud al-Dawla\tTEXT.RECONQUER\tNA\t0.0081983274118409\n'Adud al-Dawla\tTEXT.RUKN\tNA\t0.078612357636442\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Basic settings of the ESA model and the 21.7 mi rows of features","message":["%r","","summary(esa_wiki_mod)"],"enabled":true,"result":{"startTime":1734646686131,"interpreter":"r.high","endTime":1734646703589,"results":[{"message":"\nCall:\nore.createESA.wiki_model()\n\nSettings: \n                                               value\nmin.items                                         25\ntopn.features                                   1000\nvalue.threshold                            .00000001\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nodms.text.max.features                        200000\nodms.text.min.documents                            5\nodms.text.policy.name                    wiki_txtpol\nprep.auto                                         ON\n\nFeatures: \n           FEATURE_ID ATTRIBUTE_NAME COEFFICIENT\n1  Digital television     TEXT.OPTIC 0.022979720\n2            Djibouti     TEXT.OPTIC 0.008672212\n3       Dominic Cooke     TEXT.OPTIC 0.055690500\n4           Don Beyer     TEXT.OPTIC 0.032385897\n5  Downtown Connector     TEXT.OPTIC 0.042613797\n6           Dumaguete     TEXT.OPTIC 0.025493870\n7             ECHELON     TEXT.OPTIC 0.024809319\n8           Manhattan     TEXT.OPTIC 0.008325461\n9   Marcello Malpighi     TEXT.OPTIC 0.019203654\n10         Mass media     TEXT.OPTIC 0.020314891","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Examples using the Wiki ESA model"," "],"enabled":true,"result":{"startTime":1734646704012,"interpreter":"md.high","endTime":1734646704414,"results":[{"message":"<h2 id=\"examples-using-the-wiki-esa-model\">Examples using the Wiki ESA model<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Basic example of ESA features for the word 'Bank'","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"Bank\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1734646704834,"interpreter":"r.high","endTime":1734646715085,"results":[{"message":"P1\tP2\nBanking in India\t0.12073074740514\nBanking in Australia\t0.11861539327698\nBank of America\t0.113071554116955\nBank\t0.109941636462343\nCentral bank\t0.107803077904466\nNational Bank Act\t0.106470427774569\nStandard Bank\t0.105024979812532\nBankBoston\t0.104744369832675\nOnline banking\t0.102503159518508\nBank of England\t0.096106744902625\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" ESA features for the words 'NFL Games'","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"NFL Games\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1734646715503,"interpreter":"r.high","endTime":1734646726259,"results":[{"message":"P1\tP2\nNational Football League\t0.144836254979624\nNFL International Series\t0.139684141888651\nThursday Night Football\t0.114362409545099\nHistory of the National Football League championship\t0.112721298239857\nChicago College All-Star Game\t0.111212971099872\nNFL Network\t0.110076945165456\nNFL Films\t0.106270569533189\nThe NFL Today\t0.102924455670641\nNational Football League Christmas games\t0.101040928011359\nPlayoff Bowl\t0.0925872345552612\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for a text about using R on Oracle Database","message":["%r ","","TEMP_DF <- ore.push(data.frame(ID = 1, TEXT = c(\"Data scientists and broader R users can take advantage of the R ecosystem on data managed by Oracle Database. R provides a suite of software packages for data manipulation, graphics, statistical functions, and machine learning algorithms. Oracle Machine Learning for R extends R\u2019s capabilities through three primary areas: transparent access and manipulation of database data from R, in-database machine learning algorithms, ease of deployment using embedded R execution.\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1734646726685,"interpreter":"r.high","endTime":1734646736542,"results":[{"message":"P1\tP2\nDatabase\t0.138153264333878\nData mining\t0.135965584589925\nMachine learning\t0.1325152013941\nData science\t0.117836217441854\nOracle Corporation\t0.116486459958878\nSupervised learning\t0.110513880296051\nAnalysis of algorithms\t0.105911885236646\nMicrosoft Access\t0.0994831076884941\nObject database\t0.0936664783762529\nEnterprise software\t0.0935335850823769\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for a text about OML4Spark","message":["%r ","","TEMP_DF <- ore.push(data.frame(ID = 1, TEXT = c(\"OML4Spark R API provides functions for manipulating data stored in a local File System, HDFS, HIVE, Spark DataFrames, Impala, Oracle Database, and other JDBC sources. OML4Spark takes advantage of all the nodes of a Hadoop cluster for scalable, high-performance machine learning modeling in Big Data environments. \")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1734646736964,"interpreter":"r.high","endTime":1734646747714,"results":[{"message":"P1\tP2\nApache Hadoop\t0.35186953266221\nApache Spark\t0.218089549030044\nJava Database Connectivity\t0.184992809374709\nData modeling\t0.142714506107067\nDatabase\t0.140527053633504\nHigh-availability cluster\t0.136451747421796\nData warehouse\t0.119438462115326\nData mining\t0.117130383015355\nOracle Corporation\t0.111502625481305\nBig data\t0.106993497700792\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for a long text about the Oracle Machine Learning AutoML UI","message":["%r","","TEMP_DF <- ore.push(data.frame(ID = 1, TEXT = c(\"Oracle Machine Learning AutoML User Interface makes it easy for citizen data scientists and expert data scientists to build and deploy machine learning models. OML AutoML UI, a new component of Oracle Machine Learning on Oracle Autonomous Database, provides a no-code browser-based interface that automates the machine learning modeling process and simplifies deployment to just a few clicks. While data scientists may prefer to write code, perhaps using the OML4Py AutoML feature, OML AutoML UI makes machine learning possible for a much broader set of users, who may \u201cknow enough ML to be dangerous\u201d but are not familiar with specific algorithm details and the modeling process to refine usable models. At the same time, OML AutoML UI delivers significant productivity improvements for data scientists as a modeling accelerator.\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1734646748137,"interpreter":"r.high","endTime":1734646758301,"results":[{"message":"P1\tP2\nMachine learning\t0.155704473503718\nData modeling\t0.1409575620346\nUser interface\t0.128292546465855\nOne UI\t0.124529147700334\nData mining\t0.121863746936165\nSoftware design\t0.109564690786559\nBusiness process modeling\t0.106090024431756\nDatabase\t0.0965120800867556\nData science\t0.0891308488999173\nImplementation\t0.0817726804242852\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Comparing Strings for Similarity of Text using the Wikipedia Model"],"enabled":true,"result":{"startTime":1734646758725,"interpreter":"md.high","endTime":1734646759136,"results":[{"message":"<h3 id=\"comparing-strings-for-similarity-of-text-using-the-wikipedia-model\">Comparing Strings for Similarity of Text using the Wikipedia Model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare \"fish\" vs \"salmon\" ","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"fish\", \"salmon\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1734646759561,"interpreter":"r.high","endTime":1734646769836,"results":[{"message":"SIMILARITY\n0.146447776027241\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare \"fish\" vs \"hawk\" ","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"fish\", \"hawk\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1734646770268,"interpreter":"r.high","endTime":1734646780921,"results":[{"message":"SIMILARITY\n0.000369489611985352\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare two short texts about Machine Learning ","message":["%r ","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"Regression models predict values on a continuous range.\", \"Classification algorithms assign data to two or more classes.\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1734646781342,"interpreter":"r.high","endTime":1734646791395,"results":[{"message":"SIMILARITY\n0.168889290068795\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare text about Machine Learning and Geography ","message":["%r ","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"Regression models predict values on a continuous range.\", \"The U.S. is bordered by Canada and Mexico.\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1734646791814,"interpreter":"r.high","endTime":1734646802568,"results":[{"message":"SIMILARITY\n0.0102609559472403\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare text about OML4R and SailGP ","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"OML4R has data types that map R data types to SQL data types. In an R session, when you create database objects from R objects or you create R objects from database data, OML4R translates R data types to SQL data types and the reverse where possible. OML4R creates objects that are instances of OML4R classes. OML4R overloads many standard R functions so that they use OML4R classes and data types. R language constructs and syntax are supported for objects that are mapped to Oracle Database objects.\", \"In the SailGP league, thousands of sensors are attached to the catamarans and their crew members, generating millions of data points that are shared with broadcast partners, fans, race officials, and importantly, across the fleet. Each team has full access to one another\u2019s data for post-race analysis, with the goal that sailing performance on the water, not technology, should determine the winner. \")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1734646803016,"interpreter":"r.high","endTime":1734646813735,"results":[{"message":"SIMILARITY\n0.23962673310945\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare text about OML4R and OML4Py ","message":["%r ","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"OML4R has data types that map R data types to SQL data types. In an R session, when you create database objects from R objects or you create R objects from database data, OML4R translates R data types to SQL data types and the reverse where possible. OML4R creates objects that are instances of OML4R classes. OML4R overloads many standard R functions so that they use OML4R classes and data types. R language constructs and syntax are supported for objects that are mapped to Oracle Database objects.\", \"Oracle Machine Learning for Python (OML4Py) is a component of the Oracle Database Enterprise Edition that makes open source Python ready for the enterprise and big data. OML4Py integrates Python with the Oracle Database and provides a comprehensive, database-centric environment for end-to-end analytical processing. OML4Py provides the users the capability to execute Python functions on database-resident data for data exploration and preparation, whilst seamlessly leveraging the Oracle Database as a high-performance computing (HPC) environment. Furthermore, Python users can natively invoke powerful in-database machine learning algorithms directly in Python.\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1734646814164,"interpreter":"r.high","endTime":1734646825212,"results":[{"message":"SIMILARITY\n0.591289940606868\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":{"startTime":1734646825634,"interpreter":"md.high","endTime":1734646826039,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734646826468,"interpreter":"md.high","endTime":1734646826874,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Extraction ESA","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736286869770,"interpreter":"md.low","endTime":1736286870189,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base. ESA assists in understanding the meaning of a section of text as a combination of concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for R function *ore.odmESA* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we cover the basic usage of ESA, where we extract topics from the comments provided. ",""," - Create a small sample dataset"," - Use the ESA Model to predict a feature vector on test data"," - Identify top features"," - Document similarity"," - Change model settings","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736286870621,"interpreter":"md.low","endTime":1736286871039,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base. ESA assists in understanding the meaning of a section of text as a combination of concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA improves text document categorization by calculating <code>semantic relatedness<\/code> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>The Oracle Machine Learning for R function <em>ore.odmESA<\/em> extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>In this notebook, we cover the basic usage of ESA, where we extract topics from the comments provided.<\/p>\n<ul>\n<li>Create a small sample dataset<\/li>\n<li>Use the ESA Model to predict a feature vector on test data<\/li>\n<li>Identify top features<\/li>\n<li>Document similarity<\/li>\n<li>Change model settings<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis"],"enabled":true,"result":{"startTime":1736286871461,"interpreter":"md.low","endTime":1736286871873,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736286872288,"interpreter":"r.low","endTime":1736286877333,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create and view data","message":["%r","","TITLE <- c('Aids in Africa: Planning for a long war',","                'Mars rover maneuvers for rim shot',","                'Mars express confirms presence of water at Mars south pole',","                'NASA announces major Mars rover finding',","                'Drug access, Asia threat in focus at AIDS summit',","                'NASA Mars Odyssey THEMIS image: typical crater',","                'Road blocks for Aids')","","ID <- 1:length(TITLE)","","df <- data.frame(ID, TITLE)","ESA_TXT <- ore.push(df)","","z.show(ESA_TXT)"],"enabled":true,"result":{"startTime":1736286877752,"interpreter":"r.low","endTime":1736286878427,"results":[{"message":"ID\tTITLE\n1\tAids in Africa: Planning for a long war\n2\tMars rover maneuvers for rim shot\n3\tMars express confirms presence of water at Mars south pole\n4\tNASA announces major Mars rover finding\n5\tDrug access, Asia threat in focus at AIDS summit\n6\tNASA Mars Odyssey THEMIS image: typical crater\n7\tRoad blocks for Aids\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a policy for text indexing","message":["%r","","# Drop the text policy if it exists","ore.exec(paste(\"Begin ctx_ddl.drop_policy('ESA_TXT_POLICY'); ","                EXCEPTION WHEN others THEN null; END;\",sep=\"\"))","","# Create text policy","ore.exec(\"Begin ctx_ddl.create_policy('ESA_TXT_POLICY'); End;\")"],"enabled":true,"result":{"startTime":1736286878863,"interpreter":"r.low","endTime":1736286879660,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify model settings","message":["%r","","odm <- list(case_id_column_name     = \"ID\", ","            ODMS_TEXT_POLICY_NAME   = \"ESA_TXT_POLICY\", ","            ODMS_TEXT_MIN_DOCUMENTS = 1, ","            ESAS_MIN_ITEMS          = 1, ","            model_name              = 'ESA_MOD')","","ctx <- list(TITLE = \"TEXT(POLICY_NAME:ESA_TXT_POLICY)(TOKEN_TYPE:STEM)\")"],"enabled":true,"result":{"startTime":1736286880095,"interpreter":"r.low","endTime":1736286880571,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create an ESA model object ","message":["%r","","# Delete model if it exists","ore.exec(paste(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> '\", 'ESA_MOD', \"'); ","EXCEPTION WHEN others THEN null; END;\",sep=\"\"))","","# Create model","esa_mod <- ore.odmESA(~TITLE, ESA_TXT, odm.settings = odm, ctx.settings = ctx)"],"enabled":true,"result":{"startTime":1736286880987,"interpreter":"r.low","endTime":1736286882634,"results":[{"message":"Warning message:\n\u201cThe created mining model ESA_MOD is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model settings","message":["%r","","z.show(settings(esa_mod))"],"enabled":true,"result":{"startTime":1736286883071,"interpreter":"r.low","endTime":1736286883609,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\tSETTING_TYPE\nALGO_NAME\tALGO_EXPLICIT_SEMANTIC_ANALYS\tINPUT\nESAS_MIN_ITEMS\t1\tINPUT\nESAS_TOPN_FEATURES\t1000\tDEFAULT\nESAS_VALUE_THRESHOLD\t.00000001\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\tDEFAULT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nODMS_TEXT_MAX_FEATURES\t300000\tDEFAULT\nODMS_TEXT_MIN_DOCUMENTS\t1\tINPUT\nODMS_TEXT_POLICY_NAME\tESA_TXT_POLICY\tINPUT\nPREP_AUTO\tON\tINPUT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%r","","summary(esa_mod)"],"enabled":true,"result":{"startTime":1736286884033,"interpreter":"r.low","endTime":1736286884816,"results":[{"message":"\nCall:\nore.odmESA(formula = ~TITLE, data = ESA_TXT, odm.settings = odm, \n    ctx.settings = ctx)\n\nSettings: \n                                               value\nmin.items                                          1\ntopn.features                                   1000\nvalue.threshold                            .00000001\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nodms.text.max.features                        300000\nodms.text.min.documents                            1\nodms.text.policy.name                 ESA_TXT_POLICY\nprep.auto                                         ON\n\nFeatures: \n   FEATURE_ID  ATTRIBUTE_NAME ATTRIBUTE_VALUE COEFFICIENT\n1           1    TITLE.AFRICA            <NA>   0.4885556\n2           1      TITLE.AIDS            <NA>   0.2127293\n3           1      TITLE.LONG            <NA>   0.4885556\n4           1  TITLE.PLANNING            <NA>   0.4885556\n5           1       TITLE.WAR            <NA>   0.4885556\n6           2 TITLE.MANEUVERS            <NA>   0.5347384\n7           2      TITLE.MARS            <NA>   0.1537831\n8           2       TITLE.RIM            <NA>   0.5347384\n9           2     TITLE.ROVER            <NA>   0.3442607\n10          2      TITLE.SHOT            <NA>   0.5347384\n11          3  TITLE.CONFIRMS            <NA>   0.4020610\n12          3   TITLE.EXPRESS            <NA>   0.4020610\n13          3      TITLE.MARS            <NA>   0.1734405\n14          3      TITLE.POLE            <NA>   0.4020610\n15          3  TITLE.PRESENCE            <NA>   0.4020610\n16          3     TITLE.SOUTH            <NA>   0.4020610\n17          3     TITLE.WATER            <NA>   0.4020610\n18          4 TITLE.ANNOUNCES            <NA>   0.5056155\n19          4   TITLE.FINDING            <NA>   0.5056155\n20          4     TITLE.MAJOR            <NA>   0.5056155\n21          4      TITLE.MARS            <NA>   0.1454077\n22          4      TITLE.NASA            <NA>   0.3255116\n23          4     TITLE.ROVER            <NA>   0.3255116\n24          5    TITLE.ACCESS            <NA>   0.4019471\n25          5      TITLE.AIDS            <NA>   0.1750178\n26          5      TITLE.ASIA            <NA>   0.4019471\n27          5      TITLE.DRUG            <NA>   0.4019471\n28          5     TITLE.FOCUS            <NA>   0.4019471\n29          5    TITLE.SUMMIT            <NA>   0.4019471\n30          5    TITLE.THREAT            <NA>   0.4019471\n31          6    TITLE.CRATER            <NA>   0.4265110\n32          6     TITLE.IMAGE            <NA>   0.4265110\n33          6      TITLE.MARS            <NA>   0.1226584\n34          6      TITLE.NASA            <NA>   0.2745847\n35          6   TITLE.ODYSSEY            <NA>   0.4265110\n36          6    TITLE.THEMIS            <NA>   0.4265110\n37          6   TITLE.TYPICAL            <NA>   0.4265110\n38          7      TITLE.AIDS            <NA>   0.2942601\n39          7    TITLE.BLOCKS            <NA>   0.6757999\n40          7      TITLE.ROAD            <NA>   0.6757999","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the model details for each feature","message":["%r","","esa_mod$features$COEFFICIENT <- round(esa_mod$features$COEFFICIENT, digits=4)","z.show(head(esa_mod$features, 10))"],"enabled":true,"result":{"startTime":1736286885468,"interpreter":"r.low","endTime":1736286885938,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n1\tTITLE.AFRICA\tNA\t0.4886\n1\tTITLE.AIDS\tNA\t0.2127\n1\tTITLE.LONG\tNA\t0.4886\n1\tTITLE.PLANNING\tNA\t0.4886\n1\tTITLE.WAR\tNA\t0.4886\n2\tTITLE.MANEUVERS\tNA\t0.5347\n2\tTITLE.MARS\tNA\t0.1538\n2\tTITLE.RIM\tNA\t0.5347\n2\tTITLE.ROVER\tNA\t0.3443\n2\tTITLE.SHOT\tNA\t0.5347\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model name using ALL_MINING_MODELS view","message":["%sql","","SELECT * from all_mining_models WHERE ALGORITHM = 'EXPLICIT_SEMANTIC_ANALYS'"],"enabled":true,"result":{"startTime":1736286886353,"interpreter":"sql.low","endTime":1736286888297,"results":[{"message":"OWNER\tMODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\tBUILD_DURATION\tMODEL_SIZE\tPARTITIONED\tCOMMENTS\nOMLUSER\tESA_MOD\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2025-01-07 21:54:41\t\t0\tNO\t\nOMLUSER\tWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2024-12-19 21:17:32\t0\t0\tNO\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View model name using USER_MINING_MODELS view","message":["%sql","","SELECT * from user_mining_models where model_name = 'ESA_MOD'"],"enabled":true,"result":{"startTime":1736286888731,"interpreter":"sql.low","endTime":1736286889233,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\tBUILD_DURATION\tMODEL_SIZE\tPARTITIONED\tCOMMENTS\nESA_MOD\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2025-01-07 21:54:41\t\t0\tNO\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions on data","message":["%r","","esa_pred <- predict(esa_mod, ESA_TXT, type = \"class\", supplemental.cols = c(\"ID\", \"TITLE\"))","z.show(esa_pred)"],"enabled":true,"result":{"startTime":1736286889678,"interpreter":"r.low","endTime":1736286890367,"results":[{"message":"ID\tTITLE\tFEATURE_ID\n1\tAids in Africa: Planning for a long war\t1\n2\tMars rover maneuvers for rim shot\t2\n3\tMars express confirms presence of water at Mars south pole\t3\n4\tNASA announces major Mars rover finding\t4\n5\tDrug access, Asia threat in focus at AIDS summit\t5\n6\tNASA Mars Odyssey THEMIS image: typical crater\t6\n7\tRoad blocks for Aids\t7\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Feature compare titles","message":["%r","","z.show(feature_compare(esa_mod, ESA_TXT, compare.cols = c('TITLE'), supplemental.cols = c('TITLE')))"],"enabled":true,"result":{"startTime":1736286890792,"interpreter":"r.low","endTime":1736286891318,"results":[{"message":"TITLE_A\tTITLE_B\tSIMILARITY\nAids in Africa: Planning for a long war\tMars rover maneuvers for rim shot\t0\nAids in Africa: Planning for a long war\tMars express confirms presence of water at Mars south pole\t0\nAids in Africa: Planning for a long war\tNASA announces major Mars rover finding\t0\nAids in Africa: Planning for a long war\tDrug access, Asia threat in focus at AIDS summit\t0.0312418916816983\nAids in Africa: Planning for a long war\tNASA Mars Odyssey THEMIS image: typical crater\t0\nAids in Africa: Planning for a long war\tRoad blocks for Aids\t0.0645255975774246\nMars rover maneuvers for rim shot\tMars express confirms presence of water at Mars south pole\t0.0233983356418543\nMars rover maneuvers for rim shot\tNASA announces major Mars rover finding\t0.16186140052623\nMars rover maneuvers for rim shot\tDrug access, Asia threat in focus at AIDS summit\t0\nMars rover maneuvers for rim shot\tNASA Mars Odyssey THEMIS image: typical crater\t0.0204226257202758\nMars rover maneuvers for rim shot\tRoad blocks for Aids\t0\nMars express confirms presence of water at Mars south pole\tNASA announces major Mars rover finding\t0.0215906984781613\nMars express confirms presence of water at Mars south pole\tDrug access, Asia threat in focus at AIDS summit\t0\nMars express confirms presence of water at Mars south pole\tNASA Mars Odyssey THEMIS image: typical crater\t0.0171430558490882\nMars express confirms presence of water at Mars south pole\tRoad blocks for Aids\t0\nNASA announces major Mars rover finding\tDrug access, Asia threat in focus at AIDS summit\t0\nNASA announces major Mars rover finding\tNASA Mars Odyssey THEMIS image: typical crater\t0.119051964176864\nNASA announces major Mars rover finding\tRoad blocks for Aids\t0\nDrug access, Asia threat in focus at AIDS summit\tNASA Mars Odyssey THEMIS image: typical crater\t0\nDrug access, Asia threat in focus at AIDS summit\tRoad blocks for Aids\t0.0496393530793489\nNASA Mars Odyssey THEMIS image: typical crater\tRoad blocks for Aids\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Change the settings and create new model","message":["%r","","new_setting <- list(case_id_column_name = \"ID\", ","                    ODMS_TEXT_POLICY_NAME = \"ESA_TXT_POLICY\",","                    ESAS_VALUE_THRESHOLD = 0.0001, ","                    ODMS_TEXT_MAX_FEATURES = 3, ","                    ESAS_MIN_ITEMS = 1,","                    ESAS_TOPN_FEATURES = 3,","                    model_name = 'ESA_MOD_2')","                    "],"enabled":true,"result":{"startTime":1736286891746,"interpreter":"r.low","endTime":1736286892219,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the model with new settings - 2 features maximum ","message":["%r","","# Delete if ESA model exists, then create it","","ore.exec(paste(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> '\", 'ESA_MOD_2', \"'); ","  EXCEPTION WHEN others THEN null; END;\",sep=\"\"))","","esa_mod2 <- ore.odmESA(~TITLE, ESA_TXT, odm.settings = new_setting, ctx.settings = ctx)"],"enabled":true,"result":{"startTime":1736286892656,"interpreter":"r.low","endTime":1736286893903,"results":[{"message":"Warning message:\n\u201cThe created mining model ESA_MOD_2 is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List ESA model views","message":["%sql","","SELECT view_name, view_type FROM user_mining_model_views","WHERE model_name='ESA_MOD_2'","ORDER BY view_name;"],"enabled":true,"result":{"startTime":1736286894319,"interpreter":"sql.low","endTime":1736286894786,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAESA_MOD_2\tExplicit Semantic Analysis Matrix\nDM$VFESA_MOD_2\tExplicit Semantic Analysis Features\nDM$VGESA_MOD_2\tGlobal Name-Value Pairs\nDM$VNESA_MOD_2\tNormalization and Missing Value Handling\nDM$VSESA_MOD_2\tComputed Settings\nDM$VWESA_MOD_2\tModel Build Alerts\nDM$VXESA_MOD_2\tText Features\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"The VA view shows associations among features (topics) and words","message":["%md","","*ATTRIBUTE_SUBNAME* refers to words, or tokens. The strength of the associations is measured by *COEFFICIENT*. Higher coefficient values indicate a stronger association."],"enabled":true,"result":{"startTime":1736286895202,"interpreter":"md.low","endTime":1736286895605,"results":[{"message":"<p><em>ATTRIBUTE_SUBNAME<\/em> refers to words, or tokens. The strength of the associations is measured by <em>COEFFICIENT<\/em>. Higher coefficient values indicate a stronger association.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * from DM$VAESA_MOD_2;"],"enabled":true,"result":{"startTime":1736286896025,"interpreter":"sql.low","endTime":1736286896481,"results":[{"message":"PARTITION_NAME\tFEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n\t1\tTITLE\tAIDS\t\t1.0\n\t2\tTITLE\tMARS\t\t1.0\n\t3\tTITLE\tMARS\t\t1.0\n\t4\tTITLE\tMARS\t\t1.0\n\t5\tTITLE\tAIDS\t\t1.0\n\t7\tTITLE\tAIDS\t\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the associations for selected words","message":["%sql","","SELECT ATTRIBUTE_SUBNAME, count(ATTRIBUTE_SUBNAME) FROM DM$VAESA_MOD_2","WHERE ATTRIBUTE_SUBNAME='MARS'","GROUP BY ATTRIBUTE_SUBNAME"],"enabled":true,"result":{"startTime":1736286896901,"interpreter":"sql.low","endTime":1736286897395,"results":[{"message":"ATTRIBUTE_SUBNAME\tCOUNT(ATTRIBUTE_SUBNAME)\nMARS\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the help file for ESA","message":["%r","","help(\"ore.odmESA\")"],"enabled":true,"result":{"startTime":1736286897815,"interpreter":"r.low","endTime":1736286898494,"results":[{"message":"ore.odmESA                package:OREdm                R Documentation\n\nIn-Database Explicit Semantic Analysis Models\n\nDescription:\n\n     Creates an Explicit Semantic Analysis model for feature extraction\n     using Oracle Data Mining in Oracle Database 12.2 or later.\n\nUsage:\n\n     ore.odmESA(formula,                        \n             data, \n             auto.data.prep = TRUE,\n             na.action = na.pass,\n             odm.settings = NULL,\n             ctx.settings = NULL)\n     \n     ## S3 method for class 'ore.odmESA'\n     features(object,...)\n     \n     ## S3 method for class 'ore.odmESA'\n     feature_compare(object,\n       newdata,\n       compare.cols = NULL,\n       supplemental.cols = NULL)\n     \n     ## S3 method for class 'ore.odmESA'\n     predict(object,\n       newdata,                \n       supplemental.cols = NULL,\n       type = c(\"class\",\"raw\"),\n       na.action = na.pass,\n       topN = NULL,...)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. For details of the model specification, see\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build.\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmKMeans\u2019.  In addition,\n          to perform text mining, parameter \u2018CASE_ID_COLUMN_NAME\u2019 must\n          specify the name of the column containing unique identifiers.\n          Parameters \u2018ODMS_TEXT_POLICY_NAME\u2019,\n          \u2018ODMS_TEXT_MIN_DOCUMENTS\u2019, and \u2018ODMS_TEXT_MAX_FEATURES\u2019 can\n          be used to specify the name of a valid Oracle text policy,\n          the minimal number of documents in which echo token occurs,\n          and the maximum number of distinct features for text mining,\n          respectively.\n\nctx.settings: Same as \u2018ctx.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\n  object: An object of type \u2018ore.odmESA\u2019.\n\n newdata: Data used for scoring.\n\ncompare.cols: Columns used to compare the features.  All the columns\n          are used by default.\n\nsupplemental.cols: Columns to include in the output.\n\n    type: If set to \u2018\"raw\"\u2019, provides the probability for each feature\n          returned. If set to \u2018\"class\"\u2019, the feature with the maximum\n          probability is returned.\n\n    topN: A positive integer that restricts the set of features to\n          those that have one of the specified number of highest\n          probability values.\n\n     ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n     Explicit Semantic Analysis (ESA) is a feature extraction\n     algorithm.  ESA does not discover latent features but instead uses\n     explicit features based on an existing knowledge base.\n\n     Explicit knowledge often exists in text form. Multiple knowledge\n     bases are available as collections of text documents. These\n     knowledge bases can be generic, for example, Wikipedia, or\n     domain-specific. Data preparation transforms the text into vectors\n     that capture attribute-concept associations.\n\nValue:\n\n     An object of class \"ore.odmESA\" includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of attributes used to build the model.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\n    call: The matched call.\n     The function \u2018features\u2019 returns an \u2018ore.frame\u2019 that describes each\n     feature extracted. The columns are:\n\nFEATURE_ID: The numeric identifier associated with the feature.\n\nATTRIBUTE_NAME: The name of the attribute.\n\nATTRIBUTE_VALUE: The value of the attribute.\n\nCOEFFICIENT: The coefficient associated with the attribute in a\n          particular feature.\n     The function \u2018feature_compare\u2019 returns an \u2018ore.frame\u2019 containing a\n     \u2018SIMILARITY\u2019 column that measures relatedness and supplementary\n     columns if specified.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmSVD\u2019, \u2018ore.odmNMF\u2019, \u2018partitions\u2019\n\nExamples:\n\n       title <- c('Aids in Africa: Planning for a long war',\n                  'Mars rover maneuvers for rim shot',\n                  'Mars express confirms presence of water at Mars south pole',\n                  'NASA announces major Mars rover finding',\n                  'Drug access, Asia threat in focus at AIDS summit',\n                  'NASA Mars Odyssey THEMIS image: typical crater',\n                  'Road blocks for Aids')\n     \n       # TEXT contents in character column\n       df <- data.frame(CUST_ID = seq(length(title)), TITLE = title)\n       ESA_TEXT <- ore.push(df)\n     \n       # TEXT contains in clob column\n       attr(df$TITLE, \"ora.type\") <- \"clob\"\n       ESA_TEXT_CLOB <- ore.push(df)\n     \n       # create text policy (CTXSYS.CTX_DDL privilege is required)\n       ore.exec(\"Begin ctx_ddl.create_policy('ESA_TXTPOL'); End;\")\n     \n       # specify TEXT POLICY_NAME, MIN_DOCUMENTS, MAX_FEATURES and\n       # ESA algorithm settings in odm.settings\n       esa.mod <- ore.odmESA(~ TITLE, data = ESA_TEXT_CLOB,\n         odm.settings = list(case_id_column_name = \"CUST_ID\",\n                             ODMS_TEXT_POLICY_NAME = \"ESA_TXTPOL\",\n                             ODMS_TEXT_MIN_DOCUMENTS = 1,\n                             ODMS_TEXT_MAX_FEATURES = 3,\n                             ESAS_MIN_ITEMS = 1,\n                             ESAS_VALUE_THRESHOLD = 0.0001,\n                             ESAS_TOPN_FEATURES = 3))\n       class(esa.mod)\n       summary(esa.mod)\n       settings(esa.mod)\n       features(esa.mod)\n       predict(esa.mod, ESA_TEXT, type = \"class\", supplemental.cols = \"TITLE\")\n     \n       # use ctx.settings to specify a character column as TEXT and \n       # the same above settings as well as TOKEN_TYPE \n       esa.mod2 <- ore.odmESA(~ TITLE, data = ESA_TEXT,\n         odm.settings = list(case_id_column_name = \"CUST_ID\", ESAS_MIN_ITEMS = 1),\n         ctx.settings = list(TITLE = \n           \"TEXT(POLICY_NAME:ESA_TXTPOL)(TOKEN_TYPE:STEM)(MIN_DOCUMENTS:1)(MAX_FEATURES:3)\"))\n       summary(esa.mod2)\n       settings(esa.mod2)\n       features(esa.mod2)\n       predict(esa.mod2, ESA_TEXT_CLOB, type = \"class\", supplemental.cols = \"TITLE\")\n     \n       ore.exec(\"Begin ctx_ddl.drop_policy('ESA_TXTPOL'); End;\")\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Clean-up","message":["%script","","BEGIN","  DBMS_DATA_MINING.DROP_MODEL(model_name => 'ESA_MOD');","  EXCEPTION WHEN others THEN null;","END;","/","BEGIN","  DBMS_DATA_MINING.DROP_MODEL(model_name => 'ESA_MOD_2');","  EXCEPTION WHEN others THEN null;","END;","/"],"enabled":true,"result":{"startTime":1736286898918,"interpreter":"script.low","endTime":1736286900969,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736286901404,"interpreter":"md.low","endTime":1736286901818,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736286902243,"interpreter":"md.low","endTime":1736286902662,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Extraction SVD","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737752853899,"interpreter":"md.low","endTime":1737752854385,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4R Feature Extraction: Singular Value Decomposition","","The Oracle Machine Learning for R function ore.odmSVD creates a model that uses the Singular Value Decomposition (SVD) algorithm for feature extraction.","","In this notebook, we demonstrate how to use the in-database SVD for feature extraction using the Breast Cancer dataset from the University of Wisconsin.","We show that Principal Component Analysis (PCA) can be achieved using SVD results. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737752854871,"interpreter":"md.low","endTime":1737752855353,"results":[{"message":"<h1 id=\"oml4r-feature-extraction-singular-value-decomposition\">OML4R Feature Extraction: Singular Value Decomposition<\/h1>\n<p>The Oracle Machine Learning for R function ore.odmSVD creates a model that uses the Singular Value Decomposition (SVD) algorithm for feature extraction.<\/p>\n<p>In this notebook, we demonstrate how to use the in-database SVD for feature extraction using the Breast Cancer dataset from the University of Wisconsin.\nWe show that Principal Component Analysis (PCA) can be achieved using SVD results. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-singular-value-decomposition\" target=\"_blank\">OML Singular Value Decomposition<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-singular-value-decomposition-model.html\" target=\"_blank\">OML4R Singular Value Decomposition<\/a>"],"enabled":true,"result":{"startTime":1737752855848,"interpreter":"md.low","endTime":1737752856326,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-singular-value-decomposition\" target=\"_blank\">OML Singular Value Decomposition<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-singular-value-decomposition-model.html\" target=\"_blank\">OML4R Singular Value Decomposition<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Initialize the environment","message":["%r","","library(ORE)","library(OREdplyr)","library(lattice)","options(ore.warn.order=FALSE)","","ore.attach()"],"enabled":true,"result":{"startTime":1737752856802,"interpreter":"r.low","endTime":1737752857359,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create temporary database table from mtcars and obtain proxy object","message":["%r","","MTCARS <- ore.push(mtcars)","","cat(\"Shape:\", dim(MTCARS))"],"enabled":true,"result":{"startTime":1737752857852,"interpreter":"r.low","endTime":1737752858504,"results":[{"message":"Shape: 32 11","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the ore.odmSVD help file","message":["%r","","help(\"ore.odmSVD\")"],"enabled":true,"result":{"startTime":1737752858975,"interpreter":"r.low","endTime":1737752859688,"results":[{"message":"ore.odmSVD                package:OREdm                R Documentation\n\nIn-Database Singular Value Decomposition Models\n\nDescription:\n\n     Creates a Singular Value Decomposition (SVD) model for feature\n     extraction using Oracle Data Mining in Oracle Database 12.2 or\n     later.\n\nUsage:\n\n     ore.odmSVD(formula,                        \n             data, \n             auto.data.prep = TRUE,\n             na.action = na.pass,\n             odm.settings = NULL,\n             ctx.settings = NULL)\n     \n     ## S3 method for class 'ore.odmSVD'\n     features(object,...)\n     \n     ## S3 method for class 'ore.odmSVD'\n     feature_compare(object,\n       newdata,\n       compare.cols = NULL,\n       supplemental.cols = NULL)\n     \n     ## S3 method for class 'ore.odmSVD'\n     predict(object,\n       newdata,                \n       supplemental.cols = NULL,\n       type = c(\"class\",\"raw\"),\n       na.action = na.pass,\n       topN = NULL,\n       topN.attrs = FALSE,...)\n     \n     u(object)\n     ## S3 method for class 'ore.odmSVD'\n     u(object)\n     \n     v(object)\n     ## S3 method for class 'ore.odmSVD'\n     v(object)\n     \n     d(object)\n     ## S3 method for class 'ore.odmSVD'\n     d(object)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. For details of the model specification, see\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build.\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\nctx.settings: Same as \u2018ctx.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\n  object: An object of type \u2018ore.odmSVD\u2019.\n\n newdata: The data used for scoring.\n\ncompare.cols: Columns used to compare the features.  All the columns\n          are included by default.\n\nsupplemental.cols: Columns to include in the output.\n\n    type: If set to \u2018\"raw\"\u2019, provides the probability for each feature\n          returned. If set to \u2018\"class\"\u2019, the feature with the maximum\n          probability is returned.\n\n    topN: A positive integer that restricts the set of features to\n          those that have one of the specified number of highest\n          probability values.\n\ntopN.attrs: boolean, positive integer, False (default).  Returns the\n          top N most influence attributes of the predicted target value\n          for regression if topN_attrs is not False.  Returns the top N\n          most influence attributes of the highest probability class\n          for classification if topN_attrs is not False.  N is equal to\n          the specified positive integer or 5 if topN_attrs is True.\n\n     ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n     Singular Value Decomposition (SVD) is a feature extraction\n     algorithm. SVD is orthogonal linear transformations that capture\n     the underlying variance of the data by decomposing a rectangular\n     matrix into three matrixes: \u2018U\u2019, \u2018D\u2019, and \u2018V\u2019. Matrix \u2018D\u2019 is a\n     diagonal matrix and its singular values reflect the amount of data\n     variance captured by the bases.\n\nValue:\n\n     An object of class \"ore.odmSVD\" includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of attributes used to build the model.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\n    call: The matched call.\n     The \u2018d\u2019 function returns an \u2018ore.frame\u2019 with the singular values\n     of the input data. Column \u2018FEATURE_ID\u2019 represents the singular\n     value ID. The \u2018v\u2019 function returns an \u2018ore.frame\u2019 whose columns\n     contain the right singular vectors. The column names match with\n     \u2018FEATURE_ID\u2019 values. The \u2018u\u2019 function returns an \u2018ore.frame\u2019 whose\n     columns contain the left singular vectors. \u2018CASE_ID_COLUMN_NAME\u2019\n     and \u2018SVDS_U_MATRIX_OUTPUT\u2019 must be specified in argument\n     \u2018odm.settings\u2019 to product this result.\n\n     The \u2018features\u2019 function returns an \u2018ore.frame\u2019 the same as the \u2018v\u2019\n     function does except in an (vertical) unpivot format.  The columns\n     are:\n\nFEATURE_ID: The numeric identifier associated with the singular value.\n\nATTRIBUTE_NAME: The name of the attribute.\n\nATTRIBUTE_VALUE: The value of the attribute.\n\n   VALUE: The coefficient value associated with the attribute in the\n          new space.\n     The \u2018feature_compare\u2019 function returns an \u2018ore.frame\u2019 containing a\n     \u2018SIMILARITY\u2019 column and supplementary columns if specified.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmNMF\u2019, \u2018ore.odmESA\u2019, \u2018partitions\u2019\n\nExamples:\n\n     IRIS <- ore.push(cbind(Id = seq_along(iris[[1L]]), iris))\n     \n     svd.mod <- ore.odmSVD(~. -Id, IRIS)\n     summary(svd.mod)\n     d(svd.mod)\n     v(svd.mod)\n     head(predict(svd.mod, IRIS, supplemental.cols = \"Id\"))\n     \n     svd.pmod <- ore.odmSVD(~. -Id, IRIS, \n                   odm.settings = list(odms_partition_columns = \"Species\"))\n     summary(svd.pmod)\n     d(svd.pmod)\n     v(svd.pmod)\n     head(predict(svd.pmod, IRIS, supplemental.cols = \"Id\"))\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVD model","message":["%r ","","ore.drop(model = 'SVD_PCA_MODEL', silent=TRUE)","","svd_mod <- ore.odmSVD(formula=~., ","                      data=MTCARS, ","                      auto.data.prep=TRUE, ","                      odm.settings = list(svds_scoring_mode='svds_scoring_pca', ","                                          model_name = \"SVD_PCA_MODEL\")) "],"enabled":true,"result":{"startTime":1737752860213,"interpreter":"r.low","endTime":1737752862338,"results":[{"message":"Warning message:\n\u201cThe created mining model SVD_PCA_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%r","","svd_mod"],"enabled":true,"result":{"startTime":1737752862818,"interpreter":"r.low","endTime":1737752863356,"results":[{"message":"\nCall:\nore.odmSVD(formula = ~., data = MTCARS, auto.data.prep = TRUE, \n    odm.settings = list(svds_scoring_mode = \"svds_scoring_pca\", \n        model_name = \"SVD_PCA_MODEL\"))\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nscoring.mode                             scoring.pca\nu.matrix.output                     u.matrix.disable\nd: \n   FEATURE_ID      VALUE\n1           1 760.183838\n2           2 212.399507\n3           3  17.098697\n4           4   7.275124\n5           5   5.047101\n6           6   3.694440\n7           7   1.718096\n8           8   1.592160\n9           9   1.395823\n10         10   1.172860\n11         11   1.104777","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"The D matrix contains the singular values","message":["%r","","svd_d <- d(svd_mod)","rownames(svd_d) <- svd_d$FEATURE_ID","","value_total <- sum(svd_d$VALUE)","","svd_d <- mutate(svd_d, Percent_of_Total = 100 * (svd_d['VALUE'] / value_total))","","z.show(round(svd_d, 4))"],"enabled":true,"result":{"startTime":1737752863841,"interpreter":"r.low","endTime":1737752865086,"results":[{"message":"FEATURE_ID\tVALUE\tPercent_of_Total.VALUE\n1\t760.1838\t75.0664\n2\t212.3995\t20.974\n3\t17.0987\t1.6885\n4\t7.2751\t0.7184\n5\t5.0471\t0.4984\n6\t3.6944\t0.3648\n7\t1.7181\t0.1697\n8\t1.5922\t0.1572\n9\t1.3958\t0.1378\n10\t1.1729\t0.1158\n11\t1.1048\t0.1091\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Perform dimensionality reduction","message":["%r","","result <- predict(svd_mod, MTCARS, supplemental.cols = c(\"mpg\"), type=\"raw\")","","# displaying MPG & first 3 features. ","z.show(head(result, 10))"],"enabled":true,"result":{"startTime":1737752865587,"interpreter":"r.low","endTime":1737752866386,"results":[{"message":"mpg\tX.1.\tX.2.\tX.3.\tX.4.\tX.5.\tX.6.\tX.7.\tX.8.\tX.9.\tX.10.\tX.11.\n21\t-79.596425453236\t-2.13224061202142\t-2.15333613340533\t-2.7073437384197\t0.702352244759076\t0.314861057947933\t-0.0986950180770124\tNA\t0.200009173273046\t-0.290081907289714\t0.105770558304122\n21\t-79.5985703157962\t-2.1474867487556\t-2.21512370495468\t-2.1782887820005\t0.884385862004981\t0.453438728110384\tNA\t0.0956663049878943\t0.353324348212358\t-0.192835530542163\t0.106904687861641\n22.8\t-133.894096227719\t5.05757018511095\t-2.13795030254933\t0.346032956678648\t-1.10611111845204\t-1.17298583985905\tNA\t-0.136247816823808\t0.197642257181419\t0.0763435256549508\t0.26687134554917\n21.4\t8.51655879484696\t-44.985629671091\t1.23376276548334\t0.827363116783083\t-0.424014455812422\t0.0578970455257693\tNA\t-0.221208000662214\t-0.355984416761749\t-0.0905703921208842\t0.208835379012311\n18.7\t128.686341893339\t-30.8174022216218\t3.34342057033658\t-0.521100003046397\t-0.736580140800689\t0.332909565980348\t0.106304777087882\tNA\t-0.153271428249385\t-0.188622173000037\t-0.109256315083982\n18.1\t-23.2201456102374\t-35.1065178032107\t-3.25956187214875\t1.40053601381768\t-0.802976787293567\t0.088378637420889\t0.238946304390049\t-0.423905505012183\t-0.101294370707459\tNA\t0.27576928173672\n14.3\t159.309024709251\t32.2591966863896\t0.648944088184803\t0.198709007367811\t-0.786494469288403\t-0.0687398831693289\t-0.52974726955483\tNA\t-0.220881389100918\t-0.312991103982857\t-0.100061377554039\n24.4\t-112.615804620903\t-39.7021947611648\t-0.465244131120674\t0.337595738885181\t1.24454630544522\t-0.279500171660479\tNA\t-0.319631038989466\t-0.279439231472062\t0.190170681973456\t-0.318993705404116\n22.8\t-103.534590860715\t-7.51310387381033\t-1.58930973302998\t4.02251950434923\t1.13565518372385\tNA\t0.594978629318353\t0.233493125070079\t0.12594247799336\t-0.348668642626056\t-0.35972361878522\n19.2\t-67.0468766080032\t6.20853559486837\t-3.60719845383896\t-0.319764403085997\t0.96039376487932\t0.529227412992618\t-0.0174164791820424\t0.182496917666572\t-0.542677384788577\t0.412218604946171\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare the same feature from distinct records","message":["%r","","z.show(head(feature_compare(svd_mod, MTCARS, compare.cols = c(\"mpg\"), supplemental.cols = c(\"mpg\")), 10))"],"enabled":true,"result":{"startTime":1737752866855,"interpreter":"r.low","endTime":1737752867433,"results":[{"message":"mpg_A\tmpg_B\tSIMILARITY\n21\t21\t1\n21\t22.8\t-0.800000000000001\n21\t21.4\t0.600000000000012\n21\t18.7\t-1.30000000000001\n21\t18.1\t-1.9\n21\t14.3\t-5.7\n21\t24.4\t-2.39999999999999\n21\t22.8\t-0.800000000000001\n21\t19.2\t-0.800000000000012\n21\t17.8\t-2.2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients of the principal components mapping from the original 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Using the SVD model for Principal Component projections"],"enabled":true,"result":{"startTime":1737752868970,"interpreter":"md.low","endTime":1737752869436,"results":[{"message":"<h2 id=\"using-the-svd-model-for-principal-component-projections\">Using the SVD model for Principal Component projections<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop the table if it exists","message":["%r "," ","ore.drop(table=\"MTCARS_PCA_PROJECTIONS\", silent=TRUE)"],"enabled":true,"result":{"startTime":1737752869937,"interpreter":"r.low","endTime":1737752870556,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a table that contains the projections (SQL section)","message":["%sql ","","CREATE TABLE MTCARS_PCA_PROJECTIONS AS ","SELECT \"drat\", \"disp\", \"hp\", ","       FEATURE_VALUE(SVD_PCA_MODEL, 1 USING *) PCA_PROJ1,","       FEATURE_VALUE(SVD_PCA_MODEL, 2 USING *) PCA_PROJ2,","       FEATURE_VALUE(SVD_PCA_MODEL, 3 USING *) PCA_PROJ3","FROM MTCARS","ORDER BY 1, 2","          "],"enabled":true,"result":{"startTime":1737752871084,"interpreter":"sql.low","endTime":1737752871634,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Look at the PCA projections","message":["%r"," ","ore.sync(table=\"MTCARS_PCA_PROJECTIONS\")","ore.attach()","","z.show(head(MTCARS_PCA_PROJECTIONS, 10))"],"enabled":true,"result":{"startTime":1737752872108,"interpreter":"r.low","endTime":1737752872696,"results":[{"message":"drat\tdisp\thp\tPCA_PROJ1\tPCA_PROJ2\tPCA_PROJ3\n2.76\t225\t105\t-23.2201456102374\t-35.1065178032107\t-3.25956187214875\n2.76\t318\t150\t80.159385595315\t-34.9832140477831\t-1.75865888869117\n2.93\t472\t205\t242.814893034465\t-52.5017581116589\t-0.970423657565827\n3\t460\t215\t236.369886010949\t-38.2807881610081\t-1.09733141221998\n3.07\t275.8\t180\t55.2516020141162\t10.370933554131\t-2.82101556131056\n3.07\t275.8\t180\t55.21167236521\t10.3735090872071\t-1.62415224603971\n3.07\t275.8\t180\t55.1739103347954\t10.3618931245897\t-0.725753087666449\n3.08\t258\t110\t8.51655879484696\t-44.985629671091\t1.23376276548334\n3.08\t400\t175\t164.652521654351\t-48.2398795336414\t5.05284272788993\n3.15\t304\t150\t67.5724305875189\t-28.8940668933876\t-2.50158553193078\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":600,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Original data - top 3 attributes","message":["%r","","# Convert data to local data.frame object for plotting","ore.sync(table = \"MTCARS_PCA_PROJECTIONS\")","mtcars_proj_local <- ore.pull(MTCARS_PCA_PROJECTIONS)","","","splom(~mtcars_proj_local[,c(\"drat\", \"disp\", \"hp\")], ","      groups = drat, data = mtcars_proj_local, panel = panel.superpose, ","      diag.panel = function(x, ...){","        yrng <- current.panel.limits()$ylim","        d <- density(x, na.rm=TRUE, bw=\"SJ\")","        d$y <- with(d, yrng[1] + 0.95 * diff(yrng) * y / max(y) )","        panel.lines(d)","        diag.panel.splom(x, ...)","      })"],"enabled":true,"result":{"startTime":1737752873168,"interpreter":"r.low","endTime":1737752874075,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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abv8u7nfD6CEbOZbY65tv03fvue75vRrDGNlU0o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\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7MnFgVGYiJ6epR7qXDO9OkAN2/mfkRC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\n3FjBYMULUeA3MiGV/cG8Uo3SMi+xyLPmFWlTmXX/EPZQPzusX78+EBQUlOO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ge","title":"SVD-PCA projections - top 3 principal components","message":["%r ","","splom(~mtcars_proj_local[,c(\"PCA_PROJ1\", \"PCA_PROJ2\", \"PCA_PROJ3\")], ","      groups = PCA_PROJ1, data = mtcars_proj_local, panel = panel.superpose, ","      diag.panel = function(x, ...){","        yrng <- current.panel.limits()$ylim","        d <- density(x, na.rm=TRUE, bw=\"SJ\")","        d$y <- with(d, yrng[1] + 0.95 * diff(yrng) * y / max(y) )","        panel.lines(d)","        diag.panel.splom(x, ...)","      })"],"enabled":true,"result":{"startTime":1737752874590,"interpreter":"r.low","endTime":1737752875367,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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OZa3LXNZzfHp8RPenNSNe9quR/crTLjdjN2F/VL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\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g0HFHSRXXva7uDshy8s4aRnSvNVVMt3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r1uHjg7G+fqFIJuPF+Sw8l00ZjXm9erOy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\nGcqTMEkTmvrQN4LRGqxpgzaBCKx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Create model with new parameters to produce U matrix output"],"enabled":true,"result":{"startTime":1737752875855,"interpreter":"md.low","endTime":1737752876311,"results":[{"message":"<h1 id=\"create-model-with-new-parameters-to-produce-u-matrix-output\">Create model with new parameters to produce U matrix output<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop the table if it exists","message":["%r ","","ore.drop(table=\"MTCARS_IND\", silent=TRUE)"],"enabled":true,"result":{"startTime":1737752876827,"interpreter":"r.low","endTime":1737752877430,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table with index variable","message":["%sql","","CREATE TABLE MTCARS_IND AS ","SELECT rownum case_id, ","       b.* ","FROM MTCARS b"],"enabled":true,"result":{"startTime":1737752877902,"interpreter":"sql.low","endTime":1737752878457,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the new table with index variable","message":["%r ","","ore.sync(table = \"MTCARS_IND\")","","z.show(head(MTCARS_IND, 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model with new parameters to produce U matrix","message":["%r ","","svd_mod2 <- ore.odmSVD(~., MTCARS_IND, auto.data.prep=TRUE, ","                       odm.settings = list(svds_scoring_mode='svds_scoring_pca', ","                                           svds_u_matrix_output=\"svds_u_matrix_enable\", ","                                           case_id_column_name=\"CASE_ID\"))","                        ","z.show(head(round(u(svd_mod2),3), 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End of script"],"enabled":true,"result":{"startTime":1737752882207,"interpreter":"md.low","endTime":1737752882669,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737752883165,"interpreter":"md.low","endTime":1737752883621,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R Feature Selection Algorithm-based.dsnb b/machine-learning/notebooks-oml/r/OML4R Feature Selection Algorithm-based.dsnb
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+++ b/machine-learning/notebooks-oml/r/OML4R Feature Selection Algorithm-based.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Selection Algorithm-based","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736289518451,"interpreter":"md.low","endTime":1736289518991,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Feature Selection: Supervised Algorithm","","In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms via OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we build a random forest model to predict whether the customer by insurance or not, then use feature importance to perform feature selection. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","###### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a prerequisite to the current notebook.","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736289519450,"interpreter":"md.low","endTime":1736289519929,"results":[{"message":"<h1 id=\"oml4r-feature-selection-supervised-algorithm\">OML4R Feature Selection: Supervised Algorithm<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms via OML4R.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we build a random forest model to predict whether the customer by insurance or not, then use feature importance to perform feature selection.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-prerequisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a prerequisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/random-forest.html\" target=\"_blank\">OML4Py Random Forest<\/a>"],"enabled":true,"result":{"startTime":1736289520396,"interpreter":"md.low","endTime":1736289520849,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/random-forest.html\" target=\"_blank\">OML4Py Random Forest<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736289521305,"interpreter":"r.low","endTime":1736289526284,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get a proxy object to the CUSTOMER_INSURANCE_LTV table and assign rownames for row indexing","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV', use.keys= TRUE)","ore.attach()","rownames(CUSTOMER_INSURANCE_LTV) <- CUSTOMER_INSURANCE_LTV$CUSTOMER_ID"],"enabled":true,"result":{"startTime":1736289526731,"interpreter":"r.low","endTime":1736289527686,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMER_INSURANCE_LTV table","message":["%r","","z.show(head(CUSTOMER_INSURANCE_LTV))"],"enabled":true,"result":{"startTime":1736289577510,"interpreter":"r.low","endTime":1736289578041,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nOTHER\tWA                        \t0\t4\t24250\t26200\t1\t1\t70508\tCU1                       \tF                         \tIT Staff\t900\tWest\t2\t4200\tYes\t1\t0\t59\t61.27\t38527\t8893\t5\t25\t7\tVERY HIGH\tDORRIS\t2\t1\tHOPKINS\nDIVORCED\tMN                        \t57844\t4\t2500\t4350\t6\t1\t68361\tCU10                      \tF                         \tPROF-35\t800\tWest\t1\t750\tNo\t7\t0\t73\t56.01\t19390.25\t60429\t1\t25\t6\tMEDIUM\tDWANA\t1\t1\tWINN\nMARRIED\tNY                        \t0\t1\t1500\t0\t1\t1\t60185\tCU1000                    \tM                         \tNot specified\t500\tNorthEast\t1\t1500\tNo\t1\t0\t35\t53.18\t22046.25\t547\t1\t33\t3\tHIGH\tLANCE\t1\t1\tBURDEN\nMARRIED\tCA                        \t0\t3\t1000\t650\t3\t1\t71740\tCU10000                   \tM                         \tProgrammer/Developer\t1000\tWest\t1\t1000\tYes\t3\t1\t26\t53.61\t21035\t386\t2\t25\t4\tMEDIUM\tELISHA\t1\t1\tHYDE\nDIVORCED\tFL                        \t0\t1\t3000\t0\t1\t0\t68482\tCU10001                   \tM                         \tIT Staff\t1000\tSouth\t1\t3000\tNo\t1\t1\t44\t53.08\t30020.5\t259\t1\t25\t1\tVERY HIGH\tLONNY\t1\t1\tFAIRCHILD\nDIVORCED\tMI                        \t0\t4\t2600\t0\t3\t1\t71016\tCU10003                   \tF                         \tIT Staff\t1000\tMidwest\t1\t2600\tNo\t1\t1\t46\t53.05\t23354\t0\t1\t25\t2\tHIGH\tSHERRYL\t1\t1\tBAGGETT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create Train and Test (60/40 split) datasets","message":["%r","","set.seed(2)","sampleSize <- nrow(CUSTOMER_INSURANCE_LTV)*.4","ind <- sample(1:nrow(CUSTOMER_INSURANCE_LTV),sampleSize)","group <- as.integer(1:nrow(CUSTOMER_INSURANCE_LTV) %in% ind)","","CUSTOMER_INSURANCE_LTV.train <- CUSTOMER_INSURANCE_LTV[group==FALSE,]","CUSTOMER_INSURANCE_LTV.test <- CUSTOMER_INSURANCE_LTV[group==TRUE,]"],"enabled":true,"result":{"startTime":1736289589728,"interpreter":"r.low","endTime":1736289590811,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply Attribute Importance algorithm to obtain the attribute importance","message":["%r","","ai_mod <- ore.odmAI(BUY_INSURANCE ~., CUSTOMER_INSURANCE_LTV.train)","","ai_mod$importance$variable <- row.names(ai_mod$importance)","z.show(head(ai_mod$importance), 10)"],"enabled":true,"result":{"startTime":1736289596893,"interpreter":"r.low","endTime":1736289598239,"results":[{"message":"importance\trank\tvariable\n0.209333217976261\t1\tBANK_FUNDS\n0.150952228029964\t2\tMONEY_MONTLY_OVERDRAWN\n0.150295332663922\t3\tNUM_TRANS_ATM\n0.11324307283782\t4\tNUM_TRANS_TELLER\n0.111830226045908\t5\tTOTAL_AUTOM_PAYMENTS\n0.0892722533095141\t6\tMONTHLY_CHECKS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get the top attributes from Attribute Importance algorithm greater than threshold value","message":["%r","","threshold <- 0.089","features <- rownames(ai_mod$importance)[ai_mod$importance$importance >= threshold]","","z.show(head(ai_mod$importance, n = 10))"],"enabled":true,"result":{"startTime":1736289616610,"interpreter":"r.low","endTime":1736289617082,"results":[{"message":"importance\trank\tvariable\n0.209333217976261\t1\tBANK_FUNDS\n0.150952228029964\t2\tMONEY_MONTLY_OVERDRAWN\n0.150295332663922\t3\tNUM_TRANS_ATM\n0.11324307283782\t4\tNUM_TRANS_TELLER\n0.111830226045908\t5\tTOTAL_AUTOM_PAYMENTS\n0.0892722533095141\t6\tMONTHLY_CHECKS\n0.0259473661529411\t7\tNUM_DEPENDENTS\n0.0190565008234718\t8\tCHECKING_BALANCE\n0.0187666923125637\t9\tCUSTOMER_TENURE\n0.0129675690090682\t10\tPROFESSION\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Include the target variable to attribute vector","message":["%r","","features <- c(features, \"BUY_INSURANCE\")"],"enabled":true,"result":{"startTime":1736289630714,"interpreter":"r.low","endTime":1736289631182,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a new ORE Frame with top attributes for predicting the target variable","message":["%r","","DF_NEW <- subset(CUSTOMER_INSURANCE_LTV.train, select = features)","z.show(head(DF_NEW))"],"enabled":true,"result":{"startTime":1736289637130,"interpreter":"r.low","endTime":1736289637719,"results":[{"message":"BANK_FUNDS\tMONEY_MONTLY_OVERDRAWN\tNUM_TRANS_ATM\tNUM_TRANS_TELLER\tTOTAL_AUTOM_PAYMENTS\tMONTHLY_CHECKS\tBUY_INSURANCE\n26200\t61.27\t7\t5\t8893\t1\tYes\n650\t53.61\t4\t2\t386\t3\tYes\n0\t53.08\t1\t1\t259\t1\tNo\n0\t53.05\t2\t1\t0\t1\tNo\n0\t53.06\t0\t1\t0\t0\tNo\n300\t53.13\t2\t2\t198\t10\tYes\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model to predict who will purchase insurance using the top attributes","message":["%r","","mod <- ore.odmNB(BUY_INSURANCE~., DF_NEW)","mod"],"enabled":true,"result":{"startTime":1736289644107,"interpreter":"r.low","endTime":1736289645650,"results":[{"message":"\nCall:\nore.odmNB(formula = BUY_INSURANCE ~ ., data = DF_NEW)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1736289650359,"interpreter":"md.low","endTime":1736289650800,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":0,"interpreter":"md.low","endTime":1736289605718,"results":[{"message":"The paragraph run was cancelled when running the notebook as a previous paragraph encountered an 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R Feature Selection Using Summary Statistics.dsnb b/machine-learning/notebooks-oml/r/OML4R Feature Selection Using Summary Statistics.dsnb
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+++ b/machine-learning/notebooks-oml/r/OML4R Feature Selection Using Summary Statistics.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Selection Using Summary Statistics","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737857934290,"interpreter":"md.low","endTime":1737857934798,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Feature Selection Using Summary Statistics","","In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to use OML4R to select features based on number of distinct values, null values, proportion of constant values.","","The dataset `CUSTOMER_INSURANCE_LTV_R` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737857935221,"interpreter":"md.low","endTime":1737857935669,"results":[{"message":"<h1 id=\"oml4r-feature-selection-using-summary-statistics\">OML4R Feature Selection Using Summary Statistics<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4R.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to use OML4R to select features based on number of distinct values, null values, proportion of constant values.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_R<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/1.5.1/oreug/preparing-data-in-database-using-ORE.html\" target=\"_blank\">OML4R Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1737857936103,"interpreter":"md.low","endTime":1737857936541,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/1.5.1/oreug/preparing-data-in-database-using-ORE.html\" target=\"_blank\">OML4R Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load ORE packages","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737857936997,"interpreter":"r.low","endTime":1737857942154,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get proxy object for CUSTOMER_INSURANCE_LTV table","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV_R', use.keys= FALSE)","ore.attach()","","","CUSTOMER_INSURANCE_LTV <- CUSTOMER_INSURANCE_LTV_R","","row.names(CUSTOMER_INSURANCE_LTV) <- CUSTOMER_INSURANCE_LTV$CUSTOMER_ID","","numRows <- nrow(CUSTOMER_INSURANCE_LTV)","","cat('NUmber of Records on the Dataset: ',numRows)"],"enabled":true,"result":{"startTime":1737857942649,"interpreter":"r.low","endTime":1737857943749,"results":[{"message":"NUmber of Records on the Dataset:  14880","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from the table","message":["%r","","z.show(head(CUSTOMER_INSURANCE_LTV), 10)"],"enabled":true,"result":{"startTime":1737857944214,"interpreter":"r.low","endTime":1737857944746,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nNA\tWA                        \tNA\t4\t24250\tNA\t1\t1\tNA\tCU1                       \tF                         \tIT Staff\t900\tWest\t2\t4200\tYes\t1\t0\t59\t61.27\t38527\t8893\t5\t25\t7\tVERY HIGH\tDORRIS\t2\t1\tTHANOS\nNA\tMN                        \tNA\t4\t2500\tNA\t6\t1\tNA\tCU10                      \tF                         \tPROF-35\t800\tWest\t1\t750\tNo\t7\t0\t73\t56.01\t19390.25\t60429\t1\t25\t6\tMEDIUM\tDWANA\t1\t1\tTHANOS\nNA\tNY                        \tNA\t1\t1500\tNA\t1\t1\tNA\tCU1000                    \tM                         \tNot specified\t500\tNorthEast\t1\t1500\tNo\t1\t0\t35\t53.18\t22046.25\t547\t1\t33\t3\tHIGH\tLANCE\t1\t1\tTHANOS\nNA\tCA                        \tNA\t3\t1000\tNA\t3\t1\tNA\tCU10000                   \tM                         \tProgrammer/Developer\t1000\tWest\t1\t1000\tYes\t3\t1\t26\t53.61\t21035\t386\t2\t25\t4\tMEDIUM\tELISHA\t1\t1\tTHANOS\nNA\tFL                        \tNA\t1\t3000\tNA\t1\t0\tNA\tCU10001                   \tM                         \tIT Staff\t1000\tSouth\t1\t3000\tNo\t1\t1\t44\t53.08\t30020.5\t259\t1\t25\t1\tVERY HIGH\tLONNY\t1\t1\tTHANOS\nNA\tMI                        \tNA\t4\t2600\tNA\t3\t1\tNA\tCU10003                   \tF                         \tIT Staff\t1000\tMidwest\t1\t2600\tNo\t1\t1\t46\t53.05\t23354\t0\t1\t25\t2\tHIGH\tSHERRYL\t1\t1\tTHANOS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Identify categorical columns","message":["%r","","cat_cols <- names(CUSTOMER_INSURANCE_LTV)[mapply(is.factor, CUSTOMER_INSURANCE_LTV)]","cat_cols"],"enabled":true,"result":{"startTime":1737857945205,"interpreter":"r.low","endTime":1737857945716,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'MARITAL_STATUS'<\/li><li>'STATE'<\/li><li>'CUSTOMER_ID'<\/li><li>'GENDER'<\/li><li>'PROFESSION'<\/li><li>'REGION'<\/li><li>'BUY_INSURANCE'<\/li><li>'LTV_BIN'<\/li><li>'FIRST_NAME'<\/li><li>'LAST_NAME'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Identify numeric columns","message":["%r","","num_cols <- names(CUSTOMER_INSURANCE_LTV)[mapply(Negate(is.factor), CUSTOMER_INSURANCE_LTV)]","num_cols"],"enabled":true,"result":{"startTime":1737857946145,"interpreter":"r.low","endTime":1737857946616,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'CREDIT_BALANCE'<\/li><li>'CUSTOMER_TENURE'<\/li><li>'MORTGAGE_AMOUNT'<\/li><li>'BANK_FUNDS'<\/li><li>'NUM_DEPENDENTS'<\/li><li>'HAS_CHILDREN'<\/li><li>'INCOME'<\/li><li>'CREDIT_CARD_LIMITS'<\/li><li>'HOME_OWNERSHIP'<\/li><li>'NUM_ONLINE_TRANS'<\/li><li>'MONTHLY_CHECKS'<\/li><li>'NUM_TRANS_KIOSK'<\/li><li>'AGE'<\/li><li>'MONEY_MONTLY_OVERDRAWN'<\/li><li>'LTV'<\/li><li>'TOTAL_AUTOM_PAYMENTS'<\/li><li>'NUM_TRANS_TELLER'<\/li><li>'CHECKING_BALANCE'<\/li><li>'NUM_TRANS_ATM'<\/li><li>'NUM_MORTGAGES'<\/li><li>'CAR_OWNERSHIP'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique values in each column","message":["%r","","unique_counts <- mapply(OREdplyr::n_distinct,CUSTOMER_INSURANCE_LTV)","","unique_counts_df <- as.data.frame(unique_counts)","unique_counts_df$rownames <- rownames(unique_counts_df)","colnames(unique_counts_df) <- c('UNIQUE_COUNT', 'FEATURE')","z.show(unique_counts_df[order(unique_counts_df$UNIQUE_COUNT,decreasing = TRUE) , ])"],"enabled":true,"result":{"startTime":1737857947059,"interpreter":"r.low","endTime":1737857947868,"results":[{"message":"UNIQUE_COUNT\tFEATURE\n13880\tCUSTOMER_ID\n12453\tLTV\n6375\tINCOME\n5071\tTOTAL_AUTOM_PAYMENTS\n3740\tFIRST_NAME\n2798\tCHECKING_BALANCE\n1763\tMORTGAGE_AMOUNT\n1504\tNUM_ONLINE_TRANS\n1348\tLAST_NAME\n1179\tBANK_FUNDS\n964\tCREDIT_BALANCE\n870\tMONEY_MONTLY_OVERDRAWN\n100\tPROFESSION\n69\tAGE\n33\tCREDIT_CARD_LIMITS\n24\tSTATE\n19\tMONTHLY_CHECKS\n11\tNUM_TRANS_KIOSK\n10\tNUM_TRANS_TELLER\n9\tNUM_TRANS_ATM\n8\tMARITAL_STATUS\n7\tNUM_DEPENDENTS\n5\tCUSTOMER_TENURE\n5\tREGION\n4\tLTV_BIN\n3\tHOME_OWNERSHIP\n3\tNUM_MORTGAGES\n2\tHAS_CHILDREN\n2\tGENDER\n2\tBUY_INSURANCE\n2\tCAR_OWNERSHIP\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter categorical 'identifier' columns - filter out columns with > 95% unique values","message":["%r","","ratio <- 0.95","threshold <- numRows*ratio","unique_cat_cols <- unique_counts[cat_cols]","selected_cat_cols <- names(unique_cat_cols)[unique_cat_cols < threshold & unique_cat_cols > 1]","cat('== List of selected Categorical columns ==> ',selected_cat_cols)","","cat('\\n\\n== Number of selected Categorical columns ==> ',length(selected_cat_cols))"],"enabled":true,"result":{"startTime":1737857948318,"interpreter":"r.low","endTime":1737857948808,"results":[{"message":"== List of selected Categorical columns ==>  MARITAL_STATUS STATE CUSTOMER_ID GENDER PROFESSION REGION BUY_INSURANCE LTV_BIN FIRST_NAME LAST_NAME\n\n== Number of selected Categorical columns ==>  10","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"To confirm selected column is not an 'identifier',  use summary function to display unique values","message":["%r","","options(width = 500)","","summary(CUSTOMER_INSURANCE_LTV[selected_cat_cols], maxsum = 30)"],"enabled":true,"result":{"startTime":1737857949285,"interpreter":"r.low","endTime":1737857950152,"results":[{"message":"  MARITAL_STATUS                        STATE                          CUSTOMER_ID                           GENDER                         PROFESSION         REGION     BUY_INSURANCE      LTV_BIN        FIRST_NAME        LAST_NAME    \n SINGLE  :2530   NY                        :4837   CU10027                   :    2   M                         :10177   Programmer/Developer    :2397   NorthEast:5280   No :10864     HIGH     :7127   LOGAN   :   20   THANOS   :12829  \n MARRIED :2393   CA                        :3157   CU1004                    :    2   F                         : 4703   IT Staff                :1153   West     :4201   Yes: 4016     MEDIUM   :4910   JAYSON  :   19   HARE     :    6  \n DIVORCED:2063   MI                        :2594   CU10090                   :    2                                      Clerical                : 599   Midwest  :3264                 VERY HIGH:1425   LACY    :   19   RUSSO    :    6  \n WIDOWED : 475   FL                        : 606   CU10093                   :    2                                      Nurse                   : 598   South    :1123                 LOW      :1418   JIM     :   18   BERNIER  :    5  \n M       : 359   NV                        : 521   CU1010                    :    2                                      Not specified           : 459   Southwest:1012                                  LAURENCE:   18   CALDWELL :    5  \n DIV     : 337   MN                        : 489   CU1021                    :    2                                      Author                  : 458                                                   ROBERTO :   18   DELATORRE:    5  \n OTHER   :  96   DC                        : 443   CU10213                   :    2                                      Cashier                 : 450                                                   SETH    :   18   DENNIS   :    5  \n NA's    :6627   UT                        : 335   CU10236                   :    2                                      Administrative Assistant: 447                                                   TITUS   :   18   DILLON   :    5  \n                 IL                        : 261   CU10240                   :    2                                      Waiter/Waitress         : 444                                                   WILLARD :   18   ECHOLS   :    5  \n                 OR                        : 253   CU10253                   :    2                                      Construction Laborer    : 417                                                   ALEC    :   17   ANGEL    :    4  \n                 NC                        : 252   CU10259                   :    2                                      Truck Driver            : 360                                                   ANTONIA :   17   AVILES   :    4  \n                 MS                        : 176   CU10342                   :    2                                      Veterinarian            : 283                                                   IRA     :   17   BRADSHAW :    4  \n                 WA                        : 166   CU10356                   :    2                                      Software Engineer       : 255                                                   JOEY    :   17   CAMERON  :    4  \n                 WI                        : 161   CU10367                   :    2                                      Sales Representative    : 250                                                   MELVIN  :   17   CONNORS  :    4  \n                 OH                        : 141   CU10370                   :    2                                      Lab Technician          : 245                                                   MERRILL :   17   CURRAN   :    4  \n                 CO                        : 117   CU10429                   :    2                                      First-line Manager      : 240                                                   ALTON   :   16   DENNY    :    4  \n                 TX                        :  92   CU10490                   :    2                                      Administrator           : 224                                                   BARRY   :   16   GOLDEN   :    4  \n                 MO                        :  56   CU10499                   :    2                                      Bank Teller             : 208                                                   BERNARD :   16   GRANTHAM :    4  \n                 NM                        :  51   CU10501                   :    2                                      Technical Writer        : 206                                                   BURTON  :   16   HATCH    :    4  \n                 OK                        :  51   CU1051                    :    2                                      Publisher               : 197                                                   CARL    :   16   HEATH    :    4  \n                 AL                        :  48   CU10514                   :    2                                      Law Enforcement Officer : 190                                                   EMMANUEL:   16   JEAN     :    4  \n                 LA                        :  41   CU10518                   :    2                                      Fireman                 : 188                                                   ERVIN   :   16   LEARY    :    4  \n                 AK                        :  19   CU10526                   :    2                                      Dentist                 : 178                                                   HIRAM   :   16   LIPSCOMB :    4  \n                 AZ                        :  13   CU10557                   :    2                                      School Teacher          : 170                                                   LELAND  :   16   LUDWIG   :    4  \n                                                   CU10604                   :    2                                      Secretary               : 161                                                   LEONEL  :   16   NAVA     :    4  \n                                                   CU10629                   :    2                                      DBA                     : 148                                                   LINWOOD :   16   OHARA    :    4  \n                                                   CU10639                   :    2                                      Mason                   : 147                                                   ROLANDO :   16   PARNELL  :    4  \n                                                   CU10645                   :    2                                      Accountant              : 141                                                   TYLER   :   16   SAUNDERS :    4  \n                                                   CU10650                   :    2                                      Medical Doctor          : 136                                                   WARNER  :   16   SHERIDAN :    4  \n                                                   (Other)                   :14822                                      (Other)                 :3531                                                   (Other) :14388   (Other)  : 1929  ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create an OML dataframe with selected features only","message":["%r","","SELECTED_CAT_DF <- CUSTOMER_INSURANCE_LTV[selected_cat_cols]","z.show(head(SELECTED_CAT_DF))"],"enabled":true,"result":{"startTime":1737857950592,"interpreter":"r.low","endTime":1737857951067,"results":[{"message":"MARITAL_STATUS\tSTATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nNA\tWA                        \tCU1                       \tF                         \tIT Staff\tWest\tYes\tVERY HIGH\tDORRIS\tTHANOS\nNA\tMN                        \tCU10                      \tF                         \tPROF-35\tWest\tNo\tMEDIUM\tDWANA\tTHANOS\nNA\tNY                        \tCU1000                    \tM                         \tNot specified\tNorthEast\tNo\tHIGH\tLANCE\tTHANOS\nNA\tCA                        \tCU10000                   \tM                         \tProgrammer/Developer\tWest\tYes\tMEDIUM\tELISHA\tTHANOS\nNA\tFL                        \tCU10001                   \tM                         \tIT Staff\tSouth\tNo\tVERY HIGH\tLONNY\tTHANOS\nNA\tMI                        \tCU10003                   \tF                         \tIT Staff\tMidwest\tNo\tHIGH\tSHERRYL\tTHANOS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For all columns, identify columns with > 40% missing values","message":["%r","","ratio <- 0.4","threshold <- numRows*ratio","count_na <- as.list(colSums(is.na(CUSTOMER_INSURANCE_LTV)))","count_na <- Filter(function(x) any(x>threshold), count_na)","","many_missing_cols <- names(count_na)","cat('== Columns with more than 40% missing values== \\n',many_missing_cols)"],"enabled":true,"result":{"startTime":1737857951509,"interpreter":"r.low","endTime":1737857953386,"results":[{"message":"== Columns with more than 40% missing values== \n MARITAL_STATUS CREDIT_BALANCE BANK_FUNDS INCOME","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter 'constant' columns - those with > 90% same values","message":["%r","","ratio <- 0.90","threshold <- numRows*ratio","tables <- mapply(table,CUSTOMER_INSURANCE_LTV)","tables <- Filter(function(x) any(x>threshold), tables)","","major_constant_cols <- names(tables)","cat('== Columns with more than 90% constant values== \\n',major_constant_cols)"],"enabled":true,"result":{"startTime":1737857953825,"interpreter":"r.low","endTime":1737857954897,"results":[{"message":"== Columns with more than 90% constant values== \n CAR_OWNERSHIP","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create an OML dataframe with 'constant' columns and missing value columns filtered out","message":["%r","","filtered_cols <- c(many_missing_cols,major_constant_cols)","selected_cat_cols_final <- setdiff((selected_cat_cols),(filtered_cols))","SELECTED_DF <- CUSTOMER_INSURANCE_LTV[selected_cat_cols_final]","z.show(head(SELECTED_DF))"],"enabled":true,"result":{"startTime":1737857955340,"interpreter":"r.low","endTime":1737857955825,"results":[{"message":"STATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nWA                        \tCU1                       \tF                         \tIT Staff\tWest\tYes\tVERY HIGH\tDORRIS\tTHANOS\nMN                        \tCU10                      \tF                         \tPROF-35\tWest\tNo\tMEDIUM\tDWANA\tTHANOS\nNY                        \tCU1000                    \tM                         \tNot specified\tNorthEast\tNo\tHIGH\tLANCE\tTHANOS\nCA                        \tCU10000                   \tM                         \tProgrammer/Developer\tWest\tYes\tMEDIUM\tELISHA\tTHANOS\nFL                        \tCU10001                   \tM                         \tIT Staff\tSouth\tNo\tVERY HIGH\tLONNY\tTHANOS\nMI                        \tCU10003                   \tF                         \tIT Staff\tMidwest\tNo\tHIGH\tSHERRYL\tTHANOS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes Classification model to predict who will purchase insurance using the selected attributes","message":["%r","","mod <- ore.odmNB(BUY_INSURANCE~., SELECTED_DF)","mod"],"enabled":true,"result":{"startTime":1737857956250,"interpreter":"r.low","endTime":1737857958811,"results":[{"message":"\nCall:\nore.odmNB(formula = BUY_INSURANCE ~ ., data = SELECTED_DF)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment 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10))"],"enabled":true,"result":{"startTime":1737857959264,"interpreter":"r.low","endTime":1737857960253,"results":[{"message":"PROB_OF_NO\tPROB_OF_YES\tCUSTOMER_ID\tBUY_INSURANCE\tPREDICTION\n0.596441856078405\t0.403558143921595\tCU1                       \tYes\tNo\n0.596441856078405\t0.403558143921595\tCU10                      \tNo\tNo\n0.69219757447323\t0.30780242552677\tCU1000                    \tNo\tNo\n0.731505794400344\t0.268494205599656\tCU10000                   \tYes\tNo\n0.731505794400344\t0.268494205599656\tCU10001                   \tNo\tNo\n0.54953663845901\t0.45046336154099\tCU10003                   \tNo\tNo\n0.731505794400344\t0.268494205599656\tCU10004                   \tYes\tNo\n0.731505794400344\t0.268494205599656\tCU10005                   \tNo\tNo\n0.69219757447323\t0.30780242552677\tCU10006                   \tNo\tNo\n0.69219757447323\t0.30780242552677\tCU10007                   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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Partitioned Model SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736279208920,"interpreter":"md.low","endTime":1736279209456,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"OML4Py Partitioned Model","message":["%md","# OML4R Partitioned Model SVM","","Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. ","Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The applicable sub-model is ","chosen by the system based on the individual row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models. ","","In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. ","The model is then used to predict the target only, then predicting the target with prediction details. ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736279209885,"interpreter":"md.low","endTime":1736279210351,"results":[{"message":"<h1 id=\"oml4r-partitioned-model-svm\">OML4R Partitioned Model SVM<\/h1>\n<p>Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition.\nSub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The applicable sub-model is\nchosen by the system based on the individual row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models.<\/p>\n<p>In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender.\nThe model is then used to predict the target only, then predicting the target with prediction details.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-CE621172-D8BF-48FF-9854-D0BC344E1897\" target=\"_blank\">OML4R Partitioned Models Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a>"],"enabled":true,"result":{"startTime":1736279210773,"interpreter":"md.low","endTime":1736279211214,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-CE621172-D8BF-48FF-9854-D0BC344E1897\" target=\"_blank\">OML4R Partitioned Models Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736279211651,"interpreter":"r.low","endTime":1736279216800,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare CUSTOMER_DEMO dataset using data from the SH schema","message":["%r","","ore.sync(query = c(\"SUPP_DF\" = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, ","                   OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","","ore.sync(query = c(\"CUSTOMER_DF\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, ","                   CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.attach()","","CUST_DF <- merge(SUPP_DF, CUSTOMER_DF)","row.names(CUST_DF) <- CUST_DF$CUST_ID"],"enabled":true,"result":{"startTime":1736279217241,"interpreter":"r.low","endTime":1736279218482,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display number of rows and columns","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1736279218908,"interpreter":"r.low","endTime":1736279219477,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>12<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUSTOMERS_DEMO data","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1736279219895,"interpreter":"r.low","endTime":1736279220374,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n100001\t< Bach.\t0\t2\tExec.\t3\t0\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\n100002\tBach.\t0\t2\tProf.\t4\t0\tF\tNeverM\t1962\tL: 300,000 and above\t7000\n100003\t< Bach.\t0\t2\tSales\t6\t0\tM\tNeverM\t1969\tK: 250,000 - 299,999\t7000\n100004\t< Bach.\t0\t2\tSales\t5\t0\tF\tDivorc.\t1953\tK: 250,000 - 299,999\t15000\n100005\tAssoc-A\t1\t3\tCrafts\t5\t0\tM\tMarried\t1957\tB: 30,000 - 49,999\t1500\n100006\t< Bach.\t0\t9+\tProf.\t2\t1\tF\tNeverM\t1983\tG: 130,000 - 149,999\t5000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into training and testing sets in R ","message":["%r","","set.seed(123)","rownames(CUST_DF) <- CUST_DF$CUST_ID","","sampleSize <- .6 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","","# Create training data set","CUST_DF.train <- CUST_DF[index,]","","# Create test data set","CUST_DF.test <- CUST_DF[setdiff(1:nrow(CUST_DF),index),]","","class(CUST_DF.train)","cat(\"\\nTraining data: \",dim(CUST_DF.train))","","","class(CUST_DF.test)","cat(\"\\nTest data: \", dim(CUST_DF.test))"],"enabled":true,"result":{"startTime":1736279220813,"interpreter":"r.low","endTime":1736279221672,"results":[{"message":"'ore.frame'\nTraining data:  2700 12\n","type":"HTML"},{"message":"'ore.frame'\nTest data:  1800 12","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for Partitioning (Shared Settings)","message":["%md","","### Examples of possible setting overrides for Partitioning (Shared Settings)","","If the user does not override the default settings, relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","","* Requests the building of a partitioned model. The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.  ","> 'odms_partition_columns' : '<Comma separated list of attributes>'","     ","* Controls the parallel building of partitioned models.     ","    ODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all sub-processes.","    ODMS_PARTITION_BUILD_INTER builds each partition entirely in a single sub-process, but multiple partitions may be built at the same time because multiple sub-processes are active.","    ODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.","> 'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'"],"enabled":true,"result":{"startTime":1736279222094,"interpreter":"md.low","endTime":1736279222516,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-partitioning-shared-settings\">Examples of possible setting overrides for Partitioning (Shared Settings)<\/h3>\n<p>If the user does not override the default settings, relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Requests the building of a partitioned model. The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_columns' : '<Comma separated list of attributes>'<\/p>\n<\/blockquote>\n<ul>\n<li>Controls the parallel building of partitioned models.<br />\nODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all sub-processes.\nODMS_PARTITION_BUILD_INTER builds each partition entirely in a single sub-process, but multiple partitions may be built at the same time because multiple sub-processes are active.\nODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build Partitioned Model using the SVM algorithm to predict YRS_RESIDENCE, partitioned by CUST_GENDER","message":["%r","","ore.drop(model = 'SVM_PARTITIONED_REGRESSION_MODEL')","","settings <- list(odms_partition_columns = 'CUST_GENDER',","                 model_name             = 'SVM_PARTITIONED_REGRESSION_MODEL')","","SVM_PARTITIONED_REGRESSION_MODEL <- ore.odmSVM(YRS_RESIDENCE~.-CUST_ID, ","                                               CUST_DF.train, ","                                               \"regression\", ","                                               kernel.function = \"linear\", ","                                               odm.settings=settings)","","SVM_PARTITIONED_REGRESSION_MODEL"],"enabled":true,"result":{"startTime":1736279222983,"interpreter":"r.low","endTime":1736279225434,"results":[{"message":"Warning message:\n\u201cThe created mining model SVM_PARTITIONED_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", kernel.function = \"linear\", odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.max.partitions                             1000\nodms.missing.value.treatment odms.missing.value.auto\nodms.partition.columns                   CUST_GENDER\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\nNumber of partitions: 2\nPartitions:\n  PARTITION_NAME CUST_GENDER\n1              F           F\n2              M           M","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show model details for Partitioned Model using summary","message":["%r","","summary(SVM_PARTITIONED_REGRESSION_MODEL)"],"enabled":true,"result":{"startTime":1736279225858,"interpreter":"r.low","endTime":1736279226934,"results":[{"message":"$F\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", kernel.function = \"linear\", odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.max.partitions                             1000\nodms.missing.value.treatment odms.missing.value.auto\nodms.partition.columns                   CUST_GENDER\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-7.541000 -0.367100 -0.000865  0.106000  0.422200  8.008000 \n\nCoefficients: \n   PARTITION_NAME            variable                value      estimate\n1               F         (Intercept)                      -0.0123866038\n2               F       AFFINITY_CARD                       0.0327332427\n3               F   CUST_CREDIT_LIMIT                      -0.0003003418\n4               F   CUST_INCOME_LEVEL      A: Below 30,000  0.0144772514\n5               F   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.0937785670\n6               F   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.0765888558\n7               F   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.0354640998\n8               F   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.0336308215\n9               F   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.0810219264\n10              F   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.0138736123\n11              F   CUST_INCOME_LEVEL H: 150,000 - 169,999 -0.0019072163\n12              F   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.0019811865\n13              F   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.0089657204\n14              F   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.0368971012\n15              F   CUST_INCOME_LEVEL L: 300,000 and above  0.0714359257\n16              F CUST_MARITAL_STATUS              Divorc.  0.1165117556\n17              F CUST_MARITAL_STATUS              Mabsent -0.3003009390\n18              F CUST_MARITAL_STATUS               Mar-AF  0.1844910241\n19              F CUST_MARITAL_STATUS              Married  0.0602316078\n20              F CUST_MARITAL_STATUS               NeverM  0.0429441732\n21              F CUST_MARITAL_STATUS               Separ. -0.0138072480\n22              F CUST_MARITAL_STATUS              Widowed -0.0900703736\n23              F  CUST_YEAR_OF_BIRTH                      -0.8028132834\n24              F           EDUCATION                 10th -0.2642173866\n25              F           EDUCATION                 11th -0.3017945799\n26              F           EDUCATION                 12th  0.0532097721\n27              F           EDUCATION              1st-4th -0.1228023136\n28              F           EDUCATION              5th-6th  0.0207838682\n29              F           EDUCATION              7th-8th -0.2472535694\n30              F           EDUCATION                  9th -0.1375809726\n31              F           EDUCATION              < Bach. -0.0626355753\n32              F           EDUCATION              Assoc-A  0.0875190153\n33              F           EDUCATION              Assoc-V  0.0081403956\n34              F           EDUCATION                Bach.  0.0172364889\n35              F           EDUCATION              HS-grad  0.0339944938\n36              F           EDUCATION              Masters  0.0404687332\n37              F           EDUCATION                  PhD  0.3095448834\n38              F           EDUCATION              Presch.  0.1814107429\n39              F           EDUCATION               Profsc  0.3839760039\n40              F      HOUSEHOLD_SIZE                    1 -0.0272075761\n41              F      HOUSEHOLD_SIZE                    2  0.0815021730\n42              F      HOUSEHOLD_SIZE                  4-5 -0.1208557680\n43              F      HOUSEHOLD_SIZE                  6-8  0.0574389732\n44              F      HOUSEHOLD_SIZE                   9+  0.0091221979\n45              F          OCCUPATION                    ? -0.0678976938\n46              F          OCCUPATION              Cleric.  0.0651769314\n47              F          OCCUPATION               Crafts  0.1783214854\n48              F          OCCUPATION                Exec.  0.1176253140\n49              F          OCCUPATION              Farming -0.2476850314\n50              F          OCCUPATION              Handler -0.0842033528\n51              F          OCCUPATION              House-s -0.0467816866\n52              F          OCCUPATION              Machine  0.1108528629\n53              F          OCCUPATION                Other -0.0897725814\n54              F          OCCUPATION                Prof.  0.0900524708\n55              F          OCCUPATION              Protec. -0.0615914076\n56              F          OCCUPATION                Sales  0.0469075500\n57              F          OCCUPATION              TechSup  0.0364540390\n58              F          OCCUPATION              Transp. -0.0474589000\n59              F         Y_BOX_GAMES                       0.0009125024\n\n\n$M\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", kernel.function = \"linear\", odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.max.partitions                             1000\nodms.missing.value.treatment odms.missing.value.auto\nodms.partition.columns                   CUST_GENDER\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-8.005000 -0.655000  0.003037 -0.071140  0.506400  6.722000 \n\nCoefficients: \n   PARTITION_NAME            variable                value      estimate\n1               M         (Intercept)                      -1.116601e-01\n2               M       AFFINITY_CARD                       3.916771e-02\n3               M   CUST_CREDIT_LIMIT                       1.551717e-02\n4               M   CUST_INCOME_LEVEL      A: Below 30,000  1.055325e-02\n5               M   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -5.975809e-03\n6               M   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -4.513304e-03\n7               M   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -5.041152e-02\n8               M   CUST_INCOME_LEVEL  E: 90,000 - 109,999  1.009648e-01\n9               M   CUST_INCOME_LEVEL F: 110,000 - 129,999 -9.366213e-02\n10              M   CUST_INCOME_LEVEL G: 130,000 - 149,999  1.430737e-02\n11              M   CUST_INCOME_LEVEL H: 150,000 - 169,999  8.029570e-02\n12              M   CUST_INCOME_LEVEL I: 170,000 - 189,999 -4.878279e-02\n13              M   CUST_INCOME_LEVEL J: 190,000 - 249,999 -2.401287e-02\n14              M   CUST_INCOME_LEVEL K: 250,000 - 299,999  3.280594e-02\n15              M   CUST_INCOME_LEVEL L: 300,000 and above -1.156867e-02\n16              M CUST_MARITAL_STATUS              Divorc.  6.732846e-02\n17              M CUST_MARITAL_STATUS              Mabsent -9.392896e-02\n18              M CUST_MARITAL_STATUS              Married  2.414774e-02\n19              M CUST_MARITAL_STATUS               NeverM -2.414206e-02\n20              M CUST_MARITAL_STATUS               Separ.  2.659496e-02\n21              M CUST_MARITAL_STATUS              Widowed -1.324921e-07\n22              M  CUST_YEAR_OF_BIRTH                      -6.207321e-01\n23              M           EDUCATION                 10th -3.305996e-02\n24              M           EDUCATION                 11th -2.110310e-01\n25              M           EDUCATION                 12th -1.765052e-01\n26              M           EDUCATION              1st-4th  3.462087e-01\n27              M           EDUCATION              5th-6th -9.042694e-03\n28              M           EDUCATION              7th-8th -3.180242e-01\n29              M           EDUCATION                  9th  1.282928e-02\n30              M           EDUCATION              < Bach. -8.454202e-02\n31              M           EDUCATION              Assoc-A  4.644634e-02\n32              M           EDUCATION              Assoc-V -1.351945e-03\n33              M           EDUCATION                Bach. -1.832365e-02\n34              M           EDUCATION              HS-grad -2.148696e-02\n35              M           EDUCATION              Masters  5.904819e-02\n36              M           EDUCATION                  PhD  1.926538e-01\n37              M           EDUCATION               Profsc  2.161813e-01\n38              M      HOUSEHOLD_SIZE                    1 -8.280626e-02\n39              M      HOUSEHOLD_SIZE                    2  5.424788e-02\n40              M      HOUSEHOLD_SIZE                    3  5.470263e-02\n41              M      HOUSEHOLD_SIZE                  6-8  4.689173e-03\n42              M      HOUSEHOLD_SIZE                   9+ -3.083342e-02\n43              M          OCCUPATION                    ? -3.174529e-02\n44              M          OCCUPATION              Armed-F -5.966992e-02\n45              M          OCCUPATION              Cleric.  4.177425e-02\n46              M          OCCUPATION               Crafts  2.936820e-02\n47              M          OCCUPATION                Exec.  2.206533e-01\n48              M          OCCUPATION              Farming  2.256248e-01\n49              M          OCCUPATION              Handler -2.835822e-02\n50              M          OCCUPATION              House-s -5.182864e-01\n51              M          OCCUPATION              Machine -2.844732e-02\n52              M          OCCUPATION                Other -9.918860e-02\n53              M          OCCUPATION                Prof.  6.156660e-03\n54              M          OCCUPATION              Protec.  5.581363e-02\n55              M          OCCUPATION                Sales  1.303405e-01\n56              M          OCCUPATION              TechSup -1.309249e-01\n57              M          OCCUPATION              Transp.  1.868894e-01\n58              M         Y_BOX_GAMES                      -6.745903e-02\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict on the test set and display prediction result - note the use of the top level model only","message":["%r","","RES_DF <- ore.predict(SVM_PARTITIONED_REGRESSION_MODEL, ","                      CUST_DF.test, ","                      supplemental.cols = c(\"CUST_ID\", \"YRS_RESIDENCE\") )","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1736279227369,"interpreter":"r.low","endTime":1736279227915,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100003\t6\t3.86153959466658\n100006\t2\t1.90262635628896\n100008\t5\t4.24654864723995\n100010\t3\t2.98786630175208\n100014\t5\t5.09175035393478\n100015\t6\t4.95775880233581\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict on the test set and display prediction result - note the use of the top level model only","message":["%r","","RES_DF <- ore.predict(SVM_PARTITIONED_REGRESSION_MODEL, ","                      CUST_DF.test, ","                      supplemental.cols = c(\"CUST_ID\", \"YRS_RESIDENCE\"),","                      topN.attrs = 2)","","z.show(head(RES_DF, 5))"],"enabled":true,"result":{"startTime":1736279228345,"interpreter":"r.low","endTime":1736279228874,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100003\t6\t3.86153959466658\n100006\t2\t1.90262635628896\n100008\t5\t4.24654864723995\n100010\t3\t2.98786630175208\n100014\t5\t5.09175035393478\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test dataset","message":["%r","","ore.drop(table = 'SVM_TEST_DATA')","","ore.create(CUST_DF.test, table = 'SVM_TEST_DATA')"],"enabled":true,"result":{"startTime":1736279229299,"interpreter":"r.low","endTime":1736279230374,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","      PREDICTION(SVM_PARTITIONED_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","      PREDICTION_DETAILS(SVM_PARTITIONED_REGRESSION_MODEL USING *) PD","      FROM SVM_TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","      XMLTABLE('/Details'","               PASSING OUT.PD","               COLUMNS ","               \"Attribute1\" XMLType PATH 'Attribute[1]',","               \"Attribute2\" XMLType PATH 'Attribute[2]') ","      OUTPRED"],"enabled":true,"result":{"startTime":1736279230806,"interpreter":"sql.low","endTime":1736279232652,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100003\t3.862\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".174\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".098\" \n100006\t1.903\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".022\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".001\" \n100008\t4.247\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".298\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".142\" \n100010\t2.988\t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".018\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".006\" \n100014\t5.092\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".805\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".084\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1736279233071,"interpreter":"md.low","endTime":1736279233486,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1736279233915,"interpreter":"md.low","endTime":1736279234320,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R REST API","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736292088642,"interpreter":"md.low","endTime":1736292089230,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R REST API for invoking embedded R execution","","On Oracle Autonomous Database, OML4R contains a REST API to run user-defined R functions saved in the script repository. REST APIs can be called from a web development application, improving the portability of the interface to other types of platforms, increasing project scalability, and allowing components to evolve independently. Use the OML4R REST API to invoke user-defined R functions and to list those available in the R script repository.","","To run a script, it must reside in the R script repository. An Oracle Machine Learning cloud service account **username** and **password** must be provided for authentication.","","### **REST API Workflow**","","In this workflow, we highlight a few of the features of the REST API:","","- Authenticating the database user and obtaining a token","- Loading a database table into the database","- Saving scripts in the database R script repository","- Executing the saved R scripts from the REST API using both synchronous and asynchronous modes","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736292089642,"interpreter":"md.low","endTime":1736292090079,"results":[{"message":"<h1 id=\"oml4r-rest-api-for-invoking-embedded-r-execution\">OML4R REST API for invoking embedded R execution<\/h1>\n<p>On Oracle Autonomous Database, OML4R contains a REST API to run user-defined R functions saved in the script repository. REST APIs can be called from a web development application, improving the portability of the interface to other types of platforms, increasing project scalability, and allowing components to evolve independently. Use the OML4R REST API to invoke user-defined R functions and to list those available in the R script repository.<\/p>\n<p>To run a script, it must reside in the R script repository. An Oracle Machine Learning cloud service account <strong>username<\/strong> and <strong>password<\/strong> must be provided for authentication.<\/p>\n<h3 id=\"rest-api-workflow\"><strong>REST API Workflow<\/strong><\/h3>\n<p>In this workflow, we highlight a few of the features of the REST API:<\/p>\n<ul>\n<li>Authenticating the database user and obtaining a token<\/li>\n<li>Loading a database table into the database<\/li>\n<li>Saving scripts in the database R script repository<\/li>\n<li>Executing the saved R scripts from the REST API using both synchronous and asynchronous modes<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1736292090493,"interpreter":"md.low","endTime":1736292090913,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"REST  API Authentication","message":["%md","","To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' ","                   --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"' <yourpassword>'\"}' ","                   \"<OML Service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance","","For example, Oracle Machine Learning user **OMLUSER** with password **OMLPASSWD** and cloud service url **https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com** authenticates and obtain a REST token using the ","following approach:"],"enabled":true,"result":{"startTime":1736292091325,"interpreter":"md.low","endTime":1736292091741,"results":[{"message":"<p>To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' \n               --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, &quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' \n               &quot;&lt;OML Service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>oml-cloud-service-url<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n<p>For example, Oracle Machine Learning user <strong>OMLUSER<\/strong> with password <strong>OMLPASSWD<\/strong> and cloud service url <strong><a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a><\/strong> authenticates and obtain a REST token using the\nfollowing approach:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save token to a variable","message":["%md","","Copy the `accessToken` field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1736292092159,"interpreter":"md.low","endTime":1736292092574,"results":[{"message":"<p>Copy the <code>accessToken<\/code> field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ export token='eyJhbGciOiJ...'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load an R data frame to an Oracle Database table","message":["%md","","Using the `ore.create` function, create an OML proxy object `IRIS_R` from the `iris` data frame. The OML proxy object `IRIS_R` represents a database table. We first drop the table if already exists.","","The `ore.warn.order` option specifies whether you want OML4R to display a warning message when using an ore.frame object. We turn off turn the warnings off so they do not appear in the output, and display the first 6 rows of the `IRIS_R` proxy object. "],"enabled":true,"result":{"startTime":1736292093003,"interpreter":"md.low","endTime":1736292093419,"results":[{"message":"<p>Using the <code>ore.create<\/code> function, create an OML proxy object <code>IRIS_R<\/code> from the <code>iris<\/code> data frame. The OML proxy object <code>IRIS_R<\/code> represents a database table. We first drop the table if already exists.<\/p>\n<p>The <code>ore.warn.order<\/code> option specifies whether you want OML4R to display a warning message when using an ore.frame object. We turn off turn the warnings off so they do not appear in the output, and display the first 6 rows of the <code>IRIS_R<\/code> proxy object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r ","","options(ore.warn.order=FALSE)","","suppressWarnings(ore.drop(table=\"IRIS_REST\"))","","ore.create(iris, table=\"IRIS_REST\")","","z.show(head(IRIS_REST))"],"enabled":true,"result":{"startTime":1736292093851,"interpreter":"r.low","endTime":1736292099119,"results":[{"message":"Sepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n4.6\t3.6\t1\t0.2\tsetosa\n4.3\t3\t1.1\t0.1\tsetosa\n5\t3.2\t1.2\t0.2\tsetosa\n5.8\t4\t1.2\t0.2\tsetosa\n4.4\t3\t1.3\t0.2\tsetosa\n4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an R function and save it to the R script repository","message":["%md","","Create a function `group.countR` that returns the counts for each category in the `SPECIES` column.  Save the `group.countR` function to the R script repository using `ore.scriptCreate`, overwriting any existing script with the same name. The script is invoked from the R interface, followed by the OML4R R and REST APIs for embedded R execution."],"enabled":true,"result":{"startTime":1736292099535,"interpreter":"md.low","endTime":1736292099936,"results":[{"message":"<p>Create a function <code>group.countR<\/code> that returns the counts for each category in the <code>SPECIES<\/code> column.  Save the <code>group.countR<\/code> function to the R script repository using <code>ore.scriptCreate<\/code>, overwriting any existing script with the same name. The script is invoked from the R interface, followed by the OML4R R and REST APIs for embedded R execution.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","group.countR <- function(df) {","    x <- aggregate(df[, \"Species\"], by=list(df$Species), FUN=length)","    names(x) <- c(\"SPECIES\", \"COUNT\")","    return(x)}","    ","","ore.scriptCreate(\"group.countR\", group.countR, overwrite=TRUE)"],"enabled":true,"result":{"startTime":1736292100353,"interpreter":"r.low","endTime":1736292100822,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the  script in the R script repository","message":["%md","","To check for the existence of the saved `group.countR` script from the OML4R R interface, run the function `ore.scriptList`. We set the `type` parameter to `user` to show only the scripts created by the current user."],"enabled":true,"result":{"startTime":1736292101238,"interpreter":"md.low","endTime":1736292101643,"results":[{"message":"<p>To check for the existence of the saved <code>group.countR<\/code> script from the OML4R R interface, run the function <code>ore.scriptList<\/code>. We set the <code>type<\/code> parameter to <code>user<\/code> to show only the scripts created by the current user.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptList(name = \"group.countR\", type=\"user\")"],"enabled":true,"result":{"startTime":1736292102061,"interpreter":"r.low","endTime":1736292102522,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>group.countR<\/td><td><span style=white-space:pre-wrap>function (df) \n{\n    x &lt;- aggregate(df[, \"Species\"], by = list(df$Species), FUN = length)\n    names(x) &lt;- c(\"SPECIES\", \"COUNT\")\n    return(x)\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Run the script in R","message":["%r","","group.countR(iris)"],"enabled":true,"result":{"startTime":1736292102941,"interpreter":"r.low","endTime":1736292103403,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 3 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>SPECIES<\/th><th scope=col>COUNT<\/th><\/tr>\n\t<tr><th scope=col>&lt;fct&gt;<\/th><th scope=col>&lt;int&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>setosa    <\/td><td>50<\/td><\/tr>\n\t<tr><td>versicolor<\/td><td>50<\/td><\/tr>\n\t<tr><td>virginica <\/td><td>50<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the script using groupApply","message":["%md","","The `groupApply` function runs a user-defined function on data grouped by a specified column in the database environment. In this case, the grouping column is `Species` and the script is `group.countR`."],"enabled":true,"result":{"startTime":1736292103821,"interpreter":"md.low","endTime":1736292104226,"results":[{"message":"<p>The <code>groupApply<\/code> function runs a user-defined function on data grouped by a specified column in the database environment. In this case, the grouping column is <code>Species<\/code> and the script is <code>group.countR<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","ore.groupApply(IRIS_REST, INDEX=IRIS_REST$Species, FUN.NAME=\"group.countR\")"],"enabled":true,"result":{"startTime":1736292104648,"interpreter":"r.low","endTime":1736292105566,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 3 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>SPECIES<\/th><th scope=col>COUNT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;int&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>setosa    <\/td><td>50<\/td><\/tr>\n\t<tr><td>versicolor<\/td><td>50<\/td><\/tr>\n\t<tr><td>virginica <\/td><td>50<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### **Synchronous REST API calls**"],"enabled":true,"result":{"startTime":1736292105982,"interpreter":"md.low","endTime":1736292106384,"results":[{"message":"<h3 id=\"synchronous-rest-api-calls\"><strong>Synchronous REST API calls<\/strong><\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View script in the R script repository using REST","message":["%md","","Verify the existence of `group.countR` in the script repository again, this time using the REST API with `cURL`.  Send a GET request to the `scripts` endpoint, and the scripts available to the OML user are returned in JSON format.","","Note, the default REST API execution mode is *synchronous*, so code execution will wait for the REST API call to return before additional calls may be initiated. ","","The curl invocation and result are shown below. The Linux command line JSON parser jq is used to format the returned value. To install jq on your Linux REST client, run the command sudo yum install jq."],"enabled":true,"result":{"startTime":1736292106800,"interpreter":"md.low","endTime":1736292107204,"results":[{"message":"<p>Verify the existence of <code>group.countR<\/code> in the script repository again, this time using the REST API with <code>cURL<\/code>.  Send a GET request to the <code>scripts<\/code> endpoint, and the scripts available to the OML user are returned in JSON format.<\/p>\n<p>Note, the default REST API execution mode is <em>synchronous<\/em>, so code execution will wait for the REST API call to return before additional calls may be initiated.<\/p>\n<p>The curl invocation and result are shown below. The Linux command line JSON parser jq is used to format the returned value. To install jq on your Linux REST client, run the command sudo yum install jq.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to view the scripts saved in the script repository","message":["","$ curl -X GET --header \"Authorization: Bearer <token>\" \\","              --header 'Accept: application/json' \\","              \"<OML Service URL>/oml/api/r-scripts/v1/scripts\" | jq  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"result\": [","    {","      \"SCRIPT\": \"function (df) \\n{\\n    x <- aggregate(df[, \\\"Species\\\"], by = list(df$Species), FUN = length)\\n  names(x) <- c(\\\"SPECIES\\\", \\\"COUNT\\\")\\n  return(x)\\n}\",","      \"NAME\": \"group.countR\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the script from the REST API","message":["%md","","Now invoke the `group.countR` function from the REST API using the `group-apply` endpoint, setting `parallelFlag` to `true` to leverage database parallelism."],"enabled":true,"result":{"startTime":1736292107618,"interpreter":"md.low","endTime":1736292108018,"results":[{"message":"<p>Now invoke the <code>group.countR<\/code> function from the REST API using the <code>group-apply<\/code> endpoint, setting <code>parallelFlag<\/code> to <code>true<\/code> to leverage database parallelism.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to invoke the group.countR script in synchronous mode","message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"input\":\"IRIS\",\"groupBy\":\"Species\",\"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/r-scripts/v1/group-apply/group.countR\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the result","message":["%md","","The result of the REST call is a JSON representation of the value returned from the R script. Integers, vectors, lists, matrices, arrays, and data frames are supported. The returned value is:"],"enabled":true,"result":{"startTime":1736292108439,"interpreter":"md.low","endTime":1736292108847,"results":[{"message":"<p>The result of the REST call is a JSON representation of the value returned from the R script. Integers, vectors, lists, matrices, arrays, and data frames are supported. The returned value is:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{\"result\":[{\"SPECIES\":\"setosa\",\"COUNT\":50},","           {\"SPECIES\":\"versicolor\",\"COUNT\":50},","           {\"SPECIES\":\"virginica\",\"COUNT\":50}]"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Asynchronous REST API calls","","Asynchronous calls do not wait for the result to return from the server. If the R script is performing long running computations, results will not be immediate and the user may want to use asynchronous calls to continue development on the client. ","","The asynchronous call's returned results are included in the response header. Display the response header by passing the `-i` flag to the `cURL` command. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes, and can be extended up to 12 hours. Set the `asyncFlag` to `true` to run asynchronous calls from the REST API."],"enabled":true,"result":{"startTime":1736292109274,"interpreter":"md.low","endTime":1736292109675,"results":[{"message":"<h2 id=\"asynchronous-rest-api-calls\">Asynchronous REST API calls<\/h2>\n<p>Asynchronous calls do not wait for the result to return from the server. If the R script is performing long running computations, results will not be immediate and the user may want to use asynchronous calls to continue development on the client.<\/p>\n<p>The asynchronous call's returned results are included in the response header. Display the response header by passing the <code>-i<\/code> flag to the <code>cURL<\/code> command. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes, and can be extended up to 12 hours. Set the <code>asyncFlag<\/code> to <code>true<\/code> to run asynchronous calls from the REST API.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Perform an asynchronous REST call with a timeout of 300 seconds, or 5 minutes","message":["%md","","Below is the equivalent asynchronous REST call with a timeout of 300 seconds, or 5 minutes. The output includes the HTTP 201 status code indicating the job was created, and a job id location that can be polled for status and results.","","The `timeout` flag is used to change the default timeout setting."],"enabled":true,"result":{"startTime":1736292110092,"interpreter":"md.low","endTime":1736292110493,"results":[{"message":"<p>Below is the equivalent asynchronous REST call with a timeout of 300 seconds, or 5 minutes. The output includes the HTTP 201 status code indicating the job was created, and a job id location that can be polled for status and results.<\/p>\n<p>The <code>timeout<\/code> flag is used to change the default timeout setting.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to invoke the group.countR script in asynchronous mode","message":["","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"input\":\"IRIS_REST\",\"groupBy\":\"Species\",\"asyncFlag\":true, \"timeout\":300, \"parallelFlag\":true, \"service\":\"MEDIUM\"}' ","                  \"<OML Service URL>/oml/api/r-scripts/v1/group-apply/group.countR\"","                  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[" "," Result:"," "," HTTP/1.1 201 Created"," Location: <OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>"," Cache-Control: no-cache, no-store, private"," Content-Length: 0"," X-Frame-Options: SAMEORIGIN"," Proxy-Connection: Keep-Alive"," X-XSS-Protection: 1;mode=block"," X-Content-Type-Options: nosniff"," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Poll the job status","message":["%md","","","To poll the job status, pass the job ID location to the jobs endpoint using the GET method. The HTTP response status 302 indicates the running job is found and includes a job id location for future results."],"enabled":true,"result":{"startTime":1736292110907,"interpreter":"md.low","endTime":1736292111311,"results":[{"message":"<p>To poll the job status, pass the job ID location to the jobs endpoint using the GET method. The HTTP response status 302 indicates the running job is found and includes a job id location for future results.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View the job result","message":["","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>/result\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","The return value is:","","{\"result\":[{\"SPECIES\":\"setosa\",\"COUNT\":50},","           {\"SPECIES\":\"versicolor\",\"COUNT\":50},","           {\"SPECIES\":\"virginica\",\"COUNT\":50}]}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Returning model details from the REST API","message":["%md","","Let's create a linear model script and run it from the REST API. First, we'll define our function, `build.lmRestR`, that returns a list mapping of the model attributes. Note that the model itself cannot be returned directly from REST because LinearRegression model objects are not JSON-serializable. "],"enabled":true,"result":{"startTime":1736292111727,"interpreter":"md.low","endTime":1736292112132,"results":[{"message":"<p>Let's create a linear model script and run it from the REST API. First, we'll define our function, <code>build.lmRestR<\/code>, that returns a list mapping of the model attributes. Note that the model itself cannot be returned directly from REST because LinearRegression model objects are not JSON-serializable.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create the script and save it to the R script repository","message":["%r","","build.lmRestR <- function(dat){","  dat$Species <- as.factor(dat$Species)","  mod <- lm(Petal.Length~., dat)","  res <- summary(mod)","  res.list <- list(\"Coef\"=res$coefficients, \"R2\"=res$r.squared)","  res.list","}","","ore.scriptCreate(name = 'build.lmRestR', FUN = build.lmRestR, overwrite=TRUE)"],"enabled":true,"result":{"startTime":1736292112544,"interpreter":"r.low","endTime":1736292113016,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the script in R","message":["%r","","res <- build.lmRestR(iris)","res[\"Coef\"]","res[\"R2\"]"],"enabled":true,"result":{"startTime":1736292113430,"interpreter":"r.low","endTime":1736292113891,"results":[{"message":"<strong>$Coef<\/strong> = <table class=\"dataframe\">\n<caption>A matrix: 6 x 4 of type dbl<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Estimate<\/th><th scope=col>Std. Error<\/th><th scope=col>t value<\/th><th scope=col>Pr(&gt;|t|)<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>(Intercept)<\/th><td>-1.1109888<\/td><td>0.26987329<\/td><td>-4.116705<\/td><td>6.445916e-05<\/td><\/tr>\n\t<tr><th scope=row>Sepal.Length<\/th><td> 0.6080058<\/td><td>0.05024482<\/td><td>12.100867<\/td><td>1.073592e-23<\/td><\/tr>\n\t<tr><th scope=row>Sepal.Width<\/th><td>-0.1805236<\/td><td>0.08035779<\/td><td>-2.246498<\/td><td>2.619373e-02<\/td><\/tr>\n\t<tr><th scope=row>Petal.Width<\/th><td> 0.6022215<\/td><td>0.12144281<\/td><td> 4.958890<\/td><td>1.968679e-06<\/td><\/tr>\n\t<tr><th scope=row>Speciesversicolor<\/th><td> 1.4633709<\/td><td>0.17345489<\/td><td> 8.436608<\/td><td>3.142686e-14<\/td><\/tr>\n\t<tr><th scope=row>Speciesvirginica<\/th><td> 1.9742229<\/td><td>0.24479964<\/td><td> 8.064648<\/td><td>2.600021e-13<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n","type":"HTML"},{"message":"<strong>$R2<\/strong> = 0.978593281337676","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":" Invoke the function from the REST API in asynchronous mode","message":["","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"input\":\"IRIS_REST\", \"asyncFlag\":true}' ","                  \"<OML Service URL>/oml/api/r-scripts/v1/table-apply/build.lmRestR\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":" Display the results by polling the results location","message":["","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>/result\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"The returned value is a list containing the Coeffiecient and R^2 values.","message":["","{\"result\":{\"R2\":0.9786,\"Coef\":[[-1.111,0.2699,-4.1167,1.0E-4],[0.608,0.0502,12.1009,1.0736E-23],","                               [-0.1805,0.0804,-2.2465,0.0262],[0.6022,0.1214,4.9589,1.9687E-6],","                               [1.4634,0.1735,8.4366,3.1427E-14],[1.9742,0.2448,8.0646,2.6E-13]]}}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736292114315,"interpreter":"md.low","endTime":1736292114716,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736292115136,"interpreter":"md.low","endTime":1736292115551,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Regression GLM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858422549,"interpreter":"md.low","endTime":1737858423069,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Regression Modeling to Predict Numerical Values using Generalized Linear Model","message":["%md","# Regression Modeling to Predict Numerical Values using Generalized Linear Model","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737858423520,"interpreter":"md.low","endTime":1737858423982,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-generalized-linear-model\">Regression Modeling to Predict Numerical Values using Generalized Linear Model<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737858424511,"interpreter":"md.low","endTime":1737858424965,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For mode information","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-generaized-linear-model.html\" target=\"_blank\">OML4R Generalized Linear Model (GLM)<\/a>"],"enabled":true,"result":{"startTime":1737858425402,"interpreter":"md.low","endTime":1737858425844,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-generaized-linear-model.html\" target=\"_blank\">OML4R Generalized Linear Model (GLM)<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737858426296,"interpreter":"r.low","endTime":1737858431281,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%r","","ore.sync(query = c(\"SUPP_DF\" = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, ","                   OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","ore.sync(query = c(\"CUST_DF\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, ","                   CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.attach()","                   ","DEMO_DF <- merge(SUPP_DF, CUST_DF, by='CUST_ID')","row.names(DEMO_DF) <- DEMO_DF$CUST_ID"],"enabled":true,"result":{"startTime":1737858431708,"interpreter":"r.low","endTime":1737858433088,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","z.show(head(DEMO_DF))"],"enabled":true,"result":{"startTime":1737858433513,"interpreter":"r.low","endTime":1737858434085,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n100001\t< Bach.\t0\t2\tExec.\t3\t0\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\n100002\tBach.\t0\t2\tProf.\t4\t0\tF\tNeverM\t1962\tL: 300,000 and above\t7000\n100003\t< Bach.\t0\t2\tSales\t6\t0\tM\tNeverM\t1969\tK: 250,000 - 299,999\t7000\n100004\t< Bach.\t0\t2\tSales\t5\t0\tF\tDivorc.\t1953\tK: 250,000 - 299,999\t15000\n100005\tAssoc-A\t1\t3\tCrafts\t5\t0\tM\tMarried\t1957\tB: 30,000 - 49,999\t1500\n100006\t< Bach.\t0\t9+\tProf.\t2\t1\tF\tNeverM\t1983\tG: 130,000 - 149,999\t5000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data 60/40 into training and testing sets","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","# Create training data set","rownames(DEMO_DF) <- DEMO_DF$CUST_ID","DEMO_DF.train <- DEMO_DF[group==FALSE,]","","# Create test data set","DEMO_DF.test <- DEMO_DF[group==TRUE,]","cat(\"\\n======== Records | Columns\\n\\nTrain Data: \",","    dim(DEMO_DF.train),","    \"\\n Test Data: \",","    dim(DEMO_DF.test))"],"enabled":true,"result":{"startTime":1737858434506,"interpreter":"r.low","endTime":1737858435670,"results":[{"message":"\n======== Records | Columns\n\nTrain Data:  2700 12 \n Test Data:  1800 12","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a GLM regression model to predict YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1737858436113,"interpreter":"md.low","endTime":1737858436525,"results":[{"message":"<h3 id=\"build-a-glm-regression-model-to-predict-yrs_residence\">Build a GLM regression model to predict YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%r","","mod1 <- ore.odmGLM(YRS_RESIDENCE~.-CUST_ID, ","                   DEMO_DF.train,","                   type=\"normal\")","","summary(mod1)"],"enabled":true,"result":{"startTime":1737858436961,"interpreter":"r.low","endTime":1737858439302,"results":[{"message":"\nCall:\nore.odmGLM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = DEMO_DF.train, \n    type = \"normal\")\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-6.5268 -0.5767 -0.0335  0.5296  8.0889 \n\nCoefficients:\n                                        Estimate Std. Error t value Pr(>|t|)\n(Intercept)                            1.570e+02  5.412e+00  29.012  < 2e-16\nEDUCATION10th                         -3.183e-01  1.499e-01  -2.123  0.03384\nEDUCATION11th                         -3.449e-01  1.545e-01  -2.233  0.02562\nEDUCATION12th                         -1.093e-01  2.040e-01  -0.536  0.59205\nEDUCATION1st-4th                      -4.536e-02  4.438e-01  -0.102  0.91859\nEDUCATION5th-6th                       1.686e-01  2.503e-01   0.674  0.50061\nEDUCATION7th-8th                      -2.957e-01  1.750e-01  -1.690  0.09114\nEDUCATION9th                          -1.341e-01  1.766e-01  -0.759  0.44784\nEDUCATION< Bach.                      -1.346e-01  6.670e-02  -2.018  0.04374\nEDUCATIONAssoc-A                       1.918e-01  1.301e-01   1.474  0.14052\nEDUCATIONAssoc-V                       1.179e-01  1.228e-01   0.959  0.33740\nEDUCATIONBach.                         1.487e-01  7.938e-02   1.873  0.06118\nEDUCATIONMasters                       2.109e-01  1.347e-01   1.565  0.11761\nEDUCATIONPhD                           6.231e-01  2.381e-01   2.617  0.00893\nEDUCATIONPresch.                      -1.248e+00  7.118e-01  -1.753  0.07968\nEDUCATIONProfsc                        2.925e-01  1.772e-01   1.651  0.09887\nAFFINITY_CARD                          3.078e-01  6.817e-02   4.516 6.58e-06\nHOUSEHOLD_SIZE1                       -4.096e-01  2.996e-01  -1.367  0.17159\nHOUSEHOLD_SIZE2                       -1.375e-01  3.002e-01  -0.458  0.64693\nHOUSEHOLD_SIZE4-5                     -5.965e-01  1.365e-01  -4.369 1.30e-05\nHOUSEHOLD_SIZE6-8                     -2.795e-01  3.047e-01  -0.917  0.35915\nHOUSEHOLD_SIZE9+                      -2.842e-01  3.112e-01  -0.913  0.36108\nOCCUPATION?                           -7.014e-01  1.303e-01  -5.382 8.02e-08\nOCCUPATIONCleric.                     -2.628e-01  1.018e-01  -2.580  0.00992\nOCCUPATIONCrafts                      -3.267e-01  1.016e-01  -3.217  0.00131\nOCCUPATIONFarming                     -3.201e-02  1.650e-01  -0.194  0.84621\nOCCUPATIONHandler                     -5.624e-01  1.395e-01  -4.031 5.71e-05\nOCCUPATIONHouse-s                     -1.737e+00  3.556e-01  -4.884 1.10e-06\nOCCUPATIONMachine                     -2.446e-01  1.206e-01  -2.027  0.04273\nOCCUPATIONOther                       -5.083e-01  1.073e-01  -4.738 2.28e-06\nOCCUPATIONProf.                       -2.333e-01  1.031e-01  -2.263  0.02373\nOCCUPATIONProtec.                     -3.765e-01  1.835e-01  -2.052  0.04027\nOCCUPATIONSales                       -2.713e-01  9.673e-02  -2.805  0.00507\nOCCUPATIONTechSup                     -6.770e-01  1.372e-01  -4.933 8.60e-07\nOCCUPATIONTransp.                      9.328e-04  1.389e-01   0.007  0.99464\nY_BOX_GAMES                           -4.741e-01  8.329e-02  -5.692 1.39e-08\nCUST_GENDERF                          -2.080e-01  7.261e-02  -2.865  0.00421\nCUST_MARITAL_STATUSDivorc.             2.575e-01  3.036e-01   0.848  0.39630\nCUST_MARITAL_STATUSMabsent            -3.687e-02  3.499e-01  -0.105  0.91609\nCUST_MARITAL_STATUSNeverM              2.277e-02  2.921e-01   0.078  0.93786\nCUST_MARITAL_STATUSSepar.              1.959e-01  3.287e-01   0.596  0.55109\nCUST_MARITAL_STATUSWidowed            -9.832e-01  3.354e-01  -2.932  0.00340\nCUST_YEAR_OF_BIRTH                    -7.763e-02  2.764e-03 -28.085  < 2e-16\nCUST_INCOME_LEVELA: Below 30,000       7.544e-01  1.968e-01   3.833  0.00013\nCUST_INCOME_LEVELB: 30,000 - 49,999    1.713e-01  1.370e-01   1.250  0.21128\nCUST_INCOME_LEVELC: 50,000 - 69,999   -1.197e-01  1.399e-01  -0.855  0.39237\nCUST_INCOME_LEVELD: 70,000 - 89,999   -1.883e-01  1.536e-01  -1.226  0.22019\nCUST_INCOME_LEVELE: 90,000 - 109,999  -2.393e-02  1.091e-01  -0.219  0.82632\nCUST_INCOME_LEVELF: 110,000 - 129,999 -1.709e-01  1.044e-01  -1.638  0.10157\nCUST_INCOME_LEVELG: 130,000 - 149,999 -5.854e-02  1.065e-01  -0.550  0.58247\nCUST_INCOME_LEVELH: 150,000 - 169,999  9.896e-02  1.011e-01   0.979  0.32750\nCUST_INCOME_LEVELI: 170,000 - 189,999 -1.167e-01  9.337e-02  -1.250  0.21127\nCUST_INCOME_LEVELK: 250,000 - 299,999 -5.349e-02  9.477e-02  -0.564  0.57250\nCUST_INCOME_LEVELL: 300,000 and above -1.337e-01  8.157e-02  -1.639  0.10126\nCUST_CREDIT_LIMIT                      1.669e-05  7.829e-06   2.131  0.03316\n                                         \n(Intercept)                           ***\nEDUCATION10th                         *  \nEDUCATION11th                         *  \nEDUCATION12th                            \nEDUCATION1st-4th                         \nEDUCATION5th-6th                         \nEDUCATION7th-8th                      .  \nEDUCATION9th                             \nEDUCATION< Bach.                      *  \nEDUCATIONAssoc-A                         \nEDUCATIONAssoc-V                         \nEDUCATIONBach.                        .  \nEDUCATIONMasters                         \nEDUCATIONPhD                          ** \nEDUCATIONPresch.                      .  \nEDUCATIONProfsc                       .  \nAFFINITY_CARD                         ***\nHOUSEHOLD_SIZE1                          \nHOUSEHOLD_SIZE2                          \nHOUSEHOLD_SIZE4-5                     ***\nHOUSEHOLD_SIZE6-8                        \nHOUSEHOLD_SIZE9+                         \nOCCUPATION?                           ***\nOCCUPATIONCleric.                     ** \nOCCUPATIONCrafts                      ** \nOCCUPATIONFarming                        \nOCCUPATIONHandler                     ***\nOCCUPATIONHouse-s                     ***\nOCCUPATIONMachine                     *  \nOCCUPATIONOther                       ***\nOCCUPATIONProf.                       *  \nOCCUPATIONProtec.                     *  \nOCCUPATIONSales                       ** \nOCCUPATIONTechSup                     ***\nOCCUPATIONTransp.                        \nY_BOX_GAMES                           ***\nCUST_GENDERF                          ** \nCUST_MARITAL_STATUSDivorc.               \nCUST_MARITAL_STATUSMabsent               \nCUST_MARITAL_STATUSNeverM                \nCUST_MARITAL_STATUSSepar.                \nCUST_MARITAL_STATUSWidowed            ** \nCUST_YEAR_OF_BIRTH                    ***\nCUST_INCOME_LEVELA: Below 30,000      ***\nCUST_INCOME_LEVELB: 30,000 - 49,999      \nCUST_INCOME_LEVELC: 50,000 - 69,999      \nCUST_INCOME_LEVELD: 70,000 - 89,999      \nCUST_INCOME_LEVELE: 90,000 - 109,999     \nCUST_INCOME_LEVELF: 110,000 - 129,999    \nCUST_INCOME_LEVELG: 130,000 - 149,999    \nCUST_INCOME_LEVELH: 150,000 - 169,999    \nCUST_INCOME_LEVELI: 170,000 - 189,999    \nCUST_INCOME_LEVELK: 250,000 - 299,999    \nCUST_INCOME_LEVELL: 300,000 and above    \nCUST_CREDIT_LIMIT                     *  \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.219 on 2645 degrees of freedom\nMultiple R-squared:  0.5901,\tAdjusted R-squared:  0.5817 \nF-statistic: 70.51 on 54 and 2645 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-generaized-linear-model.html\" target=\"_blank\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","- Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.","> GLMS_CONF_LEVEL = 0.95","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).","> GLMS_RIDGE_VALUE = 2","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.","> GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC","> GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.","> GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> GLMS_MAX_FEATURES = 30","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'","- Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> GLMS_REFERENCE_CLASS_NAME = '<target_value>'","- Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE","> GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> GLMS_CONV_TOLERANCE = 0.0001","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> GLMS_NUM_ITERATIONS = 200","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.","> GLMS_BATCH_ROWS = 2000","- Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)","> GLMS_SOLVER = 'GLMS_SOLVER_SGD'","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.","> GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'","","<strong> Available starting with Oracle Database 23ai <\/strong>","- Specify the `link` function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK`, `GLMS_CAUCHIT_LINK` .  For regression, only one option is available: `GLMS_IDENTITY_LINK (default)`.","> GLMS_LINK_FUNCTION = 'GLMS_IDENTITY_LINK'"],"enabled":true,"result":{"startTime":1737858439758,"interpreter":"md.low","endTime":1737858440182,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-generaized-linear-model.html\" target=\"_blank\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONF_LEVEL = 0.95<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_VALUE = 2<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_MAX_FEATURES = 30<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_REFERENCE_CLASS_NAME = '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONV_TOLERANCE = 0.0001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_NUM_ITERATIONS = 200<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_BATCH_ROWS = 2000<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SOLVER = 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> Available starting with Oracle Database 23ai <\/strong><\/p>\n<ul>\n<li>Specify the <code>link<\/code> function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code>, <code>GLMS_CAUCHIT_LINK<\/code> .  For regression, only one option is available: <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_LINK_FUNCTION = 'GLMS_IDENTITY_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}},{\"table\":{\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Feature Selection and Generation","message":["%r","settings <- list(","  GLMS_RIDGE_REGRESSION ='GLMS_RIDGE_REG_DISABLE',","  GLMS_FTR_SELECTION    ='GLMS_FTR_SELECTION_ENABLE',","  GLMS_FTR_GEN_METHOD   ='GLMS_FTR_GEN_QUADRATIC',","  GLMS_FTR_GENERATION   ='GLMS_FTR_GENERATION_ENABLE', ","  GLMS_FTR_SEL_CRIT     ='GLMS_FTR_SEL_AIC',","  GLMS_NUM_ITERATIONS   ='50',","  GLMS_MAX_FEATURES     ='30',","  GLMS_PRUNE_MODEL      ='GLMS_PRUNE_MODEL_ENABLE',","  GLMS_ROW_DIAGNOSTICS  ='GLMS_ROW_DIAG_DISABLE',","  GLMS_CONV_TOLERANCE   ='0.0001',","  GLMS_BATCH_ROWS       ='2000')","","mod2 <- ore.odmGLM(formula=YRS_RESIDENCE~.-CUST_ID, ","                   data=DEMO_DF.train, ","                   type=\"normal\",","                   odm.settings=settings)","summary(mod2)"],"enabled":true,"result":{"startTime":1737858440660,"interpreter":"r.low","endTime":1737858442628,"results":[{"message":"\nCall:\nore.odmGLM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = DEMO_DF.train, \n    type = \"normal\", odm.settings = settings)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-6.7498 -0.5918 -0.0330  0.5222  8.3606 \n\nCoefficients:\n                                      Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                          1.587e+02  5.292e+00  29.981  < 2e-16 ***\nEDUCATION10th                       -3.667e-01  1.473e-01  -2.489 0.012869 *  \nEDUCATION11th                       -4.231e-01  1.523e-01  -2.779 0.005491 ** \nEDUCATION7th-8th                    -3.934e-01  1.722e-01  -2.284 0.022433 *  \nEDUCATION< Bach.                    -1.848e-01  5.881e-02  -3.142 0.001698 ** \nEDUCATIONPhD                         5.009e-01  2.253e-01   2.224 0.026257 *  \nAFFINITY_CARD                        4.127e-01  6.236e-02   6.619 4.36e-11 ***\nHOUSEHOLD_SIZE1                     -2.924e-01  7.763e-02  -3.766 0.000169 ***\nHOUSEHOLD_SIZE4-5                   -5.630e-01  1.245e-01  -4.521 6.43e-06 ***\nOCCUPATION?                         -4.465e-01  1.126e-01  -3.964 7.56e-05 ***\nOCCUPATIONHandler                   -3.492e-01  1.197e-01  -2.919 0.003545 ** \nOCCUPATIONOther                     -3.150e-01  8.093e-02  -3.892 0.000102 ***\nOCCUPATIONTechSup                   -4.358e-01  1.226e-01  -3.553 0.000387 ***\nY_BOX_GAMES                         -5.287e-01  8.038e-02  -6.578 5.71e-11 ***\nCUST_GENDERF                        -2.419e-01  5.901e-02  -4.100 4.26e-05 ***\nCUST_MARITAL_STATUSWidowed          -1.246e+00  1.531e-01  -8.138 6.07e-16 ***\nCUST_YEAR_OF_BIRTH                  -7.860e-02  2.702e-03 -29.092  < 2e-16 ***\nCUST_INCOME_LEVELA: Below 30,000     8.341e-01  1.822e-01   4.579 4.89e-06 ***\nCUST_INCOME_LEVELB: 30,000 - 49,999  2.741e-01  1.186e-01   2.311 0.020918 *  \nCUST_CREDIT_LIMIT                    1.881e-05  6.393e-06   2.943 0.003283 ** \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.229 on 2680 degrees of freedom\nMultiple R-squared:  0.5778,\tAdjusted R-squared:  0.5748 \nF-statistic:   193 on 19 and 2680 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"numberOfItems\":10,\"lastColumns\":[\"SETTING_NAME\",\"SETTING_VALUE\",\"SETTING_TYPE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Model settings","message":["%r","","z.show(settings(mod2))"],"enabled":true,"result":{"startTime":1737858443069,"interpreter":"r.low","endTime":1737858443546,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\tSETTING_TYPE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\tINPUT\nGLMS_BATCH_ROWS\t2000\tINPUT\nGLMS_CONF_LEVEL\t.95\tDEFAULT\nGLMS_CONV_TOLERANCE\t0.0001\tINPUT\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_ENABLE\tINPUT\nGLMS_FTR_GEN_METHOD\tGLMS_FTR_GEN_QUADRATIC\tINPUT\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_ENABLE\tINPUT\nGLMS_FTR_SEL_CRIT\tGLMS_FTR_SEL_AIC\tINPUT\nGLMS_MAX_FEATURES\t30\tINPUT\nGLMS_NUM_ITERATIONS\t50\tINPUT\nGLMS_PRUNE_MODEL\tGLMS_PRUNE_MODEL_ENABLE\tINPUT\nGLMS_RIDGE_REGRESSION\tGLMS_RIDGE_REG_DISABLE\tINPUT\nGLMS_ROW_DIAGNOSTICS\tGLMS_ROW_DIAG_DISABLE\tINPUT\nGLMS_SELECT_BLOCK\tGLMS_SELECT_BLOCK_DISABLE\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_DELETE_ROW\tINPUT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nPREP_AUTO\tOFF\tINPUT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}},{\"table\":{\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Ridge Regression","message":["%r","","ore.drop(model = 'GLM_REGRESSION_MODEL')","","settings2 <- list(","  GLMS_RIDGE_REGRESSION  ='GLMS_RIDGE_REG_ENABLE',","  GLMS_FTR_SELECTION     ='GLMS_FTR_SELECTION_DISABLE',","  GLMS_ROW_DIAGNOSTICS   ='GLMS_ROW_DIAG_DISABLE',","  GLMS_CONV_TOLERANCE    ='0.0001',","  GLMS_NUM_ITERATIONS    ='50',","  GLMS_RIDGE_VALUE       ='0.1',","  GLMS_BATCH_ROWS        ='2000',","  model_name             ='GLM_REGRESSION_MODEL')  # Set model name for use below with prediction details","","mod3 <- ore.odmGLM(formula=YRS_RESIDENCE~.-CUST_ID, ","                   data=DEMO_DF.train, ","                   type=\"normal\",","                   odm.settings=settings2)","summary(mod3)"],"enabled":true,"result":{"startTime":1737858443973,"interpreter":"r.low","endTime":1737858446917,"results":[{"message":"Warning message:\n\u201cThe created mining model GLM_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nCall:\nore.odmGLM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = DEMO_DF.train, \n    type = \"normal\", odm.settings = settings2)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-6.5265 -0.5768 -0.0339  0.5295  8.0907 \n\nCoefficients:\n                                        Estimate Std. Error t value Pr(>|t|)\n(Intercept)                            1.570e+02  5.411e+00  29.014  < 2e-16\nEDUCATION10th                         -3.177e-01  1.498e-01  -2.121 0.034038\nEDUCATION11th                         -3.447e-01  1.543e-01  -2.233 0.025618\nEDUCATION12th                         -1.094e-01  2.038e-01  -0.537 0.591485\nEDUCATION1st-4th                      -4.545e-02  4.406e-01  -0.103 0.917847\nEDUCATION5th-6th                       1.682e-01  2.498e-01   0.673 0.500950\nEDUCATION7th-8th                      -2.956e-01  1.748e-01  -1.691 0.090959\nEDUCATION9th                          -1.345e-01  1.764e-01  -0.763 0.445750\nEDUCATION< Bach.                      -1.343e-01  6.667e-02  -2.015 0.044012\nEDUCATIONAssoc-A                       1.919e-01  1.300e-01   1.475 0.140242\nEDUCATIONAssoc-V                       1.180e-01  1.228e-01   0.961 0.336691\nEDUCATIONBach.                         1.490e-01  7.933e-02   1.878 0.060436\nEDUCATIONMasters                       2.109e-01  1.346e-01   1.566 0.117406\nEDUCATIONPhD                           6.209e-01  2.377e-01   2.613 0.009036\nEDUCATIONPresch.                      -1.207e+00  7.000e-01  -1.725 0.084698\nEDUCATIONProfsc                        2.919e-01  1.770e-01   1.649 0.099178\nAFFINITY_CARD                          3.083e-01  6.816e-02   4.524 6.33e-06\nHOUSEHOLD_SIZE1                       -4.066e-01  2.926e-01  -1.390 0.164692\nHOUSEHOLD_SIZE2                       -1.344e-01  2.929e-01  -0.459 0.646381\nHOUSEHOLD_SIZE4-5                     -5.961e-01  1.364e-01  -4.369 1.30e-05\nHOUSEHOLD_SIZE6-8                     -2.763e-01  2.983e-01  -0.926 0.354434\nHOUSEHOLD_SIZE9+                      -2.813e-01  3.038e-01  -0.926 0.354611\nOCCUPATION?                           -6.988e-01  1.302e-01  -5.368 8.67e-08\nOCCUPATIONCleric.                     -2.607e-01  1.017e-01  -2.563 0.010428\nOCCUPATIONCrafts                      -3.248e-01  1.014e-01  -3.203 0.001377\nOCCUPATIONFarming                     -3.049e-02  1.648e-01  -0.185 0.853239\nOCCUPATIONHandler                     -5.599e-01  1.393e-01  -4.018 6.03e-05\nOCCUPATIONHouse-s                     -1.721e+00  3.541e-01  -4.860 1.24e-06\nOCCUPATIONMachine                     -2.427e-01  1.205e-01  -2.014 0.044106\nOCCUPATIONOther                       -5.061e-01  1.071e-01  -4.723 2.44e-06\nOCCUPATIONProf.                       -2.312e-01  1.030e-01  -2.245 0.024856\nOCCUPATIONProtec.                     -3.739e-01  1.833e-01  -2.040 0.041424\nOCCUPATIONSales                       -2.693e-01  9.661e-02  -2.788 0.005345\nOCCUPATIONTechSup                     -6.745e-01  1.371e-01  -4.919 9.23e-07\nOCCUPATIONTransp.                      2.890e-03  1.388e-01   0.021 0.983382\nY_BOX_GAMES                           -4.743e-01  8.328e-02  -5.696 1.36e-08\nCUST_GENDERF                          -2.084e-01  7.258e-02  -2.871 0.004127\nCUST_MARITAL_STATUSDivorc.             2.546e-01  2.963e-01   0.859 0.390285\nCUST_MARITAL_STATUSMabsent            -4.015e-02  3.430e-01  -0.117 0.906842\nCUST_MARITAL_STATUSNeverM              1.979e-02  2.851e-01   0.069 0.944654\nCUST_MARITAL_STATUSSepar.              1.928e-01  3.217e-01   0.599 0.549012\nCUST_MARITAL_STATUSWidowed            -9.857e-01  3.283e-01  -3.003 0.002702\nCUST_YEAR_OF_BIRTH                    -7.762e-02  2.764e-03 -28.087  < 2e-16\nCUST_INCOME_LEVELA: Below 30,000       7.527e-01  1.966e-01   3.830 0.000131\nCUST_INCOME_LEVELB: 30,000 - 49,999    1.713e-01  1.368e-01   1.252 0.210633\nCUST_INCOME_LEVELC: 50,000 - 69,999   -1.192e-01  1.397e-01  -0.853 0.393732\nCUST_INCOME_LEVELD: 70,000 - 89,999   -1.876e-01  1.534e-01  -1.223 0.221495\nCUST_INCOME_LEVELE: 90,000 - 109,999  -2.371e-02  1.090e-01  -0.218 0.827824\nCUST_INCOME_LEVELF: 110,000 - 129,999 -1.705e-01  1.043e-01  -1.635 0.102089\nCUST_INCOME_LEVELG: 130,000 - 149,999 -5.836e-02  1.064e-01  -0.549 0.583305\nCUST_INCOME_LEVELH: 150,000 - 169,999  9.915e-02  1.010e-01   0.982 0.326229\nCUST_INCOME_LEVELI: 170,000 - 189,999 -1.166e-01  9.330e-02  -1.249 0.211667\nCUST_INCOME_LEVELK: 250,000 - 299,999 -5.332e-02  9.472e-02  -0.563 0.573560\nCUST_INCOME_LEVELL: 300,000 and above -1.335e-01  8.152e-02  -1.638 0.101517\nCUST_CREDIT_LIMIT                      1.668e-05  7.825e-06   2.132 0.033090\n                                         \n(Intercept)                           ***\nEDUCATION10th                         *  \nEDUCATION11th                         *  \nEDUCATION12th                            \nEDUCATION1st-4th                         \nEDUCATION5th-6th                         \nEDUCATION7th-8th                      .  \nEDUCATION9th                             \nEDUCATION< Bach.                      *  \nEDUCATIONAssoc-A                         \nEDUCATIONAssoc-V                         \nEDUCATIONBach.                        .  \nEDUCATIONMasters                         \nEDUCATIONPhD                          ** \nEDUCATIONPresch.                      .  \nEDUCATIONProfsc                       .  \nAFFINITY_CARD                         ***\nHOUSEHOLD_SIZE1                          \nHOUSEHOLD_SIZE2                          \nHOUSEHOLD_SIZE4-5                     ***\nHOUSEHOLD_SIZE6-8                        \nHOUSEHOLD_SIZE9+                         \nOCCUPATION?                           ***\nOCCUPATIONCleric.                     *  \nOCCUPATIONCrafts                      ** \nOCCUPATIONFarming                        \nOCCUPATIONHandler                     ***\nOCCUPATIONHouse-s                     ***\nOCCUPATIONMachine                     *  \nOCCUPATIONOther                       ***\nOCCUPATIONProf.                       *  \nOCCUPATIONProtec.                     *  \nOCCUPATIONSales                       ** \nOCCUPATIONTechSup                     ***\nOCCUPATIONTransp.                        \nY_BOX_GAMES                           ***\nCUST_GENDERF                          ** \nCUST_MARITAL_STATUSDivorc.               \nCUST_MARITAL_STATUSMabsent               \nCUST_MARITAL_STATUSNeverM                \nCUST_MARITAL_STATUSSepar.                \nCUST_MARITAL_STATUSWidowed            ** \nCUST_YEAR_OF_BIRTH                    ***\nCUST_INCOME_LEVELA: Below 30,000      ***\nCUST_INCOME_LEVELB: 30,000 - 49,999      \nCUST_INCOME_LEVELC: 50,000 - 69,999      \nCUST_INCOME_LEVELD: 70,000 - 89,999      \nCUST_INCOME_LEVELE: 90,000 - 109,999     \nCUST_INCOME_LEVELF: 110,000 - 129,999    \nCUST_INCOME_LEVELG: 130,000 - 149,999    \nCUST_INCOME_LEVELH: 150,000 - 169,999    \nCUST_INCOME_LEVELI: 170,000 - 189,999    \nCUST_INCOME_LEVELK: 250,000 - 299,999    \nCUST_INCOME_LEVELL: 300,000 and above    \nCUST_CREDIT_LIMIT                     *  \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.219 on 2645 degrees of freedom\nMultiple R-squared:   0.59,\tAdjusted R-squared:  0.5816 \nF-statistic: 70.48 on 54 and 2645 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"numberOfItems\":10,\"lastColumns\":[\"SETTING_NAME\",\"SETTING_VALUE\",\"SETTING_TYPE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Ridge Model settings","message":["%r","","z.show(settings(mod3))"],"enabled":true,"result":{"startTime":1737858447355,"interpreter":"r.low","endTime":1737858447902,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\tSETTING_TYPE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\tINPUT\nGLMS_BATCH_ROWS\t2000\tINPUT\nGLMS_CONF_LEVEL\t.95\tDEFAULT\nGLMS_CONV_TOLERANCE\t0.0001\tINPUT\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_DISABLE\tDEFAULT\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_DISABLE\tINPUT\nGLMS_NUM_ITERATIONS\t50\tINPUT\nGLMS_RIDGE_REGRESSION\tGLMS_RIDGE_REG_ENABLE\tINPUT\nGLMS_RIDGE_VALUE\t0.1\tINPUT\nGLMS_ROW_DIAGNOSTICS\tGLMS_ROW_DIAG_DISABLE\tINPUT\nGLMS_VIF_FOR_RIDGE\tGLMS_VIF_RIDGE_DISABLE\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_DELETE_ROW\tINPUT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nPREP_AUTO\tOFF\tINPUT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display Ridge Model fit details","message":["%r","","as.table(mod3$fit.details)"],"enabled":true,"result":{"startTime":1737858448335,"interpreter":"r.low","endTime":1737858448806,"results":[{"message":"      adjusted.r.square                     aic               coeff.var \n           5.816115e-01            1.123196e+03            3.039280e+01 \n     corrected.total.df        corrected.tot.ss          dependent.mean \n           2.699000e+03            9.583710e+03            4.010370e+00 \n               error.df       error.mean.square       error.sum.squares \n           2.645000e+03            1.485629e+00            3.929490e+03 \n                f.value                   gmsep              hocking.sp \n           7.048041e+01            1.516533e+00            5.618871e-04 \n                    j.p         model.converged                model.df \n           1.515892e+00            1.000000e+00            5.400000e+01 \n        model.f.p.value       model.mean.square       model.sum.squares \n           0.000000e+00            1.047078e+02            5.654220e+03 \n             num.params                num.rows         rank.deficiency \n           5.500000e+01            2.700000e+03            0.000000e+00 \n           root.mean.sq                    r.sq                    sbic \n           1.218864e+00            5.899824e-01            1.447752e+03 \nvalid.covariance.matrix \n           1.000000e+00 ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Ridge Model coefficients","message":["%r"," summary(mod3)$coefficients"],"enabled":true,"result":{"startTime":1737858449231,"interpreter":"r.low","endTime":1737858449787,"results":[{"message":"<table class=\"dataframe\">\n<caption>A matrix: 55 x 4 of type dbl<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Estimate<\/th><th scope=col>Std. Error<\/th><th scope=col>t value<\/th><th scope=col>Pr(&gt;|t|)<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>(Intercept)<\/th><td> 1.570045e+02<\/td><td>5.411314e+00<\/td><td> 29.01411568<\/td><td>6.221041e-161<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION10th<\/th><td>-3.176943e-01<\/td><td>1.498047e-01<\/td><td> -2.12072295<\/td><td> 3.403794e-02<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION11th<\/th><td>-3.446703e-01<\/td><td>1.543383e-01<\/td><td> -2.23321358<\/td><td> 2.561820e-02<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION12th<\/th><td>-1.093713e-01<\/td><td>2.037654e-01<\/td><td> -0.53675125<\/td><td> 5.914846e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION1st-4th<\/th><td>-4.545223e-02<\/td><td>4.406175e-01<\/td><td> -0.10315576<\/td><td> 9.178472e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION5th-6th<\/th><td> 1.681673e-01<\/td><td>2.498447e-01<\/td><td>  0.67308743<\/td><td> 5.009505e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION7th-8th<\/th><td>-2.956159e-01<\/td><td>1.748193e-01<\/td><td> -1.69097937<\/td><td> 9.095856e-02<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION9th<\/th><td>-1.345423e-01<\/td><td>1.764179e-01<\/td><td> -0.76263363<\/td><td> 4.457499e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATION&lt; Bach.<\/th><td>-1.343357e-01<\/td><td>6.666990e-02<\/td><td> -2.01493753<\/td><td> 4.401220e-02<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONAssoc-A<\/th><td> 1.918662e-01<\/td><td>1.300494e-01<\/td><td>  1.47533264<\/td><td> 1.402422e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONAssoc-V<\/th><td> 1.179869e-01<\/td><td>1.227880e-01<\/td><td>  0.96089940<\/td><td> 3.366906e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONBach.<\/th><td> 1.490063e-01<\/td><td>7.932607e-02<\/td><td>  1.87840271<\/td><td> 6.043601e-02<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONMasters<\/th><td> 2.108780e-01<\/td><td>1.346375e-01<\/td><td>  1.56626471<\/td><td> 1.174063e-01<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONPhD<\/th><td> 6.208933e-01<\/td><td>2.376506e-01<\/td><td>  2.61263056<\/td><td> 9.035696e-03<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONPresch.<\/th><td>-1.207374e+00<\/td><td>7.000470e-01<\/td><td> -1.72470373<\/td><td> 8.469770e-02<\/td><\/tr>\n\t<tr><th scope=row>EDUCATIONProfsc<\/th><td> 2.918713e-01<\/td><td>1.769527e-01<\/td><td>  1.64943081<\/td><td> 9.917815e-02<\/td><\/tr>\n\t<tr><th scope=row>AFFINITY_CARD<\/th><td> 3.083372e-01<\/td><td>6.815534e-02<\/td><td>  4.52403530<\/td><td> 6.334856e-06<\/td><\/tr>\n\t<tr><th scope=row>HOUSEHOLD_SIZE1<\/th><td>-4.066140e-01<\/td><td>2.925602e-01<\/td><td> -1.38984727<\/td><td> 1.646922e-01<\/td><\/tr>\n\t<tr><th scope=row>HOUSEHOLD_SIZE2<\/th><td>-1.343837e-01<\/td><td>2.928719e-01<\/td><td> -0.45884814<\/td><td> 6.463809e-01<\/td><\/tr>\n\t<tr><th scope=row>HOUSEHOLD_SIZE4-5<\/th><td>-5.960675e-01<\/td><td>1.364408e-01<\/td><td> -4.36869024<\/td><td> 1.298082e-05<\/td><\/tr>\n\t<tr><th scope=row>HOUSEHOLD_SIZE6-8<\/th><td>-2.763208e-01<\/td><td>2.983425e-01<\/td><td> -0.92618643<\/td><td> 3.544336e-01<\/td><\/tr>\n\t<tr><th scope=row>HOUSEHOLD_SIZE9+<\/th><td>-2.813028e-01<\/td><td>3.038337e-01<\/td><td> -0.92584464<\/td><td> 3.546112e-01<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATION?<\/th><td>-6.987949e-01<\/td><td>1.301880e-01<\/td><td> -5.36758300<\/td><td> 8.672506e-08<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONCleric.<\/th><td>-2.606785e-01<\/td><td>1.017027e-01<\/td><td> -2.56314332<\/td><td> 1.042777e-02<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONCrafts<\/th><td>-3.248242e-01<\/td><td>1.014178e-01<\/td><td> -3.20283233<\/td><td> 1.376991e-03<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONFarming<\/th><td>-3.049342e-02<\/td><td>1.648245e-01<\/td><td> -0.18500538<\/td><td> 8.532390e-01<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONHandler<\/th><td>-5.599109e-01<\/td><td>1.393488e-01<\/td><td> -4.01805276<\/td><td> 6.031820e-05<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONHouse-s<\/th><td>-1.721233e+00<\/td><td>3.541400e-01<\/td><td> -4.86031723<\/td><td> 1.240482e-06<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONMachine<\/th><td>-2.426729e-01<\/td><td>1.204904e-01<\/td><td> -2.01404448<\/td><td> 4.410593e-02<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONOther<\/th><td>-5.060750e-01<\/td><td>1.071449e-01<\/td><td> -4.72327480<\/td><td> 2.441978e-06<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONProf.<\/th><td>-2.311941e-01<\/td><td>1.029855e-01<\/td><td> -2.24491928<\/td><td> 2.485564e-02<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONProtec.<\/th><td>-3.738968e-01<\/td><td>1.832595e-01<\/td><td> -2.04025839<\/td><td> 4.142373e-02<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONSales<\/th><td>-2.693241e-01<\/td><td>9.660780e-02<\/td><td> -2.78780851<\/td><td> 5.344513e-03<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONTechSup<\/th><td>-6.744566e-01<\/td><td>1.371106e-01<\/td><td> -4.91906861<\/td><td> 9.228687e-07<\/td><\/tr>\n\t<tr><th scope=row>OCCUPATIONTransp.<\/th><td> 2.890429e-03<\/td><td>1.387577e-01<\/td><td>  0.02083076<\/td><td> 9.833822e-01<\/td><\/tr>\n\t<tr><th scope=row>Y_BOX_GAMES<\/th><td>-4.743157e-01<\/td><td>8.327552e-02<\/td><td> -5.69573954<\/td><td> 1.363953e-08<\/td><\/tr>\n\t<tr><th scope=row>CUST_GENDERF<\/th><td>-2.083622e-01<\/td><td>7.257930e-02<\/td><td> -2.87082154<\/td><td> 4.126606e-03<\/td><\/tr>\n\t<tr><th scope=row>CUST_MARITAL_STATUSDivorc.<\/th><td> 2.546166e-01<\/td><td>2.963273e-01<\/td><td>  0.85924107<\/td><td> 3.902854e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_MARITAL_STATUSMabsent<\/th><td>-4.014596e-02<\/td><td>3.430300e-01<\/td><td> -0.11703339<\/td><td> 9.068425e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_MARITAL_STATUSNeverM<\/th><td> 1.979336e-02<\/td><td>2.850905e-01<\/td><td>  0.06942833<\/td><td> 9.446539e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_MARITAL_STATUSSepar.<\/th><td> 1.927743e-01<\/td><td>3.216560e-01<\/td><td>  0.59931826<\/td><td> 5.490120e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_MARITAL_STATUSWidowed<\/th><td>-9.857392e-01<\/td><td>3.283005e-01<\/td><td> -3.00255208<\/td><td> 2.702325e-03<\/td><\/tr>\n\t<tr><th scope=row>CUST_YEAR_OF_BIRTH<\/th><td>-7.762372e-02<\/td><td>2.763681e-03<\/td><td>-28.08707544<\/td><td>3.899696e-152<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELA: Below 30,000<\/th><td> 7.527324e-01<\/td><td>1.965599e-01<\/td><td>  3.82953084<\/td><td> 1.313694e-04<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELB: 30,000 - 49,999<\/th><td> 1.713324e-01<\/td><td>1.368326e-01<\/td><td>  1.25213189<\/td><td> 2.106325e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELC: 50,000 - 69,999<\/th><td>-1.192066e-01<\/td><td>1.397486e-01<\/td><td> -0.85300742<\/td><td> 3.937324e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELD: 70,000 - 89,999<\/th><td>-1.876014e-01<\/td><td>1.534131e-01<\/td><td> -1.22285147<\/td><td> 2.214947e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELE: 90,000 - 109,999<\/th><td>-2.370622e-02<\/td><td>1.089864e-01<\/td><td> -0.21751538<\/td><td> 8.278235e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELF: 110,000 - 129,999<\/th><td>-1.705384e-01<\/td><td>1.042808e-01<\/td><td> -1.63537699<\/td><td> 1.020890e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELG: 130,000 - 149,999<\/th><td>-5.836159e-02<\/td><td>1.063767e-01<\/td><td> -0.54863112<\/td><td> 5.833049e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELH: 150,000 - 169,999<\/th><td> 9.914902e-02<\/td><td>1.009747e-01<\/td><td>  0.98191949<\/td><td> 3.262293e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELI: 170,000 - 189,999<\/th><td>-1.165578e-01<\/td><td>9.329874e-02<\/td><td> -1.24929626<\/td><td> 2.116673e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELK: 250,000 - 299,999<\/th><td>-5.331890e-02<\/td><td>9.472409e-02<\/td><td> -0.56288643<\/td><td> 5.735599e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_INCOME_LEVELL: 300,000 and above<\/th><td>-1.335467e-01<\/td><td>8.152481e-02<\/td><td> -1.63811178<\/td><td> 1.015173e-01<\/td><\/tr>\n\t<tr><th scope=row>CUST_CREDIT_LIMIT<\/th><td> 1.668266e-05<\/td><td>7.824517e-06<\/td><td>  2.13210043<\/td><td> 3.309048e-02<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%r","","PRED <- predict(mod3, DEMO_DF.test, ","                supplemental.cols=colnames(DEMO_DF.test[,c(1,6)]))","","z.show(head(PRED))"],"enabled":true,"result":{"startTime":1737858450244,"interpreter":"r.low","endTime":1737858450868,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100001\t3\t4.84067399774885\n100002\t4\t4.28482575281332\n100004\t5\t5.11047960148405\n100009\t3\t3.51876969821135\n100011\t3\t3.64715915223011\n100017\t5\t6.15820344746225\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%r","","# From the original model build","CoefficientOfDeterminationR2 <- summary(mod3)$r.squared","","# Computed on the test data","MeanAbsoluteError <- (sum(abs(PRED['YRS_RESIDENCE'] - PRED['PREDICTION'])))/nrow(PRED['YRS_RESIDENCE'])","MeanSquaredError <- (sum((PRED['YRS_RESIDENCE'] - PRED['PREDICTION'])^2))/nrow(PRED['YRS_RESIDENCE'])","RootMeanSquaredError <- sqrt(MeanSquaredError)","","cat('R^2 (Coefficient Of Determination - R Squared):  ', CoefficientOfDeterminationR2)","cat('\\nMAE (Mean Absolute Error):  ', MeanAbsoluteError)","cat('\\nMSE (Mean Squared Error):  ', MeanSquaredError)","cat('\\nRMSE (Root Mean Squared Error): ', RootMeanSquaredError)"],"enabled":true,"result":{"startTime":1737858451301,"interpreter":"r.low","endTime":1737858451980,"results":[{"message":"R^2 (Coefficient Of Determination - R Squared):   0.5899824\nMAE (Mean Absolute Error):   0.8703194\nMSE (Mean Squared Error):   1.674296\nRMSE (Root Mean Squared Error):  1.293946","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Using Lattice, plot the predicted and actual years of residence","message":["%r","","x <- ore.pull(PRED[['PREDICTION']])","y <- ore.pull(PRED[['YRS_RESIDENCE']])","n <- length(x)","","dotplot(y~x, xlab = \"PREDICTION\", ylab = \"YRS_RESIDENCE\", ","  panel = function(x,y) {","  panel.dotplot(x,y)","  panel.abline(0, 1, col=\"red\")","  }, ","  xlim=c(0,14.5), ","  ylim=c(0,14.5),","  scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","  main=\"The prediction vs. actual value for years of residence\")"],"enabled":true,"result":{"startTime":1737858452400,"interpreter":"r.low","endTime":1737858453079,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\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Lattice, plot the residuals","message":["%r","","xyplot(y-x~x, xlab = \"PREDICTION\", ylab = \"RESIDUAL\", ","        panel = function(x,y) {","          panel.xyplot(x,y)","          panel.abline(0, 0, col=\"red\")","        }, ","        xlim=c(0,8.5), ","        ylim=c(-7,8.5),","        scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","        main=\"Prediction of Years of Residence vs. 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MQsFORERExCQU7ERERERMQsFORERExCQU7ERERERMQsFORERE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\n5l0i9+803sq6m7qFMA2TBh6sbMqFnpz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VXXMJOztnmyEo28tiz6juh4KUlAJUUu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XYsjKZ2Qj9JT43QF4mEmBBj09utXmyG0+bsfNRLaEEpLAubFYyqfroak+s7szuzvR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\n2ImIiDiidcfoOpNzl6nsTUl3Dp5jZzzTt9O2Bp90xL+U0fnkruirWBEREYd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ation":"html","title":null,"message":["%md","","## Use SQL to access prediction details with model created in R"],"enabled":true,"result":{"startTime":1737858454589,"interpreter":"md.low","endTime":1737858455000,"results":[{"message":"<h2 id=\"use-sql-to-access-prediction-details-with-model-created-in-r\">Use SQL to access prediction details with model created in R<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize GLM_TEST_DATA for use in query below","message":["%r","","ore.drop(table = 'GLM_TEST_DATA', silent=TRUE)","","ore.create(DEMO_DF.test, table = 'GLM_TEST_DATA')  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ONLY"],"enabled":true,"result":{"startTime":1737858456766,"interpreter":"sql.low","endTime":1737858458613,"results":[{"message":"CUST_ID\tPREDICTION_YRS_RES\tPRED_LOWER_LIMIT\tPRED_UPPER_LIMIT\tPREDICTION_DETAILS\n100001\t4.8406739977488531\t4.4638193129646693\t5.2175286825330369\t<Details algorithm=\"Generalized Linear Model\"> <Attribute name=\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".498\" rank=\"1\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".04\" rank=\"2\"/> <Attribute name=\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\"-.016\" rank=\"3\"/> <Attribute name=\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.02\" rank=\"4\"/> <Attribute name=\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\"-.029\" rank=\"5\"/> <\/Details> \n100002\t4.2848257528133242\t4.0332501498013693\t4.5364013558252791\t<Details algorithm=\"Generalized Linear Model\"> <Attribute name=\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".154\" rank=\"1\"/> 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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Regression NN","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858493883,"interpreter":"md.low","endTime":1737858494386,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Neural Networks","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737858494826,"interpreter":"md.low","endTime":1737858495378,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-neural-networks\">Regression Modeling to Predict Numerical Values using Neural Networks<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737858495841,"interpreter":"md.low","endTime":1737858496260,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">OML4R Neural Network<\/a>"],"enabled":true,"result":{"startTime":1737858496709,"interpreter":"md.low","endTime":1737858497143,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">OML4R Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","library(lattice)","","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737858497578,"interpreter":"r.low","endTime":1737858502552,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer with demographics data","message":["%r","","ore.sync(query = c(\"QUERY_CUSTOMERS\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH AGE, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.sync(query = c(\"QUERY_DEMO_DF\" =  'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","CUST_DF <- merge(QUERY_CUSTOMERS,QUERY_DEMO_DF, by=\"CUST_ID\")"],"enabled":true,"result":{"startTime":1737858503010,"interpreter":"r.low","endTime":1737858503925,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1737858504348,"interpreter":"r.low","endTime":1737858504930,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tAGE\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100100\tF\tMarried\t1959\tJ: 190,000 - 249,999\t10000\tMasters\t1\t4-5\tProf.\t4\t0\n100200\tM\tNeverM\t1983\tL: 300,000 and above\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100300\tM\tMarried\t1961\tG: 130,000 - 149,999\t10000\tBach.\t1\t3\tProf.\t4\t0\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\n100500\tM\tNeverM\t1983\tH: 150,000 - 169,999\t15000\tHS-grad\t0\t1\tCrafts\t2\t1\n100600\tM\tMarried\t1978\tK: 250,000 - 299,999\t9000\t11th\t0\t3\tTransp.\t3\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","group <- as.integer(1:nrow(CUST_DF) %in% index)","","# Create training data set","rownames(CUST_DF) <- CUST_DF$CUST_ID","CUST_DF.train <- CUST_DF[group==FALSE,]","","# Create test data set","CUST_DF.test <- CUST_DF[group==TRUE,]","cat(\"\\n======== Records | Columns\\n\\nTrain Data: \",","    dim(CUST_DF.train),","    \"\\n Test Data: \",","    dim(CUST_DF.test))"],"enabled":true,"result":{"startTime":1737858505356,"interpreter":"r.low","endTime":1737858506702,"results":[{"message":"\n======== Records | Columns\n\nTrain Data:  2700 12 \n Test Data:  1800 12","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build NN regression model to predict YRS_RESIDENCE with default settings and assign model name","message":["%r","","ore.drop(model='NN_REGRESSION_MODEL', silent=TRUE)","","nn.mod1 <- ore.odmNN(formula=YRS_RESIDENCE~.-CUST_ID, ","                     data=CUST_DF.train, ","                     type=\"regression\", ","                     odm.settings=list(model_name=\"NN_REGRESSION_MODEL\"))"],"enabled":true,"result":{"startTime":1737858507139,"interpreter":"r.low","endTime":1737858511524,"results":[{"message":"Warning message:\n\u201cThe created mining model NN_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data using model proxy object returning predictions and actual value","message":["%r","","RES_DF <- predict(object=nn.mod1, ","                  newdata=CUST_DF.test, ","                  supplemental.cols=c('CUST_ID','YRS_RESIDENCE'))","","z.show(head(RES_DF,20))"],"enabled":true,"result":{"startTime":1737858511948,"interpreter":"r.low","endTime":1737858512694,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100001\t3\t4.71365279431152\n100002\t4\t4.90605163780748\n100004\t5\t5.80876228877983\n100009\t3\t3.85942428530475\n100011\t3\t3.64229393540884\n100017\t5\t3.36743349347281\n100018\t3\t2.43336351850082\n100025\t3\t4.09687804975349\n100027\t3\t3.12613747169009\n100031\t4\t3.85926322060545\n100035\t4\t2.74074070254068\n100036\t6\t6.25965743994955\n100037\t6\t6.24873662940347\n100039\t3\t3.56309837839062\n100041\t2\t1.13511624341159\n100044\t2\t2.14568067578043\n100045\t5\t4.18403206490439\n100046\t5\t4.78176766892836\n100048\t2\t2.65669140190384\n100049\t6\t6.14410634824773\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NN ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>' ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO' ","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`,`NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and <strong>starting from Oracle Database 23ai<\/strong> `NNET_ACTIVATIONS_RELU`.","> NNET_HIDDEN_LAYERS = 3","","> NNET_NODES_PER_LAYER = '8,8,2'","","> NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'","","> NNET_HELDASIDE_RATIO = 0.25","","> NNET_HELDASIDE_MAX_FAIL = 6","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> NNET_REG_LAMBDA = 1","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> NNET_ITERATIONS = 200","","> NNET_TOLERANCE = 0.000001","","> ODMS_RANDOM_SEED = 0","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> LBFGS_HISTORY_DEPTH = 10","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> LBFGS_GRADIENT_TOLERANCE = 0.0000001","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'","","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'"],"enabled":true,"result":{"startTime":1737858513129,"interpreter":"md.low","endTime":1737858513595,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nn\">Examples of possible setting overrides for NN<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-neural-network-model-using-ore-odmnn.html\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>,<code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and <strong>starting from Oracle Database 23ai<\/strong> <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_HIDDEN_LAYERS = 3<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_NODES_PER_LAYER = '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_ACTIVATIONS = &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_RATIO = 0.25<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_MAX_FAIL = 6<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REG_LAMBDA = 1<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_ITERATIONS = 200<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_TOLERANCE = 0.000001<\/p>\n<\/blockquote>\n<blockquote>\n<p>ODMS_RANDOM_SEED = 0<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_HISTORY_DEPTH = 10<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_GRADIENT_TOLERANCE = 0.0000001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Use a single layer with 4 neurons with specific activation function","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS = 1,","                 NNET_NODES_PER_LAYER = 4,","                 NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH'\")","                 ","nn.mod2 <- ore.odmNN(formula=YRS_RESIDENCE~.-CUST_ID, ","                     data=CUST_DF.train, ","                     type=\"regression\", ","                     odm.settings = settings)","","summary(nn.mod2)"],"enabled":true,"result":{"startTime":1737858514018,"interpreter":"r.low","endTime":1737858516177,"results":[{"message":"\nCall:\nore.odmNN(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", odm.settings = settings)\n\nSettings: \n                                                  value\nlbfgs.gradient.tolerance                     .000000001\nlbfgs.history.depth                                  20\nlbfgs.scale.hessian          LBFGS_SCALE_HESSIAN_ENABLE\nactivations                     'NNET_ACTIVATIONS_TANH'\nhidden.layers                                         1\niterations                                          200\nnodes.per.layer                                       4\ntolerance                                       .000001\nodms.details                                odms.enable\nodms.missing.value.treatment    odms.missing.value.auto\nodms.random.seed                                      0\nodms.sampling                     odms.sampling.disable\nprep.auto                                            ON\n\nNumber of Layers: \n[1] 2\n\nNodes per Layer: \n[1]  4 60\n\nWeight: \n    LAYER IDX_FROM IDX_TO      ATTRIBUTE_NAME      ATTRIBUTE_VALUE       WEIGHT\n1       0        0      0       AFFINITY_CARD                 <NA> -1.179169774\n2       0        0      1       AFFINITY_CARD                 <NA> -0.267186821\n3       0        0      2       AFFINITY_CARD                 <NA>  0.144475281\n4       0        0      3       AFFINITY_CARD                 <NA> -2.038869858\n5       0        1      0                 AGE                 <NA> -2.530742645\n6       0        1      1                 AGE                 <NA> -0.424165964\n7       0        1      2                 AGE                 <NA>  0.319335312\n8       0        1      3                 AGE                 <NA>  0.509258270\n9       0        2      0   CUST_CREDIT_LIMIT                 <NA>  0.032791689\n10      0        2      1   CUST_CREDIT_LIMIT                 <NA>  0.023581436\n11      0        2      2   CUST_CREDIT_LIMIT                 <NA>  0.064880803\n12      0        2      3   CUST_CREDIT_LIMIT                 <NA> -0.156145111\n13      0        3      0         Y_BOX_GAMES                 <NA>  0.890239716\n14      0        3      1         Y_BOX_GAMES                 <NA>  0.039347526\n15      0        3      2         Y_BOX_GAMES                 <NA>  0.050655074\n16      0        3      3         Y_BOX_GAMES                 <NA>  0.366919249\n17      0        4      0         CUST_GENDER                    F -2.722101927\n18      0        4      1         CUST_GENDER                    F  0.198945463\n19      0        4      2         CUST_GENDER                    F  1.102059007\n20      0        4      3         CUST_GENDER                    F -0.099188365\n21      0        5      0         CUST_GENDER                    M -0.321085066\n22      0        5      1         CUST_GENDER                    M -0.171378061\n23      0        5      2         CUST_GENDER                    M -0.578391194\n24      0        5      3         CUST_GENDER                    M  0.152616054\n25      0        6      0   CUST_INCOME_LEVEL      A: Below 30,000 -4.134376049\n26      0        6      1   CUST_INCOME_LEVEL      A: Below 30,000 -0.019847237\n27      0        6      2   CUST_INCOME_LEVEL      A: Below 30,000  0.147033066\n28      0        6      3   CUST_INCOME_LEVEL      A: Below 30,000 -1.455876946\n29      0        7      0   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -1.153873563\n30      0        7      1   CUST_INCOME_LEVEL   B: 30,000 - 49,999  0.144071862\n31      0        7      2   CUST_INCOME_LEVEL   B: 30,000 - 49,999  0.126651883\n32      0        7      3   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.052270144\n33      0        8      0   CUST_INCOME_LEVEL   C: 50,000 - 69,999  1.486196518\n34      0        8      1   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -0.005635074\n35      0        8      2   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -0.367658466\n36      0        8      3   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.297944516\n37      0        9      0   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -3.639083862\n38      0        9      1   CUST_INCOME_LEVEL   D: 70,000 - 89,999  0.007048308\n39      0        9      2   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.248060197\n40      0        9      3   CUST_INCOME_LEVEL   D: 70,000 - 89,999  1.105942607\n41      0       10      0   CUST_INCOME_LEVEL  E: 90,000 - 109,999 -0.786624312\n42      0       10      1   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.191063732\n43      0       10      2   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.840674043\n44      0       10      3   CUST_INCOME_LEVEL  E: 90,000 - 109,999 -0.450462878\n45      0       11      0   CUST_INCOME_LEVEL F: 110,000 - 129,999  1.213661432\n46      0       11      1   CUST_INCOME_LEVEL F: 110,000 - 129,999  0.038740579\n47      0       11      2   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.137751505\n48      0       11      3   CUST_INCOME_LEVEL F: 110,000 - 129,999  0.726743102\n49      0       12      0   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.770777643\n50      0       12      1   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.093522310\n51      0       12      2   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.202832654\n52      0       12      3   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.002140809\n53      0       13      0   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.253954351\n54      0       13      1   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.093154058\n55      0       13      2   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.235649243\n56      0       13      3   CUST_INCOME_LEVEL H: 150,000 - 169,999 -0.395710081\n57      0       14      0   CUST_INCOME_LEVEL I: 170,000 - 189,999  1.752833486\n58      0       14      1   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.012196341\n59      0       14      2   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.341516286\n60      0       14      3   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.217094421\n61      0       15      0   CUST_INCOME_LEVEL J: 190,000 - 249,999 -1.556105375\n62      0       15      1   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.017903874\n63      0       15      2   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.172493055\n64      0       15      3   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.331415325\n65      0       16      0   CUST_INCOME_LEVEL K: 250,000 - 299,999  1.581621647\n66      0       16      1   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.045369424\n67      0       16      2   CUST_INCOME_LEVEL K: 250,000 - 299,999 -0.383755296\n68      0       16      3   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.312755197\n69      0       17      0   CUST_INCOME_LEVEL L: 300,000 and above  1.469626665\n70      0       17      1   CUST_INCOME_LEVEL L: 300,000 and above  0.040711064\n71      0       17      2   CUST_INCOME_LEVEL L: 300,000 and above -0.252147257\n72      0       17      3   CUST_INCOME_LEVEL L: 300,000 and above  0.572753668\n73      0       18      0 CUST_MARITAL_STATUS              Divorc. -0.094919898\n74      0       18      1 CUST_MARITAL_STATUS              Divorc. -0.027245892\n75      0       18      2 CUST_MARITAL_STATUS              Divorc. -0.617650688\n76      0       18      3 CUST_MARITAL_STATUS              Divorc.  0.457932979\n77      0       19      0 CUST_MARITAL_STATUS              Mabsent -3.740973711\n78      0       19      1 CUST_MARITAL_STATUS              Mabsent  0.453831106\n79      0       19      2 CUST_MARITAL_STATUS              Mabsent  1.492226720\n80      0       19      3 CUST_MARITAL_STATUS              Mabsent  0.746605575\n81      0       20      0 CUST_MARITAL_STATUS              Married -0.630868852\n82      0       20      1 CUST_MARITAL_STATUS              Married  0.009079104\n83      0       20      2 CUST_MARITAL_STATUS              Married  0.090387858\n84      0       20      3 CUST_MARITAL_STATUS              Married -0.612244070\n85      0       21      0 CUST_MARITAL_STATUS               NeverM -0.901547015\n86      0       21      1 CUST_MARITAL_STATUS               NeverM -0.124040924\n87      0       21      2 CUST_MARITAL_STATUS               NeverM -0.737510443\n88      0       21      3 CUST_MARITAL_STATUS               NeverM  0.166564003\n89      0       22      0 CUST_MARITAL_STATUS               Separ. -0.185871705\n90      0       22      1 CUST_MARITAL_STATUS               Separ.  0.027478300\n91      0       22      2 CUST_MARITAL_STATUS               Separ.  0.089197300\n92      0       22      3 CUST_MARITAL_STATUS               Separ. -1.072150946\n93      0       23      0 CUST_MARITAL_STATUS              Widowed  1.573391199\n94      0       23      1 CUST_MARITAL_STATUS              Widowed -0.108970672\n95      0       23      2 CUST_MARITAL_STATUS              Widowed -0.257781118\n96      0       23      3 CUST_MARITAL_STATUS              Widowed  0.212491900\n97      0       24      0           EDUCATION                 10th  2.069968224\n98      0       24      1           EDUCATION                 10th -0.234730765\n99      0       24      2           EDUCATION                 10th -0.103187159\n100     0       24      3           EDUCATION                 10th -1.419080377\n101     0       25      0           EDUCATION                 11th -3.311021090\n102     0       25      1           EDUCATION                 11th -0.105214901\n103     0       25      2           EDUCATION                 11th  0.527506649\n104     0       25      3           EDUCATION                 11th  0.582743227\n105     0       26      0           EDUCATION                 12th -0.352308184\n106     0       26      1           EDUCATION                 12th -0.129694998\n107     0       26      2           EDUCATION                 12th  0.084432326\n108     0       26      3           EDUCATION                 12th -0.370134324\n109     0       27      0           EDUCATION              1st-4th -1.722343922\n110     0       27      1           EDUCATION              1st-4th -0.330888212\n111     0       27      2           EDUCATION              1st-4th  0.353656828\n112     0       27      3           EDUCATION              1st-4th -0.792023361\n113     0       28      0           EDUCATION              5th-6th  0.471474081\n114     0       28      1           EDUCATION              5th-6th -0.019763285\n115     0       28      2           EDUCATION              5th-6th  0.827239633\n116     0       28      3           EDUCATION              5th-6th -0.574551523\n117     0       29      0           EDUCATION              7th-8th -2.934873581\n118     0       29      1           EDUCATION              7th-8th -0.142622039\n119     0       29      2           EDUCATION              7th-8th  0.231739819\n120     0       29      3           EDUCATION              7th-8th  2.714250326\n121     0       30      0           EDUCATION                  9th -0.109499089\n122     0       30      1           EDUCATION                  9th -0.116734684\n123     0       30      2           EDUCATION                  9th -0.578164876\n124     0       30      3           EDUCATION                  9th  0.219604582\n125     0       31      0           EDUCATION              < Bach.  0.228435189\n126     0       31      1           EDUCATION              < Bach. -0.084699385\n127     0       31      2           EDUCATION              < Bach.  0.274848491\n128     0       31      3           EDUCATION              < Bach.  0.435798496\n129     0       32      0           EDUCATION              Assoc-A  0.125887126\n130     0       32      1           EDUCATION              Assoc-A  0.158527285\n131     0       32      2           EDUCATION              Assoc-A  1.543463469\n132     0       32      3           EDUCATION              Assoc-A -1.310424089\n133     0       33      0           EDUCATION              Assoc-V -2.117264509\n134     0       33      1           EDUCATION              Assoc-V -0.056247853\n135     0       33      2           EDUCATION              Assoc-V  0.247852981\n136     0       33      3           EDUCATION              Assoc-V -0.322413981\n137     0       34      0           EDUCATION                Bach. -1.349860668\n138     0       34      1           EDUCATION                Bach. -0.081842750\n139     0       34      2           EDUCATION                Bach.  0.235809773\n140     0       34      3           EDUCATION                Bach. -0.779164493\n141     0       35      0           EDUCATION              HS-grad -0.408974767\n142     0       35      1           EDUCATION              HS-grad -0.063664369\n143     0       35      2           EDUCATION              HS-grad  0.176945120\n144     0       35      3           EDUCATION              HS-grad  0.865251422\n145     0       36      0           EDUCATION              Masters  0.962751091\n146     0       36      1           EDUCATION              Masters -0.394120902\n147     0       36      2           EDUCATION              Masters -1.859797239\n148     0       36      3           EDUCATION              Masters -1.196781635\n149     0       37      0           EDUCATION                  PhD -0.293646753\n150     0       37      1           EDUCATION                  PhD -0.245652944\n151     0       37      2           EDUCATION                  PhD -2.047312975\n152     0       37      3           EDUCATION                  PhD -1.520326257\n153     0       38      0           EDUCATION              Presch.  2.371737957\n154     0       38      1           EDUCATION              Presch.  0.008024549\n155     0       38      2           EDUCATION              Presch.  0.494413853\n156     0       38      3           EDUCATION              Presch. -0.150606245\n157     0       39      0           EDUCATION               Profsc  2.977401257\n158     0       39      1           EDUCATION               Profsc  0.066220582\n159     0       39      2           EDUCATION               Profsc -0.660889149\n160     0       39      3           EDUCATION               Profsc  2.370887518\n161     0       40      0      HOUSEHOLD_SIZE                    1  0.468817025\n162     0       40      1      HOUSEHOLD_SIZE                    1  0.086693287\n163     0       40      2      HOUSEHOLD_SIZE                    1 -0.052186277\n164     0       40      3      HOUSEHOLD_SIZE                    1 -0.569550633\n165     0       41      0      HOUSEHOLD_SIZE                    2 -0.829592645\n166     0       41      1      HOUSEHOLD_SIZE                    2  0.217412084\n167     0       41      2      HOUSEHOLD_SIZE                    2  0.292376310\n168     0       41      3      HOUSEHOLD_SIZE                    2  0.107114352\n169     0       42      0      HOUSEHOLD_SIZE                    3 -0.770402074\n170     0       42      1      HOUSEHOLD_SIZE                    3  0.001313679\n171     0       42      2      HOUSEHOLD_SIZE                    3 -1.135213852\n172     0       42      3      HOUSEHOLD_SIZE                    3  0.450853288\n173     0       43      0      HOUSEHOLD_SIZE                  4-5  0.471275032\n174     0       43      1      HOUSEHOLD_SIZE                  4-5  0.011133692\n175     0       43      2      HOUSEHOLD_SIZE                  4-5  1.760972619\n176     0       43      3      HOUSEHOLD_SIZE                  4-5 -0.566499531\n177     0       44      0      HOUSEHOLD_SIZE                  6-8 -0.886502564\n178     0       44      1      HOUSEHOLD_SIZE                  6-8  0.006373470\n179     0       44      2      HOUSEHOLD_SIZE                  6-8 -0.343869448\n180     0       44      3      HOUSEHOLD_SIZE                  6-8 -0.662220418\n181     0       45      0      HOUSEHOLD_SIZE                   9+ -1.876421213\n182     0       45      1      HOUSEHOLD_SIZE                   9+  0.107083388\n183     0       45      2      HOUSEHOLD_SIZE                   9+ -0.084333405\n184     0       45      3      HOUSEHOLD_SIZE                   9+  1.229250193\n185     0       46      0          OCCUPATION                    ?  0.824495137\n186     0       46      1          OCCUPATION                    ?  0.014750513\n187     0       46      2          OCCUPATION                    ?  0.103776872\n188     0       46      3          OCCUPATION                    ? -0.464120090\n189     0       47      0          OCCUPATION              Cleric. -2.259311438\n190     0       47      1          OCCUPATION              Cleric. -0.002715110\n191     0       47      2          OCCUPATION              Cleric. -0.385705382\n192     0       47      3          OCCUPATION              Cleric.  0.261402696\n193     0       48      0          OCCUPATION               Crafts -0.442749023\n194     0       48      1          OCCUPATION               Crafts -0.090030991\n195     0       48      2          OCCUPATION               Crafts -0.840513647\n196     0       48      3          OCCUPATION               Crafts  0.734624326\n197     0       49      0          OCCUPATION                Exec. -2.048426628\n198     0       49      1          OCCUPATION                Exec.  0.011801122\n199     0       49      2          OCCUPATION                Exec. -0.457693279\n200     0       49      3          OCCUPATION                Exec. -0.222047687\n201     0       50      0          OCCUPATION              Farming  4.253055096\n202     0       50      1          OCCUPATION              Farming  0.391145021\n203     0       50      2          OCCUPATION              Farming  0.906190574\n204     0       50      3          OCCUPATION              Farming -2.337530375\n205     0       51      0          OCCUPATION              Handler -0.702809393\n206     0       51      1          OCCUPATION              Handler -0.170957625\n207     0       51      2          OCCUPATION              Handler -0.597928286\n208     0       51      3          OCCUPATION              Handler -0.512009025\n209     0       52      0          OCCUPATION              House-s -0.072017297\n210     0       52      1          OCCUPATION              House-s -0.612649083\n211     0       52      2          OCCUPATION              House-s -1.240195155\n212     0       52      3          OCCUPATION              House-s -0.948445141\n213     0       53      0          OCCUPATION              Machine -2.108899117\n214     0       53      1          OCCUPATION              Machine -0.072988287\n215     0       53      2          OCCUPATION              Machine -0.793402016\n216     0       53      3          OCCUPATION              Machine  1.171347499\n217     0       54      0          OCCUPATION                Other  0.257962912\n218     0       54      1          OCCUPATION                Other  0.140300512\n219     0       54      2          OCCUPATION                Other  0.669391215\n220     0       54      3          OCCUPATION                Other  1.071351051\n221     0       55      0          OCCUPATION                Prof. -0.144228935\n222     0       55      1          OCCUPATION                Prof.  0.175128818\n223     0       55      2          OCCUPATION                Prof.  0.803170919\n224     0       55      3          OCCUPATION                Prof.  0.039075192\n225     0       56      0          OCCUPATION              Protec. -0.889865518\n226     0       56      1          OCCUPATION              Protec.  0.314054579\n227     0       56      2          OCCUPATION              Protec.  1.012464046\n228     0       56      3          OCCUPATION              Protec.  0.128647074\n229     0       57      0          OCCUPATION                Sales  0.138170123\n230     0       57      1          OCCUPATION                Sales  0.034501728\n231     0       57      2          OCCUPATION                Sales -0.166228369\n232     0       57      3          OCCUPATION                Sales -0.378126204\n233     0       58      0          OCCUPATION              TechSup -1.251926422\n234     0       58      1          OCCUPATION              TechSup  0.173856825\n235     0       58      2          OCCUPATION              TechSup  1.095399261\n236     0       58      3          OCCUPATION              TechSup  1.461662650\n237     0       59      0          OCCUPATION              Transp.  0.116283469\n238     0       59      1          OCCUPATION              Transp.  0.033766031\n239     0       59      2          OCCUPATION              Transp. -0.323487788\n240     0       59      3          OCCUPATION              Transp.  0.006932481\n241     0       NA      0                <NA>                 <NA> -3.927720070\n242     0       NA      1                <NA>                 <NA>  0.187160105\n243     0       NA      2                <NA>                 <NA>  0.250279844\n244     0       NA      3                <NA>                 <NA>  0.455032915\n245     1        0      0                <NA>                 <NA> -1.354685783\n246     1        1      0                <NA>                 <NA>  2.005940199\n247     1        2      0                <NA>                 <NA> -0.739346683\n248     1        3      0                <NA>                 <NA> -0.727968633\n249     1       NA      0                <NA>                 <NA> -1.417556763","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use 3 layers with 8,8,2 neurons with specific activation functions","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS   = 3,","                 NNET_NODES_PER_LAYER = c('8','8','4'),","                 NNET_ACTIVATIONS     = c(\"'NNET_ACTIVATIONS_TANH'\",\"'NNET_ACTIVATIONS_LOG_SIG'\", \"'NNET_ACTIVATIONS_LINEAR'\")","                 )  ","nn.mod3 <- ore.odmNN(formula=YRS_RESIDENCE~.-CUST_ID, ","                     data=CUST_DF.train, ","                     type='regression', ","                     odm.settings = settings) "],"enabled":true,"result":{"startTime":1737858516624,"interpreter":"r.low","endTime":1737858519075,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model fit statistics","message":["%r","","summary(nn.mod3)"],"enabled":true,"result":{"startTime":1737858519536,"interpreter":"r.low","endTime":1737858520331,"results":[{"message":"\nCall:\nore.odmNN(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", odm.settings = settings)\n\nSettings: \n                                                                                                    value\nlbfgs.gradient.tolerance                                                                       .000000001\nlbfgs.history.depth                                                                                    20\nlbfgs.scale.hessian                                                            LBFGS_SCALE_HESSIAN_ENABLE\nactivations                  'NNET_ACTIVATIONS_TANH','NNET_ACTIVATIONS_LOG_SIG','NNET_ACTIVATIONS_LINEAR'\nhidden.layers                                                                                           3\niterations                                                                                            200\nnodes.per.layer                                                                                     8,8,4\ntolerance                                                                                         .000001\nodms.details                                                                                  odms.enable\nodms.missing.value.treatment                                                      odms.missing.value.auto\nodms.random.seed                                                                                        0\nodms.sampling                                                                       odms.sampling.disable\nprep.auto                                                                                              ON\n\nNumber of Layers: \n[1] 4\n\nNodes per Layer: \n[1]  8  4 60\n\nWeight: \n    LAYER IDX_FROM IDX_TO      ATTRIBUTE_NAME      ATTRIBUTE_VALUE       WEIGHT\n1       0        0      0       AFFINITY_CARD                 <NA>  0.590950370\n2       0        0      1       AFFINITY_CARD                 <NA>  0.316241264\n3       0        0      2       AFFINITY_CARD                 <NA>  0.947977304\n4       0        0      3       AFFINITY_CARD                 <NA>  0.527591765\n5       0        0      4       AFFINITY_CARD                 <NA> -0.325893998\n6       0        0      5       AFFINITY_CARD                 <NA> -0.846508324\n7       0        0      6       AFFINITY_CARD                 <NA>  0.944742739\n8       0        0      7       AFFINITY_CARD                 <NA> -2.290657520\n9       0        1      0                 AGE                 <NA> -1.501560330\n10      0        1      1                 AGE                 <NA> -3.540740728\n11      0        1      2                 AGE                 <NA>  2.601270199\n12      0        1      3                 AGE                 <NA>  1.226175427\n13      0        1      4                 AGE                 <NA> -0.327146292\n14      0        1      5                 AGE                 <NA> -2.502476215\n15      0        1      6                 AGE                 <NA> -1.159574866\n16      0        1      7                 AGE                 <NA>  0.420435518\n17      0        2      0   CUST_CREDIT_LIMIT                 <NA> -0.028647581\n18      0        2      1   CUST_CREDIT_LIMIT                 <NA>  0.195658028\n19      0        2      2   CUST_CREDIT_LIMIT                 <NA>  0.031982049\n20      0        2      3   CUST_CREDIT_LIMIT                 <NA>  0.514289379\n21      0        2      4   CUST_CREDIT_LIMIT                 <NA>  0.033381332\n22      0        2      5   CUST_CREDIT_LIMIT                 <NA>  0.119131707\n23      0        2      6   CUST_CREDIT_LIMIT                 <NA>  0.166161716\n24      0        2      7   CUST_CREDIT_LIMIT                 <NA>  0.494247615\n25      0        3      0         Y_BOX_GAMES                 <NA> -1.147580504\n26      0        3      1         Y_BOX_GAMES                 <NA>  1.726003528\n27      0        3      2         Y_BOX_GAMES                 <NA>  1.909733295\n28      0        3      3         Y_BOX_GAMES                 <NA> -2.287546158\n29      0        3      4         Y_BOX_GAMES                 <NA> -1.231375575\n30      0        3      5         Y_BOX_GAMES                 <NA>  1.914498806\n31      0        3      6         Y_BOX_GAMES                 <NA>  0.500809073\n32      0        3      7         Y_BOX_GAMES                 <NA> -0.796411991\n33      0        4      0         CUST_GENDER                    F  0.392955452\n34      0        4      1         CUST_GENDER                    F  0.236905083\n35      0        4      2         CUST_GENDER                    F -0.006138491\n36      0        4      3         CUST_GENDER                    F  1.315350175\n37      0        4      4         CUST_GENDER                    F  1.240032077\n38      0        4      5         CUST_GENDER                    F  0.224317357\n39      0        4      6         CUST_GENDER                    F  3.674928427\n40      0        4      7         CUST_GENDER                    F  0.129536659\n41      0        5      0         CUST_GENDER                    M  0.369512796\n42      0        5      1         CUST_GENDER                    M -0.177382439\n43      0        5      2         CUST_GENDER                    M  0.227777198\n44      0        5      3         CUST_GENDER                    M -0.063578732\n45      0        5      4         CUST_GENDER                    M  0.612867415\n46      0        5      5         CUST_GENDER                    M -0.315822661\n47      0        5      6         CUST_GENDER                    M -2.921751738\n48      0        5      7         CUST_GENDER                    M  1.022825480\n49      0        6      0   CUST_INCOME_LEVEL      A: Below 30,000  0.075132824\n50      0        6      1   CUST_INCOME_LEVEL      A: Below 30,000  1.155412793\n51      0        6      2   CUST_INCOME_LEVEL      A: Below 30,000  2.393180370\n52      0        6      3   CUST_INCOME_LEVEL      A: Below 30,000  4.896889687\n53      0        6      4   CUST_INCOME_LEVEL      A: Below 30,000  0.540169597\n54      0        6      5   CUST_INCOME_LEVEL      A: Below 30,000 -1.099582553\n55      0        6      6   CUST_INCOME_LEVEL      A: Below 30,000 -2.469075680\n56      0        6      7   CUST_INCOME_LEVEL      A: Below 30,000 -2.329326391\n57      0        7      0   CUST_INCOME_LEVEL   B: 30,000 - 49,999  0.099412046\n58      0        7      1   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -1.212083578\n59      0        7      2   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.941915214\n60      0        7      3   CUST_INCOME_LEVEL   B: 30,000 - 49,999  0.392726123\n61      0        7      4   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.066932611\n62      0        7      5   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.094389901\n63      0        7      6   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.490161628\n64      0        7      7   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -1.687210202\n65      0        8      0   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.888924539\n66      0        8      1   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.102584742\n67      0        8      2   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.136129826\n68      0        8      3   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -2.117274761\n69      0        8      4   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.774526477\n70      0        8      5   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.166873932\n71      0        8      6   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.140935108\n72      0        8      7   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -1.522863746\n73      0        9      0   CUST_INCOME_LEVEL   D: 70,000 - 89,999  0.122107692\n74      0        9      1   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -1.288699985\n75      0        9      2   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.344257981\n76      0        9      3   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -1.233390808\n77      0        9      4   CUST_INCOME_LEVEL   D: 70,000 - 89,999  0.069833629\n78      0        9      5   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.225506961\n79      0        9      6   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -1.048204780\n80      0        9      7   CUST_INCOME_LEVEL   D: 70,000 - 89,999  0.279159755\n81      0       10      0   CUST_INCOME_LEVEL  E: 90,000 - 109,999  1.569433689\n82      0       10      1   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.085146233\n83      0       10      2   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.614874184\n84      0       10      3   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.141757920\n85      0       10      4   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.737252414\n86      0       10      5   CUST_INCOME_LEVEL  E: 90,000 - 109,999 -0.673496425\n87      0       10      6   CUST_INCOME_LEVEL  E: 90,000 - 109,999 -1.149314523\n88      0       10      7   CUST_INCOME_LEVEL  E: 90,000 - 109,999  2.113847494\n89      0       11      0   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.178930223\n90      0       11      1   CUST_INCOME_LEVEL F: 110,000 - 129,999 -1.421998143\n91      0       11      2   CUST_INCOME_LEVEL F: 110,000 - 129,999  0.850041866\n92      0       11      3   CUST_INCOME_LEVEL F: 110,000 - 129,999 -1.402752638\n93      0       11      4   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.538923860\n94      0       11      5   CUST_INCOME_LEVEL F: 110,000 - 129,999  0.073757984\n95      0       11      6   CUST_INCOME_LEVEL F: 110,000 - 129,999 -3.475135088\n96      0       11      7   CUST_INCOME_LEVEL F: 110,000 - 129,999  1.456664443\n97      0       12      0   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.182786107\n98      0       12      1   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.018502323\n99      0       12      2   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.041019168\n100     0       12      3   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.257266462\n101     0       12      4   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.824084997\n102     0       12      5   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.024633601\n103     0       12      6   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.908447981\n104     0       12      7   CUST_INCOME_LEVEL G: 130,000 - 149,999  1.472307205\n105     0       13      0   CUST_INCOME_LEVEL H: 150,000 - 169,999 -0.385289729\n106     0       13      1   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.448804855\n107     0       13      2   CUST_INCOME_LEVEL H: 150,000 - 169,999 -0.812873304\n108     0       13      3   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.583176672\n109     0       13      4   CUST_INCOME_LEVEL H: 150,000 - 169,999 -1.053254128\n110     0       13      5   CUST_INCOME_LEVEL H: 150,000 - 169,999 -0.231593251\n111     0       13      6   CUST_INCOME_LEVEL H: 150,000 - 169,999  1.351537943\n112     0       13      7   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.242451563\n113     0       14      0   CUST_INCOME_LEVEL I: 170,000 - 189,999 -1.573165894\n114     0       14      1   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.129022405\n115     0       14      2   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.276933908\n116     0       14      3   CUST_INCOME_LEVEL I: 170,000 - 189,999 -1.444298625\n117     0       14      4   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.334701151\n118     0       14      5   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.983368456\n119     0       14      6   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.092471823\n120     0       14      7   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.157326132\n121     0       15      0   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.724145949\n122     0       15      1   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.234089583\n123     0       15      2   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.099482819\n124     0       15      3   CUST_INCOME_LEVEL J: 190,000 - 249,999  1.099076867\n125     0       15      4   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.523670554\n126     0       15      5   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.042497888\n127     0       15      6   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.355088711\n128     0       15      7   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.664436698\n129     0       16      0   CUST_INCOME_LEVEL K: 250,000 - 299,999 -0.616975904\n130     0       16      1   CUST_INCOME_LEVEL K: 250,000 - 299,999  2.183221340\n131     0       16      2   CUST_INCOME_LEVEL K: 250,000 - 299,999 -0.652430415\n132     0       16      3   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.588329196\n133     0       16      4   CUST_INCOME_LEVEL K: 250,000 - 299,999  1.939828992\n134     0       16      5   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.560326219\n135     0       16      6   CUST_INCOME_LEVEL K: 250,000 - 299,999  2.527673006\n136     0       16      7   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.198237076\n137     0       17      0   CUST_INCOME_LEVEL L: 300,000 and above  0.221676633\n138     0       17      1   CUST_INCOME_LEVEL L: 300,000 and above  0.182005793\n139     0       17      2   CUST_INCOME_LEVEL L: 300,000 and above -1.326440573\n140     0       17      3   CUST_INCOME_LEVEL L: 300,000 and above -0.745448053\n141     0       17      4   CUST_INCOME_LEVEL L: 300,000 and above -0.857117832\n142     0       17      5   CUST_INCOME_LEVEL L: 300,000 and above  0.283869117\n143     0       17      6   CUST_INCOME_LEVEL L: 300,000 and above  3.173767090\n144     0       17      7   CUST_INCOME_LEVEL L: 300,000 and above  0.965229571\n145     0       18      0 CUST_MARITAL_STATUS              Divorc.  0.517034590\n146     0       18      1 CUST_MARITAL_STATUS              Divorc. -0.936501384\n147     0       18      2 CUST_MARITAL_STATUS              Divorc.  0.250130028\n148     0       18      3 CUST_MARITAL_STATUS              Divorc.  2.930575609\n149     0       18      4 CUST_MARITAL_STATUS              Divorc.  0.439281166\n150     0       18      5 CUST_MARITAL_STATUS              Divorc.  0.822384894\n151     0       18      6 CUST_MARITAL_STATUS              Divorc.  1.446307302\n152     0       18      7 CUST_MARITAL_STATUS              Divorc.  0.013444826\n153     0       19      0 CUST_MARITAL_STATUS              Mabsent -0.370933414\n154     0       19      1 CUST_MARITAL_STATUS              Mabsent  0.851363182\n155     0       19      2 CUST_MARITAL_STATUS              Mabsent -0.374491185\n156     0       19      3 CUST_MARITAL_STATUS              Mabsent -1.848706722\n157     0       19      4 CUST_MARITAL_STATUS              Mabsent  0.374868512\n158     0       19      5 CUST_MARITAL_STATUS              Mabsent -0.637169778\n159     0       19      6 CUST_MARITAL_STATUS              Mabsent -0.144441172\n160     0       19      7 CUST_MARITAL_STATUS              Mabsent -0.627748132\n161     0       20      0 CUST_MARITAL_STATUS              Married -0.212894946\n162     0       20      1 CUST_MARITAL_STATUS              Married -1.657467246\n163     0       20      2 CUST_MARITAL_STATUS              Married -0.585223675\n164     0       20      3 CUST_MARITAL_STATUS              Married  1.302204013\n165     0       20      4 CUST_MARITAL_STATUS              Married  0.103142798\n166     0       20      5 CUST_MARITAL_STATUS              Married -0.055105790\n167     0       20      6 CUST_MARITAL_STATUS              Married  0.117994890\n168     0       20      7 CUST_MARITAL_STATUS              Married -0.129337251\n169     0       21      0 CUST_MARITAL_STATUS               NeverM  0.175125256\n170     0       21      1 CUST_MARITAL_STATUS               NeverM -0.577535868\n171     0       21      2 CUST_MARITAL_STATUS               NeverM -1.137725472\n172     0       21      3 CUST_MARITAL_STATUS               NeverM -0.393668830\n173     0       21      4 CUST_MARITAL_STATUS               NeverM  0.706501305\n174     0       21      5 CUST_MARITAL_STATUS               NeverM -0.799504399\n175     0       21      6 CUST_MARITAL_STATUS               NeverM -0.749296606\n176     0       21      7 CUST_MARITAL_STATUS               NeverM -0.877462327\n177     0       22      0 CUST_MARITAL_STATUS               Separ.  0.038355581\n178     0       22      1 CUST_MARITAL_STATUS               Separ.  0.415168971\n179     0       22      2 CUST_MARITAL_STATUS               Separ.  0.532274485\n180     0       22      3 CUST_MARITAL_STATUS               Separ.  0.247570857\n181     0       22      4 CUST_MARITAL_STATUS               Separ.  0.216607615\n182     0       22      5 CUST_MARITAL_STATUS               Separ.  0.352078825\n183     0       22      6 CUST_MARITAL_STATUS               Separ. -0.093661390\n184     0       22      7 CUST_MARITAL_STATUS               Separ.  0.229033202\n185     0       23      0 CUST_MARITAL_STATUS              Widowed  0.432881117\n186     0       23      1 CUST_MARITAL_STATUS              Widowed  1.563550711\n187     0       23      2 CUST_MARITAL_STATUS              Widowed  0.059484638\n188     0       23      3 CUST_MARITAL_STATUS              Widowed -1.880681753\n189     0       23      4 CUST_MARITAL_STATUS              Widowed -0.155361697\n190     0       23      5 CUST_MARITAL_STATUS              Widowed  0.432450473\n191     0       23      6 CUST_MARITAL_STATUS              Widowed  0.469862819\n192     0       23      7 CUST_MARITAL_STATUS              Widowed  1.815159082\n193     0       24      0           EDUCATION                 10th  0.848534763\n194     0       24      1           EDUCATION                 10th -1.885128021\n195     0       24      2           EDUCATION                 10th -0.104182400\n196     0       24      3           EDUCATION                 10th  1.500008345\n197     0       24      4           EDUCATION                 10th  0.267549664\n198     0       24      5           EDUCATION                 10th  0.032831531\n199     0       24      6           EDUCATION                 10th  1.180638790\n200     0       24      7           EDUCATION                 10th  2.683673143\n201     0       25      0           EDUCATION                 11th -0.902489722\n202     0       25      1           EDUCATION                 11th -1.413594723\n203     0       25      2           EDUCATION                 11th  0.213617221\n204     0       25      3           EDUCATION                 11th  1.467667222\n205     0       25      4           EDUCATION                 11th -0.052788794\n206     0       25      5           EDUCATION                 11th -0.784666121\n207     0       25      6           EDUCATION                 11th -2.120768785\n208     0       25      7           EDUCATION                 11th  0.471805394\n209     0       26      0           EDUCATION                 12th  0.083242550\n210     0       26      1           EDUCATION                 12th  0.022811776\n211     0       26      2           EDUCATION                 12th -0.883629203\n212     0       26      3           EDUCATION                 12th -0.737022817\n213     0       26      4           EDUCATION                 12th  0.267873496\n214     0       26      5           EDUCATION                 12th  0.285756499\n215     0       26      6           EDUCATION                 12th  1.171676755\n216     0       26      7           EDUCATION                 12th -0.036009021\n217     0       27      0           EDUCATION              1st-4th  0.048791204\n218     0       27      1           EDUCATION              1st-4th -0.450762063\n219     0       27      2           EDUCATION              1st-4th -0.135187641\n220     0       27      3           EDUCATION              1st-4th  0.903193474\n221     0       27      4           EDUCATION              1st-4th -0.442816913\n222     0       27      5           EDUCATION              1st-4th -1.524952531\n223     0       27      6           EDUCATION              1st-4th -0.299645722\n224     0       27      7           EDUCATION              1st-4th  0.216657162\n225     0       28      0           EDUCATION              5th-6th  0.404954880\n226     0       28      1           EDUCATION              5th-6th -0.061052393\n227     0       28      2           EDUCATION              5th-6th -0.147634014\n228     0       28      3           EDUCATION              5th-6th  1.223305583\n229     0       28      4           EDUCATION              5th-6th -0.087550141\n230     0       28      5           EDUCATION              5th-6th -0.695283413\n231     0       28      6           EDUCATION              5th-6th -0.309195459\n232     0       28      7           EDUCATION              5th-6th -0.154413626\n233     0       29      0           EDUCATION              7th-8th -0.862396121\n234     0       29      1           EDUCATION              7th-8th  0.376408964\n235     0       29      2           EDUCATION              7th-8th -0.267669827\n236     0       29      3           EDUCATION              7th-8th -0.144680530\n237     0       29      4           EDUCATION              7th-8th  0.648485363\n238     0       29      5           EDUCATION              7th-8th  0.424536467\n239     0       29      6           EDUCATION              7th-8th  0.101603530\n240     0       29      7           EDUCATION              7th-8th  1.221174717\n241     0       30      0           EDUCATION                  9th -0.469743758\n242     0       30      1           EDUCATION                  9th  1.239145041\n243     0       30      2           EDUCATION                  9th  0.091170423\n244     0       30      3           EDUCATION                  9th -1.007506490\n245     0       30      4           EDUCATION                  9th  0.535913825\n246     0       30      5           EDUCATION                  9th  0.747139812\n247     0       30      6           EDUCATION                  9th -0.013382096\n248     0       30      7           EDUCATION                  9th  1.078547239\n249     0       31      0           EDUCATION              < Bach.  0.641235828\n250     0       31      1           EDUCATION              < Bach. -1.047943830\n251     0       31      2           EDUCATION              < Bach.  1.242712736\n252     0       31      3           EDUCATION              < Bach. -0.095368348\n253     0       31      4           EDUCATION              < Bach. -0.908843517\n254     0       31      5           EDUCATION              < Bach.  0.100276284\n255     0       31      6           EDUCATION              < Bach.  0.120505065\n256     0       31      7           EDUCATION              < Bach. -0.949887097\n257     0       32      0           EDUCATION              Assoc-A  0.427088946\n258     0       32      1           EDUCATION              Assoc-A  1.085750103\n259     0       32      2           EDUCATION              Assoc-A  0.465230286\n260     0       32      3           EDUCATION              Assoc-A -0.688298225\n261     0       32      4           EDUCATION              Assoc-A  2.095725775\n262     0       32      5           EDUCATION              Assoc-A  0.694294810\n263     0       32      6           EDUCATION              Assoc-A  1.707172751\n264     0       32      7           EDUCATION              Assoc-A -1.110267878\n265     0       33      0           EDUCATION              Assoc-V -1.414925098\n266     0       33      1           EDUCATION              Assoc-V  1.336582661\n267     0       33      2           EDUCATION              Assoc-V -0.828816712\n268     0       33      3           EDUCATION              Assoc-V  1.701522708\n269     0       33      4           EDUCATION              Assoc-V  0.704230487\n270     0       33      5           EDUCATION              Assoc-V  0.413772166\n271     0       33      6           EDUCATION              Assoc-V -0.840726256\n272     0       33      7           EDUCATION              Assoc-V  0.873068392\n273     0       34      0           EDUCATION                Bach.  0.762573123\n274     0       34      1           EDUCATION                Bach.  0.125529662\n275     0       34      2           EDUCATION                Bach.  1.244528890\n276     0       34      3           EDUCATION                Bach.  1.511694908\n277     0       34      4           EDUCATION                Bach. -0.260997206\n278     0       34      5           EDUCATION                Bach. -0.229866996\n279     0       34      6           EDUCATION                Bach.  0.097685896\n280     0       34      7           EDUCATION                Bach. -2.153110027\n281     0       35      0           EDUCATION              HS-grad  0.274779439\n282     0       35      1           EDUCATION              HS-grad -0.142509565\n283     0       35      2           EDUCATION              HS-grad -0.804897964\n284     0       35      3           EDUCATION              HS-grad  0.224923685\n285     0       35      4           EDUCATION              HS-grad -0.644586027\n286     0       35      5           EDUCATION              HS-grad  0.077539831\n287     0       35      6           EDUCATION              HS-grad  1.110633492\n288     0       35      7           EDUCATION              HS-grad -0.200714633\n289     0       36      0           EDUCATION              Masters  0.630565464\n290     0       36      1           EDUCATION              Masters -1.267530203\n291     0       36      2           EDUCATION              Masters  0.248167917\n292     0       36      3           EDUCATION              Masters -1.113036633\n293     0       36      4           EDUCATION              Masters  0.112990320\n294     0       36      5           EDUCATION              Masters  0.101573721\n295     0       36      6           EDUCATION              Masters  0.949299574\n296     0       36      7           EDUCATION              Masters -3.887576818\n297     0       37      0           EDUCATION                  PhD  0.552824378\n298     0       37      1           EDUCATION                  PhD  0.288149357\n299     0       37      2           EDUCATION                  PhD -0.218032271\n300     0       37      3           EDUCATION                  PhD -0.574766338\n301     0       37      4           EDUCATION                  PhD  0.618824542\n302     0       37      5           EDUCATION                  PhD  0.428783923\n303     0       37      6           EDUCATION                  PhD -1.069408417\n304     0       37      7           EDUCATION                  PhD -0.057184011\n305     0       38      0           EDUCATION              Presch.  0.026091512\n306     0       38      1           EDUCATION              Presch. -0.010549515\n307     0       38      2           EDUCATION              Presch. -0.057948563\n308     0       38      3           EDUCATION              Presch. -1.935839057\n309     0       38      4           EDUCATION              Presch.  0.132909253\n310     0       38      5           EDUCATION              Presch. -0.526572526\n311     0       38      6           EDUCATION              Presch.  0.923368990\n312     0       38      7           EDUCATION              Presch.  0.406600535\n313     0       39      0           EDUCATION               Profsc -0.720241070\n314     0       39      1           EDUCATION               Profsc  2.044195175\n315     0       39      2           EDUCATION               Profsc -0.251133084\n316     0       39      3           EDUCATION               Profsc -3.150549173\n317     0       39      4           EDUCATION               Profsc -1.234677315\n318     0       39      5           EDUCATION               Profsc  1.187793732\n319     0       39      6           EDUCATION               Profsc -1.356560469\n320     0       39      7           EDUCATION               Profsc  2.105535746\n321     0       40      0      HOUSEHOLD_SIZE                    1 -0.236653611\n322     0       40      1      HOUSEHOLD_SIZE                    1  0.622542262\n323     0       40      2      HOUSEHOLD_SIZE                    1 -0.346826136\n324     0       40      3      HOUSEHOLD_SIZE                    1 -1.492181659\n325     0       40      4      HOUSEHOLD_SIZE                    1 -0.516587675\n326     0       40      5      HOUSEHOLD_SIZE                    1 -0.189128011\n327     0       40      6      HOUSEHOLD_SIZE                    1  0.808229506\n328     0       40      7      HOUSEHOLD_SIZE                    1 -0.563245714\n329     0       41      0      HOUSEHOLD_SIZE                    2  0.543193400\n330     0       41      1      HOUSEHOLD_SIZE                    2 -0.377034932\n331     0       41      2      HOUSEHOLD_SIZE                    2  0.329721659\n332     0       41      3      HOUSEHOLD_SIZE                    2  1.071891427\n333     0       41      4      HOUSEHOLD_SIZE                    2  1.361901879\n334     0       41      5      HOUSEHOLD_SIZE                    2  0.701364934\n335     0       41      6      HOUSEHOLD_SIZE                    2  0.101435080\n336     0       41      7      HOUSEHOLD_SIZE                    2  1.805770874\n337     0       42      0      HOUSEHOLD_SIZE                    3  0.307126254\n338     0       42      1      HOUSEHOLD_SIZE                    3 -0.293506593\n339     0       42      2      HOUSEHOLD_SIZE                    3 -0.844544411\n340     0       42      3      HOUSEHOLD_SIZE                    3  0.292762876\n341     0       42      4      HOUSEHOLD_SIZE                    3 -0.572222292\n342     0       42      5      HOUSEHOLD_SIZE                    3  0.281742215\n343     0       42      6      HOUSEHOLD_SIZE                    3  0.665868342\n344     0       42      7      HOUSEHOLD_SIZE                    3  0.736486077\n345     0       43      0      HOUSEHOLD_SIZE                  4-5 -0.436169267\n346     0       43      1      HOUSEHOLD_SIZE                  4-5 -0.469934881\n347     0       43      2      HOUSEHOLD_SIZE                  4-5 -0.053873062\n348     0       43      3      HOUSEHOLD_SIZE                  4-5  0.291899592\n349     0       43      4      HOUSEHOLD_SIZE                  4-5  0.284471095\n350     0       43      5      HOUSEHOLD_SIZE                  4-5 -0.122179054\n351     0       43      6      HOUSEHOLD_SIZE                  4-5 -0.399226040\n352     0       43      7      HOUSEHOLD_SIZE                  4-5 -0.520703852\n353     0       44      0      HOUSEHOLD_SIZE                  6-8 -0.719492137\n354     0       44      1      HOUSEHOLD_SIZE                  6-8  0.734428227\n355     0       44      2      HOUSEHOLD_SIZE                  6-8  0.290996790\n356     0       44      3      HOUSEHOLD_SIZE                  6-8  1.036106825\n357     0       44      4      HOUSEHOLD_SIZE                  6-8 -0.527147770\n358     0       44      5      HOUSEHOLD_SIZE                  6-8 -0.580085218\n359     0       44      6      HOUSEHOLD_SIZE                  6-8 -0.285080165\n360     0       44      7      HOUSEHOLD_SIZE                  6-8 -0.194592595\n361     0       45      0      HOUSEHOLD_SIZE                   9+  0.876510680\n362     0       45      1      HOUSEHOLD_SIZE                   9+  0.054157659\n363     0       45      2      HOUSEHOLD_SIZE                   9+  1.027339220\n364     0       45      3      HOUSEHOLD_SIZE                   9+ -0.574855983\n365     0       45      4      HOUSEHOLD_SIZE                   9+  1.024793744\n366     0       45      5      HOUSEHOLD_SIZE                   9+ -0.055252615\n367     0       45      6      HOUSEHOLD_SIZE                   9+  0.180410117\n368     0       45      7      HOUSEHOLD_SIZE                   9+ -0.410152078\n369     0       46      0          OCCUPATION                    ? -1.841053843\n370     0       46      1          OCCUPATION                    ? -1.080163360\n371     0       46      2          OCCUPATION                    ?  1.111415029\n372     0       46      3          OCCUPATION                    ? -0.798581481\n373     0       46      4          OCCUPATION                    ?  1.188638568\n374     0       46      5          OCCUPATION                    ? -0.210893288\n375     0       46      6          OCCUPATION                    ? -0.411234379\n376     0       46      7          OCCUPATION                    ? -0.611580908\n377     0       47      0          OCCUPATION              Cleric. -1.224465489\n378     0       47      1          OCCUPATION              Cleric.  1.386369228\n379     0       47      2          OCCUPATION              Cleric. -1.148598909\n380     0       47      3          OCCUPATION              Cleric.  2.547156096\n381     0       47      4          OCCUPATION              Cleric.  0.263353527\n382     0       47      5          OCCUPATION              Cleric. -0.300467908\n383     0       47      6          OCCUPATION              Cleric. -0.146576285\n384     0       47      7          OCCUPATION              Cleric.  1.091769338\n385     0       48      0          OCCUPATION               Crafts -0.594576120\n386     0       48      1          OCCUPATION               Crafts  0.054249745\n387     0       48      2          OCCUPATION               Crafts -0.220699579\n388     0       48      3          OCCUPATION               Crafts  0.443253279\n389     0       48      4          OCCUPATION               Crafts -0.249751866\n390     0       48      5          OCCUPATION               Crafts  0.882787526\n391     0       48      6          OCCUPATION               Crafts -0.373249114\n392     0       48      7          OCCUPATION               Crafts  1.258895159\n393     0       49      0          OCCUPATION                Exec.  1.273549795\n394     0       49      1          OCCUPATION                Exec.  1.635147929\n395     0       49      2          OCCUPATION                Exec.  0.180833071\n396     0       49      3          OCCUPATION                Exec.  0.636452556\n397     0       49      4          OCCUPATION                Exec.  1.178173065\n398     0       49      5          OCCUPATION                Exec. -1.671736598\n399     0       49      6          OCCUPATION                Exec. -1.225641608\n400     0       49      7          OCCUPATION                Exec. -2.280137300\n401     0       50      0          OCCUPATION              Farming  2.366607666\n402     0       50      1          OCCUPATION              Farming  1.744129658\n403     0       50      2          OCCUPATION              Farming  0.443445057\n404     0       50      3          OCCUPATION              Farming -0.283840895\n405     0       50      4          OCCUPATION              Farming  0.846410513\n406     0       50      5          OCCUPATION              Farming  1.249563098\n407     0       50      6          OCCUPATION              Farming -0.177722633\n408     0       50      7          OCCUPATION              Farming -2.233488798\n409     0       51      0          OCCUPATION              Handler  1.007749200\n410     0       51      1          OCCUPATION              Handler -1.006288409\n411     0       51      2          OCCUPATION              Handler -0.118115723\n412     0       51      3          OCCUPATION              Handler -0.907344043\n413     0       51      4          OCCUPATION              Handler  0.429510921\n414     0       51      5          OCCUPATION              Handler -0.034493256\n415     0       51      6          OCCUPATION              Handler -0.138473675\n416     0       51      7          OCCUPATION              Handler  1.918471694\n417     0       52      0          OCCUPATION              House-s -0.701893985\n418     0       52      1          OCCUPATION              House-s -1.377910495\n419     0       52      2          OCCUPATION              House-s -0.160934761\n420     0       52      3          OCCUPATION              House-s -2.041696787\n421     0       52      4          OCCUPATION              House-s  0.160102487\n422     0       52      5          OCCUPATION              House-s -1.270847917\n423     0       52      6          OCCUPATION              House-s  0.783258080\n424     0       52      7          OCCUPATION              House-s  0.656937003\n425     0       53      0          OCCUPATION              Machine  0.531644225\n426     0       53      1          OCCUPATION              Machine -0.978994727\n427     0       53      2          OCCUPATION              Machine  0.477375954\n428     0       53      3          OCCUPATION              Machine  3.466498137\n429     0       53      4          OCCUPATION              Machine  0.791514993\n430     0       53      5          OCCUPATION              Machine  0.383049011\n431     0       53      6          OCCUPATION              Machine  0.128266454\n432     0       53      7          OCCUPATION              Machine  1.326996326\n433     0       54      0          OCCUPATION                Other  0.192659944\n434     0       54      1          OCCUPATION                Other -1.018117309\n435     0       54      2          OCCUPATION                Other -0.390024215\n436     0       54      3          OCCUPATION                Other -0.330888003\n437     0       54      4          OCCUPATION                Other  0.222263291\n438     0       54      5          OCCUPATION                Other  0.122930378\n439     0       54      6          OCCUPATION                Other  0.271959007\n440     0       54      7          OCCUPATION                Other  2.058565617\n441     0       55      0          OCCUPATION                Prof. -0.498576999\n442     0       55      1          OCCUPATION                Prof.  1.231279373\n443     0       55      2          OCCUPATION                Prof.  0.069766387\n444     0       55      3          OCCUPATION                Prof.  0.737278342\n445     0       55      4          OCCUPATION                Prof. -0.441699237\n446     0       55      5          OCCUPATION                Prof. -0.442519099\n447     0       55      6          OCCUPATION                Prof.  0.770149708\n448     0       55      7          OCCUPATION                Prof. -1.792861819\n449     0       56      0          OCCUPATION              Protec. -1.178769588\n450     0       56      1          OCCUPATION              Protec. -0.167112246\n451     0       56      2          OCCUPATION              Protec. -0.415541440\n452     0       56      3          OCCUPATION              Protec. -0.984990537\n453     0       56      4          OCCUPATION              Protec. -1.595485806\n454     0       56      5          OCCUPATION              Protec.  1.019716859\n455     0       56      6          OCCUPATION              Protec.  1.083717465\n456     0       56      7          OCCUPATION              Protec. -2.049369812\n457     0       57      0          OCCUPATION                Sales  1.287309170\n458     0       57      1          OCCUPATION                Sales  2.022239447\n459     0       57      2          OCCUPATION                Sales  0.320036858\n460     0       57      3          OCCUPATION                Sales -1.731268525\n461     0       57      4          OCCUPATION                Sales -1.201633453\n462     0       57      5          OCCUPATION                Sales -0.363431245\n463     0       57      6          OCCUPATION                Sales -0.240760684\n464     0       57      7          OCCUPATION                Sales -0.351213455\n465     0       58      0          OCCUPATION              TechSup -0.228048429\n466     0       58      1          OCCUPATION              TechSup -1.259017944\n467     0       58      2          OCCUPATION              TechSup  0.023522204\n468     0       58      3          OCCUPATION              TechSup -0.247233376\n469     0       58      4          OCCUPATION              TechSup  0.651662648\n470     0       58      5          OCCUPATION              TechSup  0.081736349\n471     0       58      6          OCCUPATION              TechSup  1.190186739\n472     0       58      7          OCCUPATION              TechSup  0.881933868\n473     0       59      0          OCCUPATION              Transp.  0.567265391\n474     0       59      1          OCCUPATION              Transp. -0.362991750\n475     0       59      2          OCCUPATION              Transp. -0.331672281\n476     0       59      3          OCCUPATION              Transp. -0.330551565\n477     0       59      4          OCCUPATION              Transp. -1.282392979\n478     0       59      5          OCCUPATION              Transp.  0.724967062\n479     0       59      6          OCCUPATION              Transp. -0.297733247\n480     0       59      7          OCCUPATION              Transp.  1.374555111\n481     0       NA      0                <NA>                 <NA> -0.065561228\n482     0       NA      1                <NA>                 <NA>  0.635181129\n483     0       NA      2                <NA>                 <NA> -0.085295714\n484     0       NA      3                <NA>                 <NA>  1.019360065\n485     0       NA      4                <NA>                 <NA>  2.222212076\n486     0       NA      5                <NA>                 <NA>  0.467494220\n487     0       NA      6                <NA>                 <NA>  0.352836043\n488     0       NA      7                <NA>                 <NA>  1.556486130\n489     1        0      0                <NA>                 <NA> -0.082730480\n490     1        0      1                <NA>                 <NA> -0.286467999\n491     1        0      2                <NA>                 <NA> -0.622008324\n492     1        0      3                <NA>                 <NA>  1.650941133\n493     1        0      4                <NA>                 <NA>  0.656672657\n494     1        0      5                <NA>                 <NA>  1.019746542\n495     1        0      6                <NA>                 <NA> -0.608772457\n496     1        0      7                <NA>                 <NA>  1.471991658\n497     1        1      0                <NA>                 <NA>  0.258331031\n498     1        1      1                <NA>                 <NA> -0.209574252\n499     1        1      2                <NA>                 <NA> -1.974496603\n500     1        1      3                <NA>                 <NA> -0.676527917\n501     1        1      4                <NA>                 <NA>  0.637035251\n502     1        1      5                <NA>                 <NA>  0.904479384\n503     1        1      6                <NA>                 <NA>  0.433224857\n504     1        1      7                <NA>                 <NA> -1.456645131\n505     1        2      0                <NA>                 <NA> -0.251465321\n506     1        2      1                <NA>                 <NA> -0.604226232\n507     1        2      2                <NA>                 <NA>  1.550424337\n508     1        2      3                <NA>                 <NA> -2.145036221\n509     1        2      4                <NA>                 <NA>  0.070079558\n510     1        2      5                <NA>                 <NA>  0.405534178\n511     1        2      6                <NA>                 <NA>  1.103209257\n512     1        2      7                <NA>                 <NA> -1.269853234\n513     1        3      0                <NA>                 <NA> -0.211938009\n514     1        3      1                <NA>                 <NA>  0.239011794\n515     1        3      2                <NA>                 <NA> -0.647165000\n516     1        3      3                <NA>                 <NA>  0.130234450\n517     1        3      4                <NA>                 <NA>  0.969112337\n518     1        3      5                <NA>                 <NA>  0.551589131\n519     1        3      6                <NA>                 <NA>  0.952703774\n520     1        3      7                <NA>                 <NA>  2.163283348\n521     1        4      0                <NA>                 <NA> -0.230590537\n522     1        4      1                <NA>                 <NA> -0.577665687\n523     1        4      2                <NA>                 <NA>  3.484261751\n524     1        4      3                <NA>                 <NA>  0.577879488\n525     1        4      4                <NA>                 <NA>  0.594841301\n526     1        4      5                <NA>                 <NA>  0.383136004\n527     1        4      6                <NA>                 <NA> -1.236821651\n528     1        4      7                <NA>                 <NA> -0.447946012\n529     1        5      0                <NA>                 <NA> -0.431362540\n530     1        5      1                <NA>                 <NA>  0.884509802\n531     1        5      2                <NA>                 <NA> -1.150026083\n532     1        5      3                <NA>                 <NA> -1.112613678\n533     1        5      4                <NA>                 <NA> -0.699875057\n534     1        5      5                <NA>                 <NA> -0.693453252\n535     1        5      6                <NA>                 <NA> -0.355619788\n536     1        5      7                <NA>                 <NA>  0.152951494\n537     1        6      0                <NA>                 <NA> -0.231193706\n538     1        6      1                <NA>                 <NA>  0.022484783\n539     1        6      2                <NA>                 <NA>  1.252090335\n540     1        6      3                <NA>                 <NA>  1.228777289\n541     1        6      4                <NA>                 <NA>  0.624676466\n542     1        6      5                <NA>                 <NA> -0.305540890\n543     1        6      6                <NA>                 <NA> -0.157469720\n544     1        6      7                <NA>                 <NA> -0.289687634\n545     1        7      0                <NA>                 <NA> -0.296059161\n546     1        7      1                <NA>                 <NA>  0.023843654\n547     1        7      2                <NA>                 <NA>  0.804287970\n548     1        7      3                <NA>                 <NA>  1.240503788\n549     1        7      4                <NA>                 <NA> -1.004451871\n550     1        7      5                <NA>                 <NA> -0.453389376\n551     1        7      6                <NA>                 <NA> -0.914817035\n552     1        7      7                <NA>                 <NA> -0.769266188\n553     1       NA      0                <NA>                 <NA>  0.001172474\n554     1       NA      1                <NA>                 <NA>  0.157359913\n555     1       NA      2                <NA>                 <NA>  3.921245813\n556     1       NA      3                <NA>                 <NA>  0.279281378\n557     1       NA      4                <NA>                 <NA>  0.008148572\n558     1       NA      5                <NA>                 <NA> -0.942299664\n559     1       NA      6                <NA>                 <NA> -1.114352584\n560     1       NA      7                <NA>                 <NA> -0.919398308\n561     2        0      0                <NA>                 <NA> -0.100985073\n562     2        0      1                <NA>                 <NA>  0.124793887\n563     2        0      2                <NA>                 <NA>  0.808353066\n564     2        0      3                <NA>                 <NA> -0.088087097\n565     2        1      0                <NA>                 <NA> -0.753421247\n566     2        1      1                <NA>                 <NA>  0.200423449\n567     2        1      2                <NA>                 <NA>  1.077998638\n568     2        1      3                <NA>                 <NA> -0.642984748\n569     2        2      0                <NA>                 <NA>  1.011825204\n570     2        2      1                <NA>                 <NA> -2.432047844\n571     2        2      2                <NA>                 <NA> -0.770286620\n572     2        2      3                <NA>                 <NA>  1.195795417\n573     2        3      0                <NA>                 <NA>  0.390909463\n574     2        3      1                <NA>                 <NA> -0.487748265\n575     2        3      2                <NA>                 <NA>  0.142899588\n576     2        3      3                <NA>                 <NA> -0.163887069\n577     2        4      0                <NA>                 <NA>  0.467707247\n578     2        4      1                <NA>                 <NA>  0.226504803\n579     2        4      2                <NA>                 <NA>  0.565175056\n580     2        4      3                <NA>                 <NA>  0.199322507\n581     2        5      0                <NA>                 <NA> -0.051663503\n582     2        5      1                <NA>                 <NA>  0.754980743\n583     2        5      2                <NA>                 <NA>  0.511262119\n584     2        5      3                <NA>                 <NA>  0.105407760\n585     2        6      0                <NA>                 <NA>  0.368124455\n586     2        6      1                <NA>                 <NA> -0.738354743\n587     2        6      2                <NA>                 <NA> -0.038397092\n588     2        6      3                <NA>                 <NA> -0.184398457\n589     2        7      0                <NA>                 <NA> -0.250568390\n590     2        7      1                <NA>                 <NA>  0.116258234\n591     2        7      2                <NA>                 <NA>  0.928679645\n592     2        7      3                <NA>                 <NA>  0.242140055\n593     2       NA      0                <NA>                 <NA> -0.130918697\n594     2       NA      1                <NA>                 <NA>  1.723293781\n595     2       NA      2                <NA>                 <NA>  1.105713964\n596     2       NA      3                <NA>                 <NA> -0.944135487\n597     3        0      0                <NA>                 <NA> -0.508878469\n598     3        1      0                <NA>                 <NA>  1.862528682\n599     3        2      0                <NA>                 <NA>  0.722972453\n600     3        3      0                <NA>                 <NA> -1.118094444\n601     3       NA      0                <NA>                 <NA> -0.390708834","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%r","","RES_DF <- predict(object=nn.mod3, ","                  newdata=CUST_DF.test, ","                  supplemental.cols=c('CUST_ID','YRS_RESIDENCE'))","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1737858520787,"interpreter":"r.low","endTime":1737858521521,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100001\t3\t3.50198900928808\n100002\t4\t4.13406689016519\n100004\t5\t4.33878863155461\n100009\t3\t3.07807884422352\n100011\t3\t3.13503113498524\n100017\t5\t3.65840512306682\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST dataset","message":["%r","","MeanAbsoluteError <- (sum(abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])))/nrow(RES_DF['YRS_RESIDENCE'])","MeanSquaredError <- (sum((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])^2))/nrow(RES_DF['YRS_RESIDENCE'])","RootMeanSquaredError <- sqrt(MeanSquaredError)","","cat('\\nMAE: ', MeanAbsoluteError)","cat('\\nMSE: ', MeanSquaredError)","cat('\\nRMSE: ', RootMeanSquaredError)"],"enabled":true,"result":{"startTime":1737858521944,"interpreter":"r.low","endTime":1737858522809,"results":[{"message":"\nMAE:  1.143168\nMSE:  2.467757\nRMSE:  1.57091","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":600,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot the predicted and actual years of residence","message":["%r","","x <- ore.pull(RES_DF[['PREDICTION']])","y <- ore.pull(RES_DF[['YRS_RESIDENCE']])","n <- length(x)","","dotplot(y~x, xlab = \"PREDICTION\", ylab = \"YRS_RESIDENCE\", ","        panel = function(x,y) {","          panel.dotplot(x,y)","          panel.abline(0, 1, col=\"red\")","        }, ","        xlim=c(0,14.5), ","        ylim=c(0,14.5),","        scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","        main=\"The prediction vs. actual value for years of residence\")","        "],"enabled":true,"result":{"startTime":1737858523236,"interpreter":"r.low","endTime":1737858523914,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nJuHA1eIeLzzVWMtTqbHyZKV/ZYpK3n0uFZfTipo41GQ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the residuals","message":["%r","","xyplot(y-x~x, xlab = \"PREDICTION\", ylab = \"RESIDUAL\", ","       panel = function(x,y) {","         panel.xyplot(x,y)","         panel.abline(0, 0, col=\"red\")","       }, ","       xlim=c(0,8.5), ","       ylim=c(-7,8.5),","       scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","       main=\"Prediction of Years of Residence vs. Residuals\")"],"enabled":true,"result":{"startTime":1737858524348,"interpreter":"r.low","endTime":1737858524970,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nNmzYYBs+ISHh559/tr68+eabi/TO5SvDFRVjxbA9vnP33Xd369Ytq3c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Pm9GBpETFHmPQD4R48/0ddArDxYkTFxjzNxPXMGg+v95tdD4R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tWJRYO590e+3sTXm7IbTXBXJF/0yvtejyJXSYWd\niBimXhWuD2HaRobfSMOgnPY9Z5i8jusCaBJiXLhSEOzFvw/xRSy/7WX7Sfw9ubcJw5rT4lqj\nk0mpCapE9CA2HOffIxxKoJYfrWsQEXTlCUWKR4WdSDm2+QQrDrLzFAGe3FSdrnVxLm+dAB93\no9M3tJ3K861pXQMnEzGHeW8VF9KZ1D3vLo2LmUzdwOI9bD+JrwdNgnnohnJTG3m48EQLnmhh\ndA4pW01DaOpYv1LEbqmwEymXUjIYtpAZmy9prOTCO5154iaDMhVL25pED+KBn3l5WU7jtT7M\nuD379m+5nEqmy7esP4anC/UDOZ3M1A1M28joKF6qeKeppWWy6zTB3rpPiohkU2EnUi498DOz\nt+BswgzNr+FiJnvOkJzOk4v4eA0fd6Nr3Su/iZ3oWJvdTxBziK0nMVuICKJNDdzyuUR08HzW\nH2NUe0a0xdMFC0yOZdRyXl7GzzvpHMZ911O3St7TOpJ/j/DCUlYdIsMMUM2HJ1rwbKvyeovj\nojqZzIxNbDrBqWQaBNKxNt3q6ZQ1EVBhJ1IexRxm9hbcnfF247t+jPydzSfwdaeOP3vPsucM\n3b7jiRZ81NXooIWW9Yj0dqFXGC32GL/t5f7reaMDQHI6A+YRvRvABGuP8O8R3llJ17pM7kn1\nyqWd2jC/7qbvbJxM9KlPo6rEX+C3vYz4ndWHmH9n+TscX1S/7mbwfM6l4mTC04Xo3XwQQ5c6\nzLsDHzejw4kYrXz8uJsxY4bJZDKZTMePl/O7WomUhAU7AS5m8v6tfLyGDcd5owPxz7PnCWr7\nY4JKrkxcww2TeXkZGxxoo1lxAOC+67NfDvuF6N0MiCDMHwtYoHplsBC9m/CP+WWXcUFLU8JF\n7v8Jf09ihzFvAKPa80l3tj3GvU1YsJMvYo3OV8ri4uk/F3dn5g0gcSRJL7H3CYY25be9DPnR\n6HAidqAcFHaHDh164oknQkJ03qlItsPns/+oH8ivuxncmFHts49dujtjgeR0TLDhBGP+5obJ\nPL8USwFvV36cSwUI9gbYdZrvNtMznG0n+S8hu7fvnweJewwXJ1IzuW02PWYy/h8SLhqZucT9\nuJ2Tybzd8ZLriF2d+KwnQZWY4uiF3ZsrSM8kehD9I7Jvdhjmz5e9ubsRP+3Q01dF7L6ws1gs\n999/f7Nmzfr162d0FhF74eUKYIL1xwEGNcpun7aRHacwmfB05cFmWCwsuYebQ3l/Ne+vNixt\nCarqBbD/LMDv+7BAZCBbT/Jae/w9cHEiPpme35FhxmzBbGHxHp75jYhJrHOg/+83nwDoctk5\nlJ4u3BxKXDxmx6ji8/HbHtrWpNk1uduzLjT+bU/ZJxKxL/Ze2E2cODEmJmbKlClGBxGxI61q\nAFhg1ymAIC8As4WXfscE7s7U8c/u1gr1ZfFg6gfy1l+kOMBTHOpigo/WYIGTyQB7zwF0qMXi\nPdwcSp9ZHE3Mvpz2uirU8mPqbSSk0mMmZ1KMTF6C0s1A3heXuDtjtpDpuIVdcjqJadTyy2NQ\nbT8g+4G8IhWZXRd2O3bsGDly5JtvvhkWpkcli+ToH5Hdd7VwF8CeMwC/7+f4BSyQmkHf+uw5\ng5OJEG88XXj4Bs5f5J/DRmYuEfWqcH9TFu/hru9JzwQ4koCTibt/wGzh+mD+S2BCV4IqYYIa\nvpxO5v7rmdKb+AuM+pO5W5mztdz3aWVd87v2SO52C6w9SqhvHs/kdRierni4cDwpj0HHkgCq\neJZxIhG7Y79XxWZkZNxzzz2RkZFPPfVU4afKzMyMjo5OTU3Nc2hMTAxgNptLJqKIQbxcWTCQ\ndtPYdxbgwQWMXUXcCQAThPlzW33aTqVNDSq7A9k9HCfy+u+w3Pm0B0lpzN2a/XL1YYCzqXzX\njxmb8HChSx1eWMpN13IkMbv8rVsFZxOT/mXSv9lThfoysRu9w42Ygat2ewNeXMory2gXmv39\nZpm4hj1nGNnWuGSlzwTtQvnzAAfO5e63m74RuPKF1SIlKKucyCot8uTh4dG9e3dn53zu3lQ6\n7LewGz169KZNm2JjY4u0RJYvX967d++Cx9m6dWvBI4jYvxbXsv0xbpvNlniS0thwLLv9Wh+e\nbsVts8gwM+aW7MZjiQD+DtGZ4e7MnP4Mb85ve/lmE0cTAQY1YkAE42Pw9eC22SSl0bs+L//B\n8OZsOE7Hr7FY8POgXhXWHcUCBxO4bTYNgvimD82rGT1LRVSjMm9EMeJ3mk3msZuIrMqpZBbs\nZPYW6gfyokMXdsBLN7N0L92/Y2Y/rg8BuJjJ+Bg++ofWNehY2+h8UpFklRPjx48fP358fuMs\nXbq0U6dOZRgKk8Vij8ckDh8+XLt27azjsFktjz/++KRJk44dO1bw5bFX7LEbP3787Nmz77zz\nzpIPLWKEPWcZ/gu/77uksbI7X/WmfwSA2ULrr9h4nPjnL+ngcQCJafSexZ8HAPw9SEonPRMX\nJwY15pedXMwkbjiD5hN7FDdn0jJJy+T2BrSvxakLjFmJ2YKrE5/1YmBDPOz3R27eJq3l1WWc\ntdnV9Q5nck9CvI3LVFY+W8cTi8gwU70yAZ7sOk1KBg2DWHIP1XyM7zqsvgAAIABJREFUDicV\nyZw5c+66666nn366VatWeY5gSI+dnRZ2W7ZsadSoUZ6D6tSps2dPMS98mjdv3h133DF37twB\nAwZcRToRu7PpBNG7mbeVDcdpFMzM24msCnAqmScXMzOO51rzXmejU5aCTAuTY3l1Wc61EU4m\nzBYCPJk7gDB/an9Ex9os2w8wsx8DIwFOJdNuGttPZU/ibKJrXSZ0LWePrLiQzvpj7DxFYCWu\nD8n7kgJHFRfP5+vYeJzTydQPpGNtHroBz/JWnUt5Z59FhZ1uB5GRkbkqzkL22IlUTE2CaRLM\ni214eCFfbaDJ54T542xi71kyzNzeIOewrINxNvFocx5syuwtLDvArDgyzfSP4KOuVPNh8V6A\nVf9hgg61squ6Exdo8xV7zwI0CGL7SQI8id7Nkr0MasyLbagfaOQcFZ6XKzfX5OaaRucwQqOq\nTOpudAgRu2SnhZ2IFIOTiS970z+CmXFsPUmmmQER9I+gb4NCPUYz00L0bmKPciSRelW4OZRW\n1Us9c4lwc2ZIE4Y04ZmW9JzJ99tYsJOavhxMAHBx4mJm9j1QgOeWsPcs91/PtI1cSAM4mUyI\nD8cSmb6RmXFM7MawGwybFxGRq6HCTsTRdK1L18vuXntFO0/Tfy5b4i9p7B3ON33xLT9n5jUO\nZsujfLmefw6z9yxdwojeTbNq/H0w+9kb5y8ybyudw0jLBPgvAeDj7jx2I/9bxCf/EuLNo7+y\n7hg963FLbbz17FERKVfKzf2OPvnkE4vFouOwIqXhbCqdvmHXaV5rz7bHSBjBmgcZ3JgFO7lz\nntHhiqiyO8+0Yu4AYh9m4d3cVp+VB3Fxyn7O7O4zXMykqjeztgDZZyJmPcagXU2AQwmYLXwZ\nS5/Z+LxD0DjiTho0JyIiRaceOxFhfAyHzzPtNu67PrvlpmuZ0RcPF75cz+I9xekCtBMfd2f9\nMQ4msHQft80msBLAzM34uHE+jb1nqR9Ii2uxwGsrgNwP1T2VQuNPaRTMqgfwUe+diNi9ctNj\nJyKlZ+EurvXh3utzt798M8DLy+j4NaET6DCdF3/n8PmyD1h81/rw70P0j8AEC3YydQOAnwfV\nKgNYzEy9DScTM+PYfmnPnLPNaYlxJ/B9l0d+JUN3NxcR+6bCTkQ4cp7rAvK4wCLrFLT1x9gS\nTzUftp9i3CoafZZ995DyoqoX8wZw8Gm61MHJBHA2lR2nAD7ukX2ByNhVOeNn3TUj68FcAZWy\nGy0WJq/D+x0ejSZN5Z2I2CsVdiKCt1vOfeCsTiVz22yADrWIf56YoZx4jl/uxsnE7XPK39PW\na1Rm8WDOvsjcAVR2z+6Q+yqWedt4filbTuSMmZKBsxOpmYzqQLdLj0FfzOCztbiPpt8cVN2J\niB1SYScitK5BXHzODXuzfL6Oc6kAAyJyGnvUY9ptJFzMee5q+VLZnQERrHmQVjUA/jnCHfN4\nf3V2Tx5kd1uaLfS8Dk9nvt2c9/vM34HLm7z5V1lklv9j7y7jo7i6AA7/ZyUuJECE4BLcizsU\nt+JQoEiRClKsUErLS9EixaFIgeJQKO5W3N1JQpAAgRAjnuzuvB8yYZOwaHThPp8md+5Oz5D+\nlsOVcwVBeHcisRMEgUFVANqs43agsfHfW0jgakuXUkk6N/PE2Zoj99M1wtRVJBtHe3D1O0q7\nKi2qhO/C+M0TskyrokkSvsRsNEqf0YfovImw2LSPWBAE4d2IxE4QBMq7M6cJdwIpPpfyC2m5\nlsJzuPgESWJjh+QnzKokstskOaXUTJXIzoW+bGxP3/IUckp+90U0gVEYEu2StUg477FHWWPj\n6is4TKTaEm6IqiiCIGQCotyJIAgA335GeXf+OMm5x9wIwDMrebLwNFyp8ZZYrJ5HYVTNZWx5\nFsHyy1zyJzCKwlmpl5+mhUyPdWU2KonWRWldFKDWsiTDkFNPJu/sbE1INNE6Vl9VWqSEEb4T\nDyk+j8/zs7gFeRzTPm5BEITXECN2giAoKnqwti3eA4j6mcvfMLQK0ToWnEvebdEFwmONle22\n3cFzNsP2sfYaJx4y8zQt1tBwJS9i0jn8lPqvO809jT8+SlrVRa3CPxxZxsFSWXpIQlaXw56v\nyiBJ7L9LsbnMPM3jsHSKWRAEIRmR2AmCYNrX5SjozLB9jD+KfzjA0wgmHWPwHvJloU95gCtP\nabceay0b2xM+ktAR3P+BvuXZf5cu/2Zs+O9Ngq2d8BuMh4OJu3oDVmp0BiJisbVQ9lg4WgFk\nteFuELIMEBnHD7vJO4MR+9HLJp4jCIKQpkRiJwiCadYadnehcFZGHcR9GjbjcZvKTwco4MTu\nLthqAcYeQWdgZ2daF8VKA5DbkT+b0bUU2+5w+lHGvsGH8LDHbxCBP1Itd/LCfpZaepRFLxMe\niwwqifBYAN9gTjykYUGAyjkB8jnx+3HqLmOvD6HmNnIpCIJZE2vsBEF4rQJOXOjLxpucfIhv\nCHmzUDkn7YoZtxHs8aZ6bsq+coZz/0qsuMJeHyp5pHPIqcPZmmM9CIik13a23lQaQ6NZfMHY\nx1ar7IcNj2VBM7LZsMebotk45cedQIAjDziyEgs17Ysxr9kndCKZXuayPzcC0Kop4UKx7CZq\nXwuCkEZEYicIwptYqOlUgk4lTNyK0hEWS94sJm7FNz4NT9vY0lp2G7a057gfTUwtGYzP6my1\nuNnRuzxD9gD8exPAw55HCcvsYvWsvMrmW/zbkfr50y32DHPAl77b8Ak2tpRzZ0lLY2UZQRDS\nlJiKFQThA1lrsNaY3ijwJAzA2drErUdhJk65yMyq5SR0BNs6K3PNL0nQqxyxekq5cv4xC87j\nbK1MvD4Ko34BgK6luNmPHPZExNFiDVeemnj+x+SAL41XEhjFzzXY2okN7RlYievPqLWU66Ic\njCCkCzFiJwjCh6udl4O+3A0mf9I6cEsuKndfehzGiP1suqWsS7NUU8ad1a2TfzDTalaQqJ/x\nDWHhBWadIioOScLJCrWKs4+puRRJItaAWkIvUzsvy77AYxr2lhTJyoxGtP+HWD39d9OzDBoV\nxbNT2u1jm6A0yPTdhq0Fp3pROKvS2KYorYtSbzk/7GZf1wyNTxA+DWLEThCED/dTDeIMNFnF\nucdKS7SOCUeZdZqquaiTT2m8E0iZP1lxRcnqgBg9p/0oMIve2zCjzaP5sjCxLhf78pkHBpkp\nJ4jW4fcCVztaFCY8RjlAdlhVdt4BKO8OKLUA1RJH7tF9M13+pewCSs3n7OPX/ofM0ZlH+ATT\nr6Ixq4tXMw/tinHQ1/zOFxYEcyQSO0EQPlyN3Mxvyt1gKizCfRql5pNlEj8fpIQLG9obR6S6\nbSYwUrmulov5zVjdhlyOAIsvUGY+K68Qo8+QN/gQnlk504v9X+GZkMHcC2HtNYB8WQCidAzf\nj6stbYsBSt3j+EMsVrZmc0eGVcUniLp/c9E/A+JPI95BABVNbZep6IFBxiconSMShE+RSOwE\nQUiRPuW5+A39K+KZFZ2Bpp7MacKZ3rjbKR0u+XPKTxnKGlqVYz1pVYQ5Z3gYqnS48oyum3Cd\nwvTT5lT7rV4+bvfjVj8aFgBwtMTOAr8XAO3/ISqOVW1wsESG4fsByrohQeuitCzM5Poc6k6s\nnv47eRHDg1BzGrZ8HY0KIEZn4lasHkCrNnFLEITUJdbYCYKQUsWzM6vxa+++HJRytGRcXfQy\nzVdz7jGDq3D0vnE6MjSGwbsZsY8pDRhQMc1jTi2Fs7K7C6uuMuMUF/3RGwDyZWFrJ4plB1h2\niYAIHCzxCaasO9YJX7rl3SnlyvGHOE4C0EjkysLkz5VBPnNUyhVgr4+JV9jrg6WaItnSPyhB\n+OSIETtBENLWyyGcsm5Yqtl4g7OPGV4dWwtjVpffiUYFAGL1DNzFxGMZE+oH61ySs70J/4kG\nBQB8gvl6K0P2Un8FX28FcLYmOJphVZX+cQZarDEuTAR0Mr7BtPsH9W/K1hOzUyw71XOz7BJb\nbidpn32GA750KPEJVfIThAwkEjtBENLWy32vETqAnV5I0LEEE48a+1TLxc4uuNmRzwngl0PK\nnKZ5sdKwszOjaqJVccqPP05yyBcbDcC9EH6sRseEcoCzTrPLG5WpbbEGma+3Uvtv4gzpF3lq\n+asFWaxotZaGKxl1kBH7qfoXA3bhmZWpDTI6OEH4NIjEThCEtFUrL05WABefEBqDfzgOlpzy\nQ2cAkCSAEi5IkN+JWB3Fs6M3sOwSh++z6AJbb/Mg9E3Pz1TUEmPrEDicn2tSwQNHK7LaANTL\nx++fG7vNP4uNVtlOoZKQ4Ouy7O5C/0pKh8P36LEl3aNPMc+snO9Lu+Icvsf4o/x+nKvP6Fue\nU73IbpPRwQnCp0GssRMEIW1ZqvmzGR02oDNQbC4FnQmLVWYbJZBlLDV8WRLgWQRZrKiWm+sB\n/HbYOGQlQdtizGtKNjNJDuwtGFeHcXWUH+uv4JAv667ToThAWCw+wcbhOoPMHw0ZVBnAzY7Z\np5X2VVcYWIkKOdI59pTK5cC6tuhlfILQqsmb5WMr1ycImZxI7ARBSHPti/M8in47eRymnFRx\n5hGADBLMbUJOB0754R3EN59x7AGAlZapdSiWHf9wNt3inxvcCeTE19hoM/JFPszC5lRfQqcN\n/HmOyjkJjwGMw3WutgxIGKizTvqVvPGG+SV28dSSsRaMIAjpSSR2gmA2dAZ2eXPxCU/CKeRM\n7bxK5Vuz8N1ntC1Gy7Wce6RMwgISbO5EC09OPKTjBizVNC7En+cABlY0pjtdSjH+KKMOMuMU\nI2tkTPwpkS8L5/rw4z7+vcl/9yB+qBIAGUq6ok4Y1Dr/xPgpCXxD0jlSQRDMnkjsBME8XA+g\n3XpuPk/S2KE4S1qazSCWiw0newJs9+KrfwmORoaem7G35F4I1hoWtWD4PgAVdCmd5LMjqjPz\nFFNPsP46j8JwsaVaLiZ8TjZTx9FmQu52rGjF31/gG4JaYvxRFl8AkGXCEk7jCI1h7GG0KmUO\nWk4YwIvRM/cMm25x/RlaNSVd6Fqar0qLKU5BEEwQiZ0gmIGASOovJyiK3+rQuijudtx8zvST\nrLtOnIGN7TM6vvfUrBA3+/HTAZZdJDCKkGhc7XCxoe92ouIARtRIfizVovM8j0KWCY3BIPM8\nkhsBLLpAoawc64GLbYa8x3tTSRRwAhhTm403CI4GOOXH1js8DGXGKbyDkmyVLZ+DkGgarODs\nY7LZ8FkOYvWcecQBXzbfYn07tGL/myAISYnEThDMwOTjPAlndRs6JdTLqJaLarn4ciNrrnH4\nPjkdsFST0yFDo3wfrrYsacGI6rRcw+3nPA3naTigjFcNrZqk8yk/vt+JViJWxiAb5zEBr0Dc\nprGmjbIvwVzksOdMbz5bRGg0skzLNYAyAhe/9k6twsGCTiX4bgdnHzOqJr/UxEJNrJ45Z5h2\ngs23yDGNOnnpWZZGBTPuTQRByGREYicIZmDbbQo4GbO6l3qVY8016i9XJu+crOhamrF1cLBM\n/xg/hKczN78nOJozjwiMpHA2jtxn8B72eBtLvgFTTyBJxCaszCvgzP9qU8mDOWeZeQpZptNG\nXGypkzcDXuGDFXQmZDit1rP5ptLyMluVQCOxvBUxetZeo7knY+sABEfTaCVnHmFvgbWW4Cg2\n3+KfG3Qvw18tRfEqQRBA1LETBLPg98LEcUx3Aum0EcDZhhHVGVSZ/E7MOk21Jcocn7lwsqJh\nAb4sSXl3upTC0ZJh+/BKdGD8kQfKnKNGhYc9J76mc0kKOisHOcTXTOm9lT/PsfQS5x4rg15m\nYVN7dL/SvgT2FsqIna0lzQtzqhfNPDn5EBljjttrK2ceMbIGz4YxtCp6GUsNwLJLqMdgM56R\nBzLqPQRByCzEiJ0gmAE7C4Kikjf23EJwFEDLwkyspzROPs7w/fTYTLcyFHKmcDYzW4aV3YZF\nLei0gdLzaVGY4i48CeN5hDKapTPQv5Kx1G38haWGaB0+wXy7Q2kv4cLfX5jNlmG1xLo2ADoD\ngVG4JlovGL+vwskawCuIf2/Srhjj66KX2XQTIDzW2DlKx8RjLDiP9wClIrQgCJ8gs/rKF4RP\nVZVcnH2cpPjFtWccf0ilnACVcyqNEXE8CEWS2HKb1usoOR/7CXTZRKw+A2L+YO2K8V93yrmz\n4Qa/HmL+OdQqY3paLLuxp3cQQHTCWbQzGrGrM8Or4RNEnb+5HpC+caeYRpUkqwM87AHuBAIc\nvQ/QtTTAXxe49kzpU9yF2nkBWhUDCIoizwzWXONZRLoELQhCJiMSO0EwA4MqozfQep0xt7v8\nFODcY3I60L44QJyBpquYexZZBshuS1YbYvWsukK2KSy/Yqwel/lVz82xnoSP5Oq3PP+RPuWN\np1CEJppl/v2EciElfKpRQSZ9zt6uRMYxeE+6xpwWauQhixWzT/MihtAYSBiknHBc6fB5fi72\nVRYXRsYojWExfLkRt6mUX8hTkd4JwidGJHaCYAZq5mFyfS77U3g2lRbTah1D9wJo1Wxsj60W\nYOF5Dt8HyO8EkMOewEhlBjMshm6bKLdAGeIyF1YaSriQ1Zqfqhu3gyy+gM7AtWf03MLyS8ae\ndhYUTxjMq56bVkU4cNfE/LV5sdYwri4+wdRcypNwgLOPWXmV+8FKhyn10aqU9Yh7fIwftLPE\nWsOFJxScxcMX6R63IAgZRyR2gmAehlblcA+aevI0nJ1eWGsB+pSjoofSYcVl1Cqy2VDaFeCK\nPwMrceN7dndROtx8ToMVSVZlmYucDuzrqlTrPXwf7VhKzmfpJbRqAEkiSkef8lglWjNcPgd6\nGd8QAiKZfJymq6m6hD7bOPUoY17hg31fgUmfcyOAKccBBuyi67/KLbVEaTd8Q1h/XWnpU165\n0Ei8GEkpV8Jjab023YMWBCHjiMROEMxGjdxs6sC9H4gZxZ1+5HZk2SXuJgze3AxAb6CSB1tv\nA/xUgxmNKJqNWnmUDm2L4RvC3LMZE3wKVfTg+Y/G1YTx4vQAskydvIyrm+RW/MK7nXfIMY3h\n+9npxcmHLLpAlcW4T+PiE8zI8Gp4D2BeU2U7SNFsWKoBDLDxBnWWEadHgtKuNPNUPmKlQS2x\nti3AuSfJDywRBOEjJhI7QTBLGhXzmxISTcVFjD3CXh/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xYicIQppoURgVnH6Eb0Jt26P3k3SY0oB+lV79HFE6Om3AbqKySs8s5HZkUwdO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phb0V4d8bIkz9y2eFagp+ERT9RafJJxmmnMTrNLt6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\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RT6hbum05Wrf1USJvPPIF27EchQeyUO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\nklGrFEKYNgl2QghhEl6tTaaeDD16s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## Use SQL for scoring and reviewing Prediction Details"," ---"],"enabled":true,"result":{"startTime":1737858525410,"interpreter":"md.low","endTime":1737858525822,"results":[{"message":"<h2 id=\"use-sql-for-scoring-and-reviewing-prediction-details\">Use SQL for scoring and reviewing Prediction Details<\/h2>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize NN_TEST_DATA for use in query below","message":["%r","","ore.drop(table = 'NN_TEST_DATA', silent = TRUE)","","ore.create(CUST_DF.test, table = 'NN_TEST_DATA')"],"enabled":true,"result":{"startTime":1737858526244,"interpreter":"r.low","endTime":1737858527213,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details for the predictions (manually expand PREDICTION DETAILS)","message":["%sql","","SELECT CUST_ID,","       PREDICTION(NN_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","       PREDICTION_DETAILS(NN_REGRESSION_MODEL USING *) PREDICTION_DETAILS","       FROM NN_TEST_DATA","       WHERE CUST_ID < 100015","       ORDER BY CUST_ID"],"enabled":true,"result":{"startTime":1737858527660,"interpreter":"sql.low","endTime":1737858529437,"results":[{"message":"CUST_ID\tPREDICTION_YRS_RES\tPREDICTION_DETAILS\n100001\t4.7136527943115247\t<Details algorithm=\"Neural Network\"> <Attribute name=\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".157\" rank=\"1\"/> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".125\" rank=\"2\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".051\" rank=\"3\"/> <Attribute name=\"AGE\" actualValue=\"1941\" weight=\".043\" rank=\"4\"/> <Attribute name=\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.032\" rank=\"5\"/> <\/Details> \n100002\t4.9060516378074777\t<Details algorithm=\"Neural Network\"> <Attribute name=\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".222\" rank=\"1\"/> <Attribute name=\"CUST_GENDER\" actualValue=\"F\" weight=\".17\" rank=\"2\"/> <Attribute name=\"EDUCATION\" actualValue=\"Bach.\" weight=\".166\" rank=\"3\"/> <Attribute name=\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".082\" rank=\"4\"/> <Attribute name=\"AFFINITY_CARD\" actualValue=\"0\" weight=\".067\" rank=\"5\"/> <\/Details> \n100004\t5.80876228877983\t<Details algorithm=\"Neural Network\"> <Attribute name=\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".223\" rank=\"1\"/> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".14\" rank=\"2\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".099\" rank=\"3\"/> <Attribute name=\"AGE\" actualValue=\"1953\" weight=\".086\" rank=\"4\"/> <Attribute name=\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\"-.024\" rank=\"5\"/> <\/Details> \n100009\t3.8594242853047493\t<Details algorithm=\"Neural Network\"> <Attribute name=\"CUST_CREDIT_LIMIT\" actualValue=\"3000\" weight=\".143\" rank=\"1\"/> <Attribute name=\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".119\" rank=\"2\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".068\" rank=\"3\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Prof.\" weight=\".057\" rank=\"4\"/> <Attribute name=\"AFFINITY_CARD\" actualValue=\"1\" weight=\".036\" rank=\"5\"/> <\/Details> \n100011\t3.6422939354088393\t<Details algorithm=\"Neural Network\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".148\" rank=\"1\"/> <Attribute name=\"EDUCATION\" actualValue=\"9th\" weight=\".109\" rank=\"2\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Farming\" weight=\".046\" rank=\"3\"/> <Attribute name=\"CUST_CREDIT_LIMIT\" actualValue=\"10000\" weight=\".018\" rank=\"4\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"0\" rank=\"5\"/> <\/Details> \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details for the predictions using XMLTABLE","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(NN_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","              PREDICTION_DETAILS(NN_REGRESSION_MODEL USING *) PREDICTION_DETAILS","       FROM NN_TEST_DATA","       WHERE CUST_ID < 100015","       ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","                PASSING OUT.PREDICTION_DETAILS","                COLUMNS ","                \"Attribute1\" XMLType PATH 'Attribute[1]',","                \"Attribute2\" XMLType PATH 'Attribute[2]',","                \"Attribute3\" XMLType PATH 'Attribute[3]') ","                OUTPRED"],"enabled":true,"result":{"startTime":1737858529905,"interpreter":"sql.low","endTime":1737858530356,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t4.714\t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".157\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".125\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".051\" \n100002\t4.906\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".222\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".17\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".166\" \n100004\t5.809\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".223\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".14\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".099\" \n100009\t3.859\t\"CUST_CREDIT_LIMIT\" actualValue=\"3000\" weight=\".143\" \t\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".119\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".068\" \n100011\t3.642\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".148\" \t\"EDUCATION\" actualValue=\"9th\" weight=\".109\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\".046\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737858530788,"interpreter":"md.low","endTime":1737858531200,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858531635,"interpreter":"md.low","endTime":1737858532055,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/r/OML4R Regression SVM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Regression SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858648037,"interpreter":"md.low","endTime":1737858648597,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Regression Modeling to Predict Numerical Values using Support Vector Machine","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.","","---","","Copyright (c) 2025 Oracle Corporation","","---","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737858649073,"interpreter":"md.low","endTime":1737858649579,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-support-vector-machine\">Regression Modeling to Predict Numerical Values using Support Vector Machine<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<hr />\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737858650092,"interpreter":"md.low","endTime":1737858650557,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">OML4R Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1737858651065,"interpreter":"md.low","endTime":1737858651549,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">OML4R Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load libraries","message":["%r ","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1737858652058,"interpreter":"r.low","endTime":1737858657088,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%r","","CUSTOMERS <- ore.sync (query = c(\"QUERY_CUSTOMERS\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH AGE, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","DEMO_DF <- ore.sync (query = c(\"QUERY_DEMO_DF\" =  'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","CUST_DF <- merge(x=QUERY_CUSTOMERS,y=QUERY_DEMO_DF,by=\"CUST_ID\")"],"enabled":true,"result":{"startTime":1737858657580,"interpreter":"r.low","endTime":1737858658547,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","z.show(head(CUST_DF), 10)"],"enabled":true,"result":{"startTime":1737858659007,"interpreter":"r.low","endTime":1737858659636,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tAGE\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100100\tF\tMarried\t1959\tJ: 190,000 - 249,999\t10000\tMasters\t1\t4-5\tProf.\t4\t0\n100200\tM\tNeverM\t1983\tL: 300,000 and above\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100300\tM\tMarried\t1961\tG: 130,000 - 149,999\t10000\tBach.\t1\t3\tProf.\t4\t0\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\n100500\tM\tNeverM\t1983\tH: 150,000 - 169,999\t15000\tHS-grad\t0\t1\tCrafts\t2\t1\n100600\tM\tMarried\t1978\tK: 250,000 - 299,999\t9000\t11th\t0\t3\tTransp.\t3\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","group <- as.integer(1:nrow(CUST_DF) %in% index)","","# Create training data set","rownames(CUST_DF) <- CUST_DF$CUST_ID","CUST_DF.train <- CUST_DF[group==FALSE,]","","# Create test data set","CUST_DF.test <- CUST_DF[group==TRUE,]","cat(\"\\n======== Records | Columns\\n\\nTrain Data: \",","    dim(CUST_DF.train),","    \"\\n Test Data: \",","    dim(CUST_DF.test))"],"enabled":true,"result":{"startTime":1737858660131,"interpreter":"r.low","endTime":1737858661632,"results":[{"message":"\n======== Records | Columns\n\nTrain Data:  2700 12 \n Test Data:  1800 12","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Linear regression model for predicting YRS_RESIDENCE using default settings ","message":["%r","","svm.mod1 <- ore.odmSVM(formula=YRS_RESIDENCE~.,","                       data=CUST_DF,","                       type=\"regression\", ","                       kernel.function=\"linear\")","summary (svm.mod1)"],"enabled":true,"result":{"startTime":1737858662114,"interpreter":"r.low","endTime":1737858664318,"results":[{"message":"\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    kernel.function = \"linear\")\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-7.999000 -0.502800  0.008696 -0.016500  0.470000  7.600000 \n\nCoefficients: \n              variable                value      estimate\n1          (Intercept)                      -0.1044271713\n2        AFFINITY_CARD                       0.0475586164\n3                  AGE                      -0.6733152113\n4    CUST_CREDIT_LIMIT                       0.0112718540\n5          CUST_GENDER                    F -0.0269700701\n6          CUST_GENDER                    M  0.0269700701\n7              CUST_ID                       0.0022383403\n8    CUST_INCOME_LEVEL      A: Below 30,000  0.1331750526\n9    CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.0113927098\n10   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -0.0475515942\n11   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.0463811097\n12   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.0342647070\n13   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.0618334638\n14   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.0169818428\n15   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.0421423123\n16   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.0123289837\n17   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.0146884018\n18   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.0008983566\n19   CUST_INCOME_LEVEL L: 300,000 and above  0.0006776771\n20 CUST_MARITAL_STATUS              Divorc.  0.0827373461\n21 CUST_MARITAL_STATUS              Mabsent -0.0388352624\n22 CUST_MARITAL_STATUS               Mar-AF  0.1536190766\n23 CUST_MARITAL_STATUS              Married -0.0043046183\n24 CUST_MARITAL_STATUS               NeverM -0.0212589809\n25 CUST_MARITAL_STATUS               Separ.  0.0431305917\n26 CUST_MARITAL_STATUS              Widowed -0.2150881529\n27           EDUCATION                 10th -0.1304534414\n28           EDUCATION                 11th -0.1818194549\n29           EDUCATION                 12th -0.0987968953\n30           EDUCATION              1st-4th  0.0392788824\n31           EDUCATION              5th-6th  0.0370982836\n32           EDUCATION              7th-8th -0.1645571172\n33           EDUCATION                  9th -0.0624836898\n34           EDUCATION              < Bach. -0.0345395743\n35           EDUCATION              Assoc-A  0.0676949923\n36           EDUCATION              Assoc-V  0.0601038362\n37           EDUCATION                Bach.  0.0519667379\n38           EDUCATION              HS-grad  0.0067587787\n39           EDUCATION              Masters  0.1094099360\n40           EDUCATION                  PhD  0.1840602952\n41           EDUCATION              Presch. -0.2526628011\n42           EDUCATION               Profsc  0.3689412318\n43      HOUSEHOLD_SIZE                    1 -0.0618575735\n44      HOUSEHOLD_SIZE                    2  0.0781022302\n45      HOUSEHOLD_SIZE                    3  0.1036453327\n46      HOUSEHOLD_SIZE                  4-5 -0.1064051003\n47      HOUSEHOLD_SIZE                  6-8 -0.0028216044\n48      HOUSEHOLD_SIZE                   9+ -0.0106632847\n49          OCCUPATION                    ? -0.0776321880\n50          OCCUPATION              Armed-F -0.0313920314\n51          OCCUPATION              Cleric.  0.0666945505\n52          OCCUPATION               Crafts  0.0586732575\n53          OCCUPATION                Exec.  0.1807337766\n54          OCCUPATION              Farming  0.0948578599\n55          OCCUPATION              Handler -0.0451896307\n56          OCCUPATION              House-s -0.3701341209\n57          OCCUPATION              Machine  0.0313706712\n58          OCCUPATION                Other -0.0690296205\n59          OCCUPATION                Prof.  0.0175584968\n60          OCCUPATION              Protec.  0.0070259778\n61          OCCUPATION                Sales  0.0748004504\n62          OCCUPATION              TechSup -0.0593060183\n63          OCCUPATION              Transp.  0.1209685691\n64         Y_BOX_GAMES                      -0.0269169220\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Linear regression model for predicting YRS_RESIDENCE using specific settings ","message":["%r","","svm.mod2 <- ore.odmSVM(formula=YRS_RESIDENCE~.,","                       data=CUST_DF,","                       type=\"regression\", ","                       kernel.function=\"linear\", ","                       epsilon = .1, ","                       conv.tolerance = .0001, ","                       complexity.factor = .1)","summary (svm.mod2)"],"enabled":true,"result":{"startTime":1737858664820,"interpreter":"r.low","endTime":1737858666853,"results":[{"message":"\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    complexity.factor = 0.1, conv.tolerance = 1e-04, epsilon = 0.1, \n    kernel.function = \"linear\")\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\ncomplexity.factor                                0.1\nconv.tolerance                                 1e-04\nepsilon                                          0.1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-8.013000 -0.503700  0.008408 -0.017160  0.473300  7.604000 \n\nCoefficients: \n              variable                value      estimate\n1          (Intercept)                      -0.0961645169\n2        AFFINITY_CARD                       0.0479258642\n3                  AGE                      -0.6728391817\n4    CUST_CREDIT_LIMIT                       0.0106009055\n5          CUST_GENDER                    F -0.0279257851\n6          CUST_GENDER                    M  0.0279257851\n7              CUST_ID                       0.0031403448\n8    CUST_INCOME_LEVEL      A: Below 30,000  0.1233439462\n9    CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.0117479488\n10   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -0.0454910993\n11   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.0400438051\n12   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.0301241229\n13   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.0605895787\n14   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.0170874472\n15   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.0423200256\n16   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.0109126256\n17   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.0155146830\n18   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.0035259840\n19   CUST_INCOME_LEVEL L: 300,000 and above  0.0020731090\n20 CUST_MARITAL_STATUS              Divorc.  0.0938872523\n21 CUST_MARITAL_STATUS              Mabsent -0.0213551897\n22 CUST_MARITAL_STATUS               Mar-AF  0.0999999815\n23 CUST_MARITAL_STATUS              Married -0.0055570082\n24 CUST_MARITAL_STATUS               NeverM -0.0161532406\n25 CUST_MARITAL_STATUS               Separ.  0.0500678025\n26 CUST_MARITAL_STATUS              Widowed -0.2008895979\n27           EDUCATION                 10th -0.1365029688\n28           EDUCATION                 11th -0.1904415606\n29           EDUCATION                 12th -0.1032231030\n30           EDUCATION              1st-4th  0.0308024104\n31           EDUCATION              5th-6th  0.0331048706\n32           EDUCATION              7th-8th -0.1635947240\n33           EDUCATION                  9th -0.0680287615\n34           EDUCATION              < Bach. -0.0436187296\n35           EDUCATION              Assoc-A  0.0608612927\n36           EDUCATION              Assoc-V  0.0488267576\n37           EDUCATION                Bach.  0.0399895807\n38           EDUCATION              HS-grad -0.0005043357\n39           EDUCATION              Masters  0.0957453649\n40           EDUCATION                  PhD  0.1573158059\n41           EDUCATION              Presch. -0.0999999976\n42           EDUCATION               Profsc  0.3392680980\n43      HOUSEHOLD_SIZE                    1 -0.0647532756\n44      HOUSEHOLD_SIZE                    2  0.0757244931\n45      HOUSEHOLD_SIZE                    3  0.1083905757\n46      HOUSEHOLD_SIZE                  4-5 -0.0986784105\n47      HOUSEHOLD_SIZE                  6-8 -0.0045235366\n48      HOUSEHOLD_SIZE                   9+ -0.0161598461\n49          OCCUPATION                    ? -0.0781470033\n50          OCCUPATION              Armed-F -0.0347271519\n51          OCCUPATION              Cleric.  0.0630798342\n52          OCCUPATION               Crafts  0.0514050172\n53          OCCUPATION                Exec.  0.1726076614\n54          OCCUPATION              Farming  0.0786455571\n55          OCCUPATION              Handler -0.0495116725\n56          OCCUPATION              House-s -0.3013560556\n57          OCCUPATION              Machine  0.0262640875\n58          OCCUPATION                Other -0.0727756846\n59          OCCUPATION                Prof.  0.0204187814\n60          OCCUPATION              Protec.  0.0037760887\n61          OCCUPATION                Sales  0.0716411539\n62          OCCUPATION              TechSup -0.0620377442\n63          OCCUPATION              Transp.  0.1107171310\n64         Y_BOX_GAMES                      -0.0278004598\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Gaussian regression model for predicting YRS_RESIDENCE using default settings ","message":["%r","","svm.mod3 <- ore.odmSVM(formula = YRS_RESIDENCE~.,","                       data    = CUST_DF,","                       type    = \"regression\", ","                       kernel.function=\"gaussian\")","summary(svm.mod3)"],"enabled":true,"result":{"startTime":1737858667376,"interpreter":"r.low","endTime":1737858672786,"results":[{"message":"\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    kernel.function = \"gaussian\")\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.cache.size                              5e+07\nkernel.function                             gaussian\nnum.pivots                                       200\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-8.116000 -0.484500 -0.004475 -0.048450  0.460300  6.988000 \n\nCoefficients: \n[1] No coefficients with gaussian kernel\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column.","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","   ","- Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row.","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","  ","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'","","> SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'","   ","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> SVMS_COMPLEXITY_FACTOR = 0.1","","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.    ","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'","","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'","    ","- Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0.  The default is 0.1. ",">  SVMS_EPSILON = 0.1","     ","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> SVMS_BATCH_ROWS = 20000","   ","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","> SVMS_SOLVER = 'SVMS_SOLVER_SGD'","","> SVMS_SOLVER = 'SVMS_SOLVER_IPM'","","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> SVMS_CONV_TOLERANCE = 0.001","","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> SVMS_NUM_PIVOTS = 200"],"enabled":true,"result":{"startTime":1737858673253,"interpreter":"md.low","endTime":1737858673768,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/build-support-vector-machine-model.html\" target=\"_blank\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/shared-settings-oml4r.html\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row.<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_COMPLEXITY_FACTOR = 0.1<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0.  The default is 0.1.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_EPSILON = 0.1<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_BATCH_ROWS = 20000<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_IPM'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_CONV_TOLERANCE = 0.001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_NUM_PIVOTS = 200<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Gaussian regression model for predicting YRS_RESIDENCE using specific settings","message":["%r","","ore.drop(model = 'SVM_REGRESSION_MODEL',silent=TRUE)","","svm.mod4 <- ore.odmSVM(formula=YRS_RESIDENCE~.,","                       data=CUST_DF,","                       type=\"regression\", ","                       kernel.function=\"gaussian\", ","                       epsilon = .1, ","                       conv.tolerance = .0001, ","                       complexity.factor = .1,","                       odm.settings = list(model_name = \"SVM_REGRESSION_MODEL\"))","summary(svm.mod4)"],"enabled":true,"result":{"startTime":1737858674248,"interpreter":"r.low","endTime":1737858681151,"results":[{"message":"Warning message:\n\u201cThe created mining model SVM_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    complexity.factor = 0.1, conv.tolerance = 1e-04, epsilon = 0.1, \n    kernel.function = \"gaussian\", odm.settings = list(model_name = \"SVM_REGRESSION_MODEL\"))\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\ncomplexity.factor                                0.1\nconv.tolerance                                 1e-04\nepsilon                                          0.1\nkernel.cache.size                              5e+07\nkernel.function                             gaussian\nnum.pivots                                       200\n\nResiduals: \n    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n-8.39500 -0.52050  0.00597 -0.04049  0.50700  6.58200 \n\nCoefficients: \n[1] No coefficients with gaussian kernel\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%r","","res <- predict(object=svm.mod4, ","               newdata=CUST_DF.test, ","               type=\"raw\", ","               supplemental.cols=c(\"CUST_ID\",\"YRS_RESIDENCE\"))","","z.show(head(res,10))"],"enabled":true,"result":{"startTime":1737858681624,"interpreter":"r.low","endTime":1737858682210,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100001\t3\t5.19136862285734\n100002\t4\t4.38954834694539\n100004\t5\t4.90815267481412\n100009\t3\t3.22250460781828\n100011\t3\t3.23560058090563\n100017\t5\t5.45582484942445\n100018\t3\t2.73292472956984\n100025\t3\t2.69632761593378\n100027\t3\t3.56638261036551\n100031\t4\t3.84736400037284\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%r","","MeanAbsoluteError <- (sum(abs(res['YRS_RESIDENCE'] - res['PREDICTION'])))/nrow(res['YRS_RESIDENCE'])","MeanSquaredError <- (sum((res['YRS_RESIDENCE'] - res['PREDICTION'])^2))/nrow(res['YRS_RESIDENCE'])","RootMeanSquaredError <- sqrt(MeanSquaredError)","","","cat('MAE (Mean Absolute Error): ', MeanAbsoluteError)","cat('\\nMSE (Mean Squared Error): ', MeanSquaredError)","cat('\\nRMSE (Root Mean Squared Error): ', RootMeanSquaredError)"],"enabled":true,"result":{"startTime":1737858682690,"interpreter":"r.low","endTime":1737858683649,"results":[{"message":"MAE (Mean Absolute Error):  0.8247934\nMSE (Mean Squared Error):  1.562874\nRMSE (Root Mean Squared Error):  1.25015","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot the predicted and actual years of residence","message":["%r","","x <- ore.pull(res[['PREDICTION']])","y <- ore.pull(res[['YRS_RESIDENCE']])","","dotplot(y~x, xlab = \"PREDICTION\", ylab = \"YRS_RESIDENCE\",","        panel = function(x,y) {","          panel.dotplot(x,y)","          panel.abline(0, 1, col=\"red\")","        },","        xlim=c(0,14.5),","        ylim=c(0,14.5),","        scales=list(y=list(tick.number=8), x=list(tick.number=8)),","        main=\"The prediction vs. actual value for years of residence\")","        "],"enabled":true,"result":{"startTime":1737858684200,"interpreter":"r.low","endTime":1737858685020,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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IEdiyBf37Y8MGuLgIXZAF4TV2REREZD2ys9Gv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RvHsr3sjsXhwgWGicVCqC+da/Nse1pUzfPIWU7+\n9SMLd7E3jupBdKzFc7cR7p9njgkf/crs39h5ioq+3Hodf7uNeiFsOMyjUeyNJ9uJYeBjo2Nt\n/noL3c4cPTxvBzN/Zccp/O20qc74DjQNB0jOYnI0y8m1dVgAACAASURBVPdxKIFaFehSB6uF\nVfs4mEDNYLrVZVRrpv3MmgMcSaROCH0b8td2+Ja+DzkRkatnmGZp+k1tmmZERIRpmo0bN54+\nfXpMTEyVKlUuM3/+/PkDBw6cN2/egAEDii1kiRV9mM6f4ixN3/CSrk78gZWzIurG7/9r5Fvv\n3PpUIT5y8yqse5hAL4B3f2bM0jyV2mLwyZ080Oxyj/DiWl5eXzhh7Ba+HkSfhrk3s5z0+Jy1\nB/PMqRHExmHUDD43MvQbPt+eZ06QNxM68NzqfP48MGBqT0a34ZmVvPnfPHd5W/luMK2q0fET\nfjt52ZxWsp15RtpWZ82D+NkvsYCISJHxVAMpZZtip06d+sMPP/z73//2dJBSaeB8tbrCdOPJ\nHdEfd6idcHD4HTMLt9UBW44zKRrgaBLPrLiwCblMRi8hLv2Si59K45UNhRYm28WIKDLPdKb3\nf7mw1eXk/OuKczej/ndhqwOSMnl+Tf4rfU14egWL9/DWfy+8K9PJX77ln+vctDq4sNUBP/3J\n2z+4WUpEpCwpTVspdu3aNXHixJdffrnuFZ7xNTs7OyMjo4hSlRY7Y42YFG9Ppyg7Wv+5aens\nnoGZyYPu+XLBDf2L4im+2mG+0C7zmx3WTGc+a5ySs1i8K3tA44u6DAAzN9lcZmH+dB9PYc3e\nrNtruYD5O7zy/ZswajdJqRleVoB5v9nBevEc16X/tMh0MuUHl5nfIx9OZM7v5pkt4Vdm/g7X\n022yrmJBEZFrkZ2d7ZHnLTXFzuFwDB06tEmTJmPHjr3SZVNSUuLj44siVSmy94Q3qNgVjtsP\nrF00906L6eo7OGpFvcgiepZjycTHxx+IDYT8NyXuP5UeXyU137t2ngwu9J/ufSfSbg5KB/5M\nrJRvsct0sv94YmU/F3AkoWK+xe7y/kxyXWozQmzq1bQ6zryMV7esiMhVS0lJAVwul9uZhavU\nFLuXX35527Ztmzdvtlqv+LdFQEBASEhIUaQqReo4rvL3olzgjt3ffjl/ULrdt/fgxT9cd2vR\nPVG4PyEhIddVvOQbvlaYb0iIV7531a9U+D/atSv5hYT4AFUCLQeS8plgt1CnSrC3FaBq0NUE\nqBxo+eN0/neF+Jqn0q7mPVw5wNCPv4gUv4CAAMBiKe593kpHsTt69Oirr746ceLEpk2bXsXi\ndrvdp9yfV6zldYT5EnvpvbKkIIZs//yThQ/H+oV1H7p8e+WbivS57rze8PHx6Xcjz645d6jy\nWb42ejWy+/jkvzJveEteWleYhzBX9KVLfa+cZ+vXmB/+zGdO9/oE++f+oN19I3P/yGeOcd7B\nuRewWRjV2nLx3ntAlQD6XW+8/8vVJM95Ga9mSRGRa2C3e+a4rdJx8ERCQoLD4Xj55ZeNM6ZP\nnw5UrVq1fv36nk5XOhjwn7sxtNruGoz+edpn3zx4NKhGx4fXF3WraxjK8x0B6lTg753ymfBG\n5IWnFzlftUAea1NoYSwG03udO7Z0dBtaVbtwTqgvb523UfqeG+jb8MI5PjbGXXoV50udGXAD\nw1tcOG41eL8P/7yduu7Wu1kvenvfWIln27tZSkSkLCkdxa5JkyZmXo8//jgQExOzd+9eT6cr\nNXrWZ8PDNKiY+/vPasHfjq+Nir5E1OWjO6kbouZ3SeOjJ7+75IndYY1uG7bhQKUGAd7YDAK9\n8LNjM7Dl/UkyjNwX2YAQH0a2oGd9AuznXl4fG82rUDng3OEANgsBXlgN6oUwug0//IWKvrl3\nPd+RL++h3XUEehHsTcdaRN3H463dBJ7Wk3e6E+p75knP+85aDML88LHlRg3z5R+dGd6C64Iw\nOBfSMPC3E1GX1Q9wb5Nzi/vZ+f4hJnSgUSg2CzWDGXITv4yk4XmXxjXg60FM7sZNlfGyUtmf\nftfz43DejOCbe6kRhOXMs1gNWldn3gCeuw3gw77M6E3LqvjYCPWlR33WPcydjajkx0/DGdWK\nuiFYDeqGMKw5w1tQvyJWgzoVGN6CX0YyrDm1K2A1qF+Rp25h418I1p6lIlKelI5NsVJY2l/H\n/5645L3Dbi7GKKWIafL006x6m5YtGy9bdiQsrPgjDLyRgTde8VJjb2HsLUWQBvztvNaV17pe\nbo7NwrPt81lh1q8R/RpdcikDHm3Fo63yuSvMj/d6uwn20R1uJoiIlG0qdiKX5XQyciQff0yn\nTnz7LUFBng4kIiJySaVjU+zFpk2bZprm5S87IXKtsrIYNIiPP6ZPH5YuVasTEZESrrQWO5Ei\nl5pK374sWMDgwXz9Nb6+7hcRERHxKBU7kfzExxMZyYoVPPoos2bhoaPWRUREroiKnchFjh/n\n9tv5738ZP54ZMyj200uKiIhcHR08IZLXoUNERLB3L6+/zjPPeDqNiIjIFVCxEznPzp1ERhIT\nwwcfMGKEp9OIiIhcGRU7kTM2b6ZnTxIT+eILBg70dBoREZErpmInAsD69fTti8PBwoX07Onp\nNCIiIldDe4WLwOLF9OiBYbB8uVqdiIiUXip2Uu7NmcNddxEYyNq1dOjg6TQiIiJXT8VOyrf3\n32fIEMLCWL2a5s09nUZEROSaqNhJOTZ5MqNGUbs20dE0aeLpNCIiItdKxU7KJdNk/HgmTODG\nG9mwgbp1PR1IRESkEOioWCl/nE5GjeLf/6ZNG5YsITTU04FEREQKh4qdlDNZWQwdyrx5dOnC\nokUEBHg6kIiISKHRplgpT9LS6NePefO4804WL1arExGRMkbFTsqNlBT69mXpUoYO5auv8PHx\ndCAREZFCpmIn5UNcHF27smYNTzzBZ59h004IIiJSBqnYSTkQE0OnTvz8M+PHM3UqhuHpQCIi\nIkVC6y2krNu/n4gIDhzg7bd56ilPpxERESlCKnZSpu3YQWQkJ07w0Uc8/LCn04iIiBQtFTsp\nu37+mV69SElh3jzuvtvTaURE/r+9+w6M8X78AP6+lbvsRIZIYkQipcQoatWMhFglNrXa0qK0\nVEV/5dvh26FLqeiiqFEVK1QpaiuKmjVqSyIy5DIu43Lr90fyJYlLXMjlufF+/SXPPfc87/vk\n8eSdZ4XI7FjsyEbt2YP+/aHXY+tWREQInYaIiKg68OYJskXx8ejdG1Ipdu1iqyMiIvvBYkc2\nZ+VKDBoEDw/s24d27YROQ0REVH1Y7Mi2LFqEsWMRGIiDB9G0qdBpiIiIqhWLHdmQefMwZQqe\negoHDyIkROg0RERE1Y03T5BNMBjw5puYPx+tWmH7dnh7Cx2IiIhIACx2ZP10OkyYgB9/ROfO\n2LIFbm5CByIiIhIGix1ZObUaI0diwwb06YN16+DoKHQgIiIiwfAaO7Jmubno2xcbNmDECGzc\nyFZHRER2jsWOrJZSiYgI7NqFiROxciVkMqEDERERCYzFjqzT3bvo0gVHjiAmBosXQ8wtmYiI\niNfYkTW6eRMREbh2DZ9+irfeEjoNERGRpWCxI2tz8SIiI5GcjO++w/jxQqchIiKyICx2VuZo\nEgo0SM3FswHwUCAjH/U8kJCNtFzU90R6Pm5m4W42vJygNyBPi8O3EewFXyfkFGLzZUhE8HdD\njhoGPbxc4SNHbXfkF+Kfe0jMhJsj+oTgihL/pkOtQZAXnvbB5TRkFSJbjcx8DG6C/EJcTIVW\nj0a+uJAGVzmiGiA1B0kquDlAo0OhHlcz0L4OLqcjqwAGQK9Fc394KaA2IDELXo4o0EECdKgN\nT0ecSUGrAKTnwscZ6bmo4Yi0PNzLA0SQi+GmQKAbbiphAKQSuJ0/6RPdU5ydnfXjmkvhQ7wz\nEFIDuRocTYREhI51IRGVGq48DZJyUM8DshKnatPykFuIeh7FX15T4kIaGvvCyxHpeQhww7FE\nBHmgjjsAZBZg303U9UALv+L5dQbczIS3E9RaZKuhzIerAk95oeSaE7OhB9RaSMWQiOAmR0Y+\n6npAb8DNTPi7wlkGA3AzE84y5BTCxwlu8uL3FmhxPhUKGeQS1HQunq4sgDIfdT1wOwsOkuL8\nDhLjG0nRWmo4wkNRanq2Gml5qOfxYJQ0etzMRIArnB51gWK+FonZkEsgFiHQbM+TydciIevB\nR8vV4E6Jb1/RiDlKkVNoUuYid1XQ6hHoVvx2Nzm8eI8NEdkuFjvrkKdB1GocvAWDmVe0+nRF\nr/562cjEeQeNTFx4tGrylNT55v4tP/dT67UDh8bvuNETS4zM08gHe0bDzwXHkvD6dhy/A70B\nMjGiGmBhFH6/iv8eQEI2ALjLEeyJMynQlTOmUglEgEZX/KUIiAiGrzPWX0CBtuzMEjGGPI3F\nvbHoL8w/iox8IwuUiGAwQA+IRfB1RlYB8kssp1lNTHoWC47iQlqpd9Vxh96AxGwjSwuvj6+j\nEOr1YGJmAd7ahTXnkKcBgIbe+Dgc/Rvi92uYuQtnUwBAIcWgpzG9Hd7dix1XodFDLMKzAfiq\nJ9oEGIl9Mhmvb8efCQ+2PU9HTG+LWc9BWnVXNp66i6nbcSQBOgOkYrTyR74G51KLv32RwfB1\nwS/niz8XAJEI7QKxoCda+RtfoAGYfwSf/Ym7quJPbTBArQOA+p54rwtG8Q/OEZEtEhkM5q4K\nQoqLixsyZMi6desGDx4sdJYnUn8BbmQKHUJQff79dV3cELVE3nfE1kN1nqtgTncFto1ExIpS\ntaloelaBeUP6OiE1z7yrKMPHCScmFB9c1OjRYSmO3yk1gwiY2QGf/1m2v8rE0OhLTXGUYv84\ntC7dk86koMNS5GrwsAkt8V2fKvgIAM6lov1SqAor/UZnGQ69iOZ+Rl6K2Y1PD1f03sW9MbFV\npddIRGQioRoI7yW0AstO23urG3FuzcZfonMcXLuO3VtxqwOQVYAR68u2uqLp5lbNrQ5AWh7m\nHij+98ozZVsdAAPwxREjRyXLtDoA+VrE7Co78e3dxlsdgB9O4lxqpQMb9c4fj9PqAORq8PYf\nRqbfysIXfz7ivbN2Pzj+R0RkM+ziVKxGoykoMP9PdbNZfcbBniv4xOPfLPrttUS3wMhROy97\nP2XKWxKyzB3Kguy6ZigoUAPYcUUGGLnsTvtQhyvPgVvIzi24f+me3oA/bijKm9kA7PhX28Dt\noQZdSQZg9/Vy1/JIe24gL79AXPrayp3/SnSGR1yCl63GwRuFneuYPDpERJWh0Qjzu6NdFDuV\nSqVUKoVO8fgy8rztttjFHJr3ye5Zl72fihy187Z7HRPfZcuXFzwkM99QtHnfU9UwWuxMpzMg\nKS3LQ17cdfK1okKdsdOc/5OSWaBU5jzJGgEU6kX52orW8oi365CcnukkLfU9v5vpDDz63oo7\n93KVrlb8Kx8RWTKVSgVAr6/u3x7toti5uLh4enoKneLxNfASn6qic15WRGQwzNsd89bhz877\nNokctTPZtZbp75WIobObAzFBnijavBv4SPckPNGiPOSGen7u9w9+eQK+TobUPFF58zfyk3t6\nVsE+pJaLIVlV7loq5uNkCPDxKDOxkZ9Jvwg1CXDy9OQtskRkFi4uLgDE1f78fLsodjKZTKF4\n/HM9gpvTBXEX7esolESv+2bbxPEnf/gr4NmoF7ZnONao1Nt7BOO3K2aKZnHGNBcXbd5jW+D7\nv41sJz5OSDPt4r9RzUSOpf+njG6Oz8u5WM1TgQGNZQpFFfwltzHN8cmhx3zv6Gaih/9392qI\nWi5IVlX0xmY10aqO/DHrJBHRo8gE+kOXdnqCz7o08cUb7YQOUY0cdIVrNowYf/KHPUHdwsf8\nUdlWN7ARfh5o5MkdvRqghjmPzoiAF5+B4xP8rvQY7+3fEK89W/zvtoGY2w1lmko9D/w6Ek95\nlX1jY9+yU9oE4L/dyk58rwueM3YC3FGKFQOqbDzndELnuo+YR2Ssgj1XB+93NTLdSYbVA+Hq\nUO7SajpjVXTZsSIisgF2ccTOBnwZifAg/N8fuJgOrQ4QQSqCmxy1XFGgRbYaah00OhRqoTNY\nzbE9kQgyMUSAgxQOErg6IF8DhSbv+2UDI6/siG/Yf8SQtSKFvIYEegMKdRCJIDLAAOhF0Osh\nBlzlcFfgXj5y1BABtVzxfmeMaQ4AB8Zh2WnsvYHEbDTwQt9QRDdCsgrfnsCJO8jXIKwmutRD\n7F84mgS1FmIR3ORwlCFHDY0ObnI09YOHAjuvIUcNqRjBnljSD3karD2PMynIViOzAKpCaPVw\nkqG5Hz4KR5sAzOqA709i5zWk5CK/EA5SiAA3OTwc4eUIiRjZBQhwQ31PZBbgzwRkFgBA05ro\nFoSXnsGqs/j+JK5nQKuHgxTN/RAZDI0Op5KRkA21tniNEhHa1MaAhhjWpFQ7eacjOtXF6rO4\nkAYvJ7QNxKut4C7HiQn4/iT+TEBKLhp6Y2hjdK+PjRex9V9cuYdAN3QNwrjmRp547CzDnjFY\ncRpbLuPUXegN8HJCj2BMaIkGlevbFXGSYfdo/HQGu64jIQv1PdEjBNlq7LuJpGw08EK/p1Db\nDctPY+9NZKshFqGlP3qFYEzzUo+eLqlrPZybhO9O4O9k6Azwc4HBgKQcuDigtT8mtoaPU5Xl\nJyKyHHyOHVmSrCz06YNDhzB6NJYuhZS/eBARkVXic+zI7qWmomtXHDqEKVOwfDlbHRERUWWx\n2JFluH0bnTrh1CnExGDhQuNXVBEREVGFeFCELMD164iIwI0bmD8fb7whdBoiIiJrxWJHQjt/\nHj16ICUFS5di3Dih0xAREVkxFjsS1F9/ISoKublYtw7R0UKnISIism4sdiScPXvw/PMwGLB1\nKyIihE5DRERk9XjzBAkkPh69e0Mmw+7dbHVERERVgsWOhPDTTxg0CB4e2LcPbdsKnYaIiMhG\nsNhRtfv6a4wdi8BAHDyIpk2FTkNERGQ7WOyoes2bh6lT0bAhDh1CSIjQaYiIiGwKb56g6mIw\nYPp0fPUVWrXC9u3w9hY6EBERka1hsaNqodNh/HgsW4YuXRAfDzc3oQMRERHZIBY7Mj+1GiNG\nYONG9OmDdevg6Ch0ICIiItvEa+zIzHJz0bcvNm4s7nZsdURERGbDYkfmpFQiIgK7dmHSJKxc\nCZlM6EBERES2jMWOzObuXXTpgiNHEBOD2FiIubERERGZF6+xI/O4eRMREbh2DZ99hhkzhE5D\nRERkF1jsyAwuXkREBO7exfff4+WXhU5DRERkL1jsqKqdOIGoKGRn4+efMXiw0GmIiIjsCIsd\nVan9+9GvH7RaxMejZ0+h0xAREdkXXs9OVefXXxEVBbEYO3ey1REREVU/FjuqImvWIDoabm7Y\nuxcdOgidhoiIyB6x2FFV+OYbjBqFWrVw4ACaNxc6DRERkZ1isaMnNm8eJk1CgwY4dAihoUKn\nISIisl8sdvQEDAa89RZmzcIzz+DAAdSuLXQgIiIiu8a7Yulx6XSYOBE//ICOHbF1K9zdhQ5E\nRERk71js6LEUFmLUKKxbh6gorF8PJyehAxERERGLHT2GvDwMHIgdO9C/P9auhVwudCAiIiIC\neI0dVVpmJiIjsWMHRo9GXBxbHRERkeVgsaPKSE1F1644fBhTp2L5ckh5xJeIiMiCsNiRyW7f\nRseOOH0aMTFYsAAikdCBiIiIqBQecSHTXL6MiAgkJmL+fLzxhtBpiIiIyAgWOzLBqVPo2RP3\n7uHHHzF2rNBpiIiIyDgWO3qUY8fQqxdyc7FuHaKjhU5DRERE5WKxowrt2YPnnweAX39F9+5C\npyEiIqKKWM3NE6mpqZ9++mmbNm3c3d1r1aoVGRm5b98+oUPZus2b0asXZDLs2sVWR0REZPms\n5ojdpEmTDh8+/PHHHw8YMEClUn344Yddu3ZdtmzZWOu85CunECfuIDEboV5o4QcHCZJycCoZ\nuRqE+cJJhr+ScE0JiCAC/knDDSVyNNBqoZDB2wkiQG+AQgaDAXkaZOTjXj5EQAMv+DojVYUs\nNXI0yMmHHnCRo1CDQi2kMmTlo0AHqQjeznBzQIEOBRrUcESrWnCR4/I9GIAANyTn4Onffvr0\n55eyXL2nT9t5+nSY5AwcJHCQAkBGHtQGeMhRxw3BNdAuEHXccTMTynyIRXB2gJ8LTt7Bgdvw\ndERLPzg7QFmAG0qk58HbGfU9YACSspGnRUs/POOP3ddxIxM+jqjtjkbeeKYWFFKcTcHuG8gs\ngJ8LXByQmAWRCG0C0dofzjKcuovL6ajlipa14KEQ+tv5kLsq/J2MLDUa+yCsJozeP5yQjVPJ\nKNAiyBPZatzJQUNvtPCDVAwABVqcTMb2K7iRBV8nRIUgvD4kJt+IrDNg4wXsvw0/Z0Q/jae9\nq+qTERGRRbOaYte6devFixf7+voCcHd3j42NPXTo0OzZs62x2H13Em/tRE5h8ZcBbmhWEzuu\nQm940iXfzjQ2NafsBA2QUGLOpGycSyk1w9RjC7/Y8cZNj3qRo3Ze1YcgBUadSHp0pOWPnqUs\nTwWcHJCUbfxVuRQuMtzLL/7SWYb3u+LNdpVfjXlo9XhzJxYfh1ZfPKW1P9YMREiNB/PkafDq\nr1h9zsh3PMgDK6ORWYCxm5Ge92D6V0fh6YgdI/FswKMzbLmMoetRoC3+cs5etPbHwRchlzz+\n5yIiIqtgNadiY2JiilpdEZFIFBwcnJSUpNFoBEz1GOIu4NVfH7Q6AEnZ+O1KFbS6qhJzaN6C\n7a9f9G7UcdzBqzVCqj+AsqDcVgdArX3Q6gDkajBjJ5aeqoZcJonZjYXHHrQ6AMfvIGIlckts\npy9vwcqzxr/jNzIRuRID1pZqdUWU+ei8HEkP1fQyrisx4JcHre5+hvAVlfgURERkpaym2JWR\nk5Nz4MCBoKAgmUwmdJbK+WC/0AnKJzIY5u+Y9snuWSf8W3Uetz/JzYSjQ5bhg/2whGKsLMDX\nx4xMv5mJn84U//tKBn4+X9FC8jTQ6I2/VKDFl0cekWHKduOV8XACEsqvy0REZBus5lRsGdOn\nT8/IyPjoo49MmVmj0RQUFJg7kilyNTifanlXhAEAJHrdki0vjz29fF+9Lv2Gb8mRuwqdqBJu\nZ+HWPbWfs8Dt7ugtsUbvYPSlP2/pxjXRADh0QwI8/m8je67rCwoKK5jhVLIcxi/qw5YLmpea\n6x571UREZDqhzihaZbH76quvlixZEh0dPWHCBFPmV6lUSqXS3KlMkV0oAvyETmGEXKtes2FE\n9MWNW5/qO2TwugKphbbPCqRnZMkLBW4tmdlyoIbRl/IKCpXKTABZOY6Ax2OvQq3RV7wxa3U1\nyyt2map8pTL3sVdNRESmU6lUAPT6ck7BmI31FbulS5dOnz69Z8+ea9asEZn250pdXFw8PT3N\nHcwUnkCgqyExx7L+yqpzYe6mXwZEXNu1uunIsf2Xa8XWt1V4KgwNA9ykQl9Z8KxUJILxk8It\nAmRFG+Gz9cR4gtPxrQJEFW/MIV5ISzT+Uo+n5J6exg8oEhFR1XJxcQEgFlf3TyYr+xG+atWq\nCRMmREREbNq0SS6Xm/gumUymUFjKIajX2+KtXUKHKMEzX/nb6l5tE48ubj1pSq+v9SKhy9Fj\nmfysyMVJ+G9xsALRjbDhYtnpbnK83EqqUEgBtKuHDrVxOKHchYhEMJRzSlkkwlvPSRSKiu5u\n/SQCnZcZmV7PHa1qm/pfhoiInpBQ9wBY00/xuLi4sWPHhoeHx8fHW05Rq6zp7TCxVakpMjHq\nPf6puSfip7q7f3nntolH5z0XM7l3rIW0OpEIDhU+mENSOuaopni3s1kTVcKSfuhct9QUbyds\nGgo/l+IvRcC6wWhRzgl5hRRL++HTCCOPrJOIsLQfwnyNva2ETnXwXpey52K9HHHoJRM/ARER\nWTGrOWK3bdu2kSNHduvWbcuWLdbb6gCIRVjcGxNaYu/N4gcU9wxBbTf8fg0n7yBXg0becJDg\nSCKuZCBPA70eNzKRpUahDgYDJGLIxJCKYTBAIoEIyNdAq4dGB5EITg5wkECrR6G2eH6DCGIR\n9AaIDNADMMAAiACRCPWzbu5YHlFfeW1Wz8+XdX8zWI6sAuj1cHJAVj7ytTAYIJVAKoZeX7wc\nMWAQQ6OBAZCJ4eyAIHe0DECAK/K1uKGECPB3hUaPMym4lgGZBPU84O+K5Bwk5UCthYME9Tyh\n1iIzHwagoRcC3XEpHbez4ShFsCfaBaJ7ffg4Y8MF/PovVIWQS+HigGw1xCJ0qIOewajlit+u\n4HI6/FzQuR5a+wv9TS3BQ4G9Y/HHdRy/g8wCNPbB8w3hXvpImb8rjk/A9iv4OxkFWrjKIQLS\n8xDqhd6hCHAFgOhG2HQJGy4gPQ/ucnQJwoz28HM2KcO7nfFCU3xyCOdS4SFHjxC83gZiyzr/\nT0REZiEylHfWx8J06dJl/34jlyYlJCQEBgaW9664uLghQ4asW7du8ODB5kxnhS5cQGQk7t7F\nd9/hJR7MISIiqkpCNRCrOWLHvwxblU6cQFQUsrPx889g5SUiIrIVVlPsqMrs24d+/aDTYcsW\n9OghdBoiIiKqMhZxsTxVn61bERUFiQQ7d7LVERER2RgWO3uyejUGDoS7O/buRYcOQqchIiKi\nKsZiZzcWL8bo0fD3x4EDaN5c6DRERERU9Vjs7MO8eZg8GaGhOHgQoaFCpyEiIiKz4M0Tts5g\nwMyZ+PxzPPMMduyAj4/QgYiIiMhcWOxsmk6HV1/FkiXo2BFbt8LdXehAREREZEYsdrarsBAv\nvIC4OPTqhfXr4egodCAiIiIyL15jZ6Py8vD884iLw7Bh2LyZrY6IiMgesNjZosxMREZixw68\n8gpWr4ZMJnQgIiIiqg4sdjYnJQVdu+LwYUydim++gZjfYiIiInvBn/q25fZtdOqE06cRE4MF\nCyASCR2IiIiIqg9vnrAhly4hMhKJifjqK7z+utBpiIiIqLqx2NmKv/9Gz55QKrFsGcaMEToN\nERERCYDFziYcPIi+fVFQgHXrMGCA0GmIiIhIGCx21u+33zB4MMRi/PoruncXOg0REREJhjdP\nWLnNmxEdDYUCu3ax1REREdk5Fjtr9tNPGDwY9scx5AAAG/NJREFUNWpg3z60bSt0GiIiIhIY\ni53VWrgQY8eidm0cPIiwMKHTEBERkfBY7KzTvHl4/XU0aoSDBxEcLHQaIiIisgi8ecLaGAyY\nNg0LFqB1a2zfDi8voQMRERGRpWCxsyo6HV5+GcuXo0sXbNkCV1ehAxEREZEFYbGzHmo1hg/H\npk3o2xfr1kGhEDoQERERWRZeY2clVCr06YNNm/DCC9i4ka2OiIiIHsZiZw2USkREYPduTJ6M\nFSsg5XFWIiIiMoLFzuIlJ6NzZxw9ipgYLFoEMb9lREREZByP/Vi2GzcQEYHr1/H553jzTaHT\nEBERkUVjsbNgFy4gMhJ37+KHH/DSS0KnISIiIkvHYmepjh9Hr17IzsbatRg0SOg0REREZAVY\n7CzSvn3o1w96PbZsQY8eQqchIiIi68Ar8S3P1q2IioJEgp072eqIiIjIdCx2Fmb1akRHw90d\n+/ahfXuh0xAREZE1YbGzJLGxGD0aAQE4eBDNmgmdhoiIiKwMi53FmDcPr72G0FAcPIgGDYRO\nQ0RERNaHN09YAIMBb72FL77AM89gxw74+AgdiIiIiKwSi53QdDq88gqWLkWnTtiyBe7uWWps\nuIB/0qCQorU/eodCJsbmy/jhJM6lQCZBY18Ma4wW/th9DZfT4e+K2u5IzcUNJeq4o0UtXFfi\nQhqcZFDrAEAhQesA9AmFTIwjSZh/BJfTUMMJIhHUWqi1eKYW5nRGbTehh8I8LqXjtyu4rkRt\nd4QHoZW/WdZyJQPb/sXVDAS6oUs9tA00y1rM4V4+Nl3EP2lwcYBCCp0Bablo6I3nn0Jg+ZtE\nSi42X8KFNHgq0DYQkcEQi4pfOpyAA7eQlI1QL7QJxKlkXEiDlxPaBSIiGFkF2HgR51PhKMOz\nAejdANLKnzbQ6LH1Mk7cgVqHMF9EN4Kb/DE/PhGRjWGxE1RhIUaOxPr16N0bcXFwdDx0G9G/\nIC3vwSzNakIhxbGkB1OuK7H1MkSAoTKraloTjXzwy3kjL51MxtK/Edsbr7Z6zM9hsT48iP/s\nhb7ESE1shUW9HrSQKvHFEcTsgq7EWsY1xw/9IKnStZjDrusYGgdlgZGXZuzE930xqqmRlzZf\nwqhNUBU+mNKlHuKHwVGGMZvws7FtrEhLf9zMxL0Sm3cLP/w2En4ulcickI1eq3E+9cGUWbux\naRjaWU+ZJiIyH15jJ5y8PPTrh/XrMWwYNm2Co2NGPvqvLdXqAJxJKdXq7qtUqwNwNsV4qyui\nByZtw78ZlVyoZdt8CbP3lGp1AL45gYXHqnItO65ixs5SrQ7AstP49HBVrsUcklUY+IvxVgeg\nQIsX43Empez0a0oMW1+q1QHYdxOTtmHu/opaHYCTd0q1OgCn7mLkxkpkNgBD40q1OgApuei/\nFlnqSiyHiMhW2cURO41GU1BQzo8vgYiysmQDBoiPHNG9/LJmwQLodNDpVp6S3MuXCRXJALy7\nR7esj0aoAFXu62MORn91WfSX4dXmVdYCFpWzlti/DNNaWXTXWHpCmlNY0R5Aq0fsMd3CyFKb\nxLd/SdU6I+9aex7ucgNQ6aOUe27gdJK6oZdJv6qcShEfSXR4eHpqLtac1oxrpqvs2omIzESj\nEebnqV0UO5VKpVQqhU7xgDgtrcaIEeJ//lFNnpzzf/+HrKyi6WfuuAOCFTsAZ5L1FjVQT+if\n1JpGp19XilLuZTqIK3vQ07jzqb5GpyfliG6lZLo5VM1azOHMHY9H7gHOJuvKbBJnkz2Nvktn\nQEbBY557Pn4zr6bYpF+9TtxyBIwUOwCnkgr718l+vABERFVOpVIB0Ov11bxeuyh2Li4unp6e\nQqcoJrp922HgQNHVq9rZs6XvvFMylrOjwN8OhYPEcgbqycmlxnuGWATvGh5VdQGcQlbu9Qw+\nNTychCzqj+Di+OhwTnJpmU3CWVH1H8nTzdnT09GUOT1cJeW95Ookt6Wtl4isnYuLCwCxuLqv\nebOLYieTyRQKhdApAACXLiEyEomJWLBAOnVqmdHvWA+LTgiTq0j3YLGlDFRVaF8Ht41d8tU6\nAM6OVfYx29fG5XtGpjeriRquFj2YHepi+dlHzlN2k+hQF5suG5nTxQG1XHHF2FBUTCLCc0EO\nJm53HYPKvW2oYz2pQmEXOzQisgoymTC/2fPmiWr099/o1AnJyVi2DFOnPvx6dCM096v+WMWc\nZJjdUbC1m8Os5/DwD3qxCO92rsq1zOyAhw/LiYD3ulTlWsxhZFOEelU0g48TXnu27MTxzxh/\nMs6s5/B+l8eJMbF1Je6KreeBcS2MTG/lj75PPc7aiYhsDItddTl4EN26ITsbcXEYM8boLFIx\nfhuJyOBSUya3xnudyz44QwS4lnhwV8mHdxg9MSgVY2IrrBgAeTknsryd8OdLtvYwsGY18esI\n1HF/MMXbCaui0TOkKtfS0BvbR6J+iXOANRyxrD/6N6zKtZiDoxQ7R6FjHeOvhvli92jUdC47\n3U2O3aPRusTjABVSvNsZ/9cRw5vguz5wL7EVldzeHKV4qz3Cgx5MkYkx5Vl82aNysRf3xist\nS/2PiArBryOs4OEyRETVgGcuqsVvv2HQIEgk2LYN4eEVzFjLBb+/gOvK4ie4NqsJX2cAeLM9\nNl/GgVtwkaFdbbQLhL8rzqfiSgb8XPC0D24ocU2Juu5oWhM3M3ExHR4K1HDErcxSyxkRhq2X\n8WcC6nmgsQ/+TIRai+71y/3pbu3Cg3B1Ks6lFA9OWE2Y4zrGTnVx+TWcS8W1DAS6oWlNI8fw\nLFNddxwYh6sZOJ8KVzmCayAhC2m5CPVCY99yb3AN9cJf43EpvXgza+EHj/+dSJ3QEiOb4sxd\nJKsQUgONfXHlHi6lw1OBFrWKO991Jc6lwlmGZn7wcap0ZrkE3/bB+11xNgX5GoTVRJDHY358\nIiLbIzIYLPeuvScXFxc3ZMiQdevWDR48WLAQa9di9Gi4uuK339CmjWAxiIiIqLoI1UB4KtbM\nvvsOI0fC2xv79rHVERERkVmx2JnTvHmYOBF16+LgQYSFCZ2GiIiIbByLndnMm4dZs9CoEQ4d\nQnDwo+cnIiIiejK8ecIMDAZMm4YFC9C6NbZvh1eFj5QgIiIiqiIsdlVNq8XLL2PFCnTtivh4\nuLoKHYiIiIjsBYtdlVKrMXw4Nm1Cv3745RfY0F9xICIiIsvHa+yqjkqFPn2waRNeeAEbNrDV\nERERUTVjsasiGRno3h27d2PyZKxYASkPhRIREVF1Y7GrCsnJ6NIFx44hJgaLFkHMUSUiIiIB\n8MDSE7txAxERuH4dX3yB6dOFTkNERET2i8XuyVy4gIgIpKRgyRK8+KLQaYiIiMiusdg9gePH\nERWFnBysXYtBg4ROQ0RERPaOxe5x7d2L55+HXo+tWxEZKXQaIiIiIt488Xi2bEGvXpBIsHMn\nWx0RERFZCBa7ylu1CgMHwt0d+/ahfXuh0xAREREVY7GrpNhYjBmDgAAcPIhmzYROQ0RERPQA\ni11lzJuH115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the residuals","message":["%r","","xyplot(y-x~x, xlab = \"PREDICTION\", ylab = \"RESIDUAL\",","       panel = function(x,y) {","         panel.xyplot(x,y)","         panel.abline(0, 0, col=\"red\")","       },","       xlim=c(0,8.5),","       ylim=c(-7,8.5),","       scales=list(y=list(tick.number=8), x=list(tick.number=8)),","       main=\"Prediction of Years of Residence vs. Residuals\")","       "],"enabled":true,"result":{"startTime":1737858685500,"interpreter":"r.low","endTime":1737858686151,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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DN2/eDCQmqkNpEVOJuEnJoum0JzVG3MznciRv+bkDnLyazqCkRr/0Mp9IrkuKE0nRIl1u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NQ0S6yxd+wiWBOjiV2jRpOYqDEaS2I3UWPvvfcu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K4fGcC6L3DkrOzdChNZfjYM62jsSOpm8FKfacmMqF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\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Zo2UoDTSEV3ZjfiWQJrruacZe9HuxJc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\nAABUgmIHAACgEhQ7AAAAlaDYAQAAq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CUST_ID"],"enabled":true,"result":{"startTime":1737858743600,"interpreter":"sql.low","endTime":1737858745501,"results":[{"message":"CUST_ID\tPREDICTION_YRS_RES\tPREDICTION_DETAILS\n100001\t5.1913686228573397\t<Details algorithm=\"Support Vector Machines\"> <Attribute name=\"AGE\" actualValue=\"1941\" weight=\".428\" rank=\"1\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Exec.\" weight=\".053\" rank=\"2\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".024\" rank=\"3\"/> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\"-.012\" rank=\"4\"/> <Attribute name=\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\"-.026\" rank=\"5\"/> <\/Details> \n100002\t4.3895483469453884\t<Details algorithm=\"Support Vector Machines\"> <Attribute name=\"AGE\" actualValue=\"1962\" weight=\".218\" rank=\"1\"/> <Attribute name=\"EDUCATION\" actualValue=\"Bach.\" weight=\".101\" rank=\"2\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".093\" rank=\"3\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Prof.\" weight=\".093\" rank=\"4\"/> <Attribute name=\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".074\" rank=\"5\"/> <\/Details> \n100004\t4.9081526748141178\t<Details algorithm=\"Support Vector Machines\"> <Attribute name=\"AGE\" actualValue=\"1953\" weight=\".346\" rank=\"1\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Sales\" weight=\".082\" rank=\"2\"/> <Attribute name=\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".061\" rank=\"3\"/> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".04\" rank=\"4\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".033\" rank=\"5\"/> <\/Details> \n100009\t3.2225046078182835\t<Details algorithm=\"Support Vector Machines\"> <Attribute name=\"AFFINITY_CARD\" actualValue=\"1\" weight=\".084\" rank=\"1\"/> <Attribute name=\"CUST_CREDIT_LIMIT\" actualValue=\"3000\" weight=\".008\" rank=\"2\"/> <Attribute name=\"EDUCATION\" actualValue=\"Bach.\" weight=\"-.016\" rank=\"3\"/> <Attribute name=\"CUST_ID\" actualValue=\"100009\" weight=\"-.032\" rank=\"4\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Prof.\" weight=\"-.063\" rank=\"5\"/> <\/Details> \n100011\t3.2356005809056301\t<Details algorithm=\"Support Vector Machines\"> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".07\" rank=\"1\"/> <Attribute name=\"CUST_ID\" actualValue=\"100011\" weight=\".047\" rank=\"2\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Farming\" weight=\".044\" rank=\"3\"/> <Attribute name=\"CUST_CREDIT_LIMIT\" actualValue=\"10000\" weight=\"-.008\" rank=\"4\"/> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\"-.012\" rank=\"5\"/> <\/Details> \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL using XMLTABLE","message":["%sql","","SELECT CUST_ID,","round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(SVM_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(SVM_REGRESSION_MODEL USING *) PREDICTION_DETAILS","      FROM SVMREG_TEST","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","               XMLTABLE('/Details'","                        PASSING OUT.PREDICTION_DETAILS","                        COLUMNS ","                          \"Attribute1\" XMLType PATH 'Attribute[1]',","                          \"Attribute2\" XMLType PATH 'Attribute[2]',","                          \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","                          "],"enabled":true,"result":{"startTime":1737858746033,"interpreter":"sql.low","endTime":1737858746547,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t5.191\t\"AGE\" actualValue=\"1941\" weight=\".428\" \t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".053\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".024\" \n100002\t4.39\t\"AGE\" actualValue=\"1962\" weight=\".218\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".101\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".093\" \n100004\t4.908\t\"AGE\" actualValue=\"1953\" weight=\".346\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".082\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".061\" \n100009\t3.223\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".084\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"3000\" weight=\".008\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\"-.016\" \n100011\t3.236\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".07\" \t\"CUST_ID\" actualValue=\"100011\" weight=\".047\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\".044\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737858748050,"interpreter":"md.low","endTime":1737858748516,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858749007,"interpreter":"md.low","endTime":1737858749500,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R Statistical Functions.dsnb b/machine-learning/notebooks-oml/r/OML4R Statistical Functions.dsnb
new file mode 100644
index 00000000..4c690509
--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML4R Statistical Functions.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Statistical Functions","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737732770651,"interpreter":"md.high","endTime":1737732771078,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Statistical Functions","In this notebook, various statistical functions are highlighted using data from the SH schema through the OML4R transparency layer.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1737732771533,"interpreter":"md.high","endTime":1737732771957,"results":[{"message":"<h1 id=\"oml4r-statistical-functions\">OML4R Statistical Functions<\/h1>\n<p>In this notebook, various statistical functions are highlighted using data from the SH schema through the OML4R transparency layer.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1737732772392,"interpreter":"md.high","endTime":1737732772828,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Load ORE library and get proxy object to CUSTOMER_INSURANCE_LTV 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/r/OML4R Text Mining SVM.dsnb b/machine-learning/notebooks-oml/r/OML4R Text Mining SVM.dsnb
new file mode 100644
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--- /dev/null
+++ b/machine-learning/notebooks-oml/r/OML4R Text Mining SVM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Text Mining SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736279357954,"interpreter":"md.low","endTime":1736279358565,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Building Model using Text Mining Capability in Oracle Machine Learning","","Oracle Machine Learning handles not only structured data but also unstructured text data by enabling model building used automatically extracted features. By leveraging Oracle Text, OML in-database algorithms predictive features can automatically be extracted from the text column.","","In this notebook, we build an SVM model to predict customers most likely to be positive responders to an Affinity Card loyalty program. The data comes with a text column that contains user generated comments. With a few additional specifications, the algorithm automatically uses the text column and builds the model on both the structured data and unstructured text.","","Copyright (c) 2025 Oracle Corporation","","","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736279359000,"interpreter":"md.low","endTime":1736279359447,"results":[{"message":"<h1 id=\"building-model-using-text-mining-capability-in-oracle-machine-learning\">Building Model using Text Mining Capability in Oracle Machine Learning<\/h1>\n<p>Oracle Machine Learning handles not only structured data but also unstructured text data by enabling model building used automatically extracted features. By leveraging Oracle Text, OML in-database algorithms predictive features can automatically be extracted from the text column.<\/p>\n<p>In this notebook, we build an SVM model to predict customers most likely to be positive responders to an Affinity Card loyalty program. The data comes with a text column that contains user generated comments. With a few additional specifications, the algorithm automatically uses the text column and builds the model on both the structured data and unstructured text.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-EE6BABC0-96D3-41EE-86E4-BD18DA5E0176\" target=\"_blank\">OML4R Text Processing Model<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1736279359865,"interpreter":"md.low","endTime":1736279360293,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-EE6BABC0-96D3-41EE-86E4-BD18DA5E0176\" target=\"_blank\">OML4R Text Processing Model<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R Libraries","message":["%r","","library(ORE)","library(\"OREdplyr\")","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1736279360723,"interpreter":"r.low","endTime":1736279365882,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy objects to data using ore.sync query argument","message":["%r","","CUSTOMERS <- ore.sync (query = c(\"QUERY1\" = 'SELECT CUST_ID, COUNTRY_ID, CUST_GENDER, 2003-CUST_YEAR_OF_BIRTH AGE, CUST_MARITAL_STATUS, ","                                                    CUST_INCOME_LEVEL FROM SH.CUSTOMERS ","                                             WHERE cust_id between 100001 and 104500'))","SUPP_DEMO <- ore.sync (query = c(\"QUERY2\" =  'SELECT CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE, AFFINITY_CARD,","                                                     BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES, ","                                                     Y_BOX_GAMES,os_doc_set_kanji, comments FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","COUNTRIES <- ore.sync(query =c(\"QUERY3\" = 'SELECT COUNTRY_NAME, COUNTRY_ID FROM SH.COUNTRIES'))"],"enabled":true,"result":{"startTime":1736279366300,"interpreter":"r.low","endTime":1736279366923,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data by joining tables of customer details ","message":["%r ","","MINING_DATA <- merge(QUERY1, QUERY2)","MINING_DATA <- merge(MINING_DATA, QUERY3)"],"enabled":true,"result":{"startTime":1736279367339,"interpreter":"r.low","endTime":1736279368984,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the dataset","message":["%r","","z.show(head(MINING_DATA), 10)"],"enabled":true,"result":{"startTime":1736279369405,"interpreter":"r.low","endTime":1736279369946,"results":[{"message":"COUNTRY_ID\tCUST_ID\tCUST_GENDER\tAGE\tCUST_MARITAL_STATUS\tCUST_INCOME_LEVEL\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\tCOUNTRY_NAME\n52790\t102547\tF\t17\tNeverM\tI: 170,000 - 189,999\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t101050\tM\t17\tNeverM\tL: 300,000 and above\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t100040\tF\t17\tNeverM\tJ: 190,000 - 249,999\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t102117\tM\t19\tNeverM\tB: 30,000 - 49,999\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\tUnited States of America\n52790\t101074\tM\t17\tNeverM\tJ: 190,000 - 249,999\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t104179\tM\t17\tNeverM\tJ: 190,000 - 249,999\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Notice the text column 'COMMENTS'","message":["%r","","z.show(head(MINING_DATA[ !is.na(MINING_DATA$COMMENTS), c(\"CUST_ID\", \"COMMENTS\")]), 10)"],"enabled":true,"result":{"startTime":1736279370372,"interpreter":"r.low","endTime":1736279370907,"results":[{"message":"CUST_ID\tCOMMENTS\n102760\tI love the new on-line documentation.\n100125\tI love the new on-line documentation.\n103432\tI love the new on-line documentation.\n101602\tI love the new on-line documentation.\n103479\tI love the new on-line documentation.\n103651\tI love the new on-line documentation.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Filter rows without comments","message":["%r","","MINING_DATA <- filter(MINING_DATA,!is.na('COMMENTS'))","z.show(head(MINING_DATA), 10)"],"enabled":true,"result":{"startTime":1736279371331,"interpreter":"r.low","endTime":1736279371811,"results":[{"message":"COUNTRY_ID\tCUST_ID\tCUST_GENDER\tAGE\tCUST_MARITAL_STATUS\tCUST_INCOME_LEVEL\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\tCOUNTRY_NAME\n52790\t102547\tF\t17\tNeverM\tI: 170,000 - 189,999\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t101050\tM\t17\tNeverM\tL: 300,000 and above\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t100040\tF\t17\tNeverM\tJ: 190,000 - 249,999\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t102117\tM\t19\tNeverM\tB: 30,000 - 49,999\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\tUnited States of America\n52790\t101074\tM\t17\tNeverM\tJ: 190,000 - 249,999\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n52790\t104179\tM\t17\tNeverM\tJ: 190,000 - 249,999\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\tUnited States of America\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare, train and test datasets","message":["%r","","set.seed(6218945)","sampleSize <- .6","ind <- sample(1:nrow(MINING_DATA),sampleSize)","group <- as.integer(1:nrow(MINING_DATA) %in% ind)","row.names(MINING_DATA) <- MINING_DATA$CUST_ID","","MINING_DATA.train <- MINING_DATA[group==FALSE,]","dim(MINING_DATA.train)","class(MINING_DATA.train)","","MINING_DATA.test <- MINING_DATA[group==TRUE,]","dim(MINING_DATA.test) ","class(MINING_DATA.test)"],"enabled":true,"result":{"startTime":1736279372226,"interpreter":"r.low","endTime":1736279373221,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>20<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ore.frame'\n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>0<\/li><li>20<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ore.frame'","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop and create Oracle Text policy ","message":["%r","","tryCatch(ore.exec(\"Begin ctx_ddl.drop_policy('ESA_TXTPOL'); End;\"))","","ore.exec(\"Begin ctx_ddl.create_policy('ESA_TXTPOL'); End;\")"],"enabled":true,"result":{"startTime":1736279373656,"interpreter":"r.low","endTime":1736279376899,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build the model combining structured data with unstructured text column","message":["%r","","odm.settings = list(ODMS_TEXT_POLICY_NAME   = \"ESA_TXTPOL\",","                    ODMS_TEXT_MIN_DOCUMENTS = 1, ","                    ODMS_TEXT_MAX_FEATURES = 3000)","                    ","ctx.settings = list(COMMENTS = \"TEXT(TOKEN_TYPE:STEM)\")","","","svm.mod <- ore.odmSVM(AFFINITY_CARD~.,MINING_DATA, \"classification\", kernel.function = \"linear\", odm.settings = odm.settings, ctx.settings = ctx.settings)","summary(svm.mod)"],"enabled":true,"result":{"startTime":1736279377319,"interpreter":"r.low","endTime":1736279382027,"results":[{"message":"\nCall:\nore.odmSVM(formula = AFFINITY_CARD ~ ., data = MINING_DATA, type = \"classification\", \n    kernel.function = \"linear\", odm.settings = odm.settings, \n    ctx.settings = ctx.settings)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nodms.text.max.features                          3000\nodms.text.min.documents                            1\nodms.text.policy.name                     ESA_TXTPOL\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nkernel.function                               linear\n\nCoefficients: \n    class                variable                    value      estimate\n1       1             (Intercept)                          -1.625742e+00\n2       1                     AGE                          -1.276230e-01\n3       1 BOOKKEEPING_APPLICATION                           1.867258e-01\n4       1     BULK_PACK_DISKETTES                           9.961282e-02\n5       1             COMMENTS.10                           1.681307e+00\n6       1          COMMENTS.ABOUT                           1.454479e+00\n7       1        COMMENTS.ACCOUNT                          -1.814215e-01\n8       1        COMMENTS.ADDRESS                           1.502794e+00\n9       1     COMMENTS.ADVERTISED                          -6.586082e-01\n10      1       COMMENTS.AFFINITY                           6.848033e-01\n11      1          COMMENTS.AFTER                          -7.450179e-01\n12      1          COMMENTS.AGAIN                           1.349887e+00\n13      1        COMMENTS.ALREADY                          -1.256389e+00\n14      1             COMMENTS.AM                           1.300404e+00\n15      1        COMMENTS.AMAZING                          -1.112522e+00\n16      1       COMMENTS.ANYTHING                          -6.407385e-01\n17      1          COMMENTS.APPLY                           1.871425e+00\n18      1        COMMENTS.ARIZONA                          -2.543266e-03\n19      1            COMMENTS.ASK                          -4.967314e-01\n20      1        COMMENTS.ATTRACT                          -7.450179e-01\n21      1      COMMENTS.AUSTRALIA                           9.902330e-01\n22      1           COMMENTS.AWAY                          -1.512846e+00\n23      1         COMMENTS.BEFORE                          -1.262577e-02\n24      1         COMMENTS.BETTER                          -2.484394e-01\n25      1            COMMENTS.BIT                          -4.967314e-01\n26      1          COMMENTS.BRING                          -5.577263e-01\n27      1        COMMENTS.BROTHER                          -2.531650e-01\n28      1          COMMENTS.BUILD                          -7.183833e-01\n29      1           COMMENTS.BULK                          -8.784285e-02\n30      1            COMMENTS.BUY                          -1.155058e+00\n31      1           COMMENTS.CARD                           4.481219e-01\n32      1          COMMENTS.CARDS                          -4.450585e-01\n33      1         COMMENTS.CHANCE                           7.571360e-01\n34      1         COMMENTS.CHANGE                          -6.407385e-01\n35      1       COMMENTS.CHILDREN                          -4.967314e-01\n36      1          COMMENTS.CLOSE                          -7.183833e-01\n37      1     COMMENTS.COMPETITOR                           8.846138e-01\n38      1       COMMENTS.COMPLAIN                          -4.967314e-01\n39      1       COMMENTS.COMPUTER                          -1.989701e+00\n40      1       COMMENTS.CONSIDER                          -1.102146e+00\n41      1     COMMENTS.CONSIDERED                           9.902330e-01\n42      1    COMMENTS.CONVENIENCE                           7.571360e-01\n43      1           COMMENTS.COST                          -6.586082e-01\n44      1       COMMENTS.CUSTOMER                          -3.687383e+00\n45      1           COMMENTS.DEAL                          -8.393684e-01\n46      1           COMMENTS.DEAR                          -4.450585e-01\n47      1      COMMENTS.DELIGHTED                           1.569227e+00\n48      1           COMMENTS.DIDN                          -1.169533e+00\n49      1       COMMENTS.DIRECTLY                          -1.111771e-01\n50      1       COMMENTS.DISCOUNT                          -2.897657e-01\n51      1      COMMENTS.DISCOUNTS                          -3.474075e-01\n52      1  COMMENTS.DOCUMENTATION                          -1.290562e+00\n53      1            COMMENTS.DON                          -7.111719e-01\n54      1        COMMENTS.DRIVING                          -7.183833e-01\n55      1     COMMENTS.ELECTRONIC                          -8.393684e-01\n56      1       COMMENTS.ELIGIBLE                           7.571360e-01\n57      1           COMMENTS.ELSE                          -1.262577e-02\n58      1      COMMENTS.ELSEWHERE                          -1.512846e+00\n59      1            COMMENTS.END                          -7.639012e-01\n60      1         COMMENTS.ENOUGH                          -7.450179e-01\n61      1            COMMENTS.ETC                          -4.967314e-01\n62      1           COMMENTS.EVEN                           7.360543e-01\n63      1           COMMENTS.EVER                           9.902330e-01\n64      1          COMMENTS.EVERY                           7.589007e-01\n65      1       COMMENTS.EVERYONE                          -1.262577e-02\n66      1     COMMENTS.EVERYTHING                          -1.776251e-02\n67      1      COMMENTS.EXCELLENT                           1.871425e+00\n68      1      COMMENTS.EXPENSIVE                           2.641126e-02\n69      1            COMMENTS.FAR                          -7.183833e-01\n70      1           COMMENTS.FEEL                          -4.450585e-01\n71      1            COMMENTS.FEW                           9.902330e-01\n72      1        COMMENTS.FILLING                          -1.247638e-01\n73      1           COMMENTS.FLAT                           7.265587e-03\n74      1         COMMENTS.FORGET                           2.610031e+00\n75      1           COMMENTS.FORM                          -1.247638e-01\n76      1         COMMENTS.FRANCE                           1.502794e+00\n77      1           COMMENTS.FREE                           3.621015e-02\n78      1            COMMENTS.GET                           1.718393e+00\n79      1        COMMENTS.GETTING                           3.983573e-03\n80      1          COMMENTS.GIFTS                           9.902330e-01\n81      1       COMMENTS.GIMMICKS                           1.681307e+00\n82      1           COMMENTS.GIVE                          -2.927471e-01\n83      1         COMMENTS.GIVING                           1.054059e-01\n84      1          COMMENTS.GOING                          -1.247638e-01\n85      1           COMMENTS.GOOD                           2.583034e-01\n86      1          COMMENTS.GREAT                          -6.564111e-01\n87      1          COMMENTS.HAPPY                           6.222136e-01\n88      1         COMMENTS.HASSLE                          -5.577263e-01\n89      1          COMMENTS.HENCE                          -1.814215e-01\n90      1           COMMENTS.IDEA                           9.505355e-01\n91      1    COMMENTS.IMMEDIATELY                           1.569227e+00\n92      1        COMMENTS.IMPOSES                          -4.450585e-01\n93      1      COMMENTS.IMPRESSED                           1.013631e+00\n94      1       COMMENTS.INCLUDED                           9.909437e-01\n95      1    COMMENTS.INFORMATION                           1.054059e-01\n96      1       COMMENTS.INVASION                          -4.450585e-01\n97      1          COMMENTS.ITEMS                          -1.978529e-01\n98      1           COMMENTS.JUNK                           5.729744e-02\n99      1           COMMENTS.KEEP                           1.437738e+00\n100     1           COMMENTS.KNOW                           1.427841e-01\n101     1          COMMENTS.LARGE                           7.571360e-01\n102     1         COMMENTS.LARGER                           7.571360e-01\n103     1           COMMENTS.LAST                           8.807396e-02\n104     1           COMMENTS.LATE                           1.567307e+00\n105     1         COMMENTS.LATEST                          -6.586082e-01\n106     1            COMMENTS.LET                           2.442849e+00\n107     1           COMMENTS.LIKE                           1.827154e+00\n108     1           COMMENTS.LINE                          -1.152363e+00\n109     1           COMMENTS.LIST                          -1.163631e+00\n110     1         COMMENTS.LITTLE                           1.567307e+00\n111     1           COMMENTS.LIVE                           9.902330e-01\n112     1           COMMENTS.LOOK                          -1.814215e-01\n113     1            COMMENTS.LOT                          -6.249347e-01\n114     1          COMMENTS.LOUSY                          -2.212928e+00\n115     1           COMMENTS.LOVE                          -2.395614e+00\n116     1          COMMENTS.LOYAL                          -1.102146e+00\n117     1        COMMENTS.LOYALTY                          -1.814215e-01\n118     1              COMMENTS.M                           1.054059e-01\n119     1           COMMENTS.MADE                           1.871425e+00\n120     1           COMMENTS.MAIL                           5.729744e-02\n121     1         COMMENTS.MAKESE                          -7.183833e-01\n122     1        COMMENTS.MANAGER                          -8.691560e-01\n123     1        COMMENTS.MANUALS                          -1.111771e-01\n124     1           COMMENTS.MANY                          -7.873006e-01\n125     1     COMMENTS.MEMBERSHIP                           3.983573e-03\n126     1          COMMENTS.MODEL                          -6.586082e-01\n127     1        COMMENTS.MONITOR                           7.265587e-03\n128     1          COMMENTS.MONTH                          -5.031625e-01\n129     1           COMMENTS.MORE                          -2.185627e+00\n130     1         COMMENTS.MOSTLY                          -7.639012e-01\n131     1         COMMENTS.MOTHER                           1.008259e+00\n132     1          COMMENTS.MOUSE                          -6.561533e-02\n133     1           COMMENTS.MOVE                          -7.450179e-01\n134     1          COMMENTS.MOVED                        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COMMENTS.PAGE                          -1.247638e-01\n149     1           COMMENTS.PAIN                          -8.393684e-01\n150     1          COMMENTS.PANEL                           7.265587e-03\n151     1           COMMENTS.PAST                           3.983573e-03\n152     1       COMMENTS.PERSONAL                          -2.927471e-01\n153     1         COMMENTS.PLEASE                          -1.308324e+00\n154     1        COMMENTS.PLEASED                           1.569227e+00\n155     1          COMMENTS.PRICE                          -1.814215e-01\n156     1         COMMENTS.PRICES                           9.117379e-01\n157     1        COMMENTS.PRIVACY                          -2.565237e-01\n158     1       COMMENTS.PRODUCTS                          -4.492373e-01\n159     1        COMMENTS.PROGRAM                          -6.321187e-01\n160     1     COMMENTS.PROMOTIONS                          -1.256389e+00\n161     1       COMMENTS.PURCHASE                          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COMMENTS.RUN                           7.571360e-01\n176     1           COMMENTS.SALE                          -1.352366e+00\n177     1      COMMENTS.SATISFIED                           1.345441e+00\n178     1            COMMENTS.SAY                          -1.568567e+00\n179     1           COMMENTS.SEND                           1.216963e-01\n180     1        COMMENTS.SENDING                           2.877861e+00\n181     1          COMMENTS.SENSE                          -7.183833e-01\n182     1           COMMENTS.SENT                           1.569227e+00\n183     1        COMMENTS.SERVICE                          -1.102146e+00\n184     1        COMMENTS.SEVERAL                           1.345441e+00\n185     1           COMMENTS.SHIP                          -8.969018e-02\n186     1       COMMENTS.SHIPPING                           3.621015e-02\n187     1           COMMENTS.SHOP                          -1.878339e-01\n188     1        COMMENTS.SHOPPED                           1.569227e+00\n189     1       COMMENTS.SHOPPERS                          -7.450179e-01\n190     1       COMMENTS.SHOPPING                          -5.050761e-01\n191     1          COMMENTS.SHOPS                           3.625238e+00\n192     1         COMMENTS.SIGNED                          -5.402580e-01\n193     1         COMMENTS.SISTER                           1.415431e+00\n194     1          COMMENTS.SMALL                           7.571360e-01\n195     1          COMMENTS.SORRY                          -4.492373e-01\n196     1          COMMENTS.SPEAK                           3.983573e-03\n197     1       COMMENTS.SPECIALS                          -6.934605e-01\n198     1          COMMENTS.START                          -1.262577e-02\n199     1        COMMENTS.STARTED                          -1.776251e-02\n200     1        COMMENTS.STOPPED                          -8.393684e-01\n201     1          COMMENTS.STORE                          -1.495187e+00\n202     1         COMMENTS.STORES                           1.567307e+00\n203     1          COMMENTS.TEXAS                           5.174005e+00\n204     1          COMMENTS.THANK                           8.217133e-01\n205     1         COMMENTS.THANKS                           6.357547e-01\n206     1         COMMENTS.THINGS                          -7.639012e-01\n207     1          COMMENTS.THINK                           4.251874e-01\n208     1          COMMENTS.THREE                          -1.247638e-01\n209     1          COMMENTS.THREW                          -1.512846e+00\n210     1           COMMENTS.TIME                           5.143027e-01\n211     1          COMMENTS.TIMES                          -1.015547e+00\n212     1          COMMENTS.TIRED                           4.619163e-01\n213     1           COMMENTS.TOLD                           2.199960e+00\n214     1          COMMENTS.TRIED                          -8.393684e-01\n215     1        COMMENTS.UNHAPPY                        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COMMENTS.WEEK                          -6.586082e-01\n230     1         COMMENTS.WEEKLY                          -6.934605e-01\n231     1          COMMENTS.WEREN                          -1.111771e-01\n232     1           COMMENTS.WISH                           1.054059e-01\n233     1        COMMENTS.WITHOUT                          -2.484394e-01\n234     1           COMMENTS.WORK                           2.019138e+00\n235     1          COMMENTS.WORTH                          -7.183833e-01\n236     1          COMMENTS.WRITE                           1.345441e+00\n237     1          COMMENTS.YEARS                          -1.262577e-02\n238     1              COUNTRY_ID                           1.161457e-02\n239     1            COUNTRY_NAME                Argentina -4.889858e-01\n240     1            COUNTRY_NAME                Australia -2.072921e-01\n241     1            COUNTRY_NAME                   Brazil -1.530563e-02\n242     1            COUNTRY_NAME                   Canada  1.204957e+00\n243     1            COUNTRY_NAME                    China -6.830581e-09\n244     1            COUNTRY_NAME                  Denmark -8.936292e-08\n245     1            COUNTRY_NAME                   France -1.964772e-02\n246     1            COUNTRY_NAME                  Germany  6.855999e-08\n247     1            COUNTRY_NAME                    Italy -3.491193e-01\n248     1            COUNTRY_NAME                    Japan  6.877611e-01\n249     1            COUNTRY_NAME              New Zealand -1.124046e-08\n250     1            COUNTRY_NAME                   Poland -1.197101e+00\n251     1            COUNTRY_NAME             Saudi Arabia  1.016800e+00\n252     1            COUNTRY_NAME                Singapore -2.899315e-01\n253     1            COUNTRY_NAME             South Africa -5.407039e-09\n254     1            COUNTRY_NAME                    Spain -2.319359e-08\n255     1            COUNTRY_NAME                   Turkey -2.843122e-01\n256     1            COUNTRY_NAME           United Kingdom  2.034596e-01\n257     1            COUNTRY_NAME United States of America -2.612826e-01\n258     1             CUST_GENDER                        F -2.892341e-01\n259     1             CUST_GENDER                        M  2.892341e-01\n260     1                 CUST_ID                          -1.100395e-08\n261     1       CUST_INCOME_LEVEL          A: Below 30,000 -4.512871e-01\n262     1       CUST_INCOME_LEVEL       B: 30,000 - 49,999  6.091472e-02\n263     1       CUST_INCOME_LEVEL       C: 50,000 - 69,999  3.616398e-03\n264     1       CUST_INCOME_LEVEL       D: 70,000 - 89,999 -1.113238e-01\n265     1       CUST_INCOME_LEVEL      E: 90,000 - 109,999  1.469854e-01\n266     1       CUST_INCOME_LEVEL     F: 110,000 - 129,999  9.803379e-02\n267     1       CUST_INCOME_LEVEL     G: 130,000 - 149,999  2.051645e-01\n268     1       CUST_INCOME_LEVEL     H: 150,000 - 169,999  1.467099e-01\n269     1       CUST_INCOME_LEVEL     I: 170,000 - 189,999  2.149220e-01\n270     1       CUST_INCOME_LEVEL     J: 190,000 - 249,999  1.060489e-01\n271     1       CUST_INCOME_LEVEL     K: 250,000 - 299,999 -2.932591e-01\n272     1       CUST_INCOME_LEVEL     L: 300,000 and above -1.265256e-01\n273     1     CUST_MARITAL_STATUS                  Divorc.  1.530503e-01\n274     1     CUST_MARITAL_STATUS                  Mabsent  8.595022e-02\n275     1     CUST_MARITAL_STATUS                   Mar-AF -1.686004e+00\n276     1     CUST_MARITAL_STATUS                  Married  5.556667e-01\n277     1     CUST_MARITAL_STATUS                   NeverM  3.552227e-02\n278     1     CUST_MARITAL_STATUS                   Separ.  4.439490e-01\n279     1     CUST_MARITAL_STATUS                  Widowed  4.118660e-01\n280     1               EDUCATION                     10th -3.078712e-02\n281     1               EDUCATION                     11th  4.073615e-01\n282     1               EDUCATION                     12th -7.347483e-01\n283     1               EDUCATION                  1st-4th -5.098099e-08\n284     1               EDUCATION                  5th-6th -7.582275e-02\n285     1               EDUCATION                  7th-8th  1.929119e-02\n286     1               EDUCATION                      9th  3.551544e-01\n287     1               EDUCATION                  < Bach. -1.698697e-01\n288     1               EDUCATION                  Assoc-A  1.202329e-01\n289     1               EDUCATION                  Assoc-V -1.949963e-01\n290     1               EDUCATION                    Bach.  1.818312e-01\n291     1               EDUCATION                  HS-grad -3.888345e-01\n292     1               EDUCATION                  Masters  1.866699e-01\n293     1               EDUCATION                      PhD  1.838773e-01\n294     1               EDUCATION                  Presch. -3.010442e-09\n295     1               EDUCATION                   Profsc  1.406404e-01\n296     1      FLAT_PANEL_MONITOR                          -4.404624e-02\n297     1    HOME_THEATER_PACKAGE                           1.088039e-01\n298     1          HOUSEHOLD_SIZE                        1 -1.864782e-01\n299     1          HOUSEHOLD_SIZE                        2 -2.452245e-01\n300     1          HOUSEHOLD_SIZE                        3  6.098773e-01\n301     1          HOUSEHOLD_SIZE                      4-5  1.535818e+00\n302     1          HOUSEHOLD_SIZE                      6-8 -1.488784e+00\n303     1          HOUSEHOLD_SIZE                       9+ -2.252090e-01\n304     1              OCCUPATION                        ? -7.146214e-01\n305     1              OCCUPATION                  Armed-F  7.339433e-01\n306     1              OCCUPATION                  Cleric.  5.597177e-01\n307     1              OCCUPATION                   Crafts -5.853525e-01\n308     1              OCCUPATION                    Exec.  6.592273e-01\n309     1              OCCUPATION                  Farming -6.575969e-01\n310     1              OCCUPATION                  Handler -7.896259e-01\n311     1              OCCUPATION                  House-s -2.320039e-08\n312     1              OCCUPATION                  Machine -2.884810e-01\n313     1              OCCUPATION                    Other -8.711211e-01\n314     1              OCCUPATION                    Prof.  6.961252e-01\n315     1              OCCUPATION                  Protec.  6.290432e-01\n316     1              OCCUPATION                    Sales  4.765324e-01\n317     1              OCCUPATION                  TechSup  6.514069e-01\n318     1              OCCUPATION                  Transp. -4.991971e-01\n319     1        OS_DOC_SET_KANJI                          -2.419619e-07\n320     1           YRS_RESIDENCE                           2.274925e-01\n321     1             Y_BOX_GAMES                          -4.139935e-01\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","?ore.odmSVM"],"enabled":true,"result":{"startTime":1736279382460,"interpreter":"r.low","endTime":1736279383182,"results":[{"message":"ore.odmSVM                package:OREdm                R Documentation\n\nIn-Database Support Vector Machine Models\n\nDescription:\n\n     Fits a support vector machine using Oracle Data Mining to be used\n     for regression, classification, or anomaly detection.\n\nUsage:\n\n     ore.odmSVM(formula,                        \n             data, \n             type,\n             auto.data.prep = TRUE,\n             class.priors = NULL,\n             active.learning = TRUE,\n             complexity.factor = \"system.determined\", \n             conv.tolerance = 0.0001, \n             epsilon = \"system.determined\", \n             cache.size = 50000000, \n             kernel.function = \"system.determined\", \n             std.dev = \"system.determined\", \n             outlier.rate = 0.1,\n             na.action = na.pass,\n             odm.settings = NULL,\n             ctx.settings = NULL)\n     \n     ## S3 method for class 'ore.odmSVM'\n     predict(object,\n       newdata,                \n       supplemental.cols = NULL,\n       type = c(\"class\",\"raw\"),\n       na.action = na.pass,\n       bestN = NULL,\n       topN.attrs = FALSE,...)\n     \n     ## S3 method for class 'ore.odmSVM'\n     coef(object,...)\n     \n     ## S3 method for class 'ore.odmSVM'\n     print(x,...)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. The details of model specification are given under\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\n    type: For \u2018ore.odmSVM\u2019, the type of SVM model: \u2018\"classification\"\u2019,\n          \u2018\"regression\"\u2019, or \u2018\"anomaly.detection\"\u2019. For \u2018predict\u2019, if\n          set to \u2018\"raw\"\u2019, provides probability for each class returned.\n          If set to \u2018\"class\"\u2019, the class with the maximum probability\n          is returned.\n\n   bestN: A positive integer that restricts the returned target classes\n          to the specified number of those that have the highest\n          probability.\n\ntopN.attrs: boolean, positive integer, False (default).  Returns the\n          top N most influence attributes of the predicted target value\n          for regression if topN_attrs is not False.  Returns the top N\n          most influence attributes of the highest probability class\n          for classification if topN_attrs is not False.  N is equal to\n          the specified positive integer or 5 if topN_attrs is True.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build. The default value is \u2018TRUE\u2019.\n\nclass.priors: An optional named numerical vector that specifies the\n          priors for the target classes. The names of the vector are\n          the possible target classes. The default value is \u2018NULL\u2019\n\nactive.learning: A logical value that specifies whether to use active\n          learning. The default value is \u2018TRUE\u2019.\n\ncomplexity.factor: The complexity factor. The value must be a positive\n          number or \"system.determined\". The default value is\n          \"system.determined\".\n\nconv.tolerance: The convergence tolerance. The value must be positive.\n          The default value is 0.001.\n\n epsilon: The regularization setting for regression, similar to the\n          complexity factor. Epsilon specifies the allowable residuals,\n          or noise, in the data. The value must be a positive number or\n          \"system.determined\". The default is \"system.determined\".\n\ncache.size: If kernel.function is \"gaussian\", the setting for the\n          kernel cache size (bytes). The default value is 50,000,000.\n\nkernel.function: The setting for the kernel function. It can be\n          \"gaussian\", \"linear\", or \"system.determined\". The default\n          value is \"system.determined\".\n\n std.dev: If the kernel.function is \"gaussian\", the setting for the\n          standard deviation for the SVM Gaussian kernel. The Value\n          must be a positive number or \"system.determined\". The default\n          value is \"system.determined\".\n\noutlier.rate: For an anomaly detection model, the setting for desired\n          rate of outliers in the training data. A value in the range\n          of 0 to 1. The default value is 0.1.\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\nctx.settings: Same as \u2018ctx.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\n  object: An object of type \u2018ore.odmSVM\u2019.\n\n       x: An object of type \u2018ore.odmSVM\u2019.\n\n newdata: The data used for prediction.\n\nsupplemental.cols: The columns from \u2018newdata\u2019 to be included as the\n          columns in the \u2018ore.frame\u2019 prediction result.\n\n     ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n     This function invokes the Oracle Data Mining SVM implementation\n     that supports classification, regression, and anomaly detection\n     (one-class classification) with linear or Gaussian kernels and an\n     automatic and efficient estimation of the complexity factor (C)\n     and standard deviation (sigma).\n\n     Support Vector Machines (SVMs) for regression utilizes an\n     epsilon-insensitive loss function and works particularly well for\n     high-dimensional noisy data. The implementation also supports\n     \"active learning\" which forces the SVM algorithm to restrict\n     learning to the most informative training examples and not to\n     attempt to use the entire body of data. In most cases, the\n     resulting models have predictive accuracy comparable to that of a\n     standard (exact) SVM model. Active learning provides a significant\n     improvement in both linear and Gaussian SVM models, whether for\n     classification, regression, or anomaly detection. However, active\n     learning is especially advantageous when using the Gaussian\n     kernel, because nonlinear models can otherwise grow to be very\n     large and can place considerable demands on memory and other\n     system resources.\n\n     The SVM algorithm operates natively on numeric attributes. The\n     function automatically \"explodes\" categorical data into a set of\n     binary attributes, one per category value. For example, a\n     character column for marital status with values married or single\n     would be transformed to two numeric attributes: married and\n     single. The new attributes could have the value 1 (true) or 0\n     (false). When there are missing values in columns, SVM interprets\n     them as missing at random. The algorithm automatically replaces\n     missing categorical values with the mode and missing numerical\n     values with the mean. SVM requires the normalization of numeric\n     input. Normalization places the values of numeric attributes on\n     the same scale and prevents attributes with a large original scale\n     from biasing the solution. Normalization also minimizes the\n     likelihood of overflows and underflows. Furthermore, normalization\n     brings the numerical attributes to the same scale (0,1) as the\n     exploded categorical data. The SVM algorithm automatically handles\n     missing value treatment and the transformation of categorical\n     data, but normalization and outlier detection must be handled\n     manually.\n\n     The \u2018formula\u2019 specification has the form \u2018response ~ terms\u2019 where\n     \u2018response\u2019 is the numeric or character response vector and \u2018terms\u2019\n     is a series of terms, for example, the column names, to include in\n     the model. Multiple terms are specified using \u2018+\u2019 between column\n     names.  Use \u2018response ~ .\u2019 if all columns in \u2018data\u2019 should be used\n     for model building.  Functions can be applied to \u2018response\u2019 and\n     \u2018terms\u2019 to realize transformations. To exclude columns, use \u2018-\u2019\n     before each column name to exclude.\n\n     The function \u2018predict\u2019 computes predictions based on the input\n     data and model.\n\nValue:\n\n     The function \u2018ore.odmSVM\u2019 returns an object of class \u2018ore.odmSVM\u2019\n     including the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of predictor columns used to build the\n          model. The columns include: name, type (numerical or\n          categorical), data type, data length (size), precision and\n          scale for numeric data, and whether the variable is the\n          target.\n\nfit.values: An \u2018ore.frame\u2019 of the actual column and predicted column.\n          For regression, the columns are 'ACTUAL' and 'PREDICTED'. For\n          classification, the columns are\n          'ACTUAL','PREDICTED','PROBABILITY'. For anomaly detection,\n          the columns are 'PREDICTED' and 'PROBABILITY'.\n\nresiduals: For regression models, an \u2018ore.numeric\u2019 vector containing\n          the residual values (PREDICTED - ACTUAL).\n\n formula: The \u2018formula\u2019 used for the symbolic description of the model\n          fitted.\n\n    call: The matched call.\n     If the model built uses a linear kernel, the following is\n     additionally returned:\n\ncoefficients: The coefficients of the SVM model, one for each predictor\n          variable.  If \u2018auto.data.prep\u2019 is set to \u2018TRUE\u2019, these\n          coefficients will be in the transformed space (after\n          automatic outlier-aware normalization is applied).\n     The function \u2018predict\u2019 returns an \u2018ore.frame\u2019 with columns\n     according to the \u2018type\u2019 and \u2018supplemental.cols\u2019 parameters. For a\n     classification model, if \u2018type\u2019 is \u2018\"class\"\u2019, the result includes\n     the most likely target class and its probability. If \u2018type\u2019 is\n     \u2018\"raw\"\u2019, the result includes one column for each target class and\n     the column values reflect the probability for that class. Both can\n     be specified together. For a regression model, a column for the\n     prediction is included in the result with name \"PREDICTION\". If\n     \u2018supplemental.cols\u2019 are specified, the named columns are included\n     in the result. For an anomaly detection model, the result includes\n     a prediction and its probability. If the prediction is 1, the case\n     is considered typical. If the prediction is 0, the case is\n     considered anomalous. This behavior reflects the fact that the\n     model is trained with normal data.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     B. L. Milenova, J. S. Yarmus, and M. M. Campos. SVM in oracle\n     database 10g: removing the barriers to widespread adoption of\n     support vector machines. In Proceedings of the ''31st\n     international Conference on Very Large Data Bases'' (Trondheim,\n     Norway, August 30 - September 02, 2005). pp1152-1163,\n     ISBN:1-59593-154-6.\n\n     Milenova, B.L. Campos, M.M., Mining high-dimensional data for\n     information fusion: a database-centric approach 8th International\n     Conference on Information Fusion, 2005. Publication Date: 25-28\n     July 2005. ISBN: 0-7803-9286-8. John Shawe-Taylor & Nello\n     Cristianini. Support Vector Machines and other kernel-based\n     learning methods. Cambridge University Press, 2000.\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmGLM\u2019, \u2018ore.odmNB\u2019, \u2018ore.odmDT\u2019, \u2018partitions\u2019\n\nExamples:\n\n       x <- seq(0.1, 5, by = 0.02)\n       y <- log(x) + rnorm(x, sd = 0.2)\n       dat <-ore.push(data.frame(x=x, y=y))\n     \n       # Regression\n       svm.mod <- ore.odmSVM(y~x,dat,\"regression\",kernel.function=\"linear\")\n       summary(svm.mod)\n       coef(svm.mod)\n       svm.res <- predict(svm.mod,dat,supplemental.cols=\"x\")\n       head(svm.res,6)\n     \n       m <- mtcars\n       m$gear <- as.factor(m$gear)\n       m$cyl  <- as.factor(m$cyl)\n       m$vs   <- as.factor(m$vs)\n       m$ID   <- 1:nrow(m)\n       MTCARS <- ore.push(m)\n     \n       # Classification\n       gears.priors <- c(\"3\" = 0.2, \"4\" = 0.4, \"5\" = 0.4)\n       svm.mod  <- ore.odmSVM(gear ~ .-ID, MTCARS,\"classification\",\n                     class.priors=gears.priors)\n       summary(svm.mod)\n       coef(svm.mod)\n       svm.res  <- predict (svm.mod, MTCARS,\"gear\")\n       with(svm.res, table(gear,PREDICTION))  # generate confusion matrix\n     \n       # Anomaly Detection\n       svm.mod  <- ore.odmSVM(~ .-ID, MTCARS,\"anomaly.detection\")\n       summary(svm.mod)\n       svm.res  <- predict (svm.mod, MTCARS, \"ID\")\n       head(svm.res)\n       table(svm.res$PREDICTION)\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions on test dataset","message":["%r","","svm.res <- predict(svm.mod, MINING_DATA, type=c(\"class\",\"raw\"), supplemental.cols=\"AFFINITY_CARD\")","colnames(svm.res) <- c('PROBABILITY_OF_0', 'PROBABILITY_OF_1', 'AFFINITY_CARD','PREDICTION')","","z.show(head(svm.res))"],"enabled":true,"result":{"startTime":1736279383634,"interpreter":"r.low","endTime":1736279385975,"results":[{"message":"PROBABILITY_OF_0\tPROBABILITY_OF_1\tAFFINITY_CARD\tPREDICTION\n0.999538374745157\t0.000461625254843045\t0\t0\n0.999947202125606\t5.27978743941759e-05\t0\t0\n0.999996732193058\t3.26780694243436e-06\t0\t0\n0.997610310587985\t0.0023896894120149\t0\t0\n0.808025975747952\t0.191974024252048\t1\t0\n0.99983949141018\t0.000160508589820403\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generate confusion matrix ","message":["%r","","confusion_matrix2 <- with(svm.res, table(AFFINITY_CARD, PREDICTION))","confusion_matrix2"],"enabled":true,"result":{"startTime":1736279386385,"interpreter":"r.low","endTime":1736279388434,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 3285  143\n            1  211  861","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{}},{\"raw\":{}},{\"raw\":{}},{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":"Display ROC","message":["%r","","ans.df <- ore.pull(svm.res)  ","ans.df2 <- as.vector(ans.df[,1:2])","","sensitivity <- ans.df2[order(ans.df2$\"PROBABILITY_OF_1\"),]","","sens <- sum(sensitivity$\"PROBABILITY_OF_1\")/sum(sensitivity$\"PROBABILITY_OF_1\") - cumsum(sensitivity$\"PROBABILITY_OF_1\")/sum(sensitivity$\"PROBABILITY_OF_1\")","spec <- cumsum(sensitivity$\"PROBABILITY_OF_0\")/sum(sensitivity$\"PROBABILITY_OF_0\")","","plot(1 - spec, sens, type = \"l\", col = \"red\", ylab = \"Sensitivity\", xlab = \"1 - Specificity\", main = 'ROC Curve')","abline(c(0,0),c(1,1))","(auc <- sum(spec*diff(c(0, 1 - sens))))"],"enabled":true,"result":{"startTime":1736279388859,"interpreter":"r.low","endTime":1736279390999,"results":[{"message":"0.995223853293578\n","type":"HTML"},{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg\nAElEQVR4nOzdeVyVZf7/8Q8iAgriik64tFiaueauoeKWBwFRxC0VxS0tCSuz7Dej9P02pTXj\nkLmvmYILCMpmmLKKa5nikuZaIqgpKosc9Hh+f5y+DJkiIHCdc3g9H/OYx7kXbt9S4dvruu/r\nttDr9QIAAADTV0V1AAAAAJQNih0AAICZoNgBAACYCYodAACAmaDYAQAAmAmKHQAAgJmg2AEA\nAJgJih0AAICZoNgBAACYCYodAACAmaDYAQAAmAmKHQAAgJmg2AEAAJgJih0AAICZoNgBAACY\nCYodAACAmaDYAQAAmAmKHQAAgJmg2AEAAJgJih0AAICZoNgBAACYCYodAACAmaDYAQAAmAmK\nHQAAgJmg2AEAAJgJih0AAICZoNgBAACYCYodAACAmaDYAQAAmAmKHQAAgJmg2AEAAJgJih0A\nAICZoNgBAACYCYodAACAmaDYAQAAmAmKHQAAgJmg2AEAAJgJih0AAICZoNgBAACYiaqqAwAw\nT25ublFRUQ/ttLCwqF69uqOjY8uWLV1dXX18fGrUqPG4K+j1+piYmIiIiJSUlPT09Fu3btWo\nUaN+/fodOnTo37//qFGjbG1ti85w+vTp4ODg5OTkM2fO3Lx5Mz8/v2bNmi+88EKnTp1cXV1f\nf/11S0vLEv2myvyCAFC2LPR6veoMAMzQI4vdQ5ycnKKjo9u0afPXQ4cOHfL19T1+/PjjvtbR\n0XHBggU+Pj6PPJqRkTF9+vTw8PAifsQNGjQoMjKy6ITld0EAKA9MxQJQJi0tbdiwYffv339o\nf0hIyGuvvVZEqxORa9eujR8//r333vvrocOHD7dt2zYsLKzov7hmZ2cXM2eZXxAAygnFDkBF\n0Ov1er3+1q1b0dHRDRs2LNj/yy+/JCcnFz7zyJEjY8eOzc/PN2xaWlq+9957p06dysvLS0tL\nW7x4cZ06dQpO/ve//71s2bLCX37p0qVBgwZdu3atYE+7du02bdqUnp6en5+fkZERERExatSo\n4s+ZlvkFAaAc6QGgHAwaNOhxP2r+85//FD60fPnywkd79+5dxFG9Xn/s2LHq1asXnFCrVq1b\nt24VHB02bFjhLx86dKhWq/1rvNTU1JkzZxbnN1KKC2ZlZRX+kuDg4MJnenl5FRwaNGhQ4UMN\nGjQoOPTZZ5/p9fqoqKjevXvXrl1bRDw9PQuONmjQ4P79+w9lGDduXMEJLi4uhQ+lpaX94x//\n6NGjR7169aysrGrVqtW+fftZs2ZdvHixON8EAKaCYgegXBRR7Hbs2FH40IYNGwoOHTlypPCh\nrl27PvLi/+///b/Cp/3nP/8x7P/ll18K73dycsrKynqa30XpLvhQsdu0aVPho8Uvdv/5z38s\nLCwK9oSEhFSrVq1gc+fOnYW/9u7duzVr1nzkd3XRokXW1tbyKNWqVfvqq6+e5lsEwKgwFQug\nop07d67wZuvWrQs+x8bGFj40ZsyYR17hof0FX/XQ4xpTpkyxs7N7mqhlcsHC5az4Dh069N57\n7+kL3dVXt25dDw+Pgs2goKDC50dERNy5c8fw2cHBYejQoYbP//73v2fMmKHVah/5q+Tn5/v5\n+S1durQUCQEYIYodgIpz586dmJiY+fPnF+x5/fXXCz8Ve+LEicLnd+jQ4ZHXeemllwoXrILH\nLB4a8OvVq9dTBi6TC5au2G3btq1evXpbtmzJysq6dOnSP//5T2tr64kTJxacEBYWlpeXV7AZ\nHBxc8Hn06NGGtWAuXLjw4YcfFuz38fE5efLk3bt3z58/X/hSs2bNunr1ailCAjA2rGMHoCI8\nstz07dt38+bNhfdcv3698Gb9+vUfd7V69eoVPIX6+++/P/LLnZycSh24DC9YumJnYWERHh7e\ntWtXEbGzs/voo49E5MGDB40aNbp8+bKIZGVlRUREeHt7i8jt27ejo6MLvtbX19fwYfny5ffu\n3TN87tKly7p16wyfn3vuuZUrV+7Zs+fChQsikpOTs2nTpnfeeacUOQEYFUbsAKjx8ccff//9\n9w4ODkWcU0Ql0j9q5ZFH7nwaZX7B4uvevbuh1RVWpUqVwkv3FczGhoaGFky2tmnTpmPHjobP\nCQkJBScfOHDAopAqVaoYWl3B0fL4XQCoYBQ7AGp8+umnvr6+DzWnh4boCi8yUpher79x40bB\nZr169QwfHB0dC5+Wlpb2lCHL/ILF16VLl0fu9/X1Lei7MTExt27dkj/fb1d4jrX4gR8amwRg\noih2ACqCXq/Py8vbv39/4dvm1q5d+69//avwaa+88krhzR9//PGRVztz5kzh1YBbtWpl+NCu\nXbvCpxUeryqdMrnggwcPCm9mZmYW56vq1q37yP3PP/98wa1+Wq02JCQkIyMjPj7esKdatWpv\nvPFGKUIWzNgCMGkUOwAVxNraukuXLjt37ixcWQICAtLT0ws2BwwYUPhLNm7c+MhLPbS/4Kvc\n3NwK71+5cmVOTs7TZC7dBatU+dOP1tzc3MKb58+fL84v/dBFCiu4hU5EgoKCNm/erNPpDJue\nnp6Fv71/+9vfCj57e3sXsURCQTUEYNIodgAqVL169f7xj38UbGZnZ3/++ecFm+3atSv85GlK\nSsrq1asfusKJEyf+/e9/F2zWqlWr4LazZs2aFV5P+PLlyxMmTHjkWNTx48fffffdJ6Yt3QVt\nbW0Lv4ii8PIuu3fvvnjx4hN/3aINGzasYMm6hISEJUuWFBwq3PlEpPBqz999992vv/76lL80\nAGNX/kvlAaiMilig+O7du4VX4rW1tb169WrB0R9++MHGxqbgqKWl5axZs06fPq3Vaq9cubJ0\n6dKH5iiXLFlS+OIXL1586Ma4V199dcuWLRkZGfn5+VevXo2MjBw9erSlpWWvXr2K8xsp3QUL\nZodFpHbt2rGxsbm5ufHx8U2bNi18qSe+eeJxpk6d+tef540bN9bpdIVP++WXX6pW/e/qBy++\n+OKWLVuuXLmi1WrT0tJ+/PHHoKCgKVOmvPTSSxkZGcX5bgAwchQ7AOWiiGKn1+sXLFhQ+OgH\nH3xQ+OiWLVsKv2KhCI98LdihQ4cet05KYcUsdqW74CeffFKc/KUudo98iPXvf//7X88sPCBa\nhPT09GJ+NwAYM6ZiASgwbdq0OnXqFGwuXbr05s2bBZve3t5JSUkPPUjxkPr1669bt67wnGyB\njh07Hj161NPTs+gF5Ir/DolSXPC9995r27btX89xdXUdOHBgMX/dInTu3LnwoKCIWFhYTJgw\n4a9nzp49+z//+c/jXikGwMxQ7AAoYGdnV3g53KysrMDAwMIndO7cOTU1NSoqaurUqa1bt65b\nt27VqlUdHByaNWs2YsSIVatWXbp0qfCKbg/529/+FhYWdvLkyX/84x99+vRxcnKqXr161apV\n69at26lTp+nTp0dGRm7fvr34gUt6werVqycmJs6ePbtZs2bVqlWrVauWi4vLhg0bIiMja9So\nUZJv1WM9dDudi4vLc88998gz33nnnQsXLvzP//yPi4tLgwYNqlWrVqNGjWeffbZjx47jxo1b\ntWrVmTNnGjZsWCapAKhloVe3/CYAAADKECN2AAAAZoJiBwAAYCYodgAAAGaCYgcAAGAmKHYA\nAABmgmIHAABgJih2AAAAZoJiBwAAYCYodgAAAGaCYgcAAGAmKHYAAABmgmIHAABgJih2AAAA\nZoJiBwAAYCYodgAAAGaCYgcAAGAmKHYAAABmgmIHAABgJih2AAAAZoJiBwAAYCYodgAAAGaC\nYgcAAGAmKHYAAABmgmIHAABgJih2AAAAZoJiBwAAYCYodgAAAGaCYgcAAGAmKHYAAABmgmIH\nAABgJih2AAAAZoJiBwAAYCYodgAAAGaCYgcAAGAmKHYAAABmgmIHAABgJih2AAAAZoJiBwAA\nYCaqqg5gAm7fvv3NN9/cvXtXdRAAAGAUbG1tfXx8HBwcVAd5GMXuyTZu3PjOO++oTgEAAIzI\npUuX/vWvf6lO8TCK3ZPdu3dPRFavXt22bVvVWQAAgDL5+fnz5s2LjY0VEUdHR9VxHoFiV1zN\nmzfv0KGD6hQAAECN7OxsLy+v2NjYnj17JiYm2tjYqE70CDw8AQAA8ATp6ek9e/aMjY2dMWPG\n//7v/6qO81gUOwAAgKKcPXvW2dn5p59+mjt37ldffVWlivHWJ6ZiAQAAHuvAgQPu7u6ZmZkr\nV66cOHGi6jhPQLEDAAB4tIiIiJEjR1pYWOzYsUOj0aiO82TGO5YIAACg0Lp164YOHWpvb5+Q\nkGASrU5MccQuNzc3JCQkOjo6NTX1+vXrubm51atXd3R0bNWqlaurq7e3t62treqMAADAtM2f\nP//DDz98/vnnd+7c+eKLL6qOU1wmVuzi4uLGjh2blpZWeGdOTs7169dPnDixefPmOXPmfPvt\nty4uLqoSAgAAk6bT6d5+++1ly5Z17NgxKirKONerexxTKnapqakajUar1TZr1szX17dLly5O\nTk42NjZ5eXlpaWkHDhxYs2bN2bNnNRrN4cOHW7VqpTovAAAwMXl5eWPHjg0JCenXr9+2bdvs\n7e1VJyoZUyp2n3zyiVar9fT03LRpk7W1deFDzZs379Onz7vvvjty5Mjw8PCAgICtW7eqygkA\nAEzRzZs3Bw8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PPnzzs7Ox85cmTu3Lmr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\n6tSpYcOG/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Lift Chart","message":["%r","","decile2 <- quantile(sens, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sens, col.names = c(\"sens\"))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","barplot(table(df_sens$decile), xlab = 'Decile', ylab = 'Actual Targets', main = 'Lift 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HbFramq68847QwgXXHBBx4wHAECykg27G264ISsr6+ab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\nAIBGUO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Distribution Chart ","message":["%r","","","dx <- density(ans.df2$\"PROBABILITY_OF_1\")","dx2 <- density(ans.df2$'PROBABILITY_OF_0')","","","plot(dx, lwd = 2, col = \"mistyrose\",","     main = \"Density\")","lines(dx2, lwd = 2, col = \"deepskyblue\")","","rug(jitter(ans.df2$'0'),col='red')","rug(jitter(ans.df2$'1'),col='blue')"],"enabled":true,"result":{"startTime":1736279392457,"interpreter":"r.low","endTime":1736279393006,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nsCMiIiIyif8PK/RvVz+QZvUAAAAASUVORK5CYII=","type":"IMG"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients","message":["%r","","z.show(head(summary(svm.mod)$coefficients), 10)"],"enabled":true,"result":{"startTime":1736279393437,"interpreter":"r.low","endTime":1736279393902,"results":[{"message":"class\tvariable\tvalue\testimate\n1\t(Intercept)\t \t-1.62574230149863\n1\tAGE\t \t-0.127623024456824\n1\tBOOKKEEPING_APPLICATION\t \t0.186725840207698\n1\tBULK_PACK_DISKETTES\t \t0.0996128191465153\n1\tCOMMENTS.10\t \t1.68130704137208\n1\tCOMMENTS.ABOUT\t \t1.45447938815445\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display summary","message":["%r","","summary(svm.mod)"],"enabled":true,"result":{"startTime":1736279394315,"interpreter":"r.low","endTime":1736279394852,"results":[{"message":"\nCall:\nore.odmSVM(formula = AFFINITY_CARD ~ ., data = MINING_DATA, type = \"classification\", \n    kernel.function = \"linear\", odm.settings = odm.settings, \n    ctx.settings = ctx.settings)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nodms.text.max.features                          3000\nodms.text.min.documents                            1\nodms.text.policy.name                     ESA_TXTPOL\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nkernel.function                               linear\n\nCoefficients: \n    class                variable                    value      estimate\n1       1             (Intercept)                          -1.625742e+00\n2       1                     AGE                          -1.276230e-01\n3       1 BOOKKEEPING_APPLICATION                           1.867258e-01\n4       1     BULK_PACK_DISKETTES                           9.961282e-02\n5       1             COMMENTS.10                           1.681307e+00\n6       1          COMMENTS.ABOUT                           1.454479e+00\n7       1        COMMENTS.ACCOUNT                          -1.814215e-01\n8       1        COMMENTS.ADDRESS                           1.502794e+00\n9       1     COMMENTS.ADVERTISED                          -6.586082e-01\n10      1       COMMENTS.AFFINITY                           6.848033e-01\n11      1          COMMENTS.AFTER                          -7.450179e-01\n12      1          COMMENTS.AGAIN                           1.349887e+00\n13      1        COMMENTS.ALREADY                          -1.256389e+00\n14      1             COMMENTS.AM                           1.300404e+00\n15      1        COMMENTS.AMAZING                          -1.112522e+00\n16      1       COMMENTS.ANYTHING                          -6.407385e-01\n17      1          COMMENTS.APPLY                           1.871425e+00\n18      1        COMMENTS.ARIZONA                          -2.543266e-03\n19      1            COMMENTS.ASK                          -4.967314e-01\n20      1        COMMENTS.ATTRACT                          -7.450179e-01\n21      1      COMMENTS.AUSTRALIA                           9.902330e-01\n22      1           COMMENTS.AWAY                          -1.512846e+00\n23      1         COMMENTS.BEFORE                          -1.262577e-02\n24      1         COMMENTS.BETTER                          -2.484394e-01\n25      1            COMMENTS.BIT                          -4.967314e-01\n26      1          COMMENTS.BRING                          -5.577263e-01\n27      1        COMMENTS.BROTHER                          -2.531650e-01\n28      1          COMMENTS.BUILD                          -7.183833e-01\n29      1           COMMENTS.BULK                          -8.784285e-02\n30      1            COMMENTS.BUY                          -1.155058e+00\n31      1           COMMENTS.CARD                           4.481219e-01\n32      1          COMMENTS.CARDS                          -4.450585e-01\n33      1         COMMENTS.CHANCE                           7.571360e-01\n34      1         COMMENTS.CHANGE                          -6.407385e-01\n35      1       COMMENTS.CHILDREN                          -4.967314e-01\n36      1          COMMENTS.CLOSE                          -7.183833e-01\n37      1     COMMENTS.COMPETITOR                           8.846138e-01\n38      1       COMMENTS.COMPLAIN                          -4.967314e-01\n39      1       COMMENTS.COMPUTER                          -1.989701e+00\n40      1       COMMENTS.CONSIDER                          -1.102146e+00\n41      1     COMMENTS.CONSIDERED                           9.902330e-01\n42      1    COMMENTS.CONVENIENCE                           7.571360e-01\n43      1           COMMENTS.COST                          -6.586082e-01\n44      1       COMMENTS.CUSTOMER                          -3.687383e+00\n45      1           COMMENTS.DEAL                          -8.393684e-01\n46      1           COMMENTS.DEAR                          -4.450585e-01\n47      1      COMMENTS.DELIGHTED                           1.569227e+00\n48      1           COMMENTS.DIDN                          -1.169533e+00\n49      1       COMMENTS.DIRECTLY                          -1.111771e-01\n50      1       COMMENTS.DISCOUNT                          -2.897657e-01\n51      1      COMMENTS.DISCOUNTS                          -3.474075e-01\n52      1  COMMENTS.DOCUMENTATION                          -1.290562e+00\n53      1            COMMENTS.DON                          -7.111719e-01\n54      1        COMMENTS.DRIVING                          -7.183833e-01\n55      1     COMMENTS.ELECTRONIC                          -8.393684e-01\n56      1       COMMENTS.ELIGIBLE                           7.571360e-01\n57      1           COMMENTS.ELSE                          -1.262577e-02\n58      1      COMMENTS.ELSEWHERE                          -1.512846e+00\n59      1            COMMENTS.END                          -7.639012e-01\n60      1         COMMENTS.ENOUGH                          -7.450179e-01\n61      1            COMMENTS.ETC                          -4.967314e-01\n62      1           COMMENTS.EVEN                           7.360543e-01\n63      1           COMMENTS.EVER                           9.902330e-01\n64      1          COMMENTS.EVERY                           7.589007e-01\n65      1       COMMENTS.EVERYONE                          -1.262577e-02\n66      1     COMMENTS.EVERYTHING                          -1.776251e-02\n67      1      COMMENTS.EXCELLENT                           1.871425e+00\n68      1      COMMENTS.EXPENSIVE                           2.641126e-02\n69      1            COMMENTS.FAR                          -7.183833e-01\n70      1           COMMENTS.FEEL                          -4.450585e-01\n71      1            COMMENTS.FEW                           9.902330e-01\n72      1        COMMENTS.FILLING                          -1.247638e-01\n73      1           COMMENTS.FLAT                           7.265587e-03\n74      1         COMMENTS.FORGET                           2.610031e+00\n75      1           COMMENTS.FORM                          -1.247638e-01\n76      1         COMMENTS.FRANCE                           1.502794e+00\n77      1           COMMENTS.FREE                           3.621015e-02\n78      1            COMMENTS.GET                           1.718393e+00\n79      1        COMMENTS.GETTING                           3.983573e-03\n80      1          COMMENTS.GIFTS                           9.902330e-01\n81      1       COMMENTS.GIMMICKS                           1.681307e+00\n82      1           COMMENTS.GIVE                          -2.927471e-01\n83      1         COMMENTS.GIVING                           1.054059e-01\n84      1          COMMENTS.GOING                          -1.247638e-01\n85      1           COMMENTS.GOOD                           2.583034e-01\n86      1          COMMENTS.GREAT                          -6.564111e-01\n87      1          COMMENTS.HAPPY                           6.222136e-01\n88      1         COMMENTS.HASSLE                          -5.577263e-01\n89      1          COMMENTS.HENCE                          -1.814215e-01\n90      1           COMMENTS.IDEA                           9.505355e-01\n91      1    COMMENTS.IMMEDIATELY                           1.569227e+00\n92      1        COMMENTS.IMPOSES                          -4.450585e-01\n93      1      COMMENTS.IMPRESSED                           1.013631e+00\n94      1       COMMENTS.INCLUDED                           9.909437e-01\n95      1    COMMENTS.INFORMATION                           1.054059e-01\n96      1       COMMENTS.INVASION                          -4.450585e-01\n97      1          COMMENTS.ITEMS                          -1.978529e-01\n98      1           COMMENTS.JUNK                           5.729744e-02\n99      1           COMMENTS.KEEP                           1.437738e+00\n100     1           COMMENTS.KNOW                           1.427841e-01\n101     1          COMMENTS.LARGE                           7.571360e-01\n102     1         COMMENTS.LARGER                           7.571360e-01\n103     1           COMMENTS.LAST                           8.807396e-02\n104     1           COMMENTS.LATE                           1.567307e+00\n105     1         COMMENTS.LATEST                          -6.586082e-01\n106     1            COMMENTS.LET                           2.442849e+00\n107     1           COMMENTS.LIKE                           1.827154e+00\n108     1           COMMENTS.LINE                          -1.152363e+00\n109     1           COMMENTS.LIST                          -1.163631e+00\n110     1         COMMENTS.LITTLE                           1.567307e+00\n111     1           COMMENTS.LIVE                           9.902330e-01\n112     1           COMMENTS.LOOK                          -1.814215e-01\n113     1            COMMENTS.LOT                          -6.249347e-01\n114     1          COMMENTS.LOUSY                          -2.212928e+00\n115     1           COMMENTS.LOVE                          -2.395614e+00\n116     1          COMMENTS.LOYAL                          -1.102146e+00\n117     1        COMMENTS.LOYALTY                          -1.814215e-01\n118     1              COMMENTS.M                           1.054059e-01\n119     1           COMMENTS.MADE                           1.871425e+00\n120     1           COMMENTS.MAIL                           5.729744e-02\n121     1         COMMENTS.MAKESE                          -7.183833e-01\n122     1        COMMENTS.MANAGER                          -8.691560e-01\n123     1        COMMENTS.MANUALS                          -1.111771e-01\n124     1           COMMENTS.MANY                          -7.873006e-01\n125     1     COMMENTS.MEMBERSHIP                           3.983573e-03\n126     1          COMMENTS.MODEL                          -6.586082e-01\n127     1        COMMENTS.MONITOR                           7.265587e-03\n128     1          COMMENTS.MONTH                          -5.031625e-01\n129     1           COMMENTS.MORE                          -2.185627e+00\n130     1         COMMENTS.MOSTLY                          -7.639012e-01\n131     1         COMMENTS.MOTHER                           1.008259e+00\n132     1          COMMENTS.MOUSE                          -6.561533e-02\n133     1           COMMENTS.MOVE                          -7.450179e-01\n134     1          COMMENTS.MOVED                           9.902330e-01\n135     1           COMMENTS.MUCH                          -1.561074e+00\n136     1           COMMENTS.NAME                          -1.163631e+00\n137     1           COMMENTS.NEED                          -1.026636e+00\n138     1          COMMENTS.NEVER                           2.299200e+00\n139     1            COMMENTS.NEW                          -5.302118e-01\n140     1            COMMENTS.NOW                           7.367830e-01\n141     1          COMMENTS.OFFER                          -1.713010e+00\n142     1       COMMENTS.OFFERING                           7.212928e-01\n143     1          COMMENTS.OFTEN                          -4.450585e-01\n144     1           COMMENTS.ONCE                           1.569227e+00\n145     1          COMMENTS.OTHER                           3.625238e+00\n146     1         COMMENTS.OTHERS                          -7.183833e-01\n147     1           COMMENTS.PADS                          -6.561533e-02\n148     1           COMMENTS.PAGE                          -1.247638e-01\n149     1           COMMENTS.PAIN                          -8.393684e-01\n150     1          COMMENTS.PANEL                           7.265587e-03\n151     1           COMMENTS.PAST                           3.983573e-03\n152     1       COMMENTS.PERSONAL                          -2.927471e-01\n153     1         COMMENTS.PLEASE                          -1.308324e+00\n154     1        COMMENTS.PLEASED                           1.569227e+00\n155     1          COMMENTS.PRICE                          -1.814215e-01\n156     1         COMMENTS.PRICES                           9.117379e-01\n157     1        COMMENTS.PRIVACY                          -2.565237e-01\n158     1       COMMENTS.PRODUCTS                          -4.492373e-01\n159     1        COMMENTS.PROGRAM                          -6.321187e-01\n160     1     COMMENTS.PROMOTIONS                          -1.256389e+00\n161     1       COMMENTS.PURCHASE                           1.364388e+00\n162     1      COMMENTS.PURCHASED                           3.871045e-01\n163     1      COMMENTS.PURCHASES                           1.953902e-01\n164     1       COMMENTS.QUANTITY                           7.571360e-01\n165     1      COMMENTS.QUESTIONS                          -4.967314e-01\n166     1         COMMENTS.RARELY                          -1.814215e-01\n167     1          COMMENTS.READY                          -7.450179e-01\n168     1       COMMENTS.RECEIVED                           3.983573e-03\n169     1       COMMENTS.RECENTLY                           6.280244e-01\n170     1         COMMENTS.RECORD                          -1.568567e+00\n171     1       COMMENTS.REMEMBER                          -5.104374e-01\n172     1         COMMENTS.REMOVE                          -1.163631e+00\n173     1        COMMENTS.RESPECT                           1.054059e-01\n174     1         COMMENTS.RETURN                          -8.393684e-01\n175     1            COMMENTS.RUN                           7.571360e-01\n176     1           COMMENTS.SALE                          -1.352366e+00\n177     1      COMMENTS.SATISFIED                           1.345441e+00\n178     1            COMMENTS.SAY                          -1.568567e+00\n179     1           COMMENTS.SEND                           1.216963e-01\n180     1        COMMENTS.SENDING                           2.877861e+00\n181     1          COMMENTS.SENSE                          -7.183833e-01\n182     1           COMMENTS.SENT                           1.569227e+00\n183     1        COMMENTS.SERVICE                          -1.102146e+00\n184     1        COMMENTS.SEVERAL                           1.345441e+00\n185     1           COMMENTS.SHIP                          -8.969018e-02\n186     1       COMMENTS.SHIPPING                           3.621015e-02\n187     1           COMMENTS.SHOP                          -1.878339e-01\n188     1        COMMENTS.SHOPPED                           1.569227e+00\n189     1       COMMENTS.SHOPPERS                          -7.450179e-01\n190     1       COMMENTS.SHOPPING                          -5.050761e-01\n191     1          COMMENTS.SHOPS                           3.625238e+00\n192     1         COMMENTS.SIGNED                          -5.402580e-01\n193     1         COMMENTS.SISTER                           1.415431e+00\n194     1          COMMENTS.SMALL                           7.571360e-01\n195     1          COMMENTS.SORRY                          -4.492373e-01\n196     1          COMMENTS.SPEAK                           3.983573e-03\n197     1       COMMENTS.SPECIALS                          -6.934605e-01\n198     1          COMMENTS.START                          -1.262577e-02\n199     1        COMMENTS.STARTED                          -1.776251e-02\n200     1        COMMENTS.STOPPED                          -8.393684e-01\n201     1          COMMENTS.STORE                          -1.495187e+00\n202     1         COMMENTS.STORES                           1.567307e+00\n203     1          COMMENTS.TEXAS                           5.174005e+00\n204     1          COMMENTS.THANK                           8.217133e-01\n205     1         COMMENTS.THANKS                           6.357547e-01\n206     1         COMMENTS.THINGS                          -7.639012e-01\n207     1          COMMENTS.THINK                           4.251874e-01\n208     1          COMMENTS.THREE                          -1.247638e-01\n209     1          COMMENTS.THREW                          -1.512846e+00\n210     1           COMMENTS.TIME                           5.143027e-01\n211     1          COMMENTS.TIMES                          -1.015547e+00\n212     1          COMMENTS.TIRED                           4.619163e-01\n213     1           COMMENTS.TOLD                           2.199960e+00\n214     1          COMMENTS.TRIED                          -8.393684e-01\n215     1        COMMENTS.UNHAPPY                           9.711367e-01\n216     1             COMMENTS.UP                           4.629424e-01\n217     1       COMMENTS.UPBUYING                          -7.639012e-01\n218     1        COMMENTS.UPGRADE                          -6.586082e-01\n219     1             COMMENTS.US                          -4.967314e-01\n220     1            COMMENTS.USE                           4.019312e-01\n221     1           COMMENTS.USED                          -1.567783e+00\n222     1           COMMENTS.USES                          -2.484394e-01\n223     1        COMMENTS.USUALLY                          -1.814215e-01\n224     1        COMMENTS.VARIETY                          -7.450179e-01\n225     1           COMMENTS.WANT                          -7.335794e-01\n226     1         COMMENTS.WANTED                           1.345441e+00\n227     1          COMMENTS.WASTE                          -1.247638e-01\n228     1            COMMENTS.WAY                          -7.450179e-01\n229     1           COMMENTS.WEEK                          -6.586082e-01\n230     1         COMMENTS.WEEKLY                          -6.934605e-01\n231     1          COMMENTS.WEREN                          -1.111771e-01\n232     1           COMMENTS.WISH                           1.054059e-01\n233     1        COMMENTS.WITHOUT                          -2.484394e-01\n234     1           COMMENTS.WORK                           2.019138e+00\n235     1          COMMENTS.WORTH                          -7.183833e-01\n236     1          COMMENTS.WRITE                           1.345441e+00\n237     1          COMMENTS.YEARS                          -1.262577e-02\n238     1              COUNTRY_ID                           1.161457e-02\n239     1            COUNTRY_NAME                Argentina -4.889858e-01\n240     1            COUNTRY_NAME                Australia -2.072921e-01\n241     1            COUNTRY_NAME                   Brazil -1.530563e-02\n242     1            COUNTRY_NAME                   Canada  1.204957e+00\n243     1            COUNTRY_NAME                    China -6.830581e-09\n244     1            COUNTRY_NAME                  Denmark -8.936292e-08\n245     1            COUNTRY_NAME                   France -1.964772e-02\n246     1            COUNTRY_NAME                  Germany  6.855999e-08\n247     1            COUNTRY_NAME                    Italy -3.491193e-01\n248     1            COUNTRY_NAME                    Japan  6.877611e-01\n249     1            COUNTRY_NAME              New Zealand -1.124046e-08\n250     1            COUNTRY_NAME                   Poland -1.197101e+00\n251     1            COUNTRY_NAME             Saudi Arabia  1.016800e+00\n252     1            COUNTRY_NAME                Singapore -2.899315e-01\n253     1            COUNTRY_NAME             South Africa -5.407039e-09\n254     1            COUNTRY_NAME                    Spain -2.319359e-08\n255     1            COUNTRY_NAME                   Turkey -2.843122e-01\n256     1            COUNTRY_NAME           United Kingdom  2.034596e-01\n257     1            COUNTRY_NAME United States of America -2.612826e-01\n258     1             CUST_GENDER                        F -2.892341e-01\n259     1             CUST_GENDER                        M  2.892341e-01\n260     1                 CUST_ID                          -1.100395e-08\n261     1       CUST_INCOME_LEVEL          A: Below 30,000 -4.512871e-01\n262     1       CUST_INCOME_LEVEL       B: 30,000 - 49,999  6.091472e-02\n263     1       CUST_INCOME_LEVEL       C: 50,000 - 69,999  3.616398e-03\n264     1       CUST_INCOME_LEVEL       D: 70,000 - 89,999 -1.113238e-01\n265     1       CUST_INCOME_LEVEL      E: 90,000 - 109,999  1.469854e-01\n266     1       CUST_INCOME_LEVEL     F: 110,000 - 129,999  9.803379e-02\n267     1       CUST_INCOME_LEVEL     G: 130,000 - 149,999  2.051645e-01\n268     1       CUST_INCOME_LEVEL     H: 150,000 - 169,999  1.467099e-01\n269     1       CUST_INCOME_LEVEL     I: 170,000 - 189,999  2.149220e-01\n270     1       CUST_INCOME_LEVEL     J: 190,000 - 249,999  1.060489e-01\n271     1       CUST_INCOME_LEVEL     K: 250,000 - 299,999 -2.932591e-01\n272     1       CUST_INCOME_LEVEL     L: 300,000 and above -1.265256e-01\n273     1     CUST_MARITAL_STATUS                  Divorc.  1.530503e-01\n274     1     CUST_MARITAL_STATUS                  Mabsent  8.595022e-02\n275     1     CUST_MARITAL_STATUS                   Mar-AF -1.686004e+00\n276     1     CUST_MARITAL_STATUS                  Married  5.556667e-01\n277     1     CUST_MARITAL_STATUS                   NeverM  3.552227e-02\n278     1     CUST_MARITAL_STATUS                   Separ.  4.439490e-01\n279     1     CUST_MARITAL_STATUS                  Widowed  4.118660e-01\n280     1               EDUCATION                     10th -3.078712e-02\n281     1               EDUCATION                     11th  4.073615e-01\n282     1               EDUCATION                     12th -7.347483e-01\n283     1               EDUCATION                  1st-4th -5.098099e-08\n284     1               EDUCATION                  5th-6th -7.582275e-02\n285     1               EDUCATION                  7th-8th  1.929119e-02\n286     1               EDUCATION                      9th  3.551544e-01\n287     1               EDUCATION                  < Bach. -1.698697e-01\n288     1               EDUCATION                  Assoc-A  1.202329e-01\n289     1               EDUCATION                  Assoc-V -1.949963e-01\n290     1               EDUCATION                    Bach.  1.818312e-01\n291     1               EDUCATION                  HS-grad -3.888345e-01\n292     1               EDUCATION                  Masters  1.866699e-01\n293     1               EDUCATION                      PhD  1.838773e-01\n294     1               EDUCATION                  Presch. -3.010442e-09\n295     1               EDUCATION                   Profsc  1.406404e-01\n296     1      FLAT_PANEL_MONITOR                          -4.404624e-02\n297     1    HOME_THEATER_PACKAGE                           1.088039e-01\n298     1          HOUSEHOLD_SIZE                        1 -1.864782e-01\n299     1          HOUSEHOLD_SIZE                        2 -2.452245e-01\n300     1          HOUSEHOLD_SIZE                        3  6.098773e-01\n301     1          HOUSEHOLD_SIZE                      4-5  1.535818e+00\n302     1          HOUSEHOLD_SIZE                      6-8 -1.488784e+00\n303     1          HOUSEHOLD_SIZE                       9+ -2.252090e-01\n304     1              OCCUPATION                        ? -7.146214e-01\n305     1              OCCUPATION                  Armed-F  7.339433e-01\n306     1              OCCUPATION                  Cleric.  5.597177e-01\n307     1              OCCUPATION                   Crafts -5.853525e-01\n308     1              OCCUPATION                    Exec.  6.592273e-01\n309     1              OCCUPATION                  Farming -6.575969e-01\n310     1              OCCUPATION                  Handler -7.896259e-01\n311     1              OCCUPATION                  House-s -2.320039e-08\n312     1              OCCUPATION                  Machine -2.884810e-01\n313     1              OCCUPATION                    Other -8.711211e-01\n314     1              OCCUPATION                    Prof.  6.961252e-01\n315     1              OCCUPATION                  Protec.  6.290432e-01\n316     1              OCCUPATION                    Sales  4.765324e-01\n317     1              OCCUPATION                  TechSup  6.514069e-01\n318     1              OCCUPATION                  Transp. -4.991971e-01\n319     1        OS_DOC_SET_KANJI                          -2.419619e-07\n320     1           YRS_RESIDENCE                           2.274925e-01\n321     1             Y_BOX_GAMES                          -4.139935e-01\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### End of Script"],"enabled":true,"result":{"startTime":1736279395268,"interpreter":"md.low","endTime":1736279395672,"results":[{"message":"<h3 id=\"end-of-script\">End of 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Time Series ESM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734449346176,"interpreter":"md.low","endTime":1734449346654,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data","","Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734449347105,"interpreter":"md.low","endTime":1734449347560,"results":[{"message":"<h2 id=\"forecasting-sales-using-exponential-smoothing-algorithm-for-time-series-data\">Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data<\/h2>\n<p>Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1734449348018,"interpreter":"md.low","endTime":1734449348474,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>"],"enabled":true,"result":{"startTime":1734449348940,"interpreter":"md.low","endTime":1734449349391,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and create proxy object","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(ESM_SH_DATA = \"select TIME_ID, AMOUNT_SOLD from SH.SALES\"))","ore.attach()"],"enabled":true,"result":{"startTime":1734449349853,"interpreter":"r.low","endTime":1734449355040,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the records","message":["%r","","nrow(ESM_SH_DATA)"],"enabled":true,"result":{"startTime":1734449355488,"interpreter":"r.low","endTime":1734449356093,"results":[{"message":"918843","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the data","message":["%r","","z.show(head(ESM_SH_DATA))"],"enabled":true,"result":{"startTime":1734449356554,"interpreter":"r.low","endTime":1734449357137,"results":[{"message":"TIME_ID\tAMOUNT_SOLD\n1998-01-20\t1205.99\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Options to use when creating Time Series models","message":["%md","","### Examples of possible setting overrides for ESM ","","If the user does not override the default settings, relevant settings are determined by the algorithm.","","A complete list of settings can be found in the product documentation:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F98DEE85-4223-4F92-962D-8AF34CE887E1\" target=\"_blank\">Algorithm Settings<\/a>","","* Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is `EXSM_INTERVAL_QTR`.","> EXSM_INTERVAL = 'EXSM_INTERVAL_QTR'","* Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future","> EXSM_PREDICTION_STEP = 4","* Specify the type of exponential smoothing model to be used. EXSM_SIMPLE, EXSM_SIMPLE_MULT, EXSM_HOLT, EXSM_HOLT_DMP, EXSM_MUL_TRND, EXSM_MULTRD_DMP, EXSM_SEAS_ADD, EXSM_SEAS_MUL, EXSM_HW, EXSM_HW_DMP, EXSM_HW_ADDSEA, EXSM_DHW_ADDSEA, EXSM_HWMT, EXSM_HWMT_DMP'","> EXSM_MODEL = 'EXSM_HOLT'","* Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.","> EXSM_SEASONALITY = 4","* Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.     ","> EXSM_SETMISSING = 'EXSM_MISS_AUTO'","* This setting only applies and must be provided when the time column has datetime type. It specifies how to generate the value of the accumulated time series from the input time series. It can take the folling values: EXSM_ACCU_TOTAL, EXSM_ACCU_STD, EXSM_ACCU_MAX, EXSM_ACCU_MIN, EXSM_ACCU_AVG, EXSM_ACCU_MEDIAN, EXSM_ACCU_COUNT","> EXSM_ACCUMULATE = 'EXSM_ACCU_TOTAL'","* This setting specifies the desired optimization criterion. The optimization criterion is useful as a diagnostic for comparing models' fit to the same data.","> EXSM_OPT_CRITERION = 'EXSM_OPT_CRIT_LIK'","* This setting specifies the length of the window used in computing the error metric average mean square error (AMSE).","> EXSM_NMSE = 1"],"enabled":true,"result":{"startTime":1734449357604,"interpreter":"md.low","endTime":1734449358087,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-esm\">Examples of possible setting overrides for ESM<\/h3>\n<p>If the user does not override the default settings, relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the product documentation:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F98DEE85-4223-4F92-962D-8AF34CE887E1\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is <code>EXSM_INTERVAL_QTR<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_INTERVAL = 'EXSM_INTERVAL_QTR'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_PREDICTION_STEP = 4<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the type of exponential smoothing model to be used. EXSM_SIMPLE, EXSM_SIMPLE_MULT, EXSM_HOLT, EXSM_HOLT_DMP, EXSM_MUL_TRND, EXSM_MULTRD_DMP, EXSM_SEAS_ADD, EXSM_SEAS_MUL, EXSM_HW, EXSM_HW_DMP, EXSM_HW_ADDSEA, EXSM_DHW_ADDSEA, EXSM_HWMT, EXSM_HWMT_DMP'<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_MODEL = 'EXSM_HOLT'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_SEASONALITY = 4<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_SETMISSING = 'EXSM_MISS_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>This setting only applies and must be provided when the time column has datetime type. It specifies how to generate the value of the accumulated time series from the input time series. It can take the folling values: EXSM_ACCU_TOTAL, EXSM_ACCU_STD, EXSM_ACCU_MAX, EXSM_ACCU_MIN, EXSM_ACCU_AVG, EXSM_ACCU_MEDIAN, EXSM_ACCU_COUNT<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_ACCUMULATE = 'EXSM_ACCU_TOTAL'<\/p>\n<\/blockquote>\n<ul>\n<li>This setting specifies the desired optimization criterion. The optimization criterion is useful as a diagnostic for comparing models' fit to the same data.<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_OPT_CRITERION = 'EXSM_OPT_CRIT_LIK'<\/p>\n<\/blockquote>\n<ul>\n<li>This setting specifies the length of the window used in computing the error metric average mean square error (AMSE).<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_NMSE = 1<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a Holt-Winters Model using quarterly setting","message":["%r","","ore.drop(model = 'ESM_SALES_FORECAST_1')","","settings = list(EXSM_INTERVAL='EXSM_INTERVAL_QTR',","                EXSM_PREDICTION_STEP=4,","                MODEL_NAME='ESM_SALES_FORECAST_1',","                case_id_column_name = \"TIME_ID\")","","MOD <- ore.odmESM(AMOUNT_SOLD~.,","                  ESM_SH_DATA, ","                  odm.settings= settings)","                  "],"enabled":true,"result":{"startTime":1734449358543,"interpreter":"r.low","endTime":1734449361832,"results":[{"message":"Warning message:\n\u201cThe created mining model ESM_SALES_FORECAST_1 is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a Holt-Winters Model with additional settings","message":["%r","","ore.drop(model = 'ESM_SALES_FORECAST_1')","","settings = list(EXSM_INTERVAL = 'EXSM_INTERVAL_QTR',","                EXSM_PREDICTION_STEP = 4,","                EXSM_MODEL = 'EXSM_HW',","                EXSM_SEASONALITY = 4,","                EXSM_ACCUMULATE = 'EXSM_ACCU_TOTAL',","                MODEL_NAME='ESM_SALES_FORECAST_1',","                case_id_column_name = \"TIME_ID\")","","MOD <- ore.odmESM(AMOUNT_SOLD~.,","                  ESM_SH_DATA, ","                  odm.settings= settings)"],"enabled":true,"result":{"startTime":1734449362285,"interpreter":"r.low","endTime":1734449364671,"results":[{"message":"Warning message:\n\u201cThe created mining model ESM_SALES_FORECAST_1 is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Review the details from the model proxy object","message":["%r","","summary(MOD)"],"enabled":true,"result":{"startTime":1734449365118,"interpreter":"r.low","endTime":1734449365994,"results":[{"message":"\nCall:\nore.odmESM(formula = AMOUNT_SOLD ~ ., data = ESM_SH_DATA, odm.settings = settings)\n\nSettings: \n                                               value\naccumulate                                accu.total\nconfidence.level                                 .95\ninterval                                interval.qtr\nmodel                                        winters\nnmse                                               3\noptimization.crit                       opt.crit.lik\nprediction.step                                    4\nseasonality                                        4\nsetmissing                                 miss.auto\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nPredictions: \n      CASE_ID   VALUE PREDICTION   LOWER    UPPER\n1  1998-01-01 6480684    6402751      NA       NA\n2  1998-04-01 5593994    5868258      NA       NA\n3  1998-07-01 6071823    6205873      NA       NA\n4  1998-10-01 5937414    5978511      NA       NA\n5  1999-01-01 6093747    6223665      NA       NA\n6  1999-04-01 4925472    5535877      NA       NA\n7  1999-07-01 5827050    5515472      NA       NA\n8  1999-10-01 5373679    5601171      NA       NA\n9  2000-01-01 5984889    5644292      NA       NA\n10 2000-04-01 5371731    5317402      NA       NA\n11 2000-07-01 6121239    5845169      NA       NA\n12 2000-10-01 6287647    5920688      NA       NA\n13 2001-01-01 6547097    6500126      NA       NA\n14 2001-04-01 6922468    5930069      NA       NA\n15 2001-07-01 7195999    7376867      NA       NA\n16 2001-10-01 7470898    7154088      NA       NA\n17 2002-01-01      NA    7819637 6484414  9154860\n18 2002-04-01      NA    7151013 5591487  8710540\n19 2002-07-01      NA    7907806 5843738  9971875\n20 2002-10-01      NA    7821557 5462865 10180250","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View forecast AMOUNT_SOLD values with confidence intervals","message":["%r","","modelDetails <- summary(MOD)","","PRED <- modelDetails$predictions"," ","z.show(ore.sort(PRED, by='CASE_ID', reverse=TRUE))"],"enabled":true,"result":{"startTime":1734449366469,"interpreter":"r.low","endTime":1734449367134,"results":[{"message":"CASE_ID\tVALUE\tPREDICTION\tLOWER\tUPPER\n2002-10-01\tNA\t7821557.48372126\t5462864.87682284\t10180250.0906197\n2002-07-01\tNA\t7907806.49948464\t5843737.67150902\t9971875.32746026\n2002-04-01\tNA\t7151013.3978671\t5591487.09771536\t8710539.69801883\n2002-01-01\tNA\t7819637.04128005\t6484414.34514081\t9154859.73741928\n2001-10-01\t7470897.51999978\t7154087.56307003\tNA\tNA\n2001-07-01\t7195998.63000038\t7376866.59596637\tNA\tNA\n2001-04-01\t6922468.39000042\t5930069.14451029\tNA\tNA\n2001-01-01\t6547097.44000016\t6500126.22615749\tNA\tNA\n2000-10-01\t6287646.9199998\t5920687.51408386\tNA\tNA\n2000-07-01\t6121239.28999967\t5845169.12679054\tNA\tNA\n2000-04-01\t5371730.92000025\t5317402.43073397\tNA\tNA\n2000-01-01\t5984889.48999955\t5644291.53430069\tNA\tNA\n1999-10-01\t5373678.6700003\t5601170.87040404\tNA\tNA\n1999-07-01\t5827050.15000002\t5515472.10288213\tNA\tNA\n1999-04-01\t4925471.62999991\t5535877.14128277\tNA\tNA\n1999-01-01\t6093747.20999972\t6223664.94716229\tNA\tNA\n1998-10-01\t5937413.7100013\t5978510.77964177\tNA\tNA\n1998-07-01\t6071823.10000107\t6205873.36036312\tNA\tNA\n1998-04-01\t5593994.14000076\t5868257.56109656\tNA\tNA\n1998-01-01\t6480684.00000114\t6402750.9034226\tNA\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"line\":{\"showSeries\":[\"VALUE\",\"PREDICTION\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"VALUE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"PREDICTION\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CASE_ID\",\"VALUE\",\"PREDICTION\",\"LOWER\",\"UPPER\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"line","title":"Chart forecasted AMOUNT_SOLD values with confidence intervals","message":["%r","","z.show(PRED)"],"enabled":true,"result":{"startTime":1734449367588,"interpreter":"r.low","endTime":1734449368092,"results":[{"message":"CASE_ID\tVALUE\tPREDICTION\tLOWER\tUPPER\n1998-01-01\t6480684.00000114\t6402750.9034226\tNA\tNA\n1998-04-01\t5593994.14000076\t5868257.56109656\tNA\tNA\n1998-07-01\t6071823.10000107\t6205873.36036312\tNA\tNA\n1998-10-01\t5937413.7100013\t5978510.77964177\tNA\tNA\n1999-01-01\t6093747.20999972\t6223664.94716229\tNA\tNA\n1999-04-01\t4925471.62999991\t5535877.14128277\tNA\tNA\n1999-07-01\t5827050.15000002\t5515472.10288213\tNA\tNA\n1999-10-01\t5373678.6700003\t5601170.87040404\tNA\tNA\n2000-01-01\t5984889.48999955\t5644291.53430069\tNA\tNA\n2000-04-01\t5371730.92000025\t5317402.43073397\tNA\tNA\n2000-07-01\t6121239.28999967\t5845169.12679054\tNA\tNA\n2000-10-01\t6287646.9199998\t5920687.51408386\tNA\tNA\n2001-01-01\t6547097.44000016\t6500126.22615749\tNA\tNA\n2001-04-01\t6922468.39000042\t5930069.14451029\tNA\tNA\n2001-07-01\t7195998.63000038\t7376866.59596637\tNA\tNA\n2001-10-01\t7470897.51999978\t7154087.56307003\tNA\tNA\n2002-01-01\tNA\t7819637.04128005\t6484414.34514081\t9154859.73741928\n2002-04-01\tNA\t7151013.3978671\t5591487.09771536\t8710539.69801883\n2002-07-01\tNA\t7907806.49948464\t5843737.67150902\t9971875.32746026\n2002-10-01\tNA\t7821557.48372126\t5462864.87682284\t10180250.0906197\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734449368549,"interpreter":"md.low","endTime":1734449369003,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/README.md b/machine-learning/notebooks-oml/r/README.md
similarity index 97%
rename from machine-learning/notebooks/r/README.md
rename to machine-learning/notebooks-oml/r/README.md
index 8cbc53e8..f9bca09e 100644
--- a/machine-learning/notebooks/r/README.md
+++ b/machine-learning/notebooks-oml/r/README.md
@@ -54,6 +54,6 @@ The specific denomination "OfficeHours_" in the name of the file indicates that
 
 [Oracle Machine Learning Notebooks](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/)
 
-#### Copyright (c) 2024 Oracle Corporation and/or its affilitiates.
+#### Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
 
 ###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)
diff --git a/machine-learning/notebooks-oml/rest/OML Batch Scoring REST.dsnb b/machine-learning/notebooks-oml/rest/OML Batch Scoring REST.dsnb
new file mode 100644
index 00000000..81778e03
--- /dev/null
+++ b/machine-learning/notebooks-oml/rest/OML Batch Scoring REST.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML Batch Scoring REST","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734648994180,"interpreter":"md.low","endTime":1734648994706,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Batch Scoring REST","","On Oracle Autonomous Database, data scientists can build machine learning models that reside in the database where, for example, they can be used immediately from SQL queries for scoring data, both in batch and for real-time applications. Oracle Machine Learning Services (OML Services) enables deploying those same models through REST endpoints hosted through Oracle Autonomous Database to enable both real-time, and now batch, scoring.","","The batch scoring capability complements OML Services scoring functionality for singleton and small batch scoring using synchronous REST APIs, where input data is provided as part of the payload and the output was returned in the response. Using asynchronous requests and specified as tables or views through Autonomous data, user can score large volume data. Both the data source and scoring results remain in the user's schema and scoring occurs directly in the database for optimal efficiency. ","","OML Services supports batch scoring for regression, classification, clustering, and feature extraction.","","### **Batch Scoring workflow**","","In this notebook we highlight the following features of batch scoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a batch scoring job","* View the details and output of the batch scoring job","* Update, disable, and delete a batch scoring job","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734648995167,"interpreter":"md.low","endTime":1734648995647,"results":[{"message":"<h1 id=\"oml-batch-scoring-rest\">OML Batch Scoring REST<\/h1>\n<p>On Oracle Autonomous Database, data scientists can build machine learning models that reside in the database where, for example, they can be used immediately from SQL queries for scoring data, both in batch and for real-time applications. Oracle Machine Learning Services (OML Services) enables deploying those same models through REST endpoints hosted through Oracle Autonomous Database to enable both real-time, and now batch, scoring.<\/p>\n<p>The batch scoring capability complements OML Services scoring functionality for singleton and small batch scoring using synchronous REST APIs, where input data is provided as part of the payload and the output was returned in the response. Using asynchronous requests and specified as tables or views through Autonomous data, user can score large volume data. Both the data source and scoring results remain in the user's schema and scoring occurs directly in the database for optimal efficiency.<\/p>\n<p>OML Services supports batch scoring for regression, classification, clustering, and feature extraction.<\/p>\n<h3 id=\"batch-scoring-workflow\"><strong>Batch Scoring workflow<\/strong><\/h3>\n<p>In this notebook we highlight the following features of batch scoring through the REST interface:<\/p>\n<ul>\n<li>Authenticate the database user and obtain a token<\/li>\n<li>Create a batch scoring job<\/li>\n<li>View the details and output of the batch scoring job<\/li>\n<li>Update, disable, and delete a batch scoring job<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-batch-scoring-examples.html\" target=\"_blank\">OML Batch Scoring<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Website<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demonstration<\/a>"],"enabled":true,"result":{"startTime":1734648996128,"interpreter":"md.low","endTime":1734648996602,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-batch-scoring-examples.html\" target=\"_blank\">OML Batch Scoring<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Website<\/a><\/li>\n<li><a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demonstration<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run batch scoring jobs through a REST interface via OML Services.","","If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become acquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Lab<\/a>.","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":{"startTime":1734648997060,"interpreter":"md.low","endTime":1734648997503,"results":[{"message":"<p>This notebook walks through the steps to run batch scoring jobs through a REST interface via OML Services.<\/p>\n<p>If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become acquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Lab<\/a>.\nThe <strong>cURL<\/strong> commands provided in this notebook are for illustrative purposes only. To run REST calls, use <strong>cURL<\/strong> in a Linux or Mac terminal or another REST client such as Postman.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' \\","           --header 'Accept: application/json' \\","           -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\",\"password\":\"'<yourpassword>'\"}' ","            \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning user management cloud service instance"],"enabled":true,"result":{"startTime":1734648997966,"interpreter":"md.low","endTime":1734648998424,"results":[{"message":"<p>To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' \\\n       --header 'Accept: application/json' \\\n       -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;,&quot;password&quot;:&quot;'&lt;yourpassword&gt;'&quot;}' \n        &quot;&lt;OML service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>OML service URL<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a batch scoring job ","message":["%md","","Now that you have an access token, you can send a request to OML Services to create a batch scoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a **POST** request. Below is a listing of the parameters that can be specified for a batch scoring job, divided into those that are required and those that are optional. ","","**Required**:","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, which is MODEL_SCORING for batch scoring jobs. ","* _inputData_ is the name of the table or view to read input for the batch scoring job.","* _outputData_ is the results table in the format {jobID}_{outputData} storing the batch scoring results.","* _modelId_ is the ID for the model used for scoring.","* _supplementalColumnNames_ is an array of columns from the input table or view that is used to identify rows in the output table.","","**Optional**:","* _jobStartDate_ is the start date and time for the job run. If not specified, the job runs immediately.","* _jobEndDate_ is the end date and time for the job run.","* _disableJob_ is a flag to disable the job at submission. If not set, the the job is enabled at submission.","* _maxRuns_ is the number of times the job will run according to the schedule.","* _inputSchemaName_ is the database schema that owns the input table or view. If not specified, the input schema will be the same as the user that obtained the token.","* _outputSchemaName_ is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.","* _jobDescription_ is a description of the job provided by the user.","* _jobServiceLevel_ is the database service level for the job, which can be LOW, MEDIUM, or HIGH. ","* _recompute_ is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _topN_ filters the results for classification by returning the highest N probabilities, or for clustering, returning the N most probable cluster assignments.","* _topNDetails_ provide prediction details contributing to the probability scores.","* _recompute_ is a flag to replace the results table specified by outputData."],"enabled":true,"result":{"startTime":1734648998879,"interpreter":"md.low","endTime":1734648999386,"results":[{"message":"<p>Now that you have an access token, you can send a request to OML Services to create a batch scoring job. This request is sent to the <em>/omlmod/v1/jobs<\/em> endpoint as a <strong>POST<\/strong> request. Below is a listing of the parameters that can be specified for a batch scoring job, divided into those that are required and those that are optional.<\/p>\n<p><strong>Required<\/strong>:<\/p>\n<ul>\n<li><em>jobName<\/em> specifies the name of the submitted job.<\/li>\n<li><em>jobType<\/em> specifies the type of job to be run, which is MODEL_SCORING for batch scoring jobs.<\/li>\n<li><em>inputData<\/em> is the name of the table or view to read input for the batch scoring job.<\/li>\n<li><em>outputData<\/em> is the results table in the format {jobID}_{outputData} storing the batch scoring results.<\/li>\n<li><em>modelId<\/em> is the ID for the model used for scoring.<\/li>\n<li><em>supplementalColumnNames<\/em> is an array of columns from the input table or view that is used to identify rows in the output table.<\/li>\n<\/ul>\n<p><strong>Optional<\/strong>:<\/p>\n<ul>\n<li><em>jobStartDate<\/em> is the start date and time for the job run. If not specified, the job runs immediately.<\/li>\n<li><em>jobEndDate<\/em> is the end date and time for the job run.<\/li>\n<li><em>disableJob<\/em> is a flag to disable the job at submission. If not set, the the job is enabled at submission.<\/li>\n<li><em>maxRuns<\/em> is the number of times the job will run according to the schedule.<\/li>\n<li><em>inputSchemaName<\/em> is the database schema that owns the input table or view. If not specified, the input schema will be the same as the user that obtained the token.<\/li>\n<li><em>outputSchemaName<\/em> is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.<\/li>\n<li><em>jobDescription<\/em> is a description of the job provided by the user.<\/li>\n<li><em>jobServiceLevel<\/em> is the database service level for the job, which can be LOW, MEDIUM, or HIGH.<\/li>\n<li><em>recompute<\/em> is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed.<\/li>\n<li><em>topN<\/em> filters the results for classification by returning the highest N probabilities, or for clustering, returning the N most probable cluster assignments.<\/li>\n<li><em>topNDetails<\/em> provide prediction details contributing to the probability scores.<\/li>\n<li><em>recompute<\/em> is a flag to replace the results table specified by outputData.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Initiate a batch scoring job","","Batch scoring jobs are initiated by sending a **POST** request to the */omlmod/v1/jobs* endpoint. The details for batch scoring are specified in **jobProperties**, including:","* batch scoring job name and type","* ID of the model used for scoring","* table name for storing the batch scoring job results","* supplemental column names to return with the scoring results to identify rows","* whether to replace the results table with each job run"],"enabled":true,"result":{"startTime":1734648999841,"interpreter":"md.low","endTime":1734649000293,"results":[{"message":"<h3 id=\"initiate-a-batch-scoring-job\">Initiate a batch scoring job<\/h3>\n<p>Batch scoring jobs are initiated by sending a <strong>POST<\/strong> request to the <em>/omlmod/v1/jobs<\/em> endpoint. The details for batch scoring are specified in <strong>jobProperties<\/strong>, including:<\/p>\n<ul>\n<li>batch scoring job name and type<\/li>\n<li>ID of the model used for scoring<\/li>\n<li>table name for storing the batch scoring job results<\/li>\n<li>supplemental column names to return with the scoring results to identify rows<\/li>\n<li>whether to replace the results table with each job run<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Regression","","Here is an example of a batch scoring job submission using a regression model. To start, you will need the model ID for the model used for scoring. You can get the model ID by sending a **GET** request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client. ","","This model was trained on the CUSTOMERS360 dataset to predict `YRS_RESIDENCE`, the model URI is `REGR_MOD`, and the model ID is `074f5554-fc75-4647-9392-a376056c1f03`.","","In addition to the job name and type, we specify an hourly job schedule with 50 maximum runs, the model ID and input data for scoring, and an output table to store the job results. We also return the target column *YRS_RESIDENCE* and the customer ID column *CUST_ID* with the predictions to identify rows, and set recompute to *true* to replace the results table with each run.","","Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response."],"enabled":true,"result":{"startTime":1734649000753,"interpreter":"md.low","endTime":1734649001197,"results":[{"message":"<h3 id=\"regression\">Regression<\/h3>\n<p>Here is an example of a batch scoring job submission using a regression model. To start, you will need the model ID for the model used for scoring. You can get the model ID by sending a <strong>GET<\/strong> request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client.<\/p>\n<p>This model was trained on the CUSTOMERS360 dataset to predict <code>YRS_RESIDENCE<\/code>, the model URI is <code>REGR_MOD<\/code>, and the model ID is <code>074f5554-fc75-4647-9392-a376056c1f03<\/code>.<\/p>\n<p>In addition to the job name and type, we specify an hourly job schedule with 50 maximum runs, the model ID and input data for scoring, and an output table to store the job results. We also return the target column <em>YRS_RESIDENCE<\/em> and the customer ID column <em>CUST_ID<\/em> with the predictions to identify rows, and set recompute to <em>true<\/em> to replace the results table with each run.<\/p>\n<p>Note that we are using <em>jq<\/em>, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"$<OML service URL>/omlmod/v1/deployment/REGR_MOD\" \\","       --header \"Authorization: Bearer $<token>\" | jq '.modelId'",""," returns: \"074f5554-fc75-4647-9392-a376056c1f03\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Submit batch scoring job ","message":["","$ curl -i -X POST \"<OML Serivce URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-23T20:05:22Z\",                   # job start date and time ","\t\t\t \"jobEndDate\":   \"2023-03-25T20:05:22Z\"                    # job end date and time","             \"repeatInterval\": \"FREQ=HOURLY\",                          # job frequency         ","             \"maxRuns\": \"50\"                                           # max runs within the schedule                       ","         },","         \"jobProperties\": {","             \"jobName\": \"REG_PRED1\",                                   # job name","             \"jobType\": \"MODEL_SCORING\",                               # job type; MODEL_SCORING","             \"modelId\": \"074f5554-fc75-4647-9392-a376056c1f03\",\t\t   # ID of the model used for scoring\t ","             \"inputData\": \"CUSTOMERS360\",                              # table or view to read input for the batch scoring job","             \"outputData\": \"REG_PRED_OUT1\",                            # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\",\"YRS_RESIDENCE\"],   # array of columns from the input data used to identify rows in the output","             \"topNDetails\": 2,                                         # flag to return the top N most important predictors and their weight","\t\t\t \"recompute\": \"true\"                                       # flag to determine whether to overwrite the results table","\t\t}","     }' | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"TextBox\",\"name\":\"omlservice\",\"displayName\":null,\"defaultValue\":\"\",\"argument\":null,\"isHidden\":false,\"isProgrammatic\":false},{\"type\":\"TextBox\",\"name\":\"token\",\"displayName\":null,\"defaultValue\":\"\",\"argument\":null,\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Job ID returned from job submssion","message":["%md","","","When your job is successfully submitted, you will receive a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":{"startTime":1734649001670,"interpreter":"md.low","endTime":1734649002107,"results":[{"message":"<p>When your job is successfully submitted, you will receive a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"jobId\": \"OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%2421FACAC2_B345_4485_B287_DBDADC3A1BC0\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the details of the submitted job","message":["%md","","You can view the details of your submitted regression batch scoring job with a GET request to the */omlmod/v1/jobs/{jobID}* endpoint, where **jobid** is the ID provided in response to the successful submission of your batch scoring job in the previous step. The returned details include the parameters provided when the job was submitted  and the default values for the optional parameters not specified upon submission."],"enabled":true,"result":{"startTime":1734649002574,"interpreter":"md.low","endTime":1734649003012,"results":[{"message":"<p>You can view the details of your submitted regression batch scoring job with a GET request to the <em>/omlmod/v1/jobs/{jobID}<\/em> endpoint, where <strong>jobid<\/strong> is the ID provided in response to the successful submission of your batch scoring job in the previous step. The returned details include the parameters provided when the job was submitted  and the default values for the optional parameters not specified upon submission.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL invocation to view job details","message":["","$ export jobid='OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0'  # save job ID to variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<job ID>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T20:05:22Z\",","      \"repeatInterval\": \"FREQ=HOURLY\",","      \"jobEndDate\": \"2023-03-25T20:05:22Z\",","      \"maxRuns\": 50","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"REG_PRED_OUT1\",","      \"jobDescription\": null,","      \"jobName\": \"REG_PRED1\",","      \"disableJob\": false,","      \"jobServiceLevel\": null,","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\",","        \"YRS_RESIDENCE\"","      ],","      \"topN\": null,","      \"recompute\": true,","      \"modelId\": \"074f5554-fc75-4647-9392-a376056c1f03\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T20:04:06.157516Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%2421FACAC2_B345_4485_B287_DBDADC3A1BC0\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T20:05:22Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobID}\\_{outputData}*. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example. ","","The regression predictions are returned in the *OML$PREDICTION* column along with the supplemental column names provided by the user. OML$DETAIL1 contains the most important column in prediction details, and OML$DETAILWEIGHT1 contains weight of the most important column in prediction details. OML$DETAIL2 contains the second most important column in prediction details, and OML$DETAILWEIGHT2 contains weight of the second most important column in prediction details. ","","","For help understanding the regression batch scoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html<\/a>"],"enabled":true,"result":{"startTime":1738083323966,"interpreter":"md.low","endTime":1738083327745,"results":[{"message":"<p>Once your job has run, you can view the results in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is <em>{jobID}_{outputData}<\/em>. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.<\/p>\n<p>The regression predictions are returned in the <em>OML$PREDICTION<\/em> column along with the supplemental column names provided by the user. OML$DETAIL1 contains the most important column in prediction details, and OML$DETAILWEIGHT1 contains weight of the most important column in prediction details. OML$DETAIL2 contains the second most important column in prediction details, and OML$DETAILWEIGHT2 contains weight of the second most important column in prediction details.<\/p>\n<p>For help understanding the regression batch scoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html<\/a><\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0_REG_PRED_OUT1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Classification","Here is an example of a classification batch scoring job submission using a neural network model with model URI `NN_MOD` and ID `35a97c7b-0ff4-4940-96f5-bfb29f64d223`. The model was trained on the CUSTOMERS360 dataset to predict the customers most likely to respond positively to an affinity card loyalty program. ","","In addition to the job name and type, we specify a daily job schedule with 4 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to *true* to replace the output table with each run. We return the target column *AFFINITY_CARD* and the customer ID column *CUST_ID* with the predictions to identify rows, and the top 2 predictions wiith details contributing to those predictions. ","","We also set the database service level to *MEDIUM*. Oracle Autonomous Database supports three service levels: HIGH, MEDIUM, and LOW. LOW is the default, which results in no parallelism, but you can change your database service level to MEDIUM or HIGH. The HIGH service levels gives the greatest parallelism but significantly limits the number of concurrent jobs, while the MEDIUM level enables some parallelism but allows greater concurrency for job processing. "],"enabled":true,"result":{"startTime":1734649004375,"interpreter":"md.low","endTime":1734649004815,"results":[{"message":"<h3 id=\"classification\">Classification<\/h3>\n<p>Here is an example of a classification batch scoring job submission using a neural network model with model URI <code>NN_MOD<\/code> and ID <code>35a97c7b-0ff4-4940-96f5-bfb29f64d223<\/code>. The model was trained on the CUSTOMERS360 dataset to predict the customers most likely to respond positively to an affinity card loyalty program.<\/p>\n<p>In addition to the job name and type, we specify a daily job schedule with 4 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to <em>true<\/em> to replace the output table with each run. We return the target column <em>AFFINITY_CARD<\/em> and the customer ID column <em>CUST_ID<\/em> with the predictions to identify rows, and the top 2 predictions wiith details contributing to those predictions.<\/p>\n<p>We also set the database service level to <em>MEDIUM<\/em>. Oracle Autonomous Database supports three service levels: HIGH, MEDIUM, and LOW. LOW is the default, which results in no parallelism, but you can change your database service level to MEDIUM or HIGH. The HIGH service levels gives the greatest parallelism but significantly limits the number of concurrent jobs, while the MEDIUM level enables some parallelism but allows greater concurrency for job processing.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"<OML Service URL>/omlmod/v1/deployment/NN_MOD\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'",""," returns: \"35a97c7b-0ff4-4940-96f5-bfb29f64d223\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submit batch scoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-13T21:10:23Z\",                  # job start date and time","\t\t\t \"jobEndDate\":   \"2023-03-17T21:10:23Z\",                  # job end date and time","             \"repeatInterval\": \"FREQ=DAILY\",                          # job frequency","             \"maxRuns\": \"4\"                                           # max runs within the schedule                     ","         },","         \"jobProperties\": {","             \"jobName\": \"NN_MOD1\",                                    # job name","             \"jobType\": \"MODEL_SCORING\",                              # job type; MODEL_SCORING","             \"modelId\": \"35a97c7b-0ff4-4940-96f5-bfb29f64d223\",\t\t  # ID of the model used for scoring\t ","             \"inputData\": \"CUSTOMERS360\",                             # table or view to read input for the batch scoring job","             \"outputData\": \"CLASS_PRED1\",                             # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\", \"AFFINITY_CARD\"], # array of columns from the input data used to identify rows in the output","             \"jobServiceLevel\": \"MEDIUM\",                             # The database service level for the job. The default is LOW.","\t\t\t \"topN\": 2,                                               # filters the results by returning the highest N probabilities","\t\t\t \"topNDetails\": 2,                                        # provides prediction details contributing to the probability scores","\t\t\t \"recompute\": \"true\"                                      # flag to determine whether to overwrite the result table ","\t\t}"," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","returns:","","{","  \"jobId\": \"OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24385E413C_7219_41A4_B5A2_DC6B91B3DD8E\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the details of the submitted job","message":["%md","","You can view the details of your submitted batch scoring job with a GET request to the */omlmod/v1/jobs/{jobID}* endpoint, where **jobId** is the ID provided in response to the successful submission of your batch scoring job in the previous step. The returned details include the parameters provided when the job was submitted  and the default values for the optional parameters not specified upon submission."],"enabled":true,"result":{"startTime":1734649005271,"interpreter":"md.low","endTime":1734649005721,"results":[{"message":"<p>You can view the details of your submitted batch scoring job with a GET request to the <em>/omlmod/v1/jobs/{jobID}<\/em> endpoint, where <strong>jobId<\/strong> is the ID provided in response to the successful submission of your batch scoring job in the previous step. The returned details include the parameters provided when the job was submitted  and the default values for the optional parameters not specified upon submission.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View the batch scoring job details","message":["","$ export jobid='OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E'   # save job ID to variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","Returns:","","{","  \"jobId\": \"OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T20:32:35Z\",","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2023-03-27T20:32:35Z\",","      \"maxRuns\": 4","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"CLASS_PRED1\",","      \"jobDescription\": null,","      \"jobName\": \"NN_MOD1\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"MEDIUM\",","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\",","        \"AFFINITY_CARD\"","      ],","      \"topN\": 2,","      \"recompute\": true,","      \"modelId\": \"35a97c7b-0ff4-4940-96f5-bfb29f64d223\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T20:28:55.233217Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24385E413C_7219_41A4_B5A2_DC6B91B3DD8E\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T20:32:35Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. ","","The paragraph below queries the output table associated with this example.  The supplemental column names provided by the user are returned, along with the classification predictions. The *OML$PREDICTION1* column contains the class with highest probability and the *OML$PROBABILITY1* column contains the probability for the top class. *OML$DETAIL1* contains the most important column in the prediction details, while the *OML$PROBABILITY2* column contains the probability for the second class, and *OML$DETAIL2* contains the second most import column in the prediction details. ","","For help understanding the classification batch scoring output table, refer to the documentation:","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html<\/a>"],"enabled":true,"result":{"startTime":1734649006176,"interpreter":"md.low","endTime":1734649006619,"results":[{"message":"<p>Once your job has run, you can view the results in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details.<\/p>\n<p>The paragraph below queries the output table associated with this example.  The supplemental column names provided by the user are returned, along with the classification predictions. The <em>OML$PREDICTION1<\/em> column contains the class with highest probability and the <em>OML$PROBABILITY1<\/em> column contains the probability for the top class. <em>OML$DETAIL1<\/em> contains the most important column in the prediction details, while the <em>OML$PROBABILITY2<\/em> column contains the probability for the second class, and <em>OML$DETAIL2<\/em> contains the second most import column in the prediction details.<\/p>\n<p>For help understanding the classification batch scoring output table, refer to the documentation:<\/p>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html');\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html<\/a><\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E_CLASS_PRED1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","### Clustering","","Here is an example of a K-Means clustering batch scoring job submission with model URI `KM_CLUS_MOD` and ID `4632a963-4340-4ca5-ba30-aff77a4b857a`. The model uses the CUSTOMERS360 dataset to predict the cluster membership by customer. ","","In addition to the job name and type, we specify a daily job schedule with 4 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to *true* to replace the results table with each run. We also return the customer ID column *CUST_ID* with the predictions to identify cluster membership, request the top 2 most probable cluster assignments and details contributing to the cluster assignment predictions."],"enabled":true,"result":{"startTime":1734649007075,"interpreter":"md.low","endTime":1734649007528,"results":[{"message":"<h3 id=\"clustering\">Clustering<\/h3>\n<p>Here is an example of a K-Means clustering batch scoring job submission with model URI <code>KM_CLUS_MOD<\/code> and ID <code>4632a963-4340-4ca5-ba30-aff77a4b857a<\/code>. The model uses the CUSTOMERS360 dataset to predict the cluster membership by customer.<\/p>\n<p>In addition to the job name and type, we specify a daily job schedule with 4 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to <em>true<\/em> to replace the results table with each run. We also return the customer ID column <em>CUST_ID<\/em> with the predictions to identify cluster membership, request the top 2 most probable cluster assignments and details contributing to the cluster assignment predictions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"<OML Service URL>/omlmod/v1/deployment/KM_CLUS_MOD\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'",""," returns: \"4632a963-4340-4ca5-ba30-aff77a4b857a\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submit batch scoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-23T20:58:46Z\",                  # job start date and time","\t\t\t \"jobEndDate\":   \"2023-03-28T20:58:46Z\",                  # job end date and time","             \"repeatInterval\": \"FREQ=DAILY\",                          # job frequency          ","             \"maxRuns\": \"4\"                                           # max runs within the schedule                 ","         },","         \"jobProperties\": {","             \"jobName\": \"KM_CLUS_MOD1\",                               # job name","             \"jobType\": \"MODEL_SCORING\",                              # job type; MODEL_SCORING","             \"modelId\": \"4632a963-4340-4ca5-ba30-aff77a4b857a\",\t\t  # ID of the model used for scoring\t ","             \"inputData\": \"CUSTOMERS360\",                             # table or view to read input for the batch scoring job","             \"outputData\": \"KM_CLUS_PRED1\",                           # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\"],                  # array of columns from the input data used to identify rows in the output","\t\t\t \"topN\": 2,                                               # filters the results by returning the N most probable cluster assignments","\t\t\t \"topNDetails\": 2,                                        # provides prediction details contributing to the probability scores","\t\t\t \"recompute\": \"true\"                                      # flag to determine whether to overwrite the result table ","\t\t}","     }' | jq\t   "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","returns:","","{","  \"jobId\": \"OML$65977733_A951_42CD_9E3D_5E800604EBDB\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/jobs/OML%2465977733_A951_42CD_9E3D_5E800604EBDB\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View the batch scoring job details","message":["","$ export jobid='OML$65977733_A951_42CD_9E3D_5E800604EBDB'    # save job ID to variable","","$ curl -X GET \"<omlservice>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$65977733_A951_42CD_9E3D_5E800604EBDB\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T20:58:46Z\",","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2023-03-28T20:58:46Z\",","      \"maxRuns\": 4","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"KM_CLUS_PRED1\",","      \"jobDescription\": null,","      \"jobName\": \"KM_CLUS_MOD1\",","      \"disableJob\": false,","      \"jobServiceLevel\": null,","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\"","      ],","      \"topN\": 2,","      \"recompute\": true,","      \"modelId\": \"4632a963-4340-4ca5-ba30-aff77a4b857a\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T20:54:53.248736Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%2465977733_A951_42CD_9E3D_5E800604EBDB\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T20:58:46Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.","","The paragraph below queries the output table associated with this example. The supplemental column names provided by the user are returned, along with the cluster ID predictions. The *OML$CLUSTER_ID1* column contains the cluster with highest probability, and the *OML$DETAIL1* column contains the most important column in the prediction details. *OML$CLUSTER_ID2* contains the cluster with 2nd highest probability, and the *OML$DETAIL2* column contains the 2nd most important column in the prediction details.","","","For help understanding the clustering batch scoring output table, refer to the documentation:","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html<\/a>"],"enabled":true,"result":{"startTime":1734649007990,"interpreter":"md.low","endTime":1734649008431,"results":[{"message":"<p>Once your job has run, you can view the results in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.<\/p>\n<p>The paragraph below queries the output table associated with this example. The supplemental column names provided by the user are returned, along with the cluster ID predictions. The <em>OML$CLUSTER_ID1<\/em> column contains the cluster with highest probability, and the <em>OML$DETAIL1<\/em> column contains the most important column in the prediction details. <em>OML$CLUSTER_ID2<\/em> contains the cluster with 2nd highest probability, and the <em>OML$DETAIL2<\/em> column contains the 2nd most important column in the prediction details.<\/p>\n<p>For help understanding the clustering batch scoring output table, refer to the documentation:<\/p>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html');\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html<\/a><\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$65977733_A951_42CD_9E3D_5E800604EBDB_KM_CLUS_PRED1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Feature Extraction","Here is an example of a feature extraction batch scoring job submission with model URI `NMF_MOD` and ID `555c4718-853b-4d56-8ef9-268fd87ac25b`.","","In addition to the job name and type, we specify a monthly job schedule with 2 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to *true* to replace the results table with each run. We also specify the *HIGH* database service level, a set of supplemental columns from the input data to return with the results for row identification and request the top 2 predictions wiith details contributing to those predictions."],"enabled":true,"result":{"startTime":1734649008891,"interpreter":"md.low","endTime":1734649009337,"results":[{"message":"<h3 id=\"feature-extraction\">Feature Extraction<\/h3>\n<p>Here is an example of a feature extraction batch scoring job submission with model URI <code>NMF_MOD<\/code> and ID <code>555c4718-853b-4d56-8ef9-268fd87ac25b<\/code>.<\/p>\n<p>In addition to the job name and type, we specify a monthly job schedule with 2 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to <em>true<\/em> to replace the results table with each run. We also specify the <em>HIGH<\/em> database service level, a set of supplemental columns from the input data to return with the results for row identification and request the top 2 predictions wiith details contributing to those predictions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"<omlservice>/omlmod/v1/deployment/NMF_MOD\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'",""," returns: \"555c4718-853b-4d56-8ef9-268fd87ac25b\""," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submit batch scoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-23T21:10:40Z\",                  # job start date and time","\t\t\t \"jobEndDate\":   \"2023-05-23T21:10:40Z\",                  # job end date and time","             \"repeatInterval\": \"FREQ=MONTHLY\",                        # job frequency         ","             \"maxRuns\": \"2\"                                           # max runs within the schedule                        ","         },","         \"jobProperties\": {","             \"jobName\": \"FE_MOD1\",                                    # job name","             \"jobType\": \"MODEL_SCORING\",                              # job type; MODEL_SCORING","             \"modelId\": \"555c4718-853b-4d56-8ef9-268fd87ac25b\",\t      # ID of the model used for scoring","             \"jobServiceLevel\": \"HIGH\",\t\t                          # database service level\t ","             \"inputData\": \"CUSTOMERS360\",                             # table or view to read input for the batch scoring job","             \"outputData\": \"FE_PRED1\",                                # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\",                   # array of columns from the input data used to identify rows in the output","                                        \"AFFINITY_CARD\", ","                                        \"CUST_CREDIT_LIMIT\",","                                        \"CUST_GENDER\"],","\t\t\t \"topN\": 2,                                               # filters the results by returning the top N features with the highest probability","\t\t\t \"topNDetails\": 2,                                        # provides prediction details contributing to the probability scores","\t\t\t \"recompute\": \"true\"                                      # flag to determine whether to overwrite the results table","\t\t}","     }' | jq\t  ","     "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","Returns:","","{","  \"jobId\": \"OML$D66F5559_CA57_4B85_B832_A21A26660725\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24D66F5559_CA57_4B85_B832_A21A26660725\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View batch scoring job details","message":["","$ export jobid='OML$D66F5559_CA57_4B85_B832_A21A26660725'  # save job ID to variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$D66F5559_CA57_4B85_B832_A21A26660725\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T21:10:40Z\",","      \"repeatInterval\": \"FREQ=MONTHLY\",","      \"jobEndDate\": \"2023-05-23T21:10:40Z\",","      \"maxRuns\": 2","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"FE_PRED1\",","      \"jobDescription\": null,","      \"jobName\": \"FE_MOD1\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"HIGH\",","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\",","        \"AFFINITY_CARD\",","        \"CUST_CREDIT_LIMIT\",","        \"CUST_GENDER\"","      ],","      \"topN\": 2,","      \"recompute\": true,","      \"modelId\": \"555c4718-853b-4d56-8ef9-268fd87ac25b\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T21:05:24.644571Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24D66F5559_CA57_4B85_B832_A21A26660725\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T21:10:40Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.","","The paragraph below queries the output table associated with this example. The supplemental column names provided by the user are returned, along with the predicted features. The *OML$FEATURE_ID1* column contains the features with highest probability and the *OML$WEIGHT1* column contains the the probability for the top weighted feature. *OML$FEATURE_ID2* contains the feature with 2nd highest probability, and the *OML$WEIGHT2* column contains the probability for the 2nd highest feature.","","For help understanding the feature extraction batch scoring output table, refer to the documentation: ","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html<\/a>"],"enabled":true,"result":{"startTime":1734649009813,"interpreter":"md.low","endTime":1734649010267,"results":[{"message":"<p>Once your job has run, you can view the results in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.<\/p>\n<p>The paragraph below queries the output table associated with this example. The supplemental column names provided by the user are returned, along with the predicted features. The <em>OML$FEATURE_ID1<\/em> column contains the features with highest probability and the <em>OML$WEIGHT1<\/em> column contains the the probability for the top weighted feature. <em>OML$FEATURE_ID2<\/em> contains the feature with 2nd highest probability, and the <em>OML$WEIGHT2<\/em> column contains the probability for the 2nd highest feature.<\/p>\n<p>For help understanding the feature extraction batch scoring output table, refer to the documentation:<\/p>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html');\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html<\/a><\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$D66F5559_CA57_4B85_B832_A21A26660725_FE_PRED1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Perform actions, update, and delete batch scoring jobs"],"enabled":true,"result":{"startTime":1734649010731,"interpreter":"md.low","endTime":1734649011168,"results":[{"message":"<h3 id=\"perform-actions-update-and-delete-batch-scoring-jobs\">Perform actions, update, and delete batch scoring jobs<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Perform an action on a batch scoring job","message":["%md","","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","For example, when your job has been successfuly submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","* _DISABLE_ disables the job. After an enabled job is disabled, it no longer runs according to its schedule.","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler runs the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example action that updates the job status to **DISABLED**. A successfully submitted job receives a 204 response with no body. "],"enabled":true,"result":{"startTime":1734649011633,"interpreter":"md.low","endTime":1734649012068,"results":[{"message":"<p>OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions, <strong>DISABLE<\/strong>, <strong>RUN<\/strong>, <strong>ENABLE<\/strong>, and <strong>STOP<\/strong>:<\/p>\n<p>For example, when your job has been successfuly submitted, its state is set to <strong>ENABLED<\/strong> by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as <strong>DISABLED<\/strong>. You can do this by sending a request to the <em>/omlmod/v1/jobs/{jobid}/action<\/em> endpoint.<\/p>\n<ul>\n<li><em>DISABLE<\/em> disables the job. After an enabled job is disabled, it no longer runs according to its schedule.<\/li>\n<li><em>ENABLE<\/em> enables a job. After a disabled job is enabled, the scheduler runs the job according to its schedule.<\/li>\n<li><em>RUN<\/em> will immediately run the job as a way to test a job or run it outside of its schedule.<\/li>\n<li><em>STOP<\/em> stops a currently running job.<\/li>\n<\/ul>\n<p>Note that jobs can be set to <strong>DISABLED<\/strong> at submission by setting the <em>disableJob<\/em> flag to <em>true<\/em>.<\/p>\n<p>Below is an example action that updates the job status to <strong>DISABLED<\/strong>. A successfully submitted job receives a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Update a batch scoring job","message":["%md","","After a batch scoring job is submitted, you have the option to update the job schedule. You can do this by sending a POST request with the updated options to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can updated for a batch scoring job:","","* _jobStartDate_","* _jobEndDate_","* _repeatInterval_","* _maxRuns_","","In the example request below, we update these fields to differ from the original parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body."],"enabled":true,"result":{"startTime":1734649012529,"interpreter":"md.low","endTime":1734649012969,"results":[{"message":"<p>After a batch scoring job is submitted, you have the option to update the job schedule. You can do this by sending a POST request with the updated options to the <em>/omlmod/v1/jobs/{jobid}<\/em> endpoint. The following parameters can updated for a batch scoring job:<\/p>\n<ul>\n<li><em>jobStartDate<\/em><\/li>\n<li><em>jobEndDate<\/em><\/li>\n<li><em>repeatInterval<\/em><\/li>\n<li><em>maxRuns<\/em><\/li>\n<\/ul>\n<p>In the example request below, we update these fields to differ from the original parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-28T21:00:00Z\",","\t\t\t \"jobEndDate\":   \"2023-03-30T21:00:00Z\",","             \"repeatInterval\": \"FREQ=DAILY\",                  ","             \"maxRuns\": \"2\"                                ","         }","\t   }' | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete a batch scoring job","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":{"startTime":1734649013423,"interpreter":"md.low","endTime":1734649013865,"results":[{"message":"<p>To delete a previously submitted job, send a DELETE request along with the Job ID to the <em>/omlmod/v1/jobs<\/em> endpoint.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -X DELETE \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"To reproduce the examples in this notebook...","message":["%md","---","## Reproducing these examples","---","If you would like to reproduce the examples in this notebook, the paragraph below creates the required database table, *CUSTOMERS360*.  This table is created by selecting columns from the *CUSTOMERS* and *SUPPLEMENTARY_DEMOGRAPHICS* tables which reside in the SH schema.","","The models used in this notebook were deployed using the OML Services REST API. You can download these models in serialized format here:","","[Regression model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/REGR_MOD.mod)","[Classification model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/NN_MOD.mod)","[K-Means clustering model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/KM_CLUS_MOD.mod)","[Feature Extraction (NMF) model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/NMF_MOD.mod)","","Refer to the OML Services REST API documentation for detailed instructions on deploying models: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html<\/a>."],"enabled":true,"result":{"startTime":1734649014317,"interpreter":"md.low","endTime":1734649014758,"results":[{"message":"<p>If you would like to reproduce the examples in this notebook, the paragraph below creates the required database table, <em>CUSTOMERS360<\/em>.  This table is created by selecting columns from the <em>CUSTOMERS<\/em> and <em>SUPPLEMENTARY_DEMOGRAPHICS<\/em> tables which reside in the SH schema.<\/p>\n<p>The models used in this notebook were deployed using the OML Services REST API. You can download these models in serialized format here:<\/p>\n<p><a href=\"https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/REGR_MOD.mod\">Regression model<\/a>\n<a href=\"https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/NN_MOD.mod\">Classification model<\/a>\n<a href=\"https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/KM_CLUS_MOD.mod\">K-Means clustering model<\/a>\n<a href=\"https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/NMF_MOD.mod\">Feature Extraction (NMF) model<\/a><\/p>\n<p>Refer to the OML Services REST API documentation for detailed instructions on deploying models: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html');\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html<\/a><\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the customers 360 table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;"],"enabled":true,"result":{"startTime":1734649015212,"interpreter":"script.low","endTime":1734649017796,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the CUSTOMERS360 table","message":["%sql","","SELECT * FROM CUSTOMERS360 ","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734649018244,"interpreter":"sql.low","endTime":1734649018740,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100100\tF\tMarried\t1959\tJ: 190,000 - 249,999\t10000\tMasters\t1\t4-5\tProf.\t4\t0\n100200\tM\tNeverM\t1983\tL: 300,000 and above\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100300\tM\tMarried\t1961\tG: 130,000 - 149,999\t10000\tBach.\t1\t3\tProf.\t4\t0\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\n100500\tM\tNeverM\t1983\tH: 150,000 - 169,999\t15000\tHS-grad\t0\t1\tCrafts\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1734649019191,"interpreter":"md.low","endTime":1734649019631,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Data Bias Detector REST","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734658187504,"interpreter":"md.low","endTime":1734658188031,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## OML Data Bias Detection","","Data bias refers to systematic errors or unfair patterns present in a dataset that can adversely impact machine learning models, inadvertently disadvantaging certain groups. Bias in data can occur when some ","elements of the data are overrepresented or overweighted, leading to skewed predictions. ","","The OML Services Data Bias Detector offers REST endpoints to create bias detection jobs and calculates three key metrics: Class Imbalance (CI), Statistical Parity (SP), and Conditional Demographic Disparity (CDD). ","These metrics assist in identifying data bias and provide insights without prescribing specific actions.","","**Metrics:**","","1. **Class Imbalance (CI):** Measures the distribution of training samples between groups. Positive values indicate one group has more samples, zero suggests balance, and negative values show the opposite.","","2. **Statistical Parity (SP):** Assesses whether positive outcomes are distributed proportionally among groups. Zero indicates no bias, positive values suggest one group is favored, and negative values indicate  opposite.","","3. **Conditional Demographic Disparity (CDD):** Identifies hidden biases across subgroups. Positive values indicate one group is favored, near zero suggests no disparity, and negative values show the opposite.","","The data bias detector provides quantitative measures of data bias without predefining a threshold for what is considered bias. Data bias assessment depends on the distinctive features of the data and the particular problem that is being addressed. You must set your own acceptable bias ","levels based on the goals you want to achieve.","","In this notebook, we will:","","- Load the Adult data set to a database table","- Analyze data bias using the OML Services REST API","","<br>","","The Adult dataset is a collection of demographic information from the 1994 US Census that's used to predict whether an individual makes more than $50,000 a year. It reflects societal biases that can affect data ","fairness. It's also known as the \"Census Income\" dataset.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1734658188500,"interpreter":"md.low","endTime":1734658189006,"results":[{"message":"<h2 id=\"oml-data-bias-detection\">OML Data Bias Detection<\/h2>\n<p>Data bias refers to systematic errors or unfair patterns present in a dataset that can adversely impact machine learning models, inadvertently disadvantaging certain groups. Bias in data can occur when some\nelements of the data are overrepresented or overweighted, leading to skewed predictions.<\/p>\n<p>The OML Services Data Bias Detector offers REST endpoints to create bias detection jobs and calculates three key metrics: Class Imbalance (CI), Statistical Parity (SP), and Conditional Demographic Disparity (CDD).\nThese metrics assist in identifying data bias and provide insights without prescribing specific actions.<\/p>\n<p><strong>Metrics:<\/strong><\/p>\n<ol>\n<li>\n<p><strong>Class Imbalance (CI):<\/strong> Measures the distribution of training samples between groups. Positive values indicate one group has more samples, zero suggests balance, and negative values show the opposite.<\/p>\n<\/li>\n<li>\n<p><strong>Statistical Parity (SP):<\/strong> Assesses whether positive outcomes are distributed proportionally among groups. Zero indicates no bias, positive values suggest one group is favored, and negative values indicate  opposite.<\/p>\n<\/li>\n<li>\n<p><strong>Conditional Demographic Disparity (CDD):<\/strong> Identifies hidden biases across subgroups. Positive values indicate one group is favored, near zero suggests no disparity, and negative values show the opposite.<\/p>\n<\/li>\n<\/ol>\n<p>The data bias detector provides quantitative measures of data bias without predefining a threshold for what is considered bias. Data bias assessment depends on the distinctive features of the data and the particular problem that is being addressed. You must set your own acceptable bias\nlevels based on the goals you want to achieve.<\/p>\n<p>In this notebook, we will:<\/p>\n<ul>\n<li>Load the Adult data set to a database table<\/li>\n<li>Analyze data bias using the OML Services REST API<\/li>\n<\/ul>\n<br>\n<p>The Adult dataset is a collection of demographic information from the 1994 US Census that's used to predict whether an individual makes more than $50,000 a year. It reflects societal biases that can affect data\nfairness. It's also known as the &quot;Census Income&quot; dataset.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Step 1: Get an access token","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", ","    \"password\":\"' <yourpassword>'\"}' \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning cloud service"],"enabled":true,"result":{"startTime":1734658189478,"interpreter":"md.low","endTime":1734658189943,"results":[{"message":"<h3 id=\"step-1-get-an-access-token\">Step 1: Get an access token<\/h3>\n<p>To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, \n&quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' &quot;&lt;OML service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>OML service URL<\/em> is the REST server portion of the Oracle Machine Learning cloud service<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" ","message":["%md","","### Step 2: Create a data bias detection job","","Now that you have a token, you can submit your data bias detection job. This request is sent to the */omlmod/v1/jobs* endpoint as a POST request. There are many parameters that can be specified for ","a job request, as shown below and divided into required and optional parameters.","","*Required:*","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, and is set to DATA_BIAS for data bias detection jobs. ","* _inputData_ is the name of the input table. ","* _outputData_ is the name of the output table. The results of the job will be written to a table named {jobId}_{ouputData}.","* _sensitiveFeatures_ is a list of features on which data bias detection and mitigation is measured.","* _outcomeColumn_ is the name of the feature in the input data that is the outcome of training a machine learning model.","* _outcomeThreshold_ represents a numerical value within the outcome column that splits the data into two groups: those with values >= outcomeThreshold and those with values < outcomeThreshold. Manditory if positiveOutcome is NULL.","* _positiveOutcome_ represents a value that indicates a favorable outcome for a particular group within the dataset. Mandatory if outcomeThreshold is NULL.","","*Optional:*","* _inputSchemaName_ is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.","* _replaceResultTable_ is a logical flag to determine if the result table will be replaced when the result table name already exists.","* _pairwiseMode_ is a logical flag to determine if the metrics are calculated between all possible group pairs within a feature or between one group vs. the remaining samples.","* _strata_ is a list of columns needed to calculate the Conditional Demographic Disparity (CDD). ","* _categoricalBinNum_ is the number of bins generated for categorical variables, both for sensitiveFeatures and strata.","* _numericalBinNum_ is the number of bins generated for numerical variables, both for sensitiveFeatures and strata.  ","* _disableJob_ is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.","* _jobFlag_ is the name of a JobFlag to be used to filter jobs.","* _jobDescription_ is a text description of the job.","* _jobServiceLevel_ is the Autonomous Database service level for the job, which can be LOW, MEDIUM, or HIGH."],"enabled":true,"result":{"startTime":1734658190409,"interpreter":"md.low","endTime":1734658190883,"results":[{"message":"<h3 id=\"step-2-create-a-data-bias-detection-job\">Step 2: Create a data bias detection job<\/h3>\n<p>Now that you have a token, you can submit your data bias detection job. This request is sent to the <em>/omlmod/v1/jobs<\/em> endpoint as a POST request. There are many parameters that can be specified for\na job request, as shown below and divided into required and optional parameters.<\/p>\n<p><em>Required:<\/em><\/p>\n<ul>\n<li><em>jobName<\/em> specifies the name of the submitted job.<\/li>\n<li><em>jobType<\/em> specifies the type of job to be run, and is set to DATA_BIAS for data bias detection jobs.<\/li>\n<li><em>inputData<\/em> is the name of the input table.<\/li>\n<li><em>outputData<\/em> is the name of the output table. The results of the job will be written to a table named {jobId}_{ouputData}.<\/li>\n<li><em>sensitiveFeatures<\/em> is a list of features on which data bias detection and mitigation is measured.<\/li>\n<li><em>outcomeColumn<\/em> is the name of the feature in the input data that is the outcome of training a machine learning model.<\/li>\n<li><em>outcomeThreshold<\/em> represents a numerical value within the outcome column that splits the data into two groups: those with values &gt;= outcomeThreshold and those with values &lt; outcomeThreshold. Manditory if positiveOutcome is NULL.<\/li>\n<li><em>positiveOutcome<\/em> represents a value that indicates a favorable outcome for a particular group within the dataset. Mandatory if outcomeThreshold is NULL.<\/li>\n<\/ul>\n<p><em>Optional:<\/em><\/p>\n<ul>\n<li><em>inputSchemaName<\/em> is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.<\/li>\n<li><em>replaceResultTable<\/em> is a logical flag to determine if the result table will be replaced when the result table name already exists.<\/li>\n<li><em>pairwiseMode<\/em> is a logical flag to determine if the metrics are calculated between all possible group pairs within a feature or between one group vs. the remaining samples.<\/li>\n<li><em>strata<\/em> is a list of columns needed to calculate the Conditional Demographic Disparity (CDD).<\/li>\n<li><em>categoricalBinNum<\/em> is the number of bins generated for categorical variables, both for sensitiveFeatures and strata.<\/li>\n<li><em>numericalBinNum<\/em> is the number of bins generated for numerical variables, both for sensitiveFeatures and strata.<\/li>\n<li><em>disableJob<\/em> is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.<\/li>\n<li><em>jobFlag<\/em> is the name of a JobFlag to be used to filter jobs.<\/li>\n<li><em>jobDescription<\/em> is a text description of the job.<\/li>\n<li><em>jobServiceLevel<\/em> is the Autonomous Database service level for the job, which can be LOW, MEDIUM, or HIGH.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Initiate bias detection job","message":["%md","","Here is an example of a data bias detection job using these parameters. The job start and end dates, job frequency, and maximum number of runs are specified in the **jobSchedule** property.","Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response.","","The details for data bias detection are specified in **jobProperties**, including:","* data bias detection job name and type","* sensitive features used to detect bias","* Autonomous Database service level","* table where the bias detection details will be saved","","The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes."," "],"enabled":true,"result":{"startTime":1734658191349,"interpreter":"md.low","endTime":1734658191811,"results":[{"message":"<p>Here is an example of a data bias detection job using these parameters. The job start and end dates, job frequency, and maximum number of runs are specified in the <strong>jobSchedule<\/strong> property.\nNote that we are using <em>jq<\/em>, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response.<\/p>\n<p>The details for data bias detection are specified in <strong>jobProperties<\/strong>, including:<\/p>\n<ul>\n<li>data bias detection job name and type<\/li>\n<li>sensitive features used to detect bias<\/li>\n<li>Autonomous Database service level<\/li>\n<li>table where the bias detection details will be saved<\/li>\n<\/ul>\n<p>The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to initiate bias detection job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"jobSchedule\": {","            \"jobStartDate\": \"2024-09-04T00:30:07Z\",                 # job start date and time ","            \"repeatInterval\": \"FREQ=DAILY\",                         # job frequency","            \"jobEndDate\": \"2024-09-09T20:50:06Z\",                   # job end date and time ","            \"maxRuns\": \"4\"                                          # max runs within the schedule","         },","         \"jobProperties\": {","            \"jobName\":\"DATA_BIAS_JOB_2\",                            # job name","            \"jobType\":\"DATA_BIAS\",                                  # job type; DATA_BIAS","            \"disableJob\":false,                                     # flag to disable the job at submission","            \"jobServiceLevel\":\"MEDIUM\",                             # Autonomous Database service level; either LOW, MEDIUM, and HIGH","            \"inputData\":\"ADULT\",\t\t\t                             # input table or view","            \"outputData\":\"DATA_BIAS_JOB_RESULT\",                    # table where the job results will be saved in the format {jobID}_{outputData}","            \"replaceResultTable\":\"True\"                             # flag to determine if the result, or output table should be replaced if it exists","            \"inputSchemaName\":\"OMLUSER\",                            # database schema that owns the input table/view","            \"outputSchemaName\":\"OMLUSER\",                           # database schema that owns the output table","            \"jobDescription\":\"Data bias detector job\",              # job description","            \"sensitiveFeatures\":[\"\\\"GENDER\\\"\"],                     # list of features on which data bias detection and mitigation is measured.","\t         \"strata\":[\"\\\"MARITAL_STATUS\\\"\"],                        # name of a variable required to calculate the Conditional Demographic Disparity (CDD).","\t         \"outcomeColumn\":\"INCOME\",                               # name of the feature in the input data that is the outcome of training a machine learning model","\t         \"positiveOutcome\":\">50K\",                               # value that indicates a favorable outcome for a particular group within the dataset","\t         \"categoricalBinNum\":6,                                  # number of bins for on categorical features for sensitiveFeatures and strata.","\t        \"numericalBinNum\":10}                                    # indicates whether to perform binning on numerical features for sensitive features and strata.","            }' | jq","            "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### A successful job submission returns a job ID","","When the job is successfully submitted, you will receive a response similar to the following. Note the **jobId**, as you will use it to submit requests such as retrieving job details or performing an action on the job."," "],"enabled":true,"result":{"startTime":1734658192284,"interpreter":"md.low","endTime":1734658192738,"results":[{"message":"<h3 id=\"a-successful-job-submission-returns-a-job-id\">A successful job submission returns a job ID<\/h3>\n<p>When the job is successfully submitted, you will receive a response similar to the following. Note the <strong>jobId<\/strong>, as you will use it to submit requests such as retrieving job details or performing an action on the job.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Job ID returned","message":["","{","  \"jobId\": \"OML$8017EE54_C305_47A5_9603_0EFA84B6B624\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%248017EE54_C305_47A5_9603_0EFA84B6B624\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Step 3: View the details of a submitted job","","You can now view the details of your submitted job with a GET request to the */omlmod/v1/jobs/{jobId}* endpoint, where **jobId** is the ID provided in response to the successful submission of your ","data bias detection job in the previous step. The **jobStatus** *CREATED* indicates the job as been created. If your job has already ran at least once, you will see infomation returned about the last ","job run. "," "],"enabled":true,"result":{"startTime":1734658193195,"interpreter":"md.low","endTime":1734658193652,"results":[{"message":"<h2 id=\"step-3-view-the-details-of-a-submitted-job\">Step 3: View the details of a submitted job<\/h2>\n<p>You can now view the details of your submitted job with a GET request to the <em>/omlmod/v1/jobs/{jobId}<\/em> endpoint, where <strong>jobId<\/strong> is the ID provided in response to the successful submission of your\ndata bias detection job in the previous step. The <strong>jobStatus<\/strong> <em>CREATED<\/em> indicates the job as been created. If your job has already ran at least once, you will see infomation returned about the last\njob run.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submitted job details","message":["","# Create a jobid variable using single quotes","$ export jobid='OML$8017EE54_C305_47A5_9603_0EFA84B6B624'","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq","","returns:","","{","  \"jobId\": \"OML$8017EE54_C305_47A5_9603_0EFA84B6B624\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2024-09-04T00:30:07Z\",                 ","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2024-09-09T20:50:06Z\",","      \"maxRuns\": 5","    },","    \"jobProperties\": {","      \"jobType\": \"DATA_BIAS\",","      \"inputSchemaName\": \"OMLUSER\",","      \"outputSchemaName\": \"OMLUSER\",","      \"outputData\": \"DATA_BIAS_JOB_RESULT\",","      \"jobDescription\": \"Data bias detector job\",","      \"jobName\": \"DATA_BIAS_JOB_2\",","      \"disableJob\": false,","      \"jobServiceLevel\": null,","      \"inputData\": \"ADULT\",","      \"sensitiveFeatures\": [\"\\\"GENDER\\\"\"],","      \"strata\": [\"\\\"MARITAL_STATUS\\\"\"],","      \"outcomeColumn\": \"INCOME\",","      \"outcomeThreshold\": null,","      \"positiveOutcome\": \">50K\",","      \"replaceResultTable\": true,","      \"pairwiseMode\": null,","      \"categoricalBinNum\": 6,","      \"numericalBinNum\": 10","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2024-09-05T22:58:17.667544Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%248017EE54_C305_47A5_9603_0EFA84B6B624\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"lastStartDate\": \"2024-09-05T22:58:17.862008Z\",","  \"nextRunDate\": \"2024-09-06T22:58:17.86561Z\",","  \"runCount\": 1,","  \"lastRunDetail\": {","    \"jobRunStatus\": \"SUCCEEDED\",","    \"errorMessage\": null,","    \"requestedStartDate\": \"2024-09-05T22:58:17.818005Z\",","    \"actualStartDate\": \"2024-09-05T22:58:17.862138Z\",","    \"duration\": \"0 0:0:0.0\"","  }","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Step 4: Update a bias detection job (optional)","","After submitting an asynchronous job, you can modify it by sending a POST request to the */omlmod/v1/jobs/* endpoint with the Job ID. In the example below, the request updates specific **jobSchedule** parameters ","using the same job ID. The **maxRuns** parameter is set to 2, limiting the number of runs, while **repeatInterval** is updated to HOURLY.","","A successful update will return an HTTP 204 response with no content."," "],"enabled":true,"result":{"startTime":1734658194115,"interpreter":"md.low","endTime":1734658194560,"results":[{"message":"<h3 id=\"step-4-update-a-bias-detection-job-optional\">Step 4: Update a bias detection job (optional)<\/h3>\n<p>After submitting an asynchronous job, you can modify it by sending a POST request to the <em>/omlmod/v1/jobs/<\/em> endpoint with the Job ID. In the example below, the request updates specific <strong>jobSchedule<\/strong> parameters\nusing the same job ID. The <strong>maxRuns<\/strong> parameter is set to 2, limiting the number of runs, while <strong>repeatInterval<\/strong> is updated to HOURLY.<\/p>\n<p>A successful update will return an HTTP 204 response with no content.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Update job ","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>\" \\    ","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"jobSchedule\": {","            \"maxRuns\": \"2\",","            \"repeatInterval\": \"FREQ=HOURLY\"","            }","         }'","   "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### View data bias detection job output","","Once your job has run, either according to its schedule or by the **RUN** action, you can view its output in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobid}\\_{outputData}*. You can check if your job is finished by sending a request to view its details, as we did in step 3. If your job has run at least once you should see the **lastRunDetail** field with information on that run. The next paragraph queries the output table associated with this example. ","","For more information on the output table, refer to the <a href=\"https://docs.us.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-bias-detector-example.html\" target=\"_blank\">OML Services documentation.<\/a>"],"enabled":true,"result":{"startTime":1734658195014,"interpreter":"md.low","endTime":1734658195454,"results":[{"message":"<h3 id=\"view-data-bias-detection-job-output\">View data bias detection job output<\/h3>\n<p>Once your job has run, either according to its schedule or by the <strong>RUN<\/strong> action, you can view its output in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is <em>{jobid}_{outputData}<\/em>. You can check if your job is finished by sending a request to view its details, as we did in step 3. If your job has run at least once you should see the <strong>lastRunDetail<\/strong> field with information on that run. The next paragraph queries the output table associated with this example.<\/p>\n<p>For more information on the output table, refer to the <a href=\"https://docs.us.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-bias-detector-example.html\" target=\"_blank\">OML Services documentation.<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Query to view bias detection output ","message":["%sql","","SELECT * FROM OML$8017EE54_C305_47A5_9603_0EFA84B6B624_DATA_BIAS_JOB_RESULT"," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Query to view bias detection output - parsed JSON output without subgroups","message":["%sql","","WITH json_data AS (","    SELECT ","        JSON_QUERY(RESULT, '$.metric') AS Metric_Array,","        JSON_QUERY(RESULT, '$.cdd') AS CDD_Array,","        JSON_QUERY(RESULT, '$.reweighing_matrix') AS ReweighingMatrix_Array","    FROM ","        OML$8017EE54_C305_47A5_9603_0EFA84B6B624_DATA_BIAS_JOB_RESULT",")","SELECT ","    m.group_a AS metric_group_a,","    m.ci,","    m.sp,","    c.cdd,","    r.group_a_pos_weight,","    r.non_group_a_pos_weight,","    r.group_a_neg_weight,","    r.non_group_a_neg_weight","FROM ","    json_data,","    JSON_TABLE(json_data.Metric_Array, '$[*]'","        COLUMNS (","            group_a VARCHAR2(10) PATH '$.group_a',","            ci NUMBER PATH '$.CI',","            sp NUMBER PATH '$.SP'","        )","    ) m","JOIN ","    JSON_TABLE(json_data.CDD_Array, '$[*].result[*]'","        COLUMNS (","            group_a VARCHAR2(10) PATH '$.group_a',","            cdd NUMBER PATH '$.CDD'","        )","    ) c","ON m.group_a = c.group_a","JOIN ","    JSON_TABLE(json_data.ReweighingMatrix_Array, '$[*]'","        COLUMNS (","            group_a VARCHAR2(10) PATH '$.group_a',","            group_a_pos_weight NUMBER PATH '$.group_a_pos_weight',","            non_group_a_pos_weight NUMBER PATH '$.non_group_a_pos_weight',","            group_a_neg_weight NUMBER PATH '$.group_a_neg_weight',","            non_group_a_neg_weight NUMBER PATH '$.non_group_a_neg_weight'","        )","    ) r","ON m.group_a = r.group_a;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Query to view bias detection output - parsed JSON output with subgroups","message":["%sql","","WITH json_data AS (","    SELECT ","        JSON_QUERY(RESULT, '$.metric') AS Metric_Array,","        JSON_QUERY(RESULT, '$.cdd') AS CDD_Array,","        JSON_QUERY(RESULT, '$.reweighing_matrix') AS ReweighingMatrix_Array","    FROM ","        OML$8017EE54_C305_47A5_9603_0EFA84B6B624_DATA_BIAS_JOB_RESULT",")","-- Extracting metric, CDD and reweighing_matrix values","SELECT ","    d.subgroup,","    d.dd,","    m.group_a AS metric_group_a,","    m.ci,","    m.sp,","    c.group_a AS cdd_group_a,","    c.cdd,","    r.group_a_pos_weight,","    r.non_group_a_pos_weight,","    r.group_a_neg_weight,","    r.non_group_a_neg_weight","FROM ","    json_data,","    -- Parsing the 'metric' array","    JSON_TABLE(json_data.Metric_Array, '$[*]'","        COLUMNS (","            group_a VARCHAR2(10) PATH '$.group_a',","            ci NUMBER PATH '$.CI',","            sp NUMBER PATH '$.SP'","        )","    ) m","JOIN ","    -- Parsing the 'cdd' array (top level)","    JSON_TABLE(json_data.CDD_Array, '$[*].result[*]'","        COLUMNS (","            group_a VARCHAR2(10) PATH '$.group_a',","            cdd NUMBER PATH '$.CDD'","        )","    ) c","ON m.group_a = c.group_a","JOIN ","    -- Parsing the nested 'detail' array from 'cdd' result array","    JSON_TABLE(json_data.CDD_Array, '$[*].result[*].detail[*]'","        COLUMNS (","            subgroup VARCHAR2(50) PATH '$.subgroup',","            dd NUMBER PATH '$.DD'","        )","    ) d","ON c.group_a = m.group_a -- Joining on group_a to relate to metric","JOIN ","    -- Parsing the 'reweighing_matrix' array","    JSON_TABLE(json_data.ReweighingMatrix_Array, '$[*]'","        COLUMNS (","            group_a VARCHAR2(10) PATH '$.group_a',","            group_a_pos_weight NUMBER PATH '$.group_a_pos_weight',","            non_group_a_pos_weight NUMBER PATH '$.non_group_a_pos_weight',","            group_a_neg_weight NUMBER PATH '$.group_a_neg_weight',","            non_group_a_neg_weight NUMBER PATH '$.non_group_a_neg_weight'","        )","    ) r","ON m.group_a = r.group_a;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Data bias detection results interpretation","",""," The model identifies a slight bias in favor of males, as shown by the class imbalance and selection rates. Disparities across marital status subgroups highlight more favorable outcomes for females in "," some cases. The reweighing matrix proposes adjustments to mitigate these biases by balancing class weights.",""," Reweighing is a method used to address data bias by assigning higher weights to instances from disadvantaged groups or with positive labels. This ensures that the classifier prioritizes these weighted "," instances, promoting fairer treatment and reducing the likelihood of bias towards advantaged groups.","","#### 1. **Metric Section**:","   - **Class Imbalance (CI)**: Reflects bias between males and females. ","     - MALE: CI = 0.33841 (slight favorability towards males).","     - FEMALE: CI = -0.33841 (less favorability towards females).","   - **Statistical Parity (SP)**: Measures selection rate differences between groups.","     - MALE: SP = 0.19628 (males have a slightly higher selection rate).","     - FEMALE: SP = -0.19628 (females have a lower selection rate compared to males).","","#### 2. **Conditional Demographic Disparity (CDD) Section**:","   - Analyzes disparities across **marital status** subgroups for both genders. ","   - **Overall CDD** for MALE and FEMALE: -0.092269 (indicates bias).","   - Notable Disparities by Subgroup (DD):","     - **MARRIED-CIV-SPOUSE**: DD = 0.00367 (slightly favors males).","     - **NEVER-MARRIED**: DD = -0.11335 (favors females).","     - **SEPARATED**: DD = -0.38267 (favors females).","","#### 3. **Reweighing Matrix**:","   - Suggests adjustments to reduce bias by rebalancing class weights for both groups.","   - **For MALE**:","     - Positive class weight: 0.78764 (reduces weight for males).","     - Negative class weight: 1.0935 (increases weight for males).","   - **For FEMALE**:","     - Positive class weight: 2.2000 (increases weight for females).","     - Negative class weight: 0.85251 (decreases weight for females)."],"enabled":true,"result":{"startTime":1734658195910,"interpreter":"md.low","endTime":1734658196377,"results":[{"message":"<h3 id=\"data-bias-detection-results-interpretation\">Data bias detection results interpretation<\/h3>\n<p>The model identifies a slight bias in favor of males, as shown by the class imbalance and selection rates. Disparities across marital status subgroups highlight more favorable outcomes for females in\nsome cases. The reweighing matrix proposes adjustments to mitigate these biases by balancing class weights.<\/p>\n<p>Reweighing is a method used to address data bias by assigning higher weights to instances from disadvantaged groups or with positive labels. This ensures that the classifier prioritizes these weighted\ninstances, promoting fairer treatment and reducing the likelihood of bias towards advantaged groups.<\/p>\n<h4 id=\"1-metric-section\">1. <strong>Metric Section<\/strong>:<\/h4>\n<ul>\n<li><strong>Class Imbalance (CI)<\/strong>: Reflects bias between males and females.\n<ul>\n<li>MALE: CI = 0.33841 (slight favorability towards males).<\/li>\n<li>FEMALE: CI = -0.33841 (less favorability towards females).<\/li>\n<\/ul>\n<\/li>\n<li><strong>Statistical Parity (SP)<\/strong>: Measures selection rate differences between groups.\n<ul>\n<li>MALE: SP = 0.19628 (males have a slightly higher selection rate).<\/li>\n<li>FEMALE: SP = -0.19628 (females have a lower selection rate compared to males).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4 id=\"2-conditional-demographic-disparity-cdd-section\">2. <strong>Conditional Demographic Disparity (CDD) Section<\/strong>:<\/h4>\n<ul>\n<li>Analyzes disparities across <strong>marital status<\/strong> subgroups for both genders.<\/li>\n<li><strong>Overall CDD<\/strong> for MALE and FEMALE: -0.092269 (indicates bias).<\/li>\n<li>Notable Disparities by Subgroup (DD):\n<ul>\n<li><strong>MARRIED-CIV-SPOUSE<\/strong>: DD = 0.00367 (slightly favors males).<\/li>\n<li><strong>NEVER-MARRIED<\/strong>: DD = -0.11335 (favors females).<\/li>\n<li><strong>SEPARATED<\/strong>: DD = -0.38267 (favors females).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4 id=\"3-reweighing-matrix\">3. <strong>Reweighing Matrix<\/strong>:<\/h4>\n<ul>\n<li>Suggests adjustments to reduce bias by rebalancing class weights for both groups.<\/li>\n<li><strong>For MALE<\/strong>:\n<ul>\n<li>Positive class weight: 0.78764 (reduces weight for males).<\/li>\n<li>Negative class weight: 1.0935 (increases weight for males).<\/li>\n<\/ul>\n<\/li>\n<li><strong>For FEMALE<\/strong>:\n<ul>\n<li>Positive class weight: 2.2000 (increases weight for females).<\/li>\n<li>Negative class weight: 0.85251 (decreases weight for females).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Step 5: Perform an action on a job (optional)","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","* _DISABLE_ disables the job at submission. The *force* property can be used with this action to forcefully interrupt any running job. ","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","Below is an example request to run the job immediately. When you successfully submit your job you will receive a 204 response with no body. "],"enabled":true,"result":{"startTime":1734658196833,"interpreter":"md.low","endTime":1734658197279,"results":[{"message":"<h3 id=\"step-5-perform-an-action-on-a-job-optional\">Step 5: Perform an action on a job (optional)<\/h3>\n<p>OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, <strong>DISABLE<\/strong>, <strong>RUN<\/strong>, <strong>ENABLE<\/strong>, and <strong>STOP<\/strong>:<\/p>\n<ul>\n<li><em>DISABLE<\/em> disables the job at submission. The <em>force<\/em> property can be used with this action to forcefully interrupt any running job.<\/li>\n<li><em>ENABLE<\/em> enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule.<\/li>\n<li><em>RUN<\/em> will immediately run the job as a way to test a job or run it outside of its schedule.<\/li>\n<li><em>STOP<\/em> stops a currently running job.<\/li>\n<\/ul>\n<p>Below is an example request to run the job immediately. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Run a job immediately","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"RUN\"","         }'","         "],"enabled":false,"result":{"startTime":1725565526519,"interpreter":"md.low","endTime":1725565526584,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Step 6: Delete a data bias detection job (optional)","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":{"startTime":1734658197732,"interpreter":"md.low","endTime":1734658198177,"results":[{"message":"<h3 id=\"step-6-delete-a-data-bias-detection-job-optional\">Step 6: Delete a data bias detection job (optional)<\/h3>\n<p>To delete a previously submitted job, send a DELETE request along with the Job ID to the <em>/omlmod/v1/jobs<\/em> endpoint.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to delete a job","message":["","$ curl -X DELETE \"<omlserver>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"," "],"enabled":false,"result":{"startTime":1725565526838,"interpreter":"md.low","endTime":1725565526901,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### To reproduce the example in this notebook","","If you would like to reproduce the example in this notebook you will need to load the **ADULT** data to the database. The paragraph below creates the table in your schema. You can find the original dataset and ","detailed information about it here: <a href=\"https://archive.ics.uci.edu/ml/datasets/adult\" target=\"_blank\">UCI Machine Learning Repository - Adult Data Set<\/a>."],"enabled":true,"result":{"startTime":1734658198628,"interpreter":"md.low","endTime":1734658199080,"results":[{"message":"<h3 id=\"to-reproduce-the-example-in-this-notebook\">To reproduce the example in this notebook<\/h3>\n<p>If you would like to reproduce the example in this notebook you will need to load the <strong>ADULT<\/strong> data to the database. The paragraph below creates the table in your schema. You can find the original dataset and\ndetailed information about it here: <a href=\"https://archive.ics.uci.edu/ml/datasets/adult\" target=\"_blank\">UCI Machine Learning Repository - Adult Data Set<\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the Adult data to Python memory and create a database table","message":["%python","","import oml","","import pandas as pd","import ssl","","ssl._create_default_https_context = ssl._create_unverified_context","","# Load the dataset","url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"","columns = ['AGE', 'WORKCLASS', 'FNLWGT', 'EDUCATION', 'EDUCATION_NUM', 'MARITAL_STATUS',","           'OCCUPATION', 'RELATIONSHIP', 'RACE', 'GENDER', 'CAPITAL_GAIN', 'CAPITAL_LOSS',","           'HOURS_PER_WEEK', 'NATIVE_COUNTRY', 'INCOME']","adult_df = pd.read_csv(url, names=columns, na_values=\" ?\", skipinitialspace=True)","","# Create a table from the DataFrame","try: oml.drop(table=\"ADULT\")","except: pass","","oml.create(adult_df, table=\"ADULT\")"],"enabled":true,"result":{"startTime":1734658199538,"interpreter":"python.low","endTime":1734658203618,"results":[{"message":"       AGE         WORKCLASS  FNLWGT  ... HOURS_PER_WEEK  NATIVE_COUNTRY INCOME\n0       66      Self-emp-inc  150726  ...              1               ?  <=50K\n1       75                 ?  164849  ...              5   United-States  <=50K\n2       81           Private  114670  ...              5   United-States  <=50K\n3       17           Private  211870  ...              6   United-States  <=50K\n4       17         Local-gov   32124  ...              9   United-States  <=50K\n...    ...               ...     ...  ...            ...             ...    ...\n32556   22           Private  203715  ...             40   United-States  <=50K\n32557   37           Private  179137  ...             39   United-States  <=50K\n32558   43         State-gov  255835  ...             40   United-States  <=50K\n32559   43  Self-emp-not-inc   27242  ...             50   United-States  <=50K\n32560   22           Private  310152  ...             40   United-States  <=50K\n\n[32561 rows x 15 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the ADULT table","message":["%sql","","SELECT * FROM ADULT"],"enabled":true,"result":{"startTime":1734658204071,"interpreter":"sql.low","endTime":1734658206047,"results":[{"message":"AGE\tWORKCLASS\tFNLWGT\tEDUCATION\tEDUCATION_NUM\tMARITAL_STATUS\tOCCUPATION\tRELATIONSHIP\tRACE\tGENDER\tCAPITAL_GAIN\tCAPITAL_LOSS\tHOURS_PER_WEEK\tNATIVE_COUNTRY\tINCOME\n66\tSelf-emp-inc\t150726\t9th\t5\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t1409\t0\t1\t?\t<=50K\n75\t?\t164849\t9th\t5\tMarried-civ-spouse\t?\tHusband\tBlack\tMale\t1409\t0\t5\tUnited-States\t<=50K\n81\tPrivate\t114670\t9th\t5\tWidowed\tPriv-house-serv\tNot-in-family\tBlack\tFemale\t2062\t0\t5\tUnited-States\t<=50K\n17\tPrivate\t211870\t9th\t5\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t6\tUnited-States\t<=50K\n17\tLocal-gov\t32124\t9th\t5\tNever-married\tOther-service\tOwn-child\tBlack\tMale\t0\t0\t9\tUnited-States\t<=50K\n20\tPrivate\t190231\t9th\t5\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tFemale\t0\t0\t11\tNicaragua\t<=50K\n70\t?\t167358\t9th\t5\tWidowed\t?\tUnmarried\tWhite\tFemale\t1111\t0\t15\tUnited-States\t<=50K\n49\tPrivate\t160187\t9th\t5\tMarried-spouse-absent\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t16\tJamaica\t<=50K\n51\tSelf-emp-not-inc\t175897\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n61\tPrivate\t355645\t9th\t5\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tBlack\tMale\t0\t0\t20\tTrinadad&Tobago\t<=50K\n17\tPrivate\t191260\t9th\t5\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t1055\t0\t24\tUnited-States\t<=50K\n65\tPrivate\t109351\t9th\t5\tWidowed\tPriv-house-serv\tUnmarried\tBlack\tFemale\t0\t0\t24\tUnited-States\t<=50K\n19\t?\t124884\t9th\t5\tNever-married\t?\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n46\tPrivate\t187226\t9th\t5\tDivorced\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n55\tSelf-emp-not-inc\t59469\t9th\t5\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n22\tPrivate\t201729\t9th\t5\tNever-married\tHandlers-cleaners\tOther-relative\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n36\tSelf-emp-not-inc\t136028\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n39\tPrivate\t297847\t9th\t5\tMarried-civ-spouse\tOther-service\tWife\tBlack\tFemale\t3411\t0\t34\tUnited-States\t<=50K\n69\tPrivate\t88566\t9th\t5\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t1424\t0\t35\tUnited-States\t<=50K\n23\tPrivate\t156807\t9th\t5\tNever-married\tHandlers-cleaners\tOwn-child\tBlack\tMale\t0\t0\t36\tUnited-States\t<=50K\n53\tPrivate\t117496\t9th\t5\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t36\tCanada\t<=50K\n49\tPrivate\t68505\t9th\t5\tDivorced\tOther-service\tNot-in-family\tBlack\tMale\t0\t0\t37\tUnited-States\t<=50K\n18\tPrivate\t675421\t9th\t5\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t594\t0\t40\tUnited-States\t<=50K\n22\tPrivate\t127185\t9th\t5\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n23\tPrivate\t155919\t9th\t5\tMarried-spouse-absent\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n23\tPrivate\t407684\t9th\t5\tNever-married\tMachine-op-inspct\tOther-relative\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n24\tPrivate\t154165\t9th\t5\tDivorced\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n24\tPrivate\t172146\t9th\t5\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t1721\t40\tUnited-States\t<=50K\n26\tSelf-emp-not-inc\t117125\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tPortugal\t<=50K\n27\tPrivate\t405855\t9th\t5\tNever-married\tCraft-repair\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n28\tPrivate\t208965\t9th\t5\tNever-married\tMachine-op-inspct\tUnmarried\tOther\tMale\t0\t0\t40\tMexico\t<=50K\n29\tPrivate\t266043\t9th\t5\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t54334\t9th\t5\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t151868\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tFederal-gov\t188569\t9th\t5\tMarried-civ-spouse\tTech-support\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t488720\t9th\t5\tMarried-civ-spouse\tHandlers-cleaners\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n31\tPrivate\t247328\t9th\t5\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t3137\t0\t40\tMexico\t<=50K\n34\tPrivate\t230246\t9th\t5\tSeparated\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\t?\t<=50K\n34\tLocal-gov\t303867\t9th\t5\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n35\tFederal-gov\t76845\t9th\t5\tMarried-civ-spouse\tFarming-fishing\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n38\tPrivate\t103751\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n38\tSelf-emp-not-inc\t357962\t9th\t5\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n39\tSelf-emp-inc\t218184\t9th\t5\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t1651\t40\tMexico\t<=50K\n40\tPrivate\t216116\t9th\t5\tNever-married\tMachine-op-inspct\tUnmarried\tBlack\tFemale\t0\t0\t40\tHaiti\t<=50K\n42\tLocal-gov\t175674\t9th\t5\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n43\tSelf-emp-not-inc\t272165\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n45\t?\t144354\t9th\t5\tSeparated\t?\tOwn-child\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n45\tPrivate\t186272\t9th\t5\tMarried-civ-spouse\tAdm-clerical\tHusband\tBlack\tMale\t5178\t0\t40\tUnited-States\t>50K\n45\tPrivate\t178215\t9th\t5\tMarried-civ-spouse\tMachine-op-inspct\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t>50K\n46\tPrivate\t241844\t9th\t5\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n47\tState-gov\t141483\t9th\t5\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n47\tPrivate\t241832\t9th\t5\tMarried-spouse-absent\tHandlers-cleaners\tUnmarried\tWhite\tMale\t0\t0\t40\tEl-Salvador\t<=50K\n49\tPrivate\t115613\t9th\t5\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n49\tPrivate\t72393\t9th\t5\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n51\tLocal-gov\t146181\t9th\t5\tMarried-civ-spouse\tTransport-moving\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t270194\t9th\t5\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n52\tPrivate\t366232\t9th\t5\tDivorced\tCraft-repair\tUnmarried\tWhite\tFemale\t0\t0\t40\tCuba\t<=50K\n59\tPrivate\t381851\t9th\t5\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n61\tPrivate\t197286\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n61\tPrivate\t83045\t9th\t5\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n74\t?\t340939\t9th\t5\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t3471\t0\t40\tUnited-States\t<=50K\n76\tSelf-emp-inc\t120796\t9th\t5\tMarried-civ-spouse\tProf-specialty\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n77\t?\t232894\t9th\t5\tMarried-civ-spouse\t?\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t507875\t9th\t5\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t43\tUnited-States\t<=50K\n25\tPrivate\t333677\t9th\t5\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n54\tPrivate\t174865\t9th\t5\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t>50K\n25\tPrivate\t206600\t9th\t5\tNever-married\tMachine-op-inspct\tOther-relative\tWhite\tMale\t0\t0\t48\tMexico\t<=50K\n72\tPrivate\t74141\t9th\t5\tMarried-civ-spouse\tExec-managerial\tWife\tAsian-Pac-Islander\tFemale\t0\t0\t48\tUnited-States\t>50K\n28\tPrivate\t142264\t9th\t5\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t50\tDominican-Republic\t<=50K\n36\tPrivate\t188834\t9th\t5\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n37\tPrivate\t758700\t9th\t5\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t3781\t0\t50\tMexico\t<=50K\n43\tPrivate\t598606\t9th\t5\tSeparated\tHandlers-cleaners\tUnmarried\tBlack\tFemale\t0\t0\t50\tUnited-States\t<=50K\n47\tPrivate\t178309\t9th\t5\tNever-married\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t50\tUnited-States\t<=50K\n53\tPrivate\t95647\t9th\t5\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n36\tPrivate\t208358\t9th\t5\tDivorced\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t4650\t0\t56\tUnited-States\t<=50K\n31\tSelf-emp-not-inc\t226624\t9th\t5\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t<=50K\n33\tPrivate\t157747\t9th\t5\tDivorced\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t0\t0\t70\tUnited-States\t<=50K\n60\tSelf-emp-not-inc\t170114\t9th\t5\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t1672\t84\tUnited-States\t<=50K\n67\t?\t212759\t10th\t6\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t2\tUnited-States\t<=50K\n73\t?\t132737\t10th\t6\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t4\tUnited-States\t<=50K\n58\tPrivate\t51662\t10th\t6\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t8\tUnited-States\t<=50K\n17\tPrivate\t329783\t10th\t6\tNever-married\tSales\tOther-relative\tWhite\tFemale\t0\t0\t10\tUnited-States\t<=50K\n17\tSelf-emp-not-inc\t60116\t10th\t6\tNever-married\tAdm-clerical\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n18\tPrivate\t387568\t10th\t6\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n60\t?\t24215\t10th\t6\tDivorced\t?\tNot-in-family\tAmer-Indian-Eskimo\tFemale\t0\t0\t10\tUnited-States\t<=50K\n73\tPrivate\t123160\t10th\t6\tWidowed\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t10\tUnited-States\t<=50K\n76\tPrivate\t97077\t10th\t6\tWidowed\tSales\tUnmarried\tBlack\tFemale\t0\t0\t12\tUnited-States\t<=50K\n17\t?\t139183\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t15\tUnited-States\t<=50K\n17\tPrivate\t139183\t10th\t6\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t15\tUnited-States\t<=50K\n30\tPrivate\t504725\t10th\t6\tNever-married\tSales\tOther-relative\tWhite\tMale\t0\t0\t18\tGuatemala\t<=50K\n55\tSelf-emp-not-inc\t179715\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t18\tUnited-States\t<=50K\n17\t?\t138507\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n17\t?\t158762\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t130125\t10th\t6\tNever-married\tOther-service\tOwn-child\tAmer-Indian-Eskimo\tFemale\t1055\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t32607\t10th\t6\tNever-married\tFarming-fishing\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n66\t?\t160995\t10th\t6\tDivorced\t?\tNot-in-family\tWhite\tFemale\t1086\t0\t20\tUnited-States\t<=50K\n69\tLocal-gov\t122850\t10th\t6\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t169658\t10th\t6\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t21\tUnited-States\t<=50K\n17\tPrivate\t263746\t10th\t6\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n17\tPrivate\t130806\t10th\t6\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n36\tPrivate\t348022\t10th\t6\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n22\tPrivate\t230574\t10th\t6\tNever-married\tTransport-moving\tOwn-child\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n25\tPrivate\t345121\t10th\t6\tSeparated\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n18\tPrivate\t80616\t10th\t6\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t27\tUnited-States\t<=50K\n17\t?\t333100\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tMale\t1055\t0\t30\tUnited-States\t<=50K\n17\t?\t228373\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n17\tPrivate\t163494\t10th\t6\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n17\tPrivate\t57324\t10th\t6\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n22\tPrivate\t186452\t10th\t6\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n22\tPrivate\t228452\t10th\t6\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n41\tPrivate\t239683\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t30\t?\t<=50K\n59\tLocal-gov\t171328\t10th\t6\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n59\tPrivate\t151977\t10th\t6\tSeparated\tPriv-house-serv\tNot-in-family\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n17\t?\t304873\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tFemale\t34095\t0\t32\tUnited-States\t<=50K\n29\tPrivate\t136277\t10th\t6\tNever-married\tOther-service\tOwn-child\tBlack\tFemale\t0\t0\t32\tUnited-States\t<=50K\n26\tPrivate\t91683\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n33\tPrivate\t228528\t10th\t6\tNever-married\tCraft-repair\tUnmarried\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n37\tPrivate\t632613\t10th\t6\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t35\tMexico\t<=50K\n37\tPrivate\t182668\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n64\tPrivate\t148606\t10th\t6\tSeparated\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n21\tPrivate\t260617\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t36\tUnited-States\t<=50K\n50\tPrivate\t176924\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n17\t?\t179715\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n19\tPrivate\t446219\t10th\t6\tNever-married\tSales\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n20\t?\t121313\t10th\t6\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n20\tPrivate\t171398\t10th\t6\tNever-married\tSales\tNot-in-family\tOther\tMale\t0\t0\t40\tUnited-States\t<=50K\n20\tPrivate\t444554\t10th\t6\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n21\tPrivate\t62339\t10th\t6\tNever-married\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n22\tPrivate\t427686\t10th\t6\tDivorced\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n22\tPrivate\t214731\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n23\tPrivate\t133985\t10th\t6\tNever-married\tCraft-repair\tOwn-child\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n24\tPrivate\t611029\t10th\t6\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t255004\t10th\t6\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n27\tPrivate\t161155\t10th\t6\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n27\tPrivate\t284196\t10th\t6\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n27\tPrivate\t200641\t10th\t6\tMarried-civ-spouse\tExec-managerial\tWife\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n27\tPrivate\t148069\t10th\t6\tNever-married\tMachine-op-inspct\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n28\t?\t291374\t10th\t6\tNever-married\t?\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n28\tPrivate\t270366\t10th\t6\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n28\tPrivate\t259351\t10th\t6\tNever-married\tOther-service\tOther-relative\tAmer-Indian-Eskimo\tMale\t0\t0\t40\tMexico\t<=50K\n28\tPrivate\t312588\t10th\t6\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n29\tPrivate\t206351\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t5013\t0\t40\tUnited-States\t<=50K\n29\tPrivate\t183523\t10th\t6\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n29\tPrivate\t100405\t10th\t6\tMarried-civ-spouse\tFarming-fishing\tWife\tAmer-Indian-Eskimo\tFemale\t0\t0\t40\tUnited-States\t<=50K\n31\t?\t345497\t10th\t6\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t381153\t10th\t6\tMarried-civ-spouse\tTech-support\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n31\tPrivate\t209448\t10th\t6\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t2105\t0\t40\tMexico\t<=50K\n31\tPrivate\t288419\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\t?\t199244\t10th\t6\tDivorced\t?\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n32\tPrivate\t123429\t10th\t6\tSeparated\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n33\tLocal-gov\t298785\t10th\t6\tDivorced\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n35\tPrivate\t111128\t10th\t6\tNever-married\tAdm-clerical\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n36\tPrivate\t526968\t10th\t6\tDivorced\tExec-managerial\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n36\tPrivate\t167482\t10th\t6\tNever-married\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n38\tPrivate\t53930\t10th\t6\tNever-married\tCraft-repair\tNot-in-family\tBlack\tMale\t0\t0\t40\t?\t<=50K\n39\tPrivate\t379350\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n39\tLocal-gov\t473547\t10th\t6\tDivorced\tHandlers-cleaners\tOwn-child\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n40\tPrivate\t100584\t10th\t6\tDivorced\tCraft-repair\tNot-in-family\tAmer-Indian-Eskimo\tMale\t0\t0\t40\tUnited-States\t<=50K\n40\tPrivate\t173607\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n40\tPrivate\t85995\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n41\tPrivate\t197583\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t>50K\n42\tPrivate\t230624\t10th\t6\tNever-married\tTransport-moving\tUnmarried\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n43\tPrivate\t193494\t10th\t6\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n43\tPrivate\t238287\t10th\t6\tNever-married\tMachine-op-inspct\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n44\tPrivate\t265266\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n46\tPrivate\t161508\t10th\t6\tNever-married\tMachine-op-inspct\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n47\tPrivate\t297884\t10th\t6\tWidowed\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n48\tPrivate\t207848\t10th\t6\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n49\t?\t261059\t10th\t6\tSeparated\t?\tOwn-child\tWhite\tMale\t2176\t0\t40\tUnited-States\t<=50K\n49\tLocal-gov\t120451\t10th\t6\tSeparated\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t252903\t10th\t6\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t1977\t40\tUnited-States\t>50K\n51\tPrivate\t41474\t10th\t6\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n55\tLocal-gov\t98545\t10th\t6\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n55\tPrivate\t120970\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t>50K\n55\tPrivate\t189933\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n56\tPrivate\t31782\t10th\t6\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n56\tSelf-emp-not-inc\t157486\t10th\t6\tDivorced\tSales\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n57\tPrivate\t182062\t10th\t6\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n58\tSelf-emp-not-inc\t87510\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n58\tSelf-emp-inc\t183870\t10th\t6\tMarried-civ-spouse\tTransport-moving\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n58\tLocal-gov\t310085\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n59\tPrivate\t143372\t10th\t6\tDivorced\tAdm-clerical\tNot-in-family\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n59\tPrivate\t227386\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n59\tPrivate\t107833\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t1485\t40\tUnited-States\t>50K\n60\tPrivate\t146674\t10th\t6\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n60\tPrivate\t106850\t10th\t6\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n61\tPrivate\t232308\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n62\tPrivate\t92178\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n63\tPrivate\t183608\t10th\t6\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n63\tSelf-emp-not-inc\t159715\t10th\t6\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n64\tState-gov\t550848\t10th\t6\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n64\tPrivate\t180401\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n66\tState-gov\t41506\t10th\t6\tDivorced\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n66\tPrivate\t263637\t10th\t6\tMarried-civ-spouse\tProtective-serv\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\tPrivate\t283268\t10th\t6\tSeparated\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t42\tUnited-States\t<=50K\n49\tPrivate\t245305\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tBlack\tMale\t0\t0\t42\tUnited-States\t>50K\n57\tPrivate\t180920\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t43\tUnited-States\t<=50K\n34\tPrivate\t127651\t10th\t6\tNever-married\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t44\t?\t<=50K\n28\tPrivate\t215873\t10th\t6\tNever-married\tMachine-op-inspct\tOwn-child\tBlack\tMale\t0\t0\t45\tUnited-States\t<=50K\n46\tPrivate\t192208\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n51\tPrivate\t246519\t10th\t6\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t2105\t0\t45\tUnited-States\t<=50K\n54\tLocal-gov\t135840\t10th\t6\tMarried-civ-spouse\tProtective-serv\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n52\tPrivate\t25826\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t1887\t47\tUnited-States\t>50K\n27\tSelf-emp-not-inc\t131298\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t48\tUnited-States\t<=50K\n24\tPrivate\t280134\t10th\t6\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t49\tEl-Salvador\t<=50K\n28\tPrivate\t203776\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n30\tPrivate\t123178\t10th\t6\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n35\tSelf-emp-not-inc\t462890\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tBlack\tMale\t0\t0\t50\tUnited-States\t<=50K\n37\tPrivate\t143058\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n40\tPrivate\t155190\t10th\t6\tNever-married\tCraft-repair\tOther-relative\tBlack\tMale\t0\t0\t55\tUnited-States\t<=50K\n56\tPrivate\t184553\t10th\t6\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t56\tUnited-States\t<=50K\n39\tPrivate\t338320\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t>50K\n52\tSelf-emp-not-inc\t173839\t10th\t6\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t<=50K\n53\tSelf-emp-not-inc\t311020\t10th\t6\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t<=50K\n45\tSelf-emp-not-inc\t204205\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t65\tUnited-States\t<=50K\n54\tSelf-emp-not-inc\t104748\t10th\t6\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t65\tUnited-States\t<=50K\n21\tPrivate\t190761\t10th\t6\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t70\tUnited-States\t<=50K\n35\tPrivate\t207202\t10th\t6\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t70\tUnited-States\t<=50K\n45\tSelf-emp-not-inc\t160167\t10th\t6\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t70\tUnited-States\t<=50K\n48\tPrivate\t377757\t10th\t6\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t70\tUnited-States\t<=50K\n75\tPrivate\t101887\t10th\t6\tWidowed\tPriv-house-serv\tNot-in-family\tWhite\tFemale\t0\t0\t70\tUnited-States\t<=50K\n48\tSelf-emp-inc\t26145\t10th\t6\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t80\tUnited-States\t<=50K\n50\t?\t174964\t10th\t6\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t99\tUnited-States\t<=50K\n67\t?\t184506\t11th\t7\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t419\t3\tUnited-States\t<=50K\n17\t?\t258872\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t5\tUnited-States\t<=50K\n17\tPrivate\t46496\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t5\tUnited-States\t<=50K\n18\tState-gov\t59342\t11th\t7\tNever-married\tAdm-clerical\tOwn-child\tWhite\tFemale\t0\t0\t5\tUnited-States\t<=50K\n18\tPrivate\t130849\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t8\tUnited-States\t<=50K\n34\t?\t164309\t11th\t7\tMarried-civ-spouse\t?\tWife\tWhite\tFemale\t0\t0\t8\tUnited-States\t<=50K\n67\t?\t36135\t11th\t7\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t8\tUnited-States\t<=50K\n17\tPrivate\t317681\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n17\tPrivate\t89821\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n17\tSelf-emp-not-inc\t368700\t11th\t7\tNever-married\tFarming-fishing\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n17\tPrivate\t65368\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n17\tPrivate\t242718\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t12\tUnited-States\t<=50K\n17\tPrivate\t245918\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t12\tUnited-States\t<=50K\n25\tPrivate\t132749\t11th\t7\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n17\tPrivate\t220384\t11th\t7\tNever-married\tAdm-clerical\tOwn-child\tWhite\tMale\t0\t0\t15\tUnited-States\t<=50K\n18\t?\t346382\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t15\tUnited-States\t<=50K\n18\tPrivate\t131033\t11th\t7\tNever-married\tOther-service\tOther-relative\tBlack\tMale\t0\t0\t15\tUnited-States\t<=50K\n42\t?\t257780\t11th\t7\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t15\tUnited-States\t<=50K\n17\tPrivate\t181580\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t16\tUnited-States\t<=50K\n17\tLocal-gov\t182070\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t16\tUnited-States\t<=50K\n18\tPrivate\t25828\t11th\t7\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n17\tPrivate\t195262\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t17\tUnited-States\t<=50K\n17\t?\t114798\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t18\tUnited-States\t<=50K\n17\tPrivate\t175024\t11th\t7\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t2176\t0\t18\tUnited-States\t<=50K\n30\tPrivate\t426017\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t19\tUnited-States\t<=50K\n17\t?\t80077\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n17\t?\t213055\t11th\t7\tNever-married\t?\tNot-in-family\tOther\tFemale\t0\t0\t20\tUnited-States\t<=50K\n17\tLocal-gov\t191910\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t193830\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t198124\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t262511\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t400225\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t103851\t11th\t7\tNever-married\tAdm-clerical\tOwn-child\tWhite\tFemale\t1055\t0\t20\tUnited-States\t<=50K\n17\tPrivate\t115551\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n17\tLocal-gov\t246308\t11th\t7\tNever-married\tProf-specialty\tOwn-child\tWhite\tFemale\t0\t0\t20\tPuerto-Rico\t<=50K\n17\tSelf-emp-inc\t254859\t11th\t7\tNever-married\tProf-specialty\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t107277\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t216284\t11th\t7\tNever-married\tAdm-clerical\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t415520\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t41794\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t132652\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t283342\t11th\t7\tNever-married\tOther-service\tOther-relative\tBlack\tMale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t188476\t11th\t7\tNever-married\tExec-managerial\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n19\tPrivate\t262515\t11th\t7\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n26\tPrivate\t245628\t11th\t7\tNever-married\tHandlers-cleaners\tUnmarried\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n45\tPrivate\t462440\t11th\t7\tWidowed\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t20\tUnited-States\t<=50K\n68\tSelf-emp-not-inc\t315859\t11th\t7\tNever-married\tFarming-fishing\tUnmarried\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n69\tPrivate\t185691\t11th\t7\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n18\tPrivate\t309634\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t22\tUnited-States\t<=50K\n17\tPrivate\t197850\t11th\t7\tNever-married\tAdm-clerical\tOwn-child\tAsian-Pac-Islander\tFemale\t0\t0\t24\tUnited-States\t<=50K\n72\t?\t28929\t11th\t7\tWidowed\t?\tNot-in-family\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n17\tPrivate\t295488\t11th\t7\tNever-married\tOther-service\tOwn-child\tBlack\tFemale\t0\t0\t25\tUnited-States\t<=50K\n18\t?\t191117\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n18\tPrivate\t40829\t11th\t7\tNever-married\tSales\tOther-relative\tAmer-Indian-Eskimo\tFemale\t0\t0\t25\tUnited-States\t<=50K\n18\tLocal-gov\t153405\t11th\t7\tNever-married\tAdm-clerical\tOther-relative\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n18\tPrivate\t208103\t11th\t7\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n18\t?\t120243\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t27\tUnited-States\t<=50K\n17\tPrivate\t368700\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t28\tUnited-States\t<=50K\n17\t?\t145886\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n18\tPrivate\t193741\t11th\t7\tNever-married\tOther-service\tOther-relative\tBlack\tMale\t0\t0\t30\tUnited-States\t<=50K\n19\t?\t175499\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n39\tLocal-gov\t167864\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n46\tLocal-gov\t329752\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n66\t?\t112871\t11th\t7\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n75\tSelf-emp-not-inc\t184335\t11th\t7\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n45\tPrivate\t98881\t11th\t7\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t32\tUnited-States\t<=50K\n17\tPrivate\t200199\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n18\tPrivate\t304169\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n22\tPrivate\t311764\t11th\t7\tWidowed\tSales\tOwn-child\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n24\tPrivate\t265567\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n26\t?\t152046\t11th\t7\tNever-married\t?\tNot-in-family\tWhite\tFemale\t0\t0\t35\tGermany\t<=50K\n27\tPrivate\t335764\t11th\t7\tMarried-civ-spouse\tSales\tOwn-child\tBlack\tMale\t0\t0\t35\tUnited-States\t<=50K\n40\tPrivate\t201495\t11th\t7\tNever-married\tTransport-moving\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n18\t?\t423460\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t36\tUnited-States\t<=50K\n53\tPrivate\t209022\t11th\t7\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t37\tUnited-States\t<=50K\n23\tPrivate\t342946\t11th\t7\tNever-married\tTransport-moving\tOwn-child\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n17\t?\t202521\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\t?\t395567\t11th\t7\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t140164\t11th\t7\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t230875\t11th\t7\tNever-married\tAdm-clerical\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t156764\t11th\t7\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t98044\t11th\t7\tNever-married\tProf-specialty\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n19\tPrivate\t236396\t11th\t7\tNever-married\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n19\tPrivate\t275889\t11th\t7\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n19\tPrivate\t430471\t11th\t7\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n20\t?\t114746\t11th\t7\tMarried-spouse-absent\t?\tOwn-child\tAsian-Pac-Islander\tFemale\t0\t1762\t40\tSouth\t<=50K\n21\tPrivate\t191460\t11th\t7\tNever-married\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n21\tPrivate\t256356\t11th\t7\tNever-married\tPriv-house-serv\tOther-relative\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n22\tPrivate\t148187\t11th\t7\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n23\tPrivate\t193089\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n23\tPrivate\t207546\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n24\tPrivate\t263498\t11th\t7\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n24\tPrivate\t123983\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tAsian-Pac-Islander\tMale\t0\t0\t40\tVietnam\t<=50K\n24\tPrivate\t228613\t11th\t7\tNever-married\tMachine-op-inspct\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t73839\t11th\t7\tDivorced\tSales\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t109009\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t210184\t11th\t7\tSeparated\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n26\tPrivate\t378322\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n26\tPrivate\t157249\t11th\t7\tNever-married\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n27\tSelf-emp-not-inc\t229125\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n27\tPrivate\t211570\t11th\t7\tNever-married\tHandlers-cleaners\tOther-relative\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n28\tPrivate\t210867\t11th\t7\tDivorced\tSales\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n28\tState-gov\t130620\t11th\t7\tSeparated\tAdm-clerical\tUnmarried\tAsian-Pac-Islander\tFemale\t0\t0\t40\tIndia\t<=50K\n28\tPrivate\t185061\t11th\t7\tNever-married\tHandlers-cleaners\tOther-relative\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n28\tPrivate\t201175\t11th\t7\tNever-married\tMachine-op-inspct\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n29\tPrivate\t183627\t11th\t7\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n29\tPrivate\t267034\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tBlack\tMale\t0\t0\t40\tHaiti\t<=50K\n29\tPrivate\t285290\t11th\t7\tNever-married\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n29\tPrivate\t200381\t11th\t7\tNever-married\tExec-managerial\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t30226\t11th\t7\tDivorced\tSales\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t152453\t11th\t7\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n30\tPrivate\t194901\t11th\t7\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\t?\t672412\t11th\t7\tSeparated\t?\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t193650\t11th\t7\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t257644\t11th\t7\tNever-married\tTransport-moving\tUnmarried\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\tPrivate\t375833\t11th\t7\tNever-married\tCraft-repair\tOther-relative\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\tPrivate\t195576\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n33\tPrivate\t145434\t11th\t7\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n33\tPrivate\t78707\t11th\t7\tNever-married\tOther-service\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n33\tPrivate\t83446\t11th\t7\tDivorced\tExec-managerial\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t>50K\n34\tLocal-gov\t243867\t11th\t7\tSeparated\tMachine-op-inspct\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n35\tPrivate\t25955\t11th\t7\tNever-married\tOther-service\tUnmarried\tAmer-Indian-Eskimo\tMale\t0\t0\t40\tUnited-States\t<=50K\n35\tPrivate\t103605\t11th\t7\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n35\tPrivate\t538583\t11th\t7\tSeparated\tTransport-moving\tNot-in-family\tBlack\tMale\t3674\t0\t40\tUnited-States\t<=50K\n39\tPrivate\t33487\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n41\tPrivate\t179875\t11th\t7\tDivorced\tOther-service\tUnmarried\tOther\tFemale\t0\t0\t40\tUnited-States\t<=50K\n41\tPrivate\t184102\t11th\t7\tDivorced\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n41\tPrivate\t67339\t11th\t7\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t7688\t0\t40\tUnited-States\t>50K\n41\tPrivate\t160837\t11th\t7\tMarried-spouse-absent\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t40\tGuatemala\t<=50K\n42\tFederal-gov\t296798\t11th\t7\tNever-married\tTech-support\tNot-in-family\tWhite\tMale\t0\t1340\t40\tUnited-States\t<=50K\n42\tPrivate\t250121\t11th\t7\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n43\tPrivate\t76487\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n43\tPrivate\t117037\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t2042\t40\tUnited-States\t<=50K\n45\tPrivate\t99179\t11th\t7\tWidowed\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n45\tPrivate\t125194\t11th\t7\tNever-married\tMachine-op-inspct\tNot-in-family\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n46\tFederal-gov\t340718\t11th\t7\tWidowed\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n46\tPrivate\t166003\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n47\tPrivate\t161676\t11th\t7\tNever-married\tTransport-moving\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n47\tPrivate\t239439\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tWife\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n48\tPrivate\t176140\t11th\t7\tDivorced\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n48\tPrivate\t413363\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t2002\t40\tUnited-States\t<=50K\n48\tPrivate\t242406\t11th\t7\tNever-married\tMachine-op-inspct\tUnmarried\tWhite\tMale\t0\t0\t40\tPuerto-Rico\t<=50K\n49\tState-gov\t203039\t11th\t7\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n49\tPrivate\t240859\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tCuba\t<=50K\n50\t?\t156008\t11th\t7\tMarried-civ-spouse\t?\tOwn-child\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n50\tPrivate\t157043\t11th\t7\tDivorced\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t302146\t11th\t7\tNever-married\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n52\tPrivate\t131631\t11th\t7\tSeparated\tMachine-op-inspct\tUnmarried\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n53\tPrivate\t90624\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n53\tPrivate\t200576\t11th\t7\tDivorced\tCraft-repair\tOther-relative\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n53\tPrivate\t358056\t11th\t7\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n53\tPrivate\t234721\t11th\t7\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n56\tPrivate\t117881\t11th\t7\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n59\tPrivate\t283005\t11th\t7\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n59\tPrivate\t183606\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n60\tPrivate\t121832\t11th\t7\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n60\tPrivate\t185749\t11th\t7\tWidowed\tTransport-moving\tUnmarried\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n64\tPrivate\t181232\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t2179\t40\tUnited-States\t<=50K\n64\tLocal-gov\t244903\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t>50K\n65\tPrivate\t161400\t11th\t7\tWidowed\tOther-service\tUnmarried\tOther\tMale\t0\t0\t40\tUnited-States\t<=50K\n66\t?\t117778\t11th\t7\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n69\tPrivate\t136218\t11th\t7\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n72\tPrivate\t205343\t11th\t7\tWidowed\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n90\tPrivate\t313986\t11th\t7\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t106347\t11th\t7\tSeparated\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t42\tUnited-States\t<=50K\n32\tPrivate\t80058\t11th\t7\tDivorced\tSales\tNot-in-family\tWhite\tMale\t0\t0\t43\tUnited-States\t>50K\n46\tPrivate\t134727\t11th\t7\tDivorced\tMachine-op-inspct\tUnmarried\tAmer-Indian-Eskimo\tMale\t0\t0\t43\tGermany\t<=50K\n37\tPrivate\t186934\t11th\t7\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t3103\t0\t44\tUnited-States\t>50K\n17\tPrivate\t48610\t11th\t7\tNever-married\tFarming-fishing\tOwn-child\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n24\tLocal-gov\t289886\t11th\t7\tNever-married\tOther-service\tNot-in-family\tAsian-Pac-Islander\tMale\t0\t0\t45\tUnited-States\t<=50K\n28\tPrivate\t132870\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n30\tPrivate\t27207\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n35\tPrivate\t107991\t11th\t7\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n38\tPrivate\t187046\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n56\tPrivate\t250873\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n44\tPrivate\t109339\t11th\t7\tDivorced\tMachine-op-inspct\tUnmarried\tOther\tFemale\t0\t0\t46\tPuerto-Rico\t<=50K\n25\tPrivate\t259688\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n26\tPrivate\t31350\t11th\t7\tNever-married\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n28\tSelf-emp-inc\t210295\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t>50K\n36\tPrivate\t158592\t11th\t7\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n38\tPrivate\t28887\t11th\t7\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n42\tLocal-gov\t195124\t11th\t7\tDivorced\tSales\tUnmarried\tWhite\tMale\t7430\t0\t50\tPuerto-Rico\t>50K\n48\tPrivate\t285335\t11th\t7\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n48\tSelf-emp-not-inc\t104790\t11th\t7\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t>50K\n56\tPrivate\t77415\t11th\t7\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n18\tPrivate\t232024\t11th\t7\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tMale\t0\t0\t55\tUnited-States\t<=50K\n28\t?\t157813\t11th\t7\tDivorced\t?\tUnmarried\tWhite\tFemale\t0\t0\t58\tCanada\t<=50K\n65\tSelf-emp-not-inc\t118474\t11th\t7\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t9386\t0\t59\t?\t>50K\n19\tPrivate\t176570\t11th\t7\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tMale\t0\t0\t60\tUnited-States\t<=50K\n23\tSelf-emp-not-inc\t282604\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t7688\t0\t60\tUnited-States\t>50K\n29\tPrivate\t121523\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t<=50K\n35\t?\t296738\t11th\t7\tSeparated\t?\tNot-in-family\tWhite\tFemale\t6849\t0\t60\tUnited-States\t<=50K\n37\tPrivate\t30267\t11th\t7\tNever-married\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t60\tUnited-States\t>50K\n38\tPrivate\t241765\t11th\t7\tDivorced\tHandlers-cleaners\tNot-in-family\tWhite\tFemale\t0\t0\t60\tUnited-States\t<=50K\n44\tPrivate\t129100\t11th\t7\tSeparated\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t60\tUnited-States\t<=50K\n39\tPrivate\t328466\t11th\t7\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t2407\t0\t70\tMexico\t<=50K\n50\tPrivate\t156877\t11th\t7\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t70\tUnited-States\t>50K\n34\t?\t346762\t11th\t7\tDivorced\t?\tOwn-child\tWhite\tMale\t0\t0\t84\tUnited-States\t<=50K\n35\tPrivate\t199288\t11th\t7\tSeparated\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t90\tUnited-States\t<=50K\n18\tPrivate\t208447\t12th\t8\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t6\tUnited-States\t<=50K\n17\tPrivate\t206010\t12th\t8\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t8\tUnited-States\t<=50K\n17\tPrivate\t160118\t12th\t8\tNever-married\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t10\t?\t<=50K\n18\tPrivate\t129010\t12th\t8\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n17\tPrivate\t102726\t12th\t8\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n17\tPrivate\t316929\t12th\t8\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n23\tPrivate\t482082\t12th\t8\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tMale\t0\t0\t21\tMexico\t<=50K\n18\tPrivate\t167147\t12th\t8\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n18\t?\t165532\t12th\t8\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n18\tPrivate\t127388\t12th\t8\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n18\tLocal-gov\t36411\t12th\t8\tNever-married\tProf-specialty\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n18\tPrivate\t231193\t12th\t8\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n19\tFederal-gov\t27433\t12th\t8\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n22\t?\t87867\t12th\t8\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n28\tSelf-emp-not-inc\t33035\t12th\t8\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n68\tPrivate\t75913\t12th\t8\tWidowed\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n18\tPrivate\t266287\t12th\t8\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n36\t?\t356838\t12th\t8\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n49\tPrivate\t105444\t12th\t8\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t39\tUnited-States\t<=50K\n17\t?\t103810\t12th\t8\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t194059\t12th\t8\tNever-married\tAdm-clerical\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tLocal-gov\t134935\t12th\t8\tNever-married\tProtective-serv\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n20\tPrivate\t176321\t12th\t8\tNever-married\tAdm-clerical\tOwn-child\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n20\tPrivate\t189888\t12th\t8\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n22\t?\t291407\t12th\t8\tNever-married\t?\tOwn-child\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t374918\t12th\t8\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t302465\t12th\t8\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t1741\t40\tUnited-States\t<=50K\n29\tPrivate\t319149\t12th\t8\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n30\tPrivate\t327202\t12th\t8\tNever-married\tCraft-repair\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t219838\t12th\t8\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\tSelf-emp-not-inc\t350624\t12th\t8\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\tPrivate\t256609\t12th\t8\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n33\tPrivate\t637222\t12th\t8\tDivorced\tMachine-op-inspct\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n35\tSelf-emp-not-inc\t216129\t12th\t8\tNever-married\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t40\tTrinadad&Tobago\t<=50K\n36\tPrivate\t29984\t12th\t8\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n39\tPrivate\t74163\t12th\t8\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n40\tPrivate\t229148\t12th\t8\tMarried-civ-spouse\tOther-service\tHusband\tBlack\tMale\t0\t0\t40\tJamaica\t<=50K\n44\tPrivate\t139338\t12th\t8\tDivorced\tTransport-moving\tUnmarried\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n44\tSelf-emp-not-inc\t335183\t12th\t8\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tBlack\tMale\t0\t0\t40\tUnited-States\t>50K\n45\tPrivate\t304570\t12th\t8\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tAsian-Pac-Islander\tMale\t0\t0\t40\t?\t<=50K\n48\tPrivate\t300168\t12th\t8\tSeparated\tAdm-clerical\tOwn-child\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n52\t?\t271749\t12th\t8\tNever-married\t?\tOther-relative\tBlack\tMale\t594\t0\t40\tUnited-States\t<=50K\n56\tPrivate\t174351\t12th\t8\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tItaly\t<=50K\n58\t?\t178660\t12th\t8\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n62\tSelf-emp-not-inc\t134768\t12th\t8\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n63\tPrivate\t230823\t12th\t8\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tCuba\t<=50K\n25\tPrivate\t98281\t12th\t8\tNever-married\tHandlers-cleaners\tUnmarried\tWhite\tMale\t0\t0\t43\tUnited-States\t<=50K\n26\tPrivate\t138152\t12th\t8\tNever-married\tCraft-repair\tOther-relative\tOther\tMale\t0\t0\t48\tGuatemala\t<=50K\n30\tPrivate\t142675\t12th\t8\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n35\tPrivate\t92440\t12th\t8\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t50\tUnited-States\t>50K\n36\tPrivate\t192704\t12th\t8\tNever-married\tExec-managerial\tNot-in-family\tWhite\tMale\t4650\t0\t50\tUnited-States\t<=50K\n39\tLocal-gov\t207853\t12th\t8\tMarried-civ-spouse\tTech-support\tHusband\tWhite\tMale\t0\t0\t50\tUnited-States\t<=50K\n58\tSelf-emp-not-inc\t211547\t12th\t8\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t52\tUnited-States\t<=50K\n44\tLocal-gov\t33068\t12th\t8\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t55\tUnited-States\t<=50K\n30\tSelf-emp-not-inc\t106742\t12th\t8\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t75\tUnited-States\t<=50K\n68\t?\t38317\t1st-4th\t2\tDivorced\t?\tNot-in-family\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n56\tSelf-emp-not-inc\t236731\t1st-4th\t2\tSeparated\tExec-managerial\tNot-in-family\tWhite\tMale\t0\t0\t25\t?\t<=50K\n46\tPrivate\t308334\t1st-4th\t2\tWidowed\tOther-service\tUnmarried\tOther\tFemale\t0\t0\t30\tMexico\t<=50K\n57\tPrivate\t190942\t1st-4th\t2\tWidowed\tPriv-house-serv\tNot-in-family\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n63\tSelf-emp-not-inc\t795830\t1st-4th\t2\tWidowed\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t30\tEl-Salvador\t<=50K\n41\tPrivate\t328447\t1st-4th\t2\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t35\tMexico\t<=50K\n63\t?\t401531\t1st-4th\t2\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n33\tPrivate\t373662\t1st-4th\t2\tMarried-spouse-absent\tPriv-house-serv\tNot-in-family\tWhite\tFemale\t0\t0\t40\tGuatemala\t<=50K\n37\tPrivate\t191342\t1st-4th\t2\tMarried-civ-spouse\tCraft-repair\tHusband\tAsian-Pac-Islander\tMale\t0\t0\t40\tCambodia\t<=50K\n37\tPrivate\t405723\t1st-4th\t2\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n40\tPrivate\t153799\t1st-4th\t2\tMarried-spouse-absent\tMachine-op-inspct\tUnmarried\tWhite\tFemale\t0\t0\t40\tDominican-Republic\t<=50K\n41\tPrivate\t237321\t1st-4th\t2\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t>50K\n43\tPrivate\t188291\t1st-4th\t2\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n44\tPrivate\t367749\t1st-4th\t2\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tEl-Salvador\t<=50K\n50\tPrivate\t193374\t1st-4th\t2\tMarried-spouse-absent\tCraft-repair\tUnmarried\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n52\tPrivate\t190762\t1st-4th\t2\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n56\tPrivate\t136413\t1st-4th\t2\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n57\tPrivate\t348430\t1st-4th\t2\tDivorced\tCraft-repair\tUnmarried\tWhite\tMale\t0\t0\t40\tPortugal\t<=50K\n64\t?\t187656\t1st-4th\t2\tDivorced\t?\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n28\tPrivate\t339897\t1st-4th\t2\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t43\tMexico\t<=50K\n39\tPrivate\t324420\t1st-4th\t2\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t45\tMexico\t<=50K\n64\tPrivate\t164204\t1st-4th\t2\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t53\t?\t<=50K\n75\tPrivate\t95985\t5th-6th\t3\tWidowed\tOther-service\tUnmarried\tBlack\tMale\t0\t0\t10\tUnited-States\t<=50K\n29\tPrivate\t457402\t5th-6th\t3\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t25\tMexico\t<=50K\n55\tPrivate\t282753\t5th-6th\t3\tDivorced\tOther-service\tUnmarried\tBlack\tMale\t0\t0\t25\tUnited-States\t<=50K\n53\t?\t251804\t5th-6th\t3\tWidowed\t?\tUnmarried\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n60\t?\t131852\t5th-6th\t3\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t>50K\n37\tPrivate\t327323\t5th-6th\t3\tSeparated\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t32\tGuatemala\t<=50K\n27\tPrivate\t127796\t5th-6th\t3\tNever-married\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t35\tMexico\t<=50K\n19\tPrivate\t130431\t5th-6th\t3\tNever-married\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t36\tMexico\t<=50K\n37\tPrivate\t336598\t5th-6th\t3\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t36\tMexico\t<=50K\n50\tPrivate\t237735\t5th-6th\t3\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t37\tMexico\t<=50K\n42\tPrivate\t274198\t5th-6th\t3\tMarried-civ-spouse\tMachine-op-inspct\tWife\tWhite\tFemale\t0\t0\t38\tMexico\t<=50K\n45\tPrivate\t199590\t5th-6th\t3\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t38\tMexico\t<=50K\n20\tPrivate\t221955\t5th-6th\t3\tMarried-spouse-absent\tFarming-fishing\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n22\tPrivate\t399087\t5th-6th\t3\tMarried-civ-spouse\tMachine-op-inspct\tOther-relative\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n23\tPrivate\t403107\t5th-6th\t3\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t40\tEl-Salvador\t<=50K\n23\tPrivate\t197387\t5th-6th\t3\tMarried-civ-spouse\tTransport-moving\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n24\tPrivate\t238917\t5th-6th\t3\tNever-married\tCraft-repair\tUnmarried\tWhite\tMale\t0\t0\t40\tEl-Salvador\t<=50K\n25\tPrivate\t503012\t5th-6th\t3\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n26\tPrivate\t202203\t5th-6th\t3\tNever-married\tAdm-clerical\tOwn-child\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n27\tPrivate\t258102\t5th-6th\t3\tNever-married\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n27\tPrivate\t150025\t5th-6th\t3\tNever-married\tHandlers-cleaners\tOther-relative\tWhite\tMale\t0\t0\t40\tGuatemala\t<=50K\n28\tPrivate\t128509\t5th-6th\t3\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\t?\t<=50K\n35\tPrivate\t63921\t5th-6th\t3\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n36\tPrivate\t252327\t5th-6th\t3\tMarried-spouse-absent\tCraft-repair\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n41\tPrivate\t243607\t5th-6th\t3\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n43\tPrivate\t128212\t5th-6th\t3\tMarried-civ-spouse\tMachine-op-inspct\tWife\tAsian-Pac-Islander\tFemale\t0\t0\t40\tVietnam\t>50K\n43\tPrivate\t222971\t5th-6th\t3\tNever-married\tMachine-op-inspct\tUnmarried\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n46\tPrivate\t216666\t5th-6th\t3\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n47\tPrivate\t185254\t5th-6th\t3\tNever-married\tPriv-house-serv\tOwn-child\tWhite\tFemale\t0\t0\t40\tEl-Salvador\t<=50K\n52\tPrivate\t188644\t5th-6th\t3\tMarried-spouse-absent\tCraft-repair\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n57\tPrivate\t188872\t5th-6th\t3\tDivorced\tTransport-moving\tUnmarried\tWhite\tMale\t6497\t0\t40\tUnited-States\t<=50K\n58\tPrivate\t132606\t5th-6th\t3\tDivorced\tMachine-op-inspct\tNot-in-family\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n60\tPrivate\t162947\t5th-6th\t3\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tPuerto-Rico\t<=50K\n61\tPrivate\t43554\t5th-6th\t3\tNever-married\tHandlers-cleaners\tNot-in-family\tBlack\tMale\t0\t2339\t40\tUnited-States\t<=50K\n66\tPrivate\t142723\t5th-6th\t3\tMarried-spouse-absent\tHandlers-cleaners\tUnmarried\tWhite\tFemale\t0\t0\t40\tPuerto-Rico\t<=50K\n69\t?\t473040\t5th-6th\t3\tDivorced\t?\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n46\tPrivate\t214955\t5th-6th\t3\tDivorced\tCraft-repair\tNot-in-family\tWhite\tFemale\t0\t2339\t45\tUnited-States\t<=50K\n17\tPrivate\t270942\t5th-6th\t3\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t48\tMexico\t<=50K\n26\tPrivate\t280093\t5th-6th\t3\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t1628\t50\tUnited-States\t<=50K\n47\tSelf-emp-inc\t248145\t5th-6th\t3\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t50\tCuba\t<=50K\n47\tSelf-emp-not-inc\t121124\t5th-6th\t3\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t55\tItaly\t>50K\n61\tPrivate\t123273\t5th-6th\t3\tDivorced\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t1876\t56\tUnited-States\t<=50K\n38\tPrivate\t407068\t5th-6th\t3\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t75\tMexico\t<=50K\n74\t?\t29866\t7th-8th\t4\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t2\tUnited-States\t<=50K\n76\t?\t431192\t7th-8th\t4\tWidowed\t?\tNot-in-family\tWhite\tMale\t0\t0\t2\tUnited-States\t<=50K\n66\t?\t149422\t7th-8th\t4\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t4\tUnited-States\t<=50K\n82\t?\t29441\t7th-8th\t4\tWidowed\t?\tNot-in-family\tWhite\tMale\t0\t0\t5\tUnited-States\t<=50K\n68\t?\t140282\t7th-8th\t4\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t8\tUnited-States\t<=50K\n68\t?\t133758\t7th-8th\t4\tWidowed\t?\tNot-in-family\tBlack\tMale\t0\t0\t10\tUnited-States\t<=50K\n73\tLocal-gov\t232871\t7th-8th\t4\tMarried-civ-spouse\tProtective-serv\tHusband\tWhite\tMale\t2228\t0\t10\tUnited-States\t<=50K\n76\tPrivate\t316185\t7th-8th\t4\tWidowed\tProtective-serv\tNot-in-family\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n68\tSelf-emp-not-inc\t241174\t7th-8th\t4\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n55\tPrivate\t198385\t7th-8th\t4\tWidowed\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t20\t?\t<=50K\n56\tPrivate\t128764\t7th-8th\t4\tWidowed\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n58\t?\t198478\t7th-8th\t4\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n23\tPrivate\t287681\t7th-8th\t4\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t25\tMexico\t<=50K\n54\t?\t187221\t7th-8th\t4\tNever-married\t?\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n70\tPrivate\t131060\t7th-8th\t4\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n28\tPrivate\t124905\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n31\tPrivate\t235389\t7th-8th\t4\tNever-married\tHandlers-cleaners\tNot-in-family\tWhite\tFemale\t0\t0\t30\tPortugal\t<=50K\n47\tPrivate\t121958\t7th-8th\t4\tMarried-spouse-absent\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n50\tPrivate\t196963\t7th-8th\t4\tDivorced\tCraft-repair\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n51\tPrivate\t135190\t7th-8th\t4\tSeparated\tMachine-op-inspct\tNot-in-family\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n64\tFederal-gov\t199298\t7th-8th\t4\tWidowed\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t30\tPuerto-Rico\t<=50K\n66\t?\t107112\t7th-8th\t4\tNever-married\t?\tOther-relative\tBlack\tMale\t0\t0\t30\tUnited-States\t<=50K\n68\t?\t196782\t7th-8th\t4\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n80\tSelf-emp-not-inc\t184335\t7th-8th\t4\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n61\tPrivate\t147845\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t31\tUnited-States\t<=50K\n27\tPrivate\t587310\t7th-8th\t4\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t35\tGuatemala\t<=50K\n36\tSelf-emp-not-inc\t367020\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n44\tPrivate\t219288\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n47\tPrivate\t73064\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tWife\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n23\tPrivate\t238917\t7th-8th\t4\tNever-married\tCraft-repair\tOther-relative\tWhite\tMale\t0\t0\t36\tUnited-States\t<=50K\n23\tPrivate\t260019\t7th-8th\t4\tNever-married\tFarming-fishing\tUnmarried\tOther\tMale\t0\t0\t36\tMexico\t<=50K\n24\tPrivate\t296045\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t2635\t0\t38\tUnited-States\t<=50K\n61\tPrivate\t238611\t7th-8th\t4\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t38\tUnited-States\t<=50K\n19\t?\t365871\t7th-8th\t4\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n23\tPrivate\t336360\t7th-8th\t4\tNever-married\tCraft-repair\tOwn-child\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n24\tPrivate\t308205\t7th-8th\t4\tNever-married\tHandlers-cleaners\tOther-relative\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n25\tPrivate\t179462\t7th-8th\t4\tNever-married\tHandlers-cleaners\tUnmarried\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n25\tPrivate\t282063\t7th-8th\t4\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n26\tPrivate\t292353\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tOther\tMale\t0\t0\t40\tUnited-States\t<=50K\n28\tPrivate\t166481\t7th-8th\t4\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tOther\tMale\t0\t2179\t40\tPuerto-Rico\t<=50K\n30\tLocal-gov\t261319\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n30\tPrivate\t23778\t7th-8th\t4\tSeparated\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t169269\t7th-8th\t4\tNever-married\tCraft-repair\tUnmarried\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t332379\t7th-8th\t4\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tLocal-gov\t125927\t7th-8th\t4\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t407930\t7th-8th\t4\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n31\tPrivate\t259931\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\t?\t158784\t7th-8th\t4\tWidowed\t?\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n32\t?\t293936\t7th-8th\t4\tMarried-spouse-absent\t?\tNot-in-family\tWhite\tMale\t0\t0\t40\t?\t<=50K\n32\tPrivate\t408328\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n32\tLocal-gov\t49325\t7th-8th\t4\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n33\tPrivate\t189843\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n35\tPrivate\t227571\t7th-8th\t4\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n35\tPrivate\t230054\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t<=50K\n40\tLocal-gov\t289403\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tBlack\tMale\t0\t1887\t40\t?\t>50K\n40\tPrivate\t286370\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tMexico\t>50K\n45\tPrivate\t433665\t7th-8th\t4\tSeparated\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t40\tMexico\t<=50K\n48\tSelf-emp-not-inc\t213140\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tItaly\t<=50K\n48\tPrivate\t107373\t7th-8th\t4\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n50\tPrivate\t193884\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tEcuador\t<=50K\n50\tPrivate\t204518\t7th-8th\t4\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n50\tPrivate\t170050\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t120914\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t2961\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t269080\t7th-8th\t4\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t154342\t7th-8th\t4\tMarried-civ-spouse\tProtective-serv\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n51\tPrivate\t224763\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tCuba\t<=50K\n52\tPrivate\t202956\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n52\tSelf-emp-not-inc\t242341\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tAmer-Indian-Eskimo\tMale\t0\t0\t40\tUnited-States\t<=50K\n53\tPrivate\t121618\t7th-8th\t4\tNever-married\tTransport-moving\tNot-in-family\tAmer-Indian-Eskimo\tMale\t0\t0\t40\tUnited-States\t<=50K\n54\tPrivate\t101017\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n54\tSelf-emp-inc\t125417\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t>50K\n56\tSelf-emp-not-inc\t385632\t7th-8th\t4\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n56\tPrivate\t201822\t7th-8th\t4\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t2002\t40\tUnited-States\t<=50K\n56\tPrivate\t117148\t7th-8th\t4\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n60\tPrivate\t51290\t7th-8th\t4\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n61\tPrivate\t146788\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n61\tPrivate\t159046\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n62\tPrivate\t273454\t7th-8th\t4\tMarried-civ-spouse\tProtective-serv\tHusband\tWhite\tMale\t0\t0\t40\tCuba\t<=50K\n63\tPrivate\t151210\t7th-8th\t4\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n63\tPrivate\t30602\t7th-8th\t4\tMarried-spouse-absent\tOther-service\tUnmarried\tAmer-Indian-Eskimo\tFemale\t0\t0\t40\tUnited-States\t<=50K\n65\tPrivate\t174056\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n67\tSelf-emp-not-inc\t141797\t7th-8th\t4\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n27\tPrivate\t200500\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t45\tUnited-States\t<=50K\n34\tPrivate\t245487\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tAmer-Indian-Eskimo\tMale\t0\t0\t45\tMexico\t<=50K\n46\tPrivate\t111979\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t47\tUnited-States\t<=50K\n21\tPrivate\t315065\t7th-8th\t4\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t48\tMexico\t<=50K\n28\tPrivate\t162551\t7th-8th\t4\tMarried-civ-spouse\tMachine-op-inspct\tOther-relative\tAsian-Pac-Islander\tFemale\t0\t0\t48\tChina\t<=50K\n45\tPrivate\t204205\t7th-8th\t4\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t48\tUnited-States\t<=50K\n60\tPrivate\t39352\t7th-8th\t4\tNever-married\tTransport-moving\tNot-in-family\tWhite\tMale\t0\t0\t48\tUnited-States\t>50K\n50\tPrivate\t273536\t7th-8th\t4\tMarried-civ-spouse\tSales\tHusband\tOther\tMale\t0\t0\t49\tDominican-Republic\t<=50K\n53\tSelf-emp-not-inc\t174102\t7th-8th\t4\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t4386\t0\t50\tGreece\t>50K\n62\t?\t225652\t7th-8th\t4\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t3411\t0\t50\tUnited-States\t<=50K\n65\tWithout-pay\t27012\t7th-8th\t4\tWidowed\tFarming-fishing\tUnmarried\tWhite\tFemale\t0\t0\t50\tUnited-States\t<=50K\n63\tLocal-gov\t28856\t7th-8th\t4\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t55\tUnited-States\t<=50K\n47\tPrivate\t379118\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tBlack\tMale\t0\t0\t60\tUnited-States\t>50K\n53\tPrivate\t137428\t7th-8th\t4\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t>50K\n55\tPrivate\t189528\t7th-8th\t4\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t60\tUnited-States\t<=50K\n35\tSelf-emp-not-inc\t207568\t7th-8th\t4\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t75\tUnited-States\t<=50K\n28\t?\t200819\t7th-8th\t4\tDivorced\t?\tOwn-child\tWhite\tMale\t0\t0\t84\tUnited-States\t<=50K\n76\t?\t211453\tHS-grad\t9\tWidowed\t?\tNot-in-family\tBlack\tFemale\t0\t0\t2\tUnited-States\t<=50K\n18\tPrivate\t301948\tHS-grad\t9\tNever-married\tProtective-serv\tOwn-child\tWhite\tMale\t34095\t0\t3\tUnited-States\t<=50K\n19\tPrivate\t171306\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tMale\t0\t0\t3\tUnited-States\t<=50K\n61\tPrivate\t92691\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t3\tUnited-States\t<=50K\n67\t?\t150516\tHS-grad\t9\tWidowed\t?\tUnmarried\tWhite\tMale\t0\t0\t3\tUnited-States\t<=50K\n64\t?\t29825\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t5\tUnited-States\t<=50K\n70\tPrivate\t121993\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t5\tUnited-States\t<=50K\n61\t?\t190997\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t6\tUnited-States\t<=50K\n63\tPrivate\t86108\tHS-grad\t9\tWidowed\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t6\tUnited-States\t<=50K\n77\tLocal-gov\t144608\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t6\tUnited-States\t<=50K\n23\tPrivate\t153044\tHS-grad\t9\tNever-married\tHandlers-cleaners\tUnmarried\tBlack\tFemale\t0\t0\t7\tUnited-States\t<=50K\n18\tPrivate\t152545\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t8\tUnited-States\t<=50K\n45\tPrivate\t26781\tHS-grad\t9\tMarried-civ-spouse\tProf-specialty\tHusband\tAmer-Indian-Eskimo\tMale\t0\t0\t8\tUnited-States\t<=50K\n47\t?\t331650\tHS-grad\t9\tMarried-civ-spouse\t?\tWife\tWhite\tFemale\t0\t0\t8\tUnited-States\t>50K\n65\tLocal-gov\t205024\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t8\tUnited-States\t<=50K\n69\t?\t183958\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t8\tUnited-States\t<=50K\n72\t?\t177226\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t8\tUnited-States\t<=50K\n72\t?\t213255\tHS-grad\t9\tWidowed\t?\tNot-in-family\tWhite\tFemale\t0\t0\t8\tUnited-States\t<=50K\n47\tPrivate\t379118\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tBlack\tMale\t0\t0\t9\tUnited-States\t<=50K\n18\t?\t297396\tHS-grad\t9\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n25\tPrivate\t73895\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t10\tUnited-States\t<=50K\n30\tPrivate\t35644\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n37\tSelf-emp-not-inc\t168496\tHS-grad\t9\tDivorced\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n45\t?\t28359\tHS-grad\t9\tSeparated\t?\tUnmarried\tWhite\tFemale\t0\t0\t10\tUnited-States\t<=50K\n48\tPrivate\t107682\tHS-grad\t9\tWidowed\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t10\tUnited-States\t<=50K\n65\tPrivate\t169047\tHS-grad\t9\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t10\tUnited-States\t<=50K\n18\t?\t139003\tHS-grad\t9\tNever-married\t?\tOther-relative\tOther\tFemale\t0\t0\t12\tUnited-States\t<=50K\n18\tPrivate\t183930\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t12\tUnited-States\t<=50K\n19\t?\t20469\tHS-grad\t9\tNever-married\t?\tOther-relative\tAsian-Pac-Islander\tFemale\t0\t0\t12\tSouth\t<=50K\n19\t?\t37332\tHS-grad\t9\tNever-married\t?\tOwn-child\tWhite\tFemale\t1055\t0\t12\tUnited-States\t<=50K\n26\tPrivate\t195216\tHS-grad\t9\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n31\tPrivate\t165148\tHS-grad\t9\tSeparated\tExec-managerial\tUnmarried\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n35\t?\t200426\tHS-grad\t9\tMarried-civ-spouse\t?\tWife\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n37\tPrivate\t166744\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n37\tPrivate\t26898\tHS-grad\t9\tDivorced\tExec-managerial\tUnmarried\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n58\t?\t142158\tHS-grad\t9\tWidowed\t?\tNot-in-family\tWhite\tFemale\t0\t0\t12\tUnited-States\t<=50K\n19\tPrivate\t262601\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t14\tUnited-States\t<=50K\n77\t?\t147284\tHS-grad\t9\tWidowed\t?\tNot-in-family\tWhite\tFemale\t0\t0\t14\tUnited-States\t<=50K\n18\tPrivate\t57957\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t15\tUnited-States\t<=50K\n18\tPrivate\t201901\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tFemale\t0\t1719\t15\tUnited-States\t<=50K\n45\tPrivate\t123283\tHS-grad\t9\tSeparated\tMachine-op-inspct\tUnmarried\tBlack\tFemale\t0\t0\t15\tUnited-States\t<=50K\n50\tPrivate\t144968\tHS-grad\t9\tNever-married\tTech-support\tOwn-child\tWhite\tMale\t0\t0\t15\tUnited-States\t<=50K\n71\tPrivate\t78277\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t15\tUnited-States\t<=50K\n73\tPrivate\t267408\tHS-grad\t9\tWidowed\tSales\tOther-relative\tWhite\tFemale\t0\t0\t15\tUnited-States\t<=50K\n21\tPrivate\t32616\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t1719\t16\tUnited-States\t<=50K\n23\tPrivate\t199070\tHS-grad\t9\tNever-married\tProtective-serv\tOwn-child\tBlack\tMale\t0\t0\t16\tUnited-States\t<=50K\n23\tPrivate\t129583\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tBlack\tFemale\t0\t0\t16\tUnited-States\t<=50K\n31\tPrivate\t162572\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n35\tPrivate\t148903\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t16\tUnited-States\t>50K\n59\t?\t192130\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n61\t?\t347089\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n65\tPrivate\t111095\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t16\tUnited-States\t<=50K\n67\tSelf-emp-not-inc\t116057\tHS-grad\t9\tDivorced\tExec-managerial\tNot-in-family\tWhite\tFemale\t3273\t0\t16\tUnited-States\t<=50K\n72\tPrivate\t38360\tHS-grad\t9\tWidowed\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t16\tUnited-States\t<=50K\n73\tPrivate\t247355\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t16\tCanada\t<=50K\n28\t?\t308493\tHS-grad\t9\tMarried-civ-spouse\t?\tWife\tWhite\tFemale\t0\t0\t17\tHonduras\t<=50K\n74\tFederal-gov\t181508\tHS-grad\t9\tWidowed\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t17\tUnited-States\t<=50K\n31\tPrivate\t118966\tHS-grad\t9\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t18\tUnited-States\t<=50K\n36\tPrivate\t101460\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t18\tUnited-States\t<=50K\n59\tPrivate\t99131\tHS-grad\t9\tWidowed\tProf-specialty\tNot-in-family\tWhite\tFemale\t0\t0\t18\tUnited-States\t<=50K\n64\tPrivate\t114994\tHS-grad\t9\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t0\t0\t18\tUnited-States\t<=50K\n66\t?\t190324\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tBlack\tMale\t0\t0\t18\tUnited-States\t<=50K\n18\tPrivate\t177722\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n19\tPrivate\t475028\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n19\tPrivate\t383637\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n20\tPrivate\t400443\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n20\tPrivate\t323573\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n21\tPrivate\t197387\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n22\tSelf-emp-not-inc\t199011\tHS-grad\t9\tNever-married\tCraft-repair\tOther-relative\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n22\tPrivate\t169188\tHS-grad\t9\tNever-married\tExec-managerial\tOwn-child\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n23\tPrivate\t183327\tHS-grad\t9\tNever-married\tCraft-repair\tOwn-child\tBlack\tFemale\t0\t1594\t20\tUnited-States\t<=50K\n24\tPrivate\t293579\tHS-grad\t9\tNever-married\tSales\tOwn-child\tBlack\tFemale\t0\t0\t20\tUnited-States\t<=50K\n29\tLocal-gov\t95393\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n31\tPrivate\t323069\tHS-grad\t9\tSeparated\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t20\t?\t<=50K\n33\tPrivate\t217235\tHS-grad\t9\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n33\tPrivate\t85043\tHS-grad\t9\tNever-married\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n34\tPrivate\t82623\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n36\tSelf-emp-not-inc\t288585\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tWife\tAsian-Pac-Islander\tFemale\t0\t0\t20\tSouth\t<=50K\n39\tSelf-emp-not-inc\t211785\tHS-grad\t9\tNever-married\tCraft-repair\tOwn-child\tBlack\tFemale\t0\t0\t20\tUnited-States\t<=50K\n40\tPrivate\t105936\tHS-grad\t9\tMarried-civ-spouse\tSales\tWife\tWhite\tFemale\t5013\t0\t20\tUnited-States\t<=50K\n41\tSelf-emp-not-inc\t196001\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n41\tPrivate\t308296\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tWife\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n49\tPrivate\t197418\tHS-grad\t9\tWidowed\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n51\t?\t163998\tHS-grad\t9\tMarried-spouse-absent\t?\tNot-in-family\tWhite\tMale\t0\t0\t20\tUnited-States\t>50K\n53\tPrivate\t104413\tHS-grad\t9\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t20\tUnited-States\t<=50K\n54\tFederal-gov\t175083\tHS-grad\t9\tNever-married\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n54\tPrivate\t176681\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tHusband\tBlack\tMale\t0\t0\t20\tUnited-States\t<=50K\n54\tPrivate\t302146\tHS-grad\t9\tSeparated\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t20\tUnited-States\t<=50K\n54\tPrivate\t96710\tHS-grad\t9\tMarried-civ-spouse\tPriv-house-serv\tOther-relative\tBlack\tFemale\t0\t0\t20\tUnited-States\t<=50K\n55\t?\t205527\tHS-grad\t9\tDivorced\t?\tNot-in-family\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n55\tSelf-emp-not-inc\t189721\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n56\tPrivate\t156052\tHS-grad\t9\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t594\t0\t20\tUnited-States\t<=50K\n58\tPrivate\t188982\tHS-grad\t9\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tBlack\tMale\t0\t0\t20\tUnited-States\t>50K\n64\tPrivate\t114994\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n65\tLocal-gov\t221026\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n66\tSelf-emp-not-inc\t212456\tHS-grad\t9\tWidowed\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n71\tSelf-emp-inc\t133821\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t20\tUnited-States\t>50K\n71\tPrivate\t105200\tHS-grad\t9\tMarried-civ-spouse\tProtective-serv\tHusband\tWhite\tMale\t6767\t0\t20\tUnited-States\t<=50K\n72\t?\t303588\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t20\tUnited-States\t<=50K\n77\tSelf-emp-not-inc\t145329\tHS-grad\t9\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t401\t0\t20\tUnited-States\t<=50K\n81\tSelf-emp-not-inc\t137018\tHS-grad\t9\tWidowed\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n83\t?\t251951\tHS-grad\t9\tWidowed\t?\tNot-in-family\tWhite\tFemale\t0\t0\t20\tUnited-States\t<=50K\n43\tPrivate\t122975\tHS-grad\t9\tMarried-civ-spouse\tMachine-op-inspct\tWife\tBlack\tFemale\t0\t0\t21\tTrinadad&Tobago\t<=50K\n22\t?\t35448\tHS-grad\t9\tMarried-civ-spouse\t?\tWife\tWhite\tFemale\t0\t0\t22\tUnited-States\t<=50K\n25\tPrivate\t104662\tHS-grad\t9\tNever-married\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t22\tUnited-States\t<=50K\n18\tPrivate\t280298\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n20\tPrivate\t70240\tHS-grad\t9\tNever-married\tSales\tOwn-child\tAsian-Pac-Islander\tFemale\t0\t0\t24\tPhilippines\t<=50K\n22\tPrivate\t103440\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n26\tPrivate\t153434\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n26\tPrivate\t333541\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n34\tPrivate\t441454\tHS-grad\t9\tNever-married\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n36\tPrivate\t108293\tHS-grad\t9\tWidowed\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n39\tPrivate\t680390\tHS-grad\t9\tSeparated\tMachine-op-inspct\tUnmarried\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n40\tPrivate\t163985\tHS-grad\t9\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n42\tSelf-emp-not-inc\t121718\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n59\tSelf-emp-not-inc\t80467\tHS-grad\t9\tDivorced\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n62\tPrivate\t159841\tHS-grad\t9\tWidowed\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n67\tLocal-gov\t258973\tHS-grad\t9\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n79\tSelf-emp-not-inc\t158319\tHS-grad\t9\tWidowed\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t24\tUnited-States\t<=50K\n80\t?\t107762\tHS-grad\t9\tWidowed\t?\tNot-in-family\tWhite\tMale\t0\t0\t24\tUnited-States\t<=50K\n19\tPrivate\t544091\tHS-grad\t9\tMarried-AF-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n19\tPrivate\t291429\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n20\tPrivate\t218343\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n23\tPrivate\t396099\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n23\tSelf-emp-not-inc\t519627\tHS-grad\t9\tNever-married\tFarming-fishing\tNot-in-family\tWhite\tMale\t0\t0\t25\tMexico\t<=50K\n25\tPrivate\t174592\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n26\tPrivate\t256263\tHS-grad\t9\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n27\tPrivate\t253814\tHS-grad\t9\tMarried-spouse-absent\tSales\tUnmarried\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n29\tSelf-emp-inc\t260729\tHS-grad\t9\tMarried-civ-spouse\tSales\tWife\tWhite\tFemale\t0\t1977\t25\tUnited-States\t>50K\n29\tPrivate\t32897\tHS-grad\t9\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n29\tPrivate\t131088\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n34\tPrivate\t187251\tHS-grad\t9\tDivorced\tProf-specialty\tUnmarried\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n37\tLocal-gov\t34173\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tWife\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n40\t?\t78255\tHS-grad\t9\tDivorced\t?\tNot-in-family\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n40\tPrivate\t282155\tHS-grad\t9\tSeparated\tOther-service\tOther-relative\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n46\tLocal-gov\t102076\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n46\tSelf-emp-not-inc\t317253\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n47\tPrivate\t188330\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n55\tPrivate\t326297\tHS-grad\t9\tSeparated\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n55\tSelf-emp-not-inc\t161334\tHS-grad\t9\tWidowed\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t25\tNicaragua\t<=50K\n56\t?\t656036\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t25\tUnited-States\t<=50K\n63\tPrivate\t134699\tHS-grad\t9\tWidowed\tProf-specialty\tNot-in-family\tWhite\tFemale\t0\t0\t25\tUnited-States\t<=50K\n19\tPrivate\t184759\tHS-grad\t9\tNever-married\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t26\tUnited-States\t<=50K\n30\tPrivate\t195750\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t27\tUnited-States\t<=50K\n58\tPrivate\t233193\tHS-grad\t9\tWidowed\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t27\tUnited-States\t<=50K\n18\tPrivate\t148644\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t28\tUnited-States\t<=50K\n20\tPrivate\t275691\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t28\tUnited-States\t<=50K\n32\t?\t282622\tHS-grad\t9\tDivorced\t?\tUnmarried\tWhite\tFemale\t0\t0\t28\tUnited-States\t<=50K\n38\tPrivate\t412296\tHS-grad\t9\tDivorced\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t28\tUnited-States\t<=50K\n45\tPrivate\t45857\tHS-grad\t9\tDivorced\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t28\tUnited-States\t<=50K\n46\tPrivate\t27669\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t28\tUnited-States\t<=50K\n46\tPrivate\t190487\tHS-grad\t9\tDivorced\tPriv-house-serv\tUnmarried\tWhite\tFemale\t0\t0\t28\tEcuador\t<=50K\n60\tPrivate\t181953\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t28\tUnited-States\t<=50K\n18\tPrivate\t446839\tHS-grad\t9\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n18\tPrivate\t97963\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n18\tPrivate\t226956\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t30\t?\t<=50K\n19\t?\t117201\tHS-grad\t9\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n19\tPrivate\t104112\tHS-grad\t9\tNever-married\tSales\tUnmarried\tBlack\tMale\t0\t0\t30\tHaiti\t<=50K\n19\tPrivate\t376540\tHS-grad\t9\tNever-married\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n19\tPrivate\t39026\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n19\tPrivate\t354104\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n20\tPrivate\t138768\tHS-grad\t9\tNever-married\tTransport-moving\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n20\tSelf-emp-inc\t182200\tHS-grad\t9\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n21\t?\t224209\tHS-grad\t9\tMarried-civ-spouse\t?\tWife\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n21\tPrivate\t174503\tHS-grad\t9\tNever-married\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n21\tPrivate\t57711\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n22\tPrivate\t24896\tHS-grad\t9\tDivorced\tTech-support\tUnmarried\tWhite\tFemale\t0\t0\t30\tGermany\t<=50K\n23\t?\t196816\tHS-grad\t9\tNever-married\t?\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n24\tPrivate\t106085\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tBlack\tMale\t0\t1721\t30\tUnited-States\t<=50K\n24\tPrivate\t184400\tHS-grad\t9\tNever-married\tTransport-moving\tOwn-child\tAsian-Pac-Islander\tMale\t0\t0\t30\t?\t<=50K\n25\tPrivate\t106889\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n25\tPrivate\t220098\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n25\tPrivate\t241626\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n25\tSelf-emp-not-inc\t189027\tHS-grad\t9\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n25\tPrivate\t204338\tHS-grad\t9\tNever-married\tFarming-fishing\tUnmarried\tWhite\tMale\t0\t0\t30\t?\t<=50K\n25\tPrivate\t60726\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tBlack\tMale\t0\t0\t30\tUnited-States\t<=50K\n26\tState-gov\t197156\tHS-grad\t9\tDivorced\tAdm-clerical\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n26\tPrivate\t167350\tHS-grad\t9\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n27\tPrivate\t93235\tHS-grad\t9\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n27\tPrivate\t262478\tHS-grad\t9\tNever-married\tFarming-fishing\tOwn-child\tBlack\tMale\t0\t0\t30\tUnited-States\t<=50K\n28\tPrivate\t159001\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n29\tPrivate\t31166\tHS-grad\t9\tDivorced\tProf-specialty\tNot-in-family\tOther\tFemale\t0\t0\t30\tGermany\t<=50K\n31\tPrivate\t185732\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n31\tPrivate\t185814\tHS-grad\t9\tNever-married\tTransport-moving\tUnmarried\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n32\tPrivate\t172579\tHS-grad\t9\tSeparated\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n33\tPrivate\t143653\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n34\tPrivate\t114973\tHS-grad\t9\tSeparated\tExec-managerial\tUnmarried\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n34\tPrivate\t289984\tHS-grad\t9\tDivorced\tPriv-house-serv\tUnmarried\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n34\tPrivate\t509364\tHS-grad\t9\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n35\tSelf-emp-not-inc\t267776\tHS-grad\t9\tNever-married\tOther-service\tOther-relative\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n35\tPrivate\t332588\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n38\tPrivate\t296125\tHS-grad\t9\tSeparated\tPriv-house-serv\tUnmarried\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n38\tPrivate\t234962\tHS-grad\t9\tMarried-civ-spouse\tHandlers-cleaners\tHusband\tWhite\tMale\t2829\t0\t30\tMexico\t<=50K\n41\tPrivate\t31221\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n42\tPrivate\t209370\tHS-grad\t9\tSeparated\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n44\tPrivate\t174491\tHS-grad\t9\tWidowed\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n46\tPrivate\t189763\tHS-grad\t9\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n48\tPrivate\t185039\tHS-grad\t9\tDivorced\tSales\tUnmarried\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n50\tSelf-emp-not-inc\t124793\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n54\t?\t32385\tHS-grad\t9\tDivorced\t?\tNot-in-family\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n54\tSelf-emp-not-inc\t32778\tHS-grad\t9\tMarried-civ-spouse\tSales\tWife\tWhite\tFemale\t0\t0\t30\tUnited-States\t<=50K\n55\tSelf-emp-not-inc\t149168\tHS-grad\t9\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n57\tPrivate\t74156\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n58\tPrivate\t140363\tHS-grad\t9\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t3325\t0\t30\tUnited-States\t<=50K\n59\tPrivate\t226922\tHS-grad\t9\tDivorced\tSales\tUnmarried\tWhite\tFemale\t0\t1762\t30\tUnited-States\t<=50K\n59\tPrivate\t252714\tHS-grad\t9\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t30\tItaly\t<=50K\n65\tPrivate\t118779\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n68\tPrivate\t166149\tHS-grad\t9\tWidowed\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t2206\t30\tUnited-States\t<=50K\n70\tPrivate\t35494\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n70\tPrivate\t30713\tHS-grad\t9\tMarried-civ-spouse\tFarming-fishing\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n72\t?\t113044\tHS-grad\t9\tWidowed\t?\tNot-in-family\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n79\t?\t144533\tHS-grad\t9\tWidowed\t?\tNot-in-family\tBlack\tFemale\t0\t0\t30\tUnited-States\t<=50K\n81\tSelf-emp-not-inc\t136063\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t30\tUnited-States\t<=50K\n24\tPrivate\t85088\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t1762\t32\tUnited-States\t<=50K\n25\tSelf-emp-not-inc\t209384\tHS-grad\t9\tNever-married\tOther-service\tOther-relative\tWhite\tMale\t0\t0\t32\tUnited-States\t<=50K\n27\tPrivate\t288341\tHS-grad\t9\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tFemale\t0\t0\t32\tUnited-States\t<=50K\n34\tPrivate\t148291\tHS-grad\t9\tMarried-civ-spouse\tTech-support\tWife\tWhite\tFemale\t0\t0\t32\tUnited-States\t<=50K\n37\tPrivate\t218188\tHS-grad\t9\tDivorced\tMachine-op-inspct\tOther-relative\tWhite\tFemale\t0\t0\t32\tUnited-States\t<=50K\n42\t?\t51795\tHS-grad\t9\tDivorced\t?\tUnmarried\tBlack\tFemale\t0\t0\t32\tUnited-States\t<=50K\n50\tSelf-emp-not-inc\t114117\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t32\tUnited-States\t<=50K\n51\tPrivate\t191965\tHS-grad\t9\tWidowed\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t32\tUnited-States\t<=50K\n53\tPrivate\t335481\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tBlack\tFemale\t0\t0\t32\tUnited-States\t<=50K\n54\tPrivate\t377701\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tHusband\tWhite\tMale\t0\t0\t32\tMexico\t<=50K\n56\tPrivate\t56331\tHS-grad\t9\tNever-married\tMachine-op-inspct\tNot-in-family\tWhite\tMale\t0\t0\t32\tUnited-States\t<=50K\n60\t?\t199947\tHS-grad\t9\tMarried-civ-spouse\t?\tHusband\tWhite\tMale\t0\t0\t32\tUnited-States\t<=50K\n60\tPrivate\t225526\tHS-grad\t9\tSeparated\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t32\tUnited-States\t<=50K\n63\tSelf-emp-not-inc\t194638\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t32\tUnited-States\t<=50K\n34\tLocal-gov\t167999\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t33\tUnited-States\t<=50K\n32\tPrivate\t183916\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t34\tUnited-States\t<=50K\n19\tPrivate\t55284\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n19\tLocal-gov\t167816\tHS-grad\t9\tDivorced\tExec-managerial\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n20\t?\t137300\tHS-grad\t9\tNever-married\t?\tOther-relative\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n21\tPrivate\t152540\tHS-grad\t9\tNever-married\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n21\tPrivate\t193219\tHS-grad\t9\tNever-married\tAdm-clerical\tOwn-child\tBlack\tFemale\t0\t0\t35\tJamaica\t<=50K\n21\tPrivate\t296158\tHS-grad\t9\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n21\tPrivate\t34506\tHS-grad\t9\tSeparated\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n21\tPrivate\t286853\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n22\t?\t175586\tHS-grad\t9\tNever-married\t?\tUnmarried\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n22\tPrivate\t100345\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n23\tPrivate\t197904\tHS-grad\t9\tNever-married\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n23\tPrivate\t275244\tHS-grad\t9\tNever-married\tMachine-op-inspct\tNot-in-family\tBlack\tMale\t0\t0\t35\tUnited-States\t<=50K\n25\tPrivate\t76978\tHS-grad\t9\tNever-married\tSales\tUnmarried\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n25\tLocal-gov\t44216\tHS-grad\t9\tDivorced\tAdm-clerical\tNot-in-family\tAmer-Indian-Eskimo\tFemale\t0\t0\t35\tUnited-States\t<=50K\n25\tPrivate\t358975\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n25\tSelf-emp-not-inc\t176756\tHS-grad\t9\tNever-married\tFarming-fishing\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n25\tPrivate\t195118\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOther-relative\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n25\tPrivate\t289980\tHS-grad\t9\tNever-married\tHandlers-cleaners\tNot-in-family\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n27\tPrivate\t146719\tHS-grad\t9\tDivorced\tSales\tOwn-child\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n27\tPrivate\t163942\tHS-grad\t9\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t35\tIreland\t<=50K\n27\tPrivate\t219371\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n28\tPrivate\t430084\tHS-grad\t9\tDivorced\tOther-service\tOwn-child\tBlack\tMale\t0\t0\t35\tUnited-States\t<=50K\n28\tLocal-gov\t25864\tHS-grad\t9\tNever-married\tExec-managerial\tUnmarried\tAmer-Indian-Eskimo\tFemale\t0\t0\t35\tUnited-States\t<=50K\n29\tPrivate\t228075\tHS-grad\t9\tNever-married\tAdm-clerical\tUnmarried\tWhite\tMale\t0\t0\t35\tMexico\t<=50K\n29\tSelf-emp-not-inc\t110663\tHS-grad\t9\tSeparated\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n30\tPrivate\t117747\tHS-grad\t9\tMarried-civ-spouse\tSales\tWife\tAsian-Pac-Islander\tFemale\t0\t1573\t35\t?\t<=50K\n30\tPrivate\t221043\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n30\tPrivate\t219318\tHS-grad\t9\tNever-married\tMachine-op-inspct\tOther-relative\tWhite\tFemale\t0\t0\t35\tPuerto-Rico\t<=50K\n31\tPrivate\t175548\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tOther\tFemale\t0\t0\t35\tUnited-States\t<=50K\n32\tPrivate\t153963\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n33\tPrivate\t262439\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t35\tUnited-States\t>50K\n34\tPrivate\t137900\tHS-grad\t9\tNever-married\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n34\tPrivate\t182177\tHS-grad\t9\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t3325\t0\t35\tUnited-States\t<=50K\n34\tPrivate\t202046\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t>50K\n34\tPrivate\t196164\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n34\tPrivate\t235124\tHS-grad\t9\tNever-married\tOther-service\tUnmarried\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n35\tPrivate\t82552\tHS-grad\t9\tNever-married\tCraft-repair\tUnmarried\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n35\tPrivate\t182074\tHS-grad\t9\tDivorced\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n37\tState-gov\t48211\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n37\tLocal-gov\t118909\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n38\tSelf-emp-not-inc\t120985\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t4386\t0\t35\tUnited-States\t<=50K\n40\tPrivate\t347934\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n41\tPrivate\t349221\tHS-grad\t9\tNever-married\tCraft-repair\tOwn-child\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n41\tPrivate\t179533\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n42\tPrivate\t111949\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n42\tPrivate\t358677\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tBlack\tMale\t0\t0\t35\tUnited-States\t<=50K\n42\tPrivate\t209392\tHS-grad\t9\tDivorced\tProtective-serv\tNot-in-family\tBlack\tMale\t0\t0\t35\tUnited-States\t<=50K\n43\tSelf-emp-not-inc\t180599\tHS-grad\t9\tNever-married\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n43\tSelf-emp-not-inc\t277647\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n46\tPrivate\t179048\tHS-grad\t9\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t0\t0\t35\t?\t<=50K\n47\tPrivate\t386136\tHS-grad\t9\tMarried-civ-spouse\tTransport-moving\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n48\tPrivate\t62605\tHS-grad\t9\tDivorced\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n49\tPrivate\t371299\tHS-grad\t9\tNever-married\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n49\tPrivate\t119182\tHS-grad\t9\tSeparated\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n52\tPrivate\t91048\tHS-grad\t9\tDivorced\tMachine-op-inspct\tOwn-child\tBlack\tFemale\t0\t0\t35\tUnited-States\t<=50K\n53\tPrivate\t346253\tHS-grad\t9\tDivorced\tSales\tOwn-child\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n53\tState-gov\t156877\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t15024\t0\t35\tUnited-States\t>50K\n54\tPrivate\t143865\tHS-grad\t9\tDivorced\tSales\tUnmarried\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n56\tPrivate\t203580\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t35\t?\t<=50K\n56\tSelf-emp-not-inc\t115422\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n57\tState-gov\t39380\tHS-grad\t9\tSeparated\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n58\tSelf-emp-inc\t210563\tHS-grad\t9\tMarried-civ-spouse\tSales\tWife\tWhite\tFemale\t15024\t0\t35\tUnited-States\t>50K\n60\tPrivate\t177665\tHS-grad\t9\tMarried-civ-spouse\tCraft-repair\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n61\tPrivate\t153048\tHS-grad\t9\tWidowed\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n61\tLocal-gov\t101265\tHS-grad\t9\tWidowed\tAdm-clerical\tUnmarried\tWhite\tFemale\t1471\t0\t35\tUnited-States\t<=50K\n61\tSelf-emp-inc\t84409\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t>50K\n64\tLocal-gov\t266080\tHS-grad\t9\tDivorced\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n67\tPrivate\t318533\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t35\tUnited-States\t<=50K\n71\tSelf-emp-not-inc\t130731\tHS-grad\t9\tWidowed\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t35\tUnited-States\t<=50K\n18\tPrivate\t174732\tHS-grad\t9\tNever-married\tOther-service\tOther-relative\tBlack\tMale\t0\t0\t36\tUnited-States\t<=50K\n21\tPrivate\t259510\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t36\tUnited-States\t<=50K\n25\tPrivate\t304032\tHS-grad\t9\tNever-married\tAdm-clerical\tOwn-child\tWhite\tMale\t0\t0\t36\tUnited-States\t<=50K\n29\tPrivate\t452205\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t36\tUnited-States\t<=50K\n30\tPrivate\t191571\tHS-grad\t9\tSeparated\tOther-service\tOwn-child\tWhite\tFemale\t0\t0\t36\tUnited-States\t<=50K\n35\tPrivate\t95551\tHS-grad\t9\tSeparated\tExec-managerial\tNot-in-family\tWhite\tFemale\t0\t0\t36\tUnited-States\t<=50K\n39\tPrivate\t343403\tHS-grad\t9\tMarried-civ-spouse\tSales\tWife\tWhite\tFemale\t0\t0\t36\tUnited-States\t<=50K\n41\tPrivate\t343121\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t36\tUnited-States\t<=50K\n43\tPrivate\t116852\tHS-grad\t9\tMarried-civ-spouse\tExec-managerial\tWife\tWhite\tFemale\t0\t0\t36\tPortugal\t>50K\n27\tPrivate\t39665\tHS-grad\t9\tDivorced\tSales\tUnmarried\tWhite\tFemale\t0\t0\t37\tUnited-States\t<=50K\n32\tLocal-gov\t286101\tHS-grad\t9\tNever-married\tTransport-moving\tUnmarried\tBlack\tFemale\t0\t0\t37\tUnited-States\t<=50K\n46\tPrivate\t109089\tHS-grad\t9\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t37\tUnited-States\t<=50K\n48\tPrivate\t45612\tHS-grad\t9\tNever-married\tAdm-clerical\tUnmarried\tBlack\tFemale\t0\t0\t37\tUnited-States\t<=50K\n48\tPrivate\t207058\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t37\tUnited-States\t<=50K\n53\tPrivate\t227602\tHS-grad\t9\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t37\tMexico\t<=50K\n73\tPrivate\t29778\tHS-grad\t9\tWidowed\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t37\tUnited-States\t<=50K\n24\tPrivate\t29810\tHS-grad\t9\tDivorced\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n24\tPrivate\t124971\tHS-grad\t9\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n27\tSelf-emp-inc\t233724\tHS-grad\t9\tSeparated\tAdm-clerical\tNot-in-family\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n31\tPrivate\t51471\tHS-grad\t9\tDivorced\tAdm-clerical\tUnmarried\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n32\tPrivate\t242150\tHS-grad\t9\tNever-married\tAdm-clerical\tOwn-child\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n32\tPrivate\t169240\tHS-grad\t9\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n38\tState-gov\t354591\tHS-grad\t9\tNever-married\tOther-service\tUnmarried\tBlack\tFemale\t114\t0\t38\tUnited-States\t<=50K\n39\tPrivate\t187046\tHS-grad\t9\tMarried-civ-spouse\tSales\tHusband\tWhite\tMale\t4064\t0\t38\tUnited-States\t<=50K\n41\tPrivate\t130408\tHS-grad\t9\tDivorced\tSales\tUnmarried\tBlack\tFemale\t0\t0\t38\tUnited-States\t<=50K\n44\tPrivate\t378251\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n44\tLocal-gov\t145178\tHS-grad\t9\tMarried-civ-spouse\tOther-service\tWife\tBlack\tFemale\t0\t0\t38\tJamaica\t>50K\n46\tPrivate\t171550\tHS-grad\t9\tDivorced\tMachine-op-inspct\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n47\tPrivate\t34248\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tHusband\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n48\tPrivate\t54759\tHS-grad\t9\tDivorced\tProf-specialty\tUnmarried\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n50\tPrivate\t112351\tHS-grad\t9\tDivorced\tExec-managerial\tUnmarried\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n51\tPrivate\t35576\tHS-grad\t9\tDivorced\tCraft-repair\tNot-in-family\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n51\tState-gov\t196504\tHS-grad\t9\tDivorced\tCraft-repair\tUnmarried\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n51\tPrivate\t161482\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n52\tPrivate\t87205\tHS-grad\t9\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n52\tPrivate\t158993\tHS-grad\t9\tDivorced\tOther-service\tOther-relative\tBlack\tFemale\t0\t0\t38\tUnited-States\t<=50K\n52\tSelf-emp-inc\t101017\tHS-grad\t9\tDivorced\tExec-managerial\tUnmarried\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n53\tPrivate\t99476\tHS-grad\t9\tDivorced\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n53\tPrivate\t144361\tHS-grad\t9\tMarried-civ-spouse\tMachine-op-inspct\tHusband\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n54\tState-gov\t198186\tHS-grad\t9\tNever-married\tOther-service\tNot-in-family\tBlack\tFemale\t0\t0\t38\tUnited-States\t<=50K\n55\tPrivate\t116442\tHS-grad\t9\tNever-married\tSales\tNot-in-family\tWhite\tMale\t0\t0\t38\tUnited-States\t<=50K\n55\tPrivate\t276229\tHS-grad\t9\tWidowed\tSales\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n55\tLocal-gov\t219775\tHS-grad\t9\tDivorced\tOther-service\tUnmarried\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n56\tPrivate\t311249\tHS-grad\t9\tWidowed\tAdm-clerical\tUnmarried\tBlack\tFemale\t0\t0\t38\tUnited-States\t<=50K\n59\tLocal-gov\t420537\tHS-grad\t9\tMarried-civ-spouse\tAdm-clerical\tWife\tWhite\tFemale\t0\t0\t38\tUnited-States\t>50K\n64\tState-gov\t201293\tHS-grad\t9\tWidowed\tAdm-clerical\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n64\tPrivate\t170483\tHS-grad\t9\tWidowed\tOther-service\tNot-in-family\tWhite\tFemale\t0\t0\t38\tUnited-States\t<=50K\n17\tPrivate\t354201\tHS-grad\t9\tNever-married\tOther-service\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t243313\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t263024\tHS-grad\t9\tNever-married\tSales\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t104704\tHS-grad\t9\tNever-married\tAdm-clerical\tOwn-child\tBlack\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tSelf-emp-not-inc\t40293\tHS-grad\t9\tNever-married\tFarming-fishing\tOther-relative\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t168288\tHS-grad\t9\tNever-married\tHandlers-cleaners\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n18\tPrivate\t240747\tHS-grad\t9\tNever-married\tMachine-op-inspct\tOwn-child\tWhite\tFemale\t0\t0\t40\tDominican-Republic\t<=50K\n19\t?\t80710\tHS-grad\t9\tNever-married\t?\tOwn-child\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n19\t?\t170653\tHS-grad\t9\tNever-married\t?\tOwn-child\tWhite\tMale\t0\t0\t40\tItaly\t<=50K\n19\t?\t241616\tHS-grad\t9\tNever-married\t?\tUnmarried\tWhite\tMale\t0\t2001\t40\tUnited-States\t<=50K\n19\tFederal-gov\t30559\tHS-grad\t9\tMarried-AF-spouse\tSales\tWife\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n19\tFederal-gov\t234151\tHS-grad\t9\tNever-married\tAdm-clerical\tUnmarried\tBlack\tFemale\t0\t0\t40\tUnited-States\t<=50K\n19\tPrivate\t168294\tHS-grad\t9\tNever-married\tCraft-repair\tOwn-child\tWhite\tMale\t0\t0\t40\tUnited-States\t<=50K\n19\tPrivate\t246226\tHS-grad\t9\tNever-married\tCraft-repair\tNot-in-family\tWhite\tFemale\t0\t0\t40\tUnited-States\t<=50K\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1734658206544,"interpreter":"md.low","endTime":1734658206983,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Data Drift Monitoring REST","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1718324949473,"interpreter":"md.low","endTime":1718324949626,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Monitoring - Data Drift Detection","","Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines. Identifying and understanding data drift is an essential part of maintaining the health of any organization's data in supporting data-driven decision making and business-critical applications. ","","In addition, understanding how data evolves over time is a key input when evaluating how your machine learning models are able to meet business objectives. As such, data monitoring is complementary to model monitoring, where understanding changes in data used for prediction helps understand changes in a model's efficacy. ","","Data drift can be quantified using basic descriptive statistics. For numeric columns these include:","* Mean","* Standard deviation","* Range (minimum, maximum)","* Number of nulls","","For categorical columns these include:","* Number of unique values","* Number of nulls","","","### **Data monitoring workflow**","","In this notebook we highlight the following features of data monitoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a data monitoring job","* View the details and output of the data monitoring job","* Update, disable, and delete a data monitoring job","","The dataset used in this example is from the UCI Machine Learning Repository. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1718324949691,"interpreter":"md.low","endTime":1718324949838,"results":[{"message":"<h1 id=\"oml-monitoring---data-drift-detection\">OML Monitoring - Data Drift Detection<\/h1>\n<p>Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines. Identifying and understanding data drift is an essential part of maintaining the health of any organization's data in supporting data-driven decision making and business-critical applications.<\/p>\n<p>In addition, understanding how data evolves over time is a key input when evaluating how your machine learning models are able to meet business objectives. As such, data monitoring is complementary to model monitoring, where understanding changes in data used for prediction helps understand changes in a model's efficacy.<\/p>\n<p>Data drift can be quantified using basic descriptive statistics. For numeric columns these include:<\/p>\n<ul>\n<li>Mean<\/li>\n<li>Standard deviation<\/li>\n<li>Range (minimum, maximum)<\/li>\n<li>Number of nulls<\/li>\n<\/ul>\n<p>For categorical columns these include:<\/p>\n<ul>\n<li>Number of unique values<\/li>\n<li>Number of nulls<\/li>\n<\/ul>\n<h3 id=\"data-monitoring-workflow\"><strong>Data monitoring workflow<\/strong><\/h3>\n<p>In this notebook we highlight the following features of data monitoring through the REST interface:<\/p>\n<ul>\n<li>Authenticate the database user and obtain a token<\/li>\n<li>Create a data monitoring job<\/li>\n<li>View the details and output of the data monitoring job<\/li>\n<li>Update, disable, and delete a data monitoring job<\/li>\n<\/ul>\n<p>The dataset used in this example is from the UCI Machine Learning Repository. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring.html\" target=\"_blank\">OML Data Monitoring Documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API Documentation<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demo<\/a>","","For model monitoring, refer to the Model Monitoring notebook in the template notebook examples."],"enabled":true,"result":{"startTime":1718324949939,"interpreter":"md.low","endTime":1718324950007,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring.html\" target=\"_blank\">OML Data Monitoring Documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API Documentation<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demo<\/a><\/li>\n<\/ul>\n<p>For model monitoring, refer to the Model Monitoring notebook in the template notebook examples.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run a data monitoring job through a REST interface via OML Services.","","If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become aquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" onclick=\"return ! window.open('https://asktom.oracle.com/pls/apex/asktom.search?oh=13662');\">OML Services Lab<\/a>","","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":{"startTime":1718324950075,"interpreter":"md.low","endTime":1718324950147,"results":[{"message":"<p>This notebook walks through the steps to run a data monitoring job through a REST interface via OML Services.<\/p>\n<p>If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become aquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" onclick=\"return ! window.open('https://asktom.oracle.com/pls/apex/asktom.search?oh=13662');\">OML Services Lab<\/a><\/p>\n<p>The <strong>cURL<\/strong> commands provided in this notebook are for illustrative purposes only. To run REST calls, use <strong>cURL<\/strong> in a Linux or Mac terminal or another REST client such as Postman.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 1: Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' ","                   --header 'Accept: application/json' ","                   -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"'<yourpassword>'\"}' ","                   \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning user management cloud service instance"],"enabled":true,"result":{"startTime":1718324950214,"interpreter":"md.low","endTime":1718324950285,"results":[{"message":"<p>To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' \n               --header 'Accept: application/json' \n               -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, &quot;password&quot;:&quot;'&lt;yourpassword&gt;'&quot;}' \n               &quot;&lt;OML service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>OML service URL<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 2: Create a data monitoring job","message":["%md","","Now that you have an access token, you can send a request to OML Services to create a data monitoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a POST request. Below is a listing of the parameters that can be specified for a data monitoring job, divided into those that are required and those that are optional. ","","*Required:*","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, and is set to DATA_MONITORING for data monitoring jobs. ","* _outputData_ is the output data identifier. The results of the job will be written to a table named {jobID}_{outputData}.","* _baselineData_ is a table or view name that contains baseline data to monitor. At least 100 rows are required for data monitoring, otherwise analysis is skipped.","* _newData_ is a table or view name with new data to be compared against the baseline. At least 100 rows per period are required for data monitoring, otherwise analysis is skipped. ","","","*Optional:*","* _disableJob_ is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.","* _inputSchemaName_ is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.","* _outputSchemaName_ is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.","* _jobDescription_ is a description of the job.","* _jobServiceLevel_ is the service level for the job, which can be LOW, MEDIUM, or HIGH.","* _timeColumn_ is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.","* _startDate_ is the start date and time of monitoring in newData. If not provided the earliest date and time is used. ","* _endDate_ is the end date and time of the monitoring in newData. If not provided the last date and time is used.","* _frequency_ indicates the time window unit on which the monitoring is performed on the new data. This can be \"day\", \"week\", \"month\", or \"year\". If not provided, the whole \"new\" data is used as a single time period. ","* _threshold_ is the threshold to trigger a drift alert. The default is 0.7.","* _recompute_ is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _caseidColumn_ is a case identifier column in the baseline and new data. Providing it improves the reproducability of results. ","* _featureList_ is a list of features to monitor. The features need to be either numeric or categorical. The default maximum number of features in the list is 250. Monitoring will run on all features if the feature list is not specified, and and error is returned if there are more than 250 features. ","* _anchorColumn_ is the anchor column in the baseline and new data used for bivariate analysis. The target column in supervised problems can be passed as the anchor column. "],"enabled":true,"result":{"startTime":1718324950359,"interpreter":"md.low","endTime":1718324950460,"results":[{"message":"<p>Now that you have an access token, you can send a request to OML Services to create a data monitoring job. This request is sent to the <em>/omlmod/v1/jobs<\/em> endpoint as a POST request. Below is a listing of the parameters that can be specified for a data monitoring job, divided into those that are required and those that are optional.<\/p>\n<p><em>Required:<\/em><\/p>\n<ul>\n<li><em>jobName<\/em> specifies the name of the submitted job.<\/li>\n<li><em>jobType<\/em> specifies the type of job to be run, and is set to DATA_MONITORING for data monitoring jobs.<\/li>\n<li><em>outputData<\/em> is the output data identifier. The results of the job will be written to a table named {jobID}_{outputData}.<\/li>\n<li><em>baselineData<\/em> is a table or view name that contains baseline data to monitor. At least 100 rows are required for data monitoring, otherwise analysis is skipped.<\/li>\n<li><em>newData<\/em> is a table or view name with new data to be compared against the baseline. At least 100 rows per period are required for data monitoring, otherwise analysis is skipped.<\/li>\n<\/ul>\n<p><em>Optional:<\/em><\/p>\n<ul>\n<li><em>disableJob<\/em> is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.<\/li>\n<li><em>inputSchemaName<\/em> is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.<\/li>\n<li><em>outputSchemaName<\/em> is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.<\/li>\n<li><em>jobDescription<\/em> is a description of the job.<\/li>\n<li><em>jobServiceLevel<\/em> is the service level for the job, which can be LOW, MEDIUM, or HIGH.<\/li>\n<li><em>timeColumn<\/em> is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.<\/li>\n<li><em>startDate<\/em> is the start date and time of monitoring in newData. If not provided the earliest date and time is used.<\/li>\n<li><em>endDate<\/em> is the end date and time of the monitoring in newData. If not provided the last date and time is used.<\/li>\n<li><em>frequency<\/em> indicates the time window unit on which the monitoring is performed on the new data. This can be &quot;day&quot;, &quot;week&quot;, &quot;month&quot;, or &quot;year&quot;. If not provided, the whole &quot;new&quot; data is used as a single time period.<\/li>\n<li><em>threshold<\/em> is the threshold to trigger a drift alert. The default is 0.7.<\/li>\n<li><em>recompute<\/em> is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed.<\/li>\n<li><em>caseidColumn<\/em> is a case identifier column in the baseline and new data. Providing it improves the reproducability of results.<\/li>\n<li><em>featureList<\/em> is a list of features to monitor. The features need to be either numeric or categorical. The default maximum number of features in the list is 250. Monitoring will run on all features if the feature list is not specified, and and error is returned if there are more than 250 features.<\/li>\n<li><em>anchorColumn<\/em> is the anchor column in the baseline and new data used for bivariate analysis. The target column in supervised problems can be passed as the anchor column.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Example Data Monitoring job","message":["%md","","","Data monitoring jobs are initiated by sending a **POST** request to the */omlmod/v1/jobs* endpoint, and numeric and categorical features are supported. Monitoring will run on all features by default, or a list of features specified by the user, and there is a maximum of 250 features that can be monitored.","","The details for data monitoring are specified in **jobProperties**, including:","* data monitoring job name and type","* Autonomous Database service level","* table where the data monitoring details will be saved","* drift alert trigger threshold","* maximum number of runs","* baseline and new data to be used","* performance metric","","The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by **newData** and contained in the **timeColumn** field. When the start and end dates are not specified, the earliest and latest dates and times in the **timeColumn** are used. The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.","","Here is an example of a data monitoring job request using these parameters. Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems to extract relevant components from the response."],"enabled":true,"result":{"startTime":1718324950531,"interpreter":"md.low","endTime":1718324950583,"results":[{"message":"<p>Data monitoring jobs are initiated by sending a <strong>POST<\/strong> request to the <em>/omlmod/v1/jobs<\/em> endpoint, and numeric and categorical features are supported. Monitoring will run on all features by default, or a list of features specified by the user, and there is a maximum of 250 features that can be monitored.<\/p>\n<p>The details for data monitoring are specified in <strong>jobProperties<\/strong>, including:<\/p>\n<ul>\n<li>data monitoring job name and type<\/li>\n<li>Autonomous Database service level<\/li>\n<li>table where the data monitoring details will be saved<\/li>\n<li>drift alert trigger threshold<\/li>\n<li>maximum number of runs<\/li>\n<li>baseline and new data to be used<\/li>\n<li>performance metric<\/li>\n<\/ul>\n<p>The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by <strong>newData<\/strong> and contained in the <strong>timeColumn<\/strong> field. When the start and end dates are not specified, the earliest and latest dates and times in the <strong>timeColumn<\/strong> are used. The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.<\/p>\n<p>Here is an example of a data monitoring job request using these parameters. Note that we are using <em>jq<\/em>, a command-line JSON processor available on Linux and Mac OS systems to extract relevant components from the response.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to initiate data monitoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {","             \"jobStartDate\": \"2023-03-24T20:30:26Z\",            # job start date and time ","             \"repeatInterval\": \"FREQ=HOURLY\",                   # job frequency","             \"jobEndDate\": \"2023-03-24T23:30:26Z\",              # job end date and time","             \"maxRuns\": \"10\"                                    # max runs within the schedule","         },","         \"jobProperties\": {","             \"jobName\": \"HouseholdPowerDataMonitoring\",         # job name","             \"jobType\": \"DATA_MONITORING\",                      # job type; DATA_MONITORING","             \"disableJob\": false,                               # flag to disable the job at submission","             \"outputData\": \"householdPowerConsumption\",         # table where the job results will be saved in the format {jobID}_{outputData}","             \"baselineData\": \"HOUSEHOLD_POWER_BASE\",            # table/view containing baseline data ","             \"newData\": \"HOUSEHOLD_POWER_NEW\",                  # table/view with new data to compare against baseline","             \"inputSchemaName\": \"OMLUSER\",                      # database schema that owns the input table/view","             \"outputSchemaName\": \"OMLUSER\",                     # database schema that owns the output table/view","             \"jobDescription\": \"Monitor household power\",       # job description","             \"jobServiceLevel\": \"LOW\",                          # Autonomous Database service level; either LOW, MEDIUM, or HIGH","             \"timeColumn\": \"DATES\",                             # date or timestamp column in newData","             \"startDate\": \"2008-01-01T00:00:00Z\",               # the start date of the monitoring in the new data","             \"endDate\": \"2010-11-26T00:00:00Z\",                 # the end date of the monitoring in the new data","             \"frequency\": \"Year\",                               # the time window unit on which monitoring is performed on the new data","             \"threshold\": 0.8,                                  # threshold to trigger drift alert","             \"recompute\": false,                                # flag to determine whether to replace the output table","             \"caseidColumn\": null,                              # case identifier column in the baseline and new data","             \"anchorColumn\": null,                              # anchor column for bivariate analysis","             \"featureList\": [                                   # features to perform data monitoring on","                \"GLOBAL_ACTIVE_POWER\",","                \"GLOBAL_REACTIVE_POWER\",","                \"VOLTAGE\",","                \"SUB_METERING_1\",","                \"SUB_METERING_2\",","                \"SUB_METERING_3\"","            ]","         }","     }' | jq","     "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Job ID returned with successful job submission","message":["%md","","When your job is successfully submitted, you will recieve a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":{"startTime":1718324950649,"interpreter":"md.low","endTime":1718324950701,"results":[{"message":"<p>When your job is successfully submitted, you will recieve a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"jobId\": \"OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%247ABB6308_1664_4CB4_84B1_598A6EA599D1\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 3: View the details of a submitted job","message":["%md","","You can now view the details of your submitted job by sending GET request with the job ID to the */omlmod/v1/jobs* endpoint, where **jobid** is the ID provided in response to the successful submission of your data monitoring job in the previous step."],"enabled":true,"result":{"startTime":1718324950777,"interpreter":"md.low","endTime":1718324950830,"results":[{"message":"<p>You can now view the details of your submitted job by sending GET request with the job ID to the <em>/omlmod/v1/jobs<\/em> endpoint, where <strong>jobid<\/strong> is the ID provided in response to the successful submission of your data monitoring job in the previous step.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Use the job ID to view job details","message":["","$ export jobid='OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1'  # save the job ID to a single-quoted variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-24T20:30:26Z\",","      \"repeatInterval\": \"FREQ=HOURLY\",","      \"jobEndDate\": \"2023-03-24T23:30:26Z\",","      \"maxRuns\": 3","    },","    \"jobProperties\": {","      \"jobType\": \"DATA_MONITORING\",","      \"inputSchemaName\": \"OMLUSER\",","      \"outputSchemaName\": \"OMLUSER\",","      \"outputData\": \"householdPowerConsumption\",","      \"jobDescription\": \"Monitor household power\",","      \"jobName\": \"HouseholdPowerDataMonitoring\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"LOW\",","      \"baselineData\": \"HOUSEHOLD_POWER_BASE\",","      \"newData\": \"HOUSEHOLD_POWER_NEW\",","      \"timeColumn\": \"DATES\",","      \"startDate\": \"2008-01-01T00:00:00Z\",","      \"endDate\": \"2010-11-26T00:00:00Z\",","      \"frequency\": \"Year\",","      \"threshold\": 0.8,","      \"recompute\": false,","      \"caseidColumn\": null,","      \"featureList\": [","        \"GLOBAL_ACTIVE_POWER\",","        \"GLOBAL_REACTIVE_POWER\",","        \"VOLTAGE\",","        \"SUB_METERING_1\",","        \"SUB_METERING_2\",","        \"SUB_METERING_3\"","      ],","      \"anchorColumn\": null","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-24T20:23:31.53651Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%247ABB6308_1664_4CB4_84B1_598A6EA599D1\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-24T20:30:26Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 4: Edit a data monitoring job (optional)","message":["%md","","After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the *updateProperties* field to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can updated for a data monitoring job:","","* _startDate_","* _endDate_","* _threshold_","* _recompute_","* _featureList_","","In the example request below, we update some of these fields to differ from the origional parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body."],"enabled":true,"result":{"startTime":1718324950897,"interpreter":"md.low","endTime":1718324950950,"results":[{"message":"<p>After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the <em>updateProperties<\/em> field to the <em>/omlmod/v1/jobs/{jobid}<\/em> endpoint. The following parameters can updated for a data monitoring job:<\/p>\n<ul>\n<li><em>startDate<\/em><\/li>\n<li><em>endDate<\/em><\/li>\n<li><em>threshold<\/em><\/li>\n<li><em>recompute<\/em><\/li>\n<li><em>featureList<\/em><\/li>\n<\/ul>\n<p>In the example request below, we update some of these fields to differ from the origional parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Update the properties of a data monitoring job","message":["","$ curl -i -X POST \"<OML Service URL>omlmod/v1/jobs/<jobid>\" \\","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"updateProperties\": {","                \"startDate\": \"2022-01-01T19:40:24Z\",","                \"endDate\":   \"2023-01-01T19:20:24Z\",","                \"threshold\":  0.80,","                \"featureList\": [\"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\"]","            }","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 5: Perform an action on a data monitoring job (optional)","message":["%md","","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","* _DISABLE_ disables the job at submission. The *force* property can be used with this action to forcefully interrupt any running job. ","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","For example, when your job has been successfuly submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example request to update the job status to **DISABLED**. When you successfully submit your job you will receive a 204 response with no body. "],"enabled":true,"result":{"startTime":1718324951016,"interpreter":"md.low","endTime":1718324951069,"results":[{"message":"<p>OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, <strong>DISABLE<\/strong>, <strong>RUN<\/strong>, <strong>ENABLE<\/strong>, and <strong>STOP<\/strong>:<\/p>\n<ul>\n<li><em>DISABLE<\/em> disables the job at submission. The <em>force<\/em> property can be used with this action to forcefully interrupt any running job.<\/li>\n<li><em>ENABLE<\/em> enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule.<\/li>\n<li><em>RUN<\/em> will immediately run the job as a way to test a job or run it outside of its schedule.<\/li>\n<li><em>STOP<\/em> stops a currently running job.<\/li>\n<\/ul>\n<p>For example, when your job has been successfuly submitted, its state is set to <strong>ENABLED<\/strong> by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as <strong>DISABLED<\/strong>. You can do this by sending a request to the <em>/omlmod/v1/jobs/{jobid}/action<\/em> endpoint.<\/p>\n<p>Note that jobs can be set to <strong>DISABLED<\/strong> at submission by setting the <em>disableJob<\/em> flag to <em>true<\/em>.<\/p>\n<p>Below is an example request to update the job status to <strong>DISABLED<\/strong>. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Disable data monitoring job","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 6: View the data monitoring job output","message":["%md","","Once your job has run, either according to its schedule or by the **RUN** action, you can view its output in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobid}\\_{outputData}*. You can check if your job is finished by sending a request to view its details, as we did in step #3. If your job has run at least once you should see the **lastRunDetail** field with information on that run. The paragraph below queries the output table associated with this example. ","","If you scroll down on the output table, you can see that there is information for the baseline time period and newdata time period for each of the dataset features being monitored for drift. Many of the columns are empty for the baseline rows as monitoring is only done on the new data, not the baseline data. For help understanding the data monitoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html<\/a>"],"enabled":true,"result":{"startTime":1718324951134,"interpreter":"md.low","endTime":1718324951215,"results":[{"message":"<p>Once your job has run, either according to its schedule or by the <strong>RUN<\/strong> action, you can view its output in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is <em>{jobid}_{outputData}<\/em>. You can check if your job is finished by sending a request to view its details, as we did in step #3. If your job has run at least once you should see the <strong>lastRunDetail<\/strong> field with information on that run. The paragraph below queries the output table associated with this example.<\/p>\n<p>If you scroll down on the output table, you can see that there is information for the baseline time period and newdata time period for each of the dataset features being monitored for drift. Many of the columns are empty for the baseline rows as monitoring is only done on the new data, not the baseline data. For help understanding the data monitoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html');\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html<\/a><\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT START_TIME, END_TIME, IS_BASELINE, THRESHOLD, HAS_DRIFT, round(DRIFT, 3), ","       FEATURE_NAME, ROUND(IMPORTANCE_VALUE, 3) ","FROM OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1_householdPowerConsumption","ORDER BY FEATURE_NAME, IS_BASELINE DESC"],"enabled":false,"result":{"startTime":1700068961590,"interpreter":"sql.low","endTime":1700068963129,"results":[{"message":"START_TIME\tEND_TIME\tIS_BASELINE\tTHRESHOLD\tHAS_DRIFT\tROUND(DRIFT,3)\tFEATURE_NAME\tROUND(IMPORTANCE_VALUE,3)\n\t\t1\t\t\t\tGLOBAL_ACTIVE_POWER\t\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tGLOBAL_ACTIVE_POWER\t2.158\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tGLOBAL_ACTIVE_POWER\t2.631\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tGLOBAL_ACTIVE_POWER\t2.181\n\t\t1\t\t\t\tGLOBAL_REACTIVE_POWER\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tGLOBAL_REACTIVE_POWER\t1.639\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tGLOBAL_REACTIVE_POWER\t1.897\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tGLOBAL_REACTIVE_POWER\t1.389\n\t\t1\t\t\t\tSUB_METERING_1\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_1\t0.362\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_1\t0.294\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_1\t0.234\n\t\t1\t\t\t\tSUB_METERING_2\t\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_2\t0.631\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_2\t0.56\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_2\t0.558\n\t\t1\t\t\t\tSUB_METERING_3\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_3\t1.854\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_3\t1.922\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_3\t1.888\n\t\t1\t\t\t\tVOLTAGE\t\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tVOLTAGE\t2.145\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tVOLTAGE\t2.269\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tVOLTAGE\t2.576\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 7: Delete a data monitoring job (optional)","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":{"startTime":1718324951281,"interpreter":"md.low","endTime":1718324951334,"results":[{"message":"<p>To delete a previously submitted job, send a DELETE request along with the Job ID to the <em>/omlmod/v1/jobs<\/em> endpoint.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","$ curl -X DELETE \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":{"startTime":1700068963474,"interpreter":"md.low","endTime":1700068963566,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Reproducing this example","message":["%md","","If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, *HOUSEHOLD_POWER_BASE* and *HOUSEHOLD_POWER_NEW*. These tables are created using the Individual Household Electric Power Consumption data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption).","","The code to read and transform the data and create database tables using R is provided below."],"enabled":true,"result":{"startTime":1718324951400,"interpreter":"md.low","endTime":1718324951451,"results":[{"message":"<p>If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, <em>HOUSEHOLD_POWER_BASE<\/em> and <em>HOUSEHOLD_POWER_NEW<\/em>. These tables are created using the Individual Household Electric Power Consumption data from the <a href=\"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\">UCI Machine Learning Repository<\/a>.<\/p>\n<p>The code to read and transform the data and create database tables using R is provided below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop tables if they exist and suppress warnings","message":["%r","","options(warn=-1)","","try(ore.drop(table=\"HOUSEHOLD_POWER_BASE\"))","try(ore.drop(table=\"HOUSEHOLD_POWER_NEW\"))"],"enabled":true,"result":{"startTime":1718324951516,"interpreter":"r.low","endTime":1718324956809,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Read and transform data","message":["%r","","test <- read.csv(\"https://objectstorage.us-sanjose-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/household_power_consumption.txt\", sep=\";\")","","test <- transform(test, Date = as.Date(Date, format = \"%d/%m/%Y\"))","test <- transform(test, Global_active_power = as.numeric(Global_active_power))","test <- transform(test, Global_reactive_power = as.numeric(Global_reactive_power))","test <- transform(test, Voltage = as.numeric(Voltage))","test <- transform(test, Global_intensity = as.numeric(Global_intensity))","test <- transform(test, Sub_metering_1 = as.numeric(Sub_metering_1))","test <- transform(test, Sub_metering_2 = as.numeric(Sub_metering_2))","test <- transform(test, Sub_metering_3 = as.numeric(Sub_metering_3))","","colnames(test) <- c(\"DATES\", \"TIMES\", \"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\", \"GLOBAL_INTENSITY\", \"SUB_METERING_1\", \"SUB_METERING_2\", \"SUB_METERING_3\") "],"enabled":true,"result":{"startTime":1718324956875,"interpreter":"r.low","endTime":1718324966676,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create baseline and new data ","message":["%r","","test_base <- test[test$DATES < \"2008-01-01\",]","test_new <- test[test$DATES > \"2007-12-31\",]","","# Create OML proxy objects","","ore.create(test_base, table=\"HOUSEHOLD_POWER_BASE\")","ore.create(test_new, table=\"HOUSEHOLD_POWER_NEW\")"],"enabled":true,"result":{"startTime":1718324966741,"interpreter":"r.low","endTime":1718324982245,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the baseline data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_BASE","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1718324982315,"interpreter":"sql.low","endTime":1718324983809,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2006-12-16 00:00:00\t17:24:00\t4.216\t0.418\t234.84\t18.4\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:25:00\t5.36\t0.436\t233.63\t23.0\t0.0\t1.0\t16.0\n2006-12-16 00:00:00\t17:26:00\t5.374\t0.498\t233.29\t23.0\t0.0\t2.0\t17.0\n2006-12-16 00:00:00\t17:27:00\t5.388\t0.502\t233.74\t23.0\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:28:00\t3.666\t0.528\t235.68\t15.8\t0.0\t1.0\t17.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View new data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_NEW","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1718324983879,"interpreter":"sql.low","endTime":1718324983968,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2008-01-01 00:00:00\t00:00:00\t1.62\t0.07\t241.25\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:01:00\t1.626\t0.072\t241.74\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:02:00\t1.622\t0.072\t241.52\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:03:00\t1.612\t0.07\t240.82\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:04:00\t1.612\t0.07\t240.8\t6.6\t0.0\t0.0\t18.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1718324984039,"interpreter":"md.low","endTime":1718324984093,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1718324984163,"interpreter":"md.low","endTime":1718324984217,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML Data Monitoring REST","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734653782800,"interpreter":"md.low","endTime":1734653783300,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Data Monitoring REST","","Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines. Identifying and understanding data drift is an essential part of maintaining the health of any organization's data in supporting data-driven decision making and business-critical applications. ","","In addition, understanding how data evolves over time is a key input when evaluating how your machine learning models are able to meet business objectives. As such, data monitoring is complementary to model monitoring, where understanding changes in data used for prediction helps understand changes in a model's efficacy. ","","Data drift can be quantified using basic descriptive statistics. For numeric columns these include:","* Mean","* Standard deviation","* Range (minimum, maximum)","* Number of nulls","","For categorical columns these include:","* Number of unique values","* Number of nulls","","","### **Data monitoring workflow**","","In this notebook we highlight the following features of data monitoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a data monitoring job","* View the details and output of the data monitoring job","* Update, disable, and delete a data monitoring job","","The dataset used in this example is from the UCI Machine Learning Repository. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734725210595,"interpreter":"md.low","endTime":1734725211050,"results":[{"message":"<h1 id=\"oml-data-monitoring-rest\">OML Data Monitoring REST<\/h1>\n<p>Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines. Identifying and understanding data drift is an essential part of maintaining the health of any organization's data in supporting data-driven decision making and business-critical applications.<\/p>\n<p>In addition, understanding how data evolves over time is a key input when evaluating how your machine learning models are able to meet business objectives. As such, data monitoring is complementary to model monitoring, where understanding changes in data used for prediction helps understand changes in a model's efficacy.<\/p>\n<p>Data drift can be quantified using basic descriptive statistics. For numeric columns these include:<\/p>\n<ul>\n<li>Mean<\/li>\n<li>Standard deviation<\/li>\n<li>Range (minimum, maximum)<\/li>\n<li>Number of nulls<\/li>\n<\/ul>\n<p>For categorical columns these include:<\/p>\n<ul>\n<li>Number of unique values<\/li>\n<li>Number of nulls<\/li>\n<\/ul>\n<h3 id=\"data-monitoring-workflow\"><strong>Data monitoring workflow<\/strong><\/h3>\n<p>In this notebook we highlight the following features of data monitoring through the REST interface:<\/p>\n<ul>\n<li>Authenticate the database user and obtain a token<\/li>\n<li>Create a data monitoring job<\/li>\n<li>View the details and output of the data monitoring job<\/li>\n<li>Update, disable, and delete a data monitoring job<\/li>\n<\/ul>\n<p>The dataset used in this example is from the UCI Machine Learning Repository. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring.html\" target=\"_blank\">OML Data Monitoring Documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API Documentation<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demo<\/a>","","For model monitoring, refer to the Model Monitoring notebook in the template notebook examples."],"enabled":true,"result":{"startTime":1734653784600,"interpreter":"md.low","endTime":1734653785021,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring.html\" target=\"_blank\">OML Data Monitoring Documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API Documentation<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demo<\/a><\/li>\n<\/ul>\n<p>For model monitoring, refer to the Model Monitoring notebook in the template notebook examples.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run a data monitoring job through a REST interface via OML Services.","","If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become aquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Lab<\/a>.","","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":{"startTime":1734653785441,"interpreter":"md.low","endTime":1734653785861,"results":[{"message":"<p>This notebook walks through the steps to run a data monitoring job through a REST interface via OML Services.<\/p>\n<p>If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become aquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Lab<\/a>.<\/p>\n<p>The <strong>cURL<\/strong> commands provided in this notebook are for illustrative purposes only. To run REST calls, use <strong>cURL<\/strong> in a Linux or Mac terminal or another REST client such as Postman.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 1: Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' ","                   --header 'Accept: application/json' ","                   -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"'<yourpassword>'\"}' ","                   \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning user management cloud service instance"],"enabled":true,"result":{"startTime":1734653786294,"interpreter":"md.low","endTime":1734653786724,"results":[{"message":"<p>To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' \n               --header 'Accept: application/json' \n               -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, &quot;password&quot;:&quot;'&lt;yourpassword&gt;'&quot;}' \n               &quot;&lt;OML service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>OML service URL<\/em> is the REST server portion of the Oracle Machine Learning user management cloud service instance<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 2: Create a data monitoring job","message":["%md","","Now that you have an access token, you can send a request to OML Services to create a data monitoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a POST request. Below is a listing of the parameters that can be specified for a data monitoring job, divided into those that are required and those that are optional. ","","*Required:*","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, and is set to DATA_MONITORING for data monitoring jobs. ","* _outputData_ is the output data identifier. The results of the job will be written to a table named {jobID}_{outputData}.","* _baselineData_ is a table or view name that contains baseline data to monitor. At least 100 rows are required for data monitoring, otherwise analysis is skipped.","* _newData_ is a table or view name with new data to be compared against the baseline. At least 100 rows per period are required for data monitoring, otherwise analysis is skipped. ","","","*Optional:*","* _disableJob_ is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.","* _inputSchemaName_ is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.","* _outputSchemaName_ is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.","* _jobDescription_ is a description of the job.","* _jobServiceLevel_ is the service level for the job, which can be LOW, MEDIUM, or HIGH.","* _timeColumn_ is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.","* _startDate_ is the start date and time of monitoring in newData. If not provided the earliest date and time is used. ","* _endDate_ is the end date and time of the monitoring in newData. If not provided the last date and time is used.","* _frequency_ indicates the time window unit on which the monitoring is performed on the new data. This can be \"day\", \"week\", \"month\", or \"year\". If not provided, the whole \"new\" data is used as a single time period. ","* _threshold_ is the threshold to trigger a drift alert. The default is 0.7.","* _recompute_ is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _caseidColumn_ is a case identifier column in the baseline and new data. Providing it improves the reproducability of results. ","* _featureList_ is a list of features to monitor. The features need to be either numeric or categorical. The default maximum number of features in the list is 250. Monitoring will run on all features if the feature list is not specified, and and error is returned if there are more than 250 features. ","* _anchorColumn_ is the anchor column in the baseline and new data used for bivariate analysis. The target column in supervised problems can be passed as the anchor column. "],"enabled":true,"result":{"startTime":1734653787146,"interpreter":"md.low","endTime":1734653787587,"results":[{"message":"<p>Now that you have an access token, you can send a request to OML Services to create a data monitoring job. This request is sent to the <em>/omlmod/v1/jobs<\/em> endpoint as a POST request. Below is a listing of the parameters that can be specified for a data monitoring job, divided into those that are required and those that are optional.<\/p>\n<p><em>Required:<\/em><\/p>\n<ul>\n<li><em>jobName<\/em> specifies the name of the submitted job.<\/li>\n<li><em>jobType<\/em> specifies the type of job to be run, and is set to DATA_MONITORING for data monitoring jobs.<\/li>\n<li><em>outputData<\/em> is the output data identifier. The results of the job will be written to a table named {jobID}_{outputData}.<\/li>\n<li><em>baselineData<\/em> is a table or view name that contains baseline data to monitor. At least 100 rows are required for data monitoring, otherwise analysis is skipped.<\/li>\n<li><em>newData<\/em> is a table or view name with new data to be compared against the baseline. At least 100 rows per period are required for data monitoring, otherwise analysis is skipped.<\/li>\n<\/ul>\n<p><em>Optional:<\/em><\/p>\n<ul>\n<li><em>disableJob<\/em> is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.<\/li>\n<li><em>inputSchemaName<\/em> is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.<\/li>\n<li><em>outputSchemaName<\/em> is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.<\/li>\n<li><em>jobDescription<\/em> is a description of the job.<\/li>\n<li><em>jobServiceLevel<\/em> is the service level for the job, which can be LOW, MEDIUM, or HIGH.<\/li>\n<li><em>timeColumn<\/em> is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.<\/li>\n<li><em>startDate<\/em> is the start date and time of monitoring in newData. If not provided the earliest date and time is used.<\/li>\n<li><em>endDate<\/em> is the end date and time of the monitoring in newData. If not provided the last date and time is used.<\/li>\n<li><em>frequency<\/em> indicates the time window unit on which the monitoring is performed on the new data. This can be &quot;day&quot;, &quot;week&quot;, &quot;month&quot;, or &quot;year&quot;. If not provided, the whole &quot;new&quot; data is used as a single time period.<\/li>\n<li><em>threshold<\/em> is the threshold to trigger a drift alert. The default is 0.7.<\/li>\n<li><em>recompute<\/em> is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed.<\/li>\n<li><em>caseidColumn<\/em> is a case identifier column in the baseline and new data. Providing it improves the reproducability of results.<\/li>\n<li><em>featureList<\/em> is a list of features to monitor. The features need to be either numeric or categorical. The default maximum number of features in the list is 250. Monitoring will run on all features if the feature list is not specified, and and error is returned if there are more than 250 features.<\/li>\n<li><em>anchorColumn<\/em> is the anchor column in the baseline and new data used for bivariate analysis. The target column in supervised problems can be passed as the anchor column.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Example Data Monitoring job","message":["%md","","","Data monitoring jobs are initiated by sending a **POST** request to the */omlmod/v1/jobs* endpoint, and numeric and categorical features are supported. Monitoring will run on all features by default, or a list of features specified by the user, and there is a maximum of 250 features that can be monitored.","","The details for data monitoring are specified in **jobProperties**, including:","* data monitoring job name and type","* Autonomous Database service level","* table where the data monitoring details will be saved","* drift alert trigger threshold","* maximum number of runs","* baseline and new data to be used","* performance metric","","The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by **newData** and contained in the **timeColumn** field. When the start and end dates are not specified, the earliest and latest dates and times in the **timeColumn** are used. The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.","","Here is an example of a data monitoring job request using these parameters. Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems to extract relevant components from the response."],"enabled":true,"result":{"startTime":1734653788011,"interpreter":"md.low","endTime":1734653788441,"results":[{"message":"<p>Data monitoring jobs are initiated by sending a <strong>POST<\/strong> request to the <em>/omlmod/v1/jobs<\/em> endpoint, and numeric and categorical features are supported. Monitoring will run on all features by default, or a list of features specified by the user, and there is a maximum of 250 features that can be monitored.<\/p>\n<p>The details for data monitoring are specified in <strong>jobProperties<\/strong>, including:<\/p>\n<ul>\n<li>data monitoring job name and type<\/li>\n<li>Autonomous Database service level<\/li>\n<li>table where the data monitoring details will be saved<\/li>\n<li>drift alert trigger threshold<\/li>\n<li>maximum number of runs<\/li>\n<li>baseline and new data to be used<\/li>\n<li>performance metric<\/li>\n<\/ul>\n<p>The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by <strong>newData<\/strong> and contained in the <strong>timeColumn<\/strong> field. When the start and end dates are not specified, the earliest and latest dates and times in the <strong>timeColumn<\/strong> are used. The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.<\/p>\n<p>Here is an example of a data monitoring job request using these parameters. Note that we are using <em>jq<\/em>, a command-line JSON processor available on Linux and Mac OS systems to extract relevant components from the response.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to initiate data monitoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {","             \"jobStartDate\": \"2023-03-24T20:30:26Z\",            # job start date and time ","             \"repeatInterval\": \"FREQ=HOURLY\",                   # job frequency","             \"jobEndDate\": \"2023-03-24T23:30:26Z\",              # job end date and time","             \"maxRuns\": \"10\"                                    # max runs within the schedule","         },","         \"jobProperties\": {","             \"jobName\": \"HouseholdPowerDataMonitoring\",         # job name","             \"jobType\": \"DATA_MONITORING\",                      # job type; DATA_MONITORING","             \"disableJob\": false,                               # flag to disable the job at submission","             \"outputData\": \"householdPowerConsumption\",         # table where the job results will be saved in the format {jobID}_{outputData}","             \"baselineData\": \"HOUSEHOLD_POWER_BASE\",            # table/view containing baseline data ","             \"newData\": \"HOUSEHOLD_POWER_NEW\",                  # table/view with new data to compare against baseline","             \"inputSchemaName\": \"OMLUSER\",                      # database schema that owns the input table/view","             \"outputSchemaName\": \"OMLUSER\",                     # database schema that owns the output table/view","             \"jobDescription\": \"Monitor household power\",       # job description","             \"jobServiceLevel\": \"LOW\",                          # Autonomous Database service level; either LOW, MEDIUM, or HIGH","             \"timeColumn\": \"DATES\",                             # date or timestamp column in newData","             \"startDate\": \"2008-01-01T00:00:00Z\",               # the start date of the monitoring in the new data","             \"endDate\": \"2010-11-26T00:00:00Z\",                 # the end date of the monitoring in the new data","             \"frequency\": \"Year\",                               # the time window unit on which monitoring is performed on the new data","             \"threshold\": 0.8,                                  # threshold to trigger drift alert","             \"recompute\": false,                                # flag to determine whether to replace the output table","             \"caseidColumn\": null,                              # case identifier column in the baseline and new data","             \"anchorColumn\": null,                              # anchor column for bivariate analysis","             \"featureList\": [                                   # features to perform data monitoring on","                \"GLOBAL_ACTIVE_POWER\",","                \"GLOBAL_REACTIVE_POWER\",","                \"VOLTAGE\",","                \"SUB_METERING_1\",","                \"SUB_METERING_2\",","                \"SUB_METERING_3\"","            ]","         }","     }' | jq","    "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Job ID returned with successful job submission","message":["%md","","When your job is successfully submitted, you will recieve a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":{"startTime":1734653788866,"interpreter":"md.low","endTime":1734653789284,"results":[{"message":"<p>When your job is successfully submitted, you will recieve a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"jobId\": \"OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%247ABB6308_1664_4CB4_84B1_598A6EA599D1\"","    }","  ]"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 3: View the details of a submitted job","message":["%md","","You can now view the details of your submitted job by sending GET request with the job ID to the */omlmod/v1/jobs* endpoint, where **jobid** is the ID provided in response to the successful submission of your data monitoring job in the previous step."],"enabled":true,"result":{"startTime":1734653789701,"interpreter":"md.low","endTime":1734653790106,"results":[{"message":"<p>You can now view the details of your submitted job by sending GET request with the job ID to the <em>/omlmod/v1/jobs<\/em> endpoint, where <strong>jobid<\/strong> is the ID provided in response to the successful submission of your data monitoring job in the previous step.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Use the job ID to view job details","message":["","$ export jobid='OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1'  # save the job ID to a single-quoted variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-24T20:30:26Z\",","      \"repeatInterval\": \"FREQ=HOURLY\",","      \"jobEndDate\": \"2023-03-24T23:30:26Z\",","      \"maxRuns\": 3","    },","    \"jobProperties\": {","      \"jobType\": \"DATA_MONITORING\",","      \"inputSchemaName\": \"OMLUSER\",","      \"outputSchemaName\": \"OMLUSER\",","      \"outputData\": \"householdPowerConsumption\",","      \"jobDescription\": \"Monitor household power\",","      \"jobName\": \"HouseholdPowerDataMonitoring\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"LOW\",","      \"baselineData\": \"HOUSEHOLD_POWER_BASE\",","      \"newData\": \"HOUSEHOLD_POWER_NEW\",","      \"timeColumn\": \"DATES\",","      \"startDate\": \"2008-01-01T00:00:00Z\",","      \"endDate\": \"2010-11-26T00:00:00Z\",","      \"frequency\": \"Year\",","      \"threshold\": 0.8,","      \"recompute\": false,","      \"caseidColumn\": null,","      \"featureList\": [","        \"GLOBAL_ACTIVE_POWER\",","        \"GLOBAL_REACTIVE_POWER\",","        \"VOLTAGE\",","        \"SUB_METERING_1\",","        \"SUB_METERING_2\",","        \"SUB_METERING_3\"","      ],","      \"anchorColumn\": null","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-24T20:23:31.53651Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%247ABB6308_1664_4CB4_84B1_598A6EA599D1\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-24T20:30:26Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 4: Edit a data monitoring job (optional)","message":["%md","","After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the *updateProperties* field to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can updated for a data monitoring job:","","* _startDate_","* _endDate_","* _threshold_","* _recompute_","* _featureList_","","In the example request below, we update some of these fields to differ from the origional parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body."],"enabled":true,"result":{"startTime":1734653790531,"interpreter":"md.low","endTime":1734653790948,"results":[{"message":"<p>After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the <em>updateProperties<\/em> field to the <em>/omlmod/v1/jobs/{jobid}<\/em> endpoint. The following parameters can updated for a data monitoring job:<\/p>\n<ul>\n<li><em>startDate<\/em><\/li>\n<li><em>endDate<\/em><\/li>\n<li><em>threshold<\/em><\/li>\n<li><em>recompute<\/em><\/li>\n<li><em>featureList<\/em><\/li>\n<\/ul>\n<p>In the example request below, we update some of these fields to differ from the origional parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Update the properties of a data monitoring job","message":["","$ curl -i -X POST \"<OML Service URL>omlmod/v1/jobs/<jobid>\" \\","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"updateProperties\": {","                \"startDate\": \"2022-01-01T19:40:24Z\",","                \"endDate\":   \"2023-01-01T19:20:24Z\",","                \"threshold\":  0.80,","                \"featureList\": [\"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\"]","            }","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 5: Perform an action on a data monitoring job (optional)","message":["%md","","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","* _DISABLE_ disables the job at submission. The *force* property can be used with this action to forcefully interrupt any running job. ","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","For example, when your job has been successfuly submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example request to update the job status to **DISABLED**. When you successfully submit your job you will receive a 204 response with no body. "],"enabled":true,"result":{"startTime":1734653791369,"interpreter":"md.low","endTime":1734653791772,"results":[{"message":"<p>OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, <strong>DISABLE<\/strong>, <strong>RUN<\/strong>, <strong>ENABLE<\/strong>, and <strong>STOP<\/strong>:<\/p>\n<ul>\n<li><em>DISABLE<\/em> disables the job at submission. The <em>force<\/em> property can be used with this action to forcefully interrupt any running job.<\/li>\n<li><em>ENABLE<\/em> enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule.<\/li>\n<li><em>RUN<\/em> will immediately run the job as a way to test a job or run it outside of its schedule.<\/li>\n<li><em>STOP<\/em> stops a currently running job.<\/li>\n<\/ul>\n<p>For example, when your job has been successfuly submitted, its state is set to <strong>ENABLED<\/strong> by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as <strong>DISABLED<\/strong>. You can do this by sending a request to the <em>/omlmod/v1/jobs/{jobid}/action<\/em> endpoint.<\/p>\n<p>Note that jobs can be set to <strong>DISABLED<\/strong> at submission by setting the <em>disableJob<\/em> flag to <em>true<\/em>.<\/p>\n<p>Below is an example request to update the job status to <strong>DISABLED<\/strong>. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Disable data monitoring job","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 6: View the data monitoring job output","message":["%md","","Once your job has run, either according to its schedule or by the **RUN** action, you can view its output in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobid}\\_{outputData}*. You can check if your job is finished by sending a request to view its details, as we did in step #3. If your job has run at least once you should see the **lastRunDetail** field with information on that run. The paragraph below queries the output table associated with this example. ","","If you scroll down on the output table, you can see that there is information for the baseline time period and newdata time period for each of the dataset features being monitored for drift. Many of the columns are empty for the baseline rows as monitoring is only done on the new data, not the baseline data. For help understanding the data monitoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html<\/a>"],"enabled":true,"result":{"startTime":1738083009720,"interpreter":"md.low","endTime":1738083011979,"results":[{"message":"<p>Once your job has run, either according to its schedule or by the <strong>RUN<\/strong> action, you can view its output in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is <em>{jobid}_{outputData}<\/em>. You can check if your job is finished by sending a request to view its details, as we did in step #3. If your job has run at least once you should see the <strong>lastRunDetail<\/strong> field with information on that run. The paragraph below queries the output table associated with this example.<\/p>\n<p>If you scroll down on the output table, you can see that there is information for the baseline time period and newdata time period for each of the dataset features being monitored for drift. Many of the columns are empty for the baseline rows as monitoring is only done on the new data, not the baseline data. For help understanding the data monitoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html<\/a><\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT START_TIME, END_TIME, IS_BASELINE, THRESHOLD, HAS_DRIFT, round(DRIFT, 3), ","       FEATURE_NAME, ROUND(IMPORTANCE_VALUE, 3) ","FROM OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1_householdPowerConsumption","ORDER BY FEATURE_NAME, IS_BASELINE DESC"],"enabled":false,"result":{"startTime":1700068961590,"interpreter":"sql.low","endTime":1700068963129,"results":[{"message":"START_TIME\tEND_TIME\tIS_BASELINE\tTHRESHOLD\tHAS_DRIFT\tROUND(DRIFT,3)\tFEATURE_NAME\tROUND(IMPORTANCE_VALUE,3)\n\t\t1\t\t\t\tGLOBAL_ACTIVE_POWER\t\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tGLOBAL_ACTIVE_POWER\t2.158\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tGLOBAL_ACTIVE_POWER\t2.631\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tGLOBAL_ACTIVE_POWER\t2.181\n\t\t1\t\t\t\tGLOBAL_REACTIVE_POWER\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tGLOBAL_REACTIVE_POWER\t1.639\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tGLOBAL_REACTIVE_POWER\t1.897\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tGLOBAL_REACTIVE_POWER\t1.389\n\t\t1\t\t\t\tSUB_METERING_1\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_1\t0.362\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_1\t0.294\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_1\t0.234\n\t\t1\t\t\t\tSUB_METERING_2\t\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_2\t0.631\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_2\t0.56\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_2\t0.558\n\t\t1\t\t\t\tSUB_METERING_3\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_3\t1.854\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_3\t1.922\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_3\t1.888\n\t\t1\t\t\t\tVOLTAGE\t\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tVOLTAGE\t2.145\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tVOLTAGE\t2.269\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tVOLTAGE\t2.576\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 7: Delete a data monitoring job (optional)","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":{"startTime":1734653793052,"interpreter":"md.low","endTime":1734653793458,"results":[{"message":"<p>To delete a previously submitted job, send a DELETE request along with the Job ID to the <em>/omlmod/v1/jobs<\/em> endpoint.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","$ curl -X DELETE \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":{"startTime":1700068963474,"interpreter":"md.low","endTime":1700068963566,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Reproducing this example","message":["%md","","If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, *HOUSEHOLD_POWER_BASE* and *HOUSEHOLD_POWER_NEW*. These tables are created using the Individual Household Electric Power Consumption data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption).","","The code to read and transform the data and create database tables using R is provided below."],"enabled":true,"result":{"startTime":1734653793878,"interpreter":"md.low","endTime":1734653794287,"results":[{"message":"<p>If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, <em>HOUSEHOLD_POWER_BASE<\/em> and <em>HOUSEHOLD_POWER_NEW<\/em>. These tables are created using the Individual Household Electric Power Consumption data from the <a href=\"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\">UCI Machine Learning Repository<\/a>.<\/p>\n<p>The code to read and transform the data and create database tables using R is provided below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop tables if they exist and suppress warnings","message":["%r","","options(warn=-1)","","try(ore.drop(table=\"HOUSEHOLD_POWER_BASE\"))","try(ore.drop(table=\"HOUSEHOLD_POWER_NEW\"))"],"enabled":true,"result":{"startTime":1734653794702,"interpreter":"r.low","endTime":1734653800048,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Read and transform data","message":["%r","","test <- read.csv(\"https://objectstorage.us-sanjose-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/household_power_consumption.txt\", sep=\";\")","","test <- transform(test, Date = as.Date(Date, format = \"%d/%m/%Y\"))","test <- transform(test, Global_active_power = as.numeric(Global_active_power))","test <- transform(test, Global_reactive_power = as.numeric(Global_reactive_power))","test <- transform(test, Voltage = as.numeric(Voltage))","test <- transform(test, Global_intensity = as.numeric(Global_intensity))","test <- transform(test, Sub_metering_1 = as.numeric(Sub_metering_1))","test <- transform(test, Sub_metering_2 = as.numeric(Sub_metering_2))","test <- transform(test, Sub_metering_3 = as.numeric(Sub_metering_3))","","colnames(test) <- c(\"DATES\", \"TIMES\", \"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\", \"GLOBAL_INTENSITY\", \"SUB_METERING_1\", \"SUB_METERING_2\", \"SUB_METERING_3\") "],"enabled":true,"result":{"startTime":1734653800480,"interpreter":"r.low","endTime":1734653810836,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create baseline and new data ","message":["%r","","test_base <- test[test$DATES < \"2008-01-01\",]","test_new <- test[test$DATES > \"2007-12-31\",]","","# Create OML proxy objects","","ore.create(test_base, table=\"HOUSEHOLD_POWER_BASE\")","ore.create(test_new, table=\"HOUSEHOLD_POWER_NEW\")"],"enabled":true,"result":{"startTime":1734653811256,"interpreter":"r.low","endTime":1734653825811,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the baseline data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_BASE","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734653826227,"interpreter":"sql.low","endTime":1734653828070,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2006-12-16 00:00:00\t17:24:00\t4.216\t0.418\t234.84\t18.4\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:25:00\t5.36\t0.436\t233.63\t23.0\t0.0\t1.0\t16.0\n2006-12-16 00:00:00\t17:26:00\t5.374\t0.498\t233.29\t23.0\t0.0\t2.0\t17.0\n2006-12-16 00:00:00\t17:27:00\t5.388\t0.502\t233.74\t23.0\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:28:00\t3.666\t0.528\t235.68\t15.8\t0.0\t1.0\t17.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View new data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_NEW","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734653828486,"interpreter":"sql.low","endTime":1734653828933,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2008-01-01 00:00:00\t00:00:00\t1.62\t0.07\t241.25\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:01:00\t1.626\t0.072\t241.74\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:02:00\t1.622\t0.072\t241.52\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:03:00\t1.612\t0.07\t240.82\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:04:00\t1.612\t0.07\t240.8\t6.6\t0.0\t0.0\t18.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1734653829358,"interpreter":"md.low","endTime":1734653829773,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML Model Monitoring REST","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734654464105,"interpreter":"md.low","endTime":1734654464647,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Model Monitoring REST","","Model monitoring plays an important role in MLOps by allowing users to monitor the performance of production machine learning models. Monitoring gives you the ability to identify when a model's performance is no longer satisfactory to meet business objectives and needs to be replaced with an updated model. The degradation of production models is a normal and expected event that can happen for several reasons. One of these reasons is data drift, where there is a change in the profile of the data on which predictions are being made. Another is concept drift, where the expectations of what constitutes correct predictions can change over time. For example, the statistical properties of a target variable, such as stocking inventory, may change over time as consumer preferences change. The large variety of factors that contribute to data and concept drift make model monitoring an important task. It enables users to be aware when these changes compromise the prediction quality of production models. ","","OML Services model monitoring is supported for classification and regression models and performance is tracked using model accuracy metrics. For classification, these quality metrics include:","* Accuracy","* Balanced accuracy","* AUC","* F1","* Precision","* Recall","","For regression, these accuracy metrics include:","* Mean squared error","* Mean absolute error","* Mean square log error","* Median absolute error","","### **Model monitoring workflow**","","In this notebook we highlight the following features of model monitoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a model monitoring job","* View the details and output of the model monitoring job","* Update, disable, and delete a model monitoring job","","The dataset used in this example is from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption). It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1734725171903,"interpreter":"md.low","endTime":1734725172472,"results":[{"message":"<h1 id=\"oml-model-monitoring-rest\">OML Model Monitoring REST<\/h1>\n<p>Model monitoring plays an important role in MLOps by allowing users to monitor the performance of production machine learning models. Monitoring gives you the ability to identify when a model's performance is no longer satisfactory to meet business objectives and needs to be replaced with an updated model. The degradation of production models is a normal and expected event that can happen for several reasons. One of these reasons is data drift, where there is a change in the profile of the data on which predictions are being made. Another is concept drift, where the expectations of what constitutes correct predictions can change over time. For example, the statistical properties of a target variable, such as stocking inventory, may change over time as consumer preferences change. The large variety of factors that contribute to data and concept drift make model monitoring an important task. It enables users to be aware when these changes compromise the prediction quality of production models.<\/p>\n<p>OML Services model monitoring is supported for classification and regression models and performance is tracked using model accuracy metrics. For classification, these quality metrics include:<\/p>\n<ul>\n<li>Accuracy<\/li>\n<li>Balanced accuracy<\/li>\n<li>AUC<\/li>\n<li>F1<\/li>\n<li>Precision<\/li>\n<li>Recall<\/li>\n<\/ul>\n<p>For regression, these accuracy metrics include:<\/p>\n<ul>\n<li>Mean squared error<\/li>\n<li>Mean absolute error<\/li>\n<li>Mean square log error<\/li>\n<li>Median absolute error<\/li>\n<\/ul>\n<h3 id=\"model-monitoring-workflow\"><strong>Model monitoring workflow<\/strong><\/h3>\n<p>In this notebook we highlight the following features of model monitoring through the REST interface:<\/p>\n<ul>\n<li>Authenticate the database user and obtain a token<\/li>\n<li>Create a model monitoring job<\/li>\n<li>View the details and output of the model monitoring job<\/li>\n<li>Update, disable, and delete a model monitoring job<\/li>\n<\/ul>\n<p>The dataset used in this example is from the <a href=\"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\">UCI Machine Learning Repository<\/a>. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring.html\" target=\"_blank\">OML Model Monitoring<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services demonstration<\/a>","","For data monitoring, refer to the Data Monitoring notebook in the template notebook examples."],"enabled":true,"result":{"startTime":1734654466097,"interpreter":"md.low","endTime":1734654466555,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring.html\" target=\"_blank\">OML Model Monitoring<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a><\/li>\n<li><a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services demonstration<\/a><\/li>\n<\/ul>\n<p>For data monitoring, refer to the Data Monitoring notebook in the template notebook examples.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run a model monitoring job through a REST interface on a model deployed to OML Services. To follow these steps, you must already have your own model deployed to OML Services. ","","If you do not know how to deploy a model to OML Services you can:","* Use the example notebook \"OML4SQL Exporting Serialized Models\" for a guide on how to deploy a model to OML Services using the notebook interface.","* Deploy from OML AutoML UI as described here: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html\">https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html<\/a>","","","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":{"startTime":1738085480020,"interpreter":"md.low","endTime":1738085480166,"results":[{"message":"<p>This notebook walks through the steps to run a model monitoring job through a REST interface on a model deployed to OML Services. To follow these steps, you must already have your own model deployed to OML Services.<\/p>\n<p>If you do not know how to deploy a model to OML Services you can:<\/p>\n<ul>\n<li>Use the example notebook &quot;OML4SQL Exporting Serialized Models&quot; for a guide on how to deploy a model to OML Services using the notebook interface.<\/li>\n<li>Deploy from OML AutoML UI as described here: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html\">https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html<\/a><\/a><\/li>\n<\/ul>\n<p>The <strong>cURL<\/strong> commands provided in this notebook are for illustrative purposes only. To run REST calls, use <strong>cURL<\/strong> in a Linux or Mac terminal or another REST client such as Postman.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 1: Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", ","    \"password\":\"' <yourpassword>'\"}' \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning cloud service"],"enabled":true,"result":{"startTime":1734654467940,"interpreter":"md.low","endTime":1734654468395,"results":[{"message":"<p>To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, \n&quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' &quot;&lt;OML service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>OML service URL<\/em> is the REST server portion of the Oracle Machine Learning cloud service<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 2: Create a model monitoring job","message":["%md","","Now that you have a token, you can submit your model monitoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a POST request. There are many parameters that can be specified for a job request, as shown below and divided into required and optional parameters.","","*Required:*","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, and is set to MODEL_MONITORING for model monitoring jobs. ","* _outputData_ is the output data identifier. The results of the job will be written to a table named {jobId}_{ouputData}.","* _baselineData_ is a table/view that contains baseline data to monitor. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped. ","* _newData_ is a table/view with new data to be compared against the baseline. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped. ","* _modelList_ is a list of models to be monitored, identified by their modelIds. By default, up to 20 models can be monitored by a single job.","","*Optional:*","* _disableJob_ is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.","* _outputSchemaName_ is the database schema that owns the output table. If not specifiedthe output schema will be the same as the input schema.","* _inputSchemaName_ is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.","* _jobFlag_ is the name of a JobFlag to be used to filter jobs.","* _timeColumn_ is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period. ","* _frequency_ indicates the time window unit that the monitoring is done on in the new data, which can be \"day\", \"week\", \"month\", or \"year\". If not provided, the entire \"new\" data is used as a single time period. ","* _threshold_ is the threshold to trigger a drift alert. ","* _recompute_ is a flag on whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _performanceMetric_ is The metric used to measure model performance. This is defaulted to MEAN\\_SQUARED\\_ERROR for regression models and BALANCED_ACCURACY for classification models.","* _caseidColumn_ is a case identifier column in the baseline and new data. Providing it improves the reproducability of results. ","* _startDate_ is used when timeColumn is specified. It is the start date and time of monitoring in newData. If not provided, the earliest date and time is used. ","* _endDate_ is used when timeColumn is specified. It is the end date and time of the monitoring in newData. If not provided, the last date and time is used.","* _jobDescription_ is a text description of the job.","* _jobServiceLevel_ is the service level for the job, which can be LOW, MEDIUM, or HIGH."],"enabled":true,"result":{"startTime":1734654468876,"interpreter":"md.low","endTime":1734654469335,"results":[{"message":"<p>Now that you have a token, you can submit your model monitoring job. This request is sent to the <em>/omlmod/v1/jobs<\/em> endpoint as a POST request. There are many parameters that can be specified for a job request, as shown below and divided into required and optional parameters.<\/p>\n<p><em>Required:<\/em><\/p>\n<ul>\n<li><em>jobName<\/em> specifies the name of the submitted job.<\/li>\n<li><em>jobType<\/em> specifies the type of job to be run, and is set to MODEL_MONITORING for model monitoring jobs.<\/li>\n<li><em>outputData<\/em> is the output data identifier. The results of the job will be written to a table named {jobId}_{ouputData}.<\/li>\n<li><em>baselineData<\/em> is a table/view that contains baseline data to monitor. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped.<\/li>\n<li><em>newData<\/em> is a table/view with new data to be compared against the baseline. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped.<\/li>\n<li><em>modelList<\/em> is a list of models to be monitored, identified by their modelIds. By default, up to 20 models can be monitored by a single job.<\/li>\n<\/ul>\n<p><em>Optional:<\/em><\/p>\n<ul>\n<li><em>disableJob<\/em> is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.<\/li>\n<li><em>outputSchemaName<\/em> is the database schema that owns the output table. If not specifiedthe output schema will be the same as the input schema.<\/li>\n<li><em>inputSchemaName<\/em> is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.<\/li>\n<li><em>jobFlag<\/em> is the name of a JobFlag to be used to filter jobs.<\/li>\n<li><em>timeColumn<\/em> is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.<\/li>\n<li><em>frequency<\/em> indicates the time window unit that the monitoring is done on in the new data, which can be &quot;day&quot;, &quot;week&quot;, &quot;month&quot;, or &quot;year&quot;. If not provided, the entire &quot;new&quot; data is used as a single time period.<\/li>\n<li><em>threshold<\/em> is the threshold to trigger a drift alert.<\/li>\n<li><em>recompute<\/em> is a flag on whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed.<\/li>\n<li><em>performanceMetric<\/em> is The metric used to measure model performance. This is defaulted to MEAN_SQUARED_ERROR for regression models and BALANCED_ACCURACY for classification models.<\/li>\n<li><em>caseidColumn<\/em> is a case identifier column in the baseline and new data. Providing it improves the reproducability of results.<\/li>\n<li><em>startDate<\/em> is used when timeColumn is specified. It is the start date and time of monitoring in newData. If not provided, the earliest date and time is used.<\/li>\n<li><em>endDate<\/em> is used when timeColumn is specified. It is the end date and time of the monitoring in newData. If not provided, the last date and time is used.<\/li>\n<li><em>jobDescription<\/em> is a text description of the job.<\/li>\n<li><em>jobServiceLevel<\/em> is the service level for the job, which can be LOW, MEDIUM, or HIGH.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Example model monitoring job","message":["%md","","Here is an example of a model monitoring job request using these parameters. To start, you will need the model ID(s) for the model(s) that you want to monitor. You can get the model ID by sending a GET request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client. Here, the model URI is **HousePowerNN**.  ","","Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response."],"enabled":true,"result":{"startTime":1734654469803,"interpreter":"md.low","endTime":1734654470266,"results":[{"message":"<p>Here is an example of a model monitoring job request using these parameters. To start, you will need the model ID(s) for the model(s) that you want to monitor. You can get the model ID by sending a GET request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client. Here, the model URI is <strong>HousePowerNN<\/strong>.<\/p>\n<p>Note that we are using <em>jq<\/em>, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Retrieve model ID","message":["","$ curl -X GET \"<OML Service URL>/omlmod/v1/deployment/HousePowerNN\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'","","returns the model Id:","","  \"modelId\": \"0bf13d1f-86a6-465d-93d1-8985afd1bbdb"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Inititate model monitoring job","message":["%md","","","Model monitoring jobs are initiated by sending a POST request to the */omlmod/v1/jobs* endpoint. The request body may include a single model, or a list of up to 20 models identified by their model IDs. The job start and end dates, job frequency, and maximum number of runs are specified in the **jobSchedule** property.","","The details for model monitoring are specified in **jobProperties**, including:","* model monitoring job name and type","* Autonomous Database service level","* table where the model monitoring details will be saved","* drift alert trigger threshold","* maximum number of runs","* baseline and new data to be used","* chosen balanced accuracy for the performance metric","","The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by **newData** and contained in the **timeColumn** field. ","","The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes."],"enabled":true,"result":{"startTime":1734654470730,"interpreter":"md.low","endTime":1734654471169,"results":[{"message":"<p>Model monitoring jobs are initiated by sending a POST request to the <em>/omlmod/v1/jobs<\/em> endpoint. The request body may include a single model, or a list of up to 20 models identified by their model IDs. The job start and end dates, job frequency, and maximum number of runs are specified in the <strong>jobSchedule<\/strong> property.<\/p>\n<p>The details for model monitoring are specified in <strong>jobProperties<\/strong>, including:<\/p>\n<ul>\n<li>model monitoring job name and type<\/li>\n<li>Autonomous Database service level<\/li>\n<li>table where the model monitoring details will be saved<\/li>\n<li>drift alert trigger threshold<\/li>\n<li>maximum number of runs<\/li>\n<li>baseline and new data to be used<\/li>\n<li>chosen balanced accuracy for the performance metric<\/li>\n<\/ul>\n<p>The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by <strong>newData<\/strong> and contained in the <strong>timeColumn<\/strong> field.<\/p>\n<p>The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"cURL command to initiate model monitoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"jobSchedule\": {","            \"jobStartDate\": \"2023-03-25T00:30:07Z\",                 # job start date and time ","            \"repeatInterval\": \"FREQ=DAILY\",                         # job frequency","            \"jobEndDate\": \"2023-03-30T20:50:06Z\",                   # job end date and time ","            \"maxRuns\": \"5\"                                          # max runs within the schedule","         },","         \"jobProperties\": {","            \"jobName\": \"MY_MODEL_MONITOR1\",                         # job name","            \"jobType\": \"MODEL_MONITORING\",                          # job type; MODEL_MONITORING","            \"disableJob\": false,                                    # flag to disable the job at submission","            \"jobServiceLevel\": \"LOW\",                               # Autonomous Database service level; either LOW, MEDIUM, and HIGH","            \"inputSchemaName\": \"OMLUSER\",                           # database schema that owns the input table/view","            \"outputSchemaName\": \"OMLUSER\",                          # database schema that owns the output table","            \"outputData\": \"Global_Active_Power_Monitor\",            # table where the job results will be saved in the format {jobID}_{outputData}","            \"jobDescription\": \"Global active power monitoring job\", # job description","            \"baselineData\": \"HOUSEHOLD_POWER_BASE\",                 # table/view containing baseline data ","            \"newData\": \"HOUSEHOLD_POWER_NEW\",                       # table/view with new data to compare against baseline","            \"frequency\": \"Year\",                                    # time window unit that the monitoring is done on in the new data","            \"threshold\": 0.15,                                      # threshold to trigger drift alert","            \"timeColumn\": \"DATES\",                                  # date or timestamp column in newData","            \"startDate\": \"2008-01-01T00:00:00Z\",                    # the start date and time of monitoring in the new data","            \"endDate\": \"2010-11-26T00:00:00Z\",                      # the end date and time of monitoring in the new data","            \"caseidColumn\": null,                                   # case identifier column in the baseline and new data","            \"performanceMetric\": \"MEAN_SQUARED_ERROR\",              # metric used to measure model performance","            \"modelList\": [                                          # model ID or list of model IDs to be monitored","                \"0bf13d1f-86a6-465d-93d1-8985afd1bbdb\"","             ],","            \"recompute\": false                                      # flag to determine whether to overwrite the results table","         }","    }' | jq"],"enabled":false,"result":{"startTime":1700071260844,"interpreter":"md.low","endTime":1700071260937,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"A successful job submission returns a job ID","message":["%md","","When the job is successfully submitted, you will receive a response similar to the following. Note the **jobId**, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":{"startTime":1734654471626,"interpreter":"md.low","endTime":1734654472061,"results":[{"message":"<p>When the job is successfully submitted, you will receive a response similar to the following. Note the <strong>jobId<\/strong>, as you will use it to submit requests such as retrieving job details or performing an action on the job.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Job ID returned ","message":["","{","  \"jobId\": \"OML$736F509B_FC1A_400A_AC75_553F1D6C5D97\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/v1/jobs/OML%24736F509B_FC1A_400A_AC75_553F1D6C5D97\"","    }","  ]"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 3: View the details of a submitted job","message":["%md","","You can now view the details of your submitted job with a GET request to the */omlmod/v1/jobs/{jobId}* endpoint, where **jobId** is the ID provided in response to the successful submission of your model monitoring job in the previous step. The **jobStatus** *CREATED* indicates the job as been created. If your job has already ran at least once, you will see infomation returned about the last job run. "],"enabled":true,"result":{"startTime":1734654472515,"interpreter":"md.low","endTime":1734654472956,"results":[{"message":"<p>You can now view the details of your submitted job with a GET request to the <em>/omlmod/v1/jobs/{jobId}<\/em> endpoint, where <strong>jobId<\/strong> is the ID provided in response to the successful submission of your model monitoring job in the previous step. The <strong>jobStatus<\/strong> <em>CREATED<\/em> indicates the job as been created. If your job has already ran at least once, you will see infomation returned about the last job run.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submitted job details","message":["","$ export jobid='OML$736F509B_FC1A_400A_AC75_553F1D6C5D97'   # define the Job ID as a single-quoted variable ","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq","","returns:","","{","  \"jobId\": \"OML$736F509B_FC1A_400A_AC75_553F1D6C5D97\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-25T00:30:07Z\",","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2023-03-30T00:30:07Z\",","      \"maxRuns\": 5","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_MONITORING\",","      \"inputSchemaName\": \"OMLUSER\",","      \"outputSchemaName\": \"OMLUSER\",","      \"outputData\": \"Global_Active_Power_Monitor\",","      \"jobDescription\": \"Global active power monitoring job\",","      \"jobName\": \"MY_MODEL_MONITOR1\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"LOW\",","      \"baselineData\": \"HOUSEHOLD_POWER_BASE\",","      \"newData\": \"HOUSEHOLD_POWER_NEW\",","      \"timeColumn\": \"DATES\",","      \"startDate\": \"2008-01-01T00:00:00Z\",","      \"endDate\": \"2010-11-26T00:00:00Z\",","      \"frequency\": \"Year\",","      \"threshold\": 0.15,","      \"recompute\": false,","      \"caseidColumn\": null,","      \"modelList\": [","        \"0bf13d1f-86a6-465d-93d1-8985afd1bbdb\"","      ],","      \"performanceMetric\": \"MEAN_SQUARED_ERROR\"","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-25T00:26:16.127906Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24736F509B_FC1A_400A_AC75_553F1D6C5D97\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-25T00:30:07Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 4: Update a model monitoring job (optional)","message":["%md","","After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the *updateProperties* field to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can be updated with this request for a model monitoring job:","","* _startDate_","* _endDate_","* _threshold_","* _recompute_","* _modelList_","","In the example request below, using the same job ID, we update some of these original parameters in the **updateProperties** field. The trigger for the drift alert is updated to 0.20, and the flag *recompute* is set to update already computed periods so that each job run will recalculate all time periods present in the specified **timeColumn** in the data.","","A successful update will receive an HTTP 204 response with no content."],"enabled":true,"result":{"startTime":1734654473407,"interpreter":"md.low","endTime":1734654473868,"results":[{"message":"<p>After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the <em>updateProperties<\/em> field to the <em>/omlmod/v1/jobs/{jobid}<\/em> endpoint. The following parameters can be updated with this request for a model monitoring job:<\/p>\n<ul>\n<li><em>startDate<\/em><\/li>\n<li><em>endDate<\/em><\/li>\n<li><em>threshold<\/em><\/li>\n<li><em>recompute<\/em><\/li>\n<li><em>modelList<\/em><\/li>\n<\/ul>\n<p>In the example request below, using the same job ID, we update some of these original parameters in the <strong>updateProperties<\/strong> field. The trigger for the drift alert is updated to 0.20, and the flag <em>recompute<\/em> is set to update already computed periods so that each job run will recalculate all time periods present in the specified <strong>timeColumn<\/strong> in the data.<\/p>\n<p>A successful update will receive an HTTP 204 response with no content.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Update job properties","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>\" \\    ","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"updateProperties\": {","                \"threshold\": 0.20,","                \"recompute\": \"true\"","            }","         }'","         "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 5: View model monitoring job output","message":["%md","","Once your job has run, either according to its schedule or by the **RUN** action, you can view its output in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobid}\\_{outputData}*. You can check if your job is finished by sending a request to view its details, as we did in step 3. If your job has run at least once you should see the **lastRunDetail** field with information on that run. The next paragraph queries the output table associated with this example. ","","It is important to note that the first row of results is the baseline time period. That is the reason the **HAS_DRIFT**, **DRIFT**, **THRESHOLD** columns are empty for this row, as drift is not calculated on data in the baseline time period. ","","","For more information on the output table, refer to the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring-output-table.html\" target=\"_blank\">OML Services documentation.<\/a>"],"enabled":true,"result":{"startTime":1734654474323,"interpreter":"md.low","endTime":1734654474785,"results":[{"message":"<p>Once your job has run, either according to its schedule or by the <strong>RUN<\/strong> action, you can view its output in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is <em>{jobid}_{outputData}<\/em>. You can check if your job is finished by sending a request to view its details, as we did in step 3. If your job has run at least once you should see the <strong>lastRunDetail<\/strong> field with information on that run. The next paragraph queries the output table associated with this example.<\/p>\n<p>It is important to note that the first row of results is the baseline time period. That is the reason the <strong>HAS_DRIFT<\/strong>, <strong>DRIFT<\/strong>, <strong>THRESHOLD<\/strong> columns are empty for this row, as drift is not calculated on data in the baseline time period.<\/p>\n<p>For more information on the output table, refer to the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring-output-table.html\" target=\"_blank\">OML Services documentation.<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Query to view model monitoring output","message":["%sql","","","SELECT IS_BASELINE, MODEL_ID, round(METRIC, 4), HAS_DRIFT, round(DRIFT, 4), MODEL_TYPE, ","       THRESHOLD, MODEL_METRICS ","FROM OML$736F509B_FC1A_400A_AC75_553F1D6C5D97_Global_Active_Power_Monitor"],"enabled":false,"result":{"startTime":1700071262025,"interpreter":"sql.low","endTime":1700071263764,"results":[{"message":"IS_BASELINE\tMODEL_ID\tROUND(METRIC,4)\tHAS_DRIFT\tROUND(DRIFT,4)\tMODEL_TYPE\tTHRESHOLD\tMODEL_METRICS\n0\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0017\t1\t0.1608\tREGRESSION\t0.15\t{\"R2\":0.99809,\"Mean squared error\":0.0017396,\"Mean absolute error\":0.027297,\"Median absolute error\":0.019593,\"prediction_statistics\":{\"population_stability_index\":0.025958,\"mean_value\":1.0815,\"min_value\":0.12529,\"max_value\":9.5666,\"standard_deviation\":0.94483,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.87908},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.10714},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.012538},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.001173},{\"bin_upper_boundary\":null,\"bin_percentage\":0.000068328}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1016.4},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":4.1294},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.48653},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.32686},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.19106},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.072071}]}\n1\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0015\t\t\tREGRESSION\t\t{\"R2\":0.99893,\"Mean squared error\":0.0014987,\"Mean absolute error\":0.02293,\"Median absolute error\":0.016582,\"prediction_statistics\":{\"mean_value\":1.1484,\"min_value\":0.093934,\"max_value\":10.073,\"standard_deviation\":1.1787,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.82757},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.14257},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.02613},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.0035318},{\"bin_upper_boundary\":null,\"bin_percentage\":0.00020088}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1754.9},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":3.0614},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.92329},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.28173},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.22032},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.10669}]}\n0\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0015\t0\t0.0103\tREGRESSION\t0.15\t{\"R2\":0.99865,\"Mean squared error\":0.0015141,\"Mean absolute error\":0.025352,\"Median absolute error\":0.019308,\"prediction_statistics\":{\"population_stability_index\":0.011901,\"mean_value\":1.0436,\"min_value\":0.045116,\"max_value\":9.8458,\"standard_deviation\":1.0626,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.8657},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.11415},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.017624},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.0023127},{\"bin_upper_boundary\":null,\"bin_percentage\":0.00020623}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1389.6},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":3.7846},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.5429},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.33221},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.21555},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.089874}]}\n0\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0015\t0\t0.0164\tREGRESSION\t0.15\t{\"R2\":0.9985,\"Mean squared error\":0.0015232,\"Mean absolute error\":0.025032,\"Median absolute error\":0.018151,\"prediction_statistics\":{\"population_stability_index\":0.017769,\"mean_value\":1.0709,\"min_value\":0.09097,\"max_value\":10.3,\"standard_deviation\":1.006,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.87348},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.10839},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.016178},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.0018322},{\"bin_upper_boundary\":null,\"bin_percentage\":0.00012747}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1280.4},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":4.4025},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.47583},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.35687},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.20914},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.082932}]}\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 6: Perform an action on a model monitoring job (optional)","message":["%md","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","* _DISABLE_ disables the job at submission. The *force* property can be used with this action to forcefully interrupt any running job. ","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","For example, when your job has been successfully submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state, such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example request to update the job status to **DISABLED**. When you successfully submit your job you will receive a 204 response with no body. "],"enabled":true,"result":{"startTime":1734654475239,"interpreter":"md.low","endTime":1734654475683,"results":[{"message":"<p>OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, <strong>DISABLE<\/strong>, <strong>RUN<\/strong>, <strong>ENABLE<\/strong>, and <strong>STOP<\/strong>:<\/p>\n<ul>\n<li><em>DISABLE<\/em> disables the job at submission. The <em>force<\/em> property can be used with this action to forcefully interrupt any running job.<\/li>\n<li><em>ENABLE<\/em> enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule.<\/li>\n<li><em>RUN<\/em> will immediately run the job as a way to test a job or run it outside of its schedule.<\/li>\n<li><em>STOP<\/em> stops a currently running job.<\/li>\n<\/ul>\n<p>For example, when your job has been successfully submitted, its state is set to <strong>ENABLED<\/strong> by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state, such as <strong>DISABLED<\/strong>. You can do this by sending a request to the <em>/omlmod/v1/jobs/{jobid}/action<\/em> endpoint.<\/p>\n<p>Note that jobs can be set to <strong>DISABLED<\/strong> at submission by setting the <em>disableJob<\/em> flag to <em>true<\/em>.<\/p>\n<p>Below is an example request to update the job status to <strong>DISABLED<\/strong>. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Disable a job","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\",","            \"force\": \"false\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 7: Delete a model monitoring job (optional)","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":{"startTime":1734654476136,"interpreter":"md.low","endTime":1734654476574,"results":[{"message":"<p>To delete a previously submitted job, send a DELETE request along with the Job ID to the <em>/omlmod/v1/jobs<\/em> endpoint.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to delete a job","message":["","$ curl -X DELETE \"<omlserver>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Reproducing this example","message":["%md","","If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, *HOUSEHOLD_POWER_BASE* and *HOUSEHOLD_POWER_NEW*. These tables are created using the Individual Household Electric Power Consumption data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption).","","The code to read and transform the data and create database tables using R is provided below."],"enabled":true,"result":{"startTime":1734654477037,"interpreter":"md.low","endTime":1734654477491,"results":[{"message":"<p>If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, <em>HOUSEHOLD_POWER_BASE<\/em> and <em>HOUSEHOLD_POWER_NEW<\/em>. These tables are created using the Individual Household Electric Power Consumption data from the <a href=\"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\">UCI Machine Learning Repository<\/a>.<\/p>\n<p>The code to read and transform the data and create database tables using R is provided below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop tables if they exist and suppress warnings","message":["%r","","options(warn=-1)","","try(ore.drop(table=\"HOUSEHOLD_POWER_BASE\"))","try(ore.drop(table=\"HOUSEHOLD_POWER_NEW\"))"],"enabled":true,"result":{"startTime":1734654477967,"interpreter":"r.low","endTime":1734654483245,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Read and transform data","message":["%r","","test <- read.csv(\"https://objectstorage.us-sanjose-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/household_power_consumption.txt\", sep=\";\")","","test <- transform(test, Date = as.Date(Date, format = \"%d/%m/%Y\"))","test <- transform(test, Global_active_power = as.numeric(Global_active_power))","test <- transform(test, Global_reactive_power = as.numeric(Global_reactive_power))","test <- transform(test, Voltage = as.numeric(Voltage))","test <- transform(test, Global_intensity = as.numeric(Global_intensity))","test <- transform(test, Sub_metering_1 = as.numeric(Sub_metering_1))","test <- transform(test, Sub_metering_2 = as.numeric(Sub_metering_2))","test <- transform(test, Sub_metering_3 = as.numeric(Sub_metering_3))","","colnames(test) <- c(\"DATES\", \"TIMES\", \"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\", \"GLOBAL_INTENSITY\", \"SUB_METERING_1\", \"SUB_METERING_2\", \"SUB_METERING_3\") "],"enabled":true,"result":{"startTime":1734654483710,"interpreter":"r.low","endTime":1734654494004,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create baseline and new data","message":["%r","","test_base <- test[test$DATES < \"2008-01-01\",]","test_new <- test[test$DATES > \"2007-12-31\",]","","# Create OML proxy objects","","ore.create(test_base, table=\"HOUSEHOLD_POWER_BASE\")","ore.create(test_new, table=\"HOUSEHOLD_POWER_NEW\")"],"enabled":true,"result":{"startTime":1734654494467,"interpreter":"r.low","endTime":1734654508972,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the baseline data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_BASE","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734654509432,"interpreter":"sql.low","endTime":1734654511216,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2006-12-16 00:00:00\t17:24:00\t4.216\t0.418\t234.84\t18.4\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:25:00\t5.36\t0.436\t233.63\t23.0\t0.0\t1.0\t16.0\n2006-12-16 00:00:00\t17:26:00\t5.374\t0.498\t233.29\t23.0\t0.0\t2.0\t17.0\n2006-12-16 00:00:00\t17:27:00\t5.388\t0.502\t233.74\t23.0\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:28:00\t3.666\t0.528\t235.68\t15.8\t0.0\t1.0\t17.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the new data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_NEW","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734654511675,"interpreter":"sql.low","endTime":1734654512177,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2008-01-01 00:00:00\t00:00:00\t1.62\t0.07\t241.25\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:01:00\t1.626\t0.072\t241.74\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:02:00\t1.622\t0.072\t241.52\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:03:00\t1.612\t0.07\t240.82\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:04:00\t1.612\t0.07\t240.8\t6.6\t0.0\t0.0\t18.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734654512642,"interpreter":"md.low","endTime":1734654513088,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Monitoring Model Drift REST","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1718325526059,"interpreter":"md.low","endTime":1718325526206,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Model Monitoring","","Model monitoring plays an important role in MLOps by allowing users to monitor the performance of production machine learning models. Monitoring gives you the ability to identify when a model's performance is no longer satisfactory to meet business objectives and needs to be replaced with an updated model. The degradation of production models is a normal and expected event that can happen for several reasons. One of these reasons is data drift, where there is a change in the profile of the data on which predictions are being made. Another is concept drift, where the expectations of what constitutes correct predictions can change over time. For example, the statistical properties of a target variable, such as stocking inventory, may change over time as consumer preferences change. The large variety of factors that contribute to data and concept drift make model monitoring an important task. It enables users to be aware when these changes compromise the prediction quality of production models. ","","OML Services model monitoring is supported for classification and regression models and performance is tracked using model accuracy metrics. For classification, these quality metrics include:","* Accuracy","* Balanced accuracy","* AUC","* F1","* Precision","* Recall","","For regression, these accuracy metrics include:","* Mean squared error","* Mean absolute error","* Mean square log error","* Median absolute error","","### **Model monitoring workflow**","","In this notebook we highlight the following features of model monitoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a model monitoring job","* View the details and output of the model monitoring job","* Update, disable, and delete a model monitoring job","","The dataset used in this example is from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption). It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1718325526281,"interpreter":"md.low","endTime":1718325526385,"results":[{"message":"<h1 id=\"model-monitoring\">Model Monitoring<\/h1>\n<p>Model monitoring plays an important role in MLOps by allowing users to monitor the performance of production machine learning models. Monitoring gives you the ability to identify when a model's performance is no longer satisfactory to meet business objectives and needs to be replaced with an updated model. The degradation of production models is a normal and expected event that can happen for several reasons. One of these reasons is data drift, where there is a change in the profile of the data on which predictions are being made. Another is concept drift, where the expectations of what constitutes correct predictions can change over time. For example, the statistical properties of a target variable, such as stocking inventory, may change over time as consumer preferences change. The large variety of factors that contribute to data and concept drift make model monitoring an important task. It enables users to be aware when these changes compromise the prediction quality of production models.<\/p>\n<p>OML Services model monitoring is supported for classification and regression models and performance is tracked using model accuracy metrics. For classification, these quality metrics include:<\/p>\n<ul>\n<li>Accuracy<\/li>\n<li>Balanced accuracy<\/li>\n<li>AUC<\/li>\n<li>F1<\/li>\n<li>Precision<\/li>\n<li>Recall<\/li>\n<\/ul>\n<p>For regression, these accuracy metrics include:<\/p>\n<ul>\n<li>Mean squared error<\/li>\n<li>Mean absolute error<\/li>\n<li>Mean square log error<\/li>\n<li>Median absolute error<\/li>\n<\/ul>\n<h3 id=\"model-monitoring-workflow\"><strong>Model monitoring workflow<\/strong><\/h3>\n<p>In this notebook we highlight the following features of model monitoring through the REST interface:<\/p>\n<ul>\n<li>Authenticate the database user and obtain a token<\/li>\n<li>Create a model monitoring job<\/li>\n<li>View the details and output of the model monitoring job<\/li>\n<li>Update, disable, and delete a model monitoring job<\/li>\n<\/ul>\n<p>The dataset used in this example is from the <a href=\"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\">UCI Machine Learning Repository<\/a>. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring.html\" target=\"_blank\">OML Model Monitoring<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services demonstration<\/a>","","For data monitoring, refer to the Data Monitoring notebook in the template notebook examples."],"enabled":true,"result":{"startTime":1718325526465,"interpreter":"md.low","endTime":1718325526535,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring.html\" target=\"_blank\">OML Model Monitoring<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a><\/li>\n<li><a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services demonstration<\/a><\/li>\n<\/ul>\n<p>For data monitoring, refer to the Data Monitoring notebook in the template notebook examples.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run a model monitoring job through a REST interface on a model deployed to OML Services. To follow these steps, you must already have your own model deployed to OML Services. ","","If you do not know how to deploy a model to OML Services you can:","* Use the example notebook \"OML4SQL Exporting Serialized Models\" for a guide on how to deploy a model to OML Services using the notebook interface.","* Deploy from OML AutoML UI as described here: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html<\/a>","","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":{"startTime":1718325526606,"interpreter":"md.low","endTime":1718325526675,"results":[{"message":"<p>This notebook walks through the steps to run a model monitoring job through a REST interface on a model deployed to OML Services. To follow these steps, you must already have your own model deployed to OML Services.<\/p>\n<p>If you do not know how to deploy a model to OML Services you can:<\/p>\n<ul>\n<li>Use the example notebook &quot;OML4SQL Exporting Serialized Models&quot; for a guide on how to deploy a model to OML Services using the notebook interface.<\/li>\n<li>Deploy from OML AutoML UI as described here: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html');\"><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html\">https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/deploy.html<\/a><\/a><\/li>\n<\/ul>\n<p>The <strong>cURL<\/strong> commands provided in this notebook are for illustrative purposes only. To run REST calls, use <strong>cURL<\/strong> in a Linux or Mac terminal or another REST client such as Postman.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 1: Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", ","    \"password\":\"' <yourpassword>'\"}' \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning cloud service"],"enabled":true,"result":{"startTime":1718325526746,"interpreter":"md.low","endTime":1718325526815,"results":[{"message":"<p>To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use <strong>cURL<\/strong> with the <strong>-d<\/strong> option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint <em>/oauth2/v1/token<\/em>.<\/p>\n<pre><code>$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;grant_type&quot;:&quot;password&quot;, &quot;username&quot;:&quot;'&lt;yourusername&gt;'&quot;, \n&quot;password&quot;:&quot;' &lt;yourpassword&gt;'&quot;}' &quot;&lt;OML service URL&gt;/omlusers/api/oauth2/v1/token&quot;\n<\/code><\/pre>\n<p>where<\/p>\n<ul>\n<li><em>-X POST<\/em> specifies to use a POST request when communicating with the HTTP server<\/li>\n<li><em>--header<\/em> defines the headers required for the request (application/json)<\/li>\n<li><em>-d <data><\/em> sends the username and password authentication credentials as data in a POST request to the HTTP server<\/li>\n<li><em>Content-Type<\/em> and <em>Accept<\/em> define the response format (JSON)<\/li>\n<li><em>yourusername<\/em> is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role<\/li>\n<li><em>yourpassword<\/em> is the password for the user name<\/li>\n<li><em>OML service URL<\/em> is the REST server portion of the Oracle Machine Learning cloud service<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 2: Create a model monitoring job","message":["%md","","Now that you have a token, you can submit your model monitoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a POST request. There are many parameters that can be specified for a job request, as shown below and divided into required and optional parameters.","","*Required:*","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, and is set to MODEL_MONITORING for model monitoring jobs. ","* _outputData_ is the output data identifier. The results of the job will be written to a table named {jobId}_{ouputData}.","* _baselineData_ is a table/view that contains baseline data to monitor. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped. ","* _newData_ is a table/view with new data to be compared against the baseline. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped. ","* _modelList_ is a list of models to be monitored, identified by their modelIds. By default, up to 20 models can be monitored by a single job.","","*Optional:*","* _disableJob_ is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.","* _outputSchemaName_ is the database schema that owns the output table. If not specifiedthe output schema will be the same as the input schema.","* _inputSchemaName_ is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.","* _jobFlag_ is the name of a JobFlag to be used to filter jobs.","* _timeColumn_ is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period. ","* _frequency_ indicates the time window unit that the monitoring is done on in the new data, which can be \"day\", \"week\", \"month\", or \"year\". If not provided, the entire \"new\" data is used as a single time period. ","* _threshold_ is the threshold to trigger a drift alert. ","* _recompute_ is a flag on whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _performanceMetric_ is The metric used to measure model performance. This is defaulted to MEAN\\_SQUARED\\_ERROR for regression models and BALANCED_ACCURACY for classification models.","* _caseidColumn_ is a case identifier column in the baseline and new data. Providing it improves the reproducability of results. ","* _startDate_ is used when timeColumn is specified. It is the start date and time of monitoring in newData. If not provided, the earliest date and time is used. ","* _endDate_ is used when timeColumn is specified. It is the end date and time of the monitoring in newData. If not provided, the last date and time is used.","* _jobDescription_ is a text description of the job.","* _jobServiceLevel_ is the service level for the job, which can be LOW, MEDIUM, or HIGH."],"enabled":true,"result":{"startTime":1718325526887,"interpreter":"md.low","endTime":1718325526972,"results":[{"message":"<p>Now that you have a token, you can submit your model monitoring job. This request is sent to the <em>/omlmod/v1/jobs<\/em> endpoint as a POST request. There are many parameters that can be specified for a job request, as shown below and divided into required and optional parameters.<\/p>\n<p><em>Required:<\/em><\/p>\n<ul>\n<li><em>jobName<\/em> specifies the name of the submitted job.<\/li>\n<li><em>jobType<\/em> specifies the type of job to be run, and is set to MODEL_MONITORING for model monitoring jobs.<\/li>\n<li><em>outputData<\/em> is the output data identifier. The results of the job will be written to a table named {jobId}_{ouputData}.<\/li>\n<li><em>baselineData<\/em> is a table/view that contains baseline data to monitor. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped.<\/li>\n<li><em>newData<\/em> is a table/view with new data to be compared against the baseline. At least 50 rows per period are required for model monitoring, otherwise analysis is skipped.<\/li>\n<li><em>modelList<\/em> is a list of models to be monitored, identified by their modelIds. By default, up to 20 models can be monitored by a single job.<\/li>\n<\/ul>\n<p><em>Optional:<\/em><\/p>\n<ul>\n<li><em>disableJob<\/em> is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.<\/li>\n<li><em>outputSchemaName<\/em> is the database schema that owns the output table. If not specifiedthe output schema will be the same as the input schema.<\/li>\n<li><em>inputSchemaName<\/em> is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.<\/li>\n<li><em>jobFlag<\/em> is the name of a JobFlag to be used to filter jobs.<\/li>\n<li><em>timeColumn<\/em> is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.<\/li>\n<li><em>frequency<\/em> indicates the time window unit that the monitoring is done on in the new data, which can be &quot;day&quot;, &quot;week&quot;, &quot;month&quot;, or &quot;year&quot;. If not provided, the entire &quot;new&quot; data is used as a single time period.<\/li>\n<li><em>threshold<\/em> is the threshold to trigger a drift alert.<\/li>\n<li><em>recompute<\/em> is a flag on whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed.<\/li>\n<li><em>performanceMetric<\/em> is The metric used to measure model performance. This is defaulted to MEAN_SQUARED_ERROR for regression models and BALANCED_ACCURACY for classification models.<\/li>\n<li><em>caseidColumn<\/em> is a case identifier column in the baseline and new data. Providing it improves the reproducability of results.<\/li>\n<li><em>startDate<\/em> is used when timeColumn is specified. It is the start date and time of monitoring in newData. If not provided, the earliest date and time is used.<\/li>\n<li><em>endDate<\/em> is used when timeColumn is specified. It is the end date and time of the monitoring in newData. If not provided, the last date and time is used.<\/li>\n<li><em>jobDescription<\/em> is a text description of the job.<\/li>\n<li><em>jobServiceLevel<\/em> is the service level for the job, which can be LOW, MEDIUM, or HIGH.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Example model monitoring job","message":["%md","","Here is an example of a model monitoring job request using these parameters. To start, you will need the model ID(s) for the model(s) that you want to monitor. You can get the model ID by sending a GET request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client. Here, the model URI is **HousePowerNN**.  ","","Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response."],"enabled":true,"result":{"startTime":1718325527049,"interpreter":"md.low","endTime":1718325527102,"results":[{"message":"<p>Here is an example of a model monitoring job request using these parameters. To start, you will need the model ID(s) for the model(s) that you want to monitor. You can get the model ID by sending a GET request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client. Here, the model URI is <strong>HousePowerNN<\/strong>.<\/p>\n<p>Note that we are using <em>jq<\/em>, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Retrieve model ID","message":["","$ curl -X GET \"<OML Service URL>/omlmod/v1/deployment/HousePowerNN\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'","","returns the model Id:","","  \"modelId\": \"0bf13d1f-86a6-465d-93d1-8985afd1bbdb\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Inititate model monitoring job","message":["%md","","","Model monitoring jobs are initiated by sending a POST request to the */omlmod/v1/jobs* endpoint. The request body may include a single model, or a list of up to 20 models identified by their model IDs. The job start and end dates, job frequency, and maximum number of runs are specified in the **jobSchedule** property.","","The details for model monitoring are specified in **jobProperties**, including:","* model monitoring job name and type","* Autonomous Database service level","* table where the model monitoring details will be saved","* drift alert trigger threshold","* maximum number of runs","* baseline and new data to be used","* chosen balanced accuracy for the performance metric","","The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by **newData** and contained in the **timeColumn** field. ","","The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes."],"enabled":true,"result":{"startTime":1718325527168,"interpreter":"md.low","endTime":1718325527224,"results":[{"message":"<p>Model monitoring jobs are initiated by sending a POST request to the <em>/omlmod/v1/jobs<\/em> endpoint. The request body may include a single model, or a list of up to 20 models identified by their model IDs. The job start and end dates, job frequency, and maximum number of runs are specified in the <strong>jobSchedule<\/strong> property.<\/p>\n<p>The details for model monitoring are specified in <strong>jobProperties<\/strong>, including:<\/p>\n<ul>\n<li>model monitoring job name and type<\/li>\n<li>Autonomous Database service level<\/li>\n<li>table where the model monitoring details will be saved<\/li>\n<li>drift alert trigger threshold<\/li>\n<li>maximum number of runs<\/li>\n<li>baseline and new data to be used<\/li>\n<li>chosen balanced accuracy for the performance metric<\/li>\n<\/ul>\n<p>The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by <strong>newData<\/strong> and contained in the <strong>timeColumn<\/strong> field.<\/p>\n<p>The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"cURL command to initiate model monitoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"jobSchedule\": {","            \"jobStartDate\": \"2023-03-25T00:30:07Z\",                 # job start date and time ","            \"repeatInterval\": \"FREQ=DAILY\",                         # job frequency","            \"jobEndDate\": \"2023-03-30T20:50:06Z\",                   # job end date and time ","            \"maxRuns\": \"5\"                                          # max runs within the schedule","         },","         \"jobProperties\": {","            \"jobName\": \"MY_MODEL_MONITOR1\",                         # job name","            \"jobType\": \"MODEL_MONITORING\",                          # job type; MODEL_MONITORING","            \"disableJob\": false,                                    # flag to disable the job at submission","            \"jobServiceLevel\": \"LOW\",                               # Autonomous Database service level; either LOW, MEDIUM, and HIGH","            \"inputSchemaName\": \"OMLUSER\",                           # database schema that owns the input table/view","            \"outputSchemaName\": \"OMLUSER\",                          # database schema that owns the output table","            \"outputData\": \"Global_Active_Power_Monitor\",            # table where the job results will be saved in the format {jobID}_{outputData}","            \"jobDescription\": \"Global active power monitoring job\", # job description","            \"baselineData\": \"HOUSEHOLD_POWER_BASE\",                 # table/view containing baseline data ","            \"newData\": \"HOUSEHOLD_POWER_NEW\",                       # table/view with new data to compare against baseline","            \"frequency\": \"Year\",                                    # time window unit that the monitoring is done on in the new data","            \"threshold\": 0.15,                                      # threshold to trigger drift alert","            \"timeColumn\": \"DATES\",                                  # date or timestamp column in newData","            \"startDate\": \"2008-01-01T00:00:00Z\",                    # the start date and time of monitoring in the new data","            \"endDate\": \"2010-11-26T00:00:00Z\",                      # the end date and time of monitoring in the new data","            \"caseidColumn\": null,                                   # case identifier column in the baseline and new data","            \"performanceMetric\": \"MEAN_SQUARED_ERROR\",              # metric used to measure model performance","            \"modelList\": [                                          # model ID or list of model IDs to be monitored","                \"0bf13d1f-86a6-465d-93d1-8985afd1bbdb\"","             ],","            \"recompute\": false                                      # flag to determine whether to overwrite the results table","         }","    }' | jq","    "],"enabled":false,"result":{"startTime":1700071260844,"interpreter":"md.low","endTime":1700071260937,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"A successful job submission returns a job ID","message":["%md","","When the job is successfully submitted, you will receive a response similar to the following. Note the **jobId**, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":{"startTime":1718325527292,"interpreter":"md.low","endTime":1718325527346,"results":[{"message":"<p>When the job is successfully submitted, you will receive a response similar to the following. Note the <strong>jobId<\/strong>, as you will use it to submit requests such as retrieving job details or performing an action on the job.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Job ID returned ","message":["","{","  \"jobId\": \"OML$736F509B_FC1A_400A_AC75_553F1D6C5D97\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/v1/jobs/OML%24736F509B_FC1A_400A_AC75_553F1D6C5D97\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 3: View the details of a submitted job","message":["%md","","You can now view the details of your submitted job with a GET request to the */omlmod/v1/jobs/{jobId}* endpoint, where **jobId** is the ID provided in response to the successful submission of your model monitoring job in the previous step. The **jobStatus** *CREATED* indicates the job as been created. If your job has already ran at least once, you will see infomation returned about the last job run. "],"enabled":true,"result":{"startTime":1718325527414,"interpreter":"md.low","endTime":1718325527471,"results":[{"message":"<p>You can now view the details of your submitted job with a GET request to the <em>/omlmod/v1/jobs/{jobId}<\/em> endpoint, where <strong>jobId<\/strong> is the ID provided in response to the successful submission of your model monitoring job in the previous step. The <strong>jobStatus<\/strong> <em>CREATED<\/em> indicates the job as been created. If your job has already ran at least once, you will see infomation returned about the last job run.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submitted job details","message":["","$ export jobid='OML$736F509B_FC1A_400A_AC75_553F1D6C5D97'   # define the Job ID as a single-quoted variable ","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq","","returns:","","{","  \"jobId\": \"OML$736F509B_FC1A_400A_AC75_553F1D6C5D97\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-25T00:30:07Z\",","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2023-03-30T00:30:07Z\",","      \"maxRuns\": 5","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_MONITORING\",","      \"inputSchemaName\": \"OMLUSER\",","      \"outputSchemaName\": \"OMLUSER\",","      \"outputData\": \"Global_Active_Power_Monitor\",","      \"jobDescription\": \"Global active power monitoring job\",","      \"jobName\": \"MY_MODEL_MONITOR1\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"LOW\",","      \"baselineData\": \"HOUSEHOLD_POWER_BASE\",","      \"newData\": \"HOUSEHOLD_POWER_NEW\",","      \"timeColumn\": \"DATES\",","      \"startDate\": \"2008-01-01T00:00:00Z\",","      \"endDate\": \"2010-11-26T00:00:00Z\",","      \"frequency\": \"Year\",","      \"threshold\": 0.15,","      \"recompute\": false,","      \"caseidColumn\": null,","      \"modelList\": [","        \"0bf13d1f-86a6-465d-93d1-8985afd1bbdb\"","      ],","      \"performanceMetric\": \"MEAN_SQUARED_ERROR\"","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-25T00:26:16.127906Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24736F509B_FC1A_400A_AC75_553F1D6C5D97\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-25T00:30:07Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 4: Update a model monitoring job (optional)","message":["%md","","After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the *updateProperties* field to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can be updated with this request for a model monitoring job:","","* _startDate_","* _endDate_","* _threshold_","* _recompute_","* _modelList_","","In the example request below, using the same job ID, we update some of these original parameters in the **updateProperties** field. The trigger for the drift alert is updated to 0.20, and the flag *recompute* is set to update already computed periods so that each job run will recalculate all time periods present in the specified **timeColumn** in the data.","","A successful update will receive an HTTP 204 response with no content."],"enabled":true,"result":{"startTime":1718325527547,"interpreter":"md.low","endTime":1718325527602,"results":[{"message":"<p>After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the <em>updateProperties<\/em> field to the <em>/omlmod/v1/jobs/{jobid}<\/em> endpoint. The following parameters can be updated with this request for a model monitoring job:<\/p>\n<ul>\n<li><em>startDate<\/em><\/li>\n<li><em>endDate<\/em><\/li>\n<li><em>threshold<\/em><\/li>\n<li><em>recompute<\/em><\/li>\n<li><em>modelList<\/em><\/li>\n<\/ul>\n<p>In the example request below, using the same job ID, we update some of these original parameters in the <strong>updateProperties<\/strong> field. The trigger for the drift alert is updated to 0.20, and the flag <em>recompute<\/em> is set to update already computed periods so that each job run will recalculate all time periods present in the specified <strong>timeColumn<\/strong> in the data.<\/p>\n<p>A successful update will receive an HTTP 204 response with no content.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Update job properties","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>\" \\    ","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"updateProperties\": {","                \"threshold\": 0.20,","                \"recompute\": \"true\"","            }","         }'","         "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 5: View model monitoring job output","message":["%md","","Once your job has run, either according to its schedule or by the **RUN** action, you can view its output in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobid}\\_{outputData}*. You can check if your job is finished by sending a request to view its details, as we did in step 3. If your job has run at least once you should see the **lastRunDetail** field with information on that run. The next paragraph queries the output table associated with this example. ","","It is important to note that the first row of results is the baseline time period. That is the reason the **HAS_DRIFT**, **DRIFT**, **THRESHOLD** columns are empty for this row, as drift is not calculated on data in the baseline time period. ","","","For more information on the output table, refer to the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring-output-table.html\" target=\"_blank\">OML Services documentation.<\/a>"],"enabled":true,"result":{"startTime":1718325527667,"interpreter":"md.low","endTime":1718325527720,"results":[{"message":"<p>Once your job has run, either according to its schedule or by the <strong>RUN<\/strong> action, you can view its output in the table you specified in your job request with the <strong>outputData<\/strong> parameter. The full name of the table is <em>{jobid}_{outputData}<\/em>. You can check if your job is finished by sending a request to view its details, as we did in step 3. If your job has run at least once you should see the <strong>lastRunDetail<\/strong> field with information on that run. The next paragraph queries the output table associated with this example.<\/p>\n<p>It is important to note that the first row of results is the baseline time period. That is the reason the <strong>HAS_DRIFT<\/strong>, <strong>DRIFT<\/strong>, <strong>THRESHOLD<\/strong> columns are empty for this row, as drift is not calculated on data in the baseline time period.<\/p>\n<p>For more information on the output table, refer to the <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-model-monitoring-output-table.html\" target=\"_blank\">OML Services documentation.<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Query to view model monitoring output","message":["%sql","","","SELECT IS_BASELINE, MODEL_ID, round(METRIC, 4), HAS_DRIFT, round(DRIFT, 4), MODEL_TYPE, ","       THRESHOLD, MODEL_METRICS ","FROM OML$736F509B_FC1A_400A_AC75_553F1D6C5D97_Global_Active_Power_Monitor"],"enabled":false,"result":{"startTime":1700071262025,"interpreter":"sql.low","endTime":1700071263764,"results":[{"message":"IS_BASELINE\tMODEL_ID\tROUND(METRIC,4)\tHAS_DRIFT\tROUND(DRIFT,4)\tMODEL_TYPE\tTHRESHOLD\tMODEL_METRICS\n0\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0017\t1\t0.1608\tREGRESSION\t0.15\t{\"R2\":0.99809,\"Mean squared error\":0.0017396,\"Mean absolute error\":0.027297,\"Median absolute error\":0.019593,\"prediction_statistics\":{\"population_stability_index\":0.025958,\"mean_value\":1.0815,\"min_value\":0.12529,\"max_value\":9.5666,\"standard_deviation\":0.94483,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.87908},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.10714},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.012538},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.001173},{\"bin_upper_boundary\":null,\"bin_percentage\":0.000068328}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1016.4},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":4.1294},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.48653},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.32686},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.19106},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.072071}]}\n1\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0015\t\t\tREGRESSION\t\t{\"R2\":0.99893,\"Mean squared error\":0.0014987,\"Mean absolute error\":0.02293,\"Median absolute error\":0.016582,\"prediction_statistics\":{\"mean_value\":1.1484,\"min_value\":0.093934,\"max_value\":10.073,\"standard_deviation\":1.1787,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.82757},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.14257},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.02613},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.0035318},{\"bin_upper_boundary\":null,\"bin_percentage\":0.00020088}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1754.9},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":3.0614},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.92329},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.28173},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.22032},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.10669}]}\n0\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0015\t0\t0.0103\tREGRESSION\t0.15\t{\"R2\":0.99865,\"Mean squared error\":0.0015141,\"Mean absolute error\":0.025352,\"Median absolute error\":0.019308,\"prediction_statistics\":{\"population_stability_index\":0.011901,\"mean_value\":1.0436,\"min_value\":0.045116,\"max_value\":9.8458,\"standard_deviation\":1.0626,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.8657},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.11415},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.017624},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.0023127},{\"bin_upper_boundary\":null,\"bin_percentage\":0.00020623}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1389.6},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":3.7846},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.5429},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.33221},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.21555},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.089874}]}\n0\t0bf13d1f-86a6-465d-93d1-8985afd1bbdb\t0.0015\t0\t0.0164\tREGRESSION\t0.15\t{\"R2\":0.9985,\"Mean squared error\":0.0015232,\"Mean absolute error\":0.025032,\"Median absolute error\":0.018151,\"prediction_statistics\":{\"population_stability_index\":0.017769,\"mean_value\":1.0709,\"min_value\":0.09097,\"max_value\":10.3,\"standard_deviation\":1.006,\"bin_distribution\":[{\"bin_upper_boundary\":2.0897,\"bin_percentage\":0.87348},{\"bin_upper_boundary\":4.0855,\"bin_percentage\":0.10839},{\"bin_upper_boundary\":6.0813,\"bin_percentage\":0.016178},{\"bin_upper_boundary\":8.0771,\"bin_percentage\":0.0018322},{\"bin_upper_boundary\":null,\"bin_percentage\":0.00012747}]},\"most_impactful_features\":[{\"feature_name\":\"GLOBAL_INTENSITY\",\"feature_impact\":1280.4},{\"feature_name\":\"SUB_METERING_3\",\"feature_impact\":4.4025},{\"feature_name\":\"VOLTAGE\",\"feature_impact\":0.47583},{\"feature_name\":\"GLOBAL_REACTIVE_POWER\",\"feature_impact\":0.35687},{\"feature_name\":\"SUB_METERING_1\",\"feature_impact\":0.20914},{\"feature_name\":\"SUB_METERING_2\",\"feature_impact\":0.082932}]}\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 6: Perform an action on a model monitoring job (optional)","message":["%md","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","* _DISABLE_ disables the job at submission. The *force* property can be used with this action to forcefully interrupt any running job. ","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","For example, when your job has been successfully submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state, such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example request to update the job status to **DISABLED**. When you successfully submit your job you will receive a 204 response with no body. "],"enabled":true,"result":{"startTime":1718325527787,"interpreter":"md.low","endTime":1718325527842,"results":[{"message":"<p>OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, <strong>DISABLE<\/strong>, <strong>RUN<\/strong>, <strong>ENABLE<\/strong>, and <strong>STOP<\/strong>:<\/p>\n<ul>\n<li><em>DISABLE<\/em> disables the job at submission. The <em>force<\/em> property can be used with this action to forcefully interrupt any running job.<\/li>\n<li><em>ENABLE<\/em> enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule.<\/li>\n<li><em>RUN<\/em> will immediately run the job as a way to test a job or run it outside of its schedule.<\/li>\n<li><em>STOP<\/em> stops a currently running job.<\/li>\n<\/ul>\n<p>For example, when your job has been successfully submitted, its state is set to <strong>ENABLED<\/strong> by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state, such as <strong>DISABLED<\/strong>. You can do this by sending a request to the <em>/omlmod/v1/jobs/{jobid}/action<\/em> endpoint.<\/p>\n<p>Note that jobs can be set to <strong>DISABLED<\/strong> at submission by setting the <em>disableJob<\/em> flag to <em>true<\/em>.<\/p>\n<p>Below is an example request to update the job status to <strong>DISABLED<\/strong>. When you successfully submit your job you will receive a 204 response with no body.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Disable a job","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\",","            \"force\": \"false\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 7: Delete a model monitoring job (optional)","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":{"startTime":1718325527908,"interpreter":"md.low","endTime":1718325527960,"results":[{"message":"<p>To delete a previously submitted job, send a DELETE request along with the Job ID to the <em>/omlmod/v1/jobs<\/em> endpoint.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to delete a job","message":["","$ curl -X DELETE \"<omlserver>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Reproducing this example","message":["%md","","If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, *HOUSEHOLD_POWER_BASE* and *HOUSEHOLD_POWER_NEW*. These tables are created using the Individual Household Electric Power Consumption data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption).","","The code to read and transform the data and create database tables using R is provided below."],"enabled":true,"result":{"startTime":1718325528026,"interpreter":"md.low","endTime":1718325528080,"results":[{"message":"<p>If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, <em>HOUSEHOLD_POWER_BASE<\/em> and <em>HOUSEHOLD_POWER_NEW<\/em>. These tables are created using the Individual Household Electric Power Consumption data from the <a href=\"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\">UCI Machine Learning Repository<\/a>.<\/p>\n<p>The code to read and transform the data and create database tables using R is provided below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop tables if they exist and suppress warnings","message":["%r","","options(warn=-1)","","try(ore.drop(table=\"HOUSEHOLD_POWER_BASE\"))","try(ore.drop(table=\"HOUSEHOLD_POWER_NEW\"))"],"enabled":true,"result":{"startTime":1718325528147,"interpreter":"r.low","endTime":1718325533642,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Read and transform data","message":["%r","","test <- read.csv(\"https://objectstorage.us-sanjose-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/household_power_consumption.txt\", sep=\";\")","","test <- transform(test, Date = as.Date(Date, format = \"%d/%m/%Y\"))","test <- transform(test, Global_active_power = as.numeric(Global_active_power))","test <- transform(test, Global_reactive_power = as.numeric(Global_reactive_power))","test <- transform(test, Voltage = as.numeric(Voltage))","test <- transform(test, Global_intensity = as.numeric(Global_intensity))","test <- transform(test, Sub_metering_1 = as.numeric(Sub_metering_1))","test <- transform(test, Sub_metering_2 = as.numeric(Sub_metering_2))","test <- transform(test, Sub_metering_3 = as.numeric(Sub_metering_3))","","colnames(test) <- c(\"DATES\", \"TIMES\", \"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\", \"GLOBAL_INTENSITY\", \"SUB_METERING_1\", \"SUB_METERING_2\", \"SUB_METERING_3\") "],"enabled":true,"result":{"startTime":1718325533711,"interpreter":"r.low","endTime":1718325543714,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create baseline and new data","message":["%r","","test_base <- test[test$DATES < \"2008-01-01\",]","test_new <- test[test$DATES > \"2007-12-31\",]","","# Create OML proxy objects","","ore.create(test_base, table=\"HOUSEHOLD_POWER_BASE\")","ore.create(test_new, table=\"HOUSEHOLD_POWER_NEW\")"],"enabled":true,"result":{"startTime":1718325543782,"interpreter":"r.low","endTime":1718325559589,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the baseline data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_BASE","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1718325559659,"interpreter":"sql.low","endTime":1718325561152,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2006-12-16 00:00:00\t17:24:00\t4.216\t0.418\t234.84\t18.4\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:25:00\t5.36\t0.436\t233.63\t23.0\t0.0\t1.0\t16.0\n2006-12-16 00:00:00\t17:26:00\t5.374\t0.498\t233.29\t23.0\t0.0\t2.0\t17.0\n2006-12-16 00:00:00\t17:27:00\t5.388\t0.502\t233.74\t23.0\t0.0\t1.0\t17.0\n2006-12-16 00:00:00\t17:28:00\t3.666\t0.528\t235.68\t15.8\t0.0\t1.0\t17.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the new data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_NEW","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1718325561223,"interpreter":"sql.low","endTime":1718325561313,"results":[{"message":"DATES\tTIMES\tGLOBAL_ACTIVE_POWER\tGLOBAL_REACTIVE_POWER\tVOLTAGE\tGLOBAL_INTENSITY\tSUB_METERING_1\tSUB_METERING_2\tSUB_METERING_3\n2008-01-01 00:00:00\t00:00:00\t1.62\t0.07\t241.25\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:01:00\t1.626\t0.072\t241.74\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:02:00\t1.622\t0.072\t241.52\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:03:00\t1.612\t0.07\t240.82\t6.6\t0.0\t0.0\t18.0\n2008-01-01 00:00:00\t00:04:00\t1.612\t0.07\t240.8\t6.6\t0.0\t0.0\t18.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1718325561380,"interpreter":"md.low","endTime":1718325561434,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1718325561506,"interpreter":"md.low","endTime":1718325561560,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/rest/README.md b/machine-learning/notebooks-oml/rest/README.md
similarity index 95%
rename from machine-learning/notebooks/rest/README.md
rename to machine-learning/notebooks-oml/rest/README.md
index a2c493da..f064eeb7 100644
--- a/machine-learning/notebooks/rest/README.md
+++ b/machine-learning/notebooks-oml/rest/README.md
@@ -28,6 +28,6 @@ Monitoring
 
 [Oracle Machine Learning Services](https://docs.oracle.com/en/database/oracle/machine-learning/omlss/index.html)
 
-#### Copyright (c) 2024 Oracle Corporation and/or its affilitiates.
+#### Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
 
 ###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)
diff --git a/machine-learning/notebooks-oml/sql/OML Export and Import Serialized Models.dsnb b/machine-learning/notebooks-oml/sql/OML Export and Import Serialized Models.dsnb
new file mode 100644
index 00000000..7b50cc92
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML Export and Import Serialized Models.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Export and Import Serialized Models","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734652313455,"interpreter":"md.medium","endTime":1734652313954,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Exporting and Importing Serialized Models","","Oracle Machine Learning provides APIs to streamline the process of migrating models across databases and platforms. The focus of this notebook is exporting and importing native serialized models using the `DBMS_DATA_MINING.EXPORT_SERMODEL` and `DBMS_DATA_MINING.IMPORT_SERMODEL` procedures. ","","","---","#### Serialized model format","---","The serialized model format was introduced in Oracle Database 18c as a lightweight approach to support scoring. `DBMS_DATA_MINING.EXPORT_SERMODEL` exports a single model to a serialized BLOB so it can be imported and scored in a separate Oracle Machine Learning (OML) database instance or to OML Services. `DBMS_DATA_MINING.IMPORT_SERMODEL` takes the serialized BLOB and creates the model in the target database. ","","As a result of building models, each model has a set of model detail views that provide information about the model such as model statistics for evaluation. These model detail views can be queried by the user. With serialized models, only the model data and metadata required for scoring are available in the serialized model, so it is more compact and transfers faster to the target environment than dump files produced by the `DBMS_DATA_MINING.EXPORT_MODEL` procedure.","","If full model details are required, use the `DBMS_DATA_MINING.EXPORT_MODEL` and  `DBMS_DATA_MINING.IMPORT_MODEL` procedures.  Serialized model export support only models that produce scores. Specifically, it does not support Attribute Importance, Association Rules, Exponential Smoothing, or O-Cluster (although O-Cluster does support scoring). Use `DBMS_DATA_MINING.EXPORT_MODEL` to export these models and scenarios when the full model details are required.","","---","#### Deployment Scenarios","---","","The same machine learning algorithms are available in **both Autonomous Database and on-premises**. Users have the option to build models on premises and deploy to the cloud, and vice versa.","","Serialized models can also be deployed to OML Services, which provides REST endpoints hosted on Autonomous Database. These endpoints enable storing OML models and creating scoring endpoints. The REST API for OML Services supports both OML models and ONNX format models, and enables cognitive text functionality.","","In this notebook we demonstrate the following scenarios using `DBMS_DATA_MINING.EXPORT_SERMODEL` and `DBMS_DATA_MINING.IMPORT_SERMODEL`:","","* Migrating models between two schemas in the same database","* Migrating models between two separate database instances","    * Autonomous Database to Autonomous Database","    * Autonomous Database to Oracle Database","    * Oracle Database to Autonomous Database","    * Oracle Database to Oracle Database","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1734652314401,"interpreter":"md.medium","endTime":1734652314916,"results":[{"message":"<h1 id=\"exporting-and-importing-serialized-models\">Exporting and Importing Serialized Models<\/h1>\n<p>Oracle Machine Learning provides APIs to streamline the process of migrating models across databases and platforms. The focus of this notebook is exporting and importing native serialized models using the <code>DBMS_DATA_MINING.EXPORT_SERMODEL<\/code> and <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code> procedures.<\/p>\n<hr />\n<h4 id=\"serialized-model-format\">Serialized model format<\/h4>\n<hr />\n<p>The serialized model format was introduced in Oracle Database 18c as a lightweight approach to support scoring. <code>DBMS_DATA_MINING.EXPORT_SERMODEL<\/code> exports a single model to a serialized BLOB so it can be imported and scored in a separate Oracle Machine Learning (OML) database instance or to OML Services. <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code> takes the serialized BLOB and creates the model in the target database.<\/p>\n<p>As a result of building models, each model has a set of model detail views that provide information about the model such as model statistics for evaluation. These model detail views can be queried by the user. With serialized models, only the model data and metadata required for scoring are available in the serialized model, so it is more compact and transfers faster to the target environment than dump files produced by the <code>DBMS_DATA_MINING.EXPORT_MODEL<\/code> procedure.<\/p>\n<p>If full model details are required, use the <code>DBMS_DATA_MINING.EXPORT_MODEL<\/code> and  <code>DBMS_DATA_MINING.IMPORT_MODEL<\/code> procedures.  Serialized model export support only models that produce scores. Specifically, it does not support Attribute Importance, Association Rules, Exponential Smoothing, or O-Cluster (although O-Cluster does support scoring). Use <code>DBMS_DATA_MINING.EXPORT_MODEL<\/code> to export these models and scenarios when the full model details are required.<\/p>\n<hr />\n<h4 id=\"deployment-scenarios\">Deployment Scenarios<\/h4>\n<hr />\n<p>The same machine learning algorithms are available in <strong>both Autonomous Database and on-premises<\/strong>. Users have the option to build models on premises and deploy to the cloud, and vice versa.<\/p>\n<p>Serialized models can also be deployed to OML Services, which provides REST endpoints hosted on Autonomous Database. These endpoints enable storing OML models and creating scoring endpoints. The REST API for OML Services supports both OML models and ONNX format models, and enables cognitive text functionality.<\/p>\n<p>In this notebook we demonstrate the following scenarios using <code>DBMS_DATA_MINING.EXPORT_SERMODEL<\/code> and <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code>:<\/p>\n<ul>\n<li>Migrating models between two schemas in the same database<\/li>\n<li>Migrating models between two separate database instances\n<ul>\n<li>Autonomous Database to Autonomous Database<\/li>\n<li>Autonomous Database to Oracle Database<\/li>\n<li>Oracle Database to Autonomous Database<\/li>\n<li>Oracle Database to Oracle Database<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Scenario #1: Single database, different schemas ","message":["%md","","This example builds a model and exports it from one schema to another in the same database. The user that builds the model exports it as a serialized model using a BLOB column in a table, and the second user imports the model from that table into their schema. ","","Note, this is an uncommon use case because users can grant select privileges on models to other database users for scoring.","","This example applies to **both Autonomous Database and Oracle Database**."],"enabled":true,"result":{"startTime":1734652315356,"interpreter":"md.medium","endTime":1734652315778,"results":[{"message":"<p>This example builds a model and exports it from one schema to another in the same database. The user that builds the model exports it as a serialized model using a BLOB column in a table, and the second user imports the model from that table into their schema.<\/p>\n<p>Note, this is an uncommon use case because users can grant select privileges on models to other database users for scoring.<\/p>\n<p>This example applies to <strong>both Autonomous Database and Oracle Database<\/strong>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a database table CARS using the mtcars data in R","message":["%r","","# suppress row order warnings","options(ore.warn.order=FALSE)","","# convert to a data frame","cars <- as.data.frame(mtcars) ","","# add case ID column ","cars$CID <- 1:nrow(cars)","","# convert variable names to upper case for SQL compatibility","names(cars) <- toupper(names(cars))","","# drop table if it exists","if (ore.exists(\"CARS\")) ore.drop(table=\"CARS\")","","# create database table","ore.create(cars, table=\"CARS\", overwrite=TRUE)","head(CARS)"],"enabled":true,"result":{"startTime":1734652316208,"interpreter":"r.medium","endTime":1734652321974,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 12<\/caption>\n<thead>\n\t<tr><th scope=col>MPG<\/th><th scope=col>CYL<\/th><th scope=col>DISP<\/th><th scope=col>HP<\/th><th scope=col>DRAT<\/th><th scope=col>WT<\/th><th scope=col>QSEC<\/th><th scope=col>VS<\/th><th scope=col>AM<\/th><th scope=col>GEAR<\/th><th scope=col>CARB<\/th><th scope=col>CID<\/th><\/tr>\n\t<tr><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;int&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>21.0<\/td><td>6<\/td><td>160<\/td><td>110<\/td><td>3.90<\/td><td>2.620<\/td><td>16.46<\/td><td>0<\/td><td>1<\/td><td>4<\/td><td>4<\/td><td>1<\/td><\/tr>\n\t<tr><td>21.0<\/td><td>6<\/td><td>160<\/td><td>110<\/td><td>3.90<\/td><td>2.875<\/td><td>17.02<\/td><td>0<\/td><td>1<\/td><td>4<\/td><td>4<\/td><td>2<\/td><\/tr>\n\t<tr><td>22.8<\/td><td>4<\/td><td>108<\/td><td> 93<\/td><td>3.85<\/td><td>2.320<\/td><td>18.61<\/td><td>1<\/td><td>1<\/td><td>4<\/td><td>1<\/td><td>3<\/td><\/tr>\n\t<tr><td>21.4<\/td><td>6<\/td><td>258<\/td><td>110<\/td><td>3.08<\/td><td>3.215<\/td><td>19.44<\/td><td>1<\/td><td>0<\/td><td>3<\/td><td>1<\/td><td>4<\/td><\/tr>\n\t<tr><td>18.7<\/td><td>8<\/td><td>360<\/td><td>175<\/td><td>3.15<\/td><td>3.440<\/td><td>17.02<\/td><td>0<\/td><td>0<\/td><td>3<\/td><td>2<\/td><td>5<\/td><\/tr>\n\t<tr><td>18.1<\/td><td>6<\/td><td>225<\/td><td>105<\/td><td>2.76<\/td><td>3.460<\/td><td>20.22<\/td><td>1<\/td><td>0<\/td><td>3<\/td><td>1<\/td><td>6<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop, then build a model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","  v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","  v_setlst('PREP_AUTO') := 'ON';","  v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","  v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_DIAG';","    ","  DBMS_DATA_MINING.CREATE_MODEL2(","    model_name          => 'GLM_MOD',","    mining_function     => 'REGRESSION',","    data_query          => 'SELECT * FROM CARS',","    set_list            =>  v_setlst,","    case_id_column_name => 'CID',","    target_column_name  => 'MPG');","END"],"enabled":true,"result":{"startTime":1734652322415,"interpreter":"script.medium","endTime":1734652327265,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model in the database","message":["","%sql","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE ","FROM   ALL_MINING_MODELS ","WHERE  MODEL_NAME='GLM_MOD'"],"enabled":true,"result":{"startTime":1734652327690,"interpreter":"sql.medium","endTime":1734652328289,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\nGLM_MOD\tREGRESSION\tGENERALIZED_LINEAR_MODEL\t2024-12-19 23:52:04\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table to store the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE MODEL_EXPORT';  -- drop table if it exists","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE MODEL_EXPORT(MY_MODEL BLOB"],"enabled":true,"result":{"startTime":1734652328727,"interpreter":"script.medium","endTime":1734652329242,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable MODEL_EXPORT created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Export the  model to the table","message":["%script"," ","DECLARE","  BLOB_MODEL BLOB;","BEGIN","  DBMS_LOB.CREATETEMPORARY(BLOB_MODEL, FALSE);","  DBMS_DATA_MINING.EXPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD');","  INSERT INTO MODEL_EXPORT values (BLOB_MODEL);   -- serialized model BLOB saved in table MODEL_EXPORT","  commit;","  DBMS_LOB.FREETEMPORARY(BLOB_MODEL);","END"],"enabled":true,"result":{"startTime":1734652329670,"interpreter":"script.medium","endTime":1734652330144,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confirm the model was exported by looking at the length of the BLOB","message":["%sql","","SELECT LENGTH(MY_MODEL) AS MODEL_LEN from MODEL_EXPOR"],"enabled":true,"result":{"startTime":1734652330560,"interpreter":"sql.medium","endTime":1734652332501,"results":[{"message":"MODEL_LEN\n2290\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Grant the second user access to the table","message":["%sql","","-- replace OMLUSER02 with the target OML user","","GRANT SELECT ON MODEL_EXPORT TO OMLUSER0"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import the serialized model from the table in the target schema","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD'); -- drop the model if it exists","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","  BLOB_MODEL BLOB;","BEGIN","  SELECT MY_MODEL INTO BLOB_MODEL FROM MODEL_EXPORT;","  DBMS_DATA_MINING.IMPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD');","END;"],"enabled":true,"result":{"startTime":1734652332934,"interpreter":"script.medium","endTime":1734652333922,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Confirm the model was imported","message":["%sql","","SELECT * FROM ALL_MINING_MODELS WHERE MODEL_NAME='GLM_MOD'"],"enabled":true,"result":{"startTime":1734652334340,"interpreter":"sql.medium","endTime":1734652334786,"results":[{"message":"OWNER\tMODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\tBUILD_DURATION\tMODEL_SIZE\tPARTITIONED\tCOMMENTS\nOMLUSER\tGLM_MOD\tREGRESSION\tGENERALIZED_LINEAR_MODEL\tNATIVE\t2024-12-19 23:52:13\t\t0\tNO\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model description","message":["%sql","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE ","FROM   ALL_MINING_MODELS ","WHERE  MODEL_NAME='GLM_MOD'"],"enabled":true,"result":{"startTime":1734652335217,"interpreter":"sql.medium","endTime":1734652335662,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\nGLM_MOD\tREGRESSION\tGENERALIZED_LINEAR_MODEL\t2024-12-19 23:52:13\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE ","FROM   ALL_MINING_MODEL_SETTINGS ","WHERE  MODEL_NAME='GLM_MOD'"],"enabled":true,"result":{"startTime":1734652336081,"interpreter":"sql.medium","endTime":1734652336525,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\nPREP_AUTO\tON\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_DISABLE\nGLMS_CONF_LEVEL\t.95\nODMS_DETAILS\tODMS_ENABLE\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_DISABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nGLMS_DIAGNOSTICS_TABLE_NAME\tGLMR_DIAG\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score the CARS data using the imported model","message":["%sql","","SELECT CID, ROUND(PREDICTION(GLM_MOD USING *), 1) AS PREDICTED_MPG, ","       MPG AS ACTUAL_MPG ","FROM   CARS ","ORDER BY CID;","FETCH FIRST 5 ROWS ONLY"],"enabled":true,"result":{"startTime":1734652336945,"interpreter":"sql.medium","endTime":1734652338102,"results":[{"message":"CID\tPREDICTED_MPG\tACTUAL_MPG\n1\t22.6\t21.0\n2\t22.1\t21.0\n3\t26.3\t22.8\n4\t21.2\t21.4\n5\t17.7\t18.7\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Granting select access to models","message":["%md","","An alternative to migrating models between schemas is to grant select privileges on the model for scoring. The following statement allows OMLUSER02 to score the CARS data on model *GLM_MOD*. ","","    GRANT SELECT ON MINING MODEL GLM_MOD TO OMLUSER02;"],"enabled":true,"result":{"startTime":1734652338553,"interpreter":"md.medium","endTime":1734652338958,"results":[{"message":"<p>An alternative to migrating models between schemas is to grant select privileges on the model for scoring. The following statement allows OMLUSER02 to score the CARS data on model <em>GLM_MOD<\/em>.<\/p>\n<pre><code>GRANT SELECT ON MINING MODEL GLM_MOD TO OMLUSER02;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","---","## Scenario #2: Separate source and target databases ","---","","In this scenario, we use separate databases for the development and testing of the machine learning model and ultimately production. Here, data scientists build models in one environment, and when the machine learning model produces satisfactory results, it can be exported from the development database and imported into the production database."],"enabled":true,"result":{"startTime":1734652339390,"interpreter":"md.medium","endTime":1734652339808,"results":[{"message":"<p>In this scenario, we use separate databases for the development and testing of the machine learning model and ultimately production. Here, data scientists build models in one environment, and when the machine learning model produces satisfactory results, it can be exported from the development database and imported into the production database.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Prerequisites"],"enabled":true,"result":{"startTime":1734652340226,"interpreter":"md.medium","endTime":1734652340638,"results":[{"message":"<h3 id=\"prerequisites\">Prerequisites<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a procedure to write the model to a serialized BLOB ","message":["%md","","Define the `write_serialized_model` procedure to export a model as a serialized BLOB to a directory in the database environment."],"enabled":true,"result":{"startTime":1734652341068,"interpreter":"md.medium","endTime":1734652341475,"results":[{"message":"<p>Define the <code>write_serialized_model<\/code> procedure to export a model as a serialized BLOB to a directory in the database environment.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" ","message":["%script","","CREATE OR REPLACE PROCEDURE write_serialized_model (v_blob blob, dirName varchar2, filName varchar2) IS","    v_output utl_file.file_type;","    v_amt binary_integer := 2000;","    v_raw raw(2000);","    v_pos binary_integer := 1;","    v_len binary_integer;","BEGIN","    v_output := utl_file.fopen(dirName, filName, 'wb', 32760);","    v_len := dbms_lob.getlength(v_blob);","WHILE (v_pos <= v_len) loop","  IF (v_len - v_pos + 1) < 2000 then","    v_amt := v_len - v_pos + 1;","  END IF;","  dbms_lob.read(v_blob,v_amt,v_pos,v_raw);","  utl_file.put_raw(v_output, v_raw);","  utl_file.fflush(v_output);","  v_pos := v_pos + v_amt;"," END LOOP;"," utl_file.fclose(v_output);","END;"],"enabled":true,"result":{"startTime":1734652341902,"interpreter":"script.medium","endTime":1734652342403,"results":[{"message":"\nProcedure WRITE_SERIALIZED_MODEL compiled\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"DBMS_CLOUD package","message":["%md","","To interact with other cloud services, the Oracle Autonomous Database Cloud service provides the `DBMS_CLOUD` package. This enables the database engine to transfer and store objects between Oracle Autonomous Database and Oracle Cloud Object Storage. Object Storage holds data as objects within *buckets*, i.e., containers, that are identified with a URI having the following format:","","    https://objectstorage.region.oraclecloud.com/v1/tenantname/bucketname/objectname","    ","A public bucket enables you to freely share data stored in Object Storage. Anyone with knowledge of the public bucket name and associated namespace can anonymously read data, list objects, or get the object metadata. Buckets are private by default, and the bucket properties must be explicitly set to make a bucket public. Note that anonymous operations to save data to a public bucket is not supported.","","Access to the objects stored within private buckets is protected. To access an object in the Oracle ADB Cloud service, one must to provide identification information stored into a credential object. Refer to the Autonomous Database User's Guide for more information on the `DBMS_CLOUD` procedure.","","<a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/user/load-data-cloud.html#GUID-07900054-CB65-490A-AF3C-39EF45505802\" target=\"_blank\">Oracle Autonomous Database User's Guide<\/a>"],"enabled":true,"result":{"startTime":1734652342832,"interpreter":"md.medium","endTime":1734652343240,"results":[{"message":"<p>To interact with other cloud services, the Oracle Autonomous Database Cloud service provides the <code>DBMS_CLOUD<\/code> package. This enables the database engine to transfer and store objects between Oracle Autonomous Database and Oracle Cloud Object Storage. Object Storage holds data as objects within <em>buckets<\/em>, i.e., containers, that are identified with a URI having the following format:<\/p>\n<pre><code>https://objectstorage.region.oraclecloud.com/v1/tenantname/bucketname/objectname\n<\/code><\/pre>\n<p>A public bucket enables you to freely share data stored in Object Storage. Anyone with knowledge of the public bucket name and associated namespace can anonymously read data, list objects, or get the object metadata. Buckets are private by default, and the bucket properties must be explicitly set to make a bucket public. Note that anonymous operations to save data to a public bucket is not supported.<\/p>\n<p>Access to the objects stored within private buckets is protected. To access an object in the Oracle ADB Cloud service, one must to provide identification information stored into a credential object. Refer to the Autonomous Database User's Guide for more information on the <code>DBMS_CLOUD<\/code> procedure.<\/p>\n<p><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/user/load-data-cloud.html#GUID-07900054-CB65-490A-AF3C-39EF45505802\" target=\"_blank\">Oracle Autonomous Database User's Guide<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create cloud credential","message":["%md","","To start, create a cloud user credential in ADB to connect to Cloud Storage using the `DBMS_CLOUD.CREATE_CREDENTIAL` procedure. The `CREATE_CREDENTIAL` procedure creates a credential into the current schema. The username and password correspond to the OML user account credentials. In this example, we create a cloud credential for user *OMLUSER* with password *Welcome12345*."],"enabled":true,"result":{"startTime":1734652343663,"interpreter":"md.medium","endTime":1734652344069,"results":[{"message":"<p>To start, create a cloud user credential in ADB to connect to Cloud Storage using the <code>DBMS_CLOUD.CREATE_CREDENTIAL<\/code> procedure. The <code>CREATE_CREDENTIAL<\/code> procedure creates a credential into the current schema. The username and password correspond to the OML user account credentials. In this example, we create a cloud credential for user <em>OMLUSER<\/em> with password <em>Welcome12345<\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%script","","-- IMPORTANT: replace username and password with your OML username and password ","","BEGIN","  DBMS_CLOUD.CREATE_CREDENTIAL(","    credential_name => 'MY_CLOUD_CRED',","    username => 'OMLUSER',","    password => 'Welcome12345'","  );","END;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Oracle Cloud pre-authenticated requests (PARs)","message":["%md","","","PARs are used to share and upload Object Storage data. ","","When you create a PAR, a unique PAR URL is generated. Users with this URL can access the resources identified in the PAR using the **DBMS_CLOUD** procedures ","and the standard HTTP tools `cURL` and `wget`.  ","","You can define PARs on both buckets and objects. Bucket PARs can be used to write data into a specific bucket, however object PARs can be used both to read and write ","data. When creating a PAR you need to specify a PAR name, the resource object or bucket, the authorized operations (read, write, read/write) and the PAR expiration date. ","","For example, here is a PAR defined on object *iris.csv* stored in the bucket *MY_DEMO*:","","    https://objectstorage.us-phoenix-1.oraclecloud.com/p/1evW9h43H9IjwkrlEJgCwaUZkj87buyNDWTc/n/samplenamespace/b/MY_DEMO/o/iris.csv","","Refer to the Oracle Cloud Infrastructure documentation for more information on PARs.","<a href=\"https://docs.cloud.oracle.com/en-us/iaas/Content/Object/Tasks/usingpreauthenticatedrequests.htm\" target=\"_blank\">Using Pre-Authenticated Requests<\/a>"],"enabled":true,"result":{"startTime":1734652344488,"interpreter":"md.medium","endTime":1734652344909,"results":[{"message":"<p>PARs are used to share and upload Object Storage data.<\/p>\n<p>When you create a PAR, a unique PAR URL is generated. Users with this URL can access the resources identified in the PAR using the <strong>DBMS_CLOUD<\/strong> procedures\nand the standard HTTP tools <code>cURL<\/code> and <code>wget<\/code>.<\/p>\n<p>You can define PARs on both buckets and objects. Bucket PARs can be used to write data into a specific bucket, however object PARs can be used both to read and write\ndata. When creating a PAR you need to specify a PAR name, the resource object or bucket, the authorized operations (read, write, read/write) and the PAR expiration date.<\/p>\n<p>For example, here is a PAR defined on object <em>iris.csv<\/em> stored in the bucket <em>MY_DEMO<\/em>:<\/p>\n<pre><code>https://objectstorage.us-phoenix-1.oraclecloud.com/p/1evW9h43H9IjwkrlEJgCwaUZkj87buyNDWTc/n/samplenamespace/b/MY_DEMO/o/iris.csv\n<\/code><\/pre>\n<p>Refer to the Oracle Cloud Infrastructure documentation for more information on PARs.\n<a href=\"https://docs.cloud.oracle.com/en-us/iaas/Content/Object/Tasks/usingpreauthenticatedrequests.htm\" target=\"_blank\">Using Pre-Authenticated Requests<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Directory for persisting objects","message":["%md","","When exporting models from Autonomous Database, we persist a serialized model to the data pump directory prior to sending it to Object Storage. Autonomous Database includes a predefined data pump directory in the database where you can place files. You can also create directories, drop directories, and attach network file systems. Refer to the Autonomous Database User's Guide for more information:","","###### <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/create-manage-directory.html#GUID-5EC418F4-2084-4884-8D1E-BACA56F143F9\" target=\"_blank\">Creating and Managing Directories on Autonomous Database<\/a>"],"enabled":true,"result":{"startTime":1734652345336,"interpreter":"md.medium","endTime":1734652345741,"results":[{"message":"<p>When exporting models from Autonomous Database, we persist a serialized model to the data pump directory prior to sending it to Object Storage. Autonomous Database includes a predefined data pump directory in the database where you can place files. You can also create directories, drop directories, and attach network file systems. Refer to the Autonomous Database User's Guide for more information:<\/p>\n<h6 id=\"creating-and-managing-directories-on-autonomous-database\"><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/create-manage-directory.html#GUID-5EC418F4-2084-4884-8D1E-BACA56F143F9\" target=\"_blank\">Creating and Managing Directories on Autonomous Database<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Required grants","message":["%md","","Apply the following grants as ADMIN to the OML user that will be importing and exporting models:","","    SQL> GRANT EXECUTE ON DBMS_CLOUD TO OMLUSER;                   -- grant access to run the procedures in the `DBMS_CLOUD` package","    SQL> GRANT READ, WRITE ON DIRECTORY DATA_PUMP_DIR TO OMLUSER;  -- grant read and write access to the data pump directory for saving objects"],"enabled":true,"result":{"startTime":1734652346159,"interpreter":"md.medium","endTime":1734652346567,"results":[{"message":"<p>Apply the following grants as ADMIN to the OML user that will be importing and exporting models:<\/p>\n<pre><code>SQL&gt; GRANT EXECUTE ON DBMS_CLOUD TO OMLUSER;                   -- grant access to run the procedures in the `DBMS_CLOUD` package\nSQL&gt; GRANT READ, WRITE ON DIRECTORY DATA_PUMP_DIR TO OMLUSER;  -- grant read and write access to the data pump directory for saving objects\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export a serialized model from one Autonomous Database to another Autonomous Database","message":["%md","","1. Export the model from Autonomous Database to the data pump directory using `DBMS_DATA_MINING.EXPORT_SERMODEL`. Provide the name of the in-database model and define the name of the corresponding serialized model.","","2. Save the serialized model in Object Storage. Pass your cloud credential to the `DBMS_CLOUD.PUT_OBJECT` procedure, along with the model name, directory name, and the Oracle Cloud PAR as the `LOCATION_URI` parameter, along with the name of the serialized model. ","","In this example, the name of the in-database model is *GLM_MOD* and the name of the serialized model is *GLM_MOD.mod*."],"enabled":true,"result":{"startTime":1734652346989,"interpreter":"md.medium","endTime":1734652347399,"results":[{"message":"<ol>\n<li>\n<p>Export the model from Autonomous Database to the data pump directory using <code>DBMS_DATA_MINING.EXPORT_SERMODEL<\/code>. Provide the name of the in-database model and define the name of the corresponding serialized model.<\/p>\n<\/li>\n<li>\n<p>Save the serialized model in Object Storage. Pass your cloud credential to the <code>DBMS_CLOUD.PUT_OBJECT<\/code> procedure, along with the model name, directory name, and the Oracle Cloud PAR as the <code>LOCATION_URI<\/code> parameter, along with the name of the serialized model.<\/p>\n<\/li>\n<\/ol>\n<p>In this example, the name of the in-database model is <em>GLM_MOD<\/em> and the name of the serialized model is <em>GLM_MOD.mod<\/em>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop, then build a model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","  v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","  v_setlst('PREP_AUTO') := 'ON';","  v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","  v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_DIAG';","    ","  DBMS_DATA_MINING.CREATE_MODEL2(","    model_name          => 'GLM_MOD',","    mining_function     => 'REGRESSION',","    data_query          => 'SELECT * FROM CARS',","    set_list            =>  v_setlst,","    case_id_column_name => 'CID',","    target_column_name  => 'MPG');","END;"],"enabled":true,"result":{"startTime":1734652347823,"interpreter":"script.medium","endTime":1734652350873,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View model in the database","message":["%sql","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE ","FROM   USER_MINING_MODELS ","WHERE  MODEL_NAME='GLM_MOD';"],"enabled":true,"result":{"startTime":1734652351297,"interpreter":"sql.medium","endTime":1734652351725,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\nGLM_MOD\tREGRESSION\tGENERALIZED_LINEAR_MODEL\t2024-12-19 23:52:28\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","  MODNAME VARCHAR2(100) := 'GLM_MOD';","  SER_MODNAME VARCHAR2(100);","  LOCATION_URI VARCHAR2(200) := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eUs89dFd_m_5OoGJ1PLj50bS-glojzHXZAZ3csGg4Iw9kQwKHzkZ8jorPD0si_ls/n/adwc4pm/b/OML-Resources/o/'; ","  BLOB_MODEL BLOB;","","-----------------------------------------","-- Define a model name to store the model","-----------------------------------------"," ","BEGIN","  SELECT CONCAT(MODNAME,'.mod') INTO SER_MODNAME from dual;","  DBMS_OUTPUT.PUT_LINE('Serialized model will be exported with name: '||SER_MODNAME);","","-----------------------------------------------------------------------","-- Read the Serialized Model from Object Storage into a binary object","-----------------------------------------------------------------------","","  DBMS_LOB.CREATETEMPORARY(BLOB_MODEL, FALSE);","  DBMS_DATA_MINING.EXPORT_SERMODEL(BLOB_MODEL, MODNAME);   ","  ","----------------------------------------------------------------------","-- Convenience function to write a model to a BLOB","----------------------------------------------------------------------","","  write_serialized_model(BLOB_MODEL, 'DATA_PUMP_DIR', SER_MODNAME);  ","  DBMS_LOB.FREETEMPORARY(BLOB_MODEL);","","----------------------------------------------------------------------","-- Save serialized model in the data pump directory to Object Storage","----------------------------------------------------------------------","  ","  DBMS_CLOUD.PUT_OBJECT(                                            ","    credential_name => 'MY_CLOUD_CRED',","    object_uri => LOCATION_URI||SER_MODNAME,","    directory_name => 'DATA_PUMP_DIR',","\tfile_name => SER_MODNAME);\t","\t     ","    SELECT CONCAT(MODNAME,'.mod') INTO SER_MODNAME from dual;","    DBMS_OUTPUT.PUT_LINE('Serialized model saved in Object Storage with the name: '||SER_MODNAME);","END;"],"enabled":true,"result":{"startTime":1734652352148,"interpreter":"script.medium","endTime":1734652353003,"results":[{"message":"Serialized model will be exported with name: GLM_MOD.mod\nSerialized model saved in Object Storage with the name: GLM_MOD.mod\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"A note about OML Services","message":["%md","","At this point, the serialized model has been saved to Object Storage and can be imported into the target database. It can also be deployed to OML Services for scoring. For more information on deploying OML models to OML Services, see:","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/use-case-oml.html\" target=\"_blank\">Deploy Oracle Machine Learning Models to OML Services<\/a>"],"enabled":true,"result":{"startTime":1734652353422,"interpreter":"md.medium","endTime":1734652353832,"results":[{"message":"<p>At this point, the serialized model has been saved to Object Storage and can be imported into the target database. It can also be deployed to OML Services for scoring. For more information on deploying OML models to OML Services, see:<\/p>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/use-case-oml.html\" target=\"_blank\">Deploy Oracle Machine Learning Models to OML Services<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import the serialized model","message":["%md","---","### Import the serialized model to the target Autonomous Database","---","","Import the model using the `DBMS_DATA_MINING.IMPORT_SERMODEL` procedure. Provide the name of the serialized model and the Oracle Cloud PAR corresponding to the exported serialized model."],"enabled":true,"result":{"startTime":1734652354268,"interpreter":"md.medium","endTime":1734652354679,"results":[{"message":"<p>Import the model using the <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code> procedure. Provide the name of the serialized model and the Oracle Cloud PAR corresponding to the exported serialized model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","    SER_MODNAME VARCHAR2(100) := 'GLM_MOD.mod';","    MODNAME VARCHAR2(500);","    LOCATION_URI VARCHAR2(200) := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eUs89dFd_m_5OoGJ1PLj50bS-glojzHXZAZ3csGg4Iw9kQwKHzkZ8jorPD0si_ls/n/adwc4pm/b/OML-Resources/o/';","    BLOB_MODEL BLOB;","","BEGIN","    DBMS_OUTPUT.PUT_LINE('Serialized model name in Object Storage is: '||SER_MODNAME);","  ","--------------------------------------------------","-- Define a model name to save the imported model","--------------------------------------------------","","    SELECT UPPER(REGEXP_SUBSTR(SER_MODNAME, '[^.]+')) INTO MODNAME from dual;","    DBMS_OUTPUT.PUT_LINE('Model will be saved with name: '||MODNAME);","","------------------------------------------------------------------------","-- Read the serialized model from Object Storage into a binary variable","------------------------------------------------------------------------","","    BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name => MODNAME);","    EXCEPTION WHEN OTHERS THEN NULL; END;","","    BLOB_MODEL := DBMS_CLOUD.GET_OBJECT(                            ","                      credential_name => 'MY_CLOUD_CRED',","                      object_uri => LOCATION_URI||SER_MODNAME);","-----------------------------------------------","-- Import the serialized model to the database","-----------------------------------------------                    ","  ","    DBMS_DATA_MINING.IMPORT_SERMODEL(BLOB_MODEL, MODNAME);","    DBMS_OUTPUT.PUT_LINE('New model successfully imported with name: '||MODNAME);","","END;"],"enabled":true,"result":{"startTime":1734652355096,"interpreter":"script.medium","endTime":1734652356171,"results":[{"message":"Serialized model name in Object Storage is: GLM_MOD.mod\nModel will be saved with name: GLM_MOD\nNew model successfully imported with name: GLM_MOD\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model description","message":["%sql","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE FROM ALL_MINING_MODELS WHERE MODEL_NAME='GLM_MOD'"],"enabled":true,"result":{"startTime":1734652356593,"interpreter":"sql.medium","endTime":1734652357029,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\nGLM_MOD\tREGRESSION\tGENERALIZED_LINEAR_MODEL\t2024-12-19 23:52:36\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE ","FROM   ALL_MINING_MODEL_SETTINGS ","WHERE  MODEL_NAME='GLM_MOD'"],"enabled":true,"result":{"startTime":1734652357454,"interpreter":"sql.medium","endTime":1734652357893,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\nPREP_AUTO\tON\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_DISABLE\nGLMS_CONF_LEVEL\t.95\nODMS_DETAILS\tODMS_ENABLE\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_DISABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nGLMS_DIAGNOSTICS_TABLE_NAME\tGLMR_DIAG\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score using the CARS table","message":["%sql","","SELECT CID, ROUND(PREDICTION(GLM_MOD USING *), 1) AS PREDICTED_MPG, ","       MPG AS ACTUAL_MPG ","FROM   CARS ORDER BY CID;","FETCH FIRST 5 ROWS ONLY"],"enabled":true,"result":{"startTime":1734652358319,"interpreter":"sql.medium","endTime":1734652358784,"results":[{"message":"CID\tPREDICTED_MPG\tACTUAL_MPG\n1\t22.6\t21.0\n2\t22.1\t21.0\n3\t26.3\t22.8\n4\t21.2\t21.4\n5\t17.7\t18.7\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export a serialized model from an Autonomous Database and import into an Oracle Database","message":["%md","","1. Export a model to serialized format from Autonomous Database to Object Storage. For instructions, refer to the section titled \"Export a serialized model from one Autonomous Database to another Autonomous Database\" above. ","","2. Import the serialized model saved in Object Storage to Oracle Database:","   a. Download the serialized model from Object Storage to the target database server using the serialized model's Oracle Cloud PAR.","   b. In SQL, define the directory from which the serialized model will be imported.","   c. Import the model using `DBMS_DATA_MINING.IMPORT_SERMODEL`.","","In this example, the name of the in-database model is *GLM_MOD* and the name of the serialized model is *GLM_MOD*.","","The following commands will run in a SQL prompt on the database server or in a SQL Developer instance connected to the database server as the OML user:"],"enabled":true,"result":{"startTime":1734652359209,"interpreter":"md.medium","endTime":1734652359615,"results":[{"message":"<ol>\n<li>\n<p>Export a model to serialized format from Autonomous Database to Object Storage. For instructions, refer to the section titled &quot;Export a serialized model from one Autonomous Database to another Autonomous Database&quot; above.<\/p>\n<\/li>\n<li>\n<p>Import the serialized model saved in Object Storage to Oracle Database:\na. Download the serialized model from Object Storage to the target database server using the serialized model's Oracle Cloud PAR.\nb. In SQL, define the directory from which the serialized model will be imported.\nc. Import the model using <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code>.<\/p>\n<\/li>\n<\/ol>\n<p>In this example, the name of the in-database model is <em>GLM_MOD<\/em> and the name of the serialized model is <em>GLM_MOD<\/em>.<\/p>\n<p>The following commands will run in a SQL prompt on the database server or in a SQL Developer instance connected to the database server as the OML user:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Download the serialized model","message":["%md","","    $ wget https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/w7YxZS6CVkjScObhRTbPzttGgdJnaD5A8_S_9uHgXc3j2YOx5OZpuCqBfMjSXPoU/n/adwc4pm/b/OML-Resources/o/GLM_MOD.mod"],"enabled":true,"result":{"startTime":1734652360046,"interpreter":"md.medium","endTime":1734652360455,"results":[{"message":"<p>$ wget <a href=\"https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/w7YxZS6CVkjScObhRTbPzttGgdJnaD5A8_S_9uHgXc3j2YOx5OZpuCqBfMjSXPoU/n/adwc4pm/b/OML-Resources/o/GLM_MOD.mod\">https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/w7YxZS6CVkjScObhRTbPzttGgdJnaD5A8_S_9uHgXc3j2YOx5OZpuCqBfMjSXPoU/n/adwc4pm/b/OML-Resources/o/GLM_MOD.mod<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import the serialized model to the target database","message":["%md","    ","    ----------------------------","    -- Drop table if it exists","    ----------------------------","    BEGIN EXECUTE IMMEDIATE 'DROP TABLE MODEL_IMPORT';         ","    EXCEPTION WHEN OTHERS THEN NULL; END;","    /","    ","    ----------------------------","    -- Drop model if it exists","    ----------------------------","    BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD');            ","    EXCEPTION WHEN OTHERS THEN NULL; END;","    /","    ","    ----------------------------------------------------","    -- Define directory where serialized model is saved","    ----------------------------------------------------","    CREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle';    ","   ","    -----------------------------------------------","    -- Create table to temporarily store the model","    -----------------------------------------------","    CREATE TABLE MODEL_IMPORT(MY_MODEL BLOB);  ","    ","     ---------------------------","     -- import serialized model","     --------------------------","    DECLARE","      BLOB_MODEL BLOB;","    BEGIN","      INSERT INTO MODEL_IMPORT values (BFILENAME('MYDIR','GLM_MOD.mod'));","      commit;","      SELECT MY_MODEL INTO BLOB_MODEL FROM MODEL_IMPORT;","      DBMS_DATA_MINING.IMPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD'); ","    END;","    /","","    Test the model:","    -----------------------------","    -- Verify model was imported","     ----------------------------","    SELECT * FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';    ","    -----------------------------","    -- Model description","     ----------------------------","    SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';  ","    -----------------------------","    -- Model settings","     ----------------------------","    SELECT SETTING_NAME, SETTING_VALUE FROM ALL_MINING_MODEL_SETTINGS WHERE MODEL_NAME='GLM_MOD'; ","    -----------------------------","    -- Perform scoring","     ----------------------------","    SELECT CID, ROUND(PREDICTION(GLM_MOD USING *), 1)  ","    AS PREDICTED_MPG, MPG AS ACTUAL_MPG FROM CARS ORDER BY CID;"],"enabled":true,"result":{"startTime":1734652360884,"interpreter":"md.medium","endTime":1734652361313,"results":[{"message":"<hr />\n<pre><code>-- Drop table if it exists\n----------------------------\nBEGIN EXECUTE IMMEDIATE 'DROP TABLE MODEL_IMPORT';         \nEXCEPTION WHEN OTHERS THEN NULL; END;\n/\n\n----------------------------\n-- Drop model if it exists\n----------------------------\nBEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD');            \nEXCEPTION WHEN OTHERS THEN NULL; END;\n/\n\n----------------------------------------------------\n-- Define directory where serialized model is saved\n----------------------------------------------------\nCREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle';    \n\n-----------------------------------------------\n-- Create table to temporarily store the model\n-----------------------------------------------\nCREATE TABLE MODEL_IMPORT(MY_MODEL BLOB);  \n\n ---------------------------\n -- import serialized model\n --------------------------\nDECLARE\n  BLOB_MODEL BLOB;\nBEGIN\n  INSERT INTO MODEL_IMPORT values (BFILENAME('MYDIR','GLM_MOD.mod'));\n  commit;\n  SELECT MY_MODEL INTO BLOB_MODEL FROM MODEL_IMPORT;\n  DBMS_DATA_MINING.IMPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD'); \nEND;\n/\n\nTest the model:\n-----------------------------\n-- Verify model was imported\n ----------------------------\nSELECT * FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';    \n-----------------------------\n-- Model description\n ----------------------------\nSELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';  \n-----------------------------\n-- Model settings\n ----------------------------\nSELECT SETTING_NAME, SETTING_VALUE FROM ALL_MINING_MODEL_SETTINGS WHERE MODEL_NAME='GLM_MOD'; \n-----------------------------\n-- Perform scoring\n ----------------------------\nSELECT CID, ROUND(PREDICTION(GLM_MOD USING *), 1)  \nAS PREDICTED_MPG, MPG AS ACTUAL_MPG FROM CARS ORDER BY CID;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" Export a serialized model from an Oracle Database and import into an Autonomous Database","message":["%md","","1. Export the model to a serialized BLOB from Oracle Database to the database server Operating System.","   a. Define the `write_serialized_model` procedure under \"Prerequisites\" to export a model as a serialized BLOB to a directory.","   b. Define the directory where the serialized model will be exported in SQL.","   c. Export the model using `DBMS_DATA_MINING.EXPORT_SERMODEL`.","","2. To import the model into Autonomous Database:","   a. Upload the exported model to the Object Storage bucket associated with your Autonomous Database.","   b. Generate a PAR corresponding to the serialized model file in Object Storage.","   c. Import the model using `DBMS_DATA_MINING.IMPORT_SERMODEL` as shown in the section above.","","In this example, the name of the in-database model is *GLM_MOD* and the name of the serialized model is *GLM_MOD.mod*.","","The following commands will run in a SQL prompt on the database server or in a SQL Developer instance connected to the database server as the OML user:"],"enabled":true,"result":{"startTime":1734652361751,"interpreter":"md.medium","endTime":1734652362162,"results":[{"message":"<ol>\n<li>\n<p>Export the model to a serialized BLOB from Oracle Database to the database server Operating System.\na. Define the <code>write_serialized_model<\/code> procedure under &quot;Prerequisites&quot; to export a model as a serialized BLOB to a directory.\nb. Define the directory where the serialized model will be exported in SQL.\nc. Export the model using <code>DBMS_DATA_MINING.EXPORT_SERMODEL<\/code>.<\/p>\n<\/li>\n<li>\n<p>To import the model into Autonomous Database:\na. Upload the exported model to the Object Storage bucket associated with your Autonomous Database.\nb. Generate a PAR corresponding to the serialized model file in Object Storage.\nc. Import the model using <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code> as shown in the section above.<\/p>\n<\/li>\n<\/ol>\n<p>In this example, the name of the in-database model is <em>GLM_MOD<\/em> and the name of the serialized model is <em>GLM_MOD.mod<\/em>.<\/p>\n<p>The following commands will run in a SQL prompt on the database server or in a SQL Developer instance connected to the database server as the OML user:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export model from Oracle Database","message":["%md","","    --------------------------------------------------------","    -- define directory where serialized model will be saved","    --------------------------------------------------------","    CREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle';           ","","    DECLARE","       -------------------------------------","       -- name of the model to be imported","       -------------------------------------","      MODNAME VARCHAR2(100) := 'GLM_MOD';                       ","      SER_MODNAME VARCHAR2(100);","      BLOB_MODEL BLOB;","    BEGIN","      DBMS_LOB.CREATETEMPORARY(BLOB_MODEL, FALSE);","      SELECT CONCAT(MODNAME,'.mod') INTO SER_MODNAME from dual;","       ---------------------------","       -- export serialized model","       ---------------------------","      DBMS_DATA_MINING.EXPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD');  ","       ---------------------------------------------------","       -- Convenience function to write a model to a BLOB","       ---------------------------------------------------","      write_serialized_model(BLOB_MODEL, 'MYDIR', SER_MODNAME);    ","      DBMS_LOB.FREETEMPORARY(BLOB_MODEL);","    END;","    /"],"enabled":true,"result":{"startTime":1734652362590,"interpreter":"md.medium","endTime":1734652363004,"results":[{"message":"<hr />\n<pre><code>-- define directory where serialized model will be saved\n--------------------------------------------------------\nCREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle';           \n\nDECLARE\n   -------------------------------------\n   -- name of the model to be imported\n   -------------------------------------\n  MODNAME VARCHAR2(100) := 'GLM_MOD';                       \n  SER_MODNAME VARCHAR2(100);\n  BLOB_MODEL BLOB;\nBEGIN\n  DBMS_LOB.CREATETEMPORARY(BLOB_MODEL, FALSE);\n  SELECT CONCAT(MODNAME,'.mod') INTO SER_MODNAME from dual;\n   ---------------------------\n   -- export serialized model\n   ---------------------------\n  DBMS_DATA_MINING.EXPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD');  \n   ---------------------------------------------------\n   -- Convenience function to write a model to a BLOB\n   ---------------------------------------------------\n  write_serialized_model(BLOB_MODEL, 'MYDIR', SER_MODNAME);    \n  DBMS_LOB.FREETEMPORARY(BLOB_MODEL);\nEND;\n/\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export a serialized model from an Oracle Database and import to another Oracle Database","message":["%md","","","1. Export the model to a serialized BLOB from Oracle Database to the database server Operating System.","   a. Define the `write_serialized_model` procedure under \u201cPrerequisites\u201d to export a model as a serialized BLOB to a directory.","   b. Define the directory where the serialized model will be exported in SQL.","   c. Export the model using `DBMS_DATA_MINING.EXPORT_SERMODEL`.","","2. Import the serialized model to the target Oracle Database","   a. Download the serialized model to the target database server.","   b. In SQL, define the directory from which the serialized model will be imported.","   c. Import the model using `DBMS_DATA_MINING.IMPORT_SERMODEL`.","","In this example, the name of the in-database model is *GLM_MOD* and the name of the serialized model is *GLM_MOD.mod*.","","The following commands will run in a SQL prompt on the database server or in a SQL Developer instance connected to the database server as the OML user:"],"enabled":true,"result":{"startTime":1734652363427,"interpreter":"md.medium","endTime":1734652363837,"results":[{"message":"<ol>\n<li>\n<p>Export the model to a serialized BLOB from Oracle Database to the database server Operating System.\na. Define the <code>write_serialized_model<\/code> procedure under \u201cPrerequisites\u201d to export a model as a serialized BLOB to a directory.\nb. Define the directory where the serialized model will be exported in SQL.\nc. Export the model using <code>DBMS_DATA_MINING.EXPORT_SERMODEL<\/code>.<\/p>\n<\/li>\n<li>\n<p>Import the serialized model to the target Oracle Database\na. Download the serialized model to the target database server.\nb. In SQL, define the directory from which the serialized model will be imported.\nc. Import the model using <code>DBMS_DATA_MINING.IMPORT_SERMODEL<\/code>.<\/p>\n<\/li>\n<\/ol>\n<p>In this example, the name of the in-database model is <em>GLM_MOD<\/em> and the name of the serialized model is <em>GLM_MOD.mod<\/em>.<\/p>\n<p>The following commands will run in a SQL prompt on the database server or in a SQL Developer instance connected to the database server as the OML user:<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export the model to a file on the database server","message":["%md","","    ----------------------------------------------------","    -- Define directory where serialized model is saved","    ----------------------------------------------------","    CREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle';       ","","    DECLARE","      MODNAME VARCHAR2(100) := 'GLM_MOD';","      SER_MODNAME VARCHAR2(100);","      BLOB_MODEL BLOB;","    BEGIN","      DBMS_LOB.CREATETEMPORARY(BLOB_MODEL, FALSE);","      SELECT CONCAT(MODNAME,'.mod') INTO SER_MODNAME from dual;","      ---------------------------","      -- export serialized model ","      ---------------------------","      DBMS_DATA_MINING.EXPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD'); ","      ---------------------------------------------------","      -- convenience function to write a model to a BLOB","      ---------------------------------------------------","      write_serialized_model(BLOB_MODEL, 'MYDIR', SER_MODNAME);   ","      DBMS_LOB.FREETEMPORARY(BLOB_MODEL);","    END;","    /"],"enabled":true,"result":{"startTime":1734652364279,"interpreter":"md.medium","endTime":1734652364692,"results":[{"message":"<hr />\n<pre><code>-- Define directory where serialized model is saved\n----------------------------------------------------\nCREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle';       \n\nDECLARE\n  MODNAME VARCHAR2(100) := 'GLM_MOD';\n  SER_MODNAME VARCHAR2(100);\n  BLOB_MODEL BLOB;\nBEGIN\n  DBMS_LOB.CREATETEMPORARY(BLOB_MODEL, FALSE);\n  SELECT CONCAT(MODNAME,'.mod') INTO SER_MODNAME from dual;\n  ---------------------------\n  -- export serialized model \n  ---------------------------\n  DBMS_DATA_MINING.EXPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD'); \n  ---------------------------------------------------\n  -- convenience function to write a model to a BLOB\n  ---------------------------------------------------\n  write_serialized_model(BLOB_MODEL, 'MYDIR', SER_MODNAME);   \n  DBMS_LOB.FREETEMPORARY(BLOB_MODEL);\nEND;\n/\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Copy the serialized model to the target database server and import the model","message":["%md","","    ---------------------------","    -- Drop table if it exists","    ---------------------------","    BEGIN EXECUTE IMMEDIATE 'DROP TABLE MODEL_IMPORT';           ","    EXCEPTION WHEN OTHERS THEN NULL; END;","    /","    ","    ---------------------------","    -- Drop model if it exists","    ---------------------------","    BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD');             ","    EXCEPTION WHEN OTHERS THEN NULL; END;","    /","    ","    ----------------------------------------------------","    -- Define directory where serialized model is saved","    ----------------------------------------------------","    CREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle'; ","    ","    -----------------------------------------------","    -- Create table to temporarily store the model","    -----------------------------------------------","    CREATE TABLE MODEL_IMPORT(MY_MODEL BLOB)                     ","","    DECLARE","      BLOB_MODEL BLOB;","    BEGIN","      INSERT INTO MODEL_IMPORT values (BFILENAME('MYDIR','GLM_MOD.mod'));","      commit;","      SELECT MY_MODEL INTO BLOB_MODEL FROM MODEL_IMPORT;","      --------------------------","      -- Import serialized model","      --------------------------","      DBMS_DATA_MINING.IMPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD');  ","    END;","    /","","    Test the model:","    -----------------------------","    -- Verify model was imported","     ----------------------------","    SELECT * FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';    ","    -----------------------------","    -- Model description","     ----------------------------","    SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';  ","    -----------------------------","    -- Model settings","     ----------------------------","    SELECT SETTING_NAME, SETTING_VALUE FROM ALL_MINING_MODEL_SETTINGS WHERE MODEL_NAME='GLM_MOD'; ","    -----------------------------","    -- Perform scoring","     ----------------------------","    SELECT CID, ROUND(PREDICTION(GLM_MOD USING *), 1)  ","    AS PREDICTED_MPG, MPG AS ACTUAL_MPG FROM CARS ORDER BY CID;"],"enabled":true,"result":{"startTime":1734652365119,"interpreter":"md.medium","endTime":1734652365562,"results":[{"message":"<hr />\n<pre><code>-- Drop table if it exists\n---------------------------\nBEGIN EXECUTE IMMEDIATE 'DROP TABLE MODEL_IMPORT';           \nEXCEPTION WHEN OTHERS THEN NULL; END;\n/\n\n---------------------------\n-- Drop model if it exists\n---------------------------\nBEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_MOD');             \nEXCEPTION WHEN OTHERS THEN NULL; END;\n/\n\n----------------------------------------------------\n-- Define directory where serialized model is saved\n----------------------------------------------------\nCREATE OR REPLACE DIRECTORY MYDIR AS '/home/oracle'; \n\n-----------------------------------------------\n-- Create table to temporarily store the model\n-----------------------------------------------\nCREATE TABLE MODEL_IMPORT(MY_MODEL BLOB)                     \n\nDECLARE\n  BLOB_MODEL BLOB;\nBEGIN\n  INSERT INTO MODEL_IMPORT values (BFILENAME('MYDIR','GLM_MOD.mod'));\n  commit;\n  SELECT MY_MODEL INTO BLOB_MODEL FROM MODEL_IMPORT;\n  --------------------------\n  -- Import serialized model\n  --------------------------\n  DBMS_DATA_MINING.IMPORT_SERMODEL(BLOB_MODEL, 'GLM_MOD');  \nEND;\n/\n\nTest the model:\n-----------------------------\n-- Verify model was imported\n ----------------------------\nSELECT * FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';    \n-----------------------------\n-- Model description\n ----------------------------\nSELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE FROM USER_MINING_MODELS WHERE MODEL_NAME='GLM_MOD';  \n-----------------------------\n-- Model settings\n ----------------------------\nSELECT SETTING_NAME, SETTING_VALUE FROM ALL_MINING_MODEL_SETTINGS WHERE MODEL_NAME='GLM_MOD'; \n-----------------------------\n-- Perform scoring\n ----------------------------\nSELECT CID, ROUND(PREDICTION(GLM_MOD USING *), 1)  \nAS PREDICTED_MPG, MPG AS ACTUAL_MPG FROM CARS ORDER BY CID;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Clean up"],"enabled":true,"result":{"startTime":1734652365990,"interpreter":"md.medium","endTime":1734652366392,"results":[{"message":"<h2 id=\"clean-up\">Clean up<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Drop Cloud credential (optional)","message":["%script","","BEGIN","  DBMS_CLOUD.DROP_CREDENTIAL(","    credential_name => 'MY_CLOUD_CRED');","END;","/"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Drop tables (optional)","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CARS'; 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML GPU Vector Search - Sentence Transformer.dsnb b/machine-learning/notebooks-oml/sql/OML GPU Vector Search - Sentence Transformer.dsnb
new file mode 100644
index 00000000..d56e7cbc
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML GPU Vector Search - Sentence Transformer.dsnb	
@@ -0,0 +1 @@
+[{"layout":"zeppelin","template":"dsrgmn3y","templateConfig":"{\"visualization\":{\"filters\":[{\"_id\":1583324064459,\"type\":\"styling\",\"enabled\":true,\"conditions\":{\"operator\":\"and\",\"conditions\":[{\"property\":\"hiddenConnection\",\"operator\":\"*\",\"value\":\"\"}]},\"component\":\"edge\",\"target\":\"edge\",\"properties\":{\"colors\":[\"rgba(0, 0, 0, 0.1)\"],\"style\":[\"dashed\"],\"legendTitle\":[\"Hidden Connection\"]}},{\"_id\":1590499315755,\"type\":\"aggregation\",\"enabled\":true,\"conditions\":{\"operator\":\"and\",\"conditions\":[]},\"component\":\"vertex\",\"target\":\"vertex\",\"properties\":{},\"aggregation\":[{\"source\":\"\",\"type\":\"average\"}]}],\"version\":4}}","name":"OML GPU Vector Search - Sentence Transformer","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736976257119,"interpreter":"md.gpu","endTime":1736976257673,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md\r","\r","## GPU-enhanced semantic similarity search \r","### using the `all-MiniLM-L12-v2` transformer\r","\r","The `all-MiniLM-L12-v2` transformer model from the Hugging Face repository is a powerful tool for semantic search tasks,\r","offering a compact yet effective solution for embedding-based information retrieval. As part of the MiniLM (Miniature Language Model) series, \r","it balances computational efficiency with high performance.\r","\r","Key features of `all-MiniLM-L12-v2` include:\r","* Transformer-based architecture optimized for dense vector representations\r","* Capture semantic content of sentences in 384-dimensional embeddings\r","* Comparison and retrieval based on semantic similarity rather than exact keyword matches\r","\r","The model can be used to build a semantic search system with the following steps:\r","* Set up the environment and load the model\r","* Preprocess and load a text corpus\r","* Generate embeddings for the corpus and user queries\r","* Store embeddings in Oracle Autonomous Database\r","* Perform semantic search by comparing question embeddings to source document embeddings\r","\r","This approach allows for more nuanced and context-aware search results compared to traditional keyword-based methods.\r","\r","Copyright (c) 2025 Oracle Corporation \r","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736976258148,"interpreter":"md.gpu","endTime":1736976258615,"results":[{"message":"<h2 id=\"gpu-enhanced-semantic-similarity-search\">GPU-enhanced semantic similarity search<\/h2>\n<h3 id=\"using-the-all-minilm-l12-v2-transformer\">using the <code>all-MiniLM-L12-v2<\/code> transformer<\/h3>\n<p>The <code>all-MiniLM-L12-v2<\/code> transformer model from the Hugging Face repository is a powerful tool for semantic search tasks,\noffering a compact yet effective solution for embedding-based information retrieval. As part of the MiniLM (Miniature Language Model) series,\nit balances computational efficiency with high performance.<\/p>\n<p>Key features of <code>all-MiniLM-L12-v2<\/code> include:<\/p>\n<ul>\n<li>Transformer-based architecture optimized for dense vector representations<\/li>\n<li>Capture semantic content of sentences in 384-dimensional embeddings<\/li>\n<li>Comparison and retrieval based on semantic similarity rather than exact keyword matches<\/li>\n<\/ul>\n<p>The model can be used to build a semantic search system with the following steps:<\/p>\n<ul>\n<li>Set up the environment and load the model<\/li>\n<li>Preprocess and load a text corpus<\/li>\n<li>Generate embeddings for the corpus and user queries<\/li>\n<li>Store embeddings in Oracle Autonomous Database<\/li>\n<li>Perform semantic search by comparing question embeddings to source document embeddings<\/li>\n<\/ul>\n<p>This approach allows for more nuanced and context-aware search results compared to traditional keyword-based methods.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Commands run by ADMIN to create and upload the conda environment to Object Storage","message":["%md","","    #### Commands previously run by ADMIN to create and upload the conda environment","    create -n gpuenv python=3.12.3 pandas==2.1.1 numpy==1.26.4","    activate gpuenv","    install cudatoolkit=11.8 -c conda-forge","    install cudnn=8.9 -c conda-forge","    install numpy -c pytorch","    install pytorch-cuda=11.8 -c pytorch -c nvidia","    install pytorch=*=*cuda11.8* -c pytorch -c nvidia","    install sentence-transformers -c conda-forge","","    # 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"],"enabled":true,"result":{"startTime":1736976467872,"interpreter":"md.gpu","endTime":1736976468326,"results":[{"message":"<h2 id=\"load-our-text-document-corpus-into-python-memory\">Load our text document corpus into Python memory<\/h2>\n<p>Pull the database table <code>OML_BLOG_CONTENT<\/code> containing titles and content from Oracle Machine Learning blogs to a Python Pandas DataFrame.\nWe'll generate vector embeddings in Python and perform semantic search in the database, where we can find relevant topics based on the context and meaning\nof our search terms, not just keywords.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Selection of Oracle Machine Learning blog content","message":["%python","","document_texts = {","    \"TITLE\": [","        \"Getting started with Oracle Machine Learning for Python\",","        \"Introducing Oracle Machine Learning for Python\",","        \"Hands-On Lab: Introduction to Oracle Machine Learning for Python\",","        \"OML4Py: Leveraging ONNX and Hugging Face for AI Vector Search\",","        \"Building Oracle Machine Learning Models with OML4Py and Deploying in Oracle Analytics Cloud\",","        \"How to Perform In-Database Text Analytics Using a Pre-Built ML Model\",","        \"Embedded Intelligence: Storing and Retrieving Embeddings in a Feature Store\",","        \"Automated Machine Learning with Oracle Machine Learning for Python\",","        \"ICYMI: Learn How to Invoke Your Python Functions from SQL with OML4Py\",","        \"Leveraging ONNX and Hugging Face for AI Vector Search\",","        \"Storing Named R and Python Functions in the Oracle Machine Learning (OML) Script Repository\",","        \"Announcing custom third-party Python and R packages for use on Autonomous Database\"","    ],","    \"CONTENT\": [","        \"Oracle Machine Learning for Python (OML4Py) is a powerful tool for data scientists and analysts. It allows you to leverage the computational power of Oracle Database to perform machine learning tasks efficiently. In this blog post, we'll walk through the basics of getting started with OML4Py.\",","        \"Designed for problems involving both large and small data volumes, Oracle Machine Learning for Python integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for data exploration, statistical analysis, and machine learning on database tables and views using Python syntax. Familiar Python functions are overloaded to translate Python functionality into SQL for in-database processing, achieving performance and scalability transparently.\",","        \"Join us for this Introductory Oracle Machine Learning workshop focusing on the Python API - OML4Py. You will learn to provision your Autonomous Database and create OML users, load sample data, manipulate database tables as Pandas DataFrame proxy objects, build powerful machine learning models, and use AutoML through the Python API and no-code OML AutoML UI.\",","        \"AI Vector Search is a powerful feature in Oracle Database that allows for efficient similarity search on vector embeddings. When combined with OML4Py and ONNX models from Hugging Face, it opens up a world of possibilities for natural language processing and machine learning tasks. This blog post will explore how to leverage these technologies for advanced text analysis and similarity search.\",","        \"Oracle Machine Learning for Python (OML4Py) provides a powerful platform for building and deploying machine learning models. In this blog post, we'll walk through the process of building a model with OML4Py and deploying it in Oracle Analytics Cloud (OAC). This integration allows for seamless incorporation of machine learning insights into your analytics workflows.\",","        \"In-database text analytics can be a powerful tool for extracting insights from large volumes of text data. In this blog post, we'll explore how to use a pre-built machine learning model for text analysis directly within Oracle Database. This approach allows you to leverage sophisticated text analysis techniques without moving your data out of the database.\",","        \"Embeddings have become a crucial component in many machine learning workflows, especially in natural language processing tasks. In this blog post, we'll explore how to store and retrieve embeddings efficiently using a feature store in Oracle Database. This approach can significantly improve the efficiency of your machine learning pipelines, particularly when dealing with large-scale text data.\",","        \"Automated Machine Learning (AutoML) simplifies the process of model selection and hyperparameter tuning, making it accessible for users without extensive machine learning expertise. In this blog post, we will delve into how to use AutoML capabilities in Oracle Machine Learning for Python to streamline your machine learning projects and improve model performance.\",","        \"Oracle Machine Learning for Python allows users to run user-defined Python functions in Python engines spawned and managed by the database environment - from Python (of course), but also SQL. The SQL interface makes it easy for application and dashboard developers to deploy Python-based solutions. This feature, called Embedded Python Execution (EPE), also allows you to take advantage of system-provided data-parallelism for those embarrassingly parallel use cases. Supporting OML4Py EPE is the ability to store user-defined Python functions in the database script repository as well as Python objects in the database datastore \u2013 no more separate flat files to manage, back up, and secure. Join us to learn about this powerful capability.\",","        \"Oracle Database 23ai introduces Oracle AI Vector Search, revolutionizing semantic similarity search by generating vectors using transformer models and managing them at scale within the database. This empowers users to find relevant information based on meaning and context, eliminating the pain point of transferring data to separate vector databases, thus reducing complexity and operational overhead. Starting with OML4Py 2.0, the client converts pre-trained transformer models from Hugging Face to the Open Neural Network Exchange (ONNX) format for AI Vector Search. Hugging Face is an open-source Python library for deep learning, offering thousands of pre-trained models for natural language processing (NLP), computer vision, audio, and more. ONNX is an open format for representing various machine learning models, including transformers, classification, regression, and other types. The OML4Py client streamlines the path to embedding generation in the database by downloading pre-trained models from the Hugging Face repository, augmenting them with pre- and post-processing steps, converting the augmented model to ONNX format, and loading it into an in-database model or exporting it to a file. Once loaded in ONNX format, you can seamlessly use the in-database ONNX Runtime to generate vector embeddings for AI Vector Search. Combined with the OML4Py client, AI Vector Search offers a comprehensive solution for leveraging vector embeddings in Oracle Database, enhancing text data analysis, search capabilities, and machine learning applications. In this blog, we outline the steps to install the OML4Py client, provide examples of converting pre-configured and customized models to ONNX format, and load the models into Oracle Database. This applies to Oracle Database on-premises or on the cloud and Autonomous Database.\",","        \"Oracle Machine Learning for R (OML4R) and Oracle Machine Learning for Python (OML4Py) each provide a script repository to store user defined R or Python functions in an Oracle Database or Autonomous Database instance. OML4R provides functions ore.scriptCreate and ore.scriptLoad, and OML4Py provides functions  oml.script.create and oml.script.load. These functions save R and Python UDFs into the script repository and reload the function back into the current local session. Functions are stored in the script repository specified as global or private, and given user-defined access privileges. If the global parameter is set to FALSE (the default) specifies that the script is not public and is visible only to the owner or to users to whom the owner has granted read privilege access; TRUE specifies that the script is public and therefore visible to all users. Functions in the script repository can be listed based on name, pattern, and type. (List options include user created scripts, global scripts, scripts granted to the user, scripts the user has granted access to, and all scripts). Scripts in the repository can also be dropped based on access level.\",","        \"We are pleased to announce the ability to use custom third-party packages on Oracle Autonomous Database. As we continue to expand Autonomous Database as a platform for data science and machine learning, users of Oracle Machine Learning (OML) Notebooks and OML4Py and OML4R embedded execution can now use custom-installed third-party packages through conda environments. With this feature, users can leverage the broader Python and R package ecosystems to augment database functionality on Oracle Autonomous Database in OML Notebooks. Run Python and R user-defined functions with third-party package functionality in engines spawned and managed by the database environment, while optionally taking advantage of OML4Py and OML4R system-provided data parallelism and task parallelism. This enables, for example, using native Python and R machine learning algorithms to build models, store them in the database, and make predictions from SQL and REST APIs. Creating and using conda environments. OML Notebooks introduces a conda interpreter that enables a user with administrator privileges to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as OML4R and OML4Py embedded execution invocations. Python, R, and REST interfaces for embedded execution are currently available, with the SQL embedded execution API coming soon. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party Python and R packages. With conda environments, you can install and update packages and their dependencies, as well as switch between environments to use project-specific packages. Users with administrator privileges create conda environments, install packages, and upload conda environments to Object Storage. These environments can then be accessed by non-administrator users and loaded into their own notebooks for use.\"","    ]","}","","document_df = pd.DataFrame(document_texts)","z.show(document_df)"],"enabled":true,"result":{"startTime":1736976468810,"interpreter":"python.gpu","endTime":1736976469299,"results":[{"message":"TITLE\tCONTENT\nGetting started with Oracle Machine Learning for Python\tOracle Machine Learning for Python (OML4Py) is a powerful tool for data scientists and analysts. It allows you to leverage the computational power of Oracle Database to perform machine learning tasks efficiently. In this blog post, we'll walk through the basics of getting started with OML4Py.\nIntroducing Oracle Machine Learning for Python\tDesigned for problems involving both large and small data volumes, Oracle Machine Learning for Python integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for data exploration, statistical analysis, and machine learning on database tables and views using Python syntax. Familiar Python functions are overloaded to translate Python functionality into SQL for in-database processing, achieving performance and scalability transparently.\nHands-On Lab: Introduction to Oracle Machine Learning for Python\tJoin us for this Introductory Oracle Machine Learning workshop focusing on the Python API - OML4Py. You will learn to provision your Autonomous Database and create OML users, load sample data, manipulate database tables as Pandas DataFrame proxy objects, build powerful machine learning models, and use AutoML through the Python API and no-code OML AutoML UI.\nOML4Py: Leveraging ONNX and Hugging Face for AI Vector Search\tAI Vector Search is a powerful feature in Oracle Database that allows for efficient similarity search on vector embeddings. When combined with OML4Py and ONNX models from Hugging Face, it opens up a world of possibilities for natural language processing and machine learning tasks. This blog post will explore how to leverage these technologies for advanced text analysis and similarity search.\nBuilding Oracle Machine Learning Models with OML4Py and Deploying in Oracle Analytics Cloud\tOracle Machine Learning for Python (OML4Py) provides a powerful platform for building and deploying machine learning models. In this blog post, we'll walk through the process of building a model with OML4Py and deploying it in Oracle Analytics Cloud (OAC). This integration allows for seamless incorporation of machine learning insights into your analytics workflows.\nHow to Perform In-Database Text Analytics Using a Pre-Built ML Model\tIn-database text analytics can be a powerful tool for extracting insights from large volumes of text data. In this blog post, we'll explore how to use a pre-built machine learning model for text analysis directly within Oracle Database. This approach allows you to leverage sophisticated text analysis techniques without moving your data out of the database.\nEmbedded Intelligence: Storing and Retrieving Embeddings in a Feature Store\tEmbeddings have become a crucial component in many machine learning workflows, especially in natural language processing tasks. In this blog post, we'll explore how to store and retrieve embeddings efficiently using a feature store in Oracle Database. This approach can significantly improve the efficiency of your machine learning pipelines, particularly when dealing with large-scale text data.\nAutomated Machine Learning with Oracle Machine Learning for Python\tAutomated Machine Learning (AutoML) simplifies the process of model selection and hyperparameter tuning, making it accessible for users without extensive machine learning expertise. In this blog post, we will delve into how to use AutoML capabilities in Oracle Machine Learning for Python to streamline your machine learning projects and improve model performance.\nICYMI: Learn How to Invoke Your Python Functions from SQL with OML4Py\tOracle Machine Learning for Python allows users to run user-defined Python functions in Python engines spawned and managed by the database environment - from Python (of course), but also SQL. The SQL interface makes it easy for application and dashboard developers to deploy Python-based solutions. This feature, called Embedded Python Execution (EPE), also allows you to take advantage of system-provided data-parallelism for those embarrassingly parallel use cases. Supporting OML4Py EPE is the ability to store user-defined Python functions in the database script repository as well as Python objects in the database datastore \u2013 no more separate flat files to manage, back up, and secure. Join us to learn about this powerful capability.\nLeveraging ONNX and Hugging Face for AI Vector Search\tOracle Database 23ai introduces Oracle AI Vector Search, revolutionizing semantic similarity search by generating vectors using transformer models and managing them at scale within the database. This empowers users to find relevant information based on meaning and context, eliminating the pain point of transferring data to separate vector databases, thus reducing complexity and operational overhead. Starting with OML4Py 2.0, the client converts pre-trained transformer models from Hugging Face to the Open Neural Network Exchange (ONNX) format for AI Vector Search. Hugging Face is an open-source Python library for deep learning, offering thousands of pre-trained models for natural language processing (NLP), computer vision, audio, and more. ONNX is an open format for representing various machine learning models, including transformers, classification, regression, and other types. The OML4Py client streamlines the path to embedding generation in the database by downloading pre-trained models from the Hugging Face repository, augmenting them with pre- and post-processing steps, converting the augmented model to ONNX format, and loading it into an in-database model or exporting it to a file. Once loaded in ONNX format, you can seamlessly use the in-database ONNX Runtime to generate vector embeddings for AI Vector Search. Combined with the OML4Py client, AI Vector Search offers a comprehensive solution for leveraging vector embeddings in Oracle Database, enhancing text data analysis, search capabilities, and machine learning applications. In this blog, we outline the steps to install the OML4Py client, provide examples of converting pre-configured and customized models to ONNX format, and load the models into Oracle Database. This applies to Oracle Database on-premises or on the cloud and Autonomous Database.\nStoring Named R and Python Functions in the Oracle Machine Learning (OML) Script Repository\tOracle Machine Learning for R (OML4R) and Oracle Machine Learning for Python (OML4Py) each provide a script repository to store user defined R or Python functions in an Oracle Database or Autonomous Database instance. OML4R provides functions ore.scriptCreate and ore.scriptLoad, and OML4Py provides functions  oml.script.create and oml.script.load. These functions save R and Python UDFs into the script repository and reload the function back into the current local session. Functions are stored in the script repository specified as global or private, and given user-defined access privileges. If the global parameter is set to FALSE (the default) specifies that the script is not public and is visible only to the owner or to users to whom the owner has granted read privilege access; TRUE specifies that the script is public and therefore visible to all users. Functions in the script repository can be listed based on name, pattern, and type. (List options include user created scripts, global scripts, scripts granted to the user, scripts the user has granted access to, and all scripts). Scripts in the repository can also be dropped based on access level.\nAnnouncing custom third-party Python and R packages for use on Autonomous Database\tWe are pleased to announce the ability to use custom third-party packages on Oracle Autonomous Database. As we continue to expand Autonomous Database as a platform for data science and machine learning, users of Oracle Machine Learning (OML) Notebooks and OML4Py and OML4R embedded execution can now use custom-installed third-party packages through conda environments. With this feature, users can leverage the broader Python and R package ecosystems to augment database functionality on Oracle Autonomous Database in OML Notebooks. Run Python and R user-defined functions with third-party package functionality in engines spawned and managed by the database environment, while optionally taking advantage of OML4Py and OML4R system-provided data parallelism and task parallelism. This enables, for example, using native Python and R machine learning algorithms to build models, store them in the database, and make predictions from SQL and REST APIs. Creating and using conda environments. OML Notebooks introduces a conda interpreter that enables a user with administrator privileges to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as OML4R and OML4Py embedded execution invocations. Python, R, and REST interfaces for embedded execution are currently available, with the SQL embedded execution API coming soon. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party Python and R packages. With conda environments, you can install and update packages and their dependencies, as well as switch between environments to use project-specific packages. Users with administrator privileges create conda environments, install packages, and upload conda environments to Object Storage. These environments can then be accessed by non-administrator users and loaded into their own notebooks for use.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a database table containing blog content","message":["%python","","try:","    oml.drop(table='OML_BLOG_CONTENT') ","except:","    pass","","oml.create(document_df, table=\"OML_BLOG_CONTENT\")"],"enabled":true,"result":{"startTime":1736976469809,"interpreter":"python.gpu","endTime":1736976470711,"results":[{"message":"                                                TITLE                                            CONTENT\n0   Getting started with Oracle Machine Learning f...  Oracle Machine Learning for Python (OML4Py) is...\n1      Introducing Oracle Machine Learning for Python  Designed for problems involving both large and...\n2   Hands-On Lab: Introduction to Oracle Machine L...  Join us for this Introductory Oracle Machine L...\n3   OML4Py: Leveraging ONNX and Hugging Face for A...  AI Vector Search is a powerful feature in Orac...\n4   Building Oracle Machine Learning Models with O...  Oracle Machine Learning for Python (OML4Py) pr...\n5   How to Perform In-Database Text Analytics Usin...  In-database text analytics can be a powerful t...\n6   Embedded Intelligence: Storing and Retrieving ...  Embeddings have become a crucial component in ...\n7   Automated Machine Learning with Oracle Machine...  Automated Machine Learning (AutoML) simplifies...\n8   ICYMI: Learn How to Invoke Your Python Functio...  Oracle Machine Learning for Python allows user...\n9   Leveraging ONNX and Hugging Face for AI Vector...  Oracle Database 23ai introduces Oracle AI Vect...\n10  Storing Named R and Python Functions in the Or...  Oracle Machine Learning for R (OML4R) and Orac...\n11  Announcing custom third-party Python and R pac...  We are pleased to announce the ability to use ...\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use OML4Py to pull OML blog text into Python memory","message":["%python","","document_table = oml.sync(table=\"OML_BLOG_CONTENT\")","document_df = document_table.pull()","z.show(document_df)"],"enabled":true,"result":{"startTime":1736976471192,"interpreter":"python.gpu","endTime":1736976471682,"results":[{"message":"TITLE\tCONTENT\nGetting started with Oracle Machine Learning for Python\tOracle Machine Learning for Python (OML4Py) is a powerful tool for data scientists and analysts. It allows you to leverage the computational power of Oracle Database to perform machine learning tasks efficiently. In this blog post, we'll walk through the basics of getting started with OML4Py.\nIntroducing Oracle Machine Learning for Python\tDesigned for problems involving both large and small data volumes, Oracle Machine Learning for Python integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for data exploration, statistical analysis, and machine learning on database tables and views using Python syntax. Familiar Python functions are overloaded to translate Python functionality into SQL for in-database processing, achieving performance and scalability transparently.\nHands-On Lab: Introduction to Oracle Machine Learning for Python\tJoin us for this Introductory Oracle Machine Learning workshop focusing on the Python API - OML4Py. You will learn to provision your Autonomous Database and create OML users, load sample data, manipulate database tables as Pandas DataFrame proxy objects, build powerful machine learning models, and use AutoML through the Python API and no-code OML AutoML UI.\nOML4Py: Leveraging ONNX and Hugging Face for AI Vector Search\tAI Vector Search is a powerful feature in Oracle Database that allows for efficient similarity search on vector embeddings. When combined with OML4Py and ONNX models from Hugging Face, it opens up a world of possibilities for natural language processing and machine learning tasks. This blog post will explore how to leverage these technologies for advanced text analysis and similarity search.\nBuilding Oracle Machine Learning Models with OML4Py and Deploying in Oracle Analytics Cloud\tOracle Machine Learning for Python (OML4Py) provides a powerful platform for building and deploying machine learning models. In this blog post, we'll walk through the process of building a model with OML4Py and deploying it in Oracle Analytics Cloud (OAC). This integration allows for seamless incorporation of machine learning insights into your analytics workflows.\nHow to Perform In-Database Text Analytics Using a Pre-Built ML Model\tIn-database text analytics can be a powerful tool for extracting insights from large volumes of text data. In this blog post, we'll explore how to use a pre-built machine learning model for text analysis directly within Oracle Database. This approach allows you to leverage sophisticated text analysis techniques without moving your data out of the database.\nEmbedded Intelligence: Storing and Retrieving Embeddings in a Feature Store\tEmbeddings have become a crucial component in many machine learning workflows, especially in natural language processing tasks. In this blog post, we'll explore how to store and retrieve embeddings efficiently using a feature store in Oracle Database. This approach can significantly improve the efficiency of your machine learning pipelines, particularly when dealing with large-scale text data.\nAutomated Machine Learning with Oracle Machine Learning for Python\tAutomated Machine Learning (AutoML) simplifies the process of model selection and hyperparameter tuning, making it accessible for users without extensive machine learning expertise. In this blog post, we will delve into how to use AutoML capabilities in Oracle Machine Learning for Python to streamline your machine learning projects and improve model performance.\nICYMI: Learn How to Invoke Your Python Functions from SQL with OML4Py\tOracle Machine Learning for Python allows users to run user-defined Python functions in Python engines spawned and managed by the database environment - from Python (of course), but also SQL. The SQL interface makes it easy for application and dashboard developers to deploy Python-based solutions. This feature, called Embedded Python Execution (EPE), also allows you to take advantage of system-provided data-parallelism for those embarrassingly parallel use cases. Supporting OML4Py EPE is the ability to store user-defined Python functions in the database script repository as well as Python objects in the database datastore \u2013 no more separate flat files to manage, back up, and secure. Join us to learn about this powerful capability.\nLeveraging ONNX and Hugging Face for AI Vector Search\tOracle Database 23ai introduces Oracle AI Vector Search, revolutionizing semantic similarity search by generating vectors using transformer models and managing them at scale within the database. This empowers users to find relevant information based on meaning and context, eliminating the pain point of transferring data to separate vector databases, thus reducing complexity and operational overhead. Starting with OML4Py 2.0, the client converts pre-trained transformer models from Hugging Face to the Open Neural Network Exchange (ONNX) format for AI Vector Search. Hugging Face is an open-source Python library for deep learning, offering thousands of pre-trained models for natural language processing (NLP), computer vision, audio, and more. ONNX is an open format for representing various machine learning models, including transformers, classification, regression, and other types. The OML4Py client streamlines the path to embedding generation in the database by downloading pre-trained models from the Hugging Face repository, augmenting them with pre- and post-processing steps, converting the augmented model to ONNX format, and loading it into an in-database model or exporting it to a file. Once loaded in ONNX format, you can seamlessly use the in-database ONNX Runtime to generate vector embeddings for AI Vector Search. Combined with the OML4Py client, AI Vector Search offers a comprehensive solution for leveraging vector embeddings in Oracle Database, enhancing text data analysis, search capabilities, and machine learning applications. In this blog, we outline the steps to install the OML4Py client, provide examples of converting pre-configured and customized models to ONNX format, and load the models into Oracle Database. This applies to Oracle Database on-premises or on the cloud and Autonomous Database.\nStoring Named R and Python Functions in the Oracle Machine Learning (OML) Script Repository\tOracle Machine Learning for R (OML4R) and Oracle Machine Learning for Python (OML4Py) each provide a script repository to store user defined R or Python functions in an Oracle Database or Autonomous Database instance. OML4R provides functions ore.scriptCreate and ore.scriptLoad, and OML4Py provides functions  oml.script.create and oml.script.load. These functions save R and Python UDFs into the script repository and reload the function back into the current local session. Functions are stored in the script repository specified as global or private, and given user-defined access privileges. If the global parameter is set to FALSE (the default) specifies that the script is not public and is visible only to the owner or to users to whom the owner has granted read privilege access; TRUE specifies that the script is public and therefore visible to all users. Functions in the script repository can be listed based on name, pattern, and type. (List options include user created scripts, global scripts, scripts granted to the user, scripts the user has granted access to, and all scripts). Scripts in the repository can also be dropped based on access level.\nAnnouncing custom third-party Python and R packages for use on Autonomous Database\tWe are pleased to announce the ability to use custom third-party packages on Oracle Autonomous Database. As we continue to expand Autonomous Database as a platform for data science and machine learning, users of Oracle Machine Learning (OML) Notebooks and OML4Py and OML4R embedded execution can now use custom-installed third-party packages through conda environments. With this feature, users can leverage the broader Python and R package ecosystems to augment database functionality on Oracle Autonomous Database in OML Notebooks. Run Python and R user-defined functions with third-party package functionality in engines spawned and managed by the database environment, while optionally taking advantage of OML4Py and OML4R system-provided data parallelism and task parallelism. This enables, for example, using native Python and R machine learning algorithms to build models, store them in the database, and make predictions from SQL and REST APIs. Creating and using conda environments. OML Notebooks introduces a conda interpreter that enables a user with administrator privileges to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as OML4R and OML4Py embedded execution invocations. Python, R, and REST interfaces for embedded execution are currently available, with the SQL embedded execution API coming soon. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party Python and R packages. With conda environments, you can install and update packages and their dependencies, as well as switch between environments to use project-specific packages. Users with administrator privileges create conda environments, install packages, and upload conda environments to Object Storage. These environments can then be accessed by non-administrator users and loaded into their own notebooks for use.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Produce vector embeddings from text documents","","We encode the `Content` column of our documents into embeddings using the `all-MiniLM-L12-v2` transformer to support semantic similarity search. ","","Vector embeddings are numerical vectors in a multi-dimensional space, placing semantically similar items closer together. ","In semantic search, these embeddings capture the meaning ","and context of queries and data, enabling more accurate retrieval than traditional methods. ","","Both the data and queries are converted into vectors prior to performing a similarity search in the database. ","After encoding, we print the shape of the first embedding and display the first 50 elements of the vector to get an idea of its structure. ","","Then, we check if a GPU is available, and use it to speed up the computations. "],"enabled":true,"result":{"startTime":1736976472158,"interpreter":"md.gpu","endTime":1736976472635,"results":[{"message":"<h2 id=\"produce-vector-embeddings-from-text-documents\">Produce vector embeddings from text documents<\/h2>\n<p>We encode the <code>Content<\/code> column of our documents into embeddings using the <code>all-MiniLM-L12-v2<\/code> transformer to support semantic similarity search.<\/p>\n<p>Vector embeddings are numerical vectors in a multi-dimensional space, placing semantically similar items closer together.\nIn semantic search, these embeddings capture the meaning\nand context of queries and data, enabling more accurate retrieval than traditional methods.<\/p>\n<p>Both the data and queries are converted into vectors prior to performing a similarity search in the database.\nAfter encoding, we print the shape of the first embedding and display the first 50 elements of the vector to get an idea of its structure.<\/p>\n<p>Then, we check if a GPU is available, and use it to speed up the computations.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Encode the text document corpus","message":["%python","","# Extract titles and content","document_title = document_df['TITLE'].tolist()","document = document_df['CONTENT'].tolist()","","# Use the model's encode method on GPU if available","document_embeddings = transformer.encode(document, ","                                 convert_to_tensor=True, ","                                 device='cuda' if torch.cuda.is_available() else 'cpu')","","# Show the first few dimension embeddings","print(\"First document embedding shape:\", document_embeddings[0].shape)","print(\"First document embedding (truncated):\\n\", document_embeddings[0][:50])"," "],"enabled":true,"result":{"startTime":1736976473117,"interpreter":"python.gpu","endTime":1736976473789,"results":[{"message":"First document embedding shape: torch.Size([384])\nFirst document embedding (truncated):\n tensor([-0.0269, -0.0582, -0.0508, -0.0401, -0.0839, -0.0173, -0.0207,  0.0481,\n         0.0600,  0.0278,  0.0344, -0.0272, -0.0253,  0.0169, -0.1096, -0.0070,\n        -0.0455,  0.1265,  0.0286, -0.0452,  0.0021, -0.0403, -0.0033, -0.0148,\n         0.0113,  0.0615,  0.0253, -0.0342, -0.0259, -0.0181,  0.0617,  0.0608,\n         0.0395,  0.0568, -0.0633,  0.0581, -0.0542, -0.0840, -0.0171,  0.0890,\n         0.0160, -0.1129, -0.0477,  0.0476, -0.0621, -0.0804, -0.0037,  0.0144,\n        -0.0400,  0.0355], device='cuda:0')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define and encode search queries and compute similarities","message":["%md","","## Produce embeddings for search queries ","","Next, we define a set of questions related to Oracle Machine learning blogs and encode them into embeddings, just as we did with the text document corpus. ","The output displays the shape and a truncated view of the first question's embedding, demonstrating that these queries are converted to a format ","ready for comparison with the corpus embeddings."],"enabled":true,"result":{"startTime":1736976474270,"interpreter":"md.gpu","endTime":1736976474739,"results":[{"message":"<h2 id=\"produce-embeddings-for-search-queries\">Produce embeddings for search queries<\/h2>\n<p>Next, we define a set of questions related to Oracle Machine learning blogs and encode them into embeddings, just as we did with the text document corpus.\nThe output displays the shape and a truncated view of the first question's embedding, demonstrating that these queries are converted to a format\nready for comparison with the corpus embeddings.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generate embeddings for questions","message":["%python","","questions = [","    \"How can I get started with Oracle Machine Learning for Python?\",","    \"What is AI Vector Search in Oracle Database?\",","    \"How do I deploy machine learning models in Oracle Database?\",","    \"What is embedded Python execution?\",","    \"How can I store and retrieve vector embeddings in Oracle Database?\"","]","","# Encode queries and compute similarities","question_embeddings = transformer.encode(questions, convert_to_tensor=True, device='cuda' if torch.cuda.is_available() else 'cpu')","","# Display the shape and the first 50 values of the first query embedding","print(f\"Shape of the first embedding: {question_embeddings[0].shape}\")","print(f\"First first question embedding: {question_embeddings[0].cpu().numpy()[:50]}\")"],"enabled":true,"result":{"startTime":1736976475219,"interpreter":"python.gpu","endTime":1736976475721,"results":[{"message":"Shape of the first embedding: torch.Size([384])\nFirst first question embedding: [-0.00356166 -0.05928049 -0.03184474 -0.02883271 -0.07278952  0.00629583\n -0.07346774  0.04524846 -0.04019503  0.05069361  0.03364429 -0.01588399\n -0.00663405 -0.03393978 -0.09670004 -0.0014147  -0.00961969  0.04968738\n  0.09694596 -0.07178237 -0.03168638  0.00643608 -0.00852807 -0.0410638\n -0.02738184  0.07471086  0.10773238  0.0527027  -0.00356048  0.00907027\n  0.0916478  -0.01331964  0.02236893  0.04496038 -0.09692895  0.06584504\n -0.04154566 -0.08506645 -0.02326242  0.07355611  0.03774089 -0.10345948\n -0.04736552  0.02915335 -0.02160331 -0.02685373 -0.00624387  0.06318692\n  0.01756678 -0.01528064]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"UDF to compare CPU and GPU performance - speedup factor","message":["%python","","# Number of times to replicate the corpus","n = 1000","","# Replicate the corpus n times","replicated_document_df = pd.concat([document_df] * n, ignore_index=True)","","# Convert the \"Content\" column of the replicated corpus DataFrame to a list of texts","document = replicated_document_df['CONTENT'].tolist()","","def benchmark(device):","    # Encode corpus only","    start_time = time.time()","    document_embeddings = transformer.encode(document, convert_to_tensor=True, device=device)","    encode_document_time = time.time() - start_time","    ","    # Return only corpus encoding time","    return encode_document_time","","def compare_cpu_gpu_benchmarks():","    results = {}","    ","    # CPU benchmarks","    for threads in [1, 4, 8, 12]:","        torch.set_num_threads(threads)","        results[f'CPU ({threads} threads)'] = benchmark('cpu')","    ","    # GPU benchmark","    if torch.cuda.is_available():","        results['GPU'] = benchmark('cuda')","    else:","        print(\"CUDA is not available. Skipping GPU benchmark.\")","        return","    ","    # Print CPU vs GPU Comparison","    print(\"\\nCPU vs GPU Comparison (GPU is X times faster):\")","    print(f\"{'CPU Configuration':<20} {'Encode Corpus':^15}\")","    print(\"-\" * 40)","    ","    gpu_time = results['GPU']","    ","    for cpu_config, cpu_time in results.items():","        if cpu_config != 'GPU':","            time_faster = cpu_time / gpu_time if gpu_time > 0 else float('inf')","            print(f\"{cpu_config:<20} {time_faster:^15.1f}\")","","compare_cpu_gpu_benchmarks()"],"enabled":true,"result":{"startTime":1736976476206,"interpreter":"python.gpu","endTime":1736977096465,"results":[{"message":"\nCPU vs GPU Comparison (GPU is X times faster):\nCPU Configuration     Encode Corpus \n----------------------------------------\nCPU (1 threads)           52.5      \nCPU (4 threads)           15.2      \nCPU (8 threads)            8.4      \nCPU (12 threads)           6.2      \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Perform similarity search in the database","","Create a database table and insert each embedding into the VECTOR column along with its document title. ","This maintains a connection between the semantic representation and the source material. ","To optimize search performance, we create a vector index on the VECTOR column, which enables processing large volume and high-dimensional vector data ","efficiently. It enables faster approximate nearest neighbor searches.","","We use the vector operator VECTOR_DISTANCE to compare vector embeddings. These operators compute the distance, or semantic closeness, ","between vectors. This allows you to retrieve the most relevant results, rather than relying on keyword matches."],"enabled":true,"result":{"startTime":1736977097001,"interpreter":"md.gpu","endTime":1736977097465,"results":[{"message":"<h2 id=\"perform-similarity-search-in-the-database\">Perform similarity search in the database<\/h2>\n<p>Create a database table and insert each embedding into the VECTOR column along with its document title.\nThis maintains a connection between the semantic representation and the source material.\nTo optimize search performance, we create a vector index on the VECTOR column, which enables processing large volume and high-dimensional vector data\nefficiently. It enables faster approximate nearest neighbor searches.<\/p>\n<p>We use the vector operator VECTOR_DISTANCE to compare vector embeddings. These operators compute the distance, or semantic closeness,\nbetween vectors. This allows you to retrieve the most relevant results, rather than relying on keyword matches.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table for vector embeddings","message":["%script","","BEGIN","  EXECUTE IMMEDIATE 'DROP TABLE DOCUMENT_EMBEDDINGS_TAB';","  EXCEPTION WHEN OTHERS THEN NULL;  ","END;","/","CREATE TABLE DOCUMENT_EMBEDDINGS_TAB(","   id               NUMBER PRIMARY KEY,","   document_title   VARCHAR(1000), ","   document_embeddings CLOB)"],"enabled":true,"result":{"startTime":1736977097946,"interpreter":"script.gpu","endTime":1736977099462,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable DOCUMENT_EMBEDDINGS_TAB created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use python-oracledb to populate vector table","","The `python-oracledb` package enables interacting with database data from Python. ","In OML Notebooks, we have immediate access to the database connection using `oml.cursor`. ","Insert the document titles and their embeddings into a database table named `DOCUMENT_EMBEDDINGS_TAB`. "],"enabled":true,"result":{"startTime":1736977099952,"interpreter":"md.gpu","endTime":1736977100431,"results":[{"message":"<h2 id=\"use-python-oracledb-to-populate-vector-table\">Use python-oracledb to populate vector table<\/h2>\n<p>The <code>python-oracledb<\/code> package enables interacting with database data from Python.\nIn OML Notebooks, we have immediate access to the database connection using <code>oml.cursor<\/code>.\nInsert the document titles and their embeddings into a database table named <code>DOCUMENT_EMBEDDINGS_TAB<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Insert embeddings into database table","message":["%python","","# Create a database connection","cr = oml.cursor()","","# Insert document titles and embeddings into the database","for i, (document_title, document_embeddings) in enumerate(zip(document_title, document_embeddings)):","    ","    # Convert the embeddings to properly formatted strings for SQL insertion","    embedding_list = '[' + ','.join(map(str, document_embeddings.tolist())) + ']'","    ","    # Check if the ID already exists","    cr.execute(\"SELECT COUNT(*) FROM document_embeddings_tab WHERE id = :id\", {'id': i+1})","    if cr.fetchone()[0] == 0:  # If the ID does not exist, insert the new row","        sql = \"\"\"","            INSERT INTO document_embeddings_tab (id, document_title, document_embeddings)","            VALUES (:id, :document_title, :document_embeddings)","        \"\"\"","        cr.execute(sql, {","            'id': i+1, ","            'document_title': document_title,","            'document_embeddings': embedding_list, ","        })","    else:","        print(f\"ID {i+1} already exists. Skipping insertion.\")","   ","# Commit the transaction","cr.connection.commit()","","# Close the cursor","cr.close()"],"enabled":true,"result":{"startTime":1736977100917,"interpreter":"python.gpu","endTime":1736977101658,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Convert embeddings to VECTOR column","message":["%script","","BEGIN","  EXECUTE IMMEDIATE 'DROP TABLE DOCUMENT_EMBEDDINGS_TAB_1';","  EXCEPTION WHEN OTHERS THEN NULL;  ","END;","/","CREATE TABLE DOCUMENT_EMBEDDINGS_TAB_1 AS","  SELECT id, document_title, TO_VECTOR(document_embeddings) AS embedding","  FROM DOCUMENT_EMBEDDINGS_TAB;"],"enabled":true,"result":{"startTime":1736977102146,"interpreter":"script.gpu","endTime":1736977103664,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable DOCUMENT_EMBEDDINGS_TAB_1 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a vector index for efficient similarity search","message":["%script","","-- Oracle Machine Learning's in-database KMeans algorithm is used to identify the partitions","","BEGIN","  EXECUTE IMMEDIATE 'DROP INDEX vector_idx';","  EXCEPTION WHEN OTHERS THEN NULL;  ","END;","/","CREATE VECTOR INDEX vector_idx ON","  DOCUMENT_EMBEDDINGS_TAB_1 svd_output(embedding)","  ORGANIZATION NEIGHBOR PARTITIONS","  DISTANCE COSINE WITH TARGET ACCURACY 95;"],"enabled":true,"result":{"startTime":1736977104143,"interpreter":"script.gpu","endTime":1736977107947,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nVector INDEX created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the table containing the embeddings","message":["%sql","","SELECT * FROM DOCUMENT_EMBEDDINGS_TAB_1"],"enabled":true,"result":{"startTime":1736977108430,"interpreter":"sql.gpu","endTime":1736977108960,"results":[{"message":"ID\tDOCUMENT_TITLE\tEMBEDDING\n1\tGetting started with Oracle Machine Learning for 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Oracle Machine Learning for 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Lab: Introduction to Oracle Machine Learning for 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Leveraging ONNX and Hugging Face for AI Vector 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Oracle Machine Learning Models with OML4Py and Deploying in Oracle Analytics 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to Perform In-Database Text Analytics Using a Pre-Built ML 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Intelligence: Storing and Retrieving Embeddings in a Feature 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Machine Learning with Oracle Machine Learning for 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Learn How to Invoke Your Python Functions from SQL with 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ONNX and Hugging Face for AI Vector 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Named R and Python Functions in the Oracle Machine Learning (OML) Script 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Similarity search using VECTOR_DISTANCE","","Perform semantic similarity search by calculating the distance between two vectors, in this case, using cosine similarity to identify the most ","relevant documents for each question. ","This allows us to efficiently retrieve and rank content based on the distance measure between the queries and the document embeddings - indicating their similarity. ","","Note, the ALL_MINILM_L12_V2 model was preloaded into the database using `DBMS_VECTOR.LOAD_ONNX_MODEL`."],"enabled":true,"result":{"startTime":1736977109453,"interpreter":"md.gpu","endTime":1736977109915,"results":[{"message":"<h2 id=\"similarity-search-using-vector_distance\">Similarity search using VECTOR_DISTANCE<\/h2>\n<p>Perform semantic similarity search by calculating the distance between two vectors, in this case, using cosine similarity to identify the most\nrelevant documents for each question.\nThis allows us to efficiently retrieve and rank content based on the distance measure between the queries and the document embeddings - indicating their similarity.<\/p>\n<p>Note, the ALL_MINILM_L12_V2 model was preloaded into the database using <code>DBMS_VECTOR.LOAD_ONNX_MODEL<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","    question VARCHAR2(1000) := 'How can I get started with Oracle Machine Learning for Python';","BEGIN","    dbms_output.put_line('Question: ' || question);","    dbms_output.put_line('------------------------------------------------------------------------------');","    dbms_output.put_line('Document Title                                                        Distance');","    dbms_output.put_line('------------------------------------------------------------------------------');","","    FOR i IN (","        SELECT document_title, VECTOR_DISTANCE(embedding, TO_VECTOR(VECTOR_EMBEDDING(ALL_MINILM_L12_V2 USING question AS data)), COSINE) AS distance","        FROM DOCUMENT_EMBEDDINGS_TAB_1","        ORDER BY distance","        FETCH FIRST 3 ROWS ONLY","    ) LOOP","        -- Format the output with fixed-width columns for clarity","        dbms_output.put_line(","            RPAD(i.document_title, 65) || ' ' || ","            TO_CHAR(ROUND(i.distance, 2), '999.99')","        );","    END LOOP;","END;","/"],"enabled":true,"result":{"startTime":1736977110399,"interpreter":"script.gpu","endTime":1736977111452,"results":[{"message":"Question: How can I get started with Oracle Machine Learning for Python\n------------------------------------------------------------------------------\nDocument Title                                                        Distance\n------------------------------------------------------------------------------\nGetting started with Oracle Machine Learning for Python               .26\nIntroducing Oracle Machine Learning for Python                        .28\nHands-On Lab: Introduction to Oracle Machine Learning for Python      .31\n\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736977111933,"interpreter":"md.gpu","endTime":1736977118060,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML Import Wiki ESA Model","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1738084507089,"interpreter":"md.low","endTime":1738084507653,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Import Pre-Built Wiki ESA Model","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base. ESA improves text document categorization by calculating semantic relatedness (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","This pre-built ESA model is based on millions of Wikipedia articles reduced to about 161,000 topics. The model was built under database version 19c using the July 1, 2021 <a href=\"https://dumps.wikimedia.org/enwiki/\" target=\"_blank\">Wikipedia dump.<\/a>","","The Oracle Machine Learning for SQL function`ESA`, Oracle Machine Learning for Python function  `oml.esa` and Oracle Machine Learning for R function `ore.odmESA` extract text-based features from a corpus of documents and performs document similarity comparisons. In this notebook, we'll import the wiki ESA model to Autonomous Database for use with the OML template notebook examples:","","- OML4SQL Feature Extraction ESA Wiki Model","- OML4Py Feature Extraction ESA Wiki Model","- OML4R Feature Extraction ESA Wiki Model","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1738084508104,"interpreter":"md.low","endTime":1738084508571,"results":[{"message":"<h1 id=\"import-pre-built-wiki-esa-model\">Import Pre-Built Wiki ESA Model<\/h1>\n<p>Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base. ESA improves text document categorization by calculating semantic relatedness (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>This pre-built ESA model is based on millions of Wikipedia articles reduced to about 161,000 topics. The model was built under database version 19c using the July 1, 2021 <a href=\"https://dumps.wikimedia.org/enwiki/\" target=\"_blank\">Wikipedia dump.<\/a><\/p>\n<p>The Oracle Machine Learning for SQL function<code>ESA<\/code>, Oracle Machine Learning for Python function  <code>oml.esa<\/code> and Oracle Machine Learning for R function <code>ore.odmESA<\/code> extract text-based features from a corpus of documents and performs document similarity comparisons. In this notebook, we'll import the wiki ESA model to Autonomous Database for use with the OML template notebook examples:<\/p>\n<ul>\n<li>OML4SQL Feature Extraction ESA Wiki Model<\/li>\n<li>OML4Py Feature Extraction ESA Wiki Model<\/li>\n<li>OML4R Feature Extraction ESA Wiki Model<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Download the model zip file","message":["%md","_________________________________________________________________________________________________________________________________________________________________________________","The ESA Wiki Model is a free, open source model available for download from Oracle's Open Source Software (OSS) portal. The model is stored in a dump file, *wiki_model19.0.0.0.0.dmp*, contained in the zip file *esa_en_2.0.zip*.","","- **Option 1**: Use the model dump file already saved in the public Object Storage bucket provided in this example. ","","- **Option 2**: Download and unzip the file, and upload the model dump file to an Object Storage Bucket. The zip file is available for download here:","","    #### [Wiki ESA model 2.0](https://oss.oracle.com/machine-learning/esa_en_2.0.zip)"],"enabled":true,"result":{"startTime":1738084509033,"interpreter":"md.low","endTime":1738084509465,"results":[{"message":"<hr />\n<p>The ESA Wiki Model is a free, open source model available for download from Oracle's Open Source Software (OSS) portal. The model is stored in a dump file, <em>wiki_model19.0.0.0.0.dmp<\/em>, contained in the zip file <em>esa_en_2.0.zip<\/em>.<\/p>\n<ul>\n<li>\n<p><strong>Option 1<\/strong>: Use the model dump file already saved in the public Object Storage bucket provided in this example.<\/p>\n<\/li>\n<li>\n<p><strong>Option 2<\/strong>: Download and unzip the file, and upload the model dump file to an Object Storage Bucket. The zip file is available for download here:<\/p>\n<h4 id=\"wiki-esa-model-20\"><a href=\"https://oss.oracle.com/machine-learning/esa_en_2.0.zip\">Wiki ESA model 2.0<\/a><\/h4>\n<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Prerequisites "],"enabled":true,"result":{"startTime":1738084509920,"interpreter":"md.low","endTime":1738084510337,"results":[{"message":"<h2 id=\"prerequisites\">Prerequisites<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Required Grants","message":["","%md","_______________________________________________________________________________________________________________________________________________________________________________","Copy the model dump file from the Object Storage Bucket to the Autonomous Database data pump directory, and then import the model to the database. ","","The Database *ADMIN* user must grant the OML user read and write access to the data pump directory, and the OML user also requires sevaral text policy grants to work with the model. ","","In this example, the OML user is *OMLUSER*.","","Make sure the Database *ADMIN* user grants these to the OML user:","","    GRANT read, write ON DIRECTORY DATA_PUMP_DIR TO OMLUSER;","    GRANT EXECUTE ON CTXSYS.CTX_DDL TO OMLUSER;","    GRANT EXECUTE ON CTXSYS.CTX_OUTPUT TO OMLUSER;","    GRANT EXECUTE ON CTXSYS.CTX_THES TO OMLUSER;","    GRANT EXECUTE ON CTXSYS.CTX_ULEXER TO OMLUSER;"],"enabled":true,"result":{"startTime":1738084510775,"interpreter":"md.low","endTime":1738084511199,"results":[{"message":"<hr />\n<p>Copy the model dump file from the Object Storage Bucket to the Autonomous Database data pump directory, and then import the model to the database.<\/p>\n<p>The Database <em>ADMIN<\/em> user must grant the OML user read and write access to the data pump directory, and the OML user also requires sevaral text policy grants to work with the model.<\/p>\n<p>In this example, the OML user is <em>OMLUSER<\/em>.<\/p>\n<p>Make sure the Database <em>ADMIN<\/em> user grants these to the OML user:<\/p>\n<pre><code>GRANT read, write ON DIRECTORY DATA_PUMP_DIR TO OMLUSER;\nGRANT EXECUTE ON CTXSYS.CTX_DDL TO OMLUSER;\nGRANT EXECUTE ON CTXSYS.CTX_OUTPUT TO OMLUSER;\nGRANT EXECUTE ON CTXSYS.CTX_THES TO OMLUSER;\nGRANT EXECUTE ON CTXSYS.CTX_ULEXER TO OMLUSER;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Object Storage","message":["%md","_________________________________________________________________________________________________________________________________________________________________________________","Oracle Autonomous Database provides the `DBMS_CLOUD` package. This enables the transfer of objects between Autonomous Database and Object Storage. Object Storage stores data as objects within buckets, or containers, and are identified with an URI having the following format:","","    https://objectstorage.region.oraclecloud.com/v1/tenantname/bucketname/objectname","","This example is using a public bucket, but access to the objects stored within buckets is protected for non-public buckets. To access objects in a non-public bucket, one must to provide identification information stored into a credential object. Refer to the ADB User's Guide more information on the `DBMS_CLOUD` package and credentials.","#### [Oracle Autonomous Data Warehouse Cloud User's Guide](https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/user/load-data-cloud.html#GUID-07900054-CB65-490A-AF3C-39EF45505802)"],"enabled":true,"result":{"startTime":1738084511655,"interpreter":"md.low","endTime":1738084512076,"results":[{"message":"<hr />\n<p>Oracle Autonomous Database provides the <code>DBMS_CLOUD<\/code> package. This enables the transfer of objects between Autonomous Database and Object Storage. Object Storage stores data as objects within buckets, or containers, and are identified with an URI having the following format:<\/p>\n<pre><code>https://objectstorage.region.oraclecloud.com/v1/tenantname/bucketname/objectname\n<\/code><\/pre>\n<p>This example is using a public bucket, but access to the objects stored within buckets is protected for non-public buckets. To access objects in a non-public bucket, one must to provide identification information stored into a credential object. Refer to the ADB User's Guide more information on the <code>DBMS_CLOUD<\/code> package and credentials.<\/p>\n<h4 id=\"oracle-autonomous-data-warehouse-cloud-users-guide\"><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/user/load-data-cloud.html#GUID-07900054-CB65-490A-AF3C-39EF45505802\">Oracle Autonomous Data Warehouse Cloud User's Guide<\/a><\/h4>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Import the model"],"enabled":true,"result":{"startTime":1738084512525,"interpreter":"md.low","endTime":1738084512956,"results":[{"message":"<h2 id=\"import-the-model\">Import the model<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Copy the model dump file from Object Storage to the Autonomous Database data pump directory","message":["%md","","Use the `DBMS_CLOUD.GET_OBJECT` procedure to copy the model dump file to the data pump directory. In this example, the *credential_name* parameter is set to NULL because the bucket is public. For non-public buckets, a credential name must be provided, and can be created using the `CREATE_CREDENTIAL` procedure. The *object_uri* parameter provides the model dump file's URI in the Object Storage bucket, and the *directory_name* parameter indicates we want to copy the object to the data pump directory.","","For more information on the `CREATE_CREDENTIAL` procedure, refer to the Autonomous Database User's Guide:","","#### [CREATE_CREDENTIAL Procedure](https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/dbms-cloud-subprograms.html#GUID-742FC365-AA09-48A8-922C-1987795CF36A)"],"enabled":true,"result":{"startTime":1738084513385,"interpreter":"md.low","endTime":1738084513858,"results":[{"message":"<p>Use the <code>DBMS_CLOUD.GET_OBJECT<\/code> procedure to copy the model dump file to the data pump directory. In this example, the <em>credential_name<\/em> parameter is set to NULL because the bucket is public. For non-public buckets, a credential name must be provided, and can be created using the <code>CREATE_CREDENTIAL<\/code> procedure. The <em>object_uri<\/em> parameter provides the model dump file's URI in the Object Storage bucket, and the <em>directory_name<\/em> parameter indicates we want to copy the object to the data pump directory.<\/p>\n<p>For more information on the <code>CREATE_CREDENTIAL<\/code> procedure, refer to the Autonomous Database User's Guide:<\/p>\n<h4 id=\"create_credential-procedure\"><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/dbms-cloud-subprograms.html#GUID-742FC365-AA09-48A8-922C-1987795CF36A\">CREATE_CREDENTIAL Procedure<\/a><\/h4>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","BEGIN","   DBMS_CLOUD.GET_OBJECT(","   credential_name => NULL,","   object_uri => 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/wiki_model19.0.0.0.0.dmp',","   directory_name => 'DATA_PUMP_DIR');","END;","/"],"enabled":true,"result":{"startTime":1738084514278,"interpreter":"script.low","endTime":1738084546286,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Verify the model dump file exists in the data pump directory","message":["%sql","","SELECT OBJECT_NAME, BYTES, CREATED FROM DBMS_CLOUD.LIST_FILES('DATA_PUMP_DIR') where OBJECT_NAME='wiki_model19.0.0.0.0.dmp';"],"enabled":true,"result":{"startTime":1738084546755,"interpreter":"sql.low","endTime":1738084547516,"results":[{"message":"OBJECT_NAME\tBYTES\tCREATED\nwiki_model19.0.0.0.0.dmp\t904327168\t2023-11-01 18:08:48.0 +0:00\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import the model","message":["%md","","The `DBMS_DATA_MINING` package provides the `DBMS_DATA_MINING.IMPORT_MODEL` function to import a model to the database. ","","We import the model, first dropping it if it already exists.  For the model import, pass the model dump file name to the *filename* parameter. Provide the name of the directory containing the file, the data pump directory, to the *directory* parameter. Remap the tablespace used in the development database, DMUSER, to the current user's tablespace, OMLUSER."],"enabled":true,"result":{"startTime":1738084547999,"interpreter":"md.low","endTime":1738084548541,"results":[{"message":"<p>The <code>DBMS_DATA_MINING<\/code> package provides the <code>DBMS_DATA_MINING.IMPORT_MODEL<\/code> function to import a model to the database.<\/p>\n<p>We import the model, first dropping it if it already exists.  For the model import, pass the model dump file name to the <em>filename<\/em> parameter. Provide the name of the directory containing the file, the data pump directory, to the <em>directory<\/em> parameter. Remap the tablespace used in the development database, DMUSER, to the current user's tablespace, OMLUSER.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","BEGIN","    DBMS_DATA_MINING.DROP_MODEL('WIKI_MODEL', TRUE);","END;","","-- replace OMLUSER with your OML username in schema_remap","","BEGIN","    DBMS_DATA_MINING.IMPORT_MODEL(","        filename => 'wiki_model19.0.0.0.0.dmp',","        directory =>'DATA_PUMP_DIR',","        schema_remap => 'DMUSER:OMLUSER');","END; "],"enabled":true,"result":{"startTime":1738084549059,"interpreter":"script.low","endTime":1738084636479,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Confirm the model is imported correctly","message":["%sql","","select MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE from USER_MINING_MODELS where MODEL_NAME='WIKI_MODEL';"],"enabled":true,"result":{"startTime":1738084637035,"interpreter":"sql.low","endTime":1738084637477,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\nWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\t2025-01-28 17:17:12\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","__________________________________________________________________________________________________________________________________________________________________________","Explore the imported model `WIKI_MODEL` by looking at the view `DM$VAWIKI_MODEL`. For Feature Extraction, this view contains model attribute coefficients per feature. For Classification, this view contains model attribute coefficients per target class."],"enabled":true,"result":{"startTime":1738084637935,"interpreter":"md.low","endTime":1738084638399,"results":[{"message":"<hr />\n<p>Explore the imported model <code>WIKI_MODEL<\/code> by looking at the view <code>DM$VAWIKI_MODEL<\/code>. For Feature Extraction, this view contains model attribute coefficients per feature. For Classification, this view contains model attribute coefficients per target class.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","select * from DM$VAWIKI_MODEL","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1738084638859,"interpreter":"sql.low","endTime":1738084651125,"results":[{"message":"PARTITION_NAME\tFEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n\t'Adud al-Dawla\tTEXT\tABBAD\t\t0.019127417418656693\n\t'Adud al-Dawla\tTEXT\tABBASID\t\t0.024915162555824922\n\t'Adud al-Dawla\tTEXT\tABU\t\t0.059714023220151985\n\t'Adud al-Dawla\tTEXT\tADUD\t\t0.565944673491387\n\t'Adud al-Dawla\tTEXT\tALABAMA\t\t0.22831900492863966\n\t'Adud al-Dawla\tTEXT\tARDASHIR\t\t0.01119409039571321\n\t'Adud al-Dawla\tTEXT\tAYYAD\t\t0.024976028211566435\n\t'Adud al-Dawla\tTEXT\tBADR\t\t0.00986964445080499\n\t'Adud al-Dawla\tTEXT\tBAGHDAD\t\t0.0459066966195087\n\t'Adud al-Dawla\tTEXT\tBAHA\t\t0.010458900627604538\n\t'Adud al-Dawla\tTEXT\tBAHRAM\t\t0.01612360967035841\n\t'Adud al-Dawla\tTEXT\tBAKR\t\t0.01343652703559916\n\t'Adud al-Dawla\tTEXT\tBALOCH\t\t0.0104772299872558\n\t'Adud al-Dawla\tTEXT\tBAM\t\t0.008878294407706207\n\t'Adud al-Dawla\tTEXT\tBANI\t\t0.009756462680585037\n\t'Adud al-Dawla\tTEXT\tBAQILLANI\t\t0.01446461123512926\n\t'Adud al-Dawla\tTEXT\tBARDAS\t\t0.012836149935899568\n\t'Adud al-Dawla\tTEXT\tBEDOUIN\t\t0.008722205675010091\n\t'Adud al-Dawla\tTEXT\tBUYID\t\t0.08465945748504172\n\t'Adud al-Dawla\tTEXT\tBUYIDS\t\t0.01945871493656864\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Set up model text policy","message":["%md","______________________________________________________________________________________________________________________________________________________________________________","All queries against `WIKI_MODEL` score textual data. Create a text policy identical to one used in building the ESA model from Wikipedia. These are the settings used for building the model, and scoring will be incorrect if the policy does not match the one used when the model was created."],"enabled":true,"result":{"startTime":1738084651575,"interpreter":"md.low","endTime":1738084652018,"results":[{"message":"<hr />\n<p>All queries against <code>WIKI_MODEL<\/code> score textual data. Create a text policy identical to one used in building the ESA model from Wikipedia. These are the settings used for building the model, and scoring will be incorrect if the policy does not match the one used when the model was created.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop the text policies if they exist","message":["%script","","BEGIN","    ctx_ddl.DROP_POLICY('wiki_txtpol');","    ctx_ddl.DROP_PREFERENCE( 'wiki_lexer');","  EXCEPTION WHEN OTHERS THEN NULL;","END;"],"enabled":true,"result":{"startTime":1738084652450,"interpreter":"script.low","endTime":1738084653191,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create text policies","message":["%script","","exec ctx_ddl.CREATE_PREFERENCE('wiki_lexer', 'BASIC_LEXER'); ","exec ctx_ddl.SET_ATTRIBUTE('wiki_lexer', 'INDEX_STEMS', 'ENGLISH');","exec ctx_ddl.CREATE_POLICY(policy_name => 'wiki_txtpol', lexer => 'wiki_lexer'); "],"enabled":true,"result":{"startTime":1738084653612,"interpreter":"script.low","endTime":1738084656789,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","You are now ready to run scoring queries for the `WIKI_MODEL`. Refer to the template notebook examples mentioned at the beginning of this notebook for examples."],"enabled":true,"result":{"startTime":1738084657210,"interpreter":"md.low","endTime":1738084657659,"results":[{"message":"<p>You are now ready to run scoring queries for the <code>WIKI_MODEL<\/code>. Refer to the template notebook examples mentioned at the beginning of this notebook for examples.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1738084658079,"interpreter":"md.low","endTime":1738084658520,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML Run-me-first","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1737138360559,"interpreter":"md.low","endTime":1737138361104,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Run-me-first: Data preparation and creation for examples","","In this notebook, we will load data used in other notebooks that require the `CUSTOMER_INSURANCE_LTV` table, and will also prepare the dataset used by the `Data Preparation` notebooks, in particular those for data cleaning and feature selection.","","The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.","","We will take the original table and will tweak the data, with some original values being replaced by `NULL`, and adding `duplicated rows` artificially to generate a resulting table we will call `CUSTOMER_INSURANCE_LTV_NOISE`. ","","The source of the data is a CSV file stored in <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\"> Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the `DBMS_CLOUD.CREATE_EXTERNAL_TABLE` function among others.","","###### `IMPORTANT` Please run this notebook before running any of the Data Preparation notebooks.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737138361570,"interpreter":"md.low","endTime":1737138362088,"results":[{"message":"<h1 id=\"oml-run-me-first-data-preparation-and-creation-for-examples\">OML Run-me-first: Data preparation and creation for examples<\/h1>\n<p>In this notebook, we will load data used in other notebooks that require the <code>CUSTOMER_INSURANCE_LTV<\/code> table, and will also prepare the dataset used by the <code>Data Preparation<\/code> notebooks, in particular those for data cleaning and feature selection.<\/p>\n<p>The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We will take the original table and will tweak the data, with some original values being replaced by <code>NULL<\/code>, and adding <code>duplicated rows<\/code> artificially to generate a resulting table we will call <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code>.<\/p>\n<p>The source of the data is a CSV file stored in <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\"> Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the <code>DBMS_CLOUD.CREATE_EXTERNAL_TABLE<\/code> function among others.<\/p>\n<h6 id=\"important-please-run-this-notebook-before-running-any-of-the-data-preparation-notebooks\"><code>IMPORTANT<\/code> Please run this notebook before running any of the Data Preparation notebooks.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Cleanup if tables exist","message":["%script","","-- DELETE PREVIOUS DATA IF IT EXISTS","BEGIN ","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE EXT_CUSTOMER_INSURANCE_LTV';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_NOISE';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","   BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_SQL';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","  BEGIN","       EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_PY';","       EXCEPTION WHEN OTHERS THEN NULL;","  END;","END;  "],"enabled":true,"result":{"startTime":1737138362545,"interpreter":"script.low","endTime":1737138365251,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV` table ","","This table will be used in other notebooks and allow us to build the `CUSTOMER_INSURANCE_LTV_NOISE` that will also be used.","","We will pull the data from OML dataset storage in GitHub using the `DBMS_CLOUD` capabilities.","","More details on `DBMS_CLOUD` can be found <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">in the Documentation<\/a>"],"enabled":true,"result":{"startTime":1737138365721,"interpreter":"md.low","endTime":1737138366172,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv-table\">Create the <code>CUSTOMER_INSURANCE_LTV<\/code> table<\/h4>\n<p>This table will be used in other notebooks and allow us to build the <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code> that will also be used.<\/p>\n<p>We will pull the data from OML dataset storage in GitHub using the <code>DBMS_CLOUD<\/code> capabilities.<\/p>\n<p>More details on <code>DBMS_CLOUD<\/code> can be found <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">in the Documentation<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the data from Object Storage","message":["%script","","-- LOADS THE DATA FROM GITHUB BY CREATING AN EXTERNAL TABLE AND THEN CREATING A TABLE WITH THAT","DECLARE","     uri_data        varchar2(1000) := 'https://raw.githubusercontent.com/oracle/oracle-db-examples/master/machine-learning/datasets/CUST_INSUR_LTV_APPLY.csv';","     csv_format      varchar2(1000) := '{\"dateformat\":\"YYYY-MM-DD\", \"skipheaders\":\"1\", \"delimiter\":\",\", \"ignoreblanklines\":\"true\", \"removequotes\":\"true\", \"blankasnull\":\"true\", \"trimspaces\":\"lrtrim\", \"truncatecol\":\"true\", \"ignoremissingcolumns\":\"true\"}';","BEGIN","DBMS_CLOUD.CREATE_EXTERNAL_TABLE(","            TABLE_NAME => 'EXT_CUSTOMER_INSURANCE_LTV',","            FILE_URI_LIST => uri_data,","            FORMAT => csv_format,","            COLUMN_LIST => 'MARITAL_STATUS          VARCHAR2(26),","                            STATE                   CHAR(26),","                            CREDIT_BALANCE          NUMBER(8,0),","                            CUSTOMER_TENURE         NUMBER(3,0),","                            MORTGAGE_AMOUNT         NUMBER(7,0),","                            BANK_FUNDS              NUMBER(7,0),","                            NUM_DEPENDENTS          NUMBER(3,0),","                            HAS_CHILDREN            NUMBER(3,0),","                            INCOME                  NUMBER(7,0),","                            CUSTOMER_ID             CHAR(26),","                            GENDER                  CHAR(26),","                            PROFESSION              VARCHAR2(35),","                            CREDIT_CARD_LIMITS      NUMBER(6,0),","                            REGION                  VARCHAR2(26),","                            HOME_OWNERSHIP          NUMBER(3,0),","                            NUM_ONLINE_TRANS        NUMBER(6,0),","                            BUY_INSURANCE           VARCHAR2(26),","                            MONTHLY_CHECKS          NUMBER(4,0),","                            NUM_TRANS_KIOSK         NUMBER(3,0),","                            AGE                     NUMBER(4,0),","                            MONEY_MONTLY_OVERDRAWN  NUMBER(6,2),","                            LTV                     NUMBER(9,2),","                            TOTAL_AUTOM_PAYMENTS    NUMBER(8,0),","                            NUM_TRANS_TELLER        NUMBER(3,0),","                            CHECKING_BALANCE        NUMBER(7,0),","                            NUM_TRANS_ATM           NUMBER(3,0),","                            LTV_BIN                 VARCHAR2(26),","                            FIRST_NAME              VARCHAR2(26),","                            NUM_MORTGAGES           NUMBER(3,0),","                            CAR_OWNERSHIP           NUMBER(3,0),","                            LAST_NAME               VARCHAR2(26)');","                            ","-- WRITE A TABLE INTO THE CURRENT USER WITH THE NAME \"CUSTOMER_INSURANCE_LTV\"                             ","  EXECUTE IMMEDIATE 'create table CUSTOMER_INSURANCE_LTV as select * from EXT_CUSTOMER_INSURANCE_LTV';","  EXCEPTION WHEN OTHERS THEN NULL;","","END;"],"enabled":true,"result":{"startTime":1737138366645,"interpreter":"script.low","endTime":1737138369552,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the number of records","message":["%sql","","SELECT count(*) FROM CUSTOMER_INSURANCE_LTV;"],"enabled":true,"result":{"startTime":1737138370067,"interpreter":"sql.low","endTime":1737138370613,"results":[{"message":"COUNT(*)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the contents of the CUSTOMER_INSURANCE_LTV table","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737138371093,"interpreter":"sql.low","endTime":1737138371596,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV_NOISE` table","","---"],"enabled":true,"result":{"startTime":1737138372065,"interpreter":"md.low","endTime":1737138372531,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv_noise-table\">Create the <code>CUSTOMER_INSURANCE_LTV_NOISE<\/code> table<\/h4>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table with noise from the customer insurance LTV table","message":["%script","","CREATE TABLE CUSTOMER_INSURANCE_LTV_NOISE AS","SELECT * FROM CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1737138372995,"interpreter":"script.low","endTime":1737138374786,"results":[{"message":"\nTable CUSTOMER_INSURANCE_LTV_NOISE created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Insert duplicated rows into the table with noise","message":["%script","","BEGIN ","    INSERT INTO CUSTOMER_INSURANCE_LTV_NOISE","    SELECT * FROM CUSTOMER_INSURANCE_LTV ","    WHERE ORA_HASH(CUSTOMER_ID, 13, 10) = 0","    FETCH FIRST 1000 ROWS ONLY;","    COMMIT;","END;"],"enabled":true,"result":{"startTime":1737138375250,"interpreter":"script.low","endTime":1737138376373,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For customer id  < 'CU2', change INCOME, BANK_FUNDS, CREDIT_BALANCE, MARITAL_STATUS to null ","message":["%sql","","UPDATE  CUSTOMER_INSURANCE_LTV_NOISE","SET INCOME = NULL, BANK_FUNDS = NULL, CREDIT_BALANCE = NULL, MARITAL_STATUS = NULL","WHERE CUSTOMER_ID < 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COMMIT;","END;"],"enabled":true,"result":{"startTime":1737138379481,"interpreter":"script.low","endTime":1737138380033,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV_SQL` and the `CUSTOMER_INSURANCE_LTV_PY` tables ","","---"],"enabled":true,"result":{"startTime":1737138380501,"interpreter":"md.low","endTime":1737138380947,"results":[{"message":"<h4 id=\"create-the-customer_insurance_ltv_sql-and-the-customer_insurance_ltv_py-tables\">Create the <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> and the <code>CUSTOMER_INSURANCE_LTV_PY<\/code> tables<\/h4>\n<hr 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4Py Spatial AI SpatialDataFrame to OML DataFrame.dsnb b/machine-learning/notebooks-oml/sql/OML4Py Spatial AI SpatialDataFrame to OML DataFrame.dsnb
new file mode 100644
index 00000000..916f009e
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4Py Spatial AI SpatialDataFrame to OML DataFrame.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Spatial AI SpatialDataFrame to OML DataFrame","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# SpatialDataFrame to OML DataFrame - Spatial AI in Oracle Machine Learning for Python (OML4Py)","","Oracle Machine Learning for Python (OML4Py) makes the Python environment ready for the enterprise. OML4Py integrates Python with Oracle Autonomous Database, enabling the use of Python for machine learning and visualization from database tables and views. Spatial AI is a feature of OML4Py providing algorithms and APIs to analyze, make predictions, and detect patterns from geospatial data. ","","In this notebook, you learn how a SpatialDataFrame can be transformed to and from an OML DataFrame. 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This is useful for leveraging OML DataFrame data manipulation and analysis in Spatial AI workflows.<\/p>\n<p><strong>Copyright (c) 2025 Oracle Corporation<\/strong>\n<a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","Run the notebook **OML4Py Spatial AI Run-me-first** which loads data used in all sample notebooks."],"enabled":true,"result":{"startTime":1737665263463,"interpreter":"md.low","endTime":1737665264002,"results":[{"message":"<p>Run the notebook <strong>OML4Py Spatial AI Run-me-first<\/strong> which loads data used in all sample 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For example use `describe()` to show feature statistics."],"enabled":true,"result":{"startTime":1737665272154,"interpreter":"md.low","endTime":1737665272640,"results":[{"message":"<p>You may now use OML Dataframe functions and methods on your data. For example use <code>describe()<\/code> to show feature statistics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply OML DataFrame methods","message":["%python","","z.show(df.describe().reset_index())"],"enabled":true,"result":{"startTime":1737665273150,"interpreter":"python.low","endTime":1737665274071,"results":[{"message":"index\tMEAN_INCOME\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tPER_MALE\tPER_FEMALE\tCOMPUTER\tINTERNET\tHOUSE_VALUE\tIND_AGGR\tIND_CONSTR\tIND_MANU\tIND_RETAIL\tIND_TRANSP\tIND_INFO\tIND_FINAN\tIND_EDU\tIND_HEALTH\tIND_ENTER\tIND_PADMIN\ncount\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\t3437.0\nmean\t80003.10223816388\t69962.4084957812\t9.817747524326473\t37.68904761865219\t0.4909985316922785\t0.5090014683077215\t0.8806816465426919\t0.7855957012842326\t603320.3375036368\t0.002364321671095843\t0.052106847532813125\t0.157386900943463\t0.10176306245169582\t0.053789837337874805\t0.05410122296116851\t0.06231341588395844\t0.07968731777643581\t0.09840743552842247\t0.05803486474113851\t0.030407205589473112\nstd\t33112.091563003625\t40246.952648018276\t2.0537298197977107\t5.596124829897268\t0.054549332016207576\t0.05454933201620758\t0.09465504953890733\t0.14271324196534974\t379778.4960574246\t0.00580577830149433\t0.03389222312643915\t0.08843238294690901\t0.041018932892306466\t0.03687621487266088\t0.04914906964371605\t0.03826513250833895\t0.04313747159192474\t0.04393084104657485\t0.039477158667807606\t0.030153414589800715\nmin\t21608.260245901638\t11938.0\t4.963302752293578\t21.93948655256724\t0.17682721455035366\t0.2642663043478261\t0.42735042735042733\t0.17647058823529413\t10900.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\n25%\t53664.32584269663\t41336.0\t8.048780487804878\t33.67862654320987\t0.4579097193981293\t0.47625602202339984\t0.824438202247191\t0.6883720930232559\t357100.0\t0.0\t0.03162650602409638\t0.10158311345646438\t0.08045977011494253\t0.030379746835443037\t0.023880597014925373\t0.03807106598984772\t0.053742802303262956\t0.07520891364902507\t0.034482758620689655\t0.014311270125223614\n50%\t72705.22538293217\t58185.0\t9.894398530762167\t37.51750972762646\t0.4915176524530032\t0.5084823475469968\t0.9\t0.807909604519774\t464300.0\t0.0\t0.05282357485308457\t0.15441601544247543\t0.10356442639470376\t0.05227545506182165\t0.050957566809393805\t0.06552378518340898\t0.08325333153713312\t0.09861487399025333\t0.058107559228302665\t0.032422912037214285\n75%\t99809.18734793187\t86786.0\t11.641509433962264\t41.2542270531401\t0.5237439779766001\t0.5420902806018707\t0.9567430025445293\t0.9013157894736842\t711700.0\t0.0026998498954085115\t0.05949367088607595\t0.1839080459770115\t0.11359026369168357\t0.06331877729257641\t0.05818181818181818\t0.07152317880794702\t0.08919667590027701\t0.11296534017971759\t0.0664819944598338\t0.0327613104524181\nmax\t196494.70309278351\t250000.0\t13.948453608247423\t63.67406273909717\t0.735733695652174\t0.8231727854496463\t1.0\t1.0\t2000000.0\t0.08417508417508418\t0.3157894736842105\t1.0\t0.6956521739130435\t0.26262626262626265\t0.6666666666666666\t0.5625\t0.487146529562982\t0.3917808219178082\t0.3877159309021113\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","As an example of an operation on the OML Dataframe, you next calculate feature correlation using `oml.DataFrame.corr`, which performs correlation analysis across columns"],"enabled":true,"result":{"startTime":1737665274547,"interpreter":"md.low","endTime":1737665274998,"results":[{"message":"<p>As an example of an operation on the OML Dataframe, you next calculate feature correlation using <code>oml.DataFrame.corr<\/code>, which performs correlation analysis across columns<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply an OML DataFrame method","message":["%python","","z.show(df.corr().reset_index())"],"enabled":true,"result":{"startTime":1737665275486,"interpreter":"python.low","endTime":1737665276988,"results":[{"message":"index\tMEAN_INCOME\tMEDIAN_INCOME\tMEAN_EDUCATION_LEVEL\tMEAN_AGE\tPER_MALE\tPER_FEMALE\tCOMPUTER\tINTERNET\tHOUSE_VALUE\tIND_AGGR\tIND_CONSTR\tIND_MANU\tIND_RETAIL\tIND_TRANSP\tIND_INFO\tIND_FINAN\tIND_EDU\tIND_HEALTH\tIND_ENTER\tIND_PADMIN\nMEAN_INCOME\t1.0\t0.9520036367651651\t0.7856424923121363\t0.5409290934552723\t0.019860998377949406\t-0.01986099837794936\t0.5925466556276282\t0.7009688674418161\t0.7491306840949725\t-0.035463443030321125\t-0.25352063252685436\t-0.3969879305629417\t-0.14483874938594501\t-0.1802218747268549\t0.3980205931378391\t0.39433074946526064\t0.21388120831710059\t0.11585208440858275\t-0.2922325488236088\t0.05478259722714204\nMEDIAN_INCOME\t0.9520036367651651\t1.0\t0.6945178987782278\t0.46993415358162477\t0.013211376353693356\t-0.013211376353693315\t0.5318192177381785\t0.623556994021767\t0.7360860795342679\t-0.030049043104869434\t-0.22313052094172167\t-0.3376137963176389\t-0.15275284130451017\t-0.17266310820886308\t0.34071574320901105\t0.36705608984656773\t0.18309029529102955\t0.1132143754639592\t-0.28473672272814654\t0.040201898892484815\nMEAN_EDUCATION_LEVEL\t0.7856424923121363\t0.6945178987782278\t1.0\t0.6250295625617058\t-0.053934080379895444\t0.05393408037989549\t0.6256924268855376\t0.7268057698560363\t0.6889038622870629\t-0.06569056529442194\t-0.31114004983502996\t-0.5779144318055983\t-0.11862730843127135\t-0.1794786792133113\t0.48378127058233206\t0.4372974833345171\t0.3073265615369018\t0.1636735686588157\t-0.24402267783270157\t0.12411588289262925\nMEAN_AGE\t0.5409290934552723\t0.46993415358162477\t0.6250295625617058\t1.0\t-0.07079462259400594\t0.07079462259400598\t0.27467661599669774\t0.4040483036986475\t0.4527967497021918\t-0.06201968860490018\t-0.23359440556630906\t-0.418086713405628\t-0.0780999565549909\t-0.09640203049174352\t0.3030979081400221\t0.33823799691674833\t0.230082288525231\t0.17141640602389452\t-0.2117830072211085\t0.15156710407634394\nPER_MALE\t0.019860998377949406\t0.013211376353693356\t-0.053934080379895444\t-0.07079462259400594\t1.0\t-1.0\t0.01891824133857573\t0.03281998788161601\t-0.008517130465083636\t0.006017635893458174\t0.043613868555168696\t0.06539285114693255\t0.008120715508370593\t-0.06305830799713799\t0.0025619050065386812\t-0.059360073134774226\t-0.10631780263567753\t-0.05994486968723651\t0.09096291524517205\t-0.07607629573416619\nPER_FEMALE\t-0.01986099837794936\t-0.013211376353693315\t0.05393408037989549\t0.07079462259400598\t-1.0\t1.0\t-0.018918241338575698\t-0.03281998788161597\t0.008517130465083664\t-0.006017635893458193\t-0.043613868555168724\t-0.06539285114693258\t-0.008120715508370624\t0.06305830799713799\t-0.0025619050065386574\t0.05936007313477423\t0.10631780263567754\t0.05994486968723654\t-0.09096291524517204\t0.07607629573416623\nCOMPUTER\t0.5925466556276282\t0.5318192177381785\t0.6256924268855376\t0.27467661599669774\t0.01891824133857573\t-0.018918241338575698\t1.0\t0.8428239675466156\t0.4448434857993781\t-0.026268397264199165\t-0.172519128703843\t-0.34774068631687843\t-0.062266971053521275\t-0.13440022039488633\t0.31399156210005763\t0.2734195190851723\t0.17218247269707918\t0.06354795579392776\t-0.13706527592645226\t0.042749084481171214\nINTERNET\t0.7009688674418161\t0.623556994021767\t0.7268057698560363\t0.4040483036986475\t0.03281998788161601\t-0.03281998788161597\t0.8428239675466156\t1.0\t0.5167736915122524\t-0.022197484580212236\t-0.19499394597885578\t-0.4031441099788719\t-0.07046045652820022\t-0.15755037797488095\t0.372785411567845\t0.33675762650831154\t0.19491373401886006\t0.08566890477224255\t-0.19121403017972655\t0.051061508136982806\nHOUSE_VALUE\t0.7491306840949725\t0.7360860795342679\t0.6889038622870629\t0.4527967497021918\t-0.008517130465083636\t0.008517130465083664\t0.4448434857993781\t0.5167736915122524\t1.0\t-0.059105689542857213\t-0.2340642068121792\t-0.3755670736398413\t-0.15523584222589043\t-0.2686128403892686\t0.39563859164872633\t0.3322466497373544\t0.15056980969690928\t0.05983280169136577\t-0.1743735843844158\t-0.03133392046152342\nIND_AGGR\t-0.035463443030321125\t-0.030049043104869434\t-0.06569056529442194\t-0.06201968860490018\t0.006017635893458174\t-0.006017635893458193\t-0.026268397264199165\t-0.022197484580212236\t-0.059105689542857213\t1.0\t0.0378075922998076\t0.07154794243098266\t-0.01546545363504018\t0.020562702227719245\t-0.05881341784700666\t-0.08013365769959108\t-0.0486991462484294\t-0.055137613465325606\t0.007209991540197723\t-0.02207018744911222\nIND_CONSTR\t-0.25352063252685436\t-0.22313052094172167\t-0.31114004983502996\t-0.23359440556630906\t0.043613868555168696\t-0.043613868555168724\t-0.172519128703843\t-0.19499394597885578\t-0.2340642068121792\t0.0378075922998076\t1.0\t0.1927923868356311\t-0.005239797497474294\t-0.025143911916218828\t-0.23278634030423134\t-0.21015092964180257\t-0.22345970803865545\t-0.1536155402068982\t0.11962825843856885\t-0.13401094948966644\nIND_MANU\t-0.3969879305629417\t-0.3376137963176389\t-0.5779144318055983\t-0.418086713405628\t0.06539285114693255\t-0.06539285114693258\t-0.34774068631687843\t-0.4031441099788719\t-0.3755670736398413\t0.07154794243098266\t0.1927923868356311\t1.0\t0.00313350021373656\t0.0566913436960838\t-0.4975374931409926\t-0.44274921528232025\t-0.34595213741583924\t-0.294850023945911\t0.05950458100451086\t-0.18047951821906152\nIND_RETAIL\t-0.14483874938594501\t-0.15275284130451017\t-0.11862730843127135\t-0.0780999565549909\t0.008120715508370593\t-0.008120715508370624\t-0.062266971053521275\t-0.07046045652820022\t-0.15523584222589043\t-0.01546545363504018\t-0.005239797497474294\t0.00313350021373656\t1.0\t-0.04321845036533889\t-0.1667505044650445\t-0.1426995120670633\t-0.15611689721685737\t-0.09118002237672591\t0.05299389834818355\t-0.1287431646609338\nIND_TRANSP\t-0.1802218747268549\t-0.17266310820886308\t-0.1794786792133113\t-0.09640203049174352\t-0.06305830799713799\t0.06305830799713799\t-0.13440022039488633\t-0.15755037797488095\t-0.2686128403892686\t0.020562702227719245\t-0.025143911916218828\t0.0566913436960838\t-0.04321845036533889\t1.0\t-0.2775928802112495\t-0.153976882685548\t-0.01820323773941207\t0.05365217062247045\t-0.03224230374755789\t0.14940717833705094\nIND_INFO\t0.3980205931378391\t0.34071574320901105\t0.48378127058233206\t0.3030979081400221\t0.0025619050065386812\t-0.0025619050065386574\t0.31399156210005763\t0.372785411567845\t0.39563859164872633\t-0.05881341784700666\t-0.23278634030423134\t-0.4975374931409926\t-0.1667505044650445\t-0.2775928802112495\t1.0\t0.24076503605549668\t0.09452826290188618\t-0.058731047528153536\t-0.19598590370331082\t-0.037167947544243914\nIND_FINAN\t0.39433074946526064\t0.36705608984656773\t0.4372974833345171\t0.33823799691674833\t-0.059360073134774226\t0.05936007313477423\t0.2734195190851723\t0.33675762650831154\t0.3322466497373544\t-0.08013365769959108\t-0.21015092964180257\t-0.44274921528232025\t-0.1426995120670633\t-0.153976882685548\t0.24076503605549668\t1.0\t0.1362441915333901\t0.07080023598801419\t-0.2337398192053145\t0.038677070363223445\nIND_EDU\t0.21388120831710059\t0.18309029529102955\t0.3073265615369018\t0.230082288525231\t-0.10631780263567753\t0.10631780263567754\t0.17218247269707918\t0.19491373401886006\t0.15056980969690928\t-0.0486991462484294\t-0.22345970803865545\t-0.34595213741583924\t-0.15611689721685737\t-0.01820323773941207\t0.09452826290188618\t0.1362441915333901\t1.0\t0.12162793820860274\t-0.2665723484258818\t0.15348617016884994\nIND_HEALTH\t0.11585208440858275\t0.1132143754639592\t0.1636735686588157\t0.17141640602389452\t-0.05994486968723651\t0.05994486968723654\t0.06354795579392776\t0.08566890477224255\t0.05983280169136577\t-0.055137613465325606\t-0.1536155402068982\t-0.294850023945911\t-0.09118002237672591\t0.05365217062247045\t-0.058731047528153536\t0.07080023598801419\t0.12162793820860274\t1.0\t-0.16942257562881843\t0.09318579690750398\nIND_ENTER\t-0.2922325488236088\t-0.28473672272814654\t-0.24402267783270157\t-0.2117830072211085\t0.09096291524517205\t-0.09096291524517204\t-0.13706527592645226\t-0.19121403017972655\t-0.1743735843844158\t0.007209991540197723\t0.11962825843856885\t0.05950458100451086\t0.05299389834818355\t-0.03224230374755789\t-0.19598590370331082\t-0.2337398192053145\t-0.2665723484258818\t-0.16942257562881843\t1.0\t-0.13056751028278551\nIND_PADMIN\t0.05478259722714204\t0.040201898892484815\t0.12411588289262925\t0.15156710407634394\t-0.07607629573416619\t0.07607629573416623\t0.042749084481171214\t0.051061508136982806\t-0.03133392046152342\t-0.02207018744911222\t-0.13401094948966644\t-0.18047951821906152\t-0.1287431646609338\t0.14940717833705094\t-0.037167947544243914\t0.038677070363223445\t0.15348617016884994\t0.09318579690750398\t-0.13056751028278551\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","To remove correlation effects, remove the column MEAN_INCOME."],"enabled":true,"result":{"startTime":1737665277493,"interpreter":"md.low","endTime":1737665277940,"results":[{"message":"<p>To remove correlation effects, remove the column MEAN_INCOME.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop a column","message":["%python","","df = df.drop(\"MEAN_INCOME\")"],"enabled":true,"result":{"startTime":1737665278423,"interpreter":"python.low","endTime":1737665278878,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","","Finally merge the DataFrame back to a SpatialDataFrame where you would continue your spatial workflow. You transform to SpatialDataframe using `merge_oml_dataframe()` which reinstates the geometry from the original SpatialDataframe."],"enabled":true,"result":{"startTime":1737665279415,"interpreter":"md.low","endTime":1737665279904,"results":[{"message":"<p>Finally merge the DataFrame back to a SpatialDataFrame where you would continue your spatial workflow. You transform to SpatialDataframe using <code>merge_oml_dataframe()<\/code> which reinstates the geometry from the original SpatialDataframe.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Merge SpatialDataFrame with OML DataDrame","message":["%python","","","sdf_from_df =sdf['GEOID'].merge_oml_dataframe(df, 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\ No newline at end of file
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@@ -0,0 +1 @@
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","In this notebook, we highlight the use of the Expectation Maximization algorithm, which is already supported for clustering. ","The use of EM expands on the One-Class SVM model approach for anomaly detection. EM can capture the underlying data distribution and flag records ","that do not fit the learned data distribution well. An object is identified as an outlier in an EM Anomaly Detection model if its anomaly ","probability is greater than the specified limit, with a default of 0.05. A label of 1 denotes normal, while a label of 0 denotes anomaly. ","We use the customer and demographics data to predict anomalous customers using prob_anomalous.","The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737733678319,"interpreter":"md.low","endTime":1737733678816,"results":[{"message":"<h1 id=\"anomaly-detection-to-detect-unusual-or-rare-occurrences\">Anomaly Detection to Detect Unusual or Rare Occurrences<\/h1>\n<p>Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.).\nIn this notebook, we highlight the use of the Expectation Maximization algorithm, which is already supported for clustering.\nThe use of EM expands on the One-Class SVM model approach for anomaly detection. EM can capture the underlying data distribution and flag records\nthat do not fit the learned data distribution well. An object is identified as an outlier in an EM Anomaly Detection model if its anomaly\nprobability is greater than the specified limit, with a default of 0.05. A label of 1 denotes normal, while a label of 0 denotes anomaly.\nWe use the customer and demographics data to predict anomalous customers using prob_anomalous.\nThe entire machine learning methodology runs inside Oracle Autonomous Database (ADB).<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","<dl>","<img 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href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-anomaly-detection\" target=\"_blank\">OML Anomaly Detection<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1737733680230,"interpreter":"md.low","endTime":1737733680685,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web 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SH.SUPPLEMENTARY_DEMOGRAPHICS;","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","  SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, a.CUST_YEAR_OF_BIRTH, ","         a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, b.EDUCATION, b.AFFINITY_CARD, ","         b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","  FROM   SH.CUSTOMERS a, DEMOGRAPHICS_V b","  WHERE  a.CUST_ID = b.CUST_ID;"],"enabled":true,"result":{"startTime":1737733681152,"interpreter":"script.low","endTime":1737733681682,"results":[{"message":"\nView DEMOGRAPHICS_V created.\n\n\n---------------------------\n\nView CUSTOMERS360_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the EM anomaly detection model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('CUSTOMERS360MODEL_AD');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","  v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","  v_setlst('ALGO_NAME')         := 'ALGO_EXPECTATION_MAXIMIZATION';","  v_setlst('PREP_AUTO')         := 'ON';","  v_setlst('EMCS_OUTLIER_RATE') := '0.1';","        ","  DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'CUSTOMERS360MODEL_AD',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMERS360_V',","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        SET_LIST            => v_setlst,","        TARGET_COLUMN_NAME  => NULL); -- NULL target indicates anomaly detection      ","END;"],"enabled":true,"result":{"startTime":1737733682168,"interpreter":"script.low","endTime":1737733684570,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Evaluate the model"],"enabled":true,"result":{"startTime":1737733685052,"interpreter":"md.low","endTime":1737733685522,"results":[{"message":"<h2 id=\"evaluate-the-model\">Evaluate the model<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model details","message":["%sql","","SELECT * ","FROM  TABLE(dbms_data_mining.get_model_details_global('CUSTOMERS360MODEL_AD'))","ORDER BY global_detail_name;"],"enabled":true,"result":{"startTime":1737733686061,"interpreter":"sql.low","endTime":1737733686695,"results":[{"message":"GLOBAL_DETAIL_NAME\tGLOBAL_DETAIL_VALUE\nLOGLIKELIHOOD\t-3.4040876550830728\nNUM_COMPONENTS\t20\nRANDOM_SEED\t0\nREMOVED_COMPONENTS\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the top 5 most anomalous customers","message":["%sql","","SELECT * ","FROM (SELECT CUST_ID, round(prob_anomalous,2) prob_anomalous,  ","             YRS_RESIDENCE, CUST_MARITAL_STATUS, ","             rank() OVER (ORDER BY prob_anomalous DESC) rnk ","      FROM (SELECT CUST_ID, HOUSEHOLD_SIZE, YRS_RESIDENCE, CUST_GENDER, CUST_MARITAL_STATUS, ","                   prediction_probability(CUSTOMERS360MODEL_AD, '0' USING *) prob_anomalous","            FROM CUSTOMERS360_V))","WHERE rnk <= 5","ORDER BY prob_anomalous DESC;"],"enabled":true,"result":{"startTime":1737733687193,"interpreter":"sql.low","endTime":1737733687727,"results":[{"message":"CUST_ID\tPROB_ANOMALOUS\tYRS_RESIDENCE\tCUST_MARITAL_STATUS\tRNK\n102926\t1\t2\tMarried\t1\n102555\t1\t14\tNeverM\t2\n102287\t1\t2\tMar-AF\t5\n101478\t1\t1\tMarried\t4\n101693\t1\t0\tMarried\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view of customers in descending order of anomaly probability","message":["%script","","CREATE OR REPLACE VIEW EM_ANOMALOUS_RESULTS AS","SELECT * ","FROM (SELECT CUST_ID, anomalous, round(prob_anomalous,2) prob_anomalous, ","             YRS_RESIDENCE, HOUSEHOLD_SIZE, CUST_GENDER,","             CUST_MARITAL_STATUS, ","             RANK() OVER (ORDER BY prob_anomalous DESC) rnk ","      FROM (SELECT CUST_ID, HOUSEHOLD_SIZE, YRS_RESIDENCE, ","                   CUST_GENDER, CUST_MARITAL_STATUS, ","                   prediction(CUSTOMERS360MODEL_AD using *) anomalous,","                   prediction_probability(CUSTOMERS360MODEL_AD, '0' USING *) prob_anomalous","            FROM CUSTOMERS360_V))","ORDER BY prob_anomalous DESC;"," "],"enabled":true,"result":{"startTime":1737733688203,"interpreter":"script.low","endTime":1737733688699,"results":[{"message":"\nView EM_ANOMALOUS_RESULTS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View a few rows of the result","message":["%sql","","SELECT * ","FROM   EM_ANOMALOUS_RESULTS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737733689176,"interpreter":"sql.low","endTime":1737733689677,"results":[{"message":"CUST_ID\tANOMALOUS\tPROB_ANOMALOUS\tYRS_RESIDENCE\tHOUSEHOLD_SIZE\tCUST_GENDER\tCUST_MARITAL_STATUS\tRNK\n102926\t0\t1\t2\t4-5\tF\tMarried\t1\n102555\t0\t1\t14\t2\tM\tNeverM\t2\n101693\t0\t1\t0\t4-5\tF\tMarried\t3\n101478\t0\t1\t1\t1\tF\tMarried\t4\n102287\t0\t1\t2\t4-5\tF\tMar-AF\t5\n102258\t0\t1\t9\t2\tF\tNeverM\t6\n103684\t0\t1\t4\t2\tM\tMabsent\t7\n104403\t0\t1\t1\t1\tF\tMarried\t8\n102689\t0\t1\t6\t9+\tM\tDivorc.\t9\n102363\t0\t1\t3\t1\tF\tMarried\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View prediction details of top 3 attributes to explain prediction","message":["%sql","","SELECT CUST_ID, PREDICTION,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID, ","             PREDICTION(CUSTOMERS360MODEL_AD USING *) PREDICTION,","             PREDICTION_DETAILS(CUSTOMERS360MODEL_AD, '0' USING *) PREDICTION_DETAILS ","      FROM   CUSTOMERS360_V","      WHERE  PREDICTION_PROBABILITY(CUSTOMERS360MODEL_AD, '0' USING *) > 0.50","      AND    OCCUPATION = 'TechSup'","      ORDER BY CUST_ID) OUT,","      XMLTABLE('/Details'","                PASSING OUT.PREDICTION_DETAILS","                COLUMNS ","                   \"Attribute1\" XMLType PATH 'Attribute[1]',","                   \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737733690151,"interpreter":"sql.low","endTime":1737733690685,"results":[{"message":"CUST_ID\tPREDICTION\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100061\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".466\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.036\" \n100646\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".794\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.053\" \n100941\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1948\" weight=\".539\" \t\"EDUCATION\" actualValue=\"9th\" weight=\"-.371\" \n101097\t0\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.001\" \t\"YRS_RESIDENCE\" actualValue=\"6\" weight=\"-.001\" \n101706\t0\t\t\n101970\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1973\" weight=\".315\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.013\" \n102862\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1952\" weight=\"-.022\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.024\" \n102907\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1939\" weight=\".637\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.025\" \n103341\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".506\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.048\" \n103441\t0\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".401\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.008\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737733691177,"interpreter":"md.low","endTime":1737733691619,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737733692088,"interpreter":"md.low","endTime":1737733692583,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Anomaly Detection MSET","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737998275003,"interpreter":"md.low","endTime":1737998275541,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning MSET-SPRT Algorithm for Anomaly Detection","","The Multivariate State Estimation Technique-Sequential Probability Ratio Test (MSET-SPRT) algorithm is a nonlinear, nonparametric anomaly detection technique for monitoring critical processes.","","The DBMS_DATA_MINING.ALGO_MSET_SPRT algorithm detects subtle anomalies while producing minimal false alarms. It calibrates expected behavior from available, historical data of the normal operational sequence of monitored signals. It incorporates the learned behavior of the system into a persistent MSET-SPRT model. You can apply the model to new records to detect anomalous behavior.","","MSET is a nonlinear, nonparametric anomaly detection machine learning technique that calibrates the expected behavior of a system based on historical data from the normal operational sequence of monitored signals. It incorporates the learned behavior of a system into a persistent model that represents the normal estimated behavior. You can deploy the model to evaluate a subsequent stream of live signal vectors using OML4SQL scoring functions. To form a hypothesis as to the overall health of the system, these functions calculate the difference between the estimated and the actual signal values (residuals) and use SPRT calculations to determine whether any of the signals have become degraded.","","To build a good model, MSET requires sufficient historical data that adequately captures all normal modes of behavior of the system. Incomplete data results in false alerts when the system enters a mode of operation that was poorly represented in the historical data. MSET assumes that the characteristics of the data being monitored do not change over time. Once deployed, MSET is a stationary model and does not evolve as it monitors a data stream.","","Both MSET and SPRT operate on continuous time-ordered sensor data. If the raw data stream needs to be pre-processed or sampled, you must do that before you pass the data to the MSET-SPRT model.","","The ALGO_MSET_SPRT algorithm is designated as a classification machine learning function. It generates a model in which each data row is labeled as either normal or anomalous. For anomalous predictions, the prediction details provide a list of the sensors that show the anomaly and a weight.","","When creating an MSET-SPRT model with the DBMS_DATA_MINING.CREATE_MODEL function, use the case_id argument to provide a unique row identifier for the time-ordered data that the algorithm requires. The build is then able to sort the training data and create windows for sampling and variance estimation. If you do not provide a case_id, then an exception occurs.","","MSET-SPRT supports only numeric data. An exception occurs if other column types are in the build data.","","When the number of sensors is very high, MSET-SPRT leverages random projections to improve the scalability and robustness of the algorithm. Random projections is a technique that reduces dimensionality while preserving pairwise distances. By randomly projecting the sensor data, the problem is solved in a distance-preserving, lower-dimension space. The MSET hypothesis testing approach is applied on the projected data where each random projection can be viewed as a Monte Carlo simulation of system health. The overall probability of an anomaly follows a binomial distribution with the number of projections as the number of trials and the number of alerting projections as the number of successes.","","","Copyright (c) 2025 Oracle Corporation                             ","###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)","---"],"enabled":true,"result":{"startTime":1737998276028,"interpreter":"md.low","endTime":1737998276544,"results":[{"message":"<h1 id=\"oracle-machine-learning-mset-sprt-algorithm-for-anomaly-detection\">Oracle Machine Learning MSET-SPRT Algorithm for Anomaly Detection<\/h1>\n<p>The Multivariate State Estimation Technique-Sequential Probability Ratio Test (MSET-SPRT) algorithm is a nonlinear, nonparametric anomaly detection technique for monitoring critical processes.<\/p>\n<p>The DBMS_DATA_MINING.ALGO_MSET_SPRT algorithm detects subtle anomalies while producing minimal false alarms. It calibrates expected behavior from available, historical data of the normal operational sequence of monitored signals. It incorporates the learned behavior of the system into a persistent MSET-SPRT model. You can apply the model to new records to detect anomalous behavior.<\/p>\n<p>MSET is a nonlinear, nonparametric anomaly detection machine learning technique that calibrates the expected behavior of a system based on historical data from the normal operational sequence of monitored signals. It incorporates the learned behavior of a system into a persistent model that represents the normal estimated behavior. You can deploy the model to evaluate a subsequent stream of live signal vectors using OML4SQL scoring functions. To form a hypothesis as to the overall health of the system, these functions calculate the difference between the estimated and the actual signal values (residuals) and use SPRT calculations to determine whether any of the signals have become degraded.<\/p>\n<p>To build a good model, MSET requires sufficient historical data that adequately captures all normal modes of behavior of the system. Incomplete data results in false alerts when the system enters a mode of operation that was poorly represented in the historical data. MSET assumes that the characteristics of the data being monitored do not change over time. Once deployed, MSET is a stationary model and does not evolve as it monitors a data stream.<\/p>\n<p>Both MSET and SPRT operate on continuous time-ordered sensor data. If the raw data stream needs to be pre-processed or sampled, you must do that before you pass the data to the MSET-SPRT model.<\/p>\n<p>The ALGO_MSET_SPRT algorithm is designated as a classification machine learning function. It generates a model in which each data row is labeled as either normal or anomalous. For anomalous predictions, the prediction details provide a list of the sensors that show the anomaly and a weight.<\/p>\n<p>When creating an MSET-SPRT model with the DBMS_DATA_MINING.CREATE_MODEL function, use the case_id argument to provide a unique row identifier for the time-ordered data that the algorithm requires. The build is then able to sort the training data and create windows for sampling and variance estimation. If you do not provide a case_id, then an exception occurs.<\/p>\n<p>MSET-SPRT supports only numeric data. An exception occurs if other column types are in the build data.<\/p>\n<p>When the number of sensors is very high, MSET-SPRT leverages random projections to improve the scalability and robustness of the algorithm. Random projections is a technique that reduces dimensionality while preserving pairwise distances. By randomly projecting the sensor data, the problem is solved in a distance-preserving, lower-dimension space. The MSET hypothesis testing approach is applied on the projected data where each random projection can be viewed as a Monte Carlo simulation of system health. The overall probability of an anomaly follows a binomial distribution with the number of projections as the number of trials and the number of alerting projections as the number of successes.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-mset-sprt\" target=\"_blank\">OML MSET-SPRT<\/a>"],"enabled":true,"result":{"startTime":1737998277034,"interpreter":"md.low","endTime":1737998277499,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-mset-sprt\" target=\"_blank\">OML MSET-SPRT<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","##  MSET-SPRT algorithm for multi-variate time-series anomaly detection","","#### SAMPLE PROBLEM","Given the two-variate time series data, the daily sold quantity and amount from January 1, 1998 to December 31, 1999, predict the anomalies of sold data from January 1, 2000 to December 31 2001.","","#### DATA SETUP","The data for this sample is from SH Schema, specifically, subtables of sh.sales.","","Two views are 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Anomaly Detection SVM.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Anomaly Detection SVM.dsnb
new file mode 100644
index 00000000..e8ba0c51
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Anomaly Detection SVM.dsnb	
@@ -0,0 +1 @@
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RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","       FROM (SELECT CUST_ID, PREDICTION_DETAILS(CUSTOMERS360MODEL, '0' USING *) PREDICTION_DETAILS FROM CUSTOMERS360_V","           WHERE PREDICTION_PROBABILITY(CUSTOMERS360MODEL, '0' USING *) > 0.50","           AND OCCUPATION = 'TechSup'","           ORDER BY CUST_ID) OUT,","           XMLTABLE('/Details'","                PASSING OUT.PREDICTION_DETAILS","                COLUMNS ","                   \"Attribute1\" XMLType PATH 'Attribute[1]',","                   \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED","FETCH FIRST 10 ROWS ONLY;","       "],"enabled":true,"result":{"startTime":1740588212993,"interpreter":"sql.medium","endTime":1740588213503,"results":[{"message":"CUST_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100646\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".226\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".118\" \n102922\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".222\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1931\" weight=\".169\" \n103441\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".222\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".117\" \n104286\t\"EDUCATION\" actualValue=\"9th\" weight=\".165\" \t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".146\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1740588213952,"interpreter":"md.medium","endTime":1740588214369,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4SQL Association Rules Apriori","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738092670708,"interpreter":"md.low","endTime":1738092670794,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Association Rules (Market Basket Analysis)","","The Association Rules machine learning technique, also known as Market Basket Analysis, can be used when looking for co-occurring items, ","states that lead to failures, or non-obvious events, e.g., as in manufacturing. In his notebook, you will build associations rules ","models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1738092671031,"interpreter":"md.low","endTime":1738092671272,"results":[{"message":"<h1 id=\"association-rules-market-basket-analysis\">Association Rules (Market Basket Analysis)<\/h1>\n<p>The Association Rules machine learning technique, also known as Market Basket Analysis, can be used when looking for co-occurring items,\nstates that lead to failures, or non-obvious events, e.g., as in manufacturing. In his notebook, you will build associations rules\nmodels using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1738092671486,"interpreter":"md.low","endTime":1738092671594,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules"],"enabled":true,"result":{"startTime":1738092671724,"interpreter":"md.low","endTime":1738092671817,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display SALES data","message":["%sql","","-- Note that a transaction is defined by a set of products purchased for a given customer at a given time. ","","SELECT CUST_ID, TIME_ID, PROD_ID, QUANTITY_SOLD, AMOUNT_SOLD, CHANNEL_ID, PROMO_ID ","FROM   SH.SALES","ORDER BY CUST_ID, TIME_ID, PROD_ID","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738092671972,"interpreter":"sql.low","endTime":1738092675230,"results":[{"message":"CUST_ID\tTIME_ID\tPROD_ID\tQUANTITY_SOLD\tAMOUNT_SOLD\tCHANNEL_ID\tPROMO_ID\n2\t1998-01-02 00:00:00\t31\t1\t9.71\t3\t999\n2\t1998-01-02 00:00:00\t140\t1\t41.99\t3\t999\n2\t1998-01-02 00:00:00\t146\t1\t16.79\t3\t999\n2\t1998-01-02 00:00:00\t148\t1\t29.39\t3\t999\n2\t1998-01-04 00:00:00\t130\t1\t125.99\t3\t999\n2\t1998-01-05 00:00:00\t14\t1\t1259.99\t2\t999\n2\t1998-01-05 00:00:00\t14\t1\t1259.99\t3\t999\n2\t1998-01-05 00:00:00\t123\t1\t64.99\t3\t999\n2\t1998-01-05 00:00:00\t125\t1\t16.63\t3\t999\n2\t1998-01-05 00:00:00\t126\t1\t30.15\t3\t999\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display PRODUCTS with product name and associated data for each PROD_ID","message":["%sql","","SELECT * ","FROM   SH.PRODUCTS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738092675373,"interpreter":"sql.low","endTime":1738092675603,"results":[{"message":"PROD_ID\tPROD_NAME\tPROD_DESC\tPROD_SUBCATEGORY\tPROD_SUBCATEGORY_ID\tPROD_SUBCATEGORY_DESC\tPROD_CATEGORY\tPROD_CATEGORY_ID\tPROD_CATEGORY_DESC\tPROD_WEIGHT_CLASS\tPROD_UNIT_OF_MEASURE\tPROD_PACK_SIZE\tSUPPLIER_ID\tPROD_STATUS\tPROD_LIST_PRICE\tPROD_MIN_PRICE\tPROD_TOTAL\tPROD_TOTAL_ID\tPROD_SRC_ID\tPROD_EFF_FROM\tPROD_EFF_TO\tPROD_VALID\n13\t5MP Telephoto Digital Camera\t5MP Telephoto Digital Camera\tCameras\t2044\tCameras\tPhoto\t204\tPhoto\t1\tU\tP\t1\tSTATUS\t899.99\t899.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n14\t17\" LCD w/built-in HDTV Tuner\t17\" LCD w/built-in HDTV Tuner\tMonitors\t2035\tMonitors\tPeripherals and Accessories\t203\tPeripherals and Accessories\t1\tU\tP\t1\tSTATUS\t999.99\t999.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n15\tEnvoy 256MB - 40GB\tEnvoy 256MB - 40Gb\tDesktop PCs\t2021\tDesktop PCs\tHardware\t202\tHardware\t1\tU\tP\t1\tSTATUS\t999.99\t999.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n16\tY Box\tY Box\tGame Consoles\t2011\tGame Consoles\tElectronics\t201\tElectronics\t1\tU\tP\t1\tSTATUS\t299.99\t299.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n17\tMini DV Camcorder with 3.5\" Swivel LCD\tMini DV Camcorder with 3.5\" Swivel LCD\tCamcorders\t2041\tCamcorders\tPhoto\t204\tPhoto\t1\tU\tP\t1\tSTATUS\t1099.99\t1099.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n18\tEnvoy Ambassador\tEnvoy Ambassador\tPortable PCs\t2022\tPortable PCs\tHardware\t202\tHardware\t1\tU\tP\t1\tSTATUS\t1299.99\t1299.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n19\tLaptop carrying case\tLaptop carrying case\tAccessories\t2051\tAccessories\tSoftware/Other\t205\tSoftware/Other\t1\tU\tP\t1\tSTATUS\t55.99\t55.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n20\tHome Theatre Package with DVD-Audio/Video Play\tHome Theatre Package with DVD-Audio/Video Play\tHome Audio\t2012\tHome Audio\tElectronics\t201\tElectronics\t1\tU\tP\t1\tSTATUS\t599.99\t599.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n21\t18\" Flat Panel Graphics Monitor\t18\" Flat Panel Graphics Monitor\tMonitors\t2035\tMonitors\tPeripherals and Accessories\t203\tPeripherals and Accessories\t1\tU\tP\t1\tSTATUS\t899.99\t899.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n22\tEnvoy External Keyboard\tEnvoy External Keyboard\tAccessories\t2031\tAccessories\tPeripherals and Accessories\t203\tPeripherals and Accessories\t1\tU\tP\t1\tSTATUS\t24.99\t24.99\tTOTAL\t1\t\t1998-01-01 00:00:00\t\tA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Prepare Sales data for market basket analysis","message":["%script","","-- Join data from the SH.SALES and SH.PRODUCTS tables to provide ","-- the product names and categories purchased by each customer","","CREATE OR REPLACE VIEW SALES_TRANS_CUST AS","  SELECT DISTINCT CUST_ID, PROD_NAME, PROD_CATEGORY","    FROM (SELECT A.CUST_ID, B.PROD_NAME, B.PROD_CATEGORY","    FROM SH.SALES A, SH.PRODUCTS B","    WHERE A.PROD_ID = B.PROD_ID AND","      A.CUST_ID BETWEEN 100001 AND 104500);","      "],"enabled":true,"result":{"startTime":1738092675763,"interpreter":"script.low","endTime":1738092676479,"results":[{"message":"\nView SALES_TRANS_CUST created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display view SALES_TRANS_CUST","message":["%sql","","SELECT * ","FROM   SALES_TRANS_CUST","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738092676649,"interpreter":"sql.low","endTime":1738092676898,"results":[{"message":"CUST_ID\tPROD_NAME\tPROD_CATEGORY\n100001\tKeyboard Wrist Rest\tSoftware/Other\n100001\tO/S Documentation Set - 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This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') : 'ODMS_MISSING_VALUE_AUTO';","","* Specify the Minimum support for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","    v_setlst('ASSO_MIN_SUPPORT') := '0.1';","","* Specify the Minimum confidence for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","    v_setlst('ASSO_MIN_CONFIDENCE') := '0.1';","","* Specify the Maximum rule length for association rules.","    The default value is 4.  It requires a number between 2 and 20 (inclusive).","    v_setlst('ASSO_MAX_RULE_LENGTH') := '4';","    ","* Specify the name of a column that contains the items in a transaction. ","    When you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:","    - Case ID, either categorical or numeric","    - Item ID, either categorical or numeric","    v_setlst('ODMS_ITEM_ID_COLUMN_NAME') := '<column name>';","    "],"enabled":true,"result":{"startTime":1738092677557,"interpreter":"md.low","endTime":1738092677683,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-association-rules-a-priori\">Examples of possible setting overrides for Association Rules (A Priori)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column\nv_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.\nv_setlst('ODMS_MISSING_VALUE_TREATMENT') : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/li>\n<li>\n<p>Specify the Minimum support for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).\nv_setlst('ASSO_MIN_SUPPORT') := '0.1';<\/p>\n<\/li>\n<li>\n<p>Specify the Minimum confidence for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).\nv_setlst('ASSO_MIN_CONFIDENCE') := '0.1';<\/p>\n<\/li>\n<li>\n<p>Specify the Maximum rule length for association rules.\nThe default value is 4.  It requires a number between 2 and 20 (inclusive).\nv_setlst('ASSO_MAX_RULE_LENGTH') := '4';<\/p>\n<\/li>\n<li>\n<p>Specify the name of a column that contains the items in a transaction.\nWhen you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:<\/p>\n<ul>\n<li>Case ID, either categorical or numeric<\/li>\n<li>Item ID, either categorical or numeric\nv_setlst('ODMS_ITEM_ID_COLUMN_NAME') := '<column name>';<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create Associations Rules model","message":["%script ","","BEGIN DBMS_DATA_MINING.DROP_MODEL('AR_SH_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')               := 'ALGO_APRIORI_ASSOCIATION_RULES';","    v_setlst('PREP_AUTO')               := 'ON';","    v_setlst('ASSO_MIN_SUPPORT')        := '0.04';","    v_setlst('ASSO_MIN_CONFIDENCE')     := '0.1';","    v_setlst('ASSO_MAX_RULE_LENGTH')    := '2';","    v_setlst('ODMS_ITEM_ID_COLUMN_NAME'):= 'PROD_NAME';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME        => 'AR_SH_SAMPLE',","        MINING_FUNCTION   => 'ASSOCIATION',","        DATA_QUERY        => 'select * from SALES_TRANS_CUST',","        SET_LIST          => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1738092677952,"interpreter":"script.low","endTime":1738092680313,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully 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"],"enabled":true,"result":{"startTime":1738092680455,"interpreter":"sql.low","endTime":1738092680589,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_APRIORI_ASSOCIATION_RULES\nASSO_MAX_RULE_LENGTH\t2\nASSO_MIN_CONFIDENCE\t0.1\nASSO_MIN_REV_CONFIDENCE\t0\nASSO_MIN_SUPPORT\t0.04\nASSO_MIN_SUPPORT_INT\t1\nODMS_DETAILS\tODMS_ENABLE\nODMS_ITEM_ID_COLUMN_NAME\tPROD_NAME\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display top 10 most frequent items purchased","message":["%sql ","","SELECT I.ATTRIBUTE_SUBNAME AS ITEM,","       round (F.SUPPORT,3) SUPPORT,","       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Monitor\t0.154\t0.485\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display top 10 Association Rules using model views","message":["%sql ","","SELECT RULE_ID, ","       ANTECEDENT_PREDICATE ANTECEDENT,","       CONSEQUENT_PREDICATE CONSEQUENT,","       ROUND(RULE_SUPPORT,3) SUPP, ","       ROUND(RULE_CONFIDENCE,3) CONF, ","       NUMBER_OF_ITEMS NUM_ITMES","FROM   DM$VAAR_SH_SAMPLE","ORDER BY RULE_ID, ANTECEDENT, CONSEQUENT","FETCH FIRST 10 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1738092682019,"interpreter":"sql.low","endTime":1738092682159,"results":[{"message":"RULE_ID\tANTECEDENT\tCONSEQUENT\tSUPP\tCONF\tNUM_ITMES\n1\tCD-RW, High Speed Pack of 5\t18\" Flat Panel Graphics Monitor\t0.091\t0.313\t2\n2\t18\" Flat Panel Graphics Monitor\tCD-RW, High Speed Pack of 5\t0.091\t0.295\t2\n3\tExtension Cable\t18\" Flat Panel Graphics Monitor\t0.09\t0.29\t2\n4\t18\" Flat Panel Graphics Monitor\tExtension Cable\t0.09\t0.291\t2\n5\tExternal 8X CD-ROM\t18\" Flat Panel Graphics Monitor\t0.083\t0.272\t2\n6\t18\" Flat Panel Graphics Monitor\tExternal 8X CD-ROM\t0.083\t0.267\t2\n7\tKeyboard Wrist Rest\t18\" Flat Panel Graphics Monitor\t0.06\t0.316\t2\n8\t18\" Flat Panel Graphics Monitor\tKeyboard Wrist Rest\t0.06\t0.192\t2\n9\tModel SM26273 Black Ink Cartridge\t18\" Flat Panel Graphics Monitor\t0.067\t0.309\t2\n10\t18\" Flat Panel Graphics Monitor\tModel SM26273 Black Ink Cartridge\t0.067\t0.216\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"ANTECEDENT\",\"CONSEQUENT\"],\"showSeries\":[\"CONF\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"CONF\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Antecedent & Consequent \"},\"y\":{\"title\":\"Confidence\"}},\"lastColumns\":[\"ANTECEDENT\",\"CONSEQUENT\",\"SUPP\",\"CONF\",\"NUM\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot top 12 Association Rules confidence using model views","message":["%sql ","","SELECT ANTECEDENT_PREDICATE ANTECEDENT,","       CONSEQUENT_PREDICATE CONSEQUENT,","       RULE_SUPPORT SUPP, RULE_CONFIDENCE CONF, NUMBER_OF_ITEMS NUM","FROM DM$VAAR_SH_SAMPLE","ORDER BY RULE_CONFIDENCE DESC","FETCH FIRST 12 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1738092682281,"interpreter":"sql.low","endTime":1738092682443,"results":[{"message":"ANTECEDENT\tCONSEQUENT\tSUPP\tCONF\tNUM\nExtension Cable\tStandard Mouse\t0.18085106382978725\t0.58020477815699656\t2\nStandard Mouse\tMouse Pad\t0.18404255319148935\t0.57284768211920534\t2\nMouse Pad\tStandard Mouse\t0.18404255319148935\t0.57095709570957098\t2\nExtension Cable\tMouse Pad\t0.17765957446808511\t0.56996587030716728\t2\nStandard Mouse\tExtension Cable\t0.18085106382978725\t0.5629139072847682\t2\nMouse Pad\tExtension Cable\t0.17765957446808511\t0.55115511551155116\t2\nCD-RW, High Speed Pack of 5\tExternal 8X CD-ROM\t0.15319148936170213\t0.52363636363636368\t2\nExternal 8X CD-ROM\tCD-RW, High Speed Pack of 5\t0.15319148936170213\t0.50174216027874563\t2\n18\" Flat Panel Graphics Monitor\tSIMM- 16MB PCMCIAII card\t0.15425531914893617\t0.49657534246575341\t2\nSIMM- 16MB PCMCIAII card\t18\" Flat Panel Graphics Monitor\t0.15425531914893617\t0.48494983277591974\t2\nModel SM26273 Black Ink Cartridge\tStandard Mouse\t0.079787234042553196\t0.36764705882352944\t2\nModel SM26273 Black Ink Cartridge\tMouse Pad\t0.077659574468085107\t0.35784313725490197\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Recommending items","In the following example, ***number_of_items*** refers to the total number of items in the rule - across antecedent and consequent. ","Assocation Rules supports only a single consequent item. This query is looking for rules of length two. ","","Here, we use two predicates:","- number_of_items to control for the size of the rule","- EXTRACT to filter on the antecedent","","For example, if the antecedent should include \"Mouse Pad\", then use ***extract(antecedent, '//item[item_name=\"Mouse Pad\"]') IS NOT NULL***"],"enabled":true,"result":{"startTime":1738092682589,"interpreter":"md.low","endTime":1738092682686,"results":[{"message":"<h3 id=\"recommending-items\">Recommending items<\/h3>\n<p>In the following example, <em><strong>number_of_items<\/strong><\/em> refers to the total number of items in the rule - across antecedent and consequent.\nAssocation Rules supports only a single consequent item. This query is looking for rules of length two.<\/p>\n<p>Here, we use two predicates:<\/p>\n<ul>\n<li>number_of_items to control for the size of the rule<\/li>\n<li>EXTRACT to filter on the antecedent<\/li>\n<\/ul>\n<p>For example, if the antecedent should include &quot;Mouse Pad&quot;, then use <em><strong>extract(antecedent, '//item[item_name=&quot;Mouse Pad&quot;]') IS NOT NULL<\/strong><\/em><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Recommend top 5 items based on \"Mouse Pad\"","message":["%sql","","SELECT ROWNUM RANK,","       CONSEQUENT_NAME RECOMMENDATION,","       NUMBER_OF_ITEMS NUM,","       ROUND(RULE_SUPPORT, 3) SUPPORT, ","       ROUND(RULE_CONFIDENCE, 3) CONFIDENCE,","       ROUND(RULE_LIFT, 3) LIFT,","       ROUND(RULE_REVCONFIDENCE, 3) REVERSE_CONFIDENCE","FROM (SELECT * FROM DM$VRAR_SH_SAMPLE","      WHERE NUMBER_OF_ITEMS = 2","      AND EXTRACT(antecedent, '//item[item_name=\"Mouse Pad\"]') IS NOT NULL","      ORDER BY NUMBER_OF_ITEMS)","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738092682825,"interpreter":"sql.low","endTime":1738092682953,"results":[{"message":"RANK\tRECOMMENDATION\tNUM\tSUPPORT\tCONFIDENCE\tLIFT\tREVERSE_CONFIDENCE\n1\t18\" Flat Panel Graphics Monitor\t2\t0.095\t0.294\t0.946\t0.305\n2\tCD-RW, High Speed Pack of 5\t2\t0.076\t0.234\t0.801\t0.258\n3\tExtension Cable\t2\t0.178\t0.551\t1.768\t0.57\n4\tExternal 8X CD-ROM\t2\t0.103\t0.32\t1.049\t0.338\n5\tKeyboard Wrist Rest\t2\t0.047\t0.145\t0.771\t0.249\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","In the following example, we assume the antecedent item is \"Standard Mouse\". ","Select the top 5 rules, ordered by the rule lift to see \"Extension Cable\" as the top recommendation. ","You can change sort to RULE_SUPPORT to see \"Mouse Pad\" as top recommendation."],"enabled":true,"result":{"startTime":1738092683076,"interpreter":"md.low","endTime":1738092683165,"results":[{"message":"<p>In the following example, we assume the antecedent item is &quot;Standard Mouse&quot;.\nSelect the top 5 rules, ordered by the rule lift to see &quot;Extension Cable&quot; as the top recommendation.\nYou can change sort to RULE_SUPPORT to see &quot;Mouse Pad&quot; as top recommendation.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"What items should we recommend when customer has \"Standard Mouse\" in basket?","message":["%sql","","SELECT ROWNUM RANK,","       CONSEQUENT_NAME RECOMMENDATION,","       NUMBER_OF_ITEMS NUM,","       ROUND(RULE_SUPPORT, 3) SUPPORT, ","       ROUND(RULE_CONFIDENCE, 3) CONFIDENCE,","       ROUND(RULE_LIFT, 3) LIFT,","       ROUND(RULE_REVCONFIDENCE, 3) REVERSE_CONFIDENCE","FROM (SELECT * FROM DM$VRAR_SH_SAMPLE","      WHERE NUMBER_OF_ITEMS = 2","      AND EXTRACT(antecedent, '//item[item_name=\"Standard Mouse\"]') IS NOT NULL","      ORDER BY RULE_LIFT DESC, NUMBER_OF_ITEMS)","FETCH FIRST 5 ROWS ONLY;","","---this example recommends \"Extension Cable\" and \"Mouse Pad\"."],"enabled":true,"result":{"startTime":1738092683279,"interpreter":"sql.low","endTime":1738092683430,"results":[{"message":"RANK\tRECOMMENDATION\tNUM\tSUPPORT\tCONFIDENCE\tLIFT\tREVERSE_CONFIDENCE\n1\tExtension Cable\t2\t0.181\t0.563\t1.806\t0.58\n2\tMouse Pad\t2\t0.184\t0.573\t1.777\t0.571\n3\tModel SM26273 Black Ink Cartridge\t2\t0.08\t0.248\t1.144\t0.368\n4\t18\" Flat Panel Graphics Monitor\t2\t0.096\t0.298\t0.959\t0.308\n5\tMultimedia speakers- 3\" cones\t2\t0.057\t0.179\t0.913\t0.293\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md    ","## Alternative way to create AR Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1738092683602,"interpreter":"md.low","endTime":1738092683707,"results":[{"message":"<h2 id=\"alternative-way-to-create-ar-model-using-settings-table\">Alternative way to create AR Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create settings table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP Table AR_SH_SAMPLE_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE AR_SH_SAMPLE_SETTINGS (","  SETTING_NAME  VARCHAR2(30),","  SETTING_VALUE VARCHAR2(4000));","  "],"enabled":true,"result":{"startTime":1738092683825,"interpreter":"script.low","endTime":1738092683966,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable AR_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define AR model settings","message":["%script","","BEGIN       ","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ASSO_MIN_SUPPORT,0.04);","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ASSO_MIN_CONFIDENCE,0.1);","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ASSO_MAX_RULE_LENGTH,2);","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ODMS_ITEM_ID_COLUMN_NAME, 'PROD_NAME');","    COMMIT;","END;"],"enabled":true,"result":{"startTime":1738092684091,"interpreter":"script.low","endTime":1738092684235,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model settings table","message":["%sql","","SELECT * FROM AR_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1738092684357,"interpreter":"sql.low","endTime":1738092684466,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nASSO_MIN_SUPPORT\t.04\nASSO_MIN_CONFIDENCE\t.1\nASSO_MAX_RULE_LENGTH\t2\nODMS_ITEM_ID_COLUMN_NAME\tPROD_NAME\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create model using SALES data with settings table","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('AR_SH_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'AR_SH_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.ASSOCIATION,","    DATA_TABLE_NAME     => 'SALES_TRANS_CUST',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'AR_SH_SAMPLE_SETTINGS'","    );","END;"],"enabled":true,"result":{"startTime":1738092684579,"interpreter":"script.low","endTime":1738092685639,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1738092685761,"interpreter":"md.low","endTime":1738092685853,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1738092685978,"interpreter":"md.low","endTime":1738092686063,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/sql/OML4SQL Attribute Importance MDL.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4SQL Attribute Importance MDL","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738093302710,"interpreter":"md.low","endTime":1738093302810,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Identify key factors","","Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we build an Attribute Importance model, which uses the Minimum Description Length algorithm on data from the SH schema. All functionality runs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1738093302913,"interpreter":"md.low","endTime":1738093303025,"results":[{"message":"<h2 id=\"identify-key-factors\">Identify key factors<\/h2>\n<p>Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we build an Attribute Importance model, which uses the Minimum Description Length algorithm on data from the SH schema. 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responders","message":["%sql","","-- NOTE: The last few attributes have importance value zero, meaning they do not appear to have any predictive value to the target","","SELECT ATTRIBUTE_NAME, ","       round(ATTRIBUTE_IMPORTANCE_VALUE,3) as IMPORTANCE_VALUE, ","       ATTRIBUTE_RANK as RANK ","FROM   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\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification DT.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification DT","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737755711724,"interpreter":"md.medium","endTime":1737755712156,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification Modeling to Predict Target Customers using Decision Tree","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification decision tree models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737755712692,"interpreter":"md.medium","endTime":1737755713126,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-decision-tree\">Classification Modeling to Predict Target Customers using Decision Tree<\/h2>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification decision tree models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" 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<a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a>"],"enabled":true,"result":{"startTime":1737755714659,"interpreter":"md.medium","endTime":1737755715102,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 20 ROWS 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SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1737755716474,"interpreter":"sql.medium","endTime":1737755719566,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"COUNT\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%sql","","SELECT AFFINITY_CARD, count(*) COUNT","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","GROUP BY AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1737755720025,"interpreter":"sql.medium","endTime":1737755720541,"results":[{"message":"AFFINITY_CARD\tCOUNT\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"NUM_CUSTOMERS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Household Size grouped by Affinity Card Response\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1},\"table\":{\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1},\"area\":{\"version\":1},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD ","message":["%sql","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD","ORDER BY HOUSEHOLD_SIZE;"],"enabled":true,"result":{"startTime":1737755720971,"interpreter":"sql.medium","endTime":1737755721441,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n681\t1\t0\n11\t1\t1\n1040\t2\t0\n109\t2\t1\n973\t3\t0\n814\t3\t1\n112\t4-5\t0\n107\t4-5\t1\n146\t6-8\t0\n2\t6-8\t1\n476\t9+\t0\n29\t9+\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Household Size\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1},\"table\":{\"columnsToShow\":[\"NUM_CUSTOMERS\"],\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1},\"area\":{\"version\":1},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD ","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1737755721871,"interpreter":"sql.medium","endTime":1737755722340,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","## Create and display view DEMOGRAPHICS4_V with selected columns"],"enabled":true,"result":{"startTime":1737755722771,"interpreter":"md.medium","endTime":1737755723234,"results":[{"message":"<h2 id=\"create-and-display-view-demographics4_v-with-selected-columns\">Create and display view DEMOGRAPHICS4_V with selected columns<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, CUST_ID, FLAT_PANEL_MONITOR, ","            BOOKKEEPING_APPLICATION, BULK_PACK_DISKETTES, ","            EDUCATION, HOME_THEATER_PACKAGE, ","            HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1737755723700,"interpreter":"script.medium","endTime":1737755724149,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql ","","SELECT * ","FROM DEMOGRAPHICS4_V","FETCH FIRST 20 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737755724584,"interpreter":"sql.medium","endTime":1737755725065,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tFLAT_PANEL_MONITOR\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t102547\t1\t0\t1\t10th\t0\t1\tOther\t0\t1\t0\t1\n0\t101050\t1\t0\t1\t10th\t0\t1\tOther\t0\t1\t0\t1\n0\t100040\t1\t0\t1\t11th\t0\t1\tSales\t0\t1\t0\t1\n0\t102117\t0\t1\t0\tHS-grad\t0\t1\tFarming\t0\t1\t0\t1\n0\t101074\t1\t0\t1\t10th\t0\t1\tHandler\t0\t1\t1\t1\n0\t104179\t1\t0\t1\t10th\t0\t1\tHandler\t0\t1\t1\t1\n0\t100417\t0\t0\t0\t11th\t0\t1\tHandler\t0\t1\t1\t1\n0\t101146\t1\t1\t1\t< Bach.\t0\t1\t?\t0\t1\t1\t1\n0\t103420\t1\t1\t1\t< Bach.\t0\t1\t?\t0\t1\t1\t1\n0\t101987\t1\t1\t1\t< Bach.\t0\t1\tOther\t0\t1\t1\t1\n0\t101279\t0\t1\t0\t< Bach.\t0\t1\tOther\t0\t1\t1\t1\n0\t102106\t0\t1\t0\t< Bach.\t0\t1\tCleric.\t0\t1\t1\t1\n0\t102591\t1\t1\t1\tHS-grad\t0\t1\t?\t0\t1\t1\t1\n0\t101869\t1\t1\t1\tHS-grad\t0\t1\tOther\t0\t1\t1\t1\n0\t103169\t0\t1\t0\tHS-grad\t0\t1\tSales\t0\t1\t1\t1\n0\t103552\t1\t1\t1\tHS-grad\t0\t1\tHandler\t0\t1\t1\t1\n0\t101616\t0\t1\t0\tBach.\t0\t1\tTechSup\t0\t1\t2\t1\n0\t104096\t1\t1\t1\t< Bach.\t0\t1\tOther\t0\t1\t2\t1\n0\t102171\t0\t1\t0\t< Bach.\t0\t1\tCleric.\t0\t1\t2\t1\n0\t100846\t1\t1\t1\t< Bach.\t0\t1\tHandler\t0\t1\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1737755725490,"interpreter":"script.medium","endTime":1737755725921,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for DT ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Decision Tree<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Specify Tree impurity metric for Decision Tree. Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","- Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node). For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> v_setlst('TREE_TERM_MAX_DEPTH') := '7';","- Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data. It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","- Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows. It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","- Specify The minimum number of rows in a node. It requires a number greater than or equal to zero. The default is 10.     ","> v_setlst('TREE_TERM_MINREC_NODE') := '10';","- Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","> v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","- Specify the maximum number of bins for each attribute. For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","> v_setlst('CLAS_MAX_SUP_BINS') := '32';    "],"enabled":true,"result":{"startTime":1737755726385,"interpreter":"md.medium","endTime":1737755726831,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-dt\">Examples of possible setting overrides for DT<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify Tree impurity metric for Decision Tree. Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node). For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MAX_DEPTH') := '7';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data. It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows. It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node. It requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_NODE') := '10';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_SPLIT') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute. For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLAS_MAX_SUP_BINS') := '32';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","  v_setlst('PREP_AUTO') := 'ON';","  v_setlst('ALGO_NAME') := 'ALGO_DECISION_TREE';","    ","  DBMS_DATA_MINING.CREATE_MODEL2(","    MODEL_NAME          => 'DT_CLASS_MODEL',","    MINING_FUNCTION     => 'CLASSIFICATION',","    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customer.","","SELECT ROUND(PREDICTION_PROBABILITY(DT_CLASS_MODEL, '1' USING ","                                    '3' AS HOUSEHOLD_SIZE, ","                                     5 AS YRS_RESIDENCE, ","                                     1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;","  "],"enabled":true,"result":{"startTime":1737755744912,"interpreter":"sql.medium","endTime":1737755745346,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.462\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(DT_CLASS_MODEL USING *) PREDICTION_YRS_RES,","     PREDICTION_DETAILS(DT_CLASS_MODEL USING *) PD","    FROM TEST_DATA_CLAS","    WHERE CUST_ID < 100025","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]')  OUTPRED","FETCH FIRST 10 ROWS ONLY","    "],"enabled":true,"result":{"startTime":1737755745767,"interpreter":"sql.medium","endTime":1737755746276,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".178\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".04\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".178\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".015\" \n100006\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".178\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".04\" \n100010\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".135\" \t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".123\" \n100012\t1\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".244\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".216\" \n100014\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".178\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".015\" \n100017\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".178\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".015\" \n100023\t0\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".123\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".091\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1737755746703,"interpreter":"md.medium","endTime":1737755747127,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_DECISION_TREE');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL('CLASS_MODEL_2', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","  DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1737755747550,"interpreter":"script.medium","endTime":1737755751620,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1737755752043,"interpreter":"md.medium","endTime":1737755752492,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737755752944,"interpreter":"md.medium","endTime":1737755753392,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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new file mode 100644
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--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification GLM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification GLM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858833109,"interpreter":"md.low","endTime":1737858833625,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Generalized Linear Model","","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification generalized linear models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737858834044,"interpreter":"md.low","endTime":1737858834494,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-generalized-linear-model\">Classification Modeling to Predict Target Customers using Generalized Linear Model<\/h1>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification generalized linear models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737858834930,"interpreter":"md.low","endTime":1737858835366,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html\" target=\"_blank\">OML4SQL Generalized Linear Model<\/a>"],"enabled":true,"result":{"startTime":1737858835792,"interpreter":"md.low","endTime":1737858836220,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html\" target=\"_blank\">OML4SQL Generalized Linear Model<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS 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SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1737858839084,"interpreter":"sql.low","endTime":1737858839641,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"COUNT\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD 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AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1737858841004,"interpreter":"sql.low","endTime":1737858841464,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n2\t6-8\t1\n1040\t2\t0\n109\t2\t1\n476\t9+\t0\n29\t9+\t1\n814\t3\t1\n107\t4-5\t1\n112\t4-5\t0\n11\t1\t1\n973\t3\t0\n681\t1\t0\n146\t6-8\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"AFFINITY_0_COUNT\",\"AFFINITY_1_COUNT\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1737858841905,"interpreter":"sql.low","endTime":1737858842352,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view DEMOGRAPHICS4_V with desired columns for analysis","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","AS SELECT AFFINITY_CARD, CUST_ID, BOOKKEEPING_APPLICATION,","          BULK_PACK_DISKETTES, EDUCATION,","          FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, ","          HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","          PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","   FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1737858842849,"interpreter":"script.low","endTime":1737858843535,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * FROM DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737858843974,"interpreter":"sql.low","endTime":1737858844425,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t102547\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t101050\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t100040\t0\t1\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t102117\t1\t0\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t101074\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t104179\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t100417\t0\t0\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t101146\t1\t1\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t103420\t1\t1\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t101987\t1\t1\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (80/20) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (80) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1737858844881,"interpreter":"script.low","endTime":1737858845367,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build a Generalized Linear Model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1737858845826,"interpreter":"md.low","endTime":1737858846241,"results":[{"message":"<h2 id=\"build-a-generalized-linear-model-for-predicting-affinity_card\">Build a Generalized Linear Model for predicting AFFINITY_CARD<\/h2>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1737858846673,"interpreter":"script.low","endTime":1737858849115,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" target=\"_blank\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> v_setlst('GLMS_RIDGE_VALUE') := '2';","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`","> v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> v_setlst('GLMS_MAX_FEATURES') := '30';","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> v_setlst('GLMS_REFERENCE_CLASS_NAME') := '<target_value>';","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> v_setlst('GLMS_NUM_ITERATIONS') := '200';","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> v_setlst('GLMS_BATCH_ROWS') := '2000';","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.  ","> v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';","","<strong> For Oracle Database 23ai and newer. <\/strong>","- Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function. ","   For classification, the options are `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK` or `GLMS_CAUCHIT_LINK`.  For regression, the default is `GLMS_IDENTITY_LINK (default)`.","> v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';"],"enabled":true,"result":{"startTime":1737858849575,"interpreter":"md.low","endTime":1737858850003,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" target=\"_blank\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_VALUE') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_MAX_FEATURES') := '30';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_REFERENCE_CLASS_NAME') := '&lt;target_value&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_NUM_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_BATCH_ROWS') := '2000';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';<\/p>\n<\/blockquote>\n<p><strong> For Oracle Database 23ai and newer. <\/strong><\/p>\n<ul>\n<li>Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function.\nFor classification, the options are <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code> or <code>GLMS_CAUCHIT_LINK<\/code>.  For regression, the default is <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generalized Linear Model with feature gen. and feature selection","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","    v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';","    v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';","    v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE'; ","    v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_AIC';","    v_setlst('GLMS_NUM_ITERATIONS') := '50';","    v_setlst('GLMS_MAX_FEATURES') := '30';","    v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';","    v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","    v_setlst('GLMS_BATCH_ROWS') := '2000';","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_DELETE_ROW';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1737858850434,"interpreter":"script.low","endTime":1737858852389,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generalized Linear Model with sparse, SGD solver","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';","    v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';","    v_setlst('GLMS_NUM_ITERATIONS') := '50';","    v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","    v_setlst('GLMS_BATCH_ROWS') := '2000';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1737858852842,"interpreter":"script.low","endTime":1737858854598,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model with ridge regression","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_ENABLE';","    v_setlst('GLMS_RIDGE_VALUE') := '2';","    v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_DISABLE';","    v_setlst('GLMS_NUM_ITERATIONS') := '50';","    v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","    v_setlst('GLMS_BATCH_ROWS') := '2000';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1737858855021,"interpreter":"script.low","endTime":1737858857075,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='GLM_CLASS_MODEL'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1737858857517,"interpreter":"sql.low","endTime":1737858857990,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAGLM_CLASS_MODEL\tGLM Classification Row Diagnostics\nDM$VCGLM_CLASS_MODEL\tScoring Cost Matrix\nDM$VDGLM_CLASS_MODEL\tGLM Classification Attribute Diagnostics\nDM$VGGLM_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VNGLM_CLASS_MODEL\tNormalization and Missing Value Handling\nDM$VSGLM_CLASS_MODEL\tComputed Settings\nDM$VTGLM_CLASS_MODEL\tClassification Targets\nDM$VWGLM_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get model fit statistics","message":["%sql","","SELECT NAME, round(NUMERIC_VALUE, 4) NUMERIC_VALUE, STRING_VALUE","FROM   DM$VGGLM_CLASS_MODEL;","ORDER BY 1;"],"enabled":true,"result":{"startTime":1737858858430,"interpreter":"sql.low","endTime":1737858858969,"results":[{"message":"NAME\tNUMERIC_VALUE\tSTRING_VALUE\nAIC_INTERCEPT\t3903.4939\t\nAIC_MODEL\t2562.2387\t\nCONVERGED\t\tNO\nDEPENDENT_MEAN\t0.2323\t\nITERATIONS\t7\t\nLR_CHI_SQ\t1423.2552\t\nLR_CHI_SQ_P_VALUE\t0\t\nLR_DF\t41\t\nNEG2_LL_INTERCEPT\t3901.4939\t\nNEG2_LL_MODEL\t2478.2387\t\nNUM_PARAMS\t42\t\nNUM_ROWS\t3599\t\nPCT_CORRECT\t0.8422\t\nPCT_INCORRECT\t0.1578\t\nPCT_TIED\t0\t\nPSEUDO_R_SQ_CS\t0.3266\t\nPSEUDO_R_SQ_N\t0.4936\t\nRANK_DEFICIENCY\t1\t\nSC_INTERCEPT\t3909.6823\t\nSC_MODEL\t2822.152\t\nVALID_COVARIANCE_MATRIX\t\tYES\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get fit details for each attribute","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE, round(COEFFICIENT,3) COEFFICIENT, round(P_VALUE , 3) P_VALUE","FROM   DM$VDGLM_CLASS_MODEL","ORDER BY FEATURE_EXPRESSION;","  "],"enabled":true,"result":{"startTime":1737858859408,"interpreter":"sql.low","endTime":1737858859886,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\tP_VALUE\n\t\t-1.926\t0\nOCCUPATION\tTransp.\t-0.818\t1\nBULK_PACK_DISKETTES\t\t0.239\t0.24\nFLAT_PANEL_MONITOR\t\t-0.282\t0.155\nHOME_THEATER_PACKAGE\t\t0.171\t0.263\nOS_DOC_SET_KANJI\t\t-3.792\t0.44\nPRINTER_SUPPLIES\t\t0\t1\nYRS_RESIDENCE\t\t0.16\t0\nY_BOX_GAMES\t\t-0.98\t0\nEDUCATION\t10th\t0.41\t0.386\nEDUCATION\t11th\t0.658\t0.152\nEDUCATION\t12th\t-0.162\t0.779\nEDUCATION\t1st-4th\t-0.366\t0.555\nEDUCATION\t5th-6th\t-0.436\t0.471\nEDUCATION\t7th-8th\t-0.07\t0.878\nEDUCATION\t9th\t-0.036\t0.94\nEDUCATION\t< Bach.\t0.323\t0.018\nEDUCATION\tAssoc-A\t0.671\t0.004\nEDUCATION\tAssoc-V\t0.289\t0.201\nEDUCATION\tBach.\t0.831\t0\nEDUCATION\tMasters\t1.223\t0\nEDUCATION\tPhD\t1.246\t0.001\nEDUCATION\tPresch.\t-0.101\t0.882\nEDUCATION\tProfsc\t1.113\t0\nHOUSEHOLD_SIZE\t1\t-2.273\t0\nHOUSEHOLD_SIZE\t2\t-2.151\t0\nHOUSEHOLD_SIZE\t4-5\t0.388\t0.032\nHOUSEHOLD_SIZE\t6-8\t-2.188\t0\nHOUSEHOLD_SIZE\t9+\t-2.323\t0\nOCCUPATION\t?\t-1.126\t0\nOCCUPATION\tArmed-F\t0.103\t0.88\nOCCUPATION\tCleric.\t-0.408\t0.035\nOCCUPATION\tCrafts\t-0.75\t0\nOCCUPATION\tFarming\t-1.222\t0\nOCCUPATION\tHandler\t-1.283\t0\nOCCUPATION\tHouse-s\t-0.389\t0.524\nOCCUPATION\tMachine\t-0.741\t0.001\nOCCUPATION\tOther\t-1.277\t0\nOCCUPATION\tProf.\t0.127\t0.44\nOCCUPATION\tProtec.\t-0.035\t0.903\nOCCUPATION\tSales\t-0.441\t0.007\nOCCUPATION\tTechSup\t-0.221\t0.358\nBOOKKEEPING_APPLICATION\t\t1.023\t0.006\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","  DBMS_DATA_MINING.APPLY('GLM_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');","  DBMS_DATA_MINING.COMPUTE_LIFT('APPLY_RESULT','TEST_DATA_CLAS','CUST_ID','AFFINITY_CARD',","                                'LIFT_TABLE','1','PREDICTION','PROBABILITY',100);","END;"],"enabled":true,"result":{"startTime":1737858860330,"interpreter":"script.low","endTime":1737858862188,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"line\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"height\":400,\"lastColumns\":[\"QUANTILE_NUMBER\",\"GAIN_CUMULATIVE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"line","title":"View model's cumulative gains (lift) chart","message":["%sql","","SELECT QUANTILE_NUMBER, ","       ROUND(GAIN_CUMULATIVE,3) GAIN_CUMULATIVE","FROM LIFT_TABLE;"],"enabled":true,"result":{"startTime":1737858862625,"interpreter":"sql.low","endTime":1737858863092,"results":[{"message":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t0.03\n2\t0.068\n3\t0.106\n4\t0.136\n5\t0.169\n6\t0.199\n7\t0.233\n8\t0.254\n9\t0.284\n10\t0.301\n11\t0.322\n12\t0.347\n13\t0.369\n14\t0.394\n15\t0.411\n16\t0.445\n17\t0.466\n18\t0.483\n19\t0.508\n20\t0.525\n21\t0.547\n22\t0.568\n23\t0.593\n24\t0.61\n25\t0.621\n26\t0.636\n27\t0.64\n28\t0.653\n29\t0.665\n30\t0.669\n31\t0.686\n32\t0.698\n33\t0.725\n34\t0.737\n35\t0.746\n36\t0.754\n37\t0.771\n38\t0.788\n39\t0.809\n40\t0.809\n41\t0.818\n42\t0.826\n43\t0.843\n44\t0.86\n45\t0.873\n46\t0.877\n47\t0.898\n48\t0.903\n49\t0.903\n50\t0.911\n51\t0.924\n52\t0.924\n53\t0.932\n54\t0.932\n55\t0.941\n56\t0.941\n57\t0.949\n58\t0.958\n59\t0.962\n60\t0.962\n61\t0.966\n62\t0.97\n63\t0.975\n64\t0.975\n65\t0.979\n66\t0.979\n67\t0.983\n68\t0.983\n69\t0.983\n70\t0.987\n71\t0.987\n72\t0.987\n73\t0.987\n74\t0.987\n75\t0.987\n76\t0.987\n77\t0.987\n78\t0.987\n79\t0.987\n80\t0.987\n81\t0.987\n82\t0.992\n83\t0.996\n84\t0.996\n85\t0.996\n86\t0.996\n87\t0.996\n88\t0.996\n89\t0.996\n90\t1\n91\t1\n92\t1\n93\t1\n94\t1\n95\t1\n96\t1\n97\t1\n98\t1\n99\t1\n100\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display 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"],"enabled":true,"result":{"startTime":1737858866218,"interpreter":"sql.low","endTime":1737858866653,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.245\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details (manually expand the PREDICTION DETAILS)","message":["%sql","","SELECT CUST_ID,","       PREDICTION(GLM_CLASS_MODEL USING *) PREDICTION_AFFINITY_CARD,","       PREDICTION_DETAILS(GLM_CLASS_MODEL USING *) PREDICTION_DETAILS","       FROM TEST_DATA_CLAS","       ORDER BY CUST_ID","       FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737858867100,"interpreter":"sql.low","endTime":1737858867600,"results":[{"message":"CUST_ID\tPREDICTION_AFFINITY_CARD\tPREDICTION_DETAILS\n100015\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".462\" rank=\"1\"/> <Attribute name=\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".017\" rank=\"2\"/> <Attribute name=\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.001\" rank=\"3\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\"-.01\" rank=\"4\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\"-.011\" rank=\"5\"/> <\/Details> \n100017\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".255\" rank=\"1\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Farming\" weight=\".083\" rank=\"2\"/> <Attribute name=\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".005\" rank=\"3\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\"-.003\" rank=\"4\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\"-.003\" rank=\"5\"/> <\/Details> \n100019\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"OCCUPATION\" actualValue=\"Transp.\" weight=\".195\" rank=\"1\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".034\" rank=\"2\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".021\" rank=\"3\"/> <Attribute name=\"YRS_RESIDENCE\" actualValue=\"4\" weight=\".001\" rank=\"4\"/> <Attribute name=\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.002\" rank=\"5\"/> <\/Details> \n100020\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".432\" rank=\"1\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Other\" weight=\".185\" rank=\"2\"/> <Attribute name=\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".011\" rank=\"3\"/> <Attribute name=\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.001\" rank=\"4\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\"-.006\" rank=\"5\"/> <\/Details> \n100025\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".335\" rank=\"1\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".062\" rank=\"2\"/> <Attribute name=\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".012\" rank=\"3\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".007\" rank=\"4\"/> <Attribute name=\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.001\" rank=\"5\"/> <\/Details> \n100028\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".42\" rank=\"1\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".05\" rank=\"2\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".017\" rank=\"3\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".011\" rank=\"4\"/> <Attribute name=\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.001\" rank=\"5\"/> <\/Details> \n100029\t1\t<Details algorithm=\"Generalized Linear Model\" class=\"1\"> <Attribute name=\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".165\" rank=\"1\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".074\" rank=\"2\"/> <Attribute name=\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".037\" rank=\"3\"/> <Attribute name=\"BOOKKEEPING_APPLICATION\" actualValue=\"1\" weight=\".028\" rank=\"4\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".021\" rank=\"5\"/> <\/Details> \n100030\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".047\" rank=\"1\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Other\" weight=\".016\" rank=\"2\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".006\" rank=\"3\"/> <Attribute name=\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".002\" rank=\"4\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".001\" rank=\"5\"/> <\/Details> \n100034\t1\t<Details algorithm=\"Generalized Linear Model\" class=\"1\"> <Attribute name=\"EDUCATION\" actualValue=\"PhD\" weight=\".277\" rank=\"1\"/> <Attribute name=\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".058\" rank=\"2\"/> <Attribute name=\"OCCUPATION\" actualValue=\"Prof.\" weight=\".023\" rank=\"3\"/> <Attribute name=\"BOOKKEEPING_APPLICATION\" actualValue=\"1\" weight=\".021\" rank=\"4\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".015\" rank=\"5\"/> <\/Details> \n100037\t0\t<Details algorithm=\"Generalized Linear Model\" class=\"0\"> <Attribute name=\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".48\" rank=\"1\"/> <Attribute name=\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".027\" rank=\"2\"/> <Attribute name=\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.002\" rank=\"3\"/> <Attribute name=\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\"-.016\" rank=\"4\"/> <Attribute name=\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\"-.019\" rank=\"5\"/> <\/Details> \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details using XMLTABLE","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_AFFINITY_CARD,3) PRED_AFFINITY_CARD,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(GLM_CLASS_MODEL USING *) PREDICTION_AFFINITY_CARD,","     PREDICTION_DETAILS(GLM_CLASS_MODEL USING *) PD","    FROM TEST_DATA_CLAS","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') ","    OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737858868051,"interpreter":"sql.low","endTime":1737858868556,"results":[{"message":"CUST_ID\tPRED_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100015\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".462\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".017\" \t\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.001\" \n100017\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".255\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\".083\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".005\" \n100019\t0\t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".195\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".034\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".021\" \n100020\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".432\" \t\"OCCUPATION\" actualValue=\"Other\" weight=\".185\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".011\" \n100025\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".335\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".062\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".012\" \n100028\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".42\" \t\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".05\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".017\" \n100029\t1\t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".165\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".074\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".037\" \n100030\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".047\" \t\"OCCUPATION\" actualValue=\"Other\" weight=\".016\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".006\" \n100034\t1\t\"EDUCATION\" actualValue=\"PhD\" weight=\".277\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".058\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".023\" \n100037\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".48\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".027\" \t\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.002\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1737858869018,"interpreter":"md.low","endTime":1737858869427,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. 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\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification NB.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification NB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733184168526,"interpreter":"md.low","endTime":1733184168583,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Naive Bayes","","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification naive bayes models using the SH schema data. 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This notebook builds and applies classification naive bayes models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" 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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a>"],"enabled":true,"result":{"startTime":1733184168935,"interpreter":"md.low","endTime":1733184168997,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle Github<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML 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created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Naive Bayes model for predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1733184170331,"interpreter":"md.low","endTime":1733184170392,"results":[{"message":"<h3 id=\"build-a-naive-bayes-model-for-predicting-affinity_card\">Build a Naive Bayes model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NB_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NAIVE_BAYES';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'NB_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1733184170468,"interpreter":"script.low","endTime":1733184171870,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of setting overrides for Naive Bayes","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A04C5F4E-1303-44DC-A7DA-185C969330C8\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","* Specify the value of pairwise threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0. ","> v_setlst('NABS_PAIRWISE_THRESHOLD') := '0';","* Specify the value of singleton threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0. ","> v_setlst('NABS_SINGLETON_THRESHOLD') := '0';"],"enabled":true,"result":{"startTime":1733184171949,"interpreter":"md.low","endTime":1733184172005,"results":[{"message":"<h3 id=\"examples-of-setting-overrides-for-naive-bayes\">Examples of setting overrides for Naive Bayes<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A04C5F4E-1303-44DC-A7DA-185C969330C8\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of pairwise threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NABS_PAIRWISE_THRESHOLD') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of singleton threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NABS_SINGLETON_THRESHOLD') := '0';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes Model using explicit settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NB_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NAIVE_BAYES';","","    v_setlst('NABS_PAIRWISE_THRESHOLD') := '0';","    v_setlst('NABS_SINGLETON_THRESHOLD') := '0';","","","    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"],"enabled":true,"result":{"startTime":1733184173483,"interpreter":"sql.low","endTime":1733184173615,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VBNB_CLASS_MODEL\tAutomatic Data Preparation Binning\nDM$VCNB_CLASS_MODEL\tScoring Cost Matrix\nDM$VGNB_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VPNB_CLASS_MODEL\tNaive Bayes Target Priors\nDM$VSNB_CLASS_MODEL\tComputed Settings\nDM$VTNB_CLASS_MODEL\tClassification Targets\nDM$VVNB_CLASS_MODEL\tNaive Bayes Conditional Probabilities\nDM$VWNB_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get attribute information","message":["%sql","","SELECT * FROM DM$VBNB_CLASS_MODEL"],"enabled":true,"result":{"startTime":1733184173693,"interpreter":"sql.low","endTime":1733184173784,"results":[{"message":"PARTITION_NAME\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tBIN_ID\tLOWER_BIN_BOUNDARY\tUPPER_BIN_BOUNDARY\tATTRIBUTE_VALUE\n\tBOOKKEEPING_APPLICATION\t\t0\t\t0.5\t\n\tBOOKKEEPING_APPLICATION\t\t1\t0.5\t\t\n\tHOME_THEATER_PACKAGE\t\t0\t\t0.5\t\n\tHOME_THEATER_PACKAGE\t\t1\t0.5\t\t\n\tYRS_RESIDENCE\t\t0\t\t2.5\t\n\tYRS_RESIDENCE\t\t1\t2.5\t3.5\t\n\tYRS_RESIDENCE\t\t2\t3.5\t\t\n\tY_BOX_GAMES\t\t0\t\t0.5\t\n\tY_BOX_GAMES\t\t1\t0.5\t\t\n\tEDUCATION\t\t0\t\t\t'10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', '< Bach.', 'Assoc-V', 'HS-grad', 'Presch.'\n\tEDUCATION\t\t1\t\t\t'Masters', 'PhD', 'Profsc'\n\tEDUCATION\t\t2\t\t\t'Assoc-A', 'Bach.'\n\tHOUSEHOLD_SIZE\t\t0\t\t\t'3', '4-5'\n\tHOUSEHOLD_SIZE\t\t1\t\t\t'2', '9+'\n\tHOUSEHOLD_SIZE\t\t2\t\t\t'1', '6-8'\n\tOCCUPATION\t\t0\t\t\t'Exec.', 'Prof.', 'Protec.'\n\tOCCUPATION\t\t1\t\t\t'Cleric.', 'Crafts', 'Machine', 'Sales', 'TechSup', 'Transp.'\n\tOCCUPATION\t\t2\t\t\t'?', 'Armed-F', 'Farming', 'Handler', 'House-s', 'Other'\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get conditional probabilities for attributes","message":["%sql","","SELECT * FROM DM$VVNB_CLASS_MODEL"],"enabled":true,"result":{"startTime":1733184173860,"interpreter":"sql.low","endTime":1733184173977,"results":[{"message":"PARTITION_NAME\tTARGET_NAME\tTARGET_VALUE\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tCONDITIONAL_PROBABILITY\tCOUNT\n\tAFFINITY_CARD\t0\tYRS_RESIDENCE\t\t(3.5;  )\t0.4762611275964392\t963\n\tAFFINITY_CARD\t1\tYRS_RESIDENCE\t\t(3.5;  )\t0.8538812785388128\t561\n\tAFFINITY_CARD\t0\tHOUSEHOLD_SIZE\t\t'2', '9+'\t0.4431256181998022\t896\n\tAFFINITY_CARD\t1\tHOUSEHOLD_SIZE\t\t'2', '9+'\t0.1354642313546423\t89\n\tAFFINITY_CARD\t0\tYRS_RESIDENCE\t\t(2.5; 3.5]\t0.2507418397626113\t507\n\tAFFINITY_CARD\t1\tYRS_RESIDENCE\t\t(2.5; 3.5]\t0.12176560121765602\t80\n\tAFFINITY_CARD\t0\tOCCUPATION\t\t'Exec.', 'Prof.', 'Protec.'\t0.19090009891196838\t386\n\tAFFINITY_CARD\t1\tOCCUPATION\t\t'Exec.', 'Prof.', 'Protec.'\t0.512937595129376\t337\n\tAFFINITY_CARD\t0\tEDUCATION\t\t'Masters', 'PhD', 'Profsc'\t0.040059347181008904\t81\n\tAFFINITY_CARD\t1\tEDUCATION\t\t'Masters', 'PhD', 'Profsc'\t0.19634703196347034\t129\n\tAFFINITY_CARD\t0\tHOUSEHOLD_SIZE\t\t'3', '4-5'\t0.3224530168150347\t652\n\tAFFINITY_CARD\t1\tHOUSEHOLD_SIZE\t\t'3', '4-5'\t0.8508371385083714\t559\n\tAFFINITY_CARD\t0\tOCCUPATION\t\t'Cleric.', 'Crafts', 'Machine', 'Sales', 'TechSup', 'Transp.'\t0.5316518298714145\t1075\n\tAFFINITY_CARD\t1\tOCCUPATION\t\t'Cleric.', 'Crafts', 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the model to a single new record","message":["%sql","","-- Predict the probability of being a high AFFINITY_CARD responder (1) ","--    by providing specific attribute values for a customer.","","SELECT ROUND(PREDICTION_PROBABILITY(NB_CLASS_MODEL, '1' USING ","                                    '3' AS HOUSEHOLD_SIZE, ","                                     5 AS YRS_RESIDENCE, ","                                     1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;","  "],"enabled":true,"result":{"startTime":1733184175645,"interpreter":"sql.low","endTime":1733184175721,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.194\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(NB_CLASS_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(NB_CLASS_MODEL USING *) PD","      FROM TEST_DATA_CLAS","      WHERE CUST_ID < 100025","      ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details' PASSING OUT.PD COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1733184175798,"interpreter":"sql.low","endTime":1733184175889,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".268\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".256\" \n100009\t0\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".499\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".487\" \n100011\t0\t\"OCCUPATION\" actualValue=\"Farming\" weight=\".3\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"0\" weight=\".282\" \n100012\t1\t\"EDUCATION\" actualValue=\"PhD\" weight=\"1.321\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".926\" \n100013\t1\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".912\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".585\" \n100014\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".268\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".256\" \n100018\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".487\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".333\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1733184175969,"interpreter":"md.low","endTime":1733184176028,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_NAIVE_BAYES');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL('CLASS_MODEL_2', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","  DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1733184176108,"interpreter":"script.low","endTime":1733184177603,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1733184177678,"interpreter":"md.low","endTime":1733184177737,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1733184177814,"interpreter":"md.low","endTime":1733184177871,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Classification NN.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Classification NN.dsnb
new file mode 100644
index 00000000..f029d03e
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification NN.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4SQL Classification NN","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733786748258,"interpreter":"md.low","endTime":1733786748715,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Neural Network","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification neural network models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733786749165,"interpreter":"md.low","endTime":1733786749606,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1733786750061,"interpreter":"md.low","endTime":1733786750508,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>"],"enabled":true,"result":{"startTime":1733786750971,"interpreter":"md.low","endTime":1733786751418,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the demographics data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1733786751883,"interpreter":"sql.low","endTime":1733786752343,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in demographics table","message":["%sql","","SELECT COUNT(*) FROM SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1733786752804,"interpreter":"sql.low","endTime":1733786753257,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"COUNT\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%sql","","-- In this data set, hyper-responders to the Affinity Card loyalty program are represented as 1's. ","","SELECT AFFINITY_CARD, COUNT(*) COUNT ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","GROUP BY AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1733786753721,"interpreter":"sql.low","endTime":1733786754186,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"NUM_CUSTOMERS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Household Size slit by Affinity Card Responder\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD","ORDER BY HOUSEHOLD_SIZE, AFFINITY_CARD"],"enabled":true,"result":{"startTime":1733786754647,"interpreter":"sql.low","endTime":1733786755106,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Household Size\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1733786755562,"interpreter":"sql.low","endTime":1733786756023,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view with selected demographics columns","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, BOOKKEEPING_APPLICATION,BULK_PACK_DISKETTES, CUST_ID, EDUCATION,","            FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE,HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1733786756488,"interpreter":"script.low","endTime":1733786756958,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * ","FROM DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1733786757408,"interpreter":"sql.low","endTime":1733786757863,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","  ","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS ","  SELECT * FROM DEMOGRAPHICS4_V ","  MINUS ","  SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1733786758326,"interpreter":"script.low","endTime":1733786758786,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network classification model for predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1733786759240,"interpreter":"md.low","endTime":1733786759686,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a NN model using default settings","message":["%script","","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","        ","END;"],"enabled":true,"result":{"startTime":1733786760141,"interpreter":"script.low","endTime":1733786763626,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Neural Network","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4');\">Neural Network<\/a>","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`, `NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and <strong>starting from Oracle Database 23ai<\/strong> `NNET_ACTIVATIONS_RELU`.","> v_setlst('NNET_HIDDEN_LAYERS') := '3';","","> v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","","> v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';","    ","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","","> v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","","> v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> v_setlst('NNET_REG_LAMBDA') := '1';","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> v_setlst('NNET_ITERATIONS') := '200';","","> v_setlst('NNET_TOLERANCE') := '0.000001';","","> v_setlst('ODMS_RANDOM_SEED') := '0';","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","    v_setlst('LBFGS_HISTORY_DEPTH') := '10';","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';","","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';","","* Specify the lower bound of the region where weights are randomly initialized. `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` must be set together. Setting one and not setting the other raises an error. `NNET_WEIGHT_LOWER_BOUND` must not be greater than `NNET_WEIGHT_UPPER_BOUND`. The default value is -sqrt(6/(l_nodes+r_nodes)). ","   The value of l_nodes for `input layer dense attributes` is (1+number of dense attributes), `input layer sparse attributes` is number of sparse attributes, and `each hidden layer` is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.","   If not specified, the values of `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` are system determined.","> v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'","> v_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'","    ","```"],"enabled":true,"result":{"startTime":1733786764088,"interpreter":"md.low","endTime":1733786764555,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use 3 hidden layers and specific activation functions ","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ","                                     ''NNET_ACTIVATIONS_LOG_SIG'', ","                                     ''NNET_ACTIVATIONS_ARCTAN''';","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_CLASS_MODEL2',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","        ","END;"],"enabled":true,"result":{"startTime":1733786765028,"interpreter":"script.low","endTime":1733786767406,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use held aside regularization and early stopping ","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL3');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS') := '3';","    v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ","                                     ''NNET_ACTIVATIONS_LOG_SIG'', ","                                     ''NNET_ACTIVATIONS_ARCTAN''';","    v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","    v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","    v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    v_setlst('NNET_ITERATIONS') := '200';","    v_setlst('NNET_TOLERANCE') := '0.000001';","    v_setlst('ODMS_RANDOM_SEED') := '0';    ","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'NN_CLASS_MODEL3',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1733786767863,"interpreter":"script.low","endTime":1733786769545,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='NN_CLASS_MODEL'","ORDER BY VIEW_NAME;","  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We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1733786781532,"interpreter":"md.low","endTime":1733786781976,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create NN model settings table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (","    setting_name VARCHAR2(30),","    setting_value VARCHAR2(4000));","/","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_NEURAL_NETWORK');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","END;"],"enabled":true,"result":{"startTime":1733786782437,"interpreter":"script.low","endTime":1733786782972,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL3');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  DBMS_DATA_MINING.CREATE_MODEL('NN_CLASS_MODEL3', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1733786783429,"interpreter":"script.low","endTime":1733786787015,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1733786787481,"interpreter":"md.low","endTime":1733786787928,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733786788398,"interpreter":"md.low","endTime":1733786788844,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Classification RF.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Classification RF.dsnb
new file mode 100644
index 00000000..896b64b9
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification RF.dsnb	
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HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1734626386346,"interpreter":"sql.low","endTime":1734626386785,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view with desired columns","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, BOOKKEEPING_APPLICATION,","            BULK_PACK_DISKETTES, CUST_ID, EDUCATION,","            FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, ","            HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1734626387204,"interpreter":"script.low","endTime":1734626388002,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * ","FROM   DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734626388426,"interpreter":"sql.low","endTime":1734626388872,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t0\t1\t102547\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t101050\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t100040\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t1\t0\t102117\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t0\t1\t101074\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t1\t104179\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t0\t100417\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t1\t1\t101146\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t103420\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t101987\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40)","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS  AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1734626389292,"interpreter":"script.low","endTime":1734626389734,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build Random Forest model to Predict AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1734626390152,"interpreter":"md.low","endTime":1734626390565,"results":[{"message":"<h3 id=\"build-random-forest-model-to-predict-affinity_card\">Build Random Forest model to Predict AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_RANDOM_FOREST';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'RF_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","END;"],"enabled":true,"result":{"startTime":1734626390988,"interpreter":"script.low","endTime":1734626393829,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Random Forest ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","**NOTE:** Random Forest makes use of the Decision Tree settings to configure the construction of individual trees. ","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" target=\"_blank\">Algorithm Settings - Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Algorithm Settings - Decision Tree<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","#### These settings configure the behavior of the Random Forest algorithm ","","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.","> v_setlst('RFOR_NUM_TREES') := '20';","","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)","> v_setlst('RFOR_SAMPLING_RATIO') := '0.5';","","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.","> v_setlst('RFOR_MTRY') := '0';","","#### Decision Tree settings to configure the construction of individual trees.","    ","* Specify Tree impurity metric for each Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","    ","* Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ","> v_setlst('TREE_TERM_MINREC_NODE') := '10';","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","> v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","> v_setlst('CLAS_MAX_SUP_BINS') := '32';    ","    "],"enabled":true,"result":{"startTime":1734626394257,"interpreter":"md.low","endTime":1734626394709,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-random-forest\">Examples of possible setting overrides for Random Forest<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p><strong>NOTE:<\/strong> Random Forest makes use of the Decision Tree settings to configure the construction of individual trees.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" target=\"_blank\">Algorithm Settings - Random Forest<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Algorithm Settings - Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<h4 id=\"these-settings-configure-the-behavior-of-the-random-forest-algorithm\">These settings configure the behavior of the Random Forest algorithm<\/h4>\n<ul>\n<li>Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_NUM_TREES') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_SAMPLING_RATIO') := '0.5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_MTRY') := '0';<\/p>\n<\/blockquote>\n<h4 id=\"decision-tree-settings-to-configure-the-construction-of-individual-trees\">Decision Tree settings to configure the construction of individual trees.<\/h4>\n<ul>\n<li>Specify Tree impurity metric for each Tree.\nTree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\nFor Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MAX_DEPTH') := '7';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\nIt requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\nIt requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node.\nIt requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_NODE') := '10';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\nNo split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_SPLIT') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute.\nFor Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLAS_MAX_SUP_BINS') := '32';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest Model specifying additional settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')           := 'ON';","    v_setlst('ALGO_NAME')           := 'ALGO_RANDOM_FOREST';","    v_setlst('RFOR_MTRY')           := '3';","    v_setlst('RFOR_NUM_TREES')      := '100';","    v_setlst('RFOR_SAMPLING_RATIO') := '0.5';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'RF_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","END;"],"enabled":true,"result":{"startTime":1734626395146,"interpreter":"script.low","endTime":1734626396891,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='RF_CLASS_MODEL'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1734626397316,"interpreter":"sql.low","endTime":1734626397785,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VARF_CLASS_MODEL\tVariable Importance\nDM$VCRF_CLASS_MODEL\tScoring Cost Matrix\nDM$VGRF_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VSRF_CLASS_MODEL\tComputed Settings\nDM$VTRF_CLASS_MODEL\tClassification Targets\nDM$VWRF_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get attribute importance from RF model","message":["%sql","","SELECT ATTRIBUTE_NAME, round(ATTRIBUTE_IMPORTANCE, 3) IMPORTANCE ","FROM   DM$VARF_CLASS_MODEL","ORDER BY ATTRIBUTE_IMPORTANCE 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"],"enabled":true,"result":{"startTime":1734626409413,"interpreter":"sql.low","endTime":1734626409869,"results":[{"message":"CUST_ID\tPREDICTION_AFFINITY_CARD\tPROBABILITY\n100004\t0\t0.915\n100009\t0\t0.637\n100011\t0\t0.964\n100012\t1\t0.759\n100013\t0\t0.528\n100014\t0\t0.88\n100018\t0\t0.839\n100026\t0\t0.554\n100027\t0\t0.809\n100033\t0\t0.915\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_AFFINITY_CARD,3) PRED,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(RF_CLASS_MODEL USING *) PREDICTION_AFFINITY_CARD,","             PREDICTION_DETAILS(RF_CLASS_MODEL USING *) PD","      FROM TEST_DATA_CLAS","      WHERE CUST_ID < 100025","      ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details' PASSING OUT.PD COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734626410310,"interpreter":"sql.low","endTime":1734626410752,"results":[{"message":"CUST_ID\tPRED\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100004\t0\t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".822\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".157\" \n100009\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\"13.078\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"4.37\" \n100011\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"4.745\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\"2.305\" \n100012\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"2.515\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\"1.931\" \n100013\t0\t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".046\" \t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".041\" \n100014\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".235\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".161\" \n100018\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\"8.169\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"4.641\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1734626411181,"interpreter":"md.low","endTime":1734626411620,"results":[{"message":"<h2 id=\"create-classification-model-using-a-settings-table\">Create Classification model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create settings table and build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_RANDOM_FOREST');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL('CLASS_MODEL_2', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1734626412060,"interpreter":"script.low","endTime":1734626413718,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734626414150,"interpreter":"md.low","endTime":1734626414570,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1734626414995,"interpreter":"md.low","endTime":1734626415416,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Classification SVM.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Classification SVM.dsnb
new file mode 100644
index 00000000..6d79c801
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification SVM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4SQL Classification SVM","description":null,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1740588054429,"interpreter":"md.medium","endTime":1740588054887,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification Modeling to Predict Target Customers using Support Vector Machine","","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. ","High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or ","affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  ","This notebook builds and applies classification support vector machine models using the SH schema data. ","All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1740588055394,"interpreter":"md.medium","endTime":1740588055846,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine\">Classification Modeling to Predict Target Customers using Support Vector Machine<\/h2>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program.\nHigh Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or\naffinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.<br />\nThis notebook builds and applies classification support vector machine models using the SH schema data.\nAll processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1740588056351,"interpreter":"md.medium","endTime":1740588056803,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1740588057270,"interpreter":"md.medium","endTime":1740588057725,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS 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Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1740588067241,"interpreter":"script.medium","endTime":1740588067713,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Linear classification model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1740588068183,"interpreter":"md.medium","endTime":1740588068631,"results":[{"message":"<h3 id=\"build-a-svm-linear-classification-model-for-predicting-affinity_card\">Build a SVM Linear classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1740588069103,"interpreter":"script.medium","endTime":1740588073185,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" target=\"_blank\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","   ","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian.","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';","   ","- Value of complexity factor for SVM algorithm (both classification and regression).  Default value estimated from the data by the algorithm.","> v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","   ","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L2';","","> v_setlst('SVMS_EPSILON') := '0.1';","   ","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> v_setlst('SVMS_BATCH_ROWS') := '20000';","   ","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';"],"enabled":true,"result":{"startTime":1740588073671,"interpreter":"md.medium","endTime":1740588074160,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" target=\"_blank\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression).  Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L2';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_EPSILON') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_BATCH_ROWS') := '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVM model using linear kernel with options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","    v_setlst('SVMS_SOLVER')          := 'SVMS_SOLVER_SGD';","    v_setlst('SVMS_CONV_TOLERANCE')  := '0.001'; ","    v_setlst('SVMS_BATCH_ROWS')      := '2000';","    v_setlst('SVMS_REGULARIZER')     := 'SVMS_REGULARIZER_L1';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1740588074630,"interpreter":"script.medium","endTime":1740588076613,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Gaussian classification model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1740588077085,"interpreter":"md.medium","endTime":1740588077527,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-classification-model-for-predicting-affinity_card\">Build a SVM Gaussian classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVM model using Gaussian kernel with options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN'; ","    v_setlst('SVMS_CONV_TOLERANCE')  := '0.001'; ","    v_setlst('SVMS_NUM_PIVOTS')      := '200';","   ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1740588077998,"interpreter":"script.medium","endTime":1740588081083,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","  FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='SVM_CLASS_MODEL'","  ORDER BY 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DM$VGSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1740588084425,"interpreter":"sql.medium","endTime":1740588084894,"results":[{"message":"PARTITION_NAME\tNAME\tNUMERIC_VALUE\tSTRING_VALUE\n\tNUM_ROWS\t2679\t\n\tCONVERGED\t\tYES\n\tITERATIONS\t16\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","  DBMS_DATA_MINING.APPLY('SVM_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');","  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below visual settings","","SELECT QUANTILE_NUMBER, GAIN_CUMULATIVE FROM 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SQL query to dynamically score and select customers with >70% probability of being AFFINITY_CARD responders","message":["%sql","","SELECT * ","FROM (SELECT CUST_ID, PREDICTION_PROBABILITY(SVM_CLASS_MODEL, '1'  USING A.*) PROBABILITY","      FROM TEST_DATA_CLAS A)","WHERE PROBABILITY > 0.7","ORDER BY PROBABILITY DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1740588092624,"interpreter":"sql.medium","endTime":1740588093811,"results":[{"message":"CUST_ID\tPROBABILITY\n104307\t0.9999997383737623\n102326\t0.9999997383737623\n100035\t0.9996180055888884\n104364\t0.999459476354767\n101148\t0.9992592828901383\n101801\t0.9992592828901383\n100486\t0.9992338480865643\n101863\t0.9989400089989482\n101620\t0.9989260016118111\n103873\t0.9989144945184819\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply the model to a single new record","message":["%sql","","-- Predict the probability of being a high AFFINITY_CARD responder (1) ","--    by providing specific attribute values for a customer.","","SELECT ROUND(PREDICTION_PROBABILITY(SVM_CLASS_MODEL, '1' USING ","                                    '3' AS HOUSEHOLD_SIZE, ","                                     5 AS YRS_RESIDENCE, ","                                     1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;","  "],"enabled":true,"result":{"startTime":1740588094289,"interpreter":"sql.medium","endTime":1740588094740,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.182\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(SVM_CLASS_MODEL USING *) PREDICTION_YRS_RES,","     PREDICTION_DETAILS(SVM_CLASS_MODEL USING *) PD","    FROM TRAIN_DATA_CLAS","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1740588095207,"interpreter":"sql.medium","endTime":1740588098392,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".913\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".066\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".036\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".839\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".018\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".016\" \n100005\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".337\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".013\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".011\" \n100007\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".024\" \t\t\n100008\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".029\" \t\t\n100009\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".638\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".38\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".373\" \n100011\t0\t\"OCCUPATION\" actualValue=\"Farming\" weight=\".084\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".007\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\"-.002\" \n100013\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".088\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".027\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".016\" \n100015\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".898\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".428\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".025\" \n100016\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".756\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".003\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".002\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1740588098867,"interpreter":"md.medium","endTime":1740588099312,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_SUPPORT_VECTOR_MACHINES');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'CLASS_MODEL_2', ","    MINING_FUNCTION     => 'CLASSIFICATION', ","    DATA_TABLE_NAME     => 'TRAIN_DATA_CLAS', ","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'AFFINITY_CARD', ","    SETTINGS_TABLE_NAME => 'N1_BUILD_SETTINGS');","  DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1740588099782,"interpreter":"script.medium","endTime":1740588101861,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1740588102334,"interpreter":"md.medium","endTime":1740588102769,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1740588103238,"interpreter":"md.medium","endTime":1740588103679,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Classification XGBoost.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Classification XGBoost.dsnb
new file mode 100644
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--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Classification XGBoost.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification XGBoost","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738085090770,"interpreter":"md.low","endTime":1738085091329,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","--","# Classification Modeling to Predict Target Customers using XGBoost","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification xgboost models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation                             ","###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)","---"],"enabled":true,"result":{"startTime":1738085091866,"interpreter":"md.low","endTime":1738085092336,"results":[{"message":"<p>--<\/p>\n<h1 id=\"classification-modeling-to-predict-target-customers-using-xgboost\">Classification Modeling to Predict Target Customers using XGBoost<\/h1>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification xgboost models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1738085092886,"interpreter":"md.low","endTime":1738085093347,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-xgboost\" target=\"_blank\">OML XGBoost<\/a>"],"enabled":true,"result":{"startTime":1738085093850,"interpreter":"md.low","endTime":1738085094297,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-xgboost\" target=\"_blank\">OML XGBoost<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * FROM SH.SUPPLEMENTARY_DEMOGRAPHICS FETCH FIRST 10 ROWS 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Bach.\tOther\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in SUPPLEMENTARY_DEMOGRAPHICS table","message":["%sql","","SELECT COUNT(*) FROM ","  SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1738085095813,"interpreter":"sql.low","endTime":1738085096290,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"NUM_CUSTOMERS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%sql","","-- Create a Bar chart of HOUSEHOLD_SIZE (Keys), AFFINITY_CARD (Groups), NUM_CUSTOMERS (SUM) (Values)","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1738085096808,"interpreter":"sql.low","endTime":1738085097286,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n681\t1\t0\n11\t1\t1\n1040\t2\t0\n109\t2\t1\n973\t3\t0\n814\t3\t1\n476\t9+\t0\n29\t9+\t1\n112\t4-5\t0\n107\t4-5\t1\n146\t6-8\t0\n2\t6-8\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view DEMOGRAPHICS4_V with desired columns for analysis","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, BOOKKEEPING_APPLICATION,","            BULK_PACK_DISKETTES, CUST_ID, EDUCATION,","            FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, ","            HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","  FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1738085097795,"interpreter":"script.low","endTime":1738085098289,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * FROM DEMOGRAPHICS4_V FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738085098869,"interpreter":"sql.low","endTime":1738085099342,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t0\t1\t102547\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t101050\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t100040\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t1\t0\t102117\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t0\t1\t101074\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t1\t104179\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t0\t100417\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t1\t1\t101146\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t103420\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t101987\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS ","AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_CLAS ","AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1738085099886,"interpreter":"script.low","endTime":1738085100384,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":600,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for XGBoost ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-443B3D58-8B74-422E-8E51-C8F609249B2C\" target=\"_blank\">XGBoost<\/a>","","   ","* Switch between booster - gblinear, gbtree, dart","  By default the system uses the gbtree. The parameters are similar for gbtree and dart, but has more difference comparing to gblinear. The dart and gbtree boosters use tree-based models whereas gblinear uses linear functions.","> v_setlst('booster') := 'gblinear';","","> v_setlst('booster') := 'gbtree';","","> v_setlst('booster') := 'dart';","","* The number of rounds for boosting. The default value is 10. ","> v_setlst('xgboost_num_round') := '10'","","* Regularization parameters for all boosters","> v_setlst('alpha')     := '0.1';","","> v_setlst('eta')       := '0.3';","","> v_setlst('lambda')    := '0.3';","    ","* Regularization parameters for tree based boosters: gbtree, dart","> v_setlst('gamma')           := '0.1';","","> v_setlst('max_delta_step')  := '0.1';","","> v_setlst('max_depth')       := '6';","","> v_setlst('max_leaves')      := '10';","","> v_setlst('min_child_weight'):= '1';","    ","* Subsampling methods for tree based boosters: gbtree, dart ","> v_setlst('colsample_bylevel') := '1';","","> v_setlst('colsample_bynode')  := '1';","","> v_setlst('colsample_bytree')  := '1';","    ","* Num of iteration rounds ","> v_setlst('num_round') := '100';","    ","* For binary classification tasks, the objective is ","> v_setlst('objective') := 'binary:logistic';","","* This setting specifies permitted interactions in the model. Specify the constrains in the form of a nested list where each inner list is a group of features (column names) that are allowed to interact with each other. If a single column is passed in the interactions then, the input is ignored.  ","","  In the following example, features x0, x1, and x2 are allowed to interact with each other but with no other feature. Similarly, x0 and x4 are allowed to interact with each other but with no other feature and so on. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.","> vsetlst('xgboost_interaction_constraints') := '[[x0,x1,x2],[x0,x4],[x5,x6]]'","","* This setting specifies the features (column names) that must obey increasing constraint. The feature names are separated by a comma. ","","  For example, setting value 'x0,x3' sets increasing constraint on features x0 and x3. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.","> vsetlst('xgboost_increase_constraints') := 'x0,x3'"],"enabled":true,"result":{"startTime":1738085100940,"interpreter":"md.low","endTime":1738085101392,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-xgboost\">Examples of possible setting overrides for XGBoost<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-443B3D58-8B74-422E-8E51-C8F609249B2C\" target=\"_blank\">XGBoost<\/a><\/p>\n<\/li>\n<li>\n<p>Switch between booster - gblinear, gbtree, dart\nBy default the system uses the gbtree. The parameters are similar for gbtree and dart, but has more difference comparing to gblinear. The dart and gbtree boosters use tree-based models whereas gblinear uses linear functions.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('booster') := 'gblinear';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('booster') := 'gbtree';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('booster') := 'dart';<\/p>\n<\/blockquote>\n<ul>\n<li>The number of rounds for boosting. The default value is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('xgboost_num_round') := '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization parameters for all boosters<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('alpha')     := '0.1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('eta')       := '0.3';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('lambda')    := '0.3';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization parameters for tree based boosters: gbtree, dart<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('gamma')           := '0.1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('max_delta_step')  := '0.1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('max_depth')       := '6';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('max_leaves')      := '10';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('min_child_weight'):= '1';<\/p>\n<\/blockquote>\n<ul>\n<li>Subsampling methods for tree based boosters: gbtree, dart<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('colsample_bylevel') := '1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('colsample_bynode')  := '1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('colsample_bytree')  := '1';<\/p>\n<\/blockquote>\n<ul>\n<li>Num of iteration rounds<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('num_round') := '100';<\/p>\n<\/blockquote>\n<ul>\n<li>For binary classification tasks, the objective is<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('objective') := 'binary:logistic';<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>This setting specifies permitted interactions in the model. Specify the constrains in the form of a nested list where each inner list is a group of features (column names) that are allowed to interact with each other. If a single column is passed in the interactions then, the input is ignored.<\/p>\n<p>In the following example, features x0, x1, and x2 are allowed to interact with each other but with no other feature. Similarly, x0 and x4 are allowed to interact with each other but with no other feature and so on. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>vsetlst('xgboost_interaction_constraints') := '[[x0,x1,x2],[x0,x4],[x5,x6]]'<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>This setting specifies the features (column names) that must obey increasing constraint. The feature names are separated by a comma.<\/p>\n<p>For example, setting value 'x0,x3' sets increasing constraint on features x0 and x3. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>vsetlst('xgboost_increase_constraints') := 'x0,x3'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a XGBoost model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('XGBOOST_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')  := 'ALGO_XGBOOST';","    v_setlst('PREP_AUTO')  := 'ON';","   ","    dbms_data_mining.create_model2(","        MODEL_NAME          => 'XGBOOST_CLASS_MODEL',","        MINING_FUNCTION     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WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')  := 'ALGO_XGBOOST';","    v_setlst('PREP_AUTO')  := 'ON';","    v_setlst('objective')  := 'binary:logistic';","    v_setlst('booster')    := 'gblinear';","    v_setlst('alpha')      := '0.1';","    v_setlst('eval_metric'):= 'error,auc'; ","    v_setlst('num_round')  := '10'; ","  ","    dbms_data_mining.create_model2(","        MODEL_NAME          => 'XGBOOST_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD',","        SET_LIST            => v_setlst);","END;","/"],"enabled":true,"result":{"startTime":1738085103400,"interpreter":"script.low","endTime":1738085104335,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a XGBoost model using dart booster","message":["%script ","","BEGIN DBMS_DATA_MINING.DROP_MODEL('XGBOOST_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')       := 'ON';","    v_setlst('ALGO_NAME')       := 'ALGO_XGBOOST';","    v_setlst('objective')       := 'binary:logistic';","    v_setlst('booster')         := 'dart';","    v_setlst('eval_metric')     := 'error,auc'; ","    v_setlst('num_round')       := '20';","    ","    v_setlst('alpha')           := '0.1';","    v_setlst('eta')             := '0.3';","    v_setlst('lambda')          := '1';","    v_setlst('gamma')           := '0';","    v_setlst('max_delta_step')  := '0';","    v_setlst('max_depth')       := '6';","    v_setlst('min_child_weight') := '1';","","    v_setlst('colsample_bylevel') := '0.9';","    v_setlst('colsample_bynode')  := '0.9';","    v_setlst('colsample_bytree')  := '0.9';","    ","    v_setlst('one_drop')        := '0';","    v_setlst('normalize_type')  := 'tree';","    v_setlst('rate_drop')       := '0';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'XGBOOST_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'affinity_card',","        SET_LIST            => v_setlst);","END;","/"],"enabled":true,"result":{"startTime":1738085104849,"interpreter":"script.low","endTime":1738085105847,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a XGBoost model using tree booster","message":["%script","","","BEGIN DBMS_DATA_MINING.DROP_MODEL('XGBOOST_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')       := 'ALGO_XGBOOST';","    v_setlst('PREP_AUTO')       := 'ON';","    v_setlst('objective')       := 'binary:logistic';","    v_setlst('booster')         := 'gbtree';","    v_setlst('eval_metric')     := 'error,auc'; ","    v_setlst('num_round')       := '10'; ","    ","    v_setlst('max_depth')       := '5';","    v_setlst('eta')             := '0.5';","    v_setlst('gamma')           := '0.1';","    v_setlst('max_delta_step')  := '0';","    v_setlst('min_child_weight'):= '2';","    ","    v_setlst('colsample_bylevel') := '0.9';","    v_setlst('colsample_bynode')  := '0.9';","    v_setlst('colsample_bytree')  := '0.9';","  ","    dbms_data_mining.create_model2(","        MODEL_NAME          => 'XGBOOST_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'select * from TRAIN_DATA_CLAS',","        CASE_ID_COLUMN_NAME => 'cust_id',","        TARGET_COLUMN_NAME  => 'affinity_card',","        SET_LIST            => v_setlst);","END;","/"],"enabled":true,"result":{"startTime":1738085106311,"interpreter":"script.low","endTime":1738085107400,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"XGBoost model using tree booster and monotone increase constraints","message":["%script","","","BEGIN DBMS_DATA_MINING.DROP_MODEL('XGBOOST_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')       := 'ALGO_XGBOOST';","    v_setlst('PREP_AUTO')       := 'ON';","    v_setlst('objective')       := 'binary:logistic';","    v_setlst('booster')         := 'gbtree';","    v_setlst('eval_metric')     := 'error,auc'; ","    v_setlst('num_round')       := '10'; ","    ","    v_setlst('max_depth')       := '5';","    v_setlst('eta')             := '0.5';","    v_setlst('gamma')           := '0.1';","    v_setlst('max_delta_step')  := '0';","    v_setlst('min_child_weight'):= '2';","    ","    v_setlst('colsample_bylevel') := '0.9';","    v_setlst('colsample_bynode')  := '0.9';","    v_setlst('colsample_bytree')  := '0.9';","","    v_setlst('monotone_increase_constraints') := 'YRS_RESIDENCE';","  ","    dbms_data_mining.create_model2(","        MODEL_NAME          => 'XGBOOST_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'select * from TRAIN_DATA_CLAS',","        CASE_ID_COLUMN_NAME => 'cust_id',","        TARGET_COLUMN_NAME  => 'affinity_card',","        SET_LIST            => v_setlst);","END;","/"],"enabled":true,"result":{"startTime":1738085107933,"interpreter":"script.low","endTime":1738085108917,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"XGBoost model using tree booster and interaction constraints","message":["%script","","","BEGIN DBMS_DATA_MINING.DROP_MODEL('XGBOOST_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')       := 'ALGO_XGBOOST';","    v_setlst('PREP_AUTO')       := 'ON';","    v_setlst('objective')       := 'binary:logistic';","    v_setlst('booster')         := 'gbtree';","    v_setlst('eval_metric')     := 'error,auc'; ","    v_setlst('num_round')       := '10'; ","    ","    v_setlst('max_depth')       := '5';","    v_setlst('eta')             := '0.5';","    v_setlst('gamma')           := '0.1';","    v_setlst('max_delta_step')  := '0';","    v_setlst('min_child_weight'):= '2';","    ","    v_setlst('colsample_bylevel') := '0.9';","    v_setlst('colsample_bynode')  := '0.9';","    v_setlst('colsample_bytree')  := '0.9';","","    v_setlst('interaction_constraints') := ","          '[[BULK_PACK_DISKETTES,FLAT_PANEL_MONITOR],[PRINTER_SUPPLIES, YRS_RESIDENCE]]';","  ","    dbms_data_mining.create_model2(","        MODEL_NAME          => 'XGBOOST_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'select * from TRAIN_DATA_CLAS',","        CASE_ID_COLUMN_NAME => 'cust_id',","        TARGET_COLUMN_NAME  => 'affinity_card',","        SET_LIST            => v_setlst);","END;","/"],"enabled":true,"result":{"startTime":1738085109385,"interpreter":"script.low","endTime":1738085110279,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT   VIEW_NAME, VIEW_TYPE ","FROM     USER_MINING_MODEL_VIEWS","WHERE    MODEL_NAME='XGBOOST_CLASS_MODEL'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1738085110749,"interpreter":"sql.low","endTime":1738085111247,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VCXGBOOST_CLASS_MODEL\tScoring Cost Matrix\nDM$VGXGBOOST_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VIXGBOOST_CLASS_MODEL\tXGBoost Attribute Importance\nDM$VSXGBOOST_CLASS_MODEL\tComputed Settings\nDM$VTXGBOOST_CLASS_MODEL\tClassification Targets\nDM$VWXGBOOST_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get attribute importance of XGBoost model","message":["%sql","","SELECT * from DM$VIXGBOOST_CLASS_MODEL","FETCH FIRST 20 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738085111837,"interpreter":"sql.low","endTime":1738085112312,"results":[{"message":"PNAME\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tGAIN\tCOVER\tFREQUENCY\n\tY_BOX_GAMES\t\t\t0.19856157459519402\t0.2289863715046695\t0.2\n\tOCCUPATION\t\tProf.\t0.09545031894357436\t0.08897481696422534\t0.1\n\tHOUSEHOLD_SIZE\t\t9+\t0.13541251561051335\t0.17629728132991954\t0.2\n\tOCCUPATION\t\tExec.\t0.09265920556631542\t0.09552183197096405\t0.1\n\tHOUSEHOLD_SIZE\t\t1\t0.05871113044716624\t0.09203059994414532\t0.1\n\tYRS_RESIDENCE\t\t\t0.2446205496805007\t0.13037728827490308\t0.1\n\tHOUSEHOLD_SIZE\t\t2\t0.174584705156736\t0.1878118100111733\t0.2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get target information ","message":["%sql","","SELECT * from DM$VTXGBOOST_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1738085112775,"interpreter":"sql.low","endTime":1738085113249,"results":[{"message":"PARTITION_NAME\tTARGET_VALUE\tTARGET_COUNT\tTARGET_WEIGHT\n\t0\t2022\t\n\t1\t657\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get global settings","message":["%sql","","SELECT * from DM$VGXGBOOST_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1738085113715,"interpreter":"sql.low","endTime":1738085114189,"results":[{"message":"PARTITION_NAME\tNAME\tNUMERIC_VALUE\tSTRING_VALUE\n\tNUM_ROWS\t2679\t\n\terror\t0.18738335199701381\t\n\tauc\t0.84630668431123701\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","  DBMS_DATA_MINING.APPLY('XGBOOST_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');","  DBMS_DATA_MINING.COMPUTE_LIFT('APPLY_RESULT','TEST_DATA_CLAS','CUST_ID','AFFINITY_CARD',","                                'LIFT_TABLE','1','PREDICTION','PROBABILITY',100);","END;","/"],"enabled":true,"result":{"startTime":1738085114665,"interpreter":"script.low","endTime":1738085115631,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"area\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"aggregationOption\":\"Sum\",\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"QUANTILE_NUMBER\",\"GAIN_CUMULATIVE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"area","title":"View model's cumulative gains (lift) chart","message":["%sql","","-- Create a Line chart of QUANTILE_NUMBER (Keys), GAIN_CUMULATIVE(SUM) (Values); 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LIFT_TABLE;"],"enabled":true,"result":{"startTime":1738085116132,"interpreter":"sql.low","endTime":1738085116610,"results":[{"message":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t3.21008176688688337349397590361445783133E-02\n2\t6.4201635337737669879518072289156626506E-02\n3\t9.63024553046169060240963855421686746988E-02\n4\t1.28403270675475339759036144578313253012E-01\n5\t1.60374836749341113253012048192771084337E-01\n6\t1.92168674698795180722891566265060240964E-01\n7\t2.2396251264824924819277108433734939759E-01\n8\t2.55756350597703325301204819277108433735E-01\n9\t2.87550206931240578313253012048192771084E-01\n10\t3.19344026496611445783132530120481927711E-01\n11\t3.49953672110316265060240963855421686747E-01\n12\t3.7161982892507530120481927710843373494E-01\n13\t3.91548984022025590361445783132530120482E-01\n14\t4.10607027720256024096385542168674698795E-01\n15\t4.29665071418486457831325301204819277108E-01\n16\t4.48723115116716867469879518072289156627E-01\n17\t4.67781158814947277108433734939759036145E-01\n18\t4.86839239281344120481927710843373493976E-01\n19\t5.05897282979574554216867469879518072289E-01\n20\t5.24955326677804963855421686746987951807E-01\n21\t5.44013370376035397590361445783132530121E-01\n22\t5.62068378494446530120481927710843373494E-01\n23\t5.80123349844691277108433734939759036145E-01\n24\t5.98178357963102409638554216867469879518E-01\n25\t6.16233366081513542168674698795180722892E-01\n26\t6.3428837419992469879518072289156626506E-01\n27\t6.52343308782003012048192771084337349398E-01\n28\t6.70398316900414168674698795180722891566E-01\n29\t6.8845332501882530120481927710843373494E-01\n30\t7.06508333137236433734939759036144578313E-01\n31\t7.24563341255647590361445783132530120482E-01\n32\t7.42618349374058746987951807228915662651E-01\n33\t7.60673283956137060240963855421686746988E-01\n34\t7.78728292074548192771084337349397590361E-01\n35\t7.96787124082266554216867469879518072289E-01\n36\t8.14320171310240963855421686746987951807E-01\n37\t8.30430290497929204819277108433734939759E-01\n38\t8.40635295086596385542168674698795180723E-01\n39\t8.50492914038968385542168674698795180723E-01\n40\t8.58519772449171686746987951807228915663E-01\n41\t8.64027570241905108433734939759036144578E-01\n42\t8.68346138460090361445783132530120481928E-01\n43\t8.74942494587725903614457831325301204819E-01\n44\t8.83227318453501493975903614457831325301E-01\n45\t8.91512142319277108433734939759036144578E-01\n46\t8.99796966185052722891566265060240963855E-01\n47\t9.08081790050828313253012048192771084337E-01\n48\t9.17516207407756024096385542168674698795E-01\n49\t9.27710843373493975903614457831325301205E-01\n50\t9.29282314806099397590361445783132530121E-01\n51\t9.32111036921121975903614457831325301205E-01\n52\t9.34939759036144578313253012048192771084E-01\n53\t9.37768481151167180722891566265060240964E-01\n54\t9.40597203266189759036144578313253012048E-01\n55\t9.43425851844879518072289156626506024096E-01\n56\t9.46254573959902120481927710843373493976E-01\n57\t9.4908329607492469879518072289156626506E-01\n58\t9.51912018189947277108433734939759036145E-01\n59\t9.54740666768637060240963855421686746988E-01\n60\t9.56626506024096385542168674698795180723E-01\n61\t9.58575880671121975903614457831325301205E-01\n62\t9.60525255318147590361445783132530120482E-01\n63\t9.62474629965173204819277108433734939759E-01\n64\t9.64424004612198795180722891566265060241E-01\n65\t9.66265060240963855421686746987951807229E-01\n66\t9.66265060240963855421686746987951807229E-01\n67\t9.68674698795180722891566265060240963855E-01\n68\t9.70450233551393060240963855421686746988E-01\n69\t9.7364619611257530120481927710843373494E-01\n70\t9.7684208513742469879518072289156626506E-01\n71\t9.80038047698606939759036144578313253012E-01\n72\t9.83234010259789156626506024096385542169E-01\n73\t9.85851094808923180722891566265060240964E-01\n74\t9.86963258306664144578313253012048192771E-01\n75\t9.87951807228915662650602409638554216868E-01\n76\t9.87951807228915662650602409638554216868E-01\n77\t9.87951807228915662650602409638554216868E-01\n78\t9.87951807228915662650602409638554216868E-01\n79\t9.87951807228915662650602409638554216868E-01\n80\t9.87951807228915662650602409638554216868E-01\n81\t9.88136309887989445783132530120481927711E-01\n82\t9.88331695924322289156626506024096385542E-01\n83\t9.88527081960655132530120481927710843374E-01\n84\t9.8872246799698795180722891566265060241E-01\n85\t9.88917854033320771084337349397590361446E-01\n86\t9.89113240069653614457831325301204819277E-01\n87\t9.89308552569653614457831325301204819277E-01\n88\t9.89503938605986457831325301204819277108E-01\n89\t9.89699324642319277108433734939759036145E-01\n90\t9.89894710678652096385542168674698795181E-01\n91\t9.90090096714984939759036144578313253012E-01\n92\t9.90285482751317783132530120481927710843E-01\n93\t9.91156594150037638554216867469879518072E-01\n94\t9.9245781955948795180722891566265060241E-01\n95\t9.93759044968938265060240963855421686747E-01\n96\t9.95060270378388554216867469879518072289E-01\n97\t9.96361422251506024096385542168674698795E-01\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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Clustering EM.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Clustering EM.dsnb
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--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Clustering EM.dsnb	
@@ -0,0 +1,1521 @@
+[
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+    "name" : "OML4SQL Clustering EM",
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+        "message" : [
+          "%md",
+          "# Identifying Customer Segments using Expectation Maximization Clustering",
+          "",
+          "Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization. ",
+          "In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using ",
+          "the unsupervised learning Expectation Maximization algorithm. The data exploration, preparation, and machine learning runs inside ",
+          "Oracle Autonomous Database.",
+          "",
+          "See the documentation link below for details on the Expectation Maximization in-database algortihm.",
+          "",
+          "Copyright (c) 2025 Oracle Corporation ",
+          "###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a>",
+          "---"
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+              "message" : "<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering</h1>\n<p>Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization.\nIn this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using\nthe unsupervised learning Expectation Maximization algorithm. The data exploration, preparation, and machine learning runs inside\nOracle Autonomous Database.</p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.</p>\n<p>Copyright (c) 2025 Oracle Corporation</p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0</a></h6>\n<hr />\n",
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+        "width" : 12,
+        "title" : "For more information...",
+        "hasTitle" : true,
+        "message" : [
+          "%md ",
+          "",
+          "* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation</a>",
+          "* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub</a>",
+          "* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page</a>",
+          "* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering</a>",
+          "* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization</a>"
+        ],
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+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Create view joining CUSTOMERS and DEMOGRAPHICS",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "CREATE OR REPLACE VIEW CUSTOMERS360_V AS",
+          "   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, ",
+          "          a.CUST_YEAR_OF_BIRTH, a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, ",
+          "          b.EDUCATION, b.AFFINITY_CARD, ",
+          "          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES",
+          "   FROM SH.CUSTOMERS a, SH.SUPPLEMENTARY_DEMOGRAPHICS b",
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+              "message" : "\nView CUSTOMERS360_V created.\n\n\n---------------------------\n",
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+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT COUNT(*) COUNT",
+          "FROM CUSTOMERS360_V;"
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+          "BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_CLUS_SAMPLE_1');",
+          "EXCEPTION WHEN OTHERS THEN NULL; END;",
+          "/",
+          "DECLARE",
+          "    v_setlst DBMS_DATA_MINING.SETTING_LIST;",
+          "BEGIN",
+          "    v_setlst('ALGO_NAME')        := 'ALGO_EXPECTATION_MAXIMIZATION';",
+          "",
+          "    DBMS_DATA_MINING.CREATE_MODEL2(",
+          "        MODEL_NAME          => 'EM_SH_CLUS_SAMPLE_1',",
+          "        MINING_FUNCTION     => 'CLUSTERING',",
+          "        DATA_QUERY          => 'select * from CUSTOMERS360_V',",
+          "        SET_LIST            => v_setlst,",
+          "        CASE_ID_COLUMN_NAME => 'CUST_ID');",
+          "END;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
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+        "dynamicFormParams" : "{}"
+      },
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+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "### Examples of possible setting overrides for Expectation-Maximization clustering",
+          "",
+          "If the user does not override the default settings, then relevant settings are determined by the algorithm.",
+          "",
+          "A complete list of settings can be found in the Documentation link:",
+          "",
+          "* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" target=\"_blank\">Algorithm Settings</a>",
+          "* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings</a>",
+          "",
+          "* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.",
+          "> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';  ",
+          "*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components. For EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10. ",
+          "> v_setlst('CLUS_NUM_CLUSTERS')       := '5';",
+          "",
+          "#### Expectation Maximization Settings for Learning     ",
+          "* Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected. It requires `EMCS_MODEL_SEARCH_ENABLE` or `EMCS_MODEL_SEARCH_DISABLE`.  The default is `EMCS_MODEL_SEARCH_DISABLE`.      ",
+          "> v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';",
+          "* Specify the maximum number of iterations in the EM algorithm. It requires a number larger than 1.  The default is 100. ",
+          "> v_setlst('EMCS_NUM_ITERATIONS')     := '20';",
+          "   ",
+          "#### Expectation Maximization Settings for Cluster Statistics",
+          "*  Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and `GET_MODEL_DETAILS_EM` only returns taxonomy (hierarchy) and cluster counts. It requires either `EMCS_CLUS_STATS_ENABLE` or `EMCS_CLUS_STATS_DISABLE`.  Default is `EMCS_CLUS_STATS_ENABLE`.     ",
+          "> v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';",
+          "     ",
+          "#### Expectation Maximization Settings for Data Preparation and Analysis",
+          "* Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. It requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.",
+          "> v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094467169,
+          "endTime" : 1738094467261,
+          "interpreter" : "md.low",
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation-Maximization clustering</h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.</p>\n<p>A complete list of settings can be found in the Documentation link:</p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" target=\"_blank\">Algorithm Settings</a></p>\n</li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings</a></p>\n</li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO</code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE</code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO</code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT</code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW</code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.</p>\n</li>\n</ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';</p>\n</blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components. For EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10.</li>\n</ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS')       := '5';</p>\n</blockquote>\n<h4 id=\"expectation-maximization-settings-for-learning\">Expectation Maximization Settings for Learning</h4>\n<ul>\n<li>Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected. It requires <code>EMCS_MODEL_SEARCH_ENABLE</code> or <code>EMCS_MODEL_SEARCH_DISABLE</code>.  The default is <code>EMCS_MODEL_SEARCH_DISABLE</code>.</li>\n</ul>\n<blockquote>\n<p>v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';</p>\n</blockquote>\n<ul>\n<li>Specify the maximum number of iterations in the EM algorithm. It requires a number larger than 1.  The default is 100.</li>\n</ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_ITERATIONS')     := '20';</p>\n</blockquote>\n<h4 id=\"expectation-maximization-settings-for-cluster-statistics\">Expectation Maximization Settings for Cluster Statistics</h4>\n<ul>\n<li>Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and <code>GET_MODEL_DETAILS_EM</code> only returns taxonomy (hierarchy) and cluster counts. It requires either <code>EMCS_CLUS_STATS_ENABLE</code> or <code>EMCS_CLUS_STATS_DISABLE</code>.  Default is <code>EMCS_CLUS_STATS_ENABLE</code>.</li>\n</ul>\n<blockquote>\n<p>v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';</p>\n</blockquote>\n<h4 id=\"expectation-maximization-settings-for-data-preparation-and-analysis\">Expectation Maximization Settings for Data Preparation and Analysis</h4>\n<ul>\n<li>Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. It requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.</li>\n</ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';</p>\n</blockquote>\n",
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+            }
+          ],
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+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 5,
+        "title" : "Build the EM Clustering model with explicit settings",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "-- Additional settings are available in the documentation",
+          "",
+          "BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_CLUS_SAMPLE_2');",
+          "EXCEPTION WHEN OTHERS THEN NULL; END;",
+          "/",
+          "DECLARE",
+          "    v_setlst DBMS_DATA_MINING.SETTING_LIST;",
+          "BEGIN",
+          "    v_setlst('ALGO_NAME')               := 'ALGO_EXPECTATION_MAXIMIZATION';",
+          "    v_setlst('PREP_AUTO')               := 'ON';",
+          "    v_setlst('CLUS_NUM_CLUSTERS')       := '5';",
+          "    v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';",
+          "    v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';",
+          "    v_setlst('EMCS_NUM_ITERATIONS')     := '20';",
+          "    v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';",
+          "    v_setlst('EMCS_RANDOM_SEED')        := '123456';",
+          "",
+          "    DBMS_DATA_MINING.CREATE_MODEL2(",
+          "        MODEL_NAME          => 'EM_SH_CLUS_SAMPLE_2',",
+          "        MINING_FUNCTION     => 'CLUSTERING',",
+          "        DATA_QUERY          => 'select * from CUSTOMERS360_V',",
+          "        SET_LIST            => v_setlst,",
+          "        CASE_ID_COLUMN_NAME => 'CUST_ID');",
+          "END;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
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+          "startTime" : 1738094467371,
+          "endTime" : 1738094469601,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 7,
+        "title" : "Display the model metadata",
+        "hasTitle" : true,
+        "message" : [
+          "%sql ",
+          "",
+          "SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE, PARTITIONED",
+          "FROM   USER_MINING_MODELS",
+          "WHERE  MODEL_NAME LIKE 'EM_SH_CLUS_SAMPLE_%';"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094469709,
+          "endTime" : 1738094469814,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\tPARTITIONED\nEM_SH_CLUS_SAMPLE_1\tCLUSTERING\tEXPECTATION_MAXIMIZATION\t2025-01-28 20:01:03\tNO\nEM_SH_CLUS_SAMPLE_2\tCLUSTERING\tEXPECTATION_MAXIMIZATION\t2025-01-28 20:01:07\tNO\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display the model signature",
+        "hasTitle" : true,
+        "message" : [
+          "%sql ",
+          "",
+          "-- The model signature includes the attributes used in the model training data,",
+          "--   and the attributes expected to apply the model to new data for scoring. ",
+          "",
+          "SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE",
+          "FROM   USER_MINING_MODEL_ATTRIBUTES",
+          "WHERE  MODEL_NAME = 'EM_SH_CLUS_SAMPLE_2'",
+          "ORDER BY ATTRIBUTE_NAME;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094469962,
+          "endTime" : 1738094470109,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Display the actual settings used by the algorithm",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT SETTING_NAME, SETTING_VALUE",
+          "FROM   USER_MINING_MODEL_SETTINGS",
+          "WHERE  MODEL_NAME = 'EM_SH_CLUS_SAMPLE_2'",
+          "ORDER BY SETTING_NAME;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094470222,
+          "endTime" : 1738094470382,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPECTATION_MAXIMIZATION\nCLUS_NUM_CLUSTERS\t5\nEMCS_CLUSTER_COMPONENTS\tEMCS_CLUSTER_COMP_ENABLE\nEMCS_CLUSTER_STATISTICS\tEMCS_CLUS_STATS_ENABLE\nEMCS_CLUSTER_THRESH\t2\nEMCS_LINKAGE_FUNCTION\tEMCS_LINKAGE_SINGLE\nEMCS_LOGLIKE_IMPROVEMENT\t.001\nEMCS_MAX_NUM_ATTR_2D\t50\nEMCS_MIN_PCT_ATTR_SUPPORT\t.1\nEMCS_MODEL_SEARCH\tEMCS_MODEL_SEARCH_ENABLE\nEMCS_NUM_COMPONENTS\t20\nEMCS_NUM_DISTRIBUTION\tEMCS_NUM_DISTR_SYSTEM\nEMCS_NUM_EQUIWIDTH_BINS\t11\nEMCS_NUM_ITERATIONS\t20\nEMCS_NUM_PROJECTIONS\t50\nEMCS_NUM_QUANTILE_BINS\t10\nEMCS_RANDOM_SEED\t123456\nEMCS_REMOVE_COMPONENTS\tEMCS_REMOVE_COMPS_ENABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Display the EM model detail views",
+        "hasTitle" : true,
+        "message" : [
+          "%sql ",
+          "",
+          "-- Display the list of cluster model detail views with content as shown",
+          "--   in the VIEW_TYPE column.",
+          "",
+          "SELECT VIEW_NAME, VIEW_TYPE ",
+          "FROM   USER_MINING_MODEL_VIEWS",
+          "WHERE  MODEL_NAME='EM_SH_CLUS_SAMPLE_2'",
+          "ORDER BY VIEW_NAME;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094470509,
+          "endTime" : 1738094470622,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "VIEW_NAME\tVIEW_TYPE\nDM$VAEM_SH_CLUS_SAMPLE_2\tClustering Attribute Statistics\nDM$VBEM_SH_CLUS_SAMPLE_2\tAttribute Pair Kullback-Leibler Divergence\nDM$VDEM_SH_CLUS_SAMPLE_2\tClustering Description\nDM$VFEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Bernoulli parameters\nDM$VGEM_SH_CLUS_SAMPLE_2\tGlobal Name-Value Pairs\nDM$VHEM_SH_CLUS_SAMPLE_2\tClustering Histograms\nDM$VIEM_SH_CLUS_SAMPLE_2\tUnsupervised Attribute Importance\nDM$VMEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Gaussian parameters\nDM$VNEM_SH_CLUS_SAMPLE_2\tNormalization and Missing Value Handling\nDM$VOEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Components\nDM$VPEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Projections\nDM$VREM_SH_CLUS_SAMPLE_2\tClustering Rules\nDM$VSEM_SH_CLUS_SAMPLE_2\tComputed Settings\nDM$VWEM_SH_CLUS_SAMPLE_2\tModel Build Alerts\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Show Cluster details",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "-- For each cluster_id, the DM$VD* view provides the number of records in each cluster,",
+          "--   the parent cluster id, and the level in the hierarchy. ",
+          "",
+          "SELECT CLUSTER_ID, RECORD_COUNT, PARENT, TREE_LEVEL",
+          "FROM   DM$VDEM_SH_CLUS_SAMPLE_2",
+          "ORDER BY CLUSTER_ID;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094470732,
+          "endTime" : 1738094470839,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tRECORD_COUNT\tPARENT\tTREE_LEVEL\n1\t4500\t\t1\n2\t4310\t1\t2\n3\t190\t1\t2\n4\t2725\t2\t3\n5\t1585\t2\t3\n6\t1322\t4\t4\n7\t1403\t4\t4\n8\t1170\t7\t5\n9\t233\t7\t5\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Show leaf clusters IDs",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CLUSTER_ID",
+          "FROM   DM$VDEM_SH_CLUS_SAMPLE_2",
+          "WHERE  LEFT_CHILD_ID IS NULL AND RIGHT_CHILD_ID IS NULL",
+          "ORDER BY CLUSTER_ID"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
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+          "startTime" : 1738094470946,
+          "endTime" : 1738094471050,
+          "interpreter" : "sql.low",
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+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\n3\n5\n6\n8\n9\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Show Cluster details for cluster 9",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "-- For cluster_id 9, this output shows statistics for each attribute per centroid, with the mean and variance (for numericals) ",
+          "--   or mode (for categoricals).",
+          "",
+          "SELECT CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, ROUND(MEAN,3) MEAN, ROUND(VARIANCE,3) VARIANCE, MODE_VALUE",
+          "FROM   DM$VAEM_SH_CLUS_SAMPLE_2",
+          "WHERE  CLUSTER_ID = 9",
+          "ORDER BY ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094471159,
+          "endTime" : 1738094471263,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tMEAN\tVARIANCE\tMODE_VALUE\n9\tAFFINITY_CARD\t\t0.167\t0.14\t\n9\tCUST_CREDIT_LIMIT\t\t8291.845\t16218338.39\t\n9\tCUST_GENDER\t\t\t\tM\n9\tCUST_INCOME_LEVEL\t\t\t\tJ: 190,000 - 249,999\n9\tCUST_MARITAL_STATUS\t\t\t\tMarried\n9\tCUST_YEAR_OF_BIRTH\t\t1976.506\t4.941\t\n9\tEDUCATION\t\t\t\tHS-grad\n9\tHOUSEHOLD_SIZE\t\t\t\t3\n9\tOCCUPATION\t\t\t\tCrafts\n9\tYRS_RESIDENCE\t\t2.987\t0.659\t\n9\tY_BOX_GAMES\t\t1\t0\t\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 8,
+        "title" : "Compare histograms for attribute YRS_RESIDENCE of clusters 6, 8, and 9",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "-- Use the DM$VH view for cluster histogram data. Pivot the data. ",
+          "",
+          "SELECT * ",
+          "FROM (SELECT CLUSTER_ID, BIN_ID, COUNT",
+          "      FROM   DM$VHEM_SH_CLUS_SAMPLE_2",
+          "      WHERE  (CLUSTER_ID = 6 OR CLUSTER_ID = 9 OR CLUSTER_ID = 8) ",
+          "      AND ATTRIBUTE_NAME = 'YRS_RESIDENCE') A",
+          "PIVOT (SUM(COUNT) FOR CLUSTER_ID IN(6 AS cluster_6, 9 AS cluster_9, 8 AS cluster_8))",
+          "ORDER BY BIN_ID"
+        ],
+        "selectedVisualization" : "bar",
+        "visualizationConfig" : "[{\"bar\":{\"showSeries\":[\"CLUSTER_6\",\"CLUSTER_8\",\"CLUSTER_9\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"CLUSTER_6\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"CLUSTER_8\",\"lineType\":\"straight\",\"borderColor\":\"rgb(249, 183, 14)\",\"borderWidth\":0,\"color\":\"rgb(249, 183, 14)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(249, 183, 14)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"CLUSTER_9\",\"lineType\":\"straight\",\"borderColor\":\"rgb(249, 183, 14)\",\"borderWidth\":0,\"color\":\"rgb(249, 183, 14)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(249, 183, 14)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Bin ID\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"BIN_ID\",\"CLUSTER_6\",\"CLUSTER_9\",\"CLUSTER_8\"],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094471392,
+          "endTime" : 1738094471525,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "BIN_ID\tCLUSTER_6\tCLUSTER_9\tCLUSTER_8\n1\t17\t7\t282\n2\t71\t41\t468\n3\t235\t145\t363\n4\t383\t32\t46\n5\t308\t4\t10\n6\t176\t4\t1\n7\t72\t0\t0\n8\t30\t0\t0\n9\t30\t0\t0\n10\t0\t0\t0\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "View bin boundaries for YRS_RESIDENCE",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CLUSTER_ID, ATTRIBUTE_NAME, BIN_ID, LOWER_BIN_BOUNDARY, UPPER_BIN_BOUNDARY, COUNT ",
+          "FROM   DM$VHEM_SH_CLUS_SAMPLE_2",
+          "WHERE  ATTRIBUTE_NAME = 'YRS_RESIDENCE'",
+          "ORDER BY CLUSTER_ID DESC, BIN_ID;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094471644,
+          "endTime" : 1738094471750,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tATTRIBUTE_NAME\tBIN_ID\tLOWER_BIN_BOUNDARY\tUPPER_BIN_BOUNDARY\tCOUNT\n9\tYRS_RESIDENCE\t1\t0.0\t1.0\t7\n9\tYRS_RESIDENCE\t2\t1.0\t2.0\t41\n9\tYRS_RESIDENCE\t3\t2.0\t3.0\t145\n9\tYRS_RESIDENCE\t4\t3.0\t4.0\t32\n9\tYRS_RESIDENCE\t5\t4.0\t5.0\t4\n9\tYRS_RESIDENCE\t6\t5.0\t6.0\t4\n9\tYRS_RESIDENCE\t7\t6.0\t7.0\t0\n9\tYRS_RESIDENCE\t8\t7.0\t8.0\t0\n9\tYRS_RESIDENCE\t9\t8.0\t14.0\t0\n9\tYRS_RESIDENCE\t10\t\t\t0\n8\tYRS_RESIDENCE\t1\t0.0\t1.0\t282\n8\tYRS_RESIDENCE\t2\t1.0\t2.0\t468\n8\tYRS_RESIDENCE\t3\t2.0\t3.0\t363\n8\tYRS_RESIDENCE\t4\t3.0\t4.0\t46\n8\tYRS_RESIDENCE\t5\t4.0\t5.0\t10\n8\tYRS_RESIDENCE\t6\t5.0\t6.0\t1\n8\tYRS_RESIDENCE\t7\t6.0\t7.0\t0\n8\tYRS_RESIDENCE\t8\t7.0\t8.0\t0\n8\tYRS_RESIDENCE\t9\t8.0\t14.0\t0\n8\tYRS_RESIDENCE\t10\t\t\t0\n7\tYRS_RESIDENCE\t1\t0.0\t1.0\t289\n7\tYRS_RESIDENCE\t2\t1.0\t2.0\t509\n7\tYRS_RESIDENCE\t3\t2.0\t3.0\t508\n7\tYRS_RESIDENCE\t4\t3.0\t4.0\t78\n7\tYRS_RESIDENCE\t5\t4.0\t5.0\t14\n7\tYRS_RESIDENCE\t6\t5.0\t6.0\t5\n7\tYRS_RESIDENCE\t7\t6.0\t7.0\t0\n7\tYRS_RESIDENCE\t8\t7.0\t8.0\t0\n7\tYRS_RESIDENCE\t9\t8.0\t14.0\t0\n7\tYRS_RESIDENCE\t10\t\t\t0\n6\tYRS_RESIDENCE\t1\t0.0\t1.0\t17\n6\tYRS_RESIDENCE\t2\t1.0\t2.0\t71\n6\tYRS_RESIDENCE\t3\t2.0\t3.0\t235\n6\tYRS_RESIDENCE\t4\t3.0\t4.0\t383\n6\tYRS_RESIDENCE\t5\t4.0\t5.0\t308\n6\tYRS_RESIDENCE\t6\t5.0\t6.0\t176\n6\tYRS_RESIDENCE\t7\t6.0\t7.0\t72\n6\tYRS_RESIDENCE\t8\t7.0\t8.0\t30\n6\tYRS_RESIDENCE\t9\t8.0\t14.0\t30\n6\tYRS_RESIDENCE\t10\t\t\t0\n5\tYRS_RESIDENCE\t1\t0.0\t1.0\t11\n5\tYRS_RESIDENCE\t2\t1.0\t2.0\t28\n5\tYRS_RESIDENCE\t3\t2.0\t3.0\t170\n5\tYRS_RESIDENCE\t4\t3.0\t4.0\t418\n5\tYRS_RESIDENCE\t5\t4.0\t5.0\t396\n5\tYRS_RESIDENCE\t6\t5.0\t6.0\t284\n5\tYRS_RESIDENCE\t7\t6.0\t7.0\t151\n5\tYRS_RESIDENCE\t8\t7.0\t8.0\t67\n5\tYRS_RESIDENCE\t9\t8.0\t14.0\t60\n5\tYRS_RESIDENCE\t10\t\t\t0\n4\tYRS_RESIDENCE\t1\t0.0\t1.0\t306\n4\tYRS_RESIDENCE\t2\t1.0\t2.0\t580\n4\tYRS_RESIDENCE\t3\t2.0\t3.0\t743\n4\tYRS_RESIDENCE\t4\t3.0\t4.0\t461\n4\tYRS_RESIDENCE\t5\t4.0\t5.0\t322\n4\tYRS_RESIDENCE\t6\t5.0\t6.0\t181\n4\tYRS_RESIDENCE\t7\t6.0\t7.0\t72\n4\tYRS_RESIDENCE\t8\t7.0\t8.0\t30\n4\tYRS_RESIDENCE\t9\t8.0\t14.0\t30\n4\tYRS_RESIDENCE\t10\t\t\t0\n3\tYRS_RESIDENCE\t1\t0.0\t1.0\t7\n3\tYRS_RESIDENCE\t2\t1.0\t2.0\t17\n3\tYRS_RESIDENCE\t3\t2.0\t3.0\t48\n3\tYRS_RESIDENCE\t4\t3.0\t4.0\t58\n3\tYRS_RESIDENCE\t5\t4.0\t5.0\t39\n3\tYRS_RESIDENCE\t6\t5.0\t6.0\t8\n3\tYRS_RESIDENCE\t7\t6.0\t7.0\t8\n3\tYRS_RESIDENCE\t8\t7.0\t8.0\t4\n3\tYRS_RESIDENCE\t9\t8.0\t14.0\t1\n3\tYRS_RESIDENCE\t10\t\t\t0\n2\tYRS_RESIDENCE\t1\t0.0\t1.0\t317\n2\tYRS_RESIDENCE\t2\t1.0\t2.0\t608\n2\tYRS_RESIDENCE\t3\t2.0\t3.0\t913\n2\tYRS_RESIDENCE\t4\t3.0\t4.0\t879\n2\tYRS_RESIDENCE\t5\t4.0\t5.0\t718\n2\tYRS_RESIDENCE\t6\t5.0\t6.0\t465\n2\tYRS_RESIDENCE\t7\t6.0\t7.0\t223\n2\tYRS_RESIDENCE\t8\t7.0\t8.0\t97\n2\tYRS_RESIDENCE\t9\t8.0\t14.0\t90\n2\tYRS_RESIDENCE\t10\t\t\t0\n1\tYRS_RESIDENCE\t1\t0.0\t1.0\t324\n1\tYRS_RESIDENCE\t2\t1.0\t2.0\t625\n1\tYRS_RESIDENCE\t3\t2.0\t3.0\t961\n1\tYRS_RESIDENCE\t4\t3.0\t4.0\t937\n1\tYRS_RESIDENCE\t5\t4.0\t5.0\t757\n1\tYRS_RESIDENCE\t6\t5.0\t6.0\t473\n1\tYRS_RESIDENCE\t7\t6.0\t7.0\t231\n1\tYRS_RESIDENCE\t8\t7.0\t8.0\t101\n1\tYRS_RESIDENCE\t9\t8.0\t14.0\t91\n1\tYRS_RESIDENCE\t10\t\t\t0\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "### Cluster Rules",
+          "In the following example, the view shows attribute level details for the rule associated with each cluster id. ",
+          "For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ",
+          "Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.",
+          "The larger the number, the more relevant the attribute.",
+          " ",
+          "The query below reverse-transforms the data to its original values, since the data used for building the model was normalized."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094471865,
+          "endTime" : 1738094471950,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h3 id=\"cluster-rules\">Cluster Rules</h3>\n<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.\nFor an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.\nConfidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.\nThe larger the number, the more relevant the attribute.</p>\n<p>The query below reverse-transforms the data to its original values, since the data used for building the model was normalized.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Display rule details for leaf clusters",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR,",
+          "       NUMERIC_VALUE, ATTRIBUTE_VALUE, SUPPORT, ROUND(CONFIDENCE,3) CONFIDENCE",
+          "FROM   DM$VREM_SH_CLUS_SAMPLE_2",
+          "WHERE  cluster_id IN (SELECT cluster_id",
+          "                      FROM   DM$VDEM_SH_CLUS_SAMPLE_2",
+          "                      WHERE  LEFT_CHILD_ID is NULL and RIGHT_CHILD_ID is NULL)",
+          "ORDER BY CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR, NUMERIC_VALUE, ATTRIBUTE_VALUE;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094472068,
+          "endTime" : 1738094472222,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tOPERATOR\tNUMERIC_VALUE\tATTRIBUTE_VALUE\tSUPPORT\tCONFIDENCE\n3\tAFFINITY_CARD\t\t<=\t1.0\t\t190\t0.079\n3\tAFFINITY_CARD\t\t>=\t0.0\t\t190\t0.079\n3\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t190\t0.001\n3\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t190\t0.001\n3\tCUST_GENDER\t\t=\t\tF\t190\t0.457\n3\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t173\t0.006\n3\tCUST_MARITAL_STATUS\t\t=\t\tMarried\t188\t0.125\n3\tCUST_YEAR_OF_BIRTH\t\t<=\t1978.7\t\t181\t0.045\n3\tCUST_YEAR_OF_BIRTH\t\t>\t1942.2\t\t181\t0.045\n3\tEDUCATION\t\tIN\t\t< Bach.\t167\t0.004\n3\tEDUCATION\t\tIN\t\tAssoc-A\t167\t0.004\n3\tEDUCATION\t\tIN\t\tAssoc-V\t167\t0.004\n3\tEDUCATION\t\tIN\t\tBach.\t167\t0.004\n3\tEDUCATION\t\tIN\t\tHS-grad\t167\t0.004\n3\tEDUCATION\t\tIN\t\tMasters\t167\t0.004\n3\tHOUSEHOLD_SIZE\t\t=\t\t4-5\t188\t0.321\n3\tOCCUPATION\t\tIN\t\t?\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tCleric.\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tExec.\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tMachine\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tOther\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tProf.\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tSales\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tTechSup\t174\t0.019\n3\tYRS_RESIDENCE\t\t<=\t5.0\t\t162\t0.01\n3\tYRS_RESIDENCE\t\t>\t1.0\t\t162\t0.01\n3\tY_BOX_GAMES\t\t<=\t0.0\t\t186\t0.129\n3\tY_BOX_GAMES\t\t>=\t0.0\t\t186\t0.129\n5\tAFFINITY_CARD\t\t<=\t1.0\t\t1585\t0.051\n5\tAFFINITY_CARD\t\t>=\t0.0\t\t1585\t0.051\n5\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t1585\t0\n5\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t1585\t0\n5\tCUST_GENDER\t\t=\t\tM\t1585\t0.192\n5\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t1432\t0.001\n5\tCUST_MARITAL_STATUS\t\t=\t\tMarried\t1583\t0.123\n5\tCUST_YEAR_OF_BIRTH\t\t<=\t1978.7\t\t1522\t0.041\n5\tCUST_YEAR_OF_BIRTH\t\t>\t1934.9\t\t1522\t0.041\n5\tEDUCATION\t\tIN\t\t< Bach.\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tAssoc-A\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tAssoc-V\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tBach.\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tHS-grad\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tMasters\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tProfsc\t1405\t0.003\n5\tHOUSEHOLD_SIZE\t\t=\t\t3\t1573\t0.147\n5\tOCCUPATION\t\tIN\t\t?\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tCleric.\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tCrafts\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tExec.\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tFarming\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tMachine\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tOther\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tProf.\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tSales\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tTechSup\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tTransp.\t1514\t0.009\n5\tYRS_RESIDENCE\t\t<=\t7.0\t\t1419\t0.031\n5\tYRS_RESIDENCE\t\t>\t2.0\t\t1419\t0.031\n5\tY_BOX_GAMES\t\t<=\t0.0\t\t1585\t0.177\n5\tY_BOX_GAMES\t\t>=\t0.0\t\t1585\t0.177\n6\tAFFINITY_CARD\t\t<=\t0.0\t\t1195\t0.027\n6\tAFFINITY_CARD\t\t>=\t0.0\t\t1195\t0.027\n6\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t1322\t0\n6\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t1322\t0\n6\tCUST_GENDER\t\tIN\t\tF\t1322\t0.039\n6\tCUST_GENDER\t\tIN\t\tM\t1322\t0.039\n6\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t1201\t0\n6\tCUST_MARITAL_STATUS\t\tIN\t\tDivorc.\t1255\t0.11\n6\tCUST_MARITAL_STATUS\t\tIN\t\tNeverM\t1255\t0.11\n6\tCUST_MARITAL_STATUS\t\tIN\t\tSepar.\t1255\t0.11\n6\tCUST_MARITAL_STATUS\t\tIN\t\tWidowed\t1255\t0.11\n6\tCUST_YEAR_OF_BIRTH\t\t<=\t1978.7\t\t1270\t0.038\n6\tCUST_YEAR_OF_BIRTH\t\t>\t1934.9\t\t1270\t0.038\n6\tEDUCATION\t\tIN\t\t< Bach.\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tAssoc-A\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tAssoc-V\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tBach.\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tHS-grad\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tMasters\t1143\t0.001\n6\tHOUSEHOLD_SIZE\t\tIN\t\t2\t1169\t0.12\n6\tHOUSEHOLD_SIZE\t\tIN\t\t9+\t1169\t0.12\n6\tOCCUPATION\t\tIN\t\t?\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tCleric.\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tCrafts\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tExec.\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tHandler\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tMachine\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tOther\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tProf.\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tSales\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tTechSup\t1213\t0.002\n6\tYRS_RESIDENCE\t\t<=\t6.0\t\t1102\t0.013\n6\tYRS_RESIDENCE\t\t>\t2.0\t\t1102\t0.013\n6\tY_BOX_GAMES\t\t<=\t0.0\t\t1322\t0.177\n6\tY_BOX_GAMES\t\t>=\t0.0\t\t1322\t0.177\n8\tAFFINITY_CARD\t\t<=\t0.0\t\t1149\t0.091\n8\tAFFINITY_CARD\t\t>=\t0.0\t\t1149\t0.091\n8\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t1170\t0.001\n8\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t1170\t0.001\n8\tCUST_GENDER\t\tIN\t\tF\t1170\t0.015\n8\tCUST_GENDER\t\tIN\t\tM\t1170\t0.015\n8\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t1084\t0.002\n8\tCUST_MARITAL_STATUS\t\t=\t\tNeverM\t1041\t0.104\n8\tCUST_YEAR_OF_BIRTH\t\t<=\t1986.0\t\t1170\t0.135\n8\tCUST_YEAR_OF_BIRTH\t\t>\t1971.4\t\t1170\t0.135\n8\tEDUCATION\t\tIN\t\t10th\t1050\t0.008\n8\tEDUCATION\t\tIN\t\t11th\t1050\t0.008\n8\tEDUCATION\t\tIN\t\t< Bach.\t1050\t0.008\n8\tEDUCATION\t\tIN\t\tAssoc-V\t1050\t0.008\n8\tEDUCATION\t\tIN\t\tBach.\t1050\t0.008\n8\tEDUCATION\t\tIN\t\tHS-grad\t1050\t0.008\n8\tHOUSEHOLD_SIZE\t\tIN\t\t1\t1065\t0.11\n8\tHOUSEHOLD_SIZE\t\tIN\t\t2\t1065\t0.11\n8\tHOUSEHOLD_SIZE\t\tIN\t\t9+\t1065\t0.11\n8\tOCCUPATION\t\tIN\t\t?\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tCleric.\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tCrafts\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tExec.\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tHandler\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tMachine\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tOther\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tProf.\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tSales\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tTechSup\t1103\t0.011\n8\tYRS_RESIDENCE\t\t<=\t3.0\t\t1113\t0.093\n8\tYRS_RESIDENCE\t\t>=\t0.0\t\t1113\t0.093\n8\tY_BOX_GAMES\t\t<=\t1.0\t\t1169\t0.476\n8\tY_BOX_GAMES\t\t>\t0.0\t\t1169\t0.476\n9\tAFFINITY_CARD\t\t<=\t0.0\t\t194\t0.006\n9\tAFFINITY_CARD\t\t>=\t0.0\t\t194\t0.006\n9\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t233\t0.004\n9\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t233\t0.004\n9\tCUST_GENDER\t\t=\t\tM\t220\t0.098\n9\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tC: 50,000 - 69,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t225\t0.005\n9\tCUST_MARITAL_STATUS\t\t=\t\tMarried\t232\t0.123\n9\tCUST_YEAR_OF_BIRTH\t\t<=\t1986.0\t\t233\t0.145\n9\tCUST_YEAR_OF_BIRTH\t\t>\t1971.4\t\t233\t0.145\n9\tEDUCATION\t\tIN\t\t11th\t201\t0.006\n9\tEDUCATION\t\tIN\t\t< Bach.\t201\t0.006\n9\tEDUCATION\t\tIN\t\tAssoc-V\t201\t0.006\n9\tEDUCATION\t\tIN\t\tBach.\t201\t0.006\n9\tEDUCATION\t\tIN\t\tHS-grad\t201\t0.006\n9\tHOUSEHOLD_SIZE\t\t=\t\t3\t214\t0.12\n9\tOCCUPATION\t\tIN\t\tCleric.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tCrafts\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tExec.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tHandler\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tMachine\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tProf.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tProtec.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tSales\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tTransp.\t211\t0.016\n9\tYRS_RESIDENCE\t\t<=\t4.0\t\t218\t0.067\n9\tYRS_RESIDENCE\t\t>\t1.0\t\t218\t0.067\n9\tY_BOX_GAMES\t\t<=\t1.0\t\t233\t0.48\n9\tY_BOX_GAMES\t\t>\t0.0\t\t233\t0.48\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Apply model to CUSTOMERS360_V and display customer count per cluster",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "-- For a descriptive ML technique like clustering, \"scoring\" involves assigning each record to a cluster with a certain probability. ",
+          "-- Hover over bars in the chart to view the size of each cluster. ",
+          "",
+          "SELECT CLUSTER_ID(EM_SH_CLUS_SAMPLE_2 USING *) AS CLUSTER_ID, COUNT(*) AS CNT ",
+          "FROM   CUSTOMERS360_V",
+          "GROUP BY CLUSTER_ID(EM_SH_CLUS_SAMPLE_2 USING *)"
+        ],
+        "selectedVisualization" : "bar",
+        "visualizationConfig" : "[{\"bar\":{\"showSeries\":[\"CNT\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"CNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Cluster ID\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"CLUSTER_ID\",\"CNT\"],\"version\":1}}]",
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094472331,
+          "endTime" : 1738094472465,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CLUSTER_ID\tCNT\n6\t1322\n8\t1170\n5\t1585\n9\t233\n3\t190\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 5,
+        "title" : "List ten customers most likely in cluster 6",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CUST_ID, ",
+          "       ROUND(CLUSTER_PROBABILITY(EM_SH_CLUS_SAMPLE_2, 6 USING *),4) PROB",
+          "FROM CUSTOMERS360_V",
+          "ORDER BY PROB DESC",
+          "FETCH FIRST 10 ROWS ONLY"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094472599,
+          "endTime" : 1738094472749,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CUST_ID\tPROB\n100134\t1\n102893\t1\n101392\t1\n101297\t1\n103829\t1\n102740\t1\n100804\t1\n103791\t1\n103948\t1\n102828\t1\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "List most relevant predictors and their values for specific customers cluster assignments",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "-- For customer id, pick among 101362 or 100456, then rerun the paragraph.  ",
+          "--   These represent customers with > 20% likelihood of assignment to their cluster.",
+          "",
+          "SELECT CUST_ID,",
+          "       CLUSTER_ID,",
+          "       ROUND(PROB*100,2) PROB_PCT,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE",
+          "FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ",
+          "             CLUSTER_DETAILS(EM_SH_CLUS_SAMPLE_2 USING T.*) DETAIL",
+          "      FROM (SELECT V.*, CLUSTER_SET(EM_SH_CLUS_SAMPLE_2, NULL, 0.2 USING *) PSET",
+          "            FROM CUSTOMERS360_V V",
+          "            WHERE cust_id = ${CUST_ID ='101362','101362'|'100456'}) T,",
+          "                  TABLE(T.PSET) S",
+          "            ORDER BY 2 DESC) OUT,",
+          "            XMLTABLE('/Details'",
+          "                      PASSING OUT.DETAIL",
+          "                      COLUMNS ",
+          "                      \"Attribute1\" XMLType PATH 'Attribute[1]',",
+          "                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
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+        "enabled" : true,
+        "forms" : "[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"paragraphId\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isNotebook\":false,\"isProgrammatic\":false}]",
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+          "startTime" : 1738094472884,
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+            {
+              "message" : "CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n101362\t8\t100\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" weight=\"1\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".015\" \n",
+              "type" : "TABLE"
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+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{\"CUST_ID\":\"'101362'\"}"
+      },
+      {
+        "row" : 0,
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+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## Cluster data using analytic functions",
+          "",
+          "In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic functions.",
+          "",
+          "In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. ",
+          "Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly. ",
+          "Rerunning the same query with different input will result in a different segmentation. ",
+          "It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.",
+          "",
+          "Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers."
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
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+        "enabled" : true,
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+        "result" : {
+          "startTime" : 1738094473408,
+          "endTime" : 1738094473491,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"cluster-data-using-analytic-functions\">Cluster data using analytic functions</h2>\n<p>In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic functions.</p>\n<p>In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments.\nNote that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.\nRerunning the same query with different input will result in a different segmentation.\nIt also provides the main reasons (attributes) why a given customer is placed into a specific cluster.</p>\n<p>Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Build and apply a transient model using analytic functions and show prediction details",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT CUST_ID,",
+          "       CLUSTER_ID,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,",
+          "       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE",
+          "FROM (SELECT * ",
+          "      FROM (SELECT CUST_ID,",
+          "                   CLUSTER_ID(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ",
+          "                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,",
+          "                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_ID,",
+          "                   CLUSTER_DETAILS(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ",
+          "                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,",
+          "                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_DET",
+          "            FROM CUSTOMERS360_V)",
+          "            ORDER By 1) OUT,",
+          "            XMLTABLE('/Details'",
+          "                      PASSING OUT.CLUSTER_DET",
+          "                      COLUMNS ",
+          "                      \"Attribute1\" XMLType PATH 'Attribute[1]',",
+          "                      \"Attribute2\" XMLType PATH 'Attribute[2]',",
+          "                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED",
+          "FETCH FIRST 10 ROWS ONLY;",
+          "                      "
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094473595,
+          "endTime" : 1738094473853,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "CUST_ID\tCLUSTER_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t3\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".125\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".084\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".081\" \n100002\t4\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".087\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".083\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \n100003\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".081\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".075\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \n100004\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".096\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".079\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".079\" \n100005\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".28\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".05\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".048\" \n100006\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".276\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1983\" weight=\".204\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".158\" \n100007\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".087\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".082\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".046\" \n100008\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".089\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".086\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".073\" \n100009\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".236\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".035\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".025\" \n100010\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".229\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\".152\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".122\" \n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "",
+          "## Build a clustering model using a settings table",
+          "",
+          "The settings table is an alternative way to specify algorithm settings to build a model.",
+          "First, drop the table, create it, and then populate it with settings before building the model. "
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094473980,
+          "endTime" : 1738094474061,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"build-a-clustering-model-using-a-settings-table\">Build a clustering model using a settings table</h2>\n<p>The settings table is an alternative way to specify algorithm settings to build a model.\nFirst, drop the table, create it, and then populate it with settings before building the model.</p>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : "Create and populate model settings table",
+        "hasTitle" : true,
+        "message" : [
+          "%script ",
+          "",
+          "BEGIN EXECUTE IMMEDIATE 'DROP TABLE EM_SH_SAMPLE_SETTINGS PURGE';",
+          "EXCEPTION WHEN OTHERS THEN NULL; END;",
+          "/",
+          "CREATE TABLE EM_SH_SAMPLE_SETTINGS (SETTING_NAME  VARCHAR2(30),",
+          "                                    SETTING_VALUE VARCHAR2(4000));",
+          "",
+          "BEGIN       ",
+          "   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME,               DBMS_DATA_MINING.ALGO_EXPECTATION_MAXIMIZATION);",
+          "   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.EMCS_MODEL_SEARCH,       DBMS_DATA_MINING.EMCS_MODEL_SEARCH_ENABLE);",
+          "   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO,               DBMS_DATA_MINING.PREP_AUTO_ON);",
+          "   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.EMCS_CLUSTER_STATISTICS, DBMS_DATA_MINING.EMCS_CLUS_STATS_ENABLE);",
+          "END;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094474182,
+          "endTime" : 1738094474344,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable EM_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "View the model settings table content",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "-- NOTE: The settings table can be reused or modified in multiple model builds. ",
+          "",
+          "SELECT * FROM EM_SH_SAMPLE_SETTINGS;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094474469,
+          "endTime" : 1738094474573,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPECTATION_MAXIMIZATION\nEMCS_MODEL_SEARCH\tEMCS_MODEL_SEARCH_ENABLE\nPREP_AUTO\tON\nEMCS_CLUSTER_STATISTICS\tEMCS_CLUS_STATS_ENABLE\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Build an EM Clustering model",
+        "hasTitle" : true,
+        "message" : [
+          "%script",
+          "",
+          "BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_CLUS_SAMPLE_3');",
+          "EXCEPTION WHEN OTHERS THEN NULL; END;",
+          "/",
+          "",
+          "BEGIN",
+          "DBMS_DATA_MINING.CREATE_MODEL(",
+          "    MODEL_NAME          => 'EM_SH_CLUS_SAMPLE_3',",
+          "    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,",
+          "    DATA_TABLE_NAME     => 'CUSTOMERS360_V',",
+          "    CASE_ID_COLUMN_NAME => 'CUST_ID',",
+          "    SETTINGS_TABLE_NAME => 'EM_SH_SAMPLE_SETTINGS');",
+          "END;"
+        ],
+        "selectedVisualization" : "raw",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094474678,
+          "endTime" : 1738094479009,
+          "interpreter" : "script.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n",
+              "type" : "TEXT"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 6,
+        "title" : "Display the actual settings used by the algorithm",
+        "hasTitle" : true,
+        "message" : [
+          "%sql",
+          "",
+          "SELECT SETTING_NAME, SETTING_VALUE",
+          "FROM   USER_MINING_MODEL_SETTINGS",
+          "WHERE  MODEL_NAME = 'EM_SH_CLUS_SAMPLE_3'",
+          "ORDER BY SETTING_NAME;"
+        ],
+        "selectedVisualization" : "table",
+        "visualizationConfig" : null,
+        "hideCode" : false,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : false,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094479107,
+          "endTime" : 1738094479211,
+          "interpreter" : "sql.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPECTATION_MAXIMIZATION\nCLUS_NUM_CLUSTERS\t10\nEMCS_CLUSTER_COMPONENTS\tEMCS_CLUSTER_COMP_ENABLE\nEMCS_CLUSTER_STATISTICS\tEMCS_CLUS_STATS_ENABLE\nEMCS_CLUSTER_THRESH\t2\nEMCS_LINKAGE_FUNCTION\tEMCS_LINKAGE_SINGLE\nEMCS_LOGLIKE_IMPROVEMENT\t.001\nEMCS_MAX_NUM_ATTR_2D\t50\nEMCS_MIN_PCT_ATTR_SUPPORT\t.1\nEMCS_MODEL_SEARCH\tEMCS_MODEL_SEARCH_ENABLE\nEMCS_NUM_COMPONENTS\t20\nEMCS_NUM_DISTRIBUTION\tEMCS_NUM_DISTR_SYSTEM\nEMCS_NUM_EQUIWIDTH_BINS\t11\nEMCS_NUM_ITERATIONS\t100\nEMCS_NUM_PROJECTIONS\t50\nEMCS_RANDOM_SEED\t0\nEMCS_REMOVE_COMPONENTS\tEMCS_REMOVE_COMPS_ENABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n",
+              "type" : "TABLE"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md",
+          "## End of Script"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : false,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094479308,
+          "endTime" : 1738094479383,
+          "interpreter" : "md.low",
+          "taskStatus" : "SUCCESS",
+          "status" : "SUCCESS",
+          "results" : [
+            {
+              "message" : "<h2 id=\"end-of-script\">End of Script</h2>\n",
+              "type" : "HTML"
+            }
+          ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      },
+      {
+        "row" : 0,
+        "col" : 0,
+        "sizeX" : 0,
+        "width" : 12,
+        "title" : null,
+        "hasTitle" : false,
+        "message" : [
+          "%md"
+        ],
+        "selectedVisualization" : "html",
+        "visualizationConfig" : null,
+        "hideCode" : true,
+        "hideResult" : true,
+        "hideGutter" : true,
+        "hideVizConfig" : true,
+        "hideInIFrame" : false,
+        "enabled" : true,
+        "forms" : "[]",
+        "result" : {
+          "startTime" : 1738094479482,
+          "endTime" : 1738094479557,
+          "interpreter" : "md.low",
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+          "status" : "SUCCESS",
+          "results" : [ ],
+          "forms" : "[]"
+        },
+        "relations" : [ ],
+        "dynamicFormParams" : "{}"
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Clustering KM.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Clustering KM.dsnb
new file mode 100644
index 00000000..74383132
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Clustering KM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4SQL Clustering KM","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738093729904,"interpreter":"md.low","endTime":1738093730058,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Identifying Customer Segments using K-means Clustering","","Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization. ","In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema","using the unsupervised learning K-means algorithm. The data exploration, preparation, and machine learning runs inside","Oracle Autonomous Database.","","See the documentation link below for details on the K-means in-database algortihm.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>---"],"enabled":true,"result":{"startTime":1738093730179,"interpreter":"md.low","endTime":1738093730292,"results":[{"message":"<h2 id=\"identifying-customer-segments-using-k-means-clustering\">Identifying Customer Segments using K-means Clustering<\/h2>\n<p>Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization.\nIn this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema\nusing the unsupervised learning K-means algorithm. The data exploration, preparation, and machine learning runs inside\nOracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the K-means in-database algortihm.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10---\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>---<\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1738093730421,"interpreter":"md.low","endTime":1738093730510,"results":[{"message":"<p><img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a>"],"enabled":true,"result":{"startTime":1738093730615,"interpreter":"md.low","endTime":1738093730705,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view joining CUSTOMERS and DEMOGRAPHICS","message":["%script","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, ","          a.CUST_YEAR_OF_BIRTH, a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, ","          b.EDUCATION, b.AFFINITY_CARD, ","          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","   FROM SH.CUSTOMERS a, SH.SUPPLEMENTARY_DEMOGRAPHICS b","   WHERE a.CUST_ID = b.CUST_ID;","   "],"enabled":true,"result":{"startTime":1738093730815,"interpreter":"script.low","endTime":1738093733446,"results":[{"message":"\nView CUSTOMERS360_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in CUSTOMERS360_V","message":["%sql","","SELECT COUNT(*) FROM CUSTOMERS360_V;"],"enabled":true,"result":{"startTime":1738093733546,"interpreter":"sql.low","endTime":1738093733693,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data in view CUSTOMERS360_V","message":["%sql","","SELECT * ","FROM CUSTOMERS360_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738093733801,"interpreter":"sql.low","endTime":1738093733927,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\n100804\tF\tDivorc.\t1943\tA: Below 30,000\t1500\t< Bach.\t0\t2\tProf.\t6\t0\n101610\tM\tNeverM\t1985\tI: 170,000 - 189,999\t3000\t11th\t0\t1\tHandler\t0\t1\n102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\n100593\tM\tMarried\t1963\tG: 130,000 - 149,999\t1500\tHS-grad\t1\t3\tProf.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a K-means model using the CUSTOMERS360_V view"],"enabled":true,"result":{"startTime":1738093734033,"interpreter":"md.low","endTime":1738093734112,"results":[{"message":"<h3 id=\"build-a-k-means-model-using-the-customers360_v-view\">Build a K-means model using the CUSTOMERS360_V view<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the K-means Clustering model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')   := 'ALGO_KMEANS';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'KM_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1738093734218,"interpreter":"script.low","endTime":1738093736044,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for K-means clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';  ","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced K-means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') := '5';","* Specify the distance function for K-means. It requires either the function `KMNS_COSINE` or `KMNS_EUCLIDEAN`.  The default distance function is `KMNS_EUCLIDEAN`. ","> v_setlst('KMNS_DISTANCE') := 'KMNS_EUCLIDEAN';  ","* Specify this setting which determines the level of cluster detail that are computed during the build. It requires one of the following options:","> `KMNS_DETAILS_NONE`: No cluster details are computed. Only the scoring information is persisted.","> `KMNS_DETAILS_HIERARCHY`: Cluster hierarchy and cluster record counts are computed. This is the default value.","> `KMNS_DETAILS_ALL`: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.","> v_setlst('KMNS_DETAILS') := 'KMNS_DETAILS_ALL';  ","* Specify the Maximum number of iterations for K-means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in `KMNS_CONV_TOLERANCE`, is satisfied. It requires a positive integer, and the default number of iterations is 20. ","> v_setlst('KMNS_ITERATIONS') := '3';","* Specify the number of bins in the attribute histogram produced by K-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. It requires a positive integer, and the default number of histogram bins is 11.     ","> v_setlst('KMNS_NUM_BINS') := '10';     "],"enabled":true,"result":{"startTime":1738093736169,"interpreter":"md.low","endTime":1738093736265,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-k-means-clustering\">Examples of possible setting overrides for K-means clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced K-means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') := '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the distance function for K-means. It requires either the function <code>KMNS_COSINE<\/code> or <code>KMNS_EUCLIDEAN<\/code>.  The default distance function is <code>KMNS_EUCLIDEAN<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('KMNS_DISTANCE') := 'KMNS_EUCLIDEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify this setting which determines the level of cluster detail that are computed during the build. It requires one of the following options:<\/li>\n<\/ul>\n<blockquote>\n<p><code>KMNS_DETAILS_NONE<\/code>: No cluster details are computed. Only the scoring information is persisted.\n<code>KMNS_DETAILS_HIERARCHY<\/code>: Cluster hierarchy and cluster record counts are computed. This is the default value.\n<code>KMNS_DETAILS_ALL<\/code>: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.\nv_setlst('KMNS_DETAILS') := 'KMNS_DETAILS_ALL';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Maximum number of iterations for K-means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in <code>KMNS_CONV_TOLERANCE<\/code>, is satisfied. It requires a positive integer, and the default number of iterations is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('KMNS_ITERATIONS') := '3';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of bins in the attribute histogram produced by K-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. It requires a positive integer, and the default number of histogram bins is 11.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('KMNS_NUM_BINS') := '10';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a K-means Clustering model using explicit settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')        := 'ALGO_KMEANS';","    V_setlst('PREP_AUTO')        := 'ON';","    V_setlst('KMNS_DISTANCE')    := 'KMNS_EUCLIDEAN';","    V_setlst('KMNS_DETAILS')     := 'KMNS_DETAILS_ALL';","    V_setlst('KMNS_ITERATIONS')  := '3';","    V_setlst('KMNS_NUM_BINS')    := '10';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'KM_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1738093736389,"interpreter":"script.low","endTime":1738093737949,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model signature","message":["%sql ","","-- The model signature includes the attributes used in model training data,","-- and also corresponds to the attributes expected to apply the model to new data. ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE'","ORDER BY ATTRIBUTE_NAME;"],"enabled":true,"result":{"startTime":1738093738055,"interpreter":"sql.low","endTime":1738093738182,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model metadata","message":["%sql ","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE, PARTITIONED","FROM   USER_MINING_MODELS","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE';"],"enabled":true,"result":{"startTime":1738093738284,"interpreter":"sql.low","endTime":1738093738385,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\tPARTITIONED\nKM_SH_CLUS_SAMPLE\tCLUSTERING\tKMEANS\t2025-01-28 19:48:56\tNO\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1738093738483,"interpreter":"sql.low","endTime":1738093738585,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_KMEANS\nCLUS_NUM_CLUSTERS\t10\nKMNS_CONV_TOLERANCE\t.001\nKMNS_DETAILS\tKMNS_DETAILS_ALL\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nKMNS_ITERATIONS\t3\nKMNS_MIN_PCT_ATTR_SUPPORT\t.1\nKMNS_NUM_BINS\t10\nKMNS_RANDOM_SEED\t0\nKMNS_SPLIT_CRITERION\tKMNS_VARIANCE\nKMNS_WINSORIZE\tKMNS_WINSORIZE_DISABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the K-means model detail views","message":["%sql ","","-- Display the list of available cluster model detail views as indicated in VIEW_TYPE","-- Cluster details are presented as separate views depending on the desired information.","","SELECT VIEW_NAME, VIEW_TYPE FROM USER_MINING_MODEL_VIEWS","WHERE MODEL_NAME='KM_SH_CLUS_SAMPLE'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1738093738684,"interpreter":"sql.low","endTime":1738093738785,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAKM_SH_CLUS_SAMPLE\tClustering Attribute Statistics\nDM$VCKM_SH_CLUS_SAMPLE\tk-Means Scoring Centroids\nDM$VDKM_SH_CLUS_SAMPLE\tClustering Description\nDM$VGKM_SH_CLUS_SAMPLE\tGlobal Name-Value Pairs\nDM$VHKM_SH_CLUS_SAMPLE\tClustering Histograms\nDM$VNKM_SH_CLUS_SAMPLE\tNormalization and Missing Value Handling\nDM$VRKM_SH_CLUS_SAMPLE\tClustering Rules\nDM$VSKM_SH_CLUS_SAMPLE\tComputed Settings\nDM$VWKM_SH_CLUS_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details","message":["%sql","","-- For each cluster_id, this DM$VD* view provides the number of records ","--   in the cluster, the parent cluster id, the level in the hierarchy, and dispersion.","-- Dispersion is a measure of cluster quality, and computationally, ","--   the sum of square errors.","","SELECT CLUSTER_ID CLU_ID, RECORD_COUNT RECORD_CNT, PARENT, ","       TREE_LEVEL, ROUND(TO_NUMBER(DISPERSION),3) DISPERSION","FROM   DM$VDKM_SH_CLUS_SAMPLE","ORDER BY 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IDs","message":["%sql","","-- Show leaf IDs to know for which clusters to show detail","","SELECT CLUSTER_ID","FROM   DM$VDKM_SH_CLUS_SAMPLE","WHERE  LEFT_CHILD_ID IS NULL AND RIGHT_CHILD_ID IS NULL","ORDER BY CLUSTER_ID"],"enabled":true,"result":{"startTime":1738093739081,"interpreter":"sql.low","endTime":1738093739194,"results":[{"message":"CLUSTER_ID\n6\n7\n8\n9\n11\n13\n15\n17\n18\n19\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details for cluster 18","message":["%sql","","-- For cluster_id 18, this output lists all the attributes that constitute the centroid, with the mean (for numericals) ","-- or mode (for categoricals). It also includes the variance.","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, MEAN, VARIANCE, MODE_VALUE","FROM   DM$VAKM_SH_CLUS_SAMPLE","WHERE  CLUSTER_ID = 18","ORDER BY ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME;"],"enabled":true,"result":{"startTime":1738093739295,"interpreter":"sql.low","endTime":1738093739397,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tMEAN\tVARIANCE\tMODE_VALUE\n18\tAFFINITY_CARD\t\t0.0\t0.0\t\n18\tCUST_CREDIT_LIMIT\t\t9802.784222737806\t5061015.48589003\t\n18\tCUST_GENDER\t\t\t\tF\n18\tCUST_INCOME_LEVEL\t\t\t\tJ: 190,000 - 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BIN_ID"],"enabled":true,"result":{"startTime":1738093739498,"interpreter":"sql.low","endTime":1738093739600,"results":[{"message":"BIN_ID\tCLUSTER_6\tCLUSTER_9\tCLUSTER_8\n1\t8\t195\t94\n2\t121\t620\t374\n3\t271\t36\t28\n4\t239\t11\t3\n5\t2\t0\t0\n6\t0\t0\t0\n7\t0\t0\t0\n8\t0\t0\t0\n9\t0\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show bin boundaries for YRS_RESIDENCE","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, BIN_ID, COUNT,","       ROUND(LOWER_BIN_BOUNDARY,3) LOWER_BIN_BOUNDARY, ROUND(UPPER_BIN_BOUNDARY,3) UPPER_BIN_BOUNDARY","FROM DM$VHKM_SH_CLUS_SAMPLE","WHERE ATTRIBUTE_NAME = 'YRS_RESIDENCE'","ORDER BY BIN_ID","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1738093739700,"interpreter":"sql.low","endTime":1738093739801,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tBIN_ID\tCOUNT\tLOWER_BIN_BOUNDARY\tUPPER_BIN_BOUNDARY\n1\tYRS_RESIDENCE\t1\t324\t0\t1.556\n19\tYRS_RESIDENCE\t1\t0\t0\t1.556\n18\tYRS_RESIDENCE\t1\t10\t0\t1.556\n17\tYRS_RESIDENCE\t1\t4\t0\t1.556\n16\tYRS_RESIDENCE\t1\t10\t0\t1.556\n15\tYRS_RESIDENCE\t1\t1\t0\t1.556\n14\tYRS_RESIDENCE\t1\t14\t0\t1.556\n13\tYRS_RESIDENCE\t1\t0\t0\t1.556\n12\tYRS_RESIDENCE\t1\t15\t0\t1.556\n11\tYRS_RESIDENCE\t1\t12\t0\t1.556\n10\tYRS_RESIDENCE\t1\t15\t0\t1.556\n9\tYRS_RESIDENCE\t1\t195\t0\t1.556\n8\tYRS_RESIDENCE\t1\t94\t0\t1.556\n7\tYRS_RESIDENCE\t1\t0\t0\t1.556\n6\tYRS_RESIDENCE\t1\t8\t0\t1.556\n5\tYRS_RESIDENCE\t1\t8\t0\t1.556\n4\tYRS_RESIDENCE\t1\t27\t0\t1.556\n3\tYRS_RESIDENCE\t1\t289\t0\t1.556\n2\tYRS_RESIDENCE\t1\t35\t0\t1.556\n1\tYRS_RESIDENCE\t2\t1586\t1.556\t3.111\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.","The larger the number, the more relevant the attribute."," ","The query below reverse-transforms the data to its original values, since the data used for building the model was normalized."],"enabled":true,"result":{"startTime":1738093739900,"interpreter":"md.low","endTime":1738093739977,"results":[{"message":"<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.<\/p>\n<p>For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.\nConfidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.\nThe larger the number, the more relevant the attribute.<\/p>\n<p>The query below reverse-transforms the data to its original values, since the data used for building the model was normalized.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, OPERATOR,","       ROUND(NUMERIC_VALUE,3) NUMERIC_VALUE, ATTRIBUTE_VALUE, ROUND(SUPPORT,3) SUPPORT, ROUND(CONFIDENCE,3) CONFIDENCE","FROM   DM$VRKM_SH_CLUS_SAMPLE ","WHERE  cluster_id IN (SELECT cluster_id","                      FROM DM$VDKM_SH_CLUS_SAMPLE","                      WHERE LEFT_CHILD_ID is NULL and RIGHT_CHILD_ID is NULL)","ORDER BY CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR, NUMERIC_VALUE, ATTRIBUTE_VALUE","FETCH FIRST 20 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738093740082,"interpreter":"sql.low","endTime":1738093740214,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tOPERATOR\tNUMERIC_VALUE\tATTRIBUTE_VALUE\tSUPPORT\tCONFIDENCE\n6\tAFFINITY_CARD\t<=\t1\t\t641\t0.177\n6\tAFFINITY_CARD\t>\t0.889\t\t641\t0.177\n6\tCUST_CREDIT_LIMIT\t<=\t15000\t\t641\t0.002\n6\tCUST_CREDIT_LIMIT\t>=\t1500\t\t641\t0.002\n6\tCUST_GENDER\t=\t\tM\t517\t0.019\n6\tCUST_INCOME_LEVEL\tIN\t\tB: 30,000 - 49,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tC: 50,000 - 69,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t610\t0.003\n6\tCUST_MARITAL_STATUS\t=\t\tMarried\t576\t0.07\n6\tCUST_YEAR_OF_BIRTH\t<=\t1977.889\t\t627\t0.047\n6\tCUST_YEAR_OF_BIRTH\t>\t1945.444\t\t627\t0.047\n6\tEDUCATION\tIN\t\t< Bach.\t604\t0.015\n6\tEDUCATION\tIN\t\tAssoc-A\t604\t0.015\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Last\",\"visualizations\":{\"sortingValue\":\"descending\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"CLUS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Cluster ID\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"CLUSTER_ID\",\"CNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Apply model to CUSTOMERS360_V and display customer count per cluster","message":["%sql","","-- For a descriptive machine learning technique like Clustering, \"scoring\" involves assigning each record to a cluster, with a certain probability. ","-- However, you can also obtain the probability of a record belonging to each cluster.","-- Hover over bars in the bar chart to view the size of each cluster. ","","SELECT CLUSTER_ID(KM_SH_CLUS_SAMPLE USING *) AS CLUSTER_ID, ","       COUNT(*) AS CNT ","FROM   CUSTOMERS360_V","GROUP BY CLUSTER_ID(KM_SH_CLUS_SAMPLE USING *)","ORDER BY CNT DESC;"],"enabled":true,"result":{"startTime":1738093740316,"interpreter":"sql.low","endTime":1738093740438,"results":[{"message":"CLUSTER_ID\tCNT\n9\t862\n6\t641\n17\t533\n8\t499\n18\t431\n7\t401\n15\t391\n19\t358\n13\t257\n11\t127\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top ten customers most likely in cluster 6","message":["%sql","","SELECT CUST_ID, ","       ROUND(CLUSTER_PROBABILITY(KM_SH_CLUS_SAMPLE, 6 USING *),3) PROB","FROM CUSTOMERS360_V","ORDER BY PROB DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1738093740551,"interpreter":"sql.low","endTime":1738093740678,"results":[{"message":"CUST_ID\tPROB\n101386\t0.245\n104474\t0.245\n102605\t0.243\n103381\t0.243\n103923\t0.243\n101530\t0.241\n100101\t0.24\n102250\t0.24\n100838\t0.239\n101968\t0.239\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List three most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. These represent customers ","--    with > 20% likelihood of assignment to their cluster.","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,2) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(KM_SH_CLUS_SAMPLE USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(KM_SH_CLUS_SAMPLE, NULL, 0.2 USING *) PSET","            FROM CUSTOMERS360_V V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED;"],"enabled":true,"result":{"startTime":1738093740781,"interpreter":"sql.low","endTime":1738093740959,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n101362\t9\t22.23\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".148\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" weight=\".115\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"11000\" weight=\".082\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'101362'\",\"cluster_id\":\"'5'\",\"s.cluster_id\":\"'5'\",\"cust_id\":\"'100456'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"paragraphId\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isNotebook\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 5 rows that are anomalous compared to their Clusters","message":["%sql","","-- List top 5 rows that are most unusual as measured by their distance from the cluster centroids. ","-- A row that is far from all cluster centroids may be anomalous.","","SELECT *","FROM (SELECT CUST_ID, RANK() OVER (ORDER BY CLUSTER_DISTANCE(KM_SH_CLUS_SAMPLE USING *) DESC) RANK_ANOMALOUS,","             round(CLUSTER_DISTANCE(KM_SH_CLUS_SAMPLE USING *),3) DISTANCE_TO_CENTROID","      FROM CUSTOMERS360_V)","WHERE RANK_ANOMALOUS <= 5","ORDER BY RANK_ANOMALOUS;"],"enabled":true,"result":{"startTime":1738093741057,"interpreter":"sql.low","endTime":1738093741179,"results":[{"message":"CUST_ID\tRANK_ANOMALOUS\tDISTANCE_TO_CENTROID\n102555\t1\t5.064\n103165\t2\t4.636\n100210\t3\t4.43\n102011\t4\t4.386\n103921\t5\t4.216\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach for building clustering model using an analytic function","In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.","","In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.","","Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers."],"enabled":true,"result":{"startTime":1738093741278,"interpreter":"md.low","endTime":1738093741365,"results":[{"message":"<h2 id=\"alternative-approach-for-building-clustering-model-using-an-analytic-function\">Alternative approach for building clustering model using an analytic function<\/h2>\n<p>In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.<\/p>\n<p>In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.<\/p>\n<p>Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Build and apply a transient model using analytic functions","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT * ","      FROM (SELECT CUST_ID,","                   CLUSTER_ID(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_ID,","                   CLUSTER_DETAILS(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_DET","            FROM CUSTOMERS360_V)","            ORDER By 1) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.CLUSTER_DET","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;","                      "],"enabled":true,"result":{"startTime":1738093741518,"interpreter":"sql.low","endTime":1738093741786,"results":[{"message":"CUST_ID\tCLUSTER_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t3\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".125\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".084\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".081\" \n100002\t4\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".087\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".083\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \n100003\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".081\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".075\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \n100004\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".096\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".079\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".079\" \n100005\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".28\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".05\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".048\" \n100006\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".276\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1983\" weight=\".204\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".158\" \n100007\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".087\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".082\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".046\" \n100008\t4\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".089\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".086\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".073\" \n100009\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".236\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".035\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".025\" \n100010\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".229\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\".152\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".122\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "],"enabled":true,"result":{"startTime":1738093741896,"interpreter":"md.low","endTime":1738093741978,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Populate settings table","message":["%script ","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE KM_SH_SAMPLE_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE KM_SH_SAMPLE_SETTINGS (SETTING_NAME  VARCHAR2(30),","                                    SETTING_VALUE VARCHAR2(4000));","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","-- See documentation for additional settings","","BEGIN       ","   INSERT INTO KM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.KMNS_DISTANCE,DBMS_DATA_MINING.KMNS_EUCLIDEAN);","   INSERT INTO KM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO,DBMS_DATA_MINING.PREP_AUTO_ON);","   INSERT INTO KM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.KMNS_DETAILS, DBMS_DATA_MINING.KMNS_DETAILS_ALL);","   -- Some other settings:","   -- (dbms_data_mining.kmns_iterations,3);","   -- (dbms_data_mining.kmns_block_growth,2);","   -- (dbms_data_mining.kmns_conv_tolerance,0.01);","   -- (dbms_data_mining.kmns_split_criterion,dbms_data_mining.kmns_variance);","   -- (dbms_data_mining.kmns_min_pct_attr_support,0.1);","   -- (dbms_data_mining.kmns_num_bins,10);","END;","/"],"enabled":true,"result":{"startTime":1738093742086,"interpreter":"script.low","endTime":1738093742623,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable KM_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the contents of the model settings table","message":["%sql","","-- NOTE: The settings table can be reused or modified across multiple model builds. ","","SELECT * FROM KM_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1738093742738,"interpreter":"sql.low","endTime":1738093742850,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nPREP_AUTO\tON\nKMNS_DETAILS\tKMNS_DETAILS_ALL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the k-Means Clustering model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'KM_SH_CLUS_SAMPLE2',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMERS360_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'KM_SH_SAMPLE_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1738093743045,"interpreter":"script.low","endTime":1738093744419,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE2'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1738093744525,"interpreter":"sql.low","endTime":1738093744635,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_KMEANS\nCLUS_NUM_CLUSTERS\t10\nKMNS_CONV_TOLERANCE\t.001\nKMNS_DETAILS\tKMNS_DETAILS_ALL\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nKMNS_ITERATIONS\t20\nKMNS_MIN_PCT_ATTR_SUPPORT\t.1\nKMNS_NUM_BINS\t11\nKMNS_RANDOM_SEED\t0\nKMNS_SPLIT_CRITERION\tKMNS_VARIANCE\nKMNS_WINSORIZE\tKMNS_WINSORIZE_DISABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1738093744753,"interpreter":"md.low","endTime":1738093744836,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1738093744960,"interpreter":"md.low","endTime":1738093745044,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"7","snapshot":false,"tags":null}]
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Clustering OC","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733160050014,"interpreter":"md.low","endTime":1733160050071,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identifying Customer Segments using Orthogonal Partitioning Clustering","","Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization. ","In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema","using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.","","See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733160050168,"interpreter":"md.low","endTime":1733160050224,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-orthogonal-partitioning-clustering\">Identifying Customer Segments using Orthogonal Partitioning Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms, including K-means, O-Cluster, and Expectation Maximization.\nIn this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema\nusing the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1733160050299,"interpreter":"md.low","endTime":1733160050359,"results":[{"message":"<p><img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a>"],"enabled":true,"result":{"startTime":1733160050438,"interpreter":"md.low","endTime":1733160050493,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view joining CUSTOMERS and DEMOGRAPHICS","message":["%script","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, ","          a.CUST_YEAR_OF_BIRTH, a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, ","          b.EDUCATION, b.AFFINITY_CARD, ","          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","   FROM SH.CUSTOMERS a, SH.SUPPLEMENTARY_DEMOGRAPHICS b","   WHERE a.CUST_ID = b.CUST_ID;","   "],"enabled":true,"result":{"startTime":1733160050571,"interpreter":"script.low","endTime":1733160050639,"results":[{"message":"\nView CUSTOMERS360_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in CUSTOMERS360_V","message":["%sql","","SELECT COUNT(*) FROM CUSTOMERS360_V;"],"enabled":true,"result":{"startTime":1733160050716,"interpreter":"sql.low","endTime":1733160050786,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data in view CUSTOMERS360_V","message":["%sql","","SELECT * ","FROM CUSTOMERS360_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1733160050864,"interpreter":"sql.low","endTime":1733160050936,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\n100804\tF\tDivorc.\t1943\tA: Below 30,000\t1500\t< Bach.\t0\t2\tProf.\t6\t0\n101610\tM\tNeverM\t1985\tI: 170,000 - 189,999\t3000\t11th\t0\t1\tHandler\t0\t1\n102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\n100593\tM\tMarried\t1963\tG: 130,000 - 149,999\t1500\tHS-grad\t1\t3\tProf.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an O-Cluster model using the CUSTOMERS360_V view"],"enabled":true,"result":{"startTime":1733160051010,"interpreter":"md.low","endTime":1733160051064,"results":[{"message":"<h3 id=\"build-an-o-cluster-model-using-the-customers360_v-view\">Build an O-Cluster model using the CUSTOMERS360_V view<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the OC Clustering model with default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')        := 'ALGO_O_CLUSTER';","    V_setlst('PREP_AUTO')        := 'ON';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'OC_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1733160051145,"interpreter":"script.low","endTime":1733160053757,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') := '5';","* Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5. ","v_setlst('OCLT_SENSITIVITY') := '0.5';"],"enabled":true,"result":{"startTime":1733160053844,"interpreter":"md.low","endTime":1733160053939,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-orthogonal-partitioning-cluster-clustering\">Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') := '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5.\nv_setlst('OCLT_SENSITIVITY') := '0.5';<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the OC Clustering model with explicit settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')         := 'ALGO_O_CLUSTER';","    V_setlst('PREP_AUTO')         := 'ON';","    V_setlst('CLUS_NUM_CLUSTERS') := '5';","    V_setlst('OCLT_SENSITIVITY')  := '0.5';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'OC_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1733160054039,"interpreter":"script.low","endTime":1733160056232,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model metadata","message":["%sql ","","SELECT MINING_FUNCTION, ALGORITHM","FROM   USER_MINING_MODELS","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE';"],"enabled":true,"result":{"startTime":1733160056318,"interpreter":"sql.low","endTime":1733160056389,"results":[{"message":"MINING_FUNCTION\tALGORITHM\nCLUSTERING\tO_CLUSTER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model signature","message":["%sql ","","-- The model signature includes the attributes used in model training data,","-- and also corresponds to the attributes expected to apply the model to new data. ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE'","ORDER BY ATTRIBUTE_NAME;"],"enabled":true,"result":{"startTime":1733160056477,"interpreter":"sql.low","endTime":1733160056550,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1733160056627,"interpreter":"sql.low","endTime":1733160056700,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_O_CLUSTER\nCLUS_NUM_CLUSTERS\t5\nOCLT_SENSITIVITY\t0.5\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the O-Cluster model details","message":["%sql ","","-- Display the list of available cluster model details as indicated in VIEW_TYPE","-- Cluster details are presented as separate views depending on the desired information.","","SELECT VIEW_NAME, VIEW_TYPE FROM USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='OC_SH_CLUS_SAMPLE'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1733160056780,"interpreter":"sql.low","endTime":1733160056870,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAOC_SH_CLUS_SAMPLE\tClustering Attribute Statistics\nDM$VBOC_SH_CLUS_SAMPLE\tAutomatic Data Preparation Binning\nDM$VDOC_SH_CLUS_SAMPLE\tClustering Description\nDM$VGOC_SH_CLUS_SAMPLE\tGlobal Name-Value Pairs\nDM$VHOC_SH_CLUS_SAMPLE\tClustering Histograms\nDM$VROC_SH_CLUS_SAMPLE\tClustering Rules\nDM$VSOC_SH_CLUS_SAMPLE\tComputed Settings\nDM$VWOC_SH_CLUS_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details","message":["%sql","","-- For each cluster_id, this DM$VD* view provides the number of records ","--   in the cluster, the parent cluster id, the level in the hierarchy. ","","SELECT CLUSTER_ID, RECORD_COUNT, PARENT, TREE_LEVEL","FROM   DM$VDOC_SH_CLUS_SAMPLE","ORDER BY CLUSTER_ID;"],"enabled":true,"result":{"startTime":1733160056956,"interpreter":"sql.low","endTime":1733160057102,"results":[{"message":"CLUSTER_ID\tRECORD_COUNT\tPARENT\tTREE_LEVEL\n1\t4500\t\t1\n2\t2316\t1\t2\n3\t2184\t1\t2\n4\t1690\t3\t3\n5\t494\t3\t3\n6\t991\t4\t4\n7\t699\t4\t4\n8\t993\t2\t3\n9\t1323\t2\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show leaf clusters IDs","message":["%sql","","-- Show leaf IDs to know for which clusters to show detail","","SELECT CLUSTER_ID","FROM   DM$VDOC_SH_CLUS_SAMPLE","WHERE  LEFT_CHILD_ID IS NULL AND RIGHT_CHILD_ID IS NULL","ORDER BY CLUSTER_ID"],"enabled":true,"result":{"startTime":1733160057180,"interpreter":"sql.low","endTime":1733160057275,"results":[{"message":"CLUSTER_ID\n5\n6\n7\n8\n9\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details for cluster 9","message":["%sql","","-- For cluster_id 9, this output lists all the attributes that constitute the centroid, with the mean (for numericals) ","-- or mode (for categoricals). It also includes the variance.","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, round(MEAN,3) MEAN, round(VARIANCE,3) VARIANCE, MODE_VALUE","FROM  DM$VAOC_SH_CLUS_SAMPLE","WHERE CLUSTER_ID = 9","ORDER BY ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME;"],"enabled":true,"result":{"startTime":1733160057353,"interpreter":"sql.low","endTime":1733160057444,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tMEAN\tVARIANCE\tMODE_VALUE\n9\tAFFINITY_CARD\t0.4\t0.24\t\n9\tCUST_CREDIT_LIMIT\t6938.398\t3213108.474\t\n9\tCUST_GENDER\t\t\tM\n9\tCUST_INCOME_LEVEL\t\t\tG: 130,000 - 149,999\n9\tCUST_MARITAL_STATUS\t\t\tMarried\n9\tCUST_YEAR_OF_BIRTH\t1963.322\t181.359\t\n9\tEDUCATION\t\t\tHS-grad\n9\tHOUSEHOLD_SIZE\t\t\t3\n9\tOCCUPATION\t\t\tSales\n9\tYRS_RESIDENCE\t4.203\t3.811\t\n9\tY_BOX_GAMES\t0.252\t0.189\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT CLUSTER_ID, BIN_ID, COUNT","      FROM   DM$VHOC_SH_CLUS_SAMPLE","      WHERE  (CLUSTER_ID = 6 OR CLUSTER_ID = 9 OR CLUSTER_ID = 8) ","      AND ATTRIBUTE_NAME = 'YRS_RESIDENCE'"],"enabled":true,"result":{"startTime":1733160057526,"interpreter":"sql.low","endTime":1733160057617,"results":[{"message":"CLUSTER_ID\tBIN_ID\tCOUNT\n6\t1\t0.0070635721493440967\n6\t2\t0.073662966700302729\n6\t3\t0.1846619576185671\n6\t4\t0.2391523713420787\n6\t5\t0.18668012108980828\n6\t6\t0.14732593340060546\n6\t7\t0.094853683148335019\n6\t8\t0.032290615539858729\n6\t9\t0.02119071644803229\n6\t10\t0.0080726538849646822\n6\t11\t0.0010090817356205853\n6\t12\t0.0020181634712411706\n6\t13\t0.0010090817356205853\n6\t14\t0\n6\t15\t0.0010090817356205853\n8\t1\t0.0080563947633434038\n8\t2\t0.0553877139979859\n8\t3\t0.12185297079556898\n8\t4\t0.19436052366565962\n8\t5\t0.2014098690835851\n8\t6\t0.2014098690835851\n8\t7\t0.096676737160120846\n8\t8\t0.065458207452165157\n8\t9\t0.024169184290030211\n8\t10\t0.016112789526686808\n8\t11\t0.010070493454179255\n8\t12\t0.0030211480362537764\n8\t13\t0.0010070493454179255\n8\t14\t0\n8\t15\t0.0010070493454179255\n9\t1\t0.017384731670445956\n9\t2\t0.050642479213907785\n9\t3\t0.10959939531368103\n9\t4\t0.19198790627362056\n9\t5\t0.22826908541194255\n9\t6\t0.17082388510959939\n9\t7\t0.12547241118669691\n9\t8\t0.057445200302343159\n9\t9\t0.024187452758881331\n9\t10\t0.0098261526832955411\n9\t11\t0.0098261526832955411\n9\t12\t0.0022675736961451248\n9\t13\t0\n9\t14\t0.0015117157974300832\n9\t15\t0.00075585789871504159\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"CLUSTER_6\",\"CLUSTER_8\",\"CLUSTER_9\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"CLUSTER_6\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"CLUSTER_8\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"CLUSTER_9\",\"lineType\":\"straight\",\"borderColor\":\"rgb(249, 183, 14)\",\"borderWidth\":0,\"color\":\"rgb(249, 183, 14)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(249, 183, 14)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Bin ID\"},\"y\":{\"title\":\"Proportion in cluster\"}},\"lastColumns\":[\"BIN_ID\",\"CLUSTER_6\",\"CLUSTER_9\",\"CLUSTER_8\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Compare histograms for attribute YRS_RESIDENCE of clusters 6, 8, and 9","message":["%sql","","-- Use the DM$VH view for cluster histogram data. Pivot the data. ","","SELECT * ","FROM (SELECT CLUSTER_ID, BIN_ID, COUNT","      FROM   DM$VHOC_SH_CLUS_SAMPLE","      WHERE  (CLUSTER_ID = 6 OR CLUSTER_ID = 9 OR CLUSTER_ID = 8) ","      AND ATTRIBUTE_NAME = 'YRS_RESIDENCE') A","PIVOT (SUM(COUNT) FOR CLUSTER_ID IN(6 AS cluster_6, 9 AS cluster_9, 8 AS cluster_8))","ORDER BY BIN_ID"],"enabled":true,"result":{"startTime":1733160057702,"interpreter":"sql.low","endTime":1733160057777,"results":[{"message":"BIN_ID\tCLUSTER_6\tCLUSTER_9\tCLUSTER_8\n1\t0.0070635721493440967\t0.017384731670445956\t0.0080563947633434038\n2\t0.073662966700302729\t0.050642479213907785\t0.0553877139979859\n3\t0.1846619576185671\t0.10959939531368103\t0.12185297079556898\n4\t0.2391523713420787\t0.19198790627362056\t0.19436052366565962\n5\t0.18668012108980828\t0.22826908541194255\t0.2014098690835851\n6\t0.14732593340060546\t0.17082388510959939\t0.2014098690835851\n7\t0.094853683148335019\t0.12547241118669691\t0.096676737160120846\n8\t0.032290615539858729\t0.057445200302343159\t0.065458207452165157\n9\t0.02119071644803229\t0.024187452758881331\t0.024169184290030211\n10\t0.0080726538849646822\t0.0098261526832955411\t0.016112789526686808\n11\t0.0010090817356205853\t0.0098261526832955411\t0.010070493454179255\n12\t0.0020181634712411706\t0.0022675736961451248\t0.0030211480362537764\n13\t0.0010090817356205853\t0\t0.0010070493454179255\n14\t0\t0.0015117157974300832\t0\n15\t0.0010090817356205853\t0.00075585789871504159\t0.0010070493454179255\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.","The larger the number, the more relevant the attribute."," ","The query below reverse-transforms the data to its original values, since the data used for building the model was normalized."],"enabled":true,"result":{"startTime":1733160057861,"interpreter":"md.low","endTime":1733160057916,"results":[{"message":"<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.\nFor an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.\nConfidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.\nThe larger the number, the more relevant the attribute.<\/p>\n<p>The query below reverse-transforms the data to its original values, since the data used for building the model was normalized.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, OPERATOR,","       NUMERIC_VALUE, ATTRIBUTE_VALUE, SUPPORT, ROUND(CONFIDENCE,3) CONFIDENCE","FROM   DM$VROC_SH_CLUS_SAMPLE ","WHERE  cluster_id IN (SELECT cluster_id","                      FROM DM$VDOC_SH_CLUS_SAMPLE","                      WHERE LEFT_CHILD_ID is NULL ","                      AND RIGHT_CHILD_ID is NULL)","ORDER BY CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR, NUMERIC_VALUE, ATTRIBUTE_VALUE;"],"enabled":true,"result":{"startTime":1733160057991,"interpreter":"sql.low","endTime":1733160058142,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tOPERATOR\tNUMERIC_VALUE\tATTRIBUTE_VALUE\tSUPPORT\tCONFIDENCE\n5\tAFFINITY_CARD\t<=\t\t1\t494\t0.001\n5\tAFFINITY_CARD\t>=\t\t0\t494\t0.001\n5\tCUST_CREDIT_LIMIT\t=\t\t(14035.7; 15000]\t469\t0.166\n5\tCUST_GENDER\tIN\t\tF\t494\t0.001\n5\tCUST_GENDER\tIN\t\tM\t494\t0.001\n5\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t444\t0.055\n5\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t444\t0.055\n5\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t444\t0.055\n5\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t444\t0.055\n5\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t458\t0\n5\tCUST_MARITAL_STATUS\tIN\t\tMarried\t458\t0\n5\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t458\t0\n5\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t468\t0.003\n5\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t468\t0.003\n5\tEDUCATION\tIN\t\t< Bach.\t412\t0.004\n5\tEDUCATION\tIN\t\tAssoc-V\t412\t0.004\n5\tEDUCATION\tIN\t\tBach.\t412\t0.004\n5\tEDUCATION\tIN\t\tHS-grad\t412\t0.004\n5\tEDUCATION\tIN\t\tMasters\t412\t0.004\n5\tEDUCATION\tIN\t\tProfsc\t412\t0.004\n5\tHOUSEHOLD_SIZE\tIN\t\t1\t411\t0.001\n5\tHOUSEHOLD_SIZE\tIN\t\t2\t411\t0.001\n5\tHOUSEHOLD_SIZE\tIN\t\t3\t411\t0.001\n5\tOCCUPATION\tIN\t\t?\t485\t0.002\n5\tOCCUPATION\tIN\t\tCleric.\t485\t0.002\n5\tOCCUPATION\tIN\t\tCrafts\t485\t0.002\n5\tOCCUPATION\tIN\t\tExec.\t485\t0.002\n5\tOCCUPATION\tIN\t\tHandler\t485\t0.002\n5\tOCCUPATION\tIN\t\tMachine\t485\t0.002\n5\tOCCUPATION\tIN\t\tOther\t485\t0.002\n5\tOCCUPATION\tIN\t\tProf.\t485\t0.002\n5\tOCCUPATION\tIN\t\tProtec.\t485\t0.002\n5\tOCCUPATION\tIN\t\tSales\t485\t0.002\n5\tOCCUPATION\tIN\t\tTechSup\t485\t0.002\n5\tOCCUPATION\tIN\t\tTransp.\t485\t0.002\n5\tYRS_RESIDENCE\t<=\t\t7.46667\t478\t0.001\n5\tYRS_RESIDENCE\t>=\t\t.933333\t478\t0.001\n5\tY_BOX_GAMES\t<=\t\t1\t494\t0.001\n5\tY_BOX_GAMES\t>=\t\t0\t494\t0.001\n6\tAFFINITY_CARD\t=\t\t[0; .5]\t991\t0.131\n6\tCUST_CREDIT_LIMIT\t<=\t\t10178.6\t969\t0.12\n6\tCUST_CREDIT_LIMIT\t>=\t\t8250\t969\t0.12\n6\tCUST_GENDER\tIN\t\tF\t991\t0.003\n6\tCUST_GENDER\tIN\t\tM\t991\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t960\t0.017\n6\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t891\t0.006\n6\tCUST_MARITAL_STATUS\tIN\t\tMarried\t891\t0.006\n6\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t891\t0.006\n6\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t934\t0.002\n6\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t934\t0.002\n6\tEDUCATION\tIN\t\t10th\t870\t0.006\n6\tEDUCATION\tIN\t\t< Bach.\t870\t0.006\n6\tEDUCATION\tIN\t\tAssoc-A\t870\t0.006\n6\tEDUCATION\tIN\t\tAssoc-V\t870\t0.006\n6\tEDUCATION\tIN\t\tBach.\t870\t0.006\n6\tEDUCATION\tIN\t\tHS-grad\t870\t0.006\n6\tHOUSEHOLD_SIZE\tIN\t\t1\t913\t0.007\n6\tHOUSEHOLD_SIZE\tIN\t\t2\t913\t0.007\n6\tHOUSEHOLD_SIZE\tIN\t\t3\t913\t0.007\n6\tHOUSEHOLD_SIZE\tIN\t\t9+\t913\t0.007\n6\tOCCUPATION\tIN\t\t?\t910\t0.002\n6\tOCCUPATION\tIN\t\tCleric.\t910\t0.002\n6\tOCCUPATION\tIN\t\tCrafts\t910\t0.002\n6\tOCCUPATION\tIN\t\tExec.\t910\t0.002\n6\tOCCUPATION\tIN\t\tHandler\t910\t0.002\n6\tOCCUPATION\tIN\t\tMachine\t910\t0.002\n6\tOCCUPATION\tIN\t\tOther\t910\t0.002\n6\tOCCUPATION\tIN\t\tProf.\t910\t0.002\n6\tOCCUPATION\tIN\t\tSales\t910\t0.002\n6\tOCCUPATION\tIN\t\tTransp.\t910\t0.002\n6\tYRS_RESIDENCE\t<=\t\t6.53333\t918\t0.002\n6\tYRS_RESIDENCE\t>=\t\t.933333\t918\t0.002\n6\tY_BOX_GAMES\t<=\t\t1\t991\t0.004\n6\tY_BOX_GAMES\t>=\t\t0\t991\t0.004\n7\tAFFINITY_CARD\t=\t\t[0; .5]\t562\t0.002\n7\tCUST_CREDIT_LIMIT\t=\t\t(10178.6; 11142.9]\t699\t0.172\n7\tCUST_GENDER\tIN\t\tF\t699\t0.001\n7\tCUST_GENDER\tIN\t\tM\t699\t0.001\n7\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t695\t0.077\n7\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t695\t0.077\n7\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t695\t0.077\n7\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t695\t0.077\n7\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t645\t0.001\n7\tCUST_MARITAL_STATUS\tIN\t\tMarried\t645\t0.001\n7\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t645\t0.001\n7\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t656\t0.002\n7\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t656\t0.002\n7\tEDUCATION\tIN\t\t10th\t635\t0.002\n7\tEDUCATION\tIN\t\t11th\t635\t0.002\n7\tEDUCATION\tIN\t\t< Bach.\t635\t0.002\n7\tEDUCATION\tIN\t\tAssoc-A\t635\t0.002\n7\tEDUCATION\tIN\t\tAssoc-V\t635\t0.002\n7\tEDUCATION\tIN\t\tBach.\t635\t0.002\n7\tEDUCATION\tIN\t\tHS-grad\t635\t0.002\n7\tEDUCATION\tIN\t\tMasters\t635\t0.002\n7\tHOUSEHOLD_SIZE\tIN\t\t1\t643\t0.001\n7\tHOUSEHOLD_SIZE\tIN\t\t2\t643\t0.001\n7\tHOUSEHOLD_SIZE\tIN\t\t3\t643\t0.001\n7\tHOUSEHOLD_SIZE\tIN\t\t9+\t643\t0.001\n7\tOCCUPATION\tIN\t\t?\t649\t0.001\n7\tOCCUPATION\tIN\t\tCleric.\t649\t0.001\n7\tOCCUPATION\tIN\t\tCrafts\t649\t0.001\n7\tOCCUPATION\tIN\t\tExec.\t649\t0.001\n7\tOCCUPATION\tIN\t\tHandler\t649\t0.001\n7\tOCCUPATION\tIN\t\tMachine\t649\t0.001\n7\tOCCUPATION\tIN\t\tOther\t649\t0.001\n7\tOCCUPATION\tIN\t\tProf.\t649\t0.001\n7\tOCCUPATION\tIN\t\tSales\t649\t0.001\n7\tOCCUPATION\tIN\t\tTransp.\t649\t0.001\n7\tYRS_RESIDENCE\t<=\t\t7.46667\t666\t0.001\n7\tYRS_RESIDENCE\t>=\t\t.933333\t666\t0.001\n7\tY_BOX_GAMES\t<=\t\t1\t699\t0.002\n7\tY_BOX_GAMES\t>=\t\t0\t699\t0.002\n8\tAFFINITY_CARD\t<=\t\t1\t993\t0.002\n8\tAFFINITY_CARD\t>=\t\t0\t993\t0.002\n8\tCUST_CREDIT_LIMIT\t<=\t\t3428.57\t977\t0.141\n8\tCUST_CREDIT_LIMIT\t>=\t\t1500\t977\t0.141\n8\tCUST_GENDER\tIN\t\tF\t993\t0\n8\tCUST_GENDER\tIN\t\tM\t993\t0\n8\tCUST_INCOME_LEVEL\tIN\t\tA: Below 30,000\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tB: 30,000 - 49,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tC: 50,000 - 69,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tD: 70,000 - 89,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t993\t0.08\n8\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t914\t0.001\n8\tCUST_MARITAL_STATUS\tIN\t\tMarried\t914\t0.001\n8\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t914\t0.001\n8\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t934\t0.001\n8\tCUST_YEAR_OF_BIRTH\t>=\t\t1939.55\t934\t0.001\n8\tEDUCATION\tIN\t\t< Bach.\t886\t0.003\n8\tEDUCATION\tIN\t\tAssoc-A\t886\t0.003\n8\tEDUCATION\tIN\t\tAssoc-V\t886\t0.003\n8\tEDUCATION\tIN\t\tBach.\t886\t0.003\n8\tEDUCATION\tIN\t\tHS-grad\t886\t0.003\n8\tEDUCATION\tIN\t\tMasters\t886\t0.003\n8\tHOUSEHOLD_SIZE\tIN\t\t1\t916\t0\n8\tHOUSEHOLD_SIZE\tIN\t\t2\t916\t0\n8\tHOUSEHOLD_SIZE\tIN\t\t3\t916\t0\n8\tHOUSEHOLD_SIZE\tIN\t\t9+\t916\t0\n8\tOCCUPATION\tIN\t\t?\t939\t0.001\n8\tOCCUPATION\tIN\t\tCleric.\t939\t0.001\n8\tOCCUPATION\tIN\t\tCrafts\t939\t0.001\n8\tOCCUPATION\tIN\t\tExec.\t939\t0.001\n8\tOCCUPATION\tIN\t\tFarming\t939\t0.001\n8\tOCCUPATION\tIN\t\tMachine\t939\t0.001\n8\tOCCUPATION\tIN\t\tOther\t939\t0.001\n8\tOCCUPATION\tIN\t\tProf.\t939\t0.001\n8\tOCCUPATION\tIN\t\tSales\t939\t0.001\n8\tOCCUPATION\tIN\t\tTechSup\t939\t0.001\n8\tOCCUPATION\tIN\t\tTransp.\t939\t0.001\n8\tYRS_RESIDENCE\t<=\t\t7.46667\t930\t0.001\n8\tYRS_RESIDENCE\t>=\t\t.933333\t930\t0.001\n8\tY_BOX_GAMES\t<=\t\t1\t993\t0.002\n8\tY_BOX_GAMES\t>=\t\t0\t993\t0.002\n9\tAFFINITY_CARD\t<=\t\t1\t1323\t0.022\n9\tAFFINITY_CARD\t>=\t\t0\t1323\t0.022\n9\tCUST_CREDIT_LIMIT\t<=\t\t10178.6\t1293\t0.079\n9\tCUST_CREDIT_LIMIT\t>=\t\t4392.86\t1293\t0.079\n9\tCUST_GENDER\tIN\t\tF\t1323\t0.001\n9\tCUST_GENDER\tIN\t\tM\t1323\t0.001\n9\tCUST_INCOME_LEVEL\tIN\t\tC: 50,000 - 69,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t1224\t0.011\n9\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t1244\t0.003\n9\tCUST_MARITAL_STATUS\tIN\t\tMarried\t1244\t0.003\n9\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t1244\t0.003\n9\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t1223\t0.001\n9\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t1223\t0.001\n9\tEDUCATION\tIN\t\t10th\t1215\t0.002\n9\tEDUCATION\tIN\t\t11th\t1215\t0.002\n9\tEDUCATION\tIN\t\t< Bach.\t1215\t0.002\n9\tEDUCATION\tIN\t\tAssoc-A\t1215\t0.002\n9\tEDUCATION\tIN\t\tAssoc-V\t1215\t0.002\n9\tEDUCATION\tIN\t\tBach.\t1215\t0.002\n9\tEDUCATION\tIN\t\tHS-grad\t1215\t0.002\n9\tEDUCATION\tIN\t\tMasters\t1215\t0.002\n9\tHOUSEHOLD_SIZE\tIN\t\t1\t1209\t0.003\n9\tHOUSEHOLD_SIZE\tIN\t\t2\t1209\t0.003\n9\tHOUSEHOLD_SIZE\tIN\t\t3\t1209\t0.003\n9\tHOUSEHOLD_SIZE\tIN\t\t9+\t1209\t0.003\n9\tOCCUPATION\tIN\t\t?\t1231\t0.001\n9\tOCCUPATION\tIN\t\tCleric.\t1231\t0.001\n9\tOCCUPATION\tIN\t\tCrafts\t1231\t0.001\n9\tOCCUPATION\tIN\t\tExec.\t1231\t0.001\n9\tOCCUPATION\tIN\t\tMachine\t1231\t0.001\n9\tOCCUPATION\tIN\t\tOther\t1231\t0.001\n9\tOCCUPATION\tIN\t\tProf.\t1231\t0.001\n9\tOCCUPATION\tIN\t\tSales\t1231\t0.001\n9\tOCCUPATION\tIN\t\tTechSup\t1231\t0.001\n9\tOCCUPATION\tIN\t\tTransp.\t1231\t0.001\n9\tYRS_RESIDENCE\t<=\t\t7.46667\t1236\t0.001\n9\tYRS_RESIDENCE\t>=\t\t.933333\t1236\t0.001\n9\tY_BOX_GAMES\t<=\t\t1\t1323\t0.003\n9\tY_BOX_GAMES\t>=\t\t0\t1323\t0.003\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"COUNT\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CLUS\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Apply model to CUSTOMERS360_V and display customer count per cluster","message":["%sql","","-- For a descriptive machine learning technique like Clustering, \"scoring\" involves assigning each record to a cluster, with a certain probability. ","-- However, one can also obtain the probability of a record belonging to each cluster.","-- Hover over bars in the bar chart to view the size of each cluster. ","","SELECT CLUSTER_ID(OC_SH_CLUS_SAMPLE USING *) AS CLUS, COUNT(*) AS COUNT ","FROM   CUSTOMERS360_V","GROUP BY CLUSTER_ID(OC_SH_CLUS_SAMPLE USING *)","ORDER BY COUNT DESC;"],"enabled":true,"result":{"startTime":1733160058246,"interpreter":"sql.low","endTime":1733160058340,"results":[{"message":"CLUS\tCOUNT\n9\t1323\n8\t993\n6\t991\n7\t699\n5\t494\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List ten customers most likely in cluster 6","message":["%sql","","SELECT CUST_ID, ","       ROUND(CLUSTER_PROBABILITY(OC_SH_CLUS_SAMPLE, 6 USING *),4) PROB","FROM   CUSTOMERS360_V","ORDER BY PROB DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1733160058418,"interpreter":"sql.low","endTime":1733160058511,"results":[{"message":"CUST_ID\tPROB\n104171\t0.9999\n103255\t0.9999\n100885\t0.9999\n101929\t0.9999\n100105\t0.9999\n103786\t0.9999\n104082\t0.9999\n104400\t0.9999\n100432\t0.9999\n104470\t0.9999\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List four most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. These represent customers ","--    with > 20% likelihood of assignment to their cluster.","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,2) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(OC_SH_CLUS_SAMPLE USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(OC_SH_CLUS_SAMPLE, NULL, 0.2 USING *) PSET","            FROM CUSTOMERS360_V V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;"],"enabled":true,"result":{"startTime":1733160058586,"interpreter":"sql.low","endTime":1733160058765,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n101362\t6\t98.43\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".983\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"11000\" operator=\"between\" range=\"(1.018E+004:1.114E+004]\" weight=\".\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'101362'\",\"cluster_id\":\"'5'\",\"s.cluster_id\":\"'5'\",\"cust_id\":\"'100456'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"paragraphId\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isNotebook\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach for building clustering model using an analytic function","In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.","","In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.","","Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers."],"enabled":true,"result":{"startTime":1733160059023,"interpreter":"md.low","endTime":1733160059080,"results":[{"message":"<h2 id=\"alternative-approach-for-building-clustering-model-using-an-analytic-function\">Alternative approach for building clustering model using an analytic function<\/h2>\n<p>In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.<\/p>\n<p>In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.<\/p>\n<p>Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Build and apply a transient model using analytic functions","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT * ","      FROM (SELECT CUST_ID,","                   CLUSTER_ID(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_ID,","                   CLUSTER_DETAILS(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_DET","            FROM CUSTOMERS360_V)","            WHERE CUST_ID <= 100010","            ORDER By 1) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.CLUSTER_DET","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;","                      "],"enabled":true,"result":{"startTime":1733160059159,"interpreter":"sql.low","endTime":1733160059339,"results":[{"message":"CUST_ID\tCLUSTER_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t3\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".059\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".038\" \n100002\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".024\" \n100003\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".018\" \n100004\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".038\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".028\" \n100005\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".157\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".009\" \n100006\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".131\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1983\" weight=\".086\" \n100007\t4\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".027\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \n100008\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".035\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \n100009\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".137\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".01\" \n100010\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".115\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\".046\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. ","","NOTE: The settings table can be reused or modified in multiple model build executions. "],"enabled":true,"result":{"startTime":1733160059415,"interpreter":"md.low","endTime":1733160059471,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n<p>NOTE: The settings table can be reused or modified in multiple model build executions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and populate model settings table","message":["%script ","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE OC_SH_SAMPLE_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE OC_SH_SAMPLE_SETTINGS (SETTING_NAME  VARCHAR2(30),","                                    SETTING_VALUE VARCHAR2(4000));","/","BEGIN       ","   INSERT INTO OC_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.OCLT_SENSITIVITY,'0.5');","   INSERT INTO OC_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO,DBMS_DATA_MINING.PREP_AUTO_ON);","   INSERT INTO OC_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME,DBMS_DATA_MINING.ALGO_OCLUSTER);","END;"],"enabled":true,"result":{"startTime":1733160059547,"interpreter":"script.low","endTime":1733160059698,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable OC_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the contents of the model settings table","message":["%sql","","SELECT * FROM OC_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1733160059785,"interpreter":"sql.low","endTime":1733160059862,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nOCLT_SENSITIVITY\t0.5\nPREP_AUTO\tON\nALGO_NAME\tALGO_O_CLUSTER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the OC Clustering model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_CLUS_SAMPLE2');","EXCEPTION WHEN OTHERS THEN NULL; END;","","BEGIN","DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'OC_SH_CLUS_SAMPLE2',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMERS360_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'OC_SH_SAMPLE_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1733160059944,"interpreter":"script.low","endTime":1733160062240,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE2'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1733160062316,"interpreter":"sql.low","endTime":1733160062386,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_O_CLUSTER\nCLUS_NUM_CLUSTERS\t10\nOCLT_SENSITIVITY\t0.5\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1733160062463,"interpreter":"md.low","endTime":1733160062517,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1733160062595,"interpreter":"md.low","endTime":1733160062664,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Duplicates Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736293164669,"interpreter":"md.low","endTime":1736293165071,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Duplicates Removal","In this notebook, we demonstrate how to remove duplicate records using Oracle SQL.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736293165504,"interpreter":"md.low","endTime":1736293165904,"results":[{"message":"<h1 id=\"oml4sql-data-cleaning-duplicates-removal\">OML4SQL Data Cleaning: Duplicates Removal<\/h1>\n<p>In this notebook, we demonstrate how to remove duplicate records using Oracle SQL.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-prerequisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a prerequisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md"," ","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1736293166320,"interpreter":"md.low","endTime":1736293166722,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the number of distinct customer id","message":["%sql","","SELECT COUNT(DISTINCT CUSTOMER_ID)","FROM CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1736293167141,"interpreter":"sql.low","endTime":1736293167565,"results":[{"message":"COUNT(DISTINCTCUSTOMER_ID)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the number of total rows in the table. There are duplicated rows","message":["%sql","","SELECT COUNT(1) FROM CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1736293167988,"interpreter":"sql.low","endTime":1736293168410,"results":[{"message":"COUNT(1)\n14880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view with all distinct rows","message":["%sql","","CREATE OR REPLACE VIEW CUSTOMER_INSURANCE_LTV_SQL_UNIQUE AS ","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","WHERE rowid IN (","    SELECT max(rowid) row_id","    FROM (SELECT customer_id, rowid","          FROM CUSTOMER_INSURANCE_LTV_SQL)","    GROUP BY customer_id"," ) "," "],"enabled":true,"result":{"startTime":1736293168826,"interpreter":"sql.low","endTime":1736293169244,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the number of rows in the table. All rows are distinct.","message":["%sql","","SELECT COUNT(1) FROM CUSTOMER_INSURANCE_LTV_SQL_UNIQUE;"],"enabled":true,"result":{"startTime":1736293169660,"interpreter":"sql.low","endTime":1736293170091,"results":[{"message":"COUNT(1)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the number of distinct CUSTOMER_ID in the table","message":["%sql","","SELECT COUNT(DISTINCT CUSTOMER_ID) FROM CUSTOMER_INSURANCE_LTV_SQL_UNIQUE;"],"enabled":true,"result":{"startTime":1736293170511,"interpreter":"sql.low","endTime":1736293170929,"results":[{"message":"COUNT(DISTINCTCUSTOMER_ID)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736293171351,"interpreter":"md.low","endTime":1736293171752,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736293172170,"interpreter":"md.low","endTime":1736293172581,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"isRunnable":true,"template":null,"templateConfig":null,"isEditable":true,"name":"OML4SQL Data Cleaning Missing Data","description":null,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736295403912,"interpreter":"md.low","endTime":1736295404325,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Fill in Missing Data","","In this notebook, we demonstrate how to replace missing values using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may *not* be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a>","","Copyright (c) 2025 Oracle Corporation  ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1738093413046,"interpreter":"md.low","endTime":1738093413227,"results":[{"message":"<h1 id=\"oml4sql-data-cleaning-fill-in-missing-data\">OML4SQL Data Cleaning: Fill in Missing Data<\/h1>\n<p>In this notebook, we demonstrate how to replace missing values using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<hr />\n<p><strong>Note<\/strong>: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may <em>not<\/em> be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL DBMS_DATA_MINNG_TRANSFORM Documentation<\/a>"],"enabled":true,"result":{"startTime":1736295405559,"interpreter":"md.low","endTime":1736295405973,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL DBMS_DATA_MINNG_TRANSFORM Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV_SQL data","message":["%sql","","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Data Cleaning Outlier Removal.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Data Cleaning Outlier Removal.dsnb
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index 00000000..aa642f5c
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+++ b/machine-learning/notebooks-oml/sql/OML4SQL Data Cleaning Outlier Removal.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Outlier Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1717446189305,"interpreter":"md.low","endTime":1717446189449,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Outlier Removal","","In this notebook, we demonstrate how to remove outliers using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV_SQL, we focus on numeric values and remove records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1717446189523,"interpreter":"md.low","endTime":1717446189617,"results":[{"message":"<h1 id=\"oml4sql-data-cleaning-outlier-removal\">OML4SQL Data Cleaning: Outlier Removal<\/h1>\n<p>In this notebook, we demonstrate how to remove outliers using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>In the dataset CUSTOMER_INSURANCE_LTV_SQL, we focus on numeric values and remove records with values in the top and bottom 5%.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL DBMS_DATA_MINNG_TRANSFORM 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CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1717446191445,"interpreter":"sql.low","endTime":1717446191516,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the min and max values for column INCOME","message":["%sql","","SELECT MIN(INCOME), MAX(INCOME)","FROM   CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1717446191582,"interpreter":"sql.low","endTime":1717446191952,"results":[{"message":"MIN(INCOME)\tMAX(INCOME)\n37107\t111605\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generate table with clip information with tail fraction of 0.05","message":["%script","","BEGIN","    EXECUTE IMMEDIATE 'DROP TABLE  clip_tbl';","    EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","BEGIN","   dbms_data_mining_transform.CREATE_CLIP(","      clip_table_name    => 'clip_tbl');","   dbms_data_mining_transform.INSERT_CLIP_TRIM_TAIL(","      clip_table_name    => 'clip_tbl',","      data_table_name    => 'CUSTOMER_CLIP_V',","      tail_frac      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Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1717446193604,"interpreter":"md.low","endTime":1717446193658,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Outlier Removal_1","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736299561478,"interpreter":"md.low","endTime":1736299561926,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Outlier Removal","","In this notebook, we demonstrate how to remove outliers using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV_SQL, we focus on numeric values and remove records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2025 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736299562391,"interpreter":"md.low","endTime":1736299562832,"results":[{"message":"<h1 id=\"oml4sql-data-cleaning-outlier-removal\">OML4SQL Data Cleaning: Outlier Removal<\/h1>\n<p>In this notebook, we demonstrate how to remove outliers using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>In the dataset CUSTOMER_INSURANCE_LTV_SQL, we focus on numeric values and remove records with values in the top and bottom 5%.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 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Documentation<\/a>"],"enabled":true,"result":{"startTime":1736299563297,"interpreter":"md.low","endTime":1736299563736,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL DBMS_DATA_MINNG_TRANSFORM Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV_SQL data","message":["%sql","","SELECT * ","FROM   CUSTOMER_INSURANCE_LTV_SQL ","FETCH FIRST 10 ROWS 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   => 0.05,","      exclude_list       => DBMS_DATA_MINING_TRANSFORM.COLUMN_LIST('CUSTOMER_ID'));","END;","/"," "],"enabled":true,"result":{"startTime":1736299566918,"interpreter":"script.low","endTime":1736299567452,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View clip boundaries","message":["%sql","","SELECT col, lcut, rcut","FROM   clip_tbl","ORDER BY col 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=> xforms,","          data_table_name    => 'CUSTOMER_INSURANCE_LTV_SQL',","          xform_view_name    => 'CUSTOMER_INSURANCE_LTV_SQL_STACK');"," END;"," /"],"enabled":true,"result":{"startTime":1736299568840,"interpreter":"script.low","endTime":1736299569452,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check new min and max of INCOME","message":["%sql"," ","SELECT 'PREVIOUS' STATE, MIN(INCOME), MAX(INCOME)","FROM   CUSTOMER_INSURANCE_LTV_SQL_STACK","UNION","SELECT 'AFTER' STATE, MIN(INCOME), MAX(INCOME)","FROM   CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1736299569903,"interpreter":"sql.low","endTime":1736299570380,"results":[{"message":"STATE\tMIN(INCOME)\tMAX(INCOME)\nAFTER\t37107\t111605\nPREVIOUS\t57063\t75363\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736299570834,"interpreter":"md.low","endTime":1736299571272,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736299571894,"interpreter":"md.low","endTime":1736299572330,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Data Cleaning Recode Synonymous Values.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Data Cleaning Recode Synonymous Values.dsnb
new file mode 100644
index 00000000..f18a6cc2
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Data Cleaning Recode Synonymous Values.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Recode Synonymous Values","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736374244900,"interpreter":"md.low","endTime":1736374245373,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Recode Synonymous Values","In this notebook, we demonstrate how to recode synonymous value of a column using Oracle SQL. ","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, ","and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` has recoded values generated by the `\"OML Run-me-first\"` notebook artificially, ","which `MUST` be run before this notebook. Specifically, ***MARITAL_STATUS*** has divorced status coded as 'DIV', married coded as 'M'. ","","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736374245828,"interpreter":"md.low","endTime":1736374246299,"results":[{"message":"<h1 id=\"oml4sql-data-cleaning-recode-synonymous-values\">OML4SQL Data Cleaning: Recode Synonymous Values<\/h1>\n<p>In this notebook, we demonstrate how to recode synonymous value of a column using Oracle SQL.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value,\nand whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> has recoded values generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook artificially,\nwhich <code>MUST<\/code> be run before this notebook. Specifically, <em><strong>MARITAL_STATUS<\/strong><\/em> has divorced status coded as 'DIV', married coded as 'M'.<\/p>\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md"," ","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1736374246757,"interpreter":"md.low","endTime":1736374247228,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check distinct MARITAL_STATUS values that may need recoding, e.g., DIV and M","message":["%sql","","SELECT DISTINCT MARITAL_STATUS","FROM   CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1736374247682,"interpreter":"sql.low","endTime":1736374249663,"results":[{"message":"MARITAL_STATUS\nWIDOWED\nM\nOTHER\nDIV\nDIVORCED\n\nMARRIED\nSINGLE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create view with recoded MARITAL_STATUS","message":["%sql","","CREATE OR REPLACE VIEW customer_recode_v AS","SELECT a.MARITAL_STATUS_CONVERTED, b.*","FROM (","    SELECT CUSTOMER_ID, ","           CASE WHEN MARITAL_STATUS = 'DIV' THEN 'DIVORCED' ELSE ","          (CASE WHEN MARITAL_STATUS = 'M'   THEN 'MARRIED'  ELSE ","          (CASE WHEN MARITAL_STATUS IS NULL THEN 'UNKNOWN'  ELSE","           MARITAL_STATUS  ","           END) ","           END)","        END AS MARITAL_STATUS_CONVERTED","    FROM CUSTOMER_INSURANCE_LTV_SQL ) a","JOIN CUSTOMER_INSURANCE_LTV_SQL b ","ON a.CUSTOMER_ID = b.CUSTOMER_ID"],"enabled":true,"result":{"startTime":1736374250112,"interpreter":"sql.low","endTime":1736374250587,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT(1)\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"MARITAL_STATUS_CONVERTED\",\"MARITAL_STATUS\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Display summary of recoded MARITAL_STATUS values","message":["%sql","","SELECT MARITAL_STATUS_CONVERTED, MARITAL_STATUS, count(1) COUNT","FROM   CUSTOMER_RECODE_V","GROUP BY MARITAL_STATUS_CONVERTED, MARITAL_STATUS;"],"enabled":true,"result":{"startTime":1736374251038,"interpreter":"sql.low","endTime":1736374251538,"results":[{"message":"MARITAL_STATUS_CONVERTED\tMARITAL_STATUS\tCOUNT\nDIVORCED\tDIVORCED\t2335\nWIDOWED\tWIDOWED\t523\nUNKNOWN\t\t7525\nDIVORCED\tDIV\t389\nMARRIED\tM\t407\nMARRIED\tMARRIED\t2703\nOTHER\tOTHER\t108\nSINGLE\tSINGLE\t2890\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check distinct values after conversion","message":["%sql","","SELECT DISTINCT MARITAL_STATUS_CONVERTED ","FROM   CUSTOMER_RECODE_V;"],"enabled":true,"result":{"startTime":1736374251986,"interpreter":"sql.low","endTime":1736374252472,"results":[{"message":"MARITAL_STATUS_CONVERTED\nWIDOWED\nOTHER\nDIVORCED\nMARRIED\nSINGLE\nUNKNOWN\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736374252921,"interpreter":"md.low","endTime":1736374253361,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736374253816,"interpreter":"md.low","endTime":1736374254257,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Transformation Binning.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Data Transformation Binning.dsnb
old mode 100755
new mode 100644
similarity index 59%
rename from machine-learning/notebooks/sql/OML4SQL Data Transformation Binning.dsnb
rename to machine-learning/notebooks-oml/sql/OML4SQL Data Transformation Binning.dsnb
index 443d9278..f5552624
--- a/machine-learning/notebooks/sql/OML4SQL Data Transformation Binning.dsnb	
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Data Transformation Binning.dsnb	
@@ -1 +1 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Transformation Binning","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715348372322,"interpreter":"md.low","endTime":1715348372487,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Data Transformation: Binning","In this notebook, we demonstrate how to bin numeric columns using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to bin a numerical column and visualize the distribution.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. ","Supervised binning uses decision trees to create the optimal bin boundaries. ","More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A04\" target=\"_blank\">Naive Bayes<\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715348429017,"interpreter":"md.low","endTime":1715348429078,"results":[{"message":"<h1 id=\"oml4sql-data-transformation-binning\">OML4SQL Data Transformation: Binning<\/h1>\n<p>In this notebook, we demonstrate how to bin numeric columns using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to bin a numerical column and visualize the distribution.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning.\nSupervised binning uses decision trees to create the optimal bin boundaries.\nMore details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A04\" target=\"_blank\">Naive Bayes<\/a><\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle 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dbms_data_mining_transform.COLUMN_LIST('customer_id'));","END;"],"enabled":true,"result":{"startTime":1715348375036,"interpreter":"script.low","endTime":1715348376049,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Describe the table to see data types","message":["%script","","descr bin_num_tbl"],"enabled":true,"result":{"startTime":1715348376133,"interpreter":"script.low","endTime":1715348376866,"results":[{"message":"Name Null? Type           \n---- ----- -------------- \nCOL       VARCHAR2(128) \nATT       VARCHAR2(4000) \nVAL       NUMBER  \nBIN       VARCHAR2(4000) \n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check mapping from value of the column to bin","message":["%sql","","SELECT COL, VAL, BIN","FROM   bin_num_tbl","FETCH FIRST 10 ROWS 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dbms_data_mining_transform.STACK_BIN_NUM (","      bin_table_name\t    => 'bin_num_tbl',","      xform_list\t        =>  mining_data_stack);","   dbms_data_mining_transform.XFORM_STACK (","      xform_list\t        =>  mining_data_stack,","      data_table_name   => 'CUSTOMER_BIN_NUM_V',","      xform_view_name   => 'CUSTOMER_BIN_NUM_STACK');","END;","/"],"enabled":true,"result":{"startTime":1715348377132,"interpreter":"script.low","endTime":1715348377407,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"series\":{\"availableSeriesElements\":[{\"id\":\"AGE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 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Script"],"enabled":true,"result":{"startTime":1715348377814,"interpreter":"md.low","endTime":1715348377873,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715348377961,"interpreter":"md.low","endTime":1715348378021,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Transformation Binning","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733781024761,"interpreter":"md.low","endTime":1733781025200,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Data Transformation: Binning","In this notebook, we demonstrate how to bin numeric columns using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to bin a numerical column and visualize the distribution.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. ","Supervised binning uses decision trees to create the optimal bin boundaries. ","More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A04\" target=\"_blank\">Naive Bayes<\/a>","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733781025664,"interpreter":"md.low","endTime":1733781026118,"results":[{"message":"<h1 id=\"oml4sql-data-transformation-binning\">OML4SQL Data Transformation: Binning<\/h1>\n<p>In this notebook, we demonstrate how to bin numeric columns using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to bin a numerical column and visualize the distribution.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning.\nSupervised binning uses decision trees to create the optimal bin boundaries.\nMore details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A04\" target=\"_blank\">Naive Bayes<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle 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\ No newline at end of file
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+++ b/machine-learning/notebooks-oml/sql/OML4SQL Data Transformation Categorical.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Transformation Categorical","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736374957478,"interpreter":"md.low","endTime":1736374957920,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Data Transformation: convert variables between categorical and numerical","","In this notebook, we demonstrate how to convert a categorical variable to a numeric variable and vice versa using Oracle SQL.","","We use the customer insurance lifetime value data set, which contains customer financial information, ","lifetime value, and whether or not the customer bought insurance.","","We show how to convert a categorical variable with each distinct level/value coded to an integer. ","Also, we show how to create an indicator variable based on a simple predicate. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736374958336,"interpreter":"md.low","endTime":1736374958742,"results":[{"message":"<h1 id=\"oml4sql-data-transformation-convert-variables-between-categorical-and-numerical\">OML4SQL Data Transformation: convert variables between categorical and numerical<\/h1>\n<p>In this notebook, we demonstrate how to convert a categorical variable to a numeric variable and vice versa using Oracle SQL.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information,\nlifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to convert a categorical variable with each distinct level/value coded to an integer.\nAlso, we show how to create an indicator variable based on a simple predicate.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md"," "," * <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1736374959169,"interpreter":"md.low","endTime":1736374959575,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV_SQL data","message":["%sql","","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS 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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Transformation Normalization and Scale","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734626881436,"interpreter":"md.low","endTime":1734626881861,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Transformation: Normalization and Scale","","In this notebook, we demonstrate how to normalize and scale data using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and ","whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms.  ","For more details, see * <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a>","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734626882275,"interpreter":"md.low","endTime":1734626882694,"results":[{"message":"<h1 id=\"oml4sql-data-transformation-normalization-and-scale\">OML4SQL Data Transformation: Normalization and Scale<\/h1>\n<p>In this notebook, we demonstrate how to normalize and scale data using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and\nwhether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms.<br />\nFor more details, see * <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL ORACLE_DATA_MINING_TRANSFORM Documentation<\/a>"],"enabled":true,"result":{"startTime":1734626883110,"interpreter":"md.low","endTime":1734626883537,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL ORACLE_DATA_MINING_TRANSFORM Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display customer lifetime value data","message":["%sql","","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS 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table_name = tbl and data_type = 'NUMBER' ) LOOP","    fmt := fmt||'case when '||rec.column_name||' <= 0 then 0 else log(10,'||rec.column_name||') end as log_'||rec.column_name||',';","  END LOOP;","    ","  fmt := 'create or replace view customer_log_v as ' ||substr(fmt, 0, length(fmt)-1)||' from '||tbl;","  EXECUTE IMMEDIATE fmt;","END;"],"enabled":true,"result":{"startTime":1734626897444,"interpreter":"script.low","endTime":1734626898056,"results":[{"message":"\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check view that contains log transformed value","message":["%sql","","SELECT ROUND(LOG_CREDIT_BALANCE, 4) 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Feature Engineering Aggregation and Time.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Feature Engineering Aggregation and Time.dsnb
new file mode 100644
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--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Feature Engineering Aggregation and Time.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Engineering Aggregation and Time","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736378326057,"interpreter":"md.low","endTime":1736378326488,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Engineering: Aggregation and Time","In this notebook, we demonstrate how to generate aggregated features and also extract date and time features using Oracle SQL.","","We are using the SALES table from the SH schema. This dataset contains transaction records for each cusotmer and products sold to each customer and product pair. To create features for each customer and product, we create features by aggregating the amount sold.","","We also show how to extract date and time features from the field TIME_ID.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL)"],"enabled":true,"result":{"startTime":1736378386643,"interpreter":"md.low","endTime":1736378387108,"results":[{"message":"<h1 id=\"oml4sql-feature-engineering-aggregation-and-time\">OML4SQL Feature Engineering: Aggregation and Time<\/h1>\n<p>In this notebook, we demonstrate how to generate aggregated features and also extract date and time features using Oracle SQL.<\/p>\n<p>We are using the SALES table from the SH schema. This dataset contains transaction records for each cusotmer and products sold to each customer and product pair. To create features for each customer and product, we create features by aggregating the amount sold.<\/p>\n<p>We also show how to extract date and time features from the field TIME_ID.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL)<\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," ","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1736378327744,"interpreter":"md.low","endTime":1736378328160,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from the SH.SALES table","message":["%sql","","SELECT * FROM SH.SALES FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1736378328596,"interpreter":"sql.low","endTime":1736378330635,"results":[{"message":"PROD_ID\tCUST_ID\tTIME_ID\tCHANNEL_ID\tPROMO_ID\tQUANTITY_SOLD\tAMOUNT_SOLD\n13\t524\t1998-01-20 00:00:00\t2\t999\t1\t1205.99\n13\t2128\t1998-04-05 00:00:00\t2\t999\t1\t1250.25\n13\t3212\t1998-04-05 00:00:00\t2\t999\t1\t1250.25\n13\t3375\t1998-04-05 00:00:00\t2\t999\t1\t1250.25\n13\t5204\t1998-04-05 00:00:00\t2\t999\t1\t1250.25\n13\t7082\t1998-04-05 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","    EXTRACT(SECOND FROM TIME_STAMP) SECOND","FROM SALES_TS","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1736378336494,"interpreter":"sql.low","endTime":1736378336933,"results":[{"message":"CUST_ID\tTIME_STAMP\tYEAR\tMONTH\tDAY_OF_MONTH\tWEEK_OF_YEAR\tDAY_OF_WEEK\tHOUR\tMINUTE\tSECOND\n524\t1998-01-28 04:03:40\t1998\t1\t28\t04\t4\t4\t3\t40\n2128\t1998-04-11 16:24:10\t1998\t4\t11\t15\t7\t16\t24\t10\n3212\t1998-04-12 19:36:41\t1998\t4\t12\t15\t1\t19\t36\t41\n3375\t1998-04-10 03:50:20\t1998\t4\t10\t15\t6\t3\t50\t20\n5204\t1998-04-14 07:35:31\t1998\t4\t14\t15\t3\t7\t35\t31\n7082\t1998-04-12 00:41:49\t1998\t4\t12\t15\t1\t0\t41\t49\n9316\t1998-04-06 20:01:50\t1998\t4\t6\t14\t2\t20\t1\t50\n12782\t1998-04-10 06:32:52\t1998\t4\t10\t15\t6\t6\t32\t52\n13869\t1998-04-14 21:38:23\t1998\t4\t14\t15\t3\t21\t38\t23\n1949\t1998-07-08 06:40:58\t1998\t7\t8\t27\t4\t6\t40\t58\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736378337349,"interpreter":"md.low","endTime":1736378337765,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1736378338182,"interpreter":"md.low","endTime":1736378338584,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Extraction ESA Wiki Model","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1734647569103,"interpreter":"md.low","endTime":1734647569648,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in  text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating *semantic relatedness* (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","Oracle Machine Learning for SQL ESA extracts text-based features from a corpus of documents and performs document similarity comparisons. In this notebook, we'll use the Wikipedia Model as an example:","","- Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model ","- Examples of using the Wiki ESA model","- Basic ESA Wiki model features for words","- Basic ESA Wiki model features for phrases","- ESA Wiki Model document similarity on phrases","- ESA Wiki Model document similarity on long phrases","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734647570105,"interpreter":"md.low","endTime":1734647570579,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in  text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <em>semantic relatedness<\/em> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>Oracle Machine Learning for SQL ESA extracts text-based features from a corpus of documents and performs document similarity comparisons. In this notebook, we'll use the Wikipedia Model as an example:<\/p>\n<ul>\n<li>Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model<\/li>\n<li>Examples of using the Wiki ESA model<\/li>\n<li>Basic ESA Wiki model features for words<\/li>\n<li>Basic ESA Wiki model features for phrases<\/li>\n<li>ESA Wiki Model document similarity on phrases<\/li>\n<li>ESA Wiki Model document similarity on long phrases<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis"],"enabled":true,"result":{"startTime":1734647571050,"interpreter":"md.low","endTime":1734647571504,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using the Wikipedia model as an example","","The following example shows the results of a model that was pre-loaded to the Oracle Autonomous Database.","","You have to load the Wiki Model to your schema **first** before you can run the examples below.","","You can do it in two ways:","1. Easy way: Go to the OML UI Main Menu -> Templates -> Examples and make sure to import and fully run the `OML Import Wiki ESA Model` notebook.","","2. <p>Follow the Setup from Oracle blogs:<\/p>","<ul>","   <li><a href=\"https://blogs.oracle.com/machinelearning/post/wiki-esa-model-available-for-database-19c\" target=\"_blank\">Wiki ESA Model Available for Database 19c<\/a><\/li>","   <li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" target=\"_blank\">Text Analytics using a pre-built Wikipedia-based Topic Model<\/a><\/li>","   <li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" target=\"_blank\">Explicit Semantic Analysis (ESA) for Text Analytics using R<\/a><\/li>","<\/ul>"],"enabled":true,"result":{"startTime":1734647571955,"interpreter":"md.low","endTime":1734647572409,"results":[{"message":"<h2 id=\"using-the-wikipedia-model-as-an-example\">Using the Wikipedia model as an example<\/h2>\n<p>The following example shows the results of a model that was pre-loaded to the Oracle Autonomous Database.<\/p>\n<p>You have to load the Wiki Model to your schema <strong>first<\/strong> before you can run the examples below.<\/p>\n<p>You can do it in two ways:<\/p>\n<ol>\n<li>\n<p>Easy way: Go to the OML UI Main Menu -&gt; Templates -&gt; Examples and make sure to import and fully run the <code>OML Import Wiki ESA Model<\/code> notebook.<\/p>\n<\/li>\n<li>\n<p>Follow the Setup from Oracle blogs:<\/p>\n<\/li>\n<\/ol>\n<ul>\n   <li><a href=\"https://blogs.oracle.com/machinelearning/post/wiki-esa-model-available-for-database-19c\" target=\"_blank\">Wiki ESA Model Available for Database 19c<\/a><\/li>\n   <li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" target=\"_blank\">Text Analytics using a pre-built Wikipedia-based Topic Model<\/a><\/li>\n   <li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" target=\"_blank\">Explicit Semantic Analysis (ESA) for Text Analytics using R<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete existing policies and create new policies for the Wiki Lexer","message":["%script","","BEGIN CTX_DDL.DROP_POLICY('WIKI_TXTPOL'); ","EXCEPTION WHEN OTHERS THEN NULL; END; ","/","BEGIN CTX_DDL.DROP_PREFERENCE( 'WIKI_LEXER'); ","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN ","  CTX_DDL.CREATE_PREFERENCE('WIKI_LEXER', ","                            'BASIC_LEXER'); ","","  CTX_DDL.SET_ATTRIBUTE('WIKI_LEXER', ","                        'INDEX_STEMS',  ","        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USER_MINING_MODELS WHERE MODEL_NAME='WIKI_MODEL'"],"enabled":true,"result":{"startTime":1734647575989,"interpreter":"sql.low","endTime":1734647576462,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\nWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2024-12-19 21:17:32\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE, SETTING_TYPE AS TYPE","  FROM USER_MINING_MODEL_SETTINGS"," WHERE MODEL_NAME = 'WIKI_MODEL'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1734647576923,"interpreter":"sql.low","endTime":1734647577375,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\tTYPE\nALGO_NAME\tALGO_EXPLICIT_SEMANTIC_ANALYS\tINPUT\nESAS_MIN_ITEMS\t25\tINPUT\nESAS_TOPN_FEATURES\t1000\tINPUT\nESAS_VALUE_THRESHOLD\t.00000001\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\tDEFAULT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nODMS_TEXT_MAX_FEATURES\t200000\tINPUT\nODMS_TEXT_MIN_DOCUMENTS\t5\tINPUT\nODMS_TEXT_POLICY_NAME\twiki_txtpol\tINPUT\nPREP_AUTO\tON\tDEFAULT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a list of model views","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='WIKI_MODEL'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1734647577840,"interpreter":"sql.low","endTime":1734647578379,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAWIKI_MODEL\tExplicit Semantic Analysis Matrix\nDM$VFWIKI_MODEL\tExplicit Semantic Analysis Features\nDM$VGWIKI_MODEL\tGlobal Name-Value Pairs\nDM$VNWIKI_MODEL\tNormalization and Missing Value Handling\nDM$VSWIKI_MODEL\tComputed Settings\nDM$VWWIKI_MODEL\tModel Build Alerts\nDM$VXWIKI_MODEL\tText Features\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples using the Wiki ESA model"],"enabled":true,"result":{"startTime":1734647578824,"interpreter":"md.low","endTime":1734647579276,"results":[{"message":"<h3 id=\"examples-using-the-wiki-esa-model\">Examples using the Wiki ESA model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Basic example of ESA features for the word 'Bank'  using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE FROM","  (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","   FROM (SELECT 'BANK' AS TEXT FROM DUAL)) T,","        TABLE(T.FSET) S ","   ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1734647579730,"interpreter":"sql.low","endTime":1734647585319,"results":[{"message":"FEATURE_ID\tVALUE\nBanking in India\t0.12073074740513993\nBanking in Australia\t0.1186153932769802\nBank of America\t0.11307155411695473\nBank\t0.10994163646234326\nCentral bank\t0.10780307790446561\nNational Bank Act\t0.10647042777456947\nStandard Bank\t0.10502497981253221\nBankBoston\t0.10474436983267525\nOnline banking\t0.10250315951850819\nBank of England\t0.09610674490262502\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for the words 'NFL games'  using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'NFL Games' AS TEXT FROM DUAL)) T,","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1734647585774,"interpreter":"sql.low","endTime":1734647590955,"results":[{"message":"FEATURE_ID\tVALUE\nNational Football League\t0.1448362549796241\nNFL International Series\t0.13968414188865144\nThursday Night Football\t0.11436240954509867\nHistory of the National Football League championship\t0.11272129823985683\nChicago College All-Star Game\t0.11121297109987173\nNFL Network\t0.11007694516545648\nNFL Films\t0.10627056953318934\nThe NFL Today\t0.10292445567064071\nNational Football League Christmas games\t0.10104092801135917\nPlayoff Bowl\t0.09258723455526119\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for text about Astronomy  using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'A group of European-led astronomers has made a photograph of what appears to be a planet orbiting another star. If so, it would be the first confirmed picture of a world beyond our solar system.' AS TEXT FROM DUAL)) T, ","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1734647591412,"interpreter":"sql.low","endTime":1734647596995,"results":[{"message":"FEATURE_ID\tVALUE\nSolar System\t0.1783977405440812\nPlanets beyond Neptune\t0.16384415493902063\nPlanet Nine\t0.1556042629299215\nPlanet\t0.14360577141174127\nExoplanet\t0.14146374523098973\nFormation and evolution of the Solar System\t0.1380857932488385\nTerrestrial planet\t0.12349262676588602\nAstronomer\t0.11968229035028131\nSolar analog\t0.11919958268012823\nPlanetary habitability\t0.11168273107745952\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for a text about diet and health using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'Nearly 70 percent of Americans say they are careful about what they eat, and even more say diet is essential to good health, according to a new nationwide health poll in which obesity ranked second among the biggest health concerns.' AS TEXT FROM DUAL)) T,","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1734647597453,"interpreter":"sql.low","endTime":1734647602531,"results":[{"message":"FEATURE_ID\tVALUE\nObesity\t0.2381281204952319\nChildhood obesity\t0.21566128131005566\nRobert Wood Johnson Foundation\t0.1790547081969936\nHealth\t0.1723830168425817\nHealthy diet\t0.16671289147333318\nDieting\t0.142162115212373\nPaleolithic diet\t0.1386597097885653\nHealth education\t0.12498303889516335\nHealth equity\t0.1149008443204024\nPlant-based diet\t0.11425470702731559\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for a long text on stocks and the Fed using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'The more things change... Yes, I''m inclined to agree, especially with regards to the historical relationship between stock prices and bond yields. The two have generally traded together, rising during periods of economic growth and falling during periods of contraction. Consider the period from 1998 through 2010, during which the U.S. economy experienced two expansions as well as two recessions: Then central banks came to the rescue. Fed Chairman Ben Bernanke led from Washington with the help of the bank''s current $3.6T balance sheet. He''s accompanied by Mario Draghi at the European Central Bank and an equally forthright Shinzo Abe in Japan. Their coordinated monetary expansion has provided all the sugar needed for an equities moonshot, while they vowed to hold global borrowing costs at record lows' AS TEXT FROM DUAL)) T,","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1734647602991,"interpreter":"sql.low","endTime":1734647608175,"results":[{"message":"FEATURE_ID\tVALUE\nBen Bernanke\t0.17064524253160657\nRecession\t0.15889239754197812\nMario Draghi\t0.1491065027348536\nCOVID-19 recession\t0.145309094406638\nGreat Recession\t0.1290856902681663\nLong Depression\t0.12204405109050054\nGreat Depression in the United States\t0.11521859419773026\nMonetary policy\t0.1034355579106447\nGreat Recession in the United States\t0.10193175875567674\nGroup of Thirty\t0.09912373068492472\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Comparing Strings for Similarity of Text using the Wikipedia Model"],"enabled":true,"result":{"startTime":1734647608632,"interpreter":"md.low","endTime":1734647609076,"results":[{"message":"<h3 id=\"comparing-strings-for-similarity-of-text-using-the-wikipedia-model\">Comparing Strings for Similarity of Text using the Wikipedia 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DUAL"],"enabled":true,"result":{"startTime":1734647609525,"interpreter":"sql.low","endTime":1734647619311,"results":[{"message":"SIMILARITY\n0.1936898590428866\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare 'street' vs. 'farm' ","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING 'street' AS TEXT ","                         AND USING 'farm' AS TEXT) SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1734647619760,"interpreter":"sql.low","endTime":1734647629354,"results":[{"message":"SIMILARITY\n0.007127425352303707\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare two simple texts about Golf","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING 'There are several PGA tour golfers from South Africa' TEXT ","                         AND USING 'Nick Price won the 2002 Mastercard Colonial Open' TEXT) ","                         SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1734647629890,"interpreter":"sql.low","endTime":1734647639591,"results":[{"message":"SIMILARITY\n0.10969292803597186\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare texts about Golf and Football","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING 'There are several PGA tour golfers from South Africa' TEXT ","                         AND USING 'John Elway played quarterback for the Denver Broncos' TEXT) SIMILARITY","FROM DUAL"],"enabled":true,"result":{"startTime":1734647640041,"interpreter":"sql.low","endTime":1734647650033,"results":[{"message":"SIMILARITY\n0.003917419745909201\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and SpaceX Moon Gateway","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING ","'NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.' AS TEXT","                         AND USING ","'NASA has selected SpaceX to deliver the first two segments of the moon-orbiting Gateway space station for its upcoming Artemis program, which aims to put astronauts back on the moon. The elements will launch atop a Falcon Heavy rocket, sometime in 2024. The flight, which is the second to be awarded to SpaceX this week will carry the Gateway''s power and habitation modules.  Once deposited in lunar orbit, the Gateway will serve as an outpost for astronauts and equipment heading to the moon as part of NASA Artemis program. Roughly one-sixth the size of the International Space Station, the Gateway will support research investigations, crew, and expeditions to the lunar surface.' AS TEXT) SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1734647650509,"interpreter":"sql.low","endTime":1734647660404,"results":[{"message":"SIMILARITY\n0.48847744577133123\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and Tennis Naomi Osaka","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING ","'NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.' AS TEXT","                         AND USING ","'Naomi Osaka, who hasnt lost an official match since last February, put together a dominating performance in the Australian Open semifinals, beating Williams 6-3, 6-4 and ending hopes that the 39-year old American would tie Margaret Court all-time Slam record at this tournament. Osaka, seeking her fourth major title and second Australian Open championship, will play the winner of the second semifinal between American Jennifer Brady, the No. 22 seed, and No. 25 seed Karolina Muchova.' AS TEXT) SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1734647660890,"interpreter":"sql.low","endTime":1734647670795,"results":[{"message":"SIMILARITY\n0.026660806390262026\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1734647671280,"interpreter":"md.low","endTime":1734647671728,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Extraction NMF","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## OML4SQL Feature Extraction: Non-negative Matrix Factorization (NMF)","","In this notebook, we demonstrate how to perform dimensionality reduction using the in-database non-negative matrix factorization algorithm.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to convert a table with many columns to a reduced feature set. NMF produces non-negative coefficients.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1737858902716,"interpreter":"md.low","endTime":1737858903262,"results":[{"message":"<h2 id=\"oml4sql-feature-extraction-non-negative-matrix-factorization-nmf\">OML4SQL Feature Extraction: Non-negative Matrix Factorization (NMF)<\/h2>\n<p>In this notebook, we demonstrate how to perform dimensionality reduction using the in-database non-negative matrix factorization algorithm.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to convert a table with many columns to a reduced feature set. NMF produces non-negative coefficients.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>  ","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle Github<\/a>  ","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a>  ","* <a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a>  ","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/non-negative-matrix-factorization.html\" target=\"_blank\">OML4SQL NMF Documentation<\/a>  "],"enabled":true,"result":{"startTime":1737858903687,"interpreter":"md.low","endTime":1737858904117,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle Github<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/non-negative-matrix-factorization.html\" target=\"_blank\">OML4SQL NMF Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV data","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858904559,"interpreter":"sql.low","endTime":1737858906418,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define a view with a subset of the numerical and categorical columns","message":["%script","","CREATE OR REPLACE VIEW CUST_V AS","SELECT CUSTOMER_ID, CREDIT_BALANCE, CUSTOMER_TENURE, MORTGAGE_AMOUNT, MARITAL_STATUS, GENDER, REGION","FROM CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1737858906852,"interpreter":"script.low","endTime":1737858907554,"results":[{"message":"\nView CUST_V created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":800,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NMF ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-E5CBCC4F-819A-4BD8-9C2B-BC4A9EE870AD\" target=\"_blank\">Non-Negative Matrix Factorization<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","* Specify the Convergence tolerance for NMF algorithm","  The value required is between 0 and 0.5 (excluding 0), and the default is 0.05",">    v_setlst('NMFS_CONV_TOLERANCE') := '0.05';","   ","* Specify whether negative numbers should be allowed in scoring results. ","   When set to NMFS_NONNEG_SCORING_ENABLE, negative feature values will be replaced with zeros. When set to NMFS_NONNEG_SCORING_DISABLE, negative feature values will be allowed.","   The default is NMFS_NONNEG_SCORING_ENABLE",">    v_setlst('NMFS_NONNEGATIVE_SCORING') := 'NMFS_NONNEG_SCORING_ENABLE';","    ","* Specify the number of iterations for NMF algorithm","   The value required is between 1 and 500 (including the edges), and the default is 50",">    v_setlst('NMFS_NUM_ITERATIONS') := '50';","    ","* Specify the Random seed for the NMF algorithm.","   The default value is \u20131. ",">    v_setlst('NMFS_RANDOM_SEED') := '-1';","    ","--- ","","#### Other interesting overrides for Data Preparation for NMF","","Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled. ","","A complete list of settings can be found in the Documentation link:","","* Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" target=\"_blank\">Automatic Data Preparation<\/a>","","* This setting is used to specify Automatic Data Preparation.","   The value required is either ON or OFF, and the default is ON. ",">    v_setlst('PREP_AUTO') := 'ON';","    ","The model uses heuristics to transform the build data according to the requirements of the algorithm. ","","#### User defined transformations","","Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS. ","","* This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.","   PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].",">    v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';","","* This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.","   PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.",">    v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';","","* This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].",">    v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';","","---"],"enabled":true,"result":{"startTime":1737858907997,"interpreter":"md.low","endTime":1737858908436,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nmf\">Examples of possible setting overrides for NMF<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-E5CBCC4F-819A-4BD8-9C2B-BC4A9EE870AD\" target=\"_blank\">Non-Negative Matrix Factorization<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the Convergence tolerance for NMF algorithm\nThe value required is between 0 and 0.5 (excluding 0), and the default is 0.05<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NMFS_CONV_TOLERANCE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether negative numbers should be allowed in scoring results.\nWhen set to NMFS_NONNEG_SCORING_ENABLE, negative feature values will be replaced with zeros. When set to NMFS_NONNEG_SCORING_DISABLE, negative feature values will be allowed.\nThe default is NMFS_NONNEG_SCORING_ENABLE<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NMFS_NONNEGATIVE_SCORING') := 'NMFS_NONNEG_SCORING_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations for NMF algorithm\nThe value required is between 1 and 500 (including the edges), and the default is 50<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NMFS_NUM_ITERATIONS') := '50';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Random seed for the NMF algorithm.\nThe default value is \u20131.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NMFS_RANDOM_SEED') := '-1';<\/p>\n<\/blockquote>\n<hr />\n<h4 id=\"other-interesting-overrides-for-data-preparation-for-nmf\">Other interesting overrides for Data Preparation for NMF<\/h4>\n<p>Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" target=\"_blank\">Automatic Data Preparation<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to specify Automatic Data Preparation.\nThe value required is either ON or OFF, and the default is ON.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_AUTO') := 'ON';<\/p>\n<\/blockquote>\n<p>The model uses heuristics to transform the build data according to the requirements of the algorithm.<\/p>\n<h4 id=\"user-defined-transformations\">User defined transformations<\/h4>\n<p>Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS.<\/p>\n<ul>\n<li>This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.\nPREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.\nPREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create NMF model","message":["%script","","BEGIN ","    DBMS_DATA_MINING.DROP_MODEL('NMF_LTV_MDL');","    EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    V_SETLST('ALGO_NAME')  := 'ALGO_NONNEGATIVE_MATRIX_FACTOR';","    V_SETLST('PREP_AUTO')  := 'ON';","    V_SETLST('FEAT_NUM_FEATURES')  := '5';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NMF_LTV_MDL',","        MINING_FUNCTION     => 'FEATURE_EXTRACTION',","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        DATA_QUERY          => 'SELECT * FROM CUST_V',","        SET_LIST            => V_SETLST);","END;"],"enabled":true,"result":{"startTime":1737858908865,"interpreter":"script.low","endTime":1737858910927,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List the NMF model views available","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='NMF_LTV_MDL'","  ORDER BY VIEW_NAME"],"enabled":true,"result":{"startTime":1737858911369,"interpreter":"sql.low","endTime":1737858911841,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VENMF_LTV_MDL\tNon-Negative Matrix Factorization H Matrix\nDM$VGNMF_LTV_MDL\tGlobal Name-Value Pairs\nDM$VINMF_LTV_MDL\tNon-Negative Matrix Factorization Inverse H Matrix\nDM$VNNMF_LTV_MDL\tNormalization and Missing Value Handling\nDM$VSNMF_LTV_MDL\tComputed Settings\nDM$VWNMF_LTV_MDL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check coefficients of original columns associated to each NMF feature","message":["%sql","","SELECT FEATURE_ID,","       ATTRIBUTE_NAME,","       ATTRIBUTE_VALUE,","       COEFFICIENT","FROM DM$VENMF_LTV_MDL","ORDER BY FEATURE_ID,ATTRIBUTE_NAME,ATTRIBUTE_VALUE","FETCH FIRST 30 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858912282,"interpreter":"sql.low","endTime":1737858912760,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n1\tCREDIT_BALANCE\t\t0.001340184597950889\n1\tCUSTOMER_TENURE\t\t0.08491322814520694\n1\tGENDER\tF                         \t0.09780370828038705\n1\tGENDER\tM                         \t0.3298818924269928\n1\tMARITAL_STATUS\tDIVORCED\t0.044296713537437596\n1\tMARITAL_STATUS\tMARRIED\t0.0784670024345143\n1\tMARITAL_STATUS\tOTHER\t0.004534870161403671\n1\tMARITAL_STATUS\tSINGLE\t0.03556709261641716\n1\tMARITAL_STATUS\tWIDOWED\t0.007785204835084698\n1\tMORTGAGE_AMOUNT\t\t0.0013599283262101575\n1\tREGION\tMidwest\t0.08304934840432189\n1\tREGION\tNorthEast\t0.036824028128878035\n1\tREGION\tSouth\t0.023342685762574207\n1\tREGION\tSouthwest\t0.028066134954678388\n1\tREGION\tWest\t0.00845475426219793\n2\tCREDIT_BALANCE\t\t4.934352950880333E-4\n2\tCUSTOMER_TENURE\t\t0.024174121965280678\n2\tGENDER\tF                         \t0.14096637840904322\n2\tGENDER\tM                         \t0.206074157025542\n2\tMARITAL_STATUS\tDIVORCED\t0.2223087117597371\n2\tMARITAL_STATUS\tMARRIED\t0.1391199365865613\n2\tMARITAL_STATUS\tOTHER\t0.001527958893974671\n2\tMARITAL_STATUS\tSINGLE\t0.08021795904162299\n2\tMARITAL_STATUS\tWIDOWED\t0.004037871485728927\n2\tMORTGAGE_AMOUNT\t\t0.01131762053894168\n2\tREGION\tMidwest\t0.07999242704296103\n2\tREGION\tNorthEast\t0.12416388255313135\n2\tREGION\tSouth\t0.021676219489230035\n2\tREGION\tSouthwest\t0.014610794420396827\n2\tREGION\tWest\t0.13188443902187472\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check first four NMF features for each customer","message":["%sql ","","SELECT CUSTOMER_ID, ","       ROUND(FEATURE_VALUE(NMF_LTV_MDL, 1 USING *),4) NMF_PROJ1,","       ROUND(FEATURE_VALUE(NMF_LTV_MDL, 2 USING *),4) NMF_PROJ2,","       ROUND(FEATURE_VALUE(NMF_LTV_MDL, 3 USING *),4) NMF_PROJ3,","       ROUND(FEATURE_VALUE(NMF_LTV_MDL, 4 USING *),4) NMF_PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858913217,"interpreter":"sql.low","endTime":1737858913695,"results":[{"message":"CUSTOMER_ID\tNMF_PROJ1\tNMF_PROJ2\tNMF_PROJ3\tNMF_PROJ4\nCU1                       \t1.5535\t1.8228\t0\t8.4612\nCU10                      \t0\t5.6516\t0\t6.4315\nCU1000                    \t1.3552\t0\t5.5198\t0\nCU10000                   \t1.9768\t0\t0\t0\nCU10001                   \t0.1962\t1.9594\t7.668\t0\nCU10003                   \t0\t7.2598\t0\t8.9182\nCU10004                   \t1.555\t0\t0\t5.1539\nCU10005                   \t1.6082\t0\t0\t2.6702\nCU10006                   \t1.3\t1.7619\t2.9731\t0\nCU10007                   \t0.1598\t0\t3.8602\t0\nCU10008                   \t0.1614\t0\t3.8596\t0\nCU1001                    \t0.5763\t0\t3.6229\t0\nCU10010                   \t1.5532\t0\t0\t5.1554\nCU10011                   \t0\t5.8542\t0\t8.4789\nCU10012                   \t0.7239\t0.0094\t3.7144\t0.0192\nCU10013                   \t0\t4.7695\t0\t5.4605\nCU10014                   \t1.5529\t0\t0\t5.1556\nCU10015                   \t1.555\t0\t0\t5.1539\nCU10016                   \t0\t3.0397\t0\t1.3067\nCU10017                   \t1.5532\t0\t0\t5.1554\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"NMF Feature vector 1","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(COEFFICIENT,4) COEFF","FROM DM$VENMF_LTV_MDL","WHERE FEATURE_ID = 1","ORDER BY COEFFICIENT DESC","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858914124,"interpreter":"sql.low","endTime":1737858914574,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFF\nGENDER\tM                         \t0.3299\nGENDER\tF                         \t0.0978\nCUSTOMER_TENURE\t\t0.0849\nREGION\tMidwest\t0.083\nMARITAL_STATUS\tMARRIED\t0.0785\nMARITAL_STATUS\tDIVORCED\t0.0443\nREGION\tNorthEast\t0.0368\nMARITAL_STATUS\tSINGLE\t0.0356\nREGION\tSouthwest\t0.0281\nREGION\tSouth\t0.0233\nREGION\tWest\t0.0085\nMARITAL_STATUS\tWIDOWED\t0.0078\nMARITAL_STATUS\tOTHER\t0.0045\nMORTGAGE_AMOUNT\t\t0.0014\nCREDIT_BALANCE\t\t0.0013\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"NMF Feature vector 2","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(COEFFICIENT,4) COEFF","FROM DM$VENMF_LTV_MDL","WHERE FEATURE_ID = 2","ORDER BY COEFFICIENT DESC","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858915002,"interpreter":"sql.low","endTime":1737858915448,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFF\nMARITAL_STATUS\tDIVORCED\t0.2223\nGENDER\tM                         \t0.2061\nGENDER\tF                         \t0.141\nMARITAL_STATUS\tMARRIED\t0.1391\nREGION\tWest\t0.1319\nREGION\tNorthEast\t0.1242\nMARITAL_STATUS\tSINGLE\t0.0802\nREGION\tMidwest\t0.08\nCUSTOMER_TENURE\t\t0.0242\nREGION\tSouth\t0.0217\nREGION\tSouthwest\t0.0146\nMORTGAGE_AMOUNT\t\t0.0113\nMARITAL_STATUS\tWIDOWED\t0.004\nMARITAL_STATUS\tOTHER\t0.0015\nCREDIT_BALANCE\t\t0.0005\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"NMF Feature vector 3","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(COEFFICIENT,4) COEFF","FROM DM$VENMF_LTV_MDL","WHERE FEATURE_ID = 3","ORDER BY COEFFICIENT DESC","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858915872,"interpreter":"sql.low","endTime":1737858916343,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFF\nGENDER\tM                         \t0.316\nCUSTOMER_TENURE\t\t0.1972\nMARITAL_STATUS\tSINGLE\t0.1713\nMARITAL_STATUS\tMARRIED\t0.1478\nREGION\tNorthEast\t0.1198\nREGION\tMidwest\t0.0538\nGENDER\tF                         \t0.052\nMARITAL_STATUS\tDIVORCED\t0.0519\nREGION\tSouth\t0.0466\nREGION\tWest\t0.0368\nREGION\tSouthwest\t0.0162\nMARITAL_STATUS\tWIDOWED\t0.0118\nMORTGAGE_AMOUNT\t\t0.0082\nMARITAL_STATUS\tOTHER\t0.0009\nCREDIT_BALANCE\t\t0.0008\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Create an NMF Feature Extraction Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.","","---"],"enabled":true,"result":{"startTime":1737858916775,"interpreter":"md.low","endTime":1737858917188,"results":[{"message":"<h2 id=\"create-an-nmf-feature-extraction-model-using-settings-table\">Create an NMF Feature Extraction Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop old setting table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE nmf_ltv_settings';","EXCEPTION WHEN OTHERS THEN NULL; END;"],"enabled":true,"result":{"startTime":1737858917637,"interpreter":"script.low","endTime":1737858918098,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create setting table","message":["%sql","","CREATE TABLE NMF_LTV_SETTINGS (","   SETTING_NAME  VARCHAR2(30),","   SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1737858918544,"interpreter":"sql.low","endTime":1737858919083,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Specify settings","message":["%script","","BEGIN","  INSERT INTO NMF_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES","  (DBMS_DATA_MINING.PREP_AUTO,DBMS_DATA_MINING.PREP_AUTO_ON);","  INSERT INTO NMF_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES","  (DBMS_DATA_MINING.FEAT_NUM_FEATURES, 5);","","END;"],"enabled":true,"result":{"startTime":1737858919536,"interpreter":"script.low","endTime":1737858920002,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create NMF model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NMF_LTV_MDL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'NMF_LTV_MDL',","    MINING_FUNCTION     => DBMS_DATA_MINING.FEATURE_EXTRACTION,","    DATA_TABLE_NAME     => 'CUST_V',","    CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","    SETTINGS_TABLE_NAME => 'NMF_LTV_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1737858920444,"interpreter":"script.low","endTime":1737858921907,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1737858922340,"interpreter":"md.low","endTime":1737858922760,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737858923183,"interpreter":"md.low","endTime":1737858923611,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Feature Extraction SVD.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Feature Extraction SVD.dsnb
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+++ b/machine-learning/notebooks-oml/sql/OML4SQL Feature Extraction SVD.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Extraction SVD","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737858969371,"interpreter":"md.low","endTime":1737858969890,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Extraction: Singular Value Decomposition","","In this notebook, we demonstrate how to use the in-database SVD decomposion for dimensionality reduction.","","We use the customer insurance lifetime value data. This dataset contains financial information about the customer and the decision of buying insurance or not. ","","We show that Principal Component Analysis can be achieved using SVD results.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","","---"],"enabled":true,"result":{"startTime":1737858970308,"interpreter":"md.low","endTime":1737858970753,"results":[{"message":"<h1 id=\"oml4sql-feature-extraction-singular-value-decomposition\">OML4SQL Feature Extraction: Singular Value Decomposition<\/h1>\n<p>In this notebook, we demonstrate how to use the in-database SVD decomposion for dimensionality reduction.<\/p>\n<p>We use the customer insurance lifetime value data. This dataset contains financial information about the customer and the decision of buying insurance or not.<\/p>\n<p>We show that Principal Component Analysis can be achieved using SVD results.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/singular-value-decomposition.html\" target=\"_blank\">OML4SQL SVD Documentation<\/a>"],"enabled":true,"result":{"startTime":1737858971187,"interpreter":"md.low","endTime":1737858971646,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/singular-value-decomposition.html\" target=\"_blank\">OML4SQL SVD Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858972116,"interpreter":"sql.low","endTime":1737858973971,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a view that contains a set of numerical and categorical columns","message":["%script","","CREATE OR REPLACE VIEW CUST_V AS","SELECT CUSTOMER_ID, CREDIT_BALANCE, CUSTOMER_TENURE, MORTGAGE_AMOUNT, MARITAL_STATUS, GENDER, REGION","FROM CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1737858974424,"interpreter":"script.low","endTime":1737858975107,"results":[{"message":"\nView CUST_V created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from CUST_V","message":["%sql","","SELECT * FROM CUST_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858975539,"interpreter":"sql.low","endTime":1737858975982,"results":[{"message":"CUSTOMER_ID\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tMARITAL_STATUS\tGENDER\tREGION\nCU15154                   \t0\t3\t0\tSINGLE\tM                         \tWest\nCU15155                   \t0\t4\t0\tSINGLE\tM                         \tNorthEast\nCU15157                   \t0\t3\t1000\tMARRIED\tM                         \tMidwest\nCU15286                   \t0\t5\t1200\tMARRIED\tF                         \tSouthwest\nCU15287                   \t0\t4\t1800\tMARRIED\tF                         \tSouthwest\nCU15289                   \t0\t1\t1400\tMARRIED\tM                         \tSouthwest\nCU15290                   \t0\t3\t578\tSINGLE\tM                         \tSouthwest\nCU15291                   \t0\t3\t0\tSINGLE\tM                         \tSouthwest\nCU15292                   \t0\t5\t117\tSINGLE\tF                         \tSouthwest\nCU15294                   \t0\t3\t0\tSINGLE\tM                         \tSouthwest\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVD","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117');\">Singular Value Decompositionn<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","* This setting is used to prune features. Define the minimum value the eigenvalue of a feature as a share of the first eigenvalue to not to prune. Default value is data driven. ","   The value required is between 0 and 1 (including the edges), and the default data driven.","> v_setlst('SVDS_TOLERANCE') := '0.05';","","* Setting to define whether to use SVD or PCA scoring for the model.  When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices.","   The value required is either SVDS_SCORING_SVD or SVDS_SCORING_PCA, and the default is SVDS_SCORING_SVD. ","> v_setlst('SVDS_SCORING_MODE') := 'SVDS_SCORING_SVD';","","* Specify the random seed value used for initializing the sampling matrix used by the Stochastic SVD solver. The SVD Solver must be set to SSVD or STEIGEN. ","   The value required is between 0 and 4,294,967,296 (including the edges), and the default is 0.","> v_setlst('SVDS_RANDOM_SEED') := '0';","    ","* This setting is configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features plus the oversampling setting. The SVD Solver must be set to SSVD or STEIGEN. ","   The value required is between 1 and 5,000 (including the edges).","> v_setlst('SVDS_OVER_SAMPLING') := '100';","","* The power iteration setting improves the accuracy of the SSVD solver. The SVD Solver must be set to SSVD or STEIGEN. ","   The value required is between 0 and 20 (including the edges), and the default is 2.","> v_setlst('SVDS_POWER_ITERATIONS') := '2';","   ","* Indicates whether or not to persist the U Matrix produced by SVD.","   The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when SVDS_U_MATRIX_OUTPUT is enabled.","   When SVDS_U_MATRIX_OUTPUT is enabled, the build data must include a case ID. If no case ID is present and the U matrix is requested, then an exception is raised.","   The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE. ","> v_setlst('SVDS_U_MATRIX_OUTPUT') := 'SVDS_U_MATRIX_DISABLE';","    ","* The setting SVDS_SOLVER indicates the solver to be used for computing SVD of the data. In the case of PCA, the solver setting indicates the type of SVD solver used to compute the PCA for the data. When this setting is not specified the solver type selection is data driven. If the number of attributes is greater than 3,240, then the default wide solver is used. Otherwise, the default narrow solver is selected.","   The following are the group of solvers:","   * Narrow data solvers: for matrices with up to 11500 attributes (TSEIGEN) or up to 8100 attributes (TSSVD).","   * Wide data solvers: for matrices up to 1 million attributes.","","   For narrow data solvers:","   * Tall-Skinny SVD uses QR computation TSVD (SVDS_SOLVER_TSSVD)","   * Tall-Skinny SVD uses eigenvalue computation, TSEIGEN (SVDS_SOLVER_TSEIGEN), is the default solver for narrow data.","","   For wide data solvers:","   * Stochastic SVD uses QR computation SSVD (SVDS_SOLVER_SSVD), is the default solver for wide data solvers.","   * Stochastic SVD uses eigenvalue computations, STEIGEN (SVDS_SOLVER_STEIGEN).","","   The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE. ","     v_setlst('SVDS_SOLVER') := 'SVDS_SOLVER_STEIGEN';","","---","","#### Other interesting overrides for Data Preparation for SVD","","Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled. ","","A complete list of settings can be found in the Documentation link:","","* Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a>","","* This setting is used to specify Automatic Data Preparation.","   The value required is either ON or OFF, and the default is ON. ","> v_setlst('PREP_AUTO') := 'ON';","    ","The model uses heuristics to transform the build data according to the requirements of the algorithm. ","","#### User defined transformations","","Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS. ","","* This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.","   PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].","> v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';","","* This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.","   PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.","> v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';","","* This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].","> v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';","","---"],"enabled":true,"result":{"startTime":1737858976433,"interpreter":"md.low","endTime":1737858976892,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svd\">Examples of possible setting overrides for SVD<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117');\">Singular Value Decompositionn<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to prune features. Define the minimum value the eigenvalue of a feature as a share of the first eigenvalue to not to prune. Default value is data driven.\nThe value required is between 0 and 1 (including the edges), and the default data driven.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_TOLERANCE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Setting to define whether to use SVD or PCA scoring for the model.  When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices.\nThe value required is either SVDS_SCORING_SVD or SVDS_SCORING_PCA, and the default is SVDS_SCORING_SVD.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_SCORING_MODE') := 'SVDS_SCORING_SVD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the random seed value used for initializing the sampling matrix used by the Stochastic SVD solver. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 0 and 4,294,967,296 (including the edges), and the default is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_RANDOM_SEED') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting is configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features plus the oversampling setting. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 1 and 5,000 (including the edges).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_OVER_SAMPLING') := '100';<\/p>\n<\/blockquote>\n<ul>\n<li>The power iteration setting improves the accuracy of the SSVD solver. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 0 and 20 (including the edges), and the default is 2.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_POWER_ITERATIONS') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Indicates whether or not to persist the U Matrix produced by SVD.\nThe U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when SVDS_U_MATRIX_OUTPUT is enabled.\nWhen SVDS_U_MATRIX_OUTPUT is enabled, the build data must include a case ID. If no case ID is present and the U matrix is requested, then an exception is raised.\nThe value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_U_MATRIX_OUTPUT') := 'SVDS_U_MATRIX_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>The setting SVDS_SOLVER indicates the solver to be used for computing SVD of the data. In the case of PCA, the solver setting indicates the type of SVD solver used to compute the PCA for the data. When this setting is not specified the solver type selection is data driven. If the number of attributes is greater than 3,240, then the default wide solver is used. Otherwise, the default narrow solver is selected.\nThe following are the group of solvers:<\/p>\n<ul>\n<li>Narrow data solvers: for matrices with up to 11500 attributes (TSEIGEN) or up to 8100 attributes (TSSVD).<\/li>\n<li>Wide data solvers: for matrices up to 1 million attributes.<\/li>\n<\/ul>\n<p>For narrow data solvers:<\/p>\n<ul>\n<li>Tall-Skinny SVD uses QR computation TSVD (SVDS_SOLVER_TSSVD)<\/li>\n<li>Tall-Skinny SVD uses eigenvalue computation, TSEIGEN (SVDS_SOLVER_TSEIGEN), is the default solver for narrow data.<\/li>\n<\/ul>\n<p>For wide data solvers:<\/p>\n<ul>\n<li>Stochastic SVD uses QR computation SSVD (SVDS_SOLVER_SSVD), is the default solver for wide data solvers.<\/li>\n<li>Stochastic SVD uses eigenvalue computations, STEIGEN (SVDS_SOLVER_STEIGEN).<\/li>\n<\/ul>\n<p>The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE.\nv_setlst('SVDS_SOLVER') := 'SVDS_SOLVER_STEIGEN';<\/p>\n<\/li>\n<\/ul>\n<hr />\n<h4 id=\"other-interesting-overrides-for-data-preparation-for-svd\">Other interesting overrides for Data Preparation for SVD<\/h4>\n<p>Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to specify Automatic Data Preparation.\nThe value required is either ON or OFF, and the default is ON.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_AUTO') := 'ON';<\/p>\n<\/blockquote>\n<p>The model uses heuristics to transform the build data according to the requirements of the algorithm.<\/p>\n<h4 id=\"user-defined-transformations\">User defined transformations<\/h4>\n<p>Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS.<\/p>\n<ul>\n<li>This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.\nPREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.\nPREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create SVD model with manual Data Preparation options","message":["%script","","BEGIN ","    DBMS_DATA_MINING.DROP_MODEL('SVD_CUST_LTV_MDL');","    EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    V_SETLST('ALGO_NAME')         := 'ALGO_SINGULAR_VALUE_DECOMP';","    V_SETLST('SVDS_SCORING_MODE') := 'SVDS_SCORING_PCA';","    V_SETLST('PREP_AUTO')         := 'OFF';","    V_SETLST('PREP_SCALE_2DNUM')  := 'PREP_SCALE_STDDEV';","    V_SETLST('PREP_SHIFT_2DNUM')  := 'PREP_SHIFT_MEAN';","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVD_CUST_LTV_MDL',","        MINING_FUNCTION     => 'FEATURE_EXTRACTION',","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        DATA_QUERY          => 'SELECT * FROM CUST_V',","        SET_LIST            => V_SETLST);","END;"],"enabled":true,"result":{"startTime":1737858977332,"interpreter":"script.low","endTime":1737858979490,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully 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Value Decomposition V Matrix\nDM$VWSVD_CUST_LTV_MDL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the Percentage of Cumulative Variance vs Feature #","message":["%sql","","SELECT FEATURE_ID, round(PCT_CUM_VARIANCE,3) PCT_CUM_VARIANCE","FROM 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ONLY"],"enabled":true,"result":{"startTime":1737858981753,"interpreter":"sql.low","endTime":1737858982227,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tVALUE\n1\tCREDIT_BALANCE\t\t-0.32246583912875243\n1\tCUSTOMER_TENURE\t\t0.15464257875072684\n1\tGENDER\tF                         \t-0.058883099178144716\n1\tGENDER\tM                         \t0.5325070626401884\n1\tMARITAL_STATUS\tDIVORCED\t0.029778428687589696\n1\tMARITAL_STATUS\tWIDOWED\t-0.03202096481497196\n1\tMARITAL_STATUS\tSINGLE\t0.35381996357540335\n1\tMARITAL_STATUS\tOTHER\t-0.023346721190063344\n1\tMARITAL_STATUS\tMARRIED\t0.1453932572040862\n1\tMORTGAGE_AMOUNT\t\t-0.6122800207733815\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Feature 1 - PCA coefficients","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(VALUE,4) COEFF","FROM DM$VVSVD_CUST_LTV_MDL","WHERE FEATURE_ID = 1","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS 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coefficients","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(VALUE,4) COEFF","FROM DM$VVSVD_CUST_LTV_MDL","WHERE FEATURE_ID = 3","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858984440,"interpreter":"sql.low","endTime":1737858984884,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFF\nCREDIT_BALANCE\t\t0.3481\nCUSTOMER_TENURE\t\t0.9332\nGENDER\tF                         \t0.0283\nGENDER\tM                         \t-0.0004\nMARITAL_STATUS\tDIVORCED\t-0.0037\nMARITAL_STATUS\tWIDOWED\t0.0042\nMARITAL_STATUS\tSINGLE\t0.0149\nMARITAL_STATUS\tOTHER\t0.005\nMARITAL_STATUS\tMARRIED\t0.0075\nMORTGAGE_AMOUNT\t\t0.069\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first four principal component projections for customers","message":["%sql","","SELECT CUSTOMER_ID, ","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 1 USING *),4) PCA_PROJ1,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 2 USING *),4) PCA_PROJ2,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 3 USING *),4) PCA_PROJ3,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 4 USING *),4) PCA_PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 10 ROWS 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\t1.4601\t0.5639\t0.3639\t0.0668\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create an SVD Feature Extraction Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.","","---"],"enabled":true,"result":{"startTime":1737858986227,"interpreter":"md.low","endTime":1737858986642,"results":[{"message":"<h3 id=\"create-an-svd-feature-extraction-model-using-settings-table\">Create an SVD Feature Extraction Model using Settings Table<\/h3>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Clean up old settings table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE SVD_CUSTOMER_LTV_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/"],"enabled":true,"result":{"startTime":1737858987070,"interpreter":"script.low","endTime":1737858987538,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create settings table","message":["%sql","","CREATE TABLE SVD_CUSTOMER_LTV_SETTINGS (","   SETTING_NAME  VARCHAR2(30),","   SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1737858988009,"interpreter":"sql.low","endTime":1737858988458,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Specify settings","message":["%script","","BEGIN       ","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SINGULAR_VALUE_DECOMP);","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_OFF);  ","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVDS_SCORING_MODE, DBMS_DATA_MINING.SVDS_SCORING_PCA);","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_SHIFT_2DNUM, DBMS_DATA_MINING.PREP_SHIFT_MEAN); ","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_SCALE_2DNUM, DBMS_DATA_MINING.PREP_SCALE_STDDEV);   ","END;"],"enabled":true,"result":{"startTime":1737858988897,"interpreter":"script.low","endTime":1737858989389,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVD model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_CUST_LTV_MDL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'SVD_CUST_LTV_MDL',","    MINING_FUNCTION     => DBMS_DATA_MINING.FEATURE_EXTRACTION,","    DATA_TABLE_NAME     => 'CUST_V',","    CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","    SETTINGS_TABLE_NAME => 'SVD_CUSTOMER_LTV_SETTINGS');","END;","/"],"enabled":true,"result":{"startTime":1737858989812,"interpreter":"script.low","endTime":1737858991254,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients of the principal components mapping from original attributes","message":["%sql","","SELECT FEATURE_ID, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, VALUE","FROM DM$VVSVD_CUST_LTV_MDL","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858991683,"interpreter":"sql.low","endTime":1737858992146,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tVALUE\n1\tCREDIT_BALANCE\t\t-0.32246583912875243\n1\tCUSTOMER_TENURE\t\t0.15464257875072684\n1\tGENDER\tF                         \t-0.058883099178144716\n1\tGENDER\tM                         \t0.5325070626401884\n1\tMARITAL_STATUS\tDIVORCED\t0.029778428687589696\n1\tMARITAL_STATUS\tWIDOWED\t-0.03202096481497196\n1\tMARITAL_STATUS\tSINGLE\t0.35381996357540335\n1\tMARITAL_STATUS\tOTHER\t-0.023346721190063344\n1\tMARITAL_STATUS\tMARRIED\t0.1453932572040862\n1\tMORTGAGE_AMOUNT\t\t-0.6122800207733815\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first four principal component projections for customers","message":["%sql","","SELECT CUSTOMER_ID, ","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 1 USING *),4) PROJ1,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 2 USING *),4) PROJ2,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 3 USING *),4) PROJ3,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 4 USING *),4) PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737858992598,"interpreter":"sql.low","endTime":1737858993099,"results":[{"message":"CUSTOMER_ID\tPROJ1\tPROJ2\tPROJ3\tPROJ4\nCU1                       \t-3.9247\t2.8396\t1.6307\t4.3253\nCU10                      \t-0.7384\t1.8562\t2.2444\t-1.4873\nCU1000                    \t0.8149\t1.1414\t-1.1088\t0.0841\nCU10000                   \t1.1401\t0.6253\t0.3622\t0.2917\nCU10001                   \t0.2785\t1.0078\t-1.1183\t0.2\nCU10003                   \t0.2\t0.366\t1.1067\t0.8741\nCU10004                   \t1.4874\t0.3463\t0.2974\t-0.0874\nCU10005                   \t1.539\t0.4053\t0.3482\t-0.0923\nCU10006                   \t0.7132\t0.5235\t0.3479\t0.3988\nCU10007                   \t1.4601\t0.5639\t0.3639\t0.0668\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737858993561,"interpreter":"md.low","endTime":1737858993982,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737858994411,"interpreter":"md.low","endTime":1737858994830,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Selection Algorithm Based","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1737859244968,"interpreter":"md.low","endTime":1737859245523,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Selection: Supervised Algorithm","In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We first build a random forest model to predict if the customer will buy insurance, then use feature importance values for feature selection. ","","We then build a decision tree model for the same classification task and obtain split nodes. For the top splitting nodes with highest support, we select features associated with those nodes.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1737859245997,"interpreter":"md.low","endTime":1737859246508,"results":[{"message":"<h1 id=\"oml4sql-feature-selection-supervised-algorithm\">OML4SQL Feature Selection: Supervised Algorithm<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms.<\/p>\n<p>We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We first build a random forest model to predict if the customer will buy insurance, then use feature importance values for feature selection.<\/p>\n<p>We then build a decision tree model for the same classification task and obtain split nodes. For the top splitting nodes with highest support, we select features associated with those nodes.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h6 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h6>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-attribute-importance\" target=\"_blank\">OML Attribute Importance<\/a>","* <a href=\"https://www.oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://www.oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a>"],"enabled":true,"result":{"startTime":1737859246986,"interpreter":"md.low","endTime":1737859247451,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-attribute-importance\" target=\"_blank\">OML Attribute Importance<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV data","message":["%sql","","SELECT *","FROM   CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859247928,"interpreter":"sql.low","endTime":1737859249812,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":500,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for Attribute Importance","message":["%md","","## Examples of possible setting overrides for Expectation-Maximization clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of shared settings can be found in the Documentation link:","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","* Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" target=\"_blank\"> ML settings <\/a>","","---","","##### Other interesting overrides for data preparation","","Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The `PREP_*` settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled.","","A complete list of settings can be found in the Documentation link:","","* Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" target=\"_blank\">Automatic Data Preparation<\/a>","","* This setting is used to specify Automatic Data Preparation.  ","  The value required is either `ON` or `OFF`, and the default is `ON`.","",">    % sql","     v_setlst('PREP_AUTO') := 'ON';","","The model uses heuristics to transform the build data according to the requirements of the algorithm. ","","##### User defined transformations","","Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS. ","","*  This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.","   PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].",">    v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';","","* This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.","   PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.",">    v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';","","* This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].",">    v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';","","---"],"enabled":true,"result":{"startTime":1737859250310,"interpreter":"md.low","endTime":1737859250812,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation-Maximization clustering<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of shared settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" target=\"_blank\"> ML settings <\/a><\/p>\n<\/li>\n<\/ul>\n<hr />\n<h5 id=\"other-interesting-overrides-for-data-preparation\">Other interesting overrides for data preparation<\/h5>\n<p>Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The <code>PREP_*<\/code> settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" target=\"_blank\">Automatic Data Preparation<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to specify Automatic Data Preparation.<br />\nThe value required is either <code>ON<\/code> or <code>OFF<\/code>, and the default is <code>ON<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>% sql\nv_setlst('PREP_AUTO') := 'ON';<\/p>\n<\/blockquote>\n<p>The model uses heuristics to transform the build data according to the requirements of the algorithm.<\/p>\n<h5 id=\"user-defined-transformations\">User defined transformations<\/h5>\n<p>Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS.<\/p>\n<ul>\n<li>This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.\nPREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.\nPREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use in-database Attribute Importance ","message":["\r","%script\r","\r","BEGIN DBMS_DATA_MINING.DROP_MODEL('AI_FS_MODEL');\r","EXCEPTION WHEN OTHERS THEN NULL; END;\r","/\r","DECLARE\r","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;\r","BEGIN\r","    V_SETLST('ALGO_NAME')  := 'ALGO_AI_MDL';\r","    V_SETLST('PREP_AUTO')  := 'ON';\r","\r","    DBMS_DATA_MINING.CREATE_MODEL2(\r","        MODEL_NAME          => 'AI_FS_MODEL',\r","        MINING_FUNCTION     => 'ATTRIBUTE_IMPORTANCE',\r","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',\r","        SET_LIST            => V_SETLST,\r","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',\r","        TARGET_COLUMN_NAME  => 'BUY_INSURANCE');\r","END;\r"],"enabled":true,"result":{"startTime":1737859251309,"interpreter":"script.low","endTime":1737859253494,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the model view output with attribute rankings","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_IMPORTANCE_VALUE, ATTRIBUTE_RANK ","FROM   DM$VAAI_FS_MODEL ","WHERE  ATTRIBUTE_IMPORTANCE_VALUE > 0","ORDER BY ATTRIBUTE_IMPORTANCE_VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859253961,"interpreter":"sql.low","endTime":1737859254438,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_IMPORTANCE_VALUE\tATTRIBUTE_RANK\nBANK_FUNDS\t0.2105984620957382\t1\nMONEY_MONTLY_OVERDRAWN\t0.15007198908446304\t2\nNUM_TRANS_ATM\t0.14838252230225377\t3\nNUM_TRANS_TELLER\t0.11572527598455709\t4\nTOTAL_AUTOM_PAYMENTS\t0.10805697528319519\t5\nMONTHLY_CHECKS\t0.08916425624499165\t6\nNUM_DEPENDENTS\t0.028549990022342858\t7\nCUSTOMER_TENURE\t0.022654928543333067\t8\nCHECKING_BALANCE\t0.022525045499602096\t9\nPROFESSION\t0.016638375277904405\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Plot attributes by importance for predicting people who buy insurance","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_IMPORTANCE_VALUE, ATTRIBUTE_RANK ","FROM   DM$VAAI_FS_MODEL ","WHERE  ATTRIBUTE_IMPORTANCE_VALUE > 0","ORDER BY ATTRIBUTE_IMPORTANCE_VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859254931,"interpreter":"sql.low","endTime":1737859255398,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_IMPORTANCE_VALUE\tATTRIBUTE_RANK\nBANK_FUNDS\t0.2105984620957382\t1\nMONEY_MONTLY_OVERDRAWN\t0.15007198908446304\t2\nNUM_TRANS_ATM\t0.14838252230225377\t3\nNUM_TRANS_TELLER\t0.11572527598455709\t4\nTOTAL_AUTOM_PAYMENTS\t0.10805697528319519\t5\nMONTHLY_CHECKS\t0.08916425624499165\t6\nNUM_DEPENDENTS\t0.028549990022342858\t7\nCUSTOMER_TENURE\t0.022654928543333067\t8\nCHECKING_BALANCE\t0.022525045499602096\t9\nPROFESSION\t0.016638375277904405\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Feature importance using Random Forest","---"],"enabled":true,"result":{"startTime":1737859255866,"interpreter":"md.low","endTime":1737859256303,"results":[{"message":"<h4 id=\"feature-importance-using-random-forest\">Feature importance using Random Forest<\/h4>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for Random Forest","message":["%md","","## Examples of possible setting overrides for Random Forest","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","**NOTE:** Random Forest makes use of the Decision Tree settings to configure the construction of individual trees. ","","A complete list of settings can be found in the Documentation link:","","Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" target=\"_blank\">Random Forest<\/a>","","Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Decision Tree<\/a>","","Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","* Specify a row weight column ",">    v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.",">    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","##### These settings configure the behavior of the Random Forest algorithm ","","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.",">    v_setlst('RFOR_NUM_TREES') := '20';","","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)",">    v_setlst('RFOR_SAMPLING_RATIO') := '0.5';","","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.",">    v_setlst('RFOR_MTRY') := '0';","","##### Decision Tree settings to configure the construction of individual trees","    ","* Specify Tree impurity metric for each Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.",">    v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.",">    v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ",">    v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","    ","* Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ",">    v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ",">    v_setlst('TREE_TERM_MINREC_NODE') := '10';","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ",">    v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.",">    v_setlst('CLAS_MAX_SUP_BINS') := '32';    ","    "],"enabled":true,"result":{"startTime":1737859256759,"interpreter":"md.low","endTime":1737859257228,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-random-forest\">Examples of possible setting overrides for Random Forest<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p><strong>NOTE:<\/strong> Random Forest makes use of the Decision Tree settings to configure the construction of individual trees.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" target=\"_blank\">Random Forest<\/a><\/p>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Decision Tree<\/a><\/p>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<ul>\n<li>Specify a row weight column<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<h5 id=\"these-settings-configure-the-behavior-of-the-random-forest-algorithm\">These settings configure the behavior of the Random Forest algorithm<\/h5>\n<ul>\n<li>Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_NUM_TREES') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_SAMPLING_RATIO') := '0.5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_MTRY') := '0';<\/p>\n<\/blockquote>\n<h5 id=\"decision-tree-settings-to-configure-the-construction-of-individual-trees\">Decision Tree settings to configure the construction of individual trees<\/h5>\n<ul>\n<li>Specify Tree impurity metric for each Tree.\nTree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\nFor Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MAX_DEPTH') := '7';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\nIt requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\nIt requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node.\nIt requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_NODE') := '10';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\nNo split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_SPLIT') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute.\nFor Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLAS_MAX_SUP_BINS') := '32';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest model for Feature Selection","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_FEAT_SELECTION');","      EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    V_SETLST('PREP_AUTO')           := 'ON';","    V_SETLST('ALGO_NAME')           := 'ALGO_RANDOM_FOREST';","    V_SETLST('RFOR_MTRY')           := '3';","    V_SETLST('RFOR_NUM_TREES')      := '25';","    V_SETLST('RFOR_SAMPLING_RATIO') := '0.5';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'RF_FEAT_SELECTION',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',","        SET_LIST            => V_SETLST,","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        TARGET_COLUMN_NAME  => 'BUY_INSURANCE'","        );","END;"],"enabled":true,"result":{"startTime":1737859257705,"interpreter":"script.low","endTime":1737859260613,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"ATTRIBUTE_IMPORTANCE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"ATTRIBUTE_NAME\",\"ATTRIBUTE_IMPORTANCE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot attributes by importance for predicting people who buy insurance","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_IMPORTANCE","FROM   DM$VARF_FEAT_SELECTION ","ORDER BY ATTRIBUTE_IMPORTANCE DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737859261083,"interpreter":"sql.low","endTime":1737859261566,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_IMPORTANCE\nMONEY_MONTLY_OVERDRAWN\t0.8668095070288839\nTOTAL_AUTOM_PAYMENTS\t0.8381233447543975\nBANK_FUNDS\t0.7117235921865998\nNUM_TRANS_ATM\t0.6763216009007427\nCHECKING_BALANCE\t0.5736764978371948\nNUM_TRANS_TELLER\t0.531403630259799\nNUM_ONLINE_TRANS\t0.5312016007612806\nMONTHLY_CHECKS\t0.49122807973702387\nNUM_TRANS_KIOSK\t0.47397358205654727\nAGE\t0.4482307544478193\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Feature importance using Decision Tree","---"],"enabled":true,"result":{"startTime":1737859262038,"interpreter":"md.low","endTime":1737859262500,"results":[{"message":"<h3 id=\"feature-importance-using-decision-tree\">Feature importance using Decision Tree<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for DT","message":["%md","","## Examples of possible setting overrides for DT","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Decision Tree<\/a>","","Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","* Specify a row weight column ",">    v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.",">    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","* Specify Tree impurity metric for Decision Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.",">    v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.",">    v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ",">   v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","    ","* Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ",">    v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ",">    v_setlst('TREE_TERM_MINREC_NODE') := '10';","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ",">    v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.",">    v_setlst('CLAS_MAX_SUP_BINS') := '32';    ","    ","---"],"enabled":true,"result":{"startTime":1737859262986,"interpreter":"md.low","endTime":1737859263439,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-dt\">Examples of possible setting overrides for DT<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Decision Tree<\/a><\/p>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<ul>\n<li>Specify a row weight column<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify Tree impurity metric for Decision Tree.\nTree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\nFor Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MAX_DEPTH') := '7';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\nIt requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\nIt requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node.\nIt requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_NODE') := '10';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\nNo split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_SPLIT') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute.\nFor Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLAS_MAX_SUP_BINS') := '32';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree model for Feature Selection","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_FEAT_SELECTION');","      EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    V_SETLST('PREP_AUTO')             := 'ON';","    V_SETLST('ALGO_NAME')             := 'ALGO_DECISION_TREE';","    V_SETLST('TREE_TERM_MAX_DEPTH')   := '10';","    V_SETLST('TREE_TERM_MINPCT_NODE') := '0.01';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'DT_FEAT_SELECTION',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',","        SET_LIST            => V_SETLST,","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        TARGET_COLUMN_NAME  => 'BUY_INSURANCE'","        );","END;"],"enabled":true,"result":{"startTime":1737859264072,"interpreter":"script.low","endTime":1737859266889,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display Decision Tree nodes with top support","message":["%sql","","SELECT * ","FROM (SELECT NODE, ","             NODE_SUPPORT, ","             PREDICTED_TARGET_VALUE, ","             PARENT,","             ATTRIBUTE_NAME,","             OPERATOR, ","             SPLIT.VAL","      FROM DM$VODT_FEAT_SELECTION A,","           XMLTABLE( '/Element' PASSING A.VALUE","                     COLUMNS","                     VAL VARCHAR2(20) PATH '.') 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Selection Unsupervised Attribute Importance","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737859701347,"interpreter":"md.low","endTime":1737859701845,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Selection: Unsupervised Attribute Importance","In this notebook, we demonstrate how to perform feature selection using the in-database unsupervised algorithm Expectation Maximization (EM).","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737859702280,"interpreter":"md.low","endTime":1737859702749,"results":[{"message":"<h1 id=\"oml4sql-feature-selection-unsupervised-attribute-importance\">OML4SQL Feature Selection: Unsupervised Attribute Importance<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using the in-database unsupervised algorithm Expectation Maximization (EM).<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/expectation-maximization.html\" target=\"_blank\">OML4R Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1737859703204,"interpreter":"md.low","endTime":1737859703644,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/expectation-maximization.html\" target=\"_blank\">OML4R Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Expectation-Maximization clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" target=\"_blank\"> Expectation-Maximization clustering<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a>","","* Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" target=\"_blank\"> ML settings <\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.",">    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","    ","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. ","  The algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components.  ","  For EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS')       := '5';","","#### Expectation Maximization Settings for Learning     ","* Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected.","  It requires `EMCS_MODEL_SEARCH_ENABLE` or `EMCS_MODEL_SEARCH_DISABLE`.  The default is `EMCS_MODEL_SEARCH_DISABLE`.      ","> v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';","","* Specify the maximum number of iterations in the EM algorithm.","  It requires a number larger than 1.  The default is 100. ","> v_setlst('EMCS_NUM_ITERATIONS')     := '20';","","#### Expectation Maximization Settings for Cluster Statistics","*  Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and `GET_MODEL_DETAILS_EM` only returns taxonomy (hierarchy) and cluster counts.","  It requires either `EMCS_CLUS_STATS_ENABLE` or `EMCS_CLUS_STATS_DISABLE`.  Default is `EMCS_CLUS_STATS_ENABLE`.     ","> v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';","     ","#### Expectation Maximization Settings for Data Preparation and Analysis","* Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions.","  It requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.","> v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';","","---"],"enabled":true,"result":{"startTime":1737859704103,"interpreter":"md.low","endTime":1737859704549,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation-Maximization clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" target=\"_blank\"> Expectation-Maximization clustering<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#ARPLS-GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" target=\"_blank\"> ML settings <\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm.\nThe algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components.<br />\nFor EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS')       := '5';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-learning\">Expectation Maximization Settings for Learning<\/h4>\n<ul>\n<li>Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected.\nIt requires <code>EMCS_MODEL_SEARCH_ENABLE<\/code> or <code>EMCS_MODEL_SEARCH_DISABLE<\/code>.  The default is <code>EMCS_MODEL_SEARCH_DISABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations in the EM algorithm.\nIt requires a number larger than 1.  The default is 100.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_ITERATIONS')     := '20';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-cluster-statistics\">Expectation Maximization Settings for Cluster Statistics<\/h4>\n<ul>\n<li>Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and <code>GET_MODEL_DETAILS_EM<\/code> only returns taxonomy (hierarchy) and cluster counts.\nIt requires either <code>EMCS_CLUS_STATS_ENABLE<\/code> or <code>EMCS_CLUS_STATS_DISABLE<\/code>.  Default is <code>EMCS_CLUS_STATS_ENABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-data-preparation-and-analysis\">Expectation Maximization Settings for Data Preparation and Analysis<\/h4>\n<ul>\n<li>Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions.\nIt requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the EM Clustering model with explicit settings of 1 cluster (for Feature Selection)","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_FEAT_SELECTION');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    V_SETLST('ALGO_NAME')               := 'ALGO_EXPECTATION_MAXIMIZATION';","    V_SETLST('PREP_AUTO')               := 'ON';","    V_SETLST('CLUS_NUM_CLUSTERS')       := '1';","    V_SETLST('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';","    V_SETLST('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';","    V_SETLST('EMCS_NUM_ITERATIONS')     := '20';","    V_SETLST('EMCS_NUM_QUANTILE_BINS')  := '10';","    V_SETLST('EMCS_RANDOM_SEED')        := '123456';","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'EM_FEAT_SELECTION',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',","        SET_LIST            => V_SETLST,","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID');","END;"],"enabled":true,"result":{"startTime":1737859705008,"interpreter":"script.low","endTime":1737859712662,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" Check the attribute ranks in the model view","message":["%sql","","SELECT   ATTRIBUTE_RANK,","         ATTRIBUTE_NAME, ","         ROUND(ATTRIBUTE_IMPORTANCE_VALUE, 4) IMPORTANCE_VALUE ","FROM     DM$VIEM_FEAT_SELECTION","ORDER BY ATTRIBUTE_RANK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737859713116,"interpreter":"sql.low","endTime":1737859713570,"results":[{"message":"ATTRIBUTE_RANK\tATTRIBUTE_NAME\tIMPORTANCE_VALUE\n1\tHOME_OWNERSHIP\t0.3408\n2\tNUM_MORTGAGES\t0.3406\n3\tMORTGAGE_AMOUNT\t0.3359\n4\tCUSTOMER_TENURE\t0.2928\n5\tLTV\t0.2893\n6\tLTV_BIN\t0.2876\n7\tNUM_DEPENDENTS\t0.2874\n8\tMARITAL_STATUS\t0.2725\n9\tNUM_ONLINE_TRANS\t0.2476\n10\tNUM_TRANS_ATM\t0.2422\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"IMPORTANCE_VALUE\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"IMPORTANCE_VALUE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"height\":400,\"lastColumns\":[\"ATTRIBUTE_RANK\",\"ATTRIBUTE_NAME\",\"IMPORTANCE_VALUE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":" Check the top 10 attributes by importance","message":["%sql","","SELECT   ATTRIBUTE_RANK,","         ATTRIBUTE_NAME, ","         ROUND(ATTRIBUTE_IMPORTANCE_VALUE, 4) IMPORTANCE_VALUE ","FROM     DM$VIEM_FEAT_SELECTION","ORDER BY ATTRIBUTE_RANK","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859714004,"interpreter":"sql.low","endTime":1737859714430,"results":[{"message":"ATTRIBUTE_RANK\tATTRIBUTE_NAME\tIMPORTANCE_VALUE\n1\tHOME_OWNERSHIP\t0.3408\n2\tNUM_MORTGAGES\t0.3406\n3\tMORTGAGE_AMOUNT\t0.3359\n4\tCUSTOMER_TENURE\t0.2928\n5\tLTV\t0.2893\n6\tLTV_BIN\t0.2876\n7\tNUM_DEPENDENTS\t0.2874\n8\tMARITAL_STATUS\t0.2725\n9\tNUM_ONLINE_TRANS\t0.2476\n10\tNUM_TRANS_ATM\t0.2422\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Alternative approach to creating an Expectation Maximization clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. ","","---"],"enabled":true,"result":{"startTime":1737859714891,"interpreter":"md.low","endTime":1737859715304,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-an-expectation-maximization-clustering-model-using-a-settings-table\">Alternative approach to creating an Expectation Maximization clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create setting table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE EM_LTV_SAMPLE_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE EM_LTV_SAMPLE_SETTINGS (","   SETTING_NAME  VARCHAR2(30),","   SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1737859715735,"interpreter":"script.low","endTime":1737859716213,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable EM_LTV_SAMPLE_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Add settings, set the number of maximum clusters to be 1","message":["%script","","BEGIN       ","   INSERT INTO EM_LTV_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_EXPECTATION_MAXIMIZATION);","   INSERT INTO EM_LTV_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);","   INSERT INTO EM_LTV_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.CLUS_NUM_CLUSTERS,1);","END;","/"],"enabled":true,"result":{"startTime":1737859716642,"interpreter":"script.low","endTime":1737859717124,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create EM model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_LTV_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","","BEGIN","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'EM_LTV_CLUS_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMER_INSURANCE_LTV_SQL',","    CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","    SETTINGS_TABLE_NAME => 'EM_LTV_SAMPLE_SETTINGS');","END;","/"],"enabled":true,"result":{"startTime":1737859717563,"interpreter":"script.low","endTime":1737859724854,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" The attribute ranks can be found in the model view","message":["%sql"," ","SELECT   ATTRIBUTE_RANK, ATTRIBUTE_NAME, ROUND(ATTRIBUTE_IMPORTANCE_VALUE, 4) IMPORTANCE_VALUE","FROM     DM$VIEM_LTV_CLUS_SAMPLE ","ORDER BY ATTRIBUTE_RANK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737859725288,"interpreter":"sql.low","endTime":1737859725741,"results":[{"message":"ATTRIBUTE_RANK\tATTRIBUTE_NAME\tIMPORTANCE_VALUE\n1\tHOME_OWNERSHIP\t0.3372\n2\tNUM_MORTGAGES\t0.3369\n3\tMORTGAGE_AMOUNT\t0.3007\n4\tLTV\t0.2985\n5\tLTV_BIN\t0.2966\n6\tCUSTOMER_TENURE\t0.2919\n7\tNUM_DEPENDENTS\t0.2863\n8\tNUM_TRANS_ATM\t0.2372\n9\tNUM_ONLINE_TRANS\t0.2366\n10\tSTATE\t0.2365\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737859726170,"interpreter":"md.low","endTime":1737859726586,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737859727028,"interpreter":"md.low","endTime":1737859727438,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Selection Using Summary Statistics","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737859754058,"interpreter":"md.low","endTime":1737859754509,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Selection: Summary Information","In this notebook, we demonstrate how to do feature selection using summary statistics via Oracle SQL.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to use Oracle SQL to select features based on number of distinct values, null values, proportion of constant values. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737859754983,"interpreter":"md.low","endTime":1737859755442,"results":[{"message":"<h1 id=\"oml4sql-feature-selection-summary-information\">OML4SQL Feature Selection: Summary Information<\/h1>\n<p>In this notebook, we demonstrate how to do feature selection using summary statistics via Oracle SQL.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to use Oracle SQL to select features based on number of distinct values, null values, proportion of constant values.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_STATS.html#GUID-CA6A56B9-0540-45E9-B1D7-D78769B7714C\" target=\"_blank\">SQL DBMS_STATS.GATHER_TABLE_STATS Documentation<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a>"],"enabled":true,"result":{"startTime":1737859755930,"interpreter":"md.low","endTime":1737859756384,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_STATS.html#GUID-CA6A56B9-0540-45E9-B1D7-D78769B7714C\" target=\"_blank\">SQL DBMS_STATS.GATHER_TABLE_STATS Documentation<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%sql\r","\r","SELECT *\r","FROM CUSTOMER_INSURANCE_LTV_SQL\r","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859756888,"interpreter":"sql.low","endTime":1737859757377,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         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\tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view pointing to the same table","message":["%sql","","CREATE OR REPLACE VIEW CUSTOMER_INSURANCE_LTV_SQL_V AS ","SELECT * ","FROM CUSTOMER_INSURANCE_LTV_SQL"],"enabled":true,"result":{"startTime":1737859757855,"interpreter":"sql.low","endTime":1737859758354,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Call gather_table_stats procedure to collect the table statistics","message":["%script","","BEGIN","  DBMS_STATS.GATHER_TABLE_STATS (USER, TABNAME => 'CUSTOMER_INSURANCE_LTV_SQL');","END;"],"enabled":true,"result":{"startTime":1737859758813,"interpreter":"script.low","endTime":1737859760309,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check some of the table's statistics generated","message":["%sql","","SELECT column_name, num_distinct, density, num_nulls","FROM USER_TAB_COL_STATISTICS","WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL'","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859760785,"interpreter":"sql.low","endTime":1737859761681,"results":[{"message":"COLUMN_NAME\tNUM_DISTINCT\tDENSITY\tNUM_NULLS\nMARITAL_STATUS\t7\t0.0000605840300496789\t6627\nSTATE\t24\t0.0000336021505376344\t0\nCREDIT_BALANCE\t952\t0.000228\t6627\nCUSTOMER_TENURE\t5\t0.0000336021505376344\t0\nMORTGAGE_AMOUNT\t1748\t0.000219\t0\nBANK_FUNDS\t1205\t0.00049\t6627\nNUM_DEPENDENTS\t7\t0.0000336021505376344\t0\nHAS_CHILDREN\t2\t0.0000336021505376344\t0\nINCOME\t6455\t0.000155\t6627\nCUSTOMER_ID\t14076\t0.000071\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter categorical 'identifier' columns - filter out columns with > 95% unique values","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","    SELECTED_COLS VARCHAR2(1000);","BEGIN","    ","    RATIO := 0.95;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL;","","    DBMS_OUTPUT.PUT_LINE('== Access data from TABLE CUSTOMER_INSURANCE_LTV_SQL ==');","    DBMS_OUTPUT.PUT_LINE('== Categorical columns with less than 95% unique values ==');","    DBMS_OUTPUT.PUT_LINE('== Creating a new VIEW called SELECTED_NO_IDENTIFIED_V with them ==');","    DBMS_OUTPUT.PUT_LINE('___________________________________________________________________');","","    ","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE FROM USER_TAB_COLS WHERE ","                TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL' ","                AND DATA_TYPE IN ('CHAR', 'VARCHAR2'))","    LOOP","        ","        SELECT NUM_DISTINCT","        INTO CNT","        FROM USER_TAB_COL_STATISTICS","        WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL'","        AND COLUMN_NAME = REC.COLUMN_NAME;","        IF CNT > 1 AND CNT < RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","            IF SELECTED_COLS IS NULL THEN ","                SELECTED_COLS := REC.COLUMN_NAME;","            ELSE","                SELECTED_COLS := SELECTED_COLS||','||REC.COLUMN_NAME;","            END IF;","            ","        END IF;","        ","    END LOOP;","    EXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW SELECTED_NO_IDENTIFIED_V AS SELECT '||SELECTED_COLS||' FROM CUSTOMER_INSURANCE_LTV_SQL';","END;"],"enabled":true,"result":{"startTime":1737859762198,"interpreter":"script.low","endTime":1737859763161,"results":[{"message":"\n---------------------------\n== Access data from TABLE CUSTOMER_INSURANCE_LTV_SQL ==\n== Categorical columns with less than 95% unique values ==\n== Creating a new VIEW called SELECTED_NO_IDENTIFIED_V with them ==\n___________________________________________________________________\nMARITAL_STATUS\nSTATE\nCUSTOMER_ID\nGENDER\nPROFESSION\nREGION\nBUY_INSURANCE\nLTV_BIN\nFIRST_NAME\nLAST_NAME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use a View - without table statistics - filter categorical 'identifier' columns - filter columns with > 95% unique values ","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","    SELECTED_COLS VARCHAR2(1000);","BEGIN","    ","    RATIO := 0.95;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL_V;","","    DBMS_OUTPUT.PUT_LINE('== Access data from *VIEW* CUSTOMER_INSURANCE_LTV_SQL_V ==');","    DBMS_OUTPUT.PUT_LINE('== Categorical columns with less than 95% unique values ==');","    DBMS_OUTPUT.PUT_LINE('== Creating a new VIEW called SELECTED_NO_IDENTIFIED_V with them ==');","    DBMS_OUTPUT.PUT_LINE('___________________________________________________________________');","    ","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE FROM USER_TAB_COLS ","                WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL_V' ","                AND DATA_TYPE IN ('CHAR', 'VARCHAR2'))","    LOOP","        EXECUTE IMMEDIATE 'SELECT COUNT(DISTINCT '||REC.COLUMN_NAME||') FROM CUSTOMER_INSURANCE_LTV_SQL_V' INTO CNT;","        IF CNT > 1 AND CNT < RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","            IF SELECTED_COLS IS NULL THEN ","                SELECTED_COLS := REC.COLUMN_NAME;","            ELSE","                SELECTED_COLS := SELECTED_COLS||','||REC.COLUMN_NAME;","            END IF;","            ","        END IF;","        ","    END LOOP;","    EXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW SELECTED_NO_IDENTIFIED_V AS SELECT '||SELECTED_COLS||' FROM CUSTOMER_INSURANCE_LTV_SQL_V';","END;"],"enabled":true,"result":{"startTime":1737859763667,"interpreter":"script.low","endTime":1737859764401,"results":[{"message":"\n---------------------------\n== Access data from *VIEW* CUSTOMER_INSURANCE_LTV_SQL_V ==\n== Categorical columns with less than 95% unique values ==\n== Creating a new VIEW called SELECTED_NO_IDENTIFIED_V with them ==\n___________________________________________________________________\nMARITAL_STATUS\nSTATE\nCUSTOMER_ID\nGENDER\nPROFESSION\nREGION\nBUY_INSURANCE\nLTV_BIN\nFIRST_NAME\nLAST_NAME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the view with selected columns only","message":["%sql","","SELECT * FROM SELECTED_NO_IDENTIFIED_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859764896,"interpreter":"sql.low","endTime":1737859765376,"results":[{"message":"MARITAL_STATUS\tSTATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nSINGLE\tCA                        \tCU8720                    \tM                         \tWaiter/Waitress\tWest\tYes\tMEDIUM\tKENNETH\tNADEAU\nDIVORCED\tWA                        \tCU8721                    \tM                         \tSoftware Engineer\tWest\tYes\tMEDIUM\tJARVIS\tCLARKE\nDIVORCED\tNY                        \tCU8722                    \tF                         \tIT Staff\tNorthEast\tNo\tVERY HIGH\tSTACEY\tHENSON\nMARRIED\tMI                        \tCU8665                    \tF                         \tDentist\tMidwest\tYes\tHIGH\tMARGRET\tRYAN\nMARRIED\tMI                        \tCU8666                    \tM                         \tIT Staff\tMidwest\tNo\tHIGH\tJOAN\tBAUER\nDIVORCED\tNC                        \tCU9108                    \tF                         \tPROF-15\tSouth\tYes\tMEDIUM\tMARGART\tCALDWELL\nMARRIED\tTX                        \tCU9113                    \tM                         \tProgrammer/Developer\tSouthwest\tYes\tMEDIUM\tNICKY\tBARON\nSINGLE\tNV                        \tCU9114                    \tM                         \tSales Representative\tSouthwest\tNo\tLOW\tHUNG\tFINNEY\nSINGLE\tMI                        \tCU8462                    \tM                         \tPROF-31\tMidwest\tNo\tMEDIUM\tFREDDIE\tMAYBERRY\nDIVORCED\tMI                        \tCU8506                    \tM                         \tPlumber\tMidwest\tNo\tVERY HIGH\tJON\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For all columns, identify columns with > 40% missing values","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","BEGIN ","    RATIO := 0.4;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL;","","    DBMS_OUTPUT.PUT_LINE('== Access data from TABLE CUSTOMER_INSURANCE_LTV_SQL ==');","    DBMS_OUTPUT.PUT_LINE('== Show Columns with more than 40% missing values ==');","    DBMS_OUTPUT.PUT_LINE('___________________________________________________________________');","","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE ","                FROM USER_TAB_COLS ","                WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL' )","    LOOP","        ","        SELECT NUM_NULLS","        INTO CNT","        FROM USER_TAB_COL_STATISTICS","        WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL'","        AND COLUMN_NAME = REC.COLUMN_NAME;","        IF CNT > RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","        END IF;","        ","    END LOOP;","END;"],"enabled":true,"result":{"startTime":1737859765925,"interpreter":"script.low","endTime":1737859766818,"results":[{"message":"\n---------------------------\n== Access data from TABLE CUSTOMER_INSURANCE_LTV_SQL ==\n== Show Columns with more than 40% missing values ==\n___________________________________________________________________\nMARITAL_STATUS\nCREDIT_BALANCE\nBANK_FUNDS\nINCOME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use a view - without table statistics - for all columns, identify columns with > 40% missing values","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","BEGIN ","    RATIO := 0.4;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL_V;","","    DBMS_OUTPUT.PUT_LINE('== Access data from *VIEW* CUSTOMER_INSURANCE_LTV_SQL_V ==');","    DBMS_OUTPUT.PUT_LINE('== Show Columns with more than 40% missing values ==');","    DBMS_OUTPUT.PUT_LINE('___________________________________________________________________');","","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE ","                FROM USER_TAB_COLS ","                WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL_V' )","    LOOP","        ","        EXECUTE IMMEDIATE 'SELECT COUNT(1) FROM CUSTOMER_INSURANCE_LTV_SQL_V WHERE '|| REC.COLUMN_NAME||' IS NULL' INTO CNT;","","        IF CNT > RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","        END IF;","        ","    END LOOP;","END;"],"enabled":true,"result":{"startTime":1737859767306,"interpreter":"script.low","endTime":1737859768047,"results":[{"message":"\n---------------------------\n== Access data from *VIEW* CUSTOMER_INSURANCE_LTV_SQL_V ==\n== Show Columns with more than 40% missing values ==\n___________________________________________________________________\nMARITAL_STATUS\nCREDIT_BALANCE\nBANK_FUNDS\nINCOME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter 'constant' columns - filter columns where > 90% of the values are same","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","    TBL VARCHAR2(100);","    FMT VARCHAR2(2000);","    STMT VARCHAR2(2000);","    SELECTED_COLS VARCHAR2(1000);","BEGIN","    RATIO := 0.90;","    TBL := 'CUSTOMER_INSURANCE_LTV_SQL';","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL;","","    DBMS_OUTPUT.PUT_LINE('== Access data from TABLE CUSTOMER_INSURANCE_LTV_SQL ==');","    DBMS_OUTPUT.PUT_LINE('== Remove columns with more than 90% constant values ==');","    DBMS_OUTPUT.PUT_LINE('== Creating a new VIEW called SELECTED_NO_CONSTANT_V *without* them ==');","    DBMS_OUTPUT.PUT_LINE('___________________________________________________________________');","","    FMT := 'SELECT MAX(CNT) FROM ( SELECT <COL>, COUNT(1) CNT FROM <TBL> GROUP BY <COL> ) A';","","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE ","                FROM USER_TAB_COLS ","                WHERE TABLE_NAME = TBL )","    LOOP","        STMT := REPLACE(FMT, '<COL>', REC.COLUMN_NAME);","        STMT := REPLACE(STMT, '<TBL>', TBL);","        EXECUTE IMMEDIATE STMT INTO CNT;","        IF CNT > TOTAL*RATIO THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","        ELSE","            IF SELECTED_COLS IS NULL THEN ","                SELECTED_COLS := REC.COLUMN_NAME;","            ELSE","                SELECTED_COLS := SELECTED_COLS||','||REC.COLUMN_NAME;","            END IF;","        END IF;","        ","    END LOOP;","    EXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW SELECTED_NO_CONSTANT_V AS SELECT '||SELECTED_COLS||' FROM CUSTOMER_INSURANCE_LTV_SQL';","END;"],"enabled":true,"result":{"startTime":1737859768527,"interpreter":"script.low","endTime":1737859769361,"results":[{"message":"\n---------------------------\n== Access data from TABLE CUSTOMER_INSURANCE_LTV_SQL ==\n== Remove columns with more than 90% constant values ==\n== Creating a new VIEW called SELECTED_NO_CONSTANT_V *without* them ==\n___________________________________________________________________\nCAR_OWNERSHIP\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the view with 'constant' columns removed","message":["%sql","","SELECT * FROM SELECTED_NO_CONSTANT_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859769841,"interpreter":"sql.low","endTime":1737859770310,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         \tPROF-15\t800\tSouth\t1\t1600\tYes\t1\t1\t66\t54.97\t16933\t1923\t4\t25\t5\tMEDIUM\tMARGART\t1\tCALDWELL\nMARRIED\tTX                        \t0\t3\t845\t450\t3\t1\t62061\tCU9113                    \tM                         \tProgrammer/Developer\t1500\tSouthwest\t1\t845\tYes\t8\t6\t26\t53.01\t18615.25\t226\t2\t25\t2\tMEDIUM\tNICKY\t1\tBARON\nSINGLE\tNV                        \t0\t2\t0\t0\t0\t0\t74394\tCU9114                    \tM                         \tSales Representative\t1500\tSouthwest\t0\t0\tNo\t0\t1\t0\t53.06\t0\t0\t0\t25\t0\tLOW\tHUNG\t0\tFINNEY\nSINGLE\tMI                        \t0\t3\t0\t0\t3\t0\t64255\tCU8462                    \tM                         \tPROF-31\t1500\tMidwest\t0\t0\tNo\t1\t1\t28\t53.14\t14363.75\t0\t0\t25\t2\tMEDIUM\tFREDDIE\t0\tMAYBERRY\nDIVORCED\tMI                        \t0\t1\t2513\t1150\t1\t0\t70749\tCU8506                    \tM                         \tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check column LAST_NAME which is suggested to be removed, most rows have the same last name 'THANOS'","message":["%sql","","SELECT LAST_NAME, COUNT(1)","FROM CUSTOMER_INSURANCE_LTV_SQL","GROUP BY LAST_NAME","ORDER BY COUNT(1) DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737859770785,"interpreter":"sql.low","endTime":1737859771247,"results":[{"message":"LAST_NAME\tCOUNT(1)\nTHANOS\t12829\nRUSSO\t6\nHARE\t6\nDENNIS\t5\nDELATORRE\t5\nECHOLS\t5\nBERNIER\t5\nCALDWELL\t5\nDILLON\t5\nBRADSHAW\t4\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check column CAR_OWNERSHIP which is suggested to be removed, most rows have CAR_OWNERSHIP = 1","message":["%sql","","SELECT A.CAR_OWNERSHIP, ","       A.COUNT,","       ROUND(A.COUNT/B.TOTAL_COUNT,4)*100 PCT_TOTAL","       FROM ","       (SELECT CAR_OWNERSHIP, COUNT(1) COUNT","               FROM CUSTOMER_INSURANCE_LTV_SQL","               GROUP BY CAR_OWNERSHIP) A,","       (SELECT COUNT(*) TOTAL_COUNT FROM CUSTOMER_INSURANCE_LTV_SQL) B","       ORDER BY PCT_TOTAL DESC"],"enabled":true,"result":{"startTime":1737859771731,"interpreter":"sql.low","endTime":1737859772226,"results":[{"message":"CAR_OWNERSHIP\tCOUNT\tPCT_TOTAL\n1\t13859\t93.14\n0\t1021\t6.86\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1737859772705,"interpreter":"md.low","endTime":1737859773187,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737859773698,"interpreter":"md.low","endTime":1737859774207,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Nested Columns.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Nested Columns.dsnb
new file mode 100644
index 00000000..33a8ce8e
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Nested Columns.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Nested Columns","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736383470328,"interpreter":"md.low","endTime":1736383470794,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Nested Columns","","Oracle Machine Learning can work with the nested column format that enables building models with more than 1000 predictors. Machine learning projects can involve datasets with high dimensional features. Such features are usually encoded using one-hot encoding and can make for very wide data sets. Oracle Database provides a convenient data format called nested columns to handle such wide data. Such data can be stored in transactional form, or exist externally such as a CSV file. When in transactional format, it is straightforward to create nested columns. ","","In this notebook, we use the customer insurance lifetime value dataset as an example to show how to generate nested columns for model building in OML4SQL. While this table clearly fits in the database, we first illustrate converting this to transactional format before focusing on creating nested columns. We also provide examples of how to handle multiple nested columns in both numerical and categorical types. The entire workflow of model training and testing are also demonstrated for the dataset with nested columns. ","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736383471248,"interpreter":"md.low","endTime":1736383471706,"results":[{"message":"<h1 id=\"oml4sql-nested-columns\">OML4SQL Nested Columns<\/h1>\n<p>Oracle Machine Learning can work with the nested column format that enables building models with more than 1000 predictors. Machine learning projects can involve datasets with high dimensional features. Such features are usually encoded using one-hot encoding and can make for very wide data sets. Oracle Database provides a convenient data format called nested columns to handle such wide data. Such data can be stored in transactional form, or exist externally such as a CSV file. When in transactional format, it is straightforward to create nested columns.<\/p>\n<p>In this notebook, we use the customer insurance lifetime value dataset as an example to show how to generate nested columns for model building in OML4SQL. While this table clearly fits in the database, we first illustrate converting this to transactional format before focusing on creating nested columns. We also provide examples of how to handle multiple nested columns in both numerical and categorical types. The entire workflow of model training and testing are also demonstrated for the dataset with nested columns.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle Nested Columns Table Documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/use-nested-data.html\" target=\"_blank\">OML using Nested Data Documentation<\/a>"],"enabled":true,"result":{"startTime":1736383472161,"interpreter":"md.low","endTime":1736383472621,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle Nested Columns Table Documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/use-nested-data.html\" target=\"_blank\">OML using Nested Data Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%sql","","SELECT * ","FROM   CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS 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\tProgrammer/Developer\tSINGLE\tM                         \tMEDIUM\tNY                        \nCU15157                   \tProgrammer/Developer\tMARRIED\tM                         \tHIGH\tMI                        \nCU15286                   \tFireman\tMARRIED\tF                         \tMEDIUM\tUT                        \nCU15287                   \tPROF-26\tMARRIED\tF                         \tVERY HIGH\tUT                        \nCU15289                   \tProfessor\tMARRIED\tM                         \tVERY HIGH\tUT                        \nCU15290                   \tSales Representative\tSINGLE\tM                         \tMEDIUM\tUT                        \nCU15291                   \tConstruction Laborer\tSINGLE\tM                         \tMEDIUM\tUT                        \nCU15292                   \tPROF-3\tSINGLE\tF                         \tMEDIUM\tUT                        \nCU15294                   \tProgrammer/Developer\tSINGLE\tM                         \tLOW\tUT                        \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Unpivot categorical columns to create transaction-like records","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUST_CATS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE CUST_CATS AS ","SELECT customer_id, key, val","FROM CUSTOMER_INSURANCE_LTV","UNPIVOT(","    val","    FOR key","    IN (PROFESSION,","        MARITAL_STATUS, ","        GENDER,","        LTV_BIN,","        STATE)",");"],"enabled":true,"result":{"startTime":1736383476544,"interpreter":"script.low","endTime":1736383477918,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUST_CATS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the transactional form ","message":["%sql","","SELECT * ","FROM   CUST_CATS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736383478371,"interpreter":"sql.low","endTime":1736383478848,"results":[{"message":"CUSTOMER_ID\tKEY\tVAL\nCU15154                   \tPROFESSION\tNurse\nCU15154                   \tMARITAL_STATUS\tSINGLE\nCU15154                   \tGENDER\tM                         \nCU15154                   \tLTV_BIN\tMEDIUM\nCU15154                   \tSTATE\tCA                        \nCU15155                   \tPROFESSION\tProgrammer/Developer\nCU15155                   \tMARITAL_STATUS\tSINGLE\nCU15155                   \tGENDER\tM                         \nCU15155                   \tLTV_BIN\tMEDIUM\nCU15155                   \tSTATE\tNY                        \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a nested column for categorical columns","message":["%script ","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUST_NESTED_COL_CAT';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE  CUST_NESTED_COL_CAT ","  NESTED TABLE cat_nested store AS cat_nested_tab AS ","SELECT m.customer_id, m.BUY_INSURANCE, cat_nested","    FROM CUSTOMER_INSURANCE_LTV m,","  ( SELECT features.customer_id,","           cast(COLLECT(dm_nested_categorical(features.key,features.val)) ","                        as dm_nested_categoricals)  cat_nested","    FROM CUST_CATS features","    GROUP BY features.customer_id) n","    WHERE m.customer_id = n.customer_id(+)   "],"enabled":true,"result":{"startTime":1736383479295,"interpreter":"script.low","endTime":1736383485075,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUST_NESTED_COL_CAT created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Unpivot numerical columns to create transaction like records","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE 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nested column for numerical columns","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUST_NESTED_COL_NUM';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE  CUST_NESTED_COL_NUM","  NESTED TABLE num_nested store AS num_nested_tab AS ","SELECT m.customer_id, m.BUY_INSURANCE, num_nested","  FROM CUSTOMER_INSURANCE_LTV m,","  ( SELECT features.customer_id,","           cast(COLLECT(dm_nested_numerical(features.key,features.val)) ","                        as dm_nested_numericals)  num_nested","    FROM CUST_NUMS features","    GROUP BY features.customer_id) n","  WHERE m.customer_id = n.customer_id(+)   "],"enabled":true,"result":{"startTime":1736383487794,"interpreter":"script.low","endTime":1736383492976,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUST_NESTED_COL_NUM 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n.CUSTOMER_ID"],"enabled":true,"result":{"startTime":1736383493431,"interpreter":"sql.low","endTime":1736383493934,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train/test split views","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS ","SELECT * from CUST_NESTED_V","WHERE CUSTOMER_ID < 'CU8';","","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS ","SELECT * from CUST_NESTED_V","WHERE CUSTOMER_ID >= 'CU8';"],"enabled":true,"result":{"startTime":1736383494384,"interpreter":"script.low","endTime":1736383494885,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NESTED_GLM_CLAS');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","","BEGIN","   ","    v_setlst(dbms_data_mining.algo_name) := dbms_data_mining.algo_generalized_linear_model;","    v_setlst(dbms_data_mining.prep_auto) :=  dbms_data_mining.prep_auto_on;","   ","    DBMS_DATA_MINING.CREATE_MODEL2(","        model_name => 'NESTED_GLM_CLAS',","        mining_function => dbms_data_mining.classification,","        data_query => 'select * from TRAIN_DATA_CLAS',","        set_list           => v_setlst,","        case_id_column_name => 'CUSTOMER_ID',","        target_column_name => 'BUY_INSURANCE');","END;","/ "],"enabled":true,"result":{"startTime":1736383495340,"interpreter":"script.low","endTime":1736383498925,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score on test data","message":["%sql","","SELECT CUSTOMER_ID, ","       ROUND(PREDICTION_PROBABILITY(NESTED_GLM_CLAS, 'Yes' USING *),4) prob, ","       PREDICTION(NESTED_GLM_CLAS USING *) PRED, buy_insurance","FROM   TEST_DATA_CLAS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736383499383,"interpreter":"sql.low","endTime":1736383499928,"results":[{"message":"CUSTOMER_ID\tPROB\tPRED\tBUY_INSURANCE\nCU8                       \t0.1354\tNo\tNo\nCU80                      \t0.2431\tNo\tYes\nCU8000                    \t0.0619\tNo\tNo\nCU8001                    \t0.0957\tNo\tNo\nCU8002                    \t0.3008\tNo\tNo\nCU8003                    \t0.3512\tNo\tNo\nCU8004                    \t0.3092\tNo\tNo\nCU8005                    \t0.267\tNo\tNo\nCU8006                    \t0.3269\tNo\tNo\nCU8007                    \t0.0524\tNo\tNo\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the columns in the model","message":["%sql","","SELECT   attribute_name, attribute_type","FROM     user_mining_model_attributes","WHERE    model_name = 'NESTED_GLM_CLAS'","ORDER BY 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\nCU8002                    \tNo\t\"&quot;CAT_NESTED&quot;.PROFESSION\" actualValue=\"Programmer/Developer\" weight=\".17\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"57709\" weight=\".092\" \nCU8003                    \tNo\t\"&quot;CAT_NESTED&quot;.PROFESSION\" actualValue=\"Programmer/Developer\" weight=\".192\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"69082\" weight=\".119\" \nCU8004                    \tNo\t\"&quot;CAT_NESTED&quot;.MARITAL_STATUS\" actualValue=\"DIVORCED\" weight=\".123\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"61964\" weight=\".099\" \nCU8005                    \tNo\t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"62276\" weight=\".09\" \t\"&quot;CAT_NESTED&quot;.MARITAL_STATUS\" actualValue=\"MARRIED\" weight=\".082\" \nCU8006                    \tNo\t\"&quot;CAT_NESTED&quot;.PROFESSION\" actualValue=\"Programmer/Developer\" weight=\".182\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"61397\" weight=\".102\" \nCU8007                 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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Partitioned Model SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734705374680,"interpreter":"md.low","endTime":1734705375178,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Partitioned Model","","Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. ","Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The applicable sub-model is ","chosen by the system based on the individual row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models. ","","In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. ","The model is then used to predict the target only, then predicting the target with prediction details. ","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734705375608,"interpreter":"md.low","endTime":1734705376059,"results":[{"message":"<h1 id=\"oml4sql-partitioned-model\">OML4SQL Partitioned Model<\/h1>\n<p>Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition.\nSub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The applicable sub-model is\nchosen by the system based on the individual row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models.<\/p>\n<p>In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender.\nThe model is then used to predict the target only, then predicting the target with prediction details.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a>"],"enabled":true,"result":{"startTime":1734705376488,"interpreter":"md.low","endTime":1734705376906,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create dataset with data from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS_DEMO';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS_DEMO AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM   SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE  A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1734705377332,"interpreter":"script.low","endTime":1734705380429,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS_DEMO created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows","message":["%sql","","SELECT COUNT(*) ","FROM   CUSTOMERS_DEMO;"],"enabled":true,"result":{"startTime":1734705380849,"interpreter":"sql.low","endTime":1734705381465,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View rows from CUSTOMERS_DEMO table","message":["%sql","","SELECT * ","FROM CUSTOMERS_DEMO","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1734705381883,"interpreter":"sql.low","endTime":1734705382327,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100100\tF\tMarried\t1959\tJ: 190,000 - 249,999\t10000\tMasters\t1\t4-5\tProf.\t4\t0\n100200\tM\tNeverM\t1983\tL: 300,000 and above\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100300\tM\tMarried\t1961\tG: 130,000 - 149,999\t10000\tBach.\t1\t3\tProf.\t4\t0\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\n100500\tM\tNeverM\t1983\tH: 150,000 - 169,999\t15000\tHS-grad\t0\t1\tCrafts\t2\t1\n100600\tM\tMarried\t1978\tK: 250,000 - 299,999\t9000\t11th\t0\t3\tTransp.\t3\t1\n100700\tM\tMarried\t1947\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t3\tCrafts\t6\t0\n100800\tM\tMarried\t1935\tK: 250,000 - 299,999\t11000\tBach.\t0\t3\tSales\t5\t0\n100900\tM\tMarried\t1959\tF: 110,000 - 129,999\t1500\tHS-grad\t0\t3\tExec.\t4\t0\n101000\tM\tMarried\t1936\tG: 130,000 - 149,999\t7000\t7th-8th\t0\t3\tCrafts\t7\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Partitioning (Shared Settings)","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Requests the building of a partitioned model. ","   The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. ","   If `XFORM_LIST` parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.  ","> v_setlst('ODMS_PARTITION_COLUMNS') := '<Comma separated list of attributes>'","     ","* Controls the parallel building of partitioned models.     ","   `ODMS_PARTITION_BUILD_INTRA` builds each partition in parallel using all slaves.","   `ODMS_PARTITION_BUILD_INTER` builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.","   `ODMS_PARTITION_BUILD_HYBRID` combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.","> v_setlst('ODMS_PARTITION_BUILD_TYPE') := 'ODMS_PARTITION_BUILD_HYBRID'"],"enabled":true,"result":{"startTime":1734705382746,"interpreter":"md.low","endTime":1734705383160,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-partitioning-shared-settings\">Examples of possible setting overrides for Partitioning (Shared Settings)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Requests the building of a partitioned model.\nThe setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model.\nIf <code>XFORM_LIST<\/code> parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_PARTITION_COLUMNS') := '<Comma separated list of attributes>'<\/p>\n<\/blockquote>\n<ul>\n<li>Controls the parallel building of partitioned models.<br />\n<code>ODMS_PARTITION_BUILD_INTRA<\/code> builds each partition in parallel using all slaves.\n<code>ODMS_PARTITION_BUILD_INTER<\/code> builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.\n<code>ODMS_PARTITION_BUILD_HYBRID<\/code> combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_PARTITION_BUILD_TYPE') := 'ODMS_PARTITION_BUILD_HYBRID'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build SVM Linear model partitioned by CUST_GENDER to predict YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_MOD_PARTITIONED');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')              := 'ALGO_SUPPORT_VECTOR_MACHINES'; -- choose SVM algorithm","    v_setlst('SVMS_KERNEL_FUNCTION')   := 'SVMS_LINEAR';      -- choose linear kernel, which provides coefficients","    v_setlst('ODMS_PARTITION_COLUMNS') := 'CUST_GENDER';      -- choose column on which to partition the data","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_MOD_PARTITIONED',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMERS_DEMO',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE');","END;"],"enabled":true,"result":{"startTime":1734705383578,"interpreter":"script.low","endTime":1734705385816,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully 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diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Procedure for Importing Data to ADB.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Procedure for Importing Data to ADB.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Procedure for Importing Data to ADB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736456552764,"interpreter":"md.low","endTime":1736456553207,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Importing Data from Oracle Cloud Object Storage","","So, you've signed up for an Oracle Cloud account and you want to start exploring Oracle Machine Learning in Autonomous Database. The first step is to import your data, and there are a few different options to choose from. For example, Oracle Database Actions provides a friendly web-based interface to load data from local files, cloud storage, or from remote databases. Another option for loading data from local files is to use SQL*Loader. For moving data between Oracle Database and Autonomous Database, Oracle Data Pump offers very fast data movement. ","","But how do you import data into Oracle Autonomous Database without leaving the Oracle Machine Learning Notebooks environment?  The answer is to use the `DBMS_CLOUD` package.","","`DBMS_CLOUD` provides a simple way to interact with an object store from the database. It supports loading data files from Oracle Cloud Infrastructure Object Storage, Github repositories, Amazon AWS S3, Microsoft Azure Cloud Storage, and Google Cloud Storage. ","","In this example, we demonstrate using a custom procedure `IMPORT_DATA_OBJSTORE_DATASET`, which leverages `DBMS_CLOUD`, to import data from an Oracle Cloud Object Storage bucket to an Autonomous Database table. When using `DBMS_CLOUD.COPY_DATA` alone, the target table must be created in advance. With `IMPORT_DATA_OBJSTORE_DATASET`, you simply provide the dataset name, and the table will be created and the data inserted in a single step. This custom procedure was created to facilitate easy import of the datasets used in Oracle Machine Learning notebooks and demos.","","For a one-off approach to importing specific datasets, refer to the `OML Run-me-first` notebook in the OML Notebooks template examples.","","### Prerequisites:","- An Oracle Machine Learning account with grant EXECUTE privileges on DBMS_CLOUD","","To run `DBMS_CLOUD` subprograms with a user other than ADMIN you need to grant EXECUTE privileges to that user. For example, run the following command as ADMIN to grant privileges to OMLUSER:","","    GRANT EXECUTE ON DBMS_CLOUD TO OMLUSER;","","- A public Oracle Cloud Object Storage bucket","","Copyright (c) 2025 Oracle Corporation                             ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1736456553644,"interpreter":"md.low","endTime":1736456554094,"results":[{"message":"<h1 id=\"importing-data-from-oracle-cloud-object-storage\">Importing Data from Oracle Cloud Object Storage<\/h1>\n<p>So, you've signed up for an Oracle Cloud account and you want to start exploring Oracle Machine Learning in Autonomous Database. The first step is to import your data, and there are a few different options to choose from. For example, Oracle Database Actions provides a friendly web-based interface to load data from local files, cloud storage, or from remote databases. Another option for loading data from local files is to use SQL*Loader. For moving data between Oracle Database and Autonomous Database, Oracle Data Pump offers very fast data movement.<\/p>\n<p>But how do you import data into Oracle Autonomous Database without leaving the Oracle Machine Learning Notebooks environment?  The answer is to use the <code>DBMS_CLOUD<\/code> package.<\/p>\n<p><code>DBMS_CLOUD<\/code> provides a simple way to interact with an object store from the database. It supports loading data files from Oracle Cloud Infrastructure Object Storage, Github repositories, Amazon AWS S3, Microsoft Azure Cloud Storage, and Google Cloud Storage.<\/p>\n<p>In this example, we demonstrate using a custom procedure <code>IMPORT_DATA_OBJSTORE_DATASET<\/code>, which leverages <code>DBMS_CLOUD<\/code>, to import data from an Oracle Cloud Object Storage bucket to an Autonomous Database table. When using <code>DBMS_CLOUD.COPY_DATA<\/code> alone, the target table must be created in advance. With <code>IMPORT_DATA_OBJSTORE_DATASET<\/code>, you simply provide the dataset name, and the table will be created and the data inserted in a single step. This custom procedure was created to facilitate easy import of the datasets used in Oracle Machine Learning notebooks and demos.<\/p>\n<p>For a one-off approach to importing specific datasets, refer to the <code>OML Run-me-first<\/code> notebook in the OML Notebooks template examples.<\/p>\n<h3 id=\"prerequisites\">Prerequisites:<\/h3>\n<ul>\n<li>An Oracle Machine Learning account with grant EXECUTE privileges on DBMS_CLOUD<\/li>\n<\/ul>\n<p>To run <code>DBMS_CLOUD<\/code> subprograms with a user other than ADMIN you need to grant EXECUTE privileges to that user. For example, run the following command as ADMIN to grant privileges to OMLUSER:<\/p>\n<pre><code>GRANT EXECUTE ON DBMS_CLOUD TO OMLUSER;\n<\/code><\/pre>\n<ul>\n<li>A public Oracle Cloud Object Storage bucket<\/li>\n<\/ul>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-oracle-machine-learning.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA\" target=\"_blank\">Getting Started with Oracle Machine Learning Notebooks<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/load-data.html#GUID-1351807C-E3F7-4C6D-AF83-2AEEADE2F83E\" target=\"_blank\">Loading Data with Autonomous Database<\/a>","* <a href=\"https://docs.oracle.com/en-us/iaas/Content/Object/Concepts/objectstorageoverview.htm\" target=\"_blank\">Object Storage Bucket Overview<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">DBMS_CLOUD Overview<\/a>"],"enabled":true,"result":{"startTime":1736456554547,"interpreter":"md.low","endTime":1736456554970,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-oracle-machine-learning.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA\" target=\"_blank\">Getting Started with Oracle Machine Learning Notebooks<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/load-data.html#GUID-1351807C-E3F7-4C6D-AF83-2AEEADE2F83E\" target=\"_blank\">Loading Data with Autonomous Database<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en-us/iaas/Content/Object/Concepts/objectstorageoverview.htm\" target=\"_blank\">Object Storage Bucket Overview<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">DBMS_CLOUD Overview<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Custom procedure for loading data ","message":["%md","","The custom procedure `IMPORT_DATA_OBJSTORE_DATASET` imports the data from Object Storage directly to a database table using a combination of PL/SQL commands and the `DBMS_CLOUD.COPY_DATA` procedure. Simply provide the dataset name, and the table will be created and the data inserted to the target table.","","#### Prerequisites:","","- The CSV file containing the data and a text file with the table definition must be saved in the Object Storage bucket","-- Note, the default Object Storage bucket already contains the CSV and text files needed to create the OML demo datasets","- Both the CSV and text files must match the name of the input dataset","-- For example, *CUSTOMER_INSURANCE_LTV2.txt* and *CUSTOMER_INSURANCE_LTV2.csv*","- The Object Storage bucket must be public","-- Either the default bucket or the user's public bucket may be used","- The OML ADMIN user must grant execute on the DBMS_CLOUD procedures to the OML user ","-- Use the command *grant execute on DBMS_CLOUD to OMLUSER*, where OMLUSER is the OML username","","#### Input variables:","- DATASET: The name of CSV data set residing in Object Storage","- VERBOSE: Optional. Controls the amount of information returned from the procedure","-- Default is FALSE, which returns the table name and number of rows in the table","-- When TRUE, returns detailed object information, including the default return values, the file names in Object Storage, and the table definition","- LOCATION_URI: Optional. The Object Storage URL where the files reside. The default value is the Oracle Machine Learning public bucket.","","#### Basic usage:","To run the procedure, pass the dataset name as the `DATASET` parameter:","","    EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2')"],"enabled":true,"result":{"startTime":1736456555400,"interpreter":"md.low","endTime":1736456555822,"results":[{"message":"<p>The custom procedure <code>IMPORT_DATA_OBJSTORE_DATASET<\/code> imports the data from Object Storage directly to a database table using a combination of PL/SQL commands and the <code>DBMS_CLOUD.COPY_DATA<\/code> procedure. Simply provide the dataset name, and the table will be created and the data inserted to the target table.<\/p>\n<h4 id=\"prerequisites\">Prerequisites:<\/h4>\n<ul>\n<li>The CSV file containing the data and a text file with the table definition must be saved in the Object Storage bucket\n-- Note, the default Object Storage bucket already contains the CSV and text files needed to create the OML demo datasets<\/li>\n<li>Both the CSV and text files must match the name of the input dataset\n-- For example, <em>CUSTOMER_INSURANCE_LTV2.txt<\/em> and <em>CUSTOMER_INSURANCE_LTV2.csv<\/em><\/li>\n<li>The Object Storage bucket must be public\n-- Either the default bucket or the user's public bucket may be used<\/li>\n<li>The OML ADMIN user must grant execute on the DBMS_CLOUD procedures to the OML user\n-- Use the command <em>grant execute on DBMS_CLOUD to OMLUSER<\/em>, where OMLUSER is the OML username<\/li>\n<\/ul>\n<h4 id=\"input-variables\">Input variables:<\/h4>\n<ul>\n<li>DATASET: The name of CSV data set residing in Object Storage<\/li>\n<li>VERBOSE: Optional. Controls the amount of information returned from the procedure\n-- Default is FALSE, which returns the table name and number of rows in the table\n-- When TRUE, returns detailed object information, including the default return values, the file names in Object Storage, and the table definition<\/li>\n<li>LOCATION_URI: Optional. The Object Storage URL where the files reside. The default value is the Oracle Machine Learning public bucket.<\/li>\n<\/ul>\n<h4 id=\"basic-usage\">Basic usage:<\/h4>\n<p>To run the procedure, pass the dataset name as the <code>DATASET<\/code> parameter:<\/p>\n<pre><code>EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2')\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define the custom procedure","message":["%script\r","\r","CREATE OR REPLACE PROCEDURE IMPORT_DATA_OBJSTORE_DATASET(\r","  DATASET IN VARCHAR2,\r","  VERBOSE IN BOOLEAN DEFAULT FALSE,\r","  LOCATION_URI IN VARCHAR2 DEFAULT 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/') AUTHID CURRENT_USER AS\r","dataset_upper VARCHAR2(100);\r","file_name VARCHAR2(1000);\r","table_name varchar2(1000);\r","obj varchar2(1000);\r","sql_file varchar2(1000);\r","b_data BLOB;\r","v_data VARCHAR2(5000);\r","v_rownum INTEGER;\r","\r","------------------------------------------------------\r","-- Declare a new variable to convert the input\r","-- dataset to upper case.  \r","\r","------------------------------------------------------\r","\r","BEGIN\r","    dataset_upper := upper(dataset);\r","\r","------------------------------------------------------\r","-- Define file name from input dataset\r","------------------------------------------------------\r","\r","SELECT CONCAT(dataset_upper, '.csv') INTO FILE_NAME from dual;\r","\r","IF VERBOSE THEN\r","   DBMS_OUTPUT.PUT_LINE('Dataset name is: '||dataset_upper);\r","   DBMS_OUTPUT.PUT_LINE('CSV file name is: '||file_name);\r","END IF;\r","\r","------------------------------------------------------\r","-- Select file from Object Storage\r","------------------------------------------------------\r","\r","SELECT object_name INTO obj FROM DBMS_CLOUD.LIST_OBJECTS(NULL, LOCATION_URI) where object_name = FILE_NAME;\r","\r","------------------------------------------------------\r","-- Define table name\r","------------------------------------------------------\r","\r","SELECT REGEXP_SUBSTR(obj, '[^.]+') INTO table_name from dual;\r","\r","   DBMS_OUTPUT.PUT_LINE('Table name is: '||table_name);\r","\r","------------------------------------------------------\r","-- Define file containing the CREATE TABLE statement,\r","-- which is saved in a TXT file. DBMS_CLOUD does not\r","-- support reading SQL files.\r","------------------------------------------------------\r","\r","SELECT table_name||'.txt' INTO sql_file from dual;\r","\r","IF VERBOSE THEN\r","   DBMS_OUTPUT.PUT_LINE('Text file is: '||sql_file);\r","END IF;\r","\r","------------------------------------------------------\r","-- Read the SQL statement from the text file into\r","-- a binary variable and execute the statement\r","------------------------------------------------------\r","\r","BEGIN\r","  b_data := DBMS_CLOUD.GET_OBJECT(\r","      credential_name => NULL,\r","      object_uri => LOCATION_URI||sql_file);\r","  v_data :=  utl_raw.cast_to_varchar2(b_data);\r","  IF VERBOSE THEN\r","  DBMS_OUTPUT.PUT_LINE('Table definition is: '||v_data);\r","  END IF;\r","  EXECUTE IMMEDIATE v_data;\r","END;\r","\r","------------------------------------------------------\r","-- Write data to the final table specified as dataset\r","-- input. If the table already exists, an error is\r","-- returned.\r","------------------------------------------------------\r","\r","BEGIN\r","    DBMS_CLOUD.COPY_DATA (\r","       credential_name => NULL,\r","       table_name => table_name,\r","       file_uri_list   => LOCATION_URI||file_name,\r","  format => json_object('type' value 'CSV', 'skipheaders' value 1));\r","END;\r","\r","------------------------------------------------------\r","-- Return the number of rows in the table\r","------------------------------------------------------\r","\r","EXECUTE IMMEDIATE 'SELECT count(*) FROM ' || table_name INTO v_rownum;\r","DBMS_OUTPUT.PUT_LINE ('Table created with number of rows = ' || v_rownum);\r","\r","END;\r"],"enabled":true,"result":{"startTime":1736456556255,"interpreter":"script.low","endTime":1736456558603,"results":[{"message":"\nProcedure IMPORT_DATA_OBJSTORE_DATASET compiled\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete the table if it exists","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV2';","EXCEPTION WHEN OTHERS THEN NULL; END;"],"enabled":true,"result":{"startTime":1736456559033,"interpreter":"script.low","endTime":1736456560202,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the procedure in default mode","message":["%md","","In default mode, the only required input is the dataset name, `CUSTOMER_INSURANCE_LTV2`. The table `CUSTOMER_INSURANCE_LTV2` is created and the table name and number of rows in the table is returned."],"enabled":true,"result":{"startTime":1736456560626,"interpreter":"md.low","endTime":1736456561026,"results":[{"message":"<p>In default mode, the only required input is the dataset name, <code>CUSTOMER_INSURANCE_LTV2<\/code>. The table <code>CUSTOMER_INSURANCE_LTV2<\/code> is created and the table name and number of rows in the table is returned.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2')"],"enabled":true,"result":{"startTime":1736456561450,"interpreter":"script.low","endTime":1736456569395,"results":[{"message":"Table name is: CUSTOMER_INSURANCE_LTV2\nTable created with number of rows = 13880\n\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 10 rows of the table","message":["%sql","","SELECT * ","FROM  CUSTOMER_INSURANCE_LTV2 ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736456569818,"interpreter":"sql.low","endTime":1736456570281,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the custom procedure in verbose mode","message":["%md","","In verbose mode, additional information is returned, including the dataset, CSV, and text file names, and the table definition. Verbose mode is enabled by specified by setting the `VERBOSE` parameter to `TRUE`."],"enabled":true,"result":{"startTime":1736456570746,"interpreter":"md.low","endTime":1736456571150,"results":[{"message":"<p>In verbose mode, additional information is returned, including the dataset, CSV, and text file names, and the table definition. Verbose mode is enabled by specified by setting the <code>VERBOSE<\/code> parameter to <code>TRUE<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete the previously created table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV2';","EXCEPTION WHEN OTHERS THEN NULL; END;"],"enabled":true,"result":{"startTime":1736456571581,"interpreter":"script.low","endTime":1736456572052,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2', TRUE)"],"enabled":true,"result":{"startTime":1736456572486,"interpreter":"script.low","endTime":1736456578540,"results":[{"message":"Dataset name is: CUSTOMER_INSURANCE_LTV2\nCSV file name is: CUSTOMER_INSURANCE_LTV2.csv\nTable name is: CUSTOMER_INSURANCE_LTV2\nText file is: CUSTOMER_INSURANCE_LTV2.txt\nTable definition is: CREATE TABLE CUSTOMER_INSURANCE_LTV2(MARITAL_STATUS\nVARCHAR2(26 BYTE), STATE CHAR(26 BYTE), CREDIT_BALANCE NUMBER(8,0),\nCUSTOMER_TENURE NUMBER(3,0), MORTGAGE_AMOUNT NUMBER(7,0), BANK_FUNDS\nNUMBER(7,0), NUM_DEPENDENTS NUMBER(3,0), HAS_CHILDREN NUMBER(3,0), INCOME\nNUMBER(7,0), CUSTOMER_ID CHAR(26 BYTE), GENDER CHAR(26 BYTE), PROFESSION\nVARCHAR2(35 BYTE), CREDIT_CARD_LIMITS NUMBER(6,0), REGION VARCHAR2(26 BYTE),\nHOME_OWNERSHIP NUMBER(3,0), NUM_ONLINE_TRANS NUMBER(6,0), BUY_INSURANCE\nVARCHAR2(26 BYTE), MONTHLY_CHECKS NUMBER(4,0), NUM_TRANS_KIOSK NUMBER(3,0), AGE\nNUMBER(4,0), MONEY_MONTLY_OVERDRAWN NUMBER(6,2), LTV NUMBER(9,2),\nTOTAL_AUTOM_PAYMENTS NUMBER(8,0), NUM_TRANS_TELLER NUMBER(3,0), \r\nCHECKING_BALANCE NUMBER(7,0), NUM_TRANS_ATM NUMBER(3,0), LTV_BIN VARCHAR2(26\nBYTE), FIRST_NAME VARCHAR2(26 BYTE), NUM_MORTGAGES NUMBER(3,0), CAR_OWNERSHIP\nNUMBER(3,0), LAST_NAME VARCHAR2(26 BYTE)) \r\n\nTable created with number of rows = 13880\n\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 10 rows of the table","message":["%sql","","SELECT * ","FROM  CUSTOMER_INSURANCE_LTV2 ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736456578973,"interpreter":"sql.low","endTime":1736456579424,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT                        \t0\t3\t578\t0\t3\t0\t51452\tCU15290                   \tM                         \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT                        \t0\t3\t0\t0\t3\t0\t63181\tCU15291                   \tM                         \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT                        \t0\t5\t117\t0\t5\t0\t66654\tCU15292                   \tF                         \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT                        \t0\t3\t0\t250\t3\t0\t61716\tCU15294                   \tM                         \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the procedure using your own Object Storage bucket","message":["%md","","To use your own Object Storage bucket with `IMPORT_DATA_OBJSTORE_DATASET`, pass the bucket URI to the `LOCATION_URI` parameter. Recall that the CSV file containing the data and the text file with the table definition should first be uploaded to the bucket. The example below demonstrates this functionality in default mode, and replacing NULL for TRUE will enable verbose mode."],"enabled":true,"result":{"startTime":1736456579853,"interpreter":"md.low","endTime":1736456580259,"results":[{"message":"<p>To use your own Object Storage bucket with <code>IMPORT_DATA_OBJSTORE_DATASET<\/code>, pass the bucket URI to the <code>LOCATION_URI<\/code> parameter. Recall that the CSV file containing the data and the text file with the table definition should first be uploaded to the bucket. The example below demonstrates this functionality in default mode, and replacing NULL for TRUE will enable verbose mode.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%script","","EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2', NULL, '<your bucket URI here>')"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Try it with your data","message":["%md","","To use the `IMPORT_DATA_OBJSTORE_DATASET` procedure with your own dataset, create the text file containing the table definition (in a single line), save the corresponding CSV data file to your public Object Storage bucket, and run the procedure with the dataset name as input. ","","For example, for the IRIS data, the bucket will have IRIS.csv containing the data, and IRIS.txt with table definition:","","    CREATE TABLE IRIS(SEPAL_LENGTH number, SEPAL_WIDTH number, PETAL_LENGTH number, PETAL_WIDTH number, SPECIES varchar2(4000))","","Run the procedure using your bucket:","","    EXEC IMPORT_DATA_OBJSTORE_DATASET('IRIS', NULL, '<your bucket URI>')"],"enabled":true,"result":{"startTime":1736456580699,"interpreter":"md.low","endTime":1736456581115,"results":[{"message":"<p>To use the <code>IMPORT_DATA_OBJSTORE_DATASET<\/code> procedure with your own dataset, create the text file containing the table definition (in a single line), save the corresponding CSV data file to your public Object Storage bucket, and run the procedure with the dataset name as input.<\/p>\n<p>For example, for the IRIS data, the bucket will have IRIS.csv containing the data, and IRIS.txt with table definition:<\/p>\n<pre><code>CREATE TABLE IRIS(SEPAL_LENGTH number, SEPAL_WIDTH number, PETAL_LENGTH number, PETAL_WIDTH number, SPECIES varchar2(4000))\n<\/code><\/pre>\n<p>Run the procedure using your bucket:<\/p>\n<pre><code>EXEC IMPORT_DATA_OBJSTORE_DATASET('IRIS', NULL, '&lt;your bucket URI&gt;')\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1736456581575,"interpreter":"md.low","endTime":1736456581981,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1736456582435,"interpreter":"md.low","endTime":1736456582844,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression GLM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737736011722,"interpreter":"md.medium","endTime":1737736012187,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Generalized Linear Model","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737736012669,"interpreter":"md.medium","endTime":1737736013115,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-generalized-linear-model\">Regression Modeling to Predict Numerical Values using Generalized Linear Model<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737736013605,"interpreter":"md.medium","endTime":1737736014069,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a>"],"enabled":true,"result":{"startTime":1737736014529,"interpreter":"md.medium","endTime":1737736014985,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360 joining tables from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, ","         B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1737736015462,"interpreter":"script.medium","endTime":1737736016362,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% train and 40% test sample","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V AS ","  SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V AS ","  SELECT * FROM CUSTOMERS360 MINUS SELECT * FROM TRAIN_DATA_V;"],"enabled":true,"result":{"startTime":1737736016839,"interpreter":"script.medium","endTime":1737736017337,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a GLM regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1737736017853,"interpreter":"md.medium","endTime":1737736018330,"results":[{"message":"<h3 id=\"build-a-glm-regression-model-for-predicting-yrs_residence\">Build a GLM regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('PREP_AUTO')                   := 'ON';","    v_setlst('ALGO_NAME')                   := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1737736018813,"interpreter":"script.medium","endTime":1737736022741,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> v_setlst('GLMS_RIDGE_VALUE') := '2';","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`","> v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> v_setlst('GLMS_MAX_FEATURES') := '30';","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> v_setlst('GLMS_REFERENCE_CLASS_NAME') := '<target_value>';","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> v_setlst('GLMS_NUM_ITERATIONS') := '200';","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> v_setlst('GLMS_BATCH_ROWS') := '2000';","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.  ","> v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';","","<strong> For Oracle release 23ai and newer. <\/strong>","- Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function. ","   For classification, the options are `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK` or `GLMS_CAUCHIT_LINK`.  For regression, the default is `GLMS_IDENTITY_LINK (default)`.","> v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';"],"enabled":true,"result":{"startTime":1737736023253,"interpreter":"md.medium","endTime":1737736023741,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_VALUE') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_MAX_FEATURES') := '30';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_REFERENCE_CLASS_NAME') := '&lt;target_value&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_NUM_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_BATCH_ROWS') := '2000';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';<\/p>\n<\/blockquote>\n<p><strong> For Oracle release 23ai and newer. <\/strong><\/p>\n<ul>\n<li>Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function.\nFor classification, the options are <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code> or <code>GLMS_CAUCHIT_LINK<\/code>.  For regression, the default is <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Feature Selection and Generation","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')             := 'ON';","    v_setlst('ALGO_NAME')             := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","    v_setlst('GLMS_FTR_SELECTION')    := 'GLMS_FTR_SELECTION_ENABLE';","    v_setlst('GLMS_FTR_GEN_METHOD')   := 'GLMS_FTR_GEN_QUADRATIC';","    v_setlst('GLMS_FTR_GENERATION')   := 'GLMS_FTR_GENERATION_ENABLE'; ","    v_setlst('GLMS_FTR_SEL_CRIT')     := 'GLMS_FTR_SEL_AIC';","    v_setlst('GLMS_NUM_ITERATIONS')   := '50';","    v_setlst('GLMS_MAX_FEATURES')     := '30';","    v_setlst('GLMS_PRUNE_MODEL')      := 'GLMS_PRUNE_MODEL_ENABLE';","    v_setlst('GLMS_ROW_DIAGNOSTICS')  := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE')   := '0.0001';","    v_setlst('GLMS_BATCH_ROWS')       := '2000';","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_DELETE_ROW';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1737736024234,"interpreter":"script.medium","endTime":1737736028643,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Ridge Regression","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')             := 'ON';","    v_setlst('ALGO_NAME')             := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_ENABLE';","    v_setlst('GLMS_FTR_SELECTION')    := 'GLMS_FTR_SELECTION_DISABLE';","    v_setlst('GLMS_NUM_ITERATIONS')   := '50';","    v_setlst('GLMS_ROW_DIAGNOSTICS')  := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE')   := '0.0001';","    v_setlst('GLMS_BATCH_ROWS')       := '2000';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE2',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1737736029174,"interpreter":"script.medium","endTime":1737736034172,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display GLM model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","  FROM USER_MINING_MODEL_SETTINGS","  WHERE MODEL_NAME = 'GLMR_SH_REGR_SAMPLE2'","  ORDER BY SETTING_NAME;","  "],"enabled":true,"result":{"startTime":1737736034669,"interpreter":"sql.medium","endTime":1737736035132,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\nGLMS_BATCH_ROWS\t2000\nGLMS_CONF_LEVEL\t.95\nGLMS_CONV_TOLERANCE\t0.0001\nGLMS_DIAGNOSTICS_TABLE_NAME\tGLMR_SH_SAMPLE_DIAG\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_DISABLE\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_DISABLE\nGLMS_NUM_ITERATIONS\t50\nGLMS_RIDGE_REGRESSION\tGLMS_RIDGE_REG_ENABLE\nGLMS_RIDGE_VALUE\t.01\nGLMS_ROW_DIAGNOSTICS\tGLMS_ROW_DIAG_DISABLE\nGLMS_VIF_FOR_RIDGE\tGLMS_VIF_RIDGE_DISABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display GLM model signature","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","  FROM USER_MINING_MODEL_ATTRIBUTES","  WHERE MODEL_NAME = 'GLMR_SH_REGR_SAMPLE2'","  ORDER BY ATTRIBUTE_NAME;","  "],"enabled":true,"result":{"startTime":1737736035603,"interpreter":"sql.medium","endTime":1737736036088,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display list of different model views available","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","  FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='GLMR_SH_REGR_SAMPLE'","  ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1737736036572,"interpreter":"sql.medium","endTime":1737736037056,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAGLMR_SH_REGR_SAMPLE\tGLM Regression Row Diagnostics\nDM$VDGLMR_SH_REGR_SAMPLE\tGLM Regression Attribute Diagnostics\nDM$VGGLMR_SH_REGR_SAMPLE\tGlobal Name-Value Pairs\nDM$VNGLMR_SH_REGR_SAMPLE\tNormalization and Missing Value Handling\nDM$VSGLMR_SH_REGR_SAMPLE\tComputed Settings\nDM$VWGLMR_SH_REGR_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View GLM model summary statistics","message":["%sql ","","SELECT NAME, ROUND(NUMERIC_VALUE,8), STRING_VALUE ","  FROM DM$VGGLMR_SH_REGR_SAMPLE","  ORDER BY NAME;","  "],"enabled":true,"result":{"startTime":1737736037537,"interpreter":"sql.medium","endTime":1737736039549,"results":[{"message":"NAME\tROUND(NUMERIC_VALUE,8)\tSTRING_VALUE\nADJUSTED_R_SQUARE\t0.57000474\t\nAIC\t1222.58201173\t\nCOEFF_VAR\t31.13330764\t\nCONVERGED\t\tYES\nCORRECTED_TOTAL_DF\t2692\t\nCORRECTED_TOT_SS\t9781.40586706\t\nDEPENDENT_MEAN\t4.01485332\t\nERROR_DF\t2672\t\nERROR_MEAN_SQUARE\t1.56239159\t\nERROR_SUM_SQUARES\t4174.71033585\t\nF_VALUE\t179.42670574\t\nGMSEP\t1.57467503\t\nHOCKING_SP\t0.00058495\t\nJ_P\t1.57457511\t\nMODEL_DF\t20\t\nMODEL_F_P_VALUE\t0\t\nMODEL_MEAN_SQUARE\t280.33477656\t\nMODEL_SUM_SQUARES\t5606.69553121\t\nNUM_PARAMS\t21\t\nNUM_ROWS\t2693\t\nROOT_MEAN_SQ\t1.24995664\t\nR_SQ\t0.57319935\t\nSBIC\t1346.44864468\t\nVALID_COVARIANCE_MATRIX\t\tYES\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View GLM model coefficient statistics","message":["%sql ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE, ROUND(COEFFICIENT,3) COEF, ","       ROUND(STD_ERROR,3) STD_ERROR, ROUND(TEST_STATISTIC,3) TEST_STATISTIC,","       ROUND(P_VALUE,3) P_VALUE, ROUND(STD_COEFFICIENT,3) STD_COEF, ","       ROUND(LOWER_COEFF_LIMIT,3) LOWER_COEF_LIMIT, ROUND(UPPER_COEFF_LIMIT,3) UPPER_COEF_LIMIT","  FROM DM$VDGLMR_SH_REGR_SAMPLE","  ORDER BY 1;","  "],"enabled":true,"result":{"startTime":1737736040027,"interpreter":"sql.medium","endTime":1737736040721,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEF\tSTD_ERROR\tTEST_STATISTIC\tP_VALUE\tSTD_COEF\tLOWER_COEF_LIMIT\tUPPER_COEF_LIMIT\nAFFINITY_CARD\t\t0.434\t0.064\t6.733\t0\t0.097\t0.307\t0.56\nCUST_GENDER\tF\t-0.25\t0.066\t-3.805\t0\t-0.062\t-0.379\t-0.121\nCUST_MARITAL_STATUS\tWidowed\t-0.85\t0.162\t-5.263\t0\t-0.075\t-1.167\t-0.534\nCUST_MARITAL_STATUS\tMabsent\t0.618\t0.195\t3.176\t0.002\t0.042\t0.236\t0.999\nCUST_MARITAL_STATUS\tDivorc.\t0.225\t0.082\t2.728\t0.006\t0.04\t0.063\t0.386\nCUST_YEAR_OF_BIRTH\t\t-0.075\t0.003\t-27.608\t0\t-0.545\t-0.081\t-0.07\nEDUCATION\t7th-8th\t-0.481\t0.166\t-2.908\t0.004\t-0.038\t-0.806\t-0.157\nEDUCATION\t< Bach.\t-0.171\t0.059\t-2.897\t0.004\t-0.038\t-0.287\t-0.055\nEDUCATION\t11th\t-0.45\t0.144\t-3.133\t0.002\t-0.04\t-0.732\t-0.168\nEDUCATION\tPhD\t0.502\t0.229\t2.189\t0.029\t0.028\t0.052\t0.952\nHOUSEHOLD_SIZE\t1\t-0.362\t0.081\t-4.49\t0\t-0.069\t-0.521\t-0.204\nHOUSEHOLD_SIZE\t4-5\t-0.634\t0.126\t-5.035\t0\t-0.073\t-0.881\t-0.387\nHOUSEHOLD_SIZE\t9+\t-0.241\t0.09\t-2.679\t0.007\t-0.04\t-0.417\t-0.064\nOCCUPATION\tOther\t-0.269\t0.081\t-3.315\t0.001\t-0.044\t-0.428\t-0.11\nOCCUPATION\t?\t-0.313\t0.113\t-2.76\t0.006\t-0.036\t-0.535\t-0.091\nOCCUPATION\tExec.\t0.416\t0.078\t5.35\t0\t0.071\t0.263\t0.568\nOCCUPATION\tHouse-s\t-1.584\t0.383\t-4.13\t0\t-0.053\t-2.335\t-0.832\nOCCUPATION\tTechSup\t-0.399\t0.127\t-3.144\t0.002\t-0.04\t-0.648\t-0.15\nOCCUPATION\tTransp.\t0.331\t0.13\t2.546\t0.011\t0.033\t0.076\t0.586\nY_BOX_GAMES\t\t-0.509\t0.082\t-6.209\t0\t-0.124\t-0.67\t-0.349\n\t\t152.34\t5.347\t28.49\t0\t0\t141.855\t162.825\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show 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Bach.\t-0.171\t0.004\nOCCUPATION\tExec.\t0.416\t0\nOCCUPATION\tOther\t-0.269\t0.001\nCUST_MARITAL_STATUS\tDivorc.\t0.225\t0.006\nEDUCATION\t11th\t-0.45\t0.002\nHOUSEHOLD_SIZE\t4-5\t-0.634\t0\nOCCUPATION\tTechSup\t-0.399\t0.002\nHOUSEHOLD_SIZE\t9+\t-0.241\t0.007\nOCCUPATION\tTransp.\t0.331\t0.011\nOCCUPATION\t?\t-0.313\t0.006\nCUST_MARITAL_STATUS\tWidowed\t-0.85\t0\nEDUCATION\tPhD\t0.502\t0.029\nCUST_MARITAL_STATUS\tMabsent\t0.618\t0.002\nEDUCATION\t7th-8th\t-0.481\t0.004\nCUST_YEAR_OF_BIRTH\t\t-0.075\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply GLM model to predict YRS_RESIDENCE with Prediction_Details","message":["%sql","","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) 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     OUTPRED","FETCH FIRST 10 ROWS ONLY; ","       "],"enabled":true,"result":{"startTime":1737736042243,"interpreter":"sql.medium","endTime":1737736043096,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tLOWER_PRED\tHIGHER_PRED\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100003\t3.806\t3.663\t3.949\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".205\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.135\" \n100006\t1.751\t1.543\t1.959\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.041\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\"-.068\" \n100008\t4.333\t4.199\t4.468\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".304\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".254\" \n100014\t5.082\t4.907\t5.257\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".518\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".147\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show GLM model predictions vs actuals","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","FROM TEST_DATA_V","WHERE CUST_ID <= 100030","ORDER BY CUST_ID;","  "],"enabled":true,"result":{"startTime":1737736043577,"interpreter":"sql.medium","endTime":1737736044092,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\n100003\t3.8\t6\n100006\t1.8\t2\n100008\t4.3\t5\n100014\t5.1\t5\n100016\t4.1\t4\n100018\t1.9\t3\n100023\t5.5\t8\n100026\t3.7\t3\n100027\t4\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model quality statistics on the TEST data","message":["%sql","","SELECT ROUND(R2,3) COEF_OF_DETERMINATION_R2, ","       ROUND(MAE,3) MEAN_ABSOLUTE_ERROR, ","       ROUND(MSE,3) MEAN_SQUARED_ERROR, ","       ROUND(SQRT(MSE),3) ROOT_MEAN_SQUARED_ERROR","FROM  (SELECT AVG(ABS(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE)) MAE,","               AVG(POWER(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE,2)) MSE,","               1-(SUM(POWER(ACTUAL_YRS_RESIDENCE - PRED_YRS_RESIDENCE,2)))/(SUM(POWER(ACTUAL_YRS_RESIDENCE - ","                 (SELECT AVG(ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1)) FROM TEST_DATA_V),2))) R2              ","                 FROM (SELECT CUST_ID,","                            ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","                            YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","                       FROM TEST_DATA_V))","                       "],"enabled":true,"result":{"startTime":1737736044595,"interpreter":"sql.medium","endTime":1737736045172,"results":[{"message":"COEF_OF_DETERMINATION_R2\tMEAN_ABSOLUTE_ERROR\tMEAN_SQUARED_ERROR\tROOT_MEAN_SQUARED_ERROR\n0.576\t0.848\t1.572\t1.254\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"ACTUAL_YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"showLabel\":\"no\",\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"ACTUAL_YRS_RESIDENCE\",\"HOUSEHOLD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot GLM model predictions vs actuals grouped by HOUSE_HOLD_SIZE","message":["%sql","","SELECT CUST_ID,","    ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE, ","    CASE HOUSEHOLD_SIZE WHEN '1' THEN '1'","    WHEN '2' THEN '2'","    ELSE '3 or more' END HOUSEHOLD","FROM TEST_DATA_V","FETCH FIRST 100 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1737736045652,"interpreter":"sql.medium","endTime":1737736046270,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\tHOUSEHOLD\n100008\t4.3\t5\t2\n100014\t5.1\t5\t2\n100016\t4.1\t4\t3 or more\n100018\t1.9\t3\t3 or more\n100035\t3.2\t4\t3 or more\n100040\t1.1\t0\t1\n100041\t1.9\t2\t1\n100049\t5.5\t6\t2\n100052\t5.7\t6\t3 or more\n100054\t4.2\t4\t2\n100056\t4.3\t4\t3 or more\n100082\t3\t3\t2\n100083\t2.6\t3\t3 or more\n100085\t6.3\t6\t3 or more\n100087\t3.9\t5\t2\n100088\t3.4\t4\t3 or more\n100089\t7.4\t7\t3 or more\n100095\t3.8\t4\t3 or more\n100098\t2\t3\t1\n100103\t2.6\t3\t1\n100105\t2.6\t3\t1\n100116\t3.5\t4\t3 or more\n100118\t5.8\t6\t3 or 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more\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"groupByColumns\":[],\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"RESIDUALS\"],\"label\":[],\"extraDescription\":[],\"showLabel\":\"no\",\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"RESIDUALS\"],\"version\":1},\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot GLM model predictions vs residuals","message":["%sql","","SELECT CUST_ID,","       ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","       ROUND(ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *),1) - YRS_RESIDENCE,2) RESIDUALS","FROM   TEST_DATA_V","FETCH FIRST 100 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1737736046772,"interpreter":"sql.medium","endTime":1737736047328,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tRESIDUALS\n100003\t3.8\t-2.2\n100006\t1.8\t-0.2\n100023\t5.5\t-2.5\n100026\t3.7\t0.7\n100027\t4\t1\n100031\t4.1\t0.1\n100033\t4.8\t-0.2\n100036\t5.5\t-0.5\n100037\t5.3\t-0.7\n100044\t2.6\t0.6\n100046\t5.2\t0.2\n100047\t2.3\t0.3\n100048\t2.3\t0.3\n100070\t4.9\t-0.1\n100074\t4.1\t-0.9\n100084\t2.8\t-0.2\n100086\t1.8\t0.8\n100094\t5.3\t0.3\n100096\t4.6\t-2.4\n100101\t4.2\t1.2\n100110\t2.8\t-1.2\n100111\t3.7\t1.7\n100128\t4\t1\n100130\t4.9\t-0.1\n100131\t2.7\t-0.3\n100133\t2.4\t0.4\n100134\t4.3\t2.3\n100135\t3.7\t-1.3\n100154\t5.1\t0.1\n100168\t2.5\t-0.5\n100180\t2\t-1\n100185\t5.6\t-2.4\n100193\t2.7\t1.7\n100206\t2.9\t-2.1\n100214\t1.7\t0.7\n100216\t6.1\t0.1\n100221\t4.5\t-0.5\n100224\t5.8\t-2.2\n100230\t5\t-1\n100233\t5.6\t-0.4\n100236\t4.8\t-2.2\n100237\t4.5\t-0.5\n100238\t3.7\t0.7\n100245\t5.5\t0.5\n100253\t2.5\t1.5\n100256\t5.7\t-3.3\n100261\t3.6\t0.6\n100268\t4.9\t-0.1\n100269\t4.7\t-0.3\n100272\t5.1\t1.1\n100283\t5\t-1\n100287\t2.4\t-0.6\n100294\t1.5\t-0.5\n100296\t5.7\t1.7\n100303\t4.1\t0.1\n100305\t4.2\t0.2\n100310\t5\t-1\n100312\t3.8\t0.8\n100313\t5.9\t-1.1\n100325\t3.7\t-0.3\n100331\t3.5\t0.5\n100334\t5.5\t-4.5\n100338\t5.1\t-0.9\n100339\t5.1\t0.1\n100346\t2.5\t-0.5\n100348\t2.9\t-0.1\n100349\t1.5\t0.5\n100352\t4\t0\n100354\t3.5\t-1.5\n100356\t2\t0\n100359\t4.4\t-0.6\n100365\t3.9\t-0.1\n100367\t4.1\t-0.9\n100373\t4.9\t-1.1\n100381\t1.6\t-0.4\n100383\t1.5\t-1.5\n100384\t3.6\t0.6\n100387\t4.3\t3.3\n100395\t5.2\t-0.8\n100400\t6.4\t0.4\n100404\t6.3\t1.3\n100414\t3\t0\n100417\t1.4\t0.4\n100425\t6.9\t0.9\n100426\t4\t1\n100429\t4.4\t0.4\n100432\t2.4\t-0.6\n100433\t5\t-2\n100434\t4.2\t0.2\n100437\t7.2\t0.2\n100439\t2\t0\n100446\t5.4\t0.4\n100454\t1.6\t-0.4\n100476\t6.1\t0.1\n100480\t3.9\t0.9\n100486\t3.6\t-0.4\n100489\t1.8\t-1.2\n100492\t1.8\t-0.2\n100499\t5.8\t-0.2\n100511\t4.9\t-1.1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Another option: create Regression Model using Settings Table","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1737736047820,"interpreter":"md.medium","endTime":1737736048277,"results":[{"message":"<h2 id=\"another-option-create-regression-model-using-settings-table\">Another option: create Regression Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create GLM model settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLMR_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","-- Drop any previous Model Diagnostics table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLMR_DIAG';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE GLMR_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1737736048765,"interpreter":"script.medium","endTime":1737736049262,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable GLMR_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings for automated data preparation and feature selection","message":["%script ","","BEGIN ","    INSERT INTO GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_GENERALIZED_LINEAR_MODEL);","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- output row diagnostic statistics into a table named GLMR_SH_SAMPLE_DIAG  ","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_DIAGNOSTICS_TABLE_NAME, 'GLMR_SH_SAMPLE_DIAG');  ","    ","-- turn ridge regression on or off. By default the system turns it on if there is a multicollinearity.  Ridge regression cannot be used with feature selection and feature generation, so we will keep it off in this example","     INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_RIDGE_REGRESSION, 'GLMS_RIDGE_REG_DISABLE');    ","     ","-- turn on feature selection. By default the system does not do feature selection. Some options for feature selection are show below too","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_SELECTION, DBMS_DATA_MINING.GLMS_FTR_SELECTION_ENABLE);","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_MAX_FEATURES, '30');","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_PRUNE_MODEL, DBMS_DATA_MINING.GLMS_PRUNE_MODEL_ENABLE);  ","    ","-- turn on feature generation. We will do Quadratic features, but Cubic is also available. Feature generation requires feature selection to be enabled.","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GENERATION, DBMS_DATA_MINING.GLMS_FTR_GENERATION_ENABLE);","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GEN_METHOD, DBMS_DATA_MINING.GLMS_FTR_GEN_QUADRATIC);","--    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GEN_METHOD, DBMS_DATA_MINING.GLMS_FTR_GEN_CUBIC);","    ","END;"],"enabled":true,"result":{"startTime":1737736049734,"interpreter":"script.medium","endTime":1737736050235,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM regression model for predicting YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.REGRESSION,","    DATA_TABLE_NAME     => 'TRAIN_DATA_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","    SETTINGS_TABLE_NAME => 'GLMR_SETTINGS',","    XFORM_LIST          => V_XLST);","END;"],"enabled":true,"result":{"startTime":1737736050718,"interpreter":"script.medium","endTime":1737736053023,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1737736053521,"interpreter":"md.medium","endTime":1737736053990,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737736054463,"interpreter":"md.medium","endTime":1737736054928,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression NN","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1733785039141,"interpreter":"md.medium","endTime":1733785039594,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Regression Modeling to Predict Numerical Values using Neural Networks","This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Neural Networks algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1733785040045,"interpreter":"md.medium","endTime":1733785040473,"results":[{"message":"<h2 id=\"regression-modeling-to-predict-numerical-values-using-neural-networks\">Regression Modeling to Predict Numerical Values using Neural Networks<\/h2>\n<p>This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Neural Networks algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1733785040908,"interpreter":"md.medium","endTime":1733785041318,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1733785041741,"interpreter":"md.medium","endTime":1733785042145,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;"],"enabled":true,"result":{"startTime":1733785042568,"interpreter":"script.medium","endTime":1733785043731,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V AS SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V AS ","  SELECT * FROM CUSTOMERS360 ","  MINUS ","  SELECT * FROM TRAIN_DATA_V"],"enabled":true,"result":{"startTime":1733785044159,"interpreter":"script.medium","endTime":1733785044597,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network regression model to predict YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1733785045015,"interpreter":"md.medium","endTime":1733785045421,"results":[{"message":"<h3 id=\"build-a-neural-network-regression-model-to-predict-yrs_residence\">Build a Neural Network regression model to predict YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons and default activation function","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS')   := '1';","    v_setlst('NNET_NODES_PER_LAYER') := '4';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1733785045839,"interpreter":"script.medium","endTime":1733785047785,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Neural Network","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`, `NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and <strong>starting from Oracle Database 23ai<\/strong> `NNET_ACTIVATIONS_RELU`.","> v_setlst('NNET_HIDDEN_LAYERS') := '3';","","> v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","","> v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';","    ","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","","> v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","","> v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> v_setlst('NNET_REG_LAMBDA') := '1';","","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> v_setlst('NNET_ITERATIONS') := '200';","","> v_setlst('NNET_TOLERANCE') := '0.000001';","","> v_setlst('ODMS_RANDOM_SEED') := '0';","","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","    v_setlst('LBFGS_HISTORY_DEPTH') := '10';","","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';","","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';","","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';","","","* Specify the lower bound of the region where weights are randomly initialized. `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` must be set together. Setting one and not setting the other raises an error. `NNET_WEIGHT_LOWER_BOUND` must not be greater than `NNET_WEIGHT_UPPER_BOUND`. The default value is -sqrt(6/(l_nodes+r_nodes)). ","   The value of l_nodes for `input layer dense attributes` is (1+number of dense attributes), `input layer sparse attributes` is number of sparse attributes, and `each hidden layer` is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.","   If not specified, the values of `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` are system determined.","","> v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'","","> v_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'","","    "],"enabled":true,"result":{"startTime":1733785048230,"interpreter":"md.medium","endTime":1733785048690,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-neural-network\">Examples of possible setting overrides for Neural Network<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>, <code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and <strong>starting from Oracle Database 23ai<\/strong> <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_HIDDEN_LAYERS') := '3';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_HELDASIDE_RATIO') := '0.25';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_REG_LAMBDA') := '1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_TOLERANCE') := '0.000001';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('ODMS_RANDOM_SEED') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.\nv_setlst('LBFGS_HISTORY_DEPTH') := '10';<\/p>\n<\/li>\n<li>\n<p>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the lower bound of the region where weights are randomly initialized. <code>NNET_WEIGHT_LOWER_BOUND<\/code> and <code>NNET_WEIGHT_UPPER_BOUND<\/code> must be set together. Setting one and not setting the other raises an error. <code>NNET_WEIGHT_LOWER_BOUND<\/code> must not be greater than <code>NNET_WEIGHT_UPPER_BOUND<\/code>. The default value is -sqrt(6/(l_nodes+r_nodes)).\nThe value of l_nodes for <code>input layer dense attributes<\/code> is (1+number of dense attributes), <code>input layer sparse attributes<\/code> is number of sparse attributes, and <code>each hidden layer<\/code> is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.\nIf not specified, the values of <code>NNET_WEIGHT_LOWER_BOUND<\/code> and <code>NNET_WEIGHT_UPPER_BOUND<\/code> are system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS') := '1';","    v_setlst('NNET_NODES_PER_LAYER') := '4';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_LOG_SIG''';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1733785049129,"interpreter":"script.medium","endTime":1733785051076,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 8,8,2 neurons","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS') := '3';","    v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';","    v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","    v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","    v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    v_setlst('NNET_ITERATIONS') := '200';","    v_setlst('NNET_TOLERANCE') := '0.000001';","    v_setlst('ODMS_RANDOM_SEED') := '0';","    v_setlst('LBFGS_HISTORY_DEPTH') := '10';","    v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';","    v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1733785051495,"interpreter":"script.medium","endTime":1733785053245,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 16,8,4 neurons ","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS')   := '3';","    v_setlst('NNET_NODES_PER_LAYER') := '16,8,4';","    v_setlst('NNET_ACTIVATIONS')     := '''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_LOG_SIG''';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1733785053692,"interpreter":"script.medium","endTime":1733785056743,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display NN model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","  FROM USER_MINING_MODEL_SETTINGS","  WHERE MODEL_NAME = 'NN_SH_REGR_SAMPLE'","  ORDER BY SETTING_NAME;","  "],"enabled":true,"result":{"startTime":1733785057167,"interpreter":"sql.medium","endTime":1733785057593,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_NEURAL_NETWORK\nLBFGS_GRADIENT_TOLERANCE\t.000000001\nLBFGS_HISTORY_DEPTH\t20\nLBFGS_SCALE_HESSIAN\tLBFGS_SCALE_HESSIAN_ENABLE\nNNET_ACTIVATIONS\t'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_LOG_SIG'\nNNET_HIDDEN_LAYERS\t3\nNNET_ITERATIONS\t200\nNNET_NODES_PER_LAYER\t16,8,4\nNNET_TOLERANCE\t.000001\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_RANDOM_SEED\t0\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display NN model signature","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'NN_SH_REGR_SAMPLE'","ORDER BY ATTRIBUTE_NAME;","  "],"enabled":true,"result":{"startTime":1733785058012,"interpreter":"sql.medium","endTime":1733785058434,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display list of available model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='NN_SH_REGR_SAMPLE'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1733785058851,"interpreter":"sql.medium","endTime":1733785059323,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VANN_SH_REGR_SAMPLE\tNeural Network Weights\nDM$VGNN_SH_REGR_SAMPLE\tGlobal Name-Value Pairs\nDM$VNNN_SH_REGR_SAMPLE\tNormalization and Missing Value Handling\nDM$VSNN_SH_REGR_SAMPLE\tComputed Settings\nDM$VWNN_SH_REGR_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%script"," "],"enabled":true,"result":{"startTime":1733785059751,"interpreter":"script.medium","endTime":1733785060158,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View NN global statistics","message":["%sql ","","SELECT NAME, NUMERIC_VALUE, STRING_VALUE","FROM   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"],"enabled":true,"result":{"startTime":1733785061471,"interpreter":"sql.medium","endTime":1733785062141,"results":[{"message":"LAYER\tIDX_FROM\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tIDX_TO\tWEIGHT\n0\t0\tAFFINITY_CARD\t\t0\t-4.370718\n0\t0\tAFFINITY_CARD\t\t3\t-2.754181\n0\t0\tAFFINITY_CARD\t\t5\t-0.114992\n0\t0\tAFFINITY_CARD\t\t6\t4.999988\n0\t0\tAFFINITY_CARD\t\t7\t-0.312799\n0\t0\tAFFINITY_CARD\t\t9\t0.555576\n0\t0\tAFFINITY_CARD\t\t10\t4.148281\n0\t0\tAFFINITY_CARD\t\t12\t0.370817\n0\t1\tCUST_CREDIT_LIMIT\t\t0\t-2.983026\n0\t1\tCUST_CREDIT_LIMIT\t\t1\t-0.515985\n0\t1\tCUST_CREDIT_LIMIT\t\t2\t0.954484\n0\t1\tCUST_CREDIT_LIMIT\t\t3\t0.668855\n0\t1\tCUST_CREDIT_LIMIT\t\t6\t0.17862\n0\t1\tCUST_CREDIT_LIMIT\t\t10\t-0.293414\n0\t1\tCUST_CREDIT_LIMIT\t\t14\t-2.530024\n0\t2\tCUST_YEAR_OF_BIRTH\t\t4\t-17.342764\n0\t2\tCUST_YEAR_OF_BIRTH\t\t6\t12.133262\n0\t2\tCUST_YEAR_OF_BIRTH\t\t10\t-6.513813\n0\t2\tCUST_YEAR_OF_BIRTH\t\t11\t9.259549\n0\t2\tCUST_YEAR_OF_BIRTH\t\t12\t3.103116\n0\t2\tCUST_YEAR_OF_BIRTH\t\t14\t-4.147187\n0\t2\tCUST_YEAR_OF_BIRTH\t\t15\t5.798874\n0\t3\tY_BOX_GAMES\t\t0\t-0.663085\n0\t3\tY_BOX_GAMES\t\t1\t-1.136859\n0\t3\tY_BOX_GAMES\t\t4\t8.246291\n0\t3\tY_BOX_GAMES\t\t7\t-4.332516\n0\t3\tY_BOX_GAMES\t\t11\t2.389499\n0\t3\tY_BOX_GAMES\t\t12\t-4.588606\n0\t3\tY_BOX_GAMES\t\t14\t5.187882\n0\t4\tCUST_GENDER\tF\t2\t1.094618\n0\t4\tCUST_GENDER\tF\t3\t0.807001\n0\t4\tCUST_GENDER\tF\t4\t4.192404\n0\t4\tCUST_GENDER\tF\t6\t3.466992\n0\t4\tCUST_GENDER\tF\t9\t0.249527\n0\t4\tCUST_GENDER\tF\t10\t-1.7571\n0\t4\tCUST_GENDER\tF\t11\t-1.459916\n0\t4\tCUST_GENDER\tF\t12\t-1.012123\n0\t4\tCUST_GENDER\tF\t15\t-1.363979\n0\t5\tCUST_GENDER\tM\t0\t0.923102\n0\t5\tCUST_GENDER\tM\t3\t-0.291582\n0\t5\tCUST_GENDER\tM\t4\t-0.512081\n0\t5\tCUST_GENDER\tM\t6\t-3.999676\n0\t5\tCUST_GENDER\tM\t7\t0.593675\n0\t5\tCUST_GENDER\tM\t8\t0.219558\n0\t5\tCUST_GENDER\tM\t14\t-0.860847\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t1\t-0.645932\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t2\t0.270675\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t4\t-0.215753\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t6\t5.180177\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t7\t-1.421873\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t9\t0.090732\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t12\t1.361445\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t14\t-8.464113\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t15\t-0.342572\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t1\t-1.674362\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t3\t0.387121\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t6\t-2.613074\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t8\t0.538498\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t9\t0.451317\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t10\t0.697204\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t12\t-1.858593\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t13\t0.51535\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t15\t1.431941\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t0\t1.538561\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t1\t2.911381\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t2\t0.331131\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t3\t-0.076852\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t4\t1.688624\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t6\t0.348746\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t7\t-1.151075\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t8\t-0.317991\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t9\t-0.012372\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t10\t-0.97207\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t12\t-0.496961\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t0\t-0.167138\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t1\t0.239216\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t2\t0.639802\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t3\t0.645074\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t5\t-0.240825\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t6\t-0.004196\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t12\t-0.160634\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t13\t0.233262\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t0\t-2.501912\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t1\t-2.6397\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t2\t-1.045197\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t4\t2.282456\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t5\t1.367199\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t9\t0.001595\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t11\t2.696625\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t12\t0.210299\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t13\t2.791282\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t15\t-2.05787\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t0\t1.742736\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t1\t1.662655\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t3\t1.115479\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t5\t4.363862\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t8\t-0.232538\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t9\t-0.981853\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t11\t-4.324379\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t14\t3.53081\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t1\t1.20767\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t3\t0.833542\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t5\t3.455422\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t9\t0.115863\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t14\t1.173207\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t15\t0.048094\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t0\t0.793212\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t2\t2.032964\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t4\t0.333492\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t6\t-2.778432\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t8\t1.468686\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t10\t-0.14017\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t11\t-0.252753\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t13\t0.314727\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t0\t-1.529188\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t2\t0.59194\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t3\t-1.096321\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t6\t-1.686337\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t7\t1.346118\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t9\t-0.721188\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t10\t0.245026\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t13\t-1.310881\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t15\t0.451455\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t0\t-3.109061\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t3\t-1.354413\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t4\t-0.826297\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t5\t-1.79243\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t6\t0.88164\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t7\t-0.156147\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t13\t1.612249\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t14\t-3.254153\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t0\t0.136384\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t3\t-0.04902\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t5\t-0.698721\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t7\t3.726714\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t9\t-0.002608\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t13\t-1.785723\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t14\t2.632333\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t0\t3.216302\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t1\t0.822609\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t2\t-0.366342\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t5\t2.041722\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t9\t0.392803\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t11\t1.360076\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t13\t-0.075826\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t3\t0.517879\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t5\t-3.717031\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t6\t-3.831395\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t7\t-0.205994\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t10\t0.04676\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t14\t-1.623999\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t0\t0.177199\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t1\t-1.911652\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t5\t-0.490909\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t7\t-4.539592\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t8\t1.197382\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t9\t0.022573\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t0\t-0.359479\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t1\t0.239479\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t4\t0.591566\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t6\t-0.084018\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t7\t-0.108049\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t8\t0.274907\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t10\t-0.170436\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t12\t0.420979\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t14\t-0.269288\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t15\t0.011866\n0\t21\tCUST_MARITAL_STATUS\tMarried\t1\t1.191345\n0\t21\tCUST_MARITAL_STATUS\tMarried\t2\t-1.703969\n0\t21\tCUST_MARITAL_STATUS\tMarried\t7\t3.082819\n0\t21\tCUST_MARITAL_STATUS\tMarried\t8\t-2.768465\n0\t21\tCUST_MARITAL_STATUS\tMarried\t11\t3.008321\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t0\t0.470252\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t1\t1.205716\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t2\t0.26877\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t5\t3.370685\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t6\t-1.886776\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t8\t0.934858\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t9\t0.169039\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t13\t0.044237\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t14\t-0.087237\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t15\t2.129044\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t2\t-1.208448\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t3\t0.867838\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t4\t-0.541946\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t5\t-1.514535\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t6\t1.246277\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t10\t-0.326668\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t13\t0.170186\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t14\t-4.450866\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t15\t-0.751243\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t0\t2.820536\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t2\t0.903746\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t3\t-0.30247\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t4\t-0.185084\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t5\t2.549767\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t6\t-0.678404\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t10\t-0.40184\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t11\t-2.751425\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t12\t2.79687\n0\t25\tEDUCATION\t10th\t0\t3.01459\n0\t25\tEDUCATION\t10th\t4\t-0.080635\n0\t25\tEDUCATION\t10th\t7\t3.094327\n0\t25\tEDUCATION\t10th\t8\t2.435068\n0\t25\tEDUCATION\t10th\t9\t0.391557\n0\t25\tEDUCATION\t10th\t12\t0.941991\n0\t25\tEDUCATION\t10th\t13\t0.261061\n0\t26\tEDUCATION\t11th\t0\t-1.526694\n0\t26\tEDUCATION\t11th\t1\t-2.252542\n0\t26\tEDUCATION\t11th\t2\t-0.756755\n0\t26\tEDUCATION\t11th\t5\t-1.024993\n0\t26\tEDUCATION\t11th\t6\t1.475696\n0\t26\tEDUCATION\t11th\t7\t-0.522831\n0\t26\tEDUCATION\t11th\t8\t0.595305\n0\t26\tEDUCATION\t11th\t9\t-1.75127\n0\t26\tEDUCATION\t11th\t10\t0.181098\n0\t26\tEDUCATION\t11th\t12\t-0.327113\n0\t26\tEDUCATION\t11th\t15\t1.256902\n0\t27\tEDUCATION\t12th\t0\t0.699319\n0\t27\tEDUCATION\t12th\t2\t0.556254\n0\t27\tEDUCATION\t12th\t3\t0.48573\n0\t27\tEDUCATION\t12th\t7\t2.521376\n0\t27\tEDUCATION\t12th\t10\t-0.320705\n0\t27\tEDUCATION\t12th\t11\t0.758282\n0\t28\tEDUCATION\t1st-4th\t0\t0.06462\n0\t28\tEDUCATION\t1st-4th\t1\t1.831618\n0\t28\tEDUCATION\t1st-4th\t3\t-0.053691\n0\t28\tEDUCATION\t1st-4th\t4\t1.567369\n0\t28\tEDUCATION\t1st-4th\t6\t-0.301836\n0\t28\tEDUCATION\t1st-4th\t7\t-2.15654\n0\t28\tEDUCATION\t1st-4th\t10\t0.287394\n0\t28\tEDUCATION\t1st-4th\t12\t-1.450529\n0\t28\tEDUCATION\t1st-4th\t13\t1.384922\n0\t28\tEDUCATION\t1st-4th\t15\t-1.168873\n0\t29\tEDUCATION\t5th-6th\t2\t-0.445466\n0\t29\tEDUCATION\t5th-6th\t3\t0.016895\n0\t29\tEDUCATION\t5th-6th\t5\t-0.19229\n0\t29\tEDUCATION\t5th-6th\t14\t-1.412016\n0\t30\tEDUCATION\t7th-8th\t0\t0.535462\n0\t30\tEDUCATION\t7th-8th\t1\t-1.004906\n0\t30\tEDUCATION\t7th-8th\t2\t2.984431\n0\t30\tEDUCATION\t7th-8th\t6\t1.411558\n0\t30\tEDUCATION\t7th-8th\t7\t0.52613\n0\t30\tEDUCATION\t7th-8th\t10\t0.193104\n0\t30\tEDUCATION\t7th-8th\t13\t-2.498344\n0\t30\tEDUCATION\t7th-8th\t15\t0.946301\n0\t31\tEDUCATION\t9th\t1\t-1.300518\n0\t31\tEDUCATION\t9th\t2\t0.018711\n0\t31\tEDUCATION\t9th\t3\t0.290016\n0\t31\tEDUCATION\t9th\t5\t0.553853\n0\t31\tEDUCATION\t9th\t6\t-0.387604\n0\t31\tEDUCATION\t9th\t8\t0.398375\n0\t31\tEDUCATION\t9th\t10\t0.495744\n0\t31\tEDUCATION\t9th\t11\t-0.877707\n0\t31\tEDUCATION\t9th\t12\t0.47289\n0\t31\tEDUCATION\t9th\t13\t-1.484824\n0\t31\tEDUCATION\t9th\t15\t-0.123522\n0\t32\tEDUCATION\t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Below 30,000\t0\t-2.795784\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t3\t-0.638093\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t5\t0.936458\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t8\t-0.89149\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t10\t-0.024065\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t11\t0.105752\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t13\t0.31516\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t0\t3.552544\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t2\t-2.984459\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t4\t-1.604061\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t5\t-3.342623\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t7\t-2.958796\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t11\t-3.429199\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t14\t-0.713821\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t5\t-0.778118\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t11\t1.030409\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t13\t0.458661\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t14\t0.671026\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t15\t-1.306856\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t4\t-1.140672\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t7\t1.909895\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t8\t-0.352091\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t9\t0.366689\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t10\t0.142346\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t11\t1.187929\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t14\t0.861036\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t15\t-0.166369\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t3\t-0.381633\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t6\t1.968783\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t7\t4.00789\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t8\t2.048697\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t10\t0.426314\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t14\t0.093808\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t2\t0.114442\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t4\t3.771539\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t6\t1.810326\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t7\t-1.514267\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t10\t-0.484287\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t12\t-0.183472\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t13\t-1.824475\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t15\t2.804409\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t0\t0.436532\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t2\t-0.970457\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t4\t-0.070045\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t6\t0.044509\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t7\t0.939164\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t8\t0.769206\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t10\t0.537906\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t11\t-1.305832\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t12\t-0.82704\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t13\t0.129921\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t1\t0.451637\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t3\t0.009938\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t5\t-1.484803\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t7\t0.510912\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t9\t-0.353828\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t12\t-2.014986\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t14\t-1.780015\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t15\t-1.285551\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t1\t-0.705671\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t4\t0.467917\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t5\t-2.033662\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t8\t-1.297387\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t11\t1.099564\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t12\t2.509772\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t14\t-0.286114\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t1\t-1.083221\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t2\t1.040539\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t8\t-3.216131\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t9\t-0.52478\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t10\t-1.468855\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t11\t3.261779\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t12\t0.023415\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t15\t0.238835\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t1\t0.747309\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t2\t0.664071\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t4\t-1.962498\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t6\t-3.029832\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t8\t-0.04784\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t10\t-1.752476\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t11\t-0.844652\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t12\t-1.035538\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t15\t-1.191872\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t3\t0.767305\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t4\t-0.197536\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t6\t-1.119399\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t7\t-2.755122\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t8\t1.779947\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t10\t0.80326\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t12\t2.402843\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t14\t1.371209\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t15\t0.588509\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t0\t-1.893142\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t1\t0.361724\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t2\t2.065753\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t4\t2.210591\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t8\t-0.981001\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t9\t0.573287\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t11\t-0.60152\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t12\t-1.031443\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t13\t1.029063\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t15\t-0.72449\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t2\t0.911451\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t3\t0.031786\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t4\t0.336472\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t6\t6.158056\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t10\t-0.66134\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t11\t0.766782\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t12\t-0.208484\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t13\t1.306201\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t14\t0.185756\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t15\t-2.157917\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t2\t0.298147\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t3\t0.228559\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t5\t0.11917\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t9\t0.156349\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t11\t-0.594522\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t13\t0.163254\n0\t21\tCUST_MARITAL_STATUS\tMarried\t0\t-1.592411\n0\t21\tCUST_MARITAL_STATUS\tMarried\t3\t-2.135448\n0\t21\tCUST_MARITAL_STATUS\tMarried\t4\t1.489806\n0\t21\tCUST_MARITAL_STATUS\tMarried\t5\t0.363937\n0\t21\tCUST_MARITAL_STATUS\tMarried\t6\t-1.45242\n0\t21\tCUST_MARITAL_STATUS\tMarried\t9\t0.471076\n0\t21\tCUST_MARITAL_STATUS\tMarried\t10\t0.567977\n0\t21\tCUST_MARITAL_STATUS\tMarried\t12\t-1.933803\n0\t21\tCUST_MARITAL_STATUS\tMarried\t13\t-0.088197\n0\t21\tCUST_MARITAL_STATUS\tMarried\t14\t-3.199551\n0\t21\tCUST_MARITAL_STATUS\tMarried\t15\t-1.28998\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t3\t1.393826\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t4\t-0.431805\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t7\t3.675436\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t10\t-0.936421\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t11\t-0.793851\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t12\t0.711581\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t0\t1.255459\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t1\t-0.718612\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t7\t0.084254\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t8\t1.410096\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t9\t-0.431748\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t11\t1.443982\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t12\t-1.659883\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t1\t0.168855\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t7\t1.695462\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t8\t0.878711\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t9\t-1.236549\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t13\t-1.058783\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t14\t4.830394\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t15\t2.451037\n0\t25\tEDUCATION\t10th\t1\t3.39653\n0\t25\tEDUCATION\t10th\t2\t-1.606708\n0\t25\tEDUCATION\t10th\t3\t-0.604229\n0\t25\tEDUCATION\t10th\t5\t0.08797\n0\t25\tEDUCATION\t10th\t6\t1.19025\n0\t25\tEDUCATION\t10th\t10\t1.199085\n0\t25\tEDUCATION\t10th\t11\t-0.19739\n0\t25\tEDUCATION\t10th\t14\t4.435546\n0\t25\tEDUCATION\t10th\t15\t0.809115\n0\t26\tEDUCATION\t11th\t3\t0.358657\n0\t26\tEDUCATION\t11th\t4\t-1.011804\n0\t26\tEDUCATION\t11th\t11\t0.846514\n0\t26\tEDUCATION\t11th\t13\t0.060375\n0\t26\tEDUCATION\t11th\t14\t0.87011\n0\t27\tEDUCATION\t12th\t1\t-0.815128\n0\t27\tEDUCATION\t12th\t4\t0.373556\n0\t27\tEDUCATION\t12th\t5\t0.253758\n0\t27\tEDUCATION\t12th\t6\t0.777817\n0\t27\tEDUCATION\t12th\t8\t-0.797664\n0\t27\tEDUCATION\t12th\t9\t0.045231\n0\t27\tEDUCATION\t12th\t12\t-0.362112\n0\t27\tEDUCATION\t12th\t13\t-1.188026\n0\t27\tEDUCATION\t12th\t14\t1.388636\n0\t27\tEDUCATION\t12th\t15\t0.65282\n0\t28\tEDUCATION\t1st-4th\t2\t-0.080957\n0\t28\tEDUCATION\t1st-4th\t5\t-0.686853\n0\t28\tEDUCATION\t1st-4th\t8\t0.104929\n0\t28\tEDUCATION\t1st-4th\t9\t0.370631\n0\t28\tEDUCATION\t1st-4th\t11\t0.481988\n0\t28\tEDUCATION\t1st-4th\t14\t-0.769753\n0\t29\tEDUCATION\t5th-6th\t0\t0.307438\n0\t29\tEDUCATION\t5th-6th\t1\t0.719427\n0\t29\tEDUCATION\t5th-6th\t4\t-0.225577\n0\t29\tEDUCATION\t5th-6th\t6\t0.870237\n0\t29\tEDUCATION\t5th-6th\t7\t-0.711251\n0\t29\tEDUCATION\t5th-6th\t8\t-0.186799\n0\t29\tEDUCATION\t5th-6th\t9\t-0.072992\n0\t29\tEDUCATION\t5th-6th\t10\t-0.015476\n0\t29\tEDUCATION\t5th-6th\t11\t0.901155\n0\t29\tEDUCATION\t5th-6th\t12\t1.170572\n0\t29\tEDUCATION\t5th-6th\t13\t0.37885\n0\t29\tEDUCATION\t5th-6th\t15\t-0.624854\n0\t30\tEDUCATION\t7th-8th\t3\t0.207821\n0\t30\tEDUCATION\t7th-8th\t4\t0.119981\n0\t30\tEDUCATION\t7th-8th\t5\t0.834353\n0\t30\tEDUCATION\t7th-8th\t8\t-0.804528\n0\t30\tEDUCATION\t7th-8th\t9\t-1.523057\n0\t30\tEDUCATION\t7th-8th\t11\t0.517904\n0\t30\tEDUCATION\t7th-8th\t12\t-2.805672\n0\t30\tEDUCATION\t7th-8th\t14\t-1.587155\n0\t31\tEDUCATION\t9th\t0\t-0.058235\n0\t31\tEDUCATION\t9th\t4\t-0.369962\n0\t31\tEDUCATION\t9th\t7\t1.371721\n0\t31\tEDUCATION\t9th\t9\t-0.368623\n0\t31\tEDUCATION\t9th\t14\t2.690402\n0\t32\tEDUCATION\t< Bach.\t3\t0.618335\n0\t32\tEDUCATION\t< Bach.\t6\t1.791538\n0\t32\tEDUCATION\t< Bach.\t7\t2.133367\n0\t32\tEDUCATION\t< Bach.\t10\t0.53719\n0\t32\tEDUCATION\t< Bach.\t12\t-0.394357\n0\t32\tEDUCATION\t< 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\n0\t46\tHOUSEHOLD_SIZE\t9+\t12\t2.412346\n0\t47\tOCCUPATION\t?\t3\t-1.883662\n0\t47\tOCCUPATION\t?\t5\t-1.915461\n0\t47\tOCCUPATION\t?\t7\t-0.201495\n0\t47\tOCCUPATION\t?\t10\t1.186719\n0\t47\tOCCUPATION\t?\t11\t-1.400024\n0\t47\tOCCUPATION\t?\t12\t0.839678\n0\t47\tOCCUPATION\t?\t14\t2.941971\n0\t47\tOCCUPATION\t?\t15\t0.755981\n0\t48\tOCCUPATION\tCleric.\t1\t-2.392272\n0\t48\tOCCUPATION\tCleric.\t3\t0.118772\n0\t48\tOCCUPATION\tCleric.\t5\t-1.150695\n0\t48\tOCCUPATION\tCleric.\t8\t-1.536036\n0\t48\tOCCUPATION\tCleric.\t10\t0.123883\n0\t48\tOCCUPATION\tCleric.\t12\t0.400654\n0\t48\tOCCUPATION\tCleric.\t14\t-5.348234\n0\t48\tOCCUPATION\tCleric.\t15\t-1.560794\n0\t49\tOCCUPATION\tCrafts\t1\t-1.53897\n0\t49\tOCCUPATION\tCrafts\t3\t0.120594\n0\t49\tOCCUPATION\tCrafts\t4\t1.846845\n0\t49\tOCCUPATION\tCrafts\t6\t-2.051881\n0\t49\tOCCUPATION\tCrafts\t7\t1.067477\n0\t49\tOCCUPATION\tCrafts\t9\t-0.07399\n0\t49\tOCCUPATION\tCrafts\t10\t0.439061\n0\t49\tOCCUPATION\tCrafts\t12\t3.862192\n0\t49\tOCCUPATION\tCrafts\t14\t2.497244\n0\t50\tOCCUPATION\tExec.\t1\t3.45466\n0\t50\tOCCUPATION\tExec.\t2\t-2.261971\n0\t50\tOCCUPATION\tExec.\t3\t0.161554\n0\t50\tOCCUPATION\tExec.\t4\t-1.844018\n0\t50\tOCCUPATION\tExec.\t7\t2.506464\n0\t50\tOCCUPATION\tExec.\t8\t1.847934\n0\t50\tOCCUPATION\tExec.\t9\t0.453843\n0\t50\tOCCUPATION\tExec.\t12\t1.172993\n0\t51\tOCCUPATION\tFarming\t0\t-4.709769\n0\t51\tOCCUPATION\tFarming\t1\t2.980672\n0\t51\tOCCUPATION\tFarming\t2\t-1.179094\n0\t51\tOCCUPATION\tFarming\t4\t2.507804\n0\t51\tOCCUPATION\tFarming\t5\t-2.705038\n0\t51\tOCCUPATION\tFarming\t9\t2.210696\n0\t51\tOCCUPATION\tFarming\t13\t2.342675\n0\t51\tOCCUPATION\tFarming\t14\t-6.066386\n0\t51\tOCCUPATION\tFarming\t15\t-1.377158\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict YRS_RESIDENCE with prediction details","message":["%sql","","-- Make predictions with upper and lower bounds and include explanatory prediction details","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","       FROM (SELECT CUST_ID,","               PREDICTION(NN_SH_REGR_SAMPLE USING *) PREDICTION_YRS_RES,","               PREDICTION_DETAILS(NN_SH_REGR_SAMPLE USING *) PD","             FROM TEST_DATA_V","             ORDER BY CUST_ID) OUT,","             XMLTABLE('/Details'","                      PASSING OUT.PD","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') ","       OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1733785062576,"interpreter":"sql.medium","endTime":1733785063106,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t3.972\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".084\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".078\" \n100002\t3.223\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".256\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"7000\" weight=\".004\" \n100005\t5.596\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".264\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".221\" \n100007\t5.42\t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".198\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".158\" \n100012\t5.063\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".504\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".103\" \n100013\t4.46\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".543\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1961\" weight=\".206\" \n100017\t4.925\t\"CUST_GENDER\" actualValue=\"F\" weight=\".379\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1935\" weight=\".087\" \n100021\t4.83\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".397\" \t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".394\" \n100023\t8.287\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1949\" weight=\".389\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".347\" \n100025\t2.831\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".113\" \t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".086\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show NN model predictions vs actuals","message":["%sql","","-- Show predictions vs. actuals.","","SELECT CUST_ID,","    round(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","  FROM TEST_DATA_V","  WHERE rownum < 10","  ORDER BY CUST_ID;","  "],"enabled":true,"result":{"startTime":1733785063524,"interpreter":"sql.medium","endTime":1733785066878,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\n100001\t4\t3\n100002\t3.2\t4\n100005\t5.6\t5\n100007\t5.4\t5\n100017\t4.9\t5\n100021\t4.8\t4\n100028\t4.1\t4\n100041\t2.1\t2\n100043\t2.1\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model quality statistics on the TEST data","message":["%sql","","select ROUND(R2,6) COEF_OF_DETERMINATION_R2, ","       ROUND(MAE,6) MEAN_ABSOLUTE_ERROR, ","       ROUND(MSE,6) MEAN_SQUARED_ERROR, ","       ROUND(SQRT(MSE),6) ROOT_MEAN_SQUARED_ERROR","       FROM ","       (SELECT AVG(ABS(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE)) MAE,","               AVG(POWER(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE,2)) MSE,","               1-(SUM(POWER(ACTUAL_YRS_RESIDENCE - PRED_YRS_RESIDENCE,2)))/(SUM(POWER(ACTUAL_YRS_RESIDENCE - ","                 (SELECT AVG(ROUND(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1)) FROM TEST_DATA_V),2))) R2              ","                 FROM (SELECT CUST_ID,","                            ROUND(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","                            YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","                       FROM TEST_DATA_V))"],"enabled":true,"result":{"startTime":1733785067612,"interpreter":"sql.medium","endTime":1733785068233,"results":[{"message":"COEF_OF_DETERMINATION_R2\tMEAN_ABSOLUTE_ERROR\tMEAN_SQUARED_ERROR\tROOT_MEAN_SQUARED_ERROR\n0.694174\t0.705215\t1.118231\t1.057465\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"groupByColumns\":[\"HOUSEHOLD\"],\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"ACTUAL_YRS_RESIDENCE\"],\"showLabel\":\"no\",\"showDescription\":\"no\",\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"ACTUAL_YRS_RESIDENCE\",\"HOUSEHOLD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot NN model predictions vs actuals grouped by HOUSE_HOLD_SIZE","message":["%sql","","SELECT CUST_ID,","    ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE, ","    CASE HOUSEHOLD_SIZE WHEN '1' THEN '1'","    WHEN '2' THEN '2'","    ELSE '3 or more' END HOUSEHOLD","FROM TEST_DATA_V","FETCH FIRST 100 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1733785068656,"interpreter":"sql.medium","endTime":1733785069120,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\tHOUSEHOLD\n100012\t4.2\t4\t3 or more\n100013\t5.2\t5\t3 or more\n100023\t6.1\t8\t3 or more\n100025\t2.8\t3\t2\n100027\t3.3\t3\t3 or more\n100029\t4.8\t5\t3 or more\n100032\t4.5\t4\t2\n100037\t5.8\t6\t2\n100038\t2.5\t2\t3 or more\n100039\t3.8\t3\t3 or more\n100046\t4.7\t5\t3 or more\n100051\t4.5\t6\t2\n100059\t6.3\t6\t2\n100067\t4.8\t4\t2\n100071\t3\t4\t3 or more\n100077\t4.4\t4\t3 or 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PRED_YRS_RESIDENCE,","    round(round(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1) - YRS_RESIDENCE,2) RESIDUALS","FROM TEST_DATA_V","FETCH FIRST 100 ROWS ONLY;","  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Create Regression Neural Network model using a settings table","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1733785070468,"interpreter":"md.medium","endTime":1733785070875,"results":[{"message":"<h2 id=\"create-regression-neural-network-model-using-a-settings-table\">Create Regression Neural Network model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create NN model settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE NNR_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE NNR_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1733785071300,"interpreter":"script.medium","endTime":1733785071760,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable NNR_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings","message":["%script ","","BEGIN ","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_NEURAL_NETWORK);","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- Specify a missing value treatment method:","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ODMS_MISSING_VALUE_TREATMENT, DBMS_DATA_MINING.ODMS_MISSING_VALUE_DELETE_ROW);","","-- Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS.","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_HIDDEN_LAYERS, '3');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_NODES_PER_LAYER, '8,8,2');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_ACTIVATIONS, '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''');","    ","-- Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_REGULARIZER, DBMS_DATA_MINING.NNET_REGULARIZER_HELDASIDE);","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_HELDASIDE_RATIO, '0.25');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_HELDASIDE_MAX_FAIL, '6');","    ","-- Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_REG_LAMBDA, '1');","","-- Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_ITERATIONS, '200');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_TOLERANCE, '0.000001');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ODMS_RANDOM_SEED, '0');","","-- Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_HISTORY_DEPTH, '10');","    ","-- Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN, DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN_ENABLE);","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN, DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN_DISABLE);","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_GRADIENT_TOLERANCE, '0.0000001');","   ","END;"],"enabled":true,"result":{"startTime":1733785072182,"interpreter":"script.medium","endTime":1733785072649,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a 3-Layer (8,8,2) NN regression model for predicting YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.REGRESSION,","    DATA_TABLE_NAME     => 'TRAIN_DATA_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","    SETTINGS_TABLE_NAME => 'NNR_SETTINGS',","    XFORM_LIST          => V_XLST);","END;"],"enabled":true,"result":{"startTime":1733785073075,"interpreter":"script.medium","endTime":1733785074916,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1733785075353,"interpreter":"md.medium","endTime":1733785075774,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1733785076199,"interpreter":"md.medium","endTime":1733785076607,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Regression SVM.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Regression SVM.dsnb
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index 00000000..c32fb534
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Regression SVM.dsnb	
@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737734648713,"interpreter":"md.medium","endTime":1737734649222,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Support Vector Machine","This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737734649708,"interpreter":"md.medium","endTime":1737734650157,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-support-vector-machine\">Regression Modeling to Predict Numerical Values using Support Vector Machine<\/h1>\n<p>This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737734650670,"interpreter":"md.medium","endTime":1737734651151,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1737734651622,"interpreter":"md.medium","endTime":1737734652065,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360 joining tables from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, ","         B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1737734652553,"interpreter":"script.medium","endTime":1737734653694,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V AS ","  SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V AS ","  SELECT * FROM CUSTOMERS360 MINUS SELECT * FROM TRAIN_DATA_V;"],"enabled":true,"result":{"startTime":1737734654311,"interpreter":"script.medium","endTime":1737734654892,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Linear regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1737734655356,"interpreter":"md.medium","endTime":1737734655800,"results":[{"message":"<h3 id=\"build-a-svm-linear-regression-model-for-predicting-yrs_residence\">Build a SVM Linear regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build SVM model using default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1737734656286,"interpreter":"script.medium","endTime":1737734658128,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> v_setlst('SVMS_EPSILON') := '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> v_setlst('SVMS_BATCH_ROWS') := '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> v_setlst('SVMS_CONV_TOLERANCE') := '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> v_setlst('SVMS_NUM_PIVOTS') := '200' "],"enabled":true,"result":{"startTime":1737734658631,"interpreter":"md.medium","endTime":1737734659118,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_EPSILON') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_BATCH_ROWS') := '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_CONV_TOLERANCE') := '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_NUM_PIVOTS') := '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Build SVM model using specific settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')              := 'ON';","    v_setlst('ALGO_NAME')              := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","    v_setlst('SVMS_REGULARIZER')       := 'SVMS_REGULARIZER_L1';","    v_setlst('SVMS_EPSILON')           := '0.1';","    v_setlst('SVMS_BATCH_ROWS')        := '20000';","    v_setlst('SVMS_SOLVER')            := 'SVMS_SOLVER_SGD';","    v_setlst('SVMS_CONV_TOLERANCE')    := '0.0001';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1737734659597,"interpreter":"script.medium","endTime":1737734661793,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build a SVM Gaussian regression model for predicting YRS_RESIDENCE","---"],"enabled":true,"result":{"startTime":1737734662327,"interpreter":"md.medium","endTime":1737734662785,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-regression-model-for-predicting-yrs_residence\">Build a SVM Gaussian regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1737734663280,"interpreter":"script.medium","endTime":1737734670077,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using specific settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')              := 'ON';","    v_setlst('ALGO_NAME')              := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION')   := 'SVMS_GAUSSIAN';","    v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","    v_setlst('SVMS_EPSILON')           := '0.1';","    v_setlst('SVMS_NUM_PIVOTS')        := '200';","    v_setlst('SVMS_SOLVER')            := 'SVMS_SOLVER_IPM';","    v_setlst('SVMS_CONV_TOLERANCE')    := '0.0001';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1737734670549,"interpreter":"script.medium","endTime":1737734674242,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL 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Another option: create SVM Linear Regression Model using Settings Table","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1737734686041,"interpreter":"md.medium","endTime":1737734686510,"results":[{"message":"<h2 id=\"another-option-create-svm-linear-regression-model-using-settings-table\">Another option: create SVM Linear Regression Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create SVM model settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE SVMLR_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE SVMLR_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));","    "],"enabled":true,"result":{"startTime":1737734686977,"interpreter":"script.medium","endTime":1737734687532,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable SVMLR_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings for automated data preparation and feature selection","message":["%script ","","BEGIN ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SUPPORT_VECTOR_MACHINES);","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- Specify a missing value treatment method:","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ODMS_MISSING_VALUE_TREATMENT, DBMS_DATA_MINING.ODMS_MISSING_VALUE_DELETE_ROW);","   ","-- Switch between Kernel Types - Linear or Gaussian.  By default the system uses the Linear Kernel.","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_KERNEL_FUNCTION, DBMS_DATA_MINING.SVMS_LINEAR);","--    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_KERNEL_FUNCTION, DBMS_DATA_MINING.SVMS_GAUSSIAN);","   ","-- Value of complexity factor for SVM algorithm (both classification and regression).  Default value estimated from the data by the algorithm.","   INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_COMPLEXITY_FACTOR, 0.1);","   ","-- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). ","-- The default is system determined because it depends on the potential model size.  ","-- Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_REGULARIZER, DBMS_DATA_MINING.SVMS_REGULARIZER_L1);","--     INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_REGULARIZER, DBMS_DATA_MINING.SVMS_REGULARIZER_L2);","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_EPSILON, 0.1);","   ","-- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20000. ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_BATCH_ROWS, 20000);","   ","-- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_SOLVER, DBMS_DATA_MINING.SVMS_SOLVER_SGD);","--    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_SOLVER, DBMS_DATA_MINING.SVMS_SOLVER_IPM);","","END;"],"enabled":true,"result":{"startTime":1737734688017,"interpreter":"script.medium","endTime":1737734688521,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Linear regression model for predicting YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.REGRESSION,","    DATA_TABLE_NAME     => 'TRAIN_DATA_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","    SETTINGS_TABLE_NAME => 'SVMLR_SETTINGS',","    XFORM_LIST          => V_XLST);","END;"],"enabled":true,"result":{"startTime":1737734688989,"interpreter":"script.medium","endTime":1737734691491,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1737734691956,"interpreter":"md.medium","endTime":1737734692407,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1737734692875,"interpreter":"md.medium","endTime":1737734693336,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression XGBoost","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1738095085220,"interpreter":"md.low","endTime":1738095085667,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Regression Modeling to Predict Numerical Values using XGBoost","This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the XGBoost algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2025 Oracle Corporation                             ","###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)","---"],"enabled":true,"result":{"startTime":1738095086191,"interpreter":"md.low","endTime":1738095086630,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-xgboost\">Regression Modeling to Predict Numerical Values using XGBoost<\/h1>\n<p>This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the XGBoost algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1738095087140,"interpreter":"md.low","endTime":1738095087594,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"http://www.oracle.com/goto/ml-xgboost\" target=\"_blank\">OML XGBoost<\/a>"],"enabled":true,"result":{"startTime":1738095088110,"interpreter":"md.low","endTime":1738095088575,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"http://www.oracle.com/goto/ml-xgboost\" target=\"_blank\">OML XGBoost<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360 joining tables from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","       A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","       B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, ","       B.YRS_RESIDENCE, B.Y_BOX_GAMES","FROM   SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","WHERE  A.CUST_ID = B.CUST_ID;"],"enabled":true,"result":{"startTime":1738095089081,"interpreter":"script.low","endTime":1738095089897,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V ","AS SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V ","AS SELECT * FROM CUSTOMERS360 MINUS SELECT * FROM TRAIN_DATA_V;"],"enabled":true,"result":{"startTime":1738095090392,"interpreter":"script.low","endTime":1738095090864,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the TRAIN_DATA_V View","message":["%sql","","","SELECT * FROM TRAIN_DATA_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1738095091394,"interpreter":"sql.low","endTime":1738095091890,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n102258\tF\tNeverM\t1913\tG: 130,000 - 149,999\t11000\t9th\t0\t2\tCleric.\t9\t0\n102555\tM\tNeverM\t1913\tB: 30,000 - 49,999\t3000\tBach.\t0\t2\tExec.\t14\t0\n101256\tM\tMarried\t1913\tK: 250,000 - 299,999\t9000\t< Bach.\t0\t3\tFarming\t9\t0\n103921\tM\tMarried\t1913\tK: 250,000 - 299,999\t15000\t< Bach.\t0\t3\tFarming\t9\t0\n102955\tM\tMarried\t1921\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t3\tCrafts\t8\t0\n100199\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\n100930\tF\tWidowed\t1922\tF: 110,000 - 129,999\t5000\tBach.\t0\t2\tProf.\t0\t0\n103098\tF\tDivorc.\t1922\tL: 300,000 and above\t11000\t< Bach.\t0\t2\tCleric.\t4\t0\n102224\tM\tMarried\t1923\tI: 170,000 - 189,999\t1500\tHS-grad\t0\t3\t?\t2\t0\n101693\tF\tMarried\t1924\tE: 90,000 - 109,999\t7000\tAssoc-A\t1\t4-5\t?\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a XGBoost regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1738095092395,"interpreter":"md.low","endTime":1738095092875,"results":[{"message":"<h3 id=\"build-a-xgboost-regression-model-for-predicting-yrs_residence\">Build a XGBoost regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('XGBOOST_REGRESSION_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')  := 'ALGO_XGBOOST';","    v_setlst('PREP_AUTO')  := 'ON';","   ","    dbms_data_mining.create_model2(","        MODEL_NAME          => 'XGBOOST_REGRESSION_MODEL',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","        SET_LIST            => v_setlst);","END;"],"enabled":true,"result":{"startTime":1738095093390,"interpreter":"script.low","endTime":1738095094278,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for XGBoost ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-443B3D58-8B74-422E-8E51-C8F609249B2C\" target=\"_blank\">XGBoost<\/a>","","   ","- Switch between booster - gblinear, gbtree, dart","  By default the system uses the gbtree. The parameters are similar for gbtree and dart, but has more difference comparing to gblinear","","> v_setlst('booster') := 'gblinear';",">","> v_setlst('booster') := 'gbtree';",">","> v_setlst('booster') := 'dart';","  ","- regularization parameters for all boosters","> v_setlst('alpha')     := '0.1';",">","> v_setlst('eta')       := '0.3';",">","> v_setlst('lambda')    := '0.3';","    ","- regularization parameters for tree based boosters: gbtree, dart","> v_setlst('gamma')           := '0.1';",">","> v_setlst('max_delta_step')  := '0.1';",">","> v_setlst('max_depth')       := '6';",">","> v_setlst('max_leaves')      := '10';",">","> v_setlst('min_child_weight'):= '1';","    ","- subsampling methods for tree based boosters: gbtree, dart ","> v_setlst('colsample_bylevel') := '1';",">","> v_setlst('colsample_bynode')  := '1';",">","> v_setlst('colsample_bytree')  := '1';","    ","- Num of iteration rounds ","> v_setlst('num_round') := '100';","    ","- For regression tasks, the default objective is reg:linear","> v_setlst('objective') := 'reg:linear'","","* This setting specifies permitted interactions in the model. Specify the constrains in the form of a nested list where each inner list is a group of features (column names) that are allowed to interact with each other. If a single column is passed in the interactions then, the input is ignored.  ","","  In the following example, features x0, x1, and x2 are allowed to interact with each other but with no other feature. Similarly, x0 and x4 are allowed to interact with each other but with no other feature and so on. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.","> vsetlst('xgboost_interaction_constraints') := '[[x0,x1,x2],[x0,x4],[x5,x6]]'","","* This setting specifies the features (column names) that must obey increasing constraint. The feature names are separated by a comma. ","","  For example, setting value 'x0,x3' sets increasing constraint on features x0 and x3. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.","> vsetlst('xgboost_increase_constraints') := 'x0,x3' "],"enabled":true,"result":{"startTime":1738095094799,"interpreter":"md.low","endTime":1738095095240,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-xgboost\">Examples of possible setting overrides for XGBoost<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-443B3D58-8B74-422E-8E51-C8F609249B2C\" target=\"_blank\">XGBoost<\/a><\/p>\n<\/li>\n<li>\n<p>Switch between booster - gblinear, gbtree, dart\nBy default the system uses the gbtree. The parameters are similar for gbtree and dart, but has more difference comparing to gblinear<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('booster') := 'gblinear';<\/p>\n<p>v_setlst('booster') := 'gbtree';<\/p>\n<p>v_setlst('booster') := 'dart';<\/p>\n<\/blockquote>\n<ul>\n<li>regularization parameters for all boosters<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('alpha')     := '0.1';<\/p>\n<p>v_setlst('eta')       := '0.3';<\/p>\n<p>v_setlst('lambda')    := '0.3';<\/p>\n<\/blockquote>\n<ul>\n<li>regularization parameters for tree based boosters: gbtree, dart<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('gamma')           := '0.1';<\/p>\n<p>v_setlst('max_delta_step')  := '0.1';<\/p>\n<p>v_setlst('max_depth')       := '6';<\/p>\n<p>v_setlst('max_leaves')      := '10';<\/p>\n<p>v_setlst('min_child_weight'):= '1';<\/p>\n<\/blockquote>\n<ul>\n<li>subsampling methods for tree based boosters: gbtree, dart<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('colsample_bylevel') := '1';<\/p>\n<p>v_setlst('colsample_bynode')  := '1';<\/p>\n<p>v_setlst('colsample_bytree')  := '1';<\/p>\n<\/blockquote>\n<ul>\n<li>Num of iteration rounds<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('num_round') := '100';<\/p>\n<\/blockquote>\n<ul>\n<li>For regression tasks, the default objective is reg:linear<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('objective') := 'reg:linear'<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>This setting specifies permitted interactions in the model. Specify the constrains in the form of a nested list where each inner list is a group of features (column names) that are allowed to interact with each other. If a single column is passed in the interactions then, the input is ignored.<\/p>\n<p>In the following example, features x0, x1, and x2 are allowed to interact with each other but with no other feature. Similarly, x0 and x4 are allowed to interact with each other but with no other feature and so on. This setting is applicable to 2-Dimensional features. An error occurs if you pass columns of non-supported type and non-existing feature names.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>vsetlst('xgboost_interaction_constraints') := '[[x0,x1,x2],[x0,x4],[x5,x6]]'<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>This setting specifies the features (column names) that must obey increasing constraint. The feature names are separated by a comma.<\/p>\n<p>For example, setting value 'x0,x3' sets increasing constraint on features x0 and x3. This setting is applicable to 2-Dimensional features. 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"],"enabled":true,"result":{"startTime":1738095116288,"interpreter":"sql.low","endTime":1738095116804,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t5.141\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".806\" \t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".062\" \n100004\t5.459\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1953\" weight=\".868\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".017\" \n100007\t3.836\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1963\" weight=\".358\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".054\" \n100010\t3.217\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.043\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\"-.957\" \n100012\t4.28\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1968\" weight=\".512\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".457\" \n100013\t4.415\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1961\" weight=\".906\" \t\"OCCUPATION\" actualValue=\"Transp.\" weight=\"-.017\" \n100015\t4.825\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".841\" \t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".117\" \n100017\t5.415\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1935\" weight=\".826\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\".027\" \n100020\t5.556\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1946\" weight=\".848\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.015\" \n100022\t4.544\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1965\" weight=\".497\" \t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".268\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"scatterplot\":{\"groupByColumns\":[],\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"ACTUAL_YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"height\":400,\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"ACTUAL_YRS_RESIDENCE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Show XGBoost model predictions vs actuals","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","FROM  TEST_DATA_V","ORDER BY 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XGBoost model predictions vs actuals","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) PRED_YRS_RESIDENCE,","    round(round(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) - YRS_RESIDENCE,2) RESIDUALS","FROM  TEST_DATA_V","ORDER BY CUST_ID;"],"enabled":true,"result":{"startTime":1738095118521,"interpreter":"sql.low","endTime":1738095119236,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tRESIDUALS\n100001\t5.1\t2.1\n100004\t5.5\t0.5\n100007\t3.8\t-1.2\n100010\t3.2\t0.2\n100012\t4.3\t0.3\n100013\t4.4\t-0.6\n100015\t4.8\t-1.2\n100017\t5.4\t0.4\n100020\t5.6\t-0.4\n100022\t4.5\t-0.5\n100025\t2.8\t-0.2\n100026\t3.5\t0.5\n100028\t3.2\t-0.8\n100034\t4.3\t0.3\n100035\t3.3\t-0.7\n100036\t5.7\t-0.3\n100038\t2\t0\n100041\t1.8\t-0.2\n100042\t4.4\t3.4\n100046\t4.4\t-0.6\n100048\t2.2\t0.2\n100049\t5.7\t-0.3\n100050\t2.3\t0.3\n100053\t5.7\t0.7\n100055\t2.6\t1.6\n100057\t4.3\t0.3\n100058\t4.6\t-1.4\n100059\t5.7\t-0.3\n100065\t5.3\t0.3\n100070\t4.3\t-0.7\n100072\t3.4\t0.4\n100073\t4.9\t0.9\n100074\t4.3\t-0.7\n100078\t3.2\t0.2\n100081\t5.7\t-1.3\n100086\t1.3\t0.3\n100087\t3.5\t-1.5\n100091\t4.3\t0.3\n100094\t5.5\t0.5\n100095\t4.2\t0.2\n100096\t4.8\t-2.2\n100097\t3\t0\n100100\t4.6\t0.6\n100101\t3.6\t0.6\n100102\t4.2\t0.2\n100103\t3\t0\n100105\t3\t0\n100106\t3.9\t-0.1\n100110\t3.4\t-0.6\n100113\t1.1\t0.1\n100115\t2.2\t0.2\n100116\t3.9\t-0.1\n100117\t3.8\t-0.2\n100118\t5.7\t-0.3\n100121\t3.2\t-0.8\n100122\t5.1\t1.1\n100124\t5.9\t2.9\n100127\t1.9\t-0.1\n100128\t4\t1\n100130\t5.4\t0.4\n100132\t4.1\t0.1\n100140\t2\t1\n100145\t2.8\t-0.2\n100148\t5.4\t2.4\n100149\t5.7\t-0.3\n100151\t5.4\t0.4\n100153\t2.9\t-0.1\n100156\t4.4\t-0.6\n100157\t1\t0\n100161\t6\t-4\n100163\t4.2\t-1.8\n100165\t4.5\t0.5\n100167\t1.4\t0.4\n100168\t2.7\t-0.3\n100170\t5.6\t2.6\n100172\t1.6\t0.6\n100178\t5.5\t0.5\n100183\t6.1\t0.1\n100186\t1.4\t0.4\n100192\t4.3\t0.3\n100193\t3.1\t2.1\n100194\t1.9\t-0.1\n100195\t1.5\t0.5\n100196\t5.9\t2.9\n100200\t1.9\t-0.1\n100201\t2.1\t-2.9\n100205\t2.2\t0.2\n100209\t4.1\t0.1\n100211\t2.3\t0.3\n100213\t2.6\t-0.4\n100214\t1.3\t0.3\n100215\t4.7\t0.7\n100218\t3.2\t0.2\n100219\t5.3\t0.3\n100220\t1.9\t-1.1\n100224\t5.7\t-2.3\n100225\t2.3\t0.3\n100231\t1.7\t-0.3\n100232\t4\t1\n100233\t5.9\t-0.1\n100234\t1.8\t-0.2\n100235\t4.5\t-2.5\n100236\t5.1\t-1.9\n100238\t3.5\t0.5\n100242\t1.9\t0.9\n100243\t3.3\t0.3\n100244\t5.2\t-0.8\n100246\t2.9\t0.9\n100248\t3\t0\n100251\t4.3\t2.3\n100252\t3.8\t1.8\n100254\t3.8\t-0.2\n100256\t5.6\t-3.4\n100259\t3.6\t0.6\n100262\t3.9\t-0.1\n100265\t4.7\t0.7\n100266\t5.5\t-1.5\n100267\t5.7\t-0.3\n100269\t5.1\t0.1\n100271\t4.3\t-0.7\n100274\t4.3\t-0.7\n100276\t6.1\t-0.9\n100277\t5.7\t-0.3\n100285\t4.1\t0.1\n100287\t2.9\t-0.1\n100290\t5.1\t-0.9\n100292\t1.9\t-0.1\n100293\t4.5\t-0.5\n100295\t5.8\t-0.2\n100297\t1.7\t0.7\n100298\t3.5\t0.5\n100301\t5.6\t-1.4\n100303\t3.9\t-0.1\n100304\t3.2\t0.2\n100307\t2.1\t0.1\n100308\t2.1\t-1.9\n100312\t3.4\t0.4\n100319\t2.1\t-0.9\n100320\t2\t0\n100324\t3.5\t-0.5\n100325\t3.2\t-0.8\n100327\t3.1\t0.1\n100328\t5.4\t0.4\n100331\t3.1\t0.1\n100337\t2.6\t0.6\n100343\t4.5\t0.5\n100345\t5.4\t0.4\n100348\t3\t0\n100350\t4.6\t-0.4\n100351\t5.4\t0.4\n100352\t3.8\t-0.2\n100353\t2.8\t1.8\n100355\t4.6\t-1.4\n100357\t4.3\t-0.7\n100358\t2.6\t-0.4\n100359\t4.2\t-0.8\n100361\t3.1\t1.1\n100362\t1.7\t-0.3\n100363\t5.9\t-4.1\n100364\t3\t-1\n100368\t3.8\t0.8\n100369\t3.2\t0.2\n100372\t6.2\t-3.8\n100376\t4\t-1\n100377\t4.3\t0.3\n100378\t4.2\t0.2\n100380\t3.9\t-0.1\n100384\t3.4\t0.4\n100388\t4.4\t0.4\n100389\t4\t0\n100392\t2.9\t-0.1\n100395\t5.7\t-0.3\n100396\t5.1\t-1.9\n100398\t4\t0\n100401\t4.9\t-1.1\n100402\t1.6\t-0.4\n100404\t5.6\t0.6\n100409\t5.2\t-0.8\n100410\t2.3\t-1.7\n100415\t5.9\t-2.1\n100420\t5.6\t0.6\n100422\t5.2\t2.2\n100425\t5.6\t-0.4\n100426\t3.2\t0.2\n100428\t1.3\t0.3\n100430\t5.4\t-1.6\n100431\t5.9\t-1.1\n100439\t1.9\t-0.1\n100441\t4.2\t0.2\n100442\t6.1\t-0.9\n100443\t5.7\t-1.3\n100448\t3.4\t0.4\n100450\t5.9\t-0.1\n100452\t3.8\t-0.2\n100453\t1.3\t0.3\n100455\t1.7\t-0.3\n100457\t3\t1\n100466\t4.7\t0.7\n100469\t2.6\t-0.4\n100473\t5\t0\n100480\t3.5\t0.5\n100483\t3.5\t0.5\n100488\t5.4\t-0.6\n100490\t4.2\t-0.8\n100493\t4.3\t0.3\n100494\t5.7\t0.7\n100501\t2.5\t-0.5\n100504\t1.4\t-0.6\n100505\t1.7\t-0.3\n100509\t2.2\t0.2\n100512\t5.3\t-0.7\n100513\t3.6\t-0.4\n100519\t3.6\t-0.4\n100521\t4.2\t0.2\n100524\t3.5\t-1.5\n100526\t4.1\t0.1\n100527\t4.4\t-0.6\n100529\t5.2\t0.2\n100533\t3.8\t-0.2\n100534\t5.4\t-1.6\n100535\t2\t1\n100538\t4.8\t-0.2\n100542\t4.1\t-0.9\n100544\t5.7\t0.7\n100548\t2.7\t-0.3\n100549\t3.2\t2.2\n100552\t2.6\t-2.4\n100553\t3.6\t0.6\n100555\t2.3\t0.3\n100556\t3.2\t-0.8\n100558\t4.4\t0.4\n100560\t5.9\t0.9\n100563\t5.1\t-0.9\n100564\t5.6\t-2.4\n100567\t2.3\t-0.7\n100568\t1.7\t0.7\n100575\t5.3\t0.3\n100582\t3.5\t0.5\n100583\t2.3\t0.3\n100589\t2.2\t0.2\n100591\t3.8\t-0.2\n100594\t2.8\t1.8\n100595\t5.7\t0.7\n100598\t5.9\t-0.1\n100599\t4\t0\n100602\t5.3\t-0.7\n100605\t3.3\t2.3\n100610\t2.9\t1.9\n100613\t5.7\t-1.3\n100614\t1.1\t1.1\n100615\t5.1\t1.1\n100616\t3.8\t-0.2\n100617\t1.1\t0.1\n100621\t5.7\t0.7\n100622\t2.9\t-0.1\n100623\t1.3\t-0.7\n100626\t6\t-2\n100628\t3.9\t-0.1\n100632\t5.8\t0.8\n100634\t1.4\t-0.6\n100635\t5.8\t-1.2\n100637\t1.9\t0.9\n100640\t3\t0\n100641\t4.3\t0.3\n100643\t2.9\t-0.1\n100646\t5\t3\n1006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quality statistics on the TEST data","message":["%sql","","select ROUND(R2,6) COEF_OF_DETERMINATION_R2, ","       ROUND(MAE,6) MEAN_ABSOLUTE_ERROR, ","       ROUND(MSE,6) MEAN_SQUARED_ERROR, ","       ROUND(SQRT(MSE),6) ROOT_MEAN_SQUARED_ERROR","       FROM ","       (SELECT AVG(ABS(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE)) MAE,","               AVG(POWER(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE,2)) MSE,","               1-(SUM(POWER(ACTUAL_YRS_RESIDENCE - PRED_YRS_RESIDENCE,2)))/(SUM(POWER(ACTUAL_YRS_RESIDENCE - ","                 (SELECT AVG(ROUND(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1)) FROM TEST_DATA_V),2))) R2              ","                 FROM (SELECT CUST_ID,","                            ROUND(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) PRED_YRS_RESIDENCE,","                            YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","                       FROM TEST_DATA_V))"],"enabled":true,"result":{"startTime":1738095119747,"interpreter":"sql.low","endTime":1738095120569,"results":[{"message":"COEF_OF_DETERMINATION_R2\tMEAN_ABSOLUTE_ERROR\tMEAN_SQUARED_ERROR\tROOT_MEAN_SQUARED_ERROR\n0.616665\t0.755323\t1.374898\t1.17256\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"tree\":{},\"scatterplot\":{\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"ACTUAL_YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"ACTUAL_YRS_RESIDENCE\",\"CUST_GENDER\"],\"version\":1},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"XGBoost model predictions vs actuals grouped by GENDER","message":["%sql","","-- Show predictions vs. actuals in a scatter plot grouped by CUST_GENDER.  ","-- Click the scatter plot icon and select CUST_GENDER as GroupBy, ","-- PRED_YRS_RESIDENCE as the \"Series to Show on X Axis\" and","-- ACTUAL_YRS_RESIDENCE as \"Series to Show on Y Axis\".","-- You can mouse over each Gender label to see the different plot","","SELECT CUST_ID,","    round(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE, CUST_GENDER","FROM TEST_DATA_V","ORDER BY CUST_ID;","  "],"enabled":true,"result":{"startTime":1738095121064,"interpreter":"sql.low","endTime":1738095121758,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\tCUST_GENDER\n100001\t5.1\t3\tF\n100004\t5.5\t5\tF\n100007\t3.8\t5\tF\n100010\t3.2\t3\tM\n100012\t4.3\t4\tM\n100013\t4.4\t5\tM\n100015\t4.8\t6\tM\n100017\t5.4\t5\tF\n100020\t5.6\t6\tF\n100022\t4.5\t5\tM\n100025\t2.8\t3\tF\n100026\t3.5\t3\tM\n100028\t3.2\t4\tF\n100034\t4.3\t4\tM\n100035\t3.3\t4\tF\n100036\t5.7\t6\tM\n100038\t2\t2\tF\n100041\t1.8\t2\tF\n100042\t4.4\t1\tM\n100046\t4.4\t5\tM\n100048\t2.2\t2\tM\n100049\t5.7\t6\tF\n100050\t2.3\t2\tM\n100053\t5.7\t5\tM\n100055\t2.6\t1\tF\n100057\t4.3\t4\tM\n100058\t4.6\t6\tM\n100059\t5.7\t6\tM\n100065\t5.3\t5\tM\n100070\t4.3\t5\tM\n100072\t3.4\t3\tM\n100073\t4.9\t4\tM\n100074\t4.3\t5\tM\n100078\t3.2\t3\tM\n100081\t5.7\t7\tM\n100086\t1.3\t1\tF\n100087\t3.5\t5\tM\n100091\t4.3\t4\tM\n100094\t5.5\t5\tM\n100095\t4.2\t4\tM\n100096\t4.8\t7\tF\n100097\t3\t3\tF\n100100\t4.6\t4\tF\n100101\t3.6\t3\tM\n100102\t4.2\t4\tM\n100103\t3\t3\tM\n100105\t3\t3\tM\n100106\t3.9\t4\tF\n100110\t3.4\t4\tM\n100113\t1.1\t1\tF\n100115\t2.2\t2\tM\n100116\t3.9\t4\tF\n100117\t3.8\t4\tF\n100118\t5.7\t6\tM\n100121\t3.2\t4\tM\n100122\t5.1\t4\tF\n100124\t5.9\t3\tM\n100127\t1.9\t2\tF\n100128\t4\t3\tF\n100130\t5.4\t5\tM\n100132\t4.1\t4\tF\n100140\t2\t1\tM\n100145\t2.8\t3\tM\n100148\t5.4\t3\tF\n100149\t5.7\t6\tM\n100151\t5.4\t5\tM\n100153\t2.9\t3\tM\n100156\t4.4\t5\tM\n100157\t1\t1\tF\n100161\t6\t10\tM\n100163\t4.2\t6\tM\n100165\t4.5\t4\tF\n100167\t1.4\t1\tF\n100168\t2.7\t3\tF\n100170\t5.6\t3\tF\n100172\t1.6\t1\tM\n100178\t5.5\t5\tM\n100183\t6.1\t6\tM\n100186\t1.4\t1\tM\n100192\t4.3\t4\tM\n100193\t3.1\t1\tM\n100194\t1.9\t2\tF\n100195\t1.5\t1\tM\n100196\t5.9\t3\tM\n100200\t1.9\t2\tM\n100201\t2.1\t5\tM\n100205\t2.2\t2\tM\n100209\t4.1\t4\tF\n100211\t2.3\t2\tM\n100213\t2.6\t3\tF\n100214\t1.3\t1\tF\n100215\t4.7\t4\tM\n100218\t3.2\t3\tF\n100219\t5.3\t5\tM\n100220\t1.9\t3\tF\n100224\t5.7\t8\tM\n100225\t2.3\t2\tM\n100231\t1.7\t2\tM\n100232\t4\t3\tF\n100233\t5.9\t6\tM\n100234\t1.8\t2\tF\n100235\t4.5\t7\tM\n100236\t5.1\t7\tM\n100238\t3.5\t3\tM\n100242\t1.9\t1\tF\n100243\t3.3\t3\tF\n100244\t5.2\t6\tM\n100246\t2.9\t2\tM\n100248\t3\t3\tM\n100251\t4.3\t2\tM\n100252\t3.8\t2\tF\n100254\t3.8\t4\tM\n100256\t5.6\t9\tM\n100259\t3.6\t3\tM\n100262\t3.9\t4\tF\n100265\t4.7\t4\tM\n100266\t5.5\t7\tM\n100267\t5.7\t6\tM\n100269\t5.1\t5\tF\n100271\t4.3\t5\tM\n100274\t4.3\t5\tM\n100276\t6.1\t7\tM\n100277\t5.7\t6\tF\n100285\t4.1\t4\tF\n100287\t2.9\t3\tM\n100290\t5.1\t6\tM\n100292\t1.9\t2\tM\n100293\t4.5\t5\tM\n100295\t5.8\t6\tM\n100297\t1.7\t1\tM\n100298\t3.5\t3\tM\n100301\t5.6\t7\tF\n100303\t3.9\t4\tF\n100304\t3.2\t3\tF\n100307\t2.1\t2\tF\n100308\t2.1\t4\tF\n100312\t3.4\t3\tM\n100319\t2.1\t3\tM\n100320\t2\t2\tF\n100324\t3.5\t4\tF\n100325\t3.2\t4\tF\n100327\t3.1\t3\tF\n100328\t5.4\t5\tM\n100331\t3.1\t3\tF\n100337\t2.6\t2\tF\n100343\t4.5\t4\tM\n100345\t5.4\t5\tM\n100348\t3\t3\tM\n100350\t4.6\t5\tM\n100351\t5.4\t5\tM\n100352\t3.8\t4\tF\n100353\t2.8\t1\tM\n100355\t4.6\t6\tM\n100357\t4.3\t5\tM\n100358\t2.6\t3\tF\n100359\t4.2\t5\tM\n100361\t3.1\t2\tF\n100362\t1.7\t2\tF\n100363\t5.9\t10\tM\n100364\t3\t4\tF\n100368\t3.8\t3\tM\n100369\t3.2\t3\tF\n100372\t6.2\t10\tM\n100376\t4\t5\tF\n100377\t4.3\t4\tM\n100378\t4.2\t4\tM\n100380\t3.9\t4\tM\n100384\t3.4\t3\tM\n100388\t4.4\t4\tM\n100389\t4\t4\tF\n100392\t2.9\t3\tF\n100395\t5.7\t6\tF\n100396\t5.1\t7\tF\n100398\t4\t4\tM\n100401\t4.9\t6\tF\n100402\t1.6\t2\tF\n100404\t5.6\t5\tM\n100409\t5.2\t6\tF\n100410\t2.3\t4\tM\n100415\t5.9\t8\tM\n100420\t5.6\t5\tM\n100422\t5.2\t3\tF\n100425\t5.6\t6\tM\n100426\t3.2\t3\tF\n100428\t1.3\t1\tM\n100430\t5.4\t7\tF\n100431\t5.9\t7\tM\n100439\t1.9\t2\tF\n100441\t4.2\t4\tM\n100442\t6.1\t7\tM\n100443\t5.7\t7\tM\n100448\t3.4\t3\tM\n100450\t5.9\t6\tM\n100452\t3.8\t4\tF\n100453\t1.3\t1\tM\n100455\t1.7\t2\tF\n100457\t3\t2\tM\n100466\t4.7\t4\tM\n100469\t2.6\t3\tF\n100473\t5\t5\tF\n100480\t3.5\t3\tM\n100483\t3.5\t3\tM\n100488\t5.4\t6\tM\n100490\t4.2\t5\tM\n100493\t4.3\t4\tM\n100494\t5.7\t5\tM\n100501\t2.5\t3\tM\n100504\t1.4\t2\tF\n100505\t1.7\t2\tF\n100509\t2.2\t2\tM\n100512\t5.3\t6\tM\n100513\t3.6\t4\tM\n100519\t3.6\t4\tM\n100521\t4.2\t4\tM\n100524\t3.5\t5\tM\n100526\t4.1\t4\tM\n100527\t4.4\t5\tM\n100529\t5.2\t5\tF\n100533\t3.8\t4\tF\n100534\t5.4\t7\tM\n100535\t2\t1\tM\n100538\t4.8\t5\tF\n100542\t4.1\t5\tF\n100544\t5.7\t5\tM\n100548\t2.7\t3\tF\n100549\t3.2\t1\tM\n100552\t2.6\t5\tF\n100553\t3.6\t3\tM\n100555\t2.3\t2\tM\n100556\t3.2\t4\tM\n100558\t4.4\t4\tM\n100560\t5.9\t5\tM\n100563\t5.1\t6\tM\n100564\t5.6\t8\tF\n100567\t2.3\t3\tM\n100568\t1.7\t1\tF\n100575\t5.3\t5\tF\n100582\t3.5\t3\tM\n100583\t2.3\t2\tM\n100589\t2.2\t2\tM\n100591\t3.8\t4\tF\n100594\t2.8\t1\tF\n100595\t5.7\t5\tM\n100598\t5.9\t6\tM\n100599\t4\t4\tM\n100602\t5.3\t6\tM\n100605\t3.3\t1\tM\n100610\t2.9\t1\tF\n100613\t5.7\t7\tM\n100614\t1.1\t0\tM\n100615\t5.1\t4\tF\n100616\t3.8\t4\tM\n100617\t1.1\t1\tF\n100621\t5.7\t5\tM\n100622\t2.9\t3\tF\n100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model predictions vs residuals by GENDER","message":["%sql","","-- Show predictions vs. residuals in a scatter plot grouped by HOUSE_HOLD_SIZE.  ","-- Click the scatter plot icon and select CUST_GENDER as GroupBy, ","-- PRED_YRS_RESIDENCE as the \"Series to Show on X Axis\" and","-- RESIDUALS as \"Series to Show on Y Axis\".","-- You can mouse over each Gender label to see the different plot","","SELECT CUST_ID,","    round(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) PRED_YRS_RESIDENCE,","    round(round(PREDICTION(XGBOOST_REGRESSION_MODEL USING *), 1) - YRS_RESIDENCE,2) RESIDUALS,","    CUST_GENDER","FROM TEST_DATA_V;","  "],"enabled":true,"result":{"startTime":1738095122251,"interpreter":"sql.low","endTime":1738095123131,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tRESIDUALS\tCUST_GENDER\n104076\t4.5\t-3.5\tM\n100163\t4.2\t-1.8\tM\n100996\t4.3\t0.3\tM\n101286\t4.5\t0.5\tM\n103447\t4.3\t-2.7\tF\n101137\t5.3\t2.3\tF\n100017\t5.4\t0.4\tF\n102587\t5.7\t-1.3\tM\n104318\t5.7\t-1.3\tM\n103276\t6.2\t-1.8\tM\n100244\t5.2\t-0.8\tM\n103991\t5.5\t0.5\tF\n101317\t5.9\t2.9\tM\n102289\t5.9\t-0.1\tM\n102015\t5.8\t-0.2\tM\n104307\t6.1\t-4.9\tM\n103940\t5.9\t-0.1\tM\n104135\t5.9\t-2.1\tM\n102794\t5.8\t-0.2\tM\n100442\t6.1\t-0.9\tM\n103419\t5.8\t-0.2\tM\n104141\t5.9\t-0.1\tM\n102376\t6.2\t0.2\tM\n100276\t6.1\t-0.9\tM\n101499\t6.1\t-3.9\tM\n103467\t6.3\t-1.7\tM\n103767\t5.9\t-0.1\tM\n100700\t6\t0\tM\n101078\t6.3\t0.3\tM\n100219\t5.3\t0.3\tM\n102934\t5\t0\tF\n102753\t5.5\t-1.5\tM\n101593\t5.8\t-2.2\tM\n102230\t5.7\t-2.3\tM\n101797\t5.5\t0.5\tM\n103581\t5.3\t0.3\tF\n102767\t5.7\t-2.3\tM\n102516\t5.2\t0.2\tF\n102373\t5.2\t0.2\tF\n100792\t5.3\t0.3\tF\n100719\t5.2\t1.2\tF\n103139\t5.5\t0.5\tM\n104489\t5.6\t-2.4\tM\n103560\t5.6\t-2.4\tM\n102184\t5.6\t0.6\tM\n102848\t5.7\t0.7\tM\n102079\t5.7\t0.7\tM\n103073\t5.6\t-0.4\tM\n103708\t5.7\t0.7\tM\n101771\t5.4\t-1.6\tM\n102232\t5.6\t0.6\tM\n101464\t5.4\t1.4\tM\n104380\t5.1\t0.1\tF\n103253\t5\t0\tF\n103202\t5.4\t0.4\tM\n100701\t5.7\t-1.3\tM\n103615\t5.6\t-0.4\tM\n100118\t5.7\t-0.3\tM\n101822\t5.7\t0.7\tM\n104244\t5.5\t0.5\tM\n102283\t5.7\t-1.3\tM\n102333\t5.5\t0.5\tM\n103668\t5.1\t2.1\tF\n104189\t5.3\t-0.7\tM\n101041\t5.1\t0.1\tM\n100997\t5.3\t0.3\tM\n102574\t4.8\t2.8\tF\n101838\t5.3\t0.3\tM\n101816\t5\t1\tM\n100015\t4.8\t-1.2\tM\n104019\t4.9\t2.9\tM\n103112\t4.9\t0.9\tM\n100215\t4.7\t0.7\tM\n100252\t3.8\t1.8\tF\n103900\t4.2\t-1.8\tM\n100378\t4.2\t0.2\tM\n102173\t4.3\t0.3\tM\n101894\t4.4\t-0.6\tM\n100388\t4.4\t0.4\tM\n100919\t4.1\t0.1\tM\n103857\t4.3\t2.3\tM\n104306\t4.1\t-0.9\tM\n101017\t4.1\t0.1\tM\n103755\t4\t1\tF\n103599\t4.3\t0.3\tM\n101885\t4.1\t-0.9\tF\n101753\t3.9\t-1.1\tF\n100558\t4.4\t0.4\tM\n101904\t4.2\t0.2\tM\n100986\t4.2\t-0.8\tM\n104091\t4.2\t-0.8\tM\n103025\t4.1\t0.1\tF\n103083\t4.5\t-0.5\tM\n101404\t4.2\t-0.8\tM\n101534\t4.2\t0.2\tM\n103414\t4\t2\tF\n101684\t4.3\t-0.7\tM\n101624\t4.1\t0.1\tM\n103758\t4.2\t0.2\tM\n104475\t4.2\t-0.8\tM\n103905\t4.2\t0.2\tM\n101475\t4.2\t0.2\tM\n102595\t4.4\t-0.6\tM\n104319\t3.8\t-0.2\tF\n101430\t3.9\t-1.1\tF\n102738\t4.1\t0.1\tM\n101108\t4\t-1\tF\n101938\t3.9\t-0.1\tF\n103478\t4.1\t0.1\tM\n102842\t4.1\t0.1\tM\n103127\t4.3\t0.3\tM\n102897\t4.2\t0.2\tM\n102076\t4.3\t0.3\tM\n104393\t4.1\t0.1\tM\n101173\t4.1\t1.1\tM\n102821\t3.6\t-0.4\tM\n100783\t3.5\t0.5\tM\n101573\t3.5\t0.5\tM\n102226\t3.7\t-2.3\tM\n102350\t3.2\t0.2\tF\n103503\t3.3\t0.3\tF\n100097\t3\t0\tF\n101170\t3.6\t0.6\tM\n100860\t3.2\t-1.8\tF\n102432\t3.1\t0.1\tF\n103577\t3\t1\tF\n103192\t3.2\t0.2\tF\n101955\t3.5\t0.5\tM\n101386\t3.7\t0.7\tM\n100072\t3.4\t0.4\tM\n100747\t3.3\t0.3\tM\n100384\t3.4\t0.4\tM\n103200\t3.1\t0.1\tF\n102465\t3.3\t-0.7\tM\n101107\t3.4\t0.4\tM\n101372\t3.6\t-1.4\tM\n102129\t3.6\t0.6\tM\n101968\t3.8\t0.8\tM\n100304\t3.2\t0.2\tF\n102162\t3\t1\tF\n102218\t3.5\t0.5\tM\n100298\t3.5\t0.5\tM\n102534\t3.3\t-0.7\tM\n102005\t3.3\t0.3\tM\n104271\t3.3\t0.3\tM\n103705\t3.3\t0.3\tM\n101414\t3.2\t0.2\tM\n104405\t3.2\t2.2\tM\n101531\t3.2\t0.2\tM\n104011\t3\t1\tM\n102458\t3.2\t-1.8\tM\n103513\t2.7\t-0.3\tF\n101893\t3\t0\tM\n103208\t2.7\t-0.3\tF\n100715\t2.8\t-0.2\tF\n102338\t3\t0\tM\n101395\t3.2\t0.2\tM\n103993\t3\t0\tM\n102425\t2.9\t-0.1\tF\n102355\t2.9\t-0.1\tM\n101245\t2.8\t-0.2\tF\n102132\t2.8\t-0.2\tF\n101005\t2.7\t-0.3\tF\n101140\t2.7\t-0.3\tF\n103522\t3\t0\tM\n100916\t3\t0\tM\n100799\t3\t0\tM\n102900\t2.7\t-0.3\tM\n102758\t2.6\t-0.4\tF\n101253\t2.9\t-0.1\tM\n100225\t2.3\t0.3\tM\n100127\t1.9\t-0.1\tF\n102225\t2.3\t0.3\tM\n101379\t2.3\t0.3\tM\n100320\t2\t0\tF\n101276\t2\t0\tF\n101869\t1.9\t0.9\tF\n100439\t1.9\t-0.1\tF\n102092\t2.3\t0.3\tM\n103996\t2.3\t0.3\tM\n101486\t2.2\t0.2\tM\n100201\t2.1\t-2.9\tM\n100637\t1.9\t0.9\tF\n101888\t2\t0\tF\n101923\t2.3\t0.3\tM\n100211\t2.3\t0.3\tM\n103032\t2.2\t0.2\tM\n100048\t2.2\t0.2\tM\n102530\t2.3\t0.3\tM\n100937\t2.3\t-1.7\tM\n101360\t2.3\t0.3\tM\n104448\t2.3\t0.3\tM\n101262\t2\t0\tF\n102554\t2.3\t0.3\tM\n100307\t2.1\t0.1\tF\n103241\t2.1\t1.1\tM\n100675\t2.2\t1.2\tM\n103610\t2.2\t0.2\tM\n100194\t1.9\t-0.1\tF\n100929\t1.9\t-0.1\tF\n101051\t1.8\t-0.2\tF\n101588\t2\t1\tM\n101284\t1.8\t-0.2\tF\n103089\t2.1\t1.1\tM\n100886\t1.9\t-0.1\tM\n100505\t1.7\t-0.3\tF\n103881\t1.7\t0.7\tF\n103121\t1.7\t-0.3\tF\n104238\t1.7\t0.7\tF\n103946\t2\t0\tM\n103629\t1.3\t-0.7\tF\n100086\t1.3\t0.3\tF\n101669\t1.3\t0.3\tF\n102952\t1.5\t0.5\tM\n101908\t1.6\t-0.4\tM\n102761\t1\t-1\tF\n101204\t1.3\t0.3\tM\n102375\t1.1\t0.1\tF\n100428\t1.3\t0.3\tM\n101064\t1.4\t0.4\tM\n101525\t1.1\t0.1\tF\n102604\t1.3\t-0.7\tM\n102996\t1.1\t0.1\tM\n102547\t0.8\t0.8\tF\n100910\t2.4\t-1.6\tM\n102451\t5.1\t-4.9\tM\n100235\t4.5\t-2.5\tM\n104001\t4.2\t0.2\tF\n102020\t4.5\t2.5\tM\n103333\t5.7\t0.7\tM\n1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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Statistical Functions","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Statistical Functions","","In this notebook, you will use the SH schema data to to explore descriptive and comparative statistics performed inside Oracle Autonomous Database. For the machine learning examples, please see companion notebooks.","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737737630426,"interpreter":"md.low","endTime":1737737630867,"results":[{"message":"<h1 id=\"oml4sql-statistical-functions\">OML4SQL Statistical Functions<\/h1>\n<p>In this notebook, you will use the SH schema data to to explore descriptive and comparative statistics performed inside Oracle Autonomous Database. For the machine learning examples, please see companion notebooks.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/statistics-5663173.jpg\" alt=\"OML Notebooks\" width=\"270\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1737737631355,"interpreter":"md.low","endTime":1737737631820,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/statistics-5663173.jpg\" alt=\"OML Notebooks\" width=\"270\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/sql-statistical-functions.html\" target=\"_blank\">Statistical Function Documentation<\/a>"],"enabled":true,"result":{"startTime":1737737632300,"interpreter":"md.low","endTime":1737737632776,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/sql-statistical-functions.html\" target=\"_blank\">Statistical Function 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total\t52806\t\n52783\tMY\tMalaysia\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52769\tSG\tSingapore\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52791\tZA\tSouth Africa\tAfrica\t52792\tAfrica\t52800\tWorld total\t52806\t\n52774\tAU\tAustralia\tAustralia\t52794\tOceania\t52805\tWorld total\t52806\t\n52785\tNZ\tNew Zealand\tAustralia\t52794\tOceania\t52805\tWorld total\t52806\t\n52787\tSA\tSaudi Arabia\tMiddle East\t52796\tMiddle East\t52804\tWorld total\t52806\t\n52786\tPL\tPoland\tEastern Europe\t52795\tEurope\t52803\tWorld total\t52806\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display SH.CUSTOMERS data","message":["%sql","","SELECT * FROM SH.CUSTOMERS","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737737634195,"interpreter":"sql.low","endTime":1737737634676,"results":[{"message":"CUST_ID\tCUST_FIRST_NAME\tCUST_LAST_NAME\tCUST_GENDER\tCUST_YEAR_OF_BIRTH\tCUST_MARITAL_STATUS\tCUST_STREET_ADDRESS\tCUST_POSTAL_CODE\tCUST_CITY\tCUST_CITY_ID\tCUST_STATE_PROVINCE\tCUST_STATE_PROVINCE_ID\tCOUNTRY_ID\tCUST_MAIN_PHONE_NUMBER\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tCUST_EMAIL\tCUST_TOTAL\tCUST_TOTAL_ID\tCUST_SRC_ID\tCUST_EFF_FROM\tCUST_EFF_TO\tCUST_VALID\n49671\tAbigail\tRuddy\tM\t1976\tmarried\t27 North Sagadahoc Boulevard\t60332\tEde\t51442\tGelderland\t52610\t52770\t519-236-6123\tG: 130,000 - 149,999\t1500\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n3228\tAbigail\tRuddy\tM\t1964\t\t37 West Geneva Street\t55406\tHoofddorp\t51669\tNoord-Holland\t52683\t52770\t327-194-5008\tG: 130,000 - 149,999\t7000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tA\n6783\tAbigail\tRuddy\tM\t1942\tsingle\t47 Toa Alta Road\t34077\tSchimmert\t52297\tLimburg\t52646\t52770\t288-613-9676\tG: 130,000 - 149,999\t11000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n10338\tAbigail\tRuddy\tM\t1977\tmarried\t47 South Kanabec Road\t72996\tScheveningen\t52296\tZuid-Holland\t52771\t52770\t222-269-1259\tG: 130,000 - 149,999\t1500\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n13894\tAbigail\tRuddy\tM\t1949\t\t57 North 3rd Drive\t67644\tJoinville\t51698\tSanta Catarina\t52734\t52775\t675-133-2226\tG: 130,000 - 149,999\t9000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n17449\tAbigail\tRuddy\tM\t1950\tsingle\t67 East Mcintosh Avenue\t83786\tNagoya\t51971\tAichi\t52543\t52782\t183-207-2933\tG: 130,000 - 149,999\t9000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tA\n21005\tAbigail\tRuddy\tM\t1946\tmarried\t77 Bradford Avenue\t52773\tSantos\t52293\tSao Paulo\t52735\t52775\t129-379-7148\tG: 130,000 - 149,999\t3000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n24561\tAbigail\tRuddy\tM\t1978\t\t77 North Packard Avenue\t37400\tYokohama\t52526\tKanagawa\t52634\t52782\t689-236-7611\tG: 130,000 - 149,999\t7000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tA\n28116\tAbigail\tRuddy\tM\t1949\tsingle\t87 West Coshocton Avenue\t71349\tHaarlem\t51644\tNoord-Holland\t52683\t52770\t208-194-6025\tG: 130,000 - 149,999\t11000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n31671\tAbigail\tRuddy\tM\t1951\tmarried\t97 Sagadahoc Avenue\t55056\tBolton\t51205\tEngland - Greater Manchester\t52590\t52789\t391-613-8000\tG: 130,000 - 149,999\t1500\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display mode value for CUST_MARITAL_STATUS","message":["%sql ","","SELECT STATS_MODE(CUST_MARITAL_STATUS) ","FROM 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SH.CUSTOMERS;"],"enabled":true,"result":{"startTime":1737737636118,"interpreter":"sql.low","endTime":1737737636649,"results":[{"message":"MEDIAN(CUST_CREDIT_LIMIT)\n7000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display median CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS in descendig order","message":["%sql ","","SELECT CUST_MARITAL_STATUS, MEDIAN(CUST_CREDIT_LIMIT) ","FROM SH.CUSTOMERS ","GROUP BY CUST_MARITAL_STATUS","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737737637107,"interpreter":"sql.low","endTime":1737737637601,"results":[{"message":"CUST_MARITAL_STATUS\tMEDIAN(CUST_CREDIT_LIMIT)\nDivorc.\t9000\nMabsent\t9000\nMar-AF\t11000\nMarried\t9000\nNeverM\t9000\nSepar.\t9000\nWidowed\t7000\ndivorced\t7000\nmarried\t5000\nsingle\t7000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display mean CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS in descending order","message":["%sql ","SELECT CUST_MARITAL_STATUS, ROUND(AVG(CUST_CREDIT_LIMIT),2)","FROM SH.CUSTOMERS ","GROUP BY CUST_MARITAL_STATUS ","ORDER BY AVG(CUST_CREDIT_LIMIT) DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737737638065,"interpreter":"sql.low","endTime":1737737638537,"results":[{"message":"CUST_MARITAL_STATUS\tROUND(AVG(CUST_CREDIT_LIMIT),2)\nMar-AF\t10333.33\nSepar.\t8279.85\nMabsent\t8233.33\nNeverM\t8168.66\nMarried\t7800.64\nDivorc.\t7768.29\n\t7365.56\nWidowed\t7202.21\nwidow\t6400\nsingle\t6349.95\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"STATS_MODE with dynamic data","message":["%md","","","Note that STATS_MODE can only work with materialized tables, instead of dynamic data like a view or tables joined together. ","","In that case, we recommend using the max count. For example, in the following paragraph we want to find the mode of the *COUNTRY_NAME*, which is obtained from joining two tables. In this case, it is better to achieve that by taking the max count. "],"enabled":true,"result":{"startTime":1737737639040,"interpreter":"md.low","endTime":1737737639512,"results":[{"message":"<p>Note that STATS_MODE can only work with materialized tables, instead of dynamic data like a view or tables joined together.<\/p>\n<p>In that case, we recommend using the max count. For example, in the following paragraph we want to find the mode of the <em>COUNTRY_NAME<\/em>, which is obtained from joining two tables. In this case, it is better to achieve that by taking the max count.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find mode of COUNTRY_NAME, i.e., country with most customers","message":["%sql","SELECT COUNTRY_NAME, RANK","FROM (","  SELECT COUNTRY_NAME,  ","         ROW_NUMBER() OVER( ORDER BY COUNT DESC) RANK","  FROM (","    SELECT COUNTRY_NAME, COUNT(*) COUNT","    FROM SH.CUSTOMERS CUST, SH.COUNTRIES CO","    WHERE  CUST.COUNTRY_ID = CO.COUNTRY_ID","    GROUP BY COUNTRY_NAME ) A ) B","WHERE RANK =1"],"enabled":true,"result":{"startTime":1737737640003,"interpreter":"sql.low","endTime":1737737640513,"results":[{"message":"COUNTRY_NAME\tRANK\nUnited States of America\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display counts of COUNTRY_NAME to highlight mode visually","message":["%sql","","SELECT COUNTRY_NAME, COUNT(*) COUNT","FROM SH.CUSTOMERS CUST, SH.COUNTRIES CO","WHERE  CUST.COUNTRY_ID = CO.COUNTRY_ID","GROUP BY COUNTRY_NAME","ORDER BY COUNT DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737737641003,"interpreter":"sql.low","endTime":1737737641470,"results":[{"message":"COUNTRY_NAME\tCOUNT\nUnited States of America\t18520\nGermany\t8173\nItaly\t7780\nUnited Kingdom\t7557\nFrance\t3833\nSpain\t2039\nCanada\t2010\nBrazil\t832\nAustralia\t831\nChina\t712\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check 1st, 2nd, and 3rd quantile of amount sold - PERCENTILE_CONT resolves ties by interpolation. PERCENTILE_DISC outputs first value when seeing a tie","message":["%sql","SELECT PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"First Quantile cont\",","       PERCENTILE_DISC(0.25) WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"First Quantile disc\",","       PERCENTILE_CONT(0.5)  WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"Second Quantile cont\",","       PERCENTILE_DISC(0.5)  WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"Second Quantile disc\",","       PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"Third Quantile cont\",","       PERCENTILE_DISC(0.75) WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"Third Quantile disc\"","FROM SH.SALES"],"enabled":true,"result":{"startTime":1737737641953,"interpreter":"sql.low","endTime":1737737642853,"results":[{"message":"First Quantile cont\tFirst Quantile disc\tSecond Quantile cont\tSecond Quantile disc\tThird Quantile cont\tThird Quantile disc\n17.38\t17.38\t34.24\t34.24\t53.89\t53.89\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using a t-test","message":["%md","#### Use SQL t-test function to perform an in-database t-test with SALES and CUSTOMERS tables","","See more information at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html\" target=\"_blank\">STAT_T_TEST<\/a>.","","The t-test measures the significance of a difference of means. You use it to compare the means of two groups or the means of one group with a constant. A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution under the null hypothesis. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a Normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's t distribution. ","For more details on the background of t-tests , see <a href=\"https://en.wikipedia.org/wiki/Student%27s_t-test\" target=\"_blank\">Student t-test<\/a>.","","Hint: P_Values < 0.05 show statistically significantly differences (over 95% confidence) in the amounts purchased by men vs. women"],"enabled":true,"result":{"startTime":1737737643317,"interpreter":"md.low","endTime":1737737643770,"results":[{"message":"<h4 id=\"use-sql-t-test-function-to-perform-an-in-database-t-test-with-sales-and-customers-tables\">Use SQL t-test function to perform an in-database t-test with SALES and CUSTOMERS tables<\/h4>\n<p>See more information at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html\" target=\"_blank\">STAT_T_TEST<\/a>.<\/p>\n<p>The t-test measures the significance of a difference of means. You use it to compare the means of two groups or the means of one group with a constant. A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution under the null hypothesis. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a Normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's t distribution.\nFor more details on the background of t-tests , see <a href=\"https://en.wikipedia.org/wiki/Student%27s_t-test\" target=\"_blank\">Student t-test<\/a>.<\/p>\n<p>Hint: P_Values &lt; 0.05 show statistically significantly differences (over 95% confidence) in the amounts purchased by men vs. women<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare average SOLD_TO_MEN versus average SOLD_TO_WOMEN grouped by INCOME_LEVEL - determine significance","message":["%sql ","","SELECT SUBSTR(CUST_INCOME_LEVEL,1,22) INCOME_LEVEL,  ","       ROUND(AVG(DECODE(CUST_GENDER,'M',AMOUNT_SOLD,NULL)),2) SOLD_TO_MEN,  ","\t   ROUND(AVG(DECODE(CUST_GENDER,'F',AMOUNT_SOLD,NULL)),2) SOLD_TO_WOMEN,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD, 'STATISTIC','F'),2) T_OBSERVED,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD),2) TWO_SIDED_P_VALUE","FROM SH.CUSTOMERS C, SH.SALES S  ","WHERE C.CUST_ID=S.CUST_ID  ","GROUP BY CUST_INCOME_LEVEL ","ORDER BY 1"],"enabled":true,"result":{"startTime":1737737644237,"interpreter":"sql.low","endTime":1737737645545,"results":[{"message":"INCOME_LEVEL\tSOLD_TO_MEN\tSOLD_TO_WOMEN\tT_OBSERVED\tTWO_SIDED_P_VALUE\nA: Below 30,000\t105.28\t99.43\t-1.99\t0.05\nB: 30,000 - 49,999\t102.6\t109.83\t3.04\t0\nC: 50,000 - 69,999\t105.63\t110.13\t2.36\t0.02\nD: 70,000 - 89,999\t106.63\t110.47\t2.28\t0.02\nE: 90,000 - 109,999\t103.4\t101.61\t-1.25\t0.21\nF: 110,000 - 129,999\t106.76\t105.98\t-0.6\t0.55\nG: 130,000 - 149,999\t108.88\t107.31\t-0.85\t0.39\nH: 150,000 - 169,999\t110.99\t107.15\t-1.91\t0.06\nI: 170,000 - 189,999\t102.81\t107.44\t2.18\t0.03\nJ: 190,000 - 249,999\t108.04\t115.34\t2.58\t0.01\nK: 250,000 - 299,999\t112.38\t108.2\t-1.41\t0.16\nL: 300,000 and above\t120.97\t112.22\t-2.06\t0.04\n\t107.12\t113.8\t0.69\t0.49\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare average SOLD_TO_MEN versus average SOLD_TO_WOMEN grouped by INCOME_LEVEL - determine significance","message":["%sql ","","SELECT SUBSTR(CUST_INCOME_LEVEL,1,22) INCOME_LEVEL,  ","       ROUND(AVG(DECODE(CUST_GENDER,'M',AMOUNT_SOLD,NULL)),2) SOLD_TO_MEN,  ","\t   ROUND(AVG(DECODE(CUST_GENDER,'F',AMOUNT_SOLD,NULL)),2) SOLD_TO_WOMEN,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD, 'STATISTIC','F'),2) T_OBSERVED,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD),2) TWO_SIDED_P_VALUE","FROM SH.CUSTOMERS C, SH.SALES S  ","WHERE C.CUST_ID=S.CUST_ID  ","GROUP BY CUST_INCOME_LEVEL  ","ORDER BY TWO_SIDED_P_VALUE DESC"],"enabled":true,"result":{"startTime":1737737646025,"interpreter":"sql.low","endTime":1737737647232,"results":[{"message":"INCOME_LEVEL\tSOLD_TO_MEN\tSOLD_TO_WOMEN\tT_OBSERVED\tTWO_SIDED_P_VALUE\nF: 110,000 - 129,999\t106.76\t105.98\t-0.6\t0.55\n\t107.12\t113.8\t0.69\t0.49\nG: 130,000 - 149,999\t108.88\t107.31\t-0.85\t0.39\nE: 90,000 - 109,999\t103.4\t101.61\t-1.25\t0.21\nK: 250,000 - 299,999\t112.38\t108.2\t-1.41\t0.16\nH: 150,000 - 169,999\t110.99\t107.15\t-1.91\t0.06\nA: Below 30,000\t105.28\t99.43\t-1.99\t0.05\nL: 300,000 and above\t120.97\t112.22\t-2.06\t0.04\nI: 170,000 - 189,999\t102.81\t107.44\t2.18\t0.03\nC: 50,000 - 69,999\t105.63\t110.13\t2.36\t0.02\nD: 70,000 - 89,999\t106.63\t110.47\t2.28\t0.02\nJ: 190,000 - 249,999\t108.04\t115.34\t2.58\t0.01\nB: 30,000 - 49,999\t102.6\t109.83\t3.04\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Displayed rows where INCOME_LEVEL is NULL","message":["%sql","","SELECT CUST_ID, CUST_FIRST_NAME, CUST_LAST_NAME, CUST_INCOME_LEVEL ","FROM SH.CUSTOMERS ","WHERE CUST_INCOME_LEVEL IS NULL","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737737647724,"interpreter":"sql.low","endTime":1737737648205,"results":[{"message":"CUST_ID\tCUST_FIRST_NAME\tCUST_LAST_NAME\tCUST_INCOME_LEVEL\n803\tThatcher\tTyler\t\n963\tFabian\tLusk\t\n1043\tForrest\tBanfield\t\n723\tTrevor\tNielley\t\n270\tVerna\tWalsh\t\n590\tWadswworth\tWhite\t\n1017\tWashington\tDally\t\n31\tWilla\tFitz\t\n297\tJacinta\tMathews\t\n617\tJed\tHardy\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using an F-test","message":["%md","#### Use an F-test to compare whether variances of two groups are significantly different","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html\" target=\"_blank\">STATS_F_TEST<\/a>.","","The F-test tests whether variances of two groups are significantly different. The null hypothesis is that the variances of two groups are the same. The observed value of *F* is the ratio of one variance to the other, so values very different from 1 usually indicate significant differences. ","If the p value is very small (such as p < 0.05), then we reject the null hypothesis and we say that it is statistically significant (at more than 95% confidence) that the variances of the two groups are different.","","For more details on the background of F-Tests, see <a href=\"https://en.wikipedia.org/wiki/F-test\" target=\"_blank\">F-test<\/a>.","","In the following example, we compare the credit limit in two gender groups. We use F-test to check if the variances of the two gropus are the same."],"enabled":true,"result":{"startTime":1737737648682,"interpreter":"md.low","endTime":1737737649136,"results":[{"message":"<h4 id=\"use-an-f-test-to-compare-whether-variances-of-two-groups-are-significantly-different\">Use an F-test to compare whether variances of two groups are significantly different<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html\" target=\"_blank\">STATS_F_TEST<\/a>.<\/p>\n<p>The F-test tests whether variances of two groups are significantly different. The null hypothesis is that the variances of two groups are the same. The observed value of <em>F<\/em> is the ratio of one variance to the other, so values very different from 1 usually indicate significant differences.\nIf the p value is very small (such as p &lt; 0.05), then we reject the null hypothesis and we say that it is statistically significant (at more than 95% confidence) that the variances of the two groups are different.<\/p>\n<p>For more details on the background of F-Tests, see <a href=\"https://en.wikipedia.org/wiki/F-test\" target=\"_blank\">F-test<\/a>.<\/p>\n<p>In the following example, we compare the credit limit in two gender groups. We use F-test to check if the variances of the two gropus are the same.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare variances of cust_credit_limit for men and women - determine if variances are significantly different","message":["%sql","","SELECT ROUND(VARIANCE(DECODE(CUST_GENDER, 'M', CUST_CREDIT_LIMIT, NULL)),2) VAR_MEN,","       ROUND(VARIANCE(DECODE(CUST_GENDER, 'F', CUST_CREDIT_LIMIT, NULL)),2) VAR_WOMEN,","       ROUND(STATS_F_TEST(CUST_GENDER, CUST_CREDIT_LIMIT, 'STATISTIC', 'F'),2) F_STATISTIC,","       ROUND(STATS_F_TEST(CUST_GENDER, CUST_CREDIT_LIMIT),2) TWO_SIDED_P_VALUE","  FROM SH.CUSTOMERS;"],"enabled":true,"result":{"startTime":1737737649605,"interpreter":"sql.low","endTime":1737737650119,"results":[{"message":"VAR_MEN\tVAR_WOMEN\tF_STATISTIC\tTWO_SIDED_P_VALUE\n12879896.68\t13046865.01\t1.01\t0.31\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion for F-test","message":["%md","","Result interpretation:","","The p-value is greater than 0.05, so the result above shows that the variances of the CUST_CREDIT_LIMIT for men and women has no statistical difference."],"enabled":true,"result":{"startTime":1737737650601,"interpreter":"md.low","endTime":1737737651079,"results":[{"message":"<p>Result interpretation:<\/p>\n<p>The p-value is greater than 0.05, so the result above shows that the variances of the CUST_CREDIT_LIMIT for men and women has no statistical difference.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using one way ANOVA test","message":["%md","#### Using one way ANOVA (analysis of variance) test to verify differences in means (for groups or variables) for statistical significance","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html\" target=\"_blank\">STATS_ONE_WAY_ANOVA<\/a>.","","The one-way analysis of variance function (STATS_ONE_WAY_ANOVA) tests differences in means (for groups or variables) for statistical significance by comparing two different estimates of variance. One estimate is based on the variances within each group or category. This is known as the mean squares within or mean square error. The other estimate is based on the variances among the means of the groups. This is known as the mean squares between. If the means of the groups are significantly different, then the mean squares between will be larger than expected and will not match the mean squares within. If the mean squares of the groups are consistent, then the two variance estimates will be about the same.","","For more information and background on ANOVA, see <a href=\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\" target=\"_blank\">One-way_analysis_of_variance<\/a>.","","In the following example, we apply the one way ANOVA to test for each gender, whether the amount sold varies according to the income level. "],"enabled":true,"result":{"startTime":1737737651545,"interpreter":"md.low","endTime":1737737652003,"results":[{"message":"<h4 id=\"using-one-way-anova-analysis-of-variance-test-to-verify-differences-in-means-for-groups-or-variables-for-statistical-significance\">Using one way ANOVA (analysis of variance) test to verify differences in means (for groups or variables) for statistical significance<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html\" target=\"_blank\">STATS_ONE_WAY_ANOVA<\/a>.<\/p>\n<p>The one-way analysis of variance function (STATS_ONE_WAY_ANOVA) tests differences in means (for groups or variables) for statistical significance by comparing two different estimates of variance. One estimate is based on the variances within each group or category. This is known as the mean squares within or mean square error. The other estimate is based on the variances among the means of the groups. This is known as the mean squares between. If the means of the groups are significantly different, then the mean squares between will be larger than expected and will not match the mean squares within. If the mean squares of the groups are consistent, then the two variance estimates will be about the same.<\/p>\n<p>For more information and background on ANOVA, see <a href=\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\" target=\"_blank\">One-way_analysis_of_variance<\/a>.<\/p>\n<p>In the following example, we apply the one way ANOVA to test for each gender, whether the amount sold varies according to the income level.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one way ANOVA test to different income levels and the amount sold, for each gender group.","message":["%sql","","SELECT CUST_GENDER,","       ROUND(STATS_ONE_WAY_ANOVA(CUST_INCOME_LEVEL, AMOUNT_SOLD, 'MEAN_SQUARES_BETWEEN'),2) MEAN_SQUARES_BETWEEN ,","       ROUND(STATS_ONE_WAY_ANOVA(CUST_INCOME_LEVEL, AMOUNT_SOLD, 'MEAN_SQUARES_WITHIN'),2) MEAN_SQUARES_WITHIN,","       STATS_ONE_WAY_ANOVA(CUST_INCOME_LEVEL, AMOUNT_SOLD, 'SIG') P_VALUE","  FROM SH.CUSTOMERS C, SH.SALES S","  WHERE C.CUST_ID = S.CUST_ID","  GROUP BY CUST_GENDER;"],"enabled":true,"result":{"startTime":1737737652477,"interpreter":"sql.low","endTime":1737737653463,"results":[{"message":"CUST_GENDER\tMEAN_SQUARES_BETWEEN\tMEAN_SQUARES_WITHIN\tP_VALUE\nF\t379803.89\t67878.25\t0.0000000047839510702026533\nM\t624317.68\t67228.52\t0.000000000000000067139449119250277\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of one way ANOVA test","message":["%md","Result interpretation:","","From ANOVA test result, we can see that for each gender group, the mean value of the amount sold is significantly different across income levels. "],"enabled":true,"result":{"startTime":1737737653948,"interpreter":"md.low","endTime":1737737654408,"results":[{"message":"<p>Result interpretation:<\/p>\n<p>From ANOVA test result, we can see that for each gender group, the mean value of the amount sold is significantly different across income levels.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare a view that consists of the gender and the amount sold","message":["%sql","","CREATE OR REPLACE VIEW CUST_GENDER_AMOUNT_SOLD_V AS","SELECT CUST_GENDER, AMOUNT_SOLD","  FROM SH.CUSTOMERS C, SH.SALES S","  WHERE C.CUST_ID = S.CUST_ID"],"enabled":true,"result":{"startTime":1737737654882,"interpreter":"sql.low","endTime":1737737655346,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from CUST_GENDER_AMOUNT_SOLD_V","message":["%sql","","SELECT * FROM CUST_GENDER_AMOUNT_SOLD_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1737737655825,"interpreter":"sql.low","endTime":1737737656290,"results":[{"message":"CUST_GENDER\tAMOUNT_SOLD\nF\t1205.99\nM\t1250.25\nM\t1250.25\nF\t1250.25\nM\t1250.25\nM\t1250.25\nM\t1250.25\nF\t1250.25\nM\t1250.25\nM\t1210.21\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Kolmogorov-Smirnov test","message":["%md","","#### Use a Kolmogorov-Smirnov test to compare whether two samples come from the same population","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html\" target=\"_blank\">STATS_KS_TEST<\/a>.","","Kolmogorov-Smirnov test compares two samples to test whether they are from the same population or from populations that have the same distribution. It does not assume that the population from which the samples were taken is normally distributed. The null hypothesis is that the two groups of data come from the same population or distribution. If the p-value < 0.05, then we reject the null hypothesis and determine that the two groups of data come from the different populations with more than 95% confidence.","","For more details and background on K-S tests, see <a href=\"https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test\" target=\"_blank\">Kolmogorov-Smirnov_test<\/a>.","","In the following example, we check whether the distributions of the amount sold by two gender groups is different in distribution with statistical significance."],"enabled":true,"result":{"startTime":1737737656755,"interpreter":"md.low","endTime":1737737657205,"results":[{"message":"<h4 id=\"use-a-kolmogorov-smirnov-test-to-compare-whether-two-samples-come-from-the-same-population\">Use a Kolmogorov-Smirnov test to compare whether two samples come from the same population<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html\" target=\"_blank\">STATS_KS_TEST<\/a>.<\/p>\n<p>Kolmogorov-Smirnov test compares two samples to test whether they are from the same population or from populations that have the same distribution. It does not assume that the population from which the samples were taken is normally distributed. The null hypothesis is that the two groups of data come from the same population or distribution. If the p-value &lt; 0.05, then we reject the null hypothesis and determine that the two groups of data come from the different populations with more than 95% confidence.<\/p>\n<p>For more details and background on K-S tests, see <a href=\"https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test\" target=\"_blank\">Kolmogorov-Smirnov_test<\/a>.<\/p>\n<p>In the following example, we check whether the distributions of the amount sold by two gender groups is different in distribution with statistical significance.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Kolmogorov-Smirnov test - determine if distributions of amount sold by two gender groups are statistically different","message":["%sql","","SELECT ROUND(STATS_KS_TEST(CUST_GENDER, AMOUNT_SOLD, 'STATISTIC'),5) KS_STATISTIC,","       ROUND(STATS_KS_TEST(CUST_GENDER, AMOUNT_SOLD),5) P_VALUE","  FROM CUST_GENDER_AMOUNT_SOLD_V"],"enabled":true,"result":{"startTime":1737737657685,"interpreter":"sql.low","endTime":1737737658589,"results":[{"message":"KS_STATISTIC\tP_VALUE\n0.00384\t0.00408\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of   Kolmogorov-Smirnov test","message":["%md","","Result interpretation:"," ","Since the p-value < 0.05, we reject the null hypothesis that the distributions of two gender groups are the same. Kolmogorov-Smirnov test result shows that the difference of distributions of two gender groups is significant at the more than 95% confidence. "],"enabled":true,"result":{"startTime":1737737659069,"interpreter":"md.low","endTime":1737737659598,"results":[{"message":"<p>Result interpretation:<\/p>\n<p>Since the p-value &lt; 0.05, we reject the null hypothesis that the distributions of two gender groups are the same. Kolmogorov-Smirnov test result shows that the difference of distributions of two gender groups is significant at the more than 95% confidence.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Mann Whitney test","message":["%md","","","#### Use a Mann Whitney test to check whether two populations have the same distribution function","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html\" target=\"_blank\">STATS_MW_TEST<\/a>.","","For more details and background on the Mann Whitney test, see <a href=\"https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test\" target=\"_blank\">Mann-Whitney_U_test<\/a>.","","It compares two independent samples to test the null hypothesis that two populations have the same distribution function against the alternative hypothesis that the two distribution functions are different."],"enabled":true,"result":{"startTime":1737737660090,"interpreter":"md.low","endTime":1737737660567,"results":[{"message":"<h4 id=\"use-a-mann-whitney-test-to-check-whether-two-populations-have-the-same-distribution-function\">Use a Mann Whitney test to check whether two populations have the same distribution function<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html\" target=\"_blank\">STATS_MW_TEST<\/a>.<\/p>\n<p>For more details and background on the Mann Whitney test, see <a href=\"https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test\" target=\"_blank\">Mann-Whitney_U_test<\/a>.<\/p>\n<p>It compares two independent samples to test the null hypothesis that two populations have the same distribution function against the alternative hypothesis that the two distribution functions are different.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Mann Whitney Test - determine if distributions of amount sold by two gender groups are statistically different","message":["%sql","","SELECT ROUND(STATS_MW_TEST(CUST_GENDER, CUST_CREDIT_LIMIT, 'STATISTIC'),2) Z_STATISTIC,","       ROUND(STATS_MW_TEST(CUST_GENDER, CUST_CREDIT_LIMIT, 'TWO_SIDED_SIG', 'F'),2) TWO_SIDED_P_VALUE","  FROM SH.CUSTOMERS C, SH.SALES S","  WHERE C.CUST_ID = S.CUST_ID;"],"enabled":true,"result":{"startTime":1737737661045,"interpreter":"sql.low","endTime":1737737661665,"results":[{"message":"Z_STATISTIC\tTWO_SIDED_P_VALUE\n-0.78\t0.43\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of Mann Whitney Test","message":["%md","","Result interpretation:","","Since the p-value is much larger than 0.05, we do not reject the hypothesis that the distributions of the amount sold in the two groups is the same. This means that the difference observed from the data is not significant. "],"enabled":true,"result":{"startTime":1737737662172,"interpreter":"md.low","endTime":1737737662658,"results":[{"message":"<p>Result interpretation:<\/p>\n<p>Since the p-value is much larger than 0.05, we do not reject the hypothesis that the distributions of the amount sold in the two groups is the same. This means that the difference observed from the data is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view that contains income level and the amount sold to men and women","message":["%sql","","CREATE OR REPLACE VIEW INCOME_GENDER_SOLD_V AS","SELECT SUBSTR(CUST_INCOME_LEVEL,1,22) INCOME_LEVEL,  ","\tROUND(AVG(DECODE(CUST_GENDER,'M',AMOUNT_SOLD,NULL)),2) SOLD_TO_MEN,  ","\tROUND(AVG(DECODE(CUST_GENDER,'F',AMOUNT_SOLD,NULL)),2) SOLD_TO_WOMEN","    FROM SH.CUSTOMERS C, SH.SALES S  ","    WHERE C.CUST_ID=S.CUST_ID  ","    AND SUBSTR(CUST_INCOME_LEVEL,1,22) IS NOT NULL","    GROUP BY CUST_INCOME_LEVEL"],"enabled":true,"result":{"startTime":1737737663144,"interpreter":"sql.low","endTime":1737737663659,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the view","message":["%sql","","SELECT * ","FROM INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1737737664130,"interpreter":"sql.low","endTime":1737737664824,"results":[{"message":"INCOME_LEVEL\tSOLD_TO_MEN\tSOLD_TO_WOMEN\nE: 90,000 - 109,999\t103.4\t101.61\nJ: 190,000 - 249,999\t108.04\t115.34\nD: 70,000 - 89,999\t106.63\t110.47\nL: 300,000 and above\t120.97\t112.22\nC: 50,000 - 69,999\t105.63\t110.13\nG: 130,000 - 149,999\t108.88\t107.31\nH: 150,000 - 169,999\t110.99\t107.15\nB: 30,000 - 49,999\t102.6\t109.83\nI: 170,000 - 189,999\t102.81\t107.44\nF: 110,000 - 129,999\t106.76\t105.98\nA: Below 30,000\t105.28\t99.43\nK: 250,000 - 299,999\t112.38\t108.2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Wilcoxon Signed Rank Test","message":["%md","","#### Use Wilcoxon Signed Rank test to determine whether the median of the differences between the samples are the same","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html\" target=\"_blank\">STATS_WSR_TEST<\/a>.","","Wilcoxon Signed Ranks test of paired samples to determine whether the median of the differences between the samples is significantly different from zero. The absolute values of the differences are ordered and assigned ranks. Then the null hypothesis states that the sum of the ranks of the positive differences is equal to the sum of the ranks of the negative differences.","","For more information and background on Wilcoxon Signed Rank tests, see <a href=\"https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test\" target=\"_blank\">Wilcoxon_signed-rank_test<\/a>.","","In the following example, we go through each income level and obtain average amount sold to each gender group. We are interested to know whether there is a difference between the amount sold to men or women for each income level. WSR test focuses on the median of the differences of the amount sold in the two gender groups and do a hypothesis testing on whether the median is significantly different. The null hypothesis is that the median is the same. "],"enabled":true,"result":{"startTime":1737737665328,"interpreter":"md.low","endTime":1737737665791,"results":[{"message":"<h4 id=\"use-wilcoxon-signed-rank-test-to-determine-whether-the-median-of-the-differences-between-the-samples-are-the-same\">Use Wilcoxon Signed Rank test to determine whether the median of the differences between the samples are the same<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html\" target=\"_blank\">STATS_WSR_TEST<\/a>.<\/p>\n<p>Wilcoxon Signed Ranks test of paired samples to determine whether the median of the differences between the samples is significantly different from zero. The absolute values of the differences are ordered and assigned ranks. Then the null hypothesis states that the sum of the ranks of the positive differences is equal to the sum of the ranks of the negative differences.<\/p>\n<p>For more information and background on Wilcoxon Signed Rank tests, see <a href=\"https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test\" target=\"_blank\">Wilcoxon_signed-rank_test<\/a>.<\/p>\n<p>In the following example, we go through each income level and obtain average amount sold to each gender group. We are interested to know whether there is a difference between the amount sold to men or women for each income level. WSR test focuses on the median of the differences of the amount sold in the two gender groups and do a hypothesis testing on whether the median is significantly different. The null hypothesis is that the median is the same.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Wilcoxon Signed Ranks Test - determine if median of difference between amount sold to men and amount sold to women is statistically different from zero ","message":["%sql","","SELECT ROUND(STATS_WSR_TEST(SOLD_TO_MEN, SOLD_TO_WOMEN, 'STATISTIC'),2) Z_STATISTIC,","       ROUND(STATS_WSR_TEST(SOLD_TO_MEN, SOLD_TO_WOMEN, 'TWO_SIDED_SIG'),2) TWO_SIDED_P_VALUE","  FROM INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1737737666261,"interpreter":"sql.low","endTime":1737737666852,"results":[{"message":"Z_STATISTIC\tTWO_SIDED_P_VALUE\n-0.12\t0.91\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of Wilcoxon Signed Rank Test","message":["%md","","","The p-value is much greater than 0.05. We do not reject the null hypothesis. This means that the difference between the median of the amount sold to two gender groups is not significant. "],"enabled":true,"result":{"startTime":1737737667382,"interpreter":"md.low","endTime":1737737667855,"results":[{"message":"<p>The p-value is much greater than 0.05. We do not reject the null hypothesis. This means that the difference between the median of the amount sold to two gender groups is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Pearson's correlation","message":["%md","","#### Using Pearson's correlation coefficient to measure the correlation between two columns ","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html\" target=\"_blank\">CORR<\/a>.","","This is most popular correlation coefficient and widely used for measuring correlation of a set of number pairs.","For more information and background on Pearson's correlation, see <a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\" target=\"_blank\">Pearson_correlation_coefficient<\/a>."],"enabled":true,"result":{"startTime":1737737668335,"interpreter":"md.low","endTime":1737737668799,"results":[{"message":"<h4 id=\"using-pearsons-correlation-coefficient-to-measure-the-correlation-between-two-columns\">Using Pearson's correlation coefficient to measure the correlation between two columns<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html\" target=\"_blank\">CORR<\/a>.<\/p>\n<p>This is most popular correlation coefficient and widely used for measuring correlation of a set of number pairs.\nFor more information and background on Pearson's correlation, see <a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\" target=\"_blank\">Pearson_correlation_coefficient<\/a>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute Pearson's correlation between amount sold to the two gender groups","message":["%sql","","SELECT ROUND(CORR(SOLD_TO_MEN, SOLD_TO_WOMEN),2) CORRELATION_COEF","FROM INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1737737669272,"interpreter":"sql.low","endTime":1737737669870,"results":[{"message":"CORRELATION_COEF\n0.38\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Spearman's rho correlation coefficient","message":["%md","","#### Using Spearman's rho correlation coefficient to measure the correlation between two columns","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" target=\"_blank\">CORR_A<\/a>.","","Spearman's rho correlation coefficient can find the nonlinear relationship between two variables by focusing the rank of each variables. The Spearman's rho correlation also provides a significance indicator like the p-value. If p-value is small (< 0.05), then it means that there are significant corrleation between the two groups. ","","For more details and background on Spearman's rho correlation, see <a href=\"https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient\" target=\"_blank\">Spearman's_rank_correlation_coefficient<\/a>.","","In the following example, we compute Spearman's rho coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women. "],"enabled":true,"result":{"startTime":1737737670344,"interpreter":"md.low","endTime":1737737670802,"results":[{"message":"<h4 id=\"using-spearmans-rho-correlation-coefficient-to-measure-the-correlation-between-two-columns\">Using Spearman's rho correlation coefficient to measure the correlation between two columns<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" target=\"_blank\">CORR_A<\/a>.<\/p>\n<p>Spearman's rho correlation coefficient can find the nonlinear relationship between two variables by focusing the rank of each variables. The Spearman's rho correlation also provides a significance indicator like the p-value. If p-value is small (&lt; 0.05), then it means that there are significant corrleation between the two groups.<\/p>\n<p>For more details and background on Spearman's rho correlation, see <a href=\"https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient\" target=\"_blank\">Spearman's_rank_correlation_coefficient<\/a>.<\/p>\n<p>In the following example, we compute Spearman's rho coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compute Spearman's rho correlation coefficient for income level and the amount sold to men,  income level and the amount sold to women","message":["%sql","","SELECT  ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_MEN, 'COEFFICIENT'),2) MEN_COEFFICIENT,  ","        ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_MEN, 'TWO_SIDED_SIG'),2) MEN_TWO_SIDED_SIG,","        ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_WOMEN, 'COEFFICIENT'),2) WOMEN_COEFFICIENT,  ","        ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_WOMEN, 'TWO_SIDED_SIG'),2) WOMEN_TWO_SIDED_SIG","FROM  INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1737737671271,"interpreter":"sql.low","endTime":1737737671876,"results":[{"message":"MEN_COEFFICIENT\tMEN_TWO_SIDED_SIG\tWOMEN_COEFFICIENT\tWOMEN_TWO_SIDED_SIG\n0.71\t0.01\t0.38\t0.22\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of Spearman's rho correlation","message":["%md","","Result interpretation:","","For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant. "],"enabled":true,"result":{"startTime":1737737672351,"interpreter":"md.low","endTime":1737737672816,"results":[{"message":"<p>Result interpretation:<\/p>\n<p>For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Kendall's tau-b correlation","message":["%md","","","#### Using Kendall's tau-b correlation to measure the correlation between two columns","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" target=\"_blank\">CORR_A<\/a>.","","Kendall's tau-b correlation coefficient also reveals the nonlinear relationship between two variables. To calculate the coefficient, the number of concordant and discordant pairs is computed. A pair of observations is concordant if the observation with the larger x also has a larger value of y. A pair of observations is discordant if the observation with the larger x has a smaller y. ","The Kendall's tau-b also provides a significance indicator like the p-value. If p-value is small (< 0.05), then it means that there are significant corrleation between the two groups. ","For more details and background on Kendall's tau-b correlation, see <a href=\"https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient\" target=\"_blank\">Kendall_rank_correlation_coefficient<\/a>.","","In the following example, we compute Kendall's tau-b  coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women. "],"enabled":true,"result":{"startTime":1737737673300,"interpreter":"md.low","endTime":1737737673762,"results":[{"message":"<h4 id=\"using-kendalls-tau-b-correlation-to-measure-the-correlation-between-two-columns\">Using Kendall's tau-b correlation to measure the correlation between two columns<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" target=\"_blank\">CORR_A<\/a>.<\/p>\n<p>Kendall's tau-b correlation coefficient also reveals the nonlinear relationship between two variables. To calculate the coefficient, the number of concordant and discordant pairs is computed. A pair of observations is concordant if the observation with the larger x also has a larger value of y. A pair of observations is discordant if the observation with the larger x has a smaller y.\nThe Kendall's tau-b also provides a significance indicator like the p-value. If p-value is small (&lt; 0.05), then it means that there are significant corrleation between the two groups.\nFor more details and background on Kendall's tau-b correlation, see <a href=\"https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient\" target=\"_blank\">Kendall_rank_correlation_coefficient<\/a>.<\/p>\n<p>In the following example, we compute Kendall's tau-b  coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Kendall's tau-b correlation coefficient  for income level and amount sold to men, and income level and amount sold to women","message":["%sql","","SELECT ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_MEN, 'COEFFICIENT'),2) MEN_COEFFICIENT,  ","       ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_MEN, 'TWO_SIDED_SIG'),2) MEN_TWO_SIDED_SIG,","       ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_WOMEN, 'COEFFICIENT'),2) WOMEN_COEFFICIENT,  ","       ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_WOMEN, 'TWO_SIDED_SIG'),2) WOMEN_TWO_SIDED_SIG","FROM  INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1737737674234,"interpreter":"sql.low","endTime":1737737674860,"results":[{"message":"MEN_COEFFICIENT\tMEN_TWO_SIDED_SIG\tWOMEN_COEFFICIENT\tWOMEN_TWO_SIDED_SIG\n0.61\t0.01\t0.36\t0.1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion for Kendall's  tau-b correlation","message":["%md","","Result interpretation:","","For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant."],"enabled":true,"result":{"startTime":1737737675326,"interpreter":"md.low","endTime":1737737675796,"results":[{"message":"<p>Result interpretation:<\/p>\n<p>For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":{"startTime":1737737676286,"interpreter":"md.low","endTime":1737737676734,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1737737677211,"interpreter":"md.low","endTime":1737737677677,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Text Mining SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736279591609,"interpreter":"md.low","endTime":1736279592142,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Building Model using Text Mining Capability in Oracle Machine Learning ","","Oracle Machine Learning handles not only structured data, but also unstructured text data by enabling model building used automatically extracted features. By leveraging Oracle Text, OML in-database algorithms predictive features can automatically be extracted from the text column. ","","In this notebook, we build an SVM model to predict customers most likely to be positive responders to an Affinity Card loyalty program. The data comes with a text column that contains user generated comments. With a few additional specifications, the algorithm automatically uses the text column and builds the model on both the structured data and unstructured text. ","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736279592607,"interpreter":"md.low","endTime":1736279593087,"results":[{"message":"<h1 id=\"building-model-using-text-mining-capability-in-oracle-machine-learning\">Building Model using Text Mining Capability in Oracle Machine Learning<\/h1>\n<p>Oracle Machine Learning handles not only structured data, but also unstructured text data by enabling model building used automatically extracted features. By leveraging Oracle Text, OML in-database algorithms predictive features can automatically be extracted from the text column.<\/p>\n<p>In this notebook, we build an SVM model to predict customers most likely to be positive responders to an Affinity Card loyalty program. The data comes with a text column that contains user generated comments. With a few additional specifications, the algorithm automatically uses the text column and builds the model on both the structured data and unstructured text.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/ml-operations-unstructured-text.html\" target=\"_blank\">OML Text Mining<\/a>"],"enabled":true,"result":{"startTime":1736279593546,"interpreter":"md.low","endTime":1736279594008,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/ml-operations-unstructured-text.html\" target=\"_blank\">OML Text Mining<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Prepare data","message":["%script","","CREATE OR REPLACE VIEW mining_data AS","SELECT"," a.CUST_ID, a.CUST_GENDER,"," 2003-a.CUST_YEAR_OF_BIRTH AGE,"," a.CUST_MARITAL_STATUS, c.COUNTRY_NAME, "," a.CUST_INCOME_LEVEL, b.EDUCATION,"," b.OCCUPATION, b.HOUSEHOLD_SIZE, "," b.YRS_RESIDENCE, b.AFFINITY_CARD,"," b.BULK_PACK_DISKETTES, b.FLAT_PANEL_MONITOR, "," b.HOME_THEATER_PACKAGE,"," b.BOOKKEEPING_APPLICATION, b.PRINTER_SUPPLIES, "," b.Y_BOX_GAMES, b.os_doc_set_kanji, b.comments ","FROM"," sh.customers a,"," sh.supplementary_demographics b,"," sh.countries c","WHERE"," a.CUST_ID = b.CUST_ID"," AND a.country_id = c.country_id"," AND a.cust_id between 100001 and 104500;"],"enabled":true,"result":{"startTime":1736279594468,"interpreter":"script.low","endTime":1736279596684,"results":[{"message":"\nView MINING_DATA created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the view of train dataset","message":["%script","","CREATE OR REPLACE VIEW mining_train_text AS","SELECT *","FROM   mining_data","WHERE cust_id between 101501 and 103000;","/","CREATE OR REPLACE VIEW mining_test_text AS","SELECT *","FROM   mining_data","WHERE cust_id between 100001 and 101500;","/"],"enabled":true,"result":{"startTime":1736279597139,"interpreter":"script.low","endTime":1736279597614,"results":[{"message":"\nView MINING_TRAIN_TEXT created.\n\n\n---------------------------\n\n---------------------------\n\nView MINING_TEST_TEXT created.\n\n\n---------------------------\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the text column","message":["%sql","","SELECT CUST_ID, COMMENTS ","FROM    mining_train_text","WHERE   COMMENTS IS NOT NULL","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736279598078,"interpreter":"sql.low","endTime":1736279598583,"results":[{"message":"CUST_ID\tCOMMENTS\n102828\tI am not going to waste my time filling up this three page form. Lousy idea.\n101610\tGreat program.  Love the discount. Signed happy customer.\n102308\tI love the discounts. But I mostly end upbuying  things I do not need.\n102740\tAffinity card is great. I think it is a hassle to have to remember to bring it in every time though.\n101798\tThis Affinity Card is great.I am delighted that you sent me one as I 've only shopped at your store once. I was so pleased to be included in your program that I immediately purchased a new flat panel monitor.\n101940\tHow much would it cost to upgrade my computer to the latest model you advertised this week?\n102060\tThe more times that I shop at your store, the more times I am impressed.  Don't change anything\n101893\tI just purchased a computer from your store last month.  Why didn't you offer this discount then?\n102893\tIt is a good way to attract new shoppers. After shopping at your store for more than a month, I am ready to move on though. Not enough variety\n102276\tI am not going to waste my time filling up this three page form. Lousy idea.\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create policy needed to use Oracle Text","message":["%script","","BEGIN"," ctx_ddl.drop_policy('dmdemo_svm_policy');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","EXECUTE ctx_ddl.create_policy('dmdemo_svm_policy');"],"enabled":true,"result":{"startTime":1736279599033,"interpreter":"script.low","endTime":1736279602211,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create SVM Linear model based on data with text column","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('T_SVM_Clas_sample');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE"," v_setlst DBMS_DATA_MINING.SETTING_LIST;"," xformlist dbms_data_mining_transform.TRANSFORM_LIST;","","BEGIN"," v_setlst(dbms_data_mining.algo_name)              := dbms_data_mining.algo_support_vector_machines;"," v_setlst(dbms_data_mining.prep_auto)              := dbms_data_mining.prep_auto_on;"," v_setlst(dbms_data_mining.svms_kernel_function)   := dbms_data_mining.svms_linear;"," v_setlst(dbms_data_mining.svms_complexity_factor) := '100';"," v_setlst(dbms_data_mining.odms_text_policy_name)  := 'DMDEMO_SVM_POLICY';"," v_setlst(dbms_data_mining.svms_solver)            := dbms_data_mining.svms_solver_sgd;"," "," dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'comments', null, 'comments', null, 'TEXT');"," "," DBMS_DATA_MINING.CREATE_MODEL2(model_name => 'T_SVM_CLAS_SAMPLE',","                                mining_function => dbms_data_mining.classification,","                                data_query => 'select * from mining_train_text',","                                set_list => v_setlst,","                                case_id_column_name => 'cust_id',","                                target_column_name => 'affinity_card',","                                xform_list => xformlist);","END;","/ "],"enabled":true,"result":{"startTime":1736279602661,"interpreter":"script.low","endTime":1736279605637,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display Model Settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'T_SVM_CLAS_SAMPLE'","ORDER BY SETTING_NAME;","  "],"enabled":true,"result":{"startTime":1736279606090,"interpreter":"sql.low","endTime":1736279606592,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_SUPPORT_VECTOR_MACHINES\nCLAS_WEIGHTS_BALANCED\tOFF\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nODMS_TEXT_MAX_FEATURES\t3000\nODMS_TEXT_MIN_DOCUMENTS\t1\nODMS_TEXT_POLICY_NAME\tDMDEMO_SVM_POLICY\nPREP_AUTO\tON\nSVMS_COMPLEXITY_FACTOR\t100\nSVMS_CONV_TOLERANCE\t.0001\nSVMS_KERNEL_FUNCTION\tSVMS_LINEAR\nSVMS_SOLVER\tSVMS_SOLVER_SGD\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply model and compute lift","message":["%script","","BEGIN"," EXECUTE IMMEDIATE 'DROP TABLE MINING_APPLY_RESULT PURGE';"," EXECUTE IMMEDIATE 'DROP TABLE T_LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN"," DBMS_DATA_MINING.APPLY('T_SVM_CLAS_SAMPLE',","                        'mining_test_text',","                        'CUST_ID',","                        'MINING_APPLY_RESULT');"," DBMS_DATA_MINING.COMPUTE_LIFT('MINING_APPLY_RESULT',","                               'mining_test_text',","                               'CUST_ID',","                               'AFFINITY_CARD',","                               'T_LIFT_TABLE',","                               '1',","                               'PREDICTION',","                               'PROBABILITY',","                               100);","END;"],"enabled":true,"result":{"startTime":1736279607052,"interpreter":"script.low","endTime":1736279609535,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure 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attributes generated during model building - note extracted text terms from COMMENTS column","message":["%sql","","SELECT * ","FROM (SELECT target_value, attribute_name, attribute_subname, ","             attribute_value, round(coefficient,3) coefficient,","             rank() over (order by abs(coefficient) desc) rnk","      FROM DM$VLT_SVM_CLAS_SAMPLE)","WHERE rnk <= 10","ORDER BY rnk, attribute_name, attribute_subname;"],"enabled":true,"result":{"startTime":1736279611859,"interpreter":"sql.low","endTime":1736279612376,"results":[{"message":"TARGET_VALUE\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tATTRIBUTE_VALUE\tCOEFFICIENT\tRNK\n1\tEDUCATION\t\tHS-grad\t-0.707\t1\n1\tCOMMENTS\tTEXAS\t\t0.543\t2\n1\tOCCUPATION\t\tExec.\t0.485\t3\n1\tCOMMENTS\tMORE\t\t-0.468\t4\n1\tOCCUPATION\t\tOther\t-0.463\t5\n1\tCOMMENTS\tOTHER\t\t0.448\t6\n1\tCOMMENTS\tSHOPS\t\t0.448\t6\n1\tCUST_MARITAL_STATUS\t\tMarried\t0.44\t8\n1\t\t\t\t-0.431\t9\n1\tHOUSEHOLD_SIZE\t\t4-5\t0.412\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions","message":["%sql","","SELECT AFFINITY_CARD,","       PREDICTION(T_SVM_Clas_sample USING *) AS PREDICTION, ","       ROUND(PREDICTION_PROBABILITY(T_SVM_Clas_sample, 1 USING *), 4) AS PREDICTION_PROBABILITY, ","       CUST_ID, CUST_GENDER, AGE","FROM   mining_test_text","ORDER BY PREDICTION_PROBABILITY DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736279612825,"interpreter":"sql.low","endTime":1736279613789,"results":[{"message":"AFFINITY_CARD\tPREDICTION\tPREDICTION_PROBABILITY\tCUST_ID\tCUST_GENDER\tAGE\n1\t1\t0.9894\t100962\tM\t60\n0\t1\t0.9858\t100210\tM\t55\n1\t1\t0.984\t100573\tM\t59\n1\t1\t0.9833\t100668\tM\t40\n1\t1\t0.9821\t100309\tM\t52\n1\t1\t0.9815\t100544\tM\t51\n1\t1\t0.9802\t100476\tM\t51\n1\t1\t0.9796\t101010\tM\t42\n1\t1\t0.9776\t100052\tM\t46\n1\t1\t0.9741\t101108\tF\t36\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create view that contains probability score for plotting","message":["%sql","","CREATE OR REPLACE VIEW SVMTEXT_RESULT_WITH_PROB_V AS ","SELECT CUST_ID, AFFINITY_CARD, ","       PREDICTION(T_SVM_Clas_sample USING *) AS PREDICTION, ","       PREDICTION_PROBABILITY(T_SVM_Clas_sample, '1' USING *) AS PROBABILITY_OF_1 ","FROM   mining_test_text A"],"enabled":true,"result":{"startTime":1736279614259,"interpreter":"sql.low","endTime":1736279614737,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display ROC, gains and lift charts using Oracle Machine Learning for Python","message":["%python","","import pandas as pd","import oml","","import warnings","warnings.simplefilter(action='ignore', category=FutureWarning)"," ","RES_DF = oml.sync(view = 'SVMTEXT_RESULT_WITH_PROB_V')","","def evaluate_model(pred_data='', ","    settings_name={''}, ","    name='',","    target=''","    ):","    \"\"\"Build the models by passing an OML algorithm function to \"alg\"","     Pass the Settings of the model to \"settings\"","     Pass Train X, Train y and Test datasets","     Supply the target column name for evaluation","     for computing the confusion matrix with the test dataset\"\"\"","    import oml","    import numpy as np","    import matplotlib.pyplot as plt","    from sklearn.metrics import auc","    from sklearn.metrics import roc_curve","","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)"," "," # Statistics","    cf_local = conf_matrix.pull()","    TN = cf_local.iloc[0,1]","    TP = cf_local.iloc[1,2]","    FP = cf_local.iloc[0,2]","    FN = cf_local.iloc[1,1]","    TPR = TP/(TP+FN)","    FPR = FP/(FP+TN)","    TNR = TN/(TN+FP)","    FNR = FN/(FN+TP)","    Precision = TP/(TP+FP)","    Accuracy = (TP+TN)/(TP+TN+FP+FN)","    NPV = TN/(FN+TN)","    DetectionRate = TN/(FN+TN)","    BalancedAccuracy = (TPR+TNR)/2","# Estimated AUC via Triangle (not very precise) could be","# AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR","# Compute real AUC using roc_curve by loading the","# data locally and using the roc_curve() function","    pred_local = pred_data.pull()","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])","    AUC = auc(fpr, tpr)","    opt_index = np.argmax(tpr - fpr)","    FPR_OPT = fpr[opt_index]","    TPR_OPT = tpr[opt_index]","","    F1Score = 2*Precision*TPR/(Precision+TPR)","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5","# Store all statistics to export","    statistics = {'Algorithm' : name,","    'Algorithm_setting' : settings_name,","    'TN' : TN,","    'TP' : TP,","    'FP' : FP,","    'FN' : FN,","    'TPR' : TPR,","    'FPR' : FPR,","    'TNR' : TNR,","    'FNR' : FNR,","    'Precision' : Precision,","    'Accuracy' : Accuracy,","    'NPV' : NPV,","    'DetectionRate' : DetectionRate,","    'BalancedAccuracy' : BalancedAccuracy,","    'AUC' : AUC,","    'F1Score' : F1Score,","    'MathewsCorrCoef' : MathewsCorrCoef","    }","    # Nice round stats for printing to screen","    TOTAL = TP+TN+FP+FN","    TN_P = (TN/TOTAL*100).round(2)","    FP_P = (FP/TOTAL*100).round(2)","    FN_P = (FN/TOTAL*100).round(2)","    TP_P = (TP/TOTAL*100).round(2)","    # Print the output nicely on Zeppelin native Table","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+","    \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+","    \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+","    \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+","    \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+","    \"Accuracy: \"+str((Accuracy*100).round(4))+\"%\\t\"+","    \"AUC: \"+str((AUC).round(4))+\"\\t\"+","    \"F1Score: \"+str(F1Score.round(4))","    )","    ","    # Multiple Charts for Evaluation","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])","    ax1, ax2, ax3, ax4 = axes.flatten()","    fig.suptitle('Evaluation of the Model '+str(name), size=16)","    ","    # Statistics","    ax1.axis('off')","    ","    # Function to return rounded numbers if the string is float, return","    # integers otherwise and return characters if not a number","    def round_if_float(content):","        try:","            val = float(content)","        except ValueError:","            return(content)","        else:","            if val.is_integer():","                return(int(content))","            else:","                return(round(float(content),4))","","    for num, name in enumerate(statistics):","        ax1.text(0.01, ","        (-num*0.06+0.94),","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),","        ha='left', ","        va='bottom', ","        fontsize=12)","    ","    # Produce Lift Chart","    ax2.set_title('Lift Chart')","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)","    data['row_id'] = range(0,0+len(data))","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)","    lift = data.groupby('decile')[target].agg(['count','sum'])","    lift.columns = ['count', target]","    lift['decile'] = range(1,11)","    ","    data_ideal = pred_local.sort_values(by=target, ascending=False)","    data_ideal['row_id'] = range(0,0+len(data))","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])","    lift_ideal.columns = ['count', 'IDEAL']","    lift['IDEAL']=lift_ideal['IDEAL']","    ","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],","    color='darkorange', label='Ideal')","    ax2.bar(lift['decile'],lift[target]/lift['count'],","    color='blue', alpha=0.6, label='Model')","    ax2.axhline((lift[target]/lift['count']).mean(), ","    color='grey', linestyle='--', label='Avg TARGET')","    ax2.set_ylim(0,1.15)","    ax2.set_xlabel('Decile', size=13)","    ax2.set_ylabel('Percent of Actual Targets', size=13)","    # Print labels.","    for dec in lift['decile']:","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, ","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",","        ha='center', va='bottom')","    ax2.legend(loc=\"upper right\")","    ","    # Produce Gains Chart","    ax3.set_title('Distributions of Predictions')","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')","    ax3.set_xlim([0,1])","    ax3.set_xlabel('Probability of 1', size=13)","    ax3.set_ylabel('Density', size=13)","    ax3.legend(loc=\"upper right\")","    ","    # ROC curve Chart","    ax4.set_title('ROC Curve')","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')","    ax4.plot(FPR_OPT, TPR_OPT, color='orange', markersize=6)","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(TPR_OPT.round(2))+' FPR: '+str(FPR_OPT.round(2)),","    fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), ","    textcoords='data', ","    arrowprops=dict(facecolor='gray', shrink=0.1, width=2,","    connectionstyle='arc3, rad=0.3'), ","    horizontalalignment='right', verticalalignment='top')","    ax4.annotate('AUC ='+str((AUC).round(4)), xy=(0.5, 0.35), ","    xycoords='axes fraction', size=13)","    ax4.annotate('Precision ='+str((Precision).round(4)), xy=(0.45, 0.3), ","    xycoords='axes fraction', size=13)","    ax4.annotate('Recall ='+str((TPR).round(4)), xy=(0.4, 0.25), ","    xycoords='axes fraction', size=13)","    ax4.annotate('Accuracy ='+str((Accuracy).round(4)), xy=(0.35, 0.2),","    xycoords='axes fraction', size=13)","    ax4.annotate('F1 Score ='+str((F1Score).round(4)), xy=(0.3, 0.15), ","    xycoords='axes fraction', size=13)","    ax4.set_xlim([-0.02, 1.02])","    ax4.set_ylim([0.0, 1.02])","    ax4.set_xlabel('False Positive Rate', size=13)","    ax4.set_ylabel('True Positive Rate', size=13)","    ax4.legend(loc=\"lower right\")","    ","    return(statistics, pred_local)","_ = evaluate_model(pred_data=RES_DF, settings_name='Default', name='Na?ve Bayes', target='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1736279615196,"interpreter":"python.low","endTime":1736279623002,"results":[{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1076 (71.73%)\tFalse Positive: 78 (5.2%)\nACTUAL 1\tFalse Negative: 121 (8.07%)\tTrue Positive: 225 (15.0%)\nAccuracy: 86.7333%\tAUC: 0.9189\tF1Score: 0.6934\n","type":"TABLE"},{"message":"<div 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PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736279623482,"interpreter":"sql.low","endTime":1736279624956,"results":[{"message":"CUST_ID\tPRED_AFFINITY\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t0\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".058\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".053\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".022\" \n100002\t0\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".223\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".081\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".043\" \n100003\t0\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".045\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".014\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".013\" \n100004\t0\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".109\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".061\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".055\" \n100005\t1\t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".439\" \t\"CUST_INCOME_LEVEL\" actualValue=\"B: 30,000 - 49,999\" weight=\".087\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".072\" \n100006\t0\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".029\" \t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".009\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".005\" \n100007\t0\t\"OCCUPATION\" actualValue=\"Other\" weight=\".088\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".041\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".022\" \n100008\t0\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".065\" \t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".042\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".021\" \n100009\t1\t\"EDUCATION\" actualValue=\"Bach.\" weight=\".377\" \t\"&quot;COMMENTS&quot;.SENDING\" actualValue=\"5.819E-001\" weight=\".085\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"0\" weight=\".068\" \n100010\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".148\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".028\" \t\"&quot;COMMENTS&quot;.CUSTOMER\" actualValue=\"2.763E-001\" weight=\".014\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736279625409,"interpreter":"md.low","endTime":1736279625851,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Time Series ESM.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Time Series ESM.dsnb
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@@ -0,0 +1 @@
+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Time Series ESM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1734449271336,"interpreter":"md.low","endTime":1734449271786,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data","","Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1734449272238,"interpreter":"md.low","endTime":1734449272678,"results":[{"message":"<h1 id=\"forecasting-sales-using-exponential-smoothing-algorithm-for-time-series-data\">Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data<\/h1>\n<p>Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1734449273135,"interpreter":"md.low","endTime":1734449273578,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>"],"enabled":true,"result":{"startTime":1734449274032,"interpreter":"md.low","endTime":1734449274479,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a view for model building","message":["%script","","CREATE OR REPLACE VIEW ESM_SH_DATA AS  ","SELECT TIME_ID, AMOUNT_SOLD FROM SH.SALES;"],"enabled":true,"result":{"startTime":1734449274934,"interpreter":"script.low","endTime":1734449275384,"results":[{"message":"\nView ESM_SH_DATA created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the records on the new view","message":["%script","","SELECT count(*) from ESM_SH_DATA;"],"enabled":true,"result":{"startTime":1734449275848,"interpreter":"script.low","endTime":1734449276299,"results":[{"message":"COUNT(*)   \n    918843 \n\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the data","message":["%sql","","SELECT * from ESM_SH_DATA;"],"enabled":true,"result":{"startTime":1734449276748,"interpreter":"sql.low","endTime":1734449277282,"results":[{"message":"TIME_ID\tAMOUNT_SOLD\n1998-01-20 00:00:00\t1205.99\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-30 00:00:00\t1222.87\n1998-12-30 00:00:00\t1222.87\n1998-12-31 00:00:00\t1222.87\n1998-12-31 00:00:00\t1222.87\n1999-01-10 00:00:00\t1222.87\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-23 00:00:00\t925.1\n1999-01-23 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-02-05 00:00:00\t1222.87\n1999-02-05 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-23 00:00:00\t925.1\n1999-02-23 00:00:00\t925.1\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-12-10 00:00:00\t914.34\n1999-12-10 00:00:00\t914.34\n1999-12-10 00:00:00\t914.34\n1999-12-14 00:00:00\t914.34\n1999-12-14 00:00:00\t914.34\n1999-12-14 00:00:00\t914.34\n1999-12-16 00:00:00\t914.34\n1999-12-20 00:00:00\t914.34\n1999-12-20 00:00:00\t914.34\n1999-12-20 00:00:00\t914.34\n1999-12-22 00:00:00\t914.34\n1999-12-22 00:00:00\t914.34\n1999-12-22 00:00:00\t914.34\n1999-12-23 00:00:00\t914.34\n1999-12-30 00:00:00\t927.14\n1999-12-30 00:00:00\t927.14\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-15 00:00:00\t1052.99\n2000-02-15 00:00:00\t1052.99\n2000-02-15 00:00:00\t1052.99\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-03-03 00:00:00\t1043.37\n2000-03-16 00:00:00\t1043.37\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-28 00:00:00\t1037.93\n2000-04-28 00:00:00\t1037.93\n2000-04-28 00:00:00\t1037.93\n2000-04-28 00:00:00\t1037.93\n2000-05-03 00:00:00\t1060.63\n2000-05-03 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-16 00:00:00\t1060.63\n2000-05-16 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-26 00:00:00\t1060.63\n2000-05-26 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-07-02 00:00:00\t1033.21\n2000-07-02 00:00:00\t1033.21\n2000-07-02 00:00:00\t1033.21\n2000-07-02 00:00:00\t1033.21\n2000-07-13 00:00:00\t1043.5\n2000-07-13 00:00:00\t1043.5\n2000-07-13 00:00:00\t1043.5\n2000-07-17 00:00:00\t1043.5\n2000-07-17 00:00:00\t1043.5\n2000-07-17 00:00:00\t1043.5\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-23 00:00:00\t1033.21\n2000-07-23 00:00:00\t1033.21\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-10 00:00:00\t1044.53\n2000-08-10 00:00:00\t1044.53\n2000-08-10 00:00:00\t1044.53\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-09-03 00:00:00\t920.04\n2000-09-19 00:00:00\t910.52\n2000-09-19 00:00:00\t910.52\n2000-09-19 00:00:00\t910.52\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-26 00:00:00\t920.04\n2000-09-26 00:00:00\t920.04\n2000-09-26 00:00:00\t920.04\n2000-10-10 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-23 00:00:00\t1030.39\n2000-10-23 00:00:00\t1030.39\n2000-10-23 00:00:00\t1030.39\n2000-10-23 00:00:00\t1030.39\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-11-03 00:00:00\t1043.5\n2000-11-03 00:00:00\t1043.5\n2000-11-03 00:00:00\t1043.5\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-14 00:00:00\t1029.1\n2000-11-14 00:00:00\t1029.1\n2000-11-16 00:00:00\t1029.1\n2000-11-16 00:00:00\t1029.1\n2000-11-16 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-12-02 00:00:00\t983.67\n2000-12-02 00:00:00\t983.67\n2000-12-02 00:00:00\t983.67\n2000-12-02 00:00:00\t983.67\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-10 00:00:00\t983.67\n2000-12-13 00:00:00\t983.67\n2000-12-13 00:00:00\t983.67\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-16 00:00:00\t970.1\n2000-12-16 00:00:00\t970.1\n2000-12-16 00:00:00\t970.1\n2000-12-16 00:00:00\t970.1\n2000-12-17 00:00:00\t983.67\n2000-12-18 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2001-01-13 00:00:00\t1173.97\n2001-01-13 00:00:00\t1173.97\n2001-01-13 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-18 00:00:00\t1173.97\n2001-01-23 00:00:00\t1173.97\n2001-01-23 00:00:00\t1173.97\n2001-01-23 00:00:00\t1173.97\n2001-01-24 00:00:00\t1173.97\n2001-01-24 00:00:00\t1173.97\n2001-01-24 00:00:00\t1173.97\n2001-01-30 00:00:00\t1173.97\n2001-01-30 00:00:00\t1173.97\n2001-01-30 00:00:00\t1173.97\n2001-02-02 00:00:00\t1185.17\n2001-02-02 00:00:00\t1185.17\n2001-02-02 00:00:00\t1185.17\n2001-02-02 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-13 00:00:00\t1185.17\n2001-02-13 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-23 00:00:00\t1185.17\n2001-02-23 00:00:00\t1185.17\n2001-02-28 00:00:00\t1185.17\n2001-02-28 00:00:00\t1185.17\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-05-03 00:00:00\t1126.93\n2001-05-03 00:00:00\t1126.93\n2001-05-14 00:00:00\t1126.93\n2001-05-14 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-17 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-26 00:00:00\t1126.93\n2001-05-27 00:00:00\t1126.93\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-07-02 00:00:00\t1112.18\n2001-07-02 00:00:00\t1112.18\n2001-07-02 00:00:00\t1112.18\n2001-07-13 00:00:00\t1112.18\n2001-07-13 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-23 00:00:00\t1110.86\n2001-08-23 00:00:00\t1110.86\n2001-08-27 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-09-02 00:00:00\t1101.48\n2001-09-03 00:00:00\t1101.48\n2001-09-03 00:00:00\t1101.48\n2001-09-17 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-20 00:00:00\t1101.48\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Options to use when creating Time Series models","message":["%md","","### Examples of possible setting overrides for ESM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is `EXSM_INTERVAL_QTR`.","> v_setlst('EXSM_INTERVAL') := 'EXSM_INTERVAL_QTR';","    ","* Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future",">  v_setlst('EXSM_PREDICTION_STEP') := '4';","    ","* Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.","","> v_setlst('EXSM_MODEL') := 'EXSM_WINTERS';","    ","* Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.","> v_setlst('EXSM_SEASONALITY') := '4';","","* Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.     ","> v_setlst('EXSM_SETMISSING') := 'EXSM_MISS_AUTO';"],"enabled":true,"result":{"startTime":1734449277747,"interpreter":"md.low","endTime":1734449278201,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-esm\">Examples of possible setting overrides for ESM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is <code>EXSM_INTERVAL_QTR<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_INTERVAL') := 'EXSM_INTERVAL_QTR';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_PREDICTION_STEP') := '4';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_MODEL') := 'EXSM_WINTERS';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_SEASONALITY') := '4';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_SETMISSING') := 'EXSM_MISS_AUTO';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a Holt-Winters Model using quarterly parameters","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('ESM_SALES_FORECAST_1');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    ","    v_setlst('ALGO_NAME')            := 'ALGO_EXPONENTIAL_SMOOTHING';","    v_setlst('EXSM_INTERVAL')        := 'EXSM_INTERVAL_QTR'; -- accumulation int'l = quarter","    v_setlst('EXSM_PREDICTION_STEP') := '4';                 -- prediction step = 4 quarters","    v_setlst('EXSM_MODEL')           := 'EXSM_WINTERS';      -- ESM model = Holt-Winters","    v_setlst('EXSM_SEASONALITY')     := '4';                 -- seasonal cycle = 4 quarters    ","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'ESM_SALES_FORECAST_1',","        MINING_FUNCTION     => 'TIME_SERIES',","        DATA_QUERY          => 'select * from ESM_SH_DATA',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'TIME_ID',","        TARGET_COLUMN_NAME  =>'AMOUNT_SOLD');","END;"],"enabled":true,"result":{"startTime":1734449278660,"interpreter":"script.low","endTime":1734449280639,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the forecast model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = UPPER('ESM_SALES_FORECAST_1')","ORDER BY 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forecast model","message":["%sql","","SELECT NAME, round(NUMERIC_VALUE,4), STRING_VALUE ","FROM   DM$VGESM_SALES_FORECAST_1","ORDER BY NAME;"],"enabled":true,"result":{"startTime":1734449282209,"interpreter":"sql.low","endTime":1734449282688,"results":[{"message":"NAME\tROUND(NUMERIC_VALUE,4)\tSTRING_VALUE\n-2 LOG-LIKELIHOOD\t-227.4099\t\nAIC\t472.8199\t\nAICC\t502.8199\t\nALPHA\t0.7469\t\nAMSE\t254707632297.6781\t\nBETA\t0.0559\t\nBIC\t479.7732\t\nCONVERGED\t\tYES\nGAMMA\t0.0001\t\nINITIAL LEVEL\t6110213.9241\t\nINITIAL SEASON 1\t0.998\t\nINITIAL SEASON 2\t1.0231\t\nINITIAL SEASON 3\t0.9383\t\nINITIAL SEASON 4\t1.0407\t\nINITIAL 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\ No newline at end of file
diff --git a/machine-learning/notebooks-oml/sql/OML4SQL Time Series Regression ESM plus GLM XGB.dsnb b/machine-learning/notebooks-oml/sql/OML4SQL Time Series Regression ESM plus GLM XGB.dsnb
new file mode 100644
index 00000000..2d758c23
--- /dev/null
+++ b/machine-learning/notebooks-oml/sql/OML4SQL Time Series Regression ESM plus GLM XGB.dsnb	
@@ -0,0 +1 @@
+[{"layout":"zeppelin","template":"dsrgmn3y","templateConfig":"{\"visualization\":{\"filters\":[{\"_id\":1583324064459,\"type\":\"styling\",\"enabled\":true,\"conditions\":{\"operator\":\"and\",\"conditions\":[{\"property\":\"hiddenConnection\",\"operator\":\"*\",\"value\":\"\"}]},\"component\":\"edge\",\"target\":\"edge\",\"properties\":{\"colors\":[\"rgba(0, 0, 0, 0.1)\"],\"style\":[\"dashed\"],\"legendTitle\":[\"Hidden Connection\"]}},{\"_id\":1590499315755,\"type\":\"aggregation\",\"enabled\":true,\"conditions\":{\"operator\":\"and\",\"conditions\":[]},\"component\":\"vertex\",\"target\":\"vertex\",\"properties\":{},\"aggregation\":[{\"source\":\"\",\"type\":\"average\"}]}],\"version\":4}}","name":"OML4SQL Time Series Regression ESM plus GLM XGB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736280222090,"interpreter":"md.low","endTime":1736280222551,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Time Series Regression using Multiple Time Series Exponential Smoothing with GLM and XGBoost Regression (23ai feature)","","Oracle Machine Learning supports multiple time series for conveniently generating backcasts and forecasts on one or more time series in the same table or view. ","The identified \u201ctarget\u201d time series also has confidence bounds computed for it. The output of multiple time series serves as an input to other machine learning techniques, like regression. ","","Time series regression is a powerful technique that supports one or more time series as predictors, along with flag variables, like holidays or events.","","","Copyright (c) 2025 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1736280223028,"interpreter":"md.low","endTime":1736280223483,"results":[{"message":"<h1 id=\"time-series-regression-using-multiple-time-series-exponential-smoothing-with-glm-and-xgboost-regression-23ai-feature\">Time Series Regression using Multiple Time Series Exponential Smoothing with GLM and XGBoost Regression (23ai feature)<\/h1>\n<p>Oracle Machine Learning supports multiple time series for conveniently generating backcasts and forecasts on one or more time series in the same table or view.\nThe identified \u201ctarget\u201d time series also has confidence bounds computed for it. The output of multiple time series serves as an input to other machine learning techniques, like regression.<\/p>\n<p>Time series regression is a powerful technique that supports one or more time series as predictors, along with flag variables, like holidays or events.<\/p>\n<p>Copyright (c) 2025 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"," "],"enabled":true,"result":{"startTime":1736280223977,"interpreter":"md.low","endTime":1736280224430,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>"," "],"enabled":true,"result":{"startTime":1736280224901,"interpreter":"md.low","endTime":1736280225349,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Scenario","","Perform Time Series Regression using a combination of ESM, GLM, and XGBoost algorithms.","","* Create the time series dataset","* Build an ESM model using multiple time series forecasting","* Build a GLM model using the ESM result","* Explore the model views","* Compare the regression forecast to the baseline (ESM) forecast","* Build an XGBOOST model and compare to ESM forecast"],"enabled":true,"result":{"startTime":1736280225829,"interpreter":"md.low","endTime":1736280226289,"results":[{"message":"<h2 id=\"scenario\">Scenario<\/h2>\n<p>Perform Time Series Regression using a combination of ESM, GLM, and XGBoost algorithms.<\/p>\n<ul>\n<li>Create the time series dataset<\/li>\n<li>Build an ESM model using multiple time series forecasting<\/li>\n<li>Build a GLM model using the ESM result<\/li>\n<li>Explore the model views<\/li>\n<li>Compare the regression forecast to the baseline (ESM) forecast<\/li>\n<li>Build an XGBOOST model and compare to ESM forecast<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the time series dataset","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE EUSTOCK PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE EUSTOCK(","    \"DAX\" NUMBER(38,2), ","\t\"SMI\" NUMBER(38,1), ","\t\"CAC\" NUMBER(38,1), ","\t\"FTSE\" NUMBER(38,1), ","\t\"DATES\" DATE","   ) SEGMENT CREATION IMMEDIATE ","  PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 "," NOCOMPRESS LOGGING","  STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645","  PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1","  BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)","  TABLESPACE \"USERS\" ;","REM INSERTING into EUSTOCK","SET DEFINE OFF;","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.69,2062.7,1818.8,2748.7,to_date('06-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1580.64,2089.9,1812.2,2773.3,to_date('07-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1593.35,2102.9,1820.4,2781.2,to_date('08-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.28,2086,1779.9,2771.9,to_date('09-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1575.59,2085.9,1792.6,2835.7,to_date('10-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.78,2064.1,1777.4,2832.5,to_date('11-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.68,2072.7,1780.6,2816.9,to_date('12-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1574.04,2091,1772.2,2807.2,to_date('13-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.33,2120.2,1785.9,2851.6,to_date('14-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1584.14,2120.4,1787.3,2834.4,to_date('15-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.91,2117.6,1824.1,2873.8,to_date('16-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1615.98,2123.7,1854.4,2865.9,to_date('17-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1643.83,2132.2,1908.2,2862.9,to_date('18-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1646.85,2137,1904.7,2870,to_date('19-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1639.12,2134.8,1894.1,2831.3,to_date('20-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1642.8,2121.4,1893.3,2816.4,to_date('21-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1659.07,2127.5,1905.6,2834.3,to_date('22-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.64,2135.5,1912,2843,to_date('23-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.93,2144.8,1899.5,2845.9,to_date('24-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1651.6,2131,1878.2,2812.2,to_date('25-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.35,2112.9,1905,2814,to_date('26-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1670.9,2131.3,1926.5,2837.7,to_date('27-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.3,2117.8,1937.2,2840,to_date('28-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.41,2096.1,1959.2,2838.3,to_date('29-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.09,2051.5,1944.1,2818,to_date('30-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.92,2065.7,1953.4,2817,to_date('01-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1661.96,2061,1944.6,2828.7,to_date('02-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.02,2100.6,1983.7,2868,to_date('03-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4781.62,7273,3446.7,5820.6,to_date('04-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4759.62,7259.5,3414.9,5807.7,to_date('05-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4690.52,7130.5,3381.3,5733.1,to_date('06-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4676.42,7077.3,3395.8,5695.6,to_date('07-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4762.71,7197.2,3483.2,5782.9,to_date('08-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4828.89,7187.5,3525.9,5818.9,to_date('09-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4852.22,7246.5,3521.5,5828.5,to_date('10-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4862.41,7276.7,3539.4,5829.8,to_date('11-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4838.67,7267.9,3526.6,5794.8,to_date('12-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4872.24,7328,3540.2,5782.3,to_date('13-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4905.59,7261.2,3598.3,5785.1,to_date('14-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4945.91,7236.5,3661.3,5834.9,to_date('15-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4908.55,7132.4,3652.5,5903.6,to_date('16-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4949.91,7143.8,3688.7,5997.9,to_date('17-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5045.16,7300.5,3688.9,5956.3,to_date('18-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5014.13,7341,3680.1,5947,to_date('19-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5064.35,7407.4,3738.5,5983.7,to_date('20-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5114.13,7472.1,3818.7,5967.8,to_date('21-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5029,7415.9,3783.8,5905.6,to_date('22-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5066.9,7530.3,3810.2,5939.3,to_date('23-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5069.89,7536.3,3800.2,5911.9,to_date('24-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5097.25,7585.5,3875.3,5932.2,to_date('25-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5135.35,7615.5,3883.3,6017.6,to_date('26-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5179.04,7638.8,3935.9,6052.8,to_date('27-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5254.32,7725.9,3932,6064.2,to_date('28-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5345.89,7827.7,3986.8,6105.8,to_date('29-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5309.67,7744.3,3903.3,6094,to_date('30-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5267.35,7588.1,3873.9,6055.2,to_date('31-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('01-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('02-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('03-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5367.98,7662.9,3867.7,6104.1,to_date('04-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5359.24,7616.3,3884.6,6074.1,to_date('05-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5292.97,7500.1,3845.9,6002,to_date('06-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5326.63,7453.7,3861.6,5922.2,to_date('07-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5407.93,7500.1,3885.7,5954.1,to_date('08-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2173.6,2674.5,1998.2,3241.5,to_date('09-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2158.07,2652.4,1961.5,3205.4,to_date('10-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2167.68,2662.5,1964.2,3203.9,to_date('11-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2178.1,2664.1,1983.4,3180,to_date('12-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2155.58,2642.1,1948.8,3139.3,to_date('13-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2155.81,2643,1966.8,3128.8,to_date('14-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2157.15,2658.8,1969.4,3121.4,to_date('15-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.17,2637.7,1952.9,3079.8,to_date('16-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.36,2628.6,1977.3,3112.7,to_date('17-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.45,2603.3,1924.6,3065.1,to_date('18-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.55,2614.7,1922.9,3079.1,to_date('19-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2059.15,2593,1919.3,3037.3,to_date('20-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2067.17,2594,1897.2,3014.8,to_date('21-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.81,2602.9,1899.4,3021.2,to_date('22-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.33,2609.1,1927.4,3028.2,to_date('23-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.83,2586.1,1902.7,2999.8,to_date('24-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.89,2581,1901.3,3008.5,to_date('25-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.36,2590,1905,3038.7,to_date('26-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.08,2558,1876.2,2992.5,to_date('27-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2002.3,2534.4,1879.3,3026.3,to_date('28-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2002.3,2499.5,1852.8,2983.5,to_date('29-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1988.67,2522.5,1876.1,3001.8,to_date('30-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1946.49,2480.4,1833.7,2956.3,to_date('31-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1965.41,2484.8,1843.4,2984.4,to_date('01-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1977.67,2496.9,1856.4,2998.7,to_date('02-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.56,2553.4,1898.3,3032.3,to_date('03-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2087.71,2570.2,1919,3073,to_date('04-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.68,2562.7,1918.1,3100.5,to_date('05-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.08,2593.3,1955.6,3141.9,to_date('06-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.52,2585.3,1933,3106.7,to_date('07-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.58,2575.6,1906.4,3120.2,to_date('08-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.58,2542.5,1898.6,3085.3,to_date('09-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2055.94,2529.8,1876.3,3060.8,to_date('10-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.15,2530.9,1867.4,3063.2,to_date('11-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.6,2508.6,1842.1,3032.8,to_date('12-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.64,2525.2,1841.6,3029.1,to_date('13-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.85,2494.7,1824.4,3000.9,to_date('14-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2009.45,2477.2,1831.5,2999.9,to_date('15-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.37,2458.6,1858.1,3029.6,to_date('16-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.86,2490.5,1905.7,3083.8,to_date('17-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.58,2506.5,1905.7,3097.4,to_date('18-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2066.18,2503.9,1905.7,3096.3,to_date('19-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2039.91,2500,1873.6,3081.3,to_date('20-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.37,2534.8,1911.1,3104.4,to_date('21-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.39,2541,1931.7,3097.6,to_date('22-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2043.3,2557.9,1906.1,3065.8,to_date('23-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.36,2543.9,1921.5,3063.8,to_date('24-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2090.78,2590.4,1943.9,3099.6,to_date('25-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2098.04,2602.7,1948.4,3103.5,to_date('26-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.67,2597.8,1948.4,3075.9,to_date('27-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.04,2590.9,1941.1,3095.3,to_date('28-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.21,2591.9,1954.5,3135.4,to_date('29-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.21,2609.4,1950.2,3146.5,to_date('30-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.94,2600.9,1927.5,3127.5,to_date('01-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.48,2606.5,1926.5,3131,to_date('02-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.2,2607,1927.8,3121,to_date('03-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.29,2571.8,1911.4,3078.7,to_date('04-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.05,2546.1,1893.1,3027.5,to_date('05-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2058.79,2568.6,1934.7,3036.6,to_date('06-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.27,2572.5,1945.9,3033.5,to_date('07-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.82,2579.4,1952.4,3047.1,to_date('08-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.24,2584.7,1940,3061.1,to_date('09-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.08,2594.9,1975.9,3081.4,to_date('10-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2045.54,2576,1964,3039.6,to_date('11-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2042.38,2577.5,1982.7,3017.3,to_date('12-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2067.26,2611,1973.7,3033.5,to_date('13-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2046.99,2589.1,1969,3016.1,to_date('14-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2044.04,2584.3,1969.8,3012.5,to_date('15-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2041.85,2589.3,1954.1,3013.8,to_date('16-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.19,2576,1937,2977.3,to_date('17-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2003.64,2562.8,1919.3,2943.4,to_date('18-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2019.13,2566,1917.1,2946.4,to_date('19-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.94,2581.4,1930.2,2980.6,to_date('20-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2054.23,2598,1931.1,2973.4,to_date('21-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.9,2604.7,1924.2,3013.6,to_date('22-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2066.59,2600.7,1928.1,3034.4,to_date('23-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2080.16,2614.7,1924.7,3058.1,to_date('24-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.5,2636.1,1940.9,3070.4,to_date('25-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2102.25,2649.8,1952.1,3091.7,to_date('26-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.98,2651,1949.9,3083.4,to_date('27-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.98,2651,1966.6,3083.4,to_date('28-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2110.77,2673.5,1956,3083.4,to_date('29-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.34,2656.2,1927.8,3095.8,to_date('30-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2074.68,2628.8,1894.2,3065.6,to_date('31-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.51,2628.8,1881.2,3065.5,to_date('01-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2079.19,2628.8,1881.2,3065.5,to_date('02-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2068.92,2612.3,1885.9,3065.7,to_date('03-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.9,2632.4,1901.8,3051.6,to_date('04-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2051.46,2613.1,1871.5,3032.3,to_date('05-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2058.2,2622.3,1886.4,3065,to_date('06-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2053.41,2617.3,1864.2,3055.8,to_date('07-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2062.08,2600.4,1859.2,3060.4,to_date('08-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.76,2597,1849.1,3049.4,to_date('09-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2059.68,2600.5,1844.1,3033.2,to_date('10-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.14,2600.2,1854,3048.3,to_date('11-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.25,2591.6,1872.8,3076.7,to_date('12-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2081.39,2582.8,1856.9,3054,to_date('13-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2085.62,2593.6,1860.3,3054.9,to_date('14-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2079.6,2595.5,1837.1,3028.6,to_date('15-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.86,2574.6,1813.3,2995.9,to_date('16-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2013.03,2525.3,1772.8,2954.2,to_date('17-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2027.67,2534.8,1780.2,2969,to_date('18-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.97,2525.5,1802.5,2982.2,to_date('19-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.79,2554.7,1826.3,3007.3,to_date('20-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2030.56,2552.4,1814.1,3022.2,to_date('21-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.68,2540.2,1813.4,2995.9,to_date('22-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.82,2536.9,1797.9,2991.6,to_date('23-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.44,2560.8,1827.8,3017.3,to_date('24-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2038.46,2563.7,1816.1,3034.7,to_date('25-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2077.94,2589.4,1842.4,3059.7,to_date('26-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2085.08,2621.4,1872.1,3062,to_date('27-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2093.01,2628,1870.4,3072.7,to_date('28-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2087.78,2617.4,1850.9,3072.5,to_date('29-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.8,2636.9,1874.4,3099,to_date('30-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.56,2635.4,1869.4,3109.9,to_date('01-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.96,2636,1850.1,3081.1,to_date('02-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.33,2642.4,1856.1,3071.3,to_date('03-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.49,2636,1861.9,3074.9,to_date('04-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.06,2621,1835,3051.1,to_date('05-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.29,2601,1822.5,3044.2,to_date('06-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.98,2597.2,1802.2,3018.6,to_date('07-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.82,2609.6,1805.6,3023.4,to_date('08-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2096.26,2610,1804.4,3019.5,to_date('09-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2126.44,2632.1,1827,3049.3,to_date('10-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.59,2623.3,1805.7,3037.7,to_date('11-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2106.05,2607.5,1802.2,3025.3,to_date('12-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.85,2600.4,1776.9,3009.3,to_date('13-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.21,2619.2,1808.4,3041.2,to_date('14-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.64,2617.2,1807.4,3038.2,to_date('15-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2094.16,2586.5,1795.2,3025.1,to_date('16-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.95,2553.2,1773.3,3001.9,to_date('17-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2041.26,2543.1,1748.6,2977,to_date('18-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2029.38,2515.8,1756.8,2992.1,to_date('19-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1989.2,2461.5,1727.1,2986.9,to_date('20-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2008.85,2467.7,1743.1,3021.1,to_date('21-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1974.14,2450.3,1721.8,3011.8,to_date('22-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.5,2463.3,1769,3050.6,to_date('23-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.22,2475,1738.6,3047,to_date('24-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.15,2491.7,1785.8,3094.1,to_date('25-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1979.52,2497.5,1788.8,3089.3,to_date('26-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1984.99,2516,1811.6,3124.2,to_date('27-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1984.15,2505.5,1813.8,3135,to_date('28-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.07,2501.9,1818,3139.7,to_date('29-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1935.08,2480.7,1795.7,3136.4,to_date('30-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1935.87,2526.6,1817.3,3153.4,to_date('31-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1928.82,2518.1,1836.1,3149.8,to_date('01-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1911.7,2495.6,1837.2,3128.3,to_date('02-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1918.85,2495,1852.4,3142.3,to_date('03-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1949.76,2533.1,1893,3176.2,to_date('04-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1914.69,2508.9,1859.5,3137.9,to_date('05-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1934.96,2511.1,1864,3143.1,to_date('06-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1965.68,2536.2,1882.8,3188.1,to_date('07-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1972.59,2536.3,1872.9,3190.2,to_date('08-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1976.52,2556.1,1890.2,3200.9,to_date('09-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.71,2559.3,1900.4,3210.9,to_date('10-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1980.81,2561.2,1880.9,3204.2,to_date('11-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1979.26,2563.1,1869.3,3190.9,to_date('12-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1991.08,2562.4,1871.9,3209.8,to_date('13-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1691.37,2120.5,1998.8,2882.6,to_date('01-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1701.46,2130.9,2001.5,2882.3,to_date('02-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.48,2142.4,1995.2,2918.6,to_date('03-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.46,2139.5,1986.8,2904.8,to_date('04-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.15,2134.6,1995.1,2916.6,to_date('05-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1702.27,2132.2,2004.3,2957.3,to_date('06-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.91,2150.1,2009.7,2949.9,to_date('07-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.48,2157,1992.4,2956.7,to_date('08-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1708.65,2165,1988.9,2953.4,to_date('09-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1688.74,2127.2,1965.2,2915.9,to_date('10-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1705.05,2157.2,1986,2922.4,to_date('11-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1700.28,2150.8,1975.3,2919.3,to_date('12-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1689.71,2139.1,1967.3,2889.9,to_date('13-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1696.38,2154.7,1963.5,2883.3,to_date('14-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.57,2182.4,1962.7,2900.1,to_date('15-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.59,2161.3,1939.3,2863.9,to_date('16-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.2,2166.3,1952.2,2861.1,to_date('17-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1666.72,2146.8,1954.6,2860.6,to_date('18-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1663.27,2135.7,2001.4,2852.8,to_date('19-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1667.26,2160.9,2025.8,2852.9,to_date('20-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1675.18,2175.7,2033.9,2846.5,to_date('21-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.64,2188.9,2035.9,2861,to_date('22-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1676.84,2190.3,2031.4,2878.7,to_date('23-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1670.04,2188.4,2005.9,2878.4,to_date('24-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1657.06,2190.3,1990.8,2869.9,to_date('25-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.36,2184,1974.7,2838.8,to_date('26-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1667.64,2196,1995.3,2832.2,to_date('27-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.6,2184.9,1984,2822.1,to_date('28-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('29-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('30-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('31-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.54,2181.7,2018.1,2846.8,to_date('01-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.95,2165.7,2015.4,2842.1,to_date('02-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.67,2160.5,1988.6,2839.7,to_date('03-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1678.65,2162.9,1986.7,2824.4,to_date('04-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.14,2166.3,1968.9,2830,to_date('05-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.06,2170.8,1949.3,2856.1,to_date('06-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1666.49,2178.1,1931.9,2869.6,to_date('07-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.01,2177.3,1944.5,2881.1,to_date('08-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.03,2162.3,1916.6,2843.8,to_date('09-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1643.53,2140.4,1911.6,2822.3,to_date('10-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.59,2124.7,1927.4,2832.7,to_date('11-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.88,2138.2,1942.5,2797.3,to_date('12-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.6,2123.2,1920.6,2786.8,to_date('13-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.83,2129.7,1939,2813.1,to_date('14-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632,2152.8,1937,2813.1,to_date('15-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.92,2160,1923.6,2812.6,to_date('16-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.88,2165.8,1926.3,2796.5,to_date('17-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.74,2165.4,1920.5,2786.3,to_date('18-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.7,2162.6,1878.6,2793.7,to_date('19-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.45,2179.1,1877.2,2829.7,to_date('20-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.46,2191.1,1854.5,2836.1,to_date('21-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.99,2191.5,1872.7,2860.8,to_date('22-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.02,2183.3,1879.9,2849.3,to_date('23-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.35,2186.3,1851.7,2847,to_date('24-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.37,2205.2,1835.7,2858.1,to_date('25-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.26,2227.2,1846.4,2847.3,to_date('26-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.64,2227,1836.8,2819.7,to_date('27-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.64,2227,1836.8,2816.8,to_date('28-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.98,2232.9,1836.8,2812.2,to_date('01-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1608.58,2237.4,1861.4,2825.6,to_date('02-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.05,2243.5,1891.1,2837.7,to_date('03-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.18,2247.5,1890.4,2846.9,to_date('04-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.18,2267.1,1904.6,2855.3,to_date('05-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.21,2271.6,1888.7,2840.7,to_date('06-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.21,2271.6,1888.7,2840.7,to_date('07-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.59,2253.8,1872.8,2849.2,to_date('08-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.53,2259.8,1875.8,2863,to_date('09-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.56,2269,1867.9,2852.8,to_date('10-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.47,2284.2,1859.7,2829.9,to_date('11-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1660.88,2309.7,1887.9,2844.8,to_date('12-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1662.28,2294.5,1893.7,2844.4,to_date('13-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.69,2312.5,1915.2,2866.9,to_date('14-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.69,2309.2,1911.2,2860,to_date('15-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.85,2308.6,1920.4,2861.8,to_date('16-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.44,2293.8,1916.8,2885.5,to_date('17-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1684.57,2274,1897.9,2870,to_date('18-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1689.93,2294.6,1918.8,2883,to_date('19-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1681.47,2323.4,1900.3,2875.7,to_date('20-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.14,2318,1910.3,2879.4,to_date('21-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1697.26,2329.5,1929.2,2903.4,to_date('22-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.33,2335,1935.3,2907.6,to_date('23-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.96,2323.9,1942.4,2900.7,to_date('24-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1692.16,2335.8,1963.3,2894.7,to_date('25-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1699.52,2347.3,1960.8,2887.5,to_date('26-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1712.33,2369.8,1991,2897,to_date('27-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1703.05,2371.9,1977.5,2886,to_date('28-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1700.93,2376.2,1971.9,2900,to_date('29-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.36,2375.5,1960.2,2888.8,to_date('30-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1697.39,2368.8,1941.2,2857.7,to_date('31-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1694.83,2364.4,1925.4,2838.5,to_date('01-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1705.66,2390.9,1935.1,2848.1,to_date('02-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1739.48,2372,1943.7,2848.3,to_date('03-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.63,2397.1,1980.4,2845.9,to_date('04-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.36,2403,1985.7,2843.2,to_date('05-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1808.74,2408.7,1992.2,2830.9,to_date('06-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1806.52,2418.1,1991.2,2837.1,to_date('07-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1815.63,2410.5,1991.2,2832.3,to_date('08-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1807.12,2399.9,1963.1,2831.7,to_date('09-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1829.36,2396.4,1974.9,2833,to_date('10-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1835.09,2381.8,1981.7,2842.9,to_date('11-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1826.45,2324.5,1968.4,2823.9,to_date('12-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1821.28,2313.7,1947.5,2814.1,to_date('13-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1828.53,2340.2,1965.7,2820.1,to_date('14-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1830.61,2350.5,1995,2827.7,to_date('15-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1859.49,2388.7,2006.2,2844.2,to_date('16-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1846.02,2398,1998.1,2879.4,to_date('17-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1832.2,2408.7,1989.5,2884.2,to_date('18-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1823.4,2401.5,2036,2917.6,to_date('19-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1823.07,2400.9,2085.9,2926.5,to_date('20-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1818.1,2400.7,2129,2941.7,to_date('21-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1857.36,2429.8,2110.6,2945,to_date('22-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1861.22,2431.5,2101.4,2941.3,to_date('23-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.8,2424.9,2115.3,2943.4,to_date('24-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1878.94,2423.3,2149.8,2969.8,to_date('25-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.24,2420.5,2138.5,2986.4,to_date('26-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1864.51,2384.5,2139.8,2971.6,to_date('27-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1894.56,2411.1,2167.4,3006.1,to_date('28-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1908.69,2449.5,2161.9,3009.1,to_date('29-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1917.69,2461,2148,3010.1,to_date('30-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1903.44,2478.7,2148,3008.3,to_date('01-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1918.75,2464.9,2136.3,3025,to_date('02-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1930.29,2488,2160.8,3073.6,to_date('03-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1937.77,2480.6,2139.2,3065.5,to_date('04-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1909.53,2474.5,2128.2,3057.6,to_date('05-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1893.48,2467.3,2111.4,3042,to_date('06-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1907.65,2472.4,2123.4,3049.3,to_date('07-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1915.59,2499.7,2159.3,3079.2,to_date('08-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1890.58,2475.2,2173.6,3079.2,to_date('09-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1909.54,2478.6,2183.9,3100.6,to_date('10-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1929.56,2481,2205.7,3100.6,to_date('11-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1931.88,2488.5,2216.5,3100,to_date('12-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1923.67,2470.4,2191.9,3085.1,to_date('13-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1928.63,2463.8,2185.1,3072.6,to_date('14-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1920.43,2438.8,2156.1,3057.3,to_date('15-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1911.15,2392.5,2158,3055.4,to_date('16-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1878.77,2403.4,2137.3,3038.6,to_date('17-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.32,2397.9,2129.4,3035.4,to_date('18-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.46,2382.1,2108.8,3031.2,to_date('19-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1868.28,2363.6,2108.4,3037,to_date('20-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1874.38,2365.6,2119.1,3024.8,to_date('21-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1869.25,2388.3,2134.1,3028,to_date('22-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1852.81,2361.8,2078.5,2989.4,to_date('23-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1862.62,2374.4,2075.6,3003.9,to_date('24-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1884.67,2385.2,2099.5,3005.5,to_date('25-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1922.69,2418.4,2107.4,3004.5,to_date('26-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1922.05,2431.8,2094.4,3001.6,to_date('27-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1899.54,2414.6,2080,3007.5,to_date('28-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1902.14,2425.8,2057.5,3001.3,to_date('29-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1890.18,2445.9,2092.6,3005.2,to_date('30-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1914.4,2482.4,2108.6,3026.3,to_date('31-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1915.61,2478.3,2120,3036.9,to_date('01-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1908.97,2485.4,2126.8,3030.1,to_date('02-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1910.23,2473.1,2114.6,3037.5,to_date('03-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1920.46,2481.6,2116.7,3039.3,to_date('04-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1934.99,2490.3,2128.7,3067.7,to_date('05-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1973.45,2521.3,2158.8,3085.2,to_date('06-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1994.09,2534.8,2164.5,3100.8,to_date('07-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1991.95,2528,2147.4,3092.4,to_date('08-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2015.71,2533.6,2156.4,3108.6,to_date('09-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2009.28,2548.5,2138.7,3102.2,to_date('10-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2004.11,2552.5,2126.9,3094.7,to_date('11-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1999.6,2571,2127.3,3080.9,to_date('12-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.35,2586.4,2113.9,3086.3,to_date('13-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2023.26,2612.6,2139.3,3120.8,to_date('14-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2032.25,2663.6,2145,3137.6,to_date('15-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.93,2660.7,2147.3,3129.6,to_date('16-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2029.87,2668.4,2149.7,3156.3,to_date('17-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.05,2690.1,2199.7,3188.3,to_date('18-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.62,2701.4,2231.9,3199,to_date('19-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.18,2685,2227.7,3184.8,to_date('20-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.75,2700.2,2210.4,3165.3,to_date('21-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.97,2715.9,2192,3154.3,to_date('22-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2053.66,2720.9,2196,3163,to_date('23-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.98,2723.2,2182,3171,to_date('24-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2068.51,2727.1,2182,3164.4,to_date('25-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.4,2742.9,2169.7,3164.1,to_date('26-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.22,2749.9,2171.2,3162.3,to_date('27-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.1,2706.6,2136,3149,to_date('28-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2013.65,2654.2,2081,3085.6,to_date('29-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2007.5,2675.3,2084.8,3077.6,to_date('30-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.74,2727.1,2112.9,3096,to_date('01-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.11,2742.1,2087.3,3098.5,to_date('02-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2021.81,2720.1,2087.3,3099.7,to_date('03-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2023.06,2721.1,2096.9,3099.1,to_date('04-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.2,2717.4,2117.9,3093.3,to_date('05-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.17,2712.3,2115.9,3097.5,to_date('06-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.17,2711.1,2148,3120,to_date('07-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2075.99,2727.1,2149.7,3125.5,to_date('08-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.13,2733.9,2145.2,3108,to_date('09-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.36,2696.5,2082.6,3070.6,to_date('10-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5373.8,7369.1,3860.4,5955,to_date('11-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.28,7308.9,3835.1,5931.1,to_date('12-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5262.57,7265.5,3822.1,5898.1,to_date('13-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5144.42,7232.3,3788.7,5863.9,to_date('14-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5002.71,7053.5,3689.4,5722.4,to_date('15-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5110.88,7180.1,3777.2,5806.6,to_date('16-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5083.8,7241.8,3726.2,5833.1,to_date('17-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5241.23,7401.4,3867.9,5928.3,to_date('18-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5241.23,7401.4,3867.9,6011.3,to_date('19-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5337.75,7640.8,3979.3,6011.3,to_date('20-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5226.2,7596.2,3945.5,5986.5,to_date('21-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5264.62,7610.8,3947.5,5992.4,to_date('22-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5164.89,7536,3912.8,5938,to_date('23-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5270.61,7587.1,3912.8,5969.8,to_date('24-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5348.75,7677.5,4007.3,6028.3,to_date('25-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5307.82,7627.3,3986.1,5956.7,to_date('26-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5371.99,7582.8,4018.5,5972.9,to_date('27-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5374.11,7550.6,4012,5948.5,to_date('28-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5414.31,7519.4,3990.2,5917.8,to_date('29-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5343.66,7371.4,3945.3,5826.2,to_date('30-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5441,7483.2,3980.8,5877.8,to_date('31-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5514.51,7495.8,4047.9,5907.4,to_date('01-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5514.51,7495.8,4047.9,5935.6,to_date('02-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5530.19,7542.7,4049.8,5955.6,to_date('03-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5592.46,7657.1,4108.7,5955.6,to_date('04-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5639.89,7731.9,4115.9,5970.7,to_date('05-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5466.88,7633.5,4017.4,5870.2,to_date('06-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5507.36,7605,4014.9,5862.3,to_date('07-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5556.99,7656.1,4041.2,5870.7,to_date('08-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5556.99,7656.1,4041.2,5837.9,to_date('09-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5583.83,7657.5,4087,5842.3,to_date('10-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5640.42,7676.3,4149.4,5898.4,to_date('11-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5605.38,7592.9,4119,5860.8,to_date('12-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5724.75,7699.5,4185.1,5947.3,to_date('13-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5787.05,7743.4,4204.6,6037.8,to_date('14-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5773.77,7716.8,4201.9,6019.8,to_date('15-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5799.22,7652.6,4208.6,5987.4,to_date('16-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5799.22,7498.4,4141.6,5852.5,to_date('17-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5631.34,7417.4,4050.8,5769.8,to_date('18-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5581.24,7342.7,4005.3,5715.7,to_date('19-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5621.71,7388.7,4013.3,5729.7,to_date('20-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5742.83,7562.7,4092.9,5832.7,to_date('21-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5689.89,7488,4052.3,5812.1,to_date('22-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5644.22,7518.6,4027.3,5748.1,to_date('23-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5648.11,7511.8,4018.6,5712.4,to_date('24-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5748.34,7624.8,4065,5772,to_date('25-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5784.4,7667.9,4126.3,5804.9,to_date('26-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5886.72,7794.7,4203.8,5858.9,to_date('27-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5870.49,7816.9,4215.7,5877.4,to_date('28-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5933.73,7881.9,4248.2,5884.5,to_date('29-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5841.83,7882,4203.5,5832.5,to_date('30-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5910.51,8038.2,4260.7,5919.9,to_date('31-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5905.15,8047.3,4252.1,5960.2,to_date('01-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5961.45,8099,4304.4,5988.4,to_date('02-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5942.06,8166,4311.1,5990.3,to_date('03-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5975.88,8160,4333.1,6003.4,to_date('04-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6018.89,8227.2,4339.9,6009.6,to_date('05-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6000.84,8205,4319.2,5969.7,to_date('06-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6001.24,8192.4,4256.4,5927.9,to_date('07-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6023.31,8141.9,4256.4,5958.2,to_date('08-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6101.9,8180.5,4256.4,6100.2,to_date('09-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6106.1,8158.1,4344.3,6151.5,to_date('10-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6108,8126.5,4358.1,6116.8,to_date('11-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6162.86,8288.2,4388.5,6174,to_date('12-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6186.09,8400.8,4368.9,6179,to_date('13-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6184.1,8412,4322.1,6132.7,to_date('14-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6081.11,8340.7,4220.1,5989.6,to_date('15-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6043.82,8229.2,4235.9,5976.2,to_date('16-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6040.58,8205.7,4205.4,5892.3,to_date('17-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5854.35,7998.7,4139.5,5836.1,to_date('18-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5867.52,8093,4122.4,5835.8,to_date('19-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5828.74,8102.7,4139.2,5844.1,to_date('20-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5906.33,8205.5,4197.6,5910.7,to_date('21-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5861.19,8239.5,4177.3,5837,to_date('22-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5774.38,8139.2,4095,5809.7,to_date('23-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5718.7,8170.2,4047.9,5736.1,to_date('24-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5614.77,7943.2,3976.4,5632.5,to_date('25-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5528.12,7846.2,3968.6,5594.1,to_date('26-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5598.32,7952.9,4041.9,5680.4,to_date('27-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5460.43,7721.3,3939.5,5587.6,to_date('28-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5285.78,7447.9,3846,5432.8,to_date('29-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5386.94,7607.5,3945.7,5462.2,to_date('30-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5355.03,7552.6,3951.7,5399.5,to_date('01-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5473.72,7676.3,3995,5455,to_date('02-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2224.85,2884,2145.3,3149.4,to_date('03-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.19,2887.8,2148.6,3159.1,to_date('04-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2215.19,2877.7,2152.4,3145.8,to_date('05-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2198.24,2843.7,2139.1,3131.7,to_date('06-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.92,2872.9,2159.6,3168.3,to_date('07-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.37,2869.1,2160.1,3138.2,to_date('08-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.89,2837.1,2136,3128,to_date('09-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.09,2818.7,2102.7,3098.3,to_date('10-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.41,2781.6,2092,3101.2,to_date('11-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.13,2787.9,2135.2,3133.7,to_date('12-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2208.68,2763.5,2116.3,3106.1,to_date('13-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.36,2776.2,2130.9,3125.3,to_date('14-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2256.98,2791.3,2147.3,3150,to_date('15-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2237.82,2768.9,2150.3,3129.9,to_date('16-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2252.51,2736.3,2166,3125.3,to_date('17-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.72,2765.2,2186.2,3125.3,to_date('18-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2261.71,2755.9,2179,3100,to_date('19-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.85,2705.6,2141.6,3070.5,to_date('20-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.78,2682.2,2162.6,3106,to_date('21-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2233.55,2641.4,2158.2,3106,to_date('22-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.77,2569.5,2139.4,3097.8,to_date('23-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.34,2629,2165,3136.3,to_date('24-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2248.02,2633,2176.7,3130.5,to_date('25-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2248.02,2633,2176.7,3137.8,to_date('26-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2257.33,2678.7,2187,3119.2,to_date('27-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2272.96,2709,2187.8,3115.6,to_date('28-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.11,2727.1,2195.2,3123.5,to_date('29-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2254.21,2740.6,2184,3116.5,to_date('30-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2245.79,2725.8,2165.4,3122.8,to_date('31-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2238.97,2732.7,2155.4,3127.3,to_date('01-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2238.97,2732.7,2155.4,3108.4,to_date('02-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.01,2692,2133.3,3089.1,to_date('03-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.56,2673.1,2084.4,3020.7,to_date('04-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2146,2689.2,2091.9,3019.7,to_date('05-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.8,2711.9,2050.7,2966.4,to_date('06-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.76,2742.9,2052.5,2966.4,to_date('07-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.34,2722.9,2029.9,2970.5,to_date('08-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.62,2731.5,1979.7,2931.9,to_date('09-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2120.23,2725.9,2007.4,2980.8,to_date('10-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2158.88,2728.4,2041.7,2997.8,to_date('11-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.59,2781.4,2037.2,3009.4,to_date('12-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2131.8,2778.1,2023.7,3004.8,to_date('13-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.93,2801.1,2046.8,3038.2,to_date('14-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2131.14,2777.2,2028.4,3028.9,to_date('15-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.58,2763.8,2020.7,3055.9,to_date('16-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2084.42,2744.2,1977.7,3016.3,to_date('17-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.44,2740,1992,3039.6,to_date('18-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.21,2701.8,1966.4,3045.8,to_date('19-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.29,2666.6,1942.8,3030.1,to_date('20-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2031.8,2627.6,1936,3022.9,to_date('21-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1986.42,2545,1903,2971.1,to_date('22-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1957.08,2544.2,1890.8,2940.2,to_date('23-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2004.93,2595.7,1917,2960.4,to_date('24-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2032.52,2626.7,1939,2942.4,to_date('25-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2005.07,2577.2,1907,2876.6,to_date('26-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2000.48,2561.4,1911.6,2899.9,to_date('27-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.25,2604.4,1925.8,2909,to_date('28-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2042.45,2631.2,1936.3,2946.3,to_date('29-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.85,2608.8,1892,2919.2,to_date('30-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.69,2588.9,1872.9,2936.4,to_date('01-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.7,2636.4,1866.2,2970.4,to_date('02-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2034.64,2609.7,1878.7,2965,to_date('03-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2031.33,2598.9,1889,2946.7,to_date('04-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2049.1,2590.5,1920.8,2964.4,to_date('05-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.83,2560.3,1920.8,2962.4,to_date('06-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.46,2562.5,1949.8,2983.8,to_date('07-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.57,2508,1942.1,2963.9,to_date('08-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2051.25,2474.5,1974.6,3005.3,to_date('09-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.71,2528.3,1974.6,3050.4,to_date('10-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2103.54,2514.9,1974.6,3074.8,to_date('11-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.96,2494.7,2025.1,3082,to_date('12-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.86,2521.6,2052.3,3091.3,to_date('13-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2120.97,2562.3,2043.7,3077.2,to_date('14-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2126.75,2579.4,2053.8,3095.1,to_date('15-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2148.23,2599.2,2041.4,3114.7,to_date('16-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.21,2601.2,2059.8,3106.1,to_date('17-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.32,2604.4,2076.8,3117.2,to_date('18-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.93,2544.9,2055.7,3082.3,to_date('19-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2134.12,2559.3,2053.4,3095.9,to_date('20-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2152.19,2579.5,2075,3082.6,to_date('21-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2161.5,2579.5,2069.6,3097.4,to_date('22-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.63,2629.7,2117.2,3157.5,to_date('23-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.83,2620.7,2115,3160.4,to_date('24-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.66,2615.3,2096.5,3150.5,to_date('25-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.81,2600.8,2107.1,3167.5,to_date('26-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.32,2617.5,2106.3,3171.9,to_date('27-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2160.56,2585.5,2074.5,3168.6,to_date('28-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.51,2591.4,2064.2,3167,to_date('29-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2156.31,2588.8,2038.9,3138.2,to_date('30-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2133.74,2580.5,2007,3142.3,to_date('31-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2134.34,2570.6,2007,3142.2,to_date('01-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2152.65,2588.7,2012.4,3147.3,to_date('02-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.56,2599.3,2035,3190.3,to_date('03-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2151.44,2582.7,2010.5,3182.6,to_date('04-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.84,2557,2001.3,3191.4,to_date('05-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.37,2532.4,1972.6,3171.3,to_date('06-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2121.25,2530.9,2000.6,3175.1,to_date('07-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.98,2541.1,2006.3,3205.2,to_date('08-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2153.48,2551.1,2026.5,3234.2,to_date('09-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.58,2581.3,2062.7,3265.1,to_date('10-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2215.72,2635.2,2075.3,3265.1,to_date('11-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2205.82,2635.7,2060.4,3249.6,to_date('12-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2207.09,2645.6,2069.1,3251.3,to_date('13-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2185.78,2628.4,2034.9,3216.5,to_date('14-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.38,2672,2020.4,3222.7,to_date('15-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2027.99,2702.1,2071.5,3069.3,to_date('16-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.45,2717.8,2070.6,3067.2,to_date('17-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.8,2726.8,2118.4,3093.1,to_date('18-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2045.25,2741.9,2120.6,3111.4,to_date('19-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2052.09,2738.2,2119.3,3135.8,to_date('20-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2052.92,2738.5,2110.1,3166.9,to_date('21-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.77,2774.3,2154,3233.2,to_date('22-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.76,2787.2,2160.5,3223.9,to_date('23-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.3,2819,2188.4,3234.2,to_date('24-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.01,2836.4,2186.7,3237.3,to_date('25-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.39,2834.8,2176.1,3237.3,to_date('26-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.95,2843.8,2205.3,3277.4,to_date('27-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.58,2858.4,2211.4,3271.6,to_date('28-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.5,2861.2,2198.1,3261.3,to_date('29-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.83,2876.9,2196.3,3254.6,to_date('30-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.07,2867.8,2156.5,3248.4,to_date('31-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.52,2850.6,2162.6,3278.8,to_date('01-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.03,2867.7,2160.3,3311.2,to_date('02-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.76,2889.5,2196.4,3337.1,to_date('03-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.18,2909,2223.5,3364.9,to_date('04-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.78,2918,2215.9,3342.4,to_date('05-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2209.17,2942.4,2225.8,3355.7,to_date('06-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2227.63,2967.4,2243,3396.5,to_date('07-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2227.63,2967.4,2251.5,3412.3,to_date('08-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.7,2972.6,2276.6,3412.3,to_date('09-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.91,2930.9,2264.6,3412.3,to_date('10-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2229.62,2934.2,2281.9,3462,to_date('11-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2255.29,2957.6,2281.2,3428.8,to_date('12-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2255.29,2957.6,2268.2,3418.4,to_date('13-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2274.62,2996.2,2290.6,3418.4,to_date('14-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.85,2999.2,2274.3,3408.5,to_date('15-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2233.61,3009.4,2249.6,3379.2,to_date('16-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2220.63,3021.9,2275.1,3403,to_date('17-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2224.95,3042.9,2307.6,3446,to_date('18-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2225,3015.9,2317.3,3440.6,to_date('19-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2228.1,3026.6,2331.3,3413.8,to_date('20-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.06,2999,2281.9,3372,to_date('21-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.37,2949.9,2252.2,3360,to_date('22-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2151.05,2990.6,2262.3,3400.6,to_date('23-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2115.56,3011.1,2234.8,3407.8,to_date('24-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.35,3037.5,2247.4,3437,to_date('25-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.52,3049.4,2274.7,3475.1,to_date('26-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2098.36,3045.9,2257.8,3470,to_date('27-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.94,3039.3,2244,3484.2,to_date('28-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2107.29,3041.3,2274.5,3481.4,to_date('01-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2090.78,3066.5,2278.3,3444,to_date('02-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.66,3091.3,2282.4,3436.1,to_date('03-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2123.31,3095.2,2281,3427.3,to_date('04-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2156.61,3140.7,2313.2,3447.4,to_date('05-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2192.6,3178.4,2334.4,3491.8,to_date('06-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2181.88,3148.7,2331.3,3481.5,to_date('07-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.05,3169.1,2355.9,3520.3,to_date('08-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.08,3151.9,2322,3491.5,to_date('09-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.9,3166.6,2329.2,3475.4,to_date('10-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.11,3089.8,2287.1,3419.1,to_date('11-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.57,3097.2,2299.9,3440.2,to_date('12-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.43,3054.2,2302.1,3429.1,to_date('13-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.69,3012.2,2296.8,3407,to_date('14-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.77,3012.2,2275.1,3378.9,to_date('15-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.93,2947.1,2281.6,3363.5,to_date('16-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.71,2947.1,2258,3393.2,to_date('17-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.25,2947.1,2258,3417.7,to_date('18-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2162.29,3033.4,2281.2,3425.3,to_date('19-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2133.85,3025.9,2251.8,3382.6,to_date('20-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.06,2997.6,2215.2,3350.3,to_date('21-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.64,2982.8,2226.7,3333.7,to_date('22-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.25,3027.5,2252,3341.9,to_date('23-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2082.9,2958.4,2208.3,3267.5,to_date('24-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2075.33,2929.3,2199,3281.2,to_date('25-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2103.24,2888.2,2238.1,3328.1,to_date('26-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.2,2847.4,2183.1,3270.6,to_date('27-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2018.69,2768.5,2144.7,3248.1,to_date('28-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2044.45,2803.4,2144.7,3246.5,to_date('29-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2076.76,2865.2,2178.7,3278,to_date('30-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.12,2918.7,2219.9,3305.9,to_date('31-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2125.47,2902.4,2216.4,3264.4,to_date('01-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.01,2858.5,2199.7,3246.7,to_date('02-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.51,2861.2,2184.6,3233.9,to_date('03-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.89,2831.6,2175,3191.9,to_date('04-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2169.4,2870.5,2215,3233.4,to_date('05-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2178.91,2906.9,2258.5,3267.4,to_date('06-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2168.11,2887.1,2242.7,3242.9,to_date('07-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2160.45,2887.7,2247.8,3255.7,to_date('08-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.39,2843.5,2221.3,3218.1,to_date('09-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.55,2804.3,2202.7,3198,to_date('10-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2141.7,2824.7,2200.7,3201.5,to_date('11-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2162.96,2857,2200.2,3155.3,to_date('12-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.36,2850.8,2152.6,3121.7,to_date('13-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144,2831.8,2136.6,3129,to_date('14-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2167.72,2862.4,2144.5,3129.5,to_date('15-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2162.82,2827.4,2123.4,3123.4,to_date('16-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2151.84,2814.5,2083.9,3092.4,to_date('17-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.88,2794.8,2081.9,3086.4,to_date('18-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.88,2794.8,2081.9,3086.4,to_date('19-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.88,2794.8,2081.9,3086.4,to_date('20-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2177.09,2807.3,2100.3,3116.2,to_date('21-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.89,2830.2,2128.2,3131.5,to_date('22-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2202.57,2860.9,2119.6,3129,to_date('23-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2203.18,2873.4,2114.8,3120.8,to_date('24-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.75,1678.1,1772.8,2443.6,to_date('25-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.63,1688.5,1750.5,2460.2,to_date('26-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.51,1678.6,1718,2448.2,to_date('27-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.04,1684.1,1708.1,2470.4,to_date('28-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.16,1686.6,1723.1,2484.7,to_date('29-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1610.61,1671.6,1714.3,2466.8,to_date('30-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.75,1682.9,1734.5,2487.9,to_date('01-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1640.17,1703.6,1757.4,2508.4,to_date('02-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1635.47,1697.5,1754,2510.5,to_date('03-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1645.89,1716.3,1754.3,2497.4,to_date('04-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1647.84,1723.8,1759.8,2532.5,to_date('05-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.35,1730.5,1755.5,2556.8,to_date('06-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1629.93,1727.4,1758.1,2561,to_date('07-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.49,1733.3,1757.5,2547.3,to_date('08-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.74,1734,1763.5,2541.5,to_date('09-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.63,1728.3,1762.8,2558.5,to_date('10-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.99,1737.1,1768.9,2587.9,to_date('11-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.18,1723.1,1778.1,2580.5,to_date('12-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.42,1723.6,1780.1,2579.6,to_date('13-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.95,1719,1767.7,2589.3,to_date('14-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.75,1721.2,1757.9,2595,to_date('15-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.67,1725.3,1756.6,2595.6,to_date('16-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.29,1727.2,1754.7,2588.8,to_date('17-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1620.49,1727.2,1766.8,2591.7,to_date('18-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.67,1731.6,1766.5,2601.7,to_date('19-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.07,1724.1,1762.2,2585.4,to_date('20-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.98,1716.9,1759.5,2573.3,to_date('21-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.87,1723.4,1782.4,2597.4,to_date('22-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.37,1723,1789.5,2600.6,to_date('23-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.47,1728.4,1783.5,2570.6,to_date('24-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.55,1722.1,1780.4,2569.4,to_date('25-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1650.43,1724.5,1808.8,2584.9,to_date('26-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1650.06,1733.6,1820.3,2608.8,to_date('27-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.11,1739,1820.3,2617.2,to_date('28-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.6,1726.2,1820.3,2621,to_date('29-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1501.82,1587.4,1687.5,2540.5,to_date('30-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1524.28,1630.6,1725.6,2554.5,to_date('31-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.65,1685.5,1792.9,2601.9,to_date('01-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.49,1701.3,1819.1,2623,to_date('02-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.68,1718,1833.5,2640.7,to_date('03-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.1,1726.2,1853.4,2640.7,to_date('04-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1645.81,1716.6,1849.7,2619.8,to_date('05-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1650.36,1725.8,1851.8,2624.2,to_date('06-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1651.55,1737.4,1857.7,2638.2,to_date('07-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.88,1736.6,1864.3,2645.7,to_date('08-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.52,1732.4,1863.5,2679.6,to_date('09-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1657.51,1731.2,1873.2,2669,to_date('10-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.55,1726.9,1860.8,2664.6,to_date('11-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.09,1727.8,1868.7,2663.3,to_date('12-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1646.41,1720.2,1860.4,2667.4,to_date('13-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.65,1715.4,1855.9,2653.2,to_date('14-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.8,1708.7,1840.5,2630.8,to_date('15-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.64,1713,1842.6,2626.6,to_date('16-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.22,1713.5,1861.2,2641.9,to_date('17-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.65,1718,1876.2,2625.8,to_date('18-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.17,1701.7,1878.3,2606,to_date('19-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1635.8,1701.7,1878.4,2594.4,to_date('20-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.27,1684.9,1869.4,2583.6,to_date('21-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.7,1687.2,1880.4,2588.7,to_date('22-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.13,1690.6,1885.5,2600.3,to_date('23-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.12,1684.3,1888.4,2579.5,to_date('24-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.8,1679.9,1885.2,2576.6,to_date('25-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.79,1672.9,1877.9,2597.8,to_date('26-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1614.8,1663.1,1876.5,2595.6,to_date('27-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1612.8,1669.3,1883.8,2599,to_date('28-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.47,1664.7,1880.6,2621.7,to_date('29-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1609.32,1672.3,1887.4,2645.6,to_date('30-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.48,1687.7,1878.3,2644.2,to_date('01-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.48,1686.8,1867.1,2625.6,to_date('02-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.89,1686.6,1851.9,2624.6,to_date('03-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.12,1675.8,1843.6,2596.2,to_date('04-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1582.27,1677.4,1848.1,2599.5,to_date('05-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1567.99,1673.2,1843.4,2584.1,to_date('06-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1568.16,1665,1843.6,2570.8,to_date('07-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1569.71,1671.3,1833.8,2555,to_date('08-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.74,1672.4,1833.4,2574.5,to_date('09-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1585.41,1676.2,1856.9,2576.7,to_date('10-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.01,1692.6,1863.4,2579,to_date('11-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.89,1696.5,1855.5,2588.7,to_date('12-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1565.18,1716.1,1864.2,2601.1,to_date('13-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.34,1713.3,1846,2575.7,to_date('14-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577,1705.1,1836.8,2559.5,to_date('15-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.29,1711.3,1830.4,2561.1,to_date('16-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.72,1709.8,1831.6,2528.3,to_date('17-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.07,1688.6,1834.8,2514.7,to_date('18-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.19,1698.9,1852.1,2558.5,to_date('19-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1588.73,1700,1849.8,2553.3,to_date('20-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1586.01,1693,1861.8,2577.1,to_date('21-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.77,1683.9,1856.7,2566,to_date('22-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.58,1679.2,1856.7,2549.5,to_date('23-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1568.09,1673.9,1841.5,2527.8,to_date('24-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1578.21,1683.9,1846.9,2540.9,to_date('25-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1573.94,1688.4,1836.1,2534.2,to_date('26-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1582.06,1693.9,1838.6,2538,to_date('27-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1610.18,1720.9,1857.6,2559,to_date('28-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.16,1717.9,1857.6,2554.9,to_date('29-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.84,1733.6,1858.4,2575.5,to_date('30-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1615.26,1729.7,1846.8,2546.5,to_date('31-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.08,1735.6,1868.5,2561.6,to_date('01-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.97,1734.1,1863.2,2546.6,to_date('02-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.7,1699.3,1808.3,2502.9,to_date('03-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.7,1678.6,1765.1,2463.1,to_date('04-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.7,1675.5,1763.5,2472.6,to_date('05-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.26,1670.1,1766,2463.5,to_date('06-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1599.75,1652.2,1741.3,2446.3,to_date('07-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.86,1635,1743.3,2456.2,to_date('08-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.5,1654.9,1769,2471.5,to_date('09-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.86,1642,1757.9,2447.5,to_date('10-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1587.92,1638.7,1754.9,2428.6,to_date('11-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.06,1622.6,1739.7,2420.2,to_date('12-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1549.81,1596.1,1708.8,2414.9,to_date('13-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1549.36,1612.4,1722.2,2420.2,to_date('14-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.65,1625,1713.9,2423.8,to_date('15-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.52,1610.5,1703.2,2407,to_date('16-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1555.31,1606.6,1685.7,2388.7,to_date('17-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1559.76,1610.7,1663.4,2409.6,to_date('18-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1548.44,1603.1,1636.9,2392,to_date('19-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.99,1591.5,1645.6,2380.2,to_date('20-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.21,1605.2,1671.6,2423.3,to_date('21-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.03,1621.4,1688.3,2451.6,to_date('22-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1551.78,1622.5,1696.8,2440.8,to_date('23-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1562.89,1626.6,1711.7,2432.9,to_date('24-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.28,1627.4,1706.2,2413.6,to_date('25-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1559.26,1614.9,1684.2,2391.6,to_date('26-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.87,1602.3,1648.5,2358.1,to_date('27-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1598.3,1633.6,2345.4,to_date('28-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1699.1,2384.4,to_date('29-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1699.1,2384.4,to_date('30-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1722.5,2384.4,to_date('31-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1564.27,1655.7,1720.7,2418.7,to_date('01-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1741.9,2420,to_date('02-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1765.7,2493.1,to_date('03-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1765.7,2493.1,to_date('04-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1598.19,1670.1,1749.9,2492.8,to_date('05-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.05,1704,1770.3,2504.1,to_date('06-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.69,1711.8,1787.6,2493.2,to_date('07-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1593.65,1700.5,1778.7,2482.9,to_date('08-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1581.68,1690.3,1785.6,2467.1,to_date('09-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1599.14,1715.4,1833.9,2497.9,to_date('10-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.82,1723.5,1837.4,2477.9,to_date('11-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1620.45,1719.4,1824.3,2490.1,to_date('12-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1629.51,1734.4,1843.8,2516.3,to_date('13-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1663.7,1772.8,1873.6,2537.1,to_date('14-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1664.09,1760.3,1860.2,2541.6,to_date('15-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1669.29,1747.2,1860.2,2536.7,to_date('16-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.14,1750.2,1865.9,2544.9,to_date('17-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.07,1755.3,1867.9,2543.4,to_date('18-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.13,1754.6,1841.3,2522,to_date('19-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.84,1751.2,1838.7,2525.3,to_date('20-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1669.52,1752.5,1849.9,2510.4,to_date('21-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.71,1769.4,1869.3,2539.9,to_date('22-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.51,1767.6,1890.6,2552,to_date('23-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.01,1750,1879.6,2546.5,to_date('24-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.06,1747.1,1873.9,2550.8,to_date('25-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.7,1753.5,1875.3,2571.2,to_date('26-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.66,1752.8,1857,2560.2,to_date('27-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1678.77,1752.9,1856.5,2556.8,to_date('28-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.85,1764.7,1865.8,2547.1,to_date('29-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.71,1776.8,1860.6,2534.3,to_date('30-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.59,1779.3,1861.6,2517.2,to_date('01-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.73,1785.1,1865.6,2538.4,to_date('02-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.14,1798.2,1864.1,2537.1,to_date('03-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.03,1794.1,1861.6,2523.7,to_date('04-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.81,1795.2,1876.5,2522.6,to_date('05-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1676.17,1780.4,1865.1,2513.9,to_date('06-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1688.46,1789.5,1882.1,2541,to_date('07-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1696.55,1794.2,1912.2,2555.9,to_date('08-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.24,1784.4,1915.4,2536.7,to_date('09-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.35,1800.1,1951.2,2543.4,to_date('10-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.29,1804,1962.4,2542.3,to_date('11-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.86,1816.2,1976.5,2559.7,to_date('12-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.63,1810.5,1953.5,2546.8,to_date('13-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1743.36,1821.9,1981.3,2565,to_date('14-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.17,1828.2,1985.1,2562,to_date('15-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.76,1840.6,1983.4,2562.1,to_date('16-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1749.29,1841.1,1979.7,2554.3,to_date('17-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1763.86,1846.3,1983.8,2565.4,to_date('18-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.27,1850,1988.1,2558.4,to_date('19-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.29,1839,1973,2538.3,to_date('20-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.77,1820.2,1966.9,2533.1,to_date('21-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.5,1815.2,1976.3,2550.7,to_date('22-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.21,1820.6,1993.9,2574.8,to_date('23-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1739.88,1807.1,1968,2522.4,to_date('24-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.92,1791.4,1941.8,2493.3,to_date('25-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1734.42,1806.2,1947.1,2476,to_date('26-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.13,1798.7,1929.2,2470.7,to_date('27-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1732.92,1818.2,1943.6,2491.2,to_date('28-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.89,1820.5,1928.2,2464.7,to_date('29-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1725.74,1833.3,1922,2467.6,to_date('30-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1730.9,1837.1,1919.1,2456.6,to_date('31-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.17,1818.2,1884.6,2441,to_date('01-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.2,1824.1,1896.3,2458.7,to_date('02-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1719.06,1830.1,1928.3,2464.9,to_date('03-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1718.21,1835.6,1934.8,2472.2,to_date('04-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.84,1828.7,1923.5,2447.9,to_date('05-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.76,1839.2,1943.8,2452.9,to_date('06-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1718.35,1837.2,1942.4,2440.1,to_date('07-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1706.69,1826.7,1928.1,2408.6,to_date('08-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.37,1838,1942,2405.4,to_date('09-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.18,1829.1,1942.7,2382.7,to_date('10-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1738.78,1843.1,1974.8,2400.9,to_date('11-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1737.41,1850.5,1975.4,2404.2,to_date('12-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.77,1827.1,1907.5,2393.2,to_date('13-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1724.24,1829.1,1943.6,2436.4,to_date('14-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.77,1848,1974.1,2572.6,to_date('15-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.96,1840.5,1963.3,2591,to_date('16-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1734.46,1853.8,1972.3,2600.5,to_date('17-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1744.35,1874.1,1990.7,2640.2,to_date('18-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('19-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('20-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('21-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1747.47,1860.5,1980.4,2625.8,to_date('22-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.1,1874.7,1983.7,2607.8,to_date('23-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.17,1880.1,1978.1,2609.8,to_date('24-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.72,1874.7,1984.9,2643,to_date('25-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1742.92,1875.6,1995.7,2658.2,to_date('26-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.68,1859.5,2006.6,2651,to_date('27-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.18,1874.2,2036.7,2664.9,to_date('28-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1728.09,1880.1,2031.1,2654.1,to_date('29-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1728.09,1880.1,2031.1,2659.8,to_date('30-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.29,1907.7,2041.6,2659.8,to_date('01-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.82,1920.5,2046.9,2662.2,to_date('02-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.78,1937.3,2047.2,2698.7,to_date('03-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.57,1936.8,2063.4,2701.9,to_date('04-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1748.13,1949.1,2063.4,2725.7,to_date('05-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1750.7,1963.7,2077.5,2737.8,to_date('06-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1747.91,1950.8,2063.6,2722.4,to_date('07-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.79,1953.5,2053.2,2720.5,to_date('08-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1735.34,1945,2017,2694.7,to_date('09-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1719.92,1921.1,2024,2682.6,to_date('10-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1763.59,1939.1,2051.6,2703.6,to_date('11-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1766.76,1928,2023.1,2700.6,to_date('12-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1785.4,1933.4,2030.8,2711.9,to_date('13-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1783.56,1925.7,2016.8,2702,to_date('14-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1804.42,1931.7,2045.1,2715,to_date('15-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1812.33,1928.7,2046.3,2715,to_date('16-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1799.51,1924.5,2029.6,2704.6,to_date('17-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1792.8,1914.2,2014.1,2698.6,to_date('18-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1792.8,1914.2,2014.1,2694.2,to_date('19-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1806.36,1920.6,2033.3,2707.6,to_date('20-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.23,1923.3,2017.4,2697.6,to_date('21-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1800.62,1930.4,2024.9,2705.9,to_date('22-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1786.19,1915.2,1992.6,2680.9,to_date('23-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1791.35,1916.9,1994.9,2681.9,to_date('24-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.05,1913.8,1981.6,2668.5,to_date('25-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.05,1913.8,1981.6,2645.8,to_date('26-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1784.71,1899.7,1962.2,2635.4,to_date('27-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.45,1888,1953.7,2636.1,to_date('28-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1779.74,1868.8,1928.8,2614.1,to_date('29-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1786.97,1879.9,1928.3,2603.7,to_date('30-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.25,1865.7,1918.1,2593.6,to_date('31-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1781.62,1881.3,1931.4,2616.3,to_date('01-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.75,1873.1,1908.8,2598.4,to_date('02-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.75,1862.5,1891.8,2562.7,to_date('03-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1776.34,1869.3,1913.9,2584.8,to_date('04-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1770.72,1846.9,1885.8,2550.3,to_date('05-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1772.39,1847.1,1895.8,2560.6,to_date('06-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.55,1838.3,1899.6,2532.6,to_date('07-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1764.35,1845.8,1920.3,2557.3,to_date('08-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.83,1835.5,1915.3,2534.1,to_date('09-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1755.98,1846.6,1907.3,2515.8,to_date('10-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1754.95,1854.8,1900.6,2521.2,to_date('11-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1759.9,1845.3,1880.9,2493.9,to_date('12-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1759.84,1854.5,1873.5,2476.1,to_date('13-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1776.5,1870.5,1883.6,2497.1,to_date('14-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1769.98,1862.6,1868.5,2469,to_date('15-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1766.98,1856.6,1879.1,2493.7,to_date('16-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.29,1837.6,1847.8,2472.6,to_date('17-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1760.17,1846.7,1861.8,2497.9,to_date('18-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1750.32,1856.5,1859.4,2490.8,to_date('19-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.44,1841.8,1859.4,2478.3,to_date('20-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1735.51,1835,1859.4,2484,to_date('21-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.84,1844.4,1853.3,2486.4,to_date('22-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1730.78,1838.9,1851.2,2483.4,to_date('23-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1699.46,1805.6,1801.8,2431.9,to_date('24-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.71,1756.6,1767.9,2403.7,to_date('25-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.09,1786.1,1762.7,2415.6,to_date('26-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.81,1757.1,1727.5,2387.9,to_date('27-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.81,1762.8,1734.6,2399.5,to_date('28-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.36,1756.8,1734.6,2377.2,to_date('29-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.78,1761.9,1755.4,2348,to_date('30-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.18,1778.5,1769,2373.4,to_date('31-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1637.62,1812.7,1801.6,2423.2,to_date('01-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.2,1806.1,1782.6,2411.6,to_date('02-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1608.49,1798.1,1754.7,2399.6,to_date('03-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.11,1794.9,1784.4,2420.2,to_date('04-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1609.61,1805.4,1787.6,2407.5,to_date('05-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.94,1820.3,1798,2392.8,to_date('06-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.07,1819.6,1793.8,2377.6,to_date('07-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1611.96,1809.6,1777.3,2350.1,to_date('08-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1578.95,1799.9,1755.2,2325.7,to_date('09-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.39,1800.3,1737.8,2309.6,to_date('10-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.87,1793.3,1730.1,2303.1,to_date('11-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1548.63,1784.8,1722.4,2318,to_date('12-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1560.16,1791.7,1753.5,2356.8,to_date('13-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.76,1800.2,1757.3,2376.1,to_date('14-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1531.87,1788.6,1736.7,2354.7,to_date('15-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.14,1775.7,1734.2,2363.5,to_date('16-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1509.03,1753.5,1724.2,2359.4,to_date('17-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.03,1768.2,1744.2,2365.7,to_date('18-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.97,1727.9,1689.7,2311.1,to_date('19-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1464.03,1709.6,1667.7,2281,to_date('20-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1475.11,1704.6,1667.8,2285,to_date('21-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1516.12,1740.6,1687.6,2311.6,to_date('22-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1519.69,1745.7,1687.5,2312.6,to_date('23-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.97,1751.7,1684.9,2312.6,to_date('24-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1516.44,1747.3,1674.2,2298.4,to_date('25-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1515.53,1757.8,1711.4,2313,to_date('26-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.89,1774.2,1780.5,2381.9,to_date('27-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1534.72,1774.4,1779,2362.2,to_date('28-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.66,1788.3,1779.3,2372.2,to_date('01-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.71,1788,1763.7,2337.7,to_date('02-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.83,1779.1,1756.8,2327.5,to_date('03-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.1,1792.8,1774.2,2340.6,to_date('04-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.2,1812,1802,2370.9,to_date('05-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1601.5,1872.1,1873.6,2422.1,to_date('06-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1580.29,1851.4,1836.2,2370,to_date('07-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1595.09,1873.4,1859.8,2378.3,to_date('08-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.47,1889.6,1852.7,2483.9,to_date('09-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1600.59,1897.5,1882.9,2567,to_date('10-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1566,1888.8,1826.1,2560.1,to_date('11-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.01,1900.4,1832.8,2586,to_date('12-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.74,1913.4,1828.9,2580.5,to_date('13-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.3,1909.9,1829.5,2621.2,to_date('14-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1510.66,1910.8,1843.5,2601,to_date('15-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1481.03,1879.2,1770.3,2560,to_date('16-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1483.83,1880.2,1731.9,2565.5,to_date('17-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1470.09,1878.3,1736.7,2553,to_date('18-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.78,1885.2,1724,2572.3,to_date('19-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1475.41,1867.6,1683.3,2549.7,to_date('20-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1402.34,1788,1611,2446.3,to_date('21-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1421.49,1820.5,1612.5,2488.4,to_date('22-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1434.61,1858.2,1654.2,2517.1,to_date('23-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1446.32,1870.3,1673.9,2538.8,to_date('24-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1437.65,1878.4,1657.3,2541.2,to_date('25-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1441.57,1881.5,1655.1,2557.2,to_date('26-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1471.64,1893.2,1685.1,2584.7,to_date('27-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1453.95,1889.3,1667.9,2574.7,to_date('28-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1453.79,1877.3,1650,2546.6,to_date('29-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1458.02,1884,1664.2,2563.9,to_date('30-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1479.59,1904.7,1679.1,2562.2,to_date('31-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1504.89,1922.7,1731.3,2617,to_date('01-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1496.54,1908.5,1722.2,2645.7,to_date('02-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1511,1911.4,1730.7,2658.1,to_date('03-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1528.86,1921.1,1766.4,2669.7,to_date('04-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1534.02,1930.8,1770.7,2661.6,to_date('05-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.6,1927.8,1774.5,2669.8,to_date('06-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1508.19,1908.3,1749.9,2650.4,to_date('07-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1493.54,1905.9,1730.9,2642.3,to_date('08-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1489.68,1911.1,1742.4,2658.3,to_date('09-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1482.44,1921.6,1742.4,2687.8,to_date('10-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1483.34,1933.6,1786.9,2705.6,to_date('11-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1470.57,1942,1804.1,2691.7,to_date('12-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.84,1951.5,1804.7,2711.1,to_date('13-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1487.71,1955.7,1793.6,2702.7,to_date('14-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1508.63,1957.4,1786.7,2695.4,to_date('15-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1515.27,1962.3,1798.5,2714.6,to_date('16-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1509.84,1946.1,1798.5,2696.8,to_date('17-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.28,1950.2,1821.5,2726.4,to_date('18-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1541.79,1929.7,1796.8,2697.5,to_date('19-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.48,1913.4,1772.7,2679.6,to_date('20-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.27,1889.5,1764.4,2679.2,to_date('21-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.27,1882.8,1759.2,2704,to_date('22-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.37,1895.4,1722.3,2706.2,to_date('23-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.84,1897.9,1724.2,2732.4,to_date('24-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.62,1891.5,1674.8,2722.9,to_date('25-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.68,1880.1,1720.6,2727.1,to_date('26-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1513.42,1887,1721,2709.6,to_date('27-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.02,1891.4,1739.7,2741.8,to_date('28-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.69,1914.6,1749.7,2760.1,to_date('29-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.12,1931.2,1771.4,2778.8,to_date('30-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1546.82,1929.2,1792.3,2792,to_date('01-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1528.12,1924.3,1783.3,2764.1,to_date('02-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.65,1927,1799.4,2771,to_date('03-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.25,1935,1781.7,2759.4,to_date('04-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1519.48,1955.4,1788.6,2754.5,to_date('05-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1506.65,1962.2,1765.9,2769.8,to_date('06-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1504.3,1980.7,1791.2,2750.7,to_date('07-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1480.65,1987.7,1769.5,2726.5,to_date('08-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1476.7,1993.7,1758.7,2716.2,to_date('09-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1478.07,2015.7,1738.3,2721.8,to_date('10-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1479.62,2005,1744.8,2717.9,to_date('11-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1477.55,2023.9,1736.7,2732.8,to_date('12-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1472.59,2028.5,1735.2,2740.3,to_date('13-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1495.6,2044.9,1760.1,2789.7,to_date('14-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1517.45,2045.8,1786.3,2807.7,to_date('15-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1520.93,2057.3,1824.4,2842,to_date('16-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1821.1,2827.4,to_date('17-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1854.6,2827.5,to_date('18-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1854.6,2827.5,to_date('19-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.51,2092.3,1857.5,2827.5,to_date('20-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.82,2090.1,1870.3,2847.8,to_date('21-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1858.8,2832.5,to_date('22-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1857.8,2846.5,to_date('23-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1857.8,2846.5,to_date('24-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.04,2117.7,1843.1,2861.5,to_date('25-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.03,2128.2,1850.8,2833.6,to_date('26-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1551.17,2124.7,1859.6,2826,to_date('27-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.37,2079.9,1844.5,2816.5,to_date('28-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.1,2074.9,1852.6,2799.2,to_date('29-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1522.26,2046.4,1814.6,2773.4,to_date('30-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1533.79,2079.8,1796.8,2757.9,to_date('31-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1510.18,2076.7,1782.5,2745.3,to_date('01-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.91,2104.5,1803.5,2759.2,to_date('02-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1555.52,2101.3,1827.1,2765.1,to_date('03-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1581.49,2084,1837.5,2763.1,to_date('04-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.61,2063.9,1837.7,2737.6,to_date('05-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2387.6,3270.6,1939.7,3744.3,to_date('23-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2373.01,3243.5,1932.4,3714.6,to_date('24-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2401.59,3253,1936.9,3725.6,to_date('25-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2421.93,3264.4,1953.3,3740,to_date('26-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.52,3296.2,1976.9,3740.3,to_date('27-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2438.73,3298.6,1960.9,3704.2,to_date('28-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.09,3309.9,1974.5,3715.9,to_date('29-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2486.95,3360.5,1996.9,3738.2,to_date('30-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2485.18,3354.8,1990.8,3727.6,to_date('31-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2488.85,3385.5,2017.2,3752.7,to_date('01-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2486.83,3400.6,2015.3,3768.6,to_date('02-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.52,3383.4,2001.9,3777.1,to_date('03-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2471.38,3408.3,2005.9,3758.9,to_date('04-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2479.38,3581.9,2008,3758.2,to_date('05-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2448.8,3549.7,1975.5,3710.3,to_date('06-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2419.72,3506.2,1948.9,3674.5,to_date('07-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2415.29,3540.5,1932.1,3639.5,to_date('08-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2417,3558.3,1944.3,3640.3,to_date('09-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2432.46,3578.4,1962.4,3681.8,to_date('10-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2458,3561.1,1950.1,3644.8,to_date('11-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.55,3583.6,1965,3669.6,to_date('12-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2484.74,3605.1,1967.2,3693,to_date('13-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2491.73,3611.1,1969.8,3685.4,to_date('14-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.32,3629.3,1976.9,3698.3,to_date('15-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2479.84,3647.7,1974.4,3707,to_date('16-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2514.8,3677,2003.8,3681.9,to_date('17-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.78,3658.1,2008,3660.9,to_date('18-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2523.81,3671,2030.5,3672.4,to_date('19-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2502.94,3640.3,2020,3672.6,to_date('20-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2489.35,3646.5,2044.8,3699.7,to_date('21-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2500.75,3654.3,2055.6,3718.4,to_date('22-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2508.11,3648.3,2070.4,3728.5,to_date('23-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2489.52,3606.7,2064,3725.1,to_date('24-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('25-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('26-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('27-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2510.81,3594.4,2081.7,3758.6,to_date('28-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2525.59,3601.5,2093.9,3767.4,to_date('29-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2508.12,3566.9,2072.5,3744.2,to_date('30-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2526.74,3575.5,2074.7,3766.8,to_date('01-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2540.79,3589.6,2080.6,3790.5,to_date('02-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2547.32,3607.5,2097.3,3825.3,to_date('03-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2519.82,3603.9,2075.1,3805.6,to_date('04-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2524.18,3601.2,2086,3820.7,to_date('05-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2535.86,3628.1,2092.5,3857.1,to_date('06-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.27,3673.2,2116.5,3852.7,to_date('07-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.12,3647.3,2112,3833,to_date('08-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2524.84,3668.4,2122.1,3817.6,to_date('09-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2538.68,3668.1,2116.4,3819.3,to_date('10-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2539.88,3696,2138.4,3832.8,to_date('11-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.97,3658.2,2130.8,3809.2,to_date('12-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2492.63,3651,2146.8,3817.9,to_date('13-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2492.63,3651,2146.8,3806,to_date('14-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2465.49,3635.8,2136.8,3776.4,to_date('15-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.43,3611.2,2115.4,3751.6,to_date('16-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2473.52,3591.6,2090.4,3751.6,to_date('17-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2476.79,3583.6,2083.7,3723,to_date('18-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2466.21,3534.1,2083.7,3707.3,to_date('19-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2470.57,3521.6,2085.4,3728.3,to_date('20-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2499.02,3556.9,2114.8,3754.4,to_date('21-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2496.33,3557.8,2100.9,3739.2,to_date('22-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2528.2,3582.4,2122.1,3759.7,to_date('23-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.4,3572.5,2124.6,3776.2,to_date('24-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.4,3572.5,2124.6,3753.6,to_date('25-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2552.29,3593,2136.9,3789.6,to_date('26-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2541.98,3584,2120.6,3778.2,to_date('27-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2564.12,3587.3,2129.7,3789.4,to_date('28-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.79,3550,2103.5,3764.2,to_date('29-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2546.55,3547.4,2114.5,3747,to_date('30-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.53,3572.2,2117.7,3752.1,to_date('31-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.53,3572.2,2117.7,3752.1,to_date('01-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.15,3583.8,2132.9,3760.2,to_date('02-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.53,3558.2,2117.1,3775.7,to_date('03-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2535.78,3537.7,2108.4,3746.7,to_date('04-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2523.81,3556.1,2110.1,3747.8,to_date('05-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2543.99,3544.6,2121.1,3739.2,to_date('06-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2550.42,3568.1,2111,3755.2,to_date('07-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2551.03,3572,2115.4,3753.4,to_date('08-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.02,3585.5,2133.2,3760.3,to_date('09-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2536.03,3547.9,2102,3706.8,to_date('10-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2552.96,3575.8,2120.8,3728.8,to_date('11-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2571.1,3575.6,2137.5,3755.7,to_date('12-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2569.9,3600.9,2137.3,3769.2,to_date('13-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2566.13,3607.4,2126.2,3761.7,to_date('14-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2544.9,3593.9,2111.8,3753.6,to_date('15-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.71,3600.1,2113,3761.5,to_date('16-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2554.12,3628.3,2107.9,3756.4,to_date('17-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2546.04,3643.6,2100.7,3753.2,to_date('18-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.22,3655.4,2077.1,3727.5,to_date('19-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2547.78,3687.3,2084.2,3722.3,to_date('20-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.19,3701.5,2097.6,3710.8,to_date('21-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2578.36,3699.6,2118.4,3679.5,to_date('22-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2572.06,3723.5,2113.3,3695.5,to_date('23-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2551.8,3703,2112.9,3678.8,to_date('24-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2570.44,3732.9,2123.7,3711,to_date('25-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2573.44,3728.2,2118.8,3725.6,to_date('26-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2565.32,3750.8,2111.8,3725.7,to_date('27-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2566.32,3757,2114,3714.1,to_date('28-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2578.74,3771.3,2126.8,3760.6,to_date('29-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.79,3731.2,2098.8,3743.2,to_date('30-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2561.51,3732.8,2079.1,3741.5,to_date('01-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.24,3773.4,2076.6,3752.3,to_date('02-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2573,3788,2081.9,3765.8,to_date('03-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2561.95,3810,2073.4,3749,to_date('04-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.97,3785.8,2050.6,3728.3,to_date('05-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2529.5,3755.4,2029.5,3698.3,to_date('06-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2475.98,3678.1,1989.5,3632.3,to_date('07-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2497.69,3656.2,1995.1,3658.2,to_date('08-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.56,3649.2,2007.3,3693.4,to_date('09-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.35,3661.5,1992.8,3710.5,to_date('10-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2468.32,3566.1,1960.3,3681.3,to_date('11-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2488.82,3585.2,1982.2,3708.4,to_date('12-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2459.13,3482.6,1954.1,3668.8,to_date('13-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2464.51,3537.4,1974.7,3684.7,to_date('14-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2469.51,3537.8,1962.9,3673.3,to_date('15-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.25,3549.5,1961.1,3678.8,to_date('16-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2466.84,3518.2,1968.5,3668.5,to_date('17-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2491.5,3494.4,1995.9,3703.2,to_date('18-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2504.16,3494.4,2009.9,3734.4,to_date('19-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2520.26,3604.6,2023.4,3770.6,to_date('20-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2520.52,3623.3,2013.2,3788.3,to_date('21-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2527.73,3629,1999.3,3788.4,to_date('22-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2541.41,3662.6,1996.7,3811.1,to_date('23-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.63,3653.4,1997.4,3811.4,to_date('24-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.96,3643.2,1985.4,3810.7,to_date('25-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2529.14,3623.1,1978.2,3803.3,to_date('26-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2545.65,3630.2,1980.6,3823.4,to_date('27-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2544.61,3650.7,1979.5,3830.3,to_date('28-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2542.75,3646.3,1979.5,3837.4,to_date('29-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2567.96,3670.5,1979.5,3872.9,to_date('30-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2556.25,3664.4,1986.2,3863.7,to_date('31-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.12,3698.2,2019.3,3883.2,to_date('01-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.44,3683.8,2000.6,3872.1,to_date('02-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.42,3713,2017.8,3891.1,to_date('03-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.12,3725.4,2020.8,3907.5,to_date('04-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2554.42,3714.6,2020.4,3907.5,to_date('05-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2563.59,3722.4,2018,3905.7,to_date('06-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2556.64,3692.6,2002.9,3918.7,to_date('07-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.84,3682.6,1977.6,3885,to_date('08-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.49,3646.3,1970.6,3867.6,to_date('09-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2538.34,3654.7,1977,3884.4,to_date('10-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2528.73,3623.9,1971.4,3855.9,to_date('11-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2526.18,3634.2,1984.8,3872.7,to_date('12-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.55,3627.1,1996.2,3887.2,to_date('13-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2542.74,3637.8,2004.8,3893,to_date('14-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.71,3684.4,2020.3,3910.8,to_date('15-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.77,3694.2,2042.1,3916.1,to_date('16-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.7,3678.9,2038.1,3905.6,to_date('17-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2588.04,3687.2,2065.4,3932.6,to_date('18-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2614.5,3716.8,2080.4,3967.9,to_date('19-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2630.24,3722.7,2086.2,3977.2,to_date('20-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2626.43,3690,2080.9,3972.3,to_date('21-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2621.2,3690.1,2072.7,3955.7,to_date('22-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2625.46,3694.9,2082.3,3974.3,to_date('23-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2641.5,3694.9,2079.5,3964.1,to_date('24-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2624.18,3635.5,2067.1,3919.7,to_date('25-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2643.42,3661.2,2081.5,3910.5,to_date('26-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2663.1,3697.7,2103.4,3935.7,to_date('27-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2664.96,3699.5,2104.1,3933.2,to_date('28-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2659.86,3696.6,2107.1,3946.4,to_date('29-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2655.49,3736.4,2132.8,3953.7,to_date('30-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2654.34,3737.5,2123.2,3992.2,to_date('31-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.29,3760.2,2141.8,4015.1,to_date('01-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.29,3771.6,2136.1,4000,to_date('02-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2704.25,3771.6,2152.7,4024.8,to_date('03-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2705.21,3813.7,2151.6,4031.5,to_date('04-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2702.6,3818.1,2161.5,4035.6,to_date('05-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.23,3807.9,2146.7,4009.3,to_date('06-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2683.52,3797.3,2135.6,3994.7,to_date('07-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2692.69,3805.2,2147.1,4028.1,to_date('08-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2700.83,3813.7,2143.6,4038.7,to_date('09-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2718.73,3822.2,2168.3,4050.8,to_date('10-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2705.06,3790.2,2158.5,4024.4,to_date('11-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2717.5,3793.2,2165.3,4042.1,to_date('12-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2727.56,3803.3,2185.2,4053.1,to_date('13-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2733.67,3788.2,2180.2,4073.1,to_date('14-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2721.74,3775.3,2175.6,4057.2,to_date('15-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2678.89,3742.9,2148.9,4028.4,to_date('16-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2681.94,3753.6,2151.6,3999.4,to_date('17-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2690.79,3758.7,2162.4,4022.4,to_date('18-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2703.33,3763.5,2150.4,4025.3,to_date('19-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2675.5,3732,2125.7,3993.5,to_date('20-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2664.72,3723.1,2124.8,3963.9,to_date('21-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2671.4,3725.3,2140.5,3979.1,to_date('22-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2670.19,3736.1,2140.5,3948.5,to_date('23-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2678.73,3749.2,2142.2,3928.1,to_date('24-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2716.16,3784.8,2187.3,3921.1,to_date('25-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2735.28,3815.4,2213.4,3935.7,to_date('26-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2724.25,3811.5,2211.8,3900.4,to_date('27-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2732.29,3812,2205.2,3910.8,to_date('28-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2730.44,3827.6,2205.2,3914.4,to_date('01-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2764,3829.8,2229.1,3934.3,to_date('02-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2770.61,3825.7,2217.2,3926.9,to_date('03-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2784.39,3856.4,2218.2,3926.1,to_date('04-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2800.6,3897.9,2240.3,3958.2,to_date('05-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2768.68,3888.3,2228.8,3962.1,to_date('06-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2781.54,3892.6,2240.1,3978.1,to_date('07-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2766.08,3883.3,2233.4,3962.8,to_date('08-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2765.29,3861.5,2233.6,3953.8,to_date('09-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2769.47,3882.8,2255.5,4018.7,to_date('10-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2800.52,3891.7,2277.1,4054.6,to_date('11-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2808.62,3878.2,2275.1,4068.4,to_date('12-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2793.86,3845.3,2270.8,4049.2,to_date('13-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2829.68,3884.1,2290.3,4050.2,to_date('14-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2848.84,3902.5,2315.7,4058,to_date('15-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2853.46,3874,2318.6,4038.5,to_date('16-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2900.76,3924.5,2349.1,4061.5,to_date('17-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.89,3910.6,2308.7,4045.2,to_date('18-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2894.43,3913.8,2292.5,4051.2,to_date('19-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2832.53,3835.5,2240.7,3963,to_date('20-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2870.3,3874,2255.8,4011.6,to_date('21-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2890.95,3885.4,2251.4,4035.7,to_date('22-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2836.36,3857.6,2213.3,3982.5,to_date('23-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2846.94,3860,2212.1,3990.7,to_date('24-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2815.77,3839.3,2203.4,3972.4,to_date('25-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2841.16,3869.2,2222.6,3993.8,to_date('26-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2808.5,3850.4,2193.7,3979.6,to_date('27-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2814.23,3874,2218.9,4018.2,to_date('28-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2824.83,3880.8,2248.7,4051.3,to_date('29-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2835.54,3890.1,2278.5,4077.6,to_date('30-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3875.5,2287.4,4087.2,to_date('31-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2289.6,4092.5,to_date('01-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2289.6,4092.5,to_date('02-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2303.8,4092.5,to_date('03-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2859.22,3922.2,2307,4091,to_date('04-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.07,3948.3,2318.6,4115.7,to_date('05-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.07,3942.2,2315.7,4118.5,to_date('06-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.07,3942.2,2315.7,4118.5,to_date('07-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2820.81,3942.2,2257,4057.4,to_date('08-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2863.26,3940.1,2282.8,4089.5,to_date('09-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2890.2,3923.8,2306.7,4106.5,to_date('10-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2876.34,3922.9,2301.7,4078.8,to_date('11-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2904.08,3944.9,2331.6,4087.5,to_date('12-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2936.69,3966.2,2349.1,4087,to_date('13-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2915.81,3947.4,2327.5,4056.6,to_date('14-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2956.78,3975.5,2361.3,4107.3,to_date('15-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2978.84,3983.6,2402.1,4168.2,to_date('16-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2976.56,3979.6,2388,4158.9,to_date('17-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2996.12,4007.1,2407.8,4197.5,to_date('18-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3006.87,4019.9,2425.1,4207.7,to_date('19-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2999.19,4009.5,2406.1,4194,to_date('20-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3000.66,4023.1,2409.9,4195.5,to_date('21-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3026.63,4115.4,2442.5,4219.1,to_date('22-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3037.28,4161,2461.3,4219.1,to_date('23-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2982.63,4125.5,2430.3,4218.8,to_date('24-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2992.55,4127.3,2435.2,4212,to_date('25-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3028.27,4182.3,2482.8,4237.4,to_date('26-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2997.95,4169.7,2465,4207.5,to_date('27-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3018.58,4209.1,2503.1,4228.4,to_date('28-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3037.7,4272.2,2516.6,4275.8,to_date('29-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3064.7,4282.8,2508.6,4257.8,to_date('30-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3067.48,4296.5,2503.1,4260.9,to_date('01-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3114.73,4305.5,2541.3,4281.5,to_date('02-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3124.78,4309.8,2558.4,4265.9,to_date('03-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3161.36,4357.9,2597.5,4307.8,to_date('04-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3185.72,4384.3,2595.4,4307.7,to_date('05-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3191.45,4408.4,2582.1,4304.3,to_date('06-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3211.01,4444.1,2599.3,4304.3,to_date('07-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3256.86,4436.3,2628.4,4327.1,to_date('08-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3249.17,4464.2,2627.4,4341,to_date('09-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3260.3,4514.6,2634.5,4337.8,to_date('10-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3230.83,4490.7,2617.5,4332.3,to_date('11-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3209.04,4525.5,2594.8,4357.4,to_date('12-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3197.09,4530.8,2575.2,4356.1,to_date('13-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3203.79,4522.5,2562.8,4336.8,to_date('14-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3180.63,4463.2,2567.9,4331.1,to_date('15-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3233.34,4503.9,2607.7,4344.7,to_date('16-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3245.02,4539,2602.2,4329.3,to_date('17-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3272.58,4519.7,2629.4,4339.2,to_date('18-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3261.04,4487.6,2607.8,4308.3,to_date('19-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3258.74,4460.1,2600.3,4307.1,to_date('20-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3345.09,4513.7,2651.7,4357.7,to_date('21-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3375.45,4547.1,2666.2,4360.1,to_date('22-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3396.55,4605.2,2698.9,4399.3,to_date('23-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3419.51,4638.9,2708.3,4420.3,to_date('24-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3426.77,4684.4,2709.2,4437.4,to_date('25-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3430.95,4677.1,2686.2,4444.3,to_date('26-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3382.4,4676.2,2641.7,4422.5,to_date('27-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3367.82,4609.9,2632.1,4397.7,to_date('28-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3404.29,4636.2,2645.6,4424.3,to_date('29-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3337.11,4556.5,2588.4,4373.3,to_date('30-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3289.59,4519.9,2574,4356.8,to_date('31-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3305.72,4535.1,2596.8,4332.2,to_date('01-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3247.03,4442.9,2553.7,4258.1,to_date('02-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3288.52,4491.3,2587.1,4254.8,to_date('03-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3302.57,4497.3,2579.3,4214.8,to_date('04-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3374.93,4558.6,2624.3,4270.7,to_date('05-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3439.22,4620.5,2648.7,4301.5,to_date('06-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('07-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('08-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('09-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3281.46,4501.7,2581.8,4248.1,to_date('10-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3210.94,4488.7,2530.3,4236.6,to_date('11-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3212.82,4463.9,2514.5,4214.6,to_date('12-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3235.35,4471.5,2518,4236.6,to_date('13-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3342.77,4588,2572.3,4271.7,to_date('14-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3328.13,4582.6,2579,4269.3,to_date('15-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3364.76,4634.9,2617.6,4292.3,to_date('16-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3352.58,4626.6,2608,4313.2,to_date('17-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3319.24,4604.2,2574.6,4270.7,to_date('18-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3297.52,4586.3,2566.1,4251.7,to_date('19-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3369.26,4643.4,2620.6,4286.8,to_date('20-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3347.54,4625.6,2621,4294.6,to_date('21-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3361.8,4665.7,2615.2,4298.9,to_date('22-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3361.2,4699.1,2547.6,4310.5,to_date('23-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3328.41,4740.1,2522.7,4328.7,to_date('24-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3348.9,4752.3,2514.7,4346.1,to_date('25-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3366.87,4781.1,2533.6,4387.7,to_date('26-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3396.49,4836.1,2539.8,4388.5,to_date('27-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3357.57,4772.3,2536.3,4369.7,to_date('28-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3372.96,4793.3,2550.3,4389.7,to_date('29-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3425.86,4855.1,2602.9,4433.2,to_date('30-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3438.09,4897.6,2639.5,4436,to_date('01-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3438.09,4897.6,2639.5,4445,to_date('02-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3491.08,4953.5,2655.3,4455.6,to_date('03-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3565.69,5029.6,2672.8,4455.6,to_date('04-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3548.52,4988.4,2651.9,4519.3,to_date('05-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3537.45,5016,2643.3,4537.5,to_date('06-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3537.45,5016,2643.3,4580.4,to_date('07-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3533.21,5004.7,2633.9,4630.9,to_date('08-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3593.14,5042.5,2693.1,4669.6,to_date('09-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3559.29,5084.2,2719.6,4691,to_date('10-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3588.57,5134.3,2774.6,4686.9,to_date('11-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3564.85,5141.7,2776,4681.2,to_date('12-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3569.26,5157.5,2784.3,4693.9,to_date('13-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3569.26,5157.5,2784.3,4645.2,to_date('14-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3516.2,5081,2751.1,4607.5,to_date('15-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3600.4,5178.6,2786.4,4642,to_date('16-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3575.44,5176.4,2741.7,4651.8,to_date('17-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3621.72,5181,2762.9,4661.8,to_date('18-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3669.31,5196.7,2654.7,4661.8,to_date('19-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3665.43,5190,2680.3,4681.6,to_date('20-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3626.6,5133.1,2583.2,4677.5,to_date('21-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3635.38,5132.1,2579.2,4672.3,to_date('22-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3562.73,5041.6,2583.9,4621.3,to_date('23-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3596.4,5150,2601.5,4562.8,to_date('24-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3655.59,5207.2,2624.5,4557.8,to_date('25-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3651.59,5238.5,2635.4,4557.1,to_date('26-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3684.6,5251.2,2690.9,4576.2,to_date('27-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3700.53,5320,2719.3,4645,to_date('28-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3668.61,5368.8,2686.2,4686.7,to_date('29-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3671.16,5361.9,2664.2,4739.6,to_date('30-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3671.87,5308.6,2696.2,4724.8,to_date('31-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3737.16,5364.2,2760.3,4757.4,to_date('01-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3752.37,5384.6,2808.5,4783.1,to_date('02-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3750.02,5362,2795.9,4745.1,to_date('03-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3721.18,5345.9,2762.6,4682.2,to_date('04-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3730.56,5405,2751.7,4657,to_date('05-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3777.56,5510.3,2739.7,4653.7,to_date('06-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3788.54,5561.8,2757.1,4593.9,to_date('07-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3748.79,5587.8,2762.2,4575.8,to_date('08-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3761.07,5576.1,2784.8,4596.3,to_date('09-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3819.52,5662.4,2867.4,4640,to_date('10-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3820.16,5669.9,2893.6,4657.9,to_date('11-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3809.92,5700.3,2891,4640.3,to_date('12-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3766.89,5620.6,2858.3,4604.6,to_date('13-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3834.84,5654.8,2944,4728.3,to_date('14-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3867.53,5674.3,2909.5,4751.4,to_date('15-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3939.73,5804.9,2937,4831.7,to_date('16-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3946.73,5846.5,2934.5,4812.8,to_date('17-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4003.35,5947,2947.7,4810.7,to_date('18-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4030.1,6012.6,2929.8,4758.5,to_date('19-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4026.97,5977.1,2950.6,4762.4,to_date('20-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4000.65,5885.4,2929.1,4767.8,to_date('21-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4074.3,5801.5,2941.6,4799.5,to_date('22-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4142.19,5845.8,2941.6,4857.4,to_date('23-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4139.68,5844.7,2950.7,4899.3,to_date('24-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4223.69,5927.5,2988,4964.2,to_date('25-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4203.91,5868.3,2958.6,4949,to_date('26-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4131.94,5737.1,2876.7,4877.2,to_date('27-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4139.96,5620.5,2874.1,4805.7,to_date('28-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4297.64,5677.1,2921.1,4846.7,to_date('29-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4384.82,5869.9,3003.5,4874.5,to_date('30-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4320.52,5849.2,2973.5,4862.9,to_date('31-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4368.54,5847,3025.9,4851.5,to_date('01-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4400.3,5888,3022.2,4862.6,to_date('02-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4377.7,5842.1,3023.6,4876.6,to_date('03-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4458.66,5929.5,3069.3,4927.3,to_date('04-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4405.52,5898.2,3075.7,4907.5,to_date('05-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4336.98,5898.2,3049.5,4899.3,to_date('06-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4302.5,5771,2992.4,4895.7,to_date('07-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4325.86,5765.2,2984.1,4960.6,to_date('08-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4364.25,5812.1,3037.1,5026.2,to_date('09-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4428.08,5922.1,3056.3,5086.8,to_date('10-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4342.31,5864.8,2996.3,5031.3,to_date('11-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4333.15,5825.6,2983.4,5031.9,to_date('12-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4377.51,5808.4,2998.6,5075.8,to_date('13-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4237.06,5682.1,2924,5003.6,to_date('14-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4195.53,5579.5,2921.8,4991.3,to_date('15-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4077.59,5498.5,2921.8,4865.8,to_date('16-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4080.55,5405.6,2870.1,4835,to_date('17-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4190.45,5580.1,2936.2,4914.2,to_date('18-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4251.93,5690.1,2979.3,4958.4,to_date('19-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4204.81,5668.8,2957.2,4978,to_date('20-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4090.14,5475.8,2904.2,4901.1,to_date('21-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4076.75,5473.9,2898.6,4901.1,to_date('22-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3993.7,5363.3,2869.3,4886.3,to_date('23-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3992.03,5409.6,2871.7,4906.9,to_date('24-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3897.43,5217.3,2828.4,4845.4,to_date('25-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3919.79,5216.7,2770.5,4817.5,to_date('26-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4001.81,5271.5,2805.8,4870.2,to_date('27-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4127.28,5447.5,2921.2,4952.2,to_date('28-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4062.13,5478.6,2918,4976.9,to_date('29-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4093.43,5478.1,2927,4991.3,to_date('30-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4073.71,5532.9,2924.5,4994.2,to_date('01-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4131.26,5505.3,2940.9,4985.2,to_date('02-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4104.57,5445.1,2919.7,4950.5,to_date('03-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4028,5356.7,2874.6,4905.2,to_date('04-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3890.24,5280.8,2843.6,4854.8,to_date('05-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3796.61,5281.9,2834.1,4848.2,to_date('06-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3869.53,5321.7,2898.6,4902.9,to_date('07-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3995.69,5417.8,2940.6,4976.4,to_date('08-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3970.44,5550.4,2944,5013.1,to_date('09-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4004.04,5629,2978.4,5046.2,to_date('10-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3983.06,5611,2977.2,5023.8,to_date('11-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4096.85,5705.1,3017.5,5075.7,to_date('12-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4091.77,5730.4,2997.2,5027.5,to_date('13-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.95,5732.5,3023.7,5077.2,to_date('14-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4104.93,5667.1,3005.4,5065.5,to_date('15-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4135.09,5716.6,2985.6,5226.3,to_date('16-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4116.52,5691.8,2989,5220.3,to_date('17-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4154.89,5673.6,3008.3,5244.2,to_date('18-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4262.98,5754.7,3054.9,5317.1,to_date('19-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.17,5825,3052.1,5296.1,to_date('20-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.17,5929,3094,5330.8,to_date('21-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4326.35,5897.4,3078,5300,to_date('22-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4311.13,5846.9,3064.4,5305.6,to_date('23-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4267.4,5822.3,3024.1,5262.1,to_date('24-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4179.92,5732.2,2960.7,5217.8,to_date('25-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4164.62,5699.5,2955.1,5227.3,to_date('26-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4225.27,5792.8,3002.9,5300.1,to_date('27-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4215.23,5836.3,3002.5,5298.9,to_date('28-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4168.62,5815.9,2992.2,5263.7,to_date('29-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4149.92,5806.8,2992.9,5287.9,to_date('30-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4049.16,5751.6,2958,5271.1,to_date('31-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4069.25,5777.2,2946.7,5211,to_date('01-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4172.47,5862.9,2989.9,5225.9,to_date('02-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4124.86,5803.2,2958.1,5148.8,to_date('03-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3976.38,5651.8,2856.9,4991.5,to_date('04-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3981.44,5689.5,2849,4970.2,to_date('05-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3871.39,5533.5,2769.6,4840.7,to_date('06-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3645.69,5279.7,2651.3,4755.4,to_date('07-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3806.66,5479,2818,4871.8,to_date('08-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3748.88,5370.9,2739.5,4801.9,to_date('09-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3753.66,5467.2,2739.3,4842.3,to_date('10-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3847.73,5581.6,2788,4906.4,to_date('11-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3784.8,5538.2,2774.9,4897.4,to_date('12-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3841.39,5601.6,2822.4,4908.3,to_date('13-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3813.88,5557.4,2781.8,4863.8,to_date('14-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3715.38,5438.6,2707.1,4764.3,to_date('15-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3728.37,5459.7,2707.1,4806.8,to_date('16-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3734.79,5483.9,2707.1,4793.7,to_date('17-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3697.48,5434,2694.5,4720.4,to_date('18-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3701.94,5418.2,2700.7,4711,to_date('19-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3676.65,5437,2698.9,4741.8,to_date('20-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3816.71,5565,2773,4867,to_date('21-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3844.14,5574.2,2782.6,4845.4,to_date('22-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3876.9,5571.7,2790.6,4830.1,to_date('23-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3931.81,5650.4,2821.2,4908.4,to_date('24-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3941.91,5725.5,2861.7,4985.8,to_date('25-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3832.1,5645.7,2802.5,4898.6,to_date('26-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3850.14,5666.3,2786.3,4863.5,to_date('27-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3926.93,5738.3,2811.7,4891.2,to_date('28-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3961.97,5772.4,2829,4889,to_date('29-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3972.08,5775.9,2854.4,4831.8,to_date('30-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4125.92,5875.1,2918.5,4921.8,to_date('01-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4096.4,5919.9,2913.1,4977.6,to_date('02-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4074.55,5922.7,2902.4,4970.7,to_date('03-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4159.72,5969.5,2914.5,5082.3,to_date('04-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4191.81,6009,2910.1,5142.9,to_date('05-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4208.14,6095.3,2932.5,5187.4,to_date('06-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4187.13,6103.2,2959.4,5177.1,to_date('07-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4116.7,6056.6,2932.2,5130.7,to_date('08-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4016.7,6021.8,2828.5,5035.9,to_date('09-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4061.91,6018.7,2830.3,5045.2,to_date('10-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4029.08,5986.6,2838.3,5121.8,to_date('11-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.31,6092.7,2912.2,5203.4,to_date('12-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4154.57,6122.1,2893.3,5190.8,to_date('13-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4162.92,6115.1,2894.5,5168.3,to_date('14-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4055.35,5989.9,2822.9,5020.2,to_date('15-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4125.54,6049.3,2869.7,5018.2,to_date('16-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6044.7,2858.1,5049.8,to_date('17-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6046.7,2874.1,5013.9,to_date('18-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6046.7,2874.1,5013.9,to_date('19-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6046.7,2875.1,5013.9,to_date('20-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.02,6190.4,2939.5,5112.4,to_date('21-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4224.3,6267.6,2975.5,5132.3,to_date('22-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4224.3,6265.5,2998.9,5135.5,to_date('23-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4224.3,6265.5,2998.9,5135.5,to_date('24-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4364.32,6265.5,3038.7,5193.5,to_date('25-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4416.95,6397,3072.8,5262.5,to_date('26-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4360.05,6375.7,3037.7,5264.4,to_date('27-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4339.98,6390,3006.7,5224.1,to_date('28-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4293.64,6330.2,2954.9,5237.1,to_date('29-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4237.75,6251.8,2919.8,5138.3,to_date('30-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4134.64,6062.1,2862.5,5068.8,to_date('31-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.01,6169.3,2902.9,5083.9,to_date('01-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4145.41,6149.8,2919.8,5106.9,to_date('02-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4140.22,6148.5,2932.8,5165.8,to_date('03-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4216.24,6274,2976.1,5263.1,to_date('04-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4290.05,6340.4,2987,5273.6,to_date('05-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4310.83,6397.5,3008.3,5278.2,to_date('06-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4250.47,6391.4,2998.1,5272.3,to_date('07-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4238.77,6356.1,2988.6,5253.1,to_date('08-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4222.16,6391,2966.2,5181.4,to_date('09-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.34,6411,3000.5,5237.2,to_date('10-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4316.05,6424,3052,5326.3,to_date('11-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4385.29,6508.7,3088.3,5372.6,to_date('12-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4444.53,6530.4,3133.8,5422.4,to_date('13-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4442.53,6582.6,3172.1,5458.5,to_date('14-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4529.88,6688,3187.5,5599,to_date('15-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4529.18,6720.7,3188.4,5612.8,to_date('16-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4509.25,6708.9,3166.3,5595.8,to_date('17-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4494.72,6772,3189.6,5606.4,to_date('18-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4536.91,6857.1,3216.7,5629.7,to_date('19-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4519.56,6828.4,3220.9,5600.9,to_date('20-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4558.62,6860.8,3235.8,5613.3,to_date('21-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4552.46,6931.6,3240,5607.9,to_date('22-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4509.37,6856,3178.7,5552.5,to_date('23-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4522.42,6898.9,3187.7,5582.3,to_date('24-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4535.56,6905.3,3225.1,5619.9,to_date('25-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4627.42,6990.5,3280.5,5709.5,to_date('26-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4611.66,6966.2,3281.7,5723.4,to_date('27-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4581.08,6953.2,3250.6,5718.5,to_date('28-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4583.03,6986.7,3262.5,5718.5,to_date('29-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4610.66,6986.1,3273.5,5702.8,to_date('30-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4604.55,6945,3262.3,5651,to_date('31-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4704.58,7065.4,3348.2,5745.1,to_date('01-FEB-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4695.78,7118.6,3397,5764.8,to_date('02-FEB-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4693.86,7153.1,3421.9,5767.3,to_date('03-FEB-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1982.99,2560.3,1881.1,3208.8,to_date('14-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1982.99,2560.3,1881.1,3208.8,to_date('15-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1982.99,2560.3,1881.1,3208.8,to_date('16-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1954.62,2547.1,1855.7,3194.5,to_date('17-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1943.88,2516.8,1874.9,3170.1,to_date('18-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1955.33,2544.2,1882,3174.7,to_date('19-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1987.7,2560.8,1928.4,3199.9,to_date('20-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.97,2554.8,1918.5,3209.3,to_date('21-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2007.57,2561.1,1945.9,3214.9,to_date('22-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2028.52,2574.4,1942.3,3226.2,to_date('23-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.25,2567,1931,3217.6,to_date('24-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2017.95,2597.2,1918.5,3216.7,to_date('25-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2017.95,2597.2,1918.5,3220.4,to_date('26-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.47,2621.4,1936.9,3248.2,to_date('27-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2037.99,2618.6,1971.1,3262.6,to_date('28-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2034.15,2641.7,1946.2,3264.3,to_date('29-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2021.22,2650.5,1927.4,3251.7,to_date('30-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2030.65,2658.8,1927.4,3251.7,to_date('31-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.66,2655.6,1988.7,3261.2,to_date('01-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.41,2661.4,1996.7,3290.1,to_date('02-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.41,2701,2003.6,3317.9,to_date('03-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2102.4,2728.2,2017.3,3310.3,to_date('04-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2092,2739,1991.5,3310.7,to_date('05-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2109.36,2746.6,2001.3,3300.8,to_date('06-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.99,2739.8,2004.7,3297.4,to_date('07-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.8,2727.7,1989.8,3285.8,to_date('08-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.42,2701,1965.5,3261,to_date('09-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2096.87,2751.2,1979.5,3284.5,to_date('10-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.55,2750.4,1965.3,3291.8,to_date('11-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2105.88,2778.5,1960.7,3327.3,to_date('12-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2105.88,2778.5,1960.7,3328.2,to_date('13-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2065.71,2751.7,1919.1,3311.1,to_date('14-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.26,2762.5,1927.8,3311.1,to_date('15-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2081.46,2779.4,1927.3,3309.9,to_date('16-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.95,2786.4,1948,3319.4,to_date('17-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.24,2807.8,1960.8,3340.6,to_date('18-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.72,2805.3,1971.3,3345,to_date('19-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.72,2805.3,1971.3,3376.6,to_date('20-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.17,2809.7,1965.9,3380,to_date('21-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2136.72,2819.6,1974.7,3370.8,to_date('22-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.21,2806.8,1951.2,3380.8,to_date('23-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2107.16,2795.3,1897,3337.7,to_date('24-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.79,2802.3,1907.8,3344.6,to_date('25-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.25,2800.9,1922.8,3348,to_date('26-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.84,2798.1,1893.7,3339.8,to_date('27-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.78,2800.2,1920.7,3370.4,to_date('28-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2125.06,2775.4,1905,3366.1,to_date('29-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.13,2803,1918.4,3381.3,to_date('01-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.36,2805.6,1896.1,3377.2,to_date('02-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2816.8,1902.8,3378.3,to_date('03-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2843.3,1925.2,3403.8,to_date('04-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2837.7,1895.1,3379.4,to_date('05-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.36,2830.3,1902.4,3309.2,to_date('06-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.99,2845.1,1877.7,3313.2,to_date('07-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.3,2836.8,1865.5,3282.7,to_date('08-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2094.68,2845.6,1861.9,3294,to_date('09-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.04,2825.3,1858.8,3314.6,to_date('10-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.68,2827.5,1879.7,3323.7,to_date('11-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2110.13,2847.5,1889.4,3349.2,to_date('12-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.63,2843.7,1907.8,3394.9,to_date('13-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2111.35,2833.7,1889.5,3388.3,to_date('14-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.37,2848.4,1950.8,3462.9,to_date('15-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.39,2842.9,1949.4,3455,to_date('16-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.15,2847.3,1961.3,3464,to_date('17-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2200.32,2867.9,1961.7,3450.6,to_date('18-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.72,2869.7,1948.3,3447.2,to_date('19-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2191.25,2863.4,1948.3,3429.2,to_date('20-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.36,2892.3,1951.5,3442.6,to_date('21-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.71,2875.1,1929,3420.7,to_date('22-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2195.31,2856.2,1931.2,3405.3,to_date('23-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.52,2822.2,1905.9,3400.4,to_date('24-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.54,2830.2,1908.9,3413.1,to_date('25-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.02,2836.6,1926.6,3431.6,to_date('26-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.23,2833.2,1941.3,3432.9,to_date('27-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2237.73,2827.1,1942.5,3454.3,to_date('28-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2239.36,2824.7,1947.9,3458.3,to_date('29-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2217.91,2833.1,1932.8,3468.9,to_date('30-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2222.51,2826.8,1920,3463.3,to_date('31-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.26,2826.8,1917.8,3449.9,to_date('01-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.6,2852.3,1960,3499.9,to_date('02-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2234.86,2814.3,1950.8,3475.6,to_date('03-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.68,2807.1,1957.1,3482.4,to_date('04-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2244.56,2800.6,1950.4,3483.5,to_date('05-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.89,2797.4,1954.7,3468.8,to_date('06-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2222.28,2781.3,1943.7,3468.3,to_date('07-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.72,2802.6,1949.2,3474.7,to_date('08-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2228.44,2820.1,1946.2,3467.5,to_date('09-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2208.41,2807.2,1946.2,3441.4,to_date('10-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.73,2838.4,1946.2,3444.4,to_date('11-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2251.64,2880.6,1970.4,3465.1,to_date('12-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2253.93,2880,1967.3,3470.6,to_date('13-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2265.86,2889.5,1971.6,3509.8,to_date('14-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2269.6,2886.7,1984.4,3535.7,to_date('15-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2258.97,2900.8,1963.3,3530.2,to_date('16-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2262.66,2896.8,1957.4,3515.9,to_date('17-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2253.91,2887.1,1939.2,3520,to_date('18-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2257.34,2893.8,1937.8,3524.9,to_date('19-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2240.31,2883.3,1890.1,3524.9,to_date('20-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2230.27,2887.3,1899.5,3502.6,to_date('21-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2251.3,2915.7,1921.4,3504,to_date('22-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2234.23,2908.8,1883.4,3477.8,to_date('23-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2242.76,2918.3,1881.7,3509.4,to_date('24-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2263.08,2952.7,1900.5,3522.7,to_date('25-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.57,2971.1,1902.5,3532.4,to_date('26-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.37,2999.6,1884.8,3557.7,to_date('27-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2263.27,2989.1,1861.7,3545.6,to_date('28-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.77,2989.2,1854.9,3554.5,to_date('29-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.26,2986.3,1869.7,3549.3,to_date('30-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.07,2990.5,1873.6,3535.9,to_date('01-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2300.72,3016.8,1898.2,3570.8,to_date('02-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2300.29,3011.6,1892.4,3565.4,to_date('03-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2305.58,3025.4,1880,3564.6,to_date('04-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2289.49,3008.1,1871.7,3533.3,to_date('05-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2306.66,3033.5,1882.5,3541.4,to_date('06-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2294.15,3029,1863.8,3561.5,to_date('07-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2275.72,3039.1,1853.9,3557.9,to_date('08-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2204.44,2994.7,1790.7,3514.8,to_date('09-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2212.97,3015.3,1800.6,3507,to_date('10-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.02,3045.5,1816.3,3523.3,to_date('11-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.9,3009.8,1791.8,3485,to_date('12-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.24,3000.6,1767.6,3479,to_date('13-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.27,3014.8,1788.4,3508.2,to_date('14-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.01,3039,1780.7,3520.2,to_date('15-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.01,3049,1800.7,3524.2,to_date('16-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.6,3036.5,1803.9,3544.1,to_date('17-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2191.54,3036.6,1800.3,3544.4,to_date('18-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.35,3054,1809.6,3526.5,to_date('19-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2157.41,3064,1785.7,3510.3,to_date('20-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.01,3050,1778,3460.1,to_date('21-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2153.56,3073,1794.4,3474.3,to_date('22-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2159.73,3083.7,1803.8,3523.8,to_date('23-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2206.11,3109.2,1817,3568,to_date('24-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.85,3116.3,1790.5,3557.3,to_date('25-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2185.52,3112.3,1779.7,3562.2,to_date('26-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.57,3135.4,1770.7,3593,to_date('27-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.09,3124.5,1757.3,3578.6,to_date('28-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2148.12,3120.4,1740.7,3551.4,to_date('29-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.86,3041.7,1721.1,3531.5,to_date('30-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2114.88,3062.3,1724.2,3535.3,to_date('31-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.74,3078.9,1764.1,3537.8,to_date('01-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.74,3080,1754.1,3519.6,to_date('02-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.01,3026.6,1742.4,3497.9,to_date('03-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2149.71,3057.9,1795.3,3510,to_date('04-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.76,3108.2,1814,3529.1,to_date('05-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.11,3128.5,1814,3518.7,to_date('06-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2180.49,3123.4,1828.7,3523,to_date('07-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2169.69,3137.6,1832.1,3500.4,to_date('08-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2168.57,3123.2,1822.6,3514.8,to_date('09-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2174.27,3133.8,1857.4,3522.4,to_date('10-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.05,3132.6,1866.3,3537.1,to_date('11-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.47,3150.1,1852.6,3541.6,to_date('12-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2174.78,3130.4,1839.8,3523.4,to_date('13-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2195.15,3121.4,1838.2,3536.8,to_date('14-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.34,3133,1838.2,3547.9,to_date('15-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.64,3124.8,1875.2,3571.4,to_date('16-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2199.29,3148.4,1905.1,3610.8,to_date('17-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.59,3152.5,1890.5,3609.2,to_date('18-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2204.01,3182.2,1881.4,3628.8,to_date('19-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.9,3183.9,1872.9,3604.1,to_date('20-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.53,3201,1875.5,3632.4,to_date('21-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.26,3209.2,1867.1,3602.5,to_date('22-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2210.92,3220.1,1891,3624,to_date('23-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2247.97,3251,1889.8,3649,to_date('24-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2242.91,3229.5,1870.3,3648.8,to_date('25-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.75,3245.9,1857.3,3655.5,to_date('26-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2254.95,3251.6,1828.3,3664.3,to_date('27-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.56,3261.2,1820.9,3664.3,to_date('28-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2261.08,3278.4,1774.9,3669.7,to_date('29-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2269.34,3278.5,1814.9,3664.2,to_date('30-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2274.94,3281.5,1834.8,3662.8,to_date('01-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.62,3245.7,1846.8,3639.5,to_date('02-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2277.7,3246.6,1856.3,3630,to_date('03-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2277.7,3261.6,1849.6,3652.1,to_date('04-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2278.64,3280.9,1848.9,3654.9,to_date('05-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2283.26,3273.4,1833.8,3662.4,to_date('06-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2286.21,3272.2,1875,3671.6,to_date('07-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2283.84,3289.8,1859.3,3642.6,to_date('08-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2240.24,3251.6,1820.5,3596.1,to_date('09-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.43,3217.7,1810.2,3576.9,to_date('10-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.07,3254.3,1831.2,3613.7,to_date('11-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.35,3255,1834.4,3633.3,to_date('12-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1873.4,3658.3,to_date('13-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1873.4,3658.3,to_date('14-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1866.7,3658.3,to_date('15-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.44,3317.1,1877,3676.4,to_date('16-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2273.9,3297.7,1879.1,3676.7,to_date('17-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.69,3297.7,1872,3689.3,to_date('18-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.69,3297.7,1872,3689.3,to_date('19-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2307.7,3297.7,1908.4,3687.9,to_date('20-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2326.18,3384.5,1943,3715.6,to_date('21-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2332.81,3395.8,1931.2,3714.1,to_date('22-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2315.66,3376.1,1917.7,3704.5,to_date('23-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2336.76,3368.1,1916.6,3720.6,to_date('24-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2351.47,3361.5,1916.3,3720.6,to_date('25-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2340.31,3314.8,1910.1,3671.5,to_date('26-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2330.98,3284.1,1897.9,3654.9,to_date('27-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2353.89,3292.9,1907.6,3657.3,to_date('28-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2361.38,3233.9,1924.4,3662.7,to_date('29-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2379.43,3254.8,1952.1,3710.6,to_date('30-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2375.63,3219.3,1966,3704.2,to_date('31-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2389.62,3257.8,1960.1,3748.7,to_date('01-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2400.58,3253.1,1964.3,3748.4,to_date('02-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2391.74,3249.9,1954.4,3754.2,to_date('03-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2393.12,3219,1934.1,3735,to_date('04-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2436.68,3221.3,1946,3758.2,to_date('05-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.02,3211.2,1950.2,3734.2,to_date('06-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.71,3191.4,1966.7,3734.7,to_date('07-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.7,3198.2,1980.7,3734.6,to_date('08-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2456.09,3236.8,2003.1,3735.3,to_date('09-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2463,3248.7,2021,3759.3,to_date('10-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.53,3279.9,2024.1,3752.8,to_date('11-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2459.81,3270,2022.2,3781.3,to_date('12-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2416.84,3221.7,1985.6,3746.6,to_date('13-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.98,3253.8,1988.5,3747.5,to_date('14-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2442.43,3262.7,1983.3,3726.1,to_date('15-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2411.49,3245.6,1956.2,3708.4,to_date('16-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2435.07,3242.7,1960.7,3716.3,to_date('17-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2428.59,3253.1,1967.7,3726.6,to_date('18-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2436.09,3260.1,1983.3,3747.6,to_date('19-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2427.77,3266.1,1956.4,3745,to_date('20-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2426.51,3272.2,1964.2,3779.8,to_date('21-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2423.6,3280.3,1952.5,3770.9,to_date('22-AUG-96','DD-MON-RR'));"],"enabled":true,"result":{"startTime":1736280226847,"interpreter":"script.low","endTime":1736280236411,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable EUSTOCK created.\n\n\n---------------------------\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the database","message":["%sql","","SELECT * ","FROM EUSTOCK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736280236912,"interpreter":"sql.low","endTime":1736280237404,"results":[{"message":"DAX\tSMI\tCAC\tFTSE\tDATES\n1976.52\t2556.1\t1890.2\t3200.9\t1996-01-09 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END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')            := 'ALGO_EXPONENTIAL_SMOOTHING';","    v_setlst('EXSM_INTERVAL')        := 'EXSM_INTERVAL_DAY';","    v_setlst('EXSM_MODEL')           := 'EXSM_ADDWINTERS_DAMPED';","    v_setlst('EXSM_SEASONALITY')     := '7';","    v_setlst('EXSM_PREDICTION_STEP') := '1';","    v_setlst('EXSM_SERIES_LIST')     := 'SMI,CAC,FTSE';","\t ","    DBMS_DATA_MINING.CREATE_MODEL2(","               MODEL_NAME          => 'MSDEMO_MODEL',","               MINING_FUNCTION     => 'TIME_SERIES',","               DATA_QUERY          => 'SELECT * FROM EUSTOCK',","               CASE_ID_COLUMN_NAME => 'DATES',","               TARGET_COLUMN_NAME  => 'DAX',","               SET_LIST            => v_setlst);","END;"],"enabled":true,"result":{"startTime":1736280237869,"interpreter":"script.low","endTime":1736280241147,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View model results - compare actuals to the prediction","message":["%sql","","SELECT * FROM DM$VRMSDEMO_MODEL ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736280241623,"interpreter":"sql.low","endTime":1736280242119,"results":[{"message":"PARTITION_NAME\tCASE_ID\tDAX\tDM$DAX\tDM$SMI\tDM$CAC\tDM$FTSE\nDM$$_PNVL\t1993-01-01 00:00:00\t1691.3699999999999\t1696.7851661517441\t2125.5661488438554\t2033.44361914249\t2912.5490660260589\nDM$$_PNVL\t1993-02-07 00:00:00\t1666.49\t1686.9091121537626\t2174.4264626901791\t1951.4991350844616\t2864.5419779378331\nDM$$_PNVL\t1993-02-17 00:00:00\t1628.8800000000001\t1605.1982669426102\t2142.2824275151752\t1917.7001906888413\t2802.3474213670765\nDM$$_PNVL\t1993-03-01 00:00:00\t1613.98\t1601.8860390105019\t2222.7267782722579\t1837.0503559863519\t2813.7859450056171\nDM$$_PNVL\t1993-03-05 00:00:00\t1633.1800000000001\t1618.2087959587548\t2246.5641550834716\t1888.50224862292\t2844.6610732420772\nDM$$_PNVL\t1993-03-14 00:00:00\t1679.6900000000001\t1668.9978157493995\t2298.7531353552745\t1895.9148012398277\t2852.9582359635824\nDM$$_PNVL\t1993-03-17 00:00:00\t1686.4400000000001\t1670.9032376634077\t2291.2277773810852\t1914.5963849865886\t2851.7603523267608\nDM$$_PNVL\t1993-03-25 00:00:00\t1692.1600000000001\t1690.5499448722337\t2316.4063822486587\t1938.9231456842112\t2901.3454607591589\nDM$$_PNVL\t1993-04-11 00:00:00\t1835.0899999999999\t1835.675764142027\t2400.2581833670929\t1977.01061870335\t2841.3891952887889\nDM$$_PNVL\t1993-04-18 00:00:00\t1832.2\t1853.0665103599558\t2401.805257295799\t2000.7444583660861\t2888.0091084109754\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create Training, Actual, and Test Datasets: forecast provided for the target column","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE tmesm_ms_train PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE tmesm_ms_train as","SELECT CASE_ID, DAX, DM$DAX, DM$SMI, DM$CAC, DM$FTSE, ","       CASE WHEN case_id < to_date('1998-02-03','YYYY-MM-DD') ","       THEN 0 ELSE 1 END AS prod ","FROM DM$VRMSDEMO_Model order by 1;"],"enabled":true,"result":{"startTime":1736280242586,"interpreter":"script.low","endTime":1736280243461,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable TMESM_MS_TRAIN created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Insert actual values into the prediction dataset to enable model evaluation","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE tmesm_ms_actual';","EXCEPTION WHEN OTHERS THEN NULL; END; ","/","CREATE TABLE tmesm_ms_actual (case_id DATE, DAX binary_double);","INSERT INTO tmesm_ms_actual VALUES(DATE '1998-02-04', 4633.008);","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE tmesm_ms_test';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE tmesm_ms_test as ","SELECT a.case_id, b.DAX, DM$DAX, DM$SMI, DM$CAC, DM$FTSE, 1 prod ","FROM   DM$VTMSDEMO_model a, tmesm_ms_actual b","WHERE  a.case_id=b.case_id;"],"enabled":true,"result":{"startTime":1736280243940,"interpreter":"script.low","endTime":1736280244591,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable TMESM_MS_ACTUAL created.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable TMESM_MS_TEST created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build GLM regression model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('MS_GLM_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')          := 'ALGO_GENERALIZED_LINEAR_MODEL';","\t","    DBMS_DATA_MINING.CREATE_MODEL2(","               MODEL_NAME          => 'MS_GLM_MODEL',","               MINING_FUNCTION     => dbms_data_mining.regression,","                DATA_QUERY         => 'SELECT * FROM tmesm_ms_train',","               CASE_ID_COLUMN_NAME => 'CASE_ID',","               TARGET_COLUMN_NAME  => 'DAX',","               SET_LIST            => v_setlst);    ","END;"],"enabled":true,"result":{"startTime":1736280245063,"interpreter":"script.low","endTime":1736280246736,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Evaluate the model"],"enabled":true,"result":{"startTime":1736280247209,"interpreter":"md.low","endTime":1736280247644,"results":[{"message":"<h2 id=\"evaluate-the-model\">Evaluate the model<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model views","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='MSDEMO_MODEL'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1736280248145,"interpreter":"sql.low","endTime":1736280248650,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VGMSDEMO_MODEL\tGlobal Name-Value Pairs\nDM$VPMSDEMO_MODEL\tExponential Smoothing Forecast\nDM$VRMSDEMO_MODEL\tExponential Smoothing Regression Build Data\nDM$VSMSDEMO_MODEL\tComputed Settings\nDM$VTMSDEMO_MODEL\tExponential Smoothing Regression Score Data\nDM$VWMSDEMO_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display backcasts","message":["%sql","","SELECT * ","FROM   DM$VRMSDEMO_MODEL","ORDER BY CASE_ID","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736280249119,"interpreter":"sql.low","endTime":1736280249604,"results":[{"message":"PARTITION_NAME\tCASE_ID\tDAX\tDM$DAX\tDM$SMI\tDM$CAC\tDM$FTSE\nDM$$_PNVL\t1993-01-01 00:00:00\t1691.3699999999999\t1696.7851661517441\t2125.5661488438554\t2033.44361914249\t2912.5490660260589\nDM$$_PNVL\t1993-01-02 00:00:00\t1701.46\t1695.7996033390459\t2134.1495501210279\t2001.6724577791497\t2888.2666654655577\nDM$$_PNVL\t1993-01-03 00:00:00\t1690.48\t1708.2127690800296\t2138.5055925374349\t2001.3130242851769\t2891.4545065601783\nDM$$_PNVL\t1993-01-04 00:00:00\t1685.46\t1686.238537210631\t2141.2155027537206\t1993.8779030518579\t2916.1994282579412\nDM$$_PNVL\t1993-01-05 00:00:00\t1686.1500000000001\t1694.4746833291151\t2161.2299128346185\t1988.9694220247859\t2907.4120770688946\nDM$$_PNVL\t1993-01-06 00:00:00\t1702.27\t1671.3173544335816\t2119.5886948061366\t1987.2778359197432\t2906.9183420265463\nDM$$_PNVL\t1993-01-07 00:00:00\t1711.9100000000001\t1701.456926915404\t2126.6203498819759\t1998.9855993031877\t2958.1359828873728\nDM$$_PNVL\t1993-01-08 00:00:00\t1714.48\t1712.7369364575434\t2150.6906591461366\t2006.3163895452492\t2948.358279522849\nDM$$_PNVL\t1993-01-09 00:00:00\t1708.6500000000001\t1718.5224134641876\t2167.7089783601191\t1996.1860828981924\t2961.8594901102406\nDM$$_PNVL\t1993-01-10 00:00:00\t1688.74\t1715.5388618037582\t2170.2102147494575\t1990.0207603254203\t2962.4867916958488\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display forecasts","message":["%sql","","SELECT * ","FROM DM$VTMSDEMO_MODEL","ORDER BY CASE_ID ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1736280250073,"interpreter":"sql.low","endTime":1736280250553,"results":[{"message":"PARTITION_NAME\tCASE_ID\tDAX\tDM$DAX\tDM$SMI\tDM$CAC\tDM$FTSE\nDM$$_PNVL\t1998-02-04 00:00:00\t\t4679.2161855773229\t7136.4668617779771\t3417.0147412943652\t5757.7332608184861\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare regression forecast (GLM) to baseline (ESM) forecast","message":["%sql","","SELECT CASE_ID, DAX, regression_forecast, baseline_forecast,","       DAX - regression_forecast AS regression_error,","       DAX - baseline_forecast AS baseline_error ","FROM (SELECT CASE_ID, DAX, ","             PREDICTION(MS_GLM_model using *) regression_forecast, ","             DM$DAX baseline_forecast","      FROM   tmesm_ms_test ","      ORDER BY CASE_ID);"],"enabled":true,"result":{"startTime":1736280251023,"interpreter":"sql.low","endTime":1736280251497,"results":[{"message":"CASE_ID\tDAX\tREGRESSION_FORECAST\tBASELINE_FORECAST\tREGRESSION_ERROR\tBASELINE_ERROR\n1998-02-04 00:00:00\t4633.008\t4669.6408226224476\t4679.2161855773229\t-36.63282262244775\t-46.20818557732309\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build XGBoost model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('MS_XGB_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')          := 'ALGO_XGBOOST';","\t","    DBMS_DATA_MINING.CREATE_MODEL2(","               MODEL_NAME          => 'MS_XGB_MODEL',","               MINING_FUNCTION     => dbms_data_mining.regression,","               DATA_QUERY          => 'SELECT * FROM tmesm_ms_train',","               CASE_ID_COLUMN_NAME => 'CASE_ID',","               TARGET_COLUMN_NAME  => 'DAX',","               SET_LIST            => v_setlst);    ","END;"],"enabled":true,"result":{"startTime":1736280251968,"interpreter":"script.low","endTime":1736280253178,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare regression forecast (XGB) to baseline (ESM) forecast","message":["%sql","","SELECT CASE_ID, DAX, regression_forecast, baseline_forecast,","       DAX - regression_forecast AS regression_error,","       DAX - baseline_forecast AS baseline_error ","FROM (SELECT CASE_ID, DAX, ","             PREDICTION(MS_XGB_model using *) regression_forecast, ","             DM$DAX baseline_forecast","      FROM   tmesm_ms_test ","      ORDER BY CASE_ID);"],"enabled":true,"result":{"startTime":1736280253644,"interpreter":"sql.low","endTime":1736280254111,"results":[{"message":"CASE_ID\tDAX\tREGRESSION_FORECAST\tBASELINE_FORECAST\tREGRESSION_ERROR\tBASELINE_ERROR\n1998-02-04 00:00:00\t4633.008\t4630.7705078125\t4679.2161855773229\t2.237492187499811\t-46.20818557732309\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1736280254594,"interpreter":"md.low","endTime":1736280255043,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1736280255526,"interpreter":"md.low","endTime":1736280255968,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/README.md b/machine-learning/notebooks-oml/sql/README.md
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rename to machine-learning/notebooks-oml/sql/README.md
diff --git a/machine-learning/notebooks/python/OML4Py -2- Data Selection and Manipulation.dsnb b/machine-learning/notebooks/python/OML4Py -2- Data Selection and Manipulation.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py -2- Data Selection and Manipulation.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py -2- Data Selection and Manipulation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714754551274,"interpreter":"md.low","endTime":1714754551333,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python - Data Selection and Manipulation","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight features of the transparency layer involving data selection and manipulation.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714754551413,"interpreter":"md.low","endTime":1714754551471,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python---data-selection-and-manipulation\">Oracle Machine Learning for Python - Data Selection and Manipulation<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight features of the transparency layer involving data selection and manipulation.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Import libraries supporting OML4Py and create a proxy object","","To use OML4Py, first import the package ***oml***. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1714754551552,"interpreter":"md.low","endTime":1714754551612,"results":[{"message":"<h3 id=\"import-libraries-supporting-oml4py-and-create-a-proxy-object\">Import libraries supporting OML4Py and create a proxy object<\/h3>\n<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>.<\/p>\n<p>Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":{"startTime":1714754551687,"interpreter":"python.low","endTime":1714754553684,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a temporary table for the IRIS data","message":["%python","","from sklearn.datasets import load_iris","import pandas as pd","","iris = load_iris()","","x = pd.DataFrame(iris.data, columns = ['SEPAL_LENGTH','SEPAL_WIDTH', 'PETAL_LENGTH','PETAL_WIDTH'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","z = pd.concat([x, y], axis=1)","","IRIS_TMP = oml.push(z)"],"enabled":true,"result":{"startTime":1714754553756,"interpreter":"python.low","endTime":1714754554131,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Select table columns using proxy object IRIS_TMP"],"enabled":true,"result":{"startTime":1714754554208,"interpreter":"md.low","endTime":1714754554267,"results":[{"message":"<h3 id=\"select-table-columns-using-proxy-object-iris_tmp\">Select table columns using proxy object IRIS_TMP<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Select specific columns by name","message":["%python","","IRIS_projected1 = IRIS_TMP[:, [\"SEPAL_LENGTH\", \"PETAL_LENGTH\"]]","IRIS_projected1.head(3)"],"enabled":true,"result":{"startTime":1714754554343,"interpreter":"python.low","endTime":1714754554528,"results":[{"message":"   SEPAL_LENGTH  PETAL_LENGTH\n0           4.6             1\n1           5.1             3\n2           6.0             4\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Select specific columns by index ","message":["%python","","IRIS_projected2 = IRIS_TMP[:, 1:4]","IRIS_projected2.head(3)"],"enabled":true,"result":{"startTime":1714754554603,"interpreter":"python.low","endTime":1714754554751,"results":[{"message":"   SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH\n0          3.6             1          0.2\n1          2.5             3          1.1\n2          2.2             4          1.0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Select specific columns by data type","message":["%python","","IRIS_projected3 = IRIS_TMP.select_types(include=[oml.String])","IRIS_projected3.head(3)"],"enabled":true,"result":{"startTime":1714754554827,"interpreter":"python.low","endTime":1714754555051,"results":[{"message":"      SPECIES\n0      setosa\n1  versicolor\n2  versicolor\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Select table rows using proxy object IRIS_TMP","","* Select sepal length and petal length where petal length is less than 1.5","* Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering","* Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0"],"enabled":true,"result":{"startTime":1714754555132,"interpreter":"md.low","endTime":1714754555192,"results":[{"message":"<h3 id=\"select-table-rows-using-proxy-object-iris_tmp\">Select table rows using proxy object IRIS_TMP<\/h3>\n<ul>\n<li>Select sepal length and petal length where petal length is less than 1.5<\/li>\n<li>Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering<\/li>\n<li>Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Simple row selection","message":["%python","","IRIS_filtered1 = IRIS_TMP[IRIS_TMP[\"PETAL_LENGTH\"] < 1.5, ","                                  [\"SEPAL_LENGTH\", \"PETAL_LENGTH\"]]","print(\"Length: \", len(IRIS_filtered1))","IRIS_filtered1.head(3)"],"enabled":true,"result":{"startTime":1714754555268,"interpreter":"python.low","endTime":1714754555417,"results":[{"message":"Length:  24\n   SEPAL_LENGTH  PETAL_LENGTH\n0           4.6           1.0\n1           4.3           1.1\n2           5.8           1.2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compound row selection with 'or'","message":["%python","","IRIS_filtered2 = IRIS_TMP[(IRIS_TMP[\"PETAL_LENGTH\"] < 1.5) | ","                          (IRIS_TMP[\"SEPAL_LENGTH\"] == 5.0), :]","print(\"Length: \", len(IRIS_filtered2))","IRIS_filtered2.head(3)"],"enabled":true,"result":{"startTime":1714754555496,"interpreter":"python.low","endTime":1714754555644,"results":[{"message":"Length:  30\n   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH SPECIES\n0           4.6          3.6           1.0          0.2  setosa\n1           4.3          3.0           1.1          0.1  setosa\n2           5.8          4.0           1.2          0.2  setosa\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compound row selection with 'and'","message":["%python","","IRIS_filtered3 = IRIS_TMP[(IRIS_TMP[\"PETAL_LENGTH\"] < 1.5) & ","                          (IRIS_TMP[\"SEPAL_LENGTH\"] > 5.0), :]","print(\"Length: \", len(IRIS_filtered3))","IRIS_filtered3.head(4)"],"enabled":true,"result":{"startTime":1714754555721,"interpreter":"python.low","endTime":1714754555870,"results":[{"message":"Length:  7\n   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH SPECIES\n0           5.8          4.0           1.2          0.2  setosa\n1           5.5          3.5           1.3          0.2  setosa\n2           5.4          3.9           1.3          0.4  setosa\n3           5.2          3.4           1.4          0.2  setosa\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Use Pandas DataFrame objects","","Let's explore operations involving Pandas DataFrames.","","We'll start by creating a temporary table from a Pandas DataFrame and use the ***append*** function. First, append an oml.Float series object to another, then append an oml.DataFrame object to another."],"enabled":true,"result":{"startTime":1714754555945,"interpreter":"md.low","endTime":1714754556005,"results":[{"message":"<h3 id=\"use-pandas-dataframe-objects\">Use Pandas DataFrame objects<\/h3>\n<p>Let's explore operations involving Pandas DataFrames.<\/p>\n<p>We'll start by creating a temporary table from a Pandas DataFrame and use the <em><strong>append<\/strong><\/em> function. First, append an oml.Float series object to another, then append an oml.DataFrame object to another.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data in temporary table","message":["%python","","my_df = pd.DataFrame({\"id\" : [1, 2, 3, 4, 5],","                      \"val\" : [\"a\", \"b\", \"c\", \"d\", \"e\"],","                      \"ch\" : [\"p\", \"q\", \"r\", \"a\", \"b\"],","                      \"num\" : [4, 3, 6.7, 7.2, 5]})","MY_DF = oml.push(my_df)","","print (my_df.dtypes)","","num1 = MY_DF['id']","num2 = MY_DF['num']","num1.append(num2)"],"enabled":true,"result":{"startTime":1714754556080,"interpreter":"python.low","endTime":1714754556229,"results":[{"message":"id       int64\nval     object\nch      object\nnum    float64\ndtype: object\n[1, 2, 3, 4, 5, 4, 3, 6.7, 7.2, 5]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Using append","message":["%python","","x = MY_DF[['id', 'val']] ","y = MY_DF[['num', 'ch']] ","","print(\"Types x:\\n\", x.dtypes)","print(\"Types y:\\n\", y.dtypes)","","x.append(y)"],"enabled":true,"result":{"startTime":1714754556306,"interpreter":"python.low","endTime":1714754556398,"results":[{"message":"Types x:\n id     <class 'oml.core.integer.Integer'>\nval      <class 'oml.core.string.String'>\ndtype: object\nTypes y:\n num      <class 'oml.core.float.Float'>\nch     <class 'oml.core.string.String'>\ndtype: object\n    id val\n0  1.0   a\n1  2.0   b\n2  3.0   c\n3  4.0   d\n4  5.0   e\n5  4.0   p\n6  3.0   q\n7  6.7   r\n8  7.2   a\n9  5.0   b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Concat","","Next, try a few examples using the ***concat*** function.","","* Create two oml.DataFrame objects and combine the objects column-wise","* Create an oml.Float object with the rounded exponential of two times the values in the num column of the oml_frame object, then concatenate it with the oml.DataFrame object y using a new column name","* Concatenate object x with multiple objects and turn on automatic name conflict resolution","* Concatenate multiple OML data objects and perform customized renaming"],"enabled":true,"result":{"startTime":1714754556471,"interpreter":"md.low","endTime":1714754556529,"results":[{"message":"<h3 id=\"concat\">Concat<\/h3>\n<p>Next, try a few examples using the <em><strong>concat<\/strong><\/em> function.<\/p>\n<ul>\n<li>Create two oml.DataFrame objects and combine the objects column-wise<\/li>\n<li>Create an oml.Float object with the rounded exponential of two times the values in the num column of the oml_frame object, then concatenate it with the oml.DataFrame object y using a new column name<\/li>\n<li>Concatenate object x with multiple objects and turn on automatic name conflict resolution<\/li>\n<li>Concatenate multiple OML data objects and perform customized renaming<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Column-wise concatentation","message":["%python","","from collections import OrderedDict","","x = MY_DF[['id', 'val']]","y = MY_DF[['num', 'ch']]","x.concat(y)"],"enabled":true,"result":{"startTime":1714754556603,"interpreter":"python.low","endTime":1714754556750,"results":[{"message":"   id val  num ch\n0   1   a  4.0  p\n1   2   b  3.0  q\n2   3   c  6.7  r\n3   4   d  7.2  a\n4   5   e  5.0  b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Concatenate new column","message":["%python","","w = (MY_DF['num']*2).exp().round(decimals=2)","y.concat({'round(exp(2*num))':w})"],"enabled":true,"result":{"startTime":1714754556824,"interpreter":"python.low","endTime":1714754556978,"results":[{"message":"   num ch  round(exp(2*num))\n0  4.0  p            2980.96\n1  3.0  q             403.43\n2  6.7  r          660003.22\n3  7.2  a         1794074.77\n4  5.0  b           22026.47\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Concatenation with name resolution","message":["%python","","z = MY_DF[:,'id']","x.concat([z, w, y], auto_name=True)"],"enabled":true,"result":{"startTime":1714754557055,"interpreter":"python.low","endTime":1714754557205,"results":[{"message":"   id val  id3         num  num5 ch\n0   1   a    1     2980.96   4.0  p\n1   2   b    2      403.43   3.0  q\n2   3   c    3   660003.22   6.7  r\n3   4   d    4  1794074.77   7.2  a\n4   5   e    5    22026.47   5.0  b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Concatenation with renaming","message":["%python","","x.concat(OrderedDict([('ID',z), ('round(exp(2*num))',w), ('New_',y)]))"],"enabled":true,"result":{"startTime":1714754557288,"interpreter":"python.low","endTime":1714754557361,"results":[{"message":"   id val  ID  round(exp(2*num))  New_num New_ch\n0   1   a   1            2980.96      4.0      p\n1   2   b   2             403.43      3.0      q\n2   3   c   3          660003.22      6.7      r\n3   4   d   4         1794074.77      7.2      a\n4   5   e   5           22026.47      5.0      b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Merge","","Next, explore merging data using ***merge***. Perform:","","* cross join","* left outer join","* right outer join"],"enabled":true,"result":{"startTime":1714754557441,"interpreter":"md.low","endTime":1714754557500,"results":[{"message":"<h3 id=\"merge\">Merge<\/h3>\n<p>Next, explore merging data using <em><strong>merge<\/strong><\/em>. Perform:<\/p>\n<ul>\n<li>cross join<\/li>\n<li>left outer join<\/li>\n<li>right outer join<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":100,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Cross join","message":["%python","","x = MY_DF[['id', 'val']]","y = MY_DF[['num', 'ch']]","","z = x.merge(y)","z"],"enabled":true,"result":{"startTime":1714754557574,"interpreter":"python.low","endTime":1714754557666,"results":[{"message":"    id_l val_l  num_r ch_r\n0      1     a    4.0    p\n1      1     a    3.0    q\n2      1     a    6.7    r\n3      1     a    7.2    a\n4      1     a    5.0    b\n5      2     b    4.0    p\n6      2     b    3.0    q\n7      2     b    6.7    r\n8      2     b    7.2    a\n9      2     b    5.0    b\n10     3     c    4.0    p\n11     3     c    3.0    q\n12     3     c    6.7    r\n13     3     c    7.2    a\n14     3     c    5.0    b\n15     4     d    4.0    p\n16     4     d    3.0    q\n17     4     d    6.7    r\n18     4     d    7.2    a\n19     4     d    5.0    b\n20     5     e    4.0    p\n21     5     e    3.0    q\n22     5     e    6.7    r\n23     5     e    7.2    a\n24     5     e    5.0    b\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Left outer join","message":["%python","","x.head(4).merge(other=MY_DF[['id', 'num']], on=\"id\", suffixes=['.l','.r'])"],"enabled":true,"result":{"startTime":1714754557742,"interpreter":"python.low","endTime":1714754557890,"results":[{"message":"   id val.l  num.r\n0   1     a    4.0\n1   2     b    3.0\n2   3     c    6.7\n3   4     d    7.2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Right outer join","message":["%python","","x.merge(other=y, left_on=\"id\", right_on=\"num\", how=\"right\")"],"enabled":true,"result":{"startTime":1714754557966,"interpreter":"python.low","endTime":1714754558061,"results":[{"message":"   id_l val_l  num_r ch_r\n0   3.0     c    3.0    q\n1   4.0     d    4.0    p\n2   5.0     e    5.0    b\n3   NaN  None    6.7    r\n4   NaN  None    7.2    a\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Dropna, drop_duplicates mand drop","","Next, try the drop functonality with ***dropna***, ***drop_duplicates***, and ***drop***:","","* drop rows with any missing values","* drop rows in which all column values are missing","* drop rows in which any numeric column values are missing","* drop duplicate rows","* drop rows that have the same value in column 'string1' and 'string2'","* drop column 'string2'"],"enabled":true,"result":{"startTime":1714754558138,"interpreter":"md.low","endTime":1714754558197,"results":[{"message":"<h3 id=\"dropna-drop_duplicates-mand-drop\">Dropna, drop_duplicates mand drop<\/h3>\n<p>Next, try the drop functonality with <em><strong>dropna<\/strong><\/em>, <em><strong>drop_duplicates<\/strong><\/em>, and <em><strong>drop<\/strong><\/em>:<\/p>\n<ul>\n<li>drop rows with any missing values<\/li>\n<li>drop rows in which all column values are missing<\/li>\n<li>drop rows in which any numeric column values are missing<\/li>\n<li>drop duplicate rows<\/li>\n<li>drop rows that have the same value in column 'string1' and 'string2'<\/li>\n<li>drop column 'string2'<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data","message":["%python","","my_df2 = pd.DataFrame({'numeric': [1, 1.4, -4, -4, 5.432, None, None],","                   'string1' : [None, None, 'a', 'a', 'a', 'b', None],","                   'string2': ['x', None, 'z', 'z', 'z', 'x', None]})","MY_DF2 = oml.push(my_df2, dbtypes = {'numeric': 'BINARY_DOUBLE',","                                     'string1':'CHAR(1)', ","                                     'string2':'CHAR(1)'})","MY_DF2  "],"enabled":true,"result":{"startTime":1714754558283,"interpreter":"python.low","endTime":1714754558431,"results":[{"message":"   numeric string1 string2\n0    1.400    None    None\n1      NaN    None    None\n2    1.000    None       x\n3      NaN       b       x\n4   -4.000       a       z\n5   -4.000       a       z\n6    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with any missing values ","message":["%python","","MY_DF2.dropna(how='any')"],"enabled":true,"result":{"startTime":1714754558506,"interpreter":"python.low","endTime":1714754558578,"results":[{"message":"   numeric string1 string2\n0   -4.000       a       z\n1   -4.000       a       z\n2    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with all missing values","message":["%python","","MY_DF2.dropna(how='all')"],"enabled":true,"result":{"startTime":1714754558652,"interpreter":"python.low","endTime":1714754558724,"results":[{"message":"   numeric string1 string2\n0    1.400    None    None\n1    1.000    None       x\n2      NaN       b       x\n3   -4.000       a       z\n4   -4.000       a       z\n5    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with any missing numeric values","message":["%python","","MY_DF2.dropna(how='any', subset=['numeric'])"],"enabled":true,"result":{"startTime":1714754558798,"interpreter":"python.low","endTime":1714754558873,"results":[{"message":"   numeric string1 string2\n0    1.400    None    None\n1    1.000    None       x\n2   -4.000       a       z\n3   -4.000       a       z\n4    5.432       a       z\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop duplicate rows","message":["%python","","MY_DF2.drop_duplicates()"],"enabled":true,"result":{"startTime":1714754558949,"interpreter":"python.low","endTime":1714754559098,"results":[{"message":"   numeric string1 string2\n0    5.432       a       z\n1    1.000    None       x\n2      NaN       b       x\n3   -4.000       a       z\n4    1.400    None    None\n5      NaN    None    None\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop rows with same value in named columns","message":["%python","","MY_DF2.drop_duplicates(subset=['string1', 'string2'])"],"enabled":true,"result":{"startTime":1714754559173,"interpreter":"python.low","endTime":1714754559323,"results":[{"message":"   numeric string1 string2\n0     -4.0       a       z\n1      1.4    None    None\n2      1.0    None       x\n3      NaN       b       x\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop specific column","message":["%python","","MY_DF2.drop('string2')"],"enabled":true,"result":{"startTime":1714754559398,"interpreter":"python.low","endTime":1714754559473,"results":[{"message":"   numeric string1\n0    1.400    None\n1      NaN    None\n2    1.000    None\n3      NaN       b\n4   -4.000       a\n5   -4.000       a\n6    5.432       a\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Split and KFold","","Next, explore the ***split*** and ***KFold*** function using the *digits* data set. After creating an OML DataFrame proxy object for the *digits* data set,","","* split the data into 20% and 80% samples","* split the data into four sets of 25% each","* perform stratification on the target column","* verify that the stratified sampling generates splits in which all of the different categories of digits (digits 0~9) are present in each split","* hash on the target column","* verify that the different categories of digits (digits 0~9) are present in only one of the splits generated by hashing on the category column","* split the data randomly into 4 consecutive folds"],"enabled":true,"result":{"startTime":1714754559548,"interpreter":"md.low","endTime":1714754559606,"results":[{"message":"<h3 id=\"split-and-kfold\">Split and KFold<\/h3>\n<p>Next, explore the <em><strong>split<\/strong><\/em> and <em><strong>KFold<\/strong><\/em> function using the <em>digits<\/em> data set. After creating an OML DataFrame proxy object for the <em>digits<\/em> data set,<\/p>\n<ul>\n<li>split the data into 20% and 80% samples<\/li>\n<li>split the data into four sets of 25% each<\/li>\n<li>perform stratification on the target column<\/li>\n<li>verify that the stratified sampling generates splits in which all of the different categories of digits (digits 0~9) are present in each split<\/li>\n<li>hash on the target column<\/li>\n<li>verify that the different categories of digits (digits 0~9) are present in only one of the splits generated by hashing on the category column<\/li>\n<li>split the data randomly into 4 consecutive folds<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data as temporary table","message":["%python","","import pandas as pd","from sklearn.datasets import load_digits","","digits = load_digits()","pd_digits = pd.DataFrame(digits.data,","                         columns=['IMG'+str(i) for i in","                         range(digits['data'].shape[1])])","pd_digits = pd.concat([pd_digits,","                       pd.Series(digits.target,","                                  name = 'target')],","                                  axis = 1)","DIGITS = oml.push(pd_digits)","print(\"Shape: \", DIGITS.shape)"],"enabled":true,"result":{"startTime":1714754559682,"interpreter":"python.low","endTime":1714754560405,"results":[{"message":"Shape:  (1797, 65)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data 80/20","message":["%python","","splits = DIGITS.split(ratio=(.2, .8), use_hash = False)","print(\"Split lengths: \", [len(split) for split in splits])"],"enabled":true,"result":{"startTime":1714754560484,"interpreter":"python.low","endTime":1714754560667,"results":[{"message":"Split lengths:  [351, 1446]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into 4 even samples","message":["%python","","splits = DIGITS.split(ratio = (.25, .25, .25, .25), use_hash = False)","print(\"Split lengths: \", [len(split) for split in splits])"],"enabled":true,"result":{"startTime":1714754560741,"interpreter":"python.low","endTime":1714754560965,"results":[{"message":"Split lengths:  [432, 460, 451, 454]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Stratified sampling","message":["%python","","splits = DIGITS.split(strata_cols=['target'])","print(\"Split lengths: \", [split.shape for split in splits])"],"enabled":true,"result":{"startTime":1714754561043,"interpreter":"python.low","endTime":1714754561478,"results":[{"message":"Split lengths:  [(1285, 65), (512, 65)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Veryify stratified samples","message":["%python","","print(\"Verify values: \", [split['target'].drop_duplicates().sort_values().pull()","for split in splits])"],"enabled":true,"result":{"startTime":1714754561549,"interpreter":"python.low","endTime":1714754561644,"results":[{"message":"Verify values:  [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Hash on target column","message":["%python","","splits = DIGITS.split(hash_cols=['target'])","print(\"Split lengths: \", [split.shape for split in splits])"],"enabled":true,"result":{"startTime":1714754561718,"interpreter":"python.low","endTime":1714754562340,"results":[{"message":"Split lengths:  [(899, 65), (898, 65)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Verify hashing","message":["%python","","print(\"Verify values: \", [split['target'].drop_duplicates().sort_values().pull()","for split in splits])"],"enabled":true,"result":{"startTime":1714754562418,"interpreter":"python.low","endTime":1714754562511,"results":[{"message":"Verify values:  [[0, 1, 3, 5, 8], [2, 4, 6, 7, 9]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Randomly split into 4 folds","message":["%python","","folds = DIGITS.KFold(n_splits=4)","","print(\"Split lengths: \", [(len(fold[0]), len(fold[1])) for fold in folds])"],"enabled":true,"result":{"startTime":1714754562590,"interpreter":"python.low","endTime":1714754563591,"results":[{"message":"Split lengths:  [(1352, 445), (1336, 461), (1379, 418), (1325, 472)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Correlation analysis","","Next, we explore the ***corr*** function. We construct a trivial, highly correlated DataFrame. First, verify that the correlation between column A and B is 1."],"enabled":true,"result":{"startTime":1714754563665,"interpreter":"md.low","endTime":1714754563722,"results":[{"message":"<h3 id=\"correlation-analysis\">Correlation analysis<\/h3>\n<p>Next, we explore the <em><strong>corr<\/strong><\/em> function. We construct a trivial, highly correlated DataFrame. First, verify that the correlation between column A and B is 1.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","import pandas as pd","","my_df2 = pd.DataFrame({'A': range(4), 'B': [2*i for i in range(4)]})","MY_DF2 = oml.push(my_df2)","MY_DF2"],"enabled":true,"result":{"startTime":1714754563797,"interpreter":"python.low","endTime":1714754564018,"results":[{"message":"   A  B\n0  0  0\n1  1  2\n2  2  4\n3  3  6\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MY_DF2.corr()"],"enabled":true,"result":{"startTime":1714754564092,"interpreter":"python.low","endTime":1714754564241,"results":[{"message":"   A  B\nA  1  1\nB  1  1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Correlation analysis with skipna","","When using ***corr***, by default, 'skipna' is True."],"enabled":true,"result":{"startTime":1714754564316,"interpreter":"md.low","endTime":1714754564375,"results":[{"message":"<h3 id=\"correlation-analysis-with-skipna\">Correlation analysis with skipna<\/h3>\n<p>When using <em><strong>corr<\/strong><\/em>, by default, 'skipna' is True.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Modify data to alter correlation results","message":["%python","","my_df3 = my_df2","","my_df3.loc[2, 'A'] = 1.5","","# Change an entry to NaN (not a number) to test the 'skipna' parameter in corr","my_df3.loc[1, 'B'] = None","","# Push my_df3 to Oracle Database using the floating point column type ","# because NaNs cannot be used in Oracle numbers","MY_DF3 = oml.push(my_df3, oranumber=False)","MY_DF3"],"enabled":true,"result":{"startTime":1714754564450,"interpreter":"python.low","endTime":1714754564566,"results":[{"message":"     A    B\n0  0.0  0.0\n1  1.0  NaN\n2  1.5  4.0\n3  3.0  6.0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Default corr skipping missing values","message":["%python","","MY_DF3.corr()"],"enabled":true,"result":{"startTime":1714754564638,"interpreter":"python.low","endTime":1714754564783,"results":[{"message":"          A         B\nA  1.000000  0.981981\nB  0.981981  1.000000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Not skipping missing values produces NaNs","message":["%python","","MY_DF3.corr(skipna=False)"],"enabled":true,"result":{"startTime":1714754564860,"interpreter":"python.low","endTime":1714754565008,"results":[{"message":"     A    B\nA  1.0  NaN\nB  NaN  1.0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Crosstab and pivot","","Next, explore the ***crosstab*** and ***pivot*** functions on a DataFrame relating gender, ","left or right handedness, and speed and accuracy on some task."],"enabled":true,"result":{"startTime":1714754565085,"interpreter":"md.low","endTime":1714754565143,"results":[{"message":"<h3 id=\"crosstab-and-pivot\">Crosstab and pivot<\/h3>\n<p>Next, explore the <em><strong>crosstab<\/strong><\/em> and <em><strong>pivot<\/strong><\/em> functions on a DataFrame relating gender,\nleft or right handedness, and speed and accuracy on some task.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%python","","my_df4 = pd.DataFrame({","     'GENDER': ['M', 'M', 'F', 'M', 'F', 'M', 'F', 'F', None, 'F', 'M', 'F'],","     'HAND': ['L', 'R', 'R', 'L', 'R', None, 'L', 'R', 'R', 'R', 'R', 'R'],","     'SPEED': [40.5, 30.4, 60.8, 51.2, 54, 29.3, 34.1, 39.6, 46.4, 12, 25.3, 37.5],","     'ACCURACY': [.92, .94, .87, .9, .85, .97, .96, .93, .89, .84, .91, .95]","    })","    ","MY_DF4 = oml.push(my_df4)"],"enabled":true,"result":{"startTime":1714754565218,"interpreter":"python.low","endTime":1714754565338,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the categories that the most entries belong to","message":["%python","","MY_DF4.crosstab('GENDER', 'HAND').sort_values('count', ascending=False)"],"enabled":true,"result":{"startTime":1714754565427,"interpreter":"python.low","endTime":1714754565862,"results":[{"message":"  GENDER  HAND  count\n0      F     R      5\n1      M     L      2\n2      M     R      2\n3      F     L      1\n4      M  None      1\n5   None     R      1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For each gender and across entries, find ratio of entries with different hand values","message":["%python","","MY_DF4.crosstab('GENDER', 'HAND', pivot = True, margins = True, normalize = 0)"],"enabled":true,"result":{"startTime":1714754565939,"interpreter":"python.low","endTime":1714754566439,"results":[{"message":"  GENDER  count_(L)  count_(R)  count_(None)\n0    All   0.250000   0.666667      0.083333\n1      F   0.166667   0.833333      0.000000\n2      M   0.400000   0.400000      0.200000\n3   None   0.000000   1.000000      0.000000\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the mean speed across all gender and hand combinations","message":["%python","","MY_DF4.pivot_table('GENDER', 'HAND', 'SPEED')"],"enabled":true,"result":{"startTime":1714754566513,"interpreter":"python.low","endTime":1714754567009,"results":[{"message":"  GENDER  mean(SPEED)_(L)  mean(SPEED)_(R)  mean(SPEED)_(None)\n0      F            34.10            40.78                 NaN\n1      M            45.85            27.85                29.3\n2   None              NaN            46.40                 NaN\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the median vlaues for accuracy and speed by gender and hand","message":["%python","","MY_DF4.pivot_table('GENDER', 'HAND', aggfunc = oml.DataFrame.median)"],"enabled":true,"result":{"startTime":1714754567090,"interpreter":"python.low","endTime":1714754567672,"results":[{"message":"  GENDER  median(ACCURACY)_(L)  ...  median(SPEED)_(R)  median(SPEED)_(None)\n0      F                  0.96  ...              39.60                   NaN\n1      M                  0.91  ...              27.85                  29.3\n2   None                   NaN  ...              46.40                   NaN\n\n[3 rows x 7 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the max and min speeds for every gender and hand combination and across all combinations","message":["%python","","MY_DF4.pivot_table('GENDER', 'HAND', 'SPEED', ","               aggfunc = [oml.DataFrame.max, oml.DataFrame.min],","               margins = True)"],"enabled":true,"result":{"startTime":1714754567749,"interpreter":"python.low","endTime":1714754568321,"results":[{"message":"  GENDER  max(SPEED)_(L)  ...  min(SPEED)_(None)  min(SPEED)_(All)\n0    All            51.2  ...               29.3              12.0\n1      F            34.1  ...                NaN              12.0\n2      M            51.2  ...               29.3              25.3\n3   None             NaN  ...                NaN              46.4\n\n[4 rows x 9 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Create new columns","","Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client Python engine."],"enabled":true,"result":{"startTime":1714754568394,"interpreter":"md.low","endTime":1714754568451,"results":[{"message":"<h3 id=\"create-new-columns\">Create new columns<\/h3>\n<p>Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client Python engine.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data as temporary table","message":["%python","","shopping_cart = pd.DataFrame({","  'Item_name': ['paper_towel', 'ground_pork', 'tofu', 'eggs',","                'pork_loin', 'whole_milk', 'egg_custard'],","  'Item_type': ['grocery', 'meat', 'grocery', 'dairy', 'meat',","                'dairy', 'bakery'],","  'Quantity': [1, 2.6, 4, 1, 1.9, 1, 1],","  'Unit_price': [1.19, 2.79, 0.99, 2.49, 3.19, 2.5, 3.99]","  })","CART = oml.push(shopping_cart)","CART"],"enabled":true,"result":{"startTime":1714754568533,"interpreter":"python.low","endTime":1714754568650,"results":[{"message":"     Item_name Item_type  Quantity  Unit_price\n0  paper_towel   grocery       1.0        1.19\n1  ground_pork      meat       2.6        2.79\n2         tofu   grocery       4.0        0.99\n3         eggs     dairy       1.0        2.49\n4    pork_loin      meat       1.9        3.19\n5   whole_milk     dairy       1.0        2.50\n6  egg_custard    bakery       1.0        3.99\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a column Price = Quantity * Unit_price","message":["%python","","","price = CART['Quantity']*(CART['Unit_price'])","print(\"Type: \", type(price))","","CART = CART.concat({'Price': price.round(2)})","","CART"],"enabled":true,"result":{"startTime":1714754568739,"interpreter":"python.low","endTime":1714754568884,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price\n0  paper_towel   grocery       1.0        1.19   1.19\n1  ground_pork      meat       2.6        2.79   7.25\n2         tofu   grocery       4.0        0.99   3.96\n3         eggs     dairy       1.0        2.49   2.49\n4    pork_loin      meat       1.9        3.19   6.06\n5   whole_milk     dairy       1.0        2.50   2.50\n6  egg_custard    bakery       1.0        3.99   3.99\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the pattern 'egg' in the Item_name column","message":["%python","","egg_pattern = CART['Item_name'].count_pattern('egg')","print(\"Type: \", type(egg_pattern))","CART.concat({'Egg_pattern': egg_pattern})"],"enabled":true,"result":{"startTime":1714754568958,"interpreter":"python.low","endTime":1714754569119,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price  Egg_pattern\n0  paper_towel   grocery       1.0        1.19   1.19            0\n1  ground_pork      meat       2.6        2.79   7.25            0\n2         tofu   grocery       4.0        0.99   3.96            0\n3         eggs     dairy       1.0        2.49   2.49            1\n4    pork_loin      meat       1.9        3.19   6.06            0\n5   whole_milk     dairy       1.0        2.50   2.50            0\n6  egg_custard    bakery       1.0        3.99   3.99            1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the start index of substring 'pork' in the Item_name column","message":["%python","","pork_startInd = CART['Item_name'].find('pork')","print(\"Type: \", type(pork_startInd))","CART.concat({'Pork_startInd': pork_startInd})"],"enabled":true,"result":{"startTime":1714754569196,"interpreter":"python.low","endTime":1714754569349,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price  Pork_startInd\n0  paper_towel   grocery       1.0        1.19   1.19             -1\n1  ground_pork      meat       2.6        2.79   7.25              7\n2         tofu   grocery       4.0        0.99   3.96             -1\n3         eggs     dairy       1.0        2.49   2.49             -1\n4    pork_loin      meat       1.9        3.19   6.06              0\n5   whole_milk     dairy       1.0        2.50   2.50             -1\n6  egg_custard    bakery       1.0        3.99   3.99             -1\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check whether items are of grocery category","message":["%python","","is_grocery=CART['Item_type']=='grocery'","print(\"Type: \", type(is_grocery))","CART.concat({'Is_grocery': is_grocery})"],"enabled":true,"result":{"startTime":1714754569427,"interpreter":"python.low","endTime":1714754569578,"results":[{"message":"Type:  <class 'oml.core.boolean.Boolean'>\n     Item_name Item_type  Quantity  Unit_price  Price  Is_grocery\n0  paper_towel   grocery       1.0        1.19   1.19        True\n1  ground_pork      meat       2.6        2.79   7.25       False\n2         tofu   grocery       4.0        0.99   3.96        True\n3         eggs     dairy       1.0        2.49   2.49       False\n4    pork_loin      meat       1.9        3.19   6.06       False\n5   whole_milk     dairy       1.0        2.50   2.50       False\n6  egg_custard    bakery       1.0        3.99   3.99       False\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Calculate the length of item names","message":["%python","","name_length=CART['Item_name'].len()","print(\"Type: \", type(name_length))","CART.concat({'Name_length': name_length})"],"enabled":true,"result":{"startTime":1714754569655,"interpreter":"python.low","endTime":1714754569803,"results":[{"message":"Type:  <class 'oml.core.float.Float'>\n     Item_name Item_type  Quantity  Unit_price  Price  Name_length\n0  paper_towel   grocery       1.0        1.19   1.19           11\n1  ground_pork      meat       2.6        2.79   7.25           11\n2         tofu   grocery       4.0        0.99   3.96            4\n3         eggs     dairy       1.0        2.49   2.49            4\n4    pork_loin      meat       1.9        3.19   6.06            9\n5   whole_milk     dairy       1.0        2.50   2.50           10\n6  egg_custard    bakery       1.0        3.99   3.99           11\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use a few other function to modify the Price column","message":["%python","","print(\"Ceiling: \", CART['Price'].ceil())","print(\"Floor: \", CART['Price'].floor())","print(\"Exponential: \", CART['Price'].exp().round(3))","print(\"logarithm: \", CART['Price'].log().round(3))","print(\"Square Root: \", CART['Price'].sqrt().round(3))"],"enabled":true,"result":{"startTime":1714754569878,"interpreter":"python.low","endTime":1714754569985,"results":[{"message":"Ceiling:  [2, 8, 4, 3, 7, 3, 4]\nFloor:  [1, 7, 3, 2, 6, 2, 3]\nExponential:  [3.287, 1408.105, 52.457, 12.061, 428.375, 12.182, 54.055]\nlogarithm:  [0.174, 1.981, 1.376, 0.912, 1.802, 0.916, 1.384]\nSquare Root:  [1.091, 2.693, 1.99, 1.578, 2.462, 1.581, 1.997]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, using a modified version of the *iris* data set, explore ***sort_values*** and ***tail*** functions.","","We modify the dataset by replacing a few entries with NaNs to test how the ***na_position*** parameter works in the ***sort_values*** method."],"enabled":true,"result":{"startTime":1714754570062,"interpreter":"md.low","endTime":1714754570124,"results":[{"message":"<p>Next, using a modified version of the <em>iris<\/em> data set, explore <em><strong>sort_values<\/strong><\/em> and <em><strong>tail<\/strong><\/em> functions.<\/p>\n<p>We modify the dataset by replacing a few entries with NaNs to test how the <em><strong>na_position<\/strong><\/em> parameter works in the <em><strong>sort_values<\/strong><\/em> method.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%python","","from sklearn.datasets import load_iris","import pandas as pd","","iris = load_iris()","","x = pd.DataFrame(iris.data, columns = ['Sepal_Length', 'Sepal_Width',","                                       'Petal_Length', 'Petal_Width'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor',","                                     2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","                   ","x['Sepal_Width'].replace({3.5: None}, inplace=True)","x['Petal_Length'].replace({1.5: None}, inplace=True)","x['Petal_Width'].replace({2.3: None}, inplace=True)","","iris_df = pd.concat([x, y], axis=1)","IRIS2 = oml.push(iris_df)"],"enabled":true,"result":{"startTime":1714754570197,"interpreter":"python.low","endTime":1714754570314,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Sort data set first by Sepal_Length then by Sepal_Width in descending order","message":["%python","","IRIS2.sort_values(by = ['Sepal_Length', 'Sepal_Width'], ascending=False).head(8)"],"enabled":true,"result":{"startTime":1714754570385,"interpreter":"python.low","endTime":1714754570659,"results":[{"message":"   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width    SPECIES\n0           7.9          3.8           6.4          2.0  virginica\n1           7.7          3.8           6.7          2.2  virginica\n2           7.7          3.0           6.1          NaN  virginica\n3           7.7          2.8           6.7          2.0  virginica\n4           7.7          2.6           6.9          NaN  virginica\n5           7.6          3.0           6.6          2.1  virginica\n6           7.4          2.8           6.1          1.9  virginica\n7           7.3          2.9           6.3          1.8  virginica\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display last 5 rows of the data set","message":["%python","","IRIS2.tail()"],"enabled":true,"result":{"startTime":1714754570735,"interpreter":"python.low","endTime":1714754570884,"results":[{"message":"   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width    SPECIES\n0           7.9          3.8           6.4          2.0  virginica\n1           7.6          3.0           6.6          2.1  virginica\n2           7.7          2.8           6.7          2.0  virginica\n3           7.7          3.8           6.7          2.2  virginica\n4           7.7          2.6           6.9          NaN  virginica\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Sort last 5 rows of the iris data by petal length and petal width","message":["%python","","IRIS2.tail().sort_values(by = ['Petal_Length', 'Petal_Width'], na_position = 'last')"],"enabled":true,"result":{"startTime":1714754570960,"interpreter":"python.low","endTime":1714754571055,"results":[{"message":"   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width    SPECIES\n0           7.9          3.8           6.4          2.0  virginica\n1           7.6          3.0           6.6          2.1  virginica\n2           7.7          2.8           6.7          2.0  virginica\n3           7.7          3.8           6.7          2.2  virginica\n4           7.7          2.6           6.9          NaN  virginica\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Other operations on columns","","Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics."],"enabled":true,"result":{"startTime":1714754571135,"interpreter":"md.low","endTime":1714754571191,"results":[{"message":"<h3 id=\"other-operations-on-columns\">Other operations on columns<\/h3>\n<p>Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create demo data as temporary table","message":["%python","","my_df4 = pd.DataFrame({'numeric_col': [1, 1.4, -4, 3.145, 5, None],","                       'string_col' : [None, None, 'a', 'a', 'a', 'b'],","                       'bytes_col'  : [b'a', b'b', b'c', b'c', b'd', b'e']})","","MY_DF4 = oml.push(my_df4, dbtypes = {'numeric_col': 'BINARY_DOUBLE',","                                     'string_col':'CHAR(1)', ","                                     'bytes_col':'RAW(1)'})","MY_DF4"],"enabled":true,"result":{"startTime":1714754571270,"interpreter":"python.low","endTime":1714754571493,"results":[{"message":"   numeric_col string_col bytes_col\n0        1.000       None      b'a'\n1        1.400       None      b'b'\n2       -4.000          a      b'c'\n3        3.145          a      b'c'\n4        5.000          a      b'd'\n5          NaN          b      b'e'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Combine a Boolean column with MY_DF4","message":["%python","","oml_bool = MY_DF4['numeric_col'] > 3","","MY_DF4 = MY_DF4.concat(oml_bool)","MY_DF4.rename({'COL4':'boolean_col'})"],"enabled":true,"result":{"startTime":1714754571575,"interpreter":"python.low","endTime":1714754571724,"results":[{"message":"   numeric_col string_col bytes_col  boolean_col\n0        1.000       None      b'a'        False\n1        1.400       None      b'b'        False\n2       -4.000          a      b'c'        False\n3        3.145          a      b'c'         True\n4        5.000          a      b'd'         True\n5          NaN          b      b'e'         True\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Describe","","Use the describe function to compute a variety of statistics on all columns. Then, exclude the Float columns."],"enabled":true,"result":{"startTime":1714754571801,"interpreter":"md.low","endTime":1714754571861,"results":[{"message":"<h3 id=\"describe\">Describe<\/h3>\n<p>Use the describe function to compute a variety of statistics on all columns. Then, exclude the Float columns.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MY_DF4.describe(include='all')"],"enabled":true,"result":{"startTime":1714754571936,"interpreter":"python.low","endTime":1714754572255,"results":[{"message":"        numeric_col string_col bytes_col boolean_col\ncount      5.000000          4         6           6\nunique          NaN          2         5           2\ntop             NaN          a      b'c'       False\nfreq            NaN          3         2           3\nmean       1.309000        NaN       NaN         NaN\nstd        3.364655        NaN       NaN         NaN\nmin       -4.000000        NaN       NaN         NaN\n25%        1.000000        NaN       NaN         NaN\n50%        1.400000        NaN       NaN         NaN\n75%        3.145000        NaN       NaN         NaN\nmax        5.000000        NaN       NaN         NaN\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","MY_DF4.describe(exclude=[oml.Float])"],"enabled":true,"result":{"startTime":1714754572336,"interpreter":"python.low","endTime":1714754572657,"results":[{"message":"       string_col bytes_col boolean_col\ncount           4         6           6\nunique          2         5           2\ntop             a      b'c'       False\nfreq            3         2           3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute the sum of values in each Float or Boolean column","message":["%python","","MY_DF4.sum()"],"enabled":true,"result":{"startTime":1714754572735,"interpreter":"python.low","endTime":1714754572961,"results":[{"message":"numeric_col    6.545\nboolean_col    3.000\ndtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Other statistics","","Find the cumulative sum of values in each Float or Boolean column after MY_DF4 is sorted by the bytes column in descending order. Then, compute skew, median, and kurtosis for float columns."],"enabled":true,"result":{"startTime":1714754573037,"interpreter":"md.low","endTime":1714754573099,"results":[{"message":"<h3 id=\"other-statistics\">Other statistics<\/h3>\n<p>Find the cumulative sum of values in each Float or Boolean column after MY_DF4 is sorted by the bytes column in descending order. Then, compute skew, median, and kurtosis for float columns.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute cummulate sum","message":["%python","","MY_DF4.cumsum(by = 'bytes_col', ascending = False)"],"enabled":true,"result":{"startTime":1714754573175,"interpreter":"python.low","endTime":1714754573249,"results":[{"message":"   numeric_col  boolean_col\n0          NaN            1\n1        5.000            2\n2        1.000            2\n3        4.145            3\n4        5.545            3\n5        6.545            3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute select statistics","message":["%python","","print(MY_DF4.skew())","print(MY_DF4.median())","print(MY_DF4.kurtosis())"],"enabled":true,"result":{"startTime":1714754573324,"interpreter":"python.low","endTime":1714754575721,"results":[{"message":"numeric_col   -0.683838\ndtype: float64\nnumeric_col    1.4\ndtype: float64\nnumeric_col   -0.582684\ndtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Boxplot and hist","","Next, use the overloaded ***boxplot*** and ***hist*** functions using the *wine* data set. The statistics supporting these plots are computed in-database, so transfer of data or client side memory limitations are avoided."],"enabled":true,"result":{"startTime":1714754575801,"interpreter":"md.low","endTime":1714754575860,"results":[{"message":"<h3 id=\"boxplot-and-hist\">Boxplot and hist<\/h3>\n<p>Next, use the overloaded <em><strong>boxplot<\/strong><\/em> and <em><strong>hist<\/strong><\/em> functions using the <em>wine<\/em> data set. The statistics supporting these plots are computed in-database, so transfer of data or client side memory limitations are avoided.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Boxplot of select columns","message":["%python","","import matplotlib.pyplot as plt","from sklearn.datasets import load_wine","","wine = load_wine()","wine_data = pd.DataFrame(wine.data, columns = wine.feature_names)","WINE = oml.push(wine_data)","","oml.graphics.boxplot(WINE[:,8:12], showmeans=True, ","                     meanline=True, patch_artist=True, ","                     labels=WINE.columns[8:12])","plt.title('Distribution of Wine Attributes')","plt.show()"],"enabled":true,"result":{"startTime":1714754575934,"interpreter":"python.low","endTime":1714754576745,"results":[{"message":"<div style='width:auto;height:auto'><img 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Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1714754577387,"interpreter":"md.low","endTime":1714754577442,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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-[{"layout":"zeppelin","template":null,"templateConfig":null,"name":"OML4Py -4- Embedded Python Execution","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714753868504,"interpreter":"md.medium","endTime":1714753868567,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python - Embedded Python Execution","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight using OML4Py Embedded Python Execution. We first build a linear model in Python directly, then create a function that will use Python engines spawned by the Autonomous Database environment.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714753868644,"interpreter":"md.medium","endTime":1714753868705,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python---embedded-python-execution\">Oracle Machine Learning for Python - Embedded Python Execution<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight using OML4Py Embedded Python Execution. We first build a linear model in Python directly, then create a function that will use Python engines spawned by the Autonomous Database environment.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Import libraries supporting OML4Py and create data table","","To use OML4Py, first import the package ***oml***. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1714753868788,"interpreter":"md.medium","endTime":1714753868847,"results":[{"message":"<h2 id=\"import-libraries-supporting-oml4py-and-create-data-table\">Import libraries supporting OML4Py and create data table<\/h2>\n<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>.<\/p>\n<p>Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import oml"],"enabled":true,"result":{"startTime":1714753868925,"interpreter":"python.medium","endTime":1714753868986,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table for the IRIS data","message":["%python","","from sklearn.datasets import load_iris","from sklearn import linear_model","import pandas as pd","","iris = load_iris()","x = pd.DataFrame(iris.data, ","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","iris_df = pd.concat([x,y], axis=1)","","try:","    oml.drop(table=\"IRIS\")","except:","    pass","IRIS = oml.create(iris_df, table=\"IRIS\")","IRIS.head(4)"],"enabled":true,"result":{"startTime":1714753869062,"interpreter":"python.medium","endTime":1714753869438,"results":[{"message":"   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH     SPECIES\n0           4.6          3.6             1          0.2      setosa\n1           5.1          2.5             3          1.1  versicolor\n2           6.0          2.2             4          1.0  versicolor\n3           5.8          2.6             4          1.2  versicolor\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build and score a linear model from sklearn directly in Python","","A best practice with embedded Python execution is to validate your Python script, and then your user-defined function before invoking the embedded Python APIs. The next few paragraphs exercise the functionality we want."],"enabled":true,"result":{"startTime":1714753869518,"interpreter":"md.medium","endTime":1714753869581,"results":[{"message":"<h2 id=\"build-and-score-a-linear-model-from-sklearn-directly-in-python\">Build and score a linear model from sklearn directly in Python<\/h2>\n<p>A best practice with embedded Python execution is to validate your Python script, and then your user-defined function before invoking the embedded Python APIs. The next few paragraphs exercise the functionality we want.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from sklearn import linear_model","lm = linear_model.LinearRegression()","X = iris_df[[\"PETAL_WIDTH\"]]","y = iris_df[[\"PETAL_LENGTH\"]]","mod = lm.fit(X, y)","print(\"Model:\",mod)"],"enabled":true,"result":{"startTime":1714753869658,"interpreter":"python.medium","endTime":1714753869734,"results":[{"message":"Model: LinearRegression()\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","pred = mod.predict(iris_df[[\"PETAL_WIDTH\"]])","pred[0:10]"],"enabled":true,"result":{"startTime":1714753869813,"interpreter":"python.medium","endTime":1714753869893,"results":[{"message":"array([[1.52954613],\n       [1.52954613],\n       [1.52954613],\n       [1.52954613],\n       [1.52954613],\n       [1.97553423],\n       [1.75254018],\n       [1.52954613],\n       [1.52954613],\n       [1.30655208]])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Assess model quality","","Assess model quality using mean squared error and R^2 and generate a scatterplot of the data along with a plot of the regression line."],"enabled":true,"result":{"startTime":1714753869968,"interpreter":"md.medium","endTime":1714753870027,"results":[{"message":"<h2 id=\"assess-model-quality\">Assess model quality<\/h2>\n<p>Assess model quality using mean squared error and R^2 and generate a scatterplot of the data along with a plot of the regression line.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute quality metrics","message":["%python","","from sklearn.metrics import mean_squared_error, r2_score","","print('Coefficients: ', mod.coef_)","print(\"Mean squared error: %.2f\"","      % mean_squared_error(iris_df[[\"PETAL_LENGTH\"]], pred))","","print('Variance score (1 is perfect prediction): %.2f' % r2_score(iris_df[[\"PETAL_LENGTH\"]], ","       pred))"],"enabled":true,"result":{"startTime":1714753870106,"interpreter":"python.medium","endTime":1714753870182,"results":[{"message":"Coefficients:  [[2.2299405]]\nMean squared error: 0.23\nVariance score (1 is perfect prediction): 0.93\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Scatterplot with regression line for petal dimensions","message":["%python","","import matplotlib.pyplot as plt","","plt.scatter(iris_df.loc[:,\"PETAL_WIDTH\"], iris_df.loc[:,\"PETAL_LENGTH\"])","plt.plot(iris_df[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)","plt.grid(True)","plt.title('Prediction of Petal Length')","#plt.xticks(()) # Disable ticks","#plt.yticks(())","plt.xlabel('Petal Width')","plt.ylabel('Petal Length')","","plt.show()"],"enabled":true,"result":{"startTime":1714753870262,"interpreter":"python.medium","endTime":1714753870412,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build this same model using embedded Python execution","","Using embedded Python execution, let's build the same linear model, but spawned Python engines under control of the Autonomous Database environment. First, we'll define our function, ***build_lm_1***, that returns the model as the result. We refer to this as a \"user-defined function.\" As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded Python execution can return images as well as structured content in the Python API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.","","We run this function to ensure it returns what we expect. In this case, both an image and a model."],"enabled":true,"result":{"startTime":1714753870766,"interpreter":"md.medium","endTime":1714753870826,"results":[{"message":"<h2 id=\"build-this-same-model-using-embedded-python-execution\">Build this same model using embedded Python execution<\/h2>\n<p>Using embedded Python execution, let's build the same linear model, but spawned Python engines under control of the Autonomous Database environment. First, we'll define our function, <em><strong>build_lm_1<\/strong><\/em>, that returns the model as the result. We refer to this as a &quot;user-defined function.&quot; As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded Python execution can return images as well as structured content in the Python API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.<\/p>\n<p>We run this function to ensure it returns what we expect. In this case, both an image and a model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define a function to build model, score data, and plot values","message":["%python","","def build_lm_1(dat):","    import oml","    from sklearn import linear_model","    import matplotlib.pyplot as plt","","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    mod = lm.fit(X, y)","    ","    pred = mod.predict(dat[[\"PETAL_WIDTH\"]])","    plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])","    plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)","    plt.grid(True)","    plt.title('Prediction of Petal Length')","    ","    plt.xlabel('Petal Width')","    plt.ylabel('Petal Length')","    plt.show()","    return mod"],"enabled":true,"result":{"startTime":1714753870912,"interpreter":"python.medium","endTime":1714753870971,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke user-defined Python function","message":["%python","","mod = build_lm_1(iris_df)"],"enabled":true,"result":{"startTime":1714753871047,"interpreter":"python.medium","endTime":1714753871197,"results":[{"message":"<div style='width:auto;height:auto'><img 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style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View returned model object","message":["%python","","print(\"\\nType:\",type(mod), \"\\n\")","print(\"Model:\",mod)","print(\"Coefficient\", mod.coef_) "],"enabled":true,"result":{"startTime":1714753871283,"interpreter":"python.medium","endTime":1714753871359,"results":[{"message":"\nType: <class 'sklearn.linear_model._base.LinearRegression'> \n\nModel: LinearRegression()\nCoefficient [[2.2299405]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the `table_apply` function","","The ***table_apply*** function takes the proxy object *IRIS* as input data and loads that data to the user-defined function as a pandas DataFrame. The user-defined function is passed as a Python string. We see that the model comes back as an OML object, which we can pull to the client and see that it's the linear model. We also display the image automatically."],"enabled":true,"result":{"startTime":1714753871437,"interpreter":"md.medium","endTime":1714753871498,"results":[{"message":"<h2 id=\"use-the-table_apply-function\">Use the <code>table_apply<\/code> function<\/h2>\n<p>The <em><strong>table_apply<\/strong><\/em> function takes the proxy object <em>IRIS<\/em> as input data and loads that data to the user-defined function as a pandas DataFrame. The user-defined function is passed as a Python string. We see that the model comes back as an OML object, which we can pull to the client and see that it's the linear model. We also display the image automatically.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","mod = oml.table_apply(data=IRIS, func = build_lm_1)","","print(\"Type:\",type(mod))","print(\"Model:\",mod)","print(\"Coefficient\", mod.coef_)"],"enabled":true,"result":{"startTime":1714753871582,"interpreter":"python.medium","endTime":1714753871705,"results":[{"message":"Type: <class 'sklearn.linear_model._base.LinearRegression'>\nModel: LinearRegression()\nCoefficient [[2.2299405]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the in-Database Python script repository","","We can also save this user-defined function in the Python script repository and invoke this function by providing the function name string."],"enabled":true,"result":{"startTime":1714753871787,"interpreter":"md.medium","endTime":1714753871846,"results":[{"message":"<h2 id=\"use-the-in-database-python-script-repository\">Use the in-Database Python script repository<\/h2>\n<p>We can also save this user-defined function in the Python script repository and invoke this function by providing the function name string.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","build_lm_1_str = \"\"\"def build_lm_1(dat):","    import oml","    from sklearn import linear_model","    import matplotlib.pyplot as plt","","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    mod = lm.fit(X, y)","    ","    pred = mod.predict(dat[[\"PETAL_WIDTH\"]])","    plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])","    plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)","    plt.grid(True)","    plt.title('Prediction of Petal Length')","    ","    plt.xlabel('Petal Width')","    plt.ylabel('Petal Length')","    plt.show()","    return mod\"\"\""],"enabled":true,"result":{"startTime":1714753871928,"interpreter":"python.medium","endTime":1714753871986,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List contents of Python script repository","message":["%python","","oml.script.create(\"build_lm_1\", build_lm_1_str, overwrite = True)","z.show(oml.script.dir())"],"enabled":true,"result":{"startTime":1714753872059,"interpreter":"python.medium","endTime":1714753872157,"results":[{"message":"name\tscript\tdescription\tdate\nMyTEST_function\tdef build_lm_str(dat):     from sklearn import linear_model     regr = linear_model.LinearRegression()     import pandas as pd     dat = pd.get_dummies(dat, drop_first=True)     X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \"SPECIES_versicolor\", \"SPECIES_virginica\"]]     y = dat[[\"SEPAL_LENGTH\"]]     regr.fit(X, y)     return regr\tNone\t2023-05-12 00:21:09\nRandomRedDots\tdef RandomRedDots (num_dots_1=100, num_dots_2=10):     import numpy as np     import pandas as pd     import matplotlib.pyplot as plt        d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}     df = pd.DataFrame(data=d)     fig = plt.figure(1)     ax = fig.add_subplot(111)     ax.scatter(range(0,int(num_dots_1)), np.random.rand(int(num_dots_1)),c='r')     fig.suptitle(\"Random Red Dots\")      fig2 = plt.figure(2)     ax2 = fig2.add_subplot(111)     ax2.scatter(range(0,int(num_dots_2)), np.random.rand(int(num_dots_2)),c='r')     fig2.suptitle(\"Random Red Dots\")     return df\tNone\t2023-12-30 00:38:59\nbuild_lm\tdef build_lm(dat, dsname):             from sklearn import linear_model             import oml             regr = linear_model.LinearRegression()             import pandas as pd             X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"]]             y = dat[[\"SEPAL_LENGTH\"]]             res = regr.fit(X, y)             res_coef = res.coef_             res.df = pd.DataFrame(res_coef, columns = ['Coef_SEPAL_WIDTH','Coef_PETAL_LENGTH','Coef_PETAL_WIDTH'])             oml.ds.save(objs={'regr':regr}, name=dsname, overwrite=True)             return res.df\tNone\t2023-12-15 18:25:43\nbuild_lm_1\tdef build_lm_1(dat):     import oml     from sklearn import linear_model     import matplotlib.pyplot as plt      lm = linear_model.LinearRegression()     X = dat[[\"PETAL_WIDTH\"]]     y = dat[[\"PETAL_LENGTH\"]]     mod = lm.fit(X, y)          pred = mod.predict(dat[[\"PETAL_WIDTH\"]])     plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])     plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)     plt.grid(True)     plt.title('Prediction of Petal Length')          plt.xlabel('Petal Width')     plt.ylabel('Petal Length')     plt.show()     return mod\tNone\t2024-05-03 16:31:12\nbuild_lm_rest\tdef build_rest(dat):             from sklearn import linear_model             import oml             regr = linear_model.LinearRegression()             import pandas as pd             dat = pd.get_dummies(dat, drop_first=True)             X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \"SPECIES_versicolor\", \"SPECIES_virginica\"]]             y = dat[[\"SEPAL_LENGTH\"]]             regr.fit(X, y)             data = {}             data[\"init_params\"] = regr.get_params()             data[\"model_params\"] = mp = {}             for p in (\"coef_\", \"intercept_\", \"_residues\", \"singular_\"):                 mp[p] = getattr(regr, p).tolist()             return data\tNone\t2023-12-12 18:45:31\nbuild_mod\tdef build_mod(df):   import oml   import keras   from keras.models import Sequential   from keras.layers import Dense   from tensorflow.keras.optimizers import Adam   import seaborn as sns   import pandas as pd   import numpy as np   import matplotlib.pyplot as plt      sns.set(style=\"ticks\")   sns.set_palette(\"husl\")   sns.pairplot(df.iloc[:,0:6], hue=\"species\")    # split the data into test and train sets   X = df.iloc[:,0:4].values   y = df.iloc[:,4].values    from sklearn.preprocessing import LabelEncoder   encoder =  LabelEncoder()   y1 = encoder.fit_transform(y)   Y = pd.get_dummies(y1).values    from sklearn.model_selection import train_test_split   X_train,X_test, y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0)     model = Sequential()   model.add(Dense(4,input_shape=(4,),activation='relu'))   model.add(Dense(3,activation='softmax'))   model.compile(Adam(learning_rate=0.04),'categorical_crossentropy',metrics=['accuracy'])   model.fit(X_train,y_train, epochs=50, verbose=0)    y_pred = model.predict(X_test, verbose=0)   y_test_class = np.argmax(y_test,axis=1)   y_pred_class = np.argmax(y_pred,axis=1)    from sklearn.metrics import classification_report,confusion_matrix   report = classification_report(y_test_class,y_pred_class, output_dict=True)   res = pd.DataFrame(report)   return(res)   plt.show()\tNone\t2024-01-17 19:20:08\ndensity_plt\tdef density_plt(dat):     dat.plot(kind='density')     return(True)\tNone\t2023-12-15 18:38:34\ngroup_count\tdef group_count(dat):   import oml   import pandas as pd   return pd.DataFrame([(dat[\"SPECIES\"][0], dat.shape[0])], columns = [\"SPECIES\", \"COUNT\"])\tNone\t2024-01-17 21:31:51\nmean_angle\tdef mean_angle(deg):     import numpy as np     temp = sum(1 *(np.cos(np.radians(d)) + np.sin(np.radians(d))*1j) for d in deg)/len(deg)     temp = np.degrees(np.arctan2(temp.imag,temp.real))     if temp <0.:         temp = temp + 360          return temp\tNone\t2022-10-04 21:23:19\nmyLinearRegressionModel\tdef fit_model(dat, modelName, datastoreName):        import oml        from sklearn import linear_model        regr = linear_model.LinearRegression()        regr.fit(dat.loc[:, [\"Sepal_Length\",                              \"Sepal_Width\",                              \"Petal_Length\"]],                 dat.loc[:,[\"Petal_Width\"]])        oml.ds.save(objs={modelName:regr},                     name=datastoreName,                    overwrite=True)        return str(regr)\tNone\t2022-02-09 18:11:59\nscore_lm\tdef score_lm(dat):     import oml     import pandas as pd     from sklearn import linear_model      obj = oml.ds.load(name=\"my_datastore_1\", to_globals=False)     pred = obj[\"lm_model\"].predict(dat[[\"PETAL_WIDTH\",\"SEPAL_WIDTH\"]])     pred = pd.DataFrame(pred, columns = [\"PREDICTED\"])     pred = pd.concat([dat[[\"PETAL_WIDTH\"]],pred], axis = 1)     pred = pred.rename(columns={\"PETAL_WIDTH\":\"ACTUAL\"})     return pred\tNone\t2023-02-16 01:45:59\nscore_mod\tdef score_mod(dat, dsname):             import oml             import pandas as pd             import matplotlib.pyplot as plt             obj_dict = oml.ds.load(name=dsname, to_globals=False)               regr = obj_dict[\"regr\"]                                                   pred = regr.predict(dat[['SEPAL_WIDTH','PETAL_LENGTH','PETAL_WIDTH']])                return pd.concat([dat[['SPECIES', 'SEPAL_LENGTH']], pd.DataFrame(pred, columns=['Pred_SEPAL_LENGTH'])], axis=1)\tNone\t2023-12-15 18:33:28\nsk_svc_fit\tdef fit(data):      from sklearn.svm import LinearSVC     x = data.drop('SPECIES', axis = 1).values     y = data['SPECIES']     mod =  LinearSVC().fit(x, y)     return mod\tNone\t2024-05-03 16:25:45\nstd_angle\tdef std_angle(deg):     import numpy as np     import oml     mean_angle = oml.script.load(name=\"mean_angle\")     temp = sum(pow(round(         np.degrees(             np.arctan2((1 *(np.cos(np.radians(d-mean_angle(deg))) + np.sin(np.radians(d-mean_angle(deg)))*1j)).imag,                        (1 *(np.cos(np.radians(d-mean_angle(deg))) + np.sin(np.radians(d-mean_angle(deg)))*1j)).real)             ))         ,2) for d in deg)     temp = temp/(max(len(deg)-1,1))     temp = np.sqrt(temp)     return temp\tNone\t2022-10-04 21:23:19\ntest_noinp_rest\tdef test_noinp():  import oml  from oml.automl import AlgorithmSelection  df = oml.sync(schema=\"OMLUSER02\", table = \"NARROW\")  train, test = df.split(ratio=(0.9, 0.1), seed = 1234)  X, y = train[:,0:-1], train[:,-1]  X_test, y_test = test[:,0:-1], test[:,-1]  asel = AlgorithmSelection(mining_function=\"classification\",score_metric=\"accuracy\", parallel=4)  scores = [ \"{0}:{1:.3f}\".format(m, s) for m, s in asel.select(X, y, k=None) ]  return(\"Algorithm selection prediction scores:{}\".format(scores))\tNone\t2022-03-08 00:32:47\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## The value passed to `func` can also be a string","","Notice the value to ***func*** is now a string, not a Python function object, however, the results are the same."],"enabled":true,"result":{"startTime":1714753872234,"interpreter":"md.medium","endTime":1714753872293,"results":[{"message":"<h2 id=\"the-value-passed-to-func-can-also-be-a-string\">The value passed to <code>func<\/code> can also be a string<\/h2>\n<p>Notice the value to <em><strong>func<\/strong><\/em> is now a string, not a Python function object, however, the results are the 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+WUCiE3+/v6Wb0GuFwWF8bu/n9fv3s2MuezcMP9X7sOV+ODApY/dSuXbvYvn0706dP73C8qKiIF198kaKiItLT09m2bRuBQIDm5mZUk9bS0tKCzWajoaGhp5vSK9TX1wOoTMNuzc3NbNu2raeb0SsEg0EaGxt1P3Zzu92AVW7hUBzMBujSeyhg9VPvvPMOp5566j71WEaNGsVvf/tbVq1axdKlSxk3bpzKNHyFyjR0pDIN7UzTxOPxMG7cuJ5uSq/Q28s0rNnRwDvrKwibcHJ+OsePSOvWIrHftEyD0+lk/fr1EW6VdBcFrH4oEAiwePFibrvttp5uiohIj1izo4H/9+o6Nlc0Y2Ly7vpK/nLBBI7PU5FYiQzVweqHHA4Hjz/+ODk5OT3dFBGRHvHSyjI2VTQRMk3CJpTWtfLYp0U93SzpQ9SD1Q8ZhqH5MiLSrwVCYb46rdQfDPdMY6RPUg+WiIj0O6eOyiA1ztX2ONZp55yJhzY3SmR/1IMlIiL9zuyxmYRNk8c/LSIcNrlo2hAuOGpwTzdL+hAFLBER6XfsNoMzJ2Rz5gT1Wkn3UMASEZGI27qrmVc+LyY+UMtGTyJnTcwmxtk9cz83VzTx3IpS3L4QJ44cwJkTsnE5NANGepYCloiIRNTWXc3c/voGNpXVclxGiH+s2UCzN8BVx+dEvM7Upoombn1tPV+WNhA2TRZs2oUnEOLiaUO6taaVSFcU8UVEJKJWFNWxoriOQNjEBJq8QR77pIju2Avik63VrNrRQMi0rtXoCTD/k8JuuJLI16MeLBERiaiwyT4lEMLh7tlqa387eHXTpQ6Z3w9/+hPU1sZy0UWQmQk2dW/0eQpYIiISUVOGJjNuYCLFVY0ARDlsXHLMEDobsAubJpt2NvH3Rdspq2/lxJHpXHVCDmlxrk6H+o4ZnsrIjHg2VzZjAtEOG5ceMySyH+gQmCaEQrBiBfzhD/DBBxAKxZOWBtOnQ3R0T7dQupsCloiIRNS4gYn86fwJ/PPTraSbjdw8KZeLp3UeerZXtXD7Gxv4oqQe04QNO5toaPVz27njcNoPHLAmD0nmjgsm8OjiQpp9QeaMzeTbR/fs/KtwGNavh//8B/7yF+uxYcCAAUHGj4eoKFfXbyJHPAUsERGJKMMwGD8oid/PG797s+fhXZ6zraqF1Tsa2ob8gmGT/67eyW/OGktniw8Nw2Dy0BT+fsVREWr9N1NTA6++CnfcAUV77bzzve/BjBl1nHCCiWGoNER/oIAlIiI9zmYzsNsMAqH2CVRO+5ExUcncPeds3Tr4n/+BDRugqcl6bsIE+MUvYN488Hr3P2dM+qYj46tXRET6tAmDkjh9bBbRDhsGkBLr5Ecn5+J09O5SC6EQlJTA978PM2bA0qVWuBo4EK6+Gj75BK64ApKTe7qlcripB0tERHrcwOQYbjx9FCMz46lq9jFpcDKnj8vC0YuX2zU0wDPPwD/+Yc25AkhKglNOgV/9yprMrlJc/ZcCloiI9ApDUmP52cyRPd2MLoXD8O67MH8+vPkmBIPW8UGD4IEH4MQTIT29Z9soPU8BS0REIi4QCtPoCeANhGjxBYl12bEd4d05oRDU1lplF/71L2hstHqokpLg2mvhmmtgyBD1WolFAUtERCKqxRfktS/LefrTrYxL8NKy2stNp48mPzP+iNy+xjShstLqtbr1Vigrs467XHDuuXDTTTBtmnXsCPx40k0UsEREJKI+L6rj4Y+30+z2MjI2zIebq4hy2Hnwsik93bRDsmABPPggvPNO+3DgtGlw+eXWRPa4uJ5tn/ROClgiIv1EOGwSCIcxTbDbDBw2o1t6lIpr3VQ0eojb/RsmbMLS7TWY0Gk1995iT9mFqiprOPDVV6GiwnouJsbqsfr+92HwYLB3UqNL+jcFLBGRfsAbCLFg4y7uXVBAZZOXk/PT+d85+eRlRH7YLj0+iuQYF8GAD7BC1cjMhIheoztVV8OHH8KPf2yVXDBNSEyEqVPh/vth3DgFK+maApaISD+wrqyR+xYUUFjjBuC9DZUYhsEDl07GEeGAddyINC49ZghvrCrBYfMzeUgy18/O7/W9V34/fPQRPP641Wu1ZzjwuOPgqqvg0ksh4cjJidLDFLBERPqBklo3xbXutsdhExYXVGOGiXjJ6bT4KK45KZcTRyTTXFXO8JGjyEmL7bUT3E0T3G74wQ+sQqE7dljHo6Lg+uutbW7y8zWBXb4eBSwRkX4gIcZJQrSTBk+g7dig5JhumxSVHOti8pBkCgN15GXEd89FviHTtMouvP02/O531t6Bpmn1Uk2cCI8+Cnl54HQqXMnXp4AlItIPHD0shW8fPYT/fLGDFm+QERlx/O+cfOy2/pkcgkF4/nnr31tvWccMA6ZMgR/9yJrE7nT2bBvlyKaAJSLSD6TFR/Gjk3M5JjcVty/I0NRYxg1MPOKLfx6KNWus7W2efhpaW61jDgf8/vcwd661QXMv3qFHjhAKWCIi/URafBSzx2T2dDN6hGlalddfeQV+/WtraDAUsnqpxoyx6lxNn24VDxWJBAUsERE5oCZPgC2VzTR5A2QmRpOXEU+088iqUdDUZE1ev+kmWLeu/fiQkT5GHtPEzTebDB0Yh9MZS3dMSjNNk/IGD1t2WjUfglFJDE6J6bWT/iUyFLBERGS/mr0Bnl1eylNLiqlu9jIiPZ5rZ47kzAlZOOxHxhjatm3w5z9bZRcaGqxjcXFw4hmt2MZvZXtwJ9f+B6YPT+WWs8d2S72u0rpW/vTWJqqrq7BhkrK+id+cNZbhA1QCvi87Mr5DRETksCuobObJJUVUNnkJmVBQ1cJ9HxTQ6g/1dNM6ZZrg8cAf/whnnQVPPmmFK7vdWhW4cCEce2kZ24I78QbD+IJhPttWy2OfFnVLe/69rJSPtlThC4TwBcMs2lLNv5YWd8u1pPdQD1Y/FA6HKS8vp66ujsTERAYPHoxTy2VE5CuafUFqmn0djpXWthIyzR5qUddaW2HFCrj2Wti4EcJha8L60KHWEOH3vgfR0fDSix58wXDbeSHTpLjG3ck7H7rSulYCofZ7FgyblNS2dsu1pPdQwOqHNm/ezMsvv0xUVBQ5OTmkpaUpYIn0A5WNXhYXVNPkDZCfmcAxw1M7nU+VkRBFXkYCW3Y1A1YZg2nDU3H1wuFB04SVK+Hf/7bqV+29OvD66+H88+H449tfP2lIMu9uqKTFZ5Vrd9oNjs1N65a2HZ2TwqItVYAV6FwOG9OGp3bLtaT3UMDqZ0KhEB999BFTpkzhzDPPxDRNbFqPLNLn7Wry8sAHW3l9zU5a/UEGJcfwv6eNYt7kgQcs1TAiPZ6fz8rj/oVbKap1My0nlf+dk09ML5rkvmdj5ttvh5degk2b2p+bPdvaT/Ccc/ZdHXjupGyqmrw8vbSYUNjk/CmD+M6xw7qljeMGJuJytP+cddkNJgxK6pZrSe+hgNXPeDweqqurWb16NS+99BInn3wyF198MbGxsW3P19fXU1VVhd/vx+Px4PP5CAQCeDyeHm597+D3+wmHw7ofuwWDQbxeL+FwuOsX9wOhUKhXfm18sb2KjzeVYzeDJDih2d3KE4u2MHtkcqcT1k8ekcwxQ6cSCps47QbRTjs+n/egrunz+QgGg912P7xeG+vWRfHLX1q9V3v2DszO9nL55SY33wyxsVY5hq82wWXA1ccP5opp2QBEOW1EObrn+/r9tTtwmEHiHGAzwEWId9eUMnXQ15vk7vP5un6R9BoKWP2MYRg0Nzcza9YsZs2axYMPPsjGjRs5+uijASgrK+P1119n+/btJCcnU1JSQiAQoKmpiZKSkh5ufe/Q3NyMzWbD7e6e+RpHmqamJsrKyrDbe0+vRk9yu9298nsl0NjE5JQAwb3mAsVFeSktLe22au7BYJDGxsaDvh91bj9fltTT5A0wJDWWCYOSDjiEuWwZvP12HC++OKTtWHY2zJgBP/95BQMG+Kip6fx6JbVu1uxoxMRkTHYiIzMSumVLnKRQI8ekhxgQbWIAyVEmKeGDvy97VFRURL5x0m0UsPqZ6OhocnNzSU5Oxmaz4XA4MPeasDpy5Eh+8YtfsGrVKpYuXcro0aNxu92UlZUxatSoHmx571FeXo7T6SQjI6Onm9IrhEIh8vLyiIqK6umm9DjTNGlpaWH06NE93ZR9eGPr2b7ay+ZKaz6V3TA4f8ogRo8e3W0By+v1UlhYeFD3o87t5/XFhTy33kejJ0BOmoPrs5I5a9zADu1rarJWBT7yCGze3H7+pEnw179a86zi44d3eb2iGjcvLt/IJ1t9hE2TyQ0BfjdyIGMGJh7SZ+3MmuZYHlu3iaExfmwGFLqdnDwtl9Gjv96QpM1mY82aNRFvn3QPTb7pZ+x2O2eddRYLFizg2muvJTU1lTFjxvR0s0Skm40blMSvzxzNpMFJZCREcdHRg7l+zkh6y1aEa8saeO7zUupbA4RNKKxxc+/CrQRC1tCzxwPr11ubMP+//2eFK5sNEhPhscfgnXfgtNMg/iD3lX7hix18XFCNLxgmEDJZWVLP/E8Ku+WzzZs8iBtmjyQrKZqsxGiunz2S86cM7pZrSe+hHqx+aNiwYdx111093QwROYycdhsn5adzUn56h+O9pZq42xekyRPscKyy0UMgaLK9AO69F/71L/D7refi4+Gyy+Dmm2F41x1W+6hp9nUYLg2bUNHYPXPFop12vj9jOGeNjMU0TbKysnrNfZfuo4DVD+kbW6R3aPQE+HBzFVt3NTMoOYY5YzPJSIzutuuVN3h4d30l9a1+JgxK4uT8DGJcvWPu3OCUWPIy4ttKQtgMOC4nnVt/a2PxIvjyy/bXnnkmXHABfP/7Vi9WUU0L76yrxBMIMXVoCjNGDsDZRSmJo3NSeW9DJU1eK9S57DZOGpne6TnfxN4/d/UzuH9QwBIR6QG+QIh/Lyvh8U+LqHP7iY92sK2qhZ/PGklKXOR3HN7Z4OHu9wt4d30l3mCIjIQorp8d4OJpQw5YpuFwGp2dwP/OyeeeBQWU1LSSUDuIpQ/nsuVLgz3TRFNS4I47rLIL2dlWXa6iGjd/emsziwuqCYbDZCfF8NuzxnDG+M57ic6amE2d28djnxQRDptcdPQQLps+9DB9WukPFLBERHpAszfIo4sLafAE2h6/uLKMsyZmc3Rc5ItQflFcz7vrrV4egF1NPh74YCsXHjUYm73nA1aUw87ssZmk+jJ45t/wt/kGZhjAYMAAmDoVnn4aMjKsXqs9PtxcxeKCavy752qVN3i4/4OtnDE+q9PrxUc5+OFJI/jBjFwA7Daj18xHk75BAUtEpAeEMfEGOu7p5w+GCYW7ZxuaQDjcNmF8D08v2lNw5054802D3/7WoLq6/fjZZ8MVV1hDgvvbcMIfDO1zzw72c9ltRretoBRRwBIR6QGxTgczR2fw7oZKwiYYwFHDUshO6p45WPkZ8YzKSmDDzibAChdnTczu0eHBPUN/K1bAL34Bq1bt2eLGJDrW5Pe/M7j8Mhg48MBtnDwkmSGpMRTv3tvPYTM4Z9LAg7i2ybLCWv7z+Q7CJpw9MZvZYzO75X6Ypsnigho+XVeEgclx4+2cMipdc7H6OAUsEZEeEBdl5+YzR5MU42RZUR2jsxL46al5DE6N7ZbrjR2YxG3njOOBD7ZS0ehh5ugMrjkpt8eGxYJBqKiw9glcsACarbnt2KL9RA2pI+P0jYTGDCIjcyRW/Ny/Y4an8vt547l/4VaavAHmTsjmBzO6Xla4dHstv3plHTvqrGD26bYa/mzS5dDioVhcUMPNL68lzd6KzYDXtrRy57cmcupo1dLryxSwRER6gGEYDEuL445vTTws17PbDI4ZnsozV08/LNfrTF2dVXLhvvuguHj3QSNM1MAGEo/dRsyIajDguS9KuG5OHo5OFjrabfsvP9GVN9dWUFbfyp7BxTq3n+dWlHZLwHrlyzJ2NXlJTQYTqG728dLKMgWsPk4BS0REDotwGF55BZ55Bt54w3oMkJQEl/2siXfr1xFOaDls7eme2W4He+2evLocDgpYIiK7maZJRaOX+YsL+XRbDcMHxPGzmXmMH5TUK0oZfBOhsMmq0nr+9uG2tiHCq2fkkhbv6va5QKEQ1NbC//4vvPUWNDRYx6Oj4fLL4cYbwZHioOZVJyuKredsBnxr6iAcXaxwDIVNlm6v4YEPttHsDXDmhGy+d0IOCdH7mRG/l7kTslm8tZryequ4aHKsk4unDen0nEAozD+XFvPsslJCJhydk8xvzxpLcmznZTXOmzKIZYW1GIYHAxgQH8W3pqqSe1+ngCUisps/GOau97bw39U7CZkm26paqG/1c8cFE8nLOMg9WHqpTRVN/O6NjawrbwRge7Ubty/E7eeOozurNOzcaU1iv/lmqKqyjhkGnHoqXHml9Q9gy64wvmD7KkfThBZvcD/v2NHnxXXc+t8NFNZYm69vrWohFDa5fvbIToPjCXlp3PmtiTy3vJSQaXLupIGc3sXw4Ktflu2e62W1q7jWDSbc9e3JnZ53yqh0/vKtiXyydhsGJsdPyGOmhgf7PAUsEZHdfKEw726oJLR7eZsJrN7RQEmt+4gPWAW7mtlU0dT2OBQ2eWPNTm49Zyz2TiaRHyrThNdec3DffRksX95+fOxYq+TCTTdZ+wjuUVjtZuNe7TOBt9dX8rt54zqtyr6qtIGS3SsIAYJhk9dWlXP97JGdts8wDE7IG8AJeQMO+jNtrXK3has9Pi+u7/I8wzA4dXQGY5LDmKZJdnbmQV9TjlwKWCIiuxlAlMNG6151lJx2G44+UCtpT82n4F41o6IcnW8n83WZpvWvqgp+9zt45RUHVVVWgHE4YOZMuP9+yM0F11dG1Rw2A4fNRiDUfu+t9nV+711263OF9tpXMNrZPdv/7O/L4EgfOpbuE9nvLhGRI1i0087/nDKCtDgXBhDnsnPupIGMyU7s8tzeLtZpJ/Yr+w6mxUd2S57KSnj2WcjJgX/8wwpasbEwZgx8+qk1sX306H3D1Z62ZCRGdTg2JjuhQ9X2/Tl1dAYz8gbgtFuV2LMSo/n5rLzIfai9nDhyACP36slMiHZw6fTO521J/6UeLBGR3Zx2G5ceM5TMhGg2VjSRmRjNmeOzunUD5sOlrjVAfWugw7Ht1e62Yp/fhM8HixbBAw/AO++w196BQX7600Z++tM0srqoflDV5KOiwdvh2NqyxraVhgeSmx7Pb88ew1trk2n1hzg6J4WT8runiOeMkencds5YPi6oIWyajMyI5wJNVpcDUMASEdlLQrSTeVMGMW/KoJ5uSq9nmtDUZK0EXLsWduxof+5nP4MzzwySk7OLrKy0Lt/LZhh8NRMd7DY2uenxXDur8zlX+2PuJ112FcxmjExnxsivV3NL+icFLBGRfmBUZjzjBiaycWcTJuCy2zh38sBD2ovPNKGx0RoOvPNOKC21jtts1qbMf/4zzJhhrRYsLDzI9mUnMHVoMksL66z3MuDbRw/B2U1LHH3BEK98Wc6jHxcSMk0umTaEq07IIdalX4sSGfpKEhHpByYPTeHWc8by6pfleAMhcgbEceVxOV87YIXD8J//wEsvWUVD9xg7Fo4/Hh56qH2Olde7//fYH7cv2GGFnmlCZaOXbtr7mnfXV3LnO5tp8FjDpvd/sJUYl53vndD1NjsiB0MBS0Skn5g+PI3pw7serjuQTZvgqaeslYA+n3XM6YRrroHvfheOOgrsh7iAr6S2lS2VzW2PTWDhpl0Ew2Fc3bAe67NtNTR62uek+YJhFmzcpYAlEaOAJSIiBxQKm7jd8PZbBjfdZK0UDAatsgspKfD88zBtGiQkfLPruOw2op12WnztvVgJ0U6MbqjRBRAf5cBmMwjt7iIzgMQuqr+LfB0q0yAiIvvV2GTy7OstjJsU5LLLoKzMClejRsFvf2tt1HzqqRAfgRqsk4Ykc8GUQSREObAZMDApmutnj8QV4Vpde5w5IZuU2PZAFeW08e1pWhEokaMeLBER2UdpKdz+e5NXX4uloXb3uJ9hkj65ksceymDGcZEt5pmeEMVPZ+YxblAS9W4/IzPjOW5E2iFNwj8YBZXNePYqKBsMmawra2TmaFVZl8hQwBIREcCaWB4Mwl/+Yk1iX7t2T++RiSPVTeqsDcQPbmLcxJOByFdLz0yM7nLD5UhZVdqAe++AFTZZur2W62YflstLP6CAJSIi+Hzw+efwm9/AZ5+BtWONiSPeR/xRxSQcVYzNGWLYgLiD6lVy+4KU1bqpc/upbPSSnhDVbb1Rh2JgcjQuuw1/yKpkajNgaFpsD7dK+hIFLBGRfsw04Ysv4PXX4a672ksruFzWykDHyGpW+suoaQkxPD2ea0/N22fLna9qaPXz7+WlvLS8kInJfqpWefnlGaOYPCS5WyqsH4qT8tN5dkUpNS1+wJpkf+b47B5ulfQlClgiIv2QaVo1rf72N3jsMdiwof25E06wKrGfd56B38xiY0UsTZ4AmYnRjMpKwGHvfOL58sI6nl5SjNfrJ5AQZllxLfcv3MoT35vWTWsCv76VJfUdViwGQmE+3VrDqaMzerBV0pcoYImIREAwFKbJG8QbCFPn9pMY7egyiITCJk3eAL5ACKfdRmKME2cX53wT/mCYRk+AgM9g1w4XP/kJrF7d3muVmQkXXQR/+AMkJlqV2QnYGD4gjrBp4rTZDmqYb2ejh+oWH/G7f8OYwJqyhm76VIemYFcz3kD7RochE9bvbOzyPG8gRKMngGlCXJSd+ChHr+mVk95FAUtE5BvyB8N8tKWKBxZu5eiEeu78YgU/nzWSU0alHzAwhcImS7bVcO/CAtaWNTJ8QBzXzszjtHFZRDsjP4Hc7Qvy2qpy7n2hiq2fptL0+Yi25zIyYPp0qwr7kL3mmDd5Ajy1pIjHPimiyRtkcHIM/3taPudOGthpeEyLc5EY7SQc9Lcdy02PQC2HCBqcEovTbhAI7a6DZUDOgLhOz2lo9fOvpSU89mkRvkCImaMzuPnM0QxL6/w86Z9UB0tE5BuqavbywAdb2VjRRDBksr68kQc+2EpNi++A5zR7A9y7sIBVpQ0EwiZbq1p48KNtlNS2dksbvyxs5q/3Bln3bD5Nn+e2Hc/Nhfnz4ZlnOoYrgE2VTTz5WXHbFjZlDR7ufr+gw+q7/bHZDn3j5sPFZmOfIqb2LnqiPthUxZOfFdPoCeANhnl3QyWPfLy9O5spRzD1YPVDfr8fv9+PYRg4HA6ioqJ6ukkiR7Rmb5DNFc2YWL0hJrCxoolW34GDiD8YZm1ZIya0/ZrfXtVCndt/wHMOhd8PBQVw2TkJ7NyZQNjvAEwMR5Cs0zbz7gNjycu17ROIANy+EPWtgQ7Hyhs8hLvYILC62UeTJ0DcXr9htu5qPvAJPaC0trVtBSFYc9K2V7d0es7WqmbqW9v//wmbsKKortvaKEc2Bax+6JFHHuHDDz8kKyuLU045hUsuuaSnmyRyRIt12RmaFktRjbvt2LDU2E6H+px2GyPS49myV/AYmBxDQnTXP5bdviBFNW6avAFSY13kDIjb51qhEGzbZvVOPfgg+Hy739cWJm5cOcnTCzlmipPhOew3XAFEO2zER9lp2Ssopsa56KozKjMxmuRYFwF/ew/emOzELj/X4TR8QBzRTlvbPCy7zSA/s/P9fganxBIf7aB5d4+eYcCYrN71uaT3UMDqh0zT5Ec/+hFnnHGGJmeKREByjIujhiZTvDtgGcC0nBQSYw78IzYh2sG1s/J48MNtbN3VzMDkGK45MZfhXcwDavUHeeXLMh5dXMjOBg8j0uP56al5zJ2Y3Tbfy++H3/8eFiyAFSvaz518rJfm1HKYtIXxg5K4Yc7ITofuBqXEMDY7kRXF9W3HTh2VTlQXc8TyMxMYmhJD4S4rYNkNmDMms9esIAS4YOpgimtaeWtdBWHT5MSRA/j+CTmdnnPa2Ey2V7Xw4soy/MEw03JS+MmpIzo9R/ovBax+yGazMX/+fBYuXMiVV17JxIkT24KWaZqEw2HC4XDb4z3/u+e/Rfdjb3vuRX++H/WtPlaWWCFkz98snxfX0dgaID5q/z9m7TaD2WMyyM+Ip37363IGxBLrsnd6L0tq3Tz2SSHl9R5MTLZVt/DQR9s4Pi+NtLgoliwx+Otf4c03rWEvMElOhl/9Cs79lh2fKw1/cDoDEqIYkhIDcMDr1bb4KK/3YND+/IadjfiDIaI62SNwS2UTO+pb2wKVacKCjZV89/hhBzzncMtOiubmM0dxyTFDME2TQSkxZCXGdHrv0xOiuHZWHmdPyiYcNslKimZgcufn7O2bfq/05++xI5Fh6v+xfqehoQHDMCgoKOD555/npptuIisrC4C1a9fy+OOPU1JSwogRI7j88ssJBoO43W6SkpJ6uOW9Q2trKzabjejo6J5uSq/Q0NBAQkICdnvkV74dKRo9AT7cvItg2GREAmxvBofNxuwxGSREO7t+g6+htsXHxwXVhPb60e2w2RhiZLFkcRbPPz8E3+6RudRUyM42ueeetSQnh3B8zT+pdzV5+WxbTYdrOW025k7I7nQT5u3VLaze0YDDMMmIgTI3xDodzJ2Q1a97zT0eDwAxMTGHdP6OHTsoKCjgpptuimSzpJuoB6ufCYfDxMbG4nA4yMzMJDo6mtbW9lVLEydO5P7772fVqlUsXbqUqVOn4na7KSsrY9SoUT3Y8t6jvLwcp9NJRoYKEgJs2LCBvLy8fr1Yoqy+lbtW+tmws5HLR4R4bruDCYOS+Om4SWQlRTaIb6tq4Z4vv2RzZTOmCaFWF7admZS/PYaQ1wpzhgGnnAI//CFccIGByzVp97nNfF5Ujz8UJiMhihPz0w/YwwawvryR5V+uZmuVNfnbGvpM5ZeTJnd6XsPmKj5cuQ63x8OMrDDv7LBzxvgspk6d2q8DVmVlJaZpkp19aBXj4+PjKSgoiHCrpLsoYPUzgUCA119/naVLlxIIBJgyZcohf7OLiMXlsJEW5+pwLC3ehdMe+TAxJDWGn80cyQMfbGX9WoO6j8bgr0gi7G/vKbvzTrjwQhg+vP287dUt/OWdzSzaUk0wbJIS6+QHM4bzP6fkHXAeVl6GNb/rbx9upaS2lalDU/jfOfnEdjEHa2hqLJlJURTu7rGxGXBMTuo3//AiRxAFrH7G5XJxxhlncMopp2AYBvHx8RrqEvmG6tx+lhV2XK6/ZFstDZ4AafGR7dmzY+eozEy876RR+QmEvVawi4+HWbOscDV8ODi/MjK5urShLVwB1LcGmL+4iB+dPAL7AaafRzvtnDUxm5Py0wmGwkQ5rcrlti6WEa7e0cDWXS1thRZDJrzwxQ6+e0JOr5roLtKdFLD6GcMwSEhIICGh8+XIInLwwmE61FQC63Gkp7g2NcG//w0PPmhj48b2HrNjjoEbboCLL7Ye728ULhg228LVHt5giK6a6LTbSP1K71xX3L4g3kCI2L1+w1Q3H7joqkhfpIAlIrKXJm+ABRt2sX5nI9mJ0Zw9aSADkzuflBwbZWdYaiwlde11sHLSOq+D9XWYJrz4Irz2Gjz/PG2hKC0NfvELuOACyM8/cD0rgPQEF2lxLmr3KmQ6Jjuhy5pWh2JkZjxDU+OobbLmbtkNg9lje1eZBpHupoAlIrJbIBTm30tLePSTQhpaA0Q77WytauHG00eRmXjgofRByTH88sxR3P1eAQ57M3npcdx4+iiyOjnnYITDUFdnbb78zDPWf4M1/DdvHtx2G4wate9w4P7YDYOvbh/YHXPEAI4alsIvzxjFox9tIT6qlQuPGsy1M0d2y7VEeisFLBGR3byBEP9YXEiDx9oexhMI8ebaCs6amN1pwHLabcwdn80Z47L58suV3Dr1KGwGh7xizjRh505Ytgx+9CMrWJmmtX/etGnwgx/A97+/ez+9g7xEZZOXquaO2/CsKWuii11vDkmUw1o1eHJeMkVFRYwZMwaDQ78fIkciBSwR6fU8/iCfbatlc2UTKbEuZo7JIDvp0GoJdSXwlblUoXC4y733wJo0vrigGk9VCxVrd3JifjopsZ3PXWryBPh0Ww3bq1vITorm5PwMBsRH8f778Le/wVtvtb92xAhrZeAvf2nVt/om2vY/7MYqiIZhYDOs7ZRtClbSDylgiUivFgqbvL6mgvsXFlDZ5CXGZWfDziaunz2SjG84BPdVNsNgYFI0W6vb51IlxzqJ66TmE0CLL8g/lxTzz2UlnJXt4e2VG/nu8TlcfeJwYl37P9cXDPGfz3cw/5NCqpt9JMU4WT26ldL3c1m0wMmOHdbrDANmzID774exY+FQy42lxLpIjnHS4Am0zYUakR570D1gIvL1KGCJSK8WDIW5b2EBFY1eANy+EP9dXc5J+emcMT4rotcKhU2Kals7HKtp8dO0e3PfA6lo8PDUkmIaPAHCpkmt288TnxZx7qSB5AzY/4/ZZm+Qhz/eTp3bT8jjpGx9Bnf+OY9Q0IYZhuhoGDQInnoKjjrKevxNwtDMMRnceFo+z32+A38wzMCkaG49ZxyO7pjlLiIKWCLSu5lAy1cCji8Yxh8M7/+Ebyj0leHAsNn1HnAh06TVH2LvMbdWf6jDFjNfFTZN3J4QnuI0WtYMpXVzNrsH7khOhhtvhO9+FwYPPtRP0pHDZuOK43K44ricyLyhiHRKAUtEejW7zeD0cVm8uqq8LbCMykogNz2uy3Nrmn28uqqcDTsbyctI4FtHDep07pbdZjB+UCLrypvajg1KjiYtvvO5VCmxLo4Znspn22oAKyYdm5tKUsz+l/eZJoQ8Tqr+O4XmsgRCTbFtz/3oR3DOOTB37jfrsRKRnqWAJSK9msNmcMNp+UQ7bXy0pZrBKTFcP3sko7M7L5bb4gvy4EfbeOGLHbT6Q0Q7bZTWufnVmaNJjdv/RCan3WDS4OQOAWtoaiwDuqjG7rLbSIntGKZS41w4v1IXwTTB7YY33oBbbrHTsD0TsIJU/pgQv/xViIsvcBEbq3AlcqRTwBKRXs3YPfH8D+eN3+d4Zxpa/by4O1wBeANh3lxTwXeOzTlgwPIFw7y+ZmeHY6tKGyisdjMs7cA9ZtXNPj7YXIW5e4jQBBZs3MXPZ41s68UKh+HVV+GFF6x/e4waBZMmwb//bcNutylYifQRClh9SH19PSUlJfj9Vq2bvLw8Ur/pem6RXuBQ6ieZJvvUeAqbZlsIOpCvnmPSdTUDk33nae39Ptu3wz//CX/+MwR3Tydzuaytbf7nf6zaVg6HkpVIX6KA1Uds376du+++m5iYGGJirDkml1xyiQKW9FtJMU5mj8ngzbUVbXWfZowc0GnBUJfDxgVTBvH85zvwh8LYDYPpw1MZ0cV8r7R4F8ePGMAHm3e1HZuRN4Aow8Wrr1q1q0pLrXBlt1vh6o03TLJyvby6oYg7Hq4jPyuBH56Uy8iM+E4DpWmabK9u4dHFhWyuaObonBR+MGM4A5NjVMhTpBdRwOoj6urqGD9+PNdccw02mzXvY8//ivRHJuyzuXEwZHa6ubHLbuO62SMZkBDFssJahqXF8YMZOQxNjT3wSUBdi59lhTVtj8MBG4uW+hh1UwhPrTU8CDBsmLUy8IYbwBET4pZXt/D62p2EwibrdzZS1eTlzxdMYHDKga9X0ejlj29uYvHWasImbKhooqbFz5/OH09C9EHsmSMih4UCVh/wzjvvUFBQwPr163n66adJSLAm/x5//PEMGTKkh1sn0jOaPAE+2lLVNrxnAksLa6lq9pKVtP9eLMMwSIuP4uezRvLzWQe/d14wbOL2hwEDT1M0DR/n01qQRai5fcXiFVfA1VfDySdbj6uag7y7obKtLETYhOVFdZTXezoNWJWNXpYW1rYNQYbCJgs2VvLruaMVsER6EQWsPsDv95OcnMxRRx2FYRj4fD4AQqFQD7dMJDJMs+PMqYPZ184wrNpP0F4vy2Ezujyv7Vq7xxUPdg8904TGJSN4oTyT5qKUtuOjR8PvfgezZ0NK+2EMDBx2AwLtx+w2o8ttZQzDet3eHDYbBhoeFOlNFLD6gLPPPputW7dSVFTEnDlzAPjss89oaGjo2YaJRIBpmpTUtvLAB1tZtKWaQSkx/O+cfGaMHLBPGYS9Jce4OGtCFi+sLMM0raB06ugMBh6g9wqsSfBfFNfxp7c2sb3aTXKMk2tn5XHelEFEOez7PScQgOJNUTT8dxpNWwfQGLaCTmYmXHUV/Pa3EBtrbcy8t4RoB1cdn8PTS4pp9gaJcdmZN2kgw7uY7zUsLY7zpwzitVXltPpDJEQ7+M6xww5Yc0tEeoYCVh+wfft2XnvtNYqKimhpacE0TdasWdMWtkSOZMGwyT0LCnhz7U7CJtS1+rnz3c2kxk1k0pDkA57nD4XZ2eBtWwJoAruavHgCB+7Z9fhD3PbfDWyqbAasWlr3LtxKbno803I6LhgxTVi5Ej78EH772ygCgQwAYmLgvPPghz+0hgMP1CEV7bTz/RnDGZgUw8aKJoamxnL2pOwua26lxrm4duZIhg+Io6S2ldFZCZw5IZsY1/4DoIj0DAWsPiAYDGKaJjExMbS2tmIYBtOnT2f8+PFdnyzSy4XCJh9u2tWh7MHWqhZKat2dBiy3L8jyotoOQ4urdzRQ2+I/4BynUNhk8+5wtUdlo5d6t7/DsUAAnnkG7rkH1q9vPz5pksmFFxZz/fXDiY/v+rOlxLq4dPrQrl/4FVlJ0Vx9Yu7XPk9EDh8FrD5g9OjRZGdnU1paSnS0NfzhcrkwTRO/34/L1fk2HyK9mWFAanwULXXtmzDHuuxd9tjYDINYlwO/p32SU4zT3uXmxlEOG95gx3lbdpuBaYLPBzt2wHe+YwUrt9t6TWoqXHYZ3HILlJTUEh8//BA+qYj0JQpYfYDNZmPbtm38/Oc/Z/DgwdhsNkpKSsjOzua8887jyiuvVH0cOWI5bTZumD2Sexdupay+lfgoBxdMHcyUISmdnmczIMZlo8HTfizaadtngvjeop12rjohh2eXl9LkDeKy25gzNoP8zAS2boVHH4X77oM960dSUuCYY+ChhyB3d4dSaek3/MAi0icoYPURDoeDyy+/nKuvvhqbzcazzz5Lamoqzz33HFdeeWVPN0/kkNlsBmdOyCY9IZrt1S0kxTg5fkQaAxI6n6sUCIWpaPR1OFbV7O90DpbLYeNHJ49gTHYitS1+op12pg5K460XYnnmGfjss/bX5ufDbbdZmzInJ1vHOquxJSL9iwJWH2EYBkVFRWzevBmn08mWLVs4/fTTNTwofUKUw8b03FSOzknBMKxerS4ZBnbDILRX6rEZXZdcSIl1MW/yIEIh2LABvnM+bNkCzc3WcKXTCXfeCWedBXl57ZPYw6ZJMGQSNsEfDOOwd11yQUT6LgWsPmLcuHHMnTuXJ554glAoxFlnncUxxxxDfn5+TzdN5BsxTZOCXS3cs2ALnxTUMDA5hhvm5DN7TAZRzgPPw4qLsjNteArLCuvajk0cnERaXOd/dITDUFwMzz4Lf/yjNe8KrO1t5s2DX/0KJk+2gtWe/BQMhVlRVMe9CwsYH1XL3V8u54bZ+UwbntrpkKSI9F0KWH3IgAEDOPHEE/H7/dTX11NTU8PQoV9/hZJIbxIMm9y7oIAFG62VhNuqW7hvYQHZSdFMHXbgeViBoGmVadhLVbMPj//AQ4ReL9x/P7z2Gixb1n585kyYM8faU3B/nWdVzT7uWVDAypJ68kdYYeueBQU8eNkUMjrZ+1BE+i4FrD5i69at/Otf/2Ls2LFtKwlF+oJQ2Gzbd2+PwuoWdtS3dhqwfMEQpXutPATY2eDF7Q92OLZnBHHNGvjDH+C//22fxJ6UBD/9qbXFzbBh+w9XYG3Ls3pHA3vqzZvAl6X1NHuDZCR+rY8rIn2EAlYf4fF4GD58OBdffDFOp1XR2eHQ/73SfUJhk4ZWP02eABWNXrJSHER3MmR3qAwDBibHsK2qpe1YYoyThKjOv75thkGM095hUnu0o+MqQtOEwkJrOPDOO6G11TqWlARZWfDRR5CWZg0PdsblsDEgPorKpvYes/SEKJwObbgu0l/pN3AfER0dzRdffIHP5yN595KmU089leHDVY9HIs80TdaVNfB/728hM1TDqg9quOL4XL49bUjENxx22mz875x87l1QQGF1C4kxTi6ZNpTJQzsv0xAXZWfykCSW7jUHa8zARFJirbRUWwsffwz/8z9QVWU9b7PBtGlw7bVw4YVwsJ3BWUnR/PCkXB77tBC7zc3g5BiuPnE4GV2sdBSRvksBq4/IyMjg3HPP7XBMKwilu4RNuG/hVpZsr+XsIWGqmr089mkRuenxnDo6I6LXstkMZo3OICsxmh31rSREOZg8NIXULiarewNhtlW7OxwrrW2l2RtkzRr4zW+ssgt7b9l5xx1w9tkwduzXa2Osy8FFRw9mdFYC1cWbOPOUMUwYlNQtPXoicmRQwOojkpKSSEpKYtGiRfzyl7+koKCg0+XopmnS2NhIVFQUMTExh7Gl0ld8WVrfYV7UriYvO/eu6hlBUU47U4eldDrn6qsCoTDVze11sMwwVNeFueQyk6rNsGuXdTw6Gk47zappNX68VYbhUCREOzluRBorG2I4esSAQ3sTEekzNEGgj9i0aRMfffQRa9aswTAMNm7cyIYNGw74+rKyMi6++GKeeOKJw9hK6UvGD0pi7wifFh9FZjetmPMHw2zc2cSHm3axrLCWxlZ/l+c47La2Xq6w307L+sFUPnss6z5ObAtX06ZZldlfew2mTLHmWql0lYhEgnqw+ohAIMCQIUOorKwkHA7j9/sP2IPl9Xp59tlnmTFjxmFupfQVhgGnjEpnWWGt9RgYmhJDbnpcxK8VNk0WF1Rzz4ICtuxqJjnGyXeOG8ZVx+eQHHvgYUKX3cag5GjKVifTui0T99ohu1sKGRnWysCrrupYLFREJFIUsPqIwYMH895777Fy5UpuvfVWAGbNmrXP60zTZNGiRWRnZxMTE0PVntm9u4XDYYLBIIFAANM0CYfDmKbZ9t9C273oz/cjFDZ5b30FNkwMrArpJbVutlQ2kZMWG9Fr+YNh7l+4hYLKZsCkodXHc8tLmDw4iZPy0/d7jmlCQz2seGkgtV8MJtzqAgxstjAnnWRyzz0Go0dDVJT12khvcdOfvzb2tufnh+6H5Zv+LNV9PLIoYPURmZmZXH/99Zx++umEQqEDlmiorq5m+fLlTJw4kaKiIhoaGmhubiYhIQGADRs28NRTT1FSUkJOTg5r1qwhGAzidrvxeLpnfs2RprW1FcMwqKio6Omm9BjThAkxDeQNDzE0HhKcIcJ4aa3YzprgrgOeFzZNKhu91LT4sNtsDEmNIbGLVYehsMmU+CbGDm8vt2AzPDSWb2ONZ+c+r9+1CzZtsnP77WNpas4F0wDDxDmgiRNOK+TmH/gAF5s37/96gVCYnQ1eGlr9xLjsDE6JJdZ18JPV6+vrWbNmzUG/vi8LhUI0Nzfj93c9pNsfeDweTNNk164Df490ZseOHRFukXQnBaw+wjAM4uLiOProowF46qmnSE9PZ9iwYR1eZ7fbGTp0KJs3b2bjxo2YpkldXV1bwJowYQJ33303q1atYunSpUyZMgW3201ZWRmjRo067J+rNyovL8fpdJKREdnVckeScNhk/sYw7xVUctaQEB/utJGaEMtZp4xmysj99yoBrC9v5NmVm/i82Eu008bl0zP5wYnDGRB/4HIG/mCYPyxtZU1ZI2AV8cxMiGLuKfkdrhUOw5Il8Oij8MYbENxdT9Qe7yV2zE4Sp2+nNi3MkFHHMm5g0n6vFQqbLNpSxRNLt7KhwkNWYjTXnJjGhZOGEB/d9Y9L0zRZuXIlU6ZM6fK1/YHX66WwsJCxX3dZZh9VWVmJaZpkZ2cf0vlxcXFs2bIlwq2S7qKA1QeYX2N8Iy0tje9973uYpsl///tfdu7cuU8IE+mKYcD1s0cSDJm4fJUMSo7hByfndbnK78EPt7Fkey0m4A+FeW5FKbnpcVx09JADnmNidigWamD1MgVDu6umm+DxwE03wfvvw7Zt7edmjW4gacZWvMk1JMQaXH1iLjlpB54n5g2EuG/hVtaXN2IC5Q0eHvukiPGDkjg6J/Vgbo2ICKCA1Sf8/ve/p7KyssOxHTt28NOf/rTT8+bOnasxfTkkhmGQn5nAA5dOYdOmDeQMH0FiXAwOe+cLkzfsbGTvPwcaPQHK6jsfejZN2PGVLW8aPQEaPQGamuCtt6w9AisqrC1unE6rCvv8+XDscYkYzsmETRObYRDttOPqpLp6KGyyubKpQxt3Nnqobw102kYRka9SwOoDrrzySnw+3z7HBw0adMBzDMNQIVL5RgzDIMZlJ8phJy7K0WW4Ajh6WCpl9eVtASYlztnlykObYTB1aEpbzxdAdmIsm1dF88aDsHelkQEDrNWBP/6xtXegVYnm4KvROOwGkwcn80VJfdu1hqXFkR6v7xUR+XoUsPoAbYcjR4rj89J4fU05u0f3SIuLYmRGQqfnOOwGN8zJJ2wW8EVxHdHhWOoXjuWeTUlU77XO4HvfgwsugLlzD7wpc1eiHXZumJPPvQsLWFXawKDkGP7n5BHkZ3XeRhGRr1LAEpHD5uUvy9rCFVhznD4vrmPswMQDnmMzDKYMTeH/zp/C2++HuOkXNlqqowiHDQwDRo6EX//aClcJCd+sppXNZnBMbioPX3EUrf4QLruNlDgnUQ5teSMiX48CVh+wfv36/ZZQGDFiBKmpmpgrvcfOBm+Hx95AiIYuqrKHQvDRRwaPPRbFf/7TfnzECJg6FZ55xpp3FalioQ6brdNVjSIiB0MBqw9YsWIFdXV1+xxPTExUwJJu4w2EWFxQTUVxHctqSjhtwmCGpHZeZHTm6Az+tbSE0O6Vr5kJ0YzNPnDvVVkZPPkk3HNP+6bMLpc1DHjDDXD88XCAkm8iIj1KP5r6gG9/+9uEw2FCoVCHVYHx8fE92Crpy0zT5M01O7nr/QKOSXGzpKqFNTvd/PKMUQxOOXDI+vHJuXj8Id5ZX0FclIPrZo1kxsh9N0b2++Hdd+E3v4GCAuuxzWaFqddfh8mTITOzGz+giMg3pIDVB8THx1NXV8ezzz7LSy+9RGxsLI2Njfzxj3/k1FNP7enmSR8UNuG+D7ZS2eTFTAZPIMT7Gyo5KT+dC486cMDKTIzmTxeM5w/njQfAYTOw2drH9rxeKCqC738fvviivVhoVpY1if366yE9XXsHikjvp4DVRxQWFtLa2srAgQP5wx/+wFtvvUVsbGT3hBPZm8cf6vA4GDYJhDqvq2YYBg7D2KdygmlCZSU88AA8/zwUF7c/d9llcOmlcPbZEWq4iMhhoIDVh6SmpjJo0CA2btxIc3MzDXsmrYhEmGHA2ROzeWZ5qfUYyMuIZ/TXLGewZxOChx6CF16ATz5pf27MGLjxRjjvPEjpvED8Ad7bpLrZxwtflLGuvIFRmQlccsxQspOiMdQFJiLdTAGrj8jNzSUhIYE5c+bw6KOPkpaWxqRJk3q6WdJHGcDPZ40kxmXHV13KMcMT+OGpo5g4OPmg3yMYhHXr4K9/hVdfhT21cgcMgHPOgXvvhfh4sB9ihQS3L8h9C7fyypdleINhPtpczY46D785e4xWCYpIt1PA6iMaGhqoqKjglFNO4U9/+hPLly+ntraWrKysnm6a9EGGYZAWH8WvzhzDhg1h8vLyiIo6+NCyfj0sXgzXXmtt0gwQGwuzZ8MvfgEnnvjN51k1eAK8trocb9C6gD8U5t0NlXx/Ro4Cloh0OwWsPmDTpk289NJLlJSUsHPnTsLhMCtXruSss85i3LhxPd086aMaPQGeXV5CuK6Kl7YGOWPSYKYPT+v0nEAAXnkF7roLVq1qD1e5uXD77dY8q0MZDjx4B78xuojIN6GA1QeEw2FM08Rms+HxeHA4HMydO5djjjmmp5smfVTYNLnr/c288HkZpw0MsKiilRWlTfxh3nimDts3Ifn9UFsL558PW7a017SKj7dWB/7iFzBkyKFvcbM/STFOzp00kFdXleMLhnHZbZw2NovspJjIXURE5AAUsPqAsWPHMmLECDZv3sy2bds455xz2LVrF6FQqOuTRQ6BacIrK63gAlbg2rCziQ07mzoELNOE0lJ47DFrhWBTk3U8Lg6mT7eOjR1rHYv0vPP4KAc3zMknOymatWWNjMpK4LLpQ0nTxs0ichgoYPUBhmFQWlrKa6+9xpIlSzjttNP48MMPGTZsmOpgSbfZ/2Bb+9FAAB5/3BoSXLCg/RVjxsDPfw6XXALJyd3XPsMwyEyM5rrZ+d13ERGRA1DA6iOampoYOHAgw4YNw+Fw4HK5CO6p0igSYQZw/Ig0PthU1XZsYFI0Q9NiCYdh0yb41a/g44+hudl63m6H3/0OvvUtyM+P7HCgiEhvo4DVR6SmpuJ2u6msrOSNN95g/fr1XHHFFT3dLOmjDAP+dP4EUuO2EN9ayZSh8Vxy7HCGutL5+9/hppusquwAUVFw8slw223WsOChll0QETmSKGD1AX6/n+rqalJSUjjppJOoqanh0ksvZfTo0T3dNOmj9gy//fXCSWzY4GDQoDxefjmKP86H5cvbX3fCCdbGzDfdBE5nz7VXRORwU8DqA5566ikKCgpwOBxkZmbywx/+EKd+m0k3M3eXYS8ri+E3vzH44ANoabGei4uz9hP82c8gL0/DgSLS/yhg9QGLFy/m73//OwA33ngjwWBQAUu6lWmarC3w8eu/tPDZK8NobrZhmlbZhexsa1J7ZiZER/d0S0VEeoYCVh/Q0NDA22+/jWEYFBYW8vLLL+N0Ojn++OMZMmRITzdP+pjGRqsK+w9+YqO6bIB10DBxpjdx/Y12/t+1ccSo1JSI9HMKWH3AVVddRUtLC6ZpcumllxIIBAgEAqqDJRFlmtbegX/8IyxcaFBfv6eelEnyiVuIGV7NyBOHEBMT16PtFBHpDRSw+oALL7ywp5sgfVg4DB4P/OY38NJLUF4OYGLYTaKG1XDeBQWsdjThw8Qe6WqhIiJHKAUsEdkv04TWVnj9dbjvPlixov25KVNg+DENbM1azYAML85KO1OHpO13mxwRkf5IAUtE9uvDD60q7LvXTwDWxPVLLoGf/cwga3Ai728ci7uymEljMzguP5Mx2Yk912ARkV5EAUtE2pimtV/gI4/AQw9BWZl13GaDCROsrW/GjIHYWAAHF0wdzIYNjeTlDSMqKqonmy4i0qsoYIkIAHV1sHUrnHGGtb1NKGRVXc/NhZtvhksvhZiYyG/KLCLSFylgifRz4TCsXAn33guvvWZNaAdISrKKhV53HQwb1qNNFBE54ihg9TN7qm/vzVCXRL9kmhAMWgFq8WLYsKH9uTlzrONz5oDLdeD3EBGR/VPA6mdM0+SVV17hlVdewWazcfHFFzN37lzs2oG33zBNq5fq/ffh1lth40ZrONBmsyaxP/AAnHYaJCREfjjQNE1afEHcvhA2A5JinEQ59bUnIn2PAlY/YxgG8+bN4/zzz2fbtm088sgjHHfccQwYMKCnmyaHQTgMn35q1bP629/aj2dkwHnnwa9/DTk53Xf9who3f313M4u2VBPrsvPDk3K5bPowkmK0tZOI9C0KWP2MYRg4HA6eeeYZlixZwvjx44mPj+/pZslhUFVlrQx86ikoLW0/fvHFcNllMHcuOLr5J8LfP9rGgo27CJvgC4Z5/NMiBqXEcu6kgd17YRGRw0wBq5+aNWsW6enprFy5Er/fT/TuXXnXrFnD448/TklJCXl5eaxcuZJgMEhraystLS093OrewePxYBgGO3bs6OmmHBSfDzZvjuPOO/MpLrbh91tDf+npQa64ooQzz2wgJQXWrDm0929sbKS1tRWbzdbla9N9u7gkN9j22DBCNJcVsDJYcWgX74Xq6+tZuXJlTzejVwiFQrS0tODZs3Kin/N6vZimyc6dOw/p/LI9dVPkiKCA1c+Ew2FCoRBZWVkALF26lKqqKhITrQKREydO5L777mP16tUsW7aMqVOn0traSllZGfn5+T3Z9F5j586dOBwOMjIyeropnQqFrNWB991nDQnuWd8wZAgceyw8+aSdmJjcbzzPasOGDYwcORLXQcyG/8eGlSws3EXYNDGApBgXkyblMnVq39mUfOXKlUydOrWnm9EreL1eioqKGDNmTE83pVfYtWsXpmm2/fz9uuLj4ykoKIhwq6S7KGD1Mz6fj0WLFrFt2zbcbjdpaWlkZma2PW8YRtu/PY/3fk4se9+j3qi6Gh57zKrCvuePXocDTj0VbrwRZs4EhyMy7d/f18qB/GxmHk3eAF+W1BPltHPR0YM5dXRmr76XX8eeVbp95fN8U1/na6M/OdT7oft4ZFHA6mdcLhdjx44lPT0du93O0KFDSUhI6OlmSQSYptVr9fbb8Ic/wNq1tA0HOp3wwgswdarVg9VTxg9M4v8unERlkxeHzWD4gDiSY1UHQkT6HgWsfsZutzNs2DCGqXJkn+L3WxPXb7jBKr/g91vHU1Lgu9+1eq0GDuz5Kuw2m8GQ1FiGpMb2bENERLqZApbIEcw0oaLC2iPwiSeguLj9uW9/G+bNs1YIiojI4aWAJXKEMk3497+tYPXRR+3HR42Cn/4ULr/c6sESEZHDTwFL5AgTCkFBAdx5p7U60O22jicnwwknWHWukpO7v6aViIgcmH4EixxBtm2z9g38wQ/aj8XFWWUXbr0VZsywtrwREZGepYAlcgQIBKzVgffcA0uWtB9PTYW777aqsPfyslwiIv2KApZILxYMQksLnHmm1XtVU2Mdj46Ga66BH/0IxoxRr5WISG+jgCXSC+1ZHfj001Yl9qoq67jTac2zuvNOOPpoq+xCT5deEBGRfSlgifQyoZBVhf3NN61/e4wbB5deaq0QTE7useaJiMhBUMAS6SVM01odeMcd8Mor0NxsHbfZrEKh3/mONRxot/dsO0VEpGsKWCI9zDShvBwWLoSf/AS8XutYdLTVa3XXXdawoNPZ0y0VEZGDpYAl0oNaW+Gdd6w5VZ9/3n586lS44AL4xS+soCUiIkcWBSyRHlJWZg39LVwItbXWMacTrrwSrrsOxo/XBHYRkSOVApbIYWSaUF8Pf/87/O1vUF1tHYuJgcGD4bXXYOhQiI/v6ZaKiMg3oYAlcpi43fDxx3D77e3DgYYBeXlWj9UPfmANB6rXSkTkyKeAJdLNTBNWr4b774f//hcaGtqfu+UWmD0bTjyxp1onIiLdQQFLpJuYJvh81lY28+dDSYl13GaDk0+GX/3K+t+oqJ5tp4iIRJ4ClkiEmaa1OvDDD62aVsuWWcfAmrh++unwl79Y9aw0HCgi0jcpYIlE2IoV8Oyz8MAD7ccyMuDcc+GXv7TmXClYiYj0bQpYIhFgmtYk9sceg4cfhq1breOGAQMHwosvWr1XCQk9204RETk8FLBEvqGmJigshFNPtUJWIGAN/w0cCLfeChddBImJ6rUSEelPFLBEDlE4DOvWwUMPWUOCbrd13OGwtrz5n/+B0aN7to0iItIzFLBEDkEoBNdeC0uXwqpV7cdPO83alPnii7V3oIhIf6aAJXKQ9pRdWLYskT/+0ca6dVbQAmsS+5//bO0fmJys4UARkf5OAUvkIJgmfPYZvPkm3Hln+z42GRlw/PFw770wbJiClYiIWBSwRLpQXQ3PPAP33GNt0AxWijr7bPj+9+Gss8Dl6tEmiohIL6OAJbIfpmmtBty0CS6/HIqKrOKhhgHR0WF+9zu48kobGRnqtRIRkX0pYIl8RTAIW7bAbbfBa6+1z7PKzLT2DLz11krS0x1kZGQoXImIyH4pYInspaEBHnkEnnrKCll7zJgBv/gFnHMOVFaa2GzquRIRkQNTwOpnzN2b4pmmibE7IRj9PCmYpvXv9detyepLl1rDg2BtxPzYY3DssTBihEKViIgcHAWsfsbv9/Piiy/y1ltv4XA4uPTSSzn99NOx2+093bQeEQxaE9dvvx1eeAE8Hut4fLxVy+q3v4WhQ8Fm69FmiojIEUYBq5+x2+3MmTOHCy+8kOLiYubPn8+xxx5LampqTzftsDJN2LnTClX33w8lJe3PnXsunHkm/OhH6rESEZFDo4DVzzgcDjIzMzFNE7fbjcPhwNUPawy88Qb87W+wcGH7sREj4Jpr4Ac/gAEDeq5tIiJy5FPA6odM06S2tpaHH36Yq6++mri4uLbn1qxZw/z58yktLSUvL48vvviCUChEa2srzc3N3dquQMik1R/ENCHKaSPGGdlhy3AYyssd/PvfQ/ngg1QaGqzj0bFh8vO8/PGPG0lPh+Ji69+BeDwebDYbpaWlEW3fkaqxsRG3241N46gA1NXV8cUXX/R0M3qFcDhMS0sLra2tPd2UXsHr9QJQXl5+SOeXWYX45AihgNXPmKbJzp07ueeee7jooouYPn16h0nuEydO5G9/+xurV69m6dKlHHXUUbS2tlJWVkZ+fn63tavZG+S5FaU88vF2vIEQs8dk8sszRjM4JSYi719WBh98AN/7XvsxwxHEldnEt35Uz19/NpDs5KMO6r127tyJ0+kkPT09Im070m3cuJG8vLx+2RO6PytXruSoow7ua6mv8/l8FBYWMmbMmJ5uSq+wa9cuTNMkKyvrkM5PTExk69atEW6VdBcFrH7G6/Xy1FNPsXr1ajIzM2lqauL0008nMTER2HdF4d6Pu3O14aItVcz/pJD6Vmv53lvrKkiKcfLH8yd8o/cNBGDBAnjwQet/29hDpMzaSMyIapZ5fDy1xM//O+vr/RLo76svv0r3o32Vru5FR7ofHel+9A8KWP2M0+nk8ssv59xzz8VutxMXF0d0dHRPN4vNlc3UtvjbHodNWLK99pDfLxSyVgTOmWMN91VWWsedToibWEL0uB24shoxDAiGYen2mm/4CURERNopYPUzDoeDnJycnm7GPrKSoomLctDiCwLWbn8jMuI7P2k/TNPaO/CFF+Avf4G9pzrMmGFVZ99qD/HAhy14dte6shmHdi0REZEDUcCSXuG0sVls29XCy1+W4Q+FmTIkhWtn5n2t9zBNmD8f3n8fXn65/fi4cVZP1u9+B4mJMLYhm6KaZt5YW0EobHJ0Tgo/PnlEhD+RiIj0ZwpY0itkJUVz3eyRnDEhi1DIZFBKDDlpcV2fiBWsioqsKuxPPglut3XcZoMf/hB+/GMrZDl2f7UPTI7hxjNGMW/KIMKmyZDUWIalxnbTJxMRkf5IAUt6jbT4KI6Pjzro15umNbfqk0+s+lVutzX3KjoasrLg8cfh+OOtx1+VkRBNRkLPzz0TEZG+SQFLeo2KBg8rS+oJhE1GZsQzJjsRu23/q21aW61g9etfw6pV7cfHjoXLLoPrrrO2uzmQsrpWVu1oIBQ2GZWZwKjsBGxdrOwxTZNtVS1sKqnHsNkZb4sjJy1WK4JERGQfCljSK5TVt3LP+wW8s76SQCjM6KwEbjtnHNOG77uFT1UVXH89fPyxtd3NHokTy/jBzVHccOmATkNPaa2bO9/dwsJNuwibJuMGJvG7c8cxaUhyp20s2NXCba+vJ9hchy8MaRubueWcsYxI1wR5ERHpSKWXpVd4b30l76yvxBMIEQybrN/ZxAMftBfUM01oaYHf/x6OPhqee253uLKFcaS0kHnFZyTN3MBHtVswu7jWf9fsZOGmXfiCYQIhkzU7Gvj7om1dtvGJz4pYUVRHIBTGHwzz6bYanl2uau4iIrIv9WBJr1DX6scbDHU4trPRA4DPB4sWwf/9n1WNfY+oFA9xE0uJO7oQwx7GMGBng6fLa9W2+PEHw22PTaCiwdvlebsavYT3Sm/BsElVU9fniYhI/6OAJb3C+IFJpMa6qHW3Fxs9dVQGK1fCE0/AM89AU5N13GaDG26ApHw3Cxt2UlJnhSWbAaeOzqCrGVGThyTzWqyTht1V4+0GnDKq621vZuQNYFlhLWBdLz7KwbG5aV2et768kQ827SJswgl5aUzLSdW8LRGRPk4BS3qFqmYvnoDVg2WaQNjgrf/E8OQSKCy0XmMY1vDgb39r1bUyHClMWJ/PPQsKqHP7mT0m46BqZw1LiyXWaacBK2DZDIPRWYldnvftaUOoc/tZv62YmLDB2cfkMm/yoE7P2bCzkVteW8+68kZME95cu5M7LpjIMfuZWyYiIn2HApb0CjsbvLT6Q4T9dnwVyTQsHkVpRTJ7JlTl51uh6u67weWywhbYOXfyQOZOyAasHiy7zeiyd2jBxl1UNfvaHgfCJs8sL2HuxOxOz0uIdvCL00dRPj4eh8NBZmbmAVc57vHC5ztYW9ZIaPcedYXVbuYvLlTAEhHp4xSwJOIKdjWzdHst/mCYzMQoTh2dQUK0s9NzTMBfHU/LmiE0r8xtO56WBmecYfVajRq1J1i129XoZfHWGlr9QUZlJXD0sFRcjs5Djy8QJmx2nArv8YcO8Op2hmFg3x3iHHZbl+EKwPuVa5mAJxDs8jwRETmyKWBJRG2rauaOtzfxydYagmGTpBgn36txc+3MkfsNJKYJgQDUfj6YmjcGEqjeM1RnkpEBzz9vMGUKJCfve62KBg/3LizgzbUV+AIhhqbGcfOZozl9XGanvVjx0XbsNoNwqD34pMR2HgAP1Un56by/sZL63fO9op02zhjfeU+ZiIgc+RSwJKJW72hkcUFN25BYoyfA458U8dNT8vYJWD6ftcXNz34Gn32WQMALGCa2GB9JJxQw4vhGTjllxj69Vnt8uq2GN9dW0Lq796mo1s09C7Zw2rjMTie617kDBEIde7Aqumk14OnjMgmEwsz/pJBQ2OSSaUO46OjB3XItERHpPRSwJKJCYbMtXO3hD4U71KYKh2HLFnjpJWtOVWOjddwW4yN2dAXxk3YQldmELSbmgOEKoNUfwhcIdzhWv9cqxAP56vCgdazL0w6Jw27jvCmDOG9K55PhRUSkb1HAkojKSIhiQLyLmpb2oDNuYCJ7Oq9ME/7xD3j6aVi+vP28uBFVxE0sJTpvF8bu8reOLiarj8pKYHBqDCW1rW3HThub1WWZhoyEKJx2o0Mv1uBk7UsoIiKRo4AlERUXZScuytEhYGUkRhEMwsYN8JvfWEVDW1qselbp6VYv1jrqeH7tLjqmo867lZo8AVp9HSeM72xsxYROQ9b3ThhOcY2bD7ZUgQkjM+L5w3kTvu5HFREROSAFLImo4prWDj1KpgnvfdbKnUXwu9t217gCMjPhzDPhzjutkHXHO+Y+qSjYxbBdaV0r1S0dhwQ/L27oso2JMU7uuXhyW3xTyU8REYk0BSyJqA5zrfx2WguyaF41jNt3tseYiy6Cyy6DuXOtmlaRuu7XCUqGYShYiYhIt1HAkogakhLD4JQYioug+r9TCTbEEvZaKWrgQLj9dpg3DzIyOp5n30/a2d+xvaXHR5EY7aDJG2wLSyMz4rpso2mafLatlic+KyIQDHPOpIFcMHUQDrv2PhcRkchQwJKIOnZEGjecPJar703Bv8sFGGRlwUknwd/+BgMG7FssFCAh2onLbuDfa+L5gPioTq8VH+0gxmmnyds+Dys9IbrLnqnPttXy61fWsqPe2hh6ZWk9YdPkkmOGHuzHFBER6ZT+ZJeIshkGxw7JIn9YFNHRBmeeCU8+Cc89Z/Va2Wz7D1gNnkCHcAWwa6/tbPanuLZ1n9csK6ztYmo8LNpSRXmDp+1xqz/Ei1+UdXGWiIjIwVMPlkRcVhY8/DBUVsLEiTBo0P5D1d4O53wom83AmoHVHsUOZtsbERGRg6WAJRFnt8PUqV/vnPSEKKKdNrx7FQ7NSYvt9JzxAxMZlRlPwa4WTMDlsDFvysAuw9rcCdl8urWGzZVNmCakxbv4wYzhX6/BIiIinVDAkl4hLsqB02bDS3vASo7tfInh9Nw0bj1nHK+tKicQCpOXEc+Vx+d0ug8hwOQhyfz5ggm8vLKMQCjMzNEZzBqTGZHPISIiAgpY0ksU17hp/krR0DU7Gro874S8AZyQN+BrX2/ykGQmD0n+2ueJiIgcDAUsiahw2GT1jgb+9uFWKhu9nDo6gx/MGE5qnKvTniWH7auzosCpsgkiInKEUsCSiNpY0cRtr29gfXkjJrC1qoUWb5Dbzh3XaV2rU0Zn8Mm2GtaVWeelxDr53oycw9RqERGRyFLAkogq2NXMpoqmtp6oYNjk9TU7ueWcsdg7mX4+LSeV284Zy/LCOsImDEuL5YzxWYen0SIiIhGmgNUPffbZZ/zjH/8gOzubO++8M6LvbbcZOGwGwXD7YF+U8+CG+qYMTWHS4GTAqqfVVWkHERGR3kqTXPqhnJwcfvKTn1BUVBTx9z5qWApzxmYS7bBhAAPiXfzs1Lwu60x5AyFe+qKMk/9vEcf86QNuf30DtW5/p+eIiIj0VurB6ocGDRpkbXbcDV1Eg1Ni+eUZoxk7MIl6t5+JQ5KYOToDWxfX+nhLNfcsKKCyyQvAv1eUEuOy8+u5YyLeRhERke6mgCUdbN68mRdeeIGioiIyMzNZv349wWCQ5uZmAoHAQb/PiQOAAQB1FBbUdfn6ypJ6pqe4CSe3H/PXlLB+fejrfoRu19LSgs1mo6qqqqeb0ivU1tYSDAax2+093ZReobGxkfXr1/d0M3qFUCik+7GX1tZWTNOktrb2kM4vLS2NcIukOylgSQe5ublce+21rFmzhrVr15Kfn09rays7d+4kLy+v2667oq6ET6ta8ASsQGUA03OTyM/P77ZrHqqKigocDgfp6ek93ZReYfPmzeTm5uJydV4Ytr9obW3tlV+3PcHn81FcXKz7sVtVVRWmaZKZeWiFjQ3DYMOGDRFulXQXBax+aMeOHSxevJjy8nI++eQTpkyZQnx8PAAulwuXy0VSUhIOhwOXy0UgEMBut3frL9A54wexZmcL722oJBgyyc9M4Een5PfKX9oOhwOn09kr29YT7Ha77sdupmlis9l0L3YLh8Pd/rPjSOJwODBN85Dvh9PpjHCLpDspYPVDgUAAh8PBVVddRWtrK+FwuOuTutmQ1Fh+efpoTslPJxAyGZWVwPhBST3dLBERkUOigNUP5ebmkpub29PN2MeglBjOTxnc080QERH5xlSmQURERCTCFLBEREREIkwBS0RERCTCFLBEREREIkwBS0RERCTCtIpQOmWa5n7/W3Q/vkr3o53uRUe6Hx3pfvQPCliyX6Zp8uqrr7JkyRL8fj81NTUMHDiwp5vVK9TX12Oz2UhKUp0usArXZmdn43DoxwlAYWFhryyD0hMCgQC7du1i8GCVXwFoaGgAIDk5+ZDO93q9HH300ZFrkHQrw1SUlgPY86VRXFzMv//9b37zm9/0cIt6h5dffpnExETmzJnT003pFX71q19x4403MmDAgJ5uSq/wne98h3/961893YxeobKykoceeog//OEPPd2UXuHtt98mGAxy7rnnfqP3MQwjQi2S7qQ/OeWAvvpNrG/qjnQ/OtL9aP+jRPeiI92PjnQ/+gcFLOlSYmIixxxzTE83o9fIy8sjOjq6p5vRa5xwwgnExMT0dDN6jTPOOKOnm9BrxMXFcdxxx/V0M3qNnJycXrE1mRweGiKULumv8o50PzrS/ejINE3di930tdGR7kf/oh4s6aC6upq3336bUCjErFmzGDZsGIZhEA6HWb58OV9++SU5OTnMmjWLqKionm5utysqKuKtt94iMzOTOXPmkJycjGEYFBcX88Ybb+B0Ohk2bBgnn3wysbGxPd3cbhUIBHj55ZfZtWsXF110UYdFDz6fjzfeeIOqqirmzJnDyJEj+/wvkaqqKt544w28Xi8//OEPcTqdAIRCIT777DNWr15NTEwM06dPZ9KkST3c2u7n9Xp5//332bFjB5mZmcycOZPU1FTAChYff/wx69evZ/z48Rx//PG4XK4ebnH3ampq4pVXXsHtdpOXl8fMmTPbvkaKiop45ZVXiIuLY/jw4cyePRu73d7DLZZIUx0saWOaJi+++CLBYBCXy8Vrr72Gx+MBoKSkhLfffpucnBxWr17NF1980eeXGofDYe655x6GDh1KQUEBy5Yta+veLy4uZsOGDUydOpURI0a0/eDs60zTZN26dZSXl3c4vmLFCr788ksGDRrE3//+934xDBIOh3G5XLz11lsEg8G248FgkDVr1mCaJlOmTCEjI6MHW3n4+P1+XC4XkydPZuvWrSxatIhQKARYgeLFF19k1KhRfPjhhxQVFfX5nx9er5eUlBQmTZrEq6++yvbt29ueKy0tZdOmTUydOpXc3Nw+/8dIf6WAJW3q6uqorKzklFNO4eSTT8btdlNZWQnApk2bGDRoEDNnzmT8+PGsW7euh1vb/YqKivD7/cydO5eTTz6ZVatWEQgE2p6Pjo5mwIABZGRk9IsSBQ6Hg3PPPZecnJx9nnv//fc5++yzOffcc9m1axfV1dWHv4GHWWZmJnPnzt1vuQ6bzUZCQgIZGRmkpaX1QOsOv4SEBObMmcNxxx1Hbm4u9fX1bUH7s88+Y/r06cyaNYvU1FSKi4v7fMBKT0/nnHPO4bjjjiMtLW2f74nY2FjS09PJyspSwOqjFLCkjcfjweFw4HK5cLlc2Gw2fD4fAG63m5iYGBwOB9HR0QQCgba/TvuqpqYmkpOTsdlsJCYm0tLS0vZLIT09ncTERJ588kkee+wx6uvre7i13c8wDGy2/f/IqKurIy0tDZvNRkpKSr++H3a7nSFDhlBYWMgjjzzCa6+9dvgb1wP23I+qqirWrVvH6NGj2/7waGhoIC0tDcMwSEhIoLm5uYdb2/32hKY1a9bQ2NjImDFj2p5LS0sjKiqKxx9/nIcffrjP/yztr/r+n91y0FwuF6FQiGAwiM1mwzTNtqGvqKgoGhsbCYfDBAIB7Hb7AX/Z9hWxsbFtocrj8RAdHd32QzM/P5/bbrsNj8fDb37zGyorK9vmm/RH8fHxbb80m5qaSEhI6OEW9RyHw8HcuXM555xzKC0t5de//jXz5s3rF3MWg8Egjz76KPn5+UydOrXt+yUuLq7t68Pj8ZCVldWTzTwsTNOkvLycF198kQsvvJCUlJS250aNGsVf/vIX3G43V111FU1NTf3650dfpYAlbfb8VbVhwwZiY2MxTZOEhAR8Ph8jRozg888/Z/v27WzZsoUxY8b0+W7t4cOH09jYyKZNm/j8888ZPXo0Pp8Pm83W1kNTW1sL0C9+eZqmSUNDA263m/r6elpbWwErmJ9wwgksXLgQl8uFw+EgMzOzh1vb/QKBAPX19fh8Purq6tp+gTqdTqqqqnC5XJSUlBAXF9cv5ugFg0HuuusuAoEAs2bNIhwO09rait1u56ijjmL+/PlMmzaNqqoqTjzxxD7/88Pr9fLnP/+Zo446itGjR+P3+zFNE5fLRU1NDQ6Hg+rqagzDUJmTPkplGqSNaZqUlZUxf/58AoEAV1xxBVVVVaSmpjJ+/HjefvttFi5cyLhx47j88suJi4vr6SZ3qz0Tuh999FGGDh3Kd7/7XZYvX87EiRPZsWMHr7/+OoZhMGvWLE499dQ+vyrK7/fz17/+lc2bN+Nyufj2t7+NYRhMmjSJhIQEHnroIcrKyvjOd77D0Ucf3ed/gZaUlPDwww9TUlLCoEGDOP300wmHwxx77LG8/PLLrFu3jri4OK655hqGDh3a5+9HVVUVd9xxB6ZpYrPZ2lbWZmZmMmHCBJ5//nk+++wzZsyYwbx58/p8qNi+fTu33XYbGRkZGIbB+eefj9vtZty4cWzZsqVtFfK5557LCSec0OdHBPojBSwRERGRCFNkFhEREYkwBSwRERGRCFPAEhEREYkwBSwR6TXefvttrrjiCn784x/z3HPPdaiQDtbCg61bt1JYWLjf891uN//5z3/YsmVL27Gnn36aVatWEQ6HeeKJJ/j3v/9NOBxm4cKFvPfee7z44osUFxe3vb6pqYlnnnmGkpIS1q1bR2VlJaZpMn/+fDZv3twtn1tE+h4FLBHpNXbt2sWcOXO4/fbbWbFiBWvWrKG6upri4mJ27dpFMBjkjTfe4M0336S6uhq3201xcTHFxcW0tLQQDAYpKSnpUMjS5XLx6aef0tDQQFFREQsXLsTn87FmzZq2VX/p6en4fD5KSkooKSlh8+bN1NfX8+yzz7Jo0SIaGhrYsmULxcXFFBYW0tDQ0OcrkYvIN6M6WCLSq0RHRxMXF0dsbCybNm3igw8+wO/34/F4uOSSS1ixYgWhUIhhw4aRk5PDW2+9RWNjI3l5eVx44YX7vN+kSZO46667mDNnDoMHD2b79u3U1tbi8XjIyMjggQce4MILL2THjh0sWbKEmJgYtm3bhtvtZtWqVezatYvMzExaW1t59913+fDDD3E6nfzxj3/sgbsjIkcKBSwR6TVCoRDPPfccK1euJDk5mfLycr744gtyc3PZsGEDkydPZvbs2cTExDBv3jzKysoAaG5u5oUXXuCCCy7Y5z1HjBhBRUUFZWVluFwuxo4dy5o1a/D7/W0bMbvdbpYtW8Y111xDTEwMd911F0lJSZx00kkcd9xxnHLKKbz++uuceuqpzJ49m3PPPRfTNPt8bSsROXQKWCLSa9hsNs4880zmzZtHfHw8jz32GPPmzWurDJ6YmMiLL77YNjz3xBNPkJ+fzznnnMNNN92032G7qKgo8vPz+fjjj5k8eTJDhw7l1VdfJTExsS1gBYPBtoraUVFRxMfHA9Z+cnve0+l0kpmZSWxsbIdNv0VE9kdzsESk1zAMg+TkZLKysoiPj+f4449n3bp1rF+/npUrV9Lc3ExGRgabN29m48aN2Gw2amtrWbZsGY2NjQd832OOOYZFixYxfPhwJk+ezMcff8zgwYPbqu8nJiYyaNAg3nvvPT744AO2b9+OYRgMGDCAVatWUVRURDgcPly3QUT6AFVyF5FeY8OGDTidTvLz8wFryPDzzz9n69atuFwuTjnlFOx2O++//z45OTlkZ2ezePFiMjIyaGxsZN68eWzYsIEhQ4Z02A+xvLycTz75hLlz5xIXF8d//vMfpk2bRl5eHp9++ikjR47E4XDw4YcfYrfbiYuLY9q0abS0tPDJJ58wYcIEGhoaGDVqFFlZWW0b+GqIUEQORAFLREREJMI0RCgiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhGmgCUiIiISYQpYIiIiIhH2/wEnE8CgMCNfuAAAAABJRU5ErkJggg== style='width=auto;height:auto'><div>\nLinearRegression()\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","print(\"\\nModel:\",res.dat)","print(\"\\nCoefficients:\", res.dat.coef_)"],"enabled":true,"result":{"startTime":1714753872684,"interpreter":"python.medium","endTime":1714753872758,"results":[{"message":"\nModel: LinearRegression()\n\nCoefficients: [[2.2299405]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `row_apply` and `group_apply` to process chuncks of rows in parallel","","The ***row_apply*** function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native Python models. In this example, the user-defined function ***score_lm_1*** takes a pandas DataFrame and a linear model. The ***row_apply*** invocation takes as arguments the proxy object *IRIS*, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.","","For illustrative purposes, you can also use the ***group_apply*** function to partition the data according to one or more columns and score as well, passing the model as an argument."],"enabled":true,"result":{"startTime":1714753872837,"interpreter":"md.medium","endTime":1714753872893,"results":[{"message":"<h2 id=\"use-row_apply-and-group_apply-to-process-chuncks-of-rows-in-parallel\">Use <code>row_apply<\/code> and <code>group_apply<\/code> to process chuncks of rows in parallel<\/h2>\n<p>The <em><strong>row_apply<\/strong><\/em> function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native Python models. In this example, the user-defined function <em><strong>score_lm_1<\/strong><\/em> takes a pandas DataFrame and a linear model. The <em><strong>row_apply<\/strong><\/em> invocation takes as arguments the proxy object <em>IRIS<\/em>, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.<\/p>\n<p>For illustrative purposes, you can also use the <em><strong>group_apply<\/strong><\/em> function to partition the data according to one or more columns and score as well, passing the model as an argument.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create used-defined Python function to score data","message":["%python","","def score_lm_1(dat, model):","    import pandas as pd","    from sklearn import linear_model","    pred = model.predict(dat[[\"PETAL_WIDTH\"]])","    return pd.concat([dat[['SPECIES', 'PETAL_LENGTH']], ","                      pd.DataFrame(pred, columns=['PRED_PETAL_LENGTH'])], axis=1)"],"enabled":true,"result":{"startTime":1714753872968,"interpreter":"python.medium","endTime":1714753873027,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Score data in parallel using DataFrame proxy object, returning  scores with data also as proxy object","message":["%python","","res = oml.row_apply(IRIS, rows=10, func=score_lm_1, model=mod, ","                    func_value=pd.DataFrame([('a', 1, 1)], ","                                            columns=['SPECIES', 'PETAL_LENGTH', 'PRED_PETAL_LENGTH']))","res.head()"],"enabled":true,"result":{"startTime":1714753873100,"interpreter":"python.medium","endTime":1714753874499,"results":[{"message":"  SPECIES  PETAL_LENGTH  PRED_PETAL_LENGTH\n0  setosa             1                  1\n1  setosa             1                  1\n2  setosa             1                  1\n3  setosa             1                  2\n4  setosa             1                  2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Illustrate using 'group apply' with same user-defined function","message":["%python","","res = oml.group_apply(IRIS, index=IRIS[['SPECIES']], func=score_lm_1, model=mod, ","                    func_value=pd.DataFrame([('a', 1, 1)], ","                                            columns=['SPECIES', 'PETAL_LENGTH', 'PRED_PETAL_LENGTH']))","","res.head()"],"enabled":true,"result":{"startTime":1714753874582,"interpreter":"python.medium","endTime":1714753876078,"results":[{"message":"  SPECIES  PETAL_LENGTH  PRED_PETAL_LENGTH\n0  setosa             1                  1\n1  setosa             1                  1\n2  setosa             1                  1\n3  setosa             1                  2\n4  setosa             1                  2\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build one model per `SPECIES` using `group_apply`","","A better use of ***group_apply*** - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function is then invoked on each group. The function ***group_apply*** makes this easy, as well as specifying the desired number of parallel Python engines using the ***parallel*** argument.","","Here, we build three models, one specific to each species, and return them as a dictionary."],"enabled":true,"result":{"startTime":1714753876153,"interpreter":"md.medium","endTime":1714753876212,"results":[{"message":"<h2 id=\"build-one-model-per-species-using-group_apply\">Build one model per <code>SPECIES<\/code> using <code>group_apply<\/code><\/h2>\n<p>A better use of <em><strong>group_apply<\/strong><\/em> - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function is then invoked on each group. The function <em><strong>group_apply<\/strong><\/em> makes this easy, as well as specifying the desired number of parallel Python engines using the <em><strong>parallel<\/strong><\/em> argument.<\/p>\n<p>Here, we build three models, one specific to each species, and return them as a dictionary.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","","def build_lm_g(dat):","    import oml","    from sklearn import linear_model","","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    mod = lm.fit(X, y)","   ","    return mod","","mod = oml.group_apply(IRIS[:,[\"PETAL_LENGTH\",\"PETAL_WIDTH\",\"SPECIES\"]], ","                      index=oml.DataFrame(IRIS['SPECIES']), ","                      func=build_lm_g, parallel=2,","                      oml_connect = True)","print(\"Type:\",type(mod))","mod"],"enabled":true,"result":{"startTime":1714753876285,"interpreter":"python.medium","endTime":1714753877782,"results":[{"message":"Type: <class 'dict'>\n{'virginica': LinearRegression(), 'versicolor': LinearRegression(), 'setosa': LinearRegression()}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Change the UDF to store the models inside the python datastore","","Next, change the user-defined function to save each model in a datastore - instead of returning it - and return ***True*** instead. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in Python, here \"mod_\" and the corresponding Species value."],"enabled":true,"result":{"startTime":1714753877858,"interpreter":"md.medium","endTime":1714753877916,"results":[{"message":"<h2 id=\"change-the-udf-to-store-the-models-inside-the-python-datastore\">Change the UDF to store the models inside the python datastore<\/h2>\n<p>Next, change the user-defined function to save each model in a datastore - instead of returning it - and return <em><strong>True<\/strong><\/em> instead. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in Python, here &quot;mod_&quot; and the corresponding Species value.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","def build_lm_2(dat, dsname):","    import oml","    from sklearn import linear_model","    lm = linear_model.LinearRegression()","    X = dat[[\"PETAL_WIDTH\"]]","    y = dat[[\"PETAL_LENGTH\"]]","    lm.fit(X, y)","    name = \"mod_\" + dat.loc[dat.index[0],'SPECIES']","    oml.ds.save(objs = {name: lm}, name=dsname, append=True) ","    return name"],"enabled":true,"result":{"startTime":1714753877991,"interpreter":"python.medium","endTime":1714753878050,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Replace an existing python datastore with the output of `group_apply`","","If the datastore exists, we delete it so that the ***group_apply*** function completes successfully. The ***group_apply*** function takes the data, the ***index*** parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the Python engine. Connecting to the database is necessary when using the datastore functionality.","","We then print the outcome, which contains a dictionary of three elements each assigned the model object name.","","When we load the datastore, we get the three models loaded into the client Python engine, assigned to their respective variables. For example we could request to see the model specifically built for the \"versicolor\" species."],"enabled":true,"result":{"startTime":1714753878126,"interpreter":"md.medium","endTime":1714753878183,"results":[{"message":"<h2 id=\"replace-an-existing-python-datastore-with-the-output-of-group_apply\">Replace an existing python datastore with the output of <code>group_apply<\/code><\/h2>\n<p>If the datastore exists, we delete it so that the <em><strong>group_apply<\/strong><\/em> function completes successfully. The <em><strong>group_apply<\/strong><\/em> function takes the data, the <em><strong>index<\/strong><\/em> parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the Python engine. Connecting to the database is necessary when using the datastore functionality.<\/p>\n<p>We then print the outcome, which contains a dictionary of three elements each assigned the model object name.<\/p>\n<p>When we load the datastore, we get the three models loaded into the client Python engine, assigned to their respective variables. For example we could request to see the model specifically built for the &quot;versicolor&quot; species.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","try:","    oml.ds.delete('ds-1')","except:","    print(\"Datastore not found\")","    ","res = oml.group_apply(IRIS[:,[\"PETAL_LENGTH\",\"PETAL_WIDTH\",\"SPECIES\"]], ","                      index=oml.DataFrame(IRIS['SPECIES']), ","                      func=build_lm_2, dsname=\"ds-1\",","                      oml_connect = True)","print(\"Outcome:\",res)"],"enabled":true,"result":{"startTime":1714753878257,"interpreter":"python.medium","endTime":1714753880755,"results":[{"message":"Outcome: {'virginica': 'mod_virginica', 'versicolor': 'mod_versicolor', 'setosa': 'mod_setosa'}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Leveraging third-party packages in Embedded Python Execution","","Embedded Python execution can also leverage functions from third-party packages. These packages need to be installed on the database server machine, but can then be used inside the user-defined function as shown here using ***LinearSVC***.","","Again, we create this script in the Python script repository and then invoke it by name using ***table_apply***. We then pull the model to the client and view its type."],"enabled":true,"result":{"startTime":1714753880840,"interpreter":"md.medium","endTime":1714753880902,"results":[{"message":"<h2 id=\"leveraging-third-party-packages-in-embedded-python-execution\">Leveraging third-party packages in Embedded Python Execution<\/h2>\n<p>Embedded Python execution can also leverage functions from third-party packages. These packages need to be installed on the database server machine, but can then be used inside the user-defined function as shown here using <em><strong>LinearSVC<\/strong><\/em>.<\/p>\n<p>Again, we create this script in the Python script repository and then invoke it by name using <em><strong>table_apply<\/strong><\/em>. We then pull the model to the client and view its type.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","fit = \"\"\"def fit(data):","","    from sklearn.svm import LinearSVC","    x = data.drop('SPECIES', axis = 1).values","    y = data['SPECIES']","    mod =  LinearSVC().fit(x, y)","    return mod\"\"\"","","oml.script.create(\"sk_svc_fit\", fit, overwrite = True)","","z.show(oml.script.dir())"],"enabled":true,"result":{"startTime":1714753880988,"interpreter":"python.medium","endTime":1714753881083,"results":[{"message":"name\tscript\tdescription\tdate\nMyTEST_function\tdef build_lm_str(dat):     from sklearn import linear_model     regr = linear_model.LinearRegression()     import pandas as pd     dat = pd.get_dummies(dat, drop_first=True)     X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \"SPECIES_versicolor\", \"SPECIES_virginica\"]]     y = dat[[\"SEPAL_LENGTH\"]]     regr.fit(X, y)     return regr\tNone\t2023-05-12 00:21:09\nRandomRedDots\tdef RandomRedDots (num_dots_1=100, num_dots_2=10):     import numpy as np     import pandas as pd     import matplotlib.pyplot as plt        d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}     df = pd.DataFrame(data=d)     fig = plt.figure(1)     ax = fig.add_subplot(111)     ax.scatter(range(0,int(num_dots_1)), np.random.rand(int(num_dots_1)),c='r')     fig.suptitle(\"Random Red Dots\")      fig2 = plt.figure(2)     ax2 = fig2.add_subplot(111)     ax2.scatter(range(0,int(num_dots_2)), np.random.rand(int(num_dots_2)),c='r')     fig2.suptitle(\"Random Red Dots\")     return df\tNone\t2023-12-30 00:38:59\nbuild_lm\tdef build_lm(dat, dsname):             from sklearn import linear_model             import oml             regr = linear_model.LinearRegression()             import pandas as pd             X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"]]             y = dat[[\"SEPAL_LENGTH\"]]             res = regr.fit(X, y)             res_coef = res.coef_             res.df = pd.DataFrame(res_coef, columns = ['Coef_SEPAL_WIDTH','Coef_PETAL_LENGTH','Coef_PETAL_WIDTH'])             oml.ds.save(objs={'regr':regr}, name=dsname, overwrite=True)             return res.df\tNone\t2023-12-15 18:25:43\nbuild_lm_1\tdef build_lm_1(dat):     import oml     from sklearn import linear_model     import matplotlib.pyplot as plt      lm = linear_model.LinearRegression()     X = dat[[\"PETAL_WIDTH\"]]     y = dat[[\"PETAL_LENGTH\"]]     mod = lm.fit(X, y)          pred = mod.predict(dat[[\"PETAL_WIDTH\"]])     plt.scatter(dat.loc[:,\"PETAL_WIDTH\"], dat.loc[:,\"PETAL_LENGTH\"])     plt.plot(dat[[\"PETAL_WIDTH\"]], pred, color='blue', linewidth=3)     plt.grid(True)     plt.title('Prediction of Petal Length')          plt.xlabel('Petal Width')     plt.ylabel('Petal Length')     plt.show()     return mod\tNone\t2024-05-03 16:31:12\nbuild_lm_rest\tdef build_rest(dat):             from sklearn import linear_model             import oml             regr = linear_model.LinearRegression()             import pandas as pd             dat = pd.get_dummies(dat, drop_first=True)             X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \"SPECIES_versicolor\", \"SPECIES_virginica\"]]             y = dat[[\"SEPAL_LENGTH\"]]             regr.fit(X, y)             data = {}             data[\"init_params\"] = regr.get_params()             data[\"model_params\"] = mp = {}             for p in (\"coef_\", \"intercept_\", \"_residues\", \"singular_\"):                 mp[p] = getattr(regr, p).tolist()             return data\tNone\t2023-12-12 18:45:31\nbuild_mod\tdef build_mod(df):   import oml   import keras   from keras.models import Sequential   from keras.layers import Dense   from tensorflow.keras.optimizers import Adam   import seaborn as sns   import pandas as pd   import numpy as np   import matplotlib.pyplot as plt      sns.set(style=\"ticks\")   sns.set_palette(\"husl\")   sns.pairplot(df.iloc[:,0:6], hue=\"species\")    # split the data into test and train sets   X = df.iloc[:,0:4].values   y = df.iloc[:,4].values    from sklearn.preprocessing import LabelEncoder   encoder =  LabelEncoder()   y1 = encoder.fit_transform(y)   Y = pd.get_dummies(y1).values    from sklearn.model_selection import train_test_split   X_train,X_test, y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0)     model = Sequential()   model.add(Dense(4,input_shape=(4,),activation='relu'))   model.add(Dense(3,activation='softmax'))   model.compile(Adam(learning_rate=0.04),'categorical_crossentropy',metrics=['accuracy'])   model.fit(X_train,y_train, epochs=50, verbose=0)    y_pred = model.predict(X_test, verbose=0)   y_test_class = np.argmax(y_test,axis=1)   y_pred_class = np.argmax(y_pred,axis=1)    from sklearn.metrics import classification_report,confusion_matrix   report = classification_report(y_test_class,y_pred_class, output_dict=True)   res = pd.DataFrame(report)   return(res)   plt.show()\tNone\t2024-01-17 19:20:08\ndensity_plt\tdef density_plt(dat):     dat.plot(kind='density')     return(True)\tNone\t2023-12-15 18:38:34\ngroup_count\tdef group_count(dat):   import oml   import pandas as pd   return pd.DataFrame([(dat[\"SPECIES\"][0], dat.shape[0])], columns = [\"SPECIES\", \"COUNT\"])\tNone\t2024-01-17 21:31:51\nmean_angle\tdef mean_angle(deg):     import numpy as np     temp = sum(1 *(np.cos(np.radians(d)) + np.sin(np.radians(d))*1j) for d in deg)/len(deg)     temp = np.degrees(np.arctan2(temp.imag,temp.real))     if temp <0.:         temp = temp + 360          return temp\tNone\t2022-10-04 21:23:19\nmyLinearRegressionModel\tdef fit_model(dat, modelName, datastoreName):        import oml        from sklearn import linear_model        regr = linear_model.LinearRegression()        regr.fit(dat.loc[:, [\"Sepal_Length\",                              \"Sepal_Width\",                              \"Petal_Length\"]],                 dat.loc[:,[\"Petal_Width\"]])        oml.ds.save(objs={modelName:regr},                     name=datastoreName,                    overwrite=True)        return str(regr)\tNone\t2022-02-09 18:11:59\nscore_lm\tdef score_lm(dat):     import oml     import pandas as pd     from sklearn import linear_model      obj = oml.ds.load(name=\"my_datastore_1\", to_globals=False)     pred = obj[\"lm_model\"].predict(dat[[\"PETAL_WIDTH\",\"SEPAL_WIDTH\"]])     pred = pd.DataFrame(pred, columns = [\"PREDICTED\"])     pred = pd.concat([dat[[\"PETAL_WIDTH\"]],pred], axis = 1)     pred = pred.rename(columns={\"PETAL_WIDTH\":\"ACTUAL\"})     return pred\tNone\t2023-02-16 01:45:59\nscore_mod\tdef score_mod(dat, dsname):             import oml             import pandas as pd             import matplotlib.pyplot as plt             obj_dict = oml.ds.load(name=dsname, to_globals=False)               regr = obj_dict[\"regr\"]                                                   pred = regr.predict(dat[['SEPAL_WIDTH','PETAL_LENGTH','PETAL_WIDTH']])                return pd.concat([dat[['SPECIES', 'SEPAL_LENGTH']], pd.DataFrame(pred, columns=['Pred_SEPAL_LENGTH'])], axis=1)\tNone\t2023-12-15 18:33:28\nsk_svc_fit\tdef fit(data):      from sklearn.svm import LinearSVC     x = data.drop('SPECIES', axis = 1).values     y = data['SPECIES']     mod =  LinearSVC().fit(x, y)     return mod\tNone\t2024-05-03 16:31:21\nstd_angle\tdef std_angle(deg):     import numpy as np     import oml     mean_angle = oml.script.load(name=\"mean_angle\")     temp = sum(pow(round(         np.degrees(             np.arctan2((1 *(np.cos(np.radians(d-mean_angle(deg))) + np.sin(np.radians(d-mean_angle(deg)))*1j)).imag,                        (1 *(np.cos(np.radians(d-mean_angle(deg))) + np.sin(np.radians(d-mean_angle(deg)))*1j)).real)             ))         ,2) for d in deg)     temp = temp/(max(len(deg)-1,1))     temp = np.sqrt(temp)     return temp\tNone\t2022-10-04 21:23:19\ntest_noinp_rest\tdef test_noinp():  import oml  from oml.automl import AlgorithmSelection  df = oml.sync(schema=\"OMLUSER02\", table = \"NARROW\")  train, test = df.split(ratio=(0.9, 0.1), seed = 1234)  X, y = train[:,0:-1], train[:,-1]  X_test, y_test = test[:,0:-1], test[:,-1]  asel = AlgorithmSelection(mining_function=\"classification\",score_metric=\"accuracy\", parallel=4)  scores = [ \"{0}:{1:.3f}\".format(m, s) for m, s in asel.select(X, y, k=None) ]  return(\"Algorithm selection prediction scores:{}\".format(scores))\tNone\t2022-03-08 00:32:47\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Invoke function on all data in DataFrame proxy object","message":["%python","","mod = oml.table_apply(data=IRIS, ","                      func = 'sk_svc_fit',","                      oml_input_type=\"pandas.DataFrame\")","print(\"Type:\",type(mod))","print('Coefficients: \\n', mod.coef_)"],"enabled":true,"result":{"startTime":1714753881164,"interpreter":"python.medium","endTime":1714753881258,"results":[{"message":"Type: <class 'sklearn.svm._classes.LinearSVC'>\nCoefficients: \n [[ 0.18423933  0.45122631 -0.80793782 -0.45071755]\n [ 0.0522114  -0.89079458  0.41245548 -0.96788955]\n [-0.85049619 -0.98655121  1.3810167   1.86541263]]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using a third-party package for prediction","","The model can now be used normally, e.g., to predict outcomes."],"enabled":true,"result":{"startTime":1714753881338,"interpreter":"md.medium","endTime":1714753881398,"results":[{"message":"<h2 id=\"using-a-third-party-package-for-prediction\">Using a third-party package for prediction<\/h2>\n<p>The model can now be used normally, e.g., to predict outcomes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","pred = mod.predict(iris_df.drop('SPECIES', axis = 1).values)","pred2 = pred.reshape((-1,1))","pred2[0:10]"],"enabled":true,"result":{"startTime":1714753881476,"interpreter":"python.medium","endTime":1714753881551,"results":[{"message":"array([['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa'],\n       ['setosa']], dtype=object)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Assessing model quality with a third-party package","","Let's assess model quality using a confusion matrix computed from the ***sklearn.metrics*** function confusion_matrix."],"enabled":true,"result":{"startTime":1714753881628,"interpreter":"md.medium","endTime":1714753881687,"results":[{"message":"<h2 id=\"assessing-model-quality-with-a-third-party-package\">Assessing model quality with a third-party package<\/h2>\n<p>Let's assess model quality using a confusion matrix computed from the <em><strong>sklearn.metrics<\/strong><\/em> function confusion_matrix.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","from sklearn.metrics import confusion_matrix","","conf_mat = confusion_matrix(iris_df[[\"SPECIES\"]], pred2)","conf_mat"],"enabled":true,"result":{"startTime":1714753881761,"interpreter":"python.medium","endTime":1714753881833,"results":[{"message":"array([[50,  0,  0],\n       [ 0, 47,  3],\n       [ 0,  2, 48]])\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Simple `group_apply` for counting each species","","The ***group_apply*** function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set."],"enabled":true,"result":{"startTime":1714753881913,"interpreter":"md.medium","endTime":1714753881973,"results":[{"message":"<h2 id=\"simple-group_apply-for-counting-each-species\">Simple <code>group_apply<\/code> for counting each species<\/h2>\n<p>The <em><strong>group_apply<\/strong><\/em> function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","def group_count(dat):","  import pandas as pd","  return pd.DataFrame([(dat[\"SPECIES\"][0], dat.shape[0])],","                      columns = [\"SPECIES\", \"CNT\"])","","index = oml.DataFrame(IRIS['SPECIES'])","res = oml.group_apply(IRIS, index, func=group_count,","                      oml_input_type=\"pandas.DataFrame\",","                      func_value=pd.DataFrame([('a', 1)],","                                              columns=[\"SPECIES\", \"CNT\"]))","res"],"enabled":true,"result":{"startTime":1714753882048,"interpreter":"python.medium","endTime":1714753883642,"results":[{"message":"      SPECIES  CNT\n0   virginica   50\n1  versicolor   50\n2      setosa   50\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using `index_apply` for repeated invocations","","The ***index_apply*** function allows the same function to invoked a specified number of times. ","The first argument to the user-defined function is an index number for the function execution. ","For example, if the \"times\" argument is 12, each function invocation will receive a distinct value between 1 and 10. ","This can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis."],"enabled":true,"result":{"startTime":1714753883718,"interpreter":"md.medium","endTime":1714753883780,"results":[{"message":"<h2 id=\"using-index_apply-for-repeated-invocations\">Using <code>index_apply<\/code> for repeated invocations<\/h2>\n<p>The <em><strong>index_apply<\/strong><\/em> function allows the same function to invoked a specified number of times.\nThe first argument to the user-defined function is an index number for the function execution.\nFor example, if the &quot;times&quot; argument is 12, each function invocation will receive a distinct value between 1 and 10.\nThis can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","def compute_random_mean(index):","    import numpy as np","    import scipy","    from statistics import mean ","    np.random.seed(index)","    res = np.random.random((100,1))*10","    return mean(res[1])","","print(\"Test: \", compute_random_mean(3))","","res = oml.index_apply(times=12, func=compute_random_mean)","res"],"enabled":true,"result":{"startTime":1714753883855,"interpreter":"python.medium","endTime":1714753884133,"results":[{"message":"Test:  7.081478226181048\n[7.203244934421581, 0.25926231827891333, 7.081478226181048, 5.4723224917572235, 8.707323061773764, 3.3197980530117723, 7.7991879224011464, 9.68540662820932, 5.018745921487388, 0.207519493594015, 0.19475241487624584, 7.400496965154048]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Multiple images returned from embedded Python execution","","Here, we create a function ***RandomRedDots*** that creates a simple DataFrame and generates two plots of random red dots. We create a function in the Python Script Repository named \"RandomRedDots\", and then run the native Python function."],"enabled":true,"result":{"startTime":1714753884210,"interpreter":"md.medium","endTime":1714753884269,"results":[{"message":"<h2 id=\"multiple-images-returned-from-embedded-python-execution\">Multiple images returned from embedded Python execution<\/h2>\n<p>Here, we create a function <em><strong>RandomRedDots<\/strong><\/em> that creates a simple DataFrame and generates two plots of random red dots. We create a function in the Python Script Repository named &quot;RandomRedDots&quot;, and then run the native Python function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","RandomRedDots = \"\"\"def RandomRedDots (num_dots_1 = 100, num_dots_2 = 10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt","       ","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","    plt.scatter(range(0,num_dots_1), np.random.rand(100),c='r')","    plt.title(\"Random Red Dots\")","    plt.show()","    plt.scatter(range(0,num_dots_2), np.random.rand(10),c='r')","    plt.title(\"Random Red Dots\")","    plt.show()","    return df\"\"\"","","oml.script.create(\"RandomRedDots\", func=RandomRedDots, is_global=True, overwrite=True)","","print(RandomRedDots)"],"enabled":true,"result":{"startTime":1714753884361,"interpreter":"python.medium","endTime":1714753884435,"results":[{"message":"def RandomRedDots (num_dots_1 = 100, num_dots_2 = 10):\n    import numpy as np\n    import pandas as pd\n    import matplotlib.pyplot as plt\n       \n    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}\n    df = pd.DataFrame(data=d)\n    plt.scatter(range(0,num_dots_1), np.random.rand(100),c='r')\n    plt.title(\"Random Red Dots\")\n    plt.show()\n    plt.scatter(range(0,num_dots_2), np.random.rand(10),c='r')\n    plt.title(\"Random Red Dots\")\n    plt.show()\n    return df\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use of the `graphics` flag","","If we simply invoke the named function, we see that the result is an OML object. Notice that we do not see images."],"enabled":true,"result":{"startTime":1714753884511,"interpreter":"md.medium","endTime":1714753884571,"results":[{"message":"<h2 id=\"use-of-the-graphics-flag\">Use of the <code>graphics<\/code> flag<\/h2>\n<p>If we simply invoke the named function, we see that the result is an OML object. Notice that we do not see images.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots\")","print(\"Type: \", type(res))","","res"],"enabled":true,"result":{"startTime":1714753884645,"interpreter":"python.medium","endTime":1714753884762,"results":[{"message":"Type:  <class 'pandas.core.frame.DataFrame'>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Returning graphics","","To ensure we get the plots, we need to modify the function using the `graphics` flag."],"enabled":true,"result":{"startTime":1714753884837,"interpreter":"md.medium","endTime":1714753884896,"results":[{"message":"<h2 id=\"returning-graphics\">Returning graphics<\/h2>\n<p>To ensure we get the plots, we need to modify the function using the <code>graphics<\/code> flag.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots\", graphics=True)","print(\"Type: \", type(res))","","res"],"enabled":true,"result":{"startTime":1714753884973,"interpreter":"python.medium","endTime":1714753885245,"results":[{"message":"Type:  <class 'oml.embed.data_image._DataImage'>\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Example invocation via REST API with the `graphicsFlag`","","Invoke the *RandomRedDots* function from the REST API with *graphicsFlag* to *true*:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots\""],"enabled":true,"result":{"startTime":1714753885324,"interpreter":"md.medium","endTime":1714753885384,"results":[{"message":"<h2 id=\"example-invocation-via-rest-api-with-the-graphicsflag\">Example invocation via REST API with the <code>graphicsFlag<\/code><\/h2>\n<p>Invoke the <em>RandomRedDots<\/em> function from the REST API with <em>graphicsFlag<\/em> to <em>true<\/em>:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/py-scripts/v1/do-eval/RandomRedDots&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A second option generating separate figures: RandomRedDots2","","Here, we create separate figure objects, add subplots, and then create the scatter plot. We'll store this in the script repository as \"RandomRedDots2\" and ","invoke the function to see the results. As expected, we get both plots.","","Now, when we invoke \"RandomRedDots2\" using embedded Python execution, we get both plots shown in the result."],"enabled":true,"result":{"startTime":1714753885466,"interpreter":"md.medium","endTime":1714753885524,"results":[{"message":"<h2 id=\"a-second-option-generating-separate-figures-randomreddots2\">A second option generating separate figures: RandomRedDots2<\/h2>\n<p>Here, we create separate figure objects, add subplots, and then create the scatter plot. We'll store this in the script repository as &quot;RandomRedDots2&quot; and\ninvoke the function to see the results. As expected, we get both plots.<\/p>\n<p>Now, when we invoke &quot;RandomRedDots2&quot; using embedded Python execution, we get both plots shown in the result.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","RandomRedDots2 = \"\"\"def RandomRedDots2 (num_dots_1 = 100, num_dots_2 = 10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt  ","","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","    fig = plt.figure(1)","    ax = fig.add_subplot(111)","    ax.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')","    fig.suptitle(\"Random Red Dots\")","","    fig2 = plt.figure(2)","    ax2 = fig2.add_subplot(111)","    ax2.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')","    fig2.suptitle(\"Random Red Dots\")","    return df\"\"\"","","oml.script.create(\"RandomRedDots2\", func=RandomRedDots2, is_global=True, overwrite=True)","","print(RandomRedDots2)"],"enabled":true,"result":{"startTime":1714753885598,"interpreter":"python.medium","endTime":1714753885674,"results":[{"message":"def RandomRedDots2 (num_dots_1 = 100, num_dots_2 = 10):\n    import numpy as np\n    import pandas as pd\n    import matplotlib.pyplot as plt  \n\n    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}\n    df = pd.DataFrame(data=d)\n    fig = plt.figure(1)\n    ax = fig.add_subplot(111)\n    ax.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')\n    fig.suptitle(\"Random Red Dots\")\n\n    fig2 = plt.figure(2)\n    ax2 = fig2.add_subplot(111)\n    ax2.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')\n    fig2.suptitle(\"Random Red Dots\")\n    return df\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","res = oml.do_eval(func=\"RandomRedDots2\", num_dots_1 = 600, num_dots_2 = 200, graphics = True)","","print(type(res))","","res"],"enabled":true,"result":{"startTime":1714753885749,"interpreter":"python.medium","endTime":1714753886128,"results":[{"message":"<class 'oml.embed.data_image._DataImage'>\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img src=data:image/png;base64,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 style='width=auto;height:auto'><div>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Equivalent invocation via REST API for RandomRedDots2","","Here is the equivalent REST API call","    ","    curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots2\""],"enabled":true,"result":{"startTime":1714753886218,"interpreter":"md.medium","endTime":1714753886277,"results":[{"message":"<h2 id=\"equivalent-invocation-via-rest-api-for-randomreddots2\">Equivalent invocation via REST API for RandomRedDots2<\/h2>\n<p>Here is the equivalent REST API call<\/p>\n<pre><code>curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/py-scripts/v1/do-eval/RandomRedDots2&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A third option with a single plot with subplots: RandomRedDots3","","","Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example."],"enabled":true,"result":{"startTime":1714753886355,"interpreter":"md.medium","endTime":1714753886413,"results":[{"message":"<h2 id=\"a-third-option-with-a-single-plot-with-subplots-randomreddots3\">A third option with a single plot with subplots: RandomRedDots3<\/h2>\n<p>Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","RandomRedDots3 = \"\"\"def RandomRedDots3 (num_dots_1 = 100, num_dots_2 = 10):","    import numpy as np","    import pandas as pd","    import matplotlib.pyplot as plt","","    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}","    df = pd.DataFrame(data=d)","","    plt.subplot(2,1, 1)","    plt.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')","    plt.title(\"Random Red Dots\")","","    plt.subplot(2, 1, 2)","    plt.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')","    plt.title(\"Random Red Dots\")","    return df\"\"\"","","oml.script.create(\"RandomRedDots3\", func=RandomRedDots3, is_global=True, overwrite=True)","oml.do_eval(func=\"RandomRedDots3\", graphics=True)"],"enabled":true,"result":{"startTime":1714753886488,"interpreter":"python.medium","endTime":1714753886757,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Equivalent invocation via REST API for RandomRedDots3","","Here is the equivalent REST API call:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/py-scripts/v1/do-eval/RandomRedDots3\""],"enabled":true,"result":{"startTime":1714753886834,"interpreter":"md.medium","endTime":1714753886898,"results":[{"message":"<h2 id=\"equivalent-invocation-via-rest-api-for-randomreddots3\">Equivalent invocation via REST API for RandomRedDots3<\/h2>\n<p>Here is the equivalent REST API call:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/py-scripts/v1/do-eval/RandomRedDots3&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Returning only the structured contents","","We can also return only the structured contents as an OML DataFrame, which keeps the result in the database - returning a proxy object to the result. We pull the results to the notebook explicitly to display it locally."],"enabled":true,"result":{"startTime":1714753886975,"interpreter":"md.medium","endTime":1714753887039,"results":[{"message":"<h2 id=\"returning-only-the-structured-contents\">Returning only the structured contents<\/h2>\n<p>We can also return only the structured contents as an OML DataFrame, which keeps the result in the database - returning a proxy object to the result. We pull the results to the notebook explicitly to display it locally.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","import pandas as pd","res = oml.do_eval(func=RandomRedDots, func_value=pd.DataFrame({\"id\":[0],\"val\":[0]}))","type(res)","","res.pull()"],"enabled":true,"result":{"startTime":1714753887118,"interpreter":"python.medium","endTime":1714753887491,"results":[{"message":"   id  val\n0   1    0\n1   2    0\n2   3    0\n3   4    0\n4   5    0\n5   6    0\n6   7    0\n7   8    0\n8   9    0\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Loading the python script from the repository","","Finally, note that a function stored in the Python Script Repository can be loaded back in the Python client engine, we can view its source, and finally invoke the function."],"enabled":true,"result":{"startTime":1714753887571,"interpreter":"md.medium","endTime":1714753887631,"results":[{"message":"<h2 id=\"loading-the-python-script-from-the-repository\">Loading the python script from the repository<\/h2>\n<p>Finally, note that a function stored in the Python Script Repository can be loaded back in the Python client engine, we can view its source, and finally invoke the function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","random_red_dots = oml.script.load('RandomRedDots2')","","print(random_red_dots.get_source())","random_red_dots()"],"enabled":true,"result":{"startTime":1714753887706,"interpreter":"python.medium","endTime":1714753887930,"results":[{"message":"def RandomRedDots2 (num_dots_1 = 100, num_dots_2 = 10):\n    import numpy as np\n    import pandas as pd\n    import matplotlib.pyplot as plt  \n\n    d = {'id': range(1,10), 'val': [x/100 for x in range(1,10)]}\n    df = pd.DataFrame(data=d)\n    fig = plt.figure(1)\n    ax = fig.add_subplot(111)\n    ax.scatter(range(0,num_dots_1), np.random.rand(num_dots_1),c='r')\n    fig.suptitle(\"Random Red Dots\")\n\n    fig2 = plt.figure(2)\n    ax2 = fig2.add_subplot(111)\n    ax2.scatter(range(0,num_dots_2), np.random.rand(num_dots_2),c='r')\n    fig2.suptitle(\"Random Red Dots\")\n    return df\n   id   val\n0   1  0.01\n1   2  0.02\n2   3  0.03\n3   4  0.04\n4   5  0.05\n5   6  0.06\n6   7  0.07\n7   8  0.08\n8   9  0.09\n","type":"TEXT"},{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks/python/OML4Py -5- AutoML.dsnb b/machine-learning/notebooks/python/OML4Py -5- AutoML.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py -5- AutoML.dsnb	
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py -5- AutoML","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714163399699,"interpreter":"md.low","endTime":1714163399847,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for Python (OML4Py)","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automated machine learning (AutoML) features.","","In this notebook, we highlight the AutoML workflow using OML4Py:","","* Automated algorithm selection - identify the most appropriate algorithm","* Automated feature selection - select the features (predictors) that best support that algorithm","* Automated model tuning - tune the model using algorithm hyperparameters and the selected features","","Using the WINE dataset form scikit-learn, we use AutoML for classification on the 'target' column, and for regression on the 'proline' column. ","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714163399921,"interpreter":"md.low","endTime":1714163400016,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-python-oml4py\">Oracle Machine Learning for Python (OML4Py)<\/h1>\n<p><em><strong>Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automated machine learning (AutoML) features.<\/p>\n<p>In this notebook, we highlight the AutoML workflow using OML4Py:<\/p>\n<ul>\n<li>Automated algorithm selection - identify the most appropriate algorithm<\/li>\n<li>Automated feature selection - select the features (predictors) that best support that algorithm<\/li>\n<li>Automated model tuning - tune the model using algorithm hyperparameters and the selected features<\/li>\n<\/ul>\n<p>Using the WINE dataset form scikit-learn, we use AutoML for classification on the 'target' column, and for regression on the 'proline' column.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries supporting OML4Py ","message":["%md","","To use OML4Py, first import the package ***oml***. ","","Users can get online help using Python's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1714163400100,"interpreter":"md.low","endTime":1714163400200,"results":[{"message":"<p>To use OML4Py, first import the package <em><strong>oml<\/strong><\/em>.<\/p>\n<p>Users can get online help using Python's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":{"startTime":1714163400274,"interpreter":"python.low","endTime":1714163402371,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create data sets as Pandas DataFrame and load into Oracle Database","message":["%md","","In this example, we load the **wine** and **iris** data sets, combining target and predictors into a single DataFrame that matches the form the data would have as a database table. These DataFrame objects can then be loaded into Oracle Database using the ***create*** function."],"enabled":true,"result":{"startTime":1714163402460,"interpreter":"md.low","endTime":1714163402523,"results":[{"message":"<p>In this example, we load the <strong>wine<\/strong> and <strong>iris<\/strong> data sets, combining target and predictors into a single DataFrame that matches the form the data would have as a database table. These DataFrame objects can then be loaded into Oracle Database using the <em><strong>create<\/strong><\/em> function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Create WINE data table","message":["%python","","from sklearn.datasets import load_wine","","wine = load_wine()","x = pd.DataFrame(wine.data, columns = wine[\"feature_names\"])","y = pd.DataFrame(list(map(lambda x: {0:'class_0', 1: 'class_1', 2:'class_2'}[x], wine.target)), ","                 columns = ['target'])","","wine_df = pd.concat([x,y], axis=1)","","try:","    oml.drop(table=\"WINE\")","except:","    pass","WINE = oml.create(wine_df, table=\"WINE\")","print(WINE.shape)","print(WINE.dtypes)"],"enabled":true,"result":{"startTime":1714163402594,"interpreter":"python.low","endTime":1714163403093,"results":[{"message":"(178, 14)\nalcohol                           <class 'oml.core.float.Float'>\nmalic_acid                        <class 'oml.core.float.Float'>\nash                               <class 'oml.core.float.Float'>\nalcalinity_of_ash                 <class 'oml.core.float.Float'>\nmagnesium                         <class 'oml.core.float.Float'>\ntotal_phenols                     <class 'oml.core.float.Float'>\nflavanoids                        <class 'oml.core.float.Float'>\nnonflavanoid_phenols              <class 'oml.core.float.Float'>\nproanthocyanins                   <class 'oml.core.float.Float'>\ncolor_intensity                   <class 'oml.core.float.Float'>\nhue                               <class 'oml.core.float.Float'>\nod280/od315_of_diluted_wines      <class 'oml.core.float.Float'>\nproline                           <class 'oml.core.float.Float'>\ntarget                          <class 'oml.core.string.String'>\ndtype: object\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the first 20 rows","message":["%python","","z.show(WINE.head(20))"],"enabled":true,"result":{"startTime":1714163403210,"interpreter":"python.low","endTime":1714163403306,"results":[{"message":"alcohol\tmalic_acid\tash\talcalinity_of_ash\tmagnesium\ttotal_phenols\tflavanoids\tnonflavanoid_phenols\tproanthocyanins\tcolor_intensity\thue\tod280/od315_of_diluted_wines\tproline\ttarget\n14.23\t1.71\t2.43\t15.6\t127\t2.8\t3.06\t0.28\t2.29\t5.64\t1.04\t3.92\t1065\tclass_0\n13.2\t1.78\t2.14\t11.2\t100\t2.65\t2.76\t0.26\t1.28\t4.38\t1.05\t3.4\t1050\tclass_0\n13.16\t2.36\t2.67\t18.6\t101\t2.8\t3.24\t0.3\t2.81\t5.68\t1.03\t3.17\t1185\tclass_0\n14.37\t1.95\t2.5\t16.8\t113\t3.85\t3.49\t0.24\t2.18\t7.8\t0.86\t3.45\t1480\tclass_0\n13.24\t2.59\t2.87\t21.0\t118\t2.8\t2.69\t0.39\t1.82\t4.32\t1.04\t2.93\t735\tclass_0\n14.2\t1.76\t2.45\t15.2\t112\t3.27\t3.39\t0.34\t1.97\t6.75\t1.05\t2.85\t1450\tclass_0\n14.39\t1.87\t2.45\t14.6\t96\t2.5\t2.52\t0.3\t1.98\t5.25\t1.02\t3.58\t1290\tclass_0\n14.06\t2.15\t2.61\t17.6\t121\t2.6\t2.51\t0.31\t1.25\t5.05\t1.06\t3.58\t1295\tclass_0\n14.83\t1.64\t2.17\t14.0\t97\t2.8\t2.98\t0.29\t1.98\t5.2\t1.08\t2.85\t1045\tclass_0\n13.86\t1.35\t2.27\t16.0\t98\t2.98\t3.15\t0.22\t1.85\t7.22\t1.01\t3.55\t1045\tclass_0\n14.1\t2.16\t2.3\t18.0\t105\t2.95\t3.32\t0.22\t2.38\t5.75\t1.25\t3.17\t1510\tclass_0\n14.12\t1.48\t2.32\t16.8\t95\t2.2\t2.43\t0.26\t1.57\t5.0\t1.17\t2.82\t1280\tclass_0\n13.75\t1.73\t2.41\t16.0\t89\t2.6\t2.76\t0.29\t1.81\t5.6\t1.15\t2.9\t1320\tclass_0\n14.75\t1.73\t2.39\t11.4\t91\t3.1\t3.69\t0.43\t2.81\t5.4\t1.25\t2.73\t1150\tclass_0\n14.38\t1.87\t2.38\t12.0\t102\t3.3\t3.64\t0.29\t2.96\t7.5\t1.2\t3.0\t1547\tclass_0\n13.63\t1.81\t2.7\t17.2\t112\t2.85\t2.91\t0.3\t1.46\t7.3\t1.28\t2.88\t1310\tclass_0\n14.3\t1.92\t2.72\t20.0\t120\t2.8\t3.14\t0.33\t1.97\t6.2\t1.07\t2.65\t1280\tclass_0\n13.83\t1.57\t2.62\t20.0\t115\t2.95\t3.4\t0.4\t1.72\t6.6\t1.13\t2.57\t1130\tclass_0\n14.19\t1.59\t2.48\t16.5\t108\t3.3\t3.93\t0.32\t1.86\t8.7\t1.23\t2.82\t1680\tclass_0\n13.64\t3.1\t2.56\t15.2\t116\t2.7\t3.03\t0.17\t1.66\t5.1\t0.96\t3.36\t845\tclass_0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Automated Machine Learning (AutoML)","The AutoML workflow consists of ","","- identify the most appropriate algorithm ","- select the features (predictors) that best support that algorithm","- tune the model using algorithm hyperparameters and the selected features","","### Automated Algorithm Selection","","Which algorithm is best to predict my target among those available with OML4Py?","","- Classification: Decision Tree, Generalized Linear Model, Na?ve Bayes, Neural Network, RandomForest, Support Vector Machine","- Regression: Generalized Linear Model, Neural Network, Support Vector Machine","","To use AutoML algorithm selection, we create an ***AlgorithmSelection*** object, here with the 'accuracy' score metric. Then, we invoke ***select*** to get the top 4 predicted algorithms by setting k=4. Automated algorithm selection ranks the algorithms from the set of supported in-database algorithms likely to produce better models using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and ***meta-learning*** techniques.","","Users can select from a variety of ***score_metric*** options:","","- binary classification \u2013 accuracy (default), f1, precision, recall, roc_auc, f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted","- multiclass classification \u2013 accuracy (default), f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted","- regression \u2013 r2 (default), neg_mean_squared_error, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error"],"enabled":true,"result":{"startTime":1714163418391,"interpreter":"md.low","endTime":1714163418467,"results":[{"message":"<h2 id=\"automated-machine-learning-automl\">Automated Machine Learning (AutoML)<\/h2>\n<p>The AutoML workflow consists of<\/p>\n<ul>\n<li>identify the most appropriate algorithm<\/li>\n<li>select the features (predictors) that best support that algorithm<\/li>\n<li>tune the model using algorithm hyperparameters and the selected features<\/li>\n<\/ul>\n<h3 id=\"automated-algorithm-selection\">Automated Algorithm Selection<\/h3>\n<p>Which algorithm is best to predict my target among those available with OML4Py?<\/p>\n<ul>\n<li>Classification: Decision Tree, Generalized Linear Model, Na?ve Bayes, Neural Network, RandomForest, Support Vector Machine<\/li>\n<li>Regression: Generalized Linear Model, Neural Network, Support Vector Machine<\/li>\n<\/ul>\n<p>To use AutoML algorithm selection, we create an <em><strong>AlgorithmSelection<\/strong><\/em> object, here with the 'accuracy' score metric. Then, we invoke <em><strong>select<\/strong><\/em> to get the top 4 predicted algorithms by setting k=4. Automated algorithm selection ranks the algorithms from the set of supported in-database algorithms likely to produce better models using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and <em><strong>meta-learning<\/strong><\/em> techniques.<\/p>\n<p>Users can select from a variety of <em><strong>score_metric<\/strong><\/em> options:<\/p>\n<ul>\n<li>binary classification \u2013 accuracy (default), f1, precision, recall, roc_auc, f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted<\/li>\n<li>multiclass classification \u2013 accuracy (default), f1_micro, f1_macro, f1_weighted, recall_micro, recall_macro, recall_weighted, precision_micro, precision_macro, precision_weighted<\/li>\n<li>regression \u2013 r2 (default), neg_mean_squared_error, neg_mean_absolute_error, neg_mean_squared_log_error, neg_median_absolute_error<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare WINE classification data","message":["%python","","WINE_X_cl,WINE_y_cl = WINE.drop('target'), WINE['target']"],"enabled":true,"result":{"startTime":1714163403517,"interpreter":"python.low","endTime":1714163403589,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare WINE regression data","message":["%python","","WINE_X_reg,WINE_y_reg = WINE.drop('proline'), WINE['proline']"],"enabled":true,"result":{"startTime":1714163403660,"interpreter":"python.low","endTime":1714163403732,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Rank classification algorithms for WINE data","message":["%python","","as_wine_cl = automl.AlgorithmSelection(mining_function='classification', ","                                       score_metric='accuracy', parallel=2)","","wine_alg_ranking_cl = as_wine_cl.select(WINE_X_cl, WINE_y_cl, k=4) ","","print(\"Ranked algorithms:\\n\", wine_alg_ranking_cl)","","selected_wine_alg_cl = next(iter(dict(wine_alg_ranking_cl).keys()))","print(\"Best algorithm: \", selected_wine_alg_cl)"],"enabled":true,"result":{"startTime":1714163403800,"interpreter":"python.low","endTime":1714163433729,"results":[{"message":"Ranked algorithms:\n [('svm_linear', 0.9830065359477125), ('svm_gaussian', 0.981992337164751), ('nn', 0.9644515277375317), ('rf', 0.9461170675166619)]\nBest algorithm:  svm_linear\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Rank regression algorithms for WINE data","message":["%python","","as_wine_reg = automl.AlgorithmSelection(mining_function='regression', ","                                        score_metric='r2', parallel=2)","","wine_alg_ranking_reg = as_wine_reg.select(WINE_X_reg, WINE_y_reg, k=4) ","","print(\"Ranked algorithms:\\n\", wine_alg_ranking_reg)","","selected_wine_alg_reg = next(iter(dict(wine_alg_ranking_reg).keys()))","print(\"Best algorithm: \", selected_wine_alg_reg)"],"enabled":true,"result":{"startTime":1714163433804,"interpreter":"python.low","endTime":1714163458963,"results":[{"message":"Ranked algorithms:\n [('svm_gaussian', 0.7233656799402366), ('svm_linear', 0.690595088776105), ('glm_ridge', 0.6839975942592191), ('glm', 0.683944877749971)]\nBest algorithm:  svm_gaussian\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Automated Feature Selection","","Next, we determine the features that best support the selected algorithm. We first define a ***FeatureSelection*** object with score metric 'accuracy' and invoke ***reduce*** specifying the desired algorithm determined above and the train and test data set proxy objects. ","","We see the set of selected columns. Try other algorithms, e.g., svm_linear, to see if different columns are selected."],"enabled":true,"result":{"startTime":1714163499374,"interpreter":"md.low","endTime":1714163499434,"results":[{"message":"<h3 id=\"automated-feature-selection\">Automated Feature Selection<\/h3>\n<p>Next, we determine the features that best support the selected algorithm. We first define a <em><strong>FeatureSelection<\/strong><\/em> object with score metric 'accuracy' and invoke <em><strong>reduce<\/strong><\/em> specifying the desired algorithm determined above and the train and test data set proxy objects.<\/p>\n<p>We see the set of selected columns. Try other algorithms, e.g., svm_linear, to see if different columns are selected.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Classification Feature Selection","message":["%python","","","fs_wine_cl = automl.FeatureSelection(mining_function = 'classification', ","                                     score_metric = 'accuracy', parallel=2)","","selected_wine_features_cl = fs_wine_cl.reduce(selected_wine_alg_cl, ","                                              WINE_X_cl, WINE_y_cl)","","WINE_X_reduced_cl = WINE_X_cl[:,selected_wine_features_cl]","","print(\"Selected columns:\", WINE_X_reduced_cl.columns)","print(\"Number of columns:\")","\"{}  reduced to {}\".format(len(WINE_X_cl.columns), len(selected_wine_features_cl))"],"enabled":true,"result":{"startTime":1714163459172,"interpreter":"python.low","endTime":1714163499316,"results":[{"message":"Selected columns: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\nNumber of columns:\n'13  reduced to 11'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Regression Feature Selection","message":["%python","","","fs_wine_reg = automl.FeatureSelection(mining_function = 'regression', ","                                     score_metric = 'r2', parallel=2)","","selected_wine_features_reg = fs_wine_reg.reduce(selected_wine_alg_reg, ","                                                WINE_X_reg, WINE_y_reg)","","WINE_X_reduced_reg = WINE_X_reg[:,selected_wine_features_reg]","","print(\"Selected columns:\",WINE_X_reduced_reg.columns)","print(\"Number of columns:\")","\"{}  reduced to {}\".format(len(WINE_X_reg.columns), len(selected_wine_features_reg))"],"enabled":true,"result":{"startTime":1714163499477,"interpreter":"python.low","endTime":1714163548626,"results":[{"message":"Selected columns: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'target']\nNumber of columns:\n'13  reduced to 13'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Automated Model Tuning","","At this point, we're ready to build and tune the models we want to use. First, we define a ***ModelTuning*** object for classification. ","We then invoke ***tune*** to produce the tuned model using the algorithm selected above and the reduced column data. ","","The result of model tuning is a dictionary with the 'best model' and 'all evals', which contains a list of all hyperparameter choices tried and their corresponding score.","","When an in-database model is created without a specific name, it is viewed as temporary, such that when the database connection terminates (or Notebook session closes) the models are dropped from the user schema. Models associated with proxy objects will not be dropped if the proxy object is stored in a datastore or if the model is assinged an explicit name. ","","Assigning an explicit name after a model is created can be accomplished by assigning a name to the `model_name` property. For example, a model proxy object referenced by variable `my_model` could be assigned a name using: ","","    my_model.model_name = 'MY_MODEL'","   "],"enabled":true,"result":{"startTime":1714163548701,"interpreter":"md.low","endTime":1714163548763,"results":[{"message":"<h4 id=\"automated-model-tuning\">Automated Model Tuning<\/h4>\n<p>At this point, we're ready to build and tune the models we want to use. First, we define a <em><strong>ModelTuning<\/strong><\/em> object for classification.\nWe then invoke <em><strong>tune<\/strong><\/em> to produce the tuned model using the algorithm selected above and the reduced column data.<\/p>\n<p>The result of model tuning is a dictionary with the 'best model' and 'all evals', which contains a list of all hyperparameter choices tried and their corresponding score.<\/p>\n<p>When an in-database model is created without a specific name, it is viewed as temporary, such that when the database connection terminates (or Notebook session closes) the models are dropped from the user schema. Models associated with proxy objects will not be dropped if the proxy object is stored in a datastore or if the model is assinged an explicit name.<\/p>\n<p>Assigning an explicit name after a model is created can be accomplished by assigning a name to the <code>model_name<\/code> property. For example, a model proxy object referenced by variable <code>my_model<\/code> could be assigned a name using:<\/p>\n<pre><code>my_model.model_name = 'MY_MODEL'\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Classification Model Tuning","message":["%python","","","mt_wine_cl = automl.ModelTuning(mining_function = 'classification', parallel=2)","","results_cl = mt_wine_cl.tune(selected_wine_alg_cl, WINE_X_reduced_cl, WINE_y_cl)","tuned_model_cl = results_cl['best_model']","tuned_model_cl"],"enabled":true,"result":{"startTime":1714163548837,"interpreter":"python.low","endTime":1714163586572,"results":[{"message":"\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: target\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1         CLAS_WEIGHTS_BALANCED                           OFF\n2                  ODMS_DETAILS                   ODMS_ENABLE\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                     PREP_AUTO                            ON\n6        SVMS_COMPLEXITY_FACTOR                            10\n7           SVMS_CONV_TOLERANCE                         .0001\n8          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n9                   SVMS_SOLVER               SVMS_SOLVER_IPM\n\nComputed Settings: \n          setting name setting value\n0  SVMS_NUM_ITERATIONS            30\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              15\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproline\ntotal_phenols\n\nPartition: NO\n\nCOEFFICIENTS: \n\n   TARGET_VALUE                ATTRIBUTE_NAME  ... ATTRIBUTE_VALUE    COEF\n0       class_0             alcalinity_of_ash  ...            None -1.0241\n1       class_0                       alcohol  ...            None  0.9540\n2       class_0                           ash  ...            None  0.7172\n3       class_0               color_intensity  ...            None -0.1460\n4       class_0                    flavanoids  ...            None  0.6934\n5       class_0                           hue  ...            None -0.0126\n6       class_0                    malic_acid  ...            None  0.3877\n7       class_0          nonflavanoid_phenols  ...            None  0.0100\n8       class_0  od280/od315_of_diluted_wines  ...            None  0.5115\n9       class_0                       proline  ...            None  1.1391\n10      class_0                 total_phenols  ...            None  0.2301\n11      class_0                          None  ...            None -0.8932\n12      class_1             alcalinity_of_ash  ...            None  0.8536\n13      class_1                       alcohol  ...            None -1.0057\n14      class_1                           ash  ...            None -1.1235\n15      class_1               color_intensity  ...            None -3.0848\n16      class_1                    flavanoids  ...            None  0.9363\n17      class_1                           hue  ...            None  0.7513\n18      class_1                    malic_acid  ...            None -0.6123\n19      class_1          nonflavanoid_phenols  ...            None  0.6385\n20      class_1  od280/od315_of_diluted_wines  ...            None -0.1823\n21      class_1                       proline  ...            None -2.5648\n22      class_1                 total_phenols  ...            None  0.0292\n23      class_1                          None  ...            None -1.8943\n24      class_2             alcalinity_of_ash  ...            None -0.0291\n25      class_2                       alcohol  ...            None  0.8029\n26      class_2                           ash  ...            None  0.4917\n27      class_2               color_intensity  ...            None  1.0072\n28      class_2                    flavanoids  ...            None -1.6001\n29      class_2                           hue  ...            None -0.7076\n30      class_2                    malic_acid  ...            None  0.0608\n31      class_2          nonflavanoid_phenols  ...            None -0.3188\n32      class_2  od280/od315_of_diluted_wines  ...            None -0.7787\n33      class_2                       proline  ...            None  0.0669\n34      class_2                 total_phenols  ...            None  0.1863\n35      class_2                          None  ...            None -1.7603\n\n[36 rows x 5 columns]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Regression Model Tuning","message":["%python","","","mt_wine_reg = automl.ModelTuning(mining_function = 'regression', parallel=2)","","results_reg = mt_wine_reg.tune(selected_wine_alg_reg, WINE_X_reduced_reg, WINE_y_reg)","tuned_model_reg = results_reg['best_model']","tuned_model_reg"],"enabled":true,"result":{"startTime":1714163586674,"interpreter":"python.low","endTime":1714163704299,"results":[{"message":"\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: proline\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5         SVMS_COMPLEXITY_FACTOR                             3\n6            SVMS_CONV_TOLERANCE                         .0001\n7                   SVMS_EPSILON           0.33367165666666665\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                  SVMS_STD_DEV            2.5495097567963922\n\nComputed Settings: \n          setting name    setting value\n0  SVMS_NUM_ITERATIONS               30\n1          SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              12\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\ntarget\ntotal_phenols\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Specify a model tuning search space","","Explicitly indicate the search space you want to explore for model building using the ***param_space*** argument to the ***tune*** function. Below, we show using parameters for the Random Forest algorithm for classification and Neural Network for regression. "],"enabled":true,"result":{"startTime":1714163704423,"interpreter":"md.low","endTime":1714163704480,"results":[{"message":"<h3 id=\"specify-a-model-tuning-search-space\">Specify a model tuning search space<\/h3>\n<p>Explicitly indicate the search space you want to explore for model building using the <em><strong>param_space<\/strong><\/em> argument to the <em><strong>tune<\/strong><\/em> function. Below, we show using parameters for the Random Forest algorithm for classification and Neural Network for regression.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Classification model tuning using search space","message":["%python","","","search_space={","        'RFOR_SAMPLING_RATIO': {","            'type': 'continuous', 'range': [0.05, 0.5]}, ","        'RFOR_NUM_TREES': {","            'type': 'discrete', ","            'range': [50, 55]}, ","        'TREE_IMPURITY_METRIC': {","            'type': 'categorical', ","            'range': ['TREE_IMPURITY_ENTROPY', 'TREE_IMPURITY_GINI']},","        }","              ","at_wine2_cl = automl.ModelTuning(mining_function='classification', ","                                 score_metric='f1_macro', parallel=2)","results2_cl = at_wine2_cl.tune('rf', WINE_X_cl, WINE_y_cl, ","                               param_space=search_space)","score2_cl, params2_cl = results2_cl['all_evals'][0]","\"{:.2}\".format(score2_cl)","","tuned_model2_cl = results2_cl['best_model']","tuned_model2_cl"],"enabled":true,"result":{"startTime":1714163704658,"interpreter":"python.low","endTime":1714163763322,"results":[{"message":"\nAlgorithm Name: Random Forest\n\nMining Function: CLASSIFICATION\n\nTarget: target\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME       ALGO_RANDOM_FOREST\n1              CLAS_MAX_SUP_BINS                       32\n2          CLAS_WEIGHTS_BALANCED                      OFF\n3                   ODMS_DETAILS              ODMS_ENABLE\n4   ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n5               ODMS_RANDOM_SEED                        0\n6                  ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n7                      PREP_AUTO                       ON\n8                 RFOR_NUM_TREES                       50\n9            RFOR_SAMPLING_RATIO                     0.35\n10          TREE_IMPURITY_METRIC       TREE_IMPURITY_GINI\n11           TREE_TERM_MAX_DEPTH                       16\n12         TREE_TERM_MINPCT_NODE                      .05\n13        TREE_TERM_MINPCT_SPLIT                       .1\n14         TREE_TERM_MINREC_NODE                       10\n15        TREE_TERM_MINREC_SPLIT                       20\n\nComputed Settings: \n  setting name setting value\n0    RFOR_MTRY             6\n\nGlobal Statistics: \n  attribute name attribute value\n0      AVG_DEPTH            3.74\n1  AVG_NODECOUNT            7.88\n2      MAX_DEPTH               5\n3  MAX_NODECOUNT              11\n4      MIN_DEPTH               4\n5  MIN_NODECOUNT               5\n6       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\nproline\ntotal_phenols\n\nPartition: NO\n\nImportance: \n\n                  ATTRIBUTE_NAME ATTRIBUTE_SUBNAME  ATTRIBUTE_IMPORTANCE\n0              alcalinity_of_ash              None              0.087762\n1                        alcohol              None              0.269680\n2                            ash              None              0.045137\n3                color_intensity              None              0.407622\n4                     flavanoids              None              0.294175\n5                            hue              None              0.138300\n6                      magnesium              None              0.027684\n7                     malic_acid              None              0.045442\n8           nonflavanoid_phenols              None              0.027490\n9   od280/od315_of_diluted_wines              None              0.149391\n10               proanthocyanins              None              0.008828\n11                       proline              None              0.223896\n12                 total_phenols              None              0.024040\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Regression model tuning using search space","message":["%python","","search_space={","        'NNET_REGULARIZER': {","            'range': ['NNET_REGULARIZER_HELDASIDE', 'NNET_REGULARIZER_L2', 'NNET_REGULARIZER_NONE'],","            'type': 'categorical',},","        'NNET_ALL_ACTIVATIONS': {","            'range': ['NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_LINEAR',  'NNET_ACTIVATIONS_BIPOLAR_SIG'],","            'type': 'categorical',},","        'NNET_HIDDEN_LAYERS': {","            'range': [1, 2],","            'type': 'discrete',},","        'NNET_NODES': {","            'range': [10, 20],","            'type': 'discrete',},","        }","","at_wine2_reg = automl.ModelTuning(mining_function='regression', ","                                  score_metric='r2', parallel=2)","results2_reg = at_wine2_reg.tune('nn', WINE_X_reg, WINE_y_reg,","                                 param_space=search_space)","score2_reg, params2_reg = results2_reg['all_evals'][0]","\"{:.2}\".format(score2_reg)","","tuned_model2_reg = results2_reg['best_model']","tuned_model2_reg"],"enabled":true,"result":{"startTime":1714163763410,"interpreter":"python.low","endTime":1714163810357,"results":[{"message":"\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: proline\n\nSettings: \n                    setting name                   setting value\n0                      ALGO_NAME             ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                      .000000001\n2            LBFGS_HISTORY_DEPTH                              20\n3            LBFGS_SCALE_HESSIAN      LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_BIPOLAR_SIG'\n5        NNET_HELDASIDE_MAX_FAIL                               6\n6           NNET_HELDASIDE_RATIO                             .25\n7             NNET_HIDDEN_LAYERS                               1\n8                NNET_ITERATIONS                             100\n9           NNET_NODES_PER_LAYER                              50\n10              NNET_REGULARIZER      NNET_REGULARIZER_HELDASIDE\n11                NNET_TOLERANCE                         .000001\n12                  ODMS_DETAILS                     ODMS_ENABLE\n13  ODMS_MISSING_VALUE_TREATMENT         ODMS_MISSING_VALUE_AUTO\n14              ODMS_RANDOM_SEED                               0\n15                 ODMS_SAMPLING           ODMS_SAMPLING_DISABLE\n16                     PREP_AUTO                              ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              25\n2     LOSS_VALUE        0.158361\n3       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\ntarget\ntotal_phenols\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_BIPOLAR_SIG\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0        0       0.0       0  ...            None         None -0.214998\n1        0       0.0       1  ...            None         None  0.142397\n2        0       0.0       2  ...            None         None -0.220107\n3        0       0.0       3  ...            None         None  0.113885\n4        0       0.0       4  ...            None         None  0.214632\n..     ...       ...     ...  ...             ...          ...       ...\n846      1      46.0       0  ...            None         None  0.079437\n847      1      47.0       0  ...            None         None -0.129681\n848      1      48.0       0  ...            None         None  0.391486\n849      1      49.0       0  ...            None         None  0.018201\n850      1       NaN       0  ...            None         None -0.055256\n\n[851 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Automated Model Selection","As a short cut, users may go directly to model selection on the training data. Automated model selection chooses the best algorithm (using Algorithm Selection)","from the set of supported algorithms, then builds, tunes and returns the model."],"enabled":true,"result":{"startTime":1714163852780,"interpreter":"md.low","endTime":1714163852840,"results":[{"message":"<h3 id=\"automated-model-selection\">Automated Model Selection<\/h3>\n<p>As a short cut, users may go directly to model selection on the training data. Automated model selection chooses the best algorithm (using Algorithm Selection)\nfrom the set of supported algorithms, then builds, tunes and returns the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Classification Model Selection","message":["%python","","","ms_wine = automl.ModelSelection(mining_function = 'classification', parallel=2)","","best_model = ms_wine.select(WINE_X_cl, WINE_y_cl, k=1, cv=2)","best_model"],"enabled":true,"result":{"startTime":1714163810594,"interpreter":"python.low","endTime":1714163852736,"results":[{"message":"(\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: target\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1          CLAS_WEIGHTS_BALANCED                           OFF\n2                   ODMS_DETAILS                   ODMS_ENABLE\n3   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                      PREP_AUTO                            ON\n6         SVMS_COMPLEXITY_FACTOR                            10\n7            SVMS_CONV_TOLERANCE                         .0001\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                  SVMS_STD_DEV            2.5495097567963922\n\nComputed Settings: \n          setting name    setting value\n0  SVMS_NUM_ITERATIONS               30\n1          SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              13\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\nproline\ntotal_phenols\n\nPartition: NO\n\n, 'svm_gaussian')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Regression Model Selection","message":["%python","","","ms_wine_reg = automl.ModelSelection(mining_function = 'regression', parallel=2)","","best_model_reg = ms_wine_reg.select(WINE_X_reg, WINE_y_reg, k=1, cv=2)","best_model_reg"],"enabled":true,"result":{"startTime":1714163852881,"interpreter":"python.low","endTime":1714163904038,"results":[{"message":"(\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: proline\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5         SVMS_COMPLEXITY_FACTOR                    0.82624875\n6            SVMS_CONV_TOLERANCE                         .0001\n7                   SVMS_EPSILON                           0.1\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                  SVMS_STD_DEV            2.5495097567963922\n\nComputed Settings: \n          setting name    setting value\n0  SVMS_NUM_ITERATIONS               30\n1          SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              10\n2       NUM_ROWS             178\n\nAttributes: \nalcalinity_of_ash\nalcohol\nash\ncolor_intensity\nflavanoids\nhue\nmagnesium\nmalic_acid\nnonflavanoid_phenols\nod280/od315_of_diluted_wines\nproanthocyanins\ntarget\ntotal_phenols\n\nPartition: NO\n\n, 'svm_gaussian')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1714163862022,"interpreter":"md.low","endTime":1714163862082,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1714163904261,"interpreter":"md.low","endTime":1714163904322,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":"zeppelin","template":null,"templateConfig":null,"name":"OML4Py Association Rules Apriori","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715292292041,"interpreter":"md.low","endTime":1715292292194,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Association Rules (Market Basket Analysis)","","The Association Rules machine learning technique, also known as Market Basket Analysis, can  be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing.  In his notebook, you will build association rules models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715292292273,"interpreter":"md.low","endTime":1715292292370,"results":[{"message":"<h1 id=\"association-rules-market-basket-analysis\">Association Rules (Market Basket Analysis)<\/h1>\n<p>The Association Rules machine learning technique, also known as Market Basket Analysis, can  be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing.  In his notebook, you will build association rules models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715292292444,"interpreter":"md.low","endTime":1715292292532,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/a>"],"enabled":true,"result":{"startTime":1715292292611,"interpreter":"md.low","endTime":1715292292686,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import oml","import pandas as pd","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1715292292760,"interpreter":"python.low","endTime":1715292294757,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get SALES proxy object and display a few rows of data","message":["%python","","SALES_DF = oml.sync(query = \"SELECT CUST_ID, PROD_ID, QUANTITY_SOLD, AMOUNT_SOLD, CHANNEL_ID, PROMO_ID FROM SH.SALES\")","z.show(SALES_DF.head())"],"enabled":true,"result":{"startTime":1715292294837,"interpreter":"python.low","endTime":1715292295065,"results":[{"message":"CUST_ID\tPROD_ID\tQUANTITY_SOLD\tAMOUNT_SOLD\tCHANNEL_ID\tPROMO_ID\n524.0\t13.0\t1.0\t1205.99\t2.0\t999.0\n2128.0\t13.0\t1.0\t1250.25\t2.0\t999.0\n3212.0\t13.0\t1.0\t1250.25\t2.0\t999.0\n3375.0\t13.0\t1.0\t1250.25\t2.0\t999.0\n5204.0\t13.0\t1.0\t1250.25\t2.0\t999.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get PRODUCTS proxy object and display a few rows of data","message":["%python","","PRODUCT_DF = oml.sync(query = 'SELECT PROD_ID, PROD_NAME, PROD_CATEGORY FROM SH.PRODUCTS')","z.show(PRODUCT_DF.head())"],"enabled":true,"result":{"startTime":1715292295146,"interpreter":"python.low","endTime":1715292295542,"results":[{"message":"PROD_ID\tPROD_NAME\tPROD_CATEGORY\n13\t5MP Telephoto Digital Camera\tPhoto\n14\t17\" LCD w/built-in HDTV Tuner\tPeripherals and Accessories\n15\tEnvoy 256MB - 40GB\tHardware\n16\tY Box\tElectronics\n17\tMini DV Camcorder with 3.5\" Swivel LCD\tPhoto\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Merge SALES with PRODUCT, creating another proxy object","message":["%python","","SALES_TRANS_CUST = PRODUCT_DF.merge(SALES_DF[(SALES_DF['CUST_ID'] >= 100001) & (SALES_DF['CUST_ID'] <= 104500)], ","                                    how = \"inner\", on = 'PROD_ID', suffixes = [\"\", \"\"])","SALES_TRANS_CUST = SALES_TRANS_CUST[['CUST_ID', 'PROD_NAME', 'PROD_CATEGORY']].drop_duplicates()"],"enabled":true,"result":{"startTime":1715292295620,"interpreter":"python.low","endTime":1715292295938,"results":[{"message":"<stdin>:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display customer purchases with associated product category","message":["%python","","z.show(SALES_TRANS_CUST.head())"],"enabled":true,"result":{"startTime":1715292296016,"interpreter":"python.low","endTime":1715292296286,"results":[{"message":"CUST_ID\tPROD_NAME\tPROD_CATEGORY\n100100\tMouse Pad\tSoftware/Other\n100100\tExternal 8X CD-ROM\tPeripherals and Accessories\n100200\tMouse Pad\tSoftware/Other\n100200\tStandard Mouse\tPeripherals and Accessories\n100200\tExtension Cable\tElectronics\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Check the distribution of PROD_CATEGORY ","message":["%python","","z.show(SALES_TRANS_CUST.crosstab(\"PROD_CATEGORY\"))"],"enabled":true,"result":{"startTime":1715292296364,"interpreter":"python.low","endTime":1715292296740,"results":[{"message":"PROD_CATEGORY\tcount\nElectronics\t294\nHardware\t4\nPeripherals and Accessories\t1568\nSoftware/Other\t938\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Association Rules (A Priori)","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3');\">Association Rules - A Priori<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","    ","- Specify the name of a column that contains the items in a transaction. ","   When you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:  (1) `Case ID`, either categorical or numeric, and (2) `Item ID`, either categorical or numeric.","> 'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'    ","","- Specify the Minimum support for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_SUPPORT' : '0.1'","","- Specify the Minimum confidence for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_CONFIDENCE' : '0.1'","","- Specify the Maximum rule length for association rules.","    The default value is 4.  It requires a number between 2 and 20 (inclusive).","> 'ASSO_MAX_RULE_LENGTH': '4'"],"enabled":true,"result":{"startTime":1715292296820,"interpreter":"md.low","endTime":1715292296908,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-association-rules-a-priori\">Examples of possible setting overrides for Association Rules (A Priori)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/association-rules.html#GUID-4D047EAC-D134-49D7-A532-AB3B8A95DFA3');\">Association Rules - A Priori<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the name of a column that contains the items in a transaction.\nWhen you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:  (1) <code>Case ID<\/code>, either categorical or numeric, and (2) <code>Item ID<\/code>, either categorical or numeric.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum support for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_SUPPORT' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum confidence for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_CONFIDENCE' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Maximum rule length for association rules.\nThe default value is 4.  It requires a number between 2 and 20 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MAX_RULE_LENGTH': '4'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify settings and create Association Rules model","message":["%python","","setting = {'ASSO_MIN_SUPPORT':'0.04', ","           'ASSO_MIN_CONFIDENCE':'0.1', ","           'ASSO_MAX_RULE_LENGTH': '2', ","           'ODMS_ITEM_ID_COLUMN_NAME':'PROD_NAME'}","","ar_mod = oml.ar(**setting)","ar_mod = ar_mod.fit(SALES_TRANS_CUST, case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715292296991,"interpreter":"python.low","endTime":1715292298689,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details","message":["%python","","ar_mod"],"enabled":true,"result":{"startTime":1715292298765,"interpreter":"python.low","endTime":1715292299200,"results":[{"message":"\nAlgorithm Name: Association Rules\n\nMining Function: ASSOCIATION\n\nSettings: \n                    setting name                   setting value\n0                      ALGO_NAME  ALGO_APRIORI_ASSOCIATION_RULES\n1           ASSO_MAX_RULE_LENGTH                               2\n2            ASSO_MIN_CONFIDENCE                             0.1\n3        ASSO_MIN_REV_CONFIDENCE                               0\n4               ASSO_MIN_SUPPORT                            0.04\n5           ASSO_MIN_SUPPORT_INT                               1\n6                   ODMS_DETAILS                     ODMS_ENABLE\n7       ODMS_ITEM_ID_COLUMN_NAME                       PROD_NAME\n8   ODMS_MISSING_VALUE_TREATMENT         ODMS_MISSING_VALUE_AUTO\n9                  ODMS_SAMPLING           ODMS_SAMPLING_DISABLE\n10                     PREP_AUTO                              ON\n\nGlobal Statistics: \n      attribute name attribute value\n0      ITEMSET_COUNT              63\n1        MAX_SUPPORT         0.32234\n2           NUM_ROWS             940\n3         RULE_COUNT             104\n4  TRANSACTION_COUNT             940\n\nAttributes: \nPROD_NAME\n\nPartition: NO\n\nItemsets: \n\n     ITEMSET_ID   SUPPORT  NUMBER_OF_ITEMS                          ITEM_NAME ITEM_VALUE\n0             1  0.310638                1    18\" Flat Panel Graphics Monitor       None\n1             2  0.292553                1        CD-RW, High Speed Pack of 5       None\n2             3  0.311702                1                    Extension Cable       None\n3             4  0.305319                1                 External 8X CD-ROM       None\n4             5  0.188298                1                Keyboard Wrist Rest       None\n5             6  0.217021                1  Model SM26273 Black Ink Cartridge       None\n6             7  0.322340                1                          Mouse Pad       None\n7             8  0.195745                1      Multimedia speakers- 3\" cones       None\n8             9  0.194681                1    O/S Documentation Set - English       None\n9            10  0.318085                1           SIMM- 16MB PCMCIAII card       None\n10           11  0.321277                1                     Standard Mouse       None\n11           12  0.091489                2    18\" Flat Panel Graphics Monitor       None\n12           12  0.091489                2        CD-RW, High Speed Pack of 5       None\n13           13  0.090426                2    18\" Flat Panel Graphics Monitor       None\n14           13  0.090426                2                    Extension Cable       None\n15           14  0.082979                2    18\" Flat Panel Graphics Monitor       None\n16           14  0.082979                2                 External 8X CD-ROM       None\n17           15  0.059574                2    18\" Flat Panel Graphics Monitor       None\n18           15  0.059574                2                Keyboard Wrist Rest       None\n19           16  0.067021                2    18\" Flat Panel Graphics Monitor       None\n20           16  0.067021                2  Model SM26273 Black Ink Cartridge       None\n21           17  0.094681                2    18\" Flat Panel Graphics Monitor       None\n22           17  0.094681                2                          Mouse Pad       None\n23           18  0.055319                2    18\" Flat Panel Graphics Monitor       None\n24           18  0.055319                2      Multimedia speakers- 3\" cones       None\n25           19  0.051064                2    18\" Flat Panel Graphics Monitor       None\n26           19  0.051064                2    O/S Documentation Set - English       None\n27           20  0.154255                2    18\" Flat Panel Graphics Monitor       None\n28           20  0.154255                2           SIMM- 16MB PCMCIAII card       None\n29           21  0.095745                2    18\" Flat Panel Graphics Monitor       None\n30           21  0.095745                2                     Standard Mouse       None\n31           22  0.068085                2        CD-RW, High Speed Pack of 5       None\n32           22  0.068085                2                    Extension Cable       None\n33           23  0.153191                2        CD-RW, High Speed Pack of 5       None\n34           23  0.153191                2                 External 8X CD-ROM       None\n35           24  0.058511                2        CD-RW, High Speed Pack of 5       None\n36           24  0.058511                2                Keyboard Wrist Rest       None\n37           25  0.057447                2        CD-RW, High Speed Pack of 5       None\n38           25  0.057447                2  Model SM26273 Black Ink Cartridge       None\n39           26  0.075532                2        CD-RW, High Speed Pack of 5       None\n40           26  0.075532                2                          Mouse Pad       None\n41           27  0.055319                2        CD-RW, High Speed Pack of 5       None\n42           27  0.055319                2      Multimedia speakers- 3\" cones       None\n43           28  0.056383                2        CD-RW, High Speed Pack of 5       None\n44           28  0.056383                2    O/S Documentation Set - English       None\n45           29  0.087234                2        CD-RW, High Speed Pack of 5       None\n46           29  0.087234                2           SIMM- 16MB PCMCIAII card       None\n47           30  0.070213                2        CD-RW, High Speed Pack of 5       None\n48           30  0.070213                2                     Standard Mouse       None\n49           31  0.081915                2                    Extension Cable       None\n50           31  0.081915                2                 External 8X CD-ROM       None\n51           32  0.047872                2                    Extension Cable       None\n52           32  0.047872                2                Keyboard Wrist Rest       None\n53           33  0.063830                2                    Extension Cable       None\n54           33  0.063830                2  Model SM26273 Black Ink Cartridge       None\n55           34  0.177660                2               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Wrist Rest       None\n67           40  0.057447                2                 External 8X CD-ROM       None\n68           40  0.057447                2  Model SM26273 Black Ink Cartridge       None\n69           41  0.103191                2                 External 8X CD-ROM       None\n70           41  0.103191                2                          Mouse Pad       None\n71           42  0.063830                2                 External 8X CD-ROM       None\n72           42  0.063830                2      Multimedia speakers- 3\" cones       None\n73           43  0.052128                2                 External 8X CD-ROM       None\n74           43  0.052128                2    O/S Documentation Set - English       None\n75           44  0.089362                2                 External 8X CD-ROM       None\n76           44  0.089362                2           SIMM- 16MB PCMCIAII card       None\n77           45  0.075532                2                 External 8X CD-ROM       None\n78           45  0.075532                2                     Standard Mouse       None\n79           46  0.046809                2                Keyboard Wrist Rest       None\n80           46  0.046809                2                          Mouse Pad       None\n81           47  0.065957                2                Keyboard Wrist Rest       None\n82           47  0.065957                2           SIMM- 16MB PCMCIAII card       None\n83           48  0.054255                2                Keyboard Wrist Rest       None\n84           48  0.054255                2                     Standard Mouse       None\n85           49  0.077660                2  Model SM26273 Black Ink Cartridge       None\n86           49  0.077660                2                          Mouse Pad       None\n87           50  0.044681                2  Model SM26273 Black Ink Cartridge       None\n88           50  0.044681                2      Multimedia speakers- 3\" cones       None\n89           51  0.043617                2  Model SM26273 Black Ink Cartridge       None\n90           51  0.043617                2    O/S Documentation Set - English       None\n91           52  0.064894                2  Model SM26273 Black Ink Cartridge       None\n92           52  0.064894                2           SIMM- 16MB PCMCIAII card       None\n93           53  0.079787                2  Model SM26273 Black Ink Cartridge       None\n94           53  0.079787                2                     Standard Mouse       None\n95           54  0.067021                2                          Mouse Pad       None\n96           54  0.067021                2      Multimedia speakers- 3\" cones       None\n97           55  0.058511                2                          Mouse Pad       None\n98           55  0.058511                2    O/S Documentation Set - English       None\n99           56  0.091489                2                          Mouse Pad       None\n100          56  0.091489                2           SIMM- 16MB PCMCIAII card       None\n101          57  0.184043                2                          Mouse Pad       None\n102          57  0.184043                2                     Standard Mouse       None\n103          58  0.044681                2      Multimedia speakers- 3\" cones       None\n104          58  0.044681                2    O/S Documentation Set - English       None\n105          59  0.059574                2      Multimedia speakers- 3\" cones       None\n106          59  0.059574                2           SIMM- 16MB PCMCIAII card       None\n107          60  0.057447                2      Multimedia speakers- 3\" cones       None\n108          60  0.057447                2                     Standard Mouse       None\n109          61  0.062766                2    O/S Documentation Set - English       None\n110          61  0.062766                2           SIMM- 16MB PCMCIAII card       None\n111          62  0.055319                2    O/S Documentation Set - English       None\n112          62  0.055319                2                     Standard Mouse       None\n113          63  0.091489                2           SIMM- 16MB PCMCIAII card       None\n114          63  0.091489                2                     Standard Mouse       None\n\nRules: \n\n     RULE_ID  NUMBER_OF_ITEMS                           LHS_NAME LHS_VALUE                           RHS_NAME RHS_VALUE   SUPPORT  CONFIDENCE  REVCONFIDENCE      LIFT\n0          1                2        CD-RW, High Speed Pack of 5      None    18\" Flat Panel Graphics Monitor      None  0.091489    0.312727       0.294521  1.006725\n1          2                2    18\" Flat Panel Graphics Monitor      None        CD-RW, High Speed Pack of 5      None  0.091489    0.294521       0.312727  1.006725\n2          3                2                    Extension Cable      None    18\" Flat Panel Graphics Monitor      None  0.090426    0.290102       0.291096  0.933891\n3          4                2    18\" Flat Panel Graphics Monitor      None                    Extension Cable      None  0.090426    0.291096       0.290102  0.933891\n4          5                2                 External 8X CD-ROM      None    18\" Flat Panel Graphics Monitor      None  0.082979    0.271777       0.267123  0.874899\n5          6                2    18\" Flat Panel Graphics Monitor      None                 External 8X CD-ROM      None  0.082979    0.267123       0.271777  0.874899\n6          7                2                Keyboard Wrist Rest      None    18\" Flat Panel Graphics Monitor      None  0.059574    0.316384       0.191781  1.018497\n7          8                2    18\" Flat Panel Graphics Monitor      None                Keyboard Wrist Rest      None  0.059574    0.191781       0.316384  1.018497\n8          9                2  Model SM26273 Black Ink Cartridge      None    18\" Flat Panel Graphics Monitor      None  0.067021    0.308824       0.215753  0.994158\n9         10                2    18\" Flat Panel Graphics Monitor      None  Model SM26273 Black Ink Cartridge      None  0.067021    0.215753       0.308824  0.994158\n10        11                2                          Mouse Pad      None    18\" Flat Panel Graphics Monitor      None  0.094681    0.293729       0.304795  0.945567\n11        12                2    18\" Flat Panel Graphics Monitor      None                          Mouse Pad      None  0.094681    0.304795       0.293729  0.945567\n12        13                2      Multimedia speakers- 3\" cones      None    18\" Flat Panel Graphics Monitor      None  0.055319    0.282609       0.178082  0.909768\n13        14                2    18\" Flat Panel Graphics Monitor      None      Multimedia speakers- 3\" cones      None  0.055319    0.178082       0.282609  0.909768\n14        15                2    O/S Documentation Set - English      None    18\" Flat Panel Graphics Monitor      None  0.051064    0.262295       0.164384  0.844375\n15        16                2    18\" Flat Panel Graphics Monitor      None    O/S Documentation Set - English      None  0.051064    0.164384       0.262295  0.844375\n16        17                2           SIMM- 16MB PCMCIAII card      None    18\" Flat Panel Graphics Monitor      None  0.154255    0.484950       0.496575  1.561140\n17        18                2    18\" Flat Panel Graphics Monitor      None           SIMM- 16MB PCMCIAII card      None  0.154255    0.496575       0.484950  1.561140\n18        19                2                     Standard Mouse      None    18\" Flat Panel Graphics Monitor      None  0.095745    0.298013       0.308219  0.959358\n19        20                2    18\" Flat Panel Graphics Monitor      None                     Standard Mouse      None  0.095745    0.308219       0.298013  0.959358\n20        21                2                    Extension Cable      None        CD-RW, High Speed Pack of 5      None  0.068085    0.218430       0.232727  0.746634\n21        22                2        CD-RW, High Speed Pack of 5      None                    Extension Cable      None  0.068085    0.232727       0.218430  0.746634\n22        23                2                 External 8X CD-ROM      None        CD-RW, High Speed Pack of 5      None  0.153191    0.501742       0.523636  1.715046\n23        24                2        CD-RW, High Speed Pack of 5      None                 External 8X CD-ROM      None  0.153191    0.523636       0.501742  1.715046\n24        25                2                Keyboard Wrist Rest      None        CD-RW, High Speed Pack of 5      None  0.058511    0.310734       0.200000  1.062147\n25        26                2        CD-RW, High Speed Pack of 5      None                Keyboard Wrist Rest      None  0.058511    0.200000       0.310734  1.062147\n26        27                2  Model SM26273 Black Ink Cartridge      None        CD-RW, High Speed Pack of 5      None  0.057447    0.264706       0.196364  0.904813\n27        28                2        CD-RW, High Speed Pack of 5      None  Model SM26273 Black Ink Cartridge      None  0.057447    0.196364       0.264706  0.904813\n28        29                2                          Mouse Pad      None        CD-RW, High Speed Pack of 5      None  0.075532    0.234323       0.258182  0.800960\n29        30                2        CD-RW, High Speed Pack of 5      None                          Mouse Pad      None  0.075532    0.258182       0.234323  0.800960\n30        31                2      Multimedia speakers- 3\" cones      None        CD-RW, High Speed Pack of 5      None  0.055319    0.282609       0.189091  0.966008\n31        32                2        CD-RW, High Speed Pack of 5      None      Multimedia speakers- 3\" cones      None  0.055319    0.189091       0.282609  0.966008\n32        33                2    O/S Documentation Set - English      None        CD-RW, High Speed Pack of 5      None  0.056383    0.289617       0.192727  0.989965\n33        34                2        CD-RW, High Speed Pack of 5      None    O/S Documentation Set - English      None  0.056383    0.192727       0.289617  0.989965\n34        35                2           SIMM- 16MB PCMCIAII card      None        CD-RW, High Speed Pack of 5      None  0.087234    0.274247       0.298182  0.937428\n35        36                2        CD-RW, High Speed Pack of 5      None           SIMM- 16MB PCMCIAII card      None  0.087234    0.298182       0.274247  0.937428\n36        37                2                     Standard Mouse      None        CD-RW, High Speed Pack of 5      None  0.070213    0.218543       0.240000  0.747020\n37        38                2        CD-RW, High Speed Pack of 5      None                     Standard Mouse      None  0.070213    0.240000       0.218543  0.747020\n38        39                2                 External 8X CD-ROM      None                    Extension Cable      None  0.081915    0.268293       0.262799  0.860734\n39        40                2                    Extension Cable      None                 External 8X CD-ROM      None  0.081915    0.262799       0.268293  0.860734\n40        41                2                Keyboard Wrist Rest      None                    Extension Cable      None  0.047872    0.254237       0.153584  0.815642\n41        42                2                    Extension Cable      None                Keyboard Wrist Rest      None  0.047872    0.153584       0.254237  0.815642\n42        43                2  Model SM26273 Black Ink Cartridge      None                    Extension Cable      None  0.063830    0.294118       0.204778  0.943586\n43        44                2                    Extension Cable      None  Model SM26273 Black Ink Cartridge      None  0.063830    0.204778       0.294118  0.943586\n44        45                2                          Mouse Pad      None                    Extension Cable      None  0.177660    0.551155       0.569966  1.768211\n45        46                2                    Extension Cable      None                          Mouse Pad      None  0.177660    0.569966       0.551155  1.768211\n46        47                2      Multimedia speakers- 3\" cones      None                    Extension Cable      None  0.047872    0.244565       0.153584  0.784612\n47        48                2                    Extension Cable      None      Multimedia speakers- 3\" cones      None  0.047872    0.153584       0.244565  0.784612\n48        49                2    O/S Documentation Set - English      None                    Extension Cable      None  0.050000    0.256831       0.160410  0.823962\n49        50                2                    Extension Cable      None    O/S Documentation Set - English      None  0.050000    0.160410       0.256831  0.823962\n50        51                2           SIMM- 16MB PCMCIAII card      None                    Extension Cable      None  0.094681    0.297659       0.303754  0.954947\n51        52                2                    Extension Cable      None           SIMM- 16MB PCMCIAII card      None  0.094681    0.303754       0.297659  0.954947\n52        53                2                     Standard Mouse      None                    Extension Cable      None  0.180851    0.562914       0.580205  1.805935\n53        54                2                    Extension Cable      None                     Standard Mouse      None  0.180851    0.580205       0.562914  1.805935\n54        55                2                Keyboard Wrist Rest      None                 External 8X CD-ROM      None  0.048936    0.259887       0.160279  0.851198\n55        56                2                 External 8X CD-ROM      None                Keyboard Wrist Rest      None  0.048936    0.160279       0.259887  0.851198\n56        57                2  Model SM26273 Black Ink Cartridge      None                 External 8X CD-ROM      None  0.057447    0.264706       0.188153  0.866981\n57        58                2                 External 8X CD-ROM      None  Model SM26273 Black Ink Cartridge      None  0.057447    0.188153       0.264706  0.866981\n58        59                2                          Mouse Pad      None                 External 8X CD-ROM      None  0.103191    0.320132       0.337979  1.048516\n59        60                2                 External 8X CD-ROM      None                          Mouse Pad      None  0.103191    0.337979       0.320132  1.048516\n60        61                2      Multimedia speakers- 3\" cones      None                 External 8X CD-ROM      None  0.063830    0.326087       0.209059  1.068020\n61        62                2                 External 8X CD-ROM      None      Multimedia speakers- 3\" cones      None  0.063830    0.209059       0.326087  1.068020\n62        63                2    O/S Documentation Set - English      None                 External 8X CD-ROM      None  0.052128    0.267760       0.170732  0.876983\n63        64                2                 External 8X CD-ROM      None    O/S Documentation Set - English      None  0.052128    0.170732       0.267760  0.876983\n64        65                2           SIMM- 16MB PCMCIAII card      None                 External 8X CD-ROM      None  0.089362    0.280936       0.292683  0.920140\n65        66                2                 External 8X CD-ROM      None           SIMM- 16MB PCMCIAII card      None  0.089362    0.292683       0.280936  0.920140\n66        67                2                     Standard Mouse      None                 External 8X CD-ROM      None  0.075532    0.235099       0.247387  0.770012\n67        68                2                 External 8X CD-ROM      None                     Standard Mouse      None  0.075532    0.247387       0.235099  0.770012\n68        69                2                          Mouse Pad      None                Keyboard Wrist Rest      None  0.046809    0.145215       0.248588  0.771196\n69        70                2                Keyboard Wrist Rest      None                          Mouse Pad      None  0.046809    0.248588       0.145215  0.771196\n70        71                2           SIMM- 16MB PCMCIAII card      None                Keyboard Wrist Rest      None  0.065957    0.207358       0.350282  1.101223\n71        72                2                Keyboard Wrist Rest      None           SIMM- 16MB PCMCIAII card      None  0.065957    0.350282       0.207358  1.101223\n72        73                2                     Standard Mouse      None                Keyboard Wrist Rest      None  0.054255    0.168874       0.288136  0.896846\n73        74                2                Keyboard Wrist Rest      None                     Standard Mouse      None  0.054255    0.288136       0.168874  0.896846\n74        75                2                          Mouse Pad      None  Model SM26273 Black Ink Cartridge      None  0.077660    0.240924       0.357843  1.110140\n75        76                2  Model SM26273 Black Ink Cartridge      None                          Mouse Pad      None  0.077660    0.357843       0.240924  1.110140\n76        77                2      Multimedia speakers- 3\" cones      None  Model SM26273 Black Ink Cartridge      None  0.044681    0.228261       0.205882  1.051790\n77        78                2  Model SM26273 Black Ink Cartridge      None      Multimedia speakers- 3\" cones      None  0.044681    0.205882       0.228261  1.051790\n78        79                2    O/S Documentation Set - English      None  Model SM26273 Black Ink Cartridge      None  0.043617    0.224044       0.200980  1.032358\n79        80                2  Model SM26273 Black Ink Cartridge      None    O/S Documentation Set - English      None  0.043617    0.200980       0.224044  1.032358\n80        81                2           SIMM- 16MB PCMCIAII card      None  Model SM26273 Black Ink Cartridge      None  0.064894    0.204013       0.299020  0.940062\n81        82                2  Model SM26273 Black Ink Cartridge      None           SIMM- 16MB PCMCIAII card      None  0.064894    0.299020       0.204013  0.940062\n82        83                2                     Standard Mouse      None  Model SM26273 Black Ink Cartridge      None  0.079787    0.248344       0.367647  1.144332\n83        84                2  Model SM26273 Black Ink Cartridge      None                     Standard Mouse      None  0.079787    0.367647       0.248344  1.144332\n84        85                2      Multimedia speakers- 3\" cones      None                          Mouse Pad      None  0.067021    0.342391       0.207921  1.062204\n85        86                2                          Mouse Pad      None      Multimedia speakers- 3\" cones      None  0.067021    0.207921       0.342391  1.062204\n86        87                2    O/S Documentation Set - English      None                          Mouse Pad      None  0.058511    0.300546       0.181518  0.932388\n87        88                2                          Mouse Pad      None    O/S Documentation Set - English      None  0.058511    0.181518       0.300546  0.932388\n88        89                2           SIMM- 16MB PCMCIAII card      None                          Mouse Pad      None  0.091489    0.287625       0.283828  0.892303\n89        90                2                          Mouse Pad      None           SIMM- 16MB PCMCIAII card      None  0.091489    0.283828       0.287625  0.892303\n90        91                2                     Standard Mouse      None                          Mouse Pad      None  0.184043    0.572848       0.570957  1.777151\n91        92                2                          Mouse Pad      None                     Standard Mouse      None  0.184043    0.570957       0.572848  1.777151\n92        93                2    O/S Documentation Set - English      None      Multimedia speakers- 3\" cones      None  0.044681    0.229508       0.228261  1.172488\n93        94                2      Multimedia speakers- 3\" cones      None    O/S Documentation Set - English      None  0.044681    0.228261       0.229508  1.172488\n94        95                2           SIMM- 16MB PCMCIAII card      None      Multimedia speakers- 3\" cones      None  0.059574    0.187291       0.304348  0.956813\n95        96                2      Multimedia speakers- 3\" cones      None           SIMM- 16MB PCMCIAII card      None  0.059574    0.304348       0.187291  0.956813\n96        97                2                     Standard Mouse      None      Multimedia speakers- 3\" cones      None  0.057447    0.178808       0.293478  0.913475\n97        98                2      Multimedia speakers- 3\" cones      None                     Standard Mouse      None  0.057447    0.293478       0.178808  0.913475\n98        99                2           SIMM- 16MB PCMCIAII card      None    O/S Documentation Set - English      None  0.062766    0.197324       0.322404  1.013579\n99       100                2    O/S Documentation Set - English      None           SIMM- 16MB PCMCIAII card      None  0.062766    0.322404       0.197324  1.013579\n100      101                2                     Standard Mouse      None    O/S Documentation Set - English      None  0.055319    0.172185       0.284153  0.884450\n101      102                2    O/S Documentation Set - English      None                     Standard Mouse      None  0.055319    0.284153       0.172185  0.884450\n102      103                2                     Standard Mouse      None           SIMM- 16MB PCMCIAII card      None  0.091489    0.284768       0.287625  0.895258\n103      104                2           SIMM- 16MB PCMCIAII card      None                     Standard Mouse      None  0.091489    0.287625       0.284768  0.895258\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display top 10 most frequent purchased items ","message":["%python","","z.show(ar_mod.itemsets.sort_values(['NUMBER_OF_ITEMS','SUPPORT'], ascending = False).head(10))"],"enabled":true,"result":{"startTime":1715292299278,"interpreter":"python.low","endTime":1715292299599,"results":[{"message":"ITEMSET_ID\tSUPPORT\tNUMBER_OF_ITEMS\tITEM_NAME\tITEM_VALUE\n57\t0.18404255319148935\t2\tMouse Pad\tNone\n57\t0.18404255319148935\t2\tStandard Mouse\tNone\n38\t0.18085106382978725\t2\tExtension Cable\tNone\n38\t0.18085106382978725\t2\tStandard Mouse\tNone\n34\t0.1776595744680851\t2\tExtension Cable\tNone\n34\t0.1776595744680851\t2\tMouse Pad\tNone\n20\t0.15425531914893617\t2\t18\" Flat Panel Graphics Monitor\tNone\n20\t0.15425531914893617\t2\tSIMM- 16MB PCMCIAII card\tNone\n23\t0.15319148936170213\t2\tCD-RW, High Speed Pack of 5\tNone\n23\t0.15319148936170213\t2\tExternal 8X CD-ROM\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"pie","title":"Display pie chart of top 10 most frequent items purchased","message":["%python","","z.show(ar_mod.itemsets.sort_values(['NUMBER_OF_ITEMS','SUPPORT'], ascending = False).head(10))"],"enabled":true,"result":{"startTime":1715292299675,"interpreter":"python.low","endTime":1715292299995,"results":[{"message":"ITEMSET_ID\tSUPPORT\tNUMBER_OF_ITEMS\tITEM_NAME\tITEM_VALUE\n57\t0.18404255319148935\t2\tMouse Pad\tNone\n57\t0.18404255319148935\t2\tStandard Mouse\tNone\n38\t0.18085106382978725\t2\tExtension Cable\tNone\n38\t0.18085106382978725\t2\tStandard Mouse\tNone\n34\t0.1776595744680851\t2\tExtension Cable\tNone\n34\t0.1776595744680851\t2\tMouse Pad\tNone\n20\t0.15425531914893617\t2\t18\" Flat Panel Graphics Monitor\tNone\n20\t0.15425531914893617\t2\tSIMM- 16MB PCMCIAII card\tNone\n23\t0.15319148936170213\t2\tCD-RW, High Speed Pack of 5\tNone\n23\t0.15319148936170213\t2\tExternal 8X CD-ROM\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display top 10 Association Rules sorted by confidence and support","message":["%python","","z.show(ar_mod.rules.sort_values(['CONFIDENCE', 'SUPPORT'], ascending = False).round(3).head(10))"],"enabled":true,"result":{"startTime":1715292300086,"interpreter":"python.low","endTime":1715292300465,"results":[{"message":"RULE_ID\tNUMBER_OF_ITEMS\tLHS_NAME\tLHS_VALUE\tRHS_NAME\tRHS_VALUE\tSUPPORT\tCONFIDENCE\tREVCONFIDENCE\tLIFT\n54\t2\tExtension Cable\tNone\tStandard Mouse\tNone\t0.181\t0.58\t0.563\t1.806\n91\t2\tStandard Mouse\tNone\tMouse Pad\tNone\t0.184\t0.573\t0.571\t1.777\n92\t2\tMouse Pad\tNone\tStandard 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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Attribute Importance MDL","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715292774055,"interpreter":"md.low","endTime":1715292774133,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identify key factors","Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we compute Attribute Importance, which uses the Minimum Description Length algorithm, on the SH schema data. All functionality runs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715292774209,"interpreter":"md.low","endTime":1715292774289,"results":[{"message":"<h1 id=\"identify-key-factors\">Identify key factors<\/h1>\n<p>Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we compute Attribute Importance, which uses the Minimum Description Length algorithm, on the SH schema data. All functionality runs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/attributeimportance-5663163.jpg\" alt=\"OML Notebooks\" 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Bach.\t1041\nAssoc-A\t172\nAssoc-V\t196\nBach.\t779\nHS-grad\t1462\nMasters\t190\nPhD\t49\nPresch.\t3\nProfsc\t94\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select the columns to consider for attribute importance analysis","message":["%python","","DATA_DF = SUPP_DF[[\"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", ","                   \"CUST_ID\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                   \"HOUSEHOLD_SIZE\", \"OCCUPATION\",   \"OS_DOC_SET_KANJI\", ","                   \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", 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the predictors and the target using proxy objects","message":["%python","","train_x = DATA_DF.drop(['AFFINITY_CARD'])","train_y = DATA_DF['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1715292778156,"interpreter":"python.low","endTime":1715292778232,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Attribute Importance (MDL) Shared Settings","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of shared settings can be found in the Documentation link:","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","    ","* Specify wheter to do sampling for the build data.  It requires the options `ODMS_SAMPLING_ENABLE` or  `ODMS_SAMPLING_DISABLE`.   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This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify wheter to do sampling for the build data.  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--- a/machine-learning/notebooks/python/OML4Py Classification SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Classification SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715313208502,"interpreter":"md.medium","endTime":1715313208663,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification modeling to predict Target Customers using Support Vector Machine Model","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification support vector machine model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715313208741,"interpreter":"md.medium","endTime":1715313208837,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine-model\">Classification modeling to predict Target Customers using Support Vector Machine Model<\/h2>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification support vector machine model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" 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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715313209076,"interpreter":"md.medium","endTime":1715313209138,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 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train and test data (60/40 split) ","message":["%python","","TRAIN, TEST = DEMO_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('AFFINITY_CARD')","TRAIN_Y = TRAIN['AFFINITY_CARD']","TEST_X = TEST","TEST_Y = TEST['AFFINITY_CARD']"],"enabled":true,"result":{"startTime":1715313216860,"interpreter":"python.medium","endTime":1715313217771,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Support Vector Machine classification model for Predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1715313217849,"interpreter":"md.medium","endTime":1715313217925,"results":[{"message":"<h3 id=\"build-a-support-vector-machine-classification-model-for-predicting-affinity_card\">Build a Support Vector Machine classification model for Predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using default settings","message":["%python","","try:","    oml.drop(model = 'SVM_CLAS_MODEL') ","except:","    pass","","settings = {}","","svm_mod = oml.svm(**settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'SVM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1715313218075,"interpreter":"python.medium","endTime":1715313222475,"results":[{"message":"\nModel Name: SVM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1         CLAS_WEIGHTS_BALANCED                           OFF\n2                  ODMS_DETAILS                   ODMS_ENABLE\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                     PREP_AUTO                            ON\n6           SVMS_CONV_TOLERANCE                         .0001\n7          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR               10\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              17\n2       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n    TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE    COEF\n0              1  BOOKKEEPING_APPLICATION              None            None  0.0773\n1              1      BULK_PACK_DISKETTES              None            None  0.0000\n2              1                EDUCATION              None            10th -0.0035\n3              1                EDUCATION              None            11th -0.0035\n4              1                EDUCATION              None            12th -0.0035\n5              1                EDUCATION              None         1st-4th -0.0035\n6              1                EDUCATION              None         5th-6th -0.0035\n7              1                EDUCATION              None         7th-8th -0.0035\n8              1                EDUCATION              None             9th -0.0035\n9              1                EDUCATION              None         < Bach. -0.2469\n10             1                EDUCATION              None         Assoc-A -0.2469\n11             1                EDUCATION              None         Assoc-V -0.2469\n12             1                EDUCATION              None           Bach. -0.2469\n13             1                EDUCATION              None         HS-grad -0.2469\n14             1                EDUCATION              None         Masters -0.2469\n15             1                EDUCATION              None             PhD  1.7531\n16             1                EDUCATION              None         Presch.  0.0000\n17             1                EDUCATION              None          Profsc -0.2469\n18             1       FLAT_PANEL_MONITOR              None            None  0.0000\n19             1     HOME_THEATER_PACKAGE              None            None  0.0000\n20             1           HOUSEHOLD_SIZE              None               1 -0.6667\n21             1           HOUSEHOLD_SIZE              None               2 -0.6667\n22             1           HOUSEHOLD_SIZE              None               3  1.3333\n23             1           HOUSEHOLD_SIZE              None             4-5  1.3333\n24             1           HOUSEHOLD_SIZE              None             6-8 -0.6667\n25             1           HOUSEHOLD_SIZE              None              9+ -0.6667\n26             1               OCCUPATION              None               ? -0.9333\n27             1               OCCUPATION              None         Armed-F  1.0667\n28             1               OCCUPATION              None         Cleric.  1.0667\n29             1               OCCUPATION              None          Crafts -0.9333\n30             1               OCCUPATION              None           Exec.  1.0667\n31             1               OCCUPATION              None         Farming -0.9333\n32             1               OCCUPATION              None         Handler -0.9333\n33             1               OCCUPATION              None         House-s -0.9333\n34             1               OCCUPATION              None         Machine -0.9333\n35             1               OCCUPATION              None           Other -0.9333\n36             1               OCCUPATION              None           Prof.  1.0667\n37             1               OCCUPATION              None         Protec.  1.0667\n38             1               OCCUPATION              None           Sales  1.0667\n39             1               OCCUPATION              None         TechSup  1.0667\n40             1               OCCUPATION              None         Transp. -0.9333\n41             1         OS_DOC_SET_KANJI              None            None -0.1082\n42             1            YRS_RESIDENCE              None            None  0.0000\n43             1              Y_BOX_GAMES              None            None -0.9203\n44             1                     None              None            None -1.7951\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test Data","message":["%python","svm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1715313222577,"interpreter":"python.medium","endTime":1715313223019,"results":[{"message":"0.84169\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> 'SVMS_EPSILON' : '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> 'SVMS_BATCH_ROWS' : '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> 'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> 'SVMS_CONV_TOLERANCE': '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> 'SVMS_NUM_PIVOTS': '200' "],"enabled":true,"result":{"startTime":1715313223099,"interpreter":"md.medium","endTime":1715313223192,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' : '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE': '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS': '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using Linear kernel","message":["%python","","","try:","    oml.drop(model = 'SVM_CLAS_MODEL') ","except:","    pass","    ","    ","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_LINEAR', ","            'SVMS_SOLVER': 'SVMS_SOLVER_SGD',","            'SVMS_CONV_TOLERANCE': '0.001', ","            'SVMS_BATCH_ROWS': '2000',","            'SVMS_REGULARIZER': 'SVMS_REGULARIZER_L1'}","","svm_mod = oml.svm('classification', **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'SVM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1715313223282,"interpreter":"python.medium","endTime":1715313226884,"results":[{"message":"\nModel Name: SVM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1          CLAS_WEIGHTS_BALANCED                           OFF\n2                   ODMS_DETAILS                   ODMS_ENABLE\n3   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                      PREP_AUTO                            ON\n6                SVMS_BATCH_ROWS                          2000\n7            SVMS_CONV_TOLERANCE                         0.001\n8           SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n9               SVMS_REGULARIZER           SVMS_REGULARIZER_L1\n10                   SVMS_SOLVER               SVMS_SOLVER_SGD\n\nComputed Settings: \n             setting name setting value\n0  SVMS_COMPLEXITY_FACTOR          1000\n1     SVMS_NUM_ITERATIONS          1000\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS            1000\n2       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nCOEFFICIENTS: \n\n    TARGET_VALUE           ATTRIBUTE_NAME ATTRIBUTE_SUBNAME ATTRIBUTE_VALUE    COEF\n0              1  BOOKKEEPING_APPLICATION              None            None  0.3749\n1              1      BULK_PACK_DISKETTES              None            None  0.0424\n2              1                EDUCATION              None            10th  0.6785\n3              1                EDUCATION              None            11th  0.6668\n4              1                EDUCATION              None            12th  0.0156\n5              1                EDUCATION              None         1st-4th -0.6141\n6              1                EDUCATION              None         5th-6th -0.7327\n7              1                EDUCATION              None         7th-8th -0.4346\n8              1                EDUCATION              None             9th  0.1763\n9              1                EDUCATION              None         < Bach. -0.3874\n10             1                EDUCATION              None         Assoc-A -0.2820\n11             1                EDUCATION              None         Assoc-V -0.4068\n12             1                EDUCATION              None           Bach. -0.2558\n13             1                EDUCATION              None         HS-grad -0.5199\n14             1                EDUCATION              None         Masters -0.1865\n15             1                EDUCATION              None             PhD  1.2106\n16             1                EDUCATION              None          Profsc -0.1575\n17             1       FLAT_PANEL_MONITOR              None            None -0.0370\n18             1     HOME_THEATER_PACKAGE              None            None  0.0559\n19             1           HOUSEHOLD_SIZE              None               1 -0.7847\n20             1           HOUSEHOLD_SIZE              None               2 -0.8389\n21             1           HOUSEHOLD_SIZE              None               3  1.0715\n22             1           HOUSEHOLD_SIZE              None             4-5  1.1580\n23             1           HOUSEHOLD_SIZE              None             6-8 -1.0365\n24             1           HOUSEHOLD_SIZE              None              9+ -0.8550\n25             1               OCCUPATION              None               ? -0.9483\n26             1               OCCUPATION              None         Armed-F  0.5928\n27             1               OCCUPATION              None         Cleric.  0.6079\n28             1               OCCUPATION              None          Crafts -0.8653\n29             1               OCCUPATION              None           Exec.  0.8812\n30             1               OCCUPATION              None         Farming -0.9189\n31             1               OCCUPATION              None         Handler -0.9143\n32             1               OCCUPATION              None         House-s -0.3442\n33             1               OCCUPATION              None         Machine -0.7477\n34             1               OCCUPATION              None           Other -0.9168\n35             1               OCCUPATION              None           Prof.  0.8336\n36             1               OCCUPATION              None         Protec.  0.6415\n37             1               OCCUPATION              None           Sales  0.6862\n38             1               OCCUPATION              None         TechSup  0.7539\n39             1               OCCUPATION              None         Transp. -0.6546\n40             1         OS_DOC_SET_KANJI              None            None -0.4410\n41             1            YRS_RESIDENCE              None            None  0.1338\n42             1              Y_BOX_GAMES              None            None -0.7248\n43             1                     None              None            None -1.3097\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on Test Data","message":["%python","svm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1715313226984,"interpreter":"python.medium","endTime":1715313227304,"results":[{"message":"0.84338\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using Gaussian kernel","message":["%python","","try:","    oml.drop(model = 'SVM_CLAS_MODEL') ","except:","    pass","    ","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_GAUSSIAN', ","            'SVMS_CONV_TOLERANCE': '0.001', ","            'SVMS_NUM_PIVOTS': '200'  }","","svm_mod = oml.svm('classification', **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUST_ID', model_name = 'SVM_CLAS_MODEL')"],"enabled":true,"result":{"startTime":1715313227381,"interpreter":"python.medium","endTime":1715313231481,"results":[{"message":"\nModel Name: SVM_CLAS_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: CLASSIFICATION\n\nTarget: AFFINITY_CARD\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1         CLAS_WEIGHTS_BALANCED                           OFF\n2                  ODMS_DETAILS                   ODMS_ENABLE\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n5                     PREP_AUTO                            ON\n6           SVMS_CONV_TOLERANCE                         0.001\n7          SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n8               SVMS_NUM_PIVOTS                           200\n\nComputed Settings: \n             setting name       setting value\n0  SVMS_COMPLEXITY_FACTOR                  10\n1     SVMS_NUM_ITERATIONS                  30\n2             SVMS_SOLVER     SVMS_SOLVER_IPM\n3            SVMS_STD_DEV  2.3452078799117149\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              14\n2       NUM_ROWS            2725\n\nAttributes: \nBOOKKEEPING_APPLICATION\nBULK_PACK_DISKETTES\nEDUCATION\nFLAT_PANEL_MONITOR\nHOME_THEATER_PACKAGE\nHOUSEHOLD_SIZE\nOCCUPATION\nOS_DOC_SET_KANJI\nPRINTER_SUPPLIES\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%python","","svm_mod.score(TEST_X, TEST_Y)"],"enabled":true,"result":{"startTime":1715313231560,"interpreter":"python.medium","endTime":1715313231999,"results":[{"message":"0.841127\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Score test data adding supplemental columns","message":["%python","","# Set the case ID attribute","case_id = 'CUST_ID'","# Gather the Predictions","RES_DF = svm_mod.predict(TEST_X, supplemental_cols = TEST_X)","# Additionally collect the PROBABILITY_OF_0 and PROBABILITY_OF_1","RES_PROB = svm_mod.predict_proba(TEST_X, supplemental_cols = TEST_X[case_id])","# Join the entire result into RES_DF","RES_DF = RES_DF.merge(RES_PROB, how = \"inner\", on = case_id, suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1715313232080,"interpreter":"python.medium","endTime":1715313232655,"results":[{"message":"<stdin>:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Display confusion matrix, lift chart and ROC curve ","message":["%python\r","\r","def evaluate_model(pred_data='', \r","    settings_name={''}, \r","    name='',\r","    target=''\r","    ):\r","    \"\"\"Evaluate the models by passing an proxy oml.Dataframe containing Predictions\r","     and the target column,\r","     The Settings name (for the charts), \r","     The name of the model used (for the charts),\r","     Supply the target column name for evaluation\r","     for computing the confusion matrix with the test dataset\"\"\"\r","    import oml\r","    import numpy as np\r","    import matplotlib.pyplot as plt\r","    from sklearn.metrics import auc\r","    from sklearn.metrics import roc_curve\r","\r","    conf_matrix = pred_data.crosstab(target,'PREDICTION',pivot=True)\r"," \r","    # Extract Statistics from the Confusion Matrix\r","    cf_local = conf_matrix.pull()\r","    TN = int(cf_local[cf_local[target]==0]['count_(0)'])\r","    FN = int(cf_local[cf_local[target]==0]['count_(1)'])\r","    TP = int(cf_local[cf_local[target]==1]['count_(1)'])\r","    FP = int(cf_local[cf_local[target]==1]['count_(0)'])\r","    TPR = TP/(TP+FN)\r","    FPR = FP/(FP+TN)\r","    TNR = TN/(TN+FP)\r","    FNR = FN/(FN+TP)\r","    Precision = TP/(TP+FP)\r","    Accuracy = (TP+TN)/(TP+TN+FP+FN)\r","    NPV = TN/(FN+TN)\r","    DetectionRate = TN/(FN+TN)\r","    BalancedAccuracy = (TPR+TNR)/2\r","    \r","    # Estimated AUC via Triangle (not very precise) could be\r","    # AUC = (1/2)*FPR*TPR + (1/2)*(1-FPR)*(1-TPR) + (1-FPR)*TPR\r","    # Compute real AUC using roc_curve by loading the\r","    # data locally and using the roc_curve() function\r","    pred_local = pred_data.pull()\r","    fpr, tpr, _ = roc_curve(pred_local[[target]],pred_local[['PROBABILITY_OF_1']])\r","    AUC = auc(fpr, tpr)\r","    opt_index = np.argmax(tpr - fpr)\r","    FPR_OPT = fpr[opt_index]\r","    TPR_OPT = tpr[opt_index]\r","    F1Score = 2*Precision*TPR/(Precision+TPR)\r","    MathewsCorrCoef = ((TP*TN)-(FP*FN))/((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))**0.5\r","    \r","    # Store all statistics to export\r","    statistics = {'Algorithm' : name,\r","                  'Algorithm_setting' : settings_name,\r","                  'TN' : TN,\r","                  'TP' : TP,\r","                  'FP' : FP,\r","                  'FN' : FN,\r","                  'TPR' : TPR,\r","                  'FPR' : FPR,\r","                  'TNR' : TNR,\r","                  'FNR' : FNR,\r","                  'Precision' : Precision,\r","                  'Accuracy' : Accuracy,\r","                  'NPV' : NPV,\r","                  'DetectionRate' : DetectionRate,\r","                  'BalancedAccuracy' : BalancedAccuracy,\r","                  'AUC' : AUC,\r","                  'F1Score' : F1Score,\r","                  'MathewsCorrCoef' : MathewsCorrCoef\r","                  }\r","    # Nice round stats for printing to screen\r","    TOTAL = TP+TN+FP+FN\r","    TN_P = round((TN/TOTAL*100),2)\r","    FP_P = round((FP/TOTAL*100),2)\r","    FN_P = round((FN/TOTAL*100),2)\r","    TP_P = round((TP/TOTAL*100),2)\r","    # Print the output nicely on Zeppelin native Table\r","    print(\"%table CONFUSION MATRIX\\tPREDICTED 0\\tPREDICTED 1\\nACTUAL 0\\t\"+\r","          \"True Negative: \"+str(TN)+\" (\"+str(TN_P)+\"%)\\t\"+\r","          \"False Positive: \"+str(FP)+\" (\"+str(FP_P)+\"%)\\nACTUAL 1\\t\"+\r","          \"False Negative: \"+str(FN)+\" (\"+str(FN_P)+\"%)\\t\"+\r","          \"True Positive: \"+str(TP)+\" (\"+str(TP_P)+\"%)\\n\"+\r","          \"Accuracy: \"+str(round(Accuracy*100,4))+\"%\\t\"+\r","          \"AUC: \"+str(round(AUC,4))+\"\\t\"+\r","          \"F1Score: \"+str(round(F1Score,4))\r","          )\r","    \r","    # Multiple Charts for Evaluation\r","    fig, axes = plt.subplots(nrows=1, ncols=4,figsize=[22,5])\r","    ax1, ax2, ax3, ax4 = axes.flatten()\r","    fig.suptitle('Evaluation of the '+str(name)+' Model, with settings: '+str(settings_name), size=16)\r","    \r","    # Statistics\r","    ax1.axis('off')\r","    \r","    # Function to return rounded numbers if the string is float, return\r","    # integers otherwise and return characters if not a number\r","    def round_if_float(content):\r","        try:\r","            val = float(content)\r","        except ValueError:\r","            return(content)\r","        else:\r","            if val.is_integer():\r","                return(int(content))\r","            else:\r","                return(round(float(content),4))\r","\r","    for num, name in enumerate(statistics):\r","        ax1.text(0.01, \r","        (-num*0.06+0.94),\r","        \"{0}: {1}\".format(name,round_if_float(statistics[name])),\r","        ha='left', \r","        va='bottom', \r","        fontsize=12)\r","    \r","    # Produce Lift Chart\r","    ax2.set_title('Lift Chart')\r","    data = pred_local.sort_values(by='PROBABILITY_OF_1', ascending=False)\r","    data['row_id'] = range(0,0+len(data))\r","    data['decile'] = ( data['row_id'] / (len(data)/10) ).astype(int)\r","    lift = data.groupby('decile')[target].agg(['count','sum'])\r","    lift.columns = ['count', target]\r","    lift['decile'] = range(1,11)\r","    \r","    data_ideal = pred_local.sort_values(by=target, ascending=False)\r","    data_ideal['row_id'] = range(0,0+len(data))\r","    data_ideal['decile'] = ( data_ideal['row_id'] / (len(data_ideal)/10) ).astype(int)\r","    lift_ideal = data_ideal.groupby('decile')[target].agg(['count','sum'])\r","    lift_ideal.columns = ['count', 'IDEAL']\r","    lift['IDEAL']=lift_ideal['IDEAL']\r","    \r","    ax2.bar(lift['decile'],lift['IDEAL']/lift['count'],\r","    color='darkorange', label='Ideal')\r","    ax2.bar(lift['decile'],lift[target]/lift['count'],\r","    color='blue', alpha=0.6, label='Model')\r","    ax2.axhline((lift[target]/lift['count']).mean(), \r","    color='grey', linestyle='--', label='Avg TARGET')\r","    ax2.set_ylim(0,1.15)\r","    ax2.set_xlabel('Decile', size=13)\r","    ax2.set_ylabel('Percent of Actual Targets', size=13)\r","    # Print labels.\r","    for dec in lift['decile']:\r","        ax2.text(dec, lift[lift.decile==dec][target]/lift[lift.decile==dec]['count'] + 0.05, \r","        (\"%.0f\" % int(round((lift[(lift.decile==dec)][target]/lift[lift.decile==dec]['count'])*100,0)))+\"%\",\r","        ha='center', va='bottom')\r","    ax2.legend(loc=\"upper right\")\r","    \r","    # Produce Gains Chart\r","    ax3.set_title('Distributions of Predictions')\r","    pred_local[pred_local[target]==1]['PROBABILITY_OF_1'].rename(\"Target = 1\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)\r","    pred_local[pred_local[target]==0]['PROBABILITY_OF_1'].rename(\"Target = 0\").plot(kind='density', bw_method=0.1, grid=True, ax=ax3)\r","    ax3.axvline(.5, color='grey', linestyle='--', label='Cutoff at 0.5')\r","    ax3.set_xlim([0,1])\r","    ax3.set_xlabel('Probability of 1', size=13)\r","    ax3.set_ylabel('Density', size=13)\r","    ax3.legend(loc=\"upper right\")\r","    \r","    # ROC curve Chart\r","    ax4.set_title('ROC Curve')\r","    ax4.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')\r","    ax4.plot(FPR_OPT, TPR_OPT, 'bo', color='orange', markersize=6)\r","    ax4.plot([0, 1], [0, 1], lw=2, linestyle='--', color='grey', label='Random guess')\r","    ax4.annotate('Optimal Cutoff:\\nTPR: '+str(round(TPR_OPT,2))+' FPR: '+str(round(FPR_OPT,2)),\r","                 fontsize=11, xy=(FPR_OPT, TPR_OPT), xycoords='data', xytext=(0.98, 0.54), \r","                 textcoords='data', \r","                 arrowprops=dict(facecolor='gray', shrink=0.1, width=2,\r","                                 connectionstyle='arc3, rad=0.3'), \r","                 horizontalalignment='right', verticalalignment='top')\r","    ax4.annotate('AUC ='+str(round(AUC,4)), xy=(0.5, 0.35), \r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('Precision ='+str(round(Precision,4)), xy=(0.45, 0.3), \r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('Recall ='+str(round(TPR,4)), xy=(0.4, 0.25), \r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('Accuracy ='+str(round(Accuracy,4)), xy=(0.35, 0.2),\r","                 xycoords='axes fraction', size=13)\r","    ax4.annotate('F1 Score ='+str(round(F1Score,4)), xy=(0.3, 0.15), \r","                 xycoords='axes fraction', size=13)\r","    ax4.set_xlim([-0.02, 1.02])\r","    ax4.set_ylim([0.0, 1.02])\r","    ax4.set_xlabel('False Positive Rate', size=13)\r","    ax4.set_ylabel('True Positive Rate', size=13) \r","    ax4.legend(loc=\"lower right\")\r","    \r","    return(statistics, pred_local)\r","_ = evaluate_model(pred_data=RES_DF, settings_name='Gaussian Kernel,Conv.Tolerance:0.001,Num Pivots:200', name='Support Vector Machine', target='AFFINITY_CARD')\r"],"enabled":true,"result":{"startTime":1715313232747,"interpreter":"python.medium","endTime":1715313239979,"results":[{"message":"<stdin>:22: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n<stdin>:23: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n<stdin>:24: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n<stdin>:25: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n/usr/local/lib/python3.12/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/usr/local/lib/python3.12/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/usr/local/lib/python3.12/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/usr/local/lib/python3.12/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/usr/local/lib/python3.12/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/usr/local/lib/python3.12/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","type":"TEXT"},{"message":"CONFUSION MATRIX\tPREDICTED 0\tPREDICTED 1\nACTUAL 0\tTrue Negative: 1234 (69.52%)\tFalse Positive: 176 (9.92%)\nACTUAL 1\tFalse Negative: 106 (5.97%)\tTrue Positive: 259 (14.59%)\nAccuracy: 84.1127%\tAUC: 0.8728\tF1Score: 0.6475\n<stdin>:143: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n<stdin>:160: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"bo\" (-> color='b'). The keyword argument will take precedence.\n","type":"TABLE"},{"message":"/usr/local/lib/python3.12/site-packages/matplotlib/text.py:758: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n  posy = float(self.convert_yunits(self._y))\n<div style='width:auto;height:auto'><img 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RES_DF.columns].head())"],"enabled":true,"result":{"startTime":1715313240090,"interpreter":"python.medium","endTime":1715313242090,"results":[{"message":"PREDICTION\tPROBABILITY_OF_1\tCUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY_OF_0\tPROBABILITY_OF_1\n1\t0.7914008688332306\t101031\t1\t1\t1\tMasters\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.20859913116676942\t0.7914008688332306\n1\t0.8264630225055785\t101822\t1\t1\t1\tMasters\t1\t1\t3\tExec.\t0\t1\t5\t0\t1\t0.17353697749442154\t0.8264630225055785\n1\t0.8252892161175174\t100023\t0\t1\t1\tHS-grad\t1\t1\t3\tSales\t0\t1\t8\t0\t1\t0.17471078388248262\t0.8252892161175174\n1\t0.8632375948140678\t103337\t1\t1\t1\tBach.\t1\t1\t3\tProf.\t0\t1\t4\t0\t1\t0.1367624051859322\t0.8632375948140678\n1\t0.8264630225055785\t100668\t1\t1\t1\tMasters\t1\t1\t3\tExec.\t0\t1\t5\t0\t1\t0.17353697749442154\t0.8264630225055785\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate 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RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(SVM_CLAS_MODEL USING *) PREDICTION_YRS_RES,","     PREDICTION_DETAILS(SVM_CLAS_MODEL USING *) PD","    FROM SVM_TEST_TABLE","    WHERE CUST_ID < 100015","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') ","    OUTPRED"],"enabled":true,"result":{"startTime":1715313250442,"interpreter":"sql.medium","endTime":1715313252040,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".674\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".037\" \t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".014\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".323\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".1\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".011\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".536\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".042\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"1\" weight=\".007\" \n100007\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".167\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".004\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".001\" \n100010\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".077\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".065\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".032\" \n100011\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".009\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\"-.003\" \t\"EDUCATION\" actualValue=\"9th\" weight=\"-.019\" 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\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Clustering EM.dsnb b/machine-learning/notebooks/python/OML4Py Clustering EM.dsnb
deleted file mode 100755
index 71d42d6a..00000000
--- a/machine-learning/notebooks/python/OML4Py Clustering EM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Clustering EM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713804680496,"interpreter":"md.low","endTime":1713804680570,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identifying Customer Segments using Expectation Maximization Clustering","Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the Expectation Maximization in-database algortihm. ","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1713804680717,"interpreter":"md.low","endTime":1713804680797,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1713804680886,"interpreter":"md.low","endTime":1713804680948,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/expectation-maximization.html\" target=\"_blank\">OML4Py Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1715292393044,"interpreter":"md.low","endTime":1715292393196,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/expectation-maximization.html\" target=\"_blank\">OML4Py Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python\r","\r","import pandas as pd\r","import numpy as np\r","import matplotlib.pyplot as plt\r","import oml\r","\r","pd.set_option('display.max_rows', 500)\r","pd.set_option('display.max_columns', 500)\r","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1713804681241,"interpreter":"python.low","endTime":1713804681322,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data by merging CUSTOMERS with SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","CUSTOMERS = oml.sync(query = \"\"\"SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT ","                                FROM SH.CUSTOMERS\"\"\")","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES","                              FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1713804681403,"interpreter":"python.low","endTime":1713804681503,"results":[{"message":"<stdin>:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 5 rows of CUST_DF data","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1713804681590,"interpreter":"python.low","endTime":1713804681752,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build an Expectation Maximization model using the CUST_DF proxy object"],"enabled":true,"result":{"startTime":1713804681868,"interpreter":"md.low","endTime":1713804681928,"results":[{"message":"<h3 id=\"build-an-expectation-maximization-model-using-the-cust_df-proxy-object\">Build an Expectation Maximization model using the CUST_DF proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build EM clustering model with 3 clusters with default settings","message":["%python","","try:","    oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:","    pass","","setting = {'emcs_num_iterations': 20, 'emcs_random_seed': 7}","em_mod = oml.em(n_clusters = 3, **setting).fit(CUST_DF, ","                                               model_name = \"CUST_CLUSTER_MODEL\", ","                                               case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1713804682003,"interpreter":"python.low","endTime":1713804688810,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build EM clustering model with 3 clusters with explicit settings","message":["%python","","try:","    oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:","    pass","","setting = {'EMCS_NUM_ITERATIONS': 20,","           'EMCS_RANDOM_SEED': 7,","           'EMCS_CLUSTER_COMPONENTS': 'EMCS_CLUSTER_COMP_ENABLE',","           'EMCS_CLUSTER_STATISTICS': 'EMCS_CLUS_STATS_ENABLE',","           'EMCS_CLUSTER_THRESH': 2,","           'EMCS_LINKAGE_FUNCTION': 'EMCS_LINKAGE_SINGLE',","           'EMCS_LOGLIKE_IMPROVEMENT': .001,","           'EMCS_MAX_NUM_ATTR_2D': 50,","           'EMCS_MIN_PCT_ATTR_SUPPORT': .1,","           'EMCS_MODEL_SEARCH': 'EMCS_MODEL_SEARCH_DISABLE',","           'EMCS_NUM_COMPONENTS': 20,","           'EMCS_NUM_DISTRIBUTION': 'EMCS_NUM_DISTR_SYSTEM',","           'EMCS_NUM_EQUIWIDTH_BINS': 11,","           'EMCS_NUM_PROJECTIONS': 50,","           'EMCS_REMOVE_COMPONENTS': 'EMCS_REMOVE_COMPS_ENABLE',","           'EMCS_ATTRIBUTE_FILTER': 'EMCS_ATTR_FILTER_ENABLE',","           'EMCS_CONVERGENCE_CRITERION': 'EMCS_CONV_CRIT_HELDASIDE',","           'EMCS_NUM_QUANTILE_BINS': 11,","           'EMCS_NUM_TOPN_BINS': 15,","           'ODMS_DETAILS': 'ODMS_ENABLE',","           'ODMS_SAMPLING': 'ODMS_SAMPLING_DISABLE',","           'PREP_AUTO': 'ON'}","em_mod = oml.em(n_clusters = 3, **setting).fit(CUST_DF, model_name = \"CUST_CLUSTER_MODEL\", case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1713804688886,"interpreter":"python.low","endTime":1713804694391,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display Expectation Maximization model details from model proxy object","message":["%python","","em_mod"],"enabled":true,"result":{"startTime":1713804694467,"interpreter":"python.low","endTime":1713804695302,"results":[{"message":"\nModel Name: CUST_CLUSTER_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Expectation Maximization\n\nMining Function: CLUSTERING\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_EXPECTATION_MAXIMIZATION\n1              CLUS_NUM_CLUSTERS                              3\n2          EMCS_ATTRIBUTE_FILTER        EMCS_ATTR_FILTER_ENABLE\n3        EMCS_CLUSTER_COMPONENTS       EMCS_CLUSTER_COMP_ENABLE\n4        EMCS_CLUSTER_STATISTICS         EMCS_CLUS_STATS_ENABLE\n5            EMCS_CLUSTER_THRESH                              2\n6     EMCS_CONVERGENCE_CRITERION       EMCS_CONV_CRIT_HELDASIDE\n7          EMCS_LINKAGE_FUNCTION            EMCS_LINKAGE_SINGLE\n8       EMCS_LOGLIKE_IMPROVEMENT                          0.001\n9           EMCS_MAX_NUM_ATTR_2D                             50\n10     EMCS_MIN_PCT_ATTR_SUPPORT                            0.1\n11             EMCS_MODEL_SEARCH      EMCS_MODEL_SEARCH_DISABLE\n12           EMCS_NUM_COMPONENTS                             20\n13         EMCS_NUM_DISTRIBUTION          EMCS_NUM_DISTR_SYSTEM\n14       EMCS_NUM_EQUIWIDTH_BINS                             11\n15           EMCS_NUM_ITERATIONS                             20\n16          EMCS_NUM_PROJECTIONS                             50\n17        EMCS_NUM_QUANTILE_BINS                             11\n18            EMCS_NUM_TOPN_BINS                             15\n19              EMCS_RANDOM_SEED                              7\n20        EMCS_REMOVE_COMPONENTS       EMCS_REMOVE_COMPS_ENABLE\n21                  ODMS_DETAILS                    ODMS_ENABLE\n22  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n23                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n24                     PREP_AUTO                             ON\n\nGlobal Statistics: \n       attribute name attribute value\n0           CONVERGED              NO\n1       LOGLIKELIHOOD        -13.5244\n2        NUM_CLUSTERS               3\n3      NUM_COMPONENTS              20\n4            NUM_ROWS            4500\n5         RANDOM_SEED               7\n6  REMOVED_COMPONENTS               0\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nClusters: \n\n   CLUSTER_ID  CLUSTER_NAME  RECORD_COUNT  PARENT  TREE_LEVEL  LEFT_CHILD_ID  RIGHT_CHILD_ID\n0           1             1          4500     NaN           1            2.0             3.0\n1           2             2          1164     1.0           2            NaN             NaN\n2           3             3          3336     1.0           2            4.0             5.0\n3           4             4          3155     3.0           3            NaN             NaN\n4           5             5           181     3.0           3            NaN             NaN\n\nTaxonomy: \n\n   PARENT_CLUSTER_ID  CHILD_CLUSTER_ID\n0                  1               2.0\n1                  1               3.0\n2                  2               NaN\n3                  3               4.0\n4                  3               5.0\n5                  4               NaN\n6                  5               NaN\n\nCentroids: \n\n    CLUSTER_ID       ATTRIBUTE_NAME         MEAN            MODE_VALUE      VARIANCE\n0            1        AFFINITY_CARD     0.238222                  None  1.815127e-01\n1            1    CUST_CREDIT_LIMIT  7924.222222                  None  1.591424e+07\n2            1          CUST_GENDER          NaN                     M           NaN\n3            1    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n4            1  CUST_MARITAL_STATUS          NaN               Married           NaN\n5            1   CUST_YEAR_OF_BIRTH  1964.624444                  None  1.871268e+02\n6            1            EDUCATION          NaN               HS-grad           NaN\n7            1       HOUSEHOLD_SIZE          NaN                     3           NaN\n8            1           OCCUPATION          NaN                Crafts           NaN\n9            1        YRS_RESIDENCE     4.022000                  None  3.617431e+00\n10           1          Y_BOX_GAMES     0.312444                  None  2.148707e-01\n11           2        AFFINITY_CARD     0.016323                  None  1.607039e-02\n12           2    CUST_CREDIT_LIMIT  8265.034364                  None  1.567110e+07\n13           2          CUST_GENDER          NaN                     M           NaN\n14           2    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n15           2  CUST_MARITAL_STATUS          NaN                NeverM           NaN\n16           2   CUST_YEAR_OF_BIRTH  1980.033505                  None  1.136517e+01\n17           2            EDUCATION          NaN               < Bach.           NaN\n18           2       HOUSEHOLD_SIZE          NaN                     1           NaN\n19           2           OCCUPATION          NaN                 Other           NaN\n20           2        YRS_RESIDENCE     2.134880                  None  8.579741e-01\n21           2          Y_BOX_GAMES     1.000000                  None  0.000000e+00\n22           3        AFFINITY_CARD     0.315647                  None  2.160789e-01\n23           3    CUST_CREDIT_LIMIT  7805.305755                  None  1.594911e+07\n24           3          CUST_GENDER          NaN                     M           NaN\n25           3    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n26           3  CUST_MARITAL_STATUS          NaN               Married           NaN\n27           3   CUST_YEAR_OF_BIRTH  1959.247902                  None  1.366873e+02\n28           3            EDUCATION          NaN               HS-grad           NaN\n29           3       HOUSEHOLD_SIZE          NaN                     3           NaN\n30           3           OCCUPATION          NaN                 Exec.           NaN\n31           3        YRS_RESIDENCE     4.680456                  None  2.904158e+00\n32           3          Y_BOX_GAMES     0.072542                  None  6.729980e-02\n33           4        AFFINITY_CARD     0.301743                  None  2.107611e-01\n34           4    CUST_CREDIT_LIMIT  7799.683043                  None  1.589185e+07\n35           4          CUST_GENDER          NaN                     M           NaN\n36           4    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n37           4  CUST_MARITAL_STATUS          NaN               Married           NaN\n38           4   CUST_YEAR_OF_BIRTH  1959.118225                  None  1.406021e+02\n39           4            EDUCATION          NaN               HS-grad           NaN\n40           4       HOUSEHOLD_SIZE          NaN                     3           NaN\n41           4           OCCUPATION          NaN                Crafts           NaN\n42           4        YRS_RESIDENCE     4.721078                  None  2.919641e+00\n43           4          Y_BOX_GAMES     0.076704                  None  7.084265e-02\n44           5        AFFINITY_CARD     0.558011                  None  2.480049e-01\n45           5    CUST_CREDIT_LIMIT  7903.314917                  None  1.703088e+07\n46           5          CUST_GENDER          NaN                     F           NaN\n47           5    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n48           5  CUST_MARITAL_STATUS          NaN               Married           NaN\n49           5   CUST_YEAR_OF_BIRTH  1961.508287                  None  6.341799e+01\n50           5            EDUCATION          NaN               HS-grad           NaN\n51           5       HOUSEHOLD_SIZE          NaN                   4-5           NaN\n52           5           OCCUPATION          NaN               Cleric.           NaN\n53           5        YRS_RESIDENCE     3.972376                  None  2.115899e+00\n54           5          Y_BOX_GAMES     0.000000                  None  0.000000e+00\n\nLeaf Cluster Counts: \n\n   CLUSTER_ID   CNT\n0           2  1164\n1           4  3155\n2           5   181\n\nAttribute Importance: \n\n         ATTRIBUTE_NAME  ATTRIBUTE_IMPORTANCE_VALUE  ATTRIBUTE_RANK\n0         AFFINITY_CARD                    0.125510               7\n1     CUST_CREDIT_LIMIT                    0.083163              10\n2           CUST_GENDER                    0.187811               6\n3     CUST_INCOME_LEVEL                    0.087468               8\n4   CUST_MARITAL_STATUS                    0.232739               4\n5    CUST_YEAR_OF_BIRTH                    0.368715               2\n6             EDUCATION                    0.057243              11\n7        HOUSEHOLD_SIZE                    0.263308               3\n8            OCCUPATION                    0.085454               9\n9         YRS_RESIDENCE                    0.232491               5\n10          Y_BOX_GAMES                    0.411961               1\n\nComponents: \n\n    COMPONENT_ID  CLUSTER_ID  PRIOR_PROBABILITY\n0              1           2           0.083151\n1              2           4           0.041165\n2              3           4           0.025857\n3              4           2           0.055326\n4              5           2           0.032430\n5              6           4           0.037727\n6              7           4           0.045825\n7              8           2           0.044490\n8              9           4           0.081876\n9             10           4           0.072249\n10            11           4           0.040540\n11            12           4           0.052175\n12            13           4           0.051327\n13            14           5           0.041011\n14            15           4           0.055620\n15            16           4           0.051606\n16            17           4           0.042597\n17            18           4           0.052004\n18            19           4           0.052609\n19            20           2           0.040414\n\nCluster Hists: \n\n     cluster.id                                variable  bin.id  lower.bound  upper.bound          label  count\n0             1                           AFFINITY_CARD       1          0.0          0.0            0:0   3428\n1             1                           AFFINITY_CARD       2          0.0          1.0            0:1   1072\n2             1                           AFFINITY_CARD       3          NaN          NaN              :      0\n3             1                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    580\n4             1                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    415\n5             1                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000    405\n6             1                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    556\n7             1                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    946\n8             1                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000    373\n9             1                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    748\n10            1                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    477\n11            1                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n12            1                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n13            1                           CUST_GENDER:F       3          NaN          NaN              :   1510\n14            1                           CUST_GENDER:M       2          NaN          NaN              :   2990\n15            1               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n16            1       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     88\n17            1    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :    222\n18            1    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :    171\n19            1    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :    133\n20            1   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :    310\n21            1  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :    369\n22            1  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :    339\n23            1  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :    383\n24            1  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :    484\n25            1  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    965\n26            1  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    393\n27            1  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    643\n28            1             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n29            1             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :    615\n30            1             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :     75\n31            1              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      3\n32            1             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :   2034\n33            1              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :   1503\n34            1              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :    134\n35            1             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :    136\n36            1                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      5\n37            1                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6     40\n38            1                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9     73\n39            1                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2    192\n40            1                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5    337\n41            1                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8    533\n42            1                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1    878\n43            1                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4    853\n44            1                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    792\n45            1                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986    797\n46            1                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n47            1                       EDUCATION:'Other'       1          NaN          NaN              :     20\n48            1                          EDUCATION:10th       8          NaN          NaN              :    122\n49            1                          EDUCATION:11th       9          NaN          NaN              :    121\n50            1                          EDUCATION:12th      13          NaN          NaN              :     52\n51            1                       EDUCATION:5th-6th      15          NaN          NaN              :     39\n52            1                       EDUCATION:7th-8th      11          NaN          NaN              :     87\n53            1                           EDUCATION:9th      12          NaN          NaN              :     76\n54            1                       EDUCATION:< Bach.       3          NaN          NaN              :   1041\n55            1                       EDUCATION:Assoc-A       7          NaN          NaN              :    172\n56            1                       EDUCATION:Assoc-V       5          NaN          NaN              :    196\n57            1                         EDUCATION:Bach.       4          NaN          NaN              :    779\n58            1                       EDUCATION:HS-grad       2          NaN          NaN              :   1462\n59            1                       EDUCATION:Masters       6          NaN          NaN              :    190\n60            1                           EDUCATION:PhD      14          NaN          NaN              :     49\n61            1                        EDUCATION:Profsc      10          NaN          NaN              :     94\n62            1                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n63            1                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    692\n64            1                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :   1149\n65            1                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :   1787\n66            1                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :    219\n67            1                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :    148\n68            1                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :    505\n69            1                      OCCUPATION:'Other'       1          NaN          NaN              :      2\n70            1                            OCCUPATION:?       9          NaN          NaN              :    236\n71            1                      OCCUPATION:Cleric.       6          NaN          NaN              :    520\n72            1                       OCCUPATION:Crafts       2          NaN          NaN              :    572\n73            1                        OCCUPATION:Exec.       4          NaN          NaN              :    559\n74            1                      OCCUPATION:Farming      13          NaN          NaN              :    122\n75            1                      OCCUPATION:Handler      12          NaN          NaN              :    168\n76            1                      OCCUPATION:House-s      15          NaN          NaN              :     19\n77            1                      OCCUPATION:Machine       8          NaN          NaN              :    276\n78            1                        OCCUPATION:Other       7          NaN          NaN              :    468\n79            1                        OCCUPATION:Prof.       5          NaN          NaN              :    545\n80            1                      OCCUPATION:Protec.      14          NaN          NaN              :     89\n81            1                        OCCUPATION:Sales       3          NaN          NaN              :    560\n82            1                      OCCUPATION:TechSup      11          NaN          NaN              :    177\n83            1                      OCCUPATION:Transp.      10          NaN          NaN              :    187\n84            1                           YRS_RESIDENCE       1          0.0          0.0            0:0     48\n85            1                           YRS_RESIDENCE       2          0.0          1.0            0:1    276\n86            1                           YRS_RESIDENCE       3          1.0          2.0            1:2    625\n87            1                           YRS_RESIDENCE       4          2.0          3.0            2:3    961\n88            1                           YRS_RESIDENCE       5          3.0          4.0            3:4    937\n89            1                           YRS_RESIDENCE       6          4.0          5.0            4:5    757\n90            1                           YRS_RESIDENCE       7          5.0          6.0            5:6    473\n91            1                           YRS_RESIDENCE       8          6.0          7.0            6:7    231\n92            1                           YRS_RESIDENCE       9          7.0          8.0            7:8    101\n93            1                           YRS_RESIDENCE      10          8.0         14.0           8:14     91\n94            1                           YRS_RESIDENCE      11          NaN          NaN              :      0\n95            1                             Y_BOX_GAMES       1          0.0          0.0            0:0   3094\n96            1                             Y_BOX_GAMES       2          0.0          1.0            0:1   1406\n97            1                             Y_BOX_GAMES       3          NaN          NaN              :      0\n98            2                           AFFINITY_CARD       1          0.0          0.0            0:0   1145\n99            2                           AFFINITY_CARD       2          0.0          1.0            0:1     19\n100           2                           AFFINITY_CARD       3          NaN          NaN              :      0\n101           2                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    129\n102           2                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    100\n103           2                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000     93\n104           2                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    139\n105           2                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    257\n106           2                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000     82\n107           2                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    226\n108           2                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    138\n109           2                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n110           2                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n111           2                           CUST_GENDER:F       3          NaN          NaN              :    553\n112           2                           CUST_GENDER:M       2          NaN          NaN              :    611\n113           2               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n114           2       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     16\n115           2    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :     58\n116           2    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :     43\n117           2    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :     25\n118           2   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :     52\n119           2  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :     95\n120           2  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :     95\n121           2  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :     75\n122           2  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :     96\n123           2  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    266\n124           2  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    126\n125           2  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    217\n126           2             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n127           2             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :     61\n128           2             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :      9\n129           2              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      0\n130           2             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :     25\n131           2              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :   1041\n132           2              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :     28\n133           2             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :      0\n134           2                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      0\n135           2                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6      0\n136           2                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9      0\n137           2                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2      0\n138           2                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5      0\n139           2                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8      0\n140           2                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1      0\n141           2                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4      0\n142           2                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    404\n143           2                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986    760\n144           2                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n145           2                       EDUCATION:'Other'       1          NaN          NaN              :      3\n146           2                          EDUCATION:10th       8          NaN          NaN              :     60\n147           2                          EDUCATION:11th       9          NaN          NaN              :     54\n148           2                          EDUCATION:12th      13          NaN          NaN              :     26\n149           2                       EDUCATION:5th-6th      15          NaN          NaN              :     10\n150           2                       EDUCATION:7th-8th      11          NaN          NaN              :      7\n151           2                           EDUCATION:9th      12          NaN          NaN              :      8\n152           2                       EDUCATION:< Bach.       3          NaN          NaN              :    380\n153           2                       EDUCATION:Assoc-A       7          NaN          NaN              :     37\n154           2                       EDUCATION:Assoc-V       5          NaN          NaN              :     42\n155           2                         EDUCATION:Bach.       4          NaN          NaN              :    162\n156           2                       EDUCATION:HS-grad       2          NaN          NaN              :    352\n157           2                       EDUCATION:Masters       6          NaN          NaN              :      6\n158           2                           EDUCATION:PhD      14          NaN          NaN              :      3\n159           2                        EDUCATION:Profsc      10          NaN          NaN              :     14\n160           2                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n161           2                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    580\n162           2                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :    384\n163           2                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :      0\n164           2                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :     13\n165           2                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :     88\n166           2                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :     99\n167           2                      OCCUPATION:'Other'       1          NaN          NaN              :      1\n168           2                            OCCUPATION:?       9          NaN          NaN              :    112\n169           2                      OCCUPATION:Cleric.       6          NaN          NaN              :    186\n170           2                       OCCUPATION:Crafts       2          NaN          NaN              :     88\n171           2                        OCCUPATION:Exec.       4          NaN          NaN              :     58\n172           2                      OCCUPATION:Farming      13          NaN          NaN              :     27\n173           2                      OCCUPATION:Handler      12          NaN          NaN              :     69\n174           2                      OCCUPATION:House-s      15          NaN          NaN              :      3\n175           2                      OCCUPATION:Machine       8          NaN          NaN              :     55\n176           2                        OCCUPATION:Other       7          NaN          NaN              :    218\n177           2                        OCCUPATION:Prof.       5          NaN          NaN              :     94\n178           2                      OCCUPATION:Protec.      14          NaN          NaN              :     12\n179           2                        OCCUPATION:Sales       3          NaN          NaN              :    165\n180           2                      OCCUPATION:TechSup      11          NaN          NaN              :     54\n181           2                      OCCUPATION:Transp.      10          NaN          NaN              :     22\n182           2                           YRS_RESIDENCE       1          0.0          0.0            0:0     33\n183           2                           YRS_RESIDENCE       2          0.0          1.0            0:1    250\n184           2                           YRS_RESIDENCE       3          1.0          2.0            1:2    477\n185           2                           YRS_RESIDENCE       4          2.0          3.0            2:3    347\n186           2                           YRS_RESIDENCE       5          3.0          4.0            3:4     46\n187           2                           YRS_RESIDENCE       6          4.0          5.0            4:5     10\n188           2                           YRS_RESIDENCE       7          5.0          6.0            5:6      1\n189           2                           YRS_RESIDENCE       8          6.0          7.0            6:7      0\n190           2                           YRS_RESIDENCE       9          7.0          8.0            7:8      0\n191           2                           YRS_RESIDENCE      10          8.0         14.0           8:14      0\n192           2                           YRS_RESIDENCE      11          NaN          NaN              :      0\n193           2                             Y_BOX_GAMES       1          0.0          0.0            0:0      0\n194           2                             Y_BOX_GAMES       2          0.0          1.0            0:1   1164\n195           2                             Y_BOX_GAMES       3          NaN          NaN              :      0\n196           3                           AFFINITY_CARD       1          0.0          0.0            0:0   2283\n197           3                           AFFINITY_CARD       2          0.0          1.0            0:1   1053\n198           3                           AFFINITY_CARD       3          NaN          NaN              :      0\n199           3                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    451\n200           3                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    315\n201           3                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000    312\n202           3                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    417\n203           3                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    689\n204           3                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000    291\n205           3                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    522\n206           3                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    339\n207           3                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n208           3                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n209           3                           CUST_GENDER:F       3          NaN          NaN              :    957\n210           3                           CUST_GENDER:M       2          NaN          NaN              :   2379\n211           3               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n212           3       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     72\n213           3    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :    164\n214           3    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :    128\n215           3    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :    108\n216           3   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :    258\n217           3  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :    274\n218           3  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :    244\n219           3  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :    308\n220           3  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :    388\n221           3  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    699\n222           3  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    267\n223           3  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    426\n224           3             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n225           3             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :    554\n226           3             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :     66\n227           3              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      3\n228           3             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :   2009\n229           3              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :    462\n230           3              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :    106\n231           3             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :    136\n232           3                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      5\n233           3                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6     40\n234           3                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9     73\n235           3                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2    192\n236           3                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5    337\n237           3                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8    533\n238           3                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1    878\n239           3                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4    853\n240           3                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    388\n241           3                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986     37\n242           3                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n243           3                       EDUCATION:'Other'       1          NaN          NaN              :     17\n244           3                          EDUCATION:10th       8          NaN          NaN              :     62\n245           3                          EDUCATION:11th       9          NaN          NaN              :     67\n246           3                          EDUCATION:12th      13          NaN          NaN              :     26\n247           3                       EDUCATION:5th-6th      15          NaN          NaN              :     29\n248           3                       EDUCATION:7th-8th      11          NaN          NaN              :     80\n249           3                           EDUCATION:9th      12          NaN          NaN              :     68\n250           3                       EDUCATION:< Bach.       3          NaN          NaN              :    661\n251           3                       EDUCATION:Assoc-A       7          NaN          NaN              :    135\n252           3                       EDUCATION:Assoc-V       5          NaN          NaN              :    154\n253           3                         EDUCATION:Bach.       4          NaN          NaN              :    617\n254           3                       EDUCATION:HS-grad       2          NaN          NaN              :   1110\n255           3                       EDUCATION:Masters       6          NaN          NaN              :    184\n256           3                           EDUCATION:PhD      14          NaN          NaN              :     46\n257           3                        EDUCATION:Profsc      10          NaN          NaN              :     80\n258           3                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n259           3                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    112\n260           3                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :    765\n261           3                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :   1787\n262           3                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :    206\n263           3                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :     60\n264           3                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :    406\n265           3                      OCCUPATION:'Other'       1          NaN          NaN              :      1\n266           3                            OCCUPATION:?       9          NaN          NaN              :    124\n267           3                      OCCUPATION:Cleric.       6          NaN          NaN              :    334\n268           3                       OCCUPATION:Crafts       2          NaN          NaN              :    484\n269           3                        OCCUPATION:Exec.       4          NaN          NaN              :    501\n270           3                      OCCUPATION:Farming      13          NaN          NaN              :     95\n271           3                      OCCUPATION:Handler      12          NaN          NaN              :     99\n272           3                      OCCUPATION:House-s      15          NaN          NaN              :     16\n273           3                      OCCUPATION:Machine       8          NaN          NaN              :    221\n274           3                        OCCUPATION:Other       7          NaN          NaN              :    250\n275           3                        OCCUPATION:Prof.       5          NaN          NaN              :    451\n276           3                      OCCUPATION:Protec.      14          NaN          NaN              :     77\n277           3                        OCCUPATION:Sales       3          NaN          NaN              :    395\n278           3                      OCCUPATION:TechSup      11          NaN          NaN              :    123\n279           3                      OCCUPATION:Transp.      10          NaN          NaN              :    165\n280           3                           YRS_RESIDENCE       1          0.0          0.0            0:0     15\n281           3                           YRS_RESIDENCE       2          0.0          1.0            0:1     26\n282           3                           YRS_RESIDENCE       3          1.0          2.0            1:2    148\n283           3                           YRS_RESIDENCE       4          2.0          3.0            2:3    614\n284           3                           YRS_RESIDENCE       5          3.0          4.0            3:4    891\n285           3                           YRS_RESIDENCE       6          4.0          5.0            4:5    747\n286           3                           YRS_RESIDENCE       7          5.0          6.0            5:6    472\n287           3                           YRS_RESIDENCE       8          6.0          7.0            6:7    231\n288           3                           YRS_RESIDENCE       9          7.0          8.0            7:8    101\n289           3                           YRS_RESIDENCE      10          8.0         14.0           8:14     91\n290           3                           YRS_RESIDENCE      11          NaN          NaN              :      0\n291           3                             Y_BOX_GAMES       1          0.0          0.0            0:0   3094\n292           3                             Y_BOX_GAMES       2          0.0          1.0            0:1    242\n293           3                             Y_BOX_GAMES       3          NaN          NaN              :      0\n294           4                           AFFINITY_CARD       1          0.0          0.0            0:0   2203\n295           4                           AFFINITY_CARD       2          0.0          1.0            0:1    952\n296           4                           AFFINITY_CARD       3          NaN          NaN              :      0\n297           4                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500    426\n298           4                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000    300\n299           4                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000    294\n300           4                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000    391\n301           4                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000    654\n302           4                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000    278\n303           4                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000    496\n304           4                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000    316\n305           4                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n306           4                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n307           4                           CUST_GENDER:F       3          NaN          NaN              :    776\n308           4                           CUST_GENDER:M       2          NaN          NaN              :   2379\n309           4               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n310           4       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :     70\n311           4    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :    159\n312           4    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :    124\n313           4    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :    104\n314           4   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :    233\n315           4  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :    260\n316           4  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :    236\n317           4  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :    293\n318           4  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :    361\n319           4  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :    658\n320           4  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :    250\n321           4  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :    407\n322           4             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n323           4             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :    554\n324           4             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :     66\n325           4              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      1\n326           4             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :   1830\n327           4              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :    462\n328           4              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :    106\n329           4             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :    136\n330           4                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      5\n331           4                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6     40\n332           4                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9     73\n333           4                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2    188\n334           4                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5    325\n335           4                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8    507\n336           4                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1    821\n337           4                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4    778\n338           4                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7    381\n339           4                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986     37\n340           4                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n341           4                       EDUCATION:'Other'       1          NaN          NaN              :     16\n342           4                          EDUCATION:10th       8          NaN          NaN              :     59\n343           4                          EDUCATION:11th       9          NaN          NaN              :     63\n344           4                          EDUCATION:12th      13          NaN          NaN              :     25\n345           4                       EDUCATION:5th-6th      15          NaN          NaN              :     29\n346           4                       EDUCATION:7th-8th      11          NaN          NaN              :     78\n347           4                           EDUCATION:9th      12          NaN          NaN              :     63\n348           4                       EDUCATION:< Bach.       3          NaN          NaN              :    628\n349           4                       EDUCATION:Assoc-A       7          NaN          NaN              :    127\n350           4                       EDUCATION:Assoc-V       5          NaN          NaN              :    144\n351           4                         EDUCATION:Bach.       4          NaN          NaN              :    581\n352           4                       EDUCATION:HS-grad       2          NaN          NaN              :   1051\n353           4                       EDUCATION:Masters       6          NaN          NaN              :    171\n354           4                           EDUCATION:PhD      14          NaN          NaN              :     44\n355           4                        EDUCATION:Profsc      10          NaN          NaN              :     76\n356           4                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n357           4                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :    110\n358           4                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :    765\n359           4                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :   1787\n360           4                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :     27\n361           4                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :     60\n362           4                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :    406\n363           4                      OCCUPATION:'Other'       1          NaN          NaN              :      1\n364           4                            OCCUPATION:?       9          NaN          NaN              :    111\n365           4                      OCCUPATION:Cleric.       6          NaN          NaN              :    291\n366           4                       OCCUPATION:Crafts       2          NaN          NaN              :    479\n367           4                        OCCUPATION:Exec.       4          NaN          NaN              :    466\n368           4                      OCCUPATION:Farming      13          NaN          NaN              :     92\n369           4                      OCCUPATION:Handler      12          NaN          NaN              :     96\n370           4                      OCCUPATION:House-s      15          NaN          NaN              :     16\n371           4                      OCCUPATION:Machine       8          NaN          NaN              :    211\n372           4                        OCCUPATION:Other       7          NaN          NaN              :    234\n373           4                        OCCUPATION:Prof.       5          NaN          NaN              :    421\n374           4                      OCCUPATION:Protec.      14          NaN          NaN              :     77\n375           4                        OCCUPATION:Sales       3          NaN          NaN              :    383\n376           4                      OCCUPATION:TechSup      11          NaN          NaN              :    116\n377           4                      OCCUPATION:Transp.      10          NaN          NaN              :    161\n378           4                           YRS_RESIDENCE       1          0.0          0.0            0:0     13\n379           4                           YRS_RESIDENCE       2          0.0          1.0            0:1     22\n380           4                           YRS_RESIDENCE       3          1.0          2.0            1:2    132\n381           4                           YRS_RESIDENCE       4          2.0          3.0            2:3    570\n382           4                           YRS_RESIDENCE       5          3.0          4.0            3:4    833\n383           4                           YRS_RESIDENCE       6          4.0          5.0            4:5    708\n384           4                           YRS_RESIDENCE       7          5.0          6.0            5:6    464\n385           4                           YRS_RESIDENCE       8          6.0          7.0            6:7    226\n386           4                           YRS_RESIDENCE       9          7.0          8.0            7:8     97\n387           4                           YRS_RESIDENCE      10          8.0         14.0           8:14     90\n388           4                           YRS_RESIDENCE      11          NaN          NaN              :      0\n389           4                             Y_BOX_GAMES       1          0.0          0.0            0:0   2913\n390           4                             Y_BOX_GAMES       2          0.0          1.0            0:1    242\n391           4                             Y_BOX_GAMES       3          NaN          NaN              :      0\n392           5                           AFFINITY_CARD       1          0.0          0.0            0:0     80\n393           5                           AFFINITY_CARD       2          0.0          1.0            0:1    101\n394           5                           AFFINITY_CARD       3          NaN          NaN              :      0\n395           5                       CUST_CREDIT_LIMIT       1       1500.0       1500.0      1500:1500     25\n396           5                       CUST_CREDIT_LIMIT       2       1500.0       3000.0      1500:3000     15\n397           5                       CUST_CREDIT_LIMIT       3       3000.0       5000.0      3000:5000     18\n398           5                       CUST_CREDIT_LIMIT       4       5000.0       7000.0      5000:7000     26\n399           5                       CUST_CREDIT_LIMIT       5       7000.0       9000.0      7000:9000     35\n400           5                       CUST_CREDIT_LIMIT       6       9000.0      10000.0     9000:10000     13\n401           5                       CUST_CREDIT_LIMIT       7      10000.0      11000.0    10000:11000     26\n402           5                       CUST_CREDIT_LIMIT       8      11000.0      15000.0    11000:15000     23\n403           5                       CUST_CREDIT_LIMIT       9          NaN          NaN              :      0\n404           5                     CUST_GENDER:'Other'       1          NaN          NaN              :      0\n405           5                           CUST_GENDER:F       3          NaN          NaN              :    181\n406           5                           CUST_GENDER:M       2          NaN          NaN              :      0\n407           5               CUST_INCOME_LEVEL:'Other'       1          NaN          NaN              :      0\n408           5       CUST_INCOME_LEVEL:A: Below 30,000      13          NaN          NaN              :      2\n409           5    CUST_INCOME_LEVEL:B: 30,000 - 49,999      10          NaN          NaN              :      5\n410           5    CUST_INCOME_LEVEL:C: 50,000 - 69,999      11          NaN          NaN              :      4\n411           5    CUST_INCOME_LEVEL:D: 70,000 - 89,999      12          NaN          NaN              :      4\n412           5   CUST_INCOME_LEVEL:E: 90,000 - 109,999       9          NaN          NaN              :     25\n413           5  CUST_INCOME_LEVEL:F: 110,000 - 129,999       7          NaN          NaN              :     14\n414           5  CUST_INCOME_LEVEL:G: 130,000 - 149,999       8          NaN          NaN              :      8\n415           5  CUST_INCOME_LEVEL:H: 150,000 - 169,999       6          NaN          NaN              :     15\n416           5  CUST_INCOME_LEVEL:I: 170,000 - 189,999       4          NaN          NaN              :     27\n417           5  CUST_INCOME_LEVEL:J: 190,000 - 249,999       2          NaN          NaN              :     41\n418           5  CUST_INCOME_LEVEL:K: 250,000 - 299,999       5          NaN          NaN              :     17\n419           5  CUST_INCOME_LEVEL:L: 300,000 and above       3          NaN          NaN              :     19\n420           5             CUST_MARITAL_STATUS:'Other'       1          NaN          NaN              :      0\n421           5             CUST_MARITAL_STATUS:Divorc.       4          NaN          NaN              :      0\n422           5             CUST_MARITAL_STATUS:Mabsent       7          NaN          NaN              :      0\n423           5              CUST_MARITAL_STATUS:Mar-AF       8          NaN          NaN              :      2\n424           5             CUST_MARITAL_STATUS:Married       2          NaN          NaN              :    179\n425           5              CUST_MARITAL_STATUS:NeverM       3          NaN          NaN              :      0\n426           5              CUST_MARITAL_STATUS:Separ.       6          NaN          NaN              :      0\n427           5             CUST_MARITAL_STATUS:Widowed       5          NaN          NaN              :      0\n428           5                      CUST_YEAR_OF_BIRTH       1       1913.0       1920.3    1913:1920.3      0\n429           5                      CUST_YEAR_OF_BIRTH       2       1920.3       1927.6  1920.3:1927.6      0\n430           5                      CUST_YEAR_OF_BIRTH       3       1927.6       1934.9  1927.6:1934.9      0\n431           5                      CUST_YEAR_OF_BIRTH       4       1934.9       1942.2  1934.9:1942.2      4\n432           5                      CUST_YEAR_OF_BIRTH       5       1942.2       1949.5  1942.2:1949.5     12\n433           5                      CUST_YEAR_OF_BIRTH       6       1949.5       1956.8  1949.5:1956.8     26\n434           5                      CUST_YEAR_OF_BIRTH       7       1956.8       1964.1  1956.8:1964.1     57\n435           5                      CUST_YEAR_OF_BIRTH       8       1964.1       1971.4  1964.1:1971.4     75\n436           5                      CUST_YEAR_OF_BIRTH       9       1971.4       1978.7  1971.4:1978.7      7\n437           5                      CUST_YEAR_OF_BIRTH      10       1978.7       1986.0    1978.7:1986      0\n438           5                      CUST_YEAR_OF_BIRTH      11          NaN          NaN              :      0\n439           5                       EDUCATION:'Other'       1          NaN          NaN              :      1\n440           5                          EDUCATION:10th       8          NaN          NaN              :      3\n441           5                          EDUCATION:11th       9          NaN          NaN              :      4\n442           5                          EDUCATION:12th      13          NaN          NaN              :      1\n443           5                       EDUCATION:5th-6th      15          NaN          NaN              :      0\n444           5                       EDUCATION:7th-8th      11          NaN          NaN              :      2\n445           5                           EDUCATION:9th      12          NaN          NaN              :      5\n446           5                       EDUCATION:< Bach.       3          NaN          NaN              :     33\n447           5                       EDUCATION:Assoc-A       7          NaN          NaN              :      8\n448           5                       EDUCATION:Assoc-V       5          NaN          NaN              :     10\n449           5                         EDUCATION:Bach.       4          NaN          NaN              :     36\n450           5                       EDUCATION:HS-grad       2          NaN          NaN              :     59\n451           5                       EDUCATION:Masters       6          NaN          NaN              :     13\n452           5                           EDUCATION:PhD      14          NaN          NaN              :      2\n453           5                        EDUCATION:Profsc      10          NaN          NaN              :      4\n454           5                  HOUSEHOLD_SIZE:'Other'       1          NaN          NaN              :      0\n455           5                        HOUSEHOLD_SIZE:1       4          NaN          NaN              :      2\n456           5                        HOUSEHOLD_SIZE:2       3          NaN          NaN              :      0\n457           5                        HOUSEHOLD_SIZE:3       2          NaN          NaN              :      0\n458           5                      HOUSEHOLD_SIZE:4-5       6          NaN          NaN              :    179\n459           5                      HOUSEHOLD_SIZE:6-8       7          NaN          NaN              :      0\n460           5                       HOUSEHOLD_SIZE:9+       5          NaN          NaN              :      0\n461           5                      OCCUPATION:'Other'       1          NaN          NaN              :      0\n462           5                            OCCUPATION:?       9          NaN          NaN              :     13\n463           5                      OCCUPATION:Cleric.       6          NaN          NaN              :     43\n464           5                       OCCUPATION:Crafts       2          NaN          NaN              :      5\n465           5                        OCCUPATION:Exec.       4          NaN          NaN              :     35\n466           5                      OCCUPATION:Farming      13          NaN          NaN              :      3\n467           5                      OCCUPATION:Handler      12          NaN          NaN              :      3\n468           5                      OCCUPATION:House-s      15          NaN          NaN              :      0\n469           5                      OCCUPATION:Machine       8          NaN          NaN              :     10\n470           5                        OCCUPATION:Other       7          NaN          NaN              :     16\n471           5                        OCCUPATION:Prof.       5          NaN          NaN              :     30\n472           5                      OCCUPATION:Protec.      14          NaN          NaN              :      0\n473           5                        OCCUPATION:Sales       3          NaN          NaN              :     12\n474           5                      OCCUPATION:TechSup      11          NaN          NaN              :      7\n475           5                      OCCUPATION:Transp.      10          NaN          NaN              :      4\n476           5                           YRS_RESIDENCE       1          0.0          0.0            0:0      2\n477           5                           YRS_RESIDENCE       2          0.0          1.0            0:1      4\n478           5                           YRS_RESIDENCE       3          1.0          2.0            1:2     16\n479           5                           YRS_RESIDENCE       4          2.0          3.0            2:3     44\n480           5                           YRS_RESIDENCE       5          3.0          4.0            3:4     58\n481           5                           YRS_RESIDENCE       6          4.0          5.0            4:5     39\n482           5                           YRS_RESIDENCE       7          5.0          6.0            5:6      8\n483           5                           YRS_RESIDENCE       8          6.0          7.0            6:7      5\n484           5                           YRS_RESIDENCE       9          7.0          8.0            7:8      4\n485           5                           YRS_RESIDENCE      10          8.0         14.0           8:14      1\n486           5                           YRS_RESIDENCE      11          NaN          NaN              :      0\n487           5                             Y_BOX_GAMES       1          0.0          0.0            0:0    181\n488           5                             Y_BOX_GAMES       2          0.0          1.0            0:1      0\n489           5                             Y_BOX_GAMES       3          NaN          NaN              :      0\n\nRules: \n\n     cluster.id  rhs.support  rhs.conf  lhr.support  lhs.conf              lhs.var  lhs.var.support  lhs.var.conf                                  predicate\n0             1         4500  1.000000         3428  0.761778        AFFINITY_CARD             3428      0.500000                         AFFINITY_CARD <= 0\n1             1         4500  1.000000         3428  0.761778        AFFINITY_CARD             3428      0.500000                         AFFINITY_CARD >= 0\n2             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN < Bach.\n3             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN Assoc-A\n4             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN Assoc-V\n5             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                         EDUCATION IN Bach.\n6             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN HS-grad\n7             1         4500  1.000000         3840  0.761778            EDUCATION             3428      0.600000                       EDUCATION IN Masters\n8             1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n9             1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n10            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n11            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n12            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n13            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n14            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n15            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n16            1         4500  1.000000         4108  0.761778    CUST_INCOME_LEVEL             3428      0.250000  CUST_INCOME_LEVEL IN L: 300,000 and above\n17            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                        HOUSEHOLD_SIZE IN 1\n18            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                        HOUSEHOLD_SIZE IN 2\n19            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                        HOUSEHOLD_SIZE IN 3\n20            1         4500  1.000000         4133  0.761778       HOUSEHOLD_SIZE             3428      0.333333                       HOUSEHOLD_SIZE IN 9+\n21            1         4500  1.000000         4152  0.761778  CUST_MARITAL_STATUS             3428      0.571429             CUST_MARITAL_STATUS IN Divorc.\n22            1         4500  1.000000         4152  0.761778  CUST_MARITAL_STATUS             3428      0.571429             CUST_MARITAL_STATUS IN Married\n23            1         4500  1.000000         4152  0.761778  CUST_MARITAL_STATUS             3428      0.571429              CUST_MARITAL_STATUS IN NeverM\n24            1         4500  1.000000         4190  0.761778   CUST_YEAR_OF_BIRTH             3428      0.400000                 CUST_YEAR_OF_BIRTH <= 1986\n25            1         4500  1.000000         4190  0.761778   CUST_YEAR_OF_BIRTH             3428      0.400000                CUST_YEAR_OF_BIRTH > 1942.2\n26            1         4500  1.000000         4260  0.761778        YRS_RESIDENCE             3428      0.300000                         YRS_RESIDENCE <= 7\n27            1         4500  1.000000         4260  0.761778        YRS_RESIDENCE             3428      0.300000                          YRS_RESIDENCE > 0\n28            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                            OCCUPATION IN ?\n29            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Cleric.\n30            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                       OCCUPATION IN Crafts\n31            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Exec.\n32            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Handler\n33            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Machine\n34            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Other\n35            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Prof.\n36            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                        OCCUPATION IN Sales\n37            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN TechSup\n38            1         4500  1.000000         4268  0.761778           OCCUPATION             3428      0.266667                      OCCUPATION IN Transp.\n39            1         4500  1.000000         4500  0.761778    CUST_CREDIT_LIMIT             3428      0.000000                 CUST_CREDIT_LIMIT <= 15000\n40            1         4500  1.000000         4500  0.761778    CUST_CREDIT_LIMIT             3428      0.000000                  CUST_CREDIT_LIMIT >= 1500\n41            1         4500  1.000000         4500  0.761778          CUST_GENDER             3428      0.000000                           CUST_GENDER IN F\n42            1         4500  1.000000         4500  0.761778          CUST_GENDER             3428      0.000000                           CUST_GENDER IN M\n43            1         4500  1.000000         4500  0.761778          Y_BOX_GAMES             3428      0.000000                           Y_BOX_GAMES <= 1\n44            1         4500  1.000000         4500  0.761778          Y_BOX_GAMES             3428      0.000000                           Y_BOX_GAMES >= 0\n45            2         1164  0.258667         1041  0.894330  CUST_MARITAL_STATUS             1041      0.106960               CUST_MARITAL_STATUS = NeverM\n46            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                          EDUCATION IN 10th\n47            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                          EDUCATION IN 11th\n48            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                       EDUCATION IN < Bach.\n49            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                       EDUCATION IN Assoc-V\n50            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                         EDUCATION IN Bach.\n51            2         1164  0.258667         1050  0.894330            EDUCATION             1041      0.008475                       EDUCATION IN HS-grad\n52            2         1164  0.258667         1063  0.894330       HOUSEHOLD_SIZE             1041      0.111346                        HOUSEHOLD_SIZE IN 1\n53            2         1164  0.258667         1063  0.894330       HOUSEHOLD_SIZE             1041      0.111346                        HOUSEHOLD_SIZE IN 2\n54            2         1164  0.258667         1063  0.894330       HOUSEHOLD_SIZE             1041      0.111346                       HOUSEHOLD_SIZE IN 9+\n55            2         1164  0.258667         1074  0.894330        YRS_RESIDENCE             1041      0.089257                         YRS_RESIDENCE <= 3\n56            2         1164  0.258667         1074  0.894330        YRS_RESIDENCE             1041      0.089257                          YRS_RESIDENCE > 0\n57            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n58            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n59            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n60            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n61            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n62            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n63            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n64            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n65            2         1164  0.258667         1080  0.894330    CUST_INCOME_LEVEL             1041      0.002256  CUST_INCOME_LEVEL IN L: 300,000 and above\n66            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                            OCCUPATION IN ?\n67            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN Cleric.\n68            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                       OCCUPATION IN Crafts\n69            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Exec.\n70            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN Handler\n71            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN Machine\n72            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Other\n73            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Prof.\n74            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                        OCCUPATION IN Sales\n75            2         1164  0.258667         1099  0.894330           OCCUPATION             1041      0.011051                      OCCUPATION IN TechSup\n76            2         1164  0.258667         1145  0.894330        AFFINITY_CARD             1041      0.093743                         AFFINITY_CARD <= 0\n77            2         1164  0.258667         1145  0.894330        AFFINITY_CARD             1041      0.093743                         AFFINITY_CARD >= 0\n78            2         1164  0.258667         1164  0.894330    CUST_CREDIT_LIMIT             1041      0.000769                 CUST_CREDIT_LIMIT <= 15000\n79            2         1164  0.258667         1164  0.894330    CUST_CREDIT_LIMIT             1041      0.000769                  CUST_CREDIT_LIMIT >= 1500\n80            2         1164  0.258667         1164  0.894330          CUST_GENDER             1041      0.014623                           CUST_GENDER IN F\n81            2         1164  0.258667         1164  0.894330          CUST_GENDER             1041      0.014623                           CUST_GENDER IN M\n82            2         1164  0.258667         1164  0.894330   CUST_YEAR_OF_BIRTH             1041      0.135051                 CUST_YEAR_OF_BIRTH <= 1986\n83            2         1164  0.258667         1164  0.894330   CUST_YEAR_OF_BIRTH             1041      0.135051                CUST_YEAR_OF_BIRTH > 1971.4\n84            2         1164  0.258667         1164  0.894330          Y_BOX_GAMES             1041      0.480401                           Y_BOX_GAMES <= 1\n85            2         1164  0.258667         1164  0.894330          Y_BOX_GAMES             1041      0.480401                            Y_BOX_GAMES > 0\n86            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN < Bach.\n87            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN Assoc-A\n88            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN Assoc-V\n89            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                         EDUCATION IN Bach.\n90            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN HS-grad\n91            3         3336  0.741333         2861  0.857614            EDUCATION             2861      0.000893                       EDUCATION IN Masters\n92            3         3336  0.741333         2955  0.857614        YRS_RESIDENCE             2861      0.013965                         YRS_RESIDENCE <= 7\n93            3         3336  0.741333         2955  0.857614        YRS_RESIDENCE             2861      0.013965                          YRS_RESIDENCE > 2\n94            3         3336  0.741333         2958  0.857614       HOUSEHOLD_SIZE             2861      0.015623                        HOUSEHOLD_SIZE IN 2\n95            3         3336  0.741333         2958  0.857614       HOUSEHOLD_SIZE             2861      0.015623                        HOUSEHOLD_SIZE IN 3\n96            3         3336  0.741333         2958  0.857614       HOUSEHOLD_SIZE             2861      0.015623                       HOUSEHOLD_SIZE IN 9+\n97            3         3336  0.741333         3025  0.857614  CUST_MARITAL_STATUS             2861      0.014089             CUST_MARITAL_STATUS IN Divorc.\n98            3         3336  0.741333         3025  0.857614  CUST_MARITAL_STATUS             2861      0.014089             CUST_MARITAL_STATUS IN Married\n99            3         3336  0.741333         3025  0.857614  CUST_MARITAL_STATUS             2861      0.014089              CUST_MARITAL_STATUS IN NeverM\n100           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n101           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n102           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n103           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n104           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n105           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n106           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n107           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n108           3         3336  0.741333         3028  0.857614    CUST_INCOME_LEVEL             2861      0.000263  CUST_INCOME_LEVEL IN L: 300,000 and above\n109           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                            OCCUPATION IN ?\n110           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN Cleric.\n111           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                       OCCUPATION IN Crafts\n112           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Exec.\n113           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN Machine\n114           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Other\n115           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Prof.\n116           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                        OCCUPATION IN Sales\n117           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN TechSup\n118           3         3336  0.741333         3048  0.857614           OCCUPATION             2861      0.001405                      OCCUPATION IN Transp.\n119           3         3336  0.741333         3094  0.857614          Y_BOX_GAMES             2861      0.071001                           Y_BOX_GAMES <= 0\n120           3         3336  0.741333         3094  0.857614          Y_BOX_GAMES             2861      0.071001                           Y_BOX_GAMES >= 0\n121           3         3336  0.741333         3181  0.857614   CUST_YEAR_OF_BIRTH             2861      0.024159               CUST_YEAR_OF_BIRTH <= 1978.7\n122           3         3336  0.741333         3181  0.857614   CUST_YEAR_OF_BIRTH             2861      0.024159                CUST_YEAR_OF_BIRTH > 1934.9\n123           3         3336  0.741333         3336  0.857614        AFFINITY_CARD             2861      0.005412                         AFFINITY_CARD <= 1\n124           3         3336  0.741333         3336  0.857614        AFFINITY_CARD             2861      0.005412                         AFFINITY_CARD >= 0\n125           3         3336  0.741333         3336  0.857614    CUST_CREDIT_LIMIT             2861      0.000094                 CUST_CREDIT_LIMIT <= 15000\n126           3         3336  0.741333         3336  0.857614    CUST_CREDIT_LIMIT             2861      0.000094                  CUST_CREDIT_LIMIT >= 1500\n127           3         3336  0.741333         3336  0.857614          CUST_GENDER             2861      0.001996                           CUST_GENDER IN F\n128           3         3336  0.741333         3336  0.857614          CUST_GENDER             2861      0.001996                           CUST_GENDER IN M\n129           4         3155  0.701111         2379  0.754041          CUST_GENDER             2379      0.007041                            CUST_GENDER = M\n130           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN < Bach.\n131           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN Assoc-A\n132           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN Assoc-V\n133           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                         EDUCATION IN Bach.\n134           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN HS-grad\n135           4         3155  0.701111         2702  0.754041            EDUCATION             2379      0.000859                       EDUCATION IN Masters\n136           4         3155  0.701111         2801  0.754041        YRS_RESIDENCE             2379      0.015000                         YRS_RESIDENCE <= 7\n137           4         3155  0.701111         2801  0.754041        YRS_RESIDENCE             2379      0.015000                          YRS_RESIDENCE > 2\n138           4         3155  0.701111         2846  0.754041  CUST_MARITAL_STATUS             2379      0.012734             CUST_MARITAL_STATUS IN Divorc.\n139           4         3155  0.701111         2846  0.754041  CUST_MARITAL_STATUS             2379      0.012734             CUST_MARITAL_STATUS IN Married\n140           4         3155  0.701111         2846  0.754041  CUST_MARITAL_STATUS             2379      0.012734              CUST_MARITAL_STATUS IN NeverM\n141           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n142           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n143           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n144           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n145           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n146           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n147           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n148           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n149           4         3155  0.701111         2857  0.754041    CUST_INCOME_LEVEL             2379      0.000231  CUST_INCOME_LEVEL IN L: 300,000 and above\n150           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                            OCCUPATION IN ?\n151           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN Cleric.\n152           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                       OCCUPATION IN Crafts\n153           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Exec.\n154           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN Machine\n155           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Other\n156           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Prof.\n157           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                        OCCUPATION IN Sales\n158           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN TechSup\n159           4         3155  0.701111         2873  0.754041           OCCUPATION             2379      0.001716                      OCCUPATION IN Transp.\n160           4         3155  0.701111         2913  0.754041          Y_BOX_GAMES             2379      0.067727                           Y_BOX_GAMES <= 0\n161           4         3155  0.701111         2913  0.754041          Y_BOX_GAMES             2379      0.067727                           Y_BOX_GAMES >= 0\n162           4         3155  0.701111         2958  0.754041       HOUSEHOLD_SIZE             2379      0.020523                        HOUSEHOLD_SIZE IN 2\n163           4         3155  0.701111         2958  0.754041       HOUSEHOLD_SIZE             2379      0.020523                        HOUSEHOLD_SIZE IN 3\n164           4         3155  0.701111         2958  0.754041       HOUSEHOLD_SIZE             2379      0.020523                       HOUSEHOLD_SIZE IN 9+\n165           4         3155  0.701111         3000  0.754041   CUST_YEAR_OF_BIRTH             2379      0.023531               CUST_YEAR_OF_BIRTH <= 1978.7\n166           4         3155  0.701111         3000  0.754041   CUST_YEAR_OF_BIRTH             2379      0.023531                CUST_YEAR_OF_BIRTH > 1934.9\n167           4         3155  0.701111         3155  0.754041        AFFINITY_CARD             2379      0.003698                         AFFINITY_CARD <= 1\n168           4         3155  0.701111         3155  0.754041        AFFINITY_CARD             2379      0.003698                         AFFINITY_CARD >= 0\n169           4         3155  0.701111         3155  0.754041    CUST_CREDIT_LIMIT             2379      0.000101                 CUST_CREDIT_LIMIT <= 15000\n170           4         3155  0.701111         3155  0.754041    CUST_CREDIT_LIMIT             2379      0.000101                  CUST_CREDIT_LIMIT >= 1500\n171           5          181  0.040222          157  0.867403        YRS_RESIDENCE              157      0.011292                         YRS_RESIDENCE <= 5\n172           5          181  0.040222          157  0.867403        YRS_RESIDENCE              157      0.011292                          YRS_RESIDENCE > 1\n173           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN < Bach.\n174           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN Assoc-A\n175           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN Assoc-V\n176           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                         EDUCATION IN Bach.\n177           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN HS-grad\n178           5          181  0.040222          159  0.867403            EDUCATION              157      0.003645                       EDUCATION IN Masters\n179           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n180           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n181           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n182           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n183           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n184           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n185           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n186           5          181  0.040222          166  0.867403    CUST_INCOME_LEVEL              157      0.005944  CUST_INCOME_LEVEL IN L: 300,000 and above\n187           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                            OCCUPATION IN ?\n188           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                      OCCUPATION IN Cleric.\n189           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Exec.\n190           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                      OCCUPATION IN Machine\n191           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Other\n192           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Prof.\n193           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                        OCCUPATION IN Sales\n194           5          181  0.040222          166  0.867403           OCCUPATION              157      0.019188                      OCCUPATION IN TechSup\n195           5          181  0.040222          170  0.867403   CUST_YEAR_OF_BIRTH              157      0.054366               CUST_YEAR_OF_BIRTH <= 1971.4\n196           5          181  0.040222          170  0.867403   CUST_YEAR_OF_BIRTH              157      0.054366                CUST_YEAR_OF_BIRTH > 1942.2\n197           5          181  0.040222          179  0.867403  CUST_MARITAL_STATUS              157      0.125248              CUST_MARITAL_STATUS = Married\n198           5          181  0.040222          179  0.867403       HOUSEHOLD_SIZE              157      0.320732                       HOUSEHOLD_SIZE = 4-5\n199           5          181  0.040222          181  0.867403        AFFINITY_CARD              157      0.078669                         AFFINITY_CARD <= 1\n200           5          181  0.040222          181  0.867403        AFFINITY_CARD              157      0.078669                         AFFINITY_CARD >= 0\n201           5          181  0.040222          181  0.867403    CUST_CREDIT_LIMIT              157      0.000944                 CUST_CREDIT_LIMIT <= 15000\n202           5          181  0.040222          181  0.867403    CUST_CREDIT_LIMIT              157      0.000944                  CUST_CREDIT_LIMIT >= 1500\n203           5          181  0.040222          181  0.867403          CUST_GENDER              157      0.456930                            CUST_GENDER = F\n204           5          181  0.040222          181  0.867403          Y_BOX_GAMES              157      0.177207                           Y_BOX_GAMES <= 0\n205           5          181  0.040222          181  0.867403          Y_BOX_GAMES              157      0.177207                           Y_BOX_GAMES >= 0\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model settings","message":["%python","","em_mod.settings"],"enabled":true,"result":{"startTime":1713804695408,"interpreter":"python.low","endTime":1713804695470,"results":[{"message":"{'emcs_num_iterations': 20, 'emcs_random_seed': 7, 'emcs_cluster_components': 'EMCS_CLUSTER_COMP_ENABLE', 'emcs_cluster_statistics': 'EMCS_CLUS_STATS_ENABLE', 'emcs_cluster_thresh': 2, 'emcs_linkage_function': 'EMCS_LINKAGE_SINGLE', 'emcs_loglike_improvement': 0.001, 'emcs_max_num_attr_2d': 50, 'emcs_min_pct_attr_support': 0.1, 'emcs_model_search': 'EMCS_MODEL_SEARCH_DISABLE', 'emcs_num_components': 20, 'emcs_num_distribution': 'EMCS_NUM_DISTR_SYSTEM', 'emcs_num_equiwidth_bins': 11, 'emcs_num_projections': 50, 'emcs_remove_components': 'EMCS_REMOVE_COMPS_ENABLE', 'emcs_attribute_filter': 'EMCS_ATTR_FILTER_ENABLE', 'emcs_convergence_criterion': 'EMCS_CONV_CRIT_HELDASIDE', 'emcs_num_quantile_bins': 11, 'emcs_num_topn_bins': 15, 'odms_details': 'ODMS_ENABLE', 'odms_sampling': 'ODMS_SAMPLING_DISABLE', 'prep_auto': 'ON', 'clus_num_clusters': 3}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster details for all clusters in the hierarchy with row counts and dispersion","message":["%python","","z.show(em_mod.clusters)"],"enabled":true,"result":{"startTime":1713804695544,"interpreter":"python.low","endTime":1713804695640,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tRECORD_COUNT\tPARENT\tTREE_LEVEL\tLEFT_CHILD_ID\tRIGHT_CHILD_ID\n1.0\t1.0\t4500.0\tnan\t1.0\t2.0\t3.0\n2.0\t2.0\t1164.0\t1.0\t2.0\tnan\tnan\n3.0\t3.0\t3336.0\t1.0\t2.0\t4.0\t5.0\n4.0\t4.0\t3155.0\t3.0\t3.0\tnan\tnan\n5.0\t5.0\t181.0\t3.0\t3.0\tnan\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Apply model to assign rows to clusters","message":["%python","","pred = em_mod.predict(CUST_DF, supplemental_cols = CUST_DF)"],"enabled":true,"result":{"startTime":1713804695714,"interpreter":"python.low","endTime":1713804695832,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use matplotlib scatterplot to view some cluster results","message":["%python","","pred_df = pred[['CUST_ID', 'CLUSTER_ID', 'CUST_YEAR_OF_BIRTH', 'YRS_RESIDENCE', 'CUST_CREDIT_LIMIT']].pull()","","fig = plt.figure()","","plt.style.use('seaborn')","","ax=fig.add_axes([0.1,0.1,0.8,0.8])","","ids = pred_df['CLUSTER_ID']","clusters = ids.drop_duplicates().values","handles = []","labs = []","colors = ['r', 'b', 'g']","for i, c in enumerate(clusters):","    xc = pred_df[pred_df['CLUSTER_ID'] == c]['YRS_RESIDENCE'].values","    yc = pred_df[pred_df['CLUSTER_ID'] == c]['CUST_YEAR_OF_BIRTH'].values","    ","    h = ax.scatter(xc, yc, color= colors[i])","    handles.append(h)","    labs.append('CLUSTER' + str(c))","ax.legend(handles, labs)","plt.title('EM Clustering')","","plt.grid(True)","","plt.xlabel('YRS_RESIDENCE')","plt.ylabel('CUST_YEAR_OF_BIRTH')","plt.show()"],"enabled":true,"result":{"startTime":1713804695904,"interpreter":"python.low","endTime":1713804696175,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Compare clusters","","Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals. "],"enabled":true,"result":{"startTime":1713804696260,"interpreter":"md.low","endTime":1713804696344,"results":[{"message":"<h3 id=\"compare-clusters\">Compare clusters<\/h3>\n<p>Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 4","message":["%python","","feature = 'CUST_YEAR_OF_BIRTH' ","","DF = pred[pred['CLUSTER_ID'] == 4].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1713804696419,"interpreter":"python.low","endTime":1713804696742,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1913\t5\n1921\t1\n1922\t6\n1923\t4\n1924\t6\n1925\t8\n1926\t12\n1927\t3\n1928\t14\n1929\t7\n1930\t4\n1931\t7\n1932\t13\n1933\t10\n1934\t18\n1935\t18\n1936\t22\n1937\t12\n1938\t26\n1939\t23\n1940\t28\n1941\t31\n1942\t28\n1943\t41\n1944\t51\n1945\t47\n1946\t45\n1947\t41\n1948\t50\n1949\t50\n1950\t56\n1951\t49\n1952\t76\n1953\t53\n1954\t91\n1955\t91\n1956\t91\n1957\t109\n1958\t100\n1959\t110\n1960\t95\n1961\t109\n1962\t107\n1963\t94\n1964\t97\n1965\t88\n1966\t112\n1967\t114\n1968\t125\n1969\t121\n1970\t103\n1971\t115\n1972\t87\n1973\t89\n1974\t51\n1975\t61\n1976\t28\n1977\t44\n1978\t21\n1979\t19\n1980\t6\n1981\t5\n1982\t2\n1983\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 5","message":["%python","","DF = pred[pred['CLUSTER_ID'] == 5].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1713804696821,"interpreter":"python.low","endTime":1713804697143,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1939\t2\n1940\t2\n1943\t2\n1944\t1\n1945\t2\n1947\t4\n1948\t2\n1949\t1\n1950\t4\n1951\t2\n1952\t7\n1953\t3\n1954\t2\n1955\t3\n1956\t5\n1957\t10\n1958\t5\n1959\t9\n1960\t5\n1961\t6\n1962\t6\n1963\t8\n1964\t8\n1965\t15\n1966\t6\n1967\t9\n1968\t12\n1969\t14\n1970\t13\n1971\t6\n1972\t4\n1973\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check cluster assignment probabilities for top cluster assignments","message":["%python","","pred_details = em_mod.predict_proba(CUST_DF, supplemental_cols = CUST_DF['CUST_ID'], topN=3 )","","z.show(pred_details.round(8).head())"],"enabled":true,"result":{"startTime":1713804697221,"interpreter":"python.low","endTime":1713804697946,"results":[{"message":"CUST_ID\tTOP_1\tTOP_1_PROB\tTOP_2\tTOP_2_PROB\tTOP_3\tTOP_3_PROB\n100134.0\t4.0\t0.9999999\t5.0\t1e-07\t2.0\t0.0\n102828.0\t4.0\t0.99999317\t5.0\t6.83e-06\t2.0\t0.0\n101232.0\t2.0\t0.99999999\t4.0\t1e-08\t5.0\t0.0\n100696.0\t4.0\t0.99999998\t5.0\t2e-08\t2.0\t0.0\n103948.0\t4.0\t1.0\t5.0\t0.0\t2.0\t0.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Accessing the prediction details for each cluster prediction","message":["%python","","pred_test = em_mod.predict(CUST_DF, supplemental_cols = CUST_DF['CUST_ID'], topN_attrs=3 )","","z.show(pred_test.round(8).head())"],"enabled":true,"result":{"startTime":1713804698028,"interpreter":"python.low","endTime":1713804703044,"results":[{"message":"CUST_ID\tCLUSTER_ID\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\n100100\t5\tHOUSEHOLD_SIZE\t4-5\t.001\tCUST_GENDER\tF\t.001\tNone\tNone\tNone\n100200\t2\tCUST_YEAR_OF_BIRTH\t1983\t1\tNone\tNone\tNone\tNone\tNone\tNone\n100500\t2\tCUST_YEAR_OF_BIRTH\t1983\t1\tNone\tNone\tNone\tNone\tNone\tNone\n101200\t2\tCUST_YEAR_OF_BIRTH\t1984\t1\tNone\tNone\tNone\tNone\tNone\tNone\n101500\t2\tCUST_YEAR_OF_BIRTH\t1981\t1\tNone\tNone\tNone\tNone\tNone\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use SQL to access prediction details","One feature of OML is the ability to use models from SQL as well as Python. Prediction details are exposed through the Python API and are available through the SQL API, which is illustrated here.","","We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view. ","","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well. "],"enabled":true,"result":{"startTime":1713804703122,"interpreter":"md.low","endTime":1713804703184,"results":[{"message":"<h2 id=\"use-sql-to-access-prediction-details\">Use SQL to access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as Python. Prediction details are exposed through the Python API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view.<\/p>\n<p>Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%python","","try:","    oml.drop(table = 'EM_CUSTOMERS360')","except:","    pass","_ = CUST_DF.materialize(table = 'EM_CUSTOMERS360')"],"enabled":true,"result":{"startTime":1713804703261,"interpreter":"python.low","endTime":1713804704457,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 3 most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,0) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(CUST_CLUSTER_MODEL USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(CUST_CLUSTER_MODEL, NULL, 0.2 USING *) PSET","            FROM EM_CUSTOMERS360 V","            WHERE cust_id = ${CUST_ID ='100676','100676'|'101219'|'102805'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1713804704533,"interpreter":"sql.low","endTime":1713804704759,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n102805\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".053\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".034\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".02\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'102805'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'100676'\",\"argument\":null,\"options\":[{\"value\":\"'100676'\",\"displayName\":null},{\"value\":\"'101219'\",\"displayName\":null},{\"value\":\"'102805'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 3 most relevant attributes in batch for customers with their likely cluster assignments","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,0) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(CUST_CLUSTER_MODEL USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(CUST_CLUSTER_MODEL, NULL, 0.2 USING *) PSET","            FROM EM_CUSTOMERS360 V) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1713804704837,"interpreter":"sql.low","endTime":1713804705214,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100100\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".001\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".001\" \t\n104485\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".023\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".016\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".007\" \n104394\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".003\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".002\" \t\n104381\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".001\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".001\" \t\n104370\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".009\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".008\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".001\" \n104364\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".01\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".006\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".003\" \n104335\t5\t100\t\t\t\n104311\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".005\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".005\" \t\n104299\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".029\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".027\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".002\" \n104286\t5\t100\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".002\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".002\" \t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1713804705290,"interpreter":"md.low","endTime":1713804705355,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
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index 5c2fd7b0..00000000
--- a/machine-learning/notebooks/python/OML4Py Clustering KM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Clustering KM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713901552051,"interpreter":"md.low","endTime":1713901552210,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Identifying Customer Segments using K-Means Clustering","Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the K-Means in-database algortihm.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1713901552440,"interpreter":"md.low","endTime":1713901552515,"results":[{"message":"<h2 id=\"identifying-customer-segments-using-k-means-clustering\">Identifying Customer Segments using K-Means Clustering<\/h2>\n<p>Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the K-Means in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1713901552602,"interpreter":"md.low","endTime":1713901552680,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a>"],"enabled":true,"result":{"startTime":1713901552755,"interpreter":"md.low","endTime":1713901552829,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import numpy as np","import matplotlib.pyplot as plt","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1713901552904,"interpreter":"python.low","endTime":1713901554901,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data by merging CUSTOMERS with SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","CUSTOMERS = oml.sync(query = \"\"\"SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT ","                                FROM SH.CUSTOMERS\"\"\")","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES","                              FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1713901554972,"interpreter":"python.low","endTime":1713901555123,"results":[{"message":"<stdin>:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 5 rows of CUST_DF data","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1713901555197,"interpreter":"python.low","endTime":1713901555349,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a k-Means model using the CUST_DF proxy object"],"enabled":true,"result":{"startTime":1713901555422,"interpreter":"md.low","endTime":1713901555481,"results":[{"message":"<h3 id=\"build-a-k-means-model-using-the-cust_df-proxy-object\">Build a k-Means model using the CUST_DF proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build k-Means model with 3 clusters with default settings","message":["%python","","try:","    oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:","    pass","","setting = {'KMNS_ITERATIONS': 20, 'KMNS_RANDOM_SEED': 7}","km_mod = oml.km(n_clusters = 3, **setting).fit(CUST_DF, model_name = \"CUST_CLUSTER_MODEL\", case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1713901555552,"interpreter":"python.low","endTime":1713901559352,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build k-Means model with 3 clusters with explicit settings","message":["%python","","try:","    oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:","    pass","","setting = {'KMNS_ITERATIONS'          : 20, ","           'KMNS_CONV_TOLERANCE'      : .001,","           'KMNS_DISTANCE'            : 'KMNS_EUCLIDEAN',","           'KMNS_MIN_PCT_ATTR_SUPPORT': .1,","           'KMNS_NUM_BINS'            : 11,","           'KMNS_SPLIT_CRITERION'     : 'KMNS_VARIANCE',","           'KMNS_RANDOM_SEED'         : 7,","           'KMNS_DETAILS'             : 'KMNS_DETAILS_ALL',","           'ODMS_DETAILS'             : 'ODMS_ENABLE',","           'ODMS_SAMPLING'            : 'ODMS_SAMPLING_DISABLE',","           'PREP_AUTO'                : 'ON'}","","km_mod = oml.km(n_clusters = 3, **setting)","km_mod = km_mod.fit(CUST_DF, ","                model_name = \"CUST_CLUSTER_MODEL\", ","                case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1713901559421,"interpreter":"python.low","endTime":1713901562417,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display k-Means model details","message":["%python","","km_mod"],"enabled":true,"result":{"startTime":1713901562489,"interpreter":"python.low","endTime":1713901563134,"results":[{"message":"\nModel Name: CUST_CLUSTER_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: K-Means\n\nMining Function: CLUSTERING\n\nSettings: \n                    setting name            setting value\n0                      ALGO_NAME              ALGO_KMEANS\n1              CLUS_NUM_CLUSTERS                        3\n2            KMNS_CONV_TOLERANCE                    0.001\n3                   KMNS_DETAILS         KMNS_DETAILS_ALL\n4                  KMNS_DISTANCE           KMNS_EUCLIDEAN\n5                KMNS_ITERATIONS                       20\n6      KMNS_MIN_PCT_ATTR_SUPPORT                      0.1\n7                  KMNS_NUM_BINS                       11\n8               KMNS_RANDOM_SEED                        7\n9           KMNS_SPLIT_CRITERION            KMNS_VARIANCE\n10                  ODMS_DETAILS              ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT  ODMS_MISSING_VALUE_AUTO\n12                 ODMS_SAMPLING    ODMS_SAMPLING_DISABLE\n13                     PREP_AUTO                       ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1       NUM_ROWS            4500\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nClusters: \n\n   CLUSTER_ID  ROW_CNT  PARENT_CLUSTER_ID  TREE_LEVEL  DISPERSION\n0           1     4500                NaN           1    6.276772\n1           2     3123                1.0           2    6.586027\n2           3     1377                1.0           2    5.575388\n3           4     2085                2.0           3    6.938511\n4           5     1038                2.0           3    5.878003\n\nTaxonomy: \n\n   PARENT_CLUSTER_ID  CHILD_CLUSTER_ID\n0                  1               2.0\n1                  1               3.0\n2                  2               4.0\n3                  2               5.0\n4                  3               NaN\n5                  4               NaN\n6                  5               NaN\n\nCentroids: \n\n    CLUSTER_ID       ATTRIBUTE_NAME         MEAN            MODE_VALUE      VARIANCE\n0            1        AFFINITY_CARD     0.238222                  None  1.815127e-01\n1            1    CUST_CREDIT_LIMIT  7924.222222                  None  1.591424e+07\n2            1          CUST_GENDER          NaN                     M           NaN\n3            1    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n4            1  CUST_MARITAL_STATUS          NaN               Married           NaN\n5            1   CUST_YEAR_OF_BIRTH  1964.624444                  None  1.871268e+02\n6            1            EDUCATION          NaN               HS-grad           NaN\n7            1       HOUSEHOLD_SIZE          NaN                     3           NaN\n8            1           OCCUPATION          NaN                Crafts           NaN\n9            1        YRS_RESIDENCE     4.022000                  None  3.617431e+00\n10           1          Y_BOX_GAMES     0.312444                  None  2.148707e-01\n11           2        AFFINITY_CARD     0.332373                  None  2.219722e-01\n12           2    CUST_CREDIT_LIMIT  7758.245277                  None  1.591287e+07\n13           2          CUST_GENDER          NaN                     M           NaN\n14           2    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n15           2  CUST_MARITAL_STATUS          NaN               Married           NaN\n16           2   CUST_YEAR_OF_BIRTH  1958.073647                  None  1.240862e+02\n17           2            EDUCATION          NaN               HS-grad           NaN\n18           2       HOUSEHOLD_SIZE          NaN                     3           NaN\n19           2           OCCUPATION          NaN                 Exec.           NaN\n20           2        YRS_RESIDENCE     4.797951                  None  2.846735e+00\n21           2          Y_BOX_GAMES     0.009606                  None  9.516917e-03\n22           3        AFFINITY_CARD     0.024691                  None  2.409920e-02\n23           3    CUST_CREDIT_LIMIT  8300.653595                  None  1.572459e+07\n24           3          CUST_GENDER          NaN                     M           NaN\n25           3    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n26           3  CUST_MARITAL_STATUS          NaN                NeverM           NaN\n27           3   CUST_YEAR_OF_BIRTH  1979.481481                  None  1.200711e+01\n28           3            EDUCATION          NaN               < Bach.           NaN\n29           3       HOUSEHOLD_SIZE          NaN                     1           NaN\n30           3           OCCUPATION          NaN                 Other           NaN\n31           3        YRS_RESIDENCE     2.262164                  None  9.028770e-01\n32           3          Y_BOX_GAMES     0.999274                  None  7.262164e-04\n33           4        AFFINITY_CARD     0.000000                  None  0.000000e+00\n34           4    CUST_CREDIT_LIMIT  7828.057554                  None  1.567640e+07\n35           4          CUST_GENDER          NaN                     M           NaN\n36           4    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n37           4  CUST_MARITAL_STATUS          NaN               Married           NaN\n38           4   CUST_YEAR_OF_BIRTH  1958.042206                  None  1.355078e+02\n39           4            EDUCATION          NaN               HS-grad           NaN\n40           4       HOUSEHOLD_SIZE          NaN                     3           NaN\n41           4           OCCUPATION          NaN                Crafts           NaN\n42           4        YRS_RESIDENCE     4.603357                  None  2.747110e+00\n43           4          Y_BOX_GAMES     0.000000                  None  0.000000e+00\n44           5        AFFINITY_CARD     1.000000                  None  0.000000e+00\n45           5    CUST_CREDIT_LIMIT  7618.015414                  None  1.637396e+07\n46           5          CUST_GENDER          NaN                     M           NaN\n47           5    CUST_INCOME_LEVEL          NaN  J: 190,000 - 249,999           NaN\n48           5  CUST_MARITAL_STATUS          NaN               Married           NaN\n49           5   CUST_YEAR_OF_BIRTH  1958.136802                  None  1.012465e+02\n50           5            EDUCATION          NaN                 Bach.           NaN\n51           5       HOUSEHOLD_SIZE          NaN                     3           NaN\n52           5           OCCUPATION          NaN                 Exec.           NaN\n53           5        YRS_RESIDENCE     5.188825                  None  2.820627e+00\n54           5          Y_BOX_GAMES     0.028902                  None  2.809349e-02\n\nLeaf Cluster Counts: \n\n   CLUSTER_ID   CNT\n0           3  1377\n1           4  2085\n2           5  1038\n\nCluster Hists: \n\n     cluster.id       variable  bin.id  lower.bound  upper.bound  label  count\n0             1  AFFINITY_CARD       1          0.0          0.1   0:.1   3428\n1             1  AFFINITY_CARD       2          0.1          0.2  .1:.2      0\n2             1  AFFINITY_CARD       3          0.2          0.3  .2:.3      0\n3             1  AFFINITY_CARD       4          0.3          0.4  .3:.4      0\n4             1  AFFINITY_CARD       5          0.4          0.5  .4:.5      0\n..          ...            ...     ...          ...          ...    ...    ...\n535           5    Y_BOX_GAMES       6          0.5          0.6  .5:.6      0\n536           5    Y_BOX_GAMES       7          0.6          0.7  .6:.7      0\n537           5    Y_BOX_GAMES       8          0.7          0.8  .7:.8      0\n538           5    Y_BOX_GAMES       9          0.8          0.9  .8:.9      0\n539           5    Y_BOX_GAMES      10          0.9          1.0   .9:1     30\n\n[540 rows x 7 columns]\n\nRules: \n\n     cluster.id  rhs.support  rhs.conf  lhr.support  lhs.conf              lhs.var  lhs.var.support  lhs.var.conf                                  predicate\n0             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.625000                       EDUCATION IN < Bach.\n1             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.625000                       EDUCATION IN Assoc-A\n2             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.625000                       EDUCATION IN Assoc-V\n3             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.625000                         EDUCATION IN Bach.\n4             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.625000                       EDUCATION IN HS-grad\n5             1         4500  1.000000         3840  0.853333            EDUCATION             3840      0.625000                       EDUCATION IN Masters\n6             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n7             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n8             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n9             1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n10            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n11            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n12            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n13            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n14            1         4500  1.000000         4108  0.853333    CUST_INCOME_LEVEL             3840      0.250000  CUST_INCOME_LEVEL IN L: 300,000 and above\n15            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                        HOUSEHOLD_SIZE IN 1\n16            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                        HOUSEHOLD_SIZE IN 2\n17            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                        HOUSEHOLD_SIZE IN 3\n18            1         4500  1.000000         4133  0.853333       HOUSEHOLD_SIZE             3840      0.333333                       HOUSEHOLD_SIZE IN 9+\n19            1         4500  1.000000         4152  0.853333  CUST_MARITAL_STATUS             3840      0.571429             CUST_MARITAL_STATUS IN Divorc.\n20            1         4500  1.000000         4152  0.853333  CUST_MARITAL_STATUS             3840      0.571429             CUST_MARITAL_STATUS IN Married\n21            1         4500  1.000000         4152  0.853333  CUST_MARITAL_STATUS             3840      0.571429              CUST_MARITAL_STATUS IN NeverM\n22            1         4500  1.000000         4190  0.853333   CUST_YEAR_OF_BIRTH             3840      0.400000                 CUST_YEAR_OF_BIRTH <= 1986\n23            1         4500  1.000000         4190  0.853333   CUST_YEAR_OF_BIRTH             3840      0.400000                CUST_YEAR_OF_BIRTH > 1942.2\n24            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                            OCCUPATION IN ?\n25            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                      OCCUPATION IN Cleric.\n26            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                       OCCUPATION IN Crafts\n27            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                        OCCUPATION IN Exec.\n28            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                      OCCUPATION IN Handler\n29            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                      OCCUPATION IN Machine\n30            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                        OCCUPATION IN Other\n31            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                        OCCUPATION IN Prof.\n32            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                        OCCUPATION IN Sales\n33            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                      OCCUPATION IN TechSup\n34            1         4500  1.000000         4268  0.853333           OCCUPATION             3840      0.266667                      OCCUPATION IN Transp.\n35            1         4500  1.000000         4409  0.853333        YRS_RESIDENCE             3840      0.400000                       YRS_RESIDENCE <= 8.4\n36            1         4500  1.000000         4409  0.853333        YRS_RESIDENCE             3840      0.400000                         YRS_RESIDENCE >= 0\n37            1         4500  1.000000         4500  0.853333        AFFINITY_CARD             3840      0.000000                         AFFINITY_CARD <= 1\n38            1         4500  1.000000         4500  0.853333        AFFINITY_CARD             3840      0.000000                         AFFINITY_CARD >= 0\n39            1         4500  1.000000         4500  0.853333    CUST_CREDIT_LIMIT             3840      0.000000                 CUST_CREDIT_LIMIT <= 15000\n40            1         4500  1.000000         4500  0.853333    CUST_CREDIT_LIMIT             3840      0.000000                  CUST_CREDIT_LIMIT >= 1500\n41            1         4500  1.000000         4500  0.853333          CUST_GENDER             3840      0.000000                           CUST_GENDER IN F\n42            1         4500  1.000000         4500  0.853333          CUST_GENDER             3840      0.000000                           CUST_GENDER IN M\n43            1         4500  1.000000         4500  0.853333          Y_BOX_GAMES             3840      0.000000                           Y_BOX_GAMES <= 1\n44            1         4500  1.000000         4500  0.853333          Y_BOX_GAMES             3840      0.000000                           Y_BOX_GAMES >= 0\n45            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001083                       EDUCATION IN < Bach.\n46            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001083                       EDUCATION IN Assoc-A\n47            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001083                       EDUCATION IN Assoc-V\n48            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001083                         EDUCATION IN Bach.\n49            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001083                       EDUCATION IN HS-grad\n50            2         3123  0.694000         2682  0.858790            EDUCATION             2682      0.001083                       EDUCATION IN Masters\n51            2         3123  0.694000         2772  0.858790       HOUSEHOLD_SIZE             2682      0.014310                        HOUSEHOLD_SIZE IN 2\n52            2         3123  0.694000         2772  0.858790       HOUSEHOLD_SIZE             2682      0.014310                        HOUSEHOLD_SIZE IN 3\n53            2         3123  0.694000         2772  0.858790       HOUSEHOLD_SIZE             2682      0.014310                       HOUSEHOLD_SIZE IN 9+\n54            2         3123  0.694000         2812  0.858790  CUST_MARITAL_STATUS             2682      0.012524             CUST_MARITAL_STATUS IN Divorc.\n55            2         3123  0.694000         2812  0.858790  CUST_MARITAL_STATUS             2682      0.012524             CUST_MARITAL_STATUS IN Married\n56            2         3123  0.694000         2812  0.858790  CUST_MARITAL_STATUS             2682      0.012524              CUST_MARITAL_STATUS IN NeverM\n57            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n58            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n59            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n60            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n61            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n62            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n63            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n64            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n65            2         3123  0.694000         2836  0.858790    CUST_INCOME_LEVEL             2682      0.000437  CUST_INCOME_LEVEL IN L: 300,000 and above\n66            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                            OCCUPATION IN ?\n67            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                      OCCUPATION IN Cleric.\n68            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                       OCCUPATION IN Crafts\n69            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                        OCCUPATION IN Exec.\n70            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                      OCCUPATION IN Machine\n71            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                        OCCUPATION IN Other\n72            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                        OCCUPATION IN Prof.\n73            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                        OCCUPATION IN Sales\n74            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                      OCCUPATION IN TechSup\n75            2         3123  0.694000         2868  0.858790           OCCUPATION             2682      0.001381                      OCCUPATION IN Transp.\n76            2         3123  0.694000         2882  0.858790        YRS_RESIDENCE             2682      0.015238                       YRS_RESIDENCE <= 8.4\n77            2         3123  0.694000         2882  0.858790        YRS_RESIDENCE             2682      0.015238                        YRS_RESIDENCE > 2.8\n78            2         3123  0.694000         3005  0.858790   CUST_YEAR_OF_BIRTH             2682      0.037672               CUST_YEAR_OF_BIRTH <= 1978.7\n79            2         3123  0.694000         3005  0.858790   CUST_YEAR_OF_BIRTH             2682      0.037672                CUST_YEAR_OF_BIRTH > 1934.9\n80            2         3123  0.694000         3093  0.858790          Y_BOX_GAMES             2682      0.045059                          Y_BOX_GAMES <= .1\n81            2         3123  0.694000         3093  0.858790          Y_BOX_GAMES             2682      0.045059                           Y_BOX_GAMES >= 0\n82            2         3123  0.694000         3123  0.858790        AFFINITY_CARD             2682      0.002368                         AFFINITY_CARD <= 1\n83            2         3123  0.694000         3123  0.858790        AFFINITY_CARD             2682      0.002368                         AFFINITY_CARD >= 0\n84            2         3123  0.694000         3123  0.858790    CUST_CREDIT_LIMIT             2682      0.000147                 CUST_CREDIT_LIMIT <= 15000\n85            2         3123  0.694000         3123  0.858790    CUST_CREDIT_LIMIT             2682      0.000147                  CUST_CREDIT_LIMIT >= 1500\n86            2         3123  0.694000         3123  0.858790          CUST_GENDER             2682      0.001089                           CUST_GENDER IN F\n87            2         3123  0.694000         3123  0.858790          CUST_GENDER             2682      0.001089                           CUST_GENDER IN M\n88            3         1377  0.306000         1153  0.837328       HOUSEHOLD_SIZE             1153      0.041464                        HOUSEHOLD_SIZE IN 1\n89            3         1377  0.306000         1153  0.837328       HOUSEHOLD_SIZE             1153      0.041464                        HOUSEHOLD_SIZE IN 2\n90            3         1377  0.306000         1153  0.837328       HOUSEHOLD_SIZE             1153      0.041464                        HOUSEHOLD_SIZE IN 3\n91            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006582                          EDUCATION IN 10th\n92            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006582                          EDUCATION IN 11th\n93            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006582                       EDUCATION IN < Bach.\n94            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006582                       EDUCATION IN Assoc-V\n95            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006582                         EDUCATION IN Bach.\n96            3         1377  0.306000         1231  0.837328            EDUCATION             1153      0.006582                       EDUCATION IN HS-grad\n97            3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n98            3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n99            3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n100           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n101           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n102           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n103           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n104           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n105           3         1377  0.306000         1272  0.837328    CUST_INCOME_LEVEL             1153      0.002354  CUST_INCOME_LEVEL IN L: 300,000 and above\n106           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                            OCCUPATION IN ?\n107           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                      OCCUPATION IN Cleric.\n108           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                       OCCUPATION IN Crafts\n109           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                        OCCUPATION IN Exec.\n110           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                      OCCUPATION IN Handler\n111           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                      OCCUPATION IN Machine\n112           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                        OCCUPATION IN Other\n113           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                        OCCUPATION IN Prof.\n114           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                        OCCUPATION IN Sales\n115           3         1377  0.306000         1277  0.837328           OCCUPATION             1153      0.007270                      OCCUPATION IN TechSup\n116           3         1377  0.306000         1279  0.837328  CUST_MARITAL_STATUS             1153      0.050995             CUST_MARITAL_STATUS IN Married\n117           3         1377  0.306000         1279  0.837328  CUST_MARITAL_STATUS             1153      0.050995              CUST_MARITAL_STATUS IN NeverM\n118           3         1377  0.306000         1343  0.837328        AFFINITY_CARD             1153      0.024646                        AFFINITY_CARD <= .1\n119           3         1377  0.306000         1343  0.837328        AFFINITY_CARD             1153      0.024646                         AFFINITY_CARD >= 0\n120           3         1377  0.306000         1359  0.837328        YRS_RESIDENCE             1153      0.065320                       YRS_RESIDENCE <= 4.2\n121           3         1377  0.306000         1359  0.837328        YRS_RESIDENCE             1153      0.065320                         YRS_RESIDENCE >= 0\n122           3         1377  0.306000         1376  0.837328          Y_BOX_GAMES             1153      0.143419                           Y_BOX_GAMES <= 1\n123           3         1377  0.306000         1376  0.837328          Y_BOX_GAMES             1153      0.143419                           Y_BOX_GAMES > .9\n124           3         1377  0.306000         1377  0.837328    CUST_CREDIT_LIMIT             1153      0.000766                 CUST_CREDIT_LIMIT <= 15000\n125           3         1377  0.306000         1377  0.837328    CUST_CREDIT_LIMIT             1153      0.000766                  CUST_CREDIT_LIMIT >= 1500\n126           3         1377  0.306000         1377  0.837328          CUST_GENDER             1153      0.005175                           CUST_GENDER IN F\n127           3         1377  0.306000         1377  0.837328          CUST_GENDER             1153      0.005175                           CUST_GENDER IN M\n128           3         1377  0.306000         1377  0.837328   CUST_YEAR_OF_BIRTH             1153      0.133039                 CUST_YEAR_OF_BIRTH <= 1986\n129           3         1377  0.306000         1377  0.837328   CUST_YEAR_OF_BIRTH             1153      0.133039                CUST_YEAR_OF_BIRTH > 1971.4\n130           4         2085  0.463333         1802  0.864269  CUST_MARITAL_STATUS             1802      0.010504             CUST_MARITAL_STATUS IN Divorc.\n131           4         2085  0.463333         1802  0.864269  CUST_MARITAL_STATUS             1802      0.010504             CUST_MARITAL_STATUS IN Married\n132           4         2085  0.463333         1802  0.864269  CUST_MARITAL_STATUS             1802      0.010504              CUST_MARITAL_STATUS IN NeverM\n133           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                       EDUCATION IN 7th-8th\n134           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                       EDUCATION IN < Bach.\n135           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                       EDUCATION IN Assoc-A\n136           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                       EDUCATION IN Assoc-V\n137           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                         EDUCATION IN Bach.\n138           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                       EDUCATION IN HS-grad\n139           4         2085  0.463333         1842  0.864269            EDUCATION             1802      0.002405                       EDUCATION IN Masters\n140           4         2085  0.463333         1844  0.864269       HOUSEHOLD_SIZE             1802      0.011541                        HOUSEHOLD_SIZE IN 2\n141           4         2085  0.463333         1844  0.864269       HOUSEHOLD_SIZE             1802      0.011541                        HOUSEHOLD_SIZE IN 3\n142           4         2085  0.463333         1844  0.864269       HOUSEHOLD_SIZE             1802      0.011541                       HOUSEHOLD_SIZE IN 9+\n143           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n144           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n145           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n146           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n147           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n148           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n149           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n150           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n151           4         2085  0.463333         1887  0.864269    CUST_INCOME_LEVEL             1802      0.000278  CUST_INCOME_LEVEL IN L: 300,000 and above\n152           4         2085  0.463333         1909  0.864269        YRS_RESIDENCE             1802      0.011783                       YRS_RESIDENCE <= 8.4\n153           4         2085  0.463333         1909  0.864269        YRS_RESIDENCE             1802      0.011783                        YRS_RESIDENCE > 2.8\n154           4         2085  0.463333         1989  0.864269   CUST_YEAR_OF_BIRTH             1802      0.038027               CUST_YEAR_OF_BIRTH <= 1978.7\n155           4         2085  0.463333         1989  0.864269   CUST_YEAR_OF_BIRTH             1802      0.038027                CUST_YEAR_OF_BIRTH > 1934.9\n156           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                            OCCUPATION IN ?\n157           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                      OCCUPATION IN Cleric.\n158           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                       OCCUPATION IN Crafts\n159           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                        OCCUPATION IN Exec.\n160           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                      OCCUPATION IN Farming\n161           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                      OCCUPATION IN Handler\n162           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                      OCCUPATION IN Machine\n163           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                        OCCUPATION IN Other\n164           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                        OCCUPATION IN Prof.\n165           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                        OCCUPATION IN Sales\n166           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                      OCCUPATION IN TechSup\n167           4         2085  0.463333         2031  0.864269           OCCUPATION             1802      0.001454                      OCCUPATION IN Transp.\n168           4         2085  0.463333         2085  0.864269        AFFINITY_CARD             1802      0.039364                        AFFINITY_CARD <= .1\n169           4         2085  0.463333         2085  0.864269        AFFINITY_CARD             1802      0.039364                         AFFINITY_CARD >= 0\n170           4         2085  0.463333         2085  0.864269    CUST_CREDIT_LIMIT             1802      0.000065                 CUST_CREDIT_LIMIT <= 15000\n171           4         2085  0.463333         2085  0.864269    CUST_CREDIT_LIMIT             1802      0.000065                  CUST_CREDIT_LIMIT >= 1500\n172           4         2085  0.463333         2085  0.864269          CUST_GENDER             1802      0.001412                           CUST_GENDER IN F\n173           4         2085  0.463333         2085  0.864269          CUST_GENDER             1802      0.001412                           CUST_GENDER IN M\n174           4         2085  0.463333         2085  0.864269          Y_BOX_GAMES             1802      0.053345                          Y_BOX_GAMES <= .1\n175           4         2085  0.463333         2085  0.864269          Y_BOX_GAMES             1802      0.053345                           Y_BOX_GAMES >= 0\n176           5         1038  0.230667          891  0.858382          CUST_GENDER              891      0.038201                            CUST_GENDER = M\n177           5         1038  0.230667          909  0.858382  CUST_MARITAL_STATUS              891      0.057819              CUST_MARITAL_STATUS = Married\n178           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                      OCCUPATION IN Cleric.\n179           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                       OCCUPATION IN Crafts\n180           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                        OCCUPATION IN Exec.\n181           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                      OCCUPATION IN Machine\n182           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                        OCCUPATION IN Prof.\n183           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                        OCCUPATION IN Sales\n184           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                      OCCUPATION IN TechSup\n185           5         1038  0.230667          946  0.858382           OCCUPATION              891      0.021280                      OCCUPATION IN Transp.\n186           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487    CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n187           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487   CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n188           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n189           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n190           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n191           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n192           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n193           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n194           5         1038  0.230667          949  0.858382    CUST_INCOME_LEVEL              891      0.001487  CUST_INCOME_LEVEL IN L: 300,000 and above\n195           5         1038  0.230667          973  0.858382        YRS_RESIDENCE              891      0.028267                       YRS_RESIDENCE <= 8.4\n196           5         1038  0.230667          973  0.858382        YRS_RESIDENCE              891      0.028267                        YRS_RESIDENCE > 2.8\n197           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                       EDUCATION IN < Bach.\n198           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                       EDUCATION IN Assoc-A\n199           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                       EDUCATION IN Assoc-V\n200           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                         EDUCATION IN Bach.\n201           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                       EDUCATION IN HS-grad\n202           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                       EDUCATION IN Masters\n203           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                           EDUCATION IN PhD\n204           5         1038  0.230667         1003  0.858382            EDUCATION              891      0.015113                        EDUCATION IN Profsc\n205           5         1038  0.230667         1003  0.858382       HOUSEHOLD_SIZE              891      0.066799                        HOUSEHOLD_SIZE IN 2\n206           5         1038  0.230667         1003  0.858382       HOUSEHOLD_SIZE              891      0.066799                        HOUSEHOLD_SIZE IN 3\n207           5         1038  0.230667         1003  0.858382       HOUSEHOLD_SIZE              891      0.066799                      HOUSEHOLD_SIZE IN 4-5\n208           5         1038  0.230667         1008  0.858382          Y_BOX_GAMES              891      0.035169                          Y_BOX_GAMES <= .1\n209           5         1038  0.230667         1008  0.858382          Y_BOX_GAMES              891      0.035169                           Y_BOX_GAMES >= 0\n210           5         1038  0.230667         1016  0.858382   CUST_YEAR_OF_BIRTH              891      0.039020               CUST_YEAR_OF_BIRTH <= 1978.7\n211           5         1038  0.230667         1016  0.858382   CUST_YEAR_OF_BIRTH              891      0.039020                CUST_YEAR_OF_BIRTH > 1934.9\n212           5         1038  0.230667         1038  0.858382        AFFINITY_CARD              891      0.169369                         AFFINITY_CARD <= 1\n213           5         1038  0.230667         1038  0.858382        AFFINITY_CARD              891      0.169369                         AFFINITY_CARD > .9\n214           5         1038  0.230667         1038  0.858382    CUST_CREDIT_LIMIT              891      0.000585                 CUST_CREDIT_LIMIT <= 15000\n215           5         1038  0.230667         1038  0.858382    CUST_CREDIT_LIMIT              891      0.000585                  CUST_CREDIT_LIMIT >= 1500\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model settings","message":["%python","","km_mod.settings"],"enabled":true,"result":{"startTime":1713901563226,"interpreter":"python.low","endTime":1713901563302,"results":[{"message":"{'kmns_iterations': 20, 'kmns_conv_tolerance': 0.001, 'kmns_distance': 'KMNS_EUCLIDEAN', 'kmns_min_pct_attr_support': 0.1, 'kmns_num_bins': 11, 'kmns_split_criterion': 'KMNS_VARIANCE', 'kmns_random_seed': 7, 'kmns_details': 'KMNS_DETAILS_ALL', 'odms_details': 'ODMS_ENABLE', 'odms_sampling': 'ODMS_SAMPLING_DISABLE', 'prep_auto': 'ON', 'clus_num_clusters': 3}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster details for all clusters in the hierarchy with row counts and dispersion","message":["%python","","z.show(km_mod.clusters)"],"enabled":true,"result":{"startTime":1713901563372,"interpreter":"python.low","endTime":1713901563465,"results":[{"message":"CLUSTER_ID\tROW_CNT\tPARENT_CLUSTER_ID\tTREE_LEVEL\tDISPERSION\n1.0\t4500.0\tnan\t1.0\t6.276771678127229\n2.0\t3123.0\t1.0\t2.0\t6.5860271201254195\n3.0\t1377.0\t1.0\t2.0\t5.575388420784927\n4.0\t2085.0\t2.0\t3.0\t6.9385112173435965\n5.0\t1038.0\t2.0\t3.0\t5.878002705192953\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define function to format the cluster centroid rule","message":["%python","","def display_rule(model, cluster_id, dat):","  rules = model.rules","  cluster_rules = rules[rules['cluster.id'] == cluster_id]","  vars = cluster_rules['lhs.var'].drop_duplicates().pull()","  res = []","","  for var in vars:","    rule = []","    var_rules = cluster_rules[cluster_rules['lhs.var'] == var, 'predicate'].pull()","    if isinstance(dat[var], oml.String):","      for r in var_rules:","        v = r.split(\" IN \")[1]","        if len(v) > 0:","          rule.append(v)","      rule = var + \" IN {\" + ','.join(rule) + \"}\"","    else:","      rule = \" and \".join(var_rules)","    res.append(rule + \"\\n\")","","  print(\"if \" + \" and \".join(res) + \"then cluster \" + str(cluster_id))"],"enabled":true,"result":{"startTime":1713901606100,"interpreter":"python.low","endTime":1713901606162,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display formatted rule for cluster 3","message":["%python","","display_rule(model      = km_mod, ","             cluster_id = 3, ","             dat        = CUST_DF)"],"enabled":true,"result":{"startTime":1713901609220,"interpreter":"python.low","endTime":1713901610031,"results":[{"message":"if CUST_CREDIT_LIMIT <= 15000 and CUST_CREDIT_LIMIT >= 1500\n and CUST_INCOME_LEVEL IN {B: 30,000 - 49,999,E: 90,000 - 109,999,F: 110,000 - 129,999,G: 130,000 - 149,999,H: 150,000 - 169,999,I: 170,000 - 189,999,J: 190,000 - 249,999,K: 250,000 - 299,999,L: 300,000 and above}\n and HOUSEHOLD_SIZE IN {1,2,3}\n and OCCUPATION IN {?,Cleric.,Crafts,Exec.,Handler,Machine,Other,Prof.,Sales,TechSup}\n and YRS_RESIDENCE <= 4.2 and YRS_RESIDENCE >= 0\n and CUST_YEAR_OF_BIRTH <= 1986 and CUST_YEAR_OF_BIRTH > 1971.4\n and AFFINITY_CARD <= .1 and AFFINITY_CARD >= 0\n and CUST_MARITAL_STATUS IN {Married,NeverM}\n and Y_BOX_GAMES <= 1 and Y_BOX_GAMES > .9\n and EDUCATION IN {10th,11th,< Bach.,Assoc-V,Bach.,HS-grad}\n and CUST_GENDER IN {F,M}\nthen cluster 3\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Apply clustering model to assign rows to clusters","message":["%python","","pred = km_mod.predict(CUST_DF, supplemental_cols = CUST_DF)"],"enabled":true,"result":{"startTime":1713901619500,"interpreter":"python.low","endTime":1713901619619,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use matplotlib to view cluster results using birth year and years residence","message":["%python","","pred_df = pred[['CUST_ID', 'CLUSTER_ID', 'CUST_YEAR_OF_BIRTH', 'YRS_RESIDENCE', 'CUST_CREDIT_LIMIT']].pull()","","fig = plt.figure()","ax=fig.add_axes([0.1,0.1,0.8,0.8])","","ids = pred_df['CLUSTER_ID']","clusters = ids.drop_duplicates().values","handles = []","labs = []","colors = ['r', 'b', 'g']","for i, c in enumerate(clusters):","    xc = pred_df[pred_df['CLUSTER_ID'] == c]['YRS_RESIDENCE'].values","    yc = pred_df[pred_df['CLUSTER_ID'] == c]['CUST_YEAR_OF_BIRTH'].values","    ","    h = ax.scatter(xc, yc, color= colors[i])","    handles.append(h)","    labs.append('CLUSTER' + str(c))","ax.legend(handles, labs)","plt.title('K-Means Clustering')","","plt.grid(True)","","plt.xlabel('YRS_RESIDENCE')","plt.ylabel('CUST_YEAR_OF_BIRTH')","plt.show()"],"enabled":true,"result":{"startTime":1713901619696,"interpreter":"python.low","endTime":1713901619964,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Compare clusters","","Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals. "],"enabled":true,"result":{"startTime":1713901620056,"interpreter":"md.low","endTime":1713901620124,"results":[{"message":"<h3 id=\"compare-clusters\">Compare clusters<\/h3>\n<p>Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"y\":{\"max\":100}},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 4","message":["%python","","feature = 'CUST_YEAR_OF_BIRTH' ","","DF = pred[pred['CLUSTER_ID'] == 4].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1713901620192,"interpreter":"python.low","endTime":1713901620512,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1913\t5\n1921\t1\n1922\t6\n1923\t4\n1924\t2\n1925\t6\n1926\t10\n1927\t3\n1928\t14\n1929\t6\n1930\t4\n1931\t6\n1932\t9\n1933\t8\n1934\t12\n1935\t13\n1936\t12\n1937\t10\n1938\t17\n1939\t22\n1940\t22\n1941\t24\n1942\t16\n1943\t32\n1944\t31\n1945\t32\n1946\t33\n1947\t35\n1948\t34\n1949\t34\n1950\t38\n1951\t25\n1952\t49\n1953\t20\n1954\t61\n1955\t50\n1956\t63\n1957\t71\n1958\t66\n1959\t72\n1960\t50\n1961\t75\n1962\t81\n1963\t70\n1964\t83\n1965\t59\n1966\t86\n1967\t92\n1968\t93\n1969\t95\n1970\t89\n1971\t93\n1972\t78\n1973\t63\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"y\":{\"max\":100}},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 5","message":["%python","","feature = 'CUST_YEAR_OF_BIRTH' ","","DF = pred[pred['CLUSTER_ID'] == 5].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1713901620589,"interpreter":"python.low","endTime":1713901620859,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1924\t4\n1925\t2\n1926\t2\n1929\t1\n1931\t1\n1932\t4\n1933\t2\n1934\t6\n1935\t5\n1936\t10\n1937\t2\n1938\t9\n1939\t3\n1940\t8\n1941\t7\n1942\t12\n1943\t11\n1944\t21\n1945\t17\n1946\t12\n1947\t10\n1948\t18\n1949\t17\n1950\t22\n1951\t26\n1952\t34\n1953\t36\n1954\t32\n1955\t44\n1956\t33\n1957\t48\n1958\t39\n1959\t47\n1960\t50\n1961\t40\n1962\t32\n1963\t32\n1964\t22\n1965\t44\n1966\t32\n1967\t31\n1968\t44\n1969\t40\n1970\t27\n1971\t28\n1972\t12\n1973\t29\n1974\t10\n1975\t13\n1976\t2\n1977\t4\n1978\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Using SQL access prediction details","One feature of OML is the ability to use models from SQL as well as Python. While OML4Py v1.0 does not expose prediction details through a Python API, such details are available through the SQL API, which is illustrated here.","","We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view. ","","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well. "],"enabled":true,"result":{"startTime":1713901620932,"interpreter":"md.low","endTime":1713901620991,"results":[{"message":"<h3 id=\"using-sql-access-prediction-details\">Using SQL access prediction details<\/h3>\n<p>One feature of OML is the ability to use models from SQL as well as Python. While OML4Py v1.0 does not expose prediction details through a Python API, such details are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view.<\/p>\n<p>Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%python","","try:","    oml.drop(table = 'CUSTOMERS360')","except:","    pass","_ = CUST_DF.materialize(table = 'CUSTOMERS360')"],"enabled":true,"result":{"startTime":1713901621210,"interpreter":"python.low","endTime":1713901621859,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 3 most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,0) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(CUST_CLUSTER_MODEL USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(CUST_CLUSTER_MODEL, NULL, 0.2 USING *) PSET","            FROM CUSTOMERS360 V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1713901621928,"interpreter":"sql.low","endTime":1713901623724,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100456\t5\t29\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".032\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".027\" \t\"YRS_RESIDENCE\" actualValue=\"6\" weight=\".017\" \n100456\t4\t50\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".032\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".027\" \t\"YRS_RESIDENCE\" actualValue=\"6\" weight=\".017\" \n100456\t3\t21\t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".032\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".027\" \t\"YRS_RESIDENCE\" actualValue=\"6\" weight=\".017\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'100456'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1713901623797,"interpreter":"md.low","endTime":1713901623858,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1713901623929,"interpreter":"md.low","endTime":1713901623989,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Data Cleaning Duplicates Removal.dsnb b/machine-learning/notebooks/python/OML4Py Data Cleaning Duplicates Removal.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py Data Cleaning Duplicates Removal.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Cleaning Duplicates Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Cleaning: Duplicates Removal","In this notebook, we demonstrate how to remove duplicate records using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance. ","","The dataset `CUSTOMER_INSURANCE_LTV_PY` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/clean-data.html\" target=\"_blank\">OML4Py Data Cleaning<\/a>","","","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries ","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of unique customer IDs","message":["%python","","CUST_DF['CUSTOMER_ID'].nunique()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show table dimensions - note there are more rows than customer IDs","message":["%python","","CUST_DF.shape"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Remove duplicate rows","message":["%python","","CUST_DF = CUST_DF.drop_duplicates()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check number of unique customer IDs","message":["%python","","CUST_DF['CUSTOMER_ID'].nunique()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check dimension of the OML dataframe - duplicated rows removed","message":["%python","","CUST_DF.shape"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Data Cleaning Missing Data.dsnb b/machine-learning/notebooks/python/OML4Py Data Cleaning Missing Data.dsnb
deleted file mode 100755
index 21fb4f0e..00000000
--- a/machine-learning/notebooks/python/OML4Py Data Cleaning Missing Data.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Cleaning Missing Data","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%python"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Data Cleaning: fill in missing data","In this notebook, we demonstrate how to fill in missing values using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_PY` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may *not* be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/prepare-and-explore-data1.html\" target=\"_blank\">OML4Py Data Preparation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries ","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data Overview","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Columns MARITAL_STATUS,  CREDIT_BALANCE,  BANK_FUNDS, INCOME have missing values","message":["%python","","z.show(CUST_DF[CUST_DF['CUSTOMER_ID'] < 'CU2'][['CUSTOMER_ID','MARITAL_STATUS', 'CREDIT_BALANCE', 'BANK_FUNDS', 'INCOME']].head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find mode for the categorical column MARITAL_STATUS","message":["%python","","def col_mode(DF, col):","    SUMMARY_DF = CUST_DF.dropna().crosstab([col]).sort_values('count').tail(1)","    return SUMMARY_DF.pull()[col].values[0]","","mode = col_mode(CUST_DF, 'MARITAL_STATUS')","","print(mode)","     "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Replace None with 'SINGLE' and then check the counts","message":["%python","","CUST_CAT_DF = CUST_DF.replace(old = [None], new = [mode], columns = ['MARITAL_STATUS'])    ","","z.show(CUST_CAT_DF.crosstab('MARITAL_STATUS'))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Replace the missing numerical columns using the mean value","message":["%python","","num_cols = ['CREDIT_BALANCE', 'BANK_FUNDS', 'INCOME']","","mean_vals = [float(CUST_DF[col].mean()) for col in num_cols]","CUST_NUM_DF = CUST_DF","","print(mean_vals)","for i, col in enumerate(num_cols):","    CUST_NUM_DF = CUST_NUM_DF.replace(old = [None], new = [mean_vals[i]], columns = [col])    "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the OML proxy object after the replacement","message":["%python","","z.show(CUST_NUM_DF[CUST_NUM_DF['CUSTOMER_ID'] <'CU2'][ ['CUSTOMER_ID'] + num_cols].head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Data Cleaning Outlier Removal.dsnb b/machine-learning/notebooks/python/OML4Py Data Cleaning Outlier Removal.dsnb
deleted file mode 100755
index 11649af7..00000000
--- a/machine-learning/notebooks/python/OML4Py Data Cleaning Outlier Removal.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Cleaning Outlier Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py  Data Cleaning: Outlier Removal","In this notebook, we demonstrate how to exclude records with outliers using OML4Py. ","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV, we focus on numerical values and remove the records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows in the table","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the percentiles","message":["%python","","CUST_DF['INCOME'].describe(percentiles = [0.05,0.95])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the 5 and 95 percentile","message":["%python","","df = CUST_DF['INCOME'].describe(percentiles = [0.05,0.95])","df = pd.DataFrame(df)","df = df.reset_index()","","lower_bound = df[df['index'] == '5%']['INCOME'].values[0]","upper_bound = df[df['index'] == '95%']['INCOME'].values[0]","","print(\"Lower: \", lower_bound)","print(\"Upper: \", upper_bound)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Remove data with INCOME out of bounds","message":["%python","","CLEAN_DF = CUST_DF[ (CUST_DF['INCOME'] < upper_bound) & (CUST_DF['INCOME'] > lower_bound)]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","z.show(CUST_DF.head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the distribution after removal","message":["%python","","CLEAN_DF['INCOME'].describe()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Plot the distribution before outlier removal using OML  graphics","message":["%python\r","\r","import matplotlib.pyplot as plt\r","plt.style.use('seaborn')\r","\r","plt.figure(figsize=[20,6])\r","oml.graphics.hist(CUST_DF['INCOME'], bins=10, color='C0',\r","                 linestyle='solid', edgecolor='black')\r","\r","plt.title('Histogram of INCOME before outlier removeal')\r","plt.xlabel('INCOME')\r","plt.ylabel('Customer Counts')\r","_ = plt.show()\r"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Distribution of INCOME after outlier removal","message":["%python","","import matplotlib.pyplot as plt","plt.style.use('seaborn')","","plt.figure(figsize=[20,6])","oml.graphics.hist(CLEAN_DF['INCOME'], bins=10, color='C0',","                 linestyle='solid', edgecolor='black')","","plt.title('Histogram of INCOME after outlier removeal')","plt.xlabel('INCOME')","plt.ylabel('Customer Counts')","_ = plt.show()"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Data Cleaning Recode Synonymous Values.dsnb b/machine-learning/notebooks/python/OML4Py Data Cleaning Recode Synonymous Values.dsnb
deleted file mode 100755
index 91232986..00000000
--- a/machine-learning/notebooks/python/OML4Py Data Cleaning Recode Synonymous Values.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Cleaning Recode Synonymous Values","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Cleaning: Recode Synonymous Values","In this notebook, we demonstrate how to recode synonymous value using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_PY` has special values generated by the `\"OML Run-me-first\"` notebook artificially, which `MUST` be run before this notebook.  Specifically, MARITAL_STATUS has divorced status coded as DIV, married coded as 'M'. ","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4Py Data Preparation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the distinct values","message":["%python","","z.show(CUST_DF.crosstab(['MARITAL_STATUS']))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Recode the synonymous values 'M' and 'DIV'","message":["%python","","CUST_DF = CUST_DF.replace(old = ['M', 'DIV', None], new = ['MARRIED', 'DIVORCED', 'UNKNOWN'],  columns = ['MARITAL_STATUS'])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the recoded values","message":["%python","","z.show(CUST_DF.crosstab(['MARITAL_STATUS']))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Data Transformation Binning.dsnb b/machine-learning/notebooks/python/OML4Py Data Transformation Binning.dsnb
deleted file mode 100755
index 05721681..00000000
--- a/machine-learning/notebooks/python/OML4Py Data Transformation Binning.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Binning","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715292123840,"interpreter":"md.low","endTime":1715292123927,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Transformation: Binning","In this notebook, we demonstrate how to bin numerical columns using OML4Py. Equi-width and quantile binning are shown. ","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to bin a numerical column and visualize the distribution.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","","**Note**: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. ","Supervised binning uses decision trees to create optimal bin boundaries. ","More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html');\">Naive Bayes<\/a>  ","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715292124000,"interpreter":"md.low","endTime":1715292124109,"results":[{"message":"<h1 id=\"oml4py-data-transformation-binning\">OML4Py Data Transformation: Binning<\/h1>\n<p>In this notebook, we demonstrate how to bin numerical columns using OML4Py. Equi-width and quantile binning are shown.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to bin a numerical column and visualize the distribution.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning.\nSupervised binning uses decision trees to create optimal bin boundaries.\nMore details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html');\">Naive Bayes<\/a><\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a>"],"enabled":true,"result":{"startTime":1715292124187,"interpreter":"md.low","endTime":1715292124264,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/summarize-data.html\" target=\"_blank\">OML4Py Data Summarization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":{"startTime":1715292124340,"interpreter":"python.low","endTime":1715292126443,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to CUSTOMER_INSURANCE_LTV and view data","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1715292126518,"interpreter":"python.low","endTime":1715292126897,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Bin INCOME column into 10 bins","message":["%python","","CUST_BIN_DF = CUST_DF.concat(CUST_DF['INCOME'].cut(bins = 10))"],"enabled":true,"result":{"startTime":1715292126976,"interpreter":"python.low","endTime":1715292127203,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Count\",\"series\":{\"availableSeriesElements\":[{\"id\":\"INCOME\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"INCOME\",\"Bin(INCOME)\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"View binned INCOME column values","message":["%python","","z.show(CUST_BIN_DF[['INCOME', 'Bin(INCOME)']].head())"],"enabled":true,"result":{"startTime":1715292127277,"interpreter":"python.low","endTime":1715292127429,"results":[{"message":"INCOME\tBin(INCOME)\n65871\t(59456.4, 66906.2]\n68747\t(66906.2, 74356.0]\n68684\t(66906.2, 74356.0]\n59354\t(52006.6, 59456.4]\n84801\t(81805.8, 89255.6]\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"count_l\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"Bin(INCOME)\",\"count_l\",\"mean(INCOME)_r\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot the distribution of binned INCOME","message":["%python","","INCOME_MEAN = CUST_BIN_DF.crosstab('Bin(INCOME)', values = 'INCOME', aggfunc = oml.DataFrame.mean).sort_values('mean(INCOME)')","","OUTPUT = CUST_BIN_DF.crosstab('Bin(INCOME)').merge(INCOME_MEAN, on = 'Bin(INCOME)').sort_values('mean(INCOME)_r')","","z.show(OUTPUT)"],"enabled":true,"result":{"startTime":1715292127509,"interpreter":"python.low","endTime":1715292128159,"results":[{"message":"Bin(INCOME)\tcount_l\tmean(INCOME)_r\n(37032.502, 44556.8]\t23\t41487.217391304344\n(44556.8, 52006.6]\t134\t48909.5\n(52006.6, 59456.4]\t2070\t57577.33381642512\n(59456.4, 66906.2]\t7053\t63166.39472564866\n(66906.2, 74356.0]\t3651\t69725.86469460421\n(74356.0, 81805.8]\t636\t76719.74056603774\n(81805.8, 89255.6]\t179\t84790.54189944133\n(89255.6, 96705.4]\t84\t92527.70238095238\n(96705.4, 104155.2]\t32\t100052.25\n(104155.2, 111605.0]\t18\t108535.5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get quantiles for INCOME column","message":["%python","","CUST_DF['INCOME'].describe()[-5:]"],"enabled":true,"result":{"startTime":1715292128238,"interpreter":"python.low","endTime":1715292128390,"results":[{"message":"min     37107.0\n25%     60719.5\n50%     64426.0\n75%     68482.0\nmax    111605.0\nName: INCOME, dtype: float64\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"Bin(INCOME)\",\"count\"],\"version\":1},\"area\":{\"version\":1},\"tree\":{},\"pyramid\":{\"version\":1},\"boxplot\":{}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Use quantiles for bin boundaries","message":["%python","","pct = CUST_DF['INCOME'].describe(percentiles = [0.25, 0.5, 0.75])[-5:]","","pct_list = list(pct)","pct_list.sort()","CUST_SET_DF = CUST_DF.concat(CUST_DF['INCOME'].cut(bins = pct_list))","","OUTPUT = CUST_SET_DF.crosstab('Bin(INCOME)')","z.show(OUTPUT[OUTPUT['Bin(INCOME)'].isnull() == False])"],"enabled":true,"result":{"startTime":1715292128469,"interpreter":"python.low","endTime":1715292128913,"results":[{"message":"Bin(INCOME)\tcount\n(37107.0, 60719.5]\t3469\n(60719.5, 64426.0]\t3470\n(64426.0, 68482.0]\t3472\n(68482.0, 111605.0]\t3468\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715292128993,"interpreter":"md.low","endTime":1715292129055,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715292129149,"interpreter":"md.low","endTime":1715292129212,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/python/OML4Py Data Transformation Categorical.dsnb b/machine-learning/notebooks/python/OML4Py Data Transformation Categorical.dsnb
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Categorical","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Transformation: convert categorical variables to numeric variables","","In this notebook, we demonstrate how to convert categorical variables to numeric variables using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to convert a categorical variable with each distinct level/value coded to an integer data type. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/prepare-and-explore-data1.html\" target=\"_blank\">OML4Py Data Preparation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')","z.show(CUST_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the counts by state","message":["%python","","z.show(CUST_DF.crosstab(['STATE']).sort_values('count'))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Replace the state names with integers","message":["%python","","state_names = CUST_DF['STATE'].drop_duplicates().pull()","","state_ids = [0]*len(state_names)","","for i in range(len(state_names)):","    state_ids[i] = float(i)","    ","CUST_DF = CUST_DF.replace(old = state_names, new = state_ids, default = 0.0, columns = ['STATE'])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique states, now encoded to integers","message":["%python","","z.show(CUST_DF.crosstab(['STATE']).sort_values('count'))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/python/OML4Py Data Transformation Date Datatypes.dsnb b/machine-learning/notebooks/python/OML4Py Data Transformation Date Datatypes.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py Data Transformation Date Datatypes.dsnb	
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Date Datatypes","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714497363869,"interpreter":"md.medium","endTime":1714497364025,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Date and Time Classes","","Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","OML4Py provides three classes for manipulating dates and times: `oml.Datetime`, `oml.Timezone`, and `oml.Timedelta`.","","- `oml.Datetime` represents a table column of dates to support DATE or TIMESTAMP in Oracle Database. ","- `oml.Timezone` represents a table column of time zone data to support TIMESTAMP WITH TIMEZONE.","- `oml.Timedelta` represents a single column of differences between two dates or times to support INTERVAL DAY TO SECOND in Oracle Database.   ","","These classes are based on the <a href=\"https://docs.python.org/3.10/library/datetime.html\" onclick=\"return ! window.open('https://docs.python.org/3.10/library/datetime.html');\">Python datetime library.<\/a>","","In this notebook, we highlight features of OML4Py's date and time functionality.","","---","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714497386279,"interpreter":"md.medium","endTime":1714497386340,"results":[{"message":"<h1 id=\"oml4py-date-and-time-classes\">OML4Py Date and Time Classes<\/h1>\n<p>Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>OML4Py provides three classes for manipulating dates and times: <code>oml.Datetime<\/code>, <code>oml.Timezone<\/code>, and <code>oml.Timedelta<\/code>.<\/p>\n<ul>\n<li><code>oml.Datetime<\/code> represents a table column of dates to support DATE or TIMESTAMP in Oracle Database.<\/li>\n<li><code>oml.Timezone<\/code> represents a table column of time zone data to support TIMESTAMP WITH TIMEZONE.<\/li>\n<li><code>oml.Timedelta<\/code> represents a single column of differences between two dates or times to support INTERVAL DAY TO SECOND in Oracle Database.<\/li>\n<\/ul>\n<p>These classes are based on the <a href=\"https://docs.python.org/3.10/library/datetime.html\" onclick=\"return ! window.open('https://docs.python.org/3.10/library/datetime.html');\">Python datetime library.<\/a><\/p>\n<p>In this notebook, we highlight features of OML4Py's date and time functionality.<\/p>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","","import pandas as pd","import numpy as np","","import datetime","from datetime import datetime, timezone, timedelta"],"enabled":true,"result":{"startTime":1714497364485,"interpreter":"python.medium","endTime":1714497366590,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## OML Datetime","","To create a date in Python, we use the `datetime` class of the `datetime` module, which is included in Python. The `datetime` class requires three parameters: *year*, *month*, and *day*. It also contains optional parameters for time and timezone, including *hour*, *minute*, *second*, *microsecond*, and *tzone*.","","Create a datetime object with the year, month, and day, then create a temporary proxy object using `oml.push`. The `oml.push `function requires a data frame or list as input, so we first convert the date object to a list. The resulting object is an object of class `oml.Datetime`.","","*Note: oml.Datetime is an alias to oml.core.datetime.Datetime. They are interchangeable functionally, as are aliases for the other `oml` classes, e.g., `oml.DateFrame`, `oml.Float`, etc.*"],"enabled":true,"result":{"startTime":1714497366681,"interpreter":"md.medium","endTime":1714497366761,"results":[{"message":"<h2 id=\"oml-datetime\">OML Datetime<\/h2>\n<p>To create a date in Python, we use the <code>datetime<\/code> class of the <code>datetime<\/code> module, which is included in Python. The <code>datetime<\/code> class requires three parameters: <em>year<\/em>, <em>month<\/em>, and <em>day<\/em>. It also contains optional parameters for time and timezone, including <em>hour<\/em>, <em>minute<\/em>, <em>second<\/em>, <em>microsecond<\/em>, and <em>tzone<\/em>.<\/p>\n<p>Create a datetime object with the year, month, and day, then create a temporary proxy object using <code>oml.push<\/code>. The <code>oml.push <\/code>function requires a data frame or list as input, so we first convert the date object to a list. The resulting object is an object of class <code>oml.Datetime<\/code>.<\/p>\n<p><em>Note: oml.Datetime is an alias to oml.core.datetime.Datetime. They are interchangeable functionally, as are aliases for the other <code>oml<\/code> classes, e.g., <code>oml.DateFrame<\/code>, <code>oml.Float<\/code>, etc.<\/em><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"datetime ","message":["%python","","d1 = datetime(year=2004, month=7, day=24)","","print('d1:', d1)","print('type:', type(d1))","print('year:', d1.year)","print('month:', d1.month)","print('day:', d1.day)"],"enabled":true,"result":{"startTime":1714497366848,"interpreter":"python.medium","endTime":1714497367002,"results":[{"message":"d1: 2004-07-24 00:00:00\ntype: <class 'datetime.datetime'>\nyear: 2004\nmonth: 7\nday: 24\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"oml.Datetime","message":["%python","","d1_lst = [d1] ","D1 = oml.push(d1_lst)","","print('D1:', D1)","print('type:', type(D1))","print('year:', D1.year)","print('month:', D1.month)","print('day:', D1.day)"],"enabled":true,"result":{"startTime":1714497367089,"interpreter":"python.medium","endTime":1714497367529,"results":[{"message":"D1: [datetime.datetime(2004, 7, 24, 0, 0)]\ntype: <class 'oml.core.datetime.Datetime'>\nyear: [2004]\nmonth: [7]\nday: [24]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Datetime method `fromisoformat`","The datetime method `fromisoformat` constructs a date or datetime object from a string containing date in ISO format, e.g., yyyy-mm-dd. Create a datetime object with the year, month, day, and time, then create a temporary proxy object using `oml.push`. The resulting object is an object of class `oml.Datetime`.","","---"],"enabled":true,"result":{"startTime":1714497367611,"interpreter":"md.medium","endTime":1714497367674,"results":[{"message":"<h2 id=\"datetime-method-fromisoformat\">Datetime method <code>fromisoformat<\/code><\/h2>\n<p>The datetime method <code>fromisoformat<\/code> constructs a date or datetime object from a string containing date in ISO format, e.g., yyyy-mm-dd. Create a datetime object with the year, month, day, and time, then create a temporary proxy object using <code>oml.push<\/code>. The resulting object is an object of class <code>oml.Datetime<\/code>.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","d2 = datetime.fromisoformat('2004-07-24 00:05:23+04:00')","d2_lst=[d2]","","D2 = oml.push(d2_lst)","","print('D2', D2)","print('type', type(D2))"],"enabled":true,"result":{"startTime":1714497367754,"interpreter":"python.medium","endTime":1714497367942,"results":[{"message":"D2 [datetime.datetime(2004, 7, 24, 0, 5, 23, tzinfo=datetime.timezone(datetime.timedelta(seconds=14400)))]\ntype <class 'oml.core.datetime.Datetime'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","datetime.now()"],"enabled":true,"result":{"startTime":1714497368036,"interpreter":"python.medium","endTime":1714497368116,"results":[{"message":"datetime.datetime(2024, 4, 30, 17, 16, 8, 102199)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","## Date format codes","","The `strftime` method converts a datetime object to a string, and `strptime` converts a string to a datetime object. Dates have a default representation of: **YYYY-MM-DD:HH:MM:SS.mmmmmm**","but you may want to print them in a specific format. In that case, you can get a custom string representation using the available <a href=\"https://docs.python.org/3/library/datetime.html\" onclick=\"return ! window.open('https://docs.python.org/3/library/datetime.html');\">format codes.<\/a>","","The format codes for the default date representation are shown in the table below.","","<table>","  <tr>","    <th>Code<\/th>","    <th>Example<\/th>","    <th>Description<\/th>","  <\/tr>","  <tr>","    <td>%a<\/td>","    <td>Sun<\/td>","    <td>Day abbreviated<\/td>","  <\/tr>","  <tr>","    <td>%b<\/td>","    <td>Sep<\/td>","    <td>Month abbreviated<\/td>","  <\/tr>","  <tr>","    <td>%d<\/td>","    <td>08<\/td>","    <td>Decimal Month<\/td>","  <\/tr>","  <tr>","    <td>%H<\/td>","    <td>07<\/td>","    <td>Decimal Hour<\/td>","  <\/tr>","  <tr>","    <td>%M<\/td>","    <td>06<\/td>","    <td>Decimal Minute<\/td>","  <\/tr>","  <tr>","    <td>%S<\/td>","    <td>05<\/td>","    <td>Decimal Second<\/td>","  <\/tr>","  <tr>","    <td>%Y<\/td>","    <td>2015<\/td>","    <td>Decimal Year<\/td>","  <\/tr>","<\/table>","<br>","","---"],"enabled":true,"result":{"startTime":1714497368201,"interpreter":"md.medium","endTime":1714497368280,"results":[{"message":"<h2 id=\"date-format-codes\">Date format codes<\/h2>\n<p>The <code>strftime<\/code> method converts a datetime object to a string, and <code>strptime<\/code> converts a string to a datetime object. Dates have a default representation of: <strong>YYYY-MM-DD:HH:MM:SS.mmmmmm<\/strong>\nbut you may want to print them in a specific format. In that case, you can get a custom string representation using the available <a href=\"https://docs.python.org/3/library/datetime.html\" onclick=\"return ! window.open('https://docs.python.org/3/library/datetime.html');\">format codes.<\/a><\/p>\n<p>The format codes for the default date representation are shown in the table below.<\/p>\n<table>\n  <tr>\n    <th>Code<\/th>\n    <th>Example<\/th>\n    <th>Description<\/th>\n  <\/tr>\n  <tr>\n    <td>%a<\/td>\n    <td>Sun<\/td>\n    <td>Day abbreviated<\/td>\n  <\/tr>\n  <tr>\n    <td>%b<\/td>\n    <td>Sep<\/td>\n    <td>Month abbreviated<\/td>\n  <\/tr>\n  <tr>\n    <td>%d<\/td>\n    <td>08<\/td>\n    <td>Decimal Month<\/td>\n  <\/tr>\n  <tr>\n    <td>%H<\/td>\n    <td>07<\/td>\n    <td>Decimal Hour<\/td>\n  <\/tr>\n  <tr>\n    <td>%M<\/td>\n    <td>06<\/td>\n    <td>Decimal Minute<\/td>\n  <\/tr>\n  <tr>\n    <td>%S<\/td>\n    <td>05<\/td>\n    <td>Decimal Second<\/td>\n  <\/tr>\n  <tr>\n    <td>%Y<\/td>\n    <td>2015<\/td>\n    <td>Decimal Year<\/td>\n  <\/tr>\n<\/table>\n<br>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Convert date to string","message":["#%python","","D3 = D2.strftime()","","print('D3', D3)","print('type', type(D3))"],"enabled":true,"result":{"startTime":1714497368361,"interpreter":"md.medium","endTime":1714497368440,"results":[{"message":"<p>#%python<\/p>\n<p>D3 = D2.strftime()<\/p>\n<p>print('D3', D3)\nprint('type', type(D3))<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Convert string to date","message":["%python","","d4 = \"14-Jul-05 20:01:01\"","d4_lst = [d4]","","# create a temporary proxy object","D4 = oml.push(d4_lst)","","# convert the string to an oml.Datetime object with the provided format","D5 = oml.Datetime.strptime(D4, format=\"DD-Mon-RR HH24:MI:SS\")","","print('D5', D5)","print('type', type(D5))"],"enabled":true,"result":{"startTime":1714497368522,"interpreter":"python.medium","endTime":1714497368708,"results":[{"message":"D5 [datetime.datetime(2005, 7, 14, 20, 1, 1)]\ntype <class 'oml.core.datetime.Datetime'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## OML Timedelta","","`oml.Timedelta` objects represent a column of differences between two dates or times and can be used to perform simple arithmetic operations on `oml.Datetime` objects. `oml.Timedelta` objects can be multiplied by an integer value or a floating point value. Subtracting dates produces an `oml.Timedelta` object that can be added or subtracted by an `oml.Timedate` object to produce another date.","","Create a time-based object using the current date and time, and create an `oml.Timedelta` object named DELT1 to determine a past and future dates.","","---"],"enabled":true,"result":{"startTime":1714497368793,"interpreter":"md.medium","endTime":1714497368873,"results":[{"message":"<h2 id=\"oml-timedelta\">OML Timedelta<\/h2>\n<p><code>oml.Timedelta<\/code> objects represent a column of differences between two dates or times and can be used to perform simple arithmetic operations on <code>oml.Datetime<\/code> objects. <code>oml.Timedelta<\/code> objects can be multiplied by an integer value or a floating point value. Subtracting dates produces an <code>oml.Timedelta<\/code> object that can be added or subtracted by an <code>oml.Timedate<\/code> object to produce another date.<\/p>\n<p>Create a time-based object using the current date and time, and create an <code>oml.Timedelta<\/code> object named DELT1 to determine a past and future dates.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"timedelta","message":["%python","","today = datetime.now()","print('today:', today)","","delt1 = timedelta(days=1, hours=2, seconds=5)","print('delt1:', delt1)","print('type:', type(delt1))"],"enabled":true,"result":{"startTime":1714497368958,"interpreter":"python.medium","endTime":1714497369039,"results":[{"message":"today: 2024-04-30 17:16:09.021485\ndelt1: 1 day, 2:00:05\ntype: <class 'datetime.timedelta'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"oml.Timedelta","message":["%python","","dat = pd.DataFrame({'datetime': [today], 'timedelta': [delt1]})","","DAT = oml.push(dat, dbtypes = ['TIMESTAMP', 'INTERVAL DAY TO SECOND'])","print(DAT)","","TODAY = DAT['datetime']","DELT1 = DAT['timedelta']","","print('TODAY:', today)","print('DELT1:', DELT1)","print('type:', type(DELT1))"],"enabled":true,"result":{"startTime":1714497369120,"interpreter":"python.medium","endTime":1714497369348,"results":[{"message":"                    datetime       timedelta\n0 2024-04-30 17:16:09.021485 1 days 02:00:05\nTODAY: 2024-04-30 17:16:09.021485\nDELT1: [datetime.timedelta(days=1, seconds=7205)]\ntype: <class 'oml.core.timedelta.Timedelta'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"timedelta operations","message":["%python","","past_date1 = today - delt1","print('past date 1:', past_date1)","","past_date2 = today - (delt1 *3)","print('past date 2:', past_date2)","","future_date1 = today + delt1","print('future date 1:', future_date1)","","future_date2 = today + (delt1 *3)","print('future date 2:', future_date2)"],"enabled":true,"result":{"startTime":1714497369439,"interpreter":"python.medium","endTime":1714497369518,"results":[{"message":"past date 1: 2024-04-29 15:16:04.021485\npast date 2: 2024-04-27 11:15:54.021485\nfuture date 1: 2024-05-01 19:16:14.021485\nfuture date 2: 2024-05-03 23:16:24.021485\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"oml.Timedelta operations","message":["%python","","past_date3 = TODAY - DELT1","print('past date 3:', past_date1)","","past_date4 = TODAY - (DELT1 *3)","print('past date 4:', past_date2)","","future_date3 = TODAY + DELT1","print('future date 3:', future_date1)","","future_date4 = TODAY + (DELT1 *3)","print('future date 4:', future_date2)"],"enabled":true,"result":{"startTime":1714497369605,"interpreter":"python.medium","endTime":1714497369683,"results":[{"message":"past date 3: 2024-04-29 15:16:04.021485\npast date 4: 2024-04-27 11:15:54.021485\nfuture date 3: 2024-05-01 19:16:14.021485\nfuture date 4: 2024-05-03 23:16:24.021485\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Value Comparison","","Dates and datetime objects can be compared using standard arithmetic comparison operators (like <, >, <=, >=, != etc.). ","","The examples below compare `oml.Datetime` columns in a proxy object."],"enabled":true,"result":{"startTime":1714497369764,"interpreter":"md.medium","endTime":1714497369826,"results":[{"message":"<h2 id=\"value-comparison\">Value Comparison<\/h2>\n<p>Dates and datetime objects can be compared using standard arithmetic comparison operators (like &lt;, &gt;, &lt;=, &gt;=, != etc.).<\/p>\n<p>The examples below compare <code>oml.Datetime<\/code> columns in a proxy object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create Proxy data frame with two oml.Datetime columns","message":["%python","","d1 = datetime(2005, 7, 14, 5, 10, 30)","d2 = datetime(2004, 6, 30, 1, 22, 46)","d3 = datetime(2003, 12, 10, 12, 50, 25)","d4 = datetime(2002, 3, 20, 20, 42, 59)","","d3 = pd.DataFrame({'X': [d1, d2], 'Y': [d3, d4]})                                                              ","D3 = oml.push(d3, dbtypes = ['TIMESTAMP', 'TIMESTAMP'])","","print(D3)"],"enabled":true,"result":{"startTime":1714497369912,"interpreter":"python.medium","endTime":1714497370116,"results":[{"message":"                    X                   Y\n0 2005-07-14 05:10:30 2003-12-10 12:50:25\n1 2004-06-30 01:22:46 2002-03-20 20:42:59\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["","%python","","D4 = D3['X']","D5 = D3['Y']","","print(\"D4:\", D4)","print(\"D4 type:\", type(D4))","","print(\"D5:\", D5)","print(\"D5 type:\", type(D5))"],"enabled":true,"result":{"startTime":1714497370204,"interpreter":"python.medium","endTime":1714497370478,"results":[{"message":"D4: [datetime.datetime(2005, 7, 14, 5, 10, 30), datetime.datetime(2004, 6, 30, 1, 22, 46)]\nD4 type: <class 'oml.core.datetime.Datetime'>\nD5: [datetime.datetime(2003, 12, 10, 12, 50, 25), datetime.datetime(2002, 3, 20, 20, 42, 59)]\nD5 type: <class 'oml.core.datetime.Datetime'>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"equal to","message":["%python","","# equal to","print(D4 == D5)","","# not equal to","print(D4 != D5) "],"enabled":true,"result":{"startTime":1714497370564,"interpreter":"python.medium","endTime":1714497370721,"results":[{"message":"[False, False]\n[True, True]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"greater than","message":["%python","","# greater than","print(D4 > D5) ","","# greater than or equal to","print(D4 >= D5) ## "],"enabled":true,"result":{"startTime":1714497370806,"interpreter":"python.medium","endTime":1714497370959,"results":[{"message":"[True, True]\n[True, True]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"less than","message":["%python","","# less than","print(D4 < D5)","","# less than or equal to","print(D4 <= D5)"],"enabled":true,"result":{"startTime":1714497371040,"interpreter":"python.medium","endTime":1714497371192,"results":[{"message":"[False, False]\n[False, False]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"max/min","message":["%python","","print(\"max:\", D3['X'].max())","","print(\"min:\", D3['Y'].min())"],"enabled":true,"result":{"startTime":1714497371278,"interpreter":"python.medium","endTime":1714497371400,"results":[{"message":"max: 2005-07-14 05:10:30\nmin: 2002-03-20 20:42:59\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Value Replacement","","Update specific or all elements of an `oml.Datetime` object, such as year, month, and day."],"enabled":true,"result":{"startTime":1714497371484,"interpreter":"md.medium","endTime":1714497371548,"results":[{"message":"<h2 id=\"value-replacement\">Value Replacement<\/h2>\n<p>Update specific or all elements of an <code>oml.Datetime<\/code> object, such as year, month, and day.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","D4 = D3['X'].replace(year=2000)","print(\"D4:\", D4)","","D5 = D3['X'].replace(month=11)","print(\"D5:\", D5)","","D6 = D3['X'].replace(day=6)","print(\"D6:\", D6)"],"enabled":true,"result":{"startTime":1714497371630,"interpreter":"python.medium","endTime":1714497371817,"results":[{"message":"D4: [datetime.datetime(2000, 7, 14, 5, 10, 30), datetime.datetime(2000, 6, 30, 1, 22, 46)]\nD5: [datetime.datetime(2005, 11, 14, 5, 10, 30), datetime.datetime(2004, 11, 30, 1, 22, 46)]\nD6: [datetime.datetime(2005, 7, 6, 5, 10, 30), datetime.datetime(2004, 6, 6, 1, 22, 46)]\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date Exploration","","Create an OML proxy object containing a datetime column and explore the object using the transparency layer functions `describe`, `nunique`, `min` and `max`.","","---"],"enabled":true,"result":{"startTime":1714497371906,"interpreter":"md.medium","endTime":1714497371970,"results":[{"message":"<h2 id=\"date-exploration\">Date Exploration<\/h2>\n<p>Create an OML proxy object containing a datetime column and explore the object using the transparency layer functions <code>describe<\/code>, <code>nunique<\/code>, <code>min<\/code> and <code>max<\/code>.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","date_today = datetime.now()","ndays = 10","","df = pd.DataFrame({'Date': [date_today + timedelta(days=x) for x in range(ndays)], ","                   'Val': pd.Series(np.random.randn(ndays))})","","DF = oml.push(df)","DF.head()"],"enabled":true,"result":{"startTime":1714497372052,"interpreter":"python.medium","endTime":1714497372431,"results":[{"message":"                        Date       Val\n0 2024-04-30 17:16:12.113756 -0.204708\n1 2024-05-01 17:16:12.113756  0.478943\n2 2024-05-02 17:16:12.113756 -0.519439\n3 2024-05-03 17:16:12.113756 -0.555730\n4 2024-05-04 17:16:12.113756  1.965781\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"describe date column","message":["%python","","DF['Date'].describe()"],"enabled":true,"result":{"startTime":1714497372513,"interpreter":"python.medium","endTime":1714497372635,"results":[{"message":"count                             10\nunique                            10\ntop       2024-05-09 17:16:12.113756\nfreq                               1\nName: Date, dtype: object\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"number of unique dates","message":["%python","","DF['Date'].nunique()"],"enabled":true,"result":{"startTime":1714497372715,"interpreter":"python.medium","endTime":1714497372812,"results":[{"message":"10\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"earliest date","message":["%python","","DF['Date'].min()"],"enabled":true,"result":{"startTime":1714497372892,"interpreter":"python.medium","endTime":1714497372989,"results":[{"message":"Timestamp('2024-04-30 17:16:12.113756')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"latest date","message":["%python","","DF['Date'].max()"],"enabled":true,"result":{"startTime":1714497373072,"interpreter":"python.medium","endTime":1714497373194,"results":[{"message":"Timestamp('2024-05-09 17:16:12.113756')\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date sorting","","Explore the `sort_values`, `head`, and  `tail` functions by sorting values by Date in ascending and descending order.","","---"],"enabled":true,"result":{"startTime":1714497373272,"interpreter":"md.medium","endTime":1714497373337,"results":[{"message":"<h2 id=\"date-sorting\">Date sorting<\/h2>\n<p>Explore the <code>sort_values<\/code>, <code>head<\/code>, and  <code>tail<\/code> functions by sorting values by Date in ascending and descending order.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"ascending order - first five rows","message":["%python","","DF.sort_values(by = ['Date'], ascending=False).head(5)"],"enabled":true,"result":{"startTime":1714497373414,"interpreter":"python.medium","endTime":1714497374158,"results":[{"message":"                        Date       Val\n0 2024-05-09 17:16:12.113756  1.246435\n1 2024-05-08 17:16:12.113756  0.769023\n2 2024-05-07 17:16:12.113756  0.281746\n3 2024-05-06 17:16:12.113756  0.092908\n4 2024-05-05 17:16:12.113756  1.393406\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"descending order - last 5 rows","message":["%python","","DF.sort_values(by = ['Date'], ascending=False).tail(5)"],"enabled":true,"result":{"startTime":1714497374240,"interpreter":"python.medium","endTime":1714497374968,"results":[{"message":"                        Date       Val\n0 2024-05-04 17:16:12.113756  1.965781\n1 2024-05-03 17:16:12.113756 -0.555730\n2 2024-05-02 17:16:12.113756 -0.519439\n3 2024-05-01 17:16:12.113756  0.478943\n4 2024-04-30 17:16:12.113756 -0.204708\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1714497375051,"interpreter":"md.medium","endTime":1714497375114,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714497375231,"interpreter":"md.medium","endTime":1714497375294,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation Normalization and Scaling","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715292959086,"interpreter":"md.low","endTime":1715292959164,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","%md","","# OML4Py Data Transformation: Normalization and Scaling","","In this notebook, we demonstrate how to normalize and scale data using OML4Py.","","Using the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","","**Note** : when building or applying a model using in-database Oracle Machine Learning algorithms, ","automatic data preparatio will normalize data automatically as needed by specific algorithms. ","For instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715292959237,"interpreter":"md.low","endTime":1715292959310,"results":[{"message":"<h1 id=\"oml4py-data-transformation-normalization-and-scaling\">OML4Py Data Transformation: Normalization and Scaling<\/h1>\n<p>In this notebook, we demonstrate how to normalize and scale data using OML4Py.<\/p>\n<p>Using the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong> : when building or applying a model using in-database Oracle Machine Learning algorithms,\nautomatic data preparatio will normalize data automatically as needed by specific algorithms.\nFor instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1715292959389,"interpreter":"md.low","endTime":1715292959466,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl","","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1715292959551,"interpreter":"python.low","endTime":1715292961550,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for customer lifetime value table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')"],"enabled":true,"result":{"startTime":1715292961623,"interpreter":"python.low","endTime":1715292961812,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1715292961887,"interpreter":"python.low","endTime":1715292962072,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271.0\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalization by z-score - using mean and standard deviation","message":["%python","","income_mean = CUST_DF['INCOME'].mean()","income_std  = CUST_DF['INCOME'].std()","","CUST_DF = CUST_DF.concat({'INCOME_NORMALIZED':(CUST_DF['INCOME'] - income_mean)/income_std})","","z.show(CUST_DF[['INCOME', 'INCOME_NORMALIZED']].head().round(4))"],"enabled":true,"result":{"startTime":1715292962152,"interpreter":"python.low","endTime":1715292962427,"results":[{"message":"INCOME\tINCOME_NORMALIZED\n65871.0\t0.1148\n68747.0\t0.5464\n68684.0\t0.5369\n59354.0\t-0.8632\n84801.0\t2.9556\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalize INCOME using min-max scaling","message":["%python","","income_min = CUST_DF['INCOME'].min()","income_max = CUST_DF['INCOME'].max()","","CUST_DF = CUST_DF.concat({'INCOME_MINMAX_SCALED': (CUST_DF['INCOME'] - int(income_min))/ int(income_max - income_min)})","","z.show(CUST_DF[['INCOME', 'INCOME_MINMAX_SCALED']].head().round(4))"],"enabled":true,"result":{"startTime":1715292962506,"interpreter":"python.low","endTime":1715292962884,"results":[{"message":"INCOME\tINCOME_MINMAX_SCALED\n65871.0\t0.3861\n68747.0\t0.4247\n68684.0\t0.4239\n59354.0\t0.2986\n84801.0\t0.6402\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Filter zero values from column TOTAL_AUTOM_PAYMENTS, then transform using base 10 logarithm","message":["%python","","CUST_DF_FILTERED = CUST_DF[CUST_DF['TOTAL_AUTOM_PAYMENTS'] > 0]","CUST_DF_FILTERED = CUST_DF_FILTERED.concat({'LOG_TOTAL_AUTOM_PAYMENTS': CUST_DF_FILTERED['TOTAL_AUTOM_PAYMENTS'].log(10)})","","z.show(CUST_DF_FILTERED[['TOTAL_AUTOM_PAYMENTS', 'LOG_TOTAL_AUTOM_PAYMENTS']].head().round(4))"],"enabled":true,"result":{"startTime":1715292962967,"interpreter":"python.low","endTime":1715292963156,"results":[{"message":"TOTAL_AUTOM_PAYMENTS\tLOG_TOTAL_AUTOM_PAYMENTS\n287.0\t2.4579\n132.0\t2.1206\n628.0\t2.798\n2143.0\t3.331\n3707.0\t3.569\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Histogram on the original column","message":["%python","","import matplotlib.pyplot as plt","plt.style.use('seaborn')","","plt.figure(figsize=[20,6])","oml.graphics.hist(CUST_DF['TOTAL_AUTOM_PAYMENTS'], bins=10, color='C0',","                 linestyle='solid', edgecolor='black')","","plt.title('Histogram of original TOTAL_AUTOM_PAYMENTS')","plt.xlabel('TOTAL_AUTOM_PAYMENTS')","plt.ylabel('Customer Counts')","_ = plt.show()"],"enabled":true,"result":{"startTime":1715292963236,"interpreter":"python.low","endTime":1715292963671,"results":[{"message":"<div style='width:auto;height:auto'><img 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plt.show()"],"enabled":true,"result":{"startTime":1715292963769,"interpreter":"python.low","endTime":1715292966172,"results":[{"message":"<div style='width:auto;height:auto'><img 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diff --git a/machine-learning/notebooks/python/OML4Py Data Transformation One Hot Encoding.dsnb b/machine-learning/notebooks/python/OML4Py Data Transformation One Hot Encoding.dsnb
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Data Transformation One Hot Encoding","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Data Transformation: One Hot Encoding","In this notebook, we demonstrate how to do one hot encoding using OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","**Note**: If the user plans to use the in-database algorithms, one hot encoding is automatically applied for those algorithms requiring it. The in-databae algorithms automatically explode the categorical columns and fit the model on the prepared data internally. For more details, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/cross-tabulate-data.html\" target=\"_blank\">OML4Py Cross-Tabulation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML Folder on Oracle Github<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries ","message":["%python","","import pandas as pd","import oml"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to CUSTOMER_INSURANCE_LTV and subset of columns","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV')","cat_cols = ['MARITAL_STATUS', 'STATE', 'GENDER', 'REGION']","CUST_CAT_DF = CUST_DF[['CUSTOMER_ID'] + cat_cols]","z.show(CUST_CAT_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create function for one hot encoding","message":["%python","","def encode(DF, ids, col):","    assert isinstance(ids, list)","    CNT_DF = DF.crosstab(ids, col)","    PIVOT_DF = CNT_DF.pivot_table(ids, col, 'count', aggfunc = oml.DataFrame.count)","    cols = PIVOT_DF.columns[len(ids):]","    new_columns = [ col + '_' + c.split('_')[1][1:-1] for c in cols]","    new_columns = ids + new_columns","    PIVOT_DF.rename(columns = new_columns)","    return PIVOT_DF","    "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one hot encoding to MARITAL_STATUS column and check the transformed data","message":["%python","","ONEHOT_DF = encode(CUST_CAT_DF, ['CUSTOMER_ID'], 'MARITAL_STATUS')    ","z.show(ONEHOT_DF.head())","    "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one hot encoding to all selected categorical columns","message":["%python","","RES_DF = CUST_CAT_DF[['CUSTOMER_ID']]","for i, col in enumerate(cat_cols):","    if col == 'CUSTOMER_ID':","        continue","    ONEHOT_DF = encode(CUST_CAT_DF, ['CUSTOMER_ID'], col)","    RES_DF = RES_DF.merge(ONEHOT_DF, on ='CUSTOMER_ID', how = 'inner', suffixes = ['', ''])","    ","z.show(RES_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Replace None and nan values with 0","message":["%python","","cols = RES_DF.columns[1:]","for col in cols:","    RES_DF = RES_DF.replace(old = [None], new = [0.0], default = 1.0, columns = [col])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the result","message":["%python","","z.show(RES_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/python/OML4Py Dataset Creation.dsnb b/machine-learning/notebooks/python/OML4Py Dataset Creation.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py Dataset Creation.dsnb	
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Dataset Creation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Demonstration Dataset Creation","","This script loads the sample datasets `BREASTCANCER` and `IRIS` provided with the SciKit-Learn Python package and imports into them into the Oracle Database by using the oml.create() function. ","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","","from sklearn import datasets","import pandas as pd"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load breast cancer data and create table","message":["%python","","bc = datasets.load_breast_cancer()","bc_data = bc.data.astype(float)","X = pd.DataFrame(bc_data, columns = bc.feature_names)","y = pd.DataFrame(bc.target, columns = ['TARGET'])","","try:","    oml.drop(table='BREASTCANCER')","except:","    pass","BREASTCANCER = oml.create(pd.concat([X, y], axis=1), table = 'BREASTCANCER')","print(\"Shape:\",BREASTCANCER.shape)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load IRIS dataset and create table","message":["%python","","iris = datasets.load_iris()","X = pd.DataFrame(iris.data, ","                 columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), ","                 columns = ['SPECIES'])","","try:","    oml.drop(table='IRIS')","except:","    pass","IRIS = oml.create(pd.concat([X, y], axis=1), table = 'IRIS')","print(\"Shape:\",IRIS.shape)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/python/OML4Py Feature Engineering Aggregation.dsnb b/machine-learning/notebooks/python/OML4Py Feature Engineering Aggregation.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py Feature Engineering Aggregation.dsnb	
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Engineering Aggregation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Feature Engineering: Aggregation","In this notebook, we demonstrate how to perform aggregation for min, max, mean, and count using OML4Py.","","We use the SH schema SALES table, which contains transaction records for each customer and products purchased. We create features by aggregating the amount sold for each customer and product pair.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/cross-tabulate-data.html\" target=\"_blank\">OML4Py Cross-Tabulation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/combine-data.html\" target=\"_blank\">OML4Py Combining Data<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import Python libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to SALES data and view a few records","message":["%python","","SALES_DF = oml.sync(query = 'select cust_id, prod_id, amount_sold from sh.sales')","","z.show(SALES_DF.head())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View multiple records for each customer and product pair with count","message":["%python","","z.show(SALES_DF.crosstab(['CUST_ID', 'PROD_ID']).head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Compute cross tabulation using min, max, mean, and count for each customer and product","message":["%python","","MAX_DF   = SALES_DF.crosstab(['CUST_ID', 'PROD_ID'], values = 'AMOUNT_SOLD', aggfunc = oml.DataFrame.max)","MIN_DF   = SALES_DF.crosstab(['CUST_ID', 'PROD_ID'], values = 'AMOUNT_SOLD', aggfunc = oml.DataFrame.min)","MEAN_DF  = SALES_DF.crosstab(['CUST_ID', 'PROD_ID'], values = 'AMOUNT_SOLD', aggfunc = oml.DataFrame.mean)","COUNT_DF = SALES_DF.crosstab(['CUST_ID', 'PROD_ID'], values = 'AMOUNT_SOLD', aggfunc = oml.DataFrame.count)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Join all results","message":["%python","","AGG_DF = MAX_DF.merge(MIN_DF,   on = ['CUST_ID', 'PROD_ID'], how = 'inner', suffixes =['', '']) ","AGG_DF = AGG_DF.merge(MEAN_DF,  on = ['CUST_ID', 'PROD_ID'], how = 'inner', suffixes =['', ''])","AGG_DF = AGG_DF.merge(COUNT_DF, on = ['CUST_ID', 'PROD_ID'], how = 'inner', suffixes =['', ''])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View aggregated data","message":["%python","","z.show(AGG_DF.round(2).head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/python/OML4Py Feature Extraction ESA Wiki Model.dsnb b/machine-learning/notebooks/python/OML4Py Feature Extraction ESA Wiki Model.dsnb
deleted file mode 100755
index e31929ac..00000000
--- a/machine-learning/notebooks/python/OML4Py Feature Extraction ESA Wiki Model.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Extraction ESA Wiki Model","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714408604280,"interpreter":"md.high","endTime":1714408604432,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for Python function *oml.esa* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we'll use the Wikipedia Model as an example:",""," - Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model "," - Examples of using the Wiki ESA model"," - Basic ESA Wiki model features for words"," - Basic ESA Wiki model features for phrases"," - ESA Wiki Model document similarity on phrases"," - ESA Wiki Model document similarity on long phrases","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714408604506,"interpreter":"md.high","endTime":1714408604607,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <code>semantic relatedness<\/code> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>The Oracle Machine Learning for Python function <em>oml.esa<\/em> extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>In this notebook, we'll use the Wikipedia Model as an example:<\/p>\n<ul>\n<li>Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model<\/li>\n<li>Examples of using the Wiki ESA model<\/li>\n<li>Basic ESA Wiki model features for words<\/li>\n<li>Basic ESA Wiki model features for phrases<\/li>\n<li>ESA Wiki Model document similarity on phrases<\/li>\n<li>ESA Wiki Model document similarity on long phrases<\/li>\n<\/ul>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a>"],"enabled":true,"result":{"startTime":1714408604686,"interpreter":"md.high","endTime":1714408604904,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Using the Wikipedia model as an example","","The model was pre-loaded to the Oracle Autonomous Database because it is a large 800 MB model object.","","Follow the Setup from the template notebook titled `OML Import Wiki ESA Model`.","","Related Oracle blogs:","* <a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" onclick=\"return ! window.open('https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2');\"> New Wikipedia-Based Cognitive Model Available for Text Processing <\/a>","* <a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" onclick=\"return ! window.open('https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model');\"> Text Analytics using a pre-built Wikipedia-based Topic Model <\/a>","* <a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" onclick=\"return ! window.open('https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics');\"> Explicit Semantic Analysis (ESA) for Text Analytics <\/a>","","We need to create the proxy for the existing model into OML4Py"," - If the model is available on the same user, you can use the code:","    In Python: \"esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname') \""," - If the model is available on a different schema, you need to:","    1. Run as SYSDBA a \"grant execute on model to <user> or public\" in sql; then","    2. In Python \"esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname', model_owner = 'model owner schema name') \""],"enabled":true,"result":{"startTime":1714408609395,"interpreter":"md.high","endTime":1714408609461,"results":[{"message":"<h2 id=\"using-the-wikipedia-model-as-an-example\">Using the Wikipedia model as an example<\/h2>\n<p>The model was pre-loaded to the Oracle Autonomous Database because it is a large 800 MB model object.<\/p>\n<p>Follow the Setup from the template notebook titled <code>OML Import Wiki ESA Model<\/code>.<\/p>\n<p>Related Oracle blogs:<\/p>\n<ul>\n<li><a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" onclick=\"return ! window.open('https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2');\"> New Wikipedia-Based Cognitive Model Available for Text Processing <\/a><\/li>\n<li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" onclick=\"return ! window.open('https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model');\"> Text Analytics using a pre-built Wikipedia-based Topic Model <\/a><\/li>\n<li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" onclick=\"return ! window.open('https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics');\"> Explicit Semantic Analysis (ESA) for Text Analytics <\/a><\/li>\n<\/ul>\n<p>We need to create the proxy for the existing model into OML4Py<\/p>\n<ul>\n<li>If the model is available on the same user, you can use the code:\nIn Python: &quot;esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname') &quot;<\/li>\n<li>If the model is available on a different schema, you need to:\n<ol>\n<li>Run as SYSDBA a &quot;grant execute on model to <user> or public&quot; in sql; then<\/li>\n<li>In Python &quot;esa_wiki_mod = oml.esa(model_name = 'in-db wiki modelname', model_owner = 'model owner schema name') &quot;<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Delete existing policies and create new policies for the Wiki Lexer","message":["%python","","# Should be executed in a separate session, otherwise it will not work","cur = cursor()","cur.execute(\"BEGIN ctx_ddl.drop_policy('wiki_txtpol'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","cur.execute(\"BEGIN ctx_ddl.drop_preference( 'wiki_lexer'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","cur.execute(\"BEGIN ctx_ddl.create_preference('wiki_lexer', 'BASIC_LEXER'); END;\")","cur.execute(\"BEGIN ctx_ddl.set_attribute('wiki_lexer', 'INDEX_STEMS', 'ENGLISH'); END;\")","cur.execute(\"BEGIN ctx_ddl.create_policy(policy_name => 'wiki_txtpol', lexer => 'wiki_lexer'); END;\")","cur.close()"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","import pandas as pd"],"enabled":true,"result":{"startTime":1714408605314,"interpreter":"python.high","endTime":1714408607514,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check to see if the WIKI_MODEL is available","message":["%sql","","select MODEL_NAME, MINING_FUNCTION, ALGORITHM, ALGORITHM_TYPE, CREATION_DATE from USER_MINING_MODELS where MODEL_NAME='WIKI_MODEL'"],"enabled":true,"result":{"startTime":1714408607589,"interpreter":"sql.high","endTime":1714408609088,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\nWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2023-11-08 19:36:42\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model ","message":["%python","","esa_wiki_mod = oml.esa(model_name = 'wiki_model')"],"enabled":true,"result":{"startTime":1714408609164,"interpreter":"python.high","endTime":1714408613665,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the first 100 rows of the 21.7 mi rows of features","message":["%python","","z.show(esa_wiki_mod.features.head(100).round(4))"],"enabled":true,"result":{"startTime":1714408613750,"interpreter":"python.high","endTime":1714408653998,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n'Adud al-Dawla\tTEXT.ABBAD\tNone\t0.0191\n'Adud al-Dawla\tTEXT.ABBASID\tNone\t0.0249\n'Adud al-Dawla\tTEXT.ABU\tNone\t0.0597\n'Adud al-Dawla\tTEXT.ADUD\tNone\t0.5659\n'Adud al-Dawla\tTEXT.ALABAMA\tNone\t0.2283\n'Adud al-Dawla\tTEXT.ARDASHIR\tNone\t0.0112\n'Adud al-Dawla\tTEXT.AYYAD\tNone\t0.025\n'Adud al-Dawla\tTEXT.BADR\tNone\t0.0099\n'Adud al-Dawla\tTEXT.BAGHDAD\tNone\t0.0459\n'Adud al-Dawla\tTEXT.BAHA\tNone\t0.0105\n'Adud al-Dawla\tTEXT.BAHRAM\tNone\t0.0161\n'Adud al-Dawla\tTEXT.BAKR\tNone\t0.0134\n'Adud al-Dawla\tTEXT.BALOCH\tNone\t0.0105\n'Adud al-Dawla\tTEXT.BAM\tNone\t0.0089\n'Adud al-Dawla\tTEXT.BANI\tNone\t0.0098\n'Adud al-Dawla\tTEXT.BAQILLANI\tNone\t0.0145\n'Adud al-Dawla\tTEXT.BARDAS\tNone\t0.0128\n'Adud al-Dawla\tTEXT.BEDOUIN\tNone\t0.0087\n'Adud al-Dawla\tTEXT.BUYID\tNone\t0.0847\n'Adud al-Dawla\tTEXT.BUYIDS\tNone\t0.0195\n'Adud al-Dawla\tTEXT.BYZANTINES\tNone\t0.0202\n'Adud al-Dawla\tTEXT.BYZANTIUM\tNone\t0.0082\n'Adud al-Dawla\tTEXT.CALIPH\tNone\t0.0232\n'Adud al-Dawla\tTEXT.CARAVANSERAI\tNone\t0.0114\n'Adud al-Dawla\tTEXT.CHILDLESS\tNone\t0.008\n'Adud al-Dawla\tTEXT.CHOSROES\tNone\t0.0135\n'Adud al-Dawla\tTEXT.CONCUBINE\tNone\t0.008\n'Adud al-Dawla\tTEXT.CONSUMMATE\tNone\t0.0082\n'Adud al-Dawla\tTEXT.DAWLA\tNone\t0.7239\n'Adud al-Dawla\tTEXT.DAYLAMITE\tNone\t0.0502\n'Adud al-Dawla\tTEXT.DAYLAMITES\tNone\t0.0143\n'Adud al-Dawla\tTEXT.DESPOT\tNone\t0.0094\n'Adud al-Dawla\tTEXT.DINAR\tNone\t0.0096\n'Adud al-Dawla\tTEXT.DIYAR\tNone\t0.0191\n'Adud al-Dawla\tTEXT.EMBELLISH\tNone\t0.0079\n'Adud al-Dawla\tTEXT.EMIR\tNone\t0.0315\n'Adud al-Dawla\tTEXT.ENRICHMENT\tNone\t0.0081\n'Adud al-Dawla\tTEXT.EUPHRATES\tNone\t0.0087\n'Adud al-Dawla\tTEXT.FADL\tNone\t0.0227\n'Adud al-Dawla\tTEXT.FAKHR\tNone\t0.0276\n'Adud al-Dawla\tTEXT.FARAJ\tNone\t0.0117\n'Adud al-Dawla\tTEXT.GOR\tNone\t0.0113\n'Adud al-Dawla\tTEXT.GORGAN\tNone\t0.0122\n'Adud al-Dawla\tTEXT.GRAMMARIAN\tNone\t0.0093\n'Adud al-Dawla\tTEXT.HAMADAN\tNone\t0.017\n'Adud al-Dawla\tTEXT.HAMDANID\tNone\t0.0188\n'Adud al-Dawla\tTEXT.HAMDANIDS\tNone\t0.0259\n'Adud al-Dawla\tTEXT.HARUN\tNone\t0.0101\n'Adud al-Dawla\tTEXT.HASAN\tNone\t0.0151\n'Adud al-Dawla\tTEXT.HORMUZ\tNone\t0.0109\n'Adud al-Dawla\tTEXT.HUSAIN\tNone\t0.0149\n'Adud al-Dawla\tTEXT.HUSAYN\tNone\t0.0096\n'Adud al-Dawla\tTEXT.IBN\tNone\t0.0843\n'Adud al-Dawla\tTEXT.ILYAS\tNone\t0.017\n'Adud al-Dawla\tTEXT.IMAD\tNone\t0.0333\n'Adud al-Dawla\tTEXT.IMAM\tNone\t0.0117\n'Adud al-Dawla\tTEXT.IMPALE\tNone\t0.009\n'Adud al-Dawla\tTEXT.IMRAN\tNone\t0.0095\n'Adud al-Dawla\tTEXT.IRRIGATE\tNone\t0.0081\n'Adud al-Dawla\tTEXT.ISFAHAN\tNone\t0.0144\n'Adud al-Dawla\tTEXT.ISFANDIYAR\tNone\t0.0147\n'Adud al-Dawla\tTEXT.ISHAQ\tNone\t0.01\n'Adud al-Dawla\tTEXT.IZZ\tNone\t0.1291\n'Adud al-Dawla\tTEXT.KAKI\tNone\t0.0116\n'Adud al-Dawla\tTEXT.KARBALA\tNone\t0.011\n'Adud al-Dawla\tTEXT.KARD\tNone\t0.0138\n'Adud al-Dawla\tTEXT.KARUN\tNone\t0.0115\n'Adud al-Dawla\tTEXT.KERMAN\tNone\t0.0322\n'Adud al-Dawla\tTEXT.KERMANSHAH\tNone\t0.0117\n'Adud al-Dawla\tTEXT.KHALAF\tNone\t0.0117\n'Adud al-Dawla\tTEXT.KHORRAMSHAHR\tNone\t0.0135\n'Adud al-Dawla\tTEXT.KHUSRAW\tNone\t0.0776\n'Adud al-Dawla\tTEXT.KHUZESTAN\tNone\t0.011\n'Adud al-Dawla\tTEXT.KURD\tNone\t0.013\n'Adud al-Dawla\tTEXT.LAQAB\tNone\t0.0138\n'Adud al-Dawla\tTEXT.MAKAN\tNone\t0.0229\n'Adud al-Dawla\tTEXT.MAKRAN\tNone\t0.0178\n'Adud al-Dawla\tTEXT.MANSUR\tNone\t0.009\n'Adud al-Dawla\tTEXT.MARSHY\tNone\t0.0085\n'Adud al-Dawla\tTEXT.MARZUBAN\tNone\t0.0221\n'Adud al-Dawla\tTEXT.MOBAD\tNone\t0.0146\n'Adud al-Dawla\tTEXT.MOSUL\tNone\t0.0191\n'Adud al-Dawla\tTEXT.MU\tNone\t0.0489\n'Adud al-Dawla\tTEXT.MUDAR\tNone\t0.0131\n'Adud al-Dawla\tTEXT.MUFID\tNone\t0.0136\n'Adud al-Dawla\tTEXT.MUQADDASI\tNone\t0.012\n'Adud al-Dawla\tTEXT.NAJAF\tNone\t0.0108\n'Adud al-Dawla\tTEXT.NASIR\tNone\t0.0088\n'Adud al-Dawla\tTEXT.NISHAPUR\tNone\t0.0115\n'Adud al-Dawla\tTEXT.NOMAD\tNone\t0.0077\n'Adud al-Dawla\tTEXT.NOWRUZ\tNone\t0.0115\n'Adud al-Dawla\tTEXT.OMAN\tNone\t0.0116\n'Adud al-Dawla\tTEXT.PATRONIZE\tNone\t0.0116\n'Adud al-Dawla\tTEXT.PERSEPOLIS\tNone\t0.0103\n'Adud al-Dawla\tTEXT.POLEMIC\tNone\t0.0085\n'Adud al-Dawla\tTEXT.QABUS\tNone\t0.0516\n'Adud al-Dawla\tTEXT.QAZVIN\tNone\t0.0116\n'Adud al-Dawla\tTEXT.RAHBA\tNone\t0.0145\n'Adud al-Dawla\tTEXT.RECONQUER\tNone\t0.0082\n'Adud al-Dawla\tTEXT.RUKN\tNone\t0.0786\n'Adud al-Dawla\tTEXT.RULER\tNone\t0.048\n'Adud al-Dawla\tTEXT.SAFFARID\tNone\t0.0129\n'Adud al-Dawla\tTEXT.SAFFARIDS\tNone\t0.0124\n'Adud al-Dawla\tTEXT.SAHIB\tNone\t0.0134\n'Adud al-Dawla\tTEXT.SAHL\tNone\t0.0168\n'Adud al-Dawla\tTEXT.SAMANIDS\tNone\t0.0176\n'Adud al-Dawla\tTEXT.SAMARRA\tNone\t0.0113\n'Adud al-Dawla\tTEXT.SAMSAM\tNone\t0.021\n'Adud al-Dawla\tTEXT.SASANIAN\tNone\t0.0182\n'Adud al-Dawla\tTEXT.SASANIANS\tNone\t0.0111\n'Adud al-Dawla\tTEXT.SECTARIAN\tNone\t0.0076\n'Adud al-Dawla\tTEXT.SELJUQ\tNone\t0.0105\n'Adud al-Dawla\tTEXT.SHAHANSHAH\tNone\t0.0179\n'Adud al-Dawla\tTEXT.SHAHRAM\tNone\t0.0139\n'Adud al-Dawla\tTEXT.SHAM\tNone\t0.0079\n'Adud al-Dawla\tTEXT.SHATT\tNone\t0.0179\n'Adud al-Dawla\tTEXT.SHAYBAN\tNone\t0.0138\n'Adud al-Dawla\tTEXT.SHIA\tNone\t0.0115\n'Adud al-Dawla\tTEXT.SHIITE\tNone\t0.0196\n'Adud al-Dawla\tTEXT.SHIRAZ\tNone\t0.0352\n'Adud al-Dawla\tTEXT.SKLEROS\tNone\t0.0142\n'Adud al-Dawla\tTEXT.SOHAR\tNone\t0.0135\n'Adud al-Dawla\tTEXT.SULAIMAN\tNone\t0.0152\n'Adud al-Dawla\tTEXT.SUNNIS\tNone\t0.0146\n'Adud al-Dawla\tTEXT.TABARISTAN\tNone\t0.0185\n'Adud al-Dawla\tTEXT.TAGHLIB\tNone\t0.0335\n'Adud al-Dawla\tTEXT.TAHIR\tNone\t0.0101\n'Adud al-Dawla\tTEXT.TAJ\tNone\t0.0129\n'Adud al-Dawla\tTEXT.TEGIN\tNone\t0.021\n'Adud al-Dawla\tTEXT.TIGRIS\tNone\t0.0091\n'Adud al-Dawla\tTEXT.TRAMPLE\tNone\t0.0088\n'Adud al-Dawla\tTEXT.VIZIER\tNone\t0.0212\n'Adud al-Dawla\tTEXT.ZOROASTRIAN\tNone\t0.0135\n'Ndrangheta\tTEXT.ACOLYTE\tNone\t0.0102\n'Ndrangheta\tTEXT.ACTIVITY\tNone\t0.0285\n'Ndrangheta\tTEXT.ADORATION\tNone\t0.0089\n'Ndrangheta\tTEXT.ANARCHIC\tNone\t0.0102\n'Ndrangheta\tTEXT.ANDR??S\tNone\t0.0079\n'Ndrangheta\tTEXT.ANTIMAFIA\tNone\t0.0345\n'Ndrangheta\tTEXT.ARGENTINIAN\tNone\t0.0085\n'Ndrangheta\tTEXT.ARREST\tNone\t0.0688\n'Ndrangheta\tTEXT.ASPROMONTE\tNone\t0.0138\n'Ndrangheta\tTEXT.AUC\tNone\t0.012\n'Ndrangheta\tTEXT.AUTONOMOUSLY\tNone\t0.0095\n'Ndrangheta\tTEXT.BANDITRY\tNone\t0.0107\n'Ndrangheta\tTEXT.BARBARO\tNone\t0.0347\n'Ndrangheta\tTEXT.BENVENUTO\tNone\t0.0114\n'Ndrangheta\tTEXT.BERLUSCONI\tNone\t0.0101\n'Ndrangheta\tTEXT.BERNARDO\tNone\t0.008\n'Ndrangheta\tTEXT.BIS\tNone\t0.0091\n'Ndrangheta\tTEXT.BKA\tNone\t0.0202\n'Ndrangheta\tTEXT.BLACKMAIL\tNone\t0.0115\n'Ndrangheta\tTEXT.BLEAK\tNone\t0.0082\n'Ndrangheta\tTEXT.BND\tNone\t0.0125\n'Ndrangheta\tTEXT.BOCCIA\tNone\t0.014\n'Ndrangheta\tTEXT.BONANNO\tNone\t0.0287\n'Ndrangheta\tTEXT.BOSS\tNone\t0.0476\n'Ndrangheta\tTEXT.BROKERAGE\tNone\t0.0091\n'Ndrangheta\tTEXT.BUNDESNACHRICHTENDIENST\tNone\t0.0206\n'Ndrangheta\tTEXT.BUSINESSPEOPLE\tNone\t0.0104\n'Ndrangheta\tTEXT.CALABRIA\tNone\t0.2034\n'Ndrangheta\tTEXT.CALABRIAN\tNone\t0.0951\n'Ndrangheta\tTEXT.CAMORRA\tNone\t0.0244\n'Ndrangheta\tTEXT.CANNED\tNone\t0.0087\n'Ndrangheta\tTEXT.CANS\tNone\t0.0086\n'Ndrangheta\tTEXT.CANTO\tNone\t0.0086\n'Ndrangheta\tTEXT.CAPO\tNone\t0.014\n'Ndrangheta\tTEXT.CARABINIERI\tNone\t0.0221\n'Ndrangheta\tTEXT.CARMELO\tNone\t0.0152\n'Ndrangheta\tTEXT.CARMINE\tNone\t0.0181\n'Ndrangheta\tTEXT.CARTEL\tNone\t0.0125\n'Ndrangheta\tTEXT.CATANZARO\tNone\t0.0189\n'Ndrangheta\tTEXT.CISTERNA\tNone\t0.0131\n'Ndrangheta\tTEXT.CLAN\tNone\t0.0965\n'Ndrangheta\tTEXT.CLANDESTINE\tNone\t0.0077\n'Ndrangheta\tTEXT.COCAINE\tNone\t0.0904\n'Ndrangheta\tTEXT.COHESION\tNone\t0.0082\n'Ndrangheta\tTEXT.COLLEGIAL\tNone\t0.011\n'Ndrangheta\tTEXT.COLUCCIO\tNone\t0.0349\n'Ndrangheta\tTEXT.COMMISSO\tNone\t0.0811\n'Ndrangheta\tTEXT.CONTRABAND\tNone\t0.0094\n'Ndrangheta\tTEXT.CORRADO\tNone\t0.0105\n'Ndrangheta\tTEXT.CORRIERE\tNone\t0.0111\n'Ndrangheta\tTEXT.COSA\tNone\t0.0311\n'Ndrangheta\tTEXT.COSENZA\tNone\t0.0124\n'Ndrangheta\tTEXT.COSIMO\tNone\t0.0357\n'Ndrangheta\tTEXT.COSMIN\tNone\t0.0197\n'Ndrangheta\tTEXT.COUNCILORS\tNone\t0.0137\n'Ndrangheta\tTEXT.COURAGEOUS\tNone\t0.008\n'Ndrangheta\tTEXT.CRIME\tNone\t0.064\n'Ndrangheta\tTEXT.CRIMINAL\tNone\t0.0497\n'Ndrangheta\tTEXT.CRIMINOLOGIST\tNone\t0.0162\n'Ndrangheta\tTEXT.DEALERSHIP\tNone\t0.0081\n'Ndrangheta\tTEXT.DEBTOR\tNone\t0.009\n'Ndrangheta\tTEXT.DECADENT\tNone\t0.0092\n'Ndrangheta\tTEXT.DEFIANT\tNone\t0.0088\n'Ndrangheta\tTEXT.DEFRAUD\tNone\t0.0092\n'Ndrangheta\tTEXT.DEMARIA\tNone\t0.0223\n'Ndrangheta\tTEXT.DEMETRIO\tNone\t0.0117\n'Ndrangheta\tTEXT.DIA\tNone\t0.0093\n'Ndrangheta\tTEXT.DIENST\tNone\t0.0124\n'Ndrangheta\tTEXT.DIREZIONE\tNone\t0.015\n'Ndrangheta\tTEXT.DISASSEMBLE\tNone\t0.0088\n'Ndrangheta\tTEXT.DNR\tNone\t0.0191\n'Ndrangheta\tTEXT.DOMENICO\tNone\t0.0124\n'Ndrangheta\tTEXT.DOUGH\tNone\t0.0092\n'Ndrangheta\tTEXT.DRUG\tNone\t0.0576\n'Ndrangheta\tTEXT.ECSTASY\tNone\t0.013\n'Ndrangheta\tTEXT.EDITORE\tNone\t0.0123\n'Ndrangheta\tTEXT.EMBEZZLE\tNone\t0.0097\n'Ndrangheta\tTEXT.ENNOBLE\tNone\t0.0095\n'Ndrangheta\tTEXT.ERICA\tNone\t0.0088\n'Ndrangheta\tTEXT.ERNESTO\tNone\t0.0126\n'Ndrangheta\tTEXT.ESTE\tNone\t0.0083\n'Ndrangheta\tTEXT.EUROPOL\tNone\t0.0185\n'Ndrangheta\tTEXT.EVASION\tNone\t0.0082\n'Ndrangheta\tTEXT.EXCOMMUNICATE\tNone\t0.0083\n'Ndrangheta\tTEXT.EXCOMMUNICATION\tNone\t0.009\n'Ndrangheta\tTEXT.EXONERATE\tNone\t0.0084\n'Ndrangheta\tTEXT.EXPORTATION\tNone\t0.0104\n'Ndrangheta\tTEXT.EXTORT\tNone\t0.009\n'Ndrangheta\tTEXT.EXTORTION\tNone\t0.0471\n'Ndrangheta\tTEXT.EXTRADITE\tNone\t0.0137\n'Ndrangheta\tTEXT.EXTRADITION\tNone\t0.0178\n'Ndrangheta\tTEXT.FAIRFAX\tNone\t0.008\n'Ndrangheta\tTEXT.FAMILIAL\tNone\t0.0081\n'Ndrangheta\tTEXT.FAUSTO\tNone\t0.0103\n'Ndrangheta\tTEXT.FELICE\tNone\t0.0093\n'Ndrangheta\tTEXT.FERRARIS\tNone\t0.0108\n'Ndrangheta\tTEXT.FIANC??E\tNone\t0.0079\n'Ndrangheta\tTEXT.FICO\tNone\t0.0125\n'Ndrangheta\tTEXT.FINANZA\tNone\t0.0191\n'Ndrangheta\tTEXT.FORZA\tNone\t0.0097\n'Ndrangheta\tTEXT.FRAUDULENT\tNone\t0.0078\n'Ndrangheta\tTEXT.FRAUDULENTLY\tNone\t0.01\n'Ndrangheta\tTEXT.FRAUENFELD\tNone\t0.0202\n'Ndrangheta\tTEXT.FREELOAD\tNone\t0.0144\n'Ndrangheta\tTEXT.FREEMASONRY\tNone\t0.0094\n'Ndrangheta\tTEXT.GAMBINO\tNone\t0.0209\n'Ndrangheta\tTEXT.GANGLAND\tNone\t0.0115\n'Ndrangheta\tTEXT.GERACE\tNone\t0.0137\n'Ndrangheta\tTEXT.GETTY\tNone\t0.0182\n'Ndrangheta\tTEXT.GIACOMO\tNone\t0.008\n'Ndrangheta\tTEXT.GIANCARLO\tNone\t0.0097\n'Ndrangheta\tTEXT.GIOIA\tNone\t0.0167\n'Ndrangheta\tTEXT.GIORGI\tNone\t0.0104\n'Ndrangheta\tTEXT.GIORNALE\tNone\t0.0126\n'Ndrangheta\tTEXT.GIOVANI\tNone\t0.0122\n'Ndrangheta\tTEXT.GIUSEPPE\tNone\t0.0476\n'Ndrangheta\tTEXT.GONZI\tNone\t0.0451\n'Ndrangheta\tTEXT.GRASSO\tNone\t0.0122\n'Ndrangheta\tTEXT.GTA\tNone\t0.0111\n'Ndrangheta\tTEXT.GUARDIA\tNone\t0.0141\n'Ndrangheta\tTEXT.GUIDO\tNone\t0.0162\n'Ndrangheta\tTEXT.HANDOVER\tNone\t0.0089\n'Ndrangheta\tTEXT.HELVETICA\tNone\t0.012\n'Ndrangheta\tTEXT.HEROIN\tNone\t0.0115\n'Ndrangheta\tTEXT.HEROISM\tNone\t0.0085\n'Ndrangheta\tTEXT.HIERARCHICALLY\tNone\t0.0107\n'Ndrangheta\tTEXT.ILLICIT\tNone\t0.0116\n'Ndrangheta\tTEXT.ILVA\tNone\t0.0143\n'Ndrangheta\tTEXT.IMPORTATION\tNone\t0.008\n'Ndrangheta\tTEXT.IMPORTER\tNone\t0.0083\n'Ndrangheta\tTEXT.INCONSPICUOUS\tNone\t0.0105\n'Ndrangheta\tTEXT.INDICTABLE\tNone\t0.0118\n'Ndrangheta\tTEXT.INEFFICIENT\tNone\t0.0078\n'Ndrangheta\tTEXT.INFILTRATE\tNone\t0.0207\n'Ndrangheta\tTEXT.INFILTRATION\tNone\t0.0243\n'Ndrangheta\tTEXT.INFORMANT\tNone\t0.008\n'Ndrangheta\tTEXT.INTIMIDATION\tNone\t0.0077\n'Ndrangheta\tTEXT.INVESTIGATION\tNone\t0.0323\n'Ndrangheta\tTEXT.IONIAN\tNone\t0.009\n'Ndrangheta\tTEXT.IONIC\tNone\t0.0083\n'Ndrangheta\tTEXT.IRB\tNone\t0.015\n'Ndrangheta\tTEXT.ITALIAN\tNone\t0.0672\n'Ndrangheta\tTEXT.ITALIANO\tNone\t0.015\n'Ndrangheta\tTEXT.ITALY\tNone\t0.0721\n'Ndrangheta\tTEXT.JOERG\tNone\t0.0143\n'Ndrangheta\tTEXT.J??N\tNone\t0.0104\n'Ndrangheta\tTEXT.KIDNAPPING\tNone\t0.0146\n'Ndrangheta\tTEXT.KILOGRAM\tNone\t0.0082\n'Ndrangheta\tTEXT.KIN\tNone\t0.0078\n'Ndrangheta\tTEXT.LAUNDER\tNone\t0.0643\n'Ndrangheta\tTEXT.LEGITIMATIZING\tNone\t0.0102\n'Ndrangheta\tTEXT.LEGITIMIZE\tNone\t0.0082\n'Ndrangheta\tTEXT.LEPORE\tNone\t0.0126\n'Ndrangheta\tTEXT.LETIZIA\tNone\t0.0121\n'Ndrangheta\tTEXT.LIBRI\tNone\t0.0101\n'Ndrangheta\tTEXT.LINCHPIN\tNone\t0.0115\n'Ndrangheta\tTEXT.LOCALE\tNone\t0.0191\n'Ndrangheta\tTEXT.LOCRI\tNone\t0.0199\n'Ndrangheta\tTEXT.LOMBARDY\tNone\t0.0173\n'Ndrangheta\tTEXT.LUCA\tNone\t0.0422\n'Ndrangheta\tTEXT.LUMIA\tNone\t0.013\n'Ndrangheta\tTEXT.MAFIA\tNone\t0.1348\n'Ndrangheta\tTEXT.MAFIOSO\tNone\t0.0163\n'Ndrangheta\tTEXT.MAIDA\tNone\t0.0103\n'Ndrangheta\tTEXT.MALICIOUS\tNone\t0.0081\n'Ndrangheta\tTEXT.MANCUSO\tNone\t0.0118\n'Ndrangheta\tTEXT.MANLY\tNone\t0.0086\n'Ndrangheta\tTEXT.MARTINO\tNone\t0.0093\n'Ndrangheta\tTEXT.MATERIALIZED\tNone\t0.0094\n'Ndrangheta\tTEXT.MAXI\tNone\t0.0089\n'Ndrangheta\tTEXT.METICULOUSLY\tNone\t0.009\n'Ndrangheta\tTEXT.MILITIAMAN\tNone\t0.0091\n'Ndrangheta\tTEXT.MOBSTER\tNone\t0.0087\n'Ndrangheta\tTEXT.MONTEVIDEO\tNone\t0.0141\n'Ndrangheta\tTEXT.MORABITO\tNone\t0.0221\n'Ndrangheta\tTEXT.MULTIMILLIONAIRE\tNone\t0.0119\n'Ndrangheta\tTEXT.MUSICA\tNone\t0.0094\n'Ndrangheta\tTEXT.M??RIA\tNone\t0.0132\n'Ndrangheta\tTEXT.NARCOTIC\tNone\t0.0119\n'Ndrangheta\tTEXT.NATIONALE\tNone\t0.0079\n'Ndrangheta\tTEXT.NDRANGHETA\tNone\t0.8697\n'Ndrangheta\tTEXT.NEAPOLITAN\tNone\t0.009\n'Ndrangheta\tTEXT.NOSTRA\tNone\t0.03\n'Ndrangheta\tTEXT.OMERT??\tNone\t0.014\n'Ndrangheta\tTEXT.ONORATA\tNone\t0.0149\n'Ndrangheta\tTEXT.ONORE\tNone\t0.0202\n'Ndrangheta\tTEXT.OPERAZIONE\tNone\t0.0143\n'Ndrangheta\tTEXT.ORDEAL\tNone\t0.0083\n'Ndrangheta\tTEXT.ORDINATE\tNone\t0.0087\n'Ndrangheta\tTEXT.OVERHEAR\tNone\t0.008\n'Ndrangheta\tTEXT.PADRINO\tNone\t0.0139\n'Ndrangheta\tTEXT.PALERMO\tNone\t0.0082\n'Ndrangheta\tTEXT.PALMI\tNone\t0.0143\n'Ndrangheta\tTEXT.PAOLI\tNone\t0.0109\n'Ndrangheta\tTEXT.PAPALIA\tNone\t0.0149\n'Ndrangheta\tTEXT.PARLOR\tNone\t0.0079\n'Ndrangheta\tTEXT.PASQUALE\tNone\t0.0144\n'Ndrangheta\tTEXT.PASTRY\tNone\t0.0086\n'Ndrangheta\tTEXT.PDL\tNone\t0.0114\n'Ndrangheta\tTEXT.PELLE\tNone\t0.0173\n'Ndrangheta\tTEXT.PENTITO\tNone\t0.0137\n'Ndrangheta\tTEXT.PESCE\tNone\t0.0126\n'Ndrangheta\tTEXT.PINO\tNone\t0.0095\n'Ndrangheta\tTEXT.PIZZERIA\tNone\t0.0107\n'Ndrangheta\tTEXT.PLANCK\tNone\t0.0091\n'Ndrangheta\tTEXT.POL??CIA\tNone\t0.0131\n'Ndrangheta\tTEXT.POMACE\tNone\t0.0137\n'Ndrangheta\tTEXT.POROUS\tNone\t0.0088\n'Ndrangheta\tTEXT.PREEMINENCE\tNone\t0.0107\n'Ndrangheta\tTEXT.PRESTI\tNone\t0.014\n'Ndrangheta\tTEXT.PRIMORDIAL\tNone\t0.0085\n'Ndrangheta\tTEXT.PRINCIPALE\tNone\t0.0135\n'Ndrangheta\tTEXT.PROVINCIA\tNone\t0.0159\n'Ndrangheta\tTEXT.PUNTA\tNone\t0.0089\n'Ndrangheta\tTEXT.QUAIL\tNone\t0.0142\n'Ndrangheta\tTEXT.QUESTIONNAIRE\tNone\t0.0089\n'Ndrangheta\tTEXT.QUINTAL\tNone\t0.0132\n'Ndrangheta\tTEXT.QUINTINO\tNone\t0.0136\n'Ndrangheta\tTEXT.RACKETEER\tNone\t0.0091\n'Ndrangheta\tTEXT.RAFFAELE\tNone\t0.0103\n'Ndrangheta\tTEXT.RAPACIOUS\tNone\t0.0118\n'Ndrangheta\tTEXT.RCMP\tNone\t0.01\n'Ndrangheta\tTEXT.RECHERCHE\tNone\t0.0104\n'Ndrangheta\tTEXT.REGGIO\tNone\t0.0522\n'Ndrangheta\tTEXT.REMO\tNone\t0.0093\n'Ndrangheta\tTEXT.ROCCO\tNone\t0.0223\n'Ndrangheta\tTEXT.ROSMINI\tNone\t0.0142\n'Ndrangheta\tTEXT.SALVATORE\tNone\t0.0213\n'Ndrangheta\tTEXT.SANGU\tNone\t0.0213\n'Ndrangheta\tTEXT.SAVIANO\tNone\t0.013\n'Ndrangheta\tTEXT.SAW\tNone\t0.0089\n'Ndrangheta\tTEXT.SCANDALOUS\tNone\t0.0087\n'Ndrangheta\tTEXT.SEBASTIANO\tNone\t0.0102\n'Ndrangheta\tTEXT.SECRETIVE\tNone\t0.0086\n'Ndrangheta\tTEXT.SEMINARA\tNone\t0.0148\n'Ndrangheta\tTEXT.SERGI\tNone\t0.024\n'Ndrangheta\tTEXT.SERUM\tNone\t0.008\n'Ndrangheta\tTEXT.SHARKING\tNone\t0.0193\n'Ndrangheta\tTEXT.SICILIAN\tNone\t0.0468\n'Ndrangheta\tTEXT.SILENCER\tNone\t0.0109\n'Ndrangheta\tTEXT.SILI\tNone\t0.0131\n'Ndrangheta\tTEXT.SILVIO\tNone\t0.0089\n'Ndrangheta\tTEXT.SIMONETTA\tNone\t0.013\n'Ndrangheta\tTEXT.SINDICATO\tNone\t0.0212\n'Ndrangheta\tTEXT.SKIM\tNone\t0.0195\n'Ndrangheta\tTEXT.SLOVAK\tNone\t0.0118\n'Ndrangheta\tTEXT.SMARTPHONE\tNone\t0.0084\n'Ndrangheta\tTEXT.SMER\tNone\t0.0149\n'Ndrangheta\tTEXT.SMUGGLE\tNone\t0.026\n'Ndrangheta\tTEXT.SOCIET??\tNone\t0.0103\n'Ndrangheta\tTEXT.SOLLIMA\tNone\t0.014\n'Ndrangheta\tTEXT.SOTTO\tNone\t0.0108\n'Ndrangheta\tTEXT.SOUTH_DAKOTA\tNone\t0.008\n'Ndrangheta\tTEXT.SPARKASSE\tNone\t0.0137\n'Ndrangheta\tTEXT.STEFANO\tNone\t0.0121\n'Ndrangheta\tTEXT.SUBSERVIENT\tNone\t0.0138\n'Ndrangheta\tTEXT.SUBSTANTIATE\tNone\t0.0085\n'Ndrangheta\tTEXT.SUBUNIT\tNone\t0.0091\n'Ndrangheta\tTEXT.SUPERORDINATE\tNone\t0.0337\n'Ndrangheta\tTEXT.SUPERVISORY\tNone\t0.0082\n'Ndrangheta\tTEXT.TARANTO\tNone\t0.0099\n'Ndrangheta\tTEXT.TAURO\tNone\t0.0198\n'Ndrangheta\tTEXT.THURINGIA\tNone\t0.0094\n'Ndrangheta\tTEXT.TRAFFIC\tNone\t0.0424\n'Ndrangheta\tTEXT.TRAFFICKER\tNone\t0.0096\n'Ndrangheta\tTEXT.TRANSFERAL\tNone\t0.0131\n'Ndrangheta\tTEXT.TRANSNATIONAL\tNone\t0.0084\n'Ndrangheta\tTEXT.TROUBLEMAKER\tNone\t0.01\n'Ndrangheta\tTEXT.TUNA\tNone\t0.0087\n'Ndrangheta\tTEXT.TYRRHENIAN\tNone\t0.0107\n'Ndrangheta\tTEXT.ULM\tNone\t0.0098\n'Ndrangheta\tTEXT.UMBILICAL\tNone\t0.0103\n'Ndrangheta\tTEXT.UNBREAKABLE\tNone\t0.0098\n'Ndrangheta\tTEXT.UNDERCOVER\tNone\t0.0183\n'Ndrangheta\tTEXT.UNDERSCORE\tNone\t0.0082\n'Ndrangheta\tTEXT.UNLICENSED\tNone\t0.0087\n'Ndrangheta\tTEXT.UNODC\tNone\t0.0122\n'Ndrangheta\tTEXT.UNPROFESSIONAL\tNone\t0.0103\n'Ndrangheta\tTEXT.UNWITTINGLY\tNone\t0.0089\n'Ndrangheta\tTEXT.UOMINI\tNone\t0.0127\n'Ndrangheta\tTEXT.URIBE\tNone\t0.0113\n'Ndrangheta\tTEXT.USURY\tNone\t0.0161\n'Ndrangheta\tTEXT.VALAIS\tNone\t0.0165\n'Ndrangheta\tTEXT.VALENTE\tNone\t0.0173\n'Ndrangheta\tTEXT.VALIANT\tNone\t0.0081\n'Ndrangheta\tTEXT.VINCENZO\tNone\t0.0125\n'Ndrangheta\tTEXT.VIOLI\tNone\t0.0223\n'Ndrangheta\tTEXT.VITO\tNone\t0.0089\n'Ndrangheta\tTEXT.WHOLESALER\tNone\t0.0097\n'Ndrangheta\tTEXT.WIKILEAKS\tNone\t0.0095\n'Ndrangheta\tTEXT.WIRETAP\tNone\t0.0141\n'Ndrangheta\tTEXT.WOODBRIDGE\tNone\t0.0096\n'Ndrangheta\tTEXT.ZETA\tNone\t0.0082\n'Ndrangheta\tTEXT.ZITO\tNone\t0.0115\n'Ndrangheta\tTEXT.??LVARO\tNone\t0.009\n'Ndrangheta\tTEXT.?????????????\tNone\t0.0146\n'Ndrangheta\tTEXT.?????????????????????\tNone\t0.013\n'Pataphysics\tTEXT.ABERRATION\tNone\t0.022\n'Pataphysics\tTEXT.ABSOLU\tNone\t0.0343\n'Pataphysics\tTEXT.ABSOLUTE\tNone\t0.0286\n'Pataphysics\tTEXT.AGAMBEN\tNone\t0.0306\n'Pataphysics\tTEXT.ALFORTVILLE\tNone\t0.0339\n'Pataphysics\tTEXT.AMOK\tNone\t0.0257\n'Pataphysics\tTEXT.ANIMATRONIC\tNone\t0.0369\n'Pataphysics\tTEXT.ANOMALY\tNone\t0.0264\n'Pataphysics\tTEXT.ANTINOMY\tNone\t0.0475\n'Pataphysics\tTEXT.APOSTROPHE\tNone\t0.0572\n'Pataphysics\tTEXT.APPENDAGE\tNone\t0.0217\n'Pataphysics\tTEXT.ARNAUD\tNone\t0.0219\n'Pataphysics\tTEXT.ARRABAL\tNone\t0.0305\n'Pataphysics\tTEXT.ARTICULATION\tNone\t0.0187\n'Pataphysics\tTEXT.ASGER\tNone\t0.0315\n'Pataphysics\tTEXT.AUTOLUX\tNone\t0.0339\n'Pataphysics\tTEXT.AUTOMATON\tNone\t0.0243\n'Pataphysics\tTEXT.BAUDRILLARD\tNone\t0.0281\n'Pataphysics\tTEXT.BEAUMONT\tNone\t0.0177\n'Pataphysics\tTEXT.BLANCHOT\tNone\t0.0477\n'Pataphysics\tTEXT.BOSSE\tNone\t0.0278\n'Pataphysics\tTEXT.BREWTON\tNone\t0.0475\n'Pataphysics\tTEXT.BRYARS\tNone\t0.0441\n'Pataphysics\tTEXT.BUMBLE\tNone\t0.0212\n'Pataphysics\tTEXT.B??K\tNone\t0.0615\n'Pataphysics\tTEXT.CAHIERS\tNone\t0.0242\n'Pataphysics\tTEXT.CELESTIAL\tNone\t0.0167\n'Pataphysics\tTEXT.CHANDELIER\tNone\t0.0213\n'Pataphysics\tTEXT.CHESSBOARD\tNone\t0.0263\n'Pataphysics\tTEXT.CHICO\tNone\t0.0195\n'Pataphysics\tTEXT.CHIME\tNone\t0.0195\n'Pataphysics\tTEXT.CHIPMUNK\tNone\t0.0221\n'Pataphysics\tTEXT.CHRISTOPHE\tNone\t0.0301\n'Pataphysics\tTEXT.COLLEGIUM\tNone\t0.0228\n'Pataphysics\tTEXT.COLL??GE\tNone\t0.1029\n'Pataphysics\tTEXT.CONCEIT\tNone\t0.0229\n'Pataphysics\tTEXT.CONSCIOUSLY\tNone\t0.0178\n'Pataphysics\tTEXT.CORBUSIER\tNone\t0.0233\n'Pataphysics\tTEXT.CROCODILE\tNone\t0.0177\n'Pataphysics\tTEXT.CURATED\tNone\t0.0178\n'Pataphysics\tTEXT.CURATOR\tNone\t0.0241\n'Pataphysics\tTEXT.CURIE\tNone\t0.0219\n'Pataphysics\tTEXT.CURIOSITY\tNone\t0.0169\n'Pataphysics\tTEXT.CZECHOSLOVAKIA\tNone\t0.0242\n'Pataphysics\tTEXT.DELETION\tNone\t0.0197\n'Pataphysics\tTEXT.DIGEST\tNone\t0.0164\n'Pataphysics\tTEXT.DOGMA\tNone\t0.0189\n'Pataphysics\tTEXT.DOSSIER\tNone\t0.0221\n'Pataphysics\tTEXT.DUALITY\tNone\t0.0202\n'Pataphysics\tTEXT.DUCHAMP\tNone\t0.0348\n'Pataphysics\tTEXT.ENLARGER\tNone\t0.0317\n'Pataphysics\tTEXT.ESTEBAN\tNone\t0.0206\n'Pataphysics\tTEXT.EUG??NE\tNone\t0.0272\n'Pataphysics\tTEXT.EXHIBIT\tNone\t0.0321\n'Pataphysics\tTEXT.EXIST\tNone\t0.0206\n'Pataphysics\tTEXT.EXPOUND\tNone\t0.0187\n'Pataphysics\tTEXT.EXPRESSIONISM\tNone\t0.0214\n'Pataphysics\tTEXT.FAUST\tNone\t0.0195\n'Pataphysics\tTEXT.FERREIRA\tNone\t0.0213\n'Pataphysics\tTEXT.FIGURATIVE\tNone\t0.029\n'Pataphysics\tTEXT.FIRESIGN\tNone\t0.0292\n'Pataphysics\tTEXT.FREAK\tNone\t0.0173\n'Pataphysics\tTEXT.GAVIN\tNone\t0.0242\n'Pataphysics\tTEXT.GENET\tNone\t0.0243\n'Pataphysics\tTEXT.GESTATION\tNone\t0.0205\n'Pataphysics\tTEXT.GLIMPSE\tNone\t0.0172\n'Pataphysics\tTEXT.GRAFFITI\tNone\t0.0177\n'Pataphysics\tTEXT.GREGORIAN\tNone\t0.0372\n'Pataphysics\tTEXT.GROUCHO\tNone\t0.0233\n'Pataphysics\tTEXT.HAHA\tNone\t0.0291\n'Pataphysics\tTEXT.HARNESS\tNone\t0.0162\n'Pataphysics\tTEXT.HARPO\tNone\t0.0251\n'Pataphysics\tTEXT.HEIGHTEN\tNone\t0.0157\n'Pataphysics\tTEXT.HILLYER\tNone\t0.0282\n'Pataphysics\tTEXT.HIRSUTISM\tNone\t0.0326\n'Pataphysics\tTEXT.HOLOGRAPHIC\tNone\t0.0225\n'Pataphysics\tTEXT.HUGILL\tNone\t0.0773\n'Pataphysics\tTEXT.ILLUSTR??\tNone\t0.0315\n'Pataphysics\tTEXT.IMAGINARY\tNone\t0.0904\n'Pataphysics\tTEXT.IMAGINATIVE\tNone\t0.0179\n'Pataphysics\tTEXT.INCOMPATIBLE\tNone\t0.0161\n'Pataphysics\tTEXT.INTERNATIONALIZATION\tNone\t0.0224\n'Pataphysics\tTEXT.IONESCO\tNone\t0.0395\n'Pataphysics\tTEXT.IR??N??E\tNone\t0.0322\n'Pataphysics\tTEXT.JARRY\tNone\t0.278\n'Pataphysics\tTEXT.JORN\tNone\t0.0306\n'Pataphysics\tTEXT.JULIEN\tNone\t0.0184\n'Pataphysics\tTEXT.JUXTAPOSE\tNone\t0.0198\n'Pataphysics\tTEXT.KAFKA\tNone\t0.022\n'Pataphysics\tTEXT.LESCURE\tNone\t0.0331\n'Pataphysics\tTEXT.LIL\tNone\t0.0175\n'Pataphysics\tTEXT.LINEAMENT\tNone\t0.0309\n'Pataphysics\tTEXT.LITT??RAIRE\tNone\t0.0286\n'Pataphysics\tTEXT.LI??GE\tNone\t0.0201\n'Pataphysics\tTEXT.LUC\tNone\t0.0178\n'Pataphysics\tTEXT.MAGNIFICENCE\tNone\t0.0233\n'Pataphysics\tTEXT.MARCEL\tNone\t0.0239\n'Pataphysics\tTEXT.META\tNone\t0.0172\n'Pataphysics\tTEXT.METAPHOR\tNone\t0.1008\n'Pataphysics\tTEXT.METAPHORICAL\tNone\t0.041\n'Pataphysics\tTEXT.METAPHYSIC\tNone\t0.1063\n'Pataphysics\tTEXT.MINUS\tNone\t0.0162\n'Pataphysics\tTEXT.MIREILLE\tNone\t0.0257\n'Pataphysics\tTEXT.MIR??\tNone\t0.0235\n'Pataphysics\tTEXT.MOLLET\tNone\t0.0439\n'Pataphysics\tTEXT.MONTFORT\tNone\t0.0218\n'Pataphysics\tTEXT.MUS??E\tNone\t0.0275\n'Pataphysics\tTEXT.MYRIAD\tNone\t0.018\n'Pataphysics\tTEXT.NAGE\tNone\t0.0306\n'Pataphysics\tTEXT.NICKLE\tNone\t0.0285\n'Pataphysics\tTEXT.NIGHTINGALE\tNone\t0.0191\n'Pataphysics\tTEXT.NO??L\tNone\t0.0188\n'Pataphysics\tTEXT.OCCULTATION\tNone\t0.0259\n'Pataphysics\tTEXT.OFFSHOOT\tNone\t0.017\n'Pataphysics\tTEXT.OLFACTORY\tNone\t0.0228\n'Pataphysics\tTEXT.ORACLE\tNone\t0.0171\n'Pataphysics\tTEXT.ORNAMENTATION\tNone\t0.0195\n'Pataphysics\tTEXT.ORTHOGRAPHY\tNone\t0.0186\n'Pataphysics\tTEXT.OULIPO\tNone\t0.05\n'Pataphysics\tTEXT.OUTLANDISH\tNone\t0.0217\n'Pataphysics\tTEXT.PA\tNone\t0.0181\n'Pataphysics\tTEXT.PARADOX\tNone\t0.0173\n'Pataphysics\tTEXT.PATAPHYSICS\tNone\t0.8172\n'Pataphysics\tTEXT.PATE\tNone\t0.0364\n'Pataphysics\tTEXT.PATTE\tNone\t0.0317\n'Pataphysics\tTEXT.PEANUT\tNone\t0.0176\n'Pataphysics\tTEXT.PEIXOTO\tNone\t0.0297\n'Pataphysics\tTEXT.PERIPHERY\tNone\t0.0178\n'Pataphysics\tTEXT.PERRON\tNone\t0.0274\n'Pataphysics\tTEXT.PHYSIQUE\tNone\t0.0401\n'Pataphysics\tTEXT.PLASTICITY\tNone\t0.023\n'Pataphysics\tTEXT.PRANK\tNone\t0.0185\n'Pataphysics\tTEXT.PREFERABLE\tNone\t0.0187\n'Pataphysics\tTEXT.PSYCHEDELIC\tNone\t0.0172\n'Pataphysics\tTEXT.PUN\tNone\t0.0473\n'Pataphysics\tTEXT.PURPORTEDLY\tNone\t0.0174\n'Pataphysics\tTEXT.P??TE\tNone\t0.0334\n'Pataphysics\tTEXT.QUACKERY\tNone\t0.0276\n'Pataphysics\tTEXT.QUENEAU\tNone\t0.0441\n'Pataphysics\tTEXT.REALITY\tNone\t0.0358\n'Pataphysics\tTEXT.REALIZING\tNone\t0.0183\n'Pataphysics\tTEXT.RECONSTITUTE\tNone\t0.0174\n'Pataphysics\tTEXT.RECONSTRUCTIVE\tNone\t0.0232\n'Pataphysics\tTEXT.REITERATE\tNone\t0.0158\n'Pataphysics\tTEXT.RENNES\tNone\t0.0228\n'Pataphysics\tTEXT.REPRESS\tNone\t0.0173\n'Pataphysics\tTEXT.ROI\tNone\t0.0194\n'Pataphysics\tTEXT.ROLANDO\tNone\t0.0229\n'Pataphysics\tTEXT.SABLE\tNone\t0.0197\n'Pataphysics\tTEXT.SANDOMIR\tNone\t0.0509\n'Pataphysics\tTEXT.SCP\tNone\t0.0414\n'Pataphysics\tTEXT.SEPARATION\tNone\t0.0282\n'Pataphysics\tTEXT.SERENDIPITY\tNone\t0.0269\n'Pataphysics\tTEXT.SHATTUCK\tNone\t0.0398\n'Pataphysics\tTEXT.SIMILARITY\tNone\t0.0229\n'Pataphysics\tTEXT.SITUATIONIST\tNone\t0.0269\n'Pataphysics\tTEXT.SK??ODOWSKA\tNone\t0.0312\n'Pataphysics\tTEXT.SLY\tNone\t0.029\n'Pataphysics\tTEXT.SNORKEL\tNone\t0.0228\n'Pataphysics\tTEXT.SPECULATIVE\tNone\t0.0253\n'Pataphysics\tTEXT.SPOOF\tNone\t0.0166\n'Pataphysics\tTEXT.STAROSTA\tNone\t0.029\n'Pataphysics\tTEXT.STRIDE\tNone\t0.0175\n'Pataphysics\tTEXT.SUBCOMMITTEE\tNone\t0.0187\n'Pataphysics\tTEXT.SUBLIMINAL\tNone\t0.0243\n'Pataphysics\tTEXT.SUPPLEMENTARY\tNone\t0.0171\n'Pataphysics\tTEXT.SWERVE\tNone\t0.0339\n'Pataphysics\tTEXT.SYMBOLICALLY\tNone\t0.0184\n'Pataphysics\tTEXT.SYMMETRY\tNone\t0.0176\n'Pataphysics\tTEXT.SYZYGY\tNone\t0.0603\n'Pataphysics\tTEXT.TA\tNone\t0.0336\n'Pataphysics\tTEXT.TANYA\tNone\t0.0199\n'Pataphysics\tTEXT.TENUOUS\tNone\t0.0201\n'Pataphysics\tTEXT.TRANSCEND\tNone\t0.0173\n'Pataphysics\tTEXT.TRANSCENDENT\tNone\t0.0206\n'Pataphysics\tTEXT.TROLL\tNone\t0.0199\n'Pataphysics\tTEXT.TROUPE\tNone\t0.0242\n'Pataphysics\tTEXT.TUSCALOOSA\tNone\t0.0239\n'Pataphysics\tTEXT.T??\tNone\t0.0306\n'Pataphysics\tTEXT.UBU\tNone\t0.0385\n'Pataphysics\tTEXT.UNDERTONE\tNone\t0.0214\n'Pataphysics\tTEXT.UNPREDICTABLE\tNone\t0.0171\n'Pataphysics\tTEXT.UNRELATED\tNone\t0.0206\n'Pataphysics\tTEXT.UNSETTLE\tNone\t0.0184\n'Pataphysics\tTEXT.UNUSUALLY\tNone\t0.0205\n'Pataphysics\tTEXT.VIAN\tNone\t0.0428\n'Pataphysics\tTEXT.VIGIL\tNone\t0.0196\n'Pataphysics\tTEXT.VILLAZ??N\tNone\t0.0313\n'Pataphysics\tTEXT.VILNIUS\tNone\t0.0306\n'Pataphysics\tTEXT.VIRTUALITY\tNone\t0.0312\n'Pataphysics\tTEXT.VULGATE\tNone\t0.022\n'Pataphysics\tTEXT.ZL??N\tNone\t0.0296\n'Pataphysics\tTEXT.??CHO\tNone\t0.0312\n'Pataphysics\tTEXT.??TIENNE\tNone\t0.0185\n'Salem's Lot\tTEXT.ADAPT\tNone\t0.0309\n'Salem's Lot\tTEXT.ADELINE\tNone\t0.06\n'Salem's Lot\tTEXT.ALOUD\tNone\t0.0473\n'Salem's Lot\tTEXT.AMBITION\tNone\t0.0311\n'Salem's Lot\tTEXT.ANTIQUE\tNone\t0.0376\n'Salem's Lot\tTEXT.AUDIOBOOK\tNone\t0.0516\n'Salem's Lot\tTEXT.AWAY\tNone\t0.0243\n'Salem's Lot\tTEXT.BARLOW\tNone\t0.3005\n'Salem's Lot\tTEXT.BEN\tNone\t0.1167\n'Salem's Lot\tTEXT.BRIEFLY\tNone\t0.0414\n'Salem's Lot\tTEXT.BRUSH\tNone\t0.0368\n'Salem's Lot\tTEXT.BRYANT\tNone\t0.0391\n'Salem's Lot\tTEXT.BUNCH\tNone\t0.0401\n'Salem's Lot\tTEXT.BURKE\tNone\t0.0364\n'Salem's Lot\tTEXT.CAB\tNone\t0.0393\n'Salem's Lot\tTEXT.CALLA\tNone\t0.0582\n'Salem's Lot\tTEXT.CALLAHAN\tNone\t0.2102\n'Salem's Lot\tTEXT.CENTIPEDE\tNone\t0.0599\n'Salem's Lot\tTEXT.CINEMA\tNone\t0.0396\n'Salem's Lot\tTEXT.CLASSMATE\tNone\t0.038\n'Salem's Lot\tTEXT.CLOTH\tNone\t0.0346\n'Salem's Lot\tTEXT.COALESCE\tNone\t0.0472\n'Salem's Lot\tTEXT.CODY\tNone\t0.0438\n'Salem's Lot\tTEXT.COHEN\tNone\t0.036\n'Salem's Lot\tTEXT.COINCIDE\tNone\t0.027\n'Salem's Lot\tTEXT.CONFESS\tNone\t0.0344\n'Salem's Lot\tTEXT.COREY\tNone\t0.0429\n'Salem's Lot\tTEXT.CORPSE\tNone\t0.0931\n'Salem's Lot\tTEXT.CUSTODIAN\tNone\t0.0482\n'Salem's Lot\tTEXT.DANGLE\tNone\t0.0554\n'Salem's Lot\tTEXT.DANNY\tNone\t0.0911\n'Salem's Lot\tTEXT.DANSE\tNone\t0.0577\n'Salem's Lot\tTEXT.DEAVER\tNone\t0.0723\n'Salem's Lot\tTEXT.DELETE\tNone\t0.0357\n'Salem's Lot\tTEXT.DELIBERATELY\tNone\t0.0314\n'Salem's Lot\tTEXT.DELUXE\tNone\t0.0404\n'Salem's Lot\tTEXT.DIGGER\tNone\t0.049\n'Salem's Lot\tTEXT.DISAPPEAR\tNone\t0.0256\n'Salem's Lot\tTEXT.DISAPPEARANCE\tNone\t0.0378\n'Salem's Lot\tTEXT.DRACULA\tNone\t0.0713\n'Salem's Lot\tTEXT.DREAM\tNone\t0.0426\n'Salem's Lot\tTEXT.DUD\tNone\t0.0613\n'Salem's Lot\tTEXT.DUMP\tNone\t0.0349\n'Salem's Lot\tTEXT.EDGAR\tNone\t0.0327\n'Salem's Lot\tTEXT.EMMY\tNone\t0.0479\n'Salem's Lot\tTEXT.ENOUGH\tNone\t0.023\n'Salem's Lot\tTEXT.ENTITLE\tNone\t0.0289\n'Salem's Lot\tTEXT.EPILOGUE\tNone\t0.0476\n'Salem's Lot\tTEXT.FAMILIAR\tNone\t0.0278\n'Salem's Lot\tTEXT.FANTASY\tNone\t0.0567\n'Salem's Lot\tTEXT.FAVORITE\tNone\t0.0314\n'Salem's Lot\tTEXT.FERNDALE\tNone\t0.0658\n'Salem's Lot\tTEXT.FICTIONAL\tNone\t0.0506\n'Salem's Lot\tTEXT.FOLIO\tNone\t0.0498\n'Salem's Lot\tTEXT.FORESHADOW\tNone\t0.0451\n'Salem's Lot\tTEXT.FURNITURE\tNone\t0.033\n'Salem's Lot\tTEXT.GARBAGE\tNone\t0.0421\n'Salem's Lot\tTEXT.GLICK\tNone\t0.0971\n'Salem's Lot\tTEXT.GRAVEYARD\tNone\t0.0428\n'Salem's Lot\tTEXT.GRIFFEN\tNone\t0.0746\n'Salem's Lot\tTEXT.HAMPDEN\tNone\t0.051\n'Salem's Lot\tTEXT.HANG\tNone\t0.0366\n'Salem's Lot\tTEXT.HARDCOVER\tNone\t0.0473\n'Salem's Lot\tTEXT.HITMAN\tNone\t0.0524\n'Salem's Lot\tTEXT.HORROR\tNone\t0.0435\n'Salem's Lot\tTEXT.HOSTAGE\tNone\t0.0356\n'Salem's Lot\tTEXT.HUBERT\tNone\t0.0391\n'Salem's Lot\tTEXT.HUBIE\tNone\t0.1043\n'Salem's Lot\tTEXT.ILLUSTRATE\tNone\t0.0352\n'Salem's Lot\tTEXT.IMPALE\tNone\t0.0519\n'Salem's Lot\tTEXT.INSIDE\tNone\t0.0257\n'Salem's Lot\tTEXT.INSPIRATION\tNone\t0.0383\n'Salem's Lot\tTEXT.INSTALLMENT\tNone\t0.0389\n'Salem's Lot\tTEXT.INTENT\tNone\t0.0294\n'Salem's Lot\tTEXT.INTERVIEW\tNone\t0.0338\n'Salem's Lot\tTEXT.INTERVIEWER\tNone\t0.0446\n'Salem's Lot\tTEXT.JEFFERY\tNone\t0.0528\n'Salem's Lot\tTEXT.JEREMIAH\tNone\t0.0438\n'Salem's Lot\tTEXT.JERRY\tNone\t0.0411\n'Salem's Lot\tTEXT.JERUSALEM\tNone\t0.1924\n'Salem's Lot\tTEXT.JIMMY\tNone\t0.0376\n'Salem's Lot\tTEXT.KING\tNone\t0.1281\n'Salem's Lot\tTEXT.KNIFE\tNone\t0.035\n'Salem's Lot\tTEXT.KURT\tNone\t0.0337\n'Salem's Lot\tTEXT.LEADERLESS\tNone\t0.0615\n'Salem's Lot\tTEXT.LEAN\tNone\t0.0323\n'Salem's Lot\tTEXT.LOCUS\tNone\t0.0455\n'Salem's Lot\tTEXT.LOT\tNone\t0.2608\n'Salem's Lot\tTEXT.LOWE\tNone\t0.0404\n'Salem's Lot\tTEXT.LUCKY\tNone\t0.0339\n'Salem's Lot\tTEXT.MACABRE\tNone\t0.0535\n'Salem's Lot\tTEXT.MAINE\tNone\t0.0597\n'Salem's Lot\tTEXT.MARJORIE\tNone\t0.0463\n'Salem's Lot\tTEXT.MARK\tNone\t0.0492\n'Salem's Lot\tTEXT.MARKER\tNone\t0.0327\n'Salem's Lot\tTEXT.MCDOUGALL\tNone\t0.0568\n'Salem's Lot\tTEXT.MEARS\tNone\t0.1424\n'Salem's Lot\tTEXT.MENACE\tNone\t0.0401\n'Salem's Lot\tTEXT.MINI-SERIES\tNone\t0.0511\n'Salem's Lot\tTEXT.MOHAWK\tNone\t0.0476\n'Salem's Lot\tTEXT.NAOMI\tNone\t0.0438\n'Salem's Lot\tTEXT.NEWBORN\tNone\t0.0417\n'Salem's Lot\tTEXT.NORTON\tNone\t0.0372\n'Salem's Lot\tTEXT.NOVEL\tNone\t0.1022\n'Salem's Lot\tTEXT.OBSESS\tNone\t0.0406\n'Salem's Lot\tTEXT.OBVIOUSLY\tNone\t0.0368\n'Salem's Lot\tTEXT.OFFSET\tNone\t0.0361\n'Salem's Lot\tTEXT.ORDEAL\tNone\t0.0472\n'Salem's Lot\tTEXT.ORGANISM\tNone\t0.035\n'Salem's Lot\tTEXT.ORONO\tNone\t0.0717\n'Salem's Lot\tTEXT.OSTENSIBLY\tNone\t0.0392\n'Salem's Lot\tTEXT.OVERVIEW\tNone\t0.0301\n'Salem's Lot\tTEXT.OVERWHELM\tNone\t0.0293\n'Salem's Lot\tTEXT.PAPERBACK\tNone\t0.0417\n'Salem's Lot\tTEXT.PASSIONATE\tNone\t0.0372\n'Salem's Lot\tTEXT.PATROLMAN\tNone\t0.0587\n'Salem's Lot\tTEXT.PERFECT\tNone\t0.0358\n'Salem's Lot\tTEXT.PERSUASION\tNone\t0.0463\n'Salem's Lot\tTEXT.PETRIE\tNone\t0.0532\n'Salem's Lot\tTEXT.PLASTIC\tNone\t0.0303\n'Salem's Lot\tTEXT.PLAYBOY\tNone\t0.0417\n'Salem's Lot\tTEXT.PREVENT\tNone\t0.0229\n'Salem's Lot\tTEXT.PRIEST\tNone\t0.0376\n'Salem's Lot\tTEXT.PRIMETIME\tNone\t0.0552\n'Salem's Lot\tTEXT.PROBABLY\tNone\t0.0365\n'Salem's Lot\tTEXT.PROLOGUE\tNone\t0.0452\n'Salem's Lot\tTEXT.PROSAIC\tNone\t0.0612\n'Salem's Lot\tTEXT.RALPHIE\tNone\t0.0761\n'Salem's Lot\tTEXT.RANDY\tNone\t0.0343\n'Salem's Lot\tTEXT.RECALL\tNone\t0.0337\n'Salem's Lot\tTEXT.REPAIRMAN\tNone\t0.0625\n'Salem's Lot\tTEXT.RESIST\tNone\t0.0267\n'Salem's Lot\tTEXT.RETURN\tNone\t0.0237\n'Salem's Lot\tTEXT.REVISIT\tNone\t0.0383\n'Salem's Lot\tTEXT.RIBBON\tNone\t0.0362\n'Salem's Lot\tTEXT.RIG\tNone\t0.0377\n'Salem's Lot\tTEXT.ROT\tNone\t0.0437\n'Salem's Lot\tTEXT.RYERSON\tNone\t0.0577\n'Salem's Lot\tTEXT.SAFE\tNone\t0.0346\n'Salem's Lot\tTEXT.SALEM\tNone\t0.354\n'Salem's Lot\tTEXT.SCREAM\tNone\t0.0351\n'Salem's Lot\tTEXT.SEASIDE\tNone\t0.0413\n'Salem's Lot\tTEXT.SECT\tNone\t0.0376\n'Salem's Lot\tTEXT.SEQUEL\tNone\t0.0424\n'Salem's Lot\tTEXT.SHAKER\tNone\t0.0507\n'Salem's Lot\tTEXT.SHORTEN\tNone\t0.0284\n'Salem's Lot\tTEXT.SIXTEEN\tNone\t0.0257\n'Salem's Lot\tTEXT.SLEEPY\tNone\t0.0487\n'Salem's Lot\tTEXT.SLIPCASE\tNone\t0.0723\n'Salem's Lot\tTEXT.SMASH\tNone\t0.0337\n'Salem's Lot\tTEXT.SNAP\tNone\t0.0361\n'Salem's Lot\tTEXT.STAIRCASE\tNone\t0.0398\n'Salem's Lot\tTEXT.STAKE\tNone\t0.0323\n'Salem's Lot\tTEXT.STEPHEN\tNone\t0.0415\n'Salem's Lot\tTEXT.STORY\tNone\t0.0855\n'Salem's Lot\tTEXT.STRAKER\tNone\t0.0741\n'Salem's Lot\tTEXT.SUDDENLY\tNone\t0.0299\n'Salem's Lot\tTEXT.SUPERFINE\tNone\t0.0752\n'Salem's Lot\tTEXT.SUPPER\tNone\t0.0456\n'Salem's Lot\tTEXT.SUPPOSE\tNone\t0.0239\n'Salem's Lot\tTEXT.SURE\tNone\t0.0284\n'Salem's Lot\tTEXT.SUSAN\tNone\t0.0607\n'Salem's Lot\tTEXT.SUSANNAH\tNone\t0.0542\n'Salem's Lot\tTEXT.SUSPENSE\tNone\t0.0463\n'Salem's Lot\tTEXT.TABITHA\tNone\t0.0615\n'Salem's Lot\tTEXT.TAXI\tNone\t0.0335\n'Salem's Lot\tTEXT.THICK\tNone\t0.0288\n'Salem's Lot\tTEXT.THINK\tNone\t0.0341\n'Salem's Lot\tTEXT.TNT\tNone\t0.0454\n'Salem's Lot\tTEXT.TOWNSPEOPLE\tNone\t0.0463\n'Salem's Lot\tTEXT.TRUNCATE\tNone\t0.0467\n'Salem's Lot\tTEXT.TURN\tNone\t0.0311\n'Salem's Lot\tTEXT.UNCLEAN\tNone\t0.0558\n'Salem's Lot\tTEXT.UNINHABITED\tNone\t0.0442\n'Salem's Lot\tTEXT.UNSIGNED\tNone\t0.0712\n'Salem's Lot\tTEXT.USELESS\tNone\t0.0418\n'Salem's Lot\tTEXT.VAMPIRE\tNone\t0.2512\n'Salem's Lot\tTEXT.VERMONT\tNone\t0.0366\n'Salem's Lot\tTEXT.WAN\tNone\t0.0449\n'Salem's Lot\tTEXT.WEIGH\tNone\t0.0299\n'Salem's Lot\tTEXT.WIFE\tNone\t0.0317\n'Tis Pity She's a Whore\tTEXT.ADAPTATION\tNone\t0.0554\n'Tis Pity She's a Whore\tTEXT.ADDIO\tNone\t0.0514\n'Tis Pity She's a Whore\tTEXT.ADOLPHUS\tNone\t0.0365\n'Tis Pity She's a Whore\tTEXT.ALAIN\tNone\t0.0294\n'Tis Pity She's a Whore\tTEXT.AMBIGUITY\tNone\t0.0288\n'Tis Pity She's a Whore\tTEXT.ANDERSSON\tNone\t0.0386\n'Tis Pity She's a Whore\tTEXT.ANGELIQUE\tNone\t0.0403\n'Tis Pity She's a Whore\tTEXT.ANNABELLA\tNone\t0.5899\n'Tis Pity She's a Whore\tTEXT.ANNO\tNone\t0.0372\n'Tis Pity She's a Whore\tTEXT.APPRECIATION\tNone\t0.0243\n'Tis Pity She's a Whore\tTEXT.ARROGANT\tNone\t0.0305\n'Tis Pity She's a Whore\tTEXT.AVENGE\tNone\t0.0275\n'Tis Pity She's a Whore\tTEXT.BADGER\tNone\t0.0315\n'Tis Pity She's a Whore\tTEXT.BANDIT\tNone\t0.0422\n'Tis Pity She's a Whore\tTEXT.BANDITTI\tNone\t0.0535\n'Tis Pity She's a Whore\tTEXT.BANISH\tNone\t0.0281\n'Tis Pity She's a Whore\tTEXT.BARBICAN\tNone\t0.0362\n'Tis Pity She's a Whore\tTEXT.BEFRIEND\tNone\t0.0247\n'Tis Pity She's a Whore\tTEXT.BEG\tNone\t0.0248\n'Tis Pity She's a Whore\tTEXT.BETROTH\tNone\t0.0346\n'Tis Pity She's a Whore\tTEXT.BIBI\tNone\t0.0392\n'Tis Pity She's a Whore\tTEXT.BILLINGTON\tNone\t0.0409\n'Tis Pity She's a Whore\tTEXT.BLACKSTAR\tNone\t0.0511\n'Tis Pity She's a Whore\tTEXT.BOLOGNA\tNone\t0.0292\n'Tis Pity She's a Whore\tTEXT.BONAVENTURA\tNone\t0.1161\n'Tis Pity She's a Whore\tTEXT.BOOKSELLER\tNone\t0.0338\n'Tis Pity She's a Whore\tTEXT.BOWIE\tNone\t0.0283\n'Tis Pity She's a Whore\tTEXT.BUCKLEY\tNone\t0.0303\n'Tis Pity She's a Whore\tTEXT.CANDLELIGHT\tNone\t0.0387\n'Tis Pity She's a Whore\tTEXT.CARDINAL\tNone\t0.0589\n'Tis Pity She's a Whore\tTEXT.CHEAT\tNone\t0.0261\n'Tis Pity She's a Whore\tTEXT.CHEEK\tNone\t0.0279\n'Tis Pity She's a Whore\tTEXT.CHERIE\tNone\t0.0416\n'Tis Pity She's a Whore\tTEXT.COCKPIT\tNone\t0.0294\n'Tis Pity She's a Whore\tTEXT.CONDEMNATION\tNone\t0.0279\n'Tis Pity She's a Whore\tTEXT.CONFINE\tNone\t0.0207\n'Tis Pity She's a Whore\tTEXT.CONSUMMATE\tNone\t0.0325\n'Tis Pity She's a Whore\tTEXT.CONVENT\tNone\t0.0258\n'Tis Pity She's a Whore\tTEXT.CONVINCE\tNone\t0.0223\n'Tis Pity She's a Whore\tTEXT.CRANHAM\tNone\t0.0512\n'Tis Pity She's a Whore\tTEXT.DAMIEN\tNone\t0.0474\n'Tis Pity She's a Whore\tTEXT.DAMN\tNone\t0.0264\n'Tis Pity She's a Whore\tTEXT.DASS\tNone\t0.044\n'Tis Pity She's a Whore\tTEXT.DECLAN\tNone\t0.0565\n'Tis Pity She's a Whore\tTEXT.DELON\tNone\t0.0461\n'Tis Pity She's a Whore\tTEXT.DESPERATE\tNone\t0.024\n'Tis Pity She's a Whore\tTEXT.DICE\tNone\t0.0337\n'Tis Pity She's a Whore\tTEXT.DISGUISE\tNone\t0.0228\n'Tis Pity She's a Whore\tTEXT.DISTRAUGHT\tNone\t0.0329\n'Tis Pity She's a Whore\tTEXT.DONNELLAN\tNone\t0.0731\n'Tis Pity She's a Whore\tTEXT.DRIFT\tNone\t0.024\n'Tis Pity She's a Whore\tTEXT.D??SSELDORF\tNone\t0.0326\n'Tis Pity She's a Whore\tTEXT.EINE\tNone\t0.0365\n'Tis Pity She's a Whore\tTEXT.ESQUE\tNone\t0.0347\n'Tis Pity She's a Whore\tTEXT.EUPHEMISTIC\tNone\t0.0464\n'Tis Pity She's a Whore\tTEXT.FEAST\tNone\t0.046\n'Tis Pity She's a Whore\tTEXT.FIANC??\tNone\t0.0326\n'Tis Pity She's a Whore\tTEXT.FIANC??E\tNone\t0.031\n'Tis Pity She's a Whore\tTEXT.FINALLY\tNone\t0.0196\n'Tis Pity She's a Whore\tTEXT.FLORIO\tNone\t0.1254\n'Tis Pity She's a Whore\tTEXT.FORD\tNone\t0.0605\n'Tis Pity She's a Whore\tTEXT.FRATELLO\tNone\t0.0488\n'Tis Pity She's a Whore\tTEXT.FRIAR\tNone\t0.1008\n'Tis Pity She's a Whore\tTEXT.FURIOUS\tNone\t0.0261\n'Tis Pity She's a Whore\tTEXT.GINA\tNone\t0.0322\n'Tis Pity She's a Whore\tTEXT.GIOVANNI\tNone\t0.252\n'Tis Pity She's a Whore\tTEXT.GIUSEPPE\tNone\t0.0266\n'Tis Pity She's a Whore\tTEXT.GREENAWAY\tNone\t0.0426\n'Tis Pity She's a Whore\tTEXT.GRIFFI\tNone\t0.0588\n'Tis Pity She's a Whore\tTEXT.GRIMALDI\tNone\t0.1439\n'Tis Pity She's a Whore\tTEXT.GUILT\tNone\t0.0266\n'Tis Pity She's a Whore\tTEXT.HENRIETTA\tNone\t0.0327\n'Tis Pity She's a Whore\tTEXT.HERSELF\tNone\t0.0343\n'Tis Pity She's a Whore\tTEXT.INCEST\tNone\t0.0532\n'Tis Pity She's a Whore\tTEXT.INCESTUOUS\tNone\t0.0598\n'Tis Pity She's a Whore\tTEXT.INSULT\tNone\t0.0231\n'Tis Pity She's a Whore\tTEXT.INTERVENE\tNone\t0.0211\n'Tis Pity She's a Whore\tTEXT.JACOBIAN\tNone\t0.0378\n'Tis Pity She's a Whore\tTEXT.JAMIESON\tNone\t0.0398\n'Tis Pity She's a Whore\tTEXT.JESSIE\tNone\t0.0302\n'Tis Pity She's a Whore\tTEXT.JOFF??\tNone\t0.0528\n'Tis Pity She's a Whore\tTEXT.JOWL\tNone\t0.0488\n'Tis Pity She's a Whore\tTEXT.JUSTLY\tNone\t0.0384\n'Tis Pity She's a Whore\tTEXT.KILLING\tNone\t0.0267\n'Tis Pity She's a Whore\tTEXT.LIBRETTO\tNone\t0.03\n'Tis Pity She's a Whore\tTEXT.LONGHURST\tNone\t0.0479\n'Tis Pity She's a Whore\tTEXT.LOOSELY\tNone\t0.0224\n'Tis Pity She's a Whore\tTEXT.LOVER\tNone\t0.0292\n'Tis Pity She's a Whore\tTEXT.LUCHINO\tNone\t0.0441\n'Tis Pity She's a Whore\tTEXT.LUNGHI\tNone\t0.0572\n'Tis Pity She's a Whore\tTEXT.LYDIA\tNone\t0.0308\n'Tis Pity She's a Whore\tTEXT.MAETERLINCK\tNone\t0.047\n'Tis Pity She's a Whore\tTEXT.MARRY\tNone\t0.0307\n'Tis Pity She's a Whore\tTEXT.MASQUE\tNone\t0.0372\n'Tis Pity She's a Whore\tTEXT.MEANWHILE\tNone\t0.0246\n'Tis Pity She's a Whore\tTEXT.MIDSOMER\tNone\t0.0432\n'Tis Pity She's a Whore\tTEXT.MISTAKENLY\tNone\t0.0266\n'Tis Pity She's a Whore\tTEXT.MOLONY\tNone\t0.0777\n'Tis Pity She's a Whore\tTEXT.MORDAUNT\tNone\t0.0453\n'Tis Pity She's a Whore\tTEXT.MUNBY\tNone\t0.056\n'Tis Pity She's a Whore\tTEXT.NEWLYWED\tNone\t0.0372\n'Tis Pity She's a Whore\tTEXT.NIECE\tNone\t0.0378\n'Tis Pity She's a Whore\tTEXT.NOBLEMAN\tNone\t0.0296\n'Tis Pity She's a Whore\tTEXT.NUNCIO\tNone\t0.0394\n'Tis Pity She's a Whore\tTEXT.OBVIOUSLY\tNone\t0.0254\n'Tis Pity She's a Whore\tTEXT.OFFEND\tNone\t0.0255\n'Tis Pity She's a Whore\tTEXT.OKE\tNone\t0.0458\n'Tis Pity She's a Whore\tTEXT.OMIT\tNone\t0.0227\n'Tis Pity She's a Whore\tTEXT.OPERNHAUS\tNone\t0.0506\n'Tis Pity She's a Whore\tTEXT.ORMEROD\tNone\t0.0493\n'Tis Pity She's a Whore\tTEXT.PARAMOUR\tNone\t0.0441\n'Tis Pity She's a Whore\tTEXT.PARMA\tNone\t0.0809\n'Tis Pity She's a Whore\tTEXT.PASSIONATE\tNone\t0.0257\n'Tis Pity She's a Whore\tTEXT.PATRONI\tNone\t0.0572\n'Tis Pity She's a Whore\tTEXT.PAULINE\tNone\t0.0283\n'Tis Pity She's a Whore\tTEXT.PEPYS\tNone\t0.0388\n'Tis Pity She's a Whore\tTEXT.PERSUADE\tNone\t0.0265\n'Tis Pity She's a Whore\tTEXT.PETERBOROUGH\tNone\t0.0315\n'Tis Pity She's a Whore\tTEXT.PITTY\tNone\t0.0556\n'Tis Pity She's a Whore\tTEXT.PITY\tNone\t0.1775\n'Tis Pity She's a Whore\tTEXT.POGGIO\tNone\t0.066\n'Tis Pity She's a Whore\tTEXT.POISON\tNone\t0.0318\n'Tis Pity She's a Whore\tTEXT.PREGNANCY\tNone\t0.0372\n'Tis Pity She's a Whore\tTEXT.PRETEND\tNone\t0.0248\n'Tis Pity She's a Whore\tTEXT.PROPHECY\tNone\t0.0284\n'Tis Pity She's a Whore\tTEXT.QUARTO\tNone\t0.0406\n'Tis Pity She's a Whore\tTEXT.RAMPLING\tNone\t0.046\n'Tis Pity She's a Whore\tTEXT.REPENT\tNone\t0.0351\n'Tis Pity She's a Whore\tTEXT.REPENTANCE\tNone\t0.0357\n'Tis Pity She's a Whore\tTEXT.REVEAL\tNone\t0.0312\n'Tis Pity She's a Whore\tTEXT.REVENGE\tNone\t0.0425\n'Tis Pity She's a Whore\tTEXT.REVIVE\tNone\t0.0252\n'Tis Pity She's a Whore\tTEXT.RID\tNone\t0.0262\n'Tis Pity She's a Whore\tTEXT.ROMY\tNone\t0.0436\n'Tis Pity She's a Whore\tTEXT.SALISBURY\tNone\t0.0283\n'Tis Pity She's a Whore\tTEXT.SCHADE\tNone\t0.0546\n'Tis Pity She's a Whore\tTEXT.SCHNEIDER\tNone\t0.0292\n'Tis Pity She's a Whore\tTEXT.SCHREIER\tNone\t0.0487\n'Tis Pity She's a Whore\tTEXT.SERVANT\tNone\t0.038\n'Tis Pity She's a Whore\tTEXT.SHAFTESBURY\tNone\t0.0373\n'Tis Pity She's a Whore\tTEXT.SHEE\tNone\t0.0504\n'Tis Pity She's a Whore\tTEXT.SIE\tNone\t0.0418\n'Tis Pity She's a Whore\tTEXT.SKEWER\tNone\t0.0413\n'Tis Pity She's a Whore\tTEXT.SPOUT\tNone\t0.0382\n'Tis Pity She's a Whore\tTEXT.STAB\tNone\t0.0652\n'Tis Pity She's a Whore\tTEXT.STOPPARD\tNone\t0.0401\n'Tis Pity She's a Whore\tTEXT.SUITOR\tNone\t0.1026\n'Tis Pity She's a Whore\tTEXT.SYLLABLE\tNone\t0.0285\n'Tis Pity She's a Whore\tTEXT.SYNOPSIS\tNone\t0.0297\n'Tis Pity She's a Whore\tTEXT.TALENTED\tNone\t0.0228\n'Tis Pity She's a Whore\tTEXT.TARANTINO\tNone\t0.0359\n'Tis Pity She's a Whore\tTEXT.TERRIBLE\tNone\t0.0237\n'Tis Pity She's a Whore\tTEXT.TH????TRE\tNone\t0.0506\n'Tis Pity She's a Whore\tTEXT.TI\tNone\t0.1913\n'Tis Pity She's a Whore\tTEXT.TOAST\tNone\t0.0311\n'Tis Pity She's a Whore\tTEXT.TOBIAS\tNone\t0.0319\n'Tis Pity She's a Whore\tTEXT.TRAGEDY\tNone\t0.0344\n'Tis Pity She's a Whore\tTEXT.TUMULTUOUS\tNone\t0.0314\n'Tis Pity She's a Whore\tTEXT.TURVEY\tNone\t0.0493\n'Tis Pity She's a Whore\tTEXT.UNAVOIDABLE\tNone\t0.0352\n'Tis Pity She's a Whore\tTEXT.UNCLE\tNone\t0.0354\n'Tis Pity She's a Whore\tTEXT.UNEDITED\tNone\t0.0386\n'Tis Pity She's a Whore\tTEXT.UNFORTUNATELY\tNone\t0.022\n'Tis Pity She's a Whore\tTEXT.UNSETTLE\tNone\t0.0316\n'Tis Pity She's a Whore\tTEXT.UNSTOPPABLE\tNone\t0.0371\n'Tis Pity She's a Whore\tTEXT.VASQUES\tNone\t0.2621\n'Tis Pity She's a Whore\tTEXT.VERBALLY\tNone\t0.0474\n'Tis Pity She's a Whore\tTEXT.VICKERS\tNone\t0.0304\n'Tis Pity She's a Whore\tTEXT.VILLAINY\tNone\t0.0431\n'Tis Pity She's a Whore\tTEXT.VIRTUOUS\tNone\t0.0326\n'Tis Pity She's a Whore\tTEXT.VISCONTI\tNone\t0.0363\n'Tis Pity She's a Whore\tTEXT.VISUALLY\tNone\t0.0269\n'Tis Pity She's a Whore\tTEXT.WANAMAKER\tNone\t0.0407\n'Tis Pity She's a Whore\tTEXT.WHORE\tNone\t0.1987\n'Tis Pity She's a Whore\tTEXT.WIELD\tNone\t0.0267\n'Tis Pity She's a Whore\tTEXT.WILLINGLY\tNone\t0.032\n'Tis Pity She's a Whore\tTEXT.??UVRE\tNone\t0.0456\n'night, Mother\tTEXT.AFTERMATH\tNone\t0.0264\n'night, Mother\tTEXT.ALLISON\tNone\t0.0413\n'night, Mother\tTEXT.ANYTHING\tNone\t0.0356\n'night, Mother\tTEXT.ARTIFICIAL\tNone\t0.0292\n'night, Mother\tTEXT.ASSUME\tNone\t0.028\n'night, Mother\tTEXT.ASSURE\tNone\t0.0317\n'night, Mother\tTEXT.ATTENBOROUGH\tNone\t0.0566\n'night, Mother\tTEXT.BANCROFT\tNone\t0.0775\n'night, Mother\tTEXT.BATE\tNone\t0.0818\n'night, Mother\tTEXT.BEG\tNone\t0.037\n'night, Mother\tTEXT.BLACKBURN\tNone\t0.043\n'night, Mother\tTEXT.BLAIR\tNone\t0.0372\n'night, Mother\tTEXT.BLETHYN\tNone\t0.1136\n'night, Mother\tTEXT.BRENDA\tNone\t0.0463\n'night, Mother\tTEXT.BROADWAY\tNone\t0.1258\n'night, Mother\tTEXT.BURST\tNone\t0.0355\n'night, Mother\tTEXT.CALMLY\tNone\t0.1124\n'night, Mother\tTEXT.CAREFULLY\tNone\t0.033\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{},\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Basic settings of the ESA Wiki model and the 21.7 mi rows of features","message":["%python","","esa_wiki_mod.set_params"],"enabled":true,"result":{"startTime":1714408654097,"interpreter":"python.high","endTime":1714408743007,"results":[{"message":"<bound method odmModel.set_params of \nModel Name: WIKI_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Explicit Semantic Analysis\n\nMining Function: FEATURE_EXTRACTION\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_EXPLICIT_SEMANTIC_ANALYS\n1                 ESAS_MIN_ITEMS                             25\n2             ESAS_TOPN_FEATURES                           1000\n3           ESAS_VALUE_THRESHOLD                      .00000001\n4                   ODMS_DETAILS                    ODMS_ENABLE\n5   ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n6                  ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n7         ODMS_TEXT_MAX_FEATURES                         200000\n8        ODMS_TEXT_MIN_DOCUMENTS                              5\n9          ODMS_TEXT_POLICY_NAME                    wiki_txtpol\n10                     PREP_AUTO                             ON\n\nGlobal Statistics: \n  attribute name  attribute value\n0       NUM_ROWS           161616\n\nAttributes: \nTEXT\n\nPartition: NO\n\nFeatures: \n\n                       FEATURE_ID ATTRIBUTE_NAME ATTRIBUTE_VALUE  COEFFICIENT\n0                  'Adud al-Dawla     TEXT.ABBAD            None     0.019127\n1                  'Adud al-Dawla   TEXT.ABBASID            None     0.024915\n2                  'Adud al-Dawla       TEXT.ABU            None     0.059714\n3                  'Adud al-Dawla      TEXT.ADUD            None     0.565945\n4                  'Adud al-Dawla   TEXT.ALABAMA            None     0.228319\n...                           ...            ...             ...          ...\n24359888  Zyuden Sentai Kyoryuger    TEXT.YAMATO            None     0.062645\n24359889  Zyuden Sentai Kyoryuger    TEXT.YUSUKE            None     0.056347\n24359890  Zyuden Sentai Kyoryuger    TEXT.ZYUDEN            None     0.296543\n24359891  Zyuden Sentai Kyoryuger  TEXT.ZYUOHGER            None     0.046699\n24359892  Zyuden Sentai Kyoryuger    TEXT.??MORI            None     0.068806\n\n[24359893 rows x 4 columns]\n\n>\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Examples using the Wiki ESA model"],"enabled":true,"result":{"startTime":1714408743129,"interpreter":"md.high","endTime":1714408743191,"results":[{"message":"<h3 id=\"examples-using-the-wiki-esa-model\">Examples using the Wiki ESA model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Basic example of ESA features for the word 'Bank' ","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['Bank']}))","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1714408743267,"interpreter":"python.high","endTime":1714408755382,"results":[{"message":"UserWarning:Result is not pivoted because the feature number is larger than the pivot limit 100\n","type":"TEXT"},{"message":"FEATURE_ID\tVALUE\nBanking in India\t0.12073074740513993\nBanking in Australia\t0.1186153932769802\nBank of America\t0.11307155411695473\nBank\t0.10994163646234326\nCentral bank\t0.10780307790446561\nNational Bank Act\t0.10647042777456947\nStandard Bank\t0.10502497981253221\nBankBoston\t0.10474436983267525\nOnline banking\t0.10250315951850819\nBank of England\t0.09610674490262502\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"ESA Features for the words 'NFL games' ","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['NFL Games']}))","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1714408755468,"interpreter":"python.high","endTime":1714408767283,"results":[{"message":"UserWarning:Result is not pivoted because the feature number is larger than the pivot limit 100\n","type":"TEXT"},{"message":"FEATURE_ID\tVALUE\nNational Football League\t0.1448362549796241\nNFL International Series\t0.13968414188865144\nThursday Night Football\t0.11436240954509867\nHistory of the National Football League championship\t0.11272129823985683\nChicago College All-Star Game\t0.11121297109987173\nNFL Network\t0.11007694516545648\nNFL Films\t0.10627056953318934\nThe NFL Today\t0.10292445567064071\nNational Football League Christmas games\t0.10104092801135917\nPlayoff Bowl\t0.09258723455526119\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract features on text about Astronomy","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['A group of European-led astronomers has made a photograph of what appears to be a planet orbiting another star. If so, it would be the first confirmed picture of a world beyond our solar system.']}))","","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1714408767371,"interpreter":"python.high","endTime":1714408779388,"results":[{"message":"UserWarning:Result is not pivoted because the feature number is larger than the pivot limit 100\n","type":"TEXT"},{"message":"FEATURE_ID\tVALUE\nSolar System\t0.1783977405440812\nPlanets beyond Neptune\t0.16384415493902063\nPlanet Nine\t0.1556042629299215\nPlanet\t0.14360577141174127\nExoplanet\t0.14146374523098973\nFormation and evolution of the Solar System\t0.1380857932488385\nTerrestrial planet\t0.12349262676588602\nAstronomer\t0.11968229035028131\nSolar analog\t0.11919958268012823\nPlanetary habitability\t0.11168273107745952\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract features on text about diet and health","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['Nearly 70 percent of Americans say they are careful about what they eat, and even more say diet is essential to good health, according to a new nationwide health poll in which obesity ranked second among the biggest health concerns.']}))","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1714408779474,"interpreter":"python.high","endTime":1714408791790,"results":[{"message":"UserWarning:Result is not pivoted because the feature number is larger than the pivot limit 100\n","type":"TEXT"},{"message":"FEATURE_ID\tVALUE\nObesity\t0.2381281204952319\nChildhood obesity\t0.21566128131005566\nRobert Wood Johnson Foundation\t0.1790547081969936\nHealth\t0.1723830168425817\nHealthy diet\t0.16671289147333318\nDieting\t0.142162115212373\nPaleolithic diet\t0.1386597097885653\nHealth education\t0.12498303889516335\nHealth equity\t0.1149008443204024\nPlant-based diet\t0.11425470702731559\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract features on text about stocks and the Fed","message":["%python","","temp_pd = oml.push(pd.DataFrame(","    {'TEXT':['The more things change... Yes, I''m inclined to agree, especially with regards to the historical relationship between stock prices and bond yields. The two have generally traded together, rising during periods of economic growth and falling during periods of contraction. Consider the period from 1998 through 2010, during which the U.S. economy experienced two expansions as well as two recessions: Then central banks came to the rescue. Fed Chairman Ben Bernanke led from Washington with the help of the bank''s current $3.6T balance sheet. He''s accompanied by Mario Draghi at the European Central Bank and an equally forthright Shinzo Abe in Japan. Their coordinated monetary expansion has provided all the sugar needed for an equities moonshot, while they vowed to hold global borrowing costs at record lows']}))","","z.show(esa_wiki_mod.transform(temp_pd))"],"enabled":true,"result":{"startTime":1714408791878,"interpreter":"python.high","endTime":1714408804391,"results":[{"message":"UserWarning:Result is not pivoted because the feature number is larger than the pivot limit 100\n","type":"TEXT"},{"message":"FEATURE_ID\tVALUE\nBen Bernanke\t0.17031334068743392\nRecession\t0.15858335723440023\nMario Draghi\t0.14881649785122952\nCOVID-19 recession\t0.14502647137906452\nGreat Recession\t0.12883462217838215\nLong Depression\t0.12180667662946132\nGreat Depression in the United States\t0.1149944972327347\nMonetary policy\t0.10323437884992531\nGreat Recession in the United States\t0.10173350481024938\nGroup of Thirty\t0.09893093930605422\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Comparing Strings for Similarity of Text using the Wikipedia Model"],"enabled":true,"result":{"startTime":1714408804480,"interpreter":"md.high","endTime":1714408804543,"results":[{"message":"<h3 id=\"comparing-strings-for-similarity-of-text-using-the-wikipedia-model\">Comparing Strings for Similarity of Text using the Wikipedia Model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare 'street' vs. 'avenue' ","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['street','avenue']}))","","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))"],"enabled":true,"result":{"startTime":1714408804623,"interpreter":"python.high","endTime":1714408816183,"results":[{"message":"SIMILARITY\n0.1936898590428866\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare 'street' vs. 'farm'","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['street','farm']}))","","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))"],"enabled":true,"result":{"startTime":1714408816317,"interpreter":"python.high","endTime":1714408827627,"results":[{"message":"SIMILARITY\n0.007127425352303707\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare short texts about Golf","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['There are several PGA tour golfers from South Africa',","                                         'Nick Price won the 2002 Mastercard Colonial Open']}))","                                         ","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))","                                    "],"enabled":true,"result":{"startTime":1714408827711,"interpreter":"python.high","endTime":1714408838920,"results":[{"message":"SIMILARITY\n0.10969292803597186\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare texts about Golf and Football","message":["%python","","temp_pd = oml.push(pd.DataFrame({'TEXT':['There are several PGA tour golfers from South Africa',","                                         'John Elway played quarterback for the Denver Broncos']}))","                                         ","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))"],"enabled":true,"result":{"startTime":1714408839002,"interpreter":"python.high","endTime":1714408850312,"results":[{"message":"SIMILARITY\n0.003917419745909201\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and SpaceX Moon Gateway","message":["%python","","temp_pd = oml.push(pd.DataFrame(","    {'TEXT':['NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.',","             'NASA has selected SpaceX to deliver the first two segments of the moon-orbiting Gateway space station for its upcoming Artemis program, which aims to put astronauts back on the moon. The elements will launch atop a Falcon Heavy rocket, sometime in 2024. The flight, which is the second to be awarded to SpaceX this week will carry the Gateway''s power and habitation modules.  Once deposited in lunar orbit, the Gateway will serve as an outpost for astronauts and equipment heading to the moon as part of NASA Artemis program. Roughly one-sixth the size of the International Space Station, the Gateway will support research investigations, crew, and expeditions to the lunar surface.']}))","z.show(esa_wiki_mod.feature_compare(temp_pd,","                                    compare_cols = ['TEXT']))","                                    "],"enabled":true,"result":{"startTime":1714408850402,"interpreter":"python.high","endTime":1714408861849,"results":[{"message":"SIMILARITY\n0.48847744577133123\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and Tennis Naomi Osaka","message":["%python","","temp_pd = oml.push(pd.DataFrame(","    {'TEXT':['NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.',","             'Naomi Osaka, who hasnt lost an official match since last February, put together a dominating performance in the Australian Open semifinals, beating Williams 6-3, 6-4 and ending hopes that the 39-year old American would tie Margaret Court all-time Slam record at this tournament. Osaka, seeking her fourth major title and second Australian Open championship, will play the winner of the second semifinal between American Jennifer Brady, the No. 22 seed, and No. 25 seed Karolina Muchova.']}))","","z.show(esa_wiki_mod.feature_compare(temp_pd, compare_cols = ['TEXT']))","                                    "],"enabled":true,"result":{"startTime":1714408861931,"interpreter":"python.high","endTime":1714408873344,"results":[{"message":"SIMILARITY\n0.026660806390262026\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1714408873427,"interpreter":"md.high","endTime":1714408873498,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714408873585,"interpreter":"md.high","endTime":1714408873653,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Extraction ESA","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for Python function *oml.esa* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we cover the basic usage of ESA, where we extract topics from the comments provided. ",""," - Create a small sample dataset"," - Use the ESA Model to predict a feature vector on test data"," - Identify top features"," - Document similarity"," - Change model settings","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","import pandas as pd"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create test and training data","message":["%python","","dat = oml.push(pd.DataFrame(","  {'COMMENTS':['Aids in Africa: Planning for a long war',","               'Mars rover maneuvers for rim shot',","               'Mars express confirms presence of water at Mars south pole',","               'NASA announces major Mars rover finding',","               'Drug access, Asia threat in focus at AIDS summit',","               'NASA Mars Odyssey THEMIS image: typical crater',","               'Road blocks for Aids'],","    'ID':[1,2,3,4,5,6,7]})).split(ratio=(0.7,0.3), seed = 1234)","","train_dat = dat[0]","test_dat = dat[1]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the test data","message":["%python","","z.show(test_dat)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the training data","message":["%python","","z.show(train_dat)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the help file for ESA","message":["%python","","help(oml.esa)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a policy for text indexing","message":["%script","","-- Drop the policy if it exists","BEGIN ctx_ddl.drop_policy('ESA_TXT_POLICY'); ","   exception when others then NULL;","END;","","BEGIN ctx_ddl.create_policy('ESA_TXT_POLICY'); ","   exception when others then NULL;","END;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify model settings","message":["%python","","odm_settings = {'odms_text_policy_name': 'ESA_TXT_POLICY',","                '\"ODMS_TEXT_MIN_DOCUMENTS\"': 1,","                '\"ESAS_MIN_ITEMS\"': 1}","","ctx_settings = {'COMMENTS':","                'TEXT(POLICY_NAME:ESA_TXT_POLICY)(TOKEN_TYPE:STEM)'}"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create an ESA model object ","message":["%python","","# Delete if ESA model exists","try:","    oml.drop(model = 'ESA_MOD')","except:","    print('No such model')","    ","esa_mod = oml.esa(**odm_settings)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"View the model settings","message":["%python","","esa_mod.settings"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" Fit the model according to the training data and parameter settings","message":["%python","","esa_mod = esa_mod.fit(train_dat, case_id='ID', ctx_settings = ctx_settings)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%python","","esa_mod"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details for each feature","message":["%python","","z.show(esa_mod.features.round(4))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predicted top 2 features","message":["%python","","z.show(esa_mod.transform(test_dat, ","                         supplemental_cols = test_dat[:, ['ID', 'COMMENTS']], ","                         topN = 2).sort_values(by = ['ID']).round(4))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model name internal to the database","message":["%sql","","SELECT * from all_mining_models WHERE ALGORITHM='EXPLICIT_SEMANTIC_ANALYS'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Rename the model in the database","message":["%python","","esa_mod.model_name='ESA_MOD'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View new model name in the database","message":["%sql","","select * from USER_MINING_MODELS where model_name='ESA_MOD'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View renamed model in Python","message":["%python","","oml.esa(model_name = 'ESA_MOD') "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions on the test data","message":["%python","","esa_pred = esa_mod.predict(test_dat, supplemental_cols = test_dat[:, ['ID', 'COMMENTS']])","z.show(esa_pred)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Change the settings and refit the model","message":["%python","","new_setting = {'ESAS_VALUE_THRESHOLD': '0.01',","               'ODMS_TEXT_MAX_FEATURES': '2',","               'FEAT_NUM_FEATURES': '2',","               'ESAS_TOPN_FEATURES': '2'}","               "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Refit the model with new settings - 2 features maximum ","message":["%python","","# Delete if ESA model exists","try:","    oml.drop(model = 'ESA_MOD_2')","except:","    print('No such model')","    ","esa_mod.set_params(**new_setting).fit(train_dat, case_id = 'ID', ","                                      ctx_settings = ctx_settings,","                                      model_name='ESA_MOD_2')","","z.show(esa_mod.feature_compare(test_dat,","                        compare_cols = ['COMMENTS'],","                        supplemental_cols = ['COMMENTS']))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List ESA model views","message":["%sql","","SELECT view_name, view_type FROM user_mining_model_views","WHERE model_name='ESA_MOD_2'","ORDER BY view_name;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"The VA view shows associations among features (topics) and words","message":["%md","","*ATTRIBUTE_SUBNAME* refers to words, or tokens. The strength of the associations is measured by *COEFFICIENT*. Higher coefficient values indicate a stronger association."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * from dm$vaesa_mod_2 FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the associations for selected words","message":["%sql","","SELECT ATTRIBUTE_SUBNAME, count(ATTRIBUTE_SUBNAME) FROM dm$vaesa_mod_2","WHERE ATTRIBUTE_SUBNAME='MARS'","GROUP BY ATTRIBUTE_SUBNAME"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Clean-up","message":["%python","","try:","    oml.drop(model = 'ESA_MOD')","except:","    print('No such model')","","try:","    oml.drop(model = 'ESA_MOD_2')","except:","    print('No such model')","    "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Feature Selection Algorithm-based.dsnb b/machine-learning/notebooks/python/OML4Py Feature Selection Algorithm-based.dsnb
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--- a/machine-learning/notebooks/python/OML4Py Feature Selection Algorithm-based.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Selection Algorithm-based","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py Feature Selection: Supervised Algorithm","","In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms via OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We build a random forest model to predict whether the customer by insurance or not, then use feature importance to perform feature selection. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a prerequisite to the current notebook.","","---","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/classification.html#GUID-9F922514-0F8D-42F5-BEB1-F59A09FA1CD2\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/random-forest.html\" target=\"_blank\">OML4Py Random Forest Documentation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMER_INSURANCE_LTV table","message":["%python","","CUST_INSUR_LTV_DF = oml.sync(table ='CUSTOMER_INSURANCE_LTV')","z.show(CUST_INSUR_LTV_DF.head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create Train and Test (60/40 split) datasets","message":["%python","","TRAIN, TEST = CUST_INSUR_LTV_DF.split(ratio = (0.6,0.4))","TRAIN_X     = TRAIN.drop('BUY_INSURANCE')","TRAIN_Y     = TRAIN['BUY_INSURANCE']","TEST_X      = TEST","TEST_Y      = TEST['BUY_INSURANCE']"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply Attribute Importance algorithm to obtain the attribute importance","message":["%python","","setting = {'ODMS_SAMPLING':'ODMS_SAMPLING_DISABLE'}","ai_mod = oml.ai(**setting)","ai_mod = ai_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUSTOMER_ID')","z.show(ai_mod.importance.head(10).round(4))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Plot the top 10 most important attributes","message":["%python","","z.show(ai_mod.importance.head(10).round(4))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest Model to predict who will purchase insurance","message":["%python","","settings = dict()","rf_mod = oml.rf(**settings)","rf_mod.fit(TRAIN_X, TRAIN_Y, case_id = 'CUSTOMER_ID')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the attribute importance from the trained random forest model","message":["%python","","z.show(rf_mod.importance.sort_values('ATTRIBUTE_IMPORTANCE', ascending = False).head(10).round(4))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Plot the top 10 most important features","message":["%python","","z.show(rf_mod.importance.sort_values('ATTRIBUTE_IMPORTANCE', ascending = False).head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Feature Selection Using Summary Statistics.dsnb b/machine-learning/notebooks/python/OML4Py Feature Selection Using Summary Statistics.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/python/OML4Py Feature Selection Using Summary Statistics.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Feature Selection Using Summary Statistics","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715709329768,"interpreter":"md.low","endTime":1715709329889,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Feature Selection: Summary Statistics","","In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4Py.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to use OML4Py to select features based on number of distinct values, null values, proportion of constant values. ","","The dataset `CUSTOMER_INSURANCE_LTV_PY` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715709329963,"interpreter":"md.low","endTime":1715709330037,"results":[{"message":"<h1 id=\"oml4py-feature-selection-summary-statistics\">OML4Py Feature Selection: Summary Statistics<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4Py.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to use OML4Py to select features based on number of distinct values, null values, proportion of constant values.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_PY<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/about-preparing-data.html\" target=\"_blank\">OML Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1715709330123,"interpreter":"md.low","endTime":1715709330197,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/about-preparing-data.html\" target=\"_blank\">OML Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import warnings","warnings.filterwarnings('ignore')","","import pandas as pd","import oml","from oml import automl","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1715709330274,"interpreter":"python.low","endTime":1715709333375,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_PY table","message":["%python","","CUST_DF = oml.sync(table = 'CUSTOMER_INSURANCE_LTV_PY')"],"enabled":true,"result":{"startTime":1715709333448,"interpreter":"python.low","endTime":1715709333633,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from the table","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1715709333704,"interpreter":"python.low","endTime":1715709333888,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146.0\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076.0\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877.0\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Identify categorical columns","message":["%python","","cat_cols = CUST_DF.select_types([oml.core.string.String]).columns","print(cat_cols)"],"enabled":true,"result":{"startTime":1715709333968,"interpreter":"python.low","endTime":1715709334092,"results":[{"message":"['MARITAL_STATUS', 'STATE', 'CUSTOMER_ID', 'GENDER', 'PROFESSION', 'REGION', 'BUY_INSURANCE', 'LTV_BIN', 'FIRST_NAME', 'LAST_NAME']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Identify numeric columns","message":["%python","","num_cols = CUST_DF.select_types([oml.core.string.Float]).columns","print(num_cols)"],"enabled":true,"result":{"startTime":1715709334166,"interpreter":"python.low","endTime":1715709334245,"results":[{"message":"['MONEY_MONTLY_OVERDRAWN', 'LTV']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique values in each column","message":["%python","","unique_counts = CUST_DF.nunique()","unique_df = pd.DataFrame(unique_counts)","unique_df = unique_df.reset_index()","unique_df.columns = ['FEATURE', 'CNT']","z.show(unique_df.head(10))"],"enabled":true,"result":{"startTime":1715709334325,"interpreter":"python.low","endTime":1715709334648,"results":[{"message":"FEATURE\tCNT\nMARITAL_STATUS\t7\nSTATE\t24\nCREDIT_BALANCE\t963\nCUSTOMER_TENURE\t5\nMORTGAGE_AMOUNT\t1763\nBANK_FUNDS\t1178\nNUM_DEPENDENTS\t7\nHAS_CHILDREN\t2\nINCOME\t6374\nCUSTOMER_ID\t13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter categorical 'identifier' columns - filter out columns with > 95% unique values","message":["%python","","selected_cat_cols = []","total = CUST_DF.shape[0]","ratio = 0.95","for col in cat_cols:","    cnt = unique_df[unique_df['FEATURE'] == col]['CNT'].values[0]","    if cnt > 1 and cnt < ratio*total:","        selected_cat_cols.append(col)","print(selected_cat_cols)","print(len(selected_cat_cols))"],"enabled":true,"result":{"startTime":1715709334729,"interpreter":"python.low","endTime":1715709334927,"results":[{"message":"['MARITAL_STATUS', 'STATE', 'CUSTOMER_ID', 'GENDER', 'PROFESSION', 'REGION', 'BUY_INSURANCE', 'LTV_BIN', 'FIRST_NAME', 'LAST_NAME']\n10\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"To confirm selected column is not an 'identifier',  use crosstab function to display unique values","message":["%python","","col = 'MARITAL_STATUS'","CUST_DF.crosstab([col])"],"enabled":true,"result":{"startTime":1715709335003,"interpreter":"python.low","endTime":1715709335325,"results":[{"message":"  MARITAL_STATUS  count\n0        WIDOWED    475\n1              M    359\n2          OTHER     96\n3            DIV    337\n4       DIVORCED   2063\n5           None   6627\n6        MARRIED   2393\n7         SINGLE   2530\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create an OML dataframe with selected features only","message":["%python","","SELECTED_CAT_DF = CUST_DF[selected_cat_cols]","z.show(SELECTED_CAT_DF.head())"],"enabled":true,"result":{"startTime":1715709335404,"interpreter":"python.low","endTime":1715709335554,"results":[{"message":"MARITAL_STATUS\tSTATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nSINGLE\tCA                        \tCU8720                    \tM                         \tWaiter/Waitress\tWest\tYes\tMEDIUM\tKENNETH\tNADEAU\nDIVORCED\tWA                        \tCU8721                    \tM                         \tSoftware Engineer\tWest\tYes\tMEDIUM\tJARVIS\tCLARKE\nDIVORCED\tNY                        \tCU8722                    \tF                         \tIT Staff\tNorthEast\tNo\tVERY HIGH\tSTACEY\tHENSON\nMARRIED\tMI                        \tCU8665                    \tF                         \tDentist\tMidwest\tYes\tHIGH\tMARGRET\tRYAN\nMARRIED\tMI                        \tCU8666                    \tM                         \tIT Staff\tMidwest\tNo\tHIGH\tJOAN\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For all columns, identify columns with > 40% missing values","message":["%python","","many_missing_cols = []","ratio = 0.4","total = CUST_DF.shape[0]","threshold = total*ratio","for col in CUST_DF.columns:","    grp = CUST_DF.crosstab([col])","    if grp[ grp[col].isnull()]:","        if grp[ grp[col].isnull()].pull()['count'][0] > threshold:","            many_missing_cols.append(col)","print(many_missing_cols)    "],"enabled":true,"result":{"startTime":1715709335630,"interpreter":"python.low","endTime":1715709342436,"results":[{"message":"['MARITAL_STATUS', 'CREDIT_BALANCE', 'BANK_FUNDS', 'INCOME']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter 'constant' columns - those with > 90% same values","message":["%python","","total = CUST_DF.shape[0]","","ratio = 0.90","","major_constant_cols = []","for col in CUST_DF.columns:","    GROUP = CUST_DF.crosstab([col])","    ","    if GROUP[GROUP['count'] > total*ratio]:","        print(GROUP[GROUP['count'] > total*ratio])","        major_constant_cols.append(col)","","print(major_constant_cols)"],"enabled":true,"result":{"startTime":1715709342514,"interpreter":"python.low","endTime":1715709349617,"results":[{"message":"   CAR_OWNERSHIP  count\n0              1  13859\n['CAR_OWNERSHIP']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create an OML dataframe with 'constant' columns filtered out","message":["%python","","selected_cols = [ col for col in CUST_DF.columns if col not in major_constant_cols ]","SELECTED_DF = CUST_DF[selected_cat_cols]","z.show(SELECTED_DF.head())"],"enabled":true,"result":{"startTime":1715709349702,"interpreter":"python.low","endTime":1715709349854,"results":[{"message":"MARITAL_STATUS\tSTATE\tCUSTOMER_ID\tGENDER\tPROFESSION\tREGION\tBUY_INSURANCE\tLTV_BIN\tFIRST_NAME\tLAST_NAME\nSINGLE\tCA                        \tCU8720                    \tM                         \tWaiter/Waitress\tWest\tYes\tMEDIUM\tKENNETH\tNADEAU\nDIVORCED\tWA                        \tCU8721                    \tM                         \tSoftware Engineer\tWest\tYes\tMEDIUM\tJARVIS\tCLARKE\nDIVORCED\tNY                        \tCU8722                    \tF                         \tIT Staff\tNorthEast\tNo\tVERY HIGH\tSTACEY\tHENSON\nMARRIED\tMI                        \tCU8665                    \tF                         \tDentist\tMidwest\tYes\tHIGH\tMARGRET\tRYAN\nMARRIED\tMI                        \tCU8666                    \tM                         \tIT Staff\tMidwest\tNo\tHIGH\tJOAN\tBAUER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715709349938,"interpreter":"md.low","endTime":1715709349996,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715709350083,"interpreter":"md.low","endTime":1715709350140,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Importing Wide Datasets.dsnb b/machine-learning/notebooks/python/OML4Py Importing Wide Datasets.dsnb
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--- a/machine-learning/notebooks/python/OML4Py Importing Wide Datasets.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Importing Wide Datasets","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Importing Wide Datasets using OML4Py","","Oracle Database has a limit of 1,000 columns in a table or view. Machine learning projects may involve datasets, perhaps stored as CSV files, with a higher number of columns. OML4SQL in-database algorithms can handle such wide datasets by converting the columns to a special format known as \"nested columns\". This allows an efficient handling of high dimensional and sparse features, as well as accomodating more than 1000 predictor columns, when building OML machine learning models. However, importing a dataset with the more than 1,000 columns from a data file, such as a CSV file in Object Storage, involves some preprocessing. ","","In this notebook, we demonstrate the process of importing a wide dataset with 2,002 columns from CSV format into an Oracle Database table with nested columns. ","","The approach is to read the file by chunks, convert each chunk to a transactional format into a database table, and then pivot the data into the nested column format using SQL. Note that the nested column format can be used only through OML4SQL, not OML4Py. ","","To improve the efficiency of the CSV loading, the \"index apply\" functionality of OML4Py is also used to speed up the process and make it parallel.","","Copyright (c) 2024 Oracle Corporation                             ","###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML website<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle nested columns table documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmprg/using-nested-dita.html\" target=\"_blank\">OML using nested data documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/embedded-python-execution1.html\" target=\"_blank\">OML4Py embedded Python execution documentation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import Python libraries","message":["%python","","import pandas as pd","import oml"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table to hold the data in transactional format","message":["%script ","","BEGIN","     EXECUTE IMMEDIATE 'DROP TABLE TXN_TBL';","     EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","CREATE TABLE TXN_TBL( ID NUMBER, ","                      NAME VARCHAR2(32),","                      VALUE NUMBER","                    );"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table that holds the label","message":["%script","","BEGIN","     EXECUTE IMMEDIATE 'DROP TABLE WIDE_LABEL_TBL';","     EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","CREATE TABLE WIDE_LABEL_TBL( ID NUMBER, ","                             CLASS INTEGER","                            );","/"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create supporting user-defined Python functions","* Function to insert multiple records into a database table","* Function to read the data in partitions and pivot the data into transactions","* Function for generating the partitions for the data","","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Functions to insert multiple records into the database table","message":["%python","","import oracledb","","# Insert records using the provided cursor, the statement, and the records","","def insert_records(cr, records, statement):","    try:        ","        cr.executemany(statement, records)","        cr.connection.commit()","    except cx_Oracle.Error as error:","        print('Insert error:')","        print(error)","","# Append the rows in a partition and call the insert_records function","","def insertDF(df, statement):","    cr = oml.cursor()","","    records = []","    for row in df.itertuples(index=False):","        records.append(row)","    insert_records(cr, records, statement)","    cr.close()    "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Function that writes each partition into transactional format","message":["%python","","import gc ","","def import_helper(idx, url, columns, ncol, partitions, chunk_size):","    # Verifies the partition to read","    read_rows = partitions[idx-1][0]","    # Check the largest index of that partition","    total = partitions[idx-1][1] + 1","    ","    while read_rows < total:","        offset = read_rows + 1","        # Reads the data from the defined chunks","        df = pd.read_csv(url, skiprows = offset, nrows = chunk_size, header = None)","        read_rows += df.shape[0]","        df.columns = columns","        # Pivots the data","        txn_df = pd.melt(df, id_vars = ['ID'], value_vars = columns[:ncol])","        txn_df.columns = ['ID', 'NAME', 'VALUE']","        print('Rows '+str(min(txn_df['ID']))+' to '+str(max(txn_df['ID']))+","              ': data pivoted')","        # Write the pivotted data to a database table ","        statement = \"INSERT INTO TXN_TBL (ID, NAME, VALUE) VALUES(:ID, :NAME, :VALUE)\"","        insertDF(txn_df, statement)","        statement = \"INSERT INTO WIDE_LABEL_TBL (ID, CLASS) VALUES(:ID, :CLASS)\"","        label_df = df[['ID','CLASS']]","        insertDF(label_df, statement)","        print('Rows '+str(min(txn_df['ID']))+' to '+str(max(txn_df['ID']))+","              ': inserted into TXN_TBL '+str(txn_df.shape[0])+' rows and '+str(txn_df.shape[1])+' columns')","        # Cleanup temporary elements used","        del df","        del txn_df","        del label_df","        gc.collect()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Function for generating the partitions for the data","message":["%python","","def generate_part(rows_to_process, num_partitions):","    segment_size = int(rows_to_process/num_partitions)","    result = []","    for i in range(num_partitions):","        result.append([i*segment_size, (i+1)*segment_size-1])","    result[len(result)-1][1] = rows_to_process-1","    return result"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Non-parallel processing","","In the following example, we process the data in chunks sequentially. This takes a significant amount of time. Later, we show doing this in parallel using embedded Python execution. ","","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check that the table is empty before inserting","message":["%sql","","SELECT COUNT(1) FROM TXN_TBL "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create separate partition slices to take advantage of parallelism ","message":["%python","","# Define the total number or rows to process from the dataset","# to a fixed number for this example","rows_to_process = 2000","","# Specify the number of partitions to create","num_partitions = 5","","partitions = generate_part(rows_to_process, num_partitions)","print(\"Generated partitions:\\n\")","print(partitions)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Sequential run processing the first 2000 rows","message":["%python","","import ssl","","ssl._create_default_https_context = ssl._create_unverified_context","","","url = 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/wide.csv'","","df = pd.read_csv(url, nrows = 0, skiprows = 0)","columns = list(df.columns)","","# Number of numerical columns in the dataset to be processed","ncol = 2000","# Set the number of records to process at a time (per chunk)","chunk_size = 100","","for i in range(1,6):","    import_helper(i, url, columns, ncol, partitions, chunk_size)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show a sample of the resulting transactions","message":["%sql","","select * from TXN_TBL FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Parallel processing using Embedded Python Execution","","The performance improvement is relative to the degree of parallelism enabled. Depending on your Autonomous Database instances CPU allocation, you can explicitly choose the number of Python engines to spawn in parallel. ","","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Remove all data before parallel run","message":["%script","","DELETE FROM WIDE_LABEL_TBL;","DELETE FROM TXN_TBL;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Run in parallel using oml.index_apply","message":["%python","","url = 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/wide.csv'","","df = pd.read_csv(url, nrows = 0, skiprows = 0)","columns = list(df.columns)","","ncol = 2000","chunk_size = 100","_ = oml.index_apply(times = 5, func = import_helper, url = url, columns = columns, ","                    ncol = ncol, partitions = partitions, chunk_size = 100, ","                    graphics = False, parallel = True)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the total data loaded","message":["%sql","","select count(1)","from txn_tbl"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the table with id = 0","message":["%sql","","SELECT *","FROM txn_tbl","WHERE id = 0","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check if there are any duplicates","message":["%sql","","SELECT count(distinct name)","FROM TXN_TBL","where id = 0"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the label/class and the id","message":["%sql","","SELECT * FROM WIDE_LABEL_TBL FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create the nested column from the transactional data"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the nested column table and join the labels","message":["%script","","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE TXN_NESTED_COL_NUM';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE  TXN_NESTED_COL_NUM","  NESTED TABLE txn_nested store AS txn_nested_tab AS ","SELECT m.id, m.CLASS, txn_nested","  FROM WIDE_LABEL_TBL m,","  ( SELECT features.id,","           cast(COLLECT(dm_nested_numerical(features.name,features.value)) ","                        as dm_nested_numericals)  txn_nested","    FROM TXN_TBL features","    group by features.id) n","  WHERE m.id = n.id  ","/"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check that the table contains our nested column","message":["%script","","SELECT * FROM TXN_NESTED_COL_NUM FETCH FIRST 5 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Building a Machine Learning model with a Nested Column as input"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a model using the nested column via OML4SQL","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLAS_USING_NESTED_COLUMN');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","","BEGIN","   ","    v_setlst(dbms_data_mining.algo_name) := dbms_data_mining.algo_generalized_linear_model;","    v_setlst(dbms_data_mining.prep_auto) :=  dbms_data_mining.prep_auto_on;","   ","   ","    DBMS_DATA_MINING.CREATE_MODEL2(","        model_name          => 'GLM_CLAS_USING_NESTED_COLUMN',","        mining_function     => dbms_data_mining.classification,","        data_query          => 'select * from TXN_NESTED_COL_NUM',","        set_list            => v_setlst,","        case_id_column_name => 'ID',","        target_column_name  => 'CLASS');","END;","/ "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the attributes used in building the model","message":["%sql","","SELECT attribute_name, attribute_type","  FROM user_mining_model_attributes"," WHERE model_name = 'GLM_CLAS_USING_NESTED_COLUMN'","ORDER BY attribute_name;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List of available model views with various model details","message":["%sql","","SELECT *","  FROM user_mining_model_views"," WHERE model_name = 'GLM_CLAS_USING_NESTED_COLUMN'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/python/OML4Py Partitioned Model SVM.dsnb b/machine-learning/notebooks/python/OML4Py Partitioned Model SVM.dsnb
deleted file mode 100755
index 569eef1a..00000000
--- a/machine-learning/notebooks/python/OML4Py Partitioned Model SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Partitioned Model SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715293158560,"interpreter":"md.low","endTime":1715293158625,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Partitioned Model","Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The proper sub-model is chosen by the system based on partition value(s) in the row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models. ","","In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. The model is then used to predict the target, then predict the target with prediction details. ","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715293158702,"interpreter":"md.low","endTime":1715293158764,"results":[{"message":"<h1 id=\"oml4py-partitioned-model\">OML4Py Partitioned Model<\/h1>\n<p>Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The proper sub-model is chosen by the system based on partition value(s) in the row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models.<\/p>\n<p>In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. The model is then used to predict the target, then predict the target with prediction details.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html\" target=\"_blank\">OML4Py Partitioned Models Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a>"],"enabled":true,"result":{"startTime":1715293158848,"interpreter":"md.low","endTime":1715293158910,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html\" target=\"_blank\">OML4Py Partitioned Models Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import oml","import pandas as pd","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1715293158989,"interpreter":"python.low","endTime":1715293159053,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare CUSTOMER_DEMO dataset using data from the SH schema","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","SUPPLEMENTARY_DEMOGRAPHICS = oml.sync(query = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS')","CUSTOMERS_DEMO = CUSTOMERS.merge(SUPPLEMENTARY_DEMOGRAPHICS, how = \"inner\", on = 'CUST_ID', suffixes = [\"\", \"\"])"],"enabled":true,"result":{"startTime":1715293159130,"interpreter":"python.low","endTime":1715293159207,"results":[{"message":"<stdin>:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%python","","CUSTOMERS_DEMO.shape"],"enabled":true,"result":{"startTime":1715293159284,"interpreter":"python.low","endTime":1715293159378,"results":[{"message":"(4500, 12)\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUSTOMERS_DEMO data","message":["%python","","z.show(CUSTOMERS_DEMO.head())"],"enabled":true,"result":{"startTime":1715293159452,"interpreter":"python.low","endTime":1715293159575,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create data for training and testing datasets and drop missing values","message":["%python","","dat = CUSTOMERS_DEMO.split(ratio=(0.8, 0.2), seed = 6218945)","","dat[0] = dat[0].dropna()","","train_x = dat[0].drop('YRS_RESIDENCE')","train_y = dat[0]['YRS_RESIDENCE']","test_dat = dat[1]"],"enabled":true,"result":{"startTime":1715293159651,"interpreter":"python.low","endTime":1715293159845,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Partitioning (Shared Settings)","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Requests the building of a partitioned model. ","    The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.  ","> 'odms_partition_columns' : '<Comma separated list of attributes>'","     ","* Controls the parallel building of partitioned models.     ","    ODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all slaves.","    ODMS_PARTITION_BUILD_INTER builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.","    ODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.","> 'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'"],"enabled":true,"result":{"startTime":1715293159924,"interpreter":"md.low","endTime":1715293159995,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-partitioning-shared-settings\">Examples of possible setting overrides for Partitioning (Shared Settings)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Requests the building of a partitioned model.\nThe setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_columns' : '<Comma separated list of attributes>'<\/p>\n<\/blockquote>\n<ul>\n<li>Controls the parallel building of partitioned models.<br />\nODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all slaves.\nODMS_PARTITION_BUILD_INTER builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.\nODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build Partitioned Model using the SVM algorithm to predict YRS_RESIDENCE, partitioned by CUST_GENDER","message":["%python","","try:","    oml.drop(model = 'SVM_PARTITIONED_REGRESSION_MODEL') ","except:","    pass","","setting = {'svms_kernel_function'  :'dbms_data_mining.svms_linear',","           'odms_partition_columns':'CUST_GENDER'}","           ","svm_mod = oml.svm(\"regression\", **setting)"," ","svm_mod.fit(train_x, train_y , model_name = 'SVM_PARTITIONED_REGRESSION_MODEL')"],"enabled":true,"result":{"startTime":1715293160084,"interpreter":"python.low","endTime":1715293161283,"results":[{"message":"\nModel Name: SVM_PARTITIONED_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2           ODMS_MAX_PARTITIONS                          1000\n3  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n4        ODMS_PARTITION_COLUMNS                   CUST_GENDER\n5                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n6                     PREP_AUTO                            ON\n7           SVMS_CONV_TOLERANCE                         .0001\n8                  SVMS_EPSILON                            .1\n9          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n  partition name            setting name    setting value\n0              F  SVMS_COMPLEXITY_FACTOR                3\n1              F     SVMS_NUM_ITERATIONS               30\n2              F             SVMS_SOLVER  SVMS_SOLVER_IPM\n3              M  SVMS_COMPLEXITY_FACTOR                3\n4              M     SVMS_NUM_ITERATIONS               30\n5              M             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  partition name attribute name attribute value\n0              F      CONVERGED             YES\n1              F     ITERATIONS              15\n2              F       NUM_ROWS            1213\n3              M      CONVERGED             YES\n4              M     ITERATIONS              16\n5              M       NUM_ROWS            2381\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_ID\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: YES\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict on the test set and display prediction result - note the use of the top level model only","message":["%python","","y_pred_part = svm_mod.predict(test_dat, test_dat)","","z.show(y_pred_part[['PARTITION_NAME', 'PREDICTION'] + y_pred_part.columns].round(4).head())"],"enabled":true,"result":{"startTime":1715293161357,"interpreter":"python.low","endTime":1715293161628,"results":[{"message":"PARTITION_NAME\tPREDICTION\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPARTITION_NAME\nF\t4.3611\t100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\t4.3611\tF\nF\t3.4255\t102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\t3.4255\tF\nM\t3.1581\t103401\tM\tDivorc.\t1975\tI: 170,000 - 189,999\t10000\tHS-grad\t0\t2\tCrafts\t4\t1\t3.1581\tM\nM\t3.6503\t104077\tM\tNeverM\t1975\tL: 300,000 and above\t9000\tProfsc\t0\t2\tProf.\t3\t1\t3.6503\tM\nM\t1.9144\t101940\tM\tNeverM\t1981\tJ: 190,000 - 249,999\t7000\t< Bach.\t0\t1\t?\t2\t1\t1.9144\tM\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show model global statistics for each partitioned sub-model","message":["%python","z.show(svm_mod.global_stats)"],"enabled":true,"result":{"startTime":1715293161705,"interpreter":"python.low","endTime":1715293161782,"results":[{"message":"partition name\tattribute name\tattribute value\nF\tCONVERGED\tYES\nF\tITERATIONS\t15.0F\tNUM_ROWS\t1213.0\nM\tCONVERGED\tYES\nM\tITERATIONS\t16.0\nM\tNUM_ROWS\t2381.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test dataset","message":["%python","","try:","    oml.drop(table = 'TEST_DATA')","except:","    pass","_ = test_dat.materialize(table = 'TEST_DATA')"],"enabled":true,"result":{"startTime":1715293161855,"interpreter":"python.low","endTime":1715293162178,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details","message":["%python","","y_pred_part = svm_mod.predict(test_dat, ","                              supplemental_cols = test_dat[['CUST_ID', 'YRS_RESIDENCE']], ","                              topN_attrs = True)","                              ","z.show(y_pred_part.head())"],"enabled":true,"result":{"startTime":1715293162253,"interpreter":"python.low","endTime":1715293167159,"results":[{"message":"PARTITION_NAME\tCUST_ID\tYRS_RESIDENCE\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\nF\t103200\t3\t3.5245890391003205\tCUST_MARITAL_STATUS\tDivorc.\t.108\tEDUCATION\tAssoc-A\t.101\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t.095\tY_BOX_GAMES\t0\t.011\tCUST_CREDIT_LIMIT\t5000\t-.006\nF\t103600\t1\t2.349263509449977\tCUST_CREDIT_LIMIT\t9000\t.002\tY_BOX_GAMES\t1\t-.013\tAFFINITY_CARD\t0\t-.02\tOCCUPATION\tSales\t-.075\tHOUSEHOLD_SIZE\t6-8\t-.114\nF\t100007\t5\t4.09247311406186\tCUST_YEAR_OF_BIRTH\t1963\t.319\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.128\tCUST_MARITAL_STATUS\tDivorc.\t.126\tY_BOX_GAMES\t0\t.013\tCUST_CREDIT_LIMIT\t9000\t.004\nF\t100041\t2\t1.765930950259441\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.049\tCUST_CREDIT_LIMIT\t15000\t.007\tY_BOX_GAMES\t1\t-.009\tAFFINITY_CARD\t0\t-.013\tOCCUPATION\tHandler\t-.141\nF\t100077\t4\t4.138059998358802\tCUST_YEAR_OF_BIRTH\t1959\t.397\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t.086\tCUST_MARITAL_STATUS\tMarried\t.062\tCUST_CREDIT_LIMIT\t15000\t.01\tY_BOX_GAMES\t0\t.007\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface so score data and display prediction details","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","      PREDICTION(SVM_PARTITIONED_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","      PREDICTION_DETAILS(SVM_PARTITIONED_REGRESSION_MODEL USING *) PD","      FROM TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","      XMLTABLE('/Details'","               PASSING OUT.PD","               COLUMNS ","               \"Attribute1\" XMLType PATH 'Attribute[1]',","               \"Attribute2\" XMLType PATH 'Attribute[2]') ","      OUTPRED"],"enabled":true,"result":{"startTime":1715293167236,"interpreter":"sql.low","endTime":1715293167331,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100007\t4.092\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1963\" weight=\".319\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".128\" \n100010\t2.978\t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".004\" \t\"CUST_ID\" actualValue=\"100010\" weight=\"-.001\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715293167407,"interpreter":"md.low","endTime":1715293167471,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715293167565,"interpreter":"md.low","endTime":1715293167627,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py REST API","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4Py REST API for invoking embedded Python execution","","OML4Py contains a REST API to run user-defined Python functions saved in the script repository. The REST API is used when separation between the client and the Database server is beneficial. For example, REST APIs can be called from a web development application, improving the portability of the interface to other types of platforms, increasing project scalability, and allowing components to evolve independently. Use the OML4Py REST API to build, train, deploy, and manage scripts.","","To run a script, it must reside in the OML4Py script repository. An Oracle Machine Learning cloud service account ***username*** and ***password*** must be provided for authentication.","","### **REST API Workflow**","","In this workflow, we highlight a few of the features of the REST API:","","- authenticating the Database user and obtaining a token","- loading a database table into the Database","- saving scripts in the Database Python script repository","- executing the saved Python scripts from the REST API using both synchronous and asynchronous modes","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"REST API Authentication","message":["%md","","To authenticate and obtain a token, use ***cURL*** with the ***-d*** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' \\","                   -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"' <yourpassword>'\"}' ","                   \"<OML Service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance","","For example, Oracle Machine Learning user ***OMLUSER*** with password ***OMLPASSWD*** and cloud service url ***https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com*** authenticates and obtain a REST token using the following approach:"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Export token to a variable","message":["%md ","","Copy the \"accessToken\" field from the response and assign it to variable token, surrounded by single quotes.","A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ export token='eyJhbGciOiJ...'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an OML Dataframe and load it to an Oracle Database table","message":["%md","","In this example, we load the **iris** data in Python and load the Dataframe into Oracle Database using the ***create*** function. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%python","","import oml","import pandas as pd","","from sklearn import datasets","from sklearn.datasets import load_iris","","iris = datasets.load_iris()","x = pd.DataFrame(iris.data, columns = ['SEPAL_LENGTH','SEPAL_WIDTH','PETAL_LENGTH','PETAL_WIDTH'])","y = pd.DataFrame(list(map(lambda x: {0: 'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), columns = ['SPECIES'])","","try:","    oml.drop(table=\"IRIS\")","except:","    pass","    ","oml_iris = oml.create(pd.concat([x, y], axis=1), table = 'IRIS')","index = oml.DataFrame(oml_iris['SPECIES'])","z.show(oml_iris.head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a Python script and save it to the OML4Py Script Repository","message":["%md","","In this script, we return the counts for each category in the **SPECIES** column and return the results sorted by the **SEPAL_LENGTH** column using ***oml.group_apply***.","Notice the value for ***group_count*** is a string, not a Python function object, however, the results are the same. The script is invoked from the Python interface, followed by the REST API."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke script from Python","message":["%python","","group_count = \"\"\"def group_count(dat):","  import oml","  import pandas as pd","  return pd.DataFrame([(dat[\"SPECIES\"][0], dat[\"SEPAL_LENGTH\"][0], dat.shape[0])], \\","  columns = [\"SPECIES\",\"SEPAL_LENGTH\", \"COUNT\"])\"\"\"","","try:","    oml.script.drop(\"group_count\")","except: ","    pass","oml.script.create(\"group_count\", func=group_count, overwrite=True) "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Invoke script from the Python API for embedded Python execution","message":["%python","","res = oml.group_apply(oml_iris, index, func=group_count, oml_input_type=\"pandas.DataFrame\",\\","         func_value=pd.DataFrame([('a', 1, 1)], columns=[\"SPECIES\", \"SEPAL_LENGTH\",\"COUNT\"]),\\","             orderby=oml_iris[\"SEPAL_LENGTH\"])","","z.show(res.head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the Python script in the OML4Py Script Repository","message":["%md","","We can check for the existence of the saved ***group_count*** script from the Python and REST interfaces.  From Python, run the OML4Py function ***oml.script.dir***."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","oml.script.dir(sctype='all')[['name', 'owner']]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Synchronous REST API calls","Now verify the existence of the ***group_count*** function in the script repository from the REST API with the ***cURL*** command. The default REST API execution mode is *synchronous*, so code execution will wait for the REST API call to return before additional calls may be initiated. ","","The Linux command line JSON parser ***jq*** is used to return the script name and owner.  To install jq on your Linux REST client, run the command ***sudo yum install jq***."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","$ curl -X GET --header \"Authorization: Bearer <token>\" \\","              --header 'Accept: application/json' ","              \"<OML Service URL>/oml/api/py-scripts/v1/scripts\" | jq '.result[] | .name + \" \" + .owner'","","  HTTP/1.1 200 OK","  Date: Wed, 09 Mar 2022 21:44:39 GMT","  Content-Type: application/json","  Transfer-Encoding: chunked","  Connection: keep-alive","  Cache-Control: no-cache, no-store, private","  X-Frame-Options: SAMEORIGIN","  X-XSS-Protection: 1;mode=block","  Strict-Transport-Security: max-age=31536000; includeSubDomains","  X-Content-Type-Options: nosniff","","  \"group_count OWNER\"","  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"TextBox\",\"name\":\"omlserver\",\"displayName\":null,\"defaultValue\":\"\",\"argument\":null,\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Invoke the ***group_count*** function in the script repository from the REST API. Set ***parallelFlag*** to true to leverage Database parallelism."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X POST --header \"Authorization: Bearer <token>\" \\","                  --header 'Content-Type: application/json' \\","                  --header 'Accept: application/json' \\","                  -d '{\"input\":\"select * from IRIS\", \"parameters\":\"{\\\"oml_input_type\\\":\\\"pandas.DataFrame\\\"}\", \"groupBy\":\"SPECIES\", \"orderBy\":\"SEPAL_LENGTH\", \"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/group-apply/group_count\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","The result of the REST execution is a JSON representation of the value returned from the Python script. Dataframes, lists, and dictionaries and ","PNG images are supported. The first portion of the returned value is:"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","{\"result\":{\"versicolor_8\":{\"SEPAL_LENGTH\":{\"0\":5.7},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":5}},\"versicolor_7\":{\"SEPAL_LENGTH\":{\"0\":5.6},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":2}},\"virginica_20\":{\"SEPAL_LENGTH\":{\"0\":6.7},\"SPECIES\":{\"0\":\"virginica\"},....."],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### **Asynchronous REST API calls**","","*Asynchronous* calls do not wait for the REST API call to return from the server. If the Python script is performing long running computations, results will not be immediate and the user may continue development on the client. The Asynchronous call's returned results are included in the response header. Display the response header by passing the ***-i*** flag to the ***cURL*** command. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes, and can be extended up to 12 hours. Set the ***asyncFlag*** to ***true*** to run asynchronous calls from the REST API.","","  "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"asyncFlag\":true, \"input\":\"select * from IRIS\", \"parameter\":\"{\\\"oml_input_type\\\":\\\u201cpandas.DataFrame\\\"}\", \"groupBy\":\"SPECIES\", \"orderBy\":\"SEPAL_LENGTH\", \"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/group-apply/group_count\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Setting the timeout flag for asychronous jobs","message":["%md","","The ***timeout*** flag is used to change the default timeout setting. ","","Below is the equivalent asynchronous REST call with a timeout of 300 seconds, or 5 minutes. The output includes the HTTP 201 status code indicating the job was created and a job id location that can be polled for status and results."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"asyncFlag\":true,\"timeout\":300, \"input\":\"select * from IRIS\", \"parameter\":\"{\\\"oml_input_type\\\":\\\"pandas.DataFrame\\\"}\", \"groupBy\":\"SPECIES\", \"orderBy\":\"SEPAL_LENGTH\", \"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/group-apply/group_count\"","","  HTTP/1.1 201 Created","  Location: <OML Service URL>/oml/tenants/MYTENANT/databases/MYADW/api/py-scripts/v1/<job id location>","  Cache-Control: no-cache, no-store, private","  Content-Length: 0","  X-Frame-Options: SAMEORIGIN","  Proxy-Connection: Keep-Alive","  X-XSS-Protection: 1;mode=block","  X-Content-Type-Options: nosniff"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Poll the job status","message":["%md","","To poll the job status, pass the job location to ***cURL*** using the GET method. The HTTP response status 302 indicates the currently executing job is found ","and includes a job id location for future results."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","","$ curl -i -X GET --header \"Authorization: Bearer <token>\" \\","                 --header 'Accept: application/json' \\","                 \"<OML Service URL>/oml/api/py-scripts/v1/jobs/<job id location>\"","","  HTTP/1.1 302 Found","  Date: Wed, 09 Mar 2022 13:04:04 GMT","  Server: nginx/1.18.0","  Cache-Control: no-cache, no-store, private","  Content-Length: 0","  X-Frame-Options: SAMEORIGIN","  Content-Location: http://<oml-cloud-service-instance-url>/oml/tenants/TENANT1/databases/ADWP/api/py-scripts/v1/<job id location>/result","  Proxy-Connection: Keep-Alive","  X-XSS-Protection: 1;mode=block","  X-Content-Type-Options: nosniff","  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Poll the job at the results location","message":["  ","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/py-scripts/v1/jobs/<job id location>/result\"","","","  HTTP/1.1 200 OK","  Date: Wed, 09 Mar 2022 13:06:05 GMT","  Server: nginx/1.18.0","  Content-Type: application/json","  Cache-Control: no-cache, no-store, private","  Content-Length: 1038","  X-Frame-Options: SAMEORIGIN","  Proxy-Connection: Keep-Alive","  X-XSS-Protection: 1;mode=block","  X-Content-Type-Options: nosniff","  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"A portion of the returned value","message":["","","{\"result\":{\"versicolor_8\":{\"SEPAL_LENGTH\":{\"0\":5.7},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":5}},\"versicolor_7\":{\"SEPAL_LENGTH\":{\"0\":5.6},\"SPECIES\":{\"0\":\"versicolor\"},\"CNT\":{\"0\":2}},\"virginica_20\":{\"SEPAL_LENGTH\":{\"0\":6.7},\"SPECIES\":{\"0\":\"virginica\"},....."],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### **Returning Model Details from the REST API**","","Let's create a linear model script and run it from the REST API. First, we'll define our function, ***build_lm_rest***, that returns a dict mapping of the model attributes. Note that the model itself cannot be returned directly from REST because LinearRegression models are not JSON-serializable. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%python","","build_lm_rest = \"\"\"def build_rest(dat):","            from sklearn import linear_model","            import oml","            regr = linear_model.LinearRegression()","            import pandas as pd","            dat = pd.get_dummies(dat, drop_first=True)","            X = dat[[\"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\", \"SPECIES_versicolor\", \"SPECIES_virginica\"]]","            y = dat[[\"SEPAL_LENGTH\"]]","            regr.fit(X, y)","            data = {}","            data[\"init_params\"] = regr.get_params()","            data[\"model_params\"] = mp = {}","            for p in (\"coef_\", \"intercept_\", \"_residues\", \"singular_\"):","                mp[p] = getattr(regr, p).tolist()","            return data\"\"\""],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save the buld_lm_rest script to the Python script repository","message":["%python","","try:","    oml.script.drop(\"build_lm_rest\")","except: ","    pass","oml.script.create(\"build_lm_rest\", func=build_lm_rest, overwrite=True) "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Invoke the build_lm_rest function in the script repository from the REST API in asynchronous mode.","message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" \\","                  --header 'Content-Type: application/json' \\","                  --header 'Accept: application/json' \\","                  -d '{\"input\":\"select * from IRIS\", \"parameters\":\"{\\\"oml_input_type\\\":\\\"pandas.DataFrame\\\"}\"}' ","                  \"<OML Service URL>/oml/api/py-scripts/v1/table-apply/build_lm_rest\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"The returned value is a dict containing model attributes","message":["","{\"result\":{\"init_params\":{\"fit_intercept\":true,\"n_jobs\":null,\"normalize\":false,\"copy_X\":true},\"model_params\":{\"intercept_\":[2.1712662921550736],\"_residues\":[13.556485081974792],\"coef_\":[[0.4958889383885511,0.8292439122348054,-0.315155173326473,-0.7235619577807288,-1.0234978144908309]],\"singular_\":[23.873627650888057,7.279716799977891,4.192696791126727,2.194192042042924,0.7289542732694926]}}}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Regression GLM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Regression Modeling to Predict Numerical Values using Generalized Linear Model","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1704380959872,"interpreter":"md.low","endTime":1704380959962,"results":[{"message":"<h2 id=\"regression-modeling-to-predict-numerical-values-using-generalized-linear-model\">Regression Modeling to Predict Numerical Values using Generalized Linear Model<\/h2>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1704380960098,"interpreter":"md.low","endTime":1704380960187,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a>"],"enabled":true,"result":{"startTime":1704380960339,"interpreter":"md.low","endTime":1704380960439,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1704380960578,"interpreter":"python.low","endTime":1704380960687,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES"," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1704380960807,"interpreter":"python.low","endTime":1704380960913,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1704380961040,"interpreter":"python.low","endTime":1704380961190,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%python","","TRAIN, TEST = CUST_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('YRS_RESIDENCE')","TRAIN_Y = TRAIN['YRS_RESIDENCE']","TEST_X = TEST.drop('YRS_RESIDENCE')","TEST_Y = TEST['YRS_RESIDENCE']"],"enabled":true,"result":{"startTime":1704380961322,"interpreter":"python.low","endTime":1704380961497,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a GLM regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1704380961611,"interpreter":"md.low","endTime":1704380961700,"results":[{"message":"<h3 id=\"build-a-glm-regression-model-for-predicting-yrs_residence\">Build a GLM regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%python","","settings = {}","","try:","    oml.drop(model = 'GLM_REGRESSION_MODEL')","except:","    print('No such model')"," ","glm_mod = oml.glm(\"regression\", **settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'GLM_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1704380961817,"interpreter":"python.low","endTime":1704380963842,"results":[{"message":"\nModel Name: GLM_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                  setting value\n0                     ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1               GLMS_CONF_LEVEL                            .95\n2           GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n3            GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n4                  ODMS_DETAILS                    ODMS_ENABLE\n5  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n6                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n7                     PREP_AUTO                             ON\n\nComputed Settings: \n            setting name            setting value\n0    GLMS_CONV_TOLERANCE  .0000050000000000000004\n1    GLMS_NUM_ITERATIONS                       30\n2  GLMS_RIDGE_REGRESSION   GLMS_RIDGE_REG_DISABLE\n3            GLMS_SOLVER         GLMS_SOLVER_CHOL\n\nGlobal Statistics: \n             attribute name attribute value\n0         ADJUSTED_R_SQUARE        0.577297\n1                       AIC         1133.61\n2                 COEFF_VAR         30.4233\n3                 CONVERGED             YES\n4        CORRECTED_TOTAL_DF            2708\n5          CORRECTED_TOT_SS         9534.73\n6            DEPENDENT_MEAN         4.00997\n7                  ERROR_DF            2652\n8         ERROR_MEAN_SQUARE         1.48832\n9         ERROR_SUM_SQUARES         3947.01\n10                  F_VALUE         67.0427\n11                    GMSEP         1.52032\n12               HOCKING_SP     0.000561417\n13                      J_P         1.51963\n14                 MODEL_DF              56\n15          MODEL_F_P_VALUE               0\n16        MODEL_MEAN_SQUARE         99.7807\n17        MODEL_SUM_SQUARES         5587.72\n18               NUM_PARAMS              57\n19                 NUM_ROWS            2709\n20          RANK_DEFICIENCY               0\n21             ROOT_MEAN_SQ         1.21997\n22                     R_SQ        0.586038\n23                     SBIC         1470.16\n24  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n         attribute name       attribute value  coefficient  std error    t value       p value significance code\n0           (Intercept)                  None   153.324901   5.433089  28.220575  0.000000e+00               ***\n1         AFFINITY_CARD                  None     0.340645   0.068606   4.965264  7.295958e-07               ***\n2     CUST_CREDIT_LIMIT                  None     0.000016   0.000008   2.034881  4.196216e-02                 *\n3           CUST_GENDER                     F    -0.113475   0.071621  -1.584376  1.132274e-01                  \n4     CUST_INCOME_LEVEL       A: Below 30,000     0.640696   0.198163   3.233176  1.239233e-03                **\n5     CUST_INCOME_LEVEL    B: 30,000 - 49,999     0.144060   0.131455   1.095885  2.732286e-01                  \n6     CUST_INCOME_LEVEL    C: 50,000 - 69,999     0.217899   0.142770   1.526218  1.270749e-01                  \n7     CUST_INCOME_LEVEL    D: 70,000 - 89,999    -0.011105   0.153972  -0.072125  9.425082e-01                  \n8     CUST_INCOME_LEVEL   E: 90,000 - 109,999     0.205234   0.113004   1.816173  6.945669e-02                 .\n9     CUST_INCOME_LEVEL  F: 110,000 - 129,999    -0.081483   0.106074  -0.768174  4.424523e-01                  \n10    CUST_INCOME_LEVEL  G: 130,000 - 149,999     0.164336   0.103530   1.587333  1.125565e-01                  \n11    CUST_INCOME_LEVEL  H: 150,000 - 169,999     0.289081   0.102576   2.818204  4.865106e-03                **\n12    CUST_INCOME_LEVEL  I: 170,000 - 189,999    -0.066614   0.094168  -0.707398  4.793812e-01                  \n13    CUST_INCOME_LEVEL  K: 250,000 - 299,999     0.012108   0.093756   0.129141  8.972561e-01                  \n14    CUST_INCOME_LEVEL  L: 300,000 and above    -0.003457   0.080803  -0.042782  9.658787e-01                  \n15  CUST_MARITAL_STATUS               Divorc.     0.255132   0.324671   0.785816  4.320453e-01                  \n16  CUST_MARITAL_STATUS               Mabsent    -0.231022   0.368937  -0.626183  5.312489e-01                  \n17  CUST_MARITAL_STATUS                Mar-AF    -0.975312   0.720500  -1.353660  1.759602e-01                  \n18  CUST_MARITAL_STATUS                NeverM    -0.012757   0.314184  -0.040604  9.676143e-01                  \n19  CUST_MARITAL_STATUS                Separ.    -0.040635   0.344544  -0.117939  9.061248e-01                  \n20  CUST_MARITAL_STATUS               Widowed    -0.945589   0.351749  -2.688250  7.227677e-03                **\n21   CUST_YEAR_OF_BIRTH                  None    -0.075924   0.002775 -27.358344  0.000000e+00               ***\n22            EDUCATION                  10th    -0.055915   0.154137  -0.362762  7.168118e-01                  \n23            EDUCATION                  11th    -0.281752   0.141721  -1.988072  4.690639e-02                 *\n24            EDUCATION                  12th    -0.191792   0.204292  -0.938815  3.479110e-01                  \n25            EDUCATION               1st-4th     0.187000   0.416084   0.449428  6.531600e-01                  \n26            EDUCATION               5th-6th     0.312164   0.246440   1.266694  2.053761e-01                  \n27            EDUCATION               7th-8th    -0.528300   0.173677  -3.041848  2.374333e-03                **\n28            EDUCATION                   9th    -0.480972   0.176666  -2.722501  6.521376e-03                **\n29            EDUCATION               < Bach.    -0.205311   0.067205  -3.055003  2.272976e-03                **\n30            EDUCATION               Assoc-A     0.213468   0.127929   1.668638  9.530715e-02                 .\n31            EDUCATION               Assoc-V     0.039609   0.124333   0.318572  7.500761e-01                  \n32            EDUCATION                 Bach.     0.099102   0.079059   1.253519  2.101272e-01                  \n33            EDUCATION               Masters     0.335463   0.135236   2.480566  1.317900e-02                 *\n34            EDUCATION                   PhD     0.784167   0.263927   2.971147  2.993555e-03                **\n35            EDUCATION               Presch.    -1.130164   0.712683  -1.585789  1.129065e-01                  \n36            EDUCATION                Profsc     0.610080   0.176570   3.455173  5.585775e-04               ***\n37       HOUSEHOLD_SIZE                     1    -0.438462   0.321774  -1.362637  1.731127e-01                  \n38       HOUSEHOLD_SIZE                     2    -0.115546   0.321970  -0.358872  7.197194e-01                  \n39       HOUSEHOLD_SIZE                   4-5    -0.696323   0.134620  -5.172492  2.482309e-07               ***\n40       HOUSEHOLD_SIZE                   6-8    -0.351825   0.326693  -1.076929  2.816100e-01                  \n41       HOUSEHOLD_SIZE                    9+    -0.289468   0.332312  -0.871073  3.837929e-01                  \n42           OCCUPATION                     ?    -0.432258   0.127862  -3.380671  7.335587e-04               ***\n43           OCCUPATION               Armed-F    -0.524535   1.229877  -0.426494  6.697826e-01                  \n44           OCCUPATION               Cleric.     0.061129   0.096605   0.632768  5.269398e-01                  \n45           OCCUPATION                Crafts    -0.043418   0.096684  -0.449077  6.534131e-01                  \n46           OCCUPATION                 Exec.     0.302740   0.096055   3.151724  1.641210e-03                **\n47           OCCUPATION               Farming     0.217480   0.162922   1.334878  1.820308e-01                  \n48           OCCUPATION               Handler    -0.327132   0.149622  -2.186389  2.887428e-02                 *\n49           OCCUPATION               House-s    -1.237814   0.331445  -3.734594  1.919768e-04               ***\n50           OCCUPATION               Machine    -0.056752   0.118432  -0.479196  6.318385e-01                  \n51           OCCUPATION                 Other    -0.257805   0.101369  -2.543220  1.103994e-02                 *\n52           OCCUPATION                 Prof.    -0.140482   0.105139  -1.336155  1.816134e-01                  \n53           OCCUPATION               Protec.     0.028069   0.181098   0.154996  8.768363e-01                  \n54           OCCUPATION               TechSup    -0.338445   0.132818  -2.548177  1.088468e-02                 *\n55           OCCUPATION               Transp.     0.296478   0.136628   2.169960  3.009840e-02                 *\n56          Y_BOX_GAMES                  None    -0.463632   0.083046  -5.582842  2.606260e-08               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0         ADJUSTED_R_SQUARE     0.577297\n1                       AIC  1133.612459\n2                 COEFF_VAR    30.423332\n3        CORRECTED_TOTAL_DF  2708.000000\n4          CORRECTED_TOT_SS  9534.730897\n5            DEPENDENT_MEAN     4.009967\n6                  ERROR_DF  2652.000000\n7         ERROR_MEAN_SQUARE     1.488316\n8         ERROR_SUM_SQUARES  3947.013467\n9                   F_VALUE    67.042673\n10                    GMSEP     1.520316\n11               HOCKING_SP     0.000561\n12                      J_P     1.519631\n13          MODEL_CONVERGED     1.000000\n14                 MODEL_DF    56.000000\n15          MODEL_F_P_VALUE     0.000000\n16        MODEL_MEAN_SQUARE    99.780668\n17        MODEL_SUM_SQUARES  5587.717430\n18               NUM_PARAMS    57.000000\n19                 NUM_ROWS  2709.000000\n20          RANK_DEFICIENCY     0.000000\n21             ROOT_MEAN_SQ     1.219965\n22                     R_SQ     0.586038\n23                     SBIC  1470.159545\n24  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n57\n\nDeviance: \n\n3947.013467\n\nAIC: \n\n1134\n\nNull Deviance: \n\n9534.730897\n\nDF Residual: \n\n2652.0\n\nDF Null: \n\n2708.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","- Algorithm Settings: <a href=\"https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> 'GLMS_CONF_LEVEL' : '0.95'","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> 'GLMS_RIDGE_REGRESSION' : 'GLMS_RIDGE_REG_DISABLE'","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> 'GLMS_RIDGE_VALUE' : '2'","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> 'GLMS_FTR_SELECTION' : 'GLMS_FTR_SELECTION_ENABLE'","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> 'GLMS_FTR_GENERATION' : 'GLMS_FTR_GENERATION_ENABLE'","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`","> 'GLMS_FTR_GEN_METHOD' : 'GLMS_FTR_GEN_QUADRATIC'","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> 'GLMS_FTR_SEL_CRIT' : 'GLMS_FTR_SEL_ALPHA_INV'","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> 'GLMS_MAX_FEATURES' : '30'","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> 'GLMS_PRUNE_MODEL' : 'GLMS_PRUNE_MODEL_ENABLE'","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> 'GLMS_REFERENCE_CLASS_NAME' : '<target_value>'","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> 'GLMS_ROW_DIAGNOSTICS' : 'GLMS_ROW_DIAG_ENABLE'","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> 'GLMS_CONV_TOLERANCE' : '0.0001'","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> 'GLMS_NUM_ITERATIONS' : '200'","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> 'GLMS_BATCH_ROWS' : '2000'","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> 'GLMS_SOLVER' : 'GLMS_SOLVER_SGD'","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.  ","> 'GLMS_SPARSE_SOLVER' : 'GLMS_SPARSE_SOLVER_ENABLE'","","","<strong> For Oracle release 23ai and newer. <\/strong>","- Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function. ","   For classification, the options are `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK` or `GLMS_CAUCHIT_LINK`.  For regression, the default is `GLMS_IDENTITY_LINK (default)`.","> 'GLMS_LINK_FUNCTION' : 'GLMS_LOGIT_LINK'"],"enabled":true,"result":{"startTime":1715317718368,"interpreter":"md.medium","endTime":1715317718560,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_CONF_LEVEL' : '0.95'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_RIDGE_REGRESSION' : 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_RIDGE_VALUE' : '2'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_SELECTION' : 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_GENERATION' : 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_GEN_METHOD' : 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_FTR_SEL_CRIT' : 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_MAX_FEATURES' : '30'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_PRUNE_MODEL' : 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_REFERENCE_CLASS_NAME' : '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_ROW_DIAGNOSTICS' : 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_CONV_TOLERANCE' : '0.0001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_NUM_ITERATIONS' : '200'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_BATCH_ROWS' : '2000'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_SOLVER' : 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_SPARSE_SOLVER' : 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> For Oracle release 23ai and newer. <\/strong><\/p>\n<ul>\n<li>Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function.\nFor classification, the options are <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code> or <code>GLMS_CAUCHIT_LINK<\/code>.  For regression, the default is <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'GLMS_LINK_FUNCTION' : 'GLMS_LOGIT_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Feature Selection and Generation","message":["%python","","settings = {'GLMS_RIDGE_REGRESSION':'GLMS_RIDGE_REG_DISABLE',","            'GLMS_FTR_SELECTION':'GLMS_FTR_SELECTION_ENABLE',","            'GLMS_FTR_GEN_METHOD':'GLMS_FTR_GEN_QUADRATIC',","            'GLMS_FTR_GENERATION': 'GLMS_FTR_GENERATION_ENABLE', ","            'GLMS_FTR_SEL_CRIT':'GLMS_FTR_SEL_AIC',","            'GLMS_NUM_ITERATIONS':'50',","            'GLMS_MAX_FEATURES':'30',","            'GLMS_PRUNE_MODEL':'GLMS_PRUNE_MODEL_ENABLE',","            'GLMS_ROW_DIAGNOSTICS':'GLMS_ROW_DIAG_DISABLE',","            'GLMS_CONV_TOLERANCE':'0.0001',","            'GLMS_BATCH_ROWS':'2000'}","","try:","    oml.drop(model = 'GLM_REGRESSION_MODEL')","except:","    print('No such model')"," ","glm_mod = oml.glm(\"regression\", **settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'GLM_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1704380964208,"interpreter":"python.low","endTime":1704380966242,"results":[{"message":"\nModel Name: GLM_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1                GLMS_BATCH_ROWS                           2000\n2                GLMS_CONF_LEVEL                            .95\n3            GLMS_CONV_TOLERANCE                         0.0001\n4            GLMS_FTR_GENERATION     GLMS_FTR_GENERATION_ENABLE\n5            GLMS_FTR_GEN_METHOD         GLMS_FTR_GEN_QUADRATIC\n6             GLMS_FTR_SELECTION      GLMS_FTR_SELECTION_ENABLE\n7              GLMS_FTR_SEL_CRIT               GLMS_FTR_SEL_AIC\n8              GLMS_MAX_FEATURES                             30\n9            GLMS_NUM_ITERATIONS                             50\n10              GLMS_PRUNE_MODEL        GLMS_PRUNE_MODEL_ENABLE\n11         GLMS_RIDGE_REGRESSION         GLMS_RIDGE_REG_DISABLE\n12          GLMS_ROW_DIAGNOSTICS          GLMS_ROW_DIAG_DISABLE\n13             GLMS_SELECT_BLOCK      GLMS_SELECT_BLOCK_DISABLE\n14                  ODMS_DETAILS                    ODMS_ENABLE\n15  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n16                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n17                     PREP_AUTO                             ON\n\nGlobal Statistics: \n             attribute name attribute value\n0         ADJUSTED_R_SQUARE        0.570989\n1                       AIC         1141.24\n2                 COEFF_VAR         30.6495\n3                 CONVERGED             YES\n4        CORRECTED_TOTAL_DF            2708\n5          CORRECTED_TOT_SS         9534.73\n6            DEPENDENT_MEAN         4.00997\n7                  ERROR_DF            2685\n8         ERROR_MEAN_SQUARE         1.51053\n9         ERROR_SUM_SQUARES         4055.76\n10                  F_VALUE         157.704\n11                    GMSEP         1.52403\n12               HOCKING_SP     0.000562789\n13                      J_P         1.52391\n14                 MODEL_DF              23\n15          MODEL_F_P_VALUE               0\n16        MODEL_MEAN_SQUARE         238.216\n17        MODEL_SUM_SQUARES         5478.97\n18               NUM_PARAMS              24\n19                 NUM_ROWS            2709\n20             ROOT_MEAN_SQ         1.22903\n21                     R_SQ        0.574633\n22                     SBIC         1282.95\n23  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nAFFINITY_CARD\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n         attribute name       attribute value  coefficient  std error    t value       p value significance code\n0           (Intercept)                  None   151.619049   5.325376  28.471050  0.000000e+00               ***\n1         AFFINITY_CARD                  None     0.405005   0.065140   6.217413  5.842160e-10               ***\n2           CUST_GENDER                     F    -0.156305   0.064863  -2.409772  1.602937e-02                 *\n3     CUST_INCOME_LEVEL       A: Below 30,000     0.480111   0.178824   2.684823  7.301432e-03                **\n4     CUST_INCOME_LEVEL  F: 110,000 - 129,999    -0.185843   0.088004  -2.111773  3.479808e-02                 *\n5     CUST_INCOME_LEVEL  H: 150,000 - 169,999     0.210351   0.086174   2.440988  1.471132e-02                 *\n6   CUST_MARITAL_STATUS               Divorc.     0.224238   0.079465   2.821836  4.810077e-03                **\n7   CUST_MARITAL_STATUS               Widowed    -1.072453   0.153037  -7.007801  3.049532e-12               ***\n8    CUST_YEAR_OF_BIRTH                  None    -0.075006   0.002719 -27.585654  0.000000e+00               ***\n9             EDUCATION               < Bach.    -0.167355   0.058745  -2.848861  4.421013e-03                **\n10            EDUCATION               Masters     0.240118   0.121931   1.969301  4.902121e-02                 *\n11            EDUCATION                   PhD     0.724599   0.254657   2.845393  4.469288e-03                **\n12            EDUCATION                Profsc     0.507417   0.160317   3.165075  1.567788e-03                **\n13       HOUSEHOLD_SIZE                     1    -0.380655   0.080496  -4.728864  2.374468e-06               ***\n14       HOUSEHOLD_SIZE                   4-5    -0.671540   0.126990  -5.288148  1.335068e-07               ***\n15       HOUSEHOLD_SIZE                   6-8    -0.292035   0.136270  -2.143066  3.219746e-02                 *\n16       HOUSEHOLD_SIZE                    9+    -0.210189   0.088008  -2.388277  1.699627e-02                 *\n17           OCCUPATION                     ?    -0.433972   0.114666  -3.784672  1.572781e-04               ***\n18           OCCUPATION                 Exec.     0.372300   0.075989   4.899416  1.018327e-06               ***\n19           OCCUPATION               Handler    -0.331908   0.136608  -2.429631  1.517937e-02                 *\n20           OCCUPATION                 Other    -0.274482   0.081512  -3.367383  7.695453e-04               ***\n21           OCCUPATION               TechSup    -0.268835   0.119675  -2.246382  2.476053e-02                 *\n22           OCCUPATION               Transp.     0.302057   0.121823   2.479479  1.321838e-02                 *\n23          Y_BOX_GAMES                  None    -0.483562   0.081466  -5.935759  3.301191e-09               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0         ADJUSTED_R_SQUARE     0.570989\n1                       AIC  1141.242506\n2                 COEFF_VAR    30.649500\n3        CORRECTED_TOTAL_DF  2708.000000\n4          CORRECTED_TOT_SS  9534.730897\n5            DEPENDENT_MEAN     4.009967\n6                  ERROR_DF  2685.000000\n7         ERROR_MEAN_SQUARE     1.510527\n8         ERROR_SUM_SQUARES  4055.763666\n9                   F_VALUE   157.703930\n10                    GMSEP     1.524033\n11               HOCKING_SP     0.000563\n12                      J_P     1.523909\n13          MODEL_CONVERGED     1.000000\n14                 MODEL_DF    23.000000\n15          MODEL_F_P_VALUE     0.000000\n16        MODEL_MEAN_SQUARE   238.215967\n17        MODEL_SUM_SQUARES  5478.967231\n18               NUM_PARAMS    24.000000\n19                 NUM_ROWS  2709.000000\n20             ROOT_MEAN_SQ     1.229035\n21                     R_SQ     0.574633\n22                     SBIC  1282.946542\n23  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n24\n\nDeviance: \n\n4055.763666\n\nAIC: \n\n1141\n\nNull Deviance: \n\n9534.730897\n\nDF Residual: \n\n2685.0\n\nDF Null: \n\n2708.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Ridge Regression","message":["%python","","settings = {'GLMS_RIDGE_REGRESSION':'GLMS_RIDGE_REG_ENABLE',","            'GLMS_FTR_SELECTION':'GLMS_FTR_SELECTION_DISABLE',","            'GLMS_ROW_DIAGNOSTICS':'GLMS_ROW_DIAG_DISABLE',","            'GLMS_CONV_TOLERANCE':'0.0001',","            'GLMS_NUM_ITERATIONS':'50',","            'GLMS_RIDGE_VALUE':'0.1',","            'GLMS_BATCH_ROWS':'2000'}","","try:","    oml.drop(model = 'GLM_REGRESSION_MODEL')","except:","    print('No such model')"," ","glm_mod = oml.glm(\"regression\", **settings)","glm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'GLM_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1704380966365,"interpreter":"python.low","endTime":1704380968291,"results":[{"message":"\nModel Name: GLM_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Generalized Linear Model\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                  setting value\n0                      ALGO_NAME  ALGO_GENERALIZED_LINEAR_MODEL\n1                GLMS_BATCH_ROWS                           2000\n2                GLMS_CONF_LEVEL                            .95\n3            GLMS_CONV_TOLERANCE                         0.0001\n4            GLMS_FTR_GENERATION    GLMS_FTR_GENERATION_DISABLE\n5             GLMS_FTR_SELECTION     GLMS_FTR_SELECTION_DISABLE\n6            GLMS_NUM_ITERATIONS                             50\n7          GLMS_RIDGE_REGRESSION          GLMS_RIDGE_REG_ENABLE\n8               GLMS_RIDGE_VALUE                            0.1\n9           GLMS_ROW_DIAGNOSTICS          GLMS_ROW_DIAG_DISABLE\n10            GLMS_VIF_FOR_RIDGE         GLMS_VIF_RIDGE_DISABLE\n11                  ODMS_DETAILS                    ODMS_ENABLE\n12  ODMS_MISSING_VALUE_TREATMENT        ODMS_MISSING_VALUE_AUTO\n13                 ODMS_SAMPLING          ODMS_SAMPLING_DISABLE\n14                     PREP_AUTO                             ON\n\nComputed Settings: \n  setting name     setting value\n0  GLMS_SOLVER  GLMS_SOLVER_CHOL\n\nGlobal Statistics: \n             attribute name attribute value\n0         ADJUSTED_R_SQUARE        0.577186\n1                       AIC         1134.32\n2                 COEFF_VAR         30.4273\n3                 CONVERGED             YES\n4        CORRECTED_TOTAL_DF            2708\n5          CORRECTED_TOT_SS         9534.73\n6            DEPENDENT_MEAN         4.00997\n7                  ERROR_DF            2652\n8         ERROR_MEAN_SQUARE         1.48871\n9         ERROR_SUM_SQUARES         3948.05\n10                  F_VALUE         67.0127\n11                    GMSEP         1.52071\n12               HOCKING_SP     0.000561564\n13                      J_P         1.52003\n14                 MODEL_DF              56\n15          MODEL_F_P_VALUE               0\n16        MODEL_MEAN_SQUARE         99.7622\n17        MODEL_SUM_SQUARES         5586.68\n18               NUM_PARAMS              57\n19                 NUM_ROWS            2709\n20          RANK_DEFICIENCY               0\n21             ROOT_MEAN_SQ         1.22013\n22                     R_SQ         0.58593\n23                     SBIC         1470.87\n24  VALID_COVARIANCE_MATRIX             YES\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nCoefficients: \n\n         attribute name       attribute value  coefficient  std error    t value       p value significance code\n0           (Intercept)                  None   153.286770   5.432447  28.216893  0.000000e+00               ***\n1         AFFINITY_CARD                  None     0.341061   0.068599   4.971812  7.055932e-07               ***\n2     CUST_CREDIT_LIMIT                  None     0.000016   0.000008   2.031948  4.225836e-02                 *\n3           CUST_GENDER                     F    -0.113944   0.071593  -1.591559  1.116029e-01                  \n4     CUST_INCOME_LEVEL       A: Below 30,000     0.638712   0.197875   3.227863  1.262389e-03                **\n5     CUST_INCOME_LEVEL    B: 30,000 - 49,999     0.143608   0.131329   1.093496  2.742753e-01                  \n6     CUST_INCOME_LEVEL    C: 50,000 - 69,999     0.217494   0.142641   1.524762  1.274377e-01                  \n7     CUST_INCOME_LEVEL    D: 70,000 - 89,999    -0.011218   0.153834  -0.072924  9.418718e-01                  \n8     CUST_INCOME_LEVEL   E: 90,000 - 109,999     0.205015   0.112914   1.815669  6.953406e-02                 .\n9     CUST_INCOME_LEVEL  F: 110,000 - 129,999    -0.081756   0.105989  -0.771362  4.405610e-01                  \n10    CUST_INCOME_LEVEL  G: 130,000 - 149,999     0.164024   0.103451   1.585523  1.129668e-01                  \n11    CUST_INCOME_LEVEL  H: 150,000 - 169,999     0.288714   0.102497   2.816809  4.886220e-03                **\n12    CUST_INCOME_LEVEL  I: 170,000 - 189,999    -0.066784   0.094095  -0.709746  4.779238e-01                  \n13    CUST_INCOME_LEVEL  K: 250,000 - 299,999     0.012075   0.093706   0.128866  8.974734e-01                  \n14    CUST_INCOME_LEVEL  L: 300,000 and above    -0.003902   0.080750  -0.048323  9.614627e-01                  \n15  CUST_MARITAL_STATUS               Divorc.     0.253653   0.315765   0.803296  4.218758e-01                  \n16  CUST_MARITAL_STATUS               Mabsent    -0.232063   0.360465  -0.643788  5.197688e-01                  \n17  CUST_MARITAL_STATUS                Mar-AF    -0.942407   0.708334  -1.330455  1.834827e-01                  \n18  CUST_MARITAL_STATUS                NeverM    -0.014300   0.305534  -0.046804  9.626733e-01                  \n19  CUST_MARITAL_STATUS                Separ.    -0.041920   0.335911  -0.124795  9.006955e-01                  \n20  CUST_MARITAL_STATUS               Widowed    -0.946343   0.343085  -2.758334  5.849528e-03                **\n21   CUST_YEAR_OF_BIRTH                  None    -0.075905   0.002775 -27.354516  0.000000e+00               ***\n22            EDUCATION                  10th    -0.055951   0.154018  -0.363277  7.164271e-01                  \n23            EDUCATION                  11th    -0.281551   0.141632  -1.987909  4.692445e-02                 *\n24            EDUCATION                  12th    -0.191125   0.204022  -0.936783  3.489552e-01                  \n25            EDUCATION               1st-4th     0.185111   0.413668   0.447488  6.545592e-01                  \n26            EDUCATION               5th-6th     0.311115   0.245960   1.264899  2.060185e-01                  \n27            EDUCATION               7th-8th    -0.527250   0.173509  -3.038746  2.398826e-03                **\n28            EDUCATION                   9th    -0.480464   0.176480  -2.722481  6.521764e-03                **\n29            EDUCATION               < Bach.    -0.205203   0.067175  -3.054761  2.274804e-03                **\n30            EDUCATION               Assoc-A     0.212537   0.127838   1.662556  9.651949e-02                 .\n31            EDUCATION               Assoc-V     0.039585   0.124268   0.318543  7.500980e-01                  \n32            EDUCATION                 Bach.     0.098861   0.079016   1.251158  2.109872e-01                  \n33            EDUCATION               Masters     0.334172   0.135078   2.473920  1.342612e-02                 *\n34            EDUCATION                   PhD     0.780157   0.263323   2.962736  3.076294e-03                **\n35            EDUCATION               Presch.    -1.093416   0.700902  -1.560013  1.188762e-01                  \n36            EDUCATION                Profsc     0.608335   0.176374   3.449124  5.711853e-04               ***\n37       HOUSEHOLD_SIZE                     1    -0.436662   0.313081  -1.394725  1.632155e-01                  \n38       HOUSEHOLD_SIZE                     2    -0.113825   0.312996  -0.363662  7.161393e-01                  \n39       HOUSEHOLD_SIZE                   4-5    -0.696452   0.134493  -5.178353  2.406347e-07               ***\n40       HOUSEHOLD_SIZE                   6-8    -0.349792   0.318628  -1.097805  2.723895e-01                  \n41       HOUSEHOLD_SIZE                    9+    -0.287699   0.323291  -0.889908  3.735959e-01                  \n42           OCCUPATION                     ?    -0.431388   0.127740  -3.377076  7.431725e-04               ***\n43           OCCUPATION               Armed-F    -0.475247   1.171912  -0.405531  6.851201e-01                  \n44           OCCUPATION               Cleric.     0.061619   0.096504   0.638513  5.231951e-01                  \n45           OCCUPATION                Crafts    -0.043042   0.096574  -0.445692  6.558559e-01                  \n46           OCCUPATION                 Exec.     0.303436   0.095954   3.162299  1.582974e-03                **\n47           OCCUPATION               Farming     0.217302   0.162729   1.335361  1.818730e-01                  \n48           OCCUPATION               Handler    -0.326269   0.149467  -2.182882  2.913193e-02                 *\n49           OCCUPATION               House-s    -1.228783   0.330235  -3.720932  2.026102e-04               ***\n50           OCCUPATION               Machine    -0.056461   0.118308  -0.477237  6.332329e-01                  \n51           OCCUPATION                 Other    -0.257109   0.101257  -2.539175  1.116810e-02                 *\n52           OCCUPATION                 Prof.    -0.139071   0.105009  -1.324372  1.854937e-01                  \n53           OCCUPATION               Protec.     0.028677   0.180879   0.158544  8.740406e-01                  \n54           OCCUPATION               TechSup    -0.337437   0.132700  -2.542856  1.105141e-02                 *\n55           OCCUPATION               Transp.     0.296709   0.136495   2.173768  2.981078e-02                 *\n56          Y_BOX_GAMES                  None    -0.463980   0.083037  -5.587607  2.536575e-08               ***\n\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 '\n\nFit Details: \n\n                       name        value\n0         ADJUSTED_R_SQUARE     0.577186\n1                       AIC  1134.321375\n2                 COEFF_VAR    30.427312\n3        CORRECTED_TOTAL_DF  2708.000000\n4          CORRECTED_TOT_SS  9534.730897\n5            DEPENDENT_MEAN     4.009967\n6                  ERROR_DF  2652.000000\n7         ERROR_MEAN_SQUARE     1.488705\n8         ERROR_SUM_SQUARES  3948.046492\n9                   F_VALUE    67.012740\n10                    GMSEP     1.520714\n11               HOCKING_SP     0.000562\n12                      J_P     1.520029\n13          MODEL_CONVERGED     1.000000\n14                 MODEL_DF    56.000000\n15          MODEL_F_P_VALUE     0.000000\n16        MODEL_MEAN_SQUARE    99.762222\n17        MODEL_SUM_SQUARES  5586.684405\n18               NUM_PARAMS    57.000000\n19                 NUM_ROWS  2709.000000\n20          RANK_DEFICIENCY     0.000000\n21             ROOT_MEAN_SQ     1.220125\n22                     R_SQ     0.585930\n23                     SBIC  1470.868461\n24  VALID_COVARIANCE_MATRIX     1.000000\n\nRank: \n\n57\n\nDeviance: \n\n3948.046492\n\nAIC: \n\n1134\n\nNull Deviance: \n\n9534.730897\n\nDF Residual: \n\n2652.0\n\nDF Null: \n\n2708.0\n\nConverged: \n\nTrue\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model fit details","message":["%python","","z.show(glm_mod.fit_details.round(6))"],"enabled":true,"result":{"startTime":1704380968416,"interpreter":"python.low","endTime":1704380968541,"results":[{"message":"name\tvalue\nADJUSTED_R_SQUARE\t0.577186\nAIC\t1134.321375\nCOEFF_VAR\t30.427312\nCORRECTED_TOTAL_DF\t2708.0\nCORRECTED_TOT_SS\t9534.730897\nDEPENDENT_MEAN\t4.009967\nERROR_DF\t2652.0\nERROR_MEAN_SQUARE\t1.488705\nERROR_SUM_SQUARES\t3948.046492\nF_VALUE\t67.01274\nGMSEP\t1.520714\nHOCKING_SP\t0.000562\nJ_P\t1.520029\nMODEL_CONVERGED\t1.0\nMODEL_DF\t56.0\nMODEL_F_P_VALUE\t0.0\nMODEL_MEAN_SQUARE\t99.762222\nMODEL_SUM_SQUARES\t5586.684405\nNUM_PARAMS\t57.0\nNUM_ROWS\t2709.0\nRANK_DEFICIENCY\t0.0\nROOT_MEAN_SQ\t1.220125\nR_SQ\t0.58593\nSBIC\t1470.868461\nVALID_COVARIANCE_MATRIX\t1.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the coefficients","message":["%python","","z.show(glm_mod.coef.head(10).round(3))"],"enabled":true,"result":{"startTime":1704380968658,"interpreter":"python.low","endTime":1704380968782,"results":[{"message":"attribute name\tattribute value\tcoefficient\tstd error\tt value\tp value\n(Intercept)\tNone\t153.287\t5.432\t28.217\t0.0\nAFFINITY_CARD\tNone\t0.341\t0.069\t4.972\t0.0\nCUST_CREDIT_LIMIT\tNone\t0.0\t0.0\t2.032\t0.042\nCUST_YEAR_OF_BIRTH\tNone\t-0.076\t0.003\t-27.355\t0.0\nY_BOX_GAMES\tNone\t-0.464\t0.083\t-5.588\t0.0\nCUST_GENDER\tF\t-0.114\t0.072\t-1.592\t0.112\nCUST_INCOME_LEVEL\tA: Below 30,000\t0.639\t0.198\t3.228\t0.001\nCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t0.144\t0.131\t1.093\t0.274\nCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t0.217\t0.143\t1.525\t0.127\nCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t-0.011\t0.154\t-0.073\t0.942\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%python","","RES_DF = glm_mod.predict(TEST_X, supplemental_cols = TEST)","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE'] + RES_DF.columns].head(10).round(2))"],"enabled":true,"result":{"startTime":1704380968897,"interpreter":"python.low","endTime":1704380969196,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\n4.57\t2\t100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\t4.57\n1.84\t2\t101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\t1.84\n5.31\t5\t103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\t5.31\n2.23\t2\t102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\t2.23\n4.71\t4\t100558\tM\tMarried\t1964\tJ: 190,000 - 249,999\t11000\tAssoc-V\t1\t3\tMachine\t4\t0\t4.71\n3.1\t4\t103401\tM\tDivorc.\t1975\tI: 170,000 - 189,999\t10000\tHS-grad\t0\t2\tCrafts\t4\t1\t3.1\n6.83\t0\t102740\tF\tDivorc.\t1929\tK: 250,000 - 299,999\t15000\t10th\t0\t2\tOther\t0\t0\t6.83\n3.75\t3\t103829\tM\tDivorc.\t1973\tE: 90,000 - 109,999\t7000\tHS-grad\t0\t9+\tMachine\t3\t0\t3.75\n3.39\t3\t104077\tM\tNeverM\t1975\tL: 300,000 and above\t9000\tProfsc\t0\t2\tProf.\t3\t1\t3.39\n5.56\t5\t101798\tM\tMarried\t1960\tG: 130,000 - 149,999\t15000\tHS-grad\t1\t3\tExec.\t5\t0\t5.56\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%python","","import numpy as np","","# The coefficient of determination R?2 is part of the \"score\" method for OML models","CoefficientOfDeterminationR2 = glm_mod.score(TEST_X, TEST_Y)","    ","# Compute other statistics by hand - no need to pull data locally to Python","# RES_DF[['PREDICTION', 'YRS_RESIDENCE']","MeanAbsoluteError = (abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION']).sum())/RES_DF['YRS_RESIDENCE'].count()","MeanSquaredError =  ((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])**2).sum()/RES_DF['YRS_RESIDENCE'].count()","RootMeanSquaredError =  np.sqrt(MeanSquaredError)","","print('R^2:  ', CoefficientOfDeterminationR2.round(4))","print('MAE:  ', MeanAbsoluteError.round(4))","print('MSE:  ', MeanSquaredError.round(4))","print('RMSE: ', RootMeanSquaredError.round(4))"],"enabled":true,"result":{"startTime":1704380969311,"interpreter":"python.low","endTime":1704380969671,"results":[{"message":"R^2:   0.5569\nMAE:   0.8651\nMSE:   1.6673\nRMSE:  1.2912\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"groupByColumns\":[],\"showSeriesX\":[\"YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"showLabel\":\"no\",\"showDescription\":\"no\",\"lastColumns\":[\"PREDICTION\",\"YRS_RESIDENCE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot the predicted and actual years of residence ","message":["%python","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE']].head(100))"],"enabled":true,"result":{"startTime":1704380969798,"interpreter":"python.low","endTime":1704380969983,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\n4.57191443901974\t2.0\n1.8358546077171392\t2.0\n5.312181183257389\t5.0\n2.2323988564511543\t2.0\n4.707440334622631\t4.0\n3.0985366454325205\t4.0\n6.827973805136314\t0.0\n3.7514697241400805\t3.0\n3.389984135786385\t3.0\n5.558543569472224\t5.0\n4.931785043571118\t5.0\n3.821346504334991\t1.0\n4.671141595251779\t4.0\n4.637060248997707\t5.0\n3.928007733127923\t4.0\n4.685315294174711\t5.0\n4.272350467119804\t5.0\n3.856041579641236\t3.0\n4.763705072196844\t5.0\n4.393157118465297\t4.0\n2.576899442953469\t4.0\n4.201836156665195\t3.0\n1.2322666658095578\t1.0\n4.304581897574273\t3.0\n3.8575624269465103\t3.0\n5.460466693888613\t5.0\n4.615890688182985\t5.0\n1.752029200165881\t2.0\n5.523615432036287\t5.0\n3.1286523072376253\t3.0\n4.017237980750553\t5.0\n3.649534665295635\t2.0\n4.209938366084536\t6.0\n4.183053991337589\t4.0\n4.720103459218842\t3.0\n1.7869371148460438\t2.0\n5.428604534507215\t6.0\n3.373842654253\t5.0\n4.0534245034282\t5.0\n5.179803954965174\t10.0\n1.3760803288660053\t1.0\n4.965887241808666\t7.0\n7.015572559176409\t7.0\n2.763471212249958\t2.0\n5.727870296291183\t7.0\n2.913572577855662\t4.0\n4.787647909451225\t5.0\n3.269514294134353\t3.0\n5.251748264297419\t7.0\n4.337527610211263\t4.0\n4.403361748758342\t5.0\n5.530763155593056\t4.0\n2.8202897431844773\t3.0\n2.743044928352869\t3.0\n4.351015663708916\t4.0\n3.0686750672400356\t3.0\n2.3807790690896296\t2.0\n7.156367191586237\t6.0\n4.440458514903465\t9.0\n1.6489176427546925\t1.0\n6.250639807930804\t6.0\n4.367295798221904\t4.0\n5.42467435536096\t6.0\n5.9403541537286\t6.0\n2.6442704547250946\t2.0\n3.2130959589247268\t2.0\n4.3974729014767195\t4.0\n5.736500253879448\t6.0\n1.4447064621960093\t0.0\n3.922643064613891\t3.0\n4.183053991337589\t4.0\n4.865308075564007\t7.0\n4.286962609718261\t4.0\n4.993338829966633\t6.0\n3.9027181635342836\t3.0\n3.3670569631617067\t4.0\n6.08959229229739\t6.0\n2.45238843601096\t1.0\n3.787275137527118\t3.0\n2.538570849790202\t2.0\n2.5111421778298344\t3.0\n3.732700729526654\t4.0\n3.3893333377073587\t4.0\n2.5793226011902117\t3.0\n5.761236072739153\t5.0\n1.0438523152603947\t2.0\n1.8154284561596836\t0.0\n1.9777145389965205\t2.0\n4.073812374671\t3.0\n3.6146461321885575\t3.0\n1.6360232192990298\t2.0\n5.457614570091144\t5.0\n3.9034462503274163\t5.0\n2.5393831245365903\t3.0\n4.727773483841452\t4.0\n5.607211323465545\t6.0\n5.95095004671569\t4.0\n1.4972476421551408\t1.0\n5.958070954521609\t5.0\n1.6347720515702422\t3.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using matplotlib, plot the predicted and actual years of residence","message":["%python","","import numpy as np","import matplotlib.pyplot as plt","plt.style.use('seaborn')","plt.figure(figsize=[9,7])","plt.margins(0.05)","","x = RES_DF[['PREDICTION']].pull()","y = RES_DF[['YRS_RESIDENCE']].pull()","n = len(x)","","plt.plot(x, y, '.', c='black', alpha=0.8,  ms=7)","","plt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],","          c='red', alpha=0.8,  ms=3,","          label='Perfect prediction reference line')","","plt.legend(frameon=True, facecolor='lightgray')         ","plt.xlabel('PREDICTION')","plt.ylabel('YRS_RESIDENCE')","plt.title('The prediction vs. actual value for years of residence',","          fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1704380970110,"interpreter":"python.low","endTime":1704380970415,"results":[{"message":"<div style='width:auto;height:auto'><img 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","plt.xlabel('PREDICTION')","plt.ylabel('RESIDUAL')","plt.title('Prediction of Years of Residence vs. Residuals', fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1704380970533,"interpreter":"python.low","endTime":1704380970749,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details in python","message":["%python","","","RES_DF = glm_mod.predict(TEST, supplemental_cols = TEST[['CUST_ID']], proba = True, topN_attrs = True)","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1704380970872,"interpreter":"python.low","endTime":1704380979602,"results":[{"message":"CUST_ID\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100100\t4.472758165280582\tCUST_YEAR_OF_BIRTH\t1959\t.201\tEDUCATION\tMasters\t.155\tAFFINITY_CARD\t1\t.12\tY_BOX_GAMES\t0\t.067\tCUST_CREDIT_LIMIT\t10000\t.015\n100200\t1.5283330601173908\tCUST_CREDIT_LIMIT\t9000\t.006\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-.001\tCUST_MARITAL_STATUS\tNeverM\t-.005\tAFFINITY_CARD\t0\t-.03\tEDUCATION\t< Bach.\t-.075\n100300\t5.060057240486081\tCUST_YEAR_OF_BIRTH\t1961\t.25\tAFFINITY_CARD\t1\t.231\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t.147\tY_BOX_GAMES\t0\t.129\tEDUCATION\tBach.\t.089\n100400\t6.096717138233105\tCUST_YEAR_OF_BIRTH\t1944\t.503\tOCCUPATION\tTransp.\t.095\tCUST_MARITAL_STATUS\tDivorc.\t.081\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t.07\tY_BOX_GAMES\t0\t.046\n100900\t4.83448125559028\tCUST_YEAR_OF_BIRTH\t1959\t.376\tOCCUPATION\tExec.\t.265\tY_BOX_GAMES\t0\t.126\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-.072\tAFFINITY_CARD\t0\t-.072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize GLM_TEST_DATA for use in query below","message":["%python","","try:","    oml.drop(table = 'GLM_TEST_DATA')","except:","    pass","_ = TEST.materialize(table = 'GLM_TEST_DATA')"],"enabled":true,"result":{"startTime":1704380979723,"interpreter":"python.low","endTime":1704380980079,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL","message":["%sql","","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","       round(PRED_LOWER_LIMIT,1) LOWER_BOUND,","       round(PRED_UPPER_LIMIT,1) UPPER_BOUND,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(GLM_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_BOUNDS(GLM_REGRESSION_MODEL USING *).LOWER PRED_LOWER_LIMIT,","             PREDICTION_BOUNDS(GLM_REGRESSION_MODEL USING *).UPPER PRED_UPPER_LIMIT,","             PREDICTION_DETAILS(GLM_REGRESSION_MODEL USING *) PD","      FROM GLM_TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details'","              PASSING OUT.PD","              COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]',","               \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1704380980198,"interpreter":"sql.low","endTime":1704380980459,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tLOWER_BOUND\tUPPER_BOUND\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t4.186\t3.9\t4.4\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".219\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".155\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".107\" \n100003\t3.619\t3.4\t3.9\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".157\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".013\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\"-.015\" \n100005\t5.419\t5.1\t5.8\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".392\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".174\" \t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".143\" \n100009\t3.499\t3.2\t3.8\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".146\" \t\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".093\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".056\" \n100010\t3.006\t2.8\t3.2\t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".013\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\"-.003\" \t\"OCCUPATION\" actualValue=\"Crafts\" weight=\"-.034\" \n100012\t5.414\t4.9\t5.9\t\"EDUCATION\" actualValue=\"PhD\" weight=\".395\" \t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".146\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".131\" \n100014\t5.247\t5\t5.5\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".449\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".141\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".08\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# End of Script"],"enabled":true,"result":{"startTime":1704380980584,"interpreter":"md.low","endTime":1704380980681,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1704380980822,"interpreter":"md.low","endTime":1704380980918,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Regression NN","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715291746965,"interpreter":"md.medium","endTime":1715291747043,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Neural Networks","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715291747123,"interpreter":"md.medium","endTime":1715291747208,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-neural-networks\">Regression Modeling to Predict Numerical Values using Neural Networks<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715291747305,"interpreter":"md.medium","endTime":1715291747395,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1715291747479,"interpreter":"md.medium","endTime":1715291747563,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1715291747646,"interpreter":"python.medium","endTime":1715291751451,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer with demographics data","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","DEMO_DF = oml.sync(query = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS')","","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1715291751542,"interpreter":"python.medium","endTime":1715291753544,"results":[{"message":"<stdin>:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1715291753630,"interpreter":"python.medium","endTime":1715291760537,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n102691\tF\tSepar.\t1971\tL: 300,000 and above\t11000\t9th\t0\t9+\tOther\t3\t0\n103808\tM\tMarried\t1980\tE: 90,000 - 109,999\t1500\t5th-6th\t0\t3\tHandler\t2\t1\n102558\tM\tMarried\t1975\tL: 300,000 and above\t15000\t7th-8th\t0\t3\tFarming\t3\t1\n104105\tM\tMarried\t1936\tG: 130,000 - 149,999\t9000\t7th-8th\t0\t3\tCrafts\t7\t0\n104066\tM\tMarried\t1956\tE: 90,000 - 109,999\t7000\tBach.\t0\t3\tExec.\t6\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%python","","TRAIN, TEST = CUST_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('YRS_RESIDENCE')","TRAIN_Y = TRAIN['YRS_RESIDENCE']","TEST_X = TEST.drop('YRS_RESIDENCE')","TEST_Y = TEST['YRS_RESIDENCE']"],"enabled":true,"result":{"startTime":1715291760624,"interpreter":"python.medium","endTime":1715291761012,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1715291761095,"interpreter":"md.medium","endTime":1715291761157,"results":[{"message":"<h3 id=\"build-a-neural-network-regression-model-for-predicting-yrs_residence\">Build a Neural Network regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons and default activation function","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","setting = {'NNET_HIDDEN_LAYERS'  :'1',","           'NNET_NODES_PER_LAYER':'4'}","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715291761244,"interpreter":"python.medium","endTime":1715291770258,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                  .000000001\n2            LBFGS_HISTORY_DEPTH                          20\n3            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n5             NNET_HIDDEN_LAYERS                           1\n6                NNET_ITERATIONS                         200\n7                 NNET_TOLERANCE                     .000001\n8                   ODMS_DETAILS                 ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n10              ODMS_RANDOM_SEED                           0\n11                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n12                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.105377\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0         0       0.0       0  ...            None         None -0.367653\n1         0       0.0       1  ...            None         None -0.000152\n2         0       0.0       2  ...            None         None  0.323073\n3         0       0.0       3  ...            None         None  0.445828\n4         0       0.0       4  ...            None         None -0.602031\n...     ...       ...     ...  ...             ...          ...       ...\n3196      1      46.0       0  ...            None         None  0.273744\n3197      1      47.0       0  ...            None         None -0.241160\n3198      1      48.0       0  ...            None         None -1.050772\n3199      1      49.0       0  ...            None         None -1.147731\n3200      1       NaN       0  ...            None         None -0.602011\n\n[3201 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NN ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html');\"> Neural Network <\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html');\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>' ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`,`NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and starting from Oracle Database 23ai `NNET_ACTIVATIONS_RELU`.","> 'NNET_HIDDEN_LAYERS' = '3'","","> 'NNET_NODES_PER_LAYER' = '8,8,2'","","> 'NNET_ACTIVATIONS' = \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> 'NNET_REGULARIZER' = 'NNET_REGULARIZER_HELDASIDE'","","> 'NNET_HELDASIDE_RATIO' = '0.25'","","> 'NNET_HELDASIDE_MAX_FAIL' = '6'  ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> 'NNET_REG_LAMBDA' = '1'","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> 'NNET_ITERATIONS' = '200'","","> 'NNET_TOLERANCE' = '0.000001'","","> 'ODMS_RANDOM_SEED' = '0'","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> 'LBFGS_HISTORY_DEPTH' = '10'","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> 'LBFGS_GRADIENT_TOLERANCE' = '0.0000001'","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> 'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_ENABLE'","","> 'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_DISABLE'"],"enabled":true,"result":{"startTime":1715311258700,"interpreter":"md.medium","endTime":1715311258853,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nn\">Examples of possible setting overrides for NN<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/shared-settings-23c.html');\"> Neural Network <\/a><\/li>\n<\/ul>\n<ul>\n<li>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlugp/neural-network.html');\">All algorithms<\/a><\/li>\n<\/ul>\n<ul>\n<li>Specify a row weight column<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>,<code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and starting from Oracle Database 23ai <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_HIDDEN_LAYERS' = '3'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_NODES_PER_LAYER' = '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_ACTIVATIONS' = &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_REGULARIZER' = 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_HELDASIDE_RATIO' = '0.25'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_HELDASIDE_MAX_FAIL' = '6'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_REG_LAMBDA' = '1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>'NNET_ITERATIONS' = '200'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'NNET_TOLERANCE' = '0.000001'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'ODMS_RANDOM_SEED' = '0'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_HISTORY_DEPTH' = '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_GRADIENT_TOLERANCE' = '0.0000001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'LBFGS_SCALE_HESSIAN' = 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons and specific activation function","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","setting = {'NNET_HIDDEN_LAYERS'  :'1',","           'NNET_NODES_PER_LAYER':'4',","           'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_BIPOLAR_SIG'\"","}","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715291770545,"interpreter":"python.medium","endTime":1715291778254,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                  .000000001\n2            LBFGS_HISTORY_DEPTH                          20\n3            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n5             NNET_HIDDEN_LAYERS                           1\n6                NNET_ITERATIONS                         200\n7                 NNET_TOLERANCE                     .000001\n8                   ODMS_DETAILS                 ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n10              ODMS_RANDOM_SEED                           0\n11                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n12                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.105377\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0         0       0.0       0  ...            None         None -0.367653\n1         0       0.0       1  ...            None         None -0.000152\n2         0       0.0       2  ...            None         None  0.323073\n3         0       0.0       3  ...            None         None  0.445828\n4         0       0.0       4  ...            None         None -0.602031\n...     ...       ...     ...  ...             ...          ...       ...\n3196      1      46.0       0  ...            None         None  0.273744\n3197      1      47.0       0  ...            None         None -0.241160\n3198      1      48.0       0  ...            None         None -1.050772\n3199      1      49.0       0  ...            None         None -1.147731\n3200      1       NaN       0  ...            None         None -0.602011\n\n[3201 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 8,8,2 neurons and specific activation functions","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","setting = {'NNET_HIDDEN_LAYERS':'3',","           'NNET_NODES_PER_LAYER':'8,8,2',","           'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\"","           }","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715291778359,"interpreter":"python.medium","endTime":1715291789372,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME         ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                  .000000001\n2            LBFGS_HISTORY_DEPTH                          20\n3            LBFGS_SCALE_HESSIAN  LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_LOG_SIG'\n5             NNET_HIDDEN_LAYERS                           1\n6                NNET_ITERATIONS                         200\n7                 NNET_TOLERANCE                     .000001\n8                   ODMS_DETAILS                 ODMS_ENABLE\n9   ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n10              ODMS_RANDOM_SEED                           0\n11                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n12                     PREP_AUTO                          ON\n\nComputed Settings: \n       setting name          setting value\n0  NNET_REGULARIZER  NNET_REGULARIZER_NONE\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS             200\n2     LOSS_VALUE        0.105377\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE       ACTIVATION_FUNCTION\n0                0        50  NNET_ACTIVATIONS_LOG_SIG\n\nWeights: \n\n      LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0         0       0.0       0  ...            None         None -0.367653\n1         0       0.0       1  ...            None         None -0.000152\n2         0       0.0       2  ...            None         None  0.323073\n3         0       0.0       3  ...            None         None  0.445828\n4         0       0.0       4  ...            None         None -0.602031\n...     ...       ...     ...  ...             ...          ...       ...\n3196      1      46.0       0  ...            None         None  0.273744\n3197      1      47.0       0  ...            None         None -0.241160\n3198      1      48.0       0  ...            None         None -1.050772\n3199      1      49.0       0  ...            None         None -1.147731\n3200      1       NaN       0  ...            None         None -0.602011\n\n[3201 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 8,8,2 Neurons and multiple default options","message":["%python","","settings = {}","","try:","    oml.drop(model = 'NN_REGRESSION_MODEL')","except:","    print('No such model')","","# User specified settings.","settings = {'NNET_HIDDEN_LAYERS':'3',","            'NNET_NODES_PER_LAYER':'8,8,2',","            'NNET_ACTIVATIONS':\"'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BIPOLAR_SIG','NNET_ACTIVATIONS_TANH'\",","            'NNET_HELDASIDE_MAX_FAIL':'6',","            'NNET_HELDASIDE_RATIO':'0.25',","            'NNET_ITERATIONS':'200',","            'NNET_REGULARIZER':'NNET_REGULARIZER_HELDASIDE',","            'NNET_TOLERANCE':'0.000001',","            'ODMS_RANDOM_SEED':'0'","            }","","nn_mod = oml.nn(\"regression\", **settings)","nn_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'NN_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715291789483,"interpreter":"python.medium","endTime":1715291798287,"results":[{"message":"\nModel Name: NN_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                                      setting value\n0                      ALGO_NAME                                ALGO_NEURAL_NETWORK\n1       LBFGS_GRADIENT_TOLERANCE                                         .000000001\n2            LBFGS_HISTORY_DEPTH                                                 20\n3            LBFGS_SCALE_HESSIAN                         LBFGS_SCALE_HESSIAN_ENABLE\n4               NNET_ACTIVATIONS  'NNET_ACTIVATIONS_ARCTAN','NNET_ACTIVATIONS_BI...\n5        NNET_HELDASIDE_MAX_FAIL                                                  6\n6           NNET_HELDASIDE_RATIO                                               0.25\n7             NNET_HIDDEN_LAYERS                                                  3\n8                NNET_ITERATIONS                                                200\n9           NNET_NODES_PER_LAYER                                              8,8,2\n10              NNET_REGULARIZER                         NNET_REGULARIZER_HELDASIDE\n11                NNET_TOLERANCE                                           0.000001\n12                  ODMS_DETAILS                                        ODMS_ENABLE\n13  ODMS_MISSING_VALUE_TREATMENT                            ODMS_MISSING_VALUE_AUTO\n14              ODMS_RANDOM_SEED                                                  0\n15                 ODMS_SAMPLING                              ODMS_SAMPLING_DISABLE\n16                     PREP_AUTO                                                 ON\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              22\n2     LOSS_VALUE        0.248765\n3       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\nTopology: \n\n   HIDDEN_LAYER_ID  NUM_NODE           ACTIVATION_FUNCTION\n0                0         8       NNET_ACTIVATIONS_ARCTAN\n1                1         8  NNET_ACTIVATIONS_BIPOLAR_SIG\n2                2         2         NNET_ACTIVATIONS_TANH\n\nWeights: \n\n     LAYER  IDX_FROM  IDX_TO  ... ATTRIBUTE_VALUE TARGET_VALUE    WEIGHT\n0        0       0.0       0  ...            None         None -0.377668\n1        0       0.0       1  ...            None         None -0.042808\n2        0       0.0       2  ...            None         None  0.475154\n3        0       0.0       3  ...            None         None  0.253198\n4        0       0.0       4  ...            None         None -0.476047\n..     ...       ...     ...  ...             ...          ...       ...\n592      2       NaN       0  ...            None         None  0.286667\n593      2       NaN       1  ...            None         None  0.355751\n594      3       0.0       0  ...            None         None  0.721844\n595      3       1.0       0  ...            None         None  1.160136\n596      3       NaN       0  ...            None         None -0.452993\n\n[597 rows x 8 columns]\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model fit statistics","message":["%python","","z.show(nn_mod.global_stats.round(6))"],"enabled":true,"result":{"startTime":1715291798384,"interpreter":"python.medium","endTime":1715291798473,"results":[{"message":"attribute name\tattribute value\nCONVERGED\tYESITERATIONS\t22.0\nLOSS_VALUE\t0.24876522611293034\nNUM_ROWS\t2709.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%python","","RES_DF = nn_mod.predict(TEST_X, supplemental_cols = TEST)","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE'] + RES_DF.columns].round(2).head(20))"],"enabled":true,"result":{"startTime":1715291798549,"interpreter":"python.medium","endTime":1715291798987,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\n3.66\t4\t100924\tM\tNeverM\t1963\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t2\tCrafts\t4\t0\t3.66\n1.47\t2\t104047\tM\tNeverM\t1983\tL: 300,000 and above\t7000\tHS-grad\t0\t1\tOther\t2\t1\t1.47\n5.01\t5\t102510\tM\tMarried\t1956\tL: 300,000 and above\t15000\tAssoc-A\t1\t3\tProtec.\t5\t0\t5.01\n2.08\t3\t100799\tM\tNeverM\t1977\tG: 130,000 - 149,999\t7000\tHS-grad\t0\t1\tCrafts\t3\t1\t2.08\n1.76\t2\t102045\tM\tNeverM\t1981\tG: 130,000 - 149,999\t5000\tBach.\t0\t1\tSales\t2\t1\t1.76\n2.08\t3\t103363\tF\tDivorc.\t1974\tA: Below 30,000\t1500\tBach.\t0\t9+\tOther\t3\t1\t2.08\n3.77\t4\t100061\tM\tDivorc.\t1959\tD: 70,000 - 89,999\t5000\tAssoc-V\t0\t9+\tTechSup\t4\t0\t3.77\n5.39\t7\t102523\tF\tDivorc.\t1945\tJ: 190,000 - 249,999\t11000\t12th\t0\t2\tSales\t7\t0\t5.39\n2.03\t2\t103477\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\tAssoc-A\t0\t1\tExec.\t2\t1\t2.03\n2.81\t3\t102425\tF\tMarried\t1977\tE: 90,000 - 109,999\t7000\tBach.\t1\t4-5\tExec.\t3\t1\t2.81\n2.07\t3\t102305\tF\tDivorc.\t1976\tK: 250,000 - 299,999\t11000\t10th\t0\t2\tOther\t3\t1\t2.07\n1.77\t1\t103994\tM\tNeverM\t1982\tE: 90,000 - 109,999\t10000\t< Bach.\t0\t1\tExec.\t1\t1\t1.77\n4.87\t3\t100699\tF\tWidowed\t1941\tA: Below 30,000\t1500\tHS-grad\t0\t2\tSales\t3\t0\t4.87\n4.79\t4\t102685\tM\tMarried\t1960\tI: 170,000 - 189,999\t10000\tHS-grad\t1\t3\tMachine\t4\t0\t4.79\n2.57\t3\t103443\tF\tMarried\t1970\tF: 110,000 - 129,999\t5000\tBach.\t1\t4-5\tExec.\t3\t0\t2.57\n1.45\t2\t104472\tM\tNeverM\t1983\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t1\tOther\t2\t1\t1.45\n3.09\t5\t102217\tM\tDivorc.\t1968\tD: 70,000 - 89,999\t10000\tHS-grad\t0\t1\tFarming\t5\t0\t3.09\n4.27\t4\t102357\tF\tDivorc.\t1959\tJ: 190,000 - 249,999\t11000\t10th\t0\t9+\tCleric.\t4\t0\t4.27\n4.88\t5\t103949\tM\tMarried\t1957\tF: 110,000 - 129,999\t3000\tMasters\t1\t3\tProf.\t5\t0\t4.88\n5.67\t4\t101286\tM\tMarried\t1928\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t3\tFarming\t4\t0\t5.67\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST dataset","message":["%python","","import numpy as np","","# The coefficient of determination R?2 is part of the \"score\" method for OML models","CoefficientOfDeterminationR2 = nn_mod.score(TEST_X, TEST_Y)","    ","# Compute other statistics by hand - no need to pull data locally to Python","# RES_DF[['PREDICTION', 'YRS_RESIDENCE']","MeanAbsoluteError = (abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION']).sum())/RES_DF['YRS_RESIDENCE'].count()","MeanSquaredError =  ((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])**2).sum()/RES_DF['YRS_RESIDENCE'].count()","RootMeanSquaredError =  np.sqrt(MeanSquaredError)","","print('R^2: ', CoefficientOfDeterminationR2.round(4))","print('MAE: ', MeanAbsoluteError.round(4))","print('MSE: ', MeanSquaredError.round(4))","print('RMSE: ', RootMeanSquaredError.round(4))"],"enabled":true,"result":{"startTime":1715291799073,"interpreter":"python.medium","endTime":1715291799725,"results":[{"message":"R^2:  0.5019\nMAE:  0.9438\nMSE:  1.8741\nRMSE:  1.369\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using matplotlib, plot the predicted and actual years of residence","message":["%python","","import numpy as np","import matplotlib.pyplot as plt","plt.style.use('seaborn')","plt.figure(figsize=[9,7])","plt.margins(0.05)","","x = RES_DF[['PREDICTION']].pull()","y = RES_DF[['YRS_RESIDENCE']].pull()","n = len(x)","","plt.plot(x, y, '.', c='black', alpha=0.8,  ms=7)","","plt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],","          c='red', alpha=0.8,  ms=3,","          label='Perfect prediction reference line')","","plt.legend(frameon=True, facecolor='lightgray')         ","plt.xlabel('PREDICTION')","plt.ylabel('YRS_RESIDENCE')","plt.title('The prediction vs. actual value for years of residence',","          fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1715291799809,"interpreter":"python.medium","endTime":1715291800137,"results":[{"message":"<div style='width:auto;height:auto'><img 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","plt.xlabel('PREDICTION')","plt.ylabel('RESIDUAL')","plt.title('Prediction of Years of Residence vs. Residuals', fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1715291800238,"interpreter":"python.medium","endTime":1715291800473,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details using Python","message":["%python","","","RES_DF = nn_mod.predict(TEST, supplemental_cols = TEST[['CUST_ID']], proba = True, topN_attrs = True)","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1715291800565,"interpreter":"python.medium","endTime":1715291803872,"results":[{"message":"CUST_ID\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n102400\t4.113687529967522\tOCCUPATION\tExec.\t.236\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.134\tCUST_CREDIT_LIMIT\t11000\t-.023\tAFFINITY_CARD\t1\t-.033\tY_BOX_GAMES\t0\t-.075\n104400\t3.04591837972683\tCUST_MARITAL_STATUS\tNeverM\t.15\tCUST_CREDIT_LIMIT\t10000\t-.006\tAFFINITY_CARD\t0\t-.051\tY_BOX_GAMES\t0\t-.065\tCUST_YEAR_OF_BIRTH\t1966\t-.094\n100002\t3.983898551928878\tCUST_YEAR_OF_BIRTH\t1962\t.204\tEDUCATION\tBach.\t.099\tCUST_INCOME_LEVEL\tL: 300,000 and above\t.01\tCUST_MARITAL_STATUS\tNeverM\t-.009\tCUST_CREDIT_LIMIT\t7000\t-.009\n100012\t3.8711776516605023\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t.241\tOCCUPATION\tProf.\t.028\tY_BOX_GAMES\t0\t-.022\tAFFINITY_CARD\t1\t-.024\tCUST_CREDIT_LIMIT\t15000\t-.178\n100018\t1.5106497589019439\tY_BOX_GAMES\t1\t.016\tCUST_GENDER\tF\t.005\tEDUCATION\t< Bach.\t-.013\tAFFINITY_CARD\t0\t-.053\tCUST_INCOME_LEVEL\tA: Below 30,000\t-.058\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test data into a table for use in query below","message":["%python","","try:","    oml.drop(table = 'NN_TEST_DATA')","except:","    print(\"Table not exist\")","_ = TEST.materialize(table = 'NN_TEST_DATA')"],"enabled":true,"result":{"startTime":1715291803950,"interpreter":"python.medium","endTime":1715291806853,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details using SQL","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(NN_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","              PREDICTION_DETAILS(NN_REGRESSION_MODEL USING *) PD","       FROM NN_TEST_DATA","       WHERE CUST_ID < 100015","       ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","                PASSING OUT.PD","                COLUMNS ","                \"Attribute1\" XMLType PATH 'Attribute[1]',","                \"Attribute2\" XMLType PATH 'Attribute[2]',","                \"Attribute3\" XMLType PATH 'Attribute[3]') ","                OUTPRED"],"enabled":true,"result":{"startTime":1715291806926,"interpreter":"sql.medium","endTime":1715291808724,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t3.984\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".204\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".099\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".01\" \n100003\t3.293\t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".125\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".085\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"7000\" weight=\".021\" \n100005\t5.032\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".435\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".248\" \t\"CUST_INCOME_LEVEL\" actualValue=\"B: 30,000 - 49,999\" weight=\".114\" \n100009\t3.573\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".247\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".173\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".04\" \n100010\t2.662\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".054\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".024\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".003\" \n100012\t3.871\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".241\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".028\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.022\" \n100014\t4.499\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".456\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\"-.009\" \t\"CUST_INCOME_LEVEL\" actualValue=\"B: 30,000 - 49,999\" weight=\"-.016\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715291808807,"interpreter":"md.medium","endTime":1715291808874,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715291808965,"interpreter":"md.medium","endTime":1715291809030,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Regression SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715285795585,"interpreter":"md.medium","endTime":1715285795740,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Support Vector Machine","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715319473059,"interpreter":"md.medium","endTime":1715319522415,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-support-vector-machine\">Regression Modeling to Predict Numerical Values using Support Vector Machine<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715285795999,"interpreter":"md.medium","endTime":1715285796079,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715285796158,"interpreter":"md.medium","endTime":1715285796234,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1715285796311,"interpreter":"python.medium","endTime":1715285798410,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%python","","CUSTOMERS = oml.sync(query = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES"," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1715285798485,"interpreter":"python.medium","endTime":1715285798758,"results":[{"message":"<stdin>:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%python","","z.show(CUST_DF.head(10))"],"enabled":true,"result":{"startTime":1715285798837,"interpreter":"python.medium","endTime":1715285806039,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n101888\tF\tMarried\t1980\tG: 130,000 - 149,999\t7000\t< Bach.\t0\t4-5\tSales\t2\t1\n101188\tF\tDivorc.\t1971\tG: 130,000 - 149,999\t3000\t< Bach.\t0\t9+\tSales\t2\t0\n103073\tM\tMarried\t1955\tK: 250,000 - 299,999\t11000\t11th\t0\t3\tExec.\t6\t0\n100444\tM\tMarried\t1979\tK: 250,000 - 299,999\t9000\t< Bach.\t0\t3\tHandler\t2\t1\n101714\tM\tNeverM\t1977\tJ: 190,000 - 249,999\t9000\tHS-grad\t0\t1\tHandler\t3\t1\n100888\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n104299\tF\tMarried\t1965\tI: 170,000 - 189,999\t5000\tProfsc\t0\t4-5\tSales\t4\t0\n101031\tM\tMarried\t1968\tI: 170,000 - 189,999\t10000\tMasters\t1\t3\tProf.\t4\t0\n102981\tF\tMarried\t1960\tJ: 190,000 - 249,999\t15000\tAssoc-V\t1\t4-5\tProf.\t4\t0\n100274\tM\tMarried\t1967\tD: 70,000 - 89,999\t1500\tBach.\t0\t3\tExec.\t5\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%python","","TRAIN, TEST = CUST_DF.split(ratio = (0.6,0.4))","TRAIN_X = TRAIN.drop('YRS_RESIDENCE')","TRAIN_Y = TRAIN['YRS_RESIDENCE']","TEST_X = TEST.drop('YRS_RESIDENCE')","TEST_Y = TEST['YRS_RESIDENCE']"],"enabled":true,"result":{"startTime":1715285806117,"interpreter":"python.medium","endTime":1715285806497,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Build a SVM Linear regression model for predicting YRS_RESIDENCE","---"],"enabled":true,"result":{"startTime":1715285806575,"interpreter":"md.medium","endTime":1715285806632,"results":[{"message":"<h3 id=\"build-a-svm-linear-regression-model-for-predicting-yrs_residence\">Build a SVM Linear regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using default settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_LINEAR'}","","try:","    oml.drop(model = 'SVML_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVML_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715285806716,"interpreter":"python.medium","endTime":1715285810312,"results":[{"message":"\nModel Name: SVML_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                            ON\n5           SVMS_CONV_TOLERANCE                         .0001\n6                  SVMS_EPSILON                            .1\n7          SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n\nComputed Settings: \n             setting name    setting value\n0  SVMS_COMPLEXITY_FACTOR                3\n1     SVMS_NUM_ITERATIONS               30\n2             SVMS_SOLVER  SVMS_SOLVER_IPM\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              16\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","- Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> 'SVMS_EPSILON' : '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> 'SVMS_BATCH_ROWS' : '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> 'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> 'SVMS_CONV_TOLERANCE': '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> 'SVMS_NUM_PIVOTS': '200' "],"enabled":true,"result":{"startTime":1715286124658,"interpreter":"md.medium","endTime":1715286124733,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' : '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' : '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' : 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE': '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS': '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using specific settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_LINEAR', ","            'SVMS_EPSILON':'0.1',","            'SVMS_CONV_TOLERANCE':'0.0001',","            'SVMS_COMPLEXITY_FACTOR':'0.1',","            'SVMS_BATCH_ROWS':'20000',","            'SVMS_REGULARIZER':'SVMS_REGULARIZER_L1',","            'SVMS_SOLVER':'SVMS_SOLVER_SGD'}","","try:","    oml.drop(model = 'SVML_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVML_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715285810547,"interpreter":"python.medium","endTime":1715285816447,"results":[{"message":"\nModel Name: SVML_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5                SVMS_BATCH_ROWS                         20000\n6         SVMS_COMPLEXITY_FACTOR                           0.1\n7            SVMS_CONV_TOLERANCE                        0.0001\n8                   SVMS_EPSILON                           0.1\n9           SVMS_KERNEL_FUNCTION                   SVMS_LINEAR\n10              SVMS_REGULARIZER           SVMS_REGULARIZER_L1\n11                   SVMS_SOLVER               SVMS_SOLVER_SGD\n\nComputed Settings: \n          setting name setting value\n0  SVMS_NUM_ITERATIONS          1000\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED              NO\n1     ITERATIONS            1000\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","### Build a SVM Gaussian regression model for predicting YRS_RESIDENCE","---"],"enabled":true,"result":{"startTime":1715285816526,"interpreter":"md.medium","endTime":1715285816585,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-regression-model-for-predicting-yrs_residence\">Build a SVM Gaussian regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using default settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_GAUSSIAN'}","","try:","    oml.drop(model = 'SVMG_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVMG_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715285816666,"interpreter":"python.medium","endTime":1715285821166,"results":[{"message":"\nModel Name: SVMG_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                   setting name                 setting value\n0                     ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                  ODMS_DETAILS                   ODMS_ENABLE\n2  ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                 ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                     PREP_AUTO                            ON\n5           SVMS_CONV_TOLERANCE                         .0001\n6                  SVMS_EPSILON                            .1\n7          SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n8               SVMS_NUM_PIVOTS                           200\n\nComputed Settings: \n             setting name       setting value\n0  SVMS_COMPLEXITY_FACTOR                   3\n1     SVMS_NUM_ITERATIONS                  30\n2             SVMS_SOLVER     SVMS_SOLVER_IPM\n3            SVMS_STD_DEV  2.2360679774997898\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              16\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Using specific settings","message":["%python","","settings = {'SVMS_KERNEL_FUNCTION': 'SVMS_GAUSSIAN', ","            'SVMS_EPSILON':'0.1',","            'SVMS_CONV_TOLERANCE':'0.0001',","            'SVMS_COMPLEXITY_FACTOR':'0.1',","            'SVMS_NUM_PIVOTS':'200',","            'SVMS_SOLVER':'SVMS_SOLVER_IPM',","            'ODMS_DETAILS':'ODMS_ENABLE'}","","try:","    oml.drop(model = 'SVMG_REGRESSION_MODEL')","except:","    pass"," ","svm_mod = oml.svm(\"regression\", **settings)","svm_mod.fit(TRAIN_X, TRAIN_Y, model_name = 'SVMG_REGRESSION_MODEL', case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1715285821247,"interpreter":"python.medium","endTime":1715285825547,"results":[{"message":"\nModel Name: SVMG_REGRESSION_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Support Vector Machine\n\nMining Function: REGRESSION\n\nTarget: YRS_RESIDENCE\n\nSettings: \n                    setting name                 setting value\n0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES\n1                   ODMS_DETAILS                   ODMS_ENABLE\n2   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO\n3                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE\n4                      PREP_AUTO                            ON\n5         SVMS_COMPLEXITY_FACTOR                           0.1\n6            SVMS_CONV_TOLERANCE                        0.0001\n7                   SVMS_EPSILON                           0.1\n8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN\n9                SVMS_NUM_PIVOTS                           200\n10                   SVMS_SOLVER               SVMS_SOLVER_IPM\n\nComputed Settings: \n          setting name       setting value\n0  SVMS_NUM_ITERATIONS                  30\n1         SVMS_STD_DEV  2.2360679774997898\n\nGlobal Statistics: \n  attribute name attribute value\n0      CONVERGED             YES\n1     ITERATIONS              13\n2       NUM_ROWS            2709\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nY_BOX_GAMES\n\nPartition: NO\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model fit details","message":["%python","","z.show(svm_mod.global_stats)"],"enabled":true,"result":{"startTime":1715285825624,"interpreter":"python.medium","endTime":1715285825700,"results":[{"message":"attribute name\tattribute value\nCONVERGED\tYESITERATIONS\t13.0\nNUM_ROWS\t2709.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display computed settings","message":["%python","","svm_mod.settings"],"enabled":true,"result":{"startTime":1715285825777,"interpreter":"python.medium","endTime":1715285825838,"results":[{"message":"{'svms_kernel_function': 'SVMS_GAUSSIAN', 'svms_epsilon': '0.1', 'svms_conv_tolerance': '0.0001', 'svms_complexity_factor': '0.1', 'svms_num_pivots': '200', 'svms_solver': 'SVMS_SOLVER_IPM', 'odms_details': 'ODMS_ENABLE'}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model attributes","message":["%python","","svm_mod.attributes"],"enabled":true,"result":{"startTime":1715285825921,"interpreter":"python.medium","endTime":1715285825985,"results":[{"message":"['AFFINITY_CARD', 'CUST_CREDIT_LIMIT', 'CUST_GENDER', 'CUST_INCOME_LEVEL', 'CUST_MARITAL_STATUS', 'CUST_YEAR_OF_BIRTH', 'EDUCATION', 'HOUSEHOLD_SIZE', 'OCCUPATION', 'Y_BOX_GAMES']\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%python","","RES_DF = svm_mod.predict(TEST.drop('YRS_RESIDENCE'), supplemental_cols = TEST)","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE'] + RES_DF.columns].head(10))"],"enabled":true,"result":{"startTime":1715285826062,"interpreter":"python.medium","endTime":1715285828862,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\tCUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\n4.2663274587089\t4\t100924\tM\tNeverM\t1963\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t2\tCrafts\t4\t0\t4.2663274587089\n1.702204909542246\t2\t104047\tM\tNeverM\t1983\tL: 300,000 and above\t7000\tHS-grad\t0\t1\tOther\t2\t1\t1.702204909542246\n4.956150480623611\t5\t102510\tM\tMarried\t1956\tL: 300,000 and above\t15000\tAssoc-A\t1\t3\tProtec.\t5\t0\t4.956150480623611\n2.229131287679455\t3\t100799\tM\tNeverM\t1977\tG: 130,000 - 149,999\t7000\tHS-grad\t0\t1\tCrafts\t3\t1\t2.229131287679455\n2.0636858340724986\t2\t102045\tM\tNeverM\t1981\tG: 130,000 - 149,999\t5000\tBach.\t0\t1\tSales\t2\t1\t2.0636858340724986\n2.820208792541976\t3\t103363\tF\tDivorc.\t1974\tA: Below 30,000\t1500\tBach.\t0\t9+\tOther\t3\t1\t2.820208792541976\n4.501882372227659\t4\t100061\tM\tDivorc.\t1959\tD: 70,000 - 89,999\t5000\tAssoc-V\t0\t9+\tTechSup\t4\t0\t4.501882372227659\n5.810152580639107\t7\t102523\tF\tDivorc.\t1945\tJ: 190,000 - 249,999\t11000\t12th\t0\t2\tSales\t7\t0\t5.810152580639107\n2.307498626465452\t2\t103477\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\tAssoc-A\t0\t1\tExec.\t2\t1\t2.307498626465452\n3.099095639671172\t3\t102425\tF\tMarried\t1977\tE: 90,000 - 109,999\t7000\tBach.\t1\t4-5\tExec.\t3\t1\t3.099095639671172\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%python","","import numpy as np","","# The coefficient of determination R?2 is part of the \"score\" method for OML models","CoefficientOfDeterminationR2 = svm_mod.score(TEST_X, TEST_Y)","    ","# Compute other statistics by hand - no need to pull data locally to Python","# RES_DF[['PREDICTION', 'YRS_RESIDENCE']","MeanAbsoluteError = (abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION']).sum())/RES_DF['YRS_RESIDENCE'].count()","MeanSquaredError =  ((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])**2).sum()/RES_DF['YRS_RESIDENCE'].count()","RootMeanSquaredError =  np.sqrt(MeanSquaredError)","","print('R^2: ', CoefficientOfDeterminationR2.round(4))","print('MAE: ', MeanAbsoluteError.round(4))","print('MSE: ', MeanSquaredError.round(4))","print('RMSE: ', RootMeanSquaredError.round(4))"],"enabled":true,"result":{"startTime":1715285828938,"interpreter":"python.medium","endTime":1715285829749,"results":[{"message":"R^2:  0.5754\nMAE:  0.8357\nMSE:  1.5975\nRMSE:  1.2639\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Plot the predicted and actual years of residence ","message":["%python","","z.show(RES_DF[['PREDICTION', 'YRS_RESIDENCE']].head(10))"],"enabled":true,"result":{"startTime":1715285829845,"interpreter":"python.medium","endTime":1715285830073,"results":[{"message":"PREDICTION\tYRS_RESIDENCE\n4.2663274587089\t4.0\n1.702204909542246\t2.0\n4.956150480623611\t5.0\n2.229131287679455\t3.0\n2.0636858340724986\t2.0\n2.820208792541976\t3.0\n4.501882372227659\t4.0\n5.810152580639107\t7.0\n2.307498626465452\t2.0\n3.099095639671172\t3.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"Using matplotlib, plot the predicted and actual years of residence","message":["%python","","import numpy as np","import matplotlib.pyplot as plt","plt.style.use('seaborn')","plt.figure(figsize=[9,7])","plt.margins(0.05)","","x = RES_DF[['PREDICTION']].pull()","y = RES_DF[['YRS_RESIDENCE']].pull()","n = len(x)","","plt.plot(x, y, '.', c='black', alpha=0.8,  ms=7)","","plt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],","          c='red', alpha=0.8,  ms=3,","          label='Perfect prediction reference line')","","plt.legend(frameon=True, facecolor='lightgray')         ","plt.xlabel('PREDICTION')","plt.ylabel('YRS_RESIDENCE')","plt.title('The prediction vs. actual value for years of residence',","          fontsize=16)","plt.grid(True)","plt.show()"],"enabled":true,"result":{"startTime":1715285830150,"interpreter":"python.medium","endTime":1715285830592,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction details in python","message":["%python","","","RES_DF = svm_mod.predict(TEST, supplemental_cols = TEST[['CUST_ID']], proba = True, topN_attrs = True)","","z.show(RES_DF.head())"],"enabled":true,"result":{"startTime":1715285831142,"interpreter":"python.medium","endTime":1715285840456,"results":[{"message":"CUST_ID\tPREDICTION\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\tNAME_4\tVALUE_4\tWEIGHT_4\tNAME_5\tVALUE_5\tWEIGHT_5\n100200\t1.6824188766968198\tCUST_CREDIT_LIMIT\t9000\t.014\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-.013\tEDUCATION\t< Bach.\t-.019\tAFFINITY_CARD\t0\t-.023\tOCCUPATION\tOther\t-.04\n102600\t3.1168095365912896\tY_BOX_GAMES\t0\t.115\tHOUSEHOLD_SIZE\t2\t.01\tCUST_MARITAL_STATUS\tNeverM\t-.061\tAFFINITY_CARD\t0\t-.065\tCUST_GENDER\tF\t-.148\n100009\t3.4180687571679202\tAFFINITY_CARD\t1\t.179\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t.039\tEDUCATION\tBach.\t.034\tCUST_CREDIT_LIMIT\t3000\t.033\tY_BOX_GAMES\t1\t-.101\n100010\t3.060480391621104\tOCCUPATION\tCrafts\t.032\tCUST_CREDIT_LIMIT\t9000\t.012\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-.01\tAFFINITY_CARD\t0\t-.022\tY_BOX_GAMES\t1\t-.099\n100018\t2.5139479326255447\tCUST_CREDIT_LIMIT\t1500\t.074\tY_BOX_GAMES\t1\t.016\tAFFINITY_CARD\t0\t-.009\tCUST_INCOME_LEVEL\tA: Below 30,000\t-.019\tOCCUPATION\tHandler\t-.044\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize SVM_TEST_DATA for use in query below","message":["%python","","try:","    oml.drop(table = 'SVM_TEST_DATA')","except:","    pass","_ = TEST.materialize(table = 'SVM_TEST_DATA')"],"enabled":true,"result":{"startTime":1715285840535,"interpreter":"python.medium","endTime":1715285841035,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(SVMG_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","              PREDICTION_DETAILS(SVMG_REGRESSION_MODEL USING *) PD","              FROM SVM_TEST_DATA","              WHERE CUST_ID < 100015","              ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","       PASSING OUT.PD","       COLUMNS ","       \"Attribute1\" XMLType PATH 'Attribute[1]',","       \"Attribute2\" XMLType PATH 'Attribute[2]',","       \"Attribute3\" XMLType PATH 'Attribute[3]') ","       OUTPRED"],"enabled":true,"result":{"startTime":1715285841109,"interpreter":"sql.medium","endTime":1715285842806,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t4.473\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".273\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".105\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".103\" \n100003\t3.617\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".158\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".048\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".007\" \n100005\t4.904\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".444\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".072\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".067\" \n100009\t3.418\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".179\" \t\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".039\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".034\" \n100010\t3.06\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".032\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".012\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\"-.01\" \n100012\t4.064\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".142\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".096\" \t\"EDUCATION\" actualValue=\"PhD\" weight=\".012\" \n100014\t5.122\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".537\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".072\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".057\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715286305965,"interpreter":"md.medium","endTime":1715286306026,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715285843047,"interpreter":"md.medium","endTime":1715285843115,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Time Series ESM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713822668941,"interpreter":"md.low","endTime":1713822669004,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data","","Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1713822669084,"interpreter":"md.low","endTime":1713822669156,"results":[{"message":"<h1 id=\"forecasting-sales-using-exponential-smoothing-algorithm-for-time-series-data\">Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data<\/h1>\n<p>Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1713822669231,"interpreter":"md.low","endTime":1713822669290,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>"],"enabled":true,"result":{"startTime":1713822669366,"interpreter":"md.low","endTime":1713822669553,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view for model building","message":["%python","","import pandas as pd","import oml","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","","ESM_SH_DATA = oml.sync(query=\"SELECT CAST(TIME_ID AS TIMESTAMP) as A, AMOUNT_SOLD as B FROM SH.SALES\")","  ","  "],"enabled":true,"result":{"startTime":1713822669629,"interpreter":"python.low","endTime":1713822669708,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the records from the view","message":["%python","","len(ESM_SH_DATA)"],"enabled":true,"result":{"startTime":1713822669782,"interpreter":"python.low","endTime":1713822669932,"results":[{"message":"918843\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the data","message":["%python","","z.show(ESM_SH_DATA.head())"],"enabled":true,"result":{"startTime":1713822670141,"interpreter":"python.low","endTime":1713822670216,"results":[{"message":"A\tB\n1998-01-20 00:00:00\t1205.99\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Options to use when creating Time Series models","message":["%md","","### Examples of possible setting overrides for ESM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/exponential-smoothing-method.html\" target=\"_blank\">OML Time Series<\/a>","","* Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is `EXSM_INTERVAL_QTR`.","> 'EXSM_INTERVAL' : 'EXSM_INTERVAL_QTR';","* Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future","> 'EXSM_PREDICTION_STEP' : '4';","* Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.","> 'EXSM_MODEL' : 'EXSM_WINTERS';","* Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.","> 'EXSM_SEASONALITY' : '4'","* Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.     ","> 'EXSM_SETMISSING' : 'EXSM_MISS_AUTO';"],"enabled":true,"result":{"startTime":1715292613935,"interpreter":"md.low","endTime":1715292614014,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-esm\">Examples of possible setting overrides for ESM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/exponential-smoothing-method.html\" target=\"_blank\">OML Time Series<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is <code>EXSM_INTERVAL_QTR<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_INTERVAL' : 'EXSM_INTERVAL_QTR';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_PREDICTION_STEP' : '4';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_MODEL' : 'EXSM_WINTERS';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_SEASONALITY' : '4'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.<\/li>\n<\/ul>\n<blockquote>\n<p>'EXSM_SETMISSING' : 'EXSM_MISS_AUTO';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","help(oml.esm)"],"enabled":true,"result":{"startTime":1713822670456,"interpreter":"python.low","endTime":1713822670744,"results":[{"message":"Help on class esm in module oml.algo.esm:\n\nclass esm(oml.algo.model.odmModel)\n |  esm(model_name=None, model_owner=None, **params)\n |\n |  In-database `Exponential Smoothing <https://www.oracle.com/machine-learning/exponential-smoothing>`_ Model\n |\n |  Exponential Smoothing methods have been widely used in forecasting for over half\n |  a century. It has applications at the strategic, tactical, and operation level.\n |  For example, at a strategic level, forecasting is used for projecting return on\n |  investment, growth and the effect of innovations. At a tactical level, forecasting\n |  is used for projecting costs, inventory requirements, and customer satisfaction.\n |  At an operational level, forecasting is used for setting targets and predicting\n |  quality and conformance with standards.\n |\n |  In its simplest form, Exponential Smoothing is a moving average method with a\n |  single parameter which models an exponentially decreasing effect of past levels\n |  on future values. With a variety of extensions, Exponential Smoothing covers a\n |  broader class of models than competitors, such as the Box-Jenkins auto-regressive\n |  integrated moving average (ARIMA) approach. Oracle Data Mining implements\n |  Exponential Smoothing using a state of the art state space method that\n |  incorporates a single source of error (SSOE) assumption which provides theoretical\n |  and performance advantages.\n |\n |  :Attributes:\n |\n |      **prediction** : oml.DataFrame\n |\n |        The prediction information.\n |        It includes the following components:\n |\n |        - time_seq: The time sequence used as case id\n |        - value: The real value in the training data\n |        - prediction: The predicted value by the model\n |        - lower: The lower bound of the predicted value\n |        - upper: The upper bound of the predicted value\n |\n |  Method resolution order:\n |      esm\n |      oml.algo.model.odmModel\n |      builtins.object\n |\n |  Methods defined here:\n |\n |  __init__(self, model_name=None, model_owner=None, **params)\n |      Initializes an instance of esm object.\n |\n |      Parameters\n |      ----------\n |      model_name : string or None (default)\n |          The name of an existing database Decision Tree model to create an oml.esm object from.\n |          The specified database model is not dropped when the oml.esm object is deleted.\n |      model_owner: string or None (default)\n |          The owner name of the existing Decision Tree model\n |          The current database user by default\n |      params : key-value pairs or dict\n |          Oracle Machine Learning parameter settings. Each dict element's name and\n |          value refer to the parameter setting name and value, respectively.\n |          The setting value must be numeric or string. Refer to `Oracle Data Mining\n |          Model Settings <https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmprg/specify-model-settings.html#GUID-F1083AFB-E357-4BC8-B629-1ABD85EBFA38>`_\n |          for applicable parameters and valid values. Global and Automatic Data\n |          Preparation Settings in Table 4-10 apply generally to the model.\n |          Mining Function Settings for Classification and `Algorithm-specific\n |          Settings <https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566>`_\n |          are applicable to Decision Tree model.\n |\n |  __repr__(self)\n |\n |  fit(self, x, y, time_seq, model_name=None)\n |      Fits a exponential smoothing model according to the training data and parameter settings.\n |\n |      Parameters\n |      ----------\n |      x : an OML object\n |          Attribute values for building the model.\n |      y : a single column OML object, or string\n |          Target values.\n |          If y is a single column OML object, target values specified by y must be combinable with x.\n |          If y is a string, y is the name of the column in x that specifies the target values.\n |      time_seq: string\n |         The name of the column in x used as time sequence to build the model.\n |         The column must be oml.Integer, oml.Float (Number based), or oml.Datetime\n |      model_name : string or None (default)\n |          User-specified model name.\n |          The user-specified database model is not dropped when oml.esm object is deleted.\n |          If None, a system-generated model name will be used.\n |          The system-generated model is dropped when oml.esm object is deleted\n |          unless oml.esm object is saved into a datastore.\n |\n |  ----------------------------------------------------------------------\n |  Methods inherited from oml.algo.model.odmModel:\n |\n |  export_sermodel(self, table=None, partition=None)\n |      Export model.\n |\n |      Parameters\n |      ----------\n |      table : string or None (default)\n |          A name for the new table where the serialized model is saved.\n |          If None, the serialized model will be saved to a temporary table.\n |      partition : string or None (default)\n |          Name of the partition that needs to be exported.\n |          If partition is None, all partitions are exported\n |\n |      Returns\n |      -------\n |      oml_bytes : an oml.Bytes object\n |        Contains the BLOB content from the model export\n |\n |  get_params(self, params=None, deep=False)\n |      Fetches parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : iterable of strings, None (default)\n |          Names of parameters to fetch. If ``params`` is None,\n |          fetches all settings.\n |      deep : boolean, False (default)\n |          Includes the computed and default parameters or not.\n |\n |      Returns\n |      -------\n |      settings : dict mapping str to str\n |\n |  set_params(self, **params)\n |      Changes parameters of the model.\n |\n |      Parameters\n |      ----------\n |      params : dict object mapping str to str\n |          The key should be the name of the setting, and the value should be\n |          the new setting.\n |\n |      Returns\n |      -------\n |      model : the model itself.\n |\n |  ----------------------------------------------------------------------\n |  Readonly properties inherited from oml.algo.model.odmModel:\n |\n |  model_owner\n |      The owner name of database mining model\n |\n |  ----------------------------------------------------------------------\n |  Data descriptors inherited from oml.algo.model.odmModel:\n |\n |  __dict__\n |      dictionary for instance variables (if defined)\n |\n |  __weakref__\n |      list of weak references to the object (if defined)\n |\n |  model_name\n |      The given name of database mining model\n |\n |  pivot_limit\n |      The maximum number of classes, clusters, or features for which the predicted probabilities are presented after pivoted\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a Holt-Winters Model using quarterly setting","message":["%python","","try:","    oml.drop(model = 'ESM_SALES_FORECAST_1') ","except:","    print(\"No such model\")","    ","","","settings = {'EXSM_INTERVAL'       : 'EXSM_INTERVAL_QTR', # accumulation int'l = quarter","            'EXSM_PREDICTION_STEP': 4,                   # prediction step = 4 quarters","            'EXSM_MODEL'          : 'EXSM_WINTERS',      # ESM model = Holt-Winters","            'EXSM_SEASONALITY'    : '4'}                 # seasonal cycle = 4 quarters ","","train_x=ESM_SH_DATA[:,0]","train_y=ESM_SH_DATA[:,1]","","ESM_MOD = oml.esm(**settings).fit(train_x, train_y, time_seq = 'A',model_name='ESM_SALES_FORECAST_1',)"],"enabled":true,"result":{"startTime":1713822670824,"interpreter":"python.low","endTime":1713822674523,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Review the forecast model settings","message":["%python","","z.show(ESM_MOD)"],"enabled":true,"result":{"startTime":1713822674620,"interpreter":"python.low","endTime":1713822674742,"results":[{"message":"\nModel Name: ESM_SALES_FORECAST_1\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Exponential Smoothing\n\nMining Function: TIME_SERIES\n\nTarget: B\n\nSettings: \n                    setting name               setting value\n0                      ALGO_NAME  ALGO_EXPONENTIAL_SMOOTHING\n1                EXSM_ACCUMULATE             EXSM_ACCU_TOTAL\n2          EXSM_CONFIDENCE_LEVEL                         .95\n3                  EXSM_INTERVAL           EXSM_INTERVAL_QTR\n4                     EXSM_MODEL                EXSM_WINTERS\n5                      EXSM_NMSE                           3\n6         EXSM_OPTIMIZATION_CRIT           EXSM_OPT_CRIT_LIK\n7           EXSM_PREDICTION_STEP                           4\n8               EXSM_SEASONALITY                           4\n9                EXSM_SETMISSING              EXSM_MISS_AUTO\n10                  ODMS_DETAILS                 ODMS_ENABLE\n11  ODMS_MISSING_VALUE_TREATMENT     ODMS_MISSING_VALUE_AUTO\n12                 ODMS_SAMPLING       ODMS_SAMPLING_DISABLE\n13                     PREP_AUTO                          ON\n\nGlobal Statistics: \n       attribute name attribute value\n0   -2 LOG-LIKELIHOOD         -227.41\n1                 AIC          472.82\n2                AICC          502.82\n3               ALPHA        0.746948\n4                AMSE     2.54708e+11\n5                BETA       0.0559241\n6                 BIC         479.773\n7           CONVERGED             YES\n8               GAMMA     0.000100143\n9       INITIAL LEVEL     6.11021e+06\n10   INITIAL SEASON 1        0.997969\n11   INITIAL SEASON 2         1.02306\n12   INITIAL SEASON 3        0.938255\n13   INITIAL SEASON 4         1.04071\n14      INITIAL TREND         42050.1\n15                MAE          273859\n16                MSE     1.30577e+11\n17           NUM_ROWS          918843\n18              SIGMA       0.0871202\n19                STD       0.0871202\n\nAttributes: \n\nPartition: NO\n\nPrediction: \n\n     TIME_SEQ         VALUE    PREDICTION         LOWER         UPPER\n0  1998-01-01  6.480684e+06  6.402751e+06           NaN           NaN\n1  1998-04-01  5.593994e+06  5.868258e+06           NaN           NaN\n2  1998-07-01  6.071823e+06  6.205873e+06           NaN           NaN\n3  1998-10-01  5.937414e+06  5.978511e+06           NaN           NaN\n4  1999-01-01  6.093747e+06  6.223665e+06           NaN           NaN\n5  1999-04-01  4.925472e+06  5.535877e+06           NaN           NaN\n6  1999-07-01  5.827050e+06  5.515472e+06           NaN           NaN\n7  1999-10-01  5.373679e+06  5.601171e+06           NaN           NaN\n8  2000-01-01  5.984889e+06  5.644292e+06           NaN           NaN\n9  2000-04-01  5.371731e+06  5.317402e+06           NaN           NaN\n10 2000-07-01  6.121239e+06  5.845169e+06           NaN           NaN\n11 2000-10-01  6.287647e+06  5.920688e+06           NaN           NaN\n12 2001-01-01  6.547097e+06  6.500126e+06           NaN           NaN\n13 2001-04-01  6.922468e+06  5.930069e+06           NaN           NaN\n14 2001-07-01  7.195999e+06  7.376867e+06           NaN           NaN\n15 2001-10-01  7.470898e+06  7.154088e+06           NaN           NaN\n16 2002-01-01           NaN  7.819637e+06  6.484414e+06  9.154860e+06\n17 2002-04-01           NaN  7.151013e+06  5.591487e+06  8.710540e+06\n18 2002-07-01           NaN  7.907806e+06  5.843738e+06  9.971875e+06\n19 2002-10-01           NaN  7.821557e+06  5.462865e+06  1.018025e+07\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review global diagnostics and model quality","message":["%python","","z.show(ESM_MOD.global_stats)"],"enabled":true,"result":{"startTime":1713822674823,"interpreter":"python.low","endTime":1713822674900,"results":[{"message":"attribute name\tattribute value\n-2 LOG-LIKELIHOOD\t-227.40992629779686\nAIC\t472.8198525955937\nAICC\t502.8198525955937\nALPHA\t0.7469478132599485\nAMSE\t254707632303.23087\nBETA\t0.05592410989553674\nBIC\t479.77315109575176CONVERGED\tYES\nGAMMA\t0.00010014301976231642\nINITIAL LEVEL\t6110213.924209908\nINITIAL SEASON 1\t0.9979691764644829\nINITIAL SEASON 2\t1.0230610589110443\nINITIAL SEASON 3\t0.9382551830027352\nINITIAL SEASON 4\t1.0407145816217378\nINITIAL TREND\t42050.122611142295\nMAE\t273858.8587428648\nMSE\t130577271283.0982\nNUM_ROWS\t918843.0\nSIGMA\t0.08712023294121807\nSTD\t0.08712023294121807\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Forecast AMOUNT_SOLD","message":["%python","","# Sort results by descending time sequence so latest points are shown first.","# The model predicts 4 values into the future with LOWER and UPPER condifence bounds.","","z.show(ESM_MOD.prediction.sort_values(by='TIME_SEQ', ascending=False))"],"enabled":true,"result":{"startTime":1713822675156,"interpreter":"python.low","endTime":1713822675309,"results":[{"message":"TIME_SEQ\tVALUE\tPREDICTION\tLOWER\tUPPER\n2002-10-01 00:00:00\tnan\t7821557.483706625\t5462864.876826696\t10180250.090586554\n2002-07-01 00:00:00\tnan\t7907806.499472267\t5843737.671510185\t9971875.32743435\n2002-04-01 00:00:00\tnan\t7151013.397858991\t5591487.0977140665\t8710539.698003916\n2002-01-01 00:00:00\tnan\t7819637.041272708\t6484414.345133956\t9154859.737411462\n2001-10-01 00:00:00\t7470897.51999978\t7154087.5630659675\tnan\tnan\n2001-07-01 00:00:00\t7195998.630000379\t7376866.59594857\tnan\tnan\n2001-04-01 00:00:00\t6922468.390000418\t5930069.144508536\tnan\tnan\n2001-01-01 00:00:00\t6547097.440000162\t6500126.226147046\tnan\tnan\n2000-10-01 00:00:00\t6287646.919999797\t5920687.514077313\tnan\tnan\n2000-07-01 00:00:00\t6121239.28999967\t5845169.126780912\tnan\tnan\n2000-04-01 00:00:00\t5371730.920000249\t5317402.430729034\tnan\tnan\n2000-01-01 00:00:00\t5984889.489999551\t5644291.534295818\tnan\tnan\n1999-10-01 00:00:00\t5373678.6700003\t5601170.870396041\tnan\tnan\n1999-07-01 00:00:00\t5827050.150000022\t5515472.1028802795\tnan\tnan\n1999-04-01 00:00:00\t4925471.629999909\t5535877.141277681\tnan\tnan\n1999-01-01 00:00:00\t6093747.209999715\t6223664.947154543\tnan\tnan\n1998-10-01 00:00:00\t5937413.710001296\t5978510.779636239\tnan\tnan\n1998-07-01 00:00:00\t6071823.100001069\t6205873.360362768\tnan\tnan\n1998-04-01 00:00:00\t5593994.140000758\t5868257.561111362\tnan\tnan\n1998-01-01 00:00:00\t6480684.000001145\t6402750.903513829\tnan\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View forecast values with confidence intervals using SQL query using model detail view","message":["%sql","","SELECT TO_CHAR(CASE_ID,'YYYY-MON') DATE_ID, VALUE ACTUAL_SOLD, ","       round(PREDICTION,2) FORECAST_SOLD, ","       round(LOWER,2) LOWER_BOUND, round(UPPER,2) UPPER_BOUND","FROM   DM$VPESM_SALES_FORECAST_1","ORDER BY CASE_ID DESC;"],"enabled":true,"result":{"startTime":1713822675404,"interpreter":"sql.low","endTime":1713822675678,"results":[{"message":"DATE_ID\tACTUAL_SOLD\tFORECAST_SOLD\tLOWER_BOUND\tUPPER_BOUND\n2002-OCT\t\t7821557.48\t5462864.88\t10180250.09\n2002-JUL\t\t7907806.5\t5843737.67\t9971875.33\n2002-APR\t\t7151013.4\t5591487.1\t8710539.7\n2002-JAN\t\t7819637.04\t6484414.35\t9154859.74\n2001-OCT\t7470897.5199997798\t7154087.56\t\t\n2001-JUL\t7195998.6300003789\t7376866.6\t\t\n2001-APR\t6922468.3900004178\t5930069.14\t\t\n2001-JAN\t6547097.4400001625\t6500126.23\t\t\n2000-OCT\t6287646.9199997969\t5920687.51\t\t\n2000-JUL\t6121239.2899996703\t5845169.13\t\t\n2000-APR\t5371730.9200002486\t5317402.43\t\t\n2000-JAN\t5984889.4899995513\t5644291.53\t\t\n1999-OCT\t5373678.6700002998\t5601170.87\t\t\n1999-JUL\t5827050.1500000218\t5515472.1\t\t\n1999-APR\t4925471.6299999086\t5535877.14\t\t\n1999-JAN\t6093747.209999715\t6223664.95\t\t\n1998-OCT\t5937413.7100012964\t5978510.78\t\t\n1998-JUL\t6071823.1000010688\t6205873.36\t\t\n1998-APR\t5593994.1400007578\t5868257.56\t\t\n1998-JAN\t6480684.0000011446\t6402750.9\t\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"table\":{\"version\":1},\"line\":{\"showSeries\":[\"VALUE\",\"PREDICTION\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"VALUE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 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00:00:00\t6480684.000001145\t6402750.903513829\tnan\tnan\n1998-04-01 00:00:00\t5593994.140000758\t5868257.561111362\tnan\tnan\n1998-07-01 00:00:00\t6071823.100001069\t6205873.360362768\tnan\tnan\n1998-10-01 00:00:00\t5937413.710001296\t5978510.779636239\tnan\tnan\n1999-01-01 00:00:00\t6093747.209999715\t6223664.947154543\tnan\tnan\n1999-04-01 00:00:00\t4925471.629999909\t5535877.141277681\tnan\tnan\n1999-07-01 00:00:00\t5827050.150000022\t5515472.1028802795\tnan\tnan\n1999-10-01 00:00:00\t5373678.6700003\t5601170.870396041\tnan\tnan\n2000-01-01 00:00:00\t5984889.489999551\t5644291.534295818\tnan\tnan\n2000-04-01 00:00:00\t5371730.920000249\t5317402.430729034\tnan\tnan\n2000-07-01 00:00:00\t6121239.28999967\t5845169.126780912\tnan\tnan\n2000-10-01 00:00:00\t6287646.919999797\t5920687.514077313\tnan\tnan\n2001-01-01 00:00:00\t6547097.440000162\t6500126.226147046\tnan\tnan\n2001-04-01 00:00:00\t6922468.390000418\t5930069.144508536\tnan\tnan\n2001-07-01 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of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1713822676286,"interpreter":"md.low","endTime":1713822676350,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R -2- Data Selection and Manipulation","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","# Oracle Machine Learning for R - Data Selection and Manipulation","","***Oracle Machine Learning for R*** (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight features of the transparency layer involving data selection and manipulation.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Import libraries supporting OML4R and create data table"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"raw\":{\"height\":300,\"lastColumns\":[\"'data.frame':\",\"150 obs. of  5 variables:\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Obtain proxy object for IRIS_R table and view proxy object structure","message":["%r","","ore.sync(table=\"IRIS_R\")","","IRIS <- IRIS_R","","str(IRIS)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"boxplot\":{\"showSeries\":[\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\"],\"title\":{\"text\":\"Boxplot of IRIS species flower dimensions\",\"color\":\"rgb(1, 154, 4)\"},\"lastColumns\":[\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\",\"Species\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"boxplot","title":"Compare flower dimension distribution across iris species","message":["%r","","z.show(IRIS)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Select table columns using proxy object IRIS"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select columns by name","message":["%r","","IRIS_projected1 <- IRIS[, c(\"Sepal.Length\", \"Petal.Length\")]","z.show(head(IRIS_projected1))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select columns by index range","message":["%r","","IRIS_projected2 <- IRIS[1:3]","z.show(head(IRIS_projected2))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select columns by data type","message":["%r","","IRIS_projected3 <- IRIS[,sapply(as.list(IRIS), is.numeric)]","z.show(head(IRIS_projected3))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Select table rows using proxy object IRIS","","* Select sepal length and petal length where petal length is less than 1.5","* Select all rows in which petal length is less than 1.5 or sepal length is 5.0, using the AND and OR conditions in filtering","* Select all rows in which petal length is less than 1.5 and sepal length is larger than 5.0"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select rows based on simple filter","message":["%r","","IRIS_projected4 <- IRIS_projected1[IRIS_projected1$Petal.Length <1.5 ,]","z.show(head(IRIS_projected4))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compound row selection with 'or'","message":["%r","","IRIS_projected5 <- IRIS[IRIS$Petal.Length <1.5 | IRIS$Sepal.Length == 5 ,]","z.show(head(IRIS_projected5))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compound row selection with 'and'","message":["%r","","IRIS_projected6 <- IRIS[IRIS$Petal.Length <1.5 & IRIS$Sepal.Length > 5 ,]","z.show(head(IRIS_projected6))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Use DataFrame objects","","Let's explore operations involving DataFrames."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create demo data in temporary table","message":["%r","","my_df <- data.frame(id  = c(1,2,3,4,5),","                    val = c(\"a\", \"b\", \"c\", \"d\", \"e\"),","                    ch  = c(\"p\", \"q\", \"r\", \"a\", \"b\"),","                    num = c(4,3,6.7,7.2,5))","","MY_DF <- ore.push(my_df)","","NUM1 <- MY_DF$id","NUM2 <- MY_DF$num","","NUM_APPENDED <- cbind(NUM1,NUM2)","z.show(NUM_APPENDED)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using concat()","message":["%md","","Next, try a few examples using the ***concat*** function.","","* Create two ore.frame proxy objects and combine the objects column-wise","* Create an derived column with the rounded exponential of two times the values in the num column of the ore.frame proxy object, then concatenate it with another ore.frame proxy object using a new column name","* Concatenate an ore.frame proxy object with multiple objects "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Column-wise concatenation","message":["%r","","X <- MY_DF[, c(\"id\", \"val\")]","Y <- MY_DF[, c(\"num\", \"ch\")]","","XY_APPEND <- cbind(X,Y)","z.show(XY_APPEND)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Concatenate new derived column","message":["%r","","W <- round((exp(MY_DF$num*2)),2)","","YW_APPEND <- cbind(Y,W)","z.show(YW_APPEND)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Concatenate with renaming","message":["%r","","colnames(Y) <- c(\"New_Num\", \"New_ch\")","","XWY_APPEND <- cbind(X,W,Y)","z.show(XWY_APPEND)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, explore merging data using ***merge***. Perform:","","* cross join","* left outer join","* right outer join"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Cross Join","message":["%r","","JOIN <- merge(X,Y)","z.show(JOIN)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Left outer join","message":["%r","","LEFT_JOIN <- merge(X,MY_DF, by = \"id\", all.x = TRUE)","z.show(LEFT_JOIN)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Right Outer Join","message":["%r","","RIGHT_JOIN <- merge(MY_DF,X, by = \"id\", all.y = TRUE)","z.show(RIGHT_JOIN)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, try the drop functonality with ***na.omit***, ***is.na***, ***unique***, and ***distinct***."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create demo data","message":["%r","","my_df2 <- data.frame(numeric1 = c(1,1.4,-4,-4,5.432,NA,NA), ","                     string1 = c(NA,NA,\"a\",\"a\",\"a\",\"b\",NA),","                     string2 = c(\"x\",NA,\"z\",\"z\",\"z\",\"x\",NA))","","MY_DF2 <- ore.push(my_df2)","z.show(MY_DF2)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Drop rows with any missing values","message":["%r","","MISS_VAL <- na.omit(MY_DF2)","z.show(MISS_VAL)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Drop rows with any missing values in column 'numeric1'","message":["%r","","MISS_VAL2 <- MY_DF2[!is.na(MY_DF2$numeric),]","z.show(MISS_VAL2)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Drop rows where all column values missing","message":["%r","","MISS_VAL3 <- rowSums(MY_DF2[-1] == \"NA\") == ncol(MY_DF2[-1])","z.show(MY_DF2[!MISS_VAL3,])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Drop duplicate rows","message":["%r","","MISS_VAL4 <- unique(MY_DF2)","z.show(MISS_VAL4)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Drop rows with same value in named columns","message":["%r","","z.show(distinct(my_df2, string1, string2, .keep_all= TRUE))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Drop specific columns using column index","message":["%r","","DROP <- MY_DF2[,-3]","","z.show(DROP)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Splitting Data","","Next, explore splitting data into train and test sets using ore.frame proxy objects using the digitis dataset from sklearn.datasets, loaded using OML4Py. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Use digits data from Python scikit-learn datasets and create temporary table with proxy object in DIGITS","message":["%python","","import pandas as pd","import oml","from sklearn.datasets import load_digits","","digits = load_digits()","pd_digits = pd.DataFrame(digits.data,","                         columns=['IMG'+str(i) for i in","                         range(digits['data'].shape[1])])","pd_digits = pd.concat([pd_digits,","                       pd.Series(digits.target,","                                  name = 'target')],","                                  axis = 1)","oml.drop(table=\"DIGITS\")","oml.create(pd_digits, table=\"DIGITS\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data 80/20","message":["%r","","ore.sync(table=\"DIGITS\")","","rownames(DIGITS) <- DIGITS$target","sampleSize       <- .8 * nrow(DIGITS)","index            <- sample(1:nrow(DIGITS),sampleSize)","group            <- as.integer(1:nrow(DIGITS) %in% index)","","# Create train","DIGITS.train <- DIGITS[group==TRUE,]","cat(paste(\"TRAIN:\",nrow(DIGITS.train)))","","","# Create test","DIGITS.test <- DIGITS[group==FALSE,]","cat(paste(\"\\nTEST:\",nrow(DIGITS.test)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Stratified sampling","message":["%r","","set.seed(1)","N <- nrow(DIGITS)","sampleSize <- 1000","stratifiedSample <-","  do.call(rbind,","          lapply(split(DIGITS, DIGITS$target),","                 function(y) {","                   ny <- nrow(y)","                   y[sample(ny, sampleSize*ny/N),,drop = FALSE]","                 }))","class(stratifiedSample)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View stratified data with group column","message":["%r","","z.show(head(stratifiedSample[,c(1:3,64:65)], 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Verify stratified data","message":["%r","","table(stratifiedSample$target)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Explore overloaded ***cor*** function to compute correlation on a small ore.frame proxy object. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table and compute correlation \u200b","message":["%r","","my_df3 <- data.frame(","  A = c(0,1,2,3),","  B = c(0,2,4,6))","MY_DF3 <- ore.push(my_df3)","","cor(MY_DF3)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Modify data to alter correlation results","message":["%r","","my_df3[\"A\"][my_df3[\"A\"] == 2] <- 1.5","my_df3[\"B\"][my_df3[\"B\"] == 2] <- NA","MY_NDF3 <- ore.push(my_df3)","","z.show(MY_NDF3)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Default corr skipping missing values","message":["%r","","OMIT <- na.omit(MY_NDF3)","cor(OMIT)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Not skipping missing values produces NaNs","message":["%r","","cor(MY_NDF3)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, explore the ***table*** function and ***dplyr***-related functions on a DataFrame relating gender, ","left or right handedness, and speed and accuracy on some task."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%r","","my_df4 = data.frame(","  GENDER   = c('M', 'M', 'F', 'M', 'F', 'M', 'F', 'F', NA, 'F', 'M', 'F'),","  HAND     = c('L', 'R', 'R', 'L', 'R', NA, 'L', 'R', 'R', 'R', 'R', 'R'),","  SPEED    = c(40.5, 30.4, 60.8, 51.2, 54, 29.3, 34.1, 39.6, 46.4, 12, 25.3, 37.5),","  ACCURACY = c(0.92, 0.94, 0.87, 0.90, 0.85, 0.97, 0.96, 0.93, 0.89, 0.84, 0.91, 0.95))","  ","MY_DF4 = ore.push(my_df4)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Find the categories that the most entries belonged to","message":["%r","","CROSSTAB <- table(MY_DF4$GENDER, MY_DF4$HAND) ","CROSSTAB"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"For each gender and across entries, find ratio of entries with different hand values","message":["%r","","round(CROSSTAB/nrow(MY_DF4),3)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find the mean speed across all gender and hand combinations","message":["%r","","group <- MY_DF4 %>% group_by(GENDER, HAND)","","z.show(group %>% summarise(meanSPEED = mean(SPEED)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find the median vlaues for accuracy and speed by gender and hand","message":["%r","","group2 <- MY_DF4 %>% group_by(GENDER, HAND)","","z.show(group2 %>% summarise(mSPEED = median(SPEED), mACCURACY = median(ACCURACY)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find the max and min speeds for every gender and hand combination and across all combinations","message":["%r","","group3 <- MY_DF4 %>% group_by(GENDER, HAND)","","z.show(group3 %>% summarise(minSPEED = min(SPEED), maxSPEED = max(SPEED)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Create new columns","","Next, explore creating new columns using a simple shopping cart data set. Note, that using the transparency layer, all these computations occur in-database. Neither the input nor the result needs to be brought to the client engine."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%r","","shopping_cart <- data.frame(","  ItemName = c('paper_towel', 'ground_pork', 'tofu', 'eggs',","                'pork_loin', 'whole_milk', 'egg_custard'),","  ItemType = c('grocery', 'meat', 'grocery', 'dairy', 'meat',","                'dairy', 'bakery'),","  Quantity = c(1, 2.6, 4, 1, 1.9, 1, 1),","  UnitPrice = c(1.19, 2.79, 0.99, 2.49, 3.19, 2.5, 3.99)",")","","SHOPPING_CART <- ore.push(shopping_cart)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a column Total = Quantity * Unit_price","message":["%r","","SHOPPING_CART$Total <- with(SHOPPING_CART, Quantity * UnitPrice)","z.show(SHOPPING_CART)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Count the pattern 'egg' in the Item_name column","message":["%r","","char <- as.character(shopping_cart$\"ItemName\") # MH: there should be no need to use \" around the col name","EggPattern <- lengths(regmatches(char, gregexpr(\"egg\", char)))","LCHAR_FRAME <- ore.frame(EggPattern)","EGG_PATTERN <- cbind(SHOPPING_CART,LCHAR_FRAME)","","z.show(EGG_PATTERN)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check whether items are of grocery category","message":["%r","","SHOPPING_CART$IsGrocery <- ifelse(SHOPPING_CART$ItemType == \"grocery\",\"YES\") ","z.show(SHOPPING_CART)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Calculate the length of item names","message":["%r","","SHOPPING_CART$NameLength <- nchar(as.character(SHOPPING_CART$ItemName))","z.show(SHOPPING_CART)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Calculate ceiling of UnitPrice","message":["%r","","SHOPPING_CART$UnitPriceCeiling <- ceiling(SHOPPING_CART$UnitPrice)","z.show(SHOPPING_CART)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use a few other functions to modify the Price column","message":["%r","","SHOPPING_CART$UnitPriceFloor <- floor(SHOPPING_CART$UnitPrice)","SHOPPING_CART$UnitPriceExp   <- exp(SHOPPING_CART$UnitPrice)","SHOPPING_CART$UnitPriceLog   <- log(SHOPPING_CART$UnitPrice)","SHOPPING_CART$UnitPriceSqrt  <- sqrt(SHOPPING_CART$UnitPrice)","z.show(SHOPPING_CART)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, using a modified version of the *iris* data set, explore ***sort_values*** and ***tail*** functions.","","We modify the dataset by replacing a few entries with NaNs to test how the ***na_position*** parameter works in the ***sort_values*** method."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Sort data set by Sepal.Length then by Sepal.Width in descending order","message":["%r","","res <- ore.sort(data = IRIS, by = c(\"Sepal.Length\", \"Sepal.Width\"), reverse = TRUE)","z.show(head(res))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display last 5 rows of the data set","message":["%r","","z.show(tail(IRIS))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, we explore column creation/renaming, describing data statistics, and computing a variety of statistics."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create demo data as temporary table","message":["%r","","my_df5 <- data.frame(","  numeric_col = c(1, 1.4, -4, 3.145, 5, NA),","  string_col = c(NA, NA, 'a', 'a', 'a', 'b'),","  byte_col = c('ba', 'bb', 'bc', 'bc', 'bd', 'be'))","","MY_DF5 <- ore.push(my_df5)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Combine a Boolean column with MY_DF4","message":["%r","","boolean_col <- MY_DF5$numeric_col > 3","MY_NDF5 <- cbind(MY_DF5,boolean_col)","","z.show(MY_NDF5)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Use the `summary` function to compute a variety of statistics on all columns. Then, exclude the Float columns."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get summary statistics on all columns","message":["%r","","summary(MY_NDF5)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Compute the sum of values in each Float or Boolean column","message":["%r","","sum(MY_NDF5$numeric_col, na.rm = TRUE)","sum(MY_NDF5$boolean_col, na.rm = TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","Find the cumulative sum of values in each Float or Boolean column after MY_DF5 is sorted by the bytes column in descending order. Then, compute skew, median, and kurtosis for float columns."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compute cumulative sum","message":["%r","","MY_CDF5 <- MY_NDF5[, c(\"numeric_col\", \"boolean_col\")]","z.show(cumsum(MY_CDF5))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","Next, use the overloaded ***boxplot*** and ***hist*** functions using the *wine* data set. The statistics supporting these plots are computed in-database, so transfer of data or client side memory limitations are avoided."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create WINE data table from Python Scikit-learn","message":["%python","","import pandas as pd","import oml","import matplotlib.pyplot as plt","from sklearn.datasets import load_wine","","wine = load_wine()","wine_data = pd.DataFrame(wine.data, columns = wine.feature_names)","","oml.drop(\"WINE\")","","WINE = oml.create(wine_data, \"WINE\")","z.show(WINE.head(10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Boxplot of proanthocyanins and histogram of proline","message":["%r","","ore.sync(table=\"WINE\")","","par(mfrow=c(1,2))","boxplot(WINE$proanthocyanins,main = \"Proanthocyanins\\nDistribution\", col=\"darkred\")","hist(WINE$proline, breaks = 10, col = 'darkgreen', ","     xlab = 'proline content', ","     ylab = '# of wine instances', main = 'Proline content' )"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R -3- Datastore and Script Repository","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for R - Datastore and Script Repository","","***Oracle Machine Learning for R*** (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we highlight the datastore and script repository features of OML4R.","","With a datastore, you can store R objects in a named datastore entry. This named datastore can then be used in subsequent R sessions and even be made available to other users or programs by granting/revoking read permissions.","","R objects, including OML4R proxy objects, exist for the duration of the current R session unless you explicitly save them. You can save one or more R objects, including OML4R proxy objects, to a named datastore and then load those objects in a later R session. This is also useful when using embedded R execution. Datastores exist in the user\u2019s Oracle Database schema. A datastore, and the objects it contains, persist in the database until explicitly deleted.","","Using a datastore, you can:","","* Save OML4R and other R objects in one R session and load them in another R session","* Pass non-scalar arguments to R functions for use in embedded R execution from both the R, and more importantly, the SQL API where only scalar values can be passed to functions","* List available datastores and explore the contents of a datastore","","With the script repository, users can:","","* Create and store user-defined R functions as scripts in Oracle Database","* Grant or revoke the read/execute privilege to a script","* List available scripts","* Load a script function into the R environment","* Drop a script from the script repository","","You can make scripts either private or global. A private script is available only to the owner. A global script is available to any user. For private scripts, the owner of the script may grant the read privilege to other users or revoke that privilege.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries and create a data table","message":["","%md","","To use OML4R, first import the library ORE.","","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(MASS)  # for Boston data set"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the help file for the ore.move function","message":["%r","","?ore.move"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete datastore myDataStore1 if it exists","message":["%r","","try(ore.delete(\"myDatastore1\"), silent=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete datastore mynewDataStore1 if it exists","message":["%r","","try(ore.delete(\"myNewDatastore1\"), silent=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create datastore myDatastore1","message":["%r","","# create simple example R objects","x <- stats::runif(20)","y <- list(a = 1, b = TRUE, c = \"value\")","z <- ore.push(x)  # create a temporary object for x in the database and return proxy object","","# save all objects in current R environment to datastore 'myDatastore' in user's schema","ore.save(x, y, z, name='myDatastore1',","         description = 'my first datastore')","","# list information about datastores in user's schema","ore.datastore('myDatastore1')","","# show detailed object information about datastore 'myDatastore'","ore.datastoreSummary(name='myDatastore1')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Rename the datastore and object within the datastore","message":["%r","","#rename a datastore","ore.move(name='myDatastore1', newname='myNewDatastore1')","","#rename objects within a datastore, overwriting existing objects of the same name","ore.move(name='myNewDatastore1', ","         object.names=c(\"x\", \"y\"), ","         object.newnames=c(\"x.new\", \"y.new\"),","         overwrite = TRUE)","         ","ore.datastoreSummary(name='myNewDatastore1')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create dataframes and load them into Autonomous Database","","We load three data sets, then invoke create and display the columns for each. These will be used in exploring datastore functionality."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create IRIS temporary database table and get proxy object","message":["%r","","IRIS_TMP <- ore.push(iris)","","z.show(colnames(IRIS_TMP))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create MTCARS temporary data table","message":["%r","","MTCARS_TMP <- ore.push(mtcars)  ","","z.show(colnames(MTCARS_TMP))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create AIR temporary data table","message":["%r","","AIR_TMP <- ore.push(airquality)","","z.show(colnames(AIR_TMP))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Save R objects to datastore","","Save the original *iris* data set and the temporary AIR proxy object to a datastore named \u201cds_rdata\u201d, overwriting if the named datastore already exists. Note that you can store actual data objects in a datastore, but large data objects should remain as database tables for performance and scalability.","","By storing the AIR_TMP object, the temporary table will not be deleted when the session terminates."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.save(iris, AIR_TMP, name=\"ds_rdata\", description=\"r datasets\", overwrite=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Add a third object, the temporary MTCARS proxy object, to that same datastore","message":["%r","","ore.save(MTCARS_TMP, name=\"ds_rdata\", append=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save the IRIS_TMP data frame to a new datastore and list datastores","message":["%md","","Save the *IRIS_TMP* data frame to a new datastore, and then list the datastores. Notice we see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. Our two datastores *ds_iris_proxy* and *ds_rdata* are present, with the latter containing three objects."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.save(IRIS_TMP, name=\"ds_iris_proxy\", description=\"iris data.frame proxy object\", overwrite=TRUE)","","ore.datastore()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Store model objects in a datastore","","To illustrate storing other types of objects in datastores, we'll create regression models using base R and OML4R."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","regr1 <- lm(medv ~ ., data = Boston)","","regr2 <- ore.odmGLM(formula = Ozone ~ Wind+Temp, data = AIR_TMP)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save objects to datastore and grant read privilege","message":["%md","","Save objects to a datastore and allow the read privilege to be granted to them. ","","Then grant the read privilege to all users by specifying user=None. Finally list the datastores to which the read privilege has been granted."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save objects to datastore","message":["%r","","ore.save(regr1, regr2, name=\"ds_rmodel\", grantable=TRUE, overwrite=TRUE)","","ore.datastore()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Grant read privilege to datastore","message":["%r","","ore.grant(name=\"ds_rmodel\", type=\"datastore\", user=NULL)","","ore.datastore(type=\"grantable\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Load datastore objects into memory","","Load all R objects from a datastore to the global workspace and sort the result by name, first removing the objects regr1 and regr2 from the current environment prior to loading them from the datastore. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Remove objects from current environment","message":["%r","","rm(regr1, regr2)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load objects from datastore","message":["%r","","z.show(sort(ore.load(name=\"ds_rdata\")))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Load R object from datastore to the current workspace and verify it is loaded","message":["%r","","ore.load(name=\"ds_rmodel\", list=c(\"regr2\"))","'regr2' %in% ls()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Load object regr1 from the datastore to a new environment named 'e'","message":["%r","","e <- new.env()","ore.load(name=\"ds_rmodel\", list=c(\"regr1\"), envir=e)","ls(envir=e)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show datastores and other details","message":["%r","","ore.datastore(type=\"all\")[,c(\"owner\", \"datastore.name\", \"object.count\")]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show datastores where other users have been granted read privilege","message":["%r","","ore.datastore(type=\"grant\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show datastores where the user has been granted read privilege (there are none)","message":["%r","","ore.datastore(type=\"granted\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show datstores whose names match a pattern","message":["%r","","ore.datastore(pattern=\"rdata\")[, c(\"datastore.name\", \"object.count\")]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Describe the contents of ds_iris_proxy datastore","message":["%r","","ore.datastoreSummary(name=\"ds_iris_proxy\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### User privileges on a datastore","","Revoke the read privilege from every user, and again show datastores to which read privilege has been granted. The result is empty."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.revoke(name=\"ds_rmodel\", type=\"datastore\", user=NULL)","","ore.datastore(type=\"grant\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Grant read privilege to user OMLUSER2","message":["%r","","ore.grant(name=\"ds_rmodel\", type=\"datastore\", user=\"OMLUSER2\")","","ore.datastore(type=\"grant\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Delete datastore content","","Delete some objects from the datastore. Delete a datastore. Show the existing datastores again."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","ore.delete(name=\"ds_rdata\", list=c(\"iris\", \"AIR_TMP\"))","","ore.delete(name=\"ds_rdata\")","","ore.delete(name=\"ds_rmodel\") ","","ore.datastore()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### R Script Repository","","To illustrate using the R Script Repository, we first define a function ***build_lm1*** that will fit a regression model. With this function, we create a script named \u201cMyLM_function1\u201d. OML4R stores named user-defined functions -- the scripts -- in the repository."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define function object","message":["%r","","build_lm1 <- function(dat){","  ","  regr <- lm(Sepal.Length  ~ ., data=dat)","  ","  return(regr)","}"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View function","message":["%r","","build_lm1"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create scripts in repository","","Create ***MyLM_function1*** in the script repository"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptDrop(\"MyLM_function1\", silent=TRUE)","","ore.scriptCreate(\"MyLM_function1\", FUN=build_lm1, overwrite=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List scripts available only to the current user","message":["%r","","ore.scriptList(type=\"user\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Grant read privilege to the MyLM_function1 script to user OMLUSER2","message":["%r","","ore.grant(name=\"MyLM_function1\", type=\"rqscript\", \"OMLUSER2\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"List the scripts which read privilege has been granted","message":["%r","","ore.scriptList(type=\"grant\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Revoke the read privilege to the MyLM_function1 script from the user OMLUSER2","message":["%r","","ore.revoke(name=\"MyLM_function1\", type=\"rqscript\", user=\"OMLUSER2\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Invoke function using embedded R execution","","We'll use embedded R execution to invoke this function. First we get a proxy object to the IRIS table. then we use ***tableApply***, providing the proxy object and function name. We'll view the result and then pull the coefficients."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get IRIS proxy object","message":["%r","","ore.sync(table=\"IRIS_R\")","","IRIS <- IRIS_R"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use tableApply to provide full IRIS data set to function","message":["%r","","res <- ore.tableApply(IRIS, FUN.NAME=\"MyLM_function1\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show model coefficients","message":["%r","","res_df <- data.frame(attribute = names(res$coefficients), coefficients = res$coefficients)","","z.show(res_df)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Storing functions as 'global'","","Let's define and save another function ***build_lm2***, but this time as global. We'll then invoke that function to build another model."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","build_lm2 <- function(dat){","  ","  regr <- lm(Petal.Width  ~ Petal.Length, data=dat)","  ","  return(regr)","}"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Store function as a global script","message":["%r","","ore.scriptCreate(\"MyGlobalLM_function\", FUN=build_lm2, overwrite=TRUE, global=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Invoke tableApply on the global script","message":["%r","","res <- ore.tableApply(IRIS, FUN.NAME=\"MyGlobalLM_function\")","","res"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List scripts available to the current user","message":["%r","","ore.scriptList()"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"OWNER\",\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List all scripts available to the current user","message":["%r","","ore.scriptList(type='all')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":"List scripts available to all users","message":["%r","","ore.scriptList(type=\"global\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Loading functions from the script repository","","Load the MyLM_function1 and MyGlobalLM_function scripts, and pull the models to the local R session. Build the models on the IRIS data set and pull the coefficients."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ore.scriptLoad(name=\"MyLM_function1\", newname = \"MYLM\")","ore.scriptLoad(name=\"MyGlobalLM_function\", newname = \"GlobalMYLM\")","","cat(\"MYLM: \\n\")","MYLM(iris)","cat(\"\\n\\nGlobalMYLM: \\n\")","GlobalMYLM(iris)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Drop scripts from repository","","Drop one of the private scripts.","","Drop the global script.","","List the available scripts again."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"OWNER\",\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptDrop(\"MyLM_function1\")","ore.scriptDrop(\"MyGlobalLM_function\", global=TRUE)","ore.scriptList(type=\"all\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":false,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R -4- Embedded R Execution.dsnb b/machine-learning/notebooks/r/OML4R -4- Embedded R Execution.dsnb
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--- a/machine-learning/notebooks/r/OML4R -4- Embedded R Execution.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R -4- Embedded R Execution","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714695647398,"interpreter":"md.medium","endTime":1714695647460,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Oracle Machine Learning for R - Embedded R Execution","","***Oracle Machine Learning for R*** (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.","","In this notebook, we highlight using OML4R embedded R execution. We first build a linear model in R directly, then create user-define R function to build the linear model, save the function to the script repository and then score data in parallel using R engines spawned by the Autonomous Database environment. We then show the invokation of these scripts using the SQL interface with embedded R execution. ","","To use the SQL API for embedded R execution, a user-defined R function must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account `USERNAME`, `PASSWORD`, and `URL` must be provided to obtain an authentication token.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714695647534,"interpreter":"md.medium","endTime":1714695647622,"results":[{"message":"<h1 id=\"oracle-machine-learning-for-r---embedded-r-execution\">Oracle Machine Learning for R - Embedded R Execution<\/h1>\n<p><em><strong>Oracle Machine Learning for R<\/strong><\/em> (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database, as well as new automatic machine learning capabilities.<\/p>\n<p>In this notebook, we highlight using OML4R embedded R execution. We first build a linear model in R directly, then create user-define R function to build the linear model, save the function to the script repository and then score data in parallel using R engines spawned by the Autonomous Database environment. We then show the invokation of these scripts using the SQL interface with embedded R execution.<\/p>\n<p>To use the SQL API for embedded R execution, a user-defined R function must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account <code>USERNAME<\/code>, <code>PASSWORD<\/code>, and <code>URL<\/code> must be provided to obtain an authentication token.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Import libraries supporting OML4R and create a data table","","To use OML4R, first load the ***ORE*** library.","","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":{"startTime":1714695647704,"interpreter":"md.medium","endTime":1714695647766,"results":[{"message":"<h2 id=\"import-libraries-supporting-oml4r-and-create-a-data-table\">Import libraries supporting OML4R and create a data table<\/h2>\n<p>To use OML4R, first load the <em><strong>ORE<\/strong><\/em> library.<\/p>\n<p>Users can get online help using R's help function and specifying the function of interest.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries","message":["%r","","library(ORE)","library(lattice)","","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1714695647851,"interpreter":"r.medium","endTime":1714695647975,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get a proxy object for the IRIS_R table","message":["%r","","ore.sync(table = 'IRIS_R')","IRIS <- IRIS_R","head(IRIS)"],"enabled":true,"result":{"startTime":1714695648051,"interpreter":"r.medium","endTime":1714695648322,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 5<\/caption>\n<thead>\n\t<tr><th scope=col>Sepal.Length<\/th><th scope=col>Sepal.Width<\/th><th scope=col>Petal.Length<\/th><th scope=col>Petal.Width<\/th><th scope=col>Species<\/th><\/tr>\n\t<tr><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;fct&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>4.6<\/td><td>3.6<\/td><td>1.0<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><td>4.3<\/td><td>3.0<\/td><td>1.1<\/td><td>0.1<\/td><td>setosa<\/td><\/tr>\n\t<tr><td>5.0<\/td><td>3.2<\/td><td>1.2<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><td>5.8<\/td><td>4.0<\/td><td>1.2<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><td>4.4<\/td><td>3.0<\/td><td>1.3<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><td>4.4<\/td><td>3.2<\/td><td>1.3<\/td><td>0.2<\/td><td>setosa<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build and score a linear model directly in R","","A best practice with embedded R execution is to validate your R script, and then your user-defined function before invoking the embedded R APIs. The next few paragraphs exercise the functionality we want.","","We will create a linear model and pull the data to our local environment to invoke the base R linear model function. "],"enabled":true,"result":{"startTime":1714695648397,"interpreter":"md.medium","endTime":1714695648459,"results":[{"message":"<h2 id=\"build-and-score-a-linear-model-directly-in-r\">Build and score a linear model directly in R<\/h2>\n<p>A best practice with embedded R execution is to validate your R script, and then your user-defined function before invoking the embedded R APIs. The next few paragraphs exercise the functionality we want.<\/p>\n<p>We will create a linear model and pull the data to our local environment to invoke the base R linear model function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","iris_df <- ore.pull(IRIS)","","lm_mod <- lm(Petal.Length~Petal.Width, iris_df)"],"enabled":true,"result":{"startTime":1714695648534,"interpreter":"r.medium","endTime":1714695648655,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","prediction <- predict(lm_mod,iris_df)","RES_DF <- cbind(prediction,iris_df)","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1714695648725,"interpreter":"r.medium","endTime":1714695648845,"results":[{"message":"prediction\tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n1.52954613187489\t4.6\t3.6\t1\t0.2\tsetosa\n1.3065520823627\t4.3\t3\t1.1\t0.1\tsetosa\n1.52954613187489\t5\t3.2\t1.2\t0.2\tsetosa\n1.52954613187489\t5.8\t4\t1.2\t0.2\tsetosa\n1.52954613187489\t4.4\t3\t1.3\t0.2\tsetosa\n1.52954613187489\t4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Model quality assessment","","Assess model quality using mean squared error and R^2, and generate a scatterplot of the data along with a plot of the regression line."],"enabled":true,"result":{"startTime":1714695648917,"interpreter":"md.medium","endTime":1714695648978,"results":[{"message":"<h2 id=\"model-quality-assessment\">Model quality assessment<\/h2>\n<p>Assess model quality using mean squared error and R^2, and generate a scatterplot of the data along with a plot of the regression line.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","summary(lm_mod)"],"enabled":true,"result":{"startTime":1714695649052,"interpreter":"r.medium","endTime":1714695649205,"results":[{"message":"\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = iris_df)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-1.33542 -0.30347 -0.02955  0.25776  1.39453 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept)  1.08356    0.07297   14.85   <2e-16 ***\nPetal.Width  2.22994    0.05140   43.39   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 0.4782 on 148 degrees of freedom\nMultiple R-squared:  0.9271,\tAdjusted R-squared:  0.9266 \nF-statistic:  1882 on 1 and 148 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","","","x <- ore.pull(RES_DF[['Petal.Width']])","y <- ore.pull(RES_DF[['Petal.Length']])","","xyplot(y~x, xlab = \"Petal Width\", ylab = \"Petal Length\", ","  panel = function(x,y) {","  panel.dotplot(x,y)","  panel.abline(lm(y ~ x))","  }, ","  xlim=c(0,2.7), ","  ylim=c(0,7.5),","  main=\"Prediction of Petal Length\")"," "],"enabled":true,"result":{"startTime":1714695659187,"interpreter":"r.medium","endTime":1714695659504,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg\nAElEQVR4nOzdZ1wU9/o28GsXFpAiRQTU2HvFAlhjL7FFYwQLEmNU1GN7TjxRE08sxyRq9G/U\nRGM3YmyQWGPFxK4oKlixxIKKBVCqsMCW5wUGEQaEZdkZhuv7wg87szt7LzPsXP5m5h6FXq8H\nEREREZV8SrELICIiIiLjYLAjIiIikgkGOyIiIiKZYLAjIiIikgkGOyIiIiKZYLAjIiIikgkG\nOyIiIiKZYLAjIiIikgkGOyITGTt2rOIfHh4e2We5ubllzZo/f36xlmHK9zKloKCgHj16uLq6\nqlSqrA/47NkzsesqXnJdm0bHXxSVHgx2VOL16dNHIcTS0tLV1bVz587z5s17/vy52GWawuzZ\ns7M+frVq1cQux3S+/vprHx+fw4cPR0dHazSagr/QZBuPpFZNjk8tbjFFJ6nfLZHoGOxIttLT\n06Ojo48ePfrVV1/VqVNn27ZtYldExSImJmbBggXGXSY3HiIqoczFLoDIFBITE4cNG1auXLlu\n3bqJXYsAUx4xlN/RyQsXLmRkZGQ93LFjR58+fVQqlbGWL/GNh4goO47Ykdzo9Xq9Xq/RaG7e\nvDlw4MCs6Vqt9j//+Y+IhVExSUxMzP6wb9++Bqc6bjxEVNIx2JE8mZmZ1a1bd8uWLTVr1sya\neOXKlcjIyMyfc59MvX///k6dOjk5OSkUimPHjmW96smTJ7NmzWrXrl358uUtLCwcHR2bN28+\nderUrEXloNVqly9f7uHhYWNjY29v7+XltXTp0uxDSrm988zuFy9eLFy4sEePHhUrVixTpoyT\nk1PTpk3HjRt35syZzCcMHDhQoVDMmTMn6yWRkZHZz6P65ZdfCvhekZGRX331VatWrZydnVUq\nlaOjY5MmTcaPHx8eHv7OytVq9aJFizw8POzs7GxsbJo3b75s2TKdTpfPZxdUwBqmT5+uUCgG\nDx6cfWLWxRMdO3Ys7PtmeufGk6mAG0bBV01cXNyOHTsmTJjg5eWVucwyZcq4ubl17Njxf//7\nX1RUlGEfx1gK9Ydg8Iah1WpXrFjh6elpa2ub/c9H8Nqjgv9uczDWhkokRXqiEq537975bNKf\nfvpp9rlHjx7NnO7q6po1cd68eUuWLMl+FnnW03788UdLS0vBvx0LC4tly5bleLuUlJSuXbvm\nfrKnp2f2EaAWLVpkf1WOYnIsc/369ba2tnn9Cd+5c0ev13/88cd5PSHThg0bCvJeCxYsMDfP\n8wyNsWPHqtXqvCofO3ZsgwYNcr9q2LBhBV+bhaph2rRp+XzkDh06vPO9DNt49IXZMAq+arp0\n6ZLP0+zs7Hbt2pWjwvzXpmGfWlBh/xAM2zBSUlIEj3fn9edj2GZvrA2VSJo4YkeE0NDQKVOm\n6PX6HNMXL148ceLEtLQ0wVelp6dPmjTp559/zj5xwoQJR44cEXyL3377zYDaVqxY8dlnnyUn\nJxvw2sL69ttvp02bls9VpStXrhwxYkQ+c2/cuJF7+q+//nr48GHT1GAaBmwYRZeUlDRkyJCH\nDx8ad7EFUcTPW/ANY8KECcHBwbmfafCfTxHrISqRxE6WREWVz/BDenp69qNpAO7fv585K/v/\n4AG4uroGBgYmJSVFRkZ+9913Z86cuXfvXvZTtYYPH37jxo3U1NR79+6NHDkya7qNjc2zZ88y\nl3nt2rXsy2zUqNH58+fT0tLCw8NbtGiRfVYBR+wePHiQfZjEyspq4cKFjx49UqvVf//9986d\nO/v373/37t2s58+aNSvryVWrVhX8deX1Xrdv3zYzM8ua1bBhw7Nnz6alpd26dat79+7Zi9+7\nd6/g0gC0bt366tWrCQkJK1euzL60zz77rCCr0rAatm7dmn1WRkZGQd4rkwEbjwEbhr5gq8bX\n13fcuHEHDx6MiIiIi4tTq9WPHj1atWqVnZ1d1mu//PLL7C8xwYidYZ/XgA0jx59PvXr1QkJC\n1Gr1pUuX3N3ds8/K8edT2M2+gPUQlVAMdlTiCe6lcp//DqBx48ZZr8r+Ra9QKM6ePZtjsdkP\n87Vs2TL7LJ1OV7169ay5S5Ysyf0SAOHh4VkvuXPnjlL5ZoC8gMHuyy+/zL7A33//Pf9fRVGC\n3RdffJH9vS5fvpw1Kz4+Pnu26NWrl+DSLC0tnz59mjWrR48eWbNatWqVf+VFqcHowS7/jceA\nDUNfsFWTl0mTJmW9tl27dtlnmSDYGfZ5Ddgwcvz5hIaGZs26ceNG9jMlihjsir6hEkkZ252Q\n3OTVcFWpVC5cuFBwVps2bVq1apVj4vHjx7N+PnfuXD59XM+dO5fjBwANGzbMPsxQq1atVq1a\nZV3rUEBHjx7N+rlu3boDBgwo1MsLJfvnbdasWZMmTbIe2tvb9+/ff9OmTZkPT5w4IbiEPn36\nuLm5ZT2sUaNG1s8JCQmmqaGICrLxGLBhFFxwcPC2bdsuXrwYGRn56tWr3NfcPHnypLDLLKKi\nf94CbhjZX16/fv3sd2epX79+y5YtQ0JCClm7sBz1ZM+mBdxQiaSM59hRqWBnZxcQEJD9v+bZ\ntWzZMvfEgl+EGBMTk/lD9hZxVatWzfG03FPeKftePMfRKKN7+vRp1s+52/dnn5KcnJyUlJR7\nCbVq1cr+0MrKKuvnAl5vWPQaikOOjceADaMgNBqNt7d39+7d169ff/ny5fj4eMErqVNSUgq+\nTKMo+uct4IaR/c8n99rPHr+KKEc9ZcqUEayHqITiiB3JlkqlcnBwaNCgQbdu3T777LMKFSrk\n9cxy5coV5Y2ydsD6XJdfFFH2BRb3rZ/yf6+CfDQHB4fsD7MfejZZDcZS8I0nH/n3uMlh6dKl\nBbk+wJS/hMLK6/MWcMMw2dov+oZKJGXcoEluss4zyLwr1LFjx2bMmJH/jlnwmz37S7y9vfM5\noSGr6V324zu5r17Mq+9dPipVqpT18+XLlwv78kKpWLFi1s/379/PMTd78ba2ttlPd5NTDQXZ\neAzYMAoi+13LKlasuH///vj4+Mzl5N/VpbgV0+fN/41y//k8ePCgKAsnKj0Y7IiEZW9ve+jQ\noYK0mch+SPfatWtXrlzJenj37l0DzhDq1KlT1s83b97ctWtX/s/P3v4tr+YUeenQoUPWz2Fh\nYdmLT0hI2L17d9bD999/v1BLLlk1vJMBGwYKsGqyH/EcOnRoz5497e3tAWi1WsEGOiZj2Oc1\nQPbzXK9du3b16tWshzdv3sznhMWibPZE8sNgRyRs9OjRWTuMxMTErl27BgUFPX36ND09/cmT\nJ2FhYVu3bh0zZkzdunWfP3+e+TQ/P7/sS/Dz87t48WJGRsaVK1eGDBliwOk7/v7+FhYWWQ+H\nDh26ePHiqKio9PT0Bw8e7Nmzp2fPnvfu3ct6grOzc9bPz58/379/f3p6esE/b/a+D76+vufO\nnUtPT799+/agQYOy37brX//6V2E/SAmq4Z0M2DBQgFVTvnz5rJ937tx58eJFtVp948aNQYMG\nXbx4sZg/U34M+7wGyOvPJywsbMiQIfkcii3KZk8kQ/mMqxOVCAa00dcXrE+E4B23csveOiHH\nvQryUvA7Tyxbtiz/RWXeeSLTpUuX8npaQd5r7ty576w8cxdbkKVNmTIla1bdunULuF4MqKGY\n+tjlw4AN452rZubMmQVZpqura/ZKjNLuJC9Zd9ow4PMatmFkb4yXjxx/PkXc7A3bUIkkiyN2\nRHmaNm3akiVL8rqTkqCffvop+/HTLDnuiVRwEydOXL16tY2NTUGe3KxZs7yu/C2I//73v/Pn\nz8/ndl5jxozZsGGDwcsvKTW8kwEbxjtXzdSpU7M3+MhSs2bNAv5vofgY8HkNs2zZsrzuyJf9\n7mE5Lq0o4mZPJDMMdkT5mTx58v379+fOndupUydXV1cLCwsbG5tq1ap5eHh88skna9euvX37\ndvZrJmxsbA4fPrxs2bLmzZtbW1vb2dl5enouWbLk9OnTBl97O3r06AcPHixYsKBr165ubm6W\nlpYODg5NmjQZN27cqVOncvRu2Llz58yZMxs1amRtbW3Ae02bNu3OnTtffvmll5eXk5OTubl5\n2bJlGzVqNG7cuEuXLq1cudIEe3cp1PBOhd0w8K5VY2Njc+LEidmzZ9erV8/CwsLW1rZJkyaz\nZs0KDw83oFGO0RnweQ1gbW198ODBn376qUWLFjn+fF69epX1tByXtaLImz2RnCj0Er54noiI\n6NGjR40aNco6yXLixInvPEWBqNTiiB0REUnCpUuX3n///R9//PHSpUsxMTFarfb58+c7duz4\n4IMPsl86M3jwYBGLJJI4NigmIiJJ0Ol0p06dOnXqVD7PGT16dJs2bUxWElGJwxE7IiIqAczN\nzadOnbpy5UqxCyGSNJ5jR0REkqDX60NCQnbt2nXixImoqKjnz5+bm5s7Ojo2aNCgffv2vr6+\nRrxjLJFcMdgRERERyQQPxRIRERHJBIMdERERkUww2BERERHJBIMdERERkUww2BERERHJBIMd\nERERkUww2BERERHJBIMdERERkUww2BERERHJBIMdERERkUww2BERERHJBIMdERERkUww2BER\nERHJBIMdERERkUww2BERERHJBIMdERERkUww2BERERHJBIMdERERkUww2BERERHJBIMdERER\nkUww2BERERHJBIMdERERkUww2BERERHJBIMdERERkUww2BERERHJBIMdERERkUww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BtaoUUPsikQgtUiXiRdPEBFRaTe5JVxsBKZ/0xlM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Build this same model using embedded R execution","","Using embedded R execution, let's build the same linear model, but with spawned R engines under control of the Autonomous Database environment. First, we'll define our function, ***buildLM.1***, that returns the model as the result. We refer to this as a \"user-defined function.\" As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded R execution can return images as well as structured content in the R API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.","","We run this function to ensure it returns what we expect. In this case, both an image and a model."],"enabled":true,"result":{"startTime":1714677286470,"interpreter":"md.medium","endTime":1714677286527,"results":[{"message":"<h2 id=\"build-this-same-model-using-embedded-r-execution\">Build this same model using embedded R execution<\/h2>\n<p>Using embedded R execution, let's build the same linear model, but with spawned R engines under control of the Autonomous Database environment. First, we'll define our function, <em><strong>buildLM.1<\/strong><\/em>, that returns the model as the result. We refer to this as a &quot;user-defined function.&quot; As we did above, we'll go further and score the training data and plot the model against the data points. Note that embedded R execution can return images as well as structured content in the R API. In the SQL API, as you'll see in another script, you choose between structured data, images, or XML output.<\/p>\n<p>We run this function to ensure it returns what we expect. In this case, both an image and a model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.1 <- function(dat){","  library(lattice)","  ","  regr <- lm(Petal.Length~Petal.Width, dat)","","  x <- dat[['Petal.Width']]","  y <- dat[['Petal.Length']]","","  print(xyplot(y~x, xlab = \"Petal Width\", ylab = \"Petal Length\", ","    panel = function(x,y) {","    panel.dotplot(x,y)","    panel.abline(lm(y ~ x))","    }, ","    xlim=c(0,2.7), ","    ylim=c(0,7.5),","    main=\"Prediction of Petal Length\"))","    ","    ","    return(regr)","}"],"enabled":true,"result":{"startTime":1714677286596,"interpreter":"r.medium","endTime":1714677286717,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"image\":{\"height\":700,\"lastColumns\":[]}},{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","buildLM.1(iris_df)"],"enabled":true,"result":{"startTime":1714677286788,"interpreter":"r.medium","endTime":1714677286974,"results":[{"message":"\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n      1.084        2.230  \n\n","type":"TEXT"},{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nLiSFq0NSu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xKoPVfeHXRLyaJEmv1y9d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CPayEKEjJgkei9W8oEb87/mkHvJnqjh49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\nTWrduvWePXs0pLq4JXdSXaGdZ2Xg1xpLarvwTqqzuJwt0R9buS7QHofT5Mk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Use the `tableApply` function","","________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***tableApply*** function takes the proxy object *IRIS* as input data and loads that data to the user-defined function as a data frame. The user-defined function is passed as a string. We see that the model comes back as an OML object and see that it's the linear model. "],"enabled":true,"result":{"startTime":1714677287068,"interpreter":"md.medium","endTime":1714677287128,"results":[{"message":"<h2 id=\"use-the-tableapply-function\">Use the <code>tableApply<\/code> function<\/h2>\n<hr />\n<p>The <em><strong>tableApply<\/strong><\/em> function takes the proxy object <em>IRIS<\/em> as input data and loads that data to the user-defined function as a data frame. The user-defined function is passed as a string. We see that the model comes back as an OML object and see that it's the linear model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.1 <- function(dat,dsname){","  library(lattice)","  ","  regr <- lm(Petal.Length~Petal.Width, dat)","","  x <- dat[['Petal.Width']]","  y <- dat[['Petal.Length']]","","  xyplot(y~x, xlab = \"Petal Width\", ylab = \"Petal Length\", ","    panel = function(x,y) {","    panel.dotplot(x,y)","    panel.abline(lm(y ~ x))","    }, ","    xlim=c(0,2.7), ","    ylim=c(0,7.5),","    main=\"Prediction of Petal Length\")","    ","  return(regr)","}"],"enabled":true,"result":{"startTime":1714677287202,"interpreter":"r.medium","endTime":1714677287323,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","mod <- ore.tableApply(IRIS, ","                      buildLM.1, ","                      dsname = 'ds-00')","","cat(\"Type :\",class(mod))","cat(\"\\n\")","cat(\"Coefficient :\",(mod$coefficient))"],"enabled":true,"result":{"startTime":1714677287390,"interpreter":"r.medium","endTime":1714677287574,"results":[{"message":"Type : lm\nCoefficient : 1.083558 2.22994","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Store the fuction in the script repository","","We can now save this user-defined function in the script repository and invoke this function by providing the function name."],"enabled":true,"result":{"startTime":1714677287645,"interpreter":"md.medium","endTime":1714677287705,"results":[{"message":"<h2 id=\"store-the-fuction-in-the-script-repository\">Store the fuction in the script repository<\/h2>\n<p>We can now save this user-defined function in the script repository and invoke this function by providing the function name.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[\"NAME\",\"SCRIPT\"]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptCreate(name = 'buildLM.1', ","                 FUN  = buildLM.1,     ","                 overwrite = TRUE)","                 ","ore.scriptList(name = \"buildLM.1\")"],"enabled":true,"result":{"startTime":1714677287773,"interpreter":"r.medium","endTime":1714677287891,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>buildLM.1<\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    regr &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    x &lt;- dat[[\"Petal.Width\"]]\n    y &lt;- dat[[\"Petal.Length\"]]\n    xyplot(y ~ x, xlab = \"Petal Width\", ylab = \"Petal Length\", panel = function(x, y) {\n        panel.dotplot(x, y)\n        panel.abline(lm(y ~ x))\n    }, xlim = c(0, 2.7), ylim = c(0, 7.5), main = \"Prediction of Petal Length\")\n    return(regr)\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Store the output of the function to the data store","","Notice the value to ***func*** is now a string, not a R function object, however, the results are the same. That's because we are now referencing the script in the script repository. We will save the model outputted by the tableApply to the datastore ds-0."],"enabled":true,"result":{"startTime":1714677287964,"interpreter":"md.medium","endTime":1714677288025,"results":[{"message":"<h2 id=\"store-the-output-of-the-function-to-the-data-store\">Store the output of the function to the data store<\/h2>\n<p>Notice the value to <em><strong>func<\/strong><\/em> is now a string, not a R function object, however, the results are the same. That's because we are now referencing the script in the script repository. We will save the model outputted by the tableApply to the datastore ds-0.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","mod <- ore.tableApply(IRIS, ","                      FUN.NAME = 'buildLM.1', ","                      dsname = 'ds-0')","","cat(\"Type :\",class(mod))","cat(\"\\n\")","cat(\"Coefficient :\",(mod$coefficient))","","ore.save(mod, name=\"ds1\", overwrite = TRUE)"],"enabled":true,"result":{"startTime":1714677288304,"interpreter":"r.medium","endTime":1714677288626,"results":[{"message":"Type : lm\nCoefficient : 1.083558 2.22994","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Apply a function to a chunck of rows with `rowApply` and `groupApply`","______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***ore.rowApply*** function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native R models. In this example, the user-defined function ***scoreLM.1*** takes a data frame and a model name. The ***ore.rowApply*** invocation takes as arguments the proxy object *IRIS*, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.","","For illustrative purposes, you can also use the ***ore.groupApply*** function to partition the data according to one or more columns and score as well, passing the model as an argument, and invoke the model on each partition."],"enabled":true,"result":{"startTime":1714677288704,"interpreter":"md.medium","endTime":1714677288762,"results":[{"message":"<h2 id=\"apply-a-function-to-a-chunck-of-rows-with-rowapply-and-groupapply\">Apply a function to a chunck of rows with <code>rowApply<\/code> and <code>groupApply<\/code><\/h2>\n<hr />\n<p>The <em><strong>ore.rowApply<\/strong><\/em> function can be used to invoke a user-defined function on chunks of rows, which is useful to perform scoring in parallel for native R models. In this example, the user-defined function <em><strong>scoreLM.1<\/strong><\/em> takes a data frame and a model name. The <em><strong>ore.rowApply<\/strong><\/em> invocation takes as arguments the proxy object <em>IRIS<\/em>, that we want 10 rows scored at a time (resulting in 15 function invocations), the user-defined functions, the linear model object, and that we want the result to be returned as a single table.<\/p>\n<p>For illustrative purposes, you can also use the <em><strong>ore.groupApply<\/strong><\/em> function to partition the data according to one or more columns and score as well, passing the model as an argument, and invoke the model on each partition.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","scoreLM.1 <- function(dat, dsname){","  library(lattice)","  ore.load(dsname)","  dat$Petal.Length_prediction <- predict(mod, newdata = dat)","  dat[,c(\"Petal.Length_prediction\",\"Petal.Length\",\"Species\")]","}","","ore.scriptCreate(name = 'scoreLM.1', ","                 FUN  = scoreLM.1,     ","                 overwrite = TRUE)"],"enabled":true,"result":{"startTime":1714677288833,"interpreter":"r.medium","endTime":1714677288956,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","res <- ore.rowApply(IRIS, ","                    scoreLM.1,","                    dsname = \"ds1\", ","                    rows = 75, ","                    parallel = 2)","","head(res)"],"enabled":true,"result":{"startTime":1714677566743,"interpreter":"r.medium","endTime":1714677585755,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 3<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Petal.Length_prediction<\/th><th scope=col>Petal.Length<\/th><th scope=col>Species<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>1.529546<\/td><td>1.0<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>1.306552<\/td><td>1.1<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>1.529546<\/td><td>1.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>1.529546<\/td><td>1.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>1.529546<\/td><td>1.3<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>1.529546<\/td><td>1.3<\/td><td>setosa<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","res <- ore.groupApply(IRIS, ","                      INDEX = IRIS$Species, ","                      scoreLM.1,","                      dsname = \"ds1\", ","                      parallel = 2)","","head(res)"],"enabled":true,"result":{"startTime":1714677648641,"interpreter":"r.medium","endTime":1714677705503,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 3<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>Petal.Length_prediction<\/th><th scope=col>Petal.Length<\/th><th scope=col>Species<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>1<\/th><td>1.529546<\/td><td>1.0<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>2<\/th><td>1.306552<\/td><td>1.1<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>3<\/th><td>1.529546<\/td><td>1.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>4<\/th><td>1.529546<\/td><td>1.2<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>5<\/th><td>1.529546<\/td><td>1.3<\/td><td>setosa<\/td><\/tr>\n\t<tr><th scope=row>6<\/th><td>1.529546<\/td><td>1.3<\/td><td>setosa<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Build one model per SPECIES using Group Apply","","A better use of ***groupApply*** - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function can be invoked on each group. The function ***groupApply*** makes this easy, as well as specifying the desired number of parallel R engines using the ***parallel*** argument.","","Here, we build three models, one specific to each species, and return them."],"enabled":true,"result":{"startTime":1714677776324,"interpreter":"md.medium","endTime":1714677776387,"results":[{"message":"<h2 id=\"build-one-model-per-species-using-group-apply\">Build one model per SPECIES using Group Apply<\/h2>\n<p>A better use of <em><strong>groupApply<\/strong><\/em> - the ability to automatically split the table data based on values in one or more columns - is for model building. The user-defined function can be invoked on each group. The function <em><strong>groupApply<\/strong><\/em> makes this easy, as well as specifying the desired number of parallel R engines using the <em><strong>parallel<\/strong><\/em> argument.<\/p>\n<p>Here, we build three models, one specific to each species, and return them.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.group <- function(dat){","  library(lattice)","  mod <- lm(Petal.Length~Petal.Width, dat)","  return(mod)","}","","res <- ore.groupApply(IRIS[,c(\"Petal.Length\",\"Petal.Width\",\"Species\")], ","                      INDEX = IRIS$Species, ","                      buildLM.group,","                      parallel = 2)","                      ","                                           ","","ore.scriptCreate(name = 'buildLM.group', ","                 FUN  =  buildLM.group,     ","                 overwrite = TRUE)"],"enabled":true,"result":{"startTime":1714677793625,"interpreter":"r.medium","endTime":1714677850391,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Save each model in a datastore","_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","Next, change the user-defined function to save each model in a datastore. The datastore allows storing R objects in the database under the provided name. The object assumes the name it is assigned in R, here mod and the corresponding linear model."],"enabled":true,"result":{"startTime":1714670641301,"interpreter":"md.medium","endTime":1714670641362,"results":[{"message":"<h2 id=\"save-each-model-in-a-datastore\">Save each model in a datastore<\/h2>\n<hr />\n<p>Next, change the user-defined function to save each model in a datastore. The datastore allows storing R objects in the database under the provided name. The object assumes the name it is assigned in R, here mod and the corresponding linear model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.2 <- function(dat,dsname){","  library(lattice)","  mod <- lm(Petal.Length~Petal.Width, dat)","  ore.save(mod, name=dsname, overwrite = TRUE)","  return(mod)","}"],"enabled":true,"result":{"startTime":1714678133988,"interpreter":"r.medium","endTime":1714678134106,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `groupApply` to build one model per `SPECIES`","_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***groupApply*** function takes the data, the ***index*** parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the R engine. ","","We then return the outcome, which contains three elements each assigned the model object."],"enabled":true,"result":{"startTime":1714678145008,"interpreter":"md.medium","endTime":1714678145071,"results":[{"message":"<h2 id=\"use-groupapply-to-build-one-model-per-species\">Use <code>groupApply<\/code> to build one model per <code>SPECIES<\/code><\/h2>\n<hr />\n<p>The <em><strong>groupApply<\/strong><\/em> function takes the data, the <em><strong>index<\/strong><\/em> parameter that specifies the column or columns to partition on, the user-defined function, and that we wish to automatically connect to the database from the R engine.<\/p>\n<p>We then return the outcome, which contains three elements each assigned the model object.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ore.groupApply(IRIS[,c(\"Petal.Length\",\"Petal.Width\",\"Species\")], ","                      INDEX = IRIS$Species,","                      dsname = 'ds-1',","                      buildLM.2,","                      parallel = 2)","                      "],"enabled":true,"result":{"startTime":1714670641800,"interpreter":"r.medium","endTime":1714670642707,"results":[{"message":"$setosa\n\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n     1.3276       0.5465  \n\n\n$versicolor\n\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n      1.781        1.869  \n\n\n$virginica\n\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n     4.2407       0.6473  \n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Load the models from the datastore","","When we view the datastore, we get the models loaded into the client R engine, assigned to their respective variables."],"enabled":true,"result":{"startTime":1714670642789,"interpreter":"md.medium","endTime":1714670642853,"results":[{"message":"<h2 id=\"load-the-models-from-the-datastore\">Load the models from the datastore<\/h2>\n<p>When we view the datastore, we get the models loaded into the client R engine, assigned to their respective variables.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","ore.datastore(\"ds-1\")","ore.datastoreSummary(\"ds-1\")"],"enabled":true,"result":{"startTime":1714670642939,"interpreter":"r.medium","endTime":1714670643667,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 5<\/caption>\n<thead>\n\t<tr><th scope=col>datastore.name<\/th><th scope=col>object.count<\/th><th scope=col>size<\/th><th scope=col>creation.date<\/th><th scope=col>description<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dttm&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>ds-1<\/td><td>1<\/td><td>17492<\/td><td>2024-05-02 17:24:02<\/td><td>NA<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n","type":"HTML"},{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 6<\/caption>\n<thead>\n\t<tr><th scope=col>object.name<\/th><th scope=col>class<\/th><th scope=col>size<\/th><th scope=col>length<\/th><th scope=col>row.count<\/th><th scope=col>col.count<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>mod<\/td><td>lm<\/td><td>17492<\/td><td>12<\/td><td>NA<\/td><td>NA<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Create a script that stores the model in the datastore and run `tableApply` ","","Again, we create this script in the R script repository and then invoke it by name using  ***tableApply***. We then pull the model to the client and view its type."],"enabled":true,"result":{"startTime":1714671305634,"interpreter":"md.medium","endTime":1714671305693,"results":[{"message":"<h2 id=\"create-a-script-that-stores-the-model-in-the-datastore-and-run-tableapply\">Create a script that stores the model in the datastore and run <code>tableApply<\/code><\/h2>\n<p>Again, we create this script in the R script repository and then invoke it by name using  <em><strong>tableApply<\/strong><\/em>. We then pull the model to the client and view its type.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","buildLM.2 <- function(dat,dsname){","  library(lattice)","  mod <- lm(Petal.Length~Petal.Width, dat)","  ore.save(mod, name=dsname, overwrite = TRUE)","  return(mod)","}","","","ore.scriptCreate(name = 'buildLM.2', ","                 FUN  =  buildLM.2,     ","                 overwrite = TRUE)","","ore.scriptList(name = 'buildLM.2')"],"enabled":true,"result":{"startTime":1714670643903,"interpreter":"r.medium","endTime":1714670644027,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 1 x 2<\/caption>\n<thead>\n\t<tr><th scope=col>NAME<\/th><th scope=col>SCRIPT<\/th><\/tr>\n\t<tr><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>buildLM.2<\/td><td><span style=white-space:pre-wrap>function (dat, dsname) \n{\n    library(lattice)\n    mod &lt;- lm(Petal.Length ~ Petal.Width, dat)\n    ore.save(mod, name = dsname, overwrite = TRUE)\n    return(mod)\n}<\/span><\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":[" %r","","mod <- ore.tableApply(IRIS, ","                      FUN.NAME = 'buildLM.2', ","                      dsname = 'ds-3')","","cat(\"Type :\",class(mod))","cat(\"\\n\")","cat(\"Coefficient :\",(mod$coefficient))","","mod"],"enabled":true,"result":{"startTime":1714670644111,"interpreter":"r.medium","endTime":1714670644431,"results":[{"message":"Type : lm\nCoefficient : 1.083558 2.22994\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nCoefficients:\n(Intercept)  Petal.Width  \n      1.084        2.230  \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use the loaded model normally to make predictions","","The model can now be used normally, e.g., to predict outcomes."],"enabled":true,"result":{"startTime":1714671700920,"interpreter":"md.medium","endTime":1714671700980,"results":[{"message":"<h2 id=\"use-the-loaded-model-normally-to-make-predictions\">Use the loaded model normally to make predictions<\/h2>\n<p>The model can now be used normally, e.g., to predict outcomes.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","prediction <- predict(mod,iris_df)","RES_DF <- cbind(prediction,iris_df)","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1714670644670,"interpreter":"r.medium","endTime":1714670644793,"results":[{"message":"prediction\tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\n1.52954613187489\t4.6\t3.6\t1\t0.2\tsetosa\n1.3065520823627\t4.3\t3\t1.1\t0.1\tsetosa\n1.52954613187489\t5\t3.2\t1.2\t0.2\tsetosa\n1.52954613187489\t5.8\t4\t1.2\t0.2\tsetosa\n1.52954613187489\t4.4\t3\t1.3\t0.2\tsetosa\n1.52954613187489\t4.4\t3.2\t1.3\t0.2\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Model quality assessment","","Let's assess model quality using summary statistics computed from the model."],"enabled":true,"result":{"startTime":1714671779272,"interpreter":"md.medium","endTime":1714671779340,"results":[{"message":"<h2 id=\"model-quality-assessment\">Model quality assessment<\/h2>\n<p>Let's assess model quality using summary statistics computed from the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","summary(mod)"],"enabled":true,"result":{"startTime":1714670645031,"interpreter":"r.medium","endTime":1714670645154,"results":[{"message":"\nCall:\nlm(formula = Petal.Length ~ Petal.Width, data = dat)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-1.33542 -0.30347 -0.02955  0.25776  1.39453 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept)  1.08356    0.07297   14.85   <2e-16 ***\nPetal.Width  2.22994    0.05140   43.39   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 0.4782 on 148 degrees of freedom\nMultiple R-squared:  0.9271,\tAdjusted R-squared:  0.9266 \nF-statistic:  1882 on 1 and 148 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `groupApply` to count `SPECIES` separately","","The ***group_apply*** function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set."],"enabled":true,"result":{"startTime":1714671836817,"interpreter":"md.medium","endTime":1714671836876,"results":[{"message":"<h2 id=\"use-groupapply-to-count-species-separately\">Use <code>groupApply<\/code> to count <code>SPECIES<\/code> separately<\/h2>\n<p>The <em><strong>group_apply<\/strong><\/em> function can be used for simple or complex computations. For example, here it is used to simply count the number of each species in the data set.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","groupCount <- function(dat){","  table(dat$Species)","}","","ore.groupApply(IRIS, ","               INDEX = IRIS$Species, ","               groupCount, ","               parallel = 2)","                    "],"enabled":true,"result":{"startTime":1714678751670,"interpreter":"r.medium","endTime":1714678791415,"results":[{"message":"$setosa\n\nsetosa \n    50 \n\n$versicolor\n\nversicolor \n        50 \n\n$virginica\n\nvirginica \n       50 \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `indexApply` to invoke a function a number of times","_________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________","The ***ore.indexApply*** function allows the same function to invoked a specified number of times. The first argument to the user-defined function is an index number for the function execution. For example, if the \"times\" argument is 12, each function invocation will receive a distinct value between 1 and 10. This can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis."],"enabled":true,"result":{"startTime":1714671929431,"interpreter":"md.medium","endTime":1714671929506,"results":[{"message":"<h2 id=\"use-indexapply-to-invoke-a-function-a-number-of-times\">Use <code>indexApply<\/code> to invoke a function a number of times<\/h2>\n<hr />\n<p>The <em><strong>ore.indexApply<\/strong><\/em> function allows the same function to invoked a specified number of times. The first argument to the user-defined function is an index number for the function execution. For example, if the &quot;times&quot; argument is 12, each function invocation will receive a distinct value between 1 and 10. This can be used, e.g., for selecting behavior within the function or setting a random seed for Monte Carlo analysis.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":300,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","computeMean <- function(index){","    set.seed(index)","    x <- round(runif(100,2,10),4)","    return(mean(x))","}","","ore.indexApply(12, computeMean)"],"enabled":true,"result":{"startTime":1714678823065,"interpreter":"r.medium","endTime":1714678823186,"results":[{"message":"<dl>\n\t<dt>$`1`<\/dt>\n\t\t<dd>6.142776<\/dd>\n\t<dt>$`2`<\/dt>\n\t\t<dd>5.932833<\/dd>\n\t<dt>$`3`<\/dt>\n\t\t<dd>5.872673<\/dd>\n\t<dt>$`4`<\/dt>\n\t\t<dd>6.383635<\/dd>\n\t<dt>$`5`<\/dt>\n\t\t<dd>6.147493<\/dd>\n\t<dt>$`6`<\/dt>\n\t\t<dd>6.251832<\/dd>\n\t<dt>$`7`<\/dt>\n\t\t<dd>6.07391<\/dd>\n\t<dt>$`8`<\/dt>\n\t\t<dd>5.981312<\/dd>\n\t<dt>$`9`<\/dt>\n\t\t<dd>5.927451<\/dd>\n\t<dt>$`10`<\/dt>\n\t\t<dd>5.562602<\/dd>\n\t<dt>$`11`<\/dt>\n\t\t<dd>5.320832<\/dd>\n\t<dt>$`12`<\/dt>\n\t\t<dd>5.837725<\/dd>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Use the SQL API for embedded R execution","","On Autonomous Database, OML4R provides a SQL interfaces for setting authorization tokens, and running UDFs to database tables directly from SQL. Supporting OML4R embedded R execution is the ability to load and store UDFs in the database script repository and R objects in the database datastore as displayed previously in the notebook. The datastore also enables loading saved objects for use in UDFs. Since it\u2019s under database control and invoked through SQL, deploying R-based solution in production systems is greatly simplified. Further, scheduling R functions to run is easily achieved using DBMS_SCHEDULER or other scheduling interfaces that invoke SQL.","","The service levels  `LOW`, `MEDIUM`, and `HIGH` provide different levels of performance and concurrency for an Autonomous Database instance. In this case, we use service level `MEDIUM` to enable moderate parallelism.","","The `OUT_FMT` parameter controls the format of output returned by the embedded R execution functions.","","The supported output formats are:","","- JSON","- Relational","- XML","- PNG","- Asynchronous Mode Output","","To use the SQL API for embedded R execution, a UDF must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account `USERNAME` `PASSWORD` and `URL` must be provided for an authentication token.","","## SQL API Workflow","","In the coming steps, we will provide a typical workflow for using the OML4R SQL API for embedded R execution with Oracle Autonomous Database:","","- Connect to Autonomous Database as the ADMIN user and add the OML user to cloud host ACL list","- Confirm the host ACL list contains an entry for the OML user","- Obtain an authorization token to access the SQL API","- Confirm the token is set","- Invoke UDFs using embedded R execution from the OML4R R and SQL APIs"],"enabled":true,"result":{"startTime":1714671958571,"interpreter":"md.medium","endTime":1714671958637,"results":[{"message":"<h1 id=\"use-the-sql-api-for-embedded-r-execution\">Use the SQL API for embedded R execution<\/h1>\n<p>On Autonomous Database, OML4R provides a SQL interfaces for setting authorization tokens, and running UDFs to database tables directly from SQL. Supporting OML4R embedded R execution is the ability to load and store UDFs in the database script repository and R objects in the database datastore as displayed previously in the notebook. The datastore also enables loading saved objects for use in UDFs. Since it\u2019s under database control and invoked through SQL, deploying R-based solution in production systems is greatly simplified. Further, scheduling R functions to run is easily achieved using DBMS_SCHEDULER or other scheduling interfaces that invoke SQL.<\/p>\n<p>The service levels  <code>LOW<\/code>, <code>MEDIUM<\/code>, and <code>HIGH<\/code> provide different levels of performance and concurrency for an Autonomous Database instance. In this case, we use service level <code>MEDIUM<\/code> to enable moderate parallelism.<\/p>\n<p>The <code>OUT_FMT<\/code> parameter controls the format of output returned by the embedded R execution functions.<\/p>\n<p>The supported output formats are:<\/p>\n<ul>\n<li>JSON<\/li>\n<li>Relational<\/li>\n<li>XML<\/li>\n<li>PNG<\/li>\n<li>Asynchronous Mode Output<\/li>\n<\/ul>\n<p>To use the SQL API for embedded R execution, a UDF must reside in the OML4R script repository, and an Oracle Machine Learning (OML) cloud account <code>USERNAME<\/code> <code>PASSWORD<\/code> and <code>URL<\/code> must be provided for an authentication token.<\/p>\n<h2 id=\"sql-api-workflow\">SQL API Workflow<\/h2>\n<p>In the coming steps, we will provide a typical workflow for using the OML4R SQL API for embedded R execution with Oracle Autonomous Database:<\/p>\n<ul>\n<li>Connect to Autonomous Database as the ADMIN user and add the OML user to cloud host ACL list<\/li>\n<li>Confirm the host ACL list contains an entry for the OML user<\/li>\n<li>Obtain an authorization token to access the SQL API<\/li>\n<li>Confirm the token is set<\/li>\n<li>Invoke UDFs using embedded R execution from the OML4R R and SQL APIs<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Obtain an authorization token","message":["%md","","The OML4R SQL API uses tokens to authenticate an OML user. To authenticate and obtain an access token, use `cURL` or another REST client, such as Postman, to exchange the OML user credentials for an authorization token. Send a POST request to the token URL endpoint */oauth2/v1/token* with your OML username and password. The token URL can be obtained from the OML service console in Autonomous Database:","","- Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.","- Click the hamburger menu and select the provisioned Autonomous Database instance. ","- Click **Service Console** and then click **Development**.","- Scroll down to **Oracle Machine Learning RESTful Services** tile and click Copy to obtain the following URLs for:","- Obtaining the REST authentication token for REST APIs provided by OML:","","    *oml-cloud-service-location-url*/omlusers/","","The URL *oml-cloud-service-location-url* includes the tenancy ID, location, and database name. For example, https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com.","","For example,","","- qtraya2braestch is the tenancy ID","- omldb is the database name","- us-sanjose-1 is the datacenter region","- oraclecloudapps.com is the root domain"],"enabled":true,"result":{"startTime":1714670934559,"interpreter":"md.medium","endTime":1714670934618,"results":[{"message":"<p>The OML4R SQL API uses tokens to authenticate an OML user. To authenticate and obtain an access token, use <code>cURL<\/code> or another REST client, such as Postman, to exchange the OML user credentials for an authorization token. Send a POST request to the token URL endpoint <em>/oauth2/v1/token<\/em> with your OML username and password. The token URL can be obtained from the OML service console in Autonomous Database:<\/p>\n<ul>\n<li>\n<p>Sign into your Oracle Cloud Infrastructure account. You will need your OCI user name and password.<\/p>\n<\/li>\n<li>\n<p>Click the hamburger menu and select the provisioned Autonomous Database instance.<\/p>\n<\/li>\n<li>\n<p>Click <strong>Service Console<\/strong> and then click <strong>Development<\/strong>.<\/p>\n<\/li>\n<li>\n<p>Scroll down to <strong>Oracle Machine Learning RESTful Services<\/strong> tile and click Copy to obtain the following URLs for:<\/p>\n<\/li>\n<li>\n<p>Obtaining the REST authentication token for REST APIs provided by OML:<\/p>\n<p><em>oml-cloud-service-location-url<\/em>/omlusers/<\/p>\n<\/li>\n<\/ul>\n<p>The URL <em>oml-cloud-service-location-url<\/em> includes the tenancy ID, location, and database name. For example, <a href=\"https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com\">https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com<\/a>.<\/p>\n<p>For example,<\/p>\n<ul>\n<li>qtraya2braestch is the tenancy ID<\/li>\n<li>omldb is the database name<\/li>\n<li>us-sanjose-1 is the datacenter region<\/li>\n<li>oraclecloudapps.com is the root domain<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define get_token2 function that obtains access token","message":["%script","","create or replace function get_token2 (","  p_url       varchar2,","  p_username  varchar2,","  p_password  varchar2,","  p_verbose   number default 0) ","  return varchar2","  authid definer as pragma autonomous_transaction;","  l_buffer            varchar2(4000);","  l_request_content   clob;","  l_response_content  clob;","  l_request           utl_http.req;","  l_response          utl_http.resp;","  l_url               varchar2(1000);","  l_smt               varchar2(1000);","  l_access_token      varchar2(4000);","begin","  l_url := p_url;","  l_request_content := q'[{\"grant_type\":\"password\", \"username\":\"]' || p_username || q'[\", \"password\":\"]' || p_password ||","  q'[\"}]';","  begin","    utl_http.set_wallet('');","    l_request := utl_http.begin_request(l_url,'POST',' HTTP/1.1');","    utl_http.set_header(l_request,'Content-Type','application/json');","    utl_http.set_header(l_request,'Accept','application/json');","    utl_http.set_header(l_request,'Content-Length',length(l_request_content));","    utl_http.write_text(l_request,l_request_content);","    l_response := utl_http.get_response(l_request);","    if (l_response.status_code != utl_http.HTTP_OK) THEN","      RAISE_APPLICATION_ERROR(-20999, 'Token request failed with status code: '||l_response.status_code||', reason: '||l_response.reason_phrase);","    end if;","    if p_verbose > 0 then","      dbms_output.put_line('HTTP response status code: ' || l_response.status_code);","      dbms_output.put_line('HTTP response reason phrase: ' || l_response.reason_phrase);","    end if;","    begin","      loop","        utl_http.read_line(l_response,l_buffer);","        l_access_token := regexp_substr(l_buffer,'\"accessToken\":\"([^\"]+)\"',1,1,'i',1);","        if p_verbose > 0 then","          dbms_output.put_line('l_access_token: [' || l_access_token || ']');","        end if;","      end loop;","      utl_http.end_response(l_response);","    exception","      when utl_http.end_of_body then utl_http.end_response(l_response);","    end;","  end;","  return(l_access_token);","  dbms_output.put_line('Access token is: ' || l_access_token);","end get_token2;","/"],"enabled":true,"result":{"startTime":1714670934702,"interpreter":"script.medium","endTime":1714670936709,"results":[{"message":"\nFunction GET_TOKEN2 compiled\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Create access details table to support token acquisition","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE ACCESS_DETAILS';","EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","CREATE TABLE ACCESS_DETAILS (","  NAME  VARCHAR2(20),","  VALUE VARCHAR2(1000)","  );","/","","-- Replace the OML token URL, username, and password with your database-specific values.","-- For example, the OML token URL is the OML access URL for ADB broker token authentication:","-- https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com/omlusers/api/oauth2/v1/token","","INSERT INTO ACCESS_DETAILS VALUES ('URL', '<YOUR OML TOKEN URL>');","INSERT INTO ACCESS_DETAILS VALUES ('USERNAME', '<YOUR OMLUSER>');","INSERT INTO ACCESS_DETAILS VALUES ('PASSWORD', '<YOUR PASSWORD>');"],"enabled":true,"result":{"startTime":1714670936798,"interpreter":"script.medium","endTime":1714670937024,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable ACCESS_DETAILS created.\n\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","## OML4R APIs to persist the authorization token","","OML4R provides APIs to persist the authorization token for use with the upcoming SQL calls:","","- The `rqSetAuthToken` procedure sets the access token in the token store.","- The `rqIsTokenSet` function confirms if the authorization token status. ","","The following example shows how to set the token in the token store and confirm it's status. A token has a lifecycle of 3,600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":{"startTime":1714691744270,"interpreter":"md.medium","endTime":1714691744344,"results":[{"message":"<h2 id=\"oml4r-apis-to-persist-the-authorization-token\">OML4R APIs to persist the authorization token<\/h2>\n<p>OML4R provides APIs to persist the authorization token for use with the upcoming SQL calls:<\/p>\n<ul>\n<li>The <code>rqSetAuthToken<\/code> procedure sets the access token in the token store.<\/li>\n<li>The <code>rqIsTokenSet<\/code> function confirms if the authorization token status.<\/li>\n<\/ul>\n<p>The following example shows how to set the token in the token store and confirm it's status. A token has a lifecycle of 3,600 seconds, or 1 hour, and it can be refreshed for up to 8 hours.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","DECLARE","   access_token VARCHAR2(2000);","   is_set BOOLEAN;","BEGIN","   with url_string  as (select VALUE from ACCESS_DETAILS where NAME = 'URL'), ","        username    as (select VALUE from ACCESS_DETAILS where NAME = 'USERNAME'),","        pswd        as (select VALUE from ACCESS_DETAILS where NAME = 'PASSWORD')","   SELECT get_token2(url_string.VALUE, username.VALUE, pswd.VALUE) INTO access_token ","   FROM url_string, username, pswd ","   WHERE localtimestamp=localtimestamp;","","   rqSetAuthToken(access_token);","     is_set := rqIsTokenSet();","     IF (is_set) THEN","        DBMS_OUTPUT.put_line ('token is set');","       --enable to return token","       --DBMS_OUTPUT.put_line (''|| access_token);","    END IF;","END;"],"enabled":true,"result":{"startTime":1714691294515,"interpreter":"script.medium","endTime":1714691297316,"results":[{"message":"token is set\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the same function using the SQL API table function rqTableEval","message":["%md","","## Use `rqTableEval2` to run the `buildLM.2` UDF","","Invoke the `buildLM.2` UDF that was created in R with the SQL API by running a SELECT statement that uses `rqTableEval2`. ","","The `rqTableEval2` function runs a user-defined R function on data from an Oracle Database table.","","The `inp_nam` parameter of `rqTableEval2` specifies the IRIS table as the data to pass to the UDF. The `par_lst` parameter specifies the names of the model and datastore to pass to the UDF. The `out_fmt` parameter specifies returning the value as an XML string and the `scr_name` parameter specifies the script name `buildLM.2` UDF in the script repository to invoke. For this invocation, we persist the model to a new datastore, `ds2`."],"enabled":true,"result":{"startTime":1714691709415,"interpreter":"md.medium","endTime":1714691709483,"results":[{"message":"<h2 id=\"use-rqtableeval2-to-run-the-buildlm2-udf\">Use <code>rqTableEval2<\/code> to run the <code>buildLM.2<\/code> UDF<\/h2>\n<p>Invoke the <code>buildLM.2<\/code> UDF that was created in R with the SQL API by running a SELECT statement that uses <code>rqTableEval2<\/code>.<\/p>\n<p>The <code>rqTableEval2<\/code> function runs a user-defined R function on data from an Oracle Database table.<\/p>\n<p>The <code>inp_nam<\/code> parameter of <code>rqTableEval2<\/code> specifies the IRIS table as the data to pass to the UDF. The <code>par_lst<\/code> parameter specifies the names of the model and datastore to pass to the UDF. The <code>out_fmt<\/code> parameter specifies returning the value as an XML string and the <code>scr_name<\/code> parameter specifies the script name <code>buildLM.2<\/code> UDF in the script repository to invoke. For this invocation, we persist the model to a new datastore, <code>ds2<\/code>.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","  ","SELECT *  FROM table(rqTableEval2(","        inp_nam  => 'IRIS_R',","        par_lst  => '{\"dsname\":\"ds2\", \"ore_service_level\":\"MEDIUM\"}',","--         par_lst  => '{\"dsname\":\"ds2\"}',","        out_fmt  => 'XML',","        scr_name => 'buildLM.2'));","    "],"enabled":true,"result":{"startTime":1714679298968,"interpreter":"script.medium","endTime":1714679368492,"results":[{"message":"NAME   VALUE                                                                              \n       <root><lm_obj><coefficients><ROW-coefficients><value>1.08355803285051<\/value><\/R   \n\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `rqRowEval2` to process chunks of data in parallel","","* The `rqRowEval2` function chunks data into sets of rows and then runs a user-defined R function on each chunk.","* The `rRowEval2` function passes the data specified by the `inp_nam` parameter to the R function specified by the `scr_name` parameter. You can pass arguments to the R function with the `par_lst` parameter.","* The `row_num` parameter specifies the maximum number of rows to pass to each invocation of the R function. The last set of rows may have fewer rows than the number specified."],"enabled":true,"result":{"startTime":1714691800700,"interpreter":"md.medium","endTime":1714691800855,"results":[{"message":"<h2 id=\"use-rqroweval2-to-process-chunks-of-data-in-parallel\">Use <code>rqRowEval2<\/code> to process chunks of data in parallel<\/h2>\n<ul>\n<li>The <code>rqRowEval2<\/code> function chunks data into sets of rows and then runs a user-defined R function on each chunk.<\/li>\n<li>The <code>rRowEval2<\/code> function passes the data specified by the <code>inp_nam<\/code> parameter to the R function specified by the <code>scr_name<\/code> parameter. You can pass arguments to the R function with the <code>par_lst<\/code> parameter.<\/li>\n<li>The <code>row_num<\/code> parameter specifies the maximum number of rows to pass to each invocation of the R function. The last set of rows may have fewer rows than the number specified.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Run the same function using the SQL API table function rqRowEval with structured table output","message":["%sql","","SELECT *  FROM table(rqRowEval2(","        inp_nam  => 'IRIS_R',","        par_lst  => '{\"dsname\":\"ds2\", \"ore_parallel_flag\":true, \"ore_service_level\":\"MEDIUM\"}',","        out_fmt  => '{\"Petal.Length_prediction\":\"number\", \"Petal.Length\":\"number\", \"Species\":\"varchar2(20)\"}',","        row_num  => 50,","        scr_name => 'scoreLM.1'))","        FETCH FIRST 10 ROWS ONLY;","        "],"enabled":true,"result":{"startTime":1714679835464,"interpreter":"sql.medium","endTime":1714679922669,"results":[{"message":"Petal.Length_prediction\tPetal.Length\tSpecies\n1.5295\t1\tsetosa\n1.3066\t1.1\tsetosa\n1.5295\t1.2\tsetosa\n1.5295\t1.2\tsetosa\n1.5295\t1.3\tsetosa\n1.5295\t1.3\tsetosa\n1.5295\t1.3\tsetosa\n1.5295\t1.3\tsetosa\n1.7525\t1.3\tsetosa\n1.7525\t1.3\tsetosa\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use `rqGroupEval2` to evaluate groups of rows in parallel","","Invoke the `buildLM.group` function that's stored in the script repository from the SQL API. ","* As in we did in the R API, group data by the `Species` column and set the `ore_parallel_flag` to true to leverage multiple R engines in parallel. ","* Specify the Autonomous Database service level to use with the optional `ore_service_level` argument. The service names are `LOW`, `MEDIUM`, and `HIGH`. ","* The service names provide different levels of performance and concurrency for an Autonomous Database. In this case, we use service level `MEDIUM` to enable moderate parallelism."],"enabled":true,"result":{"startTime":1714691827067,"interpreter":"md.medium","endTime":1714691827135,"results":[{"message":"<h2 id=\"use-rqgroupeval2-to-evaluate-groups-of-rows-in-parallel\">Use <code>rqGroupEval2<\/code> to evaluate groups of rows in parallel<\/h2>\n<p>Invoke the <code>buildLM.group<\/code> function that's stored in the script repository from the SQL API.<\/p>\n<ul>\n<li>As in we did in the R API, group data by the <code>Species<\/code> column and set the <code>ore_parallel_flag<\/code> to true to leverage multiple R engines in parallel.<\/li>\n<li>Specify the Autonomous Database service level to use with the optional <code>ore_service_level<\/code> argument. The service names are <code>LOW<\/code>, <code>MEDIUM<\/code>, and <code>HIGH<\/code>.<\/li>\n<li>The service names provide different levels of performance and concurrency for an Autonomous Database. In this case, we use service level <code>MEDIUM<\/code> to enable moderate parallelism.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM table(rqGroupEval2(","            inp_nam  => 'IRIS_R',","            par_lst  => '{\"ore_parallel_flag\":true, \"ore_service_level\":\"MEDIUM\"}',","            out_fmt  => 'XML',","            grp_col  => 'Species',","            scr_name => 'buildLM.group'));","    ","            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Multiple images returned from embedded R execution","","Here, we create a function ***RandomRedDots*** that creates a simple data frame and generates two plots of random red dots. ","We create a function in the R Script Repository named \"RandomRedDots\", and then run the native R function."],"enabled":true,"result":{"startTime":1714695243459,"interpreter":"md.medium","endTime":1714695243543,"results":[{"message":"<h2 id=\"multiple-images-returned-from-embedded-r-execution\">Multiple images returned from embedded R execution<\/h2>\n<p>Here, we create a function <em><strong>RandomRedDots<\/strong><\/em> that creates a simple data frame and generates two plots of random red dots.\nWe create a function in the R Script Repository named &quot;RandomRedDots&quot;, and then run the native R function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","RandomRedDots <- function(num_dots_1 = 100, num_dots_2=10){","","    library(lattice)","    x <- sample(num_dots_1)","    y <- sample(num_dots_1)","","    print(xyplot(y~x,","      main=\"Random Red Dots\",","      pch = 19,","      col = \"red\"))","    ","    x1 <- sample(num_dots_2)","    y1 <- sample(num_dots_2) ","      ","    print(xyplot(y1~x1,","      pch = 19,","      col = \"red\",","      main=\"Random Red Dots\"))","      ","}","","","ore.scriptCreate(name = 'RandomRedDots', ","                 FUN  =  RandomRedDots,     ","                 overwrite = TRUE)","                 "],"enabled":true,"result":{"startTime":1714691901182,"interpreter":"r.medium","endTime":1714691905882,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}},{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","RandomRedDots()"],"enabled":true,"result":{"startTime":1714691915726,"interpreter":"r.medium","endTime":1714691916045,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nvfc8T6dzOvXrr/r0U9MD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lzOOWU8PzzSc8EANRtDZIeoF5atCgce2z44IOQSoV0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Using the `graphicsFlag` option for the REST API ","","If we simply invoke the named function, we see that the result is an ORE object. 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47h1FPDfvslPRZAthB2QLXSvHm4+OKkhwDIUt6KBQCI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voOHz587tw5MVXx7t274+Pjfr8/GAymUqlUKpXJ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\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Wxs6Lo+NDR05syZ6upqj8djk7dW4glS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08rEokM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\n63vhhReOHz/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Call the `RandomRedDots` function from a REST API","","Invoke the *RandomRedDots* function from the REST API with *graphicsFlag* to *true*:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/r-scripts/v1/do-eval/RandomRedDots\"%md"],"enabled":true,"result":{"startTime":1714695858586,"interpreter":"md.medium","endTime":1714695858644,"results":[{"message":"<h2 id=\"call-the-randomreddots-function-from-a-rest-api\">Call the <code>RandomRedDots<\/code> function from a REST API<\/h2>\n<p>Invoke the <em>RandomRedDots<\/em> function from the REST API with <em>graphicsFlag<\/em> to <em>true<\/em>:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/r-scripts/v1/do-eval/RandomRedDots&quot;%md\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A second option creating separate figure objects: `RandomRedDots2`","","Here, we create separate figure objects and then create the scatter plot. ","* We'll store this in the script repository as \"RandomRedDots2\" and invoke the function to see the results. ","* Then, when we invoke \"RandomRedDots2\" using embedded R execution, we get both plots shown in the result."],"enabled":true,"result":{"startTime":1714695950311,"interpreter":"md.medium","endTime":1714695950371,"results":[{"message":"<h2 id=\"a-second-option-creating-separate-figure-objects-randomreddots2\">A second option creating separate figure objects: <code>RandomRedDots2<\/code><\/h2>\n<p>Here, we create separate figure objects and then create the scatter plot.<\/p>\n<ul>\n<li>We'll store this in the script repository as &quot;RandomRedDots2&quot; and invoke the function to see the results.<\/li>\n<li>Then, when we invoke &quot;RandomRedDots2&quot; using embedded R execution, we get both plots shown in the result.<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","RandomRedDots.2 <- function(num_dots_1 = 100, num_dots_2=10){","","    library(lattice)","    x <- sample(num_dots_1)","    y <- sample(num_dots_1)","","    fig1 <- xyplot(y~x, ","      pch = 19,","      col = \"red\", ","      main=\"Random Red Dots\")","      ","    print(fig1)","    ","    x1 <- sample(num_dots_2)","    y1 <- sample(num_dots_2) ","      ","    fig2 <- xyplot(y1~x1,","      pch = 19,","      col = \"red\",","      main=\"Random Red Dots\")","      ","    print(fig2)","","}","","ore.scriptCreate(name = 'RandomRedDots.2', ","                 FUN  =  RandomRedDots.2,     ","                 overwrite = TRUE)","                 "],"enabled":true,"result":{"startTime":1714695966300,"interpreter":"r.medium","endTime":1714695966419,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","res <- ore.doEval(FUN.NAME = 'RandomRedDots.2', num_dots_1=600, num_dots_2 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4c9mr48JTloHWv22kWx/7lF25jbW9Y2JHtu3MHzZqhQgV06oS+fVGv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\nCQmcOqVXFE+X7rGoOnXqzJ8//9y5c5988kmGDBlGjBhRsGDB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z5Yt3LvHyZPcv8+BA3ToYHQm7yaFnQDgxg1WrGD0aKZNY+tWJ1u8P7lD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\npFAmoixssSN1OHPG6ClRxF9/4cEDM0NbiF7c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x6L5dKhQghrwIFdoTst8nJyX/8x39kezGFw2EWsbW2tl66dOn0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pARvYlJdTVqXCOfcfysrLnjXvEirSahDT26rcsuleZ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3kDsxNkkGEx9IUu92WBQM/ImU5rYpbGH\nqIBGo9Hr9Xq9fvsJYZ1Op9PptvKG0Wg0Wq3WYDBYXktbqafXpb0eCIfp7qanh2Bws2OIxJ4O\nD5ORQTD4VCa9TwmPh8pKuroYHNwQHiUwGsnKIivryZGvej0OB01N/Jf/Qmcn/f2qgWuzkZ9P\nSQlnz6pQ8JQwGHC7OXwYnY4LFwgEVCXDYlGqOJ/vyQVag4HMTCXUexzybC/NqiMSIRBgYIAH\nD1RryWzG5aKsDIdDVRp0OmXFkhym0WqV0+Hj3eQk4nGWl9d48zYIhejvZ3o6dQkzFmNykuHh\nV5+W+4YgHmdlhZs3+eQTbt+mu1uFJcq4tzC8mRnOniUv71W/VwAMBg4eVPI4jUZZrGu1JBJY\nrZSUcPQoJ09SUJD6z2Mx2tu5epWeHpUlLTXjaFSl3La28vHH/OQnnD699ldeL5cuodczM6NK\n1Osh27P2dj78kBMnOH0aj2fPhUmm8XLxxlCENF4JhBWJYj2pi0q2L/X6J4hpwmF6e1NHS0m5\nJRhEq2Vubk2a8+LIzaWujlu3sFrXwqNYN+9WUIDXS0bGk6dWdTrq6ykv59YtJYtcXCQ7m7o6\nTp4kN3e7KTb586oqqqrU0ZP5XIvlGZi92UxeHpOTTE+nfgmXi+zsXe/gSz1sfp4HD/juOz75\nhPFxFhbIyqK0VFnDCJMzGiksJD9fGbKs7ytt//3Kr55YTJXCXiiU+rciV5qd3d0SZnLCN9mX\n38uSsl2FNDSvX+f//B81SZDE1BRzcyqApKqKnJw9cZQMBurqMJsZH1f1Y9lSJhK43TQ1cfIk\nR45s6Rm0skJrKz/+qBzLk2uL0LV4nIEB/u3f8Ho3EDufj3ffVVLjxUU1kJvccErFOhSio4Oh\nIVwu6urSxO4NR5rYpbHLSCQYH6ejQ80BAFYrRUVUVlJT84TGgXTHHveB2wTpQezUbGx2NvX1\nHD3KzAydnczPr3nmmUyUlPDLX3Lu3NNOH+t0mExUV+N2K5poMpGVpVTST4n10bTP9Bm9Xt55\nh2iUsbHNAxzintrQwKlTux71IV569+/zpz/R3MzAgDKmjsXUNENmpqqAihF3UREXLlBbi8+n\nJot9PsrKGBpSOWPrIYPhOTk4nU++98uYy1Y9gac0mnlByKeW+W6zGYsFq/XVU5ZXgrk5enqY\nnEwxXST+28EgY2OqC7lHjpJWS24u779PQwPj44RCqgwvQ0h+/3YrQzyuSsLhcOoQ5MVFRkeZ\nn9/w75mZlJby3ns4HPz4I21tSnuw/mllk9DZyf/9v/y7f7elwCONNwNpYrfnIdUOtnXH2LOI\nRBgfp7WVq1dpbaWnB8Bup7qaqSlWVyks3E7+n6ztyQd/3PVD+nQ7e0zEDq2pieVlMjPVXSeZ\nLF5Tw8WL1NY+bZVLCm9eL17v878lIXPPwTZycjh5kokJRkZUCSSZku52U1jI0aMcOrTr+/t4\nnP5+btzgu+9UmpkgGiUUYnRUTRMnT2+/X/kGu93Kpa+4mNpaOjqYnWVxUVUskmMoOTk0NODz\nPZn12mwUFzMxwcTEZiYhHsUijd+lyYlwmPl5pqaYmlJ9eYuFrCw8HrKzcbufrO98zSAR2MFg\napNt2dfNzKgUlt1T18hLy9koEWGyx7DZ1Mx78tKTU87hoKGB2lrCYRYWCIdxOpXmYfuLNOmW\nkjLvW96DCO/Ww2zGbKaxUW0Op6ZUdTP5DMn+7MgIS0s0NKjL/I06l9JYhzSx2/MQrQkoZ/z9\nJbCbmuLjj7l6lbY2AgG1EzUYmJhgcJCODn7xC957b8s/l2gprxezeYPlehLiGpUMW9spGAw0\nNanQs/FxpqYwGLDZ8Pnw+fB690rx4Imw26mo4J13sNv57jva2pifx2DA6eTkSc6epakJr3fX\nJ0BXVmhp4coVpqZSW7cktYxyVEdH+fhjtFoOHFDC8KoqIhFaWpiZUV0w8ZqRMZSqKn7729Rz\niJuQm8vZs2rGNqkNEIhzzcGDHD26W0av4+Ncv861azQ3r31klwu/n3ff5d13nyzcfM2wssLc\nHOFwaj8goSZChhYXcbt3sZIajzM7y7179PQwNKQEsgcPUlm5ZXFd1gSTiXh8LbZke0jVf5tZ\nECnwJy2T1sPhoLAQnw+3W4XWPA4J4F5YIBLZYROoNPYV9hVLeKOwuMjcHP39jI0RCqnbmMOB\n201JCTk5e8gCICUSCSIRxsa4fZvmZiVIT5YepcE6M0N1NSdOYLGk7l/IfGJ5OW638qRdX6rR\naCgpobGRvLwd5rtiKeJwkJ/P7CzT0+j1WCxkZ5OZuZ/qppLPUVWF2YzdTk0Ni4vodNjtHDzI\noUPk5e1KhpiUmUMhpqdZWmJ6mvv36e1lcXG79LMkwmGGhhgcZHwciwWnE4eD8nJ+8hOys+nv\nZ26OpSVMJmw2XC6OHePIkaeaY3W7aWhgbIypKRXFIURTr8fno7ycEyc4eJAdt0aS9Lz2dr76\nilu3aG9XZ1Eigc1Gfz9WK1lZVFc/2YrvdULSBjLlBZWUIWZkbPmYF4fMLrS20trK/fv09zMy\nosp1chIeOUJhIVlZm2mlaD9SkrCtIMX7khLGx1XhedNvnU5KSpTD+SYYjdhsKjRyK4Irn0XK\nn1tdaGm8AUgTu72K6WkePOAPf+DqVUIhVbovKqKmhn//7zl1Cqt1TxO71VVmZxkZobeXkZEN\nCTzizyn9CDGvkqi0x2G1UlvL0BA+H5EIKyuKHSYdQxoa+O1vKS3dlY+g12O3k5mJ16t45D71\niMrOJiuLmpoN0ajC+XapBCICqbEx7t5lfFwJ7ILBZxhK0GgIhxkexuVS4rm8PH77W06fpquL\nkRGCQex28vKoqSE/P4UNbEo4HNhsLC1hNPLJJywuqvFGi4X6et55h3PnqKnZ+br40hJ9fdy+\nzVdfMTFBNLo27SEleTHf/qu/erOIncVCfj5Op4rAfrxoJxe7y6UCZnYDomz7+GM+/pjJSRYW\nVIdEp+P2bSorCYU4e5bMzB24WPR6KisZHubBA4LBDcMi0vzNy6OpCb9/y2cwGJ5w2SbNNffj\nSpXGDiFN7PYeolEiEa5f59/+jeZmBgfV+JXYMYTDWK2EQpw7t1csAFJChvADAZaWUpiGyANi\nMebmGB/H6SQrK8WTaLXKtu13v6Ozk95epqYIhzGbyc7G7+fkSerqdkv7L0wO9ln7+3FIy/Ll\nZM1JVtLQEN99R3s7Q0NqtlGCUrYfgtkEYYfCREEVUXw+MjIoKmJxUQWv5eQoHd7T3MnkUBQX\nq5voO++sJU+UlFBRgd+/KzPCi4s8eMCDB0xPb7Bj5JFXy9AQJhPvvvu0LomvBzIzKSujuJi8\nPKanWVhYWyu0WkwmyspoaCAnh4yMHTgmkQjhsBrFkJkbl4vZWfr76epiaEhNQiQb9OI59+mn\ndHdTUKBWYIuFnBxqaigoeOaEEr2e4mKOHmVsDKORzk611VldVTZGJ07wzjuUl6f+c5kiKi2l\npYWpqc3jFxoNVituN1lZe72fk8YuY5/fsV5LiKns9ev88Y/KnSuJYFBtKMNhamr2igVASiRl\nwrLnTgmNRvXptrINk09XUUFZGR0dtLbS38/sLHY75eVqxd/tic40ngmJBLOztLXx4Ye0tKiB\nZZk+kc7j0yNliVTiKF4QEvLb1KRSTGDX2+vhMD09ahz4ceOeeJxAAK2W+fknW/HtfawPBWHj\n1FfKb7O8nIoKBgYAtQ8EzGaysjhyhBMn8HiereP5OMRxKRhkfJy+Pqam0GjIzaW0lNFR2tp4\n+FDNKa8/RWWI9euvuXIFk4lIhOVlXC5qavjFLzh1Sg1MPD2FEmam16vUClEUyLFyuThyhNOn\nOX8em22tqZpkusIg8/OpqsLjWWvmylSZjJG5XEoUuN/3omm8GNJf/96DxEDJ1N6mQpdsGcUC\nYHaWSGTv+ozLhliaLNtALGqfWEzSavH5sFioqVFG7XY7bvczOIak8XIQjXL9Op9/zuioch+U\n+/QziX6Eb5nN+P27NceQxMvZGknkgDgRbvUAYUKvAbETSzaZZk0kMBpxu7FatyRnR49isXD3\nLu3tPHzI0hJ6PaWlVFfT2Eht7Q5s3gIBurq4fp0bN5Q5tswx2GwYjSQSTE6myIbm0ZIr6hFp\nFs/P09XFP/0TMzPqFH3WnGW7naYm8vO5cIFQiKUlJYHNzVUT2RKlePcunZ0MD7O0hEaD309J\nCUVFVFdz/jw6nYrFExc90Y00NPCrX9HQ8KKHK419jjSx23tYXOThQ7UmbmUBMDurpuJfTn/t\nOaDVKu4lC/r64XyBxKA5nWRnP2EqU+5z0m4zGlXNIxZjdlbZvu+Ug10aL46VFdrauHmTQCD1\nnRLWvjKZeXzc6sJsxuulsJC8vJ2fY1iPl1bwlgkAMbVOadwjl4NOt78175IHMz3N6CgTE6pd\naDKRm0teHl4vNtuGzZgc/NJSvF5ycvD76esjHFZz0HV1lJY+IQ15PWTzIIuDVLBkWYjHGR7m\nu+/46iuuXlWvK9/C6qoympmeTmEsJ9gU9rC0pDJUTCaqqjAan5nYmc2KoiUHfuWwJOvTw8P0\n9PDtt9y4QW8voRBaLeXl1Ndz7hwFBTQ2otdjMCiPTyGp2dmcPs2FC+k0vDTSxG7vQdzYt5Km\ngbodit/SVoFUrxxaLS4XXq/KV1he3nCbl6n+rCzy8igsfKrC2+wsQ0Pcvk1PD6EQViv5+TQ1\n0dCAybTr2QlpPCUSCQIBlfiZktXJ1ItYcxmNxOPMzakUqeQ8QX4+773H+fM4nS/ag9sjMJsp\nKsLvV7Kqxz1fbDZyc9XuZf8iEmFkhB9+4E9/UkZ9PAozPXSIn/2MmhqKizf/lbBeUa3Juidy\nMbGRe3rIJkH8BKQaJ/vehQUePODjj+nrS1E0DQZV2UxKX0/5QisrjIxw5QpuN9XVz/Amk5AT\nXgwB1lt1rq5y4wa//z19fYyOsrCgdHjLy4yPMzFBUxNvvUVdHW+9RTDI7Cwmkyr45eaSk5Ne\nDNNIE7u9h2S+pyw0W3G7jAwMhj3dtcnIID+fM2fQarl/n8lJZmfhUdxqaSn19dTXY7M9od4m\nFcrmZm7dorVVCZVEwjw5ydgYR45QUrK2QU9jx5FIsLTEwoIK7Y3FsFqx23G5VEUteR7KPW+b\nXFfJfqivp7YWg4FYjOlp5udVwLlWi822ljzxTAKmvYzkfPfdu6ronuy6SuWyvJyzZ8nP39NX\n9DaQVnIwyA8/8OWX/Pgj4fDa2rW6qhJcEglyc5XrWxKaRxnBz91yjcUYH2d0lOFhpqcJhTAa\nsVrJycHhwGBgaIihIVXj3wQ5V6X2//SCgdVV5ubo7SUYfM73LF+0wbBh67K0pFq9t26poLDk\n9l4+l0TXnD+P309BAfPzzM8rbzyHQ1G6fXoKpbFzSBO7vQerFb8ftxuDIUXkMyhPO5cLh2NP\n3/ZEnvyb3+D3YzZz757aLtvtVFVx/jw//zkFBU/+CLOzKhv7o4/WLNd1Onp6uH+fr7/mv/93\n8vMxm/f00djXSCSYmaGvj+ZmxsYIhykspKKCw4c3ezVLfsM2rmMSX/vzn/PXf41Go4xvRkcZ\nGCAWw2CgtFTplszm10cDnpnJoUNMTPDDD0oUL3xCLMd1Oo4c4S//cn8nQcVijI7yL//Cjz8q\nJcl6dHUxNkZWFkePkpm5w99sLMb9+3zzDV99xfi4YpA6HXV11NRQVcXk5JY7DfEZkT7sVt3Y\nlFheJhBQG5KdQihEf7+ip+vDZHnU5h4dpa+P8XGKi3G7VQc2GcGSRhpAmtjtRdjtVFYq3Ukw\nyPz8hsGojAwOHOD0aTyePX3bS+5H3W4OHsRo5NQpAgFALUYlJRQWPlWEw9AQf/4zzc0Eg2tm\nV7EYKyusrLC6yv37yuEvPSG7G5ic5OFDrl6lpYWHD1Xn1O2mpYW2Ng4c4PhxJYSS0kthIZWV\ntLdv1i3JbyX+q6xMKZPicWw2MjPJzlbDfW63io7d103JTRDzjro6fvc7ursZGFDD4BYLbjde\nLydOUFy8u4LCXYVQ/4kJZmaU8GtT6WtlhcVFdS4VF+/k2JN4Tf/wA99/T3+/Kg1KNXR1lWBQ\nSZal3f84pFNcWorZTH+/+l6SxVRIHXcmXdScnCd8ZVICnJtjYkIJbKS0lpen8ic2vaVIhKkp\n5udTR3FIWVRsExYWcLleE6FCGjuNPcwM3lhkZpKZSVUVVVX09yun1qRC1uXi1CkuXMDj2Qdb\nNLENKyujrOx5/lzW09FRvviChw832NuKSlriHTs6KCjA708Tux2GHP/hYa5e5R//kRs31nqs\nBgNmM83NnDyJ309WlrrH6PWUlXHwoHIcXN+P0+mwWiko4OjRNQtWyR6wWPa0KeOLQ2oqEqPS\n28uDB4yMEApht1NSwoED2O27Huy2qxCbm0Bgu8lfjYa5OYaG8HjweHbspcfHaW7mxx+5c2ez\nBmBoSPmbaDRbxrMCGRkcOoTXS0YGg4NrpjNarfLa3PSh5Fd2O6WlW05OyGutrBAO8/Ahra2M\njzMzg9uN38+BA2owaJOhjySUiD41JZsUfY6MXGzTNV7vOCPkMhmrnW7UvgFIE7u9CmlYtLYq\n58zlZTIyKC6mspKGBqqq9vHm/ukh7C0SYXExdWiBuD1NTjI6uqUZXhovAimI/tM/0dOj6qMC\n+V4CAQYGaG/HbqeoSAmGDh5kdZWFBbRaenuJx9XdyG6ntpZTpxQXfGMhdrtlZar1LBu516D0\nIi4n20AKTikLYC+CkRFu3WJ8PPUUdjzOwoKq3m319vR6Kio4d44TJ1RdTdjP1BQPHnD5MkND\nG5yTdToMBgoKuHhxSzNhUMk6H39MaytjY8zPs7SE1YrDQW4uJ07w85+TlbXB2cBkwuNRsuPH\nGZgcQNEj2u1P2NjLpndiQgk6XS48ni3DG9N4vZAmdnsPSQsAv1/5jA8MKMu6ykqqq8nNfYNK\nU7KWiYlUyt9KIlPS1zSNHUQ8zsoKg4PcurXBkZ9HlHpujrEx5cvv92MwoNNRUIBORyBARgZZ\nWaraodHgdtPYyIkTKrv2DYRc2nb73h1mf26ItlLyIbbibaurKnN5ZzUkgQA9PczOpl4BpHcJ\nigatJ0PydXg8lJdTVcXhwxiNavpHZABwo2i6AAAgAElEQVRDQzQ3MzuL0UgwqBKAzGYyM/F4\nOHKEhoYtK83xOFNTtLby6ae0tLC4qP5cNj8mE6GQ8urz+db+ShSoLhcmk/KAXH8wxUwgMxO3\nG7t9y9rb8jKLiwwPMzys4jRiMXJy8PkoKCA3l8xMeFQdlLbv6ioGg3IelbG8NPYz0sRuD0OE\n5GKRL0NbVitW6xs0zS6hh7IU6nSpF26dDodjrRWYxg4iGmV2Vpnjp0QsRijE8PCGgCOtFo+H\nd9+lqUmJgZaWsNtxOPB4cLneoBP4zYGII3NyMBhSEzv5x6ysnZcSinrvcQeZTZBRlWQ7Mvnf\n0aP8/OdUV2M0KtqX7Ffm53P0KCYTt29z/TqBAOEweXmUl3PqFAcPkpe3ZQN9ZYXmZr7+mocP\nWVhQPeLVVUXvYjG6uvjoIxKJDcTOZqOkBJ8Pl0tVNzcJrPPzKSkhP5/MzC0rdsEg3d386U/c\nvKmyi4S3ZWZy8SKnT3PoEFotY2PcvMnNm2pY2O2mvp7Tp8nPf2ZnvjT2GNLEbq9CFpdXvrkX\nX6iZGSYn1RqRmYnTqTy3djuISQ6Cw0FFBfE44+ObpSdi+1JYSHn5s1lepfH02MYDIpmXkGR1\ncj6II39eHtEoi4tEIsqTzGDYB8LQNJ4D4jyXn8/Bg8zN0d2thhWSZ47bTVER5eXk5u5wvVYG\nUMbGnvzIpHJOzlIpvB0+zMmT5OWtDesk/4/FQn4+RiNOJzk5zMwQieDxUFDAoUPk5W03iR+L\n0ddHW5uaxkj2iMVsL5FQ0sDDhzf8ldGIXk99PT/9Ke3t9PczM6Nq3m43eXk0NHDixJZmdbI8\nPnjAZ5+psOb1n13ssVZWMBpZWeHWLe7c4d495udJJMjKYmSEYJDjx2lsVDaTaexPpIldGttC\nFGz37vHjjwQCLC1RUkJdHadPK63xS0BODidPEo0yPa32u0nodJjNVFdz8KDqL6Sxg0g2zrbx\nyjYaFWl7nOJL5ygjQ2m306rt1xtGI14vP/0pq6vKE0cgZ05REe++y+HDZGXtMLl3uykvZ2iI\nkZEnOAwLqRJ+E4/jcHDoEAcOUF6+5Qi20UhODi4Xhw8r2iT2KNJD2OaUFuo2OEgolNqyan6e\nvj6mpzf/Sqvl+HEKC/n0U374gc5O5ufR6aiu5sgR3n6bw4e3XOukj3z7Nn//9ypzcv2volFu\n3mR+HpuNYJB/+ic14SSfa3KSwUGuXWNuDr+f3Nw0sdu/SBO7NLZAPM7EBF1dfP89ra309akJ\nhqEhenvp7OTYMS5cUINduwqvl7ffVo5fvb1MTKjVWUxfjxyhsTG9DO0KhDe73fh8BAIsLGym\ndyIPED3o48Kp9Ajem4NkDayuDo2G7Gw1BJrMihXXw9LSZ1guREEbCjEyoux5JWJBOpWS1gAU\nF3PuHL299PURiWzH7eRXyVLi7Czd3XR0UFGhnjPl55KV55lmloNBBgaUHV3Kqa+tIIfRasXn\n4+xZCgtVq0SjIScHr5fycuz2LY/h0hLT00r/8PgUsMgNx8f54QcWFhgb2yBNlgZxJKIcAc+d\n2xDTnHyq9BW9H5AmdmlsgViM/n5++IHf/141VuTaHhyktZUrVwgEqK/HYNj1HmhuLtnZquQj\nzreRCBYL2dm89RY/+xklJWRl7e57eDMhbjV5eVRVARty4aSlJeb+cl98cX4vJoXrIxnSBg37\nC2YzpaUq66+/n/5+EgmsVsrKlHLrWVnd3BzDwzQ309fHxARZWXi9HDlCZaUKWtBolB3mlSu0\ntjI5uaFM9TjWW77LXsXnw+9XTlIvDhEnjI3R0sLwsOpypoRer3K0H4eoipuaaGp6tlcXz+RQ\naLvHzM+rWSip1a2HTET19PDNN1RUUFmpvEKld5z0TJF+8fYFyzReKdLELo0tEI1y7Rqff874\n+AaHgnhcTZkNDXH9Oo2N6q6/q9BqqajAZuP4cTX+JntoydV5E5xfXiFEBvTJJ9y4oUJgZQTS\nZuPcOS5coKJiOyn302N2lqkpgkHCYcxmPB51x93LRtxpPA6DAYeDsjJyctZk+yLJfXosLzMz\nw5/+xJUryvd4YUFNpF65Qn09v/wl5eUqnsRu54MPcLn48EN6ep7WTkUqWJLzVlVFRcXzfdwN\niESYn+fKFf74R7q7U78TqQLm5CjzvB3H9nwrHicc3mBdtAkLC4yPs7TE3Bzt7bS10dqqRmul\nlHjsGJWVqYv0aewNpL+YNFJBlryuLlpaNm/sxHxkdZXxcVpa8Hp3ndjJOpWX95p72O5ZlJTg\ndLK0hNGoNEOrq5jNZGVx6RKnTuHzPb8cXu4uCwtMTTEwoOKSFheVEr+sjMJCvN7N6aLP/UKS\nYyuSQaNRKaXS8xzbQ6pQMkQVDhOJKGpisahh503HUHqXZrPKI3k+BIPKQ+7LL4lGVelIp0Ov\nx2jk4UMKCrBYKC5WTYNjx8jIoKWFhQUCgScPyfKoKBgI0N//hCrX00N8Ru7d4+rVLS2R9Xoc\nDkpKOHGCwsKdeV2BaCeemCG+VQS5IBYjHCYYpL+fK1f44QeuXVOuKJmZlJQwN0ckotKi0654\nexJpYpdGKojYIhJJneYuGuS5OQYGmJl5Fe8vjZcIgwGXi3ff5ehRFhbUWWGxqBwwp3MHIhN6\ne/noI9ralExKNJRmMzYbv/wlv/oVbvcO1GXjcebnlbQoFiM3F5dLmcGmsQ0SCSIRurv5/nvl\nl67V4nBQWcmRI5w8uSvH8MEDPv6Y3l71ZckqJIxcdpVffUVGhiL9gF5PXh4/+xk6HZ9+mnpk\nISVisc0ejS+C2Vna2xkb286H2WSiqIjGRt55h4KCnXldgdjgicXdVtxOp8NmIxolEkn9Di0W\nnE76+xkf57PP6Oxkbk59BaGQEluHQrjdlJWlN9t7E2lil0YqyLogNhZbuVLtrH18GnsWorTz\n+cjPJxZTKb0ZGTsTgRWNEgzS1saVK3R1MT6+loAk/2VlkZXFyZPP3ylbXVUhGZ2dDA0xMaE0\n4y4XeXlq8iM/P91XSgG5xmdnuXuXGze4coX+fsbG0GjIzGR4mJkZlpepq6OycodfenSUO3eY\nmNicJyHubtIlrKpSlTlZr5xOmppYWWF5WfESqfOJ0+/jzcdkMlhOzo7luS0tMT7O3NyWVTE5\npRsbaWqipASrdWdeVyACifJyTp6kvZ3R0bVfra6i01FSgteLw6Hq4ktLG96kHBC/n7o6JiYY\nG+PBAyYn18qfsRjz84TD3LlDQQEDA6rbbjTicikjQ70+rb175UivZWmkgphhbiOPTQbXpt3j\n3hyItE7UjTu1di8u0tFBSwudnQQCG3Lh5G596xaLi+TmPj+xSySYm6O1lb/7O1pbNwRx5uVx\n8SIXL+LxpIldasgowB/+wNWrjIywtKRs1RYWmJlhdJSuLv7Df9h5Yjc7y+AgCwupvULCYUZG\nmJzcUGmzWqmuxmbD5+P777lyhfl5QiGVxPD4NlWrxWAgN5eqqh3L8llZYX5eDedu1YfNyeH8\neY4exWzeYQ4kLfLGRoAPP9zgpZJIkJHBsWMcPYrNxp079PYq4puEuBdVVnLuHH/4A7duEQxu\nbmqLo0pfHx99hM2mzIycTg4coKkJux2LZdd9EtJ4EtJrWRobIYtRby937zI6mrqdIcuHx8Oh\nQ+Tnv+Q3uLcg7lCiEH/tlzO5Ce3srSgcpq+PgQHm5zdHrUvpbmaGhw9VM0hqeM+KaJSrV/ni\nC1pbGRlZ65FJh/HKFcxmiovx+Tb4O6QBJBIEAgwOMjjI+LhS0K8PmJ+YIJFgYIBgEKv1mete\n8iRjY8oZZGkJk4nsbAoKWFp6wsbycXGk8JKcHNVtrKlRs58LC7S28s036v0L5RI5mt1OXR1n\nzpCb+1wH6DFYLHi9Kggn2UHe9CYzMpQ33o6bO8qz5ebS2EgsRkODMlWOx9fcmIuLlfekOJt0\ndysaarFQVERdHRcvUlaGVsvs7JZeLQsLDAwox5nVVSwWJiYYHmZwkKamza7Labx0pIldGhsh\nsxFtbXzyCUNDW246MzMpKKChYVemuvYIhFgkY8vlZiAD/7JtFSV+OEw8rtK1xQggHW729Fhe\nZniY0VF1+3kcouMOh5Xw7lkhfdirV/nzn5ma2qwrmpnhzh3VGjOb08RuMxIJpqZ4+FCZaGzS\njYnJ3MoKY2NMTSk/6qeEPI8E1Xd2cvMmvb3MzuJwUFXFsWOEQthsW0p4hbqltMW22bDZKCpS\nP0os3ief0N/P5KSqOK6uqiDjvDwOHeL48R2r19psFBeTk4PZvFbdTEKrVVZ8TucON2HXw+nE\n4aCwkJkZBgbUpsjrpbAQm03NOTmd2O1kZal6djyO283Ro/zsZ1RXY7djMGw5/AFEIiwvr/1W\nq2VwkN5e2ttVcsbz7cHS2CGkiV0aGzE1RVcXV69y8yaTkykaCkYjfj8XLvDWW2qleF0hFZ3J\nSQYGCIWIRsnOJicHv5+5OdrauHePjg6lATIYKCri4EHq63e+LfUaI9ne3f4x65OgngmSaTY7\ny9xcCpWVjAEFg3R2kpdHSckzP//rjUSChQVmZ9U9fivzjnCYQOCZu5nxOK2tfPQR3d0MDqra\nktFIezs//oheT1ERCwssLGz+4vR6srI4cICKiidzfZ2OzExOnMBqpa+Phw/VWLfLhd9PWRnV\n1Wtexy8Op5O6Olpa8PuZnFw766TEaLFw5gxvv0129s683DYwGHA6KStTC5TFssE2T95nVhan\nTql3mJGh0tIcDuJxRfsk7Ccl1n8jsvsVFj4wwOioGktK4xUhTezS2IhAgJs3aWmhpye13Nho\nJDeX8+c5cQKP5/WsTq2usrzM3Bz9/fT08OCByueRXW9lJYEA165x5Qq3b6+J/cvLGRtT9wyb\nbcfk2K83DAaysnA40OtTVwgyMpSU5/luvfE4kQjh8AaT/SSS2Qajo8zPP8/zv/aQQIJtJkxl\nRvXOHebn8XpVf1N6kduIE1ZWmJ2ltZWPP2Z4WLG3eFwRIL2eqirKy3G5VJqCpC8AGg1ZWZSU\n0NREZeWTK23Sci0vp6iIgQF6exWxE5fEkhLlcrxTsFqxWKiv58QJ+voYHWVhgVhMme3l5nLh\nAqdP43Tuesq2mM48roGWOZJ4nIwMSkupqFhzSEm+pYUFCgooL6e9nZmZp5qTE3u8eJyxMUZH\nMZnSxO4VIk3s0tiIYJA7d3j4MLXRCWAwYLdTXEx+/murKkskCAa5d49/+Ac6OgiFlAeHzYbD\ngcdDJMLoKJOTa1xEo2FoiMuXMRqxWqmvx+9/1R9jP8BioaKCri4sFpaXNyi1pUrnclFcjN3+\nnBW7ZNKA3Le2GvGW0Is0NkGGRl0uMjJSH0DJKmhuZmQEqxWXi5ISjh7lwgVcru3cDUMh7t+n\nrY2pKVUOl+Of/C5GR4nHKSggO5vmZmZm0OlIJNDpqKzk9GnOn6e6+mk3ljIn4ffjdqtzzGjE\nZNqVfalGw7Fj+Hzcu0dnJ4ODRKOYzVRWUldHVRV+/y72YZ+IlRWmppicZHISvV61rbOzNyga\njUbVDQ8G14SJoDZX21sihEJMTLzp2utXjTSxS2Mj1o/rp4RIzWRj+rpiZYXWVi5f5to1BgfX\nbjZijmqxrLm3J4/S6ioLCywtce8eHg+5uTtD7GIxolGWl4lGMRiUycjrxKclhKqujro6ursZ\nH18jEAYDJhN1dZw7h9f7nBU7vV4dtIyMFOwt2SDLyUkXGFJAq8XtxutV8++x2ObBUhGhSl6I\nRoPVyuAgc3NEozQ2Ul+/paFacmhGUk3Xfy/CGxYWMBo5f14V22ZmiMWUjK+mhro6fD5CIR48\nYGGBcFhxlNxc3O4U2WXyRYv8blchH1b8Ha1W/H7GxlhZUd51xcU4na9s5ZSUSClbjo8TDCpv\nZ7+f4mJqa8nOVqVxvZ6yMpaXmZ4mN5eREcJholFMJjXPtJUiFp5fNZHGziFN7NLYCGldbePb\nLlTj9TaxW17m++/5+GNGRzd8WIlNlDDKx9WHUr3o68Nk4vjxnXkn0Sjz8wQCzM6qDpfsrV8b\nyA2voYHRUVZXmZ5WlQMZtRMN0F/+5fNrkjIyyMxUkVabKoI86li53VRVvQzZ076DVktODsXF\n5OXhdCrJfPKOLjdv0SmKjGxlRQ27dHTwn/4TtbVb3uOXlxkdZXx8S4ogJLK2ll/8AiAcZmZG\nlczFUEPc1D79lN5exsawWiks5MwZZbrxyq8Rg4GSEgoK1KeTM+3VxpwsL3P1Kp9/zr17BIPE\n42oUTLZP//k/09SExwOPvnezGbeb27f57jvGx5mfJyuLWIyODgKB1N+aRqMyaYzGl/zh0liP\nNLFLYyNkfQwEmJ7ezF2Sd8Hc3Oe8bmUvHgrx8CFjY0xOqsKMz4fXS17enqgCShhuIMDk5IbJ\nL4FU77aB3NjW+7E9K5KusK2tdHXR36/yHjIysNvxeqmtpbFxs1+UEG65xZrN+2Zhlem5wkLe\nfhu/nxMn1KCl0YjTSU4OR46Qm/v8yUViuHjyJHNzfPedEkEme0nShzp4kOpqdUtLYxP0evLz\n+cUvyMvjzh2Gh9VMVTyuwtmSq4RcGpGIUmUNDTE2hsuVuu0ofGIb6aQ8p8WCy4Vez8qK6ghL\n5XV4mM8+4+ZNWlsJBJifx2hkaoq5OSYnCYUoL9/hUIdngnBZg2EPSZCnpujvp7mZlhZGRlhc\nhEf6BKGbf/4zKytcuqRmjZNdWrH6kyXIZGJ8nOVlxe/Xl281GrWJkvGyV9hrTiNN7NLYDLEb\nGBvj4UNVoEpCBhi9XoqKnicbVO6m4TCjo9y4wd27dHSoca3GRo4cUWrrndUyPwfE8GVxkYWF\n5wka2pGEopUVxse5fJlvv+XOHbW3jscxmyko4Kc/paBA8WC5m4oFq3S14nGyslSMo4z77f22\nSG4uubkcPsz8POPjhMNYrXg85OXtgJG90cipUxgMTEwo5bgI76QsIedeeflrdStaf7t9ESQb\niz/7GYWFOJ3cv09Xl5o1np4mHE6x85HBo8lJRkbIyEh9YGWyVYZmUmofdTqlPRDyJ9JVIB5n\nYYHubv74R5qbVcFP/lws96amiMfR6V4lsduDmJigpYXWVnp7N/e+NRpGRvjsM2w2zp1bs02W\nLEGXi/p6eLSAd3TQ1cXMDNEoS0vK6E6+ILud/Hz8fny+V18xfbORJnZpbITPx6VLBAK0t7O0\nxPIyKytrM1ZOJ6dP8/bbuN3P8+SRCF9+yQ8/0NHB8DCBAKur6PVMTKjS1NGjNDa+4mBpkZhs\nE7a4DSTX6AU3rLEY3d3cuMGtWyorM5FQN79EgpERmpvJzubsWY4cIRRicJBvv6WnR9nEi9Sp\nsJCzZ6msVCE/+wIZGTidZGSwsqJu6jtye5AJjIYG/ut/pb+f0VE1Ievx4PVSXq4sW18nCN2X\nRtuLQ4oxJSX8xV9w6pSKh29r4/vvleFzSkjzdKvStdVKTQ09PXz/vUqDXf9yOp2qTIuL73rE\nYnR20tysckrWF41kS/bwIV98QVER5869+Ed/fTA1pcJdNkW08ShMYnpafV/x+JYrhkZDbi4f\nfIDXy3ffqbFfwGLB5+PwYc6d4+jR7Zyl03gp2CcrfhovDdI66e5mYEBJuxYX0WqVi0F+PqdO\ncfjw82iQRYp7/TqffsrYmDI4EExMKL8JvZ6aGiVGeVUQFmu1YrOptuDjD0gK/NcPiMl91Ofj\n4EFcrud/A7EYvb3cvs2DB0xMbPCRkoJTVxdGI14vBw/S38/163z8MW1tBIPKeMVopLhYmRqI\nvOzx4yk0cRN3N5tVLfaVrMvSutrxdrxWq8xsvV6mphgYYHGReJz8fKW1fz2MaSRoS5K+ROou\nRS+HA7sdh0O1BZ/jm5V2eU4OOTkA0Sj376PTce8eo6Op621yXWyVNA2YzZSUUFNDZSV9fUxN\nrbV0ZampreX0afLyNrxhafW2t6+lXW16/nicQICFBUZHnz+q5LXEwoLKDtlK0RgKsbiY4pAm\nIV+E08mJE9hsaq2T2TKxZT52jLfewu1OH/NXjjSxS2MjxBtdUmU6OujvJxBQwdWVlVRXK1Pi\n5yBe09OKL0r+9Po9+soKwSDNzRQWMjeH2fzqiZ3XS3GxirpavxRKP1r6m6I0kruXmG8ZDNTX\n8/Ofv1AbSIbXOjpSRPrIzTIYpK2NiQkiES5f5l//le5ugsE1ChiN0t/PP/4jkQglJanFi7JN\nHxxkeJjZWXQ6nE5KSigq2h/d22eFfHEeDzab+sokKWTvqKBeEPE4g4Pcvcs33zA4yOKimgNt\nauLYMY4d27HpEK0Wp3MtXXcrHmC1kpu7pWZDvosjR1ha4osv+PZbtcEA3G6qq3n7bX76082u\nGeJ+19LCzZvMzm5nXiNhYibT61aLfW48cVg1adu0PcQXsKaG3FwWF1laWvtHhwO3+/W5oPYz\n0sQujY2QzZbPR3Y2LhelpczOotWSmUlREQUFz19Om51laIhgkMXFzdbHkrg6M8PUFFNT2O2v\nsogiJsyHDhEIACqJXIb+VldVl1MqPcGgUmonEkoTlp/P2bMqify5sbrK/LwyZU1ZC4lEmJ5m\ndpbZWXp6FAVc3/OSwICBAXp6ePgQs3kDsRM+2t1Nezv9/YyMKGLncFBUpMxH/P6dtOPfC5AG\nX7Ik+ZpBLMeuXePbb7l+nZERIhFVaBc3kIwMamqUZvEFIXqD/Hzy8hgaYnp6w+Us9jEul3rA\nVvVXnU7J4E6fVp7nEsCl05GdTWkpTU2Ulm5+t2Njyjt9cvIJMlb5up97fyK2zGJgKXsAq1UF\nBu5TOBz4/Wpe+PFDJymRmZlq17oNZN/rdD5z0Mg2kPqu6GvFOkcksFYrOTkUFqrAxjSeGvv2\nNE1jVyGqmvJySkrW/F1FZvfcWFhgbEz1Ah7nK/L80SgTEyoh+xXCaOT0aTIzmZsjElGqFK2W\nREKJ206c4NAhWlu5eVONieXnU1/PqVMqAvxFKJGUHB6P/dj0ALmXz8woB9GUD5ubo7sbt3tD\n5UPqGV9/zf/+3+rPl5dVA9dmIz+f//bf8HiwWF4rYvd6Y3qajg4++4zLl5V+QE6SeJyWFhYW\n0OtZXcXt3gFqIhU7yeMSwaJUf3mkT83KoqxMeZhvfwo5HNhslJXxq1+p/rhMSxiN6n83obub\nzz9naCiF2VASUpoyGteGAJ4DsRiLiyokNxbD5aKwELt9HxO7nBzq6rh9G4NByTCSkMnx3Fw8\nHozGV3PVr64yOsr333P9Os3NLC9jNCre//77+HxpYvdM2LenaRq7it0Y15e5NqNRbRkfH6YT\nW3l5zKuFVCgrK/ngAxoamJhQmmKLhexsamrUTUtWQ4kMyswkL4+iotSCtmeCTkduLoWFBIMp\ngrA0Gmw28vIwm7ekdDw6npEIc/+fvTd9ajPN0rx/gBCSQGhBAiH2zezGG3h3ZqUzs+xauqMz\nqia6OiaqYubLzKeJ+W8mZmJiIma6J7qm3uiuqq6s3NPp9J5esDEGDGZfJTaB0A6I98O5UywW\nGAwG2eiKjIyqNEaPpOe573Ofcy3zGwnsLhdtbbS2MjSkOHaxKCcp8u7cITt71dQqMSGnfEmR\nj0lGxKnh3ZsjvxIjI9y9y8CAYg6s1ROIDv3BAxwOzpzZg4al3Cp2O1euqFiIoSEmJgAMBoqK\nqK3l+HGOHdtW3pcI4TMzMRqVlEomhnG/xOlpenqYn9/KRzP2hL5egSLnpcePVdyF3080quaM\nDQ0cPUph4cEcO8Umc3BQ+cjIDDQ3V5FGbLZXTFrtdhoaOHGC2Vn6+/H5Vj+fjAyKi/noI86e\nRac7gMdHorfv3+fGDXp7GRlRZtTz8wSDTE7y4Yf85Cdvd8d0f5H8mJL4kV0hmjIxppKFVfxH\n9opxpderlKG4IxJ5RdFFHjiZXaaxBQUUFBAI4PWqoYzNhsmkalPAZOLIkb1/dRlRHTnCwABz\nc+uaE6LPsFiorsZqVa3Ezb4dGblu8KwBxsf59lva25mfXxcDKg0e4MEDMjOpqNhxYReTksRi\nPd8Q5FLlqxGuYWoqNhs2G3q9Msg4VOXd6KhqHr9c6C8vMzfH06fU1GxlPL5T5ORw+TIlJeTn\nK+siwGLh6FGamzl7dmciGHn2X4n5ecbGVNjrZr/H6eT996mo2MGrC+R3ejx0dvLnP/Mv/7Lu\nj6JRLl8mFFIOi/t/fhCDwNZW7t3j6VM8HlJSVA0djaLTkZm5Vd2Tk4Nez6lTirk7NaWuXw6x\ndXX8/OccO7bfh+rYZ37zJteu8eDBusOqpJM9foxez4kTyhwniW0g+TElAaDk7s+fMzLC1BQZ\nGZhM1NZSXKxKmd3DZqOqCquV9HRltxaDKDbsdjUOSASb4hjEn0lixDIyXs1B2T1EgREI8OKF\nEjlK+SX6DK2Wmho++YTqajIzlV9r3NG2LNlFRRvZMF4vvb1MTcVRwEWjKhJgZEQFbOwIi4tK\nYCuzsDe3883OMjLC99/z7BmBgNoJMjPJyeGDDzh27N3Rum4TweBW3iIx+fPeBsakpuJwcP48\ntbXMzQFotVit2O3odG/kMZHe3ma7e2oqGRmUlCjt104hERo9Pfzf/8vjx+vuf/ncnj0jGsVq\nxeEgM3O/VQKtreo8Jg+vXJ60Y0dGaGnh8mWczq1+g1bLyZPY7Zw5g9uNx4NGg8GgRg3CDN7/\nOWw0yvw8bW08f04gsG5fiEaV7/roKE+e0NiYWFtDAiNZ2B16SId/aIjubh4+5MULxsfR6cjJ\nwe1eNeXfwiN+m8jOpqiI6mrVbBe+v9hziICutpbq6j2rI/cKu2QWbh9LSywuEgwSDJKSQm4u\nR46wtMTMjHKvEAa3ycTZs1y4gNVKMKg8OyYnFdVJIB1HkwmHg9JSLJZ1LxQOMzXFwkKcOGDZ\n2+bnFSN+O5BG7/y8spkIBADS08nKwmJRrJ093ALFW3hwkNu3+ctfePRotaOp0WAyKTbkyZOq\nsjw8fbut3+yefw7ycntLon8lRO3YWQIAACAASURBVAEwM8Ps7MYiNUYUq6igrm7jPb8dSA94\naIjbt3G54tz/o6OEQrz/Pk1N+6qnluKmo4PPPlP6sxgBd36e8XFl6VdfT07OVgcqjYayMgoK\nqK9Xwi8plPPzMZsPrMm9tMTCgpovv9xRlu1JjE6Li/cmgPsQIFnYHXosLTE3x7/+K19/zdSU\nMq4T4kt7O/X1/PrXnDhBUdFuCzvZd69exWrl88/p6MDlIholPZ2SEk6c4OpVjh9/N0WL20Eo\nxMyMSkYXa/iGBurqiESYmWF+nuxscnNVJrqkuqWlcfo08/Ncu6asYmW5F/eyI0eoq6O8fOPW\nGxuyx0VMJbPNVV6sah484K9/ZWwMjwdQ/deWFn7+c/LzMZl288Gsg2T13rvH//k/jI6uSz5I\nSSES4YsvWFhQ2aaHx3nBbKaoiMlJlQG6AenpKubhbVfDFBVx+jSTk4yPb1QXSdrhqVPU1r7m\nyTASYXIyTpTiWkSjuN2MjGCx7F/3KBDA7WZoSCn0175x4dFOTNDXx+goBQXYbK/g+Go0ZGej\n15Obq+iSuz+0vzZWVvD71Ww9LS1+YSciMLdb0QqT2AaShd1bgnAYn4/RUXVqXFwkLY3sbOx2\n1bN57ebE5CRdXbS2qumD0OyAlBSmpggEsNuVH8EuG1cyKKmqUhVJbS3T06qwE0pZczNO5x5E\nSL11CIeZmaG3l/Z2+vsZHWVxEZ0OpxObDbOZI0cwm8nMxGKhqEhNTISW1NDA8jLp6bx4oWzl\nAauVwkIaG2luxmLZyF6SCMjpaWUrsCEOWKtVjOxXcp6EeDQzw61bXL/O99+rRiA/VhI+Hykp\nXLjAyZNqPrt7CJlanFxCoY07gaSI9vYyMaEisA5JtFFhIc3NjI8zNkZKyup0PtZXO3GCmpq3\nnqLkdNLczMgIXi8jI/j9yoQoNZX8fOrquHCBhgbS019nDZEiaeuB9coKwaAS8L45CDvW61XO\nUB4PbreyFIhENhpFLS8TDKoMt7k5rNZX3PNCk4hFtB04RECz2fcl68Ye5tAcDrzlz/nhgc/H\nyAiffcbnn+N24/erIun0aT74QJ3SXq8eGhjg5k2GhhRXKTaeEwsDl0tlCF64sAcn1JQUsrOp\nraW8XJH65bkVi/xDK3ry+ejs5Ouv+eMfFVNK9iqdjpISGhr4m7/hvffUZ7W28NVoqKxUQ9vn\nz2lvx+9nZYUjR6iupq4Omy0O1cxq5ehRJicZGtpIdpTwifJyReB7JZaWGB3lD3/g7l01IZL7\nZ3kZt5t79+jvR6OhqWnPvlmfj8FB1ReMC+GTud3MzirXrsOAigo0Gp48ob1d/ZfYw6XRkJfH\nRx9x5sxb7xmRn4/RqLy4AwFF6IxG0WioquLCBS5fpqLi9bkc28nYXRs284YgVNfhYVXCer2q\nZbXFVQlnQ7RWbxFEzG40KjOplyFVnfgmHtphzs5xKPfRtwtLSwQCtLfz6acqZkrObRqNGksJ\nC6G+noKC1b81M4PLRW8v4+OEQmg0ZGVRVUVVlerDr/3J3t5Vhv4GyHhC0nuEvbQbSP9Azouy\nPh4qFtTLkKbX1BTXrnHzpvqyYsaBgYDqvtTXMzsbRy8c49KVlpKVRWGhOtDb7dhsisP+MvLy\nuHhRHfHHx5UBtVyJzUZJCefOcebMq+en0q4bG2NqCq93XS9BGgmBANPTuFxMTJCTg9G4+w9M\nPQ5rY9Y2INbD2HOtQCIjM5PCQn75S+x21bCcnUWnIzubykqOHqWlBadzqzJXnsdwGK9XtUKz\nsjAaE+u4JVl/EmnY2MjMDOGwOo2UlFBRsa1O82ZITycnR7HNNoOIr/Pz3xQPOBIhFOLhQ374\nAZdLGR6Fw6rO28LbUpjKu7HuOyhI5s2xY8zP09ur3JcEUtXpdBQV0dR0wOambxUS5olNYjMI\nh+nxY37/eyYnVym9y8uMjzM1pQ6vJpOSREkbf3SU1la++YYnT1RIV24uH35ISgrl5cqFUpYA\nr5fxcRWf8DLECFfESnu7TR7yki4GsR64dUvZcsYQG3QuLNDTw9CQ2tVehmxIOTlUV2/rFe12\ncnKYmmJigvR05RwmL1deztGjnD/PyZOvLuLl8iYmNmrZYhAnZI+H8XH0+r0p7OTXioRiCwNn\n+YE9x9p04NgNLNq9paVVDoOQFKUe2p+bXOSoP/sZDQ3cuEFXFyMjGI0qCqWpadMqXyCLRiCA\nx8PoqPI+lIm82UxWVgLFkKSnU13NkSPKom9+HoNBhRzusjsrKWc2G0YjHg+hkPruYrdZerqS\nGjidb0pzLc3mb77hf/0vIhFFjJEDsFj8vHxXS19WjKKk9fUWIZY/e/o0Pp+SbQlxNmaZbjZT\nUUF9/Vvfb95HJAu7hIfM6V68IBjc+FSLIHFwkLQ0fvIT9fzPzTE0xNdf8/XXDA+rym9hQbFu\n+/r41a84dw6TST0nen0cGlYMQrSPGdolsbeIRpmbY3paWQS/nKcuO+7sLEND5OWRl7c3r5ua\nSlMTJpPqt/n9pKZiMJCfT0EB5eXbkshJuJlM8Lco+oUeulcOahLpZjZvmhMqgznJfNvzO1b0\nidJEMRrR60lNxe3mxQsVzgYqEL2iQtFJ9w0iCxUXt2PH8PtJT1dmFhbLK7puYhj29dfqHChd\nE/E8amnh+HElV08oyPuVWfOerE7yq44c4e//nmvXuHNn9RGQ+6q+nnPnqK19g4Zqg4N8+in3\n76tHZsNxOjbiiG0Esj6bTOTmUlDwRu75fYDJxOnT6PXk5NDeTk+PSp5wOKir49QpmpuTHLsd\nIVnYJTyCQQYGGBkhHN5Y2MnUSSJN5+ZYXiYtjakpHjzg1i1u315VQoCKHx0aUp6i1dWqmLNY\nKCmhtzdOVSH0/JwcLJZdBS8msRmkNpKSfbOPd2WFQIC5ua3mjzuCvJAk/0rFH8uKzc6On3L7\ncptKkJq6upHEjbWVrWgPnRQMBpxOnE4cDqanN3rVpqWRk0NBAQ7HnolA5V2IZlky3Px+QiHV\nytJqGRmhtZWuLvUQmUzU1CjbWJG/7A9kj7dYXsfpQ/yNP/uMe/dWR+rRKFqtIrQJDeCNehPu\nCHIfyqhuD3+nRkNRER9/rFhu4bCakAgJ+Px5PviA8vI3ooeVz3xiguvX6e5W7kUbnimhI6em\nrhZ84n9UWUljI04nWVmJ8gXtCHo9FRXo9WRnq+NrOKxEdceOcf48FsshUrjvBZKFXcJjaYnZ\nWRUSsHVfJBRCr2dsjC+/pLNTWWZs+FV+P21t5OUpPwigqIiWFjo76emJ4yBgNtPURFVV8rl6\nIxCSXEZGnKo69gPiR6DX732TQJjL0phJeVUWsNwba31SUlOxWsnLU+mTL0P6HEYjBQV7thdm\nZGC10tjI++/zww/09q7OZCXzvrmZDz6goGAvw5GWl5mY4PPPaW2lr09x9mNBKYEAo6N4PHi9\nAFotw8P09tLdzZUrfPjh3lzDG8XDh/zP/0l/P17vRvnUw4esrFBerjT473zXJDubI0fIyqKl\nhdFRpRw3m3E6KSqisDD+yWev4PczMoLHE59IsLKiInRlL0hJoaiImho+/JAzZ8jPfyuruhhy\ncjh+nIoKfvpTxefW6TAaD5dv0R4hWdglPGS70unQaDZq3QWy98eyv7xeXrxgaiq+/ay4eA8M\nrHoC2e3U19PcjNdLf7+qIIG0NPLzqanh0iUaG5OP1htBSgoGAyYTBgNabZzvV9oSJtNe1kZr\nf7kUc5sRhubnlfTB7VaKV+ngFhZit2OxqA2vpAS3e/VnBMKJzs+nqOgVBK8dQW716mo+/hiz\nmZISPB7VVpGB1IULnDq1N2n3gqUl+vt59IibN3nyhOFhRQCSLBCjUfFQ1/oEeb0q7qysjJYW\n1etKTExPMzTEw4e0tSm3o7WIRpW5TF+fykh95ws7kXbp9TiduFzMz6vDSV6eUifsLXy+VVuT\ntDRGR/F6N00QEVeBwkLKyjAaSUlRDuSnTlFZqc6HbyPksnU6ZYyfxK6RLOwSHhkZyqkuI4Nw\nOA5XSeYRer2SREUizM9vmgcVI3XF5npCtvv4Y/R6/vxnXrxQ4QF6PdXVvPceV68qvUUSe46U\nFIxGrFbMZgwGAoH4J3W7XcmZ9xkuF99/z3ffceeOahJkZHD0KFeucPYsVismkxqXeDwsLKxK\ncOSedDo5c4aaGiyWPab+iP6xvp6BAXp6WFggNZWKCo4cUUyjPTyHRCI8eMAXX9DaytiYEttK\nhzUlRe3Ba5Uc0ugSPePAAJOT5OYm7uMzNMQf/8jDh5sGsIqF7OAgVVVvJBk5MSGiIoNhVaL+\nhrgo09P09qp6zmDgxQtFt42L1FRycjh5kt/9jspKYDVm8BDafyaxOZKFXcIjM5O6OuU2J5tK\nbBeRjkt1Nc3NFBSovVOkW1pt/Eio1FSystaRf9PS0OmorFRk1akpgkElsyouprycgoK3UkUP\nqsaVgkOrxWDAalXC0sR5O6mp5OVx9So6Hbdu4fGoSDHp1ZWWcvIkzc1YrfsqCvP7GR7m7l2+\n/ppnz5iYUDucbG8i0NPrcTiw2Xj/faxWKisZHsblUm3IwkJqa5Uv7p63eSSxNz+fcJiBASXO\nHRzk6VOKiigvp66O3Ny9SSxdXqavj/Z2Fdwkj17s35sZ1caS1lwusrIOoCh/JcQ4ZmiIH35Q\nbci4EM1vKBR/XPCuIvYAvgnIxzg4qOyr+vqYnSUYRKdbzVd42ddNRjdlZdTVkZ+f7GwlsQWS\nhV3CIzOTmhoGBnA6lR25aNaEnpWZyfHjXL1KUZH6eZ2O3FxF8X4ZaWnYbDgc66qEtDQKCigo\n4OxZFhcJh9VB8I2ubm8OMUeuuTmGh5mYYHKSrCysVsrLcThW+WoHXt7JBdhsXL2KXs/MDMPD\nKk9d+NrHj/PrX9PYuN+VwcICbW3cvMn33zM3t85kR6q3rCwVa1ZWxvnzlJdTW0tHBz09pKRg\nsdDYSE2NOjC8CcipRuae/f08eaJs/8rKOHlSJRQ5HLt11ZHSbXSUvr5Vi8HtX2EgwMzM6rO5\nbxAxtXiAxwiIGo0qiOWJlizBkRE6OpiaevXvPPCH5Z2BJDL39PDP/0xnJyMjisMg5x85Ksh3\nJJoq+QaFC1tZuV3z8CQOMZKFXcJD7Hyamviv/5XWVtrbVbaMxBLU19PSwokTqwe4ggKuXCEt\nLY7ncMze8/TpraK7V1ZUXSjmkGbzW2YgJDLSa9e4f5+hIWVJJe8lL4+6Oq5coaQkgaZjIlI5\nfZqcHKanVwXOQq2rqHgdneMusbDA48d0dMRJTxJD7N5ebt2iqIiyMgCTiepqcnM5cUKpCiSc\n9M1524bD3LzJjRs8fEh/P3NziqUwMkIkwuwsly/z619jNu/2GqS221q6tBn0eszmA7jT5ucZ\nHaWzk+fPVQM+J4cjRzh+HItFlQXiPe7xvOLwJv3XkhIcjnefYLc/8PsZGKC9nd5epqaU3UHM\nIlEYnEJmiFkgRaMUFioydF1dsrBLYmskC7uEh3QmJPfd4SA3VxEyMjOpqODkScrKlDWxwOHg\n4kXm5picZHoar5flZbVMSK7AiRPxl4bFRXw+FVkh5UV6OllZ5OYqCXpGRgJ50MeFbL2yq924\nwVdfMT6uGjlAairZ2QwOkp9PRgYFBYnydtLSMBioqKCiAp9PWViJ+EAI0fvfLAkE6O9neDhO\nm0oKnYkJOjpWo70MBmWDtz8QN93WVr79lsFBvN5V7unMDF4vExNkZXHxIlrtHjQ7pde1Iyu+\ntDT10nsoHNkOFhfx++nrU+kFjx4pk0KHg2PHCIWor6eyEo1GfYZiw7tFUqesAKWlycJuzxAI\n0NtLTw/j46tiNVh3gtJoyM9Xx2+p88RDp6FhXcJQAkL8GXw+gkGWl5U2y2hUVizJvu++IDE2\ntiReibQ09HoaGiguVoE/Ug1syAcDzGbq64lEsFi4fZuuLnw+JWY8d45Ll2hqis8u9/no6ODO\nHa5dY2FBZU2KocOZM3zyCfn5iUgV2oCVFbq7uXGDJ08YG1NyM4EYe/b38+WXrKzw858nSmG3\nFjqdcg+JjRoPBGLrHwpt2qaS7Km4PM59QGzOPjSEz7duR5QpZDjM9DTPnytzrN0gNRWLBbsd\nl2tVS74WG/IJ+JG3ajaTl6coqvsGv5+uLr7/nr/8hdFRZmcVbcPjYWKC1lb+/u+VwFMWEPFY\n3gzi61ZXR2XlofA62R+EQgwPMzqqSp+40Om4fJkTJ4hEMBgUf+atoNZ5PIyM8PQpAwMqEkkC\nwRoalJV0Em8eibexJREXcmizWl+dl5eRQUYGtbUYDFgs1NcTCKgc5aYmjh3Dat3YQpDc9LEx\nxam6d49wWI0AZPUXn/3z5zl+/M29xV1B/GPHxxkeprWVR4/o7VXl6dodNxRiepq2NsrK9iwL\nYW+xtZncviFmsrOhbBJIxW80HpgJTjCofOPi5l6IcEGy8srLd/VC4vpbX8/QELdv43Ktqpdi\nrLWMDHQ6/H58PgCtFrOZsjIaG6mvJytrX1mqXi/373P7Nh0d6767UAivF5eL2lqamjhyBJNJ\nybG12vjbrQRMHz/OhQs4nckIdtWrFldRv1/ZOooj9I4SdaNRQiFFld7s4KTRUFXF+fNKpWSx\nkJX1RoyR9xALC8zM0NbGo0cq1C4UIjubggImJpiYoLGRoqI969vFPrpkF/AlJMAWksSbQG4u\nViv19atWliK2kJTYDVhZYW6Onh6++orW1o3GB6EQjx8zPY1Ol7iFnd9PRwfffsunn6qMrEgk\nTukWjeLzMTjI+PimZ+UkAK1W3UIez8YPSihZFgtFRQfG9ZHCTvrWLy/rIuQU4YJ49+wGGRmc\nPw8wMKBeVLKexGMoOxuLhZwcxsYYGgIwGqmo4P33+bu/o7h4v7VHHg83b6pMqg2NTOFvCTnS\nZMJuV9QO8ch8GVotFguXLnHlyp7l/L7VEB/Q/n6eP2d4mOVlTCYaGjh6lOzsHRR24jmwdYxH\nSgo5OZSXr/MoTXDMzPDwIX/9K198gd+vJOTp6XR00NpKaSn/5b/gdO6ZcUyMj5jES0gWdu8o\nZDnYpuhheZnxcfr6mJ6Ok0i7vMzCggoVlanu9vs08ux5vUxOqmJLMnBsNtU13P0TvryMx0NX\nF199xZ07DA9vNeDgR0na63HhDw/MZi5cwOfD5VLDzZi4UhTT4ne/lty5n5AehhDFNrPs1uux\nWvegyZGait3OiRP87neqD7GwQCikUjfKysjJISuL+XklZ87IwG6nvJzSUjIz928/lmdNhCNr\nAyTW/kAsd1ic/7Raqqv5zW+4c4dHj/D5lP9lRoZSWZ09S1MT2dmHffuUsvjRI+7do7eXkRGm\np4lGMRhoa+P2bS5epLFxuzozvZ7ycoqK0OmIRDYuVmJIXliofttbkeUoz+DICJ99xqNHzMys\nLrASryzvsauLsjKKi18/vSMcxuOhv5+ODubmCASUSXhhIRUVKifzkN+rQLKwSwJgeRmXi+Hh\nTQ1yxRDO68XnUzYcr4Q81ULlHhmhq0sle9rtFBZSXY3NpoY7u1y2olFcLp4949tvVZDa1ojF\nLbz8ujFzslh7Qybgh5DzK4Xd/DyPHpGaqmidKytoteh0GAw0NfHBB1tpq98oMjOx2TAY4ncy\npGQxGsnP34NWk2huamooLqa/n8eP1QmnoIDycsVYTZCGihxawuFNPedWVvD5mJpSmgmZ95nN\n6PXMzzM5qVLRjEbsdj78kE8+ITf3YLgBcvqKncFiybBrHVv2DcvLhELcu8d//+/MzDA/rz5h\njUbVYUtLGI2rAdxbw2CgslKp3cNh1VSOUV80GhwOqqvV0eVtQTTK2BjffIPLtY56K/dkIMDs\nLD09VFSog9BOIQuy309/P9eu8a//qnL8NBry8mhp4fJlbDZycpKcAZKFXRIKsXPVZomlYksb\ny03aDoJBBgf59FM6O1fHo8L8Kyzk9Gk14tklnVYSn549WzW82AJyvBPX5c1ed36e2VnFoTGZ\nMJnIzj50nN/0dKxWzp0jI4OnT3n+nPl5lpfVeKi+nhMnDsbIQ6DRYDRSU8PRo/T2qgmpQIQL\n0vOoqcFu35tXlOTKoiIMBhW9lZmJ0YjRmEAdAqnVdDoyMuILXyTpJC9vdfPT6bDb+fhjqqpY\nWFDZ83o9RiOlpWpQeyCYmFC+0ENDhEJqLhxjCb/RwNaXMTXF8+fqdCp6LDkALy2RmorPx40b\naDTk5GzrqJORgdPJ8eP87Gfcu0d7+6prnSyPFy/ys59RUfFm39QeQlLIZc3c7AdCISYnmZjY\nNDBta6yssLBATw9/+AP37jE8rJyYVlaYneXJE9WuPneOkyd381beDSQLuyQAVCdms7Z/LI52\nm3MBOaWNjSnKRUeHCkOLqd8tFkIhqqspKXm1HGRriPXG4OBqnlVcyIlftrGmJqqq1p2GpUs3\nP4/bzegoExMsLBCNkpOjhI12u7rORGjM7AOEOF9djdNJcTFFRczOsrREbi41NaqqO8CTscQ9\nNTQwO0t6OoODigsohYvY+oiEfK903NIFzMlJXFminL50OvLzsduZmNjIN5BHwG6nsnL1Y5Ee\nWEMD9fWEw6rXIg/pQd3qYpbR0cHt29y7R3c3fj96PTYbbjfhME1NlJSo1t3+wOOhrY2BAebn\n1wUiy3Q7GKSjA5OJX/5SWQpv/dGJ5KK6mkBAad2Wl9VfzM7G4eDSJS5eTHSpxFq8kt8iP7Cw\noKKHRfOk0SjXhcxM1fvcAtGoKq9v3aK9fXUyI0nNg4OqdhTWBIdmod4EycIuCZRHv1Af0tLi\nqyDT0lR4wzanA+Ewt27x+eeq5JJnXtZB+aeri2++4b33aGnZ1cWLHXFc8eZayCJisVBZyccf\n09KybmgSjRIO8/Qp/9//x+Ag09OqAyRuUlVV/PSnfPDBoVss0tMxmTh+nMpKFhfVKDYzc2dU\n8TcErZaWFux2CgqUcbcYFkpD8cIFJQw/bF+ZycSZMywsqH/W9rCFRFFezvnz5OXF+bsSkMpB\nb4pTU7S389VXfP31qhOn34/Xy8ICXV38x/+IybSvxuleL319TE6uq+pikCXI61WMhW1SkHNz\nOXeOykp+9jMVKSZ21nY7ublqKX5bIOYJOh3RaPzTtewgGg2zs3z6KaEQc3OYzTidnD1LXR0m\n0yva/zKZEWrdhqVeqsbJSUIhLl0iGk2gJvoB4aBX5yQSAWlp5OVRVUV5OR6PWr9igqOUFBwO\n6uspL99qJY1xt4NBhobo7ua773j8mNnZdYwfiUiPRhkfp7WVqqrdXnxKihJkiOD35WVFqDmV\nlRw5gsNBXR1NTRvdVn0+uru5c4dbt1S7Lna1IyO4XNjtlJSQm4vJtNsLfosgPdrEbFNJ80nY\n5bm5VFYq4n9hIeXlNDZisynv/kOF7GxOniQcJhymv5/xcWVIaTDgcFBWRnMz5eVxqIey9SYC\n3G5u3ODBA/r61hVS4TDBIF4vbW0UF9PQsH+FnRjobAjyWYuXs0nkf0cieDzMzTE3p0LDrFYs\nFuU/KhqgsjLm5wmHlWYloYb724T0s7OzcTrVW5btYO0PAFNTiicnNXp2Nrm5Kui5pQWnU/mx\nx4V07MbG4sj7gJUVgkGWlvB6iURWjyiHFYf6zSehIMb01dU0NTE3h89HIKDGpvJAVlfzD//w\naq8TMROZmODTT/nznxkdXSePikEOWLOzdHdvK6Rya6SlkZ9PSQmdnczOxnnm09OVCd+vf60C\nPGJZsTHMzvLtt1y/zsiI6i/GLnVmhmCQR49wODh79nAVdokPo5HGRmprV/na0hhIT0+UMmWf\nYTRy/DiZmWRm8v33hMP4/aolf/Ysv/gFtbXk5CR0vTs+zjff0N8fh4wlaRnPnimGwL5F7UkG\nzxZ1pIy5Xzah9Pt58YKODjo6WF5Gp6Opifp6qqpUg0o8e2O/OUFUOK8BOQE2NBCJxDEQFWlI\nXx/9/cqISsrc/n7a23n6VNFVt3hso1G8XjyeTYVBMgEXuV5WVrKwS+LQQzjXublcvkx+Pk1N\nTE6uRorl5dHQwMmT5OXFWXSWlggGefaMFy+YmFDunR0d9PevS314GTHx6S4hyr6pKdraVMSt\nnPJlqU1JobSUM2eoqMDrpbcXlwuTSZHnYpTBQIAXL+jvj2N4K7zgwUHa2qiu3u3VJrFXkC9O\nrJLfrizjNwrxSCss5OxZHA7OnCESUdTDkhJqa7FaE7fkledOzMb9/vg/sLTE1NTrc/BfDxYL\njY309dHWpvhwMYiFtdNJaem60f/iIm1ttLXx9Cn9/YyNEY2Snq6UXidP0tDAkSPKze6ta9Ft\ngLzr4mJ++UvMZtLT1aleyBsGg3Ih8PsJhVYleqLGi0QYHOTOHfR6zp3b9OZMTVVzar1+K75Q\nRkacc/vhw2F//0msQtRY9fUMDysL34wMbDZqa3E4NjXQDwZxu7l+na++oqND9cy2U64J/31H\n0QVr7UjkVeSUVlZGKMStW8zOKiu+cFidntPTqajgvfdYXKSnR9l3FRaqJdVoVDVBKMToKC5X\nnOOgvJbLpZShSSSxD1i784kUfa3lh7AO4j6PssVKRE1T075e855ASjchq8WFjAVExLBvsFo5\nepS2NrKzVXUSg5x+KyqoqVFxqPyYyHf3rvIEmJ5eLUOfPaOtjfFxIhElAXlLW3Qvo7CQ3Fwy\nMohEVokxmZnk5BCJqEHQhq9Vvu6ZGX74gbw8Tp3aVIWdmkpeHsXFGI1oNHF2GZnMZGUdmGt6\nIiFZ2CWxHpmZFBUp0ZbISM3mrSRyT59y7Rq3b9Pbq3Spr6zq5Iybm0tT0+s43Eop6fHg82Ew\nKA/9wkJ++1tOnaKri4kJZmbIylJ/FAzyxz/i96uCDzCZ1An78mUuXVIb5AZSyAasNelNIon9\nweAgnZ10dDA8rDZLk4ljxzh58p2le0pAjtHI/Hz8ZF6NBqsVu31frXYyMyku5tw55uf54Qf6\n+lTwdDRKbi7l5fz0p7z3uIDbcgAAIABJREFUHjab+nmvl5ERurt58WJjDbq4yMwMjx6Rn8+l\nS2g0747vmjDtjh7FauXqVTWQTU/HYOCvf+WPf4xfi6+sEA7jdjMzs5WtgUZDZSUzM9y+zcQE\nsMq/lImTqILq69/Me3vLkCzsDiWkTFlaYnFRZQlI8LzfvyqmW1lRLt6b9QakEurp4csvGRhg\ncnK7pU8sTPPUKQoKdnDNS0tMTDA6ql5ufh6jkZwcSkspKuL4cUpKKC1ldJSpKbKzlUqrtZUf\nfmBhQalx+VHsabdjMlFbq2zqJBBdAhw3QIJTs7MPLBo1icOGmErgu++4f58XLwiFlE+Q9JVP\nnVIZMAk7V30NyOkxO5uKCsJhRkc37vRSOhQWUla2r24gOh06HUePqtliTs4qYaOoiJoaLlyg\nsXG11vR6GR1lZET5s6x9F9Eofj9DQwwNMTWF0fhOFXapqRQXU1wMP+4yMiHt6dk4wl4L8X+W\nCLLNIDzRmhqOH2dpCZeLhQWVPKHTYbXS3KzsGJNIFnaHFJISNjfH7Cw6HUYjVitLS3R20tbG\n48fKYqCggOpqLlygpASjcWPTTp5bt5vubuU2sp2qLjWVrCyqqjh1iosXyc/f7jUvLTE3x5//\nzPXrTE7i87G4qM67+fkcO8avf01hIc3NNDayuEhaGsPD/PM/q6pOVo3YMNfrVe/3wQOampT3\nbH4+gcDGhVjI+E4n9fUHFrSQxGGD282DB3zzDdevq8gWOdiEw3z3HYODRKNkZ2OzvVOFnaCg\ngCtXANxuZX4pEHWCwUBjI83Ne+ZQuH0UFmIyUVenmMSAXo/JhMWCzbYuhtvrZWyMQGDTJVHS\nXMbHVVjCOwkp6WTXyMjAaCQSiZ8MlJ6O2fzq5DqNhuJifvc7jh/nhx8YGGB8nMxMnE5OnKCp\niaNHk0u0IFnYHSYIoWF4mOFhRkaYmcHrVeTWnByWl+nupquLjg5VqDkcDA8zP09LC83NZGau\n20XkmOXzsbCg7FG2hgxZKiqoreXYMU6fprBwB6fV8XE6Orh7VxVqQoaTtWN0lGCQykq0WkVb\nkRpuelqdm18+NEtnbniYjg6Ki7HbaW5mfn41LygGWbtramhu3rMYgySS2BouFzdu0NrK0NCq\ny4bUduPj+P08fUpJiTIPS1jIgiPDASG2byedLzeXs2cJBIhEVCrr4iLp6YolcuQIJ06ooNV9\nRmYmBgO5uYoxJnbQWm2cRn7sDLmZ37uoRPdEPZawWPtdiyavvV2JJ+L+mNvN7dsUFJCTg8Wy\nkSonPyZ5M2KlOT7O9LRyrq6tpajowOLvEg/JT+GQIRLh3j2++YbWVmZmSE1V5VFmJqmpyl4o\nlhgrTJEnTxgfp6ICrXZdYSdyUQnV2Q7/VyyOf/ITrlyhsVHRbLeP58/5y1+Up8naZDMhu/T1\n8f33ZGSQn6/W2eVllRgtGRIbEI0SieB209PD+fPU1PDRRywu8uyZmpXI0pySgtms5rwXLyb0\nJprEuwS3m5s3GRqK0+GQ5M2uLjWR3GVwyxuF0KeCQYJBZQ8eN6N5A6xWsrPRasnN5bvvePoU\nr5esLAoK+OADLl9WgSIHpTkQIbYsMpvVqdKgkh7eZtcpZP9DIuguL+eDD5ifx+VSFW0M0qWT\ns4rLxdGjSjIcVwMh3YGSEpxORa2R+a9wEt697vXrIlnYvetYqwlwuejv5/59WlsZHlZO/fIz\nIs4SE85YzSTD1sVFZfaxskJZ2epvFjnYFkFkMQiNz+mkqko9tLm5O2aWuFx0dDA1tTGvVlyR\n5+Z4/pzq6nUFX+zdbYbYsECjwWKhuZn/9J8YHsbtVmMUCZIvKeHkSTIz3x39WhIJC3lgw2Hm\n5lQ8/MtYXGRoiLY2KitJScHp3FbBtG8QD4uODp4+ZXpapQxLy622lvp68vK2Un7IDl1URHo6\ndjsXL6qsWJOJigrKytQp9EAQ+5C3riHMZoqLMZnify9paSpkQtwGDgPEf8frRafj+fNVmbAU\nvouLim8wOsrkJP39DA5y/DjV1RtHq/JhSg5eEpsjWdi9oxDFlnAaYqOc7m4ePKC1lZ4eIpF1\nx6a41AeZpABuN21t2O0bC7uYwlymLRsQc2fIyECno7qa06c5epTS0td5Rx6Pymp8WdwgDgjD\nw0xMbGTkiAX5Zvw/nU5JImSpbWykvp6hIfVC0agKii0uTq4jSewfJHkvEtk0JW95mbEx9Hp1\nZ2ZkYDKpdvK+lXfyQMnZT0aK0jtJSyMUYnqamzf5wx8YGcHjIS1N/enVq/zd33HypLJP2uJq\nJe/kLRU5ms0qqCY7Wy2/scVWaME2Gw4HeXmHpbDLy8NsZnlZFeW9vczNre5NUtjJHSUnlokJ\nfD6lb+OgM+7eQiQLu3cUwSCzs9y5Q3u7CtEDFhZUeRQ38TAuZI/x+Rgfx+uN8wMNDXzyCTdv\n8vz5qu5JiioZvlRVUVJCQQE1NVRXU1Ly+m9qa4LOhj+VGKWyMkWz21DIyhXKjDVGXo6lpIs8\nFtDrMRiSHf4k9hViMmwy4fXGb9pJgNLICF9+yYsX3LzJ5cv85Cf7uv/JyjAzw8iIikk1mbDZ\nKCykr4+//pU7dxgaUgRciTUDHj0iHCYtjezsV8eDvr2QN3jlCno9331Hf786KMryUlXFhx9y\n/vxBth73HxoNFRUYjTQ0MDLCyAi3b3Pv3kYnKQkX6ezEZOLiRZUzlsQOkSzs3jnIKGdsjPZ2\nvviCu3cZH1fbg1aLRhOHvvrKXyhyvLiWoVVVXLmi3O09HuUsKomHNhtFRTQ2qtrO4cBiYXaW\nzk6lURWFrITJyGR2i51JchUDgThaMyFemEyrBqHyb6ORpiYVRzE3RzC4+vvl8qqrqa9fDSaS\n0jA7+wAEd0kkIZBb1GSiqopwmOHh+GcwaXJ4vQwN8ewZFgtHj2I07gcNNHbYGxmhv5+eHmZn\n8fux2Sgo4MgROjv58kv6+lQ26NqnVXrhx49TVaWUB7uHjAtkgYqNCDYzadofiH2S8DdWVnA4\nmJ5WIY35+TQ08NOfqk/g8PSiJJE8L4/GRkZHefaMoaFVt/kYRNk2McHgIFNT+P3vmqfPviBZ\n2L1zkAPQ3bv80z8xMIDbTSiknpxweCNB7ZUQFlpWFg5HnOBwwOHAYCAzk6NHaW/H5cLvx+mk\nvJyjRykvx2QiMxOdjlCI4WG++47Hj3G7WVwkI4P6eo4f58QJSktfscbl5VFXRzgcJ4hQo8Fk\nUu3AtaqonBw+/hitFo+H/n7cbrVARKMUF3PhAmfPUlGRlEQkkXAoKuIXvyAlhYmJdbTXtVhZ\nYXFRaYN6enjyhLq6HRhDvjZWVggE6O7mn/6JZ8/UkSkSwWAgO5u8PObmlDFv3LD2SIShIXp7\nycuLv6TsFKEQXi8TE3i9pKdjtVJQsGdV42sjNRWTiYYGHA7m59XROjVVKTrt9nX5Y4cNwSAu\nl+LVvHyTyKYj/WCPR/mMJrETJAu7dw5Cu+7ro71djUhiNdAGOdJ2ICPLnBxqasjNjfMDBgMZ\nGTQ2qvO6JHrZ7RQUUFmJw0FqqlJgtLdz5w5379LRoc6v6elMTjI9rbiARUVbpcGUlnLpkrLf\nk/lO7ArNZkpLaW6mpmYdGU6no6SElhYCAQYHcblUhqxWq+yRxZ347YVsk5GIMprW6eLEkCfx\nNsJm4/RpZdYoah6vN45xv7Q3olFGR+npoaAgfmEn8vDpaSYnFSdBrCudThWXvqMKY2mJnh41\nR+vpUZNWyXTPyFD+LHGvlh8dWyT6Ly6vd0fwepmcpK+P3l6mp1V3x2QiN5fqao4cUUfKA0Es\nxdhqVQugDASS4k2BfBqvtL85tLXv7pDcA945iIxgelpNRbfpk7RWQxr7K8I502rJy+PkyU2b\nAampWK2YTFRWKraELF4ajZqGSF/h+nX+x/9QtKFY9uXTp4yMqBjBK1e2KuxEITU6ytiY6hmI\nF5RMN+rree896uvXFXZS69TXU1nJ3Bxzc/j9pKSQk0N2NllZbz3FR8JC5ueZn0enw2YjMzNZ\n2L0LMJvVXLW0lG+/VTFWXm+cx1lqu+lpBgZobo7/2wIBenp48IC7d5mbY3FRNdQ//JCysler\n2jdALJO++IKRkdVnGZT9hFQwmx0gZYwbDOL372x0EBeTk9y5w1dfcf366n+UleeTT/jNbygu\nToh+vJAmk4hBmDOyWL3M8JEGRGoqRuPhoiHuHRJ0D4hGo9Fo9PHjx0+ePElNTbXb7WfOnMnN\nzQVmZ2fdbndnZ+fIyMji4qLJZCosLKyuri4vLwfC4fD09HR/f39nZ2cgEIhGo4WFhWVlZdXV\n1SaTCUh5508AkQgez6oX3ctYe1SKadk0GoxGSkqIRBgbU9Nbi4XCQhobuXgRh2NTg5LYMXSz\nOsnjoauL/n5mZpQSSiAc8GiUJ08wGjl7VtWOm1lD5eby8ccUFDA6qgZAOh1ZWRQXKxrfBs5K\nzPRc/jGZVJMgM1N58r2lN4PsoJImPjHB7CyRiBL2lpdTVcWRIzuI9EhiHyDnJfEB0Wpfvc3L\nAyWmjCKi/P3vWViIf04Tvx4hlm3A4iL9/XR0cOsWz57R16eeuJkZXC5GRzl3jsuXVd9umxBN\n7sBAnLwZqdW2sBmSrp7dTl7ermqdxUV8Prq6+PJLnjxhcnJd7yclhdu3SUvjk0+wWrflivxG\n8ZauM28ORiPl5djtpKerjm8McjObzeTmYrOt0qaT2AkStLBbXFwMBAL379///e9/v7KyUltb\nW1FRYbfbAZfL9fjx4y+++OLJkyehUMjhcDQ2NkoBp9FoAoFAb2/vjRs3vvjiC4/HE41GGxsb\nz549a7VaMzMzNYehmbG0pCgvm6Xay5NjMJCaSjSKRoNWi16P06kM3x8/VuyH4mKamvjFL6ip\nwWJ5/QmCx8PTp4yOxlHjRqMEg7x4gcWC16sOanGRlkZmJu+/T0sLExNMTzM7q4R4W9tiCaS2\nezcgHdC7d/nnf2ZoiNlZ0tKUxuXUKS5dIiNDhU0lD7sHC3kGZVweiahOlcGg3GvFi2eL78hk\nIjuboiLMZm7cYHhYPddrIW11gwGrdeMdLqXks2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7ubkZG8HrR68nNpbKSqiqcTlUGxbC0xMAAz54xOUkgsFrV8aOQdmgIv5/qaj788OCX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\ntdqpqanh4eGhoSGn0+n3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GCeK112XZC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8R8pLqamJpqLmp6uPGW02iNDX/YLEZMd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\nsVqtNputtbW1u7tboVBks1lMsMcV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CnYAyuWy3++/du3axsaGSCTq6upyOp1N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zKE3FJeu01dDrm5ggEpG+FzUahQCTC3Bzz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\nsyRJRqNx//79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Call the `RandomRedDots2` function from a REST API","","Here is the equivalent REST API call:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/r-scripts/v1/do-eval/RandomRedDots2\""],"enabled":true,"result":{"startTime":1714696155316,"interpreter":"md.medium","endTime":1714696155375,"results":[{"message":"<h2 id=\"call-the-randomreddots2-function-from-a-rest-api\">Call the <code>RandomRedDots2<\/code> function from a REST API<\/h2>\n<p>Here is the equivalent REST API call:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/r-scripts/v1/do-eval/RandomRedDots2&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## A third option creating subplots: `RandomRedDots3`","","Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example."],"enabled":true,"result":{"startTime":1714696076683,"interpreter":"md.medium","endTime":1714696076743,"results":[{"message":"<h2 id=\"a-third-option-creating-subplots-randomreddots3\">A third option creating subplots: <code>RandomRedDots3<\/code><\/h2>\n<p>Another way to approach this is to create a single plot composed to two subplots. This approach is somewhat simpler, but yields only a single image. Generating two images may be desired when using the SQL API so that each image is returned as a separate row. See the SQL API demo script for an example.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}},{\"image\":{\"height\":700,\"lastColumns\":[]},\"raw\":{}},{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","RandomRedDots.3 <- function(num_dots_1 = 100, num_dots_2=10){","","    library(lattice)","    fig1 <- xyplot(sample(num_dots_1)~sample(num_dots_1), ","      pch = 19,","      col = \"red\", ","      main=\"Random Red Dots\")","      ","    fig2 <- xyplot(sample(num_dots_2)~sample(num_dots_2),","      pch = 19,","      col = \"red\", ","      main=\"Random Red Dots\")","      ","      print(fig1)","      print(fig2)","}","","ore.scriptCreate(name = 'RandomRedDots.3', ","                 FUN  =  RandomRedDots.3,     ","                 overwrite = TRUE)","                 ","ore.doEval(FUN.NAME = 'RandomRedDots.3')"],"enabled":true,"result":{"startTime":1714692072556,"interpreter":"r.medium","endTime":1714692073369,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAIog7GAzWVlZW7duzcrKcvYgyAWfjsvio3FlfDqujE8nV4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MnxdNTGjWSQYNk4EDR6Zw9GYCiytLJE6tWrRo1atTFixe3bt364osv5lp1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6u+nw+ceCFQqGmpsZms2m1WvGSierNYsWPKJUihvidTqfoAnx5pfuICIDk5S22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RSQSWFvb5B66fo19bQ1jY2hvL8X6yEGl\nn/5zHAoFWCxgGGgarFZ4vXj5ZZw5g/p6Y6QQeSLbDXYul+vIkSMej6ejo0NRFEEQent7Gxsb\nrVar0+lsbGz0eDw1NTWVlZU7ulxCHkUUkU7jhx/www+Ix5HNwmJBTQ3a29HaioYGSnXkANEL\nDPWPNw+jfxCi8kOym/SLdL298Pnw9tvGgay+Y1dbi2iU2tc9pceoOtE0rba2tqmpiWEYs9ns\ndrttNhsAPdjpX7BTyyTk0fT3sOVlDA3h/HlcvIiFBaTT4Hk0NiIWQz4PnofPR/c2yEGhz9zj\nOHAcVHWT9Ka/v9rtdOZFdhXDgGEQiRgXAPQfTr0elj57PwvbDXaLi4t/+MMffD7f3/7t3zoc\nDpPJZP6xFNnlcjU3N589e/brr7/+9a9//fzzz+/Yagl5CL31UX8//vt/x8QElpaMRqzZLCQJ\ny8tIJJBK4dQptLSUeq2E7AqOg90OpxMOB2R5Y7DTM5/Ph5YWBAIlWiIhP85BIc/OdoNdKpXq\n7++vra1lGEa4vwiZ4ziz2by0tHT58uXXXnttBxZJyFZEETMzGBnBjRuIx41xXrpiEek0LBZo\nGhob0dwM0E07cgAwDAQBnZ04dgzXryMeN1pL6IezgoBgEI2N6OhARUWp10oOpLuvw/SC/Ew9\nRoPixcVFvePJhoc0TVNVNZlMzs/P53K5Z71CQrYhk8HwMG7fRi63sX5eVSFJGB9HNou3397i\nyhEh+wnP4+WXwbJYXkYmY9xYUFUwDNxuNDejpwc9PXC7S71QQsgzs0WwW1xcXFxcBHDr1q1U\nKhWPxwcGBrxe70+/Jp/PJ5PJWCzmcDiojx0pDUnC0hJWViBJm98E1ysEs1nkcjQik2zX+jpW\nV5FIQBRhscDjQVUVBKFsLqXpEyaOH4csY3wcy8vI541ZsdXVaG5GV5cxFoUQsl9s8fc5Fotd\nvXoVwJ07dzKZzOrq6rVr1xz3V6ykUqnFxcXl5eWamhq9nIKQ3SbLSCaRSm3crrvLZILJBEky\n3qEJeRj950eWUShgdhZjY5iZQTIJux3RKLq6UFEBr9e4AL7HMQy8XrhcaGpCLIbRUaRSKBaN\ne+vhMNUSEbL/bBHspqenL1y4AGB1dTWVSqmq+u233/I8rz/60wFijY2N9fX1dXV1O7VSQh7B\nYkEwaPS0lKRNphDq79aCALvdaGhHyCOMj+PLLzE6iokJJJMoFMDzcLng9eKVV/DqqwgGy2Zw\nnMkEi8XYaywWoaqw26kYlpD9aotgxzCMfroqCILe38Risfz0vNVkMvE8b7fbe3t7Dx8+XF1d\nvbPrJWRTgoBIBDU1EASIIiTpvkcZBi4Xqqrg8YDn6Y4deRRZRi6H0VGcPYtbtzA3B1mGqoJl\njV06WYbbjWPHyibY6ct2u+kuHSEHwRbB7uTJk01NTQBmZmb++Mc/+v3+999/337/7j3DMCzL\nulwul8t1dzOPkF2lj7ycmIDDgWzWuCGu79LpkydaW3HqFEIhSnVkC7kcbt3C4CBu38byslFh\nrQ810X94rl1DsQifD/X1pV4rIYRstEWwq6ys1IdJVFRUZLNZt9vd29trNpuLxaIkSZqmcRyn\n7+GZ6P2SlJB+FNvVhTfewMgIpqeRSqFQAMvC50NNDU6dwosvorr6aYOd/h6vKFAUsCyoWmj/\nyecxNYXJSWN4yd1jff2PHsDiIsxmxOOQJJjNZXDTjhBykGy3GCoQCPz85z9nGIbn+Vwul06n\nE4mELMsej8flcpnNZgp2pJQYBjyP3l5UVuKLL/DFF7hzB4kErFa0t+OFF3DiBA4degaXivQJ\nm5KEfB42GwW7fUjviTg3h3x+8wprSUI6jUwGogibjYIdIWRPeYwqd5Zlp6enb968OT09PT8/\nL4qiqqqCIASDwXA43N3d3d7erjcr3rnlAigUCul0emhoaHJycm1tLZ/PK4rS09PT19fn9/s5\njovH45OTk8PDw7lcTlXVcDhcX1/f2trqdrtxf8EH2T/0oleHA+EwTp1CZSXW15HPGzt59fWo\nqYHV+uTPL8vI5zEwgNu3sbBgnPYKAlwudHejqQkVFU/1/GTvMJmM63QPc/dYliIdIWTv2W4I\nk2U5kUgMDAx8+OGHIyMjsVhMT0iapgUCgfr6+g8++KCystLj8ex0sEulUrOzs+fOnfv6669j\nsVgqlSoUCn/3d39XUVEhCILFYhkfH79w4cKnn366vr6uqmp3d/fzzz/v8/nsdvtOr42UmD4W\n89AhHDr0jJ85n8fSEs6dw1//iuFhpFLGnE23G7/6Fd54A1arUZZBnxzKHcvCbofNBpaFybRJ\n9xyGMU7hOY6yHSFkr9lu0FlfXz9//vy1a9ey2ezJkycbGxudTqfZbE6n07Ozs2NjY4ODgyzL\nvvTSS21tbTu64h9++OEvf/lLOp1ua2t75513PB4Py7INDQ2NjY12u312dvaTTz5ZW1t76623\n3G43x3E3bty4devW0NAQy7K1tbXUQpk8iYEBfPklLl3CxAQyGcgyTCajROPcOeTzsFrR1QW3\nm3qplD2bDU1NGB2FIGw8jdWDu9eLaBRuN80sJ4TsQY8xK/bq1avT09NVVVUnT558+eWX9c25\nRCLR39+vKEo8Hr9w4UJLS8vOBTtFUYrF4q1bty5cuHD48OHDhw+/+OKL4XBYEASTyaSqajab\nXV5eHhoa8ng8L7zwQjQatVgsq6uro6OjExMTPp8vFApRsCOPR78yPzaGv/4Vk5NYWoKiGLs4\nioJiEUNDANDXh4oKOJ0U7MqeIKCuDi0tqK+HyYTVVQDG1p3ZDEFAQwOOHUNlJf1ZE0L2oO0G\nu3w+Pz09bbFY3n///ba2tsrKSr0SluO4vr4+n8/3xz/+8eLFi8lkcufWKori2tra0tJSIpHo\n7Ox85ZVXgsGg5cfr8Kqqrq6urq6u2u32cDjc1NQUCARkWe7s7FQUZW1tbX5+Xtn0KjQhj6DX\nwC4tYXQUmcy9VHeXqiKZxMgIamsRjVI5RdnjeVRXo68Py8s4fx6XLxsBTu/r6/fjxRfx/vto\naCj1QgkhZBPbDXaKoqTT6YqKirq6usrKyrujw8xms9/vZ1nWZrMlk8lisbhjS0WhUFhfXxdF\n0WQyra+vj46OXr9+Xf9fRyKR6urqRCKRzWb1Xsp6O+VCoRAMBgOBwNjYWDKZVB8cSAAAGB0d\nHR0dlSRJluUHH52YmFhdXW2g1/GDSVUhSchmkUxuPq9MVSGKWFxEPL55ESUpLywLmw3RKF5+\nGXY7ampQKEBRjB6/FRU4dQodHVQrs8/pJTLZLJaWsL6OdBosC55HRQV8Pjgc9BHugJMk6Ztv\nvmE2u2Wr9w+JRCJdXV0lae772MUED34bJpOJYRiTyaRpmrbpmM5nRJKkZDLJMIzT6bx8+fKV\nK1cmJyfNZnM4HH7vvffeeOONVColSZIgCPrhrL42/V8lSRJF8WHL++qrr373u9+tr69ns9kH\nH5VlOZvNHjt2bOe+NbJ3KQpEEbIMhtn8QpWmoVhEKoVsdvMxtaQc6QNhu7qQSmF5GbkcbDZ4\nvQgEYLPR/JIDoVjEygq++QYDA5icBM/D7zcaJ9He/IGXy+V+//vff/jhhw8+xPO8x+M5c+ZM\nQ0PDng52PM+HQqF8Pv/ZZ58dPXq0u7vbarUyDCOK4uTk5PXr11dWVurq6pw7OWOHZVme54vF\nYjqdPnz4cHNz83PPPZdIJFZWVubn5y9evOhyuTiOkyRJb54MQNM0/V/NZvMjuij39vb+9re/\nzefz0oZRVACAubm5s2fP7tz3RfY0loXVCkEAxxl9iR/8ApsNVVXw+6lGcv9gWWNmicMBlwuS\nBIsFgkCN6w4ERUEuh3Pn8N13uHULs7NYWYHZDJsN8/MYG8OLL6K1FaEQqNPCQcXz/Ouvv97T\n0/PgQ/pBYkdHhyAIu78wbD/YWa3WxsbGoaGhzz//XFEUl8vl9Xo5jkskEjdu3Pjkk08Yhmlv\nb3fv5CxCjuMcDoeqqul0urGx8d13362oqBgfHz979uzKysqlS5dOnjzJ87woirlcrlgsyrKs\nKEomk8lkMoIg2Gy2hwW7EydOnDhx4mH/3ytXrgwMDOzYt0X2NrMZZjNcLrjdUFVsuGxgMoHj\n4Hajrg7V1XSbfr/R//RL9OpMSkaSsLqKTz/Fn/5kdKK++xd/ZAQjI8bgmYoKCnYHltVq/eCD\nD37zm9+UeiGb2O4Ppc/nO336NM/z58+f/+KLLy5duiQIgr5jVygURFE8ceLESy+9FIlEdm6t\nVqu1srIyEAh4PB7LAyMEWJb1+/08z2ez2Vgsdvv27XA4bDabh4aGxsbGGhsbQ6EQS++75Ml0\nd+Nv/xbnzmFoyOhyAhjNzDwe1NSgpwcNDfQqT8h+EIvhhx8wO4ts1rhheZcsY2kJn38OhwNH\njlDoJ3vQdt+HHA5HV1dXoVCYn5+/devWxMSEXohgMpmCwWB9fX1PT8/Ro0fvFlXsBI7jPB5P\nJBJpaWmRJGlsbGxxcXF+fj6VSlmtVq/XW1VVxbJsIBAoFArXr1+fn5+3WCyTk5O5XC4cDofD\nYQp25Ak1NeGNN5DPQ9OQSBi37ux2uFwIh3H0KFpbUVlZTod0imLMRtP3HsxmarBMiGFpCQMD\nWFoy9up+endWVZFKYWgInZ3I5+Fw0D492Wu2G+wYhrHZbL29vaFQaGlpaWVlJZ1OK4ricDj8\nfn9lZWVFRYXD4djR5GQymUwmU09PjyRJN27cuHjxYi6X0xf24osvnj59OhKJKIry1ltvXb9+\n/ezZs6IoAggEAu3t7Z2dnbW1tTR5gjwhfWKY1YpDh3DzJhYXkc2iuhr19ejtRWsrwuFySnUA\nRBHpNJaWkMvBaoXPh8pKcBwFO0KQTGJmBsnkxlSn01sgFQrIZOByYSe3Mwh5ApsEHVVVZVne\ntDOI3W632+3BYDCTyWSzWVVVbTabw+FwOp16i+BNS3+fFf2GXDgcZhhGVVWO49LptNVqDQaD\nPT09TU1NNptNUZS+vj69TiKdTquq2tTU1N7eXlNT43A4dm5tZJ/TJ4Z1dSEYRHU14nHk8wgE\nEAqhsdHYqyuXSJRIYH4eU1OYnsbaGkQRPA+vF1VVaG5GUxMsFqr4IwfdI/46651QNI12uMne\ntHmwy+fzm3Z002maZrVa9XIPfRctl8vp/2y323f6uNPr9bpcrvr6+mKxqGma3myF53mLxcIw\njKZpdXV1NTU1p0+f1gtj9XrYB+/kEfJ4TCa43XA6EY0aL+v6zFCzucz26ubn8fnnOH8eV64Y\n/8VkAs/D6cQ//AN8Pvh8FOzIgWazwe+HIGw+LBiApnT2dWUAACAASURBVBnl8PTOQvaeTYLd\n/Pz8xx9/PDMz87jPZbPZ3nvvvcOHDz+LhT0Uy7Isyz4sqOk5j+O4Hb3tRw4ik8logVG+oadQ\nwOoqBgaMKpDl5XtbDiyLtTV8/TV4Hq+9hs7OUq+VkNKpqkJPD374AWbzxtNYhoHDYYyZoXaG\nZE/aJNitrKx89NFHV69e/el/1DRN7x4iy/LdnnD68assy8ViUa9aaGtr2+lgRwh5Qvk8ZmZw\n8yYuX8bq6sbWLaKI779HIoHGRnR0AI88jSJkH6uqQm8vamvhciGdhiQZ2c5kgiCgogK9vWhp\ngcVSZrv15GDYJNiFw+F//ud/PnPmzE//YzabnZmZmZqampycbGlp6e7udjqdLMum0+nx8fH+\n/v6qqqqOjo4dbXdCCHkq+TwmJjAzg3x+k07LqmqUU+jvZFRIQQ4smw2hEN5+G3Y7Ll3C9DTS\naQDgebS24vBhvPYaDh1CKYYKELKlTYKd3+9//fXXN9yxW1xc/OqrrywWi9/vP378+MmTJz0e\nD8uyyWTyxo0bPM/bbLZwOGy323dr5YSQxyRJWF5GPA5JwqZzk0URySTyecgyWJZ2I8gBpZcT\nHT8Oux0ch9u3sb4OkwlWK7q7ceQITp7cvCG5vrGXySCVQjqNfB6qCrMZFgs8Hng84Djqdkl2\n2iY/YXoDkQ1jVefn5wcGBhiG+eUvf9nc3Ky3DjGZTH6/3+FwhEKh8+fPf/vtt0ePHt2tlRNC\nHpPepuHBvboNVHXz2EfIwaEPlrDZ0NBgtClmmHsj5rzezdvXaRo0DRMTuHoVP/yAO3cgSXA4\nUFmJU6fw4ovw+UD9GcgO2zzYPdi1RFGUxcVFv9/f2NgYDoddLtdPv95isXz99dfT09OZTGZn\n10sIeWIWC/x+o+i1UNgkvVkssNvB8zCb6RyWHGj6/pzVimDQ6G9iMm29h72+jpkZXLqEixcx\nPIxYDMWiUWObzSKdxsmTaG0Fx9F2ONk5290TVhQlnU7b7XaO4zY0NNG7jciynEqlJEnagUUS\nQp4FQUB9PWprYbUah0QbWK3weuF0UrkfIffZZg6LxXD2LC5exPffG/t8+PGGw8oKfvgBgoCa\nGjid1CeF7JztfmiwWCwVFRWiKJ47d254eDibzepFsvl8fmpq6rPPPlteXg6FQnTHjpC9Sz9X\n6upCTw9CIXCc8ctigSDAZkNfH375S9TXl1O/ZUJ2mt4V6NF/I/RdvaUlXLqE0VGkUigUjJNZ\nRYEkIZXC8jLGxjA2hlxut5ZODqLt7tgJglBXVzc2NvbFF19omub1evUBYplMZnBw8NNPP83n\n862trW63e0eXSwh5coKASARdXTh+HJp2b7q53mbZYsHzz+ODD1BVVeqFElKGVNXYllta2uQm\nq6Ign8fUFMbGEInA4ynFEp/a3aiqKMY/61VWVGu1l2w32Omlsi6X69KlS2fPnj137pzVamUY\nRhTFfD4viuKRI0dOnjwZjUZ3dLmEkKcVjeIXv0B3N6amjNloPG+MR/v/7N3Xd1v3lS/w76nA\nQScaCRCsYhM7JdlqcZGtOInjZCbJrDvJWuN5yKzM2zzOvzGveblvM+tOjdM8jpzEthJH1bKo\nQoq9d4IASPRyyn04xyoUJYM2IQDk/iw92CRMb4ogzsbv7NLdjbo6SFK5QySk2igKUilks888\n29M0FAqIxbC1tXuEZBXRv82NDSwsIJ2GLMPnQ10d6utB9+sqRrGJnd1uHxwclGV5bW1tampq\ncnJSb7DQNM3tdjc3Nw8MDJw9e5ZuxRJS6bxeeL1oaUE4jPV1JJMwm1Fbi+ZmmM1VvFeDkDL6\n0gWy+qAJWYYs772mrMJpGrJZRKOYncXUFCYmkEigUEB9PZqbcfw4Ghrg94PjqIqj7IpN7DiO\ns9lsJ06caGho2NjY2NzcTKVSiqJYrVaPx1NbW+v3++12O08TegipCna7MUNfUcCyEEVIEt1M\nIeQr4jhYrc877eY4iCK8Xvj9Vdk5oShYX8fwMH7xCyOry+ehKLDZ4PPh2DG8/jp++EM6t6sE\nxeZh+gwUj8fj8XhCoVAymXyY2NlsNrvdvmtCytbW1vz8fG1tbUNDQwnCJoR8PaJoDDchhHx9\nDANBgNeLnh6wLFZWjAO8xz9rtaK5GW1tqMZV5oUC7t7FH/+I69exvGxMu9Q08DzW1xEOw+HA\n0BAaGqq1fPAQ+SoHbBaLRZIkj8cDgGEYlmWZp45e5+fn/+d//ufVV1+lxI4QQsiREAzi4kUA\n2Np61JzEMEZzks2Gzk709FTljOJcDp9+it/+1lhd81ChgEQCsoyJCdy9C56nxK7svkpipx/O\ncXvO3f5COBy+fv16c3PzVwuLELIH/QCgUEA2a7Sy8vzeE/AJIS+SfrpRV4dXX4UgwOnEzAzW\n1h4NKO7qwsAABgZgs1Xf72w+j2QSsRi2t/fo/NAbfsNhTE6CGigrQKlK4mKx2MjIyPr6eom+\nPiFHiz5ioFBALodkEjs7EARIEiwWmM0wmYxLBZUtE1JGPh98PjgcqK01Btql03C50NKCCxfw\n2muw2aqywK5QQDqNbHbvNdOaBlnGzg4WF7G9XY74yBOo14GQaqAoWFjA+DiuXcPqKrJZABAE\nhELo7MQ3voH6+qq8YBBy+NTV4eWX0dSE7W3IMkwm2GwIBmGzVWvXud758ZzmqodFhPQqVAEo\nsSOk4ulVLCMj+OQTfPghlpagqkY3a3MzTpyAzQaGQUMDqC2dkLJzueByobW13HEcHJ6H2Wws\nz1VVyPLuB7AsrFbU1lZl+eChQ5cBQipeIoGFBfzxj/jwQ6yvI5s1Zr4zDFZWAIBhkErhBz+g\nV1VCyMFjWZhMqK1FMIiVFSSTT9yQ1btDPB709tLemkpAiR0hFW9rCxMTmJjAwgIKhUczFDQN\nqRRWVsAwaGzE22/DYqFZdISQA6YndgMDCIdx7RqWlpBKPXohstnQ2orBQbS3w+0ua6AEoMSO\nkCqwtoZ797C1tcfMelVFKoX5eaysIJeDqlJiRwg5eCYTXnkFdjsSCWSzxmvRw17gixdx4QJa\nWqpyRN+hQ4kdIRUvk0E0ikxm701EekuaPgNFlqnMjrw4eqf2wgLm5oynqCjC70dzM3w+Orw5\nVFgWLhe6u/F//g9eegnhsNEha7Ggtha9vWhthdlMbywrAV0DCKl4etKmKHsndgwDljVWjOsD\nUQkpNf2pmMkgFsPwMP78Z8zOIhaD1Yrjx/GNb6C3F1YrzVk8PBgGZjMaG9HYiHQa8TjSaeTz\n8HrhcIDnKaWrHJTYEVLx3G60tmJsDKL4RI2djmGMTa9VOiKLVCP9jcTdu/jlLzE+bgwwy2Qg\nCNjcxPQ0XnkFFy6grQ21teWOlRw0UYTDAYsFqgqzGTxPEzQrSqkSO5fL1dvbW0cNMoR8fT4f\nurrw+eewWpFKPZHbMQwsFjQ2IhSC1VqtU7JI1SkUEIlgZATvv4/1daTTUBSjU3JzEzMzRlWA\nzUaJ3SGk77whlapUP5u+vr5//ud/bj1Mg3wIKZdgEAyDa9cwNgYAqRTyeTCMMWWgrg5vv41v\nfAMmU7kDJUdGMomREYyPIx5/VEqv02sGRkeRy6G3FwMDZQ2UkCOn2MRO0zQAqVRqe3s7lUpl\nMhlVVbUnbwlxHNfQ0ODxeAD4fL7Tp09LknTgERNy5FitCARw7hw0DXNzWF9HLAaOg9kMvx9d\nXXj9dXR2Vu5xnaYhl0Mmg3QamgaOg80Gq9XITUk1ymQwP4+lJaTTe5QHqCpiMSwsIBZDLgdB\noAIsQl6YfZzYaZoWjUZHRkaWl5fX19dlWVaerNSWJOmdd97REzuTySQIAkOv2oQcCIsF3/kO\nBgdx5w4mJjA7C5MJNTU4cQLHj6O+vqI3i6sqEgmEw1hZgSxDktDYCJOJSnOqWD6PjQ2Ew8aQ\nnafpy1H0bN5mo8SOkBem2MRue3v7/hdUVeX3ur+ey+Xy+bz+zwzDcBV7mSGkuujZj9Vq3JMN\nhTA0BJ6HJKG+Hl6v0X5YmWZmMD5u1NcnElBVCAI8HgQCOHsWnZ2wWCr3rJE8y8NjV44Dwzxz\nEI8gfMmOUULIQSv2YhCNRj/55JMbN24sLCyEQqGWlhae59knf13piI6QUnmY27W1oa2t3NEU\nRz+zGRvDb36D69cxMwOWNb4RUYTbDVmG04lQiBK76iOKxqQ6/dj16cSO42AywWym2WaEvGDF\nJnbpdHpyctJkMv3DP/xDU1NTXV0dwzC70jie55ubmw8+RkJINYrHsbqK27dx4wbW1pDLPSqq\nKxSgqvjjH6Gq+Ju/QUtLuWMl+6TPq5uehiQhnX7ibizLguPQ1IT+fvj9lNUR8oIVm9jl8/mN\njQ2fz3fhwoXm5uaampqShkUIqXo7O5iYwNgYJid3D08uFCDLuHMHFgvefBNNTdRIUWXMZjQ1\n4fhxdHdjdhabm8jnoShgGNhsqKlBfz9eeQV1dfRjJeQFK/a9FMMwoiiazWaLxSLSEFRCyJfS\nE7v1deN8bhdFwc4OIhFjegupLjwPhwNDQ/jZz3DxIoJBo9BTkhAK4fx5fPvb+O530dBQ7kAJ\nOXKKPbGTJKmlpYVl2ampKZ7nm5qaWJZl6YydkAOnadA0ZDJYW8PmJjY3jXESkgS3G42NcLsh\nSVVwhyufx86OMbr2afrqglzOSPseLhQnVYFlIYoIBHDmDHgeHg9iMWSz4HkEg+jsRH8/mpqq\n4FlKyKFTbGLncDiGhobm5uY++ugjTdMCgYAoipTYEVISmoZoFFev4soV3LiBeByKgkAAfX34\n679Gfz8Cgeq4ZO7ZLEkODasVkgSfD2+++Sg75zjwPESxcufvEHKoFZvYmc3m5ubm1dXVa9eu\nbWxs3LhxQxTFXUNPzGbzW2+91dvbW4I4CTkyCgWMjuL2bVy+jJERzM8jm4WqIpVCOo1sFuvr\nePtt1NTAbC53rM9lt+PYMYyOQhQhy7vvxrIsbDY4nZAkCAId11UllgXLgudhtZY7FEKIodjE\nThAEl8sFYG5ubnh4WNM0QRB2JXYul6upqYkSO0K+Ov0G5fAwfvc73LyJjY1H9WeRCLa3MTOD\nXA4DA8YsiUrmdKKzE/X1sFqRTiOXMz6u90k8nGZnt9O4E0IIOSjFJnZbW1uXLl168OBBR0dH\nIBAIhUKCIOwaQSxJUkdHRwmCJOQoyecxOYk7dxCLQZaf+JSmQZaxuYnbt2Eywe0uU4jFcTjQ\n0YHBQczOYnIS6+vGsZx+t85ux8WL+Na34POVO1BCCDk8ik3sEonE8PDwzs7O6dOn+/r62tvb\nnx5QzHFcMBgsQZCEHBmKgmwWa2tYWtp7BaeiIBrF+HgVjCm2WGCxoL8f0SicTszPG6ePHAer\nFT4fLlzAmTNwucodKCGHnSyjUDBqORQFPA+TCRaLcepPhRCHS7GJnSzL8Xjc7Xa//fbbjY2N\nTqfz6SUTDMOYTKaDjpCQoySfRyoFWTbuVz7dfKAoSKexuYlkshzx7V93N/x+nDmDlRWsr0NR\njIkYDQ2or4fbTfdhCSm5TMZ4Q7i8jHQadjvq63HsGM0GP5SKTexYljWbzXa73e12u1wuu91e\n0rAIOaL0UvSHq7f2fIAgQJIqdznsLi4XHA54PGhpwdYWFAVmM/x++HzguOro7SWkemUyiMcx\nOop79zA7i7U1ZDKw2RAIoLMTXV3o7ERdHUDndodHsdcGURS9Xq8kSdFotKamhhI7QkrCZILd\nDlHc+0WWYYzBsKEQnM4XHtxXxTBwOmG3Qy/VYBhj6xQhpNTicUxN4de/xv/8D3I5Y0EIz8Ni\nQX09urrwD/+A2lrK6g6TfSR2gUBgbW3tV7/6VU9PT1dX19PjTvQaO9o2RshXxzAQRfT1YXYW\no6OIRh9N99UP88xmBAI4cQL19WUNdD/028p6/ISQF2lhAb/8JW7cQDgMVTXGDbIsMhnjn0dH\n0dCAYBAWS7ljJQdjH+NO3G73+Pj4tWvX5ufnNzY2rFbrroo6k8lkMpkosSPkaxFFDA5iZwfJ\npFHvrE+AE0VYLHA60dKCoSFqJiWP6NtK9IrMh3e36QzmiNOfEouLeP99LC+jUHj0KT3Di8Wg\naRgfR2srnE5K7A6NYhO7XC63tLQUDocFQdAHFPM8v2vcic1ma21tbav8Zj1CKhnPo7MTggCb\nDXfuYHQUiQRkGYEAWltx4gROnoTLRadf5JF8HrkcslkwDGw2iCIVLxIoCjIZpFLI5fZe66c/\nYHUVi4vo63vh8ZFSKfbawPO8y+VqbGxsbGx81mNYln26VZYQsj8ch0AANhtsNtTVwe/H9jYK\nBTQ0oLMTZ8+ivh5mM125jzpNg6IgkUA4jO1t7OwgkwHDwOWC2w2fDzYbncEcafrYy0IBhcLe\niZ2qQpaRSCAe3z0yk1SzYhO7QCDw7rvvptPp5zyG5tgRcmAkCa2t8Plw6pQx0M5shtUKpxMm\nE91lI8Zxy717eP99o9sRXxRi9vbi4kX09lbBsENSOiwLk8kYJ7Tn1ma98lWSYLFQM9Nhso9d\nsU1NTdqXrfTm6MlByIHgedjtoPZz8iyJBO7fx5/+hD/9CUtLiESMza0AIhEUClBVeDywWEDj\nRY8mfXGf1QqvF6kU4vHd6Z3eqlVbi/p6epIcJsUmdqqq5nI5Zc/j3C8wDGM2mym3I4SQktva\nwgcf4PJlTE4inTZK4/Wj3OlpbGzAZkNXFwIBumYfUfp0JJcLLS1IJpFKQVGeyO04DiYTmpvR\n0QGrtXyBkgNWbGK3trb229/+dnFx8TmPsVgs3/ve94aGhg4iMEIIIXvRNKgqEglMT2NhAen0\nowIp/bKdz2NnB4uLGBmBJIEmFRxNepbf1IQf/ABOJz79FNEoUilj/BDDoKsLp05hcBB1dRDF\ncodLDkyxid3m5uYvf/nLzz77bNfHNU1TFCWfz7Ms6/V6Ozo6KLEjhJDSkmWkUlhbw9bWHmXv\nelH8+jrGxtDeXo749knPRxXFKPPXbw3p9YI8TxtKvpbGRng80DREIpiff3TLnuPw8sv4/vfR\n1wePp9xRkoNUbGJXX1//s5/97Hvf+97jH1QUJZvNzs7O3r592+PxdHR0PKdnlhBCyAHQNKPV\nEc8eVqeqyOWQSj0xvaySZbPY3MTdu5ifx8aGMe7n+HG0tSEUohuFXx3Pw2rFuXMIBBCJIB5H\nPg9RhN2OxkY0NcHtLneI5IAVm9h5PJ6LFy8WnnyNUBQlnU6PjIxIkiQIgt/vt9lsJQiSEELI\nkxjmeUdZ+pGMIFTBWZeqIp/H7Czu38eVKxgbw9ISBAEuF5aWjOUr9fWw26vge6lAHAeOQ0cH\n2tqQzSKTQSYDUTRmYdJf6WFUbGLHcZzdblf1Cfhf0DTN5XKJoujxeD7++OPr16+fPHmyBEES\nQgj5AsMYs28kCaKIfH53t6Oe0vn9OHYMDkeZoiyaLGN7G7/+NS5dwsYGtreNOrCVFayvY2oK\n0SjOnsXAAHWBfC0MA5PJOMDTb3PT1KRDqtjEjmVZ9hmpvdfrFUXx8uXL8/PzyWTy4GIjhBDy\nFL3bsaYGQ0PY3sb9+8jlHq0U47hHq+e6u+FylTvcLxOJYHISd+/i3j1ks8agFl0yiUwGJhOs\nVhw/Tond16If8dLYiiPgAI5hOY4zm82FQiEajeZyua//BQkhhHwJrxff+Q5eew0OBwQBHGfM\nmxVFOJ0IhdDVhYGBKqigWl7GZ59hZWV3VgdAlhEO489/xuefI58vX4iEVJNiT+yy2WwkEtmV\nt+ktseFweH5+fmVlxefzmc3mEgRJSkyfnpDJGM3w+TzsdjgcsNlAP1BCKpMkoa0Nb74JsxkL\nC1hbQzYLloXPh+ZmtLfj1Knq+P3d3sb8PLa3Ict4stoHABQF6TSSSSSTsNno0I6QL1VsYpfJ\nZGZmZqLR6OMf1DStUCjMz8/fvn07nU63trbaaVB+1VFVKAqSSYTDmJnB1hbSaWMWeTAItxui\nSKf3hFQcsxn19XC50NuL0VGMjCCZhCCguRmdnejpgShWRxFVMonNTaTTe+8zxRc7TzMZFAqU\n2BHypYpN7CKRyKVLl6amph7/IMMwDMNomqaqan9//8DAQEtLSwmCJKWkv6p++CFu3cL2tvHq\naTbD4UBvLwYGcPo0fL5yR0kI2YvJBJcLPT0IBlEogGVhs8HpBF/sa3v52WyorYXFApbd48RO\nrx3kOFitNESXkGLsb6VYJpN5/IMMw/A873Q6/X7/yZMnz5w543Q6SxAkKaW1Ndy+jd/9Dleu\nPLoVomngeSwuIh5HMAiLBZJEjfGEVBx9hK/Vivr6cofyVbndaGnB7dvguEctIDq9kdPlQk0N\nJMnYZ08Iea59DCj+x3/8x1Qqtevjem4niqLL5XK5XHwVvU0kuuFh/Nu/4cEDpFJQ1UevqoUC\npqYgSejuhs2GlhZK7AghBy8YxIkT+MtfwPNGZchDevPvwAC6uui4jpAiFZuHWa3Wzs5OTdMA\nyLJcKBRUVdWeHJ5UKBRYluWoHqta6K+hy8u4exfb27sn1KsqtrextISJCYRCaGykt8uEkIPn\ndKK1FWfOIJHA7KxR5sswEEUEAujsxGuvob+/akoGCSm3r3LAls1mE4lEoVBQnqx15TjO4/EI\ndPmvFoqCXA7xOKLRvUcJyDLicUxPo7Nzj32UhBDy9ZnNqK3FO+8gEMCvf41797C2Bo6D3Y4T\nJ/DKK3jzTTQ10YkdIUXa37iT6enpe/fuRSKRZDKpKMquEzuLxfLDH/7w1KlTJYiTlIymQVF2\nT65/+Cl9m/izutUIKRf9yRmPY2oK8/NYXjaG9NpsqKszmgkcDmrorgIsC0FAMAiGgSThtdcQ\njxvbNZqa0NKCQABmMx3XEVKkfYw7mZ6e/sMf/vDee+/pg4iZp37NampqBgcHKbGrGgxjTDTl\nuL1Tt4fzTql0klQaTUMmg+Vl/OlP+POfcfMmEgkoCurq0NeHH/4QL78MSTKew6TCsSxcLrhc\nOH683KEQUvWKvWBHo9GPP/54fn7+5MmTjY2NjY2NPM/vWjJmMploV2w1YVmYTHA64fcjEkEq\ntfvcjufhdKKtDQ0NlNuRypLP47PPcPUqPv0Uk5NIJIyVqdEoRkaQzWJzEz/+Mfx+WCzljpUQ\nQl6cYq/WyWTy/v37+Xz+W9/61qlTp3p7e59O7BiGoc6JasKyYFmEQhgYwOgolpaMrlg9vWNZ\n1NSgoQGdnQiF6JYWqSCahkIBd+/i44/x4AEikUetP/p20c1N2Gx4+WWIIiV2hJAjpdjETpbl\nZDLp8XjOnTvX1NQkSZI+nXjXw57+CKl0Q0NgGPy//4edHeTzRr2dPsfu+HGcOYO+PgSDlNiR\nCqJpyOcxM4OREezs7O7s0QtDNzcxPAyHA8FgmaIkhJAyKDax4zjOYrHYbDaLxWIymWhe3eER\nCIDnEYnA60UshnQasgyTCQ4H+vsxOIhQCFbrV/zi+uFfMoloFIkEUikIAiwWeDzG2keqfyJf\ngb5ganvbGNOzq4RA7weKxzE/j/7+MoVICCHlUWx+JopibW2tJEnhcNjlctFO2MPDYkEohB//\nGN/8JubnEY0inYbfj2AQfj9crq87ZUBVEYngzh3MzGB5GTabMY+0uRle7wF9D+SIyeeRSkFR\njAagp3u6VRXpNMJhPDVTnRBCDrdiEzuTyRQMBldWVn7zm9/09PQcP35cFMVd53YcxwWDwZqa\nmhLESUqGZcEwcDhgMsFsRjqNQgE2GxwOSNJXX7mtX2s3N3H9Ou7fx+Qk1tcRjRoLgu7dw8AA\nzp9HMAiP5wC/G3Ik6Oe+grD3ia/eCatvILXZXnhwhBBSTvs4sfN4PGNjY5cvX56bm9vY2LDZ\nbKYnr/omk8lkMlFiV330Yw9JOuB1k/k8lpfxi1/g+nVsbCCTQaEAhgHHgedx7hxEEadPU2JH\n9s1kgs0Gmw2S9GjB8UP60gKXCy0toJcjQsgRU2xil06nZ2dn19bWRFHc3Ny8efMmz/O7emBt\nNltra2tbW1sJ4iTVRpYxN4d797C4iGgU2awxKu/hPOSpKfz3f0OS0NNT7lhJFRJFnDplHAmv\nrz/qn+A44zwvFMKpUwiFyholIYS8aMUmdjzP2+32UCgUevYLJcuy1BVLDIqCpSWMj2N93Zgx\n9pCe221sIJHAK6+gUADHURcF2R9BQH8/Mhlks5iYQCQCWYamwWpFTQ3q6tDfj44OuN3lDhQA\njE5zRXl0aK3/IYSQg1ZsYhcIBN599910Ov2cx+g1dgcRFal+qorNTSwtIZF4NGNs1wMKBWQy\nSCRgtX71Yj5yNPE82tthtcLtxvAwPv8cOzsoFNDYiK4unDmD7m44nZXyhkEfvJdKIRaDIMBs\nht0OSSp3WISQQ6jYxM5isdA9VrIP+vmEohhDj/d8wNPVUYQUiWXhcIDnIYrweFBfj2QShQIC\nATQ3o7cXXi8EofwLRhUFW1tYW8P8PLa2EI+D52EywetFMIi2Nrhc4Pnyx0kIOSxoHB0pDb0b\nw2o1Lq5P53b6EneehyTRPSnyFZnNaGhAIIDTp43bnXprjiBUypNKljE1hU8/xf/+L5aWAEBV\nwbIIBnHyJN59F93dsFopsSOEHJQ9ErtwOHz9+nVN01pbW2traz0ez55LJh7SNE3TtEgksrGx\nMTs7C+DMmTN+v7+EUZPKx3EIhdDWhhs3wHG7dwPoxy0+HzyeZw6tIORL6WvxBKFCb2vG41hf\nx1/+go8+wvQ0YrFHb3KyWTAM3G4kkzh3rkLjJ4RUoT0Su62trUuXLuXz+fPnz3d3dzMMIwgC\nx3Esy+5aDquqqqqqiqIUCoW5ubmxsbErV65wHNfW1kaJ3VHH80a1k9+P5WUkk4/uurIsRBE+\nH3p7EQyCtpiQw2p7G9PTuHIFV64gn3+i8GBrC/k8cjkIAk6ehNlMh3aEkAOxxzXV5XKdP39+\nfHz86tWrV65cEUUxEAj4/f6amhp9dh3DMJqm3jolugAAIABJREFU5XK5ZDIZi8U2NzdXV1cL\nhQLDMIFAoLOzk0bZETAMXC50deH734fDgStXkEpBVaGqxsC8M2fw9tu08YkcZuvruH8f4bDR\nsfs4TUMmg+VlrK0hl4OqVsq9Y0JIldsjsXM4HENDQwC2trZWVlZisdja2prb7XY6nZIkPRxK\nnMvlMpnMzs5ONBrd2tpyuVz19fWdnZ1DQ0O0cIyAZWG1IhTCa69BEKCqRtMiAJsNLS04exav\nvgqXq9yBlkMuh3Qa8bhxkMkwMJths8HlqqDiMPL1xWKYn0c8bsxufJymIZ9HLIadHeRyUBT6\nuRNCDsQeiZ3JZGpoaHC5XIODg6urqysrK8vLy2tra6urq8lkMpPJqKrKsqwkSXa73ePxnDp1\nKhQK1dfXBwKBmpoau91uNptf/HdCKpHFgrY2eDw4exbxONJpmEyQJDgccLuNPOYIisUwOYnb\nt3H/PrJZCALq69Hfj298AzU1sFjKHR85IIWCsaNvz8ZwnaoinzfGdxNCyNe2R2LH87zNZrPZ\nbIFAIBAINDU1LS4urqysbG1txePxdDqtaRrDMBaLxeFw+Hy+YDDY1NTk9XpdR/P0hTwHz8Ph\ngN2OxkZjlqwoQhCObsNEJoONDYyM4OpV3LuH8XGjyqq21til+9JL6O+n6bWHhMUCjwcWCzhu\nj9RNLzbVfyOO5q8DIaQEvqRu3el02my2UCikKIreKqF98daTYRi9nULfLcbRdYg8n9kMk8nY\nS3tkxeP4/HN89BE+/BBbW0inoWlgWWxsYHYWn36Kf/xHdHTAbKbE7jDweNDWhuFhCIIx3+dx\nLAtJgs0GqxWiWKYQCSGHzZckdnrqJnxxv0zP6lKplH5PluM4n88niqJKY2bJc+iZ3FHO5wDj\nZtz2Nq5dw82b2Nh4tD9Xn+SsKMhmMT2Ne/fQ1oa6uvLGSw5AXR0GBjA8jPl5RCLIZKAoYBiw\nLBgGfj++8Q2cPk0tsYSQA1TspAl9WF0+n89ms5ubm+vr67FYzGw2WywWlmVTqZQgCGazWZ+K\nUtKICalWmoadHdy6hfv3n1ieiy8WrGkaFhZw+7ax7ZRUO78fkoSeHiwsQBAQjSKbNQbvCQLa\n2vD22zh7lvbpkUNFfxOrLx/SNw/p8yb1P6T0ik3sZFlOpVIjIyOXL19eXl4Oh8OpVKqlpaWh\noSEcDn/88ceBQKCtrS0UCrkrZOs2IRVFH2+RSj2zTF7fsRaLYWUFz13KTKqJ2YyLF9HYiIkJ\nLC1hYwOCAJsNzc1ob8fAAGpr6bY7OWwKBSSTWF1FPI58Hi4XvF7U1MBqLXdkR0KxiV0mk5mb\nm7tz587Vq1fT6bQsy1NTU9FoNB6Pcxw3Pz+vt1ZIkkSJHSF7U9U9xl48pH+8UHh0i5ZUO4aB\nIKCnB42NOHYMS0tYXYUgwOFAZydCIWPnHiGHRqGATAZLS1hcxPy8cUqtb0ZubkZ9Pdxu0NyM\nEis2sYtGox999NHs7GwoFBoaGmpra/uXf/mXSCQCoK6u7vXXX//ss88++uijlpaW9vb2UgZM\nSHXSh9VZLHtvztUfwHFwOFBbSwumDhuLBa2tCAaNW7E8D4uFWmTIIRSPY3ER//3fuHoVqZQx\no1EUYbOhvx8vv4yLF1FfX+4oD7liE7tUKvXgwYNcLvfKK6+cPn26oaHB4/HoiZ3T6ezs7Lx5\n8+bY2Nj29nYpoyWkmvE87HY0N2NpCevryOefyPD0631dHbq64HSWL0pyoPSuCP1HT5PbyaE3\nM4NPPsGnn2J42Ciw02ewCwISCSgKWlthscDhoHc1pVNsYpfL5VZWVmpra1977bWGhoZcLvfw\nU5Ik1dbWAlhfX89kMiUJk5DDweHA0BBiMcTjRsOETj+uE0U0NuLkSXg8ZY2SEEK+kuFh/Nu/\nYXUVmQw07dF710IBMzMwmTAwAJcLnZ2U2JXOPvav66NPGIYBwDzWnC/Lsj612GKx0DQ7Qvam\n/8q4XDh3DjwPkwmTk1hcRKEAjoPLhZYW9PXh/Hl4PNQmSQh5nocnYZXTZyrLKBQQi2FtzdgM\n/jhVRTaLSAQzM2htRVtbmaI8EopN7DiO0yebxGKxmpoajuP0mXaapqXT6fX1dUVR3G43LRMj\n5HkcDrz0Elwu42YEYKwUC4Vw7hy+/300NsJmK3eUhJDKoydzsox8HoUCZBksC44Dz4PnIYpG\nkleumYiKgkwGySSSyUf3Ih4ny0gmsbiI9XXqDyupYhM7i8Vy7NixcDj8wQcfnD9/vqOjo1Ao\nqKqayWRmZmb++Mc/bm9v9/X11dTUlDRcQqoey6KuDq+8go4OfO97UBSwLKxW+P2U1RFCnklV\nsbGBmRlcu4bFRSQSYBiYTGhvx/HjOHECPl8572/q9STPOUHUzxef/xhyEIpN7KxWa19f3507\nd0ZGRnie397eDofDyWRyZGSEZdnh4eFgMDgwMOD1eksaLiHV7eENWb3KhBBCiiHLSKUwNoYr\nV3DpEqamsLMDAGYz+vuxvg6zGcePw+8Hv48Kq4Okt3+JIsxmaNruGez4YvqP0wmbjXK7kir2\nGeByuc6fP89x3Nra2uXLl997773l5eVcLvfzn//c7/e7XK6Ojo433njD7/eXNFxCCCHkyEml\nsLKCDz7ApUtYW0MyCVkGgEwGExPI5VAoIJHAW2+VM7Ezm+F0wutFOGys0nkcz8PhQFsbGhqo\nc6Kkin0GmM3mYDDY29u7vb09MTExPz8fDAZVVTWZTHV1dW1tbQMDAw0NDXy5nlKEEELIYbW1\nhfFxPHiA6eknciZ9XY3eSOH34/XXIUnlKbPTD+Ha2vDGG7hxA9PTkOVH68U4DqEQenvR1YVA\ngBK7kio2D2MYxmQydXV1tbS0xGKxaDQajUYLhUJNTY3b7fZ4PJIkiaJY0lgJIYSQo2htDcPD\n2Nx8lC09pCjY2cGDB+jtNaZjlqt/AsDQEGw2ZLPY2kI6DUUBw0BVIQjo78crr6Cnh9boldo+\nEjsAoiiKoigIgsPhqK2tVRRFkiRJksxmM0u3zAkhRUqlsLODlRVsbSGXgyTB7UYwiLo6sGw5\nL0uEVKZkEhsbSKf33lujD8XM5ZDJoFAo57wkpxNtbfjBD9DZaeR2hQIsFrhc6O5GVxc8nrLd\nLD4y9vj7VVVVlmV11xCaJ5nN5scnm+TzeQAMw/A8T6PsCCHPpM9rCIcxN4dbtzA5iXgcNTVo\na8OJE5AkWCzGZYnSO0IeyuWQSOxeV/MQwxgHY9ksZLmciZ0kwWzG97+PN9/E8jJiMaTT8PsR\nCMBup2WJL8YeiV0ymZyamorH4/v9WoIgtLW11dXVHURghJDDKBbD0hIuX8bly9jYQCyGfB6S\nhJER3LmDjg5897sYHKSmOUKe4HKhqQkTExCEPe7GAuA4mEyw2VAJNVF6I0UgAI8HsmysyRaE\ncod1VOyd2I2MjKysrOz3a0mS5HK5KLEjhDxTOIxr1/DHP+KjjyDLUBSjsFoQMDODiQk0N6Op\nCQ5HRVyfCKkQXi86OjA8DJMJmmb84ugYBpKEYBCBQEXkT/pZuyjSr3C57J3Y3b9/f2JiYr9f\ny263Dw4OHkRUhJBDan4e772HsTHjppJ+cdKvUvE4NjcxNobmZvT10VWBkEdCIagqrlyB1QrA\nmG8CgGHA86itxcWLOHOGthES7JnYWSyWjo4Oi8Wy368lSZLb7T6IqAghh45e3x2NYnoaW1t7\nrJLM5RCPY2EBc3M4dgy0xmZPimLsbpLlR/NgqR7x0LPZ0NCA8+ehKJifx+YmYjGwLCQJgQC6\nu3HhAo4fL/9xHakAeyR2dXV1P/nJT5T9r3JjGEai0khCyJ4UBem00SW358uLnq+srWF5Gbnc\nC4+vSsgyMhlsbiKdNpoNRZGGRxx+LAu7HX/1VxgYwKef4v59TE/DZILXi7NncfIk2tpQU0MN\npwR7JnY8z9vt9j0fLctyPp8Ph8PhcDibzaqqajaba2pqamtrzWbzC5tjp2maqqrhcPjGjRtb\nW1ssyw4ODg4NDQHIZrORSGR2dnZsbCydTquqGgqFmpqaOjs7nU4nvpjbQgh50fSFQhwHVd27\nsw8AyxpHUNQ88bRIBAsLGB/HxARSKRQK4Hk4nfD7MTCA/n6IIh3YHFr6olWnE83N0DS0tSES\nAcfBakVzM4JBOJ300ye6YrN7TdMURUkmk5FIZGJiYnJyMh6PK4pit9ubmpq6u7v9fn9NTQ3L\nsi9goJ2maZlMZmFh4be//e34+DiAn/70p3p5Xzqdnpqa+vOf/3zp0qVYLKaqal9f39mzZ91u\nt9VqpcUYhJQNy8JkgskEQQDL7nFopy8RdzjgctHBwxP0YvnVVfzlL/jwQ1y+bBzRqSpsNvh8\n+PGPUVsLn48u7YeZXk7n8cDjKXcopKIV++qZzWY3Nzdv3br1+9//fnt7W5ZlURRZls3lcjdv\n3jSZTK+99trrr7/u9/sdDkdJIwZQKBSGh4dHRkZqa2vz+fzi4qL+cUVR1tfXP/jgg2g0+p3v\nfMfpdAqCcPfu3YmJidHRUY7jGhoaBHrhI6Qs9LzN7UZXFzQNa2tPdPYB4DiYzWhtRVeXUSFO\ndLkcNjdx5w4++AATE8jnjaI6TTPmAn76KQBjUgwh5GgrNrFLpVJjY2OfffbZZ599Zrfbg8Gg\n0+nkeX5nZ2djY2N1ddVisbhcrpMnT5Y6sctkMrFYbG5ubmNjIxgMAtAns+jHeJubm6Ojoy6X\n6/z5801NTaIoRiKR8fHxmZkZt9sdDAYpsSOkPPQZqrW1OHsWmmYMXM1mjU+xLHw+tLSgqwvN\nzTTI9AnZLJaWMDqK4WFEo8b2d52iIJfD/fvI5zE4iP5+4++ZEHJUFZvYxWKxTz75ZGVl5fz5\n8ydPnhwaGuJ5nmXZQqEwPT19/fr1zc3N3/zmNz6fr6WlpaQRb2xsTE9Py7Ls8/lCoVA+n9d3\nXaiqGolEIpGI1WoNhUJtbW1er1eW5Z6eHkVRotHo6urqV+gIIYQcpKYm/OhHsFhQKGBqChsb\nYFnwPEwmnD6Nt97CiRPweOiW4hPSaUxNYX4e2ezubmJ9ZEwshsVF7OwYrbKU2BFyhBWb2KXT\n6ZmZGYZhzpw5c/Lkya6uroefqqmpEUXxf//3fz/77LPt7e3SxAl80bqxsrIyNTXl8/kaGxvN\nZvPDQcqapiWTyVQqZTabnU6n0+m0WCy5XM7n83m93snJyZ2dnWftSRsfHx8fH8/n8/Ljb4W/\nMDMzE4lEWltbS/etEXJUOByQJJw7B57HwoIx90QQYLHg1Cm8/DJCITy2rnB/NA35POJxLC8j\nGkUqBYaBKBr1Zx5PtS4r08fEbG+jUNid2OnyeSSTxjop6pAlpPTy+fzVq1f3bCpgWdZkMjU2\nNvb29prKMVmw2MROluXt7e26urpTp06FQqHHP+Xz+U6fPn3jxo1IJJIr5ZCCfD6/s7OztLQ0\nOzvb39/f1dX1+HoMTdNyuVw+n9f32OrdrwzD6P+az+ez2az2jF68P/3pTz//+c9jsVgqlXr6\ns7Isp1Kpl156qUTfFyFHCMeBZXHqFPr6kM8jm0U2C0GAzQaz+etO7tA0pFKYm8Pvfoe7d7Gw\nAEGAy4Xz53H6NE6cqNbxrfqQv2ftCdUxDBQFhQINdibkBUin0//6r//6X//1X09/ymQyuVyu\nv/qrv2ptba3oxI7neZfLZTabE4lENpu1PlbarOdbLMt6PJ6Sfg/RaPT+/fs3b968du3azs6O\n3+/f3t5eWFiYnZ395JNPNE2rqakRBCGfz+fzeT2H0zRN/1ee5wVBeNask/7+/nfffTeTyeTz\n+ac/u7q6eunSpdJ9X4QcIXoFmNn8aDNSoWA0zH7Ng7RCAYkE/vAH/OUvGBnB0hKiUWOCay6H\ncBjxOPr60Npaffd5TSYEg6ithcmEfH6PQztRhNUKi+UA/hoJIUUwmUwXL17s6+t7+lMcx1ks\nlp6eHvNXvvnw9RSb2Imi6Pf7NU2bm5uTJEmSJP0EUtO0SCQyPT1dKBSCwaAoio8f2nEcd4AT\nRnZ2diYmJqampiYmJvSNZ4qi6PdnNU2LRqPvvPNOTU1NNptNp9OFQkGWZX1ESzKZNJvNFovl\nWYnd2bNnz549+6z/782bN0dGRg7quyCEAF9keCx7YGlWNouNDXzwAX7zG2Mxw8Oa2nAYS0vI\nZKBpCIWqrwpNkowVunY7cjmj4+RxNhv8fjgcX/0uNiFkPyRJ+tGPfvR3f/d35Q5kD8VmXSaT\nqb6+fmxs7N///d97e3v7+/ttNhvHcYlEYnp6+vPPP+c4LhQKhcPhW7duPfyvGhoaGhsbDyrW\nYDB48eLF7u7uv/3bvwWQzWa3trbGx8c///zzCxcufPe73w0Gg+vr66lUanl5eWpqKhQK8Tw/\nOjo6OTl57NixYDDIUfUJIYfV8jJu3cLaGnI5yPITx1qFAsJhXLuGujpcvIj970ssM4sFHR1Y\nWkJHBzgO6+tQFKiqcV+bYXDyJL77XRw7Vu5ACSHlV2xix7KsIAg7Ozv37t3b3t6OxWIOh0Mf\ndzIzM3P37t1jx4653e6ZmZmNjY2H/9W5c+cOMLGrqampqanp7e3V/zWZTM7PzzscjtXV1cHB\nwbfeeks/pfN6vblc7vbt26urq6Iozs7OptPpUCgUCoUosSPk0NrcxIMH2NoyOgweL0dTVSST\nmJ7GwgKyWShKlQ1AFkXU1aG7G+fPw+XC7KyxeUIUIUmwWvHqq3jzTQQC5Q6UHDqFglHfmc+D\n4yAIkCTwvPGOglSkYl/dMpnM3NxcOBy22WyJROL+/ft6yVqhUMhkMna7PZFIjIyMiKL4ePIU\nCATOnz9fmsj3wPN8XV3dd77zndu3b//ud7/LZrMAvF7v8ePHe3p6GhoaaPMEIYdWPI61NSST\nu+ceP65QQDwOh6PKEjtdWxv+/u8xPY2pKaytIZGAxYJgEO3taGlBQ0O1toaQSpZKYXUV6+vY\n2oIkweNBSwtqaujJVsmKfXWz2Wx9fX0ul2tfX72urm7/IRWL53mn09ne3n7hwoXW1laGYRiG\ncTqdQ0NDetKZSCRUVW1razt+/Hh9fb3NZitdMISQMtOPE551Kq+nevr2i6pbRKsfjTgcxp9g\nEFtbSKdhMsHnQ2MjrFa60JIDlkhgdhYzM5iexsYGolFIEmpqEAqhowO9vcboIlJ5ik3sAoHA\nu+++u+eYt+coaUuIyWQKBAJer/fll19+2I1rNpubm5vr6+tff/11vTFW74cVaQQAIYeb3Y5A\nADYbeB6yvMehnaaB52G3V3cO5PHA5YKqQlWNPLUaU1VS+cJhvP8+rlzB2BjSaWP2Nc9DEPD6\n6/jZz9DWRoldZdrHuBO73V7SUPaLYRie53melx57brEsq5cDWqquPpoQ8nUEgxgcxOefQxCg\nqnh8zQzHwelEays6O6t+IIieyRFSOoqC9XWMjuLOHYyPY2MDhQI0DSxrlNbdvYv33sPbb6O2\nttyxkj0Um9jpp1+qqiqKoqqqvsJh17xfhmFEUaQ6NkJIGQQCYBg0NaGmBvG4sYZBH6pitSIY\nxNmz6OuDKFZ3Ylctdp2Y0t95FZFlLCzg3j2Mj2N1FQ9HmD18szQ9je1thEJ44w2AfrgVZx9J\nmD4rbnFxcWtra3t7W5blXRu6TCbTqVOnaPUWIaQMRBEeD773Pfh8uH4ds7PY2gLLwmpFTw9O\nnMArr+D4cVrM8OLoA6j1O+B07a8iioLVVczNIR5HobDHA/J57OwYW+wEoSpbkQ61Yn8euVwu\nEolMTU3du3cvEokkEgn96O7xx1gslpaWFkrsCCFloNfPvfwyvF7YbBgfx9oaOA52O06cwNAQ\nBgbgdlOGUUKaBk1DMolYDIkEUinj0NRshsMBj8fYjUEqnKYhHkckgmx2793EsoxUinYTV6xi\nE7toNPqHP/zh5s2bo6OjDofD5/NxHLdrkQPP88rjdS2EEPIiMQzsdnR0wOtFKoVcDgwDnofD\nAbsddjtldaWlaSgUMDODTz7B/fuYmjKO6xwODA7iW99CayuCwXJHSYqgN0k8pylH04zNMdS4\nU3mKTezi8fjt27eXl5cbvqC3KTz+GJPJ5PF4ShAkIYQUgWEgihBFOJ3lDuVISiYxOYmrV/Hx\nxxgfx/w8APA8LBZEoygUcOEC7HaYzdW3rvdIYVl4PAgEIElg2d2HdgwDkwl2O6xWuslemYpN\n7NLp9OTkpN1u/+lPf9ra2rpnAscwjImO2Qkh5GiKRPCHP+DyZQwPIx43au0VBYUC7tzB7CxY\nFp2d8HgosatoPI+GBrS3w+kEzyOff+KzDAObDaEQ3G6qWK1M+2ueMJvNfr/f7XZX2ugTQggh\nZaNX18XjePAAk5O7i+5V1djktrCA0VH094OuIJWMZREIoKcHZ85A0zA1ZdRKaho4DhYLenpw\n8SL6+ui4rjIVm9hxHGe1WiVJYhhG0zR90AlDP1RCykjTjIFt+s57fdAUx4HnjYpm+g0lL4yi\nIJnE3ByWl/H0KHtNgyxjdRUjI2hoQENDOUIkxeE4+Hw4fhyvvgpZRiKBbNZ4MRFF1NTgpZfw\nN39D5ZIVq9jEzm63nzhxYnt7+6OPPnrppZdOnDjB8zxH7TCElJGqIhrF6iomJxGJIJOB14tA\nAB0dCATAcZTYkRdE05DLIZs1aur3fICqIpVCNPpoLhqpZA4HXn4ZdXU4exbRKBIJmEyw2VBb\ni5YW1NailJulyNdRbGJntVq7u7vv3r179erVbDZbKBSenkXM83xDQwP1TxDyImQy2NnB2BhG\nRnDnDtbWkEohGERLC7a30dODUAj6fmRK78gLoE8kfk6bpD4smlQLiwXHjiEUwuAgolHEYjCb\nYbfD54PZTD/NSlZsYmc2m0Oh0NjY2Ojo6MjIyC9+8YunEzu73f5P//RP3/zmN0sQJyHkSWtr\nePAAv/0trl9HLIZMBoqC2Vncu4dbt3DyJN59Fx0dNGWKvAgMA0mC1QpRNLZO7do8oVcIeL1o\nbjbeb5CqIAiw2WAywes11tnR7paKV2xil8lk5ubm1tfXTSaTvjdWEIRdiZ3NZhOo14mQF2N6\nGh9+iOvXMTYGRTFGEsTj2NpCOAwAL78MpxO1tTQXnpScPi/Q5UJvL7a2MDmJfP7RmAyWhSjC\nakVDA7q74XKVNVayH/oRLF3Zq0qxr/iRSOTSpUuLi4sdHR19fX3d3d2CIOyqsRMEoaOjowRB\nEkKeMjKC997D9vYT7YcPM7y1Ndy7B48HHg8lduQF8Xjw5pvI57G2hu1t46nIMBAEWCyoqUFb\nG06coNosQkqq2Ff8fD4fDoctFstbb73V3t4eDAaf3jzBsqyT5oKSI0UftZ/NIpGAqhq3LaxW\noJSVbbKMXA6JBOLx3SOmHoYUj2NhAevroGUw5IXR1/LKMiQJ09NYXEQuB56Hz4eWFnR24uxZ\nWCx0I4+Qkio2sVNVNZPJ1NfXv/7664FAgAYRE4JCwViGHY1iYwOKArMZtbVGv5heiVKKa5gs\nI51GLgdZ3l3JpFMUpNPY2EA0uveqR0JKQS+310/mbt3C558jmYQg4NgxDA3hzBnYbFT0SUip\nFZvYCYLg9XolSYpGow6HgxI7QjA3h5ER3L6NmRnkcsb0zpoaBIN4800MDMBiKUltij4jlOeN\nqbBP029+OZ2w2eh0hLxoVitCIZjN6OpCoQCWhcMBjwcWC+0VJeQFKDaxE0XR7/cXCoWRkZFC\nodDU1PT0rViGYWw2G+V85PArFJDLYWwMv/89rl7F1NSjTkCbDX4/TCZYLMZOngPHccZAKafT\nmB26K71jWUgS6uqMRjZCXiSTCSYT3G5QyTUh5VBsYmcymQKBwMjIyH/8x3/09fUNDQ1ZLBbx\nyT1xgiAcP348SNOoyaGXTGJlBZ99ho8+QjiMfN44GNM0YzPm73+PXA7vvluSxI5hjJ0/fX2Y\nnMTaGhTlidyO4+B0orsbLS3UOUEIIUfKPmrsstlsNBpdXFwEkMlkzGbzruEmkiR5PB5K7Mjh\nt7ODyUlMT2NpCYXCE3Vs+pSHyUl4PNjZgaIYh3kHSC/da2/HN78JSQLLIhxGJgMAggBJQlsb\nTp9GVxf8fjqxI4SQI6XYxC6bzS4tLW1ubgqCsLGxEYlEGIbZdSvW4XCcOnWqBEESUmG2t/Hg\nAVZXd2d1+GInZiSCjQ0kkygUUKLihN5e+P3geaMtt1CApsFigd+Pt97CxYvo7ITTSTV2hBBy\npBSb2Hm93nfeeeell156zmNMJlNra+tBREVIZZNlpFLI5/fuXdB7GhQFsmzcJC1FdiVJqK3F\nK6+grg5vvIFkEooCSYLTib4+tLXBbv/qtep64+3yMsbHEYkgkYAgwOFAUxMaG9HQAJ6nlJEQ\nQipQsYldbW3tj370o5KGQsjhoZfBPatr9UBwHKxWvPwyXnoJqmrkkTyPJytf900POJvF2hpu\n3sT772N6GuvrMJsRCuH8eZw/D6cTdvvX/R8RQggpASqsJmT/HA60t+PBA4jiHndj9aZUu93Y\nsfgCTrb0k7nn7F/fl2wWs7P4r//CzZuYmcHODtJppFLI5ZDNIhpFPI4TJ9DdfQD/L0IIIQeq\n2MRO0zQA+Xw+lUql0+lsNivLsqqq2mMHEhzH1dXVuWgPIDn0nE50dqKpyeiQyGSMgy69rUEQ\n0NhozDp5ATsW9cTxoJokVBXRKCYncfkyhoeRy0FVje8ulUI0inQa+TycThw//uj/TgghpDLs\n78QuEolMTExMT0/Pz88nEolsNvt4bme1Wn/yk5+cPXu2BHESUklcLnR3o7cXo6OYmTHWTjAM\nOA4MA5sNb7yBt9+Gz1fuQPdPljEzg5ERxGKQ5UdZHb5YVra8jEwGJ06UqnaQEELI11BsYpfJ\nZNbW1h48ePD555/H4/FUKjUxMbGzs1NfXy/L8sbGhs1mq6+vz2azJQ2XkIqgj2AdGEAyifFx\nLC0Zu2JFEU4n6urw+uvo6YHNVu5A908j+TomAAAgAElEQVRRsL6OxUUkk3usLNM0pNMoFBCN\nIpuFKJZwTp5eNbizg3gcsgyWhdUKm+1rNYUQQshhV+yL8s7OzvXr169du3br1q3Ozs62trbh\n4eHt7e3z589ns9n5+Xmfz9fY2GirxisZIV/N4CDa2zE7i/l5LC+jUIDFgrY2dHTA7YbVWpUz\n5FQVsRg2N5HJPHPPLMMgn0ciAYejhImd3pk7O4vJSWQy4Hk0NKCpiTZTEULIcxT7opxMJu/f\nv59MJl977bXu7u5AIHDlypVkMjkwMGC3291udyQSiUQidGJHjhCTCTyPlha4XGhpgaJAEOD1\nwuuFIFTrygeWhd0OlwuiCJbdO7fTNGMScoky13we29sYG8PVq1hawuYmcjmwLNxuNDaiowMD\nA+jpAajCjxBCdiv22pNKpcbGxhwOx/e///329nae530+XywW6+npaW9vb2tre//9969du5ZK\npUoaLiGVhePg8cDjKXccB4dl4fUiGDROHJ9O7DgOZjMsFthspcqrMhksLODjj/F//y92doxh\nLno2WVeH9naoKtrbwfNVeSZKCCGlVOwdDVmW4/G4qqq1tbX6/Vae5zmOk2VZFMX6+noAy8vL\nlNgRUt14HseOoa8PbjcEwWgH0ekNvw0NuHABbW0lPC3b2sL77+Pjj7Gzg1wO+bwx6jmXQzSK\nqSmMjhrtHYQQQp60v12xmqY5HA5JknK5nMlkEgRBn37C83yhUNjZ2SkUCiUNlxBSFH31hT5+\nRf9TJJZFIICuLvT1IZvF+jpSKWSzxkGd242BAbzxBo4dK0lip2lGkd+tWxgZQTq9u3sjkUAu\nh+lpjI3B6YTXe/AxEEJINdtHGZA+1kTfD8swjN1ulyRpbW3NZrOZTKZUKiVJEkd3RgipBIqC\nbBYMA1F84tTtSzEMTCa0tODv/x6dnbh8GfPz2NyE2Yy6Opw6hdOnce4c/P5SRZ7LIZVCMok9\nC3b1c7v1dUxN0YRkQgh5WrGJnSAIPp+vpqaG4ziGYXieb25u3tzcvHPnzsTEBM/zsVisu7ub\nphMTUk6JBGIxLCwYJ216XVpNDbxeNDXB6/3yJE+fxud0orcXZjNsNqyuIhKByQSvF93daG9H\nY2OpBi/r53OqClneY9iKTlWRzyOdhiyXJAZCCKlmxSZ2kiS1tLQEAgH9TE4QhN7e3nA4/Ktf\n/WphYUHTtHPnzr355pt1dXWljJYQ8lzhMEZH8Ytf4Pp1I/XRNHR2YnAQP/gBHA6YTEU1HPA8\nHA709aGzE4oCVTW23wpCaRt+9bTy+V+fYcDzxX4jhBByxBT7Au31et9++239risAjuNaW1tV\nVZUkaWNjQ1GUnp6ewcFBSuwIKY9sFskkbtzABx9geBhLS8YSW30/RC4Hmw2ZDE6dKqouTS/L\n0+cwv2AcB4sFfj/cbkSju4/lWBYch9padHbC6XzRsRFCSMXbR2L37W9/+9F/xvMNDQ11dXUD\nAwP63liHw0H3YQkpm0wGa2u4ehW/+AXyeSjKo09tbiIeN6rTWlqM4SyVOQFOP42z2dDUhIUF\npNPIZIwuEHwxP89qRWMjurtBLziEEPKUr3VLhWEYURQZhlFVVRTFg4qJELJv0Sju3cPKyh6l\naaqKXA6Li5ieNlafVfjmBq8X77wDSUIuh40NJJNGwKqKpiacPInTp9HUVJUb2wghpMSKTewU\nRcnn85lMJp1O2+12h8ORz+fj8bg+u05VVb/fX1dXJ0mS6cXfuyHkOQoF5HJIJpFKGWc/ZjOs\nVtjt4LhKT3GKl0hgdhZbW0ZJ3OM0DbKMSAQbG0bhXYW/DXM4cPKkEfPSEqJR4Ivyu85OvPoq\n+vtp0AkhhOyp2MQun89vbm7Ozc2Nj48PDQ2dOnUqFovdu3fvP//zP6empmRZfvPNN7/97W+3\ntbXV1taWNGJC9ieVwuoq7t/H6CjSaWPlaHc3hoZgtR6exC6fx87Olyx41TRks8jlIAgVeitW\nx3Gw2XDqFEIhbG5iawvZLFgWHg/8ftTWUnUdIYQ8S7GJXSKRuHPnztramj6CWP7/7N3pc1vn\nlefxL3CxXqwECBIkuJMSSUkktVi7vDvuKO5OUllmnEmnqqt65p/of6S7ql9MdaVrpieT1Ewn\n0213nFiWLUe29p3URnER9xUg9osLzIt7LVoUtdgRCRI4n9IbC7D8UCaBH57nOecUCiMjI3fu\n3Emn01ar1Wq1jo2NnT171u/3S7ATW4UxqOD2bbPb7d275iz5xkbGxpicZGCA3l6z3nO7s9vx\n+XC5zDlgTzYKsVqx2bbHGC6r1Yxx4TAtLWZTYouFmhrcbhyOLZ1KhRCirF402C0tLZ06dUpR\nlO985zvRaLRQKAwODj548ODgwYMul0vX9evXr//hD3/o6+vr6+vb0BUL8aJSKYaG+P3v+c1v\nmJsjk6FUwmrl7l2uXOGjj/iv/9UcOVoBvF7a2ohEcDjWFk/wtRYhXi9u93YKRl4vqmrW9lqt\n22nlQghRDi/6lpbNZh8+fBgKhRoaGgKBQLFYnJ+fTyaTO3bsiMViuq6Pjo5OTEzIrFixhSwt\ncfo0n3/O1JRZXAlYLGgamkY6zZ07XL9OayuRSLnX+mcLhejv5/p1ampYWTG/XmPfzpgGduAA\nr75KOLxttieNDFcZ+6lCCLFZXjTYaZq2sLCgqmpNTY3L5Uqn04lEIpvNtra29vb2aprm9XqX\nl5fz+fyGLleIF1IqUSqxtMRnn3HhAtns6tGkMUQ1m0XXGR7m0iW83koIdjU1+P309NDayswM\nS0tks2YBrDHj9bXXeOcdQqFyL1SIbct4GTFeXowLD8a1gW80jlmIDfaiwU5RFFVVdV2fnJzM\n5/OaphWLRVVVFUXRNC0ejwN+v1+anogtwagSSCZXm/SuUSyiaSwsMDpKMlmOJb5sRtHo8eME\nAty8yd27ZusTVaWzk54e9u+nqwtVLfdChdjOjNeWxUVSKXI5/P7Vq59CbA0vGuwcDkc0Gs1m\nsxcuXPD7/Ubvumg06nQ6U6nU+Pg40NLS4vF4NnK1QrwwXTd/rVsl+mjfLpmskJGjxp7Bzp20\ntNDSQkcHY2MUCng87NpFby+RCD5fuVcpxLZVKJDPMzXF9DRTU8TjZLPU1FBXR2MjkQiBgMQ7\nsRW8aLDz+/379u378ssv/+mf/qlYLHo8nqNHjw4MDPj9/oWFhYsXL1osloMHD0Yq4EhLVABj\nHJbLZfb4WPcJFgseD6FQGaZmbRzj4LW7m6Ym8wDaaB3i8chbjhB/lkyG6Wl+/Ws+/5x4nHye\nYtGcgHf0KEePcugQdXXlXqUQLxzsfD7f/v37i8Wipmmapnk8nt27d/f09Hg8nsXFRYvF0tbW\nVldXJ8FObBU2Gx6P+Ul6fn7tPAajSrSujp078fvLt8qXzTiQDQZl3JYQL9nwMGfP8umn5rVd\no9t5sYjNZoa8ujqcTnw+KfcR5fUNduwOHDjQ3t5+/PhxY4BYQ0NDTU2Noig+n6+zszMajba2\ntrpcrg1drhAvymLB66Wnh6kpUilz4sLXH7XZaGpi715zdqoQQjzDlSv88pc8eMDy8mM3d/N5\nrl2jVKK7m2AQVZVgJ8rrRYOd1Wp1Op2hUMjpdJZKJUVR3G633W4H/H5/V1eXz+fzeDwWqQwS\nW4HxfRgK8frr2GzY7dy/z8SEeTTpdtPezt69HDtGNIrbXe7lCiG2MF2nUGBujpEREonVRkKG\nUolMhrk57twxb7ja7eVbqxAvHOwMTqfzyVGwPp/PJ5eyxRYUDHLiBF4vhQJ2u1kha7dTU8OR\nI/z4x/T0UFNT7lUKAcUixSL5PIWCuRtkbCobH0uMHSD52Fwuuk4uRyKxzqUOQ6HAygoPHjA5\nWSHFWGI7q4ie+0Ksy3hrbG/nhz/k2DEWF9F1rFbcburraWuTi2hiq0ilmJ/n/HkGB1lYQNex\n2+nqoreX7m4aGyXVldmjtP00RqH9mokvQpSDBDtRuYxKgro6s1TNeOU1TmPlEozYIopFdJ3R\nUa5e5Y9/5OJFpqfRNFwu9uxhagpdR1EqrXx7ezHGn9jt2O3m3uoaxqOqalbiC1FWEuxENVGU\n53zsFmKTaRrJJKdP88//zPQ0CwvkchSLpFJcu8bsLMkkiQQnThCNlnut1croaeL1EgiwtISm\nrX2CzYbPR0cHsRiKUo4lCrFKgp2oGo9mjwqxdSQSDA9z8ybXr5t37B5d4ZqfJ5XC6cTppK+P\n+nqQb+ByMOaGdXby6qtcusTIiDlV7NEs5ro6c75LUxM2eVcVZSbfgkIIUT4LC1y6xNgYmra2\n3FLXSaW4eROvl5/8RDaby2zvXmw2s02xceWuWMRiweGgo4O9e+nro6lJduxE2UmwE0JUH11n\nbo7FRZaX0XUcDvMupsOx2b0q0mkmJlhaWr/cslQinyebNSeTOp2S7comFGLXLn76U3p6zCPy\nfB6fj3CY7m527aK+XrbrxFYg34VCiKphJKd8nlSKe/e4e9fcKjN6We/eTW0tPh+Ksnn5KZdj\neZl0ev2hxmCON8jlzGAnysXnw+slGuX117lzh7k50mmiUZqbicWkcZLYOiTYCSGqzPnznD7N\nvXuMjxOPUyzidPLZZzQ08N3vcvAgdXWb17ZaVWlsJBjEZlt7FPuIcXnf7ZbtuvIzGmH29NDW\nRqGA243Xi6qWe1lCrJJgJ4SoGvk8ySSXL/O73/HwIQsLZoWjcTve58PhwOXi8OHNC3Z+P52d\nXLuGw2EWTzxi9OsJhYhGzbWJ8nrUNdrjKfdShHgqCXZCiKqxuMjQEIODjIywsmLObgfzFnwi\nwenTlEp0dBCJbNKSwmEOHODyZZxOs9DyUZNbRcHrZf9+jh2Tkz4hxAuSYCeEqBrxOLdvMzbG\n8rI5Ys5gJCpNY3yce/eIx9E0bLbNOPpUVRoaOHCAkyfNmVTGNFKbjWiUtjbefJPDhwkE5BxW\nbHu6bu6R22zmNrnYABLshBBVY2WF+/eZnSWXW+fRUolcjlSKZJJcbpNKKBQFt5vXX6ejgw8/\n5IsvGBkhn0dVOXiQV1/lwAE6OuQcVlQCXSeZBFDV1SHI4mWTYCeEqBrGHalnxDWjP9lm7iVY\nLFgsBALYbLz1Fp2d5lBjh4PmZtraaGzE5dqkxQixEebnmZzkxg1GR0mnzbmOdXXEYuzZQ0uL\nuYEnXhIJdkKIqmG34/fjdmO1rtNexBgJqig4HJva8QTM8RJHj3L06Ob9R4XYaMZo3YcP+eIL\nfvMbzp1D181fnZ0MDPCf/zPBIH6/7Em/RBLshBBVo6aG3bu5fh27/bE7doDFgt1OfT1NTXg8\n0mlWiJcgk2FxkTNn+M1vuHePTMa8z1osMjPDtWv4/aRSvPUWjY3lXmvlkBcvIUTVCATYuZMd\nO2htZXaWRAKLxWwd53Dg89HTQ18fwaAMhhLiJVhZ4cEDrl7liy/MoXmPxONksygKNhv79tHQ\nADIK+eWQYCeEqBqqSnMzR46QTHLqFENDWK3m/kEoRHMz3/0ub79NbW25FypERVha4vp1Hj6k\nUFh7+aFYJJ9nfJzhYVIpikW5ZveySLATotyMayjLy8zMEI+TSmG3o6rU1VFTg88nr3cvjd2O\n3U53N4DPx65d5HKUSlgsRCI0NXHsGJ2dUqwgxMthjEI2JjI/OVVF10mlSCTIZCgU5JrdyyLB\nTohyM7psjI7y6afcucP4OF6vGTJ270ZVJdi9ZC0tRKMcOEA8ztwcpRJuN6EQNTW4XNjtch4k\nxMuRzxOPm1fr1mXchcjnyeflR+9lkWAnRPkYYwampvjkEy5fZnCQ6WkWF3E6GR5mYoL79zlx\ngrY28wKKeClsNrP01eslEACw23G7ZaNOiJdMVYnFCIWw2ykU1sY7o/2Q04nLhcMhqe5lkWAn\nRPkYe3UjI/yv/8X58ySTq9NCrVauXWNszLx6IsHu5Xo09FPGtwuxcfx+urq4cQOnEzDHTjxi\nDGiuqcHrNZ8gXgY54hGifAoF7t/n2jXm5kinyedXq8aKRQoFHjzg//0/BgfLukohhPhWwmH2\n72fnTiIRvN7VbTnjk5XXy6uvcvKklCu9XLJjJ6qSrlMooGloGlareRxgdLjYzOMAXWdsjDt3\nWFwklzP36r7+6NwciQTj4+Tz0pxdCLHNeL2oKn19HD7Mw4fMzLCyQrGI3Y7PRzTKG29w9CjB\nYLkXWlEk2ImqlM+TSDA/b15o8/tpbMTj2ezkVCyaw3aSybWpzmB04shmWVnB45FLYEKI7cSY\n5nLsGM3NXL3K0BDj4xQKeDz09LBnD93dxGK43eVeaEWRYCeqjFF+PzrK8DDz8yQSZmfahgba\n2+np2ezbHsao0Gcwrhs/92lCCLHVGC9c9fWEQmax/+wshQKqSlsbHR3miD/xUkmwE1VmaYkz\nZ/jkE86cIZsFzKNYVeXNN/lv/42Wls0LdlYrfj81NTidqyMQvs7YsbPbZciVEGK7slhwOOjq\nor3dvEb8qHpJPq9uAHmrEFVD11lY4PZtzp7l8mWmptB182XFyHaXLvHrX/Puu7z66iYtSVFo\naaG7m7NnUZS1ndkVhVCIWIyGBrlgJ4TYroyXWek/vFkk2ImqYXSMu3GD8+e5c4dc7rFHLRYG\nB1laIhrlxAnzdzaaotDayq5dRKNMTJBIrPZnt9lwu2lq4tAhWlpkdKkQQogXIcFOVA1d5+FD\n7t8nkVinUqFUIpNhbs4cTW23b8bRp9WK18uOHbz/PrEYZ84wN8fKCjYbwSDd3Rw9yttvmyOw\nhBBCiOeRYCeqRrHI4iIzM6TTq+3ivk7TWFkhnUbTNmmHzGLB5aKhgTfeMGtyp6ZYXsZmo7bW\n7BFw5Agez2YsRgghtgjj4CKdJpFgZYVUimLRvKvn9xMK4XRit5d7lVuUBDtRTYxahGcwjl+f\n/ZyXzukkGuX119m9m1zObK1n9HkKBGRWrBCiGuk6ExOcO8fVq9y8iaZht1NXx/79vPUWjY2E\nQuVe4hYlwU5UDauVYJBIBLcbRVln086ojTXGwG9mllIUVBVVJRpdLYyVYjEhRNVKpRgd5cIF\nPvmE69e5dw9Nw2YjHGZhgVSK48c5eHCT7sxsN/I3IqqGohCL0dmJ34/Ntk6wczqpqTH7Kkmu\nEkKIclla4vRpTp3i7FmWlshkACwWMhkWF7l6lUKBnh58Pgl2T5K/EVE1jGC3ezf9/WSzjI1R\nKKy2O7Fa2bGDN99kz55yHn1KoBRCVLNSiVKJRIIrV7h+3Ry3aBxlGHdpUik0jeFhbtygp0f6\nGz9Jgp2oGopCNEo+z8GDZDLkcmQylEooCoqCw8G+ffyn/0RHR7kXKoRYj/HuruvoOsUipRIW\nC4pi9rmVD0UVo1gkkWBwkPv31+lgoOtoGhMTXLtGXR2NjeVY4pYmwU5UmVCIt9+mvZ1XX2V2\nluXl1VmxXV20teH1lnuJQoinKBQYH2digrk5dB2Xi5YWWlpQ1U2dBCg2jtF5KpWiVFr/8KRU\nolAgkWB21pweJB4nwU5UGa+XXbtoaaGvj+lpFhZwuQgGaWnB78dul8/9Qmw5xl7d/DyTk9y8\nyb17TEyYI0d37KCnh85OolFcLmnlXQmKxee3JigWKRQ2u4PBNiHBTlQll4u6OoJB8nmzt4jL\nJZdwhdi6SiW+/JJ//VdGRpiaYmWFUgm7Ha+Xujp++lNef52mJmn6uO1ZLKgqXu9TM7oxAbKm\nhlhM/nevS97JRFUy5k/LrVshtoVkkoUFrl3j88+Znycep1Awj+qsVsbGqK3F6eSdd+SdvhLY\nbAQC7NjB9DRjY+TzqztzFgs2Gy4XjY309hIMlnWhW5QEOyGEEFvb4iKXLzM0xMQE2Sz5vPn7\nRiHFygqnT6PrDAwQi5V1oeIlCQY5fpx0msVF4nEz2BmpzulEVWlr48AByfHrkmAnxMYzqvQz\nGXTd7Kgpl/mEeHGJBPfuMTVl/hCtYRRRLiyQTJLL4XDID9c2Zvy/8/t55RWsVjwebt9mZMRs\nUFxbS3s7u3Zx4gQ+n1ypXJcEOyE2krGjYLRWWVoilzOHTHg8OJ3mO5C8CQnxbKkU4+MsLKBp\n6zxaKpHPk06TyZiDp+Rnarvz+ejrIxSioYGzZzl3jnQah4OODg4f5p13CIVwOMq9yi1Kgp0Q\nG2lpidFRzp3j4kWyWQoFrFbz2u/Roxw6hMMho6yFeA6jSMLpxGJZHbv3dcZvGtvhkuoqRjDI\n7t1EIhw9ar54+nxEItTUSKp7Bgl2QmwMo4vm+DhnzvAf/8Gnn5oHssUiNTW0tFAoEAjQ2ko4\nXO61CrG1ud1Eo/j9WK1ma+Kvs1pxu83kJ2dzlcTjweORe5PflAQ7ITaGpjE7y+XL/OpXDA+T\nza5OxYnHuX+fU6dIpfjJTyTYCfEcNTXs2cPVq9jtaNpj1+ysVhwOGhpoaXlWjwwhqoYEOyE2\nRibD6ChDQ9y9y9LSY29FuRyaxp07lEq8+iq6jtUq50dCPJXPR0cHfX3s38+DB8zNmb9fLOL1\nEg5z6BDHjlFTU85Bz0JsDRLshNgY6TR37zIyQja7to6vVELXmZvDYjEzn5RQCPEMqkpTE0eO\nkM/zu9+xsgJQLKLrhMPs3Ml3v8ubb8rmtxBIsBNioxQKxOOsrKDr61/3Nkbi5PPkcmafVbER\njAw9Pc3ICPE4+TyBALW15hAqu13+5rcB4wekrQ2LhXCY0VHSaUols/9FYyP79xMKyfAYIZBg\nJ8RGMVowaNpTpxlaLFit6LrZQ19shEKBTIbhYS5d4swZxsdJpWhqYudO3n4bu51QyBweLzum\nW180Sn09Bw6QyTA/T6mE12verxdCfEWCnRAbw+mkqYnGRtzutde9DcZgHI8HVZUb3xtlbIxb\nt/j4Yy5eZGKClRUKBZaXmZxkdJQDB/jRj+jslJ2e7cRqxeWitha+mg0ohPga+ZEQYmO43bS3\n09ZGTQ2aRjr92LhDi4VIhB07CIfNHSPxchmboMPD/PGPfPIJt26haeZvxuPmyezSktkENRyW\nbL09GD87Vqt0fxTiaSTYCbExPB56ehgbo7nZ3LHL59F1FAWrFUVh/36+/306Osq90Io2NMS/\n/zuzs6upDsxbj9ks8/Ncu0Y4TCAgwU4IURkk2AmxMWw2gkF6e/ne9xgaMveHMhmcTgIBIhHe\nfJMjR4hEyr3QCmU0iF5YYHycfH7tLcZikXyeRIKRESYnGRiQfdPtyqiNTSZJpykUsNvxeHC5\n5H+oqFoS7ITYMBYLPT20tnLzJtev8+ABy8v4/XR0sHcvzc3U1ckNoY2iaayskM0+9Qm6TirF\n1BTz808tcBFbX7FILsfEBFNTpNP4fDQ1UVsrwU5Ure30plIqlUql0tjY2IMHD8bGxhYWFkql\nUk1NTXNzc1dXV3t7O5DNZhcWFoaHhwcHB9PpdLFYbGpqam1t7e7uDgQCgEVq38TmML7THA4c\nDrq68Hrp7iabxekkHKaxEa9Xxh1uIKM2xWZ7VlWyMYHU45GS2G0pmyWV4tIlLlxgft7sZeNw\nEAjQ1UVPDz09NDSUe5VCbLbtFOyKxWKhUBgeHj516tT58+eHh4dLpVJTU1N/f//Jkyebm5ut\nVms6nb579+6nn3764YcfLi0tFYvFvr6+o0ePhkIhj8djk90RscmMxFBfT319uZdSZWw2PB7c\nbhwO87RuDaO4sq5OxhVsV6kUo6N88AH//M+rhefGROaBAY4fx+UiEpEmkaLabKegk8lk5ufn\n4/G43W5/7733XC6XpmkjIyOXL19ubm7u7OwMh8PT09P//u//vri4ePLkyUAgYLfbr169evv2\n7Zs3byqK0tzcbJdaKiGqhNVKczOHDnHnDtPTj/ULtFjMS5C7d9PZKSWW29L9+/zqV5w9y8oK\nxeLqLGZgZARFoauLujoaGlDV8q5UiM20nYKdruv5fN7tdjc3N+/duzcWi+Xz+d///vcfffTR\n9PT04uKioiizs7M3b94MBoPHjx9vbW11OBwLCwtDQ0P3798PhUKNjY0S7ISoCsZeaUcHr79u\nHsguLZHLUSxit+N2E42yaxe9vTQ2SknsNmNsy01OcuoUDx6sc5Nybg5NY2iIzk6CQQl2oqps\np2Dn8XhisVgoFMrlcj6fz+VyFYvFcDgcCAS8Xq/T6YzH4wsLCx6Pp6mpqaurq7a2tlAo7N69\nW9f1xcXFyclJ/cnjGCFEBevpweczu0BfucLcHPk8oRCxGN/5Dq++yo4dqKrcsdtmjIKJlRUW\nF0mn13mCrpPNMjrK8DDd3TJDVlSV7RTsbDabzWbzfDU9Jp1OT01Nrays1NXV1dfXB4PB2dnZ\nVCrlcrkCgUAgEFBVNZfLRSKR2traO3fuxOPx4lOuUQ8NDQ0NDeXz+UKh8OSj9+/fX1hY6JB+\nY0JsOzU1uN2k0/j9dHWxuIimEQhQX8+RI/T2Egpt9jmsMSN4aorpaebmyGTQdVwuvF5aW6mv\nR1WlVvo5SiWznU0ux3ov2pRKFAqkUuawZiFetnw+/6c//cm63vVNq9XqdDpbWlr27NnjLEd1\n9jZ++YjH419++eWDBw+Mutf6+vq5ubl8Pu9yuVwul1H9arFYjH/M5/PZbLb0lImcp0+f/vu/\n//ulpaVUKvXko4VCIZVKHTx4cGO/HiHES2ex4HKxbx979phze0sls0G0w4HdXoZDWKM38qVL\nfPop584xNUUuR20tbW388IecOEFjowS7b0B2W0U5pNPpX/7yl7/61a+efMjpdAaDwR/84Acd\nHR0S7F5IqVTSNO327dt37tyZmJgIBoP79u3buXOnoihOp9Nut+fz+Xw+b2S4Uqlk/KPNZrPb\n7U/rddLf3/+LX/wik8nk8/knH52cnPzwww839qsSQmwEYwKV07mFuprdu8fVq5w+zYULjIwQ\nj6/uLVmtzM/zve/R3o7LJbWcT2W14nDg8VBTQzLJysraJygKLheNjbS04HKVY4miwjmdznfe\neaevr+/JhxRFUVV19+7drjJ97+/EXKsAACAASURBVG2nYGdktXw+H4/HL126dPPmTbfb3dnZ\n+dZbb6mqqmma2+12Op3ZbDadTmuaVigUdF1PJpPJZNLlcqmq+rRgd/To0aNHjz7tv3vu3Lkb\nN25s1FclhKgSxonB7dv89rdcvMiDB+YmIhCPk0iY57NdXTJB+DmsVpxOgkFiMVZWVgcxPzqT\nMbrZtbfT0YHbXcaVikrldrt//OMf//Vf/3W5F7KObRbsdF2/dOnS+fPnU6lULBbr6+vr6Ohw\nOByAoiihUCgcDqdSqYcPH969e7epqclms928efPOnTudnZ2NjY2K1L4JIcqoWGR8nAsXzPG1\nX7/1a9wbi8e5eZP6empqpFb3OTo7+fnP+bd/46OPKBQe62PX3s7AAP39tLZKsBPVZjsFO03T\nksnk1atX/+3f/q2+vr6joyOTyczOzs7OztbU1IRCIYfDUVtbW1tbm8vlLl26NDk56XA4hoeH\n0+l0U1NTU1OTBDshRNnoOoUC8/OMjKxNdY+ekEzy4AETEzK+9vmiUd58k2SSeJxkkmwWXcdq\nxWajv59Dh+jpoba23Kv8ah8xkyGdNhdpnBR7PNjtcp9SvHTb6VtqZWVldHT01q1bFy9edDgc\nqqr+7ne/M/rSvfHGG9/5zne6u7uj0ejJkycvXbr0wQcfZLNZoLa2tre3d/fu3c3NzTJ5QghR\nNoUCySSaZt78e5LRxWNhgXicp1R6iVXGYIn33mNggPFxs8TY46G+nuZmYjFCoXIv8StG1707\nd5iYYGUFj4fmZnp7CYfx+8u9OFFptlPQsdlsqqr29va+++67ax6qr6/3eDwOh8Pr9e7bt8+o\nk1hZWSkWi11dXb29vbFYzOv1lmXZQggBX427UJSnFnIau01ut+zVvRCbDZuN5maiUZqbzQbU\nqmqmpa3wgl8qUSoxNcXgIEND3L7N1BTJJKpKQwO3b9Pfz549+P1yXixeou0U7Px+v9F8+Kc/\n/emahxwOh9PptNlsFoulra0tFou98cYbRrGFUQ/rkGnrQojyMs7dnE6s1vWznaKgqjQ2Ulsr\nF+xelFFIEYvR0ECphMWytYbDlkoMDvIP/8C9e0xNkclQKJjddlwufvQjXC66uiTYiZdoOwU7\nq9VqtVrtdrvP53v20+x2uyozZIQQW4pxAtvVxRtvcPMmDx+i6+aRq8Vi3ruqrWXXLjo65OrV\nizIi8tb860qnefiQmze5e5eJCRIJdJ1iEYuFTIaVFc6fx+3mJz+hvr7caxWVY0v+MAghROUx\nIsjOnXzve+g6KyukUhQKlErm/k0wSEsLe/bQ1rZFk8q3ZuRX42iSrzJuxfcWTqUYHGRwkKkp\nEgk0zfx9owJa17lxg+VlDh7klVeq4i9EbIrKeu0QQogtrrmZ117D4aC9nZs3mZ0llyMSobmZ\nffvYt4/W1kpLdXwVZZJJUilcLpxO3O4K/DLXyGZ5+JDJSXNw3JM0jXSadJpMBpdLzt/FS1Hp\nP1dCCLGlhMMEAjidRKNEo+a7fjRKRweHDrFzZ6W9wWsaqRSLi8zPs7REImEOxq2pobaW2try\nDHbbHLpOIsHKCoXCOt1tjCfk82ja6qG8EH82CXZCCLG5rFYaGwkG6e0ll0PXzR2sQKACJ4ml\nUty4wdmzfPwxyaS5caWqxGIcO8Zf/iXhcMUGO0XB60VVzVLop0U3mw2HQ85hxcsiwU4IITaX\n1YrHg8dDJFLupWykYpF0mtFRPvuM06f54gtyOcC8UxiJoGn4/ezbR09Pude6MZxOmppoaMDh\nIJNZG+wsFnw+olG8Xmy2Sgv0onwk2AkhhNgAxSLz8wwO8uGHXLlCMrlaQqFp5POcO0cyicVS\nscHO46G7m6EhXC6SycdOY42mhnV17NxJKFSxe5aiHOQjghBCiA2g64yPc/s2s7Ok0xSLq1Wx\nxSL5PPPz3LrF9PRTr6Btdw4H9fUMDPC977F7N04nDod5p9Dvp62N48f5wQ9oaZGSWPESyY6d\nEEKIDaDrPHzIvXtm/7Y1SiVWVkinWVigUMBuL8cSN5jDQW0t/f1YLNjtLC9TKJit7Gpr6ejg\n8GFefx2Xi3gcRcFmw25/av9qIV6MBDshhBAboFQimyWdflbJp8ViVoZarRV7HBkK0ddHIMBr\nr5FMks/jcJjFwrOz/MM/mNUz9fXs2MHevfh8MlNO/Dkk2AkhhNgYRr3n02pCrdZK7nXyiFEo\n09TE4cMkEqTT5POMjXH1Ktevc/Uq2Sx2u9nIMJ+nu5u2Ntm3E9+aBDshhBAbQFFoaKC1FZ+P\n+Xny+bVPsNlQVTweVLUqakIVBZ+PbJbBQT7/nI8/ZmqKxUWKRRSF+XnGxzl/nvff52c/w+Go\n/AbOYmPI940QQogNoCg0N9Pby/nzLC4Sj5tFEo/miUWjDAxUy2BcY8/SZiOT4eJF/vQnBgdJ\npVZvH6bTJBJMTbFnD8PDxGLU1JR1xWK7qoIfJyGEEJvPZqO9nXSazk5mZsjnyWTQNBTF/LVz\nJ++/z9695V7o5lpe5vPPuXjRrBR+xLhrCIyMcOECTqcEO/HtSLATQgjQdTSNhQVzWLvTSSCA\nz4fHU+6VbWd2O7EY3/8+HR3cucPsLMvLuFwEAjQ2MjDA3r3U1ZV7lZsomyWZNK/ZPdnhpVg0\nvwnHxhgYKMf6RCWQYCeEqG5Gf7VkkuVl7t41h7f6fDQ309hojg2ohrPCl864+x+J8N577NvH\nrVuMjDA1hcdDQwMDAzQ2EghUxe06g1EmnMk8tWlfqUShQCLB3BzZ7OYuTlQOebUSQlS3bJbl\nZT75hI8/ZmmJZBJNw243xwb09XH4MJ2d5V7lNhcMsmsXLS2k02bNRCiEqlZX4afFgtttftXr\n9n+xWLBa8fupq8Pt3vT1iQohwU4IUd0WFrh2jY8+4v/+X3TdbLpmvO/ev8/0NOGw+UYr+3bf\nghHdjJYfwuHA6yUcxu8nHl/b4c9qxWajtpb2drze8q1SbG/yOiWEqG7Dw/yP/8Hly6RS5rHs\nI+Pj6Dq9vTQ00N6Oz1e+VYqKYLHg9zMwwOIi166RTlMorD5kNPYzGtqFw2VdqNjGJNgJIapV\nsYiuMzfH9etMTKBpa5+wssLUFPfv8+AB0eg3DnaPMmJVHTiKpzG+DYJBjhwx276MjTEzg66j\nKHg8NDbS2cn+/cRiqGq5lyu2Kwl2QohqVSySy5FMsrhIOr3OEwoFMhnGxhge/pZViqWSpDrx\nmGCQ116jpgaXi7NnyWbJZnE4iMU4fpzvf5/ubvx++bYR35oEOyFEdSuV1p7APvnokzPsn/bM\n5WXu3GFkhIkJcjlKJbxeolF27yYWIxCo/Ala4tkUBVWlvZ233qKzkzfeoFBAUQgEaGtj1y6C\nwSqqFBYbQIKdEKJaGbeajPHzVuv66c1iwW7H4Xj+DkqpRCbDw4d8/DGffcb586RSlEpEIuzZ\nw49/zOHDZkWkvG1Xs0dTN6JRjh0r92pEBZJgJ4SoVlYrDofZXSKVYnl57b6douB209pKZ+fz\n7zzl83z5JWfOcOYMd++SSpmX9paWuHWLfJ65Od5/n/p6qQ8VQmwcCXZCiGplsZjdJQYGKBRI\np9F181a7kfACAZqb6eqivf05fcVKJfJ5rl7l1CkGB1lcXK12TKfJZpmbw+fj8GGcTgl2QoiN\nI8FOCFHdOjv5+c9xOpmZIZs1A5lxYa6zk6NH2buXlhZcrmf9IUawGx7m1i0SibWnuqWSWX57\n5Qp+P7HYBn45QojqJsFOCLEdGE2DjQrWRIJUCrsdVaW2Fq8Xl+vbX1wLhejvZ2kJi8WcPGG0\nn3C72bWLffvo6Hh+t1ijfjYeJx5H09Ye6RrBLpFgdJSlpW+5TiGEeAES7IQQ20SpxNwcly9z\n7x4PH+L1Eovxyiu0t+N0fvs/1unEbucv/oJjxxgbY3aWdBqfj8ZGIhFCIRyO5/8h+TypFLpu\nXo1/ssa2WCSTYW6OVOrbL1UIIZ5Hgp0QYmsz9upmZzl7lmvXuHOH6WkWF3E6CQa5cYO9ezlx\ngsbGb9ms32Ix28O6XNjt1NejaTid+P243c85gX3EmC1rt6+/cWhUwno81NfLqCghxIaSYCeE\n2PI0jfFxfv1rvviCuTmyWTRtdQTT3bs4HBw+/GdNYTL6nkQiRCLf5l93OvF68XpxuykUKBYf\ne9RiweGgpoaODhkVJYTYUNJOSQixtRUK3L/P1auMj7O0RDZrliYY9Q2axr17/O//zfXrZV6n\nw8HBg7z9NvX12L72mVlRzNjX1MSBA1I5IYTYULJjJ4TY2nSdsTGGhpiZIZl8bKKrUZQwM8PK\nCq+9hqaZrYbLwm6nv9+cEDU0ZHY8MSZP1NTQ0MDAADt3UlNTnuWtYRxwFwpomrlbabPJVAwh\nKoAEOyHE1qbrzM4yPs7KympzuK8rFsnnyWRIJlHVP6uQ4s9hs9HVhcdDKMTly1y6RDxOoUBL\nC93dHDlCTw9+/1YJT0Z/lpUVlpaw23G78ftl8LwQFUCCnRBiyzOmtT5touujR8vLasXvN+eP\nhcM0NZFMUigQjdLayu7dhMPY7eUf7m501JuaYmSEuTkSCRQFl4tIhMZGuroIBrfEOoUQ34oE\nOyHE1maxoKr4fGbaWDfblUrYbLjdj11uKwunk+ZmGho4dMjMmoqCouBwbJURsYUCt2/z2Wd8\n8AHj4wClElYr0Sj79/OLX7BnDzabBDshtqlyvwgKIcSzKQrNzezYwZdfoihrT2OtVgIB6uqI\nRLZEHHl0X+0F+6RsskSCqSk+/5yPP+bePZaXzaxssZDJYLEQDpNKceLEc0aoCSG2Kgl2Qoit\nzWajpYWeHurrmZwkmVw9k7VacTqpr6evj8bG8m/XbX1LS9y9y5/+xNmz5POPnV8vLKBp5HLY\n7Rw8iMtV/pQshPjm5HVQCLG1WSwEg/T08IMfEAxy5ow546FYRFVpaeHIEb77Xfr6yr3Q7WBm\nhhs3mJ83K3a/rlQik2FiwpyZ6/NtlToPIcQ3IcFOCLG1GTMbmpp4/XUcDkollpbI5ymV8Pvp\n6uLoUV59lWCw3AvdDpaWGB0lkUDX1wl2+TzLyywvk8+bA3OrgfH3UCiQSpFMmsXXpRJOJx4P\nwSAuF4oi+5diu5BgJ4TYDlSVzk7CYY4eJZEglcLpRFXx+6mpIRiUc9gXUiiQyayzXfd1pRKa\nZnaBrh6pFIODXLvG5cskEhQKNDXR3c3x47S04PVKsBPbhbwUCiG2A5sNvx+/n5YWMhmyWZxO\n7PZydiTejtxuwmHcbhRlnehm3Fl0OrHZquhvVdeZnjavHl6+zM2bq31qxsZYXubgQQ4eRFWr\nZQtTbHMS7IQQ240RPkA2Ub6x2lp27ODyZex2cybb11mtuN14PHg8OBxlWuKmKxS4cYNTp/jo\nIx48IJUy/1qWlxkd5dIlJibo7DQ/RQix5VXNZzIhRMUwWopYrVgsku2+mfp6+vvZsYPGRrxe\ns0GM0Z/Fbqe+nrff5siRKiqJNW4WXrvGmTPmdBPjGFrXyeVIJpmbY2SEq1eZmir3WoV4IbJj\nJ4QQVaOuDlVl927GxrDZWFggm0VRsNux2+nq4r33OHq0bGPZNl+pRC7H4CAXL5oVOV9/yGia\nOD3NlSuEw7S3b/ba+GqwijHb12o17x5USewW34oEOyGEqCYuF2+/TUsLt28zPs7MDHY7Ph+t\nrezYQX8/kUgVnTnmcuYunbH7+2SlsK6TTDI5SSKx2WszdhMXF5mYIJ2mUCASobaWUGiLtr8W\nW4MEOyGEqBoWC3Y7u3fT2kpnJw8fMjmJw4HPx86dxGJ4PNVVX1wsPr9GuFAgm1078mRDGZEu\nHmd8nNFRhodJJtE0GhpoaqK9nYYGwuEqyt/im6imH2AhhBAGl4u2NqJRcjnzjp2q4nJVUTGs\nwQi1NtvqOJOvs1hQFDweolF8vs1bVbHIwgLXr/OrX3H7NqkUmkaxiMtFJMKePZw4wV/8Baq6\neUsS24cEOyGEqD42Gz7fpoaVrcmY6huL0drK9PRqSazBiLzhMD09RCKbtypN4/p1/vAH/vQn\nxsbM0GmM9PX5WF7G7aa3l8ZG/P7NW5XYJiTYCSGEqGIOB729vPIKZ8+Sy5HPm79v1AsbxcL7\n99PUtHlLyuU4fZp//VcmJ8lkgNXdRF1naIj6em7cMJs7CvE4CXbbjdF6qlSSETdCCPHnslhw\nOBgYoFRCVbl+nQcPyGQoFqmpIRajr4/XXiMaxe3epCVpGqkU8/PMzZHLrT0g1nUyGWZmuH2b\nWIyurk1aldg+JNhtE0bFu6aRy5mXLWw285fDgd0O0qxVCCG+OZuNPXuIRPB4CARQFOJxCgVa\nWujv56/+it7eTa1U0DQyGVIp0ul1poMYA9+Wl3nwgMXFTVqS2FYk2G0TySQzM5w7x8WLrKyY\nrafq6ujsZP9+BgZQFKmQEkKIb8NiIRDgwAGamnjjDbOhncdDKERLC37/pn5sNprVwVNrdR+d\nEVdbpYt4MRLstjyjGn9iggsX+I//4NQpEgmyWSwWYjH27EHT8PmIRgkGy71WIYTYbozQpqq0\ntdHWVubFgBnaXC6cztUmyWue4HYTCklVrFiXBLstr1BgeZnLl/nv/51791hYoFAw67bm5rh0\nCSCR4L33JNgJIcS2Z7PhdhMOE4kwM2OOnXjEYjELdXftor6+fKsUW5cEuy0vk2F0lKEhbt0y\nU90j6TS5HDdvUixy4AB9fTI6UwghtjerFaeT/n4mJ/niC6anVwt1jQqP5mZ276a7m9rasi5U\nbFES7La8dJrbtxkeJpt9rMESX7VEn5xE15mfR9elVFYIIbY9p5PXXsPtZn6eTIZk0mxip+uE\nwxw5wrFj7NiB11vuhYqtSILdlqdpLC6ytISmrX+XVtfJ58lkyGZxu+U6rRDiJSgW0TQePmRq\nioUFc+xBQwONjfh8m9f7ozpZrYTD9Pfz13/N8DDz8+RyFIvmDIxdu9i5E1WVgjmxLgl2W56u\nk0qZfZXWDXbG8avRCcXp3PT1CSEqi/E6k8mwtMSVK1y9yr17FAr4/ezdy4EDtLZis6Eo8jFy\no1iteDx0ddHRwfIyk5OsrJizYiMRvF6zxZUQ65Fgt+U5nTQ2Eo3icqFp67Q1ejTN0OuVD3BC\niD+X0QX988/58EMePGBigqUldB2Hg9u3OXuWd9/l0CGam+UocMMZCS8WM9uXGvN85XVePJME\nuy3P5aK1leZmfD5yObLZx/btrFZCIdraCIVwucq3SiFEpchmice5eJHf/palJZJJ8x6IxcLw\nMLduYbNhtxMMSrDbWMaFaZdLXtvFNyLBbsvzetm1i5ERwmHzQNb46AYoCjYb/f28+y6treVe\nqBCiIiwscO0ad++yuEgms3q716jWWlnhzBksFvr6aGgo91qFEGtJsNvy7HZCIXbt4nvfY2iI\nsTGWlshksNupqaGhgddf58QJotFyL1QIURHicW7fZmKCVIpC4bEjAiPbTU0xOkoiQT6P3S6V\n+GJTGU37s1lyOXPz+NHxtHwrAhLstgdFYc8emps5f54vvuD+fRYX8Xrp7uboUXp6aGvDJv8r\nhRAvQzLJyAjz86vt076uVELXyeVIJslmsdnk3VRsKiPVzc0xP4/Hg99PJCL3Dr9O0sCW9+ia\nhd1OXx+BgNnZyOGgro7WVkIhKYYVQrw0Nhsej7kVt24lvvGbdrt8nhSbamWFkRHu3GFwkHjc\nPLlSVerq6O7mlVfw+eTdEAl224bVitVKezvt7eVeihCiorlc1Nbi82G1rtNlySjDN86/HA7Z\nrhObwajUXlri3Dk++oiPPiKfN78/HQ5CIb7zHSIR2tok2CHBTgghxGNCIQYGuHEDm21tX3Sr\nFYeDlhZ27DCTnxCboFhkZoYbNzh1isuXSaXQdXNH2agmvHQJt5uTJ3nnnXKvtfwk2AkhhPga\nv5+dO+npYccOpqdZXl7dt1NVwmEGBti3j0BAgp3YJLrO1BSDg1y7xoMHaNrqQ4UCmsa9e2Qy\ndHXx1lsyM12CnRBCiK9xuYhEOHEC4MMPuXKFbJZCAaCxkT17eO89jh0jHC7vMkUV0XXGxrhz\nh3h8nS79xSIrK4yNsbCAppljUaqYBDshnlAqkcuRSPDwIYuLpFJYLGa1Sl0d4bDZL7S6PxSK\nimWzYbPR1YXdjsPBzp2k0+g6VitNTezYwSuv0NIiU63E5ikWicdZXDRn5j6pUCCVMj+BWK0S\n7IQQjysWSaW4f58PPuDqVcbGsNnw+zlxgiNHOHAAp1NSnahwtbUEg/T0kM2STpufbRwOnE4c\nDkl1YrMVCuTz66c6g8Vitrir+m9OCXZCPE7TSCT4/e85c4br13n4kKUlLBbcbgoFFhZIJOjv\nN/czhKhUioKi4HTi9ZpXmoyWdXKvTmw+q5VIhKYmvF4WF9c5jbXb8XrxeHA6q3y7Dgl2QqyV\nyTAzwwcf8NvfkslQKJifEZeWmJ9nfJxUilKJlhZpzSqqgtUqLSREmSkKsRidnYRCzM6a0zUN\nxouwqhKNUlMjc3UB+ewlxOMmJrh4kelp8nl0/bGdf2PH7ssvuX6dXK58SxRCiGqiKLS20t9P\nTw+xGG43djuKYt4NcLno7uZnP2PfvnIvdEuQHTvxwopFdJ102kw8Npv5Q1Vhn+ZnZ7l1y6yu\nWnOfw6i9Gh5mfJxs1vxLEEIIsaGsVmpr2bmTw4exWLhzh5UVslnzPcjj4dgx3n2XtrZyL3RL\nkLcl8cI0zSwpmJoim8Xrpb6eWIxotNwre6lWVpiaIplE19eZp2R0SNI0VlYIBCTYCSHEJolE\n+P73GRjg7l0mJ5mfx+0mHGbHDtrbaW1FVcu9xC1B3pbEC8jlSKcZGuLmTcbHmZsjm0VVqa2l\ns5OuLjo7iUSgIjqAGNv7z7ghXiqZU5Uq4IsVQoitz3ixdbtpbSUYpL6e+XmWlnC5CARoaSEQ\nkGYFj0iwEy8gnWZigt/+lv/5P9E0CgWzV5DdTlcX/f38l/9iBrsK4PPR2IjPh81GobB2065U\nolTCZsPnk1u6Qgix2Xw+VJXWVopFs0zbZpNi7a+TYCdewOgov/sdX3zB9LSZbIyfKOOHqlSi\nv59olGi0EnbCGxoYGODCBex2SqXHDmQVhUCAzk66u+XToRBClIHVam4riKeQYCeeycg0IyP8\nn//DgwfrlILOzaFp3LxJayt+f4UEO6uV1lZCIRKJ1WFKViuqSizGsWP09+NwSLATYuta94Ks\nEFVAgp14pmKRfJ5UinicbHadJxQKZDI8fMjICLt2bfr6NoDDQSjE979PXR3nznH/PnNzWK14\nPOzezf79nDhBTw8OR7kXKoR4Jk2jVEJRsFol1YnqIcFOPJNxFpnPk8mY3efXMJLf0hKLi+s/\nYdsx7s8dOkQkgt9PLMbUFIqCz8f+/ezbR18fNTXyPiHEVpTPk82yuMjiorndbrfjduP1UlND\nMCghT1Q8CXbimYzyz2ffS7VYsNmw2yvn5dJiweulq4tQiHffJZcz/x78fvx+vN7K+UqFqDCJ\nBKOjnDrFJ5+QSpHPY7HQ0EBvL6+9xokT2GzSpUhUNvn+Fs9khDZVpaaGZJJkcu3NFasVh4O6\nOhoaKqdTscWC3Y7djt9f7qUIIV5MoUA2y717/PGPfPYZX35JNmseI9TVMT2NxYLTyY4dldZ6\nU4jHSbATz2QEO7+fpiZWVkinKRYfy3aKgstFWxs7duDxlG+hQojqpmksLHDxIr/8JZOTJJPw\nVQnF/LzZcjyR4P33JdiJyibBTjyTcebY2soPf4jXy5kz5kgxi8Xse9LRQV8f/f00N0tfNyFE\n2aTT3L3LvXvMz5NOP/b5s1AgnWZ0FIeDt99e7dYkRCWSYCfWY4Q2oxFxsWheNUskGBtjeZl0\n2nxNLJUYGODtt+nvp6Gh3IsWQlQxI9gZXZnWTHk2isBmZigWWVxE12VyjKhgEuzEeopFslmG\nhnjwgOlpslkUBaeTn/+cYpFCgXwep5NAgNZWWlvlaEMIUWbGSevTpjzz1fmDppHN4nbLrAJR\nqSTYiccZe3XT04yMcP48N24wNkY+j6qyZw/9/dTXEwya7T9qa3G75QRWCLElGAesxq91GxQr\nCqzXu1iICiLBTjzOOIH97DN+9SsmJpibY2UFXcdm4+5dvviCd9/l2DF27qSmBofDfKEUQojy\ncjqJxYhGcbnI5dD1tU9QFBwOVBVVle06UcEk2InHrawwM8P165w/TyJBJkOhYH7AXVxkdhaf\nD4+Hjg6pgRVCbCGqSlcXnZ0Eg2ZP9Ucl/MZU65YW9u6lvl762InKJt/f4nELC1y6xL17LC+T\nyz02TELXSSY5exbg9ddpayvTEoUQ4gnG0L/RUSIR0mkKBTTN3LdTFOx2+vv50Y/khUtUPAl2\nFceobDB6ztntuFz4fOZg0xepAksmGRlhbm79s4xikUyGRMLczHO5pLJMCLElKApuN3v28Dd/\nw+Agd+8yN0cqhctFXR1tbRw9yt69hMPlXqgQG0uCXQUpFtE0kkmWlpiZYX4et5tgkMZGgkFc\nLqzW598syWSYnmZ5+amDX42q2EyGXA6nU4KdEGJLMGojurvp6uLKFc6f5/59lpbw++nu5tAh\nmpupry/3KoXYcBLsKkg8zo0bXL3KpUskEuTz5kfYcJh9+3jnHWprUdXn/CFOJ6EQHg9W69pe\nUIZSCYsFl6tyBogJISqG8dm1vR2vl1deIZfD4SAQIBKRa8GiSkiwqwilEtks09N88QWnTvGn\nP6FpWK2USthseDzMzBAK0d9PR8dz/ihVpamJmhqz3HVNtlMUPB4CAVQVu12264QQW4uxbxeJ\nEImUeylClIcEu4qg68zOcvMmp09z5QqplDkzB7BYyOW4epVikZ/97PnBLhRiYIALF7DZ1kl1\nTidtbezcid8vjU6EEEKIrUaCXUXQdSYnuX2bBw+Yn6dQWPvo1BSaxokTaBqK8qybdh4Pzc3s\n3curr3LvHlNT5PNmy4BQ9RleVQAAIABJREFUiMZGjh/n2DGCQdmuE0IIIbYaCXYVQdcZH+fe\nPRKJtakOKJVIp5meZmnJrHh4RrBzuXA4eO01/H5+8xs+/5x4HE3DYqGpiYMHee89Dh3C59vQ\nL0gIIYQQ34IEu4pQKpHLkck8dUiiMQPb+PXscTrGDZVolP37sdk4dMg82DV+s7mZ3l45hxVV\nxxi1t7jI2Bijozx8SKGAopjjknt6CARwu8u9SiGEkGBXeZ49JPFRK/ZnCwYJBtm502yhYrFg\ns5kTGIWoQsUiuRxjY5w5w+efc+4cmQwOB83NHD+OotDRQUOD/IwIIcpOgl1FUBQaG+no4MoV\n5ufXaUHncODz4fOhqt9snI4R6Xix5sZCVKrlZS5f5uxZPv2UkRHm59F1rFY0jWyWkRF+8AP+\n6q/MmwxCCFE+EuwqgqIQi7FjB42NLCywvGzuzD3aPwiFzEe/afO5R1t9QlQnY4c7HufLL/nk\nEy5eJJVavcmaTrO0xL17xGK88gr19RLshBDlJcGuIigKzc0kk/T1ma2JczlKJXPUhNVKTw/v\nv8++feVeqBDbUKlEPM7Fi9y4QSr12Ki9UolCAYuF8XEuXeLQIYLB8i1UCCEqLtjl8/lEIjEz\nMzM2NpbL5UqlUjgcrq+vb2pqUlUVsFTkkaLVarYpMapZ29pYXjZnxaoqwSAHDnD0KI2N5V6o\nENuQppFKMTfH0tI6VefGkL25Oe7fp7e3HOsTQohVlRbsstns6Ojop59++tvf/nZhYUHX9Vde\neeX48eMnT55UnztNa7urreXkSfbvZ2SEsTGmp1FVIhF6emhoIBjEbi/3EoXYboyxLl9v+v3k\nEwoF4nGmpkinN319QgjxmMoJdqVSqVgszs7O/uEPfxgfHx8YGHA4HDabbWZm5tatW+3t7Yqi\n1NXV2b5R6cD2oiioKvX1OJ3U17Oygt2O10tdHR6PWdYqhPhGLBYcDlyup3Z/tFiwWvF6CYdx\nuTZ3cUIIsVZFpZx8Pj8zM/PJJ594vd6/+Zu/6ejocLlc//iP/zg0NDQ4OOjz+cLhcCUHO4Oq\noqpy6irES+Ny4fWiqjgcFAprR+1ZrdjthEK0tMiYeSFE2VVOyikWiwsLCwsLC6qqxmKxrq6u\n+vr6YrG4a9cu46HJycn+/v5yL1MIsQ0FAhw5QjrN1auk06v1E0aqczppbeXAAWpry7pKIYSo\nrGCXSCQSiYSqqrW1tdFoNBAI5HK5lpaWpaWl4eFh48rduv/u9PT09PR0oVAorvksDsDNmzdT\nqdQGL18IsSUZFxgCAQ4dIpslk2FqiuVldB1Fwe0mFCIaZc8eurrkKFaIKqHr+v3798+dO/fk\nQxaLxWazhUKhWCxWlkPCygl2pVIpm83m83mn0+lyuYzqV4vF4nK5XC5XPp/PZrOlpwxd+OST\nT/7lX/4lHo9nMpknH02lUiMjI319fRv7BQghtqxAgIMH8XqpqeHCBa5dI5vFbicWY98+Xn+d\n3btxu581hVkIUUFSqdQvf/nLDz744MmHHA6H3+9/++23//Zv/9bv92/+2ion2AFWq9VisRSL\nxWKxaGQ4o6KiWCxaLBbj0XX/xUAg0NzcHAqFstnsk48uLCxMTU1t7NKFEFuZ3U4kAuBwUF/P\njh3kcths1NfT3c3+/QSD32ymixBiOzPKMdva2p58yG63ezyecDhsLdMnvcp5JbJYLE6n0+Fw\n5HK5r2/OZbPZbDbrcDicTufTgt0bb7xx+PDhdc9hgUuXLv3d3/3dRq1bCLFdhMMEAvT1rU7t\nUxRsNhwO2asToqqoqvqLX/zipz/96bqPWq1Wh8NRriZrlRPsrFZrKBSqqalJpVJTU1Ojo6P5\nfN5isdy5c2d4eNi4dfe0+Ox2u91u99P+5GAwWPm1tEKIZzA+EyoKivKN5/IJISqOxWLx+Xy1\nW7JeqnLyitVqrampCYfDpVJpbm7u1q1b6XTa5XINDQ2Nj4/v2rWrXNcYhRBCCCE2R0UFHUVR\notHoyZMnb9++bVxpNK7c7dy5c/fu3c3Nzcr/b+9OY+M47/uBz87MzszO3vd98trlbZKSLDmS\nHUe2E0eOD9gNiqJ90aIvigJFX7dAkVcFerxK0SKv6qao08Sp4DaSIie2ZMkSJVGiKB4SKd5L\ncpfc+75mdo7/i+dvgpB8SLJISqvf54WBkJOZZ4ePOF8+x2/gffYAAAAAaF2tsy5EoVAQBGEy\nmY4cOdLb2ytJUi6XS6fTDodjYGAgFAqZTKb9WskIAAAAALAHWmrEDsMwlUoVDAZNJtOBAwcE\nQZBlWaPR6HQ6o9H4VTsnAAAAAABaQ6sFO5IktVqtVqv1eDz73RYAAAAAgD0FU5MAAAAAAC0C\ngh0AAAAAQIuAYAcAAAAA0CIg2AEAAAAAtAgIdgAAAAAALQKCHQAAAABAi4BgBwAAAADQIiDY\nAQAAAAC0CAh2AAAAAAAtAoIdAAAAAECLgGAHAAAAANAiINgBAAAAALQICHYAAAAAAC0Cgh0A\nAAAAQIuAYAcAAAAA0CIg2AEAAAAAtAgIdgAAAAAALQKCHQAAAABAi4BgBwAAAADQIiDYAQAA\nAAC0CAh2AAAAAAAtAoIdAAAAAECLgGAHAAAAANAiINgBAAAAALQICHYAAAAAAC2C3O8GPB0I\ngsjn8xMTEwaDYb/bAgAAAIB9s7m5SZKkQqHY74Z8OQh23wzHcZqmZ2Zm/vZv/5YgiP1uDgAA\nAAD2TTqdZhjmic0DEOy+mdlsPn78uN/v53leluXdu1A2m71586ZGoxkeHqZpevcuBJCZmZmF\nhYXh4eFAILDfbWl90L332O3bt+fn56F77w3o3ntsf7t3V1eXwWB4Yv9lQbD7Zl6v9y//8i+b\nzeZuX2hiYuJv/uZv/H7/3//935vN5t2+HPjHf/zHWCz2x3/8x++9995+t6X1QffeY//8z/+8\nsbEB3XtvQPfeY/vevXEcV6vV+3LpbwTB7puRJKnX6/fgQgaDgSRJmqbNZrPFYtmDKz7jWJZV\nKBRarRbu9h6A7r3HoHvvJejeewy699eAXbEAAAAAAC0Cgh0AAAAAQIuAYAcAAAAA0CIg2AEA\nAAAAtAgIdgAAAAAALQKCHQAAAABAi4Bg9wQhCEKr1aJd3PvdlmcCwzB6vZ6iqP1uyDMBuvce\ng+69l6B77zHo3l8Dgt0ThKZpp9NptVpxHH4ue8FgMPh8vie2yGSLge69x/R6PXTvPQPde49B\n9/4axE9+8pP9bgP4/5RKpdvtHhgY8Hg8JAm1o3edWq3u7u7u7+/fmwLUzzjo3ntMo9FA994z\n0L33GHTvr6HY1ZefAgAAAACAPQODxgAAAAAALQKCHQAAAABAi4BgBwAAAADQIiDYAQAAAAC0\nCAh2AAAAAAAtAoIdAAAAAECLgHI7TwRJkhqNRqVSyeVyjUZDkiSWZTUajdFoZFkWwzCoZv7I\nUEGfcrlcKpXK5XKj0ZBlmWEYjUZjMBh0Oh2GYYIgNBqNUqmUy+UEQZBlWaPRaLVao9GIKpvD\n/X8EsizLspxOpzOZjEKhUKlUdrsd9edGo1Gv13O5XKVSEUWRpumdPw642w9LFEWe53O5XKlU\najQaqA+bzWar1cowDI7j0L0fo2azWfqCKIro94lWqzWbzSqVSqFQcBwH3fuRCYIQi8VyuZwo\nijiOK5VKu91ut9u3D+B5vtFo5PP5QqEgSRJBEBqNRqfTGY1GHMdlWYaHKQS7J4IkSZubm7dv\n3z5//vz6+nqj0ejo6Ojr6zt27FhXV9d+t+7phuLF0tLSjRs3Jicn19fXZVl2u919fX1HjhwZ\nGRnBMKzRaKytrd24ceP8+fPol0VPT8/w8PDRo0cdDsd+f4KnlSRJoiiOjo6eOXMGx/FQKPTj\nH/84FAphGJbJZJaXl8+fPz8zM9NoNOx2e29v73e+852DBw/ud6ufSo1GI5PJnDt37saNG7FY\nrFQq8Tz/2muv/ehHP/L5fAzDQPd+jEql0sTExPj4+I0bNyqViizLPp+vv7//+9//fiAQoCgq\nm80uLS3d071feOGFQ4cO7XfbnwL1ev3Xv/71J598UqvVGIYxm83vvvvue++9t31AqVRaW1u7\ndOnS5cuXG40Gy7K9vb2HDh06evSoSqW6/2Ha3t7e39//TD1M4c0T+0+SpHq9Pjo6ev369UKh\ngP7+4DgunU5bLBaDwaBUKqGU+SPjOK5cLi8sLNy9e5fjOJIkaZpuNpuxWEyv17vdbgzDstns\nhQsXZmdna7UaTdMsy1YqlVqtZrPZ1Go1TdPwmqBHkMvl5ufnp6enl5aWEomELMsjIyMWi0WS\npImJic8++yyVSsmyzLKsLMvJZFKr1dpsNoIg4P2PD2thYeHatWu3b99OpVIsy+r1eoPBEIlE\n2tvbtVptsVjc7t4URUH3fmSyLAuCEI/Hf/e73y0uLpIkqdFo1Gp1tVptNBpoxI5l2cnJyfPn\nz9/TvTUaDXTvByEIwurqarFYVCqVtVptfX09HA6jP/lkWZYkaXl5+ZNPPllbW+N5nmVZpVKZ\ny+UUCoXdbidJUpZleJi2/id88omiWKlUPv/88+np6bfffruvr89ut586derjjz+enZ01mUzd\n3d00Te93M59W9Xp9a2urUCiQJPn666/7fL5Go3Hp0qWf//znHo8nEom43e5EInH27FmCIN55\n5x00wvHBBx/Mz88HAgGWZbVaLUEQ+/05nj7o+SdJ0uDg4MzMDPqiLMvNZvPWrVsfffTR22+/\n/c4779hstsnJyQ8++GBubs7tdnd3d8P7Hx/W5OTkL37xC7PZHA6Hjx8/7vf7GYZhGIamaUmS\nlpaWoHs/Fmiab2tra3R0VK/X//mf/7nT6VQoFCdPnlxcXJyZmVEqlUajcXJy8ku7t8fjge79\njVQq1Xvvvffaa69lMpmLFy9+8MEH299CwXppaenDDz88cODAH/7hH9rt9kwms92fJUkymUz3\nPExPnz599uzZZ+phCsFu/5XL5UQi0Ww2dTpde3t7V1eXVqttb28Ph8O1Wi0ajQaDQaPRuN/N\nfFrRNG2xWHp6elwul9/vN5lMgiAsLi7SNE2SJI7jxWIxm83KsmyxWCKRCPpN3dXVJQhCKpWK\nx+PPzgD+41Kv15PJZCKRIAjCZDJptdrV1VX0rVqtls1ma7WaSqXy+/2RSESj0ZRKpd7eXhzH\nFxYW3G43GkYFD4LjuGq1urm5ubm5eeDAgWPHjrW3t5tMJqVSqVAoRFHM5/PQvR8jHMfRQi6S\nJNG6LoVCQVGUQqFQKpU8zycSiWq1en/3JggCuveDwHFco9GQJCmKolqt3rkkjuf5TCZTLBYJ\ngnA6nb29vWjENBKJ5PP5lZUVhmF4noeHKQS7/VcqldAj0OFw+P1+l8uFYVgwGBwYGFhfX0er\nBPa7jU8xlmVZlt1eSyRJUrPZRGtpdTodwzBoUTnLsk6nMxAIGAwGjuO6uroajcb6+noikRBF\ncX8/wlMErWisVqtLS0vpdNpsNttsNq1Wy7JsvV7HMKxer29uboqiaLPZvF6v1+vFMMztdg8P\nD6+srCwvL6NVj+ABodV1mUymXC4HAoG+vj4cx2u1Go7jKG3kcjno3o+LQqEgSZJhGJ1OR1FU\npVIpFAoKhYLneZIk9Xo9QRDolkL33g08z6fT6Wq1ajQa3W53IBDAMEyW5b6+vrm5ubW1NZVK\nRZIkPEwh2O2/ZrNZq9VIkkQ9En2RoiiNRiOKYr1elyRpf1vYSorF4vT09Orqqtvt9vv9dru9\nVqvxPE/TNMMw238dopksjuPQLtr9bfNTRJblWq2WSqWWlpZomj548KAsy7lcbvsAQRCq1SqG\nYWq1enuxC1qrhGFYtVptNpv70vKnFM/zKFtoNJqxsbFYLJZMJkmSNJvNBw8efO6559BGCuje\njwu6t4cOHZqbm/vXf/1XhUJBEESj0XC5XG1tbSaTKZlMYl/WvRUKBXTvbwmtR0cjedszqjiO\no5V2tVqtWq3CwxSDYPckkCRJkiTFF9AXcRwnCEKWZbSdfn9b2ALQqtt8Pr+2tnbnzp1KpdLX\n1xcKhXQ6HUEQkiShGRZ0/xUKBfqf6EcD9//BSZKUTqeXl5dv376t1+uDwWClUllfX08mk81m\nc3Fx0el08jyPYRhBENtr9tHTEcMw6O0PC41Ay7KM43gmk0ErwARBYBiGoiitVlutVqF7P0aC\nIGAYptPpFApFPB5vNpsEQej1eqVSSRAEmv7GoHvvDvRrXJZldKvRF9HtRXdeFEV4mGIQ7J4E\nFEWpVCpBEJrN5va0CM/zlUqFIAiVSgV71h4LnufHx8dv375dqVT8fv+RI0fsdrtCoUBrzHeO\nXqAl0o1Gg6IomqafhbpHjwtav3jlypUrV64UCoVPPvkE1QjM5XI0TQuCMDw8jDa47Ry9EAQB\nlY1gWfZZ2LP2GJEkybJss9ksFApdXV2o4kM8Hp+YmMhkMp9++mlnZyd078cF/XG4srJy9epV\niqL++q//2mAwKBSK8fHxVCo1Pj7udDqtVisG3Xt3EATBMAxBENVqFf19iGGYKIq1Wq3ZbLIs\nq1ar73+Ychz3rD1MoZPtP7VabbFYBEHIZrOpVMrhcKhUqq2traWlJYqi7Hb7s7CLZ/egP/I2\nNjZWVlaWlpYajYbP5wuHw4FAAEUNnU6n1WprtVomk0kmk2iV2MbGRjwe1+l0ZrMZ9gw+OBzH\n0UDdkSNH0AxsrVYrl8uiKJIkabfbXS6XzWbDcTybzaLaxQzDZDKZ+fn5ZrPpdrthz+BDoWna\nbDazLEsQhNlsDoVCqIjJ8vIyWo3U09NDURR078dClmVUeTiZTPp8vp6eHrR4F82AJxIJtVrd\n1dUF3XuXkCRpMpkYhikWi+l0OplMqlSqYrG4srJSKBQcDofL5br/YZpIJJ61hykEu/2n1+ud\nTqcoipubm8vLy0aj0W63Ly0tjY+Pv/jii4FAQKVS7Xcbn2KoTO7ExMTHH39stVrb29uPHTvm\n8XhQNSn0ODSbzdVqNRaLLS4uiqLIMMzs7Ozi4uKxY8dcLhc8+R6cUqkcHBzs7u5+99130XIW\n1Kt/+9vfchz3p3/6p5FIBE2dJJPJaDS6urpqt9s3NjbGxsYikcjIyAiqzg8eEHqfh9Vq1ev1\n97xGAt1ns9lMURR078dClmWe5zmOu3+pFnp7EEmSLpeLJMn7u3c4HIbu/S3RNG2329VqdTab\nXV9fX15ettvtqVTq1q1bGIYdO3asra3NarXe8zBdXl5+1h6mEOz2H9o2PzIyolAopqenFxYW\nGIZJpVJOp7O9vb2tre0Z6Yu7pFqtptPpO3fuXLt2Ta/X3717d3Z2VqvVYhgWDod7enrQ+2qO\nHj0ai8U+/fRThmFIkkwkEm63OxwOe71eePI9OIVCQdP0zj+L0bZN1Id1Op1OpxNFsaen57vf\n/e7W1taHH37IMEy5XNZqtaFQKBKJGAyG/Wv+0we9c6m7u/v48ePJZPKXv/wljuOVSiWdTvt8\nvkgk4vF4FAoFdO/HAsdxk8nk9/vb29uLxeIvfvELiqJQ/WEcx/v6+sLhsFar7e7uhu79yDiO\nGxsbm5mZicfjs7OzsVjs7Nmz5XI5HA63t7d7PJ5gMPjaa681m82TJ08yDNNsNnme7+jo6Onp\ncbvdDMPAwxSC3f4jCEKtVh8+fJgkyVOnTi0tLdVqtd7e3oMHD0YikUAgAItgvo16vZ5IJDY3\nN2OxWCwWu3PnzmeffYa+deLECbRhymazvfLKK59//vmpU6ey2awoisPDwwcOHAiHwy6XC+7/\nt4EqRLAsi5Ywo2X7/f39OI6fOnXq0qVL9Xrd4/EMDQ1FIpHOzk642w8FLRLv6elRKpUfffTR\nlStXcrmcRqPx+/2HDx9+8cUX9Xp9s9mE7v1Y4DhusVhCoVBfX9/o6OipU6fQ4jm/3z80NDQ4\nOBiJRFiW7evrUygU0L0fDc/zY2Njp06dikajlUoFw7CrV69OTU2dOHFCkiSbzRYKhd5+++2P\nP/7497//fa1W02q1w8PDkUgkEomo1WpBEO55mPb09Bw6dOiZepgqnpFNIk84SZLQooH19fVy\nuSwIgtFotFgsXq/XZDJhz8Z7i3dJvV4vlUpLS0vbNXK3eb3eQCCA5qqKxWIikVhfX+c4Dr1A\n3W63e71etCYG7v8jq1ar5XJ5Y2NDFMVwOIxGLEqlUqFQWF9fT6fToiiyLGuxWDwej9PpxOBu\nP7xqtVosFtfW1lKpFMdxSqVSrVb7fD6fz0dRFPr1At3720OPy1qttrGxkUwms9ks2iGh0WjM\nZnMwGDQYDKi+XT6fv6d7u91uVFYN7vbXazabc3NzGxsb6FGIvkgQhM/nc7vddrtdluVisRiL\nxeLxuCiKSqXSbDY7nU6v14teKXbPw9RgMFit1mfqYQrBDgAAAACgRTwTW38BAAAAAJ4FEOwA\nAAAAAFoEBDsAAAAAgBYBwQ4AAAAAoEVAsAMAAAAAaBEQ7AAAAAAAWgQEOwAAAACAFgHBDgAA\nAACgRUCwAwAAAABoERDsAAAAAABaBAQ7AAAAAIAWAcEOAAAAAKBFQLADAAAAAGgREOwAAAAA\nAFoEBDsAAAAAgBZB7ncDAAAAwzBsaWkpGo1avsAwzMOeQZKkcrmcy+Xi8bhOpwuHwxRF7UZT\n95csy81mM5PJTE9Pq9XqgYEBlmVJ8mn9ZR6NRtfX1y0Wi8lkMhqNNE3vd4sAeLoRP/nJT/a7\nDQAAgJ05c+b9998vl8skSVosFrVa/bBnEARhY2Pj5s2b//M//5PL5QYHBx8hHT75ZFmuVqtT\nU1P/8A//sLKyMjw8zLKsUqnc73Y9onPnzv33f/93o9FQKpUmk0mlUu13iwB4uj2tf+QBAFoM\nx3HFYrFWq/E8L8vyI5yh2WxOT09PTU2ZzWaPx/P0DmJ9IzRoVygUDAaDKIqPdrueEDabraOj\nY319vV6vezweg8GAYZhCodjvdgHwtII1dgCAViBJUqPRmJ6enp2d7ejo6O3tfXoHsXaVJEm1\nWq1cLtfr9WazuUuhkOf5arVaKBRyO9Tr9fuPDAaDzz//fCaTuX79ejabFUVxN9oDwLOjZf+i\nBQA8U0qlUjweTyQSHMd5PB63200QxH436knUaDR++9vfrq6uhsPhjo6Otra23UjAc3NzN27c\nmJ+f39rawjBMoVAoFIq33nrrnXfeuedIo9EYDAZZlk0kEmtraw6Hw+l0tvBoKwC7Df7xAABa\nQS6XW11drVarNE3bbDaTyYTjMCPxJZrN5tTU1M2bN0VR1Ol0gUBgN4JdtVpNJpPRaHRlZaVe\nrxcKhUwmEwqF7g92arXaarUaDA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YFCn0/n9/o2NjUgkIkcNp6en5aq+eDyu1+s3NzdlbibYATuBYAfsDhnvjh8/rtPp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\nf19fH7REiUQiCwsLxWLxwoULHR0dnZ2dHMcdHh5aLBa/3+/x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Call the RandomRedDots3 function from a REST API","","Here is the equivalent REST API call:","","    $ curl -X POST --header \"Authorization: Bearer <token>\" --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"graphicsFlag\":true}' \"<omlserver>/oml/api/r-scripts/v1/do-eval/RandomRedDots3\""],"enabled":true,"result":{"startTime":1714696250096,"interpreter":"md.medium","endTime":1714696250158,"results":[{"message":"<h2 id=\"call-the-randomreddots3-function-from-a-rest-api\">Call the RandomRedDots3 function from a REST API<\/h2>\n<p>Here is the equivalent REST API call:<\/p>\n<pre><code>$ curl -X POST --header &quot;Authorization: Bearer &lt;token&gt;&quot; --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{&quot;graphicsFlag&quot;:true}' &quot;&lt;omlserver&gt;/oml/api/r-scripts/v1/do-eval/RandomRedDots3&quot;\n<\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Load a function from the R script repository","","Finally, note that a function stored in the R Script Repository can be loaded back in the R client engine, we can view its source, and finally invoke the function."],"enabled":true,"result":{"startTime":1714696285948,"interpreter":"md.medium","endTime":1714696286012,"results":[{"message":"<h2 id=\"load-a-function-from-the-r-script-repository\">Load a function from the R script repository<\/h2>\n<p>Finally, note that a function stored in the R Script Repository can be loaded back in the R client engine, we can view its source, and finally invoke the function.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{},\"image\":{\"height\":700,\"lastColumns\":[]}},{\"image\":{\"height\":700,\"lastColumns\":[]}},{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r","","ore.scriptLoad(name = 'RandomRedDots.3')","RandomRedDots.3","RandomRedDots.3()"],"enabled":true,"result":{"startTime":1714692122736,"interpreter":"r.medium","endTime":1714692123011,"results":[{"message":"<pre class=language-r><code>function (num_dots_1 = 100, num_dots_2 = 10) \n{\n<span style=white-space:pre-wrap>    library(lattice)<\/span>\n<span style=white-space:pre-wrap>    fig1 &lt;- xyplot(sample(num_dots_1) ~ sample(num_dots_1), pch = 19, <\/span>\n<span style=white-space:pre-wrap>        col = \"red\", main = \"Random Red Dots\")<\/span>\n<span style=white-space:pre-wrap>    fig2 &lt;- xyplot(sample(num_dots_2) ~ sample(num_dots_2), pch = 19, <\/span>\n<span style=white-space:pre-wrap>        col = \"red\", main = \"Random Red Dots\")<\/span>\n<span style=white-space:pre-wrap>    print(fig1)<\/span>\n<span style=white-space:pre-wrap>    print(fig2)<\/span>\n}<\/code><\/pre>\n","type":"HTML"},{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nJ1xkPOFasbAzFX/++WenTp3+/PNPqYNYEZ5zkfGEi4wnXGQ8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zMS4cfjhB7i6vhqRy9GvH/btg4ODdLGIii8pCbt3a1+aFsCWLbwhS1Ri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\nvvnmG7lzERmTWrVw4YLGh3aentKmISIifTKfJ3aHDx/ev39/UFBQixYt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AAAIog7AAAABRB2AEAACiCsAMAAFAE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\nywg7AAAARRB2AAAAiiDs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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Anomaly Detection SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715726114891,"interpreter":"md.medium","endTime":1715726114954,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Detect Unusual or Rare Occurrences using SVM Anomaly Detection","Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.) in your data using the semi-supervised learning algorithm One-Class Support Vector Machine. In this notebook, you will build a One-Class SVM model and then use it to flag unusual or suspicious records. The data used in this example is from the SH schema. The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).","","Copyright (c) 2024 Oracle Corporation","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715726115024,"interpreter":"md.medium","endTime":1715726115082,"results":[{"message":"<h1 id=\"detect-unusual-or-rare-occurrences-using-svm-anomaly-detection\">Detect Unusual or Rare Occurrences using SVM Anomaly Detection<\/h1>\n<p>Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.) in your data using the semi-supervised learning algorithm One-Class Support Vector Machine. In this notebook, you will build a One-Class SVM model and then use it to flag unusual or suspicious records. The data used in this example is from the SH schema. The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715726115157,"interpreter":"md.medium","endTime":1715726115217,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715726115294,"interpreter":"r.medium","endTime":1715726115412,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create OML Data Frames for tables CUSTOMERS and SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","ore.sync(query= c(\"CUSTOMERS\" =  'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS')) ","ore.sync(query= c(\"SUPP_DEM\" = \"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()"],"enabled":true,"result":{"startTime":1715726115483,"interpreter":"r.medium","endTime":1715726115602,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMERS","message":["%r","","z.show(head(CUSTOMERS, 10))"],"enabled":true,"result":{"startTime":1715726115674,"interpreter":"r.medium","endTime":1715726115858,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n48809\tM\tNA\t1973\tE: 90,000 - 109,999\t9000\n2365\tM\tmarried\t1971\tE: 90,000 - 109,999\t1500\n5920\tM\tNA\t1937\tE: 90,000 - 109,999\t7000\n9475\tM\tNA\t1945\tE: 90,000 - 109,999\t9000\n13031\tM\tmarried\t1963\tE: 90,000 - 109,999\t1500\n16587\tM\tNA\t1948\tE: 90,000 - 109,999\t7000\n20142\tM\tNA\t1969\tE: 90,000 - 109,999\t9000\n23698\tM\tmarried\t1951\tE: 90,000 - 109,999\t1500\n27253\tM\tNA\t1972\tE: 90,000 - 109,999\t7000\n30808\tM\tNA\t1965\tE: 90,000 - 109,999\t9000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPP_DEM","message":["%r","","z.show(head(SUPP_DEM, 10))"],"enabled":true,"result":{"startTime":1715726115936,"interpreter":"r.medium","endTime":1715726116053,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n102547\t10th\t0\t1\tOther\t0\t1\n101050\t10th\t0\t1\tOther\t0\t1\n100040\t11th\t0\t1\tSales\t0\t1\n102117\tHS-grad\t0\t1\tFarming\t0\t1\n101074\t10th\t0\t1\tHandler\t1\t1\n104179\t10th\t0\t1\tHandler\t1\t1\n100417\t11th\t0\t1\tHandler\t1\t1\n101146\t< Bach.\t0\t1\t?\t1\t1\n103420\t< Bach.\t0\t1\t?\t1\t1\n101987\t< Bach.\t0\t1\tOther\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Merge CUSTOMERS and SUPP_DEM on CUST_ID column using proxy objects ","message":["%r","","CUST_DF <- merge(CUSTOMERS, SUPP_DEM, by = \"CUST_ID\")  "],"enabled":true,"result":{"startTime":1715726116129,"interpreter":"r.medium","endTime":1715726116780,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUST_DF","message":["%r","","z.show(head(CUST_DF, 10))"],"enabled":true,"result":{"startTime":1715726116854,"interpreter":"r.medium","endTime":1715726119651,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n104088\tM\tNeverM\t1980\tL: 300,000 and above\t11000\tBach.\t0\t1\tExec.\t1\t1\n104089\tM\tMarried\t1970\tH: 150,000 - 169,999\t5000\t< Bach.\t0\t3\tCrafts\t3\t0\n104090\tM\tMarried\t1953\tG: 130,000 - 149,999\t7000\tMasters\t1\t3\tProf.\t6\t0\n104091\tM\tMarried\t1964\tJ: 190,000 - 249,999\t15000\tHS-grad\t0\t3\tCrafts\t5\t0\n104092\tF\tNeverM\t1958\tL: 300,000 and above\t15000\tBach.\t0\t2\tSales\t5\t0\n104093\tM\tMarried\t1956\tF: 110,000 - 129,999\t1500\tBach.\t1\t3\tProf.\t7\t0\n104094\tM\tMarried\t1970\tI: 170,000 - 189,999\t9000\t< Bach.\t1\t3\tTransp.\t3\t0\n104095\tM\tNeverM\t1981\tK: 250,000 - 299,999\t11000\tHS-grad\t0\t2\tOther\t2\t1\n104096\tM\tNeverM\t1982\tJ: 190,000 - 249,999\t15000\t< Bach.\t0\t1\tOther\t2\t1\n104097\tM\tMabsent\t1981\tL: 300,000 and above\t11000\tHS-grad\t0\t2\tSales\t3\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"showSeriesX\":[\"sum.YRS_RESIDENCE.\"],\"extraDescription\":[],\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL.STATUS\",\"sum.YRS_RESIDENCE.\"],\"version\":1},\"bar\":{\"groupByColumns\":[\"CUST_MARITAL.STATUS\",\"CUST_YEAR_OF_BIRTH\"],\"showSeries\":[\"sum.YRS_RESIDENCE.\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"sum.YRS_RESIDENCE.\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL.STATUS\",\"sum.YRS_RESIDENCE.\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Visualize the distribution of birth date, marital status, and years of residence","message":["%r","","DF <- aggregate(CUST_DF$YRS_RESIDENCE, by = list(CUST_YEAR_OF_BIRTH = CUST_DF$CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS = CUST_DF$CUST_MARITAL_STATUS), FUN = sum)","colnames(DF) <- c('CUST_YEAR_OF_BIRTH','CUST_MARITAL STATUS', 'sum(YRS_RESIDENCE)')","z.show(head(DF, 20))"],"enabled":true,"result":{"startTime":1715726119724,"interpreter":"r.medium","endTime":1715726119909,"results":[{"message":"CUST_YEAR_OF_BIRTH\tCUST_MARITAL.STATUS\tsum.YRS_RESIDENCE.\n1913\tMarried\t29\n1913\tNeverM\t23\n1921\tMarried\t8\n1922\tDivorc.\t8\n1922\tWidowed\t4\n1923\tMarried\t10\n1923\tNeverM\t4\n1924\tMabsent\t4\n1924\tMarried\t20\n1925\tMarried\t40\n1925\tWidowed\t10\n1926\tMabsent\t8\n1926\tMarried\t34\n1926\tNeverM\t4\n1926\tWidowed\t8\n1927\tWidowed\t14\n1928\tDivorc.\t8\n1928\tMabsent\t6\n1928\tMar-AF\t2\n1928\tMarried\t16\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build an Anomaly Detection Model","message":["%md","### Build an Anomaly Detection model","","Build a one-class SVM anomaly detection model using ore.odmSVM.  Specify the outlier rate, which is the proportion of rows that should be flagged as outliers. ","","Note that settings can be specified as explicit arguments - as indicated in the R documentation ?ore.odmSVM - or through a list of name-value pairs specified in the odm.settings argument. "],"enabled":true,"result":{"startTime":1715726119996,"interpreter":"md.medium","endTime":1715726120234,"results":[{"message":"<h3 id=\"build-an-anomaly-detection-model\">Build an Anomaly Detection model<\/h3>\n<p>Build a one-class SVM anomaly detection model using ore.odmSVM.  Specify the outlier rate, which is the proportion of rows that should be flagged as outliers.<\/p>\n<p>Note that settings can be specified as explicit arguments - as indicated in the R documentation ?ore.odmSVM - or through a list of name-value pairs specified in the odm.settings argument.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build the One-Class SVM model","message":["%r","","SVM.MOD <- ore.odmSVM(~., data = CUST_DF, type = \"anomaly.detection\", outlier.rate = 0.01)"],"enabled":true,"result":{"startTime":1715726120310,"interpreter":"r.medium","endTime":1715726126211,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Examples of possible setting overrides for SVM using the name-value settings specification with odm.settings","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: [Support Vector Machine](https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB)","","* Shared Settings: [All algorithms](https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html)","","* Specify a row weight column","","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is ODMS_MISSING_VALUE_AUTO. The option ODMS_MISSING_VALUE_MEAN_MODE replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option ODMS_MISSING_VALUE_AUTO performs different strategies for different algorithms. When ODMS_MISSING_VALUE_TREATMENT is set to ODMS_MISSING_VALUE_DELETE_ROW, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","","* Switch between Kernel Types - Linear or Gaussian","By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR'","","> 'SVMS_KERNEL_FUNCTION' : 'SVMS_GAUSSIAN'","* Value of complexity factor for SVM algorithm (both classification and regression).","Default value estimated from the data by the algorithm.","> 'SVMS_COMPLEXITY_FACTOR' : '0.1'","*  Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1'","","> 'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L2'","*  Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0. The default is 0.1.","> 'SVMS_EPSILON' : '0.1'  ","*  Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20000.","> 'SVMS_BATCH_ROWS' : '20000'","*  Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.","> 'SVMS_SOLVER' : 'SVMS_SOLVER_SGD'","> 'SVMS_SOLVER' : 'SVMS_SOLVER_IPM'","*  Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.","> 'SVMS_CONV_TOLERANCE' : '0.001'","*  Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.","> 'SVMS_NUM_PIVOTS' : '200'"],"enabled":true,"result":{"startTime":1715726126298,"interpreter":"md.medium","endTime":1715726126365,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm-using-the-name-value-settings-specification-with-odmsettings\">Examples of possible setting overrides for SVM using the name-value settings specification with odm.settings<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is ODMS_MISSING_VALUE_AUTO. The option ODMS_MISSING_VALUE_MEAN_MODE replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option ODMS_MISSING_VALUE_AUTO performs different strategies for different algorithms. When ODMS_MISSING_VALUE_TREATMENT is set to ODMS_MISSING_VALUE_DELETE_ROW, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian\nBy default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_LINEAR'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'SVMS_KERNEL_FUNCTION' : 'SVMS_GAUSSIAN'<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression).\nDefault value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_COMPLEXITY_FACTOR' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L1'<\/p>\n<\/blockquote>\n<blockquote>\n<p>'SVMS_REGULARIZER' : 'SVMS_REGULARIZER_L2'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0. The default is 0.1.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_EPSILON' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20000.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_BATCH_ROWS' : '20000'<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_SOLVER' : 'SVMS_SOLVER_SGD'\n'SVMS_SOLVER' : 'SVMS_SOLVER_IPM'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_CONV_TOLERANCE' : '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>'SVMS_NUM_PIVOTS' : '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build the SVM model using the odm.settings","message":["%r","","settings <- list(svms_outlier_rate = '0.01', svms_regularizer = 'SVMS_REGULARIZER_L1', svms_kernel_function = 'SVMS_LINEAR', svms_conv_tolerance = 0.001 )","","SVM.MOD <- ore.odmSVM(~., data = CUST_DF, ","                      type = \"anomaly.detection\",","                      odm.settings = settings)","summary(SVM.MOD)"],"enabled":true,"result":{"startTime":1715726126447,"interpreter":"r.medium","endTime":1715726128944,"results":[{"message":"\nCall:\nore.odmSVM(formula = ~., data = CUST_DF, type = \"anomaly.detection\", \n    odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 0.001\nkernel.cache.size                              5e+07\nkernel.function                               linear\noutlier.rate                                    0.01\nregularizer                           regularizer.l1\n\nCoefficients: \n              variable                value     estimate\n1          (Intercept)                      -1.000000491\n2        AFFINITY_CARD                       0.533187088\n3    CUST_CREDIT_LIMIT                       0.904174856\n4          CUST_GENDER                    F  0.944724238\n5          CUST_GENDER                    M  1.086436436\n6              CUST_ID                       0.974924793\n7    CUST_INCOME_LEVEL      A: Below 30,000  0.069078694\n8    CUST_INCOME_LEVEL   B: 30,000 - 49,999  0.145265468\n9    CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.098754992\n10   CUST_INCOME_LEVEL   D: 70,000 - 89,999  0.080186591\n11   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.169994663\n12   CUST_INCOME_LEVEL F: 110,000 - 129,999  0.200750695\n13   CUST_INCOME_LEVEL G: 130,000 - 149,999  0.175458584\n14   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.200093355\n15   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.226143730\n16   CUST_INCOME_LEVEL J: 190,000 - 249,999  0.289046239\n17   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.158834634\n18   CUST_INCOME_LEVEL L: 300,000 and above  0.217553029\n19 CUST_MARITAL_STATUS              Divorc.  0.434445683\n20 CUST_MARITAL_STATUS              Mabsent  0.059256134\n21 CUST_MARITAL_STATUS               Mar-AF  0.002444605\n22 CUST_MARITAL_STATUS              Married  0.716268228\n23 CUST_MARITAL_STATUS               NeverM  0.600822589\n24 CUST_MARITAL_STATUS               Separ.  0.105091063\n25 CUST_MARITAL_STATUS              Widowed  0.112832373\n26  CUST_YEAR_OF_BIRTH                       1.411096227\n27           EDUCATION                 10th  0.080537332\n28           EDUCATION                 11th  0.083574205\n29           EDUCATION                 12th  0.035647395\n30           EDUCATION              1st-4th  0.012169450\n31           EDUCATION              5th-6th  0.025580999\n32           EDUCATION              7th-8th  0.063932059\n33           EDUCATION                  9th  0.057288066\n34           EDUCATION              < Bach.  0.418922975\n35           EDUCATION              Assoc-A  0.108468443\n36           EDUCATION              Assoc-V  0.125578850\n37           EDUCATION                Bach.  0.312883893\n38           EDUCATION              HS-grad  0.518107344\n39           EDUCATION              Masters  0.111040748\n40           EDUCATION                  PhD  0.025214822\n41           EDUCATION              Presch.  0.001690826\n42           EDUCATION               Profsc  0.050523266\n43      HOUSEHOLD_SIZE                    1  0.313540719\n44      HOUSEHOLD_SIZE                    2  0.547847331\n45      HOUSEHOLD_SIZE                    3  0.552514933\n46      HOUSEHOLD_SIZE                  4-5  0.151945929\n47      HOUSEHOLD_SIZE                  6-8  0.095757336\n48      HOUSEHOLD_SIZE                   9+  0.369554425\n49          OCCUPATION                    ?  0.140320370\n50          OCCUPATION              Armed-F  0.001552633\n51          OCCUPATION              Cleric.  0.243564911\n52          OCCUPATION               Crafts  0.204346175\n53          OCCUPATION                Exec.  0.220996584\n54          OCCUPATION              Farming  0.070156757\n55          OCCUPATION              Handler  0.082790910\n56          OCCUPATION              House-s  0.014856271\n57          OCCUPATION              Machine  0.130661379\n58          OCCUPATION                Other  0.230752921\n59          OCCUPATION                Prof.  0.253894918\n60          OCCUPATION              Protec.  0.038816702\n61          OCCUPATION                Sales  0.218183276\n62          OCCUPATION              TechSup  0.097716939\n63          OCCUPATION              Transp.  0.082549927\n64       YRS_RESIDENCE                       0.747704907\n65         Y_BOX_GAMES                       0.595857354\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Invoke predict() to score data for anomalies","message":["%r","","RES_DF <- predict(SVM.MOD, CUST_DF, supplemental.cols = colnames(CUST_DF), proba = TRUE, type = \"class\")"],"enabled":true,"result":{"startTime":1715726129029,"interpreter":"r.medium","endTime":1715726129297,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Anomalies are indicated by PREDICTION = 0, display a few rows flagged as anomalies","message":["%r","","z.show(head(RES_DF[RES_DF$PREDICTION == 0,], 10))"],"enabled":true,"result":{"startTime":1715726129371,"interpreter":"r.medium","endTime":1715726129558,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY\n104275\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\tBach.\t0\t2\tProf.\t0\t0\t0\t0.516454809427265\n104359\tF\tWidowed\t1927\tD: 70,000 - 89,999\t1500\t7th-8th\t0\t2\tOther\t6\t0\t0\t0.542283559936595\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\t0\t0.562704326544703\n100001\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\t< Bach.\t0\t2\tExec.\t3\t0\t0\t0.557876401433934\n100017\tF\tMabsent\t1935\tI: 170,000 - 189,999\t10000\t< Bach.\t0\t9+\tFarming\t5\t0\t0\t0.542615745076834\n100031\tF\tDivorc.\t1967\tH: 150,000 - 169,999\t1500\tBach.\t0\t6-8\tSales\t4\t0\t0\t0.500269901330298\n100072\tM\tMabsent\t1971\tJ: 190,000 - 249,999\t9000\t5th-6th\t0\t6-8\tCrafts\t3\t0\t0\t0.524271250281955\n100132\tF\tWidowed\t1926\tI: 170,000 - 189,999\t3000\t< Bach.\t0\t2\tHouse-s\t4\t0\t0\t0.637477532369609\n100148\tF\tWidowed\t1936\tI: 170,000 - 189,999\t9000\t7th-8th\t0\t2\tOther\t3\t0\t0\t0.553179536335528\n100170\tF\tWidowed\t1943\tF: 110,000 - 129,999\t3000\t7th-8th\t0\t9+\t?\t3\t0\t0\t0.701078977491907\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Plot the probability distribution to be an anomaly against marital status","message":["%r","","rownames(RES_DF) <- RES_DF['CUST_ID']","RES_DF <- mutate(RES_DF,\"Bin_PROBABILITY\" = cut(RES_DF$PROBABILITY, breaks = 10, dig.lab=3)) ","temp <- ore.crosstab(CUST_MARITAL_STATUS ~ Bin_PROBABILITY, RES_DF)","","z.show(head(temp, 10))"],"enabled":true,"result":{"startTime":1715726129642,"interpreter":"r.medium","endTime":1715726130641,"results":[{"message":"CUST_MARITAL_STATUS\tBin_PROBABILITY\tORE.FREQ\tORE.STRATA\tORE.GROUP\nDivorc.\t(0.5,0.545]\t8\t1\t1\nDivorc.\t(0.545,0.59]\t14\t1\t1\nDivorc.\t(0.59,0.635]\t20\t1\t1\nDivorc.\t(0.635,0.68]\t35\t1\t1\nDivorc.\t(0.68,0.725]\t55\t1\t1\nDivorc.\t(0.725,0.769]\t109\t1\t1\nDivorc.\t(0.769,0.814]\t140\t1\t1\nDivorc.\t(0.814,0.859]\t153\t1\t1\nDivorc.\t(0.859,0.904]\t73\t1\t1\nDivorc.\t(0.904,0.949]\t8\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the top 5 most anomalous cases","message":["%r","","z.show(head(ore.sort(RES_DF[RES_DF$PREDICTION == 0,], by = c('PROBABILITY'), reverse =TRUE),5)  )"],"enabled":true,"result":{"startTime":1715726130720,"interpreter":"r.medium","endTime":1715726131223,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tPREDICTION\tPROBABILITY\tBin_PROBABILITY\n100199\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.803307844886286\t(0.769,0.814]\n100170\tF\tWidowed\t1943\tF: 110,000 - 129,999\t3000\t7th-8th\t0\t9+\t?\t3\t0\t0\t0.701078977491907\t(0.68,0.725]\n103154\tF\tWidowed\t1922\tF: 110,000 - 129,999\t1500\t9th\t0\t2\tHouse-s\t2\t0\t0\t0.684590840278712\t(0.68,0.725]\n101137\tF\tWidowed\t1935\tH: 150,000 - 169,999\t1500\t10th\t0\t9+\tOther\t3\t0\t0\t0.67996173775069\t(0.68,0.725]\n100646\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\tBach.\t0\t9+\tTechSup\t2\t0\t0\t0.667046911688017\t(0.635,0.68]\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Assign explicit model names","We can assign a model name explicitly at the time we build the model. This is facilitated through the use of `odm.settings`. We'll rebuild the model with the assigned name 'SVM_ANOMALY_DETECTION_MODEL' for use in the query below."],"enabled":true,"result":{"startTime":1715726131301,"interpreter":"md.medium","endTime":1715726131360,"results":[{"message":"<h3 id=\"assign-explicit-model-names\">Assign explicit model names<\/h3>\n<p>We can assign a model name explicitly at the time we build the model. This is facilitated through the use of <code>odm.settings<\/code>. We'll rebuild the model with the assigned name 'SVM_ANOMALY_DETECTION_MODEL' for use in the query below.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","ore.drop(model = 'SVM_ANOMALY_DETECTION_MODEL')","","MOD.SVM2 <- ore.odmSVM(formula = ~., data = CUST_DF, type = 'anomaly.detection', odm.settings = list(model_name = \"SVM_ANOMALY_DETECTION_MODEL\"))"],"enabled":true,"result":{"startTime":1715726131437,"interpreter":"r.medium","endTime":1715726135838,"results":[{"message":"Warning message:\n\u201cThe created mining model SVM_ANOMALY_DETECTION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize CUST_DF data frame into table CUSTOMER360_TAB for use in the PREDICTION_DETAILS query","message":["%r","","ore.drop(\"CUSTOMERS360_TAB\")","ore.create(CUST_DF, \"CUSTOMERS360_TAB\")"],"enabled":true,"result":{"startTime":1715726135914,"interpreter":"r.medium","endTime":1715726136725,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View PREDICTION_DETAILS in SQL of top 3 attributes that explain factors leading to prediction","message":["%sql","","SELECT CUST_ID, "," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE"," FROM (SELECT CUST_ID, PREDICTION_DETAILS(SVM_ANOMALY_DETECTION_MODEL, '0' USING *) PREDICTION_DETAILS FROM CUSTOMERS360_TAB","       WHERE PREDICTION_PROBABILITY(SVM_ANOMALY_DETECTION_MODEL, '0' USING *) > 0.50","       AND OCCUPATION = 'TechSup'","       ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","                PASSING OUT.PREDICTION_DETAILS","                COLUMNS ","                \"Attribute1\" XMLType PATH 'Attribute[1]',","                \"Attribute2\" XMLType PATH 'Attribute[2]') ","       OUTPRED","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726136796,"interpreter":"sql.medium","endTime":1715726137021,"results":[{"message":"CUST_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100061\t\"EDUCATION\" actualValue=\"Assoc-V\" weight=\".039\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".039\" \n100489\t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".043\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".037\" \n100646\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".066\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".022\" \n100792\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".055\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".033\" \n100941\t\"EDUCATION\" actualValue=\"9th\" weight=\".051\" \t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".033\" \n101706\t\"CUST_MARITAL_STATUS\" actualValue=\"Mabsent\" weight=\".081\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".039\" \n102254\t\"CUST_MARITAL_STATUS\" actualValue=\"Separ.\" weight=\".079\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".053\" \n102862\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".055\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".043\" \n102922\t\"CUST_MARITAL_STATUS\" actualValue=\"Widowed\" weight=\".064\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".023\" \n103341\t\"EDUCATION\" actualValue=\"Assoc-V\" weight=\".039\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".039\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View prediction details in transactional format","message":["%sql","","WITH d AS"," (SELECT CUST_ID, PREDICTION_DETAILS(SVM_ANOMALY_DETECTION_MODEL, '0' using *) anomalyDetails FROM CUSTOMERS360_TAB)","  SELECT d.CUST_ID, x.name, x.weight","         from d,","         XMLTable('/Details'","                  PASSING d.anomalyDetails","                  COLUMNS","                  class varchar2(20) PATH '@class') c,","         XMLTable('/Details/Attribute'","                  PASSING d.anomalyDetails","                  COLUMNS","                  name varchar2(20) PATH '@name',","                  weight varchar2(30) PATH '@weight') x","    WHERE c.class='0'","ORDER BY CUST_ID, name","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715726137103,"interpreter":"sql.medium","endTime":1715726148512,"results":[{"message":"CUST_ID\tNAME\tWEIGHT\n100001\tCUST_CREDIT_LIMIT\t.023\n100001\tCUST_ID\t.028\n100001\tCUST_INCOME_LEVEL\t.018\n100001\tCUST_MARITAL_STATUS\t.064\n100001\tY_BOX_GAMES\t.015\n100002\tCUST_GENDER\t.012\n100002\tCUST_ID\t.028\n100002\tCUST_INCOME_LEVEL\t.01\n100002\tEDUCATION\t.021\n100002\tY_BOX_GAMES\t.017\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","BEGIN","DBMS_DATA_MINING.DROP_MODEL(model_name => 'SVM_ANOMALY_DETECTION_MODEL');","EXCEPTION WHEN others THEN null;","END;"],"enabled":true,"result":{"startTime":1715726148592,"interpreter":"script.medium","endTime":1715726149405,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715726149483,"interpreter":"md.medium","endTime":1715726149541,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715726149622,"interpreter":"md.medium","endTime":1715726149681,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/r/OML4R Association Rules Apriori.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Association Rules Apriori","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715301045665,"interpreter":"md.low","endTime":1715301045739,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Association Rules (Market Basket Analysis)","The Association Rules machine learning technique, also known as Market Basket Analysis, can be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing. In his notebook, you will build association rules models using the A Priori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715301045829,"interpreter":"md.low","endTime":1715301045930,"results":[{"message":"<h1 id=\"association-rules-market-basket-analysis\">Association Rules (Market Basket Analysis)<\/h1>\n<p>The Association Rules machine learning technique, also known as Market Basket Analysis, can be used when looking for co-occurring items, states that lead to failures, or non-obvious cobinations of events, e.g., as in manufacturing. In his notebook, you will build association rules models using the A Priori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/a>"],"enabled":true,"result":{"startTime":1715301046014,"interpreter":"md.low","endTime":1715301046092,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715301046172,"interpreter":"r.low","endTime":1715301052379,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get SALES proxy object and display a few rows of data","message":["%r","","ore.sync(query = c(\"SALES_DF\" = \"SELECT CUST_ID, PROD_ID, QUANTITY_SOLD, AMOUNT_SOLD, CHANNEL_ID, PROMO_ID FROM SH.SALES\"))","ore.attach()","z.show(head(SALES_DF, 10))"],"enabled":true,"result":{"startTime":1715301052460,"interpreter":"r.low","endTime":1715301052686,"results":[{"message":"CUST_ID\tPROD_ID\tQUANTITY_SOLD\tAMOUNT_SOLD\tCHANNEL_ID\tPROMO_ID\n524\t13\t1\t1205.99\t2\t999\n2128\t13\t1\t1250.25\t2\t999\n3212\t13\t1\t1250.25\t2\t999\n3375\t13\t1\t1250.25\t2\t999\n5204\t13\t1\t1250.25\t2\t999\n7082\t13\t1\t1250.25\t2\t999\n9316\t13\t1\t1250.25\t2\t999\n12782\t13\t1\t1250.25\t2\t999\n13869\t13\t1\t1250.25\t2\t999\n1949\t13\t1\t1210.21\t2\t999\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get PRODUCTS proxy object and display a few rows of data","message":["%r","","ore.sync(query = c(\"PRODUCT_DF\" = \"SELECT PROD_ID, PROD_NAME, PROD_CATEGORY FROM SH.PRODUCTS\"))","ore.attach()","","z.show(head(PRODUCT_DF, 10))"],"enabled":true,"result":{"startTime":1715301052774,"interpreter":"r.low","endTime":1715301052961,"results":[{"message":"PROD_ID\tPROD_NAME\tPROD_CATEGORY\n13\t5MP Telephoto Digital Camera\tPhoto\n14\t17\" LCD w/built-in HDTV Tuner\tPeripherals and Accessories\n15\tEnvoy 256MB - 40GB\tHardware\n16\tY Box\tElectronics\n17\tMini DV Camcorder with 3.5\" Swivel LCD\tPhoto\n18\tEnvoy Ambassador\tHardware\n19\tLaptop carrying case\tSoftware/Other\n20\tHome Theatre Package with DVD-Audio/Video Play\tElectronics\n21\t18\" Flat Panel Graphics Monitor\tPeripherals and Accessories\n22\tEnvoy External Keyboard\tPeripherals and Accessories\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Merge SALES with PRODUCT, creating another proxy object","message":["%r","","SALES_TRANS_CUST <- merge(PRODUCT_DF, SALES_DF[(SALES_DF$CUST_ID >= 100001 & SALES_DF$CUST_ID <=104500),])","SALES_TRANS_CUST <- unique(SALES_TRANS_CUST[c(\"CUST_ID\", \"PROD_NAME\",\"PROD_CATEGORY\")])"],"enabled":true,"result":{"startTime":1715301053042,"interpreter":"r.low","endTime":1715301053771,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display customer purchases with associated product category","message":["%r","","z.show(head(SALES_TRANS_CUST, 10))"],"enabled":true,"result":{"startTime":1715301053850,"interpreter":"r.low","endTime":1715301053978,"results":[{"message":"CUST_ID\tPROD_NAME\tPROD_CATEGORY\n100100\tMouse Pad\tSoftware/Other\n100100\tExternal 8X CD-ROM\tPeripherals and Accessories\n100200\tMouse Pad\tSoftware/Other\n100200\tStandard Mouse\tPeripherals and Accessories\n100200\tExtension Cable\tElectronics\n100200\tCD-RW, High Speed Pack of 5\tSoftware/Other\n100300\tSIMM- 16MB PCMCIAII card\tPeripherals and Accessories\n100300\t18\" Flat Panel Graphics Monitor\tPeripherals and Accessories\n100400\tMouse Pad\tSoftware/Other\n100400\tStandard Mouse\tPeripherals and Accessories\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Check the distribution of PROD_CATEGORY","message":["%r","","barplot(table(SALES_TRANS_CUST$PROD_CATEGORY), 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sAAAiIewAACIh7AAAIiHsAAAiIewAACIh7AAAIiHsAAAiIewAACIh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\nHQBAJP4/8P/isAoaM10AAAAASUVORK5CYII=","type":"IMG"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Examples of possible setting overrides for Association Rules (A Priori)","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-3BAEC55B-BC9F-4D73-8809-B2D219F5CF89\" target=\"_blank\">Algorithm settings<\/a>","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared settings<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`. The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms. When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'","* Specify the name of a column that contains the items in a transaction.","When you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns: (1) Case ID, either categorical or numeric, and (2) Item ID, either categorical or numeric.","> 'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'    ","* Specify the Minimum support for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_SUPPORT' : '0.1'","* Specify the Minimum confidence for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","> 'ASSO_MIN_CONFIDENCE' : '0.1'","* Specify the Maximum rule length for association rules. The default value is 4.  It requires a number between 2 and 20 (inclusive).","> 'ASSO_MAX_RULE_LENGTH': '4'"],"enabled":true,"result":{"startTime":1715301054494,"interpreter":"md.low","endTime":1715301054562,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-association-rules-a-priori\">Examples of possible setting overrides for Association Rules (A Priori)<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-3BAEC55B-BC9F-4D73-8809-B2D219F5CF89\" target=\"_blank\">Algorithm settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>. The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms. When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the name of a column that contains the items in a transaction.\nWhen you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns: (1) Case ID, either categorical or numeric, and (2) Item ID, either categorical or numeric.<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ITEM_ID_COLUMN_NAME' : '<column name>'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum support for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_SUPPORT' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Minimum confidence for association rules. The default value is 0.1.  It requires a number between 0 and 1 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MIN_CONFIDENCE' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Maximum rule length for association rules. The default value is 4.  It requires a number between 2 and 20 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>'ASSO_MAX_RULE_LENGTH': '4'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify settings and create Association Rules model","message":["%r","","AR.MOD <- ore.odmAssocRules(~., SALES_TRANS_CUST, ","                            case.id.column = \"CUST_ID\", ","                            item.id.column = \"PROD_NAME\", ","                            min.support = 0.04, ","                            min.confidence = 0.1, ","                            max.rule.length = 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           0.1\nasso.min.rev.confidence                            0\nasso.min.support                                0.04\nasso.min.support.int                               1\nodms.details                             odms.enable\nodms.item.id.column.name                   PROD_NAME\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"pie","title":"Display pie chart of top 10 most frequent purchased items","message":["%r","","itemsets <- AR.MOD$itemsets","purchased_items = itemsets[order(itemsets$NUMBER_OF_ITEMS, itemsets$SUPPORT, decreasing = 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\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Attribute Importance MDL","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713823950115,"interpreter":"md.medium","endTime":1713824060306,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Identify Key Factors","message":["%md","# Identify Key Factors","","Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we compute Attribute Importance, which uses the Minimum Description Length algorithm, on the SH schema data. All functionality executes inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1713824060383,"interpreter":"md.medium","endTime":1713824060442,"results":[{"message":"<h1 id=\"identify-key-factors\">Identify Key Factors<\/h1>\n<p>Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we compute Attribute Importance, which uses the Minimum Description Length algorithm, on the SH schema data. All functionality executes inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a>"],"enabled":true,"result":{"startTime":1713824060521,"interpreter":"md.medium","endTime":1713824060591,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1713824060659,"interpreter":"r.medium","endTime":1713824060776,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Obtain proxy object for SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","ore.sync(schema=\"SH\",table=\"SUPPLEMENTARY_DEMOGRAPHICS\")","ore.attach(\"SH\")","","SUPP_DF <- SUPPLEMENTARY_DEMOGRAPHICS"],"enabled":true,"result":{"startTime":1713824060844,"interpreter":"r.medium","endTime":1713824061343,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View first few rows of SUPP_DF","message":["%r","z.show(head(SUPP_DF, 10))"],"enabled":true,"result":{"startTime":1713824061412,"interpreter":"r.medium","endTime":1713824061595,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n100001\t< Bach.\tExec.\t2\t3\t0\t0\t0\t1\t1\t1\t0\t0\tThanks a lot for my new affinity card. I love the discounts and have since started shopping at your store for everything.\n100002\tBach.\tProf.\t2\t4\t0\t1\t1\t1\t1\t1\t0\t0\tThe more times that I shop at your store, the more times I am impressed.  Don't change anything\n100003\t< Bach.\tSales\t2\t6\t0\t1\t1\t0\t1\t1\t0\t0\tIt is a good way to attract new shoppers. After shopping at your store for more than a month, I am ready to move on though. Not enough variety\n100004\t< Bach.\tSales\t2\t5\t0\t1\t1\t1\t1\t1\t0\t0\tThanks but even with your discounts, your products are too expensive. Sorry.\n100005\tAssoc-A\tCrafts\t3\t5\t1\t0\t0\t1\t1\t1\t0\t0\tAffinity card is a great idea. But your store is still too expensive. I am tired of your lousy junk mail.\n100006\t< Bach.\tProf.\t9+\t2\t0\t0\t0\t0\t1\t1\t1\t0\tI am not going to waste my time filling up this three page form. Lousy idea.\n100007\tHS-grad\tOther\t2\t5\t0\t1\t1\t1\t1\t1\t0\t0\tThanks but even with your discounts, your products are too expensive. Sorry.\n100008\t< Bach.\tCrafts\t2\t5\t0\t1\t1\t1\t1\t1\t0\t0\tI shop your store a lot.  I love your weekly specials.\n100009\tBach.\tProf.\t3\t3\t1\t0\t0\t0\t1\t1\t1\t0\tThank you! But I am very unhappy with all the junk mail you keep sending.\n100010\tHS-grad\tCrafts\t3\t3\t0\t1\t1\t0\t1\t1\t1\t0\tI am unhappy with the service at your store. Do not consider me a loyal customer just because I use your Affinity Card\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"pie","title":"Display a pie chart for education levels","message":["%r","","z.show(head(ore.crosstab(~EDUCATION, SUPP_DF), 10))"],"enabled":true,"result":{"startTime":1713824061666,"interpreter":"r.medium","endTime":1713824061849,"results":[{"message":"EDUCATION\tORE.FREQ\tORE.STRATA\tORE.GROUP\n10th\t122\t1\t1\n11th\t121\t1\t1\n12th\t52\t1\t1\n1st-4th\t17\t1\t1\n5th-6th\t39\t1\t1\n7th-8th\t87\t1\t1\n9th\t76\t1\t1\n< Bach.\t1041\t1\t1\nAssoc-A\t172\t1\t1\nAssoc-V\t196\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display the number of rows and columns","message":["%r","","dim(SUPP_DF)"],"enabled":true,"result":{"startTime":1713824061923,"interpreter":"r.medium","endTime":1713824062296,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>14<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select the columns to consider for attribute importance analysis","message":["%r ","","DATA_DF <- SUPP_DF[, c(\"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", ","                       \"CUST_ID\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", ","                       \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\",   ","                       \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","z.show(head(DATA_DF, 10))"],"enabled":true,"result":{"startTime":1713824062366,"interpreter":"r.medium","endTime":1713824062551,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t1\t0\t100001\t< Bach.\t0\t1\t2\tExec.\t0\t1\t3\t0\n0\t1\t1\t100002\tBach.\t1\t1\t2\tProf.\t0\t1\t4\t0\n0\t1\t1\t100003\t< Bach.\t1\t0\t2\tSales\t0\t1\t6\t0\n0\t1\t1\t100004\t< Bach.\t1\t1\t2\tSales\t0\t1\t5\t0\n1\t1\t0\t100005\tAssoc-A\t0\t1\t3\tCrafts\t0\t1\t5\t0\n0\t1\t0\t100006\t< Bach.\t0\t0\t9+\tProf.\t0\t1\t2\t1\n0\t1\t1\t100007\tHS-grad\t1\t1\t2\tOther\t0\t1\t5\t0\n0\t1\t1\t100008\t< Bach.\t1\t1\t2\tCrafts\t0\t1\t5\t0\n1\t1\t0\t100009\tBach.\t0\t0\t3\tProf.\t0\t1\t3\t1\n0\t1\t1\t100010\tHS-grad\t1\t0\t3\tCrafts\t0\t1\t3\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Examples of possible setting overrides for Attribute Importance","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of shared settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","* Specifies whether Oracle Data Mining should invoke automatic data preparation for the build","> auto.data.prep = FALSE","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. To handle missing values, either `na.pass` to allow missing values or `na.omit` to remove rows with missing values. The default value is `na.pass`","> na.action = na.pass or na.omit"],"enabled":true,"result":{"startTime":1715346283313,"interpreter":"md.medium","endTime":1715346283390,"results":[{"message":"<h2 id=\"examples-of-possible-setting-overrides-for-attribute-importance\">Examples of possible setting overrides for Attribute Importance<\/h2>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of shared settings can be found in the Documentation link:<\/p>\n<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/li>\n<li>Specifies whether Oracle Data Mining should invoke automatic data preparation for the build<\/li>\n<\/ul>\n<blockquote>\n<p>auto.data.prep = FALSE<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. To handle missing values, either <code>na.pass<\/code> to allow missing values or <code>na.omit<\/code> to remove rows with missing values. The default value is <code>na.pass<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>na.action = na.pass or na.omit<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Compute Attribute Importance to rank variables most predictive of AFFINITY_CARD","message":["%r","","AI_MOD <- ore.odmAI(AFFINITY_CARD ~., SUPP_DF)","","class(AI_MOD)"],"enabled":true,"result":{"startTime":1713824062772,"interpreter":"r.medium","endTime":1713824063965,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>'ore.odmAI'<\/li><li>'ore.model'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model object","message":["%r","","AI_MOD"],"enabled":true,"result":{"startTime":1713824064038,"interpreter":"r.medium","endTime":1713824064158,"results":[{"message":"\nCall:\nore.odmAI(formula = AFFINITY_CARD ~ ., data = SUPP_DF)\n\nImportance: \n                        importance rank\nCOMMENTS                0.34022157    1\nHOUSEHOLD_SIZE          0.17020901    2\nYRS_RESIDENCE           0.09263171    3\nY_BOX_GAMES             0.08047519    4\nOCCUPATION              0.07326430    5\nHOME_THEATER_PACKAGE    0.05947446    6\nEDUCATION               0.05524484    7\nBOOKKEEPING_APPLICATION 0.01579748    8\nOS_DOC_SET_KANJI        0.00000000    9\nCUST_ID                 0.00000000    9\nPRINTER_SUPPLIES        0.00000000    9\nBULK_PACK_DISKETTES     0.00000000    9\nFLAT_PANEL_MONITOR      0.00000000    9\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display ranked attributes that most influence target","message":["%r","","AI_MOD$importance$attribute <- row.names(AI_MOD$importance)","z.show(AI_MOD$importance)"],"enabled":true,"result":{"startTime":1713824064227,"interpreter":"r.medium","endTime":1713824064345,"results":[{"message":"importance\trank\tattribute\n0.340221572217487\t1\tCOMMENTS\n0.170209014224814\t2\tHOUSEHOLD_SIZE\n0.0926317122260526\t3\tYRS_RESIDENCE\n0.0804751905273532\t4\tY_BOX_GAMES\n0.0732643019481289\t5\tOCCUPATION\n0.0594744616913272\t6\tHOME_THEATER_PACKAGE\n0.0552448356309264\t7\tEDUCATION\n0.0157974797190234\t8\tBOOKKEEPING_APPLICATION\n0\t9\tOS_DOC_SET_KANJI\n0\t9\tCUST_ID\n0\t9\tPRINTER_SUPPLIES\n0\t9\tBULK_PACK_DISKETTES\n0\t9\tFLAT_PANEL_MONITOR\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Last\",\"visualizations\":{\"orientationValue\":\"horizontal\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"importance\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"importance\",\"rank\",\"attribute\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph the attributes that most influence AFFINITY_CARD high responders","message":["%r","","z.show(AI_MOD$importance)"],"enabled":true,"result":{"startTime":1713824064419,"interpreter":"r.medium","endTime":1713824064536,"results":[{"message":"importance\trank\tattribute\n0.340221572217487\t1\tCOMMENTS\n0.170209014224814\t2\tHOUSEHOLD_SIZE\n0.0926317122260526\t3\tYRS_RESIDENCE\n0.0804751905273532\t4\tY_BOX_GAMES\n0.0732643019481289\t5\tOCCUPATION\n0.0594744616913272\t6\tHOME_THEATER_PACKAGE\n0.0552448356309264\t7\tEDUCATION\n0.0157974797190234\t8\tBOOKKEEPING_APPLICATION\n0\t9\tOS_DOC_SET_KANJI\n0\t9\tCUST_ID\n0\t9\tPRINTER_SUPPLIES\n0\t9\tBULK_PACK_DISKETTES\n0\t9\tFLAT_PANEL_MONITOR\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a data.frame proxy object with the top attributes","message":["%r","","selectedAttributes <- AI_MOD$importance[AI_MOD$importance$importance > 0.05, 'attribute']","","DF <- SUPP_DF[, c('CUST_ID',selectedAttributes)]","","z.show(head(DF, 10))"],"enabled":true,"result":{"startTime":1713824064608,"interpreter":"r.medium","endTime":1713824064791,"results":[{"message":"CUST_ID\tCOMMENTS\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tY_BOX_GAMES\tOCCUPATION\tHOME_THEATER_PACKAGE\tEDUCATION\n100001\tThanks a lot for my new affinity card. I love the discounts and have since started shopping at your store for everything.\t2\t3\t0\tExec.\t1\t< Bach.\n100002\tThe more times that I shop at your store, the more times I am impressed.  Don't change anything\t2\t4\t0\tProf.\t1\tBach.\n100003\tIt is a good way to attract new shoppers. After shopping at your store for more than a month, I am ready to move on though. Not enough variety\t2\t6\t0\tSales\t0\t< Bach.\n100004\tThanks but even with your discounts, your products are too expensive. Sorry.\t2\t5\t0\tSales\t1\t< Bach.\n100005\tAffinity card is a great idea. But your store is still too expensive. I am tired of your lousy junk mail.\t3\t5\t0\tCrafts\t1\tAssoc-A\n100006\tI am not going to waste my time filling up this three page form. Lousy idea.\t9+\t2\t1\tProf.\t0\t< Bach.\n100007\tThanks but even with your discounts, your products are too expensive. Sorry.\t2\t5\t0\tOther\t1\tHS-grad\n100008\tI shop your store a lot.  I love your weekly specials.\t2\t5\t0\tCrafts\t1\t< Bach.\n100009\tThank you! But I am very unhappy with all the junk mail you keep sending.\t3\t3\t1\tProf.\t0\tBach.\n100010\tI am unhappy with the service at your store. Do not consider me a loyal customer just because I use your Affinity Card\t3\t3\t1\tCrafts\t0\tHS-grad\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1713824064863,"interpreter":"md.medium","endTime":1713824064922,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713824065007,"interpreter":"md.medium","endTime":1713824065068,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null}],"version":"6","snapshot":false,"tags":null}]
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification DT","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715731202430,"interpreter":"md.low","endTime":1715731202500,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification modeling to predict Target Customers using a Decision Tree Model","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2023 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715731202614,"interpreter":"md.low","endTime":1715731202685,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-a-decision-tree-model\">Classification modeling to predict Target Customers using a Decision Tree Model<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2023 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715731202770,"interpreter":"md.low","endTime":1715731202839,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a>"],"enabled":true,"result":{"startTime":1715731202929,"interpreter":"md.low","endTime":1715731202994,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(\"SUP_DEM\" = \"select * from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()"],"enabled":true,"result":{"startTime":1715731203076,"interpreter":"r.low","endTime":1715731203217,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","z.show(head(SUP_DEM))"],"enabled":true,"result":{"startTime":1715731203299,"interpreter":"r.low","endTime":1715731203425,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows and columns","message":["%r","","z.show(dim(SUP_DEM))"],"enabled":true,"result":{"startTime":1715731203514,"interpreter":"r.low","endTime":1715731203705,"results":[{"message":"4500 14","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show distribution of AFFINITY_CARD responders","message":["%r","","z.show(ore.crosstab(~AFFINITY_CARD,SUP_DEM))"],"enabled":true,"result":{"startTime":1715731203796,"interpreter":"r.low","endTime":1715731203927,"results":[{"message":"AFFINITY_CARD\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t3428\t1\t1\n1\t1072\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%r","","z.show(ore.crosstab(AFFINITY_CARD~HOUSEHOLD_SIZE,SUP_DEM))"],"enabled":true,"result":{"startTime":1715731204012,"interpreter":"r.low","endTime":1715731204140,"results":[{"message":"AFFINITY_CARD\tHOUSEHOLD_SIZE\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t1\t681\t1\t1\n0\t2\t1040\t1\t1\n0\t3\t973\t1\t1\n0\t4-5\t112\t1\t1\n0\t6-8\t146\t1\t1\n0\t9+\t476\t1\t1\n1\t1\t11\t1\t1\n1\t2\t109\t1\t1\n1\t3\t814\t1\t1\n1\t4-5\t107\t1\t1\n1\t6-8\t2\t1\t1\n1\t9+\t29\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","DEMO_DF <- SUP_DEM[,c(\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                      \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","                      "],"enabled":true,"result":{"startTime":1715731204226,"interpreter":"r.low","endTime":1715731204348,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Create train and test data (60/40 split)","message":["%r","","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","rownames(DEMO_DF) <- DEMO_DF$CUST_ID","DEMO_DF.train <- DEMO_DF[group==FALSE,]","class(DEMO_DF.train)","cat(\"\\nTraining data: \")","dim(DEMO_DF.train)","","DEMO_DF.test <- DEMO_DF[group==TRUE,]","class(DEMO_DF.test)","cat(\"\\nTest data: \")","dim(DEMO_DF.test)"],"enabled":true,"result":{"startTime":1715731204425,"interpreter":"r.low","endTime":1715731205531,"results":[{"message":"'ore.frame'\nTraining data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>2700<\/li><li>13<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ore.frame'\nTest data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>1800<\/li><li>13<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Build a Decision Tree Model for Predicting AFFINITY_CARD"],"enabled":true,"result":{"startTime":1715731205637,"interpreter":"md.low","endTime":1715731205702,"results":[{"message":"<h2 id=\"build-a-decision-tree-model-for-predicting-affinity_card\">Build a Decision Tree Model for Predicting AFFINITY_CARD<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","?ore.odmDT"],"enabled":true,"result":{"startTime":1715731205786,"interpreter":"r.low","endTime":1715731206060,"results":[{"message":"ore.odmDT                package:OREdm                 R Documentation\n\nIn-Database Decision Tree Models\n\nDescription:\n\n     Fits a classification tree using Oracle Data Mining.\n\nUsage:\n\n     ore.odmDT(formula, \n            data, \n            auto.data.prep = TRUE,\n            cost.matrix = NULL,\n            impurity.metric = \"gini\",\n            max.depth = 7, \n            min.rec.split = 20,\n            min.pct.split = 0.1,\n            min.rec.node = 10,\n            min.pct.node = 0.05,\n            na.action = na.pass,\n            odm.settings = NULL)\n     \n     \n     ## S3 method for class 'ore.odmDT'\n     predict(object,\n       newdata,                \n       supplemental.cols = NULL,\n       type = c(\"class\",\"raw\"),\n       na.action = na.pass,\n       bestN = NULL,\n       topN.attrs = FALSE,...)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. The details of model specification are given under\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build.\n\ncost.matrix: An optional numerical square matrix that specifies the\n          costs for incorrectly predicting the target values.\n          Specifying the row and column names of the matrix is\n          required. The values of the names are possible target values:\n          the row names represent actual target values and the column\n          names represent predicted target values. The vectors of the\n          row and column names must be the same. In general, the\n          diagonal entries of the matrix are zeros. The default value\n          is \u2018NULL\u2019.\n\nimpurity.metric: Tree impurity metric \"gini\" or \"entropy\". The default\n          value is \"gini\".\n\nmax.depth: The maximum depth of the tree, from root to leaf inclusive.\n          A value in the range of 2 to 20. The default value is 7.\n\nmin.rec.split: The minimum number of cases required in a node for a\n          further split to be possible. The value must be positive. The\n          default value is 20.\n\nmin.pct.split: The minimum number of cases required in a node for a\n          further split to be possible. The value is expressed as a\n          percentage of all rows in the training data, and must be in\n          the range of 0 to 20. The default value is 0.1 (0.1 percent).\n\nmin.rec.node: The optional minimum number of cases required in a child\n          node. The value must be positive. The default value is 10.\n\nmin.pct.node: The optional minimum number of cases required in a child\n          node. The value is expressed as a percentage of the rows in\n          the training data, and must be in the range of 0 to 10. The\n          default value is 0.05 (0.05 percent).\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\n  object: An object of type \u2018ore.odmDT\u2019.\n\n newdata: The data used for scoring.\n\nsupplemental.cols: The columns from \u2018newdata\u2019 to include as the columns\n          in the \u2018ore.frame\u2019 prediction result.\n\n    type: If set to \u2018\"raw\"\u2019, provides probability for each class\n          returned. If set to \u2018\"class\"\u2019, the class with the maximum\n          probability is returned. The default value is\n          \u2018c(\"class\",\"raw\")\u2019.\n\n   bestN: A positive integer that restricts the returned target classes\n          to the specified number of those that have the highest\n          probability.\n\ntopN.attrs: boolean, positive integer, False (default).  Returns the\n          top N most influence attributes of the predicted target value\n          for regression if topN_attrs is not False.  Returns the top N\n          most influence attributes of the highest probability class\n          for classification if topN_attrs is not False.  N is equal to\n          the specified positive integer or 5 if topN_attrs is True.\n\n     ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n     The Decision Tree algorithm can be used for both binary and\n     multiclass classification problems.  The tree structure, created\n     in the model build, is used for a series of simple tests. Each\n     test is based on a single predictor. It is a membership test:\n     either IN or NOT IN a list of values (categorical predictor); or\n     LESS THAN or EQUAL TO some value (numeric predictor).\n\n     The \u2018formula\u2019 specification has the form \u2018response ~ terms\u2019 where\n     \u2018response\u2019 is the numeric or character response vector and \u2018terms\u2019\n     is a series of terms, for example, the column names, to include in\n     the model. Multiple terms are specified using \u2018+\u2019 between column\n     names. Use \u2018response ~ .\u2019 if all columns in \u2018data\u2019 should be used\n     for model building. Functions can be applied to \u2018response\u2019 and\n     \u2018terms\u2019 to realize transformations. To exclude columns, use \u2018-\u2019\n     before each column name to exclude.\n\n     The function \u2018predict\u2019 computes predictions based on the input\n     data and model. Results are specified in the section on Value.\n\nValue:\n\n     The function \u2018ore.odmDT\u2019 returns an object of class \u2018ore.odmDT\u2019,\n     which includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of attributes used to build the model. The\n          columns include: name, type (numerical or categorical), data\n          type, data length (size), precision and scale for numeric\n          data, and whether the variable is the target.\n\ndistributions: The target class distributions at each tree node.\n\n   nodes: The node summary information. See \u2018summary.ore.odmDT\u2019.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\n    call: The matched call.\n     The function \u2018predict\u2019 returns an \u2018ore.frame\u2019 with columns\n     according to the \u2018type\u2019 and \u2018supplemental.cols\u2019 parameters. If\n     \u2018type\u2019 is \u2018\"class\"\u2019, the result includes the most likely target\n     class and its probability. If \u2018type\u2019 is \u2018\"raw\"\u2019, the result\n     includes one column for each target class and the column values\n     reflect the probability for that class. Both can be specified\n     together. If \u2018supplemental.cols\u2019 are specified, the named columns\n     are included in the result.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmSVM\u2019, \u2018ore.odmGLM\u2019, \u2018ore.odmNB\u2019, \u2018partitions\u2019\n\nExamples:\n\n       m <- mtcars\n       m$gear <- as.factor(m$gear)\n       m$cyl  <- as.factor(m$cyl)\n       m$vs   <- as.factor(m$vs)\n       m$ID   <- 1:nrow(m)\n       MTCARS <- ore.push(m)\n       row.names(MTCARS) <- MTCARS\n     \n       dt.mod  <- ore.odmDT(gear ~ ., MTCARS)\n       summary(dt.mod)\n     \n       dt.res  <- predict (dt.mod, MTCARS,\"gear\")\n       with(dt.res, table(gear,PREDICTION))  # generate confusion matrix\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree Model using default settings","message":["%r","","ore.exec(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> 'DT_CLASSIFICATION_MODEL'); EXCEPTION WHEN others THEN null; END;\")","","MOD <- ore.odmDT(AFFINITY_CARD~., DEMO_DF.train, odm.settings=list(model_name=\"DT_CLASSIFICATION_MODEL\"))    ","","RES <- predict(MOD, DEMO_DF.test, ","               type= c(\"raw\",\"class\"), norm.votes=TRUE, cache.model=TRUE, ","               supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"EDUCATION\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"YRS_RESIDENCE\")) "],"enabled":true,"result":{"startTime":1715731206143,"interpreter":"r.low","endTime":1715731207845,"results":[{"message":"Warning message:\n\u201cThe created mining model DT_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix","message":["%r","","CMATRIX <- with(RES, table(AFFINITY_CARD,PREDICTION))   ","CMATRIX"],"enabled":true,"result":{"startTime":1715731207929,"interpreter":"r.low","endTime":1715731208582,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 1324   90\n            1  180  206","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%r","","ACCURACY <- CMATRIX/sum(CMATRIX)","round(sum(diag(ACCURACY)),3)"],"enabled":true,"result":{"startTime":1715731208667,"interpreter":"r.low","endTime":1715731208790,"results":[{"message":"0.85","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Decision Tree ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566');\">Decision Tree<\/a> ","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Specify a row weight column ","> 'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'","   ","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'","","* Specify Tree impurity metric for Decision Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> 'TREE_IMPURITY_METRIC' : 'TREE_IMPURITY_GINI'","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> 'TREE_TERM_MAX_DEPTH' : '7'","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> 'TREE_TERM_MINPCT_NODE' : '0.05'","    ","* Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> 'TREE_TERM_MINPCT_SPLIT' : '0.1'","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ","> 'TREE_TERM_MINREC_NODE' : '10'","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ",">  'TREE_TERM_MINREC_SPLIT' : '20'","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.",". 'CLAS_MAX_SUP_BINS' : '32'"],"enabled":true,"result":{"startTime":1715731208876,"interpreter":"md.low","endTime":1715731208950,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-decision-tree\">Examples of possible setting overrides for Decision Tree<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566');\">Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' : '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify Tree impurity metric for Decision Tree.\nTree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_IMPURITY_METRIC' : 'TREE_IMPURITY_GINI'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\nFor Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MAX_DEPTH' : '7'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\nIt requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINPCT_NODE' : '0.05'<\/p>\n<\/blockquote>\n<ul>\n<li>Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\nIt requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINPCT_SPLIT' : '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node.\nIt requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINREC_NODE' : '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\nNo split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>'TREE_TERM_MINREC_SPLIT' : '20'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute.\nFor Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.\n. 'CLAS_MAX_SUP_BINS' : '32'<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction results","message":["%r","","z.show(ore.sort(RES[(RES$\"'1'\" > 0.5),], by = 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CHART","decile2 <- quantile(sensitivity$\"'1'\", probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity$\"'1'\", col.names = c(\"sens\"))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","","# DISTRIBUTION CHART","dx <- density(res$\"'0'\")","dx2 <- density(res$\"'1'\")","","# PLOTS 3x1","par(mfrow=c(3,3))","plot(1 - spec, sens, type = \"l\", col = \"darkred\", ylab = \"Sensitivity\", xlab = \"1 - Specificity\", main = 'ROC Curve')","abline(c(0,0),c(1,1))","paste(\"AUC: \", round(sum(spec*diff(c(0, 1 - sens))),3)) ","","barplot(table(df_sens$decile), xlab = 'Decile', ylab = 'Actual Targets', main = 'Lift Chart', col = \"darkred\")","","plot(dx, lwd = 2, col = \"burlywood\",","     main = \"Density\")","lines(dx2, lwd = 2, col = \"darkred\")","# Add the data-poins with noise in the X-axis","","#rug(jitter(res$\"'0'\"),col='burlywood')","#rug(jitter(res$\"'1'\"),col='darkred')"],"enabled":true,"result":{"startTime":1715731209641,"interpreter":"r.low","endTime":1715731211242,"results":[{"message":"<span style=white-space:pre-wrap>'AUC:  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sSDkhE1ewjczUG5mqaUWBltZMmW/hE5I2\neHL8+O/r18uEwuGbNr0ZGNhJdolok7t37y5cuDAzM9Pb2/ubb75puJ5Aj6kpq4POtAgJIW3T\nQj1ItkDQxdy8JCEBAPLv3u3v5UVTjKgxxQQ7lqGAzW2pNIHiUSwASGtKMbFrPzMzMwAoLy9v\n9tUWPiEJhUI1hvUqwuLi31atSouMNOnRY1p0tP24cTQGo52ox6+bNm1is9lBQUEqeRCpQ8LD\nw48fP56env78+XMA2L1796pVq6ytm1+Q1XqdYRESQtqphXqQAGAzYgSV2BXcu6fpyJBy/66c\naHG4DgBYBjwm21AukwCAVFimcwUstCixazjHvKkWPiHxeDw1h6ZUxuXLv/r61hQUuCxePOnw\nYUMzHcvrNaCoqMjHx+fKlSv9+/ePiIgY2Mk21fjuu+8+/vjjDz74IDY2ljpiYWHx6aefBgUF\ndbBnvV+EhJCOshk+/OkPPwBA0aNHRG0tC7dg1g6Sirq9HA1NX10tlcMzl1QWAAC1PFa3KE3s\n5s6d6+vrO23aNJampv/v3LmzhVdb/oSkeTKR6MbGjfFHjhiams4IC+u3aBEtYWi5a9eueXl5\nFRQUeHt7Hz16lMYUnC4HDx48e/bsmDFjAgICqCPTp08PDAzseGKnx4uQENJptiNHUg1CIil+\n9Mhm+HB640EAIBWWyaUiqt2axI5dn9jJ9Cmx43A48+bNs7Cw8PHx8fX17YMzQBsojIu75OVV\nmpTUY/z4aSdOdNqtwFogk8l2794dGBgoEAjCw8M9PT3pjogeGRkZw4cPBwDF2iBzc/PSUhUs\nodfXRUgI6TqrQYPYXK5MLAaA/Hv3MLHTBv+unGAwWjliRzV0ccRO6aqFiIiIvLy8bdu2Xbly\npW/fvqNHj/7hhx+onYs6Li4uztvb28nJSSAQ8Pl8Jycnb2/vuLg4lXSuViRB3P3ss1Ovv16R\nkTF2//75sbGY1TWVlZU1duzYXbt2ubm5PXz4sNNmdQBgZ2dHzYFT5FuXLl1ydnZWx7WYTCZm\ndQjRjsnhWNcv+S/45x96g0EURWLH4Vkw2a9e3cjm1U2skokqQBUbBWlSS8tRLSws1q1b9/Dh\nwwcPHgwZMmT16tU2NjYrVqx4+vRpRy4ZFRXl7u6enJw8f/78Xbt2BQYGzp8/PykpidrmsiM9\nq1tFRkb4uHF/+ftb9O3rde/e8I0bGXq3BVbHnT9/fsiQIbdv3/bz87t582avXr3ojohOa9as\nWbZsWWxsLIPBSEhI+OKLL9asWbN27Vq640IIqZFilA7XT2iJ2n9XTrRqOzOzy5QAACAASURB\nVHLFiB0pJ2TiSnWFpR6vXjyRl5d35cqV3377jclkzpo1Kysra/Dgwfv27fvoo4/ad0l/f/99\n+/Zt2LCh0fGDBw9u27at2Yl02iDhxIlrfn61VVVu69eP/uwzdn0VSqQgFou3bNkSHBxsZWV1\n8eLF6dOn0x0R/T766KOqqqpZs2YRBDFw4EAjI6PNmze///77dMeFEFIjRWJXmpxcW1lpgJv+\n0Yok5ZKqIqrdysSuYYkTmaicbWSqlsjUQ+mAk1QqPXfu3IwZM3r06BEeHr5u3br8/PyTJ0/+\n9ttvV69eDQwMbPclU1NTly5d2vT4smXLUlNT292tGpFk7PvvX1661MDYeP5vv40/dAizuqaS\nk5Pd3d2Dg4PHjx8fHx+PWR2FwWDs2rXr5cuXDx48uH//fklJScuLhBBCekCxfoKUywvu36c3\nGCStKiYJKdV+Za0TCttQwGDWjXxJhc1XYdNaSkfsunXrJpFIFi1adPv27eH/nfs5YcKEjlSX\n7dGjR3R0tI+PT6Pj0dHRDg4O7e5WTUi5/Orq1Y+/+85h0qR3IiK4+riraceFhoauXbtWJBIF\nBARs375dYyupdQWXy9XvbTYQQg2ZOztzzc3FZWUAkH/vXo8JE+iOqFNTTLBjMFkGxq2rIcpg\nsI1MpTUvAUCxEZmuUJrYLVu2LCAggM/nNzyYlJTUr18/AMjJyWn3Jbdv3758+fLz58+PHz/e\nysoKAIqLi2/cuBEdHX38+PF2d6sOpFz+28qVT0JCHKdOnXX+PA7UNVVVVfX++++fPHmye/fu\nMTExo0ePpjsi7bJixYqmBw0NDXv16rVgwQJ7XHmDEN3i4uKCgoJu3bpVWFhIkqSNjc2oUaPW\nr1/v5ubW/k4ZDJvhwzN/+w1w/YQWkFTWJXYGxtYMZmvHHTg8s/rErkJdkamH0sRu//79+/bt\na3TQxcWF7PDyEB8fH2tr6wMHDvj7+1PLbPl8PrVyYurUqR3sXIVIufxXX9+EEyd6TZ8+69w5\n3CWsKWrFa2pq6syZM0NCQrp06UJ3RFqnpKTkwoUL9vb2gwcPZjAY8fHx2dnZU6ZMiY2N3bFj\nx59//tmhPx4IoY6Jiory8PBwdXWdP3++lZUVg8EoKiq6du2au7v7uXPnOjLn22bEiLrEDtdP\n0E3SxpUTFMW8OqnejNg1VV1d3WgAr92mTp06depUkiSrqqoAwNjYWNvKNJAEcXnZssSffnJ6\n552ZP/+MWV1ToaGhq1evlslke/fu3bx5s7b9H9QSVlZWGzdu3Lt3L/V4miCIzZs3V1VVXb58\neePGjRs3brx+/TrdMSLUealvMZ/tiBFUoyonpzo3V2Bn1/4oUQfIZbW11SVUu5UT7CiK9RP6\nMGKn2Nqr4R5fcrk8Pj5+6NChKrw2g8Ew0cq1QiRBxPj4PAsLc541650zZ3BDmEYqKipWrlz5\n888/9+zZ8/Tp04rt51FTly5devr0qWLSIYvF8vf3f+211xgMxqZNm/r3709veAjpBPXthNTC\nYr5t27Z1pGfF+gkAyL97tzdu9EeT2sp8RSG6NiZ2dSN2hKSGJKQMFkf1walHM6ti09LS0tLS\nFA1Kdna2m5tbaGioxiPUNLlMdmnJkmdhYb3nzp3588+Y1TVy7949V1fXn3/+2cPD4+HDh5jV\ntayqqiorK6vhkaysrMrKSgBgMBjGxsY0xYWQLqF2QrK3t9+6dWtKSooKe6YW8zU93vHFfDxr\na1NHR6qdf/duR7pCHSGuXznB5HA5/DYsf+Q0KHGiW6Xsmhmxu3jxIgAsXbr0xx9/1HQ4dJNL\npZeWLEn++ec+8+bNCAtjcnQmQ9cAkiSDg4M3bdrEZrODgoI+/PBDuiPSAR4eHgsWLNizZ8+w\nYcNIkoyLi9u2bdu8efMA4MyZM3PxQzxCrRAREVFaWnrq1KmQkJDPP//8zTff9PX1XbBgQcdn\nB6l1MZ/tyJEVGRkAkHfnTge7Qu0mKc+lGm2aYAf/LWUnFZZz+DoziVxpHbvOmdVFL1yY/PPP\n/Tw9Z5w+jVldQ0VFRdOnT1+/fr2zs/Pdu3cxq2ulb7755q233vLy8urVq5eTk5OXl9fUqVMP\nHz4MAAMGDPj000/pDhAh3aCmnZB8fHyio6Orqqr8/f0XL168ePFif3//ysrKZmtytZXiaWxh\nXJxcJutgb6h9GqycaMNzWABgcriKzcd0a5pd4xG7Dz74AAC+/vprqtHU119/rfag6EDU1kZ7\neqZFRvZbtOjtn35iYCW2Bn7//XcvL6/8/Hxvb++jR4/yeDy6I9IZPB7v66+/3rdvX3p6OoPB\ncHR0VPzrTcDSVgi1kcp3QgJ1Lubr9vrrVENaU1P86FFXXAKvcTJROSGp2+Oea9bm9StsI9Pa\nqiIAkIl1ObFTFKjrSKU6nUMSRPSCBWkXLvT38pr244+Y1SnIZLLdu3cHBgYKBIKwsLBFixbR\nHZGOGT9+/PXr13k83sCBA+mOBQCAIIiYmBixWNzw4O3btwFALpfTFBRCryCVSqOjo0NCQn79\n9deBAweuW7fOy8vLzMwMAH7//fe5c+d2JLGjtGkxXwv3UUPWrq5sLlcmFgNA3u3bmNhpnrj+\nOSwwGIZmbRuxg4aJnU6P2EVGRjZqdAbX1q3DrK6p7OzsxYsX37x5c9iwYeHh4U5OTnRHpHvi\n4uKqq6s7slOLal2/fn3mzJnNvpSQkKDhYBBqJfXthKQMlTWWlzdfwKyF+0goFCraLAODrsOG\n5d68CQC5t265KnkOhtRHMcHOgN+FyW7zFgOKhbG6ndgpzJo1y8vL65133uHq+3YL/+zfH3/k\niMOkSVNDQjCrU7hw4YKvr29ZWZmfn9/+/fsNcHVwu0ybNu306dMrV66kO5A648ePj4qKajrS\ncOjQoQEDBtAVFUItU99OSMo0rPbVVAv3UaOZKt1ef51K7PJu3VJ5kOiVxGX1Kyfa/hwWANjc\nukFcPUns+Hz+0qVLORyOh4eHl5fX2LFjmUylKy10V/LPP/+5davloEEzz57F1RIUiUSyefPm\nr776qkuXLtHR0W+//TbdEekwY2Pj995775dffunfv3/D5Hjv3r20xMNisZqtuXro0CG9vMGR\nflDfTkjK7Ny5s4VXW7iPGh2xe+ONf/bvB4DKFy8qs7JMevRQXYzoFeQySW11EdXmmnVvRw//\nlrKrFepQKTuliV1YWFhNTc358+dPnTr11ltv2djYLF682MvLa9CgQZqMT61y//77so8P38bG\n49IlQ1PTV7+hE0hJSfH09IyPjx87duypU6fssFp6x2RmZk6YMEEmkz1+/JjuWBDSHyrcCUmt\n7N54AxgMqkBu7s2bJosX0x1RJyIpz/23NLF5ezbmZv+3lJ2uVDxpaUsxPp/v5eXl5eVVVFQU\nERFx9OjRffv2qe8TkobVVlVdmDuXyeHMvXjRGPdiBwCA0NDQtWvXikSigICA7du3q7zIeycU\nGxtLdwgI6TAN7ISUk5MTGxtramo6ZcqUhg9Sd+7c2fK4XWsYWVp2cXF5mZgIADl//eWCiZ0G\nicuyqQbLUMDhmbV8crPY3AaJnUgvEjuKUCi8du3alStXUlJSundvz2CmdnoWFiYsKpp89Ki1\nqyvdsdBPJBJt3bo1ODi4a9eu586dmzx5Mt0RIYQQUNsgNWwAAIfDcXNzU0k1zfv370+ePLm6\nupogCHt7+7NnzyoWZ+zatavjiR0AdB8zpi6x++OPjveGWk9cWrfrD7ddw3UAwDIwYrIM5EQt\n6FTFE6WJnUwmi42NPXXqVGRkJIvF8vDwuHLlyrhx4zQY23+0cnl56z06etTQ1LS/l1eHQ9N5\niYmJnp6eT58+nTx5cmhoqI2NDd0R6RWZTJaYmJiVlSVrUKF09uzZNIaEkK5Q905IH3/88YIF\nC4KDg8vKyj788MOJEyfGxMS8+eabKryE/dixj44eBYCXSUk1BQV8/AWrESQhldRvJsa1aP/U\nRraRSW11CejU+gmliZ2dnV15efm0adN++OGHGTNm0L42tpXLy1sp/+7dovh417VrObowS0Ot\nQkND16xZU1tbGxAQsGPHDpxBr1qpqamzZ89OTk4mCILD4UilUgDg8/nV1dV0h4aQzlDfTkj/\n/PPPqVOnDA0NbWxsIiIi/P39p02bFhMTM3r0aFVdwn7s2LoWSWZfv94Pq4FqhLgsmyTranMa\ndSCxY3FNQG8Su8DAwPnz55ubm2symha0fnl5azw6dgwABr/3nsri00GVlZWrVq2KiIhwcHA4\nffr06/VF0pEKrV+/fuzYsQ8ePOByuWKxOD4+fvny5UuXLqU7LoR0gAZ2QuJwOARBKL789NNP\nORzO9OnTL1++3MGeFfi2tl3696eexr64dg0TO80QvXxBNViGgo7MjWtQyq5SBWFphNLEbtWq\nVZqM45Vav7z8lSTl5ckREd1GjbLUoxW+bfXPP/8sXLgwPT197ty533//vfZk8Hrmzp07P/zw\ng6GhIQAQBDF06NATJ07MmTMHN9tF6JU0sBPSuHHj/vzzz/nz5yuO7Nq1i8lkTps2TYVXcZg0\nqS6xw9VUmiJ6mUE1jLr07Eg//yZ2ujvHrjPsFZsQGioVCoesXk13IPQgSTI4OHjz5s1MJjMo\nKAgzDLUqLS21trYGAEtLy4KCAnt7eycnp9zc3Fe+ESGkgZ2QNm/eHBAQ0DCxA4CAgAA2mx0Y\nGKiqqzhMmvQgOBgAKl+8KE1KsujXT1U9o2YRkmpqKzDoeGKnqFEsqSZJOYOhA7OVGoeYk5ND\nfTbKUYKOIFXs0bFjXAuLPvPm0R0IDYqLi99+++3169f36tXr7t27mNVpzIgRI/bu3ZuWlrZn\nzx5nZ2e6w0FIlxAEER8fT7UTExM3btx45MgRldTecnNzo9ZnNPLxxx83mvnTET3Gj2fV1ydP\nj4lRVbdIGWFJuqJt1MWxI139W8qOJAmxbjyNVbpX7N69e/s1+VSRlJSkiaDUKeevv14mJrpt\n2MA2MqI7Fk27fv26l5dXXl6et7f3kSNHdKK8p657r34e5969e2fMmPHNN9+Ym5ufOXOG3qgQ\n0i1ffPFFeXn5kCFDRCLR5MmT7ezsTp48WVxcvGPHDrpDaxWOQNB9zBjqOWz6xYvD/vc/uiPS\nc6Li51TD0MSGZdihv3Sc+hE7AJCJKtlG7amHp2FKBxVdXFxaeVC3PDp2DBiMzrZsgiCInTt3\nTp48uaqq6uTJk6GhoZjVacbRo0epxqBBgzIzM7OzswsLCydNmkRvVAjplm+//Zb6jHTt2jVL\nS8t79+7FxMSob6msOjjNmEE1cv76S1xWRm8w+o2Uy0Ql9RPsrDr6eIRlKGAw62r168o0uzY8\nLdaVLVxaICopSTl71n7sWIu+femORXNycnLGjx+/a9euIUOGPHjwYMmSJXRH1CmsbjKJk8Fg\ndO/enYNbEiPURnl5eVZWVgDw+++/U6voXFxcCgoK6I6rDZzqy3XJZbLnUVH0BqPfRCUZVElh\nAOBbd3jeC4Px7zQ7Hal40syqWA1s4UKXpz/+SEgkgzvTsomoqChfX9/S0lI/P7/9+/c33Ice\nqdWxY8cUw3UIoY7o27dvSEjIzJkzIyIiTp8+DQApKSl9derzuamjo7Wra9HDhwCQFBEx4N13\n6Y5Ib9UUPKMabCMzAxMVlINmG5lKhWUAINXdxE7dW7jQhiQff/stz9q695w5dIeiCVKpdPv2\n7fv27bOwsIiKippR/yAAIYR0y549e+bNm7du3boZM2ZQpYMPHz78/vvv0x1X2/Tz9KQSuxdX\nrwqLinjW1nRHpIfkRG1NUSrV5tuoZvVxgxE73Vw8AerfwoUuWb//XpaaOmLLFlYnGLXKzMxc\ntGjRnTt3Ro4cGR4e3rNnT7ojQqoXFxcXFBR069atwsJCkiRtbGxGjRq1fv16Nzc3ukNDSJWm\nT59eVFRUVFTk6OjIYDAAwNfXV7Gpq67ot3DhX/7+pFwul8kST57EJRTqUFOQRBJSqi2w7a+S\nPhULYwkdmWOntECxnmV1ABB/9CiDyRysZYWX1eGXX35ZsWJFRUWFn5/fgQMHcFIXXVrYiK/j\nlRSioqI8PDxcXV3nz59vZWXFYDCKioquXbvm7u5+7ty5Zqt5I6S7BAKBQCBQfOnu7k5jMO1j\n4uBgP25c1u+/A8CjY8eGbdgADAbdQembquyHVMPAxMbAWDVjog1qFFcBSWr//7XOUqC4pqAg\n7cIFh8mTTXv1ojsWNRKJRFu3bg0ODra2tr58+fKUKVPojqhTO3DggPo69/f337dv34YNGxod\nP3jw4LZt2zCxQ/pEJpOdOHHi77//Li0tbXhcfYWL1WTw6tVUYleWkvL84kUnvE9VSlyWI6nI\np9om9kNU1a3iUSwpJ2SSajbXWFU9q0njxE4DW7jQ4mlIiFwq1e8qJ8+ePfP09Hzy5MnEiRNP\nnjxpY6OCSaOoI5R9OlKJ1NTUZvecXbZs2bZt29R3XYQ078MPPzx16tTbb7/dvXt3umMBACAI\nIiYmpune5a98Y+/Zs43t7auyswHgdmCg04wZ2j/8o0PKn9+kGkwOV2A7QFXd/lujGEAmrtC9\nxE4DW7hoHimXP/7uO0G3bnr88Sg0NHTNmjW1tbUBAQE7duxgMnVg2xPUET169IiOjvbx8Wl0\nPDo62sHBgZaQEFKTM2fOxMbGDhs2jO5A6ly/fn1mffmSRoRCYQtvZHI4wzdt+t3PDwAK/vkn\n8dSp/l5eagmx8xEWPxe9zKTapg7DGSyVzUFicY0ZDCZJyoGqeGKmFZ8uWqB0jh1BEE+ePBky\nZAgAJCYmhoSEODk5rV69mqGGjxeZmZlqnd2f8euvFZmZr+/YwWQr/X51V2Vl5erVq0+fPt2j\nR4+wsLA33niD7oiQJmzfvn358uXnz58fP348VeKruLj4xo0b0dHRx48fpzs6hFSJyWQ23QmJ\nRuPHj4+Kimo6Ynfo0CEej9fye19bufL+gQOVWVkAcH3DBvtx44y1YxhSpxG1opeJV6g2y8DI\nxEGVnwEYDCaLa0wVsdOJUnZKEx1NbuHi6Oiokl3/lHl07BiDxXptxQr1XYIu9+/fX7hw4fPn\nz+fMmXP8+HFzc3O6I0J1qqqq1Nq/j4+PtbX1gQMH/P39a2pqAIDP57u7u0dHR0+dOlWtl0ZI\nwyZPnnzu3Lmm49N0YbFYzU5jPXTo0Cvfy+Zyxx08GDVvHgCISkp+mTbN4/JlzO06gqgVFj74\nWVa/kat573FMtqFqL8E2MtWHxO7bb7+9du0aNNjC5cGDB/Pmzet4Yjdv3rwWDp49e7aD/TdS\nlZ2dfulSr+nTje3tVdszvUiSDA4O3rx5M5PJDAoK0u0Sg/qo4Qo+NZk6derUqVNJkqSSSGNj\nY3UMqCNEOy6X6+vre+HCBWdn54Y/5Hv37qUxqnbr4+HR39s78aefAKDk6dMfBw4c4ONj7erK\nEQjYXC7X3LxL//5cCwu6w9RScpmktrpELhWRhIyUS2uriqvznhC1IupVnpWzcffBKr/ovwtj\ndTqxU98WLr/88suIESOozhU6Xv1BmSfHj5MEoWfLJkpKSpYuXXrp0qW+fftGREQMHqz6n2Ok\nKxgMhomJyavPQ0hnZWZmjhs3rqKiIi4uju5YVOOtY8eqsrKy//gDACQVFQ+++uo/LzMYtiNH\nDtuwoe/8+bi6QkFYnFaRcVdcngNKHvEZmthYvaaWmfQc/Ujs1LeFy5dffvnFF18EBARMnz6d\nOsJgMKiqyConl8kef/+9iYODox49nPrjjz+WLFmSm5vr7e39zTffaGBkCGknLFCMOonY2Fi6\nQ1AxtpHR3JiY2DVrEkJDm3mZJPPv3In29Ew4ceLtU6cMzcw0HqB2IQlp8dNLNQVJLZzD79rX\ncuB0lT+EpTQoZVep/aXslK6d3LNnz+bNmx0cHIYOHaraLVz8/PwiIyP/97//ffDBByKRqOMd\ntiD94sXq3NzXVq5ksFhqvZBmEASxc+fOiRMnVlZW/vTTT6GhoZjVdVpRUVHu7u7Jycnz58/f\ntWtXYGDg/Pnzk5KSqGl2dEeHEHoFDo837cSJJXfuDFq+3KJfP7aRUdNz0mNiIsaPF5eVaT48\n7UES0oL7Ec1mdQwmm8MzF3QbaDtiifWQOWrK6gCAza1L7Eg5IZOod/50xykdsVPrFi6urq73\n799fu3atm5tbWFiYSvps1qNjx5gcziBfX/VdQmMKCwu9vb2vXr3q6uoaERHRu3dvuiNCSr2y\ngl3HC323r0BxC/W35HJ5B0NCSH3Cw8OPHz+enp7+/PlzANi9e/eqVausdX+7VduRI21HjlR8\nKROJavLzM3/77fbu3dW5uQBQFB8f7ek57/Jl/RieaIfiJxfF5XWFdVkGPLNeo4ysnNiGAhUW\nNHklNu/fQVOZqEJRslg7tVT+Q61buAgEghMnTvz000+TJk1SYbcNVb54kfnbb73nzOHb2qrp\nEhpz9epVb29vKrc7duyYUXOf7ZD20EB97/YVKG6h/lZCQoKqYkNItb777ruPP/74gw8+UDyT\ntbCw+PTTT4OCgugNTOXYRkamvXoNXr2674IF52bMyLt9GwBeXL167/PPR/r70x0dDSqzH9YU\nJlNtA2NrGzdPliFf82GwDY0ZTBYpJwBAJioHc61ei6k0sdPMFi7e3t5vvvnmo0ePVNinQtHD\nh6Rc7qTkz5iukMlku3fvDgwMNDY2joiIWLBgAd0RoVfTQH3v9hUobqH+1oABKivUjpBqHTx4\n8OzZs2PGjAkICKCOTJ8+PTAwUP8SOwWuhcXcS5dOjhhRnpYGALc++aTP/PnmnexBDSGpLku5\nTrVZXGMbtwW0ZHUAAAwGm2siFZYBgFRYTk8MraY0sdPYFi6Ojo6Ojo6vPK0dW7hIKisBgKvL\npd1evHixaNGi27dvjxgxIjw8vDX/UKiTaF+B4hbqb+FuJUhrZWRkUBOBFLVOzM3NGw066B+u\nufnbp06FjRpFEgQhkdzYuHHOhQt0B6VRpSl/yGW1VNtq4NssQzrnlLONzKjETvsXxipN7DS/\nhYuZmRkAlJc3nwu3YwuX2spKADDQ2UoQ586dW7FiRXl5uZ+f34EDBzgczc0nQCokk8kSExOz\nsrJkMpni4OzZszvYLRYoRp2HnZ1dQkLCsGHDFIndpUuXnJ2dVdK5Nq8utx0xwvX996liKM+j\nonL+uGbiaA6k3MjSkcPvQnd0alH+/HnG5csAYD/hjer8p9RBQbeBRl160hkWAJtnCi8BAGS6\nO2Kn+S1c1q9f38Kr7djChUrsDHUwsROLxVu2bAkODra2to6JicG/07orNTV19uzZycnJBEFw\nOBypVAoAfD6/urq6451jgWLUSaxZs2bZsmWHDh1iMBgJCQm//vrrJ5988tlnn3W856ioKA8P\nD1dX1/nz51tZWTEYjKKiomvXrrm7u587d07ZIiRNGrVzZ0JoqKSiAgCub1z75u6lAAAMhpmj\nu3nvsfTGpnL3Pv/85v/9n1wmAwAmmzVw+bTec95gMNna8J1yjOrWT0hFOpvYaX4Ll507d7bw\naju2cJHo5ohdUlKSp6fn48ePJ0yYcPLkSVvdX/nRma1fv37s2LEPHjzgcrlisTg+Pn758uXN\nLnpoN0WBYnXvuYwQXT766KOqqqpZs2YRBDFw4EAjI6PNmzerpPxW+1aXaxLXwsJtw4ZbO3cC\nQOH95PLn+WZOtkCS5em3gcE0dx5Nd4AqE3fo0J9btyq+lMuIx8cucowMXntvDZtrTGNgFMXC\nWEJSTRJSTa7JbSuliZ0ebOGii49iQ0ND33//fYlEEhAQsGPHDpz2pOvu3Lnzww8/GBoaAgBB\nEEOHDj1x4sScOXPUsQWcuvdcRoguDAZj165d27Zte/bsmVwud3FxUfagpq3at7pcwwavWn7v\n889kIgkApEX+PXzTQlIuA4Dy9FtGlo5cM33YZ7b48WNFVscyMCDlcmrcLv5I9GsrA2gNrY5i\nxA4ApKIKA4EljcG0TGlip9YtXDQzp0GiU49iq6qq1qxZc+rUKXt7+7CwsDfffJPuiJAKlJaW\nUqW2LC0tCwoK7O3tnZyccnNzO96zhvdcRoh2XC7X1dVVtX22b3W5holePu75llvahVsAkPvn\n07eOTitLv0wSMiDJl8+u2rkv1fKNEFrjdz8/orYWAJhs9txL0fn3zv79f9+TJElIpDf/b+es\nc+foDhDYvH8XYsqEZTqZ2KlvCxeNzWmoraxkGRqyDNVVilqF4uLiFi5cmJaWNmvWrJCQEAvc\n/lnvjBgxYu/evRs2bAgJCVHJpG8N77mMEF1EIlFwcPC5c+cyMjIYDIajo6OHh8e6deu4XG7H\nO2/f6nJNkgrLq/OeOs18PS3qNpCkTCJJOXe5j8fo0pTrAFBbWVhTlMzv2p4J8SRBPPr227TI\nSJIknWfOHLx6NZPdUmnb5rogq3If1xQmAyk3snIysR/KYLaninLWtWvUtrkAMHzTJot+XeVE\nL8e3R6ZfvAMAqZGRRQ8fWqs6p28rJtuQZcAjaoUAQC2P1Vpt/L+oChqb01BbWan9w3UkSQYH\nB2/evJnBYAQFBfn5+eH8d33y3nvvUY29e/fOmDHjm2++MTc3P3PmTMd71uSeywjRRSqVTpo0\nKS4ubtq0aePHjweApKSk7du3R0dH//777+y2JiJNaP/q8soX/5CkXGBnae3qXPQgFQCefP/9\nsA3rK7MfykTlAFCRfqcdiZ1MJIqcPTvzt9+oL19cvZpy9uzcS5c4/NYWiiPlRFH8eWFxGvWl\n6GWmsCC5q9v8duzrdad+HQzP2trd37844RwA9PeelHXtoUwkAZK8f/Dg9J9+amu3KsfmmVOJ\nnUy7E7uWpnCFh4dPnjzZycmJ+nL37t1FRUUdv2QLcxpSU1M73r+CpLJSyyfYvXz5cubMmevX\nr+/Zs+ft27c//PBDzOr0zNGjR6nGoEGDMjMzs7OzCwsLVbLbiib3ehRRdQAAIABJREFUXEaI\nLseOHcvNzX327Nn58+f37t27d+/eyMjIhISEzMzM77//XiWXmDp1amxsbFVVVUVFRUVFRVVV\nVWxsrJZkdXKitjrvCdV2WTyXapQmJ+fduWvmWLcRmaSyQFzW5q1ufnvvPUVWR8n+448rK1a0\nvofSpFhFVkcRl+cUP27zRtUlCQlZ165Rbbf16wHEkvI8ADA05bssmU8dTz5zRqiK9KODOPVP\nY7V8xE5pYvfdd9/5+fmNHj06PT2dOkJt4dLxS1JzGpoeV/mchlrtTuzu3r07bNiwixcvent7\nx8XFqXzuCNI2DAaje/fuKqxHSO25XFVV5ebmFh8fr6puEdIeZ8+e/eyzzxrVZndycvrss89U\nMvKtQK0uNzExac2na4IgoqOjf/6vlgvmt09N/jNFhd6BPu9z62fpJISGCroNYnLq9pasynnY\npm7TLlxIrB8AM3N2tqgvbZYUHp56/nxrehCXvqjMrrso28hUMeFMWJxWnfukTcE8PnaManB4\nvMHvvVeVV1e7jsFiD9/kz2AyAYCorU0IDW1Tt+rQILHT6oonSsex1beFi8bmNNRWVhqreduM\n9qEev27atInNZgcFBaljgSTSEiuUfAJW1WCDBvZcRohGCQkJEyZMaHp84sSJTefzqJDKC+a3\nT3V9lmNoYmNk1aPfwoXx33wDAMlnzkwMDja2G1SReQ8AagqTu7hMZrJbNelQLpXe2LiRahtZ\nWnpev85ksU4MHiwsLgaAP7dscXrnnVdOtitNrtvpi8k2tBm2iMk2zLsdIhNXAUBp6h98m36t\nrAYiE4sTT52i2n09Pbnm5sVP6jat5lv3sejj0mPixBdXrwJAwokTw+vDpoti/YRMXEHKifZN\nKNQApf/z1LeFi8bmNGjno9iioiIfH58rV670798/IiJi4MCBdEeE1KhhIWK5XJ6amhofH6/y\n4lhq3XMZIRqVlZU1WiFEsba2LitT4+MwlRfMbweZqEJclk21BXavAcAAb28qsZOUl6dfutRz\nylgqsSMJWU1BsnH3wa3pNvHkSWr/WQAYu28fNfwxdv/+y0uXAkBZauqzsLABLZawFRanSSoL\nqLa585scnhkAdHGZXPjwHAAQkurK7IemPUe0Jpj0ixfF9XnFaytWiMuyqOwQAATdBgLAwKVL\nqcSu5OnT4kePrAa36ntUE8WIHZCkTFSutZt/KE3s1LqFiwYq5hMSCSGRaFtid+3aNS8vr4KC\nAm9v76NHj6rwVwDSTuHh4Y2OBAYGFhcXq/xCrdxzGSHdQhBEs+U8mUxmwz36VE7lBfPboabg\nGdVgMJh8m34AYDtypJmzM5WWPQsL6z13rqGpraQiHwCq8xNaldiR5L19+6im5YABA959l2r3\n9/K69/nnL589A4D7Bw+2nNhR2SQAsLnGxvZDqTbPug/XzE5cngsAFS/+Menh1poBrWdhYVTD\nvE+fbqNGlSRcpr5kGfC4XXoCgPOsWRyBQFpdDQBJ4eE0J3b8fyueSGteam1ip3SOHbWFS2xs\nLLWFyxdffLFmzZq1a9eq8NptmtPQVnVF7ExNVd5z+8hksp07d7711ltCoTA8PDw0NBSzus5p\n3bp151s3iwUhBADjlKA7LrWrKUiiGtwuPVkGPAAABsNl8WLqYHpMTG1lpcB2APWluCybqB/r\nakHGlSulSXXdjty2jVGfNDNYrGH1DzqLHz3K/ftvZT1Iq0vEpVlU28RheMPszbTXKKpBiKtq\nCpNfGYykoiI9JoZquyxeTMoJxbv4Ni4MBhMAOHy+U30OnUx3eU4mm8syqFs1LK1RwQNMNVGa\n2H300Udz585VbOGyffv2DRs2qGQLF82o23bCmP59SAAgKytr3Lhxu3btcnNze/jwoaenJ90R\nIdoUFBSoZKNYhDqDd999t6cS79aPNqmJsgmymiETVSged1LDdRSXRYvqTxClRkbybVzqqhOT\nZGtyqfgjR6iGoFu3vgsWNHzJZdEixeKMx999p6yHypy6KR8MFsfY7rWGL/GsnDj8uh6qsl+9\nniPtwgVCIlFcXfQyQy6te7otsO2vOK3v/Lq1seVpacV0TzhRfIPanNgpfRSrvi1cNKNWa7ad\nOH/+/PLly8vLy/38/Pbv329gYEB3REhzDhw40PDLsrKyU6dOzZgxg654ENItP/74I12XPn78\nuKoWObWDsKiu+BeDweRZ9VYct+jXz2rwYCq/SY6IGODjwzXvIS59AQA1BUkmDsNa6LM6Ly+j\nfoTstVWrmP9doc82Mhrg4xMXFAQAKb/8Munw4aY17UhSXpNfv7ihaz8mp/FyDRP7oS+TYgFA\nXJb9yoeVKT//TDWshwwx79NHUSqFbWRqaGanOK3nlCkcPl9aUwMAqZGRdD+N7UJNfJTWvKQx\njJa9YuULtYULQRA6VyiLehRrQOujWLFYvGXLluDgYCsrq4sXLyoKyaLO4+TJkw2/NDc39/Hx\n2bx5M13xIIQaanSHao+a+sTO0NyeZWDU8KV+CxdSiV3m1avi0lK+TT8qsRNX5BKSapahQFmf\nCaGh1AasDBZr0PLlTU8YuHQpldhJq6tTz5/v7+XV6ARRSQZVoRcAjO0GNe1B0G1gacoNaivb\nqtwnFn3GKQumtrIy8+pVqt13/nySkAmL675lvo1LwzM5PF7PKVNSz50DgLTIyFEBdG4dqxix\nq9WtEbuKiooff/xRUYPjyJEjGzduFIlEkyZNioiIMDc3b/oWLUT7iF1ycrKnp+ejR4/Gjx9/\n8uTJbt260RUJolF4eHi/fo2LwiclJTU9iBDSPG9vb7pDaIZcKpbUr4flW/du9Gq/BQv+8vcH\nkpRLpannzvX3XvTy2W9AkkCSNUWpJvZKS6ImnDhBNXpOntxsLTCrwYMtBw0qefIEAJLCw5sm\ndjUFiVSDzTXhmts37YHJ4fK79qnOTwSA6rynFr3HKtvHNv3SJcVz2N4eHsKS54qKfQKbxr8e\nnWfNohK7okePKrOyTHr0UPY9qpuiYp9cKiJqhXVzH7VMM3Psvvzyy4qKCqqdkpLi5+e3YMGC\nb7/9NicnZ8+ePZoNr/3q5tjRlNiFhoYOGzbs6dOnAQEBV69exayu03JxcWnlQYSQ5g0ZMuT+\n/ftkc2iMSliSTpJyqs2zblyMwrRXL9sRdcVEnoWHswz53PoHl0Ll0+zy791TLJsYuGyZstMU\nizNeXL0q/m9BGVIuExbV1Unh2/ZXlrEJ6ifeEZJq0ctMZRdSVEK2HDDAom/fmvy6JcAcvoWB\niU2jk3u9/TaDxQIAIMnnzW1woDENHy5Lq0tojKQFzSR2Z86cUczuP3/+fK9evUJCQlasWHH8\n+PELFy5oNrz2k9CU2FVVVXl7e7/77rtmZmbXr1/fuXMni6WlNQwRLaqrq/mt3o0RIaRWa9as\nUez7pz0UDyUNBJZsI7OmJ/Sr/xudfeOGsLCQ17Uv9aW4NEux/qARxVYThmZmTkqqKwNAv/oV\nFURt7fOoqIYviUoy5LK6MTZ+k0E1BSMLBxa3btmiosByIzKxOP1yXWWT3nPnymW1wpL6lNGm\nmc+9Rl26dHv9dapNb2LH5pooai/Xamti18yj2PT0dMXWXrdu3XrrrbeociRDhgzJzc3VaHQd\nQMujWGrFa2pq6syZM0NCQrp00dIiN0gDFAVOG1Y6lcvl8fHxQ4cOpSkohNB/LFmypLa2ttmX\nrtbPANMwkpSLSjKoNs+q+dqxfRcsuLFxIymXkwSRFBHx2kqf0qRr1HuFxWlUad+G5FJpUn1N\nzT7z5rG5SveoMO3Vq+vQoYUPHgBAytmzAxosPa4prBvwYxuZGTYZVPsXgyGw7V+RcRcAaopS\nuhC1TFbjJYMvYmOl9cUBnGfPFhalkERdVUJBc4kdADi9807uzZsAkH3jhrS6miNQOpVQvRgM\nA4ElVTtQWqOliV0zI3bW1tYJCQkAIJVKb968+Xp9mlxSUmJpaanR6DpA8yN2oaGhb7zxRmZm\nJrVNNWZ1nVxaWlpaWpqiQcnOznZzcwvVgk0PEUIAwOfzP/jgg2ZfomubPklZjmLUzajJBDuK\nwM6u+5gxVPtZWBjbyMzA2Jr6sqYopen5Gb/+Kiqpy0IGvGpaYZ9586hG5tWrtVV1tfFIUi4s\nek61+TZ9W+5BYFuXWZKEVFjYTDxp9c9hTRwcurq6UnPyAMDA2JojaD7NUFSzIySSF7GxLQeg\nVgaCuq1QaqtUX2peJZoZsZs5c6avr+9HH310/fp1iUQyZcoU6vi9e/cUSZ720+SIXUVFxapV\nq86cOdOzZ8/Tp0+7u7tr4KJIy128eBEAli5dSmO9BoSQzhGW1OVPLAMjrqnS+dkuixZl37gB\nAPl375alpvKs+9RWFQGAqCSDJGQM1n/+uCuew5r27Nl99OiWA+g9d+5f/v4AQEgk6TEx1GNf\n8csXclldusm3fkViZ2BsZWBsTcVTnfe00QgiSRCKx6nOs2YRUqG4fipew/J1jXRxcTHt1asi\nPR0Anl+65Dx7dssxqI8i9dTaxK6ZEbtPPvmkR48ey5cvP3/+/HfffacYefryyy/XrVun2fDa\nT2OLJ+7du+fq6nrmzBkPD4+HDx9iVocawqwOIdQmigUKRpa9lC1QAIA+8+axDA2pduJPP/G7\n9qHaJCEVvcxoeKa4rCytfraci5dXC31SLPr27VK/wEsxtFZTVLcsg8U1NjR79XJARTInKn0h\nE1c2fCn377+F9dsq9p4zpzo/sW6lCIPBV57YAYDT229TjfRLl4C+1S2KwVG5TNzoW9MSzYzY\nmZmZRUdHSyQSAwODhpt9Xb161bD+x0j7SSormRwO28jo1ae2F0mSwcHBmzZtYrPZQUFBigIx\nCCncvHnz1KlTR+qrvQPAmjVrlixZ8uabb9IYlZoQBBETE9NoZ/QWcLnc6dOn4+oihBSkwnJF\n5VtlE+woXAsLpxkz/r+9O42PqjwYPnwmC9nZZFNkF5eqFVnEBTewiktVVKxaUB6h1LZPBfel\n+qKP2CpWrdiqrYJC0VbrUkHF1iKKqCgiCFYUFBdAQPaEkLAk834YTFOWMAlZ4Pa6fnyYzJzl\nnpDJ+efMnHPmPfNMFEX/Hjv26FtuSctquLloTRRFhd/Myy73Hu7Hf/nLlhOLxGIVXwS2TMdz\nzll5++1RFC146aWSDRtS69X7z/GwO3h3eCu5ex+8et5r8XhpFI+v+/rDht9ebSyKovl//3vi\nRlaTJi179Fjy7paPpmQ1ap2WWdG+mPZnnPH+/fdHUVS4ZMmy999v3qVLMiOpdmVhF0XRxoJv\nKh5zndjhCYq3bbi6rbrtbjPefvvtHU2/MT+/Rt+H/eabby655JKXX375oIMOevLJJw89dDun\naoQrr7zygQceKH/PpZde+stf/nLatGl1Mp4KXkelpaW7uPCJEyeeueOj7bZrwoQJrsMBZYqW\nb+mnWCwlq0m7iic++JJLEmGX/+WXX/7rX/Xb7L/2i3ejKFr/zafxeGniWqtRFM0ZNSpxo+XR\nRzfqmFSW7Xf22dNuvz2Koo0FBV9OmtTy6MNKNmw51iG72f7JLCE1IyerSfv1yz+Noqhg0eyG\n7Y4qu/RZ2V7ADj/84abC5Yl3bKMoyt3eGY/La3X88em5uYmjLj6bMKGuwi61XnZqRm7iG7Ix\nf1nF/V0ndnLlid3H5MmTd7TNWL9+/bZ3bszPr7n3YV999dV+/fotWbKkf//+Dz300J51sTVq\n0+zZsw844L8+j3LAAQfMnj27rsZTwesoccjUrkhcn+a4KGqTxMRfRtGUb2cBEtZ/G3YZjVql\npO3w2NWEdqeemrvPPuu+/jqKog/++MdTRt2XCLvSTUXFq77K2qttFEVLp09PHOIaRdEhl16a\n5DBadOmS16pVwcKFURTNf+65hvttee8rJS0zs3GyJwfOa/n9xNPZXLSmaNUXWXu1i6Jo2fvv\nr/3ii8QEHc85p2DRrG+XnJHTfCcf3UvNyGh78smJMxV/NmHC0bfckuRIql29vGZFG9ZFUbQh\nf1ldjaECe0zYnXjiiePHj992T8O999673a7aUDNht3nz5uHDh9922225ublPPPHEhd9ejxm2\nq1WrVq+99toPvz2eK4qiyZMn1+EJqyt4HR188MHVsoo2UfT9nU8FbK1084biVV8lbm97XuJt\npaSlHXLppdOGD4+i6LPx4zeuvSc1I6dkQ2EURYXLPkmE3cw//CExcUaDBmVnv9u5WKxjnz7v\njxwZRdFnzz//vX6Hl42qbEfgTmU1269sz1b+V+8nwi6xizGKonp5ea2OP+brd0Ynvszd5+Cy\n88NVoMMPf5gIu2UzZxYsWrTd62fUgowGLYpWLIiiaGP+0joZQMX2mLBLTU0tv3Usc++99253\n+o35+TnNm1fvGBYuXHjRRRdNnTq1a9euf/3rXzt06FC9yyc8l1122cCBA4cPH37cccfF4/Ep\nU6bcdNNN1113XV2Np4LXUUpKsr+vgZqwfvln/7ngRHJv8B02ePC7d9xRunlz6ebN748ceejA\nk/MXzowSl6A46OTCpUvLTl938MUXp1fm1Oj7nX12IuzWL1++dPoHTQ5tF0VRdnIfsEuIxVLy\n9j1szWdvRlG0fvmnmwpXpec0/uRvf0s82v7009d/MzdesinxZf1WSZ3dM3EJinhJSRSPfzZ+\nfKef/zz58VSjstP4bS7OL9lYmFpv9zrnfLC/yjesXVu9e+yef/75Tp06vfnmm5dffvmbb76p\n6kjGlVde+ZOf/GTo0KEHHXTQ9773vSuuuGLw4MFXXXVVXY8L2O2s//YUdOm5TdKzk7ose16r\nVmWnnfvgT39KSdtyirWSjeuLVn81/be/TRw2EUtJObySJ7XY99hjs5tuWdriqR9GURRLTc9u\n0r5SC6nfqnMsZculwNZ+Pu2bmTPXfLrlveb9z+mT/9X0xO2sJu12dPq6rWQ3bbrPt6eeKDsI\no/Zl1N+77HbiZMW7lT1mj12llG7atLmoqLrCbsOGDddee+3999+/1157TZgw4fRvj7iGnYrF\nYrfffvuvfvWruXPnxmKxAw880CcyYU9X2YP5khEv3Zx4dy9K+sjThCOuu+6Tp56Kl5ZuWrdu\nxn2j9j/vwJKNhVEULZvx+qxvj8fveM45SR42USYlLa3DWWfNeeSRKIoWT/3w+5edkdOkfTLv\nlpaXmpGTu8+hiQ/Srfv6w89f/kfi/vTc3MYHNir4esvzbdiuEqfI3e/ssxe/+WYURQtfe614\n9erMRkkVcPVKzcxLy8zbXFwQRdGGNYt3t+Mnwgy7DdV3duJ58+ZdcMEFM2fOPP744x9//PGW\nLVvu+jL5rsnOzu7y7QFcpaWlkyZN+sEPflC3QwKqrLIH8yWjaOUXpZu3XNwse2eHEZTXrFOn\njn36JD679sEf/9jymN9m7hVF8fib/+/OzUVFURTFUlOrdpzBAeedlwi7opX5qz76svlhVTkn\ncMP2R637ek68tCReWvLxXx5P3Nn+1FPWLd1y2ERW4zbJH5ARRVHHPn1ev+aaKIpKN236bMKE\nJE/gUu0yGuyzufiTKIqK13xdJwOoQJhvxSaugrLre+zGjh3bpUuX2bNnDxs2bNKkSaqOXbFw\n4cL/+7//a9++/cknn1zXYwGqLnEQ0lP/7YorroiiqMq75AuXll2JtUFFV2LdnuPuuCNx+dd4\nScnkK3+zet6i2Y9MXDJty3W6Dr300iZVOjSqdc+eGQ22bEYXvfFhVtOqfAApLatB/TZdoyha\n8e8vC5duOUVfs87N46UlURTFYimND+xZqQU27NCh6WGHJW7Pe/rpKgypWmQ03NIDG9Z+XfbJ\nyN1EmHvsNq5dG+3aHruioqLrr79+5MiRzZs3f/bZZ+1foco2bdo0fvz4Rx555J///Oexxx47\nZMiQPn361PWgdhelpaUTJkxwTmP2LJU9mG+n4qWb138zP3E7p/mBlZ294X77HXPbbYn9WOuX\nffPq5X8oeyhv332Pu+OOqo0qJT295bHfX/DC1CiKFr3x7yrvCWrUocf6b+Z/+cq3x8PWz2l6\n6JYzAzRo171eXqUPczzw/POXf/BBFEVf/OMfdfVubGajVokb8ZJNG9YuyWy4G+33CTTsdm2P\n3UcfffSjH/3oww8/POmkk/785z+3aFG5P54gYe7cuaNGjRo7dmxKSsqAAQNefvnl1157ra4H\ntXuZOXPmnXfeWalZnNOY8Kxf/lnp5g2J2zktKh12URR1u+qq5R988NG4ceXvTMvKOOOvT2Q2\nbly1UW1Y+/W+PQ5MhF3xyjVfTZrU9ttrx1dKLDW90f69F72x5eJMrU74fiwtNYqi7GYdG+63\nkwvXbtf+ffu+8atfRVFUsnHj/GefPXTgwCosZBfVq988JS0j8b9WvOqr3SrswnwrdsPatVFV\nw27s2LHdunX7+OOPhw0b9o9//EPVUTXHHHPMIYccMnv27AceeGDhwoV3VPWP5rBt3LgxiqLj\noqh/Ev+Oi6LIOY0J0bqvt5wePD27UUaDvSueePtisVMfe+zIX/2q7EKaDdq1OO6uwbktq37J\nqHVff9jksPZZe23Zkv577NgqL+rzFydtXr9lx3ybk7vGUlIbtOnW7LCzkz8rXnmNOnZs0bVr\n4vZHf/5zlUe1K2KxlLKddkUrv6iTMexIoHvsqnTwRH5+/uDBg5988sk2bdr85S9/OeqoShyn\nA1t56623unXrNnTo0N69eztFXMWc05jvspKNRUUrPkvcztm76ucJj6Wm9hg+vOuVVy6fM2fd\n4rczG8ejWKxg0awGbY+owtLipZvXLfkoFou17tnpk79NiaJo/nPPbVizJqNhwyos7YOHHkrc\naHrYod+78Or03Cap9Xbp/ADf69dv6XvvRVG0cMqUtZ9/3qDdTi6/VhOy9mqTuLTGhjWLSks2\npqTWq/0xbFeY25vEUbGV2mM3ffr0ww8//MknnzznnHNmzpyp6thFM2bM6Nq160UXXdS2bdtb\nb7114cKFdT0iYHe0bsmHiSMJoijKa3nILi4ts3HjVscf37rn2Ylrs24qXFW04vMqLKdw6Sel\nm4qjKGpz8pZ9Y5uLiv5dpd1jS959d8m77yZud/rZLzIbt97Fqoui6MCLLkqtVy+Koige//DR\nR3dxaVWT9e1Z/eKlJcUrvqiTMWxXmGG3sTJhF4/H77vvvh49enz99de/+93vnnnmmUZ18UlM\nAtO5c+cHHnhgyZIlt99++6uvvtquXbsoip588sn8/Py6HhqwGylYODNxI7Nxm7SsquwP21Z2\n8/1TM3ITtxMXkK38qLZcYXavgw5q2aNH4vYHDz0UxeOVXdSMe+5J3Mho2PB7P/5xFQazreym\nTdt/+1nb2Y88UrppU7UstlLSc/YqO4904bfHvuwOwnwrNvnz2C1fvnzAgAEvvfTSgQce+OST\nT37/+94RojplZWX179+/f//+8+fPHzVq1NChQy+++OKePXtOnDixroe2B9vu6WF3JHEgbRRF\nlZ3FsbfUgqIVCzYVrkrcrt+qU3UtNhZLqd+68+r5U6IoKlr5+Yb8pZU6hcqGtUuK1yxO3M5r\ndXiny7IWT50aRdHKjz764pVX2lbmhE1rPv30k29PSnLowIHpubnJz1uxw37608R1YwuXLJn3\n9NMH1sWl27ObdUx08/rl8+OlJVsus1HXwgy7JPfYTZ48uV+/fl9//XX//v0ffPDBnMpcRw8q\npWPHjnfcccfw4cNffPHFRx55pK6Hs2ebOHHijk4Pu10TJkyIoqiyszj2llpQtjstNSM3u9n+\n1bjk+q06r10wrbRkYxRFaz57s/nh51ZiVJ+/k7iRkpaRu8+h+/c99LVrrilcsiSKondHjKhU\n2L09fHi8pCSKotR69boMHVqJJ7AzbX/wg0b777963rwoiqbffXedhF1O8wMS/4Olm4qLVnxW\nvf+DVVY3YTdjxozf/e53b7311rJly+LxeIsWLY4++uihQ4eWnZ1/F+304ImSkpLbbrtt+PDh\n2dnZ48aN+3E17RyGiqWlpZ111llnnXVWXQ9kz5Y4MPa4KGqzsym/jKIp5Q6krcIsUHM2rPm6\n7IDK+q27VO/+npT0zLzWndd+Pi2KovXfzC9esyiz4b7JzLhx3fLCbz5J3M5rdXhKWr0oijpf\nfvkbN9wQRdFXkyYtnjq17M3Ziq2YM6fsDCwHDxiQt29SA0hWLNZl6NB//fznURQtmzHj84kT\n2516anUuPwkZDVumZTXcXLQmiqKCRbN3k7Crg8/YjR8//sgjj/zkk0/69u1766233nbbbX37\n9v3444+PPPLIxB/Wu25Dfn4sNTV9B3vgFi1adOKJJ956662HHXbY+++/r+pgT5Q4lrbif212\neRaCN2PGjP79+3fo0CE3NzcnJ6dDhw79+/efMWNGLax69fzXEzdS0jLqtz682pffsF33lLQt\npztZOfdfSX48bvW81xNTxlLTGrTZcuREp5/9rOx42CnXX5/UouLxV4cMSeyuS8vMPOqmmyr/\nDHbikAEDcr49JdnUm2+Ol9bBFSDyWh6auLF+xWebi9bW/gC2VQdhd+ONN44YMeLdd9+94447\nrrrqqiuvvPKOO+6YPn36nXfeecMNN1TLKjbm59fLy0scE7SV8ePHd+rUaerUqZdffvlbb721\n336717V7Aag1tbCjYUfWL5tXtOrLxO0GbY9IScus9lWkpGc1bL/lDA8b85eu/XLnR1GsX/5Z\n4hQeURTVb92l7AiMjAYNjrj22sTtxW+++dHjj+90UXNGj/5q8uTE7c5DhuS1alXZ8e9UWlZW\n92+zYdmMGf8eM6baV7FTeS2/v+VsfPH42i/eqf0BbKsO3oqdP3/+gAEDtr3/f/7nf6ox7LZ9\nH3bTpk0333zziBEjGjduPH78eB+gASpQhUM0HG+xx0nsaEhc5rW8e+6554YbbtjudcOqRenm\n4pUfv5K4nVovp36bbjW0ovptuhUsnrOpcGUURavnT8ls3KYANX+KAAAgAElEQVSCoyhKNxWv\n/Ogfidsp6VkN2/3Xab86Dxky68EHCxYujKJo8hVXtOnVK2fvHZ5LeeXcuZO//a7mtWp15K9+\ntevPZbsO++lPZ9x339oFC6Ioev2aa9qdempO7V5WIDUzL2fvgxKnmC5Y9EGDtt3TshrU5gC2\nVQdh17p16wkTJlx88cVb3T9hwoQ2barnbZAN+flbHTnxxRdfXHjhhdOmTevevftf//rXtm3b\nVsuKgFBV4RCNU089tRYO163pWaLvUqfWwo6G7Vrx4cTNxQWJ240PODHxObaaEEtJbXLIaUve\nHRfF4/HSkmUzn9nniH7bLY94vPSb2eM3F+f/Z1Tp/7UTMT07u+d99z1/zjlRFBWtWDG+b9/z\nJ01KzdjOlS0Kly597swzE9f2jGKxk//0p3p5edX+1BJSMzJ63nvvc2edFUVR0cqVL/Xvf97L\nL8dq90e3Yfuj1y35KPEdXvnxvyp1nEpNqIOwu/nmmwcOHPjcc8+deOKJTZs2jaJo+fLlr732\n2oQJE0aNGlUtq9iYn1+/XLo988wzgwYNWrt27eWXX/7b3/42PT29WtYCBKwKh2jUzuG6tTBL\n9J05LrgWdjRsa9W81wqXbTk6IatJ+9x9dvWkxBXLbNiyUYceqz99I4qikuKCJe8+3rzzefXy\nmpWfJl66efmcF4pWLEh8md20Q17L7Zz8q2OfPgdddNHcJ56Iomjxm2/+vU+fM596aqszmKye\nN+/ZH/5wzadb3s/tMnRou969a+J5lelw5pkHXnDBx3/9axRFX/7rX6/8/OcnP/TQdj+LVUPS\nc/bKa3lYwaJZURSt/2Z+/sKZ9VtV/ycmk1cHYXfxxRc3a9bst7/97Y033lhYWBhFUU5OTuID\nDb2r6b9/w7dvxRYVFV1//fUjR45s1qzZxIkTT6nSBYyB76xKXe6sNg/XraFZtp0rbLWwo6G8\nxB6dsjMSp2bkNj309Gpfy7Yadjhm47oVhUvnRlG0uTj/62ljG7Ttlrdvp7SsBvF4adGKBavn\nv76xYHli4rSshk0P3WHT/+Chh5bPnr3iww+jKPp84sSxnTsf+5vftD/99LTMzHWLF88ZPfrd\nESM2rVuXmLhd797HjxhR888v+sGDDy6dPn3NZ59FUTT7T3/atG7dyQ8/nJ69q9e3SF7j/Y9f\n/838ko2FURStmvtKanpWTosDa23tW6mb05307t27d+/e8Xi8oKAgiqK8vLxY9cV1vKRkU2Fh\nvfr1586d+6Mf/WjOnDm9evUaN25ci9p93x34bqrCpW9321mCVws7GraIx9d/M3/V/NcTH3eL\noiglrV7zzuel1qul86c2PfSMKF6a2FMYL928ZsHbaxa8nZKWES/dXHZBsyiKUjPzWnT9UUp6\n1o6WUy8v79yXXnqyZ8/EPrnV8+ePP++8lPT09OzsDWv/65jQNieddNazz6ak1UZmZDRs2GfC\nhL8ee2zRypVRFM194omvp03rcdttB/Ttm1Ir79GlpGc1/f4ZS2c8FcXj8XjpNx/8vf7qLg07\n9Eitt8PvZA0OpvZXWSYWi9WvX79+/frVWHVRFG0sKIji8SnLlnXt2nXu3LnDhg375z//qeoA\n2Fbv3r3/9a9/FRQUrF27du3atQUFBf/617+qq+pKNxcXLJy5fM6LX73++2Wznv1P1aVnNu/y\no0pdDWIXxVJSmx12dsP2R5d/j7J084byVZdRv8U+R/Qru0zWjuS1anXhG2/se+yx/1nOpk3/\nVXWxWOchQ8596aW0rNrLmr0OOuj8SZOymzdPfLl2wYIXf/zjB/fZ56X+/d+///6iFStqegBZ\ne7Vr8r3/vCuY/9WMRVMeLP72wOfatBtdeaJhw4ZRFK1Zs2a7j273CLW333572ylXfP31E1E0\n8/XXW7du/cQTTxxzzDE1MVoAgpHY0ZDkxMlvj5a9/3Tx6kVb3ZlRv0XTw87aaT9Vv1isUcfj\nclocsPrTqeuXf1r+XHRpmXkN2nbPa915y5k7dianRYsfTZ48Z9Sod+68M3FE6pY1pKS0+cEP\njrnllr2PPLL6x78zTQ87rP/06S9cdFHiAmhRFBWtWPHRuHEfjRs3+49/HPDhhzU9gLx9O8VS\n663498R4yaYoikpLNhYsnpPZuLZPjrkbhd3QCi82Mnny5B19+Lf8Z0Hee++9H/XtuyCKenbq\n9PSrrzZqVOuvHKgtNX0FF/jOqnhHw462R02aNGnevHniI0YJBesK47H/vNmampGb1/L7Wc32\nKy5JKS43We3Kzt7v5My2x29at6JkY2EUS03Pbpie3TiKxdatK6zUgtpdeGG7Cy5Y+e9/r/7o\no02Fhdl7792sc+esZs2iKCqoq2fXsOHpL7ww/8kn3x8xYu1nn5XdvXr+/LWrVtXG27K5rRoc\ndkH+l+8Vr/oqisXi9duWfSuKi4ubNGlS4wPYrcLulltuqeDRE088cfz48Vv9hVRaWvrTn/60\nZcuWURTF4/GRI0dee+21KSkp99x11xVXX12jo4W6NX78+HPPPffwww/v27dv06ZNY7HYN998\nM2nSpCOPPPLZZ5+tufNvwXdBxTsaKtgebdq0Ka/cqT1iBx6z5rM3U9KzMhq2zG7aIatxm9o8\nWrNCeVGj6omMvKOOanvUUTufrhZ1GTy486BBi6dO/WzChK/ffnvdkiXdrrqqQePGtbT6vLxG\nTVtuLlobxWJpmf/ZDbxu3boVNf+OcLRbhV3FUlNTt7utuuaaa2Kx2IoVKwYMGPDiiy8ecMAB\nTz755GGHHVb7I4TaVLUTq1bwFlLp9q7Gk8zHQ76s8Mtk5qrCLEnOZZbKzpL8ZGGreEdDxduj\n8vfk7nNITZ/NhO2KpaTse9xx+x53XF0NoA5PU1w3YVe9byHl5+d36tRp8eLF/fv3f+CBB3L/\n+5w6EKSqnVi1go80zJ8/v/yXWVlZURRNSXo8Wd9+Sjr5WaIoSuzbqNQsVViRWSo7S/m5gD1L\nHYRd9b6FVFBQsHDhwvT09BNPPDErK+uqq64qLS2dNWtWgwYNqvdg263E4/FVq1Y1btx4T19L\nME8ksZa1a9d26tQpJWXL53/Xr19fc6urW1U7seqO3kL65JNPrv32QpAJp5566rZT7khmZuap\np54aRVHysyTmOuWUU7p27VqpWSq7IrNU7b8mMdd3QfXuaFiwYMFPf/rTKg+mprdfAWxTaucp\nbLUpqRa1tj2KxcsdF1M7DjnkkIEDB273LaTRo0d/WMnjVho1arSjz7dCwu9+97shQ4bU9Siq\n2dixYwcOHHjGGWds98Sq2wYfsK2yHQ09e/Ysv6Nh1qxZVdjRcPzxx0+ZUqkdo3zn1ML2qA7C\nLiMjY+nSpdser7p69eoWLVps2LChUksrKSnJz88vf89zzz03cODAn/3sZ0ccccSujnXH3n33\n3QcffDCAtQTzRMrWMmrUqD59+pTdmZGRkV2L5x+vTS+//PJvf/vbadOmlT+x6tVXX13NJ1aF\ncFXvjoZtt0eVVdPbrwC2KbX2FLbalFSLWtoexWvdfvvtN2bMmG3vHzNmTMeOHXd9+U899VQU\nRU899dSuL+q7sJZgnkitrWV3U1pamjixamlpaV2PBfYw9erVW7Vq1bb3r1q1ql69erU/npr+\nJRbANiWAp1DT6uAzdrV8bT4IW6VOrAqUV7XPqsLurA7CrvauzQcAO2ZHA+Gpm9Od9O7du3fv\n3vF4PHFG5ry8vBo9WBIAtmVHA+GpyxMUewsJgLplRwOB2WOuPAEANcSOBoJRnSff200kTphe\n06dND2YtwTyRWlsLQA2p6V9iAWxTAngKNa0OzmNX00pKSiZNmtSrV6/U1FRr2R1WEdhaAGpI\nTf8SC2CbEsBTqGkBhh0AwHdTgG/FAgB8Nwk7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCA\nQAg7AIBACDsAgEDs2WE3duzY/fffPyMj45BDDpkwYcIuTlaHKjXCkSNHxmKx8847r3bGVlnJ\nPJeioqIrrriidevWmZmZ7du3v/nmm0tKSmp5nFRKYWHhww8/3Llz51gsNm7cuJpb0ZIlS4YM\nGdK2bdvc3NzOnTs//vjjNbGW4cOHx8pp2LBhTazlwAMPjP23c845p9rXkp+fP2TIkNatW2dl\nZR155JFvvvlmta+CapfM78n333//Rz/6UYsWLfbaa6/evXvPnDmzlgdZsSQ3W4sXL7744oub\nNGnStGnTIUOGFBYW1uYgK5D877TdPyG2Ft9jTZw4MS0tbcyYMatWrbr77rvT0tLee++9Kk9W\nhyo1wpkzZ7Zu3fqII44499xza3OQSUryuVx++eUtW7Z87733ioqKJk+eXL9+/d/85je1P1qS\nd//99w8cOPC9996LoujPf/5zza3o5ptvfuSRRxYtWpSfn/+nP/0pJSXl5Zdfrva13Hbbbd27\nd6/2xVbgk08+iaLo8ccfr/Yl9+nT56CDDpo9e3ZBQcHvf//7nJycTz/9tNrXQjVK8vfk6aef\n/tRTTy1duvSbb74ZNGhQo0aNFi1aVPuj3a4kn8LKlSvbtGlz5plnfv755+vWrXvggQeee+65\n2h/tdiX5O233T4ht7cFh16tXrz59+pR92bVr1379+lV5sjqU/AgLCgoOOOCAF1988fTTT989\nwy7J59KjR4+BAweWfXnaaaedd955tTE+dllNh91WWrVqdcstt1T7Yms/7K699trGjRsXFRVV\n72ILCwtTUlLGjRtXdk/Xrl2HDBlSvWuhelVhq1RcXJyWlvboo4/W7MiSluRTuOaaa1q0aLF+\n/fpaHFqlVfw7bfdPiG3tqW/FxuPxadOmnXDCCWX39OrV66233qraZHWoUiP8xS9+cdJJJ512\n2mm1NLhKSv65XHDBBf/4xz9mzpy5YcOGKVOmTJs27cILL6y9gbInKCwsTPyVfMYZZ9TE8j/4\n4IO8vLymTZuefvrpc+bMqYlVlNm8efPYsWP79++fmZlZvUtO/B6PxWLl75w6dWr1roVqVLWt\n0urVq0tKSho0aFCzg0tO8k/h73//e58+fbKysmpvcNVq90+I7dpTw66goKCwsLBp06Zl9zRr\n1mzp0qVVm6wOJT/CcePGTZ8+/a677qrF0VVO8s/lF7/4Rd++fTt37pyZmXniiSdee+21NfHZ\nI/ZQH374YSwWy83Nveyyy/74xz926dKl2lfRokWL0aNHf/XVV++++26DBg169OixcOHCal9L\nmRdffHHp0qU/+clPqn3JOTk5p5566q9//esPP/ywsLDwoYceev/995csWVLtK6K6VG2rNGTI\nkFatWp1yyik1PLqkJP8UPv/888aNG5922mlZWVlt2rS56qqrdp/P2CVj90+I7dpTw25b2/7Z\nuiuT1aHtjvDzzz8fOnTo448/vmf96bOj7/Z11133zDPPTJs2rbCw8JVXXhkxYsTIkSNrf3js\nng455JB4PL569er77rvvf/7nf8aPH1/tqxg0aNCFF17YqFGjdu3aPfbYY7m5uQ8//HC1r6XM\nqFGjjjrqqIMPPrgmFj5mzJju3bv/4Ac/aNq06T//+c/LLrssNTW1JlZEDdnpVulXv/rVSy+9\n9PTTT2dnZ9faqCplR08hHo/fddddAwYMWLFixdNPP/23v/1tyJAhtT+8arT7J0S054ZdXl5e\nTk7O8uXLy+5Zvnx58+bNqzZZHUpyhB988MHKlSsTx+/EYrEXX3zxmWeeicViX3zxRa0Ot0JJ\nPpfS0tL777//yiuv7N69e3Z2ds+ePQcPHnzvvffW7mDZ3TVs2HDw4MGnnXbaQw89VKMrqlev\n3v777z9//vwaWv6SJUsmTpxYE7vrEpo0aTJq1KglS5asX7/+2WefXbJkSfv27WtoXey6ym6V\nbrnllpEjR06cOLFbt261MsCdS/4ptGjR4pRTTjn//PNzcnK6des2dOjQp59+uhZHuqt2/4TY\nrj017GKx2JFHHjl58uSye1599dWjjz66apPVoSRHePbZZ5f/aGTZwRNt27at1eFWKPn/l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visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Use SQL interface for prediction details"],"enabled":true,"result":{"startTime":1715731211343,"interpreter":"md.low","endTime":1715731211415,"results":[{"message":"<h3 id=\"use-sql-interface-for-prediction-details\">Use SQL interface for prediction details<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table = \"DT_TEST_TABLE\")","ore.create(DEMO_DF, table =\"DT_TEST_TABLE\")"],"enabled":true,"result":{"startTime":1715731211538,"interpreter":"r.low","endTime":1715731212353,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface so score data and display prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(DT_CLASSIFICATION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(DT_CLASSIFICATION_MODEL USING *) PD","      FROM 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actualValue=\"Transp.\" weight=\".051\" \n100500\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".188\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".054\" \n100600\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".175\" \t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".11\" \n100700\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".062\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".05\" \n100800\t1\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".225\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".211\" \n100900\t1\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".23\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".225\" \n101000\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".062\" \t\"EDUCATION\" actualValue=\"7th-8th\" weight=\".05\" \n101100\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".188\" \t\"OCCUPATION\" actualValue=\"Other\" weight=\".051\" \n101200\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".188\" \t\"YRS_RESIDENCE\" actualValue=\"1\" 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diff --git a/machine-learning/notebooks/r/OML4R Classification GLM.dsnb b/machine-learning/notebooks/r/OML4R Classification GLM.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/r/OML4R Classification GLM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a>"],"enabled":true,"result":{"startTime":1715318918386,"interpreter":"md.medium","endTime":1715318918445,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a 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DEMO_DF)","summary(GLM_MOD)"],"enabled":true,"result":{"startTime":1715318925178,"interpreter":"r.medium","endTime":1715318928581,"results":[{"message":"\nCall:\nore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-0.84764 -0.24629 -0.02794  0.15066  1.07182 \n\nCoefficients:\n                          Estimate Std. Error t value Pr(>|t|)\n(Intercept)              3.757e-01         NA      NA       NA\nCUST_ID                 -2.260e-06         NA      NA       NA\nBOOKKEEPING_APPLICATION  5.446e-02         NA      NA       NA\nBULK_PACK_DISKETTES      4.360e-02         NA      NA       NA\nEDUCATION10th            9.177e-02         NA      NA       NA\nEDUCATION11th            7.772e-02         NA      NA       NA\nEDUCATION12th            7.900e-02         NA      NA       NA\nEDUCATION1st-4th        -1.936e-01         NA      NA       NA\nEDUCATION5th-6th        -4.596e-02         NA      NA       NA\nEDUCATION7th-8th        -5.803e-02         NA      NA       NA\nEDUCATION9th            -3.550e-02         NA      NA       NA\nEDUCATION< Bach.         4.872e-02         NA      NA       NA\nEDUCATIONAssoc-A         1.046e-01         NA      NA       NA\nEDUCATIONAssoc-V         3.079e-02         NA      NA       NA\nEDUCATIONBach.           1.174e-01         NA      NA       NA\nEDUCATIONMasters         2.474e-01         NA      NA       NA\nEDUCATIONPhD             2.991e-01         NA      NA       NA\nEDUCATIONPresch.         0.000e+00         NA      NA       NA\nEDUCATIONProfsc          2.717e-01         NA      NA       NA\nFLAT_PANEL_MONITOR      -4.220e-02         NA      NA       NA\nHOME_THEATER_PACKAGE     3.719e-02         NA      NA       NA\nHOUSEHOLD_SIZE1         -2.702e-01         NA      NA       NA\nHOUSEHOLD_SIZE2         -3.167e-01         NA      NA       NA\nHOUSEHOLD_SIZE4-5        5.633e-02         NA      NA       NA\nHOUSEHOLD_SIZE6-8       -3.039e-01         NA      NA       NA\nHOUSEHOLD_SIZE9+        -3.369e-01         NA      NA       NA\nOCCUPATION?             -1.267e-02         NA      NA       NA\nOCCUPATIONArmed-F        2.131e-01         NA      NA       NA\nOCCUPATIONCleric.        5.130e-02         NA      NA       NA\nOCCUPATIONExec.          1.801e-01         NA      NA       NA\nOCCUPATIONFarming       -6.059e-02         NA      NA       NA\nOCCUPATIONHandler       -3.002e-02         NA      NA       NA\nOCCUPATIONHouse-s        2.578e-02         NA      NA       NA\nOCCUPATIONMachine        2.303e-03         NA      NA       NA\nOCCUPATIONOther          6.940e-03         NA      NA       NA\nOCCUPATIONProf.          1.387e-01         NA      NA       NA\nOCCUPATIONProtec.        8.145e-02         NA      NA       NA\nOCCUPATIONSales          9.057e-02         NA      NA       NA\nOCCUPATIONTechSup        9.051e-02         NA      NA       NA\nOCCUPATIONTransp.       -1.960e-02         NA      NA       NA\nOS_DOC_SET_KANJI        -7.417e-02         NA      NA       NA\nPRINTER_SUPPLIES         0.000e+00         NA      NA       NA\nYRS_RESIDENCE            2.164e-02         NA      NA       NA\nY_BOX_GAMES             -5.034e-02         NA      NA       NA\n\nResidual standard error: 0.3476 on 4458 degrees of freedom\nMultiple R-squared:  0.3404,\tAdjusted R-squared:  0.3343 \nF-statistic: 56.11 on 41 and 4458 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Getting model predictions","message":["%r","","GLM_RES <- predict(GLM_MOD, DEMO_DF, c('AFFINITY_CARD', \"CUST_ID\"))","","GLM_RES$PREDICTED_CARD <- 1*(GLM_RES$PREDICTION >0.5)","names(GLM_RES) <- c(\"AFFINITY_CARD\",\"CUST_ID\",\"PROBABILITY_OF_1\",\"PREDICTION\")","GLM_RES$PROBABILITY_OF_0 <- 1 - (GLM_RES$PROBABILITY_OF_1)"],"enabled":true,"result":{"startTime":1715318928663,"interpreter":"r.medium","endTime":1715318928932,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(GLM_RES, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","","accuracy <- confusion_matrix/sum(confusion_matrix)","sum(diag(accuracy))"],"enabled":true,"result":{"startTime":1715318929004,"interpreter":"r.medium","endTime":1715318929239,"results":[{"message":"0.834444444444444","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA');\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All algorithms<\/a>","","- Specify a row weight column","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","- Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.","> GLMS_CONF_LEVEL = '0.95'","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).","> GLMS_RIDGE_VALUE = 2","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.","> GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC","> GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.","> GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> GLMS_MAX_FEATURES = 30","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'","- Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> GLMS_REFERENCE_CLASS_NAME = '<target_value>'","- Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE","> GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> GLMS_CONV_TOLERANCE = 0.0001","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> GLMS_NUM_ITERATIONS = 200","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.","> GLMS_BATCH_ROWS = 2000","- Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)","> GLMS_SOLVER = 'GLMS_SOLVER_SGD'","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.","> GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'","","<strong> Available starting with Oracle Database 23ai <\/strong>","- Specify the `link` function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK`, `GLMS_CAUCHIT_LINK` .  For regression, only one option is available: `GLMS_IDENTITY_LINK (default)`.","> GLMS_LINK_FUNCTION = 'GLMS_LOGIT_LINK'"],"enabled":true,"result":{"startTime":1715318929324,"interpreter":"md.medium","endTime":1715318929412,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA');\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONF_LEVEL = '0.95'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_VALUE = 2<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_MAX_FEATURES = 30<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_REFERENCE_CLASS_NAME = '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONV_TOLERANCE = 0.0001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_NUM_ITERATIONS = 200<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_BATCH_ROWS = 2000<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SOLVER = 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> Available starting with Oracle Database 23ai <\/strong><\/p>\n<ul>\n<li>Specify the <code>link<\/code> function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code>, <code>GLMS_CAUCHIT_LINK<\/code> .  For regression, only one option is available: <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_LINK_FUNCTION = 'GLMS_LOGIT_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Model using ridge regression","message":["%r","","GLM_MOD2 <- ore.odmGLM(AFFINITY_CARD~.,DEMO_DF, ridge = TRUE, ridge.value = 2)","summary(GLM_MOD2)"],"enabled":true,"result":{"startTime":1715318929497,"interpreter":"r.medium","endTime":1715318932695,"results":[{"message":"\nCall:\nore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF, ridge = TRUE, \n    ridge.value = 2)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-0.84334 -0.24683 -0.03063  0.15205  1.07341 \n\nCoefficients:\n                        Estimate\n(Intercept)                0.373\nCUST_ID                    0.000\nBOOKKEEPING_APPLICATION    0.057\nBULK_PACK_DISKETTES        0.043\nEDUCATION10th              0.091\nEDUCATION11th              0.078\nEDUCATION12th              0.077\nEDUCATION1st-4th          -0.172\nEDUCATION5th-6th          -0.043\nEDUCATION7th-8th          -0.055\nEDUCATION9th              -0.033\nEDUCATION< Bach.           0.048\nEDUCATIONAssoc-A           0.102\nEDUCATIONAssoc-V           0.029\nEDUCATIONBach.             0.116\nEDUCATIONMasters           0.244\nEDUCATIONPhD               0.285\nEDUCATIONPresch.           0.000\nEDUCATIONProfsc            0.264\nFLAT_PANEL_MONITOR        -0.041\nHOME_THEATER_PACKAGE       0.037\nHOUSEHOLD_SIZE1           -0.267\nHOUSEHOLD_SIZE2           -0.314\nHOUSEHOLD_SIZE4-5          0.058\nHOUSEHOLD_SIZE6-8         -0.297\nHOUSEHOLD_SIZE9+          -0.334\nOCCUPATION?               -0.015\nOCCUPATIONArmed-F          0.106\nOCCUPATIONCleric.          0.048\nOCCUPATIONExec.            0.178\nOCCUPATIONFarming         -0.062\nOCCUPATIONHandler         -0.032\nOCCUPATIONHouse-s          0.021\nOCCUPATIONMachine          0.000\nOCCUPATIONOther            0.004\nOCCUPATIONProf.            0.138\nOCCUPATIONProtec.          0.078\nOCCUPATIONSales            0.088\nOCCUPATIONTechSup          0.088\nOCCUPATIONTransp.         -0.022\nOS_DOC_SET_KANJI          -0.062\nPRINTER_SUPPLIES           0.000\nYRS_RESIDENCE              0.022\nY_BOX_GAMES               -0.051\n\nResidual standard error: 0.3481 on 4457 degrees of freedom\nMultiple R-squared:  0.3385,\tAdjusted R-squared:  0.3323 \nF-statistic: 54.31 on 42 and 4457 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Getting model predictions","message":["","%r","","GLM_RES <- predict(GLM_MOD2, DEMO_DF, c('AFFINITY_CARD', \"CUST_ID\"))","","GLM_RES$PREDICTED_CARD <- 1*(GLM_RES$PREDICTION >0.5)","names(GLM_RES) <- c(\"AFFINITY_CARD\",\"CUST_ID\",\"PROBABILITY_OF_1\",\"PREDICTION\")","GLM_RES$PROBABILITY_OF_0 <- 1 - (GLM_RES$PROBABILITY_OF_1)"],"enabled":true,"result":{"startTime":1715318932777,"interpreter":"r.medium","endTime":1715318933000,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(GLM_RES, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")"],"enabled":true,"result":{"startTime":1715318933075,"interpreter":"r.medium","endTime":1715318933722,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM Model using specific settings","message":["%r","","settings <-  list(GLMS_FTR_SELECTION  = \"GLMS_FTR_SELECTION_ENABLE\", ","                  GLMS_FTR_GENERATION = \"GLMS_FTR_GENERATION_ENABLE\",  ","                  GLMS_FTR_GEN_METHOD = \"GLMS_FTR_GEN_CUBIC\", ","                  GLMS_FTR_SEL_CRIT   = \"GLMS_FTR_SEL_AIC\", ","                  GLMS_MAX_FEATURES   = 100)","                       ","GLM_MOD2 <- ore.odmGLM(AFFINITY_CARD~.,DEMO_DF, odm.settings = settings) ","summary(GLM_MOD2)"],"enabled":true,"result":{"startTime":1715318933801,"interpreter":"r.medium","endTime":1715318937901,"results":[{"message":"\nCall:\nore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF, odm.settings = settings)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-0.97020 -0.23298 -0.02389  0.05969  1.10652 \n\nCoefficients:\n                                             Estimate Std. Error t value\n(Intercept)                                 2.534e-01  1.089e-02  23.270\nY_BOX_GAMES                                -1.537e-01  1.920e-02  -8.004\nHOUSEHOLD_SIZE_1                           -2.729e-01  3.533e-02  -7.725\nEDUCATION_Bach.                             1.507e-01  2.731e-02   5.519\nOCCUPATION_Prof.                            2.687e-01  2.397e-02  11.208\nOCCUPATION_Cleric.                         -9.905e-02  4.557e-02  -2.174\nHOUSEHOLD_SIZE_4-5                          1.807e-01  4.011e-02   4.506\nEDUCATION_Profsc                           -2.806e-01  1.064e-01  -2.637\nEDUCATION_PhD                               3.045e-01  5.138e-02   5.927\nOCCUPATION_TechSup                         -7.024e-01  2.105e-01  -3.336\nOCCUPATION_Protec.                          1.415e-01  3.694e-02   3.832\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_2   -1.231e-01  2.017e-02  -6.104\nY_BOX_GAMES*HOUSEHOLD_SIZE_1                1.492e-01  3.967e-02   3.762\nCUST_ID*EDUCATION_< Bach.                   4.613e-07  1.286e-07   3.589\nBOOKKEEPING_APPLICATION*OCCUPATION_Exec.    3.266e-01  2.365e-02  13.809\nHOME_THEATER_PACKAGE*HOUSEHOLD_SIZE_2      -1.614e-01  2.237e-02  -7.214\nEDUCATION_Bach.*BULK_PACK_DISKETTES         7.082e-02  2.577e-02   2.748\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_9+  -2.742e-01  2.331e-02 -11.762\nHOME_THEATER_PACKAGE*OCCUPATION_Sales       1.879e-01  2.330e-02   8.064\nHOME_THEATER_PACKAGE*EDUCATION_Masters      2.338e-01  3.416e-02   6.845\nY_BOX_GAMES*OCCUPATION_Cleric.              1.425e-01  5.215e-02   2.733\nHOUSEHOLD_SIZE_2*EDUCATION_Bach.           -2.261e-01  3.224e-02  -7.014\nBULK_PACK_DISKETTES*HOUSEHOLD_SIZE_4-5     -1.855e-01  4.863e-02  -3.815\nYRS_RESIDENCE*HOUSEHOLD_SIZE_6-8           -7.450e-02  8.166e-03  -9.123\nEDUCATION_Bach.*OCCUPATION_Sales            8.819e-02  3.557e-02   2.479\nY_BOX_GAMES*OCCUPATION_Prof.               -1.924e-01  4.387e-02  -4.385\nHOME_THEATER_PACKAGE*EDUCATION_Profsc       4.546e-01  8.515e-02   5.338\nBULK_PACK_DISKETTES*EDUCATION_Assoc-A       1.595e-01  3.172e-02   5.028\nY_BOX_GAMES*HOUSEHOLD_SIZE_9+               1.252e-01  4.166e-02   3.005\nOCCUPATION_Prof.*EDUCATION_Profsc           2.741e-01  8.731e-02   3.139\nHOME_THEATER_PACKAGE*OCCUPATION_TechSup     2.921e-01  5.277e-02   5.534\nHOUSEHOLD_SIZE_2*OCCUPATION_Exec.          -1.272e-01  3.826e-02  -3.325\nBOOKKEEPING_APPLICATION*OCCUPATION_TechSup  6.002e-01  2.017e-01   2.975\nHOUSEHOLD_SIZE_1*EDUCATION_Bach.           -1.503e-01  4.718e-02  -3.185\nOCCUPATION_Exec.*EDUCATION_Masters          1.324e-01  6.085e-02   2.176\nFLAT_PANEL_MONITOR*OCCUPATION_TechSup       1.601e-01  5.192e-02   3.085\nY_BOX_GAMES*OCCUPATION_Exec.               -1.526e-01  4.767e-02  -3.202\nEDUCATION_Bach.*HOUSEHOLD_SIZE_9+          -2.100e-01  5.733e-02  -3.663\nHOME_THEATER_PACKAGE*OCCUPATION_Cleric.     2.185e-01  4.983e-02   4.384\nHOUSEHOLD_SIZE_1*OCCUPATION_Prof.          -1.451e-01  6.782e-02  -2.139\nOCCUPATION_Prof.*HOUSEHOLD_SIZE_9+         -1.963e-01  5.393e-02  -3.640\nOCCUPATION_Exec.*HOUSEHOLD_SIZE_9+         -2.382e-01  5.896e-02  -4.039\nHOUSEHOLD_SIZE_1*OCCUPATION_Exec.          -2.725e-01  7.232e-02  -3.768\n                                           Pr(>|t|)    \n(Intercept)                                 < 2e-16 ***\nY_BOX_GAMES                                1.52e-15 ***\nHOUSEHOLD_SIZE_1                           1.38e-14 ***\nEDUCATION_Bach.                            3.60e-08 ***\nOCCUPATION_Prof.                            < 2e-16 ***\nOCCUPATION_Cleric.                         0.029789 *  \nHOUSEHOLD_SIZE_4-5                         6.77e-06 ***\nEDUCATION_Profsc                           0.008402 ** \nEDUCATION_PhD                              3.33e-09 ***\nOCCUPATION_TechSup                         0.000856 ***\nOCCUPATION_Protec.                         0.000129 ***\nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_2   1.12e-09 ***\nY_BOX_GAMES*HOUSEHOLD_SIZE_1               0.000171 ***\nCUST_ID*EDUCATION_< Bach.                  0.000336 ***\nBOOKKEEPING_APPLICATION*OCCUPATION_Exec.    < 2e-16 ***\nHOME_THEATER_PACKAGE*HOUSEHOLD_SIZE_2      6.35e-13 ***\nEDUCATION_Bach.*BULK_PACK_DISKETTES        0.006027 ** \nBOOKKEEPING_APPLICATION*HOUSEHOLD_SIZE_9+   < 2e-16 ***\nHOME_THEATER_PACKAGE*OCCUPATION_Sales      9.38e-16 ***\nHOME_THEATER_PACKAGE*EDUCATION_Masters     8.66e-12 ***\nY_BOX_GAMES*OCCUPATION_Cleric.             0.006292 ** \nHOUSEHOLD_SIZE_2*EDUCATION_Bach.           2.66e-12 ***\nBULK_PACK_DISKETTES*HOUSEHOLD_SIZE_4-5     0.000138 ***\nYRS_RESIDENCE*HOUSEHOLD_SIZE_6-8            < 2e-16 ***\nEDUCATION_Bach.*OCCUPATION_Sales           0.013201 *  \nY_BOX_GAMES*OCCUPATION_Prof.               1.19e-05 ***\nHOME_THEATER_PACKAGE*EDUCATION_Profsc      9.85e-08 ***\nBULK_PACK_DISKETTES*EDUCATION_Assoc-A      5.15e-07 ***\nY_BOX_GAMES*HOUSEHOLD_SIZE_9+              0.002666 ** \nOCCUPATION_Prof.*EDUCATION_Profsc          0.001707 ** \nHOME_THEATER_PACKAGE*OCCUPATION_TechSup    3.31e-08 ***\nHOUSEHOLD_SIZE_2*OCCUPATION_Exec.          0.000891 ***\nBOOKKEEPING_APPLICATION*OCCUPATION_TechSup 0.002942 ** \nHOUSEHOLD_SIZE_1*EDUCATION_Bach.           0.001458 ** \nOCCUPATION_Exec.*EDUCATION_Masters         0.029622 *  \nFLAT_PANEL_MONITOR*OCCUPATION_TechSup      0.002051 ** \nY_BOX_GAMES*OCCUPATION_Exec.               0.001374 ** \nEDUCATION_Bach.*HOUSEHOLD_SIZE_9+          0.000253 ***\nHOME_THEATER_PACKAGE*OCCUPATION_Cleric.    1.19e-05 ***\nHOUSEHOLD_SIZE_1*OCCUPATION_Prof.          0.032478 *  \nOCCUPATION_Prof.*HOUSEHOLD_SIZE_9+         0.000276 ***\nOCCUPATION_Exec.*HOUSEHOLD_SIZE_9+         5.45e-05 ***\nHOUSEHOLD_SIZE_1*OCCUPATION_Exec.          0.000167 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 0.3384 on 4457 degrees of freedom\nMultiple R-squared:  0.3751,\tAdjusted R-squared:  0.3692 \nF-statistic:  63.7 on 42 and 4457 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Getting model predictions","message":["%r","","GLM_RES <- predict(GLM_MOD2, DEMO_DF, c('AFFINITY_CARD', \"CUST_ID\"))","","GLM_RES$PREDICTED_CARD <- 1*(GLM_RES$PREDICTION >0.5)","names(GLM_RES) <- c(\"AFFINITY_CARD\",\"CUST_ID\",\"PROBABILITY_OF_1\",\"PREDICTION\")","GLM_RES$PROBABILITY_OF_0 <- 1 - (GLM_RES$PROBABILITY_OF_1)"],"enabled":true,"result":{"startTime":1715318937975,"interpreter":"r.medium","endTime":1715318938197,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","confusion_matrix <- with(GLM_RES, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","","accuracy <- confusion_matrix/sum(confusion_matrix)","sum(diag(accuracy))"],"enabled":true,"result":{"startTime":1715318938266,"interpreter":"r.medium","endTime":1715318938455,"results":[{"message":"0.848","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data adding supplemental columns","message":["%r","","merged <- merge(GLM_RES, DEMO_DF, all.x = TRUE)","","predicted_results <- ore.sort(merged[(merged$PROBABILITY_OF_1 > 0.5),], by = c('PREDICTION','PROBABILITY_OF_1'), reverse = TRUE)","z.show(head(predicted_results,n=20))"],"enabled":true,"result":{"startTime":1715318938535,"interpreter":"r.medium","endTime":1715318941937,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tPROBABILITY_OF_1\tPREDICTION\tPROBABILITY_OF_0\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n1\t100442\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t7\t0\n1\t100601\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t5\t0\n1\t102037\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\n1\t102025\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\n0\t101990\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t5\t0\n1\t101935\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t6\t0\n1\t101863\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t9\t0\n1\t101697\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\n1\t101581\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t4\t0\n1\t101570\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\n1\t101560\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\n1\t101526\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t4\t0\n1\t101426\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\n1\t101189\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t6\t0\n1\t101145\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t7\t0\n1\t101086\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\n1\t101049\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t5\t0\n1\t100962\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t0\t1\t3\tProf.\t0\t1\t11\t0\n1\t100607\t0.970199945026003\t1\t0.029800054973997\t1\t1\tProfsc\t1\t1\t3\tProf.\t0\t1\t4\t0\n1\t102189\t0.970199945026003\t1\t0.029800054973997\t1\t0\tProfsc\t0\t1\t3\tProf.\t0\t1\t7\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Graph ROC curve, lift, and probability densities","message":["%r","","df <- ore.pull(GLM_RES)","options(warn = -1)","","# ROC PLOT","sensitivity <-df[order(df$PROBABILITY_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROBABILITY_OF_0)/sum(sensitivity$PROBABILITY_OF_0) - cumsum(sensitivity$PROBABILITY_OF_0)/sum(sensitivity$PROBABILITY_OF_0))","spec <- cumsum(sensitivity$PROBABILITY_OF_1)/sum(sensitivity$PROBABILITY_OF_1)","","# LIFT CHART","sensitivity2 <- df[order(df$PROBABILITY_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROBABILITY_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROBABILITY_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DISTRIBUTION CHART","dx <- density(df$PROBABILITY_OF_0)","dx2 <- density(df$PROBABILITY_OF_1)","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","par(mar = c(6, 4, 4, 2))","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.4),","       horiz = TRUE)  "],"enabled":true,"result":{"startTime":1715318942015,"interpreter":"r.medium","endTime":1715318942337,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAFoCAIAAAAXZAVmAAAABmJLR0QA/wD/AP+gvaeTAAAg\nAElEQVR4nOzdd1wU19oH8Gd3WXpv0osoioKgWIgKdgW7ol77tUcTk+jVGCUx0Zh4vfa8N4nG\nnve1oBE1oNhA1NiVYkMEEaT3uix1d94/zjJuYIHts8jz/dzP/ZzMzM48Ksv+9syZc1gURQFC\nCCGEEEJIOmymC0AIIYQQQqg9wQCNEEIIIYSQDDBAI4QQQgghJAMM0AghhBBCCMkAAzRCCCGE\nEEIywACNEEIIIYSQDDBAI4QQQgghJAMM0AghhBBCCMkAAzRCCCGEEEIywACNEEIIIYSQDDBA\nI4QQQgghJAMM0AghhBBCCMkAAzRCCCGEEEIywACNEEIIIYSQDDBAI4QQQgghJAMM0AghhBBC\nCMkAAzRCCCGEEEIywACNEEIIIYSQDDBAI4QQQgghJAMM0AghhBBCCMkAAzRCCCGEEEIywACN\nEEIIIYSQDDBAI4QQQgghJAMM0AghhBBCCMkAAzRCCCGEEEIywADNvPXr17PEaGtr29vbT5s2\nLT4+vsmRjx8/njt3rrOzs46OjrGxsa+v7/fff19WVtb8nGlpaatXr+7Zs6eRkZGhoWG3bt0W\nLVp09+7d1iuR71UIIRr9dv7hhx+a7505cybZ++bNG7Ll8uXLAwYMMDY2JtuzsrJkvWLrb9vm\nV0SIcU0+9TgcjrW19aRJk+7fv6/4yRX/mW/zDE0OaP0/lUX8L01bW9vc3NzLy2vJkiWPHj1S\n4lXaJOWfriP85sEArXHq6+tzcnLCwsIGDx6clpZGb9+xY4efn9+JEycyMjLq6uoqKyvj4uK+\n++47b2/vV69eiZ/h9OnTPXv23Lt3b2JiIo/Hq6qqSk5OPnr06JYtW1q5rnyvQgjJraCgIDg4\n+NGjR5WVlfTGQ4cOkQ+e/fv3t3kGBt+2MtWJUCuEQmFhYWF4eLi/v//ly5eZLkfJVPFOqa+v\nLy0tffHixeHDhwcMGLB+/XplnVlWHfn3AAZoDbJlyxaKojIyMvr16wcAfD7/woULZFdkZOS6\ndeuEQqGhoWFoaCifz8/Kylq4cCEAZGRkTJo0qa6ujhz54MGDuXPnVldXs1isDRs2ZGRk8Pn8\nFy9e7Nu3r0uXLi1dWr5XIYRkEhoaSlEURVHkbfXs2bPq6moAWLRokUAgoCjKwcFB+rPh2xa1\nd+RTr7S0lHycCQSCdevWMV1U25q8kWXaq7gtW7YIhcKcnJxffvnFyMgIAP7zn//s2bNHFddq\nTso/nar/EjQChZj21VdfkX8L8quEoqiQkBCyZfv27WSLn58f2bJjxw76hQKBwN3dnWz//fff\nycbhw4eTLStWrJC+BmleNWTIEHJMWVlZky2VlZUURf3jH/8g/5mUlPTll19aWlp27979iy++\nIBvv379Pn6p///4AYGBgUFFRQVFUfHz8jBkzbGxsuFyutbX1P/7xjxcvXkhfPEKao/nbWRz9\nHklJSZk0aVKT38b29vYDBgxo/lv68uXLEq8lzduWvuLhw4cDAwM7depkYWExa9asoqIicsCa\nNWvoC3G5XAcHhzlz5iQnJzc/g/j7WqY6EWqi+dtEfPASn8+nWvjBIwc/efJk2rRpnTp10tLS\nsra2njp16pMnT+iT0y/87bffRowYYWVlZWRkNGvWrIKCAnKA9D/zLZ1B/I3c+n+29E5ZsWIF\nacfFxdHX9fLyAgArK6va2lpp/tIoijp16hTZaGpqyuPxyMbWP1Lb/J2QnJw8ZcoUa2trLS0t\nZ2fnKVOmnD9/nnzDl/JP1/zvpM2qWr+oZsIAzbwm74rMzMy+ffsCAIvFevr0KUVRZWVlbLbo\nXkF+fr74a+lxlnPmzKEoqqKigsPhkC2pqalSFiDlq+i4XF5e3mRLkwAdHBxMGt26dXv27Blp\nf/755+RVqampZMs///lPiqIuX76sra3d5B2or6//4MEDGf8iEWKe2gK0lG9b+opNzJgxgxwg\nHiZoVlZWxcXFTc4g/r7GAI0U0fxtkpmZSf8gNQnQ4j94FEVdvHiRy+U2+dnjcrkXL14kp2rp\nZ97Hx4cEU+l/5ls6g+IBOjExkbRXrlxJLpqcnEy2rF27Vsq/NMLJyYk+LSXFR2rrvxOEQqGr\nq2vzvfHx8U1eLlOAbr2qNi+qmXAIhwbZuHEji8VydHR88uSJo6NjaGhor169ACArK0soFAKA\nvr6+tbW1+Evon7mMjAwAyMzMFAgEAKCrqyvxx1EiWV9Fp/mWPH/+/NGjRw0NDUlJSV5eXr6+\nvgBw5swZcpWTJ0+Sw8ht66VLl9bV1bm7uz99+rS2tvbhw4cmJiZ8Pn/16tVS1o9Qe3ThwoWI\niAjS/u677yiKysrKevDgwcGDB8nGffv2kV/TgYGBzV8u69vW29s7NTU1MTHR3NwcAMLCwhoa\nGgBg586d5CpCobCsrGzTpk0AUFhYePbs2SZnEH9fS18nQm0qKyv79ttvSdvT01NPT098r/gP\nXkNDw7Jly+rr61ks1smTJysrK0+dOsViserr6z/++GPyI03z8/PLyMjIyMggt3ATEhLIp4/0\nP/MtnUF6Lb1TPDw8Ro4cCQAnT56sra0FgD/++IMctnTpUpku0b17d9JIS0uT6SNV4u+EjIwM\n8vDVxo0bq6urs7Ozw8PDx44dK/FzX8rfA21WJdNFNYdGF9eRCYVCe3v7Ng+jKIo0WCyWiiuS\nwdatW/v160d3jy1atAgA8vLyYmJiAIDccurSpUtAQEBcXBy5c5ecnOzt7a2jozNgwIDy8nIA\nePDgAfm1ghBS3KZNmzp37uzh4UGigEAgyMnJAYDMzMylS5e6urrq6uqampqSMAEA6enpTc7Q\n5H2NkOJIt5GZmdnRo0cBgM1mb9u2rckx4j948fHx5Oc2ICBg1qxZhoaGM2fO9Pf3B4Ds7OyE\nhATxF3733XeOjo6Ojo7ffPMN2RIdHQ2y/My3dAal+PzzzwGgpKSEPOxE4vvQoUPpkZlSIv1r\nhEwfqRJ/J9jY2BgaGgLArVu3Tpw4kZqa6u/vf+nSJdKdJ582q1LFRdUAA7QG2bJlS01NDXkU\nIDs7Ozg4mDyb7+DgQL6H8fn8goIC8ZfQb3hHR0fy/+S3TE1NjfgMHq2T71UAUF9fL3F7jx49\nxP9z9uzZurq6AHDy5MmnT5+SW1ckVTf544ijKIrH40lfDEIdiqxv227dupEGeTMCQF1dXW1t\n7ZAhQw4dOpSenk4/iEzvbXKGJu9rhJSFzWZbWlqOHz/+1q1b48aNa7JX/AeP/sggH3kEPYah\nyQcKfQz9bG5RUZFMP/MSzyDbn61l48aNI/eOjhw5kpaWRiauXbZsmUwnoSjq9evXpO3q6irT\nR6rE3wk6OjqnTp1ydXW9ffv2kiVLAgICzM3Nx4wZU1hYKFNh4tqsShUXVQMM0JpFR0dn1apV\n5Nmg/Pz8//73vwBgYmJCnroDgP/93/+lDxYKhfR/jh49GgCMjIzocck7d+6U8qJSvooedsbn\n8wFAIBDQo5lbOpIwNTWdPHkyAJw7d+7YsWMAwOFw/vnPfwIAPSJlypQpzQcYWVhYSPlHQOiD\nIeXdJFnf7PS7Uvz89+/fJ+F7yJAh2dnZFEWdOXOmzTPIVCdCrSDDeQUCQWFhYURExODBg5sf\nI/6DZ2VlRRriY6bJCEYQ+0Bpcgz9hKKlpaVMP/MSzyDDHw8AWn6nsNnsTz/9FACioqJ2794N\nABYWFlOnTpXp5CdPniRFmpqaBgQEyPSRKvF3AgCMHz/+7du36enp169f37ZtG5vNvnbt2nff\nfSfTn06cNFXJdFENgQFaE61du5Y0fvnlF9LLS98/+v7778+cOVNTU5OTk7NkyRLy2EGXLl1m\nzpxJDtiyZYuWlhYA7N+/PyQkJCsrq6am5vXr1wcOHPjss89auqI0r6K/5f/+++8VFRXff/99\nfn6+lH8i0t9cXl7+888/A8CYMWPs7OwAoHfv3qTx559/7t+/v7y8vKSk5OHDh1999RX5zYJQ\nR2NmZkYaiYmJTcZ0NiHfm10ceTkA6OnpGRoapqSk/Pvf/1Z6nQgpS58+fchHxu3bt0+dOsXj\n8UJDQ//66y8AsLOz8/HxET/4+++/z8zMzMzMpJ+2Hz58uEw/8xLPIGvNrbxTFi9ebGBgIBQK\nf/nlFwCYP3++jo6ONOekKCovL++XX35Zvnw52fLtt9/q6+sr/pFaW1vr7+9/+vRpgUAQEBAw\nZswYkrNTUlJk/dPR2qxK1otqChkfOkTK1/zRWqFQSI+COnnyJNn4n//8R+JXPUdHx5cvX4qf\n8Pjx4/QdGXFjxoxppYw2X3X16tWWfoqazMJBT1tDEwgEzs7O9PFnz56ld0VERDR/pBoAgoOD\nFft7RYgB9Nu5iUGDBlHN3iNNHiIk0tLS6M948feXRG2+bZu/K+k5DVJSUvh8Pv3FuIk1a9a0\ndAY56kRIXOuT1RAt/eCFh4c3+cEDAC0trfDw8CYvbKJXr141NTUy/cxLPEPz2lr/z9bfKR9/\n/DG9/dWrV9L8pTW3fv16+rA2P1Jb/51AZqZvbuvWrRJf3tKfrvnvulaqavOimgl7oDURi8Ui\njxcAwE8//UQa69ate/DgwezZsx0dHbW1tQ0NDXv37r1p06anT582GZs4Z86cFy9efP755x4e\nHvr6+vr6+u7u7gsWLKC7sSVq81WjR4/et29f165d9fX1PT09f/zxx4EDB0r5J2Kz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WBwO06VpkPv37wcGBgoEAkzPUpJpIZVhw4aFh4c3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\n0F4UQ9tFD3R2qSjls1lsWxPb5gcY6xnbmdiRdmJOYk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Ih6blr1643btzA9KxpGhoaEhMTMzIyGhqf4wGAyZMnM1gSQqh1OTk5VlZWAHDj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prediction results","message":["%r","","z.show(head(ore.sort(GLM_RES[(GLM_RES$PROBABILITY_OF_1 > 0.5),], by = c('PREDICTION','PROBABILITY_OF_1'), reverse = TRUE),n=20))"],"enabled":true,"result":{"startTime":1715318942427,"interpreter":"r.medium","endTime":1715318942863,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tPROBABILITY_OF_1\tPREDICTION\tPROBABILITY_OF_0\n0\t101990\t0.970199945026003\t1\t0.029800054973997\n1\t100442\t0.970199945026003\t1\t0.029800054973997\n1\t102037\t0.970199945026003\t1\t0.029800054973997\n1\t102025\t0.970199945026003\t1\t0.029800054973997\n1\t101935\t0.970199945026003\t1\t0.029800054973997\n1\t101863\t0.970199945026003\t1\t0.029800054973997\n1\t101697\t0.970199945026003\t1\t0.029800054973997\n1\t101581\t0.970199945026003\t1\t0.029800054973997\n1\t101570\t0.970199945026003\t1\t0.029800054973997\n1\t101560\t0.970199945026003\t1\t0.029800054973997\n1\t101526\t0.970199945026003\t1\t0.029800054973997\n1\t101426\t0.970199945026003\t1\t0.029800054973997\n1\t101189\t0.970199945026003\t1\t0.029800054973997\n1\t101145\t0.970199945026003\t1\t0.029800054973997\n1\t101086\t0.970199945026003\t1\t0.029800054973997\n1\t101049\t0.970199945026003\t1\t0.029800054973997\n1\t100962\t0.970199945026003\t1\t0.029800054973997\n1\t100607\t0.970199945026003\t1\t0.029800054973997\n1\t100601\t0.970199945026003\t1\t0.029800054973997\n1\t102189\t0.970199945026003\t1\t0.029800054973997\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test data into a table","message":["%r","","ore.drop(\"DEMO_DF_GLM\")","ore.create(DEMO_DF, \"DEMO_DF_GLM\")","","ore.drop(model = 'GLM_CLASSIFICATION_MODEL')","","MOD <- ore.odmGLM(formula = AFFINITY_CARD ~ ., data = DEMO_DF, odm.settings = list(model_name = \"GLM_CLASSIFICATION_MODEL\"))","PRED <- predict(MOD, newdata=DEMO_DF, supplemental.cols=\"AFFINITY_CARD\")"],"enabled":true,"result":{"startTime":1715318942954,"interpreter":"r.medium","endTime":1715318946457,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,","round(PREDICTION_AFFINITY_CARD,3) PREDICTION_AFFINITY_CARD,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","      PREDICTION(GLM_CLASSIFICATION_MODEL USING *) PREDICTION_AFFINITY_CARD,","      PREDICTION_DETAILS(GLM_CLASSIFICATION_MODEL USING *) PD","      FROM DEMO_DF_GLM","      ORDER BY CUST_ID","      FETCH FIRST 10 ROWS ONLY) OUT,","XMLTABLE('/Details'","         PASSING OUT.PD","         COLUMNS","         \"Attribute1\" XMLType PATH 'Attribute[1]',","         \"Attribute2\" XMLType PATH 'Attribute[2]')","OUTPRED;"],"enabled":true,"result":{"startTime":1715318946542,"interpreter":"sql.medium","endTime":1715318950443,"results":[{"message":"CUST_ID\tPREDICTION_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t0.218\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".271\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".074\" \n100002\t0.269\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".214\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".18\" \n100003\t0.158\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".156\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".084\" \n100004\t0.174\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".164\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".088\" \n100005\t0.454\t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".475\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"0\" weight=\".109\" \n100006\t0.048\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".202\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".071\" \n100007\t0.041\t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".05\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".038\" \n100008\t0.083\t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".106\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".045\" \n100009\t0.475\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".35\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".295\" \n100010\t0.22\t\"BULK_PACK_DISKETTES\" actualValue=\"1\" weight=\".13\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"1\" weight=\".049\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715318950527,"interpreter":"md.medium","endTime":1715318950600,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r"," "],"enabled":true,"result":{"startTime":1715318950695,"interpreter":"r.medium","endTime":1715318950814,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Classification NB.dsnb b/machine-learning/notebooks/r/OML4R Classification NB.dsnb
deleted file mode 100755
index 8b59399d..00000000
--- a/machine-learning/notebooks/r/OML4R Classification NB.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification NB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715346499186,"interpreter":"md.low","endTime":1715346499248,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Predicting Target Customers using Classification using Naive Bayes","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.","","This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation","","[The Universal Permissive License (UPL), Version 1.0](https://gcc59e2cf7a6f5f-oml2.adb-compdev1.us-phoenix-1.oraclecloudapps.com/oml/zep/%20//oss.oracle.com/licenses/upl/)"],"enabled":true,"result":{"startTime":1715346499325,"interpreter":"md.low","endTime":1715346499383,"results":[{"message":"<h1 id=\"predicting-target-customers-using-classification-using-naive-bayes\">Predicting Target Customers using Classification using Naive Bayes<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<p><a href=\"https://gcc59e2cf7a6f5f-oml2.adb-compdev1.us-phoenix-1.oraclecloudapps.com/oml/zep/%20//oss.oracle.com/licenses/upl/\">The Universal Permissive License (UPL), Version 1.0<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715346499458,"interpreter":"md.low","endTime":1715346499517,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a>"],"enabled":true,"result":{"startTime":1715346499590,"interpreter":"md.low","endTime":1715346499652,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715346499741,"interpreter":"r.low","endTime":1715346499862,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from 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SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()","z.show(head(DEMO))"],"enabled":true,"result":{"startTime":1715346499934,"interpreter":"r.low","endTime":1715346500055,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display number of rows and columns","message":["%r","","dim(DEMO)"],"enabled":true,"result":{"startTime":1715346500131,"interpreter":"r.low","endTime":1715346500340,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>14<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"ORE.FREQ\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"ORE.FREQ\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD 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116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\",\"ORE.FREQ\",\"ORE.STRATA\",\"ORE.GROUP\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD 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\"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", ","                   \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","","rownames(DEMO_DF) <- (DEMO_DF$CUST_ID)"],"enabled":true,"result":{"startTime":1715346500938,"interpreter":"r.low","endTime":1715346501312,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first few records of DEMO_DF","message":["%r","","z.show(head(DEMO_DF))"],"enabled":true,"result":{"startTime":1715346501384,"interpreter":"r.low","endTime":1715346501504,"results":[{"message":"CUST_ID\tAFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n100001\t0\t1\t0\t< Bach.\t0\t1\t2\tExec.\t0\t1\t3\t0\n100002\t0\t1\t1\tBach.\t1\t1\t2\tProf.\t0\t1\t4\t0\n100003\t0\t1\t1\t< Bach.\t1\t0\t2\tSales\t0\t1\t6\t0\n100004\t0\t1\t1\t< Bach.\t1\t1\t2\tSales\t0\t1\t5\t0\n100005\t1\t1\t0\tAssoc-A\t0\t1\t3\tCrafts\t0\t1\t5\t0\n100006\t0\t1\t0\t< Bach.\t0\t0\t9+\tProf.\t0\t1\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Split data into train and test sets (60/40)","message":["%r","","set.seed(123)","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)","","# Create training data set","DEMO_DF.train <- DEMO_DF[group==FALSE,]","","# Create test data set","DEMO_DF.test <- DEMO_DF[group==TRUE,]"],"enabled":true,"result":{"startTime":1715346501581,"interpreter":"r.low","endTime":1715346502391,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build a Naive Bayes model to predict AFFINITY_CARD","<hr>"],"enabled":true,"result":{"startTime":1715346502467,"interpreter":"md.low","endTime":1715346502528,"results":[{"message":"<h3 id=\"build-a-naive-bayes-model-to-predict-affinity_card\">Build a Naive Bayes model to predict AFFINITY_CARD<\/h3>\n<hr>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model using default settings","message":["%r","","nb_mod <- ore.odmNB(AFFINITY_CARD ~., DEMO_DF.train)","summary(nb_mod)"],"enabled":true,"result":{"startTime":1715346502601,"interpreter":"r.low","endTime":1715346504097,"results":[{"message":"\nCall:\nore.odmNB(formula = AFFINITY_CARD ~ ., data = DEMO_DF.train)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nApriori: \n        0         1 \n0.7614815 0.2385185 \n\nTables: \n$BOOKKEEPING_APPLICATION\n    ( ; .5]   (.5;  )\n0 0.1473735 0.8526265\n1 0.0326087 0.9673913\n\n$EDUCATION\n  '10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', 'Presch.'\n0                                                                0.14737354\n1                                                                0.03260870\n  '< Bach.', 'Assoc-A', 'Assoc-V', 'HS-grad'    'Bach.'\n0                                 0.67558366 0.13813230\n1                                 0.50155280 0.26708075\n  'Masters', 'PhD', 'Profsc'\n0                 0.03891051\n1                 0.19875776\n\n$HOME_THEATER_PACKAGE\n    ( ; .5]   (.5;  )\n0 0.5155642 0.4844358\n1 0.1832298 0.8167702\n\n$HOUSEHOLD_SIZE\n  '1', '6-8'  '2', '9+' '3', '4-5'\n0 0.24562257 0.43968872 0.31468872\n1 0.01397516 0.12267081 0.86335404\n\n$OCCUPATION\n  '?', 'Cleric.', 'Farming', 'Handler', 'House-s', 'Other'\n0                                                0.4061284\n1                                                0.1366460\n  'Crafts', 'Machine', 'Sales', 'TechSup', 'Transp.'\n0                                          0.4056420\n1                                          0.3571429\n  'Exec.', 'Prof.', 'Protec.'\n0                   0.1882296\n1                   0.5062112\n\n$YRS_RESIDENCE\n    ( ; 2.5] (2.5; 3.5] (3.5; 6.5]   (6.5;  )\n0 0.26702335 0.25000000 0.41926070 0.06371595\n1 0.02173913 0.11180124 0.68167702 0.18478261\n\n$Y_BOX_GAMES\n     ( ; .5]    (.5;  )\n0 0.60846304 0.39153696\n1 0.95031056 0.04968944\n\n\nLevels: \n[1] \"0\" \"1\"\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data using model proxy object returning predictions and class probabilities","message":["%r","","nb_res <- predict(nb_mod, DEMO_DF.test, c('AFFINITY_CARD', \"CUST_ID\"))","","z.show(head(nb_res, 10))"],"enabled":true,"result":{"startTime":1715346504174,"interpreter":"r.low","endTime":1715346504745,"results":[{"message":"X.0.\tX.1.\tAFFINITY_CARD\tCUST_ID\tPREDICTION\n0.810949132919523\t0.189050867080477\t0\t100001\t0\n0.311778159206504\t0.688221840793496\t0\t100002\t1\n0.782792530320276\t0.217207469679724\t0\t100004\t0\n0.907224899426563\t0.0927751005734371\t1\t100009\t0\n0.999642796119885\t0.00035720388011539\t0\t100011\t0\n0.904127874316971\t0.0958721256830293\t0\t100017\t0\n0.995111585869023\t0.00488841413097739\t0\t100018\t0\n0.996022921801772\t0.0039770781982284\t0\t100025\t0\n0.990888839545903\t0.0091111604540966\t0\t100027\t0\n0.871552932090672\t0.128447067909328\t0\t100031\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Show model accuracy on data","message":["%r","","","confusion_matrix <- with(nb_res, table(AFFINITY_CARD, PREDICTION))","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1715346504822,"interpreter":"r.low","endTime":1715346505143,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1095  277\n     1  103  325\n\nAccuracy:\n","type":"TEXT"},{"message":"0.7889","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for NB","message":["%md","### Examples of possible setting overrides for Naive Bayes","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-65D8FF60-6760-4815-B307-AD9A6E49247D\" target=\"_blank\">Algorithm Settings<\/a>","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","","* Enable Sampling, this allows the use to request sampling of the build data","> ODMS_SAMPLING = 'ODMS_SAMPLING_DISABLE'","","* Specify the value of pairwise threshold for the Naive Bayes algorithm","The value required is between 0 and 1 (including the edges), and the default value is 0.","> NABS_PAIRWISE_THRESHOLD = 0","* Specify the value of a singleton threshold for the NB algorithm","The value required is between 0 and 1 (including the edges), and the default value is 0.","> NABS_SINGLETON_THRESHOLD = 0"],"enabled":true,"result":{"startTime":1715346505225,"interpreter":"md.low","endTime":1715346505289,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-naive-bayes\">Examples of possible setting overrides for Naive Bayes<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-65D8FF60-6760-4815-B307-AD9A6E49247D\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Enable Sampling, this allows the use to request sampling of the build data<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_SAMPLING = 'ODMS_SAMPLING_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of pairwise threshold for the Naive Bayes algorithm\nThe value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>NABS_PAIRWISE_THRESHOLD = 0<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of a singleton threshold for the NB algorithm\nThe value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>NABS_SINGLETON_THRESHOLD = 0<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes Model using explicit settings","message":["%r","","settings <- list(NABS_PAIRWISE_THRESHOLD = 0, NABS_SINGLETON_THRESHOLD = 0)","nb_mod2 <- ore.odmNB(AFFINITY_CARD ~., DEMO_DF.train, odm.settings = settings)","summary(nb_mod2)"],"enabled":true,"result":{"startTime":1715346505430,"interpreter":"r.low","endTime":1715346506829,"results":[{"message":"\nCall:\nore.odmNB(formula = AFFINITY_CARD ~ ., data = DEMO_DF.train, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclas.weights.balanced                            OFF\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nApriori: \n        0         1 \n0.7614815 0.2385185 \n\nTables: \n$BOOKKEEPING_APPLICATION\n    ( ; .5]   (.5;  )\n0 0.1473735 0.8526265\n1 0.0326087 0.9673913\n\n$EDUCATION\n  '10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', 'Presch.'\n0                                                                0.14737354\n1                                                                0.03260870\n  '< Bach.', 'Assoc-A', 'Assoc-V', 'HS-grad'    'Bach.'\n0                                 0.67558366 0.13813230\n1                                 0.50155280 0.26708075\n  'Masters', 'PhD', 'Profsc'\n0                 0.03891051\n1                 0.19875776\n\n$HOME_THEATER_PACKAGE\n    ( ; .5]   (.5;  )\n0 0.5155642 0.4844358\n1 0.1832298 0.8167702\n\n$HOUSEHOLD_SIZE\n  '1', '6-8'  '2', '9+' '3', '4-5'\n0 0.24562257 0.43968872 0.31468872\n1 0.01397516 0.12267081 0.86335404\n\n$OCCUPATION\n  '?', 'Cleric.', 'Farming', 'Handler', 'House-s', 'Other'\n0                                                0.4061284\n1                                                0.1366460\n  'Crafts', 'Machine', 'Sales', 'TechSup', 'Transp.'\n0                                          0.4056420\n1                                          0.3571429\n  'Exec.', 'Prof.', 'Protec.'\n0                   0.1882296\n1                   0.5062112\n\n$YRS_RESIDENCE\n    ( ; 2.5] (2.5; 3.5] (3.5; 6.5]   (6.5;  )\n0 0.26702335 0.25000000 0.41926070 0.06371595\n1 0.02173913 0.11180124 0.68167702 0.18478261\n\n$Y_BOX_GAMES\n     ( ; .5]    (.5;  )\n0 0.60846304 0.39153696\n1 0.95031056 0.04968944\n\n\nLevels: \n[1] \"0\" \"1\"\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy on data","message":["%r","","nb_res <- predict(nb_mod2, DEMO_DF.test, c('AFFINITY_CARD', \"CUST_ID\"))","confusion_matrix <- with(nb_res, table(AFFINITY_CARD, PREDICTION))","colnames(nb_res) <- c( 'PROBABILITY_OF_0', 'PROBABILITY_OF_1', 'AFFINITY_CARD', 'CUST_ID' ,'PREDICTION')","df <- ore.push(nb_res)","","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\", round(sum(diag(accuracy)),4))"],"enabled":true,"result":{"startTime":1715346506915,"interpreter":"r.low","endTime":1715346507640,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 1095  277\n            1  103  325\n\nAccuracy: 0.7889","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Display ROC Curve, Lift Chart and Probability Density","message":["%r","","df <- ore.pull(df)","","# BAR PLOT","sensitivity <-df[order(df$PROBABILITY_OF_1, decreasing = TRUE),]","sens <- (sum(sensitivity$PROBABILITY_OF_0)/sum(sensitivity$PROBABILITY_OF_0) - cumsum(sensitivity$PROBABILITY_OF_0)/sum(sensitivity$PROBABILITY_OF_0))","spec <- cumsum(sensitivity$PROBABILITY_OF_1)/sum(sensitivity$PROBABILITY_OF_1)","","# LIFT CHART","sensitivity2 <- df[order(df$PROBABILITY_OF_1,decreasing = TRUE), ]","decile <- quantile(sensitivity2$PROBABILITY_OF_1, probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity2$PROBABILITY_OF_1, col.names = c('sens'))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DISTRIBUTION CHART","dx <- density(df$PROBABILITY_OF_0)","dx2 <- density(df$PROBABILITY_OF_1)","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","","plot(1 - spec, sens, type = 'l', col = 'darkred', ","     ylab = 'Sensitivity', xlab = '1 - Specificity', main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col='darkred')","        ","plot(dx, lwd = 2, col = 'darkgreen', main = 'Probability Densities')","lines(dx2, lwd = 2, col = 'darkred')","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE) 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EGiFk4uSEE2iYR6hQWaUfPp84WjiKOZNEjkDz+LxSTqmlodq/a9OwDjRSjXUo2ZmZL0+d\n6jJypGtgoDKvC+RHEMS6deu2bdvm5eUVHR0N2TMAAEiFwWAYGRkZGRmpbPaMEKqWsoQDiZRB\nwwi0QpEj0JaGloa6hpLchRyBRgjls/MVEpZy0TACrSLrUCb9/DMhEHhv3qycywGqEASxZs2a\nH3/8cciQIX/++acRfHsAAADyET+JX3GrYIkh5PNrSxqrbCVfYRYSaOUgR6AlHH5GCNmYfCiz\nzK/M72vfl/qwlIuGBFoV1qFs4HJfnjpl4e7eZfhwJVwOUEUgECxZsuTkyZPDhg2LiopisVh0\nRwQAAGpP/CR+Ra2CJVZNQQEhFOK25CUcZAJd9fYtIRQyJFgkF8hAhgTaWM+YpcPi8DiovSzE\nQUMCrQrrUKadO1dXUTHk++8lWRkHqAiBQLBw4cL//e9//v7+V65c0dPTozsiAABoD8RP4lfQ\nKljikTMIkVQj0O/3UhHweJy8PEn2LwQykCGBRgh1Nu6cUZyBoIRDZqqwDuWLU6eYLFbPoCDl\nXA7Ir76+fvbs2VeuXJkwYcKlS5d0dHTojggAADoERayC1SY8FxCToYQDIVSZlQUJtCIQBJFd\nlo3b0iXQJu8T6EpIoGVC+zqUlVlZ+Q8f9pwzR9tQosp3QDsej/fJJ5/88ccfn3zyyW+//cZk\nMumOSNUJBIIXL154eHgghFJSUk6ePOns7Lx06VK6JgyJqaEUvv+WFgCgBLm5uTExMcbGxmPG\njBEdPw4JCRE/Dq1k5AxCHRMTpsTj3Ia2tpra2oL6eoRQZXa23dChioqvAyuuLq7h1eC2hGvY\nYZ2NGz8IFbALxJ+pFmhIoGlfhzL13DlEEG6zZyvhWkB+XC538uTJ0dHRc+bM+fXXX7W0OvTa\nixLau3cvm8328PCora0dPXq0ra3tmTNnSkpKvv32W1riEVNDmZycrORgAOiwnjx5Mnr0aA6H\nIxAI7O3tL1++PGDAAHzTtm3bVCqBJkegWdKs8c/Q1DS0t2dnZiKYR6gwMqxhh5HzCGEEWnb0\nrkOZeu6cvpWVw+jRSrsikFlNTc3EiRNv3779+eef//zzzxowI0Qyv/zyS2xsLEIoNjbWwsLi\n0aNHiYmJ06dPpyuBFlND6e7uTktIAHRA33zzTWBg4I8//lhRUbF69epRo0Zdv359yJAhdMfV\ngprCQtyQvH4DM3ZyggRaocgEWlND095UiiIZGIGmDF6HUskXLXn2rCwlxXP5cg0YyFR5bDbb\n398/Pj5++fLlP/30kyqvV6pq8vPzcX3U7du3cfGim5tb4fs3JOUTU0MJH4oAUJrHjx+fPXtW\nR0fH2to6PDw8ODjY39//+vXrPj4+dIfW1IdtCKVNoLt2xQ1IoBWETKBtTWy1tbQlvyOZQBdV\nFQmEAk0NTeqDU6IO99aVeu4cQgjqN1RfRUWFn59ffHz8hg0bDh48CNmzVFxdXU+ePJmTkxMe\nHu7n54cQSk9Pd3V1pTsuAACdmEymQCAgf9y5c+eXX345bty4+/fv0xhVi8gSDqlHoGEpaAXL\nLs3GDanqN5BICQdfyC/llFIblfJ1rASaEArTLlwwdnS0+fhjumMB4hQVFQ0bNuzx48chISGh\noaF0h6N+duzYsX79egcHh379+uGxpUOHDn3xxRd0xwUAoNPw4cPj4uJEj2zbtu2rr77y9/en\nK6TWfCjhsLaW6o5kAs3Jy8OzCQG1yCU4ulp0leqO1kYf/ikLKtW+iqNjJdD5Dx9W5eT0mDkT\nln9WZQUFBSNHjnz58uXevXu3bt1KdzhqycnJqbi4ODMzMyIiAg/eL1y4cNCgQXTHBQCg0/r1\n60+fPt3k4NatWzdu3KhSa4MSAgH3/TaEUpdwvE+gCaGwOieH4siASAlHV/OuUt1RdDNCSKDV\nTPrlywgh18BAugMBrXr79q2Pj09qauqBAwe+/PJLusNRV25ubiwWy8nJiSx98fb27tWrF71R\nAQDo5eXlFRUV1fz4N99802SOL71qioqI96UmBtKswoGaLQVNZVgAISEhzClv/Fgi1Rp2CCET\nfRM9ZuMOaO1gHmHHSqBfR0QYOzpaeXjQHQhoWXp6uo+PT3Z29vHjx1etWkV3OO0Kh8MxMDCg\nOwoAAGjbf3ZRkbKEQ9/Kisli4TYk0JTLrcit5zcWxkhbA40QsjZu/NdsByPQHWglivK0tMo3\nb/qtXEl3IKBlaWlpo0aNKioqOnnyZPONKoGE1qxZ06SBEBIKhUlJSf369aMpKAAAkEKNyJJB\n0pZwIISMHRxKk5MRJNAKIPMi0Ji1sTXuobCKtlWhqNKBEuh39+4hhBx8fekOBLQgKSnJz8+v\nsrIyPDx82rRpdIejxl6/ft2kgRBiMpleXl6rV6+mKSgAAJACmUAz9fW1pV/u1tjRERJoBSGX\n4NDW0iaXpZNce1oKugMl0Hn37zM0NGwGD6Y7ENBUQkLCmDFjOBzOxYsXJ02aRHc46g0XOM6f\nP//XX3+lOxYAAJAFmUDrS1m/gcFKdopDjkB3Mesiw0LOZAINI9DqJO/+fXM3Nz1zc7oDAf9x\n//798ePH8/n8yMjI0bA9JEUgewYAqC+Z17DDPiTQ2dlUhQQwMoGWoX4DidRAF1aqfQLdUSYR\nVr97V5mdbauSG5Z2ZPfu3Rs3bpxAIIiKioLsmVoXLlwYPXq0s7Mz/vH7778vLi6mNyQAAJAE\nOYlQhgJohJDR+80IucXFDRwOVVEBhNCbkje4Ie0SHBi5FHQ7mETYURLovPv3EUKQQKuUGzdu\n+Pv7a2pqxsTEjBgxgu5w2pVjx46tWrXKx8fnzZvGFzszM7OdO3fSGxUAAEjiQwlHp04y3P0/\nK9nBIDSlPuyiIuUi0Bg5As2t51bXVVMUFD1aTaCnTp0aFRUluuenWsu9fx8hZOfjQ3cgoFFU\nVNTUqVN1dXVv3brl7e1Ndzjtzb59+y5fvvztt9+SR8aNGxceHk5jSAAAICGqSjgQlEFTisfn\n5bPzcVvGEg6RzQjVvQy61QSayWROnz7d3t5+48aN6enpyoxJEfLu3ze0tzdycKA7EIAQQuHh\n4VOnTjU2Nr53796AAQPoDqcdysrKwn9YciMVU1PT8vJyWoMCAACJkAm0bCUcOsbGumZmuA0J\nNIWyS7OFhBC3ZSzhMBZJoNW8DLrVBDo8PDw/P3/Tpk03b950dXX18fE5depUTU2NMoOjCo/N\nLn35Euo3VMS5c+fmzp1rYWFx+/bt3r170x1O+2Rra5ucnIxEEug///zTxcWF1qAAAKBt9dXV\nDe+TDdlW4UCwEIdiiC4C7WQhSwJtZWhFviu12wQaIWRmZrZy5cqnT58mJiZ6eHgsXbrU2tp6\n8eLFL1++VFp8lMj75x9CKLSDBFoF/PLLL0FBQba2tn///XfPnj3pDqfdWrZs2YIFC2JiYhgM\nRnJy8t69e5ctW7Z8+XK64wIAgDZwi4rItoFMNdAIIROnxvSO/X4eCJAfmUAb6hqas2RZ00xb\nS9vMoPHLgaKqIvEnq7i2l7HLz8+/efPmrVu3NDQ0Jk2alJOT07dv37CwsHXr1ikhPkrk/fMP\nghmEKuDnn39esWJFt27dYmJi7O3t6Q6nPVu3bl11dfWkSZMEAkGvXr309PTWr1//xRdf0B0X\nAAC0QXQbQtlqoBGMQCvGm9LGTyOyFUBj1kbWZZwy1I5roBsaGq5evRoQENClS5cLFy6sXLmy\noKDgzJkzt27dio6O3r59uzKjlFPe/fs6JiYWvXrRHUiHtmvXruXLl7u6ut65cweyZ0VjMBjb\ntm0rKytLTEx88uRJaWlpSEgI3UEBAEDbOO/XsEMMhmyrcCDRBBpGoKkj5xp2GFkGre4j0K0m\n0DY2NvPnz7e1tX348GFSUtKKFStMTEzwTSNHjmSxWDJfsqam5tq1a7jN4XAWLVpkYmKCy0V4\nPJ7M3bZGwOMVPHpkO3gwQ6OjrNmngnbt2rVx40YPD4+4uDgbGxu6w+kodHV1PT09vby89PX1\n6Y4FAAAkQpZw6JmZaWpry9aJ8fsSjoaaGtGaECAPOXdRwToZNX4oUvca6FZLOBYsWLB161YD\nAwPRg2lpaT169EAI5ebmynzJ4OBgNzc33N68eXN0dPSWLVsQQgcOHDAxMaF8bLvwyRMBjwf1\nG3QhCOLrr7/eu3evl5fXzZs3zWEnSKVYvHhx84M6OjpOTk6BgYHwDQAAQGXJuYYd1mQlO5lH\nsoEoMoGWbQYhRq5k125LOHbv3t0ke0YIkYmvPM6ePTtz5kzcvnLlypUrV9atW7du3brLly+f\nPXtW/v6byH/4ECFkO2gQ5T2DNhEEsWbNmr179w4ZMuT27duQPStNaWnpiRMnbt26VVRUVFxc\nfOvWrRMnTmRmZv7yyy89evRISEigO0AAAGhZzfsBY3myXuOuXRmamrgN8wgpUV5TzuaycZuS\nEg51H4GWoqqBw+E0T6llQBBEZWUlbpeUlLi7u+N2r1698vPz5e+/ifwHDzSYTGtYbFjphELh\n4sWLf/zxx2HDht24ccPIyIjuiDoQS0vLr776KisrKzIyMiIiIisr68svv+zSpUtaWtrSpUu/\n+uorugMEAICWcakYgdZgMg3t7HAbyqApkVmSSbblSaDJEo7i6mKCIOQNiz4tlHCsWbOmSQMh\nJBQKk5KS+vXrJ/8lx40bt3bt2nPnzunq6g4dOjQqKiowMBAhFBERgetDqFXw77+Wffpo6elR\n3jMQQyAQLFq06PTp0/7+/leuXNGDv79y/fnnny9fvtR8PwCjqakZHBzcp08fBoPx9ddfwwKC\nAKiyqVOnLly40N/fn3wKdygfSjhk2kWFZOLkVPX2LYIRaIqQMwg1GBqy7eONkSUc9fz68ppy\n2ZbDUwUtJNCvX79u0kAIMZlMLy+v1atXy3/J0NDQwYMHOzk5TZ48ecCAAUFBQWfOnCEIIiYm\n5sqVK/L3L4rHZnPy8x1Gj6a2WyBefX397Nmzr1y5EhAQcOnSJV1dXboj6nCqq6tzcnLM3u/F\nhRDKycmpqqpCCDEYDENDQ/pCAwC0Ae8EbGZmNm/evIULF3bv3l0RV8nJyTl27Njr169tbGyW\nLFni6uqqiKvI4EMCLV/hsrGzM7pzB8EINEXIEWhbU1tdpuxv6//ZjLCqsF0l0FFRUQih+fPn\n//rrr4q4pK2tbWJiYmho6LVr17KysgiCSExMHDx4cFxcHOW7Ole8fo0QMu3WjdpugRg8Hm/m\nzJnXrl375JNPfvvtNyaTSXdEHdG0adMCAwN37NjRv39/giASEhI2bdo0ffp0hNDFixenTp1K\nd4AAgFaFh4eXl5efPXv25MmTu3btGjJkyMKFCwMDA+WvojQxMWGz2QihZ8+eDR06lMVide/e\nPS4u7ujRo/Hx8b1UYbFXguAWF+OmnDP/Puylkpkp/kwgCXIEWp4lOJBICQdCqKiqyN3GXa6w\n6NNqDbSCsmfMzMwsLCwsPT29oaGBz+fn5uaGh4dTnj2j9587TZydKe8ZtIjL5U6YMOHatWtz\n5sw5c+YMZM90+fnnn/38/ObOnevk5OTs7Dx37tyxY8ceOnQIIeTu7r5z5055Ok9ISAgKCnJ2\ndmaxWAYGBs7OzkFBQTAxEQAKKWgnYHIC0saNG8ePH5+VlXXnzp2srKyJEydu3ryZisDlVVte\nLqivx215aqCRyEp21Xl5/NpaeSPr8MgRaGdLuXIqS0NLLY3G0Vu1nkfYdAR6xYoVCKGDBw/i\nRnMHDx5UeFDUwZ87yWcRUKiampqJEyfevn17yZIlhw8f1oCFt+mjr69/8ODBsLCwN2/eMBgM\nR0dHcinokSNHytNzRETEtGnTPD09Z8yYYWlpyWAwiouLY2Njvb29r169OmHCBCrCBwAgpMid\ngB8/fvz48WNtbW2EkLa29o4dO4aoxmKv/9nHW74E+sPYGUFUZmebU7GMWEdGVQKtwdCwMLTA\nqbNa76XSNIEmF3iWZ6Vn2eCNWvC3S80JBILr16/X1dWJHnz48KH4PvHUARNIoBWPzWaPGzfu\n4cOHy5cv/+mnnxgMBt0RdWgjRoy4c+eOvr4+5d/JBgcHh4WFrV27tsnxffv2bdq0CRJoAOTX\n0NAQGRl58uTJv/76q1evXitXrpw7dy5+i7x9+/bUqVPlT6C5XK6pqSn5o4WFRU1NjZx9UqJG\nJIHWt7KSpyvRL5/Zr19DAi0PHp+XV5GH2/IswYFZG1njBFqtl4JumkCTewSSDaURXfSjuTt3\n7kycOLHFm7hcbmv3qszK0jYy0rOwkDc4IFZFRcXYsWMfPXq0YcOG0NBQusMBKCEhgcPhyLNj\naGsyMjLmz5/f/PiCBQs2bdpE+eUA6IBsbGx4PN6sWbMePnzYpLhRzp2AEULe3t4IIYIgbt26\nhZfAQgglJibi47Sreb+PN0NTU9/SUp6udE1Ndc3M6srLEZRByy2rNEtICHHbxcpFzt7IMuh2\nNQJNmjRp0ty5cydMmKC0JRRCQkLE3DpixIiIiIjmI9D79+8Xs01xVXa2cdeuFAUIWlZUVOTn\n5/f8+XPInlWHv7//+fPnP/vsM8p77tKlS2Rk5Lx585ocj4yMdHBwoPxyAHRAitsJmNzrNyAg\nAC/Lgx08eBBvCUw7cgahnoUFQ+5V/ExdXAoePUKQQMsts1hkEWg5tiHE2sdeKq0m0AYGBvPn\nz2cymdOmTZs7d+6wYcPoLWnV1NRs8dvh/fv3t3YXQiCoyslx9PdXZFwdXWFhoa+vb0pKyt69\ne7/88ku6wwGNDA0NP//88ytXrvTs2ROXOWLyf8LZsmXLokWLfv/99xEjRlhaWiKESkpK7t69\nGxkZeeLECTk7BwAghHbv3h0WFtbkoJubm/y7TrQ2U/DSpUty9kwVsgZazgJozNjZGRJoSrwp\nbVyCw1TfVP6F59r5CPS5c+dqamp+//33s2fP+vn5WVtbz549e+7cub1795b/qgkJCQcOHHjw\n4EFRURFBENbW1oMGDVqzZo2Xl5f8nZNqCguFDQ3GMCqmMG/fvvX19c3MzDxw4MCqVavoDgd8\nkJ2dPXLkSD6f//z5c2p7njdvnpWV1Z49e4KDg3HRpIGBgbe3d2Rk5NixY6m9FgAAo2onYNnI\nNgdJNpz3JRyUJNCmLo3FBpBAy+l1cePGIPLXbyCRvVTaVQ20KAMDg7lz586dO7e4uDg8PPzI\nkSNhYWHyfwJW2iz+6nfvEEKG9vZUdQhEZWVljRo1Kicn5/jx4wsXLqQ7HPAfMTExiut87Nix\nY8eOJQiiuroaIWRoaAhzRgGghKJ3Am6N+En8ss1Bks2HEWj5FoHGTN4n0JVZWUI+X0NLXM4D\nxKA2gSZHoEuqSwRCgaaGWu642faDicvlxsbG3rx5Mz093e79zvLyUNos/urcXAQJtGKkpaX5\n+voWFhaePHmyeTks6AgYDIaRkRHdUQDQrih6J+DWiJ/EL9scJNmQz8QQtgAAIABJREFUq3DI\nuYsKRo5ACxsaqt6+hU0hZJZRlIEb1IxAv6+BFggFpZxS0a1V1EirCTSfz4+JiTl79uy1a9c0\nNTWnTZt28+bN4cOHy39Jpc3i5+TlIYRYtrYU9gkQQklJSX5+fpWVleHh4dOmTaM7HNAyPp+f\nkpKSk5PD5/PJg5MnT5a/Z+WUYAHQASl6J+DWiJ/EL8McJJnVUFrCQY5AI4TYr19DAi0bvpCf\nXZaN292sKNjauclmhO0tgba1tWWz2f7+/qdOnQoICKBwLQ6lzeKvKSxEFD0JASkhIWHMmDEc\nDufixYuTJk2iOxzQsoyMjMmTJ7969UogEDCZzIaGBoSQgYEBh8ORs2fYSAUARVNy9qw6CKGw\ntrQUtykp4dC3stIxNuZVViKEKjIyuo4ZI3+fHVB2aXaDoAG3qa2BRggVVhb2sesjf5/K12oC\nvX379hkzZogutE4Vpc3ix6vhUPIkBNg///wzbtw4Pp8fGRk5evRousMBrVqzZs2wYcMSExN1\ndXXr6uqSkpIWLVrU4jc/0pKtBEvMJCShUCh/VAC0A/TuBLx48eLjx48rrn9J1JaWCt9/Y0bV\n4JeJi0tRQgJCqCIjg5IOO6CM4g9/OkoSaDMDM20t7Xp+PVLneYStJtBLlixR0CWVNoufW1Ki\nqaOjDWWaFImLiwsICBAKhZGRkXJuBw0ULT4+/tSpUzo6OgghgUDQr1+/06dPT5kyRf4yStlK\nsMRMQkpOTpYzJADaBxp3AkYInThxgvYEWnQfb0pqoBFCpt26QQItJ7IA2lTf1NJQrt1tMAaD\nYWVolVuRi9R5JbumCbRyPgErZxZ/bVmZrpkZ5d12TH/99dfUqVN1dHRu3rz58ccf0x0OaEN5\nebmVlRVCyMLCorCw0N7e3tnZOS8vT/6eZSvBEjMJyd3dXf6oAGgHlLAT8JkzZxTUMyVw4SVG\n1Qi0affuuFGRnk5Jhx1QelHjn65bJwoKoDFrY2ucQKvvXipNE2hlfgJW9Cx+Hputq4ASlA4o\nKipqxowZenp6f/3110cffUR3OEAKH330UWho6Nq1a0+ePOniQsFXb7KVYImZhETvDk0AqCCB\nQPDixQsPDw+EUEpKysmTJ52dnZcuXSr/SFNQUBAVASoKmUBrMJl65vLu1oGRC3FUZmcL6us1\nRTaWAhIiE+junbpT1SdZBt1+RqDJD76hoaF411BRaWlpygiKIjw22wj28ZbbxYsX586da2Zm\nFh0dTck2OkAJPv/8c9wIDQ0NCAj4+eefTU1NL168KH/PsJEKAIq2d+9eNpvt4eFRW1s7evRo\nW1vbM2fOlJSUfPvtt3L27OHhcfz48RYXzFGF1dzJBNqgUydEUTzkCDQhEFS+eWPWLLEBbfow\nAk3FEhwYufJGO6yBbnHXUEq2ElWaeg5H29CQ7ijU27lz5z799FNLS8vo6Gj4ql2NHDlyBDd6\n9+6dnZ2dl5fXqVMnJpNJSeewkQoACvXLL7/ExsYihGJjYy0sLB49epSYmDh9+nT5E+hly5Yd\nOXLk2LFjVIRJPTz1HyGkb2VFVZ9mrq5ku/zVK0igpVXbUPuu/B1ud7embgT6/VLQ6lvCIcWX\np/RuJSo1gmiABFo+v/zyS1BQkK2t7d9//w3Zs7pYunRpkyMMBsPOzo6q7Fm0WyMjIyMjI8ie\nAaBWfn4+ro+6ffs2rn1yc3MrLKQgz5gzZ07fvn1bvCk6Olr+/uX0YQSauvVndUxMyPmI5a9e\nUdVtx5FRlCEkGtdKcu3kKv5kybXPEWi6thKlVkNtLSEUMtUo41cxP//884oVK7p27RobG+vo\n6Eh3OEBSR48eJYefFaGmpiY6OhpvyMLhcFavXn3lyhUNDY05c+bs2bMHr/sBAJCHq6vryZMn\nJ06cGB4efv78eYRQenq6qysFuYuBgUFrKwT4+vrK37+cFJFAI4TMXF3x+h4VkEBLL62wsXaX\nwWAooga6vKa8nl+vraV+tektJNB0bSVKLT6XixBiUr3LaAcRFha2YcOGHj16xMbG2tjY0B0O\nUCHBwcFubm64vXnz5ujo6C1btiCEDhw4YGJisn37dlqjA6A92LFjx/Tp01euXBkQEODj44MQ\nOnTo0BdffEF3XApHJtD6VCfQuXFxCKEytZrHpSJeFTZ+6rAztTPQoWxQkizhIAiiuLrYztSO\nqp6VpoUEmq6tRKnFr61FCGnp6dEdiPrZtWvXxo0bPTw8bt26hb9GBIB09uxZ8qP1lStXrl69\nOmDAAITQkCFDZs2aBQk0APIbN25ccXFxcXGxo6MjLpFauHAhfqK1b4obgcaNckigpUeOQLt1\ndqOwWzKBRggVVha2kwQaU+vsGUECLastW7Z8//33Xl5eN2/eNKdoFSGgZLq6uq3d1GQlZhkQ\nBFFZWWliYoIQKikpIYvje/XqlZ+fL2fnAACMxWKxWCzyR29vbxqDUQ5hQ0NdeTluU7uFsHnP\nnrhRV17OLSnRh4EhaZAj0D2sqZx/+Z/dvNWzDJqejVSUABJoaREEsXbt2h9++GHw4MHXr19X\n6BLdQKH27NmjuM7HjRu3du3ac+fO6erqDh06NCoqKjAwECEUERHRfOFLAIAM+Hz+6dOn//nn\nn/L3CSWmuA1WVEFNUREhbJysZtC5M4U9/2chjrQ0SKAlRxCEghJoQ11DAx2DGl4NUtuFOOjc\nSEWh+HV1CCGt1ofigCihULhkyZITJ04MGzYsMjLSEFYvUWetffqlRGho6ODBg52cnCZPnjxg\nwICgoKAzZ84QBBETE3PlyhXFXReAjmP16tVnz54dP368nZ36fa8tM9F9vKkdgTbq2lVLTw8P\nq5Wnptr5+FDYefv2ruIdh8fBbWoTaISQtZF1Zkkmajcj0ErYSlQ5BDweQkgT1gSQgEAgWLRo\n0enTp8eOHXv16lU9GLYHrbO1tU1MTAwNDb127VpWVhZBEImJiYMHD46Li+sINZoAKMHFixdj\nYmL69+9PdyBKxSkoINvU1kAzNDTMXF2Lk5IQQqUpKRT23O6l5H/4c1FbA40Q6mTUCSfQaroZ\nYas10IrbSlQ5cAINI9BtamhomD179uXLlwMCAi5duiSmfBYAzMzMLCwsLCwsjO5AAGifNDQ0\nOmBBFDkCzdTX16a6htC8Z0+cQJdBAi2NlILGP5eZgZnotD9KqPteKq1upLJ371688S/eSjQu\nLm7btm1qNMUel3DACLR49fX1gYGBly9fDgwMvHr1KmTP7QDeHRAAoL5Gjx599epVuqNQtpr3\nI9DUrmGHkfMIy5KTKe+8HSNHoCkffkYi8wgLKgvEn6maWh2BVtxWosoBJRxt4nK5U6ZMuXXr\n1uzZs0+fPq2l1eqDAagR0Zn7AAB1pKuru3Dhwj/++MPFxUX0W9/Q0FAao1I0soSDpYDNB8gE\nmpOfX1dermtmRvkl2qWXeS9xw92G+t2I1X0EutWcSXFbiSoHJNDi1dTUTJw48fbt20uWLDl8\n+LCGhhSbugMAAFCc7Ozs4cOHV1ZWJiQk0B2L8nxYBJrSGYSYhfuH/K8sNdV28GDKL9H+EASR\nWpiK2z1telLef2fjxrVW2skkQpLithJVDliFQ4zKykp/f/+HDx9+8cUXBw8eVJe6dgAA6Ahi\nYmLoDoEGHxJoSteww0ycncmFOEpfvoQEWhLvKt5V1Vbhdi+bXpT3T45A1/BqqmqrjPTUbPHc\nVscdd+zYsX79egcHh379+qnjVqLChgaEkAaTSXcgKqeiosLPz+/hw4cbNmw4dOgQZM8AAABo\nR9ZAU7sEB8bQ1DR3a6zihTJoCZH1GwihXrbUJ9DkCDRSzzLoVkeg1X0rUUF9PUJIU1ub7kBU\nS3Fx8ejRo58/f75hw4b2XU7XMbW5ArS6bIQEQAd34cKFEydOvHnzJjMzEyH0/fffL1myxMrK\niu64FEihI9AIIYtevYoSExFCJS9eKKL/9udFXuMfytLQspMR9XU1ogl0YVWhq7Xa1Dhg4uaN\nKW0r0ezs7K5du1LbJ4xAN1dYWOjr65ucnLx9+/bNmzfTHQ6gnrrvfwQAQAgdO3bsm2++WbFi\nBVnLYWZmtnPnzgMHDtAbmOLUVVTg+gqkmEmECCGL3r1xo+T5c0X03/48z238Q/Wx66OI/q2M\nrDQ1NAVCAWpnI9DK3ErU0dGRIAhq+xTy+QghDVhZ4r2cnJxRo0ZlZmYeOHBg9erVdIcDFELd\n9z8CACCE9u3bd/ny5aFDh27duhUfGTdu3Pbt29txAl0jskSBIko4EEIWvRqLEOrKyzl5eSxb\nW0VcpT15kds4At3btrci+tfS0LJgWeBdVArY7SiBVtxWotOnTxdz8PLly5RchRAIECTQ72Vl\nZY0aNert27fHjh1btGgR3eEAAABoVVZWFi6YJOeomJqaNhnMamdqFLYNIcmyz4dh1OJnzyCB\nFo/H56UVpuG2ghJohFBn486NCXR7GoFW3FaiV65c+eijj/AaeaS6ujpqr4JHoBmamtR2q47S\n0tJ8fX0LCwtPnjz56aef0h0OUBI+n5+SkpKTk8Pn88mDkydPpjEkAIAkbG1tk5OT+/fvTybQ\nf/75p4uLC71RKRSZQGtoaekrptSbZWOjb2nJLSlBCJU8f+40bpwirtJupOSnNAgacLuvfV8F\nXaWzceekd0monSXQittK9Icffti7d+/WrVvHvX/4MhiMqKgoaq8ihBFohBBCKSkpvr6+ZWVl\nFy5caHHsH7RLGRkZkydPfvXqlUAgYDKZDQ0NCCEDAwMOh0N3aACANixbtmzBggX79+9nMBjJ\nycl//fXXd99993//93+UdJ6QkHDgwIEHDx4UFRURBGFtbT1o0KA1a9Z4eXlR0r9sOPn5uKHf\nqRNDYfsSWHp4vI2ORgiVJCUp6BLtxtOcp7ihpaGliF1UsM4mjfMI21UCjbcSnTdvHuWXXLVq\nlY+Pz6xZs65fv7579249PT3KL4FgBBohhFBiYqKfn191dXV4eDgMPXYoa9asGTZsWGJioq6u\nbl1dXVJS0qJFi+bPn093XACAtq1bt666unrSpEkCgaBXr156enrr16+nZBnZiIiIadOmeXp6\nzpgxw9LSksFgFBcXx8bGent7X716FW+aRguyBpqlmCU4MKv3CXQxJNBtIWcQunV202UqaksN\nciGOdpVAK3QrUU9PzydPnixfvtzLy+vcuXPyd9hcYw10B06g//nnn/Hjxzc0NERGRvr5+dEd\nDlCq+Pj4U6dO6ejoIIQEAkG/fv1Onz49ZcoUmD8KgOpjMBjbtm3btGlTamqqUCh0c3PT19en\npOfg4OCwsLC1a9c2Ob5v375NmzbRmECTI9AKWsMOs/LwwI2KjIwGDocpss4YaCIxJxE3PLp4\nKO4qZAKdz85X3FUUpNUEWtFbibJYrNOnT//222++vr6K6B8n0B12BDouLi4gIEAoFEZGRo4c\nOZLucICylZeX4yVj/5+9O4+LaX0DAP6eaV+1i5SIhGwlQiiFSgjVvfwqkSXXEiE72fflWqNF\nJTtFqRtJJN2kIlulyJbSvk37zPn9cWruaBkzNXOmqef78cc7Z95z3qfMdJ55512UlJRyc3PV\n1dW1tLSys7P5HRcAgF3i4uIjRozg7jUzMjJa/CZqwYIFmzZt4m5bHPlvFxWeJtCNv0+cTs9L\nSYH9CFtDx+kp31OI8gh1Lr8ImfWUa1iysKSypKquSkKEJ0MSeKTVBJqcrUQdHByMjIxSUlK4\nfmV6F06gIyIiZs+eLSoqev/+/TFjxvA7HMBPo0aNOnDgwJo1a3x9fTv3JCQAOoeqqqqTJ08G\nBQVlZWVhGNanT585c+asXLlSXJwLX6NraGiEhoY2H5wZGhrau3fv9l+/zRg90DxaBJqgoK0t\nIiVVR6UihH4mJUEC3ZrMvEzGJt56vfV41xDzXio/Sn5oKWvxri2u4/8cuz59+vTp0+e31Wg0\nWnh4eJPFOv7999/W6uN0OuqSQzjCwsJsbGwkJCQiIiJGjRrF73AAfyxdupQoHDhwwMrK6uzZ\ns/Ly8jdu3OBvVAAA1urq6szMzJKSkiwsLExMTBBCaWlp27ZtCw0NffTokXC7p8Vv27bN2dk5\nODjYxMSEWAsrPz//8ePHoaGhPj4+XPgB2uq/BJqXPdCYkJDKiBHZsbEIoZ+8+Xa9c0j60vDL\noWAUcnqgEUI5pTmdJ4EmeStROTk5hFBJSUmLz0ZHR8+YMaPFpyorK5sfbBjCwbPJvB3TjRs3\n7O3t5eXlIyMjhw7lydZBQCB4enoShSFDhnz+/Dk7O7t79+4isDEnAB3b+fPns7OzU1NTmfuV\nPn78aGJi4u3t7eLi0s7rOzo6qqioHDlyZPPmzVQqFSEkJSVlaGgYGhpqbm7ezou3WW1ZGdEr\njBDi9fLM3fX1iQQ6NzGRpw0JtBefXxCF/t37y0rI8q4hVVlVCkah43QkgHuptJpAk7+V6OrV\nq1k8a2JiEhIS0rwH+vjx4y3OriB6oLvUEI4rV67Mnz9fSUnp4cOHgwfzatEZIHAwDOP6dkgA\nAF64devW/v37m3wrq6WltX//fh8fn/Yn0Aghc3Nzc3NzHMfLy8sRQjIyMszrBPAFo/sZ8XgI\nB0JItXF3i6K0tNryclEZGZ42J6ASPzd8uhjZm/ubgTATFRZVllEm9lL5USpg8whbTaDJ30rU\nw8ODxbNCQkItThA+fvx4i/W7Wg+0l5eXi4tLr169oqKiYKgrWLRoUYvHvb29SY4EAMC+d+/e\ntTjt29TUtPnSGe2BYZisLLs9i5wOoeQUqQm0gQFRwOn0n0lJ6sbGPG1OENHoNMYSHAZ9DHjd\nXE+5nkQCnV0sYNPcW02gBX0r0YYe6K6RQJ87d2758uWamppRUVHsDCgHnR7zhil0Oj0jI+PV\nq1d8XKMKAMCO4uLiJtv0ElRUVIqLi3nXLneHUHKKkUBTREQkWvrxuUhBW1tMTq6mpAQhlPP8\nOSTQzb378Y5a0zCixkCTjASa2LRF4FayazWB5ulWoiTshITjOOoaCfShQ4c2bNigo6Pz8OFD\nNR6PHgOC4tq1a02O7N69Oz8/ny/BAADYRKPRKC3dtigUSn19Pe/a5e4QSk5VNK6wKd2jB8/v\n2himamBAbKeS8/w5b9sSTM+zGn4tIkIiIzR4OIOQoCbXkLdkl3SWHmjebSVK0k5IXaMH+uDB\ngxs3bhw0aNDDhw978HLyMhB0K1euHDJkyMmTJ/kdCACAFWN+9Ilydwglp/7bRYXH4zcIPUaP\nhgSahfhP8URhmPowEhZmZizE0Xl6oHm3lSg5OyERPdCI33MjeGrbtm179uzR19e/f/++oqIi\nv8MBHVpubi7zuA4AQAc0f/781p7S1NQkMRBSlX//ThRkSJnu3LNxe4SKHz/KvnyR5esC2B1Q\nXGYcURirNZaE5hg90N+Lv5PQHBe1mkDzbitRknZC6tQJNI7jbm5uJ06cGDduXHh4OPtzQUAX\nceTIEeaHxcXFly9ftrKy4lc8LCYh0el0PgUFQIfj5+fHr6YXLVrEr0nGjG0IeboINENPQ0OE\nYUSSkP3sGSTQzAorCtN/phNlw76GJLSoJt+QQFfWVpZUlshJypHQKFf8ZlV2YitRGo1WVVXF\nrSbJ2QmpYQx0Z0ygcRxfuXLlmTNnJkyYcO/ePRlYhQc0ExgYyPxQXl7e0dHR3d2dX/GwmIT0\n7t07koMBADTn4+PDrwT6vzHQpEzjEVdQUNTRKUxNRQj9iIsbOG8eCY0KiriPcQ1f4JPeA40Q\nyi7JFuwEurS01M/Pz9XVlXh47ty5devWVVVVmZmZXb9+XV5evp1NkrQTUiftgabRaM7Ozv7+\n/ubm5kFBQRISgrRxPCDNtWvXdHR0mhxMS0trfpAcLCYhwZrlAJCmyUfrjgCn0ysYPdBkzYPv\nOW4ckUB/j40lp0VB8SzzGVHoJd+rtyIZffO/JNDF2YN7CswdoYUE+u+//2aUP3z4sGrVKnt7\n+3Hjxh07dmzv3r1NvhpuA3J2QuqUPdB1dXXz5s27deuWlZXVzZs3xcXF+R0R6KAGDhzI6EVg\nfZAcLCYhtbjmAACAFxwcHPgdQlOVP3/S6+qIMjljoBFCvYyM3nh7I4QK3rypKSkRkxOYXk9e\ne5rxlCiM7z+enBYVpBQkRCSq6qqQoA2DbiGBvnHjxu3bt4lycHBw3759fX19MQwbPHiwo6Nj\n+xNoRM5OSHxKFHintrb2jz/+uHPnjp2dXWBgIGzLDDhSUVEhJSXF7ygAAPw0fPhwb2/vFleM\n5Vd/E2MGISKxB7rXhAlEAafTvz99qgVr5COEEKqsrUz80rAHIWkJNIZhavJqmXmZSNBWsmsh\ngf706RNjLHJcXNyUKVOI99Xw4cOzs7n5s3G0E1Kb2+Dt9clSWVk5a9asBw8ezJs3z9/fX1j4\nN4PXQZfFWM+VeWFXOp3+6tUrPT09PgUFAOgQli1b5unp6eXlxe9A/sOXBLpbnz6yGhplX78i\nhL49fgwJNOFZ5rPa+lqiPFF7Imntqsk1JNDfir6R1mj7tZCHqaiovHv3Tl9fv66uLjY29tSp\nU8TxgoICJSUlcsNru840hINKpc6cOTMqKmrx4sWenp7wlTdgITMzs0kBISQiIqKvr8+Y2AAA\n6Jr+97//1dbWtvhUZGQkycEQGAm0hKKiCJcW+2JHr4kT31+6hBD69vgxaY12cNHp0URBRUZl\nYI+BpLWrrqBOFAR+CMeMGTMWLly4du3a6OjompqaqVOnEscTEhLGNK6eKAA6yyTC0tJSS0vL\nuLi4ZcuWnTlzpnN8JAC8c+/ePYSQk5MTH9fDAgB0TFJSUitWrGjxKTMzM5KDIVQ0JtDSZA2A\nJmiYmBAJdN6rV9VFReIKCmS23jFFpzUk0MYDjMlMNnrJN/zXC1YC3UJf5q5duzQ0NIiFMry8\nvBg7dPz9998rV64kN7yurri4eMqUKXFxce7u7mfPnoXsGbAJsmcAgEAo+9bwrb2sujqZ7WpM\nmkQUcDr9a3Q0mU13TKVVpYmfGwZAT9KZRGbTjAT6W7EgDeFoIYGWk5MLDQ2tqKgoLi6eO3cu\n43hkZKSRkRGJsbVLJxjCkZeXZ2xsnJCQsGHDhoMHD/I7HCBIYmNjly1bxnxk2bJlsbBgEwCg\ngylvTKBlyE2gZXv3luvXjyh/efiQzKY7psfpj+vp9UTZdKApmU0zhnCUVJaUV5eT2XR7tDqa\nVkxMrEn2KSYmxvt4QIPc3NxJkya9fv169+7dBw4c4Hc4QMC4ubk5OzszH1m4cOG6dev4FQ8A\nALSIXwk0Qkhz8mSi8PnBA5Kb7oAevGv4JWgqavZT6Udm0+ry//3XC9Aojs47HU2Ql7H7+vXr\n+PHj379/f+LEia1bt/I7HCB4Xr9+PWDAAOYjAwYMeP36Nb/iAQCA5nAajbENIR8S6ClTiELp\np0/FGRkkt97RPHjfkEBP1Z1KctOMHmiE0NeiryS33madN4EmCOAQjs+fP5uYmHz8+PHUqVOw\nbAJoG3V19ce/Ti2Pjo7u2bMnn8IBAIAWVPz4Qa9vGDZAfgKtYWoqJCpKlLP++Yfk1juUjLwM\nYiE5hNDUwWQn0IpSipKiDQuwQAIN2ig9Pd3IyOjLly8XL15cvnw5v8MBgsrFxcXZ2fnChQtp\naWmpqannz59ftGiRi4sLv+MCAID/ECsxE2Q1NEhuXVRGpufYsUS5iyfQ4W/CiYKIkIipDqkD\noBFCGIYxOqEFaCnozrsfhwBOInz//r2ZmVlBQcG1a9dsbGz4HQ4QYG5ubkVFRatXr66qqkII\nSUhIrFmzZu3atfyOCwAA/sMYAI1RKKTt482sj4UFsQ70t8eP66hUka66XWvY6zCiYNTfSFaC\nxzvctURDQSM9Nx0JVA90502gBU1ycvLUqVPLyspu3LhhbW3N73CAYMMwbO/evVu2bElNTcUw\nTEdHR5LEHQoAAIAdjB5oqR49KCIi7b9gSWXJg/cP3v94X0+vV5dXn6A9gfWGIFpWVjEbNiCE\n6qurPz940H/WrPbHIHBKq0qffHhClKcP/c2mjJV5eV8fPSpKT6fV1nbr3bu3mVm3vn3bH4OG\nQsP3D5BAA868ePHC3Ny8uro6NDR0SuO0BgDaSVJSUl9fnyjT6fSoqKjJjbPOAQCA78o+fyYK\nsr17t/NS1BrqztCdZ6LPVNZWMh/XVdNdOWnl/LHzxYRbWElMcdAgOS2tko8fEUKZd+8SCXRN\nfc3TjKevvr4qqChQlFYc2XvkBO0JQhShdkbId4Wpqd+io8u/fxcWF1cYOFDDxERCSQkhFP4m\nnLGDt9Uwq9ZO//LwYeKxY58fPMBptP+OYpjW9Okmx47JaWm1JzZGAv2l8Et7rkOmTptA44Kz\nE2FMTIyVlRWdTg8JCTE1JXvsEej0vn37dvHiRV9f3y9fvuCCvDoNAKCTKfvSkC21cwD0p/xP\n009Pf//jffOn3ma/XXpp6d6wvR4zPBzHODbPg/vNnJl47BhC6NO9e++/vT315MyVhCtlVWXM\ndfoq9907a++fBn+2J0g+yk9JiV6zpsl+MZiQUB9z8xErVtz9fIc4MqjnoP4q/Zufnv3s2dNN\nm74/fdrCpXH8Y0jIt+joaZcva03/Te81C70VGz5BfSv6RqPTBOLjCkwi5LOIiAhzc3MKhRIZ\nGQnZM+Ciurq627dvW1hYaGpqPnr0yNXVNSsri99BAQDAfxhDOLpparb5Ip8LP088PJE5e+6n\n0k9XTVdUWJRx5GvR14V+C4d4DLmacJWxXUhD5cYxk1WFhbNchno+8WySPSOEPuV/mnth7nzf\n+TX1NW2Ok19eX7gQOGpU890WcRrtU1jYbQsL9a239DOREB3NGtF0BEvO8+dBVlZXjYx+yZ4x\nTE5LS2nIEMaom9ry8ruzZ2feudPmIBkJdB2tLqc0p83XIVOn7YEWCGFhYTY2NhISEhEREaNG\njeJ3OKCTSE1N9fHxCQgIoFAoTk5OERERTZa0AwCAjqCUMYSItwETAAAgAElEQVSjrQl0RU2F\n1Ukrxu4bY7XGnrM/N7TXUIQQtYZ6O/n28cjjr769Ip5NzUmd5zXP/Zb7HwZ/jOs3rrts98KK\nwheFCSLSwhIV9QihIZ/wD42rfSpKK2oqamYVZBVRi4gjAf8GFFQUBP8VzJyad3CJx449Zpo+\nLiwuLj9gQG1ZWSlTf0r3Qvqfj9H0eKRdm55Wc12mV6/a8vK8V68ygoJyX7xgvpqkioreypW6\nCxZIq6khhCrz859t357i6YkQotfX35s3b+7Tp90bxw1ypI9SH0b5c+FnxubeHRkk0Hxz8+bN\n//3vf/Ly8pGRkUOHDuV3OKCTGDduXHx8vKmp6dmzZ2fOnCkiIgL7wAMAOqDK/Py6igqi3OYe\n6L8u//XuxzuibKNvc3nRZUZ2KyUm5TjG0cHQ4Xby7a13thKLPCCEvhd/P/rg6NEHRxkXmaWB\nxr5HCKEhn1HERIl5Y+0XjV9koGmAYRiNTruReMP1mmt+eT5CKPxN+JJLS/wW+LUtWpKlXb36\nuHEDWkxIaPTGjaPc3UVlZRFCZV+/vg8IeHXuXMWPH0QFqWqUHXgrO/BWi5cSk5MbtX69nqsr\n80IlksrKk8+dUx469OHy5QjH66uqQuzs5r96JSojw2moavJqIkIidbQ6hNDngs9G/Yw4vQL5\nYAgHf1y9enXevHmKioqPHj2C7BlwUVxcnL6+/urVq2fPni3CjVntAADAC4wZhKitPdAhKSGX\n/r1ElI36GTFnzwwYhtno27zd+dZvgV+LA3wRQimNvZ9S1SjW/OIFxwuj+owilsEVogjNHTU3\nYUtCX+WGtSb84/zPPj7bhmhJVvD27f1Fi4glfYXExKzv3DHas4fInhFCshoahlu3/i/93d1J\nYj/lWF1HrFs3w61bF3/8OHrz5haX+Ru+bNmYbduIcumnT8SqJpwSpggzep2zCgRjtGHnTaA7\n8GQpLy8ve3v7nj17Pn36dPDgwfwOB3QqSUlJI0eOnDdvnqam5s6dO799E5hF6QEAXQpjFAFG\nobRhFY7K2sqVV1YSZQUphatLrrIYWSFMEZ4/dn7anrRw13DHMY7Me0d3l+0+3HKWkJI88TDn\nTgs7qmgqaj50e6gio0I8XHtjLaPbu2Oi1dTcmzu3rrISIYQwzPLSJS2rFpbXCEuNiO1bc9QG\nnbNCvf5nI6+tjVEa0kIxObk+FhZTvbyWfvtmtHu3uIICi+bG7tihbmxMlFPOn89JSGhDzIxR\nHJBAdwAdcgmOc+fOubi4aGhoPH78uF+/fvwOB3Q2enp6Z8+ezcnJ2bt376NHj/r06YMQun79\nellZ02kxAADAR4wEWqpHD2FxcU5PP3z/MGPN4ON/HGdn1CwFo1joWvgv9P968GvF6YqvB7+W\nnSrLPZp7+6+g4Q5ORJ2MoKCGvPNXfZT6XF1ylVgdorqu2umiE41Oa16tg4jbubPg7VuiPHrD\nhgG2ti1Wuxx/GSGEIyQ2YuCfgTed09NdqVSX7OwVhYUri4vnhIcPWbSInfEYGIVi7usrIimJ\nEMLp9OjVq9vQiQkJNFu+f//u5+cXHBxc+evL1MPDgy/xkObw4cN//fWXtrZ2bGwskdkAwAsS\nEhIODg5PnjxJTU3dsGHD6tWrlZWVLSws+B0XAAA0KPn0iSjIcb4TR1553pH7R4iyUT8jB0MH\nTq8gJSalrqAuI96QHQ6ytycKteXlra0mMUln0vqp64ly4ufEv6P+5rRRchS8efPiSMMvR9XA\nYNyuXS1W+1n2M+JtBFG2N2z48YXFxaV79mTd39yibn36jN60iSj/+PffjOBgTq/ASKA/5X/i\n9Fy+4EMCnZiYOGTIkMWLF8+ZM2fgwIEvmOZ47ty5k/x4SHPw4EF3d/dBgwY9evRITU2N3+GA\nLqF///4HDhz49u3bjRs3hIVh0jAAoMHXr1+3bds2d+7ctWvXpqenkx9AaWMC3Y3z7qTD9w9X\n1FQghDAMO2p3FGv3F87d9fSUGkdUvvXza62axwwPxtaGHiEeP0p+tLNd7sPxh8uX0+vqEEJC\noqLmvr6tbfEYGB9IrOhHwSiMBLo9Rq5dK6PeMDbmmYcHp53QjFHm2SXZjI1dOjI+JNBbtmyx\ns7OrqKj48eOHoaGhqalpbGws+WGQbPv27Rs3btTT03vy5EmPHj34HQ7oWoSFhWfOnBkaGsrv\nQAAA/CQn1zBfLCUlZciQIb6+vrm5udeuXdPX13/b+I0/aRg90JzuBV1QUXDu8TmibD3celQf\n7iwCO9jJiSh8jYoqZZrgyExMWMzT3pPI18uryzcGbeRK01yUevUqY83mkWvXKunqtlbT95kv\nUTDRMWFsBNgewhISjNmEBW/eZN69y9HpjASaRqd9Lvzc/nh4jQ8J9IsXL/bu3SsmJqaqqnr9\n+vUVK1ZYWFg8bXGHm04Bx/E1a9bs3r3bwMAgMjJSSUmJ3xEBAADoikpLS4nCxo0bp02blpWV\nFR0dnZWVNWPGjK1bt5IZCb2urrxxFxVOd4E+9egUtYaKEMIwbPv07dwKaZC9PdFZi9Ppb319\nW6s2QXvCvFHziHJgfGBCVlsmzPFIXWVlzMaGnJ5YZ6O1ms8ynzG2nlk4biG3AtB1cmLMB31+\n4ABH52op//cy+Jj/kVsh8Q4fEmgREREa00bq+/btc3Nzs7S07JT90DiOr1q16sSJExMmTIiK\nilLgfFwRAB1NUlKSg4ODlpaWtLS0lJSUlpaWg4NDUlISv+MCALCL6MkSFRVFCImKiu7du/fF\nr1tm8FrZly/0+oYdATlKoKvqqs5GN6wiN23ItOHqw7kVkpSqKmMz6jc+PsQoiBYdtDkoLSaN\nEMJx3O2GG95hVv1KPHq0vHHlpQmHDhGz+lp0PuY8UVCUVpytN5tbAVBERAwaV57Oef685d2/\nW6EkrdRNohtR/pgHCXRLjI2NY2JimI/s3Llz3bp1nW+GE41GW7hw4enTp83NzSMiImQ4X1q8\nDV6+fIlh2Lhx45ocNzQ0XLRoEfORwMBADMOqq6uJhwUFBW5ubv369RMXF+/Vq9f06dN5sX1d\nQECAtra2mJiYrq5uayMKysrKXF1dNTQ0JCQkDA0Nnz171rzOyZMnMQyzsbFhHMnJyXF1ddXU\n1JSWltbT07t8+TLXgwcIoZCQEENDw/T0dFtb2507d+7evdvW1jYtLc3Q0BCGiAAgKCorK+Xl\n5RkPlZSUqFQqmQEUZ2YyynKcLEgVGB9YUFFAlNdNXcfdqIYtXUoUKn78YDECQU1Ozd3cnSg/\ny3x2O/k2d8NoG2pubsKhQ0RZbdw4HTu71moWVBTcTLxJlB3HOIqLcLwECgu6CxdKKCoS5aQT\nJzg6t3/3hoW6M/MyWdfsCPiQQLu7u/v7+zc5uGPHjo0bN4qJiXGrFb5/Iqyvr3dycvLz85s2\nbVpwcLCEhAQ57Xp5eRkYGMTHx6emprJ/VnZ2tr6+/qtXrwIDA0tKSl68eLF8+fLdu3czf1fQ\nfhEREc7Ozlu3bs3NzV24cOHs2bNb7LZ0cnKKjIwMCwvLz893cHCYOnXqx4+/fBh99erV0aNH\nm2x+fu7cuaFDhz579iwnJ2fZsmWOjo7379/nYvCAsHnz5kOHDiUkJBw4cGDt2rVubm4HDhx4\n8eLFwYMHNzVOwQYAdFiGhoaGhoY4jj948IBxMDk52dDQkMwwijMyiIKYnJyksjL7J56KOkUU\n9HvrT9SeyN2oepuZMbL55FOnWNRcO2UtY+G8jbc3doRJb8+2b2/Y2RHDjI8dY7GSr/dT7+q6\naoQQhmFLJyzlbhgikpJDGz+HZN69W/blC/vn9lNu+OVn5GVwNype4EMCra+vf+/evebHt2zZ\nwugN5Yr2T8tts9raWjs7u8DAQDs7u+DgYHHOV7hsm6qqqitXrnh4eJiamvr4+LB/4sqVKxFC\n4eHhhoaG4uLiPXr0MDc3j4qKEhIS4mJ4R44cmT59uqOjo7y8vJub2/Dhw080+3haWVl59+7d\nLVu2DBkyRFpaevny5QMHDjzF9IesoqLizz//PHfunPKvf3N37drl7OyspqYmIyOzePFiNTW1\n+Ph4LgYPCBkZGU6NU22YLViwICNDAP7kAdCV7d6928rKysrKasuWLcxrw58+fXpb4/QvcpQ0\n/rmQ56T7OeZDzJvsN0R5lekqrkeFUSgj/vqLKH+Picl7+bK1mpKiknus9xDlj/kfT0ef5now\nHCl4+/ZN47htHTu7HqNanVhZR6s7E32GKE8eNHmA6gCuBzPir78aRpPTaCnnz7N/IqMH+sPP\nD1yPiusEZiMVGo0WGhp681f//vsvv+NqQWVl5YwZM4KDg+fOnXv58mUyt1O+deuWrKysubn5\nkiVLAgIC6lofwsWsoqIiNDT0r7/+YifR37p1K9YK1kM+cByPj483btysCCFkamoaFxfXvBqO\n400+/DCPj1++fLmZmZmlpWVrDVGpVH9//6KiIquWNl4C7aShodHiUI3Q0NDenO8lBgAg01Ym\nzIP6bt682XzgH08VfWjIkOS1tdk/y/OJJ1FQklayG9nqEIX20F24kLF1SOLx4yxqOoxx0NPQ\nI8p77u0prCjkRTxsinZzw2k0hJCwuPj4/ftZ1LyReON78XeivGoS9z+EIISk1dT6W1sT5Tc+\nPrRadrvntbs3vBg+F3zuCJ36rHWgdWGJ5XVKSkpafDY6OnrGjBktPlXZ0qZB/EKlUmfOnBkV\nFbV48WJPT08KhdSPKN7e3s7OzhQKZebMmStWrAgJCZkzZ85vz/r06VN9fb02e3/F9uzZs2fP\nnjbEVl5eTqVSmbuNVVRUcnNzm1STkpKysLDYt2/f0KFD+/Tpc+nSpeTkZFVVVeLZwMDAFy9e\ntDZf7e3bt0OGDEEIiYuLe3t76+vrtyFOwNq2bducnZ2Dg4NNTEyI/838/PzHjx+HhoZy9KUH\nAKArK25ceZr9BDq/PD8oOYgoLxi3gLsjdxnEunXTXbAg+eRJhFDatWtGu3e3ts04BaMcsT0y\n6egkhFBxZfH2u9vP/O8ML0L6rY+hoV8iI4my3qpVLNbVxnH88P3DRHmA6gCLIbyaezZs2bL0\nmzcRQpV5eRnBwTp//MHOWYwe6Hp6/aeCTzqqOjwKjys6UAK9evVqFs+amJiEhIQ0GePx77//\nHj9+XLL1eaYkKy0ttbS0jIuLW7Zs2ZkzZ0geQ5KZmfns2bPAwECEkIiIyIIFC7y9vdlJoInx\n4uSPeGne00zw9/ffsGHD5MmTS0tLzc3NXVxciC7PrKys1atXR0ZGtjagXFdXF8fxkpKSGzdu\nLFiwQEZGprUPXaDNHB0dVVRUjhw5snnzZmLWkZSUFDGD0NzcnN/RAQDagnUHFo1GCw8Pb37/\nbXNz9VVVZY1r2CmwnUD7x/nX1NcghDAMWzJhSZtb/y39NWtenT1Lr6+n19W9OHLEtPXB0CY6\nJtYjrO+8vIMQOh9zfvGExVxcFYRN9dXV0WvWEGXJ7t0Nt2xhUTn8TXjKtxSivG7KOgrGqz4+\nDWNjhQEDitLTEUJvvL3ZTKAHdP9vPEl6bjok0OxivY+3kJDQ9Mb1ZZgdZ/kNC5mKi4stLCye\nP3/u7u5+8OBB8gPw9vam0WgaGv8th06hUL59+6auro4QkpGRKS8vZ65fXl4uJiYmKirat29f\nYWFhNnei2rp16969e1t8Kjo6mnmERhMyMjJSUlL5+fmMI/n5+d27d29eU0lJibkvc/bs2X37\n9kUIpaSkFBYW6unpMVfGMCwrK0tTU5NxRE5ObsmSJeHh4Z6enpBA84K5ubm5uTmO48QrSkZG\nho/zDQAA7ce6A4vr3wAXZ2TgdDpRVtBhN0libPwxSWdSPxUORk5zqpum5oA//ki9fBkh9Mbb\n23DzZqnWtz87Ynvknzf/1NTX0Oi0ZYHLnm18xrustEXP9+8vaZxnP37vXlFZWRaVd9/bTRR6\nyvV0GMPx/uccwLChixc/XrcOIfT10aPSrCx29puUk5TrLtv9Z9lPhFB6Lh92x+SIwIyB7uDy\n8vJMTEyeP3++YcMGvmTP9fX1/v7+165dw5kYGRldvHiRqDBgwIA3b94wn/L69ev+/ftTKBQZ\nGRkrK6uzZ8+yM4lzz549eCtYZM8IIQzDDA0No6OjGUcePXo0duxY1s0VFhY+ePDA2toaIWRt\nbc3c3LRp0+bMmYPjOHP2zFBXVwdZHU9hGCYrKysrKwu/ZwAEnYeHB4s+LOIb4Bu/WrNmDUKo\nbd8AF6WlEQWMQpEfwNYkttjM2NSchqWlFo9f3IZGOTJ60yaMQkEI1VdXx+/bx6KmlrIWY0m7\n+E/xpx+ROpuw4N27hMaUo8fo0boLFrCoHPY67HnWc6K8fup6MWGurXvWokGOjkKiogghnE5/\n0/rGNE0wdkpPy03jVWRcwp8EupNtxJCbm2tqapqSkrJr164DHG69wy1hYWFFRUVN1tK2trb2\n9fWl0+kIoZUrV378+HHr1q0/f/4sKyu7fv26v7//usYFz0+ePEmn0y0tLZ8/f15TU5ObmxsR\nEWFmZsbdZezWrVt37969gICA4uLi48ePv3z5ktHt4eHhwdhj1sfH5+rVq+Xl5enp6TY2Ntra\n2suWLfvtxW1tbWNjY8vLy/Py8o4fPx4REdHiYhGg/TrZ+xcAwBrxDbDtr8aMGdPmCxa+b9gD\nT1ZDg8VmH8wuPmvoDFKUVrQeYd3mptmkNHjwAFtbovzay6u1nb0Jmyw3MXrENwdvJm0FCVpt\n7T/z59NqahBCFGHhyZ6eWOvTrug4fdvdhoVWenTrwdMxMARJZWWtxi8u3vn74+ylE4xhG4zP\nSx0WHxLoTrYRw9evX8ePH//u3bvjx4+TvAwQMx8fn0mTJsn++t3NrFmzvnz58vDhQ4TQgAED\nYmJiEhIShg4d2qtXr8OHD1+4cGH+/PlETXV19aSkpKFDh86bN09WVlZfX//06dNbtmzh7jJ2\n5ubmPj4+u3fvVlVV9fHxuX37dovz/Kytre/fv6+urj5hwgQdHZ2oqCh2FghftWrV7t271dXV\nBw8efPfu3bt379o2/vkDXNTJ3r8AdDUd4QMwI4FWGDiQnfoVNRU3Xtwgyv8b/T9ed50Sxu7c\niQkJIYRoNTWxLPc5lxCR8HL0IkZuUGuocy/MJVZZ5rWnmzb9bPyPM1i3TmU4q+HXV55fefm1\nYVW+zZabJUXJmDw2ZGHDJuHl3759bpzmyBqjB/p9znu+b+jBGh/GQBMbMaxpHPPOcOzYsU2b\nNrU40LnD+vz5s6mpaVZW1qlTp5YvX87HSEJCQpof1NTUZH79jR49mnnl/CZUVFROnDjRfGFm\n7nJ0dHR0dGx+nPkLREVFRT8/v99eqslq4uPHj4edU0jQtvcvi0lI9MahkAAAXiOWZhoxYoSt\nra2ysjKGYXl5eVFRUYaGhkFBQaTdfwveviUKSoMHs1P/ZuLNipoKorxw3EJehfUrhQEDdJ2c\n3vj4IIRSr1zRW7GiR+t7zRgPMF5luurEwxMIoeSvyYv8F11yvsTT4W3vAgISjx0jysrDho1l\nOYuMWkPdFNSw0VUfpT4kdD8TNKdMkVZTq8jORgi9vXixDxsTzQf1HEQUyqrKvhZ97a3YcVdH\n5UMCzWIjBsHaySw9Pd3U1DQ3N9fX1xdGC4Auom3vXxaTkN69e8et2AAArHWEDixaTQ1jG0JF\n9hJoxvgNPQ29YerDeBVZM+N27Uq7fr2uogLh+MMVK+yfP8da/1Z2/+z9TzOeJn1JQghdfn5Z\nXkr+5J8nWefQ1UVFr6PD05OfFeb/qMPowkoKPYYNN5hgpaXan3VgGcHB9xuX8RaVkbG6elWI\n5fe0e8L2MNZ+PjDngKiwKOvrcwsmJDTY0fH5/v0Iocy7d6uLisQVFFifottTl1F+9+NdR06g\n+TCEo3NsxPD+/XsTE5OfP3/6+flB9gy6jra9f1lMQhrM3h0UANB+HWEn0cLUVHp9PVFWHjLk\nt/Uz8zJjMxv20nIa58S7wJqT7tnTcPNmovwzKYlYHLo14iLiQX8FqXZr2LXg9KPTNp42RdSi\n5jUryoove6zaPaDHKSXFGBuHn/s8671CsAv3aPsCvv/hdlVDe/kA6T2rZn7KfNP8XJxGSzh4\nMMTWll5XhxDCKBTLgABFliNhXn9/ffTBUaI8QXuCrT6pgxt1FywgNhWn1dSkXrny2/qq3VSV\nZRr2i2DsOtkx8aEHuhNsxJCcnDx16tSysrIbN27MmjWL3+EAQJ62vX9ZLENJ8mZDAHRlxAfg\n5uPoyOzAKmhcDwoTElIcNOi39f3i/IixiKLCovNGzeNtcM2MdHN7FxBALBsSu3VrH0tLhdaX\nDdFQ0AhbFTb52GQibw5KDnry4cmSCUtMdUzlJOXKqspSPid/8A1QCXsjXYm3vJ0BQuJ1qM8H\nKvoQcut0SF3/HqPn2PcZM166Z09aTU1uYuLrCxcKGN/aYZjpqVP9rFlNqaytr11wcUEdrQ4h\nJCIkcmYe2TtUyPfvrzZuXHZsLELorZ/fiBUrfnvKELUhj9IeIYRef3/N8/jagQ8JtKBvxPDi\nxQtzc/Pq6urQ0NApU6bwOxwASCXo718AurKO0IGV/7ohK1LQ1hZuZVcsBhqdFvBvAFGeMWyG\norQib4NrRkhMbMqFC9eNjXE6va6yMmzevHlxcSzGS+hp6D10e2h1yupHyQ+EUGFF4f7w/fvD\n9yOE+uSiObGob7PNaqpFUZUERZSGSVJpGNOsOQqOxD7kvNp/+BU63LwhiojI5LNnhzDtx96i\nLXe2JH9NJsobzDfoqumyrs8LugsWEAn0z6SkgjdvlH73tcPQXkMhgW6V4G7E8PTp02nTptHp\n9JCQEFNTU36HAwAfCO77F4AuriN8AM579YooKA/7/Wjmh6kPvxV9I8oLxrFa5Jh3eo0fr7dq\nVdKJEwihn8nJUatWTTl/nkX9ERojkrclL7209O6ru8QR0XpkmYDGvkfMfyjzu4sKWYwZaecw\nYfxMJWklhFB9VdXHuJhnt/y+hkdIfS1h8VdVQUfH4uJFFpMaCUHJQYzBG0N7Dd1qxWotEd4Z\nYGv7aNWqOioVIfTG19fkd/vfMXZzTMtJq66r5tGe7e3Hz50IiY0Y+BgApx4/fjx9+nQhIaGI\niIjf7gACQOcmcO9fAADi+wdgHM972bCYGutl1wi+sQ0bcKjJqU0dPJWHgbE0fv/+b0+eEJG/\nvnBBvn9/g8ZdFFrUXbb7neV3nmY8PRN95n1k2LQHFYpl/z1L1+w+YueWyY4rm5wlLCExwHTq\nANOpCKFXL594HV5DjXvZOw/JVDVUwISFexkZ6To5DZw3jyIiwjrm51nPHXwciNEvUmJSVxdf\nJWf5v+ZEZWS0bWze+fsjhFIvX55w8CCxwUprGAl0Pb3+9ffXo/qMIiNKzsHoQ3aFhYVZWFiI\niIjcv38fsmfQlX3//t3Pzy84OLjJLr4sdjIDAHQo/NpJtPTz56rCQqLcvaV9AJgVVhQyOnEd\nxzoKUbi5LwFHhMXFZ9y8yVhB4om7e/Lff//2LMOeekveq84PqmRkzyJSUibHjq3L+N48e25i\n+IiJZ64k2/8TGbi673YHdOAPtO9P5O5Yf94KE7c0Yid7tjhhUVlbiRDCMOyCwwXG8nB8wVgQ\nujI//2NLq+4yG9RzEKPXmVjVpGOCBJotN2/enDVrlqysbHR09OjRo/kdDgB8k5iYOGTIkMWL\nF8+ZM2fgwIEvXrxgPLVz504+BgYA6PhyGX8xMKy7nh7rypfiL9XU1yCEMAwjbfnn1shpac28\nfbth9DOOP1q9+tGqVcQugC3A8fSbNy8OGpT8999441L3vSZMcHr9Wn/NGoowu1/+mw00e+Px\nZpHFyiJZrFga0SkoOi16yI4hO0N3UmuorZ11+fnlSUcmFVcWEw+3WW2bN5rsyZdN9Bo/Xr5/\nw9p8r729WVcWERIZ1qtheE/il0TeRtYOkED/3tWrV+fNm6eoqBgVFTWMjTFbAHRiW7ZssbOz\nq6io+PHjh6GhoampaWxsLL+DAgAIhpyEBKIgp6X12yWBvZ56EYWJ2hMZe2Xzkbqx8YwbNxjD\nD5JPnQoYMeLDrVuMVfkQQnUVFe8vXQrQ1w+1syv7+pU4KCwhYXLs2B/R0d369uW0UUlRyZNz\nTz50e6ipqEkcqaqr8gjx0NqstTdsL2N1Z0Li58Tpp6bbe9sTfc8IIVdT150zOkDXBoYNcXYm\nil8iI1lvjY4QYgzbSMhK4Glc7cHPMdACwdvbe+nSpb169YqKiurXj/9vYAD468WLF5cvXxYT\nE1NVVb1+/frmzZstLCzCw8PHjx/P79AAAB1dzvPnREHVwIB1zWeZz97/aNjxe/H4xbwNi21a\nM2bMCgkJsbOrLStDCBWmpobY2orJyXUfMUJcQaHs69f8lBRabS3zKerGxlPOn5fX1m5Pu5N0\nJr32eL0leMuZ6DN0nI4Q+ln2c+udrdvubhvQfYB2d20aTnub/fZL4RfGKRiGbZ22tUNkzwgh\nhAbPnx+7bRu9rg6n0994exvt2cOisoFmw8vj/Y/3pVWl3SS6kRIjZ6AHmhVPT8+lS5dqaGhE\nR0dD9gwAQkhERIRGozEe7tu3z83NzdLSEvqhAQCs0WprfyY1DGnt+bsVJDyfeBIFRWnF2Xqz\neRsZJzSnTrVPSGAewF1TUvI1OvrD7du5L14wZ8+yGhqWly798ehRO7Nngoy4zMm5J59vfj6+\n/3+9FTiOp+WmhaSEhL0OY86elWWUg5YF7Zq5q+MskSSlqtqvcT/aNz4+xEYwrTHUanh50HF6\nh+2EhgS6VYcPH162bFn//v1jY2P7cv61C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prediction results","message":["%r","","predicted_results <- ore.sort(nb_res[(nb_res$PROBABILITY_OF_1 > 0.5),], ","                              by = c('PREDICTION','PROBABILITY_OF_1'), ","                              reverse = TRUE)","","z.show(predicted_results)"],"enabled":true,"result":{"startTime":1715346508127,"interpreter":"r.low","endTime":1715346508787,"results":[{"message":"PROBABILITY_OF_0\tPROBABILITY_OF_1\tAFFINITY_CARD\tCUST_ID\tPREDICTION\n0.00968170643844318\t0.990318293561557\t1\t100179\t1\n0.00968170643844318\t0.990318293561557\t1\t100780\t1\n0.00968170643844318\t0.990318293561557\t1\t101559\t1\n0.00968170643844318\t0.990318293561557\t1\t101560\t1\n0.00968170643844318\t0.990318293561557\t1\t101708\t1\n0.00968170643844318\t0.990318293561557\t1\t101831\t1\n0.00968170643844318\t0.990318293561557\t0\t101861\t1\n0.00968170643844318\t0.990318293561557\t0\t102687\t1\n0.00968170643844318\t0.990318293561557\t1\t102734\t1\n0.00968170643844318\t0.990318293561557\t1\t103543\t1\n0.00968170643844318\t0.990318293561557\t1\t103801\t1\n0.00968170643844318\t0.990318293561557\t1\t104245\t1\n0.00968170643844318\t0.990318293561557\t1\t104307\t1\n0.0171390831422237\t0.982860916857776\t1\t100052\t1\n0.0171390831422237\t0.982860916857776\t1\t100100\t1\n0.0171390831422237\t0.982860916857776\t1\t100245\t1\n0.0171390831422237\t0.982860916857776\t1\t100544\t1\n0.0171390831422237\t0.982860916857776\t1\t100601\t1\n0.0171390831422237\t0.982860916857776\t1\t100633\t1\n0.0171390831422237\t0.982860916857776\t1\t100668\t1\n0.0171390831422237\t0.982860916857776\t1\t100816\t1\n0.0171390831422237\t0.982860916857776\t1\t100889\t1\n0.0171390831422237\t0.982860916857776\t1\t100985\t1\n0.0171390831422237\t0.982860916857776\t1\t101010\t1\n0.0171390831422237\t0.982860916857776\t1\t101026\t1\n0.0171390831422237\t0.982860916857776\t1\t101044\t1\n0.0171390831422237\t0.982860916857776\t1\t101049\t1\n0.0171390831422237\t0.982860916857776\t1\t101189\t1\n0.0171390831422237\t0.982860916857776\t1\t101208\t1\n0.0171390831422237\t0.982860916857776\t1\t101426\t1\n0.0171390831422237\t0.982860916857776\t1\t101494\t1\n0.0171390831422237\t0.982860916857776\t0\t101733\t1\n0.0171390831422237\t0.982860916857776\t0\t101990\t1\n0.0171390831422237\t0.982860916857776\t1\t102025\t1\n0.0171390831422237\t0.982860916857776\t1\t102037\t1\n0.0171390831422237\t0.982860916857776\t1\t102083\t1\n0.0171390831422237\t0.982860916857776\t1\t102266\t1\n0.0171390831422237\t0.982860916857776\t0\t102330\t1\n0.0171390831422237\t0.982860916857776\t1\t102380\t1\n0.0171390831422237\t0.982860916857776\t0\t102585\t1\n0.0171390831422237\t0.982860916857776\t1\t102669\t1\n0.0171390831422237\t0.982860916857776\t1\t102881\t1\n0.0171390831422237\t0.982860916857776\t1\t103127\t1\n0.0171390831422237\t0.982860916857776\t1\t103149\t1\n0.0171390831422237\t0.982860916857776\t1\t103266\t1\n0.0171390831422237\t0.982860916857776\t1\t103294\t1\n0.0171390831422237\t0.982860916857776\t1\t103332\t1\n0.0171390831422237\t0.982860916857776\t1\t103353\t1\n0.0171390831422237\t0.982860916857776\t1\t103439\t1\n0.0171390831422237\t0.982860916857776\t1\t103463\t1\n0.0171390831422237\t0.982860916857776\t1\t103613\t1\n0.0171390831422237\t0.982860916857776\t1\t103873\t1\n0.0171390831422237\t0.982860916857776\t1\t103949\t1\n0.0171390831422237\t0.982860916857776\t1\t104136\t1\n0.0171390831422237\t0.982860916857776\t0\t104410\t1\n0.0171390831422237\t0.982860916857776\t1\t104419\t1\n0.0251774955533605\t0.974822504446639\t1\t100081\t1\n0.0251774955533605\t0.974822504446639\t1\t100089\t1\n0.0251774955533605\t0.974822504446639\t1\t100224\t1\n0.0251774955533605\t0.974822504446639\t1\t100256\t1\n0.0251774955533605\t0.974822504446639\t1\t100988\t1\n0.0251774955533605\t0.974822504446639\t1\t101442\t1\n0.0251774955533605\t0.974822504446639\t0\t101445\t1\n0.0251774955533605\t0.974822504446639\t1\t101499\t1\n0.0251774955533605\t0.974822504446639\t1\t101662\t1\n0.0251774955533605\t0.974822504446639\t0\t103165\t1\n0.0251774955533605\t0.974822504446639\t1\t103276\t1\n0.0251774955533605\t0.974822504446639\t1\t103361\t1\n0.0251774955533605\t0.974822504446639\t1\t104093\t1\n0.0251774955533605\t0.974822504446639\t0\t104223\t1\n0.0289963166924954\t0.971003683307505\t1\t100817\t1\n0.0289963166924954\t0.971003683307505\t1\t100953\t1\n0.0440397870012385\t0.955960212998762\t0\t100036\t1\n0.0440397870012385\t0.955960212998762\t0\t100344\t1\n0.0440397870012385\t0.955960212998762\t1\t100355\t1\n0.0440397870012385\t0.955960212998762\t1\t100406\t1\n0.0440397870012385\t0.955960212998762\t0\t100621\t1\n0.0440397870012385\t0.955960212998762\t1\t100778\t1\n0.0440397870012385\t0.955960212998762\t1\t101078\t1\n0.0440397870012385\t0.955960212998762\t1\t101139\t1\n0.0440397870012385\t0.955960212998762\t1\t101144\t1\n0.0440397870012385\t0.955960212998762\t0\t101191\t1\n0.0440397870012385\t0.955960212998762\t1\t101233\t1\n0.0440397870012385\t0.955960212998762\t1\t101260\t1\n0.0440397870012385\t0.955960212998762\t1\t101469\t1\n0.0440397870012385\t0.955960212998762\t1\t101605\t1\n0.0440397870012385\t0.955960212998762\t1\t101606\t1\n0.0440397870012385\t0.955960212998762\t1\t101621\t1\n0.0440397870012385\t0.955960212998762\t0\t101635\t1\n0.0440397870012385\t0.955960212998762\t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the proxy object into a table","message":["%r","","ore.drop(table=\"DEMO_DF_NB\")","ore.create(DEMO_DF, \"DEMO_DF_NB\")"],"enabled":true,"result":{"startTime":1715346508868,"interpreter":"r.low","endTime":1715346509442,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create named model to be used in SQL interface","message":["%r","","ore.drop(model = 'NB_CLASSIFICATION_MODEL')","","mod <- ore.odmNB(formula = AFFINITY_CARD ~ ., ","                 data = DEMO_DF.train, ","                 odm.settings = list(model_name = \"NB_CLASSIFICATION_MODEL\"))"],"enabled":true,"result":{"startTime":1715346509517,"interpreter":"r.low","endTime":1715346511213,"results":[{"message":"Warning message:\n\u201cThe created mining model NB_CLASSIFICATION_MODEL is explicitly named. 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\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Classification NN.dsnb b/machine-learning/notebooks/r/OML4R Classification NN.dsnb
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--- a/machine-learning/notebooks/r/OML4R Classification NN.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification NN","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715346866839,"interpreter":"md.medium","endTime":1715346866904,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Neural Network","In this notebook, we predict customers most likely to be positive responders to an Affinity Card ","loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, ","when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification neural network model using the ","Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715346866984,"interpreter":"md.medium","endTime":1715346867064,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-neural-network\">Classification Modeling to Predict Target Customers using Neural Network<\/h1>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card\nloyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who,\nwhen given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification neural network model using the\nSales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715346867144,"interpreter":"md.medium","endTime":1715346867206,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1715346867284,"interpreter":"md.medium","endTime":1715346867342,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","library(lattice)","","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715346867418,"interpreter":"r.medium","endTime":1715346871918,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare dataset combining customer and demographics data","message":["%r","","ore.sync(query = c(\"QUERY_CUSTOMERS\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH AGE, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.sync(query = c(\"QUERY_DEMO_DF\" =  'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","CUST_DF <- merge(QUERY_CUSTOMERS,QUERY_DEMO_DF)"],"enabled":true,"result":{"startTime":1715346871995,"interpreter":"r.medium","endTime":1715346873195,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","head(CUST_DF)"],"enabled":true,"result":{"startTime":1715346873270,"interpreter":"r.medium","endTime":1715346873592,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 12<\/caption>\n<thead>\n\t<tr><th scope=col>CUST_ID<\/th><th scope=col>CUST_GENDER<\/th><th scope=col>CUST_MARITAL_STATUS<\/th><th scope=col>AGE<\/th><th scope=col>CUST_INCOME_LEVEL<\/th><th scope=col>CUST_CREDIT_LIMIT<\/th><th scope=col>EDUCATION<\/th><th scope=col>AFFINITY_CARD<\/th><th scope=col>HOUSEHOLD_SIZE<\/th><th scope=col>OCCUPATION<\/th><th scope=col>YRS_RESIDENCE<\/th><th scope=col>Y_BOX_GAMES<\/th><\/tr>\n\t<tr><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;int&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th 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<\/td><td>5<\/td><td>0<\/td><\/tr>\n\t<tr><td>104093<\/td><td>M<\/td><td>Married<\/td><td>1956<\/td><td>F: 110,000 - 129,999<\/td><td> 1500<\/td><td>Bach.  <\/td><td>1<\/td><td>3<\/td><td>Prof. <\/td><td>7<\/td><td>0<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Split data into randomly selected 60% sample for train and 40% test","message":["%r","","set.seed(123)","rownames(CUST_DF) <- CUST_DF$CUST_ID","","sampleSize <- .6 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","","# Create training data set","CUST_DF.train <- CUST_DF[index,]","","# Create test data set","CUST_DF.test <- CUST_DF[setdiff(1:nrow(CUST_DF),index),]","","class(CUST_DF.train)","cat(\"\\nTraining data: \")","dim(CUST_DF.train)","","class(CUST_DF.test)","cat(\"\\nTest data: \")","dim(CUST_DF.test)"],"enabled":true,"result":{"startTime":1715346873668,"interpreter":"r.medium","endTime":1715346875366,"results":[{"message":"'ore.frame'\nTraining data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>2700<\/li><li>12<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ore.frame'\nTest data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>1800<\/li><li>12<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a NN classification model to predict AFFINITY_CARD using default model settings","message":["%r","","ore.drop(model=\"NN_CLASSIFICATION_MODEL\", silent=TRUE)","","nn.mod1 <- ore.odmNN(AFFINITY_CARD~.-CUST_ID, ","                     CUST_DF.train, ","                     \"classification\", ","                     odm.settings=list(model_name=\"NN_CLASSIFICATION_MODEL\"))"],"enabled":true,"result":{"startTime":1715346875462,"interpreter":"r.medium","endTime":1715346883365,"results":[{"message":"Warning message:\n\u201cThe created mining model NN_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict outcomes using TEST dataset","message":["%r","","RES_DF <- predict(nn.mod1, CUST_DF.test, supplemental.cols='AFFINITY_CARD')","","z.show(head(RES_DF,20))"],"enabled":true,"result":{"startTime":1715346883444,"interpreter":"r.medium","endTime":1715346884843,"results":[{"message":"X.0.\tX.1.\tAFFINITY_CARD\tPREDICTION\n1\t6.25295902785289e-18\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n0\t1\t0\t1\n1\t0\t0\t0\n3.54698492799344e-11\t0.99999999996453\t1\t1\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n0\t1\t1\t1\n1\t0\t0\t0\n1\t0\t0\t0\n0.000490078726147258\t0.999509921273853\t1\t1\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n1\t0\t0\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NN ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-77D9BF0F-E724-48F4-A062-B14A8F361AE6\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>' ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO' ","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS.  The options for the activation functions are one or more of the following strings: NNET_ACTIVATIONS_ARCTAN, NNET_ACTIVATIONS_BIPOLAR_SIG, NNET_ACTIVATIONS_LINEAR, NNET_ACTIVATIONS_LOG_SIG, NNET_ACTIVATIONS_TANH and <strong>starting from Oracle Database 23ai<\/strong> NNET_ACTIVATIONS_RELU.","> NNET_HIDDEN_LAYERS = '3'","","> NNET_NODES_PER_LAYER = '8,8,2'","","> NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'","","> NNET_HELDASIDE_RATIO = '0.25'","","> NNET_HELDASIDE_MAX_FAIL = '6'","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> NNET_REG_LAMBDA = '1'","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> NNET_ITERATIONS = '200'","","> NNET_TOLERANCE = '0.000001'","","> ODMS_RANDOM_SEED = '0'","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> LBFGS_HISTORY_DEPTH = '10'","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> LBFGS_GRADIENT_TOLERANCE = '0.0000001'","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'","","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'"],"enabled":true,"result":{"startTime":1715346884933,"interpreter":"md.medium","endTime":1715346885018,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nn\">Examples of possible setting overrides for NN<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-77D9BF0F-E724-48F4-A062-B14A8F361AE6\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS.  The options for the activation functions are one or more of the following strings: NNET_ACTIVATIONS_ARCTAN, NNET_ACTIVATIONS_BIPOLAR_SIG, NNET_ACTIVATIONS_LINEAR, NNET_ACTIVATIONS_LOG_SIG, NNET_ACTIVATIONS_TANH and <strong>starting from Oracle Database 23ai<\/strong> NNET_ACTIVATIONS_RELU.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_HIDDEN_LAYERS = '3'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_NODES_PER_LAYER = '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_ACTIVATIONS = &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_RATIO = '0.25'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_MAX_FAIL = '6'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REG_LAMBDA = '1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_ITERATIONS = '200'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_TOLERANCE = '0.000001'<\/p>\n<\/blockquote>\n<blockquote>\n<p>ODMS_RANDOM_SEED = '0'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_HISTORY_DEPTH = '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_GRADIENT_TOLERANCE = '0.0000001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use a single layer with 4 neurons and specific activation function","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS = 1,","                 NNET_NODES_PER_LAYER = '4',","                 NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH'\")","                 ","nn.mod2 <- ore.odmNN(AFFINITY_CARD~.-CUST_ID, ","                     CUST_DF.train, ","                     \"classification\", ","                     odm.settings = settings)"],"enabled":true,"result":{"startTime":1715346885107,"interpreter":"r.medium","endTime":1715346888408,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use 3 layers with 8,8,2 neurons and specific activation functions","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS   = 3,","                 NNET_NODES_PER_LAYER = c('8','8','4'),","                 NNET_ACTIVATIONS     = c(\"'NNET_ACTIVATIONS_TANH'\",","                                          \"'NNET_ACTIVATIONS_LOG_SIG'\", ","                                          \"'NNET_ACTIVATIONS_LINEAR'\")","                 )  ","nn.mod3 <- ore.odmNN(AFFINITY_CARD~.-CUST_ID, ","                     CUST_DF.train, ","                     'classification', ","                     odm.settings = settings) "],"enabled":true,"result":{"startTime":1715346888495,"interpreter":"r.medium","endTime":1715346892695,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model fit statistics","message":["%r","","summary(nn.mod3)"],"enabled":true,"result":{"startTime":1715346892768,"interpreter":"r.medium","endTime":1715346893671,"results":[{"message":"\nCall:\nore.odmNN(formula = AFFINITY_CARD ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"classification\", odm.settings = settings)\n\nSettings: \n                                                                                                    value\nclas.weights.balanced                                                                                 OFF\nlbfgs.gradient.tolerance                                                                       .000000001\nlbfgs.history.depth                                                                                    20\nlbfgs.scale.hessian                                                            LBFGS_SCALE_HESSIAN_ENABLE\nactivations                  'NNET_ACTIVATIONS_TANH','NNET_ACTIVATIONS_LOG_SIG','NNET_ACTIVATIONS_LINEAR'\nhidden.layers                                                                                           3\niterations                                                                                            200\nnodes.per.layer                                                                                     8,8,4\ntolerance                                                                                         .000001\nodms.details                                                                                  odms.enable\nodms.missing.value.treatment                                                      odms.missing.value.auto\nodms.random.seed                                                                                        0\nodms.sampling                                                                       odms.sampling.disable\nprep.auto                                                                                              ON\n\nNumber of Layers: \n[1] 4\n\nNodes per Layer: \n[1]  4  8 62\n\nWeight: \n    LAYER IDX_FROM IDX_TO      ATTRIBUTE_NAME      ATTRIBUTE_VALUE\n1       0        0      0                 AGE                 <NA>\n2       0        0      1                 AGE                 <NA>\n3       0        0      2                 AGE                 <NA>\n4       0        0      3                 AGE                 <NA>\n5       0        0      4                 AGE                 <NA>\n6       0        0      5                 AGE                 <NA>\n7       0        0      6                 AGE                 <NA>\n8       0        0      7                 AGE                 <NA>\n9       0        1      0   CUST_CREDIT_LIMIT                 <NA>\n10      0        1      1   CUST_CREDIT_LIMIT                 <NA>\n11      0        1      2   CUST_CREDIT_LIMIT                 <NA>\n12      0        1      3   CUST_CREDIT_LIMIT                 <NA>\n13      0        1      4   CUST_CREDIT_LIMIT                 <NA>\n14      0        1      5   CUST_CREDIT_LIMIT                 <NA>\n15      0        1      6   CUST_CREDIT_LIMIT                 <NA>\n16      0        1      7   CUST_CREDIT_LIMIT                 <NA>\n17      0        2      0       YRS_RESIDENCE                 <NA>\n18      0        2      1       YRS_RESIDENCE                 <NA>\n19      0        2      2       YRS_RESIDENCE                 <NA>\n20      0        2      3       YRS_RESIDENCE                 <NA>\n21      0        2      4       YRS_RESIDENCE                 <NA>\n22      0        2      5       YRS_RESIDENCE                 <NA>\n23      0        2      6       YRS_RESIDENCE                 <NA>\n24      0        2      7       YRS_RESIDENCE                 <NA>\n25      0        3      0         Y_BOX_GAMES                 <NA>\n26      0        3      1         Y_BOX_GAMES                 <NA>\n27      0        3      2         Y_BOX_GAMES                 <NA>\n28      0        3      3         Y_BOX_GAMES                 <NA>\n29      0        3      4         Y_BOX_GAMES                 <NA>\n30      0        3      5         Y_BOX_GAMES                 <NA>\n31      0        3      6         Y_BOX_GAMES                 <NA>\n32      0        3      7         Y_BOX_GAMES                 <NA>\n33      0        4      0         CUST_GENDER                    F\n34      0        4      1         CUST_GENDER                    F\n35      0        4      2         CUST_GENDER                    F\n36      0        4      3         CUST_GENDER                    F\n37      0        4      4         CUST_GENDER                    F\n38      0        4      5         CUST_GENDER                    F\n39      0        4      6         CUST_GENDER                    F\n40      0        4      7         CUST_GENDER                    F\n41      0        5      0         CUST_GENDER                    M\n42      0        5      1         CUST_GENDER                    M\n43      0        5      2         CUST_GENDER                    M\n44      0        5      3         CUST_GENDER                    M\n45      0        5      4         CUST_GENDER                    M\n46      0        5      5         CUST_GENDER                    M\n47      0        5      6         CUST_GENDER                    M\n48      0        5      7         CUST_GENDER                    M\n49      0        6      0   CUST_INCOME_LEVEL      A: Below 30,000\n50      0        6      1   CUST_INCOME_LEVEL      A: Below 30,000\n51      0        6      2   CUST_INCOME_LEVEL      A: Below 30,000\n52      0        6      3   CUST_INCOME_LEVEL      A: Below 30,000\n53      0        6      4   CUST_INCOME_LEVEL      A: Below 30,000\n54      0        6      5   CUST_INCOME_LEVEL      A: Below 30,000\n55      0        6      6   CUST_INCOME_LEVEL      A: Below 30,000\n56      0        6      7   CUST_INCOME_LEVEL      A: Below 30,000\n57      0        7      0   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n58      0        7      1   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n59      0        7      2   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n60      0        7      3   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n61      0        7      4   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n62      0        7      5   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n63      0        7      6   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n64      0        7      7   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n65      0        8      0   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n66      0        8      1   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n67      0        8      2   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n68      0        8      3   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n69      0        8      4   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n70      0        8      5   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n71      0        8      6   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n72      0        8      7   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n73      0        9      0   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n74      0        9      1   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n75      0        9      2   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n76      0        9      3   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n77      0        9      4   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n78      0        9      5   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n79      0        9      6   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n80      0        9      7   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n81      0       10      0   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n82      0       10      1   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n83      0       10      2   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n84      0       10      3   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n85      0       10      4   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n86      0       10      5   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n87      0       10      6   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n88      0       10      7   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n89      0       11      0   CUST_INCOME_LEVEL F: 110,000 - 129,999\n90      0       11      1   CUST_INCOME_LEVEL F: 110,000 - 129,999\n91      0       11      2   CUST_INCOME_LEVEL F: 110,000 - 129,999\n92      0       11      3   CUST_INCOME_LEVEL F: 110,000 - 129,999\n93      0       11      4   CUST_INCOME_LEVEL F: 110,000 - 129,999\n94      0       11      5   CUST_INCOME_LEVEL F: 110,000 - 129,999\n95      0       11      6   CUST_INCOME_LEVEL F: 110,000 - 129,999\n96      0       11      7   CUST_INCOME_LEVEL F: 110,000 - 129,999\n97      0       12      0   CUST_INCOME_LEVEL G: 130,000 - 149,999\n98      0       12      1   CUST_INCOME_LEVEL G: 130,000 - 149,999\n99      0       12      2   CUST_INCOME_LEVEL G: 130,000 - 149,999\n100     0       12      3   CUST_INCOME_LEVEL G: 130,000 - 149,999\n101     0       12      4   CUST_INCOME_LEVEL G: 130,000 - 149,999\n102     0       12      5   CUST_INCOME_LEVEL G: 130,000 - 149,999\n103     0       12      6   CUST_INCOME_LEVEL G: 130,000 - 149,999\n104     0       12      7   CUST_INCOME_LEVEL G: 130,000 - 149,999\n105     0       13      0   CUST_INCOME_LEVEL H: 150,000 - 169,999\n106     0       13      1   CUST_INCOME_LEVEL H: 150,000 - 169,999\n107     0       13      2   CUST_INCOME_LEVEL H: 150,000 - 169,999\n108     0       13      3   CUST_INCOME_LEVEL H: 150,000 - 169,999\n109     0       13      4   CUST_INCOME_LEVEL H: 150,000 - 169,999\n110     0       13      5   CUST_INCOME_LEVEL H: 150,000 - 169,999\n111     0       13      6   CUST_INCOME_LEVEL H: 150,000 - 169,999\n112     0       13      7   CUST_INCOME_LEVEL H: 150,000 - 169,999\n113     0       14      0   CUST_INCOME_LEVEL I: 170,000 - 189,999\n114     0       14      1   CUST_INCOME_LEVEL I: 170,000 - 189,999\n115     0       14      2   CUST_INCOME_LEVEL I: 170,000 - 189,999\n116     0       14      3   CUST_INCOME_LEVEL I: 170,000 - 189,999\n117     0       14      4   CUST_INCOME_LEVEL I: 170,000 - 189,999\n118     0       14      5   CUST_INCOME_LEVEL I: 170,000 - 189,999\n119     0       14      6   CUST_INCOME_LEVEL I: 170,000 - 189,999\n120     0       14      7   CUST_INCOME_LEVEL I: 170,000 - 189,999\n121     0       15      0   CUST_INCOME_LEVEL J: 190,000 - 249,999\n122     0       15      1   CUST_INCOME_LEVEL J: 190,000 - 249,999\n123     0       15      2   CUST_INCOME_LEVEL J: 190,000 - 249,999\n124     0       15      3   CUST_INCOME_LEVEL J: 190,000 - 249,999\n125     0       15      4   CUST_INCOME_LEVEL J: 190,000 - 249,999\n126     0       15      5   CUST_INCOME_LEVEL J: 190,000 - 249,999\n127     0       15      6   CUST_INCOME_LEVEL J: 190,000 - 249,999\n128     0       15      7   CUST_INCOME_LEVEL J: 190,000 - 249,999\n129     0       16      0   CUST_INCOME_LEVEL K: 250,000 - 299,999\n130     0       16      1   CUST_INCOME_LEVEL K: 250,000 - 299,999\n131     0       16      2   CUST_INCOME_LEVEL K: 250,000 - 299,999\n132     0       16      3   CUST_INCOME_LEVEL K: 250,000 - 299,999\n133     0       16      4   CUST_INCOME_LEVEL K: 250,000 - 299,999\n134     0       16      5   CUST_INCOME_LEVEL K: 250,000 - 299,999\n135     0       16      6   CUST_INCOME_LEVEL K: 250,000 - 299,999\n136     0       16      7   CUST_INCOME_LEVEL K: 250,000 - 299,999\n137     0       17      0   CUST_INCOME_LEVEL L: 300,000 and above\n138     0       17      1   CUST_INCOME_LEVEL L: 300,000 and above\n139     0       17      2   CUST_INCOME_LEVEL L: 300,000 and above\n140     0       17      3   CUST_INCOME_LEVEL L: 300,000 and above\n141     0       17      4   CUST_INCOME_LEVEL L: 300,000 and above\n142     0       17      5   CUST_INCOME_LEVEL L: 300,000 and above\n143     0       17      6   CUST_INCOME_LEVEL L: 300,000 and above\n144     0       17      7   CUST_INCOME_LEVEL L: 300,000 and above\n145     0       18      0 CUST_MARITAL_STATUS              Divorc.\n146     0       18      1 CUST_MARITAL_STATUS              Divorc.\n147     0       18      2 CUST_MARITAL_STATUS              Divorc.\n148     0       18      3 CUST_MARITAL_STATUS              Divorc.\n149     0       18      4 CUST_MARITAL_STATUS              Divorc.\n150     0       18      5 CUST_MARITAL_STATUS              Divorc.\n151     0       18      6 CUST_MARITAL_STATUS              Divorc.\n152     0       18      7 CUST_MARITAL_STATUS              Divorc.\n153     0       19      0 CUST_MARITAL_STATUS              Mabsent\n154     0       19      1 CUST_MARITAL_STATUS              Mabsent\n155     0       19      2 CUST_MARITAL_STATUS              Mabsent\n156     0       19      3 CUST_MARITAL_STATUS              Mabsent\n157     0       19      4 CUST_MARITAL_STATUS              Mabsent\n158     0       19      5 CUST_MARITAL_STATUS              Mabsent\n159     0       19      6 CUST_MARITAL_STATUS              Mabsent\n160     0       19      7 CUST_MARITAL_STATUS              Mabsent\n161     0       20      0 CUST_MARITAL_STATUS               Mar-AF\n162     0       20      1 CUST_MARITAL_STATUS               Mar-AF\n163     0       20      2 CUST_MARITAL_STATUS               Mar-AF\n164     0       20      3 CUST_MARITAL_STATUS               Mar-AF\n165     0       20      4 CUST_MARITAL_STATUS               Mar-AF\n166     0       20      5 CUST_MARITAL_STATUS               Mar-AF\n167     0       20      6 CUST_MARITAL_STATUS               Mar-AF\n168     0       20      7 CUST_MARITAL_STATUS               Mar-AF\n169     0       21      0 CUST_MARITAL_STATUS              Married\n170     0       21      1 CUST_MARITAL_STATUS              Married\n171     0       21      2 CUST_MARITAL_STATUS              Married\n172     0       21      3 CUST_MARITAL_STATUS              Married\n173     0       21      4 CUST_MARITAL_STATUS              Married\n174     0       21      5 CUST_MARITAL_STATUS              Married\n175     0       21      6 CUST_MARITAL_STATUS              Married\n176     0       21      7 CUST_MARITAL_STATUS              Married\n177     0       22      0 CUST_MARITAL_STATUS               NeverM\n178     0       22      1 CUST_MARITAL_STATUS               NeverM\n179     0       22      2 CUST_MARITAL_STATUS               NeverM\n180     0       22      3 CUST_MARITAL_STATUS               NeverM\n181     0       22      4 CUST_MARITAL_STATUS               NeverM\n182     0       22      5 CUST_MARITAL_STATUS               NeverM\n183     0       22      6 CUST_MARITAL_STATUS               NeverM\n184     0       22      7 CUST_MARITAL_STATUS               NeverM\n185     0       23      0 CUST_MARITAL_STATUS               Separ.\n186     0       23      1 CUST_MARITAL_STATUS               Separ.\n187     0       23      2 CUST_MARITAL_STATUS               Separ.\n188     0       23      3 CUST_MARITAL_STATUS               Separ.\n189     0       23      4 CUST_MARITAL_STATUS               Separ.\n190     0       23      5 CUST_MARITAL_STATUS               Separ.\n191     0       23      6 CUST_MARITAL_STATUS               Separ.\n192     0       23      7 CUST_MARITAL_STATUS               Separ.\n193     0       24      0 CUST_MARITAL_STATUS              Widowed\n194     0       24      1 CUST_MARITAL_STATUS              Widowed\n195     0       24      2 CUST_MARITAL_STATUS              Widowed\n196     0       24      3 CUST_MARITAL_STATUS              Widowed\n197     0       24      4 CUST_MARITAL_STATUS              Widowed\n198     0       24      5 CUST_MARITAL_STATUS              Widowed\n199     0       24      6 CUST_MARITAL_STATUS              Widowed\n200     0       24      7 CUST_MARITAL_STATUS              Widowed\n201     0       25      0           EDUCATION                 10th\n202     0       25      1           EDUCATION                 10th\n203     0       25      2           EDUCATION                 10th\n204     0       25      3           EDUCATION                 10th\n205     0       25      4           EDUCATION                 10th\n206     0       25      5           EDUCATION                 10th\n207     0       25      6           EDUCATION                 10th\n208     0       25      7           EDUCATION                 10th\n209     0       26      0           EDUCATION                 11th\n210     0       26      1           EDUCATION                 11th\n211     0       26      2           EDUCATION                 11th\n212     0       26      3           EDUCATION                 11th\n213     0       26      4           EDUCATION                 11th\n214     0       26      5           EDUCATION                 11th\n215     0       26      6           EDUCATION                 11th\n216     0       26      7           EDUCATION                 11th\n217     0       27      0           EDUCATION                 12th\n218     0       27      1           EDUCATION                 12th\n219     0       27      2           EDUCATION                 12th\n220     0       27      3           EDUCATION                 12th\n221     0       27      4           EDUCATION                 12th\n222     0       27      5           EDUCATION                 12th\n223     0       27      6           EDUCATION                 12th\n224     0       27      7           EDUCATION                 12th\n225     0       28      0           EDUCATION              1st-4th\n226     0       28      1           EDUCATION              1st-4th\n227     0       28      2           EDUCATION              1st-4th\n228     0       28      3           EDUCATION              1st-4th\n229     0       28      4           EDUCATION              1st-4th\n230     0       28      5           EDUCATION              1st-4th\n231     0       28      6           EDUCATION              1st-4th\n232     0       28      7           EDUCATION              1st-4th\n233     0       29      0           EDUCATION              5th-6th\n234     0       29      1           EDUCATION              5th-6th\n235     0       29      2           EDUCATION              5th-6th\n236     0       29      3           EDUCATION              5th-6th\n237     0       29      4           EDUCATION              5th-6th\n238     0       29      5           EDUCATION              5th-6th\n239     0       29      6           EDUCATION              5th-6th\n240     0       29      7           EDUCATION              5th-6th\n241     0       30      0           EDUCATION              7th-8th\n242     0       30      1           EDUCATION              7th-8th\n243     0       30      2           EDUCATION              7th-8th\n244     0       30      3           EDUCATION              7th-8th\n245     0       30      4           EDUCATION              7th-8th\n246     0       30      5           EDUCATION              7th-8th\n247     0       30      6           EDUCATION              7th-8th\n248     0       30      7           EDUCATION              7th-8th\n249     0       31      0           EDUCATION                  9th\n250     0       31      1           EDUCATION                  9th\n251     0       31      2           EDUCATION                  9th\n252     0       31      3           EDUCATION                  9th\n253     0       31      4           EDUCATION                  9th\n254     0       31      5           EDUCATION                  9th\n255     0       31      6           EDUCATION                  9th\n256     0       31      7           EDUCATION                  9th\n257     0       32      0           EDUCATION              < Bach.\n258     0       32      1           EDUCATION              < Bach.\n259     0       32      2           EDUCATION              < Bach.\n260     0       32      3           EDUCATION              < Bach.\n261     0       32      4           EDUCATION              < Bach.\n262     0       32      5           EDUCATION              < Bach.\n263     0       32      6           EDUCATION              < Bach.\n264     0       32      7           EDUCATION              < Bach.\n265     0       33      0           EDUCATION              Assoc-A\n266     0       33      1           EDUCATION              Assoc-A\n267     0       33      2           EDUCATION              Assoc-A\n268     0       33      3           EDUCATION              Assoc-A\n269     0       33      4           EDUCATION              Assoc-A\n270     0       33      5           EDUCATION              Assoc-A\n271     0       33      6           EDUCATION              Assoc-A\n272     0       33      7           EDUCATION              Assoc-A\n273     0       34      0           EDUCATION              Assoc-V\n274     0       34      1           EDUCATION              Assoc-V\n275     0       34      2           EDUCATION              Assoc-V\n276     0       34      3           EDUCATION              Assoc-V\n277     0       34      4           EDUCATION              Assoc-V\n278     0       34      5           EDUCATION              Assoc-V\n279     0       34      6           EDUCATION              Assoc-V\n280     0       34      7           EDUCATION              Assoc-V\n281     0       35      0           EDUCATION                Bach.\n282     0       35      1           EDUCATION                Bach.\n283     0       35      2           EDUCATION                Bach.\n284     0       35      3           EDUCATION                Bach.\n285     0       35      4           EDUCATION                Bach.\n286     0       35      5           EDUCATION                Bach.\n287     0       35      6           EDUCATION                Bach.\n288     0       35      7           EDUCATION                Bach.\n289     0       36      0           EDUCATION              HS-grad\n290     0       36      1           EDUCATION              HS-grad\n291     0       36      2           EDUCATION              HS-grad\n292     0       36      3           EDUCATION              HS-grad\n293     0       36      4           EDUCATION              HS-grad\n294     0       36      5           EDUCATION              HS-grad\n295     0       36      6           EDUCATION              HS-grad\n296     0       36      7           EDUCATION              HS-grad\n297     0       37      0           EDUCATION              Masters\n298     0       37      1           EDUCATION              Masters\n299     0       37      2           EDUCATION              Masters\n300     0       37      3           EDUCATION              Masters\n301     0       37      4           EDUCATION              Masters\n302     0       37      5           EDUCATION              Masters\n303     0       37      6           EDUCATION              Masters\n304     0       37      7           EDUCATION              Masters\n305     0       38      0           EDUCATION                  PhD\n306     0       38      1           EDUCATION                  PhD\n307     0       38      2           EDUCATION                  PhD\n308     0       38      3           EDUCATION                  PhD\n309     0       38      4           EDUCATION                  PhD\n310     0       38      5           EDUCATION                  PhD\n311     0       38      6           EDUCATION                  PhD\n312     0       38      7           EDUCATION                  PhD\n313     0       39      0           EDUCATION              Presch.\n314     0       39      1           EDUCATION              Presch.\n315     0       39      2           EDUCATION              Presch.\n316     0       39      3           EDUCATION              Presch.\n317     0       39      4           EDUCATION              Presch.\n318     0       39      5           EDUCATION              Presch.\n319     0       39      6           EDUCATION              Presch.\n320     0       39      7           EDUCATION              Presch.\n321     0       40      0           EDUCATION               Profsc\n322     0       40      1           EDUCATION               Profsc\n323     0       40      2           EDUCATION               Profsc\n324     0       40      3           EDUCATION               Profsc\n325     0       40      4           EDUCATION               Profsc\n326     0       40      5           EDUCATION               Profsc\n327     0       40      6           EDUCATION               Profsc\n328     0       40      7           EDUCATION               Profsc\n329     0       41      0      HOUSEHOLD_SIZE                    1\n330     0       41      1      HOUSEHOLD_SIZE                    1\n331     0       41      2      HOUSEHOLD_SIZE                    1\n332     0       41      3      HOUSEHOLD_SIZE                    1\n333     0       41      4      HOUSEHOLD_SIZE                    1\n334     0       41      5      HOUSEHOLD_SIZE                    1\n335     0       41      6      HOUSEHOLD_SIZE                    1\n336     0       41      7      HOUSEHOLD_SIZE                    1\n337     0       42      0      HOUSEHOLD_SIZE                    2\n338     0       42      1      HOUSEHOLD_SIZE                    2\n339     0       42      2      HOUSEHOLD_SIZE                    2\n340     0       42      3      HOUSEHOLD_SIZE                    2\n341     0       42      4      HOUSEHOLD_SIZE                    2\n342     0       42      5      HOUSEHOLD_SIZE                    2\n343     0       42      6      HOUSEHOLD_SIZE                    2\n344     0       42      7      HOUSEHOLD_SIZE                    2\n345     0       43      0      HOUSEHOLD_SIZE                    3\n346     0       43      1      HOUSEHOLD_SIZE                    3\n347     0       43      2      HOUSEHOLD_SIZE                    3\n348     0       43      3      HOUSEHOLD_SIZE                    3\n349     0       43      4      HOUSEHOLD_SIZE                    3\n350     0       43      5      HOUSEHOLD_SIZE                    3\n351     0       43      6      HOUSEHOLD_SIZE                    3\n352     0       43      7      HOUSEHOLD_SIZE                    3\n353     0       44      0      HOUSEHOLD_SIZE                  4-5\n354     0       44      1      HOUSEHOLD_SIZE                  4-5\n355     0       44      2      HOUSEHOLD_SIZE                  4-5\n356     0       44      3      HOUSEHOLD_SIZE                  4-5\n357     0       44      4      HOUSEHOLD_SIZE                  4-5\n358     0       44      5      HOUSEHOLD_SIZE                  4-5\n359     0       44      6      HOUSEHOLD_SIZE                  4-5\n360     0       44      7      HOUSEHOLD_SIZE                  4-5\n361     0       45      0      HOUSEHOLD_SIZE                  6-8\n362     0       45      1      HOUSEHOLD_SIZE                  6-8\n363     0       45      2      HOUSEHOLD_SIZE                  6-8\n364     0       45      3      HOUSEHOLD_SIZE                  6-8\n365     0       45      4      HOUSEHOLD_SIZE                  6-8\n366     0       45      5      HOUSEHOLD_SIZE                  6-8\n367     0       45      6      HOUSEHOLD_SIZE                  6-8\n368     0       45      7      HOUSEHOLD_SIZE                  6-8\n369     0       46      0      HOUSEHOLD_SIZE                   9+\n370     0       46      1      HOUSEHOLD_SIZE                   9+\n371     0       46      2      HOUSEHOLD_SIZE                   9+\n372     0       46      3      HOUSEHOLD_SIZE                   9+\n373     0       46      4      HOUSEHOLD_SIZE                   9+\n374     0       46      5      HOUSEHOLD_SIZE                   9+\n375     0       46      6      HOUSEHOLD_SIZE                   9+\n376     0       46      7      HOUSEHOLD_SIZE                   9+\n377     0       47      0          OCCUPATION                    ?\n378     0       47      1          OCCUPATION                    ?\n379     0       47      2          OCCUPATION                    ?\n380     0       47      3          OCCUPATION                    ?\n381     0       47      4          OCCUPATION                    ?\n382     0       47      5          OCCUPATION                    ?\n383     0       47      6          OCCUPATION                    ?\n384     0       47      7          OCCUPATION                    ?\n385     0       48      0          OCCUPATION              Armed-F\n386     0       48      1          OCCUPATION              Armed-F\n387     0       48      2          OCCUPATION              Armed-F\n388     0       48      3          OCCUPATION              Armed-F\n389     0       48      4          OCCUPATION              Armed-F\n390     0       48      5          OCCUPATION              Armed-F\n391     0       48      6          OCCUPATION              Armed-F\n392     0       48      7          OCCUPATION              Armed-F\n393     0       49      0          OCCUPATION              Cleric.\n394     0       49      1          OCCUPATION              Cleric.\n395     0       49      2          OCCUPATION              Cleric.\n396     0       49      3          OCCUPATION              Cleric.\n397     0       49      4          OCCUPATION              Cleric.\n398     0       49      5          OCCUPATION              Cleric.\n399     0       49      6          OCCUPATION              Cleric.\n400     0       49      7          OCCUPATION              Cleric.\n401     0       50      0          OCCUPATION               Crafts\n402     0       50      1          OCCUPATION               Crafts\n403     0       50      2          OCCUPATION               Crafts\n404     0       50      3          OCCUPATION               Crafts\n405     0       50      4          OCCUPATION               Crafts\n406     0       50      5          OCCUPATION               Crafts\n407     0       50      6          OCCUPATION               Crafts\n408     0       50      7          OCCUPATION               Crafts\n409     0       51      0          OCCUPATION                Exec.\n410     0       51      1          OCCUPATION                Exec.\n411     0       51      2          OCCUPATION                Exec.\n412     0       51      3          OCCUPATION                Exec.\n413     0       51      4          OCCUPATION                Exec.\n414     0       51      5          OCCUPATION                Exec.\n415     0       51      6          OCCUPATION                Exec.\n416     0       51      7          OCCUPATION                Exec.\n417     0       52      0          OCCUPATION              Farming\n418     0       52      1          OCCUPATION              Farming\n419     0       52      2          OCCUPATION              Farming\n420     0       52      3          OCCUPATION              Farming\n421     0       52      4          OCCUPATION              Farming\n422     0       52      5          OCCUPATION              Farming\n423     0       52      6          OCCUPATION              Farming\n424     0       52      7          OCCUPATION              Farming\n425     0       53      0          OCCUPATION              Handler\n426     0       53      1          OCCUPATION              Handler\n427     0       53      2          OCCUPATION              Handler\n428     0       53      3          OCCUPATION              Handler\n429     0       53      4          OCCUPATION              Handler\n430     0       53      5          OCCUPATION              Handler\n431     0       53      6          OCCUPATION              Handler\n432     0       53      7          OCCUPATION              Handler\n433     0       54      0          OCCUPATION              House-s\n434     0       54      1          OCCUPATION              House-s\n435     0       54      2          OCCUPATION              House-s\n436     0       54      3          OCCUPATION              House-s\n437     0       54      4          OCCUPATION              House-s\n438     0       54      5          OCCUPATION              House-s\n439     0       54      6          OCCUPATION              House-s\n440     0       54      7          OCCUPATION              House-s\n441     0       55      0          OCCUPATION              Machine\n442     0       55      1          OCCUPATION              Machine\n443     0       55      2          OCCUPATION              Machine\n444     0       55      3          OCCUPATION              Machine\n445     0       55      4          OCCUPATION              Machine\n446     0       55      5          OCCUPATION              Machine\n447     0       55      6          OCCUPATION              Machine\n448     0       55      7          OCCUPATION              Machine\n449     0       56      0          OCCUPATION                Other\n450     0       56      1          OCCUPATION                Other\n451     0       56      2          OCCUPATION                Other\n452     0       56      3          OCCUPATION                Other\n453     0       56      4          OCCUPATION                Other\n454     0       56      5          OCCUPATION                Other\n455     0       56      6          OCCUPATION                Other\n456     0       56      7          OCCUPATION                Other\n457     0       57      0          OCCUPATION                Prof.\n458     0       57      1          OCCUPATION                Prof.\n459     0       57      2          OCCUPATION                Prof.\n460     0       57      3          OCCUPATION                Prof.\n461     0       57      4          OCCUPATION                Prof.\n462     0       57      5          OCCUPATION                Prof.\n463     0       57      6          OCCUPATION                Prof.\n464     0       57      7          OCCUPATION                Prof.\n465     0       58      0          OCCUPATION              Protec.\n466     0       58      1          OCCUPATION              Protec.\n467     0       58      2          OCCUPATION              Protec.\n468     0       58      3          OCCUPATION              Protec.\n469     0       58      4          OCCUPATION              Protec.\n470     0       58      5          OCCUPATION              Protec.\n471     0       58      6          OCCUPATION              Protec.\n472     0       58      7          OCCUPATION              Protec.\n473     0       59      0          OCCUPATION                Sales\n474     0       59      1          OCCUPATION                Sales\n475     0       59      2          OCCUPATION                Sales\n476     0       59      3          OCCUPATION                Sales\n477     0       59      4          OCCUPATION                Sales\n478     0       59      5          OCCUPATION                Sales\n479     0       59      6          OCCUPATION                Sales\n480     0       59      7          OCCUPATION                Sales\n481     0       60      0          OCCUPATION              TechSup\n482     0       60      1          OCCUPATION              TechSup\n483     0       60      2          OCCUPATION              TechSup\n484     0       60      3          OCCUPATION              TechSup\n485     0       60      4          OCCUPATION              TechSup\n486     0       60      5          OCCUPATION              TechSup\n487     0       60      6          OCCUPATION              TechSup\n488     0       60      7          OCCUPATION              TechSup\n489     0       61      0          OCCUPATION              Transp.\n490     0       61      1          OCCUPATION              Transp.\n491     0       61      2          OCCUPATION              Transp.\n492     0       61      3          OCCUPATION              Transp.\n493     0       61      4          OCCUPATION              Transp.\n494     0       61      5          OCCUPATION              Transp.\n495     0       61      6          OCCUPATION              Transp.\n496     0       61      7          OCCUPATION              Transp.\n497     0       NA      0                <NA>                 <NA>\n498     0       NA      1                <NA>                 <NA>\n499     0       NA      2                <NA>                 <NA>\n500     0       NA      3                <NA>                 <NA>\n501     0       NA      4                <NA>                 <NA>\n502     0       NA      5                <NA>                 <NA>\n503     0       NA      6                <NA>                 <NA>\n504     0       NA      7                <NA>                 <NA>\n505     1        0      0                <NA>                 <NA>\n506     1        0      1                <NA>                 <NA>\n507     1        0      2                <NA>                 <NA>\n508     1        0      3                <NA>                 <NA>\n509     1        0      4                <NA>                 <NA>\n510     1        0      5                <NA>                 <NA>\n511     1        0      6                <NA>                 <NA>\n512     1        0      7                <NA>                 <NA>\n513     1        1      0                <NA>                 <NA>\n514     1        1      1                <NA>                 <NA>\n515     1        1      2                <NA>                 <NA>\n516     1        1      3                <NA>                 <NA>\n517     1        1      4                <NA>                 <NA>\n518     1        1      5                <NA>                 <NA>\n519     1        1      6                <NA>                 <NA>\n520     1        1      7                <NA>                 <NA>\n521     1        2      0                <NA>                 <NA>\n522     1        2      1                <NA>                 <NA>\n523     1        2      2                <NA>                 <NA>\n524     1        2      3                <NA>                 <NA>\n525     1        2      4                <NA>                 <NA>\n526     1        2      5                <NA>                 <NA>\n527     1        2      6                <NA>                 <NA>\n528     1        2      7                <NA>                 <NA>\n529     1        3      0                <NA>                 <NA>\n530     1        3      1                <NA>                 <NA>\n531     1        3      2                <NA>                 <NA>\n532     1        3      3                <NA>                 <NA>\n533     1        3      4                <NA>                 <NA>\n534     1        3      5                <NA>                 <NA>\n535     1        3      6                <NA>                 <NA>\n536     1        3      7                <NA>                 <NA>\n537     1        4      0                <NA>                 <NA>\n538     1        4      1                <NA>                 <NA>\n539     1        4      2                <NA>                 <NA>\n540     1        4      3                <NA>                 <NA>\n541     1        4      4                <NA>                 <NA>\n542     1        4      5                <NA>                 <NA>\n543     1        4      6                <NA>                 <NA>\n544     1        4      7                <NA>                 <NA>\n545     1        5      0                <NA>                 <NA>\n546     1        5      1                <NA>                 <NA>\n547     1        5      2                <NA>                 <NA>\n548     1        5      3                <NA>                 <NA>\n549     1        5      4                <NA>                 <NA>\n550     1        5      5                <NA>                 <NA>\n551     1        5      6                <NA>                 <NA>\n552     1        5      7                <NA>                 <NA>\n553     1        6      0                <NA>                 <NA>\n554     1        6      1                <NA>                 <NA>\n555     1        6      2                <NA>                 <NA>\n556     1        6      3                <NA>                 <NA>\n557     1        6      4                <NA>                 <NA>\n558     1        6      5                <NA>                 <NA>\n559     1        6      6                <NA>                 <NA>\n560     1        6      7                <NA>                 <NA>\n561     1        7      0                <NA>                 <NA>\n562     1        7      1                <NA>                 <NA>\n563     1        7      2                <NA>                 <NA>\n564     1        7      3                <NA>                 <NA>\n565     1        7      4                <NA>                 <NA>\n566     1        7      5                <NA>                 <NA>\n567     1        7      6                <NA>                 <NA>\n568     1        7      7                <NA>                 <NA>\n569     1       NA      0                <NA>                 <NA>\n570     1       NA      1                <NA>                 <NA>\n571     1       NA      2                <NA>                 <NA>\n572     1       NA      3                <NA>                 <NA>\n573     1       NA      4                <NA>                 <NA>\n574     1       NA      5                <NA>                 <NA>\n575     1       NA      6                <NA>                 <NA>\n576     1       NA      7                <NA>                 <NA>\n577     2        0      0                <NA>                 <NA>\n578     2        0      1                <NA>                 <NA>\n579     2        0      2                <NA>                 <NA>\n580     2        0      3                <NA>                 <NA>\n581     2        1      0                <NA>                 <NA>\n582     2        1      1                <NA>                 <NA>\n583     2        1      2                <NA>                 <NA>\n584     2        1      3                <NA>                 <NA>\n585     2        2      0                <NA>                 <NA>\n586     2        2      1                <NA>                 <NA>\n587     2        2      2                <NA>                 <NA>\n588     2        2      3                <NA>                 <NA>\n589     2        3      0                <NA>                 <NA>\n590     2        3      1                <NA>                 <NA>\n591     2        3      2                <NA>                 <NA>\n592     2        3      3                <NA>                 <NA>\n593     2        4      0                <NA>                 <NA>\n594     2        4      1                <NA>                 <NA>\n595     2        4      2                <NA>                 <NA>\n596     2        4      3                <NA>                 <NA>\n597     2        5      0                <NA>                 <NA>\n598     2        5      1                <NA>                 <NA>\n599     2        5      2                <NA>                 <NA>\n600     2        5      3                <NA>                 <NA>\n601     2        6      0                <NA>                 <NA>\n602     2        6      1                <NA>                 <NA>\n603     2        6      2                <NA>                 <NA>\n604     2        6      3                <NA>                 <NA>\n605     2        7      0                <NA>                 <NA>\n606     2        7      1                <NA>                 <NA>\n607     2        7      2                <NA>                 <NA>\n608     2        7      3                <NA>                 <NA>\n609     2       NA      0                <NA>                 <NA>\n610     2       NA      1                <NA>                 <NA>\n611     2       NA      2                <NA>                 <NA>\n612     2       NA      3                <NA>                 <NA>\n613     3        0      0                <NA>                 <NA>\n614     3        1      0                <NA>                 <NA>\n615     3        2      0                <NA>                 <NA>\n616     3        3      0                <NA>                 <NA>\n617     3       NA      0                <NA>                 <NA>\n           WEIGHT\n1     8.229927063\n2     5.299321175\n3     2.619868279\n4     1.625880957\n5     1.388641596\n6     4.611637115\n7     2.263547182\n8   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0.847415090\n581   4.125393867\n582  -8.075553894\n583  -0.508984149\n584  -4.410441875\n585  -3.536094904\n586   4.477013588\n587  -0.355714768\n588   0.923635244\n589  -1.165177107\n590   0.958768725\n591  -0.684877157\n592  -1.352784753\n593  -2.088316679\n594   2.709913969\n595  -0.677554369\n596  -2.227767467\n597   0.823573887\n598  -1.978428602\n599  -1.125163078\n600  -3.163433075\n601   5.128084183\n602  -7.984966755\n603  -0.911229193\n604  -3.026110411\n605  -2.930909395\n606   2.763731956\n607  -0.373686492\n608  -2.102555990\n609   2.454554081\n610  -3.523326397\n611  -1.361525893\n612  -2.874101162\n613  -3.441395998\n614   5.974472046\n615  -0.898094594\n616   5.424322605\n617  -3.896113873","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Make predictions using test data","message":["%r","","RES_DF <- predict(nn.mod3, CUST_DF.test, supplemental.cols='AFFINITY_CARD')"],"enabled":true,"result":{"startTime":1715346893754,"interpreter":"r.medium","endTime":1715346894325,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST dataset","message":["%r","","C_MATRIX <- with(RES_DF, table(AFFINITY_CARD, PREDICTION))   ","C_MATRIX"],"enabled":true,"result":{"startTime":1715346894400,"interpreter":"r.medium","endTime":1715346895598,"results":[{"message":"             PREDICTION\nAFFINITY_CARD   0   1\n            0 669 721\n            1  23 387","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute model accuracy","message":["%r","","ACCURACY <- C_MATRIX/sum(C_MATRIX)","round(sum(diag(ACCURACY)),3)"],"enabled":true,"result":{"startTime":1715346895677,"interpreter":"r.medium","endTime":1715346895798,"results":[{"message":"0.587","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Generate statistics for ROC Curve, Lift Chart, and Probability Densities","message":["%r","","# BAR PLOT","res <- ore.pull(RES_DF)","sensitivity <- res[order(res$\"'1'\",decreasing = TRUE), ]","sens <- sum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\") - cumsum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\")","spec <- cumsum(sensitivity$\"'1'\")/sum(sensitivity$\"'1'\")","","# LIFT CHART","decile2 <- quantile(sensitivity$\"'1'\", probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity$\"'1'\", col.names = c(\"sens\"))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","","# DISTRIBUTION CHART","dx <- density(res$\"'0'\")","dx2 <- density(res$\"'1'\")","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","","plot(1 - spec, sens, type = \"l\", col = \"darkred\", ","     ylab = \"Sensitivity\", xlab = \"1 - Specificity\", ","     main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col = \"darkred\")","","plot(dx, lwd = 2, col = \"darkgreen\", main = \"Probability Densities\")","lines(dx2, lwd = 2, col = \"darkred\")","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE)  "],"enabled":true,"result":{"startTime":1715346895875,"interpreter":"r.medium","endTime":1715346896447,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAFoCAIAAAAXZAVmAAAABmJLR0QA/wD/AP+gvaeTAAAg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Ghom7drVnPnqlfLCUiL1T6AJgaDo9WvTzw8kQsI4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NN_TEST_DATA for use in query below","message":["%r","","ore.drop(table = 'NN_TEST_DATA', silent=TRUE)","","ore.create(CUST_DF.test, table = 'NN_TEST_DATA')"],"enabled":true,"result":{"startTime":1715346896536,"interpreter":"r.medium","endTime":1715346897267,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use the SQL interface so score data and display prediction details","message":["%sql","","SELECT CUST_ID,"," round(AFFINITY_CARD_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(NN_CLASSIFICATION_MODEL 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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification RF","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715347522723,"interpreter":"md.low","endTime":1715347522880,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Random Forest","","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we highlight the Random Forest algorithm for classification in OML4R and predict customers most likely to be positive responders to an Affinity Card loyalty program.","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.","","This notebook builds and applies a classification random forest model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715347522956,"interpreter":"md.low","endTime":1715347523051,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-random-forest\">Classification Modeling to Predict Target Customers using Random Forest<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we highlight the Random Forest algorithm for classification in OML4R and predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification random forest model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715347523130,"interpreter":"md.low","endTime":1715347523204,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>"],"enabled":true,"result":{"startTime":1715347523284,"interpreter":"md.low","endTime":1715347523361,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(\"SUP_DEM\" = \"select * from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.attach()"],"enabled":true,"result":{"startTime":1715347523438,"interpreter":"r.low","endTime":1715347528037,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":400,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","?ore.odmRF "],"enabled":true,"result":{"startTime":1715347528117,"interpreter":"r.low","endTime":1715347528501,"results":[{"message":"ore.odmRF                package:OREdm                 R Documentation\n\nIn-Database Random Forest Models\n\nDescription:\n\n     Fits a random forest model using Oracle Data Mining,\n\nUsage:\n\n     ore.odmRF(formula, \n            data,\n            auto.data.prep = TRUE,\n            na.action = na.pass,\n            odm.settings = NULL,\n            ctx.settings = NULL)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. The details of model specification are given under\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build. The default value is \u2018TRUE\u2019.\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmRF\u2019.\n\nctx.settings: Text mining settings.\n\nDetails:\n\n     Random Forest is a popular ensemble learning technique for\n     classification and regression. By combining the ideas of bagging\n     and random selection of variables, the algorithm produces a\n     collection of decision trees with controlled variance, while\n     avoiding overfitting - a common problem for decision trees.\n\n     The \u2018formula\u2019 specification has the form \u2018attribute ~ terms\u2019 where\n     \u2018attribute\u2019 is the attribute to be used for model build and\n     \u2018terms\u2019 is a series of terms, for example, the column names, to\n     include in the model. Multiple terms are specified using \u2018+\u2019\n     between column names. Use \u2018response ~ .\u2019 if all columns in \u2018data\u2019\n     should be used for model building. To exclude columns, use \u2018-\u2019\n     before each column name to exclude.\n\nValue:\n\n     The \u2018ore.odmRF\u2019 function returns an object of class \u2018ore.odmRF\u2019,\n     which includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of variables (attribiutes) used to build the\n          model. The columns include: name, type (numerical or\n          categorical), data type, data length (size), precision and\n          scale for numeric data, and whether the variable is the\n          target.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\nimportance: Attribute importance of the fitted model\n\n    call: The matched call.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmSVM\u2019, \u2018ore.odmGLM\u2019, \u2018ore.odmDT\u2019, \u2018partitions\u2019\n\nExamples:\n\n         IRIS <- ore.push(iris)\n         mod <- ore.odmRF(Species~., IRIS)\n         mod\n         summary(mod)\n         importance(mod)\n         ans <- predict(mod, IRIS)\n         head(ans,3)\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from SUPPLEMENTARY_DEMOGRAPHICS","message":["%r","","z.show(head(SUP_DEM))"],"enabled":true,"result":{"startTime":1715347528581,"interpreter":"r.low","endTime":1715347528765,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display number of rows and columns","message":["%r","","dim(SUP_DEM)"],"enabled":true,"result":{"startTime":1715347528841,"interpreter":"r.low","endTime":1715347529026,"results":[{"message":"<style>\n.list-inline {list-style: none; 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The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> 'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_DELETE_ROW'","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.","> 'RFOR_NUM_TREES' = '20","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)","> 'RFOR_SAMPLING_RATIO' = '0.5'","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.","> 'RFOR_MTRY' = '0'"],"enabled":true,"result":{"startTime":1715347571405,"interpreter":"md.low","endTime":1715347571467,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-random-forest\">Examples of possible setting overrides for Random Forest<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-457AFEBA-8F23-447F-883D-F6DBC4B53CA7\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_ROW_WEIGHT_COLUMN_NAME' = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>'ODMS_MISSING_VALUE_TREATMENT' = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_NUM_TREES' = '20<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_SAMPLING_RATIO' = '0.5'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.<\/li>\n<\/ul>\n<blockquote>\n<p>'RFOR_MTRY' = '0'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest model using explicit settings and score data","message":["%r","","ore.drop (model = 'RF_CLASSIFICATION_MODEL')","","settings = list(RFOR_MTRY = 3, ","                RFOR_NUM_TREES = 100, ","                RFOR_SAMPLING_RATIO =0.5,","                model_name=\"RF_CLASSIFICATION_MODEL\")","","MOD2 <- ore.odmRF(AFFINITY_CARD~., DEMO_DF.train, odm.settings= settings)","","RES2 <- predict(MOD2, DEMO_DF.test, type= c(\"class\",\"raw\"), norm.votes=TRUE, cache.model=TRUE, ","                supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", ","                                    \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", \"HOUSEHOLD_SIZE\", ","                                    \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\"))"],"enabled":true,"result":{"startTime":1715347534019,"interpreter":"r.low","endTime":1715347536717,"results":[{"message":"Warning message:\n\u201cThe created mining model RF_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix","message":["%r","","confusion_matrix <- with(RES2, table(AFFINITY_CARD, PREDICTION))   ","names(dimnames(confusion_matrix)) <- c(\"ACTUAL\",\"PREDICTED\")","confusion_matrix","","accuracy <- confusion_matrix/sum(confusion_matrix)","cat(\"\\n\\nAccuracy:\") ","round(sum(diag(accuracy)),4)"],"enabled":true,"result":{"startTime":1715347536795,"interpreter":"r.low","endTime":1715347537442,"results":[{"message":"      PREDICTED\nACTUAL    0    1\n     0 1324   66\n     1  216  194\n\nAccuracy:\n","type":"TEXT"},{"message":"0.8433","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Display ROC curve, lift chart, and probability densities","message":["%r","","# BAR PLOT","res <- ore.pull(RES)","sensitivity <- res[order(res$\"'1'\",decreasing = TRUE), ]","sens <- sum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\") - cumsum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\")","spec <- cumsum(sensitivity$\"'1'\")/sum(sensitivity$\"'1'\")","","# LIFT CHART","decile2 <- quantile(sensitivity$\"'1'\", probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity$\"'1'\", col.names = c(\"sens\"))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","","# DISTRIBUTION CHART","dx <- density(res$\"'0'\")","dx2 <- density(res$\"'1'\")","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","","plot(1 - spec, sens, type = \"l\", col = \"darkred\", ","     ylab = \"Sensitivity\", xlab = \"1 - Specificity\", ","     main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, ","     paste(\" AUC = \", ","           round(sum(spec*diff(c(0, 1 - sens))),3)))","","barplot(table(df_sens$decile), ","        xlab = 'Decile', ylab = 'Actual Targets', ","        main = 'Lift Chart', col = \"darkred\")","","plot(dx, lwd = 2, col = \"darkgreen\", main = \"Probability Densities\")","lines(dx2, lwd = 2, col = \"darkred\")","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1,bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE)  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p8fHxc+fOPX78uLY2XBcAAKBIVezG4cYmCbSJ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\n8+bN+/Tpk7+/v7a29qlTpwYPHnzy5EnO1sZP+I0ePTo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6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table = \"RF_TEST_TABLE\")","ore.create(DEMO_DF, table =\"RF_TEST_TABLE\")"],"enabled":true,"result":{"startTime":1715347538047,"interpreter":"r.low","endTime":1715347538691,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Use the SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(RF_CLASSIFICATION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(RF_CLASSIFICATION_MODEL USING *) PD","      FROM RF_TEST_TABLE","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","               XMLTABLE('/Details'","                        PASSING OUT.PD","                        COLUMNS ","                        \"Attribute1\" XMLType PATH 'Attribute[1]',","                        \"Attribute2\" XMLType PATH 'Attribute[2]')  OUTPRED"],"enabled":true,"result":{"startTime":1715347538764,"interpreter":"sql.low","endTime":1715347540361,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"6.454\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".719\" \n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".19\" \t\"CUST_ID\" actualValue=\"100002\" weight=\".171\" \n100003\t0\t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\"2.914\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".127\" 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\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Classification SVM.dsnb b/machine-learning/notebooks/r/OML4R Classification SVM.dsnb
deleted file mode 100755
index e6aa1f42..00000000
--- a/machine-learning/notebooks/r/OML4R Classification SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Classification SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715347858816,"interpreter":"md.medium","endTime":1715347858970,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification modeling to predict Target Customers using Support Vector Machine Model","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. ","","High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. ","","This notebook builds and applies a classification support vector machine model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715347859174,"interpreter":"md.medium","endTime":1715347859254,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine-model\">Classification modeling to predict Target Customers using Support Vector Machine Model<\/h1>\n<p>Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.<\/p>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.<\/p>\n<p>High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.<\/p>\n<p>This notebook builds and applies a classification support vector machine model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" 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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"hhttps://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715347859479,"interpreter":"md.medium","endTime":1715347859539,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"hhttps://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(\"SUP_DEM\" = \"select * from 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\"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]","                      "],"enabled":true,"result":{"startTime":1715347866696,"interpreter":"r.medium","endTime":1715347866821,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create train and test data (60/40 split)","message":["%r","","sampleSize <- .4 * nrow(DEMO_DF)","index <- sample(1:nrow(DEMO_DF),sampleSize)","group <- as.integer(1:nrow(DEMO_DF) %in% index)"],"enabled":true,"result":{"startTime":1715347866897,"interpreter":"r.medium","endTime":1715347867546,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Create train and test data sets","message":["%r","","rownames(DEMO_DF) <- DEMO_DF$CUST_ID","DEMO_DF.train <- DEMO_DF[group==FALSE,]","class(DEMO_DF.train)","cat(\"\\nTraining data: \")","dim(DEMO_DF.train)","","DEMO_DF.test <- DEMO_DF[group==TRUE,]","class(DEMO_DF.test)","cat(\"\\nTest data: \")","dim(DEMO_DF.test)"],"enabled":true,"result":{"startTime":1715347867621,"interpreter":"r.medium","endTime":1715347869523,"results":[{"message":"'ore.frame'\nTraining data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; 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AFFINITY_CARD"],"enabled":true,"result":{"startTime":1715347869614,"interpreter":"md.medium","endTime":1715347869675,"results":[{"message":"<h2 id=\"build-a-support-vector-machine-model-for-predicting-affinity_card\">Build a Support Vector Machine Model for Predicting AFFINITY_CARD<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using default settings","message":["%r","","ore.exec(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> 'SVM_CLASSIFICATION_MODEL'); EXCEPTION WHEN others THEN null; END;\")","","MOD <- ore.odmSVM(AFFINITY_CARD~., DEMO_DF.train, \"classification\", kernel.function = \"system.determined\",odm.settings=list(model_name=\"SVM_CLASSIFICATION_MODEL\"))    ","","RES <- predict(MOD, DEMO_DF.test, ","               type= c(\"raw\",\"class\"), norm.votes=TRUE, cache.model=TRUE, ","               supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"EDUCATION\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"YRS_RESIDENCE\")) "],"enabled":true,"result":{"startTime":1715347869755,"interpreter":"r.medium","endTime":1715347872656,"results":[{"message":"Warning message:\n\u201cThe default kernel function is linear.\u201d\nWarning message:\n\u201cThe created mining model SVM_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Confusion Matrix","message":["%r","","CMATRIX <- with(RES, table(AFFINITY_CARD,PREDICTION))   ","CMATRIX"],"enabled":true,"result":{"startTime":1715347872744,"interpreter":"r.medium","endTime":1715347873743,"results":[{"message":"             PREDICTION\nAFFINITY_CARD    0    1\n            0 1270   96\n            1  207  227","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%r","","ACCURACY <- CMATRIX/sum(CMATRIX)","round(sum(diag(ACCURACY)),3)"],"enabled":true,"result":{"startTime":1715347873825,"interpreter":"r.medium","endTime":1715347873949,"results":[{"message":"0.832","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","   ","* Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","  ","* Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'","","> SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'","   ","* Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> SVMS_COMPLEXITY_FACTOR = '0.1'","","* Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.    ","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'","","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'","      ","* Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> SVMS_BATCH_ROWS = '20000'","   ","* Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","> SVMS_SOLVER = 'SVMS_SOLVER_SGD'","","> SVMS_SOLVER = 'SVMS_SOLVER_IPM'","","* Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> SVMS_CONV_TOLERANCE = '0.001'","","* Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> SVMS_NUM_PIVOTS = '200' "],"enabled":true,"result":{"startTime":1715347859017,"interpreter":"md.medium","endTime":1715347859123,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_COMPLEXITY_FACTOR = '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_BATCH_ROWS = '20000'<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_IPM'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_CONV_TOLERANCE = '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_NUM_PIVOTS = '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build a SVM Model using Linear kernel","message":["%r","","MOD2 <- ore.odmSVM(AFFINITY_CARD~., DEMO_DF.train,\"classification\", kernel.function = \"linear\")   ","","RES2 <- predict(MOD2, DEMO_DF.test, type=\"class\", supplemental.cols=\"AFFINITY_CARD\") "],"enabled":true,"result":{"startTime":1715347874186,"interpreter":"r.medium","endTime":1715347876589,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%r","","CMATRIX2 <- with(RES2, table(AFFINITY_CARD,PREDICTION)) ","ACCURACY2 <- CMATRIX2/sum(CMATRIX2)","","z.show(round(sum(diag(ACCURACY2)),3))"],"enabled":true,"result":{"startTime":1715347876682,"interpreter":"r.medium","endTime":1715347876930,"results":[{"message":"0.832","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build a SVM Model using Gaussian kernel","message":["%r","","MOD3 <- ore.odmSVM(AFFINITY_CARD~., DEMO_DF.train, \"classification\", kernel.function = \"gaussian\") ","","RES3 <- predict(MOD3, DEMO_DF.test, type=\"class\", supplemental.cols=\"AFFINITY_CARD\") "],"enabled":true,"result":{"startTime":1715347877011,"interpreter":"r.medium","endTime":1715347883125,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show model accuracy","message":["%r","","CMATRIX3 <- table(RES3$AFFINITY_CARD, RES3$PREDICTION)","ACCURACY3 <- CMATRIX3/sum(CMATRIX3)","z.show(round(sum(diag(ACCURACY3)),3))"],"enabled":true,"result":{"startTime":1715347883206,"interpreter":"r.medium","endTime":1715347883528,"results":[{"message":"0.835","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display prediction results","message":["%r","","z.show(head(ore.sort(RES[(RES$\"'1'\" > 0.5),], by = c(\"'1'\")), 10))"],"enabled":true,"result":{"startTime":1715347883609,"interpreter":"r.medium","endTime":1715347884515,"results":[{"message":"X.0.\tX.1.\tCUST_ID\tAFFINITY_CARD\tEDUCATION\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tPREDICTION\n0.0293122307513715\t0.970687769248629\t101257\t1\tHS-grad\t3\tProtec.\t3\t1\n0.02931223075137\t0.97068776924863\t101849\t0\tHS-grad\t3\tSales\t3\t1\n0.0293122307513691\t0.970687769248631\t103699\t1\t< Bach.\t3\tProtec.\t3\t1\n0.0293122307513689\t0.970687769248631\t104031\t1\tHS-grad\t3\tExec.\t4\t1\n0.0293122307513688\t0.970687769248631\t103775\t0\tHS-grad\t3\tSales\t2\t1\n0.0293122307513686\t0.970687769248631\t100305\t0\tHS-grad\t3\tExec.\t4\t1\n0.0293122307513684\t0.970687769248632\t102383\t0\t< Bach.\t3\tProf.\t3\t1\n0.0293122307513684\t0.970687769248632\t103086\t0\tHS-grad\t3\tSales\t4\t1\n0.0293122307513684\t0.970687769248632\t103244\t0\tHS-grad\t3\tSales\t4\t1\n0.0293122307513681\t0.970687769248632\t101401\t0\t< Bach.\t3\tSales\t4\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Display ROC Curve, Lift Chart, and Distribution Chart"],"enabled":true,"result":{"startTime":1715347884608,"interpreter":"md.medium","endTime":1715347884673,"results":[{"message":"<h2 id=\"display-roc-curve-lift-chart-and-distribution-chart\">Display ROC Curve, Lift Chart, and Distribution Chart<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":null,"message":["%r","","# BAR PLOT","res <- ore.pull(RES)","sensitivity <- res[order(res$\"'1'\",decreasing = TRUE), ]","sens <- sum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\") - cumsum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\")","spec <- cumsum(sensitivity$\"'1'\")/sum(sensitivity$\"'1'\")","","# LIFT CHART","decile2 <- quantile(sensitivity$\"'1'\", probs = seq(.1, .9, by = .1))","df_sens <- as.data.frame(sensitivity$\"'1'\", col.names = c(\"sens\"))","df_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))","","# DISTRIBUTION CHART","dx <- density(res$\"'0'\")","dx2 <- density(res$\"'1'\")","","# PLOTS 3 in 1 , with specific width and height ","par(mfrow=c(1,3))","options(repr.plot.width=8, repr.plot.height=3)","","plot(1 - spec, sens, type = \"l\", col = \"darkred\", ylab = \"Sensitivity\", xlab = \"1 - Specificity\", main = 'ROC Curve')","abline(c(0,0),c(1,1))","text(0.6, 0.2, paste(\" AUC = \", round(sum(spec*diff(c(0, 1 - sens))),3)))","","barplot(table(df_sens$decile), xlab = 'Decile', ylab = 'Actual Targets', main = 'Lift Chart', col = \"darkred\")","","par(mar = c(6, 4, 4, 2))","plot(dx, lwd = 2, col = \"darkgreen\",","     main = \"Probability Densities\")","lines(dx2, lwd = 2, col = \"darkred\")","legend(x = \"bottom\", legend = c(\"Negative\",\"Positive\"), ","       col = c(\"darkred\",\"darkgreen\"), ","       lwd = 1, bty = \"n\",","       xpd = TRUE,inset = c(0, -0.36),","       horiz = TRUE)  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iqI2bdqUm5s7ffp0Ly+vuLi4uLg4PT09Ly+v6Oho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\nQqiaW82uU8DtdyLS1NI1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Use SQL interface for prediction details"],"enabled":true,"result":{"startTime":1715347885746,"interpreter":"md.medium","endTime":1715347885807,"results":[{"message":"<h3 id=\"use-sql-interface-for-prediction-details\">Use SQL interface for prediction details<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table = \"SVM_TEST_TABLE\")","ore.create(DEMO_DF, table 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PREDICTION_YRS_RES,","             PREDICTION_DETAILS(SVM_CLASSIFICATION_MODEL USING *) PD","      FROM SVM_TEST_TABLE","      WHERE rownum < 20","      ORDER BY CUST_ID) OUT,","               XMLTABLE('/Details'","                        PASSING OUT.PD","                        COLUMNS ","                        \"Attribute1\" XMLType PATH 'Attribute[1]',","                        \"Attribute2\" XMLType PATH 'Attribute[2]',","                        \"Attribute3\" XMLType PATH 'Attribute[3]')  OUTPRED","FETCH FIRST 10 ROWS ONLY","                        "],"enabled":true,"result":{"startTime":1715347886691,"interpreter":"sql.medium","endTime":1715347889391,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100200\t0\t\t\t\n100800\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".178\" \t\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\".001\" \t\n101400\t0\t\"OCCUPATION\" actualValue=\"Machine\" weight=\".941\" \t\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\"-.001\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"-.026\" \n100300\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".178\" \t\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\".001\" \t\n100900\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".178\" \t\"OS_DOC_SET_KANJI\" actualValue=\"0\" weight=\".001\" \t\n101500\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".029\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".003\" \t\n100500\t0\t\t\t\n101100\t0\t\"OCCUPATION\" actualValue=\"Other\" weight=\".029\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".029\" \t\n101700\t0\t\"OCCUPATION\" actualValue=\"Machine\" weight=\".029\" \t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".029\" \t\n100600\t0\t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".029\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".003\" 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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Clustering EM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715301551351,"interpreter":"md.low","endTime":1715301551416,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Identifying Customer Segments using Expectation Maximization Clustering","Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the Expectation Maximization in-database algortihm.","","Copyright (c) 2024 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","","----"],"enabled":true,"result":{"startTime":1715301551492,"interpreter":"md.low","endTime":1715301551554,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1715301551636,"interpreter":"md.low","endTime":1715301551707,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1715301551785,"interpreter":"md.low","endTime":1715301551847,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715301551928,"interpreter":"r.low","endTime":1715301552051,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare customer demographic data using proxy objects","message":["%r","","ore.sync(query = c(\"SUPPLEMENTARY_DEMOGRAPHICS\" = \"select CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.sync(query = c(\"CUSTOMERS\" = \"select CUST_ID, CUST_GENDER, CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS from SH.CUSTOMERS\"))","","CUST_DF <- merge(SUPPLEMENTARY_DEMOGRAPHICS,CUSTOMERS, by=\"CUST_ID\")"],"enabled":true,"result":{"startTime":1715301552126,"interpreter":"r.low","endTime":1715301552774,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first 5 rows of CUST_DF data","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1715301552849,"interpreter":"r.low","endTime":1715301553580,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tCUST_GENDER\tCUST_YEAR_OF_BIRTH\tCUST_MARITAL_STATUS\n100100\tMasters\tProf.\t4-5\t4\tF\t1959\tMarried\n100200\t< Bach.\tOther\t1\t2\tM\t1983\tNeverM\n100300\tBach.\tProf.\t3\t4\tM\t1961\tMarried\n100400\t12th\tTransp.\t6-8\t6\tM\t1944\tDivorc.\n100500\tHS-grad\tCrafts\t1\t2\tM\t1983\tNeverM\n100600\t11th\tTransp.\t3\t3\tM\t1978\tMarried\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get dimensions of data set using the CUST_DF proxy object","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1715301553661,"interpreter":"r.low","endTime":1715301553890,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>8<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an Expectation Maximization model"],"enabled":true,"result":{"startTime":1715301553974,"interpreter":"md.low","endTime":1715301554039,"results":[{"message":"<h3 id=\"build-an-expectation-maximization-model\">Build an Expectation Maximization model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an EM clustering model with 3 clusters and default settings","message":["%r","","EM.MOD1 <- ore.odmEM(~.-CUST_ID, CUST_DF, num.centers = 3)","","EM.MOD1"],"enabled":true,"result":{"startTime":1715301554134,"interpreter":"r.low","endTime":1715301558239,"results":[{"message":"\nCall:\nore.odmEM(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\ncluster.components               cluster.comp.enable\ncluster.statistics                 clus.stats.enable\ncluster.thresh                                     2\nlinkage.function                      linkage.single\nloglike.improvement                             .001\nmax.num.attr.2d                                   50\nmin.pct.attr.support                              .1\nmodel.search                    model.search.disable\nnum.components                                    20\nnum.distribution                    num.distr.system\nnum.equiwidth.bins                                11\nnum.iterations                                   100\nnum.projections                                   50\nrandom.seed                                        0\nremove.components                remove.comps.enable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display EM model details","message":["%r","","summary(EM.MOD1)"],"enabled":true,"result":{"startTime":1715301558317,"interpreter":"r.low","endTime":1715301558468,"results":[{"message":"\nCall:\nore.odmEM(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\ncluster.components               cluster.comp.enable\ncluster.statistics                 clus.stats.enable\ncluster.thresh                                     2\nlinkage.function                      linkage.single\nloglike.improvement                             .001\nmax.num.attr.2d                                   50\nmin.pct.attr.support                              .1\nmodel.search                    model.search.disable\nnum.components                                    20\nnum.distribution                    num.distr.system\nnum.equiwidth.bins                                11\nnum.iterations                                   100\nnum.projections                                   50\nrandom.seed                                        0\nremove.components                remove.comps.enable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS MEAN.CUST_YEAR_OF_BIRTH\n3               NA                       NA                1963.555\n4               NA                       NA                1968.397\n5               NA                       NA                1959.571\n  MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.OCCUPATION MEAN.YRS_RESIDENCE\n3             NA                  NA              NA           3.784404\n4             NA                  NA              NA           3.430186\n5             NA                  NA              NA           4.863636\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster histogram data for clusters in the hierarchy","message":["%r","","z.show(head(clusterhists(EM.MOD1),10))"],"enabled":true,"result":{"startTime":1715301558556,"interpreter":"r.low","endTime":1715301558781,"results":[{"message":"cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_GENDER:F\t3\tNA\tNA\t:\t1510\n1\tCUST_GENDER:M\t2\tNA\tNA\t:\t2990\n1\tCUST_MARITAL_STATUS:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_MARITAL_STATUS:Divorc.\t4\tNA\tNA\t:\t615\n1\tCUST_MARITAL_STATUS:Mabsent\t7\tNA\tNA\t:\t75\n1\tCUST_MARITAL_STATUS:Mar-AF\t8\tNA\tNA\t:\t3\n1\tCUST_MARITAL_STATUS:Married\t2\tNA\tNA\t:\t2034\n1\tCUST_MARITAL_STATUS:NeverM\t3\tNA\tNA\t:\t1503\n1\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\t:\t134\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "],"enabled":true,"result":{"startTime":1715301558866,"interpreter":"md.low","endTime":1715301558931,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Expectation Maximization clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA');\"> EM clustering<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All Algorithms<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '<row_weight_column_name>';  ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') = '5';","* Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5. ","> v_setlst('OCLT_SENSITIVITY') = '0.5';","* Whether or not to include uncorrelated attributes in the model. When EMCS_ATTRIBUTE_FILTER is enabled, uncorrelated attributes are not included. Default is system-determined.","> v_setlist('EMCS_ATTRIBUTE_FILTER') = 'EMCS_ATTR_FILTER_ENABLE'","* Maximum number of correlated attributes to include in the model. Note: This setting applies only to attributes that are not nested (2D). Default is 50.","> v_setlist('EMCS_MAX_NUM_ATTR_2D') = 'TO_CHAR( numeric_expr >=1)';","* The distribution for modeling numeric attributes. Applies to the input table or view as a whole and does not allow per-attribute specifications. The options include Bernoulli, Gaussian, or system-determined distribution. When Bernoulli or Gaussian distribution is chosen, all numeric attributes are modeled using the same type of distribution. When the distribution is system-determined, individual attributes may use different distributions (either Bernoulli or Gaussian), depending on the data. Default is EMCS_NUM_DISTR_SYSTEM.","> v_setlist('EMCS_NUM_DISTRIBUTION') = 'EMCS_NUM_DISTR_SYSTEM';","* Number of equi-width bins that will be used for gathering cluster statistics for numeric columns. Default is 11.","> v_setlist('EMCS_NUM_EQUIWIDTH_BINS') = 'TO_CHAR( 1 <numeric_expr <=255)';","* Specifies the number of projections that will be used for each nested column. If a column has fewer distinct attributes than the specified number of projections, the data will not be projected. The setting applies to all nested columns. Default is 50.","> v_setlist('EMCS_NUM_PROJECTIONS') = 'TO_CHAR( numeric_expr >=1)'","* Specifies the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. Default is system-determined.","> v_setlist('EMCS_NUM_QUANTILE_BINS') = 'TO_CHAR( 1 <numeric_expr <=255)'","* Specifies the number of top-N bins that will be used for modeling categorical columns with multivalued Bernoulli distributions. Default is system-determined.","> v_setlist('EMCS_NUM_TOPN_BINS') = 'TO_CHAR( 1 <numeric_expr <=255)'"],"enabled":true,"result":{"startTime":1715301559015,"interpreter":"md.low","endTime":1715301559084,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation Maximization clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F464614F-CE80-4A1D-A3D9-7F77CD3B48BA');\"> EM clustering<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All Algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') = '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('OCLT_SENSITIVITY') = '0.5';<\/p>\n<\/blockquote>\n<ul>\n<li>Whether or not to include uncorrelated attributes in the model. When EMCS_ATTRIBUTE_FILTER is enabled, uncorrelated attributes are not included. Default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_ATTRIBUTE_FILTER') = 'EMCS_ATTR_FILTER_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Maximum number of correlated attributes to include in the model. Note: This setting applies only to attributes that are not nested (2D). Default is 50.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_MAX_NUM_ATTR_2D') = 'TO_CHAR( numeric_expr &gt;=1)';<\/p>\n<\/blockquote>\n<ul>\n<li>The distribution for modeling numeric attributes. Applies to the input table or view as a whole and does not allow per-attribute specifications. The options include Bernoulli, Gaussian, or system-determined distribution. When Bernoulli or Gaussian distribution is chosen, all numeric attributes are modeled using the same type of distribution. When the distribution is system-determined, individual attributes may use different distributions (either Bernoulli or Gaussian), depending on the data. Default is EMCS_NUM_DISTR_SYSTEM.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_DISTRIBUTION') = 'EMCS_NUM_DISTR_SYSTEM';<\/p>\n<\/blockquote>\n<ul>\n<li>Number of equi-width bins that will be used for gathering cluster statistics for numeric columns. Default is 11.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_EQUIWIDTH_BINS') = 'TO_CHAR( 1 &lt;numeric_expr &lt;=255)';<\/p>\n<\/blockquote>\n<ul>\n<li>Specifies the number of projections that will be used for each nested column. If a column has fewer distinct attributes than the specified number of projections, the data will not be projected. The setting applies to all nested columns. Default is 50.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_PROJECTIONS') = 'TO_CHAR( numeric_expr &gt;=1)'<\/p>\n<\/blockquote>\n<ul>\n<li>Specifies the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. Default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_QUANTILE_BINS') = 'TO_CHAR( 1 &lt;numeric_expr &lt;=255)'<\/p>\n<\/blockquote>\n<ul>\n<li>Specifies the number of top-N bins that will be used for modeling categorical columns with multivalued Bernoulli distributions. Default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlist('EMCS_NUM_TOPN_BINS') = 'TO_CHAR( 1 &lt;numeric_expr &lt;=255)'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build EM clustering model with 3 clusters with explicit settings","message":["%r","","ore.drop(model = 'EM_CLUSTERING_MODEL')","","settings = list(","  EMCS_NUM_ITERATIONS= 20,","  EMCS_RANDOM_SEED= 7,","  EMCS_CLUSTER_COMPONENTS= 'EMCS_CLUSTER_COMP_ENABLE',","  EMCS_CLUSTER_STATISTICS= 'EMCS_CLUS_STATS_ENABLE',","  EMCS_CLUSTER_THRESH= 2,","  EMCS_LINKAGE_FUNCTION= 'EMCS_LINKAGE_SINGLE',","  EMCS_LOGLIKE_IMPROVEMENT= .001,","  EMCS_MAX_NUM_ATTR_2D= 50,","  EMCS_MIN_PCT_ATTR_SUPPORT= .1,","  EMCS_MODEL_SEARCH= 'EMCS_MODEL_SEARCH_DISABLE',","  EMCS_NUM_COMPONENTS= 20,","  EMCS_NUM_DISTRIBUTION= 'EMCS_NUM_DISTR_SYSTEM',","  EMCS_NUM_EQUIWIDTH_BINS= 11,","  EMCS_REMOVE_COMPONENTS= 'EMCS_REMOVE_COMPS_ENABLE',","  EMCS_ATTRIBUTE_FILTER= 'EMCS_ATTR_FILTER_ENABLE',","  EMCS_CONVERGENCE_CRITERION= 'EMCS_CONV_CRIT_HELDASIDE',","  EMCS_NUM_QUANTILE_BINS= 11,","  EMCS_NUM_TOPN_BINS= 15,","  ODMS_DETAILS= 'ODMS_ENABLE',","  ODMS_SAMPLING= 'ODMS_SAMPLING_DISABLE',","  PREP_AUTO= 'ON',","  model_name= \"EM_CLUSTERING_MODEL\")","","EM.MOD2 <- ore.odmEM(~.-CUST_ID, CUST_DF, num.centers = 3,odm.settings = settings)"],"enabled":true,"result":{"startTime":1715301559180,"interpreter":"r.low","endTime":1715301563283,"results":[{"message":"Warning message:\n\u201cThe created mining model EM_CLUSTERING_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details","message":["%r","","summary(EM.MOD2)"],"enabled":true,"result":{"startTime":1715301563371,"interpreter":"r.low","endTime":1715301563526,"results":[{"message":"\nCall:\nore.odmEM(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nattribute.filter                  attr.filter.enable\ncluster.components               cluster.comp.enable\ncluster.statistics                 clus.stats.enable\ncluster.thresh                                     2\nconvergence.criterion            conv.crit.heldaside\nlinkage.function                      linkage.single\nloglike.improvement                            0.001\nmax.num.attr.2d                                   50\nmin.pct.attr.support                             0.1\nmodel.search                    model.search.disable\nnum.components                                    20\nnum.distribution                    num.distr.system\nnum.equiwidth.bins                                11\nnum.iterations                                    20\nnum.projections                                   50\nnum.quantile.bins                                 11\nnum.topn.bins                                     15\nrandom.seed                                        7\nremove.components                remove.comps.enable\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  MEAN.CUST_GENDER MEAN.CUST_MARITAL_STATUS MEAN.CUST_YEAR_OF_BIRTH\n3               NA                       NA                1959.533\n4               NA                       NA                1968.319\n5               NA                       NA                1964.435\n  MEAN.EDUCATION MEAN.HOUSEHOLD_SIZE MEAN.OCCUPATION MEAN.YRS_RESIDENCE\n3             NA                  NA              NA           4.867778\n4             NA                  NA              NA           3.434765\n5             NA                  NA              NA           3.736842\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display summary of clusters","message":["%r","","z.show(EM.MOD2$clusters)"],"enabled":true,"result":{"startTime":1715301563610,"interpreter":"r.low","endTime":1715301563730,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tRECORD_COUNT\tPARENT\tTREE_LEVEL\tLEFT_CHILD_ID\tRIGHT_CHILD_ID\n1\t1\t4500\tNA\t1\t2\t3\n2\t2\t2700\t1\t2\t4\t5\n3\t3\t1800\t1\t2\tNA\tNA\n4\t4\t2491\t2\t3\tNA\tNA\n5\t5\t209\t2\t3\tNA\tNA\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster histograms for clusters in the hierarchy with row counts","message":["%r","","z.show(head(clusterhists(EM.MOD2),10))"],"enabled":true,"result":{"startTime":1715301563808,"interpreter":"r.low","endTime":1715301563999,"results":[{"message":"cluster.id\tvariable\tbin.id\tlower.bound\tupper.bound\tlabel\tcount\n1\tCUST_GENDER:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_GENDER:F\t3\tNA\tNA\t:\t1510\n1\tCUST_GENDER:M\t2\tNA\tNA\t:\t2990\n1\tCUST_MARITAL_STATUS:'Other'\t1\tNA\tNA\t:\t0\n1\tCUST_MARITAL_STATUS:Divorc.\t4\tNA\tNA\t:\t615\n1\tCUST_MARITAL_STATUS:Mabsent\t7\tNA\tNA\t:\t75\n1\tCUST_MARITAL_STATUS:Mar-AF\t8\tNA\tNA\t:\t3\n1\tCUST_MARITAL_STATUS:Married\t2\tNA\tNA\t:\t2034\n1\tCUST_MARITAL_STATUS:NeverM\t3\tNA\tNA\t:\t1503\n1\tCUST_MARITAL_STATUS:Separ.\t6\tNA\tNA\t:\t134\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Apply model to assign rows to clusters","message":["%r","","EM.PRED <- predict(EM.MOD2, CUST_DF, supplemental.cols=c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\"))","class(EM.PRED)","","z.show(head(EM.PRED)) "],"enabled":true,"result":{"startTime":1715301564084,"interpreter":"r.low","endTime":1715301564461,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"YRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.3.\tX.4.\tX.5.\tCLUSTER_ID\n4\t1959\t4.00041843584483e-07\t2.86871551237022e-07\t0.999999313086605\t5\n2\t1983\t2.08246808532542e-10\t0.999999999791687\t6.66017104164384e-14\t4\n4\t1961\t0.999999927247647\t6.53106154009262e-08\t7.44173731283542e-09\t3\n6\t1944\t1.39252934931271e-05\t0.999986074706404\t1.02766131649819e-13\t4\n2\t1983\t9.50471234719974e-10\t0.99999999904944\t8.90591137473444e-14\t4\n3\t1978\t0.999999211550069\t7.87290929569211e-07\t1.15900106205538e-09\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compare Clusters","Compare the  distribution for years residence and year of birth across two clusters using the overloaded `hist` functions. "],"enabled":true,"result":{"startTime":1715301564542,"interpreter":"md.low","endTime":1715301564616,"results":[{"message":"<h2 id=\"compare-clusters\">Compare Clusters<\/h2>\n<p>Compare the  distribution for years residence and year of birth across two clusters using the overloaded <code>hist<\/code> functions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot histograms of select clusters and attributes","message":["%r","","dat.cluster3 <- EM.PRED[EM.PRED$CLUSTER_ID == 3,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","dat.cluster5 <- EM.PRED[EM.PRED$CLUSTER_ID == 5,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","","par(mfrow=c(2,2)) ","hist(dat.cluster3$YRS_RESIDENCE, main=\"Cluster 3 YRS_RESIDENCE\", col=\"darkred\", xlim=c(0,15), ylim=c(0,600))","hist(dat.cluster5$YRS_RESIDENCE, main=\"Cluster 5 YRS_RESIDENCE\", col=\"darkgreen\", xlim=c(0,15),ylim=c(0,600))","","hist(dat.cluster3$CUST_YEAR_OF_BIRTH, main=\"Cluster 3 CUST_YEAR_OF_BIRTH\", col=\"darkred\", xlim=c(1910,1990), ylim=c(0,250))","hist(dat.cluster5$CUST_YEAR_OF_BIRTH, main=\"Cluster 5 CUST_YEAR_OF_BIRTH\", col=\"darkgreen\", xlim=c(1910,1990), ylim=c(0,250))"],"enabled":true,"result":{"startTime":1715301564693,"interpreter":"r.low","endTime":1715301565891,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nnnnmmTNnznz99ddbtmz5888/r1+/7ubmFhAQ0KhRox49evTp08f45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KBu5DoAKmD6rtiRI0du2rTJDqEAgAOR6wCogOkjdhMmTHjvvfcu\nXLjQu3fvwMBA3UcA+Pv72zI2ALAfch0AFTBd2Ckvt3n33Xf1RvG6GwCqQa4DoAKmC7u5c+fa\nIQ4AcCxyHQAVMF3YTZw40Q5xAIBjkesAqIC5rxTbt2/f7Nmz33nnHfnjH3/8odVqbRYVADgG\nuQ5AhWa6sCsuLh4wYEDHjh0nTZqknKqYOXPm5s2bbRwbANgPuQ6ACpgu7GJjYw8dOrRhw4ab\nN28qjWPGjPn8889tGRgA2BW5DoAKmL7G7ttvv12xYkVoaKhuY0hISGpqqs2iAgB7I9cBUAHT\nR+wuXrzYvn17eVh5sJO3t3dBQYEN4wIA+yLXAVAB04Wdv7//8ePH9Rr/+OMP3nsDQE3IdQBU\nwHRhFxYWNn78+GvXrol/92JzcnJiYmLCw8NtHh0A2Au5DoAKmL7GLjY29tFHH23cuHG3bt0k\nSYqIiNi+fbubm9vGjRvtEB8A2Ae5DoAKmD5iFxQUtH///rCwsJ07d0qS9MMPP3Tt2vW3334L\nCAiwQ3wAYB/kOgAqYPqInRAiODj4u+++E0Lk5+d7eXnpvhsbAFSDXAegojOrsFN4e3vbKA5Y\npKSkJCkpqaioyKKpeGoDYCZyHYAKynRht2fPnrJGPf7441YNBuZKTk7u27evo6MAVIVcB0AF\nTBd2TzzxRFmjJEmyajAwV2FhoRCiixDBlkx1SIgMGwUEVHzkOgAqYLqw27RpkzIsSVJWVtbm\nzZurV68+fPhwWwZWfsZPU8qnI9XxVu9gIVpb0v8chR1QNnIdABUwXdgZPsPp9ddfnzp16tWr\nV20T0r1KSUkxeZry2LFj9gkGQEVBrgOgApbdPKEYO3Zs586dR44cad1orCI0NDQxMdHIXmxc\nXFzLli3tHBWAiohcB6BiKWdhd/v27aysLOuGYi2urq59+vQx0iEuLs7FxfQD/ACAXAegYrG4\nsLt9+/bRo0fffvvtkJAQWwQE1ZCvNi/HM1Y8PDx69erl6upq9ZAA85HrAFREpgs7Dw8P3Y/F\nxcVCCD8/P16zA+PkoxxxcXFxcXGWTrtp0yZe0Ak7I9cBUAHThd3rr7+u+7FmzZoPPvjgwIED\nq1WrZrOooAYlQgjLn8lyTohf/n2eC2BP5DoAKmC6sFuwYIEd4oBaWfpMFsBRyHUAVIDragEA\nAFTC9BG73bt3mzOjbt263WMoAOBA5DoAKmC6sAsNDTVnRrxyB0CFRq4DoAKmC7vk5OQvvvii\nuLi4X79+tWvXvnLlyoYNGzw8PEaPHm2H+ADAPsh1AFTAdGF39OjRNm3aTJ8+XWkZPXr0tGnT\nMjIyJkyYYMvYAMB+yHUAVMB0Ybdw4cLff/9drzEyMrJdu3YkOwCqQa4DoHMpG0MAACAASURB\nVAKm74q9evVqfn6+XuOtW7ec9sXYAFAO5DoAKmC6sGvTpk10dLT8EHZZUVHRxIkTH374YVsG\nBgB2Ra4DoAKmT8XOnj376aeffuCBB3r06CFfULx9+/bc3NydO3faIT4AsA9yHQAVMF3Yde3a\n9ddff50+ffq6deuKioo8PDxCQ0NnzJjRrl07O8QHAPZBrgOgAqYLOyFE+/btk5OTJUnKz8/3\n9vbWaDS2DgsA7I9cB6CiM/eVYvv27ZszZ86MGTPkTPfHH39otVpbBgYADkCuA1ChmS7siouL\nBwwY0LFjx0mTJs2dO1dunDlz5ubNm20cGwDYD7kOgAqYLuxiY2MPHTq0YcOGmzdvKo1jxoz5\n/PPPbRkYANgVuQ6ACpi+xu7bb79dsWKF3lsUQ0JCUlNTbRaVuHjxYmJi4pEjR7KzsyVJ8vf3\nDwkJ6devX2BgoO0WCqAyI9cBUAHThd3Fixfbt28vDyuXEnt7excUFNgiIEmSpk+fPmvWrDt3\n7uiNGj9+/OTJk99//31bLBdAJUeuA6ACpk/F+vv7Hz9+XK/xjz/+qF+/vi0CWrhw4Zw5c8aN\nG7d3797s7GytVqvVarOzs/fu3RsVFTVr1qxFixbZYrkAKjlyHQAVMF3YhYWFjR8//tq1a+Lf\nvdicnJyYmJjw8HBbBJSQkLBy5cp58+Z16tTJ19dXo9FoNBpfX99OnTrNmzdv5cqV8fHxtlgu\ngEqOXAdABcy6eeLvv/9u3LjxgAEDJEmKiIho1qzZhQsXpkyZYouAzpw506dPn7LG9unT58yZ\nM7ZYLoBKjlwHQAVMF3ZBQUH79+8PCwvbuXOnJEk//PBD165df/vtt4CAAFsEFBAQkJaWVtbY\ntLQ0Gy0XQCVHrgOgAma9eSI4OPi7774TQuTn53t5edn0aezDhg0bMmTInDlz+vbtW6NGDaU9\nLy8vMTExOjp6xIgRtls6gMqMXAegojNd2Lm5uSUkJLzyyitCCG9vb1sHFBsbe+zYsYiICBcX\nl8DAQF9fX0mScnNzL126pNVqw8PDuVMMgC2Q6wCogOnCrlatWoMGDbJDKDIvL6+kpKStW7eu\nX78+PT39+vXrQoigoKDevXsPGjTo6aeftlskACoVch0AFTBd2A0bNiwxMfHFF1+0QzSKnj17\n9uzZ055LBFDJkesAqIDpwu6dd96Jjo4+depU375969Wr5+bmpozy9/e3ZWwAYD/kOgAqYLqw\nq1evnjwwc+ZMvVGSJFk/IiEEr9kBYHfkOgAqYLqwmzt3rh3iUPCaHQAOQa4DoAKmC7uJEyfa\nIQ6F8pqdgQMHNm/e3MfHRwiRm5ubkZGxfv36WbNm1apVa9y4cfYMCUBlQK4DoAJmPcfOnuTX\n7AwZMkS3UX7NTqdOnTp06DB16lTjya6kpCQpKamoqKjUsampqUIIrVZrxZgBwFLkOgC2UGZh\n165du0OHDikfd+7c2b17dzsEZPI1Oy+88ILxOaSkpPTt29d4n2PHjpUnOACqQ64DoCZlFnZ6\n77rp0aOH7S4f1iW/Zqdz585lRWXyNTuhoaGJiYlG9mLj4uJatmx5r4ECUAVyHQA1cbpTsff+\nmh1XV1cj+8FCiLi4OBcX0y/JBQDbIdcBsAWnK+x4zQ6AyoBcB8AWnK6w4zU7ACoDch0AWzBW\n2K1Zs8bIRyHEc889Z/2IhBC8ZgeAHZHrAKiGscJu2LBhRj4KWyY7ALAbch0A1SizsFu3bp09\n4zB0584d3Xc1KjIzM+vXr2//eACoErkOgJqUWdgNHjzYnnHo+uqrr6ZNm5aVldWgQYOYmJjR\no0frjg0KCrLPwwgAVAbkOgBq4nR3wu/cuXPUqFF+fn79+/e/cePG66+/PmjQoNu3bzs6LgCw\nJnIdAFtwusJu1qxZkZGRf/75548//nj27NlXXnll/fr1/fv3L+shnABQEZHrANiC0xV26enp\nkydP1mg0Qojq1at/+eWXcXFxycnJ5DsAakKuA2ALTlfYFRYWent767a8+eabX3zxxfbt2/v2\n7Uu+A6AO5DoAtuB0hV2zZs02bNig1/jaa68tX778p59+MvnGawCoEMh1AGzB6Qq7QYMGTZo0\n6Z9//tFrf/HFF7/77rvdu3c7IigAsDJyHQBbcLrC7qWXXurcufPy5csNRz377LPr16/v2rWr\n/aMCAOsi1wGwBad7V2zt2rW///77ssaGh4eHh4fbMx4AsAVyHQBbcLrCDpWc/DzW1NTUckzr\n4eHRq1cvV1dX64YEAEBFQWEH55IlhBAiLi4uLi6uHJNv2rSJ4xwAgEqLwg7OpUQIIUQXIYIt\nnPCcEL8IUVhYaP2YAACoICjs4IyChWjt6BgAAKhwnO6uWAAAAJQPhR0AAIBKUNgBAACoBIUd\nAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAA\ngEpQ2AEAAKgEhR0AAIBKUNgBAACoRBVHB2B9JSUlSUlJRUVFpY5NTU0VQmi1WvsGBQBWRq4D\nYEiFhV1KSkrfvn2N9zl27Jh9ggEAGyHXATCkwsIuNDQ0MTHRyF5sXFxcy5Yt7RwVAFgXuQ6A\nIRUWdq6urn369DHSIS4uzsWFiwsBVGzkOgCG+M0DAACoBIUdAACASlDYAQAAqASFHQAAgEpQ\n2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASqjwAcWonCQhxL/vx7SIh4dHr169XF1drR4SAAB2\nRmEHlcgSQggRFxcXFxdn6bSbNm0KDw+3ekgAANgZhR1UokQIIUQXIYItmeqcEL8IUVhYaJOY\nANybkpKSpKSkst6HawRH4lFpUdg5WPnSVjlOOFYSwUK0dnQMAKwlOTm5b9++5Zs2Jiambdu2\nlk5FRYiKjsLOwe4lbQGAuv3vaHqIEAGWTHZKiLNi9uzZ5Vso12agQqOwczA5bVl6AvGQEBk2\nCggAnE2AEA0s6f+PEMLyclCeMJ1rM1CxUdg5BUtPIJ6jsAMA4ywtBwFV4Dl2AAAAKkFhBwAA\noBIUdgAAACrBNXYAAAgh/vcGG15ggwqNwg4AACGEENeF4AU2qOAo7FCp8YZZAP9HK4Sw/Dkp\nPCQFzoTCDpUab5gFoI/npKAio7BDpcYbZgH74PWJgH1Q2AG8YRawOV6fCNgHhR0AwObK+dbX\nv4Q4b5uAAJWisAMA2Ev53voKwGwUdoDFuJcWAOCcKOyso3zXBQsuDa6YuJcWAOCcKOysg+uC\nKxXupQUAOCcnLewuXryYmJh45MiR7OxsSZL8/f1DQkL69esXGBjo6NBKJ/9vben/9EKIQ0Jk\n2CIg2B730uLeVbhcB8DJOV1hJ0nS9OnTZ82adefOHb1R48ePnzx58vvvv++IuMxSjv/pz1HY\nVRrlvjJPcHGeGlXQXMdlJ6Uo1xtmtVrt0aNHW7Vq5eLiYp8JSSOVhNMVdgsXLpwzZ864ceMG\nDhzYvHlzHx8fIURubm5GRsb69etnzZpVq1atcePG2S4AnqIJG7mXK/OEEDExMW3btjW/P9nf\nyVXQXJeWljZ79mwbhVRR3cMbZu2Ma3wrA6cr7BISElauXDlkyBDdRl9f306dOnXq1KlDhw5T\np041nuyMZyu5AtNqtWVNfi9Xy52zfJKr5ZrQnlPZeXEqDvKyEEKIYCFqWj7hP0LY839Tsr8d\nVOhcJwKE8LZwkmwhblj++JIbQgh7TVXuCXOEEJavE3mFlHtNWjphvhD/cI1v5SA5GTc3t8LC\nwrLGFhYWuru7G5/Djh07TH7rmTNnljX5pk2b7LDaAWe2adOm8v+GYR5yHeyPn3Zl4HRH7AIC\nAtLS0jp37lzq2LS0tIAAE48tDw0NTUxMLGsvVqvVnjhx4p133ilr8rCwMCOTl8XOV0vYcyqC\ndOxU9l+ch4dHWFiYRZOgHMh1zjaVuoMU/LQrDY0kSY6O4f8THR397bffzpkzp2/fvjVq1FDa\n8/LyEhMTo6OjR4wYwRUeACo6ch0AW3C6wq6goGDw4MHJyckuLi6BgYG+vr6SJOXm5l66dEmr\n1YaHh69du9bT09PRYQLAPSHXAbAFpyvsZFu3bl2/fn16evr169eFEH5+fq1btx40aNDTTz/t\n6NAAwGrIdQCsy0kLOwAAAFjKsksvAQAA4LQo7AAAAFTC6R534nC3b9+eNWtW06ZNLb2TXMW0\nWm16enpISAjrRMYKMSQ/XGPSpEnu7u6OjgVmIdcZ4qethxViyPlzHYWdvjlz5kyfPt3RUQAV\nkouLy3vvvefoKGAWch1Qbs6c6yjs9DVu3FgIMWHChI4dOzo6FmeRmpoaFxfHOlGwQgzJ60T+\n+aBCINcZ4qethxViyPlzHYWdPvmAc8eOHfXe4VjJxcXFsU50sUIMxcXFcb6mAiHXlYqfth5W\niCEnz3XOGxkAAAAsQmEHAACgEhR2AAAAKkFhBwAAoBIUdgAAACpBYQcAAKASFHYAAAAqQWGn\nz9PTU/kXMtaJHlaIIdZJhcOfzBDrRA8rxJDzrxONJEmOjsG5lJSU7Nq166mnnnJ1dXV0LM6C\ndaKHFWKIdVLh8CczxDrRwwox5PzrhMIOAABAJTgVCwAAoBIUdgAAACpBYQcAAKASFHYAAAAq\nQWEHAACgEhR2AAAAKkFhBwAAoBIUdgAAACpBYQcAAKASFHYAAAAqQWEHAACgEhR2AAAAKkFh\nBwAAoBIUdv+nsLAwJiYmKCioatWqzZo1W7RokaMjcrCjR49qDCxevNjRcdnVwYMHJ06cGBwc\nrNFo3nvvPcMOlW2zMb5C2GYqhMq20ZrEdivIdQYqcK6T8K/w8PA6ders2bOnsLBw7dq1VatW\nnT59uqODcqT09HQhxPLlyx0diMOcPXv20UcfnTt37rZt24QQU6ZMMexTqTYbkyuEbaZCqFQb\nrTnYbsl1eip0rqviuJLSuWzfvn3z5s0rV67s3LmzEGLIkCH79+//+OOPR48eHRgY6Ojo4BjB\nwcEHDhwQQhw/frzUDpVtszG5QuD8KttGC3OQ6/RU6FzHqdj/+eGHH1xcXPr27au0DBw48Pbt\n2xs3bnRgVHBybDaocNhoUQ5sNhUIhd3/HDlyJDAwsEaNGkpL8+bNhRDy4dbKbOLEiVWrVq1R\no8YTTzzx/fffOzoc58JmUyq2GWfGRlsWtlsj2GxK5ZzbDIXd/1y/ft3Hx0e3Rf547do1B0Xk\neK6urmPGjNm+fXt2dvaBAwcaNGgwZMiQ2bNnOzouJ8Jmo4dtxvmx0RpiuzWJzUaPM28zFHb/\nR6PRmNlYSTRv3jw+Pv6RRx7x9vZu1qzZihUrnnzyyWnTpl2/ft3RoTkRNhtdbDMVAhutHrZb\nc7DZ6HLmbYbC7n/8/PxycnJ0W3Jzc4UQtWrVclBEzqhfv363b98+ePCgowNxFmw2JrHNOBs2\nWnOw3ephszHJebYZCrv/CQkJuXTpUl5entKSkZEhhGjdurXjgoKzY7NBhcNGi3Jgs6lAKOz+\nZ+DAgVqtNjExUWlZv369m5ub7k1ASExMdHd3b9eunaMDcRZsNiaxzTgbNlpzsN3qYbMxyYm2\nGUc/SM+JhIWFBQYG6j59cerUqY4OypGioqIWLVp0/PjxgoKC48ePjxw5Ugjx4YcfOjouB5D3\nTUt9aGfl3GzKWiFsMxVC5dxojWC7VZDr9FTEXEdh93/y8/MnTpxYr149Nze3Jk2axMXFabVa\nRwflSJmZmTExMS1atKhataqPj8+TTz75ww8/ODooe6tXr57h7lBKSorSobJtNsZXCNtMhVDZ\nNlqT2G4lcp2BipvrNJIklf9wHwAAAJwG19gBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEA\nAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACo\nBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASF\nHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYWc3WrVs1Gs3u3bvtsKylS5dqNJqz\nZ8/aYVlWd+3atW7dujk6CgDlRK4zE7kODkFh5xiLFy/WaDSZmZmODsRqkdy5c2f16tVPP/10\nQEBAzZo1H3rooblz5xYWFpo5+cCBA729vU+ePKnX3rNnz5o1a549e/b777/X/MvV1TUgIKBf\nv35paWl6/Q8ePDho0KDg4GBPT88mTZpERET8+uuv5gRgzvx1++h6//33zQxAnsOaNWsMZ+ju\n7n7fffd16tRp8uTJp06dsmi5cgcXFxe9gCMjIzUaza1bt3Qbz5w5M27cuGbNmnl7e/v4+LRu\n3fqNN944fPiw+d8RMJ/6ct3Ro0cNfyCLFy82c3JyHbnO1ijsYB1bt25NSUmZMmXKyZMnT58+\nHRUVNWXKlMGDB+v2OXLkyIABA1q0aPHzzz8/+OCDb775ZlZWljxqyZIl1atXf+GFF+7evav0\nj4+P37Zt26JFix544AG5ZfXq1ZIkFRUVbdmy5dSpU126dNHNj1u2bOnYsWNJScnmzZuzs7PX\nr19/9+7dLl26XL582cxvYXz+un10KYmgfAHIMywsLDx27Ni77767b9++Fi1afP755+YvVyZJ\nUnR0tPEvmJiYGBIScvTo0YSEhMuXL2dmZi5YsODEiROdO3e2aFlAJbd8+XLdH0hkZKTuWHKd\nkYWS62yNwg7W0adPnyVLlnTt2tXHx8fPz++VV16JiIhISkpS9o9PnTrVuXPnWrVqpaSkdOnS\nJSkpqUGDBnFxcfJYf3//ZcuWHTx4cObMmXLLyZMno6OjBw0a9OKLL+oty83N7dFHH50/f35B\nQcGyZcuU9tmzZ3t7e69duzYkJMTT07NVq1arV6+Oi4tzcbFsOy9r/ibdSwDyrnN4ePhPP/00\nYMCAN9544+eff7Yo7LCwsJSUlKSkpLI6nDx58rnnnuvateuOHTtCQ0OrV69erVq1J598cufO\nnSazJAAzkeuMI9fZGoVd+e3Zs6dTp06enp7333+/8qNVbN68WTm06+3t/eijj37zzTfyqIkT\nJ0ZFRQkhgoKC5A47d+4sdREXLlx49dVX77//fg8PjxYtWsybN+/27duG3d58800PDw/Dpe/Z\ns0f+eO3atTFjxijHzN96663r168bjyQzM3PUqFF169Z1d3dv0KDB1KlTlUXPmzdPo9FcvHhx\n4sSJderUcXNzKzV4Nzc3jUbj6ekpf1yzZk1RUVF8fHzt2rU1Gk3Tpk3Hjx8/b948pX/v3r1H\njx790Ucf7d+//+7duxERETVr1vziiy/KWP2icePGcpxKS3Z2tr+/v7u7u97KCQgIKGsmRhjO\n3ySrBKDRaOQ1PGfOHPOnEkK89tprTZs2jYmJKSkpKbXDnDlzCgsL58+f7+rqqrfE6dOnW7Qs\nVCrkOuO5Tg+5zkzkOhuhsCunQ4cOde/evU6dOseOHTt06FBOTo7ezzI8PFw+rqvVak+dOjV4\n8OCXX375xx9/FELMmzfv008/FUJcuHBB7tO9e3fDRZw5c6Zt27ZpaWnffffd1atX169f/88/\n/1i6ZyOLiIjYvXv3hg0bcnJytm7d+sADDyxfvtxIJOfOnWvXrl1GRkZiYmJOTs6yZcu+/vrr\n559/Xnee0dHRrVq1ysjI+Oyzz/QWd+PGjdWrV69YsSI6OtrPz09uzM/PLykpyc3NNRLnJ598\n0qBBg4iIiPfee+/AgQPLly9XJjd04sQJIcT999+vtDz++OOnT5/+8ssvJUmyaP2YOX+TrBVA\n/fr15ZM4Fk1VpUqVjz/++OjRo19//XWpHbZu3dqwYcOmTZveS2yobMh1pea6iRMnVq1atUaN\nGk888cT333+v259cZz5ynU1IKJewsLDatWsXFhYqLT179hRCpKSklDVJ9+7de/bsKQ/rpZhS\nDR48uFq1aleuXDEc9eWXXwohzpw5I38cP3581apVdTts2rRJCPHrr7/KHz08PCZPnlzqUkqN\nZNiwYT4+PlevXlVaNmzYIIRITU2VJGnu3LlCiKlTpxrObfz48fJ2pdFo3nzzzbt37yqjUlNT\nXVxcWrRoMXfuXPnijFLj2bdvn7yPFRkZqdu+bt068e/1ELdv396/f3+LFi18fX1PnTql9MnO\nzu7Ro4cQ4r777uvfv/+MGTMOHDhQ6lIMmTN/uY8h5Y9uMgDdpRh+1CVvTjdu3DBnuXKHTZs2\nSZLUuXPnunXr5ufnS5I0duxYIcTNmzclSbp7965Go+nWrZs568HIslDZkOv0ct1///vfMWPG\npKWl3bp1KyMjIyIiQggxa9YspQO5TiLXORRH7MpDkqSUlJSnn35a96RA//79dfuUlJTMmzfv\n4Ycf9vb2Vo78//333+YvJTk5uXv37uU7tK7noYceWrJkSXx8/Pnz583pv2nTpm7duvn7+yst\nTz31lBBCd7+qb9++hhMuWLBAkqQrV64sW7Zs6dKl/fr1k/7dn3vsscc2btzo5uYWHR2dmprq\n5+f3wgsvnD59Wm8OHTp06NOnjxDigw8+MJz/sGHD5JuqOnTokJ2dvWfPnoYNGypjfX19t2/f\nnpGRERsbGxgYuHLlyvbt2/fq1UvvVikjjM9fZpiblCca3HsACnm9aTQac5ara+7cuRcvXpw/\nf36pMzSTmcuC6pHrhEGua968eXx8/COPPOLt7d2sWbMVK1Y8+eST06ZNk8/5CnKdhch1Vkdh\nVx75+flFRUW1a9fWbdT7GBMT8+6770ZFRZ0+fVo+cNW/f/87d+6Yv4j8/Px69eqVL0K9LXvd\nunXPPPPMpEmTgoODGzZs+Pbbbys5qNRF37p1a+PGjVWqVHF1dXV1dXVxcalevboQQncqI7EF\nBASMHDkyNjZ2y5YtW7duVdrDw8P//PPPjIyMxo0bDx069IcffnjiiSeys7P1Jq9ataryrx75\nR1hQUJCYmFhUVCQfFNTr06xZszFjxsTHx588eXL27NnJycnmX1RhzvxNupcAFJmZmdWrV5dX\nu0U6duw4aNCgOXPmXL16Vbe9SpUqdevWNfN/O0BGrhNGc52sX79+t2/fPnjwoNJCrjMfuc7q\nKOzKw9vb28PD48qVK7qNeh9XrFghX2tSu3Zt+Xj7mTNnzF+El5eXl5eXcoe8cTVr1iwuLta9\n1lhvwqCgoG+//TY7OzstLW3kyJEJCQmDBg0ysmhPT8/hw4ffvXu3pKSkpKREq9XKuzK6l7ia\nvI64devWQgjD/VR/f/+6det+/vnn33zzzcWLF1NSUsz5jro8PT379OkTHx+/Y8cOw0u5dUVH\nR3t6epr5eKdyzN+k8gWQmZmZkZHRtWvX8i30448/Lioqio2N1Wvv2bPn6dOnDR9qAJSFXCfM\nyHVlIdeZRK6zBQq78pDP3+/YsaO4uFhp3Lhxo1433f2wI0eOHDlyRPno7e0thNCd3HARvXv3\n3rVr1z///GMyngcffFAIcezYMaVl8+bNht2qVKnyyCOPTJ069fnnn9+zZ4+8p2sYiUajCQ8P\n37FjR05OjslFG3HgwAEhRPPmzY2Hrdw2a6lhw4Z16NDhww8/VPaDJ0+erHci4Pr160VFRT4+\nPlaZv0lWCUCSpIkTJwohYmJizJ9KV+PGjUePHv3FF1/89ddfuu1y5p04caJWq9Vbogoe3QRb\nINeZIzEx0d3dvV27dsbDJtfpIdfZCIVdOcXGxmZnZ7/wwgtnz569evVqbGys3uH0vn37rl27\ndtu2bfn5+b/++utrr72mu1PSqlUrIcSWLVt0dz3//PNP+Z4D+ePs2bM9PDzCwsL27Nlz69at\nEydOvPPOOzt27DAMZuDAgX5+fhMnTrxw4cKVK1emTp2qe1j7+vXr3bt337hxY1ZWVnFx8f79\n+3fu3Nm1a1f5moZSI5kzZ46Li0vv3r1//fXXmzdvXrlyZefOnYMGDfrjjz/KWiGRkZHyAfmi\noqLMzMyFCxfOmDEjPDw8NDRU7jB16tQpU6b8/vvveXl5kiT9/vvv48aNa9KkSbn31YQQH3zw\nwY0bN2bPni1/3LRpU6tWrb7++utr167l5+fv27dv4MCBLi4ub731llXmb9K9BKDVaq9evbp5\n8+annnpqw4YNn3/++eOPP16+sIUQ06ZN8/Ly2r59u25j06ZNV69evWvXrmeeeWb37t23bt26\ndevWTz/91L17d91nMQC6yHV6xo0b9+mnn544caKwsPDEiRMvv/zyrl27pk+frlyoR64zjlxn\nc6XdUQGz7N69u0OHDlWrVq1fv/7cuXOTk5OFzg01eXl5o0ePrl27tre395NPPnnkyJGhQ4cG\nBwcrk0+bNq1u3bry4xx37NghSZKcSsaPH6/0OXfu3MiRIwMDA6tWrdqyZct58+YVFxdLBneK\nSZL066+/tmvXTn4O05dffql3p9hPP/00cODAevXqeXp6NmrUKDo6Oicnx0gkkiRdunRp7Nix\nDzzwgJubW926dXv27Pnjjz/Kt3fJd4rp3kcmSdKFCxdiYmJatmzp4eFRvXr1Dh06LFy48Pbt\n20qHa9euzZ49u0OHDjVr1nRxcWnSpMnYsWMvXrxouGKHDh0qhNC9C08q+6aq0NBQT0/PrKws\nSZLOnDkzY8aMDh06+Pn5yZdZDBw4cO/evUb+iBbNv6y7qIYOHSp3NhlAqXeKydzc3Pz8/B57\n7LFJkyb9/fffhrEZWa7unWKKDz/8UO4m3ymmOHXqVGRkZJMmTTw8PGrUqBESEjJmzJjDhw+b\nuSxUQuQ63bWRmZkZExPTokWLqlWr+vj4PPnkkz/88INuB3Kd4VLIdfakkazxFBzAfJcvX37u\nuefs8wZxAHAUch0cglOxsDdLX3oDABURuQ4OwWYHe9N9XhEAqBW5Dg5BYQd7u+++++x/buLo\n0aOaMgwePNjOwQCoDMh1cAiusQMAAFAJjtgBAACoBIUdAACASlDYOgvZggAAIABJREFUAQAA\nqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgE\nhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUd\nAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAA\ngEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBK\nUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDY\nAQAAqASFHQAAgEpQ2AEAAKgEhR0AAIBKUNgBAACoBIUdAACASlDYAQAAqASFHQAAgEpUmMKu\noKAgPj4+PDy8fv36np6eNWrUaNSoUc+ePT/55JOLFy/KfTZs2KD519GjRx0bsC2kp6dPmTLl\nscceCwwMdHNz8/HxadOmzYQJE06fPm3+TMxZk8ePH1fW5Ouvv647+apVq5RRn3/+ue6oPXv2\nDB069P77769ataqXl1edOnVatWrVv3//SZMmyR3q1KmjMYPSv1RFRUVNmjSRe7q4uKSmpuqO\nlSSpS5cuyqxWr16t+10MValSpdSlDBgwQLdbRkaGXgfD2bq4uFSrVq158+avvvrqkSNHzPhT\n6Lt48eLUqVMfe+wxf39/Nzc3f3//Dh06vPfee8rfxcjSzflShsr6Fk2aNBk+fPiuXbvK6qxs\nFeavB4v++ka2QCFEo0aN5FF16tSxaA1XCOQ6IcQDDzxQ1hYya9YsM2dCrtNFriv1W6gz10kV\nwbZt22rXrl3WV0hISJC7/fjjj0pjenq6jYJRFjF9+nQbLaJUhYWFZa0BLy+v3bt3mzMTM9ek\n7m979OjRunNYuXKlYX9JkuLi4jQaTamzdXV1lfsYWbSumJgY498iJSVFWVZISMidO3eUUcuW\nLVPmExYWpvddjMSmKzs7293dXbfbu+++q9fH+Gzd3NySkpLM+Ysovvjii6pVq5Y6t6pVq37x\nxRfmL73UL1Uq4/MRQnzyySeldla2CvPXg0V/fSNboCRJDz74oDyqdu3aFq1k50eukwUHB5e1\nEj7++GNz5kCuKys2XeQ6hZpyXQU4Yrdly5awsLArV64IIerWrbtixYqrV6/m5+cfP358w4YN\nL730kqenp6NjtAeNRtO+ffuvvvoqIyOjoKDg/PnzUVFR8qiCgoKJEyeanIPt1uSpU6eio6Ml\nSRJCREVFnTt3rqio6MSJEx9++KGfn5/S7fLly8qWl5OTo7T37t1bd6M0uUferVu3UaNGycPp\n6enz58+Xh69du/bOO+/Iw97e3gkJCXoTGv5s7t69azj/tWvX3r59W7fl22+/lXT+nyt1tjdv\n3ly5cqWche/cuTN+/Hjj30LXkiVLRo8eXVxcLITo2bPnkSNHioqKjhw5EhYWJoQoLi4ePXr0\nkiVLjCzd5JcySZ5PSUnJvn37atWqJTdOmTLl1q1bFs2hrPVgrb++ipHrDJ05c0Zv8zZ+lEtG\nriPXGaH+XCc5t+zsbB8fHzlUHx+f06dPG+ms+r1YQ/fff78cTLVq1Yz3tGhNWroXq6QbX19f\nrVar2z83N3fEiBGGizCyuZsjJycnMDBQntzLy0vO/i+++KIyzwULFpj8LmV5/PHH5f66u5W/\n/PKLbp+yZhsaGqq0X7161ZzFZWZmKgtq06ZNcXGxMqq4uLhNmzZKMFlZWeX+UqUqaz5jxoxR\n2g8cOGCkc/nWg8m/fiU8Ykeu06UcsTMs7Ewi15mfFsh1qsx1zn7ELiEhITc3Vx5+9913GzRo\nYOkcFixYoJzAvnbtmtLu4+MjN7755ptK44YNG3r16lWnTh13d3d/f/927dq98cYbu3bt0mq1\nQoju3bvrHoGPjY1V5pyZmam0p6amRkRENGjQwMPDo1q1aq1bt548efI///yjG5XeJTJr1659\n9NFHvby82rVrZ9G3k/5NvkbOXMjufU0acfnyZWVYbxeqZs2a33zzjRWXJfPx8fn000/l4YKC\ngsjIyJ9//llZUPv27ZXDmZY6e/bs3r175eE333xT2bNftWqVOZPXq1dPGS4pKTFnkvj4eHn/\nVQgxadIk3TMj7u7uyvGJ4uLi+Ph4c2ZoXR4eHuWYqhzroZIj11kLuc5M5Do9qsl1zl7YJSUl\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gAmRK4DYA42V9hNnTpV/pyQkBAVFdWr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\n69fvl19+sUpfhCm6c/z48eHDhzdu3NjFxaVZs2aTJ0/OzMy0Vnd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Using SQL access prediction details","One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.","","We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well."],"enabled":true,"result":{"startTime":1715301565977,"interpreter":"md.low","endTime":1715301566037,"results":[{"message":"<h2 id=\"using-sql-access-prediction-details\">Using SQL access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.\nThen, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table=\"CUST_DF_EM\")","ore.create(CUST_DF, table='CUST_DF_EM')"],"enabled":true,"result":{"startTime":1715301566113,"interpreter":"r.low","endTime":1715301566840,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top 3 predictive attributes for specific customers with corresponding cluster assignments","message":["%sql\r","\r","SELECT CUST_ID,\r","       CLUSTER_ID,\r","       ROUND(PROB*100,0) PROB_PCT,\r","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,\r","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,\r","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE\r","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, \r","             CLUSTER_DETAILS(EM_CLUSTERING_MODEL USING T.*) DETAIL\r","      FROM (SELECT V.*, CLUSTER_SET(EM_CLUSTERING_MODEL, NULL, 0.2 USING *) PSET\r","            FROM CUST_DF_EM V\r","            WHERE cust_id = ${CUST_ID ='100676','100676'|'101219'|'102805'}) T,\r","                  TABLE(T.PSET) S\r","            ORDER BY 2 DESC) OUT,\r","            XMLTABLE('/Details'\r","                      PASSING OUT.DETAIL\r","                      COLUMNS \r","                      \"Attribute1\" XMLType PATH 'Attribute[1]',\r","                      \"Attribute2\" XMLType PATH 'Attribute[2]',\r","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1715301566920,"interpreter":"sql.low","endTime":1715301567147,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n102805\t5\t100\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".655\" \t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".416\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Married\" weight=\".413\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'102805'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'100676'\",\"argument\":null,\"options\":[{\"value\":\"'100676'\",\"displayName\":null},{\"value\":\"'101219'\",\"displayName\":null},{\"value\":\"'102805'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 3 top predictive attributes for customers in batch with corresponding cluster assignments","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,0) PROB_PCT,","       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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Clustering KM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713818290615,"interpreter":"md.medium","endTime":1713818290678,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identifying Customer Segments using K-Means Clustering","Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning K-Means (KM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the K-Means in-database algortihm.","","---","","Copyright (c) 2024 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1713818290755,"interpreter":"md.medium","endTime":1713818290815,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-k-means-clustering\">Identifying Customer Segments using K-Means Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning K-Means (KM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the K-Means in-database algortihm.<\/p>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny 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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a>"],"enabled":true,"result":{"startTime":1713818291034,"interpreter":"md.medium","endTime":1713818291094,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1713818291179,"interpreter":"r.medium","endTime":1713818291301,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data set for model building from customer demographic data in proxy object CUST_DF","message":["%r","","ore.sync(query = c(\"SUPPLEMENTARY_DEMOGRAPHICS\" = \"select CUST_ID, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))","ore.sync(query = c(\"CUSTOMERS\" = \"select CUST_ID, CUST_GENDER, CUST_YEAR_OF_BIRTH, CUST_MARITAL_STATUS from SH.CUSTOMERS\"))","","CUST_DF <- merge(SUPPLEMENTARY_DEMOGRAPHICS,CUSTOMERS, by=\"CUST_ID\")"],"enabled":true,"result":{"startTime":1713818291378,"interpreter":"r.medium","endTime":1713818291950,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"groupByColumns\":[\"CUST_MARITAL_STATUS\"],\"showSeriesX\":[\"CUST_YEAR_OF_BIRTH\"],\"showSeriesY\":[\"YRS_RESIDENCE\"],\"label\":[\"CUST_GENDER\"],\"extraDescription\":[],\"lastColumns\":[\"CUST_ID\",\"EDUCATION\",\"OCCUPATION\",\"HOUSEHOLD_SIZE\",\"YRS_RESIDENCE\",\"CUST_GENDER\",\"CUST_YEAR_OF_BIRTH\",\"CUST_MARITAL_STATUS\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"table","title":"Display first 5 rows of CUST_DF 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id=\"build-a-k-means-model-using-the-cust_df-proxy-object\">Build a k-Means model using the CUST_DF proxy object<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an k-Means clustering model with 3 clusters and default settings","message":["%r","","KM.MOD1 <- ore.odmKMeans(~.-CUST_ID, CUST_DF, num.centers = 3)","","KM.MOD1"],"enabled":true,"result":{"startTime":1713818292738,"interpreter":"r.medium","endTime":1713818296338,"results":[{"message":"\nCall:\nore.odmKMeans(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nblock.growth                                       2\nconv.tolerance                                  0.01\ndetails                                  details.all\ndistance                                   euclidean\niterations                                         3\nmin.pct.attr.support                             0.1\nnum.bins                                          10\nrandom.seed                                        0\nsplit.criterion                             variance\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display k-Means model details","message":["%r","","summary(KM.MOD1)"],"enabled":true,"result":{"startTime":1713818296414,"interpreter":"r.medium","endTime":1713818296535,"results":[{"message":"\nCall:\nore.odmKMeans(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nblock.growth                                       2\nconv.tolerance                                  0.01\ndetails                                  details.all\ndistance                                   euclidean\niterations                                         3\nmin.pct.attr.support                             0.1\nnum.bins                                          10\nrandom.seed                                        0\nsplit.criterion                             variance\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  CUST_YEAR_OF_BIRTH YRS_RESIDENCE CUST_GENDER CUST_MARITAL_STATUS EDUCATION\n2           1978.564      2.232196           F              NeverM   < Bach.\n4           1948.602      6.206897           M             Married   HS-grad\n5           1964.999      3.893485           M             Married   HS-grad\n  HOUSEHOLD_SIZE OCCUPATION\n2              1      Other\n4              3      Exec.\n5              3     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Examples of possible setting overrides for KMeans clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","-- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C');\"> KMeans clustering<\/a>","","-- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms<\/a>","","-- Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a>","","-- Specify a row weight column ","    v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '<row_weight_column_name>';","   ","-- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';","    ","--  Specify the maximum number of leaf clusters generated by a clustering algorithm. ","  The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. ","  For k-Means and O-Cluster, the default is 10. ","     v_setlst('CLUS_NUM_CLUSTERS') = '5';","","-- Minimum Convergence Tolerance for k-Means. The algorithm iterates until the minimum Convergence Tolerance is satisfied or until the maximum number of iterations, specified in KMNS_ITERATIONS, is reached. Decreasing the Convergence Tolerance produces a more accurate solution but may result in longer run times. The default Convergence Tolerance is 0.001.","    v_setlst('KMNS_CONV_TOLERANCE') = 'TO_CHAR(0<numeric_expr<1)';","    ","-- Distance function for k-Means. The default distance function is KMNS_EUCLIDEAN.","    v_setlist('KMNS_DISTANCE') = 'KMNS_EUCLIDEAN'","    ","-- Maximum number of iterations for k-Means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in KMNS_CONV_TOLERANCE, is satisfied. The default number of iterations is 20.","    v_setlist('KMNS_ITERATIONS') = 'TO_CHAR(positive_numeric_expr)';","    ","-- Minimum percentage of attribute values that must be non-null in order for the attribute to be included in the rule description for the cluster. If the data is sparse or includes many missing values, a minimum support that is too high can cause very short rules or even empty rules. The default minimum support is 0.1.","    v_setlist('KMNS_MIN_PCT_ATTR_SUPPORT') = 'TO_CHAR(0<=numeric_expr<=1)';","    ","-- Number of bins in the attribute histogram produced by k-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. The default number of histogram bins is 11.","    v_setlist('KMNS_NUM_BINS') = 'TO_CHAR(numeric_expr>0)';","    ","-- Split criterion for k-means. The split criterion controls the initialization of new k-Means clusters. The algorithm builds a binary tree and adds one new cluster at a time.","When the split criterion is based on size, the new cluster is placed in the area where the largest current cluster is located. When the split criterion is based on the variance, the new cluster is placed in the area of the most spread-out cluster. The default split criterion is the KMNS_VARIANCE.","    v_setlist('KMNS_SPLIT_CRITERION') = 'KMNS_VARIANCE'","    ","-- This setting controls the seed of the random generator used during the k-Means initialization. It must be a non-negative integer value. The default is 0.","    v_setlist('KMNS_RANDOM_SEED') = 'Non-negative integer'","    ","-- This setting determines the level of cluster detail that are computed during the build. KMNS_DETAILS_NONE: No cluster details are computed. Only the scoring information is persisted.","KMNS_DETAILS_HIERARCHY: Cluster hierarchy and cluster record counts are computed. This is the default value. KMNS_DETAILS_ALL: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.","    v_setlist('KMNS_DETAILS') = 'KMNS_DETAILS_HIERARCHY'"],"enabled":true,"result":{"startTime":1713818296961,"interpreter":"md.medium","endTime":1713818297029,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-kmeans-clustering\">Examples of possible setting overrides for KMeans clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<p>-- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C');\"> KMeans clustering<\/a><\/p>\n<p>-- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All Algorithms<\/a><\/p>\n<p>-- Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a><\/p>\n<p>-- Specify a row weight column\nv_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') = '&lt;row_weight_column_name&gt;';<\/p>\n<p>-- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.\nv_setlst('ODMS_MISSING_VALUE_TREATMENT') = 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<p>--  Specify the maximum number of leaf clusters generated by a clustering algorithm.\nThe algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points.\nFor k-Means and O-Cluster, the default is 10.\nv_setlst('CLUS_NUM_CLUSTERS') = '5';<\/p>\n<p>-- Minimum Convergence Tolerance for k-Means. The algorithm iterates until the minimum Convergence Tolerance is satisfied or until the maximum number of iterations, specified in KMNS_ITERATIONS, is reached. Decreasing the Convergence Tolerance produces a more accurate solution but may result in longer run times. The default Convergence Tolerance is 0.001.\nv_setlst('KMNS_CONV_TOLERANCE') = 'TO_CHAR(0&lt;numeric_expr&lt;1)';<\/p>\n<p>-- Distance function for k-Means. The default distance function is KMNS_EUCLIDEAN.\nv_setlist('KMNS_DISTANCE') = 'KMNS_EUCLIDEAN'<\/p>\n<p>-- Maximum number of iterations for k-Means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in KMNS_CONV_TOLERANCE, is satisfied. The default number of iterations is 20.\nv_setlist('KMNS_ITERATIONS') = 'TO_CHAR(positive_numeric_expr)';<\/p>\n<p>-- Minimum percentage of attribute values that must be non-null in order for the attribute to be included in the rule description for the cluster. If the data is sparse or includes many missing values, a minimum support that is too high can cause very short rules or even empty rules. The default minimum support is 0.1.\nv_setlist('KMNS_MIN_PCT_ATTR_SUPPORT') = 'TO_CHAR(0&lt;=numeric_expr&lt;=1)';<\/p>\n<p>-- Number of bins in the attribute histogram produced by k-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. The default number of histogram bins is 11.\nv_setlist('KMNS_NUM_BINS') = 'TO_CHAR(numeric_expr&gt;0)';<\/p>\n<p>-- Split criterion for k-means. The split criterion controls the initialization of new k-Means clusters. The algorithm builds a binary tree and adds one new cluster at a time.\nWhen the split criterion is based on size, the new cluster is placed in the area where the largest current cluster is located. When the split criterion is based on the variance, the new cluster is placed in the area of the most spread-out cluster. The default split criterion is the KMNS_VARIANCE.\nv_setlist('KMNS_SPLIT_CRITERION') = 'KMNS_VARIANCE'<\/p>\n<p>-- This setting controls the seed of the random generator used during the k-Means initialization. It must be a non-negative integer value. The default is 0.\nv_setlist('KMNS_RANDOM_SEED') = 'Non-negative integer'<\/p>\n<p>-- This setting determines the level of cluster detail that are computed during the build. KMNS_DETAILS_NONE: No cluster details are computed. Only the scoring information is persisted.\nKMNS_DETAILS_HIERARCHY: Cluster hierarchy and cluster record counts are computed. This is the default value. KMNS_DETAILS_ALL: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.\nv_setlist('KMNS_DETAILS') = 'KMNS_DETAILS_HIERARCHY'<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "],"enabled":true,"result":{"startTime":1713818297111,"interpreter":"md.medium","endTime":1713818297169,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build k-Means clustering model with 3 clusters with explicit settings","message":["%r","","ore.exec(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> 'KM_CLUSTERING_MODEL'); EXCEPTION WHEN others THEN null; END;\")","","settings = list(KMNS_ITERATIONS           = 20,","                KMNS_CONV_TOLERANCE       = .001,","                KMNS_DISTANCE             = 'KMNS_EUCLIDEAN',","                KMNS_MIN_PCT_ATTR_SUPPORT = 0.05,","                KMNS_NUM_BINS             = 11,","                KMNS_SPLIT_CRITERION      = 'KMNS_VARIANCE',","                KMNS_RANDOM_SEED          = 7,","                KMNS_DETAILS              = 'KMNS_DETAILS_ALL',","                ODMS_DETAILS              = 'ODMS_ENABLE',","                ODMS_SAMPLING             = 'ODMS_SAMPLING_DISABLE',","                PREP_AUTO                 = 'ON',","                model_name                ='KM_CLUSTERING_MODEL')","","KM.MOD2 <- ore.odmKMeans(~.-CUST_ID, CUST_DF, num.centers = 3,odm.settings = settings)"],"enabled":true,"result":{"startTime":1713818297242,"interpreter":"r.medium","endTime":1713818302042,"results":[{"message":"Warning message:\n\u201cThe created mining model KM_CLUSTERING_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model details from model proxy object","message":["%r","","summary(KM.MOD2)"],"enabled":true,"result":{"startTime":1713818302123,"interpreter":"r.medium","endTime":1713818302242,"results":[{"message":"\nCall:\nore.odmKMeans(formula = ~. - CUST_ID, data = CUST_DF, num.centers = 3, \n    odm.settings = settings)\n\nSettings: \n                                               value\nclus.num.clusters                                  3\nblock.growth                                       2\nconv.tolerance                                 0.001\ndetails                                  details.all\ndistance                                   euclidean\niterations                                        20\nmin.pct.attr.support                            0.05\nnum.bins                                          11\nrandom.seed                                        7\nsplit.criterion                             variance\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nCenters: \n  CUST_YEAR_OF_BIRTH YRS_RESIDENCE CUST_GENDER CUST_MARITAL_STATUS EDUCATION\n3           1977.323      2.373171           M              NeverM   HS-grad\n4           1954.856      4.985663           F             Divorc.   HS-grad\n5           1958.935      4.955849           M             Married   HS-grad\n  HOUSEHOLD_SIZE OCCUPATION\n3              1      Other\n4              2    Cleric.\n5              3     Crafts\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display summary of clusters","message":["%r","","z.show(KM.MOD2$clusters)"],"enabled":true,"result":{"startTime":1713818302317,"interpreter":"r.medium","endTime":1713818302438,"results":[{"message":"CLUSTER_ID\tROW_CNT\tPARENT_CLUSTER_ID\tTREE_LEVEL\tDISPERSION\tIS_LEAF\n1\t4500\tNA\t1\t3.78898942838354\tFALSE\n2\t2860\t1\t2\t3.83897003690909\tFALSE\n3\t1640\t1\t2\t3.7018281232719\tTRUE\n4\t1116\t2\t3\t4.7800201967741\tTRUE\n5\t1744\t2\t3\t3.23678426947253\tTRUE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Define function to output formatted rule","message":["%r","","displayRule <- function (model, cluster.id, dat) {","  rules  <- rules(model)","  cluster.rules <- rules[rules$cluster.id == cluster.id, ]","  vars   <- unique(cluster.rules$lhs.var)","  res    <- c()","","  for (var in vars) {","     rule <- c()","     var.rules <- cluster.rules[cluster.rules$lhs.var == var, 'predicate']","     if (class(dat[[var]]) == \"ore.character\") {","       for (r in var.rules)","         rule <- c(rule, tail(strsplit(r, \" IN \")[[1]], 1))","       rule <- paste(var, \" IN {'\", paste(rule, collapse=\"','\"), \"'}\", sep=\"\")","     } else","       rule <- paste(var.rules, collapse = \" and \")","     res <- c(res, paste0(rule, \"\\n\"))","  }","","  cat(paste(\"if \", paste(res, collapse = \"  and \"), \"then cluster \", cluster.id, sep=\"\"))    ","}"],"enabled":true,"result":{"startTime":1713818302516,"interpreter":"r.medium","endTime":1713818302638,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generate a formatted rule for cluster 5","message":["%r","","displayRule(model      = KM.MOD2, ","            cluster.id = 5, ","            dat        = CUST_DF)"],"enabled":true,"result":{"startTime":1713818302716,"interpreter":"r.medium","endTime":1713818303290,"results":[{"message":"if CUST_GENDER IN {'CUST_GENDER = M'}\n  and CUST_MARITAL_STATUS IN {'CUST_MARITAL_STATUS = Married'}\n  and CUST_YEAR_OF_BIRTH <= 1978.7 and CUST_YEAR_OF_BIRTH > 1934.9\n  and EDUCATION IN {'< Bach.','Assoc-A','Assoc-V','Bach.','HS-grad','Masters','Profsc'}\n  and HOUSEHOLD_SIZE IN {'HOUSEHOLD_SIZE = 3'}\n  and OCCUPATION IN {'Cleric.','Crafts','Exec.','Farming','Machine','Other','Prof.','Sales','TechSup','Transp.'}\n  and YRS_RESIDENCE <= 8.4 and YRS_RESIDENCE > 2.8\nthen cluster 5","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Apply model to assign rows to clusters","message":["%r","","KM.PRED <- predict(KM.MOD2, CUST_DF, supplemental.cols=c(\"CUST_ID\",\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\"))","class(KM.PRED)","","z.show(head(KM.PRED))"],"enabled":true,"result":{"startTime":1713818303376,"interpreter":"r.medium","endTime":1713818304194,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"CUST_ID\tYRS_RESIDENCE\tCUST_YEAR_OF_BIRTH\tX.3.\tX.4.\tX.5.\tCLUSTER_ID\n100100\t4\t1959\t0.268461350707808\t0.402447568840267\t0.329091080451925\t4\n100200\t2\t1983\t0.61336535960901\t0.194452418496826\t0.192182221894163\t3\n100300\t4\t1961\t0.238345619265775\t0.294239065905417\t0.467415314828808\t5\n100400\t6\t1944\t0.144293191177821\t0.481789311112426\t0.373917497709753\t4\n100500\t2\t1983\t0.59495753362575\t0.19980860439888\t0.20523386197537\t3\n100600\t3\t1978\t0.383929725773937\t0.225126844389175\t0.390943429836887\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Compare Clusters","Compare the  distribution for years residence and year of birth across two clusters using the overloaded `hist` functions. "],"enabled":true,"result":{"startTime":1713818304272,"interpreter":"md.medium","endTime":1713818304333,"results":[{"message":"<h2 id=\"compare-clusters\">Compare Clusters<\/h2>\n<p>Compare the  distribution for years residence and year of birth across two clusters using the overloaded <code>hist<\/code> functions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"image","title":"Plot histograms of select clusters and attributes","message":["%r","","dat.cluster3 <- KM.PRED[KM.PRED$CLUSTER_ID == 3,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","dat.cluster5 <- KM.PRED[KM.PRED$CLUSTER_ID == 5,c(\"YRS_RESIDENCE\",\"CUST_YEAR_OF_BIRTH\")]","","par(mfrow=c(2,2)) ","hist(dat.cluster3$YRS_RESIDENCE, main=\"Cluster 3 YRS_RESIDENCE\", col=\"darkred\", xlim=c(0,15), ylim=c(0,600))","hist(dat.cluster5$YRS_RESIDENCE, main=\"Cluster 5 YRS_RESIDENCE\", col=\"darkgreen\", xlim=c(0,15),ylim=c(0,600))","","hist(dat.cluster3$CUST_YEAR_OF_BIRTH, main=\"Cluster 3 CUST_YEAR_OF_BIRTH\", col=\"darkred\", xlim=c(1910,1990), ylim=c(0,250))","hist(dat.cluster5$CUST_YEAR_OF_BIRTH, main=\"Cluster 5 CUST_YEAR_OF_BIRTH\", col=\"darkgreen\", xlim=c(1910,1990), ylim=c(0,250))"],"enabled":true,"result":{"startTime":1713818304405,"interpreter":"r.medium","endTime":1713818307003,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\n1c3TTz+dmZn5zTffbN269c8//7x27ZqLi4ufn1+zZs2efPLJ/v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GvRqOZOHHi008/PXr06I4dO06ZMsVKCwIAGbkOgAqYLuzGjh178uTJ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\nU3R0tAKOs6CKItfZOCUNh1ynwVsAAACgEBR2AAAACkFhBwAA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of the year of birth for cluster 5","message":["%md","## Using SQL to access prediction details","One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.","","We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well."],"enabled":true,"result":{"startTime":1713818307099,"interpreter":"md.medium","endTime":1713818307159,"results":[{"message":"<h2 id=\"using-sql-to-access-prediction-details\">Using SQL to access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as R. Prediction details are exposed through the R API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the R proxy object CUST_DF so that SQL can reference it. This could just as easily been a database view.\nThen, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%r","","ore.drop(table=\"CUST_DF_KM\")","ore.create(CUST_DF,table=\"CUST_DF_KM\")"],"enabled":true,"result":{"startTime":1713818307233,"interpreter":"r.medium","endTime":1713818307804,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top 3 predictive attributes for specific customers and likely cluster assignments","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,0) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(KM_CLUSTERING_MODEL USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(KM_CLUSTERING_MODEL, NULL, 0.2 USING *) PSET","            FROM CUST_DF_KM V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1713818307877,"interpreter":"sql.medium","endTime":1713818308315,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n101362\t5\t21\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" weight=\".096\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".048\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Separ.\" weight=\".025\" \n101362\t4\t32\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" weight=\".096\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".048\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Separ.\" weight=\".025\" \n101362\t3\t48\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" weight=\".096\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".048\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Separ.\" weight=\".025\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'101362'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1713818308390,"interpreter":"md.medium","endTime":1713818308449,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713818308529,"interpreter":"md.medium","endTime":1713818308590,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Data Cleaning Duplicate Removal.dsnb b/machine-learning/notebooks/r/OML4R Data Cleaning Duplicate Removal.dsnb
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--- a/machine-learning/notebooks/r/OML4R Data Cleaning Duplicate Removal.dsnb	
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Duplicate Removal","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning for R - Data Cleaning - Duplicates Removal","Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.","","In this notebook, we demonstrate how to remove duplicate records using OML4R.","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","The dataset CUSTOMER_INSURANCE_LTV is generated by the \"OML Run-me-first\" notebook, which MUST be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---","","Import R libraries and obtain a proxy object for the data table to perform duplicate removal in the database.","Note that users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV table","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV_R', use.keys= FALSE)","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV_R"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the distinct rows from the dataset","message":["%r","","n_distinct(CUST_DF$CUSTOMER_ID)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Show table dimensions - note there are more rows than customer IDs","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Remove duplicate rows and check the number of unique customer IDs","message":["%r","","CUST_DF2 <- distinct(CUST_DF)","","nrow(CUST_DF2)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Check dimensions of the OML dataframe - duplicate rows removed","message":["%r","","dim(CUST_DF2)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Data Cleaning Missing Data.dsnb b/machine-learning/notebooks/r/OML4R Data Cleaning Missing Data.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/r/OML4R Data Cleaning Missing Data.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Missing Data","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4R Data Cleaning: Fill in Missing Data","","In this notebook, we demonstrate how to perform missing value replacement using OML4R.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance. ","","The dataset `CUSTOMER_INSURANCE_LTV_R` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may *not* be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---","","Import R libraries and obtain a proxy object to the data table.","To use OML4R, first import the library ORE.","Users can get online help using R's help function and specifying the function of interest."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_R table and view a few rows","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV_R', use.keys= FALSE)","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV_R","","z.show(head(CUST_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Assign row names to enable row indexing","message":["%r","","rownames(CUST_DF) <- CUST_DF$CUSTOMER_ID"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":50,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Data overview","message":["%r","","summary(CUST_DF)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Columns MARITAL_STATUS, CREDIT_BALANCE, BANK_FUNDS, and INCOME have missing values","message":["%r","","MISS_VAL <- CUST_DF[ , c(\"CUSTOMER_ID\", \"MARITAL_STATUS\", \"CREDIT_BALANCE\", \"BANK_FUNDS\", \"INCOME\")]","z.show(head(is.na(MISS_VAL)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Determine number missing in each column","message":["%r","","z.show(ore.summary(MISS_VAL, c(\"MARITAL_STATUS\", \"CREDIT_BALANCE\", \"BANK_FUNDS\", \"INCOME\"), stats = c(\"nmiss\")))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"REPLACE NA with \u2018SINGLE\u2019  for MARITAL_STATUS and missing numericals with the mean","message":["%r","","MISS_VAL$MARITAL_STATUS[is.na(MISS_VAL$MARITAL_STATUS)] <- \"SINGLE\"","","MISS_VAL$CREDIT_BALANCE[is.na(MISS_VAL$CREDIT_BALANCE)] <- round(mean(MISS_VAL$CREDIT_BALANCE, na.rm = TRUE),2)","MISS_VAL$BANK_FUNDS[is.na(MISS_VAL$BANK_FUNDS)] <- round(mean(MISS_VAL$BANK_FUNDS, na.rm = TRUE),2)","MISS_VAL$INCOME[is.na(MISS_VAL$INCOME)] <- round(mean(MISS_VAL$INCOME, na.rm = TRUE),2)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check for missing data","message":["%r","","z.show(ore.summary(MISS_VAL, c(\"MARITAL_STATUS\", \"CREDIT_BALANCE\", \"BANK_FUNDS\", \"INCOME\"), stats = c(\"nmiss\")))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the OML data frame after the replacement","message":["%r","","z.show(MISS_VAL[2053:2059, c('CUSTOMER_ID', 'MARITAL_STATUS', 'BANK_FUNDS', 'CREDIT_BALANCE', 'INCOME')])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Data Cleaning Outlier Removal.dsnb b/machine-learning/notebooks/r/OML4R Data Cleaning Outlier Removal.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/r/OML4R Data Cleaning Outlier Removal.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Outlier Removal","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R  Data Cleaning: Outlier Removal","In this notebook, we demonstrate how to exclude records with outliers using OML4R. ","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV, we focus on numerical values and remove the records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" Import libraries","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_R table","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV', use.keys= FALSE)","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from the table","message":["%r","z.show(head(CUST_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get the percentiles","message":["%r","","summary(CUST_DF$INCOME)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get the 5 and 95 percentile along with the interquartile range","message":["%r","","Q1 <- quantile(CUST_DF$INCOME, .05, na.rm=TRUE)","Q3 <- quantile(CUST_DF$INCOME, .95, na.rm=TRUE)","IQR <- IQR(CUST_DF$INCOME, na.rm=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":450,\"lastColumns\":[]},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Remove data with INCOME out of bounds and create plot","message":["%r","","par(mfrow=c(2,2))","NO_OUTLIERS <- subset(CUST_DF$INCOME, CUST_DF$INCOME > (Q1 - 1.5*IQR) & CUST_DF$INCOME < (Q3 + 1.5*IQR))","hist(NO_OUTLIERS, col=\"red\")","hist(CUST_DF$INCOME, col=\"red\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the distribution after removal","message":["%r","","summary(NO_OUTLIERS)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/r/OML4R Data Cleaning Recode Synonymous Values.dsnb b/machine-learning/notebooks/r/OML4R Data Cleaning Recode Synonymous Values.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/r/OML4R Data Cleaning Recode Synonymous Values.dsnb	
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Cleaning Recode Synonymous Values","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Cleaning: Recode Synonymous Values","In this notebook, we demonstrate how to recode synonymous value using OML4R.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_R` has special values generated by the `\"OML Run-me-first\"` notebook artificially, which `MUST` be run before this notebook.  Specifically, MARITAL_STATUS has divorced status coded as DIV, married coded as 'M'. ","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":" For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4R Data Preparation"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get proxy object for CUSTOMER_INSURANCE_LTV_R table","message":["%r","","ore.sync(table=\"CUSTOMER_INSURANCE_LTV_R\")","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV_R","z.show(head(CUST_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the distinct values","message":["%r","","crosstab <- ore.crosstab(~MARITAL_STATUS, CUST_DF)","row.names(crosstab) <- 1:nrow(crosstab)","z.show(crosstab[, c(\"MARITAL_STATUS\", \"ORE$FREQ\")])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Recode the synonymous values 'M' and 'DIV'","message":["%r","","CUST_DF$MARITAL_STATUS[CUST_DF$MARITAL_STATUS == 'M'] <- \"MARRIED\"","CUST_DF$MARITAL_STATUS[CUST_DF$MARITAL_STATUS == 'DIV'] <- 'DIVORCED'","CUST_DF$MARITAL_STATUS[is.na(CUST_DF$MARITAL_STATUS) == TRUE] <- 'UNKNOWN'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the recoded values","message":["%r","","crosstab2 <- ore.crosstab(~MARITAL_STATUS, CUST_DF)","row.names(crosstab2) <- 1:nrow(crosstab2)","z.show(crosstab2[, c(\"MARITAL_STATUS\", \"ORE$FREQ\")])"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/r/OML4R Data Transformation Binning.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Binning","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715301157671,"interpreter":"md.low","endTime":1715301157751,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4R Data Transformation: Binning","In this notebook, we demonstrate how to bin numerical columns using OML4R. Equi-width and quantile binning are shown.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to bin a numerical column and visualize the distribution.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which MUST be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. Supervised binning uses decision trees to create optimal bin boundaries. More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0');\">Naive Bayes<\/a>  ","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715301157827,"interpreter":"md.low","endTime":1715301157925,"results":[{"message":"<h1 id=\"oml4r-data-transformation-binning\">OML4R Data Transformation: Binning<\/h1>\n<p>In this notebook, we demonstrate how to bin numerical columns using OML4R. Equi-width and quantile binning are shown.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to bin a numerical column and visualize the distribution.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which MUST be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: If the user plans to use the in-database Naive Bayes algorithm later, Automatic Data Preparation (ADP) performs supervised binning. Supervised binning uses decision trees to create optimal bin boundaries. More details can be found in <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/naive-bayes.html#GUID-FD929CF9-6513-42DE-B04D-FD57310C3A0');\">Naive Bayes<\/a><\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1715301158001,"interpreter":"md.low","endTime":1715301158079,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R Libraries and set options","message":["%r","","library(ORE)","library(OREdplyr)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715301158164,"interpreter":"r.low","endTime":1715301162966,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object for CUSTOMER_INSURANCE_LTV and view data","message":["%r","","ore.sync(table = \"CUSTOMER_INSURANCE_LTV\")","ore.attach()","","CUST_DF <- CUSTOMER_INSURANCE_LTV","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1715301163044,"interpreter":"r.low","endTime":1715301166246,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Bin INCOME column into 10 bins","message":["%r","","rownames(CUST_DF) <- CUST_DF$CUSTOMER_ID # this enables row indexing","","CUST_BIN_DF <- mutate(CUST_DF, ","                      \"INCOME_BIN\" = cut(CUST_DF$INCOME, breaks=10, dig.lab=7))"],"enabled":true,"result":{"startTime":1715301166327,"interpreter":"r.low","endTime":1715301167327,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View binned INCOME column values","message":["%r","","# Update datatype of binned column for proper output format","CUST_BIN_DF['INCOME_BIN'] <- as.ore.character(CUST_BIN_DF$INCOME_BIN)","","z.show(CUST_BIN_DF[c(\"INCOME_BIN\", \"INCOME\")])"],"enabled":true,"result":{"startTime":1715301167407,"interpreter":"r.low","endTime":1715301167785,"results":[{"message":"INCOME_BIN\tINCOME\n(66906.2,74356]\t70508\n(66906.2,74356]\t68361\n(59456.4,66906.2]\t60185\n(66906.2,74356]\t71740\n(66906.2,74356]\t68482\n(66906.2,74356]\t71016\n(52006.6,59456.4]\t58641\n(66906.2,74356]\t70738\n(59456.4,66906.2]\t60554\n(59456.4,66906.2]\t66340\n(52006.6,59456.4]\t57992\n(66906.2,74356]\t69103\n(59456.4,66906.2]\t61335\n(89255.6,96705.4]\t92802\n(59456.4,66906.2]\t59480\n(52006.6,59456.4]\t57982\n(59456.4,66906.2]\t60423\n(66906.2,74356]\t68265\n(59456.4,66906.2]\t61436\n(66906.2,74356]\t71743\n(52006.6,59456.4]\t59336\n(59456.4,66906.2]\t64678\n(52006.6,59456.4]\t57852\n(74356,81805.8]\t75571\n(52006.6,59456.4]\t58664\n(59456.4,66906.2]\t62795\n(66906.2,74356]\t70323\n(66906.2,74356]\t73033\n(66906.2,74356]\t72196\n(59456.4,66906.2]\t61762\n(52006.6,59456.4]\t58179\n(59456.4,66906.2]\t62417\n(59456.4,66906.2]\t62868\n(52006.6,59456.4]\t55260\n(66906.2,74356]\t69483\n(74356,81805.8]\t74718\n(66906.2,74356]\t66988\n(59456.4,66906.2]\t63421\n(59456.4,66906.2]\t64670\n(52006.6,59456.4]\t58705\n(66906.2,74356]\t73212\n(59456.4,66906.2]\t62239\n(66906.2,74356]\t74170\n(74356,81805.8]\t76933\n(59456.4,66906.2]\t64744\n(59456.4,66906.2]\t62360\n(59456.4,66906.2]\t60409\n(59456.4,66906.2]\t61196\n(59456.4,66906.2]\t61277\n(74356,81805.8]\t80121\n(66906.2,74356]\t68972\n(52006.6,59456.4]\t57453\n(66906.2,74356]\t67152\n(66906.2,74356]\t71567\n(52006.6,59456.4]\t57340\n(59456.4,66906.2]\t62415\n(59456.4,66906.2]\t62023\n(66906.2,74356]\t70168\n(59456.4,66906.2]\t66670\n(66906.2,74356]\t70645\n(66906.2,74356]\t67814\n(52006.6,59456.4]\t58660\n(59456.4,66906.2]\t61855\n(66906.2,74356]\t73864\n(59456.4,66906.2]\t61630\n(74356,81805.8]\t76440\n(66906.2,74356]\t71273\n(66906.2,74356]\t71713\n(52006.6,59456.4]\t55361\n(59456.4,66906.2]\t62767\n(59456.4,66906.2]\t66685\n(52006.6,59456.4]\t57252\n(59456.4,66906.2]\t65678\n(52006.6,59456.4]\t58224\n(59456.4,66906.2]\t65759\n(59456.4,66906.2]\t60211\n(66906.2,74356]\t68043\n(66906.2,74356]\t69658\n(66906.2,74356]\t66966\n(59456.4,66906.2]\t62475\n(59456.4,66906.2]\t62323\n(59456.4,66906.2]\t62400\n(59456.4,66906.2]\t62744\n(59456.4,66906.2]\t60266\n(59456.4,66906.2]\t62778\n(59456.4,66906.2]\t64309\n(59456.4,66906.2]\t62850\n(59456.4,66906.2]\t65875\n(66906.2,74356]\t72612\n(52006.6,59456.4]\t55395\n(52006.6,59456.4]\t58475\n(74356,81805.8]\t75086\n(59456.4,66906.2]\t62924\n(66906.2,74356]\t67407\n(59456.4,66906.2]\t61502\n(59456.4,66906.2]\t60664\n(66906.2,74356]\t67533\n(52006.6,59456.4]\t59163\n(59456.4,66906.2]\t63481\n(59456.4,66906.2]\t64427\n(59456.4,66906.2]\t61035\n(66906.2,74356]\t70416\n(59456.4,66906.2]\t62834\n(59456.4,66906.2]\t61049\n(66906.2,74356]\t67373\n(59456.4,66906.2]\t65686\n(66906.2,74356]\t68482\n(59456.4,66906.2]\t63114\n(52006.6,59456.4]\t55549\n(59456.4,66906.2]\t61507\n(52006.6,59456.4]\t55636\n(66906.2,74356]\t69797\n(52006.6,59456.4]\t59118\n(52006.6,59456.4]\t58665\n(59456.4,66906.2]\t60922\n(59456.4,66906.2]\t61226\n(59456.4,66906.2]\t66012\n(59456.4,66906.2]\t64895\n(66906.2,74356]\t73333\n(59456.4,66906.2]\t66084\n(66906.2,74356]\t67622\n(74356,81805.8]\t76583\n(52006.6,59456.4]\t58743\n(52006.6,59456.4]\t57196\n(52006.6,59456.4]\t56393\n(66906.2,74356]\t72655\n(59456.4,66906.2]\t65930\n(59456.4,66906.2]\t60118\n(59456.4,66906.2]\t62952\n(59456.4,66906.2]\t62628\n(59456.4,66906.2]\t64310\n(59456.4,66906.2]\t65337\n(59456.4,66906.2]\t60322\n(59456.4,66906.2]\t62284\n(59456.4,66906.2]\t63371\n(59456.4,66906.2]\t62474\n(59456.4,66906.2]\t64771\n(59456.4,66906.2]\t66114\n(66906.2,74356]\t71888\n(59456.4,66906.2]\t61948\n(66906.2,74356]\t70852\n(59456.4,66906.2]\t62996\n(66906.2,74356]\t71034\n(66906.2,74356]\t70397\n(52006.6,59456.4]\t55236\n(59456.4,66906.2]\t65111\n(59456.4,66906.2]\t63947\n(66906.2,74356]\t68450\n(59456.4,66906.2]\t60556\n(81805.8,89255.6]\t88530\n(66906.2,74356]\t67365\n(66906.2,74356]\t69514\n(59456.4,66906.2]\t62622\n(59456.4,66906.2]\t66583\n(59456.4,66906.2]\t61050\n(59456.4,66906.2]\t66174\n(59456.4,66906.2]\t60543\n(66906.2,74356]\t70084\n(59456.4,66906.2]\t65030\n(66906.2,74356]\t70758\n(66906.2,74356]\t69202\n(59456.4,66906.2]\t61028\n(59456.4,66906.2]\t65544\n(59456.4,66906.2]\t60684\n(59456.4,66906.2]\t66224\n(59456.4,66906.2]\t63911\n(59456.4,66906.2]\t66381\n(59456.4,66906.2]\t63729\n(59456.4,66906.2]\t60385\n(52006.6,59456.4]\t57599\n(52006.6,59456.4]\t59147\n(66906.2,74356]\t71748\n(66906.2,74356]\t69360\n(59456.4,66906.2]\t63092\n(59456.4,66906.2]\t64404\n(59456.4,66906.2]\t64501\n(59456.4,66906.2]\t65892\n(59456.4,66906.2]\t62358\n(59456.4,66906.2]\t66302\n(59456.4,66906.2]\t63053\n(59456.4,66906.2]\t61724\n(59456.4,66906.2]\t59603\n(66906.2,74356]\t66956\n(59456.4,66906.2]\t63228\n(59456.4,66906.2]\t64591\n(52006.6,59456.4]\t58515\n(59456.4,66906.2]\t63449\n(89255.6,96705.4]\t90455\n(59456.4,66906.2]\t60650\n(66906.2,74356]\t67637\n(66906.2,74356]\t67776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6906.2,74356]\t68129\n(59456.4,66906.2]\t61294\n(66906.2,74356]\t68705\n(66906.2,74356]\t72865\n(52006.6,59456.4]\t56291\n(59456.4,66906.2]\t60441\n(59456.4,66906.2]\t66831\n(59456.4,66906.2]\t62339\n(59456.4,66906.2]\t62959\n(66906.2,74356]\t68713\n(59456.4,66906.2]\t62350\n(66906.2,74356]\t69651\n(81805.8,89255.6]\t87509\n(74356,81805.8]\t75940\n(59456.4,66906.2]\t62658\n(66906.2,74356]\t67283\n(66906.2,74356]\t68591\n(59456.4,66906.2]\t64856\n(66906.2,74356]\t68787\n(52006.6,59456.4]\t56909\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot the distribution of binned income","message":["%r","","OUTPUT <- count(CUST_BIN_DF, INCOME_BIN)","rownames(OUTPUT) <- OUTPUT$INCOME_BIN # ordering rows of OUTPUT","","output <- ore.pull(OUTPUT) # Pull output ore.frame to data.frame for plotting","","output$Bin_INCOME <- factor(output$INCOME_BIN, ","                            levels=c(\"(37032.5,44556.8]\", \"(44556.8,52006.6]\", ","                                     \"(52006.6,59456.4]\", \"(59456.4,66906.2]\", ","                                     \"(66906.2,74356]\", \"(74356,81805.8]\", ","                                     \"(81805.8,89255.6]\", \"(89255.6,96705.4]\", ","                                     \"(96705.4,104155.2]\", \"(104155.2,111679.5]\"))  # Order the output correctly","","barchart(n ~ Bin_INCOME, data = output, horiz = FALSE,","         ylab=\"Count\", scales = list(x = list(rot = 45, relation=\"free\")), col = \"blue\")"],"enabled":true,"result":{"startTime":1715301167867,"interpreter":"r.low","endTime":1715301168517,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nfW/VqlVKqZiYmM6dO5tjevbsmZ+ff+XKFfd3ffLJJ0qpXr161dYAAAAAR9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0AAIAQBDsAAAAhCHYAAABCEOwAAACEINgBAAAIQbADAAAQ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\nE68Rr433nHiNnDp1asWKFcuWLTt48GCll5z1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quantiles for INCOME column ","message":["%r","","z.show(data.frame(quantile(CUST_DF$INCOME)))"],"enabled":true,"result":{"startTime":1715301168598,"interpreter":"r.low","endTime":1715301169102,"results":[{"message":"quantile.CUST_DF.INCOME.\n37107\n60719.5\n64426\n68482\n111605\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Use quantiles for bin boundaries","message":["%r","","income_quantiles <- quantile(CUST_DF$INCOME)","","CUST_SET_DF <- transmute(CUST_DF, ","                         \"INCOME_BIN\" = cut(CUST_DF$INCOME, ","                                            breaks=income_quantiles, ","                                            dig.lab=10))","","CUST_SET_DF <- count(CUST_SET_DF, INCOME_BIN)","rownames(CUST_SET_DF) <- CUST_SET_DF$INCOME_BIN","","","cust_set_df <- ore.pull(CUST_SET_DF) # Pull data to client for plotting","","barchart(n ~ INCOME_BIN, data = cust_set_df, ","         horiz = FALSE, ylab = \"Count\", col =\" Blue\")"],"enabled":true,"result":{"startTime":1715301169179,"interpreter":"r.low","endTime":1715301170682,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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End of script"],"enabled":true,"result":{"startTime":1715301170786,"interpreter":"md.low","endTime":1715301170848,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715301170934,"interpreter":"md.low","endTime":1715301170995,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/r/OML4R Data Transformation Categorical Recode.dsnb b/machine-learning/notebooks/r/OML4R Data Transformation Categorical Recode.dsnb
deleted file mode 100755
index 52ab57e9..00000000
--- a/machine-learning/notebooks/r/OML4R Data Transformation Categorical Recode.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Categorical Recode","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation: recode categorical variable to numeric variable","","In this notebook, we demonstrate how to recode a categorical string variable to a numeric variable and string-to-string recoding using OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to convert a categorical variable with each distinct level/value coded to an integer data type. Note that ***ore.recode***, which supports this transformation, can also be used to recode string values as illustrated in the last example. ","","The dataset CUSTOMER_INSURANCE_LTV is generated by the \"OML Run-me-first\" notebook, which MUST be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%r","","ore.sync(query = c(\"CUST_DF\" = \"select * from CUSTOMER_INSURANCE_LTV\"))","ore.attach()","z.show(head(CUST_DF, 5))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the unique states by count","message":["%r","","output <- count(CUST_DF, STATE)","colnames(output)[2] <- \"count\"","z.show(arrange(output, count))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Replace the state names with integers","message":["%r","","# Get vector of unique values in STATE column and create vector of integers to replace with.","state_names <- ore.pull(c(unique(CUST_DF$STATE)))","state_nums <- c(1:length(state_names))","","# Set STATE column to ore.character datatype before replacement.","CUST_DF$STATE <- as.ore.character(CUST_DF$STATE)","","# Replace the state names. ","CUST_DF$STATE <- ore.recode(CUST_DF$STATE, state_names, state_nums)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Check the unique states encoded as integers","message":["%r","","output <- arrange(count(CUST_DF, STATE), n)","colnames(output)[2] <- \"count\"","","class(output$STATE)","z.show(output)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Simple example recoding string values on a vector","message":["%r","","vec <- ore.push(c(\"a\", \"b\", NA, \"a\", \"c\", NA, \"e\"))","","cat(class(vec), \"\\n\")","","ore.recode(vec, c(\"a\", \"b\", \"c\"), c(\"able\", \"baker\", \"charlie\"))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Recode to multiple columns with different values","message":["%r","","ore.recode(vec, c(\"a\", \"b\", \"c\"),","            cbind(lower = c(\"able\", \"baker\", \"charlie\"),","                  upper = c(\"ABLE\", \"BAKER\", \"CHARLIE\")))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Data Transformation Date Datatypes.dsnb b/machine-learning/notebooks/r/OML4R Data Transformation Date Datatypes.dsnb
deleted file mode 100755
index f9e1ec92..00000000
--- a/machine-learning/notebooks/r/OML4R Data Transformation Date Datatypes.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Date Datatypes","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714498562508,"interpreter":"md.medium","endTime":1714498562568,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation Date Datatypes","","In this notebook, we demonstrate how to perform various operations on date and time data using database table proxy objects using OML4R.","","Using a sequence of date and time data spanning from 2001/01/01 to 2001/12/31, we show how to do operations on date and time data. These operations include arithmetic, aggregation, ","comparisons, accessing details of data, coercion, as well as an example of ARIMA with a rolling mean window function.","","","For more information the Documentation can be found at <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-A1BC12C1-B431-4F71-9DC5-4ECCA0C693AC\" target=\"_blank\">OML4R documentation on preparing time series data<\/a>","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1714498562643,"interpreter":"md.medium","endTime":1714498562705,"results":[{"message":"<h1 id=\"oml4r-data-transformation-date-datatypes\">OML4R Data Transformation Date Datatypes<\/h1>\n<p>In this notebook, we demonstrate how to perform various operations on date and time data using database table proxy objects using OML4R.<\/p>\n<p>Using a sequence of date and time data spanning from 2001/01/01 to 2001/12/31, we show how to do operations on date and time data. These operations include arithmetic, aggregation,\ncomparisons, accessing details of data, coercion, as well as an example of ARIMA with a rolling mean window function.<\/p>\n<p>For more information the Documentation can be found at <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-A1BC12C1-B431-4F71-9DC5-4ECCA0C693AC\" target=\"_blank\">OML4R documentation on preparing time series data<\/a><\/p>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import R libraries and set seed","message":["%r","","library(ORE)","set.seed(100)"],"enabled":true,"result":{"startTime":1714498562783,"interpreter":"r.medium","endTime":1714498567787,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","## Create Datetime dataset","","Create datetime dataset as a database table with ore.push"],"enabled":true,"result":{"startTime":1714498567868,"interpreter":"md.medium","endTime":1714498567928,"results":[{"message":"<h2 id=\"create-datetime-dataset\">Create Datetime dataset<\/h2>\n<p>Create datetime dataset as a database table with ore.push<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%r ","","n <- 500","","mydata <- data.frame(datetime = seq(as.POSIXct(\"2001/01/01\"), as.POSIXct(\"2001/12/31\"), length.out = n), ","                     difftime = as.difftime(runif(n), units = \"mins\"), ","                     x = rnorm(n))","","MYDATA <- ore.push(mydata) # create temporary table and return proxy object","","class(MYDATA)","class(MYDATA$datetime)","","z.show(head(MYDATA, 3))"],"enabled":true,"result":{"startTime":1714498568026,"interpreter":"r.medium","endTime":1714498568675,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"'ore.datetime'\n","type":"HTML"},{"message":"datetime\tdifftime\tx\n2001-01-01 00:00:00\t18.465967\t-0.771408001996856\n2001-01-01 17:30:25\t15.46035\t-0.507342353723463\n2001-01-02 11:00:50\t33.139346\t-0.270022583786385\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time arithmetic"],"enabled":true,"result":{"startTime":1714498568755,"interpreter":"md.medium","endTime":1714498568816,"results":[{"message":"<h2 id=\"date-and-time-arithmetic\">Date and time arithmetic<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create day1Shift dataset by adding 1 day to each date in MYDATA$datetime","message":["%r  ","","options(\"width\"=200)","","day1Shift <- MYDATA$datetime + as.difftime(1, units = \"days\")","","cat(class(day1Shift), \"\\n\")","head(MYDATA$datetime, 3)","cat(\"\\n\")","head(day1Shift, 3)"],"enabled":true,"result":{"startTime":1714498568890,"interpreter":"r.medium","endTime":1714498569114,"results":[{"message":"ore.datetime \n[1] \"2001-01-01 00:00:00 UTC\" \"2001-01-01 17:30:25 UTC\" \"2001-01-02 11:00:50 UTC\"\n[1] \"2001-01-02 00:00:00 UTC\" \"2001-01-02 17:30:25 UTC\" \"2001-01-03 11:00:50 UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use diff function to get the differences in time between each value of MYDATA$datetime.","message":["%r","","lag1Diff <- diff(MYDATA$datetime)","cat(class(lag1Diff), \"\\n\")","head(lag1Diff, 3)"],"enabled":true,"result":{"startTime":1714498569190,"interpreter":"r.medium","endTime":1714498569312,"results":[{"message":"ore.difftime \nTime differences in secs\n[1] 63025.25 63025.25 63025.25","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time aggregates","","Obtain statistical aggregates of your date and time data."],"enabled":true,"result":{"startTime":1714498569390,"interpreter":"md.medium","endTime":1714498569452,"results":[{"message":"<h2 id=\"date-and-time-aggregates\">Date and time aggregates<\/h2>\n<p>Obtain statistical aggregates of your date and time data.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","min(MYDATA$datetime)"],"enabled":true,"result":{"startTime":1714498569525,"interpreter":"r.medium","endTime":1714498569710,"results":[{"message":"[1] \"2001-01-01 UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","max(MYDATA$datetime)"],"enabled":true,"result":{"startTime":1714498569785,"interpreter":"r.medium","endTime":1714498569906,"results":[{"message":"[1] \"2001-12-31 UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","range(MYDATA$datetime)"],"enabled":true,"result":{"startTime":1714498569979,"interpreter":"r.medium","endTime":1714498570098,"results":[{"message":"[1] \"2001-01-01 UTC\" \"2001-12-31 UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%r","","median(MYDATA$datetime)"],"enabled":true,"result":{"startTime":1714498570172,"interpreter":"r.medium","endTime":1714498570356,"results":[{"message":"[1] \"2001-07-02 UTC\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Calculate quantiles at the 0%, 5%, and 10% marks","message":["%r ","","quantile(MYDATA$datetime, probs = c(0, 0.05, 0.10))"],"enabled":true,"result":{"startTime":1714498570433,"interpreter":"r.medium","endTime":1714498571630,"results":[{"message":"                       0%                        5%                       10% \n\"2001-01-01 00:00:00 UTC\" \"2001-01-19 04:47:59 UTC\" \"2001-02-06 09:35:59 UTC\" ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time comparisons"],"enabled":true,"result":{"startTime":1714498571709,"interpreter":"md.medium","endTime":1714498571771,"results":[{"message":"<h2 id=\"date-and-time-comparisons\">Date and time comparisons<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use comparison to identify all data that is in Q1","message":["%r ","","isQ1 <- MYDATA$datetime < as.Date(\"2001/04/01\")","cat(class(isQ1), \"\\n\")","head(isQ1, 3)"],"enabled":true,"result":{"startTime":1714498571850,"interpreter":"r.medium","endTime":1714498572121,"results":[{"message":"ore.logical \n[1] TRUE TRUE TRUE","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use comparison and identified Q1 data to identify subset of data that is in March","message":["%r","","isMarch <- isQ1 & MYDATA$datetime > as.Date(\"2001/03/01\")","cat(class(isMarch), \"\\n\")","cat(sum(isMarch), \"\\n\")","head(isMarch[70:85,], 100)"],"enabled":true,"result":{"startTime":1714498572198,"interpreter":"r.medium","endTime":1714498572521,"results":[{"message":"ore.logical \n43 \n [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Use comparison to get subset of data that is at the end of the year","message":["%r   ","","eoySubset <- MYDATA[MYDATA$datetime > as.Date(\"2001/12/27\"), ]","class(eoySubset)","z.show(head(eoySubset))"],"enabled":true,"result":{"startTime":1714498572596,"interpreter":"r.medium","endTime":1714498572868,"results":[{"message":"'ore.frame'\n","type":"HTML"},{"message":"datetime\tdifftime\tx\n2001-12-27 08:27:53\t6.263731\t-1.33717666886641\n2001-12-28 01:58:18\t34.794086\t2.31131788765912\n2001-12-28 19:28:44\t19.985368\t-0.350491028513415\n2001-12-29 12:59:09\t0.226655\t-0.321103323805441\n2001-12-30 06:29:34\t59.675767\t-0.428961592336069\n2001-12-31 00:00:00\t22.154092\t-0.229611260023931\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time accessors","","Extract various features from your date and time data.","","Create a function to extract all details from data given a dataframe DF and column name dates.","After running this function, an attribute importance algorithm can then tell you how impactful these new columns are."],"enabled":true,"result":{"startTime":1714498572947,"interpreter":"md.medium","endTime":1714498573008,"results":[{"message":"<h2 id=\"date-and-time-accessors\">Date and time accessors<\/h2>\n<p>Extract various features from your date and time data.<\/p>\n<p>Create a function to extract all details from data given a dataframe DF and column name dates.\nAfter running this function, an attribute importance algorithm can then tell you how impactful these new columns are.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","dateFeatureExtraction <- function(dat, dateCol){","  ","  datesIndex <- which(colnames(dat) == dateCol)","  colnames(dat)[datesIndex] <- \"datesCol\"","  ","  dat <- cbind(dat, year = ore.year(dat$datesCol), month = ore.month(dat$datesCol),","               dayOfMonth = ore.mday(dat$datesCol), hour = ore.hour(dat$datesCol),","               minute = ore.minute(dat$datesCol), second = ore.second(dat$datesCol))","  ","  colnames(dat)[datesIndex] <- dateCol","  ","  return(dat)","}"],"enabled":true,"result":{"startTime":1714498573085,"interpreter":"r.medium","endTime":1714498573208,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Extract details from MYDATA and view data","message":["%r ","","DATE_DETAILS <- dateFeatureExtraction(MYDATA, \"datetime\")","","z.show(head(DATE_DETAILS))"],"enabled":true,"result":{"startTime":1714498573286,"interpreter":"r.medium","endTime":1714498573473,"results":[{"message":"datetime\tdifftime\tx\tyear\tmonth\tdayOfMonth\thour\tminute\tsecond\n2001-01-01 00:00:00\t18.465967\t-0.771408001996856\t2001\t1\t1\t0\t0\t0\n2001-01-01 17:30:25\t15.46035\t-0.507342353723463\t2001\t1\t1\t17\t30\t25.250501\n2001-01-02 11:00:50\t33.139346\t-0.270022583786385\t2001\t1\t2\t11\t0\t50.501002\n2001-01-03 04:31:15\t3.382989\t0.74811656342622\t2001\t1\t3\t4\t31\t15.751503\n2001-01-03 22:01:41\t28.112957\t0.668593810460555\t2001\t1\t3\t22\t1\t41.002004\n2001-01-04 15:32:06\t29.026244\t-0.455298660081533\t2001\t1\t4\t15\t32\t6.252505\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Date and time Coercion","","Coerce details of your date and time data to other datatypes"],"enabled":true,"result":{"startTime":1714498573551,"interpreter":"md.medium","endTime":1714498573613,"results":[{"message":"<h2 id=\"date-and-time-coercion\">Date and time Coercion<\/h2>\n<p>Coerce details of your date and time data to other datatypes<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Coercion to date only and ore.date datatype","message":["%r ","","dateOnly <- as.ore.date(MYDATA$datetime)","class(dateOnly)","head(sort(unique(dateOnly)), 3)"],"enabled":true,"result":{"startTime":1714498573690,"interpreter":"r.medium","endTime":1714498574687,"results":[{"message":"'ore.date'\n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li><time datetime=\"2001-01-01\">2001-01-01<\/time><\/li><li><time datetime=\"2001-01-02\">2001-01-02<\/time><\/li><li><time datetime=\"2001-01-03\">2001-01-03<\/time><\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Coercion to name of day and ore.character datatype","message":["%r"," ","nameOfDay <- as.ore.character(MYDATA$datetime, format = \"DAY\")","cat(class(nameOfDay), \"\\n\")","sort(unique(nameOfDay))"],"enabled":true,"result":{"startTime":1714498574767,"interpreter":"r.medium","endTime":1714498574951,"results":[{"message":"ore.character \n[1] \"FRIDAY   \" \"MONDAY   \" \"SATURDAY \" \"SUNDAY   \" \"THURSDAY \" \"TUESDAY  \" \"WEDNESDAY\"","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Coercion to day of year and ore.integer datatype","message":["%r ","","dayOfYear <- as.integer(as.character(MYDATA$datetime, format = \"DDD\"))","class(dayOfYear)","range(dayOfYear)"],"enabled":true,"result":{"startTime":1714498575028,"interpreter":"r.medium","endTime":1714498575216,"results":[{"message":"'ore.integer'\n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>1<\/li><li>365<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Coercion to week of year and ore.integer datatype","message":["%r","","weekOfYear <- as.integer(as.character(MYDATA$datetime, format = \"WW\"))","class(weekOfYear)","range(weekOfYear)"],"enabled":true,"result":{"startTime":1714498575297,"interpreter":"r.medium","endTime":1714498575484,"results":[{"message":"'ore.integer'\n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>1<\/li><li>53<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Coercion to quarter and ore.integer datatype ","message":["%r","","quarter <- as.integer(as.character(MYDATA$datetime, format = \"Q\"))","cat(class(quarter), \"\\n\")","sort(unique(quarter))"],"enabled":true,"result":{"startTime":1714498575567,"interpreter":"r.medium","endTime":1714498575753,"results":[{"message":"ore.integer \n[1] 1 2 3 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\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation Normalization and Scaling","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714084431635,"interpreter":"md.low","endTime":1714084431697,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation: Normalization and Scaling","","In this notebook, we demonstrate how to normalize and scale data using OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data using using z-score (mean and standard deviation), min-max scaling, and log scaling.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note** : when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms. ","For instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715347300328,"interpreter":"md.low","endTime":1715347300484,"results":[{"message":"<h1 id=\"oml4r-data-transformation-normalization-and-scaling\">OML4R Data Transformation: Normalization and Scaling<\/h1>\n<p>In this notebook, we demonstrate how to normalize and scale data using OML4R.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data using using z-score (mean and standard deviation), min-max scaling, and log scaling.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong> : when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms.\nFor instance, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1714084431959,"interpreter":"md.low","endTime":1714084432019,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R libraries and set display options","message":["%r","","library(ORE)","library(OREdplyr)","options(scipen = 999)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1714084432106,"interpreter":"r.low","endTime":1714084432231,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for customer lifetime value table","message":["%r","","ore.sync(query = c(\"CUST_DF\" = \"SELECT * FROM CUSTOMER_INSURANCE_LTV\"))","ore.attach()"],"enabled":true,"result":{"startTime":1714084432311,"interpreter":"r.low","endTime":1714084432442,"results":[{"message":"The following object is masked _by_ .GlobalEnv:\n\n    CUST_DF\n\n\nThe following object is masked _by_ .GlobalEnv:\n\n    CUST_DF\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%r","","z.show(head(CUST_DF, 5))"],"enabled":true,"result":{"startTime":1714084432514,"interpreter":"r.low","endTime":1714084432675,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\tINCOME_NORMALIZED\tINCOME_MINMAX_SCALED\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\t0.114776449977208\t0.386104324948321\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         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HIGH\tCHASITY\t1\t1\tELLIS\t2.95559820871758\t0.640205106177347\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalization by z-score - using mean and standard deviation","message":["%r","","income_mean <- summarise(CUST_DF, income_mean = mean(INCOME, na.rm=TRUE))","income_std  <- summarise(CUST_DF, income_std = sd(INCOME, na.rm=TRUE))","","CUST_DF <- mutate(CUST_DF, INCOME_NORMALIZED = (INCOME - income_mean) / income_std)","","z.show(head(round(CUST_DF[, c(\"INCOME\", \"INCOME_NORMALIZED\")], digits = 4), 5))"],"enabled":true,"result":{"startTime":1714084432770,"interpreter":"r.low","endTime":1714084433146,"results":[{"message":"INCOME\tINCOME_NORMALIZED\n65871\t0.1148\n68747\t0.5464\n68684\t0.5369\n59354\t-0.8632\n84801\t2.9556\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Normalize INCOME using min-max scaling","message":["%r","","income_min <- summarise(CUST_DF, income_min = min(INCOME, na.rm=TRUE))","income_max <- summarise(CUST_DF, income_max = max(INCOME, na.rm=TRUE))","  ","CUST_DF <- mutate(CUST_DF, INCOME_MINMAX_SCALED = (INCOME - income_min) / (income_max - income_min))","","z.show(head(round(CUST_DF[, c(\"INCOME\", \"INCOME_MINMAX_SCALED\")], digits = 4), 5))"],"enabled":true,"result":{"startTime":1714084433219,"interpreter":"r.low","endTime":1714084433653,"results":[{"message":"INCOME\tINCOME_MINMAX_SCALED\n65871\t0.3861\n68747\t0.4247\n68684\t0.4239\n59354\t0.2986\n84801\t0.6402\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Filter 0 values from column TOTAL_AUTOM_PAYMENTS, then transform using base 10 logarithm","message":["%r","","CUST_DF_FILTERED <- filter(CUST_DF, TOTAL_AUTOM_PAYMENTS > 0)","","CUST_DF_FILTERED <- mutate(CUST_DF_FILTERED, LOG_TOTAL_AUTOM_PAYMENTS = log10(TOTAL_AUTOM_PAYMENTS))","","z.show(head(round(CUST_DF_FILTERED[, c(\"TOTAL_AUTOM_PAYMENTS\", \"LOG_TOTAL_AUTOM_PAYMENTS\")], digits = 4), 5))"],"enabled":true,"result":{"startTime":1714084433765,"interpreter":"r.low","endTime":1714084433968,"results":[{"message":"TOTAL_AUTOM_PAYMENTS\tLOG_TOTAL_AUTOM_PAYMENTS\n287\t2.4579\n132\t2.1206\n628\t2.798\n2143\t3.331\n3707\t3.569\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":500,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Histogram on the original column","message":["%r","","hist(CUST_DF$TOTAL_AUTOM_PAYMENTS, breaks = 10,  col = \"blue\",","     xlab = \"TOTAL_AUTOM_PAYMENTS\", ylab = \"Customer Count\", ","     main = paste(\"Histogram of origional TOTAL_AUTOM_PAYMENTS\"))","     "],"enabled":true,"result":{"startTime":1714084434076,"interpreter":"r.low","endTime":1714084434449,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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IwW7QoEGHDx+uq6sbNmxYR23q6uqGDx8+aNCg\n+vp6V5bZ0tKi1+udP1j21KlTe/fuXbhwYZ8+fTpdNAAAgLcE0s0TDQ0NQojIyEgnbWxzbS1d\nYTAYpk+f7krLnj17Llq0yMXFAgAAeF8gBbuoqKh9+/aVl5dPmjSpozYVFRVCiOjoaBeXqdVq\nP/zwQ+c9dtu2bVu2bNnw4cM7VS0AAICXBVKwS0tLy83N1el0RUVFl156adsGlZWVOp1OCJGa\nmuriMkNCQpKTk0/bbNmyZT17MuYfAADwa4EU7DIzM/V6fU1Nzfjx4zUaTXx8fEREhEwms1qt\nJpNp+/btRqNRCBEXF5eZmenrYgEAALwtkIKdUqncunVrVlZWQUGB0Wi0xTgpuVyekZGRk5MT\nFhbmkwoBAAB8KJCCnRAiPDw8Pz8/NzfXYDAYjcb6+vqmpiaFQqFSqTQajVarVSqVvq4RAADA\nNwIs2Nkolcrk5GRXro0DAAA4cwTPDQG7du3atWuXr6sAAADwmYDssWvX6NGjhRB+Pt6yK+Mh\nBxm5XJ6UlBQSEuLrQgAACH7BE+wCwn//93+7OB5yMPnoo4+mTZvm6yoAAAh+gRTsevTo4Xob\n/+y6a2pqEkIIsUCIi31cipd8IcQL//nWAADAswIp2AWRi4VwdQhlAAAAFwXSzRPR0dE9e/a8\n//77Dx069EcbtjYObwEAAM4cgRTsamtr77777pdfflmr1X777bf9/y9bG4e3AAAAZ45ACnZK\npfLll1+urKzs2bPnhAkT5s2bd/z4cV8XBQAA4C8CKdjZJCQk7Ny5c/HixQUFBSNHjvz44499\nXREAAIBfCLxgJ4To3bv3okWLampqhg4dmpycPHPmzEOHDvm6KAAAAB8LyGBnExsbu2XLluXL\nl3/yyScXXHCBr8sBAADwsQAOdkKIHj16zJ07d/fu3VdeeaVMJpPJZL6uCAAAwGeCYRy7c845\nZ+3atb6uAgAAwMcCu8cOAAAAdkEY7F555ZVXXnnF11UAAAB4WzCcinVwzz33CCHmzZvn60IA\nAAC8Kgh77AAAAM5MgdRjd+LECV+XAAAA4L8CKdj17dvX1yUAAAD4L07FAgAABIlACnZDhw4V\nQnz33Xd/OuXrMgEAAHwjkILd1VdfLYT4/PPPeznl6zIBAAB8I5CC3eTJk4UQGzdu9HUhAAAA\n/iiQ+rcmTZp07bXXhoeHO2+2YsUK79QDAADgVwIp2PXv37+srOy0zTIyMrxQDAAAgL8JpFOx\nAAAAcIJgBwAAECQIdgAAAEGCYAcAABAkCHYAAABBgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAA\nAEGCYAcAABAkCHYAAABBgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAqXEBwAACAASURB\nVAcAABAkCHYAAABBgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAcAABAkCHYAAABBgmAH\nAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAcAABAkCHYAAABBgmAHAAAQJAh2AAAAQYJgBwAA\nECQIdgAAAEGCYAcAABAkCHYAAABBgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAcAABAk\nCHYAAABBgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAcAABAkCHYAAABBgmAHAAAQJAh2\nAAAAQYJgBwAAECR6+boAH2tpadHr9RaLxUmbbdu2CSFOnTrlraIAAAC64kwPdgaDYfr06a60\nrK2t9XQxAAAA3XGmBzutVvvhhx+etsdu2bJlI0eO9FpVAAAAXXCmB7uQkJDk5OTTNlu2bFnP\nnlyPCAAA/BphBQAAIEgQ7AAAAIIEwQ4AACBIEOwAAACCBMEOAAAgSBDsAAAAggTBDgAAIEgQ\n7AAAAIIEwQ4AACBIEOwAAACCBMEOAAAgSBDsAAAAggTBDgAAIEgQ7AAAAIIEwQ4AACBIEOwA\nAACCBMEOAAAgSBDsAAAAggTBDgAAIEgQ7AAAAIIEwQ4AACBIEOwAAACCBMEOAAAgSBDsAAAA\nggTBDgAAIEgQ7AAAAIIEwQ4AACBIEOwAAACCBMEOAAAgSBDsAAAAggTBDgAAIEgQ7AAAAIIE\nwQ4AACBIEOwAAACCBMEOAAAgSBDsAAAAggTBDgAAIEgQ7AAAAIIEwQ4AACBIEOwAAACCBMEO\nAAAgSBDsAAAAggTBDgAAIEgQ7AAAAIIEwQ4AACBIEOwAAACCBMEOAAAgSBDsAAAAggTBDgAA\nIEj08nUBndbY2FhaWqrX641Go9lsbmxsDA0NValUarU6KSkpNTVVoVD4ukYAAAAfCLBgZzAY\nZs+ebTKZpBNPnjxpNptra2uLi4szMzNXr16t1Wp9VSEAAICvBFKwMxqNiYmJVqs1JiZGp9Ml\nJCRERETI5XKLxWIymaqqqgoLC+vq6hITE6urq9Vqta/rBQAA8KpACnbZ2dlWqzUlJaWoqEgm\nk0lnxcbGTpw4ccGCBenp6WVlZYsXLy4pKfFVnQAAAD4RSDdPbN68WQiRl5fnkOrsZDLZ0qVL\nhRBbtmzxZmEAAAD+IJCCXUNDgxAiMjLSSRvbXFtLAACAM0ogBbuoqCghRHl5uZM2FRUVQojo\n6Ggv1QQAAOA3AinYpaWlCSF0Ol1lZWW7DSorK3U6nRAiNTXVq5UBAAD4gUC6eSIzM1Ov19fU\n1IwfP16j0cTHx0dERMhkMqvVajKZtm/fbjQahRBxcXGZmZm+LhYAAMDbAinYKZXKrVu3ZmVl\nFRQUGI1GW4yTksvlGRkZOTk5YWFhPqkQAADAhwIp2AkhwsPD8/Pzc3NzDQaD0Wisr69vampS\nKBQqlUqj0Wi1WqVS6esaAQAAfCPAgp2NUqlMTk5OTk72dSEAAAB+JJBungAAAIATgddj19jY\nWFpaqtfrjUaj2WxubGwMDQ1VqVRqtTopKSk1NVWhUPi6RgAAAB8IsGBnMBhmz55tMpmkE0+e\nPGk2m2tra4uLizMzM1evXq3Van1VIQAAgK8EUrAzGo2JiYlWqzUmJkan0yUkJERERMjlcovF\nYjKZqqqqCgsL6+rqEhMTq6ur1Wq1r+sFAADwqkAKdtnZ2VarNSUlpaioyOFxsbGxsRMnTlyw\nYEF6enpZWdnixYtLSkp8VScAAIBPBNLNE5s3bxZC5OXlOaQ6O5lMtnTpUiHEli1bvFkYAACA\nPwikYNfQ0CCEiIyMdNLGNtfWEgAA4IwSSMEuKipKCFFeXu6kTUVFhRAiOjraSzUBAAD4jUAK\ndmlpaUIInU5XWVnZboPKykqdTieESE1N9WplAAAAfiCQbp7IzMzU6/U1NTXjx4/XaDTx8fER\nEREymcxqtZpMpu3bt9ueHhsXF5eZmenrYgEAALwtkIKdUqncunVrVlZWQUGB0Wi0xTgpuVye\nkZGRk5MTFhbmkwoBAAB8KJCCnRAiPDw8Pz8/NzfXYDAYjcb6+vqmpiaFQqFSqTQajVarVSqV\nvq4RAADANwIs2Nkolcrk5OTk5GRfFwIAAOBHAunmCQAAADgReD12jY2NpaWler3eaDSazebG\nxsbQ0FCVSqVWq5OSklJTUxUKha9rBAAA8IEAC3YGg2H27Nkmk0k68eTJk2azuba2tri4ODMz\nc/Xq1Vqt1lcVAgAA+EogBTuj0ZiYmGi1WmNiYnQ6XUJCQkREhFwut1gsJpOpqqqqsLCwrq4u\nMTGxurparVb7ul4AAACvCqRgl52dbbVaU1JSioqKHB4XGxsbO3HixAULFqSnp5eVlS1evLik\npMRXdQIAAPhEIAW7zZs3CyHy8vIcUp2dTCZbunRpWVnZli1bXFxmS0uLXq+3WCxO2mzbtk0I\ncerUqc6VCwAA4F2BFOwaGhqEEJGRkU7a2ObaWrrCYDBMnz7dlZa1tbUuLhMAAMAnAinYRUVF\n7du3r7y8fNKkSR21qaioEEJER0e7uEytVvvhhx+etsdu2bJlI0eO7FS1AAAAXhZIwS4tLS03\nN1en0xUVFV166aVtG1RWVup0OiFEamqqi8sMCQlxZaDjZcuW9ezJmH8AAMCvBVKwy8zM1Ov1\nNTU148eP12g08fHxERERMpnMarWaTKbt27fbnh4bFxeXmZnp62IBAAC8LZCCnVKp3Lp1a1ZW\nVkFBgdFotMU4KblcnpGRkZOTExYW5pMKAQAAfCiQgp0QIjw8PD8/Pzc312AwGI3G+vr6pqYm\nhUKhUqk0Go1Wq1Uqlb6uEQAAwDcCLNjZKJXK5ORkV66NAwAAOHNwQwAAAECQCLweu8bGxtLS\nUr1ebzQazWZzY2NjaGioSqVSq9VJSUmpqakKhcLXNQIAAPhAgAU7g8Ewe/Zsk8kknXjy5Emz\n2VxbW1tcXJyZmbl69WqtVuurCgEAAHwlkIKd0WhMTEy0Wq0xMTE6nS4hISEiIkIul1ssFpPJ\nVFVVVVhYWFdXl5iYWF1drVarfV0vAACAVwVSsMvOzrZarSkpKUVFRQ6Pi42NjZ04ceKCBQvS\n09PLysoWL15cUlLiqzoBAAB8IpBunti8ebMQIi8vzyHV2clksqVLlwohtmzZ4s3CAAAA/EEg\nBbuGhgYhRGRkpJM2trm2lgAAAGeUQAp2UVFRQojy8nInbSoqKoQQ0dHRXqoJAADAbwRSsEtL\nSxNC6HS6ysrKdhtUVlbqdDohRGpqqlcrAwAA8AOBdPNEZmamXq+vqakZP368RqOJj4+PiIiQ\nyWRWq9VkMm3fvt329Ni4uLjMzExfFwsAAOBtgRTslErl1q1bs7KyCgoKjEajLcZJyeXyjIyM\nnJycsLAwn1QIAADgQ4EU7IQQ4eHh+fn5ubm5BoPBaDTW19c3NTUpFAqVSqXRaLRarVKp9HWN\nAAAAvhFgwc5GqVQmJycnJyf7uhAAAAA/Ekg3TwAAAMCJIAx2r7zyyiuvvOLrKgAAALwtIE/F\nOnfPPfcIIebNm+frQgAAALwqCHvsAAAAzkyB1GN34sQJX5cAAADgvwIp2PXt29fXJQAAAPgv\nTsUCAAAEiUAKdkOHDhVCfPfdd3865esyAQAAfCOQgt3VV18thPj88897OeXrMgEAAHwjkILd\n5MmThRAbN270dSEAAAD+KJD6tyZNmnTttdeGh4c7b7ZixQrv1AMAAOBXAinY9e/fv6ys7LTN\nMjIyvFAMAACAvwmkU7EAAABwgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAcAABAkCHYA\nAABBgmAHAAAQJAh2AAAAQYJgBwAAECQIdgAAAEGCYAcAABAkCHYAAABBwtVgN2nSpEmTJnV2\nFgAAALyml4vtNm3a1IVZAAAA8Jrunoptbm4WQvTq5WpABAAAgId0N9hVVlYKIYYMGeKOYgAA\nANB1p+lpGzFihJO3FovlwIEDQojExES3VwYAAIBOOU2w27t3r5O3QogBAwYkJyc/99xzbq4L\nAAAAnXSaYHf8+HHbi759+0rf2vTu3Vsmk3moMgAAAHTKaYKdUqm0vVi2bJn0LQAAAPyNq3ez\n3n///R6tAwAAAN3UuWFKtm3b9sUXXxw+fLilpcVh1rPPPuu+qgAAANBprga748ePp6SkfP75\n5x01INgBAAD4lqvBLjMz8/PPP5fL5XfccceoUaO4ZwIAAMDfuBrs1q5dK4RYuXLlDTfc4Ml6\nAAAA0EWuPnnCbDYLIaZOnerJYgAAANB1rvbYRUZG/vjjj7Ynw/pWY2NjaWmpXq83Go1ms7mx\nsTE0NFSlUqnV6qSkpNTUVIVC4esaAQAAfMDVHrsbb7xRCOHk5gnvMBgM559//pw5c4qLi3fv\n3m02m0+ePGk2m2tra4uLi+fMmTN8+HCDweDbIgEAAHzC1R67rKysrVu3Pvjgg8OGDRs7dqxH\na+qI0WhMTEy0Wq0xMTE6nS4hISEiIkIul1ssFpPJVFVVVVhYWFdXl5iYWF1drVarfVIkAACA\nr7ga7CZMmNDc3HzgwIFx48ZFR0cPGjTIoUF1dbW7a3OUnZ1ttVpTUlKKioocbsuNjY2dOHHi\nggUL0tPTy8rKFi9eXFJS4ul6AAAA/IqrwW7Hjh3217/88ssvv/zimXqc2bx5sxAiLy+vo8FW\nZDLZ0qVLy8rKtmzZ4tXKAAAA/ICrwe7gwYMercMVDQ0NQojIyEgnbWxzbS0BAADOKK4GuyFD\nhni0DldERUXt27evvLx80qRJHbWpqKgQQkRHR3uxLgAAAL/g6l2x/iAtLU0IodPpKisr221Q\nWVmp0+mEEKmpqV6tDAAAwA+42mO3fPly5w3uvPPObhdzGpmZmXq9vqamZvz48RqNJj4+PiIi\nQiaTWa1Wk8m0fft2o9EohIiLi8vMzPR0MQAAAP7G1WB31113OW/ghWCnVCq3bt2alZVVUFBg\nNBptMU5KLpdnZGTk5OSEhYV5uhgAAAB/42qwe+qppxymmM1mvV5fV1d34403jhgxwt2FtS88\nPDw/Pz83N9dgMBiNxvr6+qamJoVCoVKpNBqNVqtVKpXeqQQAAMDfuBrsFi1a1Hbis88+m5SU\npNfrs7Oz3VrVaSiVyuTk5OTkZG+uFAAAwM916+YJhUKRk5Pzxx9/cE0bAACAz7naY9eRUaNG\nCSE+++wzdxTjksbGxtLSUr1ebzQazWZzY2NjaGioSqVSq9VJSUmpqakKhcJrxQAAAPiP7ga7\nY8eOCSFOnDjhjmJOz2AwzJ4922QySSeePHnSbDbX1tYWFxdnZmauXr1aq9V6px4AAAD/0a1g\nd+rUqSVLlgghzj//fDfV44zRaExMTLRarTExMTqdLiEhISIiQi6XWywWk8lUVVVVWFhYV1eX\nmJhYXV2tVqu9UBIAAID/cDXYzZgxw2FKc3NzbW3tDz/8IISYP3++m+tqT3Z2ttVqTUlJKSoq\ncnhcbGxs7MSJExcsWJCenl5WVrZ48eKSkhIvlAQAAOA/XA1277//frvThwwZ8thjj916663u\nK6lDmzdvFkLk5eU5pDo7mUy2dOnSsrKyLVu2eKEeAAAAv+JqsNu2bZvDlJCQkIEDB5533nk9\nevRwd1Xta2hoEEJERkY6aWOba2sJAABwRnE12F188cUercMVUVFR+/btKy8vnzRpUkdtKioq\nhBDR0dFerAsAAMAvdGscOy9LS0sTQuh0usrKynYbVFZW6nQ6IURqaqpXKwMAAPADnbsr9siR\nI2vWrKmurj569Gj//v3HjRs3a9asAQMGeKg4B5mZmXq9vqamZvz48RqNJj4+PiIiQiaTWa1W\nk8m0fft229Nj4+LiGDAZAACcgToR7MrKyubMmSO9fG3VqlW2ceO883QvpVK5devWrKysgoIC\no9Foi3FScrk8IyMjJycnLCzMC/UAAAD4FVeD3a5du2bOnNnc3HzhhRfqdLqoqKj9+/e/+eab\ntbW1qampX375pUaj8WihNuHh4fn5+bm5uQaDwWg01tfXNzU1KRQKlUql0Wi0Wq1SqezUAlta\nWvR6vcVicdLGduPIqVOnulU6AACAh7ka7JYsWdLc3Dx58uT169f36vU/n7rvvvumTp26cePG\n3Nzcd99912NFOlIqlcnJyW7pJjQYDNOnT3elZW1tbfdXBwAA4DmuBjvbGHK5ubn2VCeE6NWr\n1zPPPLNx48bAHTdOq9V++OGHp+2xW7Zs2ciRI71WFQAAQBe4GuwOHz4shIiNjXWYPmLECCGE\n2Wx2b1lONDY2lpaW6vV6o9FoNpsbGxtDQ0NVKpVarU5KSkpNTVUoFK4vLSQkxJWev2XLlvXs\nGUh3EAMAgDOQq8GuX79+v//++/79+21Jzm7//v22ue4vrT0Gg2H27Nkmk0k68eTJk2azuba2\ntri42HYzh1ar9U49AAAA/sPVXijbAMV5eXkO021TLrnkEveW1S6j0ZiYmGgymWJiYp555plN\nmzbt2bPnp59+2rNnz6ZNm5555pmYmBiTyZSYmPjNN994oR4AAAC/4mqP3fz589evX79y5coj\nR45kZGTY7ootKChYt25djx495s+f79EqbbKzs61Wa0pKSlFRkcPjYmNjYydOnLhgwYL09PSy\nsrLFixeXlJR4oSQAAAD/4WqP3VVXXfX888/37Nlz3bp1U6dOHTVq1NSpU9etWxcSErJs2TLv\nnPq03cCRl5fnkOrsZDLZ0qVLhRCBezMHAABAl3VigOIFCxZcffXVK1eurK6uPnbsWL9+/caO\nHZuRkaFWqz1Xn5RtbOTIyEgnbWxzpaMoAwAAnCE690gxjUbz4osveqiU04qKitq3b195efmk\nSZM6alNRUSGEiI6O9mJdAAAAfiGQhvBIS0sTQuh0usrKynYbVFZW6nQ6IURqaqpXKwMAAPAD\nznrsTp48+dRTT/Xt2zcrK6ujNk8//fSJEycee+wxLzyeNTMzU6/X19TUjB8/XqPRxMfHR0RE\nyGQyq9VqMpm2b99ue3psXFxcZmamp4sBAADwN86C3ccff7xkyZL09HQnbb755pvi4uIxY8Z4\noZNMqVRu3bo1KyuroKDAaDTaYpyUXC7PyMjIycnxQsoEAADwN86C3dq1a4UQM2bMcNJmxowZ\nxcXFa9eu9c7Zz/Dw8Pz8/NzcXIPBYDQa6+vrm5qaFAqFSqXSaDRarVapVHqhDAAAAD/kLNjt\n3r1b/OehYR2xzbW19BqlUpmcnOzKo8AAAADOHM5unvjtt9+EEGeffbaTNra5Bw8edG9ZAAAA\n6Cxnwe7YsWNCiL59+zppY5tra+kFLS0tRUVFjz766KuvvmpfaXl5+ZQpU6KjozUazcKFC71W\nDAAAgF9xdip2wIAB9fX1v/32W1RUVEdtbL16AwYMcH9pbfz111/XXHPNpk2bbG9zc3Orqqr2\n7NkzZcqUlpYWIcT+/fu/+eYbvV7/xRdfhIaGeqEkAAAA/+Gsx+7CCy8U/3mQV0cMBoMQYuTI\nkW6tqn3vvPPOpk2bevXqlZGRkZGRcejQoUcfffShhx4KDQ1dvnz5zp07X3311bCwMKPR+PLL\nL3uhHgAAAL/irMdu6tSpmzdvzsvLmzlzZp8+fdo2sFqttmezJiUleapAiZUrVwohnn/++Xvv\nvVcIoVarH3744ebm5uXLl8+dO1cIMXr06FOnTt1zzz3vv//+woULvVASAACA/3DWY3fHHXcM\nHDjw66+/TktLO3r0qMPcP/74Y8aMGbW1tYMGDbrjjjs8WeT/2Ldvn/jP8yeEEDNnzmxubhZC\nTJ8+3d7mn//8pxBi7969XqgHAADArzjrsQsPD//3v/89bdq0Dz744Nxzz73uuutGjx4dHh7e\n0NDw1VdfrV27tqGhoXfv3mvWrHF+g4W7/PHHH0JyPV///v1tL6RX+J111llCiKamJi/UAwAA\n4FecBTshxJQpUz777LM5c+b8/PPPq1atWrVqlXTu0KFDV69ePWHCBA8WKBEREbFv3759+/bZ\nLv6zdeAJIX744QfbFNtr8Z94BwAAcEY5TbATQlxxxRXff//92rVrP/300++//76hoSE8PHz4\n8OFXX331dddd17t3by9UaTN69Oh9+/Y9+eST7777rhBi8eLFkZGRZrM5Ly+vsLBQCNHa2rpk\nyRIhxNixY71WFQAAgJ84fbATQvTu3XvmzJkzZ870dDXOPfjgg6WlpSUlJZ988okQoqGhITc3\nd9euXW+++WZdXV18fPz27dvLy8uFELZ7KQAAAM4oLgU7P5GQkPDKK688/PDDDQ0NQohbbrnl\noYce+v3333fu3FleXm6LdEKIBx98UHo7BQAAwBkikIKdEOLuu+++5ZZbvv/++3POOUelUgkh\nVCrV119/vWbNmt27d/fr1y8pKekf//iHr8sEAADwgQALdkKIsLCwuLg46RS5XH7rrbf6qh4A\nAAA/4WwcOwAAAAQQgh0AAECQINgBAAAECYIdAABAkHA12N1yyy0333yzR0sBAABAd7ga7N59\n993Vq1d7tBQAAAB0h6vBLjIyUgjx22+/ebIYAAAAdJ2rwe66664TQnz66aeeLAYAAABd52qw\ne/LJJ+Pj4x944AHbc1oBAADgb1x98sSVV15ptVrNZvM111xz1llnnXvuuT17/p9QWF1d7YHy\nAAAA4CpXg92OHTvsr48cOXLkyBHP1AMAAIAucjXYHTx40KN1AAAAoJtcDXZDhgzxaB0AAADo\nJp48AQAAECRc7bGz+fjjj4uKin744Yfm5mbb3RLvvfdec3PzzJkze/fu7ZkKAQAA4JJOBLs7\n7rhjxYoVDhPXrFlTVlbWr1+/5ORktxYGAACAznH1VOzq1atXrFgxdOjQiooK6fSbbrpJCLFm\nzRr3lwYAAIDOcDXYvfHGG0KIF154Yfz48dLparVaCFFZWen2ygAAANAprga7mpoaIcTll1/u\nMD0qKkowGAoAAIAfcDXYNTc3CyGUSqUQokePHvbpJ06cEEJw5wQAAIDPuRrszj77bCHEgQMH\nHKbv3r1bCDF06FC3VgUAAIBOczXYTZo0SQixdu1aIemxa21tfeGFF4QQU6ZM8Ux5AAAAcJWr\nwe6hhx5SKBRPPPHEf/3Xf9mmbNq06frrr//oo4/Cw8Pvv/9+j1UIAAAAl7ga7GJjY4uKinr2\n7Dl37txTp04JISZNmrRu3bqwsLD33nvPdgsFAAAAfKgTAxRPnz597969y5cvr6ioOHz4cP/+\n/cePHz9v3rzIyEjP1QcAAAAXde6RYhEREU899ZSHSgEAAEB3uHoqFgAAAH6uEz12p06dev/9\n96urq/fv328b1k6qtLTUrYUBAACgc1wNdvv3709MTKytrfVoNQAAAOgyV4Pd/Pnza2tro6Ki\nMjMzL7zwwj59+ni0LAAAAHSWq8Fu48aNQoh///vfl112mSfrAQAAQBe5evNEa2urEGLs2LGe\nLAYAAABd52qwu+iii4QQv/76qyeLcUljY+Pbb7+dnp4+cuRIlUqlVCpVKpVarU5PT3/77beb\nmpp8XSAAAIBvuBrsHn74YSHE66+/7sliTs9gMJx//vlz5swpLi7evXu32Ww+efKk2Wyura0t\nLi6eM2fO8OHDDQaDb4sEAADwCVevsZs+ffrrr79+//33Hz58+Nprrx00aJBDgwkTJri7NkdG\nozExMdFqtcbExOh0uoSEhIiICLlcbrFYTCZTVVVVYWFhXV1dYmJidXW1Wq32dD0AAAB+pRPj\n2P35558KheKtt95666232s61XYTnUdnZ2VarNSUlpaioSCaTSWfFxsZOnDhxwYIF6enpZWVl\nixcvLikp8XQ9AAAAfsXVYPfaa6/de++9QogRI0ZccMEFPhnuZPPmzUKIvLw8h1RnJ5PJli5d\nWlZWtmXLFq9WBgAA4AdcDXYvvfSSEGLRokU+fFZsQ0ODECIyMtJJG9tcW0sAAIAziqs3T/z8\n889CiPvuu8+TxZxGVFSUEKK8vNxJm4qKCiFEdHS0l2oCAADwG64Gu8GDBwshQkJCPFnMaaSl\npQkhdDpdZWVluw0qKyt1Op0QIjU11auVAQAA+AFXT8XOmDHjhRde2LJlS0pKikcLciIzM1Ov\n19fU1IwfP16j0cTHx0dERMhkMqvVajKZtm/fbjQahRBxcXGZmZm+KhIAAMBXXA122dnZlZWV\n8+fPP/fcc0ePHu3RmjqiVCq3bt2alZVVUFBgNBptMU5KLpdnZGTk5OSEhYX5pEIAAAAfcjXY\nXXHFFc3NzT/99NM//vGP6OjotuPYVVdXu7u2doSHh+fn5+fm5hoMBqPRWF9f39TUpFAoVCqV\nRqPRarVKpdILZQAAAPghV4Pdjh077K9/+eWXX375xTP1uESpVCYnJycnJ/uwBgAAAH/jarA7\nePCgR+sAAABAN7ka7IYMGeLROlzX2NhYWlqq1+uNRqPZbG5sbAwNDVWpVGq1OikpKTU1VaFQ\n+LpGAAAAH+jEI8X8gcFgmD17tslkkk48efKk2Wyura0tLi7OzMxcvXq1Vqv1VYUAAAC+0rlg\nd+TIkTVr1lRXVx89erR///7jxo2bNWvWgAEDPFScA6PRmJiYaLVaY2JidDpdQkJCRESEXC63\nWCwmk6mqqqqwsLCuri4xMbG6ulqtVnunKgAAAD/RiWBXVlY2Z84c6dO6Vq1aZesh8859DNnZ\n2VarNSUlpaioyOFxsbGxsRMnTlywYEF6enpZWdnixYtLSkq8UBIAAID/cPXJE7t27Zo5c2ZD\nQ8OFF16Yl5dXXFycl5c3cuTIY8eOpaamth1SzhM2b94shMjLy3NIdXYymWzp0qVCiC1btnih\nHgAAAL/iao/dkiVLmpubJ0+evH79+l69/udT991339SpUzdu3Jibm/vuu+96rMj/YessjIyM\ndNLGNlfarQgAAHCGcLXHztZblpuba091QohevXo988wzwls9ZFFR3nhCGQAAIABJREFUUUKI\n8vJyJ20qKiqEENHR0V6oBwAAwK+4GuwOHz4shIiNjXWYPmLECCGE2Wx2b1ntSktLE0LodLrK\nysp2G1RWVup0OiFEamqqF+oBAADwK66eiu3Xr9/vv/++f/9+W5Kz279/v22u+0trIzMzU6/X\n19TUjB8/XqPRxMfHR0REyGQyq9VqMpm2b99uu9QvLi4uMzPTC/UAAAD4FVeD3cUXX7x+/fq8\nvLyCggLp9Ly8PCHEJZdc4v7S2lAqlVu3bs3KyiooKDAajW3v2JDL5RkZGTk5OWFhYV6oBwAA\nwK+4Guzmz5+/fv36lStXHjlyJCMjIyoqav/+/QUFBevWrevRo8f8+fM9WqVdeHh4fn5+bm6u\nwWAwGo319fVNTU0KhUKlUmk0Gq1Wq1QqO7XAlpYWvV5vsVictNm2bZsQ4tSpU90qHQAAwMNc\nDXZXXXXV888//9BDD61bt27dunX26SEhIc8//7yXn/SgVCqTk5PdMniewWCYPn26Ky1ra2u7\nvzoAAADP6cQAxQsWLLj66qtXrlxZXV197Nixfv36jR07NiMjI6Cf8aDVaj/88MPT9tgtW7Zs\n5MiRXqsKAACgCzr3SDGNRvPiiy96qBQXNTY2lpaW6vV6o9FoNpsbGxtDQ0NVKpVarU5KSkpN\nTVUoFK4vLSQkxJWev2XLlvXs6eodxAAAAD7harCbNGmSEOKzzz7r1Cy3MxgMs2fPNplM0okn\nT540m821tbXFxcW2R5x5+dQwAACAP3A12G3atKkLs9zLaDQmJiZardaYmBidTpeQkBARESGX\nyy0Wi8lkqqqqKiwsrKurS0xMrK6uDugTxAAAAF3QuVOxbTU3NwshpI+j8Jzs7Gyr1ZqSklJU\nVOTwuNjY2NiJEycuWLAgPT29rKxs8eLFJSUlXigJAADAf3T3ujHbQyCGDBnijmJOw/ZYs7y8\nPIdUZyeTyZYuXSq89YgzAAAAv3KanjaH50w4vLVYLAcOHBBCJCYmur2ythoaGoQQkZGRTtrY\n5tpaAgAAnFFOE+z27t3r5K0QYsCAAcnJyc8995yb62pPVFTUvn37ysvLbbdrtKuiokIIER0d\n7YV6AAAA/MppTsUe/w+HtzYWi+XIkSNvvfVW//79PV+qSEtLE0LodDrb+d+2KisrdTqdECI1\nNdUL9QAAAPiV0/TY2Z/QtWzZMulbn8jMzNTr9TU1NePHj9doNPHx8RERETKZzGq1mkym7du3\n254eGxcXl5mZ6cM6AQAAfMLVu1nvv/9+j9bhCqVSuXXr1qysrIKCAqPRaItxUnK5PCMjIycn\nJywszCcVAgAA+JCrwe6vv/7asGFDnz59Jk+ebJvy+uuvv/rqq6dOnXrkkUfmzJnjsQr/j/Dw\n8Pz8/NzcXIPBYDQa6+vrm5qaFAqFSqXSaDRarda3fYoAAAA+5Gqwe++992688cabbrrJFuw+\n+eSTf/3rX7ZZt956a0xMzPjx4z1VYxtKpTI5OdmVR4EBAACcOVwdx66wsFAIkZGRYXv7xhtv\njBs37rfffrv77rtbW1tfffVVTxXoslOnTvm6BAAAAF9yNdjt2bNHCHHBBRcIIVpbWzdv3nz7\n7bcPHjzYdu3d9u3bPVei1IkTJ06ePCmdYjab77jjDpVKFRISMmTIkHvvvffYsWPeKQYAAMCv\nuBrsDh8+LITo16+fEOLnn3/+448/EhIShBBRUVFCiF9//dVjFf6vzZs39+3bd/r06fYpx44d\nGz9+/IoVK8xmsxDi0KFD+fn5Wq3WYrF4oR4AAAC/4mqwGzx4sBDil19+EUJUVlbK5XLbUyiO\nHj0qhOjbt6/HKvxf+fn5Qoj58+fbpyxZsuT7778fPHjwBx988Ouvv37wwQeDBw/+6quvbIOz\nAAAAnFFcDXZjxowRQixdunT//v2vvvrq5Zdf3qdPHyHETz/9JIQYNmyYxyr8X1VVVUKIcePG\n2aesW7dOCPHSSy9Nnz797LPPnj59+ksvvSSEeO+997xQDwAAgF9xNdjdf//9PXr0WLFiRXR0\ndGVlpb3bbP369UKIqVOneqpACdvpYGnv4I8//iiEkD5h7KqrrhJCfPfdd16oBwAAwK+4Guwu\nv/zyDz/8cOrUqVOnTi0uLr7mmmts07/44othw4bdcMMNHqvwf0VGRgohduzYYZ8SGhoqhOjd\nu7d9iq0fEQAA4AzkarATQkybNu3jjz/++OOPbc9stdm4cWNdXd15553ngdocJSYmCiEyMzOt\nVqttii1fbtu2zd7G9hjZ2NhYL9QDAAD+f3v3HxdVge9//AMmMwMjleloDpDtolwVFDcDzX4s\nZG2w0oNMkEq3dWKz37lst2y0bdFYM3x8uVe3si6SLdsurr8w945uWiNKpF5KaUSvK7cfa1Mm\nFYr8Gn/A94/zuDy4gCMKzJlz5vX8o4ec8+Gczzkfi3dnOOfAr1xCsFPdv/7rv4aGhpaXl99y\nyy1Op7OtrS0/Pz8iIuLpp58+fPiwiBw+fFh5/IrP3oQBAADgP3r65olVq1Z5L3jkkUd63cxF\nREVFrVu37t577/2v//qv5ORki8Vy4403JiQkbNq0aezYsWFhYcoj7lJTUx9//PH+bgYAAMDf\n9DTYPfroo94LfBDsRCQ1NfXjjz9+5plntm3bduLECeXWDUVjY+Pw4cNzcnLmz59/xRU9PS4A\nAADd6GkAWrJkSacltbW1DoejpqbmgQceUJ5p5xtjx451OBxfffXVhx9++Pnnnzc0NJhMpmHD\nho0fP37SpEnBwVr6cBkAAKAP9TTYLVq0qOvCl19+OTU11eFwLF68uE+7uriIiIhZs2b5eKcA\nAAD+rFfXt0wmU15eXl1dnd1u76uGAAAAcHl6+8Hl+PHjRWTHjh190QwAAAAuX2+D3alTp0Sk\noaGhL5oBAADA5etVsGttbV22bJmIjB49uo/6AQAAwGXq6c0TM2fO7LTkzJkz1dXVn332mYi0\nvzoWAAAAaulpsNuwYUO3y4cPH/7CCy/MnTu371oCAADA5ehpsOv4PlbFgAEDrrnmmuuvvz4o\nKKivuwIAAMAl62mwmzx5cr/2AQAAgF7iPQ0AAAA64S3YNTY2LliwIC8vz0vNSy+9tGDBgsbG\nxr5uDAAAAJfGW7D729/+tmzZsoMHD3qpOXjw4LJlyxwOR183BgAAgEvjLdht3LhRunvQSUfK\nWqUSAAAAKvIW7A4dOiQi//Iv/+KlRlmrVAIAAEBF3oLd8ePHReTaa6/1UqOs/eabb/q2LQAA\nAFwqb8FOeQ/soEGDvNQoa5VKAAAAqMhbsLv66qvlf6/bXYiyVqkEAACAirwFu7Fjx4rIzp07\nvdQ4nU4RGTduXJ92BQAAgEvmLdj9/Oc/F5Hly5efOXOm2wKPx5Ofny8iqamp/dEcAAAAes5b\nsHv44YevueaaTz/9NDMz8+TJk53W1tXVzZw5s7q6eujQoQ8//HB/NgkAAICL8/au2PDw8Hfe\neWf69OmbN2++7rrrZsyYMXHixPDw8Pr6+v3792/cuLG+vn7gwIF/+ctfvN9gAQAAAB/wFuxE\n5Gc/+9mOHTsefPDBL7/8cs2aNWvWrOm4duTIkcXFxTfffHM/NggAAICeuUiwE5Hbbrvt6NGj\nGzdu3L59+9GjR+vr68PDw0eNGnXHHXfMmDFj4MCBPugSAAAAF3XxYCciAwcOnDVr1qxZs/q7\nGwAAAFw2bzdPAAAAQEMIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDY\nAQAA6ATBDgAAQCd69OYJv9LU1LR+/XqHw+FyuWpra5uamkJDQy0WS2xsbGpqakZGhslkUrtH\nAAAAFWgs2Dmdzjlz5rjd7o4LGxsba2trq6ur165da7fbi4uLk5KS1OoQAABALVoKdi6XKyUl\nxePxREdH22y2xMREq9VqNBpbWlrcbvfevXuLiopqampSUlIqKytjY2PV7hcAAMCntBTsFi9e\n7PF40tPTS0pKDAZDx1UxMTHJyck5OTlZWVmlpaW5ubnr1q1Tq08AAABVaOnmiZ07d4rI8uXL\nO6W6dgaDIT8/X0TKysp82RgAAIA/0FKwq6+vF5GIiAgvNcpapRIAACCgaCnYRUZGisju3bu9\n1JSXl4tIVFSUj3oCAADwG1oKdpmZmSJis9kqKiq6LaioqLDZbCKSkZHh084AAAD8gJZunrDb\n7Q6Ho6qqaurUqXFxcQkJCVar1WAweDwet9u9b98+l8slIvHx8Xa7Xe1mAQAAfE1Lwc5sNu/a\ntWvhwoWFhYUul0uJcR0Zjcbs7Oy8vLywsDBVOgQAAFCRloKdiISHh69cuXLp0qVOp9Plcp04\ncaK5udlkMlkslri4uKSkJLPZrHaPAAAA6tBYsFOYzea0tLS0tDS1GwEAAPAjWrp5AgAAAF5o\n74pdU1PT+vXrHQ6Hy+Wqra1tamoKDQ21WCyxsbGpqakZGRkmk0ntHgEAAFSgsWDndDrnzJnj\ndrs7LmxsbKytra2url67dq3dbi8uLk5KSlKrQwAAALVoKdi5XK6UlBSPxxMdHW2z2RITE61W\nq9FobGlpcbvde/fuLSoqqqmpSUlJqaysjI2NVbtfAAAAn9JSsFu8eLHH40lPTy8pKen0utiY\nmJjk5OScnJysrKzS0tLc3Nx169ap1ScAAIAqtHTzxM6dO0Vk+fLlnVJdO4PBkJ+fLyJlZWW+\nbAwAAMAfaCnY1dfXi0hERISXGmWtUgkAABBQtBTsIiMjRWT37t1easrLy0UkKirKRz0BAAD4\nDS0Fu8zMTBGx2WwVFRXdFlRUVNhsNhHJyMjwaWcAAAB+QEs3T9jtdofDUVVVNXXq1Li4uISE\nBKvVajAYPB6P2+3et2+f8vbY+Ph4u92udrMAAAC+pqVgZzabd+3atXDhwsLCQpfLpcS4joxG\nY3Z2dl5eXlhYmCodAgAAqEhLwU5EwsPDV65cuXTpUqfT6XK5Tpw40dzcbDKZLBZLXFxcUlKS\n2WxWu0cAAAB1aCzYKcxmc1paWlpaWu83df78eYfD0dLS4qXmo48+EpHW1tbe7w4AAKD/aDLY\n9SGn03n33Xf3pLK6urq/mwEAAOgN7QW7pqam9evXOxwOl8tVW1vb1NQUGhpqsVhiY2NTU1Mz\nMjJMJlPPt5aUlPTuu+9e9IpdQUHBuHHjet07AABAP9JYsHM6nXPmzHG73R0XNjY21tbWVldX\nr1271m63FxcXJyUl9XCDAwYM6MlHugUFBcHBWno0DAAACEBaCnYulyslJcXj8URHR9tstsTE\nRKvVajQaW1pa3G733r17i4qKampqUlJSKisrY2Nj1e4XAADAp7QU7BYvXuzxeNLT00tKSjq9\nLjYmJiY5OTknJycrK6u0tDQ3N3fdunVq9QkAAKAKLX28uHPnThFZvnx5p1TXzmAw5Ofni0hZ\nWZkvGwMAAPAHWgp29fX1IhIREeGlRlmrVAIAAAQULQW7yMhIEdm9e7eXmvLychGJioryUU8A\nAAB+Q0vBLjMzU0RsNltFRUW3BRUVFTabTUQyMjJ82hkAAIAf0NLNE3a73eFwVFVVTZ06NS4u\nLiEhwWq1GgwGj8fjdrv37dunvD02Pj7ebrer3SwAAICvaSnYmc3mXbt2LVy4sLCw0OVyKTGu\nI6PRmJ2dnZeXFxYWpkqHAAAAKtJSsBOR8PDwlStXLl261Ol0ulyuEydONDc3m0wmi8USFxeX\nlJRkNpvV7hEAAEAdGgt2CrPZnJaW1pM3RgAAAAQOLd084d2BAwcOHDigdhcAAACq0eQVu25N\nnDhRRNra2tRuBAAAQB36uWIHAAAQ4LR0xS4oKKjnNVy6AwAAgYYrdgAAADqhpWAXFRUVHBw8\nf/78b7/9tq4LpabTlwAAAIFDS8Guurr68ccfX7FiRVJS0uHDh6/6v5SaTl8CAAAEDi0FO7PZ\nvGLFioqKiuDg4JtvvvmJJ544ffq02k0BAAD4Cy0FO0ViYuInn3ySm5tbWFg4bty4v/3tb2p3\nBAAA4Be0F+xEZODAgYsWLaqqqho5cmRaWtqsWbO+/fZbtZsCAABQmSaDnSImJqasrGzVqlV/\n//vfx4wZo3Y7AAAAKtNwsBORoKCgefPmHTp06Kc//anBYDAYDGp3BAAAoBotPaD4QkaMGLFx\n40a1uwAAAFCZtq/YAQAAoB3BDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACg\nEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7\nAAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAA\nnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnbhC\n7QYuWVNT0/r16x0Oh8vlqq2tbWpqCg0NtVgssbGxqampGRkZJpNJ7R4BAABUoLFg53Q658yZ\n43a7Oy5sbGysra2trq5eu3at3W4vLi5OSkpSq0MAAAC1aCnYuVyulJQUj8cTHR1ts9kSExOt\nVqvRaGxpaXG73Xv37i0qKqqpqUlJSamsrIyNjVW7XwAAAJ/SUrBbvHixx+NJT08vKSkxGAwd\nV8XExCQnJ+fk5GRlZZWWlubm5q5bt06tPgEAAFShpZsndu7cKSLLly/vlOraGQyG/Px8ESkr\nK/NlYwAAAP5AS8Guvr5eRCIiIrzUKGuVSgAAgICipWAXGRkpIrt37/ZSU15eLiJRUVE+6gkA\nAMBvaCnYZWZmiojNZquoqOi2oKKiwmaziUhGRoZPOwMAAPADWrp5wm63OxyOqqqqqVOnxsXF\nJSQkWK1Wg8Hg8Xjcbve+fftcLpeIxMfH2+12tZsFAADwNS0FO7PZvGvXroULFxYWFrpcLiXG\ndWQ0GrOzs/Py8sLCwlTpEAAAQEVaCnYiEh4evnLlyqVLlzqdTpfLdeLEiebmZpPJZLFY4uLi\nkpKSzGaz2j0CAACoQ2PBTmE2m9PS0tLS0tRuBAAAwI9o6eYJAAAAeKG9K3ZNTU3r1693OBwu\nl6u2trapqSk0NNRiscTGxqampmZkZJhMJrV7BAAAUIHGgp3T6ZwzZ47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on the Log Transformed column","message":["%r","","hist(CUST_DF_FILTERED$LOG_TOTAL_AUTOM_PAYMENTS, breaks = 10, col = \"blue\", ","     xlab = \"LOG_TOTAL_AUTOM_PAYMENTS\", ylab = \"Customer Count\", ","     main = paste(\"Histogram of LOG_TOTAL_AUTOM_PAYMENTS\"))","     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h4YGBgdXV1SaTaffu3atWrTp27FhsbGxxcXFU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\nAEARFDsAAABFU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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Data Transformation One-Hot Encoding","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Data Transformation: One-Hot Encoding","In this notebook, we demonstrate how to perform one-hot encoding using OML4R using the customer insurance lifetime value dataset, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","","**Note**: If the user plans to use the in-database algorithms, one hot encoding is automatically applied for those algorithms requiring it. The in-databae algorithms automatically explode the categorical columns and fit the model on the prepared data internally. For more details, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html\" target=\"_blank\">OML4R documentation on Data Preparation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to CUSTOMER_INSURANCE_LTV and subset of columns","message":["%r","","ore.sync(query = c(\"CUST_DF\" = \"SELECT * FROM CUSTOMER_INSURANCE_LTV\"))","ore.attach()","","CUST_CAT_DF <- CUST_DF[, c(\"CUSTOMER_ID\", \"MARITAL_STATUS\", \"STATE\", \"GENDER\", \"REGION\")]","","z.show(head(CUST_CAT_DF, 5))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create function for one hot encoding","message":["%r ","","encode <- function(DF, ids, col) {","  uniqueValues <- ore.pull(unique(DF[col])) # Get the unique values of column col","  RESULT <- cbind(ids, DF[col]) # RESULT begins as ids and col to be encoded","","  # Rename columns","  col_index <- which(colnames(RESULT) == col)","  colnames(RESULT)[col_index] <- \"target_col\""," ","  # Add One Hot Encoded columns to RESULT","  for (x in 1:nrow(uniqueValues)){","    RESULT <- cbind(RESULT, new_col = ifelse(RESULT$target_col == uniqueValues[x,], 1, 0)) # Add encoded column","    # Rename encoded column","    index <- which(colnames(RESULT) == \"new_col\")","    colnames(RESULT)[index] <- paste(col, gsub(\" \", \"\", uniqueValues[x,1]), sep='.')","  }"," ","  # Now that One Hot Encoded columns have been added, remove original column","  RESULT <- RESULT[-c(col_index)]"," ","  return(RESULT)","}"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one hot encoding to MARITAL_STATUS column and check the transformed data","message":["%r","","rownames(CUST_CAT_DF) <- CUST_CAT_DF$CUSTOMER_ID # Ordering rows of CUST_CAT_DF before applying function","","ONEHOT_DF <- encode(CUST_CAT_DF, CUST_CAT_DF['CUSTOMER_ID'], \"MARITAL_STATUS\")","","z.show(head(ONEHOT_DF))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one-hot encoding to all selected categorical columns","message":["%r","","RES_DF <- CUST_CAT_DF[\"CUSTOMER_ID\"]","","for (col in colnames(CUST_CAT_DF)[-c(1)]){","  ","  ONEHOT_DF <- encode(CUST_CAT_DF, CUST_CAT_DF[\"CUSTOMER_ID\"], col)","  ","  RES_DF <- full_join(RES_DF, ONEHOT_DF)","}","","rownames(RES_DF) <- 1:nrow(RES_DF) # Renaming rows of RES_DF","","z.show(head(RES_DF))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/r/OML4R Dataset Creation.dsnb b/machine-learning/notebooks/r/OML4R Dataset Creation.dsnb
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--- a/machine-learning/notebooks/r/OML4R Dataset Creation.dsnb	
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Dataset Creation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Dataset Creation","","This script loads the sample datasets `MTCARS` and `IRIS` and imports them into your Oracle Autonomous Database instnace using the ore.create() function.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use mtcars dataset to create table","message":["%r","","options(warn=-1)","ore.sync(use.keys=FALSE)","ore.attach()","","if (ore.exists(\"MTCARS\")) ore.drop(table=\"MTCARS\")","","ore.create(mtcars, table=\"MTCARS\")","","ore.exec(\"ANALYZE TABLE MTCARS COMPUTE STATISTICS\")","","z.show(cat(\"Shape:\", dim(MTCARS)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use iris dataset to create table.","message":["%r","","if (ore.exists(\"IRIS_R\")) ore.drop(table=\"IRIS_R\")","","ore.create(iris, table = \"IRIS_R\")","","ore.exec(\"ANALYZE TABLE IRIS_R COMPUTE STATISTICS\")","","z.show(cat(\"Shape:\", dim(IRIS_R)))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/r/OML4R Feature Engineering Aggregation.dsnb b/machine-learning/notebooks/r/OML4R Feature Engineering Aggregation.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/r/OML4R Feature Engineering Aggregation.dsnb	
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Engineering Aggregation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Feature Engineering: Aggregation","In this notebook, we demonstrate how to perform aggregation for min, max, mean, and count using OML4R.","","Using the SH schema SALES table, which contains transaction records for each customer and products purchased, we create features by aggregating the amount sold for each customer and product pair.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/prepare-and-explore-data-database.html#GUID-9EA7BB02-A334-4ADF-A37A-7042370FB258\" target=\"_blank\">OML4R Data Preparation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Import R libraries","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a proxy object to the SALES data and view a few records.","message":["%r","","ore.sync(query = c(\"SALES_DF\" = \"select cust_id, prod_id, amount_sold from sh.sales\"))","ore.attach()","","z.show(head(SALES_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View multiple records for each customer and product pair with count","message":["%r","","OUTPUT <- rename(count(SALES_DF, CUST_ID, PROD_ID), \"count\" = n)","","z.show(head(OUTPUT, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Compute cross tabulation using min, max, mean, and count for each customer and product ","message":["%r"," ","MAX_DF <- summarise(group_by(SALES_DF, CUST_ID, PROD_ID), \"max(AMOUNT_SOLD)\" = max(AMOUNT_SOLD))","MIN_DF <- summarise(group_by(SALES_DF, CUST_ID, PROD_ID), \"min(AMOUNT_SOLD)\" = min(AMOUNT_SOLD))","MEAN_DF <-summarise(group_by(SALES_DF, CUST_ID, PROD_ID), \"mean(AMOUNT_SOLD)\" = mean(AMOUNT_SOLD))","COUNT_DF <- summarise(group_by(SALES_DF, CUST_ID, PROD_ID), \"count(AMOUNT_SOLD)\" = n())"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","z.show(head(MAX_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","z.show(head(MIN_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","z.show(head(MEAN_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","z.show(head(COUNT_DF, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Join all results","message":["%r","","AGG_DF <- merge(MAX_DF, MIN_DF)","AGG_DF <- merge(AGG_DF, MEAN_DF)","AGG_DF <- merge(AGG_DF, COUNT_DF)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View aggregated data","message":["%r","","z.show(head(round(AGG_DF, 2), 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/r/OML4R Feature Extraction ESA Wiki Model.dsnb b/machine-learning/notebooks/r/OML4R Feature Extraction ESA Wiki Model.dsnb
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--- a/machine-learning/notebooks/r/OML4R Feature Extraction ESA Wiki Model.dsnb	
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Extraction ESA Wiki Model","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714409258524,"interpreter":"md.high","endTime":1714409258585,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for R function *ore.odmESA* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we'll use the Wikipedia Model as an example:",""," - Create an OML4R proxy ESA model object for the pre-loaded Wikipedia model "," - Examples of using the Wiki ESA model"," - Basic ESA Wiki model features for words"," - Basic ESA Wiki model features for phrases"," - ESA Wiki Model document similarity on phrases"," - ESA Wiki Model document similarity on long phrases","","*Note: The pre-built Wikipedia model must be installed in your Autonomous Database instance to run this notebook. See instructions below.*","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714409258662,"interpreter":"md.high","endTime":1714409258737,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base.  It is the process of understanding the meaning of a section of text as a combination of the concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <code>semantic relatedness<\/code> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>The Oracle Machine Learning for R function <em>ore.odmESA<\/em> extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>In this notebook, we'll use the Wikipedia Model as an example:<\/p>\n<ul>\n<li>Create an OML4R proxy ESA model object for the pre-loaded Wikipedia model<\/li>\n<li>Examples of using the Wiki ESA model<\/li>\n<li>Basic ESA Wiki model features for words<\/li>\n<li>Basic ESA Wiki model features for phrases<\/li>\n<li>ESA Wiki Model document similarity on phrases<\/li>\n<li>ESA Wiki Model document similarity on long phrases<\/li>\n<\/ul>\n<p><em>Note: The pre-built Wikipedia model must be installed in your Autonomous Database instance to run this notebook. See instructions below.<\/em><\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a>"],"enabled":true,"result":{"startTime":1714409258824,"interpreter":"md.high","endTime":1714409258889,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using the Wikipedia model as an example","","The pre-built Wikipedia model must be loaded to the Oracle Autonomous Database. It is a relatively large model object at 800 MB.","","Follow the setup in template notebook `OML Import ESA Wiki Model`.","","Relevant Oracle blogs:","* <a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" onclick=\"return ! window.open('https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2');\"> New Wikipedia-Based Cognitive Model Available for Text Processing <\/a> ","* <a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" onclick=\"return ! window.open('https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model');\"> Text Analytics using a pre-built Wikipedia-based Topic Model <\/a> ","* <a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" onclick=\"return ! window.open('https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics');\"> Explicit Semantic Analysis (ESA) for Text Analytics <\/a>","","We will also need to create a model proxy object for the Wiki model into OML4R"," - Use the included function ore.createESA.wiki_model() to create our proxy object"," - This function retrieves information about the Wikipedia model and uses that information to create our ESA model using the ore.odmESA function"],"enabled":true,"result":{"startTime":1714409258968,"interpreter":"md.high","endTime":1714409259029,"results":[{"message":"<h2 id=\"using-the-wikipedia-model-as-an-example\">Using the Wikipedia model as an example<\/h2>\n<p>The pre-built Wikipedia model must be loaded to the Oracle Autonomous Database. It is a relatively large model object at 800 MB.<\/p>\n<p>Follow the setup in template notebook <code>OML Import ESA Wiki Model<\/code>.<\/p>\n<p>Relevant Oracle blogs:<\/p>\n<ul>\n<li><a href=\"https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2\" onclick=\"return ! window.open('https://blogs.oracle.com/machinelearning/how-to-get-started-using-oracle-advanced-analytics-122-new-feature%3a-explicit-semantic-analysis-v2');\"> New Wikipedia-Based Cognitive Model Available for Text Processing <\/a><\/li>\n<li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" onclick=\"return ! window.open('https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model');\"> Text Analytics using a pre-built Wikipedia-based Topic Model <\/a><\/li>\n<li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" onclick=\"return ! window.open('https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics');\"> Explicit Semantic Analysis (ESA) for Text Analytics <\/a><\/li>\n<\/ul>\n<p>We will also need to create a model proxy object for the Wiki model into OML4R<\/p>\n<ul>\n<li>Use the included function ore.createESA.wiki_model() to create our proxy object<\/li>\n<li>This function retrieves information about the Wikipedia model and uses that information to create our ESA model using the ore.odmESA function<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete existing policies and create new policies for the Wiki Lexer with settings","message":["%r ","","# Should be executed in a separate session, otherwise it will not work","","ore.exec(\"BEGIN ctx_ddl.drop_policy('wiki_txtpol'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","ore.exec(\"BEGIN ctx_ddl.drop_preference( 'wiki_lexer'); EXCEPTION WHEN OTHERS THEN NULL; END;\")","ore.exec(\"BEGIN ctx_ddl.create_preference('wiki_lexer', 'BASIC_LEXER'); END;\")","ore.exec(\"BEGIN ctx_ddl.set_attribute('wiki_lexer', 'INDEX_STEMS', 'ENGLISH'); END;\")","ore.exec(\"BEGIN ctx_ddl.create_policy(policy_name => 'wiki_txtpol', lexer => 'wiki_lexer'); END;\")"],"enabled":true,"result":{"startTime":1714409259109,"interpreter":"r.high","endTime":1714409259488,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1714409259569,"interpreter":"r.high","endTime":1714409259693,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" Check to see if the WIKI_MODEL is available","message":["%sql","","select MODEL_NAME, MINING_FUNCTION, ALGORITHM, ALGORITHM_TYPE, CREATION_DATE from USER_MINING_MODELS where MODEL_NAME='WIKI_MODEL'"],"enabled":true,"result":{"startTime":1714409259771,"interpreter":"sql.high","endTime":1714409259868,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\nWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2023-11-08 19:36:42\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create function for getting a proxy model object for the pre-loaded Wikipedia Model","message":["%r"," ","ore.createESA.wiki_model <- function () {","  model.name <- \"WIKI_MODEL\"","  attr(model.name, \"owner\") <- OREbase:::.ore.schema()","  formula <- \"~ TEXT\"","  formula <- as.formula(formula, env = parent.frame())","  env.lst <- list()","  model.settings   <- OREdm:::.ore.getModelSettings(model.name,\"esas_\")","  model.attributes <- OREdm:::.ore.getModelAttributes(model.name)","  query <- paste(","    \"SELECT \",","    \"FEATURE_ID, \",","    \"nvl2(attribute_subname, \", \"attribute_name\",\"||\",\"'.'\", \"||\",","    \"attribute_subname,  attribute_name) attribute_name,\",","    \"COEFFICIENT \",","    \"FROM \", \"DM$VAWIKI_MODEL\",","    \" where ROWNUM <= 10 order by 1,2,3\",","    sep=\"\");","  features.all <- OREdm:::.ore.frame4query(query, extRef = env.lst)","  esa.mod <- OREdm:::.create.ore.odmESA(model.name,","                                        model.settings,","                                        model.attributes,","                                        formula,","                                        env.lst,","                                        match.call(),","                                        features=features.all)","}"],"enabled":true,"result":{"startTime":1714409259950,"interpreter":"r.high","endTime":1714409260073,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Call the function to obtain proxy object","message":["%r ","","esa_wiki_mod <- ore.createESA.wiki_model()"],"enabled":true,"result":{"startTime":1714409260152,"interpreter":"r.high","endTime":1714409260291,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the first 100 rows of the 21.7 mi rows of features","message":["%r"," ","z.show(head(features(esa_wiki_mod), 100))"],"enabled":true,"result":{"startTime":1714409260370,"interpreter":"r.high","endTime":1714409321847,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\n'Adud al-Dawla\tTEXT.ABBAD\tNA\t0.0191274174186567\n'Adud al-Dawla\tTEXT.ABBASID\tNA\t0.0249151625558249\n'Adud al-Dawla\tTEXT.ABU\tNA\t0.059714023220152\n'Adud al-Dawla\tTEXT.ADUD\tNA\t0.565944673491387\n'Adud al-Dawla\tTEXT.ALABAMA\tNA\t0.22831900492864\n'Adud al-Dawla\tTEXT.ARDASHIR\tNA\t0.0111940903957132\n'Adud al-Dawla\tTEXT.AYYAD\tNA\t0.0249760282115664\n'Adud al-Dawla\tTEXT.BADR\tNA\t0.00986964445080499\n'Adud al-Dawla\tTEXT.BAGHDAD\tNA\t0.0459066966195087\n'Adud al-Dawla\tTEXT.BAHA\tNA\t0.0104589006276045\n'Adud al-Dawla\tTEXT.BAHRAM\tNA\t0.0161236096703584\n'Adud al-Dawla\tTEXT.BAKR\tNA\t0.0134365270355992\n'Adud al-Dawla\tTEXT.BALOCH\tNA\t0.0104772299872558\n'Adud al-Dawla\tTEXT.BAM\tNA\t0.00887829440770621\n'Adud al-Dawla\tTEXT.BANI\tNA\t0.00975646268058504\n'Adud al-Dawla\tTEXT.BAQILLANI\tNA\t0.0144646112351293\n'Adud al-Dawla\tTEXT.BARDAS\tNA\t0.0128361499358996\n'Adud al-Dawla\tTEXT.BEDOUIN\tNA\t0.00872220567501009\n'Adud al-Dawla\tTEXT.BUYID\tNA\t0.0846594574850417\n'Adud al-Dawla\tTEXT.BUYIDS\tNA\t0.0194587149365686\n'Adud al-Dawla\tTEXT.BYZANTINES\tNA\t0.0202176515677629\n'Adud al-Dawla\tTEXT.BYZANTIUM\tNA\t0.00820039029135046\n'Adud al-Dawla\tTEXT.CALIPH\tNA\t0.0232450776566311\n'Adud al-Dawla\tTEXT.CARAVANSERAI\tNA\t0.0114335306364663\n'Adud al-Dawla\tTEXT.CHILDLESS\tNA\t0.00801411366862151\n'Adud al-Dawla\tTEXT.CHOSROES\tNA\t0.0134748733114124\n'Adud al-Dawla\tTEXT.CONCUBINE\tNA\t0.00802143665804979\n'Adud al-Dawla\tTEXT.CONSUMMATE\tNA\t0.00815164021102195\n'Adud al-Dawla\tTEXT.DAWLA\tNA\t0.723942878242064\n'Adud al-Dawla\tTEXT.DAYLAMITE\tNA\t0.0501999484162784\n'Adud al-Dawla\tTEXT.DAYLAMITES\tNA\t0.0143428417394205\n'Adud al-Dawla\tTEXT.DESPOT\tNA\t0.00935953765459191\n'Adud al-Dawla\tTEXT.DINAR\tNA\t0.00961407929094202\n'Adud al-Dawla\tTEXT.DIYAR\tNA\t0.0191274174186567\n'Adud al-Dawla\tTEXT.EMBELLISH\tNA\t0.00786795749121632\n'Adud al-Dawla\tTEXT.EMIR\tNA\t0.0314917677186082\n'Adud al-Dawla\tTEXT.ENRICHMENT\tNA\t0.00811412374362538\n'Adud al-Dawla\tTEXT.EUPHRATES\tNA\t0.00874861239537755\n'Adud al-Dawla\tTEXT.FADL\tNA\t0.0226663780212495\n'Adud al-Dawla\tTEXT.FAKHR\tNA\t0.0275993124557871\n'Adud al-Dawla\tTEXT.FARAJ\tNA\t0.0116971284535617\n'Adud al-Dawla\tTEXT.GOR\tNA\t0.0112543478978115\n'Adud al-Dawla\tTEXT.GORGAN\tNA\t0.0122048966987822\n'Adud al-Dawla\tTEXT.GRAMMARIAN\tNA\t0.00925693943414468\n'Adud al-Dawla\tTEXT.HAMADAN\tNA\t0.0169517398437838\n'Adud al-Dawla\tTEXT.HAMDANID\tNA\t0.0188381765657075\n'Adud al-Dawla\tTEXT.HAMDANIDS\tNA\t0.0259449538696399\n'Adud al-Dawla\tTEXT.HARUN\tNA\t0.0101103503589411\n'Adud al-Dawla\tTEXT.HASAN\tNA\t0.0151329041024834\n'Adud al-Dawla\tTEXT.HORMUZ\tNA\t0.0109490678074295\n'Adud al-Dawla\tTEXT.HUSAIN\tNA\t0.0149480328455242\n'Adud al-Dawla\tTEXT.HUSAYN\tNA\t0.00956774133608969\n'Adud al-Dawla\tTEXT.IBN\tNA\t0.0842530506638853\n'Adud al-Dawla\tTEXT.ILYAS\tNA\t0.0170241892897465\n'Adud al-Dawla\tTEXT.IMAD\tNA\t0.0332821869454185\n'Adud al-Dawla\tTEXT.IMAM\tNA\t0.0116784256335568\n'Adud al-Dawla\tTEXT.IMPALE\tNA\t0.00899550414807461\n'Adud al-Dawla\tTEXT.IMRAN\tNA\t0.0095032072007147\n'Adud al-Dawla\tTEXT.IRRIGATE\tNA\t0.00806235431550003\n'Adud al-Dawla\tTEXT.ISFAHAN\tNA\t0.0144289732453719\n'Adud al-Dawla\tTEXT.ISFANDIYAR\tNA\t0.0147419758625679\n'Adud al-Dawla\tTEXT.ISHAQ\tNA\t0.00995878319735963\n'Adud al-Dawla\tTEXT.IZZ\tNA\t0.129121646709541\n'Adud al-Dawla\tTEXT.KAKI\tNA\t0.0116170421624621\n'Adud al-Dawla\tTEXT.KARBALA\tNA\t0.0109616928162925\n'Adud al-Dawla\tTEXT.KARD\tNA\t0.0137655251180668\n'Adud al-Dawla\tTEXT.KARUN\tNA\t0.0115225213027311\n'Adud al-Dawla\tTEXT.KERMAN\tNA\t0.0322142654226283\n'Adud al-Dawla\tTEXT.KERMANSHAH\tNA\t0.0116971284535617\n'Adud al-Dawla\tTEXT.KHALAF\tNA\t0.0117388107268482\n'Adud al-Dawla\tTEXT.KHORRAMSHAHR\tNA\t0.0134748733114124\n'Adud al-Dawla\tTEXT.KHUSRAW\tNA\t0.0776226895619185\n'Adud al-Dawla\tTEXT.KHUZESTAN\tNA\t0.0110264383618927\n'Adud al-Dawla\tTEXT.KURD\tNA\t0.0129519058817242\n'Adud al-Dawla\tTEXT.LAQAB\tNA\t0.0137655251180668\n'Adud al-Dawla\tTEXT.MAKAN\tNA\t0.0228670612729326\n'Adud al-Dawla\tTEXT.MAKRAN\tNA\t0.017806772201188\n'Adud al-Dawla\tTEXT.MANSUR\tNA\t0.00904863982642141\n'Adud al-Dawla\tTEXT.MARSHY\tNA\t0.00847075416697272\n'Adud al-Dawla\tTEXT.MARZUBAN\tNA\t0.0221129637938519\n'Adud al-Dawla\tTEXT.MOBAD\tNA\t0.0145969810444171\n'Adud al-Dawla\tTEXT.MOSUL\tNA\t0.0190851912529848\n'Adud al-Dawla\tTEXT.MU\tNA\t0.0488914103923194\n'Adud al-Dawla\tTEXT.MUDAR\tNA\t0.0131222336053104\n'Adud al-Dawla\tTEXT.MUFID\tNA\t0.0136132687777778\n'Adud al-Dawla\tTEXT.MUQADDASI\tNA\t0.0119662927857039\n'Adud al-Dawla\tTEXT.NAJAF\tNA\t0.0108399242519064\n'Adud al-Dawla\tTEXT.NASIR\tNA\t0.00877548570835643\n'Adud al-Dawla\tTEXT.NISHAPUR\tNA\t0.0114509174976689\n'Adud al-Dawla\tTEXT.NOMAD\tNA\t0.00768822108128912\n'Adud al-Dawla\tTEXT.NOWRUZ\tNA\t0.0114509174976689\n'Adud al-Dawla\tTEXT.OMAN\tNA\t0.0116225388283156\n'Adud al-Dawla\tTEXT.PATRONIZE\tNA\t0.0116340695331498\n'Adud al-Dawla\tTEXT.PERSEPOLIS\tNA\t0.0103280488741183\n'Adud al-Dawla\tTEXT.POLEMIC\tNA\t0.00847075416697272\n'Adud al-Dawla\tTEXT.QABUS\tNA\t0.0515969173816329\n'Adud al-Dawla\tTEXT.QAZVIN\tNA\t0.0116170421624621\n'Adud al-Dawla\tTEXT.RAHBA\tNA\t0.0144646112351293\n'Adud al-Dawla\tTEXT.RECONQUER\tNA\t0.0081983274118409\n'Adud al-Dawla\tTEXT.RUKN\tNA\t0.078612357636442\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Basic settings of the ESA model and the 21.7 mi rows of features","message":["%r","","summary(esa_wiki_mod)"],"enabled":true,"result":{"startTime":1714409321951,"interpreter":"r.high","endTime":1714409345077,"results":[{"message":"\nCall:\nore.createESA.wiki_model()\n\nSettings: \n                                               value\nmin.items                                         25\ntopn.features                                   1000\nvalue.threshold                            .00000001\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nodms.text.max.features                        200000\nodms.text.min.documents                            5\nodms.text.policy.name                    wiki_txtpol\nprep.auto                                         ON\n\nFeatures: \n             FEATURE_ID  ATTRIBUTE_NAME COEFFICIENT\n1     French Resistance TEXT.TRANQUILLE 0.009966666\n2      It's Oh So Quiet TEXT.TRANQUILLE 0.125462949\n3         Jacques Marin TEXT.TRANQUILLE 0.061751623\n4           Jean Lesage TEXT.TRANQUILLE 0.057978394\n5  Jean-Jacques Goldman TEXT.TRANQUILLE 0.064475395\n6             La Fouine TEXT.TRANQUILLE 0.025642043\n7           Marcel Mule TEXT.TRANQUILLE 0.046433684\n8     Maurice Duplessis TEXT.TRANQUILLE 0.053106132\n9         Quebec French TEXT.TRANQUILLE 0.023032906\n10       Thompson River TEXT.TRANQUILLE 0.050367835","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Examples using the Wiki ESA model"," "],"enabled":true,"result":{"startTime":1714409345158,"interpreter":"md.high","endTime":1714409345222,"results":[{"message":"<h2 id=\"examples-using-the-wiki-esa-model\">Examples using the Wiki ESA model<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Basic example of ESA features for the word 'Bank'","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"Bank\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1714409345300,"interpreter":"r.high","endTime":1714409355109,"results":[{"message":"P1\tP2\nBanking in India\t0.12073074740514\nBanking in Australia\t0.11861539327698\nBank of America\t0.113071554116955\nBank\t0.109941636462343\nCentral bank\t0.107803077904466\nNational Bank Act\t0.106470427774569\nStandard Bank\t0.105024979812532\nBankBoston\t0.104744369832675\nOnline banking\t0.102503159518508\nBank of England\t0.096106744902625\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" ESA features for the words 'NFL Games'","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"NFL Games\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1714409355193,"interpreter":"r.high","endTime":1714409365104,"results":[{"message":"P1\tP2\nNational Football League\t0.144836254979624\nNFL International Series\t0.139684141888651\nThursday Night Football\t0.114362409545099\nHistory of the National Football League championship\t0.112721298239857\nChicago College All-Star Game\t0.111212971099872\nNFL Network\t0.110076945165456\nNFL Films\t0.106270569533189\nThe NFL Today\t0.102924455670641\nNational Football League Christmas games\t0.101040928011359\nPlayoff Bowl\t0.0925872345552612\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for a text about using R on Oracle Database","message":["%r ","","TEMP_DF <- ore.push(data.frame(ID = 1, TEXT = c(\"Data scientists and broader R users can take advantage of the R ecosystem on data managed by Oracle Database. R provides a suite of software packages for data manipulation, graphics, statistical functions, and machine learning algorithms. Oracle Machine Learning for R extends R\u2019s capabilities through three primary areas: transparent access and manipulation of database data from R, in-database machine learning algorithms, ease of deployment using embedded R execution.\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1714409365192,"interpreter":"r.high","endTime":1714409375404,"results":[{"message":"P1\tP2\nDatabase\t0.138153264333878\nData mining\t0.135965584589925\nMachine learning\t0.1325152013941\nData science\t0.117836217441854\nOracle Corporation\t0.116486459958878\nSupervised learning\t0.110513880296051\nAnalysis of algorithms\t0.105911885236646\nMicrosoft Access\t0.0994831076884941\nObject database\t0.0936664783762529\nEnterprise software\t0.0935335850823769\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for a text about OML4Spark","message":["%r ","","TEMP_DF <- ore.push(data.frame(ID = 1, TEXT = c(\"OML4Spark R API provides functions for manipulating data stored in a local File System, HDFS, HIVE, Spark DataFrames, Impala, Oracle Database, and other JDBC sources. OML4Spark takes advantage of all the nodes of a Hadoop cluster for scalable, high-performance machine learning modeling in Big Data environments. \")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1714409375499,"interpreter":"r.high","endTime":1714409385411,"results":[{"message":"P1\tP2\nApache Hadoop\t0.35186953266221\nApache Spark\t0.218089549030044\nJava Database Connectivity\t0.184992809374709\nData modeling\t0.142714506107067\nDatabase\t0.140527053633504\nHigh-availability cluster\t0.136451747421796\nData warehouse\t0.119438462115326\nData mining\t0.117130383015355\nOracle Corporation\t0.111502625481305\nBig data\t0.106993497700792\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for a long text about the Oracle Machine Learning AutoML UI","message":["%r","","TEMP_DF <- ore.push(data.frame(ID = 1, TEXT = c(\"Oracle Machine Learning AutoML User Interface makes it easy for citizen data scientists and expert data scientists to build and deploy machine learning models. OML AutoML UI, a new component of Oracle Machine Learning on Oracle Autonomous Database, provides a no-code browser-based interface that automates the machine learning modeling process and simplifies deployment to just a few clicks. While data scientists may prefer to write code, perhaps using the OML4Py AutoML feature, OML AutoML UI makes machine learning possible for a much broader set of users, who may \u201cknow enough ML to be dangerous\u201d but are not familiar with specific algorithm details and the modeling process to refine usable models. At the same time, OML AutoML UI delivers significant productivity improvements for data scientists as a modeling accelerator.\")))","","z.show(predict(esa_wiki_mod, TEMP_DF, type = c(\"class\", \"raw\"))[-c(3)])"],"enabled":true,"result":{"startTime":1714409385528,"interpreter":"r.high","endTime":1714409395346,"results":[{"message":"P1\tP2\nMachine learning\t0.155704473503718\nData modeling\t0.1409575620346\nUser interface\t0.128292546465855\nOne UI\t0.124529147700334\nData mining\t0.121863746936165\nSoftware design\t0.109564690786559\nBusiness process modeling\t0.106090024431756\nDatabase\t0.0965120800867556\nData science\t0.0891308488999173\nImplementation\t0.0817726804242852\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Comparing Strings for Similarity of Text using the Wikipedia Model"],"enabled":true,"result":{"startTime":1714409395430,"interpreter":"md.high","endTime":1714409395491,"results":[{"message":"<h3 id=\"comparing-strings-for-similarity-of-text-using-the-wikipedia-model\">Comparing Strings for Similarity of Text using the Wikipedia Model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare \"fish\" vs \"salmon\" ","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"fish\", \"salmon\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1714409395570,"interpreter":"r.high","endTime":1714409407282,"results":[{"message":"SIMILARITY\n0.146447776027241\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare \"fish\" vs \"hawk\" ","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"fish\", \"hawk\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1714409407363,"interpreter":"r.high","endTime":1714409418080,"results":[{"message":"SIMILARITY\n0.000369489611985352\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare two short texts about Machine Learning ","message":["%r ","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"Regression models predict values on a continuous range.\", \"Classification algorithms assign data to two or more classes.\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1714409418158,"interpreter":"r.high","endTime":1714409429376,"results":[{"message":"SIMILARITY\n0.168889290068795\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare text about Machine Learning and Geography ","message":["%r ","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"Regression models predict values on a continuous range.\", \"The U.S. is bordered by Canada and Mexico.\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1714409429456,"interpreter":"r.high","endTime":1714409440271,"results":[{"message":"SIMILARITY\n0.0102609559472403\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare text about OML4R and SailGP ","message":["%r","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"OML4R has data types that map R data types to SQL data types. In an R session, when you create database objects from R objects or you create R objects from database data, OML4R translates R data types to SQL data types and the reverse where possible. OML4R creates objects that are instances of OML4R classes. OML4R overloads many standard R functions so that they use OML4R classes and data types. R language constructs and syntax are supported for objects that are mapped to Oracle Database objects.\", \"In the SailGP league, thousands of sensors are attached to the catamarans and their crew members, generating millions of data points that are shared with broadcast partners, fans, race officials, and importantly, across the fleet. Each team has full access to one another\u2019s data for post-race analysis, with the goal that sailing performance on the water, not technology, should determine the winner. \")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1714409440354,"interpreter":"r.high","endTime":1714409451272,"results":[{"message":"SIMILARITY\n0.23962673310945\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare text about OML4R and OML4Py ","message":["%r ","","TEMP_DF <- ore.push(data.frame(TEXT = c(\"OML4R has data types that map R data types to SQL data types. In an R session, when you create database objects from R objects or you create R objects from database data, OML4R translates R data types to SQL data types and the reverse where possible. OML4R creates objects that are instances of OML4R classes. OML4R overloads many standard R functions so that they use OML4R classes and data types. R language constructs and syntax are supported for objects that are mapped to Oracle Database objects.\", \"Oracle Machine Learning for Python (OML4Py) is a component of the Oracle Database Enterprise Edition that makes open source Python ready for the enterprise and big data. OML4Py integrates Python with the Oracle Database and provides a comprehensive, database-centric environment for end-to-end analytical processing. OML4Py provides the users the capability to execute Python functions on database-resident data for data exploration and preparation, whilst seamlessly leveraging the Oracle Database as a high-performance computing (HPC) environment. Furthermore, Python users can natively invoke powerful in-database machine learning algorithms directly in Python.\")))","","z.show(feature_compare(esa_wiki_mod, TEMP_DF, compare.cols = \"TEXT\"))"],"enabled":true,"result":{"startTime":1714409451355,"interpreter":"r.high","endTime":1714409462771,"results":[{"message":"SIMILARITY\n0.591289940606868\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":{"startTime":1714409462862,"interpreter":"md.high","endTime":1714409462946,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714409463035,"interpreter":"md.high","endTime":1714409463103,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Extraction ESA","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) is an unsupervised algorithm for feature extraction of explicit features based on an existing knowledge base. ESA assists in understanding the meaning of a section of text as a combination of concepts found in the text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA improves text document categorization by calculating `semantic relatedness` (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","The Oracle Machine Learning for R function *ore.odmESA* extracts text-based features from a corpus of documents and performs document similarity comparisons. All processing occurs inside Oracle Autonomous Database.","","In this notebook, we cover the basic usage of ESA, where we extract topics from the comments provided. ",""," - Create a small sample dataset"," - Use the ESA Model to predict a feature vector on test data"," - Identify top features"," - Document similarity"," - Change model settings","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create and view data","message":["%r","","TITLE <- c('Aids in Africa: Planning for a long war',","                'Mars rover maneuvers for rim shot',","                'Mars express confirms presence of water at Mars south pole',","                'NASA announces major Mars rover finding',","                'Drug access, Asia threat in focus at AIDS summit',","                'NASA Mars Odyssey THEMIS image: typical crater',","                'Road blocks for Aids')","","ID <- 1:length(TITLE)","","df <- data.frame(ID, TITLE)","ESA_TXT <- ore.push(df)","","z.show(ESA_TXT)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a policy for text indexing","message":["%r","","# Drop the text policy if it exists","ore.exec(paste(\"Begin ctx_ddl.drop_policy('ESA_TXT_POLICY'); ","                EXCEPTION WHEN others THEN null; END;\",sep=\"\"))","","# Create text policy","ore.exec(\"Begin ctx_ddl.create_policy('ESA_TXT_POLICY'); End;\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Specify model settings","message":["%r","","odm <- list(case_id_column_name     = \"ID\", ","            ODMS_TEXT_POLICY_NAME   = \"ESA_TXT_POLICY\", ","            ODMS_TEXT_MIN_DOCUMENTS = 1, ","            ESAS_MIN_ITEMS          = 1, ","            model_name              = 'ESA_MOD')","","ctx <- list(TITLE = \"TEXT(POLICY_NAME:ESA_TXT_POLICY)(TOKEN_TYPE:STEM)\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create an ESA model object ","message":["%r","","# Delete model if it exists","ore.exec(paste(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> '\", 'ESA_MOD', \"'); ","EXCEPTION WHEN others THEN null; END;\",sep=\"\"))","","# Create model","esa_mod <- ore.odmESA(~TITLE, ESA_TXT, odm.settings = odm, ctx.settings = ctx)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model settings","message":["%r","","z.show(settings(esa_mod))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%r","","summary(esa_mod)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the model details for each feature","message":["%r","","esa_mod$features$COEFFICIENT <- round(esa_mod$features$COEFFICIENT, digits=4)","z.show(head(esa_mod$features, 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the model name using ALL_MINING_MODELS view","message":["%sql","","SELECT * from all_mining_models WHERE ALGORITHM = 'EXPLICIT_SEMANTIC_ANALYS'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View model name using USER_MINING_MODELS view","message":["%sql","","SELECT * from user_mining_models where model_name = 'ESA_MOD'"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions on data","message":["%r","","esa_pred <- predict(esa_mod, ESA_TXT, type = \"class\", supplemental.cols = c(\"ID\", \"TITLE\"))","z.show(esa_pred)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Feature compare titles","message":["%r","","z.show(feature_compare(esa_mod, ESA_TXT, compare.cols = c('TITLE'), supplemental.cols = c('TITLE')))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Change the settings and create new model","message":["%r","","new_setting <- list(case_id_column_name = \"ID\", ","                    ODMS_TEXT_POLICY_NAME = \"ESA_TXT_POLICY\",","                    ESAS_VALUE_THRESHOLD = 0.0001, ","                    ODMS_TEXT_MAX_FEATURES = 3, ","                    ESAS_MIN_ITEMS = 1,","                    ESAS_TOPN_FEATURES = 3,","                    model_name = 'ESA_MOD_2')","                    "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the model with new settings - 2 features maximum ","message":["%r","","# Delete if ESA model exists, then create it","","ore.exec(paste(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> '\", 'ESA_MOD_2', \"'); ","  EXCEPTION WHEN others THEN null; END;\",sep=\"\"))","","esa_mod2 <- ore.odmESA(~TITLE, ESA_TXT, odm.settings = new_setting, ctx.settings = ctx)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List ESA model views","message":["%sql","","SELECT view_name, view_type FROM user_mining_model_views","WHERE model_name='ESA_MOD_2'","ORDER BY view_name;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"The VA view shows associations among features (topics) and words","message":["%md","","*ATTRIBUTE_SUBNAME* refers to words, or tokens. The strength of the associations is measured by *COEFFICIENT*. Higher coefficient values indicate a stronger association."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * from DM$VAESA_MOD_2;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show the associations for selected words","message":["%sql","","SELECT ATTRIBUTE_SUBNAME, count(ATTRIBUTE_SUBNAME) FROM DM$VAESA_MOD_2","WHERE ATTRIBUTE_SUBNAME='MARS'","GROUP BY ATTRIBUTE_SUBNAME"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the help file for ESA","message":["%r","","help(\"ore.odmESA\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Clean-up","message":["%script","","BEGIN","  DBMS_DATA_MINING.DROP_MODEL(model_name => 'ESA_MOD');","  EXCEPTION WHEN others THEN null;","END;","/","BEGIN","  DBMS_DATA_MINING.DROP_MODEL(model_name => 'ESA_MOD_2');","  EXCEPTION WHEN others THEN null;","END;","/"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/r/OML4R Feature Extraction SVD.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Extraction SVD","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715727066074,"interpreter":"md.low","endTime":1715727066152,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4R Feature Extraction: Singular Value Decomposition","","The Oracle Machine Learning for R function ore.odmSVD creates a model that uses the Singular Value Decomposition (SVD) algorithm for feature extraction.","","In this notebook, we demonstrate how to use the in-database SVD for feature extraction using the Breast Cancer dataset from the University of Wisconsin.","We show that Principal Component Analysis (PCA) can be achieved using SVD results. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715727066231,"interpreter":"md.low","endTime":1715727066294,"results":[{"message":"<h1 id=\"oml4r-feature-extraction-singular-value-decomposition\">OML4R Feature Extraction: Singular Value Decomposition<\/h1>\n<p>The Oracle Machine Learning for R function ore.odmSVD creates a model that uses the Singular Value Decomposition (SVD) algorithm for feature extraction.<\/p>\n<p>In this notebook, we demonstrate how to use the in-database SVD for feature extraction using the Breast Cancer dataset from the University of Wisconsin.\nWe show that Principal Component Analysis (PCA) can be achieved using SVD results. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-singular-value-decomposition\" target=\"_blank\">OML Singular Value Decomposition<\/a>"],"enabled":true,"result":{"startTime":1715727066374,"interpreter":"md.low","endTime":1715727066436,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-singular-value-decomposition\" target=\"_blank\">OML Singular Value Decomposition<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Initialize the environment","message":["%r","","library(ORE)","library(OREdplyr)","library(lattice)","options(ore.warn.order=FALSE)","","ore.attach()"],"enabled":true,"result":{"startTime":1715727066519,"interpreter":"r.low","endTime":1715727066644,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create temporary database table from mtcars and obtain proxy object","message":["%r","","MTCARS <- ore.push(mtcars)","","cat(\"Shape:\", dim(MTCARS))"],"enabled":true,"result":{"startTime":1715727066724,"interpreter":"r.low","endTime":1715727066948,"results":[{"message":"Shape: 32 11","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"View the ore.odmSVD help file","message":["%r","","help(\"ore.odmSVD\")"],"enabled":true,"result":{"startTime":1715727067024,"interpreter":"r.low","endTime":1715727067400,"results":[{"message":"ore.odmSVD                package:OREdm                R Documentation\n\nIn-Database Singular Value Decomposition Models\n\nDescription:\n\n     Creates a Singular Value Decomposition (SVD) model for feature\n     extraction using Oracle Data Mining in Oracle Database 12.2 or\n     later.\n\nUsage:\n\n     ore.odmSVD(formula,                        \n             data, \n             auto.data.prep = TRUE,\n             na.action = na.pass,\n             odm.settings = NULL,\n             ctx.settings = NULL)\n     \n     ## S3 method for class 'ore.odmSVD'\n     features(object,...)\n     \n     ## S3 method for class 'ore.odmSVD'\n     feature_compare(object,\n       newdata,\n       compare.cols = NULL,\n       supplemental.cols = NULL)\n     \n     ## S3 method for class 'ore.odmSVD'\n     predict(object,\n       newdata,                \n       supplemental.cols = NULL,\n       type = c(\"class\",\"raw\"),\n       na.action = na.pass,\n       topN = NULL,\n       topN.attrs = FALSE,...)\n     \n     u(object)\n     ## S3 method for class 'ore.odmSVD'\n     u(object)\n     \n     v(object)\n     ## S3 method for class 'ore.odmSVD'\n     v(object)\n     \n     d(object)\n     ## S3 method for class 'ore.odmSVD'\n     d(object)\n     \nArguments:\n\n formula: An object of class \u2018formula\u2019 (or one that can be coerced to\n          that class): a symbolic description of the model to be\n          fitted. For details of the model specification, see\n          'Details'.\n\n    data: An \u2018ore.frame\u2019 object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n          Mining should invoke automatic data preparation for the\n          build.\n\nna.action: A function to use for handling missing values, either\n          \u2018na.pass\u2019 to allow missing values or \u2018na.omit\u2019 to remove rows\n          with missing values. The default value is \u2018na.pass\u2019.\n\nodm.settings: Same as \u2018odm.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\nctx.settings: Same as \u2018ctx.settings\u2019 in \u2018ore.odmKMeans\u2019.\n\n  object: An object of type \u2018ore.odmSVD\u2019.\n\n newdata: The data used for scoring.\n\ncompare.cols: Columns used to compare the features.  All the columns\n          are included by default.\n\nsupplemental.cols: Columns to include in the output.\n\n    type: If set to \u2018\"raw\"\u2019, provides the probability for each feature\n          returned. If set to \u2018\"class\"\u2019, the feature with the maximum\n          probability is returned.\n\n    topN: A positive integer that restricts the set of features to\n          those that have one of the specified number of highest\n          probability values.\n\ntopN.attrs: boolean, positive integer, False (default).  Returns the\n          top N most influence attributes of the predicted target value\n          for regression if topN_attrs is not False.  Returns the top N\n          most influence attributes of the highest probability class\n          for classification if topN_attrs is not False.  N is equal to\n          the specified positive integer or 5 if topN_attrs is True.\n\n     ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n     Singular Value Decomposition (SVD) is a feature extraction\n     algorithm. SVD is orthogonal linear transformations that capture\n     the underlying variance of the data by decomposing a rectangular\n     matrix into three matrixes: \u2018U\u2019, \u2018D\u2019, and \u2018V\u2019. Matrix \u2018D\u2019 is a\n     diagonal matrix and its singular values reflect the amount of data\n     variance captured by the bases.\n\nValue:\n\n     An object of class \"ore.odmSVD\" includes the following components:\n\n    name: The name of the in-database model.\n\nsettings: A \u2018data.frame\u2019 of settings used to build the model.\n\nattributes: A \u2018data.frame\u2019 of attributes used to build the model.\n\n formula: The \u2018formula\u2019 used for the model fitted.\n\n    call: The matched call.\n     The \u2018d\u2019 function returns an \u2018ore.frame\u2019 with the singular values\n     of the input data. Column \u2018FEATURE_ID\u2019 represents the singular\n     value ID. The \u2018v\u2019 function returns an \u2018ore.frame\u2019 whose columns\n     contain the right singular vectors. The column names match with\n     \u2018FEATURE_ID\u2019 values. The \u2018u\u2019 function returns an \u2018ore.frame\u2019 whose\n     columns contain the left singular vectors. \u2018CASE_ID_COLUMN_NAME\u2019\n     and \u2018SVDS_U_MATRIX_OUTPUT\u2019 must be specified in argument\n     \u2018odm.settings\u2019 to product this result.\n\n     The \u2018features\u2019 function returns an \u2018ore.frame\u2019 the same as the \u2018v\u2019\n     function does except in an (vertical) unpivot format.  The columns\n     are:\n\nFEATURE_ID: The numeric identifier associated with the singular value.\n\nATTRIBUTE_NAME: The name of the attribute.\n\nATTRIBUTE_VALUE: The value of the attribute.\n\n   VALUE: The coefficient value associated with the attribute in the\n          new space.\n     The \u2018feature_compare\u2019 function returns an \u2018ore.frame\u2019 containing a\n     \u2018SIMILARITY\u2019 column and supplementary columns if specified.\n\nAuthor(s):\n\n     Oracle\n\n     Maintainer: Oracle <email: oracle-r-enterprise@oracle.com>\n\nReferences:\n\n     Oracle R Enterprise\n\n     Oracle Data Mining Concepts\n\n     Oracle Data Mining User's Guide\n\nSee Also:\n\n     \u2018ore.odmNMF\u2019, \u2018ore.odmESA\u2019, \u2018partitions\u2019\n\nExamples:\n\n     IRIS <- ore.push(cbind(Id = seq_along(iris[[1L]]), iris))\n     \n     svd.mod <- ore.odmSVD(~. -Id, IRIS)\n     summary(svd.mod)\n     d(svd.mod)\n     v(svd.mod)\n     head(predict(svd.mod, IRIS, supplemental.cols = \"Id\"))\n     \n     svd.pmod <- ore.odmSVD(~. -Id, IRIS, \n                   odm.settings = list(odms_partition_columns = \"Species\"))\n     summary(svd.pmod)\n     d(svd.pmod)\n     v(svd.pmod)\n     head(predict(svd.pmod, IRIS, supplemental.cols = \"Id\"))\n     ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVD model","message":["%r ","","ore.drop(model = 'SVD_PCA_MODEL')","","svd_mod <- ore.odmSVD(~., MTCARS, auto.data.prep=TRUE, ","                      odm.settings = list(svds_scoring_mode='svds_scoring_pca', model_name = \"SVD_PCA_MODEL\")) "],"enabled":true,"result":{"startTime":1715727067493,"interpreter":"r.low","endTime":1715727068992,"results":[{"message":"Warning message:\n\u201cThe created mining model SVD_PCA_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show the model details","message":["%r","","svd_mod"],"enabled":true,"result":{"startTime":1715727069076,"interpreter":"r.low","endTime":1715727069195,"results":[{"message":"\nCall:\nore.odmSVD(formula = ~., data = MTCARS, auto.data.prep = TRUE, \n    odm.settings = list(svds_scoring_mode = \"svds_scoring_pca\", \n        model_name = \"SVD_PCA_MODEL\"))\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nscoring.mode                             scoring.pca\nu.matrix.output                     u.matrix.disable\nd: \n   FEATURE_ID      VALUE\n1           1 760.183838\n2           2 212.399507\n3           3  17.098697\n4           4   7.275124\n5           5   5.047101\n6           6   3.694440\n7           7   1.718096\n8           8   1.592160\n9           9   1.395823\n10         10   1.172860\n11         11   1.104777","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"The D matrix contains the singular values","message":["%r","","svd_d <- d(svd_mod)","rownames(svd_d) <- svd_d$FEATURE_ID","","value_total <- sum(svd_d$VALUE)","","svd_d <- mutate(svd_d, Percent_of_Total = 100 * (svd_d['VALUE'] / value_total))","","z.show(round(svd_d, 4))"],"enabled":true,"result":{"startTime":1715727069272,"interpreter":"r.low","endTime":1715727069781,"results":[{"message":"FEATURE_ID\tVALUE\tPercent_of_Total.VALUE\n1\t760.1838\t75.0664\n2\t212.3995\t20.974\n3\t17.0987\t1.6885\n4\t7.2751\t0.7184\n5\t5.0471\t0.4984\n6\t3.6944\t0.3648\n7\t1.7181\t0.1697\n8\t1.5922\t0.1572\n9\t1.3958\t0.1378\n10\t1.1729\t0.1158\n11\t1.1048\t0.1091\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Perform dimensionality reduction","message":["%r","","result <- predict(svd_mod, MTCARS, supplemental.cols = c(\"mpg\"), type=\"raw\")","","# Giving result a unique key","rownames(result) <- result$FEATURE_ID ","","# displaying Target & first 3 features. ","z.show(head(result[,1:4], 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projections"],"enabled":true,"result":{"startTime":1715727102753,"interpreter":"md.low","endTime":1715727102813,"results":[{"message":"<h2 id=\"using-the-svd-model-for-principal-component-projections\">Using the SVD model for Principal Component projections<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop the table if it exists","message":["%r "," ","tryCatch({ ore.drop(table=\"MTCARS_PCA_PROJECTIONS\") })"],"enabled":true,"result":{"startTime":1715727071042,"interpreter":"r.low","endTime":1715727071269,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a table that contains the projections (SQL section)","message":["%r ","","ore.exec('CREATE TABLE MTCARS_PCA_PROJECTIONS AS ","          SELECT \"drat\", \"disp\", \"hp\", ","                 FEATURE_VALUE(SVD_PCA_MODEL, 1 USING *) PCA_PROJ1,","                 FEATURE_VALUE(SVD_PCA_MODEL, 2 USING *) PCA_PROJ2,","                 FEATURE_VALUE(SVD_PCA_MODEL, 3 USING *) PCA_PROJ3","          FROM MTCARS","          ORDER BY 1, 2')","          "],"enabled":true,"result":{"startTime":1715727071345,"interpreter":"r.low","endTime":1715727071533,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Look at the PCA projections","message":["%r"," ","ore.sync(table=\"MTCARS_PCA_PROJECTIONS\")","ore.attach()","","z.show(head(MTCARS_PCA_PROJECTIONS, 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data - top 3 attributes","message":["%r","","# Convert data to local data.frame object for plotting","ore.sync(table = \"MTCARS_PCA_PROJECTIONS\")","mtcars_proj_local <- ore.pull(MTCARS_PCA_PROJECTIONS)","","","splom(~mtcars_proj_local[,c(\"drat\", \"disp\", \"hp\")], ","      groups = drat, data = mtcars_proj_local, panel = panel.superpose, ","      diag.panel = function(x, ...){","        yrng <- current.panel.limits()$ylim","        d <- density(x, na.rm=TRUE, bw=\"SJ\")","        d$y <- with(d, yrng[1] + 0.95 * diff(yrng) * y / max(y) )","        panel.lines(d)","        diag.panel.splom(x, ...)","      })"],"enabled":true,"result":{"startTime":1715727071869,"interpreter":"r.low","endTime":1715727072514,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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abv8u7nfD6CEbOZbY65tv03fvue75vRrDGNlU0o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\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7MnFgVGYiJ6epR7qXDO9OkAN2/mfkRC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\n3FjBYMULUeA3MiGV/cG8Uo3SMi+xyLPmFWlTmXX/EPZQPzusX78+EBQUlOO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ge","title":"SVD-PCA projections - top 3 principal components","message":["%r ","","splom(~mtcars_proj_local[,c(\"PCA_PROJ1\", \"PCA_PROJ2\", \"PCA_PROJ3\")], ","      groups = PCA_PROJ1, data = mtcars_proj_local, panel = panel.superpose, ","      diag.panel = function(x, ...){","        yrng <- current.panel.limits()$ylim","        d <- density(x, na.rm=TRUE, bw=\"SJ\")","        d$y <- with(d, yrng[1] + 0.95 * diff(yrng) * y / max(y) )","        panel.lines(d)","        diag.panel.splom(x, ...)","      })"],"enabled":true,"result":{"startTime":1715727072607,"interpreter":"r.low","endTime":1715727072982,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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OZa3LXNZzfHp8RPenNSNe9quR/crTLjdjN2F/VL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\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g0HFHSRXXva7uDshy8s4aRnSvNVVMt3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r1uHjg7G+fqFIJuPF+Sw8l00ZjXm9erOy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\nGcqTMEkTmvrQN4LRGqxpgzaBCKx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Create model with new parameters to produce U matrix output"],"enabled":true,"result":{"startTime":1715727073061,"interpreter":"md.low","endTime":1715727073120,"results":[{"message":"<h1 id=\"create-model-with-new-parameters-to-produce-u-matrix-output\">Create model with new parameters to produce U matrix output<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop the table if it exists","message":["%r ","","tryCatch({ ore.drop(table=\"MTCARS_IND\") })"],"enabled":true,"result":{"startTime":1715727073195,"interpreter":"r.low","endTime":1715727073407,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table with index variable","message":["%r","","ore.exec(\"CREATE TABLE MTCARS_IND as select rownum case_id, b.* from MTCARS b\")"],"enabled":true,"result":{"startTime":1715727073480,"interpreter":"r.low","endTime":1715727073665,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the new table with index variable","message":["%r ","","ore.sync(table = \"MTCARS_IND\")","","z.show(head(MTCARS_IND, 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\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Feature Selection Algorithm-based.dsnb b/machine-learning/notebooks/r/OML4R Feature Selection Algorithm-based.dsnb
deleted file mode 100755
index 2ac44f47..00000000
--- a/machine-learning/notebooks/r/OML4R Feature Selection Algorithm-based.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Selection Algorithm-based","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Feature Selection: Supervised Algorithm","","In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms via OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we build a random forest model to predict whether the customer by insurance or not, then use feature importance to perform feature selection. ","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","###### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a prerequisite to the current notebook.","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/random-forest.html\" target=\"_blank\">OML4Py Random Forest<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get a proxy object to the CUSTOMER_INSURANCE_LTV table and assign rownames for row indexing","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV', use.keys= TRUE)","ore.attach()","rownames(CUSTOMER_INSURANCE_LTV) <- CUSTOMER_INSURANCE_LTV$CUSTOMER_ID"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUSTOMER_INSURANCE_LTV table","message":["%r","","z.show(head(CUSTOMER_INSURANCE_LTV))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create Train and Test (60/40 split) datasets","message":["%r","","set.seed(2)","sampleSize <- nrow(CUSTOMER_INSURANCE_LTV)*.4","ind <- sample(1:nrow(CUSTOMER_INSURANCE_LTV),sampleSize)","group <- as.integer(1:nrow(CUSTOMER_INSURANCE_LTV) %in% ind)","","CUSTOMER_INSURANCE_LTV.train <- CUSTOMER_INSURANCE_LTV[group==FALSE,]","CUSTOMER_INSURANCE_LTV.test <- CUSTOMER_INSURANCE_LTV[group==TRUE,]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply Attribute Importance algorithm to obtain the attribute importance","message":["%r","","ai_mod <- ore.odmAI(BUY_INSURANCE ~., CUSTOMER_INSURANCE_LTV.train)","","ai_mod$importance$variable <- row.names(ai_mod$importance)","z.show(head(ai_mod$importance), 10)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get the top attributes from Attribute Importance algorithm greater than threshold value","message":["%r","","threshold <- 0.089","features <- rownames(ai_mod$importance)[ai_mod$importance$importance >= threshold]","","z.show(head(ai_mod$importance, n = 10))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Include the target variable to attribute vector","message":["%r","","features <- c(features, \"BUY_INSURANCE\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create a new ORE Frame with top attributes for predicting the target variable","message":["%r","","DF_NEW <- subset(CUSTOMER_INSURANCE_LTV.train, select = features)","z.show(head(DF_NEW))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model to predict who will purchase insurance using the top attributes","message":["%r","","mod <- ore.odmNB(BUY_INSURANCE~., DF_NEW)","mod"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Feature Selection Using Summary Statistics.dsnb b/machine-learning/notebooks/r/OML4R Feature Selection Using Summary Statistics.dsnb
deleted file mode 100755
index 4738ba11..00000000
--- a/machine-learning/notebooks/r/OML4R Feature Selection Using Summary Statistics.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Feature Selection Using Summary Statistics","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715709181436,"interpreter":"md.medium","endTime":1715709181594,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Feature Selection Using Summary Statistics","","In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4R.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to use OML4R to select features based on number of distinct values, null values, proportion of constant values.","","The dataset `CUSTOMER_INSURANCE_LTV_R` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715709181673,"interpreter":"md.medium","endTime":1715709181756,"results":[{"message":"<h1 id=\"oml4r-feature-selection-using-summary-statistics\">OML4R Feature Selection Using Summary Statistics<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using summary statistics via OML4R.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to use OML4R to select features based on number of distinct values, null values, proportion of constant values.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_R<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/1.5.1/oreug/preparing-data-in-database-using-ORE.html\" target=\"_blank\">OML4R Data Preparation<\/a>"],"enabled":true,"result":{"startTime":1715709181840,"interpreter":"md.medium","endTime":1715709182111,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/1.5.1/oreug/preparing-data-in-database-using-ORE.html\" target=\"_blank\">OML4R Data Preparation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load ORE packages","message":["%r","","library(ORE)","library(OREdplyr)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715709182187,"interpreter":"r.medium","endTime":1715709186886,"results":[{"message":"\nAttaching package: \u2018OREdplyr\u2019\n\n\nThe following objects are masked from \u2018package:dplyr\u2019:\n\n    arrange, arrange_, count, count_, cume_dist, dense_rank, desc,\n    distinct, distinct_, filter, filter_, first, full_join, group_by,\n    group_by_, group_size, groups, inner_join, last, left_join,\n    min_rank, mutate, mutate_, n, n_distinct, n_groups, nth, ntile,\n    percent_rank, rename, rename_, right_join, row_number, sample_frac,\n    sample_n, select, select_, slice, slice_, summarise, summarise_,\n    tally, top_n, transmute, transmute_, ungroup\n\n\nThe following object is masked from \u2018package:stats\u2019:\n\n    filter\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get proxy object for CUSTOMER_INSURANCE_LTV table","message":["%r","","ore.sync(table = 'CUSTOMER_INSURANCE_LTV_R', use.keys= FALSE)","ore.attach()","","","CUSTOMER_INSURANCE_LTV <- CUSTOMER_INSURANCE_LTV_R","","row.names(CUSTOMER_INSURANCE_LTV) <- CUSTOMER_INSURANCE_LTV$CUSTOMER_ID","","numRows <- nrow(CUSTOMER_INSURANCE_LTV)"],"enabled":true,"result":{"startTime":1715709186973,"interpreter":"r.medium","endTime":1715709187972,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from the table","message":["%r","","z.show(head(CUSTOMER_INSURANCE_LTV), 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class=list-inline><li>'MARITAL_STATUS'<\/li><li>'STATE'<\/li><li>'CUSTOMER_ID'<\/li><li>'GENDER'<\/li><li>'PROFESSION'<\/li><li>'REGION'<\/li><li>'BUY_INSURANCE'<\/li><li>'LTV_BIN'<\/li><li>'FIRST_NAME'<\/li><li>'LAST_NAME'<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Identify numeric columns","message":["%r","","num_cols <- names(CUSTOMER_INSURANCE_LTV)[mapply(Negate(is.factor), CUSTOMER_INSURANCE_LTV)]","num_cols"],"enabled":true,"result":{"startTime":1715709188506,"interpreter":"r.medium","endTime":1715709188625,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: 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class=list-inline><li>'MARITAL_STATUS'<\/li><li>'STATE'<\/li><li>'CUSTOMER_ID'<\/li><li>'GENDER'<\/li><li>'PROFESSION'<\/li><li>'REGION'<\/li><li>'BUY_INSURANCE'<\/li><li>'LTV_BIN'<\/li><li>'FIRST_NAME'<\/li><li>'LAST_NAME'<\/li><\/ol>\n\n","type":"HTML"},{"message":"10","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"To confirm selected column is not an 'identifier',  use summary function to display unique values","message":["%r","","options(width = 500)","summary(CUSTOMER_INSURANCE_LTV[selected_cat_cols], maxsum = 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 :3264                 VERY HIGH:1425   LACY    :   19   RUSSO    :    6  \n WIDOWED : 475   FL                        : 606   CU10093                   :    2                                      Nurse                   : 598   South    :1123                 LOW      :1418   JIM     :   18   BERNIER  :    5  \n M       : 359   NV                        : 521   CU1010                    :    2                                      Not specified           : 459   Southwest:1012                                  LAURENCE:   18   CALDWELL :    5  \n DIV     : 337   MN                        : 489   CU1021                    :    2                                      Author                  : 458                                                   ROBERTO :   18   DELATORRE:    5  \n OTHER   :  96   DC                        : 443   CU10213                   :    2                                      Cashier                 : 450                                                   SETH    :   18   DENNIS   :    5  \n NA's    :6627   UT                        : 335   CU10236                   :    2                                      Administrative Assistant: 447                                                   TITUS   :   18   DILLON   :    5  \n                 IL                        : 261   CU10240                   :    2                                      Waiter/Waitress         : 444                                                   WILLARD :   18   ECHOLS   :    5  \n                 OR                        : 253   CU10253                   :    2                                      Construction Laborer    : 417                                                   ALEC    :   17   ANGEL    :    4  \n                 NC                        : 252   CU10259                   :    2                                      Truck Driver            : 360                                                   ANTONIA :   17   AVILES   :    4  \n                 MS                        : 176   CU10342                   :    2                                      Veterinarian            : 283                                                   IRA     :   17   BRADSHAW :    4  \n                 WA                        : 166   CU10356                   :    2                                      Software Engineer       : 255                                                   JOEY    :   17   CAMERON  :    4  \n                 WI                        : 161   CU10367                   :    2                                      Sales Representative    : 250                                                   MELVIN  :   17   CONNORS  :    4  \n                 OH                        : 141   CU10370                   :    2                                      Lab Technician          : 245                                                   MERRILL :   17   CURRAN   :    4  \n                 CO                        : 117   CU10429                   :    2                                      First-line Manager      : 240                                                   ALTON   :   16   DENNY    :    4  \n                 TX                        :  92   CU10490                   :    2                                      Administrator           : 224                                                   BARRY   :   16   GOLDEN   :    4  \n                 MO                        :  56   CU10499                   :    2                                      Bank Teller             : 208                                                   BERNARD :   16   GRANTHAM :    4  \n                 NM                        :  51   CU10501                   :    2                                      Technical Writer        : 206                                                   BURTON  :   16   HATCH    :    4  \n                 OK                        :  51   CU1051                    :    2                                      Publisher               : 197                                                   CARL    :   16   HEATH    :    4  \n                 AL                        :  48   CU10514                   :    2                                      Law Enforcement Officer : 190                                                   EMMANUEL:   16   JEAN     :    4  \n                 LA                        :  41   CU10518                   :    2                                      Fireman                 : 188                                                   ERVIN   :   16   LEARY    :    4  \n                 AK                        :  19   CU10526                   :    2                                      Dentist                 : 178                                                   HIRAM   :   16   LIPSCOMB :    4  \n                 AZ                        :  13   CU10557                   :    2                                      School Teacher          : 170                                                   LELAND  :   16   LUDWIG   :    4  \n                                                   CU10604                   :    2                                      Secretary               : 161                                                   LEONEL  :   16   NAVA     :    4  \n                                                   CU10629                   :    2                                      DBA                     : 148                                                   LINWOOD :   16   OHARA    :    4  \n                                                   CU10639                   :    2                                      Mason                   : 147                                                   ROLANDO :   16   PARNELL  :    4  \n                                                   CU10645                   :    2                                      Accountant              : 141                                                   TYLER   :   16   SAUNDERS :    4  \n    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\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Partitioned Model SVM.dsnb b/machine-learning/notebooks/r/OML4R Partitioned Model SVM.dsnb
deleted file mode 100755
index 733a9fd3..00000000
--- a/machine-learning/notebooks/r/OML4R Partitioned Model SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Partitioned Model SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715347934755,"interpreter":"md.low","endTime":1715347934820,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"OML4Py Partitioned Model","message":["%md","# OML4R Partitioned Model SVM","Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The proper sub-model is chosen by the system based on partition value(s) in the row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models. ","","In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. The model is then used to predict the target, then predict the target with prediction details. ","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715347934902,"interpreter":"md.low","endTime":1715347934976,"results":[{"message":"<h1 id=\"oml4r-partitioned-model-svm\">OML4R Partitioned Model SVM<\/h1>\n<p>Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. Sub-models exist and are used as one model, which results in simplified scoring using the top-level model only. The proper sub-model is chosen by the system based on partition value(s) in the row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models.<\/p>\n<p>In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. The model is then used to predict the target, then predict the target with prediction details.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-CE621172-D8BF-48FF-9854-D0BC344E1897\" target=\"_blank\">OML4R Partitioned Models Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a>"],"enabled":true,"result":{"startTime":1715347935064,"interpreter":"md.low","endTime":1715347935134,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-CE621172-D8BF-48FF-9854-D0BC344E1897\" target=\"_blank\">OML4R Partitioned Models Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715347935256,"interpreter":"r.low","endTime":1715347935386,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare CUSTOMER_DEMO dataset using data from the SH schema","message":["%r","","ore.sync(query = c(\"SUPP_DF\" = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, ","                   OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","","ore.sync(query = c(\"CUSTOMER_DF\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, ","                   CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.attach()","","CUST_DF <- merge(SUPP_DF, CUSTOMER_DF)","row.names(CUST_DF) <- CUST_DF$CUST_ID"],"enabled":true,"result":{"startTime":1715347935477,"interpreter":"r.low","endTime":1715347936687,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Display number of rows and columns","message":["%r","","dim(CUST_DF)"],"enabled":true,"result":{"startTime":1715347936761,"interpreter":"r.low","endTime":1715347936953,"results":[{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>4500<\/li><li>12<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of CUSTOMERS_DEMO data","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1715347937028,"interpreter":"r.low","endTime":1715347937151,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n100001\t< Bach.\t0\t2\tExec.\t3\t0\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\n100002\tBach.\t0\t2\tProf.\t4\t0\tF\tNeverM\t1962\tL: 300,000 and above\t7000\n100003\t< Bach.\t0\t2\tSales\t6\t0\tM\tNeverM\t1969\tK: 250,000 - 299,999\t7000\n100004\t< Bach.\t0\t2\tSales\t5\t0\tF\tDivorc.\t1953\tK: 250,000 - 299,999\t15000\n100005\tAssoc-A\t1\t3\tCrafts\t5\t0\tM\tMarried\t1957\tB: 30,000 - 49,999\t1500\n100006\t< Bach.\t0\t9+\tProf.\t2\t1\tF\tNeverM\t1983\tG: 130,000 - 149,999\t5000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into training and testing sets in R ","message":["%r","","set.seed(123)","rownames(CUST_DF) <- CUST_DF$CUST_ID","","sampleSize <- .6 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","","# Create training data set","CUST_DF.train <- CUST_DF[index,]","","# Create test data set","CUST_DF.test <- CUST_DF[setdiff(1:nrow(CUST_DF),index),]","","class(CUST_DF.train)","cat(\"\\nTraining data: \",dim(CUST_DF.train))","","","class(CUST_DF.test)","cat(\"\\nTest data: \", dim(CUST_DF.test))"],"enabled":true,"result":{"startTime":1715347937228,"interpreter":"r.low","endTime":1715347938000,"results":[{"message":"'ore.frame'\nTraining data:  2700 12\n","type":"HTML"},{"message":"'ore.frame'\nTest data:  1800 12","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for Partitioning (Shared Settings)","message":["%md","","### Examples of possible setting overrides for Partitioning (Shared Settings)","","If the user does not override the default settings, relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","","* Requests the building of a partitioned model. The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.  ","> 'odms_partition_columns' : '<Comma separated list of attributes>'","     ","* Controls the parallel building of partitioned models.     ","    ODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all sub-processes.","    ODMS_PARTITION_BUILD_INTER builds each partition entirely in a single sub-process, but multiple partitions may be built at the same time because multiple sub-processes are active.","    ODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.","> 'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'"],"enabled":true,"result":{"startTime":1715347938080,"interpreter":"md.low","endTime":1715347938141,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-partitioning-shared-settings\">Examples of possible setting overrides for Partitioning (Shared Settings)<\/h3>\n<p>If the user does not override the default settings, relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Requests the building of a partitioned model. The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. If XFORM_LIST parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_columns' : '<Comma separated list of attributes>'<\/p>\n<\/blockquote>\n<ul>\n<li>Controls the parallel building of partitioned models.<br />\nODMS_PARTITION_BUILD_INTRA builds each partition in parallel using all sub-processes.\nODMS_PARTITION_BUILD_INTER builds each partition entirely in a single sub-process, but multiple partitions may be built at the same time because multiple sub-processes are active.\nODMS_PARTITION_BUILD_HYBRID combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.<\/li>\n<\/ul>\n<blockquote>\n<p>'odms_partition_build_type' : 'ODMS_PARTITION_BUILD_HYBRID'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build Partitioned Model using the SVM algorithm to predict YRS_RESIDENCE, partitioned by CUST_GENDER","message":["%r","","ore.drop(model = 'SVM_PARTITIONED_REGRESSION_MODEL')","","settings <- list(odms_partition_columns = 'CUST_GENDER',","                 model_name             = 'SVM_PARTITIONED_REGRESSION_MODEL')","","SVM_PARTITIONED_REGRESSION_MODEL <- ore.odmSVM(YRS_RESIDENCE~.-CUST_ID, ","                                               CUST_DF.train, ","                                               \"regression\", ","                                               kernel.function = \"linear\", ","                                               odm.settings=settings)","","SVM_PARTITIONED_REGRESSION_MODEL"],"enabled":true,"result":{"startTime":1715347938218,"interpreter":"r.low","endTime":1715347939416,"results":[{"message":"Warning message:\n\u201cThe created mining model SVM_PARTITIONED_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", kernel.function = \"linear\", odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.max.partitions                             1000\nodms.missing.value.treatment odms.missing.value.auto\nodms.partition.columns                   CUST_GENDER\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\nNumber of partitions: 2\nPartitions:\n  PARTITION_NAME CUST_GENDER\n1              F           F\n2              M           M","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Show model details for Partitioned Model using summary","message":["%r","","summary(SVM_PARTITIONED_REGRESSION_MODEL)"],"enabled":true,"result":{"startTime":1715347939500,"interpreter":"r.low","endTime":1715347940608,"results":[{"message":"$F\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", kernel.function = \"linear\", odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.max.partitions                             1000\nodms.missing.value.treatment odms.missing.value.auto\nodms.partition.columns                   CUST_GENDER\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-7.541000 -0.367100 -0.000865  0.106000  0.422200  8.008000 \n\nCoefficients: \n   PARTITION_NAME            variable                value      estimate\n1               F         (Intercept)                      -0.0123866038\n2               F       AFFINITY_CARD                       0.0327332427\n3               F   CUST_CREDIT_LIMIT                      -0.0003003418\n4               F   CUST_INCOME_LEVEL      A: Below 30,000  0.0144772514\n5               F   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.0937785670\n6               F   CUST_INCOME_LEVEL   C: 50,000 - 69,999  0.0765888558\n7               F   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.0354640998\n8               F   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.0336308215\n9               F   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.0810219264\n10              F   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.0138736123\n11              F   CUST_INCOME_LEVEL H: 150,000 - 169,999 -0.0019072163\n12              F   CUST_INCOME_LEVEL I: 170,000 - 189,999  0.0019811865\n13              F   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.0089657204\n14              F   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.0368971012\n15              F   CUST_INCOME_LEVEL L: 300,000 and above  0.0714359257\n16              F CUST_MARITAL_STATUS              Divorc.  0.1165117556\n17              F CUST_MARITAL_STATUS              Mabsent -0.3003009390\n18              F CUST_MARITAL_STATUS               Mar-AF  0.1844910241\n19              F CUST_MARITAL_STATUS              Married  0.0602316078\n20              F CUST_MARITAL_STATUS               NeverM  0.0429441732\n21              F CUST_MARITAL_STATUS               Separ. -0.0138072480\n22              F CUST_MARITAL_STATUS              Widowed -0.0900703736\n23              F  CUST_YEAR_OF_BIRTH                      -0.8028132834\n24              F           EDUCATION                 10th -0.2642173866\n25              F           EDUCATION                 11th -0.3017945799\n26              F           EDUCATION                 12th  0.0532097721\n27              F           EDUCATION              1st-4th -0.1228023136\n28              F           EDUCATION              5th-6th  0.0207838682\n29              F           EDUCATION              7th-8th -0.2472535694\n30              F           EDUCATION                  9th -0.1375809726\n31              F           EDUCATION              < Bach. -0.0626355753\n32              F           EDUCATION              Assoc-A  0.0875190153\n33              F           EDUCATION              Assoc-V  0.0081403956\n34              F           EDUCATION                Bach.  0.0172364889\n35              F           EDUCATION              HS-grad  0.0339944938\n36              F           EDUCATION              Masters  0.0404687332\n37              F           EDUCATION                  PhD  0.3095448834\n38              F           EDUCATION              Presch.  0.1814107429\n39              F           EDUCATION               Profsc  0.3839760039\n40              F      HOUSEHOLD_SIZE                    1 -0.0272075761\n41              F      HOUSEHOLD_SIZE                    2  0.0815021730\n42              F      HOUSEHOLD_SIZE                  4-5 -0.1208557680\n43              F      HOUSEHOLD_SIZE                  6-8  0.0574389732\n44              F      HOUSEHOLD_SIZE                   9+  0.0091221979\n45              F          OCCUPATION                    ? -0.0678976938\n46              F          OCCUPATION              Cleric.  0.0651769314\n47              F          OCCUPATION               Crafts  0.1783214854\n48              F          OCCUPATION                Exec.  0.1176253140\n49              F          OCCUPATION              Farming -0.2476850314\n50              F          OCCUPATION              Handler -0.0842033528\n51              F          OCCUPATION              House-s -0.0467816866\n52              F          OCCUPATION              Machine  0.1108528629\n53              F          OCCUPATION                Other -0.0897725814\n54              F          OCCUPATION                Prof.  0.0900524708\n55              F          OCCUPATION              Protec. -0.0615914076\n56              F          OCCUPATION                Sales  0.0469075500\n57              F          OCCUPATION              TechSup  0.0364540390\n58              F          OCCUPATION              Transp. -0.0474589000\n59              F         Y_BOX_GAMES                       0.0009125024\n\n\n$M\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", kernel.function = \"linear\", odm.settings = settings)\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.max.partitions                             1000\nodms.missing.value.treatment odms.missing.value.auto\nodms.partition.columns                   CUST_GENDER\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-8.005000 -0.655000  0.003037 -0.071140  0.506400  6.722000 \n\nCoefficients: \n   PARTITION_NAME            variable                value      estimate\n1               M         (Intercept)                      -1.116601e-01\n2               M       AFFINITY_CARD                       3.916771e-02\n3               M   CUST_CREDIT_LIMIT                       1.551717e-02\n4               M   CUST_INCOME_LEVEL      A: Below 30,000  1.055325e-02\n5               M   CUST_INCOME_LEVEL   B: 30,000 - 49,999 -5.975809e-03\n6               M   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -4.513304e-03\n7               M   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -5.041152e-02\n8               M   CUST_INCOME_LEVEL  E: 90,000 - 109,999  1.009648e-01\n9               M   CUST_INCOME_LEVEL F: 110,000 - 129,999 -9.366213e-02\n10              M   CUST_INCOME_LEVEL G: 130,000 - 149,999  1.430737e-02\n11              M   CUST_INCOME_LEVEL H: 150,000 - 169,999  8.029570e-02\n12              M   CUST_INCOME_LEVEL I: 170,000 - 189,999 -4.878279e-02\n13              M   CUST_INCOME_LEVEL J: 190,000 - 249,999 -2.401287e-02\n14              M   CUST_INCOME_LEVEL K: 250,000 - 299,999  3.280594e-02\n15              M   CUST_INCOME_LEVEL L: 300,000 and above -1.156867e-02\n16              M CUST_MARITAL_STATUS              Divorc.  6.732846e-02\n17              M CUST_MARITAL_STATUS              Mabsent -9.392896e-02\n18              M CUST_MARITAL_STATUS              Married  2.414774e-02\n19              M CUST_MARITAL_STATUS               NeverM -2.414206e-02\n20              M CUST_MARITAL_STATUS               Separ.  2.659496e-02\n21              M CUST_MARITAL_STATUS              Widowed -1.324921e-07\n22              M  CUST_YEAR_OF_BIRTH                      -6.207321e-01\n23              M           EDUCATION                 10th -3.305996e-02\n24              M           EDUCATION                 11th -2.110310e-01\n25              M           EDUCATION                 12th -1.765052e-01\n26              M           EDUCATION              1st-4th  3.462087e-01\n27              M           EDUCATION              5th-6th -9.042694e-03\n28              M           EDUCATION              7th-8th -3.180242e-01\n29              M           EDUCATION                  9th  1.282928e-02\n30              M           EDUCATION              < Bach. -8.454202e-02\n31              M           EDUCATION              Assoc-A  4.644634e-02\n32              M           EDUCATION              Assoc-V -1.351945e-03\n33              M           EDUCATION                Bach. -1.832365e-02\n34              M           EDUCATION              HS-grad -2.148696e-02\n35              M           EDUCATION              Masters  5.904819e-02\n36              M           EDUCATION                  PhD  1.926538e-01\n37              M           EDUCATION               Profsc  2.161813e-01\n38              M      HOUSEHOLD_SIZE                    1 -8.280626e-02\n39              M      HOUSEHOLD_SIZE                    2  5.424788e-02\n40              M      HOUSEHOLD_SIZE                    3  5.470263e-02\n41              M      HOUSEHOLD_SIZE                  6-8  4.689173e-03\n42              M      HOUSEHOLD_SIZE                   9+ -3.083342e-02\n43              M          OCCUPATION                    ? -3.174529e-02\n44              M          OCCUPATION              Armed-F -5.966992e-02\n45              M          OCCUPATION              Cleric.  4.177425e-02\n46              M          OCCUPATION               Crafts  2.936820e-02\n47              M          OCCUPATION                Exec.  2.206533e-01\n48              M          OCCUPATION              Farming  2.256248e-01\n49              M          OCCUPATION              Handler -2.835822e-02\n50              M          OCCUPATION              House-s -5.182864e-01\n51              M          OCCUPATION              Machine -2.844732e-02\n52              M          OCCUPATION                Other -9.918860e-02\n53              M          OCCUPATION                Prof.  6.156660e-03\n54              M          OCCUPATION              Protec.  5.581363e-02\n55              M          OCCUPATION                Sales  1.303405e-01\n56              M          OCCUPATION              TechSup -1.309249e-01\n57              M          OCCUPATION              Transp.  1.868894e-01\n58              M         Y_BOX_GAMES                      -6.745903e-02\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict on the test set and display prediction result - note the use of the top level model only","message":["%r","","RES_DF <- ore.predict(SVM_PARTITIONED_REGRESSION_MODEL, ","                      CUST_DF.test, ","                      supplemental.cols = c(\"CUST_ID\", \"YRS_RESIDENCE\") )","","z.show(head(RES_DF))"],"enabled":true,"result":{"startTime":1715347940716,"interpreter":"r.low","endTime":1715347940910,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100003\t6\t3.86153959466658\n100006\t2\t1.90262635628902\n100008\t5\t4.24654864723995\n100010\t3\t2.98786630175208\n100014\t5\t5.09175035393474\n100015\t6\t4.95775880233581\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize the test dataset","message":["%r","","ore.drop(table = 'SVM_TEST_DATA')","","ore.create(CUST_DF.test, table = 'SVM_TEST_DATA')"],"enabled":true,"result":{"startTime":1715347940990,"interpreter":"r.low","endTime":1715347941494,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Demonstrate using the SQL interface to score data and display prediction details","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","      PREDICTION(SVM_PARTITIONED_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","      PREDICTION_DETAILS(SVM_PARTITIONED_REGRESSION_MODEL USING *) PD","      FROM SVM_TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","      XMLTABLE('/Details'","               PASSING OUT.PD","               COLUMNS ","               \"Attribute1\" XMLType PATH 'Attribute[1]',","               \"Attribute2\" XMLType PATH 'Attribute[2]') ","      OUTPRED"],"enabled":true,"result":{"startTime":1715347941572,"interpreter":"sql.low","endTime":1715347941692,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100003\t3.862\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".174\" \t\"CUST_INCOME_LEVEL\" actualValue=\"K: 250,000 - 299,999\" weight=\".098\" \n100006\t1.903\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".022\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".001\" \n100008\t4.247\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".298\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".142\" \n100010\t2.988\t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".018\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".006\" \n100014\t5.092\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1954\" weight=\".805\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".084\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1715347941769,"interpreter":"md.low","endTime":1715347941831,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715347941914,"interpreter":"md.low","endTime":1715347941973,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R REST API","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R REST API for invoking embedded R execution","","On Oracle Autonomous Database, OML4R contains a REST API to run user-defined R functions saved in the script repository. REST APIs can be called from a web development application, improving the portability of the interface to other types of platforms, increasing project scalability, and allowing components to evolve independently. Use the OML4R REST API to invoke user-defined R functions and to list those available in the R script repository.","","To run a script, it must reside in the R script repository. An Oracle Machine Learning cloud service account **username** and **password** must be provided for authentication.","","### **REST API Workflow**","","In this workflow, we highlight a few of the features of the REST API:","","- Authenticating the database user and obtaining a token","- Loading a database table into the database","- Saving scripts in the database R script repository","- Executing the saved R scripts from the REST API using both synchronous and asynchronous modes","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"REST  API Authentication","message":["%md","","To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' ","                   --header 'Accept: application/json' -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"' <yourpassword>'\"}' ","                   \"<OML Service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _oml-cloud-service-url_ is the REST server portion of the Oracle Machine Learning user management cloud service instance","","For example, Oracle Machine Learning user **OMLUSER** with password **OMLPASSWD** and cloud service url **https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com** authenticates and obtain a REST token using the ","following approach:"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Save token to a variable","message":["%md","","Copy the `accessToken` field from the response and assign it to variable token, surrounded by single quotes. A token has a lifecycle of 3600 seconds, or 1 hour, and it can be refreshed for up to 8 hours."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ export token='eyJhbGciOiJ...'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load an R data frame to an Oracle Database table","message":["%md","","Using the `ore.create` function, create an OML proxy object `IRIS_R` from the `iris` data frame. The OML proxy object `IRIS_R` represents a database table. We first drop the table if already exists.","","The `ore.warn.order` option specifies whether you want OML4R to display a warning message when using an ore.frame object. We turn off turn the warnings off so they do not appear in the output, and display the first 6 rows of the `IRIS_R` proxy object. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r ","","options(ore.warn.order=FALSE)","","suppressWarnings(ore.drop(table=\"IRIS_REST\"))","","ore.create(iris, table=\"IRIS_REST\")","","z.show(head(IRIS_REST))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create an R function and save it to the R script repository","message":["%md","","Create a function `group.countR` that returns the counts for each category in the `SPECIES` column.  Save the `group.countR` function to the R script repository using `ore.scriptCreate`, overwriting any existing script with the same name. The script is invoked from the R interface, followed by the OML4R R and REST APIs for embedded R execution."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","group.countR <- function(df) {","    x <- aggregate(df[, \"Species\"], by=list(df$Species), FUN=length)","    names(x) <- c(\"SPECIES\", \"COUNT\")","    return(x)}","    ","","ore.scriptCreate(\"group.countR\", group.countR, overwrite=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the  script in the R script repository","message":["%md","","To check for the existence of the saved `group.countR` script from the OML4R R interface, run the function `ore.scriptList`. We set the `type` parameter to `user` to show only the scripts created by the current user."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%r","","ore.scriptList(name = \"group.countR\", type=\"user\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Run the script in R","message":["%r","","group.countR(iris)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the script using groupApply","message":["%md","","The `groupApply` function runs a user-defined function on data grouped by a specified column in the database environment. In this case, the grouping column is `Species` and the script is `group.countR`."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%r","","ore.groupApply(IRIS_REST, INDEX=IRIS_REST$Species, FUN.NAME=\"group.countR\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### **Synchronous REST API calls**"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View script in the R script repository using REST","message":["%md","","Verify the existence of `group.countR` in the script repository again, this time using the REST API with `cURL`.  Send a GET request to the `scripts` endpoint, and the scripts available to the OML user are returned in JSON format.","","Note, the default REST API execution mode is *synchronous*, so code execution will wait for the REST API call to return before additional calls may be initiated. ","","The curl invocation and result are shown below. The Linux command line JSON parser jq is used to format the returned value. To install jq on your Linux REST client, run the command sudo yum install jq."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to view the scripts saved in the script repository","message":["","$ curl -X GET --header \"Authorization: Bearer <token>\" \\","              --header 'Accept: application/json' \\","              \"<OML Service URL>/oml/api/r-scripts/v1/scripts\" | jq  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"result\": [","    {","      \"SCRIPT\": \"function (df) \\n{\\n    x <- aggregate(df[, \\\"Species\\\"], by = list(df$Species), FUN = length)\\n  names(x) <- c(\\\"SPECIES\\\", \\\"COUNT\\\")\\n  return(x)\\n}\",","      \"NAME\": \"group.countR\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the script from the REST API","message":["%md","","Now invoke the `group.countR` function from the REST API using the `group-apply` endpoint, setting `parallelFlag` to `true` to leverage database parallelism."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to invoke the group.countR script in synchronous mode","message":["","","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"input\":\"IRIS\",\"groupBy\":\"Species\",\"parallelFlag\":true}' ","                  \"<OML Service URL>/oml/api/r-scripts/v1/group-apply/group.countR\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the result","message":["%md","","The result of the REST call is a JSON representation of the value returned from the R script. Integers, vectors, lists, matrices, arrays, and data frames are supported. The returned value is:"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{\"result\":[{\"SPECIES\":\"setosa\",\"COUNT\":50},","           {\"SPECIES\":\"versicolor\",\"COUNT\":50},","           {\"SPECIES\":\"virginica\",\"COUNT\":50}]"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Asynchronous REST API calls","","Asynchronous calls do not wait for the result to return from the server. If the R script is performing long running computations, results will not be immediate and the user may want to use asynchronous calls to continue development on the client. ","","The asynchronous call's returned results are included in the response header. Display the response header by passing the `-i` flag to the `cURL` command. Asynchronous calls have a default timeout of 1800 seconds, or 30 minutes, and can be extended up to 12 hours. Set the `asyncFlag` to `true` to run asynchronous calls from the REST API."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Perform an asynchronous REST call with a timeout of 300 seconds, or 5 minutes","message":["%md","","Below is the equivalent asynchronous REST call with a timeout of 300 seconds, or 5 minutes. The output includes the HTTP 201 status code indicating the job was created, and a job id location that can be polled for status and results.","","The `timeout` flag is used to change the default timeout setting."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to invoke the group.countR script in asynchronous mode","message":["","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"input\":\"IRIS_REST\",\"groupBy\":\"Species\",\"asyncFlag\":true, \"timeout\":300, \"parallelFlag\":true, \"service\":\"MEDIUM\"}' ","                  \"<OML Service URL>/oml/api/r-scripts/v1/group-apply/group.countR\"","                  "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[" "," Result:"," "," HTTP/1.1 201 Created"," Location: <OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>"," Cache-Control: no-cache, no-store, private"," Content-Length: 0"," X-Frame-Options: SAMEORIGIN"," Proxy-Connection: Keep-Alive"," X-XSS-Protection: 1;mode=block"," X-Content-Type-Options: nosniff"," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Poll the job status","message":["%md","","","To poll the job status, pass the job ID location to the jobs endpoint using the GET method. The HTTP response status 302 indicates the running job is found and includes a job id location for future results."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View the job result","message":["","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>/result\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","The return value is:","","{\"result\":[{\"SPECIES\":\"setosa\",\"COUNT\":50},","           {\"SPECIES\":\"versicolor\",\"COUNT\":50},","           {\"SPECIES\":\"virginica\",\"COUNT\":50}]}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Returning model details from the REST API","message":["%md","","Let's create a linear model script and run it from the REST API. First, we'll define our function, `build.lmRestR`, that returns a list mapping of the model attributes. Note that the model itself cannot be returned directly from REST because LinearRegression model objects are not JSON-serializable. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create the script and save it to the R script repository","message":["%r","","build.lmRestR <- function(dat){","  dat$Species <- as.factor(dat$Species)","  mod <- lm(Petal.Length~., dat)","  res <- summary(mod)","  res.list <- list(\"Coef\"=res$coefficients, \"R2\"=res$r.squared)","  res.list","}","","ore.scriptCreate(name = 'build.lmRestR', FUN = build.lmRestR, overwrite=TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the script in R","message":["%r","","res <- build.lmRestR(iris)","res[\"Coef\"]","res[\"R2\"]"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":" Invoke the function from the REST API in asynchronous mode","message":["","$ curl -i -X POST --header \"Authorization: Bearer <token>\" ","                  --header 'Content-Type: application/json' ","                  --header 'Accept: application/json' -d '{\"input\":\"IRIS_REST\", \"asyncFlag\":true}' ","                  \"<OML Service URL>/oml/api/r-scripts/v1/table-apply/build.lmRestR\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":" Display the results by polling the results location","message":["","$ curl -i -X GET --header \"Authorization: Bearer <token>\" ","                 --header 'Accept: application/json' ","                 \"<OML Service URL>/oml/api/r-scripts/v1/jobs/<job id location>/result\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"The returned value is a list containing the Coeffiecient and R^2 values.","message":["","{\"result\":{\"R2\":0.9786,\"Coef\":[[-1.111,0.2699,-4.1167,1.0E-4],[0.608,0.0502,12.1009,1.0736E-23],","                               [-0.1805,0.0804,-2.2465,0.0262],[0.6022,0.1214,4.9589,1.9687E-6],","                               [1.4634,0.1735,8.4366,3.1427E-14],[1.9742,0.2448,8.0646,2.6E-13]]}}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/r/OML4R Regression GLM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Regression GLM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715318925800,"interpreter":"md.medium","endTime":1715318925878,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Regression Modeling to Predict Numerical Values using Generalized Linear Model","message":["%md","# Regression Modeling to Predict Numerical Values using Generalized Linear Model","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715318925957,"interpreter":"md.medium","endTime":1715318926019,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-generalized-linear-model\">Regression Modeling to Predict Numerical Values using Generalized Linear Model<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715318926105,"interpreter":"md.medium","endTime":1715318926180,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For mode information","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a>"],"enabled":true,"result":{"startTime":1715318926255,"interpreter":"md.medium","endTime":1715318926333,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%r","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715318926412,"interpreter":"r.medium","endTime":1715318926531,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%r","","ore.sync(query = c(\"SUPP_DF\" = 'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, ","                   OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","ore.sync(query = c(\"CUST_DF\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, ","                   CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.attach()","                   ","CUST_DF <- merge(SUPP_DF, CUST_DF)","row.names(CUST_DF) <- CUST_DF$CUST_ID"],"enabled":true,"result":{"startTime":1715318926603,"interpreter":"r.medium","endTime":1715318927899,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","z.show(head(CUST_DF))"],"enabled":true,"result":{"startTime":1715318927979,"interpreter":"r.medium","endTime":1715318928168,"results":[{"message":"CUST_ID\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\n100001\t< Bach.\t0\t2\tExec.\t3\t0\tF\tWidowed\t1941\tG: 130,000 - 149,999\t1500\n100002\tBach.\t0\t2\tProf.\t4\t0\tF\tNeverM\t1962\tL: 300,000 and above\t7000\n100003\t< Bach.\t0\t2\tSales\t6\t0\tM\tNeverM\t1969\tK: 250,000 - 299,999\t7000\n100004\t< Bach.\t0\t2\tSales\t5\t0\tF\tDivorc.\t1953\tK: 250,000 - 299,999\t15000\n100005\tAssoc-A\t1\t3\tCrafts\t5\t0\tM\tMarried\t1957\tB: 30,000 - 49,999\t1500\n100006\t< Bach.\t0\t9+\tProf.\t2\t1\tF\tNeverM\t1983\tG: 130,000 - 149,999\t5000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data 60/40 into training and testing sets","message":["%r","","n.rows <- nrow(CUST_DF)","ind <- sample(1:n.rows,floor(0.4*n.rows))","group <- as.integer(1:n.rows %in% ind)","","TRAIN <- CUST_DF[group==FALSE,]","TEST <- CUST_DF[group==TRUE,]","","cat(\"TRAIN: \", dim(TRAIN))","cat(\"\\nTEST:  \", dim(TEST))"],"enabled":true,"result":{"startTime":1715318928251,"interpreter":"r.medium","endTime":1715318929690,"results":[{"message":"TRAIN:  2700 12\nTEST:   1800 12","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a GLM regression model to predict YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1715318929769,"interpreter":"md.medium","endTime":1715318929847,"results":[{"message":"<h3 id=\"build-a-glm-regression-model-to-predict-yrs_residence\">Build a GLM regression model to predict YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%r","","mod1 <- ore.odmGLM(YRS_RESIDENCE~.-CUST_ID, TRAIN)","summary(mod1)"],"enabled":true,"result":{"startTime":1715318930033,"interpreter":"r.medium","endTime":1715318933134,"results":[{"message":"\nCall:\nore.odmGLM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = TRAIN)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-6.3472 -0.6124 -0.0236  0.5496  8.0440 \n\nCoefficients:\n                                        Estimate Std. Error t value Pr(>|t|)\n(Intercept)                            1.442e+02  5.655e+00  25.496  < 2e-16\nEDUCATION10th                         -2.506e-01  1.621e-01  -1.546 0.122225\nEDUCATION11th                         -3.424e-01  1.528e-01  -2.241 0.025111\nEDUCATION12th                         -1.135e-01  2.662e-01  -0.426 0.669794\nEDUCATION1st-4th                       3.232e-01  3.640e-01   0.888 0.374668\nEDUCATION5th-6th                       1.754e-01  2.792e-01   0.628 0.529923\nEDUCATION7th-8th                      -5.654e-01  1.821e-01  -3.105 0.001925\nEDUCATION9th                          -2.819e-01  1.931e-01  -1.460 0.144428\nEDUCATION< Bach.                      -1.071e-01  6.912e-02  -1.550 0.121213\nEDUCATIONAssoc-A                       1.036e-01  1.320e-01   0.785 0.432555\nEDUCATIONAssoc-V                       1.845e-01  1.284e-01   1.437 0.150711\nEDUCATIONBach.                         8.297e-02  8.106e-02   1.024 0.306116\nEDUCATIONMasters                       2.285e-01  1.395e-01   1.638 0.101569\nEDUCATIONPhD                           5.243e-01  2.402e-01   2.183 0.029144\nEDUCATIONPresch.                      -3.698e+00  1.283e+00  -2.881 0.003991\nEDUCATIONProfsc                        4.393e-01  1.923e-01   2.285 0.022379\nAFFINITY_CARD                          3.250e-01  7.035e-02   4.619 4.04e-06\nHOUSEHOLD_SIZE1                       -4.995e-01  3.193e-01  -1.564 0.117855\nHOUSEHOLD_SIZE2                       -2.566e-01  3.201e-01  -0.802 0.422740\nHOUSEHOLD_SIZE4-5                     -6.628e-01  1.402e-01  -4.728 2.39e-06\nHOUSEHOLD_SIZE6-8                     -2.943e-01  3.212e-01  -0.916 0.359676\nHOUSEHOLD_SIZE9+                      -3.064e-01  3.306e-01  -0.927 0.354139\nOCCUPATION?                           -3.378e-01  1.354e-01  -2.495 0.012670\nOCCUPATIONArmed-F                     -4.914e-01  9.078e-01  -0.541 0.588363\nOCCUPATIONCleric.                      1.650e-02  1.063e-01   0.155 0.876666\nOCCUPATIONExec.                        3.286e-01  1.038e-01   3.167 0.001559\nOCCUPATIONFarming                      3.573e-01  1.684e-01   2.122 0.033905\nOCCUPATIONHandler                     -1.738e-01  1.478e-01  -1.176 0.239651\nOCCUPATIONHouse-s                     -1.256e+00  3.272e-01  -3.840 0.000126\nOCCUPATIONMachine                     -7.705e-03  1.230e-01  -0.063 0.950040\nOCCUPATIONOther                       -3.493e-01  1.099e-01  -3.179 0.001495\nOCCUPATIONProf.                       -1.447e-01  1.163e-01  -1.244 0.213576\nOCCUPATIONProtec.                      3.314e-02  1.856e-01   0.179 0.858314\nOCCUPATIONSales                        5.116e-02  1.004e-01   0.510 0.610409\nOCCUPATIONTechSup                     -4.465e-01  1.483e-01  -3.011 0.002628\nOCCUPATIONTransp.                      3.275e-01  1.468e-01   2.232 0.025718\nY_BOX_GAMES                           -6.398e-01  8.775e-02  -7.291 4.05e-13\nCUST_GENDERF                          -1.924e-01  7.539e-02  -2.553 0.010744\nCUST_MARITAL_STATUSDivorc.             2.877e-01  3.219e-01   0.894 0.371588\nCUST_MARITAL_STATUSMabsent             7.944e-02  3.640e-01   0.218 0.827269\nCUST_MARITAL_STATUSMar-AF              5.573e-01  1.284e+00   0.434 0.664243\nCUST_MARITAL_STATUSNeverM              6.753e-02  3.130e-01   0.216 0.829217\nCUST_MARITAL_STATUSSepar.             -2.461e-04  3.479e-01  -0.001 0.999436\nCUST_MARITAL_STATUSWidowed            -7.058e-01  3.509e-01  -2.012 0.044373\nCUST_YEAR_OF_BIRTH                    -7.122e-02  2.887e-03 -24.674  < 2e-16\nCUST_INCOME_LEVELA: Below 30,000       7.078e-01  1.977e-01   3.581 0.000348\nCUST_INCOME_LEVELB: 30,000 - 49,999    1.161e-01  1.383e-01   0.839 0.401434\nCUST_INCOME_LEVELC: 50,000 - 69,999    1.161e-01  1.478e-01   0.786 0.432140\nCUST_INCOME_LEVELD: 70,000 - 89,999    4.473e-02  1.632e-01   0.274 0.784036\nCUST_INCOME_LEVELE: 90,000 - 109,999   1.303e-01  1.144e-01   1.139 0.254622\nCUST_INCOME_LEVELF: 110,000 - 129,999 -9.104e-02  1.104e-01  -0.825 0.409505\nCUST_INCOME_LEVELG: 130,000 - 149,999  1.410e-01  1.078e-01   1.308 0.190978\nCUST_INCOME_LEVELH: 150,000 - 169,999  2.787e-01  1.079e-01   2.582 0.009863\nCUST_INCOME_LEVELI: 170,000 - 189,999  3.811e-02  9.787e-02   0.389 0.696990\nCUST_INCOME_LEVELK: 250,000 - 299,999 -2.537e-02  9.855e-02  -0.257 0.796878\nCUST_INCOME_LEVELL: 300,000 and above  3.183e-02  8.393e-02   0.379 0.704544\nCUST_CREDIT_LIMIT                      1.605e-05  8.238e-06   1.948 0.051522\n                                         \n(Intercept)                           ***\nEDUCATION10th                            \nEDUCATION11th                         *  \nEDUCATION12th                            \nEDUCATION1st-4th                         \nEDUCATION5th-6th                         \nEDUCATION7th-8th                      ** \nEDUCATION9th                             \nEDUCATION< Bach.                         \nEDUCATIONAssoc-A                         \nEDUCATIONAssoc-V                         \nEDUCATIONBach.                           \nEDUCATIONMasters                         \nEDUCATIONPhD                          *  \nEDUCATIONPresch.                      ** \nEDUCATIONProfsc                       *  \nAFFINITY_CARD                         ***\nHOUSEHOLD_SIZE1                          \nHOUSEHOLD_SIZE2                          \nHOUSEHOLD_SIZE4-5                     ***\nHOUSEHOLD_SIZE6-8                        \nHOUSEHOLD_SIZE9+                         \nOCCUPATION?                           *  \nOCCUPATIONArmed-F                        \nOCCUPATIONCleric.                        \nOCCUPATIONExec.                       ** \nOCCUPATIONFarming                     *  \nOCCUPATIONHandler                        \nOCCUPATIONHouse-s                     ***\nOCCUPATIONMachine                        \nOCCUPATIONOther                       ** \nOCCUPATIONProf.                          \nOCCUPATIONProtec.                        \nOCCUPATIONSales                          \nOCCUPATIONTechSup                     ** \nOCCUPATIONTransp.                     *  \nY_BOX_GAMES                           ***\nCUST_GENDERF                          *  \nCUST_MARITAL_STATUSDivorc.               \nCUST_MARITAL_STATUSMabsent               \nCUST_MARITAL_STATUSMar-AF                \nCUST_MARITAL_STATUSNeverM                \nCUST_MARITAL_STATUSSepar.                \nCUST_MARITAL_STATUSWidowed            *  \nCUST_YEAR_OF_BIRTH                    ***\nCUST_INCOME_LEVELA: Below 30,000      ***\nCUST_INCOME_LEVELB: 30,000 - 49,999      \nCUST_INCOME_LEVELC: 50,000 - 69,999      \nCUST_INCOME_LEVELD: 70,000 - 89,999      \nCUST_INCOME_LEVELE: 90,000 - 109,999     \nCUST_INCOME_LEVELF: 110,000 - 129,999    \nCUST_INCOME_LEVELG: 130,000 - 149,999    \nCUST_INCOME_LEVELH: 150,000 - 169,999 ** \nCUST_INCOME_LEVELI: 170,000 - 189,999    \nCUST_INCOME_LEVELK: 250,000 - 299,999    \nCUST_INCOME_LEVELL: 300,000 and above    \nCUST_CREDIT_LIMIT                     .  \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.269 on 2643 degrees of freedom\nMultiple R-squared:  0.5707,\tAdjusted R-squared:  0.5616 \nF-statistic: 62.74 on 56 and 2643 DF,  p-value: < 2.2e-16\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA');\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All algorithms<\/a>","","- Specify a row weight column","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","- Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.","> GLMS_CONF_LEVEL = '0.95'","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).","> GLMS_RIDGE_VALUE = 2","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.","> GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC","> GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.","> GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> GLMS_MAX_FEATURES = 30","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'","- Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> GLMS_REFERENCE_CLASS_NAME = '<target_value>'","- Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE","> GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> GLMS_CONV_TOLERANCE = 0.0001","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> GLMS_NUM_ITERATIONS = 200","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.","> GLMS_BATCH_ROWS = 2000","- Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)","> GLMS_SOLVER = 'GLMS_SOLVER_SGD'","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.","> GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'","","<strong> Available starting with Oracle Database 23ai <\/strong>","- Specify the `link` function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK`, `GLMS_CAUCHIT_LINK` .  For regression, only one option is available: `GLMS_IDENTITY_LINK (default)`.","> GLMS_LINK_FUNCTION = 'GLMS_IDENTITY_LINK'"],"enabled":true,"result":{"startTime":1715318933236,"interpreter":"md.medium","endTime":1715318933328,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-D431C333-E1F1-4114-BDBD-1F2C89FA02DA');\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONF_LEVEL = '0.95'<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions. When ridge is enabled, prediction bounds are not produced by the PREDICTION_BOUNDS SQL function. Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_REGRESSION = 'GLMS_RIDGE_REG_DISABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm. It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_RIDGE_VALUE = 2<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SELECTION = 'GLMS_FTR_SELECTION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GENERATION = 'GLMS_FTR_GENERATION_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data. Options are GLMS_FTR_GEN_QUADRATIC or GLMS_FTR_GEN_CUBIC<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_GEN_METHOD = 'GLMS_FTR_GEN_QUADRATIC'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are GLMS_FTR_SEL_AIC, GLMS_FTR_SEL_SBIC,GLMS_FTR_SEL_RIC,GLMS_FTR_SEL_ALPHA_INV.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_FTR_SEL_CRIT = 'GLMS_FTR_SEL_ALPHA_INV'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_MAX_FEATURES = 30<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_PRUNE_MODEL = 'GLMS_PRUNE_MODEL_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the reference class in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_REFERENCE_CLASS_NAME = '&lt;target_value&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is GLMS_ROW_DIAG_DISABLE<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_ROW_DIAGNOSTICS = 'GLMS_ROW_DIAG_ENABLE'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_CONV_TOLERANCE = 0.0001<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_NUM_ITERATIONS = 200<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer. The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_BATCH_ROWS = 2000<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if GLMS_FTR_SELECTION setting is enabled. The default value is system determined, and the options are GLMS_SOLVER_SGD (Stochastic Gradient Descent), GLMS_SOLVER_CHOL (Cholesky), GLMS_SOLVER_QR (QR decomposition) and GLMS_SOLVER_LBFGS_ADMM (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SOLVER = 'GLMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_SPARSE_SOLVER = 'GLMS_SPARSE_SOLVER_ENABLE'<\/p>\n<\/blockquote>\n<p><strong> Available starting with Oracle Database 23ai <\/strong><\/p>\n<ul>\n<li>Specify the <code>link<\/code> function for building a GLM model. The link functions are specific to the mining function. For classification, the options are: <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code>, <code>GLMS_CAUCHIT_LINK<\/code> .  For regression, only one option is available: <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>GLMS_LINK_FUNCTION = 'GLMS_IDENTITY_LINK'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}},{\"table\":{\"version\":1}}]","hideInIFrame":false,"selectedVisualization":null,"title":"Using options for Feature Selection and Generation","message":["%r","settings <- list(","  GLMS_RIDGE_REGRESSION ='GLMS_RIDGE_REG_DISABLE',","  GLMS_FTR_SELECTION    ='GLMS_FTR_SELECTION_ENABLE',","  GLMS_FTR_GEN_METHOD   ='GLMS_FTR_GEN_QUADRATIC',","  GLMS_FTR_GENERATION   ='GLMS_FTR_GENERATION_ENABLE', ","  GLMS_FTR_SEL_CRIT     ='GLMS_FTR_SEL_AIC',","  GLMS_NUM_ITERATIONS   ='50',","  GLMS_MAX_FEATURES     ='30',","  GLMS_PRUNE_MODEL      ='GLMS_PRUNE_MODEL_ENABLE',","  GLMS_ROW_DIAGNOSTICS  ='GLMS_ROW_DIAG_DISABLE',","  GLMS_CONV_TOLERANCE   ='0.0001',","  GLMS_BATCH_ROWS       ='2000')","","mod2 <- ore.odmGLM(YRS_RESIDENCE~.-CUST_ID, TRAIN, odm.settings=settings)","summary(mod2)","","z.show(settings(mod2))"],"enabled":true,"result":{"startTime":1715318933415,"interpreter":"r.medium","endTime":1715318937114,"results":[{"message":"\nCall:\nore.odmGLM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = TRAIN, \n    odm.settings = settings)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-6.7363 -0.6317 -0.0141  0.5577  8.0567 \n\nCoefficients:\n                                        Estimate Std. Error t value Pr(>|t|)\n(Intercept)                           146.943426   5.478004  26.824  < 2e-16\nEDUCATION11th                          -0.328985   0.148549  -2.215 0.026867\nEDUCATION7th-8th                       -0.556492   0.177778  -3.130 0.001765\nAFFINITY_CARD                           0.405631   0.063816   6.356 2.42e-10\nHOUSEHOLD_SIZE1                        -0.318465   0.081590  -3.903 9.73e-05\nHOUSEHOLD_SIZE4-5                      -0.603631   0.127199  -4.746 2.19e-06\nOCCUPATION?                            -0.330434   0.117032  -2.823 0.004786\nOCCUPATIONExec.                         0.368157   0.076560   4.809 1.60e-06\nOCCUPATIONFarming                       0.327045   0.156462   2.090 0.036690\nOCCUPATIONHouse-s                      -1.391456   0.315933  -4.404 1.10e-05\nOCCUPATIONOther                        -0.366858   0.086532  -4.240 2.32e-05\nOCCUPATIONTechSup                      -0.435530   0.130173  -3.346 0.000832\nOCCUPATIONTransp.                       0.307338   0.133989   2.294 0.021882\nY_BOX_GAMES                            -0.702626   0.083674  -8.397  < 2e-16\nCUST_GENDERF                           -0.224278   0.061393  -3.653 0.000264\nCUST_MARITAL_STATUSWidowed             -0.990230   0.152520  -6.492 1.00e-10\nCUST_YEAR_OF_BIRTH                     -0.072571   0.002796 -25.953  < 2e-16\nCUST_INCOME_LEVELA: Below 30,000        0.555107   0.176061   3.153 0.001634\nCUST_INCOME_LEVELF: 110,000 - 129,999  -0.198546   0.091702  -2.165 0.030467\nCUST_INCOME_LEVELH: 150,000 - 169,999   0.182508   0.091464   1.995 0.046098\n                                         \n(Intercept)                           ***\nEDUCATION11th                         *  \nEDUCATION7th-8th                      ** \nAFFINITY_CARD                         ***\nHOUSEHOLD_SIZE1                       ***\nHOUSEHOLD_SIZE4-5                     ***\nOCCUPATION?                           ** \nOCCUPATIONExec.                       ***\nOCCUPATIONFarming                     *  \nOCCUPATIONHouse-s                     ***\nOCCUPATIONOther                       ***\nOCCUPATIONTechSup                     ***\nOCCUPATIONTransp.                     *  \nY_BOX_GAMES                           ***\nCUST_GENDERF                          ***\nCUST_MARITAL_STATUSWidowed            ***\nCUST_YEAR_OF_BIRTH                    ***\nCUST_INCOME_LEVELA: Below 30,000      ** \nCUST_INCOME_LEVELF: 110,000 - 129,999 *  \nCUST_INCOME_LEVELH: 150,000 - 169,999 *  \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.272 on 2680 degrees of freedom\nMultiple R-squared:  0.5624,\tAdjusted R-squared:  0.5593 \nF-statistic: 181.3 on 19 and 2680 DF,  p-value: < 2.2e-16\n\n","type":"TEXT"},{"message":"SETTING_NAME\tSETTING_VALUE\tSETTING_TYPE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\tINPUT\nGLMS_BATCH_ROWS\t2000\tINPUT\nGLMS_CONF_LEVEL\t.95\tDEFAULT\nGLMS_CONV_TOLERANCE\t0.0001\tINPUT\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_ENABLE\tINPUT\nGLMS_FTR_GEN_METHOD\tGLMS_FTR_GEN_QUADRATIC\tINPUT\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_ENABLE\tINPUT\nGLMS_FTR_SEL_CRIT\tGLMS_FTR_SEL_AIC\tINPUT\nGLMS_MAX_FEATURES\t30\tINPUT\nGLMS_NUM_ITERATIONS\t50\tINPUT\nGLMS_PRUNE_MODEL\tGLMS_PRUNE_MODEL_ENABLE\tINPUT\nGLMS_RIDGE_REGRESSION\tGLMS_RIDGE_REG_DISABLE\tINPUT\nGLMS_ROW_DIAGNOSTICS\tGLMS_ROW_DIAG_DISABLE\tINPUT\nGLMS_SELECT_BLOCK\tGLMS_SELECT_BLOCK_DISABLE\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_DELETE_ROW\tINPUT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nPREP_AUTO\tOFF\tINPUT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}},{\"table\":{\"version\":1}}]","hideInIFrame":false,"selectedVisualization":null,"title":"Using options for Ridge Regression","message":["%r","","ore.drop(model = 'GLM_REGRESSION_MODEL')","","settings2 <- list(","  GLMS_RIDGE_REGRESSION  ='GLMS_RIDGE_REG_ENABLE',","  GLMS_FTR_SELECTION     ='GLMS_FTR_SELECTION_DISABLE',","  GLMS_ROW_DIAGNOSTICS   ='GLMS_ROW_DIAG_DISABLE',","  GLMS_CONV_TOLERANCE    ='0.0001',","  GLMS_NUM_ITERATIONS    ='50',","  GLMS_RIDGE_VALUE       ='0.1',","  GLMS_BATCH_ROWS        ='2000',","  model_name             ='GLM_REGRESSION_MODEL')  # Set model name for use below with prediction details","","mod3 <- ore.odmGLM(YRS_RESIDENCE~.-CUST_ID, TRAIN, odm.settings=settings2)","summary(mod3)","z.show(settings(mod3))"],"enabled":true,"result":{"startTime":1715318937197,"interpreter":"r.medium","endTime":1715318943998,"results":[{"message":"Warning message:\n\u201cThe created mining model GLM_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nCall:\nore.odmGLM(formula = YRS_RESIDENCE ~ . - CUST_ID, data = TRAIN, \n    odm.settings = settings2)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-6.3462 -0.6127 -0.0273  0.5491  8.0443 \n\nCoefficients:\n                                        Estimate Std. Error t value Pr(>|t|)\n(Intercept)                            1.441e+02  5.654e+00  25.489  < 2e-16\nEDUCATION10th                         -2.497e-01  1.620e-01  -1.541 0.123345\nEDUCATION11th                         -3.416e-01  1.527e-01  -2.237 0.025354\nEDUCATION12th                         -1.129e-01  2.656e-01  -0.425 0.670875\nEDUCATION1st-4th                       3.201e-01  3.625e-01   0.883 0.377293\nEDUCATION5th-6th                       1.753e-01  2.785e-01   0.629 0.529167\nEDUCATION7th-8th                      -5.638e-01  1.819e-01  -3.099 0.001962\nEDUCATION9th                          -2.814e-01  1.929e-01  -1.459 0.144731\nEDUCATION< Bach.                      -1.070e-01  6.909e-02  -1.549 0.121422\nEDUCATIONAssoc-A                       1.040e-01  1.319e-01   0.789 0.430298\nEDUCATIONAssoc-V                       1.847e-01  1.283e-01   1.439 0.150255\nEDUCATIONBach.                         8.269e-02  8.102e-02   1.021 0.307570\nEDUCATIONMasters                       2.277e-01  1.394e-01   1.634 0.102468\nEDUCATIONPhD                           5.224e-01  2.398e-01   2.178 0.029461\nEDUCATIONPresch.                      -3.355e+00  1.223e+00  -2.744 0.006103\nEDUCATIONProfsc                        4.381e-01  1.920e-01   2.281 0.022623\nAFFINITY_CARD                          3.250e-01  7.034e-02   4.620 4.03e-06\nHOUSEHOLD_SIZE1                       -4.937e-01  3.116e-01  -1.584 0.113201\nHOUSEHOLD_SIZE2                       -2.503e-01  3.120e-01  -0.802 0.422593\nHOUSEHOLD_SIZE4-5                     -6.624e-01  1.401e-01  -4.729 2.37e-06\nHOUSEHOLD_SIZE6-8                     -2.886e-01  3.143e-01  -0.918 0.358487\nHOUSEHOLD_SIZE9+                      -3.013e-01  3.226e-01  -0.934 0.350329\nOCCUPATION?                           -3.354e-01  1.353e-01  -2.479 0.013220\nOCCUPATIONArmed-F                     -4.659e-01  8.856e-01  -0.526 0.598884\nOCCUPATIONCleric.                      1.839e-02  1.062e-01   0.173 0.862512\nOCCUPATIONExec.                        3.302e-01  1.036e-01   3.187 0.001457\nOCCUPATIONFarming                      3.586e-01  1.682e-01   2.132 0.033091\nOCCUPATIONHandler                     -1.721e-01  1.477e-01  -1.165 0.243959\nOCCUPATIONHouse-s                     -1.246e+00  3.262e-01  -3.821 0.000136\nOCCUPATIONMachine                     -6.319e-03  1.229e-01  -0.051 0.958982\nOCCUPATIONOther                       -3.474e-01  1.097e-01  -3.165 0.001567\nOCCUPATIONProf.                       -1.426e-01  1.161e-01  -1.228 0.219627\nOCCUPATIONProtec.                      3.475e-02  1.854e-01   0.187 0.851359\nOCCUPATIONSales                        5.291e-02  1.003e-01   0.528 0.597840\nOCCUPATIONTechSup                     -4.444e-01  1.481e-01  -3.000 0.002727\nOCCUPATIONTransp.                      3.288e-01  1.466e-01   2.242 0.025027\nY_BOX_GAMES                           -6.403e-01  8.774e-02  -7.297 3.87e-13\nCUST_GENDERF                          -1.922e-01  7.535e-02  -2.551 0.010806\nCUST_MARITAL_STATUSDivorc.             2.815e-01  3.140e-01   0.896 0.370070\nCUST_MARITAL_STATUSMabsent             7.289e-02  3.564e-01   0.204 0.837983\nCUST_MARITAL_STATUSMar-AF              5.050e-01  1.223e+00   0.413 0.679686\nCUST_MARITAL_STATUSNeverM              6.101e-02  3.053e-01   0.200 0.841623\nCUST_MARITAL_STATUSSepar.             -6.130e-03  3.402e-01  -0.018 0.985626\nCUST_MARITAL_STATUSWidowed            -7.154e-01  3.431e-01  -2.085 0.037167\nCUST_YEAR_OF_BIRTH                    -7.120e-02  2.886e-03 -24.667  < 2e-16\nCUST_INCOME_LEVELA: Below 30,000       7.065e-01  1.974e-01   3.579 0.000352\nCUST_INCOME_LEVELB: 30,000 - 49,999    1.162e-01  1.382e-01   0.840 0.400711\nCUST_INCOME_LEVELC: 50,000 - 69,999    1.163e-01  1.477e-01   0.788 0.430948\nCUST_INCOME_LEVELD: 70,000 - 89,999    4.483e-02  1.631e-01   0.275 0.783385\nCUST_INCOME_LEVELE: 90,000 - 109,999   1.305e-01  1.143e-01   1.142 0.253588\nCUST_INCOME_LEVELF: 110,000 - 129,999 -9.066e-02  1.103e-01  -0.822 0.411113\nCUST_INCOME_LEVELG: 130,000 - 149,999  1.413e-01  1.077e-01   1.312 0.189592\nCUST_INCOME_LEVELH: 150,000 - 169,999  2.788e-01  1.078e-01   2.585 0.009794\nCUST_INCOME_LEVELI: 170,000 - 189,999  3.838e-02  9.781e-02   0.392 0.694806\nCUST_INCOME_LEVELK: 250,000 - 299,999 -2.505e-02  9.851e-02  -0.254 0.799298\nCUST_INCOME_LEVELL: 300,000 and above  3.213e-02  8.388e-02   0.383 0.701693\nCUST_CREDIT_LIMIT                      1.603e-05  8.235e-06   1.947 0.051699\n                                         \n(Intercept)                           ***\nEDUCATION10th                            \nEDUCATION11th                         *  \nEDUCATION12th                            \nEDUCATION1st-4th                         \nEDUCATION5th-6th                         \nEDUCATION7th-8th                      ** \nEDUCATION9th                             \nEDUCATION< Bach.                         \nEDUCATIONAssoc-A                         \nEDUCATIONAssoc-V                         \nEDUCATIONBach.                           \nEDUCATIONMasters                         \nEDUCATIONPhD                          *  \nEDUCATIONPresch.                      ** \nEDUCATIONProfsc                       *  \nAFFINITY_CARD                         ***\nHOUSEHOLD_SIZE1                          \nHOUSEHOLD_SIZE2                          \nHOUSEHOLD_SIZE4-5                     ***\nHOUSEHOLD_SIZE6-8                        \nHOUSEHOLD_SIZE9+                         \nOCCUPATION?                           *  \nOCCUPATIONArmed-F                        \nOCCUPATIONCleric.                        \nOCCUPATIONExec.                       ** \nOCCUPATIONFarming                     *  \nOCCUPATIONHandler                        \nOCCUPATIONHouse-s                     ***\nOCCUPATIONMachine                        \nOCCUPATIONOther                       ** \nOCCUPATIONProf.                          \nOCCUPATIONProtec.                        \nOCCUPATIONSales                          \nOCCUPATIONTechSup                     ** \nOCCUPATIONTransp.                     *  \nY_BOX_GAMES                           ***\nCUST_GENDERF                          *  \nCUST_MARITAL_STATUSDivorc.               \nCUST_MARITAL_STATUSMabsent               \nCUST_MARITAL_STATUSMar-AF                \nCUST_MARITAL_STATUSNeverM                \nCUST_MARITAL_STATUSSepar.                \nCUST_MARITAL_STATUSWidowed            *  \nCUST_YEAR_OF_BIRTH                    ***\nCUST_INCOME_LEVELA: Below 30,000      ***\nCUST_INCOME_LEVELB: 30,000 - 49,999      \nCUST_INCOME_LEVELC: 50,000 - 69,999      \nCUST_INCOME_LEVELD: 70,000 - 89,999      \nCUST_INCOME_LEVELE: 90,000 - 109,999     \nCUST_INCOME_LEVELF: 110,000 - 129,999    \nCUST_INCOME_LEVELG: 130,000 - 149,999    \nCUST_INCOME_LEVELH: 150,000 - 169,999 ** \nCUST_INCOME_LEVELI: 170,000 - 189,999    \nCUST_INCOME_LEVELK: 250,000 - 299,999    \nCUST_INCOME_LEVELL: 300,000 and above    \nCUST_CREDIT_LIMIT                     .  \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.269 on 2643 degrees of freedom\nMultiple R-squared:  0.5705,\tAdjusted R-squared:  0.5614 \nF-statistic: 62.69 on 56 and 2643 DF,  p-value: < 2.2e-16\n\n","type":"TEXT"},{"message":"SETTING_NAME\tSETTING_VALUE\tSETTING_TYPE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\tINPUT\nGLMS_BATCH_ROWS\t2000\tINPUT\nGLMS_CONF_LEVEL\t.95\tDEFAULT\nGLMS_CONV_TOLERANCE\t0.0001\tINPUT\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_DISABLE\tDEFAULT\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_DISABLE\tINPUT\nGLMS_NUM_ITERATIONS\t50\tINPUT\nGLMS_RIDGE_REGRESSION\tGLMS_RIDGE_REG_ENABLE\tINPUT\nGLMS_RIDGE_VALUE\t0.1\tINPUT\nGLMS_ROW_DIAGNOSTICS\tGLMS_ROW_DIAG_DISABLE\tINPUT\nGLMS_VIF_FOR_RIDGE\tGLMS_VIF_RIDGE_DISABLE\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_DELETE_ROW\tINPUT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nPREP_AUTO\tOFF\tINPUT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model fit details","message":["%r","","as.table(mod3$fit.details)"],"enabled":true,"result":{"startTime":1715318944085,"interpreter":"r.medium","endTime":1715318944209,"results":[{"message":"      adjusted.r.square                     aic               coeff.var \n           5.614020e-01            1.344791e+03            3.115385e+01 \n     corrected.total.df        corrected.tot.ss          dependent.mean \n           2.699000e+03            9.916887e+03            4.074815e+00 \n               error.df       error.mean.square       error.sum.squares \n           2.643000e+03            1.611533e+00            4.259281e+03 \n                f.value                   gmsep              hocking.sp \n           6.269106e+01            1.646300e+00            6.099670e-04 \n                    j.p         model.converged                model.df \n           1.645554e+00            1.000000e+00            5.600000e+01 \n        model.f.p.value       model.mean.square       model.sum.squares \n           0.000000e+00            1.010287e+02            5.657606e+03 \n             num.params                num.rows         rank.deficiency \n           5.700000e+01            2.700000e+03            0.000000e+00 \n           root.mean.sq                    r.sq                    sbic \n           1.269462e+00            5.705022e-01            1.681149e+03 \nvalid.covariance.matrix \n           1.000000e+00 ","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display the coefficients","message":["%r","","stats <- summary(mod3)","as.table(stats$coefficients)"],"enabled":true,"result":{"startTime":1715318944305,"interpreter":"r.medium","endTime":1715318944455,"results":[{"message":"                                            Estimate     Std. Error\n(Intercept)                             1.441165e+02   5.654113e+00\nEDUCATION10th                          -2.497199e-01   1.620112e-01\nEDUCATION11th                          -3.416237e-01   1.526993e-01\nEDUCATION12th                          -1.128862e-01   2.656174e-01\nEDUCATION1st-4th                        3.201019e-01   3.624986e-01\nEDUCATION5th-6th                        1.753076e-01   2.785485e-01\nEDUCATION7th-8th                       -5.638466e-01   1.819406e-01\nEDUCATION9th                           -2.813627e-01   1.928687e-01\nEDUCATION< Bach.                       -1.070489e-01   6.909365e-02\nEDUCATIONAssoc-A                        1.040261e-01   1.318786e-01\nEDUCATIONAssoc-V                        1.846627e-01   1.283227e-01\nEDUCATIONBach.                          8.268651e-02   8.102303e-02\nEDUCATIONMasters                        2.277105e-01   1.393942e-01\nEDUCATIONPhD                            5.223723e-01   2.397910e-01\nEDUCATIONPresch.                       -3.355399e+00   1.222621e+00\nEDUCATIONProfsc                         4.380590e-01   1.920398e-01\nAFFINITY_CARD                           3.249566e-01   7.034327e-02\nHOUSEHOLD_SIZE1                        -4.937033e-01   3.115846e-01\nHOUSEHOLD_SIZE2                        -2.502743e-01   3.120408e-01\nHOUSEHOLD_SIZE4-5                      -6.624220e-01   1.400644e-01\nHOUSEHOLD_SIZE6-8                      -2.886144e-01   3.142536e-01\nHOUSEHOLD_SIZE9+                       -3.013082e-01   3.225596e-01\nOCCUPATION?                            -3.353997e-01   1.352717e-01\nOCCUPATIONArmed-F                      -4.658879e-01   8.856023e-01\nOCCUPATIONCleric.                       1.838961e-02   1.061789e-01\nOCCUPATIONExec.                         3.302051e-01   1.036252e-01\nOCCUPATIONFarming                       3.586328e-01   1.682067e-01\nOCCUPATIONHandler                      -1.720916e-01   1.476666e-01\nOCCUPATIONHouse-s                      -1.246394e+00   3.261622e-01\nOCCUPATIONMachine                      -6.319347e-03   1.228586e-01\nOCCUPATIONOther                        -3.473529e-01   1.097412e-01\nOCCUPATIONProf.                        -1.425572e-01   1.161068e-01\nOCCUPATIONProtec.                       3.474666e-02   1.854109e-01\nOCCUPATIONSales                         5.290800e-02   1.002857e-01\nOCCUPATIONTechSup                      -4.443788e-01   1.481375e-01\nOCCUPATIONTransp.                       3.287928e-01   1.466343e-01\nY_BOX_GAMES                            -6.402788e-01   8.774337e-02\nCUST_GENDERF                           -1.921978e-01   7.535026e-02\nCUST_MARITAL_STATUSDivorc.              2.814950e-01   3.139950e-01\nCUST_MARITAL_STATUSMabsent              7.288947e-02   3.564368e-01\nCUST_MARITAL_STATUSMar-AF               5.049858e-01   1.222912e+00\nCUST_MARITAL_STATUSNeverM               6.100713e-02   3.052837e-01\nCUST_MARITAL_STATUSSepar.              -6.129814e-03   3.402136e-01\nCUST_MARITAL_STATUSWidowed             -7.154405e-01   3.431401e-01\nCUST_YEAR_OF_BIRTH                     -7.119727e-02   2.886372e-03\nCUST_INCOME_LEVELA: Below 30,000        7.064750e-01   1.974116e-01\nCUST_INCOME_LEVELB: 30,000 - 49,999     1.161748e-01   1.382230e-01\nCUST_INCOME_LEVELC: 50,000 - 69,999     1.163341e-01   1.476900e-01\nCUST_INCOME_LEVELD: 70,000 - 89,999     4.482928e-02   1.630523e-01\nCUST_INCOME_LEVELE: 90,000 - 109,999    1.304891e-01   1.142710e-01\nCUST_INCOME_LEVELF: 110,000 - 129,999  -9.066217e-02   1.102857e-01\nCUST_INCOME_LEVELG: 130,000 - 149,999   1.413359e-01   1.077153e-01\nCUST_INCOME_LEVELH: 150,000 - 169,999   2.787544e-01   1.078395e-01\nCUST_INCOME_LEVELI: 170,000 - 189,999   3.837892e-02   9.780963e-02\nCUST_INCOME_LEVELK: 250,000 - 299,999  -2.504921e-02   9.850964e-02\nCUST_INCOME_LEVELL: 300,000 and above   3.213350e-02   8.388252e-02\nCUST_CREDIT_LIMIT                       1.602884e-05   8.234639e-06\n                                             t value       Pr(>|t|)\n(Intercept)                             2.548880e+01  2.526507e-128\nEDUCATION10th                          -1.541374e+00   1.233454e-01\nEDUCATION11th                          -2.237231e+00   2.535428e-02\nEDUCATION12th                          -4.249955e-01   6.708746e-01\nEDUCATION1st-4th                        8.830432e-01   3.772933e-01\nEDUCATION5th-6th                        6.293611e-01   5.291670e-01\nEDUCATION7th-8th                       -3.099069e+00   1.961725e-03\nEDUCATION9th                           -1.458830e+00   1.447307e-01\nEDUCATION< Bach.                       -1.549330e+00   1.214220e-01\nEDUCATIONAssoc-A                        7.888020e-01   4.302985e-01\nEDUCATIONAssoc-V                        1.439049e+00   1.502550e-01\nEDUCATIONBach.                          1.020531e+00   3.075701e-01\nEDUCATIONMasters                        1.633573e+00   1.024677e-01\nEDUCATIONPhD                            2.178448e+00   2.946079e-02\nEDUCATIONPresch.                       -2.744431e+00   6.102548e-03\nEDUCATIONProfsc                         2.281084e+00   2.262268e-02\nAFFINITY_CARD                           4.619583e+00   4.029345e-06\nHOUSEHOLD_SIZE1                        -1.584492e+00   1.132015e-01\nHOUSEHOLD_SIZE2                        -8.020562e-01   4.225925e-01\nHOUSEHOLD_SIZE4-5                      -4.729409e+00   2.370034e-06\nHOUSEHOLD_SIZE6-8                      -9.184123e-01   3.584868e-01\nHOUSEHOLD_SIZE9+                       -9.341165e-01   3.503291e-01\nOCCUPATION?                            -2.479452e+00   1.322034e-02\nOCCUPATIONArmed-F                      -5.260690e-01   5.988844e-01\nOCCUPATIONCleric.                       1.731946e-01   8.625118e-01\nOCCUPATIONExec.                         3.186534e+00   1.456674e-03\nOCCUPATIONFarming                       2.132095e+00   3.309097e-02\nOCCUPATIONHandler                      -1.165407e+00   2.439595e-01\nOCCUPATIONHouse-s                      -3.821393e+00   1.357592e-04\nOCCUPATIONMachine                      -5.143593e-02   9.589820e-01\nOCCUPATIONOther                        -3.165199e+00   1.567399e-03\nOCCUPATIONProf.                        -1.227811e+00   2.196272e-01\nOCCUPATIONProtec.                       1.874035e-01   8.513587e-01\nOCCUPATIONSales                         5.275729e-01   5.978401e-01\nOCCUPATIONTechSup                      -2.999772e+00   2.727046e-03\nOCCUPATIONTransp.                       2.242264e+00   2.502693e-02\nY_BOX_GAMES                            -7.297176e+00   3.867140e-13\nCUST_GENDERF                           -2.550725e+00   1.080582e-02\nCUST_MARITAL_STATUSDivorc.              8.964953e-01   3.700699e-01\nCUST_MARITAL_STATUSMabsent              2.044948e-01   8.379826e-01\nCUST_MARITAL_STATUSMar-AF               4.129370e-01   6.796863e-01\nCUST_MARITAL_STATUSNeverM               1.998375e-01   8.416230e-01\nCUST_MARITAL_STATUSSepar.              -1.801755e-02   9.856262e-01\nCUST_MARITAL_STATUSWidowed             -2.084981e+00   3.716681e-02\nCUST_YEAR_OF_BIRTH                     -2.466670e+01  4.428987e-121\nCUST_INCOME_LEVELA: Below 30,000        3.578690e+00   3.515306e-04\nCUST_INCOME_LEVELB: 30,000 - 49,999     8.404881e-01   4.007108e-01\nCUST_INCOME_LEVELC: 50,000 - 69,999     7.876910e-01   4.309481e-01\nCUST_INCOME_LEVELD: 70,000 - 89,999     2.749380e-01   7.833854e-01\nCUST_INCOME_LEVELE: 90,000 - 109,999    1.141927e+00   2.535877e-01\nCUST_INCOME_LEVELF: 110,000 - 129,999  -8.220667e-01   4.111131e-01\nCUST_INCOME_LEVELG: 130,000 - 149,999   1.312125e+00   1.895920e-01\nCUST_INCOME_LEVELH: 150,000 - 169,999   2.584901e+00   9.793829e-03\nCUST_INCOME_LEVELI: 170,000 - 189,999   3.923839e-01   6.948062e-01\nCUST_INCOME_LEVELK: 250,000 - 299,999  -2.542818e-01   7.992977e-01\nCUST_INCOME_LEVELL: 300,000 and above   3.830775e-01   7.016931e-01\nCUST_CREDIT_LIMIT                       1.946514e+00   5.169890e-02","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Make predictions using test data and display results","message":["%r","","PRED <- predict(mod3, TEST, supplemental.cols=colnames(TEST[,c(1,6)]))","","z.show(head(PRED))"],"enabled":true,"result":{"startTime":1715318944535,"interpreter":"r.medium","endTime":1715318946233,"results":[{"message":"CUST_ID\tYRS_RESIDENCE\tPREDICTION\n100003\t6\t3.77287396647378\n100008\t5\t4.37162671660886\n100010\t3\t3.03806002807631\n100011\t3\t3.67316669448357\n100017\t5\t6.37947098162032\n100018\t3\t2.38722771882397\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%r","","CoefficientOfDeterminationR2 <- summary(mod3)$r.squared","MeanAbsoluteError <- (sum(abs(PRED['YRS_RESIDENCE'] - PRED['PREDICTION'])))/nrow(PRED['YRS_RESIDENCE'])","MeanSquaredError <- (sum((PRED['YRS_RESIDENCE'] - PRED['PREDICTION'])^2))/nrow(PRED['YRS_RESIDENCE'])","RootMeanSquaredError <- sqrt(MeanSquaredError)","","cat('R^2:  ', CoefficientOfDeterminationR2)","cat('\\nMAE:  ', MeanAbsoluteError)","cat('\\nMSE:  ', MeanSquaredError)","cat('\\nRMSE: ', RootMeanSquaredError)"],"enabled":true,"result":{"startTime":1715318946315,"interpreter":"r.medium","endTime":1715318947129,"results":[{"message":"R^2:   0.5705022\nMAE:   0.8364171\nMSE:   1.487575\nRMSE:  1.219662","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Using Lattice, plot the predicted and actual years of residence","message":["%r","","x <- ore.pull(PRED[['PREDICTION']])","y <- ore.pull(PRED[['YRS_RESIDENCE']])","n <- length(x)","","dotplot(y~x, xlab = \"PREDICTION\", ylab = \"YRS_RESIDENCE\", ","  panel = function(x,y) {","  panel.dotplot(x,y)","  panel.abline(0, 1, col=\"red\")","  }, ","  xlim=c(0,14.5), ","  ylim=c(0,14.5),","  scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","  main=\"The prediction vs. actual value for years of residence\")"],"enabled":true,"result":{"startTime":1715318947204,"interpreter":"r.medium","endTime":1715318947643,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nbky+f//+rFmzVL3UT47DoAYx9QdMx/W6IpazTVOpyqeuiq1h2O7J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M1NdOQx2REREZCXu30dQEPbtw7RpWL0ajjzxWB6DHREREVmDtDQE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I+Pb8WYoy4/y7Cbi0nOmxzNcMy+JAQwG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:false,"selectedVisualization":"image","title":"Using Lattice, plot the residuals","message":["%r","","xyplot(y-x~x, xlab = \"PREDICTION\", ylab = \"RESIDUAL\", ","        panel = function(x,y) {","          panel.xyplot(x,y)","          panel.abline(0, 0, col=\"red\")","        }, ","        xlim=c(0,8.5), ","        ylim=c(-7,8.5),","        scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","        main=\"Prediction of Years of Residence vs. Residuals\")"],"enabled":true,"result":{"startTime":1715318947725,"interpreter":"r.medium","endTime":1715318948045,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg\nAElEQVR4nOzdZ3gU5R6G8XuTkEISAgklFOkQSkA6BFAIIB1EARFBrIiAiuWooEcseFRsQaRY\nALGhgAKCUkQpCgSE0DtSIjWhBQhJSNvzYWOyhCSk7+7s8/vAxb4zs/uf2ZnJs1PeMZnNZkRE\nRETE8bnYugARERERKRwKdiIiIiIGoWAnIiIiYhAKdiIiIiIGoWAnIiIiYhAKdiIiIiIGoWAn\nIiIiYhAKdiIiIiIGoWDn8B5//HHTv1q0aGE9KDAwMH3QO++8U6RlFOdnFaf58+d369atQoUK\nJUqUSJ/BM2fO2LougzPYYs/f1pHDpi2SieOuY0b922FDCnbZ6t27tykrHh4eFSpU6NSp09tv\nvx0VFWXrMovDa6+9lj771atXt3U5xeeVV1655557fv311+jo6OTk5JuOHxsbW7NmzfRl5erq\nGh4efuNo+/bt8/DwSB+tbNmy0dHRRVC+o8rrYk+X5Tbr4uLi4+NTs2bN3r17T5s27erVq0VX\nuRie1jGxfwp2eZaYmBgdHb169eqXXnqpbt2633//va0rkiJx9uzZiRMn5mkSHx+fmTNnmkwm\ny8vU1NRHHnkkMTHRepwbG6dMmVK+fPmCF2wM+VjsOTObzVevXj169Ogvv/wyevTooKCgnTt3\nFuL7i2gdE7viZusCHNvly5eHDh0aEBBwxx132LqWLBTnqSvHPU2WnS1btiQlJaW/XLBgQe/e\nvUuUKJHzVKGhoSNHjpw2bZrl5b59+yZMmDBhwoT0EaZMmWJ9GO/uu+++9957C7Vwx5a/xZ57\nJ0+eHDBgwN69e93cim/vZ7ytQ3KgdUxsS0fscstsNpvN5uTk5P379w8YMCC9PSUl5T//+Y8N\nC5MicvnyZeuXffr0yWW8mDhxYo0aNaxfpv98j4yMfPnll9MHBQQEpEdAscj3Yr+RZZuNiYlZ\nunRpYGBgevuhQ4fWrVtXoCpFAK1jYpcU7PLG1dU1KChozpw5tWrVSm/cuXNnZGSk5f83Xge6\ndOnS0NBQf39/k8m0Zs2a9KlOnTr16quvtm/fvly5cu7u7mXKlGnWrNkLL7yQ/laZpKSkTJ06\ntUWLFt7e3n5+fq1atfroo4+sj23c6KYXpZ4/f/69997r1q1bpUqVvLy8/P39mzRpMnLkyA0b\nNlhGGDBggMlkev3119MniYyMtL64ZPbs2bn8rMjIyJdeeqlNmzZly5YtUaJEmTJlGjduPHr0\n6O3bt9+08oSEhPfff79Fixa+vr7e3t7NmjWbPHlyampqDvOepVzWMHbsWJPJlOlAWvpV/B07\ndsz5UzKdkE1KSnrkkUdSUlKAESNGxMbGpo85ZcqUChUqZJo8TyvGxYsXFyxY8MQTT7Rq1coy\nvpeXV2BgYMeOHd94442TJ0/eOEluVlGz2Tx//vz+/fvXrFnT29vbw8OjatWqrVu3fuSRR2bP\nnp3XS0uLZ7Fnx8/Pr0ePHmPHjrVuPHjw4I1j5mnJ52kR5bx15GPTzkfBBdmmbrqjyHdVWfro\no4/SSw0MDLRsO9YeeOCB9BE6depkaSzclTZPjLeOxcbGWu/nM11xZPm7YNG7d2/rQfnYI+XA\nht+pYzNLNnr16pXDgnrwwQeth65evdrSbv13+u233540aVL6H3jr0T7++GMPD48svxF3d/fJ\nkydn+ri4uLguXbrcOHLLli2tDx82b97ceqpMxWR6z1mzZvn4+GS3Yhw6dMhsNvfv3z+7ESy+\n+OKL3HzWxIkTczgr8fjjjyckJGRX+eOPP96gQYMbpxo6dGjuv8081fDiiy/mMMsdOnTIzceN\nGjXKeqp33333yy+/tG656667bpwqrytG586dcyjV19d30aJFmSa56SqakpLSt2/fHN62devW\n9rnYc9hmFy9ebD3om2++KciSz+siymHryN+mndeCzQXYpnKzo8h3VVk6d+6cu7t7+oTLly+3\nHhofH1+qVKn0oZavsnBX2hw4yTp25coV6zG///576ze0/rvQq1cv60EF3yPlewlIOgW7bBU8\n2N19992urq43jvbBBx/ksLJaTJs2zfrjHn744ZtOQl6C3dSpU3N+q0IMdm+++eZNKx88eHB2\nledgxYoVufw281RDoQS72NhY6xOyluMc6S8DAgLOnDmTaZJ8rBg570YtnxsZGZndss1yFc0U\nQG+U+/1pMS/2HLbZsLAw60E7duwoyJLP6yLKYevI36adj1Ulf9tULncU+a4qO9aBY9iwYdaD\n5s2blz7Iz88vLi4uH99IvjnJOpYp2M2dO9f6DfMd7LjZHsm68mL7To1HwS5bOWzAiYmJ1qdi\ngaNHj1oGZdp7VqhQYd68eVeuXImMjHzrrbc2bNhw5MgR62uGHnjggb1798bHxx85cuSRRx5J\nb/f29k7/w797927r9wwODv7rr7+uXbu2ffv25s2bWw/KZbA7duyY9Q9HT0/P99577/jx4wkJ\nCX///ffChQv79et3+PDh9PFfffXV9JGrVauW5eLK7rMOHjxoHR0aNmwYHh5+7dq1AwcOdO3a\n1br4JUuWZPluQEhIyK5duy5duvTJJ59Yv9vDDz+cm68yfzV899131oOSkpJy81nWVq9ebX0w\nzNqcOXMyjZyPFcNsNg8ZMmTkyJHLly/ft2/fxYsXExISjh8//umnn/r6+qZPMm7cOOsPuukq\nOnTo0PShY8aMOXHiRGJi4unTpyMiImbPnj148OA+ffrY52LPcpu9dOlSpuufunXrVsAln9dF\nlN3Wkb9NO3+rSj62qTztKPJXVXaWLVuWPomvr298fHz6oLvuuit90MiRI/P3jeSbk6xjmYLd\nvHnzrD8rh2BXwD2SdeXF9p0aj4JdtrLcgG+8eQJo1KhR+lTW66jJZAoPD8/0ttaHJTL94EhN\nTbU+xjNp0qQbJwG2b9+ePsmhQ4dcXDIulMxlsBs3bpz1G/744485L4qCBLvnn3/e+rOsf8XG\nxMRYb+09e/bM8t08PDxOnz6dPqhbt27pg9q0aZNz5QWpoeDBzmw2jx49mhv069fvxjHzsWLk\n4Kmnnkofv3379taDbrqK3n333ekjLFiwIO8znab4F3umbTZLnTt3jomJsZ4qH0s+r4sou60j\nf5t2/laVfGxTedpRFO4KnJKSUqVKlfRJ0oNFTEyMddbcvHmzpb2wVtqbcpJ1LFOwmz9/vvVn\n5RDsspPLPZJ15cX2nRqPujvJrewOvbi4uLz33ntZDmrbtm2bNm0yNa5duzb9/5s2bcrubS1D\nM/0HaNiw4a233pr+snbt2m3atMnyEuYcrF69Ov3/QUFB1ttPobOe36ZNmzZu3Dj9pZ+fX79+\n/b7++mvLyz/++CPLd+jdu7f1T+GaNWum///SpUvFU0O+TZw4cenSpUePHk1v8ff3/+STT3Iu\nMpcrhsXKlSu///77iIiIyMjIq1ev3nhB9KlTp7J7qyxX0aCgoPT/9+/fPzg4uF69ekFBQQ0b\nNmzXrt0tt9yS3btlYsPFnp2XX375xrPD+VjyhbWI8rdp53tVSZfLbSpPO4qCV2XNxcXlgQce\n+N///md5OWfOnIEDBwI//vjjtWvXLI2NGzdOf1hCYX0jBWeMdawgCrJHsmY/36nDUbArEF9f\n3+nTp1v/3rXWunXrGxtzf1vQ2bNnLf+x7qCoWrVqmUarVq1aXrdM6+3KejsvCqdPn07//41P\nrbBuiY2NvXLlivWBHIvatWtbv/T09Ez/fy5vjC14Dfnm7e09YcIE63MKL774YpZXO+VjxUhO\nTh48ePAPP/yQ8/hxcXHZDcpyFR0xYsS0adMsf+DNZvOuXbt27dqVPjQkJOSDDz4ICQm5aZ02\nXOzZ+d///nfq1Cnre5bJ15IvrEWUv007HwVnksttKk87ioJXlcnDDz/81ltvmc1mYNmyZTEx\nMaVLl54zZ076CNYnMQvrGyk4Y6xj+VPwPZI1+/lOHY66O8mzEiVKlCtXrkOHDm+++eaBAweG\nDBmS3ZgBAQEF+aD0HzqWXVshsn7DHH47FsNn5WbWSpcubf3S+txBsdVQEH5+ftYvrW/oy5/0\nFeOjjz666T6UHGcwy1W0WrVq69at69atW5aLOjw8vHPnzvv378/T5xb/Yrd8REJCwsaNG62v\nJfriiy9ycxl7ltKXfFEsoqKQXX8WudymimhHkZueXICaNWt26NDB8v9r16798MMPZ86cSe8x\nyt3d3XrfW1jfSF45yTqW6Sf0xYsXsxyt4Hska7b6Tg1AwS630s9eWx4ptmbNmpdffrlixYo5\nTJLl6mg9ycCBA3M4TZ6+C7M+afLPP/9kesPc9w6VrnLlyun/37FjR14nz5NKlSql/9/6jKSF\ndfE+Pj5FdMzGHmq4qXysGNadS1WqVGnp0qXpV/bkfIdpuuz+ogcHBy9fvjw6OnrJkiXvvvvu\no48+av2A8Pj4+FmzZt30ze1hsXt4eLRu3Xr58uXWEfb111+3PpqYjyVPIS2i/G3a+Ss4H/K0\noyiKqqxv55wzZ87cuXPT+7Tr169fpp8lhfKN5IMh17FMe4ZMx9iOHDmS5VQF3yNlYqvv1NEp\n2BU3635WV6xYceOWdiPr82W7d++2fgrh4cOHN27cmNcaQkND0/+/f//+RYsW5Ty+dT9k6Re4\n5FL6b25g27Zt1sVfunTpp59+Sn9522235emdHauGm8rHimF9fue+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vXp13759c37nPXv2FHaxIiIi4lwscSIsLCyHGwBWrlzZpUuXYizK\njoNdampqSkrKuHHj+vfvD4SEhHz22WedOnWaOnXqa6+9lt1UoaGhixcvzuGIXVhYWMOGDYuo\nZhEREXESljjxzDPPhISEZDmCp6dnaGho8RZlx8GubNmyQIcOHdJb2rdv7+rqunnz5hymcnV1\n7dOnTw4jhIWFubjY75WFIiIi4hAscSIkJGTgwIG2riWD/UacW2+9Nct2k8lUzJWIiIiIOAT7\nDXb33HMPYN0j8fr161NSUrI74CkiIiLi5Ow32DVt2tTSid3ChQtjY2M3bdo0YsSIGjVqjB49\n2taliYiIiNgj+w12wPTp08eOHfviiy/6+/v37du3Xbt2GzZsKF26tK3rEhEREbFH9nvzBODq\n6jp27NixY8fauhARERERB2DXR+xEREREikGKmb8vEH6CmKw7THMYdn3ETkRERKRIJSTz6hqm\nb+ZKYlpLi0p82I3bqtq0rPxSsBMREREndS2Fbt/wRyTNKtK9NuVKsiOK+XsInc2Pg7gzyNb1\n5Z2CnYiIiDipjzbyRyRPt+GDrrj820/uuPZ0mM2ji+n4FH4eNq0v73SNnYiIiDipmdu4pRTv\n3pGR6oC6AUwI5Vwciw/YrrL8UrATERERZ3QthUPnub0aJW5IQ51rAuyOLv6iCkrBTkRERJxR\nqhkzuGYVhVxNaSM4HAU7ERERcUZeblT1I/x4FgFuw3GAugHFX1RBKdiJiIiIkxrSiEMX+CD8\nusboq7yyGh933RUrIiIi4jjGtmfJQV5Yybp/6FmHsiXZFcWnEZyJ5bM+lPe2dX15p2AnIiIi\nTqqUB6sf4MllzNuTcQ9sZV++68+9wTatLL8U7ERERMR5lS3Jd/2Z3IOdUZyPo15ZGpTDzWEv\nVVOwExEREWdXriSda9i6iMLgsIlURERERK6nYCciIiJiEAp2IiIiIgahYCciIiJiEAp2IiIi\nIgahYCciIiJiEAp2IiIiIgahYCciIiJiEAp2IiIiIgahJ0+IiL1LTGH/OUq4UsffgZ/zIyJS\nDBTsRMR+HYvh2RUsOUhyKoCHK4OCeb8r5UraujIREbukYCcidurAedrP4kI8PWrTpgpJqaz7\nh6928EckGx6hoo+t6xMRsT8KdiJipx5dzKUEfrqX3nUzGmdt45HFPLOc7wfYrjIREXuly1VE\nxB4dOM+6f3iwyXWpDni4KT3r8OM+LibYqDIRETumYCci9mhXFECXmlkM6lKT5FT2nS3mikRE\nHICCnYjYI8vdElneA1vCBSDFXKz1iIg4BAU7EbFHdQMAwk9kMSj8BCao41/MFYmIOAAFOxGx\nR00rUr8sn2xhZ9R17WuOMW8PnWoQqLtiRURuoLtiRcQemeCzPtzxNe1mMUloe4UAACAASURB\nVKI5baqQnMofkczcho87k3vYuj4REbukYCcidqp9VX4bxmNL+CA8o7FVZWb0pUE525UlImLH\nFOxExH61u4Xdo9h3lj1ncXOhUXlq69I6EZHsKdiJiF0zQYNyOkQnIpIrunlCRERExCAU7ERE\nRAwu1Uxiiq2LkGKhU7EiIiLGlGrm0whmb2dPNIkp1AmgT13G3Yafh60rkyKjI3YiIiIGlJTK\nnd8z6heOXqRTDe6uz7VkJq6nxWecvGLr4qTI6IidiIiIAb39Jz8f5KEmTO2FlxuAGaZv5sll\nPLiIlffbuj4pGjpiJyIiYjQpZqZuJiiAz/qkpTrABKNa8sCt/HaEPWdtWp8UGQU7ERERozl6\nkeir3FkPtxv+zt9dH+Cvk8VflBQHBTsRERGjuZoEUCqrmyT8PAGuJhZrPVJsFOxERESMpkop\nXEzsic5ikKWxql8xVyTFRMFORETEaAK8uK0qP+5jZ9R17Zeu8UE4pTzoVMNGlUkRU7ATEREx\noPe64mIi9Es+/otd0Ry5yNw9tJ3J3xd4uzM+7rauT4qGujsRERExoJaVWDKYYQt5allGo6cb\nH3ZjVEvblSVFTMFORETEmLrU5PAYVvzN7miuJhFcni41qeBt67KkKCnYiYiIGJaXG/3q0a+e\nreuQ4qJr7EREREQMQsFORERExCAU7EREREQMQsFORERExCAU7EREREQMQsFORERExCAU7ERE\nREQMQv3YiYgUlSuJTN/MqqMcOE95b5pXZFRLgsvbuqxidDWJvWc5fomaZahfDg9XWxckYnQK\ndiIiReLIRbp+zeGL+HlQN4CoWKZvYcZWpvXi0Wa2Lq7oJabw2ho+2kRcUlqLvxf/vZ2n22Cy\naWEixqZgJyJS+JJT6fc9kZeY0pMRzXFzAdh6msE/MuJngsvTpoqtSyxKZrj3Bxbup3lFBgVT\n1Y9D5/lyB8+u4MRlPuhq6/pEjEvX2ImIFL5fDrErmpduY3TLtFQHNKvIksG4mJi43qbFFb0f\n9rJwP0MasWk4z7dlUEP+ezs7R9KhGmHhbD5l6/pEjEvBTkSk8K09BvDArZnb6wYQUoU/Iou9\noOL11Q48XJncA1er065ebnzcEzN8u9N2lYkYnYKdiEjhi0kAqOCTxaAKPsQkYL6+MS6J5NRi\nqKuYHDhHg3L4e2Vub1SeUh7sP2eLmkScg66xExEpfJZId+QijW64B/bIRSp4p91AEHWV19aw\n/G8iYyjhSsNyDG/OiOa4OPj9BSYTKeasB6WaHX7uROyZjtg5hpNX+PMfjsZk/pUvIvape22A\nSRszt6+NZNvptKEHz9PkEz7ZQikPHmhCn7qcvMKoX+g/z+GP3jUsx96zRF3N3L75FLGJNHSm\nDl9EipmO2Nm7r3cyfjXHYtJelvdmbHvGtNZPXhG71qEa3WszaxslXHi+HTXLcCmBnw7wzHK8\n3Xn5dlLN3Pcj5+P4fgCDGqZNFZ9Mt29YtJ/qk2gayB21eaKVQ/7+fqgpC/czYglzB2b0XReT\nwOhfcDUx7IZLD0WksCjY2bUJfzB+NYE+jGlNbX/+ucSP+3h2BXuimdHX1sWJSI6+H8CAeXwa\nwacReLkRnwxQ3psFg6hVhvXHiTjNcyEZqS4qlpYzOH4J4OQVTl7h50P893d+vd/x+kbpU5cH\nbuXLHTSaRv8G3FKKwxf5bhenY3m9Yxanp0WksCjY2a9d0by+hhaV+PV+ynimNb4RyoB5zNzG\n3fXpWcem9YlIjvw8+PV+lv/NmmPsO0ugD00rcl8j/DwANp8E6FcvY/xG0zkbR5VSVPVjw3E+\n6s7sHWw7ze1fcOI5ype0zVzk26w7aVieiet4Z11ayy2lmN0vizuFRaQQKdjZry+3k2JmWq+M\nVAd4uvF5X6qG8cV2BTsRe2eCHrXpUTuLQZbnMfi4p7188w/OxlHHn4NP8uJvbDhO3yCeas3d\n81i4j2ELWD60+MouFC4mnm/LcyEcjUl7pFhVP1vXJOIEHPHiDWex5yylPGhZKXN7RR/ql2V3\ntC1qEpFCUq00wK5/N+TvdgF8dRfAzig8XAn0Afh+AC4m1h+3SY2FwMVErTJ0rK5UJ1JMFOzs\nV0pqtndIuLqQqvtjRRxZ99qULME764hNBDgbjwnaVOGPSH49TM86eLoBuLvgU4L4pJzfTEQk\njYKd/QoqS0wCe85mbr8Qz96zBAXYoiYRKSQBXrwRyt6zhMzk2124ghleWUPPb/F15+0uGWMm\npuKqXbWVS9fYFc3la7auQ8Qu6Ro7+zW0MVP/4qll/HJf2m93IMXMmOUkpjC0sU2LE5ECey4E\ns5nxqxm6IK3lzbXU9ufbuzN+uf1yiIRkapa2VY32Zc4uxq/h8IW0l/5ePNiEd++47sFlIk5O\nwc5+ta7Mk62ZvIkmn/Bgk7TuTr7bzZZT9KvHwIY3fwcRsXP/acuwW1lzjHX/MOUvgOfa0qpy\n2tA//mHAPIDxHW1VoB15ZTVv/pH2xA43E+6uXIjnw3AW7ifisetuMhNxZgp2di2sG1VK8daf\njPs9rcXLjRfb8Xoo+oEqYgzlvbmnIfc0JNCXl39n5M88t4Ly3lyM59I1gLvrq4sQNp7gf39g\nMlHZl5l96VwTVxP7z9PhC45epO/3/PmgrUsUsQ+OEey+/vrrYcOGAadPnw4MDLR1OcXH0l/A\nk63Yc5bDF6hWmoblMvpHEJFMEpJZeYQ90SQk07A8nWtk8Rx6u/VSe5pVZNQvHL/EsRhcTFTw\n5pUOjG5p68rswOdbMYObiRVDaVAurbFeAMuH0uxT1kWy8YTjdeMsUhQcINgdP378qaeeCgwM\nPHPmjK1rsQ1PN5pXpHlFW9chYt9WHuHBRZy6ktHiXYK3OvNUa9vVlEfda3HkKYC4ZEo6wO65\n+OyMwmSiY/WMVGfRNJAyXlyM57cjCnYiYP93xZrN5oceeqhZs2b9+/e3dS0iYr82nqDPHBKS\nmdSdhffSpSaeblxNYsxyyr/H+xtITrV1iXmhVJfJtWTM5qw7w7PcW3Y2rpgrErFT9h7sJk+e\nHB4e/vnnn9u6EBGxa//5FZOJtQ/SujL3L2DVUdpXZVRLfNw5H8/zK+n3PUkOle3EWt0AgKMX\nM7efuExULECA45xzFylSdh3s9u/fP27cuDfeeKNmzZq2rkVE7NeZWDYcZ1BD6gQwZAEmWPMg\nK+9nak/eCCXVTNda/HIo7bZTcUSDGwGsjeTghYzGFDPPrkjrrb1TDdsUJmJv7Pdwf3Jy8v33\n3x8cHPz000/nfqqUlJSlS5cmJCRkOTQ8PBxITdXPdhFDOX4ZMwSX59fDHLnI2525rWraoODy\nAP3qsfcs0zfzTBsblin5d3d92t3C+uPcOp0xrbmjFocvMGMrm08BdK5B+6o3ewuRwmaJE5Zo\nkSVPT8+ePXu6uroWY1F2HOwmTJiwY8eOiIiIPC2R1atX9+3bN+dx9uzZU7DSRMS+eLkBXL7G\nttMAvepmDLpyDcDPg841+HIHsYm6r9whmeC3YfSZw29HmbieieszBrWsxPcDbFeZODFLnAgL\nCwsLC8tunJUrV3bp0iW7oUXBToPdiRMn3nrrrXHjxjVq1ChPE4aGhi5evDiHI3ZhYWENG6pv\nXxFDqRtAaU9+OpAW6TytdmwL9wO0rMy6fwCupeBjiwql4DzdWDmMv07yQTi7o0lK4dZAetfl\nvkaUsOurisSwLHHimWeeCQkJyXIET0/P0NDQ4i3KXoNdTExMcnLyhAkTJkyYYN1esWLFWrVq\n/f3339lN6Orq2qdPnxzeOSwszMVF+wARQ3F3ZURzJq5Pu4J+62nq+GOGaZv5difda1PHn4jT\nlPF0pG7tJEutKjNXx+fEPljiREhIyMCBA21dSwY7DXbBwcFms9m65Yknnpg6daqzdVAsIrn0\nWkc2n2LVUUzw2GJ+3MeOMxw8T21/Zt3JvD38dZLhzfTIlswiL7HjDBfiqVeWWwPTTmqLiOPS\nRiwiRuDpxoqhTN7EB+GcusL8PZQvyZDG9A3irT/5ZAuVfHm9uE+J2LXTsTy2hJ8PZrSULcnE\nLjzc1HY1iUiBKdiJiEG4ufBsCM+G8PFf/HcV0XF8u5NvdwKEVOGru6ioy+v+dTGBDl/w9wUG\nB9OrLv5e7Ipi6mYeWczVJJ5sZev6RCS/HCbYTZkyZcqUKbauQkQcwJOtGHYrm06w7xxlPGlc\ngSa6guN6b/3JoQtM7cmofx9E26M2w5vTdiZjf2NgAwIVgkUck8MEOxGR3PPzoGstutbKz7Rm\nmLOL2dvZFUV8Mg3L0TeIMW0Mdf3ZnF00KMfIltc1lvHkjVDumc/iAzzW3EaViUjB6P5QEZEM\nKWYG/8DQBWw8QVBZQqpw5CLjfqfNDM4Z5WmkcUmcukKrylncStK6MsChCzcMEBEHYaBfoCIi\nBfb+Bubu4Z6GfN6HUh4Ayam89SevrqHLVzzRiuDytKqMiyPfXuvqggkSU7IYZGl0c8ncuP8c\nKWbql72uj0ARsUPaRkVE0qSambSR2v58fRfu/z7yZsVhPt8KsCOK4UsA6gYwux8hVWxWZwF5\nuBJUljXHSEzJmE2LXw8DNK6Q9vJMLM+u4Ie9JKUCuJroVZdJ3alRungrtpHoq3yyhU0niYyh\nRhlCqvB4C3WFKPZOp2JFRNIcuciZWO6qlxF3lv9Nv+9JSGZIY4BBDWlakcMXaD+LO79j6SEb\nFlsgw5tx6gpjlpNi1WHoziheXUOgD33qApy4TKvP+W43nWowIZS3O9OrLj8fpOVn7D9nq8KL\nz7p/aDiNV9fwZyTA6qO8vIqG09hyytaVieRIR+xERNJcSQQyDsmkmhn1C6U82PQokZf4didz\n9wAE+hAVy+KDLD7IkEbM7pf53KX9e7I1y//mky2sPUa32mndnSw+gBkWD057nO6Y5Zy4zNd3\nMbRxxoRLD9Hvex5bwh8P2ar24hB9lb7fkWpm/kD6N8AEqWbm7OLxn+nzHfufwM/D1iWKZMPR\n9kYiIkWmki8mMg5HbT7F0RhGt6RmGab+BdCsIpFPc/o5HmiCCXrU4dtdvLrGdhXnVwkXfr6P\n1zpyLo5JGxm/mgX7aFOF9Q/TrRbAuTh+2k+foOtSHdCzDg804c9/OHjeJoUXk6mbuZjArDsZ\n0CDtFhMXE0MbM60XZ2L5LMLG5YnkQMFORCRNBW9aVWb+3rTbQg9fAGheiZhrLNqPi4nlQ6nq\nB9C8ImZ4PoRmFQkLJzbRlmXnj7srr3Yg+nlOPsvOkVx9mTUP0qJS2lDL3RKda2QxoaVxd3Tx\nlVr8Vh8lwIt+9TK33xtMyRKsOWaDkkRyScFORCTDxDtITKHDF3y9k8vXAMKP03YmKWbuqEW5\nkmmjxScDlHTnwSbEJ7PppM0KLrhKvjQqj8f1d1Ekp8INt8dauLoA112cZzwX4gn0yeLeZ3dX\nypXkvFE6vhFDUrATEcnQoRrzBhKfzLCFjPwFYOL6tNOOAxtkjLbsEB6uNChHJV8g81/6uCSH\nPIZnrW4AJthwPItB4ccBggKKuaJiVc6bk1fS0q21+GSirlLe2xY1ieSOgp2IyHXuqsfRMczu\nx3/aUqUUJhNjWgNExqSN8O56Vh/jvkb4uqc1Wv7SxyczfjV1Psb3bUq9Tc2PeGFl2g0ZDqeS\nL6E1mLuH1ceua996mk8jaFQ+o0sUQ+pai5gEvtmZuX3mVhKSuSNfTzQRKR66K1ZEJLPSnjxw\nK8ATrWg/iw/DKeHCR5uIimXTSXZEEVye97uSkMzMbfi607oKl64ROpttZ6jtz5BGuLqw8QTv\nbWDJQdY+6JDHeD7uQbtZdPuah5vSvipuLoSfYMZWzGY+62Pr4orYqJZ8vIlRvxCXxLBb8XHn\n8jVmbGXc79QozSNNbV2fSPYU7EREslXNj4jHeOl3vtvN5Wt8thV/L54N4bWORMYwZjl7z/JO\nF7zceHIp287wekf+e3vatVlmmLSR51YweinzB9p6TvKuQTnWPczwxXwawaf/3gd6awU+7ZP2\n5DED8/Ng+VD6fsfopTyxlHLeRF8FqOPP4sGULJHthNvP8EE4f53k+CVq+dPuFl5oR80yxVa4\niIKdiEiOynszoy8z+vL0Cj7exIV4Pt3C5xFp51ifas0L7bh0ja93Elqd8R0yJjTBM234I5IF\n+zgdS0UfG81AATQsx4ZHOBrD7miSUmhUgdr+WTxh1pAaV2D3KL7ZyV8nOXKRPnVpU4UhjfHK\n/s/m1zt55CdSzDQqT5eaHLrApxHM2cXCe7O+v1ikKCjYiYjkyqRuPHgrc/ewO5qUVILLM6AB\nrSoD7IoiMYW+QVlM1TeIRfvZeppedYq53kJTo7SzPEMsEx93Hm/B4y1yNfK+czzyE7f4sXBQ\nxgWIq48xcB4D53HoKQL0LDIpFgp2IiK51SSQJoFZtFt6P/F2z2KQ5SkO8UlFWZbYgY82kpTK\n3AHX3VYSWp2HmvB+OLU/IhUalKNnHZ5pk7ZWiBQFBTsRkYKqXhpgZ1QWgyyN1a2OeP1yiB/2\nsjsaFxONyjO4kc7TGUH4CWqVyejhGTDDqF/4ZAuAiwttKrPjDONX881OVj+Q1lGOSKFTdyci\nIgVVx5/GFZi9PfODtv65xPTNVC9Ns4oAyakMWUDvOXy5nQvxRMUycxtdvuLxn0k1dH+/zuDy\ntYynDFvM2MonW+heG39PmgaybAjHn2VSd/6+wJAFNqpSnICCnYhIIfioO9eSaTeLD8PZfIqI\n00z5izYzuJjAxz3S7pMdv5o5u+hfn1PPcfgpjj1N5NN0r82nEXwQbusZkIKp7Mvhi9f1afzB\nBgJ9mN6LmGtULgXgamJMa4Y3Y80xtpyyVaVicAp2IiKFoGN1fhpMCRee+5VWn9PiM55cRlIq\n8wbSuy5AbCKTNtK8InMHEvjvHbJV/Vh0L3UDeGcdSTc850AcSJ8gLsSnnXgFzsdz4Dy96zJ5\nE6nmtHXA4t5gyOapHiIFp2vsREQKR4/aHH2aNcfYFUWqmeDydKiO9799nm08QXwyDzbB9fr+\nQjxcGdqY8avZcea6K7TEsYxuyecRPLOCU1cY0jitU5jNJ9kRRbtb6F8/Y0zLGVvLk4hFCp2C\nnYhIofFwpVstumX1yKnz8QAVs7pk3nId/YX4oqxMipiPO7/ez4B5vL2Ot9elNe6I4o6afDcg\n7Vy8heVCzCqlbFCkOAMFO3EkUVc5fIGqftoniuOxPFUs/YGz1qwfOCuOq2YZtjzG8r/ZfIp/\nLvFnJEcv8l7X63qwS0zh/Q2UcKHrDek/PpnFB9h+hovx1CvLHbVoWK44yxeDULATxzBnF+NX\nc/hi2suKPrx8O6NaOksn+GIAbarg687MbYxqiafVrvdKIl/uoII3jSpkMdWVRPad5dQVgspS\nxx83XRdt31xM9KxDzzoAW0/TbhZdvuKdLnSpiZ8H287w2ho2nWRc+8zdnaz7h3t/4OSVjBYT\njGjBxz30pUveKNiJA/jfn/x3FeW9eaIVtf2JjGHBPp5Yyu5opveydXEiuePlxku3Me53es1h\nUneCy2M2s/0Mo5fyzyU+6Z352rurSYz9jc8juJaS1lLJl7c7M+zW4q9d8qNZRRbdy/0LeHRx\nRqOLiWdDeLPTdWMeOE/Pb3Ex8XEPetTB34udUfzvDz7ZgpsLH/co5sLFsSnYib3bFc2rq2lR\niRVDM7qJerMTd8/lky3cVS+LMxoi9unF9pyJZfImGk/H151UM1eTcDHxyu2MaH7dmEmp9PqW\ntZG0r0q/elT0Yd85vtjGA4s4G8dzITaaAcmjbrU4PIZF+9kVRUwCDcvTrRb1ymYe7dXVXE1i\n7YO0r5rW0qEa7YfS9Wumbeap1tTxL+bCxYEp2Im9m72dFDPTe13X+WfJEsy8k2phzNqmYCcO\nwwSTunNfI37cx+5oXE0El2dQMLfecBL20y2sjWRMa8K6Z1xv8GwIHb7gpd+5u76TPrwVuHyN\nuKSM/mLsn6879zfOaQQz/HyQ26tlpDoLVxMvtmPVUZYeYkzrIq1RDEXBTuzd7mj8PLLoBqKy\nL0Fl2R1ti5pECqBVZVpVvsk4X+0gwIuJd1x3FWkZT97vSrdvmLubse2LskT7k5jCy6v4LCKt\nlxA3E7UCmNmXdrfYurICi0ngahJBAVkMCioLcOJyMVckjk3BTuxdSiqu2Vw77OZCYkoW7bGJ\nTvSM7RQzC/bx+xEOnKe8N00DebCJIx3PkCztP0e7qni4Zm63HNTZd46vd7LkAPvO4eVGowo8\n1CTz8R4jiU8mZAY7ogC8S1Dak3PxHDjHbbOY0otRLWxdX8F4l8CUTWc3lsZSHsVckTg2BTux\nd3UD+P0o+89lvjDlQjz7z9G9dkbLzwd5fwPbznD5GhV9uL0ar3XM4nIWIzkfz53fsf44QHlv\n1v/DvD28s45v7r6up3txOCZT1k+PtbStPMxXOyjhQp0AzsXxxTa+2MYL7XinS/FWWVz+u4od\nUXi48cPAjBX77T95eRVPLqVXHar52bS+gnF3pUUlVh7hQnzmp83O3Q0QUsUmdYmj0l3UYu+G\nNAYYszzj3kAg1czTy0lMYUijtJZX19DnO7acIqQKDzWhemnm7aHZp/x2xAY1F5t75rP+OM+F\nEPUfov5D7EssuhevEgycz56zti5OCqBBOf46SVxS5vbVRwFOxzK6JedeYM8ojoxh2+PUCWDi\nejrO5qNNaf3fGkaKmU+3AHx553U/V8bdRv8GpJp56TdblVZonmtLTAL953EmNq3FDF9s54Nw\nmgbSqUZO0566wrYzxCYWQ5niGHTETuxdu1sY2YLpW2j+KQ82oU4AkTHM2cWmk9wZxMCGAL8f\n5Y21hFRh4b1U+LeX140n6D2HwT9y8EnKeNpwDorK6mOsOsrjLXi/a1qLuyt3BlHZl9Yz+N8f\nzOlv0/qkAB5qwoifGbP8um5QzsQyZjlA55pM6ZnWuOQgDy5KO2e3NpK1kTxr4vm2vN3FIL08\nHr/E1SRKuHB3g8yDHm/BD3vZcMIWZRWqQQ3Zcor3N1DjI5oEEuDFziiOX6aqH/Pvue6pFemS\nU3l/A5M2EnUVwAQtKzOxCx2rF2/pYn8U7MQBfNyTKqWYuJ7nV6a1eLrxn7a82SntT9fHmyjh\nwryBGakOaFOFsO4MW8h3uxjVsvirLnIrDwOMvmHWWlSidWVWGvpQpeE90oyF+5mxlS2nuDOI\nQB/2nePbnWnPJUu/qiz8BAPmUcaTL/vx+1G+2sF3A5iyiYnrKVmC8R1sOAeFJiEZwNONEjec\nYSpZAiDOEAer3ruDTjWYtpmdUeyMokE5hjbm+XZZ/yhNNTNwPov2U8efwY0o782+syzYR+ev\nmNOfQQ2LvXqxJwp24gBcTbx0G2PasCeaQxeo5kfjCtddUPzXSVpUyuI5Y3cGAayJ5Phltpzi\nxGVq+9PuFka2xM/xr0eOvgpQNauri6r6sfEEKebMfd6Ko3A18dO9vL6Wjzfx+tq0xtr+3FmP\nWdsybo4Z9xsuJlY/SP2y/HMJoEFZfhtG25m8vY5RLSlb0jb1F6IqpXAxEZvI5WuZbyPYcgqg\nglFuFepRmx61bz4aMHs7i/YztDGz7szIu692JHQ2I5bQqQblHP97l3zTNXbiMLxL0KoyQxrR\nvmrm/XtcEr5ZBTUfd1xMLNzHO+uIOEUJF1YfZdzvNJpmhH5SAkoCnLqSxaBTVyjtqVTn2Nxd\n+V8nLrzI3tGseoDjz3DoSbrUBDgWA3DpGn/+Q7961C+b0VjBB083ng0hIdkgF5j6uHNrBczw\n9Irr2s/G8doagEHBtijLpj7fSmlPpve67ihmrTK815VL15i/x3aViR1QsBMjqFaaPdFZ3EX4\n6xFSzXi4smwIF15k50gujWNGX6Kv0uc74pNtUWvhCa0OMGNr5vY9Zwk/QWiOF1yLo3BzoX5Z\nQqunHZDuVIMSLkzdTHIqp6+QaqZuAMCpK/ywlyaBaVcjWBpPXMYMvx7mv6u49weeXcE3O9PO\nbDqWb+7G1cQX2wiZyVc7WX+c1/+gzmQuxFO2pDN23rsrira3ZNGpkyX374wq/orEjuhUrBjB\nXfV4fS2fbLnuWrpUM6N/AZh4R0avKK4mHmnK1UTGLOebnQxvZoNqC0vXWrSuzIfh+HvxTAhe\nbgB//sOwhQAv3Wbb6qRIVPDmqdZ8EM7dc3kmBCD6Kr8fZfQvXL6W8QRSy6V4ri50+YpVRwFc\n/u0/ZfxqFgyiSaBNys+nBuX4ZQh3fc/GE2y0ulWiki9rHnSiTivTJafiltVhGXfXtKHizBTs\nxAieDeGbnTy1jAPn6V+fSr7sP8fkTRy5iI97FrcXPNSUZ1aw5phjBzsXEwsG0f0bXl7Fa2uo\n5U9ULBcT8HRjdj+aV7R1fVI03unChXi+2M6Sg5hMfLqFT7bg4cr03vSqkzaOpf+zObvYcoqR\nLXiqNXUCmLOLjzay7QwtP6dnbQY3YlCww9w5260Wl8cRtpFVRzkdS90AetdhcKMs7qhwBnUC\niDiVRbyzpN46WT3EAriSiJtL2i9AMTB9w2IEpTz4/QHu+5HJm5i8Ka3RxYR3CRpndWTC1x0f\n96y7encslXz5azhf7mDVUfafo3klmgYyogW1yti6Mikybi7MupNHmrHkAEsPsSuaemWZ05+m\ngQDJqXwYzlc7aF6RLad4shWTe5BqZsTPzNiKq4kKPpy+wpKDLD7IW+uY1ov2DvJULjcXnm/L\n821tXYcduK8RL/3OW39ed+PzlUTG/kYJFwZc3y9MTAJvrGXhfiJjcDFRswxDG/NCOzz199+g\n9MWKQVTzY/3DbDjO1tOcjqW2P7dVZeB8/onJYuTz8Vy5dl3fKI7L040RzRnR3NZ1SPFqdwvt\nbuGtzgxfwqxttJ/FrRXw9WDHGaKu0rAcjcoTcZr/tAX4IJwZW+lVh2m9CNvIpI1pT7DYFcVt\ns6hflhl30laPN3AcY1ozfw+vriHidFpvOHvO8ukWDl/kzU7X/a472WlJjAAAIABJREFUdYUO\ns/n7Ag3K8VBTElPYcJxX17D0ECuH4et8Z7GdgYKdGErbW2hrdfihay3eXc/C/dxV77rRpvyF\nGe6oVczViZ3aHc0fkew/R0VfmgTStZbD3FDsYmJmX3rWYeZWdkdzJZEG5RjThjGtGbIAd1eq\n+pGcyrvrqePPj4N4ahmfRVDak4RkArw4eQVg3znazSQogI97ckdNW89SMYpNZM9ZjlykVhka\nlse7hK0LyrWSJfhtGE8sZe4eFh9Ia/T3YkrPzFeejPiZwxeY1ouR//Z9mGLmlVW8vY6Xfufj\nHsVathQPBTsxsmdDmLGVBxZyoTuDGuLjzrk4pm7mjbU0rsA96sbT6SUkM/IXvtyO9R3VweWZ\nO4AG5WxWVV71r0//+pkb/TxITCEmgchLnItjVEv2nuXzCLrXZu85YmKIS6JcSc7GpY1/4Dxd\nv6ZfEF/dbfwDOUmpvLGWsHCu/vvQNh93nm/LS7dlfVOCHfL3Yk5/PujGzihOX6FeWRpXSOux\nOd3RGH4+yL3BGakOcDXxVmfWHGPWNiZ2yTyJGICDrMIi+VLBm6VD8PXg0cWUepuAdyn3Hq+t\noVF5fr7PSS+7FmuPLmb2dnrW4bdhnPkPW0cwvgMHz9Plq4zE46DaVwX4dhcX4wEqeLNgH2YY\nFMw/MXi5UdGXc3EAL7anSqm0rowXHWDgPG7oOMhoHljIm39QN4APuvLybTQsR2Iyr67hlg95\nZ10Wj+i1WxV96FaLB5vQpkoWES3iFPzbT3smfYKIS2LfuSKvUIqf/rKJwbWuzL7RTOnJg01o\nVpHHW/DN3fw1nFtueEyFOJu/TvLtLu6qx5L76FyDCt40DeT1jszux+lY3lln6/oK5r5G3FKK\nF1ay7QzA0Zi0HozH/gbQvip7z9K9DkCn6jQN5GoiHarh5sKKwyw5kP37Or5fDvHdbgY0YNNw\nDl/kf39y+CKNAilbkjNXGfc7zT7lxGVbV1kYLF11emd1/NXSR4wDRVjJPQU7Mb5SHoxuyaw7\nWXk/03sxpFFab0/i5H46APBKh8xdfgwOpm5AxqVLDqpkCX4ajK87z67A1YXJG/lpP5B2lC4m\nAV93Ii/i4067qlxJpGQJ2lQhORUTLNxv29qL1jc7cTUxuQeztzNtM11rEfk0W4YzZwCAq4kD\n56kaRtNP+XKHrWstmOqlIZv+ii2NNUpf15hiZv85ftzHb0c4E1v09UnR0DV2IuKkLEdlLM/j\nyqR+WZb9DZBq5o9Itp7mbBx1A+hQjZqO05VM00B2jiRsIwv3c+AcJAN0qcGKw0Rewgx7z/F2\nZ2IT2XiCDtVISgXw9yIyhkvXmLmVjSeIvEQ1P1pX4dFmRnjCMnDgHDXKUNGH9zcQ6MOP9+Dj\nzu9HuXMOgI8HlX3Ze5ZjMTy4iPX/8Gkfh+ntL5OQKlTy5eNNPNSESr4Z7buj+XYnrSpf93zt\nZX/z5FIOX0x7aYK+QUzrdd2E4hAU7ETESVnugoxJIPCGp8jHJODjzoHzDJrPDqsDHq4mRrfi\ng64Oc4l9eW/e7szbnfkgPO0k7IrDANFXAZ4LYXhzBs4jIZlRrRi/igAv4pNJSqXhVE5eoYQL\nlUux7TT/Z+8846I6mz58baF3KQKiUkXFgoJii713jb33kryWxCQmxiTGRGP0sRuj0di72Hvv\nBcUOiA0VRUV6h1129/3AUYoYY1Tg4Lk++JM591nmXmB3du6Z/2wOYcYZ/LsJpXuiRiFHoyUq\nldsxDPPBVJ8kFb23YKSHiR7lbfmpAc1W80MDjoSx5DJNXOkuzkYrpZy5Lem2Gb+lTPgEv1Ko\ntZx8yG+n0OqY0zJ75dabdN2MpSHf1KW6A4kZHLqHfwjXIwkYiq1x4e1B4u0RyYuThISExPum\nlhOAf0he++NEzj+mugONVxISxbf1OD+Ee6PZ2ZN6ZZgXwKh9Be/suzKuNiGfM9A7l5LLpWe4\nzeXYA76szbVnXIukvjOpaq4+Iy6dv9uTPIG1naldGqWcyBQ+WU65+Sy5LO7uCi9bHsRzIxLA\n2ghgSwiRKYyuSXQaleyEPpKMTNZ0xkyfPy4UprfvSJeKrP2UVDWf7cHnL2ot5ZtDmOizpze1\nXygXpqgZuYeSJlwaxu9N6e7F0Ops6sqS9tyP54ejhboBibdHythJSEj8F64+4+ITHifiZkWd\n0riXKGyH3p4uFfn+KN8dwb1E9jThiCS6bCJDg5k+T5JY9yk9KwmXXK1o7UGrNSwO5LMaVLYr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vrDsYQKt16LVIZfR2IVfGtPEBYUMjY4G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\nTFSAQY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szlVECCPIx2L8vc3WJvRfTfWM8m7CJuZ9NtHNmuq5kKAtoUBFlzg0FPy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\nY1PCUThjBwBA8e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Use SQL to access prediction details with model created in R"],"enabled":true,"result":{"startTime":1715318948128,"interpreter":"md.medium","endTime":1715318948188,"results":[{"message":"<h2 id=\"use-sql-to-access-prediction-details-with-model-created-in-r\">Use SQL to access prediction details with model created in R<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Materialize GLM_TEST_DATA for use in query below","message":["%r","","tryCatch(ore.drop(table = 'GLM_TEST_DATA'))","","ore.create(TEST, table = 'GLM_TEST_DATA')  "],"enabled":true,"result":{"startTime":1715318948265,"interpreter":"r.medium","endTime":1715318950664,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL","message":["%sql","","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","       round(PRED_LOWER_LIMIT,1) LOWER_BOUND,","       round(PRED_UPPER_LIMIT,1) UPPER_BOUND,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(GLM_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_BOUNDS(GLM_REGRESSION_MODEL USING *).LOWER PRED_LOWER_LIMIT,","             PREDICTION_BOUNDS(GLM_REGRESSION_MODEL USING *).UPPER PRED_UPPER_LIMIT,","             PREDICTION_DETAILS(GLM_REGRESSION_MODEL USING *) PD","      FROM GLM_TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","               XMLTABLE('/Details'","                        PASSING OUT.PD","                        COLUMNS ","                           \"Attribute1\" XMLType PATH 'Attribute[1]',","                           \"Attribute2\" XMLType PATH 'Attribute[2]',","                           \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED"],"enabled":true,"result":{"startTime":1715318950738,"interpreter":"sql.medium","endTime":1715318952840,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tLOWER_BOUND\tUPPER_BOUND\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100003\t3.773\t3.5\t4.1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".174\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".054\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".047\" \n100008\t4.372\t4.1\t4.6\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".201\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".168\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".116\" \n100010\t3.038\t2.8\t3.3\t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".024\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\".012\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.061\" \n100011\t3.673\t3.2\t4.2\t\"OCCUPATION\" actualValue=\"Farming\" weight=\".189\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".103\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".032\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":{"startTime":1715318952918,"interpreter":"md.medium","endTime":1715318952982,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":null}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Regression NN","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715347080941,"interpreter":"md.medium","endTime":1715347081020,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Neural Networks","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715347081101,"interpreter":"md.medium","endTime":1715347081221,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-neural-networks\">Regression Modeling to Predict Numerical Values using Neural Networks<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Neural Network algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715347081314,"interpreter":"md.medium","endTime":1715347081390,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1715347081480,"interpreter":"md.medium","endTime":1715347081555,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%r","","library(ORE)","library(lattice)","","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715347081637,"interpreter":"r.medium","endTime":1715347086042,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer with demographics data","message":["%r","","ore.sync(query = c(\"QUERY_CUSTOMERS\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH AGE, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","ore.sync(query = c(\"QUERY_DEMO_DF\" =  'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","CUST_DF <- merge(QUERY_CUSTOMERS,QUERY_DEMO_DF)"],"enabled":true,"result":{"startTime":1715347086122,"interpreter":"r.medium","endTime":1715347087029,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","head(CUST_DF)"],"enabled":true,"result":{"startTime":1715347087104,"interpreter":"r.medium","endTime":1715347087374,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 12<\/caption>\n<thead>\n\t<tr><th scope=col>CUST_ID<\/th><th scope=col>CUST_GENDER<\/th><th scope=col>CUST_MARITAL_STATUS<\/th><th scope=col>AGE<\/th><th scope=col>CUST_INCOME_LEVEL<\/th><th scope=col>CUST_CREDIT_LIMIT<\/th><th scope=col>EDUCATION<\/th><th scope=col>AFFINITY_CARD<\/th><th scope=col>HOUSEHOLD_SIZE<\/th><th scope=col>OCCUPATION<\/th><th scope=col>YRS_RESIDENCE<\/th><th scope=col>Y_BOX_GAMES<\/th><\/tr>\n\t<tr><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;int&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;chr&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><td>104088<\/td><td>M<\/td><td>NeverM <\/td><td>1980<\/td><td>L: 300,000 and above<\/td><td>11000<\/td><td>Bach.  <\/td><td>0<\/td><td>1<\/td><td>Exec. <\/td><td>1<\/td><td>1<\/td><\/tr>\n\t<tr><td>104089<\/td><td>M<\/td><td>Married<\/td><td>1970<\/td><td>H: 150,000 - 169,999<\/td><td> 5000<\/td><td>&lt; Bach.<\/td><td>0<\/td><td>3<\/td><td>Crafts<\/td><td>3<\/td><td>0<\/td><\/tr>\n\t<tr><td>104090<\/td><td>M<\/td><td>Married<\/td><td>1953<\/td><td>G: 130,000 - 149,999<\/td><td> 7000<\/td><td>Masters<\/td><td>1<\/td><td>3<\/td><td>Prof. <\/td><td>6<\/td><td>0<\/td><\/tr>\n\t<tr><td>104091<\/td><td>M<\/td><td>Married<\/td><td>1964<\/td><td>J: 190,000 - 249,999<\/td><td>15000<\/td><td>HS-grad<\/td><td>0<\/td><td>3<\/td><td>Crafts<\/td><td>5<\/td><td>0<\/td><\/tr>\n\t<tr><td>104092<\/td><td>F<\/td><td>NeverM <\/td><td>1958<\/td><td>L: 300,000 and above<\/td><td>15000<\/td><td>Bach.  <\/td><td>0<\/td><td>2<\/td><td>Sales <\/td><td>5<\/td><td>0<\/td><\/tr>\n\t<tr><td>104093<\/td><td>M<\/td><td>Married<\/td><td>1956<\/td><td>F: 110,000 - 129,999<\/td><td> 1500<\/td><td>Bach.  <\/td><td>1<\/td><td>3<\/td><td>Prof. <\/td><td>7<\/td><td>0<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Split data into randomly selected 60% sample for train and 40% test","message":["%r","","set.seed(123)","rownames(CUST_DF) <- CUST_DF$CUST_ID","","sampleSize <- .6 * nrow(CUST_DF)","index <- sample(1:nrow(CUST_DF),sampleSize)","","# Create training data set","CUST_DF.train <- CUST_DF[index,]","","# Create test data set","CUST_DF.test <- CUST_DF[setdiff(1:nrow(CUST_DF),index),]","","class(CUST_DF.train)","cat(\"\\nTraining data: \")","dim(CUST_DF.train)","","class(CUST_DF.test)","cat(\"\\nTest data: \")","dim(CUST_DF.test)"],"enabled":true,"result":{"startTime":1715347087456,"interpreter":"r.medium","endTime":1715347088953,"results":[{"message":"'ore.frame'\nTraining data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>2700<\/li><li>12<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ore.frame'\nTest data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>1800<\/li><li>12<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build NN regression model to predict YRS_RESIDENCE with default settings and assign model name","message":["%r","","ore.exec(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> 'NN_REGRESSION_MODEL'); EXCEPTION WHEN others THEN null;END;\")","","nn.mod1 <- ore.odmNN(YRS_RESIDENCE~.-CUST_ID, ","                     CUST_DF.train, ","                     \"regression\", ","                     odm.settings=list(model_name=\"NN_REGRESSION_MODEL\"))"],"enabled":true,"result":{"startTime":1715347089040,"interpreter":"r.medium","endTime":1715347095643,"results":[{"message":"Warning message:\n\u201cThe created mining model NN_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score data using model proxy object returning predictions and actual value","message":["%r","","RES_DF <- predict(nn.mod1, CUST_DF.test, supplemental.cols='YRS_RESIDENCE')","","z.show(head(RES_DF,20))"],"enabled":true,"result":{"startTime":1715347095722,"interpreter":"r.medium","endTime":1715347096303,"results":[{"message":"YRS_RESIDENCE\tPREDICTION\n6\t3.87539589973124\n2\t0.638610224764031\n5\t4.70305087673019\n3\t3.2900563439976\n5\t5.54834473792343\n6\t4.90429059959212\n4\t4.45532422417456\n5\t5.08806743909388\n8\t5.48981849041463\n5\t4.71437380986594\n4\t3.25905487640367\n5\t3.95543451248874\n4\t3.91119151175309\n5\t4.81873623562142\n4\t5.17326719793186\n2\t2.77818576069939\n2\t2.85344031596236\n2\t2.97024526892452\n2\t3.77591773457519\n5\t3.67639137247716\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for NN ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-77D9BF0F-E724-48F4-A062-B14A8F361AE6\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>' ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO' ","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`,`NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and <strong>starting from Oracle Database 23ai<\/strong> `NNET_ACTIVATIONS_RELU`.","> NNET_HIDDEN_LAYERS = '3'","","> NNET_NODES_PER_LAYER = '8,8,2'","","> NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'\"","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'","","> NNET_HELDASIDE_RATIO = '0.25'","","> NNET_HELDASIDE_MAX_FAIL = '6'","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> NNET_REG_LAMBDA = '1'","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> NNET_ITERATIONS = '200'","","> NNET_TOLERANCE = '0.000001'","","> ODMS_RANDOM_SEED = '0'","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","> LBFGS_HISTORY_DEPTH = '10'","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> LBFGS_GRADIENT_TOLERANCE = '0.0000001'","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'","","> LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'"],"enabled":true,"result":{"startTime":1715347096395,"interpreter":"md.medium","endTime":1715347096492,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-nn\">Examples of possible setting overrides for NN<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-77D9BF0F-E724-48F4-A062-B14A8F361AE6\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings: <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>,<code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and <strong>starting from Oracle Database 23ai<\/strong> <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_HIDDEN_LAYERS = '3'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_NODES_PER_LAYER = '8,8,2'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_ACTIVATIONS = &quot;'NNET_ACTIVATIONS_TANH', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_ARCTAN'&quot;<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REGULARIZER = 'NNET_REGULARIZER_HELDASIDE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_RATIO = '0.25'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_HELDASIDE_MAX_FAIL = '6'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_REG_LAMBDA = '1'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>NNET_ITERATIONS = '200'<\/p>\n<\/blockquote>\n<blockquote>\n<p>NNET_TOLERANCE = '0.000001'<\/p>\n<\/blockquote>\n<blockquote>\n<p>ODMS_RANDOM_SEED = '0'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_HISTORY_DEPTH = '10'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_GRADIENT_TOLERANCE = '0.0000001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_ENABLE'<\/p>\n<\/blockquote>\n<blockquote>\n<p>LBFGS_SCALE_HESSIAN = 'LBFGS_SCALE_HESSIAN_DISABLE'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use a single layer with 4 neurons with specific activation function","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS = 1,","                 NNET_NODES_PER_LAYER = '4',","                 NNET_ACTIVATIONS = \"'NNET_ACTIVATIONS_TANH'\")","                 ","nn.mod2 <- ore.odmNN(YRS_RESIDENCE~.-CUST_ID, CUST_DF.train, \"regression\", odm.settings = settings)"],"enabled":true,"result":{"startTime":1715347096586,"interpreter":"r.medium","endTime":1715347099991,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use 3 layers with 8,8,2 neurons with specific activation functions","message":["%r","","settings <- list(NNET_HIDDEN_LAYERS   = 3,","                 NNET_NODES_PER_LAYER = c('8','8','4'),","                 NNET_ACTIVATIONS     = c(\"'NNET_ACTIVATIONS_TANH'\",\"'NNET_ACTIVATIONS_LOG_SIG'\", \"'NNET_ACTIVATIONS_LINEAR'\")","                 )  ","nn.mod3 <- ore.odmNN(YRS_RESIDENCE~.-CUST_ID, CUST_DF.train, 'regression', odm.settings = settings) "],"enabled":true,"result":{"startTime":1715347100066,"interpreter":"r.medium","endTime":1715347104072,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display model fit statistics","message":["%r","","summary(nn.mod3)"],"enabled":true,"result":{"startTime":1715347104152,"interpreter":"r.medium","endTime":1715347105151,"results":[{"message":"\nCall:\nore.odmNN(formula = YRS_RESIDENCE ~ . - CUST_ID, data = CUST_DF.train, \n    type = \"regression\", odm.settings = settings)\n\nSettings: \n                                                                                                    value\nlbfgs.gradient.tolerance                                                                       .000000001\nlbfgs.history.depth                                                                                    20\nlbfgs.scale.hessian                                                            LBFGS_SCALE_HESSIAN_ENABLE\nactivations                  'NNET_ACTIVATIONS_TANH','NNET_ACTIVATIONS_LOG_SIG','NNET_ACTIVATIONS_LINEAR'\nhidden.layers                                                                                           3\niterations                                                                                            200\nnodes.per.layer                                                                                     8,8,4\ntolerance                                                                                         .000001\nodms.details                                                                                  odms.enable\nodms.missing.value.treatment                                                      odms.missing.value.auto\nodms.random.seed                                                                                        0\nodms.sampling                                                                       odms.sampling.disable\nprep.auto                                                                                              ON\n\nNumber of Layers: \n[1] 4\n\nNodes per Layer: \n[1]  4  8 62\n\nWeight: \n    LAYER IDX_FROM IDX_TO      ATTRIBUTE_NAME      ATTRIBUTE_VALUE\n1       0        0      0       AFFINITY_CARD                 <NA>\n2       0        0      1       AFFINITY_CARD                 <NA>\n3       0        0      2       AFFINITY_CARD                 <NA>\n4       0        0      3       AFFINITY_CARD                 <NA>\n5       0        0      4       AFFINITY_CARD                 <NA>\n6       0        0      5       AFFINITY_CARD                 <NA>\n7       0        0      6       AFFINITY_CARD                 <NA>\n8       0        0      7       AFFINITY_CARD                 <NA>\n9       0        1      0                 AGE                 <NA>\n10      0        1      1                 AGE                 <NA>\n11      0        1      2                 AGE                 <NA>\n12      0        1      3                 AGE                 <NA>\n13      0        1      4                 AGE                 <NA>\n14      0        1      5                 AGE                 <NA>\n15      0        1      6                 AGE                 <NA>\n16      0        1      7                 AGE                 <NA>\n17      0        2      0   CUST_CREDIT_LIMIT                 <NA>\n18      0        2      1   CUST_CREDIT_LIMIT                 <NA>\n19      0        2      2   CUST_CREDIT_LIMIT                 <NA>\n20      0        2      3   CUST_CREDIT_LIMIT                 <NA>\n21      0        2      4   CUST_CREDIT_LIMIT                 <NA>\n22      0        2      5   CUST_CREDIT_LIMIT                 <NA>\n23      0        2      6   CUST_CREDIT_LIMIT                 <NA>\n24      0        2      7   CUST_CREDIT_LIMIT                 <NA>\n25      0        3      0         Y_BOX_GAMES                 <NA>\n26      0        3      1         Y_BOX_GAMES                 <NA>\n27      0        3      2         Y_BOX_GAMES                 <NA>\n28      0        3      3         Y_BOX_GAMES                 <NA>\n29      0        3      4         Y_BOX_GAMES                 <NA>\n30      0        3      5         Y_BOX_GAMES                 <NA>\n31      0        3      6         Y_BOX_GAMES                 <NA>\n32      0        3      7         Y_BOX_GAMES                 <NA>\n33      0        4      0         CUST_GENDER                    F\n34      0        4      1         CUST_GENDER                    F\n35      0        4      2         CUST_GENDER                    F\n36      0        4      3         CUST_GENDER                    F\n37      0        4      4         CUST_GENDER                    F\n38      0        4      5         CUST_GENDER                    F\n39      0        4      6         CUST_GENDER                    F\n40      0        4      7         CUST_GENDER                    F\n41      0        5      0         CUST_GENDER                    M\n42      0        5      1         CUST_GENDER                    M\n43      0        5      2         CUST_GENDER                    M\n44      0        5      3         CUST_GENDER                    M\n45      0        5      4         CUST_GENDER                    M\n46      0        5      5         CUST_GENDER                    M\n47      0        5      6         CUST_GENDER                    M\n48      0        5      7         CUST_GENDER                    M\n49      0        6      0   CUST_INCOME_LEVEL      A: Below 30,000\n50      0        6      1   CUST_INCOME_LEVEL      A: Below 30,000\n51      0        6      2   CUST_INCOME_LEVEL      A: Below 30,000\n52      0        6      3   CUST_INCOME_LEVEL      A: Below 30,000\n53      0        6      4   CUST_INCOME_LEVEL      A: Below 30,000\n54      0        6      5   CUST_INCOME_LEVEL      A: Below 30,000\n55      0        6      6   CUST_INCOME_LEVEL      A: Below 30,000\n56      0        6      7   CUST_INCOME_LEVEL      A: Below 30,000\n57      0        7      0   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n58      0        7      1   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n59      0        7      2   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n60      0        7      3   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n61      0        7      4   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n62      0        7      5   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n63      0        7      6   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n64      0        7      7   CUST_INCOME_LEVEL   B: 30,000 - 49,999\n65      0        8      0   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n66      0        8      1   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n67      0        8      2   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n68      0        8      3   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n69      0        8      4   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n70      0        8      5   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n71      0        8      6   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n72      0        8      7   CUST_INCOME_LEVEL   C: 50,000 - 69,999\n73      0        9      0   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n74      0        9      1   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n75      0        9      2   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n76      0        9      3   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n77      0        9      4   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n78      0        9      5   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n79      0        9      6   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n80      0        9      7   CUST_INCOME_LEVEL   D: 70,000 - 89,999\n81      0       10      0   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n82      0       10      1   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n83      0       10      2   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n84      0       10      3   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n85      0       10      4   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n86      0       10      5   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n87      0       10      6   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n88      0       10      7   CUST_INCOME_LEVEL  E: 90,000 - 109,999\n89      0       11      0   CUST_INCOME_LEVEL F: 110,000 - 129,999\n90      0       11      1   CUST_INCOME_LEVEL F: 110,000 - 129,999\n91      0       11      2   CUST_INCOME_LEVEL F: 110,000 - 129,999\n92      0       11      3   CUST_INCOME_LEVEL F: 110,000 - 129,999\n93      0       11      4   CUST_INCOME_LEVEL F: 110,000 - 129,999\n94      0       11      5   CUST_INCOME_LEVEL F: 110,000 - 129,999\n95      0       11      6   CUST_INCOME_LEVEL F: 110,000 - 129,999\n96      0       11      7   CUST_INCOME_LEVEL F: 110,000 - 129,999\n97      0       12      0   CUST_INCOME_LEVEL G: 130,000 - 149,999\n98      0       12      1   CUST_INCOME_LEVEL G: 130,000 - 149,999\n99      0       12      2   CUST_INCOME_LEVEL G: 130,000 - 149,999\n100     0       12      3   CUST_INCOME_LEVEL G: 130,000 - 149,999\n101     0       12      4   CUST_INCOME_LEVEL G: 130,000 - 149,999\n102     0       12      5   CUST_INCOME_LEVEL G: 130,000 - 149,999\n103     0       12      6   CUST_INCOME_LEVEL G: 130,000 - 149,999\n104     0       12      7   CUST_INCOME_LEVEL G: 130,000 - 149,999\n105     0       13      0   CUST_INCOME_LEVEL H: 150,000 - 169,999\n106     0       13      1   CUST_INCOME_LEVEL H: 150,000 - 169,999\n107     0       13      2   CUST_INCOME_LEVEL H: 150,000 - 169,999\n108     0       13      3   CUST_INCOME_LEVEL H: 150,000 - 169,999\n109     0       13      4   CUST_INCOME_LEVEL H: 150,000 - 169,999\n110     0       13      5   CUST_INCOME_LEVEL H: 150,000 - 169,999\n111     0       13      6   CUST_INCOME_LEVEL H: 150,000 - 169,999\n112     0       13      7   CUST_INCOME_LEVEL H: 150,000 - 169,999\n113     0       14      0   CUST_INCOME_LEVEL I: 170,000 - 189,999\n114     0       14      1   CUST_INCOME_LEVEL I: 170,000 - 189,999\n115     0       14      2   CUST_INCOME_LEVEL I: 170,000 - 189,999\n116     0       14      3   CUST_INCOME_LEVEL I: 170,000 - 189,999\n117     0       14      4   CUST_INCOME_LEVEL I: 170,000 - 189,999\n118     0       14      5   CUST_INCOME_LEVEL I: 170,000 - 189,999\n119     0       14      6   CUST_INCOME_LEVEL I: 170,000 - 189,999\n120     0       14      7   CUST_INCOME_LEVEL I: 170,000 - 189,999\n121     0       15      0   CUST_INCOME_LEVEL J: 190,000 - 249,999\n122     0       15      1   CUST_INCOME_LEVEL J: 190,000 - 249,999\n123     0       15      2   CUST_INCOME_LEVEL J: 190,000 - 249,999\n124     0       15      3   CUST_INCOME_LEVEL J: 190,000 - 249,999\n125     0       15      4   CUST_INCOME_LEVEL J: 190,000 - 249,999\n126     0       15      5   CUST_INCOME_LEVEL J: 190,000 - 249,999\n127     0       15      6   CUST_INCOME_LEVEL J: 190,000 - 249,999\n128     0       15      7   CUST_INCOME_LEVEL J: 190,000 - 249,999\n129     0       16      0   CUST_INCOME_LEVEL K: 250,000 - 299,999\n130     0       16      1   CUST_INCOME_LEVEL K: 250,000 - 299,999\n131     0       16      2   CUST_INCOME_LEVEL K: 250,000 - 299,999\n132     0       16      3   CUST_INCOME_LEVEL K: 250,000 - 299,999\n133     0       16      4   CUST_INCOME_LEVEL K: 250,000 - 299,999\n134     0       16      5   CUST_INCOME_LEVEL K: 250,000 - 299,999\n135     0       16      6   CUST_INCOME_LEVEL K: 250,000 - 299,999\n136     0       16      7   CUST_INCOME_LEVEL K: 250,000 - 299,999\n137     0       17      0   CUST_INCOME_LEVEL L: 300,000 and above\n138     0       17      1   CUST_INCOME_LEVEL L: 300,000 and above\n139     0       17      2   CUST_INCOME_LEVEL L: 300,000 and above\n140     0       17      3   CUST_INCOME_LEVEL L: 300,000 and above\n141     0       17      4   CUST_INCOME_LEVEL L: 300,000 and above\n142     0       17      5   CUST_INCOME_LEVEL L: 300,000 and above\n143     0       17      6   CUST_INCOME_LEVEL L: 300,000 and above\n144     0       17      7   CUST_INCOME_LEVEL L: 300,000 and above\n145     0       18      0 CUST_MARITAL_STATUS              Divorc.\n146     0       18      1 CUST_MARITAL_STATUS              Divorc.\n147     0       18      2 CUST_MARITAL_STATUS              Divorc.\n148     0       18      3 CUST_MARITAL_STATUS              Divorc.\n149     0       18      4 CUST_MARITAL_STATUS              Divorc.\n150     0       18      5 CUST_MARITAL_STATUS              Divorc.\n151     0       18      6 CUST_MARITAL_STATUS              Divorc.\n152     0       18      7 CUST_MARITAL_STATUS              Divorc.\n153     0       19      0 CUST_MARITAL_STATUS              Mabsent\n154     0       19      1 CUST_MARITAL_STATUS              Mabsent\n155     0       19      2 CUST_MARITAL_STATUS              Mabsent\n156     0       19      3 CUST_MARITAL_STATUS              Mabsent\n157     0       19      4 CUST_MARITAL_STATUS              Mabsent\n158     0       19      5 CUST_MARITAL_STATUS              Mabsent\n159     0       19      6 CUST_MARITAL_STATUS              Mabsent\n160     0       19      7 CUST_MARITAL_STATUS              Mabsent\n161     0       20      0 CUST_MARITAL_STATUS               Mar-AF\n162     0       20      1 CUST_MARITAL_STATUS               Mar-AF\n163     0       20      2 CUST_MARITAL_STATUS               Mar-AF\n164     0       20      3 CUST_MARITAL_STATUS               Mar-AF\n165     0       20      4 CUST_MARITAL_STATUS               Mar-AF\n166     0       20      5 CUST_MARITAL_STATUS               Mar-AF\n167     0       20      6 CUST_MARITAL_STATUS               Mar-AF\n168     0       20      7 CUST_MARITAL_STATUS               Mar-AF\n169     0       21      0 CUST_MARITAL_STATUS              Married\n170     0       21      1 CUST_MARITAL_STATUS              Married\n171     0       21      2 CUST_MARITAL_STATUS              Married\n172     0       21      3 CUST_MARITAL_STATUS              Married\n173     0       21      4 CUST_MARITAL_STATUS              Married\n174     0       21      5 CUST_MARITAL_STATUS              Married\n175     0       21      6 CUST_MARITAL_STATUS              Married\n176     0       21      7 CUST_MARITAL_STATUS              Married\n177     0       22      0 CUST_MARITAL_STATUS               NeverM\n178     0       22      1 CUST_MARITAL_STATUS               NeverM\n179     0       22      2 CUST_MARITAL_STATUS               NeverM\n180     0       22      3 CUST_MARITAL_STATUS               NeverM\n181     0       22      4 CUST_MARITAL_STATUS               NeverM\n182     0       22      5 CUST_MARITAL_STATUS               NeverM\n183     0       22      6 CUST_MARITAL_STATUS               NeverM\n184     0       22      7 CUST_MARITAL_STATUS               NeverM\n185     0       23      0 CUST_MARITAL_STATUS               Separ.\n186     0       23      1 CUST_MARITAL_STATUS               Separ.\n187     0       23      2 CUST_MARITAL_STATUS               Separ.\n188     0       23      3 CUST_MARITAL_STATUS               Separ.\n189     0       23      4 CUST_MARITAL_STATUS               Separ.\n190     0       23      5 CUST_MARITAL_STATUS               Separ.\n191     0       23      6 CUST_MARITAL_STATUS               Separ.\n192     0       23      7 CUST_MARITAL_STATUS               Separ.\n193     0       24      0 CUST_MARITAL_STATUS              Widowed\n194     0       24      1 CUST_MARITAL_STATUS              Widowed\n195     0       24      2 CUST_MARITAL_STATUS              Widowed\n196     0       24      3 CUST_MARITAL_STATUS              Widowed\n197     0       24      4 CUST_MARITAL_STATUS              Widowed\n198     0       24      5 CUST_MARITAL_STATUS              Widowed\n199     0       24      6 CUST_MARITAL_STATUS              Widowed\n200     0       24      7 CUST_MARITAL_STATUS              Widowed\n201     0       25      0           EDUCATION                 10th\n202     0       25      1           EDUCATION                 10th\n203     0       25      2           EDUCATION                 10th\n204     0       25      3           EDUCATION                 10th\n205     0       25      4           EDUCATION                 10th\n206     0       25      5           EDUCATION                 10th\n207     0       25      6           EDUCATION                 10th\n208     0       25      7           EDUCATION                 10th\n209     0       26      0           EDUCATION                 11th\n210     0       26      1           EDUCATION                 11th\n211     0       26      2           EDUCATION                 11th\n212     0       26      3           EDUCATION                 11th\n213     0       26      4           EDUCATION                 11th\n214     0       26      5           EDUCATION                 11th\n215     0       26      6           EDUCATION                 11th\n216     0       26      7           EDUCATION                 11th\n217     0       27      0           EDUCATION                 12th\n218     0       27      1           EDUCATION                 12th\n219     0       27      2           EDUCATION                 12th\n220     0       27      3           EDUCATION                 12th\n221     0       27      4           EDUCATION                 12th\n222     0       27      5           EDUCATION                 12th\n223     0       27      6           EDUCATION                 12th\n224     0       27      7           EDUCATION                 12th\n225     0       28      0           EDUCATION              1st-4th\n226     0       28      1           EDUCATION              1st-4th\n227     0       28      2           EDUCATION              1st-4th\n228     0       28      3           EDUCATION              1st-4th\n229     0       28      4           EDUCATION              1st-4th\n230     0       28      5           EDUCATION              1st-4th\n231     0       28      6           EDUCATION              1st-4th\n232     0       28      7           EDUCATION              1st-4th\n233     0       29      0           EDUCATION              5th-6th\n234     0       29      1           EDUCATION              5th-6th\n235     0       29      2           EDUCATION              5th-6th\n236     0       29      3           EDUCATION              5th-6th\n237     0       29      4           EDUCATION              5th-6th\n238     0       29      5           EDUCATION              5th-6th\n239     0       29      6           EDUCATION              5th-6th\n240     0       29      7           EDUCATION              5th-6th\n241     0       30      0           EDUCATION              7th-8th\n242     0       30      1           EDUCATION              7th-8th\n243     0       30      2           EDUCATION              7th-8th\n244     0       30      3           EDUCATION              7th-8th\n245     0       30      4           EDUCATION              7th-8th\n246     0       30      5           EDUCATION              7th-8th\n247     0       30      6           EDUCATION              7th-8th\n248     0       30      7           EDUCATION              7th-8th\n249     0       31      0           EDUCATION                  9th\n250     0       31      1           EDUCATION                  9th\n251     0       31      2           EDUCATION                  9th\n252     0       31      3           EDUCATION                  9th\n253     0       31      4           EDUCATION                  9th\n254     0       31      5           EDUCATION                  9th\n255     0       31      6           EDUCATION                  9th\n256     0       31      7           EDUCATION                  9th\n257     0       32      0           EDUCATION              < Bach.\n258     0       32      1           EDUCATION              < Bach.\n259     0       32      2           EDUCATION              < Bach.\n260     0       32      3           EDUCATION              < Bach.\n261     0       32      4           EDUCATION              < Bach.\n262     0       32      5           EDUCATION              < Bach.\n263     0       32      6           EDUCATION              < Bach.\n264     0       32      7           EDUCATION              < Bach.\n265     0       33      0           EDUCATION              Assoc-A\n266     0       33      1           EDUCATION              Assoc-A\n267     0       33      2           EDUCATION              Assoc-A\n268     0       33      3           EDUCATION              Assoc-A\n269     0       33      4           EDUCATION              Assoc-A\n270     0       33      5           EDUCATION              Assoc-A\n271     0       33      6           EDUCATION              Assoc-A\n272     0       33      7           EDUCATION              Assoc-A\n273     0       34      0           EDUCATION              Assoc-V\n274     0       34      1           EDUCATION              Assoc-V\n275     0       34      2           EDUCATION              Assoc-V\n276     0       34      3           EDUCATION              Assoc-V\n277     0       34      4           EDUCATION              Assoc-V\n278     0       34      5           EDUCATION              Assoc-V\n279     0       34      6           EDUCATION              Assoc-V\n280     0       34      7           EDUCATION              Assoc-V\n281     0       35      0           EDUCATION                Bach.\n282     0       35      1           EDUCATION                Bach.\n283     0       35      2           EDUCATION                Bach.\n284     0       35      3           EDUCATION                Bach.\n285     0       35      4           EDUCATION                Bach.\n286     0       35      5           EDUCATION                Bach.\n287     0       35      6           EDUCATION                Bach.\n288     0       35      7           EDUCATION                Bach.\n289     0       36      0           EDUCATION              HS-grad\n290     0       36      1           EDUCATION              HS-grad\n291     0       36      2           EDUCATION              HS-grad\n292     0       36      3           EDUCATION              HS-grad\n293     0       36      4           EDUCATION              HS-grad\n294     0       36      5           EDUCATION              HS-grad\n295     0       36      6           EDUCATION              HS-grad\n296     0       36      7           EDUCATION              HS-grad\n297     0       37      0           EDUCATION              Masters\n298     0       37      1           EDUCATION              Masters\n299     0       37      2           EDUCATION              Masters\n300     0       37      3           EDUCATION              Masters\n301     0       37      4           EDUCATION              Masters\n302     0       37      5           EDUCATION              Masters\n303     0       37      6           EDUCATION              Masters\n304     0       37      7           EDUCATION              Masters\n305     0       38      0           EDUCATION                  PhD\n306     0       38      1           EDUCATION                  PhD\n307     0       38      2           EDUCATION                  PhD\n308     0       38      3           EDUCATION                  PhD\n309     0       38      4           EDUCATION                  PhD\n310     0       38      5           EDUCATION                  PhD\n311     0       38      6           EDUCATION                  PhD\n312     0       38      7           EDUCATION                  PhD\n313     0       39      0           EDUCATION              Presch.\n314     0       39      1           EDUCATION              Presch.\n315     0       39      2           EDUCATION              Presch.\n316     0       39      3           EDUCATION              Presch.\n317     0       39      4           EDUCATION              Presch.\n318     0       39      5           EDUCATION              Presch.\n319     0       39      6           EDUCATION              Presch.\n320     0       39      7           EDUCATION              Presch.\n321     0       40      0           EDUCATION               Profsc\n322     0       40      1           EDUCATION               Profsc\n323     0       40      2           EDUCATION               Profsc\n324     0       40      3           EDUCATION               Profsc\n325     0       40      4           EDUCATION               Profsc\n326     0       40      5           EDUCATION               Profsc\n327     0       40      6           EDUCATION               Profsc\n328     0       40      7           EDUCATION               Profsc\n329     0       41      0      HOUSEHOLD_SIZE                    1\n330     0       41      1      HOUSEHOLD_SIZE                    1\n331     0       41      2      HOUSEHOLD_SIZE                    1\n332     0       41      3      HOUSEHOLD_SIZE                    1\n333     0       41      4      HOUSEHOLD_SIZE                    1\n334     0       41      5      HOUSEHOLD_SIZE                    1\n335     0       41      6      HOUSEHOLD_SIZE                    1\n336     0       41      7      HOUSEHOLD_SIZE                    1\n337     0       42      0      HOUSEHOLD_SIZE                    2\n338     0       42      1      HOUSEHOLD_SIZE                    2\n339     0       42      2      HOUSEHOLD_SIZE                    2\n340     0       42      3      HOUSEHOLD_SIZE                    2\n341     0       42      4      HOUSEHOLD_SIZE                    2\n342     0       42      5      HOUSEHOLD_SIZE                    2\n343     0       42      6      HOUSEHOLD_SIZE                    2\n344     0       42      7      HOUSEHOLD_SIZE                    2\n345     0       43      0      HOUSEHOLD_SIZE                    3\n346     0       43      1      HOUSEHOLD_SIZE                    3\n347     0       43      2      HOUSEHOLD_SIZE                    3\n348     0       43      3      HOUSEHOLD_SIZE                    3\n349     0       43      4      HOUSEHOLD_SIZE                    3\n350     0       43      5      HOUSEHOLD_SIZE                    3\n351     0       43      6      HOUSEHOLD_SIZE                    3\n352     0       43      7      HOUSEHOLD_SIZE                    3\n353     0       44      0      HOUSEHOLD_SIZE                  4-5\n354     0       44      1      HOUSEHOLD_SIZE                  4-5\n355     0       44      2      HOUSEHOLD_SIZE                  4-5\n356     0       44      3      HOUSEHOLD_SIZE                  4-5\n357     0       44      4      HOUSEHOLD_SIZE                  4-5\n358     0       44      5      HOUSEHOLD_SIZE                  4-5\n359     0       44      6      HOUSEHOLD_SIZE                  4-5\n360     0       44      7      HOUSEHOLD_SIZE                  4-5\n361     0       45      0      HOUSEHOLD_SIZE                  6-8\n362     0       45      1      HOUSEHOLD_SIZE                  6-8\n363     0       45      2      HOUSEHOLD_SIZE                  6-8\n364     0       45      3      HOUSEHOLD_SIZE                  6-8\n365     0       45      4      HOUSEHOLD_SIZE                  6-8\n366     0       45      5      HOUSEHOLD_SIZE                  6-8\n367     0       45      6      HOUSEHOLD_SIZE                  6-8\n368     0       45      7      HOUSEHOLD_SIZE                  6-8\n369     0       46      0      HOUSEHOLD_SIZE                   9+\n370     0       46      1      HOUSEHOLD_SIZE                   9+\n371     0       46      2      HOUSEHOLD_SIZE                   9+\n372     0       46      3      HOUSEHOLD_SIZE                   9+\n373     0       46      4      HOUSEHOLD_SIZE                   9+\n374     0       46      5      HOUSEHOLD_SIZE                   9+\n375     0       46      6      HOUSEHOLD_SIZE                   9+\n376     0       46      7      HOUSEHOLD_SIZE                   9+\n377     0       47      0          OCCUPATION                    ?\n378     0       47      1          OCCUPATION                    ?\n379     0       47      2          OCCUPATION                    ?\n380     0       47      3          OCCUPATION                    ?\n381     0       47      4          OCCUPATION                    ?\n382     0       47      5          OCCUPATION                    ?\n383     0       47      6          OCCUPATION                    ?\n384     0       47      7          OCCUPATION                    ?\n385     0       48      0          OCCUPATION              Armed-F\n386     0       48      1          OCCUPATION              Armed-F\n387     0       48      2          OCCUPATION              Armed-F\n388     0       48      3          OCCUPATION              Armed-F\n389     0       48      4          OCCUPATION              Armed-F\n390     0       48      5          OCCUPATION              Armed-F\n391     0       48      6          OCCUPATION              Armed-F\n392     0       48      7          OCCUPATION              Armed-F\n393     0       49      0          OCCUPATION              Cleric.\n394     0       49      1          OCCUPATION              Cleric.\n395     0       49      2          OCCUPATION              Cleric.\n396     0       49      3          OCCUPATION              Cleric.\n397     0       49      4          OCCUPATION              Cleric.\n398     0       49      5          OCCUPATION              Cleric.\n399     0       49      6          OCCUPATION              Cleric.\n400     0       49      7          OCCUPATION              Cleric.\n401     0       50      0          OCCUPATION               Crafts\n402     0       50      1          OCCUPATION               Crafts\n403     0       50      2          OCCUPATION               Crafts\n404     0       50      3          OCCUPATION               Crafts\n405     0       50      4          OCCUPATION               Crafts\n406     0       50      5          OCCUPATION               Crafts\n407     0       50      6          OCCUPATION               Crafts\n408     0       50      7          OCCUPATION               Crafts\n409     0       51      0          OCCUPATION                Exec.\n410     0       51      1          OCCUPATION                Exec.\n411     0       51      2          OCCUPATION                Exec.\n412     0       51      3          OCCUPATION                Exec.\n413     0       51      4          OCCUPATION                Exec.\n414     0       51      5          OCCUPATION                Exec.\n415     0       51      6          OCCUPATION                Exec.\n416     0       51      7          OCCUPATION                Exec.\n417     0       52      0          OCCUPATION              Farming\n418     0       52      1          OCCUPATION              Farming\n419     0       52      2          OCCUPATION              Farming\n420     0       52      3          OCCUPATION              Farming\n421     0       52      4          OCCUPATION              Farming\n422     0       52      5          OCCUPATION              Farming\n423     0       52      6          OCCUPATION              Farming\n424     0       52      7          OCCUPATION              Farming\n425     0       53      0          OCCUPATION              Handler\n426     0       53      1          OCCUPATION              Handler\n427     0       53      2          OCCUPATION              Handler\n428     0       53      3          OCCUPATION              Handler\n429     0       53      4          OCCUPATION              Handler\n430     0       53      5          OCCUPATION              Handler\n431     0       53      6          OCCUPATION              Handler\n432     0       53      7          OCCUPATION              Handler\n433     0       54      0          OCCUPATION              House-s\n434     0       54      1          OCCUPATION              House-s\n435     0       54      2          OCCUPATION              House-s\n436     0       54      3          OCCUPATION              House-s\n437     0       54      4          OCCUPATION              House-s\n438     0       54      5          OCCUPATION              House-s\n439     0       54      6          OCCUPATION              House-s\n440     0       54      7          OCCUPATION              House-s\n441     0       55      0          OCCUPATION              Machine\n442     0       55      1          OCCUPATION              Machine\n443     0       55      2          OCCUPATION              Machine\n444     0       55      3          OCCUPATION              Machine\n445     0       55      4          OCCUPATION              Machine\n446     0       55      5          OCCUPATION              Machine\n447     0       55      6          OCCUPATION              Machine\n448     0       55      7          OCCUPATION              Machine\n449     0       56      0          OCCUPATION                Other\n450     0       56      1          OCCUPATION                Other\n451     0       56      2          OCCUPATION                Other\n452     0       56      3          OCCUPATION                Other\n453     0       56      4          OCCUPATION                Other\n454     0       56      5          OCCUPATION                Other\n455     0       56      6          OCCUPATION                Other\n456     0       56      7          OCCUPATION                Other\n457     0       57      0          OCCUPATION                Prof.\n458     0       57      1          OCCUPATION                Prof.\n459     0       57      2          OCCUPATION                Prof.\n460     0       57      3          OCCUPATION                Prof.\n461     0       57      4          OCCUPATION                Prof.\n462     0       57      5          OCCUPATION                Prof.\n463     0       57      6          OCCUPATION                Prof.\n464     0       57      7          OCCUPATION                Prof.\n465     0       58      0          OCCUPATION              Protec.\n466     0       58      1          OCCUPATION              Protec.\n467     0       58      2          OCCUPATION              Protec.\n468     0       58      3          OCCUPATION              Protec.\n469     0       58      4          OCCUPATION              Protec.\n470     0       58      5          OCCUPATION              Protec.\n471     0       58      6          OCCUPATION              Protec.\n472     0       58      7          OCCUPATION              Protec.\n473     0       59      0          OCCUPATION                Sales\n474     0       59      1          OCCUPATION                Sales\n475     0       59      2          OCCUPATION                Sales\n476     0       59      3          OCCUPATION                Sales\n477     0       59      4          OCCUPATION                Sales\n478     0       59      5          OCCUPATION                Sales\n479     0       59      6          OCCUPATION                Sales\n480     0       59      7          OCCUPATION                Sales\n481     0       60      0          OCCUPATION              TechSup\n482     0       60      1          OCCUPATION              TechSup\n483     0       60      2          OCCUPATION              TechSup\n484     0       60      3          OCCUPATION              TechSup\n485     0       60      4          OCCUPATION              TechSup\n486     0       60      5          OCCUPATION              TechSup\n487     0       60      6          OCCUPATION              TechSup\n488     0       60      7          OCCUPATION              TechSup\n489     0       61      0          OCCUPATION              Transp.\n490     0       61      1          OCCUPATION              Transp.\n491     0       61      2          OCCUPATION              Transp.\n492     0       61      3          OCCUPATION              Transp.\n493     0       61      4          OCCUPATION              Transp.\n494     0       61      5          OCCUPATION              Transp.\n495     0       61      6          OCCUPATION              Transp.\n496     0       61      7          OCCUPATION              Transp.\n497     0       NA      0                <NA>                 <NA>\n498     0       NA      1                <NA>                 <NA>\n499     0       NA      2                <NA>                 <NA>\n500     0       NA      3                <NA>                 <NA>\n501     0       NA      4                <NA>                 <NA>\n502     0       NA      5                <NA>                 <NA>\n503     0       NA      6                <NA>                 <NA>\n504     0       NA      7                <NA>                 <NA>\n505     1        0      0                <NA>                 <NA>\n506     1        0      1                <NA>                 <NA>\n507     1        0      2                <NA>                 <NA>\n508     1        0      3                <NA>                 <NA>\n509     1        0      4                <NA>                 <NA>\n510     1        0      5                <NA>                 <NA>\n511     1        0      6                <NA>                 <NA>\n512     1        0      7                <NA>                 <NA>\n513     1        1      0                <NA>                 <NA>\n514     1        1      1                <NA>                 <NA>\n515     1        1      2                <NA>                 <NA>\n516     1        1      3                <NA>                 <NA>\n517     1        1      4                <NA>                 <NA>\n518     1        1      5                <NA>                 <NA>\n519     1        1      6                <NA>                 <NA>\n520     1        1      7                <NA>                 <NA>\n521     1        2      0                <NA>                 <NA>\n522     1        2      1                <NA>                 <NA>\n523     1        2      2                <NA>                 <NA>\n524     1        2      3                <NA>                 <NA>\n525     1        2      4                <NA>                 <NA>\n526     1        2      5                <NA>                 <NA>\n527     1        2      6                <NA>                 <NA>\n528     1        2      7                <NA>                 <NA>\n529     1        3      0                <NA>                 <NA>\n530     1        3      1                <NA>                 <NA>\n531     1        3      2                <NA>                 <NA>\n532     1        3      3                <NA>                 <NA>\n533     1        3      4                <NA>                 <NA>\n534     1        3      5                <NA>                 <NA>\n535     1        3      6                <NA>                 <NA>\n536     1        3      7                <NA>                 <NA>\n537     1        4      0                <NA>                 <NA>\n538     1        4      1                <NA>                 <NA>\n539     1        4      2                <NA>                 <NA>\n540     1        4      3                <NA>                 <NA>\n541     1        4      4                <NA>                 <NA>\n542     1        4      5                <NA>                 <NA>\n543     1        4      6                <NA>                 <NA>\n544     1        4      7                <NA>                 <NA>\n545     1        5      0                <NA>                 <NA>\n546     1        5      1                <NA>                 <NA>\n547     1        5      2                <NA>                 <NA>\n548     1        5      3                <NA>                 <NA>\n549     1        5      4                <NA>                 <NA>\n550     1        5      5                <NA>                 <NA>\n551     1        5      6                <NA>                 <NA>\n552     1        5      7                <NA>                 <NA>\n553     1        6      0                <NA>                 <NA>\n554     1        6      1                <NA>                 <NA>\n555     1        6      2                <NA>                 <NA>\n556     1        6      3                <NA>                 <NA>\n557     1        6      4                <NA>                 <NA>\n558     1        6      5                <NA>                 <NA>\n559     1        6      6                <NA>                 <NA>\n560     1        6      7                <NA>                 <NA>\n561     1        7      0                <NA>                 <NA>\n562     1        7      1                <NA>                 <NA>\n563     1        7      2                <NA>                 <NA>\n564     1        7      3                <NA>                 <NA>\n565     1        7      4                <NA>                 <NA>\n566     1        7      5                <NA>                 <NA>\n567     1        7      6                <NA>                 <NA>\n568     1        7      7                <NA>                 <NA>\n569     1       NA      0                <NA>                 <NA>\n570     1       NA      1                <NA>                 <NA>\n571     1       NA      2                <NA>                 <NA>\n572     1       NA      3                <NA>                 <NA>\n573     1       NA      4                <NA>                 <NA>\n574     1       NA      5                <NA>                 <NA>\n575     1       NA      6                <NA>                 <NA>\n576     1       NA      7                <NA>                 <NA>\n577     2        0      0                <NA>                 <NA>\n578     2        0      1                <NA>                 <NA>\n579     2        0      2                <NA>                 <NA>\n580     2        0      3                <NA>                 <NA>\n581     2        1      0                <NA>                 <NA>\n582     2        1      1                <NA>                 <NA>\n583     2        1      2                <NA>                 <NA>\n584     2        1      3                <NA>                 <NA>\n585     2        2      0                <NA>                 <NA>\n586     2        2      1                <NA>                 <NA>\n587     2        2      2                <NA>                 <NA>\n588     2        2      3                <NA>                 <NA>\n589     2        3      0                <NA>                 <NA>\n590     2        3      1                <NA>                 <NA>\n591     2        3      2                <NA>                 <NA>\n592     2        3      3                <NA>                 <NA>\n593     2        4      0                <NA>                 <NA>\n594     2        4      1                <NA>                 <NA>\n595     2        4      2                <NA>                 <NA>\n596     2        4      3                <NA>                 <NA>\n597     2        5      0                <NA>                 <NA>\n598     2        5      1                <NA>                 <NA>\n599     2        5      2                <NA>                 <NA>\n600     2        5      3                <NA>                 <NA>\n601     2        6      0                <NA>                 <NA>\n602     2        6      1                <NA>                 <NA>\n603     2        6      2                <NA>                 <NA>\n604     2        6      3                <NA>                 <NA>\n605     2        7      0                <NA>                 <NA>\n606     2        7      1                <NA>                 <NA>\n607     2        7      2                <NA>                 <NA>\n608     2        7      3                <NA>                 <NA>\n609     2       NA      0                <NA>                 <NA>\n610     2       NA      1                <NA>                 <NA>\n611     2       NA      2                <NA>                 <NA>\n612     2       NA      3                <NA>                 <NA>\n613     3        0      0                <NA>                 <NA>\n614     3        1      0                <NA>                 <NA>\n615     3        2      0                <NA>                 <NA>\n616     3        3      0                <NA>                 <NA>\n617     3       NA      0                <NA>                 <NA>\n           WEIGHT\n1   -1.7008633614\n2   -0.6407517195\n3    0.0430388190\n4    0.0062119532\n5   -0.1418946087\n6   -0.9720343351\n7   -1.1885712147\n8   -2.3970742226\n9   -4.0530409813\n10  -4.5183963776\n11  -0.1779582500\n12   3.3194153309\n13  -0.6384279728\n14  -3.1820111275\n15   2.3028352261\n16   0.6872273684\n17  -0.1673423052\n18  -0.1485831589\n19  -0.4454938769\n20   0.5980802178\n21   0.1047934070\n22   0.0915091261\n23  -0.1365298331\n24   0.7786470056\n25  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supplemental.cols='YRS_RESIDENCE')","","head(RES_DF)"],"enabled":true,"result":{"startTime":1715347105239,"interpreter":"r.medium","endTime":1715347106338,"results":[{"message":"<table class=\"dataframe\">\n<caption>A data.frame: 6 x 2<\/caption>\n<thead>\n\t<tr><th><\/th><th scope=col>YRS_RESIDENCE<\/th><th scope=col>PREDICTION<\/th><\/tr>\n\t<tr><th><\/th><th scope=col>&lt;dbl&gt;<\/th><th scope=col>&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n\t<tr><th scope=row>100003<\/th><td>6<\/td><td>2.921526<\/td><\/tr>\n\t<tr><th scope=row>100006<\/th><td>2<\/td><td>2.182019<\/td><\/tr>\n\t<tr><th scope=row>100008<\/th><td>5<\/td><td>3.230458<\/td><\/tr>\n\t<tr><th scope=row>100010<\/th><td>3<\/td><td>2.786458<\/td><\/tr>\n\t<tr><th scope=row>100014<\/th><td>5<\/td><td>4.406207<\/td><\/tr>\n\t<tr><th scope=row>100015<\/th><td>6<\/td><td>3.309392<\/td><\/tr>\n<\/tbody>\n<\/table>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST dataset","message":["%r","","MeanAbsoluteError <- (sum(abs(RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])))/nrow(RES_DF['YRS_RESIDENCE'])","MeanSquaredError <- (sum((RES_DF['YRS_RESIDENCE'] - RES_DF['PREDICTION'])^2))/nrow(RES_DF['YRS_RESIDENCE'])","RootMeanSquaredError <- sqrt(MeanSquaredError)","","cat('\\nMAE: ', MeanAbsoluteError)","cat('\\nMSE: ', MeanSquaredError)","cat('\\nRMSE: ', RootMeanSquaredError)"],"enabled":true,"result":{"startTime":1715347106422,"interpreter":"r.medium","endTime":1715347107625,"results":[{"message":"\nMAE:  1.30208\nMSE:  3.142453\nRMSE:  1.772697","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot the predicted and actual years of residence","message":["%r","","x <- ore.pull(RES_DF[['PREDICTION']])","y <- ore.pull(RES_DF[['YRS_RESIDENCE']])","n <- length(x)","","dotplot(y~x, xlab = \"PREDICTION\", ylab = \"YRS_RESIDENCE\", ","        panel = function(x,y) {","          panel.dotplot(x,y)","          panel.abline(0, 1, col=\"red\")","        }, ","        xlim=c(0,14.5), ","        ylim=c(0,14.5),","        scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","        main=\"The prediction vs. actual value for years of residence\")"],"enabled":true,"result":{"startTime":1715347107713,"interpreter":"r.medium","endTime":1715347108219,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nTM/1Whvb2aapmLLUmdgaxu2erLaaGLTNtPLKa4qKUtiNHTtWtRvL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ff89hgxBQAAOHUK7dkKnERILOyIiIrJncXGYMAHBwUhKQtOmQqcR\nGAs7IiIisltxcZgxA40a4ehR1K0rdBrh8a5YIiIiskNKJd59F/Pno00b7N6NSpWEDmQTWNgR\nERGRvSkuxpgxWLECoaHYtg1eXkIHshUs7IiIiMiuFBRg0CBs2YLevbF+PaRSoQPZEF5jR0RE\nRPZDJkOvXtiyBUOHYtMmVnWlsLAjIiIiO/HoEbp1w/79mDgRK1bAmSceS2NhR0RERPYgNRUh\nITh5ErGxWLgQYtYwGrDUJSIiIpt38ybCwnDjBubOxbRpQqexXSzsiIiIyLZduoSwMKSn48cf\nMWqU0GlsGgs7IiIismGnTyMyEjk5WLcO/fsLncbWsbAjIiIiW3XwIPr0gUKBhASEhwudxg7w\nwkMiIiKySQkJ6NEDTk5ITGRVpycWdkRERGR7Vq1CdDR8fHDoEF55Reg0doOFHREREdmYRYsw\nYgRq1EBSEpo3FzqNPWFhR0RERLYkLg6TJqFBAxw9ivr1hU5jZ3jzBBEREdkGpRLTp+Orr9Cq\nFXbvRuXKQgeyPyzsiIiIyAbI5Rg3DkuX4rXXkJAAb2+hA9klFnZEREQktMJCDBmCjRvRsyfi\n4+HmJnQge8Vr7IiIiEhQT54gKgobN2LQIGzZwqrOFDxiR6RB+hN8dhiHb+FuNupXQu+GeP8V\nSHWuLneyMfMwkm4jTYYGldD/JbzTHhL+dCIi0i0rCz174vhxjB+PRYsg5nbTJCzsiEq7/Riv\nLMW9nKdvz9zHmfvY8xcODNda2115iI4/4UHu07en7uHUPey/gZ2D4cxtFBGRNunpiIjA778j\nNhaffw6RSOhAdo/7HKLS3t/3rKpTOX4H35zSOsqU3c+qOpXE61iWbOZsRESOIyUFnTrh/HnE\nxWH2bFZ1ZlEhjtgVFRXl5+cLnYLsQ7EC269INfba9IdicsvCst2zC3DghtZRhr2kYRQiogpO\ndOWKS69eotTUom++kY8eDYfbTRcVFQky3QpR2MlksszMTKFTkH14lC/OL9Zcpd3PUWpckG5l\nO8uVmke5+1jOZY+IqBTJ+fP+Q4aIsrOzFi3K690bjridlMlkABQKhZWnWyEKO09PTz8/P6FT\nkH3wUsDVCQVyDb0CPUUaFySxO8QiKJQaRqnu7cRlj4hInfjoUZeYGBQUFK1eLY2K0vyz2P55\nenoCEFv9XpAKUdhJJBKp1FGXHDK/Xg2w6U8N3aNfEmtckKRShNbCgZsGjEJEVEHt3IkBA+Dk\nhJ07JV27SoSOYzkSiTBfjjdPEJU2JxxVPUp3bF0dU9ppHWV+JPzK1G8htTC6pZmzERHZsXXr\n8PrrcHPD/v3o2lXoNI6JhR1RabV9cXYcRr+Mun5wdUKTKvhXJxwaCXftvzPgJPcAACAASURB\nVL4aV8aZtzCsGWr7wtUJzavis1DsGcrn2BER/eO77zBkCAICcPgw2mn/oUymqRCnYokMVcML\nP/Y2bJQ6fvj5dcukISKyd3Fx+PBD1KqFfftQt67QaRwZjycQERGRxSiVmDEDM2bgpZdw9Cir\nOkvjETsiIiKyDKUS77yDBQvQpg1270alSkIHcnws7IiIiMgCiosxZgxWrEBoKLZtg5eX0IEq\nBBZ2REREZG4FBXjjDWzdij59sG4d+OAna+E1dkRERGRWMhl69sTWrRg6FBs3sqqzJhZ2RERE\nZD6PHqFbNxw4gEmTsGIFnHlu0KpY2BEREZGZpKaic2ecPInYWHzzDaz+D7WIdTQRERGZw82b\nCAvDjRv46iu8+67QaSooFnZERERksj/+QHg40tPx448YNUroNBUXCzsiIiIyzenTiIxETg7W\nr0e/fkKnqdBY2BEREZEJDh5Enz5QKJCQgPBwodNUdLyqkYiIiIy1fTt69ICzMxITWdXZAhZ2\nREREZJRVq9CvH3x9cfAgXnlF6DQEsLAjIiIiYyxahBEjEBSEI0fQvLnQaegpFnZERERkoLg4\nTJqEhg2RlIT69YVOQ8/w5gkiIiLSm1KJ6dPx1Vdo1Qq7d6NyZaED0XNY2BEREZF+5HK89RZ+\n+gmdO2P7dnh7Cx2ISmNhR0RERHooLMSQIdi4ET17Ij4ebm5CByINeI0dERERlefJE0RFYeNG\nDBqELVtY1dksFnZERESkU1YWwsORmIjx47FqFSQSoQORVizsiIiISLv0dISE4PhxxMbi228h\nZuVg03iNHREREWmRkoKwMPz1F+Li8MEHQqeh8rGwIyIiIk0uX0ZYGFJT8f33GDtW6DSkFxZ2\nREREVMbZs4iMxOPHWLMGMTFCpyF9sbAjIiKi5x05gqgoFBdj61ZERgqdhgzASyCJiIhIzc6d\n6N4dIhH27mVVZ3dY2BEREdE/1q7F66/DzQ2JiejYUeg0ZDAWdkRERAQA+O47DB2KgAAcPoy2\nbYVOQ8ZgYUdERERAXBwmTECtWjh6FE2aCJ2GjMTCjoiIqGJTKhEbixkz0LgxkpJQp47Qgch4\nvCuWiIioApPL8fbb+OEHtG2LXbtQqZLQgcgkLOyIiIgqqsJCDB+O9evRpQu2boWXl9CByFQ8\nFUtERFQhFRRg4ECsX48+fbBzJ6s6x8DCjoiIqOKRydCzJ7ZuxbBh2LgRUqnQgcg8WNgRERFV\nMI8eoVs3HDiASZOwYgWceV2W47Chwu706dPTp08PDg4WiUQfffRRqb4ZGRlffPFFu3btfHx8\nqlWrFh4efujQISFiEhER2bPUVHTujJMnERuLb76BSCR0IDInWynsUlJSJk6cGBgYuGTJEo0D\nvP32219//fWECRNu37595syZevXqhYaGLl++3LoxyWB3s3EzC0pzfFSxAlcf4kHu07dyJf56\nhPQn5vho4ciVuPoQf+eWP2Qptx/jVhYK5fjzAR4XWCDZP1JluJ4JhVlmIREJ7uZNdOyIP/7A\nV19h9myh05D52crR1+Dg4FOnTgG4fPmyxgHatGmzePHiKlWqAPDx8Vm0aNHRo0c/+uijkSNH\nWjMn6UkJfHUCXxxDxhMA8HHFxLb4pDNcnIz5tKx8TNuLtReRXwwAtf3QsBKOpCC3CABqeuPT\nEIx62XzprSK7ANMTseo88kq+lC9mdsGQpuWMJVdizjHMPfGswC3RIhBfRSC0ljkTfnsG/z2C\n+zkA4OmCsS3xv65ws5VtBhEZ7o8/EB6O9HQsXYo33xQ6DVmE3WykY2Nj1d+KRKK6deteuHCh\nqKhIIpEIlYq0+dcBzD767O3jAsxKQkoWVkUb/FFFCoSvxOn7z7rczMTNzGdv72Rj9HY8KcJk\n+/n/N3IlIlfj+J1nXW5mYehm5BdjtM4K9b29mH9SQ/fkNESsxL7h6BxsnoSzj+LDA8/eygrx\n9a+49ggJg8zz+URkbadOoUcPyGRYvx79+gmdhizFVk7FGionJ+fIkSO1a9dmVWeD7mbjy+Ma\nuq++gFP3DP60Veefq+q0+dcBPCky+MOFsv7ic1Wdygf7UCjXOtb1THxzSmvfIgXeTzRDNgCP\n8vCfwxq677iKfTfMMwkisqqDB9GtG/LzkZDAqs6x2c0Ru1KmTZv26NGjWbNm6TNwUVFRfn6+\npSORyr5rTsUKzQX33mvFzSoVG/Rpe69JgPLP4MoKkXSjMCRYYdCHC0Xbl3qUh19TCttW1/wt\n9l51Uih1/ZI5cx/pj/N9XE2Nd/C6OL/YRUuG4k7VDZuDRCQscUKCy/DhSqm0KCFB0a4duEO0\niqIiYQ422GVhN2/evB9//DE6Ovqtt97SZ3iZTJaZmVn+cGQmaZnugI/GXhmP8zMzcwz6tEcy\nP30KOwCpj55ketvHBuvRE61f6v5DWaab5rsh0rM8AF2FnRK4+3e2wkP7QT/9pGW6AZoLuwc5\nhZmZj038fCKyGreNG32nTVP4+z9au7aoQQNwb2gtMpkMgEJh7cMN9lfYLV26dNq0ad27d1+z\nZo1Iv5u0PT09/fz8LB2MVBpX13qKv1Ggq5+fYUtdgyqSfbf1m24Ndz8/N4M+XCj1A5yh5Zxm\n0yAPPz93jb0aVy+nwPWQoH41b+PuUFHXpIbWOfhiVReuTUT2wunbbyXTpytr1izaudOzbl2h\n41Qsnp6eAMRia1/zZmeF3apVq956662wsLAtW7a4uup7wkkikUj5TG0rimiIF3xwu8xhHW9X\nDGgqkUoNuyxy5MtYfKb8B6a0CESbF1zt5XFMI1tiwWnIy3yrV2uicTWtC3bPFxHoiTSZ1o8d\n1BTeHmZY1DvWxosBuPygdHepMwY3d5ZK7Wy7QVRBxcVhxgw0aiRKTHQNChI6TYUj1D0A9nTz\nRHx8/MiRI7t27bpt2zYWarbM1Qlr+sH3+VkkdcaKvqis+VCULq2rIy4MpSq2Um+reWJ1dOmO\ntqxZVcyNgPj5xDW9sbyvrrE8JFjTD95aCr9W1fBFmHniOYmwpl/pmSURY0kUXtB8jp2IbIlS\niWnTMGMGWrXCkSNgVVeR2M0v7507dw4ZMqRLly7bt29nVWf7Xq2Ji2/jh7M4l4pCOVoEYkxL\n1Pc38tPefwWdXsCq87j0Nyq5o10NdAhC/CVczIC3K1pXx7jWqGQf52CfmdoOr9TEyt/xx9/w\nk6JtDbzVqnQ1XFZoLVyYgB/O4rc0pD+BXA5XZ9T2Q0gtjGwBV5NPwqq8HIiLb+P7szh7H0+K\n0KwqRr2MxpXN9vlEZClyOd56Cz/9hM6dsX07vL2FDkRWJVIqbeWJ8kFBQffulX4YxsGDB0NC\nQgCEhIQcPqzhAQx37twJ0v5bJD4+PiYmZsOGDQMGDDBrWCIiIttTWIjBg7FpE3r1woYNcLO3\nn7wORKgKxIaO2N29e1dHX/5nWCIiIl2ePEF0NBITMXgwli8Hn/NaIdnTNXZERESkWWYmwsKQ\nmIgJE7ByJau6CouFHRERkZ1LS0NICE6cQGwsFi+G1R+xQbbDhk7FEhERkcFu3UJ4OP76C198\ngfffFzoNCYyFHRERkd3680+EhyM1Fd9/j7FjhU5DwmNhR0REZJ/OnkX37sjOxpo1iIkROg3Z\nBBZ2REREdujwYfTujeJibNuG7t2FTkO2gtdXEhER2ZsdOxAZCZEIe/eyqiN1LOyIiIjsypo1\niI6GlxcOHkTHjkKnIdvCwo6IiMh+fPcdhg1DtWo4cgQvvyx0GrI5LOyIiIjsRFwcJkxArVo4\neBANGwqdhmwRCzsiIiKbp1Tigw8wYwaaNEFSEurUEToQ2SjeFUtERGTb5HJMmIAlS9C2LXbv\nhr+/0IHIdrGwIyIismGFhRg2DBs2oEsXbNsGT0+hA5FN46lYIiIiW5Wbi759sWED+vTBzp2s\n6qhcLOyIiIhs0uPHiIjA7t0YPhwbN0IqFToQ2QEWdkRERLYnIwOhoTh6FJMnY/lyOPPSKdIL\nCzsiIiIbc/8+unbFb78hNhYLFkAkEjoQ2Q3+AiAiIrIlN24gLAw3b+Lrr/HOO0KnITvDwo6I\niMhm/PEHwsORno6lS/Hmm0KnIfvDwo6IiMg2nDqFHj0gk2HDBkRHC52G7BILOyIiIhvwyy/o\n0wdKJRISEBYmdBqyV7x5goiISGjbtqFnT0gk2LePVR2ZgoUdERGRoFauRP/+8PXFoUPo0EHo\nNGTfWNgREREJ55tvMGIEgoKQlIRmzYROQ3aPhR0REZFA4uIwZQpefBFHj6JePaHTkCPgzRNE\nRERWp1Tivffw9ddo3Rq7dyMgQOhA5CBY2BEREVmXXI6xY7FsGTp3xvbt8PYWOhA5DhZ2RERE\nVlRQgMGDsXkzevXChg1wcxM6EDkUXmNHRERkLU+eICoKmzc/re1Y1ZG5sbAjIiKyisxMhIVh\n3z5MmICVKyGRCB2IHBALOyIiIstLS0NICE6cQGwsFi+GmPtfsgheY0dERGRht24hLAzXr2PO\nHEyfLnQacmQs7IiIiCzpzz8RHo7UVPzwA8aMEToNOTgWdkRERBZz5gwiI5GdjTVrEBMjdBpy\nfCzsiIiILOPwYfTujeJibNuG7t2FTkMVAgs7sjN/5+LMfaTL0DAAratDYsj1x/nFOHUP1zNR\n0xttasDH1fgYWfk4fR93s1HPH22qQ6q2JhUpcOY+rjxAoCdaV8e9HBy7g3vZqOSOjjXRqjqc\nRMZPt0TKY5y9j7xiNKmC5lV1DZkmw5n7eJiHRgFoXR1ikydNRPrasQMxMXB1RWIiXn1V6DRU\nUbCwI3vy2WHMSkKB/Onb2r5Y3Q8dgvQad+c1jN6G9CdP33q5YE44xrUyJsai04jdhydFT99W\n88RPfdC9HgAcu4Ohm3Er62kvMaB4ftz6/ljXHy2rGTNdAHnFGL8DK3+H8p8uHYKwtj+CfUoP\nqVBixn7M+xVF/yR4MQBr+6FFoJGTJiIDrFmDkSPh54e9e9GihdBpqALh7dZkN+afxCeHnlV1\nAG5mofsq3Mkuf9xzqYhe/6yqA5BTiPE7sPlPg2Osu4hJu55VdQBSZei7DufTcSsLkaueVXUo\nU9UBuPYI4SuR8aRMD/1M2IGf1ao6ACfuovuq55qlxMwjmHP8WVUH4PIDhK/Eg1wjJ01E+vr2\nWwwbhmrVkJTEqo6sjIUd2Qe5ErOSNHTPLsD8X8sfPe4YCsuUPgD+e8TgJBpHKZBjznHMP4mc\nwvI/4WEeFp82eLoAUh7j5981dL/8ABsvPdclrxhfHtcw5N+5+O6MMZMmIn3FxeHtt1G/Po4e\nRYMGQqehCqdCnIotKirKz88XOgWZ5EaWKOOJ5mviTt5V5OeXU0/9escV0HB9WXIasp/kuzjp\nG+NJEf74W6olhtJfqtTzx5I+mcs6fstJCc2Pqv/1trxf/WdHEZPTxbJCFzNOmojKp1Q6/9//\nOX/9taJ586KEBGXlyuCupwIrKioqfyALqBCFnUwmy8zMFDoFmSTzsTNQWWOv/MLicudvsbwK\noKF8UwIPM7OkTsqyvTTKKRQBmi9SKyxWFBUr9Czs8gqKjFgms3OkgJ/GXk/yCjIzH6veZmZJ\ngAAzTpqIyiGX+8yY4bx6dWG7do9WrFA6O4MrWsUmk8kAKBRlL8mxrApR2Hl6evr5ad4dkr1o\n6g1vV2QXaOj1cnWncudvs0DR/esautfzU1YL8NU/hh/wgrfydraGg3/NqiLAXXwuQ6/P0Sdz\nWe1qa72ptVWQRP0DW7nDxUnz2WfjJk1EuhQWSkaNctq0SREaqtiwwdfTU+hAJDxPT08AYqv/\n77gKUdhJJBKpVPPpM7IXUmBCa8QdK91dIsbk9k5SaTknU9/tgL3XUfa43DsdRIYuG+92wLt7\nS3cUAe++4uQnxcoLKC7v55nUGRPbOUulBq99LWogvC4Sy1SogZ4Y+rJE6vrsLK1Uijdb4Puz\npYd0c8bbRk2aiLTKzcXAgdizB337itetk7qa8CAlciASieYrZyyNN0+Q3ZjZBUOaPtfF0wWr\n+6FplfLHDa+LBZFQv5ZOBLzXAW+3MTjG1PaY0u656/VcnbC4J0JroUUgVkXDQ+e67O2C+AGo\n52/wdEusjsYrNZ/rEuSNhEEansn3dXf0afhcF18pNsagtgEHKImoPFlZiIjAnj0YPhzx8WBV\nR0LjD3eyGxIxVkXjnfY4dgcZT9CgEnrUR2V3fUef1BZ9XsTev54+oDikFl7SfM1eOUTA/O4Y\n2xKHU3A3G3X90L0egryf9h3YGKG1sOsarj1CFQ+0ro77OdjzF9Jl8JEioi56NYC/mzHTLRHg\njqQ38ctNnE1FbhGaVEHP+nDXVEq6OWPrGzhxFyfu4FEeXgxAzwbw45FrIjPKyEBEBJKTMWUK\n5s2DiE8AJ+GxsCM707o6Wlc3ctya3hjT0jwxmlRBEy1HCqt4YOTzD64a8JJ5JlpCLEK3OuhW\nR6+BOwTp+wBnIjLM7dsID8eVK4iNxezZQqcheoqFHRERkYGuXEF4OO7cwddf4513hE5D9AwL\nOyIiIkNcvIjwcGRk4KefMHKk0GmInsPCjoiISG+nTiEyEk+eYMMGREcLnYaoNBZ2RERE+vnl\nF/TpA6USO3agWzeh0xBpwMedEBER6WHrVvToAYkE+/ezqiObxcKOiIioPD//jAED4OeHw4fR\nvr3QaYi0YmFHRESk0zffYORIBAUhKQlNm5Y/PJFwWNgRERFpFxeHKVPQqBGOHkW9ekKnISoH\nb54gIiLSRKnEtGmYNw+tW2P3bgQECB2IqHws7IiIiMqQyzF2LJYtQ0gItm+Hl5fQgYj0wsKO\niIjoeQUFGDwYmzcjKgobNkDK/7JMdoPX2BEREal58gRRUdi8GUOGYNMmVnVkX1jYERER/SMz\nE926Yd8+vP02fv4ZEonQgYgMw8KOiIgIAJCWhpAQ/PorYmOxaBHE3EWS/eE1dkRERMCtWwgL\nw/XrmDMH06cLnYbISCzsiIiowvvzT4SFIS0NP/yAMWOETkNkPBZ2RERUsZ05g8hIZGdj7VoM\nGCB0GiKTsLAjIqIK7NAh9OmD4mJs24bu3YVOQ2QqXhlKREQV1Y4diIyEWIzERFZ15BhY2BER\nUYW0Zg2io+Hjg4MH8eqrQqchMg8WdkREVPEsXoxhw1CtGo4cQYsWQqchMhsWdkREVMHExWHi\nRDRogKNH0aCB0GmIzIk3TxARUYWhVOKDD/Dll2jZEnv2oHJloQMRmRkLOyIiqhjkcowfjx9/\nRKdOSEiAj4/QgYjMj4UdERFVAIWFGDoU8fHo0QPx8XB3FzoQkUXwGjsiInJ0ubno0wfx8Xjj\nDWzdyqqOHBiP2BERkUPLykKvXjh2DMOHY+lSOHPHR46MR+yIiMhxpacjNBTHjmHKFCxfzqqO\nHB4LOyIiclC3b+O115CcjNhYzJ8PkUjoQEQWx98uRETkiK5cQVgY7t7FvHmYOlXoNERWwsKO\niIgczrlziIzEw4dYtgwjRgidhsh6WNgREZFjSUpCVBTy8xEfj9dfFzoNkVWxsCMiIgeyezf6\n94dYjB070K2b0GmIrI03TxARkaPYuhWvvw5XV+zbx6qOKiYWdkRE5BB+/hkDBsDfH4cPo317\nodMQCcOGCrvTp09Pnz49ODhYJBJ99NFHZQfIy8uLjY2tWbOmq6vriy++uGDBAuuHJCIiW7Rg\nAUaORM2aSEpC06ZCpyESjK1cY5eSkjJx4sSYmJglS5ZERERoHCYmJubMmTMbN25s1apVQkLC\nsGHDHj169Omnn1o3KQHAX4+w7QquPUSgJ14LRpfaQgcCAGTmY+MlnE9HkRx5xZA6o7I7Xn0B\nEXUh1uPxVYdv4euTuPYQAe7oUR/vv6r5d8+FDOy+huN38aQQ7hK8UhOR9dCsqoYh84rxQSLW\n/4GcQrhLEFEPS6LgIXlumBuZ2HYFVx+iigc6voBudVCStFCOBaew4yru5cBFDFcnPClCkQJi\nER7koqAIrhK0C8LafvCXGtxQOhTKsflPnEtFXjGaVsGAxvAz6+cTWURcHGbMwEsvITERNWoI\nnYZISLZS2AUHB586dQrA5cuXNQ6QmJi4Y8eOlStXvvrqqwAGDBhw8uTJzz//fNy4cdWqVbNq\n1gpv4Sm8uxfFimddXn8Ra/vD1Um4TEDSbUSvx4NcDb261sbWN+Dpomv04Vux8vdnb4+k4KsT\n+HNS6bLp00P47DCUal22X8GH+/FxZ/wn5Lkhrz1C029RUPz0bX4x1l5A/EWcHocW/1SBS85h\n0i4Uyp+N1bM+4mPwMBetvkeGpu+iki9H4l8I+AI7BqFHfV1D6u/2Y0SuxqW/n3X5v1+weSA6\nvWCezycyP6US06Zh3jy0aYNduxAQIHQgIoHZ0KlY3TZt2iQWi3v37q3qEh0dXVhYuG3bNgFT\nVUBJtzFl93NVHYAtl/HvgwIFAgBk5uP1dZqrOgAHbmLqHl2jL0t+rqorkfEEXVY812XTn/jP\n81VdCSXw2WHEX3rWRa7EK0ufVXUqxUq8uvRpJXfqHsbveK6qA7DzGmbsR9jKcqq6Z5NWovda\n5MvLH7L8jwIGbnyuqgPwIBevr0Nmvhk+n8j85HKMGoV58xASggMHWNURwXaO2JXr/Pnz1apV\n8/b2VnVp1KgRgAsXLpQ7blFRUX4+d03msfBXiRIaDs19f0b58SsFzgL9Ulid7PQwT6JjgFXn\n8XnnfG9XzX1nHXEFNJys/T0NGdn53v8c6lt40kXHb6FFJxVRdQpLXifdET/I1XyEMLcIOy8X\nRtZVLDopUSg1tOSSs8grUxHqIFdi3rHid9oaMo4myeniX+9qyPwwD6uTi8a0MEfxSGRGBQWS\nESOctm1T9OxZuGoVJBJwO0+2pKioSJDp2k1h9/DhQ19fX/UuJW8fPHhQ7rgymSwzM9NSySqY\nPzICoKmwe1wgunwvu4anMLv/5HvegK7CrlCO5NuypgGaV7NUWaC2EX+5kts5qKDk9Z9/a7qS\n7h+X/oZqMTt72x3Qeuo36UZBe3+ZtpY0qKorceimfER9U5fwMylu2jIn3yvMDM428fOJzEj0\n5InfqFFOSUl50dFZ8+YhLw95eUKHInqOTCYDoFAoyh3SvOymsAMg0vT/mzV2LMXT09PPz88C\niSoiNxetV9JVqeTj5172RKU1eLuXvyQH+Hn5+WmO56z93orASp5+fu4lr12ddS1sUolItZj5\neem63jDAR+rnJ9HRkobydnM2fQn399Gax9vdlWsQ2Q5RVpZkyBDxqVPy8eNFc+f6ie3mmiKq\nUDw9PQGIrb582k1hV6lSpTt37qh3ycrKAuDv71/uuBKJRCrlrX3m0TEYZ1I1dK/rhxf8tZzp\ntLzXauPrU7oGqOSGZtVdtZ0pblQZv97V0N1JjNfquKhGevUF3L6odRKvviBSLWav1dEVJrye\nRCqVdAzG0Tsa+lbzQlqOhiv5dBjczEkqNbVM7FQbYhEUmibcqbazVGo32wpycGlpCA/HhQuI\njXWaPVvQW7aIdJFIdJ1Hshy7+aHTtGnT1NTU7Oxn54P+/PNPAM2aNRMuVEU0rYPm51/M7GL1\nKGqiGqKtzkccfBICHdf/fd9L8/NQRjR/bg35Vye4aSlv3Jzxr07P3jYKQEiw5iFfDEDr6gAw\ntR0C3DUM8L8uGNhEa9SyAtzQ90UDhtempjfGttTQvU119G5ohs8nMoObN9GpEy5exJdfYvZs\nodMQ2SK7Keyio6MVCsX27dtVXTZv3iyRSNTvkyUrqOmN/cPRuPKzLn5S/BCFQYbUImbnJMKO\nwYhqoKGXpwvmhGFyW12jN6uK9f2fK9pEQExjLH1+4WpSBbuHok6Zc5K1fbFrCJpWea7jrqFo\nH1R6yAaVcHrs09eBnjgwHM3VLtvzccXCHnizBX5+Hf1f0nQ3RxmV3XF5sh7D6WdBJCa3hURt\nqxDVADuHwEmfKESWdukSOnXCzZtYsgTvvSd0GiIbZTenV7p37x4ZGfnBBx/Url275AHFCxcu\nnDFjRvXq1YWOVuG0rIbzE3D1Ia48RHUvNKmi9TiWNVV2x/ZBuJuNixlwdYaXC+7noLIHmlYp\n5wl2Jfq/hOiXkHgdR1JQ3x9RDTQfTuscjKuTcelvXH2IgmK4OqNBJbxUWUPp4+aME6ORKsPX\nv+L0fTQOwDsdUO/5orBZVfw2Hlcf4vIDVPNEkypwlwCARIz4AXiQh+1XcCsL1T3h5Yo0GR7m\noY4fbmch6TZe8MV77TU/GNloLk5YEIlPQ/B7OgqK0aQKgrzLH4vIGs6cQWQksrOxbh369xc6\nDZHtEimVwlztXlZQUNC9e/dKdTx48GBISEjJ69zc3E8++WTt2rUZGRm1a9eeMGHC1KlTdd88\nER8fHxMTs2HDhgEDBlgoNhERWdahQ+jdG3I5Nm+Gln9NRGRrhKpAbOBIyz/u3tV0+boad3f3\nOXPmzJkzxzp5iIhIeAkJiImBVIrdu/Hqq0KnIbJ1dnONHRERVTirVyM6Gj4+OHiQVR2RPljY\nERGRTVq8GMOHo0YNJCWhRQuh0xDZBxZ2RERke+LiMHEiGjRAUhLq1xc6DZHdsKFr7IiIiKBU\n4v33MXcuWrbEnj2oXLn8UYjoHyzsiIjIZsjlGDcOS5fitdewfTt8fIQORGRnWNgREZFtKCzE\n0KGIj0ePHti4EW5uQgcisj+8xo6IiGxAbi5690Z8PN54A1u3sqojMg4LOyIiElpWFsLDsXcv\nxo3D6tUQ6L+nEzkAFnZERCSo9HSEhuLYMcTG4ttvIeaOich4vMaOiIiEk5KC8HBcvYrYWMye\nLXQaIrvHwo6IiARy+TLCw3H3LubNw9SpQqchcgQs7IiISAjnzqF7d2RmYtkyjBghdBoiB8HC\njoiIrC4pCVFRyM9HfDz69hU6DZHj4DWqRERkXbt2ISICcjl27GBVR2ReLOyIiMiK1q1D375w\nc8P+/ejWTeg0RI6GhR0REVnL999jyBAEBODQIbRrJ3QaIgfEwo6IKHmNuQAAIABJREFUiKxi\n/nxMmIDgYCQloWlTodMQOSYWdkREZHlxcXjnHTRqhKNHUbeu0GmIHBbviiUiIktSKvHuu5g/\nH23aYPduVKokdCAiR8bCjoiILKa4GGPGYMUKhIZi2zZ4eQkdiMjBsbAjIiLLKCjAoEHYsgW9\ne2P9ekilQgcicny8xo6IiCxAJkOvXtiyBUOHYtMmVnVE1sHCjoiIzO3RI4SFYf9+TJyIFSvg\nzLNDRFbCwo6IiMwqNRUhIfj1V8TGYuFCiLmjIbIe/ooiIiLzuXkTYWG4cQNz52LaNKHTEFU4\nLOyIiMhMLl1CeDjS0vDjjxg1Sug0RBURCzsiIjKH06cRGYmcHKxbh/79hU5DVEGxsCMiIpMd\nOoTevaFQICEB4eFCpyGquHhNKxERmSYhAZGRcHJCYiKrOiJhsbAjIiITrFqF6Gj4+ODQIbzy\nitBpiCo6FnZERGSsRYswYgRq1EBSEpo3FzoNEbGwIyIi48TFYdIkNGiAo0dRv77QaYgIYGFH\nREQGUyoxfTpmzECrVjhyBEFBQgcioqd4VywRERlCLse4cVi6FK+9hoQEeHsLHYiInmFhR0RE\neissxJAh2LgRPXsiPh5ubkIHIqLnsLAjOyArxNzjWHEed7IgB0SAnxuGNkOQFw7ewu3HkIgh\nAgoVEIsAJRRAHT+E1cG41pCIAeBhHub/ihN38SAXjQIwuCl6NShnogpg4k7E/4GsAkAJF2dU\ndoevFM2rYnRLdA5+NuSFDIxLwOUHyJfD1w25BcgtglwBsRhyQKkAABHg6YI6/mhVDW+1Qj1/\njN+BpBRkF8BHilo+qOyBW1kI9kW3OhjfGq5OWoMdvIVlvyHpNnILoRThBW+IxZAr4C5By2qY\n3Bb1/E1vciJNnjxBv37YuxdvvIGff4ZEInQgIiqNhR3Zuown6PgTrj161kUJPMzF/F91jXUx\nA9uvYP0f2DsUqTKELMfd7Ke9ktOw9iLebY+vIrSOXqhA3fnPRgGQV4Tbj3H7Mc6nY9V5fN4N\nsa8CwNbL6LceCrXBVBSKZ6+VQE4hfk/D72lYkQwnMQrl/4wiQ5rs6esLGdhxFWsvYN9weLlo\nCDYrCR/9AqVal7+fPHt99DZ+PIftg9C1tvamITJOVhZ69sTx4xg/HosWQcxLtIlsUTlrZrdu\n3bp166beZeHChQsXLiw1WEBAQEBAgJmjEQEApic+V9UZ5OhtfH4U4xKeK9FKfP0r9t/QOqLG\nUVSUwL8O4Pd0KJQYuhkKrQNqJlc+q+o0OnkPnx3W0D05DR8ffK6qKyu3CMO3IK/YwExEuqWn\nIyQEx48jNhaLF7OqI7JZ5RyxO3DgQKkukydPBjBp0iT1jg8fPjRvLPMqKirKz88XOgUZo1CO\njZekpnzCz8nKO9kijb1WJss7Vi/S2GvrZVdA81glFEqsTi5+LVjxpEjTgTWTrT6vnNmpoFTH\nlcnOCmX5R9nv52Df1cLwOoYWnESaiW7fdunZU3T9evH//lc8bRoKSi+ZRFRWUZHm/YulVYhT\nsTKZLDMzU+gUZIy0XKe8YpMKu3sykbZDXDceybUtGLLCwHI/+a8HRa7KIsAihV2qTJTxMFPy\n/GGR6w989VxnL6fltfHLtUQwqmic//rL/403RGlpj+PicocOBbelRPqRyWQAFApr/8auEIWd\np6enn5+f0CnIGBIPiEVQ6D77qJO3izIzX/Oxt8qeTtoWDBcnUXF5K2Ogj0udypZag7xcUKVS\n6WxVvfW9Vr2Gv7ufn6u5Q1GFI05OlkRHi7Kyir7/3nXoUC5SRPrz9PQEILb6dQsVorCTSCRS\nqUlHfUgoUilerYmk28Z/Qq8GohN38Zemq/R6NHCSSjXffdqqWvkT7dHAqUttpzE7Tao7tele\nD2UX2p4N8cNv5Y8rdUZYfYlUyjsWyTRHjqB3b+TnIz5e0rcvlycig0gEum2cF8CSrZsbAamx\nP0Aqu+OzUCyIhHOZJb19EEa00Driz9GQaH/gCIBeDdCzAdwlmNreyGw6+Lvhf101dI9qiMh6\n5Y/+nxBU8TB7KKpgdu5E9+6Qy7FzJ/r2FToNEemLhR3ZujbVcXQUWleH6PkTqsG+6BT89Hlv\nzmI4iwDASQwnEQC4S9C7IU6MQS1fRNZD4jC0D3r6TLtKbpjcFonDING++NfywW/jUMtXQ69A\nT/xfJ2yMeXpvxVfh+CwU7v/8MBPpuuMCAIK88b8u2PLGc7WXmzPcJE9f9KyP46NRX9Oz6ETA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MiSmcOAiybart2ECTJhI37jJTDlPbq9O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\nnPT0PAA5eXYv8rkqLFaVVzvyCoq1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the residuals","message":["%r","","xyplot(y-x~x, xlab = \"PREDICTION\", ylab = \"RESIDUAL\", ","       panel = function(x,y) {","         panel.xyplot(x,y)","         panel.abline(0, 0, col=\"red\")","       }, ","       xlim=c(0,8.5), ","       ylim=c(-7,8.5),","       scales=list(y=list(tick.number=8), x=list(tick.number=8)), ","       main=\"Prediction of Years of Residence vs. 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G4z/nWKuDKhrUKx8ZoJX23H6On1+5lhESmNELKOXseYIDzeiXIaP\nthERsXc6YidSdD3TgsX7eG8TISfpE0SgF6EX+H4XF24wszdlvY3Ol29GNeXAZT4KodqnVPHF\n3YVDV0g00706n/UwOpyISC6osBMpujxcWP0AL63hu51sOJ7SWLkE8wYxyEEP11l92I2+Qfzw\nL3vOE5vIgDr8pzZD6qV9PoeISOGiwk6kSPNxY0Yv3u9K+EXORVEngGp+ReXRC3dX5m51oCQi\njkWFnYjg40bz8kaHEBGRXNPNEyIiIiIOQoWdiIiIiINQYSciIiLiIFTYiYiIiDgIFXYiIiIi\nDkKFnYiIiIiDUGEnIiIi4iBU2ImIiIg4CBV2IiIiIg5ChZ2IiIiIg1BhJyIiIuIgVNiJiIiI\nOAgVdiIiIiIOQoWdiIiIiINQYSciIiLiIFTYiYiIiDgIFXYiIiIiDkKFnYiIiIiDUGEnIiIi\n4iBU2ImIiIg4CBV2IiIiIg5ChZ2IiIiIg1BhJyIiIuIgVNiJiIiIOAgVdiIiIiIOQoWdiIiI\niINQYSciIiLiIFTYiYiIiDgIFXYiIiIiDsLF6AAiYoD9l/kllNALJJqpU4oBdWla1uhMIiKS\nayrsRIqc9zfz6loSzbg542Ri8T7e28TTLZh6Dyajs4mISG6osBMpWmbvZtzvNCrDtJ40L4+T\nid3neWEVn/5FaS9eaWd0PhERyQVdYydShFjgtbWU8Wbtg7SpiKsTziYal2H5/QSXZvKf3Egw\nOqKIiOSCCjuRImTfJY5H8kAD/DxuafdwYXRTbiSw+YRByUREJC+osBMpQs5HAVTxTWdQcuP5\nGwWaR0RE8pYKO5EixM8T4FxUOoPORgFpj+SJiEjhosJOpAgJLk1AMebsIS7plnazhR924epE\nm0oGJRMRkbygwk4kq6LiWRjOpI1MXM/8MCLjjA6Ufc4mxrXl8FX6/8Lp6ymNl2N4eAmbT/L4\nXTpiJyJSuKm7E5EsWRjOY8u4EpPaUsKd6b24v75xmXLk+VYcvMyX26k0laq+uDhx+CqJZvoG\nMaWr0eFERCR3VNiJ3NmqwwyeT2kvZvamTUWcTISc4q0NjFhEMVf61zY6X3aYYGZvBtRl9u6U\nJ08MrMuAOgyoq96JRUQKPRV2Inf27Eq83djySOr9pHUD6FGDRjN5diX/CcKpsNVEXavRtZrR\nIUREJK/pGjuROwi/xL5LjGyctpeQcj6MacaJSHacNSiZiIjIrVTYidzBiUiA4NLpDEpuPB5Z\noHlEREQyosJO5A6KuQJcS+8e2OQbY71cCzSPiIhIRlTYidxBw0DcnVm0L51Bi8JxNtG0XIFn\nEhERSY8KO5E7KO7OQ43YeJxX/iDRnNJotvD2RlYcYmgwAcUMzSf2JOwiX7D/1hMAACAASURB\nVG7n+VV8sIW1RzFbjA4kIkWM7ooVubMPurHrHO9u4sfdtKyAk4m/TnE8kkZl+Lyn0eHsnoUi\n0ZFKbCKP/8asXdjWco3LMHcQNUsalkpEihoVdiJ35uPGhof5OIRfQlm6H4uFWv5M7MBLbfDU\nNpSBuCQ+3cqvYYRfwslEvQDuq8+YZrg46HmCkUv4OZSeNXm2JXUDOHudX8P4KITOP7BrDCU9\njc4nIkWD/iiJZIm7My+35eW2JJqxgKuDVid5JTKOrv9l2xnKeNOhCglJbD/LUytYvI9l9zlg\nNRxyip9DubcOvw5OOTxZ3odm5ahdioeX8MEW3u1scEIRKSL010kke1ycVNXd2VPL2XaG1+7m\nxHMsG8bK4Zx8jsfv4o+jvLbW6HD5YPE+gIkd0p50frARVXxZkt6dNyIi+UF/oEQkj124wZw9\n9KjBpI6pRbCHC9N6clc5Zv5DTKKh+fLByUhMUKdU2nYT1A1I6QpRRKQAqLATkTz292mSLAys\nm7bdBAPqEp3A7vNGxMpPXm5YbvZrmEZkLN5uBR5IRIoqFXYiksei4gF8PdIZ5OcBcD29AqhQ\na1EeYGF42vaT1/j7NC0qFHwiESmiVNiJSB6rUBwg/FI6g5IbK5Yo0DwFYEgwZbx5cTXrjqU2\nnr7OwHkkmHm6hWHBRKSocbib00TEaC0rEOjFjG2Mbkopm96bT0QyaxdB/gT5Gxcuf/i4MW8Q\nvX6i0w80LkOdAM5eJ+QUcYm81ZHOVY3OJyJFho7YiUgec3Hiw26cvk6b75i3l+ORHI3gx920\n/Y5rcXzc3eh8+aNdJXaN4eFGRMbxSyh7LtChCiuH8/rdRicTkaJER+xEJO8Nb0BUPC+sZsiv\nqY0l3Jl9Lz1rGhcrn1Xz47v/AMQn4eZsdBoRKZJU2IlIvhjTjHvrsPwgey/ibKJeafrUSv+O\nCsejqk5EjKLCTkTyS2kvHmpkdAgRkaJE19iJiIiIOAgVdiIiIiIOQqdiRUQKgcg4FoUTeoHr\n8QSXpkcNapQ0OpOI2B8VdiIi9m7pfh5azNXY1BZnE+Pa8nYnTMalEhE7pMJORMSubTnJwHn4\nF+OHfnSsircb/5zh9bVM/pOjV3mhNXVKUczV6JQiYh9U2ImI2LXxv+PsxLoHqV0KIMnC9jMp\nD2f7OZSfQ/F24/9a82o7XHTVtEiRp92AiIj9uhrL5pP0r51S1QFP/MbLf1Deh0F1Ae6rTzU/\nJq7nocUGxhQRe6HCTkTEfp29jtlC0M2qbsNxvtzOPTXYOYbnWwE0Lcs/o+hfm5/2sPyggUlF\nxC7Ye2G3e/fuQYMGlS5d2sfHp0uXLhs3bjQ6kYhIwfFxB7gcnfLyx38xwbSeuDtzJQaguDuu\nTkzvhbOJH3cbllNE7IRdF3arVq1q0aJFzZo1t2/ffvny5TfffHPq1KlGhxKRwsFs4fBVfj/C\n4auYLUanyakKxalUgiX7iU0E2H+Zsj5U9wP4JRSgdUWAst5U82PfJeOCioh9sN+bJyIiIoYP\nH/7YY49Nnjw5uaVNmzZt2rQxNpWI2D8LTP+btzdy/kZKS0AxXrubp1oUvs5BTPB8K55dydBf\n+b4fJkgyk2jmoxBm76ZHDeoGpIxptuBU6GZPRPKa/RZ2P/7446VLl55++mmjg4hIIfPcSj79\niyq+jG9LNT+OXGVuKM+s5OAVPu9hdLjse6o5O8/yw79U/Bgfd87foMyHXI6hXgCz+qWMczyS\nI1cZ3sDQoCJiB+y3sNuwYYOvr+8///wzaNCgsLAwHx+frl27vvfee5UrV85kqqSkpOXLl8fG\nxqY7NCQkBDCbzfmSWETswKYTfPoXnaqydBheN3t3e/1u/vML0/5mYF3aZ7YLsUdOJmb1o1ct\nvtvJ9rMACWbe6MBLrVO6r4tO4LGlWOChRsYmFSlaksuJ5NIiXR4eHj179nR2di7AUHZc2J06\nder69etjxoz59ttvu3XrFhoaOmLEiNatW+/atSsgICCjqdatW9e3b9/M33nv3r15HVZE7MWs\nXQAze6dWdUAxV77qQ/VPmbWr8BV2yQbVTenfZNzvvL+Z73ZwI56qvhyPZP5ejkbw+F10qmp0\nSpGiJLmcmDp1aiY3AKxZs6ZLly4FGMqOCzuz2ZyUlPTyyy8PGDAAaNWq1VdffdWpU6fp06dP\nnDgxo6k6duy4dOnSTI7YTZ06tV69evmUWUQMF3aRCsWpedtzVKv6UqkEYReNyJSnpnShuh9v\nbuDDLSktpb34rAdPNjc0lkjRk1xOPPfcc61atUp3BA8Pj44dOxZsKDsu7EqVKgW0b9/e2tK2\nbVtnZ+dt27ZlMpWzs3OfPn0yGWHq1KlOTnZ9L7CI5IaFDO+QcDJhKbS3x9oa1ZRRTTkWwbEI\nKvtSxbfw3RQi4gCSy4lWrVoNGjTI6Cyp7LfEadiwYbrtJpP2YCKSodqlOHWNYxFp209e43hk\n6vMbHEAVXzpUoaqqOhGxYb+F3eDBgwHbHok3b96clJSU0QFPERFgREMs8MRy4pJSG+OTePw3\nzBYeSP8HY4oTkelUhCIihYj9nopt3Lhxcid2NWvW7Nq16969e0ePHl21atUnnnjC6GgiYr86\nVuGxJny9g8Yzub8BVX05FsFPewi7yMjGdK2WziSXY3jlD+aGEhkH4OPGoHq814WAYgUbXUQk\n1+y3sANmzJhRrVq1cePGDRkyxM/Pr1evXpMnT/b19TU6l4jYtRm9qV6S9zbx2tqUlhLuTO7M\nS+l1cH42irbfceQqLSvQthIm2HKS73by+xE2j6RC8YIMLiKSW3Zd2Dk7O48fP378+PFGBxGR\nwsTZxLg2PNuS8Iscj6RyCeoE4J5BT1JPr+DoVb7szaimqY0//MvDi3n8N5YOK5jIIiJ5w64L\nOxGRHHN3plEZGpXJbJyL0SwKp0/QLVUd8GBDlh9k/l7mh3E+ivgkgkvTrjKe2mWKiH3TXkpE\n7iwqnt8Osuc8cUnUDeCeGpT1NjpTXthzniQL99RIZ1CVElhg8PzUFl8P3uvC6KbpjCwiYidU\n2InIHSw7wMglXIpObXF3ZnJnni/8d6jHJwHpnKXdeoqpWwHua4ibifl7uZFARCxj/seEdXzW\ngyHq5lxE7JIKOxHJzJ8nGDCXkp581YcOVXB3ZtsZJq7nhdWY4Ym7CvfZyeolAf45w8jGt7Q/\nu5LknozPXmPdUeqXpn8dPFyYtIHL0Qz9lbPXebZlwecVEbmDwrxLFpH89+JqXJ3Z8DBB/ikt\nLk40Kkv4RV5czfg11CjJo014tiUu9tstZoZqlqRpWWbtYlTT1KvxTkTy12lcnahQnHVHGdGQ\nb/umzF18EhPXU82Pcb/TN4hqfgZmFxFJRyHcE4tIQTkbxV+nGVIvtaoLv0TjL5n9LxVKALSv\nQkwiL66h15yU05qFzvReAO2+59W1LD/IykNMWAdggYBieLsxrWdqzZpc/A0NJj6JOXsMSiwi\nkjEVdiKSoVPXAOoGpLw0Wxi+kIhYFg7h274AQ+px6GlGN2X1YaZsNixnbrQoz5oRlPdh8p/0\nmkOPn/jhX4BHmnD6Ok3L4uOWOvKNeIBGZXAyEXbRmMAiIplQYSciGfJyBbgam/Jy6yl2nOWp\n5vSvTUQsgLcbrk5M60nNkszYhtliWNTcaFORsCf4ZxSz+vHdf1j/EB4uhF3AYiHNs6n/dwCg\nSRmgsM6siDg2FXYikqFa/vh7sngfSRaA7WcB+gQB/BoG0KICgIsTPWpyNoqzUUYlzS0nE03L\n8mBDHm5E+8qMaMifJyjmyo6z3EhIGefrHfwSSs+anLqO2UKdgEzfUUTECCrsRCRDLk482Zyw\nizy6lOgEYhIAPF34YAtzQ+lTi+o37x5IPrYXm2hY1Lz1flcal+F4JNfiaPYlo/9H4y8ZtYwq\nvnzUnedX4eLE0GCjU4qI3EZ3xYpIZl5px/azzNrF0v1ULA5wz09cjaFuAN/+J3W0f8/j7kw5\nH6Ni5rES7mwaydt/8kkI+y6z7zLF3elWnYaBdPsvJ6/xTqfUG0pEROyHjtiJSGbcnFkylC97\nU7sURyMAouMZ15a/HyOgWMo4646x8hA9axbuPu3SKObK5E5cGceTzXF34Vocqw/zwRYSzHzT\nl1faGZ1PRCQ9DrQbFpH84WRiVNOUp6l+8Q9P/MaCMKr70bQs1+JYe5QPt1Dcnfe6GB00H3i4\n8HkP3utC+EVOX6dmSWr646pfxHcSn8RX21l2gPCLFHOlQSAjG6f/6DYRyVsq7EQkGx5vhhO8\ntIZRy1Ib6wbwY39qOe6pSS9XmpWjmdExCoursXT/kW1nKO5OvQBuJLB4H/PDeLI5n/XAdOc3\nEJGcU2EnItkzphlDg9l0gtALlPAguDRtKuKkP9dy0yNL2HaGV9vx2t14uACcjeLBRUz7m/ql\nUw79ikg+UWEnItnm60HvWvSuZXQOsT8HLrNoH0Pq8Xan1May3iwaSq3PmbJZhZ1I/tKlIiIi\nkmf+PAEwvEHadi9X+tXmyFVOXy/4UCJFiAo7ERHJM5GxAAFe6Qwq7ZU6gojkExV2IiKSZ8p4\nAxy6ks6gQ1cw3RxBRPKJrrETETsSm8jCcP48gbcbXavRrbrRgSSbulTDzZnP/mJwvVv6hTly\nlYXhtKhASU/jwokUASrsRMReTNzAOxtItKS8/HALxd2YO5h7VN4VHqW9eKEV726ix2ze7kTj\nssQksPYoz60iLpF3OxudT8TRqbATEbvw2lre+RMT3F2ZdpWISmDNIcIu0Ws26x7m7kpG55Ms\ne7sT1+OZ/jetvsXZRJIFwNuN2ffSoYrB2UQcngo7ETHetXje24STiQ0P0dZaw3XnjXW8tZH7\nfuXU80bGk2xxMvF5Dx5twm8H2HsRbzeCSzOkXsrNEyKSr1TYiYjxpv9NkoVetWyqOgDe7MiM\nfzh9nfNRBOqi+0KlYSANA40OIVL06K5YETHernMA3dO7lq5eaYCtZwo0j4hIIaUjdiJiPFdn\ngJiEdAYlJAF4OhdoHjt0NorvdrLrHOejqF2KjlUZVBcX/TYXkVtpryAixmtfGWBBWDqDdp3D\nBK0rFnAi+7J0P7Wn8dpalu7n0BW+3sF9C2j3PRejjU4mInZGhZ2IGO/hRng48/cZPgq5pb37\nbG4kUD8QbzeDktmB0AsMno+XK8uGceMVzrzApZd4qQ1bTzF4PpY7v4GIFCE6FSsixnNxYlZ/\nhv3K/63mg83UCSAmkdDz3EjAw4X/3Wd0PkO98ycJZpbdR9OyKS3+nkzpQlQ8X2xj7VE6VzU0\nn4jYEx2xExG7MKQeq0YQ6MX5G6w/xl+niE6gYSBHnqFicaPDGer3I7Qon1rVWT1+V8pQEREr\nHbETEXvRtSrn/o/oRP44TAlP2lbK4U/PBDPnoijng7MpjxMWPLOFy9HpH5OrVALg4o0CTiQi\ndk2FnYjYl2Iu9AnK4bQLwnlvE/+eI8GMhwutKvBmR9oV5qdWOJnw8+TUtXQGJTf6FyvgRCJi\n13QqVkQcxIR1DJzHoSv0r8PzrWhXmU0n6fA9Y39j/2Wjw+VCp6psPUXohbTt3+xIGSoiYqXC\nTkQcwZ8nmLSRVhU4+BT/7U9cIuuOkpCEGWb+Q+1pDJjH5RijU+bIy21xMtF7DuuOpbTcSGDS\nRqaG0KoC3dLr1VlEiiydihUpEkIv8N9/2X2emETqBtCnFj1rGp0pT83YhpOJ2fdSqhhDf2Xu\nXjpUYUwzLkfzxHLqlWZhOCcj2fwIroXt92yTsvy3PyOX0OkHirtT2otjESSaaRjIgiEU/ssI\nRSQvqbATcXwfbOHl30my4O6MxcLG48z8hzLe/HgvXRzlRN7Oc9QNoJofa48ydy8D6zJ3IE4m\nkiy8sJrqfvwniMl/8s0OxjYzOmv2DQ2mRQW+3s6Os1yMZmBdOlflgYa4F/kHcohIGirsRBzc\n/DBeWkO90lyP40Qkni44Q5KZc1F0/S/ebnzTlyH1jE6Za7GJBBQDmB8GMKULTiYAZxPuzsQl\n8vrdfP4X8/cWysIOqOrL5M5GhxARu1fYzkmISDZNXI+/J8VcOHWNGiWJScRsJtCLYq4AUfEM\n/ZXX1hqdMteq+bHvEjGJHL1KCXeq+aW0H7lKZBxV/fBwoU4ARyMMTWkHEs38c4ZZu5izh3/P\n68EVIo5GR+xEHNmZ64RdpG8tlh7g7spsPI63G389St0A5u5l6K+4uRCfyOQ/ua8+dQOMjpsL\ng+qy9ihvbcDdhdhEkiw4mzBbeGlNylAgOgGPor3P+/0Io5bdUt02COT7/9Dktt6PRaSQ0hE7\nEUd2KRogJhFg3yWAVQ+kFHDJj3OoUwqTCQuM/8OojHnjkSa0KM97mzh0hbgkvvibn0Pp+AML\nwhkaTKeqHLlK2EUalzE6qHF+P0LPn7gay4T2/HYfi4bwQisOXKbDLPbc1peKiBRSRfvXq2Tq\nwg1CLxCbSJ0Aqvjq5rtCKbn32ovRAFdiKOZK6wopg5K7tw3wxGIB+PccxyL47SChF/B0Ibg0\n/WpT0tOQ1Dnh6sSK4Yxexq9hAE+vBHBx4qnmfNCNi9HcvxCLhbF3GRvTMGYLY3/Dy42tjxLk\nn9LYrzb31qHDLJ5ewboHDc0nInlEhZ2k40QkY39jxcHU629q+TOzNx2rGJdJcqS8D7VLcfAy\ngNmMm2tKuwW+3oGTifgkPF2ISeRqDDU/J9GcOu1zq/i6D4MLz30Vfh7MG8TBK3yylZn/4Gyi\nfRVcnHhgIasPExnHhPaF+ykUufHPGQ5d4dV2qVVdstYVGRLMT7s5G0VZb4PCiUje0alYSevU\nNVp/y4qDDKrLf2rj6Qpw4DKdfsBvCtO3GZ1PsmliB24kAJhMXIsjNpFzUTy4iN+P0C+IzSdp\nWBbgejwNA1l+P5df4tTz/HQvxd25bwF/HDU2frbVLMn0nmx8mLsrs+UkU7eyZD9BpVg4hDc7\nGB3OOIeuANxVPp1BzctjgcNXCjiRiOQLHbGTtF5cw+nrzBnA7N0sPwjgbMLXkyvRRMTy5HJW\nH2bxUJ2ZLTSG1OPIVV79gyQLQIkpxCcCNC7DhhO4OXP4MkBgMdY/hLdbylT31ScuiVHL6P0T\nZXyoF0DfIEY2xqWQ/BhsU5HfR2C2cDaK0l456ZT4SgzbzrDvEqW9aFqWWv53nsSeuToDxCWm\nMyi50VVd4ok4hEKyk5aCEhXPonC6VScyNqWqG9OMK+O49CIT2gO4OLF0P59sNTamZM/Lbfnr\nMWqVBFKqOmDnOWIScHFKuQJvQofUqs4CY/7HyCWYzcQm4evO+mOM/h8df+B6vBEzkFNOJsr7\nZLuqM1t4eyPlPuae2Ty7kvsWEDQNt7eZsD5fQhaMBoEAqw6nM2jVYdycqVOqgBOJSL5QYSe3\nOBpBXBJtKzH9b4Bu1ZnRi+LuAG0rASk92b6zUd1fFTJ3lWP/U/w4IOXcerLoBK7HU9YHIMjm\n7/q0v/lyO/fU4PX2AF/24fyLPN+KTScYPJ91x1JutnVU43/n9XVpD24lJDFpA7WnkWDOYDL7\nFuRP+8r8918WhN/S/tlf/H6EocEpm7mIFHY6FSu3SD7BmmQm/BLAyMapg5IruUZl+GkPl2M4\nFkFV34IPKLkyPJjhwfy8lxUHORFB3dIMDeb0Ne5bwOWbtZoF3t9MpRIsHMKkDQDF3YmK58x1\nTLDyECsPAbSswLSeNHW4/s/CL/FxSMr/PV34sBs9auLvyZBfWXmI/Zfp/wv/u8/QiDn1TV/a\nfc+geXSsSovyJJrZeJy/ThPkz4fdjA4nInlEhZ3copofni6sP0byUYkyNnfJrTsKpPZheyWG\nqr4cucr/DrD3Iu7O1A+kf21KFSvozJJdw+oxzOZe1+ORmGDu3pQbYI9c5dQ1nm+FixMLwgko\nRgkPWn/Lkas0KcuOszzcGCz8HErb71jzQMqhXIfxa1jKxYgmWG0zd7/dR6kPuBrDbwc5cLlQ\nXnJXoyT/jOKlNSzex9qjAMXdGduMyZ3x9TA6nIjkERV2cotirgwJZtYuirtzLY4jV2hfGeCv\n03z2F3VKYb55CraMN29tYNLGWzrIeGEVX/ZhWLABySXHKpdgQF1+DWPiel69m4hYgBLujFjE\ngcu804lX/+DIVb7uQ01/OsyiWVkev4snm9NhFiOXEP4kzg50K4315tA2lW6pWZ1MtK3IsgMA\nyw8WysIOKO/DT/ditnA0AhcnKpcwOpCI5DUVdpLWlC78eZzDVwGeX83VOHaeZd5eXJyY1osn\nlwME+TNvL2+sp1k53ulE8/LEJbH+GC+t4YGF+HvSrbqxMyHZ81Ufjl7lzQ18sY06AQBvbyTB\nzKC6PNWCMh9yd2UebcJ3O4GUa/KalOW5Vry1gc0nuLuykeHzlvvNneLtD1iLvXnVXXLfzoWX\nk4nqfnceTUQKIxV2klZpL7Y8wpMrmL+XiFheWAVQrzRdqzF8AWejMMFbHXl0KbVLsf4hvG5e\njD+kHg3K0GQG9y2gWTmq+Kb0fequbhTsnp8Hmx/h879Ysp8953F1wgKf9eDJ5uy7RHQCHaoQ\nl8S0v/FwoUOVlKmSj+aGXXSowq7RzWeOHYu4pf1qLFtOpfxf9xmIiN3SXbGSjtJezBvIkWco\n55PSsvcCn2zlbBQuTnzRCy83rsfzVPPUqg7YfZ5es4lN4nIMW07y5XYeXEyTL1MO/omdc3fm\n/1rz58NcGcfioZgtfLqV5QeJigc4fY0es9l5jvFt8bt5PZbJBDja/dFD6qUctPvjCJdjUhqj\nE3hoMTdudvXSuqIx2URE7khH7CRDVX05/TxL9zN7D4cvU7IYbSvxYEOq+PL1DuCWy4wiYrln\nNhGx9K/Non2sGUF1P77ewYR19PyJf8fgoXWt8OhZk2/68sRv9J6TcqP0tzsxwbMtef3u1NH+\nPA44Wv9nJT2ZM4ABc0myEPgBHari58EfR7l6s8irX5pOVQ2NKCKSMf2xlTvoG0TfoLSNPm7A\nLZ2ZTd/G2Shm9WP/JYDi7pQqxout+e0Am0/i8y6uTpTx4bEmvNy2oKJLLjzciG7VmbeX0Aus\nPcKxSCZ14tV2qSPsucDHIVTzS/+u2NALrD5M+CVKetIwkH61Keaazmj26d7aLBjMoPkkWfjj\nyC2DAr1ZNBSnO90sEp1A+CVuxFOvNP6e+ZdURCQtFXaSE60rYoJfQhl68wbYVYco6cmQYBrO\nIKAYtfy5EkvQ5ynFn9lCopmjV3nlD37aze7Hi9xFANfiCL9EqWJU9b1zWWAnyvvwXEuAs1G0\n/IbX17LjLJ2q4unC9rPM2kWSmW/7pn3IWKKZJ5fz1fZbTtGW82HOgJRr8gqFe+uQNIEnljNv\nL5GxmKGsNw804MU2qWei03UxmudW8ktoSp8pQKsKTOtJE4fr8E9E7JMKO8mJSiUYXI+5e3lt\nLRPa4+bMhRuU8+HBmx1kOJto9y2XogkqRUQMNUqyaSTbz3LPj+y9yMB5LBxs9DwUlC0neWE1\nW22uux/TjDc7FKZz02W92fIITy5nUTgLbz63oFEZvuhFqwppR35uFV9up1t1XrubBoFciWHl\nIV75g95z+PuxQnbednpPpvfMxvgXo2n9LYeucE8NOlbBy41tp/klucO/EbTRlXkikv8Kz98W\nsTMze3PkKu/8yYx/aFSGM9eJiif0AkPqMa4tYRcJu4SfJzvHUPI9SnoCNC3LwacJ+IAl+0g0\nF5rHyefG/w7Qfy5uzoxoSINALkWz6hDvb2brKVY/UJjuFy7vw6IhXIkh9AIxidQpRaX0ukA7\ndIUvttGpKsvvT+ncroQ7Y5vRMJB23zNhHfMHFXDwAvXy7xy6wszejG56s+kunmpBx1k8vJiw\nJ4rEOi8ixtJuRnLI14NNI5nanQaB/HsOTFjgxTb8PBBnE7N2AQyqw9w9xCbSvkrqVM3KYbak\n/zByB3M9nkeWUtKTf0bxQz9eaMW7nflnFM+2ZONxpobc+R3sTUlP7q5M9+rpV3XA8oOYLbzU\nJm2Xxa0r0qFKylBHFZvIL6G0q2RT1QHQtCzPteLgFbacNCiZiBQlKuwk59ycebYl6x7k0kvs\ne4Li7ny7g/8dALgQDRAZz1MrKOPNo01Sp0ru3vZkIe/iNSuW7efCDd7qeMv5RycT73elUgm+\n3Wlcsnxz+jpAUHpPZQjyJzqBq7EFnKjgHI/kRkJqJ3+2khv3XizQPCJSNKmwk7xRzodFQzBb\n6PszJaew8iDA3FC8XFk6jBI2HboevQoZ/O13MKEXADrf1jWGqxPtK3PoSuqTDBxG8u3S1u7f\nbF2OwQTebgWcqOAkH4w0pXdnTHKbxXGPVoqI/VBhJ3mmU1X2Ps4r7WhaLuXvt4sTWx/lrnKp\n4+y7zO7zuDrTsYoxIQtS8n2RzultZMnXWiU53F/65J57fwlN234lhtWHaVquMF1WmF2VS+Dp\nktK3XxqbTgApz2oTEclXKuwkL5Xz4Z1OrHmAQ0/TqSqJZpp8xZw9xJuJimfqVhrPxGzhyeZG\nBy0QyR04h9x2ZZUFtp6ivM8tz+1wDB2q0LQsU0P4ekfq5XRno7h3LhGx/F9rQ8Pls2KuDKzL\numPM3n1L+96LfBRCFd90Ovz7+zQPLabW5/i/T53pjP2fI5+qFpGCobtiJb+sGk6zr/n3HPcv\n5P6Fqe396/BxN+NiFaC+Qfi4MWEdXapR2iu1/ZOthF/ipTbGJcs3TibmD6bTD4xaxlsbqF+a\nyzHsPk9sIv/XmiH1jM6Xz6Z0ZcNxRixi8T46V0vp8O/7nSSYWTAY11t/R7+/mfF/pJ6fvRLD\nvkt8s5Pv+vFA/YLPLiIOQoWd5BcXJ3aNZvYevvyHI1dxMVGrFK+0KxInYZMFFOPj7jy2jEYz\nGd0spbuTlYdYGE6dUrzSLrNpzRaORhB2EV8PgkvfoVNcu1LVl52j+SiEVYf48wT+nnStxuN3\ncU8No5Plv+QO/574jYXhLLjZ4V+DQL7olbYTu+UHGfc7JijpwWvtzGDtmgAAIABJREFUaV2R\niDheWMXeC4xYyIIw7q/PvXXS3lwsInJHKuwkfw2vz/AifPjh0SZ4u/F/q5m4PqXFycTAukzv\necsNJWksP8gzKzl0JXWSocF8eg+liuV33rzh68GkjkzqaHQOI5T3YfFQLsew53xKh39VfNMZ\n7b1NOIG3O3+Porofm0/y0CLORaUMXbKPJfsIDmDRMGr4FWR8ESn0VNiJ5K+hwQysS+gF9l3C\nvxj1S1PGO7Px5+1l2AJ8PXihFQ3LEBHLqkPM2cOuc2weiW/hOXRXlPl7pt/vSbJEM5tPYoZR\nTanux6lr9PoJ4Lv/UL443X9MGS30IjU/o4w349rybIsCSC0ijkCFnUi+c3GiURkalbnzmNfi\nePy3lDN61k6AKxTn4BXCLlLqfYJL07U649vq0fKF2LW4lDtLGgYCvL+ZyDiW30/nqnT8IXU0\nZyeSzJyL4rmVLArn9xFpr9ITEbmd9hMidmT5QS7H8EaHlKrOAk8u5965nLqGlxsuzlyL48Mt\nNJqZeqJWCh1fD9ycAW4kAKw+TC1/etTg879Tn04xvRdHngHoXgNXJzYep+qnPLyEjScMCi0i\nhYQKOyms4pPYdY75YWw+SWSc0WnyyL5LQGq/GLN3M30b3apz/FkeaEBcIttG8UM/zkcxeL4j\nP54rIxdu8GEIg3/l8d/4NfzO49snJxOdqgL8tAfg/I2U6/C+2ZHSv7GzE0PqUak4ziZ+P0KC\nGeD0NWbtov33uE1iYaGddxHJbzoVK4XSjH94Yx0Xo1NeujkzqilTulCskPcMl/x33VqxfRyC\nvyfzB1HcPaXRycSIhoRf4r1N/H6USsU5G0WNklQsbljmAvPQYv67O7V/kBn/4OHCZz14rEmm\nk9mlyZ1ZfZg/jzNiESXcOXud3edTynpgaD38PVl2gCQL2JTvnq7EJpBgZsA83u3CeEfsMUdE\ncklH7KTwmbiex3/D241JHZk3iE/uoXl5pv1Nn59JNBsdLnfqBgCsPwYQm8iuc3SrnlLVbThG\nOZ+Ufk/61Qbo+zN1ptPpBypNpfpnzA8zLHYBGLaAH/7F2USQPy43OwGJTWTUMlp/y5nrhobL\nvsZl+LE/ziZ+3M3xSPZcoOHMlEGlvfi0B8BTy1NaPG7+AP+gK7Gv4ecJ8MrvnIgs4NQiUgio\nsJNCJvwSb2+keXl2j+W1uxlUl2dasPFhnriLtUf5dqfR+XKnZ00CvXhrA/svExUPpPwVf3sj\n+y/zUCMAs4VX/wAo7s6E9szoxTMtuB7H4Pl89pdx0fPToavMDcXVmfvrs/8yjcvy3/5se4z+\ntQFCTtHiG1YfLmSPbbivPoeepku11MPMTiaA/rUp4c7UrRy/WbeNb5vyn1LFcHNmWk8AC4z/\no4Aji0ghoFOxUsj8vIckCx91u+Vx8ib4oBuzdzN7N6ObGhcu17xc+aYv/efS7CuGBuPmzMqD\ntPueTSdoUpaX2wL891/+OArw+t08dfPhbK/eTYdZvLSGPkFUTa/jtELtoy1Y4D9B/PdfOlVl\n5fCU+0MfaMiSfZjh/9m768Cq6veB4+9zY91jTWx0d/dopEtAWkJAQUBCVFQaUQQJFQklRRqk\nc6M7RjcbGzDYWLC8u/H7Y2fc3Tn8fZGxuwuf11+Xzz3n8pwB28Mnnic8nuYrUUp8UI45LXCz\nkCPD/i7s7QWwIoSPtpGcBrDoHCsvkaiRr7FRsfqSXKm4QSGA4m7yWyf/0a1OEARBzNgJFuZG\nNEqJWgWyjtuqqOTDjajs7rEorYuzvzdF3Vh8Do2Ou7GcDGdwVQ72kXPZ3y+gVKBS0Ka48S4P\nO2Y3J1XHn5fkkaQ0MwT/hqRvPlNKGGBaYzmrW3aRTn+hUAA4W+PjSNMirAghcJk82WlBepXn\n8hA+qY61Er0BjZb6heS3NDpuRqMzMKiKXAHxxdbSFJ15ohUEIS+zjMRuxYoVkiRJkvT48WNz\nxyKYmULCALrs9tLpDfJilqWrX4jzH/FkDCs6Yq/GRkVxdx4+53ECu+9w/AE6PZ/WyNrSoIE/\nEgTd571V5JuJ/TR8Z9FlHRcjzfQYOSe9OEj4c0AuBxidzPCd+LvIX4TKPsSnsLMH0xsTEsmM\nI+aL9b8q7Mq8ljwYRWkP0vQcCpXH0w/NtC/J7BbyyMoQ+YWvY65HKQhCnmcBid2DBw+GDx/u\n7f0/VHcV3gFlPNAb5OMFmcWlcvYhZT3NENIb4mFHz3Ls6YWrLaN2U2oBPrNosZI0PQWc+a5p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S89zKZIEDaU9aF2cgVUs+H84gmDRRGIn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\nc/MbDjdHVfElfCSdy/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hdFVOPyQVpsJ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NN_TEST_DATA for use in query below","message":["%r","","ore.drop(table = 'NN_TEST_DATA', silent = TRUE)","","ore.create(CUST_DF.test, table = 'NN_TEST_DATA')"],"enabled":true,"result":{"startTime":1715347108686,"interpreter":"r.medium","endTime":1715347109260,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE"," FROM (SELECT CUST_ID,","              PREDICTION(NN_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","              PREDICTION_DETAILS(NN_REGRESSION_MODEL USING *) PD","       FROM NN_TEST_DATA","       WHERE CUST_ID < 100015","       ORDER BY CUST_ID) OUT,","       XMLTABLE('/Details'","                PASSING OUT.PD","                COLUMNS ","                \"Attribute1\" XMLType PATH 'Attribute[1]',","                \"Attribute2\" XMLType PATH 'Attribute[2]') ","                OUTPRED"],"enabled":true,"result":{"startTime":1715347109338,"interpreter":"sql.medium","endTime":1715347111145,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100003\t3.875\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".199\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".169\" \n100006\t0.639\t\"CUST_GENDER\" actualValue=\"F\" weight=\".097\" \t\"CUST_INCOME_LEVEL\" actualValue=\"G: 130,000 - 149,999\" weight=\".052\" \n100008\t4.703\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".418\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".136\" \n100010\t3.29\t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".067\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\"-.009\" \n100014\t5.548\t\"AGE\" actualValue=\"1954\" weight=\".26\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".176\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715347111239,"interpreter":"md.medium","endTime":1715347111306,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715347111392,"interpreter":"md.medium","endTime":1715347111453,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Regression SVM.dsnb b/machine-learning/notebooks/r/OML4R Regression SVM.dsnb
deleted file mode 100755
index 27cbfd25..00000000
--- a/machine-learning/notebooks/r/OML4R Regression SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Regression SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715286430686,"interpreter":"md.medium","endTime":1715286430850,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Regression Modeling to Predict Numerical Values using Support Vector Machine","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.","","---","","Copyright (c) 2024 Oracle Corporation","","---","","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715286430929,"interpreter":"md.medium","endTime":1715286431026,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-support-vector-machine\">Regression Modeling to Predict Numerical Values using Support Vector Machine<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE - a continuous variable. We will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<hr />\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715286431103,"interpreter":"md.medium","endTime":1715286431179,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715286431257,"interpreter":"md.medium","endTime":1715286431332,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Load libraries","message":["%r ","","library(ORE)","library(lattice)","options(ore.warn.order=FALSE)"],"enabled":true,"result":{"startTime":1715286431411,"interpreter":"r.medium","endTime":1715286437316,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare the dataset combining customer data with supplemental demographics","message":["%r","","CUSTOMERS <- ore.sync (query = c(\"QUERY_CUSTOMERS\" = 'SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH AGE, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT FROM SH.CUSTOMERS'))","DEMO_DF <- ore.sync (query = c(\"QUERY_DEMO_DF\" =  'SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES FROM SH.SUPPLEMENTARY_DEMOGRAPHICS'))","CUST_DF <- merge(x=QUERY_CUSTOMERS,y=QUERY_DEMO_DF,by=\"CUST_ID\")"],"enabled":true,"result":{"startTime":1715286437392,"interpreter":"r.medium","endTime":1715286438297,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows from CUST_DF","message":["%r","","z.show(head(CUST_DF), 10)"],"enabled":true,"result":{"startTime":1715286438372,"interpreter":"r.medium","endTime":1715286438596,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tAGE\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n104088\tM\tNeverM\t1980\tL: 300,000 and above\t11000\tBach.\t0\t1\tExec.\t1\t1\n104089\tM\tMarried\t1970\tH: 150,000 - 169,999\t5000\t< Bach.\t0\t3\tCrafts\t3\t0\n104090\tM\tMarried\t1953\tG: 130,000 - 149,999\t7000\tMasters\t1\t3\tProf.\t6\t0\n104091\tM\tMarried\t1964\tJ: 190,000 - 249,999\t15000\tHS-grad\t0\t3\tCrafts\t5\t0\n104092\tF\tNeverM\t1958\tL: 300,000 and above\t15000\tBach.\t0\t2\tSales\t5\t0\n104093\tM\tMarried\t1956\tF: 110,000 - 129,999\t1500\tBach.\t1\t3\tProf.\t7\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Split data into randomly selected 60% sample for train and 40% test","message":["%r","","set.seed(2)","sampleSize <- nrow(CUST_DF)*.4","ind <- sample(1:nrow(CUST_DF),sampleSize)","group <- as.integer(1:nrow(CUST_DF) %in% ind)","rownames(CUST_DF) <- CUST_DF$CUST_ID","","CUST_DF.train <- CUST_DF[group==FALSE,]","class(CUST_DF.train)","cat(\"\\nTraining data: \")","dim(CUST_DF.train)","","CUST_DF.test <- CUST_DF[group==TRUE,]","class(CUST_DF.test)","cat(\"\\nTest data: \")","dim(CUST_DF.test) "],"enabled":true,"result":{"startTime":1715286438671,"interpreter":"r.medium","endTime":1715286440571,"results":[{"message":"'ore.frame'\nTraining data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>2700<\/li><li>12<\/li><\/ol>\n\n","type":"HTML"},{"message":"'ore.frame'\nTest data: \n","type":"HTML"},{"message":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n<\/style>\n<ol class=list-inline><li>1800<\/li><li>12<\/li><\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Linear regression model for predicting YRS_RESIDENCE using default settings ","message":["%r","","svm.mod1 <- ore.odmSVM(YRS_RESIDENCE~.,CUST_DF,\"regression\", kernel.function=\"linear\")","summary (svm.mod1)"],"enabled":true,"result":{"startTime":1715286440657,"interpreter":"r.medium","endTime":1715286444458,"results":[{"message":"\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    kernel.function = \"linear\")\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-7.999000 -0.502800  0.008696 -0.016500  0.470000  7.600000 \n\nCoefficients: \n              variable                value      estimate\n1          (Intercept)                      -0.1044271713\n2        AFFINITY_CARD                       0.0475586164\n3                  AGE                      -0.6733152113\n4    CUST_CREDIT_LIMIT                       0.0112718540\n5          CUST_GENDER                    F -0.0269700701\n6          CUST_GENDER                    M  0.0269700701\n7              CUST_ID                       0.0022383403\n8    CUST_INCOME_LEVEL      A: Below 30,000  0.1331750526\n9    CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.0113927098\n10   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -0.0475515942\n11   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.0463811097\n12   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.0342647070\n13   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.0618334638\n14   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.0169818428\n15   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.0421423123\n16   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.0123289837\n17   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.0146884018\n18   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.0008983566\n19   CUST_INCOME_LEVEL L: 300,000 and above  0.0006776771\n20 CUST_MARITAL_STATUS              Divorc.  0.0827373461\n21 CUST_MARITAL_STATUS              Mabsent -0.0388352624\n22 CUST_MARITAL_STATUS               Mar-AF  0.1536190766\n23 CUST_MARITAL_STATUS              Married -0.0043046183\n24 CUST_MARITAL_STATUS               NeverM -0.0212589809\n25 CUST_MARITAL_STATUS               Separ.  0.0431305917\n26 CUST_MARITAL_STATUS              Widowed -0.2150881529\n27           EDUCATION                 10th -0.1304534414\n28           EDUCATION                 11th -0.1818194549\n29           EDUCATION                 12th -0.0987968953\n30           EDUCATION              1st-4th  0.0392788824\n31           EDUCATION              5th-6th  0.0370982836\n32           EDUCATION              7th-8th -0.1645571172\n33           EDUCATION                  9th -0.0624836898\n34           EDUCATION              < Bach. -0.0345395743\n35           EDUCATION              Assoc-A  0.0676949923\n36           EDUCATION              Assoc-V  0.0601038362\n37           EDUCATION                Bach.  0.0519667379\n38           EDUCATION              HS-grad  0.0067587787\n39           EDUCATION              Masters  0.1094099360\n40           EDUCATION                  PhD  0.1840602952\n41           EDUCATION              Presch. -0.2526628011\n42           EDUCATION               Profsc  0.3689412318\n43      HOUSEHOLD_SIZE                    1 -0.0618575735\n44      HOUSEHOLD_SIZE                    2  0.0781022302\n45      HOUSEHOLD_SIZE                    3  0.1036453327\n46      HOUSEHOLD_SIZE                  4-5 -0.1064051003\n47      HOUSEHOLD_SIZE                  6-8 -0.0028216044\n48      HOUSEHOLD_SIZE                   9+ -0.0106632847\n49          OCCUPATION                    ? -0.0776321880\n50          OCCUPATION              Armed-F -0.0313920314\n51          OCCUPATION              Cleric.  0.0666945505\n52          OCCUPATION               Crafts  0.0586732575\n53          OCCUPATION                Exec.  0.1807337766\n54          OCCUPATION              Farming  0.0948578599\n55          OCCUPATION              Handler -0.0451896307\n56          OCCUPATION              House-s -0.3701341209\n57          OCCUPATION              Machine  0.0313706712\n58          OCCUPATION                Other -0.0690296205\n59          OCCUPATION                Prof.  0.0175584968\n60          OCCUPATION              Protec.  0.0070259778\n61          OCCUPATION                Sales  0.0748004504\n62          OCCUPATION              TechSup -0.0593060183\n63          OCCUPATION              Transp.  0.1209685691\n64         Y_BOX_GAMES                      -0.0269169220\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Linear regression model for predicting YRS_RESIDENCE using specific settings ","message":["%r","","svm.mod2 <- ore.odmSVM(YRS_RESIDENCE~.,","                       CUST_DF,\"regression\", ","                       kernel.function=\"linear\", ","                       epsilon = .1, ","                       conv.tolerance = .0001, ","                       complexity.factor = .1)","summary (svm.mod2)"],"enabled":true,"result":{"startTime":1715286444545,"interpreter":"r.medium","endTime":1715286447449,"results":[{"message":"\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    complexity.factor = 0.1, conv.tolerance = 1e-04, epsilon = 0.1, \n    kernel.function = \"linear\")\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\ncomplexity.factor                                0.1\nconv.tolerance                                 1e-04\nepsilon                                          0.1\nkernel.function                               linear\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-8.013000 -0.503700  0.008408 -0.017160  0.473300  7.604000 \n\nCoefficients: \n              variable                value      estimate\n1          (Intercept)                      -0.0961645169\n2        AFFINITY_CARD                       0.0479258642\n3                  AGE                      -0.6728391817\n4    CUST_CREDIT_LIMIT                       0.0106009055\n5          CUST_GENDER                    F -0.0279257851\n6          CUST_GENDER                    M  0.0279257851\n7              CUST_ID                       0.0031403448\n8    CUST_INCOME_LEVEL      A: Below 30,000  0.1233439462\n9    CUST_INCOME_LEVEL   B: 30,000 - 49,999 -0.0117479488\n10   CUST_INCOME_LEVEL   C: 50,000 - 69,999 -0.0454910993\n11   CUST_INCOME_LEVEL   D: 70,000 - 89,999 -0.0400438051\n12   CUST_INCOME_LEVEL  E: 90,000 - 109,999  0.0301241229\n13   CUST_INCOME_LEVEL F: 110,000 - 129,999 -0.0605895787\n14   CUST_INCOME_LEVEL G: 130,000 - 149,999 -0.0170874472\n15   CUST_INCOME_LEVEL H: 150,000 - 169,999  0.0423200256\n16   CUST_INCOME_LEVEL I: 170,000 - 189,999 -0.0109126256\n17   CUST_INCOME_LEVEL J: 190,000 - 249,999 -0.0155146830\n18   CUST_INCOME_LEVEL K: 250,000 - 299,999  0.0035259840\n19   CUST_INCOME_LEVEL L: 300,000 and above  0.0020731090\n20 CUST_MARITAL_STATUS              Divorc.  0.0938872523\n21 CUST_MARITAL_STATUS              Mabsent -0.0213551897\n22 CUST_MARITAL_STATUS               Mar-AF  0.0999999815\n23 CUST_MARITAL_STATUS              Married -0.0055570082\n24 CUST_MARITAL_STATUS               NeverM -0.0161532406\n25 CUST_MARITAL_STATUS               Separ.  0.0500678025\n26 CUST_MARITAL_STATUS              Widowed -0.2008895979\n27           EDUCATION                 10th -0.1365029688\n28           EDUCATION                 11th -0.1904415606\n29           EDUCATION                 12th -0.1032231030\n30           EDUCATION              1st-4th  0.0308024104\n31           EDUCATION              5th-6th  0.0331048706\n32           EDUCATION              7th-8th -0.1635947240\n33           EDUCATION                  9th -0.0680287615\n34           EDUCATION              < Bach. -0.0436187296\n35           EDUCATION              Assoc-A  0.0608612927\n36           EDUCATION              Assoc-V  0.0488267576\n37           EDUCATION                Bach.  0.0399895807\n38           EDUCATION              HS-grad -0.0005043357\n39           EDUCATION              Masters  0.0957453649\n40           EDUCATION                  PhD  0.1573158059\n41           EDUCATION              Presch. -0.0999999976\n42           EDUCATION               Profsc  0.3392680980\n43      HOUSEHOLD_SIZE                    1 -0.0647532756\n44      HOUSEHOLD_SIZE                    2  0.0757244931\n45      HOUSEHOLD_SIZE                    3  0.1083905757\n46      HOUSEHOLD_SIZE                  4-5 -0.0986784105\n47      HOUSEHOLD_SIZE                  6-8 -0.0045235366\n48      HOUSEHOLD_SIZE                   9+ -0.0161598461\n49          OCCUPATION                    ? -0.0781470033\n50          OCCUPATION              Armed-F -0.0347271519\n51          OCCUPATION              Cleric.  0.0630798342\n52          OCCUPATION               Crafts  0.0514050172\n53          OCCUPATION                Exec.  0.1726076614\n54          OCCUPATION              Farming  0.0786455571\n55          OCCUPATION              Handler -0.0495116725\n56          OCCUPATION              House-s -0.3013560556\n57          OCCUPATION              Machine  0.0262640875\n58          OCCUPATION                Other -0.0727756846\n59          OCCUPATION                Prof.  0.0204187814\n60          OCCUPATION              Protec.  0.0037760887\n61          OCCUPATION                Sales  0.0716411539\n62          OCCUPATION              TechSup -0.0620377442\n63          OCCUPATION              Transp.  0.1107171310\n64         Y_BOX_GAMES                      -0.0278004598\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Gaussian regression model for predicting YRS_RESIDENCE using default settings ","message":["%r","","svm.mod3 <- ore.odmSVM(formula = YRS_RESIDENCE~.,","                       data    = CUST_DF,","                       type    = \"regression\", ","                       kernel.function=\"gaussian\")","summary(svm.mod3)"],"enabled":true,"result":{"startTime":1715286447527,"interpreter":"r.medium","endTime":1715286454131,"results":[{"message":"\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    kernel.function = \"gaussian\")\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\nconv.tolerance                                 1e-04\nepsilon                                           .1\nkernel.cache.size                              5e+07\nkernel.function                             gaussian\nnum.pivots                                       200\n\nResiduals: \n     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n-8.116000 -0.484500 -0.004475 -0.048450  0.460300  6.988000 \n\nCoefficients: \n[1] No coefficients with gaussian kernel\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All algorithms<\/a>","","- Specify a row weight column.","> ODMS_ROW_WEIGHT_COLUMN_NAME = '<row_weight_column_name>'","   ","- Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row.","> ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'","  ","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'","","> SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'","   ","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> SVMS_COMPLEXITY_FACTOR = '0.1'","","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.    ","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'","","> SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'","    ","- Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0.  The default is 0.1. ",">  SVMS_EPSILON = '0.1'   ","     ","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> SVMS_BATCH_ROWS = '20000'","   ","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","> SVMS_SOLVER = 'SVMS_SOLVER_SGD'","","> SVMS_SOLVER = 'SVMS_SOLVER_IPM'","","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> SVMS_CONV_TOLERANCE = '0.001'","","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> SVMS_NUM_PIVOTS = '200' "],"enabled":true,"result":{"startTime":1715314911098,"interpreter":"md.medium","endTime":1715314911181,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-14FB8AC4-4CE5-4B91-BAA9-552A115CB5BE');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-42BDAC50-3951-48A0-A26D-693D5407DD44');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_ROW_WEIGHT_COLUMN_NAME = '&lt;row_weight_column_name&gt;'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method.  The default is to replace with mean (numeric features), mode (categorical features) or delete the row.<\/li>\n<\/ul>\n<blockquote>\n<p>ODMS_MISSING_VALUE_TREATMENT = 'ODMS_MISSING_VALUE_DELETE_ROW'<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_LINEAR'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_KERNEL_FUNCTION = 'SVMS_GAUSSIAN'<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_COMPLEXITY_FACTOR = '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L1'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_REGULARIZER = 'SVMS_REGULARIZER_L2'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a regularization setting for regression, similar to complexity factor. Epsilon specifies the allowable residuals, or noise, in the data. It takes a value larger than 0.  The default is 0.1.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_EPSILON = '0.1'<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_BATCH_ROWS = '20000'<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_SGD'<\/p>\n<\/blockquote>\n<blockquote>\n<p>SVMS_SOLVER = 'SVMS_SOLVER_IPM'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_CONV_TOLERANCE = '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>SVMS_NUM_PIVOTS = '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Gaussian regression model for predicting YRS_RESIDENCE using specific settings","message":["%r","","ore.drop(model = 'SVM_REGRESSION_MODEL')","","svm.mod4 <- ore.odmSVM(YRS_RESIDENCE~.,CUST_DF,\"regression\", ","                       kernel.function=\"gaussian\", ","                       epsilon = .1, ","                       conv.tolerance = .0001, ","                       complexity.factor = .1,","                       odm.settings = list(model_name = \"SVM_REGRESSION_MODEL\"))","summary(svm.mod4)"],"enabled":true,"result":{"startTime":1715286454575,"interpreter":"r.medium","endTime":1715286465986,"results":[{"message":"Warning message:\n\u201cThe created mining model SVM_REGRESSION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n\nCall:\nore.odmSVM(formula = YRS_RESIDENCE ~ ., data = CUST_DF, type = \"regression\", \n    complexity.factor = 0.1, conv.tolerance = 1e-04, epsilon = 0.1, \n    kernel.function = \"gaussian\", odm.settings = list(model_name = \"SVM_REGRESSION_MODEL\"))\n\nSettings: \n                                               value\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\nactive.learning                            al.enable\ncomplexity.factor                                0.1\nconv.tolerance                                 1e-04\nepsilon                                          0.1\nkernel.cache.size                              5e+07\nkernel.function                             gaussian\nnum.pivots                                       200\n\nResiduals: \n    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n-8.39500 -0.52050  0.00597 -0.04049  0.50700  6.58200 \n\nCoefficients: \n[1] No coefficients with gaussian kernel\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Make predictions using test data and display results","message":["%r","","ans <- predict(svm.mod4, CUST_DF.test, type=\"raw\", supplemental.cols=\"YRS_RESIDENCE\")"],"enabled":true,"result":{"startTime":1715286466067,"interpreter":"r.medium","endTime":1715286466297,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Model quality statistics on the TEST data","message":["%r","","MeanAbsoluteError <- (sum(abs(ans['YRS_RESIDENCE'] - ans['PREDICTION'])))/nrow(ans['YRS_RESIDENCE'])","MeanSquaredError <- (sum((ans['YRS_RESIDENCE'] - ans['PREDICTION'])^2))/nrow(ans['YRS_RESIDENCE'])","RootMeanSquaredError <- sqrt(MeanSquaredError)","","","cat('MAE: ', MeanAbsoluteError)","cat('MSE: ', MeanSquaredError)","cat('RMSE: ', RootMeanSquaredError)"],"enabled":true,"result":{"startTime":1715286466372,"interpreter":"r.medium","endTime":1715286467189,"results":[{"message":"MAE:  0.8060354MSE:  1.388552RMSE:  1.178368","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"image\":{\"height\":700,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"image","title":"Plot the predicted and actual years of residence","message":["%r","","x <- ore.pull(ans[['PREDICTION']])","y <- ore.pull(ans[['YRS_RESIDENCE']])","","dotplot(y~x, xlab = \"PREDICTION\", ylab = \"YRS_RESIDENCE\",","        panel = function(x,y) {","          panel.dotplot(x,y)","          panel.abline(0, 1, col=\"red\")","        },","        xlim=c(0,14.5),","        ylim=c(0,14.5),","        scales=list(y=list(tick.number=8), x=list(tick.number=8)),","        main=\"The prediction vs. actual value for years of residence\")","        "],"enabled":true,"result":{"startTime":1715286467275,"interpreter":"r.medium","endTime":1715286467709,"results":[{"message":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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Hcj0sLAjIiIiIyCVYto0rF6Nvn0REwNnZ6EDmSQW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bUP0bgaxQZzMSUREdUuk0ehydwjhde/e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\niMhEsNgRERERmQgWOyIiIiITwWJHREREZCJY7IiIiIhMBIsdER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the residuals","message":["%r","","xyplot(y-x~x, xlab = \"PREDICTION\", ylab = \"RESIDUAL\",","       panel = function(x,y) {","         panel.xyplot(x,y)","         panel.abline(0, 0, col=\"red\")","       },","       xlim=c(0,8.5),","       ylim=c(-7,8.5),","       scales=list(y=list(tick.number=8), x=list(tick.number=8)),","       main=\"Prediction of Years of Residence vs. Residuals\")","       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51fesWNHQRcrIiIiziUtTkRERORwA8DixYu7du1a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sbgX4GauhTIaLRUXM0U633SgYiEO9OX5OLb1ws0elYLqXoTGcjOCzf/S5Gd9TG5U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\n/TB+rsvtEHwjEKFQdnRGemyjxjImnWTfI1bf4P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QDvLGLXL2ZbQf+f/lV5uTzhCfy5m7WXgKoZk+enh/PKR361fnLND0yGQNsD+dINBFJ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","type":"IMG"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Materialize SVM_TEST_DATA for use in query","message":["%r","","ore.drop(table = 'CUST_ID.test')","ore.create(CUST_DF.test, table = 'CUST_ID.test')"],"enabled":true,"result":{"startTime":1715286468153,"interpreter":"r.medium","endTime":1715286468881,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display predictions with explanatory prediction details in SQL","message":["%sql","","SELECT CUST_ID,","round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(SVM_REGRESSION_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(SVM_REGRESSION_MODEL USING *) PD","      FROM SVM_TEST_DATA","      WHERE CUST_ID < 100015","      ORDER BY CUST_ID) OUT,","               XMLTABLE('/Details'","                        PASSING OUT.PD","                        COLUMNS ","                          \"Attribute1\" XMLType PATH 'Attribute[1]',","                          \"Attribute2\" XMLType PATH 'Attribute[2]',","                          \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","                          "],"enabled":true,"result":{"startTime":1715286468960,"interpreter":"sql.medium","endTime":1715286470562,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t4.137\t\"EDUCATION\" actualValue=\"Bach.\" weight=\".143\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".136\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".126\" \n100003\t4.221\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".29\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".255\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".048\" \n100005\t4.341\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".357\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".209\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".209\" \n100009\t3.835\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".037\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\"-.015\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"3000\" weight=\"-.02\" \n100010\t3.771\t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\"-.04\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"9000\" weight=\"-.04\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.146\" \n100012\t4.084\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".09\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".052\" \t\"EDUCATION\" actualValue=\"PhD\" weight=\".05\" \n100014\t4.151\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".293\" \t\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".136\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".072\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715287095942,"interpreter":"md.medium","endTime":1715287096005,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
diff --git a/machine-learning/notebooks/r/OML4R Statistical Functions.dsnb b/machine-learning/notebooks/r/OML4R Statistical Functions.dsnb
deleted file mode 100755
index c459c4ab..00000000
--- a/machine-learning/notebooks/r/OML4R Statistical Functions.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Statistical Functions","description":null,"readOnly":false,"type":"high","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715727897646,"interpreter":"md.high","endTime":1715727897705,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4R Statistical Functions","In this notebook, various statistical functions are highlighted using data from the SH schema through the OML4R transparency layer.","","Copyright (c) 2024 Oracle Corporation","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1715727918980,"interpreter":"md.high","endTime":1715727919048,"results":[{"message":"<h1 id=\"oml4r-statistical-functions\">OML4R Statistical Functions<\/h1>\n<p>In this notebook, various statistical functions are highlighted using data from the SH schema through the OML4R transparency layer.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":{"startTime":1715727897964,"interpreter":"md.high","endTime":1715727898025,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Load ORE library and get proxy object to CUSTOMER_INSURANCE_LTV table","message":["%r","","library(\"ORE\")","library(\"OREdplyr\")","options(ore.warn.order=FALSE)","","ore.sync(table='CUSTOMER_INSURANCE_LTV')","ore.attach()","","CUST_INSUR_LTV <- CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1715727898099,"interpreter":"r.high","endTime":1715727899798,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the dataset","message":["%r","","z.show(head(CUST_INSUR_LTV), 10)"],"enabled":true,"result":{"startTime":1715727899867,"interpreter":"r.high","endTime":1715727900049,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nMARRIED\tNY                        \t0\t3\t1000\t0\t3\t0\t68235\tCU1594                    \tM                         \tFirst-line Manager\t1000\tNorthEast\t1\t1000\tNo\t1\t0\t33\t53.08\t25858.75\t515\t1\t25\t1\tHIGH\tFERNANDO\t1\t1\tCRENSHAW\nSINGLE\tNY                        \t0\t2\t0\t0\t0\t1\t67208\tCU1595                    \tM                         \tAdministrator\t600\tNorthEast\t0\t0\tNo\t0\t0\t34\t53.06\t16202\t504\t1\t25\t0\tMEDIUM\tDANNIE\t0\t1\tGRAHAM\nSINGLE\tNY                        \t0\t2\t0\t0\t0\t0\t68100\tCU1597                    \tM                         \tAuthor\t1700\tNorthEast\t0\t0\tNo\t0\t0\t0\t53.06\t0\t505\t0\t25\t0\tLOW\tTYRELL\t0\t1\tMARIN\nDIVORCED\tNY                        \t0\t1\t4000\t3850\t1\t1\t52550\tCU1598                    \tF                         \tIT Staff\t2000\tNorthEast\t1\t1200\tYes\t3\t6\t35\t54.04\t20137.5\t2330\t1\t138\t5\tMEDIUM\tCHRISSY\t1\t1\tBLAND\nDIVORCED\tNY                        \t0\t2\t2650\t1800\t0\t0\t66711\tCU1600                    \tM                         \tFireman\t1000\tNorthEast\t1\t2650\tNo\t0\t0\t51\t53.06\t32777.75\t505\t3\t25\t0\tVERY HIGH\tRUBEN\t1\t1\tALFORD\nWIDOWED\tNY                        \t44774\t5\t5000\t4978\t5\t0\t67832\tCU1601                    \tF                         \tLaw Enforcement Officer\t1500\tNorthEast\t1\t1078\tNo\t3\t5\t61\t54.14\t25558\t68532\t1\t25\t5\tHIGH\tLETA\t1\t1\tCHAPMAN\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display median CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS","message":["%r","","res <- aggregate(CUST_INSUR_LTV$CREDIT_CARD_LIMITS, list(CUST_INSUR_LTV$MARITAL_STATUS), FUN=median)","z.show(res)"],"enabled":true,"result":{"startTime":1715727900128,"interpreter":"r.high","endTime":1715727900316,"results":[{"message":"Group.1\tx\nDIVORCED\t1000\nMARRIED\t1000\nOTHER\t1600\nSINGLE\t1000\nWIDOWED\t1300\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display median CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS in descending order","message":["%r","","median <- ore.sort(res,\"x\",reverse=TRUE)","z.show(head(median))"],"enabled":true,"result":{"startTime":1715727900394,"interpreter":"r.high","endTime":1715727900663,"results":[{"message":"Group.1\tx\nOTHER\t1600\nWIDOWED\t1300\nDIVORCED\t1000\nMARRIED\t1000\nSINGLE\t1000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display median(CUST_INSUR_LTV$CREDIT_CARD_LIMITS)","message":["%r","","median(CUST_INSUR_LTV$CREDIT_CARD_LIMITS)"],"enabled":true,"result":{"startTime":1715727900735,"interpreter":"r.high","endTime":1715727900921,"results":[{"message":"1000","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Estimating cumulative distribution function","message":["%r","","x <- ore.rank(data=CUST_INSUR_LTV, var='CUSTOMER_ID, CREDIT_CARD_LIMITS',nplus1=TRUE)","z.show(head(x, 10))"],"enabled":true,"result":{"startTime":1715727900994,"interpreter":"r.high","endTime":1715727901220,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t500\t600\t3\t1\t60504\t0.939341545998127\tM                         \tProgrammer/Developer\t0.0794611339240689\tWest\t1\t500\tNo\t4\t6\t25\t53.81\t18126\t5616\t2\t25\t4\tMEDIUM\tLUCIANO\t1\t1\tROBERTS\nMARRIED\tAL                        \t0\t4\t1772\t0\t6\t1\t60760\t0.954037893523521\tF                         \tPROF-4\t0.0794611339240689\tSouth\t1\t1772\tNo\t0\t0\t59\t52.31\t16090\t506\t1\t25\t2\tMEDIUM\tWENONA\t1\t1\tLOWERY\nMARRIED\tCA                        \t0\t2\t1500\t0\t0\t1\t65531\t0.956055039262301\tM                         \tAdministrative Assistant\t0.0794611339240689\tWest\t1\t1500\tNo\t1\t0\t64\t53.16\t28782.75\t1492\t1\t989\t2\tHIGH\tDONTE\t1\t1\tTERRY\nSINGLE\tNY                        \t0\t2\t0\t250\t0\t1\t61289\t0.961458108205461\tM                         \tProfessor\t0.0794611339240689\tNorthEast\t0\t0\tNo\t13\t0\t59\t53.52\t17222.25\t2823\t2\t2150\t4\tMEDIUM\tJOHNATHON\t0\t1\tCROUCH\nSINGLE\tNY                        \t0\t2\t0\t0\t0\t0\t65049\t0.982854261220373\tM                         \tNurse\t0.0794611339240689\tNorthEast\t0\t0\tNo\t0\t0\t0\t53.06\t0\t503\t0\t25\t0\tLOW\tMARSHALL\t0\t1\tHOSKINS\nDIVORCED\tNY                        \t0\t1\t2000\t2200\t1\t0\t59785\t0.983574670412794\tM                         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Driver\t0.0794611339240689\tWest\t1\t1400\tYes\t3\t1\t44\t53.37\t29435.25\t1277\t3\t54\t3\tHIGH\tFRED\t1\t1\tROJAS\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Rank 2 columns and report them as derived columns","message":["%r","","x <- ore.rank(data=CUST_INSUR_LTV, var='CUSTOMER_ID, CREDIT_CARD_LIMITS')","z.show(head(x), 10)"],"enabled":true,"result":{"startTime":1715727901297,"interpreter":"r.high","endTime":1715727901527,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t500\t600\t3\t1\t60504\t13039\tM                         \tProgrammer/Developer\t552\tWest\t1\t500\tNo\t4\t6\t25\t53.81\t18126\t5616\t2\t25\t4\tMEDIUM\tLUCIANO\t1\t1\tROBERTS\nMARRIED\tAL                        \t0\t4\t1772\t0\t6\t1\t60760\t13243\tF                         \tPROF-4\t552\tSouth\t1\t1772\tNo\t0\t0\t59\t52.31\t16090\t506\t1\t25\t2\tMEDIUM\tWENONA\t1\t1\tLOWERY\nMARRIED\tCA                        \t0\t2\t1500\t0\t0\t1\t65531\t13271\tM                         \tAdministrative Assistant\t552\tWest\t1\t1500\tNo\t1\t0\t64\t53.16\t28782.75\t1492\t1\t989\t2\tHIGH\tDONTE\t1\t1\tTERRY\nSINGLE\tNY                        \t0\t2\t0\t250\t0\t1\t61289\t13346\tM                         \tProfessor\t552\tNorthEast\t0\t0\tNo\t13\t0\t59\t53.52\t17222.25\t2823\t2\t2150\t4\tMEDIUM\tJOHNATHON\t0\t1\tCROUCH\nSINGLE\tNY                        \t0\t2\t0\t0\t0\t0\t65049\t13643\tM                         \tNurse\t552\tNorthEast\t0\t0\tNo\t0\t0\t0\t53.06\t0\t503\t0\t25\t0\tLOW\tMARSHALL\t0\t1\tHOSKINS\nDIVORCED\tNY                        \t0\t1\t2000\t2200\t1\t0\t59785\t13653\tM                         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900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 \n1103  653  805  692 1647 4062  120  139  164  129 2637   83   59   23   10  244 \n2100 2200 2300 2400 2500 2600 2700 2800 3000 3200 3500 3800 3900 4000 4200 4600 \n   3   10    7    2  463    3    3    2  177    1  244    3    1  171    2    1 \n5000 \n 217 \n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Perform One-Sample Kolmogorov-Smirnov Test","message":["%r","","value <- ks.test(CUST_INSUR_LTV$CREDIT_CARD_LIMIT, \"pexp\",rate=4)","(head(value))"],"enabled":true,"result":{"startTime":1715727901886,"interpreter":"r.high","endTime":1715727902210,"results":[{"message":"<dl>\n\t<dt>$statistic<\/dt>\n\t\t<dd><strong>D:<\/strong> 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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4R Time Series ESM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715301841802,"interpreter":"md.low","endTime":1715301841867,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data","","Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715301841946,"interpreter":"md.low","endTime":1715301842011,"results":[{"message":"<h2 id=\"forecasting-sales-using-exponential-smoothing-algorithm-for-time-series-data\">Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data<\/h2>\n<p>Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715301842093,"interpreter":"md.low","endTime":1715301842153,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>"],"enabled":true,"result":{"startTime":1715301842232,"interpreter":"md.low","endTime":1715301842291,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and create proxy object","message":["%r","","library(ORE)","options(ore.warn.order=FALSE)","ore.sync(query = c(ESM_SH_DATA = \"select TIME_ID, AMOUNT_SOLD from SH.SALES\"))","ore.attach()"],"enabled":true,"result":{"startTime":1715301842368,"interpreter":"r.low","endTime":1715301842491,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the records","message":["%r","","nrow(ESM_SH_DATA)"],"enabled":true,"result":{"startTime":1715301842564,"interpreter":"r.low","endTime":1715301842749,"results":[{"message":"918843","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the data","message":["%r","","z.show(head(ESM_SH_DATA))"],"enabled":true,"result":{"startTime":1715301842833,"interpreter":"r.low","endTime":1715301842954,"results":[{"message":"TIME_ID\tAMOUNT_SOLD\n1998-01-20\t1205.99\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n1998-04-05\t1250.25\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Options to use when creating Time Series models","message":["%md","","### Examples of possible setting overrides for ESM ","","If the user does not override the default settings, relevant settings are determined by the algorithm.","","A complete list of settings can be found in the product documentation:","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F98DEE85-4223-4F92-962D-8AF34CE887E1\" target=\"_blank\">Algorithm Settings<\/a>","","* Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is `EXSM_INTERVAL_QTR`.","> EXSM_INTERVAL = 'EXSM_INTERVAL_QTR'","* Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future","> EXSM_PREDICTION_STEP = 4","* Specify the type of exponential smoothing model to be used. EXSM_SIMPLE, EXSM_SIMPLE_MULT, EXSM_HOLT, EXSM_HOLT_DMP, EXSM_MUL_TRND, EXSM_MULTRD_DMP, EXSM_SEAS_ADD, EXSM_SEAS_MUL, EXSM_HW, EXSM_HW_DMP, EXSM_HW_ADDSEA, EXSM_DHW_ADDSEA, EXSM_HWMT, EXSM_HWMT_DMP'","> EXSM_MODEL = 'EXSM_HOLT'","* Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.","> EXSM_SEASONALITY = 4","* Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.     ","> EXSM_SETMISSING = 'EXSM_MISS_AUTO'","* This setting only applies and must be provided when the time column has datetime type. It specifies how to generate the value of the accumulated time series from the input time series. It can take the folling values: EXSM_ACCU_TOTAL, EXSM_ACCU_STD, EXSM_ACCU_MAX, EXSM_ACCU_MIN, EXSM_ACCU_AVG, EXSM_ACCU_MEDIAN, EXSM_ACCU_COUNT","> EXSM_ACCUMULATE = 'EXSM_ACCU_TOTAL'","* This setting specifies the desired optimization criterion. The optimization criterion is useful as a diagnostic for comparing models' fit to the same data.","> EXSM_OPT_CRITERION = 'EXSM_OPT_CRIT_LIK'","* This setting specifies the length of the window used in computing the error metric average mean square error (AMSE).","> EXSM_NMSE = 1"],"enabled":true,"result":{"startTime":1715301843032,"interpreter":"md.low","endTime":1715301843103,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-esm\">Examples of possible setting overrides for ESM<\/h3>\n<p>If the user does not override the default settings, relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the product documentation:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/2.0.0/oreug/oml4r-classes-that-provide-access-database-ml-algorithm1.html#GUID-F98DEE85-4223-4F92-962D-8AF34CE887E1\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is <code>EXSM_INTERVAL_QTR<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_INTERVAL = 'EXSM_INTERVAL_QTR'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_PREDICTION_STEP = 4<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the type of exponential smoothing model to be used. EXSM_SIMPLE, EXSM_SIMPLE_MULT, EXSM_HOLT, EXSM_HOLT_DMP, EXSM_MUL_TRND, EXSM_MULTRD_DMP, EXSM_SEAS_ADD, EXSM_SEAS_MUL, EXSM_HW, EXSM_HW_DMP, EXSM_HW_ADDSEA, EXSM_DHW_ADDSEA, EXSM_HWMT, EXSM_HWMT_DMP'<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_MODEL = 'EXSM_HOLT'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_SEASONALITY = 4<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_SETMISSING = 'EXSM_MISS_AUTO'<\/p>\n<\/blockquote>\n<ul>\n<li>This setting only applies and must be provided when the time column has datetime type. It specifies how to generate the value of the accumulated time series from the input time series. It can take the folling values: EXSM_ACCU_TOTAL, EXSM_ACCU_STD, EXSM_ACCU_MAX, EXSM_ACCU_MIN, EXSM_ACCU_AVG, EXSM_ACCU_MEDIAN, EXSM_ACCU_COUNT<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_ACCUMULATE = 'EXSM_ACCU_TOTAL'<\/p>\n<\/blockquote>\n<ul>\n<li>This setting specifies the desired optimization criterion. The optimization criterion is useful as a diagnostic for comparing models' fit to the same data.<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_OPT_CRITERION = 'EXSM_OPT_CRIT_LIK'<\/p>\n<\/blockquote>\n<ul>\n<li>This setting specifies the length of the window used in computing the error metric average mean square error (AMSE).<\/li>\n<\/ul>\n<blockquote>\n<p>EXSM_NMSE = 1<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a Holt-Winters Model using quarterly setting","message":["%r","","ore.drop(model = 'ESM_SALES_FORECAST_1')","","settings = list(EXSM_INTERVAL='EXSM_INTERVAL_QTR',","                EXSM_PREDICTION_STEP=4,","                MODEL_NAME='ESM_SALES_FORECAST_1',","                case_id_column_name = \"TIME_ID\")","","MOD <- ore.odmESM(AMOUNT_SOLD~.,","                  ESM_SH_DATA, ","                  odm.settings= settings)","                  "],"enabled":true,"result":{"startTime":1715301843185,"interpreter":"r.low","endTime":1715301845684,"results":[{"message":"Warning message:\n\u201cThe created mining model ESM_SALES_FORECAST_1 is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a Holt-Winters Model with additional settings","message":["%r","","ore.drop(model = 'ESM_SALES_FORECAST_1')","","settings = list(EXSM_INTERVAL = 'EXSM_INTERVAL_QTR',","                EXSM_PREDICTION_STEP = 4,","                EXSM_MODEL = 'EXSM_HW',","                EXSM_SEASONALITY = 4,","                EXSM_ACCUMULATE = 'EXSM_ACCU_TOTAL',","                MODEL_NAME='ESM_SALES_FORECAST_1',","                case_id_column_name = \"TIME_ID\")","","MOD <- ore.odmESM(AMOUNT_SOLD~.,","                  ESM_SH_DATA, ","                  odm.settings= settings)"],"enabled":true,"result":{"startTime":1715301845762,"interpreter":"r.low","endTime":1715301848062,"results":[{"message":"Warning message:\n\u201cThe created mining model ESM_SALES_FORECAST_1 is explicitly named. ORE does not manage this mining model's life cycle.\u201d\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Review the details from the model proxy object","message":["%r","","summary(MOD)"],"enabled":true,"result":{"startTime":1715301848140,"interpreter":"r.low","endTime":1715301848291,"results":[{"message":"\nCall:\nore.odmESM(formula = AMOUNT_SOLD ~ ., data = ESM_SH_DATA, odm.settings = settings)\n\nSettings: \n                                               value\naccumulate                                accu.total\nconfidence.level                                 .95\ninterval                                interval.qtr\nmodel                                        winters\nnmse                                               3\noptimization.crit                       opt.crit.lik\nprediction.step                                    4\nseasonality                                        4\nsetmissing                                 miss.auto\nodms.details                             odms.enable\nodms.missing.value.treatment odms.missing.value.auto\nodms.sampling                  odms.sampling.disable\nprep.auto                                         ON\n\nPredictions: \n      CASE_ID   VALUE PREDICTION   LOWER    UPPER\n1  1998-01-01 6480684    6402751      NA       NA\n2  1998-04-01 5593994    5868258      NA       NA\n3  1998-07-01 6071823    6205873      NA       NA\n4  1998-10-01 5937414    5978511      NA       NA\n5  1999-01-01 6093747    6223665      NA       NA\n6  1999-04-01 4925472    5535877      NA       NA\n7  1999-07-01 5827050    5515472      NA       NA\n8  1999-10-01 5373679    5601171      NA       NA\n9  2000-01-01 5984889    5644292      NA       NA\n10 2000-04-01 5371731    5317402      NA       NA\n11 2000-07-01 6121239    5845169      NA       NA\n12 2000-10-01 6287647    5920688      NA       NA\n13 2001-01-01 6547097    6500126      NA       NA\n14 2001-04-01 6922468    5930069      NA       NA\n15 2001-07-01 7195999    7376867      NA       NA\n16 2001-10-01 7470898    7154088      NA       NA\n17 2002-01-01      NA    7819637 6484414  9154860\n18 2002-04-01      NA    7151013 5591487  8710540\n19 2002-07-01      NA    7907806 5843738  9971875\n20 2002-10-01      NA    7821557 5462865 10180250","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View forecast AMOUNT_SOLD values with confidence intervals","message":["%r","","modelDetails <- summary(MOD)","","PRED <- modelDetails$predictions"," ","z.show(ore.sort(PRED, by='CASE_ID', 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End of Script"],"enabled":true,"result":{"startTime":1715301848885,"interpreter":"md.low","endTime":1715301848952,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML Batch Scoring REST","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Batch Scoring REST","","On Oracle Autonomous Database, data scientists can build machine learning models that reside in the database where, for example, they can be used immediately from SQL queries for scoring data, both in batch and for real-time applications. Oracle Machine Learning Services (OML Services) enables deploying those same models through REST endpoints hosted through Oracle Autonomous Database to enable both real-time, and now batch, scoring.","","The batch scoring capability complements OML Services scoring functionality for singleton and small batch scoring using synchronous REST APIs, where input data is provided as part of the payload and the output was returned in the response. Using asynchronous requests and specified as tables or views through Autonomous data, user can score large volume data. Both the data source and scoring results remain in the user's schema and scoring occurs directly in the database for optimal efficiency. ","","OML Services supports batch scoring for regression, classification, clustering, and feature extraction.","","### **Batch Scoring workflow**","","In this notebook we highlight the following features of batch scoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a batch scoring job","* View the details and output of the batch scoring job","* Update, disable, and delete a batch scoring job","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-batch-scoring-examples.html\" target=\"_blank\">OML Batch Scoring<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Website<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demonstration<\/a>"],"enabled":true,"result":{"startTime":1715376485714,"interpreter":"md.low","endTime":1715376485787,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-batch-scoring-examples.html\" target=\"_blank\">OML Batch Scoring<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Website<\/a><\/li>\n<li><a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demonstration<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run batch scoring jobs through a REST interface via OML Services.","","If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become aquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" onclick=\"return ! window.open('https://asktom.oracle.com/pls/apex/asktom.search?oh=13662');\">OML Services Lab<\/a>","","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' \\","           --header 'Accept: application/json' \\","           -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\",\"password\":\"'<yourpassword>'\"}' ","            \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning user management cloud service instance"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a batch scoring job ","message":["%md","","Now that you have an access token, you can send a request to OML Services to create a batch scoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a **POST** request. Below is a listing of the parameters that can be specified for a batch scoring job, divided into those that are required and those that are optional. ","","**Required**:","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, which is MODEL_SCORING for batch scoring jobs. ","* _inputData_ is the name of the table or view to read input for the batch scoring job.","* _outputData_ is the results table in the format {jobID}_{outputData} storing the batch scoring results.","* _modelId_ is the ID for the model used for scoring.","* _supplementalColumnNames_ is an array of columns from the input table or view that is used to identify rows in the output table.","","**Optional**:","* _jobStartDate_ is the start date and time for the job run. If not specified, the job runs immediately.","* _jobEndDate_ is the end date and time for the job run.","* _disableJob_ is a flag to disable the job at submission. If not set, the the job is enabled at submission.","* _maxRuns_ is the number of times the job will run according to the schedule.","* _inputSchemaName_ is the database schema that owns the input table or view. If not specified, the input schema will be the same as the user that obtained the token.","* _outputSchemaName_ is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.","* _jobDescription_ is a description of the job provided by the user.","* _jobServiceLevel_ is the database service level for the job, which can be LOW, MEDIUM, or HIGH. ","* _recompute_ is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _topN_ filters the results for classification by returning the highest N probabilities, or for clustering, returning the N most probable cluster assignments.","* _topNDetails_ provide prediction details contributing to the probability scores.","* _recompute_ is a flag to replace the results table specified by outputData."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Initiate a batch scoring job","","Batch scoring jobs are initiated by sending a **POST** request to the */omlmod/v1/jobs* endpoint. The details for batch scoring are specified in **jobProperties**, including:","* batch scoring job name and type","* ID of the model used for scoring","* table name for storing the batch scoring job results","* supplemental column names to return with the scoring results to identify rows","* whether to replace the results table with each job run"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Regression","","Here is an example of a batch scoring job submission using a regression model. To start, you will need the model ID for the model used for scoring. You can get the model ID by sending a **GET** request to the deployment endpoint and specifying the model URI. The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client. ","","This model was trained on the CUSTOMERS360 dataset to predict `YRS_RESIDENCE`, the model URI is `REGR_MOD`, and the model ID is `074f5554-fc75-4647-9392-a376056c1f03`.","","In addition to the job name and type, we specify an hourly job schedule with 50 maximum runs, the model ID and input data for scoring, and an output table to store the job results. We also return the target column *YRS_RESIDENCE* and the customer ID column *CUST_ID* with the predictions to identify rows, and set recompute to *true* to replace the results table with each run.","","Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems, to extract relevant components from the response."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"$<OML service URL>/omlmod/v1/deployment/REGR_MOD\" \\","       --header \"Authorization: Bearer $<token>\" | jq '.modelId'",""," returns: \"074f5554-fc75-4647-9392-a376056c1f03\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Submit batch scoring job ","message":["","$ curl -i -X POST \"<OML Serivce URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-23T20:05:22Z\",                   # job start date and time ","\t\t\t \"jobEndDate\":   \"2023-03-25T20:05:22Z\"                    # job end date and time","             \"repeatInterval\": \"FREQ=HOURLY\",                          # job frequency         ","             \"maxRuns\": \"50\"                                           # max runs within the schedule                       ","         },","         \"jobProperties\": {","             \"jobName\": \"REG_PRED1\",                                   # job name","             \"jobType\": \"MODEL_SCORING\",                               # job type; MODEL_SCORING","             \"modelId\": \"074f5554-fc75-4647-9392-a376056c1f03\",\t\t   # ID of the model used for scoring\t ","             \"inputData\": \"CUSTOMERS360\",                              # table or view to read input for the batch scoring job","             \"outputData\": \"REG_PRED_OUT1\",                            # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\",\"YRS_RESIDENCE\"],   # array of columns from the input data used to identify rows in the output","             \"topNDetails\": 2,                                         # flag to return the top N most important predictors and their weight","\t\t\t \"recompute\": \"true\"                                       # flag to determine whether to overwrite the results table","\t\t}","     }' | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"TextBox\",\"name\":\"omlservice\",\"displayName\":null,\"defaultValue\":\"\",\"argument\":null,\"isHidden\":false,\"isProgrammatic\":false},{\"type\":\"TextBox\",\"name\":\"token\",\"displayName\":null,\"defaultValue\":\"\",\"argument\":null,\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Job ID returned from job submssion","message":["%md","","","When your job is successfully submitted, you will receive a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"jobId\": \"OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%2421FACAC2_B345_4485_B287_DBDADC3A1BC0\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the details of the submitted job","message":["%md","","You can view the details of your submitted regression batch scoring job with a GET request to the */omlmod/v1/jobs/{jobID}* endpoint, where **jobid** is the ID provided in response to the successful submission of your batch scoring job in the previous step. The returned details include the parameters provided when the job was submitted  and the default values for the optional parameters not specified upon submission."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL invocation to view job details","message":["","$ export jobid='OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0'  # save job ID to variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<job ID>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T20:05:22Z\",","      \"repeatInterval\": \"FREQ=HOURLY\",","      \"jobEndDate\": \"2023-03-25T20:05:22Z\",","      \"maxRuns\": 50","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"REG_PRED_OUT1\",","      \"jobDescription\": null,","      \"jobName\": \"REG_PRED1\",","      \"disableJob\": false,","      \"jobServiceLevel\": null,","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\",","        \"YRS_RESIDENCE\"","      ],","      \"topN\": null,","      \"recompute\": true,","      \"modelId\": \"074f5554-fc75-4647-9392-a376056c1f03\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T20:04:06.157516Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%2421FACAC2_B345_4485_B287_DBDADC3A1BC0\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T20:05:22Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobID}\\_{outputData}*. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example. ","","The regression predictions are returned in the *OML$PREDICTION* column along with the supplemental column names provided by the user. OML$DETAIL1 contains the most important column in prediction details, and OML$DETAILWEIGHT1 contains weight of the most important column in prediction details. OML$DETAIL2 contains the second most important column in prediction details, and OML$DETAILWEIGHT2 contains weight of the second most important column in prediction details. ","","","For help understanding the regression batch scoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-regr-output-table-rel.html<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$21FACAC2_B345_4485_B287_DBDADC3A1BC0_REG_PRED_OUT1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Classification","Here is an example of a classification batch scoring job submission using a neural network model with model URI `NN_MOD` and ID `35a97c7b-0ff4-4940-96f5-bfb29f64d223`. The model was trained on the CUSTOMERS360 dataset to predict the customers most likely to respond positively to an affinity card loyalty program. ","","In addition to the job name and type, we specify a daily job schedule with 4 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to *true* to replace the output table with each run. We return the target column *AFFINITY_CARD* and the customer ID column *CUST_ID* with the predictions to identify rows, and the top 2 predictions wiith details contributing to those predictions. ","","We also set the database service level to *MEDIUM*. Oracle Autonomous Database supports three service levels: HIGH, MEDIUM, and LOW. LOW is the default, which results in no parallelism, but you can change your database service level to MEDIUM or HIGH. The HIGH service levels gives the greatest parallelism but significantly limits the number of concurrent jobs, while the MEDIUM level enables some parallelism but allows greater concurrency for job processing. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"<OML Service URL>/omlmod/v1/deployment/NN_MOD\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'",""," returns: \"35a97c7b-0ff4-4940-96f5-bfb29f64d223\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submit batch scoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-13T21:10:23Z\",                  # job start date and time","\t\t\t \"jobEndDate\":   \"2023-03-17T21:10:23Z\",                  # job end date and time","             \"repeatInterval\": \"FREQ=DAILY\",                          # job frequency","             \"maxRuns\": \"4\"                                           # max runs within the schedule                     ","         },","         \"jobProperties\": {","             \"jobName\": \"NN_MOD1\",                                    # job name","             \"jobType\": \"MODEL_SCORING\",                              # job type; MODEL_SCORING","             \"modelId\": \"35a97c7b-0ff4-4940-96f5-bfb29f64d223\",\t\t  # ID of the model used for scoring\t ","             \"inputData\": \"CUSTOMERS360\",                             # table or view to read input for the batch scoring job","             \"outputData\": \"CLASS_PRED1\",                             # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\", \"AFFINITY_CARD\"], # array of columns from the input data used to identify rows in the output","             \"jobServiceLevel\": \"MEDIUM\",                             # The database service level for the job. The default is LOW.","\t\t\t \"topN\": 2,                                               # filters the results by returning the highest N probabilities","\t\t\t \"topNDetails\": 2,                                        # provides prediction details contributing to the probability scores","\t\t\t \"recompute\": \"true\"                                      # flag to determine whether to overwrite the result table ","\t\t}"," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","returns:","","{","  \"jobId\": \"OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24385E413C_7219_41A4_B5A2_DC6B91B3DD8E\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the details of the submitted job","message":["%md","","You can view the details of your submitted batch scoring job with a GET request to the */omlmod/v1/jobs/{jobID}* endpoint, where **jobId** is the ID provided in response to the successful submission of your batch scoring job in the previous step. The returned details include the parameters provided when the job was submitted  and the default values for the optional parameters not specified upon submission."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View the batch scoring job details","message":["","$ export jobid='OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E'   # save job ID to variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","Returns:","","{","  \"jobId\": \"OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T20:32:35Z\",","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2023-03-27T20:32:35Z\",","      \"maxRuns\": 4","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"CLASS_PRED1\",","      \"jobDescription\": null,","      \"jobName\": \"NN_MOD1\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"MEDIUM\",","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\",","        \"AFFINITY_CARD\"","      ],","      \"topN\": 2,","      \"recompute\": true,","      \"modelId\": \"35a97c7b-0ff4-4940-96f5-bfb29f64d223\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T20:28:55.233217Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24385E413C_7219_41A4_B5A2_DC6B91B3DD8E\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T20:32:35Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. ","","The paragraph below queries the output table associated with this example.  The supplemental column names provided by the user are returned, along with the classification predictions. The *OML$PREDICTION1* column contains the class with highest probability and the *OML$PROBABILITY1* column contains the probability for the top class. *OML$DETAIL1* contains the most important column in the prediction details, while the *OML$PROBABILITY2* column contains the probability for the second class, and *OML$DETAIL2* contains the second most import column in the prediction details. ","","For help understanding the classification batch scoring output table, refer to the documentation:","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-class-output-table-rel.html<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$385E413C_7219_41A4_B5A2_DC6B91B3DD8E_CLASS_PRED1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","","### Clustering","","Here is an example of a K-Means clustering batch scoring job submission with model URI `KM_CLUS_MOD` and ID `4632a963-4340-4ca5-ba30-aff77a4b857a`. The model uses the CUSTOMERS360 dataset to predict the cluster membership by customer. ","","In addition to the job name and type, we specify a daily job schedule with 4 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to *true* to replace the results table with each run. We also return the customer ID column *CUST_ID* with the predictions to identify cluster membership, request the top 2 most probable cluster assignments and details contributing to the cluster assignment predictions."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"<OML Service URL>/omlmod/v1/deployment/KM_CLUS_MOD\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'",""," returns: \"4632a963-4340-4ca5-ba30-aff77a4b857a\""],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submit batch scoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-23T20:58:46Z\",                  # job start date and time","\t\t\t \"jobEndDate\":   \"2023-03-28T20:58:46Z\",                  # job end date and time","             \"repeatInterval\": \"FREQ=DAILY\",                          # job frequency          ","             \"maxRuns\": \"4\"                                           # max runs within the schedule                 ","         },","         \"jobProperties\": {","             \"jobName\": \"KM_CLUS_MOD1\",                               # job name","             \"jobType\": \"MODEL_SCORING\",                              # job type; MODEL_SCORING","             \"modelId\": \"4632a963-4340-4ca5-ba30-aff77a4b857a\",\t\t  # ID of the model used for scoring\t ","             \"inputData\": \"CUSTOMERS360\",                             # table or view to read input for the batch scoring job","             \"outputData\": \"KM_CLUS_PRED1\",                           # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\"],                  # array of columns from the input data used to identify rows in the output","\t\t\t \"topN\": 2,                                               # filters the results by returning the N most probable cluster assignments","\t\t\t \"topNDetails\": 2,                                        # provides prediction details contributing to the probability scores","\t\t\t \"recompute\": \"true\"                                      # flag to determine whether to overwrite the result table ","\t\t}","     }' | jq\t   "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","returns:","","{","  \"jobId\": \"OML$65977733_A951_42CD_9E3D_5E800604EBDB\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/jobs/OML%2465977733_A951_42CD_9E3D_5E800604EBDB\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View the batch scoring job details","message":["","$ export jobid='OML$65977733_A951_42CD_9E3D_5E800604EBDB'    # save job ID to variable","","$ curl -X GET \"<omlservice>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$65977733_A951_42CD_9E3D_5E800604EBDB\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T20:58:46Z\",","      \"repeatInterval\": \"FREQ=DAILY\",","      \"jobEndDate\": \"2023-03-28T20:58:46Z\",","      \"maxRuns\": 4","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"KM_CLUS_PRED1\",","      \"jobDescription\": null,","      \"jobName\": \"KM_CLUS_MOD1\",","      \"disableJob\": false,","      \"jobServiceLevel\": null,","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\"","      ],","      \"topN\": 2,","      \"recompute\": true,","      \"modelId\": \"4632a963-4340-4ca5-ba30-aff77a4b857a\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T20:54:53.248736Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%2465977733_A951_42CD_9E3D_5E800604EBDB\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T20:58:46Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.","","The paragraph below queries the output table associated with this example. The supplemental column names provided by the user are returned, along with the cluster ID predictions. The *OML$CLUSTER_ID1* column contains the cluster with highest probability, and the *OML$DETAIL1* column contains the most important column in the prediction details. *OML$CLUSTER_ID2* contains the cluster with 2nd highest probability, and the *OML$DETAIL2* column contains the 2nd most important column in the prediction details.","","","For help understanding the clustering batch scoring output table, refer to the documentation:","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-clustering-output-table.html<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$65977733_A951_42CD_9E3D_5E800604EBDB_KM_CLUS_PRED1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Feature Extraction","Here is an example of a feature extraction batch scoring job submission with model URI `NMF_MOD` and ID `555c4718-853b-4d56-8ef9-268fd87ac25b`.","","In addition to the job name and type, we specify a monthly job schedule with 2 maximum runs, the model ID and input data for scoring, an output table to store the job results, and set recompute to *true* to replace the results table with each run. We also specify the *HIGH* database service level, a set of supplemental columns from the input data to return with the results for row identification and request the top 2 predictions wiith details contributing to those predictions."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Get model ID","message":["","$ curl -X GET \"<omlservice>/omlmod/v1/deployment/NMF_MOD\" \\","       --header \"Authorization: Bearer <token>\" | jq '.modelId'",""," returns: \"555c4718-853b-4d56-8ef9-268fd87ac25b\""," "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Submit batch scoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-23T21:10:40Z\",                  # job start date and time","\t\t\t \"jobEndDate\":   \"2023-05-23T21:10:40Z\",                  # job end date and time","             \"repeatInterval\": \"FREQ=MONTHLY\",                        # job frequency         ","             \"maxRuns\": \"2\"                                           # max runs within the schedule                        ","         },","         \"jobProperties\": {","             \"jobName\": \"FE_MOD1\",                                    # job name","             \"jobType\": \"MODEL_SCORING\",                              # job type; MODEL_SCORING","             \"modelId\": \"555c4718-853b-4d56-8ef9-268fd87ac25b\",\t      # ID of the model used for scoring","             \"jobServiceLevel\": \"HIGH\",\t\t                          # database service level\t ","             \"inputData\": \"CUSTOMERS360\",                             # table or view to read input for the batch scoring job","             \"outputData\": \"FE_PRED1\",                                # output table to store the scoring results in the format {jobID}_{outputData}","             \"supplementalColumnNames\": [\"CUST_ID\",                   # array of columns from the input data used to identify rows in the output","                                        \"AFFINITY_CARD\", ","                                        \"CUST_CREDIT_LIMIT\",","                                        \"CUST_GENDER\"],","\t\t\t \"topN\": 2,                                               # filters the results by returning the top N features with the highest probability","\t\t\t \"topNDetails\": 2,                                        # provides prediction details contributing to the probability scores","\t\t\t \"recompute\": \"true\"                                      # flag to determine whether to overwrite the results table","\t\t}","     }' | jq\t  ","     "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","Returns:","","{","  \"jobId\": \"OML$D66F5559_CA57_4B85_B832_A21A26660725\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24D66F5559_CA57_4B85_B832_A21A26660725\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"View batch scoring job details","message":["","$ export jobid='OML$D66F5559_CA57_4B85_B832_A21A26660725'  # save job ID to variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$D66F5559_CA57_4B85_B832_A21A26660725\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-23T21:10:40Z\",","      \"repeatInterval\": \"FREQ=MONTHLY\",","      \"jobEndDate\": \"2023-05-23T21:10:40Z\",","      \"maxRuns\": 2","    },","    \"jobProperties\": {","      \"jobType\": \"MODEL_SCORING\",","      \"inputSchemaName\": null,","      \"outputSchemaName\": null,","      \"outputData\": \"FE_PRED1\",","      \"jobDescription\": null,","      \"jobName\": \"FE_MOD1\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"HIGH\",","      \"inputData\": \"CUSTOMERS360\",","      \"supplementalColumnNames\": [","        \"CUST_ID\",","        \"AFFINITY_CARD\",","        \"CUST_CREDIT_LIMIT\",","        \"CUST_GENDER\"","      ],","      \"topN\": 2,","      \"recompute\": true,","      \"modelId\": \"555c4718-853b-4d56-8ef9-268fd87ac25b\",","      \"topNDetails\": 2","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-23T21:05:24.644571Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%24D66F5559_CA57_4B85_B832_A21A26660725\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-23T21:10:40Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"View the batch scoring job output","message":["%md","","Once your job has run, you can view the results in the table you specified in your job request with the **outputData** parameter. The full name of the table is {jobID}_{outputData}. You can check if your job is finished by sending a request to view its details. The paragraph below queries the output table associated with this example.","","The paragraph below queries the output table associated with this example. The supplemental column names provided by the user are returned, along with the predicted features. The *OML$FEATURE_ID1* column contains the features with highest probability and the *OML$WEIGHT1* column contains the the probability for the top weighted feature. *OML$FEATURE_ID2* contains the feature with 2nd highest probability, and the *OML$WEIGHT2* column contains the probability for the 2nd highest feature.","","For help understanding the feature extraction batch scoring output table, refer to the documentation: ","","<a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-feature-extraction-output-table.html<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT * FROM OML$D66F5559_CA57_4B85_B832_A21A26660725_FE_PRED1","ORDER BY CUST_ID","FETCH FIRST 5 ROWS ONLY;"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Perform actions, update, and delete batch scoring jobs"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Perform an action on a batch scoring job","message":["%md","","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","For example, when your job has been successfuly submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","* _DISABLE_ disables the job. After an enabled job is disabled, it no longer runs according to its schedule.","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler runs the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example action that updates the job status to **DISABLED**. A successfully submitted job receives a 204 response with no body. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Update a batch scoring job","message":["%md","","After a batch scoring job is submitted, you have the option to update the job schedule. You can do this by sending a POST request with the updated options to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can updated for a batch scoring job:","","* _jobStartDate_","* _jobEndDate_","* _repeatInterval_","* _maxRuns_","","In the example request below, we update these fields to differ from the original parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {  ","\t\t     \"jobStartDate\": \"2023-03-28T21:00:00Z\",","\t\t\t \"jobEndDate\":   \"2023-03-30T21:00:00Z\",","             \"repeatInterval\": \"FREQ=DAILY\",                  ","             \"maxRuns\": \"2\"                                ","         }","\t   }' | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Delete a batch scoring job","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","$ curl -X DELETE \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"To reproduce the examples in this notebook...","message":["%md","---","## Reproducing these examples","---","If you would like to reproduce the examples in this notebook, the paragraph below creates the required database table, *CUSTOMERS360*.  This table is created by selecting columns from the *CUSTOMERS* and *SUPPLEMENTARY_DEMOGRAPHICS* tables which reside in the SH schema.","","The models used in this notebook were deployed using the OML Services REST API. You can download these models in serialized format here:","","[Regression model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/REGR_MOD.mod)","[Classification model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/NN_MOD.mod)","[K-Means clustering model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/KM_CLUS_MOD.mod)","[Feature Extraction (NMF) model](https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML-Resources/o/NMF_MOD.mod)","","Refer to the OML Services REST API documentation for detailed instructions on deploying models: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html<\/a>."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create the customers 360 table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the CUSTOMERS360 table","message":["%sql","","SELECT * FROM CUSTOMERS360 ","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML Data Drift Monitoring REST","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Monitoring - Data Drift Detection","","Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines. Identifying and understanding data drift is an essential part of maintaining the health of any organization's data in supporting data-driven decision making and business-critical applications. ","","In addition, understanding how data evolves over time is a key input when evaluating how your machine learning models are able to meet business objectives. As such, data monitoring is complementary to model monitoring, where understanding changes in data used for prediction helps understand changes in a model's efficacy. ","","Data drift can be quantified using basic descriptive statistics. For numeric columns these include:","* Mean","* Standard deviation","* Range (minimum, maximum)","* Number of nulls","","For categorical columns these include:","* Number of unique values","* Number of nulls","","","### **Data monitoring workflow**","","In this notebook we highlight the following features of data monitoring through the REST interface:","","* Authenticate the database user and obtain a token","* Create a data monitoring job","* View the details and output of the data monitoring job","* Update, disable, and delete a data monitoring job","","The dataset used in this example is from the UCI Machine Learning Repository. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715382928921,"interpreter":"md.low","endTime":1715382929109,"results":[{"message":"<h1 id=\"oml-monitoring---data-drift-detection\">OML Monitoring - Data Drift Detection<\/h1>\n<p>Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines. Identifying and understanding data drift is an essential part of maintaining the health of any organization's data in supporting data-driven decision making and business-critical applications.<\/p>\n<p>In addition, understanding how data evolves over time is a key input when evaluating how your machine learning models are able to meet business objectives. As such, data monitoring is complementary to model monitoring, where understanding changes in data used for prediction helps understand changes in a model's efficacy.<\/p>\n<p>Data drift can be quantified using basic descriptive statistics. For numeric columns these include:<\/p>\n<ul>\n<li>Mean<\/li>\n<li>Standard deviation<\/li>\n<li>Range (minimum, maximum)<\/li>\n<li>Number of nulls<\/li>\n<\/ul>\n<p>For categorical columns these include:<\/p>\n<ul>\n<li>Number of unique values<\/li>\n<li>Number of nulls<\/li>\n<\/ul>\n<h3 id=\"data-monitoring-workflow\"><strong>Data monitoring workflow<\/strong><\/h3>\n<p>In this notebook we highlight the following features of data monitoring through the REST interface:<\/p>\n<ul>\n<li>Authenticate the database user and obtain a token<\/li>\n<li>Create a data monitoring job<\/li>\n<li>View the details and output of the data monitoring job<\/li>\n<li>Update, disable, and delete a data monitoring job<\/li>\n<\/ul>\n<p>The dataset used in this example is from the UCI Machine Learning Repository. It contains measurements of household electric power consumption in one household near Paris with a one-minute sampling rate over a period of almost 4 years.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring.html\" target=\"_blank\">OML Data Monitoring Documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/index.html\" target=\"_blank\">OML Services REST API Documentation<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" target=\"_blank\">OML Services Demo<\/a>","","For model monitoring, refer to the Model Monitoring notebook in the template notebook examples."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Before you begin","message":["%md","","This notebook walks through the steps to run a data monitoring job through a REST interface via OML Services.","","If you are unfamiliar with OML Services and its REST interface, the links above as well as the following guided lab are a great way to become aquainted: <a href=\"https://asktom.oracle.com/pls/apex/asktom.search?oh=13662\" onclick=\"return ! window.open('https://asktom.oracle.com/pls/apex/asktom.search?oh=13662');\">OML Services Lab<\/a>","","The **cURL** commands provided in this notebook are for illustrative purposes only. To run REST calls, use **cURL** in a Linux or Mac terminal or another REST client such as Postman."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 1: Get an access token","message":["%md","","To send requests to OML Services, you must obtain an authentication token by using your Oracle Machine Learning account credentials. To authenticate and obtain a token, use **cURL** with the **-d** option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint */oauth2/v1/token*. ","","    $ curl -X POST --header 'Content-Type: application/json' ","                   --header 'Accept: application/json' ","                   -d '{\"grant_type\":\"password\", \"username\":\"'<yourusername>'\", \"password\":\"'<yourpassword>'\"}' ","                   \"<OML service URL>/omlusers/api/oauth2/v1/token\"","    ","where   ","","* _-X POST_ specifies to use a POST request when communicating with the HTTP server","* _--header_ defines the headers required for the request (application/json)","* _-d <data>_ sends the username and password authentication credentials as data in a POST request to the HTTP server","* _Content-Type_ and _Accept_ define the response format (JSON)","* _yourusername_ is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role","* _yourpassword_ is the password for the user name","* _OML service URL_ is the REST server portion of the Oracle Machine Learning user management cloud service instance"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 2: Create a data monitoring job","message":["%md","","Now that you have an access token, you can send a request to OML Services to create a data monitoring job. This request is sent to the */omlmod/v1/jobs* endpoint as a POST request. Below is a listing of the parameters that can be specified for a data monitoring job, divided into those that are required and those that are optional. ","","*Required:*","* _jobName_ specifies the name of the submitted job.","* _jobType_ specifies the type of job to be run, and is set to DATA_MONITORING for data monitoring jobs. ","* _outputData_ is the output data identifier. The results of the job will be written to a table named {jobID}_{outputData}.","* _baselineData_ is a table or view name that contains baseline data to monitor. At least 100 rows are required for data monitoring, otherwise analysis is skipped.","* _newData_ is a table or view name with new data to be compared against the baseline. At least 100 rows per period are required for data monitoring, otherwise analysis is skipped. ","","","*Optional:*","* _disableJob_ is a flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.","* _inputSchemaName_ is the database schema that owns the input table/view. If not specified, the input schema will be the same as the username in the request token.","* _outputSchemaName_ is the database schema that owns the output table. If not specified the output schema will be the same as the input schema.","* _jobDescription_ is a description of the job.","* _jobServiceLevel_ is the service level for the job, which can be LOW, MEDIUM, or HIGH.","* _timeColumn_ is the name of a date or timestamp column in the new data. If not provided the entire newData is treated as one period.","* _startDate_ is the start date and time of monitoring in newData. If not provided the earliest date and time is used. ","* _endDate_ is the end date and time of the monitoring in newData. If not provided the last date and time is used.","* _frequency_ indicates the time window unit on which the monitoring is performed on the new data. This can be \"day\", \"week\", \"month\", or \"year\". If not provided, the whole \"new\" data is used as a single time period. ","* _threshold_ is the threshold to trigger a drift alert. The default is 0.7.","* _recompute_ is a flag whether to update already computed periods. The default is False, meaning only time periods not present in the output result table will be computed. ","* _caseidColumn_ is a case identifier column in the baseline and new data. Providing it improves the reproducability of results. ","* _featureList_ is a list of features to monitor. The features need to be either numeric or categorical. The default maximum number of features in the list is 250. Monitoring will run on all features if the feature list is not specified, and and error is returned if there are more than 250 features. ","* _anchorColumn_ is the anchor column in the baseline and new data used for bivariate analysis. The target column in supervised problems can be passed as the anchor column. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Example Data Monitoring job","message":["%md","","","Data monitoring jobs are initiated by sending a **POST** request to the */omlmod/v1/jobs* endpoint, and numeric and categorical features are supported. Monitoring will run on all features by default, or a list of features specified by the user, and there is a maximum of 250 features that can be monitored.","","The details for data monitoring are specified in **jobProperties**, including:","* data monitoring job name and type","* Autonomous Database service level","* table where the data monitoring details will be saved","* drift alert trigger threshold","* maximum number of runs","* baseline and new data to be used","* performance metric","","The optional start date and end date correspond to a DATE or TIMESTAMP column in the table or view denoted by **newData** and contained in the **timeColumn** field. When the start and end dates are not specified, the earliest and latest dates and times in the **timeColumn** are used. The job is submitted asynchronously, so it will run as scheduled and the results can be retrieved when the job completes.","","Here is an example of a data monitoring job request using these parameters. Note that we are using *jq*, a command-line JSON processor available on Linux and Mac OS systems to extract relevant components from the response."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"cURL command to initiate data monitoring job","message":["","$ curl -X POST \"<OML Service URL>/omlmod/v1/jobs\" \\","     --header \"Authorization: Bearer <token>\" \\","     --header 'Content-Type: application/json' \\","     --data '{","         \"jobSchedule\": {","             \"jobStartDate\": \"2023-03-24T20:30:26Z\",            # job start date and time ","             \"repeatInterval\": \"FREQ=HOURLY\",                   # job frequency","             \"jobEndDate\": \"2023-03-24T23:30:26Z\",              # job end date and time","             \"maxRuns\": \"10\"                                    # max runs within the schedule","         },","         \"jobProperties\": {","             \"jobName\": \"HouseholdPowerDataMonitoring\",         # job name","             \"jobType\": \"DATA_MONITORING\",                      # job type; DATA_MONITORING","             \"disableJob\": false,                               # flag to disable the job at submission","             \"outputData\": \"householdPowerConsumption\",         # table where the job results will be saved in the format {jobID}_{outputData}","             \"baselineData\": \"HOUSEHOLD_POWER_BASE\",            # table/view containing baseline data ","             \"newData\": \"HOUSEHOLD_POWER_NEW\",                  # table/view with new data to compare against baseline","             \"inputSchemaName\": \"OMLUSER\",                      # database schema that owns the input table/view","             \"outputSchemaName\": \"OMLUSER\",                     # database schema that owns the output table/view","             \"jobDescription\": \"Monitor household power\",       # job description","             \"jobServiceLevel\": \"LOW\",                          # Autonomous Database service level; either LOW, MEDIUM, or HIGH","             \"timeColumn\": \"DATES\",                             # date or timestamp column in newData","             \"startDate\": \"2008-01-01T00:00:00Z\",               # the start date of the monitoring in the new data","             \"endDate\": \"2010-11-26T00:00:00Z\",                 # the end date of the monitoring in the new data","             \"frequency\": \"Year\",                               # the time window unit on which monitoring is performed on the new data","             \"threshold\": 0.8,                                  # threshold to trigger drift alert","             \"recompute\": false,                                # flag to determine whether to replace the output table","             \"caseidColumn\": null,                              # case identifier column in the baseline and new data","             \"anchorColumn\": null,                              # anchor column for bivariate analysis","             \"featureList\": [                                   # features to perform data monitoring on","                \"GLOBAL_ACTIVE_POWER\",","                \"GLOBAL_REACTIVE_POWER\",","                \"VOLTAGE\",","                \"SUB_METERING_1\",","                \"SUB_METERING_2\",","                \"SUB_METERING_3\"","            ]","         }","     }' | jq","     "],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Job ID returned with successful job submission","message":["%md","","When your job is successfully submitted, you will recieve a response. Note the job ID, as you will use it to submit requests such as retrieving job details or performing an action on the job."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["","{","  \"jobId\": \"OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%247ABB6308_1664_4CB4_84B1_598A6EA599D1\"","    }","  ]","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 3: View the details of a submitted job","message":["%md","","You can now view the details of your submitted job by sending GET request with the job ID to the */omlmod/v1/jobs* endpoint, where **jobid** is the ID provided in response to the successful submission of your data monitoring job in the previous step."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Use the job ID to view job details","message":["","$ export jobid='OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1'  # save the job ID to a single-quoted variable","","$ curl -X GET \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["Returns:","","{","  \"jobId\": \"OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1\",","  \"jobRequest\": {","    \"jobSchedule\": {","      \"jobStartDate\": \"2023-03-24T20:30:26Z\",","      \"repeatInterval\": \"FREQ=HOURLY\",","      \"jobEndDate\": \"2023-03-24T23:30:26Z\",","      \"maxRuns\": 3","    },","    \"jobProperties\": {","      \"jobType\": \"DATA_MONITORING\",","      \"inputSchemaName\": \"OMLUSER\",","      \"outputSchemaName\": \"OMLUSER\",","      \"outputData\": \"householdPowerConsumption\",","      \"jobDescription\": \"Monitor household power\",","      \"jobName\": \"HouseholdPowerDataMonitoring\",","      \"disableJob\": false,","      \"jobServiceLevel\": \"LOW\",","      \"baselineData\": \"HOUSEHOLD_POWER_BASE\",","      \"newData\": \"HOUSEHOLD_POWER_NEW\",","      \"timeColumn\": \"DATES\",","      \"startDate\": \"2008-01-01T00:00:00Z\",","      \"endDate\": \"2010-11-26T00:00:00Z\",","      \"frequency\": \"Year\",","      \"threshold\": 0.8,","      \"recompute\": false,","      \"caseidColumn\": null,","      \"featureList\": [","        \"GLOBAL_ACTIVE_POWER\",","        \"GLOBAL_REACTIVE_POWER\",","        \"VOLTAGE\",","        \"SUB_METERING_1\",","        \"SUB_METERING_2\",","        \"SUB_METERING_3\"","      ],","      \"anchorColumn\": null","    }","  },","  \"jobStatus\": \"CREATED\",","  \"dateSubmitted\": \"2023-03-24T20:23:31.53651Z\",","  \"links\": [","    {","      \"rel\": \"self\",","      \"href\": \"<OML Service URL>/omlmod/v1/jobs/OML%247ABB6308_1664_4CB4_84B1_598A6EA599D1\"","    }","  ],","  \"jobFlags\": [],","  \"state\": \"SCHEDULED\",","  \"enabled\": true,","  \"runCount\": 0,","  \"nextRunDate\": \"2023-03-24T20:30:26Z\"","}"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 4: Edit a data monitoring job (optional)","message":["%md","","After an asynchronous job is submitted, you have the option to update the job. You can do this by sending a POST request with the updated options in the *updateProperties* field to the */omlmod/v1/jobs/{jobid}* endpoint. The following parameters can updated for a data monitoring job:","","* _startDate_","* _endDate_","* _threshold_","* _recompute_","* _featureList_","","In the example request below, we update some of these fields to differ from the origional parameters we set with our initial request. When you successfully submit your job you will receive a 204 response with no body."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Update the properties of a data monitoring job","message":["","$ curl -i -X POST \"<OML Service URL>omlmod/v1/jobs/<jobid>\" \\","         --header \"Authorization: Bearer <token>\" \\","         --header 'Content-Type: application/json' \\","         --data '{","            \"updateProperties\": {","                \"startDate\": \"2022-01-01T19:40:24Z\",","                \"endDate\":   \"2023-01-01T19:20:24Z\",","                \"threshold\":  0.80,","                \"featureList\": [\"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\"]","            }","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 5: Perform an action on a data monitoring job (optional)","message":["%md","","","OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint, **DISABLE**, **RUN**, **ENABLE**, and **STOP**:","","* _DISABLE_ disables the job at submission. The *force* property can be used with this action to forcefully interrupt any running job. ","* _ENABLE_ enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule. ","* _RUN_ will immediately run the job as a way to test a job or run it outside of its schedule.","* _STOP_ stops a currently running job.","","For example, when your job has been successfuly submitted, its state is set to **ENABLED** by default. This means that it will run on the schedule you specified when submitting the job unless its updated to another state such as **DISABLED**. You can do this by sending a request to the */omlmod/v1/jobs/{jobid}/action* endpoint. ","","Note that jobs can be set to **DISABLED** at submission by setting the *disableJob* flag to *true*.","","Below is an example request to update the job status to **DISABLED**. When you successfully submit your job you will receive a 204 response with no body. "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":"Disable data monitoring job","message":["","$ curl -i -X POST \"<OML Service URL>/omlmod/v1/jobs/<jobid>/action\" \\","       --header \"Authorization: Bearer <token>\" \\","       --header 'Content-Type: application/json' \\","       --data '{","            \"action\": \"DISABLE\"","         }'"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 6: View the data monitoring job output","message":["%md","","Once your job has run, either according to its schedule or by the **RUN** action, you can view its output in the table you specified in your job request with the **outputData** parameter. The full name of the table is *{jobid}\\_{outputData}*. You can check if your job is finished by sending a request to view its details, as we did in step #3. If your job has run at least once you should see the **lastRunDetail** field with information on that run. The paragraph below queries the output table associated with this example. ","","If you scroll down on the output table, you can see that there is information for the baseline time period and newdata time period for each of the dataset features being monitored for drift. Many of the columns are empty for the baseline rows as monitoring is only done on the new data, not the baseline data. For help understanding the data monitoring output table, refer to the documentation: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html');\">https://docs.oracle.com/en/database/oracle/machine-learning/omlss/omlss/omls-data-monitoring-output-table.html<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql","","SELECT START_TIME, END_TIME, IS_BASELINE, THRESHOLD, HAS_DRIFT, round(DRIFT, 3), ","       FEATURE_NAME, ROUND(IMPORTANCE_VALUE, 3) ","FROM OML$7ABB6308_1664_4CB4_84B1_598A6EA599D1_householdPowerConsumption","ORDER BY FEATURE_NAME, IS_BASELINE DESC"],"enabled":false,"result":{"startTime":1700068961590,"interpreter":"sql.low","endTime":1700068963129,"results":[{"message":"START_TIME\tEND_TIME\tIS_BASELINE\tTHRESHOLD\tHAS_DRIFT\tROUND(DRIFT,3)\tFEATURE_NAME\tROUND(IMPORTANCE_VALUE,3)\n\t\t1\t\t\t\tGLOBAL_ACTIVE_POWER\t\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tGLOBAL_ACTIVE_POWER\t2.158\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tGLOBAL_ACTIVE_POWER\t2.631\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tGLOBAL_ACTIVE_POWER\t2.181\n\t\t1\t\t\t\tGLOBAL_REACTIVE_POWER\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tGLOBAL_REACTIVE_POWER\t1.639\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tGLOBAL_REACTIVE_POWER\t1.897\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tGLOBAL_REACTIVE_POWER\t1.389\n\t\t1\t\t\t\tSUB_METERING_1\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_1\t0.362\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_1\t0.294\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_1\t0.234\n\t\t1\t\t\t\tSUB_METERING_2\t\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_2\t0.631\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_2\t0.56\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_2\t0.558\n\t\t1\t\t\t\tSUB_METERING_3\t\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tSUB_METERING_3\t1.854\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tSUB_METERING_3\t1.922\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tSUB_METERING_3\t1.888\n\t\t1\t\t\t\tVOLTAGE\t\n2009-11-26 00:00:00.0 +0:00\t2010-11-26 00:00:00.0 +0:00\t0\t0.8\t1\t0.811\tVOLTAGE\t2.145\n2008-01-01 00:00:00.0 +0:00\t2008-11-25 23:59:58.0 +0:00\t0\t0.8\t0\t0.644\tVOLTAGE\t2.269\n2008-11-25 23:59:59.0 +0:00\t2009-11-25 23:59:59.0 +0:00\t0\t0.8\t0\t0.725\tVOLTAGE\t2.576\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Step 7: Delete a data monitoring job (optional)","message":["%md","","To delete a previously submitted job, send a DELETE request along with the Job ID to the */omlmod/v1/jobs* endpoint."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["","$ curl -X DELETE \"<OML Service URL>/omlmod/v1/jobs/<jobid>\"  \\","       --header 'Accept: application/json' \\","       --header 'Content-Type: application/json' \\","       --header \"Authorization: Bearer <token>\" | jq"],"enabled":false,"result":{"startTime":1700068963474,"interpreter":"md.low","endTime":1700068963566,"results":null,"taskStatus":null,"forms":null,"status":null},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Reproducing this example","message":["%md","","If you would like to reproduce the example in this notebook, the paragraph below creates the required database tables, *HOUSEHOLD_POWER_BASE* and *HOUSEHOLD_POWER_NEW*. These tables are created using the Individual Household Electric Power Consumption data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption).","","The code to read and transform the data and create database tables using R is provided below."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Drop tables if they exist and suppress warnings","message":["%r","","options(warn=-1)","","try(ore.drop(table=\"HOUSEHOLD_POWER_BASE\"))","try(ore.drop(table=\"HOUSEHOLD_POWER_NEW\"))"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Read and transform data","message":["%r","","test <- read.csv(\"https://objectstorage.us-sanjose-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/household_power_consumption.txt\", sep=\";\")","","test <- transform(test, Date = as.Date(Date, format = \"%d/%m/%Y\"))","test <- transform(test, Global_active_power = as.numeric(Global_active_power))","test <- transform(test, Global_reactive_power = as.numeric(Global_reactive_power))","test <- transform(test, Voltage = as.numeric(Voltage))","test <- transform(test, Global_intensity = as.numeric(Global_intensity))","test <- transform(test, Sub_metering_1 = as.numeric(Sub_metering_1))","test <- transform(test, Sub_metering_2 = as.numeric(Sub_metering_2))","test <- transform(test, Sub_metering_3 = as.numeric(Sub_metering_3))","","colnames(test) <- c(\"DATES\", \"TIMES\", \"GLOBAL_ACTIVE_POWER\", \"GLOBAL_REACTIVE_POWER\", \"VOLTAGE\", \"GLOBAL_INTENSITY\", \"SUB_METERING_1\", \"SUB_METERING_2\", \"SUB_METERING_3\") "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create baseline and new data ","message":["%r","","test_base <- test[test$DATES < \"2008-01-01\",]","test_new <- test[test$DATES > \"2007-12-31\",]","","# Create OML proxy objects","","ore.create(test_base, table=\"HOUSEHOLD_POWER_BASE\")","ore.create(test_new, table=\"HOUSEHOLD_POWER_NEW\")"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the baseline data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_BASE","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View new data","message":["%sql","","SELECT * FROM HOUSEHOLD_POWER_NEW","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Anomaly Detection EM.dsnb b/machine-learning/notebooks/sql/OML4SQL Anomaly Detection EM.dsnb
deleted file mode 100755
index 95d7fce0..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Anomaly Detection EM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
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","We use the customer and demographics data to predict anomalous customers using prob_anomalous.","The entire machine learning methodology runs inside Oracle Autonomous Database (ADB).","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715729469671,"interpreter":"md.low","endTime":1715729469827,"results":[{"message":"<h1 id=\"anomaly-detection-to-detect-unusual-or-rare-occurrences\">Anomaly Detection to Detect Unusual or Rare Occurrences<\/h1>\n<p>Oracle Machine Learning supports anomaly detection to identify rare or unusual records (customers, transactions, etc.).\nIn this notebook, we highlight the use of the Expectation Maximization algorithm, which is already supported for clustering.\nThe use of EM expands on the One-Class SVM model approach for anomaly detection. EM can capture the underlying data distribution and flag records\nthat do not fit the learned data distribution well. An object is identified as an outlier in an EM Anomaly Detection model if its anomaly\nprobability is greater than the specified limit, with a default of 0.05. A label of 1 denotes normal, while a label of 0 denotes anomaly.\nWe use the customer and demographics data to predict anomalous customers using prob_anomalous.\nThe entire machine learning methodology runs inside Oracle Autonomous Database (ADB).<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","<dl>","<img 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href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-anomaly-detection\" target=\"_blank\">OML Anomaly Detection<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1714398011443,"interpreter":"md.low","endTime":1714398011513,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-anomaly-detection\" target=\"_blank\">OML Anomaly Detection<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Prepare the datasets for modeling","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS_V AS","  SELECT CUST_ID, YRS_RESIDENCE, EDUCATION, AFFINITY_CARD, ","         HOUSEHOLD_SIZE, OCCUPATION, BOOKKEEPING_APPLICATION, ","         BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE,","         OS_DOC_SET_KANJI, PRINTER_SUPPLIES, Y_BOX_GAMES","  FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","  SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, a.CUST_YEAR_OF_BIRTH, ","         a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, b.EDUCATION, b.AFFINITY_CARD, ","         b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","  FROM   SH.CUSTOMERS a, DEMOGRAPHICS_V b","  WHERE  a.CUST_ID = b.CUST_ID;"],"enabled":true,"result":{"startTime":1714398012144,"interpreter":"script.low","endTime":1714398012249,"results":[{"message":"\nView DEMOGRAPHICS_V created.\n\n\n---------------------------\n\nView CUSTOMERS360_V 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  CASE_ID_COLUMN_NAME => 'CUST_ID',","        SET_LIST            => v_setlst,","        TARGET_COLUMN_NAME  => NULL); -- NULL target indicates anomaly detection      ","END;"],"enabled":true,"result":{"startTime":1714398012355,"interpreter":"script.low","endTime":1714398013789,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Evaluate the model"],"enabled":true,"result":{"startTime":1714398013902,"interpreter":"md.low","endTime":1714398013973,"results":[{"message":"<h2 id=\"evaluate-the-model\">Evaluate the 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 prediction_probability(CUSTOMERS360MODEL_AD, '0' USING *) prob_anomalous","            FROM CUSTOMERS360_V))","WHERE rnk <= 5","ORDER BY prob_anomalous DESC;"],"enabled":true,"result":{"startTime":1714398014297,"interpreter":"sql.low","endTime":1714398014432,"results":[{"message":"CUST_ID\tPROB_ANOMALOUS\tYRS_RESIDENCE\tCUST_MARITAL_STATUS\tRNK\n102926\t1\t2\tMarried\t1\n102555\t1\t14\tNeverM\t2\n102287\t1\t2\tMar-AF\t5\n101478\t1\t1\tMarried\t4\n101693\t1\t0\tMarried\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view of customers in descending order of anomaly probability","message":["%script","","CREATE OR REPLACE VIEW EM_ANOMALOUS_RESULTS AS","SELECT * ","FROM (SELECT CUST_ID, 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deleted file mode 100755
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--- a/machine-learning/notebooks/sql/OML4SQL Anomaly Detection SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Association Rules Apriori.dsnb b/machine-learning/notebooks/sql/OML4SQL Association Rules Apriori.dsnb
deleted file mode 100755
index 482deb15..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Association Rules Apriori.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Association Rules Apriori","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715348263835,"interpreter":"md.low","endTime":1715348263898,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Association Rules (Market Basket Analysis)","","The Association Rules machine learning technique, also known as Market Basket Analysis, can also be used when looking for co-occurring items, states that lead to failures, or non-obvious events, e.g., as in manufacturing. In his notebook, you will build associations rules models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715348263994,"interpreter":"md.low","endTime":1715348264059,"results":[{"message":"<h1 id=\"association-rules-market-basket-analysis\">Association Rules (Market Basket Analysis)<\/h1>\n<p>The Association Rules machine learning technique, also known as Market Basket Analysis, can also be used when looking for co-occurring items, states that lead to failures, or non-obvious events, e.g., as in manufacturing. In his notebook, you will build associations rules models using the Apriori algorithm with data from the SH schema (SH.SALES). All computation occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715348264145,"interpreter":"md.low","endTime":1715348264206,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/associationrules-5663172.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules"],"enabled":true,"result":{"startTime":1715348264287,"interpreter":"md.low","endTime":1715348264351,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-association-rules\" target=\"_blank\">OML Association Rules<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display SALES data","message":["%sql","","-- Note that a transaction is defined by a set of products purchased for a given customer at a given time. 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This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') : 'ODMS_MISSING_VALUE_AUTO';","","* Specify the Minimum support for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","    v_setlst('ASSO_MIN_SUPPORT') := '0.1';","","* Specify the Minimum confidence for association rules.","    The default value is 0.1.  It requires a number between 0 and 1 (inclusive).","    v_setlst('ASSO_MIN_CONFIDENCE') := '0.1';","","* Specify the Maximum rule length for association rules.","    The default value is 4.  It requires a number between 2 and 20 (inclusive).","    v_setlst('ASSO_MAX_RULE_LENGTH') := '4';","    ","* Specify the name of a column that contains the items in a transaction. 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This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.\nv_setlst('ODMS_MISSING_VALUE_TREATMENT') : 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/li>\n<li>\n<p>Specify the Minimum support for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).\nv_setlst('ASSO_MIN_SUPPORT') := '0.1';<\/p>\n<\/li>\n<li>\n<p>Specify the Minimum confidence for association rules.\nThe default value is 0.1.  It requires a number between 0 and 1 (inclusive).\nv_setlst('ASSO_MIN_CONFIDENCE') := '0.1';<\/p>\n<\/li>\n<li>\n<p>Specify the Maximum rule length for association rules.\nThe default value is 4.  It requires a number between 2 and 20 (inclusive).\nv_setlst('ASSO_MAX_RULE_LENGTH') := '4';<\/p>\n<\/li>\n<li>\n<p>Specify the name of a column that contains the items in a transaction.\nWhen you specify this setting, the algorithm expects the data to be presented in native transactional format, consisting of two columns:<\/p>\n<ul>\n<li>Case ID, either categorical or numeric<\/li>\n<li>Item ID, either categorical or numeric\nv_setlst('ODMS_ITEM_ID_COLUMN_NAME') := '<column name>';<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create Associations Rules model","message":["%script ","","BEGIN DBMS_DATA_MINING.DROP_MODEL('AR_SH_SAMPLE');","EXCEPTION WHEN OTHERS 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Graphics Monitor\t0.091\t0.313\t2\n2\t18\" Flat Panel Graphics Monitor\tCD-RW, High Speed Pack of 5\t0.091\t0.295\t2\n3\tExtension Cable\t18\" Flat Panel Graphics Monitor\t0.09\t0.29\t2\n4\t18\" Flat Panel Graphics Monitor\tExtension Cable\t0.09\t0.291\t2\n5\tExternal 8X CD-ROM\t18\" Flat Panel Graphics Monitor\t0.083\t0.272\t2\n6\t18\" Flat Panel Graphics Monitor\tExternal 8X CD-ROM\t0.083\t0.267\t2\n7\tKeyboard Wrist Rest\t18\" Flat Panel Graphics Monitor\t0.06\t0.316\t2\n8\t18\" Flat Panel Graphics Monitor\tKeyboard Wrist Rest\t0.06\t0.192\t2\n9\tModel SM26273 Black Ink Cartridge\t18\" Flat Panel Graphics Monitor\t0.067\t0.309\t2\n10\t18\" Flat Panel Graphics Monitor\tModel SM26273 Black Ink Cartridge\t0.067\t0.216\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"ANTECEDENT\",\"CONSEQUENT\"],\"showSeries\":[\"CONF\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"CONF\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Antecedent & Consequent \"},\"y\":{\"title\":\"Confidence\"}},\"lastColumns\":[\"ANTECEDENT\",\"CONSEQUENT\",\"SUPP\",\"CONF\",\"NUM\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Plot top 12 Association Rules 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CD-ROM\t0.15319148936170213\t0.52363636363636368\t2\nExternal 8X CD-ROM\tCD-RW, High Speed Pack of 5\t0.15319148936170213\t0.50174216027874563\t2\n18\" Flat Panel Graphics Monitor\tSIMM- 16MB PCMCIAII card\t0.15425531914893617\t0.49657534246575341\t2\nSIMM- 16MB PCMCIAII card\t18\" Flat Panel Graphics Monitor\t0.15425531914893617\t0.48494983277591974\t2\nModel SM26273 Black Ink Cartridge\tStandard Mouse\t0.079787234042553196\t0.36764705882352944\t2\nModel SM26273 Black Ink Cartridge\tMouse Pad\t0.077659574468085107\t0.35784313725490197\t2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Recommending items","In the following example, ***number_of_items*** refers to the total number of items in the rule - across antecedent and consequent. ","Assocation Rules supports only a single consequent item. This query is looking for rules of length two. ","","Here, we use two predicates:","- number_of_items to control for the size of the rule","- EXTRACT to filter on the antecedent","","For example, if the antecedent should include \"Mouse Pad\", then use ***extract(antecedent, '//item[item_name=\"Mouse Pad\"]') IS NOT NULL***"],"enabled":true,"result":{"startTime":1715348267824,"interpreter":"md.low","endTime":1715348267885,"results":[{"message":"<h3 id=\"recommending-items\">Recommending items<\/h3>\n<p>In the following example, <em><strong>number_of_items<\/strong><\/em> refers to the total number of items in the rule - across antecedent and consequent.\nAssocation Rules supports only a single consequent item. This query is looking for rules of length two.<\/p>\n<p>Here, we use two predicates:<\/p>\n<ul>\n<li>number_of_items to control for the size of the rule<\/li>\n<li>EXTRACT to filter on the antecedent<\/li>\n<\/ul>\n<p>For example, if the antecedent should include &quot;Mouse Pad&quot;, then use <em><strong>extract(antecedent, '//item[item_name=&quot;Mouse Pad&quot;]') IS NOT NULL<\/strong><\/em><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Recommend top 5 items based on \"Mouse Pad\"","message":["%sql","","SELECT ROWNUM RANK,","       CONSEQUENT_NAME RECOMMENDATION,","       NUMBER_OF_ITEMS NUM,","       ROUND(RULE_SUPPORT, 3) SUPPORT, ","       ROUND(RULE_CONFIDENCE, 3) CONFIDENCE,","       ROUND(RULE_LIFT, 3) LIFT,","       ROUND(RULE_REVCONFIDENCE, 3) REVERSE_CONFIDENCE","FROM (SELECT * FROM DM$VRAR_SH_SAMPLE","      WHERE NUMBER_OF_ITEMS = 2","      AND EXTRACT(antecedent, '//item[item_name=\"Mouse Pad\"]') IS NOT NULL","      ORDER BY NUMBER_OF_ITEMS)","FETCH FIRST 5 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715348267963,"interpreter":"sql.low","endTime":1715348268082,"results":[{"message":"RANK\tRECOMMENDATION\tNUM\tSUPPORT\tCONFIDENCE\tLIFT\tREVERSE_CONFIDENCE\n1\t18\" Flat Panel Graphics Monitor\t2\t0.095\t0.294\t0.946\t0.305\n2\tCD-RW, High Speed Pack of 5\t2\t0.076\t0.234\t0.801\t0.258\n3\tExtension Cable\t2\t0.178\t0.551\t1.768\t0.57\n4\tExternal 8X CD-ROM\t2\t0.103\t0.32\t1.049\t0.338\n5\tKeyboard Wrist Rest\t2\t0.047\t0.145\t0.771\t0.249\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","In the following example, we assume the antecedent item is \"Standard Mouse\". ","Select the top 5 rules, ordered by the rule lift to see \"Extension Cable\" as the top recommendation. ","You can change sort to RULE_SUPPORT to see \"Mouse Pad\" as top recommendation."],"enabled":true,"result":{"startTime":1715348268164,"interpreter":"md.low","endTime":1715348268224,"results":[{"message":"<p>In the following example, we assume the antecedent item is &quot;Standard Mouse&quot;.\nSelect the top 5 rules, ordered by the rule lift to see &quot;Extension Cable&quot; as the top recommendation.\nYou can change sort to RULE_SUPPORT to see &quot;Mouse Pad&quot; as top recommendation.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"What items should we recommend when customer has \"Standard Mouse\" in basket?","message":["%sql","","SELECT ROWNUM RANK,","       CONSEQUENT_NAME RECOMMENDATION,","       NUMBER_OF_ITEMS NUM,","       ROUND(RULE_SUPPORT, 3) SUPPORT, ","       ROUND(RULE_CONFIDENCE, 3) CONFIDENCE,","       ROUND(RULE_LIFT, 3) LIFT,","       ROUND(RULE_REVCONFIDENCE, 3) REVERSE_CONFIDENCE","FROM (SELECT * FROM DM$VRAR_SH_SAMPLE","      WHERE NUMBER_OF_ITEMS = 2","      AND EXTRACT(antecedent, '//item[item_name=\"Standard Mouse\"]') IS NOT NULL","      ORDER BY RULE_LIFT DESC, NUMBER_OF_ITEMS)","FETCH FIRST 5 ROWS ONLY;","","---this example recommends \"Extension Cable\" and \"Mouse Pad\"."],"enabled":true,"result":{"startTime":1715348268300,"interpreter":"sql.low","endTime":1715348268419,"results":[{"message":"RANK\tRECOMMENDATION\tNUM\tSUPPORT\tCONFIDENCE\tLIFT\tREVERSE_CONFIDENCE\n1\tExtension Cable\t2\t0.181\t0.563\t1.806\t0.58\n2\tMouse Pad\t2\t0.184\t0.573\t1.777\t0.571\n3\tModel SM26273 Black Ink Cartridge\t2\t0.08\t0.248\t1.144\t0.368\n4\t18\" Flat Panel Graphics Monitor\t2\t0.096\t0.298\t0.959\t0.308\n5\tMultimedia speakers- 3\" cones\t2\t0.057\t0.179\t0.913\t0.293\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md    ","## Alternative way to create AR Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715348268494,"interpreter":"md.low","endTime":1715348268553,"results":[{"message":"<h2 id=\"alternative-way-to-create-ar-model-using-settings-table\">Alternative way to create AR Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create settings table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP Table AR_SH_SAMPLE_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE AR_SH_SAMPLE_SETTINGS (","  SETTING_NAME  VARCHAR2(30),","  SETTING_VALUE VARCHAR2(4000));","  "],"enabled":true,"result":{"startTime":1715348268635,"interpreter":"script.low","endTime":1715348268754,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable AR_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define AR model settings","message":["%script","","BEGIN       ","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ASSO_MIN_SUPPORT,0.04);","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ASSO_MIN_CONFIDENCE,0.1);","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ASSO_MAX_RULE_LENGTH,2);","    INSERT INTO AR_SH_SAMPLE_SETTINGS VALUES (DBMS_DATA_MINING.ODMS_ITEM_ID_COLUMN_NAME, 'PROD_NAME');","    COMMIT;","END;"],"enabled":true,"result":{"startTime":1715348268829,"interpreter":"script.low","endTime":1715348268923,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model settings table","message":["%sql","","SELECT * FROM AR_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1715348269000,"interpreter":"sql.low","endTime":1715348269076,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nASSO_MIN_SUPPORT\t.04\nASSO_MIN_CONFIDENCE\t.1\nASSO_MAX_RULE_LENGTH\t2\nODMS_ITEM_ID_COLUMN_NAME\tPROD_NAME\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create model using SALES data with settings table","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('AR_SH_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'AR_SH_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.ASSOCIATION,","    DATA_TABLE_NAME     => 'SALES_TRANS_CUST',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'AR_SH_SAMPLE_SETTINGS'","    );","END;"],"enabled":true,"result":{"startTime":1715348269150,"interpreter":"script.low","endTime":1715348270448,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715348270529,"interpreter":"md.low","endTime":1715348270591,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715348270689,"interpreter":"md.low","endTime":1715348270748,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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index 03d84f6d..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Attribute Importance MDL.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Attribute Importance MDL","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715349539600,"interpreter":"md.low","endTime":1715349539756,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Identify key factors","","Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we build an Attribute Importance model, which uses the Minimum Description Length algorithm on data from the SH schema. All functionality runs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715349539841,"interpreter":"md.low","endTime":1715349539941,"results":[{"message":"<h2 id=\"identify-key-factors\">Identify key factors<\/h2>\n<p>Oracle Machine Learning supports Attribute Importance to identify key factors (a.k.a. attributes, predictors, variables) that have the most influence on a target attribute. In this example, we build an Attribute Importance model, which uses the Minimum Description Length algorithm on data from the SH schema. All functionality runs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/attributeimportance-5663163.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715349540020,"interpreter":"md.low","endTime":1715349540099,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/attributeimportance-5663163.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a>"],"enabled":true,"result":{"startTime":1715349540188,"interpreter":"md.low","endTime":1715349540268,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View rows from the SH.SUPPLEMENTARY_DEMOGRAPHICS table","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS 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\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Classification DT.dsnb b/machine-learning/notebooks/sql/OML4SQL Classification DT.dsnb
deleted file mode 100755
index 6d6fd974..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Classification DT.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification DT","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714746817267,"interpreter":"md.medium","endTime":1714746817417,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification Modeling to Predict Target Customers using Decision Tree","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification decision tree models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714746817491,"interpreter":"md.medium","endTime":1714746817584,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-decision-tree\">Classification Modeling to Predict Target Customers using Decision Tree<\/h2>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification decision tree models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" 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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a>"],"enabled":true,"result":{"startTime":1714746817794,"interpreter":"md.medium","endTime":1714746817869,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 20 ROWS 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116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1},\"table\":{\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1},\"area\":{\"version\":1},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD ","message":["%sql","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD","ORDER BY HOUSEHOLD_SIZE;"],"enabled":true,"result":{"startTime":1714746854906,"interpreter":"sql.medium","endTime":1714746855002,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n681\t1\t0\n11\t1\t1\n1040\t2\t0\n109\t2\t1\n973\t3\t0\n814\t3\t1\n112\t4-5\t0\n107\t4-5\t1\n146\t6-8\t0\n2\t6-8\t1\n476\t9+\t0\n29\t9+\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1},\"table\":{\"columnsToShow\":[\"NUM_CUSTOMERS\"],\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1},\"area\":{\"version\":1},\"raw\":{}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD ","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1714747761776,"interpreter":"sql.medium","endTime":1714747761895,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","","## Create and display a view DEMOGRAPHICS4_V with desired columns for analysis"],"enabled":true,"result":{"startTime":1714746821693,"interpreter":"md.medium","endTime":1714746821756,"results":[{"message":"<h2 id=\"create-and-display-a-view-demographics4_v-with-desired-columns-for-analysis\">Create and display a view DEMOGRAPHICS4_V with desired columns for analysis<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, CUST_ID, FLAT_PANEL_MONITOR, BOOKKEEPING_APPLICATION,","            BULK_PACK_DISKETTES, EDUCATION, HOME_THEATER_PACKAGE, ","            HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1714746821831,"interpreter":"script.medium","endTime":1714746822149,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":null,"message":["%sql ","","SELECT * ","FROM DEMOGRAPHICS4_V","FETCH FIRST 20 ROWS ONLY;"],"enabled":true,"result":{"startTime":1714746822220,"interpreter":"sql.medium","endTime":1714746822337,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tFLAT_PANEL_MONITOR\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t102547\t1\t0\t1\t10th\t0\t1\tOther\t0\t1\t0\t1\n0\t101050\t1\t0\t1\t10th\t0\t1\tOther\t0\t1\t0\t1\n0\t100040\t1\t0\t1\t11th\t0\t1\tSales\t0\t1\t0\t1\n0\t102117\t0\t1\t0\tHS-grad\t0\t1\tFarming\t0\t1\t0\t1\n0\t101074\t1\t0\t1\t10th\t0\t1\tHandler\t0\t1\t1\t1\n0\t104179\t1\t0\t1\t10th\t0\t1\tHandler\t0\t1\t1\t1\n0\t100417\t0\t0\t0\t11th\t0\t1\tHandler\t0\t1\t1\t1\n0\t101146\t1\t1\t1\t< Bach.\t0\t1\t?\t0\t1\t1\t1\n0\t103420\t1\t1\t1\t< Bach.\t0\t1\t?\t0\t1\t1\t1\n0\t101987\t1\t1\t1\t< Bach.\t0\t1\tOther\t0\t1\t1\t1\n0\t101279\t0\t1\t0\t< Bach.\t0\t1\tOther\t0\t1\t1\t1\n0\t102106\t0\t1\t0\t< Bach.\t0\t1\tCleric.\t0\t1\t1\t1\n0\t102591\t1\t1\t1\tHS-grad\t0\t1\t?\t0\t1\t1\t1\n0\t101869\t1\t1\t1\tHS-grad\t0\t1\tOther\t0\t1\t1\t1\n0\t103169\t0\t1\t0\tHS-grad\t0\t1\tSales\t0\t1\t1\t1\n0\t103552\t1\t1\t1\tHS-grad\t0\t1\tHandler\t0\t1\t1\t1\n0\t101616\t0\t1\t0\tBach.\t0\t1\tTechSup\t0\t1\t2\t1\n0\t104096\t1\t1\t1\t< Bach.\t0\t1\tOther\t0\t1\t2\t1\n0\t102171\t0\t1\t0\t< Bach.\t0\t1\tCleric.\t0\t1\t2\t1\n0\t100846\t1\t1\t1\t< Bach.\t0\t1\tHandler\t0\t1\t2\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1714746822414,"interpreter":"script.medium","endTime":1714746822506,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for DT ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566');\">Decision Tree<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Specify Tree impurity metric for Decision Tree. Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","- Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node). For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> v_setlst('TREE_TERM_MAX_DEPTH') := '7';","- Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data. It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","- Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows. It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","- Specify The minimum number of rows in a node. It requires a number greater than or equal to zero. The default is 10.     ","> v_setlst('TREE_TERM_MINREC_NODE') := '10';","- Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","> v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","- Specify the maximum number of bins for each attribute. For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","> v_setlst('CLAS_MAX_SUP_BINS') := '32';    "],"enabled":true,"result":{"startTime":1714746822590,"interpreter":"md.medium","endTime":1714746822668,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-dt\">Examples of possible setting overrides for DT<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566');\">Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify Tree impurity metric for Decision Tree. Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node). For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MAX_DEPTH') := '7';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data. It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows. It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node. It requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_NODE') := '10';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_SPLIT') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute. For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLAS_MAX_SUP_BINS') := '32';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_DECISION_TREE';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'DT_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1714746822754,"interpreter":"script.medium","endTime":1714746825452,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree Model using full settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_DECISION_TREE';","    ","    v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    v_setlst('TREE_TERM_MINPCT_NODE') := '0.05'; ","    v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","    v_setlst('TREE_TERM_MINREC_NODE') := '10';","    v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    v_setlst('CLAS_MAX_SUP_BINS') := '32';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'DT_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1714746825528,"interpreter":"script.medium","endTime":1714746827125,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully 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RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(DT_CLASS_MODEL USING *) PREDICTION_YRS_RES,","     PREDICTION_DETAILS(DT_CLASS_MODEL USING *) PD","    FROM TEST_DATA_CLAS","    WHERE CUST_ID < 100025","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]')  OUTPRED","FETCH FIRST 10 ROWS ONLY","    "],"enabled":true,"result":{"startTime":1714746834660,"interpreter":"sql.medium","endTime":1714746834808,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".178\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".04\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".015\" \n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".178\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".015\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\"-.061\" \n100006\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".178\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".04\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".015\" \n100010\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".135\" \t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".123\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".042\" \n100012\t1\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".244\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".216\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".04\" \n100014\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".178\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".015\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\"-.061\" \n100017\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".178\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\".015\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\"-.061\" \n100023\t0\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".123\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".091\" \t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".042\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1714746834885,"interpreter":"md.medium","endTime":1714746834945,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_DECISION_TREE');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL('CLASS_MODEL_2', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","  DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1714746835019,"interpreter":"script.medium","endTime":1714746836914,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":{"startTime":1714746836988,"interpreter":"md.medium","endTime":1714746837049,"results":[{"message":"<h1 id=\"end-of-script\">End of Script<\/h1>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1714746837129,"interpreter":"md.medium","endTime":1714746837186,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Classification GLM.dsnb b/machine-learning/notebooks/sql/OML4SQL Classification GLM.dsnb
deleted file mode 100755
index 009d07af..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Classification GLM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification GLM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715229629998,"interpreter":"md.medium","endTime":1715229630151,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Generalized Linear Model","","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification generalized linear models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715229630227,"interpreter":"md.medium","endTime":1715229630321,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-generalized-linear-model\">Classification Modeling to Predict Target Customers using Generalized Linear Model<\/h1>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification generalized linear models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715229647394,"interpreter":"md.medium","endTime":1715229647458,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a>"],"enabled":true,"result":{"startTime":1715224812906,"interpreter":"md.medium","endTime":1715224812975,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS 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SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1715224813375,"interpreter":"sql.medium","endTime":1715224813648,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"COUNT\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD 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  PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","   FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1715224814355,"interpreter":"script.medium","endTime":1715224814790,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * FROM DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS 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Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (80/20) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (80) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1715224815065,"interpreter":"script.medium","endTime":1715224815184,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715224815263,"interpreter":"script.medium","endTime":1715224820364,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> v_setlst('GLMS_RIDGE_VALUE') := '2';","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`","> v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> v_setlst('GLMS_MAX_FEATURES') := '30';","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> v_setlst('GLMS_REFERENCE_CLASS_NAME') := '<target_value>';","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> v_setlst('GLMS_NUM_ITERATIONS') := '200';","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> v_setlst('GLMS_BATCH_ROWS') := '2000';","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.  ","> v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';","","<strong> For Oracle Database 23ai and newer. <\/strong>","- Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function. ","   For classification, the options are `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK` or `GLMS_CAUCHIT_LINK`.  For regression, the default is `GLMS_IDENTITY_LINK (default)`.","> v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';"],"enabled":true,"result":{"startTime":1715229722249,"interpreter":"md.medium","endTime":1715229722332,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/23/arpls&id=ARPLS-GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_VALUE') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_MAX_FEATURES') := '30';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_REFERENCE_CLASS_NAME') := '&lt;target_value&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_NUM_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_BATCH_ROWS') := '2000';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';<\/p>\n<\/blockquote>\n<p><strong> For Oracle Database 23ai and newer. <\/strong><\/p>\n<ul>\n<li>Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function.\nFor classification, the options are <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code> or <code>GLMS_CAUCHIT_LINK<\/code>.  For regression, the default is <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model with feature generation and feature selection","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","    v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';","    v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';","    v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE'; ","    v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_AIC';","    v_setlst('GLMS_NUM_ITERATIONS') := '50';","    v_setlst('GLMS_MAX_FEATURES') := '30';","    v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';","    v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","    v_setlst('GLMS_BATCH_ROWS') := '2000';","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_DELETE_ROW';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715224820615,"interpreter":"script.medium","endTime":1715224822516,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model with sparse, SGD solver","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';","    v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';","    v_setlst('GLMS_NUM_ITERATIONS') := '50';","    v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","    v_setlst('GLMS_BATCH_ROWS') := '2000';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715224822604,"interpreter":"script.medium","endTime":1715224824506,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Generalized Linear Model with ridge regression","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_ENABLE';","    v_setlst('GLMS_RIDGE_VALUE') := '2';","    v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_DISABLE';","    v_setlst('GLMS_NUM_ITERATIONS') := '50';","    v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","    v_setlst('GLMS_BATCH_ROWS') := '2000';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'GLM_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715224824586,"interpreter":"script.medium","endTime":1715224826788,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='GLM_CLASS_MODEL'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1715224826865,"interpreter":"sql.medium","endTime":1715224826987,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAGLM_CLASS_MODEL\tGLM Classification Row Diagnostics\nDM$VCGLM_CLASS_MODEL\tScoring Cost Matrix\nDM$VDGLM_CLASS_MODEL\tGLM Classification Attribute Diagnostics\nDM$VGGLM_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VNGLM_CLASS_MODEL\tNormalization and Missing Value Handling\nDM$VSGLM_CLASS_MODEL\tComputed Settings\nDM$VTGLM_CLASS_MODEL\tClassification Targets\nDM$VWGLM_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get model fit statistics","message":["%sql","","SELECT NAME, round(NUMERIC_VALUE, 4) NUMERIC_VALUE, STRING_VALUE","FROM   DM$VGGLM_CLASS_MODEL;","ORDER BY 1;"],"enabled":true,"result":{"startTime":1715224827065,"interpreter":"sql.medium","endTime":1715224827185,"results":[{"message":"NAME\tNUMERIC_VALUE\tSTRING_VALUE\nAIC_INTERCEPT\t3903.4939\t\nAIC_MODEL\t2562.2387\t\nCONVERGED\t\tNO\nDEPENDENT_MEAN\t0.2323\t\nITERATIONS\t7\t\nLR_CHI_SQ\t1423.2552\t\nLR_CHI_SQ_P_VALUE\t0\t\nLR_DF\t41\t\nNEG2_LL_INTERCEPT\t3901.4939\t\nNEG2_LL_MODEL\t2478.2387\t\nNUM_PARAMS\t42\t\nNUM_ROWS\t3599\t\nPCT_CORRECT\t0.8422\t\nPCT_INCORRECT\t0.1578\t\nPCT_TIED\t0\t\nPSEUDO_R_SQ_CS\t0.3266\t\nPSEUDO_R_SQ_N\t0.4936\t\nRANK_DEFICIENCY\t1\t\nSC_INTERCEPT\t3909.6823\t\nSC_MODEL\t2822.152\t\nVALID_COVARIANCE_MATRIX\t\tYES\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get fit details for each attribute","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE, round(COEFFICIENT,3) COEFFICIENT, round(P_VALUE , 3) P_VALUE","FROM   DM$VDGLM_CLASS_MODEL","ORDER BY FEATURE_EXPRESSION;","  "],"enabled":true,"result":{"startTime":1715224827265,"interpreter":"sql.medium","endTime":1715224827450,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\tP_VALUE\n\t\t-1.926\t0\nOCCUPATION\tTransp.\t-0.818\t1\nBULK_PACK_DISKETTES\t\t0.239\t0.24\nFLAT_PANEL_MONITOR\t\t-0.282\t0.155\nHOME_THEATER_PACKAGE\t\t0.171\t0.263\nOS_DOC_SET_KANJI\t\t-3.792\t0.44\nPRINTER_SUPPLIES\t\t0\t1\nYRS_RESIDENCE\t\t0.16\t0\nY_BOX_GAMES\t\t-0.98\t0\nEDUCATION\t10th\t0.41\t0.386\nEDUCATION\t11th\t0.658\t0.152\nEDUCATION\t12th\t-0.162\t0.779\nEDUCATION\t1st-4th\t-0.366\t0.555\nEDUCATION\t5th-6th\t-0.436\t0.471\nEDUCATION\t7th-8th\t-0.07\t0.878\nEDUCATION\t9th\t-0.036\t0.94\nEDUCATION\t< Bach.\t0.323\t0.018\nEDUCATION\tAssoc-A\t0.671\t0.004\nEDUCATION\tAssoc-V\t0.289\t0.201\nEDUCATION\tBach.\t0.831\t0\nEDUCATION\tMasters\t1.223\t0\nEDUCATION\tPhD\t1.246\t0.001\nEDUCATION\tPresch.\t-0.101\t0.882\nEDUCATION\tProfsc\t1.113\t0\nHOUSEHOLD_SIZE\t1\t-2.273\t0\nHOUSEHOLD_SIZE\t2\t-2.151\t0\nHOUSEHOLD_SIZE\t4-5\t0.388\t0.032\nHOUSEHOLD_SIZE\t6-8\t-2.188\t0\nHOUSEHOLD_SIZE\t9+\t-2.323\t0\nOCCUPATION\t?\t-1.126\t0\nOCCUPATION\tArmed-F\t0.103\t0.88\nOCCUPATION\tCleric.\t-0.408\t0.035\nOCCUPATION\tCrafts\t-0.75\t0\nOCCUPATION\tFarming\t-1.222\t0\nOCCUPATION\tHandler\t-1.283\t0\nOCCUPATION\tHouse-s\t-0.389\t0.524\nOCCUPATION\tMachine\t-0.741\t0.001\nOCCUPATION\tOther\t-1.277\t0\nOCCUPATION\tProf.\t0.127\t0.44\nOCCUPATION\tProtec.\t-0.035\t0.903\nOCCUPATION\tSales\t-0.441\t0.007\nOCCUPATION\tTechSup\t-0.221\t0.358\nBOOKKEEPING_APPLICATION\t\t1.023\t0.006\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","  DBMS_DATA_MINING.APPLY('GLM_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');","  DBMS_DATA_MINING.COMPUTE_LIFT('APPLY_RESULT','TEST_DATA_CLAS','CUST_ID','AFFINITY_CARD',","                                'LIFT_TABLE','1','PREDICTION','PROBABILITY',100);","END;"],"enabled":true,"result":{"startTime":1715224827536,"interpreter":"script.medium","endTime":1715224831537,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"line\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"QUANTILE_NUMBER\",\"GAIN_CUMULATIVE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"line","title":"View model's cumulative gains (lift) chart","message":["%sql","","SELECT QUANTILE_NUMBER, ","       ROUND(GAIN_CUMULATIVE,3) GAIN_CUMULATIVE","FROM LIFT_TABLE;"],"enabled":true,"result":{"startTime":1715224831614,"interpreter":"sql.medium","endTime":1715224831737,"results":[{"message":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t0.05\n2\t0.094\n3\t0.139\n4\t0.173\n5\t0.21\n6\t0.238\n7\t0.272\n8\t0.312\n9\t0.337\n10\t0.361\n11\t0.386\n12\t0.411\n13\t0.436\n14\t0.465\n15\t0.495\n16\t0.505\n17\t0.525\n18\t0.545\n19\t0.569\n20\t0.584\n21\t0.614\n22\t0.629\n23\t0.644\n24\t0.668\n25\t0.683\n26\t0.693\n27\t0.718\n28\t0.728\n29\t0.738\n30\t0.75\n31\t0.763\n32\t0.771\n33\t0.782\n34\t0.797\n35\t0.807\n36\t0.817\n37\t0.832\n38\t0.837\n39\t0.851\n40\t0.871\n41\t0.881\n42\t0.891\n43\t0.896\n44\t0.896\n45\t0.896\n46\t0.906\n47\t0.916\n48\t0.916\n49\t0.921\n50\t0.926\n51\t0.931\n52\t0.931\n53\t0.936\n54\t0.95\n55\t0.95\n56\t0.95\n57\t0.95\n58\t0.95\n59\t0.955\n60\t0.96\n61\t0.96\n62\t0.965\n63\t0.973\n64\t0.975\n65\t0.975\n66\t0.98\n67\t0.98\n68\t0.98\n69\t0.98\n70\t0.98\n71\t0.98\n72\t0.98\n73\t0.98\n74\t0.98\n75\t0.99\n76\t1\n77\t1\n78\t1\n79\t1\n80\t1\n81\t1\n82\t1\n83\t1\n84\t1\n85\t1\n86\t1\n87\t1\n88\t1\n89\t1\n90\t1\n91\t1\n92\t1\n93\t1\n94\t1\n95\t1\n96\t1\n97\t1\n98\t1\n99\t1\n100\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display 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"],"enabled":true,"result":{"startTime":1715224831817,"interpreter":"sql.medium","endTime":1715224831938,"results":[{"message":"CUST_ID\tPRED\tPROB\tCOST\n101708\t1\t0.848\t0.15\n103031\t1\t0.841\t0.16\n104206\t1\t0.836\t0.16\n100179\t1\t0.833\t0.17\n100100\t1\t0.832\t0.17\n100985\t1\t0.829\t0.17\n101676\t1\t0.827\t0.17\n102435\t1\t0.823\t0.18\n101885\t1\t0.821\t0.18\n103579\t1\t0.817\t0.18\n100816\t1\t0.814\t0.19\n103755\t1\t0.812\t0.19\n100780\t1\t0.811\t0.19\n102585\t1\t0.808\t0.19\n102669\t1\t0.806\t0.19\n102661\t1\t0.806\t0.19\n104064\t1\t0.806\t0.19\n100889\t1\t0.805\t0.2\n103692\t1\t0.805\t0.2\n101972\t1\t0.802\t0.2\n101469\t1\t0.797\t0.2\n100988\t1\t0.793\t0.21\n102022\t1\t0.793\t0.21\n104093\t1\t0.793\t0.21\n100644\t1\t0.793\t0.21\n103320\t1\t0.792\t0.21\n101781\t1\t0.784\t0.22\n102796\t1\t0.781\t0.22\n104131\t1\t0.778\t0.22\n102266\t1\t0.778\t0.22\n104364\t1\t0.778\t0.22\n101439\t1\t0.778\t0.22\n104419\t1\t0.777\t0.22\n102177\t1\t0.777\t0.22\n103463\t1\t0.777\t0.22\n100073\t1\t0.775\t0.22\n104251\t1\t0.771\t0.23\n102087\t1\t0.767\t0.23\n103880\t1\t0.766\t0.23\n102401\t1\t0.764\t0.24\n100817\t1\t0.763\t0.24\n103838\t1\t0.763\t0.24\n100372\t1\t0.759\t0.24\n100230\t1\t0.758\t0.24\n100066\t1\t0.758\t0.24\n102615\t1\t0.756\t0.24\n101564\t1\t0.755\t0.24\n104320\t1\t0.753\t0.25\n101581\t1\t0.751\t0.25\n102447\t1\t0.742\t0.26\n103898\t1\t0.742\t0.26\n101191\t1\t0.742\t0.26\n104369\t1\t0.742\t0.26\n101635\t1\t0.742\t0.26\n103204\t1\t0.741\t0.26\n101229\t1\t0.739\t0.26\n101730\t1\t0.737\t0.26\n101793\t1\t0.736\t0.26\n102183\t1\t0.73\t0.27\n102351\t1\t0.727\t0.27\n103931\t1\t0.722\t0.28\n100173\t1\t0.72\t0.28\n100954\t1\t0.716\t0.28\n101504\t1\t0.711\t0.29\n100274\t1\t0.711\t0.29\n101260\t1\t0.711\t0.29\n100404\t1\t0.711\t0.29\n103830\t1\t0.71\t0.29\n102120\t1\t0.707\t0.29\n100272\t1\t0.704\t0.3\n103090\t1\t0.704\t0.3\n104197\t1\t0.703\t0.3\n103457\t1\t0.702\t0.3\n103708\t1\t0.702\t0.3\n100406\t1\t0.702\t0.3\n100293\t1\t0.702\t0.3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select 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the model to a single new record","message":["%sql","","-- Predict the probability of being a high AFFINITY_CARD responder (1) ","--    by providing specific attribute values for a customer.","","SELECT ROUND(PREDICTION_PROBABILITY(GLM_CLASS_MODEL, '1' USING ","                                    '3' AS HOUSEHOLD_SIZE, ","                                     5 AS YRS_RESIDENCE, ","                                     1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;","  "],"enabled":true,"result":{"startTime":1715224832454,"interpreter":"sql.medium","endTime":1715224832528,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.245\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_AFFINITY_CARD,3) PRED_AFFINITY_CARD,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(GLM_CLASS_MODEL USING *) PREDICTION_AFFINITY_CARD,","     PREDICTION_DETAILS(GLM_CLASS_MODEL USING *) PD","    FROM TEST_DATA_CLAS","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') ","    OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715224832609,"interpreter":"sql.medium","endTime":1715224832800,"results":[{"message":"CUST_ID\tPRED_AFFINITY_CARD\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".379\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".045\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".011\" \n100006\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".216\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".028\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".011\" \n100011\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".055\" \t\"OCCUPATION\" actualValue=\"Farming\" weight=\".018\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"0\" weight=\".011\" \n100016\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".357\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".008\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".006\" \n100023\t1\t\"YRS_RESIDENCE\" actualValue=\"8\" weight=\".155\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".076\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"1\" weight=\".029\" \n100032\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".304\" \t\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".029\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".007\" \n100041\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".047\" \t\"OCCUPATION\" actualValue=\"Handler\" weight=\".015\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".005\" \n100047\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".108\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".013\" \t\"OCCUPATION\" actualValue=\"Cleric.\" weight=\".007\" \n100050\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".027\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"0\" weight=\".004\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".003\" \n100051\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".213\" \t\"OCCUPATION\" actualValue=\"Handler\" weight=\".085\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".005\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715224832879,"interpreter":"md.medium","endTime":1715224832945,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop settings and diagnostics table and create the GLM model Settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLMC_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","-- Drop any previous Model Diagnostics table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLMC_DIAG';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE GLMC_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1715224833026,"interpreter":"script.medium","endTime":1715224833176,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable GLMC_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings for automated data preparation and feature selection","message":["%script ","","BEGIN ","    INSERT INTO GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_GENERALIZED_LINEAR_MODEL);","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- output row diagnostic statistics into a table named GLMC_SH_SAMPLE_DIAG  ","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_DIAGNOSTICS_TABLE_NAME, 'GLMC_SH_SAMPLE_DIAG');  ","    ","-- turn ridge regression on or off. By default the system turns it on if there is a multicollinearity.  Ridge regression cannot be used with feature selection and feature generation, so we will keep it off in this example","     INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_RIDGE_REGRESSION, 'GLMS_RIDGE_REG_DISABLE');    ","     ","-- turn on feature selection. By default the system does not do feature selection. Some options for feature selection are show below too","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_SELECTION, DBMS_DATA_MINING.GLMS_FTR_SELECTION_ENABLE);","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_MAX_FEATURES, '30');","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_PRUNE_MODEL, DBMS_DATA_MINING.GLMS_PRUNE_MODEL_ENABLE);  ","    ","-- turn on feature generation. We will do Quadratic features, but Cubic is also available. Feature generation requires feature selection to be enabled.","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GENERATION, DBMS_DATA_MINING.GLMS_FTR_GENERATION_ENABLE);","    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GEN_METHOD, DBMS_DATA_MINING.GLMS_FTR_GEN_QUADRATIC);","--    INSERT INTO  GLMC_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GEN_METHOD, DBMS_DATA_MINING.GLMS_FTR_GEN_CUBIC);","    ","END;"],"enabled":true,"result":{"startTime":1715224833261,"interpreter":"script.medium","endTime":1715224833391,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMC_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'GLMC_SH_REGR_SAMPLE',","    MINING_FUNCTION     => 'CLASSIFICATION',","    DATA_TABLE_NAME     => 'TRAIN_DATA_CLAS',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'AFFINITY_CARD',","    SETTINGS_TABLE_NAME => 'GLMC_SETTINGS',","    XFORM_LIST          => V_XLST);","","    DBMS_OUTPUT.PUT_LINE ('Created model: GLMC_SH_REGR_SAMPLE ');","END;"],"enabled":true,"result":{"startTime":1715224833494,"interpreter":"script.medium","endTime":1715224835993,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: GLMC_SH_REGR_SAMPLE \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715224836069,"interpreter":"md.medium","endTime":1715224836130,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715224836215,"interpreter":"md.medium","endTime":1715224836288,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Classification NB.dsnb b/machine-learning/notebooks/sql/OML4SQL Classification NB.dsnb
deleted file mode 100755
index ffe70ec7..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Classification NB.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification NB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715349766885,"interpreter":"md.low","endTime":1715349766977,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Classification Modeling to Predict Target Customers using Naive Bayes","","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification naive bayes models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715349767090,"interpreter":"md.low","endTime":1715349767154,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-naive-bayes\">Classification Modeling to Predict Target Customers using Naive Bayes<\/h1>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification naive bayes models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715349767231,"interpreter":"md.low","endTime":1715349767309,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle Github<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a>"],"enabled":true,"result":{"startTime":1715349767385,"interpreter":"md.low","endTime":1715349767457,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle Github<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML web page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-naive-bayes\" target=\"_blank\">OML Naive Bayes<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the demographics data ","message":["%sql","","SELECT * ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349767532,"interpreter":"sql.low","endTime":1715349769132,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\t\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\t\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\t\n100417\t11th\tHandler\t1\t1\t0\t0\t0\t0\t0\t1\t1\t0\t\n101146\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n103420\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n101987\t< Bach.\tOther\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in demographics table","message":["%sql","","SELECT COUNT(*) FROM SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1715349769210,"interpreter":"sql.low","endTime":1715349769398,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"COUNT\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%sql","","-- In this data set, hyper-responders to the Affinity Card loyalty program are represented as 1's. ","","SELECT AFFINITY_CARD, COUNT(*) COUNT","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS","GROUP BY AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1715349769492,"interpreter":"sql.low","endTime":1715349769567,"results":[{"message":"AFFINITY_CARD\tCOUNT\n1\t1072\n0\t3428\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"NUM_CUSTOMERS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1715349769650,"interpreter":"sql.low","endTime":1715349769744,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n2\t6-8\t1\n1040\t2\t0\n109\t2\t1\n476\t9+\t0\n29\t9+\t1\n814\t3\t1\n107\t4-5\t1\n112\t4-5\t0\n11\t1\t1\n973\t3\t0\n681\t1\t0\n146\t6-8\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1715349769824,"interpreter":"sql.low","endTime":1715349769905,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view with selected demographics columns","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, CUST_ID, BOOKKEEPING_APPLICATION,BULK_PACK_DISKETTES, EDUCATION,","            FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE,HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1715349769987,"interpreter":"script.low","endTime":1715349770490,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * ","FROM DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349770577,"interpreter":"sql.low","endTime":1715349770676,"results":[{"message":"AFFINITY_CARD\tCUST_ID\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t102547\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t101050\t0\t1\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t100040\t0\t1\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t102117\t1\t0\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t101074\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t104179\t0\t1\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t100417\t0\t0\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t101146\t1\t1\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t103420\t1\t1\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t101987\t1\t1\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1715349770772,"interpreter":"script.low","endTime":1715349770963,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Naive Bayes model for predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1715349771067,"interpreter":"md.low","endTime":1715349771132,"results":[{"message":"<h3 id=\"build-a-naive-bayes-model-for-predicting-affinity_card\">Build a Naive Bayes model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NB_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NAIVE_BAYES';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'NB_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715349771215,"interpreter":"script.low","endTime":1715349774118,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Naive Bayes","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A04C5F4E-1303-44DC-A7DA-185C969330C8\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","* Specify the value of pairwise threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0. ","> v_setlst('NABS_PAIRWISE_THRESHOLD') := '0';","* Specify the value of singleton threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0. ","> v_setlst('NABS_SINGLETON_THRESHOLD') := '0';"],"enabled":true,"result":{"startTime":1715349774203,"interpreter":"md.low","endTime":1715349774266,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-naive-bayes\">Examples of possible setting overrides for Naive Bayes<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A04C5F4E-1303-44DC-A7DA-185C969330C8\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of pairwise threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NABS_PAIRWISE_THRESHOLD') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the value of singleton threshold for the Naive Bayes algorithm. The value required is between 0 and 1 (including the edges), and the default value is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NABS_SINGLETON_THRESHOLD') := '0';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Naive Bayes Model using explicit settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NB_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NAIVE_BAYES';","","    v_setlst('NABS_PAIRWISE_THRESHOLD') := '0';","    v_setlst('NABS_SINGLETON_THRESHOLD') := '0';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        'NB_CLASS_MODEL',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715349774350,"interpreter":"script.low","endTime":1715349776252,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","  FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='NB_CLASS_MODEL'","  ORDER BY VIEW_NAME;"," ","  ","  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the model to a single new record","message":["%sql","","-- Predict the probability of being a high AFFINITY_CARD responder (1) ","--    by providing specific attribute values for a customer.","","SELECT ROUND(PREDICTION_PROBABILITY(NB_CLASS_MODEL, '1' USING ","                                    '3' AS HOUSEHOLD_SIZE, ","                                     5 AS YRS_RESIDENCE, ","                                     1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;","  "],"enabled":true,"result":{"startTime":1715349779504,"interpreter":"sql.low","endTime":1715349779587,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.194\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(NB_CLASS_MODEL USING *) PREDICTION_YRS_RES,","             PREDICTION_DETAILS(NB_CLASS_MODEL USING *) PD","      FROM TEST_DATA_CLAS","      WHERE CUST_ID < 100025","      ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details' PASSING OUT.PD COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349779685,"interpreter":"sql.low","endTime":1715349779833,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100004\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".268\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".256\" \n100009\t0\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".499\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".487\" \n100011\t0\t\"OCCUPATION\" actualValue=\"Farming\" weight=\".3\" \t\"BOOKKEEPING_APPLICATION\" actualValue=\"0\" weight=\".282\" \n100012\t1\t\"EDUCATION\" actualValue=\"PhD\" weight=\"1.321\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".926\" \n100013\t1\t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".912\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".585\" \n100014\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".268\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".256\" \n100018\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".487\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".333\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715349779933,"interpreter":"md.low","endTime":1715349780014,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_NAIVE_BAYES');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL('CLASS_MODEL_2', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","  DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1715349780129,"interpreter":"script.low","endTime":1715349782653,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715349782747,"interpreter":"md.low","endTime":1715349782817,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715349782911,"interpreter":"md.low","endTime":1715349783006,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Classification NN.dsnb b/machine-learning/notebooks/sql/OML4SQL Classification NN.dsnb
deleted file mode 100755
index f250c565..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Classification NN.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification NN","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715349893912,"interpreter":"md.low","endTime":1715349894068,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Neural Network","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification neural network models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715349894151,"interpreter":"md.low","endTime":1715349894246,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-neural-network\">Classification Modeling to Predict Target Customers using Neural Network<\/h1>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification neural network models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715349894329,"interpreter":"md.low","endTime":1715349894406,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>"],"enabled":true,"result":{"startTime":1715349905298,"interpreter":"md.low","endTime":1715349905399,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the demographics data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349894641,"interpreter":"sql.low","endTime":1715349896240,"results":[{"message":"CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\t\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\t\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\t\n100417\t11th\tHandler\t1\t1\t0\t0\t0\t0\t0\t1\t1\t0\t\n101146\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n103420\t< Bach.\t?\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n101987\t< Bach.\tOther\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in demographics table","message":["%sql","","SELECT COUNT(*) FROM SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1715349896323,"interpreter":"sql.low","endTime":1715349896418,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"COUNT\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%sql","","-- In this data set, hyper-responders to the Affinity Card loyalty program are represented as 1's. ","","SELECT AFFINITY_CARD, COUNT(*) COUNT ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","GROUP BY AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1715349896495,"interpreter":"sql.low","endTime":1715349896766,"results":[{"message":"AFFINITY_CARD\tCOUNT\n1\t1072\n0\t3428\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"NUM_CUSTOMERS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD","ORDER BY HOUSEHOLD_SIZE, AFFINITY_CARD"],"enabled":true,"result":{"startTime":1715349896845,"interpreter":"sql.low","endTime":1715349896921,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n681\t1\t0\n11\t1\t1\n1040\t2\t0\n109\t2\t1\n973\t3\t0\n814\t3\t1\n112\t4-5\t0\n107\t4-5\t1\n146\t6-8\t0\n2\t6-8\t1\n476\t9+\t0\n29\t9+\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"aggregationOption\":\"Last\",\"visualizations\":{\"stackValue\":\"on\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1715349897004,"interpreter":"sql.low","endTime":1715349897081,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view with selected demographics columns","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, BOOKKEEPING_APPLICATION,BULK_PACK_DISKETTES, CUST_ID, EDUCATION,","            FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE,HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1715349897159,"interpreter":"script.low","endTime":1715349897536,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * ","FROM DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349897630,"interpreter":"sql.low","endTime":1715349897726,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t0\t1\t102547\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t101050\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t100040\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t1\t0\t102117\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t0\t1\t101074\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t1\t104179\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t0\t100417\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t1\t1\t101146\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t103420\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t101987\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","  ","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS ","  SELECT * FROM DEMOGRAPHICS4_V ","  MINUS ","  SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1715349897807,"interpreter":"script.low","endTime":1715349897934,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network classification model for predicting AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1715349898018,"interpreter":"md.low","endTime":1715349898081,"results":[{"message":"<h3 id=\"build-a-neural-network-classification-model-for-predicting-affinity_card\">Build a Neural Network classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a NN model using default settings","message":["%script","","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715349898157,"interpreter":"script.low","endTime":1715349904060,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Neural Network","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4');\">Neural Network<\/a>","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`, `NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and <strong>starting from Oracle Database 23ai<\/strong> `NNET_ACTIVATIONS_RELU`.","> v_setlst('NNET_HIDDEN_LAYERS') := '3';","","> v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","","> v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';","    ","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","","> v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","","> v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> v_setlst('NNET_REG_LAMBDA') := '1';","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> v_setlst('NNET_ITERATIONS') := '200';","","> v_setlst('NNET_TOLERANCE') := '0.000001';","","> v_setlst('ODMS_RANDOM_SEED') := '0';","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","    v_setlst('LBFGS_HISTORY_DEPTH') := '10';","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';","","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';","","* Specify the lower bound of the region where weights are randomly initialized. `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` must be set together. Setting one and not setting the other raises an error. `NNET_WEIGHT_LOWER_BOUND` must not be greater than `NNET_WEIGHT_UPPER_BOUND`. The default value is -sqrt(6/(l_nodes+r_nodes)). ","   The value of l_nodes for `input layer dense attributes` is (1+number of dense attributes), `input layer sparse attributes` is number of sparse attributes, and `each hidden layer` is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.","   If not specified, the values of `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` are system determined.","> v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'","> v_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'","    ","```"],"enabled":true,"result":{"startTime":1715349904150,"interpreter":"md.low","endTime":1715349904265,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-neural-network\">Examples of possible setting overrides for Neural Network<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4');\">Neural Network<\/a><\/li>\n<li>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/li>\n<li>Specify a row weight column<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>, <code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and <strong>starting from Oracle Database 23ai<\/strong> <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_HIDDEN_LAYERS') := '3';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_HELDASIDE_RATIO') := '0.25';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_REG_LAMBDA') := '1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_TOLERANCE') := '0.000001';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('ODMS_RANDOM_SEED') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.\nv_setlst('LBFGS_HISTORY_DEPTH') := '10';<\/li>\n<li>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the lower bound of the region where weights are randomly initialized. <code>NNET_WEIGHT_LOWER_BOUND<\/code> and <code>NNET_WEIGHT_UPPER_BOUND<\/code> must be set together. Setting one and not setting the other raises an error. <code>NNET_WEIGHT_LOWER_BOUND<\/code> must not be greater than <code>NNET_WEIGHT_UPPER_BOUND<\/code>. The default value is -sqrt(6/(l_nodes+r_nodes)).\nThe value of l_nodes for <code>input layer dense attributes<\/code> is (1+number of dense attributes), <code>input layer sparse attributes<\/code> is number of sparse attributes, and <code>each hidden layer<\/code> is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.\nIf not specified, the values of <code>NNET_WEIGHT_LOWER_BOUND<\/code> and <code>NNET_WEIGHT_UPPER_BOUND<\/code> are system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'\nv_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'<\/p>\n<\/blockquote>\n<pre><code><\/code><\/pre>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use 3 hidden layers and specific activation functions ","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ","                                     ''NNET_ACTIVATIONS_LOG_SIG'', ","                                     ''NNET_ACTIVATIONS_ARCTAN''';","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_CLASS_MODEL2',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715349904350,"interpreter":"script.low","endTime":1715349907852,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use held aside regularization and early stopping ","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_CLASS_MODEL3');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS') := '3';","    v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ","                                     ''NNET_ACTIVATIONS_LOG_SIG'', ","                                     ''NNET_ACTIVATIONS_ARCTAN''';","    v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","    v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","    v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    v_setlst('NNET_ITERATIONS') := '200';","    v_setlst('NNET_TOLERANCE') := '0.000001';","    v_setlst('ODMS_RANDOM_SEED') := '0';    ","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        'NN_CLASS_MODEL3',","        'CLASSIFICATION',","        'SELECT * FROM TRAIN_DATA_CLAS',","        v_setlst,","        'CUST_ID',","        'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715349907934,"interpreter":"script.low","endTime":1715349909837,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='NN_CLASS_MODEL'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1715349909916,"interpreter":"sql.low","endTime":1715349910017,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VANN_CLASS_MODEL\tNeural Network Weights\nDM$VCNN_CLASS_MODEL\tScoring Cost Matrix\nDM$VGNN_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VNNN_CLASS_MODEL\tNormalization and Missing Value Handling\nDM$VSNN_CLASS_MODEL\tComputed Settings\nDM$VTNN_CLASS_MODEL\tClassification Targets\nDM$VWNN_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get general settings","message":["%sql","","SELECT NAME, NUMERIC_VALUE, STRING_VALUE from DM$VGNN_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715349910101,"interpreter":"sql.low","endTime":1715349910202,"results":[{"message":"NAME\tNUMERIC_VALUE\tSTRING_VALUE\nNUM_ROWS\t2679\t\nITERATIONS\t200\t\nLOSS_VALUE\t0.076290427056661489\t\nCONVERGED\t\tNO\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get information for missing value handling","message":["%sql","","SELECT * FROM DM$VNNN_CLASS_MODEL"],"enabled":true,"result":{"startTime":1715349910371,"interpreter":"sql.low","endTime":1715349910473,"results":[{"message":"PARTITION_NAME\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tNUMERIC_MISSING_VALUE\tCATEGORICAL_MISSING_VALUE\tNORMALIZATION_SHIFT\tNORMALIZATION_SCALE\n\tEDUCATION\t\t\tHS-grad\t\t\n\tHOUSEHOLD_SIZE\t\t\t3\t\t\n\tOCCUPATION\t\t\tCrafts\t\t\n\tBOOKKEEPING_APPLICATION\t\t0.8783128032848078\t\t0.8783128032848078\t0.3269852183756386\n\tBULK_PACK_DISKETTES\t\t0.6360582306830922\t\t0.6360582306830922\t0.48122198469071364\n\tFLAT_PANEL_MONITOR\t\t0.5759611795446065\t\t0.5759611795446065\t0.49428847631749057\n\tHOME_THEATER_PACKAGE\t\t0.5591638671145956\t\t0.5591638671145956\t0.49657998650332785\n\tOS_DOC_SET_KANJI\t\t0.0029861888764464357\t\t0.0029861888764464357\t0.054574566460277744\n\tPRINTER_SUPPLIES\t\t1.0\t\t1.0\t1.0\n\tYRS_RESIDENCE\t\t4.001493094438232\t\t4.001493094438232\t1.9178414346193704\n\tY_BOX_GAMES\t\t0.3154162000746549\t\t0.3154162000746549\t0.4647681695563441\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","  DBMS_DATA_MINING.APPLY('NN_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');","  DBMS_DATA_MINING.COMPUTE_LIFT('APPLY_RESULT','TEST_DATA_CLAS','CUST_ID','AFFINITY_CARD',","                                'LIFT_TABLE','1','PREDICTION','PROBABILITY',100);","END;"],"enabled":true,"result":{"startTime":1715349910556,"interpreter":"script.low","endTime":1715349911960,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"line\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"QUANTILE_NUMBER\",\"GAIN_CUMULATIVE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"line","title":"View model's cumulative gains (lift) chart","message":["%sql","","","SELECT QUANTILE_NUMBER, GAIN_CUMULATIVE FROM LIFT_TABLE;"],"enabled":true,"result":{"startTime":1715349912046,"interpreter":"sql.low","endTime":1715349912143,"results":[{"message":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t3.58302771327007253012048192771084337349E-02\n2\t7.16605542654014506024096385542168674699E-02\n3\t1.13253012048192771084337349397590361446E-01\n4\t1.59036144578313253012048192771084337349E-01\n5\t1.95180722891566265060240963855421686747E-01\n6\t2.19277108433734939759036144578313253012E-01\n7\t2.5542168674698795180722891566265060241E-01\n8\t2.93373493975903614457831325301204819277E-01\n9\t3.30120481927710843373493975903614457831E-01\n10\t3.74698795180722891566265060240963855422E-01\n11\t4.06746995305440506024096385542168674699E-01\n12\t4.30923682523060987951807228915662650602E-01\n13\t4.57831325301204819277108433734939759036E-01\n14\t4.84337349397590361445783132530120481928E-01\n15\t5.10843373493975903614457831325301204819E-01\n16\t5.37349397590361445783132530120481927711E-01\n17\t5.63052221091396843373493975903614457831E-01\n18\t5.85542168674698795180722891566265060241E-01\n19\t6.15662650602409638554216867469879518072E-01\n20\t6.31325301204819277108433734939759036145E-01\n21\t6.53012048192771084337349397590361445783E-01\n22\t6.69879518072289156626506024096385542169E-01\n23\t6.95903585043298192771084337349397590361E-01\n24\t7.17269051793109927710843373493975903615E-01\n25\t7.37349397590361445783132530120481927711E-01\n26\t7.59036144578313253012048192771084337349E-01\n27\t7.81204848691641566265060240963855421687E-01\n28\t8.0361445783132530120481927710843373494E-01\n29\t8.12048192771084337349397590361445783133E-01\n30\t8.24096385542168674698795180722891566265E-01\n31\t8.28915662650602409638554216867469879518E-01\n32\t8.4819277108433734939759036144578313253E-01\n33\t8.53012048192771084337349397590361445783E-01\n34\t8.57831325301204819277108433734939759036E-01\n35\t8.57831325301204819277108433734939759036E-01\n36\t8.65060240963855421686746987951807228916E-01\n37\t8.67469879518072289156626506024096385542E-01\n38\t8.72289156626506024096385542168674698795E-01\n39\t8.72289156626506024096385542168674698795E-01\n40\t8.74698795180722891566265060240963855422E-01\n41\t8.79518072289156626506024096385542168675E-01\n42\t8.84337349397590361445783132530120481928E-01\n43\t8.89156626506024096385542168674698795181E-01\n44\t8.98795180722891566265060240963855421687E-01\n45\t9.0361445783132530120481927710843373494E-01\n46\t9.0361445783132530120481927710843373494E-01\n47\t9.0361445783132530120481927710843373494E-01\n48\t9.10843373493975903614457831325301204819E-01\n49\t9.20481927710843373493975903614457831325E-01\n50\t9.27710843373493975903614457831325301205E-01\n51\t9.30120481927710843373493975903614457831E-01\n52\t9.30120481927710843373493975903614457831E-01\n53\t9.32530120481927710843373493975903614458E-01\n54\t9.39759036144578313253012048192771084337E-01\n55\t9.4457831325301204819277108433734939759E-01\n56\t9.46987951807228915662650602409638554217E-01\n57\t9.46987951807228915662650602409638554217E-01\n58\t9.5180722891566265060240963855421686747E-01\n59\t9.5180722891566265060240963855421686747E-01\n60\t9.61445783132530120481927710843373493976E-01\n61\t9.61445783132530120481927710843373493976E-01\n62\t9.61445783132530120481927710843373493976E-01\n63\t9.63855421686746987951807228915662650602E-01\n64\t9.66265060240963855421686746987951807229E-01\n65\t9.66265060240963855421686746987951807229E-01\n66\t9.66265060240963855421686746987951807229E-01\n67\t9.71084337349397590361445783132530120482E-01\n68\t9.71084337349397590361445783132530120482E-01\n69\t9.75903614457831325301204819277108433735E-01\n70\t9.76554852221385542168674698795180722892E-01\n71\t9.77336396366716867469879518072289156627E-01\n72\t9.78117866975715373493975903614457831325E-01\n73\t9.7889941112104669879518072289156626506E-01\n74\t9.79680881730045180722891566265060240964E-01\n75\t9.80462425875376506024096385542168674699E-01\n76\t9.81243896484374987951807228915662650602E-01\n77\t9.82025367093373493975903614457831325301E-01\n78\t9.82806911238704819277108433734939759036E-01\n79\t9.83588381847703325301204819277108433735E-01\n80\t9.8436992599303465060240963855421686747E-01\n81\t9.85151396602033132530120481927710843374E-01\n82\t9.85932940747364457831325301204819277108E-01\n83\t9.86714411356362939759036144578313253012E-01\n84\t9.87495955501694265060240963855421686747E-01\n85\t9.88277426110692771084337349397590361446E-01\n86\t9.89058970256024096385542168674698795181E-01\n87\t9.8984044086502260240963855421686746988E-01\n88\t9.90621911474021084337349397590361445783E-01\n89\t9.91403455619352409638554216867469879518E-01\n90\t9.92184926228350891566265060240963855422E-01\n91\t9.92966470373682216867469879518072289157E-01\n92\t9.93747940982680722891566265060240963855E-01\n93\t9.9452948512801204819277108433734939759E-01\n94\t9.95310955737010554216867469879518072289E-01\n95\t9.96092499882341879518072289156626506024E-01\n96\t9.96873970491340361445783132530120481928E-01\n97\t9.97655514636671686746987951807228915663E-01\n98\t9.98436985245670168674698795180722891566E-01\n99\t9.99218529391001493975903614457831325301E-01\n100\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display 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We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715349913360,"interpreter":"md.low","endTime":1715349913426,"results":[{"message":"<h2 id=\"create-classification-neural-network-model-using-a-settings-table\">Create Classification Neural Network model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. 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\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Classification RF.dsnb b/machine-learning/notebooks/sql/OML4SQL Classification RF.dsnb
deleted file mode 100755
index 1e4ce23c..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Classification RF.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification RF","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715350480225,"interpreter":"md.low","endTime":1715350480303,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Classification Modeling to Predict Target Customers using Random Forest","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification random forest models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715350480379,"interpreter":"md.low","endTime":1715350480457,"results":[{"message":"<h1 id=\"classification-modeling-to-predict-target-customers-using-random-forest\">Classification Modeling to Predict Target Customers using Random Forest<\/h1>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification random forest models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" 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href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>"],"enabled":true,"result":{"startTime":1715350480689,"interpreter":"md.low","endTime":1715350480749,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-random-forest\" 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116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD","message":["%sql","","SELECT HOUSEHOLD_SIZE,","       SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,","       SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","      FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS ","      GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1715350483073,"interpreter":"sql.low","endTime":1715350483147,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view with desired columns","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V ","  AS SELECT AFFINITY_CARD, BOOKKEEPING_APPLICATION,","            BULK_PACK_DISKETTES, CUST_ID, EDUCATION,","            FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, ","            HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,","            PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES","     FROM   SH.SUPPLEMENTARY_DEMOGRAPHICS;","     "],"enabled":true,"result":{"startTime":1715350483225,"interpreter":"script.low","endTime":1715350483724,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * ","FROM   DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350483801,"interpreter":"sql.low","endTime":1715350483898,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t0\t1\t102547\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t101050\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t100040\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t1\t0\t102117\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t0\t1\t101074\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t1\t104179\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t0\t100417\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t1\t1\t101146\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t103420\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t101987\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40)","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS  AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1715350483983,"interpreter":"script.low","endTime":1715350484078,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build Random Forest model to Predict AFFINITY_CARD","","---"],"enabled":true,"result":{"startTime":1715350484160,"interpreter":"md.low","endTime":1715350484223,"results":[{"message":"<h3 id=\"build-random-forest-model-to-predict-affinity_card\">Build Random Forest model to Predict AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_RANDOM_FOREST';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'RF_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","END;"],"enabled":true,"result":{"startTime":1715350484300,"interpreter":"script.low","endTime":1715350486297,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Random Forest ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","**NOTE:** Random Forest makes use of the Decision Tree settings to configure the construction of individual trees. ","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" target=\"_blank\">Algorithm Settings - Random Forest<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Algorithm Settings - Decision Tree<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","#### These settings configure the behavior of the Random Forest algorithm ","","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.","> v_setlst('RFOR_NUM_TREES') := '20';","","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)","> v_setlst('RFOR_SAMPLING_RATIO') := '0.5';","","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.","> v_setlst('RFOR_MTRY') := '0';","","#### Decision Tree settings to configure the construction of individual trees.","    ","* Specify Tree impurity metric for each Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","> v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","> v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","> v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","    ","* Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","> v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ","> v_setlst('TREE_TERM_MINREC_NODE') := '10';","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","> v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","> v_setlst('CLAS_MAX_SUP_BINS') := '32';    ","    "],"enabled":true,"result":{"startTime":1715350486381,"interpreter":"md.low","endTime":1715350486447,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-random-forest\">Examples of possible setting overrides for Random Forest<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p><strong>NOTE:<\/strong> Random Forest makes use of the Decision Tree settings to configure the construction of individual trees.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" target=\"_blank\">Algorithm Settings - Random Forest<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" target=\"_blank\">Algorithm Settings - Decision Tree<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<h4 id=\"these-settings-configure-the-behavior-of-the-random-forest-algorithm\">These settings configure the behavior of the Random Forest algorithm<\/h4>\n<ul>\n<li>Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_NUM_TREES') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_SAMPLING_RATIO') := '0.5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('RFOR_MTRY') := '0';<\/p>\n<\/blockquote>\n<h4 id=\"decision-tree-settings-to-configure-the-construction-of-individual-trees\">Decision Tree settings to configure the construction of individual trees.<\/h4>\n<ul>\n<li>Specify Tree impurity metric for each Tree.\nTree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini <code>TREE_IMPURITY_GINI<\/code> or entropy <code>TREE_IMPURITY_ENTROPY<\/code> as the purity metric. By default, the algorithm uses <code>TREE_IMPURITY_GINI<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\nFor Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MAX_DEPTH') := '7';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\nIt requires a number between 0 and 10.  The default is 0.05, indicating 0.05%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\nIt requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The minimum number of rows in a node.\nIt requires a number greater than or equal to zero. The default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_NODE') := '10';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\nNo split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('TREE_TERM_MINREC_SPLIT') := '20';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of bins for each attribute.\nFor Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLAS_MAX_SUP_BINS') := '32';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest Model specifying additional settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')           := 'ON';","    v_setlst('ALGO_NAME')           := 'ALGO_RANDOM_FOREST';","    v_setlst('RFOR_MTRY')           := '3';","    v_setlst('RFOR_NUM_TREES')      := '100';","    v_setlst('RFOR_SAMPLING_RATIO') := '0.5';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'RF_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD'","        );","END;"],"enabled":true,"result":{"startTime":1715350486531,"interpreter":"script.low","endTime":1715350488528,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='RF_CLASS_MODEL'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1715350488607,"interpreter":"sql.low","endTime":1715350488702,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VARF_CLASS_MODEL\tVariable Importance\nDM$VCRF_CLASS_MODEL\tScoring Cost Matrix\nDM$VGRF_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VSRF_CLASS_MODEL\tComputed Settings\nDM$VTRF_CLASS_MODEL\tClassification Targets\nDM$VWRF_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get attribute importance from RF model","message":["%sql","","SELECT ATTRIBUTE_NAME, round(ATTRIBUTE_IMPORTANCE, 3) IMPORTANCE ","FROM   DM$VARF_CLASS_MODEL","ORDER BY ATTRIBUTE_IMPORTANCE 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"],"enabled":true,"result":{"startTime":1715350492311,"interpreter":"sql.low","endTime":1715350492408,"results":[{"message":"CUST_ID\tPREDICTION_AFFINITY_CARD\tPROBABILITY\n100004\t0\t0.915\n100009\t0\t0.637\n100011\t0\t0.964\n100012\t1\t0.759\n100013\t0\t0.528\n100014\t0\t0.88\n100018\t0\t0.839\n100026\t0\t0.554\n100027\t0\t0.809\n100033\t0\t0.915\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_AFFINITY_CARD,3) PRED,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID,","             PREDICTION(RF_CLASS_MODEL USING *) PREDICTION_AFFINITY_CARD,","             PREDICTION_DETAILS(RF_CLASS_MODEL USING *) PD","      FROM TEST_DATA_CLAS","      WHERE CUST_ID < 100025","      ORDER BY CUST_ID) OUT,","     XMLTABLE('/Details' PASSING OUT.PD COLUMNS ","              \"Attribute1\" XMLType PATH 'Attribute[1]',","              \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350492482,"interpreter":"sql.low","endTime":1715350492578,"results":[{"message":"CUST_ID\tPRED\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100004\t0\t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".822\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".157\" \n100009\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\"13.078\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"4.37\" \n100011\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"4.745\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\"2.305\" \n100012\t1\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\"2.515\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\"1.931\" \n100013\t0\t\"EDUCATION\" actualValue=\"HS-grad\" weight=\".046\" \t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".041\" \n100014\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".235\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".161\" \n100018\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\"8.169\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\"4.641\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715350492662,"interpreter":"md.low","endTime":1715350492719,"results":[{"message":"<h2 id=\"create-classification-model-using-a-settings-table\">Create Classification model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create settings table and build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_RANDOM_FOREST');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL('CLASS_MODEL_2', 'CLASSIFICATION', 'TRAIN_DATA_CLAS', 'CUST_ID',' AFFINITY_CARD', 'N1_BUILD_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1715350492797,"interpreter":"script.low","endTime":1715350494395,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715350494474,"interpreter":"md.low","endTime":1715350494534,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715350494611,"interpreter":"md.low","endTime":1715350494670,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Classification SVM.dsnb b/machine-learning/notebooks/sql/OML4SQL Classification SVM.dsnb
deleted file mode 100755
index 1738a361..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Classification SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715312793797,"interpreter":"md.medium","endTime":1715312793861,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification Modeling to Predict Target Customers using Support Vector Machine","","In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification support vector machine models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715312793938,"interpreter":"md.medium","endTime":1715312794172,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine\">Classification Modeling to Predict Target Customers using Support Vector Machine<\/h2>\n<p>In this notebook, we  predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage.  This notebook builds and applies classification support vector machine models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715312794252,"interpreter":"md.medium","endTime":1715312794314,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715312794393,"interpreter":"md.medium","endTime":1715312794455,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS 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Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1715312796612,"interpreter":"script.medium","endTime":1715312796797,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Linear classification model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1715312796875,"interpreter":"md.medium","endTime":1715312796936,"results":[{"message":"<h3 id=\"build-a-svm-linear-classification-model-for-predicting-affinity_card\">Build a SVM Linear classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715312797016,"interpreter":"script.medium","endTime":1715312800616,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","   ","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian.","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';","   ","- Value of complexity factor for SVM algorithm (both classification and regression).  Default value estimated from the data by the algorithm.","> v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","   ","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L2';","","> v_setlst('SVMS_EPSILON') := '0.1';","   ","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> v_setlst('SVMS_BATCH_ROWS') := '20000';","   ","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';"],"enabled":true,"result":{"startTime":1715312800699,"interpreter":"md.medium","endTime":1715312800785,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression).  Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L2';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_EPSILON') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_BATCH_ROWS') := '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using linear kernel with options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","    v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","    v_setlst('SVMS_CONV_TOLERANCE') := '0.001'; ","    v_setlst('SVMS_BATCH_ROWS') := '2000';","    v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715312800865,"interpreter":"script.medium","endTime":1715312803164,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Gaussian classification model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1715312803246,"interpreter":"md.medium","endTime":1715312803316,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-classification-model-for-predicting-affinity_card\">Build a SVM Gaussian classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM model using Gaussian kernel with options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN'; ","    v_setlst('SVMS_CONV_TOLERANCE') := '0.001'; ","    v_setlst('SVMS_NUM_PIVOTS') := '200';","   ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_CLASS_MODEL',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_CLAS',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715312803391,"interpreter":"script.medium","endTime":1715312807289,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","  FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='SVM_CLASS_MODEL'","  ORDER BY VIEW_NAME;","  ","  ","  ","  "],"enabled":true,"result":{"startTime":1715312807372,"interpreter":"sql.medium","endTime":1715312807496,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VCSVM_CLASS_MODEL\tScoring Cost Matrix\nDM$VGSVM_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VNSVM_CLASS_MODEL\tNormalization and Missing Value Handling\nDM$VSSVM_CLASS_MODEL\tComputed Settings\nDM$VTSVM_CLASS_MODEL\tClassification Targets\nDM$VWSVM_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get coefficients of SVM model","message":["%sql","","SELECT * from DM$VNSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715312807578,"interpreter":"sql.medium","endTime":1715312807706,"results":[{"message":"PARTITION_NAME\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tNUMERIC_MISSING_VALUE\tCATEGORICAL_MISSING_VALUE\tNORMALIZATION_SHIFT\tNORMALIZATION_SCALE\n\tEDUCATION\t\t\tHS-grad\t\t\n\tHOUSEHOLD_SIZE\t\t\t3\t\t\n\tOCCUPATION\t\t\tCrafts\t\t\n\tBOOKKEEPING_APPLICATION\t\t0.8783128032848078\t\t0.8783128032848078\t0.3269852183756386\n\tBULK_PACK_DISKETTES\t\t0.6360582306830922\t\t0.6360582306830922\t0.48122198469071364\n\tFLAT_PANEL_MONITOR\t\t0.5759611795446065\t\t0.5759611795446065\t0.49428847631749057\n\tHOME_THEATER_PACKAGE\t\t0.5591638671145956\t\t0.5591638671145956\t0.49657998650332785\n\tOS_DOC_SET_KANJI\t\t0.0029861888764464357\t\t0.0029861888764464357\t0.054574566460277744\n\tPRINTER_SUPPLIES\t\t1.0\t\t1.0\t1.0\n\tYRS_RESIDENCE\t\t4.001493094438232\t\t4.001493094438232\t1.9178414346193704\n\tY_BOX_GAMES\t\t0.3154162000746549\t\t0.3154162000746549\t0.4647681695563441\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get computed settings","message":["%sql","","SELECT * from DM$VSSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715312807789,"interpreter":"sql.medium","endTime":1715312807894,"results":[{"message":"PARTITION_NAME\tSETTING_NAME\tSETTING_VALUE\n\tSVMS_SOLVER\tSVMS_SOLVER_IPM\n\tSVMS_STD_DEV\t2.3452078799117149\n\tSVMS_NUM_ITERATIONS\t30\n\tSVMS_COMPLEXITY_FACTOR\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get global settings","message":["%sql","","SELECT * from DM$VGSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715312807976,"interpreter":"sql.medium","endTime":1715312808078,"results":[{"message":"PARTITION_NAME\tNAME\tNUMERIC_VALUE\tSTRING_VALUE\n\tNUM_ROWS\t2679\t\n\tCONVERGED\t\tYES\n\tITERATIONS\t16\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN","  DBMS_DATA_MINING.APPLY('SVM_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');","  DBMS_DATA_MINING.COMPUTE_LIFT('APPLY_RESULT','TEST_DATA_CLAS','CUST_ID','AFFINITY_CARD',","                                'LIFT_TABLE','1','PREDICTION','PROBABILITY',100);","END;"],"enabled":true,"result":{"startTime":1715312808160,"interpreter":"script.medium","endTime":1715312812259,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"area\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 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QUANTILE_NUMBER, GAIN_CUMULATIVE FROM LIFT_TABLE;"],"enabled":true,"result":{"startTime":1715312812339,"interpreter":"sql.medium","endTime":1715312812468,"results":[{"message":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t3.4562211981566820276497695852534562212E-02\n2\t6.68202764976958525345622119815668202765E-02\n3\t9.71033562163603479262672811059907834101E-02\n4\t1.27496156824349258064516129032258064516E-01\n5\t1.53225806451612903225806451612903225807E-01\n6\t1.88940092165898617511520737327188940092E-01\n7\t2.21198156682027649769585253456221198157E-01\n8\t2.58064516129032258064516129032258064516E-01\n9\t2.90322580645161290322580645161290322581E-01\n10\t3.20276497695852534562211981566820276498E-01\n11\t3.41013824884792626728110599078341013825E-01\n12\t3.73271889400921658986175115207373271889E-01\n13\t4.07834101382488479262672811059907834101E-01\n14\t4.44700460829493087557603686635944700461E-01\n15\t4.67741935483870967741935483870967741936E-01\n16\t4.91935483870967741935483870967741935484E-01\n17\t5.23041474654377880184331797235023041475E-01\n18\t5.47619059338547672811059907834101382489E-01\n19\t5.71428571428571428571428571428571428571E-01\n20\t6.01382488479262672811059907834101382489E-01\n21\t6.19815668202764976958525345622119815668E-01\n22\t6.24423963133640552995391705069124423963E-01\n23\t6.40552995391705069124423963133640552995E-01\n24\t6.56682027649769585253456221198156682028E-01\n25\t6.7281105990783410138248847926267281106E-01\n26\t6.84331797235023041474654377880184331797E-01\n27\t6.98156682027649769585253456221198156682E-01\n28\t7.16589861751152073732718894009216589862E-01\n29\t7.30414746543778801843317972350230414747E-01\n30\t7.48847926267281105990783410138248847926E-01\n31\t7.60368663594470046082949308755760368664E-01\n32\t7.71889400921658986175115207373271889401E-01\n33\t7.85714285714285714285714285714285714286E-01\n34\t8.00691244239631336405529953917050691244E-01\n35\t8.04147465437788018433179723502304147465E-01\n36\t8.06451612903225806451612903225806451613E-01\n37\t8.20276497695852534562211981566820276498E-01\n38\t8.31797235023041474654377880184331797235E-01\n39\t8.38709677419354838709677419354838709677E-01\n40\t8.38709677419354838709677419354838709677E-01\n41\t8.38709677419354838709677419354838709677E-01\n42\t8.43317972350230414746543778801843317972E-01\n43\t8.47926267281105990783410138248847926267E-01\n44\t8.59447004608294930875576036866359447005E-01\n45\t8.66359447004608294930875576036866359447E-01\n46\t8.75576036866359447004608294930875576037E-01\n47\t8.82488479262672811059907834101382488479E-01\n48\t8.84792626728110599078341013824884792627E-01\n49\t8.98617511520737327188940092165898617512E-01\n50\t9.03225806451612903225806451612903225807E-01\n51\t9.12442396313364055299539170506912442396E-01\n52\t9.14746543778801843317972350230414746544E-01\n53\t9.19354838709677419354838709677419354839E-01\n54\t9.26267281105990783410138248847926267281E-01\n55\t9.35483870967741935483870967741935483871E-01\n56\t9.37788018433179723502304147465437788018E-01\n57\t9.42396313364055299539170506912442396313E-01\n58\t9.44700460829493087557603686635944700461E-01\n59\t9.47004608294930875576036866359447004608E-01\n60\t9.56221198156682027649769585253456221198E-01\n61\t9.60829493087557603686635944700460829493E-01\n62\t9.63133640552995391705069124423963133641E-01\n63\t9.65437788018433179723502304147465437788E-01\n64\t9.67741935483870967741935483870967741936E-01\n65\t9.70046082949308755760368663594470046083E-01\n66\t9.7235023041474654377880184331797235023E-01\n67\t9.74654377880184331797235023041474654378E-01\n68\t9.79262672811059907834101382488479262673E-01\n69\t9.8156682027649769585253456221198156682E-01\n70\t9.83870967741935483870967741935483870968E-01\n71\t9.83870967741935483870967741935483870968E-01\n72\t9.83870967741935483870967741935483870968E-01\n73\t9.86175115207373271889400921658986175115E-01\n74\t9.88479262672811059907834101382488479263E-01\n75\t9.88479262672811059907834101382488479263E-01\n76\t9.88479262672811059907834101382488479263E-01\n77\t9.88479262672811059907834101382488479263E-01\n78\t9.88479262672811059907834101382488479263E-01\n79\t9.88479262672811059907834101382488479263E-01\n80\t9.88479262672811059907834101382488479263E-01\n81\t9.88479262672811059907834101382488479263E-01\n82\t9.88479262672811059907834101382488479263E-01\n83\t9.9078341013824884792626728110599078341E-01\n84\t9.9078341013824884792626728110599078341E-01\n85\t9.93087557603686635944700460829493087558E-01\n86\t9.93087557603686635944700460829493087558E-01\n87\t9.93087557603686635944700460829493087558E-01\n88\t9.93087557603686635944700460829493087558E-01\n89\t9.93087557603686635944700460829493087558E-01\n90\t9.95391705069124423963133640552995391705E-01\n91\t9.95391705069124423963133640552995391705E-01\n92\t9.95391705069124423963133640552995391705E-01\n93\t9.95391705069124423963133640552995391705E-01\n94\t9.97695852534562211981566820276497695853E-01\n95\t9.97695852534562211981566820276497695853E-01\n96\t9.97695852534562211981566820276497695853E-01\n97\t1\n98\t1\n99\t1\n100\t1\n","type":"TAB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"],"enabled":true,"result":{"startTime":1715312812558,"interpreter":"sql.medium","endTime":1715312812687,"results":[{"message":"CUST_ID\tPRED\tPROB\tCOST\n102326\t1\t1\t0\n104307\t1\t1\t0\n100035\t1\t1\t0\n104364\t1\t0.999\t0\n101801\t1\t0.999\t0\n101148\t1\t0.999\t0\n100486\t1\t0.999\t0\n101863\t1\t0.999\t0\n101620\t1\t0.999\t0\n103873\t1\t0.999\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select and view likely and unlikely AFFINITY_CARD responders ","message":["%sql ","","SELECT CUST_ID, PREDICTION, ROUND(PROBABILITY,2) PROB, ROUND(COST,2) COST","FROM   APPLY_RESULT ","WHERE  PREDICTION = ${PREDICTION='1','1'|'0'} ","AND    PROBABILITY > 0.7 ","ORDER BY PROBABILITY DESC","FETCH 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ONLY;"],"enabled":true,"result":{"startTime":1715312812952,"interpreter":"sql.medium","endTime":1715312813683,"results":[{"message":"CUST_ID\tPROBABILITY\n104307\t0.9999997383737623\n102326\t0.9999997383737623\n100035\t0.9996180055888897\n104364\t0.9994594763547683\n101148\t0.9992592828901385\n101801\t0.9992592828901385\n100486\t0.9992338480865646\n101863\t0.9989400089989504\n101620\t0.9989260016118109\n103873\t0.9989144945184821\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Apply the model to a single new record","message":["%sql","","-- Predict the probability of being a high AFFINITY_CARD responder (1) ","--    by providing specific attribute values for a customer.","","SELECT ROUND(PREDICTION_PROBABILITY(SVM_CLASS_MODEL, '1' USING ","                                    '3' AS HOUSEHOLD_SIZE, ","                                     5 AS YRS_RESIDENCE, ","                                     1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;","  "],"enabled":true,"result":{"startTime":1715312813766,"interpreter":"sql.medium","endTime":1715312813844,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.182\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","    RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,","     PREDICTION(SVM_CLASS_MODEL USING *) PREDICTION_YRS_RES,","     PREDICTION_DETAILS(SVM_CLASS_MODEL USING *) PD","    FROM TRAIN_DATA_CLAS","    ORDER BY CUST_ID) OUT,","    XMLTABLE('/Details'","    PASSING OUT.PD","    COLUMNS ","    \"Attribute1\" XMLType PATH 'Attribute[1]',","    \"Attribute2\" XMLType PATH 'Attribute[2]',","    \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715312813933,"interpreter":"sql.medium","endTime":1715312816639,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".913\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".066\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".036\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".839\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".018\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".016\" \n100005\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".337\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".013\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".011\" \n100007\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".024\" \t\t\n100008\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".029\" \t\t\n100009\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".638\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".38\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".373\" \n100011\t0\t\"OCCUPATION\" actualValue=\"Farming\" weight=\".084\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".007\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\"-.002\" \n100013\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".088\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".027\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".016\" \n100015\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".898\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".428\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".025\" \n100016\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".756\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".003\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".002\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715312816725,"interpreter":"md.medium","endTime":1715312816789,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_SUPPORT_VECTOR_MACHINES');","  INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');","","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'CLASS_MODEL_2', ","    MINING_FUNCTION     => 'CLASSIFICATION', ","    DATA_TABLE_NAME     => 'TRAIN_DATA_CLAS', ","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'AFFINITY_CARD', ","    SETTINGS_TABLE_NAME => 'N1_BUILD_SETTINGS');","  DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1715312816868,"interpreter":"script.medium","endTime":1715312818966,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715312819051,"interpreter":"md.medium","endTime":1715312819112,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715312819198,"interpreter":"md.medium","endTime":1715312819259,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Clustering EM.dsnb b/machine-learning/notebooks/sql/OML4SQL Clustering EM.dsnb
deleted file mode 100755
index b311e480..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Clustering EM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Clustering EM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715349091630,"interpreter":"md.low","endTime":1715349091707,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identifying Customer Segments using Expectation Maximization Clustering","","Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.","","See the documentation link below for details on the Expectation Maximization in-database algortihm.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715349091783,"interpreter":"md.low","endTime":1715349091864,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1715349091948,"interpreter":"md.low","endTime":1715349092024,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1715349092101,"interpreter":"md.low","endTime":1715349092162,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view joining CUSTOMERS and DEMOGRAPHICS","message":["%script","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, ","          a.CUST_YEAR_OF_BIRTH, a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, ","          b.EDUCATION, b.AFFINITY_CARD, ","          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","   FROM SH.CUSTOMERS a, SH.SUPPLEMENTARY_DEMOGRAPHICS b","   WHERE a.CUST_ID = b.CUST_ID;","   "],"enabled":true,"result":{"startTime":1715349092239,"interpreter":"script.low","endTime":1715349094138,"results":[{"message":"\nView CUSTOMERS360_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in CUSTOMERS360_V","message":["%sql","","SELECT COUNT(*) COUNT","FROM CUSTOMERS360_V;"],"enabled":true,"result":{"startTime":1715349094217,"interpreter":"sql.low","endTime":1715349094368,"results":[{"message":"COUNT\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data in view CUSTOMERS360_V","message":["%sql","","SELECT * ","FROM CUSTOMERS360_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349094449,"interpreter":"sql.low","endTime":1715349094569,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\n100804\tF\tDivorc.\t1943\tA: Below 30,000\t1500\t< Bach.\t0\t2\tProf.\t6\t0\n101610\tM\tNeverM\t1985\tI: 170,000 - 189,999\t3000\t11th\t0\t1\tHandler\t0\t1\n102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\n100593\tM\tMarried\t1963\tG: 130,000 - 149,999\t1500\tHS-grad\t1\t3\tProf.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an Expectation Maximization model using the CUSTOMERS360_V view"],"enabled":true,"result":{"startTime":1715349094651,"interpreter":"md.low","endTime":1715349094713,"results":[{"message":"<h3 id=\"build-an-expectation-maximization-model-using-the-customers360_v-view\">Build an Expectation Maximization model using the CUSTOMERS360_V view<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the EM Clustering model with default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_CLUS_SAMPLE_1');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')        := 'ALGO_EXPECTATION_MAXIMIZATION';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'EM_SH_CLUS_SAMPLE_1',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1715349094796,"interpreter":"script.low","endTime":1715349100801,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Expectation-Maximization clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';  ","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components. For EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS')       := '5';","","#### Expectation Maximization Settings for Learning     ","* Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected. It requires `EMCS_MODEL_SEARCH_ENABLE` or `EMCS_MODEL_SEARCH_DISABLE`.  The default is `EMCS_MODEL_SEARCH_DISABLE`.      ","> v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';","* Specify the maximum number of iterations in the EM algorithm. It requires a number larger than 1.  The default is 100. ","> v_setlst('EMCS_NUM_ITERATIONS')     := '20';","   ","#### Expectation Maximization Settings for Cluster Statistics","*  Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and `GET_MODEL_DETAILS_EM` only returns taxonomy (hierarchy) and cluster counts. It requires either `EMCS_CLUS_STATS_ENABLE` or `EMCS_CLUS_STATS_DISABLE`.  Default is `EMCS_CLUS_STATS_ENABLE`.     ","> v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';","     ","#### Expectation Maximization Settings for Data Preparation and Analysis","* Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. It requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.","> v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';"],"enabled":true,"result":{"startTime":1715349116891,"interpreter":"md.low","endTime":1715349116983,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation-Maximization clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components. For EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS')       := '5';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-learning\">Expectation Maximization Settings for Learning<\/h4>\n<ul>\n<li>Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected. It requires <code>EMCS_MODEL_SEARCH_ENABLE<\/code> or <code>EMCS_MODEL_SEARCH_DISABLE<\/code>.  The default is <code>EMCS_MODEL_SEARCH_DISABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations in the EM algorithm. It requires a number larger than 1.  The default is 100.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_ITERATIONS')     := '20';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-cluster-statistics\">Expectation Maximization Settings for Cluster Statistics<\/h4>\n<ul>\n<li>Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and <code>GET_MODEL_DETAILS_EM<\/code> only returns taxonomy (hierarchy) and cluster counts. It requires either <code>EMCS_CLUS_STATS_ENABLE<\/code> or <code>EMCS_CLUS_STATS_DISABLE<\/code>.  Default is <code>EMCS_CLUS_STATS_ENABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-data-preparation-and-analysis\">Expectation Maximization Settings for Data Preparation and Analysis<\/h4>\n<ul>\n<li>Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions. It requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the EM Clustering model with explicit settings","message":["%script","","-- Additional settings are available in the documentation","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_CLUS_SAMPLE_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')               := 'ALGO_EXPECTATION_MAXIMIZATION';","    v_setlst('PREP_AUTO')               := 'ON';","    v_setlst('CLUS_NUM_CLUSTERS')       := '5';","    v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';","    v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';","    v_setlst('EMCS_NUM_ITERATIONS')     := '20';","    v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';","    v_setlst('EMCS_RANDOM_SEED')        := '123456';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'EM_SH_CLUS_SAMPLE_2',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1715349101049,"interpreter":"script.low","endTime":1715349105551,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model metadata","message":["%sql ","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE, PARTITIONED","FROM   USER_MINING_MODELS","WHERE  MODEL_NAME LIKE 'EM_SH_CLUS_SAMPLE_%';"],"enabled":true,"result":{"startTime":1715349105636,"interpreter":"sql.low","endTime":1715349105737,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\tPARTITIONED\nEM_SH_CLUS_SAMPLE_3\tCLUSTERING\tEXPECTATION_MAXIMIZATION\t2024-04-22 16:21:13\tNO\nEM_SH_CLUS_SAMPLE_2\tCLUSTERING\tEXPECTATION_MAXIMIZATION\t2024-05-10 13:51:42\tNO\nEM_SH_CLUS_SAMPLE_1\tCLUSTERING\tEXPECTATION_MAXIMIZATION\t2024-05-10 13:51:36\tNO\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model signature","message":["%sql ","","-- The model signature includes the attributes used in the model training data,","-- and also corresponds to the attributes expected to apply the model to new data. ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'EM_SH_CLUS_SAMPLE_2'","ORDER BY ATTRIBUTE_NAME;"],"enabled":true,"result":{"startTime":1715349105819,"interpreter":"sql.low","endTime":1715349105896,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM USER_MINING_MODEL_SETTINGS","WHERE MODEL_NAME = 'EM_SH_CLUS_SAMPLE_2'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715349105975,"interpreter":"sql.low","endTime":1715349106073,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPECTATION_MAXIMIZATION\nCLUS_NUM_CLUSTERS\t5\nEMCS_CLUSTER_COMPONENTS\tEMCS_CLUSTER_COMP_ENABLE\nEMCS_CLUSTER_STATISTICS\tEMCS_CLUS_STATS_ENABLE\nEMCS_CLUSTER_THRESH\t2\nEMCS_LINKAGE_FUNCTION\tEMCS_LINKAGE_SINGLE\nEMCS_LOGLIKE_IMPROVEMENT\t.001\nEMCS_MAX_NUM_ATTR_2D\t50\nEMCS_MIN_PCT_ATTR_SUPPORT\t.1\nEMCS_MODEL_SEARCH\tEMCS_MODEL_SEARCH_ENABLE\nEMCS_NUM_COMPONENTS\t20\nEMCS_NUM_DISTRIBUTION\tEMCS_NUM_DISTR_SYSTEM\nEMCS_NUM_EQUIWIDTH_BINS\t11\nEMCS_NUM_ITERATIONS\t20\nEMCS_NUM_PROJECTIONS\t50\nEMCS_NUM_QUANTILE_BINS\t10\nEMCS_RANDOM_SEED\t123456\nEMCS_REMOVE_COMPONENTS\tEMCS_REMOVE_COMPS_ENABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the EM model detail views","message":["%sql ","","-- Display the list of available cluster model detail views as indicated in VIEW_TYPE","-- Cluster details are presented as separate views depending on the desired information.","","SELECT VIEW_NAME, VIEW_TYPE ","FROM USER_MINING_MODEL_VIEWS","WHERE MODEL_NAME='EM_SH_CLUS_SAMPLE_2'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1715349106162,"interpreter":"sql.low","endTime":1715349106296,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAEM_SH_CLUS_SAMPLE_2\tClustering Attribute Statistics\nDM$VBEM_SH_CLUS_SAMPLE_2\tAttribute Pair Kullback-Leibler Divergence\nDM$VDEM_SH_CLUS_SAMPLE_2\tClustering Description\nDM$VFEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Bernoulli parameters\nDM$VGEM_SH_CLUS_SAMPLE_2\tGlobal Name-Value Pairs\nDM$VHEM_SH_CLUS_SAMPLE_2\tClustering Histograms\nDM$VIEM_SH_CLUS_SAMPLE_2\tUnsupervised Attribute Importance\nDM$VMEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Gaussian parameters\nDM$VNEM_SH_CLUS_SAMPLE_2\tNormalization and Missing Value Handling\nDM$VOEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Components\nDM$VPEM_SH_CLUS_SAMPLE_2\tExpectation Maximization Projections\nDM$VREM_SH_CLUS_SAMPLE_2\tClustering Rules\nDM$VSEM_SH_CLUS_SAMPLE_2\tComputed Settings\nDM$VWEM_SH_CLUS_SAMPLE_2\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details","message":["%sql","","-- For each cluster_id, this DM$VD* view provides the number of records in each cluster,","--   the parent cluster id, and the level in the hierarchy. 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YRS_RESIDENCE","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, BIN_ID, LOWER_BIN_BOUNDARY, UPPER_BIN_BOUNDARY, COUNT ","FROM DM$VHEM_SH_CLUS_SAMPLE_2","WHERE ATTRIBUTE_NAME = 'YRS_RESIDENCE'","ORDER BY CLUSTER_ID DESC, 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Cluster Rules","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.","The larger the number, the more relevant the attribute."," ","The query below reverse-transforms the data to its original values, since the data used for building the model was normalized."],"enabled":true,"result":{"startTime":1715349107536,"interpreter":"md.low","endTime":1715349107598,"results":[{"message":"<h3 id=\"cluster-rules\">Cluster Rules<\/h3>\n<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.\nFor an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.\nConfidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.\nThe larger the number, the more relevant the attribute.<\/p>\n<p>The query below reverse-transforms the data to its original values, since the data used for building the model was normalized.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR,","       NUMERIC_VALUE, ATTRIBUTE_VALUE, SUPPORT, ROUND(CONFIDENCE,3) CONFIDENCE","FROM   DM$VREM_SH_CLUS_SAMPLE_2","WHERE  cluster_id IN (SELECT cluster_id","                      FROM   DM$VDEM_SH_CLUS_SAMPLE_2","                      WHERE  LEFT_CHILD_ID is NULL and RIGHT_CHILD_ID is NULL)","ORDER BY CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR, NUMERIC_VALUE, ATTRIBUTE_VALUE;"],"enabled":true,"result":{"startTime":1715349107678,"interpreter":"sql.low","endTime":1715349107827,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tOPERATOR\tNUMERIC_VALUE\tATTRIBUTE_VALUE\tSUPPORT\tCONFIDENCE\n3\tAFFINITY_CARD\t\t<=\t1.0\t\t190\t0.079\n3\tAFFINITY_CARD\t\t>=\t0.0\t\t190\t0.079\n3\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t190\t0.001\n3\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t190\t0.001\n3\tCUST_GENDER\t\t=\t\tF\t190\t0.457\n3\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t173\t0.006\n3\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t173\t0.006\n3\tCUST_MARITAL_STATUS\t\t=\t\tMarried\t188\t0.125\n3\tCUST_YEAR_OF_BIRTH\t\t<=\t1978.7\t\t181\t0.045\n3\tCUST_YEAR_OF_BIRTH\t\t>\t1942.2\t\t181\t0.045\n3\tEDUCATION\t\tIN\t\t< Bach.\t167\t0.004\n3\tEDUCATION\t\tIN\t\tAssoc-A\t167\t0.004\n3\tEDUCATION\t\tIN\t\tAssoc-V\t167\t0.004\n3\tEDUCATION\t\tIN\t\tBach.\t167\t0.004\n3\tEDUCATION\t\tIN\t\tHS-grad\t167\t0.004\n3\tEDUCATION\t\tIN\t\tMasters\t167\t0.004\n3\tHOUSEHOLD_SIZE\t\t=\t\t4-5\t188\t0.321\n3\tOCCUPATION\t\tIN\t\t?\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tCleric.\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tExec.\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tMachine\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tOther\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tProf.\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tSales\t174\t0.019\n3\tOCCUPATION\t\tIN\t\tTechSup\t174\t0.019\n3\tYRS_RESIDENCE\t\t<=\t5.0\t\t162\t0.01\n3\tYRS_RESIDENCE\t\t>\t1.0\t\t162\t0.01\n3\tY_BOX_GAMES\t\t<=\t0.0\t\t186\t0.129\n3\tY_BOX_GAMES\t\t>=\t0.0\t\t186\t0.129\n5\tAFFINITY_CARD\t\t<=\t1.0\t\t1585\t0.051\n5\tAFFINITY_CARD\t\t>=\t0.0\t\t1585\t0.051\n5\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t1585\t0\n5\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t1585\t0\n5\tCUST_GENDER\t\t=\t\tM\t1585\t0.192\n5\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t1432\t0.001\n5\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t1432\t0.001\n5\tCUST_MARITAL_STATUS\t\t=\t\tMarried\t1583\t0.123\n5\tCUST_YEAR_OF_BIRTH\t\t<=\t1978.7\t\t1522\t0.041\n5\tCUST_YEAR_OF_BIRTH\t\t>\t1934.9\t\t1522\t0.041\n5\tEDUCATION\t\tIN\t\t< Bach.\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tAssoc-A\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tAssoc-V\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tBach.\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tHS-grad\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tMasters\t1405\t0.003\n5\tEDUCATION\t\tIN\t\tProfsc\t1405\t0.003\n5\tHOUSEHOLD_SIZE\t\t=\t\t3\t1573\t0.147\n5\tOCCUPATION\t\tIN\t\t?\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tCleric.\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tCrafts\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tExec.\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tFarming\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tMachine\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tOther\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tProf.\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tSales\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tTechSup\t1514\t0.009\n5\tOCCUPATION\t\tIN\t\tTransp.\t1514\t0.009\n5\tYRS_RESIDENCE\t\t<=\t7.0\t\t1419\t0.031\n5\tYRS_RESIDENCE\t\t>\t2.0\t\t1419\t0.031\n5\tY_BOX_GAMES\t\t<=\t0.0\t\t1585\t0.177\n5\tY_BOX_GAMES\t\t>=\t0.0\t\t1585\t0.177\n6\tAFFINITY_CARD\t\t<=\t0.0\t\t1195\t0.027\n6\tAFFINITY_CARD\t\t>=\t0.0\t\t1195\t0.027\n6\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t1322\t0\n6\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t1322\t0\n6\tCUST_GENDER\t\tIN\t\tF\t1322\t0.039\n6\tCUST_GENDER\t\tIN\t\tM\t1322\t0.039\n6\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t1201\t0\n6\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t1201\t0\n6\tCUST_MARITAL_STATUS\t\tIN\t\tDivorc.\t1255\t0.11\n6\tCUST_MARITAL_STATUS\t\tIN\t\tNeverM\t1255\t0.11\n6\tCUST_MARITAL_STATUS\t\tIN\t\tSepar.\t1255\t0.11\n6\tCUST_MARITAL_STATUS\t\tIN\t\tWidowed\t1255\t0.11\n6\tCUST_YEAR_OF_BIRTH\t\t<=\t1978.7\t\t1270\t0.038\n6\tCUST_YEAR_OF_BIRTH\t\t>\t1934.9\t\t1270\t0.038\n6\tEDUCATION\t\tIN\t\t< Bach.\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tAssoc-A\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tAssoc-V\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tBach.\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tHS-grad\t1143\t0.001\n6\tEDUCATION\t\tIN\t\tMasters\t1143\t0.001\n6\tHOUSEHOLD_SIZE\t\tIN\t\t2\t1169\t0.12\n6\tHOUSEHOLD_SIZE\t\tIN\t\t9+\t1169\t0.12\n6\tOCCUPATION\t\tIN\t\t?\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tCleric.\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tCrafts\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tExec.\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tHandler\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tMachine\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tOther\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tProf.\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tSales\t1213\t0.002\n6\tOCCUPATION\t\tIN\t\tTechSup\t1213\t0.002\n6\tYRS_RESIDENCE\t\t<=\t6.0\t\t1102\t0.013\n6\tYRS_RESIDENCE\t\t>\t2.0\t\t1102\t0.013\n6\tY_BOX_GAMES\t\t<=\t0.0\t\t1322\t0.177\n6\tY_BOX_GAMES\t\t>=\t0.0\t\t1322\t0.177\n8\tAFFINITY_CARD\t\t<=\t0.0\t\t1149\t0.091\n8\tAFFINITY_CARD\t\t>=\t0.0\t\t1149\t0.091\n8\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t1170\t0.001\n8\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t1170\t0.001\n8\tCUST_GENDER\t\tIN\t\tF\t1170\t0.015\n8\tCUST_GENDER\t\tIN\t\tM\t1170\t0.015\n8\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t1084\t0.002\n8\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t1084\t0.002\n8\tCUST_MARITAL_STATUS\t\t=\t\tNeverM\t1041\t0.104\n8\tCUST_YEAR_OF_BIRTH\t\t<=\t1986.0\t\t1170\t0.135\n8\tCUST_YEAR_OF_BIRTH\t\t>\t1971.4\t\t1170\t0.135\n8\tEDUCATION\t\tIN\t\t10th\t1050\t0.008\n8\tEDUCATION\t\tIN\t\t11th\t1050\t0.008\n8\tEDUCATION\t\tIN\t\t< Bach.\t1050\t0.008\n8\tEDUCATION\t\tIN\t\tAssoc-V\t1050\t0.008\n8\tEDUCATION\t\tIN\t\tBach.\t1050\t0.008\n8\tEDUCATION\t\tIN\t\tHS-grad\t1050\t0.008\n8\tHOUSEHOLD_SIZE\t\tIN\t\t1\t1065\t0.11\n8\tHOUSEHOLD_SIZE\t\tIN\t\t2\t1065\t0.11\n8\tHOUSEHOLD_SIZE\t\tIN\t\t9+\t1065\t0.11\n8\tOCCUPATION\t\tIN\t\t?\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tCleric.\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tCrafts\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tExec.\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tHandler\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tMachine\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tOther\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tProf.\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tSales\t1103\t0.011\n8\tOCCUPATION\t\tIN\t\tTechSup\t1103\t0.011\n8\tYRS_RESIDENCE\t\t<=\t3.0\t\t1113\t0.093\n8\tYRS_RESIDENCE\t\t>=\t0.0\t\t1113\t0.093\n8\tY_BOX_GAMES\t\t<=\t1.0\t\t1169\t0.476\n8\tY_BOX_GAMES\t\t>\t0.0\t\t1169\t0.476\n9\tAFFINITY_CARD\t\t<=\t0.0\t\t194\t0.006\n9\tAFFINITY_CARD\t\t>=\t0.0\t\t194\t0.006\n9\tCUST_CREDIT_LIMIT\t\t<=\t15000.0\t\t233\t0.004\n9\tCUST_CREDIT_LIMIT\t\t>=\t1500.0\t\t233\t0.004\n9\tCUST_GENDER\t\t=\t\tM\t220\t0.098\n9\tCUST_INCOME_LEVEL\t\tIN\t\tB: 30,000 - 49,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tC: 50,000 - 69,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tE: 90,000 - 109,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tF: 110,000 - 129,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tG: 130,000 - 149,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tH: 150,000 - 169,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tI: 170,000 - 189,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tJ: 190,000 - 249,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tK: 250,000 - 299,999\t225\t0.005\n9\tCUST_INCOME_LEVEL\t\tIN\t\tL: 300,000 and above\t225\t0.005\n9\tCUST_MARITAL_STATUS\t\t=\t\tMarried\t232\t0.123\n9\tCUST_YEAR_OF_BIRTH\t\t<=\t1986.0\t\t233\t0.145\n9\tCUST_YEAR_OF_BIRTH\t\t>\t1971.4\t\t233\t0.145\n9\tEDUCATION\t\tIN\t\t11th\t201\t0.006\n9\tEDUCATION\t\tIN\t\t< Bach.\t201\t0.006\n9\tEDUCATION\t\tIN\t\tAssoc-V\t201\t0.006\n9\tEDUCATION\t\tIN\t\tBach.\t201\t0.006\n9\tEDUCATION\t\tIN\t\tHS-grad\t201\t0.006\n9\tHOUSEHOLD_SIZE\t\t=\t\t3\t214\t0.12\n9\tOCCUPATION\t\tIN\t\tCleric.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tCrafts\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tExec.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tHandler\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tMachine\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tProf.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tProtec.\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tSales\t211\t0.016\n9\tOCCUPATION\t\tIN\t\tTransp.\t211\t0.016\n9\tYRS_RESIDENCE\t\t<=\t4.0\t\t218\t0.067\n9\tYRS_RESIDENCE\t\t>\t1.0\t\t218\t0.067\n9\tY_BOX_GAMES\t\t<=\t1.0\t\t233\t0.48\n9\tY_BOX_GAMES\t\t>\t0.0\t\t233\t0.48\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Apply model to CUSTOMERS360_V and display customer count per cluster","message":["%sql","","-- For a descriptive machine learning technique like Clustering, \"scoring\" involves assigning each record to a cluster with a certain probability. ","-- Hover over bars in the bar chart to view the size of each cluster. ","","SELECT CLUSTER_ID(EM_SH_CLUS_SAMPLE_2 USING *) AS CLUSTER_ID, COUNT(*) AS CNT ","FROM   CUSTOMERS360_V","GROUP BY CLUSTER_ID(EM_SH_CLUS_SAMPLE_2 USING *)","ORDER BY CNT DESC;"],"enabled":true,"result":{"startTime":1715349107920,"interpreter":"sql.low","endTime":1715349108044,"results":[{"message":"CLUSTER_ID\tCNT\n5\t1585\n6\t1322\n8\t1170\n9\t233\n3\t190\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List ten customers most likely in cluster 6","message":["%sql","","SELECT CUST_ID, ","       ROUND(CLUSTER_PROBABILITY(EM_SH_CLUS_SAMPLE_2, 6 USING *),4) PROB","FROM CUSTOMERS360_V","ORDER BY PROB DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715349108125,"interpreter":"sql.low","endTime":1715349108250,"results":[{"message":"CUST_ID\tPROB\n100134\t1\n102893\t1\n101392\t1\n101297\t1\n102828\t1\n102740\t1\n100804\t1\n103791\t1\n103948\t1\n103829\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List most relevant predictors and their values for specific customers cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. These represent customers ","--    with > 20% likelihood of assignment to their cluster.","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,2) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(EM_SH_CLUS_SAMPLE_2 USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(EM_SH_CLUS_SAMPLE_2, NULL, 0.2 USING *) PSET","            FROM CUSTOMERS360_V V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;"],"enabled":true,"result":{"startTime":1715349108329,"interpreter":"sql.low","endTime":1715349108606,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100456\t6\t100\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".001\" \t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'100456'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach for building clustering model using an analytic function","In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.","","In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.","","Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers."],"enabled":true,"result":{"startTime":1715349108686,"interpreter":"md.low","endTime":1715349108747,"results":[{"message":"<h2 id=\"alternative-approach-for-building-clustering-model-using-an-analytic-function\">Alternative approach for building clustering model using an analytic function<\/h2>\n<p>In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.<\/p>\n<p>In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.<\/p>\n<p>Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Build and apply a transient model using analytic functions","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT * ","      FROM (SELECT CUST_ID,","                   CLUSTER_ID(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_ID,","                   CLUSTER_DETAILS(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_DET","            FROM CUSTOMERS360_V)","            ORDER By 1) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.CLUSTER_DET","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;","                      "],"enabled":true,"result":{"startTime":1715349108826,"interpreter":"sql.low","endTime":1715349109101,"results":[{"message":"CUST_ID\tCLUSTER_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t3\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".059\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".038\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".015\" \n100002\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".024\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".019\" \n100003\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".018\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".016\" \n100004\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".038\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".028\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".028\" \n100005\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".157\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".009\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".009\" \n100006\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".131\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1983\" weight=\".086\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".056\" \n100007\t4\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".027\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".026\" \n100008\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".035\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".019\" \n100009\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".137\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".01\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".007\" \n100010\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".115\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\".046\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".028\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "],"enabled":true,"result":{"startTime":1715349109183,"interpreter":"md.low","endTime":1715349109246,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and populate model settings table","message":["%script ","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE EM_SH_SAMPLE_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE EM_SH_SAMPLE_SETTINGS (SETTING_NAME  VARCHAR2(30),","                                    SETTING_VALUE VARCHAR2(4000));","","BEGIN       ","   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME,               DBMS_DATA_MINING.ALGO_EXPECTATION_MAXIMIZATION);","   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.EMCS_MODEL_SEARCH,       DBMS_DATA_MINING.EMCS_MODEL_SEARCH_ENABLE);","   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO,               DBMS_DATA_MINING.PREP_AUTO_ON);","   INSERT INTO EM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.EMCS_CLUSTER_STATISTICS, DBMS_DATA_MINING.EMCS_CLUS_STATS_ENABLE);","END;"],"enabled":true,"result":{"startTime":1715349109325,"interpreter":"script.low","endTime":1715349109550,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable EM_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the contents of the model settings table","message":["%sql","-- NOTE: The settings table can be reused or modified in multiple model builds. ","","SELECT * FROM EM_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1715349109628,"interpreter":"sql.low","endTime":1715349109704,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPECTATION_MAXIMIZATION\nEMCS_MODEL_SEARCH\tEMCS_MODEL_SEARCH_ENABLE\nPREP_AUTO\tON\nEMCS_CLUSTER_STATISTICS\tEMCS_CLUS_STATS_ENABLE\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop, then build the EM Clustering model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_CLUS_SAMPLE_3');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'EM_SH_CLUS_SAMPLE_3',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMERS360_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'EM_SH_SAMPLE_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1715349109784,"interpreter":"script.low","endTime":1715349116788,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'EM_SH_CLUS_SAMPLE_3'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715349117034,"interpreter":"sql.low","endTime":1715349117112,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPECTATION_MAXIMIZATION\nCLUS_NUM_CLUSTERS\t10\nEMCS_CLUSTER_COMPONENTS\tEMCS_CLUSTER_COMP_ENABLE\nEMCS_CLUSTER_STATISTICS\tEMCS_CLUS_STATS_ENABLE\nEMCS_CLUSTER_THRESH\t2\nEMCS_LINKAGE_FUNCTION\tEMCS_LINKAGE_SINGLE\nEMCS_LOGLIKE_IMPROVEMENT\t.001\nEMCS_MAX_NUM_ATTR_2D\t50\nEMCS_MIN_PCT_ATTR_SUPPORT\t.1\nEMCS_MODEL_SEARCH\tEMCS_MODEL_SEARCH_ENABLE\nEMCS_NUM_COMPONENTS\t20\nEMCS_NUM_DISTRIBUTION\tEMCS_NUM_DISTR_SYSTEM\nEMCS_NUM_EQUIWIDTH_BINS\t11\nEMCS_NUM_ITERATIONS\t100\nEMCS_NUM_PROJECTIONS\t50\nEMCS_RANDOM_SEED\t0\nEMCS_REMOVE_COMPONENTS\tEMCS_REMOVE_COMPS_ENABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715349117203,"interpreter":"md.low","endTime":1715349117273,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715349117362,"interpreter":"md.low","endTime":1715349117426,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/sql/OML4SQL Clustering KM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Clustering KM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715349415993,"interpreter":"md.low","endTime":1715349416157,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Identifying Customer Segments using K-Means Clustering","","Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning executes inside Oracle Autonomous Database.","","See the documentation link below for details on the K-Means in-database algortihm.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715349416281,"interpreter":"md.low","endTime":1715349416384,"results":[{"message":"<h2 id=\"identifying-customer-segments-using-k-means-clustering\">Identifying Customer Segments using K-Means Clustering<\/h2>\n<p>Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning executes inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the K-Means in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1715349416467,"interpreter":"md.low","endTime":1715349416540,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a>"],"enabled":true,"result":{"startTime":1715349416617,"interpreter":"md.low","endTime":1715349416689,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-k-means\" target=\"_blank\">OML K-Means<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view joining CUSTOMERS and DEMOGRAPHICS","message":["%script","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, ","          a.CUST_YEAR_OF_BIRTH, a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, ","          b.EDUCATION, b.AFFINITY_CARD, ","          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","   FROM SH.CUSTOMERS a, SH.SUPPLEMENTARY_DEMOGRAPHICS b","   WHERE a.CUST_ID = b.CUST_ID;","   "],"enabled":true,"result":{"startTime":1715349416771,"interpreter":"script.low","endTime":1715349418672,"results":[{"message":"\nView CUSTOMERS360_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in CUSTOMERS360_V","message":["%sql","","SELECT COUNT(*) FROM CUSTOMERS360_V;"],"enabled":true,"result":{"startTime":1715349418746,"interpreter":"sql.low","endTime":1715349418894,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data in view CUSTOMERS360_V","message":["%sql","","SELECT * ","FROM CUSTOMERS360_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349418976,"interpreter":"sql.low","endTime":1715349419071,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\n100804\tF\tDivorc.\t1943\tA: Below 30,000\t1500\t< Bach.\t0\t2\tProf.\t6\t0\n101610\tM\tNeverM\t1985\tI: 170,000 - 189,999\t3000\t11th\t0\t1\tHandler\t0\t1\n102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\n100593\tM\tMarried\t1963\tG: 130,000 - 149,999\t1500\tHS-grad\t1\t3\tProf.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a k-Means model using the CUSTOMERS360_V view"],"enabled":true,"result":{"startTime":1715349419149,"interpreter":"md.low","endTime":1715349419211,"results":[{"message":"<h3 id=\"build-a-k-means-model-using-the-customers360_v-view\">Build a k-Means model using the CUSTOMERS360_V view<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the k-Means Clustering model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')   := 'ALGO_KMEANS';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'KM_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1715349419295,"interpreter":"script.low","endTime":1715349422293,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for k-Means clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';  ","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') := '5';","* Specify the distance function for k-Means. It requires either the function `KMNS_COSINE` or `KMNS_EUCLIDEAN`.  The default distance function is `KMNS_EUCLIDEAN`. ","> v_setlst('KMNS_DISTANCE') := 'KMNS_EUCLIDEAN';  ","* Specify this setting which determines the level of cluster detail that are computed during the build. It requires one of the following options:","> `KMNS_DETAILS_NONE`: No cluster details are computed. Only the scoring information is persisted.","> `KMNS_DETAILS_HIERARCHY`: Cluster hierarchy and cluster record counts are computed. This is the default value.","> `KMNS_DETAILS_ALL`: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.","> v_setlst('KMNS_DETAILS') := 'KMNS_DETAILS_ALL';  ","* Specify the Maximum number of iterations for k-Means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in `KMNS_CONV_TOLERANCE`, is satisfied. It requires a positive integer, and the default number of iterations is 20. ","> v_setlst('KMNS_ITERATIONS') := '3';","* Specify the number of bins in the attribute histogram produced by k-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. It requires a positive integer, and the default number of histogram bins is 11.     ","> v_setlst('KMNS_NUM_BINS') := '10';     "],"enabled":true,"result":{"startTime":1715349422377,"interpreter":"md.low","endTime":1715349422457,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-k-means-clustering\">Examples of possible setting overrides for k-Means clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7010593E-C323-4DFC-8468-D85CE41A0C3C\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') := '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the distance function for k-Means. It requires either the function <code>KMNS_COSINE<\/code> or <code>KMNS_EUCLIDEAN<\/code>.  The default distance function is <code>KMNS_EUCLIDEAN<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('KMNS_DISTANCE') := 'KMNS_EUCLIDEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify this setting which determines the level of cluster detail that are computed during the build. It requires one of the following options:<\/li>\n<\/ul>\n<blockquote>\n<p><code>KMNS_DETAILS_NONE<\/code>: No cluster details are computed. Only the scoring information is persisted.\n<code>KMNS_DETAILS_HIERARCHY<\/code>: Cluster hierarchy and cluster record counts are computed. This is the default value.\n<code>KMNS_DETAILS_ALL<\/code>: Cluster hierarchy, record counts, descriptive statistics (means, variances, modes, histograms, and rules) are computed.\nv_setlst('KMNS_DETAILS') := 'KMNS_DETAILS_ALL';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Maximum number of iterations for k-Means. The algorithm iterates until either the maximum number of iterations is reached or the minimum Convergence Tolerance, specified in <code>KMNS_CONV_TOLERANCE<\/code>, is satisfied. It requires a positive integer, and the default number of iterations is 20.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('KMNS_ITERATIONS') := '3';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of bins in the attribute histogram produced by k-means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin. It requires a positive integer, and the default number of histogram bins is 11.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('KMNS_NUM_BINS') := '10';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a k-Means Clustering model using explicit settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')        := 'ALGO_KMEANS';","    V_setlst('PREP_AUTO')        := 'ON';","    V_setlst('KMNS_DISTANCE')    := 'KMNS_EUCLIDEAN';","    V_setlst('KMNS_DETAILS')     := 'KMNS_DETAILS_ALL';","    V_setlst('KMNS_ITERATIONS')  := '3';","    V_setlst('KMNS_NUM_BINS')    := '10';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'KM_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1715349422544,"interpreter":"script.low","endTime":1715349425542,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model signature","message":["%sql ","","-- The model signature includes the attributes used in model training data,","-- and also corresponds to the attributes expected to apply the model to new data. ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE'","ORDER BY ATTRIBUTE_NAME;"],"enabled":true,"result":{"startTime":1715349425617,"interpreter":"sql.low","endTime":1715349425713,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model metadata","message":["%sql ","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, CREATION_DATE, PARTITIONED","FROM   USER_MINING_MODELS","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE';"],"enabled":true,"result":{"startTime":1715349425791,"interpreter":"sql.low","endTime":1715349425886,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tCREATION_DATE\tPARTITIONED\nKM_SH_CLUS_SAMPLE\tCLUSTERING\tKMEANS\t2024-05-10 13:57:03\tNO\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715349425964,"interpreter":"sql.low","endTime":1715349426059,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_KMEANS\nCLUS_NUM_CLUSTERS\t10\nKMNS_CONV_TOLERANCE\t.001\nKMNS_DETAILS\tKMNS_DETAILS_ALL\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nKMNS_ITERATIONS\t3\nKMNS_MIN_PCT_ATTR_SUPPORT\t.1\nKMNS_NUM_BINS\t10\nKMNS_RANDOM_SEED\t0\nKMNS_SPLIT_CRITERION\tKMNS_VARIANCE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the k-Means model detail views","message":["%sql ","","-- Display the list of available cluster model detail views as indicated in VIEW_TYPE","-- Cluster details are presented as separate views depending on the desired information.","","SELECT VIEW_NAME, VIEW_TYPE FROM USER_MINING_MODEL_VIEWS","WHERE MODEL_NAME='KM_SH_CLUS_SAMPLE'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1715349426148,"interpreter":"sql.low","endTime":1715349426241,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAKM_SH_CLUS_SAMPLE\tClustering Attribute Statistics\nDM$VCKM_SH_CLUS_SAMPLE\tk-Means Scoring Centroids\nDM$VDKM_SH_CLUS_SAMPLE\tClustering Description\nDM$VGKM_SH_CLUS_SAMPLE\tGlobal Name-Value Pairs\nDM$VHKM_SH_CLUS_SAMPLE\tClustering Histograms\nDM$VNKM_SH_CLUS_SAMPLE\tNormalization and Missing Value Handling\nDM$VRKM_SH_CLUS_SAMPLE\tClustering Rules\nDM$VSKM_SH_CLUS_SAMPLE\tComputed Settings\nDM$VWKM_SH_CLUS_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details","message":["%sql","","-- For each cluster_id, this DM$VD* view provides the number of records ","--   in the cluster, the parent cluster id, the level in the hierarchy, and dispersion.","-- Dispersion is a measure of cluster quality, and computationally, ","--   the sum of square errors.","","SELECT CLUSTER_ID CLU_ID, RECORD_COUNT RECORD_CNT, PARENT, ","       TREE_LEVEL, ROUND(TO_NUMBER(DISPERSION),3) DISPERSION","FROM   DM$VDKM_SH_CLUS_SAMPLE","ORDER BY 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IDs","message":["%sql","","-- Show leaf IDs to know for which clusters to show detail","","SELECT CLUSTER_ID","FROM   DM$VDKM_SH_CLUS_SAMPLE","WHERE  LEFT_CHILD_ID IS NULL AND RIGHT_CHILD_ID IS NULL","ORDER BY CLUSTER_ID"],"enabled":true,"result":{"startTime":1715349426522,"interpreter":"sql.low","endTime":1715349426619,"results":[{"message":"CLUSTER_ID\n6\n7\n8\n9\n11\n13\n15\n17\n18\n19\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details for cluster 18","message":["%sql","","-- For cluster_id 18, this output lists all the attributes that constitute the centroid, with the mean (for numericals) ","-- or mode (for categoricals). It also includes the variance.","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, MEAN, VARIANCE, MODE_VALUE","FROM   DM$VAKM_SH_CLUS_SAMPLE","WHERE  CLUSTER_ID = 18","ORDER BY ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME;"],"enabled":true,"result":{"startTime":1715349426693,"interpreter":"sql.low","endTime":1715349426786,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tMEAN\tVARIANCE\tMODE_VALUE\n18\tAFFINITY_CARD\t\t0.0\t0.0\t\n18\tCUST_CREDIT_LIMIT\t\t9802.784222737806\t5061015.48589003\t\n18\tCUST_GENDER\t\t\t\tF\n18\tCUST_INCOME_LEVEL\t\t\t\tJ: 190,000 - 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BIN_ID;"],"enabled":true,"result":{"startTime":1715349427033,"interpreter":"sql.low","endTime":1715349427110,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tBIN_ID\tCOUNT\tLOWER_BIN_BOUNDARY\tUPPER_BIN_BOUNDARY\n9\tYRS_RESIDENCE\t1\t195\t0\t1.556\n9\tYRS_RESIDENCE\t2\t620\t1.556\t3.111\n9\tYRS_RESIDENCE\t3\t36\t3.111\t4.667\n9\tYRS_RESIDENCE\t4\t11\t4.667\t6.222\n9\tYRS_RESIDENCE\t5\t0\t6.222\t7.778\n9\tYRS_RESIDENCE\t6\t0\t7.778\t9.333\n9\tYRS_RESIDENCE\t7\t0\t9.333\t10.889\n9\tYRS_RESIDENCE\t8\t0\t10.889\t12.444\n9\tYRS_RESIDENCE\t9\t0\t12.444\t14\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show bin boundaries for YRS_RESIDENCE","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, BIN_ID, COUNT,","       ROUND(LOWER_BIN_BOUNDARY,3) LOWER_BIN_BOUNDARY, ROUND(UPPER_BIN_BOUNDARY,3) UPPER_BIN_BOUNDARY","FROM DM$VHKM_SH_CLUS_SAMPLE","WHERE ATTRIBUTE_NAME = 'YRS_RESIDENCE'","ORDER BY BIN_ID","FETCH FIRST 20 ROWS ONLY"],"enabled":true,"result":{"startTime":1715349427190,"interpreter":"sql.low","endTime":1715349427288,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tBIN_ID\tCOUNT\tLOWER_BIN_BOUNDARY\tUPPER_BIN_BOUNDARY\n1\tYRS_RESIDENCE\t1\t324\t0\t1.556\n19\tYRS_RESIDENCE\t1\t0\t0\t1.556\n18\tYRS_RESIDENCE\t1\t10\t0\t1.556\n17\tYRS_RESIDENCE\t1\t4\t0\t1.556\n16\tYRS_RESIDENCE\t1\t10\t0\t1.556\n15\tYRS_RESIDENCE\t1\t1\t0\t1.556\n14\tYRS_RESIDENCE\t1\t14\t0\t1.556\n13\tYRS_RESIDENCE\t1\t0\t0\t1.556\n12\tYRS_RESIDENCE\t1\t15\t0\t1.556\n11\tYRS_RESIDENCE\t1\t12\t0\t1.556\n10\tYRS_RESIDENCE\t1\t15\t0\t1.556\n9\tYRS_RESIDENCE\t1\t195\t0\t1.556\n8\tYRS_RESIDENCE\t1\t94\t0\t1.556\n7\tYRS_RESIDENCE\t1\t0\t0\t1.556\n6\tYRS_RESIDENCE\t1\t8\t0\t1.556\n5\tYRS_RESIDENCE\t1\t8\t0\t1.556\n4\tYRS_RESIDENCE\t1\t27\t0\t1.556\n3\tYRS_RESIDENCE\t1\t289\t0\t1.556\n2\tYRS_RESIDENCE\t1\t35\t0\t1.556\n1\tYRS_RESIDENCE\t2\t1586\t1.556\t3.111\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.","The larger the number, the more relevant the attribute."," ","The query below reverse-transforms the data to its original values, since the data used for building the model was normalized."],"enabled":true,"result":{"startTime":1715349427371,"interpreter":"md.low","endTime":1715349427436,"results":[{"message":"<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.<\/p>\n<p>For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.\nConfidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.\nThe larger the number, the more relevant the attribute.<\/p>\n<p>The query below reverse-transforms the data to its original values, since the data used for building the model was normalized.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, OPERATOR,","       ROUND(NUMERIC_VALUE,3) NUMERIC_VALUE, ATTRIBUTE_VALUE, ROUND(SUPPORT,3) SUPPORT, ROUND(CONFIDENCE,3) CONFIDENCE","FROM   DM$VRKM_SH_CLUS_SAMPLE ","WHERE  cluster_id IN (SELECT cluster_id","                      FROM DM$VDKM_SH_CLUS_SAMPLE","                      WHERE LEFT_CHILD_ID is NULL and RIGHT_CHILD_ID is NULL)","ORDER BY CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR, NUMERIC_VALUE, ATTRIBUTE_VALUE","FETCH FIRST 20 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349427515,"interpreter":"sql.low","endTime":1715349427636,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tOPERATOR\tNUMERIC_VALUE\tATTRIBUTE_VALUE\tSUPPORT\tCONFIDENCE\n6\tAFFINITY_CARD\t<=\t1\t\t641\t0.177\n6\tAFFINITY_CARD\t>\t0.889\t\t641\t0.177\n6\tCUST_CREDIT_LIMIT\t<=\t15000\t\t641\t0.002\n6\tCUST_CREDIT_LIMIT\t>=\t1500\t\t641\t0.002\n6\tCUST_GENDER\t=\t\tM\t517\t0.019\n6\tCUST_INCOME_LEVEL\tIN\t\tB: 30,000 - 49,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tC: 50,000 - 69,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t610\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t610\t0.003\n6\tCUST_MARITAL_STATUS\t=\t\tMarried\t576\t0.065\n6\tCUST_YEAR_OF_BIRTH\t<=\t1977.889\t\t627\t0.047\n6\tCUST_YEAR_OF_BIRTH\t>\t1945.444\t\t627\t0.047\n6\tEDUCATION\tIN\t\t< Bach.\t604\t0.014\n6\tEDUCATION\tIN\t\tAssoc-A\t604\t0.014\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"aggregationOption\":\"Last\",\"visualizations\":{\"sortingValue\":\"descending\"},\"series\":{\"availableSeriesElements\":[{\"id\":\"CLUS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Cluster ID\"},\"y\":{\"title\":\"Count\"}},\"lastColumns\":[\"CLUSTER_ID\",\"CNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Apply model to CUSTOMERS360_V and display customer count per cluster","message":["%sql","","-- For a descriptive machine learning technique like Clustering, \"scoring\" involves assigning each record to a cluster, with a certain probability. ","-- However, you can also obtain the probability of a record belonging to each cluster.","-- Hover over bars in the bar chart to view the size of each cluster. ","","SELECT CLUSTER_ID(KM_SH_CLUS_SAMPLE USING *) AS CLUSTER_ID, ","       COUNT(*) AS CNT ","FROM   CUSTOMERS360_V","GROUP BY CLUSTER_ID(KM_SH_CLUS_SAMPLE USING *)","ORDER BY CNT DESC;"],"enabled":true,"result":{"startTime":1715349427714,"interpreter":"sql.low","endTime":1715349427813,"results":[{"message":"CLUSTER_ID\tCNT\n9\t862\n6\t641\n17\t533\n8\t499\n18\t431\n7\t401\n15\t391\n19\t358\n13\t257\n11\t127\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List top ten customers most likely in cluster 6","message":["%sql","","SELECT CUST_ID, ","       ROUND(CLUSTER_PROBABILITY(KM_SH_CLUS_SAMPLE, 6 USING *),3) PROB","FROM CUSTOMERS360_V","ORDER BY PROB DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715349427893,"interpreter":"sql.low","endTime":1715349428012,"results":[{"message":"CUST_ID\tPROB\n104474\t0.245\n101386\t0.245\n103381\t0.243\n102605\t0.243\n103923\t0.243\n101530\t0.241\n100101\t0.24\n102250\t0.24\n100838\t0.239\n101968\t0.239\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List three most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. These represent customers ","--    with > 20% likelihood of assignment to their cluster.","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,2) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(KM_SH_CLUS_SAMPLE USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(KM_SH_CLUS_SAMPLE, NULL, 0.2 USING *) PSET","            FROM CUSTOMERS360_V V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED;"],"enabled":true,"result":{"startTime":1715349428096,"interpreter":"sql.low","endTime":1715349428423,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n101362\t9\t22.22\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".077\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1978\" weight=\".041\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"11000\" weight=\".024\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'101362'\",\"cluster_id\":\"'5'\",\"s.cluster_id\":\"'5'\",\"cust_id\":\"'100456'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 5 rows that are anomalous compared to their Clusters","message":["%sql","","-- List top 5 rows that are most unusual as measured by their distance from the cluster centroids. ","-- A row that is far from all cluster centroids may be anomalous.","","SELECT *","FROM (SELECT CUST_ID, RANK() OVER (ORDER BY CLUSTER_DISTANCE(KM_SH_CLUS_SAMPLE USING *) DESC) RANK_ANOMALOUS,","             round(CLUSTER_DISTANCE(KM_SH_CLUS_SAMPLE USING *),3) DISTANCE_TO_CENTROID","      FROM CUSTOMERS360_V)","WHERE RANK_ANOMALOUS <= 5","ORDER BY RANK_ANOMALOUS;"],"enabled":true,"result":{"startTime":1715349428498,"interpreter":"sql.low","endTime":1715349428626,"results":[{"message":"CUST_ID\tRANK_ANOMALOUS\tDISTANCE_TO_CENTROID\n102555\t1\t5.064\n103165\t2\t4.636\n100210\t3\t4.43\n102011\t4\t4.386\n103921\t5\t4.216\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach for building clustering model using an analytic function","In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.","","In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.","","Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers."],"enabled":true,"result":{"startTime":1715349428710,"interpreter":"md.low","endTime":1715349428777,"results":[{"message":"<h2 id=\"alternative-approach-for-building-clustering-model-using-an-analytic-function\">Alternative approach for building clustering model using an analytic function<\/h2>\n<p>In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.<\/p>\n<p>In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.<\/p>\n<p>Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Build and apply a transient model using analytic functions","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT * ","      FROM (SELECT CUST_ID,","                   CLUSTER_ID(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_ID,","                   CLUSTER_DETAILS(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_DET","            FROM CUSTOMERS360_V)","            ORDER By 1) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.CLUSTER_DET","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]',","                      \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;","                      "],"enabled":true,"result":{"startTime":1715349428859,"interpreter":"sql.low","endTime":1715349429137,"results":[{"message":"CUST_ID\tCLUSTER_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100001\t3\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".059\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".038\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".015\" \n100002\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".024\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".019\" \n100003\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".018\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".016\" \n100004\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".038\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".028\" \t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".028\" \n100005\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".157\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".009\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".009\" \n100006\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".131\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1983\" weight=\".086\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".056\" \n100007\t4\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".027\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".026\" \n100008\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".035\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".019\" \n100009\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".137\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".01\" \t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".007\" \n100010\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".115\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\".046\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".028\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. "],"enabled":true,"result":{"startTime":1715349429218,"interpreter":"md.low","endTime":1715349429278,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Populate settings table","message":["%script ","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE KM_SH_SAMPLE_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE KM_SH_SAMPLE_SETTINGS (SETTING_NAME  VARCHAR2(30),","                                    SETTING_VALUE VARCHAR2(4000));","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","-- See documentation for additional settings","","BEGIN       ","   INSERT INTO KM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.KMNS_DISTANCE,DBMS_DATA_MINING.KMNS_EUCLIDEAN);","   INSERT INTO KM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO,DBMS_DATA_MINING.PREP_AUTO_ON);","   INSERT INTO KM_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.KMNS_DETAILS, DBMS_DATA_MINING.KMNS_DETAILS_ALL);","   -- Some other settings:","   -- (dbms_data_mining.kmns_iterations,3);","   -- (dbms_data_mining.kmns_block_growth,2);","   -- (dbms_data_mining.kmns_conv_tolerance,0.01);","   -- (dbms_data_mining.kmns_split_criterion,dbms_data_mining.kmns_variance);","   -- (dbms_data_mining.kmns_min_pct_attr_support,0.1);","   -- (dbms_data_mining.kmns_num_bins,10);","END;","/"],"enabled":true,"result":{"startTime":1715349429359,"interpreter":"script.low","endTime":1715349430456,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable KM_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the contents of the model settings table","message":["%sql","","-- NOTE: The settings table can be reused or modified across multiple model builds. ","","SELECT * FROM KM_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1715349430538,"interpreter":"sql.low","endTime":1715349430638,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nPREP_AUTO\tON\nKMNS_DETAILS\tKMNS_DETAILS_ALL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the k-Means Clustering model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_CLUS_SAMPLE2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'KM_SH_CLUS_SAMPLE2',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMERS360_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'KM_SH_SAMPLE_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1715349430716,"interpreter":"script.low","endTime":1715349433721,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'KM_SH_CLUS_SAMPLE2'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715349433801,"interpreter":"sql.low","endTime":1715349433881,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_KMEANS\nCLUS_NUM_CLUSTERS\t10\nKMNS_CONV_TOLERANCE\t.001\nKMNS_DETAILS\tKMNS_DETAILS_ALL\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nKMNS_ITERATIONS\t20\nKMNS_MIN_PCT_ATTR_SUPPORT\t.1\nKMNS_NUM_BINS\t11\nKMNS_RANDOM_SEED\t0\nKMNS_SPLIT_CRITERION\tKMNS_VARIANCE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715349433964,"interpreter":"md.low","endTime":1715349434030,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715349434120,"interpreter":"md.low","endTime":1715349434181,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Clustering OC","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715350345488,"interpreter":"md.low","endTime":1715350345563,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identifying Customer Segments using Orthogonal Partitioning Clustering","","Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.","","See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715350345638,"interpreter":"md.low","endTime":1715350345713,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-orthogonal-partitioning-clustering\">Identifying Customer Segments using Orthogonal Partitioning Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms, including k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning k-Means algorithm. The data exploration, preparation, and machine learning runs inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Orthogonal Partitioning (O-Cluster) in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1715350345794,"interpreter":"md.low","endTime":1715350345870,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a>"],"enabled":true,"result":{"startTime":1715350345951,"interpreter":"md.low","endTime":1715350346014,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-o-cluster\" target=\"_blank\">OML Orthogonal Partitioning<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view joining CUSTOMERS and DEMOGRAPHICS","message":["%script","","CREATE OR REPLACE VIEW CUSTOMERS360_V AS","   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, ","          a.CUST_YEAR_OF_BIRTH, a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, ","          b.EDUCATION, b.AFFINITY_CARD, ","          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES","   FROM SH.CUSTOMERS a, SH.SUPPLEMENTARY_DEMOGRAPHICS b","   WHERE a.CUST_ID = b.CUST_ID;","   "],"enabled":true,"result":{"startTime":1715350346096,"interpreter":"script.low","endTime":1715350348093,"results":[{"message":"\nView CUSTOMERS360_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in CUSTOMERS360_V","message":["%sql","","SELECT COUNT(*) FROM CUSTOMERS360_V;"],"enabled":true,"result":{"startTime":1715350348178,"interpreter":"sql.low","endTime":1715350348328,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data in view CUSTOMERS360_V","message":["%sql","","SELECT * ","FROM CUSTOMERS360_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350348404,"interpreter":"sql.low","endTime":1715350348506,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n103791\tM\tDivorc.\t1952\tB: 30,000 - 49,999\t3000\tHS-grad\t0\t2\tProf.\t5\t0\n100804\tF\tDivorc.\t1943\tA: Below 30,000\t1500\t< Bach.\t0\t2\tProf.\t6\t0\n101610\tM\tNeverM\t1985\tI: 170,000 - 189,999\t3000\t11th\t0\t1\tHandler\t0\t1\n102308\tM\tNeverM\t1980\tJ: 190,000 - 249,999\t11000\t< Bach.\t0\t2\tProf.\t2\t1\n100593\tM\tMarried\t1963\tG: 130,000 - 149,999\t1500\tHS-grad\t1\t3\tProf.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build an O-Cluster model using the CUSTOMERS360_V view"],"enabled":true,"result":{"startTime":1715350348585,"interpreter":"md.low","endTime":1715350348649,"results":[{"message":"<h3 id=\"build-an-o-cluster-model-using-the-customers360_v-view\">Build an O-Cluster model using the CUSTOMERS360_V view<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the OC Clustering model with default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')        := 'ALGO_O_CLUSTER';","    V_setlst('PREP_AUTO')        := 'ON';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'OC_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1715350348728,"interpreter":"script.low","endTime":1715350353329,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS') := '5';","* Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5. ","v_setlst('OCLT_SENSITIVITY') := '0.5';"],"enabled":true,"result":{"startTime":1715350353415,"interpreter":"md.low","endTime":1715350353483,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-orthogonal-partitioning-cluster-clustering\">Examples of possible setting overrides for Orthogonal Partitioning Cluster clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12C659A2-0317-47C8-A05D-708FAF7DF370\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of leaf clusters generated by a clustering algorithm. The algorithm may return fewer clusters, depending on the data.  Enhanced k-Means usually produces the exact number of clusters specified by CLUS_NUM_CLUSTERS, unless there are fewer distinct data points. For k-Means and O-Cluster, the default is 10.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS') := '5';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a fraction that specifies the peak density required for separating a new cluster.  The fraction is related to the global uniform density. It requires a number between 0 and 1 (inclusive).  The default is 0.5.\nv_setlst('OCLT_SENSITIVITY') := '0.5';<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the OC Clustering model with explicit settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')         := 'ALGO_O_CLUSTER';","    V_setlst('PREP_AUTO')         := 'ON';","    V_setlst('CLUS_NUM_CLUSTERS') := '5';","    V_setlst('OCLT_SENSITIVITY')  := '0.5';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'OC_SH_CLUS_SAMPLE',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'select * from CUSTOMERS360_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID');","END;"],"enabled":true,"result":{"startTime":1715350353563,"interpreter":"script.low","endTime":1715350356165,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model metadata","message":["%sql ","","SELECT MINING_FUNCTION, ALGORITHM","FROM   USER_MINING_MODELS","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE';"],"enabled":true,"result":{"startTime":1715350356242,"interpreter":"sql.low","endTime":1715350356316,"results":[{"message":"MINING_FUNCTION\tALGORITHM\nCLUSTERING\tO_CLUSTER\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the model signature","message":["%sql ","","-- The model signature includes the attributes used in model training data,","-- and also corresponds to the attributes expected to apply the model to new data. ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE'","ORDER BY ATTRIBUTE_NAME;"],"enabled":true,"result":{"startTime":1715350356395,"interpreter":"sql.low","endTime":1715350356471,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715350356548,"interpreter":"sql.low","endTime":1715350356621,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_O_CLUSTER\nCLUS_NUM_CLUSTERS\t5\nOCLT_SENSITIVITY\t0.5\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the O-Cluster model details","message":["%sql ","","-- Display the list of available cluster model details as indicated in VIEW_TYPE","-- Cluster details are presented as separate views depending on the desired information.","","SELECT VIEW_NAME, VIEW_TYPE FROM USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='OC_SH_CLUS_SAMPLE'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1715350356697,"interpreter":"sql.low","endTime":1715350356821,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAOC_SH_CLUS_SAMPLE\tClustering Attribute Statistics\nDM$VBOC_SH_CLUS_SAMPLE\tAutomatic Data Preparation Binning\nDM$VDOC_SH_CLUS_SAMPLE\tClustering Description\nDM$VGOC_SH_CLUS_SAMPLE\tGlobal Name-Value Pairs\nDM$VHOC_SH_CLUS_SAMPLE\tClustering Histograms\nDM$VROC_SH_CLUS_SAMPLE\tClustering Rules\nDM$VSOC_SH_CLUS_SAMPLE\tComputed Settings\nDM$VWOC_SH_CLUS_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details","message":["%sql","","-- For each cluster_id, this DM$VD* view provides the number of records ","--   in the cluster, the parent cluster id, the level in the hierarchy. ","","SELECT CLUSTER_ID, RECORD_COUNT, PARENT, TREE_LEVEL","FROM   DM$VDOC_SH_CLUS_SAMPLE","ORDER BY CLUSTER_ID;"],"enabled":true,"result":{"startTime":1715350356903,"interpreter":"sql.low","endTime":1715350357060,"results":[{"message":"CLUSTER_ID\tRECORD_COUNT\tPARENT\tTREE_LEVEL\n1\t4500\t\t1\n2\t2316\t1\t2\n3\t2184\t1\t2\n4\t1690\t3\t3\n5\t494\t3\t3\n6\t991\t4\t4\n7\t699\t4\t4\n8\t993\t2\t3\n9\t1323\t2\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Show leaf clusters IDs","message":["%sql","","-- Show leaf IDs to know for which clusters to show detail","","SELECT CLUSTER_ID","FROM   DM$VDOC_SH_CLUS_SAMPLE","WHERE  LEFT_CHILD_ID IS NULL AND RIGHT_CHILD_ID IS NULL","ORDER BY CLUSTER_ID"],"enabled":true,"result":{"startTime":1715350357142,"interpreter":"sql.low","endTime":1715350357236,"results":[{"message":"CLUSTER_ID\n5\n6\n7\n8\n9\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show Cluster details for cluster 9","message":["%sql","","-- For cluster_id 9, this output lists all the attributes that constitute the centroid, with the mean (for numericals) ","-- or mode (for categoricals). It also includes the variance.","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, round(MEAN,3) MEAN, round(VARIANCE,3) VARIANCE, MODE_VALUE","FROM  DM$VAOC_SH_CLUS_SAMPLE","WHERE CLUSTER_ID = 9","ORDER BY ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME;"],"enabled":true,"result":{"startTime":1715350357312,"interpreter":"sql.low","endTime":1715350357406,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tMEAN\tVARIANCE\tMODE_VALUE\n9\tAFFINITY_CARD\t0.4\t0.24\t\n9\tCUST_CREDIT_LIMIT\t6938.398\t3213108.474\t\n9\tCUST_GENDER\t\t\tM\n9\tCUST_INCOME_LEVEL\t\t\tG: 130,000 - 149,999\n9\tCUST_MARITAL_STATUS\t\t\tMarried\n9\tCUST_YEAR_OF_BIRTH\t1963.322\t181.359\t\n9\tEDUCATION\t\t\tHS-grad\n9\tHOUSEHOLD_SIZE\t\t\t3\n9\tOCCUPATION\t\t\tSales\n9\tYRS_RESIDENCE\t4.203\t3.811\t\n9\tY_BOX_GAMES\t0.252\t0.189\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","In the following example, the view shows attribute level details for the rule associated with each cluster id. ","For an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null. ","Confidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.","The larger the number, the more relevant the attribute."," ","The query below reverse-transforms the data to its original values, since the data used for building the model was normalized."],"enabled":true,"result":{"startTime":1715350357486,"interpreter":"md.low","endTime":1715350357544,"results":[{"message":"<p>In the following example, the view shows attribute level details for the rule associated with each cluster id.\nFor an attribute, support indicates the number of records that fall within the attribute range specified in the rule antecedent where the given attribute is not null.\nConfidence is a number between 0 and 1 that indicates how relevant this attribute is in distinguishing the records in the cluster from all the records in the whole data.\nThe larger the number, the more relevant the attribute.<\/p>\n<p>The query below reverse-transforms the data to its original values, since the data used for building the model was normalized.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display rule details for leaf clusters","message":["%sql","","SELECT CLUSTER_ID, ATTRIBUTE_NAME, OPERATOR,","       NUMERIC_VALUE, ATTRIBUTE_VALUE, SUPPORT, ROUND(CONFIDENCE,3) CONFIDENCE","FROM   DM$VROC_SH_CLUS_SAMPLE ","WHERE  cluster_id IN (SELECT cluster_id","                      FROM DM$VDOC_SH_CLUS_SAMPLE","                      WHERE LEFT_CHILD_ID is NULL ","                      AND RIGHT_CHILD_ID is NULL)","ORDER BY CLUSTER_ID, ATTRIBUTE_NAME, ATTRIBUTE_SUBNAME, OPERATOR, NUMERIC_VALUE, ATTRIBUTE_VALUE;"],"enabled":true,"result":{"startTime":1715350357622,"interpreter":"sql.low","endTime":1715350357806,"results":[{"message":"CLUSTER_ID\tATTRIBUTE_NAME\tOPERATOR\tNUMERIC_VALUE\tATTRIBUTE_VALUE\tSUPPORT\tCONFIDENCE\n5\tAFFINITY_CARD\t<=\t\t1\t494\t0.001\n5\tAFFINITY_CARD\t>=\t\t0\t494\t0.001\n5\tCUST_CREDIT_LIMIT\t=\t\t(14035.7; 15000]\t469\t0.166\n5\tCUST_GENDER\tIN\t\tF\t494\t0.001\n5\tCUST_GENDER\tIN\t\tM\t494\t0.001\n5\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t444\t0.055\n5\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t444\t0.055\n5\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t444\t0.055\n5\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t444\t0.055\n5\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t458\t0\n5\tCUST_MARITAL_STATUS\tIN\t\tMarried\t458\t0\n5\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t458\t0\n5\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t468\t0.003\n5\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t468\t0.003\n5\tEDUCATION\tIN\t\t< Bach.\t412\t0.004\n5\tEDUCATION\tIN\t\tAssoc-V\t412\t0.004\n5\tEDUCATION\tIN\t\tBach.\t412\t0.004\n5\tEDUCATION\tIN\t\tHS-grad\t412\t0.004\n5\tEDUCATION\tIN\t\tMasters\t412\t0.004\n5\tEDUCATION\tIN\t\tProfsc\t412\t0.004\n5\tHOUSEHOLD_SIZE\tIN\t\t1\t411\t0.001\n5\tHOUSEHOLD_SIZE\tIN\t\t2\t411\t0.001\n5\tHOUSEHOLD_SIZE\tIN\t\t3\t411\t0.001\n5\tOCCUPATION\tIN\t\t?\t485\t0.002\n5\tOCCUPATION\tIN\t\tCleric.\t485\t0.002\n5\tOCCUPATION\tIN\t\tCrafts\t485\t0.002\n5\tOCCUPATION\tIN\t\tExec.\t485\t0.002\n5\tOCCUPATION\tIN\t\tHandler\t485\t0.002\n5\tOCCUPATION\tIN\t\tMachine\t485\t0.002\n5\tOCCUPATION\tIN\t\tOther\t485\t0.002\n5\tOCCUPATION\tIN\t\tProf.\t485\t0.002\n5\tOCCUPATION\tIN\t\tProtec.\t485\t0.002\n5\tOCCUPATION\tIN\t\tSales\t485\t0.002\n5\tOCCUPATION\tIN\t\tTechSup\t485\t0.002\n5\tOCCUPATION\tIN\t\tTransp.\t485\t0.002\n5\tYRS_RESIDENCE\t<=\t\t7.46667\t478\t0.001\n5\tYRS_RESIDENCE\t>=\t\t.933333\t478\t0.001\n5\tY_BOX_GAMES\t<=\t\t1\t494\t0.001\n5\tY_BOX_GAMES\t>=\t\t0\t494\t0.001\n6\tAFFINITY_CARD\t=\t\t[0; .5]\t991\t0.131\n6\tCUST_CREDIT_LIMIT\t<=\t\t10178.6\t969\t0.12\n6\tCUST_CREDIT_LIMIT\t>=\t\t8250\t969\t0.12\n6\tCUST_GENDER\tIN\t\tF\t991\t0.003\n6\tCUST_GENDER\tIN\t\tM\t991\t0.003\n6\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t960\t0.017\n6\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t960\t0.017\n6\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t891\t0.006\n6\tCUST_MARITAL_STATUS\tIN\t\tMarried\t891\t0.006\n6\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t891\t0.006\n6\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t934\t0.002\n6\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t934\t0.002\n6\tEDUCATION\tIN\t\t10th\t870\t0.006\n6\tEDUCATION\tIN\t\t< Bach.\t870\t0.006\n6\tEDUCATION\tIN\t\tAssoc-A\t870\t0.006\n6\tEDUCATION\tIN\t\tAssoc-V\t870\t0.006\n6\tEDUCATION\tIN\t\tBach.\t870\t0.006\n6\tEDUCATION\tIN\t\tHS-grad\t870\t0.006\n6\tHOUSEHOLD_SIZE\tIN\t\t1\t913\t0.007\n6\tHOUSEHOLD_SIZE\tIN\t\t2\t913\t0.007\n6\tHOUSEHOLD_SIZE\tIN\t\t3\t913\t0.007\n6\tHOUSEHOLD_SIZE\tIN\t\t9+\t913\t0.007\n6\tOCCUPATION\tIN\t\t?\t910\t0.002\n6\tOCCUPATION\tIN\t\tCleric.\t910\t0.002\n6\tOCCUPATION\tIN\t\tCrafts\t910\t0.002\n6\tOCCUPATION\tIN\t\tExec.\t910\t0.002\n6\tOCCUPATION\tIN\t\tHandler\t910\t0.002\n6\tOCCUPATION\tIN\t\tMachine\t910\t0.002\n6\tOCCUPATION\tIN\t\tOther\t910\t0.002\n6\tOCCUPATION\tIN\t\tProf.\t910\t0.002\n6\tOCCUPATION\tIN\t\tSales\t910\t0.002\n6\tOCCUPATION\tIN\t\tTransp.\t910\t0.002\n6\tYRS_RESIDENCE\t<=\t\t6.53333\t918\t0.002\n6\tYRS_RESIDENCE\t>=\t\t.933333\t918\t0.002\n6\tY_BOX_GAMES\t<=\t\t1\t991\t0.004\n6\tY_BOX_GAMES\t>=\t\t0\t991\t0.004\n7\tAFFINITY_CARD\t=\t\t[0; .5]\t562\t0.002\n7\tCUST_CREDIT_LIMIT\t=\t\t(10178.6; 11142.9]\t699\t0.172\n7\tCUST_GENDER\tIN\t\tF\t699\t0.001\n7\tCUST_GENDER\tIN\t\tM\t699\t0.001\n7\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t695\t0.077\n7\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t695\t0.077\n7\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t695\t0.077\n7\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t695\t0.077\n7\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t645\t0.001\n7\tCUST_MARITAL_STATUS\tIN\t\tMarried\t645\t0.001\n7\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t645\t0.001\n7\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t656\t0.002\n7\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t656\t0.002\n7\tEDUCATION\tIN\t\t10th\t635\t0.002\n7\tEDUCATION\tIN\t\t11th\t635\t0.002\n7\tEDUCATION\tIN\t\t< Bach.\t635\t0.002\n7\tEDUCATION\tIN\t\tAssoc-A\t635\t0.002\n7\tEDUCATION\tIN\t\tAssoc-V\t635\t0.002\n7\tEDUCATION\tIN\t\tBach.\t635\t0.002\n7\tEDUCATION\tIN\t\tHS-grad\t635\t0.002\n7\tEDUCATION\tIN\t\tMasters\t635\t0.002\n7\tHOUSEHOLD_SIZE\tIN\t\t1\t643\t0.001\n7\tHOUSEHOLD_SIZE\tIN\t\t2\t643\t0.001\n7\tHOUSEHOLD_SIZE\tIN\t\t3\t643\t0.001\n7\tHOUSEHOLD_SIZE\tIN\t\t9+\t643\t0.001\n7\tOCCUPATION\tIN\t\t?\t649\t0.001\n7\tOCCUPATION\tIN\t\tCleric.\t649\t0.001\n7\tOCCUPATION\tIN\t\tCrafts\t649\t0.001\n7\tOCCUPATION\tIN\t\tExec.\t649\t0.001\n7\tOCCUPATION\tIN\t\tHandler\t649\t0.001\n7\tOCCUPATION\tIN\t\tMachine\t649\t0.001\n7\tOCCUPATION\tIN\t\tOther\t649\t0.001\n7\tOCCUPATION\tIN\t\tProf.\t649\t0.001\n7\tOCCUPATION\tIN\t\tSales\t649\t0.001\n7\tOCCUPATION\tIN\t\tTransp.\t649\t0.001\n7\tYRS_RESIDENCE\t<=\t\t7.46667\t666\t0.001\n7\tYRS_RESIDENCE\t>=\t\t.933333\t666\t0.001\n7\tY_BOX_GAMES\t<=\t\t1\t699\t0.002\n7\tY_BOX_GAMES\t>=\t\t0\t699\t0.002\n8\tAFFINITY_CARD\t<=\t\t1\t993\t0.002\n8\tAFFINITY_CARD\t>=\t\t0\t993\t0.002\n8\tCUST_CREDIT_LIMIT\t<=\t\t3428.57\t977\t0.141\n8\tCUST_CREDIT_LIMIT\t>=\t\t1500\t977\t0.141\n8\tCUST_GENDER\tIN\t\tF\t993\t0\n8\tCUST_GENDER\tIN\t\tM\t993\t0\n8\tCUST_INCOME_LEVEL\tIN\t\tA: Below 30,000\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tB: 30,000 - 49,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tC: 50,000 - 69,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tD: 70,000 - 89,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t993\t0.08\n8\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t993\t0.08\n8\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t914\t0.001\n8\tCUST_MARITAL_STATUS\tIN\t\tMarried\t914\t0.001\n8\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t914\t0.001\n8\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t934\t0.001\n8\tCUST_YEAR_OF_BIRTH\t>=\t\t1939.55\t934\t0.001\n8\tEDUCATION\tIN\t\t< Bach.\t886\t0.003\n8\tEDUCATION\tIN\t\tAssoc-A\t886\t0.003\n8\tEDUCATION\tIN\t\tAssoc-V\t886\t0.003\n8\tEDUCATION\tIN\t\tBach.\t886\t0.003\n8\tEDUCATION\tIN\t\tHS-grad\t886\t0.003\n8\tEDUCATION\tIN\t\tMasters\t886\t0.003\n8\tHOUSEHOLD_SIZE\tIN\t\t1\t916\t0\n8\tHOUSEHOLD_SIZE\tIN\t\t2\t916\t0\n8\tHOUSEHOLD_SIZE\tIN\t\t3\t916\t0\n8\tHOUSEHOLD_SIZE\tIN\t\t9+\t916\t0\n8\tOCCUPATION\tIN\t\t?\t939\t0.001\n8\tOCCUPATION\tIN\t\tCleric.\t939\t0.001\n8\tOCCUPATION\tIN\t\tCrafts\t939\t0.001\n8\tOCCUPATION\tIN\t\tExec.\t939\t0.001\n8\tOCCUPATION\tIN\t\tFarming\t939\t0.001\n8\tOCCUPATION\tIN\t\tMachine\t939\t0.001\n8\tOCCUPATION\tIN\t\tOther\t939\t0.001\n8\tOCCUPATION\tIN\t\tProf.\t939\t0.001\n8\tOCCUPATION\tIN\t\tSales\t939\t0.001\n8\tOCCUPATION\tIN\t\tTechSup\t939\t0.001\n8\tOCCUPATION\tIN\t\tTransp.\t939\t0.001\n8\tYRS_RESIDENCE\t<=\t\t7.46667\t930\t0.001\n8\tYRS_RESIDENCE\t>=\t\t.933333\t930\t0.001\n8\tY_BOX_GAMES\t<=\t\t1\t993\t0.002\n8\tY_BOX_GAMES\t>=\t\t0\t993\t0.002\n9\tAFFINITY_CARD\t<=\t\t1\t1323\t0.022\n9\tAFFINITY_CARD\t>=\t\t0\t1323\t0.022\n9\tCUST_CREDIT_LIMIT\t<=\t\t10178.6\t1293\t0.079\n9\tCUST_CREDIT_LIMIT\t>=\t\t4392.86\t1293\t0.079\n9\tCUST_GENDER\tIN\t\tF\t1323\t0.001\n9\tCUST_GENDER\tIN\t\tM\t1323\t0.001\n9\tCUST_INCOME_LEVEL\tIN\t\tC: 50,000 - 69,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tE: 90,000 - 109,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tF: 110,000 - 129,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tG: 130,000 - 149,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tH: 150,000 - 169,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tI: 170,000 - 189,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tJ: 190,000 - 249,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tK: 250,000 - 299,999\t1224\t0.011\n9\tCUST_INCOME_LEVEL\tIN\t\tL: 300,000 and above\t1224\t0.011\n9\tCUST_MARITAL_STATUS\tIN\t\tDivorc.\t1244\t0.003\n9\tCUST_MARITAL_STATUS\tIN\t\tMarried\t1244\t0.003\n9\tCUST_MARITAL_STATUS\tIN\t\tNeverM\t1244\t0.003\n9\tCUST_YEAR_OF_BIRTH\t<=\t\t1986\t1223\t0.001\n9\tCUST_YEAR_OF_BIRTH\t>=\t\t1942.86\t1223\t0.001\n9\tEDUCATION\tIN\t\t10th\t1215\t0.002\n9\tEDUCATION\tIN\t\t11th\t1215\t0.002\n9\tEDUCATION\tIN\t\t< Bach.\t1215\t0.002\n9\tEDUCATION\tIN\t\tAssoc-A\t1215\t0.002\n9\tEDUCATION\tIN\t\tAssoc-V\t1215\t0.002\n9\tEDUCATION\tIN\t\tBach.\t1215\t0.002\n9\tEDUCATION\tIN\t\tHS-grad\t1215\t0.002\n9\tEDUCATION\tIN\t\tMasters\t1215\t0.002\n9\tHOUSEHOLD_SIZE\tIN\t\t1\t1209\t0.003\n9\tHOUSEHOLD_SIZE\tIN\t\t2\t1209\t0.003\n9\tHOUSEHOLD_SIZE\tIN\t\t3\t1209\t0.003\n9\tHOUSEHOLD_SIZE\tIN\t\t9+\t1209\t0.003\n9\tOCCUPATION\tIN\t\t?\t1231\t0.001\n9\tOCCUPATION\tIN\t\tCleric.\t1231\t0.001\n9\tOCCUPATION\tIN\t\tCrafts\t1231\t0.001\n9\tOCCUPATION\tIN\t\tExec.\t1231\t0.001\n9\tOCCUPATION\tIN\t\tMachine\t1231\t0.001\n9\tOCCUPATION\tIN\t\tOther\t1231\t0.001\n9\tOCCUPATION\tIN\t\tProf.\t1231\t0.001\n9\tOCCUPATION\tIN\t\tSales\t1231\t0.001\n9\tOCCUPATION\tIN\t\tTechSup\t1231\t0.001\n9\tOCCUPATION\tIN\t\tTransp.\t1231\t0.001\n9\tYRS_RESIDENCE\t<=\t\t7.46667\t1236\t0.001\n9\tYRS_RESIDENCE\t>=\t\t.933333\t1236\t0.001\n9\tY_BOX_GAMES\t<=\t\t1\t1323\t0.003\n9\tY_BOX_GAMES\t>=\t\t0\t1323\t0.003\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"COUNT\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CLUS\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Apply model to CUSTOMERS360_V and display customer count per cluster","message":["%sql","","-- For a descriptive machine learning technique like Clustering, \"scoring\" involves assigning each record to a cluster, with a certain probability. ","-- However, one can also obtain the probability of a record belonging to each cluster.","-- Hover over bars in the bar chart to view the size of each cluster. ","","SELECT CLUSTER_ID(OC_SH_CLUS_SAMPLE USING *) AS CLUS, COUNT(*) AS COUNT ","FROM   CUSTOMERS360_V","GROUP BY CLUSTER_ID(OC_SH_CLUS_SAMPLE USING *)","ORDER BY COUNT DESC;"],"enabled":true,"result":{"startTime":1715350357888,"interpreter":"sql.low","endTime":1715350357985,"results":[{"message":"CLUS\tCOUNT\n9\t1323\n8\t993\n6\t991\n7\t699\n5\t494\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List ten customers most likely in cluster 6","message":["%sql","","SELECT CUST_ID, ","       ROUND(CLUSTER_PROBABILITY(OC_SH_CLUS_SAMPLE, 6 USING *),4) PROB","FROM   CUSTOMERS360_V","ORDER BY PROB DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350358062,"interpreter":"sql.low","endTime":1715350358183,"results":[{"message":"CUST_ID\tPROB\n104171\t0.9999\n103255\t0.9999\n100885\t0.9999\n101929\t0.9999\n100105\t0.9999\n103786\t0.9999\n104082\t0.9999\n104400\t0.9999\n100432\t0.9999\n104470\t0.9999\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List four most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","-- For customer id, pick among 101362, 102087 or 100456. These represent customers ","--    with > 20% likelihood of assignment to their cluster.","","SELECT CUST_ID,","       CLUSTER_ID,","       ROUND(PROB*100,2) PROB_PCT,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, ","             CLUSTER_DETAILS(OC_SH_CLUS_SAMPLE USING T.*) DETAIL","      FROM (SELECT V.*, CLUSTER_SET(OC_SH_CLUS_SAMPLE, NULL, 0.2 USING *) PSET","            FROM CUSTOMERS360_V V","            WHERE cust_id = ${CUST_ID ='101362','101362'|'102087'| '100456'}) T,","                  TABLE(T.PSET) S","            ORDER BY 2 DESC) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.DETAIL","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;"],"enabled":true,"result":{"startTime":1715350358261,"interpreter":"sql.low","endTime":1715350358530,"results":[{"message":"CUST_ID\tCLUSTER_ID\tPROB_PCT\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n101362\t6\t98.43\t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".983\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"11000\" operator=\"between\" range=\"(1.018E+004:1.114E+004]\" weight=\".\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":"{\"CUST_ID\":\"'101362'\",\"cluster_id\":\"'5'\",\"s.cluster_id\":\"'5'\",\"cust_id\":\"'100456'\"}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[{\"type\":\"Select\",\"name\":\"CUST_ID\",\"displayName\":null,\"defaultValue\":\"'101362'\",\"argument\":null,\"options\":[{\"value\":\"'101362'\",\"displayName\":null},{\"value\":\"'102087'\",\"displayName\":null},{\"value\":\"'100456'\",\"displayName\":null}],\"isHidden\":false,\"isProgrammatic\":false}]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach for building clustering model using an analytic function","In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.","","In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.","","Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers."],"enabled":true,"result":{"startTime":1715350358607,"interpreter":"md.low","endTime":1715350358667,"results":[{"message":"<h2 id=\"alternative-approach-for-building-clustering-model-using-an-analytic-function\">Alternative approach for building clustering model using an analytic function<\/h2>\n<p>In addition to creating a persistent model that is stored as a schema object, models can be built and scored on data dynamically using Oracle's analytic function syntax.<\/p>\n<p>In the following example, we segment customers into 4 groups based on common characteristics and provide the segment assignments. Note that this query does not reference a pre-build clustering model, but rather it segments the input data on the fly.  Rerunning the same query with different input will result in a different segmentation. It also provides the main reasons (attributes) why a given customer is placed into a specific cluster.<\/p>\n<p>Note that the where clause has to be placed outside of the inline view so that the analytic function will build the clustering model on all the data, and not just the selected customers.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Build and apply a transient model using analytic functions","message":["%sql","","SELECT CUST_ID,","       CLUSTER_ID,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","FROM (SELECT * ","      FROM (SELECT CUST_ID,","                   CLUSTER_ID(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_ID,","                   CLUSTER_DETAILS(INTO 4 USING CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, ","                                              CUST_CREDIT_LIMIT, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION,","                                              YRS_RESIDENCE, Y_BOX_GAMES) OVER () CLUSTER_DET","            FROM CUSTOMERS360_V)","            WHERE CUST_ID <= 100010","            ORDER By 1) OUT,","            XMLTABLE('/Details'","                      PASSING OUT.CLUSTER_DET","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') OUTPRED;","                      "],"enabled":true,"result":{"startTime":1715350358745,"interpreter":"sql.low","endTime":1715350359015,"results":[{"message":"CUST_ID\tCLUSTER_ID\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100001\t3\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1941\" weight=\".059\" \t\"CUST_CREDIT_LIMIT\" actualValue=\"1500\" weight=\".038\" \n100002\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\".024\" \n100003\t4\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".025\" \t\"CUST_MARITAL_STATUS\" actualValue=\"NeverM\" weight=\".018\" \n100004\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".038\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".028\" \n100005\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".157\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".009\" \n100006\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".131\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1983\" weight=\".086\" \n100007\t4\t\"CUST_MARITAL_STATUS\" actualValue=\"Divorc.\" weight=\".027\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \n100008\t4\t\"CUST_CREDIT_LIMIT\" actualValue=\"15000\" weight=\".035\" \t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".026\" \n100009\t1\t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".137\" \t\"EDUCATION\" actualValue=\"Bach.\" weight=\".01\" \n100010\t2\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".115\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1975\" weight=\".046\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Alternative approach to creating clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. ","","NOTE: The settings table can be reused or modified in multiple model build executions. "],"enabled":true,"result":{"startTime":1715350359092,"interpreter":"md.low","endTime":1715350359158,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-clustering-model-using-a-settings-table\">Alternative approach to creating clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n<p>NOTE: The settings table can be reused or modified in multiple model build executions.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create and populate model settings table","message":["%script ","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE OC_SH_SAMPLE_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE OC_SH_SAMPLE_SETTINGS (SETTING_NAME  VARCHAR2(30),","                                    SETTING_VALUE VARCHAR2(4000));","/","BEGIN       ","   INSERT INTO OC_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.OCLT_SENSITIVITY,'0.5');","   INSERT INTO OC_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO,DBMS_DATA_MINING.PREP_AUTO_ON);","END;"],"enabled":true,"result":{"startTime":1715350359236,"interpreter":"script.low","endTime":1715350359420,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable OC_SH_SAMPLE_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View the contents of the model settings table","message":["%sql","","SELECT * FROM OC_SH_SAMPLE_SETTINGS;"],"enabled":true,"result":{"startTime":1715350359494,"interpreter":"sql.low","endTime":1715350359587,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nOCLT_SENSITIVITY\t0.5\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop, then build the k-Means Clustering model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_CLUS_SAMPLE2');","EXCEPTION WHEN OTHERS THEN NULL; END;","","BEGIN","DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'OC_SH_CLUS_SAMPLE2',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMERS360_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    SETTINGS_TABLE_NAME => 'OC_SH_SAMPLE_SETTINGS');","END;"],"enabled":true,"result":{"startTime":1715350359664,"interpreter":"script.low","endTime":1715350362166,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the actual settings used by the algorithm","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'OC_SH_CLUS_SAMPLE2'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715350362251,"interpreter":"sql.low","endTime":1715350362328,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_KMEANS\nCLUS_NUM_CLUSTERS\t10\nKMNS_CONV_TOLERANCE\t.001\nKMNS_DETAILS\tKMNS_DETAILS_HIERARCHY\nKMNS_DISTANCE\tKMNS_EUCLIDEAN\nKMNS_ITERATIONS\t20\nKMNS_MIN_PCT_ATTR_SUPPORT\t.1\nKMNS_NUM_BINS\t11\nKMNS_RANDOM_SEED\t0\nKMNS_SPLIT_CRITERION\tKMNS_VARIANCE\nOCLT_SENSITIVITY\t0.5\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715350362405,"interpreter":"md.low","endTime":1715350362464,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715350362543,"interpreter":"md.low","endTime":1715350362607,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Duplicates Removal.dsnb b/machine-learning/notebooks/sql/OML4SQL Data Cleaning Duplicates Removal.dsnb
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--- a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Duplicates Removal.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Duplicates Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Duplicates Removal","In this notebook, we demonstrate how to remove duplicate records using Oracle SQL.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a prerequisite to the current notebook.","","---","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md"," ","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the number of distinct customer id","message":["%sql","","SELECT COUNT(DISTINCT CUSTOMER_ID)","FROM CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the number of total rows in the table. There are duplicated rows","message":["%sql","","SELECT COUNT(1) FROM CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view with all distinct rows","message":["%sql","","CREATE OR REPLACE VIEW CUSTOMER_INSURANCE_LTV_SQL_UNIQUE AS ","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","WHERE rowid IN (","    SELECT max(rowid) row_id","    FROM (SELECT customer_id, rowid","          FROM CUSTOMER_INSURANCE_LTV_SQL)","    GROUP BY customer_id"," ) "," "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the number of rows in the table. All rows are distinct.","message":["%sql","","SELECT COUNT(1) FROM CUSTOMER_INSURANCE_LTV_SQL_UNIQUE;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check the number of distinct CUSTOMER_ID in the table","message":["%sql","","SELECT COUNT(DISTINCT CUSTOMER_ID) FROM CUSTOMER_INSURANCE_LTV_SQL_UNIQUE;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Missing Data.dsnb b/machine-learning/notebooks/sql/OML4SQL Data Cleaning Missing Data.dsnb
deleted file mode 100755
index c4f37169..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Missing Data.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Missing Data","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Data Cleaning: fill in missing data","","In this notebook, we demonstrate how to replace  missing values using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, this operation may *not* be needed separately if automatic data preparation is enabled. Automatic data preparation automatically replaces missing values of numerical attributes with the mean and missing values of categorical attributes with the mode. For example, see <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12');\"> Data Preparation for GLM <\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL DBMS_DATA_MINNG_TRANSFORM Documentation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV_SQL data","message":["%sql","","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select rows where columns MARITAL_STATUS,  CREDIT_BALANCE,  BANK_FUNDS, INCOME contain missing values","message":["%sql","","SELECT CUSTOMER_ID, MARITAL_STATUS, CREDIT_BALANCE, BANK_FUNDS, INCOME","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view to include the columns to be treated","message":["%sql","","CREATE OR REPLACE VIEW CUSTOMER_MISSING_V AS","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute mode value of each categorical variable","message":["%script","","BEGIN","    EXECUTE IMMEDIATE 'DROP TABLE missc_tbl';","    EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","BEGIN","  DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_CAT(","      MISS_TABLE_NAME   => 'MISSC_TBL');","  DBMS_DATA_MINING_TRANSFORM.INSERT_MISS_CAT_MODE(","      MISS_TABLE_NAME   => 'MISSC_TBL',","      DATA_TABLE_NAME   => 'CUSTOMER_MISSING_V');","END;","/"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Table MISSC_TBL contains the mode of each categorical variable","message":["%sql","","SELECT COL, VAL","FROM MISSC_TBL"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view CUSTOMER_INSURANCE_LTV_SQL_STACK with missing categorical columns replaced by mode","message":["%script","","BEGIN","    EXECUTE IMMEDIATE 'DROP VIEW  CUSTOMER_INSURANCE_LTV_SQL_STACK';","    EXCEPTION WHEN OTHERS THEN NULL;","END;","/","DECLARE","    xforms      dbms_data_mining_transform.TRANSFORM_LIST;","BEGIN","    DBMS_DATA_MINING_TRANSFORM.STACK_MISS_CAT (","        MISS_TABLE_NAME    => 'MISSC_TBL',","        XFORM_LIST         => xforms);","    DBMS_DATA_MINING_TRANSFORM.XFORM_STACK (","         XFORM_LIST         => xforms,","         DATA_TABLE_NAME    => 'CUSTOMER_INSURANCE_LTV_SQL',","         XFORM_VIEW_NAME    => 'CUSTOMER_INSURANCE_LTV_SQL_STACK');","END;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View categorical columns with missing values replaced","message":["%sql","","SELECT CUSTOMER_ID, MARITAL_STATUS","FROM CUSTOMER_INSURANCE_LTV_SQL_STACK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a table with the mean for each numeric variable","message":["%script","","BEGIN","    EXECUTE IMMEDIATE 'DROP TABLE miss_num_xtbl';","    EXCEPTION WHEN OTHERS THEN NULL;","END;","/","","BEGIN","   DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_NUM(","       MISS_TABLE_NAME   => 'MISS_NUM_XTBL');","   DBMS_DATA_MINING_TRANSFORM.INSERT_MISS_NUM_MEAN(","       MISS_TABLE_NAME   => 'miss_num_xtbl',","       DATA_TABLE_NAME   => 'CUSTOMER_MISSING_V',","       EXCLUDE_LIST      => 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\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Outlier Removal.dsnb b/machine-learning/notebooks/sql/OML4SQL Data Cleaning Outlier Removal.dsnb
deleted file mode 100755
index 46cb78d3..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Outlier Removal.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Outlier Removal","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Outlier Removal","","In this notebook, we demonstrate how to remove outliers using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","In the dataset CUSTOMER_INSURANCE_LTV_SQL, we focus on numeric values and remove records with values in the top and bottom 5%. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL DBMS_DATA_MINNG_TRANSFORM Documentation<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV_SQL data","message":["%sql","","SELECT * ","FROM   CUSTOMER_INSURANCE_LTV_SQL ","FETCH FIRST 10 ROWS 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"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View clip boundaries","message":["%sql","","SELECT col, lcut, rcut","FROM   clip_tbl","ORDER BY col ASC;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view with column INCOME clipped","message":["%script","","BEGIN","    EXECUTE IMMEDIATE 'DROP VIEW  CUSTOMER_INSURANCE_LTV_SQL_STACK';","    EXCEPTION WHEN OTHERS THEN NULL;","END;","/","DECLARE","     xforms      dbms_data_mining_transform.TRANSFORM_LIST;","BEGIN","     dbms_data_mining_transform.STACK_CLIP (","          clip_table_name    => 'clip_tbl',","          xform_list         => xforms);","     dbms_data_mining_transform.XFORM_STACK (","          xform_list         => xforms,","          data_table_name    => 'CUSTOMER_INSURANCE_LTV_SQL',","          xform_view_name    => 'CUSTOMER_INSURANCE_LTV_SQL_STACK');"," END;"," /"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check new min and max of INCOME","message":["%sql"," ","SELECT 'PREVIOUS' STATE, MIN(INCOME), MAX(INCOME)","FROM   CUSTOMER_INSURANCE_LTV_SQL_STACK","UNION","SELECT 'AFTER' STATE, MIN(INCOME), MAX(INCOME)","FROM   CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Recode Synonymous Values.dsnb b/machine-learning/notebooks/sql/OML4SQL Data Cleaning Recode Synonymous Values.dsnb
deleted file mode 100755
index 4db9e083..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Data Cleaning Recode Synonymous Values.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Cleaning Recode Synonymous Values","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Cleaning: Recode Synonymous Values","In this notebook, we demonstrate how to recode synonymous value of a column using Oracle SQL. ","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` has recoded values generated by the `\"OML Run-me-first\"` notebook artificially, which `MUST` be run before this notebook. Specifically, ***MARITAL_STATUS*** has divorced status coded as 'DIV', married coded as 'M'. ","","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md"," "," * <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check distinct MARITAL_STATUS values that may need recoding, e.g., DIV and M","message":["%sql","","SELECT DISTINCT MARITAL_STATUS","FROM   CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create view with recoded MARITAL_STATUS","message":["%sql","","CREATE OR REPLACE VIEW customer_recode_v AS","SELECT a.MARITAL_STATUS_CONVERTED, b.*","FROM (","    SELECT CUSTOMER_ID, ","           CASE WHEN MARITAL_STATUS = 'DIV' THEN 'DIVORCED' ELSE ","          (CASE WHEN MARITAL_STATUS = 'M'   THEN 'MARRIED'  ELSE ","          (CASE WHEN MARITAL_STATUS IS NULL THEN 'UNKNOWN'  ELSE","           MARITAL_STATUS  ","           END) ","           END)","        END AS MARITAL_STATUS_CONVERTED","    FROM CUSTOMER_INSURANCE_LTV_SQL ) a","JOIN CUSTOMER_INSURANCE_LTV_SQL b ","ON a.CUSTOMER_ID = b.CUSTOMER_ID"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{},\"table\":{\"version\":1},\"bar\":{\"series\":{\"availableSeriesElements\":[{\"id\":\"COUNT(1)\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"MARITAL_STATUS_CONVERTED\",\"MARITAL_STATUS\",\"COUNT(1)\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Display summary of recoded MARITAL_STATUS values","message":["%sql","","SELECT MARITAL_STATUS_CONVERTED, MARITAL_STATUS, count(1) COUNT","FROM   CUSTOMER_RECODE_V","GROUP BY MARITAL_STATUS_CONVERTED, MARITAL_STATUS;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Check distinct values after conversion","message":["%sql","","SELECT DISTINCT MARITAL_STATUS_CONVERTED ","FROM   CUSTOMER_RECODE_V;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Transformation Categorical.dsnb b/machine-learning/notebooks/sql/OML4SQL Data Transformation Categorical.dsnb
deleted file mode 100755
index 2fbe1825..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Data Transformation Categorical.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Transformation Categorical","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Data Transformation: convert variables from categorical to numerical","","In this notebook, we demonstrate how to convert a categorical variable to a numeric variable using Oracle SQL.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to convert a categorical variable with each distinct level/value coded to an integer. Also, we show how to create an indicator variable based on a simple predicate. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md"," "," * <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV_SQL data","message":["%sql","","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Encode categorical column STATE using rownum","message":["%sql","","SELECT b.customer_id, a.state_code, a.state","FROM (","    SELECT rownum state_code,  state","    FROM (","        SELECT distinct state","        FROM CUSTOMER_INSURANCE_LTV_SQL ) d ) a","join CUSTOMER_INSURANCE_LTV_SQL b","ON a.state = b.state","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create zero credit balance flag when the credit balance is zero","message":["%sql","","SELECT customer_id, ","       case when credit_balance = 0 then 1 else 0 end as zero_credit_balance, ","       credit_balance","FROM   CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Data Transformation Normalization and Scale.dsnb b/machine-learning/notebooks/sql/OML4SQL Data Transformation Normalization and Scale.dsnb
deleted file mode 100755
index bd27d0d9..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Data Transformation Normalization and Scale.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Data Transformation Normalization and Scale","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715350206106,"interpreter":"md.low","endTime":1715350206266,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Data Transformation: Normalization and Scale","","In this notebook, we demonstrate how to normalize and scale data using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.","","Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### IMPORTANT: The `\"OML Run-me-first\"` notebook is available under the menu `Templates -> Examples` and is a pre-requisite to the current notebook.","","---","","**Note**: when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms.  ","For more details, see * <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a>","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715350206343,"interpreter":"md.low","endTime":1715350206439,"results":[{"message":"<h1 id=\"oml4sql-data-transformation-normalization-and-scale\">OML4SQL Data Transformation: Normalization and Scale<\/h1>\n<p>In this notebook, we demonstrate how to normalize and scale data using Oracle SQL and the DBMS_DATA_MINING_TRANSFORM package.<\/p>\n<p>Using the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance, we show how to normalize data usig using z-score (mean and standard deviation), min max scaling, and log scaling.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\">IMPORTANT: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu <code>Templates -&gt; Examples<\/code> and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p><strong>Note<\/strong>: when building or applying a model using in-database Oracle Machine Learning algorithms, automatic data preparation will normalize data automatically as needed by specific algorithms.<br />\nFor more details, see * <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/generalized-linear-model.html#GUID-19B8E133-0029-4892-88BB-3E1C9E83EB12\" target=\"_blank\">Data Preparation for GLM<\/a><\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL ORACLE_DATA_MINING_TRANSFORM Documentation<\/a>"],"enabled":true,"result":{"startTime":1715350220102,"interpreter":"md.low","endTime":1715350220161,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING_TRANSFORM.html\" target=\"_blank\">OML4SQL ORACLE_DATA_MINING_TRANSFORM Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display customer lifetime value data","message":["%sql","","SELECT *","FROM CUSTOMER_INSURANCE_LTV_SQL","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350206677,"interpreter":"sql.low","endTime":1715350208378,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t400\t590\t3\t1\t61384\tCU8720                    \tM                         \tWaiter/Waitress\t1000\tWest\t1\t400\tYes\t2\t1\t23\t53.98\t18146\t208\t2\t25\t5\tMEDIUM\tKENNETH\t1\t1\tNADEAU\nDIVORCED\tWA                        \t0\t3\t2050\t650\t3\t1\t63525\tCU8721                    \tM                         \tSoftware Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         \tPROF-15\t800\tSouth\t1\t1600\tYes\t1\t1\t66\t54.97\t16933\t1923\t4\t25\t5\tMEDIUM\tMARGART\t1\t1\tCALDWELL\nMARRIED\tTX                        \t0\t3\t845\t450\t3\t1\t62061\tCU9113                    \tM                         \tProgrammer/Developer\t1500\tSouthwest\t1\t845\tYes\t8\t6\t26\t53.01\t18615.25\t226\t2\t25\t2\tMEDIUM\tNICKY\t1\t1\tBARON\nSINGLE\tNV                        \t0\t2\t0\t0\t0\t0\t74394\tCU9114                    \tM                         \tSales Representative\t1500\tSouthwest\t0\t0\tNo\t0\t1\t0\t53.06\t0\t0\t0\t25\t0\tLOW\tHUNG\t0\t0\tFINNEY\nSINGLE\tMI                        \t0\t3\t0\t0\t3\t0\t64255\tCU8462                    \tM                         \tPROF-31\t1500\tMidwest\t0\t0\tNo\t1\t1\t28\t53.14\t14363.75\t0\t0\t25\t2\tMEDIUM\tFREDDIE\t0\t0\tMAYBERRY\nDIVORCED\tMI                        \t0\t1\t2513\t1150\t1\t0\t70749\tCU8506                    \tM                         \tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table for values used in normalization","message":["%script","","BEGIN","  EXECUTE IMMEDIATE 'DROP TABLE norm_zscore_tbl';","  EXCEPTION WHEN OTHERS THEN NULL;","END;","     ","BEGIN","  dbms_data_mining_transform.CREATE_NORM_LIN (","    norm_table_name  => 'norm_zscore_tbl');","  dbms_data_mining_transform.INSERT_NORM_LIN_ZSCORE (","    norm_table_name  => 'norm_zscore_tbl',","    data_table_name  => 'CUSTOMER_INSURANCE_LTV_SQL',","    exclude_list     => dbms_data_mining_transform.COLUMN_LIST('CUSTOMER_ID'),","    round_num        => 3);","END;"],"enabled":true,"result":{"startTime":1715350208456,"interpreter":"script.low","endTime":1715350209457,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Shift (mean) and scale (standard deviation) used for normalizing numeric columns","message":["%sql","","SELECT col, shift, scale ","FROM   norm_zscore_tbl;"],"enabled":true,"result":{"startTime":1715350209533,"interpreter":"sql.low","endTime":1715350209630,"results":[{"message":"COL\tSHIFT\tSCALE\nCREDIT_BALANCE\t3940\t22300\nCUSTOMER_TENURE\t2.46\t1.25\nMORTGAGE_AMOUNT\t2010\t3170\nBANK_FUNDS\t3440\t5590\nNUM_DEPENDENTS\t2.07\t1.55\nHAS_CHILDREN\t0.498\t0.5\nINCOME\t65000\t6540\nCREDIT_CARD_LIMITS\t1260\t804\nHOME_OWNERSHIP\t0.805\t0.516\nNUM_ONLINE_TRANS\t1340\t1950\nMONTHLY_CHECKS\t4.54\t4.86\nNUM_TRANS_KIOSK\t1.92\t1.88\nAGE\t37.4\t14.6\nMONEY_MONTLY_OVERDRAWN\t53.6\t3.11\nLTV\t22200\t6770\nTOTAL_AUTOM_PAYMENTS\t5310\t21600\nNUM_TRANS_TELLER\t1.8\t1.48\nCHECKING_BALANCE\t1100\t3230\nNUM_TRANS_ATM\t2.86\t1.86\nNUM_MORTGAGES\t0.804\t0.516\nCAR_OWNERSHIP\t0.931\t0.253\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check 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dbms_data_mining_transform.STACK_NORM_LIN (","    norm_table_name    => 'norm_zscore_tbl',","    xform_list         => xforms);","  dbms_data_mining_transform.XFORM_STACK (","    xform_list         => xforms,","    data_table_name    => 'CUSTOMER_INSURANCE_LTV_SQL',","    xform_view_name    => 'CUSTOMER_INSURANCE_LTV_SQL_STACK');","END;"],"enabled":true,"result":{"startTime":1715350209972,"interpreter":"script.low","endTime":1715350210394,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check range of MORTGAGE_AMOUNT variable after normalization ","message":["%sql","","SELECT ROUND(MIN(MORTGAGE_AMOUNT),3) MIN_MORTGAGE_AMT, ROUND(MAX(MORTGAGE_AMOUNT),3) MAX_MORTGAGE_AMT","FROM   CUSTOMER_INSURANCE_LTV_SQL_STACK;"],"enabled":true,"result":{"startTime":1715350210471,"interpreter":"sql.low","endTime":1715350210549,"results":[{"message":"MIN_MORTGAGE_AMT\tMAX_MORTGAGE_AMT\n-0.634\t27.757\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table for min-max scaling","message":["%script","","BEGIN","  EXECUTE IMMEDIATE 'DROP TABLE minmax_norm_tbl';","  EXCEPTION WHEN OTHERS THEN NULL;","END;","","BEGIN","  dbms_data_mining_transform.CREATE_NORM_LIN(","    norm_table_name  => 'minmax_norm_tbl');","  dbms_data_mining_transform.INSERT_NORM_LIN_MINMAX(","    norm_table_name  => 'minmax_norm_tbl',","    data_table_name  => 'CUSTOMER_INSURANCE_LTV_SQL',","    exclude_list     => dbms_data_mining_transform.COLUMN_LIST( 'cust_id'),","    round_num        => 3);","END;"],"enabled":true,"result":{"startTime":1715350210629,"interpreter":"script.low","endTime":1715350212027,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Shift (mean) and scale (standard deviation) used for normalizing numeric columns","message":["%sql","","SELECT col, shift, scale ","FROM   minmax_norm_tbl;"],"enabled":true,"result":{"startTime":1715350212111,"interpreter":"sql.low","endTime":1715350212207,"results":[{"message":"COL\tSHIFT\tSCALE\nCREDIT_BALANCE\t0\t609000\nCUSTOMER_TENURE\t1\t4\nMORTGAGE_AMOUNT\t0\t90000\nBANK_FUNDS\t0\t105000\nNUM_DEPENDENTS\t0\t6\nHAS_CHILDREN\t0\t1\nINCOME\t37100\t74500\nCREDIT_CARD_LIMITS\t500\t4500\nHOME_OWNERSHIP\t0\t2\nNUM_ONLINE_TRANS\t0\t45000\nMONTHLY_CHECKS\t0\t18\nNUM_TRANS_KIOSK\t0\t10\nAGE\t0\t84\nMONEY_MONTLY_OVERDRAWN\t-77.3\t171\nLTV\t0\t47500\nTOTAL_AUTOM_PAYMENTS\t0\t745000\nNUM_TRANS_TELLER\t0\t9\nCHECKING_BALANCE\t25\t24700\nNUM_TRANS_ATM\t0\t8\nNUM_MORTGAGES\t0\t2\nCAR_OWNERSHIP\t0\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check range of MORTGAGE_AMOUNT variable before normalization ","message":["%sql","","SELECT MIN(MORTGAGE_AMOUNT) MIN_MORTAGE_AMT, MAX(MORTGAGE_AMOUNT) MAX_MORTGAGE_AMT","FROM   CUSTOMER_INSURANCE_LTV_SQL;"],"enabled":true,"result":{"startTime":1715350212283,"interpreter":"sql.low","endTime":1715350212471,"results":[{"message":"MIN_MORTAGE_AMT\tMAX_MORTGAGE_AMT\n0\t90000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Generate view that contains normalized columns ","message":["%script","","BEGIN","  EXECUTE IMMEDIATE 'DROP VIEW CUSTOMER_INSURANCE_LTV_SQL_STACK';","  EXCEPTION WHEN OTHERS THEN NULL;","END;","","DECLARE","  xforms dbms_data_mining_transform.TRANSFORM_LIST;","BEGIN","  dbms_data_mining_transform.STACK_NORM_LIN (","     norm_table_name    => 'minmax_norm_tbl',","     xform_list         => xforms);","  dbms_data_mining_transform.XFORM_STACK (","     xform_list         => xforms,","     data_table_name    => 'CUSTOMER_INSURANCE_LTV_SQL',","     xform_view_name    => 'CUSTOMER_INSURANCE_LTV_SQL_STACK');","END;","/"],"enabled":true,"result":{"startTime":1715350212552,"interpreter":"script.low","endTime":1715350212873,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check range of MORTGAGE_AMOUNT variable after normalization ","message":["%sql","","SELECT ROUND(MIN(MORTGAGE_AMOUNT),3) MIN_MORTGAGE_AMT, ROUND(MAX(MORTGAGE_AMOUNT),3) MAX_MORTGAGE_AMT","FROM   CUSTOMER_INSURANCE_LTV_SQL_STACK;"],"enabled":true,"result":{"startTime":1715350212950,"interpreter":"sql.low","endTime":1715350213025,"results":[{"message":"MIN_MORTGAGE_AMT\tMAX_MORTGAGE_AMT\n0\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Log scale transformation - for each numerical column apply log(10, ) to its nonzero values","message":["%script ","","SET serveroutput ON;","DECLARE","  tbl VARCHAR2(100);","  fmt VARCHAR2(2000);","BEGIN","  tbl := 'CUSTOMER_INSURANCE_LTV_SQL';","","  fmt := 'select ';","    ","  FOR rec IN (SELECT column_name, data_type FROM user_tab_cols WHERE table_name = tbl and data_type = 'NUMBER' ) LOOP","    fmt := fmt||'case when '||rec.column_name||' <= 0 then 0 else log(10,'||rec.column_name||') end as log_'||rec.column_name||',';","  END LOOP;","    ","  fmt := 'create or replace view customer_log_v as ' ||substr(fmt, 0, length(fmt)-1)||' from '||tbl;","  EXECUTE IMMEDIATE fmt;","END;"],"enabled":true,"result":{"startTime":1715350213105,"interpreter":"script.low","endTime":1715350213833,"results":[{"message":"\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check view that contains log transformed value","message":["%sql","","SELECT ROUND(LOG_CREDIT_BALANCE, 4) LOG_CREDIT_BALANCE, ","       ROUND(LOG_CUSTOMER_TENURE,4) LOG_CUSTOMER_TENURE, ","       ROUND(LOG_MORTGAGE_AMOUNT,4) LOG_MORTGAGE_AMOUNT, ","       ROUND(LOG_TOTAL_AUTOM_PAYMENTS,4) LOG_TOTAL_AUTOM_PAYMENTS,","       ROUND(LOG_BANK_FUNDS,4) LOG_BANK_FUNDS,","       ROUND(LOG_INCOME,4) LOG_INCOME","FROM   customer_log_v","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350213919,"interpreter":"sql.low","endTime":1715350214018,"results":[{"message":"LOG_CREDIT_BALANCE\tLOG_CUSTOMER_TENURE\tLOG_MORTGAGE_AMOUNT\tLOG_TOTAL_AUTOM_PAYMENTS\tLOG_BANK_FUNDS\tLOG_INCOME\n0\t0.4771\t2.6021\t2.3181\t2.7709\t4.7881\n0\t0.4771\t3.3118\t2.3838\t2.8129\t4.8029\n0\t0.6021\t3.3979\t2.5944\t2.8451\t4.8543\n0\t0\t3.9201\t2.8129\t4.0434\t4.8592\n0\t0\t3\t2.5832\t0\t4.811\n0\t0.6021\t3.6021\t3.284\t3.9445\t4.7877\n0\t0.4771\t2.9269\t2.3541\t2.6532\t4.7928\n0\t0.301\t0\t0\t0\t4.8715\n0\t0.4771\t0\t0\t0\t4.8079\n0\t0\t3.4002\t3.7957\t3.0607\t4.8497\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check original distribution of TOTAL_AUTOM_PAYMENTS - note distribution is heavily skewed","message":["%sql","","SELECT ROUND(TOTAL_AUTOM_PAYMENTS*1.0/100000, 1)*100000 TOTAL_AUTOM_PAYMENTS, COUNT(1) CNT","FROM   CUSTOMER_INSURANCE_LTV_SQL","WHERE  TOTAL_AUTOM_PAYMENTS > 0","GROUP BY ROUND(TOTAL_AUTOM_PAYMENTS*1.0/100000, 1)*100000","ORDER BY ROUND(TOTAL_AUTOM_PAYMENTS*1.0/100000, 1)*100000","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715350214097,"interpreter":"sql.low","endTime":1715350214190,"results":[{"message":"TOTAL_AUTOM_PAYMENTS\tCNT\n0\t9202\n10000\t1368\n20000\t382\n30000\t210\n40000\t132\n50000\t83\n60000\t71\n70000\t44\n80000\t27\n90000\t26\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"After applying log transformation, distribution becomes better distributed","message":["%sql","","SELECT ROUND(LOG_TOTAL_AUTOM_PAYMENTS/10,1)*10 LOG_TOTAL_AUTOM_PAYMENTS, COUNT(1) CNT","FROM   customer_log_v","WHERE  LOG_TOTAL_AUTOM_PAYMENTS > 0","GROUP BY ROUND(LOG_TOTAL_AUTOM_PAYMENTS/10,1)*10","ORDER BY ROUND(LOG_TOTAL_AUTOM_PAYMENTS/10,1)*10"],"enabled":true,"result":{"startTime":1715350214265,"interpreter":"sql.low","endTime":1715350214641,"results":[{"message":"LOG_TOTAL_AUTOM_PAYMENTS\tCNT\n2\t1726\n3\t6730\n4\t2655\n5\t541\n6\t13\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715350214727,"interpreter":"md.low","endTime":1715350214789,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715350214865,"interpreter":"md.low","endTime":1715350214926,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/sql/OML4SQL Feature Engineering Aggregation and Time.dsnb b/machine-learning/notebooks/sql/OML4SQL Feature Engineering Aggregation and Time.dsnb
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--- a/machine-learning/notebooks/sql/OML4SQL Feature Engineering Aggregation and Time.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Engineering Aggregation and Time","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Engineering: Aggregation and Time","In this notebook, we demonstrate how to generate aggregated features and also extract date and time features using Oracle SQL.","","We are using the SALES table from the SH schema. This dataset contains transaction records for each cusotmer and products sold to each customer and product pair. To create features for each customer and product, we create features by aggregating the amount sold.","","We also show how to extract date and time features from the field TIME_ID.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"," ","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from the SH.SALES table","message":["%sql","","SELECT * FROM SH.SALES FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Multiple items purchased by each customer and product pair","message":["%sql","","SELECT CUST_ID, PROD_ID, COUNT(1)","FROM SH.SALES","GROUP BY CUST_ID, PROD_ID","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Create features that summarize the amount sold ","message":["%sql","","SELECT CUST_ID, PROD_ID, MIN(AMOUNT_SOLD) MIN_AMOUNT, MAX(AMOUNT_SOLD) MAX_AMOUNT, ROUND(AVG(AMOUNT_SOLD), 2) AVG_AMOUNT, MEDIAN(AMOUNT_SOLD) MEDIAN_AMOUNT","FROM SH.SALES","GROUP BY CUST_ID, PROD_ID","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Augment original TIME_ID with random values to artificially create demo timestamp column","message":["%sql","","CREATE OR REPLACE VIEW SALES_TS AS ","SELECT CUST_ID, CAST(TIME_ID + DBMS_RANDOM.VALUE(0, 10) AS TIMESTAMP) AS  TIME_STAMP","FROM SH.SALES"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the view","message":["%sql","","SELECT * ","FROM SALES_TS","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Generate time related features: year, month, day of month, week of year, day of week, hour, minute, second","message":["%sql","","SELECT CUST_ID, TIME_STAMP, ","    EXTRACT(YEAR FROM TIME_STAMP) YEAR, ","    EXTRACT(MONTH FROM TIME_STAMP) MONTH, ","    EXTRACT(DAY FROM TIME_STAMP) DAY_OF_MONTH, ","    TO_CHAR(TIME_STAMP,'WW') WEEK_OF_YEAR, ","    TO_CHAR(TIME_STAMP,'D') DAY_OF_WEEK, ","    EXTRACT(HOUR FROM TIME_STAMP) HOUR, ","    EXTRACT(MINUTE FROM TIME_STAMP) MINUTE, ","    EXTRACT(SECOND FROM TIME_STAMP) SECOND","FROM SALES_TS","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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diff --git a/machine-learning/notebooks/sql/OML4SQL Feature Extraction ESA Wiki Model.dsnb b/machine-learning/notebooks/sql/OML4SQL Feature Extraction ESA Wiki Model.dsnb
deleted file mode 100755
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--- a/machine-learning/notebooks/sql/OML4SQL Feature Extraction ESA Wiki Model.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Extraction ESA Wiki Model","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714406166894,"interpreter":"md.low","endTime":1714406166981,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Explicit Semantic Analysis (ESA) for Text Analytics","","Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in  text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating *semantic relatedness* (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.","","Oracle Machine Learning for SQL ESA extracts text-based features from a corpus of documents and performs document similarity comparisons. In this notebook, we'll use the Wikipedia Model as an example:","","- Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model ","- Examples of using the Wiki ESA model","- Basic ESA Wiki model features for words","- Basic ESA Wiki model features for phrases","- ESA Wiki Model document similarity on phrases","- ESA Wiki Model document similarity on long phrases","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714406167079,"interpreter":"md.low","endTime":1714406167163,"results":[{"message":"<h1 id=\"explicit-semantic-analysis-esa-for-text-analytics\">Explicit Semantic Analysis (ESA) for Text Analytics<\/h1>\n<p>Explicit Semantic Analysis (ESA) supports unsupervised learning for feature extraction of explicit features based on an existing knowledge base, and supervised learning for classification. It is the process of understanding the meaning of a section of text as a combination of the concepts found in  text. Compared to other techniques such as Latent Dirichlet Association (LDA) or Term Frequency-Inverse Document Frequency (TF-IDF), ESA offers some unique benefits. Most notably, it improves text document categorization by calculating <em>semantic relatedness<\/em> (how similar in meaning two words or pieces of text are to each other) between the documents and a set of topics that are explicitly defined and described by humans.<\/p>\n<p>Oracle Machine Learning for SQL ESA extracts text-based features from a corpus of documents and performs document similarity comparisons. In this notebook, we'll use the Wikipedia Model as an example:<\/p>\n<ul>\n<li>Create an OML4Py proxy ESA model object for the pre-loaded Wikipedia model<\/li>\n<li>Examples of using the Wiki ESA model<\/li>\n<li>Basic ESA Wiki model features for words<\/li>\n<li>Basic ESA Wiki model features for phrases<\/li>\n<li>ESA Wiki Model document similarity on phrases<\/li>\n<li>ESA Wiki Model document similarity on long phrases<\/li>\n<\/ul>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis"],"enabled":true,"result":{"startTime":1714406167259,"interpreter":"md.low","endTime":1714406167345,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-explicit-semantic-analysis\" target=\"_blank\">OML Explicit Semantic Analysis<\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Using the Wikipedia model as an example","","The following example shows the results of a model that was pre-loaded to the Oracle Autonomous Database.","","You have to load the Wiki Model to your schema **first** before you can run the examples below.","","You can do it in two ways:","1. Easy way: Go to the OML UI Main Menu -> Templates -> Examples and make sure to import and fully run the `OML Import Wiki ESA Model` notebook.","","2. Follow the Setup from Oracle blogs:","    * <a href=\"https://blogs.oracle.com/machinelearning/post/wiki-esa-model-available-for-database-19c\" onclick=\"return ! window.open('https://blogs.oracle.com/machinelearning/post/wiki-esa-model-available-for-database-19c');\"> Wiki ESA Model Available for Database 19c <\/a>","    * <a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" onclick=\"return ! window.open('https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model');\"> Text Analytics using a pre-built Wikipedia-based Topic Model <\/a>","    * <a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" onclick=\"return ! window.open('https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics');\"> Explicit Semantic Analysis (ESA) for Text Analytics using R <\/a>"],"enabled":true,"result":{"startTime":1714406167463,"interpreter":"md.low","endTime":1714406167549,"results":[{"message":"<h2 id=\"using-the-wikipedia-model-as-an-example\">Using the Wikipedia model as an example<\/h2>\n<p>The following example shows the results of a model that was pre-loaded to the Oracle Autonomous Database.<\/p>\n<p>You have to load the Wiki Model to your schema <strong>first<\/strong> before you can run the examples below.<\/p>\n<p>You can do it in two ways:<\/p>\n<ol>\n<li>\n<p>Easy way: Go to the OML UI Main Menu -&gt; Templates -&gt; Examples and make sure to import and fully run the <code>OML Import Wiki ESA Model<\/code> notebook.<\/p>\n<\/li>\n<li>\n<p>Follow the Setup from Oracle blogs:<\/p>\n<ul>\n<li><a href=\"https://blogs.oracle.com/machinelearning/post/wiki-esa-model-available-for-database-19c\" onclick=\"return ! window.open('https://blogs.oracle.com/machinelearning/post/wiki-esa-model-available-for-database-19c');\"> Wiki ESA Model Available for Database 19c <\/a><\/li>\n<li><a href=\"https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model\" onclick=\"return ! window.open('https://blogs.oracle.com/r/text-analytics-using-a-pre-built-wikipedia-based-topic-model');\"> Text Analytics using a pre-built Wikipedia-based Topic Model <\/a><\/li>\n<li><a href=\"https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics\" onclick=\"return ! window.open('https://blogs.oracle.com/r/explicit-semantic-analysis-esa-for-text-analytics');\"> Explicit Semantic Analysis (ESA) for Text Analytics using R <\/a><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete existing 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completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check to see if the pre-loaded WIKI_MODEL is available","message":["%sql","","SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM, ALGORITHM_TYPE, CREATION_DATE FROM USER_MINING_MODELS WHERE MODEL_NAME='WIKI_MODEL'"],"enabled":true,"result":{"startTime":1714406291297,"interpreter":"sql.low","endTime":1714406291400,"results":[{"message":"MODEL_NAME\tMINING_FUNCTION\tALGORITHM\tALGORITHM_TYPE\tCREATION_DATE\nWIKI_MODEL\tFEATURE_EXTRACTION\tEXPLICIT_SEMANTIC_ANALYS\tNATIVE\t2023-11-08 19:36:42\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE, SETTING_TYPE AS TYPE","  FROM USER_MINING_MODEL_SETTINGS"," WHERE MODEL_NAME = 'WIKI_MODEL'","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1714406353685,"interpreter":"sql.low","endTime":1714406353788,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\tTYPE\nALGO_NAME\tALGO_EXPLICIT_SEMANTIC_ANALYS\tINPUT\nESAS_MIN_ITEMS\t25\tINPUT\nESAS_TOPN_FEATURES\t1000\tINPUT\nESAS_VALUE_THRESHOLD\t.00000001\tDEFAULT\nODMS_DETAILS\tODMS_ENABLE\tDEFAULT\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\tDEFAULT\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\tDEFAULT\nODMS_TEXT_MAX_FEATURES\t200000\tINPUT\nODMS_TEXT_MIN_DOCUMENTS\t5\tINPUT\nODMS_TEXT_POLICY_NAME\twiki_txtpol\tINPUT\nPREP_AUTO\tON\tDEFAULT\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get a list of model views","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='WIKI_MODEL'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1714406358614,"interpreter":"sql.low","endTime":1714406358746,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAWIKI_MODEL\tExplicit Semantic Analysis Matrix\nDM$VFWIKI_MODEL\tExplicit Semantic Analysis Features\nDM$VGWIKI_MODEL\tGlobal Name-Value Pairs\nDM$VNWIKI_MODEL\tNormalization and Missing Value Handling\nDM$VSWIKI_MODEL\tComputed Settings\nDM$VWWIKI_MODEL\tModel Build Alerts\nDM$VXWIKI_MODEL\tText Features\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples using the Wiki ESA model"],"enabled":true,"result":{"startTime":1714406398910,"interpreter":"md.low","endTime":1714406398978,"results":[{"message":"<h3 id=\"examples-using-the-wiki-esa-model\">Examples using the Wiki ESA model<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Basic example of ESA features for the word 'Bank'  using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE FROM","  (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","   FROM (SELECT 'BANK' AS TEXT FROM DUAL)) T,","        TABLE(T.FSET) S ","   ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1714406460992,"interpreter":"sql.low","endTime":1714406466602,"results":[{"message":"FEATURE_ID\tVALUE\nBanking in India\t0.12073074740513993\nBanking in Australia\t0.1186153932769802\nBank of America\t0.11307155411695473\nBank\t0.10994163646234326\nCentral bank\t0.10780307790446561\nNational Bank Act\t0.10647042777456947\nStandard Bank\t0.10502497981253221\nBankBoston\t0.10474436983267525\nOnline banking\t0.10250315951850819\nBank of England\t0.09610674490262502\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA features for the words 'NFL games'  using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'NFL Games' AS TEXT FROM DUAL)) T,","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1714406515072,"interpreter":"sql.low","endTime":1714406520880,"results":[{"message":"FEATURE_ID\tVALUE\nNational Football League\t0.1448362549796241\nNFL International Series\t0.13968414188865144\nThursday Night Football\t0.11436240954509867\nHistory of the National Football League championship\t0.11272129823985683\nChicago College All-Star Game\t0.11121297109987173\nNFL Network\t0.11007694516545648\nNFL Films\t0.10627056953318934\nThe NFL Today\t0.10292445567064071\nNational Football League Christmas games\t0.10104092801135917\nPlayoff Bowl\t0.09258723455526119\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for text about Astronomy  using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'A group of European-led astronomers has made a photograph of what appears to be a planet orbiting another star. If so, it would be the first confirmed picture of a world beyond our solar system.' AS TEXT FROM DUAL)) T, ","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1714406571231,"interpreter":"sql.low","endTime":1714406576942,"results":[{"message":"FEATURE_ID\tVALUE\nSolar System\t0.1783977405440812\nPlanets beyond Neptune\t0.16384415493902063\nPlanet Nine\t0.1556042629299215\nPlanet\t0.14360577141174127\nExoplanet\t0.14146374523098973\nFormation and evolution of the Solar System\t0.1380857932488385\nTerrestrial planet\t0.12349262676588602\nAstronomer\t0.11968229035028131\nSolar analog\t0.11919958268012823\nPlanetary habitability\t0.11168273107745952\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for a text about diet and health using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'Nearly 70 percent of Americans say they are careful about what they eat, and even more say diet is essential to good health, according to a new nationwide health poll in which obesity ranked second among the biggest health concerns.' AS TEXT FROM DUAL)) T,","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1714406596816,"interpreter":"sql.low","endTime":1714406602623,"results":[{"message":"FEATURE_ID\tVALUE\nObesity\t0.2381281204952319\nChildhood obesity\t0.21566128131005566\nRobert Wood Johnson Foundation\t0.1790547081969936\nHealth\t0.1723830168425817\nHealthy diet\t0.16671289147333318\nDieting\t0.142162115212373\nPaleolithic diet\t0.1386597097885653\nHealth education\t0.12498303889516335\nHealth equity\t0.1149008443204024\nPlant-based diet\t0.11425470702731559\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"ESA Features for a long text on stocks and the Fed using the Wikipedia model","message":["%sql","","SELECT S.FEATURE_ID, S.VALUE ","FROM (SELECT FEATURE_SET(WIKI_MODEL, 10 USING *) FSET ","      FROM (SELECT 'The more things change... Yes, I''m inclined to agree, especially with regards to the historical relationship between stock prices and bond yields. The two have generally traded together, rising during periods of economic growth and falling during periods of contraction. Consider the period from 1998 through 2010, during which the U.S. economy experienced two expansions as well as two recessions: Then central banks came to the rescue. Fed Chairman Ben Bernanke led from Washington with the help of the bank''s current $3.6T balance sheet. He''s accompanied by Mario Draghi at the European Central Bank and an equally forthright Shinzo Abe in Japan. Their coordinated monetary expansion has provided all the sugar needed for an equities moonshot, while they vowed to hold global borrowing costs at record lows' AS TEXT FROM DUAL)) T,","     TABLE(T.FSET) S ","ORDER BY S.VALUE DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1714406619576,"interpreter":"sql.low","endTime":1714406625083,"results":[{"message":"FEATURE_ID\tVALUE\nBen Bernanke\t0.17064524253160657\nRecession\t0.15889239754197812\nMario Draghi\t0.1491065027348536\nCOVID-19 recession\t0.145309094406638\nGreat Recession\t0.1290856902681663\nLong Depression\t0.12204405109050054\nGreat Depression in the United States\t0.11521859419773026\nMonetary policy\t0.1034355579106447\nGreat Recession in the United States\t0.10193175875567674\nGroup of Thirty\t0.09912373068492472\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Comparing Strings for Similarity of Text using the Wikipedia Model"],"enabled":true,"result":{"startTime":1714406629351,"interpreter":"md.low","endTime":1714406629415,"results":[{"message":"<h3 id=\"comparing-strings-for-similarity-of-text-using-the-wikipedia-model\">Comparing Strings for Similarity of Text using the Wikipedia 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DUAL"],"enabled":true,"result":{"startTime":1714406642702,"interpreter":"sql.low","endTime":1714406654469,"results":[{"message":"SIMILARITY\n0.1936898590428866\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare 'street' vs. 'farm' ","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING 'street' AS TEXT ","                         AND USING 'farm' AS TEXT) SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1714406667475,"interpreter":"sql.low","endTime":1714406678691,"results":[{"message":"SIMILARITY\n0.007127425352303707\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare two simple texts about Golf","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING 'There are several PGA tour golfers from South Africa' TEXT ","                         AND USING 'Nick Price won the 2002 Mastercard Colonial Open' TEXT) ","                         SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1714406683238,"interpreter":"sql.low","endTime":1714406693858,"results":[{"message":"SIMILARITY\n0.10969292803597186\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare texts about Golf and Football","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING 'There are several PGA tour golfers from South Africa' TEXT ","                         AND USING 'John Elway played quarterback for the Denver Broncos' TEXT) SIMILARITY","FROM DUAL"],"enabled":true,"result":{"startTime":1714406755581,"interpreter":"sql.low","endTime":1714406766751,"results":[{"message":"SIMILARITY\n0.003917419745909201\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and SpaceX Moon Gateway","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING ","'NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.' AS TEXT","                         AND USING ","'NASA has selected SpaceX to deliver the first two segments of the moon-orbiting Gateway space station for its upcoming Artemis program, which aims to put astronauts back on the moon. The elements will launch atop a Falcon Heavy rocket, sometime in 2024. The flight, which is the second to be awarded to SpaceX this week will carry the Gateway''s power and habitation modules.  Once deposited in lunar orbit, the Gateway will serve as an outpost for astronauts and equipment heading to the moon as part of NASA Artemis program. Roughly one-sixth the size of the International Space Station, the Gateway will support research investigations, crew, and expeditions to the lunar surface.' AS TEXT) SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1714406756884,"interpreter":"sql.low","endTime":1714406768899,"results":[{"message":"SIMILARITY\n0.48847744577133123\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compare news on Nasa Rover and Tennis Naomi Osaka","message":["%sql","","SELECT 1-FEATURE_COMPARE(WIKI_MODEL USING ","'NASA Perseverance Mars rover closed in on the red planet after a journey of 293 million miles, hurtling toward a nail-biting seven-minute descent to touchdown Thursday. The mission is an unprecedented attempt to find signs of past microbial life at the site of an ancient martian river, delta and lakebed. Mission managers at NASA Jet Propulsion Laboratory in Pasadena, California, said Wednesday that the spacecraft, still attached to its interplanetary cruise stage, is healthy and flawlessly executing its final approach to Jezero Crater.' AS TEXT","                         AND USING ","'Naomi Osaka, who hasnt lost an official match since last February, put together a dominating performance in the Australian Open semifinals, beating Williams 6-3, 6-4 and ending hopes that the 39-year old American would tie Margaret Court all-time Slam record at this tournament. Osaka, seeking her fourth major title and second Australian Open championship, will play the winner of the second semifinal between American Jennifer Brady, the No. 22 seed, and No. 25 seed Karolina Muchova.' AS TEXT) SIMILARITY ","FROM DUAL"],"enabled":true,"result":{"startTime":1714406780017,"interpreter":"sql.low","endTime":1714406790536,"results":[{"message":"SIMILARITY\n0.026660806390262026\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1714406790592,"interpreter":"md.low","endTime":1714406790668,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":0,"interpreter":"md.low","endTime":1714406170666,"results":[{"message":"The paragraph run was cancelled when running the notebook as a previous paragraph encountered an error.","type":"TEXT"},{"message":"OLDS-f7921ecf95a68a7044d7c4df50ec3c76","type":"TEXT"}],"taskStatus":"CANCELLED","forms":"null","status":"ERROR"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Extraction SVD","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715726809566,"interpreter":"md.low","endTime":1715726809624,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Extraction: Singular Value Decomposition","","In this notebook, we demonstrate how to use the in-database SVD decomposion for dimensionality reduction.","","We use the customer insurance lifetime value data. This dataset contains financial information about the customer and the decision of buying insurance or not. ","","We show that Principal Component Analysis can be achieved using SVD results.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715726809697,"interpreter":"md.low","endTime":1715726809755,"results":[{"message":"<h1 id=\"oml4sql-feature-extraction-singular-value-decomposition\">OML4SQL Feature Extraction: Singular Value Decomposition<\/h1>\n<p>In this notebook, we demonstrate how to use the in-database SVD decomposion for dimensionality reduction.<\/p>\n<p>We use the customer insurance lifetime value data. This dataset contains financial information about the customer and the decision of buying insurance or not.<\/p>\n<p>We show that Principal Component Analysis can be achieved using SVD results.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html\" target=\"_blank\">OML4SQL SVD Documentation<\/a>"],"enabled":true,"result":{"startTime":1715726809855,"interpreter":"md.low","endTime":1715726809917,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html\" target=\"_blank\">OML4SQL SVD Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726809995,"interpreter":"sql.low","endTime":1715726810071,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         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\tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a view that contains a set of numerical and categorical columns","message":["%script","","CREATE OR REPLACE VIEW CUST_V AS","SELECT CUSTOMER_ID, CREDIT_BALANCE, CUSTOMER_TENURE, MORTGAGE_AMOUNT, MARITAL_STATUS, GENDER, REGION","FROM CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1715726810179,"interpreter":"script.low","endTime":1715726810253,"results":[{"message":"\nView CUST_V created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from CUST_V","message":["%sql","","SELECT * FROM CUST_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726810330,"interpreter":"sql.low","endTime":1715726810406,"results":[{"message":"CUSTOMER_ID\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tMARITAL_STATUS\tGENDER\tREGION\nCU15154                   \t0\t3\t0\tSINGLE\tM                         \tWest\nCU15155                   \t0\t4\t0\tSINGLE\tM                         \tNorthEast\nCU15157                   \t0\t3\t1000\tMARRIED\tM                         \tMidwest\nCU15286                   \t0\t5\t1200\tMARRIED\tF                         \tSouthwest\nCU15287                   \t0\t4\t1800\tMARRIED\tF                         \tSouthwest\nCU15289                   \t0\t1\t1400\tMARRIED\tM                         \tSouthwest\nCU15290                   \t0\t3\t578\tSINGLE\tM                         \tSouthwest\nCU15291                   \t0\t3\t0\tSINGLE\tM                         \tSouthwest\nCU15292                   \t0\t5\t117\tSINGLE\tF                         \tSouthwest\nCU15294                   \t0\t3\t0\tSINGLE\tM                         \tSouthwest\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVD","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117');\">Singular Value Decompositionn<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","* This setting is used to prune features. Define the minimum value the eigenvalue of a feature as a share of the first eigenvalue to not to prune. Default value is data driven. ","   The value required is between 0 and 1 (including the edges), and the default data driven.","> v_setlst('SVDS_TOLERANCE') := '0.05';","","* Setting to define whether to use SVD or PCA scoring for the model.  When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices.","   The value required is either SVDS_SCORING_SVD or SVDS_SCORING_PCA, and the default is SVDS_SCORING_SVD. ","> v_setlst('SVDS_SCORING_MODE') := 'SVDS_SCORING_SVD';","","* Specify the random seed value used for initializing the sampling matrix used by the Stochastic SVD solver. The SVD Solver must be set to SSVD or STEIGEN. ","   The value required is between 0 and 4,294,967,296 (including the edges), and the default is 0.","> v_setlst('SVDS_RANDOM_SEED') := '0';","    ","* This setting is configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features plus the oversampling setting. The SVD Solver must be set to SSVD or STEIGEN. ","   The value required is between 1 and 5,000 (including the edges).","> v_setlst('SVDS_OVER_SAMPLING') := '100';","","* The power iteration setting improves the accuracy of the SSVD solver. The SVD Solver must be set to SSVD or STEIGEN. ","   The value required is between 0 and 20 (including the edges), and the default is 2.","> v_setlst('SVDS_POWER_ITERATIONS') := '2';","   ","* Indicates whether or not to persist the U Matrix produced by SVD.","   The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when SVDS_U_MATRIX_OUTPUT is enabled.","   When SVDS_U_MATRIX_OUTPUT is enabled, the build data must include a case ID. If no case ID is present and the U matrix is requested, then an exception is raised.","   The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE. ","> v_setlst('SVDS_U_MATRIX_OUTPUT') := 'SVDS_U_MATRIX_DISABLE';","    ","* The setting SVDS_SOLVER indicates the solver to be used for computing SVD of the data. In the case of PCA, the solver setting indicates the type of SVD solver used to compute the PCA for the data. When this setting is not specified the solver type selection is data driven. If the number of attributes is greater than 3,240, then the default wide solver is used. Otherwise, the default narrow solver is selected.","   The following are the group of solvers:","   * Narrow data solvers: for matrices with up to 11500 attributes (TSEIGEN) or up to 8100 attributes (TSSVD).","   * Wide data solvers: for matrices up to 1 million attributes.","","   For narrow data solvers:","   * Tall-Skinny SVD uses QR computation TSVD (SVDS_SOLVER_TSSVD)","   * Tall-Skinny SVD uses eigenvalue computation, TSEIGEN (SVDS_SOLVER_TSEIGEN), is the default solver for narrow data.","","   For wide data solvers:","   * Stochastic SVD uses QR computation SSVD (SVDS_SOLVER_SSVD), is the default solver for wide data solvers.","   * Stochastic SVD uses eigenvalue computations, STEIGEN (SVDS_SOLVER_STEIGEN).","","   The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE. ","     v_setlst('SVDS_SOLVER') := 'SVDS_SOLVER_STEIGEN';","","---","","#### Other interesting overrides for Data Preparation for SVD","","Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled. ","","A complete list of settings can be found in the Documentation link:","","* Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a>","","* This setting is used to specify Automatic Data Preparation.","   The value required is either ON or OFF, and the default is ON. ","> v_setlst('PREP_AUTO') := 'ON';","    ","The model uses heuristics to transform the build data according to the requirements of the algorithm. ","","#### User defined transformations","","Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS. ","","* This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.","   PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].","> v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';","","* This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.","   PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.","> v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';","","* This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].","> v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';","","---"],"enabled":true,"result":{"startTime":1715726810487,"interpreter":"md.low","endTime":1715726810561,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svd\">Examples of possible setting overrides for SVD<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117');\">Singular Value Decompositionn<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to prune features. Define the minimum value the eigenvalue of a feature as a share of the first eigenvalue to not to prune. Default value is data driven.\nThe value required is between 0 and 1 (including the edges), and the default data driven.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_TOLERANCE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Setting to define whether to use SVD or PCA scoring for the model.  When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices.\nThe value required is either SVDS_SCORING_SVD or SVDS_SCORING_PCA, and the default is SVDS_SCORING_SVD.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_SCORING_MODE') := 'SVDS_SCORING_SVD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the random seed value used for initializing the sampling matrix used by the Stochastic SVD solver. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 0 and 4,294,967,296 (including the edges), and the default is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_RANDOM_SEED') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting is configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features plus the oversampling setting. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 1 and 5,000 (including the edges).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_OVER_SAMPLING') := '100';<\/p>\n<\/blockquote>\n<ul>\n<li>The power iteration setting improves the accuracy of the SSVD solver. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 0 and 20 (including the edges), and the default is 2.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_POWER_ITERATIONS') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Indicates whether or not to persist the U Matrix produced by SVD.\nThe U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when SVDS_U_MATRIX_OUTPUT is enabled.\nWhen SVDS_U_MATRIX_OUTPUT is enabled, the build data must include a case ID. If no case ID is present and the U matrix is requested, then an exception is raised.\nThe value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_U_MATRIX_OUTPUT') := 'SVDS_U_MATRIX_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>The setting SVDS_SOLVER indicates the solver to be used for computing SVD of the data. In the case of PCA, the solver setting indicates the type of SVD solver used to compute the PCA for the data. When this setting is not specified the solver type selection is data driven. If the number of attributes is greater than 3,240, then the default wide solver is used. Otherwise, the default narrow solver is selected.\nThe following are the group of solvers:<\/p>\n<ul>\n<li>Narrow data solvers: for matrices with up to 11500 attributes (TSEIGEN) or up to 8100 attributes (TSSVD).<\/li>\n<li>Wide data solvers: for matrices up to 1 million attributes.<\/li>\n<\/ul>\n<p>For narrow data solvers:<\/p>\n<ul>\n<li>Tall-Skinny SVD uses QR computation TSVD (SVDS_SOLVER_TSSVD)<\/li>\n<li>Tall-Skinny SVD uses eigenvalue computation, TSEIGEN (SVDS_SOLVER_TSEIGEN), is the default solver for narrow data.<\/li>\n<\/ul>\n<p>For wide data solvers:<\/p>\n<ul>\n<li>Stochastic SVD uses QR computation SSVD (SVDS_SOLVER_SSVD), is the default solver for wide data solvers.<\/li>\n<li>Stochastic SVD uses eigenvalue computations, STEIGEN (SVDS_SOLVER_STEIGEN).<\/li>\n<\/ul>\n<p>The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE.\nv_setlst('SVDS_SOLVER') := 'SVDS_SOLVER_STEIGEN';<\/p>\n<\/li>\n<\/ul>\n<hr />\n<h4 id=\"other-interesting-overrides-for-data-preparation-for-svd\">Other interesting overrides for Data Preparation for SVD<\/h4>\n<p>Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to specify Automatic Data Preparation.\nThe value required is either ON or OFF, and the default is ON.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_AUTO') := 'ON';<\/p>\n<\/blockquote>\n<p>The model uses heuristics to transform the build data according to the requirements of the algorithm.<\/p>\n<h4 id=\"user-defined-transformations\">User defined transformations<\/h4>\n<p>Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS.<\/p>\n<ul>\n<li>This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.\nPREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.\nPREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create SVD model with manual Data Preparation options","message":["%script","","BEGIN ","    DBMS_DATA_MINING.DROP_MODEL('SVD_CUST_LTV_MDL');","    EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    V_SETLST('ALGO_NAME')         := 'ALGO_SINGULAR_VALUE_DECOMP';","    V_SETLST('SVDS_SCORING_MODE') := 'SVDS_SCORING_PCA';","    V_SETLST('PREP_AUTO')         := 'OFF';","    V_SETLST('PREP_SCALE_2DNUM')  := 'PREP_SCALE_STDDEV';","    V_SETLST('PREP_SHIFT_2DNUM')  := 'PREP_SHIFT_MEAN';","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVD_CUST_LTV_MDL',","        MINING_FUNCTION     => 'FEATURE_EXTRACTION',","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        DATA_QUERY          => 'SELECT * FROM CUST_V',","        SET_LIST            => V_SETLST);","END;"],"enabled":true,"result":{"startTime":1715726810645,"interpreter":"script.low","endTime":1715726812341,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List the SVD model views available","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='SVD_CUST_LTV_MDL'","  ORDER BY VIEW_NAME"],"enabled":true,"result":{"startTime":1715726812417,"interpreter":"sql.low","endTime":1715726812511,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VESVD_CUST_LTV_MDL\tSingular Value Decomposition S Matrix\nDM$VGSVD_CUST_LTV_MDL\tGlobal Name-Value Pairs\nDM$VNSVD_CUST_LTV_MDL\tNormalization and Missing Value Handling\nDM$VSSVD_CUST_LTV_MDL\tComputed Settings\nDM$VUSVD_CUST_LTV_MDL\tSingular Value Decomposition U Matrix\nDM$VVSVD_CUST_LTV_MDL\tSingular Value Decomposition V Matrix\nDM$VWSVD_CUST_LTV_MDL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the Percentage of Cumulative Variance vs Feature #","message":["%sql","","SELECT FEATURE_ID, round(PCT_CUM_VARIANCE,3) PCT_CUM_VARIANCE","FROM DM$VESVD_CUST_LTV_MDL"],"enabled":true,"result":{"startTime":1715726812586,"interpreter":"sql.low","endTime":1715726812661,"results":[{"message":"FEATURE_ID\tPCT_CUM_VARIANCE\n1\t22.909\n2\t41.255\n3\t57.786\n4\t72.747\n5\t78.784\n6\t84.083\n7\t89.268\n8\t93.247\n9\t96.391\n10\t98.001\n11\t99.177\n12\t99.892\n13\t100\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients of the principal components mapping from original attributes","message":["%sql","","SELECT FEATURE_ID, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, VALUE","FROM DM$VVSVD_CUST_LTV_MDL","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS 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\t0.5325070626402109\n1\tMARITAL_STATUS\tDIVORCED\t0.029778428687600597\n1\tMARITAL_STATUS\tWIDOWED\t-0.03202096481496996\n1\tMARITAL_STATUS\tSINGLE\t0.35381996357540935\n1\tMARITAL_STATUS\tOTHER\t-0.023346721190061374\n1\tMARITAL_STATUS\tMARRIED\t0.1453932572041015\n1\tMORTGAGE_AMOUNT\t\t-0.612280020773312\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Feature 1 - PCA coefficients","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(VALUE,4) COEFF","FROM DM$VVSVD_CUST_LTV_MDL","WHERE FEATURE_ID = 1","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS 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DM$VVSVD_CUST_LTV_MDL","WHERE FEATURE_ID = 2","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726813096,"interpreter":"sql.low","endTime":1715726813170,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFF\nCREDIT_BALANCE\t\t0.4612\nCUSTOMER_TENURE\t\t-0.2244\nGENDER\tF                         \t0.2849\nGENDER\tM                         \t0.4467\nMARITAL_STATUS\tDIVORCED\t0.2137\nMARITAL_STATUS\tWIDOWED\t0.0607\nMARITAL_STATUS\tSINGLE\t0.1661\nMARITAL_STATUS\tOTHER\t0.0252\nMARITAL_STATUS\tMARRIED\t0.2659\nMORTGAGE_AMOUNT\t\t0.3865\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Feature 3 - PCA coefficients","message":["%sql","","SELECT ATTRIBUTE_NAME, ","       ATTRIBUTE_VALUE, ","       ROUND(VALUE,4) COEFF","FROM DM$VVSVD_CUST_LTV_MDL","WHERE FEATURE_ID = 3","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726813248,"interpreter":"sql.low","endTime":1715726813323,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFF\nCREDIT_BALANCE\t\t0.3481\nCUSTOMER_TENURE\t\t0.9332\nGENDER\tF                         \t0.0283\nGENDER\tM                         \t-0.0004\nMARITAL_STATUS\tDIVORCED\t-0.0037\nMARITAL_STATUS\tWIDOWED\t0.0042\nMARITAL_STATUS\tSINGLE\t0.0149\nMARITAL_STATUS\tOTHER\t0.005\nMARITAL_STATUS\tMARRIED\t0.0075\nMORTGAGE_AMOUNT\t\t0.069\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first four principal components","message":["%sql","","SELECT CUSTOMER_ID, ","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 1 USING *),4) PCA_PROJ1,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 2 USING *),4) PCA_PROJ2,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 3 USING *),4) PCA_PROJ3,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 4 USING *),4) PCA_PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726813398,"interpreter":"sql.low","endTime":1715726813516,"results":[{"message":"CUSTOMER_ID\tPCA_PROJ1\tPCA_PROJ2\tPCA_PROJ3\tPCA_PROJ4\nCU1                       \t-3.9247\t2.8396\t1.6307\t4.3253\nCU10                      \t-0.7384\t1.8562\t2.2444\t-1.4873\nCU1000                    \t0.8149\t1.1414\t-1.1088\t0.0841\nCU10000                   \t1.1401\t0.6253\t0.3622\t0.2917\nCU10001                   \t0.2785\t1.0078\t-1.1183\t0.2\nCU10003                   \t0.2\t0.366\t1.1067\t0.8741\nCU10004                   \t1.4874\t0.3463\t0.2974\t-0.0874\nCU10005                   \t1.539\t0.4053\t0.3482\t-0.0923\nCU10006                   \t0.7132\t0.5235\t0.3479\t0.3988\nCU10007                   \t1.4601\t0.5639\t0.3639\t0.0668\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Create an SVD Feature Extraction Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.","","---"],"enabled":true,"result":{"startTime":1715726813588,"interpreter":"md.low","endTime":1715726813645,"results":[{"message":"<h3 id=\"create-an-svd-feature-extraction-model-using-settings-table\">Create an SVD Feature Extraction Model using Settings Table<\/h3>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Clean up old settings table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE SVD_CUSTOMER_LTV_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/"],"enabled":true,"result":{"startTime":1715726813720,"interpreter":"script.low","endTime":1715726813944,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create settings table","message":["%sql","","CREATE TABLE SVD_CUSTOMER_LTV_SETTINGS (","   SETTING_NAME  VARCHAR2(30),","   SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1715726814016,"interpreter":"sql.low","endTime":1715726814091,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Specify settings","message":["%script","","BEGIN       ","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SINGULAR_VALUE_DECOMP);","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_OFF);  ","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVDS_SCORING_MODE, DBMS_DATA_MINING.SVDS_SCORING_PCA);","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_SHIFT_2DNUM, DBMS_DATA_MINING.PREP_SHIFT_MEAN); ","  INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_SCALE_2DNUM, DBMS_DATA_MINING.PREP_SCALE_STDDEV);   ","END;"],"enabled":true,"result":{"startTime":1715726814163,"interpreter":"script.low","endTime":1715726814257,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVD model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_CUST_LTV_MDL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'SVD_CUST_LTV_MDL',","    MINING_FUNCTION     => DBMS_DATA_MINING.FEATURE_EXTRACTION,","    DATA_TABLE_NAME     => 'CUST_V',","    CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","    SETTINGS_TABLE_NAME => 'SVD_CUSTOMER_LTV_SETTINGS');","END;","/"],"enabled":true,"result":{"startTime":1715726814329,"interpreter":"script.low","endTime":1715726815623,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients of the principal components mapping from original attributes","message":["%sql","","SELECT FEATURE_ID, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, VALUE","FROM DM$VVSVD_CUST_LTV_MDL","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726815906,"interpreter":"sql.low","endTime":1715726816001,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tVALUE\n1\tCREDIT_BALANCE\t\t-0.3224658391288337\n1\tCUSTOMER_TENURE\t\t0.15464257875070309\n1\tGENDER\tF                         \t-0.05888309917813106\n1\tGENDER\tM                         \t0.5325070626402109\n1\tMARITAL_STATUS\tDIVORCED\t0.029778428687600597\n1\tMARITAL_STATUS\tWIDOWED\t-0.03202096481496996\n1\tMARITAL_STATUS\tSINGLE\t0.35381996357540935\n1\tMARITAL_STATUS\tOTHER\t-0.023346721190061374\n1\tMARITAL_STATUS\tMARRIED\t0.1453932572041015\n1\tMORTGAGE_AMOUNT\t\t-0.612280020773312\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first four principal components","message":["%sql","","SELECT CUSTOMER_ID, ","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 1 USING *),4) PROJ1,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 2 USING *),4) PROJ2,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 3 USING *),4) PROJ3,","       ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 4 USING *),4) PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726816089,"interpreter":"sql.low","endTime":1715726816212,"results":[{"message":"CUSTOMER_ID\tPROJ1\tPROJ2\tPROJ3\tPROJ4\nCU1                       \t-3.9247\t2.8396\t1.6307\t4.3253\nCU10                      \t-0.7384\t1.8562\t2.2444\t-1.4873\nCU1000                    \t0.8149\t1.1414\t-1.1088\t0.0841\nCU10000                   \t1.1401\t0.6253\t0.3622\t0.2917\nCU10001                   \t0.2785\t1.0078\t-1.1183\t0.2\nCU10003                   \t0.2\t0.366\t1.1067\t0.8741\nCU10004                   \t1.4874\t0.3463\t0.2974\t-0.0874\nCU10005                   \t1.539\t0.4053\t0.3482\t-0.0923\nCU10006                   \t0.7132\t0.5235\t0.3479\t0.3988\nCU10007                   \t1.4601\t0.5639\t0.3639\t0.0668\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715726816286,"interpreter":"md.low","endTime":1715726816346,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715726816430,"interpreter":"md.low","endTime":1715726816488,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/sql/OML4SQL Feature Selection Algorithm Based.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Selection Algorithm Based","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Selection: Supervised Algorithm","In this notebook, we demonstrate how to perform feature selection using in-database supervised algorithms.","","We use the customer insurance lifetime value data set, which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We first build a random forest model to predict if the customer will buy insurance, then use feature importance values for feature selection. ","","We then build a decision tree model for the same classification task and obtain split nodes. For the top splitting nodes with highest support, we select features associated with those nodes.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","###### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-attribute-importance\" target=\"_blank\">OML Attribute Importance<\/a>","* <a href=\"https://www.oracle.com/goto/ml-random-forest\" target=\"_blank\">OML Random Forest<\/a>","* <a href=\"https://www.oracle.com/goto/ml-decision-tree\" target=\"_blank\">OML Decision Tree<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display CUSTOMER_INSURANCE_LTV data","message":["%sql","","SELECT *","FROM   CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for Attribute Importance","message":["%md","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of shared settings can be found in the Documentation link:","","Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","--- ","","##### Other interesting overrides for data preparation","","Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled. ","","A complete list of settings can be found in the Documentation link:","","Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a>","","This setting is used to specify Automatic Data Preparation.","   The value required is either ON or OFF, and the default is ON. ","    v_setlst('PREP_AUTO') := 'ON';","    ","The model uses heuristics to transform the build data according to the requirements of the algorithm. ","","##### User defined transformations","","Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS. ","","*  This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.","   PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].","    v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';","","* This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:","   PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.","   PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.","    v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';","","* This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].","    v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';","","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use in-database Attribute Importance ","message":["\r","%script\r","\r","BEGIN DBMS_DATA_MINING.DROP_MODEL('AI_FS_MODEL');\r","EXCEPTION WHEN OTHERS THEN NULL; END;\r","/\r","DECLARE\r","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;\r","BEGIN\r","    V_SETLST('ALGO_NAME')  := 'ALGO_AI_MDL';\r","    V_SETLST('PREP_AUTO')  := 'ON';\r","\r","    DBMS_DATA_MINING.CREATE_MODEL2(\r","        MODEL_NAME          => 'AI_FS_MODEL',\r","        MINING_FUNCTION     => 'ATTRIBUTE_IMPORTANCE',\r","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',\r","        SET_LIST            => V_SETLST,\r","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',\r","        TARGET_COLUMN_NAME  => 'BUY_INSURANCE');\r","END;\r"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the model view output with attribute rankings","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_IMPORTANCE_VALUE, ATTRIBUTE_RANK ","FROM   DM$VAAI_FS_MODEL ","WHERE  ATTRIBUTE_IMPORTANCE_VALUE > 0","ORDER BY ATTRIBUTE_IMPORTANCE_VALUE DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"bar","title":"Plot attributes by importance for predicting people who buy insurance","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_IMPORTANCE_VALUE, ATTRIBUTE_RANK ","FROM   DM$VAAI_FS_MODEL ","WHERE  ATTRIBUTE_IMPORTANCE_VALUE > 0","ORDER BY ATTRIBUTE_IMPORTANCE_VALUE DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Feature importance using Random Forest","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for Random Forest","message":["%md","","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","**NOTE:** Random Forest makes use of the Decision Tree settings to configure the construction of individual trees. ","","A complete list of settings can be found in the Documentation link:","","Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-481B6C67-B26E-4689-AD4C-98062D5A2117');\">Random Forest<\/a>","","Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566');\">Decision Tree<\/a>","","Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","* Specify a row weight column ","    v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","##### These settings configure the behavior of the Random Forest algorithm ","","* Specify the number of trees in the forest.  It requires a number between 1 and 65535 (including the edges).  The default is 20.","    v_setlst('RFOR_NUM_TREES') := '20';","","* Specify the fraction of the training data to be randomly sampled for use in the construction of an individual tree. The default is half of the number of rows in the training data. It requires a fraction between 0 and 1 (excluding the edges)","    v_setlst('RFOR_SAMPLING_RATIO') := '0.5';","","* Specify the size of the random subset of columns to be considered when choosing a split at a node. For each node, the size of the pool remains the same, but the specific candidate columns change. The default is half of the columns in the model signature. The special value 0 indicates that the candidate pool includes all columns.  It requires a number equal to or greater than 0.","    v_setlst('RFOR_MTRY') := '0';","","##### Decision Tree settings to configure the construction of individual trees","    ","* Specify Tree impurity metric for each Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","    v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","    v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","    v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","    ","* Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","    v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ","    v_setlst('TREE_TERM_MINREC_NODE') := '10';","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","    v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","    v_setlst('CLAS_MAX_SUP_BINS') := '32';    ","    "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Random Forest model for Feature Selection","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_FEAT_SELECTION');","      EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    V_SETLST('PREP_AUTO')           := 'ON';","    V_SETLST('ALGO_NAME')           := 'ALGO_RANDOM_FOREST';","    V_SETLST('RFOR_MTRY')           := '3';","    V_SETLST('RFOR_NUM_TREES')      := '25';","    V_SETLST('RFOR_SAMPLING_RATIO') := '0.5';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'RF_FEAT_SELECTION',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',","        SET_LIST            => V_SETLST,","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        TARGET_COLUMN_NAME  => 'BUY_INSURANCE'","        );","END;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Plot attributes by importance for predicting people who buy insurance","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_IMPORTANCE","FROM   DM$VARF_FEAT_SELECTION ","ORDER BY ATTRIBUTE_IMPORTANCE DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Feature importance using Decision Tree","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Examples of possible setting overrides for DT","message":["%md","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566');\">Decision Tree<\/a>","","Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","* Specify a row weight column ","    v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","* Specify Tree impurity metric for Decision Tree. ","   Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.","    v_setlst('TREE_IMPURITY_METRIC') := 'TREE_IMPURITY_GINI';","    ","* Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).","   For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.","    v_setlst('TREE_TERM_MAX_DEPTH') := '7';","    ","* Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.","   It requires a number between 0 and 10.  The default is 0.05, indicating 0.05%. ","    v_setlst('TREE_TERM_MINPCT_NODE') := '0.05';","    ","* Specify the minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.","   It requires a number greater than 0, and smaller or equal to 20.  The default is 0.1, indicating 0.1%. ","    v_setlst('TREE_TERM_MINPCT_SPLIT') := '0.1';","","* Specify The minimum number of rows in a node.","   It requires a number greater than or equal to zero. The default is 10.      ","    v_setlst('TREE_TERM_MINREC_NODE') := '10';","    ","* Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. ","   No split is attempted if the number of records is below this value.  It requires a number greater than 1.  The default is 20.       ","    v_setlst('TREE_TERM_MINREC_SPLIT') := '20';","    ","* Specify the maximum number of bins for each attribute.","   For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.","    v_setlst('CLAS_MAX_SUP_BINS') := '32';    ","    ","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a Decision Tree model for Feature Selection","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_FEAT_SELECTION');","      EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    V_SETLST('PREP_AUTO')             := 'ON';","    V_SETLST('ALGO_NAME')             := 'ALGO_DECISION_TREE';","    V_SETLST('TREE_TERM_MAX_DEPTH')   := '10';","    V_SETLST('TREE_TERM_MINPCT_NODE') := '0.01';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'DT_FEAT_SELECTION',","        MINING_FUNCTION     => 'CLASSIFICATION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',","        SET_LIST            => V_SETLST,","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","        TARGET_COLUMN_NAME  => 'BUY_INSURANCE'","        );","END;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display Decision Tree nodes with top support","message":["%sql","","SELECT * ","FROM (SELECT NODE, ","             NODE_SUPPORT, ","             PREDICTED_TARGET_VALUE, ","             PARENT,","             ATTRIBUTE_NAME,","             OPERATOR, ","             SPLIT.VAL","      FROM DM$VODT_FEAT_SELECTION A,","           XMLTABLE( '/Element' PASSING A.VALUE","                     COLUMNS","                     VAL VARCHAR2(20) PATH '.') SPLIT  ","      ORDER BY 1,2,3,4,5,6,7)","ORDER BY NODE_SUPPORT DESC","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select the attributes and their average support","message":["%sql","","SELECT DISTINCT ATTRIBUTE_NAME, ","       ROUND(AVG(NODE_SUPPORT),2) AVG_SUPPORT","FROM (SELECT * ","      FROM (SELECT NODE, ","                   NODE_SUPPORT, ","                   PREDICTED_TARGET_VALUE, ","                   PARENT,","                   ATTRIBUTE_NAME,","                   OPERATOR, ","                   SPLIT.VAL","            FROM DM$VODT_FEAT_SELECTION A,","            XMLTABLE( '/Element' PASSING A.VALUE","                     COLUMNS","                     VAL VARCHAR2(20) PATH '.') SPLIT  ","            ORDER BY 1,2,3,4,5,6,7)","            )"," GROUP BY ATTRIBUTE_NAME"," ORDER BY AVG_SUPPORT DESC"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select the attributes and their average support","message":["%sql","","SELECT DISTINCT ATTRIBUTE_NAME, ","       ROUND(AVG(NODE_SUPPORT),2) AVG_SUPPORT","FROM (SELECT * ","      FROM (SELECT NODE, ","                   NODE_SUPPORT, ","                   PREDICTED_TARGET_VALUE, ","                   PARENT,","                   ATTRIBUTE_NAME,","                   OPERATOR, ","                   SPLIT.VAL","            FROM DM$VODT_FEAT_SELECTION A,","            XMLTABLE( '/Element' PASSING A.VALUE","                     COLUMNS","                     VAL VARCHAR2(20) PATH '.') SPLIT  ","            ORDER BY 1,2,3,4,5,6,7)","            )"," GROUP BY ATTRIBUTE_NAME"," ORDER BY AVG_SUPPORT DESC"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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--- a/machine-learning/notebooks/sql/OML4SQL Feature Selection Unsupervised Attribute Importance.dsnb	
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Selection Unsupervised Attribute Importance","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715705326403,"interpreter":"md.low","endTime":1715705326715,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Selection: Unsupervised Attribute Importance","In this notebook, we demonstrate how to perform feature selection using the in-database unsupervised algorithm Expectation Maximization (EM).","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715705579895,"interpreter":"md.low","endTime":1715705579957,"results":[{"message":"<h1 id=\"oml4sql-feature-selection-unsupervised-attribute-importance\">OML4SQL Feature Selection: Unsupervised Attribute Importance<\/h1>\n<p>In this notebook, we demonstrate how to perform feature selection using the in-database unsupervised algorithm Expectation Maximization (EM).<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1715705327026,"interpreter":"md.low","endTime":1715705327102,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Expectation-Maximization clustering","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8');\"> Expectation-Maximization clustering<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a>","","* Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a>","","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","    ","*  Specify the maximum number of leaf clusters generated by a clustering algorithm. ","  The algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components.  ","  For EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10. ","> v_setlst('CLUS_NUM_CLUSTERS')       := '5';","","#### Expectation Maximization Settings for Learning     ","* Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected.","  It requires `EMCS_MODEL_SEARCH_ENABLE` or `EMCS_MODEL_SEARCH_DISABLE`.  The default is `EMCS_MODEL_SEARCH_DISABLE`.      ","> v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';","","* Specify the maximum number of iterations in the EM algorithm.","  It requires a number larger than 1.  The default is 100. ","> v_setlst('EMCS_NUM_ITERATIONS')     := '20';","","#### Expectation Maximization Settings for Cluster Statistics","*  Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and `GET_MODEL_DETAILS_EM` only returns taxonomy (hierarchy) and cluster counts.","  It requires either `EMCS_CLUS_STATS_ENABLE` or `EMCS_CLUS_STATS_DISABLE`.  Default is `EMCS_CLUS_STATS_ENABLE`.     ","> v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';","     ","#### Expectation Maximization Settings for Data Preparation and Analysis","* Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions.","  It requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.","> v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';","","---"],"enabled":true,"result":{"startTime":1715705327186,"interpreter":"md.low","endTime":1715705327297,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-expectation-maximization-clustering\">Examples of possible setting overrides for Expectation-Maximization clustering<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-1796B451-BE1B-43BC-9839-05F5F73031C8');\"> Expectation-Maximization clustering<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/shared-settings.html#GUID-2EFF3880-F2E2-4449-A8CC-2CF516DD096B');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Machine Learning Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-CE514371-EC80-41EA-AB07-7F6501687D12');\"> ML settings <\/a><\/p>\n<\/li>\n<li>\n<p>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.\nv_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/li>\n<li>\n<p>Specify the maximum number of leaf clusters generated by a clustering algorithm.\nThe algorithm may return fewer clusters, depending on the data. Expectation maximization (EM) may return fewer clusters than the number specified by CLUS_NUM_CLUSTERS depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings (see Expectation Maximization Settings for Learning table).  Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, the number of clusters equals the number of components.<br />\nFor EM, the default value of CLUS_NUM_CLUSTERS is system-determined. For k-Means and O-Cluster, the default is 10.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('CLUS_NUM_CLUSTERS')       := '5';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-learning\">Expectation Maximization Settings for Learning<\/h4>\n<ul>\n<li>Specify this setting to enable model search in EM where different model sizes are explored and a best size is selected.\nIt requires <code>EMCS_MODEL_SEARCH_ENABLE<\/code> or <code>EMCS_MODEL_SEARCH_DISABLE<\/code>.  The default is <code>EMCS_MODEL_SEARCH_DISABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations in the EM algorithm.\nIt requires a number larger than 1.  The default is 100.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_ITERATIONS')     := '20';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-cluster-statistics\">Expectation Maximization Settings for Cluster Statistics<\/h4>\n<ul>\n<li>Specify whether to enable or disable the gathering of descriptive statistics for clusters (centroids, histograms, and rules).  When statistics are disabled, model size is reduced, and <code>GET_MODEL_DETAILS_EM<\/code> only returns taxonomy (hierarchy) and cluster counts.\nIt requires either <code>EMCS_CLUS_STATS_ENABLE<\/code> or <code>EMCS_CLUS_STATS_DISABLE<\/code>.  Default is <code>EMCS_CLUS_STATS_ENABLE<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';<\/p>\n<\/blockquote>\n<h4 id=\"expectation-maximization-settings-for-data-preparation-and-analysis\">Expectation Maximization Settings for Data Preparation and Analysis<\/h4>\n<ul>\n<li>Specify the number of quantile bins that will be used for modeling numeric columns with multivalued Bernoulli distributions.\nIt requires a number larger than 1 and up to 255 (inclusive).  The default is system-determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EMCS_NUM_QUANTILE_BINS')  := '10';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the EM Clustering model with explicit settings of 1 cluster (for Feature Selection)","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_FEAT_SELECTION');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    V_SETLST DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    V_SETLST('ALGO_NAME')               := 'ALGO_EXPECTATION_MAXIMIZATION';","    V_SETLST('PREP_AUTO')               := 'ON';","    V_SETLST('CLUS_NUM_CLUSTERS')       := '1';","    V_SETLST('EMCS_MODEL_SEARCH')       := 'EMCS_MODEL_SEARCH_ENABLE';","    V_SETLST('EMCS_CLUSTER_STATISTICS') := 'EMCS_CLUS_STATS_ENABLE';","    V_SETLST('EMCS_NUM_ITERATIONS')     := '20';","    V_SETLST('EMCS_NUM_QUANTILE_BINS')  := '10';","    V_SETLST('EMCS_RANDOM_SEED')        := '123456';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'EM_FEAT_SELECTION',","        MINING_FUNCTION     => 'CLUSTERING',","        DATA_QUERY          => 'SELECT * FROM CUSTOMER_INSURANCE_LTV',","        SET_LIST            => V_SETLST,","        CASE_ID_COLUMN_NAME => 'CUSTOMER_ID');","END;"],"enabled":true,"result":{"startTime":1715705327390,"interpreter":"script.low","endTime":1715705338714,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" Check the attribute ranks in the model view","message":["%sql","","SELECT   ATTRIBUTE_RANK,","         ATTRIBUTE_NAME, ","         ROUND(ATTRIBUTE_IMPORTANCE_VALUE, 4) IMPORTANCE_VALUE ","FROM     DM$VIEM_FEAT_SELECTION","ORDER BY ATTRIBUTE_RANK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715705338797,"interpreter":"sql.low","endTime":1715705338990,"results":[{"message":"ATTRIBUTE_RANK\tATTRIBUTE_NAME\tIMPORTANCE_VALUE\n1\tHOME_OWNERSHIP\t0.3408\n2\tNUM_MORTGAGES\t0.3406\n3\tMORTGAGE_AMOUNT\t0.3359\n4\tCUSTOMER_TENURE\t0.2928\n5\tLTV\t0.2893\n6\tLTV_BIN\t0.2876\n7\tNUM_DEPENDENTS\t0.2874\n8\tMARITAL_STATUS\t0.2725\n9\tNUM_ONLINE_TRANS\t0.2476\n10\tNUM_TRANS_ATM\t0.2422\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"IMPORTANCE_VALUE\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"IMPORTANCE_VALUE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"ATTRIBUTE_RANK\",\"ATTRIBUTE_NAME\",\"IMPORTANCE_VALUE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":" Check the top 10 attributes by importance","message":["%sql","","SELECT   ATTRIBUTE_RANK,","         ATTRIBUTE_NAME, ","         ROUND(ATTRIBUTE_IMPORTANCE_VALUE, 4) IMPORTANCE_VALUE ","FROM     DM$VIEM_FEAT_SELECTION","ORDER BY ATTRIBUTE_RANK","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715705339067,"interpreter":"sql.low","endTime":1715705339142,"results":[{"message":"ATTRIBUTE_RANK\tATTRIBUTE_NAME\tIMPORTANCE_VALUE\n1\tHOME_OWNERSHIP\t0.3408\n2\tNUM_MORTGAGES\t0.3406\n3\tMORTGAGE_AMOUNT\t0.3359\n4\tCUSTOMER_TENURE\t0.2928\n5\tLTV\t0.2893\n6\tLTV_BIN\t0.2876\n7\tNUM_DEPENDENTS\t0.2874\n8\tMARITAL_STATUS\t0.2725\n9\tNUM_ONLINE_TRANS\t0.2476\n10\tNUM_TRANS_ATM\t0.2422\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Alternative approach to creating an Expectation Maximization clustering model using a settings table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model. ","","---"],"enabled":true,"result":{"startTime":1715705516674,"interpreter":"md.low","endTime":1715705516734,"results":[{"message":"<h2 id=\"alternative-approach-to-creating-an-expectation-maximization-clustering-model-using-a-settings-table\">Alternative approach to creating an Expectation Maximization clustering model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the table, create it, and then populate it with settings before building the model.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create setting table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE EM_LTV_SAMPLE_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","CREATE TABLE EM_LTV_SAMPLE_SETTINGS (","   SETTING_NAME  VARCHAR2(30),","   SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1715705339348,"interpreter":"script.low","endTime":1715705339620,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable EM_LTV_SAMPLE_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Add settings, set the number of maximum clusters to be 1","message":["%script","","BEGIN       ","   INSERT INTO EM_LTV_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_EXPECTATION_MAXIMIZATION);","   INSERT INTO EM_LTV_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);","   INSERT INTO EM_LTV_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.CLUS_NUM_CLUSTERS,1);","END;","/"],"enabled":true,"result":{"startTime":1715705339694,"interpreter":"script.low","endTime":1715705339832,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create EM model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_LTV_CLUS_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","","BEGIN","  DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'EM_LTV_CLUS_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.CLUSTERING,","    DATA_TABLE_NAME     => 'CUSTOMER_INSURANCE_LTV_SQL',","    CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',","    SETTINGS_TABLE_NAME => 'EM_LTV_SAMPLE_SETTINGS');","END;","/"],"enabled":true,"result":{"startTime":1715705339910,"interpreter":"script.low","endTime":1715705348414,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":" The attribute ranks can be found in the model view","message":["%sql"," ","SELECT   ATTRIBUTE_RANK, ATTRIBUTE_NAME, ROUND(ATTRIBUTE_IMPORTANCE_VALUE, 4) IMPORTANCE_VALUE","FROM     DM$VIEM_LTV_CLUS_SAMPLE ","ORDER BY ATTRIBUTE_RANK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715705545506,"interpreter":"sql.low","endTime":1715705545583,"results":[{"message":"ATTRIBUTE_RANK\tATTRIBUTE_NAME\tIMPORTANCE_VALUE\n1\tHOME_OWNERSHIP\t0.3372\n2\tNUM_MORTGAGES\t0.3369\n3\tMORTGAGE_AMOUNT\t0.3007\n4\tLTV\t0.2985\n5\tLTV_BIN\t0.2966\n6\tCUSTOMER_TENURE\t0.2919\n7\tNUM_DEPENDENTS\t0.2863\n8\tNUM_TRANS_ATM\t0.2372\n9\tNUM_ONLINE_TRANS\t0.2366\n10\tSTATE\t0.2365\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715705557074,"interpreter":"md.low","endTime":1715705557133,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715705348810,"interpreter":"md.low","endTime":1715705348873,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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index 4fa1df8a..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Feature Selection Using Summary Statistics.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Selection Using Summary Statistics","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715708966660,"interpreter":"md.low","endTime":1715708966723,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Selection: Summary Information","In this notebook, we demonstrate how to do feature selection using summary statistics via Oracle SQL.","","We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.","","We show how to use Oracle SQL to select features based on number of distinct values, null values, proportion of constant values. ","","The dataset `CUSTOMER_INSURANCE_LTV_SQL` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715708966802,"interpreter":"md.low","endTime":1715708966863,"results":[{"message":"<h1 id=\"oml4sql-feature-selection-summary-information\">OML4SQL Feature Selection: Summary Information<\/h1>\n<p>In this notebook, we demonstrate how to do feature selection using summary statistics via Oracle SQL.<\/p>\n<p>We use the customer insurance lifetime value data set which contains customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n<p>We show how to use Oracle SQL to select features based on number of distinct values, null values, proportion of constant values.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV_SQL<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_STATS.html#GUID-CA6A56B9-0540-45E9-B1D7-D78769B7714C\" target=\"_blank\">SQL DBMS_STATS.GATHER_TABLE_STATS Documentation<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a>"],"enabled":true,"result":{"startTime":1715708966940,"interpreter":"md.low","endTime":1715708967002,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_STATS.html#GUID-CA6A56B9-0540-45E9-B1D7-D78769B7714C\" target=\"_blank\">SQL DBMS_STATS.GATHER_TABLE_STATS Documentation<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-selection\" target=\"_blank\">OML Attribute Importance<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%sql\r","\r","SELECT *\r","FROM CUSTOMER_INSURANCE_LTV_SQL\r","FETCH FIRST 10 ROWS 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Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         \tPROF-15\t800\tSouth\t1\t1600\tYes\t1\t1\t66\t54.97\t16933\t1923\t4\t25\t5\tMEDIUM\tMARGART\t1\t1\tCALDWELL\nMARRIED\tTX                        \t0\t3\t845\t450\t3\t1\t62061\tCU9113                    \tM                         \tProgrammer/Developer\t1500\tSouthwest\t1\t845\tYes\t8\t6\t26\t53.01\t18615.25\t226\t2\t25\t2\tMEDIUM\tNICKY\t1\t1\tBARON\nSINGLE\tNV                        \t0\t2\t0\t0\t0\t0\t74394\tCU9114                    \tM                         \tSales Representative\t1500\tSouthwest\t0\t0\tNo\t0\t1\t0\t53.06\t0\t0\t0\t25\t0\tLOW\tHUNG\t0\t0\tFINNEY\nSINGLE\tMI                        \t0\t3\t0\t0\t3\t0\t64255\tCU8462                    \tM                         \tPROF-31\t1500\tMidwest\t0\t0\tNo\t1\t1\t28\t53.14\t14363.75\t0\t0\t25\t2\tMEDIUM\tFREDDIE\t0\t0\tMAYBERRY\nDIVORCED\tMI                        \t0\t1\t2513\t1150\t1\t0\t70749\tCU8506                    \tM                         \tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view pointing to the same table","message":["%sql","","CREATE OR REPLACE VIEW CUSTOMER_INSURANCE_LTV_SQL_V AS ","SELECT * ","FROM CUSTOMER_INSURANCE_LTV_SQL"],"enabled":true,"result":{"startTime":1715708967279,"interpreter":"sql.low","endTime":1715708967361,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Call gather_table_stats procedure to collect the table statistics","message":["%script","","BEGIN","  DBMS_STATS.GATHER_TABLE_STATS (USER, TABNAME => 'CUSTOMER_INSURANCE_LTV_SQL');","END;"],"enabled":true,"result":{"startTime":1715708967431,"interpreter":"script.low","endTime":1715708968927,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check some of the table's statistics generated","message":["%sql","","SELECT column_name, num_distinct, density, num_nulls","FROM USER_TAB_COL_STATISTICS","WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL'","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715708969007,"interpreter":"sql.low","endTime":1715708969518,"results":[{"message":"COLUMN_NAME\tNUM_DISTINCT\tDENSITY\tNUM_NULLS\nMARITAL_STATUS\t7\t0.0000605840300496789\t6627\nSTATE\t24\t0.0000336021505376344\t0\nCREDIT_BALANCE\t952\t0.000228\t6627\nCUSTOMER_TENURE\t5\t0.0000336021505376344\t0\nMORTGAGE_AMOUNT\t1748\t0.000219\t0\nBANK_FUNDS\t1205\t0.00049\t6627\nNUM_DEPENDENTS\t7\t0.0000336021505376344\t0\nHAS_CHILDREN\t2\t0.0000336021505376344\t0\nINCOME\t6455\t0.000155\t6627\nCUSTOMER_ID\t14076\t0.000071\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Filter categorical 'identifier' columns - filter out columns with > 95% unique values","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","    SELECTED_COLS VARCHAR2(1000);","BEGIN","    ","    RATIO := 0.95;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL;","    ","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE FROM USER_TAB_COLS WHERE ","                TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL' ","                AND DATA_TYPE IN ('CHAR', 'VARCHAR2'))","    LOOP","        ","        SELECT NUM_DISTINCT","        INTO CNT","        FROM USER_TAB_COL_STATISTICS","        WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL'","        AND COLUMN_NAME = REC.COLUMN_NAME;","        IF CNT > 1 AND CNT < RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","            IF SELECTED_COLS IS NULL THEN ","                SELECTED_COLS := REC.COLUMN_NAME;","            ELSE","                SELECTED_COLS := SELECTED_COLS||','||REC.COLUMN_NAME;","            END IF;","            ","        END IF;","        ","    END LOOP;","    EXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW SELECTED_NO_IDENTIFIED_V AS SELECT '||SELECTED_COLS||' FROM CUSTOMER_INSURANCE_LTV_SQL';","END;"],"enabled":true,"result":{"startTime":1715708969595,"interpreter":"script.low","endTime":1715708970323,"results":[{"message":"\n---------------------------\nMARITAL_STATUS\nSTATE\nCUSTOMER_ID\nGENDER\nPROFESSION\nREGION\nBUY_INSURANCE\nLTV_BIN\nFIRST_NAME\nLAST_NAME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use a View - without table statistics - filter categorical 'identifier' columns - filter columns with > 95% unique values ","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","    SELECTED_COLS VARCHAR2(1000);","BEGIN","    ","    RATIO := 0.95;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL_V;","    ","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE FROM USER_TAB_COLS ","                WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL_V' ","                AND DATA_TYPE IN ('CHAR', 'VARCHAR2'))","    LOOP","        EXECUTE IMMEDIATE 'SELECT COUNT(DISTINCT '||REC.COLUMN_NAME||') FROM CUSTOMER_INSURANCE_LTV_SQL_V' INTO CNT;","        IF CNT > 1 AND CNT < RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","            IF SELECTED_COLS IS NULL THEN ","                SELECTED_COLS := REC.COLUMN_NAME;","            ELSE","                SELECTED_COLS := SELECTED_COLS||','||REC.COLUMN_NAME;","            END IF;","            ","        END IF;","        ","    END LOOP;","    EXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW SELECTED_NO_IDENTIFIED_V AS SELECT '||SELECTED_COLS||' FROM CUSTOMER_INSURANCE_LTV_SQL_V';","END;"],"enabled":true,"result":{"startTime":1715708970403,"interpreter":"script.low","endTime":1715708970780,"results":[{"message":"\n---------------------------\nMARITAL_STATUS\nSTATE\nCUSTOMER_ID\nGENDER\nPROFESSION\nREGION\nBUY_INSURANCE\nLTV_BIN\nFIRST_NAME\nLAST_NAME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check the view with selected columns only","message":["%sql","","SELECT * FROM SELECTED_NO_IDENTIFIED_V","FETCH FIRST 10 ROWS 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              \tF                         \tPROF-15\tSouth\tYes\tMEDIUM\tMARGART\tCALDWELL\nMARRIED\tTX                        \tCU9113                    \tM                         \tProgrammer/Developer\tSouthwest\tYes\tMEDIUM\tNICKY\tBARON\nSINGLE\tNV                        \tCU9114                    \tM                         \tSales Representative\tSouthwest\tNo\tLOW\tHUNG\tFINNEY\nSINGLE\tMI                        \tCU8462                    \tM                         \tPROF-31\tMidwest\tNo\tMEDIUM\tFREDDIE\tMAYBERRY\nDIVORCED\tMI                        \tCU8506                    \tM                         \tPlumber\tMidwest\tNo\tVERY 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REC.COLUMN_NAME;","        IF CNT > RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","        END IF;","        ","    END LOOP;","END;"],"enabled":true,"result":{"startTime":1715708971016,"interpreter":"script.low","endTime":1715708971664,"results":[{"message":"\n---------------------------\nMARITAL_STATUS\nCREDIT_BALANCE\nBANK_FUNDS\nINCOME\n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Use a view - without table statistics - for all columns, identify columns with > 40% missing values","message":["%script","","SET SERVEROUTPUT ON;","DECLARE","    CNT INT;","    TOTAL INT;","    RATIO NUMBER;","BEGIN ","    RATIO := 0.4;","    SELECT COUNT(1) ","    INTO TOTAL","    FROM CUSTOMER_INSURANCE_LTV_SQL_V;","","    FOR REC IN (SELECT COLUMN_NAME, DATA_TYPE ","                FROM USER_TAB_COLS ","                WHERE TABLE_NAME = 'CUSTOMER_INSURANCE_LTV_SQL_V' )","    LOOP","        ","        EXECUTE IMMEDIATE 'SELECT COUNT(1) FROM CUSTOMER_INSURANCE_LTV_SQL_V WHERE '|| REC.COLUMN_NAME||' IS NULL' INTO CNT;","","        IF CNT > RATIO*TOTAL THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","        END IF;","        ","    END LOOP;","END;"],"enabled":true,"result":{"startTime":1715708971752,"interpreter":"script.low","endTime":1715708972194,"results":[{"message":"\n---------------------------\nMARITAL_STATUS\nCREDIT_BALANCE\nBANK_FUNDS\nINCOME\n\n\nPL/SQL procedure successfully 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","                WHERE TABLE_NAME = TBL )","    LOOP","        STMT := REPLACE(FMT, '<COL>', REC.COLUMN_NAME);","        STMT := REPLACE(STMT, '<TBL>', TBL);","        EXECUTE IMMEDIATE STMT INTO CNT;","        IF CNT > TOTAL*RATIO THEN","            DBMS_OUTPUT.PUT_LINE(REC.COLUMN_NAME);    ","        ELSE","            IF SELECTED_COLS IS NULL THEN ","                SELECTED_COLS := REC.COLUMN_NAME;","            ELSE","                SELECTED_COLS := SELECTED_COLS||','||REC.COLUMN_NAME;","            END IF;","        END IF;","        ","    END LOOP;","    EXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW SELECTED_NO_CONSTANT_V AS SELECT '||SELECTED_COLS||' FROM CUSTOMER_INSURANCE_LTV_SQL';","END;"],"enabled":true,"result":{"startTime":1715708972270,"interpreter":"script.low","endTime":1715708972772,"results":[{"message":"\n---------------------------\nCAR_OWNERSHIP\n\n\nPL/SQL procedure successfully 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Engineer\t1000\tWest\t1\t2050\tYes\t13\t3\t22\t54.35\t18581.25\t242\t2\t25\t5\tMEDIUM\tJARVIS\t1\tCLARKE\nDIVORCED\tNY                        \t0\t4\t2500\t700\t1\t0\t71504\tCU8722                    \tF                         \tIT Staff\t1000\tNorthEast\t1\t2500\tNo\t1\t1\t42\t53.79\t32076\t393\t3\t118\t4\tVERY HIGH\tSTACEY\t1\tHENSON\nMARRIED\tMI                        \t0\t1\t8320\t11050\t1\t1\t72308\tCU8665                    \tF                         \tDentist\t4000\tMidwest\t1\t1700\tYes\t14\t1\t33\t53.97\t24877\t650\t4\t25\t5\tHIGH\tMARGRET\t1\tRYAN\nMARRIED\tMI                        \t0\t1\t1000\t0\t1\t1\t64710\tCU8666                    \tM                         \tIT Staff\t700\tMidwest\t1\t1000\tNo\t4\t1\t38\t53.17\t23477.5\t383\t0\t374\t2\tHIGH\tJOAN\t1\tBAUER\nDIVORCED\tNC                        \t0\t4\t4000\t8800\t6\t1\t61332\tCU9108                    \tF                         \tPROF-15\t800\tSouth\t1\t1600\tYes\t1\t1\t66\t54.97\t16933\t1923\t4\t25\t5\tMEDIUM\tMARGART\t1\tCALDWELL\nMARRIED\tTX                        \t0\t3\t845\t450\t3\t1\t62061\tCU9113                    \tM                         \tProgrammer/Developer\t1500\tSouthwest\t1\t845\tYes\t8\t6\t26\t53.01\t18615.25\t226\t2\t25\t2\tMEDIUM\tNICKY\t1\tBARON\nSINGLE\tNV                        \t0\t2\t0\t0\t0\t0\t74394\tCU9114                    \tM                         \tSales Representative\t1500\tSouthwest\t0\t0\tNo\t0\t1\t0\t53.06\t0\t0\t0\t25\t0\tLOW\tHUNG\t0\tFINNEY\nSINGLE\tMI                        \t0\t3\t0\t0\t3\t0\t64255\tCU8462                    \tM                         \tPROF-31\t1500\tMidwest\t0\t0\tNo\t1\t1\t28\t53.14\t14363.75\t0\t0\t25\t2\tMEDIUM\tFREDDIE\t0\tMAYBERRY\nDIVORCED\tMI                        \t0\t1\t2513\t1150\t1\t0\t70749\tCU8506                    \tM                         \tPlumber\t1000\tMidwest\t1\t2513\tNo\t11\t1\t48\t54.13\t30987.25\t6248\t3\t466\t5\tVERY HIGH\tJON\t1\tDUTTON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check column LAST_NAME which is suggested to be removed, most rows have the same last name 'THANOS'","message":["%sql","","SELECT LAST_NAME, COUNT(1)","FROM CUSTOMER_INSURANCE_LTV_SQL","GROUP BY LAST_NAME","ORDER BY COUNT(1) DESC","FETCH FIRST 10 ROWS 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DESC"],"enabled":true,"result":{"startTime":1715708973188,"interpreter":"sql.low","endTime":1715708973265,"results":[{"message":"CAR_OWNERSHIP\tCOUNT(1)\n1\t13859\n0\t1021\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715708973340,"interpreter":"md.low","endTime":1715708973402,"results":[{"message":"<h2 id=\"end-of-script\">End of 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\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Nested Columns.dsnb b/machine-learning/notebooks/sql/OML4SQL Nested Columns.dsnb
deleted file mode 100755
index a3b31346..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Nested Columns.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Nested Columns","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":{"startTime":1714430822715,"interpreter":"md.low","endTime":1714430823129,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Nested Columns","","Oracle Machine Learning can work with the nested column format that enables building models with more than 1000 predictors. Machine learning projects can involve datasets with high dimensional features. Such features are usually encoded using one-hot encoding and can make for very wide data sets. Oracle Database provides a convenient data format called nested columns to handle such wide data. Such data can be stored in transactional form, or exist externally such as a CSV file. When in transactional format, it is straightforward to create nested columns. ","","In this notebook, we use the customer insurance lifetime value dataset as an example to show how to generate nested columns for model building in OML4SQL. While this table clearly fits in the database, we first illustrate converting this to transactional format before focusing on creating nested columns. We also provide examples of how to handle multiple nested columns in both numerical and categorical types. The entire workflow of model training and testing are also demonstrated for the dataset with nested columns. ","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":{"startTime":1714430823213,"interpreter":"md.low","endTime":1714430823319,"results":[{"message":"<h1 id=\"oml4sql-nested-columns\">OML4SQL Nested Columns<\/h1>\n<p>Oracle Machine Learning can work with the nested column format that enables building models with more than 1000 predictors. Machine learning projects can involve datasets with high dimensional features. Such features are usually encoded using one-hot encoding and can make for very wide data sets. Oracle Database provides a convenient data format called nested columns to handle such wide data. Such data can be stored in transactional form, or exist externally such as a CSV file. When in transactional format, it is straightforward to create nested columns.<\/p>\n<p>In this notebook, we use the customer insurance lifetime value dataset as an example to show how to generate nested columns for model building in OML4SQL. While this table clearly fits in the database, we first illustrate converting this to transactional format before focusing on creating nested columns. We also provide examples of how to handle multiple nested columns in both numerical and categorical types. The entire workflow of model training and testing are also demonstrated for the dataset with nested columns.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle Nested Columns Table Documentation<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/use-nested-data.html\" target=\"_blank\">OML using Nested Data Documentation<\/a>"],"enabled":true,"result":{"startTime":1714430823410,"interpreter":"md.low","endTime":1714430823488,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/adobj/multilevel-collection-types.html\" target=\"_blank\">Oracle Nested Columns Table Documentation<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmapi/use-nested-data.html\" target=\"_blank\">OML using Nested Data Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Data overview","message":["%sql","","SELECT * ","FROM   CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1714430823568,"interpreter":"sql.low","endTime":1714430825268,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA                        \t0\t3\t0\t0\t3\t0\t65871\tCU15154                   \tM                         \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY                        \t0\t4\t0\t290\t4\t0\t68747\tCU15155                   \tM                         \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI                        \t0\t3\t1000\t550\t3\t0\t68684\tCU15157                   \tM                         \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT                        \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286                   \tF                         \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT                        \t0\t4\t1800\t0\t3\t0\t84801\tCU15287                   \tF                         \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT                        \t0\t1\t1400\t0\t1\t0\t73987\tCU15289                   \tM                         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\tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Select some categorical columns","message":["%sql","","SELECT CUSTOMER_ID, PROFESSION,  MARITAL_STATUS, GENDER, LTV_BIN, STATE","FROM   CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1714430825366,"interpreter":"sql.low","endTime":1714430825461,"results":[{"message":"CUSTOMER_ID\tPROFESSION\tMARITAL_STATUS\tGENDER\tLTV_BIN\tSTATE\nCU15154                   \tNurse\tSINGLE\tM                         \tMEDIUM\tCA                        \nCU15155                   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\t\"&quot;NUM_NESTED&quot;.NUM_DEPENDENTS\" actualValue=\"3\" weight=\".002\" \nCU8001                    \tNo\t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"66248\" weight=\".039\" \t\"&quot;CAT_NESTED&quot;.MARITAL_STATUS\" actualValue=\"MARRIED\" weight=\".034\" \t\"&quot;CAT_NESTED&quot;.LTV_BIN\" actualValue=\"MEDIUM\" weight=\".018\" \nCU8002                    \tNo\t\"&quot;CAT_NESTED&quot;.PROFESSION\" actualValue=\"Programmer/Developer\" weight=\".17\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"57709\" weight=\".092\" \t\"&quot;CAT_NESTED&quot;.LTV_BIN\" actualValue=\"HIGH\" weight=\".054\" \nCU8003                    \tNo\t\"&quot;CAT_NESTED&quot;.PROFESSION\" actualValue=\"Programmer/Developer\" weight=\".192\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"69082\" weight=\".119\" \t\"&quot;CAT_NESTED&quot;.LTV_BIN\" actualValue=\"HIGH\" weight=\".059\" \nCU8004                    \tNo\t\"&quot;CAT_NESTED&quot;.MARITAL_STATUS\" actualValue=\"DIVORCED\" weight=\".123\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"61964\" weight=\".099\" \t\"&quot;CAT_NESTED&quot;.GENDER\" actualValue=\"F                         \" weight=\".068\" \nCU8005                    \tNo\t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"62276\" weight=\".09\" \t\"&quot;CAT_NESTED&quot;.MARITAL_STATUS\" actualValue=\"MARRIED\" weight=\".082\" \t\"&quot;CAT_NESTED&quot;.LTV_BIN\" actualValue=\"HIGH\" weight=\".05\" \nCU8006                    \tNo\t\"&quot;CAT_NESTED&quot;.PROFESSION\" actualValue=\"Programmer/Developer\" weight=\".182\" \t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"61397\" weight=\".102\" \t\"&quot;CAT_NESTED&quot;.LTV_BIN\" actualValue=\"LOW\" weight=\".047\" \nCU8007                    \tNo\t\"&quot;NUM_NESTED&quot;.INCOME\" actualValue=\"63429\" weight=\".021\" \t\"&quot;CAT_NESTED&quot;.LTV_BIN\" actualValue=\"MEDIUM\" weight=\".01\" \t\"&quot;NUM_NESTED&quot;.NUM_DEPENDENTS\" actualValue=\"1\" weight=\".001\" 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\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Partitioned Model SVM.dsnb b/machine-learning/notebooks/sql/OML4SQL Partitioned Model SVM.dsnb
deleted file mode 100755
index 62717532..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Partitioned Model SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Partitioned Model SVM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715353612246,"interpreter":"md.low","endTime":1715353612307,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4SQL Partitioned Model","Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. Sub-models exist and are used as one model, which results in simplified scoring using top-level model only. The proper sub-model is chosen by system based on row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models. ","","In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. The model is then used to predict the target first, then predicting the target with prediction details. ","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715353612401,"interpreter":"md.low","endTime":1715353612478,"results":[{"message":"<h1 id=\"oml4sql-partitioned-model\">OML4SQL Partitioned Model<\/h1>\n<p>Oracle Machine Learning enables automatically building an ensemble model comprised of multiple sub-models, one for each data partition. Sub-models exist and are used as one model, which results in simplified scoring using top-level model only. The proper sub-model is chosen by system based on row of data to be scored. Partitioned models achieve potentially better accuracy through multiple targeted models.<\/p>\n<p>In this notebook, we build an SVM model to predict the number of years a customer resides at their residence but partitioned on customer gender. The model is then used to predict the target first, then predicting the target with prediction details.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a>"],"enabled":true,"result":{"startTime":1715353612593,"interpreter":"md.low","endTime":1715353612654,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/intro-oracle-machine-learning-SQL.html#GUID-02417CD8-80E8-4324-9E5E-2BE2DCE29527\" target=\"_blank\">OML Partitioned Models<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create dataset with data from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS_DEMO';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS_DEMO AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM   SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE  A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1715353612743,"interpreter":"script.low","endTime":1715353613848,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS_DEMO created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display number of rows","message":["%sql","","SELECT COUNT(*) ","FROM   CUSTOMERS_DEMO;"],"enabled":true,"result":{"startTime":1715353613927,"interpreter":"sql.low","endTime":1715353614111,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View rows from CUSTOMERS_DEMO table","message":["%sql","","SELECT * ","FROM CUSTOMERS_DEMO","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715353614194,"interpreter":"sql.low","endTime":1715353614315,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100100\tF\tMarried\t1959\tJ: 190,000 - 249,999\t10000\tMasters\t1\t4-5\tProf.\t4\t0\n100200\tM\tNeverM\t1983\tL: 300,000 and above\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100300\tM\tMarried\t1961\tG: 130,000 - 149,999\t10000\tBach.\t1\t3\tProf.\t4\t0\n100400\tM\tDivorc.\t1944\tC: 50,000 - 69,999\t9000\t12th\t0\t6-8\tTransp.\t6\t0\n100500\tM\tNeverM\t1983\tH: 150,000 - 169,999\t15000\tHS-grad\t0\t1\tCrafts\t2\t1\n100600\tM\tMarried\t1978\tK: 250,000 - 299,999\t9000\t11th\t0\t3\tTransp.\t3\t1\n100700\tM\tMarried\t1947\tJ: 190,000 - 249,999\t11000\tHS-grad\t0\t3\tCrafts\t6\t0\n100800\tM\tMarried\t1935\tK: 250,000 - 299,999\t11000\tBach.\t0\t3\tSales\t5\t0\n100900\tM\tMarried\t1959\tF: 110,000 - 129,999\t1500\tHS-grad\t0\t3\tExec.\t4\t0\n101000\tM\tMarried\t1936\tG: 130,000 - 149,999\t7000\t7th-8th\t0\t3\tCrafts\t7\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Partitioning (Shared Settings)","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Requests the building of a partitioned model. ","   The setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model. ","   If `XFORM_LIST` parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.  ","> v_setlst('ODMS_PARTITION_COLUMNS') := '<Comma separated list of attributes>'","     ","* Controls the parallel building of partitioned models.     ","   `ODMS_PARTITION_BUILD_INTRA` builds each partition in parallel using all slaves.","   `ODMS_PARTITION_BUILD_INTER` builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.","   `ODMS_PARTITION_BUILD_HYBRID` combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.","> v_setlst('ODMS_PARTITION_BUILD_TYPE') := 'ODMS_PARTITION_BUILD_HYBRID'"],"enabled":true,"result":{"startTime":1715353614392,"interpreter":"md.low","endTime":1715353614474,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-partitioning-shared-settings\">Examples of possible setting overrides for Partitioning (Shared Settings)<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Requests the building of a partitioned model.\nThe setting value is a comma-separated list of the machine learning attributes to be used to determine the in-list partition key values. These attributes are taken from the input columns, unless an XFORM_LIST parameter is passed to the model.\nIf <code>XFORM_LIST<\/code> parameter is passed to the model, then the attributes are taken from the attributes produced by these transformations.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_PARTITION_COLUMNS') := '<Comma separated list of attributes>'<\/p>\n<\/blockquote>\n<ul>\n<li>Controls the parallel building of partitioned models.<br />\n<code>ODMS_PARTITION_BUILD_INTRA<\/code> builds each partition in parallel using all slaves.\n<code>ODMS_PARTITION_BUILD_INTER<\/code> builds each partition entirely in a single slave, but multiple partitions may be built at the same time because multiple slaves are active.\n<code>ODMS_PARTITION_BUILD_HYBRID<\/code> combines the other two types and is recommended for most situations to adapt to dynamic environments. This is the default value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_PARTITION_BUILD_TYPE') := 'ODMS_PARTITION_BUILD_HYBRID'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build SVM Linear model partitioned by CUST_GENDER to predict YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_MOD_PARTITIONED');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')              := 'ALGO_SUPPORT_VECTOR_MACHINES'; -- choose SVM algorithm","    v_setlst('SVMS_KERNEL_FUNCTION')   := 'SVMS_LINEAR';      -- choose linear kernel, which provides coefficients","    v_setlst('ODMS_PARTITION_COLUMNS') := 'CUST_GENDER';      -- choose column on which to partition the data","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVM_MOD_PARTITIONED',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM CUSTOMERS_DEMO',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE');","END;"],"enabled":true,"result":{"startTime":1715353614549,"interpreter":"script.low","endTime":1715353616047,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View sub-models of the partitioned model","message":["%sql","","SELECT MODEL_NAME, PARTITION_NAME, COLUMN_NAME, COLUMN_VALUE","FROM   ALL_MINING_MODEL_PARTITIONS","WHERE  MODEL_NAME = 'SVM_MOD_PARTITIONED';"],"enabled":true,"result":{"startTime":1715353616127,"interpreter":"sql.low","endTime":1715353616447,"results":[{"message":"MODEL_NAME\tPARTITION_NAME\tCOLUMN_NAME\tCOLUMN_VALUE\nSVM_MOD_PARTITIONED\tF\tCUST_GENDER\tF\nSVM_MOD_PARTITIONED\tM\tCUST_GENDER\tM\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List model views","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='SVM_MOD_PARTITIONED'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1715353616526,"interpreter":"sql.low","endTime":1715353616621,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VGSVM_MOD_PARTITIONED\tGlobal Name-Value Pairs\nDM$VLSVM_MOD_PARTITIONED\tSVM Linear Coefficients\nDM$VNSVM_MOD_PARTITIONED\tNormalization and Missing Value Handling\nDM$VSSVM_MOD_PARTITIONED\tComputed Settings\nDM$VWSVM_MOD_PARTITIONED\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View model details - coefficients per partition","message":["%sql","","SELECT PARTITION_NAME, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, ","       round(COEFFICIENT,3) COEFFICIENT, ","       round(REVERSED_COEFFICIENT,3) REVERSED_COEFFICIENT ","FROM   DM$VLSVM_MOD_PARTITIONED;"],"enabled":true,"result":{"startTime":1715353616696,"interpreter":"sql.low","endTime":1715353616880,"results":[{"message":"PARTITION_NAME\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\tREVERSED_COEFFICIENT\nM\tCUST_INCOME_LEVEL\tA: Below 30,000\t0.164\t0.164\nM\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t0.063\t0.063\nM\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t-0.048\t-0.048\nM\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t-0.079\t-0.079\nM\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t0.032\t0.032\nM\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-0.098\t-0.098\nM\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t-0.006\t-0.006\nM\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t0.053\t0.053\nM\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t-0.043\t-0.043\nM\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t-0.023\t-0.023\nM\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t0.009\t0.009\nM\tCUST_INCOME_LEVEL\tL: 300,000 and above\t-0.024\t-0.024\nM\tCUST_MARITAL_STATUS\tDivorc.\t0.127\t0.127\nM\tCUST_MARITAL_STATUS\tMabsent\t-0.024\t-0.024\nM\tCUST_MARITAL_STATUS\tMarried\t0.075\t0.075\nM\tCUST_MARITAL_STATUS\tNeverM\t0.001\t0.001\nM\tCUST_MARITAL_STATUS\tSepar.\t0.059\t0.059\nM\tCUST_MARITAL_STATUS\tWidowed\t-0.238\t-0.238\nM\tEDUCATION\t10th\t-0.058\t-0.058\nM\tEDUCATION\t11th\t-0.113\t-0.113\nM\tEDUCATION\t12th\t-0.138\t-0.138\nM\tEDUCATION\t1st-4th\t0.185\t0.185\nM\tEDUCATION\t5th-6th\t0.046\t0.046\nM\tEDUCATION\t7th-8th\t-0.168\t-0.168\nM\tEDUCATION\t9th\t-0.029\t-0.029\nM\tEDUCATION\t< Bach.\t-0.019\t-0.019\nM\tEDUCATION\tAssoc-A\t0.051\t0.051\nM\tEDUCATION\tAssoc-V\t0.044\t0.044\nM\tEDUCATION\tBach.\t0.031\t0.031\nM\tEDUCATION\tHS-grad\t0.002\t0.002\nM\tEDUCATION\tMasters\t0.033\t0.033\nM\tEDUCATION\tPhD\t0.214\t0.214\nM\tEDUCATION\tPresch.\t-0.495\t-0.495\nM\tEDUCATION\tProfsc\t0.415\t0.415\nM\tHOUSEHOLD_SIZE\t1\t-0.074\t-0.074\nM\tHOUSEHOLD_SIZE\t2\t0.073\t0.073\nM\tHOUSEHOLD_SIZE\t3\t0.044\t0.044\nM\tHOUSEHOLD_SIZE\t6-8\t-0.018\t-0.018\nM\tHOUSEHOLD_SIZE\t9+\t-0.026\t-0.026\nM\tOCCUPATION\t?\t-0.045\t-0.045\nM\tOCCUPATION\tArmed-F\t-0.034\t-0.034\nM\tOCCUPATION\tCleric.\t0.041\t0.041\nM\tOCCUPATION\tCrafts\t0.046\t0.046\nM\tOCCUPATION\tExec.\t0.254\t0.254\nM\tOCCUPATION\tFarming\t0.16\t0.16\nM\tOCCUPATION\tHandler\t-0.065\t-0.065\nM\tOCCUPATION\tHouse-s\t-0.463\t-0.463\nM\tOCCUPATION\tMachine\t-0.001\t-0.001\nM\tOCCUPATION\tOther\t-0.072\t-0.072\nM\tOCCUPATION\tProf.\t0.009\t0.009\nM\tOCCUPATION\tProtec.\t0.025\t0.025\nM\tOCCUPATION\tSales\t0.158\t0.158\nM\tOCCUPATION\tTechSup\t-0.156\t-0.156\nM\tOCCUPATION\tTransp.\t0.141\t0.141\nM\tAFFINITY_CARD\t\t0.039\t0.084\nM\tCUST_CREDIT_LIMIT\t\t0.012\t0\nM\tCUST_YEAR_OF_BIRTH\t\t-0.646\t-0.048\nM\tY_BOX_GAMES\t\t-0.043\t-0.097\nF\tCUST_INCOME_LEVEL\tA: Below 30,000\t0.111\t0.111\nF\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t-0.154\t-0.154\nF\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t0.022\t0.022\nF\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t-0.012\t-0.012\nF\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t-0.022\t-0.022\nF\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-0.004\t-0.004\nF\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t-0.057\t-0.057\nF\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t0.05\t0.05\nF\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t0.011\t0.011\nF\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t-0.025\t-0.025\nF\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t0.022\t0.022\nF\tCUST_INCOME_LEVEL\tL: 300,000 and above\t0.056\t0.056\nF\tCUST_MARITAL_STATUS\tDivorc.\t0.131\t0.131\nF\tCUST_MARITAL_STATUS\tMabsent\t-0.145\t-0.145\nF\tCUST_MARITAL_STATUS\tMar-AF\t0.177\t0.177\nF\tCUST_MARITAL_STATUS\tMarried\t-0.037\t-0.037\nF\tCUST_MARITAL_STATUS\tNeverM\t0.004\t0.004\nF\tCUST_MARITAL_STATUS\tSepar.\t0.077\t0.077\nF\tCUST_MARITAL_STATUS\tWidowed\t-0.206\t-0.206\nF\tEDUCATION\t10th\t-0.23\t-0.23\nF\tEDUCATION\t11th\t-0.31\t-0.31\nF\tEDUCATION\t12th\t0.098\t0.098\nF\tEDUCATION\t1st-4th\t-0.464\t-0.464\nF\tEDUCATION\t5th-6th\t0.108\t0.108\nF\tEDUCATION\t7th-8th\t-0.287\t-0.287\nF\tEDUCATION\t9th\t-0.138\t-0.138\nF\tEDUCATION\t< Bach.\t-0.079\t-0.079\nF\tEDUCATION\tAssoc-A\t0.041\t0.041\nF\tEDUCATION\tAssoc-V\t0.058\t0.058\nF\tEDUCATION\tBach.\t0.02\t0.02\nF\tEDUCATION\tHS-grad\t0.026\t0.026\nF\tEDUCATION\tMasters\t0.182\t0.182\nF\tEDUCATION\tPhD\t0.355\t0.355\nF\tEDUCATION\tPresch.\t0.23\t0.23\nF\tEDUCATION\tProfsc\t0.39\t0.39\nF\tHOUSEHOLD_SIZE\t1\t-0.057\t-0.057\nF\tHOUSEHOLD_SIZE\t2\t0.106\t0.106\nF\tHOUSEHOLD_SIZE\t4-5\t-0.05\t-0.05\nF\tHOUSEHOLD_SIZE\t6-8\t-0.004\t-0.004\nF\tHOUSEHOLD_SIZE\t9+\t0.006\t0.006\nF\tOCCUPATION\t?\t-0.103\t-0.103\nF\tOCCUPATION\tCleric.\t0.077\t0.077\nF\tOCCUPATION\tCrafts\t0.23\t0.23\nF\tOCCUPATION\tExec.\t0.106\t0.106\nF\tOCCUPATION\tFarming\t-0.422\t-0.422\nF\tOCCUPATION\tHandler\t-0.037\t-0.037\nF\tOCCUPATION\tHouse-s\t-0.258\t-0.258\nF\tOCCUPATION\tMachine\t0.136\t0.136\nF\tOCCUPATION\tOther\t-0.089\t-0.089\nF\tOCCUPATION\tProf.\t0.073\t0.073\nF\tOCCUPATION\tProtec.\t0.072\t0.072\nF\tOCCUPATION\tSales\t0.018\t0.018\nF\tOCCUPATION\tTechSup\t0.071\t0.071\nF\tOCCUPATION\tTransp.\t0.127\t0.127\nF\tAFFINITY_CARD\t\t0.048\t0.155\nF\tCUST_CREDIT_LIMIT\t\t0\t0\nF\tCUST_YEAR_OF_BIRTH\t\t-0.796\t-0.057\nF\tY_BOX_GAMES\t\t0.012\t0.025\nM\t\t\t-0.19\t94.488\nF\t\t\t0.025\t111.124\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Score 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diff --git a/machine-learning/notebooks/sql/OML4SQL Procedure for Importing Data to ADB.dsnb b/machine-learning/notebooks/sql/OML4SQL Procedure for Importing Data to ADB.dsnb
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Procedure for Importing Data to ADB","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# Importing Data from Oracle Cloud Object Storage","","So, you've signed up for an Oracle Cloud account and you want to start exploring Oracle Machine Learning in Autonomous Database. The first step is to import your data, and there are a few different options to choose from. For example, Oracle Database Actions provides a friendly web-based interface to load data from local files, cloud storage, or from remote databases. Another option for loading data from local files is to use SQL*Loader. For moving data between Oracle Database and Autonomous Database, Oracle Data Pump offers very fast data movement. ","","But how do you import data into Oracle Autonomous Database without leaving the Oracle Machine Learning Notebooks environment?  The answer is to use the `DBMS_CLOUD` package.","","`DBMS_CLOUD` provides a simple way to interact with an object store from the database. It supports loading data files from Oracle Cloud Infrastructure Object Storage, Github repositories, Amazon AWS S3, Microsoft Azure Cloud Storage, and Google Cloud Storage. ","","In this example, we demonstrate using a custom procedure `IMPORT_DATA_OBJSTORE_DATASET`, which leverages `DBMS_CLOUD`, to import data from an Oracle Cloud Object Storage bucket to an Autonomous Database table. When using `DBMS_CLOUD.COPY_DATA` alone, the target table must be created in advance. With `IMPORT_DATA_OBJSTORE_DATASET`, you simply provide the dataset name, and the table will be created and the data inserted in a single step. This custom procedure was created to facilitate easy import of the datasets used in Oracle Machine Learning notebooks and demos.","","For a one-off approach to importing specific datasets, refer to the `OML Run-me-first` notebook in the OML Notebooks template examples.","","### Prerequisites:","- An Oracle Machine Learning account with grant EXECUTE privileges on DBMS_CLOUD","","To run `DBMS_CLOUD` subprograms with a user other than ADMIN you need to grant EXECUTE privileges to that user. For example, run the following command as ADMIN to grant privileges to OMLUSER:","","    GRANT EXECUTE ON DBMS_CLOUD TO OMLUSER;","","- A public Oracle Cloud Object Storage bucket","","Copyright (c) 2024 Oracle Corporation                             ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" target=\"_blank\">The Universal Permissive License (UPL), Version 1.0<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-oracle-machine-learning.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA\" target=\"_blank\">Getting Started with Oracle Machine Learning Notebooks<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/load-data.html#GUID-1351807C-E3F7-4C6D-AF83-2AEEADE2F83E\" target=\"_blank\">Loading Data with Autonomous Database<\/a>","* <a href=\"https://docs.oracle.com/en-us/iaas/Content/Object/Concepts/objectstorageoverview.htm\" target=\"_blank\">Object Storage Bucket Overview<\/a>","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-database/adbdu/dbms_cloud-objects-and-files.html#GUID-12C083D6-76ED-4D35-8BD6-B277D91090F9\" target=\"_blank\">DBMS_CLOUD Overview<\/a>"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Custom procedure for loading data ","message":["%md","","The custom procedure `IMPORT_DATA_OBJSTORE_DATASET` imports the data from Object Storage directly to a database table using a combination of PL/SQL commands and the `DBMS_CLOUD.COPY_DATA` procedure. Simply provide the dataset name, and the table will be created and the data inserted to the target table.","","#### Prerequisites:","","- The CSV file containing the data and a text file with the table definition must be saved in the Object Storage bucket","-- Note, the default Object Storage bucket already contains the CSV and text files needed to create the OML demo datasets","- Both the CSV and text files must match the name of the input dataset","-- For example, *CUSTOMER_INSURANCE_LTV2.txt* and *CUSTOMER_INSURANCE_LTV2.csv*","- The Object Storage bucket must be public","-- Either the default bucket or the user's public bucket may be used","- The OML ADMIN user must grant execute on the DBMS_CLOUD procedures to the OML user ","-- Use the command *grant execute on DBMS_CLOUD to OMLUSER*, where OMLUSER is the OML username","","#### Input variables:","- DATASET: The name of CSV data set residing in Object Storage","- VERBOSE: Optional. Controls the amount of information returned from the procedure","-- Default is FALSE, which returns the table name and number of rows in the table","-- When TRUE, returns detailed object information, including the default return values, the file names in Object Storage, and the table definition","- LOCATION_URI: Optional. The Object Storage URL where the files reside. The default value is the Oracle Machine Learning public bucket.","","#### Basic usage:","To run the procedure, pass the dataset name as the `DATASET` parameter:","","    EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2')"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define the custom procedure","message":["%script\r","\r","CREATE OR REPLACE PROCEDURE IMPORT_DATA_OBJSTORE_DATASET(\r","  DATASET IN VARCHAR2,\r","  VERBOSE IN BOOLEAN DEFAULT FALSE,\r","  LOCATION_URI IN VARCHAR2 DEFAULT 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/') AUTHID CURRENT_USER AS\r","dataset_upper VARCHAR2(100);\r","file_name VARCHAR2(1000);\r","table_name varchar2(1000);\r","obj varchar2(1000);\r","sql_file varchar2(1000);\r","b_data BLOB;\r","v_data VARCHAR2(5000);\r","v_rownum INTEGER;\r","\r","------------------------------------------------------\r","-- Declare a new variable to convert the input\r","-- dataset to upper case.  \r","\r","------------------------------------------------------\r","\r","BEGIN\r","    dataset_upper := upper(dataset);\r","\r","------------------------------------------------------\r","-- Define file name from input dataset\r","------------------------------------------------------\r","\r","SELECT CONCAT(dataset_upper, '.csv') INTO FILE_NAME from dual;\r","\r","IF VERBOSE THEN\r","   DBMS_OUTPUT.PUT_LINE('Dataset name is: '||dataset_upper);\r","   DBMS_OUTPUT.PUT_LINE('CSV file name is: '||file_name);\r","END IF;\r","\r","------------------------------------------------------\r","-- Select file from Object Storage\r","------------------------------------------------------\r","\r","SELECT object_name INTO obj FROM DBMS_CLOUD.LIST_OBJECTS(NULL, LOCATION_URI) where object_name = FILE_NAME;\r","\r","------------------------------------------------------\r","-- Define table name\r","------------------------------------------------------\r","\r","SELECT REGEXP_SUBSTR(obj, '[^.]+') INTO table_name from dual;\r","\r","   DBMS_OUTPUT.PUT_LINE('Table name is: '||table_name);\r","\r","------------------------------------------------------\r","-- Define file containing the CREATE TABLE statement,\r","-- which is saved in a TXT file. DBMS_CLOUD does not\r","-- support reading SQL files.\r","------------------------------------------------------\r","\r","SELECT table_name||'.txt' INTO sql_file from dual;\r","\r","IF VERBOSE THEN\r","   DBMS_OUTPUT.PUT_LINE('Text file is: '||sql_file);\r","END IF;\r","\r","------------------------------------------------------\r","-- Read the SQL statement from the text file into\r","-- a binary variable and execute the statement\r","------------------------------------------------------\r","\r","BEGIN\r","  b_data := DBMS_CLOUD.GET_OBJECT(\r","      credential_name => NULL,\r","      object_uri => LOCATION_URI||sql_file);\r","  v_data :=  utl_raw.cast_to_varchar2(b_data);\r","  IF VERBOSE THEN\r","  DBMS_OUTPUT.PUT_LINE('Table definition is: '||v_data);\r","  END IF;\r","  EXECUTE IMMEDIATE v_data;\r","END;\r","\r","------------------------------------------------------\r","-- Write data to the final table specified as dataset\r","-- input. If the table already exists, an error is\r","-- returned.\r","------------------------------------------------------\r","\r","BEGIN\r","    DBMS_CLOUD.COPY_DATA (\r","       credential_name => NULL,\r","       table_name => table_name,\r","       file_uri_list   => LOCATION_URI||file_name,\r","  format => json_object('type' value 'CSV', 'skipheaders' value 1));\r","END;\r","\r","------------------------------------------------------\r","-- Return the number of rows in the table\r","------------------------------------------------------\r","\r","EXECUTE IMMEDIATE 'SELECT count(*) FROM ' || table_name INTO v_rownum;\r","DBMS_OUTPUT.PUT_LINE ('Table created with number of rows = ' || v_rownum);\r","\r","END;\r"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete the table if it exists","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV2';","EXCEPTION WHEN OTHERS THEN NULL; END;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the procedure in default mode","message":["%md","","In default mode, the only required input is the dataset name, `CUSTOMER_INSURANCE_LTV2`. The table `CUSTOMER_INSURANCE_LTV2` is created and the table name and number of rows in the table is returned."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2')"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 10 rows of the table","message":["%sql","","SELECT * ","FROM  CUSTOMER_INSURANCE_LTV2 ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the custom procedure in verbose mode","message":["%md","","In verbose mode, additional information is returned, including the dataset, CSV, and text file names, and the table definition. Verbose mode is enabled by specified by setting the `VERBOSE`parameter to `TRUE`."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Delete the previously created table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV2';","EXCEPTION WHEN OTHERS THEN NULL; END;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%script","","EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2', TRUE)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 10 rows of the table","message":["%sql","","SELECT * ","FROM  CUSTOMER_INSURANCE_LTV2 ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Run the procedure using your own Object Storage bucket","message":["%md","","To use your own Object Storage bucket with `IMPORT_DATA_OBJSTORE_DATASET`, pass the bucket URI to the `LOCATION_URI` parameter. Recall that the CSV file containing the data and the text file with the table definition should first be uploaded to the bucket. The example below demonstrates this functionality in default mode, and replacing NULL for TRUE will enable verbose mode."],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%script","","EXEC IMPORT_DATA_OBJSTORE_DATASET('CUSTOMER_INSURANCE_LTV2', NULL, '<your bucket URI here>')"],"enabled":false,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Try it with your data","message":["%md","","To use the `IMPORT_DATA_OBJSTORE_DATASET` procedure with your own dataset, create the text file containing the table definition (in a single line), save the corresponding CSV data file to your public Object Storage bucket, and run the procedure with the dataset name as input. ","","For example, for the IRIS data, the bucket will have IRIS.csv containing the data, and IRIS.txt with table definition:","","    CREATE TABLE IRIS(SEPAL_LENGTH number, SEPAL_WIDTH number, PETAL_LENGTH number, PETAL_WIDTH number, SPECIES varchar2(4000))","","Run the procedure using your bucket:","","    EXEC IMPORT_DATA_OBJSTORE_DATASET('IRIS', NULL, '<your bucket URI>')"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression GLM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715225404062,"interpreter":"md.low","endTime":1715225404123,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Generalized Linear Model","","This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715284645174,"interpreter":"md.medium","endTime":1715284645328,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-generalized-linear-model\">Regression Modeling to Predict Numerical Values using Generalized Linear Model<\/h1>\n<p>This notebook shows how to predict numerical values using multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Generalized Linear Model algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715225404353,"interpreter":"md.low","endTime":1715225404428,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a>"],"enabled":true,"result":{"startTime":1715225404514,"interpreter":"md.low","endTime":1715225404573,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-generalized-linear-model\" target=\"_blank\">OML Generalized Linear Model (GLM)<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360 joining tables from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1715225404652,"interpreter":"script.low","endTime":1715225407052,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V AS SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V AS SELECT * FROM CUSTOMERS360 MINUS SELECT * FROM TRAIN_DATA_V;"],"enabled":true,"result":{"startTime":1715225407135,"interpreter":"script.low","endTime":1715225407285,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a GLM regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1715225407359,"interpreter":"md.low","endTime":1715225407419,"results":[{"message":"<h3 id=\"build-a-glm-regression-model-for-predicting-yrs_residence\">Build a GLM regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1715225407498,"interpreter":"script.low","endTime":1715225410000,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for GLM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95. ","> v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';","- Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the `PREDICTION_BOUNDS` SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.","> v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","- Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).","> v_setlst('GLMS_RIDGE_VALUE') := '2';","- Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below","> v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';","- Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.     ","> v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';","- Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are `GLMS_FTR_GEN_QUADRATIC` or `GLMS_FTR_GEN_CUBIC`","> v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';","- Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are `GLMS_FTR_SEL_AIC`, `GLMS_FTR_SEL_SBIC`,`GLMS_FTR_SEL_RIC`,`GLMS_FTR_SEL_ALPHA_INV`.   ","> v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';","- Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).","> v_setlst('GLMS_MAX_FEATURES') := '30';","- Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.","> v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';","- Specify the target value used as the `reference class` in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.","> v_setlst('GLMS_REFERENCE_CLASS_NAME') := '<target_value>';","- Specify whether to enable or disable row diagnostics. The default is `GLMS_ROW_DIAG_DISABLE`","> v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';","- Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).","> v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';","- Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.","> v_setlst('GLMS_NUM_ITERATIONS') := '200';","- Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.","> v_setlst('GLMS_BATCH_ROWS') := '2000';","- Specify the GLM solver to use. The solver cannot be selected if `GLMS_FTR_SELECTION` setting is enabled. The default value is system determined, and the options are `GLMS_SOLVER_SGD` (Stochastic Gradient Descent), `GLMS_SOLVER_CHOL` (Cholesky), `GLMS_SOLVER_QR` (QR decomposition) and `GLMS_SOLVER_LBFGS_ADMM` (LBFGS and ADAM)","> v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';","- Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.  ","> v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';","","<strong> For Oracle release 23ai and newer. <\/strong>","- Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function. ","   For classification, the options are `GLMS_LOGIT_LINK (default)`, `GLMS_PROBIT_LINK`, `GLMS_CLOGLOG_LINK` or `GLMS_CAUCHIT_LINK`.  For regression, the default is `GLMS_IDENTITY_LINK (default)`.","> v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';"],"enabled":true,"result":{"startTime":1715284733015,"interpreter":"md.medium","endTime":1715284733160,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-glm\">Examples of possible setting overrides for GLM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-4E3665B9-B1C2-4F6B-AB69-A7F353C70F5C');\">Generalized Linear Model<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The confidence level for coefficient confidence intervals. The value required is between 0 and 1 (excluding the edges), and the default confidence level is 0.95.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONF_LEVEL') := 'ODMS_MISSING_VALUE_DELETE_ROW';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn ridge regression on or off. Enable or disable ridge regression. Ridge applies to both regression and classification machine learning functions.  When ridge is enabled, prediction bounds are not produced by the <code>PREDICTION_BOUNDS<\/code> SQL function.  Ridge may only be enabled when feature selection is not specified, or has been explicitly disabled. If ridge regression and feature selection are both explicitly enabled, then an exception is raised. By default the system turns it on if there is a multicollinearity.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify The value of the ridge parameter. This setting is only used when the algorithm is configured to use ridge regression. If ridge regression is enabled internally by the algorithm, then the ridge parameter is determined by the algorithm.  It requires a value greater than 0 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_RIDGE_VALUE') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Turn feature selection on or off. By default the system does not do feature selection. Also, feature generation requires feature selection to be enabled. Some options for feature selection are show below<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SELECTION') := 'GLMS_FTR_SELECTION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether or not feature generation is enabled for GLM. By default, feature generation is not enabled. Feature generation can only be enabled when feature selection is also enabled.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GENERATION') := 'GLMS_FTR_GENERATION_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether feature generation is quadratic or cubic. When feature generation is enabled, the algorithm automatically chooses the most appropriate feature generation method based on the data.  Options are <code>GLMS_FTR_GEN_QUADRATIC<\/code> or <code>GLMS_FTR_GEN_CUBIC<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_GEN_METHOD') := 'GLMS_FTR_GEN_QUADRATIC';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify feature selection penalty criterion for adding a feature to the model. When feature selection is enabled, the algorithm automatically chooses the penalty criterion based on the data. Options are <code>GLMS_FTR_SEL_AIC<\/code>, <code>GLMS_FTR_SEL_SBIC<\/code>,<code>GLMS_FTR_SEL_RIC<\/code>,<code>GLMS_FTR_SEL_ALPHA_INV<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_FTR_SEL_CRIT') := 'GLMS_FTR_SEL_ALPHA_INV';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of features that can be selected for the final model (when feature selection is enabled). By default, the algorithm limits the number of features to ensure sufficient memory. It requires a number between 0 (exclusive) and 2000 (inclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_MAX_FEATURES') := '30';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether prune is enabled or disabled for features in the final model. Pruning is based on T-Test statistics for linear regression, or Wald Test statistics for logistic regression. Features are pruned in a loop until all features are statistically significant with respect to the full data. When feature selection is enabled, the algorithm automatically performs pruning based on the data.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_PRUNE_MODEL') := 'GLMS_PRUNE_MODEL_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the target value used as the <code>reference class<\/code> in a binary logistic regression model. Probabilities are produced for the other class. By default, the algorithm chooses the value with the highest prevalence (the most cases) for the reference class.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_REFERENCE_CLASS_NAME') := '&lt;target_value&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to enable or disable row diagnostics. The default is <code>GLMS_ROW_DIAG_DISABLE<\/code><\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_ROW_DIAGNOSTICS') := 'GLMS_ROW_DIAG_ENABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance setting of the GLM algorithm. The default value is system-determined, and the value required is between 0 and 1 (exclusive).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_CONV_TOLERANCE') := '0.0001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the maximum number of iterations for the GLM algorithm. The default value is system-determined, and it requires a positive integer.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_NUM_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of rows in a batch used by the SGD solver. The value of this parameter sets the size of the batch for the SGD solver. An input of 0 triggers a data driven batch size estimate, and it requires a positive integer.  The default is 2,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_BATCH_ROWS') := '2000';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the GLM solver to use. The solver cannot be selected if <code>GLMS_FTR_SELECTION<\/code> setting is enabled. The default value is system determined, and the options are <code>GLMS_SOLVER_SGD<\/code> (Stochastic Gradient Descent), <code>GLMS_SOLVER_CHOL<\/code> (Cholesky), <code>GLMS_SOLVER_QR<\/code> (QR decomposition) and <code>GLMS_SOLVER_LBFGS_ADMM<\/code> (LBFGS and ADAM)<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SOLVER') := 'GLMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify whether to use sparse solver if it is available. The default value is GLMS_SPARSE_SOLVER_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_SPARSE_SOLVER') := 'GLMS_SPARSE_SOLVER_ENABLE';<\/p>\n<\/blockquote>\n<p><strong> For Oracle release 23ai and newer. <\/strong><\/p>\n<ul>\n<li>Specify which GLM Link Function to use. This setting allows the user to specify the link function for building a GLM model. The link functions are specific to the mining function.\nFor classification, the options are <code>GLMS_LOGIT_LINK (default)<\/code>, <code>GLMS_PROBIT_LINK<\/code>, <code>GLMS_CLOGLOG_LINK<\/code> or <code>GLMS_CAUCHIT_LINK<\/code>.  For regression, the default is <code>GLMS_IDENTITY_LINK (default)<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('GLMS_LINK_FUNCTION') := 'GLMS_LOGIT_LINK';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Feature Selection and Generation","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')             := 'ON';","    v_setlst('ALGO_NAME')             := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_DISABLE';","    v_setlst('GLMS_FTR_SELECTION')    := 'GLMS_FTR_SELECTION_ENABLE';","    v_setlst('GLMS_FTR_GEN_METHOD')   := 'GLMS_FTR_GEN_QUADRATIC';","    v_setlst('GLMS_FTR_GENERATION')   := 'GLMS_FTR_GENERATION_ENABLE'; ","    v_setlst('GLMS_FTR_SEL_CRIT')     := 'GLMS_FTR_SEL_AIC';","    v_setlst('GLMS_NUM_ITERATIONS')   := '50';","    v_setlst('GLMS_MAX_FEATURES')     := '30';","    v_setlst('GLMS_PRUNE_MODEL')      := 'GLMS_PRUNE_MODEL_ENABLE';","    v_setlst('GLMS_ROW_DIAGNOSTICS')  := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE')   := '0.0001';","    v_setlst('GLMS_BATCH_ROWS')       := '2000';","    v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_DELETE_ROW';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1715225410459,"interpreter":"script.low","endTime":1715225412257,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Using options for Ridge Regression","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')             := 'ON';","    v_setlst('ALGO_NAME')             := 'ALGO_GENERALIZED_LINEAR_MODEL';","    v_setlst('GLMS_DIAGNOSTICS_TABLE_NAME') := 'GLMR_SH_SAMPLE_DIAG';","    v_setlst('GLMS_RIDGE_REGRESSION') := 'GLMS_RIDGE_REG_ENABLE';","    v_setlst('GLMS_FTR_SELECTION')    := 'GLMS_FTR_SELECTION_DISABLE';","    v_setlst('GLMS_NUM_ITERATIONS')   := '50';","    v_setlst('GLMS_ROW_DIAGNOSTICS')  := 'GLMS_ROW_DIAG_DISABLE';","    v_setlst('GLMS_CONV_TOLERANCE')   := '0.0001';","    v_setlst('GLMS_BATCH_ROWS')       := '2000';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE2',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715225412337,"interpreter":"script.low","endTime":1715225414244,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display GLM model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","  FROM USER_MINING_MODEL_SETTINGS","  WHERE MODEL_NAME = 'GLMR_SH_REGR_SAMPLE2'","  ORDER BY SETTING_NAME;","  "],"enabled":true,"result":{"startTime":1715225414320,"interpreter":"sql.low","endTime":1715225414416,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_GENERALIZED_LINEAR_MODEL\nGLMS_BATCH_ROWS\t2000\nGLMS_CONF_LEVEL\t.95\nGLMS_CONV_TOLERANCE\t0.0001\nGLMS_DIAGNOSTICS_TABLE_NAME\tGLMR_SH_SAMPLE_DIAG\nGLMS_FTR_GENERATION\tGLMS_FTR_GENERATION_DISABLE\nGLMS_FTR_SELECTION\tGLMS_FTR_SELECTION_DISABLE\nGLMS_NUM_ITERATIONS\t50\nGLMS_RIDGE_REGRESSION\tGLMS_RIDGE_REG_ENABLE\nGLMS_RIDGE_VALUE\t.01\nGLMS_ROW_DIAGNOSTICS\tGLMS_ROW_DIAG_DISABLE\nGLMS_VIF_FOR_RIDGE\tGLMS_VIF_RIDGE_DISABLE\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display GLM model signature","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","  FROM USER_MINING_MODEL_ATTRIBUTES","  WHERE MODEL_NAME = 'GLMR_SH_REGR_SAMPLE2'","  ORDER BY ATTRIBUTE_NAME;","  "],"enabled":true,"result":{"startTime":1715225414494,"interpreter":"sql.low","endTime":1715225414569,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display list of different model views available","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","  FROM USER_MINING_MODEL_VIEWS","  WHERE MODEL_NAME='GLMR_SH_REGR_SAMPLE'","  ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1715225414650,"interpreter":"sql.low","endTime":1715225414747,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VAGLMR_SH_REGR_SAMPLE\tGLM Regression Row Diagnostics\nDM$VDGLMR_SH_REGR_SAMPLE\tGLM Regression Attribute Diagnostics\nDM$VGGLMR_SH_REGR_SAMPLE\tGlobal Name-Value Pairs\nDM$VNGLMR_SH_REGR_SAMPLE\tNormalization and Missing Value Handling\nDM$VSGLMR_SH_REGR_SAMPLE\tComputed Settings\nDM$VWGLMR_SH_REGR_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View GLM model summary statistics","message":["%sql ","","SELECT NAME, ROUND(NUMERIC_VALUE,8), STRING_VALUE ","  FROM DM$VGGLMR_SH_REGR_SAMPLE","  ORDER BY NAME;","  "],"enabled":true,"result":{"startTime":1715225414822,"interpreter":"sql.low","endTime":1715225414917,"results":[{"message":"NAME\tROUND(NUMERIC_VALUE,8)\tSTRING_VALUE\nADJUSTED_R_SQUARE\t0.57023929\t\nAIC\t1150.50695627\t\nCOEFF_VAR\t30.57334125\t\nCONVERGED\t\tYES\nCORRECTED_TOTAL_DF\t2699\t\nCORRECTED_TOT_SS\t9556.72740741\t\nDEPENDENT_MEAN\t4.03481481\t\nERROR_DF\t2683\t\nERROR_MEAN_SQUARE\t1.52171395\t\nERROR_SUM_SQUARES\t4082.7585205\t\nF_VALUE\t224.8274428\t\nGMSEP\t1.53135899\t\nHOCKING_SP\t0.00056738\t\nJ_P\t1.53129511\t\nMODEL_DF\t16\t\nMODEL_F_P_VALUE\t0\t\nMODEL_MEAN_SQUARE\t342.12305543\t\nMODEL_SUM_SQUARES\t5473.9688869\t\nNUM_PARAMS\t17\t\nNUM_ROWS\t2700\t\nROOT_MEAN_SQ\t1.2335777\t\nR_SQ\t0.57278696\t\nSBIC\t1250.82407615\t\nVALID_COVARIANCE_MATRIX\t\tYES\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View GLM model coefficient statistics","message":["%sql ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE, ROUND(COEFFICIENT,3) COEF, ","       ROUND(STD_ERROR,3) STD_ERROR, ROUND(TEST_STATISTIC,3) TEST_STATISTIC,","       ROUND(P_VALUE,3) P_VALUE, ROUND(STD_COEFFICIENT,3) STD_COEF, ","       ROUND(LOWER_COEFF_LIMIT,3) LOWER_COEF_LIMIT, ROUND(UPPER_COEFF_LIMIT,3) UPPER_COEF_LIMIT","  FROM DM$VDGLMR_SH_REGR_SAMPLE","  ORDER BY 1;","  "],"enabled":true,"result":{"startTime":1715225414998,"interpreter":"sql.low","endTime":1715225415120,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEF\tSTD_ERROR\tTEST_STATISTIC\tP_VALUE\tSTD_COEF\tLOWER_COEF_LIMIT\tUPPER_COEF_LIMIT\nAFFINITY_CARD\t\t0.5\t0.062\t8.036\t0\t0.114\t0.378\t0.621\nCUST_GENDER\tF\t-0.189\t0.058\t-3.251\t0.001\t-0.047\t-0.303\t-0.075\nCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-0.32\t0.088\t-3.658\t0\t-0.047\t-0.492\t-0.149\nCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t0.194\t0.095\t2.039\t0.042\t0.026\t0.007\t0.381\nCUST_MARITAL_STATUS\tWidowed\t-0.943\t0.148\t-6.377\t0\t-0.089\t-1.233\t-0.653\nCUST_YEAR_OF_BIRTH\t\t-0.079\t0.003\t-28.553\t0\t-0.566\t-0.084\t-0.073\nEDUCATION\t< Bach.\t-0.137\t0.058\t-2.348\t0.019\t-0.031\t-0.252\t-0.023\nEDUCATION\t11th\t-0.363\t0.151\t-2.413\t0.016\t-0.031\t-0.659\t-0.068\nHOUSEHOLD_SIZE\t4-5\t-0.607\t0.123\t-4.943\t0\t-0.069\t-0.847\t-0.366\nHOUSEHOLD_SIZE\t1\t-0.344\t0.079\t-4.377\t0\t-0.065\t-0.498\t-0.19\nOCCUPATION\tExec.\t0.226\t0.077\t2.934\t0.003\t0.039\t0.075\t0.378\nOCCUPATION\tSales\t0.164\t0.073\t2.248\t0.025\t0.029\t0.021\t0.308\nOCCUPATION\t?\t-0.345\t0.112\t-3.072\t0.002\t-0.04\t-0.565\t-0.125\nOCCUPATION\tTransp.\t0.381\t0.119\t3.19\t0.001\t0.041\t0.147\t0.615\nOCCUPATION\tTechSup\t-0.346\t0.121\t-2.855\t0.004\t-0.037\t-0.583\t-0.108\nY_BOX_GAMES\t\t-0.535\t0.08\t-6.655\t0\t-0.131\t-0.693\t-0.378\n\t\t158.878\t5.399\t29.428\t0\t0\t148.292\t169.465\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show model features and p-values","message":["%sql ","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE, ROUND(COEFFICIENT,3) COEFFICIENT, ROUND(P_VALUE, 3) P_VALUE","  FROM DM$VDGLMR_SH_REGR_SAMPLE","  ORDER BY FEATURE_EXPRESSION;"],"enabled":true,"result":{"startTime":1715225415224,"interpreter":"sql.low","endTime":1715225415302,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_VALUE\tCOEFFICIENT\tP_VALUE\n\t\t158.878\t0\nHOUSEHOLD_SIZE\t4-5\t-0.607\t0\nY_BOX_GAMES\t\t-0.535\t0\nAFFINITY_CARD\t\t0.5\t0\nHOUSEHOLD_SIZE\t1\t-0.344\t0\nCUST_GENDER\tF\t-0.189\t0.001\nEDUCATION\t< Bach.\t-0.137\t0.019\nOCCUPATION\tExec.\t0.226\t0.003\nOCCUPATION\t?\t-0.345\t0.002\nCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t0.194\t0.042\nCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t-0.32\t0\nOCCUPATION\tSales\t0.164\t0.025\nOCCUPATION\tTransp.\t0.381\t0.001\nCUST_MARITAL_STATUS\tWidowed\t-0.943\t0\nOCCUPATION\tTechSup\t-0.346\t0.004\nEDUCATION\t11th\t-0.363\t0.016\nCUST_YEAR_OF_BIRTH\t\t-0.079\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply GLM model to predict YRS_RESIDENCE with Prediction_Details","message":["%sql","","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","       round(PRED_LOWER_LIMIT,3) LOWER_PRED,","       round(PRED_UPPER_LIMIT,3) HIGHER_PRED,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') 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\"Attribute4\" XMLType PATH 'Attribute[4]') ","       OUTPRED","FETCH FIRST 10 ROWS ONLY; ","       "],"enabled":true,"result":{"startTime":1715225415381,"interpreter":"sql.low","endTime":1715225415758,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tLOWER_PRED\tHIGHER_PRED\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\tFOURTH_ATTRIBUTE\n100002\t4.29\t4.173\t4.407\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".3\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".243\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.178\" \t\"CUST_GENDER\" actualValue=\"F\" weight=\"-.278\" \n100004\t5.025\t4.84\t5.211\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1953\" weight=\".54\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".098\" \t\"OCCUPATION\" actualValue=\"Sales\" weight=\".097\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.072\" \n100005\t5.372\t5.259\t5.484\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1957\" weight=\".523\" \t\"AFFINITY_CARD\" actualValue=\"1\" weight=\".332\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".145\" \t\n100008\t4.341\t4.208\t4.475\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1962\" weight=\".325\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".263\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.193\" \t\"EDUCATION\" actualValue=\"&lt; Bach.\" weight=\"-.219\" \n100013\t4.938\t4.707\t5.169\t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".401\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1961\" weight=\".298\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".174\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\"-.127\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show GLM model predictions vs actuals","message":["%sql","","SELECT CUST_ID,","    round(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","FROM TEST_DATA_V","WHERE CUST_ID <= 100030","ORDER BY CUST_ID;","  "],"enabled":true,"result":{"startTime":1715225415837,"interpreter":"sql.low","endTime":1715225415938,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\n100002\t4.3\t4\n100004\t5\t5\n100005\t5.4\t5\n100008\t4.3\t5\n100013\t4.9\t5\n100015\t4.8\t6\n100016\t4\t4\n100019\t4.8\t4\n100021\t4.5\t4\n100025\t2.6\t3\n100027\t3.9\t3\n100029\t4.6\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model quality statistics on the TEST data","message":["%sql","","select ROUND(R2,3) COEF_OF_DETERMINATION_R2, ","       ROUND(MAE,3) MEAN_ABSOLUTE_ERROR, ","       ROUND(MSE,3) MEAN_SQUARED_ERROR, ","       ROUND(SQRT(MSE),3) ROOT_MEAN_SQUARED_ERROR","       FROM ","       (SELECT AVG(ABS(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE)) MAE,","               AVG(POWER(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE,2)) MSE,","               1-(SUM(POWER(ACTUAL_YRS_RESIDENCE - PRED_YRS_RESIDENCE,2)))/(SUM(POWER(ACTUAL_YRS_RESIDENCE - ","                 (SELECT AVG(ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1)) FROM TEST_DATA_V),2))) R2              ","                 FROM (SELECT CUST_ID,","                            ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","                            YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","                       FROM TEST_DATA_V))","                       "],"enabled":true,"result":{"startTime":1715225416023,"interpreter":"sql.low","endTime":1715225416143,"results":[{"message":"COEF_OF_DETERMINATION_R2\tMEAN_ABSOLUTE_ERROR\tMEAN_SQUARED_ERROR\tROOT_MEAN_SQUARED_ERROR\n0.547\t0.872\t1.69\t1.3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"ACTUAL_YRS_RESIDENCE\"],\"label\":[],\"extraDescription\":[],\"showLabel\":\"no\",\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"ACTUAL_YRS_RESIDENCE\",\"HOUSEHOLD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot GLM model predictions vs actuals grouped by HOUSE_HOLD_SIZE","message":["%sql","","SELECT CUST_ID,","    ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE, ","    CASE HOUSEHOLD_SIZE WHEN '1' THEN '1'","    WHEN '2' THEN '2'","    ELSE '3 or more' END HOUSEHOLD","FROM TEST_DATA_V","FETCH FIRST 100 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1715227010772,"interpreter":"sql.medium","endTime":1715227013377,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\tHOUSEHOLD\n100022\t4.7\t5\t3 or more\n100033\t5\t5\t3 or more\n100036\t5.7\t6\t3 or more\n100053\t4.4\t5\t3 or more\n100057\t5.1\t4\t3 or more\n100075\t4.4\t4\t3 or more\n100086\t1.3\t1\t3 or more\n100097\t3.2\t3\t3 or more\n100100\t4.7\t4\t3 or more\n100101\t3.5\t3\t3 or more\n100128\t3.6\t3\t3 or more\n100131\t2.8\t3\t2\n100137\t4.1\t2\t3 or more\n100138\t5.5\t4\t2\n100161\t6.7\t10\t2\n100170\t4.1\t3\t3 or more\n100175\t3.2\t3\t3 or more\n100179\t6.4\t7\t3 or more\n100190\t5.5\t5\t3 or more\n100222\t3.2\t4\t2\n100225\t2.9\t2\t3 or more\n100248\t2.9\t3\t2\n100265\t4.9\t4\t2\n100275\t4.8\t4\t2\n100277\t5.9\t6\t3 or more\n100283\t5.6\t6\t3 or more\n100286\t4.5\t4\t3 or more\n100289\t5.1\t5\t3 or more\n100303\t3.7\t4\t2\n100350\t5.5\t5\t3 or more\n100356\t2.2\t2\t3 or more\n100389\t3.5\t4\t3 or more\n100394\t4.2\t5\t3 or more\n100400\t6.2\t6\t3 or more\n100421\t3.4\t4\t3 or more\n100434\t4.3\t4\t2\n100446\t6\t5\t3 or more\n100454\t1.5\t2\t1\n100467\t3.1\t3\t2\n100476\t6.3\t6\t3 or more\n100492\t1.6\t2\t1\n100494\t6\t5\t3 or more\n100514\t2.1\t2\t1\n100532\t6.3\t6\t3 or more\n100580\t4.8\t5\t2\n100582\t3.7\t3\t3 or more\n100601\t6\t5\t3 or more\n100621\t5.7\t5\t3 or more\n100628\t3.8\t4\t3 or more\n100645\t5.1\t5\t2\n100649\t2.6\t2\t1\n100653\t4.8\t5\t3 or more\n100655\t4.6\t4\t3 or more\n100665\t5.7\t6\t3 or more\n100729\t4.3\t4\t3 or more\n100734\t3.2\t3\t1\n100747\t3.3\t3\t3 or more\n100753\t3.6\t3\t3 or more\n100760\t5.5\t4\t3 or more\n100819\t1.5\t1\t1\n100834\t3.6\t3\t3 or more\n100863\t3.3\t3\t3 or 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"],"enabled":true,"result":{"startTime":1715225416400,"interpreter":"sql.low","endTime":1715225416523,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tRESIDUALS\n100002\t4.3\t0.3\n100004\t5\t0\n100005\t5.4\t0.4\n100008\t4.3\t-0.7\n100013\t4.9\t-0.1\n100015\t4.8\t-1.2\n100016\t4\t0\n100019\t4.8\t0.8\n100021\t4.5\t0.5\n100025\t2.6\t-0.4\n100027\t3.9\t0.9\n100029\t4.6\t-0.4\n100032\t4.2\t0.2\n100034\t4.9\t0.9\n100035\t3.3\t-0.7\n100041\t1.8\t-0.2\n100043\t2.3\t0.3\n100046\t4.7\t-0.3\n100050\t2.3\t0.3\n100052\t5.6\t-0.4\n100054\t4\t0\n100055\t2\t1\n100058\t4.4\t-1.6\n100059\t6\t0\n100061\t4.4\t0.4\n100064\t5.4\t0.4\n100066\t5.4\t-0.6\n100067\t4.1\t0.1\n100068\t6.8\t0.8\n100071\t3.4\t-0.6\n100073\t5.2\t1.2\n100076\t1.9\t-0.1\n100077\t3.8\t-0.2\n100080\t2.8\t-0.2\n100084\t3\t0\n100086\t2\t1\n100087\t3.8\t-1.2\n100090\t4.6\t0.6\n100091\t4.4\t0.4\n100093\t5.8\t-1.2\n100094\t5.4\t0.4\n100095\t3.8\t-0.2\n100096\t4.6\t-2.4\n100097\t3.7\t0.7\n100100\t4.4\t0.4\n100102\t4.2\t0.2\n100105\t2.5\t-0.5\n100106\t3.8\t-0.2\n100107\t5.7\t-0.3\n100111\t4.1\t2.1\n100114\t2.6\t0.6\n100115\t1.8\t-0.2\n100116\t3.6\t-0.4\n100118\t5.7\t-0.3\n100120\t4.5\t-0.5\n100121\t2.9\t-1.1\n100124\t6\t3\n100127\t1.9\t-0.1\n100129\t2.3\t1.3\n100131\t2.7\t-0.3\n100141\t2.3\t-0.7\n100145\t2.3\t-0.7\n100146\t5.2\t-0.8\n100151\t4.5\t-0.5\n100153\t1.9\t-1.1\n100155\t2.8\t-0.2\n100157\t1.2\t0.2\n100160\t3.3\t0.3\n100162\t5\t0\n100163\t7.3\t1.3\n100164\t6.4\t-2.6\n100165\t4.5\t0.5\n100166\t4.4\t0.4\n100167\t2\t1\n100168\t2.5\t-0.5\n100174\t5.7\t-0.3\n100177\t1.7\t0.7\n100180\t2\t-1\n100181\t2.1\t1.1\n100184\t4.3\t0.3\n100186\t1.4\t0.4\n100188\t1.5\t0.5\n100190\t5\t0\n100199\t6.2\t4.2\n100205\t2.2\t0.2\n100206\t2.9\t-2.1\n100212\t1.8\t-0.2\n100217\t4.9\t-3.1\n100222\t3.5\t-0.5\n100223\t2.6\t-0.4\n100224\t5.8\t-2.2\n100226\t1.9\t-0.1\n100228\t2.6\t0.6\n100231\t2.2\t0.2\n100232\t3.9\t0.9\n100233\t5.7\t-0.3\n100235\t7.2\t0.2\n100238\t3.6\t0.6\n100247\t3.4\t-0.6\n100250\t4.1\t0.1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Another option: create Regression Model using Settings Table","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715225416602,"interpreter":"md.low","endTime":1715225416661,"results":[{"message":"<h2 id=\"another-option-create-regression-model-using-settings-table\">Another option: create Regression Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create GLM model settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLMR_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","-- Drop any previous Model Diagnostics table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLMR_DIAG';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE GLMR_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1715225416739,"interpreter":"script.low","endTime":1715225416886,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable GLMR_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings for automated data preparation and feature selection","message":["%script ","","BEGIN ","    INSERT INTO GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_GENERALIZED_LINEAR_MODEL);","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- output row diagnostic statistics into a table named GLMR_SH_SAMPLE_DIAG  ","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_DIAGNOSTICS_TABLE_NAME, 'GLMR_SH_SAMPLE_DIAG');  ","    ","-- turn ridge regression on or off. By default the system turns it on if there is a multicollinearity.  Ridge regression cannot be used with feature selection and feature generation, so we will keep it off in this example","     INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_RIDGE_REGRESSION, 'GLMS_RIDGE_REG_DISABLE');    ","     ","-- turn on feature selection. By default the system does not do feature selection. Some options for feature selection are show below too","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_SELECTION, DBMS_DATA_MINING.GLMS_FTR_SELECTION_ENABLE);","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_MAX_FEATURES, '30');","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_PRUNE_MODEL, DBMS_DATA_MINING.GLMS_PRUNE_MODEL_ENABLE);  ","    ","-- turn on feature generation. We will do Quadratic features, but Cubic is also available. Feature generation requires feature selection to be enabled.","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GENERATION, DBMS_DATA_MINING.GLMS_FTR_GENERATION_ENABLE);","    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GEN_METHOD, DBMS_DATA_MINING.GLMS_FTR_GEN_QUADRATIC);","--    INSERT INTO  GLMR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.GLMS_FTR_GEN_METHOD, DBMS_DATA_MINING.GLMS_FTR_GEN_CUBIC);","    ","END;"],"enabled":true,"result":{"startTime":1715225416973,"interpreter":"script.low","endTime":1715225417073,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a GLM regression model for predicting YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'GLMR_SH_REGR_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.REGRESSION,","    DATA_TABLE_NAME     => 'TRAIN_DATA_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","    SETTINGS_TABLE_NAME => 'GLMR_SETTINGS',","    XFORM_LIST          => V_XLST);","END;"],"enabled":true,"result":{"startTime":1715225417160,"interpreter":"script.low","endTime":1715225418963,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715225419039,"interpreter":"md.low","endTime":1715225419098,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715225419177,"interpreter":"md.low","endTime":1715225419241,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression NN","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715229463230,"interpreter":"md.medium","endTime":1715229463293,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Regression Modeling to Predict Numerical Values using Neural Networks","This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Neural Networks algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715229463370,"interpreter":"md.medium","endTime":1715229463438,"results":[{"message":"<h2 id=\"regression-modeling-to-predict-numerical-values-using-neural-networks\">Regression Modeling to Predict Numerical Values using Neural Networks<\/h2>\n<p>This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Neural Networks algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715229463514,"interpreter":"md.medium","endTime":1715229463576,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a>"],"enabled":true,"result":{"startTime":1715229463667,"interpreter":"md.medium","endTime":1715229463806,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-neural-network\" target=\"_blank\">OML Neural Network<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1715229463988,"interpreter":"script.medium","endTime":1715229464717,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V AS SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V AS ","  SELECT * FROM CUSTOMERS360 ","  MINUS ","  SELECT * FROM TRAIN_DATA_V;"],"enabled":true,"result":{"startTime":1715229464793,"interpreter":"script.medium","endTime":1715229464915,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a Neural Network regression model to predict YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1715229464994,"interpreter":"md.medium","endTime":1715229465053,"results":[{"message":"<h3 id=\"build-a-neural-network-regression-model-to-predict-yrs_residence\">Build a Neural Network regression model to predict YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons and default activation function","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS')   := '1';","    v_setlst('NNET_NODES_PER_LAYER') := '4';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715229465128,"interpreter":"script.medium","endTime":1715229467626,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for Neural Network","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';","   ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","","* Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  `NNET_ACTIVATIONS_ARCTAN`, `NNET_ACTIVATIONS_BIPOLAR_SIG`, `NNET_ACTIVATIONS_LINEAR`, `NNET_ACTIVATIONS_LOG_SIG`, `NNET_ACTIVATIONS_TANH` and <strong>starting from Oracle Database 23ai<\/strong> `NNET_ACTIVATIONS_RELU`.","> v_setlst('NNET_HIDDEN_LAYERS') := '3';","","> v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","","> v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';","    ","* Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","> v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","","> v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","","> v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    ","* Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","> v_setlst('NNET_REG_LAMBDA') := '1';","","* Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","> v_setlst('NNET_ITERATIONS') := '200';","","> v_setlst('NNET_TOLERANCE') := '0.000001';","","> v_setlst('ODMS_RANDOM_SEED') := '0';","","* Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","    v_setlst('LBFGS_HISTORY_DEPTH') := '10';","","* Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9. ","> v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';","","* Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';","","> v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';","","","* Specify the lower bound of the region where weights are randomly initialized. `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` must be set together. Setting one and not setting the other raises an error. `NNET_WEIGHT_LOWER_BOUND` must not be greater than `NNET_WEIGHT_UPPER_BOUND`. The default value is -sqrt(6/(l_nodes+r_nodes)). ","   The value of l_nodes for `input layer dense attributes` is (1+number of dense attributes), `input layer sparse attributes` is number of sparse attributes, and `each hidden layer` is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.","   If not specified, the values of `NNET_WEIGHT_LOWER_BOUND` and `NNET_WEIGHT_UPPER_BOUND` are system determined.","","> v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'","","> v_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'","","    "],"enabled":true,"result":{"startTime":1715350004549,"interpreter":"md.medium","endTime":1715350004752,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-neural-network\">Examples of possible setting overrides for Neural Network<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-7793F608-2719-45EA-87F9-6F246BA800D4\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes &gt; 50).  Be careful with quotes around NNET_ACTIVATIONS. The options for the activation functions are one or more of the following strings:  <code>NNET_ACTIVATIONS_ARCTAN<\/code>, <code>NNET_ACTIVATIONS_BIPOLAR_SIG<\/code>, <code>NNET_ACTIVATIONS_LINEAR<\/code>, <code>NNET_ACTIVATIONS_LOG_SIG<\/code>, <code>NNET_ACTIVATIONS_TANH<\/code> and <strong>starting from Oracle Database 23ai<\/strong> <code>NNET_ACTIVATIONS_RELU<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_HIDDEN_LAYERS') := '3';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_HELDASIDE_RATIO') := '0.25';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_REG_LAMBDA') := '1';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_ITERATIONS') := '200';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_TOLERANCE') := '0.000001';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('ODMS_RANDOM_SEED') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10.\nv_setlst('LBFGS_HISTORY_DEPTH') := '10';<\/p>\n<\/li>\n<li>\n<p>Specify the gradient infinity norm tolerance for L-BFGS. The default value is 1E-9.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the lower bound of the region where weights are randomly initialized. <code>NNET_WEIGHT_LOWER_BOUND<\/code> and <code>NNET_WEIGHT_UPPER_BOUND<\/code> must be set together. Setting one and not setting the other raises an error. <code>NNET_WEIGHT_LOWER_BOUND<\/code> must not be greater than <code>NNET_WEIGHT_UPPER_BOUND<\/code>. The default value is -sqrt(6/(l_nodes+r_nodes)).\nThe value of l_nodes for <code>input layer dense attributes<\/code> is (1+number of dense attributes), <code>input layer sparse attributes<\/code> is number of sparse attributes, and <code>each hidden layer<\/code> is (1+number of nodes in that hidden layer).  The value of r_nodes is the number of nodes in the layer that the weight is connecting to.\nIf not specified, the values of <code>NNET_WEIGHT_LOWER_BOUND<\/code> and <code>NNET_WEIGHT_UPPER_BOUND<\/code> are system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('NNET_WEIGHT_LOWER_BOUND') := 'a real number'<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('NNET_WEIGHT_UPPER_BOUND') := 'a real number'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use a single layer with 4 neurons","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS') := '1';","    v_setlst('NNET_NODES_PER_LAYER') := '4';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_LOG_SIG''';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1715229467863,"interpreter":"script.medium","endTime":1715229471562,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 8,8,2 neurons","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS') := '3';","    v_setlst('NNET_NODES_PER_LAYER') := '8,8,2';","    v_setlst('NNET_ACTIVATIONS') := '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''';","    v_setlst('NNET_REGULARIZER') := 'NNET_REGULARIZER_HELDASIDE';","    v_setlst('NNET_HELDASIDE_RATIO') := '0.25';","    v_setlst('NNET_HELDASIDE_MAX_FAIL') := '6';","    v_setlst('NNET_ITERATIONS') := '200';","    v_setlst('NNET_TOLERANCE') := '0.000001';","    v_setlst('ODMS_RANDOM_SEED') := '0';","    v_setlst('LBFGS_HISTORY_DEPTH') := '10';","    v_setlst('LBFGS_SCALE_HESSIAN') := 'LBFGS_SCALE_HESSIAN_ENABLE';","    v_setlst('LBFGS_GRADIENT_TOLERANCE') := '0.0000001';","","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715229471633,"interpreter":"script.medium","endTime":1715229474135,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Use 3 layers with 16,8,4 neurons ","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO')            := 'ON';","    v_setlst('ALGO_NAME')            := 'ALGO_NEURAL_NETWORK';","    v_setlst('NNET_HIDDEN_LAYERS')   := '3';","    v_setlst('NNET_NODES_PER_LAYER') := '16,8,4';","    v_setlst('NNET_ACTIVATIONS')     := '''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_LOG_SIG''';","    ","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715229474221,"interpreter":"script.medium","endTime":1715229478127,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display NN model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","  FROM USER_MINING_MODEL_SETTINGS","  WHERE MODEL_NAME = 'NN_SH_REGR_SAMPLE'","  ORDER BY SETTING_NAME;","  "],"enabled":true,"result":{"startTime":1715229478204,"interpreter":"sql.medium","endTime":1715229478280,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_NEURAL_NETWORK\nLBFGS_GRADIENT_TOLERANCE\t.000000001\nLBFGS_HISTORY_DEPTH\t20\nLBFGS_SCALE_HESSIAN\tLBFGS_SCALE_HESSIAN_ENABLE\nNNET_ACTIVATIONS\t'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_LOG_SIG', 'NNET_ACTIVATIONS_LOG_SIG'\nNNET_HIDDEN_LAYERS\t3\nNNET_ITERATIONS\t200\nNNET_NODES_PER_LAYER\t16,8,4\nNNET_TOLERANCE\t.000001\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_RANDOM_SEED\t0\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display NN model signature","message":["%sql","","SELECT ATTRIBUTE_NAME, ATTRIBUTE_TYPE","FROM   USER_MINING_MODEL_ATTRIBUTES","WHERE  MODEL_NAME = 'NN_SH_REGR_SAMPLE'","ORDER BY ATTRIBUTE_NAME;","  "],"enabled":true,"result":{"startTime":1715229478356,"interpreter":"sql.medium","endTime":1715229478430,"results":[{"message":"ATTRIBUTE_NAME\tATTRIBUTE_TYPE\nAFFINITY_CARD\tNUMERICAL\nCUST_CREDIT_LIMIT\tNUMERICAL\nCUST_GENDER\tCATEGORICAL\nCUST_INCOME_LEVEL\tCATEGORICAL\nCUST_MARITAL_STATUS\tCATEGORICAL\nCUST_YEAR_OF_BIRTH\tNUMERICAL\nEDUCATION\tCATEGORICAL\nHOUSEHOLD_SIZE\tCATEGORICAL\nOCCUPATION\tCATEGORICAL\nYRS_RESIDENCE\tNUMERICAL\nY_BOX_GAMES\tNUMERICAL\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display list of available model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='NN_SH_REGR_SAMPLE'","ORDER BY VIEW_NAME;","  "],"enabled":true,"result":{"startTime":1715229478507,"interpreter":"sql.medium","endTime":1715229478599,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VANN_SH_REGR_SAMPLE\tNeural Network Weights\nDM$VGNN_SH_REGR_SAMPLE\tGlobal Name-Value Pairs\nDM$VNNN_SH_REGR_SAMPLE\tNormalization and Missing Value Handling\nDM$VSNN_SH_REGR_SAMPLE\tComputed Settings\nDM$VWNN_SH_REGR_SAMPLE\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%script"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":null},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View NN global statistics","message":["%sql ","","SELECT NAME, NUMERIC_VALUE, STRING_VALUE","FROM   DM$VGNN_SH_REGR_SAMPLE"],"enabled":true,"result":{"startTime":1715229478676,"interpreter":"sql.medium","endTime":1715229478770,"results":[{"message":"NAME\tNUMERIC_VALUE\tSTRING_VALUE\nNUM_ROWS\t2700\t\nITERATIONS\t200\t\nLOSS_VALUE\t0.091607218124184112\t\nCONVERGED\t\tNO\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"View NN attribute weights (not importance)","message":["%sql ","","SELECT LAYER, IDX_FROM, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, ","       IDX_TO, ROUND(WEIGHT,6) WEIGHT","FROM   DM$VANN_SH_REGR_SAMPLE","  "],"enabled":true,"result":{"startTime":1715229478849,"interpreter":"sql.medium","endTime":1715229479281,"results":[{"message":"LAYER\tIDX_FROM\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tIDX_TO\tWEIGHT\n0\t0\tAFFINITY_CARD\t\t2\t-0.700846\n0\t0\tAFFINITY_CARD\t\t14\t-4.400739\n0\t1\tCUST_CREDIT_LIMIT\t\t0\t-0.218048\n0\t1\tCUST_CREDIT_LIMIT\t\t9\t0.429131\n0\t1\tCUST_CREDIT_LIMIT\t\t12\t-2.040309\n0\t2\tCUST_YEAR_OF_BIRTH\t\t1\t-5.996758\n0\t2\tCUST_YEAR_OF_BIRTH\t\t5\t-2.533373\n0\t2\tCUST_YEAR_OF_BIRTH\t\t6\t11.395877\n0\t3\tY_BOX_GAMES\t\t1\t2.798235\n0\t3\tY_BOX_GAMES\t\t2\t-0.301737\n0\t3\tY_BOX_GAMES\t\t5\t0.344525\n0\t3\tY_BOX_GAMES\t\t14\t6.106965\n0\t4\tCUST_GENDER\tF\t0\t2.209274\n0\t4\tCUST_GENDER\tF\t7\t0.075032\n0\t4\tCUST_GENDER\tF\t8\t0.04749\n0\t4\tCUST_GENDER\tF\t15\t0.009701\n0\t5\tCUST_GENDER\tM\t10\t1.716043\n0\t5\tCUST_GENDER\tM\t13\t2.103473\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t2\t0.672609\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t8\t-2.004071\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t12\t0.13021\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t5\t0.305598\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t8\t0.280355\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t10\t-0.435015\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t15\t2.010618\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t7\t0.959979\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t8\t0.202981\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t10\t1.577521\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t11\t1.325173\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t1\t-1.227925\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t5\t-0.329476\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t12\t0.103534\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t13\t-0.329378\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t0\t2.632105\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t2\t-0.450904\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t10\t-1.899908\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t13\t-1.66221\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t3\t-0.84749\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t4\t1.996669\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t7\t-0.252075\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t14\t5.091374\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t2\t0.54676\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t10\t2.132803\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t11\t1.747881\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t1\t-0.963531\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t2\t-0.615778\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t4\t0.07912\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t5\t0.628663\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t12\t0.347468\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t14\t-1.728864\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t15\t0.995964\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t2\t-0.0815\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t8\t-0.253754\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t11\t0.59143\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t9\t1.003162\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t14\t-0.809032\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t4\t-2.083687\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t9\t-1.025006\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t15\t-1.628378\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t1\t0.969954\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t5\t-0.121339\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t10\t0.860695\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t10\t1.317044\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t14\t2.929526\n0\t21\tCUST_MARITAL_STATUS\tMarried\t5\t0.765216\n0\t21\tCUST_MARITAL_STATUS\tMarried\t7\t0.522983\n0\t21\tCUST_MARITAL_STATUS\tMarried\t10\t-1.145516\n0\t21\tCUST_MARITAL_STATUS\tMarried\t12\t1.325057\n0\t21\tCUST_MARITAL_STATUS\tMarried\t15\t-0.335538\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t1\t2.302338\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t11\t1.807776\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t13\t3.73105\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t3\t-0.035358\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t11\t1.187732\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t12\t-0.623874\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t15\t0.49881\n0\t25\tEDUCATION\t10th\t2\t0.296811\n0\t25\tEDUCATION\t10th\t3\t-0.680102\n0\t25\tEDUCATION\t10th\t12\t-1.290809\n0\t25\tEDUCATION\t10th\t14\t2.604749\n0\t26\tEDUCATION\t11th\t8\t-2.167163\n0\t26\tEDUCATION\t11th\t12\t0.43095\n0\t26\tEDUCATION\t11th\t14\t-2.793573\n0\t27\tEDUCATION\t12th\t11\t1.082673\n0\t27\tEDUCATION\t12th\t12\t0.039849\n0\t28\tEDUCATION\t1st-4th\t9\t-0.863035\n0\t28\tEDUCATION\t1st-4th\t13\t-0.525154\n0\t28\tEDUCATION\t1st-4th\t14\t-0.190025\n0\t30\tEDUCATION\t7th-8th\t2\t0.901652\n0\t30\tEDUCATION\t7th-8th\t9\t-0.420859\n0\t30\tEDUCATION\t7th-8th\t14\t-1.027651\n0\t31\tEDUCATION\t9th\t3\t-0.384842\n0\t31\tEDUCATION\t9th\t5\t-0.170614\n0\t31\tEDUCATION\t9th\t12\t-0.353933\n0\t32\tEDUCATION\t< Bach.\t0\t-0.93394\n0\t32\tEDUCATION\t< Bach.\t2\t0.976095\n0\t32\tEDUCATION\t< Bach.\t4\t0.930162\n0\t32\tEDUCATION\t< Bach.\t9\t0.74795\n0\t32\tEDUCATION\t< Bach.\t10\t-0.845594\n0\t33\tEDUCATION\tAssoc-A\t3\t0.893084\n0\t33\tEDUCATION\tAssoc-A\t6\t-2.096904\n0\t34\tEDUCATION\tAssoc-V\t3\t-1.376259\n0\t34\tEDUCATION\tAssoc-V\t10\t0.486891\n0\t34\tEDUCATION\tAssoc-V\t11\t0.743791\n0\t34\tEDUCATION\tAssoc-V\t12\t1.134073\n0\t34\tEDUCATION\tAssoc-V\t14\t-2.569289\n0\t35\tEDUCATION\tBach.\t13\t2.634177\n0\t36\tEDUCATION\tHS-grad\t1\t1.426372\n0\t36\tEDUCATION\tHS-grad\t6\t3.033146\n0\t36\tEDUCATION\tHS-grad\t8\t0.389384\n0\t37\tEDUCATION\tMasters\t4\t-1.967019\n0\t37\tEDUCATION\tMasters\t14\t-0.842314\n0\t38\tEDUCATION\tPhD\t13\t1.263002\n0\t39\tEDUCATION\tPresch.\t8\t-0.228082\n0\t40\tEDUCATION\tProfsc\t5\t0.51109\n0\t40\tEDUCATION\tProfsc\t10\t-0.812652\n0\t40\tEDUCATION\tProfsc\t13\t-1.264251\n0\t41\tHOUSEHOLD_SIZE\t1\t3\t-1.157198\n0\t41\tHOUSEHOLD_SIZE\t1\t7\t-1.470175\n0\t41\tHOUSEHOLD_SIZE\t1\t10\t1.194299\n0\t41\tHOUSEHOLD_SIZE\t1\t12\t-0.771096\n0\t42\tHOUSEHOLD_SIZE\t2\t4\t0.906445\n0\t42\tHOUSEHOLD_SIZE\t2\t5\t-0.372542\n0\t42\tHOUSEHOLD_SIZE\t2\t7\t-0.842413\n0\t43\tHOUSEHOLD_SIZE\t3\t2\t-0.667147\n0\t43\tHOUSEHOLD_SIZE\t3\t6\t0.816735\n0\t43\tHOUSEHOLD_SIZE\t3\t9\t0.901231\n0\t43\tHOUSEHOLD_SIZE\t3\t12\t2.196839\n0\t44\tHOUSEHOLD_SIZE\t4-5\t8\t-1.138122\n0\t45\tHOUSEHOLD_SIZE\t6-8\t7\t0.989246\n0\t45\tHOUSEHOLD_SIZE\t6-8\t10\t-0.252918\n0\t45\tHOUSEHOLD_SIZE\t6-8\t13\t0.040106\n0\t46\tHOUSEHOLD_SIZE\t9+\t3\t0.163683\n0\t47\tOCCUPATION\t?\t3\t0.785957\n0\t47\tOCCUPATION\t?\t6\t-0.173764\n0\t47\tOCCUPATION\t?\t8\t1.720932\n0\t47\tOCCUPATION\t?\t10\t-1.777847\n0\t48\tOCCUPATION\tArmed-F\t4\t0.121367\n0\t48\tOCCUPATION\tArmed-F\t7\t-0.115962\n0\t48\tOCCUPATION\tArmed-F\t8\t-0.016643\n0\t49\tOCCUPATION\tCleric.\t8\t-1.959388\n0\t49\tOCCUPATION\tCleric.\t11\t0.222894\n0\t50\tOCCUPATION\tCrafts\t6\t-1.086986\n0\t52\tOCCUPATION\tFarming\t13\t-0.889169\n0\t52\tOCCUPATION\tFarming\t14\t-2.43001\n0\t53\tOCCUPATION\tHandler\t7\t-1.315418\n0\t53\tOCCUPATION\tHandler\t14\t0.619485\n0\t54\tOCCUPATION\tHouse-s\t2\t0.26754\n0\t54\tOCCUPATION\tHouse-s\t3\t-0.064307\n0\t55\tOCCUPATION\tMachine\t4\t1.877282\n0\t55\tOCCUPATION\tMachine\t7\t0.831606\n0\t55\tOCCUPATION\tMachine\t9\t-1.116366\n0\t56\tOCCUPATION\tOther\t0\t3.436013\n0\t56\tOCCUPATION\tOther\t1\t-0.801045\n0\t56\tOCCUPATION\tOther\t3\t-0.781965\n0\t56\tOCCUPATION\tOther\t6\t-0.620598\n0\t56\tOCCUPATION\tOther\t11\t0.744104\n0\t56\tOCCUPATION\tOther\t15\t1.19346\n0\t57\tOCCUPATION\tProf.\t6\t-1.477155\n0\t57\tOCCUPATION\tProf.\t15\t0.027011\n0\t58\tOCCUPATION\tProtec.\t4\t1.188083\n0\t58\tOCCUPATION\tProtec.\t8\t-0.9624\n0\t58\tOCCUPATION\tProtec.\t11\t0.452069\n0\t58\tOCCUPATION\tProtec.\t15\t1.324636\n0\t59\tOCCUPATION\tSales\t4\t-0.510276\n0\t59\tOCCUPATION\tSales\t11\t-2.098727\n0\t59\tOCCUPATION\tSales\t13\t-1.847036\n0\t60\tOCCUPATION\tTechSup\t0\t1.255457\n0\t60\tOCCUPATION\tTechSup\t2\t1.367339\n0\t60\tOCCUPATION\tTechSup\t6\t0.954805\n0\t60\tOCCUPATION\tTechSup\t13\t0.899182\n0\t60\tOCCUPATION\tTechSup\t15\t0.677904\n0\t61\tOCCUPATION\tTransp.\t1\t-1.805707\n0\t61\tOCCUPATION\tTransp.\t6\t-0.336517\n0\t61\tOCCUPATION\tTransp.\t9\t1.569981\n0\t61\tOCCUPATION\tTransp.\t15\t0.398235\n0\t\t\t\t1\t0.85258\n0\t\t\t\t4\t2.501273\n0\t\t\t\t8\t-0.179799\n1\t0\t\t\t3\t1.382009\n1\t1\t\t\t0\t-0.988086\n1\t1\t\t\t2\t-1.692239\n1\t2\t\t\t2\t-1.124555\n1\t3\t\t\t1\t0.175172\n1\t3\t\t\t4\t-1.246933\n1\t4\t\t\t7\t-2.642747\n1\t5\t\t\t2\t0.912559\n1\t7\t\t\t1\t-1.947787\n1\t8\t\t\t1\t-0.617376\n1\t8\t\t\t3\t-1.337381\n1\t9\t\t\t4\t1.639032\n1\t9\t\t\t5\t1.887058\n1\t10\t\t\t2\t-0.545766\n1\t10\t\t\t5\t-5.676251\n1\t10\t\t\t6\t0.100306\n1\t12\t\t\t3\t1.193946\n1\t13\t\t\t5\t1.940201\n1\t14\t\t\t5\t-2.710019\n1\t15\t\t\t0\t0.497674\n1\t15\t\t\t6\t0.632413\n1\t15\t\t\t7\t1.876518\n1\t\t\t\t7\t1.872469\n2\t0\t\t\t0\t-1.813192\n2\t1\t\t\t1\t-2.482008\n2\t5\t\t\t1\t3.029949\n3\t3\t\t\t0\t3.222627\n0\t0\tAFFINITY_CARD\t\t5\t1.962951\n0\t0\tAFFINITY_CARD\t\t9\t0.72686\n0\t2\tCUST_YEAR_OF_BIRTH\t\t9\t-0.99234\n0\t3\tY_BOX_GAMES\t\t3\t-0.152405\n0\t3\tY_BOX_GAMES\t\t12\t-1.170745\n0\t5\tCUST_GENDER\tM\t8\t-0.372858\n0\t5\tCUST_GENDER\tM\t9\t0.237339\n0\t5\tCUST_GENDER\tM\t14\t0.281355\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t6\t0.855411\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t9\t2.040677\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t2\t-0.76075\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t3\t-0.713432\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t7\t-2.31023\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t12\t-0.885903\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t0\t0.884108\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t0\t-0.130207\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t2\t0.846579\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t3\t-0.431049\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t6\t1.539645\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t8\t-0.211514\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t7\t0.259937\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t9\t-0.595432\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t11\t3.229636\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t9\t-0.94743\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t12\t0.187867\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t1\t0.503887\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t15\t1.317164\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t7\t1.940775\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t13\t0.617013\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t3\t-0.708631\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t10\t-2.384049\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t0\t-1.537717\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t11\t-1.859711\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t15\t0.939331\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t2\t-1.226526\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t3\t0.272528\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t14\t-0.772003\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t9\t1.27032\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t11\t-0.180611\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t12\t0.703614\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t8\t-2.464444\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t0\t0.197028\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t3\t-0.075861\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t4\t0.559258\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t10\t1.660988\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t11\t-0.247377\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t12\t-0.743712\n0\t21\tCUST_MARITAL_STATUS\tMarried\t6\t0.182575\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t4\t-1.147096\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t6\t2.227751\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t9\t0.396034\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t0\t0.789663\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t8\t-1.602337\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t14\t-0.691458\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t0\t2.287338\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t2\t2.254324\n0\t25\tEDUCATION\t10th\t1\t1.598845\n0\t25\tEDUCATION\t10th\t15\t-1.18712\n0\t26\tEDUCATION\t11th\t0\t1.636887\n0\t26\tEDUCATION\t11th\t4\t-0.273806\n0\t27\tEDUCATION\t12th\t4\t-0.19611\n0\t27\tEDUCATION\t12th\t10\t0.463157\n0\t27\tEDUCATION\t12th\t13\t0.394353\n0\t28\tEDUCATION\t1st-4th\t5\t0.380505\n0\t29\tEDUCATION\t5th-6th\t0\t-0.149336\n0\t29\tEDUCATION\t5th-6th\t11\t0.07494\n0\t29\tEDUCATION\t5th-6th\t13\t0.18045\n0\t30\tEDUCATION\t7th-8th\t8\t-0.558568\n0\t30\tEDUCATION\t7th-8th\t15\t0.810014\n0\t31\tEDUCATION\t9th\t6\t-2.550466\n0\t32\tEDUCATION\t< Bach.\t11\t0.098036\n0\t33\tEDUCATION\tAssoc-A\t7\t-0.084643\n0\t33\tEDUCATION\tAssoc-A\t15\t-0.997925\n0\t34\tEDUCATION\tAssoc-V\t4\t0.897708\n0\t34\tEDUCATION\tAssoc-V\t5\t-0.202979\n0\t35\tEDUCATION\tBach.\t0\t-2.139048\n0\t35\tEDUCATION\tBach.\t1\t-0.530772\n0\t35\tEDUCATION\tBach.\t3\t0.900502\n0\t35\tEDUCATION\tBach.\t15\t-0.181326\n0\t36\tEDUCATION\tHS-grad\t4\t-1.496879\n0\t37\tEDUCATION\tMasters\t1\t0.73603\n0\t37\tEDUCATION\tMasters\t2\t-0.011527\n0\t37\tEDUCATION\tMasters\t3\t0.290122\n0\t37\tEDUCATION\tMasters\t8\t-1.83378\n0\t37\tEDUCATION\tMasters\t9\t-0.260523\n0\t37\tEDUCATION\tMasters\t12\t0.856835\n0\t37\tEDUCATION\tMasters\t15\t-1.442927\n0\t38\tEDUCATION\tPhD\t5\t0.089446\n0\t38\tEDUCATION\tPhD\t6\t0.360262\n0\t38\tEDUCATION\tPhD\t7\t0.081742\n0\t38\tEDUCATION\tPhD\t15\t-0.163608\n0\t39\tEDUCATION\tPresch.\t10\t-0.081801\n0\t40\tEDUCATION\tProfsc\t2\t-0.943387\n0\t40\tEDUCATION\tProfsc\t6\t-2.512521\n0\t40\tEDUCATION\tProfsc\t11\t-1.757074\n0\t40\tEDUCATION\tProfsc\t14\t3.478435\n0\t41\tHOUSEHOLD_SIZE\t1\t6\t-2.792295\n0\t41\tHOUSEHOLD_SIZE\t1\t9\t-0.170462\n0\t41\tHOUSEHOLD_SIZE\t1\t13\t-4.73303\n0\t42\tHOUSEHOLD_SIZE\t2\t1\t-3.148605\n0\t42\tHOUSEHOLD_SIZE\t2\t9\t0.860789\n0\t42\tHOUSEHOLD_SIZE\t2\t12\t-0.024556\n0\t42\tHOUSEHOLD_SIZE\t2\t13\t2.764475\n0\t43\tHOUSEHOLD_SIZE\t3\t1\t-1.363375\n0\t43\tHOUSEHOLD_SIZE\t3\t4\t0.993407\n0\t43\tHOUSEHOLD_SIZE\t3\t7\t1.416058\n0\t44\tHOUSEHOLD_SIZE\t4-5\t7\t-0.712776\n0\t45\tHOUSEHOLD_SIZE\t6-8\t0\t-1.074554\n0\t45\tHOUSEHOLD_SIZE\t6-8\t4\t-0.530747\n0\t45\tHOUSEHOLD_SIZE\t6-8\t5\t-0.055594\n0\t45\tHOUSEHOLD_SIZE\t6-8\t6\t-0.07578\n0\t45\tHOUSEHOLD_SIZE\t6-8\t11\t-0.023221\n0\t46\tHOUSEHOLD_SIZE\t9+\t0\t-1.723956\n0\t46\tHOUSEHOLD_SIZE\t9+\t6\t3.824484\n0\t47\tOCCUPATION\t?\t11\t1.405482\n0\t47\tOCCUPATION\t?\t13\t-1.680356\n0\t47\tOCCUPATION\t?\t15\t-0.518055\n0\t48\tOCCUPATION\tArmed-F\t13\t0.061691\n0\t49\tOCCUPATION\tCleric.\t4\t-2.28617\n0\t49\tOCCUPATION\tCleric.\t7\t1.4373\n0\t49\tOCCUPATION\tCleric.\t13\t-0.509414\n0\t50\tOCCUPATION\tCrafts\t7\t-0.016796\n0\t50\tOCCUPATION\tCrafts\t10\t-0.796921\n0\t51\tOCCUPATION\tExec.\t6\t2.745409\n0\t51\tOCCUPATION\tExec.\t8\t1.324765\n0\t51\tOCCUPATION\tExec.\t11\t-1.753302\n0\t51\tOCCUPATION\tExec.\t14\t-3.274109\n0\t51\tOCCUPATION\tExec.\t15\t-1.19854\n0\t52\tOCCUPATION\tFarming\t15\t1.178398\n0\t53\tOCCUPATION\tHandler\t0\t0.296073\n0\t53\tOCCUPATION\tHandler\t4\t0.495432\n0\t53\tOCCUPATION\tHandler\t5\t-0.279791\n0\t53\tOCCUPATION\tHandler\t8\t0.329027\n0\t54\tOCCUPATION\tHouse-s\t8\t-1.714529\n0\t54\tOCCUPATION\tHouse-s\t10\t-1.714549\n0\t55\tOCCUPATION\tMachine\t0\t-0.013789\n0\t55\tOCCUPATION\tMachine\t15\t0.478541\n0\t56\tOCCUPATION\tOther\t5\t0.325171\n0\t57\tOCCUPATION\tProf.\t5\t0.168239\n0\t57\tOCCUPATION\tProf.\t8\t0.032068\n0\t57\tOCCUPATION\tProf.\t12\t-0.974062\n0\t58\tOCCUPATION\tProtec.\t1\t-0.343323\n0\t58\tOCCUPATION\tProtec.\t3\t-0.062568\n0\t58\tOCCUPATION\tProtec.\t5\t-0.527262\n0\t58\tOCCUPATION\tProtec.\t6\t-0.346518\n0\t58\tOCCUPATION\tProtec.\t10\t-1.280726\n0\t58\tOCCUPATION\tProtec.\t13\t0.252791\n0\t59\tOCCUPATION\tSales\t2\t0.409013\n0\t59\tOCCUPATION\tSales\t15\t0.143584\n0\t60\tOCCUPATION\tTechSup\t5\t-0.351981\n0\t60\tOCCUPATION\tTechSup\t7\t0.062655\n0\t60\tOCCUPATION\tTechSup\t9\t-0.120142\n0\t60\tOCCUPATION\tTechSup\t14\t-1.018426\n0\t61\tOCCUPATION\tTransp.\t4\t1.9944\n0\t61\tOCCUPATION\tTransp.\t12\t1.039207\n0\t\t\t\t5\t0.524133\n0\t\t\t\t10\t-0.286056\n1\t0\t\t\t5\t0.158045\n1\t1\t\t\t7\t-1.39697\n1\t2\t\t\t3\t-0.857297\n1\t2\t\t\t4\t-0.734746\n1\t2\t\t\t7\t-1.394965\n1\t4\t\t\t5\t2.363867\n1\t5\t\t\t3\t1.015802\n1\t5\t\t\t6\t-1.030366\n1\t7\t\t\t5\t4.084356\n1\t7\t\t\t7\t0.694133\n1\t8\t\t\t6\t-1.870625\n1\t9\t\t\t1\t-1.869693\n1\t9\t\t\t7\t1.623752\n1\t10\t\t\t0\t-1.052052\n1\t11\t\t\t0\t-0.289941\n1\t11\t\t\t5\t6.892056\n1\t12\t\t\t4\t1.343592\n1\t13\t\t\t0\t-1.778798\n1\t13\t\t\t2\t-0.141137\n1\t14\t\t\t4\t-3.845195\n2\t2\t\t\t0\t-0.285234\n2\t3\t\t\t1\t-2.144296\n2\t4\t\t\t2\t0.663479\n2\t6\t\t\t2\t-2.91386\n2\t7\t\t\t0\t-0.43296\n2\t7\t\t\t3\t-0.975768\n2\t\t\t\t0\t-0.22936\n2\t\t\t\t2\t1.739216\n0\t0\tAFFINITY_CARD\t\t0\t0.073543\n0\t0\tAFFINITY_CARD\t\t3\t0.086555\n0\t0\tAFFINITY_CARD\t\t6\t3.837406\n0\t0\tAFFINITY_CARD\t\t8\t-0.803312\n0\t1\tCUST_CREDIT_LIMIT\t\t3\t-0.316984\n0\t1\tCUST_CREDIT_LIMIT\t\t4\t-0.018814\n0\t1\tCUST_CREDIT_LIMIT\t\t6\t0.743088\n0\t1\tCUST_CREDIT_LIMIT\t\t11\t0.150533\n0\t2\tCUST_YEAR_OF_BIRTH\t\t10\t-0.936426\n0\t4\tCUST_GENDER\tF\t1\t2.509081\n0\t4\tCUST_GENDER\tF\t5\t0.986287\n0\t4\tCUST_GENDER\tF\t10\t-2.311896\n0\t4\tCUST_GENDER\tF\t12\t-0.956728\n0\t4\tCUST_GENDER\tF\t14\t-2.24623\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t10\t-0.729735\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t13\t-0.05625\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t6\t0.038804\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t11\t-0.476035\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t15\t-1.499753\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t1\t0.597175\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t3\t-0.277584\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t14\t-1.899346\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t0\t3.987969\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t6\t-2.099582\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t13\t-1.531972\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t15\t0.547658\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t6\t0.655998\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t9\t0.500134\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t12\t-2.181497\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t14\t-1.607313\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t3\t-0.299316\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t6\t0.385615\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t9\t0.139487\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t11\t-0.591664\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t6\t0.822369\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t4\t0.286836\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t6\t0.863474\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t10\t1.320626\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t0\t-0.23273\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t6\t-2.261656\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t0\t5.217329\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t4\t0.249066\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t8\t2.685001\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t11\t0.068211\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t15\t-0.744694\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t6\t-1.482085\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t10\t-0.150577\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t12\t-1.716368\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t8\t0.17318\n0\t21\tCUST_MARITAL_STATUS\tMarried\t9\t-0.62466\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t0\t-2.522458\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t2\t0.531865\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t4\t1.475065\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t6\t0.690517\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t9\t0.843926\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t5\t-0.467859\n0\t25\tEDUCATION\t10th\t4\t-0.360445\n0\t25\tEDUCATION\t10th\t7\t-1.437495\n0\t25\tEDUCATION\t10th\t11\t-0.855732\n0\t26\tEDUCATION\t11th\t10\t-1.365855\n0\t27\tEDUCATION\t12th\t5\t-0.004228\n0\t27\tEDUCATION\t12th\t8\t0.430801\n0\t27\tEDUCATION\t12th\t9\t-1.218645\n0\t28\tEDUCATION\t1st-4th\t2\t-0.037988\n0\t28\tEDUCATION\t1st-4th\t3\t0.19015\n0\t28\tEDUCATION\t1st-4th\t8\t0.866755\n0\t29\tEDUCATION\t5th-6th\t3\t0.010869\n0\t29\tEDUCATION\t5th-6th\t5\t0.365755\n0\t29\tEDUCATION\t5th-6th\t15\t-0.37402\n0\t30\tEDUCATION\t7th-8th\t1\t1.536098\n0\t30\tEDUCATION\t7th-8th\t4\t0.056021\n0\t30\tEDUCATION\t7th-8th\t5\t-0.673485\n0\t30\tEDUCATION\t7th-8th\t10\t-1.213465\n0\t31\tEDUCATION\t9th\t1\t0.507639\n0\t31\tEDUCATION\t9th\t7\t-0.493867\n0\t31\tEDUCATION\t9th\t8\t0.992082\n0\t32\tEDUCATION\t< Bach.\t1\t-1.238594\n0\t32\tEDUCATION\t< Bach.\t13\t-1.256249\n0\t32\tEDUCATION\t< Bach.\t14\t0.08103\n0\t33\tEDUCATION\tAssoc-A\t2\t-0.306645\n0\t33\tEDUCATION\tAssoc-A\t9\t-0.048178\n0\t33\tEDUCATION\tAssoc-A\t10\t0.542035\n0\t33\tEDUCATION\tAssoc-A\t13\t-0.050256\n0\t33\tEDUCATION\tAssoc-A\t14\t0.146115\n0\t34\tEDUCATION\tAssoc-V\t1\t-2.318295\n0\t34\tEDUCATION\tAssoc-V\t8\t-2.421569\n0\t34\tEDUCATION\tAssoc-V\t9\t1.021141\n0\t35\tEDUCATION\tBach.\t8\t-4.413498\n0\t35\tEDUCATION\tBach.\t11\t-0.643721\n0\t36\tEDUCATION\tHS-grad\t5\t0.424275\n0\t36\tEDUCATION\tHS-grad\t9\t-0.576135\n0\t36\tEDUCATION\tHS-grad\t14\t-1.468514\n0\t37\tEDUCATION\tMasters\t5\t-0.551368\n0\t37\tEDUCATION\tMasters\t6\t-1.468439\n0\t38\tEDUCATION\tPhD\t9\t0.921943\n0\t38\tEDUCATION\tPhD\t12\t0.674493\n0\t39\tEDUCATION\tPresch.\t5\t0.096612\n0\t39\tEDUCATION\tPresch.\t9\t-0.313038\n0\t40\tEDUCATION\tProfsc\t8\t4.4291\n0\t40\tEDUCATION\tProfsc\t9\t-1.598949\n0\t41\tHOUSEHOLD_SIZE\t1\t0\t1.400397\n0\t41\tHOUSEHOLD_SIZE\t1\t14\t3.041886\n0\t42\tHOUSEHOLD_SIZE\t2\t2\t0.734556\n0\t42\tHOUSEHOLD_SIZE\t2\t3\t-0.87075\n0\t42\tHOUSEHOLD_SIZE\t2\t6\t0.027064\n0\t43\tHOUSEHOLD_SIZE\t3\t15\t0.619837\n0\t44\tHOUSEHOLD_SIZE\t4-5\t0\t1.044103\n0\t44\tHOUSEHOLD_SIZE\t4-5\t4\t-0.794015\n0\t44\tHOUSEHOLD_SIZE\t4-5\t6\t-0.453636\n0\t44\tHOUSEHOLD_SIZE\t4-5\t13\t-0.415593\n0\t44\tHOUSEHOLD_SIZE\t4-5\t14\t-0.228942\n0\t44\tHOUSEHOLD_SIZE\t4-5\t15\t-0.5137\n0\t45\tHOUSEHOLD_SIZE\t6-8\t8\t-0.571317\n0\t45\tHOUSEHOLD_SIZE\t6-8\t12\t0.769522\n0\t45\tHOUSEHOLD_SIZE\t6-8\t15\t0.303767\n0\t46\tHOUSEHOLD_SIZE\t9+\t10\t-1.583511\n0\t46\tHOUSEHOLD_SIZE\t9+\t11\t3.820104\n0\t46\tHOUSEHOLD_SIZE\t9+\t13\t0.553567\n0\t46\tHOUSEHOLD_SIZE\t9+\t15\t0.090043\n0\t47\tOCCUPATION\t?\t1\t2.627943\n0\t47\tOCCUPATION\t?\t9\t-2.836365\n0\t48\tOCCUPATION\tArmed-F\t1\t-0.217448\n0\t49\tOCCUPATION\tCleric.\t1\t0.29886\n0\t49\tOCCUPATION\tCleric.\t5\t0.15266\n0\t50\tOCCUPATION\tCrafts\t12\t-0.141543\n0\t51\tOCCUPATION\tExec.\t3\t0.24321\n0\t51\tOCCUPATION\tExec.\t4\t-2.815293\n0\t51\tOCCUPATION\tExec.\t10\t1.508865\n0\t52\tOCCUPATION\tFarming\t0\t-3.205155\n0\t52\tOCCUPATION\tFarming\t3\t-0.835388\n0\t52\tOCCUPATION\tFarming\t4\t-0.107494\n0\t52\tOCCUPATION\tFarming\t5\t0.689062\n0\t52\tOCCUPATION\tFarming\t9\t1.96607\n0\t53\tOCCUPATION\tHandler\t3\t-0.096708\n0\t53\tOCCUPATION\tHandler\t12\t0.121713\n0\t54\tOCCUPATION\tHouse-s\t0\t2.276329\n0\t54\tOCCUPATION\tHouse-s\t7\t0.023127\n0\t54\tOCCUPATION\tHouse-s\t9\t-1.568242\n0\t55\tOCCUPATION\tMachine\t2\t0.333476\n0\t55\tOCCUPATION\tMachine\t13\t2.760823\n0\t55\tOCCUPATION\tMachine\t14\t-3.366965\n0\t56\tOCCUPATION\tOther\t2\t0.962819\n0\t56\tOCCUPATION\tOther\t7\t0.570329\n0\t56\tOCCUPATION\tOther\t10\t1.472499\n0\t56\tOCCUPATION\tOther\t14\t-1.260416\n0\t57\tOCCUPATION\tProf.\t1\t-1.880581\n0\t57\tOCCUPATION\tProf.\t4\t0.071357\n0\t57\tOCCUPATION\tProf.\t10\t1.743961\n0\t57\tOCCUPATION\tProf.\t13\t1.000001\n0\t58\tOCCUPATION\tProtec.\t12\t-0.301388\n0\t59\tOCCUPATION\tSales\t0\t1.203515\n0\t59\tOCCUPATION\tSales\t6\t-3.586327\n0\t61\tOCCUPATION\tTransp.\t5\t0.185246\n0\t61\tOCCUPATION\tTransp.\t14\t-1.185681\n0\t\t\t\t0\t0.396786\n0\t\t\t\t2\t0.055804\n0\t\t\t\t7\t1.038159\n0\t\t\t\t9\t-1.623247\n1\t2\t\t\t0\t0.190096\n1\t3\t\t\t7\t-2.243152\n1\t4\t\t\t0\t-2.062104\n1\t5\t\t\t1\t-0.343003\n1\t5\t\t\t7\t0.701974\n1\t6\t\t\t1\t1.60369\n1\t6\t\t\t2\t1.021213\n1\t7\t\t\t2\t-0.677459\n1\t7\t\t\t4\t0.786424\n1\t9\t\t\t6\t-0.152422\n1\t10\t\t\t1\t-1.387017\n1\t10\t\t\t7\t2.635387\n1\t11\t\t\t3\t0.973954\n1\t11\t\t\t6\t0.567357\n1\t12\t\t\t1\t-1.439443\n1\t12\t\t\t7\t1.138854\n1\t13\t\t\t3\t-1.258146\n1\t13\t\t\t4\t-0.575292\n1\t13\t\t\t7\t0.267804\n1\t14\t\t\t2\t1.305936\n1\t14\t\t\t3\t0.322019\n1\t14\t\t\t6\t0.207359\n1\t15\t\t\t2\t1.034712\n2\t0\t\t\t3\t-0.303221\n2\t1\t\t\t3\t-0.812421\n2\t3\t\t\t0\t-0.499837\n2\t4\t\t\t3\t-0.969161\n2\t7\t\t\t2\t0.385266\n3\t0\t\t\t0\t2.018202\n0\t0\tAFFINITY_CARD\t\t1\t0.884736\n0\t0\tAFFINITY_CARD\t\t7\t-0.942296\n0\t0\tAFFINITY_CARD\t\t11\t2.177606\n0\t0\tAFFINITY_CARD\t\t15\t-2.035066\n0\t1\tCUST_CREDIT_LIMIT\t\t5\t0.117942\n0\t1\tCUST_CREDIT_LIMIT\t\t8\t-0.449424\n0\t1\tCUST_CREDIT_LIMIT\t\t10\t0.484745\n0\t2\tCUST_YEAR_OF_BIRTH\t\t4\t-8.226682\n0\t2\tCUST_YEAR_OF_BIRTH\t\t13\t0.86101\n0\t3\tY_BOX_GAMES\t\t4\t1.651227\n0\t3\tY_BOX_GAMES\t\t7\t-2.904164\n0\t4\tCUST_GENDER\tF\t6\t-0.091637\n0\t4\tCUST_GENDER\tF\t11\t0.738437\n0\t4\tCUST_GENDER\tF\t13\t-0.999346\n0\t5\tCUST_GENDER\tM\t7\t0.479247\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t1\t1.52135\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t15\t0.778912\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t14\t0.759893\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t1\t-1.152016\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t2\t0.631592\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t3\t-0.15718\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t5\t-0.364976\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t6\t-0.98823\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t12\t0.867665\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t7\t-1.493268\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t5\t0.215237\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t6\t0.439574\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t15\t-2.032789\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t1\t1.489265\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t0\t1.308523\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t10\t0.566674\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t5\t0.045557\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t13\t0.520064\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t14\t1.4718\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t15\t-0.215724\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t8\t1.008223\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t14\t0.717874\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t2\t-1.524911\n0\t17\tCUST_INCOME_LEVEL\tL: 300,000 and above\t3\t-0.11684\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t3\t-0.56756\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t0\t-1.536361\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t4\t-1.330446\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t5\t-0.660898\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t14\t0.856549\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t1\t0.293142\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t5\t-0.082308\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t13\t-1.195681\n0\t21\tCUST_MARITAL_STATUS\tMarried\t0\t1.908432\n0\t21\tCUST_MARITAL_STATUS\tMarried\t2\t-0.940947\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t3\t-1.358305\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t5\t0.347866\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t7\t-2.095726\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t8\t1.054716\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t1\t-0.822719\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t3\t0.467209\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t7\t1.114059\n0\t25\tEDUCATION\t10th\t6\t-0.569195\n0\t25\tEDUCATION\t10th\t9\t-0.378532\n0\t26\tEDUCATION\t11th\t1\t-1.250551\n0\t26\tEDUCATION\t11th\t2\t0.933884\n0\t26\tEDUCATION\t11th\t3\t0.330703\n0\t26\tEDUCATION\t11th\t11\t0.999871\n0\t26\tEDUCATION\t11th\t15\t0.655197\n0\t27\tEDUCATION\t12th\t6\t0.803289\n0\t28\tEDUCATION\t1st-4th\t1\t-2.322247\n0\t28\tEDUCATION\t1st-4th\t10\t0.13232\n0\t29\tEDUCATION\t5th-6th\t1\t0.34142\n0\t29\tEDUCATION\t5th-6th\t2\t-0.305234\n0\t30\tEDUCATION\t7th-8th\t3\t-0.163379\n0\t30\tEDUCATION\t7th-8th\t11\t1.269339\n0\t31\tEDUCATION\t9th\t4\t0.478108\n0\t31\tEDUCATION\t9th\t11\t0.128937\n0\t31\tEDUCATION\t9th\t13\t-2.369831\n0\t31\tEDUCATION\t9th\t15\t0.866844\n0\t32\tEDUCATION\t< Bach.\t3\t0.791577\n0\t32\tEDUCATION\t< Bach.\t12\t1.733375\n0\t32\tEDUCATION\t< 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Below 30,000\t0\t-3.502228\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t3\t-0.031777\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t5\t0.269896\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t14\t-2.606651\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t0\t-4.454073\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t1\t-3.186012\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t9\t2.49878\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t4\t-0.120192\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t10\t-0.943541\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t4\t1.080615\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t8\t0.629375\n0\t10\tCUST_INCOME_LEVEL\tE: 90,000 - 109,999\t12\t0.538089\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t2\t0.565611\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t8\t0.064386\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t10\t-0.529049\n0\t11\tCUST_INCOME_LEVEL\tF: 110,000 - 129,999\t11\t1.717712\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t3\t0.10902\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t5\t0.612152\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t7\t0.014202\n0\t12\tCUST_INCOME_LEVEL\tG: 130,000 - 149,999\t13\t1.270603\n0\t13\tCUST_INCOME_LEVEL\tH: 150,000 - 169,999\t8\t0.816129\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t7\t1.119132\n0\t14\tCUST_INCOME_LEVEL\tI: 170,000 - 189,999\t12\t0.50777\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t1\t-1.914715\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t2\t0.796481\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t5\t-1.250076\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t7\t0.987123\n0\t15\tCUST_INCOME_LEVEL\tJ: 190,000 - 249,999\t8\t-2.66786\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t1\t1.869756\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t10\t-0.525322\n0\t16\tCUST_INCOME_LEVEL\tK: 250,000 - 299,999\t12\t1.394814\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t1\t-2.873371\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t2\t-1.353471\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t4\t1.956675\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t5\t-0.403486\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t6\t0.734944\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t8\t0.356507\n0\t18\tCUST_MARITAL_STATUS\tDivorc.\t14\t-0.864542\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t1\t1.495609\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t11\t-2.407717\n0\t19\tCUST_MARITAL_STATUS\tMabsent\t15\t-0.844569\n0\t20\tCUST_MARITAL_STATUS\tMar-AF\t6\t-0.002903\n0\t21\tCUST_MARITAL_STATUS\tMarried\t3\t-0.692412\n0\t21\tCUST_MARITAL_STATUS\tMarried\t4\t0.249367\n0\t21\tCUST_MARITAL_STATUS\tMarried\t8\t0.837878\n0\t21\tCUST_MARITAL_STATUS\tMarried\t11\t0.275889\n0\t21\tCUST_MARITAL_STATUS\tMarried\t14\t-3.334926\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t10\t-0.58184\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t13\t-1.638715\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t14\t-2.230756\n0\t22\tCUST_MARITAL_STATUS\tNeverM\t15\t0.332555\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t11\t0.307043\n0\t23\tCUST_MARITAL_STATUS\tSepar.\t15\t0.648103\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t1\t0.713954\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t4\t0.671003\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t10\t-0.320215\n0\t24\tCUST_MARITAL_STATUS\tWidowed\t13\t-2.702157\n0\t25\tEDUCATION\t10th\t5\t-0.305697\n0\t25\tEDUCATION\t10th\t10\t-0.328198\n0\t25\tEDUCATION\t10th\t13\t1.342308\n0\t27\tEDUCATION\t12th\t2\t-0.12874\n0\t27\tEDUCATION\t12th\t7\t-0.031461\n0\t27\tEDUCATION\t12th\t14\t-0.036297\n0\t27\tEDUCATION\t12th\t15\t0.194852\n0\t28\tEDUCATION\t1st-4th\t0\t2.458921\n0\t28\tEDUCATION\t1st-4th\t4\t0.351686\n0\t28\tEDUCATION\t1st-4th\t6\t0.233208\n0\t28\tEDUCATION\t1st-4th\t11\t-0.495191\n0\t28\tEDUCATION\t1st-4th\t12\t-0.298486\n0\t28\tEDUCATION\t1st-4th\t15\t1.012826\n0\t29\tEDUCATION\t5th-6th\t4\t-0.108918\n0\t29\tEDUCATION\t5th-6th\t6\t0.621164\n0\t29\tEDUCATION\t5th-6th\t7\t-0.821135\n0\t29\tEDUCATION\t5th-6th\t8\t0.260887\n0\t29\tEDUCATION\t5th-6th\t12\t0.293233\n0\t29\tEDUCATION\t5th-6th\t14\t-0.249981\n0\t30\tEDUCATION\t7th-8th\t0\t0.706585\n0\t30\tEDUCATION\t7th-8th\t6\t-0.322496\n0\t30\tEDUCATION\t7th-8th\t7\t0.845418\n0\t30\tEDUCATION\t7th-8th\t12\t-0.250393\n0\t30\tEDUCATION\t7th-8th\t13\t-3.151631\n0\t31\tEDUCATION\t9th\t2\t0.295158\n0\t32\tEDUCATION\t< Bach.\t5\t-0.025107\n0\t32\tEDUCATION\t< Bach.\t6\t1.331049\n0\t32\tEDUCATION\t< 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Below 30,000\t4\t-0.35759\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t7\t-1.127208\n0\t6\tCUST_INCOME_LEVEL\tA: Below 30,000\t11\t-2.386848\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t4\t1.487656\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t11\t-1.001505\n0\t7\tCUST_INCOME_LEVEL\tB: 30,000 - 49,999\t13\t-0.29751\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t4\t-0.374502\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t9\t-1.008947\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t13\t-1.316241\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t14\t-0.36482\n0\t8\tCUST_INCOME_LEVEL\tC: 50,000 - 69,999\t15\t-0.709199\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t9\t0.224674\n0\t9\tCUST_INCOME_LEVEL\tD: 70,000 - 89,999\t14\t1.034053\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Predict YRS_RESIDENCE with prediction details","message":["%sql","","-- Make predictions with upper and lower bounds and include explanatory prediction details","SELECT CUST_ID,","       round(PREDICTION_YRS_RES,3) PRED_YRS_RES,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,","       RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE","       FROM (SELECT CUST_ID,","               PREDICTION(NN_SH_REGR_SAMPLE USING *) PREDICTION_YRS_RES,","               PREDICTION_DETAILS(NN_SH_REGR_SAMPLE USING *) PD","             FROM TEST_DATA_V","             ORDER BY CUST_ID) OUT,","             XMLTABLE('/Details'","                      PASSING OUT.PD","                      COLUMNS ","                      \"Attribute1\" XMLType PATH 'Attribute[1]',","                      \"Attribute2\" XMLType PATH 'Attribute[2]') ","       OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715229479378,"interpreter":"sql.medium","endTime":1715229479565,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\n100005\t5.834\t\"EDUCATION\" actualValue=\"Assoc-A\" weight=\".191\" \t\"CUST_INCOME_LEVEL\" actualValue=\"B: 30,000 - 49,999\" weight=\".152\" \n100006\t1.765\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".134\" \t\"AFFINITY_CARD\" actualValue=\"0\" weight=\".133\" \n100007\t4.042\t\"OCCUPATION\" actualValue=\"Other\" weight=\".156\" \t\"CUST_INCOME_LEVEL\" actualValue=\"L: 300,000 and above\" weight=\".086\" \n100012\t4.429\t\"EDUCATION\" actualValue=\"PhD\" weight=\".154\" \t\"CUST_INCOME_LEVEL\" actualValue=\"H: 150,000 - 169,999\" weight=\".143\" \n100013\t4.514\t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".198\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".195\" \n100015\t4.938\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1959\" weight=\".182\" \t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".176\" \n100016\t4.088\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".181\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".097\" \n100017\t5.561\t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".189\" \t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1935\" weight=\".116\" \n100018\t2.589\t\"CUST_INCOME_LEVEL\" actualValue=\"A: Below 30,000\" weight=\".116\" \t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".09\" \n100020\t5.629\t\"CUST_YEAR_OF_BIRTH\" actualValue=\"1946\" weight=\".22\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".133\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Show NN model predictions vs actuals","message":["%sql","","-- Show predictions vs. actuals.","","SELECT CUST_ID,","    round(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","  FROM TEST_DATA_V","  WHERE rownum < 10","  ORDER BY CUST_ID;","  "],"enabled":true,"result":{"startTime":1715229479641,"interpreter":"sql.medium","endTime":1715229479793,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\n100005\t5.8\t5\n100007\t4\t5\n100017\t5.6\t5\n100018\t2.6\t3\n100041\t2.1\t2\n100050\t2.7\t2\n100061\t4.3\t4\n100099\t2.9\t3\n100145\t2.2\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Model quality statistics on the TEST data","message":["%sql","","select ROUND(R2,6) COEF_OF_DETERMINATION_R2, ","       ROUND(MAE,6) MEAN_ABSOLUTE_ERROR, ","       ROUND(MSE,6) MEAN_SQUARED_ERROR, ","       ROUND(SQRT(MSE),6) ROOT_MEAN_SQUARED_ERROR","       FROM ","       (SELECT AVG(ABS(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE)) MAE,","               AVG(POWER(PRED_YRS_RESIDENCE - ACTUAL_YRS_RESIDENCE,2)) MSE,","               1-(SUM(POWER(ACTUAL_YRS_RESIDENCE - PRED_YRS_RESIDENCE,2)))/(SUM(POWER(ACTUAL_YRS_RESIDENCE - ","                 (SELECT AVG(ROUND(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1)) FROM TEST_DATA_V),2))) R2              ","                 FROM (SELECT CUST_ID,","                            ROUND(PREDICTION(NN_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","                            YRS_RESIDENCE ACTUAL_YRS_RESIDENCE","                       FROM TEST_DATA_V))"],"enabled":true,"result":{"startTime":1715229479872,"interpreter":"sql.medium","endTime":1715229482372,"results":[{"message":"COEF_OF_DETERMINATION_R2\tMEAN_ABSOLUTE_ERROR\tMEAN_SQUARED_ERROR\tROOT_MEAN_SQUARED_ERROR\n0.686895\t0.705162\t1.168054\t1.080766\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"scatterplot\":{\"showSeriesX\":[\"PRED_YRS_RESIDENCE\"],\"showSeriesY\":[\"ACTUAL_YRS_RESIDENCE\"],\"showLabel\":\"no\",\"showDescription\":\"no\",\"lastColumns\":[\"CUST_ID\",\"PRED_YRS_RESIDENCE\",\"ACTUAL_YRS_RESIDENCE\",\"HOUSEHOLD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"scatterplot","title":"Plot NN model predictions vs actuals grouped by HOUSE_HOLD_SIZE","message":["%sql","","SELECT CUST_ID,","    ROUND(PREDICTION(GLMR_SH_REGR_SAMPLE USING *), 1) PRED_YRS_RESIDENCE,","    YRS_RESIDENCE ACTUAL_YRS_RESIDENCE, ","    CASE HOUSEHOLD_SIZE WHEN '1' THEN '1'","    WHEN '2' THEN '2'","    ELSE '3 or more' END HOUSEHOLD","FROM TEST_DATA_V","FETCH FIRST 100 ROWS ONLY;","  "],"enabled":true,"result":{"startTime":1715229482456,"interpreter":"sql.medium","endTime":1715229482573,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tACTUAL_YRS_RESIDENCE\tHOUSEHOLD\n100005\t5.5\t5\t3 or more\n100007\t4.1\t5\t2\n100017\t4.7\t5\t3 or more\n100018\t2.4\t3\t3 or more\n100041\t1.7\t2\t1\n100050\t2.5\t2\t3 or more\n100061\t4.9\t4\t3 or more\n100099\t3.1\t3\t3 or more\n100145\t2.7\t3\t1\n100169\t2.6\t3\t3 or more\n100174\t5.6\t6\t2\n100192\t4.4\t4\t3 or more\n100194\t1.9\t2\t1\n100195\t1.5\t1\t3 or more\n100232\t3.7\t3\t3 or more\n100254\t4.1\t4\t3 or more\n100262\t4\t4\t3 or more\n100270\t5.8\t6\t3 or more\n100271\t4.1\t5\t3 or 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"],"enabled":true,"result":{"startTime":1715229482652,"interpreter":"sql.medium","endTime":1715229482771,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tRESIDUALS\n100005\t5.8\t0.8\n100007\t4\t-1\n100017\t5.6\t0.6\n100018\t2.6\t-0.4\n100041\t2.1\t0.1\n100050\t2.7\t0.7\n100061\t4.3\t0.3\n100099\t2.9\t-0.1\n100145\t2.2\t-0.8\n100169\t3\t0\n100174\t6.5\t0.5\n100192\t3.8\t-0.2\n100194\t1.7\t-0.3\n100195\t1.9\t0.9\n100232\t3.8\t0.8\n100254\t3.9\t-0.1\n100262\t3.8\t-0.2\n100270\t5.1\t-0.9\n100271\t4\t-1\n100276\t7.6\t0.6\n100282\t3.9\t-0.1\n100292\t2.1\t0.1\n100302\t3.1\t-0.9\n100326\t4\t2\n100351\t5.7\t0.7\n100372\t9\t-1\n100398\t3.8\t-0.2\n100409\t6\t0\n100410\t2.5\t-1.5\n100445\t5.8\t1.8\n100452\t3.2\t-0.8\n100453\t1.7\t0.7\n100469\t2.3\t-0.7\n100488\t6.5\t0.5\n100490\t4.4\t-0.6\n100504\t1.7\t-0.3\n100505\t1.6\t-0.4\n100540\t5.9\t0.9\n100544\t5.4\t0.4\n100571\t2.9\t0.9\n100576\t3.6\t-0.4\n100577\t4.8\t-0.2\n100583\t2\t0\n100587\t5.1\t0.1\n100593\t4.8\t0.8\n100630\t1.9\t-0.1\n100633\t5.4\t1.4\n100659\t2.7\t1.7\n100681\t4.3\t-2.7\n100706\t0.3\t0.3\n100720\t3.4\t0.4\n100768\t2.4\t0.4\n100789\t3.8\t-0.2\n100800\t3.9\t-1.1\n100805\t5.6\t-0.4\n100810\t1.3\t-0.7\n100815\t7\t-2\n100841\t6\t0\n100842\t4.6\t-0.4\n100855\t2\t-1\n100883\t5.4\t-1.6\n100897\t4.8\t-2.2\n100901\t4.9\t-1.1\n100906\t5\t1\n100922\t3.8\t-0.2\n100937\t2.1\t-1.9\n100997\t5.6\t0.6\n101024\t2.9\t-0.1\n101026\t3.9\t-0.1\n101031\t3.8\t-0.2\n101047\t4.3\t1.3\n101063\t6.1\t1.1\n101075\t5.5\t1.5\n101090\t1.7\t-0.3\n101096\t3.4\t0.4\n101103\t5.2\t-1.8\n101104\t2\t0\n101137\t2.7\t-0.3\n101140\t2.6\t-0.4\n101153\t1.7\t0.7\n101155\t1.9\t-0.1\n101156\t3.9\t-0.1\n101157\t0.2\t-1.8\n101161\t4\t0\n101169\t3.1\t0.1\n101175\t3\t0\n101198\t2\t-1\n101229\t3.5\t-0.5\n101256\t9.1\t0.1\n101289\t6.4\t1.4\n101330\t7.5\t-1.5\n101345\t5.8\t-0.2\n101355\t2\t1\n101361\t4.7\t-1.3\n101362\t2.6\t-0.4\n101377\t4.4\t0.4\n101399\t4.7\t0.7\n101438\t2.9\t-0.1\n101464\t4.8\t0.8\n101466\t4.7\t-0.3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Regression Neural Network model using a settings table","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715229482846,"interpreter":"md.medium","endTime":1715229482916,"results":[{"message":"<h2 id=\"create-regression-neural-network-model-using-a-settings-table\">Create Regression Neural Network model using a settings table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create NN model settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE NNR_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE NNR_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1715229482993,"interpreter":"script.medium","endTime":1715229483182,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable NNR_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings","message":["%script ","","BEGIN ","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_NEURAL_NETWORK);","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- Specify a missing value treatment method:","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ODMS_MISSING_VALUE_TREATMENT, DBMS_DATA_MINING.ODMS_MISSING_VALUE_DELETE_ROW);","","-- Specify the Neural Network architecture.  The default number of layers is 1, and the default activation function is NNET_ACTIVATIONS_LOG_SIG (Log Sigmoid). The default number of nodes per layer is the number of attributes or 50 (if the number of attributes > 50).  Be careful with quotes around NNET_ACTIVATIONS.","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_HIDDEN_LAYERS, '3');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_NODES_PER_LAYER, '8,8,2');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_ACTIVATIONS, '''NNET_ACTIVATIONS_TANH'', ''NNET_ACTIVATIONS_LOG_SIG'', ''NNET_ACTIVATIONS_ARCTAN''');","    ","-- Specify the Heldaside options for Regularization. If the total number of training rows is greater than 50000, the default is NNET_REGULARIZER_HELDASIDE, otherwise it is NNET_REGULARIZER_NONE. The held ratio for the held-aside method is 0.25 by default. With NNET_REGULARIZER_HELDASIDE, the training process is stopped early if the network performance on the validation data fails to improve for NNET_HELDASIDE_MAX_FAIL epochs in a row (default is 6).","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_REGULARIZER, DBMS_DATA_MINING.NNET_REGULARIZER_HELDASIDE);","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_HELDASIDE_RATIO, '0.25');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_HELDASIDE_MAX_FAIL, '6');","    ","-- Specify the L2 regularization parameter, with a default of 1. Cannot be used with NNET_REGULARIZER_HELDASIDE.","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_REG_LAMBDA, '1');","","-- Specify the number of iterations, which is 200 by default for LBFGS solver and 10000 for the ADAM solver. The default convergence tolerance setting is 0.000001. ","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_ITERATIONS, '200');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.NNET_TOLERANCE, '0.000001');","    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ODMS_RANDOM_SEED, '0');","","-- Specify details on the LBFGS Solver. The number of historical copies kept in L-BFGS solver is defined by LBFGS_HISTORY_DEPTH. When the number of iterations is smaller than the history depth, the Hessian computed by L-BFGS is accurate, otherwise the Hessian computed by L-BFGS is an approximation. Typically, the value is between 3 and 10. ","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_HISTORY_DEPTH, '10');","    ","-- Specify how to set the initial approximation of the inverse Hessian at the beginning of each iteration. If the value is set to be LBFGS_SCALE_HESSIAN_ENABLE, then we approximate the initial inverse Hessian with Oren-Luenberger scaling. If it is set to be LBFGS_SCALE_HESSIAN_DISABLE, then we use identity as the approximation of the inverse Hessian at the beginning of each iteration    ","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN, DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN_ENABLE);","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN, DBMS_DATA_MINING.LBFGS_SCALE_HESSIAN_DISABLE);","--    INSERT INTO NNR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.LBFGS_GRADIENT_TOLERANCE, '0.0000001');","   ","END;"],"enabled":true,"result":{"startTime":1715229483265,"interpreter":"script.medium","endTime":1715229483390,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a 3-Layer (8,8,2) NN regression model for predicting YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'NN_SH_REGR_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.REGRESSION,","    DATA_TABLE_NAME     => 'TRAIN_DATA_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","    SETTINGS_TABLE_NAME => 'NNR_SETTINGS',","    XFORM_LIST          => V_XLST);","END;"],"enabled":true,"result":{"startTime":1715229483476,"interpreter":"script.medium","endTime":1715229485276,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715229485352,"interpreter":"md.medium","endTime":1715229485410,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715229485489,"interpreter":"md.medium","endTime":1715229485548,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Regression SVM.dsnb b/machine-learning/notebooks/sql/OML4SQL Regression SVM.dsnb
deleted file mode 100755
index b6b646c3..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Regression SVM.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Regression SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715284536613,"interpreter":"md.medium","endTime":1715284536774,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Regression Modeling to Predict Numerical Values using Support Vector Machine","This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715284585042,"interpreter":"md.medium","endTime":1715284585111,"results":[{"message":"<h1 id=\"regression-modeling-to-predict-numerical-values-using-support-vector-machine\">Regression Modeling to Predict Numerical Values using Support Vector Machine<\/h1>\n<p>This notebook shows how to predict numerical values using a multiple regression. Given demographic, purchase, and affinity card data for a set of customers, predict the number of years a customer remains at the same residence, as found in column YRS_RESIDENCE. Since YRS_RESIDENCE is a continuous variable, we will use the Support Vector Machine algorithm. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md ","<dl>","<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715284537084,"interpreter":"md.medium","endTime":1715284537160,"results":[{"message":"<dl>\n<img src=\"https://www.oracle.com/technetwork/database/options/advanced-analytics/regression-5663170.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715284537236,"interpreter":"md.medium","endTime":1715284537310,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-regression\" target=\"_blank\">OML Regression<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create table CUSTOMERS360 joining tables from SH schema","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE CUSTOMERS360';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE CUSTOMERS360 AS ","  SELECT A.CUST_ID, A.CUST_GENDER, A.CUST_MARITAL_STATUS, A.CUST_YEAR_OF_BIRTH, ","         A.CUST_INCOME_LEVEL, A.CUST_CREDIT_LIMIT, ","         B.EDUCATION, B.AFFINITY_CARD, B.HOUSEHOLD_SIZE, B.OCCUPATION, B.YRS_RESIDENCE, B.Y_BOX_GAMES","  FROM SH.CUSTOMERS A, SH.SUPPLEMENTARY_DEMOGRAPHICS B","  WHERE A.CUST_ID = B.CUST_ID;","  "],"enabled":true,"result":{"startTime":1715284537387,"interpreter":"script.medium","endTime":1715284540289,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable CUSTOMERS360 created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Split data into randomly selected 60% sample for train and 40% test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_V AS SELECT * FROM CUSTOMERS360 SAMPLE (60) SEED (1);","","CREATE OR REPLACE VIEW TEST_DATA_V AS SELECT * FROM CUSTOMERS360 MINUS SELECT * FROM TRAIN_DATA_V;"],"enabled":true,"result":{"startTime":1715284540369,"interpreter":"script.medium","endTime":1715284540608,"results":[{"message":"\nView TRAIN_DATA_V created.\n\n\n---------------------------\n\nView TEST_DATA_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Linear regression model for predicting YRS_RESIDENCE","","---"],"enabled":true,"result":{"startTime":1715284540689,"interpreter":"md.medium","endTime":1715284540750,"results":[{"message":"<h3 id=\"build-a-svm-linear-regression-model-for-predicting-yrs_residence\">Build a SVM Linear regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715284540829,"interpreter":"script.medium","endTime":1715284546548,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>'; ","- Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`.  The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms.  When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: `SVMS_LINEAR` and `SVMS_GAUSSIAN`.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: `SVMS_REGULARIZER_L1` and `SVMS_REGULARIZER_L2`. ","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","- Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","> v_setlst('SVMS_EPSILON') := '0.1';","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> v_setlst('SVMS_BATCH_ROWS') := '20000';","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  `SVMS_SOLVER_SGD` and `SVMS_SOLVER_IPM`.  ","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","- Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001. ","> v_setlst('SVMS_CONV_TOLERANCE') := '0.001'","- Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.     ","> v_setlst('SVMS_NUM_PIVOTS') := '200' "],"enabled":true,"result":{"startTime":1715286177603,"interpreter":"md.medium","endTime":1715286177669,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '&lt;row_weight_column_name&gt;';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>.  The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms.  When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. Options are: <code>SVMS_LINEAR<\/code> and <code>SVMS_GAUSSIAN<\/code>.  The linear kernel also is able to return the coefficients, while this is not available on the Gaussian<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size.  Options are: <code>SVMS_REGULARIZER_L1<\/code> and <code>SVMS_REGULARIZER_L2<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<ul>\n<li>Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_EPSILON') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_BATCH_ROWS') := '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. Options are:  <code>SVMS_SOLVER_SGD<\/code> and <code>SVMS_SOLVER_IPM<\/code>.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the convergence tolerance for SVM algorithm. It requires a number larger than 0. Default is 0.0001.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_CONV_TOLERANCE') := '0.001'<\/p>\n<\/blockquote>\n<ul>\n<li>Specify an upper limit on the number of pivots used in the Incomplete Cholesky decomposition. It can be set only for non-linear kernels. It requires a value from a range of 1 to 10,000 (inclusive of the edges). The default value is 200.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_NUM_PIVOTS') := '200'<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using specific settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","    v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","    v_setlst('SVMS_EPSILON') := '0.1';","    v_setlst('SVMS_BATCH_ROWS') := '20000';","    v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","    v_setlst('SVMS_CONV_TOLERANCE') := '0.0001';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715284546828,"interpreter":"script.medium","endTime":1715284549128,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build a SVM Gaussian regression model for predicting YRS_RESIDENCE","---"],"enabled":true,"result":{"startTime":1715284549210,"interpreter":"md.medium","endTime":1715284549278,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-regression-model-for-predicting-yrs_residence\">Build a SVM Gaussian regression model for predicting YRS_RESIDENCE<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using the default options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","        ","END;"],"enabled":true,"result":{"startTime":1715284549361,"interpreter":"script.medium","endTime":1715284552862,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Using specific settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","    ","BEGIN","    v_setlst('PREP_AUTO') := 'ON';","    v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';","    v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';","    v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';","    v_setlst('SVMS_EPSILON') := '0.1';","    v_setlst('SVMS_NUM_PIVOTS') := '200';","    v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';","    v_setlst('SVMS_CONV_TOLERANCE') := '0.0001';","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","        MINING_FUNCTION     => 'REGRESSION',","        DATA_QUERY          => 'SELECT * FROM TRAIN_DATA_V',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'CUST_ID',","        TARGET_COLUMN_NAME  => 'YRS_RESIDENCE'","        );","END;"],"enabled":true,"result":{"startTime":1715284552945,"interpreter":"script.medium","endTime":1715284558852,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display SVM model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = 'SVML_SH_REGR_SAMPLE'","ORDER BY SETTING_NAME;","  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"],"enabled":true,"result":{"startTime":1715284561316,"interpreter":"sql.medium","endTime":1715284561468,"results":[{"message":"CUST_ID\tPRED_YRS_RESIDENCE\tRESIDUALS\n100004\t5\t0\n100022\t4.6\t-0.4\n100053\t6.2\t1.2\n100063\t5.7\t0.7\n100067\t4.6\t0.6\n100086\t2\t1\n100101\t3.9\t0.9\n100110\t3\t-1\n100131\t2.7\t-0.3\n100138\t4.7\t0.7\n100176\t4\t0\n100190\t5\t0\n100198\t1.2\t0.2\n100214\t1.6\t0.6\n100221\t3.8\t-1.2\n100249\t3.8\t-0.2\n100275\t4.4\t0.4\n100277\t5.7\t-0.3\n100286\t4.2\t0.2\n100289\t4.9\t-0.1\n100354\t4.1\t-0.9\n100355\t4.9\t-1.1\n100370\t3\t0\n100417\t1.5\t0.5\n100419\t1.9\t0.9\n100432\t2.4\t-0.6\n100446\t5.6\t0.6\n100484\t2\t0\n100486\t3.9\t-0.1\n100493\t4.3\t0.3\n100511\t5.2\t-0.8\n100514\t2\t0\n100518\t5.1\t-0.9\n100530\t4.7\t-0.3\n100532\t6.2\t0.2\n100534\t6.1\t-0.9\n100566\t5.5\t-0.5\n100598\t6\t0\n100609\t5.5\t-0.5\n100617\t1.4\t0.4\n100628\t3.8\t-0.2\n100648\t3.8\t0.8\n100655\t4.2\t0.2\n100684\t2.1\t1.1\n100799\t2.2\t-0.8\n100818\t5.4\t0.4\n100827\t6.2\t0.2\n100832\t2.9\t-0.1\n100834\t3.6\t0.6\n100837\t1.8\t0.8\n100838\t3.9\t-0.1\n100839\t3.8\t-0.2\n100846\t1.8\t-0.2\n100863\t3.7\t0.7\n100874\t5.4\t0.4\n100910\t5.9\t1.9\n100924\t4.3\t0.3\n100944\t4.7\t-0.3\n100951\t5.1\t0.1\n100961\t4.6\t-0.4\n100981\t1.1\t0.1\n101002\t2.4\t-0.6\n101007\t5.5\t-1.5\n101011\t3.9\t-2.1\n101016\t2.1\t0.1\n101030\t6.1\t-1.9\n101048\t2.7\t-0.3\n101080\t3.3\t0.3\n101098\t2.2\t0.2\n101109\t5\t0\n101119\t5.3\t-0.7\n101127\t4.8\t0.8\n101132\t3.4\t-0.6\n101144\t5.1\t0.1\n101152\t4.8\t0.8\n101160\t4.7\t-0.3\n101168\t5.9\t-0.1\n101173\t3.9\t0.9\n101188\t3.4\t1.4\n101212\t2.3\t0.3\n101226\t2.2\t0.2\n101264\t3.6\t-0.4\n101265\t5.2\t0.2\n101266\t5.9\t-2.1\n101271\t3.3\t3.3\n101281\t1.8\t-0.2\n101285\t6\t4\n101301\t5.6\t0.6\n101350\t3.4\t0.4\n101367\t3.8\t-1.2\n101374\t6\t0\n101418\t2.3\t1.3\n101429\t1.5\t1.5\n101444\t5.3\t-0.7\n101449\t4.7\t-1.3\n101458\t2.7\t0.7\n101487\t3\t0\n101490\t2.4\t0.4\n101505\t4.1\t-0.9\n101507\t3.1\t0.1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## 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We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715284561553,"interpreter":"md.medium","endTime":1715284561613,"results":[{"message":"<h2 id=\"another-option-create-svm-linear-regression-model-using-settings-table\">Another option: create SVM Linear Regression Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Drop previous settings and output tables and create SVM model settings table","message":["%script ","","-- Drop any previous GLM Model Settings table for notebook repeatability","BEGIN EXECUTE IMMEDIATE 'DROP TABLE SVMLR_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","--Create Model Settings Table","CREATE TABLE SVMLR_SETTINGS (","    SETTING_NAME  VARCHAR2(30),","    SETTING_VALUE VARCHAR2(4000));","    "],"enabled":true,"result":{"startTime":1715284561693,"interpreter":"script.medium","endTime":1715284561849,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable SVMLR_SETTINGS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Define model settings for automated data preparation and feature selection","message":["%script ","","BEGIN ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SUPPORT_VECTOR_MACHINES);","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_ON);  ","","-- Specify a missing value treatment method:","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ODMS_MISSING_VALUE_TREATMENT, DBMS_DATA_MINING.ODMS_MISSING_VALUE_DELETE_ROW);","   ","-- Switch between Kernel Types - Linear or Gaussian.  By default the system uses the Linear Kernel.","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_KERNEL_FUNCTION, DBMS_DATA_MINING.SVMS_LINEAR);","--    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_KERNEL_FUNCTION, DBMS_DATA_MINING.SVMS_GAUSSIAN);","   ","-- Value of complexity factor for SVM algorithm (both classification and regression).  Default value estimated from the data by the algorithm.","   INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_COMPLEXITY_FACTOR, 0.1);","   ","-- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). ","-- The default is system determined because it depends on the potential model size.  ","-- Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).     ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_REGULARIZER, DBMS_DATA_MINING.SVMS_REGULARIZER_L1);","--     INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_REGULARIZER, DBMS_DATA_MINING.SVMS_REGULARIZER_L2);","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_EPSILON, 0.1);","   ","-- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20000. ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_BATCH_ROWS, 20000);","   ","-- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.    ","    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_SOLVER, DBMS_DATA_MINING.SVMS_SOLVER_SGD);","--    INSERT INTO SVMLR_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVMS_SOLVER, DBMS_DATA_MINING.SVMS_SOLVER_IPM);","","END;"],"enabled":true,"result":{"startTime":1715284561930,"interpreter":"script.medium","endTime":1715284562067,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Linear regression model for predicting YRS_RESIDENCE","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVML_SH_REGR_SAMPLE');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    V_XLST DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;"," ","BEGIN","    DBMS_DATA_MINING.CREATE_MODEL(","    MODEL_NAME          => 'SVML_SH_REGR_SAMPLE',","    MINING_FUNCTION     => DBMS_DATA_MINING.REGRESSION,","    DATA_TABLE_NAME     => 'TRAIN_DATA_V',","    CASE_ID_COLUMN_NAME => 'CUST_ID',","    TARGET_COLUMN_NAME  => 'YRS_RESIDENCE',","    SETTINGS_TABLE_NAME => 'SVMLR_SETTINGS',","    XFORM_LIST          => V_XLST);","END;"],"enabled":true,"result":{"startTime":1715284562194,"interpreter":"script.medium","endTime":1715284564492,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of Script"],"enabled":true,"result":{"startTime":1715284564569,"interpreter":"md.medium","endTime":1715284564628,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715284564709,"interpreter":"md.medium","endTime":1715284564770,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/notebooks/sql/OML4SQL Statistical Functions.dsnb b/machine-learning/notebooks/sql/OML4SQL Statistical Functions.dsnb
deleted file mode 100755
index 2e762a30..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Statistical Functions.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
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For the machine learning examples, please see companion notebooks.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715728480473,"interpreter":"md.low","endTime":1715728480536,"results":[{"message":"<h1 id=\"oml4sql-statistical-functions\">OML4SQL Statistical Functions<\/h1>\n<p>In this notebook, you will use the SH schema data to to explore descriptive and comparative statistics performed inside Oracle Autonomous Database. For the machine learning examples, please see companion notebooks.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/statistics-5663173.jpg\" alt=\"OML Notebooks\" width=\"270\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715728480615,"interpreter":"md.low","endTime":1715728480675,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/statistics-5663173.jpg\" alt=\"OML Notebooks\" width=\"270\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/sql-statistical-functions.html\" target=\"_blank\">Statistical Function Documentation<\/a>"],"enabled":true,"result":{"startTime":1715728480774,"interpreter":"md.low","endTime":1715728480832,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/sql-statistical-functions.html\" target=\"_blank\">Statistical Function Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display SH.COUNTRIES data","message":["%sql","","SELECT * FROM SH.COUNTRIES","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715728480910,"interpreter":"sql.low","endTime":1715728480983,"results":[{"message":"COUNTRY_ID\tCOUNTRY_ISO_CODE\tCOUNTRY_NAME\tCOUNTRY_SUBREGION\tCOUNTRY_SUBREGION_ID\tCOUNTRY_REGION\tCOUNTRY_REGION_ID\tCOUNTRY_TOTAL\tCOUNTRY_TOTAL_ID\tCOUNTRY_NAME_HIST\n52771\tCN\tChina\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52781\tIN\tIndia\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52782\tJP\tJapan\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52783\tMY\tMalaysia\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52769\tSG\tSingapore\tAsia\t52793\tAsia\t52802\tWorld total\t52806\t\n52791\tZA\tSouth Africa\tAfrica\t52792\tAfrica\t52800\tWorld total\t52806\t\n52774\tAU\tAustralia\tAustralia\t52794\tOceania\t52805\tWorld total\t52806\t\n52785\tNZ\tNew Zealand\tAustralia\t52794\tOceania\t52805\tWorld total\t52806\t\n52787\tSA\tSaudi Arabia\tMiddle East\t52796\tMiddle East\t52804\tWorld total\t52806\t\n52786\tPL\tPoland\tEastern Europe\t52795\tEurope\t52803\tWorld total\t52806\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display SH.CUSTOMERS data","message":["%sql","","SELECT * FROM SH.CUSTOMERS","FETCH FIRST 10 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Avenue\t52773\tSantos\t52293\tSao Paulo\t52735\t52775\t129-379-7148\tG: 130,000 - 149,999\t3000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n24561\tAbigail\tRuddy\tM\t1978\t\t77 North Packard Avenue\t37400\tYokohama\t52526\tKanagawa\t52634\t52782\t689-236-7611\tG: 130,000 - 149,999\t7000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tA\n28116\tAbigail\tRuddy\tM\t1949\tsingle\t87 West Coshocton Avenue\t71349\tHaarlem\t51644\tNoord-Holland\t52683\t52770\t208-194-6025\tG: 130,000 - 149,999\t11000\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n31671\tAbigail\tRuddy\tM\t1951\tmarried\t97 Sagadahoc Avenue\t55056\tBolton\t51205\tEngland - Greater Manchester\t52590\t52789\t391-613-8000\tG: 130,000 - 149,999\t1500\tRuddy@company.example.com\tCustomer total\t52772\t\t1998-01-01 00:00:00\t\tI\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display mode value for CUST_MARITAL_STATUS","message":["%sql ","","SELECT STATS_MODE(CUST_MARITAL_STATUS) ","FROM SH.CUSTOMERS"],"enabled":true,"result":{"startTime":1715728481218,"interpreter":"sql.low","endTime":1715728481313,"results":[{"message":"STATS_MODE(CUST_MARITAL_STATUS)\nsingle\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display median value for CUST_CREDIT_LIMIT","message":["%sql","","SELECT MEDIAN(CUST_CREDIT_LIMIT) ","FROM SH.CUSTOMERS;"],"enabled":true,"result":{"startTime":1715728481389,"interpreter":"sql.low","endTime":1715728481481,"results":[{"message":"MEDIAN(CUST_CREDIT_LIMIT)\n7000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display median CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS in descendig order","message":["%sql ","","SELECT CUST_MARITAL_STATUS, MEDIAN(CUST_CREDIT_LIMIT) ","FROM SH.CUSTOMERS ","GROUP BY CUST_MARITAL_STATUS","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715728481560,"interpreter":"sql.low","endTime":1715728481654,"results":[{"message":"CUST_MARITAL_STATUS\tMEDIAN(CUST_CREDIT_LIMIT)\nDivorc.\t9000\nMabsent\t9000\nMar-AF\t11000\nMarried\t9000\nNeverM\t9000\nSepar.\t9000\nWidowed\t7000\ndivorced\t7000\nmarried\t5000\nsingle\t7000\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display mean CUST_CREDIT_LIMIT grouped by CUST_MARITAL_STATUS in descending order","message":["%sql ","SELECT CUST_MARITAL_STATUS, ROUND(AVG(CUST_CREDIT_LIMIT),2)","FROM SH.CUSTOMERS ","GROUP BY CUST_MARITAL_STATUS ","ORDER BY AVG(CUST_CREDIT_LIMIT) DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715728481741,"interpreter":"sql.low","endTime":1715728481816,"results":[{"message":"CUST_MARITAL_STATUS\tROUND(AVG(CUST_CREDIT_LIMIT),2)\nMar-AF\t10333.33\nSepar.\t8279.85\nMabsent\t8233.33\nNeverM\t8168.66\nMarried\t7800.64\nDivorc.\t7768.29\n\t7365.56\nWidowed\t7202.21\nwidow\t6400\nsingle\t6349.95\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"STATS_MODE with dynamic data","message":["%md","Note that STATS_MODE can only work with materialized tables, instead of dynamic data like a view or tables joined together. In that case, we recommend using the max count. For example, in the following paragraph we want to find the mode of the *COUNTRY_NAME*, which is obtained from joining two tables. In this case, it is better to achieve that by taking the max count. "],"enabled":true,"result":{"startTime":1715728481892,"interpreter":"md.low","endTime":1715728481951,"results":[{"message":"<p>Note that STATS_MODE can only work with materialized tables, instead of dynamic data like a view or tables joined together. In that case, we recommend using the max count. For example, in the following paragraph we want to find the mode of the <em>COUNTRY_NAME<\/em>, which is obtained from joining two tables. In this case, it is better to achieve that by taking the max count.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Find mode of COUNTRY_NAME, i.e., country with most customers","message":["%sql","SELECT COUNTRY_NAME,","       RANK","FROM (","    SELECT COUNTRY_NAME,  ","           ROW_NUMBER() OVER( ORDER BY COUNT DESC) RANK","    FROM (","        SELECT COUNTRY_NAME, COUNT(*) COUNT","        FROM SH.CUSTOMERS CUST, SH.COUNTRIES CO","        WHERE  CUST.COUNTRY_ID = CO.COUNTRY_ID","        GROUP BY COUNTRY_NAME ) A ) B","WHERE RANK =1"],"enabled":true,"result":{"startTime":1715728482027,"interpreter":"sql.low","endTime":1715728482103,"results":[{"message":"COUNTRY_NAME\tRANK\nUnited States of America\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display counts of COUNTRY_NAME to highlight mode visually","message":["%sql","","SELECT COUNTRY_NAME, COUNT(*) COUNT","FROM SH.CUSTOMERS CUST, SH.COUNTRIES CO","WHERE  CUST.COUNTRY_ID = CO.COUNTRY_ID","GROUP BY COUNTRY_NAME","ORDER BY COUNT DESC","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715728482180,"interpreter":"sql.low","endTime":1715728482253,"results":[{"message":"COUNTRY_NAME\tCOUNT\nUnited States of America\t18520\nGermany\t8173\nItaly\t7780\nUnited Kingdom\t7557\nFrance\t3833\nSpain\t2039\nCanada\t2010\nBrazil\t832\nAustralia\t831\nChina\t712\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check 1st, 2nd, and 3rd quantile of amount sold - PERCENTILE_CONT resolves ties by interpolation. PERCENTILE_DISC outputs first value when seeing a tie","message":["%sql","SELECT PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY AMOUNT_SOLD) \"First Quantile cont\",","    PERCENTILE_DISC(0.25) WITHIN GROUP (ORDER BY AMOUNT_SOLD)    \"First Quantile disc\",","    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY AMOUNT_SOLD)     \"Second Quantile cont\",","    PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY AMOUNT_SOLD)     \"Second Quantile disc\",","    PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY AMOUNT_SOLD)    \"Third Quantile cont\",","    PERCENTILE_DISC(0.75) WITHIN GROUP (ORDER BY AMOUNT_SOLD)    \"Third Quantile disc\"","FROM SH.SALES"],"enabled":true,"result":{"startTime":1715728482328,"interpreter":"sql.low","endTime":1715728482827,"results":[{"message":"First Quantile cont\tFirst Quantile disc\tSecond Quantile cont\tSecond Quantile disc\tThird Quantile cont\tThird Quantile disc\n17.38\t17.38\t34.24\t34.24\t53.89\t53.89\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using a t-test","message":["%md","#### Use the SQL t-test function for performing a t-test in-database using the SH.SALES and SH.CUSTOMERS tables.  ","","See more information at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html <\/a>","","The t-test measures the significance of a difference of means. You use it to compare the means of two groups or the means of one group with a constant. A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution under the null hypothesis. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a Normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's t distribution. ","For more details on the background of t-tests , see <a href=\"https://en.wikipedia.org/wiki/Student%27s_t-test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Student%27s_t-test');\"> https://en.wikipedia.org/wiki/Student%27s_t-test <\/a>","","`Hint`: P_Values < 0.05 show statistically significantly differences (over 95% confidence) in the amounts purchased by men vs. women","`Hint`: NaN is used for display when there are nulls and/or empty"],"enabled":true,"result":{"startTime":1715728482907,"interpreter":"md.low","endTime":1715728482967,"results":[{"message":"<h4 id=\"use-the-sql-t-test-function-for-performing-a-t-test-in-database-using-the-shsales-and-shcustomers-tables\">Use the SQL t-test function for performing a t-test in-database using the SH.SALES and SH.CUSTOMERS tables.<\/h4>\n<p>See more information at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_T_TEST_.html<\/a> <\/a><\/p>\n<p>The t-test measures the significance of a difference of means. You use it to compare the means of two groups or the means of one group with a constant. A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution under the null hypothesis. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a Normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's t distribution.\nFor more details on the background of t-tests , see <a href=\"https://en.wikipedia.org/wiki/Student%27s_t-test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Student%27s_t-test');\"> <a href=\"https://en.wikipedia.org/wiki/Student%27s_t-test\">https://en.wikipedia.org/wiki/Student%27s_t-test<\/a> <\/a><\/p>\n<p><code>Hint<\/code>: P_Values &lt; 0.05 show statistically significantly differences (over 95% confidence) in the amounts purchased by men vs. women\n<code>Hint<\/code>: NaN is used for display when there are nulls and/or empty<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare average amount SOLD_TO_MEN versus average amount SOLD_TO_WOMEN grouped by INCOME_LEVEL - determine if significantly different","message":["%sql ","","SELECT SUBSTR(CUST_INCOME_LEVEL,1,22) INCOME_LEVEL,  ","       ROUND(AVG(DECODE(CUST_GENDER,'M',AMOUNT_SOLD,NULL)),2) SOLD_TO_MEN,  ","\t   ROUND(AVG(DECODE(CUST_GENDER,'F',AMOUNT_SOLD,NULL)),2) SOLD_TO_WOMEN,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD, 'STATISTIC','F'),2) T_OBSERVED,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD),2) TWO_SIDED_P_VALUE","FROM SH.CUSTOMERS C, SH.SALES S  ","WHERE C.CUST_ID=S.CUST_ID  ","GROUP BY CUST_INCOME_LEVEL ","ORDER BY 1"],"enabled":true,"result":{"startTime":1715728483047,"interpreter":"sql.low","endTime":1715728483947,"results":[{"message":"INCOME_LEVEL\tSOLD_TO_MEN\tSOLD_TO_WOMEN\tT_OBSERVED\tTWO_SIDED_P_VALUE\nA: Below 30,000\t105.28\t99.43\t-1.99\t0.05\nB: 30,000 - 49,999\t102.6\t109.83\t3.04\t0\nC: 50,000 - 69,999\t105.63\t110.13\t2.36\t0.02\nD: 70,000 - 89,999\t106.63\t110.47\t2.28\t0.02\nE: 90,000 - 109,999\t103.4\t101.61\t-1.25\t0.21\nF: 110,000 - 129,999\t106.76\t105.98\t-0.6\t0.55\nG: 130,000 - 149,999\t108.88\t107.31\t-0.85\t0.39\nH: 150,000 - 169,999\t110.99\t107.15\t-1.91\t0.06\nI: 170,000 - 189,999\t102.81\t107.44\t2.18\t0.03\nJ: 190,000 - 249,999\t108.04\t115.34\t2.58\t0.01\nK: 250,000 - 299,999\t112.38\t108.2\t-1.41\t0.16\nL: 300,000 and above\t120.97\t112.22\t-2.06\t0.04\n\t107.12\t113.8\t0.69\t0.49\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare average amount SOLD_TO_MEN versus average amount SOLD_TO_WOMEN grouped by INCOME_LEVEL - determine if significantly different","message":["%sql ","","SELECT SUBSTR(CUST_INCOME_LEVEL,1,22) INCOME_LEVEL,  ","       ROUND(AVG(DECODE(CUST_GENDER,'M',AMOUNT_SOLD,NULL)),2) SOLD_TO_MEN,  ","\t   ROUND(AVG(DECODE(CUST_GENDER,'F',AMOUNT_SOLD,NULL)),2) SOLD_TO_WOMEN,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD, 'STATISTIC','F'),2) T_OBSERVED,  ","\t   ROUND(STATS_T_TEST_INDEP(CUST_GENDER, AMOUNT_SOLD),2) TWO_SIDED_P_VALUE","FROM SH.CUSTOMERS C, SH.SALES S  ","WHERE C.CUST_ID=S.CUST_ID  ","GROUP BY CUST_INCOME_LEVEL  ","ORDER BY TWO_SIDED_P_VALUE DESC"],"enabled":true,"result":{"startTime":1715728484028,"interpreter":"sql.low","endTime":1715728485024,"results":[{"message":"INCOME_LEVEL\tSOLD_TO_MEN\tSOLD_TO_WOMEN\tT_OBSERVED\tTWO_SIDED_P_VALUE\nF: 110,000 - 129,999\t106.76\t105.98\t-0.6\t0.55\n\t107.12\t113.8\t0.69\t0.49\nG: 130,000 - 149,999\t108.88\t107.31\t-0.85\t0.39\nE: 90,000 - 109,999\t103.4\t101.61\t-1.25\t0.21\nK: 250,000 - 299,999\t112.38\t108.2\t-1.41\t0.16\nH: 150,000 - 169,999\t110.99\t107.15\t-1.91\t0.06\nA: Below 30,000\t105.28\t99.43\t-1.99\t0.05\nL: 300,000 and above\t120.97\t112.22\t-2.06\t0.04\nI: 170,000 - 189,999\t102.81\t107.44\t2.18\t0.03\nC: 50,000 - 69,999\t105.63\t110.13\t2.36\t0.02\nD: 70,000 - 89,999\t106.63\t110.47\t2.28\t0.02\nJ: 190,000 - 249,999\t108.04\t115.34\t2.58\t0.01\nB: 30,000 - 49,999\t102.6\t109.83\t3.04\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Note: NaN is displayed above where INCOME_LEVEL is NULL - let's display those records","message":["%sql","","SELECT CUST_ID, CUST_FIRST_NAME, CUST_LAST_NAME, CUST_INCOME_LEVEL ","FROM SH.CUSTOMERS ","WHERE CUST_INCOME_LEVEL IS NULL","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715728485112,"interpreter":"sql.low","endTime":1715728485190,"results":[{"message":"CUST_ID\tCUST_FIRST_NAME\tCUST_LAST_NAME\tCUST_INCOME_LEVEL\n803\tThatcher\tTyler\t\n963\tFabian\tLusk\t\n1043\tForrest\tBanfield\t\n723\tTrevor\tNielley\t\n270\tVerna\tWalsh\t\n590\tWadswworth\tWhite\t\n1017\tWashington\tDally\t\n31\tWilla\tFitz\t\n297\tJacinta\tMathews\t\n617\tJed\tHardy\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using an F-test","message":["%md","#### Use an F-test to compare whether variances of two groups are significantly different","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html <\/a>","","The F-test tests whether variances of two groups are significantly different. The null hypothesis is that the variances of two groups are the same. The observed value of *F* is the ratio of one variance to the other, so values very different from 1 usually indicate significant differences. ","If the p value is very small (such as p < 0.05), then we reject the null hypothesis and we say that it is statistically significant (at more than 95% confidence) that the variances of the two groups are different.","","For more details on the background of F-Tests, see <a href=\"https://en.wikipedia.org/wiki/F-test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/F-test');\">https://en.wikipedia.org/wiki/F-test<\/a>","","In the following example, we compare the credit limit in two gender groups. We use F-test to check if the variances of the two gropus are the same."],"enabled":true,"result":{"startTime":1715728485269,"interpreter":"md.low","endTime":1715728485334,"results":[{"message":"<h4 id=\"use-an-f-test-to-compare-whether-variances-of-two-groups-are-significantly-different\">Use an F-test to compare whether variances of two groups are significantly different<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_F_TEST.html<\/a> <\/a><\/p>\n<p>The F-test tests whether variances of two groups are significantly different. The null hypothesis is that the variances of two groups are the same. The observed value of <em>F<\/em> is the ratio of one variance to the other, so values very different from 1 usually indicate significant differences.\nIf the p value is very small (such as p &lt; 0.05), then we reject the null hypothesis and we say that it is statistically significant (at more than 95% confidence) that the variances of the two groups are different.<\/p>\n<p>For more details on the background of F-Tests, see <a href=\"https://en.wikipedia.org/wiki/F-test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/F-test');\"><a href=\"https://en.wikipedia.org/wiki/F-test\">https://en.wikipedia.org/wiki/F-test<\/a><\/a><\/p>\n<p>In the following example, we compare the credit limit in two gender groups. We use F-test to check if the variances of the two gropus are the same.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare variances of cust_credit_limit for men and women - determine if variances are significantly different","message":["%sql","","SELECT ROUND(VARIANCE(DECODE(CUST_GENDER, 'M', CUST_CREDIT_LIMIT, NULL)),2) VAR_MEN,","       ROUND(VARIANCE(DECODE(CUST_GENDER, 'F', CUST_CREDIT_LIMIT, NULL)),2) VAR_WOMEN,","       ROUND(STATS_F_TEST(CUST_GENDER, CUST_CREDIT_LIMIT, 'STATISTIC', 'F'),2) F_STATISTIC,","       ROUND(STATS_F_TEST(CUST_GENDER, CUST_CREDIT_LIMIT),2) TWO_SIDED_P_VALUE","  FROM SH.CUSTOMERS;"],"enabled":true,"result":{"startTime":1715728485417,"interpreter":"sql.low","endTime":1715728485512,"results":[{"message":"VAR_MEN\tVAR_WOMEN\tF_STATISTIC\tTWO_SIDED_P_VALUE\n12879896.68\t13046865.01\t1.01\t0.31\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion for F-test","message":["%md","The p-value is greater than 0.05, so the result above shows that the variances of the CUST_CREDIT_LIMIT for men and women has no statistical difference."],"enabled":true,"result":{"startTime":1715728485591,"interpreter":"md.low","endTime":1715728485650,"results":[{"message":"<p>The p-value is greater than 0.05, so the result above shows that the variances of the CUST_CREDIT_LIMIT for men and women has no statistical difference.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using one way ANOVA test","message":["%md","#### Using one way ANOVA (analysis of variance) test to verify differences in means (for groups or variables) for statistical significance","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html <\/a>","","The one-way analysis of variance function (STATS_ONE_WAY_ANOVA) tests differences in means (for groups or variables) for statistical significance by comparing two different estimates of variance. One estimate is based on the variances within each group or category. This is known as the mean squares within or mean square error. The other estimate is based on the variances among the means of the groups. This is known as the mean squares between. If the means of the groups are significantly different, then the mean squares between will be larger than expected and will not match the mean squares within. If the mean squares of the groups are consistent, then the two variance estimates will be about the same.","","For more information and background on ANOVA, see <a href=\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/One-way_analysis_of_variance');\"> https://en.wikipedia.org/wiki/One-way_analysis_of_variance <\/a>","","In the following example, we apply the one way ANOVA to test for each gender, whether the amount sold varies according to the income level. "],"enabled":true,"result":{"startTime":1715728485735,"interpreter":"md.low","endTime":1715728485797,"results":[{"message":"<h4 id=\"using-one-way-anova-analysis-of-variance-test-to-verify-differences-in-means-for-groups-or-variables-for-statistical-significance\">Using one way ANOVA (analysis of variance) test to verify differences in means (for groups or variables) for statistical significance<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_ONE_WAY_ANOVA.html<\/a> <\/a><\/p>\n<p>The one-way analysis of variance function (STATS_ONE_WAY_ANOVA) tests differences in means (for groups or variables) for statistical significance by comparing two different estimates of variance. One estimate is based on the variances within each group or category. This is known as the mean squares within or mean square error. The other estimate is based on the variances among the means of the groups. This is known as the mean squares between. If the means of the groups are significantly different, then the mean squares between will be larger than expected and will not match the mean squares within. If the mean squares of the groups are consistent, then the two variance estimates will be about the same.<\/p>\n<p>For more information and background on ANOVA, see <a href=\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/One-way_analysis_of_variance');\"> <a href=\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\">https://en.wikipedia.org/wiki/One-way_analysis_of_variance<\/a> <\/a><\/p>\n<p>In the following example, we apply the one way ANOVA to test for each gender, whether the amount sold varies according to the income level.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Apply one way ANOVA test to different income levels and the amount sold, for each gender group.","message":["%sql","","SELECT CUST_GENDER,","       ROUND(STATS_ONE_WAY_ANOVA(CUST_INCOME_LEVEL, AMOUNT_SOLD, 'MEAN_SQUARES_BETWEEN'),2) MEAN_SQUARES_BETWEEN ,","       ROUND(STATS_ONE_WAY_ANOVA(CUST_INCOME_LEVEL, AMOUNT_SOLD, 'MEAN_SQUARES_WITHIN'),2) MEAN_SQUARES_WITHIN,","       STATS_ONE_WAY_ANOVA(CUST_INCOME_LEVEL, AMOUNT_SOLD, 'SIG') P_VALUE","  FROM SH.CUSTOMERS C, SH.SALES S","  WHERE C.CUST_ID = S.CUST_ID","  GROUP BY CUST_GENDER;"],"enabled":true,"result":{"startTime":1715728485875,"interpreter":"sql.low","endTime":1715728486601,"results":[{"message":"CUST_GENDER\tMEAN_SQUARES_BETWEEN\tMEAN_SQUARES_WITHIN\tP_VALUE\nF\t379803.89\t67878.25\t0.0000000047839510702026533\nM\t624317.68\t67228.52\t0.000000000000000067139449119250277\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of one way ANOVA test","message":["%md","From ANOVA test result, we can see that for each gender group, the mean value of the amount sold is significantly different across income levels. "],"enabled":true,"result":{"startTime":1715728486687,"interpreter":"md.low","endTime":1715728486744,"results":[{"message":"<p>From ANOVA test result, we can see that for each gender group, the mean value of the amount sold is significantly different across income levels.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare a view that consists of the gender and the amount sold","message":["%sql","","CREATE OR REPLACE VIEW CUST_GENDER_AMOUNT_SOLD_V AS","SELECT CUST_GENDER, AMOUNT_SOLD","  FROM SH.CUSTOMERS C, SH.SALES S","  WHERE C.CUST_ID = S.CUST_ID"],"enabled":true,"result":{"startTime":1715728486822,"interpreter":"sql.low","endTime":1715728486897,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from CUST_GENDER_AMOUNT_SOLD_V","message":["%sql","","SELECT * FROM CUST_GENDER_AMOUNT_SOLD_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715728486972,"interpreter":"sql.low","endTime":1715728487068,"results":[{"message":"CUST_GENDER\tAMOUNT_SOLD\nF\t1205.99\nM\t1250.25\nM\t1250.25\nF\t1250.25\nM\t1250.25\nM\t1250.25\nM\t1250.25\nF\t1250.25\nM\t1250.25\nM\t1210.21\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Kolmogorov-Smirnov test","message":["%md","","#### Use a Kolmogorov-Smirnov test to compare whether two samples come from the same population","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html <\/a>","","Kolmogorov-Smirnov test compares two samples to test whether they are from the same population or from populations that have the same distribution. It does not assume that the population from which the samples were taken is normally distributed. The null hypothesis is that the two groups of data come from the same population or distribution. If the p-value < 0.05, then we reject the null hypothesis and determine that the two groups of data come from the different populations with more than 95% confidence.","","For more details and background on K-S tests, see <a href=\"https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test');\"> https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test <\/a>","","In the following example, we check whether the distributions of the amount sold by two gender groups is different in distribution with statistical significance."],"enabled":true,"result":{"startTime":1715728487143,"interpreter":"md.low","endTime":1715728487201,"results":[{"message":"<h4 id=\"use-a-kolmogorov-smirnov-test-to-compare-whether-two-samples-come-from-the-same-population\">Use a Kolmogorov-Smirnov test to compare whether two samples come from the same population<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_KS_TEST.html<\/a> <\/a><\/p>\n<p>Kolmogorov-Smirnov test compares two samples to test whether they are from the same population or from populations that have the same distribution. It does not assume that the population from which the samples were taken is normally distributed. The null hypothesis is that the two groups of data come from the same population or distribution. If the p-value &lt; 0.05, then we reject the null hypothesis and determine that the two groups of data come from the different populations with more than 95% confidence.<\/p>\n<p>For more details and background on K-S tests, see <a href=\"https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test');\"> <a href=\"https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test\">https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test<\/a> <\/a><\/p>\n<p>In the following example, we check whether the distributions of the amount sold by two gender groups is different in distribution with statistical significance.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Kolmogorov-Smirnov test - determine if distributions of amount sold by two gender groups are statistically different","message":["%sql","","SELECT ROUND(STATS_KS_TEST(CUST_GENDER, AMOUNT_SOLD, 'STATISTIC'),5) KS_STATISTIC,","       ROUND(STATS_KS_TEST(CUST_GENDER, AMOUNT_SOLD),5) P_VALUE","  FROM CUST_GENDER_AMOUNT_SOLD_V"],"enabled":true,"result":{"startTime":1715728487278,"interpreter":"sql.low","endTime":1715728487846,"results":[{"message":"KS_STATISTIC\tP_VALUE\n0.00384\t0.00408\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of   Kolmogorov-Smirnov test","message":["%md","","Since the p-value < 0.05, we reject the null hypothesis that the distributions of two gender groups are the same. Kolmogorov-Smirnov test result shows that the difference of distributions of two gender groups is significant at the more than 95% confidence. "],"enabled":true,"result":{"startTime":1715728487928,"interpreter":"md.low","endTime":1715728487989,"results":[{"message":"<p>Since the p-value &lt; 0.05, we reject the null hypothesis that the distributions of two gender groups are the same. Kolmogorov-Smirnov test result shows that the difference of distributions of two gender groups is significant at the more than 95% confidence.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Mann Whitney test","message":["%md","","","#### Use a Mann Whitney test to check whether two populations have the same distribution function","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html <\/a>","","For more details and background on the Mann Whitney test, see <a href=\"https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test');\"> https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test <\/a>","","It compares two independent samples to test the null hypothesis that two populations have the same distribution function against the alternative hypothesis that the two distribution functions are different."],"enabled":true,"result":{"startTime":1715728488063,"interpreter":"md.low","endTime":1715728488121,"results":[{"message":"<h4 id=\"use-a-mann-whitney-test-to-check-whether-two-populations-have-the-same-distribution-function\">Use a Mann Whitney test to check whether two populations have the same distribution function<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_MW_TEST.html<\/a> <\/a><\/p>\n<p>For more details and background on the Mann Whitney test, see <a href=\"https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test');\"> <a href=\"https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test\">https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test<\/a> <\/a><\/p>\n<p>It compares two independent samples to test the null hypothesis that two populations have the same distribution function against the alternative hypothesis that the two distribution functions are different.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Mann Whitney Test - determine if distributions of amount sold by two gender groups are statistically different","message":["%sql","","SELECT ROUND(STATS_MW_TEST(CUST_GENDER, CUST_CREDIT_LIMIT, 'STATISTIC'),2) Z_STATISTIC,","       ROUND(STATS_MW_TEST(CUST_GENDER, CUST_CREDIT_LIMIT, 'TWO_SIDED_SIG', 'F'),2) TWO_SIDED_P_VALUE","  FROM SH.CUSTOMERS C, SH.SALES S","  WHERE C.CUST_ID = S.CUST_ID;"],"enabled":true,"result":{"startTime":1715728488199,"interpreter":"sql.low","endTime":1715728488383,"results":[{"message":"Z_STATISTIC\tTWO_SIDED_P_VALUE\n-0.78\t0.43\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of Mann Whitney Test","message":["%md","","Since the p-value is much larger than 0.05, we do not reject the hypothesis that the distributions of the amount sold in the two groups is the same. This means that the difference observed from the data is not significant. "],"enabled":true,"result":{"startTime":1715728488459,"interpreter":"md.low","endTime":1715728488517,"results":[{"message":"<p>Since the p-value is much larger than 0.05, we do not reject the hypothesis that the distributions of the amount sold in the two groups is the same. This means that the difference observed from the data is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create a view that contains income level and the amount sold to men and women","message":["%sql","","CREATE OR REPLACE VIEW INCOME_GENDER_SOLD_V AS","SELECT SUBSTR(CUST_INCOME_LEVEL,1,22) INCOME_LEVEL,  ","\tROUND(AVG(DECODE(CUST_GENDER,'M',AMOUNT_SOLD,NULL)),2) SOLD_TO_MEN,  ","\tROUND(AVG(DECODE(CUST_GENDER,'F',AMOUNT_SOLD,NULL)),2) SOLD_TO_WOMEN","    FROM SH.CUSTOMERS C, SH.SALES S  ","    WHERE C.CUST_ID=S.CUST_ID  ","    AND SUBSTR(CUST_INCOME_LEVEL,1,22) IS NOT NULL","    GROUP BY CUST_INCOME_LEVEL"],"enabled":true,"result":{"startTime":1715728488590,"interpreter":"sql.low","endTime":1715728488669,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the view","message":["%sql","","SELECT * ","FROM INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1715728488745,"interpreter":"sql.low","endTime":1715728489122,"results":[{"message":"INCOME_LEVEL\tSOLD_TO_MEN\tSOLD_TO_WOMEN\nE: 90,000 - 109,999\t103.4\t101.61\nJ: 190,000 - 249,999\t108.04\t115.34\nD: 70,000 - 89,999\t106.63\t110.47\nL: 300,000 and above\t120.97\t112.22\nC: 50,000 - 69,999\t105.63\t110.13\nG: 130,000 - 149,999\t108.88\t107.31\nH: 150,000 - 169,999\t110.99\t107.15\nB: 30,000 - 49,999\t102.6\t109.83\nI: 170,000 - 189,999\t102.81\t107.44\nF: 110,000 - 129,999\t106.76\t105.98\nA: Below 30,000\t105.28\t99.43\nK: 250,000 - 299,999\t112.38\t108.2\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Wilcoxon Signed Rank Test","message":["%md","","#### Use Wilcoxon Signed Rank test to determine whether the median of the differences between the samples are the same","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html <\/a>","","Wilcoxon Signed Ranks test of paired samples to determine whether the median of the differences between the samples is significantly different from zero. The absolute values of the differences are ordered and assigned ranks. Then the null hypothesis states that the sum of the ranks of the positive differences is equal to the sum of the ranks of the negative differences.","","For more information and background on Wilcoxon Signed Rank tests, see <a href=\"https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test');\"> https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test <\/a>","","In the following example, we go through each income level and obtain average amount sold to each gender group. We are interested to know whether there is a difference between the amount sold to men or women for each income level. WSR test focuses on the median of the differences of the amount sold in the two gender groups and do a hypothesis testing on whether the median is significantly different. The null hypothesis is that the median is the same. "],"enabled":true,"result":{"startTime":1715728489200,"interpreter":"md.low","endTime":1715728489260,"results":[{"message":"<h4 id=\"use-wilcoxon-signed-rank-test-to-determine-whether-the-median-of-the-differences-between-the-samples-are-the-same\">Use Wilcoxon Signed Rank test to determine whether the median of the differences between the samples are the same<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/STATS_WSR_TEST.html<\/a> <\/a><\/p>\n<p>Wilcoxon Signed Ranks test of paired samples to determine whether the median of the differences between the samples is significantly different from zero. The absolute values of the differences are ordered and assigned ranks. Then the null hypothesis states that the sum of the ranks of the positive differences is equal to the sum of the ranks of the negative differences.<\/p>\n<p>For more information and background on Wilcoxon Signed Rank tests, see <a href=\"https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test');\"> <a href=\"https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test\">https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test<\/a> <\/a><\/p>\n<p>In the following example, we go through each income level and obtain average amount sold to each gender group. We are interested to know whether there is a difference between the amount sold to men or women for each income level. WSR test focuses on the median of the differences of the amount sold in the two gender groups and do a hypothesis testing on whether the median is significantly different. The null hypothesis is that the median is the same.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Wilcoxon Signed Ranks Test - determine if median of difference between amount sold to men and amount sold to women is statistically different from zero ","message":["%sql","","SELECT ROUND(STATS_WSR_TEST(SOLD_TO_MEN, SOLD_TO_WOMEN, 'STATISTIC'),2) Z_STATISTIC,","       ROUND(STATS_WSR_TEST(SOLD_TO_MEN, SOLD_TO_WOMEN, 'TWO_SIDED_SIG'),2) TWO_SIDED_P_VALUE","  FROM INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1715728489334,"interpreter":"sql.low","endTime":1715728489531,"results":[{"message":"Z_STATISTIC\tTWO_SIDED_P_VALUE\n-0.12\t0.91\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of Wilcoxon Signed Rank Test","message":["%md","","","The p-value is much greater than 0.05. We do not reject the null hypothesis. This means that the difference between the median of the amount sold to two gender groups is not significant. "],"enabled":true,"result":{"startTime":1715728489608,"interpreter":"md.low","endTime":1715728489669,"results":[{"message":"<p>The p-value is much greater than 0.05. We do not reject the null hypothesis. This means that the difference between the median of the amount sold to two gender groups is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Pearson's correlation","message":["%md","","#### Using Pearson's correlation coefficient to measure the correlation between two columns ","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html <\/a>","","This is most popular correlation coefficient and widely used for measuring correlation of a set of number pairs.","For more information and background on Pearson's correlation, see <a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Pearson_correlation_coefficient');\"> https://en.wikipedia.org/wiki/Pearson_correlation_coefficient <\/a>"],"enabled":true,"result":{"startTime":1715728489744,"interpreter":"md.low","endTime":1715728489805,"results":[{"message":"<h4 id=\"using-pearsons-correlation-coefficient-to-measure-the-correlation-between-two-columns\">Using Pearson's correlation coefficient to measure the correlation between two columns<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR.html<\/a> <\/a><\/p>\n<p>This is most popular correlation coefficient and widely used for measuring correlation of a set of number pairs.\nFor more information and background on Pearson's correlation, see <a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Pearson_correlation_coefficient');\"> <a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\">https://en.wikipedia.org/wiki/Pearson_correlation_coefficient<\/a> <\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Compute Pearson's correlation between amount sold to the two gender groups","message":["%sql","","SELECT ROUND(CORR(SOLD_TO_MEN, SOLD_TO_WOMEN),2) CORRELATION_COEF","FROM INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1715728489880,"interpreter":"sql.low","endTime":1715728490107,"results":[{"message":"CORRELATION_COEF\n0.38\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Spearman's rho correlation coefficient","message":["%md","","#### Using Spearman's rho correlation coefficient to measure the correlation between two columns","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html <\/a>","","Spearman's rho correlation coefficient can find the nonlinear relationship between two variables by focusing the rank of each variables. The Spearman's rho correlation also provides a significance indicator like the p-value. If p-value is small (< 0.05), then it means that there are significant corrleation between the two groups. ","","For more details and background on Spearman's rho correlation, see <a href=\"https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient');\"> https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient <\/a> ","In the following example, we compute Spearman's rho coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women. "],"enabled":true,"result":{"startTime":1715728490182,"interpreter":"md.low","endTime":1715728490249,"results":[{"message":"<h4 id=\"using-spearmans-rho-correlation-coefficient-to-measure-the-correlation-between-two-columns\">Using Spearman's rho correlation coefficient to measure the correlation between two columns<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html<\/a> <\/a><\/p>\n<p>Spearman's rho correlation coefficient can find the nonlinear relationship between two variables by focusing the rank of each variables. The Spearman's rho correlation also provides a significance indicator like the p-value. If p-value is small (&lt; 0.05), then it means that there are significant corrleation between the two groups.<\/p>\n<p>For more details and background on Spearman's rho correlation, see <a href=\"https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient');\"> <a href=\"https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient\">https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient<\/a> <\/a>\nIn the following example, we compute Spearman's rho coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compute Spearman's rho correlation coefficient for income level and the amount sold to men,  income level and the amount sold to women","message":["%sql","","SELECT  ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_MEN, 'COEFFICIENT'),2) MEN_COEFFICIENT,  ","        ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_MEN, 'TWO_SIDED_SIG'),2) MEN_TWO_SIDED_SIG,","        ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_WOMEN, 'COEFFICIENT'),2) WOMEN_COEFFICIENT,  ","        ROUND(CORR_S(INCOME_LEVEL, SOLD_TO_WOMEN, 'TWO_SIDED_SIG'),2) WOMEN_TWO_SIDED_SIG","FROM  INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1715728490327,"interpreter":"sql.low","endTime":1715728490556,"results":[{"message":"MEN_COEFFICIENT\tMEN_TWO_SIDED_SIG\tWOMEN_COEFFICIENT\tWOMEN_TWO_SIDED_SIG\n0.71\t0.01\t0.38\t0.22\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion of Spearman's rho correlation","message":["%md","","For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant. "],"enabled":true,"result":{"startTime":1715728490633,"interpreter":"md.low","endTime":1715728490696,"results":[{"message":"<p>For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Using Kendall's tau-b correlation","message":["%md","","","#### Using Kendall's tau-b correlation to measure the correlation between two columns","","See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html');\"> https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html <\/a>","","Kendall's tau-b correlation coefficient also reveals the nonlinear relationship between two variables. To calculate the coefficient, the number of concordant and discordant pairs is computed. A pair of observations is concordant if the observation with the larger x also has a larger value of y. A pair of observations is discordant if the observation with the larger x has a smaller y. ","The Kendall's tau-b also provides a significance indicator like the p-value. If p-value is small (< 0.05), then it means that there are significant corrleation between the two groups. ","For more details and background on Kendall's tau-b correlation, see <a href=\"https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient');\"> https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient <\/a> ","","In the following example, we compute Kendall's tau-b  coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women. "],"enabled":true,"result":{"startTime":1715728490774,"interpreter":"md.low","endTime":1715728490840,"results":[{"message":"<h4 id=\"using-kendalls-tau-b-correlation-to-measure-the-correlation-between-two-columns\">Using Kendall's tau-b correlation to measure the correlation between two columns<\/h4>\n<p>See documentation at <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html');\"> <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html\">https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CORR_A.html<\/a> <\/a><\/p>\n<p>Kendall's tau-b correlation coefficient also reveals the nonlinear relationship between two variables. To calculate the coefficient, the number of concordant and discordant pairs is computed. A pair of observations is concordant if the observation with the larger x also has a larger value of y. A pair of observations is discordant if the observation with the larger x has a smaller y.\nThe Kendall's tau-b also provides a significance indicator like the p-value. If p-value is small (&lt; 0.05), then it means that there are significant corrleation between the two groups.\nFor more details and background on Kendall's tau-b correlation, see <a href=\"https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient\" onclick=\"return ! window.open('https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient');\"> <a href=\"https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient\">https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient<\/a> <\/a><\/p>\n<p>In the following example, we compute Kendall's tau-b  coefficient between the income level and the amount sold to men and women. We check if the correlation between the amount sold and their income level is significant for men and women.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Kendall's tau-b correlation coefficient  for income level and amount sold to men, and income level and amount sold to women","message":["%sql","","SELECT ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_MEN, 'COEFFICIENT'),2) MEN_COEFFICIENT,  ","       ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_MEN, 'TWO_SIDED_SIG'),2) MEN_TWO_SIDED_SIG,","       ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_WOMEN, 'COEFFICIENT'),2) WOMEN_COEFFICIENT,  ","       ROUND(CORR_K(INCOME_LEVEL, SOLD_TO_WOMEN, 'TWO_SIDED_SIG'),2) WOMEN_TWO_SIDED_SIG","FROM  INCOME_GENDER_SOLD_V"],"enabled":true,"result":{"startTime":1715728490919,"interpreter":"sql.low","endTime":1715728491106,"results":[{"message":"MEN_COEFFICIENT\tMEN_TWO_SIDED_SIG\tWOMEN_COEFFICIENT\tWOMEN_TWO_SIDED_SIG\n0.61\t0.01\t0.36\t0.1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Conclusion for Kendall's  tau-b correlation","message":["%md","","For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant."],"enabled":true,"result":{"startTime":1715728491184,"interpreter":"md.low","endTime":1715728491242,"results":[{"message":"<p>For the correlation between the income level and the amount sold to men, the TWO_SIDED_SIG value is very small, which indicates the correlation between the income level and the amount sold to men has significant correlation. But for women, such a relationship is not significant.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## End of script"],"enabled":true,"result":{"startTime":1715728491318,"interpreter":"md.low","endTime":1715728491377,"results":[{"message":"<h2 id=\"end-of-script\">End of script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715728491604,"interpreter":"md.low","endTime":1715728491664,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
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-[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Time Series ESM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715349263531,"interpreter":"md.low","endTime":1715349263685,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data","","Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715349263768,"interpreter":"md.low","endTime":1715349263874,"results":[{"message":"<h1 id=\"forecasting-sales-using-exponential-smoothing-algorithm-for-time-series-data\">Forecasting Sales Using Exponential Smoothing Algorithm for Time Series Data<\/h1>\n<p>Oracle Machine Learning supports time series forecasting via the Exponential Smoothing algorithm within Oracle Autonomous Database.  This sales forecasting example is based on the SH.SALES example data. All computations occur inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715349263950,"interpreter":"md.low","endTime":1715349264024,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>"],"enabled":true,"result":{"startTime":1715349264098,"interpreter":"md.low","endTime":1715349264171,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a view for model building","message":["%script","","CREATE OR REPLACE VIEW ESM_SH_DATA AS  ","SELECT TIME_ID, AMOUNT_SOLD FROM SH.SALES;"],"enabled":true,"result":{"startTime":1715349264246,"interpreter":"script.low","endTime":1715349266146,"results":[{"message":"\nView ESM_SH_DATA created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the records on the new view","message":["%script","","SELECT count(*) from ESM_SH_DATA;"],"enabled":true,"result":{"startTime":1715349266223,"interpreter":"script.low","endTime":1715349266871,"results":[{"message":"COUNT(*)   \n    918843 \n\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the data","message":["%sql","","SELECT * from ESM_SH_DATA;"],"enabled":true,"result":{"startTime":1715349266949,"interpreter":"sql.low","endTime":1715349267133,"results":[{"message":"TIME_ID\tAMOUNT_SOLD\n1998-01-20 00:00:00\t1205.99\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-04-05 00:00:00\t1250.25\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-05 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-20 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-07-30 00:00:00\t1210.21\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-05 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-10 00:00:00\t1222.87\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-08-20 00:00:00\t1210.21\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-05 00:00:00\t1210.81\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-10-20 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-05 00:00:00\t1205.99\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-10 00:00:00\t1210.81\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-20 00:00:00\t1222.87\n1998-12-30 00:00:00\t1222.87\n1998-12-30 00:00:00\t1222.87\n1998-12-31 00:00:00\t1222.87\n1998-12-31 00:00:00\t1222.87\n1999-01-10 00:00:00\t1222.87\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-20 00:00:00\t925.1\n1999-01-23 00:00:00\t925.1\n1999-01-23 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-01-30 00:00:00\t925.1\n1999-02-05 00:00:00\t1222.87\n1999-02-05 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-20 00:00:00\t1222.87\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-21 00:00:00\t914.34\n1999-02-23 00:00:00\t925.1\n1999-02-23 00:00:00\t925.1\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-02-25 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-05-20 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-07-30 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-08-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-10 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-10-20 00:00:00\t914.34\n1999-12-10 00:00:00\t914.34\n1999-12-10 00:00:00\t914.34\n1999-12-10 00:00:00\t914.34\n1999-12-14 00:00:00\t914.34\n1999-12-14 00:00:00\t914.34\n1999-12-14 00:00:00\t914.34\n1999-12-16 00:00:00\t914.34\n1999-12-20 00:00:00\t914.34\n1999-12-20 00:00:00\t914.34\n1999-12-20 00:00:00\t914.34\n1999-12-22 00:00:00\t914.34\n1999-12-22 00:00:00\t914.34\n1999-12-22 00:00:00\t914.34\n1999-12-23 00:00:00\t914.34\n1999-12-30 00:00:00\t927.14\n1999-12-30 00:00:00\t927.14\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-01-30 00:00:00\t1075.12\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-03 00:00:00\t1067.73\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-10 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-11 00:00:00\t1052.99\n2000-02-15 00:00:00\t1052.99\n2000-02-15 00:00:00\t1052.99\n2000-02-15 00:00:00\t1052.99\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-18 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-21 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-02-25 00:00:00\t1067.73\n2000-03-03 00:00:00\t1043.37\n2000-03-16 00:00:00\t1043.37\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-23 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-03-26 00:00:00\t1057.97\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-02 00:00:00\t1037.93\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-13 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-16 00:00:00\t1052.46\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-25 00:00:00\t1037.93\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-26 00:00:00\t1052.46\n2000-04-28 00:00:00\t1037.93\n2000-04-28 00:00:00\t1037.93\n2000-04-28 00:00:00\t1037.93\n2000-04-28 00:00:00\t1037.93\n2000-05-03 00:00:00\t1060.63\n2000-05-03 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-14 00:00:00\t1060.63\n2000-05-16 00:00:00\t1060.63\n2000-05-16 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-23 00:00:00\t1060.63\n2000-05-26 00:00:00\t1060.63\n2000-05-26 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-05-30 00:00:00\t1060.63\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-02 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-03 00:00:00\t1035.33\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-13 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-16 00:00:00\t1049.83\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-06-26 00:00:00\t1035.33\n2000-07-02 00:00:00\t1033.21\n2000-07-02 00:00:00\t1033.21\n2000-07-02 00:00:00\t1033.21\n2000-07-02 00:00:00\t1033.21\n2000-07-13 00:00:00\t1043.5\n2000-07-13 00:00:00\t1043.5\n2000-07-13 00:00:00\t1043.5\n2000-07-17 00:00:00\t1043.5\n2000-07-17 00:00:00\t1043.5\n2000-07-17 00:00:00\t1043.5\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-20 00:00:00\t1029.1\n2000-07-23 00:00:00\t1033.21\n2000-07-23 00:00:00\t1033.21\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-28 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-07-30 00:00:00\t1029.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-02 00:00:00\t1030.1\n2000-08-10 00:00:00\t1044.53\n2000-08-10 00:00:00\t1044.53\n2000-08-10 00:00:00\t1044.53\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-17 00:00:00\t1030.1\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-20 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-28 00:00:00\t1044.53\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-08-30 00:00:00\t1033.71\n2000-09-03 00:00:00\t920.04\n2000-09-19 00:00:00\t910.52\n2000-09-19 00:00:00\t910.52\n2000-09-19 00:00:00\t910.52\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-23 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-25 00:00:00\t907.34\n2000-09-26 00:00:00\t920.04\n2000-09-26 00:00:00\t920.04\n2000-09-26 00:00:00\t920.04\n2000-10-10 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-13 00:00:00\t1044.82\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-17 00:00:00\t1030.39\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-20 00:00:00\t1044.82\n2000-10-23 00:00:00\t1030.39\n2000-10-23 00:00:00\t1030.39\n2000-10-23 00:00:00\t1030.39\n2000-10-23 00:00:00\t1030.39\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-10-28 00:00:00\t1044.82\n2000-11-03 00:00:00\t1043.5\n2000-11-03 00:00:00\t1043.5\n2000-11-03 00:00:00\t1043.5\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-12 00:00:00\t1029.1\n2000-11-14 00:00:00\t1029.1\n2000-11-14 00:00:00\t1029.1\n2000-11-16 00:00:00\t1029.1\n2000-11-16 00:00:00\t1029.1\n2000-11-16 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-17 00:00:00\t1029.1\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-19 00:00:00\t1043.5\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-11-26 00:00:00\t1032.7\n2000-12-02 00:00:00\t983.67\n2000-12-02 00:00:00\t983.67\n2000-12-02 00:00:00\t983.67\n2000-12-02 00:00:00\t983.67\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-03 00:00:00\t970.1\n2000-12-10 00:00:00\t983.67\n2000-12-13 00:00:00\t983.67\n2000-12-13 00:00:00\t983.67\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-14 00:00:00\t973.97\n2000-12-16 00:00:00\t970.1\n2000-12-16 00:00:00\t970.1\n2000-12-16 00:00:00\t970.1\n2000-12-16 00:00:00\t970.1\n2000-12-17 00:00:00\t983.67\n2000-12-18 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-20 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-24 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2000-12-28 00:00:00\t983.67\n2001-01-13 00:00:00\t1173.97\n2001-01-13 00:00:00\t1173.97\n2001-01-13 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-17 00:00:00\t1173.97\n2001-01-18 00:00:00\t1173.97\n2001-01-23 00:00:00\t1173.97\n2001-01-23 00:00:00\t1173.97\n2001-01-23 00:00:00\t1173.97\n2001-01-24 00:00:00\t1173.97\n2001-01-24 00:00:00\t1173.97\n2001-01-24 00:00:00\t1173.97\n2001-01-30 00:00:00\t1173.97\n2001-01-30 00:00:00\t1173.97\n2001-01-30 00:00:00\t1173.97\n2001-02-02 00:00:00\t1185.17\n2001-02-02 00:00:00\t1185.17\n2001-02-02 00:00:00\t1185.17\n2001-02-02 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-10 00:00:00\t1185.17\n2001-02-13 00:00:00\t1185.17\n2001-02-13 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-14 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-17 00:00:00\t1185.17\n2001-02-23 00:00:00\t1185.17\n2001-02-23 00:00:00\t1185.17\n2001-02-28 00:00:00\t1185.17\n2001-02-28 00:00:00\t1185.17\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-14 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-16 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-23 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-26 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-03-30 00:00:00\t1172.59\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-02 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-03 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-13 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-16 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-04-26 00:00:00\t1128.28\n2001-05-03 00:00:00\t1126.93\n2001-05-03 00:00:00\t1126.93\n2001-05-14 00:00:00\t1126.93\n2001-05-14 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-16 00:00:00\t1126.93\n2001-05-17 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-23 00:00:00\t1126.93\n2001-05-26 00:00:00\t1126.93\n2001-05-27 00:00:00\t1126.93\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-02 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-03 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-13 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-16 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-26 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-06-28 00:00:00\t1124.25\n2001-07-02 00:00:00\t1112.18\n2001-07-02 00:00:00\t1112.18\n2001-07-02 00:00:00\t1112.18\n2001-07-13 00:00:00\t1112.18\n2001-07-13 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-17 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-20 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-23 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-07-30 00:00:00\t1112.18\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-02 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-10 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-13 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-17 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-20 00:00:00\t1110.86\n2001-08-23 00:00:00\t1110.86\n2001-08-23 00:00:00\t1110.86\n2001-08-27 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-08-28 00:00:00\t1110.86\n2001-09-02 00:00:00\t1101.48\n2001-09-03 00:00:00\t1101.48\n2001-09-03 00:00:00\t1101.48\n2001-09-17 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-19 00:00:00\t1101.48\n2001-09-20 00:00:00\t1101.48\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Options to use when creating Time Series models","message":["%md","","### Examples of possible setting overrides for ESM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm","","A complete list of settings can be found in the Documentation link:","","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>","","* Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is `EXSM_INTERVAL_QTR`.","> v_setlst('EXSM_INTERVAL') := 'EXSM_INTERVAL_QTR';","    ","* Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future",">  v_setlst('EXSM_PREDICTION_STEP') := '4';","    ","* Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.","","> v_setlst('EXSM_MODEL') := 'EXSM_WINTERS';","    ","* Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.","> v_setlst('EXSM_SEASONALITY') := '4';","","* Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.     ","> v_setlst('EXSM_SETMISSING') := 'EXSM_MISS_AUTO';"],"enabled":true,"result":{"startTime":1715349267231,"interpreter":"md.low","endTime":1715349267313,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-esm\">Examples of possible setting overrides for ESM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a><\/p>\n<\/li>\n<li>\n<p><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/p>\n<\/li>\n<li>\n<p>Specify the interval of the data set or a unit of interval size, like day, week, month, etc. This setting applies only to the time column with datetime type.  For example, if you want to predict for quarterly sales, the setting is <code>EXSM_INTERVAL_QTR<\/code>.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_INTERVAL') := 'EXSM_INTERVAL_QTR';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how many predictions to make. For example, if you want to display each value representing a quarter, then a value of 4 gives four values (Quarters) prediction into the future<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_PREDICTION_STEP') := '4';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the type of exponential smoothing model to be used. As an example, EXSM_WINTERS represents the Holt-Winters triple exponential smoothing model with additive trend and multiplicative seasonality. This type of model considers various combinations of additive and multiplicative trend, seasonality and error, with and without trend damping. Other options are 'EXSM_SIMPLE', 'EXSM_SIMPLE_MULT_ERR', 'EXSM_HOLT', 'EXSM_HOLT_DAMPED', 'EXSM_MULT_TREND', 'EXSM_MULT_TREND_DAMPED', 'EXSM_SEASON_ADD', 'EXSM_SEASON_MUL', 'EXSM_WINTERS', 'EXSM_WINTERS_DAMPED', 'EXSM_ADDWINTERS', 'EXSM_ADDWINTERS_DAMPED','EXSM_WINTERS_MUL_TREND', 'EXSM_WINTERS_MUL_TREND_DMP'.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_MODEL') := 'EXSM_WINTERS';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how long a season lasts. The parameter specifies a positive integer value as the length of seasonal cycle. The value it takes must be larger than 1. For example, 4 means that every group of four values forms a seasonal cycle, which makes sense if you are using 4 quarters to represent a year.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_SEASONALITY') := '4';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify how to handle missing values. In time series, the special value EXSM_MISS_AUTO indicates that, if the series contains missing values it is to be treated as an irregular time series.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('EXSM_SETMISSING') := 'EXSM_MISS_AUTO';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a Holt-Winters Model using quarterly parameters","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('ESM_SALES_FORECAST_1');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    ","    v_setlst('ALGO_NAME')            := 'ALGO_EXPONENTIAL_SMOOTHING';","    v_setlst('EXSM_INTERVAL')        := 'EXSM_INTERVAL_QTR'; -- accumulation int'l = quarter","    v_setlst('EXSM_PREDICTION_STEP') := '4';                 -- prediction step = 4 quarters","    v_setlst('EXSM_MODEL')           := 'EXSM_WINTERS';      -- ESM model = Holt-Winters","    v_setlst('EXSM_SEASONALITY')     := '4';                 -- seasonal cycle = 4 quarters    ","","    DBMS_DATA_MINING.CREATE_MODEL2(","        MODEL_NAME          => 'ESM_SALES_FORECAST_1',","        MINING_FUNCTION     => 'TIME_SERIES',","        DATA_QUERY          => 'select * from ESM_SH_DATA',","        SET_LIST            => v_setlst,","        CASE_ID_COLUMN_NAME => 'TIME_ID',","        TARGET_COLUMN_NAME  =>'AMOUNT_SOLD');","END;"],"enabled":true,"result":{"startTime":1715349267405,"interpreter":"script.low","endTime":1715349269707,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the forecast model settings","message":["%sql","","SELECT SETTING_NAME, SETTING_VALUE","FROM   USER_MINING_MODEL_SETTINGS","WHERE  MODEL_NAME = UPPER('ESM_SALES_FORECAST_1')","ORDER BY SETTING_NAME;"],"enabled":true,"result":{"startTime":1715349269787,"interpreter":"sql.low","endTime":1715349269861,"results":[{"message":"SETTING_NAME\tSETTING_VALUE\nALGO_NAME\tALGO_EXPONENTIAL_SMOOTHING\nEXSM_ACCUMULATE\tEXSM_ACCU_TOTAL\nEXSM_CONFIDENCE_LEVEL\t.95\nEXSM_INTERVAL\tEXSM_INTERVAL_QTR\nEXSM_MODEL\tEXSM_WINTERS\nEXSM_NMSE\t3\nEXSM_OPTIMIZATION_CRIT\tEXSM_OPT_CRIT_LIK\nEXSM_PREDICTION_STEP\t4\nEXSM_SEASONALITY\t4\nEXSM_SETMISSING\tEXSM_MISS_AUTO\nODMS_DETAILS\tODMS_ENABLE\nODMS_MISSING_VALUE_TREATMENT\tODMS_MISSING_VALUE_AUTO\nODMS_SAMPLING\tODMS_SAMPLING_DISABLE\nPREP_AUTO\tON\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review global diagnostics and model quality for 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diff --git a/machine-learning/notebooks/sql/OML4SQL Time Series Regression ESM plus GLM XGB.dsnb b/machine-learning/notebooks/sql/OML4SQL Time Series Regression ESM plus GLM XGB.dsnb
deleted file mode 100755
index e80a6464..00000000
--- a/machine-learning/notebooks/sql/OML4SQL Time Series Regression ESM plus GLM XGB.dsnb	
+++ /dev/null
@@ -1 +0,0 @@
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","The identified \u201ctarget\u201d time series also has confidence bounds computed for it. The output of multiple time series serves as an input to other machine learning techniques, like regression. ","","Time series regression is a powerful technique that supports one or more time series as predictors, along with flag variables, like holidays or events.","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1714398381784,"interpreter":"md.low","endTime":1714398381860,"results":[{"message":"<h1 id=\"time-series-regression-using-multiple-time-series-exponential-smoothing-with-glm-and-xgboost-regression-23ai-feature\">Time Series Regression using Multiple Time Series Exponential Smoothing with GLM and XGBoost Regression (23ai feature)<\/h1>\n<p>Oracle Machine Learning supports multiple time series for conveniently generating backcasts and forecasts on one or more time series in the same table or view.\nThe identified \u201ctarget\u201d time series also has confidence bounds computed for it. The output of multiple time series serves as an input to other machine learning techniques, like regression.<\/p>\n<p>Time series regression is a powerful technique that supports one or more time series as predictors, along with flag variables, like holidays or events.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":10,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>","<\/dl>"," "],"enabled":true,"result":{"startTime":1714398382015,"interpreter":"md.low","endTime":1714398382109,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/timeseries-5663161.jpg\" alt=\"OML Notebooks\" width=\"300\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":2,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md ","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a>"," "],"enabled":true,"result":{"startTime":1715349631517,"interpreter":"md.low","endTime":1715349631595,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-time-series\" target=\"_blank\">OML Time Series<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC\" target=\"_blank\">Algorithm Settings<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" target=\"_blank\">Shared Settings<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Scenario","","Perform Time Series Regression using a combination of ESM, GLM, and XGBoost algorithms.","","* Create the time series dataset","* Build an ESM model using multiple time series forecasting","* Build a GLM model using the ESM result","* Explore the model views","* Compare the regression forecast to the baseline (ESM) forecast","* Build an XGBOOST model and compare to ESM forecast"],"enabled":true,"result":{"startTime":1714398382658,"interpreter":"md.low","endTime":1714398382732,"results":[{"message":"<h2 id=\"scenario\">Scenario<\/h2>\n<p>Perform Time Series Regression using a 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(1575.59,2085.9,1792.6,2835.7,to_date('10-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.78,2064.1,1777.4,2832.5,to_date('11-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.68,2072.7,1780.6,2816.9,to_date('12-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1574.04,2091,1772.2,2807.2,to_date('13-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.33,2120.2,1785.9,2851.6,to_date('14-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1584.14,2120.4,1787.3,2834.4,to_date('15-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.91,2117.6,1824.1,2873.8,to_date('16-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1615.98,2123.7,1854.4,2865.9,to_date('17-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1643.83,2132.2,1908.2,2862.9,to_date('18-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1646.85,2137,1904.7,2870,to_date('19-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1639.12,2134.8,1894.1,2831.3,to_date('20-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1642.8,2121.4,1893.3,2816.4,to_date('21-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1659.07,2127.5,1905.6,2834.3,to_date('22-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.64,2135.5,1912,2843,to_date('23-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.93,2144.8,1899.5,2845.9,to_date('24-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1651.6,2131,1878.2,2812.2,to_date('25-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.35,2112.9,1905,2814,to_date('26-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1670.9,2131.3,1926.5,2837.7,to_date('27-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.3,2117.8,1937.2,2840,to_date('28-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.41,2096.1,1959.2,2838.3,to_date('29-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.09,2051.5,1944.1,2818,to_date('30-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.92,2065.7,1953.4,2817,to_date('01-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1661.96,2061,1944.6,2828.7,to_date('02-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.02,2100.6,1983.7,2868,to_date('03-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4781.62,7273,3446.7,5820.6,to_date('04-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4759.62,7259.5,3414.9,5807.7,to_date('05-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4690.52,7130.5,3381.3,5733.1,to_date('06-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4676.42,7077.3,3395.8,5695.6,to_date('07-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4762.71,7197.2,3483.2,5782.9,to_date('08-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4828.89,7187.5,3525.9,5818.9,to_date('09-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4852.22,7246.5,3521.5,5828.5,to_date('10-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4862.41,7276.7,3539.4,5829.8,to_date('11-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4838.67,7267.9,3526.6,5794.8,to_date('12-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4872.24,7328,3540.2,5782.3,to_date('13-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4905.59,7261.2,3598.3,5785.1,to_date('14-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4945.91,7236.5,3661.3,5834.9,to_date('15-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4908.55,7132.4,3652.5,5903.6,to_date('16-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4949.91,7143.8,3688.7,5997.9,to_date('17-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5045.16,7300.5,3688.9,5956.3,to_date('18-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5014.13,7341,3680.1,5947,to_date('19-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5064.35,7407.4,3738.5,5983.7,to_date('20-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5114.13,7472.1,3818.7,5967.8,to_date('21-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5029,7415.9,3783.8,5905.6,to_date('22-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5066.9,7530.3,3810.2,5939.3,to_date('23-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5069.89,7536.3,3800.2,5911.9,to_date('24-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5097.25,7585.5,3875.3,5932.2,to_date('25-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5135.35,7615.5,3883.3,6017.6,to_date('26-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5179.04,7638.8,3935.9,6052.8,to_date('27-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5254.32,7725.9,3932,6064.2,to_date('28-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5345.89,7827.7,3986.8,6105.8,to_date('29-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5309.67,7744.3,3903.3,6094,to_date('30-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5267.35,7588.1,3873.9,6055.2,to_date('31-JUL-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('01-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('02-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('03-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5367.98,7662.9,3867.7,6104.1,to_date('04-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5359.24,7616.3,3884.6,6074.1,to_date('05-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5292.97,7500.1,3845.9,6002,to_date('06-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5326.63,7453.7,3861.6,5922.2,to_date('07-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5407.93,7500.1,3885.7,5954.1,to_date('08-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2173.6,2674.5,1998.2,3241.5,to_date('09-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2158.07,2652.4,1961.5,3205.4,to_date('10-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2167.68,2662.5,1964.2,3203.9,to_date('11-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2178.1,2664.1,1983.4,3180,to_date('12-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2155.58,2642.1,1948.8,3139.3,to_date('13-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2155.81,2643,1966.8,3128.8,to_date('14-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2157.15,2658.8,1969.4,3121.4,to_date('15-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.17,2637.7,1952.9,3079.8,to_date('16-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.36,2628.6,1977.3,3112.7,to_date('17-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.45,2603.3,1924.6,3065.1,to_date('18-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.55,2614.7,1922.9,3079.1,to_date('19-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2059.15,2593,1919.3,3037.3,to_date('20-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2067.17,2594,1897.2,3014.8,to_date('21-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.81,2602.9,1899.4,3021.2,to_date('22-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.33,2609.1,1927.4,3028.2,to_date('23-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.83,2586.1,1902.7,2999.8,to_date('24-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.89,2581,1901.3,3008.5,to_date('25-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.36,2590,1905,3038.7,to_date('26-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.08,2558,1876.2,2992.5,to_date('27-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2002.3,2534.4,1879.3,3026.3,to_date('28-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2002.3,2499.5,1852.8,2983.5,to_date('29-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1988.67,2522.5,1876.1,3001.8,to_date('30-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1946.49,2480.4,1833.7,2956.3,to_date('31-AUG-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1965.41,2484.8,1843.4,2984.4,to_date('01-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1977.67,2496.9,1856.4,2998.7,to_date('02-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.56,2553.4,1898.3,3032.3,to_date('03-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2087.71,2570.2,1919,3073,to_date('04-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.68,2562.7,1918.1,3100.5,to_date('05-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.08,2593.3,1955.6,3141.9,to_date('06-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.52,2585.3,1933,3106.7,to_date('07-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.58,2575.6,1906.4,3120.2,to_date('08-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.58,2542.5,1898.6,3085.3,to_date('09-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2055.94,2529.8,1876.3,3060.8,to_date('10-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.15,2530.9,1867.4,3063.2,to_date('11-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.6,2508.6,1842.1,3032.8,to_date('12-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.64,2525.2,1841.6,3029.1,to_date('13-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.85,2494.7,1824.4,3000.9,to_date('14-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2009.45,2477.2,1831.5,2999.9,to_date('15-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.37,2458.6,1858.1,3029.6,to_date('16-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.86,2490.5,1905.7,3083.8,to_date('17-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.58,2506.5,1905.7,3097.4,to_date('18-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2066.18,2503.9,1905.7,3096.3,to_date('19-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2039.91,2500,1873.6,3081.3,to_date('20-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.37,2534.8,1911.1,3104.4,to_date('21-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.39,2541,1931.7,3097.6,to_date('22-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2043.3,2557.9,1906.1,3065.8,to_date('23-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.36,2543.9,1921.5,3063.8,to_date('24-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2090.78,2590.4,1943.9,3099.6,to_date('25-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2098.04,2602.7,1948.4,3103.5,to_date('26-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.67,2597.8,1948.4,3075.9,to_date('27-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.04,2590.9,1941.1,3095.3,to_date('28-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.21,2591.9,1954.5,3135.4,to_date('29-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.21,2609.4,1950.2,3146.5,to_date('30-SEP-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.94,2600.9,1927.5,3127.5,to_date('01-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.48,2606.5,1926.5,3131,to_date('02-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.2,2607,1927.8,3121,to_date('03-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.29,2571.8,1911.4,3078.7,to_date('04-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.05,2546.1,1893.1,3027.5,to_date('05-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2058.79,2568.6,1934.7,3036.6,to_date('06-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.27,2572.5,1945.9,3033.5,to_date('07-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.82,2579.4,1952.4,3047.1,to_date('08-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.24,2584.7,1940,3061.1,to_date('09-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.08,2594.9,1975.9,3081.4,to_date('10-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2045.54,2576,1964,3039.6,to_date('11-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2042.38,2577.5,1982.7,3017.3,to_date('12-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2067.26,2611,1973.7,3033.5,to_date('13-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2046.99,2589.1,1969,3016.1,to_date('14-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2044.04,2584.3,1969.8,3012.5,to_date('15-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2041.85,2589.3,1954.1,3013.8,to_date('16-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.19,2576,1937,2977.3,to_date('17-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2003.64,2562.8,1919.3,2943.4,to_date('18-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2019.13,2566,1917.1,2946.4,to_date('19-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.94,2581.4,1930.2,2980.6,to_date('20-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2054.23,2598,1931.1,2973.4,to_date('21-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.9,2604.7,1924.2,3013.6,to_date('22-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2066.59,2600.7,1928.1,3034.4,to_date('23-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2080.16,2614.7,1924.7,3058.1,to_date('24-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.5,2636.1,1940.9,3070.4,to_date('25-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2102.25,2649.8,1952.1,3091.7,to_date('26-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.98,2651,1949.9,3083.4,to_date('27-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.98,2651,1966.6,3083.4,to_date('28-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2110.77,2673.5,1956,3083.4,to_date('29-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.34,2656.2,1927.8,3095.8,to_date('30-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2074.68,2628.8,1894.2,3065.6,to_date('31-OCT-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.51,2628.8,1881.2,3065.5,to_date('01-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2079.19,2628.8,1881.2,3065.5,to_date('02-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2068.92,2612.3,1885.9,3065.7,to_date('03-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.9,2632.4,1901.8,3051.6,to_date('04-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2051.46,2613.1,1871.5,3032.3,to_date('05-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2058.2,2622.3,1886.4,3065,to_date('06-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2053.41,2617.3,1864.2,3055.8,to_date('07-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2062.08,2600.4,1859.2,3060.4,to_date('08-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.76,2597,1849.1,3049.4,to_date('09-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2059.68,2600.5,1844.1,3033.2,to_date('10-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.14,2600.2,1854,3048.3,to_date('11-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.25,2591.6,1872.8,3076.7,to_date('12-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2081.39,2582.8,1856.9,3054,to_date('13-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2085.62,2593.6,1860.3,3054.9,to_date('14-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2079.6,2595.5,1837.1,3028.6,to_date('15-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.86,2574.6,1813.3,2995.9,to_date('16-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2013.03,2525.3,1772.8,2954.2,to_date('17-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2027.67,2534.8,1780.2,2969,to_date('18-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.97,2525.5,1802.5,2982.2,to_date('19-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.79,2554.7,1826.3,3007.3,to_date('20-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2030.56,2552.4,1814.1,3022.2,to_date('21-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.68,2540.2,1813.4,2995.9,to_date('22-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.82,2536.9,1797.9,2991.6,to_date('23-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.44,2560.8,1827.8,3017.3,to_date('24-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2038.46,2563.7,1816.1,3034.7,to_date('25-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2077.94,2589.4,1842.4,3059.7,to_date('26-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2085.08,2621.4,1872.1,3062,to_date('27-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2093.01,2628,1870.4,3072.7,to_date('28-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2087.78,2617.4,1850.9,3072.5,to_date('29-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.8,2636.9,1874.4,3099,to_date('30-NOV-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.56,2635.4,1869.4,3109.9,to_date('01-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.96,2636,1850.1,3081.1,to_date('02-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.33,2642.4,1856.1,3071.3,to_date('03-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.49,2636,1861.9,3074.9,to_date('04-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.06,2621,1835,3051.1,to_date('05-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.29,2601,1822.5,3044.2,to_date('06-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.98,2597.2,1802.2,3018.6,to_date('07-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.82,2609.6,1805.6,3023.4,to_date('08-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2096.26,2610,1804.4,3019.5,to_date('09-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2126.44,2632.1,1827,3049.3,to_date('10-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.59,2623.3,1805.7,3037.7,to_date('11-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2106.05,2607.5,1802.2,3025.3,to_date('12-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.85,2600.4,1776.9,3009.3,to_date('13-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.21,2619.2,1808.4,3041.2,to_date('14-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.64,2617.2,1807.4,3038.2,to_date('15-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2094.16,2586.5,1795.2,3025.1,to_date('16-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.95,2553.2,1773.3,3001.9,to_date('17-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2041.26,2543.1,1748.6,2977,to_date('18-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2029.38,2515.8,1756.8,2992.1,to_date('19-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1989.2,2461.5,1727.1,2986.9,to_date('20-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2008.85,2467.7,1743.1,3021.1,to_date('21-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1974.14,2450.3,1721.8,3011.8,to_date('22-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.5,2463.3,1769,3050.6,to_date('23-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.22,2475,1738.6,3047,to_date('24-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.15,2491.7,1785.8,3094.1,to_date('25-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1979.52,2497.5,1788.8,3089.3,to_date('26-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1984.99,2516,1811.6,3124.2,to_date('27-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1984.15,2505.5,1813.8,3135,to_date('28-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.07,2501.9,1818,3139.7,to_date('29-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1935.08,2480.7,1795.7,3136.4,to_date('30-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1935.87,2526.6,1817.3,3153.4,to_date('31-DEC-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1928.82,2518.1,1836.1,3149.8,to_date('01-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1911.7,2495.6,1837.2,3128.3,to_date('02-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1918.85,2495,1852.4,3142.3,to_date('03-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1949.76,2533.1,1893,3176.2,to_date('04-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1914.69,2508.9,1859.5,3137.9,to_date('05-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1934.96,2511.1,1864,3143.1,to_date('06-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1965.68,2536.2,1882.8,3188.1,to_date('07-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1972.59,2536.3,1872.9,3190.2,to_date('08-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1976.52,2556.1,1890.2,3200.9,to_date('09-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.71,2559.3,1900.4,3210.9,to_date('10-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1980.81,2561.2,1880.9,3204.2,to_date('11-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1979.26,2563.1,1869.3,3190.9,to_date('12-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1991.08,2562.4,1871.9,3209.8,to_date('13-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1691.37,2120.5,1998.8,2882.6,to_date('01-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1701.46,2130.9,2001.5,2882.3,to_date('02-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.48,2142.4,1995.2,2918.6,to_date('03-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.46,2139.5,1986.8,2904.8,to_date('04-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.15,2134.6,1995.1,2916.6,to_date('05-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1702.27,2132.2,2004.3,2957.3,to_date('06-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.91,2150.1,2009.7,2949.9,to_date('07-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.48,2157,1992.4,2956.7,to_date('08-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1708.65,2165,1988.9,2953.4,to_date('09-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1688.74,2127.2,1965.2,2915.9,to_date('10-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1705.05,2157.2,1986,2922.4,to_date('11-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1700.28,2150.8,1975.3,2919.3,to_date('12-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1689.71,2139.1,1967.3,2889.9,to_date('13-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1696.38,2154.7,1963.5,2883.3,to_date('14-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.57,2182.4,1962.7,2900.1,to_date('15-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.59,2161.3,1939.3,2863.9,to_date('16-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.2,2166.3,1952.2,2861.1,to_date('17-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1666.72,2146.8,1954.6,2860.6,to_date('18-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1663.27,2135.7,2001.4,2852.8,to_date('19-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1667.26,2160.9,2025.8,2852.9,to_date('20-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1675.18,2175.7,2033.9,2846.5,to_date('21-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.64,2188.9,2035.9,2861,to_date('22-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1676.84,2190.3,2031.4,2878.7,to_date('23-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1670.04,2188.4,2005.9,2878.4,to_date('24-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1657.06,2190.3,1990.8,2869.9,to_date('25-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.36,2184,1974.7,2838.8,to_date('26-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1667.64,2196,1995.3,2832.2,to_date('27-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.6,2184.9,1984,2822.1,to_date('28-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('29-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('30-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('31-JAN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.54,2181.7,2018.1,2846.8,to_date('01-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.95,2165.7,2015.4,2842.1,to_date('02-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.67,2160.5,1988.6,2839.7,to_date('03-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1678.65,2162.9,1986.7,2824.4,to_date('04-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.14,2166.3,1968.9,2830,to_date('05-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.06,2170.8,1949.3,2856.1,to_date('06-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1666.49,2178.1,1931.9,2869.6,to_date('07-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.01,2177.3,1944.5,2881.1,to_date('08-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.03,2162.3,1916.6,2843.8,to_date('09-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1643.53,2140.4,1911.6,2822.3,to_date('10-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.59,2124.7,1927.4,2832.7,to_date('11-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.88,2138.2,1942.5,2797.3,to_date('12-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.6,2123.2,1920.6,2786.8,to_date('13-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.83,2129.7,1939,2813.1,to_date('14-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632,2152.8,1937,2813.1,to_date('15-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.92,2160,1923.6,2812.6,to_date('16-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.88,2165.8,1926.3,2796.5,to_date('17-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.74,2165.4,1920.5,2786.3,to_date('18-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.7,2162.6,1878.6,2793.7,to_date('19-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.45,2179.1,1877.2,2829.7,to_date('20-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.46,2191.1,1854.5,2836.1,to_date('21-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.99,2191.5,1872.7,2860.8,to_date('22-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.02,2183.3,1879.9,2849.3,to_date('23-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.35,2186.3,1851.7,2847,to_date('24-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.37,2205.2,1835.7,2858.1,to_date('25-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.26,2227.2,1846.4,2847.3,to_date('26-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.64,2227,1836.8,2819.7,to_date('27-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.64,2227,1836.8,2816.8,to_date('28-FEB-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.98,2232.9,1836.8,2812.2,to_date('01-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1608.58,2237.4,1861.4,2825.6,to_date('02-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.05,2243.5,1891.1,2837.7,to_date('03-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.18,2247.5,1890.4,2846.9,to_date('04-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.18,2267.1,1904.6,2855.3,to_date('05-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.21,2271.6,1888.7,2840.7,to_date('06-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.21,2271.6,1888.7,2840.7,to_date('07-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.59,2253.8,1872.8,2849.2,to_date('08-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.53,2259.8,1875.8,2863,to_date('09-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.56,2269,1867.9,2852.8,to_date('10-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.47,2284.2,1859.7,2829.9,to_date('11-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1660.88,2309.7,1887.9,2844.8,to_date('12-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1662.28,2294.5,1893.7,2844.4,to_date('13-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.69,2312.5,1915.2,2866.9,to_date('14-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.69,2309.2,1911.2,2860,to_date('15-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.85,2308.6,1920.4,2861.8,to_date('16-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.44,2293.8,1916.8,2885.5,to_date('17-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1684.57,2274,1897.9,2870,to_date('18-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1689.93,2294.6,1918.8,2883,to_date('19-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1681.47,2323.4,1900.3,2875.7,to_date('20-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.14,2318,1910.3,2879.4,to_date('21-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1697.26,2329.5,1929.2,2903.4,to_date('22-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.33,2335,1935.3,2907.6,to_date('23-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.96,2323.9,1942.4,2900.7,to_date('24-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1692.16,2335.8,1963.3,2894.7,to_date('25-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1699.52,2347.3,1960.8,2887.5,to_date('26-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1712.33,2369.8,1991,2897,to_date('27-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1703.05,2371.9,1977.5,2886,to_date('28-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1700.93,2376.2,1971.9,2900,to_date('29-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.36,2375.5,1960.2,2888.8,to_date('30-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1697.39,2368.8,1941.2,2857.7,to_date('31-MAR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1694.83,2364.4,1925.4,2838.5,to_date('01-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1705.66,2390.9,1935.1,2848.1,to_date('02-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1739.48,2372,1943.7,2848.3,to_date('03-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.63,2397.1,1980.4,2845.9,to_date('04-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.36,2403,1985.7,2843.2,to_date('05-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1808.74,2408.7,1992.2,2830.9,to_date('06-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1806.52,2418.1,1991.2,2837.1,to_date('07-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1815.63,2410.5,1991.2,2832.3,to_date('08-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1807.12,2399.9,1963.1,2831.7,to_date('09-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1829.36,2396.4,1974.9,2833,to_date('10-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1835.09,2381.8,1981.7,2842.9,to_date('11-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1826.45,2324.5,1968.4,2823.9,to_date('12-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1821.28,2313.7,1947.5,2814.1,to_date('13-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1828.53,2340.2,1965.7,2820.1,to_date('14-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1830.61,2350.5,1995,2827.7,to_date('15-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1859.49,2388.7,2006.2,2844.2,to_date('16-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1846.02,2398,1998.1,2879.4,to_date('17-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1832.2,2408.7,1989.5,2884.2,to_date('18-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1823.4,2401.5,2036,2917.6,to_date('19-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1823.07,2400.9,2085.9,2926.5,to_date('20-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1818.1,2400.7,2129,2941.7,to_date('21-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1857.36,2429.8,2110.6,2945,to_date('22-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1861.22,2431.5,2101.4,2941.3,to_date('23-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.8,2424.9,2115.3,2943.4,to_date('24-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1878.94,2423.3,2149.8,2969.8,to_date('25-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.24,2420.5,2138.5,2986.4,to_date('26-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1864.51,2384.5,2139.8,2971.6,to_date('27-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1894.56,2411.1,2167.4,3006.1,to_date('28-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1908.69,2449.5,2161.9,3009.1,to_date('29-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1917.69,2461,2148,3010.1,to_date('30-APR-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1903.44,2478.7,2148,3008.3,to_date('01-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1918.75,2464.9,2136.3,3025,to_date('02-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1930.29,2488,2160.8,3073.6,to_date('03-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1937.77,2480.6,2139.2,3065.5,to_date('04-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1909.53,2474.5,2128.2,3057.6,to_date('05-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1893.48,2467.3,2111.4,3042,to_date('06-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1907.65,2472.4,2123.4,3049.3,to_date('07-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1915.59,2499.7,2159.3,3079.2,to_date('08-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1890.58,2475.2,2173.6,3079.2,to_date('09-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1909.54,2478.6,2183.9,3100.6,to_date('10-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1929.56,2481,2205.7,3100.6,to_date('11-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1931.88,2488.5,2216.5,3100,to_date('12-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1923.67,2470.4,2191.9,3085.1,to_date('13-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1928.63,2463.8,2185.1,3072.6,to_date('14-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1920.43,2438.8,2156.1,3057.3,to_date('15-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1911.15,2392.5,2158,3055.4,to_date('16-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1878.77,2403.4,2137.3,3038.6,to_date('17-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.32,2397.9,2129.4,3035.4,to_date('18-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.46,2382.1,2108.8,3031.2,to_date('19-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1868.28,2363.6,2108.4,3037,to_date('20-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1874.38,2365.6,2119.1,3024.8,to_date('21-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1869.25,2388.3,2134.1,3028,to_date('22-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1852.81,2361.8,2078.5,2989.4,to_date('23-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1862.62,2374.4,2075.6,3003.9,to_date('24-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1884.67,2385.2,2099.5,3005.5,to_date('25-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1922.69,2418.4,2107.4,3004.5,to_date('26-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1922.05,2431.8,2094.4,3001.6,to_date('27-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1899.54,2414.6,2080,3007.5,to_date('28-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1902.14,2425.8,2057.5,3001.3,to_date('29-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1890.18,2445.9,2092.6,3005.2,to_date('30-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1914.4,2482.4,2108.6,3026.3,to_date('31-MAY-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1915.61,2478.3,2120,3036.9,to_date('01-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1908.97,2485.4,2126.8,3030.1,to_date('02-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1910.23,2473.1,2114.6,3037.5,to_date('03-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1920.46,2481.6,2116.7,3039.3,to_date('04-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1934.99,2490.3,2128.7,3067.7,to_date('05-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1973.45,2521.3,2158.8,3085.2,to_date('06-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1994.09,2534.8,2164.5,3100.8,to_date('07-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1991.95,2528,2147.4,3092.4,to_date('08-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2015.71,2533.6,2156.4,3108.6,to_date('09-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2009.28,2548.5,2138.7,3102.2,to_date('10-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2004.11,2552.5,2126.9,3094.7,to_date('11-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1999.6,2571,2127.3,3080.9,to_date('12-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.35,2586.4,2113.9,3086.3,to_date('13-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2023.26,2612.6,2139.3,3120.8,to_date('14-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2032.25,2663.6,2145,3137.6,to_date('15-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.93,2660.7,2147.3,3129.6,to_date('16-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2029.87,2668.4,2149.7,3156.3,to_date('17-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.05,2690.1,2199.7,3188.3,to_date('18-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.62,2701.4,2231.9,3199,to_date('19-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.18,2685,2227.7,3184.8,to_date('20-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.75,2700.2,2210.4,3165.3,to_date('21-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.97,2715.9,2192,3154.3,to_date('22-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2053.66,2720.9,2196,3163,to_date('23-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.98,2723.2,2182,3171,to_date('24-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2068.51,2727.1,2182,3164.4,to_date('25-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.4,2742.9,2169.7,3164.1,to_date('26-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.22,2749.9,2171.2,3162.3,to_date('27-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.1,2706.6,2136,3149,to_date('28-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2013.65,2654.2,2081,3085.6,to_date('29-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2007.5,2675.3,2084.8,3077.6,to_date('30-JUN-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.74,2727.1,2112.9,3096,to_date('01-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.11,2742.1,2087.3,3098.5,to_date('02-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2021.81,2720.1,2087.3,3099.7,to_date('03-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2023.06,2721.1,2096.9,3099.1,to_date('04-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.2,2717.4,2117.9,3093.3,to_date('05-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.17,2712.3,2115.9,3097.5,to_date('06-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.17,2711.1,2148,3120,to_date('07-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2075.99,2727.1,2149.7,3125.5,to_date('08-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.13,2733.9,2145.2,3108,to_date('09-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.36,2696.5,2082.6,3070.6,to_date('10-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5373.8,7369.1,3860.4,5955,to_date('11-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.28,7308.9,3835.1,5931.1,to_date('12-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5262.57,7265.5,3822.1,5898.1,to_date('13-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5144.42,7232.3,3788.7,5863.9,to_date('14-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5002.71,7053.5,3689.4,5722.4,to_date('15-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5110.88,7180.1,3777.2,5806.6,to_date('16-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5083.8,7241.8,3726.2,5833.1,to_date('17-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5241.23,7401.4,3867.9,5928.3,to_date('18-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5241.23,7401.4,3867.9,6011.3,to_date('19-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5337.75,7640.8,3979.3,6011.3,to_date('20-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5226.2,7596.2,3945.5,5986.5,to_date('21-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5264.62,7610.8,3947.5,5992.4,to_date('22-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5164.89,7536,3912.8,5938,to_date('23-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5270.61,7587.1,3912.8,5969.8,to_date('24-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5348.75,7677.5,4007.3,6028.3,to_date('25-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5307.82,7627.3,3986.1,5956.7,to_date('26-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5371.99,7582.8,4018.5,5972.9,to_date('27-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5374.11,7550.6,4012,5948.5,to_date('28-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5414.31,7519.4,3990.2,5917.8,to_date('29-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5343.66,7371.4,3945.3,5826.2,to_date('30-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5441,7483.2,3980.8,5877.8,to_date('31-JUL-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5514.51,7495.8,4047.9,5907.4,to_date('01-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5514.51,7495.8,4047.9,5935.6,to_date('02-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5530.19,7542.7,4049.8,5955.6,to_date('03-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5592.46,7657.1,4108.7,5955.6,to_date('04-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5639.89,7731.9,4115.9,5970.7,to_date('05-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5466.88,7633.5,4017.4,5870.2,to_date('06-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5507.36,7605,4014.9,5862.3,to_date('07-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5556.99,7656.1,4041.2,5870.7,to_date('08-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5556.99,7656.1,4041.2,5837.9,to_date('09-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5583.83,7657.5,4087,5842.3,to_date('10-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5640.42,7676.3,4149.4,5898.4,to_date('11-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5605.38,7592.9,4119,5860.8,to_date('12-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5724.75,7699.5,4185.1,5947.3,to_date('13-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5787.05,7743.4,4204.6,6037.8,to_date('14-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5773.77,7716.8,4201.9,6019.8,to_date('15-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5799.22,7652.6,4208.6,5987.4,to_date('16-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5799.22,7498.4,4141.6,5852.5,to_date('17-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5631.34,7417.4,4050.8,5769.8,to_date('18-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5581.24,7342.7,4005.3,5715.7,to_date('19-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5621.71,7388.7,4013.3,5729.7,to_date('20-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5742.83,7562.7,4092.9,5832.7,to_date('21-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5689.89,7488,4052.3,5812.1,to_date('22-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5644.22,7518.6,4027.3,5748.1,to_date('23-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5648.11,7511.8,4018.6,5712.4,to_date('24-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5748.34,7624.8,4065,5772,to_date('25-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5784.4,7667.9,4126.3,5804.9,to_date('26-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5886.72,7794.7,4203.8,5858.9,to_date('27-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5870.49,7816.9,4215.7,5877.4,to_date('28-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5933.73,7881.9,4248.2,5884.5,to_date('29-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5841.83,7882,4203.5,5832.5,to_date('30-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5910.51,8038.2,4260.7,5919.9,to_date('31-AUG-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5905.15,8047.3,4252.1,5960.2,to_date('01-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5961.45,8099,4304.4,5988.4,to_date('02-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5942.06,8166,4311.1,5990.3,to_date('03-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5975.88,8160,4333.1,6003.4,to_date('04-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6018.89,8227.2,4339.9,6009.6,to_date('05-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6000.84,8205,4319.2,5969.7,to_date('06-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6001.24,8192.4,4256.4,5927.9,to_date('07-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6023.31,8141.9,4256.4,5958.2,to_date('08-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6101.9,8180.5,4256.4,6100.2,to_date('09-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6106.1,8158.1,4344.3,6151.5,to_date('10-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6108,8126.5,4358.1,6116.8,to_date('11-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6162.86,8288.2,4388.5,6174,to_date('12-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6186.09,8400.8,4368.9,6179,to_date('13-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6184.1,8412,4322.1,6132.7,to_date('14-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6081.11,8340.7,4220.1,5989.6,to_date('15-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6043.82,8229.2,4235.9,5976.2,to_date('16-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (6040.58,8205.7,4205.4,5892.3,to_date('17-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5854.35,7998.7,4139.5,5836.1,to_date('18-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5867.52,8093,4122.4,5835.8,to_date('19-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5828.74,8102.7,4139.2,5844.1,to_date('20-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5906.33,8205.5,4197.6,5910.7,to_date('21-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5861.19,8239.5,4177.3,5837,to_date('22-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5774.38,8139.2,4095,5809.7,to_date('23-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5718.7,8170.2,4047.9,5736.1,to_date('24-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5614.77,7943.2,3976.4,5632.5,to_date('25-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5528.12,7846.2,3968.6,5594.1,to_date('26-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5598.32,7952.9,4041.9,5680.4,to_date('27-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5460.43,7721.3,3939.5,5587.6,to_date('28-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5285.78,7447.9,3846,5432.8,to_date('29-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5386.94,7607.5,3945.7,5462.2,to_date('30-SEP-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5355.03,7552.6,3951.7,5399.5,to_date('01-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5473.72,7676.3,3995,5455,to_date('02-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2224.85,2884,2145.3,3149.4,to_date('03-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.19,2887.8,2148.6,3159.1,to_date('04-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2215.19,2877.7,2152.4,3145.8,to_date('05-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2198.24,2843.7,2139.1,3131.7,to_date('06-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.92,2872.9,2159.6,3168.3,to_date('07-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.37,2869.1,2160.1,3138.2,to_date('08-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.89,2837.1,2136,3128,to_date('09-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.09,2818.7,2102.7,3098.3,to_date('10-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.41,2781.6,2092,3101.2,to_date('11-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.13,2787.9,2135.2,3133.7,to_date('12-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2208.68,2763.5,2116.3,3106.1,to_date('13-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.36,2776.2,2130.9,3125.3,to_date('14-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2256.98,2791.3,2147.3,3150,to_date('15-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2237.82,2768.9,2150.3,3129.9,to_date('16-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2252.51,2736.3,2166,3125.3,to_date('17-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.72,2765.2,2186.2,3125.3,to_date('18-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2261.71,2755.9,2179,3100,to_date('19-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.85,2705.6,2141.6,3070.5,to_date('20-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.78,2682.2,2162.6,3106,to_date('21-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2233.55,2641.4,2158.2,3106,to_date('22-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.77,2569.5,2139.4,3097.8,to_date('23-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.34,2629,2165,3136.3,to_date('24-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2248.02,2633,2176.7,3130.5,to_date('25-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2248.02,2633,2176.7,3137.8,to_date('26-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2257.33,2678.7,2187,3119.2,to_date('27-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2272.96,2709,2187.8,3115.6,to_date('28-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.11,2727.1,2195.2,3123.5,to_date('29-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2254.21,2740.6,2184,3116.5,to_date('30-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2245.79,2725.8,2165.4,3122.8,to_date('31-OCT-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2238.97,2732.7,2155.4,3127.3,to_date('01-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2238.97,2732.7,2155.4,3108.4,to_date('02-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.01,2692,2133.3,3089.1,to_date('03-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.56,2673.1,2084.4,3020.7,to_date('04-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2146,2689.2,2091.9,3019.7,to_date('05-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.8,2711.9,2050.7,2966.4,to_date('06-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.76,2742.9,2052.5,2966.4,to_date('07-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.34,2722.9,2029.9,2970.5,to_date('08-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.62,2731.5,1979.7,2931.9,to_date('09-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2120.23,2725.9,2007.4,2980.8,to_date('10-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2158.88,2728.4,2041.7,2997.8,to_date('11-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.59,2781.4,2037.2,3009.4,to_date('12-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2131.8,2778.1,2023.7,3004.8,to_date('13-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.93,2801.1,2046.8,3038.2,to_date('14-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2131.14,2777.2,2028.4,3028.9,to_date('15-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.58,2763.8,2020.7,3055.9,to_date('16-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2084.42,2744.2,1977.7,3016.3,to_date('17-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.44,2740,1992,3039.6,to_date('18-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.21,2701.8,1966.4,3045.8,to_date('19-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.29,2666.6,1942.8,3030.1,to_date('20-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2031.8,2627.6,1936,3022.9,to_date('21-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1986.42,2545,1903,2971.1,to_date('22-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1957.08,2544.2,1890.8,2940.2,to_date('23-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2004.93,2595.7,1917,2960.4,to_date('24-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2032.52,2626.7,1939,2942.4,to_date('25-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2005.07,2577.2,1907,2876.6,to_date('26-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2000.48,2561.4,1911.6,2899.9,to_date('27-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.25,2604.4,1925.8,2909,to_date('28-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2042.45,2631.2,1936.3,2946.3,to_date('29-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.85,2608.8,1892,2919.2,to_date('30-NOV-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.69,2588.9,1872.9,2936.4,to_date('01-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.7,2636.4,1866.2,2970.4,to_date('02-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2034.64,2609.7,1878.7,2965,to_date('03-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2031.33,2598.9,1889,2946.7,to_date('04-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2049.1,2590.5,1920.8,2964.4,to_date('05-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.83,2560.3,1920.8,2962.4,to_date('06-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.46,2562.5,1949.8,2983.8,to_date('07-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.57,2508,1942.1,2963.9,to_date('08-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2051.25,2474.5,1974.6,3005.3,to_date('09-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.71,2528.3,1974.6,3050.4,to_date('10-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2103.54,2514.9,1974.6,3074.8,to_date('11-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.96,2494.7,2025.1,3082,to_date('12-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.86,2521.6,2052.3,3091.3,to_date('13-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2120.97,2562.3,2043.7,3077.2,to_date('14-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2126.75,2579.4,2053.8,3095.1,to_date('15-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2148.23,2599.2,2041.4,3114.7,to_date('16-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.21,2601.2,2059.8,3106.1,to_date('17-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.32,2604.4,2076.8,3117.2,to_date('18-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.93,2544.9,2055.7,3082.3,to_date('19-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2134.12,2559.3,2053.4,3095.9,to_date('20-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2152.19,2579.5,2075,3082.6,to_date('21-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2161.5,2579.5,2069.6,3097.4,to_date('22-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.63,2629.7,2117.2,3157.5,to_date('23-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.83,2620.7,2115,3160.4,to_date('24-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.66,2615.3,2096.5,3150.5,to_date('25-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.81,2600.8,2107.1,3167.5,to_date('26-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.32,2617.5,2106.3,3171.9,to_date('27-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2160.56,2585.5,2074.5,3168.6,to_date('28-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.51,2591.4,2064.2,3167,to_date('29-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2156.31,2588.8,2038.9,3138.2,to_date('30-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2133.74,2580.5,2007,3142.3,to_date('31-DEC-93','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2134.34,2570.6,2007,3142.2,to_date('01-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2152.65,2588.7,2012.4,3147.3,to_date('02-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.56,2599.3,2035,3190.3,to_date('03-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2151.44,2582.7,2010.5,3182.6,to_date('04-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.84,2557,2001.3,3191.4,to_date('05-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.37,2532.4,1972.6,3171.3,to_date('06-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2121.25,2530.9,2000.6,3175.1,to_date('07-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.98,2541.1,2006.3,3205.2,to_date('08-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2153.48,2551.1,2026.5,3234.2,to_date('09-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.58,2581.3,2062.7,3265.1,to_date('10-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2215.72,2635.2,2075.3,3265.1,to_date('11-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2205.82,2635.7,2060.4,3249.6,to_date('12-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2207.09,2645.6,2069.1,3251.3,to_date('13-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2185.78,2628.4,2034.9,3216.5,to_date('14-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.38,2672,2020.4,3222.7,to_date('15-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2027.99,2702.1,2071.5,3069.3,to_date('16-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.45,2717.8,2070.6,3067.2,to_date('17-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.8,2726.8,2118.4,3093.1,to_date('18-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2045.25,2741.9,2120.6,3111.4,to_date('19-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2052.09,2738.2,2119.3,3135.8,to_date('20-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2052.92,2738.5,2110.1,3166.9,to_date('21-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.77,2774.3,2154,3233.2,to_date('22-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.76,2787.2,2160.5,3223.9,to_date('23-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.3,2819,2188.4,3234.2,to_date('24-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.01,2836.4,2186.7,3237.3,to_date('25-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.39,2834.8,2176.1,3237.3,to_date('26-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.95,2843.8,2205.3,3277.4,to_date('27-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.58,2858.4,2211.4,3271.6,to_date('28-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.5,2861.2,2198.1,3261.3,to_date('29-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.83,2876.9,2196.3,3254.6,to_date('30-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.07,2867.8,2156.5,3248.4,to_date('31-JAN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.52,2850.6,2162.6,3278.8,to_date('01-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.03,2867.7,2160.3,3311.2,to_date('02-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.76,2889.5,2196.4,3337.1,to_date('03-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.18,2909,2223.5,3364.9,to_date('04-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.78,2918,2215.9,3342.4,to_date('05-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2209.17,2942.4,2225.8,3355.7,to_date('06-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2227.63,2967.4,2243,3396.5,to_date('07-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2227.63,2967.4,2251.5,3412.3,to_date('08-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.7,2972.6,2276.6,3412.3,to_date('09-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.91,2930.9,2264.6,3412.3,to_date('10-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2229.62,2934.2,2281.9,3462,to_date('11-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2255.29,2957.6,2281.2,3428.8,to_date('12-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2255.29,2957.6,2268.2,3418.4,to_date('13-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2274.62,2996.2,2290.6,3418.4,to_date('14-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.85,2999.2,2274.3,3408.5,to_date('15-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2233.61,3009.4,2249.6,3379.2,to_date('16-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2220.63,3021.9,2275.1,3403,to_date('17-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2224.95,3042.9,2307.6,3446,to_date('18-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2225,3015.9,2317.3,3440.6,to_date('19-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2228.1,3026.6,2331.3,3413.8,to_date('20-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.06,2999,2281.9,3372,to_date('21-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.37,2949.9,2252.2,3360,to_date('22-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2151.05,2990.6,2262.3,3400.6,to_date('23-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2115.56,3011.1,2234.8,3407.8,to_date('24-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.35,3037.5,2247.4,3437,to_date('25-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.52,3049.4,2274.7,3475.1,to_date('26-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2098.36,3045.9,2257.8,3470,to_date('27-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.94,3039.3,2244,3484.2,to_date('28-FEB-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2107.29,3041.3,2274.5,3481.4,to_date('01-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2090.78,3066.5,2278.3,3444,to_date('02-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.66,3091.3,2282.4,3436.1,to_date('03-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2123.31,3095.2,2281,3427.3,to_date('04-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2156.61,3140.7,2313.2,3447.4,to_date('05-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2192.6,3178.4,2334.4,3491.8,to_date('06-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2181.88,3148.7,2331.3,3481.5,to_date('07-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.05,3169.1,2355.9,3520.3,to_date('08-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.08,3151.9,2322,3491.5,to_date('09-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.9,3166.6,2329.2,3475.4,to_date('10-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.11,3089.8,2287.1,3419.1,to_date('11-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.57,3097.2,2299.9,3440.2,to_date('12-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.43,3054.2,2302.1,3429.1,to_date('13-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.69,3012.2,2296.8,3407,to_date('14-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.77,3012.2,2275.1,3378.9,to_date('15-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.93,2947.1,2281.6,3363.5,to_date('16-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.71,2947.1,2258,3393.2,to_date('17-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.25,2947.1,2258,3417.7,to_date('18-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2162.29,3033.4,2281.2,3425.3,to_date('19-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2133.85,3025.9,2251.8,3382.6,to_date('20-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.06,2997.6,2215.2,3350.3,to_date('21-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.64,2982.8,2226.7,3333.7,to_date('22-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.25,3027.5,2252,3341.9,to_date('23-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2082.9,2958.4,2208.3,3267.5,to_date('24-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2075.33,2929.3,2199,3281.2,to_date('25-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2103.24,2888.2,2238.1,3328.1,to_date('26-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.2,2847.4,2183.1,3270.6,to_date('27-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2018.69,2768.5,2144.7,3248.1,to_date('28-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2044.45,2803.4,2144.7,3246.5,to_date('29-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2076.76,2865.2,2178.7,3278,to_date('30-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.12,2918.7,2219.9,3305.9,to_date('31-MAR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2125.47,2902.4,2216.4,3264.4,to_date('01-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.01,2858.5,2199.7,3246.7,to_date('02-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.51,2861.2,2184.6,3233.9,to_date('03-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.89,2831.6,2175,3191.9,to_date('04-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2169.4,2870.5,2215,3233.4,to_date('05-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2178.91,2906.9,2258.5,3267.4,to_date('06-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2168.11,2887.1,2242.7,3242.9,to_date('07-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2160.45,2887.7,2247.8,3255.7,to_date('08-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.39,2843.5,2221.3,3218.1,to_date('09-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.55,2804.3,2202.7,3198,to_date('10-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2141.7,2824.7,2200.7,3201.5,to_date('11-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2162.96,2857,2200.2,3155.3,to_date('12-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.36,2850.8,2152.6,3121.7,to_date('13-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144,2831.8,2136.6,3129,to_date('14-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2167.72,2862.4,2144.5,3129.5,to_date('15-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2162.82,2827.4,2123.4,3123.4,to_date('16-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2151.84,2814.5,2083.9,3092.4,to_date('17-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.88,2794.8,2081.9,3086.4,to_date('18-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.88,2794.8,2081.9,3086.4,to_date('19-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.88,2794.8,2081.9,3086.4,to_date('20-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2177.09,2807.3,2100.3,3116.2,to_date('21-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.89,2830.2,2128.2,3131.5,to_date('22-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2202.57,2860.9,2119.6,3129,to_date('23-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2203.18,2873.4,2114.8,3120.8,to_date('24-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.75,1678.1,1772.8,2443.6,to_date('25-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.63,1688.5,1750.5,2460.2,to_date('26-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.51,1678.6,1718,2448.2,to_date('27-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.04,1684.1,1708.1,2470.4,to_date('28-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.16,1686.6,1723.1,2484.7,to_date('29-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1610.61,1671.6,1714.3,2466.8,to_date('30-APR-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.75,1682.9,1734.5,2487.9,to_date('01-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1640.17,1703.6,1757.4,2508.4,to_date('02-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1635.47,1697.5,1754,2510.5,to_date('03-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1645.89,1716.3,1754.3,2497.4,to_date('04-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1647.84,1723.8,1759.8,2532.5,to_date('05-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.35,1730.5,1755.5,2556.8,to_date('06-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1629.93,1727.4,1758.1,2561,to_date('07-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.49,1733.3,1757.5,2547.3,to_date('08-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.74,1734,1763.5,2541.5,to_date('09-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.63,1728.3,1762.8,2558.5,to_date('10-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.99,1737.1,1768.9,2587.9,to_date('11-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.18,1723.1,1778.1,2580.5,to_date('12-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.42,1723.6,1780.1,2579.6,to_date('13-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.95,1719,1767.7,2589.3,to_date('14-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.75,1721.2,1757.9,2595,to_date('15-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.67,1725.3,1756.6,2595.6,to_date('16-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.29,1727.2,1754.7,2588.8,to_date('17-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1620.49,1727.2,1766.8,2591.7,to_date('18-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.67,1731.6,1766.5,2601.7,to_date('19-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.07,1724.1,1762.2,2585.4,to_date('20-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.98,1716.9,1759.5,2573.3,to_date('21-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.87,1723.4,1782.4,2597.4,to_date('22-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.37,1723,1789.5,2600.6,to_date('23-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.47,1728.4,1783.5,2570.6,to_date('24-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.55,1722.1,1780.4,2569.4,to_date('25-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1650.43,1724.5,1808.8,2584.9,to_date('26-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1650.06,1733.6,1820.3,2608.8,to_date('27-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.11,1739,1820.3,2617.2,to_date('28-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.6,1726.2,1820.3,2621,to_date('29-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1501.82,1587.4,1687.5,2540.5,to_date('30-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1524.28,1630.6,1725.6,2554.5,to_date('31-MAY-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.65,1685.5,1792.9,2601.9,to_date('01-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.49,1701.3,1819.1,2623,to_date('02-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.68,1718,1833.5,2640.7,to_date('03-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.1,1726.2,1853.4,2640.7,to_date('04-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1645.81,1716.6,1849.7,2619.8,to_date('05-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1650.36,1725.8,1851.8,2624.2,to_date('06-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1651.55,1737.4,1857.7,2638.2,to_date('07-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.88,1736.6,1864.3,2645.7,to_date('08-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.52,1732.4,1863.5,2679.6,to_date('09-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1657.51,1731.2,1873.2,2669,to_date('10-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.55,1726.9,1860.8,2664.6,to_date('11-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.09,1727.8,1868.7,2663.3,to_date('12-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1646.41,1720.2,1860.4,2667.4,to_date('13-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.65,1715.4,1855.9,2653.2,to_date('14-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.8,1708.7,1840.5,2630.8,to_date('15-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.64,1713,1842.6,2626.6,to_date('16-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.22,1713.5,1861.2,2641.9,to_date('17-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.65,1718,1876.2,2625.8,to_date('18-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.17,1701.7,1878.3,2606,to_date('19-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1635.8,1701.7,1878.4,2594.4,to_date('20-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.27,1684.9,1869.4,2583.6,to_date('21-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.7,1687.2,1880.4,2588.7,to_date('22-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.13,1690.6,1885.5,2600.3,to_date('23-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.12,1684.3,1888.4,2579.5,to_date('24-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.8,1679.9,1885.2,2576.6,to_date('25-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.79,1672.9,1877.9,2597.8,to_date('26-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1614.8,1663.1,1876.5,2595.6,to_date('27-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1612.8,1669.3,1883.8,2599,to_date('28-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.47,1664.7,1880.6,2621.7,to_date('29-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1609.32,1672.3,1887.4,2645.6,to_date('30-JUN-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.48,1687.7,1878.3,2644.2,to_date('01-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.48,1686.8,1867.1,2625.6,to_date('02-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.89,1686.6,1851.9,2624.6,to_date('03-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.12,1675.8,1843.6,2596.2,to_date('04-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1582.27,1677.4,1848.1,2599.5,to_date('05-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1567.99,1673.2,1843.4,2584.1,to_date('06-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1568.16,1665,1843.6,2570.8,to_date('07-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1569.71,1671.3,1833.8,2555,to_date('08-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.74,1672.4,1833.4,2574.5,to_date('09-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1585.41,1676.2,1856.9,2576.7,to_date('10-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.01,1692.6,1863.4,2579,to_date('11-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.89,1696.5,1855.5,2588.7,to_date('12-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1565.18,1716.1,1864.2,2601.1,to_date('13-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.34,1713.3,1846,2575.7,to_date('14-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577,1705.1,1836.8,2559.5,to_date('15-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.29,1711.3,1830.4,2561.1,to_date('16-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.72,1709.8,1831.6,2528.3,to_date('17-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.07,1688.6,1834.8,2514.7,to_date('18-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.19,1698.9,1852.1,2558.5,to_date('19-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1588.73,1700,1849.8,2553.3,to_date('20-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1586.01,1693,1861.8,2577.1,to_date('21-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.77,1683.9,1856.7,2566,to_date('22-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.58,1679.2,1856.7,2549.5,to_date('23-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1568.09,1673.9,1841.5,2527.8,to_date('24-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1578.21,1683.9,1846.9,2540.9,to_date('25-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1573.94,1688.4,1836.1,2534.2,to_date('26-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1582.06,1693.9,1838.6,2538,to_date('27-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1610.18,1720.9,1857.6,2559,to_date('28-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.16,1717.9,1857.6,2554.9,to_date('29-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.84,1733.6,1858.4,2575.5,to_date('30-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1615.26,1729.7,1846.8,2546.5,to_date('31-JUL-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.08,1735.6,1868.5,2561.6,to_date('01-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.97,1734.1,1863.2,2546.6,to_date('02-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.7,1699.3,1808.3,2502.9,to_date('03-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.7,1678.6,1765.1,2463.1,to_date('04-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.7,1675.5,1763.5,2472.6,to_date('05-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.26,1670.1,1766,2463.5,to_date('06-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1599.75,1652.2,1741.3,2446.3,to_date('07-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.86,1635,1743.3,2456.2,to_date('08-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.5,1654.9,1769,2471.5,to_date('09-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.86,1642,1757.9,2447.5,to_date('10-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1587.92,1638.7,1754.9,2428.6,to_date('11-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.06,1622.6,1739.7,2420.2,to_date('12-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1549.81,1596.1,1708.8,2414.9,to_date('13-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1549.36,1612.4,1722.2,2420.2,to_date('14-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.65,1625,1713.9,2423.8,to_date('15-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.52,1610.5,1703.2,2407,to_date('16-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1555.31,1606.6,1685.7,2388.7,to_date('17-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1559.76,1610.7,1663.4,2409.6,to_date('18-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1548.44,1603.1,1636.9,2392,to_date('19-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.99,1591.5,1645.6,2380.2,to_date('20-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.21,1605.2,1671.6,2423.3,to_date('21-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.03,1621.4,1688.3,2451.6,to_date('22-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1551.78,1622.5,1696.8,2440.8,to_date('23-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1562.89,1626.6,1711.7,2432.9,to_date('24-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.28,1627.4,1706.2,2413.6,to_date('25-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1559.26,1614.9,1684.2,2391.6,to_date('26-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.87,1602.3,1648.5,2358.1,to_date('27-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1598.3,1633.6,2345.4,to_date('28-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1699.1,2384.4,to_date('29-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1699.1,2384.4,to_date('30-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1722.5,2384.4,to_date('31-AUG-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1564.27,1655.7,1720.7,2418.7,to_date('01-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1741.9,2420,to_date('02-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1765.7,2493.1,to_date('03-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1765.7,2493.1,to_date('04-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1598.19,1670.1,1749.9,2492.8,to_date('05-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.05,1704,1770.3,2504.1,to_date('06-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.69,1711.8,1787.6,2493.2,to_date('07-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1593.65,1700.5,1778.7,2482.9,to_date('08-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1581.68,1690.3,1785.6,2467.1,to_date('09-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1599.14,1715.4,1833.9,2497.9,to_date('10-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.82,1723.5,1837.4,2477.9,to_date('11-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1620.45,1719.4,1824.3,2490.1,to_date('12-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1629.51,1734.4,1843.8,2516.3,to_date('13-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1663.7,1772.8,1873.6,2537.1,to_date('14-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1664.09,1760.3,1860.2,2541.6,to_date('15-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1669.29,1747.2,1860.2,2536.7,to_date('16-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.14,1750.2,1865.9,2544.9,to_date('17-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.07,1755.3,1867.9,2543.4,to_date('18-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.13,1754.6,1841.3,2522,to_date('19-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.84,1751.2,1838.7,2525.3,to_date('20-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1669.52,1752.5,1849.9,2510.4,to_date('21-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.71,1769.4,1869.3,2539.9,to_date('22-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.51,1767.6,1890.6,2552,to_date('23-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.01,1750,1879.6,2546.5,to_date('24-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.06,1747.1,1873.9,2550.8,to_date('25-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.7,1753.5,1875.3,2571.2,to_date('26-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.66,1752.8,1857,2560.2,to_date('27-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1678.77,1752.9,1856.5,2556.8,to_date('28-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.85,1764.7,1865.8,2547.1,to_date('29-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.71,1776.8,1860.6,2534.3,to_date('30-SEP-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.59,1779.3,1861.6,2517.2,to_date('01-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.73,1785.1,1865.6,2538.4,to_date('02-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.14,1798.2,1864.1,2537.1,to_date('03-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.03,1794.1,1861.6,2523.7,to_date('04-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.81,1795.2,1876.5,2522.6,to_date('05-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1676.17,1780.4,1865.1,2513.9,to_date('06-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1688.46,1789.5,1882.1,2541,to_date('07-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1696.55,1794.2,1912.2,2555.9,to_date('08-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.24,1784.4,1915.4,2536.7,to_date('09-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.35,1800.1,1951.2,2543.4,to_date('10-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.29,1804,1962.4,2542.3,to_date('11-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.86,1816.2,1976.5,2559.7,to_date('12-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.63,1810.5,1953.5,2546.8,to_date('13-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1743.36,1821.9,1981.3,2565,to_date('14-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.17,1828.2,1985.1,2562,to_date('15-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.76,1840.6,1983.4,2562.1,to_date('16-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1749.29,1841.1,1979.7,2554.3,to_date('17-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1763.86,1846.3,1983.8,2565.4,to_date('18-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.27,1850,1988.1,2558.4,to_date('19-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.29,1839,1973,2538.3,to_date('20-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.77,1820.2,1966.9,2533.1,to_date('21-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.5,1815.2,1976.3,2550.7,to_date('22-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.21,1820.6,1993.9,2574.8,to_date('23-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1739.88,1807.1,1968,2522.4,to_date('24-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.92,1791.4,1941.8,2493.3,to_date('25-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1734.42,1806.2,1947.1,2476,to_date('26-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.13,1798.7,1929.2,2470.7,to_date('27-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1732.92,1818.2,1943.6,2491.2,to_date('28-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.89,1820.5,1928.2,2464.7,to_date('29-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1725.74,1833.3,1922,2467.6,to_date('30-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1730.9,1837.1,1919.1,2456.6,to_date('31-OCT-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.17,1818.2,1884.6,2441,to_date('01-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.2,1824.1,1896.3,2458.7,to_date('02-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1719.06,1830.1,1928.3,2464.9,to_date('03-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1718.21,1835.6,1934.8,2472.2,to_date('04-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.84,1828.7,1923.5,2447.9,to_date('05-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.76,1839.2,1943.8,2452.9,to_date('06-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1718.35,1837.2,1942.4,2440.1,to_date('07-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1706.69,1826.7,1928.1,2408.6,to_date('08-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.37,1838,1942,2405.4,to_date('09-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.18,1829.1,1942.7,2382.7,to_date('10-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1738.78,1843.1,1974.8,2400.9,to_date('11-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1737.41,1850.5,1975.4,2404.2,to_date('12-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.77,1827.1,1907.5,2393.2,to_date('13-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1724.24,1829.1,1943.6,2436.4,to_date('14-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.77,1848,1974.1,2572.6,to_date('15-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.96,1840.5,1963.3,2591,to_date('16-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1734.46,1853.8,1972.3,2600.5,to_date('17-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1744.35,1874.1,1990.7,2640.2,to_date('18-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('19-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('20-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('21-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1747.47,1860.5,1980.4,2625.8,to_date('22-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.1,1874.7,1983.7,2607.8,to_date('23-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.17,1880.1,1978.1,2609.8,to_date('24-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.72,1874.7,1984.9,2643,to_date('25-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1742.92,1875.6,1995.7,2658.2,to_date('26-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.68,1859.5,2006.6,2651,to_date('27-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.18,1874.2,2036.7,2664.9,to_date('28-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1728.09,1880.1,2031.1,2654.1,to_date('29-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1728.09,1880.1,2031.1,2659.8,to_date('30-NOV-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.29,1907.7,2041.6,2659.8,to_date('01-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.82,1920.5,2046.9,2662.2,to_date('02-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.78,1937.3,2047.2,2698.7,to_date('03-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.57,1936.8,2063.4,2701.9,to_date('04-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1748.13,1949.1,2063.4,2725.7,to_date('05-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1750.7,1963.7,2077.5,2737.8,to_date('06-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1747.91,1950.8,2063.6,2722.4,to_date('07-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.79,1953.5,2053.2,2720.5,to_date('08-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1735.34,1945,2017,2694.7,to_date('09-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1719.92,1921.1,2024,2682.6,to_date('10-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1763.59,1939.1,2051.6,2703.6,to_date('11-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1766.76,1928,2023.1,2700.6,to_date('12-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1785.4,1933.4,2030.8,2711.9,to_date('13-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1783.56,1925.7,2016.8,2702,to_date('14-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1804.42,1931.7,2045.1,2715,to_date('15-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1812.33,1928.7,2046.3,2715,to_date('16-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1799.51,1924.5,2029.6,2704.6,to_date('17-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1792.8,1914.2,2014.1,2698.6,to_date('18-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1792.8,1914.2,2014.1,2694.2,to_date('19-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1806.36,1920.6,2033.3,2707.6,to_date('20-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.23,1923.3,2017.4,2697.6,to_date('21-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1800.62,1930.4,2024.9,2705.9,to_date('22-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1786.19,1915.2,1992.6,2680.9,to_date('23-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1791.35,1916.9,1994.9,2681.9,to_date('24-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.05,1913.8,1981.6,2668.5,to_date('25-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.05,1913.8,1981.6,2645.8,to_date('26-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1784.71,1899.7,1962.2,2635.4,to_date('27-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.45,1888,1953.7,2636.1,to_date('28-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1779.74,1868.8,1928.8,2614.1,to_date('29-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1786.97,1879.9,1928.3,2603.7,to_date('30-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.25,1865.7,1918.1,2593.6,to_date('31-DEC-94','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1781.62,1881.3,1931.4,2616.3,to_date('01-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.75,1873.1,1908.8,2598.4,to_date('02-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.75,1862.5,1891.8,2562.7,to_date('03-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1776.34,1869.3,1913.9,2584.8,to_date('04-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1770.72,1846.9,1885.8,2550.3,to_date('05-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1772.39,1847.1,1895.8,2560.6,to_date('06-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.55,1838.3,1899.6,2532.6,to_date('07-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1764.35,1845.8,1920.3,2557.3,to_date('08-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.83,1835.5,1915.3,2534.1,to_date('09-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1755.98,1846.6,1907.3,2515.8,to_date('10-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1754.95,1854.8,1900.6,2521.2,to_date('11-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1759.9,1845.3,1880.9,2493.9,to_date('12-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1759.84,1854.5,1873.5,2476.1,to_date('13-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1776.5,1870.5,1883.6,2497.1,to_date('14-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1769.98,1862.6,1868.5,2469,to_date('15-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1766.98,1856.6,1879.1,2493.7,to_date('16-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.29,1837.6,1847.8,2472.6,to_date('17-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1760.17,1846.7,1861.8,2497.9,to_date('18-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1750.32,1856.5,1859.4,2490.8,to_date('19-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.44,1841.8,1859.4,2478.3,to_date('20-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1735.51,1835,1859.4,2484,to_date('21-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.84,1844.4,1853.3,2486.4,to_date('22-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1730.78,1838.9,1851.2,2483.4,to_date('23-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1699.46,1805.6,1801.8,2431.9,to_date('24-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.71,1756.6,1767.9,2403.7,to_date('25-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.09,1786.1,1762.7,2415.6,to_date('26-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.81,1757.1,1727.5,2387.9,to_date('27-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.81,1762.8,1734.6,2399.5,to_date('28-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.36,1756.8,1734.6,2377.2,to_date('29-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.78,1761.9,1755.4,2348,to_date('30-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.18,1778.5,1769,2373.4,to_date('31-JAN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1637.62,1812.7,1801.6,2423.2,to_date('01-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.2,1806.1,1782.6,2411.6,to_date('02-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1608.49,1798.1,1754.7,2399.6,to_date('03-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.11,1794.9,1784.4,2420.2,to_date('04-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1609.61,1805.4,1787.6,2407.5,to_date('05-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.94,1820.3,1798,2392.8,to_date('06-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.07,1819.6,1793.8,2377.6,to_date('07-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1611.96,1809.6,1777.3,2350.1,to_date('08-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1578.95,1799.9,1755.2,2325.7,to_date('09-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.39,1800.3,1737.8,2309.6,to_date('10-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.87,1793.3,1730.1,2303.1,to_date('11-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1548.63,1784.8,1722.4,2318,to_date('12-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1560.16,1791.7,1753.5,2356.8,to_date('13-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.76,1800.2,1757.3,2376.1,to_date('14-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1531.87,1788.6,1736.7,2354.7,to_date('15-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.14,1775.7,1734.2,2363.5,to_date('16-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1509.03,1753.5,1724.2,2359.4,to_date('17-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.03,1768.2,1744.2,2365.7,to_date('18-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.97,1727.9,1689.7,2311.1,to_date('19-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1464.03,1709.6,1667.7,2281,to_date('20-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1475.11,1704.6,1667.8,2285,to_date('21-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1516.12,1740.6,1687.6,2311.6,to_date('22-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1519.69,1745.7,1687.5,2312.6,to_date('23-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.97,1751.7,1684.9,2312.6,to_date('24-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1516.44,1747.3,1674.2,2298.4,to_date('25-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1515.53,1757.8,1711.4,2313,to_date('26-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.89,1774.2,1780.5,2381.9,to_date('27-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1534.72,1774.4,1779,2362.2,to_date('28-FEB-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.66,1788.3,1779.3,2372.2,to_date('01-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.71,1788,1763.7,2337.7,to_date('02-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.83,1779.1,1756.8,2327.5,to_date('03-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.1,1792.8,1774.2,2340.6,to_date('04-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.2,1812,1802,2370.9,to_date('05-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1601.5,1872.1,1873.6,2422.1,to_date('06-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1580.29,1851.4,1836.2,2370,to_date('07-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1595.09,1873.4,1859.8,2378.3,to_date('08-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.47,1889.6,1852.7,2483.9,to_date('09-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1600.59,1897.5,1882.9,2567,to_date('10-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1566,1888.8,1826.1,2560.1,to_date('11-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.01,1900.4,1832.8,2586,to_date('12-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.74,1913.4,1828.9,2580.5,to_date('13-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.3,1909.9,1829.5,2621.2,to_date('14-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1510.66,1910.8,1843.5,2601,to_date('15-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1481.03,1879.2,1770.3,2560,to_date('16-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1483.83,1880.2,1731.9,2565.5,to_date('17-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1470.09,1878.3,1736.7,2553,to_date('18-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.78,1885.2,1724,2572.3,to_date('19-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1475.41,1867.6,1683.3,2549.7,to_date('20-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1402.34,1788,1611,2446.3,to_date('21-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1421.49,1820.5,1612.5,2488.4,to_date('22-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1434.61,1858.2,1654.2,2517.1,to_date('23-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1446.32,1870.3,1673.9,2538.8,to_date('24-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1437.65,1878.4,1657.3,2541.2,to_date('25-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1441.57,1881.5,1655.1,2557.2,to_date('26-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1471.64,1893.2,1685.1,2584.7,to_date('27-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1453.95,1889.3,1667.9,2574.7,to_date('28-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1453.79,1877.3,1650,2546.6,to_date('29-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1458.02,1884,1664.2,2563.9,to_date('30-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1479.59,1904.7,1679.1,2562.2,to_date('31-MAR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1504.89,1922.7,1731.3,2617,to_date('01-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1496.54,1908.5,1722.2,2645.7,to_date('02-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1511,1911.4,1730.7,2658.1,to_date('03-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1528.86,1921.1,1766.4,2669.7,to_date('04-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1534.02,1930.8,1770.7,2661.6,to_date('05-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.6,1927.8,1774.5,2669.8,to_date('06-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1508.19,1908.3,1749.9,2650.4,to_date('07-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1493.54,1905.9,1730.9,2642.3,to_date('08-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1489.68,1911.1,1742.4,2658.3,to_date('09-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1482.44,1921.6,1742.4,2687.8,to_date('10-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1483.34,1933.6,1786.9,2705.6,to_date('11-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1470.57,1942,1804.1,2691.7,to_date('12-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.84,1951.5,1804.7,2711.1,to_date('13-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1487.71,1955.7,1793.6,2702.7,to_date('14-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1508.63,1957.4,1786.7,2695.4,to_date('15-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1515.27,1962.3,1798.5,2714.6,to_date('16-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1509.84,1946.1,1798.5,2696.8,to_date('17-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.28,1950.2,1821.5,2726.4,to_date('18-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1541.79,1929.7,1796.8,2697.5,to_date('19-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.48,1913.4,1772.7,2679.6,to_date('20-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.27,1889.5,1764.4,2679.2,to_date('21-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.27,1882.8,1759.2,2704,to_date('22-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.37,1895.4,1722.3,2706.2,to_date('23-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.84,1897.9,1724.2,2732.4,to_date('24-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.62,1891.5,1674.8,2722.9,to_date('25-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.68,1880.1,1720.6,2727.1,to_date('26-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1513.42,1887,1721,2709.6,to_date('27-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.02,1891.4,1739.7,2741.8,to_date('28-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.69,1914.6,1749.7,2760.1,to_date('29-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.12,1931.2,1771.4,2778.8,to_date('30-APR-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1546.82,1929.2,1792.3,2792,to_date('01-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1528.12,1924.3,1783.3,2764.1,to_date('02-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.65,1927,1799.4,2771,to_date('03-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.25,1935,1781.7,2759.4,to_date('04-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1519.48,1955.4,1788.6,2754.5,to_date('05-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1506.65,1962.2,1765.9,2769.8,to_date('06-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1504.3,1980.7,1791.2,2750.7,to_date('07-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1480.65,1987.7,1769.5,2726.5,to_date('08-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1476.7,1993.7,1758.7,2716.2,to_date('09-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1478.07,2015.7,1738.3,2721.8,to_date('10-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1479.62,2005,1744.8,2717.9,to_date('11-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1477.55,2023.9,1736.7,2732.8,to_date('12-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1472.59,2028.5,1735.2,2740.3,to_date('13-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1495.6,2044.9,1760.1,2789.7,to_date('14-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1517.45,2045.8,1786.3,2807.7,to_date('15-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1520.93,2057.3,1824.4,2842,to_date('16-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1821.1,2827.4,to_date('17-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1854.6,2827.5,to_date('18-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1854.6,2827.5,to_date('19-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.51,2092.3,1857.5,2827.5,to_date('20-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.82,2090.1,1870.3,2847.8,to_date('21-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1858.8,2832.5,to_date('22-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1857.8,2846.5,to_date('23-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1857.8,2846.5,to_date('24-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.04,2117.7,1843.1,2861.5,to_date('25-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.03,2128.2,1850.8,2833.6,to_date('26-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1551.17,2124.7,1859.6,2826,to_date('27-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.37,2079.9,1844.5,2816.5,to_date('28-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.1,2074.9,1852.6,2799.2,to_date('29-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1522.26,2046.4,1814.6,2773.4,to_date('30-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1533.79,2079.8,1796.8,2757.9,to_date('31-MAY-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1510.18,2076.7,1782.5,2745.3,to_date('01-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.91,2104.5,1803.5,2759.2,to_date('02-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1555.52,2101.3,1827.1,2765.1,to_date('03-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1581.49,2084,1837.5,2763.1,to_date('04-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.61,2063.9,1837.7,2737.6,to_date('05-JUN-95','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2387.6,3270.6,1939.7,3744.3,to_date('23-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2373.01,3243.5,1932.4,3714.6,to_date('24-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2401.59,3253,1936.9,3725.6,to_date('25-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2421.93,3264.4,1953.3,3740,to_date('26-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.52,3296.2,1976.9,3740.3,to_date('27-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2438.73,3298.6,1960.9,3704.2,to_date('28-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.09,3309.9,1974.5,3715.9,to_date('29-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2486.95,3360.5,1996.9,3738.2,to_date('30-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2485.18,3354.8,1990.8,3727.6,to_date('31-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2488.85,3385.5,2017.2,3752.7,to_date('01-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2486.83,3400.6,2015.3,3768.6,to_date('02-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.52,3383.4,2001.9,3777.1,to_date('03-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2471.38,3408.3,2005.9,3758.9,to_date('04-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2479.38,3581.9,2008,3758.2,to_date('05-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2448.8,3549.7,1975.5,3710.3,to_date('06-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2419.72,3506.2,1948.9,3674.5,to_date('07-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2415.29,3540.5,1932.1,3639.5,to_date('08-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2417,3558.3,1944.3,3640.3,to_date('09-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2432.46,3578.4,1962.4,3681.8,to_date('10-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2458,3561.1,1950.1,3644.8,to_date('11-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.55,3583.6,1965,3669.6,to_date('12-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2484.74,3605.1,1967.2,3693,to_date('13-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2491.73,3611.1,1969.8,3685.4,to_date('14-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.32,3629.3,1976.9,3698.3,to_date('15-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2479.84,3647.7,1974.4,3707,to_date('16-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2514.8,3677,2003.8,3681.9,to_date('17-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.78,3658.1,2008,3660.9,to_date('18-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2523.81,3671,2030.5,3672.4,to_date('19-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2502.94,3640.3,2020,3672.6,to_date('20-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2489.35,3646.5,2044.8,3699.7,to_date('21-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2500.75,3654.3,2055.6,3718.4,to_date('22-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2508.11,3648.3,2070.4,3728.5,to_date('23-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2489.52,3606.7,2064,3725.1,to_date('24-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('25-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('26-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('27-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2510.81,3594.4,2081.7,3758.6,to_date('28-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2525.59,3601.5,2093.9,3767.4,to_date('29-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2508.12,3566.9,2072.5,3744.2,to_date('30-SEP-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2526.74,3575.5,2074.7,3766.8,to_date('01-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2540.79,3589.6,2080.6,3790.5,to_date('02-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2547.32,3607.5,2097.3,3825.3,to_date('03-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2519.82,3603.9,2075.1,3805.6,to_date('04-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2524.18,3601.2,2086,3820.7,to_date('05-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2535.86,3628.1,2092.5,3857.1,to_date('06-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.27,3673.2,2116.5,3852.7,to_date('07-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.12,3647.3,2112,3833,to_date('08-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2524.84,3668.4,2122.1,3817.6,to_date('09-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2538.68,3668.1,2116.4,3819.3,to_date('10-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2539.88,3696,2138.4,3832.8,to_date('11-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.97,3658.2,2130.8,3809.2,to_date('12-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2492.63,3651,2146.8,3817.9,to_date('13-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2492.63,3651,2146.8,3806,to_date('14-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2465.49,3635.8,2136.8,3776.4,to_date('15-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.43,3611.2,2115.4,3751.6,to_date('16-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2473.52,3591.6,2090.4,3751.6,to_date('17-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2476.79,3583.6,2083.7,3723,to_date('18-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2466.21,3534.1,2083.7,3707.3,to_date('19-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2470.57,3521.6,2085.4,3728.3,to_date('20-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2499.02,3556.9,2114.8,3754.4,to_date('21-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2496.33,3557.8,2100.9,3739.2,to_date('22-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2528.2,3582.4,2122.1,3759.7,to_date('23-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.4,3572.5,2124.6,3776.2,to_date('24-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.4,3572.5,2124.6,3753.6,to_date('25-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2552.29,3593,2136.9,3789.6,to_date('26-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2541.98,3584,2120.6,3778.2,to_date('27-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2564.12,3587.3,2129.7,3789.4,to_date('28-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.79,3550,2103.5,3764.2,to_date('29-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2546.55,3547.4,2114.5,3747,to_date('30-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.53,3572.2,2117.7,3752.1,to_date('31-OCT-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.53,3572.2,2117.7,3752.1,to_date('01-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.15,3583.8,2132.9,3760.2,to_date('02-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.53,3558.2,2117.1,3775.7,to_date('03-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2535.78,3537.7,2108.4,3746.7,to_date('04-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2523.81,3556.1,2110.1,3747.8,to_date('05-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2543.99,3544.6,2121.1,3739.2,to_date('06-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2550.42,3568.1,2111,3755.2,to_date('07-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2551.03,3572,2115.4,3753.4,to_date('08-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.02,3585.5,2133.2,3760.3,to_date('09-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2536.03,3547.9,2102,3706.8,to_date('10-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2552.96,3575.8,2120.8,3728.8,to_date('11-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2571.1,3575.6,2137.5,3755.7,to_date('12-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2569.9,3600.9,2137.3,3769.2,to_date('13-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2566.13,3607.4,2126.2,3761.7,to_date('14-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2544.9,3593.9,2111.8,3753.6,to_date('15-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.71,3600.1,2113,3761.5,to_date('16-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2554.12,3628.3,2107.9,3756.4,to_date('17-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2546.04,3643.6,2100.7,3753.2,to_date('18-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.22,3655.4,2077.1,3727.5,to_date('19-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2547.78,3687.3,2084.2,3722.3,to_date('20-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.19,3701.5,2097.6,3710.8,to_date('21-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2578.36,3699.6,2118.4,3679.5,to_date('22-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2572.06,3723.5,2113.3,3695.5,to_date('23-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2551.8,3703,2112.9,3678.8,to_date('24-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2570.44,3732.9,2123.7,3711,to_date('25-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2573.44,3728.2,2118.8,3725.6,to_date('26-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2565.32,3750.8,2111.8,3725.7,to_date('27-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2566.32,3757,2114,3714.1,to_date('28-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2578.74,3771.3,2126.8,3760.6,to_date('29-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.79,3731.2,2098.8,3743.2,to_date('30-NOV-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2561.51,3732.8,2079.1,3741.5,to_date('01-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.24,3773.4,2076.6,3752.3,to_date('02-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2573,3788,2081.9,3765.8,to_date('03-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2561.95,3810,2073.4,3749,to_date('04-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.97,3785.8,2050.6,3728.3,to_date('05-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2529.5,3755.4,2029.5,3698.3,to_date('06-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2475.98,3678.1,1989.5,3632.3,to_date('07-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2497.69,3656.2,1995.1,3658.2,to_date('08-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.56,3649.2,2007.3,3693.4,to_date('09-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.35,3661.5,1992.8,3710.5,to_date('10-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2468.32,3566.1,1960.3,3681.3,to_date('11-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2488.82,3585.2,1982.2,3708.4,to_date('12-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2459.13,3482.6,1954.1,3668.8,to_date('13-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2464.51,3537.4,1974.7,3684.7,to_date('14-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2469.51,3537.8,1962.9,3673.3,to_date('15-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.25,3549.5,1961.1,3678.8,to_date('16-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2466.84,3518.2,1968.5,3668.5,to_date('17-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2491.5,3494.4,1995.9,3703.2,to_date('18-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2504.16,3494.4,2009.9,3734.4,to_date('19-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2520.26,3604.6,2023.4,3770.6,to_date('20-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2520.52,3623.3,2013.2,3788.3,to_date('21-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2527.73,3629,1999.3,3788.4,to_date('22-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2541.41,3662.6,1996.7,3811.1,to_date('23-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.63,3653.4,1997.4,3811.4,to_date('24-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.96,3643.2,1985.4,3810.7,to_date('25-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2529.14,3623.1,1978.2,3803.3,to_date('26-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2545.65,3630.2,1980.6,3823.4,to_date('27-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2544.61,3650.7,1979.5,3830.3,to_date('28-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2542.75,3646.3,1979.5,3837.4,to_date('29-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2567.96,3670.5,1979.5,3872.9,to_date('30-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2556.25,3664.4,1986.2,3863.7,to_date('31-DEC-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.12,3698.2,2019.3,3883.2,to_date('01-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.44,3683.8,2000.6,3872.1,to_date('02-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.42,3713,2017.8,3891.1,to_date('03-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.12,3725.4,2020.8,3907.5,to_date('04-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2554.42,3714.6,2020.4,3907.5,to_date('05-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2563.59,3722.4,2018,3905.7,to_date('06-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2556.64,3692.6,2002.9,3918.7,to_date('07-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.84,3682.6,1977.6,3885,to_date('08-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.49,3646.3,1970.6,3867.6,to_date('09-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2538.34,3654.7,1977,3884.4,to_date('10-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2528.73,3623.9,1971.4,3855.9,to_date('11-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2526.18,3634.2,1984.8,3872.7,to_date('12-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.55,3627.1,1996.2,3887.2,to_date('13-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2542.74,3637.8,2004.8,3893,to_date('14-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.71,3684.4,2020.3,3910.8,to_date('15-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.77,3694.2,2042.1,3916.1,to_date('16-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.7,3678.9,2038.1,3905.6,to_date('17-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2588.04,3687.2,2065.4,3932.6,to_date('18-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2614.5,3716.8,2080.4,3967.9,to_date('19-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2630.24,3722.7,2086.2,3977.2,to_date('20-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2626.43,3690,2080.9,3972.3,to_date('21-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2621.2,3690.1,2072.7,3955.7,to_date('22-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2625.46,3694.9,2082.3,3974.3,to_date('23-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2641.5,3694.9,2079.5,3964.1,to_date('24-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2624.18,3635.5,2067.1,3919.7,to_date('25-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2643.42,3661.2,2081.5,3910.5,to_date('26-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2663.1,3697.7,2103.4,3935.7,to_date('27-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2664.96,3699.5,2104.1,3933.2,to_date('28-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2659.86,3696.6,2107.1,3946.4,to_date('29-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2655.49,3736.4,2132.8,3953.7,to_date('30-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2654.34,3737.5,2123.2,3992.2,to_date('31-JAN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.29,3760.2,2141.8,4015.1,to_date('01-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.29,3771.6,2136.1,4000,to_date('02-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2704.25,3771.6,2152.7,4024.8,to_date('03-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2705.21,3813.7,2151.6,4031.5,to_date('04-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2702.6,3818.1,2161.5,4035.6,to_date('05-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.23,3807.9,2146.7,4009.3,to_date('06-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2683.52,3797.3,2135.6,3994.7,to_date('07-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2692.69,3805.2,2147.1,4028.1,to_date('08-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2700.83,3813.7,2143.6,4038.7,to_date('09-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2718.73,3822.2,2168.3,4050.8,to_date('10-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2705.06,3790.2,2158.5,4024.4,to_date('11-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2717.5,3793.2,2165.3,4042.1,to_date('12-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2727.56,3803.3,2185.2,4053.1,to_date('13-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2733.67,3788.2,2180.2,4073.1,to_date('14-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2721.74,3775.3,2175.6,4057.2,to_date('15-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2678.89,3742.9,2148.9,4028.4,to_date('16-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2681.94,3753.6,2151.6,3999.4,to_date('17-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2690.79,3758.7,2162.4,4022.4,to_date('18-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2703.33,3763.5,2150.4,4025.3,to_date('19-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2675.5,3732,2125.7,3993.5,to_date('20-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2664.72,3723.1,2124.8,3963.9,to_date('21-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2671.4,3725.3,2140.5,3979.1,to_date('22-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2670.19,3736.1,2140.5,3948.5,to_date('23-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2678.73,3749.2,2142.2,3928.1,to_date('24-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2716.16,3784.8,2187.3,3921.1,to_date('25-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2735.28,3815.4,2213.4,3935.7,to_date('26-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2724.25,3811.5,2211.8,3900.4,to_date('27-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2732.29,3812,2205.2,3910.8,to_date('28-FEB-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2730.44,3827.6,2205.2,3914.4,to_date('01-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2764,3829.8,2229.1,3934.3,to_date('02-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2770.61,3825.7,2217.2,3926.9,to_date('03-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2784.39,3856.4,2218.2,3926.1,to_date('04-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2800.6,3897.9,2240.3,3958.2,to_date('05-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2768.68,3888.3,2228.8,3962.1,to_date('06-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2781.54,3892.6,2240.1,3978.1,to_date('07-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2766.08,3883.3,2233.4,3962.8,to_date('08-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2765.29,3861.5,2233.6,3953.8,to_date('09-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2769.47,3882.8,2255.5,4018.7,to_date('10-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2800.52,3891.7,2277.1,4054.6,to_date('11-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2808.62,3878.2,2275.1,4068.4,to_date('12-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2793.86,3845.3,2270.8,4049.2,to_date('13-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2829.68,3884.1,2290.3,4050.2,to_date('14-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2848.84,3902.5,2315.7,4058,to_date('15-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2853.46,3874,2318.6,4038.5,to_date('16-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2900.76,3924.5,2349.1,4061.5,to_date('17-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.89,3910.6,2308.7,4045.2,to_date('18-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2894.43,3913.8,2292.5,4051.2,to_date('19-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2832.53,3835.5,2240.7,3963,to_date('20-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2870.3,3874,2255.8,4011.6,to_date('21-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2890.95,3885.4,2251.4,4035.7,to_date('22-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2836.36,3857.6,2213.3,3982.5,to_date('23-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2846.94,3860,2212.1,3990.7,to_date('24-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2815.77,3839.3,2203.4,3972.4,to_date('25-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2841.16,3869.2,2222.6,3993.8,to_date('26-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2808.5,3850.4,2193.7,3979.6,to_date('27-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2814.23,3874,2218.9,4018.2,to_date('28-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2824.83,3880.8,2248.7,4051.3,to_date('29-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2835.54,3890.1,2278.5,4077.6,to_date('30-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3875.5,2287.4,4087.2,to_date('31-MAR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2289.6,4092.5,to_date('01-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2289.6,4092.5,to_date('02-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2303.8,4092.5,to_date('03-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2859.22,3922.2,2307,4091,to_date('04-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.07,3948.3,2318.6,4115.7,to_date('05-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.07,3942.2,2315.7,4118.5,to_date('06-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2880.07,3942.2,2315.7,4118.5,to_date('07-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2820.81,3942.2,2257,4057.4,to_date('08-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2863.26,3940.1,2282.8,4089.5,to_date('09-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2890.2,3923.8,2306.7,4106.5,to_date('10-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2876.34,3922.9,2301.7,4078.8,to_date('11-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2904.08,3944.9,2331.6,4087.5,to_date('12-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2936.69,3966.2,2349.1,4087,to_date('13-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2915.81,3947.4,2327.5,4056.6,to_date('14-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2956.78,3975.5,2361.3,4107.3,to_date('15-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2978.84,3983.6,2402.1,4168.2,to_date('16-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2976.56,3979.6,2388,4158.9,to_date('17-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2996.12,4007.1,2407.8,4197.5,to_date('18-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3006.87,4019.9,2425.1,4207.7,to_date('19-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2999.19,4009.5,2406.1,4194,to_date('20-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3000.66,4023.1,2409.9,4195.5,to_date('21-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3026.63,4115.4,2442.5,4219.1,to_date('22-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3037.28,4161,2461.3,4219.1,to_date('23-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2982.63,4125.5,2430.3,4218.8,to_date('24-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2992.55,4127.3,2435.2,4212,to_date('25-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3028.27,4182.3,2482.8,4237.4,to_date('26-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2997.95,4169.7,2465,4207.5,to_date('27-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3018.58,4209.1,2503.1,4228.4,to_date('28-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3037.7,4272.2,2516.6,4275.8,to_date('29-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3064.7,4282.8,2508.6,4257.8,to_date('30-APR-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3067.48,4296.5,2503.1,4260.9,to_date('01-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3114.73,4305.5,2541.3,4281.5,to_date('02-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3124.78,4309.8,2558.4,4265.9,to_date('03-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3161.36,4357.9,2597.5,4307.8,to_date('04-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3185.72,4384.3,2595.4,4307.7,to_date('05-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3191.45,4408.4,2582.1,4304.3,to_date('06-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3211.01,4444.1,2599.3,4304.3,to_date('07-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3256.86,4436.3,2628.4,4327.1,to_date('08-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3249.17,4464.2,2627.4,4341,to_date('09-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3260.3,4514.6,2634.5,4337.8,to_date('10-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3230.83,4490.7,2617.5,4332.3,to_date('11-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3209.04,4525.5,2594.8,4357.4,to_date('12-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3197.09,4530.8,2575.2,4356.1,to_date('13-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3203.79,4522.5,2562.8,4336.8,to_date('14-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3180.63,4463.2,2567.9,4331.1,to_date('15-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3233.34,4503.9,2607.7,4344.7,to_date('16-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3245.02,4539,2602.2,4329.3,to_date('17-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3272.58,4519.7,2629.4,4339.2,to_date('18-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3261.04,4487.6,2607.8,4308.3,to_date('19-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3258.74,4460.1,2600.3,4307.1,to_date('20-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3345.09,4513.7,2651.7,4357.7,to_date('21-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3375.45,4547.1,2666.2,4360.1,to_date('22-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3396.55,4605.2,2698.9,4399.3,to_date('23-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3419.51,4638.9,2708.3,4420.3,to_date('24-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3426.77,4684.4,2709.2,4437.4,to_date('25-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3430.95,4677.1,2686.2,4444.3,to_date('26-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3382.4,4676.2,2641.7,4422.5,to_date('27-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3367.82,4609.9,2632.1,4397.7,to_date('28-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3404.29,4636.2,2645.6,4424.3,to_date('29-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3337.11,4556.5,2588.4,4373.3,to_date('30-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3289.59,4519.9,2574,4356.8,to_date('31-MAY-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3305.72,4535.1,2596.8,4332.2,to_date('01-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3247.03,4442.9,2553.7,4258.1,to_date('02-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3288.52,4491.3,2587.1,4254.8,to_date('03-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3302.57,4497.3,2579.3,4214.8,to_date('04-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3374.93,4558.6,2624.3,4270.7,to_date('05-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3439.22,4620.5,2648.7,4301.5,to_date('06-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('07-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('08-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('09-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3281.46,4501.7,2581.8,4248.1,to_date('10-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3210.94,4488.7,2530.3,4236.6,to_date('11-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3212.82,4463.9,2514.5,4214.6,to_date('12-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3235.35,4471.5,2518,4236.6,to_date('13-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3342.77,4588,2572.3,4271.7,to_date('14-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3328.13,4582.6,2579,4269.3,to_date('15-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3364.76,4634.9,2617.6,4292.3,to_date('16-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3352.58,4626.6,2608,4313.2,to_date('17-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3319.24,4604.2,2574.6,4270.7,to_date('18-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3297.52,4586.3,2566.1,4251.7,to_date('19-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3369.26,4643.4,2620.6,4286.8,to_date('20-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3347.54,4625.6,2621,4294.6,to_date('21-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3361.8,4665.7,2615.2,4298.9,to_date('22-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3361.2,4699.1,2547.6,4310.5,to_date('23-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3328.41,4740.1,2522.7,4328.7,to_date('24-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3348.9,4752.3,2514.7,4346.1,to_date('25-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3366.87,4781.1,2533.6,4387.7,to_date('26-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3396.49,4836.1,2539.8,4388.5,to_date('27-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3357.57,4772.3,2536.3,4369.7,to_date('28-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3372.96,4793.3,2550.3,4389.7,to_date('29-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3425.86,4855.1,2602.9,4433.2,to_date('30-JUN-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3438.09,4897.6,2639.5,4436,to_date('01-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3438.09,4897.6,2639.5,4445,to_date('02-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3491.08,4953.5,2655.3,4455.6,to_date('03-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3565.69,5029.6,2672.8,4455.6,to_date('04-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3548.52,4988.4,2651.9,4519.3,to_date('05-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3537.45,5016,2643.3,4537.5,to_date('06-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3537.45,5016,2643.3,4580.4,to_date('07-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3533.21,5004.7,2633.9,4630.9,to_date('08-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3593.14,5042.5,2693.1,4669.6,to_date('09-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3559.29,5084.2,2719.6,4691,to_date('10-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3588.57,5134.3,2774.6,4686.9,to_date('11-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3564.85,5141.7,2776,4681.2,to_date('12-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3569.26,5157.5,2784.3,4693.9,to_date('13-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3569.26,5157.5,2784.3,4645.2,to_date('14-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3516.2,5081,2751.1,4607.5,to_date('15-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3600.4,5178.6,2786.4,4642,to_date('16-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3575.44,5176.4,2741.7,4651.8,to_date('17-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3621.72,5181,2762.9,4661.8,to_date('18-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3669.31,5196.7,2654.7,4661.8,to_date('19-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3665.43,5190,2680.3,4681.6,to_date('20-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3626.6,5133.1,2583.2,4677.5,to_date('21-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3635.38,5132.1,2579.2,4672.3,to_date('22-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3562.73,5041.6,2583.9,4621.3,to_date('23-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3596.4,5150,2601.5,4562.8,to_date('24-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3655.59,5207.2,2624.5,4557.8,to_date('25-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3651.59,5238.5,2635.4,4557.1,to_date('26-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3684.6,5251.2,2690.9,4576.2,to_date('27-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3700.53,5320,2719.3,4645,to_date('28-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3668.61,5368.8,2686.2,4686.7,to_date('29-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3671.16,5361.9,2664.2,4739.6,to_date('30-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3671.87,5308.6,2696.2,4724.8,to_date('31-JUL-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3737.16,5364.2,2760.3,4757.4,to_date('01-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3752.37,5384.6,2808.5,4783.1,to_date('02-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3750.02,5362,2795.9,4745.1,to_date('03-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3721.18,5345.9,2762.6,4682.2,to_date('04-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3730.56,5405,2751.7,4657,to_date('05-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3777.56,5510.3,2739.7,4653.7,to_date('06-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3788.54,5561.8,2757.1,4593.9,to_date('07-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3748.79,5587.8,2762.2,4575.8,to_date('08-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3761.07,5576.1,2784.8,4596.3,to_date('09-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3819.52,5662.4,2867.4,4640,to_date('10-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3820.16,5669.9,2893.6,4657.9,to_date('11-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3809.92,5700.3,2891,4640.3,to_date('12-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3766.89,5620.6,2858.3,4604.6,to_date('13-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3834.84,5654.8,2944,4728.3,to_date('14-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3867.53,5674.3,2909.5,4751.4,to_date('15-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3939.73,5804.9,2937,4831.7,to_date('16-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3946.73,5846.5,2934.5,4812.8,to_date('17-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4003.35,5947,2947.7,4810.7,to_date('18-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4030.1,6012.6,2929.8,4758.5,to_date('19-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4026.97,5977.1,2950.6,4762.4,to_date('20-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4000.65,5885.4,2929.1,4767.8,to_date('21-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4074.3,5801.5,2941.6,4799.5,to_date('22-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4142.19,5845.8,2941.6,4857.4,to_date('23-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4139.68,5844.7,2950.7,4899.3,to_date('24-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4223.69,5927.5,2988,4964.2,to_date('25-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4203.91,5868.3,2958.6,4949,to_date('26-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4131.94,5737.1,2876.7,4877.2,to_date('27-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4139.96,5620.5,2874.1,4805.7,to_date('28-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4297.64,5677.1,2921.1,4846.7,to_date('29-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4384.82,5869.9,3003.5,4874.5,to_date('30-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4320.52,5849.2,2973.5,4862.9,to_date('31-AUG-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4368.54,5847,3025.9,4851.5,to_date('01-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4400.3,5888,3022.2,4862.6,to_date('02-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4377.7,5842.1,3023.6,4876.6,to_date('03-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4458.66,5929.5,3069.3,4927.3,to_date('04-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4405.52,5898.2,3075.7,4907.5,to_date('05-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4336.98,5898.2,3049.5,4899.3,to_date('06-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4302.5,5771,2992.4,4895.7,to_date('07-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4325.86,5765.2,2984.1,4960.6,to_date('08-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4364.25,5812.1,3037.1,5026.2,to_date('09-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4428.08,5922.1,3056.3,5086.8,to_date('10-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4342.31,5864.8,2996.3,5031.3,to_date('11-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4333.15,5825.6,2983.4,5031.9,to_date('12-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4377.51,5808.4,2998.6,5075.8,to_date('13-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4237.06,5682.1,2924,5003.6,to_date('14-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4195.53,5579.5,2921.8,4991.3,to_date('15-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4077.59,5498.5,2921.8,4865.8,to_date('16-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4080.55,5405.6,2870.1,4835,to_date('17-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4190.45,5580.1,2936.2,4914.2,to_date('18-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4251.93,5690.1,2979.3,4958.4,to_date('19-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4204.81,5668.8,2957.2,4978,to_date('20-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4090.14,5475.8,2904.2,4901.1,to_date('21-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4076.75,5473.9,2898.6,4901.1,to_date('22-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3993.7,5363.3,2869.3,4886.3,to_date('23-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3992.03,5409.6,2871.7,4906.9,to_date('24-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3897.43,5217.3,2828.4,4845.4,to_date('25-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3919.79,5216.7,2770.5,4817.5,to_date('26-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4001.81,5271.5,2805.8,4870.2,to_date('27-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4127.28,5447.5,2921.2,4952.2,to_date('28-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4062.13,5478.6,2918,4976.9,to_date('29-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4093.43,5478.1,2927,4991.3,to_date('30-SEP-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4073.71,5532.9,2924.5,4994.2,to_date('01-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4131.26,5505.3,2940.9,4985.2,to_date('02-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4104.57,5445.1,2919.7,4950.5,to_date('03-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4028,5356.7,2874.6,4905.2,to_date('04-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3890.24,5280.8,2843.6,4854.8,to_date('05-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3796.61,5281.9,2834.1,4848.2,to_date('06-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3869.53,5321.7,2898.6,4902.9,to_date('07-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3995.69,5417.8,2940.6,4976.4,to_date('08-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3970.44,5550.4,2944,5013.1,to_date('09-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4004.04,5629,2978.4,5046.2,to_date('10-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3983.06,5611,2977.2,5023.8,to_date('11-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4096.85,5705.1,3017.5,5075.7,to_date('12-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4091.77,5730.4,2997.2,5027.5,to_date('13-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.95,5732.5,3023.7,5077.2,to_date('14-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4104.93,5667.1,3005.4,5065.5,to_date('15-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4135.09,5716.6,2985.6,5226.3,to_date('16-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4116.52,5691.8,2989,5220.3,to_date('17-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4154.89,5673.6,3008.3,5244.2,to_date('18-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4262.98,5754.7,3054.9,5317.1,to_date('19-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.17,5825,3052.1,5296.1,to_date('20-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.17,5929,3094,5330.8,to_date('21-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4326.35,5897.4,3078,5300,to_date('22-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4311.13,5846.9,3064.4,5305.6,to_date('23-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4267.4,5822.3,3024.1,5262.1,to_date('24-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4179.92,5732.2,2960.7,5217.8,to_date('25-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4164.62,5699.5,2955.1,5227.3,to_date('26-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4225.27,5792.8,3002.9,5300.1,to_date('27-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4215.23,5836.3,3002.5,5298.9,to_date('28-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4168.62,5815.9,2992.2,5263.7,to_date('29-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4149.92,5806.8,2992.9,5287.9,to_date('30-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4049.16,5751.6,2958,5271.1,to_date('31-OCT-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4069.25,5777.2,2946.7,5211,to_date('01-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4172.47,5862.9,2989.9,5225.9,to_date('02-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4124.86,5803.2,2958.1,5148.8,to_date('03-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3976.38,5651.8,2856.9,4991.5,to_date('04-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3981.44,5689.5,2849,4970.2,to_date('05-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3871.39,5533.5,2769.6,4840.7,to_date('06-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3645.69,5279.7,2651.3,4755.4,to_date('07-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3806.66,5479,2818,4871.8,to_date('08-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3748.88,5370.9,2739.5,4801.9,to_date('09-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3753.66,5467.2,2739.3,4842.3,to_date('10-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3847.73,5581.6,2788,4906.4,to_date('11-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3784.8,5538.2,2774.9,4897.4,to_date('12-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3841.39,5601.6,2822.4,4908.3,to_date('13-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3813.88,5557.4,2781.8,4863.8,to_date('14-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3715.38,5438.6,2707.1,4764.3,to_date('15-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3728.37,5459.7,2707.1,4806.8,to_date('16-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3734.79,5483.9,2707.1,4793.7,to_date('17-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3697.48,5434,2694.5,4720.4,to_date('18-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3701.94,5418.2,2700.7,4711,to_date('19-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3676.65,5437,2698.9,4741.8,to_date('20-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3816.71,5565,2773,4867,to_date('21-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3844.14,5574.2,2782.6,4845.4,to_date('22-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3876.9,5571.7,2790.6,4830.1,to_date('23-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3931.81,5650.4,2821.2,4908.4,to_date('24-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3941.91,5725.5,2861.7,4985.8,to_date('25-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3832.1,5645.7,2802.5,4898.6,to_date('26-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3850.14,5666.3,2786.3,4863.5,to_date('27-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3926.93,5738.3,2811.7,4891.2,to_date('28-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3961.97,5772.4,2829,4889,to_date('29-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3972.08,5775.9,2854.4,4831.8,to_date('30-NOV-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4125.92,5875.1,2918.5,4921.8,to_date('01-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4096.4,5919.9,2913.1,4977.6,to_date('02-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4074.55,5922.7,2902.4,4970.7,to_date('03-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4159.72,5969.5,2914.5,5082.3,to_date('04-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4191.81,6009,2910.1,5142.9,to_date('05-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4208.14,6095.3,2932.5,5187.4,to_date('06-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4187.13,6103.2,2959.4,5177.1,to_date('07-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4116.7,6056.6,2932.2,5130.7,to_date('08-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4016.7,6021.8,2828.5,5035.9,to_date('09-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4061.91,6018.7,2830.3,5045.2,to_date('10-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4029.08,5986.6,2838.3,5121.8,to_date('11-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.31,6092.7,2912.2,5203.4,to_date('12-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4154.57,6122.1,2893.3,5190.8,to_date('13-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4162.92,6115.1,2894.5,5168.3,to_date('14-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4055.35,5989.9,2822.9,5020.2,to_date('15-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4125.54,6049.3,2869.7,5018.2,to_date('16-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6044.7,2858.1,5049.8,to_date('17-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6046.7,2874.1,5013.9,to_date('18-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6046.7,2874.1,5013.9,to_date('19-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4132.79,6046.7,2875.1,5013.9,to_date('20-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.02,6190.4,2939.5,5112.4,to_date('21-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4224.3,6267.6,2975.5,5132.3,to_date('22-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4224.3,6265.5,2998.9,5135.5,to_date('23-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4224.3,6265.5,2998.9,5135.5,to_date('24-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4364.32,6265.5,3038.7,5193.5,to_date('25-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4416.95,6397,3072.8,5262.5,to_date('26-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4360.05,6375.7,3037.7,5264.4,to_date('27-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4339.98,6390,3006.7,5224.1,to_date('28-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4293.64,6330.2,2954.9,5237.1,to_date('29-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4237.75,6251.8,2919.8,5138.3,to_date('30-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4134.64,6062.1,2862.5,5068.8,to_date('31-DEC-97','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.01,6169.3,2902.9,5083.9,to_date('01-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4145.41,6149.8,2919.8,5106.9,to_date('02-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4140.22,6148.5,2932.8,5165.8,to_date('03-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4216.24,6274,2976.1,5263.1,to_date('04-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4290.05,6340.4,2987,5273.6,to_date('05-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4310.83,6397.5,3008.3,5278.2,to_date('06-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4250.47,6391.4,2998.1,5272.3,to_date('07-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4238.77,6356.1,2988.6,5253.1,to_date('08-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4222.16,6391,2966.2,5181.4,to_date('09-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.34,6411,3000.5,5237.2,to_date('10-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4316.05,6424,3052,5326.3,to_date('11-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4385.29,6508.7,3088.3,5372.6,to_date('12-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4444.53,6530.4,3133.8,5422.4,to_date('13-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4442.53,6582.6,3172.1,5458.5,to_date('14-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4529.88,6688,3187.5,5599,to_date('15-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4529.18,6720.7,3188.4,5612.8,to_date('16-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4509.25,6708.9,3166.3,5595.8,to_date('17-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4494.72,6772,3189.6,5606.4,to_date('18-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4536.91,6857.1,3216.7,5629.7,to_date('19-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4519.56,6828.4,3220.9,5600.9,to_date('20-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4558.62,6860.8,3235.8,5613.3,to_date('21-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4552.46,6931.6,3240,5607.9,to_date('22-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4509.37,6856,3178.7,5552.5,to_date('23-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4522.42,6898.9,3187.7,5582.3,to_date('24-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4535.56,6905.3,3225.1,5619.9,to_date('25-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4627.42,6990.5,3280.5,5709.5,to_date('26-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4611.66,6966.2,3281.7,5723.4,to_date('27-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4581.08,6953.2,3250.6,5718.5,to_date('28-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4583.03,6986.7,3262.5,5718.5,to_date('29-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4610.66,6986.1,3273.5,5702.8,to_date('30-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4604.55,6945,3262.3,5651,to_date('31-JAN-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4704.58,7065.4,3348.2,5745.1,to_date('01-FEB-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4695.78,7118.6,3397,5764.8,to_date('02-FEB-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4693.86,7153.1,3421.9,5767.3,to_date('03-FEB-98','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1982.99,2560.3,1881.1,3208.8,to_date('14-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1982.99,2560.3,1881.1,3208.8,to_date('15-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1982.99,2560.3,1881.1,3208.8,to_date('16-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1954.62,2547.1,1855.7,3194.5,to_date('17-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1943.88,2516.8,1874.9,3170.1,to_date('18-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1955.33,2544.2,1882,3174.7,to_date('19-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1987.7,2560.8,1928.4,3199.9,to_date('20-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.97,2554.8,1918.5,3209.3,to_date('21-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2007.57,2561.1,1945.9,3214.9,to_date('22-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2028.52,2574.4,1942.3,3226.2,to_date('23-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.25,2567,1931,3217.6,to_date('24-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2017.95,2597.2,1918.5,3216.7,to_date('25-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2017.95,2597.2,1918.5,3220.4,to_date('26-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.47,2621.4,1936.9,3248.2,to_date('27-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2037.99,2618.6,1971.1,3262.6,to_date('28-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2034.15,2641.7,1946.2,3264.3,to_date('29-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2021.22,2650.5,1927.4,3251.7,to_date('30-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2030.65,2658.8,1927.4,3251.7,to_date('31-JAN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.66,2655.6,1988.7,3261.2,to_date('01-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.41,2661.4,1996.7,3290.1,to_date('02-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.41,2701,2003.6,3317.9,to_date('03-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2102.4,2728.2,2017.3,3310.3,to_date('04-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2092,2739,1991.5,3310.7,to_date('05-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2109.36,2746.6,2001.3,3300.8,to_date('06-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.99,2739.8,2004.7,3297.4,to_date('07-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.8,2727.7,1989.8,3285.8,to_date('08-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.42,2701,1965.5,3261,to_date('09-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2096.87,2751.2,1979.5,3284.5,to_date('10-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.55,2750.4,1965.3,3291.8,to_date('11-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2105.88,2778.5,1960.7,3327.3,to_date('12-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2105.88,2778.5,1960.7,3328.2,to_date('13-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2065.71,2751.7,1919.1,3311.1,to_date('14-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.26,2762.5,1927.8,3311.1,to_date('15-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2081.46,2779.4,1927.3,3309.9,to_date('16-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.95,2786.4,1948,3319.4,to_date('17-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.24,2807.8,1960.8,3340.6,to_date('18-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.72,2805.3,1971.3,3345,to_date('19-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.72,2805.3,1971.3,3376.6,to_date('20-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.17,2809.7,1965.9,3380,to_date('21-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2136.72,2819.6,1974.7,3370.8,to_date('22-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.21,2806.8,1951.2,3380.8,to_date('23-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2107.16,2795.3,1897,3337.7,to_date('24-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.79,2802.3,1907.8,3344.6,to_date('25-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.25,2800.9,1922.8,3348,to_date('26-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.84,2798.1,1893.7,3339.8,to_date('27-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.78,2800.2,1920.7,3370.4,to_date('28-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2125.06,2775.4,1905,3366.1,to_date('29-FEB-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.13,2803,1918.4,3381.3,to_date('01-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.36,2805.6,1896.1,3377.2,to_date('02-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2816.8,1902.8,3378.3,to_date('03-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2843.3,1925.2,3403.8,to_date('04-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2837.7,1895.1,3379.4,to_date('05-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.36,2830.3,1902.4,3309.2,to_date('06-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.99,2845.1,1877.7,3313.2,to_date('07-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.3,2836.8,1865.5,3282.7,to_date('08-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2094.68,2845.6,1861.9,3294,to_date('09-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.04,2825.3,1858.8,3314.6,to_date('10-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.68,2827.5,1879.7,3323.7,to_date('11-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2110.13,2847.5,1889.4,3349.2,to_date('12-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.63,2843.7,1907.8,3394.9,to_date('13-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2111.35,2833.7,1889.5,3388.3,to_date('14-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.37,2848.4,1950.8,3462.9,to_date('15-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.39,2842.9,1949.4,3455,to_date('16-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.15,2847.3,1961.3,3464,to_date('17-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2200.32,2867.9,1961.7,3450.6,to_date('18-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.72,2869.7,1948.3,3447.2,to_date('19-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2191.25,2863.4,1948.3,3429.2,to_date('20-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.36,2892.3,1951.5,3442.6,to_date('21-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.71,2875.1,1929,3420.7,to_date('22-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2195.31,2856.2,1931.2,3405.3,to_date('23-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.52,2822.2,1905.9,3400.4,to_date('24-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.54,2830.2,1908.9,3413.1,to_date('25-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.02,2836.6,1926.6,3431.6,to_date('26-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.23,2833.2,1941.3,3432.9,to_date('27-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2237.73,2827.1,1942.5,3454.3,to_date('28-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2239.36,2824.7,1947.9,3458.3,to_date('29-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2217.91,2833.1,1932.8,3468.9,to_date('30-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2222.51,2826.8,1920,3463.3,to_date('31-MAR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.26,2826.8,1917.8,3449.9,to_date('01-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.6,2852.3,1960,3499.9,to_date('02-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2234.86,2814.3,1950.8,3475.6,to_date('03-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.68,2807.1,1957.1,3482.4,to_date('04-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2244.56,2800.6,1950.4,3483.5,to_date('05-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.89,2797.4,1954.7,3468.8,to_date('06-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2222.28,2781.3,1943.7,3468.3,to_date('07-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.72,2802.6,1949.2,3474.7,to_date('08-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2228.44,2820.1,1946.2,3467.5,to_date('09-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2208.41,2807.2,1946.2,3441.4,to_date('10-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.73,2838.4,1946.2,3444.4,to_date('11-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2251.64,2880.6,1970.4,3465.1,to_date('12-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2253.93,2880,1967.3,3470.6,to_date('13-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2265.86,2889.5,1971.6,3509.8,to_date('14-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2269.6,2886.7,1984.4,3535.7,to_date('15-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2258.97,2900.8,1963.3,3530.2,to_date('16-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2262.66,2896.8,1957.4,3515.9,to_date('17-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2253.91,2887.1,1939.2,3520,to_date('18-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2257.34,2893.8,1937.8,3524.9,to_date('19-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2240.31,2883.3,1890.1,3524.9,to_date('20-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2230.27,2887.3,1899.5,3502.6,to_date('21-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2251.3,2915.7,1921.4,3504,to_date('22-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2234.23,2908.8,1883.4,3477.8,to_date('23-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2242.76,2918.3,1881.7,3509.4,to_date('24-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2263.08,2952.7,1900.5,3522.7,to_date('25-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.57,2971.1,1902.5,3532.4,to_date('26-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.37,2999.6,1884.8,3557.7,to_date('27-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2263.27,2989.1,1861.7,3545.6,to_date('28-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.77,2989.2,1854.9,3554.5,to_date('29-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.26,2986.3,1869.7,3549.3,to_date('30-APR-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.07,2990.5,1873.6,3535.9,to_date('01-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2300.72,3016.8,1898.2,3570.8,to_date('02-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2300.29,3011.6,1892.4,3565.4,to_date('03-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2305.58,3025.4,1880,3564.6,to_date('04-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2289.49,3008.1,1871.7,3533.3,to_date('05-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2306.66,3033.5,1882.5,3541.4,to_date('06-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2294.15,3029,1863.8,3561.5,to_date('07-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2275.72,3039.1,1853.9,3557.9,to_date('08-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2204.44,2994.7,1790.7,3514.8,to_date('09-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2212.97,3015.3,1800.6,3507,to_date('10-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.02,3045.5,1816.3,3523.3,to_date('11-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.9,3009.8,1791.8,3485,to_date('12-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.24,3000.6,1767.6,3479,to_date('13-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.27,3014.8,1788.4,3508.2,to_date('14-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.01,3039,1780.7,3520.2,to_date('15-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.01,3049,1800.7,3524.2,to_date('16-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.6,3036.5,1803.9,3544.1,to_date('17-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2191.54,3036.6,1800.3,3544.4,to_date('18-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.35,3054,1809.6,3526.5,to_date('19-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2157.41,3064,1785.7,3510.3,to_date('20-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.01,3050,1778,3460.1,to_date('21-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2153.56,3073,1794.4,3474.3,to_date('22-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2159.73,3083.7,1803.8,3523.8,to_date('23-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2206.11,3109.2,1817,3568,to_date('24-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.85,3116.3,1790.5,3557.3,to_date('25-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2185.52,3112.3,1779.7,3562.2,to_date('26-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.57,3135.4,1770.7,3593,to_date('27-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.09,3124.5,1757.3,3578.6,to_date('28-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2148.12,3120.4,1740.7,3551.4,to_date('29-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.86,3041.7,1721.1,3531.5,to_date('30-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2114.88,3062.3,1724.2,3535.3,to_date('31-MAY-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.74,3078.9,1764.1,3537.8,to_date('01-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.74,3080,1754.1,3519.6,to_date('02-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.01,3026.6,1742.4,3497.9,to_date('03-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2149.71,3057.9,1795.3,3510,to_date('04-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.76,3108.2,1814,3529.1,to_date('05-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.11,3128.5,1814,3518.7,to_date('06-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2180.49,3123.4,1828.7,3523,to_date('07-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2169.69,3137.6,1832.1,3500.4,to_date('08-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2168.57,3123.2,1822.6,3514.8,to_date('09-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2174.27,3133.8,1857.4,3522.4,to_date('10-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.05,3132.6,1866.3,3537.1,to_date('11-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.47,3150.1,1852.6,3541.6,to_date('12-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2174.78,3130.4,1839.8,3523.4,to_date('13-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2195.15,3121.4,1838.2,3536.8,to_date('14-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.34,3133,1838.2,3547.9,to_date('15-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.64,3124.8,1875.2,3571.4,to_date('16-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2199.29,3148.4,1905.1,3610.8,to_date('17-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.59,3152.5,1890.5,3609.2,to_date('18-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2204.01,3182.2,1881.4,3628.8,to_date('19-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.9,3183.9,1872.9,3604.1,to_date('20-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.53,3201,1875.5,3632.4,to_date('21-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.26,3209.2,1867.1,3602.5,to_date('22-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2210.92,3220.1,1891,3624,to_date('23-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2247.97,3251,1889.8,3649,to_date('24-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2242.91,3229.5,1870.3,3648.8,to_date('25-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.75,3245.9,1857.3,3655.5,to_date('26-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2254.95,3251.6,1828.3,3664.3,to_date('27-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.56,3261.2,1820.9,3664.3,to_date('28-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2261.08,3278.4,1774.9,3669.7,to_date('29-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2269.34,3278.5,1814.9,3664.2,to_date('30-JUN-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2274.94,3281.5,1834.8,3662.8,to_date('01-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.62,3245.7,1846.8,3639.5,to_date('02-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2277.7,3246.6,1856.3,3630,to_date('03-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2277.7,3261.6,1849.6,3652.1,to_date('04-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2278.64,3280.9,1848.9,3654.9,to_date('05-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2283.26,3273.4,1833.8,3662.4,to_date('06-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2286.21,3272.2,1875,3671.6,to_date('07-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2283.84,3289.8,1859.3,3642.6,to_date('08-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2240.24,3251.6,1820.5,3596.1,to_date('09-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.43,3217.7,1810.2,3576.9,to_date('10-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.07,3254.3,1831.2,3613.7,to_date('11-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.35,3255,1834.4,3633.3,to_date('12-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1873.4,3658.3,to_date('13-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1873.4,3658.3,to_date('14-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1866.7,3658.3,to_date('15-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.44,3317.1,1877,3676.4,to_date('16-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2273.9,3297.7,1879.1,3676.7,to_date('17-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.69,3297.7,1872,3689.3,to_date('18-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.69,3297.7,1872,3689.3,to_date('19-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2307.7,3297.7,1908.4,3687.9,to_date('20-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2326.18,3384.5,1943,3715.6,to_date('21-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2332.81,3395.8,1931.2,3714.1,to_date('22-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2315.66,3376.1,1917.7,3704.5,to_date('23-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2336.76,3368.1,1916.6,3720.6,to_date('24-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2351.47,3361.5,1916.3,3720.6,to_date('25-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2340.31,3314.8,1910.1,3671.5,to_date('26-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2330.98,3284.1,1897.9,3654.9,to_date('27-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2353.89,3292.9,1907.6,3657.3,to_date('28-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2361.38,3233.9,1924.4,3662.7,to_date('29-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2379.43,3254.8,1952.1,3710.6,to_date('30-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2375.63,3219.3,1966,3704.2,to_date('31-JUL-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2389.62,3257.8,1960.1,3748.7,to_date('01-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2400.58,3253.1,1964.3,3748.4,to_date('02-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2391.74,3249.9,1954.4,3754.2,to_date('03-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2393.12,3219,1934.1,3735,to_date('04-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2436.68,3221.3,1946,3758.2,to_date('05-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.02,3211.2,1950.2,3734.2,to_date('06-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.71,3191.4,1966.7,3734.7,to_date('07-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.7,3198.2,1980.7,3734.6,to_date('08-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2456.09,3236.8,2003.1,3735.3,to_date('09-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2463,3248.7,2021,3759.3,to_date('10-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.53,3279.9,2024.1,3752.8,to_date('11-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2459.81,3270,2022.2,3781.3,to_date('12-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2416.84,3221.7,1985.6,3746.6,to_date('13-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.98,3253.8,1988.5,3747.5,to_date('14-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2442.43,3262.7,1983.3,3726.1,to_date('15-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2411.49,3245.6,1956.2,3708.4,to_date('16-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2435.07,3242.7,1960.7,3716.3,to_date('17-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2428.59,3253.1,1967.7,3726.6,to_date('18-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2436.09,3260.1,1983.3,3747.6,to_date('19-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2427.77,3266.1,1956.4,3745,to_date('20-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2426.51,3272.2,1964.2,3779.8,to_date('21-AUG-96','DD-MON-RR'));","Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2423.6,3280.3,1952.5,3770.9,to_date('22-AUG-96','DD-MON-RR'));"],"enabled":true,"result":{"startTime":1714398383093,"interpreter":"script.low","endTime":1714398388273,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable EUSTOCK created.\n\n\n---------------------------\n\n---------------------------\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n\n1 row inserted.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display a few rows of the database","message":["%sql","","SELECT * ","FROM EUSTOCK","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1714398388418,"interpreter":"sql.low","endTime":1714398388617,"results":[{"message":"DAX\tSMI\tCAC\tFTSE\tDATES\n1696.55\t1794.2\t1912.2\t2555.9\t1994-10-08 00:00:00\n1690.24\t1784.4\t1915.4\t2536.7\t1994-10-09 00:00:00\n1711.35\t1800.1\t1951.2\t2543.4\t1994-10-10 00:00:00\n1711.29\t1804\t1962.4\t2542.3\t1994-10-11 00:00:00\n1729.86\t1816.2\t1976.5\t2559.7\t1994-10-12 00:00:00\n1716.63\t1810.5\t1953.5\t2546.8\t1994-10-13 00:00:00\n1743.36\t1821.9\t1981.3\t2565\t1994-10-14 00:00:00\n1745.17\t1828.2\t1985.1\t2562\t1994-10-15 00:00:00\n1746.76\t1840.6\t1983.4\t2562.1\t1994-10-16 00:00:00\n1749.29\t1841.1\t1979.7\t2554.3\t1994-10-17 00:00:00\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build the time series ESM model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('MSDEMO_MODEL'); ","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')            := 'ALGO_EXPONENTIAL_SMOOTHING';","    v_setlst('EXSM_INTERVAL')        := 'EXSM_INTERVAL_DAY';","    v_setlst('EXSM_MODEL')           := 'EXSM_ADDWINTERS_DAMPED';","    v_setlst('EXSM_SEASONALITY')     := '7';","    v_setlst('EXSM_PREDICTION_STEP') := '1';","    v_setlst('EXSM_SERIES_LIST')     := 'SMI,CAC,FTSE';","\t ","    DBMS_DATA_MINING.CREATE_MODEL2(","               MODEL_NAME          => 'MSDEMO_MODEL',","               MINING_FUNCTION     => 'TIME_SERIES',","               DATA_QUERY          => 'SELECT * FROM EUSTOCK',","               CASE_ID_COLUMN_NAME => 'DATES',","               TARGET_COLUMN_NAME  => 'DAX',","               SET_LIST            => v_setlst);","END;"],"enabled":true,"result":{"startTime":1714398388950,"interpreter":"script.low","endTime":1714398390651,"results":[{"message":"\nPL/SQL procedure successfully 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=> 'DAX',","               SET_LIST            => v_setlst);    ","END;"],"enabled":true,"result":{"startTime":1714398393469,"interpreter":"script.low","endTime":1714398394314,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Evaluate the model"],"enabled":true,"result":{"startTime":1714398394425,"interpreter":"md.low","endTime":1714398394549,"results":[{"message":"<h2 id=\"evaluate-the-model\">Evaluate the model<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display model views","message":["%sql","","SELECT VIEW_NAME, VIEW_TYPE ","FROM   USER_MINING_MODEL_VIEWS","WHERE  MODEL_NAME='MSDEMO_MODEL'","ORDER BY VIEW_NAME;"],"enabled":true,"result":{"startTime":1714398394661,"interpreter":"sql.low","endTime":1714398394750,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VGMSDEMO_MODEL\tGlobal Name-Value Pairs\nDM$VPMSDEMO_MODEL\tExponential Smoothing Forecast\nDM$VRMSDEMO_MODEL\tExponential Smoothing Regression Build Data\nDM$VSMSDEMO_MODEL\tComputed Settings\nDM$VTMSDEMO_MODEL\tExponential Smoothing Regression Score Data\nDM$VWMSDEMO_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display backcasts","message":["%sql","","SELECT * ","FROM   DM$VRMSDEMO_MODEL","ORDER BY CASE_ID","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1714398394891,"interpreter":"sql.low","endTime":1714398394992,"results":[{"message":"PARTITION_NAME\tCASE_ID\tDAX\tDM$DAX\tDM$SMI\tDM$CAC\tDM$FTSE\nDM$$_PNVL\t1993-01-01 00:00:00\t1691.3699999999999\t1696.7851661195\t2125.5661483564318\t2033.44361914249\t2912.5490658966582\nDM$$_PNVL\t1993-01-02 00:00:00\t1701.46\t1695.7996033383492\t2134.1495501159975\t2001.6724577791497\t2888.2666654655009\nDM$$_PNVL\t1993-01-03 00:00:00\t1690.48\t1708.2127690800501\t2138.5055925402335\t2001.3130242851769\t2891.4545065613456\nDM$$_PNVL\t1993-01-04 00:00:00\t1685.46\t1686.2385372107833\t2141.2155027572016\t1993.8779030518579\t2916.1994282596975\nDM$$_PNVL\t1993-01-05 00:00:00\t1686.1500000000001\t1694.4746833291817\t2161.2299128372124\t1988.9694220247859\t2907.412077068961\nDM$$_PNVL\t1993-01-06 00:00:00\t1702.27\t1671.3173544336435\t2119.5886948057937\t1987.2778359197432\t2906.9183420273644\nDM$$_PNVL\t1993-01-07 00:00:00\t1711.9100000000001\t1701.4569269154085\t2126.6203498851664\t1998.9855993031877\t2958.1359828889231\nDM$$_PNVL\t1993-01-08 00:00:00\t1714.48\t1712.7369364576161\t2150.6906591505749\t2006.3163895452492\t2948.3582795224656\nDM$$_PNVL\t1993-01-09 00:00:00\t1708.6500000000001\t1718.5224134641501\t2167.708978361698\t1996.1860828981924\t2961.8594901114134\nDM$$_PNVL\t1993-01-10 00:00:00\t1688.74\t1715.5388618037366\t2170.2102147501191\t1990.0207603254203\t2962.4867916961498\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display forecasts","message":["%sql","","SELECT * ","FROM DM$VTMSDEMO_MODEL","ORDER BY CASE_ID ","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1714398395105,"interpreter":"sql.low","endTime":1714398395194,"results":[{"message":"PARTITION_NAME\tCASE_ID\tDAX\tDM$DAX\tDM$SMI\tDM$CAC\tDM$FTSE\nDM$$_PNVL\t1998-02-04 00:00:00\t\t4679.2161855773438\t7136.4668617788902\t3417.0147412943652\t5757.7332608192073\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare regression forecast (GLM) to baseline (ESM) forecast","message":["%sql","","SELECT CASE_ID, DAX, regression_forecast, baseline_forecast,","       DAX - regression_forecast AS regression_error,","       DAX - baseline_forecast AS baseline_error ","FROM (SELECT CASE_ID, DAX, ","             PREDICTION(MS_GLM_model using *) regression_forecast, ","             DM$DAX baseline_forecast","      FROM   tmesm_ms_test ","      ORDER BY CASE_ID);"],"enabled":true,"result":{"startTime":1714398395321,"interpreter":"sql.low","endTime":1714398396493,"results":[{"message":"CASE_ID\tDAX\tREGRESSION_FORECAST\tBASELINE_FORECAST\tREGRESSION_ERROR\tBASELINE_ERROR\n1998-02-04 00:00:00\t4633.008\t4669.6408226225494\t4679.2161855773438\t-36.63282262254961\t-46.20818557734401\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build XGBoost model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('MS_XGB_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE","    v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN","    v_setlst('ALGO_NAME')          := 'ALGO_XGBOOST';","\t","    DBMS_DATA_MINING.CREATE_MODEL2(","               MODEL_NAME          => 'MS_XGB_MODEL',","               MINING_FUNCTION     => dbms_data_mining.regression,","               DATA_QUERY          => 'SELECT * FROM tmesm_ms_train',","               CASE_ID_COLUMN_NAME => 'CASE_ID',","               TARGET_COLUMN_NAME  => 'DAX',","               SET_LIST            => v_setlst);    ","END;"],"enabled":true,"result":{"startTime":1714398396613,"interpreter":"script.low","endTime":1714398397101,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Compare regression forecast (XGB) to baseline (ESM) forecast","message":["%sql","","SELECT CASE_ID, DAX, regression_forecast, baseline_forecast,","       DAX - regression_forecast AS regression_error,","       DAX - baseline_forecast AS baseline_error ","FROM (SELECT CASE_ID, DAX, ","             PREDICTION(MS_XGB_model using *) regression_forecast, ","             DM$DAX baseline_forecast","      FROM   tmesm_ms_test ","      ORDER BY CASE_ID);"],"enabled":true,"result":{"startTime":1714398397217,"interpreter":"sql.low","endTime":1714398397302,"results":[{"message":"CASE_ID\tDAX\tREGRESSION_FORECAST\tBASELINE_FORECAST\tREGRESSION_ERROR\tBASELINE_ERROR\n1998-02-04 00:00:00\t4633.008\t4630.7705078125\t4679.2161855773438\t2.237492187499811\t-46.20818557734401\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":0,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1714398397415,"interpreter":"md.low","endTime":1714398398066,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1714398398184,"interpreter":"md.low","endTime":1714398398259,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
\ No newline at end of file
diff --git a/machine-learning/sql/26ai/README.md b/machine-learning/sql/26ai/README.md
new file mode 100644
index 00000000..8bf83f70
--- /dev/null
+++ b/machine-learning/sql/26ai/README.md
@@ -0,0 +1,59 @@
+# Oracle Database 26ai Machine Learning for SQL examples
+Oracle Machine Learning for SQL is part of the Oracle Machine Learning family of technologies, and is the fastest way to access the heart of the Machine Learning inside every Oracle Database.
+
+Following Oracle's philosophy of moving the algorithms and not the data, the execution of these 30+ parallel, scalable in-Database implementations of machine learning algorithms minimizes or eliminates data movement, achieving scalability, security, and accelerated time-to-model deployment. 
+
+Oracle also provides easy mechanism for running R-extensible scripts directly inside the Oracle Database.
+
+**The following are a list of demos containing SQL code for learning about (OML4SQL)**
+
+Files on the current folder:
+ * dmsh.sql - Setup - Creates Demo Views and Tables
+ * dmshgrants.sql - Setup - Grants Privileges to Users
+ * oml4sql-anomaly-detection-1class-svm.sql - Anomaly Detection - One Class SVM Algorithm
+ * oml4sql-association-rules.sql - Association Rules - Apriori Algorithm
+ * oml4sql-attribute-importance.sql - Attribute Importance - "EXPLAIN" MDL function
+ * oml4sql-classification-decision-tree.sql - Classification - Decision Tree Algorithm
+ * oml4sql-classification-glm.sql - Classification - Generalized Linear Model Algorithm
+ * oml4sql-classification-naive-bayes.sql - Classification - Naïve Bayes Algorithm
+ * oml4sql-classification-neural-networks.sql - Classification - Neural Networks Algorithm
+ * oml4sql-classification-random-forest.sql - Classification - Random Forest Algorithm
+ * oml4sql-classification-regression-xgboost.sql - Classification and Regression - XGBoost Algorithm
+ * oml4sql-classification-svm.sql - Classification - SVM Algorithm
+ * oml4sql-classification-text-mining-svm.sql - Classification - SVM Algorithm with Text Mining
+ * oml4sql-clustering-expectation-maximization.sql - Clustering - Expectation-Maximization Algorithm
+ * oml4sql-clustering-kmeans-star-schema.sql - Clustering - K-Means Algorithm
+ * oml4sql-clustering-kmeans.sql - Clustering - K-Means Algorithm
+ * oml4sql-clustering-ocluster.sql - Clustering - O-Cluster Algorithm
+ * oml4sql-cross-validation-decision-tree.sql - Cross-Validation - Decision Tree Algorithm
+ * oml4sql-feature-extraction-cur.sql - Feature and Row Extraction - CUR Decomposition Algorithm
+ * oml4sql-feature-extraction-nmf.sql - Feature Extraction - Non-Negative Matrix Factorization Algorithm
+ * oml4sql-feature-extraction-svd.sql - Feature Extraction - Singular Value Decomposition
+ * oml4sql-feature-extraction-text-mining-esa.sql - Feature Extraction - ESA Algorithm for Text Mining
+ * oml4sql-feature-extraction-text-mining-nmf.sql - Feature Extraction - NMF Algorithm with Text Mining
+ * oml4sql-feature-extraction-text-term-extraction.sql - Feature Extraction - Text Term Extraction
+ * oml4sql-partitioned-models-svm.sql - Partitioned Models - Support Vector Machine Algorithm
+ * oml4sql-r-extensible-algorithm-registration.sql - OML R Extensible - Algorithm Registration
+ * oml4sql-r-extensible-association-rules.sql - OML R Extensible - Association Rules Algorithm
+ * oml4sql-r-extensible-attribute-importance-via-rf.sql - OML R Extensible - Attribute Importance via RF Algorithm
+ * oml4sql-r-extensible-glm.sql - OML R Extensible - Generalized Linear Model Algorithm
+ * oml4sql-r-extensible-kmeans.sql - OML R Extensible - K-Means Algorithm
+ * oml4sql-r-extensible-principal-components.sql - OML R Extensible - Principal Components Algorithm
+ * oml4sql-r-extensible-regression-neural-networks.sql - OML R Extensible - Regression - Neural Networks Algorithm
+ * oml4sql-r-extensible-regression-tree.sql - OML R Extensible - Regression Tree Algorithm
+ * oml4sql-regression-glm.sql - Regression - Generalized Linear Model Algorithm
+ * oml4sql-regression-neural-networks.sql - Regression - Neural Networks Algorithm
+ * oml4sql-regression-random-forest.sql - Regression - Random Forest Algorithm
+ * oml4sql-regression-svm.sql - Regression - SVM Algorithm
+ * oml4sql-singular-value-decomposition.sql - SVD Algorithm
+ * oml4sql-survival-analysis-xgboost.sql - Survival Analysis with AFT (Accelerated Failure Time) via XGBoost Algorithm
+ * oml4sql-time-series-esm-auto-model-search.sql - Time Series - Exponential Smoothing with Auto Model Search
+ * oml4sql-time-series-exponential-smoothing.sql - Time Series - Exponential Smoothing Algorithm
+ * oml4sql-time-series-mset.sql - Time Series - MSET Multivariate State Estimation Technique Algorithm
+ * oml4sql-time-series-regression-dataset.sql - Time Series Regression - Dataset
+ * oml4sql-time-series-regression.sql - Time Series Regression - Using a combination of ESM and GLM algorithms
+
+#### Copyright (c) 2025 Oracle Corporation and its affiliates
+
+##### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/)
+
diff --git a/machine-learning/sql/26ai/dmsh.sql b/machine-learning/sql/26ai/dmsh.sql
new file mode 100644
index 00000000..8ea42e13
--- /dev/null
+++ b/machine-learning/sql/26ai/dmsh.sql
@@ -0,0 +1,150 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Setup - Creates Demo Views and Tables - dmsh.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+--
+--
+-- dmsh.sql
+--
+--
+--    NAME
+--    dmsh.sql
+--
+--    DESCRIPTION
+--      This script creates views and tables using SH data
+--      in the schema of the data mining user. These tables/views
+--      are the datasets used by the Oracle Data Mining demo programs.
+--      This script also creates a text policy for text mining.
+--    NOTES
+--       The script assumes that the full SH schema is already created and the
+--       necessary SELECTs have been granted (See dmshgrants.sql). This script runs in 
+--       the schema of the data mining user.
+--       mining_data_*_v views : Used for mining (no text)
+--       mining_*_text views: Used for mining with text 
+--
+--------------------------------------------------------------------------------
+--
+-- Creates data mining views on SH data
+--
+
+--------------------------------------------------------------------------------
+-- View to join and filter data
+-- CUST_YEAR_OF_BIRTH column is transformed to an AGE
+CREATE OR REPLACE VIEW mining_data AS
+SELECT
+ a.CUST_ID, a.CUST_GENDER,
+ 2003-a.CUST_YEAR_OF_BIRTH AGE,
+ a.CUST_MARITAL_STATUS, c.COUNTRY_NAME, a.CUST_INCOME_LEVEL, b.EDUCATION,
+ b.OCCUPATION, b.HOUSEHOLD_SIZE, b.YRS_RESIDENCE, b.AFFINITY_CARD,
+ b.BULK_PACK_DISKETTES, b.FLAT_PANEL_MONITOR, b.HOME_THEATER_PACKAGE,
+ b.BOOKKEEPING_APPLICATION, b.PRINTER_SUPPLIES, b.Y_BOX_GAMES,
+ b.os_doc_set_kanji, b.comments 
+FROM
+ sh.customers a,
+ sh.supplementary_demographics b,
+ sh.countries c
+WHERE
+ a.CUST_ID = b.CUST_ID
+ AND a.country_id  = c.country_id
+ AND a.cust_id between 100001 and 104500;
+
+--------------------------------------------------------------------------------
+-- Build, test, and apply views (with text)
+-- Build, test, and apply datasets are made non-overlapping by using
+-- a predicate on cust_id.
+
+CREATE OR REPLACE VIEW mining_build_text AS
+SELECT *
+FROM mining_data
+WHERE cust_id between 101501 and 103000;
+
+CREATE OR REPLACE VIEW mining_test_text AS
+SELECT *
+FROM mining_data
+WHERE cust_id between 103001 and 104500;
+
+CREATE OR REPLACE VIEW mining_apply_text AS
+SELECT *
+FROM mining_data
+WHERE cust_id between 100001 and 101500;
+
+--------------------------------------------------------------------------------
+-- Build, test, and apply views
+-- Same as above, but no text - COMMENTS column removed
+
+CREATE OR REPLACE VIEW mining_data_build_v AS
+SELECT CUST_ID, CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME,
+ CUST_INCOME_LEVEL, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE,
+ YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR,
+ HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES,
+ Y_BOX_GAMES, OS_DOC_SET_KANJI
+FROM mining_build_text;
+
+CREATE OR REPLACE VIEW mining_data_test_v AS
+SELECT CUST_ID, CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME,
+ CUST_INCOME_LEVEL, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE,
+ YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR,
+ HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES,
+ Y_BOX_GAMES, OS_DOC_SET_KANJI
+FROM mining_test_text;
+
+CREATE OR REPLACE VIEW mining_data_apply_v AS
+SELECT CUST_ID, CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME,
+ CUST_INCOME_LEVEL, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE,
+ YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR,
+ HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES,
+ Y_BOX_GAMES, OS_DOC_SET_KANJI
+FROM mining_apply_text;
+
+--------------------------------------------------------------------------------
+-- Data for one class model
+-- Only data for positive affinity card (one class) is used.
+
+CREATE OR REPLACE VIEW mining_data_one_class_v AS
+SELECT CUST_ID, CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME,
+ CUST_INCOME_LEVEL, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE, YRS_RESIDENCE
+FROM mining_data_build_v
+WHERE affinity_card = 1;
+
+--------------------------------------------------------------------------------
+--
+-- Create view mining_data_build_parallel_v with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW mining_data_build_parallel_v AS SELECT /*+ parallel (4)*/ * FROM mining_data_build_v; 
+
+--------------------------------------------------------------------------------
+--
+-- Create view  mining_data_one_class_pv with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW mining_data_one_class_pv AS SELECT /*+ parallel (4)*/ * FROM mining_data_one_class_v;
+
+--------------------------------------------------------------------------------
+--
+-- Create view mining_data_test_parallel_v with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW mining_data_test_parallel_v AS SELECT /*+ parallel (4)*/ * FROM mining_data_test_v;
+
+--------------------------------------------------------------------------------
+--
+-- Create view mining_data_apply_parallel_v with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW mining_data_apply_parallel_v AS SELECT /*+ parallel (4)*/ * FROM mining_data_apply_v;
+
+--------------------------------------------------------------------------------
+--
+-- Create view mining_test_text_parallel with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW mining_test_text_parallel AS SELECT /*+ parallel (4)*/ * FROM mining_test_text;
+
diff --git a/machine-learning/sql/26ai/dmshgrants.sql b/machine-learning/sql/26ai/dmshgrants.sql
new file mode 100644
index 00000000..8ed09ce4
--- /dev/null
+++ b/machine-learning/sql/26ai/dmshgrants.sql
@@ -0,0 +1,54 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Setup - Grants Privileges to Users - dmshgrants.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+--
+--
+-- dmshgrants.sql
+--
+-- Copyright (c) 2001, 2010, 2021, Oracle and/or its affiliates. 
+-- All rights reserved. 
+--
+--    NAME
+--    dmshgrants.sql
+--
+--    DESCRIPTION
+--      This script grants SELECT on SH tables and SYS privileges 
+--      required to run the Oracle Data Mining demo programs
+--      
+--      The script is to be run in SYS account
+--
+--    NOTES
+--       &&1    Name of the DM user
+--
+--------------------------------------------------------------------------------
+DEFINE DMUSER = &&1 
+
+grant create session to &DMUSER
+/
+grant create table to &DMUSER
+/
+grant create view to &DMUSER
+/
+grant create mining model to &DMUSER
+/
+grant execute on ctxsys.ctx_ddl to &DMUSER
+/
+
+GRANT SELECT ON sh.customers TO &DMUSER
+/
+GRANT SELECT ON sh.sales TO &DMUSER
+/
+GRANT SELECT ON sh.products TO &DMUSER
+/
+GRANT SELECT ON sh.supplementary_demographics TO &DMUSER
+/
+GRANT SELECT ON sh.countries TO &DMUSER
+/ 
diff --git a/machine-learning/sql/26ai/oml4sql-anomaly-detection-1class-svm.sql b/machine-learning/sql/26ai/oml4sql-anomaly-detection-1class-svm.sql
new file mode 100644
index 00000000..829fcc12
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-anomaly-detection-1class-svm.sql
@@ -0,0 +1,228 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Anomaly Detection - One Class SVM Algorithm 
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographics about a set of customers that are known to have 
+-- an affinity card, 1) find the most atypical members of this group 
+-- (outlier identification), 2) discover the common demographic 
+-- characteristics of the most typical customers with affinity card, 
+-- and 3) compute how typical a given new/hypothetical customer is.
+--
+-------
+-- DATA
+-------
+-- The data for this sample is composed from base tables in the SH schema
+-- (See Sample Schema Documentation) and presented through a view:
+-- mining_data_one_class_pv
+-- (See dmsh.sql for view definition).
+--
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with the same name (if any)
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVMO_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+--------------------------------
+-- PREPARE DATA
+--
+-- Automatic data preparation is used.
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table (if any)
+BEGIN
+  EXECUTE IMMEDIATE 'DROP TABLE svmo_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN
+  NULL;
+END;
+/
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE svmo_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+BEGIN       
+  -- Populate settings table
+  -- SVM needs to be selected explicitly (default classifier: Naive Bayes)
+   
+  -- Examples of other possible overrides are:
+  -- select a different rate of outliers in the data (default 0.1)
+  -- (dbms_data_mining.svms_outlier_rate, ,0.05);
+  -- select a kernel type (default kernel: selected by the algorithm)
+  -- (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_linear);
+  -- (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_gaussian);
+   
+  INSERT INTO svmo_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines);  
+  INSERT INTO svmo_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+END;
+/
+
+---------------------
+-- CREATE A MODEL
+--
+-- Build a new one-class SVM Model
+-- Note the NULL sprecification for target column name
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVMO_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_one_class_pv',
+    case_id_column_name => 'cust_id',
+    target_column_name  => NULL,
+    settings_table_name => 'svmo_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVMO_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+-- For sample code displaying SVM signature see dmsvcdem.sql.
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- Model details are available only for SVM models with linear kernel.
+-- For SVM model details sample code see dmsvcdem.sql.
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+WHERE model_name='SVMO_SH_CLAS_SAMPLE'
+ORDER BY view_name;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+-- Depending on the business case, the model can be scored against the
+-- build data (e.g, business cases 1 and 2) or against new, previously
+-- unseen data (e.g., business case 3). New apply data needs to undergo 
+-- the same transformations as the build data (see business case 3).
+
+------------------
+-- BUSINESS CASE 1
+-- Find the top 5 outliers - customers that differ the most from 
+-- the rest of the population. Depending on the application, such 
+-- atypical customers can be removed from the data (data cleansing).
+-- Explain which attributes cause them to appear different.
+--
+set long 20000
+col pd format a90
+SELECT cust_id, pd FROM
+(SELECT cust_id,
+        PREDICTION_DETAILS(SVMO_SH_Clas_sample, 0 using *) pd,
+        rank() over (order by prediction_probability(
+                     SVMO_SH_Clas_sample, 0 using *) DESC, cust_id) rnk
+ FROM mining_data_one_class_pv)
+WHERE rnk <= 5
+order by rnk;
+
+------------------
+-- BUSINESS CASE 2
+-- Find demographic characteristics of the typical affinity card members.
+-- These statistics will not be influenced by outliers and are likely to
+-- provide a more truthful picture of the population of interest than
+-- statistics computed on the entire group of affinity members.
+-- Statistics are computed on the original (non-transformed) data.
+column cust_gender format a12
+SELECT cust_gender, round(avg(age)) age, 
+       round(avg(yrs_residence)) yrs_residence,
+       count(*) cnt
+FROM mining_data_one_class_pv
+WHERE prediction(SVMO_SH_Clas_sample using *) = 1
+GROUP BY cust_gender
+ORDER BY cust_gender;  
+
+
+------------------
+-- BUSINESS CASE 3
+-- 
+-- Compute probability of a new/hypothetical customer being a typical  
+-- affinity card holder.
+-- Necessary data preparation on the input attributes is performed
+-- automatically during model scoring since the model was build with
+-- auto data prep.
+--
+column prob_typical format 9.99
+select prediction_probability(SVMO_SH_Clas_sample, 1 using 
+                             44 AS age,
+                             6 AS yrs_residence,
+                             'Bach.' AS education,
+                             'Married' AS cust_marital_status,
+                             'Exec.' AS occupation,
+                             'United States of America' AS country_name,
+                             'M' AS cust_gender,
+                             'L: 300,000 and above' AS cust_income_level,
+                             '3' AS household_size
+                             ) prob_typical
+from dual;
+
+-----------------------------------------------------------------------
+--    BUILD and APPLY a transient model using analytic functions
+-----------------------------------------------------------------------
+-- In addition to creating a persistent model that is stored as a schema
+-- object, models can be built and scored on data on the fly using
+-- Oracle's analytic function syntax.
+
+----------------------
+-- BUSINESS USE CASE 4
+-- 
+-- Identify rows that are most atypical in the input dataset.
+-- Consider each type of marital status to be separate, so the most
+-- anomalous rows per marital status group should be returned.
+-- Provide the top three attributes leading to the reason for the
+-- record being an anomaly.
+-- The partition by clause used in the analytic version of the
+-- prediction_probability function will lead to separate models
+-- being built and scored for each marital status.
+col cust_marital_status format a30
+select cust_id, cust_marital_status, rank_anom, anom_det FROM
+(SELECT cust_id, cust_marital_status, anom_det,
+        rank() OVER (PARTITION BY CUST_MARITAL_STATUS 
+                     ORDER BY ROUND(ANOM_PROB,8) DESC,cust_id) rank_anom FROM
+ (SELECT cust_id, cust_marital_status,
+        PREDICTION_PROBABILITY(OF ANOMALY, 0 USING *) 
+          OVER (PARTITION BY CUST_MARITAL_STATUS) anom_prob,
+        PREDICTION_DETAILS(OF ANOMALY, 0, 3 USING *) 
+          OVER (PARTITION BY CUST_MARITAL_STATUS) anom_det
+   FROM mining_data_one_class_pv
+ ))
+where rank_anom < 3 order by 2, 3;
diff --git a/machine-learning/sql/26ai/oml4sql-anomaly-detection-em.sql b/machine-learning/sql/26ai/oml4sql-anomaly-detection-em.sql
new file mode 100644
index 00000000..caf5a8da
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-anomaly-detection-em.sql
@@ -0,0 +1,159 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+--
+--   Expectation Maximization - EM Algorithm for Anomaly Detection
+--
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--   The Universal Permissive License (UPL), Version 1.0
+--
+--   https://oss.oracle.com/licenses/upl
+-----------------------------------------------------------------------
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Segment the demographic data into clusters and examine the anomalies.
+
+-----------------------------------------------------------------------
+--                            EXAMPLE IN THIS SCRIPT
+-----------------------------------------------------------------------
+-- Create EM model with CREATE MODEL2 
+-- View model details 
+-- View and sort anomalous customers 
+-- View prediction details
+
+-----------------------------------------------------------------------
+-- In this script, we are using an EM classification model to expand on 
+-- the One-Class SVM model approach for anomaly detection. EM 
+-- can capture the underlying data distribution and thus flag records 
+-- that do not fit the learned data distribution well. An object is 
+-- identified as an outlier in an EM Anomaly Detection model if its anomaly 
+-- probability is greater than 0.5. A label of 1 denotes normal, while 
+-- a label of 0 denotes anomaly. The customer and demographics data is 
+-- used to predict anomalous customers using prob_anomalous.
+
+-----------------------------------------------------------------------
+--                  SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+------------------------------
+-- CREATE VIEW DEMOGRAPHICS_V
+--
+CREATE OR REPLACE VIEW DEMOGRAPHICS_V AS
+  SELECT CUST_ID, YRS_RESIDENCE, EDUCATION, AFFINITY_CARD, 
+         HOUSEHOLD_SIZE, OCCUPATION, BOOKKEEPING_APPLICATION, 
+         BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE,
+         OS_DOC_SET_KANJI, PRINTER_SUPPLIES, Y_BOX_GAMES
+  FROM SH.SUPPLEMENTARY_DEMOGRAPHICS;
+
+-------------------------
+-- CREATE VIEW JOINING CUSTOMERS AND DEMOGRAPHICS_V
+--
+
+CREATE OR REPLACE VIEW CUSTOMERS360_V AS
+   SELECT a.CUST_ID, a.CUST_GENDER, a.CUST_MARITAL_STATUS, a.CUST_YEAR_OF_BIRTH, 
+          a.CUST_INCOME_LEVEL, a.CUST_CREDIT_LIMIT, b.EDUCATION, b.AFFINITY_CARD, 
+          b.HOUSEHOLD_SIZE, b.OCCUPATION, b.YRS_RESIDENCE, b.Y_BOX_GAMES
+   FROM SH.CUSTOMERS a, DEMOGRAPHICS_V b
+   WHERE a.CUST_ID = b.CUST_ID;
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-------------------------
+-- SET OUTLIER RATE IN SETTINGS TABLE - DEFAULT IS 0.05
+--
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('CUSTOMERS360MODEL_AD');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+DECLARE
+  v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+  v_setlst('ALGO_NAME')         := 'ALGO_EXPECTATION_MAXIMIZATION';
+  v_setlst('PREP_AUTO')         := 'ON';
+  v_setlst('EMCS_OUTLIER_RATE') := '0.1';
+        
+  DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'CUSTOMERS360MODEL_AD',
+        MINING_FUNCTION     => 'CLASSIFICATION',
+        DATA_QUERY          => 'SELECT * FROM CUSTOMERS360_V',
+        CASE_ID_COLUMN_NAME => 'CUST_ID',
+        SET_LIST            => v_setlst,
+        TARGET_COLUMN_NAME  => NULL); -- NULL target indicates anomaly detection      
+END;
+/
+
+-----------------------------------------------------------------------
+--                   EXAMINE THE MODEL
+-----------------------------------------------------------------------
+-------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+SELECT NAME, NUMERIC_VALUE
+FROM  DM$VGCUSTOMERS360MODEL_AD
+ORDER BY NAME;
+
+---------------------------------------------
+-- DISPLAY THE TOP 5 MOST ANOMALOUS CUSTOMERS
+--
+
+SELECT * 
+FROM (SELECT CUST_ID, round(prob_anomalous,2) prob_anomalous,  
+             YRS_RESIDENCE, CUST_MARITAL_STATUS, 
+             rank() over (ORDER BY prob_anomalous DESC) rnk 
+      FROM (SELECT CUST_ID, HOUSEHOLD_SIZE, YRS_RESIDENCE, CUST_GENDER, CUST_MARITAL_STATUS, 
+                   prediction_probability(CUSTOMERS360MODEL_AD, '0' USING *) prob_anomalous
+            FROM CUSTOMERS360_V))
+WHERE rnk <= 5
+ORDER BY prob_anomalous DESC;
+---------------------------------------------------------------
+-- CREATE VIEW OF CUSTOMERS IN DESCENDING ORDER OF ANOMALY PROBABILITY
+--
+CREATE OR REPLACE VIEW EM_ANOMALOUS_RESULTS AS
+SELECT * 
+FROM (SELECT CUST_ID, anomalous, round(prob_anomalous,2) prob_anomalous, 
+             YRS_RESIDENCE, HOUSEHOLD_SIZE, CUST_GENDER,
+             CUST_MARITAL_STATUS, 
+             RANK() OVER (ORDER BY prob_anomalous DESC) rnk 
+      FROM (SELECT CUST_ID, HOUSEHOLD_SIZE, YRS_RESIDENCE, 
+                   CUST_GENDER, CUST_MARITAL_STATUS, 
+                   prediction(CUSTOMERS360MODEL_AD using *) anomalous,
+                   prediction_probability(CUSTOMERS360MODEL_AD, '0' USING *) prob_anomalous
+            FROM CUSTOMERS360_V))
+ORDER BY prob_anomalous DESC;
+
+SELECT * 
+FROM   EM_ANOMALOUS_RESULTS
+FETCH FIRST 10 ROWS ONLY;
+
+--------------------------------------------------------------------
+-- VIEW PREDICTION DETAILS OF TOP 3 ATTRIBUTES TO EXPLAIN PREDICTION
+--
+
+SELECT CUST_ID, PREDICTION,
+       RTRIM(TRIM(SUBSTR(OUTPRED."Attribute1",17,100)),'rank="1"/>') FIRST_ATTRIBUTE,
+       RTRIM(TRIM(SUBSTR(OUTPRED."Attribute2",17,100)),'rank="2"/>') SECOND_ATTRIBUTE,
+       RTRIM(TRIM(SUBSTR(OUTPRED."Attribute3",17,100)),'rank="3"/>') THIRD_ATTRIBUTE
+FROM (SELECT CUST_ID, 
+             PREDICTION(CUSTOMERS360MODEL_AD USING *) PREDICTION,
+             PREDICTION_DETAILS(CUSTOMERS360MODEL_AD, '0' USING *) PREDICTION_DETAILS 
+      FROM   CUSTOMERS360_V
+      WHERE  PREDICTION_PROBABILITY(CUSTOMERS360MODEL_AD, '0' USING *) > 0.50
+      AND OCCUPATION = 'TechSup'
+      ORDER BY CUST_ID) OUT,
+      XMLTABLE('/Details'
+                PASSING OUT.PREDICTION_DETAILS
+                COLUMNS 
+                   "Attribute1" XMLType PATH 'Attribute[1]',
+                   "Attribute2" XMLType PATH 'Attribute[2]',
+                   "Attribute3" XMLType PATH 'Attribute[3]') OUTPRED
+FETCH FIRST 10 ROWS ONLY;
+
+
+-----------------------------------------------------------------------
+--   End of script
+-----------------------------------------------------------------------
diff --git a/machine-learning/sql/26ai/oml4sql-association-rules.sql b/machine-learning/sql/26ai/oml4sql-association-rules.sql
new file mode 100644
index 00000000..e55b73ef
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-association-rules.sql
@@ -0,0 +1,433 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Association Rules - Apriori Algorithm - dmardemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------  
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 140
+SET echo ON
+
+-- ODM API accepts data both in relational (2D) form, and
+-- transactional form for Association Rules.
+-- Transactional data is the more common form of input for
+-- this type of problem, so the demo shows examples of
+-- processing transactional input.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-- Cleanup old dataset for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW sales_trans_cust';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------
+-- DATA
+-------
+-- The data for this sample is composed from a small subset of
+-- sales transactions in the SH schema - listing the (multiple)
+-- items bought by a set of customers with ids in the range
+-- 100001-104500. It also lists the dollar amount sold for
+-- each item. Note that this data is based on customer id,
+-- not "basket" id (as in the case of true market basket data).
+--
+-- Market basket or sales datasets are transactional in nature,
+-- and form fact tables in a typical data warehouse.
+--
+CREATE VIEW sales_trans_cust AS
+ SELECT cust_id, prod_name, prod_category, amount_sold
+ FROM (SELECT a.cust_id, b.prod_name, b.prod_category,
+             a.amount_sold
+        FROM sh.sales a, sh.products b
+       WHERE a.prod_id = b.prod_id AND
+             a.cust_id between 100001 AND 104500);
+
+-----------
+-- ANALYSIS
+-----------
+-- Association Rules in ODM works best on sparse data - i.e. data where
+-- the average number of attributes/items associated with a given case is
+-- a small percentage of the total number of possible attributes/items.
+-- This is true of most market basket datasets where an average customer
+-- purchases only a small subset of items from a fairly large inventory
+-- in the store.
+--
+-- This section provides a rough outline of the analysis to be performed
+-- on data used for Association Rules model build.
+--
+-- 1. Compute the cardinality of customer id and product (940, 14)
+SELECT COUNT(DISTINCT cust_id) cc, COUNT(DISTINCT prod_name) cp
+  FROM sales_trans_cust;
+
+-- 2. Compute the density of data (21.31)
+column density format a18
+SELECT TO_CHAR((100 * ct)/(cc * cp), 99.99) density
+  FROM (SELECT COUNT(DISTINCT cust_id) cc,
+               COUNT(DISTINCT prod_name) cp,
+               COUNT(*) ct
+          FROM sales_trans_cust);
+
+-- 3. Common items are candidates for removal during model build, because
+--    if a majority of customers have bought those items, the resulting
+--    rules do not have much value. Find out most common items. For example,
+--    the query shown below determines that Mouse_Pad is most common (303).
+--
+--    Since the dataset is small, we will skip common item removal.
+--
+column prod_name format a40
+SELECT prod_name, count(prod_name) cnt
+  FROM sales_trans_cust
+GROUP BY prod_name
+ORDER BY cnt DESC, prod_name DESC;
+
+-- 4. Compute the average number of products purchased per customer (2.98)
+--    3 out of 11 corresponds to the density we computed earlier.
+--
+column avg_num_prod format a16
+SELECT TO_CHAR(AVG(cp), 999.99) avg_num_prod
+  FROM (SELECT COUNT(prod_name) cp
+          FROM sales_trans_cust
+        GROUP BY cust_id);
+
+-- 5. Compute the minimum and maximum dollar amount sold 
+--    for each item (7.99, 1299.99).
+SELECT MIN(amount_sold), MAX(amount_sold) FROM sales_trans_cust;
+
+--------------------------------------------------------------------------------
+--
+-- Create view sales_trans_cust_parallel with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW sales_trans_cust_parallel AS SELECT /*+ parallel (4)*/ * FROM sales_trans_cust;
+
+-----------------------------------------------------------------------
+--         SAMPLE PROBLEM USING TRANSACTIONAL (pair/triple) INPUT
+-----------------------------------------------------------------------
+
+-- ODM API accepts data both in relational (2D) form and
+-- transactional form for Association Rules.
+--
+-- The transactional input is a two column table of the form:
+-- (transaction_id, item_id)
+-- or a three column table of the form:
+-- (transaction_id, item_id, item_value)
+-- where we use the case_id to represent a transaction_id.
+--
+-- Example of a two column transactional table is:
+-- (transaction_id, item_id)
+-- (1, 1)
+-- (1, 4)
+-- (2, 2)
+-- or
+-- (1, 'apple')
+-- (1, 'pear')
+-- (2, 'banana')
+--
+-- Example of a three column transactional table is:
+-- (transaction_id, item_id, item_value)
+-- (1, 'apple', 2)
+-- (1, 'banana', 4)
+-- (2, 'apple', 1)
+-- (2, 'banana', 2)
+-- or
+-- (1, 'wine', 'red')
+-- (1, 'wine', 'white')
+-- (1, 'cheese', 'swiss')
+-- (2, 'cheese', 'provolone')
+-- which allows you to treat different (item_id, item_val) pairings
+-- for a given transaction essentially as different, unique items.
+--
+
+--------------------------------
+-- PREPARE BUILD (TRAINING) DATA
+--
+-- Data for AR modeling may need binning if it contains numerical data.
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE ar_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- The default (and only) algorithm for association rules is
+-- Apriori AR. However, we need a settings table 
+-- to override the default Min Support, Min Confidence,
+-- and Max items settings.
+-- Add settings for Transaction Input - the presence
+-- of an Item Id column specification indicates to the
+-- API that the input is transactional
+-- 
+set echo off
+CREATE TABLE ar_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+BEGIN       
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_min_support,0.1);
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_min_confidence,0.1);
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_max_rule_length,3);
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.odms_item_id_column_name, 'PROD_NAME');
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_aggregates, 'AMOUNT_SOLD');
+  COMMIT;
+END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('AR_SH_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+----------------------------------------------
+-- Build AR model with transactional input
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'AR_SH_SAMPLE',
+    mining_function     => DBMS_DATA_MINING.ASSOCIATION,
+    data_table_name     => 'sales_trans_cust_parallel',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'ar_sh_sample_settings'
+    );
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'AR_SH_SAMPLE'
+ORDER BY setting_name;
+
+-----------------------------------------------------------------------
+--                            TEST THE MODEL
+-----------------------------------------------------------------------
+
+-- Association rules do not have a predefined test metric.
+--
+-- Two indirect measures of modeling success are:
+--
+-- 1. Number of Rules generated: The optimal number of rules is
+--    application dependent. In general, an overwhelming number of
+--    rules is undesirable for user interpretation. More rules take
+--    longer to compute, and also consume storage and CPU cycles.
+--    You avoid too many rules by increasing the value for support.
+-- 
+-- 2. Relevance of rules
+--    This can be determined only by user inspection of rules, since
+--    it is application dependent. Ideally, we want to find rules with
+--    high confidence and with non-obvious patterns. The value for
+--    confidence is an indicator of the strength of the rule - so
+--    you could set the confidence value high in conjunction with
+--    support and see if you get high quality rules.
+-- 
+-- 3. Frequent itemsets provide an insight into co-occurrence of items.
+
+-----------------------------------------------------------------------
+--                            DISPLAY MODEL CONTENT
+-----------------------------------------------------------------------
+
+-------------------------------------------------------------
+-- Display Top-10 Frequent Itemsets
+--
+break on itemset_id skip 1;
+column item format a40
+SELECT item, support, number_of_items
+  FROM (SELECT I.attribute_subname AS item,
+               F.support,
+               F.number_of_items
+          FROM TABLE(DBMS_DATA_MINING.GET_FREQUENT_ITEMSETS(
+                       'AR_SH_SAMPLE',
+                       10)) F,
+               TABLE(F.items) I
+        ORDER BY number_of_items, support, item);
+
+----------------------------------------------------------
+-- Display Top-10 Association Rules
+--
+SET line 300
+column antecedent format a30
+column consequent format a20
+column supp format 9.999
+column conf format 9.999
+SELECT a.attribute_subname antecedent,
+       c.attribute_subname consequent,
+       rule_support supp,
+       rule_confidence conf,
+       row_number() over (partition by rule_id order by a.attribute_subname) piece
+  FROM TABLE(DBMS_DATA_MINING.GET_ASSOCIATION_RULES('AR_SH_SAMPLE', 10)) T,
+       TABLE(T.consequent) C,
+       TABLE(T.antecedent) A
+ ORDER BY conf DESC, supp DESC, piece;
+
+-----------------------------------------------------------------------
+--                        DISPLAY MODEL CONTENT USING VIEWS
+-----------------------------------------------------------------------
+
+-- In 12.2, aggregation functionality is added to Association Rules
+-- model. The model views, DM$VI<ModelName> and DM$VR<ModelName>, are
+-- provided as the new output interface.
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='AR_SH_SAMPLE'
+  ORDER BY view_name;
+
+----------------------------------------------------------
+-- Using DM$VI<ModelName> to display Top-10 Frequent Itemsets.
+-- The dollar amount sold of each item is displayed.
+--
+column item format a40
+column amount_sold format 999999.99
+column support format 9.999
+column number_of_items format 99
+
+set echo off
+SELECT items.item, items.amount_sold, support, number_of_items
+FROM
+(SELECT * FROM(
+  SELECT itemset,
+         number_of_items,
+         support
+  FROM   DM$VIAR_SH_SAMPLE
+  ORDER BY number_of_items, support)
+  WHERE  ROWNUM <=10) fis,
+  XMLTABLE ('/itemset/item'  PASSING fis.itemset
+             COLUMNS 
+             item     varchar2(40)  PATH 'item_name',
+             amount_sold  number    PATH 'ASSO_AGG0'
+         ) items
+ORDER BY number_of_items, support, item;
+set echo on
+
+----------------------------------------------------------
+-- Using DM$VR<ModelName> to display Top-10 Association Rules.
+-- For each rule, the dollar amount sold of the consequent item
+-- is displayed.
+--
+SET line 300
+column antecedent format a30
+column consequent format a20
+column supp format 9.999
+column conf format 9.999
+column con_amount format 99999.99
+column piece format 99
+
+set echo off
+SELECT ant_items.item antecedent,
+       consequent_name consequent,
+       con_rule_amount_sold con_amount, 
+       rule_support supp, rule_confidence conf,
+       ant_items.piece
+FROM   (SELECT * FROM (SELECT antecedent, consequent_name, rule_support,
+                              rule_confidence, con_rule_amount_sold
+                       FROM   DM$VRAR_SH_SAMPLE
+                       ORDER BY rule_confidence DESC, rule_support DESC)
+        WHERE  ROWNUM <=10) r,
+  XMLTABLE ('/itemset/item'  PASSING r.ANTECEDENT
+             COLUMNS 
+             item     varchar2(30)  PATH 'item_name',
+             piece  for ordinality
+         ) ant_items
+ORDER BY conf DESC, supp DESC, piece;
+set echo on
+
+--- ------------------------------------------------------------------
+--- Now we shall build the model with a 2 column transactional input table
+--- We choose only cust_id and prod_name from the sales_trans_cust view
+--- Clear the settings table and specify the settings for this model build.
+--- Use dbms_data_mining.odms_item_id_column_name to indicate transactional 
+--- input
+
+CREATE OR REPLACE VIEW sales_trans_cust_2col AS
+SELECT cust_id, prod_name from sales_trans_cust;
+
+--------------------------------------------------------------------------------
+--
+-- Create view sales_trans_2col_parallel with a parallel hint
+--
+--------------------------------------------------------------------------------
+CREATE or REPLACE VIEW sales_trans_2col_parallel AS SELECT /*+ parallel (4)*/ * FROM sales_trans_cust_2col;
+
+truncate table ar_sh_sample_settings;       
+BEGIN
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_min_support,0.1);
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_min_confidence,0.1);
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.asso_max_rule_length,3);
+  INSERT INTO ar_sh_sample_settings VALUES
+  (dbms_data_mining.odms_item_id_column_name, 'PROD_NAME');
+COMMIT;
+END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('AR_SH_SAMPLE_2COL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'AR_SH_SAMPLE_2COL',
+    mining_function     => DBMS_DATA_MINING.ASSOCIATION,
+    data_table_name     => 'sales_trans_2col_parallel',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'ar_sh_sample_settings'
+    );
+END;
+/
+
+---- Lets display the model content using views. 
+---- There are 2 additional views for transactional data
+---- i.e DM$VTAR_SH_SAMPLE_2COL and DM$VAAR_SH_SAMPLE_2COL that give 
+---- information about the frequent item sets and rules. 
+---- in addition to DM$VIAR_SH_SAMPLE_2COL and DM$VRAR_SH_SAMPLE_2COL.
+---- We shall use DM$VT and DM$VA to display the Top-10 frequent itemsets
+---- and Top-10 association rules
+
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='AR_SH_SAMPLE_2COL'
+  ORDER BY view_name;
+
+set echo off
+column item_name format a40
+select * from
+( select item_name, support, number_of_items from DM$VTAR_SH_SAMPLE_2COL
+  ORDER BY number_of_items, support
+) where rownum <=10
+ORDER BY number_of_items, support, item_name;
+
+
+select * from
+(
+SELECT antecedent_predicate antecedent,
+       consequent_predicate consequent,
+       rule_support supp, rule_confidence conf, number_of_items num
+from DM$VAAR_SH_SAMPLE_2COL
+ORDER BY rule_confidence DESC, rule_support DESC)
+WHERE  ROWNUM <=10 
+order by antecedent, consequent;
+
+set echo on
+
diff --git a/machine-learning/sql/26ai/oml4sql-attribute-importance.sql b/machine-learning/sql/26ai/oml4sql-attribute-importance.sql
new file mode 100644
index 00000000..2d1ec271
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-attribute-importance.sql
@@ -0,0 +1,55 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Attribute Importance - "EXPLAIN" function - dmaidemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+  
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given a target attribute affinity_card, find the importance of
+-- attributes that independently impact the target attribute.
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-------------------
+-- Cleanup old output table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE ai_explain_output';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- Run the EXPLAIN routine to get attribute importance results
+BEGIN
+  DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(
+    data_table_name     => 'mining_data_build_v',
+    explain_column_name => 'affinity_card',
+    result_table_name   => 'ai_explain_output');
+END;
+/
+
+------------------------
+-- DISPLAY RESULTS
+--
+-- List of attribute names ranked by their importance value.
+-- The larger the value, the more impact that attribute has
+-- on causing variation in the target column.
+--
+column attribute_name    format a40
+column explanatory_value format 9.999
+SELECT attribute_name, explanatory_value, rank
+FROM ai_explain_output
+ORDER BY rank, attribute_name;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-decision-tree.sql b/machine-learning/sql/26ai/oml4sql-classification-decision-tree.sql
new file mode 100644
index 00000000..90896e85
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-decision-tree.sql
@@ -0,0 +1,350 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - Decision Tree Algorithm - dmdtdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON
+SET pages 10000
+SET linesize 420
+SET echo ON
+SET long 2000000000
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic data about a set of customers, predict the
+-- customer response to an affinity card program using a classifier
+-- based on Decision Trees algorithm.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_parallel_v (build data)
+-- mining_data_test_parallel_v  (test data)
+-- mining_data_apply_parallel_v (apply data)
+-- (See dmsh.sql for view definitions).
+
+-----------
+-- ANALYSIS
+-----------
+-- These are the factors to consider for data analysis in decision trees:
+--
+-- 1. Missing Value Treatment for Predictors
+--
+--    See dmsvcdem.sql for a definition of missing values, and the
+--    steps to be taken for missing value imputation.
+--
+--    Decision Tree implementation in ODM handles missing predictor
+--    values (by penalizing predictors which have missing values)
+--    and missing target values (by simply discarding records with
+--    missing target values).
+--
+-- 2. Outlier Treatment for Predictors for Build data
+--
+--    See dmsvcdem.sql for a discussion on outlier treatment.
+--    For ODM decision trees, outlier treatment is not really necessary.
+--
+-- 3. Binning high cardinality data
+--    No data preparation for the types we accept is necessary - even
+--    for high cardinality predictors.  Preprocessing to reduce the
+--    cardinality (e.g., binning) can improve the performance of the build.
+--
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+--------------------------------
+-- PREPARE BUILD (TRAINING) DATA
+--
+-- The decision tree algorithm is very capable of handling data which
+-- has not been specially prepared.  For this example, no data preparation
+-- will be performed.
+--
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE dt_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE dt_sh_sample_cost';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+ 
+--------------------------
+-- CREATE A SETTINGS TABLE
+--
+-- The default classification algorithm is Naive Bayes. In order to override
+-- this, create and populate a settings table to be used as input for
+-- CREATE_MODEL.
+-- 
+set echo off
+CREATE TABLE dt_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+ 
+-- CREATE AND POPULATE A COST MATRIX TABLE
+--
+-- A cost matrix is used to influence the weighting of misclassification
+-- during model creation (and scoring).
+-- See Oracle Data Mining Concepts Guide for more details.
+--
+CREATE TABLE dt_sh_sample_cost (
+  actual_target_value           NUMBER,
+  predicted_target_value        NUMBER,
+  cost                          NUMBER);
+INSERT INTO dt_sh_sample_cost VALUES (0,0,0);
+INSERT INTO dt_sh_sample_cost VALUES (0,1,1);
+INSERT INTO dt_sh_sample_cost VALUES (1,0,8);
+INSERT INTO dt_sh_sample_cost VALUES (1,1,0);
+
+BEGIN       
+  -- Populate settings table
+  INSERT INTO dt_sh_sample_settings VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_decision_tree);
+  INSERT INTO dt_sh_sample_settings VALUES
+    (dbms_data_mining.clas_cost_table_name, 'dt_sh_sample_cost');
+
+  -- Examples of other possible settings are:
+  --(dbms_data_mining.tree_impurity_metric, 'TREE_IMPURITY_ENTROPY')
+  --(dbms_data_mining.tree_term_max_depth, 5)
+  --(dbms_data_mining.tree_term_minrec_split, 5)
+  --(dbms_data_mining.tree_term_minpct_split, 2)
+  --(dbms_data_mining.tree_term_minrec_node, 5)
+  --(dbms_data_mining.tree_term_minpct_node, 0.05)
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Build a DT model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'DT_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'dt_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'DT_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'DT_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+-- NOTE: The "&quot" characters in this XML output are owing to
+--       SQL*Plus behavior. Cut and paste this XML into a file,
+--       and open the file in a browser to see correctly formatted XML.
+--
+column dt_details format a320
+SELECT 
+ dbms_data_mining.get_model_details_xml('DT_SH_Clas_sample') 
+ AS DT_DETAILS
+FROM dual;
+
+--------------------------
+-- DISPLAY MODEL VIEWS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='DT_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+column ATTRIBUTE_NAME format a12
+column VAL format a12
+column split_type format a12
+column OPERATOR format a12
+  
+-- Information about the splits in the hierarchy
+SELECT * FROM (  
+select parent, split_type, node, attribute_name, operator, split.val 
+from DM$VPDT_SH_CLAS_sample a,
+XMLTABLE( '/Element' passing a.value
+    columns 
+    val varchar2(20) path '.') split
+  order by 1,2,3,4,5,6)
+WHERE ROWNUM < 20;
+
+-- Information about node statistics
+select node, node_support, predicted_target_value, target_value,
+  target_support 
+from DM$VIDT_SH_CLAS_SAMPLE order by 1,2,3,4,5;
+
+-- Information about the nodes in the decision tree
+SELECT * FROM (
+select node, node_support, predicted_target_value, parent,
+  attribute_name, operator, split.val
+from DM$VODT_SH_CLAS_SAMPLE a,
+XMLTABLE( '/Element' passing a.value
+    columns 
+    val varchar2(20) path '.') split  
+  order by 1,2,3,4,5,6,7)
+WHERE ROWNUM < 20;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+--------------------
+-- PREPARE TEST DATA
+--
+-- If the data for model creation has been prepared, then the data used
+-- for testing the model must be prepared to the same scale in order to
+-- obtain meaningful results.
+-- In this case, no data preparation is necessary since model creation
+-- was performed on the raw (unprepared) input.
+--
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- Other demo programs demonstrate how to use the PL/SQL API to
+-- compute a number of metrics, including lift and ROC.  This demo
+-- only computes a confusion matrix and accuracy, but it does so 
+-- using the SQL data mining functions.
+--
+-- In this example, we experiment with using the cost matrix
+-- that was provided to the create routine.  In this example, the 
+-- cost matrix reduces the problematic misclassifications, but also 
+-- negatively impacts the overall model accuracy.
+
+-- DISPLAY CONFUSION MATRIX WITHOUT APPLYING COST MATRIX
+--
+SELECT affinity_card AS actual_target_value, 
+       PREDICTION(DT_SH_Clas_sample USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_parallel_v
+GROUP BY affinity_card, PREDICTION(DT_SH_Clas_sample USING *)
+ORDER BY 1,2;
+
+-- DISPLAY CONFUSION MATRIX APPLYING THE COST MATRIX
+--
+SELECT affinity_card AS actual_target_value, 
+       PREDICTION(DT_SH_Clas_sample COST MODEL USING *) 
+         AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_parallel_v
+GROUP BY affinity_card, PREDICTION(DT_SH_Clas_sample COST MODEL USING *)
+ORDER BY 1,2;
+
+-- DISPLAY ACCURACY WITHOUT APPLYING COST MATRIX
+--
+SELECT ROUND(SUM(correct)/COUNT(*),4) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+               PREDICTION(DT_SH_Clas_sample USING *), 1, 0) AS correct
+          FROM mining_data_test_parallel_v);
+
+-- DISPLAY ACCURACY APPLYING THE COST MATRIX
+--
+SELECT ROUND(SUM(correct)/COUNT(*),4) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(DT_SH_Clas_sample COST MODEL USING *),
+                 1, 0) AS correct
+          FROM mining_data_test_parallel_v);
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+------------------
+-- BUSINESS CASE 1
+-- Find the 10 customers who live in Italy that are least expensive
+-- to be convinced to use an affinity card.
+--
+SELECT cust_id FROM
+(SELECT cust_id, 
+        rank() over (order by PREDICTION_COST(DT_SH_Clas_sample, 
+                     1 COST MODEL USING *) ASC, cust_id) rnk
+   FROM mining_data_apply_parallel_v
+  WHERE country_name = 'Italy')
+where rnk <= 10
+order by rnk;
+
+------------------
+-- BUSINESS CASE 2
+-- Find the average age of customers who are likely to use an
+-- affinity card.
+-- Include the build-time cost matrix in the prediction.
+-- Only take into account CUST_MARITAL_STATUS, EDUCATION, and
+-- HOUSEHOLD_SIZE as predictors.
+-- Break out the results by gender.
+--
+column cust_gender format a12
+SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age
+  FROM mining_data_apply_parallel_v
+ WHERE PREDICTION(dt_sh_clas_sample COST MODEL
+                 USING cust_marital_status, education, household_size) = 1
+GROUP BY cust_gender
+ORDER BY cust_gender;
+
+------------------
+-- BUSINESS CASE 3
+-- List ten customers (ordered by their id) along with likelihood and cost
+-- to use or reject the affinity card (Note: while this example has a
+-- binary target, such a query is useful in multi-class classification -
+-- Low, Med, High for example).
+--
+column prediction format 9;
+column probability format 9.999999999
+column cost format 9.999999999
+SELECT T.cust_id, S.prediction, S.probability, S.cost
+  FROM (SELECT cust_id,
+               PREDICTION_SET(dt_sh_clas_sample COST MODEL USING *) pset
+          FROM mining_data_apply_parallel_v
+         WHERE cust_id < 100011) T,
+       TABLE(T.pset) S
+ORDER BY cust_id, S.prediction;
+
+------------------
+-- BUSINESS CASE 4
+-- Find the segmentation (resulting tree node and rule) for customers who
+-- work in Tech support and are under 25.
+--
+set long 20000
+set line 300
+set pagesize 100
+column education format a30;
+SELECT cust_id, education,
+       PREDICTION_DETAILS(dt_sh_clas_sample USING *) prediction_details 
+  FROM mining_data_apply_parallel_v
+ WHERE occupation = 'TechSup' AND age < 25
+ORDER BY cust_id;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-glm.sql b/machine-learning/sql/26ai/oml4sql-classification-glm.sql
new file mode 100644
index 00000000..53caa76e
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-glm.sql
@@ -0,0 +1,523 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - Generalized Linear Model Algorithm - dmglcdem.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic and purchase data about a set of customers, predict
+-- customer's response to an affinity card program using a GLM classifier.
+--
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-------
+-- DATA
+-------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+
+-----------
+-- ANALYSIS
+-----------
+-- Data preparation in GLM is performed internally
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('GLMC_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE glmc_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE glmc_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+-- The default classification algorithm is Naive Bayes. So override
+-- this choice to GLM logistic regression using a settings table. 
+-- Turn on feature selection and generation
+--    
+BEGIN 
+-- Populate settings table
+  INSERT INTO glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_generalized_linear_model);
+  -- output row diagnostic statistics 
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.glms_row_diagnostics,
+    dbms_data_mining.glms_row_diag_enable);
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  -- turn on feature selection
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES  
+    (dbms_data_mining.glms_ftr_selection, 
+    dbms_data_mining.glms_ftr_selection_enable);
+  -- turn on feature generation
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES 
+    (dbms_data_mining.glms_ftr_generation, 
+    dbms_data_mining.glms_ftr_generation_enable); 
+  
+  /* Examples of possible overrides are shown below. If the user does not
+     override, then relevant settings are determined by the algorithm
+      
+  -- specify a row weight column 
+    (dbms_data_mining.odms_row_weight_column_name,<row_weight_column_name>);
+  -- specify a missing value treatment method:
+     Default:  replace with mean (numeric features) or 
+               mode (categorical features) 
+       or delete the row
+    (dbms_data_mining.odms_missing_value_treatment,
+      dbms_data_mining.odms_missing_value_delete_row);
+  -- turn ridge regression on or off 
+     By default the system turns it on if there is a multicollinearity
+    (dbms_data_mining.glms_ridge_regression,
+     dbms_data_mining.glms_ridge_reg_enable);      
+  */
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Force the column age to be a feature in the model using
+-- dbms_data_mining_transform
+--
+declare
+  v_xlst dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  -- Force the column age to be a feature in the model
+  dbms_data_mining_transform.set_transform(v_xlst,
+    'AGE', NULL, 'AGE', 'AGE', 'FORCE_IN');
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLMC_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'glmc_sh_sample_settings',
+    xform_list          => v_xlst);
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'GLMC_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'GLMC_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+-- IF the covariance matrix had been invalid, THEN the global details 
+-- would have had a row like:
+--
+--   VALID_COVARIANCE_MATRIX  0 
+-- 
+-- And, as a result we would only have gotten a limited set of diagnostics.
+-- This never happens with feature selecion enabled. IF the forced in feature 
+-- had caused a multi-collinearity then the model build would have failed.
+-- Note that the forced_in feature, age, was not statistically significant.
+-- However, it did not cause a multi-collinearity, hence the build succeeded.
+--
+-- With feature selection disabled, then an invalid covariance matrix is 
+-- possible. Then, multi-collinearity, if it exists will cause 
+-- RIDGE REGRESSION to kick in, unless it has been specifically disabled by 
+-- you. The build will succeed with ridge enabled. However, the covariance 
+-- matrix will be invalid since it is not computed by the ridge algorithm.
+-- 
+-- An important consequence of an invalid covariance matrix is that
+-- the model cannot predict confidence bounds - i.e. the result of
+-- PREDICTION_BOUNDS function in a SQL query is NULL.
+--
+-- If accuracy is the primary goal and interpretability not important, then we
+-- note that RIDGE REGRESSION may be preferrable to feature selection for 
+-- some datasets. You can test this by specifically enabling ridge regression.
+-- 
+-- In this demo, we compute two models. The first (above) has feature
+-- selection enabled and produces confidence bounds and a full set of 
+-- diagnostics. The second enables ridge regression. It does not produce 
+-- confidence bounds. It only produces a limited set of diagnostics.
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='GLMC_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+-- Global statistics
+column name format a30
+column numeric_value format 9999990.999
+column string_value format a20
+select name, numeric_value, string_value from DM$VGGLMC_SH_CLAS_SAMPLE
+  ORDER BY name;
+
+-- Coefficient statistics
+SET line 200
+column feature_expression format a53 
+column attr_name format a20
+col attr_val format a10  
+column coefficient format 9999990.999
+column std_error format 9999990.999 
+column test_statistic format 9999990.999  
+column p_value format 9999990.999  
+column std_coefficient format 9999990.999  
+column lower_coeff_limit format 9999990.999 
+column upper_coeff_limit format 9999990.999
+column exp_coefficient format 9999990.999  
+column exp_lower_coeff_limit format 9999990.999  
+column exp_upper_coeff_limit format 9999990.999  
+  
+SELECT attribute_name attr_name, attribute_value attr_val, 
+  coefficient, std_error, test_statistic,
+  p_value, std_coefficient, lower_coeff_limit, upper_coeff_limit,
+  exp_coefficient, exp_lower_coeff_limit, exp_upper_coeff_limit
+  FROM DM$VDGLMC_SH_CLAS_SAMPLE
+  ORDER BY 1,2;
+
+-- Show the features and their p_values
+SET lin 80
+SET pages 20
+SELECT attribute_name attr_name, attribute_value attr_val, coefficient, p_value 
+  FROM DM$VDGLMC_SH_CLAS_SAMPLE
+  ORDER BY p_value;
+    
+-- Row diagnostics
+SELECT CASE_id, TARGET_value, TARGET_value_prob, hat, 
+  working_residual, pearson_residual, deviance_residual, 
+  c, cbar, difdev, difchisq
+  FROM dm$vaGLMC_SH_Clas_sample
+ WHERE case_id <= 101510
+ ORDER BY case_id;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The queries shown below demonstrate the use of new SQL data mining functions
+-- along with analytic functions to compute the various test metrics.
+--
+-- Modelname:             glmc_sh_clas_sample
+-- Target attribute:      affinity_card
+-- Positive target value: 1
+-- (Change as appropriate for a different example)
+
+-- Compute CONFUSION MATRIX
+--
+-- This query demonstates how to generate a confusion matrix using the new
+-- SQL prediction functions for scoring. The returned columns match the
+-- schema of the table generated by COMPUTE_CONFUSION_MATRIX procedure.
+--
+SELECT affinity_card AS actual_target_value,
+       PREDICTION(glmc_sh_clas_sample USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_v
+ GROUP BY affinity_card, PREDICTION(glmc_sh_clas_sample USING *)
+ ORDER BY 1, 2;
+
+-- Compute ACCURACY
+--
+column accuracy format 9.99
+
+SELECT SUM(correct)/COUNT(*) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(glmc_sh_clas_sample USING *), 1, 0) AS correct
+          FROM mining_data_test_v);
+  
+-- Compute AUC (Area Under the roc Curve)
+-- (See notes on ROC Curve and AUC computation in dmsvcdem.sql)
+--
+column auc format 9.99
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(glmc_sh_clas_sample, 1 USING *) pos_prob,
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+tpf_fpf AS (
+SELECT  pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(pos_cnt) OVER () tpf,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(1 - pos_cnt) OVER () fpf
+  FROM pos_prob_and_counts
+),
+trapezoid_areas AS (
+SELECT 0.5 * (fpf - LAG(fpf, 1, 0) OVER (ORDER BY fpf, tpf)) *
+        (tpf + LAG(tpf, 1, 0) OVER (ORDER BY fpf, tpf)) area
+  FROM tpf_fpf
+ WHERE pos_cnt = 1
+    OR (tpf = 1 AND fpf = 1)
+)
+SELECT SUM(area) auc
+  FROM trapezoid_areas;
+
+--------------------------------------------------------------------------
+--                               SCORE DATA
+--------------------------------------------------------------------------
+
+-- Since the model has a valid covariance matrix, it is possible
+-- to obtain confidence bounds.  In addition, provide the ranked set
+-- of attributes which have the most influence on each prediction.
+set long 10000
+SELECT PREDICTION(GLMC_SH_Clas_sample USING *) pr,
+       PREDICTION_PROBABILITY(GLMC_SH_Clas_sample USING *) pb,
+       PREDICTION_BOUNDS(GLMC_SH_Clas_sample USING *).lower pl,
+       PREDICTION_BOUNDS(GLMC_SH_Clas_sample USING *).upper pu,
+       PREDICTION_DETAILS(GLMC_SH_Clas_sample USING *) pd
+  FROM mining_data_apply_v
+ WHERE CUST_ID <= 100010
+ ORDER BY CUST_ID;
+
+
+-- Next we compare to a model built with ridge regression enabled
+
+-----------------------------------------------------------------------
+--                            BUILD A NEW MODEL
+-----------------------------------------------------------------------
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Cleanup old model with same name (if any)
+BEGIN
+  DBMS_DATA_MINING.DROP_MODEL('GLMC_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN
+  NULL;
+END;
+/
+
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table 
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE glmc_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+CREATE TABLE glmc_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+-- Turn on ridge regression
+--  
+BEGIN 
+-- Populate settings table
+  INSERT INTO glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_generalized_linear_model);
+  -- output row diagnostic statistics into a table named GLMC_SH_SAMPLE_DIAG  
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.glms_row_diagnostics,
+    dbms_data_mining.glms_row_diag_enable); 
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);  
+  -- turn on ridge regression
+  INSERT INTO  glmc_sh_sample_settings (setting_name, setting_value) VALUES  
+    (dbms_data_mining.glms_ridge_regression,
+    dbms_data_mining.glms_ridge_reg_enable);  
+  INSERT INTO glmc_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.GLMS_SOLVER, 
+     dbms_data_mining.GLMS_SOLVER_QR); 
+END;
+/
+commit;
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLMC_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'glmc_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'GLMC_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'GLMC_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='GLMC_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+-- Global statistics
+column numeric_value format 9999990.999
+column string_value format a20
+select name, numeric_value, string_value from DM$VGGLMC_SH_CLAS_SAMPLE
+  ORDER BY name;
+
+-- Coefficient statistics
+SET line 120
+column class format a20
+column attribute_name format a20  
+column attribute_subname format a20
+column attribute_value format a20  
+column partition_name format a20
+  
+SELECT *
+  FROM DM$VDGLMC_SH_CLAS_SAMPLE
+ WHERE attribute_name = 'OCCUPATION'
+ ORDER BY target_value, attribute_name, attribute_value;
+    
+-- Limited row diagnostics - working_residuals only, others are NULL
+SELECT CASE_id, TARGET_value, TARGET_value_prob, working_residual
+  FROM dm$vaGLMC_SH_Clas_sample
+ WHERE case_id <= 101510
+ ORDER BY case_id;
+
+-----------------------------------------------------------------------
+--                               TEST THE NEW MODEL
+-----------------------------------------------------------------------
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The queries shown below demonstrate the use of new SQL data mining functions
+-- along with analytic functions to compute various test metrics. 
+--
+-- Modelname:             glmc_sh_clas_sample
+-- Target attribute:      affinity_card
+-- Positive target value: 1
+-- (Change these as appropriate for a different example)
+
+-- Compute CONFUSION MATRIX
+--
+-- This query demonstates how to generate a confusion matrix using the new
+-- SQL prediction functions for scoring. The returned columns match the
+-- schema of the table generated by COMPUTE_CONFUSION_MATRIX procedure.
+--
+SELECT affinity_card AS actual_target_value,
+       PREDICTION(glmc_sh_clas_sample USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_v
+ GROUP BY affinity_card, PREDICTION(glmc_sh_clas_sample USING *)
+ ORDER BY 1, 2;
+
+-- Compute ACCURACY
+--
+column accuracy format 9.99
+
+SELECT SUM(correct)/COUNT(*) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(glmc_sh_clas_sample USING *), 1, 0) AS correct
+          FROM mining_data_test_v);
+
+-- Compute AUC (Area Under the roc Curve)
+--
+-- See notes on ROC Curve and AUC computation above
+--
+column auc format 9.99
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(glmc_sh_clas_sample, 1 USING *) pos_prob,
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+tpf_fpf AS (
+SELECT  pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(pos_cnt) OVER () tpf,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(1 - pos_cnt) OVER () fpf
+  FROM pos_prob_and_counts
+),
+trapezoid_areas AS (
+SELECT 0.5 * (fpf - LAG(fpf, 1, 0) OVER (ORDER BY fpf, tpf)) *
+        (tpf + LAG(tpf, 1, 0) OVER (ORDER BY fpf, tpf)) area
+  FROM tpf_fpf
+ WHERE pos_cnt = 1
+    OR (tpf = 1 AND fpf = 1)
+)
+SELECT SUM(area) auc
+  FROM trapezoid_areas;
+
+-- Judging from the accuracy and AUC, the ridge regression model 
+-- and feature selection/generation model are of approximately equal 
+-- quality
+
+--------------------------------------------------------------------------
+--                               SCORE DATA
+--------------------------------------------------------------------------
+
+-- Now that the model has an invalid covariance matrix, it is 
+-- no longer possible to obtain confidence bounds.
+-- So the lower (PL) and upper (PU) confidence bounds are NULL
+set long 10000
+SELECT PREDICTION(GLMC_SH_Clas_sample USING *) pr,
+       PREDICTION_PROBABILITY(GLMC_SH_Clas_sample USING *) pb,
+       PREDICTION_BOUNDS(GLMC_SH_Clas_sample USING *).lower pl,
+       PREDICTION_BOUNDS(GLMC_SH_Clas_sample USING *).upper pu,
+       PREDICTION_DETAILS(GLMC_SH_Clas_sample USING *) pd
+  FROM mining_data_apply_v
+ WHERE CUST_ID <= 100010
+ ORDER BY CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-naive-bayes.sql b/machine-learning/sql/26ai/oml4sql-classification-naive-bayes.sql
new file mode 100644
index 00000000..b2f810e6
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-naive-bayes.sql
@@ -0,0 +1,605 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - Naïve Bayes Algorithm - dmnbdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic data about a set of customers, predict the
+-- customer response to an affinity card program using a classifier
+-- based on the Naive Bayes algorithm.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_parallel_v (build data)
+-- mining_data_test_parallel_v  (test data)
+-- mining_data_apply_parallel_v (apply data)
+-- (See dmsh.sql for view definitions).
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_priors';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Algorithm setting:
+-- NB is the default classifier, thus there is no need to specify 
+-- the algorithm in settings table when the mining_function parameter
+-- of the CREATE_MODEL operation specifies classification.
+
+-- CREATE AND POPULATE A PRIORS TABLE
+-- The priors represent the overall distribution of the target in
+-- the population. By default, the priors are computed from the sample
+-- (in this case, the build data). If the sample is known to be a
+-- distortion of the population target distribution (because, say,
+-- stratified sampling has been employed, or due to some other reason),
+-- then the user can override the default by providing a priors table
+-- as a setting for model creation. See Oracle Data Mining Concepts Guide
+-- for more details.
+-- 
+CREATE TABLE nb_sh_sample_priors (
+  target_value      NUMBER,
+  prior_probability NUMBER);
+INSERT INTO nb_sh_sample_priors VALUES (0,0.65);
+INSERT INTO nb_sh_sample_priors VALUES (1,0.35);
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE nb_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+BEGIN       
+  INSERT INTO nb_sh_sample_settings (setting_name, setting_value) VALUES 
+    (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
+  INSERT INTO nb_sh_sample_settings VALUES
+    (dbms_data_mining.clas_priors_table_name, 'nb_sh_sample_priors');
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('NB_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Build a new NB model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'NB_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'nb_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'NB_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'NB_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- If the build data is prepared (as in this example), then the training
+-- data has been encoded. For numeric data, this means that ranges of
+-- values have been grouped into bins.  For categorical data, the
+-- categorical values may have been grouped into subsets.
+--
+
+set line 200 
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='NB_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+-- Naive Bayes Target Priors
+column partition_name format a14 
+column target_name format a11 
+column target_value format 9999999999.9999999999
+column prior_probability format 9999999999.9999999999
+column count format 9999999999
+SELECT partition_name,
+       target_name,
+       target_value, 
+       prior_probability,
+       count
+  FROM DM$VPNB_SH_Clas_sample
+ORDER BY 1,2,3,4,5;
+
+-- Naive Bayes Conditional Probabilities
+column partition_name format a14 
+column target_name format a11 
+column target_value format 9999999999.9999999999
+column attribute_name format a14 
+column attribute_subname format a17 
+column attribute_value format a15 
+column conditional_probability format 9999999999.9999999999
+column count format 9999999999
+SELECT partition_name,
+       target_name,
+       target_value,
+       attribute_name,
+       attribute_subname,
+       attribute_value,
+       conditional_probability,
+       count
+  FROM DM$VVNB_SH_Clas_sample
+ORDER BY 1,2,3,4,5,6,7,8;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+
+-- Cleanup old test result objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_test_apply';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_confusion_matrix';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_cm_no_cost';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_lift';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_roc';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_alter_cost';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_alter_confusion_matrix';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The COMPUTE interfaces that provide the test results require two
+-- data inputs:
+-- 1. A table or view of targets - i.e. one that provides only the
+--    case identifier and target columns of your test data.
+-- 2. The table with the results of an APPLY operation on test data.
+--
+
+-- CREATE TEST TARGETS VIEW
+--
+CREATE OR REPLACE VIEW nb_sh_sample_test_targets AS
+SELECT cust_id, affinity_card
+  FROM mining_data_apply_parallel_v;
+
+-- APPLY MODEL ON TEST DATA
+--
+BEGIN
+  DBMS_DATA_MINING.APPLY(
+    model_name          => 'NB_SH_Clas_sample',
+    data_table_name     => 'mining_data_apply_parallel_v',
+    case_id_column_name => 'cust_id',
+    result_table_name   => 'nb_sh_sample_test_apply');
+END;
+/
+
+----------------------------------
+-- COMPUTE TEST METRICS, WITH COST
+--
+----------------------
+-- Specify cost matrix
+--
+-- Consider an example where it costs $10 to mail a promotion to a
+-- prospective customer and if the prospect becomes a customer, the
+-- typical sale including the promotion, is worth $100. Then the cost
+-- of missing a customer (i.e. missing a $100 sale) is 10x that of
+-- incorrectly indicating that a person is good prospect (spending
+-- $10 for the promo). In this case, all prediction errors made by
+-- the model are NOT equal. To act on what the model determines to
+-- be the most likely (probable) outcome may be a poor choice.
+--
+-- Suppose that the probability of a BUY reponse is 10% for a given
+-- prospect. Then the expected revenue from the prospect is:
+--   .10 * $100 - .90 * $10 = $1.
+-- The optimal action, given the cost matrix, is to simply mail the
+-- promotion to the customer, because the action is profitable ($1).
+--
+-- In contrast, without the cost matrix, all that can be said is
+-- that the most likely response is NO BUY, so don't send the
+-- promotion.
+--
+-- This shows that cost matrices can be very important. 
+--
+-- The caveat in all this is that the model predicted probabilities
+-- may NOT be accurate. For binary targets, a systematic approach to
+-- this issue exists. It is ROC, illustrated below. 
+--
+-- With ROC computed on a test set, the user can see how various model 
+-- predicted probability thresholds affect the action of mailing a promotion.
+-- Suppose I promote when the probability to BUY exceeds 5, 10, 15%, etc. 
+-- What return can I expect? Note that the answer to this question does
+-- not rely on the predicted probabilities being accurate, only that
+-- they are in approximately the correct rank order. 
+--
+-- Assuming that the predicted probabilities are accurate, provide the
+-- cost matrix table name as input to the RANK_APPLY procedure to get
+-- appropriate costed scoring results to determine the most appropriate
+-- action.
+
+-- Cleanup old cost matrix table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_cost';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- CREATE A COST MATRIX TABLE
+--
+CREATE TABLE nb_sh_cost (
+  actual_target_value    NUMBER,
+  predicted_target_value NUMBER,
+  cost                   NUMBER);
+
+-- POPULATE THE COST MATRIX
+--
+INSERT INTO nb_sh_cost VALUES (0,0,0);
+INSERT INTO nb_sh_cost VALUES (0,1,.35);
+INSERT INTO nb_sh_cost VALUES (1,0,.65);
+INSERT INTO nb_sh_cost VALUES (1,1,0);
+
+-- Compute Test Metrics
+DECLARE
+  v_accuracy         NUMBER;
+  v_area_under_curve NUMBER;
+BEGIN
+   DBMS_DATA_MINING.COMPUTE_CONFUSION_MATRIX (
+     accuracy                    => v_accuracy,
+     apply_result_table_name     => 'nb_sh_sample_test_apply',
+     target_table_name           => 'nb_sh_sample_test_targets',
+     case_id_column_name         => 'cust_id',
+     target_column_name          => 'affinity_card',
+     confusion_matrix_table_name => 'nb_sh_sample_confusion_matrix',
+     score_column_name           => 'PREDICTION',   -- default
+     score_criterion_column_name => 'PROBABILITY',  -- default
+     cost_matrix_table_name      => 'nb_sh_cost');
+   DBMS_OUTPUT.PUT_LINE('**** MODEL ACCURACY ****: ' || ROUND(v_accuracy,4));
+ 
+   DBMS_DATA_MINING.COMPUTE_LIFT (
+     apply_result_table_name => 'nb_sh_sample_test_apply',
+     target_table_name       => 'nb_sh_sample_test_targets',
+     case_id_column_name     => 'cust_id',
+     target_column_name      => 'affinity_card',
+     lift_table_name         => 'nb_sh_sample_lift',
+     positive_target_value   => '1',
+     num_quantiles           => '10',
+     cost_matrix_table_name  => 'nb_sh_cost');
+
+   DBMS_DATA_MINING.COMPUTE_ROC (
+     roc_area_under_curve        => v_area_under_curve,
+     apply_result_table_name     => 'nb_sh_sample_test_apply',
+     target_table_name           => 'nb_sh_sample_test_targets',
+     case_id_column_name         => 'cust_id',
+     target_column_name          => 'affinity_card',
+     roc_table_name              => 'nb_sh_sample_roc',
+     positive_target_value       => '1',
+     score_column_name           => 'PREDICTION',
+     score_criterion_column_name => 'PROBABILITY');
+   DBMS_OUTPUT.PUT_LINE('**** AREA UNDER ROC CURVE ****: ' ||
+     ROUND(v_area_under_curve,4));
+END;
+/
+
+-- TEST RESULT OBJECTS:
+-- -------------------
+-- 1. Confusion matrix Table: nb_sh_sample_confusion_matrix
+-- 2. Lift Table:             nb_sh_sample_lift
+-- 3. ROC Table:              nb_sh_sample_roc
+--
+
+-- DISPLAY CONFUSION MATRIX
+--
+-- NOTES ON COST (contd):
+-- This section illustrates the effect of the cost matrix on the per-class
+-- errors in the confusion matrix. First, compute the Confusion Matrix with
+-- costs. Our cost matrix assumes that ratio of the cost of an error in 
+-- class 1 to class 0 is 65:35 (where 1 => BUY and 0 => NO BUY).
+
+column predicted format 9;
+SELECT actual_target_value as actual, 
+       predicted_target_value as predicted, 
+       value as count
+  FROM nb_sh_sample_confusion_matrix
+ORDER BY actual_target_value, predicted_target_value;
+
+-- Confusion matrix with Cost:
+--    869  285
+--     55  291
+
+-- Compute the confusion matrix without costs for later analysis
+DECLARE
+  v_accuracy         NUMBER;
+  v_area_under_curve NUMBER;
+BEGIN
+   DBMS_DATA_MINING.COMPUTE_CONFUSION_MATRIX (
+     accuracy                    => v_accuracy,
+     apply_result_table_name     => 'nb_sh_sample_test_apply',
+     target_table_name           => 'nb_sh_sample_test_targets',
+     case_id_column_name         => 'cust_id',
+     target_column_name          => 'affinity_card',
+     confusion_matrix_table_name => 'nb_sh_sample_cm_no_cost',
+     score_column_name           => 'PREDICTION',
+     score_criterion_column_name => 'PROBABILITY');
+   DBMS_OUTPUT.PUT_LINE('** ACCURACY W/ NO COST **: ' || ROUND(v_accuracy,4));
+END;
+/
+
+-- Confusion matrix without Cost:
+--
+column predicted format 9;
+SELECT actual_target_value as actual, 
+       predicted_target_value as predicted, 
+       value as count
+  FROM nb_sh_sample_cm_no_cost
+ORDER BY actual_target_value, predicted_target_value;
+
+-- Confusion matrix without Cost:
+--    901  253
+--     60  286
+--
+-- Several points are illustrated here:
+-- 1. The cost matrix causes an increase in class 1 accuracy
+--    at the expense of class 0 accuracy
+-- 2. The overall accuracy is down
+
+-- DISPLAY ROC - TOP PROBABILITIE THRESHOLDS LEADING TO MINIMIZED COST
+--
+column prob format .9999
+column tp format 9999
+column fn format 9999
+column fp format 9999
+column tn format 9999
+column tpf format 9.9999
+column fpf format 9.9999
+column nb_cost format 9999.99
+SELECT *
+  FROM (SELECT ROUND(probability,4) prob,
+               true_positives  tp,
+               false_negatives fn,
+               false_positives fp,
+               true_negatives  tn,
+               ROUND(true_positive_fraction,4) tpf,
+               ROUND(false_positive_fraction,4) fpf,
+               .35 * false_positives + .65 * false_negatives nb_cost
+         FROM nb_sh_sample_roc)
+ WHERE nb_cost < 130
+ ORDER BY nb_cost;
+
+-- Here we see 13 different probability thresholds resulting in
+-- confusion matrices with an overall cost below 130.
+--
+-- Now, let us create a cost matrix from the optimal threshold, i.e.,
+-- one whose action is to most closely mimic the user cost matrix.
+-- Let Poptimal = Probability corresponding to the minimum cost
+--                computed from the ROC table above
+--
+-- Find the ratio of costs that causes breakeven expected cost at
+-- at the optimal probability threshold:
+--
+--    Cost(misclassify 1) = (1 - Poptimal)/Poptimal
+--    Cost(misclassify 0) = 1.0
+--
+-- The following query constructs the alternative cost matrix
+-- based on the above rationale.
+--
+CREATE TABLE nb_alter_cost AS
+WITH
+cost_q AS (
+SELECT probability,
+       (.35 * false_positives + .65 * false_negatives) nb_cost
+  FROM nb_sh_sample_roc
+),
+min_cost AS (
+SELECT MIN(nb_cost) mincost
+  FROM cost_q
+),
+prob_q AS (
+SELECT min(probability) prob
+  FROM cost_q, min_cost
+ WHERE nb_cost = mincost
+)
+SELECT 1 actual_target_value,
+       0 predicted_target_value, 
+       (1.0 - prob)/prob cost
+  FROM prob_q
+UNION ALL 
+SELECT 0 actual_target_value,
+       1 predicted_target_value,
+       1 cost
+  FROM dual
+UNION ALL 
+SELECT 0 actual_target_value,
+       0 predicted_target_value,
+       0 cost
+  FROM dual
+UNION ALL
+SELECT 1 actual_target_value,
+       1 predicted_target_value,
+       0 cost
+  FROM dual;
+
+
+column cost format 9.999999999
+SELECT ACTUAL_TARGET_VALUE, PREDICTED_TARGET_VALUE, COST
+  FROM nb_alter_cost;
+
+-- Now, use this new cost matrix to compute the confusion matrix
+--
+DECLARE
+  v_accuracy         NUMBER;
+  v_area_under_curve NUMBER;
+BEGIN
+   DBMS_DATA_MINING.COMPUTE_CONFUSION_MATRIX (
+     accuracy                    => v_accuracy,
+     apply_result_table_name     => 'nb_sh_sample_test_apply',
+     target_table_name           => 'nb_sh_sample_test_targets',
+     case_id_column_name         => 'cust_id',
+     target_column_name          => 'affinity_card',
+     confusion_matrix_table_name => 'nb_sh_alter_confusion_matrix',
+     score_column_name           => 'PREDICTION',   -- default
+     score_criterion_column_name => 'PROBABILITY',  -- default
+     cost_matrix_table_name      => 'nb_alter_cost');
+   DBMS_OUTPUT.PUT_LINE('**** MODEL ACCURACY ****: ' || ROUND(v_accuracy,4)); 
+END;
+/
+
+SELECT actual_target_value as actual, 
+       predicted_target_value as predicted, 
+       value as count
+  FROM nb_sh_alter_confusion_matrix
+  ORDER BY actual_target_value, predicted_target_value;
+
+-- DISPLAY LIFT RESULTS
+--
+SELECT quantile_number               qtl,
+       lift_cumulative               lcume,
+       percentage_records_cumulative prcume,
+       targets_cumulative            tcume,
+       target_density_cumulative     tdcume
+-- Other info in Lift results
+-- quantile_total_count,
+-- non_targets_cumulative,
+-- lift_quantile,
+-- target_density
+  FROM nb_sh_sample_lift
+ORDER BY quantile_number;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old scoring result objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_apply_result';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nb_sh_sample_apply_ranked';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+------------------
+-- APPLY THE MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.APPLY(
+    model_name          => 'NB_SH_Clas_sample',
+    data_table_name     => 'mining_data_apply_parallel_v',
+    case_id_column_name => 'cust_id',
+    result_table_name   => 'nb_sh_sample_apply_result');
+END;
+/
+
+-- APPLY RESULT OBJECTS: nb_sh_sample_apply_result
+
+------------------------
+-- DISPLAY APPLY RESULTS
+--
+-- 1. The results table contains a prediction set - i.e. ALL the predictions
+--    for a given case id, with their corresponding probability values.
+-- 2. Only the first 10 rows of the table are displayed here.
+--
+column probability format 9.99999
+column prediction format 9
+SELECT cust_id, prediction, ROUND(probability,4) probability
+  FROM nb_sh_sample_apply_result
+ WHERE cust_id <= 100005
+ORDER BY cust_id, prediction;
+   
+-----------------------------------------------------------
+-- GENERATE RANKED APPLY RESULTS (OPTIONALLY BASED ON COST)
+--
+-- ALTER APPLY RESULTS TABLE (just for demo purposes)
+--
+-- The RANK_APPLY and COMPUTE() procedures do not necessarily have
+-- to work on the result table generated from DBMS_DATA_MINING.APPLY
+-- alone. They can work on any table with similar schema and content
+-- that matches the APPLY result table. An example will be a table
+-- generated from some other tool, scoring engine or a generated result.
+--
+-- To demonstrate this, we will make a simply change the column names in
+-- the APPLY results schema table, and supply the new table as input to
+-- RANK_APPLY. The only requirement is that the new column names have to be
+-- reflected in the RANK_APPLY procedure. The table containing the ranked
+-- results will reflect these new column names.
+-- 
+ALTER TABLE nb_sh_sample_apply_result RENAME COLUMN cust_id TO customer_id;
+ALTER TABLE nb_sh_sample_apply_result RENAME COLUMN prediction TO score;
+ALTER TABLE nb_sh_sample_apply_result RENAME COLUMN probability TO chance;
+
+-- RANK APPLY RESULTS (WITH COST MATRIX INPUT)
+--
+BEGIN
+  DBMS_DATA_MINING.RANK_APPLY (
+    apply_result_table_name     => 'nb_sh_sample_apply_result',
+    case_id_column_name         => 'customer_id',
+    score_column_name           => 'score',
+    score_criterion_column_name => 'chance',
+    ranked_apply_table_name     => 'nb_sh_sample_apply_ranked',
+    top_n                       => 2,
+    cost_matrix_table_name      => 'nb_alter_cost');
+END;
+/
+
+-- RANK_APPLY RESULT OBJECTS: nb_sh_sample_apply_ranked
+
+-------------------------------
+-- DISPLAY RANKED APPLY RESULTS
+-- using altered cost matrix
+column chance format 9.99
+column cost format 9.99 
+SELECT customer_id, score, ROUND(chance,4) chance, ROUND(cost,4) cost, rank
+  FROM nb_sh_sample_apply_ranked
+ WHERE customer_id <= 100005
+ ORDER BY customer_id, rank;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-neural-networks.sql b/machine-learning/sql/26ai/oml4sql-classification-neural-networks.sql
new file mode 100644
index 00000000..7eaa4196
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-neural-networks.sql
@@ -0,0 +1,439 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - Neural Networks Algorithm - dmnncdem.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic and purchase data about a set of customers, predict
+-- customer's response to an affinity card program using a NN classifier.
+--
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-------
+-- DATA
+-------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('NNC_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+
+------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nnc_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nnc_sh_sample_class_wt';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- CREATE AND POPULATE A CLASS WEIGHTS TABLE
+--
+-- A class weights table is used to influence the weighting of target classes
+-- during model creation. For example, weights of (0.9, 0.1) for a binary
+-- problem specify that an error in the first class has significantly
+-- higher penalty that an error in the second class. Weights of (0.5, 0.5)
+-- do not introduce a differential weight and would produce the same
+-- model as when no weights are provided.
+--
+CREATE TABLE nnc_sh_sample_class_wt (
+  target_value NUMBER,
+  class_weight NUMBER);
+INSERT INTO nnc_sh_sample_class_wt VALUES (0,0.35);
+INSERT INTO nnc_sh_sample_class_wt VALUES (1,0.65);
+COMMIT;
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE nnc_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+--    
+BEGIN 
+-- Populate settings table
+  INSERT INTO nnc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.algo_name, dbms_data_mining.algo_neural_network);
+  INSERT INTO nnc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.clas_weights_table_name, 'nnc_sh_sample_class_wt');
+  INSERT INTO nnc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+
+  -- Examples of other possible settings are:
+  --(dbms_data_mining.odms_random_seed, '12');
+  --(dbms_data_mining.nnet_hidden_layers, '2');
+  --(dbms_data_mining.nnet_nodes_per_layer, '10, 30');
+  --(dbms_data_mining.nnet_iterations, '100');
+  --(dbms_data_mining.nnet_tolerance, '0.0001');
+  --(dbms_data_mining.nnet_activations, 
+  --   ''''|| dbms_data_mining.nnet_activations_log_sig ||'''');
+  --(dbms_data_mining.nnet_regularizer, 
+  --        dbms_data_mining.nnet_regularizer_heldaside);
+  --(dbms_data_mining.nnet_heldaside_ratio, '0.3');
+  --(dbms_data_mining.nnet_heldaside_max_fail, '5');
+  --(dbms_data_mining.nnet_regularizer,
+  --        dbms_data_mining.nnet_regularizer_l2);
+  --(dbms_data_mining.nnet_reg_lambda, '0.5');
+  --(dbms_data_mining.nnet_weight_upper_bound, '0.7');
+  --(dbms_data_mining.nnet_weight_lower_bound, '-0.6')
+  --(dbms_data_mining.lbfgs_history_depth, '20');
+  --(dbms_data_mining.lbfgs_scale_hessian, 
+  --        dbms_data_mining.lbfgs_scale_hessian_disable);
+  --(dbms_data_mining.lbfgs_gradient_tolerance, '0.0001');
+ 
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Build a new SVM Model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'NNC_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'nnc_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'NNC_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'NNC_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='NNC_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The queries shown below demonstrate the use of SQL data mining functions
+-- along with analytic functions to compute various test metrics. In these
+-- queries:
+--
+-- Modelname:             nnc_sh_clas_sample
+-- # of Lift Quantiles:   10
+-- Target attribute:      affinity_card
+-- Positive target value: 1
+-- (Change these as appropriate for a different example)
+
+-- Compute CONFUSION MATRIX
+--
+-- This query demonstates how to generate a confusion matrix using the
+-- SQL prediction functions for scoring. The returned columns match the
+-- schema of the table generated by COMPUTE_CONFUSION_MATRIX procedure.
+--
+SELECT affinity_card AS actual_target_value,
+       PREDICTION(nnc_sh_clas_sample USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_v
+ GROUP BY affinity_card, PREDICTION(nnc_sh_clas_sample USING *)
+ ORDER BY 1, 2;
+
+-- Compute ACCURACY
+--
+column accuracy format 9.99
+
+SELECT SUM(correct)/COUNT(*) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(nnc_sh_clas_sample USING *), 1, 0) AS correct
+          FROM mining_data_test_v);
+
+-- Compute CUMULATIVE LIFT, GAIN Charts.
+--
+-- The cumulative gain chart is a popular version of the lift chart, and
+-- it maps cumulative gain (Y axis) against the cumulative records (X axis).
+--
+-- The cumulative lift chart is another popular representation of lift, and
+-- it maps cumulative lift (Y axis) against the cumulative records (X axis).
+--
+-- The query also returns the probability associated with each quantile, so
+-- that when the cut-off point for Lift is selected, you can correlate it
+-- with a probability value (say P_cutoff). You can then use this value of
+-- P_cutoff in a prediction query as follows:
+--
+-- SELECT *
+--   FROM records_to_be_scored
+--  WHERE PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1 USING *) > P_cutoff;
+--
+-- In the query below
+--
+-- q_num     - Quantile Number
+-- pos_cnt   - # of records that predict the positive target
+-- pos_prob  - the probability associated with predicting a positive target
+--             value for a given new record
+-- cume_recs - % Cumulative Records upto quantile
+-- cume_gain - % Cumulative Gain
+-- cume_lift - Cumulative Lift
+--
+-- Note that the LIFT can also be computed using 
+-- DBMS_DATA_MINING.COMPUTE_LIFT function, see examples in dmnbdemo.sql.
+--
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(nnc_sh_clas_sample, 1 USING *) pos_prob,
+       -- hit count for positive target value
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+qtile_and_smear AS (
+SELECT NTILE(10) OVER (ORDER BY pos_prob DESC) q_num,
+       pos_prob,
+       -- smear the counts across records with the same probability to
+       -- eliminate potential biased distribution across qtl boundaries
+       AVG(pos_cnt) OVER (PARTITION BY pos_prob) pos_cnt
+  FROM pos_prob_and_counts
+),
+cume_and_total_counts AS (
+SELECT q_num,
+       -- inner sum for counts within q_num groups,
+       -- outer sum for cume counts
+       MIN(pos_prob) pos_prob,
+       SUM(COUNT(*)) OVER (ORDER BY q_num) cume_recs,
+       SUM(SUM(pos_cnt)) OVER (ORDER BY q_num) cume_pos_cnt,
+       SUM(COUNT(*)) OVER () total_recs,
+       SUM(SUM(pos_cnt)) OVER () total_pos_cnt
+  FROM qtile_and_smear
+ GROUP BY q_num
+)
+SELECT pos_prob,
+       100*(cume_recs/total_recs) cume_recs,
+       100*(cume_pos_cnt/total_pos_cnt) cume_gain,
+       (cume_pos_cnt/total_pos_cnt)/(cume_recs/total_recs) cume_lift
+  FROM cume_and_total_counts
+ ORDER BY pos_prob DESC;
+
+-- Compute ROC CURVE
+--
+-- This can be used to find the operating point for classification.
+--
+-- The ROC curve plots true positive fraction - TPF (Y axis) against
+-- false positive fraction - FPF (X axis). Note that the query picks
+-- only the corner points (top tpf switch points for a given fpf) and
+-- the last point. It should be noted that the query does not generate
+-- the first point, i.e (tpf, fpf) = (0, 0). All of the remaining points
+-- are computed, but are then filtered based on the criterion above. For
+-- example, the query picks points a,b,c,d and not points o,e,f,g,h,i,j. 
+--
+-- The Area Under the Curve (next query) is computed using the trapezoid
+-- rule applied to all tpf change points (i.e. summing up the areas of
+-- the trapezoids formed by the points for each segment along the X axis;
+-- (recall that trapezoid Area = 0.5h (A+B); h=> hieght, A, B are sides).
+-- In the example, this means the curve covering the area would trace
+-- points o,e,a,g,b,c,d.
+--
+-- |
+-- |        .c .j .d
+-- |  .b .h .i
+-- |  .g
+-- .a .f
+-- .e
+-- .__.__.__.__.__.__
+-- o
+--
+-- Note that the ROC curve can also be computed using 
+-- DBMS_DATA_MINING.COMPUTE_ROC function, see examples in dmnbdemo.sql.
+--
+column prob format 9.9999
+column fpf  format 9.9999
+column tpf  format 9.9999
+
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(nnc_sh_clas_sample, 1 USING *) pos_prob,
+       -- hit count for positive target value
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+cume_and_total_counts AS (
+SELECT pos_prob,
+       pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) cume_pos_cnt, 
+       SUM(pos_cnt) OVER () tot_pos_cnt,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) cume_neg_cnt,
+       SUM(1 - pos_cnt) OVER () tot_neg_cnt
+  FROM pos_prob_and_counts
+),
+roc_corners AS (
+SELECT MIN(pos_prob) pos_prob,
+       MAX(cume_pos_cnt) cume_pos_cnt, cume_neg_cnt,
+       MAX(tot_pos_cnt) tot_pos_cnt, MAX(tot_neg_cnt) tot_neg_cnt
+  FROM cume_and_total_counts
+ WHERE pos_cnt = 1                      -- tpf switch points
+    OR (cume_pos_cnt = tot_pos_cnt AND  -- top-right point 
+        cume_neg_cnt = tot_neg_cnt)
+ GROUP BY cume_neg_cnt
+)
+SELECT pos_prob prob,
+       cume_pos_cnt/tot_pos_cnt tpf,
+       cume_neg_cnt/tot_neg_cnt fpf,
+       cume_pos_cnt tp,
+       tot_pos_cnt - cume_pos_cnt fn,
+       cume_neg_cnt fp,
+       tot_neg_cnt - cume_neg_cnt tn
+  FROM roc_corners
+ ORDER BY fpf;
+
+
+-- Compute AUC (Area Under the roc Curve)
+--
+-- See notes on ROC Curve and AUC computation above
+--
+column auc format 9.99
+
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(nnc_sh_clas_sample, 1 USING *) pos_prob,
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+tpf_fpf AS (
+SELECT  pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(pos_cnt) OVER () tpf,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(1 - pos_cnt) OVER () fpf
+  FROM pos_prob_and_counts
+),
+trapezoid_areas AS (
+SELECT 0.5 * (fpf - LAG(fpf, 1, 0) OVER (ORDER BY fpf, tpf)) *
+        (tpf + LAG(tpf, 1, 0) OVER (ORDER BY fpf, tpf)) area
+  FROM tpf_fpf
+ WHERE pos_cnt = 1
+    OR (tpf = 1 AND fpf = 1)
+)
+SELECT SUM(area) auc
+  FROM trapezoid_areas;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- Find the 10 customers who live in Italy that are most likely 
+-- to use an affinity card.
+--
+SELECT cust_id FROM
+(SELECT cust_id, 
+        rank() over (order by PREDICTION_PROBABILITY(nnc_sh_clas_sample, 1
+                     USING *) DESC, cust_id) rnk
+   FROM mining_data_apply_v
+  WHERE country_name = 'Italy')
+where rnk <= 10
+order by rnk;
+
+------------------
+-- BUSINESS CASE 2
+-- Find the average age of customers who are likely to use an
+-- affinity card. Break out the results by gender.
+--
+column cust_gender format a12
+SELECT cust_gender,
+       COUNT(*) AS cnt,
+       ROUND(AVG(age)) AS avg_age
+  FROM mining_data_apply_v
+ WHERE PREDICTION(nnc_sh_clas_sample USING *) = 1
+GROUP BY cust_gender
+ORDER BY cust_gender;
+
+------------------
+-- BUSINESS CASE 3
+-- List ten customers (ordered by their id) along with their likelihood to
+-- use or reject the affinity card (Note: while this example has a
+-- binary target, such a query is useful in multi-class classification -
+-- Low, Med, High for example).
+--
+column prediction format 9;
+column probability format 9.999999999
+column cost format 9.999999999
+SELECT T.cust_id, S.prediction, S.probability
+  FROM (SELECT cust_id,
+               PREDICTION_SET(nnc_sh_clas_sample USING *) pset
+          FROM mining_data_apply_v
+         WHERE cust_id < 100011) T,
+       TABLE(T.pset) S
+ORDER BY cust_id, S.prediction;
+
+------------------
+-- BUSINESS CASE 4
+-- Find customers whose profession is Tech Support
+-- with > 75% likelihood of using the affinity card,
+-- and explain the attributes which make them likely
+-- to use an affinity card.
+--
+set long 20000
+SELECT cust_id, PREDICTION_DETAILS(nnc_sh_clas_sample, 1 USING *) PD
+  FROM mining_data_apply_v
+ WHERE PREDICTION_PROBABILITY(nnc_sh_clas_sample, 1 USING *) > 0.75
+       AND occupation = 'TechSup'
+ORDER BY cust_id;
+
diff --git a/machine-learning/sql/26ai/oml4sql-classification-random-forest.sql b/machine-learning/sql/26ai/oml4sql-classification-random-forest.sql
new file mode 100644
index 00000000..1110cc0b
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-random-forest.sql
@@ -0,0 +1,332 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - Random Forest Algorithm - dmrfdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON
+SET pages 10000
+SET linesize 420
+SET echo ON
+SET long 2000000000
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic data about a set of customers, predict the
+-- customer response to an affinity card program using a classifier
+-- based on Random Forests algorithm.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_parallel_v (build data)
+-- mining_data_test_parallel_v  (test data)
+-- mining_data_apply_parallel_v (apply data)
+-- (See dmsh.sql for view definitions).
+
+-----------
+-- ANALYSIS
+-----------
+-- These are the factors to consider for data analysis in random forest:
+--
+-- 1. Missing Value Treatment for Predictors
+--
+--    Decision Tree implementation in ODM handles missing predictor
+--    values (by penalizing predictors which have missing values)
+--    and missing target values (by simply discarding records with
+--    missing target values).
+--    Other options can be specified by using the ODMS_MISSING_VALUE_TREATMENT
+--    setting.
+--
+-- 2. Binning high cardinality data
+--    No data preparation for the types we accept is necessary - even
+--    for high cardinality predictors.  Preprocessing to reduce the
+--    cardinality (e.g., binning) can improve the performance of the build.
+--
+-- This demo is similar to the Decision Tree demo. But instead of building a
+-- decision tree, we build a random forest.We need some additional settings for
+-- building the random forest. The build and score steps for random forest
+-- and decision tree are identical.
+--
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_SH_Clas_Sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+--------------------------------
+-- PREPARE BUILD (TRAINING) DATA
+--
+-- The decision tree algorithm is very capable of handling data which
+-- has not been specially prepared.  For this example, no data preparation
+-- will be performed.
+--
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE rf_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE rf_sh_sample_cost';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+ 
+--------------------------
+-- CREATE A SETTINGS TABLE
+--
+-- The default classification algorithm is Naive Bayes. In order to override
+-- this, create and populate a settings table to be used as input for
+-- CREATE_MODEL.
+-- 
+set echo off
+CREATE TABLE rf_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+ 
+-- CREATE AND POPULATE A COST MATRIX TABLE
+--
+-- A cost matrix is used to influence the weighting of misclassification
+-- during model creation (and scoring).
+-- See Oracle Data Mining Concepts Guide for more details.
+--
+CREATE TABLE rf_sh_sample_cost (
+  actual_target_value           NUMBER,
+  predicted_target_value        NUMBER,
+  cost                          NUMBER);
+INSERT INTO rf_sh_sample_cost VALUES (0,0,0);
+INSERT INTO rf_sh_sample_cost VALUES (0,1,1);
+INSERT INTO rf_sh_sample_cost VALUES (1,0,8);
+INSERT INTO rf_sh_sample_cost VALUES (1,1,0);
+
+BEGIN       
+  -- Populate settings table
+  INSERT INTO rf_sh_sample_settings VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_random_forest);
+  INSERT INTO rf_sh_sample_settings VALUES
+    (dbms_data_mining.clas_cost_table_name, 'rf_sh_sample_cost');
+  INSERT INTO rf_sh_sample_settings VALUES
+    (dbms_data_mining.rfor_num_trees, 25);
+  -- Examples of other possible settings are:
+  --(dbms_data_mining.rfor_mtry, 5) ;
+  -- rfor_mtry specifies the number of attributes that are to be
+  -- randomly chosen for computing splits at each node of the trees in
+  -- the forest.
+  --(dbms_data_mining.rfor_sampling_ratio, 0.5);
+  --(dbms_data_mining.odms_random_seed, 41);
+  --(dbms_data_mining.tree_impurity_metric, 'TREE_IMPURITY_ENTROPY')
+  --(dbms_data_mining.tree_term_max_depth, 5)
+  --(dbms_data_mining.tree_term_minrec_split, 5)
+  --(dbms_data_mining.tree_term_minpct_split, 2)
+  --(dbms_data_mining.tree_term_minrec_node, 5)
+  --(dbms_data_mining.tree_term_minpct_node, 0.05)
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Build a RF model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RF_SH_Clas_Sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'rf_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'RF_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'RF_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+--------------------------
+-- DISPLAY MODEL VIEWS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='RF_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+------------------------
+-- DISPLAY MODEL metdata
+--
+column string_value format a12
+column setting_name format a12
+column setting_value format a14
+
+select name, numeric_value, string_value from DM$VGRF_SH_CLAS_SAMPLE 
+order by name;
+
+-- Get computed settings --
+select setting_name, setting_value from DM$VSRF_SH_CLAS_SAMPLE;
+
+--Get variable importance for the forest --
+column attribute_name format a30
+select attribute_name, attribute_importance
+from DM$VARF_SH_CLAS_SAMPLE 
+order by  attribute_importance desc, attribute_name asc;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+--------------------
+-- PREPARE TEST DATA
+--
+-- If the data for model creation has been prepared, then the data used
+-- for testing the model must be prepared to the same scale in order to
+-- obtain meaningful results.
+-- In this case, no data preparation is necessary since model creation
+-- was performed on the raw (unprepared) input.
+--
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- Other demo programs demonstrate how to use the PL/SQL API to
+-- compute a number of metrics, including lift and ROC.  This demo
+-- only computes a confusion matrix and accuracy, but it does so 
+-- using the SQL data mining functions.
+--
+-- In this example, we experiment with using the cost matrix
+-- that was provided to the create routine.  In this example, the 
+-- cost matrix reduces the problematic misclassifications, but also 
+-- negatively impacts the overall model accuracy.
+
+-- DISPLAY CONFUSION MATRIX WITHOUT APPLYING COST MATRIX
+--
+SELECT affinity_card AS actual_target_value, 
+       PREDICTION(RF_SH_Clas_Sample USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_parallel_v
+GROUP BY affinity_card, PREDICTION(RF_SH_Clas_Sample USING *)
+ORDER BY 1,2;
+
+-- DISPLAY CONFUSION MATRIX APPLYING THE COST MATRIX
+--
+SELECT affinity_card AS actual_target_value, 
+       PREDICTION(RF_SH_Clas_Sample COST MODEL USING *) 
+         AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_parallel_v
+GROUP BY affinity_card, PREDICTION(RF_SH_Clas_Sample COST MODEL USING *)
+ORDER BY 1,2;
+
+-- DISPLAY ACCURACY WITHOUT APPLYING COST MATRIX
+--
+SELECT ROUND(SUM(correct)/COUNT(*),4) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+               PREDICTION(RF_SH_Clas_Sample USING *), 1, 0) AS correct
+          FROM mining_data_test_parallel_v);
+
+-- DISPLAY ACCURACY APPLYING THE COST MATRIX
+--
+SELECT ROUND(SUM(correct)/COUNT(*),4) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(RF_SH_Clas_Sample COST MODEL USING *),
+                 1, 0) AS correct
+          FROM mining_data_test_parallel_v);
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+------------------
+-- BUSINESS CASE 1
+-- Find the 10 customers who live in Italy that are least expensive
+-- to be convinced to use an affinity card.
+--
+SELECT cust_id FROM
+(SELECT cust_id, 
+        rank() over (order by PREDICTION_COST(RF_SH_Clas_Sample, 
+                     1 COST MODEL USING *) ASC, cust_id) rnk
+   FROM mining_data_apply_parallel_v
+  WHERE country_name = 'Italy')
+where rnk <= 10
+order by rnk;
+
+------------------
+-- BUSINESS CASE 2
+-- Find the average age of customers who are likely to use an
+-- affinity card.
+-- Include the build-time cost matrix in the prediction.
+-- Only take into account CUST_MARITAL_STATUS, EDUCATION, and
+-- HOUSEHOLD_SIZE as predictors.
+-- Break out the results by gender.
+--
+column cust_gender format a12
+SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age
+  FROM mining_data_apply_parallel_v
+ WHERE PREDICTION(rf_sh_clas_sample COST MODEL
+                 USING cust_marital_status, education, household_size) = 1
+GROUP BY cust_gender
+ORDER BY cust_gender;
+
+------------------
+-- BUSINESS CASE 3
+-- List ten customers (ordered by their id) along with likelihood and cost
+-- to use or reject the affinity card (Note: while this example has a
+-- binary target, such a query is useful in multi-class classification -
+-- Low, Med, High for example).
+--
+column prediction format 9;
+column probability format 9.999999999
+column cost format 9.999999999
+SELECT T.cust_id, S.prediction, S.probability, S.cost
+  FROM (SELECT cust_id,
+               PREDICTION_SET(rf_sh_clas_sample COST MODEL USING *) pset
+          FROM mining_data_apply_parallel_v
+         WHERE cust_id < 100011) T,
+       TABLE(T.pset) S
+ORDER BY cust_id, S.prediction;
+
+------------------
+-- BUSINESS CASE 4
+-- Find the segmentation (prediction and rule) for customers who
+-- work in Tech support and are under 25.
+--
+set long 20000
+set line 300
+set pagesize 100
+column education format a30;
+SELECT cust_id, education,
+       PREDICTION_DETAILS(rf_sh_clas_sample USING *) prediction_details 
+  FROM mining_data_apply_parallel_v
+ WHERE occupation = 'TechSup' AND age < 25
+ORDER BY cust_id;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-regression-xgboost.sql b/machine-learning/sql/26ai/oml4sql-classification-regression-xgboost.sql
new file mode 100644
index 00000000..07a4d78f
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-regression-xgboost.sql
@@ -0,0 +1,357 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification and Regression - XGBoost Algorithm
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET LONG 20000
+-----------------------------------------------------------------------
+--                (1) Use XGBoost for classification
+-----------------------------------------------------------------------
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic and purchase data about a set of customers, predict
+-- customer's response to an affinity card program using XGboost
+--
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-------
+-- DATA
+-------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- (See dmsh.sql for view definitions).
+--
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE xgc_sh_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGC_SH_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE xgc_sh_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+BEGIN 
+-- Populate settings table
+  INSERT INTO xgc_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_xgboost);
+  -- for 0/1 target, choose binary:logistic as objective
+  INSERT INTO xgc_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_objective, 'binary:logistic');
+  INSERT INTO xgc_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_max_depth, '3');
+  INSERT INTO xgc_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_eta, '1');
+  -- choose error and auc as eval_metric to evaluate training dataset
+  INSERT INTO xgc_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_eval_metric, 'error,auc');
+  INSERT INTO xgc_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_num_round, '10');
+END;
+/
+
+
+---------------------
+-- CREATE MODEL
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'XGC_SH_MODEL',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'xgc_sh_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'XGC_SH_MODEL'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'XGC_SH_MODEL'
+ORDER BY attribute_name;
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='XGC_SH_MODEL'
+  ORDER BY view_name;
+
+-- Global statistics
+column name format a30
+column numeric_value format 9999990.999
+column string_value format a20
+select name, numeric_value, string_value 
+  from DM$VGXGC_SH_MODEL
+  ORDER BY name;
+
+--attribute importance: top 10 important features
+column attribute_name format a25
+column attribute_value format a15
+column gain format 9.999
+column cover format 9.999
+column frequency format 9.999
+
+select * from(
+select attribute_name, attribute_value, gain, cover, frequency 
+from DM$VIXGC_SH_MODEL
+order by gain desc)
+where rownum <= 10;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The queries shown below demonstrate the use of new SQL data mining functions
+-- along with analytic functions to compute the various test metrics.
+--
+-- Modelname:             xgc_sh_model
+-- Target attribute:      affinity_card
+-- Positive target value: 1
+-- (Change as appropriate for a different example)
+
+-- Compute CONFUSION MATRIX
+--
+-- This query demonstates how to generate a confusion matrix using the new
+-- SQL prediction functions for scoring. The returned columns match the
+-- schema of the table generated by COMPUTE_CONFUSION_MATRIX procedure.
+--
+SELECT affinity_card AS actual_target_value,
+       PREDICTION(XGC_SH_MODEL USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_v
+ GROUP BY affinity_card, PREDICTION(XGC_SH_MODEL USING *)
+ ORDER BY 1, 2;
+
+-- Compute ACCURACY
+--
+column accuracy format 9.99
+
+SELECT SUM(correct)/COUNT(*) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(XGC_SH_MODEL USING *), 1, 0) AS correct
+          FROM mining_data_test_v);
+
+-- Compute AUC (Area Under the roc Curve)
+column auc format 9.99
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(XGC_SH_MODEL, 1 USING *) pos_prob,
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+tpf_fpf AS (
+SELECT  pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(pos_cnt) OVER () tpf,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(1 - pos_cnt) OVER () fpf
+  FROM pos_prob_and_counts
+),
+trapezoid_areas AS (
+SELECT 0.5 * (fpf - LAG(fpf, 1, 0) OVER (ORDER BY fpf, tpf)) *
+        (tpf + LAG(tpf, 1, 0) OVER (ORDER BY fpf, tpf)) area
+  FROM tpf_fpf
+ WHERE pos_cnt = 1
+    OR (tpf = 1 AND fpf = 1)
+)
+SELECT SUM(area) auc
+  FROM trapezoid_areas;
+
+-----------------------------------------------------------------------
+--                (2) Use XGBoost for regression
+-----------------------------------------------------------------------
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic and purchase data about a set of customers, predict
+-- customer's response to an affinity card program using XGboost
+--
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-------
+-- DATA
+-------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- (See dmsh.sql for view definitions).
+--
+-----------------------------------------------------------------------
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE xgr_sh_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGR_SH_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE xgr_sh_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+BEGIN 
+-- Populate settings table
+  INSERT INTO xgr_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_xgboost);
+  -- for 0/1 target, choose binary:logistic as objective
+  INSERT INTO xgr_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_booster, 'gblinear');
+  INSERT INTO xgr_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_alpha, '0.0001');
+  INSERT INTO xgr_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_lambda, '1');
+  INSERT INTO xgr_sh_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.xgboost_num_round, '100');
+END;
+/
+
+---------------------
+-- CREATE MODEL
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'XGR_SH_MODEL',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'age',
+    settings_table_name => 'xgr_sh_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'XGR_SH_MODEL'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'XGR_SH_MODEL'
+ORDER BY attribute_name;
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='XGR_SH_MODEL'
+  ORDER BY view_name;
+
+-- Global statistics
+column name format a30
+column numeric_value format 9999990.999
+column string_value format a20
+select name, numeric_value, string_value 
+  from DM$VGXGR_SH_MODEL
+  ORDER BY name;
+
+-- attribute importance
+-- show top 5
+column ATTRIBUTE_NAME format a25;
+column ATTRIBUTE_VALUE format a15;
+column weight format 9.999
+select * from(
+select attribute_name, attribute_value, weight
+from DM$VIXGR_SH_MODEL
+order by abs(weight) desc) 
+where rownum <= 5;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The queries shown below demonstrate the use of new SQL data mining functions
+-- along with analytic functions to compute the various test metrics.
+--
+-- Modelname:             xgr_sh_model
+-- Target attribute:      age
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+
+-- 1. Root Mean Square Error - Sqrt(Mean((x - x')^2))
+-- 2. Mean Absolute Error - Mean(|(x - x')|)
+--
+column rmse format 9999.9
+column mae format 9999.9  
+SELECT SQRT(AVG((A.pred - B.age) * (A.pred - B.age))) rmse,
+       AVG(ABS(a.pred - B.age)) mae
+  FROM (SELECT cust_id, prediction(XGR_SH_MODEL using *) pred
+          FROM mining_data_test_v) A,
+       mining_data_test_v B
+  WHERE A.cust_id = B.cust_id;
+
+--- prediction
+SELECT CUST_ID, age,
+       PREDICTION(XGR_SH_MODEL USING *) pred,
+       PREDICTION_DETAILS(XGR_SH_MODEL USING *) det
+  FROM mining_data_apply_v
+ WHERE CUST_ID < 100010
+ ORDER BY CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-svm.sql b/machine-learning/sql/26ai/oml4sql-classification-svm.sql
new file mode 100644
index 00000000..d02fd51b
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-svm.sql
@@ -0,0 +1,470 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - SVM Algorithm - dmsvcdem.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic and purchase data about a set of customers, predict
+-- customer's response to an affinity card program using an SVM classifier.
+--
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-------
+-- DATA
+-------
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVMC_SH_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+
+------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svmc_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svmc_sh_sample_class_wt';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+
+-- CREATE AND POPULATE A CLASS WEIGHTS TABLE
+--
+-- A class weights table is used to influence the weighting of target classes
+-- during model creation. For example, weights of (0.9, 0.1) for a binary
+-- problem specify that an error in the first class has significantly
+-- higher penalty that an error in the second class. Weights of (0.5, 0.5)
+-- do not introduce a differential weight and would produce the same
+-- model as when no weights are provided.
+--
+CREATE TABLE svmc_sh_sample_class_wt (
+  target_value NUMBER,
+  class_weight NUMBER);
+INSERT INTO svmc_sh_sample_class_wt VALUES (0,0.35);
+INSERT INTO svmc_sh_sample_class_wt VALUES (1,0.65);
+COMMIT;
+
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE svmc_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+set echo on
+
+-- The default classification algorithm is Naive Bayes. So override
+-- this choice to SVM using a settings table.
+-- SVM chooses a kernel type automatically. This choice can be overriden
+-- by the user. Linear kernel is preferred for high dimensional data, and 
+-- Gaussian kernel for low dimensional data. Here we use linear kernel
+-- to demonstrate the coefficient model view, which applies only for
+-- linear kernel models.
+--    
+BEGIN 
+-- Populate settings table
+  INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines);
+  INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_linear);
+  INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.clas_weights_table_name, 'svmc_sh_sample_class_wt');
+  insert into svmc_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  -- Examples of other possible overrides are:
+  --(dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_gaussian);
+  --(dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_complexity_factor); 
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Build a new SVM Model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVMC_SH_Clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'svmc_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVMC_SH_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'SVMC_SH_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='SVMC_SH_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+-- The coefficient indicates the relative influence of a given
+-- (attribute, value) pair on the target value. A negative
+-- coefficient value indicates a negative influence.
+--
+-- NOTE: The row in the SVM model details output with a NULL attribute_name
+-- shows the value for SVM bias under the COEFFICIENT column.
+--
+SET line 120
+column aname format a30
+column aval  format a30
+column coeff format 9.99
+SELECT target_value, attribute_name aname, attribute_value aval, 
+    coefficient coeff
+  FROM DM$VLSVMC_SH_CLAS_SAMPLE
+WHERE ABS(coefficient) > 0.01  
+ORDER BY target_value, ABS(coefficient) DESC;
+  
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+-- The queries shown below demonstrate the use of SQL data mining functions
+-- along with analytic functions to compute various test metrics. In these
+-- queries:
+--
+-- Modelname:             svmc_sh_clas_sample
+-- # of Lift Quantiles:   10
+-- Target attribute:      affinity_card
+-- Positive target value: 1
+-- (Change these as appropriate for a different example)
+
+-- Compute CONFUSION MATRIX
+--
+-- This query demonstates how to generate a confusion matrix using the
+-- SQL prediction functions for scoring. The returned columns match the
+-- schema of the table generated by COMPUTE_CONFUSION_MATRIX procedure.
+--
+SELECT affinity_card AS actual_target_value,
+       PREDICTION(svmc_sh_clas_sample USING *) AS predicted_target_value,
+       COUNT(*) AS value
+  FROM mining_data_test_v
+ GROUP BY affinity_card, PREDICTION(svmc_sh_clas_sample USING *)
+ ORDER BY 1, 2;
+
+-- Compute ACCURACY
+--
+column accuracy format 9.99
+
+SELECT SUM(correct)/COUNT(*) AS accuracy
+  FROM (SELECT DECODE(affinity_card,
+                 PREDICTION(svmc_sh_clas_sample USING *), 1, 0) AS correct
+          FROM mining_data_test_v);
+
+-- Compute CUMULATIVE LIFT, GAIN Charts.
+--
+-- The cumulative gain chart is a popular version of the lift chart, and
+-- it maps cumulative gain (Y axis) against the cumulative records (X axis).
+--
+-- The cumulative lift chart is another popular representation of lift, and
+-- it maps cumulative lift (Y axis) against the cumulative records (X axis).
+--
+-- The query also returns the probability associated with each quantile, so
+-- that when the cut-off point for Lift is selected, you can correlate it
+-- with a probability value (say P_cutoff). You can then use this value of
+-- P_cutoff in a prediction query as follows:
+--
+-- SELECT *
+--   FROM records_to_be_scored
+--  WHERE PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1 USING *) > P_cutoff;
+--
+-- In the query below
+--
+-- q_num     - Quantile Number
+-- pos_cnt   - # of records that predict the positive target
+-- pos_prob  - the probability associated with predicting a positive target
+--             value for a given new record
+-- cume_recs - % Cumulative Records upto quantile
+-- cume_gain - % Cumulative Gain
+-- cume_lift - Cumulative Lift
+--
+-- Note that the LIFT can also be computed using 
+-- DBMS_DATA_MINING.COMPUTE_LIFT function, see examples in dmnbdemo.sql.
+--
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1 USING *) pos_prob,
+       -- hit count for positive target value
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+qtile_and_smear AS (
+SELECT NTILE(10) OVER (ORDER BY pos_prob DESC) q_num,
+       pos_prob,
+       -- smear the counts across records with the same probability to
+       -- eliminate potential biased distribution across qtl boundaries
+       AVG(pos_cnt) OVER (PARTITION BY pos_prob) pos_cnt
+  FROM pos_prob_and_counts
+),
+cume_and_total_counts AS (
+SELECT q_num,
+       -- inner sum for counts within q_num groups,
+       -- outer sum for cume counts
+       MIN(pos_prob) pos_prob,
+       SUM(COUNT(*)) OVER (ORDER BY q_num) cume_recs,
+       SUM(SUM(pos_cnt)) OVER (ORDER BY q_num) cume_pos_cnt,
+       SUM(COUNT(*)) OVER () total_recs,
+       SUM(SUM(pos_cnt)) OVER () total_pos_cnt
+  FROM qtile_and_smear
+ GROUP BY q_num
+)
+SELECT pos_prob,
+       100*(cume_recs/total_recs) cume_recs,
+       100*(cume_pos_cnt/total_pos_cnt) cume_gain,
+       (cume_pos_cnt/total_pos_cnt)/(cume_recs/total_recs) cume_lift
+  FROM cume_and_total_counts
+ ORDER BY pos_prob DESC;
+
+-- Compute ROC CURVE
+--
+-- This can be used to find the operating point for classification.
+--
+-- The ROC curve plots true positive fraction - TPF (Y axis) against
+-- false positive fraction - FPF (X axis). Note that the query picks
+-- only the corner points (top tpf switch points for a given fpf) and
+-- the last point. It should be noted that the query does not generate
+-- the first point, i.e (tpf, fpf) = (0, 0). All of the remaining points
+-- are computed, but are then filtered based on the criterion above. For
+-- example, the query picks points a,b,c,d and not points o,e,f,g,h,i,j. 
+--
+-- The Area Under the Curve (next query) is computed using the trapezoid
+-- rule applied to all tpf change points (i.e. summing up the areas of
+-- the trapezoids formed by the points for each segment along the X axis;
+-- (recall that trapezoid Area = 0.5h (A+B); h=> hieght, A, B are sides).
+-- In the example, this means the curve covering the area would trace
+-- points o,e,a,g,b,c,d.
+--
+-- |
+-- |        .c .j .d
+-- |  .b .h .i
+-- |  .g
+-- .a .f
+-- .e
+-- .__.__.__.__.__.__
+-- o
+--
+-- Note that the ROC curve can also be computed using 
+-- DBMS_DATA_MINING.COMPUTE_ROC function, see examples in dmnbdemo.sql.
+--
+column prob format 9.9999
+column fpf  format 9.9999
+column tpf  format 9.9999
+
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1 USING *) pos_prob,
+       -- hit count for positive target value
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+cume_and_total_counts AS (
+SELECT pos_prob,
+       pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) cume_pos_cnt, 
+       SUM(pos_cnt) OVER () tot_pos_cnt,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) cume_neg_cnt,
+       SUM(1 - pos_cnt) OVER () tot_neg_cnt
+  FROM pos_prob_and_counts
+),
+roc_corners AS (
+SELECT MIN(pos_prob) pos_prob,
+       MAX(cume_pos_cnt) cume_pos_cnt, cume_neg_cnt,
+       MAX(tot_pos_cnt) tot_pos_cnt, MAX(tot_neg_cnt) tot_neg_cnt
+  FROM cume_and_total_counts
+ WHERE pos_cnt = 1                      -- tpf switch points
+    OR (cume_pos_cnt = tot_pos_cnt AND  -- top-right point 
+        cume_neg_cnt = tot_neg_cnt)
+ GROUP BY cume_neg_cnt
+)
+SELECT pos_prob prob,
+       cume_pos_cnt/tot_pos_cnt tpf,
+       cume_neg_cnt/tot_neg_cnt fpf,
+       cume_pos_cnt tp,
+       tot_pos_cnt - cume_pos_cnt fn,
+       cume_neg_cnt fp,
+       tot_neg_cnt - cume_neg_cnt tn
+  FROM roc_corners
+ ORDER BY fpf;
+
+-- Compute AUC (Area Under the roc Curve)
+--
+-- See notes on ROC Curve and AUC computation above
+--
+column auc format 9.99
+
+WITH
+pos_prob_and_counts AS (
+SELECT PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1 USING *) pos_prob,
+       DECODE(affinity_card, 1, 1, 0) pos_cnt
+  FROM mining_data_test_v
+),
+tpf_fpf AS (
+SELECT  pos_cnt,
+       SUM(pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(pos_cnt) OVER () tpf,
+       SUM(1 - pos_cnt) OVER (ORDER BY pos_prob DESC) /
+         SUM(1 - pos_cnt) OVER () fpf
+  FROM pos_prob_and_counts
+),
+trapezoid_areas AS (
+SELECT 0.5 * (fpf - LAG(fpf, 1, 0) OVER (ORDER BY fpf, tpf)) *
+        (tpf + LAG(tpf, 1, 0) OVER (ORDER BY fpf, tpf)) area
+  FROM tpf_fpf
+ WHERE pos_cnt = 1
+    OR (tpf = 1 AND fpf = 1)
+)
+SELECT SUM(area) auc
+  FROM trapezoid_areas;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- Find the 10 customers who live in Italy that are most likely 
+-- to use an affinity card.
+--
+SELECT cust_id FROM
+(SELECT cust_id, 
+        rank() over (order by PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1
+                     USING *) DESC, cust_id) rnk
+   FROM mining_data_apply_v
+  WHERE country_name = 'Italy')
+where rnk <= 10
+order by rnk;
+
+------------------
+-- BUSINESS CASE 2
+-- Find the average age of customers who are likely to use an
+-- affinity card. Break out the results by gender.
+--
+column cust_gender format a12
+SELECT cust_gender,
+       COUNT(*) AS cnt,
+       ROUND(AVG(age)) AS avg_age
+  FROM mining_data_apply_v
+ WHERE PREDICTION(svmc_sh_clas_sample USING *) = 1
+GROUP BY cust_gender
+ORDER BY cust_gender;
+
+------------------
+-- BUSINESS CASE 3
+-- List ten customers (ordered by their id) along with their likelihood to
+-- use or reject the affinity card (Note: while this example has a
+-- binary target, such a query is useful in multi-class classification -
+-- Low, Med, High for example).
+--
+column prediction format 9;
+column probability format 9.999999999
+column cost format 9.999999999
+SELECT T.cust_id, S.prediction, S.probability
+  FROM (SELECT cust_id,
+               PREDICTION_SET(svmc_sh_clas_sample USING *) pset
+          FROM mining_data_apply_v
+         WHERE cust_id < 100011) T,
+       TABLE(T.pset) S
+ORDER BY cust_id, S.prediction;
+
+
+------------------
+-- BUSINESS CASE 4
+-- Find customers whose profession is Tech Support
+-- with > 75% likelihood of using the affinity card,
+-- and explain the attributes which make them likely
+-- to use an affinity card.
+--
+set long 20000
+SELECT cust_id, PREDICTION_DETAILS(svmc_sh_clas_sample, 1 USING *) PD
+  FROM mining_data_apply_v
+ WHERE PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1 USING *) > 0.75
+       AND occupation = 'TechSup'
+ORDER BY cust_id;
+
+-----------------------------------------------------------------------
+--    BUILD and APPLY a transient model using analytic functions
+-----------------------------------------------------------------------
+-- In addition to creating a persistent model that is stored as a schema
+-- object, models can be built and scored on data on the fly using
+-- Oracle's analytic function syntax.
+
+----------------------
+-- BUSINESS USE CASE 5
+-- 
+-- Identify rows that do not currently have an affinity card, but
+-- the customer pattern is like those that do have such a card.
+-- Perform this analysis by building a classification model on the
+-- fly and scoring it against the same data, and extract those
+-- customers who do not have a card, but are predicted to have a card
+-- based on observed patterns.
+-- All necessary data preparation steps are automatically performed.
+select cust_id, round(pred_prob,2) pred_prob from
+(select cust_id, affinity_card, 
+        prediction(for to_char(affinity_card) using AGE, CUST_GENDER, YRS_RESIDENCE) over () pred_card,
+        prediction_probability(for to_char(affinity_card),1 using AGE, CUST_GENDER, YRS_RESIDENCE) over () pred_prob
+ from mining_data_build_v)
+where affinity_card = 0
+  and pred_card = 1
+order by 2 desc, 1;
diff --git a/machine-learning/sql/26ai/oml4sql-classification-text-mining-svm.sql b/machine-learning/sql/26ai/oml4sql-classification-text-mining-svm.sql
new file mode 100644
index 00000000..12a8f71d
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-classification-text-mining-svm.sql
@@ -0,0 +1,167 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Classification - SVM Algorithm with Text Mining - dmtxtsvm.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-- Create a policy for text feature extraction
+BEGIN
+  ctx_ddl.drop_policy('dmdemo_svm_policy');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+EXECUTE ctx_ddl.create_policy('dmdemo_svm_policy');
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Mine text features using SVM algorithm. 
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model and objects for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('T_SVM_Clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE t_svmc_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create settings table to choose linear kernel
+CREATE TABLE t_svmc_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+ 
+BEGIN
+  -- Populate settings table
+  INSERT INTO t_svmc_sample_settings VALUES
+    (dbms_data_mining.algo_name,
+     dbms_data_mining.algo_support_vector_machines);
+  INSERT INTO t_svmc_sample_settings VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  INSERT INTO t_svmc_sample_settings VALUES
+    (dbms_data_mining.svms_kernel_function,dbms_data_mining.svms_linear);
+  INSERT INTO t_svmc_sample_settings VALUES
+    (dbms_data_mining.svms_complexity_factor, 100);
+  INSERT INTO t_svmc_sample_settings VALUES
+    (dbms_data_mining.odms_text_policy_name, 'DMDEMO_SVM_POLICY');
+  INSERT INTO t_svmc_sample_settings VALUES
+    (dbms_data_mining.svms_solver, dbms_data_mining.svms_solver_sgd);
+  COMMIT;
+END;
+/
+
+---------------
+-- CREATE MODEL
+
+-- Create SVM model
+-- Note the transform makes the 'comments' attribute 
+-- to be treated as unstructured text data
+DECLARE
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'comments', null, 'comments', null, 'TEXT');
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name => 'T_SVM_Clas_sample',
+    mining_function => dbms_data_mining.classification,
+    data_table_name => 'mining_build_text',
+    case_id_column_name => 'cust_id',
+    target_column_name => 'affinity_card',
+    settings_table_name => 't_svmc_sample_settings',
+    xform_list => xformlist);
+END;
+/ 
+ 
+-- Display the model settings
+column setting_name format a30;
+column setting_value format a30;
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'T_SVM_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+-- Display the model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'T_SVM_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+-- Display model details
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='T_SVM_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+-- Note how several text terms extracted from the COMMENTs documents
+-- show up as influential predictors.
+--
+SET line 120
+column attribute_name format a25
+column attribute_subname format a25
+column attribute_value format a25
+column coefficient format 9.99
+SELECT * from 
+(SELECT target_value, attribute_name, attribute_subname, 
+        attribute_value, coefficient,
+        rank() over (order by abs(coefficient) desc) rnk
+   FROM DM$VLT_SVM_CLAS_SAMPLE)
+WHERE rnk <= 10
+ORDER BY rnk, attribute_name, attribute_subname;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+-- See dmsvcdem.sql for examples.
+
+-----------------------------------------------------------------------
+--                SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+-----------------------------------------------------------------------
+
+------------------
+-- BUSINESS CASE 1
+--
+-- Find the 5 customers that are most likely to use an affinity card.
+-- Note that the SQL data mining functions seamless work against
+-- tables that contain textual data (comments).
+-- Also explain why they are likely to use an affinity card.
+--
+set long 20000
+SELECT cust_id, pd FROM
+( SELECT cust_id, 
+    PREDICTION_DETAILS(T_SVM_Clas_sample, 1 USING *) pd,
+    rank() over (order by PREDICTION_PROBABILITY(T_SVM_Clas_sample, 1 USING *) DESC, 
+                          cust_id) rnk
+  FROM mining_apply_text)
+WHERE rnk <= 5
+order by rnk;
+
+------------------
+-- BUSINESS CASE 2
+-- Find the average age of customers who are likely to use an
+-- affinity card. Break out the results by gender.
+--
+column cust_gender format a12
+SELECT cust_gender,
+       COUNT(*) AS cnt,
+       ROUND(AVG(age)) AS avg_age
+  FROM mining_apply_text
+ WHERE PREDICTION(T_SVM_Clas_sample USING *) = 1
+GROUP BY cust_gender
+ORDER BY cust_gender;
diff --git a/machine-learning/sql/26ai/oml4sql-clustering-expectation-maximization.sql b/machine-learning/sql/26ai/oml4sql-clustering-expectation-maximization.sql
new file mode 100644
index 00000000..c011b97e
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-clustering-expectation-maximization.sql
@@ -0,0 +1,335 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Clustering - Expectation-Maximization Algorithm - dmemdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 140
+SET echo ON
+  
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Segment the demographic data into 10 clusters and study the individual
+-- clusters.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_parallel_v (build data)
+-- mining_data_test_parallel_v  (test data)
+-- mining_data_apply_parallel_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+
+-----------
+-- ANALYSIS
+-----------
+-- For clustering using EM, perform the following on mining data.
+--
+-- 1. Use Data Auto Preparation
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('EM_SH_Clus_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE em_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- K-Means is the default Clustering algorithm. For this sample,
+-- we skip specification of any overrides to defaults
+--
+-- Uncomment the appropriate sections of the code below for
+-- changing settings values.
+-- 
+set echo on
+CREATE TABLE em_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+ 
+BEGIN       
+   INSERT INTO em_sh_sample_settings (setting_name, setting_value) VALUES
+   (dbms_data_mining.algo_name, dbms_data_mining.algo_expectation_maximization);
+   INSERT INTO em_sh_sample_settings (setting_name, setting_value) VALUES 
+   (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
+   -- Other examples of overrides are:
+   -- (dbms_data_mining.emcs_num_components,15);
+   -- (dbms_data_mining.clus_num_clusters,5);
+   -- (dbms_data_mining.emcs_cluster_comp_enable,
+   --  dbms_data_mining.emcs_cluster_comp_disable);
+   -- (dbms_data_mining.emcs_attribute_filter,
+   --  dbms_data_mining.emcs_attr_filter_disable);
+   -- (dbms_data_mining.emcs_linkage_function,
+   --  dbms_data_mining.emcs_linkage_average);
+   -- (dbms_data_mining.emcs_num_iterations,200);
+   -- (dbms_data_mining.emcs_loglike_improvement,1e-10);
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'EM_SH_Clus_sample',
+    mining_function     => dbms_data_mining.clustering,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'em_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'EM_SH_CLUS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'EM_SH_CLUS_SAMPLE'
+ORDER BY attribute_name;
+
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column mining_function format a20
+column algorithm format a30
+SELECT mining_function, algorithm
+  FROM user_mining_models
+ WHERE model_name = 'EM_SH_CLUS_SAMPLE';
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='EM_SH_CLUS_SAMPLE'
+  ORDER BY view_name;
+
+-- Attribute importance
+-- 2D attributes are ranked based on their interactions with other attributes.
+-- The ranking is based on the scaled pairwise Kullback-Leibler divergence.
+col attribute_name format a25
+col attribute_name_1 format a25
+col attribute_name_2 format a25
+col attribute_importance_value format 999.9999
+col dependency format 999.9999
+  
+SELECT attribute_name, attribute_importance_value, attribute_rank
+FROM DM$VIEM_SH_CLUS_sample ORDER BY attribute_rank;
+
+SELECT * FROM (
+SELECT attribute_name_1, attribute_name_2, dependency
+FROM DM$VBEM_SH_CLUS_sample 
+WHERE attribute_name_1 < attribute_name_2 ORDER BY dependency desc)
+WHERE ROWNUM < 11;
+
+-- Cluster details are best seen in pieces - based on the kind of
+-- associations and groupings that are needed to be observed.
+--
+-- CLUSTERS
+-- For each cluster_id, provides the number of records in the cluster,
+-- the parent cluster id, the level in the hierarchy, and dispersion -
+-- which is a measure of the quality of the cluster, and computationally,
+-- the sum of square errors.
+--
+SELECT cluster_id clu_id, record_count rec_cnt, parent, tree_level
+  FROM DM$VDEM_SH_CLUS_SAMPLE
+ ORDER BY cluster_id;
+
+-- TAXONOMY
+--
+SELECT cluster_id, left_child_id, right_child_id
+  FROM DM$VDEM_SH_CLUS_SAMPLE
+ORDER BY cluster_id;
+
+-- CENTROIDS FOR LEAF CLUSTERS
+-- For cluster_id 17, this output lists all the attributes that
+-- constitute the centroid, with the mean (for numericals) or
+-- mode (for categoricals), along with the variance from mean
+--
+column attribute_name format a20
+column attribute_subname format a20
+column mean format 9999999.999
+column variance format 9999999.999
+column lower_bin_boundary format 9999999.999
+column upper_bin_boundary format 9999999.999  
+column attribute_value format a20
+column mode_value format a20
+
+SELECT cluster_id, attribute_name, attribute_subname, mean, variance,
+    mode_value
+FROM DM$VAEM_SH_CLUS_SAMPLE
+WHERE cluster_id = 17
+ORDER BY attribute_name, attribute_subname;
+
+-- HISTOGRAM FOR ATTRIBUTE OF A LEAF CLUSTER
+-- For cluster 17, provide the histogram for the AGE attribute.
+--
+SELECT cluster_id, attribute_name, attribute_subname,
+        bin_id, lower_bin_boundary, upper_bin_boundary, attribute_value, count 
+FROM DM$VHEM_SH_CLUS_SAMPLE
+WHERE cluster_id = 17 AND attribute_name = 'AGE'
+ORDER BY bin_id;
+
+-- RULES FOR LEAF CLUSTERS
+-- A rule_id corresponds to the associated cluster_id. The support
+-- indicates the number of records (say M) that satisfies this rule.
+-- This is an upper bound on the  number of records that fall within
+-- the bounding box defined by the rule. Each predicate in the rule
+-- antecedent defines a range for an attribute, and it can be
+-- interpreted as the side of a bounding box which envelops most of
+-- the data in the cluster.
+-- Confidence = M/N, where N is the number of records in the cluster
+-- and M is the rule support defined as above.
+--
+column numeric_value format 999999.999
+column confidence format 999999.999
+column rule_confidence format 999999.999
+column support format 9999
+column rule_support format 9999
+column operator format a2
+
+SELECT distinct cluster_id, rule_support, rule_confidence
+FROM DM$VREM_SH_CLUS_SAMPLE ORDER BY cluster_id;
+
+-- RULE DETAILS FOR LEAF CLUSTERS
+-- Attribute level details of each rule/cluster id.
+-- For an attribute, support (say M) indicates the number of records that 
+-- fall in the attribute range specified in the rule antecedent where the
+-- given attribute is not null. Confidence is a number between 0 and 1
+-- that indicates how relevant this attribute is in distinguishing the 
+-- the records in the cluster from all the records in the whole data. The
+-- larger the number, more relevant the attribute.
+-- 
+-- The query shown below reverse-transforms the data to its original
+-- values, since build data was normalized.
+--
+SELECT cluster_id, attribute_name, attribute_subname, operator,
+        numeric_value, attribute_value, support, confidence 
+FROM DM$VREM_SH_CLUS_SAMPLE 
+WHERE cluster_id < 3
+ORDER BY cluster_id, attribute_name, attribute_subname, operator,
+  numeric_value, attribute_value;
+
+-- Global EM model statistics
+-- This view shows high-level model statistics, including
+-- number of components, number of clusters, and log likelihood value
+column numeric_value format 9999999.999
+column string_value format a20
+select name, numeric_value, string_value from DM$VGEM_SH_CLUS_SAMPLE
+ORDER BY name;
+
+-- EM component details
+-- Component priors and mapping to leaf clusters view
+col prior_probability format 9.999
+select component_id, cluster_id, prior_probability
+from DM$VOEM_SH_CLUS_SAMPLE
+ORDER BY component_id;
+
+-- Means and variances for attributes modeled with Gaussian distributions
+column attribute_name format a25
+select component_id, attribute_name, mean, variance
+from DM$VMEM_SH_CLUS_sample
+WHERE component_id = 11  
+ORDER BY attribute_name;  
+
+-- Frequences for attributes modeled with multivalued Bernoulli 
+-- distributions
+column attribute_value format a15 
+column frequency format 9999999.999
+select component_id, attribute_name, attribute_value, frequency
+from DM$VFEM_SH_CLUS_SAMPLE
+WHERE component_id=11    
+ORDER BY attribute_name, attribute_value;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+-- There is no specific set of testing parameters for Clustering.
+-- Examination and analysis of clusters is the main method to prove
+-- the efficacy of a clustering model.
+--
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-- For a descriptive mining function like Clustering, "Scoring" involves
+-- providing the probability values for each cluster.
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- List the clusters into which the customers in this
+-- given dataset have been grouped.
+--
+SELECT CLUSTER_ID(em_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt 
+  FROM mining_data_apply_parallel_v
+GROUP BY CLUSTER_ID(em_sh_clus_sample USING *)
+ORDER BY cnt DESC;
+
+------------------
+-- BUSINESS CASE 2
+-- List ten most representative (based on likelihood) customers of cluster 7
+--
+SELECT cust_id
+FROM (SELECT cust_id, rank() over (order by prob desc, cust_id) rnk_clus2
+  FROM (SELECT cust_id, 
+          round(CLUSTER_PROBABILITY(em_sh_clus_sample, 7 USING *),3) prob
+          FROM mining_data_apply_parallel_v))
+WHERE rnk_clus2 <= 10
+order by rnk_clus2;
+
+------------------
+-- BUSINESS CASE 3
+-- List the five most relevant attributes for likely cluster assignments
+-- for customer id 100955 (> 20% likelihood of assignment).
+--
+column prob format 9.9999
+set long 10000
+SELECT S.cluster_id, probability prob, 
+       CLUSTER_DETAILS(em_sh_clus_sample, S.cluster_id, 5 using T.*) det
+FROM 
+  (SELECT v.*, CLUSTER_SET(em_sh_clus_sample, NULL, 0.2 USING *) pset
+    FROM mining_data_apply_parallel_v v
+   WHERE cust_id = 100955) T, 
+  TABLE(T.pset) S
+order by 2 desc;
diff --git a/machine-learning/sql/26ai/oml4sql-clustering-kmeans-star-schema.sql b/machine-learning/sql/26ai/oml4sql-clustering-kmeans-star-schema.sql
new file mode 100644
index 00000000..ff5a603f
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-clustering-kmeans-star-schema.sql
@@ -0,0 +1,223 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Clustering - K-Means Algorithm - dmstardemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+
+-------------------
+-- STAR SCHEMA
+-- Bring together the data in the sh star schema.
+-- For mining, include the customer demographics, but also include the
+-- per-subcategory purchase amounts that were made for each customer.
+-- Include also the dates between which the purchases were made.  
+-- This will enhance the clustering model to account for customer
+-- behavior as well as demographics.
+create or replace view cust_with_sales as
+select c.*, v2.sales_from, v2.sales_to, v2.per_subcat_sales from
+sh.customers c,
+(select v.cust_id, min(v.sales_from) sales_from, max(v.sales_to) sales_to,
+        cast(collect(dm_nested_numerical(v.prod_subcategory, v.sum_amount_sold)) 
+             as dm_nested_numericals) per_subcat_sales
+ from 
+ (select s.cust_id, p.prod_subcategory, sum(s.amount_sold) sum_amount_sold,
+  min(s.time_id) sales_from, max(s.time_id) sales_to
+  from sh.sales s, sh.products p
+  where s.prod_id = p.prod_id
+  group by s.cust_id, p.prod_subcategory) v
+ group by v.cust_id) v2
+where c.cust_id = v2.cust_id;
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE dm_star_set';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+set echo off
+create table dm_star_set (setting_name varchar2(30), setting_value varchar2(4000));
+set echo on
+BEGIN       
+   INSERT INTO dm_star_set (setting_name, setting_value) VALUES 
+   (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
+   INSERT INTO dm_star_set (setting_name, setting_value) VALUES 
+   (dbms_data_mining.kmns_details, dbms_data_mining.kmns_details_all);
+END;
+/
+commit;
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('DM_STAR_CLUSTER');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+declare
+  xform_list dbms_data_mining_transform.TRANSFORM_LIST;
+begin
+  -- Transform the purchase dates to a numeric period
+  dbms_data_mining_transform.set_transform(xform_list,
+    'SALES_PERIOD', NULL, 'SALES_TO - SALES_FROM', NULL);
+
+  -- Transform the country to a categorical attribute since
+  -- numeric datatypes are treated as numeric attributes.
+  dbms_data_mining_transform.set_transform(xform_list,
+    'COUNTRY_ID', NULL, 'TO_CHAR(COUNTRY_ID)', NULL);
+
+  -- Eliminate columns known to be uninteresting,
+  -- which will speed up the process.
+  -- Alternatively, you can do this when creating the view.
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_EFF_TO', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_EFF_FROM', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_CITY_ID', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_STATE_PROVINCE_ID', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_STREET_ADDRESS', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_FIRST_NAME', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_LAST_NAME', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_MAIN_PHONE_NUMBER', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_EMAIL', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_TOTAL_ID', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_SRC_ID', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'SALES_TO', NULL, NULL, NULL);
+  dbms_data_mining_transform.set_transform(xform_list,
+    'SALES_FROM', NULL, NULL, NULL);
+
+  -- perform the build
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'DM_STAR_CLUSTER',
+    mining_function     => dbms_data_mining.clustering,
+    data_table_name     => 'cust_with_sales',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'dm_star_set',
+    xform_list          => xform_list);
+end;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'DM_STAR_CLUSTER'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a30
+column attribute_type format a20
+column data_type format a20
+SELECT attribute_name, attribute_type, data_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'DM_STAR_CLUSTER'
+ORDER BY attribute_name;
+
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='DM_STAR_CLUSTER'
+  ORDER BY view_name;
+
+-- Cluster details are best seen in pieces - based on the kind of
+-- associations and groupings that are needed to be observed.
+--
+-- CLUSTERS
+-- For each cluster_id, provides the number of records in the cluster,
+-- the parent cluster id, the level in the hierarchy, and dispersion -
+-- which is a measure of the quality of the cluster, and computationally,
+-- the sum of square errors.
+--
+SELECT cluster_id clu_id, record_count rec_cnt, parent, tree_level, 
+       TO_NUMBER(dispersion) dispersion
+  FROM DM$VDDM_STAR_CLUSTER
+ ORDER BY cluster_id;
+
+-- TAXONOMY
+--
+SELECT cluster_id, left_child_id, right_child_id
+  FROM DM$VDDM_STAR_CLUSTER
+ORDER BY cluster_id;
+
+-- CENTROIDS FOR LEAF CLUSTERS
+-- For cluster_id 16, this output lists all the attributes that
+-- constitute the centroid, with the mean (for numericals) or
+-- mode (for categoricals)
+-- Note that per-subcategory sales for each customer are being
+-- considered when creating clusters.
+--
+column aname format a60
+column mode_val format a40
+column mean_val format 9999999
+SELECT NVL2(C.attribute_subname, 
+            C.attribute_name || '.' || C.attribute_subname, 
+            C.attribute_name) aname,
+       C.mean mean_val,
+       C.mode_value mode_val
+  FROM DM$VADM_STAR_CLUSTER c
+WHERE cluster_id = 16
+ORDER BY aname;
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- List the clusters into which the customers in this
+-- given dataset have been grouped.
+--
+SELECT CLUSTER_ID(DM_STAR_CLUSTER USING *) AS clus, COUNT(*) AS cnt 
+  FROM cust_with_sales
+GROUP BY CLUSTER_ID(DM_STAR_CLUSTER USING *)
+ORDER BY cnt DESC;
+--
+------------------
+-- BUSINESS CASE 2
+-- List the five most relevant attributes for likely cluster assignments
+-- for customer id 100955 (> 20% likelihood of assignment).
+--
+column prob format 9.9999
+set line 150
+set long 10000
+SELECT S.cluster_id, probability prob, 
+       CLUSTER_DETAILS(DM_STAR_CLUSTER, S.cluster_id, 5 using T.*) det
+FROM 
+  (SELECT v.*, CLUSTER_SET(DM_STAR_CLUSTER, NULL, 0.2 USING *) pset
+    FROM cust_with_sales v
+   WHERE cust_id = 100949) T, 
+  TABLE(T.pset) S
+order by 2 desc;
diff --git a/machine-learning/sql/26ai/oml4sql-clustering-kmeans.sql b/machine-learning/sql/26ai/oml4sql-clustering-kmeans.sql
new file mode 100644
index 00000000..be085e70
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-clustering-kmeans.sql
@@ -0,0 +1,336 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Clustering - K-Means Algorithm - dmkmdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 140
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Segment the demographic data into 10 clusters and study the individual
+-- clusters.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+
+-----------
+-- ANALYSIS
+-----------
+-- For clustering using KM, perform the following on mining data.
+--
+-- 1. Use Data Auto Preparation
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('KM_SH_Clus_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE km_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- K-Means is the default Clustering algorithm. For this sample,
+-- we skip specification of any overrides to defaults
+--
+-- Uncomment the appropriate sections of the code below for
+-- changing settings values.
+-- 
+set echo on
+CREATE TABLE km_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+ 
+BEGIN       
+   INSERT INTO km_sh_sample_settings (setting_name, setting_value) VALUES
+   (dbms_data_mining.kmns_distance,dbms_data_mining.kmns_euclidean);
+
+   INSERT INTO km_sh_sample_settings (setting_name, setting_value) VALUES 
+   (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
+
+   INSERT INTO km_sh_sample_settings (setting_name, setting_value) VALUES
+   (dbms_data_mining.kmns_details, dbms_data_mining.kmns_details_all);
+   -- Other examples of overrides are:
+   -- (dbms_data_mining.kmns_iterations,3);
+   -- (dbms_data_mining.kmns_random_seed,2);
+   -- (dbms_data_mining.kmns_conv_tolerance,0.01);
+   -- (dbms_data_mining.kmns_split_criterion,dbms_data_mining.kmns_variance);
+   -- (dbms_data_mining.kmns_min_pct_attr_support,0.1);
+   -- (dbms_data_mining.kmns_num_bins,10);
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'KM_SH_Clus_sample',
+    mining_function     => dbms_data_mining.clustering,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'km_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'KM_SH_CLUS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'KM_SH_CLUS_SAMPLE'
+ORDER BY attribute_name;
+
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column mining_function format a20
+column algorithm format a20
+SELECT mining_function, algorithm
+  FROM user_mining_models
+ WHERE model_name = 'KM_SH_CLUS_SAMPLE';
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='KM_SH_CLUS_SAMPLE'
+  ORDER BY view_name;
+
+-- Cluster details are best seen in pieces - based on the kind of
+-- associations and groupings that are needed to be observed.
+--
+-- CLUSTERS
+-- For each cluster_id, provides the number of records in the cluster,
+-- the parent cluster id, the level in the hierarchy, and dispersion -
+-- which is a measure of the quality of the cluster, and computationally,
+-- the sum of square errors.
+-- Since centroid, histogram, and rule details are not being requested
+-- here, specify 0,0,0 as arguments to the table function to reduce
+-- the amount of work it needs to perform when fetching details.
+--
+SELECT cluster_id clu_id, record_count rec_cnt, parent, tree_level, 
+       TO_NUMBER(dispersion) dispersion
+  FROM DM$VDKM_SH_CLUS_SAMPLE
+ ORDER BY cluster_id;
+
+-- TAXONOMY
+--
+SELECT cluster_id, left_child_id, right_child_id
+  FROM DM$VDKM_SH_CLUS_SAMPLE
+ORDER BY cluster_id;
+
+-- CENTROIDS FOR LEAF CLUSTERS
+-- For cluster_id 18, this output lists all the attributes that
+-- constitute the centroid, with the mean (for numericals) or
+-- mode (for categoricals), along with the variance from mean
+--
+column attribute_name format a20
+column attribute_subname format a20
+column mean format 9999999.999
+column variance format 9999999.999
+column lower_bin_boundary format 9999999.999
+column upper_bin_boundary format 9999999.999
+column attribute_value format a20
+column mode_value format a20
+
+SELECT cluster_id, attribute_name, attribute_subname, mean, variance,
+    mode_value
+FROM DM$VAKM_SH_CLUS_SAMPLE
+WHERE cluster_id = 18
+ORDER BY attribute_name, attribute_subname;
+
+-- HISTOGRAM FOR ATTRIBUTE OF A LEAF CLUSTER
+-- For cluster 18, provide the histogram for the AGE attribute.
+--
+SELECT cluster_id, attribute_name, attribute_subname,
+        bin_id, lower_bin_boundary, upper_bin_boundary, attribute_value, count 
+FROM DM$VHKM_SH_CLUS_SAMPLE
+WHERE cluster_id = 18 AND attribute_name = 'AGE'
+ORDER BY bin_id;
+
+-- RULES FOR LEAF CLUSTERS
+-- A rule_id corresponds to the associated cluster_id. The support
+-- indicates the number of records (say M) that satisfies this rule.
+-- This is an upper bound on the number of records that fall within
+-- the bounding box defined by the rule. Each predicate in the rule
+-- antecedent defines a range for an attribute, and it can be
+-- interpreted as the side of a bounding box which envelops most of
+-- the data in the cluster.
+-- Confidence = M/N, where N is the number of records in the cluster
+-- and M is the rule support defined as above.
+--
+
+column numeric_value format 999999.999
+column confidence format 999999.999
+column rule_confidence format 999999.999
+column support format 9999
+column rule_support format 9999
+column operator format a2
+
+SELECT distinct cluster_id, rule_support, rule_confidence
+FROM DM$VRKM_SH_CLUS_SAMPLE ORDER BY cluster_id;
+
+-- RULE DETAILS FOR LEAF CLUSTERS
+-- Attribute level details of each rule/cluster id.
+-- For an attribute, support (say M) indicates the number of records that 
+-- fall in the attribute range specified in the rule antecedent where the
+-- given attribute is not null. Confidence is a number between 0 and 1
+-- that indicates how relevant this attribute is in distinguishing the 
+-- the records in the cluster from all the records in the whole data. The
+-- larger the number, more relevant the attribute.
+-- 
+-- The query shown below reverse-transforms the data to its original
+-- values, since build data was normalized.
+--
+SELECT cluster_id, attribute_name, attribute_subname, operator,
+        numeric_value, attribute_value, support, confidence 
+FROM DM$VRKM_SH_CLUS_SAMPLE 
+WHERE cluster_id < 3
+ORDER BY cluster_id, attribute_name, attribute_subname, operator,
+  numeric_value, attribute_value;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+-- There is no specific set of testing parameters for Clustering.
+-- Examination and analysis of clusters is the main method to prove
+-- the efficacy of a clustering model.
+--
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-- For a descriptive mining function like Clustering, "Scoring" involves
+-- providing the probability values for each cluster.
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- List the count per cluster into which the customers in this
+-- given dataset have been grouped.
+--
+SELECT CLUSTER_ID(km_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt 
+  FROM mining_data_apply_v
+GROUP BY CLUSTER_ID(km_sh_clus_sample USING *)
+ORDER BY cnt DESC;
+
+------------------
+-- BUSINESS CASE 2
+-- List ten most representative (based on likelihood) customers of cluster 2
+--
+SELECT cust_id
+FROM (SELECT cust_id, rank() over (order by prob desc, cust_id) rnk_clus2
+  FROM (SELECT cust_id, CLUSTER_PROBABILITY(km_sh_clus_sample, 2 USING *) prob
+          FROM mining_data_apply_v))
+WHERE rnk_clus2 <= 10
+order by rnk_clus2;
+
+------------------
+-- BUSINESS CASE 3
+-- List the five most relevant attributes for likely cluster assignments
+-- for customer id 101362 (> 20% likelihood of assignment).
+--
+column prob format 9.9999
+set long 10000
+SELECT S.cluster_id, probability prob, 
+       CLUSTER_DETAILS(km_sh_clus_sample, S.cluster_id, 5 using T.*) det
+FROM 
+  (SELECT v.*, CLUSTER_SET(km_sh_clus_sample, NULL, 0.2 USING *) pset
+    FROM mining_data_apply_v v
+   WHERE cust_id = 101362) T, 
+  TABLE(T.pset) S
+order by 2 desc;
+
+------------------
+-- BUSINESS CASE 4
+-- 
+-- List the 10 rows which are most anomalous as measured by their
+-- distance from the cluster centroids.  A row which is far from
+-- all cluster centroids may be anomalous.
+--
+SELECT cust_id
+FROM (
+  SELECT cust_id,
+         rank() over 
+           (order by CLUSTER_DISTANCE(km_sh_clus_sample USING *) desc) rnk
+    FROM mining_data_apply_v)
+WHERE rnk <= 11
+ORDER BY rnk;
+
+
+-----------------------------------------------------------------------
+--    BUILD and APPLY a transient model using analytic functions
+-----------------------------------------------------------------------
+-- In addition to creating a persistent model that is stored as a schema
+-- object, models can be built and scored on data on the fly using
+-- Oracle's analytic function syntax.
+
+------------------
+-- BUSINESS CASE 5
+-- 
+-- Segment customers into 4 groups based on common characteristics
+-- and provide the segment assignments.  Note that this query does
+-- not reference a pre-build clustering model, but rather it segment
+-- the input data on the fly.  Rerunning the same query with different
+-- input will result in a different segmentation.
+-- Also provide the main reasons (attributes) why a given customer
+-- is placed into a specific cluster.
+-- Note that the where clause has to be placed outside of the inline
+-- view so that the analytic function will build the clustering model
+-- on all the data, and not just the selected customers.
+--
+select * from (
+SELECT cust_id,
+       CLUSTER_ID(INTO 4 USING *) OVER () cluster_id,
+       CLUSTER_DETAILS(INTO 4 USING *) OVER () cluster_det
+  FROM mining_data_apply_v)
+WHERE cust_id <= 100010
+order by 1;
diff --git a/machine-learning/sql/26ai/oml4sql-clustering-ocluster.sql b/machine-learning/sql/26ai/oml4sql-clustering-ocluster.sql
new file mode 100644
index 00000000..1582f906
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-clustering-ocluster.sql
@@ -0,0 +1,307 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Clustering - O-Cluster Algorithm - dmocdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 120
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Segment the demographic data into 10 clusters and study the individual
+-- clusters. Rank the clusters on probability.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_parallel_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+
+-----------
+-- ANALYSIS
+-----------
+-- For clustering using OC, perform the following on mining data.
+--
+-- 1. Use Data Auto Preparation
+--    O-Cluster uses a special binning procedure that automatically 
+--    determines the number of bins based on data statistics.
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('OC_SH_Clus_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+--
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE oc_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- K-Means is the default clustering algorithm. Override the
+-- default to set the algorithm to O-Cluster using a settings table.
+-- 
+-- CREATE AND POPULATE A SETTINGS TABLE
+--
+set echo off
+CREATE TABLE oc_sh_sample_settings (
+  setting_name  VARCHAR2(30), 
+  setting_value VARCHAR2(4000));
+set echo on
+BEGIN
+  INSERT INTO oc_sh_sample_settings VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_ocluster);
+  INSERT INTO oc_sh_sample_settings VALUES
+    (dbms_data_mining.clus_num_clusters, 10);
+  INSERT INTO oc_sh_sample_settings VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+
+  -- Other possible settings are:
+  -- (dbms_data_mining.oclt_sensitivity, 0.5);
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Build a new OC model
+-- TO_CHAR function is used to transform columns to 
+-- categorical attributes since numeric datatypes 
+-- are treated as numeric attributes.
+DECLARE
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'AFFINITY_CARD', null, 'TO_CHAR(AFFINITY_CARD)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'BOOKKEEPING_APPLICATION', null, 'TO_CHAR(BOOKKEEPING_APPLICATION)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'BULK_PACK_DISKETTES', null, 'TO_CHAR(BULK_PACK_DISKETTES)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'FLAT_PANEL_MONITOR', null, 'TO_CHAR(FLAT_PANEL_MONITOR)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'HOME_THEATER_PACKAGE', null, 'TO_CHAR(HOME_THEATER_PACKAGE)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'OS_DOC_SET_KANJI', null, 'TO_CHAR(OS_DOC_SET_KANJI)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'PRINTER_SUPPLIES', null, 'TO_CHAR(PRINTER_SUPPLIES)', null);
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'Y_BOX_GAMES', null, 'TO_CHAR(Y_BOX_GAMES)', null);
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'OC_SH_Clus_sample',
+    mining_function     => dbms_data_mining.clustering,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'oc_sh_sample_settings',
+    xform_list => xformlist);
+END;
+/ 
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'OC_SH_CLUS_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'OC_SH_CLUS_SAMPLE'
+ORDER BY attribute_name;
+
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column mining_function format a20
+column algorithm format a20
+SELECT mining_function, algorithm
+  FROM user_mining_models
+ WHERE model_name = 'OC_SH_CLUS_SAMPLE';
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='OC_SH_CLUS_SAMPLE'
+  ORDER BY view_name;
+
+-- Binning information
+column attribute_name format a20
+column attribute_value format a20
+column lower format 99999.999
+column upper format 99999.999
+  
+select attribute_name, bin_id, lower_bin_boundary lower,
+  upper_bin_boundary upper, attribute_value
+from DM$VBOC_SH_CLUS_sample WHERE attribute_name IN ('AGE', 'CUST_GENDER');
+
+-- Cluster details are best seen in pieces - based on the kind of
+-- associations and groupings that are needed to be observed.
+--
+
+-- CLUSTERS
+-- For each cluster_id, provides the number of records in the cluster,
+-- the parent cluster id, and the level in the hierarchy.
+-- NOTE: Unlike K-means, O-Cluster does not return a value for the
+--       dispersion associated with a cluster.
+--
+column pname format a20
+SELECT cluster_id clu_id, record_count rec_cnt, parent, tree_level
+  FROM DM$VDOC_SH_CLUS_SAMPLE
+ ORDER BY cluster_id;
+
+-- TAXONOMY
+-- 
+SELECT cluster_id, left_child_id, right_child_id
+  FROM DM$VDOC_SH_CLUS_SAMPLE
+ORDER BY cluster_id;
+
+-- SPLIT PREDICATES
+-- For each cluster, the split predicate indicates the attribute
+-- and the condition used to assign records to the cluster's children
+-- during model build. It provides an important piece of information
+-- on how the population within a cluster can be divided up into
+-- two smaller clusters.
+--
+column attribute_name format a20
+column attribute_subname format a20
+column operator format a2
+column val format a20
+SELECT cluster_id, attribute_name, attribute_subname,
+       operator, splits.val
+FROM DM$VDOC_SH_CLUS_SAMPLE a,
+  XMLTABLE( '/Element' passing a.value 
+    columns 
+    val varchar2(20) path '.') splits
+where left_child_id is not NULL AND cluster_id < 5 
+ORDER BY cluster_id, val;  
+
+-- CENTROIDS FOR LEAF CLUSTERS
+-- For cluster_id 1, this output lists all the attributes that
+-- constitute the centroid, with the mean (for numericals) or
+-- mode (for categoricals). Unlike K-Means, O-Cluster does not return 
+-- the variance for numeric attributes.
+--
+column mean format 9999999.999
+column variance format 9999999.999
+column attribute_value format a20
+column mode_value format a20
+
+SELECT cluster_id, attribute_name, attribute_subname, mean, variance,
+    mode_value
+FROM DM$VAOC_SH_CLUS_SAMPLE
+WHERE cluster_id = 1
+ORDER BY attribute_name, attribute_subname;
+
+-- HISTOGRAM FOR ATTRIBUTE OF A LEAF CLUSTER
+-- For cluster 1, provide the histogram for the AGE attribute.
+-- Histogram count is represented in frequency, rather than actual count.
+column count format 9999.99
+column bin_id format 9999999
+column label format a20;
+
+SELECT cluster_id, attribute_name, attribute_subname,
+        bin_id, label, count
+FROM DM$VHOC_SH_CLUS_SAMPLE
+WHERE cluster_id = 1 AND attribute_name = 'AGE'
+ORDER BY bin_id;
+
+-- RULES FOR LEAF CLUSTERS
+-- See dmkmdemo.sql for explanation on output columns.
+column numeric_value format 999999.999
+column confidence format 999999.999
+column rule_confidence format 999999.999
+column support format 9999
+column rule_support format 9999
+
+SELECT distinct cluster_id, rule_support, rule_confidence
+FROM DM$VROC_SH_CLUS_SAMPLE ORDER BY cluster_id;
+
+-- RULE DETAILS FOR LEAF CLUSTERS
+-- See dmkmdemo.sql for explanation on output columns.
+SELECT cluster_id, attribute_name, attribute_subname, operator,
+        numeric_value, attribute_value, support, confidence 
+FROM DM$VROC_SH_CLUS_SAMPLE 
+WHERE cluster_id < 3
+ORDER BY cluster_id, attribute_name, attribute_subname, operator,
+  numeric_value, attribute_value;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+-- There is no specific set of testing parameters for Clustering.
+-- Examination and analysis of clusters is the main method to prove
+-- the efficacy of a clustering model.
+--
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-- For a descriptive mining function like Clustering, "Scoring" involves
+-- assigning the probability with which a given case belongs to a given
+-- cluster.
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- List the clusters into which the customers in this
+-- given dataset have been grouped.
+--
+SELECT CLUSTER_ID(oc_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt 
+  FROM mining_data_apply_v
+GROUP BY CLUSTER_ID(oc_sh_clus_sample USING *)
+ORDER BY cnt DESC;
+
+-- See dmkmdemo.sql for more examples
+
+------------------
+-- BUSINESS CASE 2
+-- Assign 5 customers to clusters, and provide explanations for the assingments.
+--
+set long 20000
+set line 200
+set pagesize 100
+column cust_id format 999999999
+SELECT cust_id,
+       cluster_details(oc_sh_clus_sample USING *) cluster_details
+  FROM mining_data_apply_v
+ WHERE cust_id <= 100005
+ ORDER BY cust_id;
diff --git a/machine-learning/sql/26ai/oml4sql-cross-validation-decision-tree.sql b/machine-learning/sql/26ai/oml4sql-cross-validation-decision-tree.sql
new file mode 100644
index 00000000..e9bf2b07
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-cross-validation-decision-tree.sql
@@ -0,0 +1,223 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Cross-Validation - Decision Tree Algorithm - dmdtxvlddemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET SERVEROUTPUT ON
+SET ECHO OFF
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET long 2000000000
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic data about a set of customers, compute the accuracy
+-- of predictions for customer response to an affinity card program
+-- using a classifier based on Decision Trees algorithm.
+--
+-- This demo should be viewed as an extension of the scenario 
+-- described in dmdtdemo.sql. We perform 10 fold cross-validation
+-- using the training data set alone.
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE dt_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+ 
+--------------------------
+-- CREATE A SETTINGS TABLE
+--
+-- The default classification algorithm is Naive Bayes. In order to override
+-- this, create and populate a settings table to be used as input for
+-- CREATE_MODEL.
+-- 
+CREATE TABLE dt_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+DECLARE
+ nfolds         NUMBER;
+ maxbucket      NUMBER;       -- max buckets for ORA_HASH()
+ iter           NUMBER;       -- loop itarator
+ tgtname        VARCHAR2(30); -- target column name
+ tgtval         NUMBER;       -- class value (same type as target column) 
+ case_id        VARCHAR2(30); -- case_id column name
+ tabname        VARCHAR2(30); -- training data
+ tabname_p      VARCHAR2(30); -- "partitioned" training data
+ model_name     VARCHAR2(30); -- model_name
+ sqltxt         VARCHAR2(32767);
+ type dmdtcurtyp IS REF CURSOR;
+ tgtcur         dmdtcurtyp;
+BEGIN       
+ nfolds := 10;               -- number of folds (typically 10 fold)
+ maxbucket := nfolds-1;      -- max buckets for ORA_HASH(); max of 9 means
+                             -- 10 buckets in all 0..9
+
+ model_name := 'DT_SH_Clas_Xvld_tmp';
+ tgtname := 'affinity_card';
+
+ --Training data, can be parametrized for general usage
+ tabname := 'mining_data_build_v';
+ case_id := 'cust_id';
+
+ -- "partitioned" table
+ tabname_p := tabname || '_P';
+
+ -- Populate settings table
+ INSERT INTO dt_sh_sample_settings VALUES
+   (dbms_data_mining.algo_name, dbms_data_mining.algo_decision_tree);
+
+ -- Examples of other possible settings are:
+ --(dbms_data_mining.tree_impurity_metric, 'TREE_IMPURITY_ENTROPY')
+ --(dbms_data_mining.tree_term_max_depth, 5)
+ --(dbms_data_mining.tree_term_minrec_split, 5)
+ --(dbms_data_mining.tree_term_minpct_split, 2)
+ --(dbms_data_mining.tree_term_minrec_node, 5)
+ --(dbms_data_mining.tree_term_minpct_node, 0.05)
+ BEGIN EXECUTE IMMEDIATE 'DROP TABLE dmdtxvld_c';
+ EXCEPTION WHEN OTHERS THEN NULL; END;
+
+ -- The column type of the actual and predicted should be the same as the 
+ -- target column, in this case it happens to be NUMBER
+ EXECUTE IMMEDIATE
+ 'CREATE TABLE dmdtxvld_c (' ||
+   'iter NUMBER, actual NUMBER(10), predicted NUMBER(10), count NUMBER)';
+
+ -- Create "partitioned" table. A new table is created which tags the rows
+ -- with a partition number using column 'pn'
+ --
+ -- Drop if already exists
+ BEGIN EXECUTE IMMEDIATE 'DROP TABLE '|| tabname_p;
+ EXCEPTION WHEN OTHERS THEN NULL; END;
+
+ -- Instances of each class are partitioned nfolds ways
+ sqltxt := 'CREATE TABLE ' || tabname_p || ' AS ';
+
+ -- Open tgtcur using tgtname,tabname;
+ OPEN tgtcur FOR 'SELECT DISTINCT(' || tgtname || ') FROM '|| tabname;
+
+ -- Form SQL statement with as many UNION ALL's as distinct target values
+ FETCH tgtcur INTO tgtval;
+ sqltxt := sqltxt ||
+ 'SELECT t1.*, MOD(ROWNUM,' || nfolds ||') pn ' ||
+   'FROM (SELECT t.*, ORA_HASH(rownum) randrow ' ||
+           'FROM ' || tabname || ' t ' ||
+          'WHERE ' || tgtname || ' = '''|| tgtval ||''' ORDER BY randrow) t1 ';
+ LOOP 
+   FETCH tgtcur INTO tgtval;
+   EXIT WHEN tgtcur%NOTFOUND;
+
+   sqltxt := sqltxt || 
+   'UNION ALL ' ||
+   'SELECT t1.*, MOD(ROWNUM,' || nfolds || ') pn ' ||
+     'FROM (SELECT t.*, ORA_HASH(rownum) randrow ' ||
+             'FROM ' || tabname || ' t ' ||
+            'WHERE ' || tgtname || ' = '''|| tgtval||''' ORDER BY randrow) t1 ';
+ END LOOP;
+ CLOSE tgtcur;
+
+ -- execute the statement
+ EXECUTE IMMEDIATE sqltxt;
+
+ -- Iterate nfolds times. Each time we use (nfolds-1)*R/nfolds rows for build
+ -- and the remaining R/nfolds rows for testing where R is the number of
+ -- training rows (instances)
+ --
+ FOR iter IN 0..(nfolds-1)
+ LOOP
+
+  -- Create the build view
+  -- All rows except those in current partition
+  EXECUTE IMMEDIATE
+  'CREATE OR REPLACE VIEW xvld_tmp_bld_v AS ' ||
+  'SELECT * FROM ' || tabname_p || ' WHERE pn != '|| iter;
+
+  -- We will test using rows in current partition
+  EXECUTE IMMEDIATE
+  'CREATE OR REPLACE VIEW xvld_tmp_tst_v AS ' ||
+  'SELECT * FROM ' || tabname_p || ' WHERE pn = '|| iter;
+
+  -- Build a model with this subset of data
+  BEGIN
+    -- Cleanup old model with same name for repeat runs
+    BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name);
+    EXCEPTION WHEN OTHERS THEN NULL; END;
+
+    -- Build a DT model
+    BEGIN
+      DBMS_DATA_MINING.CREATE_MODEL(
+        model_name          => model_name,
+        mining_function     => dbms_data_mining.classification,
+        data_table_name     => 'xvld_tmp_bld_v',
+        case_id_column_name => case_id,
+        target_column_name  => tgtname,
+        settings_table_name => 'dt_sh_sample_settings');
+    END;
+  
+    -- Apply the model, and compute confusion matrix
+    -- Confusion matrix is saved away in dmdtxvld_c table tagged by 
+    -- iteration number iter
+    EXECUTE IMMEDIATE 
+    'INSERT INTO dmdtxvld_c ' ||
+    'SELECT ' || iter || ',' || tgtname || ' AS actual_target_value,' ||
+           'PREDICTION('|| model_name || ' USING *) ' ||
+             'AS predicted_target_value,' ||
+           'COUNT(*) AS value ' ||
+      'FROM xvld_tmp_tst_v ' ||
+    'GROUP BY ' || tgtname || ', PREDICTION('|| model_name|| ' USING *) ' ||
+    'ORDER BY 1,2';
+  END;
+ END LOOP;
+
+ -- Drop the model and partitioned table 
+ -- remaining from the last iteration
+ BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name);
+ EXCEPTION WHEN OTHERS THEN NULL; END;
+
+ BEGIN EXECUTE IMMEDIATE 'DROP TABLE '|| TABNAME_P;
+ EXCEPTION WHEN OTHERS THEN NULL; END;
+
+ BEGIN EXECUTE IMMEDIATE 'DROP VIEW XVLD_TMP_BLD_V';
+ EXCEPTION WHEN OTHERS THEN NULL; END;
+
+ BEGIN EXECUTE IMMEDIATE 'DROP VIEW XVLD_TMP_TST_V';
+ EXCEPTION WHEN OTHERS THEN NULL; END;
+
+END;
+/
+
+-- Compute accuracy per iteration and the average for nfolds
+-- from the confusion matrix
+--
+SELECT a.iter, AVG(ROUND(correct/total,4)) AS accuracy
+  FROM (SELECT iter, SUM(count) AS correct
+          FROM dmdtxvld_c
+         WHERE actual = predicted
+        GROUP BY iter) a,
+       (SELECT iter, SUM(count) AS total
+          FROM dmdtxvld_c
+        GROUP BY iter) b
+ WHERE a.iter = b.iter
+GROUP BY ROLLUP (a.iter);
+
+-- Show confusion matrix by iteration and rolled up across iterations
+SELECT *
+  FROM (SELECT iter, actual, predicted, SUM(count) count
+          FROM dmdtxvld_c
+        GROUP BY ROLLUP (actual, predicted, iter))
+ WHERE predicted IS NOT NULL
+ORDER BY iter, actual, predicted;
diff --git a/machine-learning/sql/26ai/oml4sql-feature-extraction-cur.sql b/machine-learning/sql/26ai/oml4sql-feature-extraction-cur.sql
new file mode 100644
index 00000000..140fb52a
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-feature-extraction-cur.sql
@@ -0,0 +1,127 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Feature and Row Extraction - CUR Decomposition Algorithm - dmcurdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEMS
+-----------------------------------------------------------------------
+-- Perform CUR decomposition-based attribute and row importance for:
+-- Selecting top attributes and rows with highest importance scores
+-- (Select approximately top 10 attributes and top 50 rows)
+--
+
+-- Cleanup for repeat runs
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cur_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('CUR_SH_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Setting specification
+-- Create settings table
+CREATE TABLE cur_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+-- Populate settings table
+BEGIN
+  -- Select CUR Decomposition as the Attribute Importance algorithm
+  INSERT INTO cur_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_cur_decomposition);
+  -- Set row importance to be enabled (disabled by default)
+  INSERT INTO cur_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.curs_row_importance, dbms_data_mining.curs_row_imp_enable);
+  -- Set approximate number of attributes to be selected
+  INSERT INTO cur_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.curs_approx_attr_num, '10');
+  -- Set approximate number of rows to be selected
+  INSERT INTO cur_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.curs_approx_row_num, '50');
+  -- Set SVD rank parameter
+  INSERT INTO cur_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.curs_svd_rank, '5');
+  -- Examples of possible overrides are:
+  -- (dbms_data_mining.odms_random_seed, '1');  
+END;
+/
+
+-- Build a CUR model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'CUR_SH_SAMPLE',
+    mining_function     => dbms_data_mining.attribute_importance,
+    data_table_name     => 'MINING_DATA_BUILD_V',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'cur_sh_sample_settings');
+END;
+/
+
+-- Display model settings
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'CUR_SH_SAMPLE'
+ORDER BY setting_name;
+
+-- Display model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'CUR_SH_SAMPLE'
+ORDER BY attribute_name;
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='CUR_SH_SAMPLE'
+  ORDER BY view_name;
+
+-- Display global model details
+column name format a30
+column numeric_value format 9999999999
+SELECT name, numeric_value
+  FROM DM$VGCUR_SH_SAMPLE
+ORDER BY name;
+
+-- Attribute importance and ranks
+column attribute_name format a15
+column attribute_subname format a18
+column attribute_value format a15
+column attribute_importance format 9.99999999
+column attribute_rank format 999999
+
+SELECT attribute_name, attribute_subname, attribute_value, 
+       attribute_importance, attribute_rank
+FROM   DM$VCCUR_SH_SAMPLE
+ORDER BY attribute_rank, attribute_name, attribute_subname,
+         attribute_value;
+
+-- Row importance and ranks
+column case_id format 999999999
+column row_importance format 9.99999999
+column row_rank format 999999999
+
+SELECT case_id, row_importance, row_rank
+  FROM DM$VRCUR_SH_SAMPLE
+ORDER BY row_rank, case_id;
diff --git a/machine-learning/sql/26ai/oml4sql-feature-extraction-nmf.sql b/machine-learning/sql/26ai/oml4sql-feature-extraction-nmf.sql
new file mode 100644
index 00000000..e00722b6
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-feature-extraction-nmf.sql
@@ -0,0 +1,178 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Feature Extraction - Non-Negative Matrix Factorization Algorithm - dmnmdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 100
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic data about a set of customers, extract features
+-- from the given dataset.
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old settings table objects for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nmf_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('NMF_SH_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- CREATE A SETTINGS TABLE
+--
+-- NMF is the default Feature Extraction algorithm. For this sample,
+-- we use Data Auto Preparation.
+--
+set echo off
+CREATE TABLE nmf_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+set echo on
+ 
+BEGIN
+  -- Populate settings table
+  insert into NMF_SH_SAMPLE_SETTINGS (SETTING_NAME, SETTING_VALUE) values
+  (DBMS_DATA_MINING.PREP_AUTO,DBMS_DATA_MINING.PREP_AUTO_ON);
+  -- Other examples of possible overrides are:
+  -- (dbms_data_mining.feat_num_features, 10);
+  -- (dbms_data_mining.nmfs_conv_tolerance,0.05);
+  -- (dbms_data_mining.nmfs_num_iterations,50);
+  -- (dbms_data_mining.nmfs_random_seed,-1);
+END;
+/     
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Build NMF model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'NMF_SH_sample',
+    mining_function     => dbms_data_mining.feature_extraction,
+    DATA_TABLE_NAME     => 'mining_data_build_v',
+    CASE_ID_COLUMN_NAME => 'cust_id',
+    settings_table_name => 'nmf_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'NMF_SH_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'NMF_SH_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='NMF_SH_SAMPLE'
+  ORDER BY view_name;
+
+-- Each feature is a linear combination of the original attribute set; 
+-- the coefficients of these linear combinations are non-negative.
+-- The model details return for each feature the coefficients
+-- associated with each one of the original attributes. Categorical 
+-- attributes are described by (attribute_name, attribute_value) pairs.
+-- That is, for a given feature, each distinct value of a categorical 
+-- attribute has its own coefficient.
+--
+column attribute_name format a20;
+column attribute_value format a60;
+column coefficient format 9.99999
+SELECT feature_id,
+       attribute_name,
+       attribute_value,
+       coefficient
+  FROM DM$VENMF_SH_Sample
+WHERE feature_id = 1
+  AND attribute_name in ('AFFINITY_CARD','AGE','COUNTRY_NAME')
+ORDER BY feature_id,attribute_name,attribute_value;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+-- There is no specific set of testing parameters for feature extraction.
+-- Examination and analysis of features is the main method to prove
+-- the efficacy of an NMF model.
+--
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+--
+-- For a descriptive mining function like feature extraction, "Scoring"
+-- involves providing the probability values for each feature.
+-- During model apply, an NMF model maps the original data into the 
+-- new set of attributes (features) discovered by the model.
+-- 
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- List the features that correspond to customers in this dataset.
+-- The feature that is returned for each row is the one with the
+-- largest value based on the inputs for that row.
+-- Count the number of rows that have the same "largest" feature value.
+--
+SELECT FEATURE_ID(nmf_sh_sample USING *) AS feat, COUNT(*) AS cnt
+  FROM mining_data_apply_v
+group by FEATURE_ID(NMF_SH_SAMPLE using *)
+ORDER BY cnt DESC,FEAT DESC;
+
+------------------
+-- BUSINESS CASE 2
+-- List top (largest) 3 features that represent a customer (100002).
+-- Explain the attributes which most impact those features.
+--
+set line 120
+column fid format 999
+column val format 999.999
+set long 20000
+SELECT S.feature_id fid, value val,
+       FEATURE_DETAILS(nmf_sh_sample, S.feature_id, 5 using T.*) det
+FROM 
+  (SELECT v.*, FEATURE_SET(nmf_sh_sample, 3 USING *) fset
+    FROM mining_data_apply_v v
+   WHERE cust_id = 100002) T, 
+  TABLE(T.fset) S
+order by 2 desc;
diff --git a/machine-learning/sql/26ai/oml4sql-feature-extraction-svd.sql b/machine-learning/sql/26ai/oml4sql-feature-extraction-svd.sql
new file mode 100644
index 00000000..befa9248
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-feature-extraction-svd.sql
@@ -0,0 +1,341 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Feature Extraction - Singular Value Decomposition Demo - dmsvddemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEMS
+-----------------------------------------------------------------------
+-- Extract SVD features for:
+-- 1. Visualization 
+-- 2. Data compression
+-- Each use case will be illustrated separately.  
+--
+  
+-----------------------------------------------------------------------
+-----------------------------------------------------------------------
+-- VISUALIZATIION USE CASE
+--
+-- Goal: Produce the top two PCA projections to visualize the data  
+-----------------------------------------------------------------------  
+-- Cleanup for repeat runs
+-- Cleanup old data view
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW svd_sh_sample_build_num';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_SH_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a build data view with only numerical columns
+CREATE VIEW svd_sh_sample_build_num AS
+  SELECT CUST_ID, AGE, YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES,
+    FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION,
+    PRINTER_SUPPLIES, Y_BOX_GAMES, OS_DOC_SET_KANJI 
+  FROM MINING_DATA_BUILD_V;
+
+-- Setting specification
+-- Create settings table
+set echo off
+ CREATE TABLE svd_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+set echo on
+  
+-- Populate settings table
+BEGIN       
+  -- Select SVD as the Feature Extraction algorithm
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_singular_value_decomp);
+  -- Turn on automatic data preparation
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_off);  
+  -- Select PCA mode of scoring
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.svds_scoring_mode, dbms_data_mining.svds_scoring_pca);
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_shift_2dnum, dbms_data_mining.prep_shift_mean); 
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_scale_2dnum, dbms_data_mining.prep_scale_stddev);   
+  -- Examples of possible overrides are:
+  -- (dbms_data_mining.feat_num_features, 10);
+  -- (dbms_data_mining.svds_u_matrix_output, dbms_data_mining.svds_u_matrix_enable);
+  -- (dbms_data_mining.odms_approximate_computation, dbms_data_mining.odms_appr_comp_enable);
+END;
+/
+
+-- Build an SVD model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVD_SH_sample',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'svd_sh_sample_build_num',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'svd_sh_sample_settings');
+END;
+/
+
+-- Display model settings
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY setting_name;
+
+-- Display model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY attribute_name;
+
+-- Display model details
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='SVD_SH_SAMPLE'
+  ORDER BY view_name;
+
+-- The model details return the SVD decomposition matrices.
+-- The user can specify the type of matrix. If no matrix type is provided
+-- all stored matrices are returned.
+-- In the current use case only the S matrix (singular values and variances)
+-- and the V matrix (PCA bases) are stored in the model.
+column value format 9999999.99
+column variance format 999999999999.9
+column pct_cum_variance format 999999.9
+  
+---- S matrix
+select feature_id, VALUE, variance, pct_cum_variance 
+FROM DM$VESVD_SH_SAMPLE;
+
+---- V matrix
+select feature_id, attribute_name, value
+from DM$VVSVD_SH_sample
+ORDER BY feature_id, attribute_name;  
+
+-- Display the high-level model details 
+column name format a30;
+column string_value format 99999.99;
+column string_value format a20;
+SELECT name, string_value, numeric_value
+FROM DM$VGSVD_SH_SAMPLE
+ORDER BY name;
+
+-- Compute the top two PCA projections that will be used for visualization
+column proj1 format 9.9999999
+column proj2 format 9.9999999
+SELECT FEATURE_VALUE(svd_sh_sample, 1 USING *) proj1,
+       FEATURE_VALUE(svd_sh_sample, 2 USING *) proj2
+FROM svd_sh_sample_build_num
+WHERE CUST_ID <= 101510
+ORDER BY 1, 2;
+
+-- Identify the three input attributes that most impact the top PCA projection
+-- for customer 101501
+set long 10000
+SELECT FEATURE_DETAILS(svd_sh_sample, 1, 3 USING *) proj1det
+FROM svd_sh_sample_build_num
+WHERE CUST_ID = 101501;
+
+-----------------------------------------------------------------------
+-----------------------------------------------------------------------
+-- Compression USE CASE
+--
+-- Goal: Compress the data and measure the reconstruction error.
+-----------------------------------------------------------------------  
+-- Cleanup for repeat runs
+-- Cleanup old transactional table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_build_num_piv';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old reconstruction table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_build_num_recon';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_SH_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Setting specification
+-- Create settings table
+set echo off
+ CREATE TABLE svd_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+set echo on
+  
+-- Populate settings table
+BEGIN       
+  -- Select SVD as the Feature Extraction algorithm
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_singular_value_decomp);
+  -- Request the output of the U matrix values (not produced by default)
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.svds_u_matrix_output, dbms_data_mining.svds_u_matrix_enable);
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_off);    
+  -- Examples of possible overrides are:
+  -- (dbms_data_mining.feat_num_features, 10);
+  -- (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  -- (dbms_data_mining.svds_scoring_mode, dbms_data_mining.svds_scoring_pca);
+  -- (dbms_data_mining.odms_approximate_computation, dbms_data_mining.odms_appr_comp_enable);
+END;
+/
+
+-- Build an SVD model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVD_SH_sample',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'svd_sh_sample_build_num',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'svd_sh_sample_settings');
+END;
+/
+
+-- Display model settings
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY setting_name;
+
+-- Display model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY attribute_name;
+
+-- Display model details
+--
+-- The model details return the SVD decomposition matrices.
+-- The user can specify the type of matrix. If no matrix type is provided
+-- all stored matrices are returned.
+-- The S matrix represents the singular values.
+-- The V and U matrices represent two new sets of orthonormal bases.
+-- Usually, V is chosen as the new coordinate system and U represents
+-- the projection of the data in the new coordinates.
+column case_id format a10
+column attribute_name format a30
+column value format 9999999.99
+  
+---- S matrix
+select feature_id, VALUE FROM DM$VESVD_SH_SAMPLE;
+
+---- V matrix
+select feature_id, attribute_name, value
+from DM$VVSVD_SH_SAMPLE;  
+
+---- U matrix
+select feature_id, value 
+FROM DM$VUSVD_SH_sample
+WHERE case_id = 101501
+ORDER BY feature_id;
+
+-- To compress the data and reduce storage only a few of the projections
+-- in the new coordinate system need to be stored.
+-- In this use case, we  use only the top 5 projections. This results in
+-- two-fold compression. The SVD projection values can be obtained
+-- either by invoking the FEATURE_VALUE operator (see previous 
+-- use case) or using the get_model_details_svd U matrix output.
+
+-- Here, we compute the average reconstruction error due to compression.
+-- To facilitate the computation, we first pivot the original data into
+-- transactional format.
+
+-- Make the data transactional
+CREATE TABLE svd_sh_sample_build_num_piv as
+select * from svd_sh_sample_build_num
+  unpivot (value for attribute_name in("AGE", "YRS_RESIDENCE", "AFFINITY_CARD", 
+  "BULK_PACK_DISKETTES", "FLAT_PANEL_MONITOR", "HOME_THEATER_PACKAGE",
+  "BOOKKEEPING_APPLICATION", "PRINTER_SUPPLIES", "Y_BOX_GAMES", "OS_DOC_SET_KANJI"));
+
+
+-- Compute the average reconstruction error using the top 5 projections
+-- First compute the data reconstruction as U*S*V' using only the top five
+-- projections.
+CREATE TABLE svd_sh_sample_build_num_recon as
+WITH
+  s_mat AS (
+  SELECT feature_id, value FROM DM$VESVD_SH_SAMPLE 
+  WHERE feature_id<=5),
+  v_mat AS (
+  SELECT feature_id, attribute_name, value FROM DM$VVSVD_SH_SAMPLE
+  WHERE feature_id<=5),
+  u_mat AS (
+  SELECT feature_id, case_id, value FROM DM$VUSVD_SH_SAMPLE
+  WHERE feature_id<=5)
+SELECT case_id cust_id, attribute_name, sum(c.value*b.VALUE*a.value) value
+  FROM s_mat a, v_mat b, u_mat c
+  WHERE a.feature_id=b.feature_id AND a.feature_id=c.feature_id
+  GROUP BY case_id, attribute_name;
+
+column mae format 9999999.999
+-- Compute the mean absolute error.
+SELECT avg(abs(a.value-b.value)) mae
+FROM svd_sh_sample_build_num_recon a, svd_sh_sample_build_num_piv b
+WHERE a.cust_id=b.cust_id AND a.attribute_name=b.attribute_name; 
+
+column mape format 9999999.999
+-- Compute the mean absolute percentage error.
+SELECT avg(abs((a.value-b.value)/
+                CASE WHEN b.VALUE=0 THEN 1 ELSE b.VALUE END)) mape
+FROM svd_sh_sample_build_num_recon a, svd_sh_sample_build_num_piv b
+WHERE a.cust_id=b.cust_id AND a.attribute_name=b.attribute_name; 
+
+-----------------------------------------------------------------------
+--    BUILD and APPLY a transient model using analytic functions
+-----------------------------------------------------------------------
+-- In addition to creating a persistent model that is stored as a schema
+-- object, models can be built and scored on data on the fly using
+-- Oracle's analytic function syntax.
+
+--------------------
+-- BUSINESS USE CASE
+-- 
+-- Map customer attributes into six features and return the feature
+-- mapping for customer 100001.
+-- All data in the apply view is used to construct the feature mappings.
+-- All necessary data preparation steps are automatically performed.
+column feature_id format 999
+column value format 999.999
+select feature_id, value 
+from (
+ select cust_id, feature_set(into 6 using *) over () fset
+ from mining_data_apply_v),
+table(fset)
+where cust_id = 100001
+order by feature_id;
diff --git a/machine-learning/sql/26ai/oml4sql-feature-extraction-text-mining-esa.sql b/machine-learning/sql/26ai/oml4sql-feature-extraction-text-mining-esa.sql
new file mode 100644
index 00000000..a0428408
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-feature-extraction-text-mining-esa.sql
@@ -0,0 +1,509 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+--
+--   Feature Extraction - ESA Algorithm for Text Mining
+--
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+
+-----------------------------------------------------------------------
+--                            Prerequisites
+-----------------------------------------------------------------------
+
+-- Install the Wiki ESA model
+--   https://oss.oracle.com/machine-learning/
+-- Run the dmsh.sql script 
+--   https://github.com/oracle-samples/oracle-db-examples/blob/main/machine-learning/sql/26ai/dmsh.sql
+
+-----------------------------------------------------------------------
+--                            EXAMPLES IN THIS SCRIPT
+-----------------------------------------------------------------------
+-- Create an ESA model with CREATE_MODEL PL/SQL procedure, which uses a
+-- separate settings table as input. 
+
+-- Walk thorugh 3 ESA use cases with the model
+
+-- Create an ESA model with CREATE_MODEL2, which provides settings
+-- as part of the procedure. 
+
+-- (26ai Feature) Create an ESA model with dense projections, which is  
+-- similar to a doc2vec approach, by specifying the ESAS_EMBEDDINGS 
+-- parameter as ESAS_EMBEDDINGS_ENABLED.
+
+-- (26ai Feature) Use the dense projection scoring results to create a  
+-- clustering model. You can use such projections to improve the quality  
+-- of, e.g., classification and clustering models - a common use case 
+-- for dense projections.
+-----------------------------------------------------------------------
+
+
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+SET serveroutput ON
+SET pages 10000
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+-- Create a policy for text feature extraction
+-- The policy will include stemming
+BEGIN ctx_ddl.drop_policy('dmdemo_esa_policy');
+EXCEPTION when others then null; END;
+/
+BEGIN ctx_ddl.drop_preference('dmdemo_esa_lexer');
+EXCEPTION when others then null; END;
+/
+BEGIN
+  ctx_ddl.create_preference('dmdemo_esa_lexer', 'BASIC_LEXER');
+  ctx_ddl.set_attribute('dmdemo_esa_lexer', 'index_stems', 'ENGLISH');
+--  ctx_ddl.set_attribute('dmdemo_esa_lexer', 'index_themes', 'YES');
+END;
+/
+BEGIN
+  ctx_ddl.create_policy('dmdemo_esa_policy', lexer=>'dmdemo_esa_lexer');
+END;
+/
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model and objects for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('ESA_text_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE ESA_text_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a build data view with case ID and text column
+CREATE OR REPLACE VIEW mining_build_text_parallel AS
+  SELECT /*+ parallel (4)*/ min(cust_id) as cust_id, comments
+  FROM   mining_build_text
+  WHERE  length(comments) >= 70
+  GROUP BY comments;
+
+-- Create settings table to choose text policy and auto data prep
+CREATE TABLE ESA_text_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+  -- Populate settings table
+  INSERT INTO ESA_text_sample_settings VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_explicit_semantic_analys);
+  INSERT INTO ESA_text_sample_settings VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  INSERT INTO ESA_text_sample_settings VALUES
+    (dbms_data_mining.odms_text_policy_name, 'DMDEMO_ESA_POLICY');
+  -- lower than the default value of 100 due to the small size of data
+  INSERT INTO ESA_text_sample_settings VALUES
+    ('ESAS_MIN_ITEMS', 5);
+  -- lower than the default value of 3 due to the small size of data
+  INSERT INTO ESA_text_sample_settings VALUES
+    ('ODMS_TEXT_MIN_DOCUMENTS', 2);
+--('ODMS_TEXT_MAX_FEATURES', 10000);
+--('ESAS_TOPN_FEATURES', 500);
+--('ESAS_VALUE_THRESHOLD', 0.0001);
+  COMMIT;
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+-- Note that the transform makes the column 'comments'
+-- to be treated as unstructured text data
+--
+DECLARE
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'comments', null, 'comments', 'comments',
+     -- 'TEXT(POLICY_NAME:DMDEMO_ESA_POLICY)(TOKEN_TYPE:STEM)');
+    'TEXT(POLICY_NAME:DMDEMO_ESA_POLICY)(TOKEN_TYPE:THEME)');
+
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'ESA_text_sample',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'mining_build_text_parallel',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'ESA_text_sample_settings',
+    xform_list          => xformlist);
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30;
+column setting_value format a30;
+column setting_type format a10;
+
+SELECT setting_name, setting_value, setting_type AS type
+FROM   user_mining_model_settings
+WHERE  model_name = 'ESA_TEXT_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a30
+column attribute_type format a20
+column data_type format a20
+
+SELECT attribute_name, attribute_type, data_type
+FROM   user_mining_model_attributes
+WHERE  model_name='ESA_TEXT_SAMPLE'
+ORDER BY attribute_name;
+
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+
+SELECT view_name, view_type
+FROM   user_mining_model_views
+WHERE  model_name='ESA_TEXT_SAMPLE'
+ORDER BY view_name;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+--
+-- Unlike other feature extraction functions, ESA does not discover new features. 
+-- It treats the rows of the training data as predefined features. 
+-- Test data are expressed via these pre-defined features as a basis.
+--
+
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- List the customer comments (features) most relevant to the input text.
+-- The comments are sorted according to their relevancy.
+--
+column value format 9.99999
+column comments format a200
+
+SELECT cust_id, 
+       1-feature_compare(ESA_TEXT_SAMPLE 
+                         using 'card discount' comments AND
+                         using comments) similarity,
+       comments 
+FROM   mining_build_text_parallel
+ORDER BY similarity desc;
+
+-- Another similar example
+SELECT cust_id, 
+       1-feature_compare(ESA_TEXT_SAMPLE 
+                         using 'computer manual' comments AND
+                         using comments) similarity,
+       comments 
+FROM mining_build_text_parallel
+ORDER BY similarity DESC
+
+-- Another example with longer text
+-- The input is the following comment
+
+SELECT comments
+FROM   mining_test_text_parallel
+WHERE  cust_id = 103030;
+
+-- the most relevant comments from build data
+SELECT s.value, m.comments
+FROM   (SELECT feature_set(ESA_TEXT_SAMPLE, 5 using *) fset
+        FROM (SELECT comments FROM mining_test_text_parallel
+              WHERE cust_id = 103030)) t,
+       TABLE(t.fset) s, 
+       mining_build_text_parallel m
+WHERE  s.feature_id = m.cust_id
+ORDER BY s.value desc;
+
+------------------
+-- BUSINESS CASE 2
+-- List the attributes that represent customer comments (cust_id=101613).
+-- The attributes are sorted according to their coefficients.
+--
+column coefficient format 9.99999
+column attribute_subname format a30
+
+-- comments for cust_id=101613
+SELECT comments 
+FROM   mining_build_text_parallel 
+WHERE  cust_id = 101613;
+
+-- attributes representing the comments in the model
+SELECT attribute_subname, coefficient
+FROM   dm$vaESA_TEXT_SAMPLE
+WHERE  feature_id = 101613
+ORDER BY coefficient desc;
+
+------------------
+-- BUSINESS CASE 3
+-- Compare customer comments using the model
+--
+-- create a test data view with case ID and text column
+CREATE OR REPLACE VIEW mining_test_text_only AS
+SELECT cust_id, comments 
+FROM   mining_test_text_parallel
+WHERE  cust_id < 103005;
+
+column comments format a50
+-- test data
+SELECT cust_id, comments
+FROM mining_test_text_only
+ORDER BY cust_id;
+
+-- test data relatedness based on the model
+-- smaller values correspond to more related data rows
+
+column comp format 9.99999
+
+SELECT a.cust_id id1, b.cust_id id2,
+       feature_compare(ESA_TEXT_SAMPLE using a.comments and using b.comments) comp
+FROM  mining_test_text_only a, mining_test_text_only b
+WHERE a.cust_id < b.cust_id
+ORDER BY comp;
+
+-----------------------------------------------------------------------
+--                         CREATE MODEL WITH CREATE_MODEL2
+-----------------------------------------------------------------------
+-- This improves on the CREATE_MODEL procedure by allowing you to use a query
+-- rather than a database table as input. You also no longer need to declare
+-- and populate a settings table.
+-- Other than using CREATE_MODEL2, this model is identical to the first one.
+
+-- Cleanup old model and objects for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('ESA_text_sample2');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+DECLARE
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+  v_setlst  DBMS_DATA_MINING.SETTING_LIST;
+
+BEGIN
+  v_setlst('PREP_AUTO')               := 'ON';
+  v_setlst('ALGO_NAME')               := 'ALGO_EXPLICIT_SEMANTIC_ANALYS';
+  v_setlst('ODMS_TEXT_POLICY_NAME')   := 'DMDEMO_ESA_POLICY';
+  v_setlst('ESAS_MIN_ITEMS')          := '5';
+  v_setlst('ODMS_TEXT_MIN_DOCUMENTS') := '2';
+
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'comments', null, 'comments', 'comments',
+      'TEXT(POLICY_NAME:DMDEMO_ESA_POLICY)(TOKEN_TYPE:STEM)');
+
+  DBMS_DATA_MINING.CREATE_MODEL2(
+    model_name          => 'ESA_text_sample2',
+    mining_function     => 'FEATURE_EXTRACTION',
+    data_query          => 'SELECT * FROM mining_build_text_parallel',
+    case_id_column_name => 'cust_id',
+    set_list            => v_setlst,
+    xform_list          => xformlist);
+END;
+/
+
+-------------------------
+-- Score using the model
+
+column value format 9.99999
+column comments format a200
+
+SELECT s.feature_id, s.value 
+FROM 
+  (SELECT cust_id, comments, feature_set(ESA_TEXT_SAMPLE2, 5 using comments) fset 
+   FROM   mining_build_text_parallel) t,
+          TABLE(t.fset) s
+ORDER BY s.value desc;
+
+
+-----------------------------------------------------------------------
+--                    CREATE MODEL WITH DENSE PROJECTIONS
+-----------------------------------------------------------------------
+-- Create another model with CREATE_MODEL2, this time specifying the
+-- ESAS_EMBEDDINGS parameter and setting it to ESAS_EMBEDDINGS_ENABLE.
+-- Doing so means that scoring this model will produce dense projections with
+-- embeddings, similar to a doc2vec approach.
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('ESA_text_sample_dense');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+DECLARE
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+  v_setlst  DBMS_DATA_MINING.SETTING_LIST;
+
+BEGIN
+  v_setlst('PREP_AUTO')               := 'ON';
+  v_setlst('ALGO_NAME')               := 'ALGO_EXPLICIT_SEMANTIC_ANALYS';
+  v_setlst('ODMS_TEXT_POLICY_NAME')   := 'DMDEMO_ESA_POLICY';
+  v_setlst('ESAS_MIN_ITEMS')          := '5';
+  v_setlst('ODMS_TEXT_MIN_DOCUMENTS') := '2';
+  v_setlst('ESAS_EMBEDDINGS')         := 'ESAS_EMBEDDINGS_ENABLE';
+  v_setlst('ESAS_EMBEDDING_SIZE')     := '1024';
+
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'comments', null, 'comments', 'comments',
+      'TEXT(POLICY_NAME:DMDEMO_ESA_POLICY)(TOKEN_TYPE:STEM)');
+
+  DBMS_DATA_MINING.CREATE_MODEL2(
+    model_name          => 'ESA_text_sample_dense',
+    mining_function     => 'FEATURE_EXTRACTION',
+    data_query          => 'SELECT * FROM mining_build_text_parallel',
+    case_id_column_name => 'cust_id',
+    set_list            => v_setlst,
+    xform_list          => xformlist);
+END;
+/
+
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+-- Note the ESAS_EMBEDDINGS and ESAS_EMBEDDINGS_SIZE settings.
+--
+column setting_name format a30;
+column setting_value format a30;
+column setting_type format a10;
+
+SELECT setting_name, setting_value, setting_type AS type
+FROM   user_mining_model_settings
+WHERE  model_name = 'ESA_TEXT_SAMPLE_DENSE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a30
+column attribute_type format a20
+column data_type format a20
+
+SELECT attribute_name, attribute_type, data_type
+FROM   user_mining_model_attributes
+WHERE  model_name='ESA_TEXT_SAMPLE_DENSE'
+ORDER BY attribute_name;
+
+--------------------------
+-- Get a list of model views
+--
+col view_name format a30
+col view_type format a50
+
+SELECT view_name, view_type
+FROM   user_mining_model_views
+WHERE  model_name='ESA_TEXT_SAMPLE_DENSE'
+ORDER BY view_name;
+
+-------------------------
+-- STORE SCORING RESULTS IN TABLE
+--
+
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE esa_dense_results';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+column value format 9.99999
+column comments format a200
+
+CREATE TABLE esa_dense_results AS
+SELECT cust_id, s.feature_id, s.value, comments 
+FROM   (SELECT cust_id, comments,
+               feature_set(ESA_TEXT_SAMPLE_DENSE using comments) fset 
+        FROM   mining_build_text_parallel) t,
+       TABLE(t.fset) s;
+
+-----------------------------------------------------------------------
+--              PASS PROJECTIONS INTO CLUSTERING MODEL
+-----------------------------------------------------------------------
+-- A common use case for the dense projections created by the scoring results
+-- of the model above is to use them to train other models, such as
+-- classification or clustering. The expectation is that the addition of these
+-- results may improve model accuracy.
+-- Here we create a clustering model using the dense projections.
+
+-------------------------
+-- Prepare ESA dense results to create clustering model
+--
+
+CREATE OR REPLACE VIEW esa_dense_results_v AS
+  SELECT cust_id, 
+         comments,
+         CAST(COLLECT(dm_nested_numerical(feature_id, value))
+            AS dm_nested_numericals) AS nest
+  FROM esa_dense_results
+  GROUP BY cust_id, comments;
+
+---------------------------
+-- CREATE CLUSTERING MODEL
+-- Use the table augmented with the dense projection to create a
+-- clustering model
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('CLUSTERING_EXAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+DECLARE
+  xformlist DBMS_DATA_MINING_TRANSFORM.TRANSFORM_LIST;
+  v_setlst  DBMS_DATA_MINING.SETTING_LIST;
+
+BEGIN
+  v_setlst('PREP_AUTO')    := 'ON';
+  v_setlst('ALGO_NAME')    := 'ALGO_KMEANS';
+  v_setlst('KMNS_DETAILS') := 'KMNS_DETAILS_HIERARCHY';
+
+  DBMS_DATA_MINING.CREATE_MODEL2(
+    model_name          => 'CLUSTERING_EXAMPLE',
+    mining_function     => 'CLUSTERING',
+    data_query          => 'SELECT * FROM esa_dense_results_v',
+    case_id_column_name => 'cust_id',
+    set_list =>v_setlst);
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+
+SELECT setting_name, setting_value
+FROM   user_mining_model_settings
+WHERE  model_name = 'CLUSTERING_EXAMPLE'
+ORDER BY setting_name;
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+
+SELECT view_name, view_type
+FROM   user_mining_model_views
+WHERE  model_name='CLUSTERING_EXAMPLE'
+ORDER BY view_name;
+
+-- CLUSTERS
+-- For each cluster_id, provide the number of records in the cluster,
+-- the parent cluster id, the level in the hierarchy, and dispersion,
+-- which is a measure of individual cluster quality, and computationally,
+-- the sum of square errors.
+--
+SELECT cluster_id CLU_ID, RECORD_COUNT REC_CNT, parent, TREE_LEVEL,
+       ROUND(TO_NUMBER(dispersion),3) dispersion
+FROM   DM$VDCLUSTERING_EXAMPLE
+ORDER BY CLUSTER_ID;
+
+SELECT CLUSTER_ID CLU_ID, RECORD_COUNT REC_CNT, ROUND(TO_NUMBER(dispersion),3) DISPERSION
+FROM   DM$VDCLUSTERING_EXAMPLE
+WHERE  LEFT_CHILD_ID IS NULL AND RIGHT_CHILD_ID IS NULL
+ORDER BY CLUSTER_ID;
+
+-----------------------------------------------------------------------
+--   End of script
+-----------------------------------------------------------------------
diff --git a/machine-learning/sql/26ai/oml4sql-feature-extraction-text-mining-nmf.sql b/machine-learning/sql/26ai/oml4sql-feature-extraction-text-mining-nmf.sql
new file mode 100644
index 00000000..0dd15bc0
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-feature-extraction-text-mining-nmf.sql
@@ -0,0 +1,178 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Feature Extraction - NMF Algorithm with Text Mining - dmtxtnmf.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------  
+SET serveroutput ON
+SET trimspool ON
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Mine text features using NMF algorithm. 
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE THE DATA
+-----------------------------------------------------------------------
+-- Create a policy for text feature extraction
+-- The policy will include stemming
+begin
+  ctx_ddl.drop_policy('dmdemo_nmf_policy');
+exception when others then null;
+end;
+/
+begin
+  ctx_ddl.drop_preference('dmdemo_nmf_lexer');
+exception when others then null;
+end;
+/
+begin
+  ctx_ddl.create_preference('dmdemo_nmf_lexer', 'BASIC_LEXER');
+  ctx_ddl.set_attribute('dmdemo_nmf_lexer', 'index_stems', 'ENGLISH');
+--  ctx_ddl.set_attribute('dmdemo_nmf_lexer', 'index_themes', 'YES');
+end;
+/
+begin
+  ctx_ddl.create_policy('dmdemo_nmf_policy', lexer=>'dmdemo_nmf_lexer');
+end;
+/
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model and objects for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('T_NMF_Sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE t_nmf_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------------------------------
+-- CREATE A NEW MODEL USING SETTINGS TABLE
+-- Note the transform makes the 'comments' attribute 
+-- to be treated as unstructured text data
+--
+
+-- Create settings table to choose text policy and auto data prep
+CREATE TABLE t_nmf_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+ 
+BEGIN
+  -- Populate settings table
+  INSERT INTO t_nmf_sample_settings VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  INSERT INTO t_nmf_sample_settings VALUES(
+    dbms_data_mining.odms_text_policy_name, 'DMDEMO_NMF_POLICY');
+--(dbms_data_mining.nmfs_conv_tolerance,0.05);
+--(dbms_data_mining.nmfs_num_iterations,50);
+--(dbms_data_mining.nmfs_random_seed,-1);
+--(dbms_data_mining.nmfs_stop_criteria,dbms_data_mining.nmfs_sc_iter_or_conv);
+  COMMIT;
+END;
+/
+
+DECLARE
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'comments', null, 'comments', null, 'TEXT(TOKEN_TYPE:STEM)');
+--    xformlist, 'comments', null, 'comments', null, 'TEXT(TOKEN_TYPE:THEME)');
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name => 'T_NMF_Sample',
+    mining_function => dbms_data_mining.feature_extraction,
+    data_table_name => 'mining_build_text',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 't_nmf_sample_settings',
+    xform_list => xformlist);
+END;
+/
+    
+--------------------------------------------------------
+-- CREATE A NEW MODEL USING V_SETLST (NO SETTINGS TABLE)
+-- Note the transform makes the 'comments' attribute 
+-- to be treated as unstructured text data
+--
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+    dbms_data_mining_transform.SET_TRANSFORM(
+    xformlist, 'comments', null, 'comments', null, 'TEXT(TOKEN_TYPE:STEM)');
+--    xformlist, 'comments', null, 'comments', null, 'TEXT(TOKEN_TYPE:THEME)');
+
+    v_setlst('PREP_AUTO') := 'ON';
+    v_setlst('ALGO_NAME') := 'ALGO_NMF';
+    V_setlst('odms_text_policy_name' := 'DMDEMO_NMF_POLICY';
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        'T_NMF_Sample',
+        'FEATURE_EXTRACTION',
+        'SELECT * FROM mining_build_text',
+        v_setlst,
+        'cust_id',
+        'AFFINITY_CARD');
+END;
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30;
+column setting_value format a30;
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'T_NMF_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'T_NMF_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+WHERE model_name='T_NMF_SAMPLE'
+ORDER BY view_name;
+
+column attribute_name format a30;
+column attribute_value format a20;
+column coefficient format 9.99999;
+set pages 15;
+SET line 120;
+break ON feature_id;
+SELECT * FROM (
+SELECT feature_id,
+       nvl2(attribute_subname,
+            attribute_name||'.'||attribute_subname,
+            attribute_name) attribute_name,
+       attribute_value,
+       coefficient
+  FROM DM$VET_NMF_SAMPLE
+WHERE feature_id < 3
+ORDER BY 1,2,3,4)
+WHERE ROWNUM < 21;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-- See dmnmdemo.sql for examples.
diff --git a/machine-learning/sql/26ai/oml4sql-feature-extraction-text-term-extraction.sql b/machine-learning/sql/26ai/oml4sql-feature-extraction-text-term-extraction.sql
new file mode 100644
index 00000000..2d503ac5
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-feature-extraction-text-term-extraction.sql
@@ -0,0 +1,197 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Feature Extraction - Text Term Extraction - dmtxtfe.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+--   DESCRIPTION
+--   This script demonstrates a means to extract terms for
+--   text mining from Oracle Text documents (i.e. CLOB or
+--   VARCHAR2 columns containing text documents) using a Text
+--   Index built on those documents. 
+--
+--   For more information on term extraction for text mining,
+--   see Oracle Data Mining Application Developer s Guide.
+--
+--   This program uses the following schema object:
+--   . BUILD_TEXT_IDX - the Oracle Text Index built 
+--     on MINING_BUILD_TEXT. Please see rdbms/demo/dmsh.sql
+--     to see how MINING_BUILD_TEXT is constructed from
+--     data from the Sample Schema.
+-- 
+--   Given this Text Index, the program demonstrates how to
+--   extract terms from the documents using the Text Index.
+--   The terms are generated in an output table in the multi-record
+--   case format - i.e. in the form
+--   (sequence_id, attribute_name, attribute_value)
+--  
+--   This output table can then be used to generate a nested table
+--   column (DM_Nested_Numerical) in the final table that represents
+--   the training data that is to be used for model creation, or the
+--   scoring table that is to be used for model scoring.
+-- 
+--   Simply put, terms extracted from text documents will become
+--   generic attributes in training or scoring data. This data can
+--   then be classified, clustered or feature-extracted using the
+--   DBMS_DATA_MINING package.
+-- 
+--   Since the number of attributes (i.e. the terms) is typically
+--   greater than 1000, these attributes are composed into a nested
+--   column in the input table.
+-- 
+--   The goal of this program is to enable users to build their
+--   own term extractors, given a document table with a text index
+--   built on the documents.
+
+  
+SET serveroutput ON
+SET trimspool ON
+SET pages 10000
+SET echo ON
+
+-- Cleanup for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE txt_sample_pref_terms';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE txt_sample_tf_out';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE txt_sample_term_out';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE txt_sample_terms';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE txt_sample_text_cat';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE txt_sample_data_input';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nontxt_attrib';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN CTX_DDL.DROP_PREFERENCE(preference_name => 'txt_sample_pref');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Step 1. The sample Text document table is MINING_BUILD_TEXT
+--         This table is constructed when you run rdbms/demo/dmsh.sql.
+
+-- Step 2. The sample Text Index is BUILD_TEXT_INDEX
+--         This index is also constructed when you run rdbms/demo/dmsh.sql
+
+-- Given the presence of these two schema objects, you can now
+-- proceed to extract terms from text documents in MINING_BUILD_TEXT
+-- using the following steps:
+
+-- Step 3. Create preferences for Text term extraction
+--         (This routine must be used exclusively with ODM;
+--          it is a public, but undocumented, routine in Oracle10g)
+--
+BEGIN CTX_DDL.CREATE_PREFERENCE('txt_sample_pref', 'SVM_CLASSIFIER');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Step 4. Define an ancillary table for categories
+--         (required to execute preference choices)
+--
+CREATE TABLE txt_sample_text_cat (id NUMBER, cat NUMBER)
+/
+
+-- Step 5. Extract terms set up by preference into intermediate terms tables
+--         1. txt_sample_term_out   - terms set by preference
+--         2. txt_sample_pref_terms - term definitions
+--
+--         The parameters are:
+--         'BUILD_TEXT_IDX',        -- index_name - Text index name
+--         'CUST_ID',               -- doc_id     - the case-id
+--         'txt_sample_text_cat',   -- cattab     - category table name
+--         'id',                    -- catdocid   - docid for category tab
+--         'cat',                   -- catid      - category id
+--         'txt_sample_pref_terms', -- restab     - term definitions
+--         'txt_sample_pref'        -- preference - Text preference name
+--
+CREATE TABLE txt_sample_term_out AS
+SELECT *
+  FROM TABLE(ctxsys.drvodm.feature_prep (
+               'BUILD_TEXT_IDX',
+               'CUST_ID',
+               'txt_sample_text_cat',
+               'id',
+               'cat',
+               'txt_sample_pref_terms',
+               'txt_sample_pref'));
+
+-- At the end of this step:
+-- . txt_sample_term_out has the schema
+--   (sequence_id, value)
+--   sequence_id contains the values of CUST_ID, the case identifier
+--   column in MINING_BUILD_TEXT.
+--
+
+-- Step 6. Explain the terms in the intermediate tables to generate
+--         the final terms table. This table will contain the text
+--         elements that can be used as attributes for mining.
+--         (This drvodm routine must be used exclusively with ODM;
+--          it is a public, but undocumented, routine in Oracle10g)
+--
+CREATE TABLE txt_sample_terms AS
+SELECT A.sequence_id, B.text, A.value
+  FROM txt_sample_term_out A,
+       TABLE(ctxsys.drvodm.feature_explain(
+               'txt_sample_pref_terms')) B
+ WHERE A.attribute_id = B.id;
+
+-- Step 7. (Optional) Display the extracted terms
+--
+column text format a45
+SELECT *
+  FROM (SELECT sequence_id,text,value
+          FROM txt_sample_terms
+        ORDER BY sequence_id, text)
+ WHERE ROWNUM < 10;
+
+-- Step 8. Now, assume that you have a table containing training data
+--         with non-text attributes, with matching sequence ids
+--         (i.e. matching values of CUST_ID).
+--
+--         See MINING_BUILD_TEXT in rdbms/demo/dmsh.sql as an example
+--          
+--         Add the text mining attributes to this table as a nested table,
+--         as shown below.
+--
+
+-- Just for the sake of this demo, create a dummy table with
+-- NULL non-text attributes, using txt_sample_terms itself as
+-- the source (to prepare a table with matching case-id's)
+-- In an actual scenario, the non-text columns may be
+-- projected from a different table.
+--
+CREATE TABLE nontxt_attrib AS
+SELECT A.sequence_id case_id, 1 age, 1 salary, 1 class
+  FROM txt_sample_terms A;
+
+-- You can now create the mining data input table (or a view)
+-- with text and non-text attributes as follows:
+--
+CREATE TABLE txt_sample_data_input
+  NESTED TABLE txt_terms STORE AS txt_terms AS
+SELECT N.*,
+       CAST(MULTISET(
+       SELECT DM_Nested_Numerical (T.text, T.value)
+         FROM txt_sample_terms T
+        WHERE N.case_id = T.sequence_id)
+       AS DM_Nested_Numericals) txt_terms
+  FROM nontxt_attrib N;
+
+-- Step 9. Read rdbms/demo/dmsh.sql to revise the above steps
+--         in the context of an end-to-end demo of Text Mining.
+--         dmsh.sql performs all of the steps discussed above,
+--         against tables in the Sample Schema. The text data
+--         prepared in this manner can be classified using
+--         dmtxtsvm.sql, or feature extracted using dmtxtnmf.sql.
diff --git a/machine-learning/sql/26ai/oml4sql-partitioned-models-svm.sql b/machine-learning/sql/26ai/oml4sql-partitioned-models-svm.sql
new file mode 100644
index 00000000..b93c2b27
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-partitioned-models-svm.sql
@@ -0,0 +1,273 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Partitioned Models - Support Vector Machine Algorithm - dmpartdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET serveroutput ON
+SET pages 10000
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic data about a set of customers, predict the
+-- customer response to an affinity card program using a SVM
+-- classifier whose model is partitioned. 
+
+-------------------
+-- SPECIFY SETTINGS
+-------------------
+-- Cleanup old settings table with the same name for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE part_clas_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create settings table to choose linear kernel
+CREATE TABLE part_clas_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+ 
+BEGIN
+  -- Populate settings table
+  INSERT INTO part_clas_settings VALUES
+    (dbms_data_mining.algo_name,
+     dbms_data_mining.algo_support_vector_machines);
+  INSERT INTO part_clas_settings VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  INSERT INTO part_clas_settings VALUES
+    (dbms_data_mining.svms_kernel_function,dbms_data_mining.svms_linear);
+  -- partition column: GENDER
+  INSERT INTO part_clas_settings VALUES
+    (dbms_data_mining.odms_partition_columns, 'CUST_GENDER');
+  COMMIT;
+END;
+/
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('part_clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Build a new partitioned SVM model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'part_clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'part_clas_settings');
+END;
+/
+
+-- Display the model settings
+column setting_name format a30;
+column setting_value format a30;
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'PART_CLAS_SAMPLE'
+ORDER BY setting_name;
+
+-- Display the model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'PART_CLAS_SAMPLE'
+ORDER BY attribute_name;
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='PART_CLAS_SAMPLE'
+  ORDER BY view_name;
+
+-- Display the top ten model details per partition
+set long 20000
+column class format 9999
+column aname format a25
+column aval  format a25
+column coeff format 9.999
+-- for male customers
+SELECT target_value class, attribute_name aname, attribute_value aval, coefficient coeff
+FROM (SELECT target_value, attribute_name, attribute_value, coefficient
+  FROM DM$VLPART_CLAS_SAMPLE WHERE partition_name = 
+  (SELECT ORA_DM_PARTITION_NAME(PART_CLAS_SAMPLE using 'M' CUST_GENDER) FROM dual)
+  ORDER BY coefficient DESC) 
+WHERE ROWNUM <= 10;
+-- for female customers
+SELECT target_value class, attribute_name aname, attribute_value aval, coefficient coeff
+FROM (SELECT target_value, attribute_name, attribute_value, coefficient
+  FROM DM$VLPART_CLAS_SAMPLE WHERE partition_name = 
+  (SELECT ORA_DM_PARTITION_NAME(PART_CLAS_SAMPLE using 'F' CUST_GENDER) FROM dual)
+  ORDER BY coefficient DESC) 
+WHERE ROWNUM <= 10;
+
+-- Add another partition column 
+UPDATE part_clas_settings SET setting_value = 'CUST_GENDER,CUST_INCOME_LEVEL'
+  WHERE setting_name = 'ODMS_PARTITION_COLUMNS';
+
+-- Cleanup old model with the same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('part2_clas_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Build another partitioned model with two partition columns
+-- with three partition values for CUST_INCOME_LEVEL
+DECLARE
+  xform_list dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  dbms_data_mining_transform.set_transform(xform_list,
+    'CUST_INCOME_LEVEL', null, 
+    'CASE CUST_INCOME_LEVEL WHEN ''A: Below 30,000'' THEN ''LOW'' 
+    WHEN ''L: 300,000 and above'' THEN ''HIGH'' 
+    ELSE ''MEDIUM'' END', null);
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'part2_clas_sample',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'affinity_card',
+    settings_table_name => 'part_clas_settings',
+    xform_list          => xform_list);
+END;
+/
+
+-- Display model details for partition: 'F','MEDIUM'
+SELECT target_value class, attribute_name aname, attribute_value aval, coefficient coeff
+FROM (SELECT target_value, attribute_name, attribute_value, coefficient
+  FROM DM$VLPART2_CLAS_SAMPLE WHERE partition_name = 
+  (SELECT ORA_DM_PARTITION_NAME(PART2_CLAS_SAMPLE USING 
+  'F' CUST_GENDER, 'MEDIUM' CUST_INCOME_LEVEL) FROM dual)
+  ORDER BY coefficient DESC) 
+WHERE ROWNUM <= 10;
+
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+--                SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+-----------------------------------------------------------------------
+
+------------------
+-- BUSINESS CASE 1
+--
+-- Find the three male and five female customers that are most likely 
+-- to use an affinity card.
+-- Also explain why they are likely to use an affinity card.
+-- /*+ GROUPING */ hint forces scoring to be done completely 
+-- for each partition before advancing to the next partition.
+-- GROUPING is especially beneficial when partitions altogether
+-- do not fit into fast memory.
+column gender format a1
+column income format a30
+column rnk format 9
+SELECT cust_id, cust_gender as gender, rnk, pd FROM
+( SELECT cust_id, cust_gender,
+    PREDICTION_DETAILS(/*+ GROUPING */ PART_CLAS_SAMPLE, 1 USING *) pd,
+    rank() over (partition by cust_gender order by 
+    PREDICTION_PROBABILITY(PART_CLAS_SAMPLE, 1 USING *) desc, cust_id) rnk
+  FROM mining_data_apply_parallel_v)
+WHERE rnk <= 3 
+order by rnk, cust_gender;
+
+------------------
+-- BUSINESS CASE 2
+-- Find the average age of customers who are likely to use an
+-- affinity card. Break out the results by gender.
+--
+SELECT cust_gender as gender,
+       COUNT(*) AS cnt,
+       ROUND(AVG(age)) AS avg_age
+FROM mining_data_apply_parallel_v
+WHERE PREDICTION(PART_CLAS_SAMPLE USING *) = 1
+GROUP BY cust_gender ORDER BY cust_gender;
+
+-- compare with the average age of all customers
+SELECT cust_gender,
+       COUNT(*) AS cnt,
+       ROUND(AVG(age)) AS avg_age
+  FROM mining_data_apply_parallel_v
+GROUP BY cust_gender ORDER BY cust_gender;
+
+-- find the average age of predicted card users per gender and income
+-- for the groups containing statistically sufficient data
+-- using model PART2_CLAS_SAMPLE with two partition columns
+SELECT cust_gender as gender, cust_income_level as income, avg_age FROM
+  (SELECT cust_gender, cust_income_level,
+    COUNT(*) AS cnt,
+    ROUND(AVG(age)) AS avg_age
+  FROM mining_data_apply_parallel_v
+  WHERE PREDICTION(PART2_CLAS_SAMPLE USING *) = 1  
+  GROUP BY cust_gender, cust_income_level)
+WHERE cnt > 10 -- throw out the groups with fewer than 10 people
+ORDER BY cust_gender, cust_income_level;
+
+------------------
+-- BUSINESS CASE 3
+-- Calculate prediction accuracy per gender (expressed in percents).
+-- Expand the model and re-calculate the accuracy
+--
+column percent format 99
+SELECT t.cust_gender as gender, round(cnt/total*100) as percent FROM 
+(SELECT cust_gender, COUNT(*) AS cnt FROM mining_data_apply_parallel_v
+  WHERE PREDICTION(PART_CLAS_SAMPLE USING *) = AFFINITY_CARD 
+  GROUP BY cust_gender) p,
+(SELECT cust_gender, COUNT(*) AS total FROM mining_data_apply_parallel_v 
+  GROUP BY cust_gender) t
+WHERE p.cust_gender = t.cust_gender ORDER BY t.cust_gender;
+
+-- Suppose we have additional training data with an unknown gender
+-- For that purpose, we duplicate mining_data_build_v 
+-- with gender set to 'unknown' and ID set to a negative value
+CREATE OR replace VIEW ext_mining_data_build_v AS
+(SELECT -CUST_ID as CUST_ID, 'U' as CUST_GENDER, AGE, 
+  CUST_MARITAL_STATUS, COUNTRY_NAME,
+  CUST_INCOME_LEVEL, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE,
+  YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR,
+  HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES,
+  Y_BOX_GAMES, OS_DOC_SET_KANJI
+  FROM mining_data_build_parallel_v);
+
+-- Now we can add these data as a new partition to model PART_CLAS_SAMPLE
+BEGIN
+dbms_data_mining.add_partition('PART_CLAS_SAMPLE',
+'SELECT * FROM ext_mining_data_build_v', 'error');
+END;
+/
+
+-- And we similarly duplicate mining_data_apply_v 
+CREATE OR replace VIEW ext_mining_data_apply_v AS 
+SELECT -CUST_ID as CUST_ID, 'U' as CUST_GENDER, AGE, CUST_MARITAL_STATUS, 
+  COUNTRY_NAME, CUST_INCOME_LEVEL, EDUCATION, OCCUPATION, HOUSEHOLD_SIZE,
+  YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES, FLAT_PANEL_MONITOR,
+  HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION, PRINTER_SUPPLIES,
+  Y_BOX_GAMES, OS_DOC_SET_KANJI
+  FROM mining_data_apply_parallel_v
+UNION
+SELECT * FROM mining_data_apply_parallel_v;
+
+-- Re-calculate prediction accuracy per gender 
+-- including data with unknown gender
+SELECT t.cust_gender as gender, round(cnt/total*100) as percent FROM 
+(SELECT cust_gender, COUNT(*) AS cnt FROM ext_mining_data_apply_v
+  WHERE PREDICTION(PART_CLAS_SAMPLE USING *) = AFFINITY_CARD 
+  GROUP BY cust_gender) p,
+(SELECT cust_gender, COUNT(*) AS total FROM ext_mining_data_apply_v 
+  GROUP BY cust_gender) t
+WHERE p.cust_gender = t.cust_gender ORDER BY t.cust_gender;
+
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-algorithm-registration.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-algorithm-registration.sql
new file mode 100644
index 00000000..511fb7cb
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-algorithm-registration.sql
@@ -0,0 +1,265 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Algorithm Registration - dmralgregdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET LONG 10000
+
+column algorithm_name format a15;
+column mining_function format a15;
+column algorithm_type format a15;
+column description format a11;
+column algorithm_metadata format a20;
+
+connect sys/knl_test7 as sysdba;
+GRANT rqadmin TO dmuser;
+connect dmuser/dmuser
+
+-------------------------------------------------------------------------------
+--                        R Algorithm Registration DEMO 1
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to register a new GLM algorithm and use it to create models.
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLM_RDEMO_SETTINGS_CL';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_RDEMO_CLASSIFICATION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+  DBMS_DATA_MINING.drop_algorithm(
+    ALGORITHM_NAME => 't1',
+    CASCADE => TRUE);
+END;
+/
+
+-- Algorithm Registration -----------------------------------------------------
+
+BEGIN
+  DBMS_DATA_MINING.register_algorithm(
+    ALGORITHM_NAME         => 't1',
+    algorithm_metadata     => 
+    '{"function_language":"R",
+      "mining_function" : { "mining_function_name" : "CLASSIFICATION",
+                            "build_function" : {"function_body":
+"function(dat, formula, keep.model) { set.seed(1234); mod <- glm(formula = formula(formula), data=dat, family=binomial(logit), model = as.logical(keep.model)); mod}"},
+            
+                            "score_function" : {"function_body":
+"function(mod, dat) { res <- predict(mod, newdata = dat, type=''response''); res2=data.frame(1-res, res); names(res2) <- c(''0'', ''1''); res2}"}},
+      "algo_setting" : [{"name" : "ralg_parameter_keep.model", "data_type" : "integer","value" : "0", "optional" : "TRUE", "min_value" : {"min_value": "0", "inclusive": "TRUE"}, "max_value" : {"max_value": "1", "inclusive": "TRUE"}}]
+}',
+    algorithm_description  => 't1');
+END;
+/
+
+-- Model Settings -------------------------------------------------------------
+
+CREATE TABLE GLM_RDEMO_SETTINGS_CL (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO GLM_RDEMO_SETTINGS_CL VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.algo_name, 't1');
+END;
+/
+
+BEGIN
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.r_formula, 'AGE + EDUCATION + HOUSEHOLD_SIZE + OCCUPATION');
+END;
+/
+
+BEGIN
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES('ralg_parameter_keep.model', 1);
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R build function script user has already registered. 
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLM_RDEMO_CLASSIFICATION',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AFFINITY_CARD',
+    settings_table_name => 'GLM_RDEMO_SETTINGS_CL');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R score function script user has already registered. 
+
+SELECT CUST_ID, AFFINITY_CARD as AFFINITY_CARD_act, 
+PREDICTION(GLM_RDEMO_CLASSIFICATION USING *) AFFINITY_CARD_pred,
+round(PREDICTION_PROBABILITY(GLM_RDEMO_CLASSIFICATION USING *), 3) 
+as AFFINITY_CARD_prob 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+
+------------------------ Drop Models and Algorithms ---------------------------
+
+BEGIN
+  DBMS_DATA_MINING.drop_algorithm(
+    ALGORITHM_NAME => 't1',
+    CASCADE => TRUE);
+END;
+/
+
+-------------------------------------------------------------------------------
+--                        R Algorithm Registration DEMO 2
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to register a new DT algorithm and use it to create models.
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE DT_RDEMO_SETTINGS_CL';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_RDEMO_CLASSIFICATION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+  DBMS_DATA_MINING.drop_algorithm(
+    ALGORITHM_NAME => 't1');
+END;
+/
+
+-- Algorithm Registration -----------------------------------------------------
+
+BEGIN
+  DBMS_DATA_MINING.register_algorithm(
+    ALGORITHM_NAME         => 't1',
+    algorithm_metadata     => 
+    '{"function_language":"R",
+      "mining_function" : { "mining_function_name" : "CLASSIFICATION",
+                            "build_function" : {"function_body":
+"function(dat, form) {require(rpart); set.seed(1234); mod <- rpart(formula = formula(form), method=''class'', data=dat); mod}"},
+                            "weight_function" : {"function_body": 
+"function(mod, dat, clas) {require(rpart); v0 <- as.data.frame(predict(mod, newdata=dat, type = ''prob''));res <- data.frame(lapply(seq_along(dat),function(x, dat) {if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(NA) else dat[,x] <- as.factor(NA); vv <- as.data.frame(predict(mod, newdata = dat, type = ''prob'')); v0[[clas]] / vv[[clas]]}, dat = dat)); names(res) <- names(dat); res}"},
+                            "detail_function" : [{"function_body":
+"function(object, x) {mod.frm <- object$frame; data.frame(node = row.names(mod.frm), split = mod.frm$var, n = mod.frm$n,ln = mod.frm$yval2[,2], rn = mod.frm$yval2[,3])}",
+                                                 "view_columns": [{"NAME": "node", "TYPE": "VARCHAR2(100)"}, {"NAME": "split", "TYPE": "VARCHAR2(2000)"}, {"NAME": "NodeCnt", "TYPE": "number"}, {"NAME": "LeftNodeCnt", "TYPE": "number"},{"NAME": "RightNodeCnt", "TYPE": "number"}]},
+                                                 {"function_body":
+"function(object, x) {mod.frm <- object$frame; data.frame(node = row.names(mod.frm), split = mod.frm$var, n = mod.frm$n,ln = mod.frm$yval2[,2], rn = mod.frm$yval2[,3])}",
+                                                 "view_columns": [{"NAME": "node", "TYPE": "VARCHAR2(100)"}, {"NAME": "split", "TYPE": "VARCHAR2(2000)"}, {"NAME": "NodeCnt", "TYPE": "number"}, {"NAME": "LeftNodeCnt", "TYPE": "number"},{"NAME": "RightNodeCnt", "TYPE": "number"}]}],
+                            "score_function" : {"function_body":
+"function(mod, dat) {require(rpart);res <- data.frame(predict(mod, newdata=dat, type = ''prob'')); names(res) <- c(''0'', ''1''); res}"}}}',
+    algorithm_description  => 't1');
+END;
+/
+
+-- Model Settings -------------------------------------------------------------
+
+CREATE TABLE DT_RDEMO_SETTINGS_CL (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+
+BEGIN
+ INSERT INTO DT_RDEMO_SETTINGS_CL VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+  INSERT INTO DT_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.algo_name, 't1');
+END;
+/
+
+BEGIN
+  INSERT INTO DT_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.r_formula, 'AGE + EDUCATION + HOUSEHOLD_SIZE + OCCUPATION');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R build script user has already registered. 
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'DT_RDEMO_CLASSIFICATION',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AFFINITY_CARD',
+    settings_table_name => 'DT_RDEMO_SETTINGS_CL');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Display the model details using R detail function user has already registered
+
+column SPLIT format a12;
+select to_number(node) as node, split, nodecnt, leftnodecnt, rightnodecnt 
+from DM$V0DT_RDEMO_CLASSIFICATION
+order by node, split;
+
+column SPLIT format a12;
+select to_number(node) as node, split, nodecnt, leftnodecnt, rightnodecnt 
+from DM$V1DT_RDEMO_CLASSIFICATION
+order by node, split;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R score and weight function scripts user registered. 
+
+column pred_det format a65;
+SELECT CUST_ID, PREDICTION_DETAILS(DT_RDEMO_CLASSIFICATION, '1' USING *) pred_det
+FROM mining_data_apply_v where EDUCATION = 'Bach.' and HOUSEHOLD_SIZE = '3'
+and CUST_ID <= 100080 order by CUST_ID;
+
+------------------------ Drop Models and Algorithms ---------------------------
+
+BEGIN
+  DBMS_DATA_MINING.drop_algorithm(
+    ALGORITHM_NAME => 't1',
+    CASCADE => TRUE);
+END;
+/
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-association-rules.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-association-rules.sql
new file mode 100644
index 00000000..fb3ca09d
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-association-rules.sql
@@ -0,0 +1,239 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Association Rules Algorithm - dmrardemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 140
+SET LONG 10000
+SET echo ON
+
+
+-----------------------------------------------------------------------
+--                            SET UP THE DATA
+-----------------------------------------------------------------------
+
+-- Cleanup old training data view for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW ar_build_v';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a view for building association rules model
+-- The data for this sample is composed from a small subset of
+-- sales transactions in the SH schema - listing the (multiple)
+-- items bought by a set of customers with ids in the range
+-- 100001-104500.
+--
+CREATE VIEW ar_build_v AS
+SELECT cust_id, prod_name, prod_category, amount_sold
+FROM (SELECT a.cust_id, b.prod_name, b.prod_category,
+             a.amount_sold
+        FROM sh.sales a, sh.products b
+       WHERE a.prod_id = b.prod_id AND
+             a.cust_id between 100001 AND 104500);
+
+
+--
+-- We will build two separate models for rules and itemsets, respectively.
+--
+-----------------------------------------------------------------------
+--                          BUILD THE MODEL for RULES
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RAR_SH_AR_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table and R scripts for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE Rar_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN 
+sys.rqScriptDrop('RAR_BUILD');
+sys.rqScriptDrop('RAR_DETAILS');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+CREATE TABLE Rar_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+------------
+-- R scripts
+--
+-- The R scripts are created by users using sys.rqScriptCreate to define
+-- their own approaches in R for building Association Rules models in 
+-- ODM framework.
+
+BEGIN
+  INSERT INTO Rar_sh_sample_settings VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+ 
+-- The BUILD script will be invoked during CREATE_MODEL
+-- Our script here uses the apriori algorithm in R's arules package 
+-- to mine rules
+  sys.rqScriptCreate('RAR_BUILD', 
+    'function(dat){
+     library(arules)
+     trans <- as(split(dat[["PROD_NAME"]], dat[["CUST_ID"]]), "transactions")
+     r <- apriori(trans, parameter = list(minlen=2, supp=0.1, conf=0.5, target="rules"))
+     as(r, "data.frame")}');
+
+-- The DETAILS script, along with the FORMAT script below will be 
+-- invoked during CREATE_MODEL. A model view will be generated with 
+-- the output of the DETAILS script. We deliver the mined rules through
+-- the model view                       
+  sys.rqScriptCreate('RAR_DETAILS',
+     'function(mod) {mod}');
+    
+  INSERT INTO Rar_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_build_function, 'RAR_BUILD');
+  INSERT INTO Rar_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_function, 'RAR_DETAILS');
+  INSERT INTO Rar_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_format, 
+  'select cast(''a'' as varchar2(100)) rules, 1 support, 1 confidence, 1 lift from dual');
+END;
+/
+
+---------------
+-- CREATE MODEL
+--
+-- let case_id_column_name be NULL, as the case_id_column_name should be
+-- identified in the R BUILD script
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RAR_SH_AR_SAMPLE',
+    mining_function     => dbms_data_mining.association,
+    data_table_name     => 'AR_BUILD_V',
+    case_id_column_name => NULL,
+    settings_table_name => 'Rar_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a40
+select setting_name, setting_value from Rar_sh_sample_settings
+order by setting_name;
+      
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column model_name format a20
+column mining_function format a20
+column algorithm format a20
+select model_name, mining_function, algorithm from user_mining_models
+where model_name = 'RAR_SH_AR_SAMPLE';
+
+------------------------------------
+-- DISPLAY THE RULES USING MODEL VIEW
+-- The model view was generated during CREATE_MODEL
+--
+column partition_name format a5
+column rules format A30
+select * from DM$VDRAR_SH_AR_SAMPLE order by confidence desc;
+
+
+
+-----------------------------------------------------------------------
+--                          BUILD THE MODEL for ITEMSETS
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RAR_SH_FI_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE Rar_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+
+CREATE TABLE Rar_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+  INSERT INTO Rar_sh_sample_settings VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+ 
+-- Our script here uses the apriori algorithm in R's arules package to 
+-- mine itemsets
+  sys.rqScriptCreate('RAR_BUILD', 
+    'function(dat){
+     library(arules)
+     trans <- as(split(dat[["PROD_NAME"]], dat[["CUST_ID"]]), "transactions")
+     items <- apriori(trans, parameter = list(supp=0.1, target="frequent"))
+     as(items, "data.frame")}', v_overwrite => TRUE);
+            
+  sys.rqScriptCreate('RAR_DETAILS',
+     'function(mod) {mod}', v_overwrite => TRUE);
+    
+  INSERT INTO Rar_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_build_function, 'RAR_BUILD');
+  INSERT INTO Rar_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_function, 'RAR_DETAILS');
+  INSERT INTO Rar_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_format, 
+  'select cast(''a'' as varchar2(100)) items, 1 support from dual');
+END;
+/
+
+
+---------------
+-- CREATE MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RAR_SH_FI_SAMPLE',
+    mining_function     => dbms_data_mining.association,
+    data_table_name     => 'AR_BUILD_V',
+    case_id_column_name => NULL,
+    settings_table_name => 'Rar_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a40
+select setting_name, setting_value from Rar_sh_sample_settings
+order by setting_name;
+      
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column model_name format a20
+column mining_function format a20
+column algorithm format a20
+select model_name, mining_function, algorithm from user_mining_models
+where model_name = 'RAR_SH_FI_SAMPLE';
+
+---------------------------------------
+-- DISPLAY THE ITEMSETS USING MODEL VIEW
+--
+column partition_name format a5
+column items format a50
+select * from DM$VDRAR_SH_FI_SAMPLE order by support desc;
+
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-attribute-importance-via-rf.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-attribute-importance-via-rf.sql
new file mode 100644
index 00000000..98366a96
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-attribute-importance-via-rf.sql
@@ -0,0 +1,126 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Attribute Importance via RF Algorithm - dmraidemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+-------------------------------------------------------------------------------
+--                         ATTRIBUTE IMPORTANCE DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the attribute importance algorithm in 
+-- Oracle Data Mining using R randomForest algorithm
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE AI_RDEMO_SETTINGS';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+  sys.rqScriptDrop('AI_RDEMO_BUILD_FUNCTION', v_silent => TRUE);
+  sys.rqScriptDrop('AI_RDEMO_DETAILS_FUNCTION', v_silent => TRUE);
+END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('AI_RDEMO');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create setting table -------------------------------------------------------
+create table AI_RDEMO_SETTINGS(
+        setting_name varchar2(30),
+        setting_value varchar2(4000));
+
+BEGIN
+ INSERT INTO AI_RDEMO_SETTINGS VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named AI_RDEMO_BUILD_FUNCTION is defined. This 
+-- function builds and returns a random forest model using R randomForest 
+-- algorithm. User can also choose other R algorithm to get the attribute 
+-- importance.
+
+  sys.rqScriptCreate('AI_RDEMO_BUILD_FUNCTION', 'function(dat) {
+    require(randomForest); 
+    set.seed(1234);
+    mod <- randomForest(AFFINITY_CARD ~ ., data=dat);
+    mod}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named AI_RDEMO_DETAILS_FUNCTION 
+-- is defined. This function creates and returns an R data.frame containing the 
+-- attribute importance of the built model. User can also display other details.
+
+  sys.rqScriptCreate('AI_RDEMO_DETAILS_FUNCTION', 'function(object, x)
+   {require(randomForest); 
+   mod <- object;
+   data.frame(row_name=row.names(mod$importance), importance=mod$importance)}');
+
+   INSERT INTO AI_RDEMO_SETTINGS 
+     VALUES(dbms_data_mining.ralg_build_function, 'AI_RDEMO_BUILD_FUNCTION');
+   INSERT INTO AI_RDEMO_SETTINGS 
+     VALUES(dbms_data_mining.ralg_details_function, 'AI_RDEMO_DETAILS_FUNCTION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- attribute names and the corresponding importance.
+
+   INSERT INTO AI_RDEMO_SETTINGS 
+     VALUES(dbms_data_mining.ralg_details_format, 
+     'select cast(''a'' as varchar2(100)) name, 1 importance from dual');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- AI_RDEMO_BUILD_FUNCTION will be used to create the model AI_RDEMO, using 
+-- dataset mining_data_build_v.
+
+begin
+  dbms_data_mining.create_model(
+    model_name          => 'AI_RDEMO',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AFFINITY_CARD',
+    settings_table_name => 'AI_RDEMO_SETTINGS');
+end;
+/
+
+-------------------------------------------------------------------------------
+--                           ATTRIBUTE IMPORTANCE
+-------------------------------------------------------------------------------
+
+-- Attribute Importance
+-- Explanation:
+-- Display the model details using the R script user defined. Here R script 
+-- AI_RDEMO_DETAIL_FUNCTION will be used to provide the attribute importance.
+
+column name format a30;
+select name, round(importance, 3) as importance, 
+rank() OVER (ORDER BY importance DESC) rank 
+from DM$VDAI_RDEMO order by importance desc, name;
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-glm.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-glm.sql
new file mode 100644
index 00000000..b7127d46
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-glm.sql
@@ -0,0 +1,382 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Generalized Linear Model Algorithm - dmrglmdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET LONG 10000
+
+-------------------------------------------------------------------------------
+--                         GLM REGRESSION DEMO
+-------------------------------------------------------------------------------
+-- Explanation:
+-- This demo shows how to implement the GLM regression algorithm in Oracle Data 
+-- Mining using R glm algorithm.
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLM_RDEMO_SETTINGS_RE';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+Begin
+  sys.rqScriptDrop('GLM_RDEMO_BUILD_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('GLM_RDEMO_SCORE_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('GLM_RDEMO_DETAILS_REGRESSION', v_silent => TRUE);
+End;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_RDEMO_REGRESSION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE GLM_RDEMO_SETTINGS_RE (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO GLM_RDEMO_SETTINGS_RE VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+Begin
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named GLM_RDEMO_BUILD_REGRESSION is defined. This 
+-- function builds and returns a GLM regression model using R glm algorithm. 
+-- User can also choose other R algorithm to implement the GLM regression 
+-- algorithm.
+
+  sys.rqScriptCreate('GLM_RDEMO_BUILD_REGRESSION', 'function(dat, wgt) {
+   set.seed(1234); 
+   mod <- glm(AGE ~ ., data = dat, weights = wgt); mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named GLM_RDEMO_SCORE_REGRESSION is defined. 
+-- This function creates and returns an R data.frame containing the target 
+-- predictions with se.fit on. User can also define other prediction functions
+-- with different settings.
+
+  sys.rqScriptCreate('GLM_RDEMO_SCORE_REGRESSION', 'function(mod, dat) {
+   res <- predict(mod, newdata = dat, se.fit = TRUE); 
+   data.frame(fit=res$fit, se=res$se.fit, df=summary(mod)$df[1L]) }');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named GLM_RDEMO_DETAILS_REGRESSION
+-- is defined. This function creates and returns an R data.frame containing the 
+-- attribute coefficients of the built model. User can also display other details.
+
+  sys.rqScriptCreate('GLM_RDEMO_DETAILS_REGRESSION', 'function(object) {
+   mod <- object; 
+   data.frame(name=names(mod$coefficients), 
+   coef=mod$coefficients)}');
+
+  INSERT INTO GLM_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_build_function, 'GLM_RDEMO_BUILD_REGRESSION');
+  INSERT INTO GLM_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_score_function, 'GLM_RDEMO_SCORE_REGRESSION');
+  INSERT INTO GLM_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_details_function, 'GLM_RDEMO_DETAILS_REGRESSION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- attribute names and the corresponding coefficients.
+
+  INSERT INTO GLM_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_details_format, 
+    'select cast(''a'' as varchar2(200)) attr, 1 coef from dual');
+
+-- Column YRS_RESIDENCE has row weights.
+
+  INSERT INTO GLM_RDEMO_SETTINGS_RE 
+    VALUES('ODMS_ROW_WEIGHT_COLUMN_NAME', 'YRS_RESIDENCE');
+End;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- GLM_RDEMO_BUILD_REGRESSION will be used to create the GLM regression model 
+-- GLM_RDEMO_REGRESSION using dataset mining_data_build_v.
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLM_RDEMO_REGRESSION',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AGE',
+    settings_table_name => 'GLM_RDEMO_SETTINGS_RE');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Display the details of the built model using the R script user defined. 
+-- Here R script GLM_RDEMO_DETAIL_REGRESSION will be used to display the model 
+-- details.
+
+column attr format a40
+select attr, round(coef, 3) as coef from DM$VDGLM_RDEMO_REGRESSION 
+order by attr;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. Here R script 
+-- GLM_RDEMO_SCORE_REGRESSION will be used to do the scoring. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Show actual target value and predicted target values.
+
+SELECT CUST_ID, round(PREDICTION(GLM_RDEMO_REGRESSION USING *), 3) as AGE_pred, 
+AGE as AGE_act 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-- PREDICTION_BOUND -----------------------------------------------------------
+-- Explanation:
+-- Show actual target value, predicted target values, upper bounds, lower 
+-- bounds. 
+
+SELECT CUST_ID, AGE, 
+       round(PREDICTION(GLM_RDEMO_REGRESSION USING *), 3) as AGE_pred,
+       round(PREDICTION_BOUNDS(GLM_RDEMO_REGRESSION USING *).UPPER, 3) as upp, 
+       round(PREDICTION_BOUNDS(GLM_RDEMO_REGRESSION USING *).LOWER, 3) as low 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-- Specify Confidence Level 0.9 -----------------------------------------------
+-- Explanation:
+-- Show predicted target values, bounds, middle value. 
+
+select CUST_ID, round(AGE_pred, 3) as AGE_pred, 
+round((upp - low)/2, 3) as bound, round((low+upp)/2, 3) as pred_mid
+from (select CUST_ID, PREDICTION(GLM_RDEMO_REGRESSION USING *) AGE_pred,
+             PREDICTION_BOUNDS(GLM_RDEMO_REGRESSION, 0.9 USING *).LOWER low,
+             PREDICTION_BOUNDS(GLM_RDEMO_REGRESSION, 0.9 USING *).UPPER upp
+FROM mining_data_apply_v where CUST_ID <= 100010) 
+order by CUST_ID;
+
+-------------------------------------------------------------------------------
+--                        GLM CLASSIFICATION DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the GLM classification algorithm in Oracle 
+-- Data Mining using R glm algorithm.
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE GLM_RDEMO_SETTINGS_CL';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+Begin
+  sys.rqScriptDrop('GLM_RDEMO_BUILD_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('GLM_RDEMO_SCORE_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('GLM_RDEMO_DETAILS_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('GLM_RDEMO_WEIGHT_CLASSIFICATION', v_silent => TRUE);
+End;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('GLM_RDEMO_CLASSIFICATION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE GLM_RDEMO_SETTINGS_CL (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO GLM_RDEMO_SETTINGS_CL VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+Begin
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named GLM_RDEMO_BUILD_CLASSIFICATION is defined. 
+-- This function builds and returns a GLM classification model using R glm 
+-- algorithm. User can also choose other R algorithm to implement the GLM 
+-- classification algorithm.
+
+  sys.rqScriptCreate('GLM_RDEMO_BUILD_CLASSIFICATION', 
+                     'function(dat, form, keep.model) {
+   set.seed(1234); 
+   mod <- glm(formula = formula(form), data=dat, 
+              family=binomial(logit), model = as.logical(keep.model)); 
+   mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named GLM_RDEMO_SCORE_CLASSIFICATION is 
+-- defined. This function creates and returns an R data.frame containing the 
+-- target predictions using type response. User can also define other prediction 
+-- functions with different types.
+
+  sys.rqScriptCreate('GLM_RDEMO_SCORE_CLASSIFICATION', 'function(mod, dat) {
+   res <- predict(mod, newdata = dat, type="response"); 
+   res2=data.frame(1-res, res); names(res2) <- c("0", "1"); res2}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named 
+-- GLM_RDEMO_DETAILS_CLASSIFICATION is defined. This function creates and 
+-- returns an R data.frame containing the attribute coefficients of the built 
+-- model. User can also display other details.
+
+  sys.rqScriptCreate('GLM_RDEMO_DETAILS_CLASSIFICATION', 'function(object) {
+   mod <- object; 
+   data.frame(name=names(mod$coefficients), 
+   coef=mod$coefficients)}');
+
+-- Model Weight R Function ----------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to provide the attribute weights
+-- of the scoring data. For example, here a script named 
+-- GLM_RDEMO_WEIGHT_CLASSIFICATION is defined. This function creates and returns
+-- an R data.frame containing the weights of each attribute of the scoring data.
+-- Here we simply use the product of the attribute value with the attribute
+-- coefficients as the weight of the missing attribute. User can define their 
+-- own method to calculate the attribute weight.
+
+  sys.rqScriptCreate('GLM_RDEMO_WEIGHT_CLASSIFICATION', 'function(mod, dat, clas) {
+
+   v <- predict(mod, newdata=dat, type = "response");
+   v0 <- data.frame(v, 1-v); names(v0) <- c("0", "1");
+   res <- data.frame(lapply(seq_along(dat),
+   function(x, dat) {
+   if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(0)
+   else dat[,x] <- as.factor(NA);
+   vv <- predict(mod, newdata = dat, type = "response");
+   vv = data.frame(vv, 1-vv); names(vv) <- c("0", "1");
+   v0[[clas]] / vv[[clas]]}, dat = dat));
+   names(res) <- names(dat);
+   res}');
+
+  INSERT INTO GLM_RDEMO_SETTINGS_CL  
+    VALUES(dbms_data_mining.ralg_build_function, 'GLM_RDEMO_BUILD_CLASSIFICATION');
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_score_function, 'GLM_RDEMO_SCORE_CLASSIFICATION');
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_function, 'GLM_RDEMO_DETAILS_CLASSIFICATION');
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_weight_function, 'GLM_RDEMO_WEIGHT_CLASSIFICATION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- attribute names and the corresponding coefficients.
+
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_format, 
+    'select cast(''a'' as varchar2(200)) attr, 1 coef from dual');
+
+-- In this setting, a formula is specified,  which will be passed as a parameter 
+-- to the model build function to build the model.
+
+  INSERT INTO GLM_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_build_parameter, 
+    'select ''AFFINITY_CARD ~ AGE + EDUCATION + HOUSEHOLD_SIZE + OCCUPATION'' ' ||
+    '"form", 0 "keep.model" from dual');
+End;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- GLM_RDEMO_BUILD_CLASSIFICATION will be used to create the GLM classification
+-- model GLM_RDEMO_CLASSIFICATION using dataset mining_data_build_v.
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLM_RDEMO_CLASSIFICATION',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AFFINITY_CARD',
+    settings_table_name => 'GLM_RDEMO_SETTINGS_CL');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Display the details of the built model using the R script user defined. 
+-- Here R script GLM_RDEMO_DETAIL_CLASSIFICATION will be used to display the 
+-- model details.
+
+column attr format a40
+select attr, round(coef, 3) as coef from DM$VDGLM_RDEMO_CLASSIFICATION 
+order by attr;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Here R script GLM_RDEMO_SCORE_CLASSIFICATION is used to get the prediction 
+-- value and the prediction probability. Actual target value and predicted 
+-- target values are provided.
+
+SELECT CUST_ID, AFFINITY_CARD as AFFINITY_CARD_act, 
+PREDICTION(GLM_RDEMO_CLASSIFICATION USING *) AFFINITY_CARD_pred,
+round(PREDICTION_PROBABILITY(GLM_RDEMO_CLASSIFICATION USING *), 3) 
+as AFFINITY_CARD_prob 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-- PREDICTION_SET -------------------------------------------------------------
+-- Explanation:
+-- Here R script GLM_RDEMO_SCORE_CLASSIFICATION is used to get the 
+-- prediction set. Actual target value and predicted target values are provided.
+
+select T.CUST_ID, T.AFFINITY_CARD, S.prediction, 
+round(S.probability, 3) as probability 
+from (select CUST_ID, AFFINITY_CARD, 
+PREDICTION_SET(GLM_RDEMO_CLASSIFICATION USING *) pset 
+from mining_data_apply_v where CUST_ID <= 100010) T, TABLE(T.pset) S
+where S.probability > 0 
+order by T.CUST_ID, S.prediction;
+
+-- PREDICTION_DETAILS ---------------------------------------------------------
+-- Explanation:
+-- The R script GLM_RDEMO_WEIGHT_CLASSIFICATION is used to get the prediction 
+-- details. The CUST_ID and the prediction details with the weight of each 
+-- attribute are provided.
+
+column pred_det format a65;
+SELECT CUST_ID, PREDICTION_DETAILS(GLM_RDEMO_CLASSIFICATION, '0' USING *) pred_det
+FROM mining_data_apply_v where CUST_ID <= 100010 order by CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-kmeans.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-kmeans.sql
new file mode 100644
index 00000000..75fe9993
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-kmeans.sql
@@ -0,0 +1,338 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - K-Means Algorithm - dmrkmdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 140
+SET LONG 10000
+SET echo ON
+
+
+-----------------------------------------------------------------------
+--                            SET UP THE DATA
+-----------------------------------------------------------------------
+
+-- Cleanup old training data view for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW km_build_v';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a view for building kmeans model
+create view km_build_v as
+select cust_id, age, yrs_residence, affinity_card, bulk_pack_diskettes,
+flat_panel_monitor, home_theater_package, bookkeeping_application, y_box_games  
+from mining_data_build_v;
+
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RKM_SH_CLUS_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table and R scripts for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE Rkm_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+sys.rqScriptDrop('RKM_BUILD');
+sys.rqScriptDrop('RKM_SCORE');
+sys.rqScriptDrop('RKM_WEIGHT');
+sys.rqScriptDrop('RKM_DETAILS');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+CREATE TABLE Rkm_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+------------
+-- R scripts
+--
+-- The R scripts are created by users using sys.rqScriptCreate to define
+-- their own approaches in R for building CLUSTERING models and 
+-- scoring new data in ODM framework.
+--
+-- Here is the mapping between the R scripts and ODM functions/PROCs that
+-- invoke and use the R scripts. Please refer to user guide for details.
+--------------------------------------------------------------------------
+-- ralg_build_function           -------   CREATE_MODEL 
+-- ralg_score_function           -------   CLUSTER_ID, CLUSTER_PROBABILITY
+--                                         CLUSTER_SET, CLUSTER_DISTANCE
+-- ralg_weight_function          -------   CLUSTER_DETAILS
+-- ralg_details_function         -------   CREATE_MODEL(to generate model view)
+-- ralg_details_format           -------   CREATE_MODEL(to generate model view)
+
+BEGIN
+  INSERT INTO Rkm_sh_sample_settings VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+ 
+-- Our BUILD script here uses R's kmeans function to build a kmeans model.
+-- We centralize and normalize the training data before the model build.
+-- Predefined attribute dm$nclus must be set on the generated R model to
+-- indicate the number of clusters produced by the clustering model fit.
+  sys.rqScriptCreate('RKM_BUILD', 
+    'function(dat) {dat.scaled <- scale(dat)
+     set.seed(6543); mod <- list()
+     fit <- kmeans(dat.scaled, centers = 3L)
+     mod[[1L]] <- fit
+     mod[[2L]] <- attr(dat.scaled, "scaled:center")
+     mod[[3L]] <- attr(dat.scaled, "scaled:scale")
+     attr(mod, "dm$nclus") <- nrow(fit$centers)
+     mod}');
+
+-- Our SCORE script here calculates the probabilities and distances to
+-- each cluster of the new data. It returns a data.frame combining columns
+-- of cluster probabilities and columns of cluster distances.
+-- We calculate the probability based on the normal distribution with distance.
+-- The distance here is referred to Euclidean distance.   
+  sys.rqScriptCreate('RKM_SCORE',
+    'function(x, dat){
+     mod <- x[[1L]]; ce <- x[[2L]]; sc <- x[[3L]]
+     newdata = scale(dat, center = ce, scale = sc)
+     centers <- mod$centers
+     ss <- sapply(as.data.frame(t(centers)), 
+     function(v) rowSums(scale(newdata, center=v, scale=FALSE)^2))
+     if (!is.matrix(ss)) ss <- matrix(ss, ncol=length(ss))
+     disp <- -1 / (2* mod$tot.withinss/length(mod$cluster))
+     distr <- exp(disp*ss)
+     prob <- distr / rowSums(distr)
+     as.data.frame(cbind(prob, sqrt(ss)))}');
+
+-- Our WEIGHT script here calculates the attribute importance of new data for
+-- the specified cluster. It returns a data.frame with each column representing
+-- the weights of the corresponding attribute.
+-- We calulate the new probability without accounting for an attribute, and
+-- the attribute importance is the difference between the original probability
+-- by SCORING and the new probability.
+  sys.rqScriptCreate('RKM_WEIGHT', 
+     'function(x, dat, clus) {
+      clus <- as.numeric(clus)
+      mod <- x[[1L]]; ce <- x[[2L]]; sc <- x[[3L]]
+      newdata <- scale(dat, center = ce, scale = sc)
+      centers <- mod$centers
+      ss <- sapply(as.data.frame(t(centers)),
+      function(v) rowSums(scale(newdata, center=v, scale=FALSE)^2))
+      if (!is.matrix(ss)) ss <- matrix(ss, ncol=length(ss))
+      disp <- -1 / (2* mod$tot.withinss/length(mod$cluster))
+      distr <- exp(disp*ss)
+      prob0 <- distr[, clus] / rowSums(distr)
+      for (iattr in 1:ncol(newdata)) {
+      newd <- newdata[, -iattr]
+      if(!is.matrix(newd)) newd <- matrix(newd, ncol=length(newd))
+      ss <- sapply(as.data.frame(t(centers[, -iattr])),
+      function(v) rowSums(scale(newd, center=v, scale=FALSE)^2))
+      if (!is.matrix(ss)) ss <- matrix(ss, ncol=length(ss))
+      distr <- exp(disp*ss)
+      prob <- distr[, clus] / rowSums(distr)
+      w <- prob0-prob
+      if (iattr == 1) res <- as.data.frame(w)
+      else res <- cbind(res, w)
+      }
+      colnames(res) <- colnames(mod$centers)
+      res}');
+
+-- The DETAILS script, along with the FORMAT script below will be 
+-- invoked during CREATE_MODEL. A model view will be generated with 
+-- the output of the DETAILS script.
+-- Our DETAILS script returns a data.frame containing the sum of squares 
+-- within clusters and the cluster size of the model.        
+  sys.rqScriptCreate('RKM_DETAILS',
+     'function(x) {
+      mod <- x[[1L]]
+      data.frame(clus = seq(length(mod$size)), withinss=mod$withinss, 
+      clussize=mod$size)}');
+    
+  INSERT INTO Rkm_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_build_function, 'RKM_BUILD');
+  INSERT INTO Rkm_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_score_function, 'RKM_SCORE');
+  INSERT INTO Rkm_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_weight_function, 'RKM_WEIGHT');
+  INSERT INTO Rkm_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_function, 'RKM_DETAILS');
+  INSERT INTO Rkm_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_format, 
+  'select 1 clus, 1 withinss, 1 clussize from dual');
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RKM_SH_CLUS_SAMPLE',
+    mining_function     => dbms_data_mining.clustering,
+    data_table_name     => 'KM_BUILD_V',
+    case_id_column_name => 'CUST_ID',
+    settings_table_name => 'Rkm_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+select setting_name, setting_value from Rkm_sh_sample_settings
+order by setting_name;
+      
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column model_name format a20
+column mining_function format a20
+column algorithm format a20
+select model_name, mining_function, algorithm from user_mining_models
+where model_name = 'RKM_SH_CLUS_SAMPLE';
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+column partition_name format a20
+select * from DM$VDRKM_SH_CLUS_SAMPLE order by clus;
+
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-- For a descriptive mining function like Clustering, "Scoring" involves
+-- providing the probability values, distances for each cluster.
+
+-- List the count per cluster into which the customers in this
+-- given dataset have been grouped.
+--
+SELECT CLUSTER_ID(RKM_SH_CLUS_SAMPLE USING *) AS clus, COUNT(*) AS cnt
+  FROM mining_data_apply_v
+GROUP BY CLUSTER_ID(RKM_SH_CLUS_SAMPLE USING *)
+ORDER BY clus;
+
+-- List the cluster and the corresponding probabilities for 15 new customers
+--
+SELECT cust_id, clus, prob, prob_1, prob_2, prob_3, prob_1+prob_2+prob_3 prob_tot
+FROM (SELECT cust_id, CLUSTER_ID(RKM_SH_CLUS_SAMPLE USING *) clus,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE USING *) prob,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE, 1 USING *) prob_1,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE, 2 USING *) prob_2,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE, 3 USING *) prob_3            
+      FROM mining_data_apply_v
+      where cust_id <= 100015
+      ORDER BY cust_id);
+      
+-- List the probabilities for each cluster for 15 new customers 
+-- using CLUSTER_SET      
+--
+select T.cust_id, S.cluster_id, S.probability
+from (select cust_id, CLUSTER_SET(RKM_SH_CLUS_SAMPLE USING *) pset 
+      FROM mining_data_apply_v) T, TABLE(T.pset) S
+where T.cust_id <= 100015
+order by T.cust_id, S.cluster_id;
+
+-- List the cluster and distance to its centroid for 15 new customers 
+--
+select cust_id, CLUSTER_ID(RKM_SH_CLUS_SAMPLE USING *) clus, 
+CLUSTER_DISTANCE(RKM_SH_CLUS_SAMPLE USING *) dis
+from mining_data_apply_v
+where cust_id <= 100015 order by cust_id;
+
+-- List the 5 most important attributes for each row for 15 new customers
+--
+column clus_det format a60
+SELECT cust_id, CLUSTER_DETAILS(RKM_SH_CLUS_SAMPLE USING *) clus_det
+FROM mining_data_apply_v where cust_id <= 100015 order by cust_id;
+
+-- List the 10 rows which are most anomalous as measured by their
+-- distance from the cluster centroids.  A row which is far from
+-- all cluster centroids may be anomalous.
+--
+select cust_id, dist
+from(
+select cust_id, CLUSTER_DISTANCE(RKM_SH_CLUS_SAMPLE USING *) dist,
+       rank() over (order by CLUSTER_DISTANCE(RKM_SH_CLUS_SAMPLE USING *) desc) rnk 
+from mining_data_apply_v)
+where rnk <=10
+order by rnk;
+
+
+
+-----------------------------------------------------------------------
+--                      BUILD A MODEL USING SAMPLING
+-----------------------------------------------------------------------
+-- This example illustrates building a kmeans model by sampling the
+-- training data. We use the same settings table in the above example 
+-- with additional sampling settings.
+
+-- Enable sampling and specify sample size in setting table
+INSERT INTO Rkm_sh_sample_settings VALUES
+('ODMS_SAMPLING', 'ODMS_SAMPLING_ENABLE');
+INSERT INTO Rkm_sh_sample_settings VALUES 
+('ODMS_SAMPLE_SIZE', 1000);
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RKM_SH_CLUS_SAMPLE_S');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-----------------------------------
+-- CREATE A NEW MODEL WITH SAMPLING
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RKM_SH_CLUS_SAMPLE_S',
+    mining_function     => dbms_data_mining.clustering,
+    data_table_name     => 'KM_BUILD_V',
+    case_id_column_name => 'CUST_ID',
+    settings_table_name => 'Rkm_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+select setting_name, setting_value from Rkm_sh_sample_settings
+order by setting_name;
+      
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+select model_name, mining_function, algorithm from user_mining_models
+where model_name = 'RKM_SH_CLUS_SAMPLE_S';
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+column partition_name format a20
+select * from DM$VDRKM_SH_CLUS_SAMPLE_S order by clus;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+
+-- List the cluster and the corresponding probabilities for 15 new customers
+--
+SELECT cust_id, clus, prob, prob_1, prob_2, prob_3, prob_1+prob_2+prob_3 prob_tot
+FROM (SELECT cust_id, CLUSTER_ID(RKM_SH_CLUS_SAMPLE_S USING *) clus,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE_S USING *) prob,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE_S, 1 USING *) prob_1,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE_S, 2 USING *) prob_2,
+             CLUSTER_PROBABILITY(RKM_SH_CLUS_SAMPLE_S, 3 USING *) prob_3            
+      FROM mining_data_apply_v
+      where cust_id <= 100015
+      ORDER BY cust_id);
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-principal-components.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-principal-components.sql
new file mode 100644
index 00000000..7541fe18
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-principal-components.sql
@@ -0,0 +1,316 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Principal Components Algorithm - dmrpcademo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET linesize 140
+SET LONG 10000
+SET echo ON
+
+
+-----------------------------------------------------------------------
+--                            SET UP THE DATA
+-----------------------------------------------------------------------
+
+-- Cleanup old training data view for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW pca_build_v';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a view for building PCA model
+create view pca_build_v as
+select cust_id, age, yrs_residence, affinity_card, bulk_pack_diskettes,
+flat_panel_monitor, home_theater_package, bookkeeping_application, y_box_games  
+from mining_data_build_v;
+
+-----------------------------------------------------------------------
+--                            BUILD A PCA MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RPCA_SH_FE_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table and R scripts for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE Rpca_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+sys.rqScriptDrop('RPCA_BUILD');
+sys.rqScriptDrop('RPCA_SCORE');
+sys.rqScriptDrop('RPCA_WEIGHT');
+sys.rqScriptDrop('RPCA_DETAILS');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+CREATE TABLE Rpca_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+------------
+-- R scripts
+--
+-- The R scripts are created by users using sys.rqScriptCreate to define
+-- their own approaches in R for building FEATURE EXTRACTION models and 
+-- scoring new data in ODM framework.
+--
+-- Here is the mapping between the R scripts and ODM functions/PROCs that
+-- invoke and use the R scripts. Please refer to user guide for details.
+--------------------------------------------------------------------------
+-- ralg_build_function           -------   CREATE_MODEL 
+-- ralg_score_function           -------   FEATURE_VALUE, FEATURE_SET
+-- ralg_weight_function          -------   FEATURE_DETAILS
+-- ralg_details_function         -------   CREATE_MODEL(to generate model view)
+-- ralg_details_format           -------   CREATE_MODEL(to generate model view)
+
+BEGIN
+  INSERT INTO Rpca_sh_sample_settings VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+  
+-- Our BUILD script here uses R's prcomp function to build a PCA model  
+-- Predefined attribute dm$nfeat must be set on the generated R model to
+-- indicate the number of features extracted by the model fit.
+  sys.rqScriptCreate('RPCA_BUILD', 
+    'function(dat) {
+     mod <- prcomp(dat, retx = FALSE)
+     attr(mod, "dm$nfeat") <- ncol(mod$rotation)
+     mod}');
+
+-- Our SCORE script here uses the predict method for prcomp to generate
+-- the mapped feature values of the new data. It returns a data.frame
+-- with each column representing a projected new feature.   
+  sys.rqScriptCreate('RPCA_SCORE',
+    'function(mod, dat) { 
+     res <- predict(mod, dat)
+     as.data.frame(res)}');
+
+-- Our WEIGHT script here calculates the contribution of each attribute
+-- to the specified feature. It returns a data.frame with each column
+-- representing the weight of the corresponding attribute.
+  sys.rqScriptCreate('RPCA_WEIGHT', 
+    'function(mod, dat, feature) {
+     feature <- as.numeric(feature)
+     dat <- scale(dat, center = mod$center, scale = FALSE)
+     v <- mod$rotation[, feature]
+     as.data.frame(t(apply(dat, 1L, function(u) v*u)))}');
+
+-- The DETAILS script, along with the FORMAT script below will be 
+-- invoked during CREATE_MODEL. A model view will be generated with 
+-- the output of the DETAILS script.                                                                            
+-- Our DETAILS script returns a data.frame containing the standard 
+-- deviation of the new features.           
+  sys.rqScriptCreate('RPCA_DETAILS',
+    'function(mod){
+     data.frame(feature = seq(length(mod$sdev)), sd = mod$sdev)}');
+    
+  INSERT INTO Rpca_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_build_function, 'RPCA_BUILD');
+  INSERT INTO Rpca_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_score_function, 'RPCA_SCORE');
+  INSERT INTO Rpca_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_weight_function, 'RPCA_WEIGHT');
+  INSERT INTO Rpca_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_function, 'RPCA_DETAILS');
+  INSERT INTO Rpca_sh_sample_settings VALUES
+  (dbms_data_mining.ralg_details_format, 
+  'select 1 feature, 1 sd from dual');
+END;
+/
+
+
+---------------------
+-- CREATE A NEW MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RPCA_SH_FE_SAMPLE',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'PCA_BUILD_V',
+    case_id_column_name => 'CUST_ID',
+    settings_table_name => 'Rpca_sh_sample_settings');
+END;
+/
+
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a40
+select setting_name, setting_value from Rpca_sh_sample_settings
+order by setting_name;
+
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+column model_name format a20
+column mining_function format a20
+column algorithm format a20
+select model_name, mining_function, algorithm from user_mining_models
+where model_name = 'RPCA_SH_FE_SAMPLE';
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+column partition_name format a20
+select * from DM$VDRPCA_SH_FE_SAMPLE order by feature;
+
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-- For a descriptive mining function like Feature Extraction, "Scoring"
+-- involves providing the projected values of each feature.
+
+-- List the PCA projection values of the top four features for 15 customers 
+--
+SELECT cust_id, round(FEATURE_VALUE(RPCA_SH_FE_SAMPLE, 1 USING *), 3) 
+AS PROJV1, round(FEATURE_VALUE(RPCA_SH_FE_SAMPLE, 2 USING *), 3) 
+AS PROJV2, round(FEATURE_VALUE(RPCA_SH_FE_SAMPLE, 3 USING *), 3) AS PROJV3,
+round(FEATURE_VALUE(RPCA_SH_FE_SAMPLE, 4 USING *), 3) AS PROJV4
+FROM mining_data_apply_v
+WHERE cust_id <= 100015
+ORDER BY cust_ID;
+
+-- List the PCA projection values of the top three features for 10 customers 
+-- using FEATURE_SET
+--
+SELECT cust_id, S.feature_id fid, round(S.value, 3) value 
+FROM  (SELECT cust_id, FEATURE_SET(RPCA_SH_FE_SAMPLE USING *) fset
+                FROM mining_data_apply_v v where cust_id <= 100010) T,
+               TABLE(T.fset) S
+WHERE S.feature_id <= 3
+ORDER BY cust_id, fid;
+
+-- List the 2 most important attributes for the top feature for each row 
+-- for 10 new customers
+--
+column feat_det format a60
+SELECT cust_id,
+       FEATURE_DETAILS(RPCA_SH_FE_SAMPLE, 1, 2 USING *) feat_det
+FROM mining_data_apply_v 
+WHERE CUST_ID < = 100010
+ORDER BY cust_id;
+
+
+
+-----------------------------------------------------------------------
+--                       BUILD A PCA MODEL BY PARTITION
+-----------------------------------------------------------------------
+-- This example illustrates building a partitioned PCA model by 
+-- a specified partition column in parallel. We use the same settings 
+-- table in the above example with an additional partition column
+-- setting.
+
+
+-- Cleanup old training data view for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW pca_build_partition_v';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a view for building PCA models by partition in parallel
+create view pca_build_partition_v as
+select /*+parallel */ cust_id, cust_gender, age, yrs_residence, 
+affinity_card, bulk_pack_diskettes, flat_panel_monitor, 
+home_theater_package, bookkeeping_application, y_box_games  
+from mining_data_build_v;
+
+-- Check the customer gender distribution of the training data
+select cust_gender gender, count(*) CNT 
+from pca_build_partition_v group by cust_gender order by cust_gender;
+
+-- Specify the column CUST_GENDER as the partition column in
+-- setting table
+INSERT INTO Rpca_sh_sample_settings VALUES
+('ODMS_PARTITION_COLUMNS', 'CUST_GENDER');
+
+
+-- Cleanup old model with same name for repeat runs
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RPCA_SH_FE_SAMPLE_P');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+----------------------------------
+-- CREATE A NEW MODEL BY PARTITION
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'RPCA_SH_FE_SAMPLE_P',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'PCA_BUILD_PARTITION_V',
+    case_id_column_name => 'CUST_ID',
+    settings_table_name => 'Rpca_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+select setting_name, setting_value from Rpca_sh_sample_settings
+order by setting_name;
+
+
+-------------------------
+-- DISPLAY MODEL METADATA
+--
+select model_name, mining_function, algorithm from user_mining_models
+where model_name = 'RPCA_SH_FE_SAMPLE_P';
+
+
+------------------------------
+-- DISPLAY MODEL PARTITION INFO
+--
+column model_name format a20
+column partition_name format a15
+column column_name format a12
+column column_value format a10
+
+select * from user_mining_model_partitions
+where model_name= 'RPCA_SH_FE_SAMPLE_P'
+order by partition_name;
+
+
+------------------------------------
+-- DISPLAY MODEL DETAILS BY PARTITION
+--
+select * from DM$VDRPCA_SH_FE_SAMPLE_P order by partition_name, feature;
+
+
+-----------------------------------------------------------------------
+--                     APPLY THE PARTITIONED MODEL
+-----------------------------------------------------------------------
+
+-- List the corresponding partition names of the first 10 new customers
+column cust_gender format a15
+select cust_id, cust_gender, age, 
+       ora_dm_partition_name(RPCA_SH_FE_SAMPLE_P using *) partition_name
+from mining_data_apply_v
+where cust_id < = 100010
+order by cust_id;
+
+
+-- List the PCA projection values of the top two features for 15 customers
+-- using the partitioned model. 
+-- Each row of new data automatically uses its corresponding partitioned
+-- model for scoring.
+--
+SELECT cust_id, round(FEATURE_VALUE(RPCA_SH_FE_SAMPLE_P, 1 USING *), 3) 
+AS PROJV1, round(FEATURE_VALUE(RPCA_SH_FE_SAMPLE_P, 2 USING *), 3) 
+AS PROJV2
+FROM mining_data_apply_v
+WHERE cust_id <= 100015
+ORDER BY cust_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-regression-neural-networks.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-regression-neural-networks.sql
new file mode 100644
index 00000000..2e57804f
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-regression-neural-networks.sql
@@ -0,0 +1,347 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Regression - Neural Networks Algorithm - dmrnndemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET LONG 10000
+
+-------------------------------------------------------------------------------
+--                         NEURAL NETWORK REGRESSION DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the neural network regression algorithm in 
+-- Oracle Data Mining using R nnet algorithm.
+
+-- Cleanup old output table for repeat runs -----------------------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE NN_RDEMO_SETTINGS_RE';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+Begin
+  sys.rqScriptDrop('NN_RDEMO_BUILD_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('NN_RDEMO_SCORE_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('NN_RDEMO_DETAILS_REGRESSION', v_silent => TRUE);
+End;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE NN_RDEMO_SETTINGS_RE (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO NN_RDEMO_SETTINGS_RE VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+Begin
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named NN_RDEMO_BUILD_REGRESSION is defined. This 
+-- function builds and returns a neural network regression model using R nnet 
+-- algorithm. User can also choose other R algorithm to implement the neural
+-- network regression algorithm.
+
+  sys.rqScriptCreate('NN_RDEMO_BUILD_REGRESSION', 'function(dat) {
+   require(nnet); 
+   set.seed(1234); 
+   mod <- nnet(formula=AGE ~ ., data=dat, 
+               size=0, skip=TRUE, linout=TRUE, trace=FALSE); mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named NN_RDEMO_SCORE_REGRESSION is defined. 
+-- This function creates and returns an R data.frame containing the target 
+-- predictions. User can also define other prediction functions with different 
+-- settings.
+
+  sys.rqScriptCreate('NN_RDEMO_SCORE_REGRESSION', 'function(mod, dat) {
+   require(nnet); 
+   res <- predict(mod, newdata = dat);
+   data.frame(pred=res)}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named NN_RDEMO_DETAILS_REGRESSION
+-- is defined. This function creates and returns an R data.frame containing the 
+-- weights of the built neural network regression model. User can also display 
+-- other details.
+
+  sys.rqScriptCreate('NN_RDEMO_DETAILS_REGRESSION', 'function(object, x) {
+    mod <- object; 
+    data.frame(wts=mod$wts)}');
+
+  INSERT INTO NN_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_build_function, 'NN_RDEMO_BUILD_REGRESSION');
+  INSERT INTO NN_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_score_function, 'NN_RDEMO_SCORE_REGRESSION');
+  INSERT INTO NN_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_details_function, 'NN_RDEMO_DETAILS_REGRESSION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- weights of the built neural network regression model.
+
+  INSERT INTO NN_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_details_format, 'select 1 wts from dual');
+End;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_RDEMO_REGRESSION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- NN_RDEMO_BUILD_REGRESSION will be used to create the neural network 
+-- regression model NN_RDEMO_REGRESSION using dataset mining_data_build_v.
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'NN_RDEMO_REGRESSION',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AGE',
+    settings_table_name => 'NN_RDEMO_SETTINGS_RE');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Display the details of the built model using the R script user defined. 
+-- Here R script NN_RDEMO_DETAIL_REGRESSION will be used to display the model 
+-- details.
+
+select round(wts, 3) as wts from DM$VDNN_RDEMO_REGRESSION where wts >= 4 
+order by wts;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. Here R script 
+-- NN_RDEMO_SCORE_REGRESSION will be used to do the scoring. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Show actual target value and predicted target values.
+
+SELECT CUST_ID, AGE as AGE_act, 
+round(PREDICTION(NN_RDEMO_REGRESSION USING *), 3) as AGE_pred
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-------------------------------------------------------------------------------
+--                        NEURAL NETWORK CLASSIFICATION DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the neural network classification algorithm
+-- in Oracle Data Mining using R nnet algorithm.
+
+-- Cleanup old output table for repeat runs -----------------------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE NN_RDEMO_SETTINGS_CL';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+Begin
+  sys.rqScriptDrop('NN_RDEMO_BUILD_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('NN_RDEMO_SCORE_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('NN_RDEMO_DETAILS_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('NN_RDEMO_WEIGHT_CLASSIFICATION', v_silent => TRUE);
+End;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE NN_RDEMO_SETTINGS_CL (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO NN_RDEMO_SETTINGS_CL VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+Begin
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named NN_RDEMO_BUILD_CLASSIFICATION is defined. 
+-- This function builds and returns a neural network classification model using 
+-- R nnet algorithm. User can also choose other R algorithm to implement the
+-- neural network classification algorithm.
+
+  sys.rqScriptCreate('NN_RDEMO_BUILD_CLASSIFICATION', 'function(dat) {
+   require(nnet); 
+   set.seed(1234); 
+   mod <- nnet(formula=HOUSEHOLD_SIZE ~ ., data=dat, 
+               size=0, skip=TRUE, linout=TRUE, trace=FALSE); 
+   mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named NN_RDEMO_SCORE_CLASSIFICATION is 
+-- defined. This function creates and returns an R data.frame containing the 
+-- target predictions. User can also define other prediction functions with 
+-- different types.
+
+  sys.rqScriptCreate('NN_RDEMO_SCORE_CLASSIFICATION', 'function(mod, dat) {
+   require(nnet); 
+   res <- predict(mod, newdata = dat); 
+   res=data.frame(res);
+   names(res) <- sort(mod$lev); res}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named 
+-- NN_RDEMO_DETAILS_CLASSIFICATION is defined. This function creates and 
+-- returns an R data.frame containing the weights of the built neural network 
+-- classification model. User can also display other details.
+
+  sys.rqScriptCreate('NN_RDEMO_DETAILS_CLASSIFICATION', 'function(object, x) {
+   mod <- object; 
+   data.frame(wts=mod$wts)}');
+
+-- Weight R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to provide the attribute weights
+-- of the scoring data. For example, here a script named 
+-- NN_RDEMO_WEIGHT_CLASSIFICATION is defined. This function creates and returns
+-- an R data.frame containing the weights of each attribute of the scoring data.
+-- Here we simply use the ratio of the predicted target probability with all
+-- attribute values present to the predicted target probability with one 
+-- attribute value missing as the weight of the missing attribute. User can 
+-- define their own method to calculate the attribute weight.
+
+  sys.rqScriptCreate('NN_RDEMO_WEIGHT_CLASSIFICATION', 'function(mod, dat, clas) {
+   require(nnet);
+   Sys.setlocale(, "C");
+   v0 <- as.data.frame(predict(mod, newdata=dat, type = "raw"));
+   res <- data.frame(lapply(seq_along(dat),
+   function(x, dat) {
+   if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(0)
+   else dat[,x] <- mod$xlevels[[names(dat[x])]][1];
+   vv <- as.data.frame(predict(mod, newdata = dat, type = "raw"));
+   max((v0[[clas]]-vv[[clas]])/v0[[clas]], 0)}, dat = dat));
+   res <- res[,order(names(dat))];
+   names(res) <- sort(names(dat)); 
+   res}');
+
+  INSERT INTO NN_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_build_function, 'NN_RDEMO_BUILD_CLASSIFICATION');
+  INSERT INTO NN_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_score_function, 'NN_RDEMO_SCORE_CLASSIFICATION');
+  INSERT INTO NN_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_function, 'NN_RDEMO_DETAILS_CLASSIFICATION');
+  INSERT INTO NN_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_weight_function, 'NN_RDEMO_WEIGHT_CLASSIFICATION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- weights of the built neural network classification model.
+
+  INSERT INTO NN_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_format, 'select 1 wts from dual');
+End;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('NN_RDEMO_CLASSIFICATION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- NN_RDEMO_BUILD_CLASSIFICATION will be used to create the neural network 
+-- classification model NN_RDEMO_CLASSIFICATION using dataset mining_data_build_v.
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'NN_RDEMO_CLASSIFICATION',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'HOUSEHOLD_SIZE',
+    settings_table_name => 'NN_RDEMO_SETTINGS_CL');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Display the details of the built model using the R script user defined. 
+-- Here R script NN_RDEMO_DETAIL_CLASSIFICATION will be used to display the 
+-- model details.
+
+select round(wts, 3) as wts from DM$VDNN_RDEMO_CLASSIFICATION where wts >= 10 
+order by wts;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Here R script NN_RDEMO_SCORE_CLASSIFICATION is used to get the prediction 
+-- value and the prediction probability. Actual target value and predicted 
+-- target values are provided.
+
+SELECT CUST_ID, HOUSEHOLD_SIZE as HOUSEHOLD_SIZE_act, 
+PREDICTION(NN_RDEMO_CLASSIFICATION USING *) HOUSEHOLD_SIZE_pred,
+round(PREDICTION_PROBABILITY(NN_RDEMO_CLASSIFICATION USING *), 3) 
+as HOUSEHOLD_SIZE_prob 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-- PREDICTION_SET -------------------------------------------------------------
+-- Explanation:
+-- Here R script NN_RDEMO_SCORE_CLASSIFICATION is used to get the 
+-- prediction set. Actual target value and predicted target values are provided. 
+
+select T.CUST_ID, T.HOUSEHOLD_SIZE, S.prediction, 
+round(S.probability, 3) as probability 
+from (select CUST_ID, HOUSEHOLD_SIZE, 
+PREDICTION_SET(NN_RDEMO_CLASSIFICATION USING *) pset 
+from mining_data_apply_v where CUST_ID <= 100005) T, TABLE(T.pset) S
+where S.probability > 0 
+order by T.CUST_ID, S.prediction;
+
+-- PREDICTION_DETAILS ---------------------------------------------------------
+-- Explanation:
+-- The R script NN_RDEMO_WEIGHT_CLASSIFICATION is used to get the prediction 
+-- details. The HOUSEHOLD_SIZE and the prediction details with the weight of each 
+-- attribute are provided.
+
+column pred_det format a60;
+SELECT CUST_ID, 
+PREDICTION_DETAILS(NN_RDEMO_CLASSIFICATION, '2' USING *) pred_det
+FROM mining_data_apply_v where CUST_ID <= 100010 order by CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-r-extensible-regression-tree.sql b/machine-learning/sql/26ai/oml4sql-r-extensible-regression-tree.sql
new file mode 100644
index 00000000..5dda179d
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-r-extensible-regression-tree.sql
@@ -0,0 +1,357 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   OML R Extensible - Regression Tree Algorithm - dmrdtdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET LONG 10000
+
+-------------------------------------------------------------------------------
+--                         REGRESSION TREE DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the regression tree algorithm in Oracle Data 
+-- Mining using R rpart algorithm
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE DT_RDEMO_SETTINGS_RE';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+ sys.rqScriptDrop('DT_RDEMO_BUILD_REGRESSION', v_silent => TRUE);
+ sys.rqScriptDrop('DT_RDEMO_SCORE_REGRESSION', v_silent => TRUE);
+ sys.rqScriptDrop('DT_RDEMO_DETAILS_REGRESSION', v_silent => TRUE);
+ sys.rqScriptDrop('DT_RDEMO_WEIGHT_REGRESSION', v_silent => TRUE);
+END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_RDEMO_REGRESSION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE DT_RDEMO_SETTINGS_RE (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO DT_RDEMO_SETTINGS_RE VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named DT_RDEMO_BUILD_REGRESSION is defined. This 
+-- function builds and returns a regression tree model using R rpart algorithm. 
+-- User can also choose other R algorithm to implement the regression tree 
+-- algorithm.
+
+  sys.rqScriptCreate('DT_RDEMO_BUILD_REGRESSION', 'function(dat) {
+   require(rpart); 
+   set.seed(1234); 
+   mod <- rpart(AGE ~ ., data=dat, method="anova"); 
+   mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named DT_RDEMO_SCORE_REGRESSION is defined. 
+-- This function creates and returns an R data.frame containing the target 
+-- predictions using vector type. User can also define other prediction function
+-- with different types.
+
+  sys.rqScriptCreate('DT_RDEMO_SCORE_REGRESSION', 'function(mod, dat) {
+   require(rpart); 
+   res <- predict(mod, newdata=dat, type = "vector"); 
+   data.frame(res)}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named DT_RDEMO_DETAILS_REGRESSION
+-- is defined. This function creates and returns an R data.frame containing the 
+-- split attributes, node counts, weights, deviation and mean of the built model. 
+-- User can also display other details.
+
+  sys.rqScriptCreate('DT_RDEMO_DETAILS_REGRESSION', 'function(object, x) {
+   mod.frm <- object$frame
+   data.frame(node = row.names(mod.frm), split = mod.frm$var, n = mod.frm$n,
+   wt = mod.frm$wt, dev = mod.frm$dev, yval = mod.frm$yval)}');
+
+  INSERT INTO DT_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_build_function, 'DT_RDEMO_BUILD_REGRESSION');
+  INSERT INTO DT_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_score_function, 'DT_RDEMO_SCORE_REGRESSION');
+  INSERT INTO DT_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_details_function, 'DT_RDEMO_DETAILS_REGRESSION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- split attributes, node counts, weights, deviation and mean of the built model
+
+  INSERT INTO DT_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_details_format, 
+     'select cast(''a'' as varchar2(20)) node, ' ||
+     'cast(''a'' as varchar2(20)) split, ' ||
+     '1 NodeCnt, 1 wt, 1 deviation, 1 mean from dual'); 
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- DT_RDEMO_BUILD_REGRESSION will be used to create the regression tree model 
+-- DT_RDEMO_REGRESSION using dataset mining_data_build_v.
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'DT_RDEMO_REGRESSION',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AGE',
+    settings_table_name => 'DT_RDEMO_SETTINGS_RE');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Display the details of the built model using the R script user defined. 
+-- Here R script DT_RDEMO_DETAIL_REGRESSION will be used to display the model 
+-- details.
+
+column SPLIT format a12;
+select to_number(node) as node, split, NodeCnt, wt, 
+round(deviation, 3) as deviation, round(mean, 3) as mean 
+from DM$VDDT_RDEMO_REGRESSION 
+order by node, split;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. Here R script 
+-- DT_RDEMO_SCORE_REGRESSION will be used to do the scoring. Actual target value
+-- and predicted target values are provided. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+SELECT CUST_ID, AGE as AGE_act, round(PREDICTION(DT_RDEMO_REGRESSION USING *),3)
+ AGE_pred FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-------------------------------------------------------------------------------
+--                        CLASSIFICATION TREE DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the classification tree algorithm in Oracle 
+-- Data Mining using R rpart algorithm
+
+-- Cleanup old output tables/scripts/models for repeat runs -------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE DT_RDEMO_SETTINGS_CL';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+Begin
+  sys.rqScriptDrop('DT_RDEMO_BUILD_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('DT_RDEMO_SCORE_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('DT_RDEMO_DETAILS_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('DT_RDEMO_WEIGHT_CLASSIFICATION', v_silent => TRUE);
+End;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('DT_RDEMO_CLASSIFICATION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE DT_RDEMO_SETTINGS_CL (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+BEGIN
+ INSERT INTO DT_RDEMO_SETTINGS_CL VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named DT_RDEMO_BUILD_CLASSIFICATION is defined. 
+-- This function builds and returns a classification tree model using R rpart 
+-- algorithm. User can also choose other R algorithm to implement the 
+-- classification tree algorithm.
+
+  sys.rqScriptCreate('DT_RDEMO_BUILD_CLASSIFICATION', 'function(dat) {
+   require(rpart); 
+   set.seed(1234); 
+   mod <- rpart(AFFINITY_CARD ~ ., method="class", data=dat); 
+   mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named DT_RDEMO_SCORE_CLASSIFICATION is 
+-- defined. This function creates and returns an R data.frame containing the 
+-- target predictions using prob type. User can also define other prediction 
+-- function with different types.
+
+  sys.rqScriptCreate('DT_RDEMO_SCORE_CLASSIFICATION', 'function(mod, dat) {
+   require(rpart);
+   res <- data.frame(predict(mod, newdata=dat, type = "prob"));
+   names(res) <- c("0", "1");
+   res}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named 
+-- DT_RDEMO_DETAILS_CLASSIFICATION is defined. This function creates and 
+-- returns an R data.frame containing the split attributes, node counts, left 
+-- node counts, right node counts of the built model. User can also display 
+-- other details.
+
+  sys.rqScriptCreate('DT_RDEMO_DETAILS_CLASSIFICATION', 'function(object, x) {
+   mod.frm <- object$frame
+   data.frame(node = row.names(mod.frm), split = mod.frm$var, n = mod.frm$n,
+   ln = mod.frm$yval2[,2], rn = mod.frm$yval2[,3])}');
+
+-- Weight R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to provide attribute relative 
+-- contribution to the prediction. For example, here a script named 
+-- DT_RDEMO_WEIGHT_CLASSIFICATION is defined. This function creates and returns
+-- an R data.frame containing the contribution weight of each attribute to the 
+-- prediction probability of the specified class. Here we simply use the ratio 
+-- of the predicted target probability with all attribute values present to the 
+-- predicted target probability with one attribute value missing as the weight 
+-- of the missing attribute for the specified class. User can define their own 
+-- method to calculate the attribute weight.
+
+  sys.rqScriptCreate('DT_RDEMO_WEIGHT_CLASSIFICATION', 'function(mod, dat, clas) {
+   require(rpart); 
+
+   v0 <- as.data.frame(predict(mod, newdata=dat, type = "prob"));
+   res <- data.frame(lapply(seq_along(dat),
+   function(x, dat) {
+   if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(NA)
+   else dat[,x] <- as.factor(NA);
+   vv <- as.data.frame(predict(mod, newdata = dat, type = "prob"));
+   v0[[clas]] / vv[[clas]]}, dat = dat));
+   names(res) <- names(dat);
+   res}');
+
+  INSERT INTO DT_RDEMO_SETTINGS_CL  
+    VALUES(dbms_data_mining.ralg_build_function, 'DT_RDEMO_BUILD_CLASSIFICATION');
+  INSERT INTO DT_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_score_function, 'DT_RDEMO_SCORE_CLASSIFICATION');
+  INSERT INTO DT_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_function, 'DT_RDEMO_DETAILS_CLASSIFICATION');
+  INSERT INTO DT_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_weight_function, 'DT_RDEMO_WEIGHT_CLASSIFICATION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- split attributes, node counts, left node counts, right node counts of 
+-- the built model
+
+  INSERT INTO DT_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_format, 
+      'select cast(''a'' as varchar2(20)) node, ' ||
+      'cast(''a'' as varchar2(20)) split, ' ||
+      '1 NodeCnt, 1 LeftNodeCnt, 1 RightNodeCnt from dual');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- DT_RDEMO_BUILD_CLASSIFICATION will be used to create the classification tree 
+-- model DT_RDEMO_CLASSIFICATION using dataset mining_data_build_v.
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'DT_RDEMO_CLASSIFICATION',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AFFINITY_CARD',
+    settings_table_name => 'DT_RDEMO_SETTINGS_CL');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Display the details of the built model using the R script user defined. 
+-- Here R script DT_RDEMO_DETAIL_CLASSIFICATION will be used to display the model 
+-- details.
+
+column SPLIT format a12;
+select to_number(node) as node, split, nodecnt, leftnodecnt, rightnodecnt 
+from DM$VDDT_RDEMO_CLASSIFICATION
+order by node, split;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Here R script DT_RDEMO_SCORE_CLASSIFICATION is used to get the prediction 
+-- value and the prediction probability. Actual target value and predicted 
+-- target values are provided. 
+
+SELECT cust_id, affinity_card as affinity_card_act, 
+PREDICTION(DT_RDEMO_CLASSIFICATION USING *) affinity_card_pred,
+round(PREDICTION_PROBABILITY(DT_RDEMO_CLASSIFICATION USING *), 3) 
+affinity_card_prob 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by cust_id;
+
+-- PREDICTION_SET -------------------------------------------------------------
+-- Explanation:
+-- Here R script DT_RDEMO_SCORE_CLASSIFICATION is used to get the 
+-- prediction set. Actual target value and predicted target values are provided. 
+
+SELECT T.CUST_ID, T.affinity_card, S.prediction, 
+round(S.probability, 3) as probability
+FROM (SELECT CUST_ID, affinity_card, 
+PREDICTION_SET(DT_RDEMO_CLASSIFICATION USING *) pset 
+from mining_data_apply_v where CUST_ID <= 100010) T, TABLE(T.pset) S 
+where S.probability > 0 order by T.CUST_ID, S.prediction;
+
+-- PREDICTION_DETAILS ---------------------------------------------------------
+-- Explanation:
+-- The R script DT_RDEMO_WEIGHT_CLASSIFICATION is used to get the prediction 
+-- details. The CUST_ID and the prediction details with the weight of each 
+-- attribute are provided.
+
+column pred_det format a65;
+SELECT CUST_ID, PREDICTION_DETAILS(DT_RDEMO_CLASSIFICATION, '1' USING *) pred_det
+FROM mining_data_apply_v where EDUCATION = 'Bach.' and HOUSEHOLD_SIZE = '3'
+and CUST_ID <= 100080 order by CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-regression-glm.sql b/machine-learning/sql/26ai/oml4sql-regression-glm.sql
new file mode 100644
index 00000000..e43686a4
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-regression-glm.sql
@@ -0,0 +1,438 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Regression - Generalized Linear Model Algorithm - dmglrdem.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic, purchase, and affinity card membership data for a 
+-- set of customers, predict customer's age. Since age is a continuous 
+-- variable, this is a regression problem.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in the SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+-----------
+-- ANALYSIS
+-----------
+-- Data preparation for GLM is performed internally
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name (if any)
+BEGIN
+  DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_Regr_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE glmr_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+CREATE TABLE glmr_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+-- Turn on feature selection and generation
+--  
+BEGIN 
+-- Populate settings table
+  INSERT INTO glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_generalized_linear_model);
+  -- output row diagnostic statistics
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.glms_row_diagnostics,
+    dbms_data_mining.glms_row_diag_enable); 
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);  
+  -- turn on feature selection
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES  
+    (dbms_data_mining.glms_ftr_selection, 
+    dbms_data_mining.glms_ftr_selection_enable);
+  -- turn on feature generation
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES 
+    (dbms_data_mining.glms_ftr_generation, 
+    dbms_data_mining.glms_ftr_generation_enable); 
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES 
+    (dbms_data_mining.glms_ftr_gen_method, 
+    dbms_data_mining.glms_ftr_gen_quadratic);
+  /* Examples of possible overrides are shown below. If the user does not
+     override, then relevant settings are determined by the algorithm
+      
+  -- specify a row weight column 
+    (dbms_data_mining.odms_row_weight_column_name,<row_weight_column_name>);
+  -- specify a missing value treatment method:
+     Default:  replace with mean (numeric features) or 
+               mode (categorical features) 
+       or delete the row
+    (dbms_data_mining.odms_missing_value_treatment,
+      dbms_data_mining.odms_missing_value_delete_row);
+  -- turn ridge regression on or off 
+     By default the system turns it on if there is a multicollinearity
+    (dbms_data_mining.glms_ridge_regression,
+     dbms_data_mining.glms_ridge_reg_enable);  
+  */  
+END;
+/ 
+
+CREATE OR replace VIEW mdb_rdemo_v AS 
+  SELECT cust_id, age, bookkeeping_application, bulk_pack_diskettes,
+    cust_marital_status, home_theater_package, household_size, occupation,
+    yrs_residence, y_box_games FROM mining_data_build_v;
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Force the column affinity_card to be a feature in the model using
+-- dbms_data_mining_transform
+--
+declare
+  v_xlst dbms_data_mining_transform.TRANSFORM_LIST;
+BEGIN
+  -- Force the column affinity_card to be a feature in the model
+  dbms_data_mining_transform.set_transform(v_xlst,
+    'HOME_THEATER_PACKAGE', NULL, 'HOME_THEATER_PACKAGE', 
+    'HOME_THEATER_PACKAGE', 'FORCE_IN');
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLMR_SH_Regr_sample',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mdb_rdemo_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'age',
+    settings_table_name => 'glmr_sh_sample_settings',
+    xform_list          => v_xlst);
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'GLMR_SH_REGR_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'GLMR_SH_REGR_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- IF the covariance matrix had been invalid, THEN the global details 
+-- would have had a row like:
+--
+--   VALID_COVARIANCE_MATRIX  0 
+-- 
+-- And, as a result we would only have gotten a limited set of diagnostics.
+-- This never happens with feature selecion enabled. IF the forced in feature 
+-- had caused a multi-collinearity then the model build would have failed.
+-- Note that the forced_in feature, age, was not statistically significant.
+-- However, it did not cause a multi-collinearity, hence the build succeeded.
+--
+-- With feature selection disabled, then an invalid covariance matrix is 
+-- possible. Then, multi-collinearity, if it exists will cause 
+-- RIDGE REGRESSION to kick in, unless it has been specifically disabled by 
+-- you. The build will succeed with ridge enabled. However, the covariance 
+-- matrix will be invalid since it is not computed by the ridge algorithm.
+-- 
+-- An important consequence of an invalid covariance matrix is that
+-- the model cannot predict confidence bounds - i.e. the result of
+-- PREDICTION_BOUNDS function in a SQL query is NULL.
+--
+-- If accuracy is the primary goal and interpretability not important, then we
+-- note that RIDGE REGRESSION may be preferrable to feature selection for 
+-- some datasets. You can test this by specifically enabling ridge regression.
+-- 
+-- In this demo, we compute two models. The first (above) has feature
+-- selection enabled and produces confidence bounds and a full set of 
+-- diagnostics. The second enables ridge regression. It does not produce 
+-- confidence bounds. It only produces a limited set of diagnostics.
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='GLMR_SH_REGR_SAMPLE'
+  ORDER BY view_name;
+
+-- Global GLM model statistics
+column numeric_value format 9999990.999
+column string_value format a20
+select name, numeric_value, string_value from DM$VGGLMR_SH_REGR_SAMPLE
+  ORDER BY name;
+
+-- Coefficient statistics
+SET line 120
+column feature_expression format a53 
+column coefficient format 9999990.999
+column std_error format 9999990.999 
+column test_statistic format 9999990.999  
+column p_value format 9999990.999  
+column std_coefficient format 9999990.999  
+column lower_coeff_limit format 9999990.999 
+column upper_coeff_limit format 9999990.999  
+  SELECT feature_expression, coefficient, std_error, test_statistic,
+  p_value, std_coefficient, lower_coeff_limit, upper_coeff_limit
+  FROM DM$VDGLMR_SH_REGR_SAMPLE
+  ORDER BY 1;
+
+-- Show the features and their p_values
+SET lin 80
+SET pages 20
+  SELECT feature_expression, coefficient, p_value 
+  FROM DM$VDGLMR_SH_REGR_SAMPLE
+  ORDER BY feature_expression;
+
+-- Row diagnostics
+SELECT CASE_id, TARGET_value, PREDICTED_TARGET_value, hat, 
+  residual, std_err_residual, studentized_residual, pred_res, cooks_d
+  FROM dm$vaGLMR_SH_Regr_sample
+ WHERE case_id <= 101510
+ ORDER BY case_id;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+
+-- 1. Root Mean Square Error - Sqrt(Mean((x - x')^2))
+-- 2. Mean Absolute Error - Mean(|(x - x')|)
+--
+column rmse format 9999.9
+column mae format 9999.9  
+SELECT SQRT(AVG((A.pred - B.age) * (A.pred - B.age))) rmse,
+       AVG(ABS(a.pred - B.age)) mae
+  FROM (SELECT cust_id, prediction(GLMR_SH_Regr_sample using *) pred
+          FROM mining_data_test_v) A,
+       mining_data_test_v B
+  WHERE A.cust_id = B.cust_id;
+
+-----------------------------------------------------------------------
+--                               SCORE NEW DATA
+-----------------------------------------------------------------------
+
+-- Since the model has a valid covariance matrix, it is possible
+-- to obtain confidence bounds.
+-- In addition, provide details to help explain the prediction
+set long 20000
+set line 200
+set pagesize 100
+SELECT CUST_ID,
+       PREDICTION(GLMR_SH_Regr_sample USING *) pr,
+       PREDICTION_BOUNDS(GLMR_SH_Regr_sample USING *).lower pl,
+       PREDICTION_BOUNDS(GLMR_SH_Regr_sample USING *).upper pu,
+       PREDICTION_DETAILS(GLMR_SH_Regr_sample USING *) pd
+  FROM mining_data_apply_v
+ WHERE CUST_ID < 100010
+ ORDER BY CUST_ID;
+
+-- Next we compare to a model built with ridge regression enabled
+
+
+-----------------------------------------------------------------------
+--                            BUILD A NEW MODEL
+-----------------------------------------------------------------------
+
+---------------------
+-- CREATE A NEW MODEL
+--
+-- Cleanup old model with same name (if any)
+BEGIN
+  DBMS_DATA_MINING.DROP_MODEL('GLMR_SH_Regr_sample');
+EXCEPTION WHEN OTHERS THEN
+  NULL;
+END;
+/
+
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table 
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE glmr_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+CREATE TABLE glmr_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+
+-- Turn on ridge regression
+--  
+BEGIN 
+-- Populate settings table
+  INSERT INTO glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_generalized_linear_model);
+  -- output row diagnostic statistics
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.glms_row_diagnostics,
+    dbms_data_mining.glms_row_diag_enable); 
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);  
+  -- turn on ridge regression
+  INSERT INTO  glmr_sh_sample_settings (setting_name, setting_value) VALUES  
+    (dbms_data_mining.glms_ridge_regression,
+    dbms_data_mining.glms_ridge_reg_enable);  
+  INSERT INTO glmr_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.GLMS_SOLVER, 
+    dbms_data_mining.GLMS_SOLVER_QR); 
+END;
+/
+
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'GLMR_SH_Regr_sample',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'age',
+    settings_table_name => 'glmr_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'GLMR_SH_REGR_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'GLMR_SH_REGR_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='GLMR_SH_REGR_SAMPLE'
+  ORDER BY view_name;
+
+-- Global GLM model statistics
+column name format a30
+column numeric_value format 9999990.999
+column string_value format a20
+select name, numeric_value, string_value from DM$VGGLMR_SH_REGR_SAMPLE
+  ORDER BY name;
+
+-- Coefficient statistics
+SET line 120
+column attribute_name format a20  
+column attribute_subname format a20
+column attribute_value format a20 
+column partition_name format a20
+column vif format 9999990.999 
+column exp_coefficient format 9999990.999  
+column exp_lower_coeff_limit format 9999990.999  
+column exp_upper_coeff_limit format 9999990.999
+  SELECT *
+  FROM DM$VDGLMR_SH_REGR_SAMPLE
+ WHERE attribute_name in ('AFFINITY_CARD','BULK_PACK_DISKETTES','COUNTRY_NAME')
+ORDER BY attribute_name, attribute_value;
+    
+-- Limited row diagnostics - residuals only, others are NULL
+SELECT CASE_id, TARGET_value, PREDICTED_TARGET_value, residual
+  FROM dm$vaGLMR_SH_Regr_sample
+ WHERE case_id <= 101510
+ ORDER BY case_id;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+
+-- 1. Root Mean Square Error - Sqrt(Mean((x - x')^2))
+-- 2. Mean Absolute Error - Mean(|(x - x')|)
+--
+column rmse format 9999.9
+column mae format 9999.9
+SELECT SQRT(AVG((A.pred - B.age) * (A.pred - B.age))) rmse,
+       AVG(ABS(a.pred - B.age)) mae
+  FROM (SELECT cust_id, prediction(GLMR_SH_Regr_sample using *) pred
+          FROM mining_data_test_v) A,
+       mining_data_test_v B
+ WHERE A.cust_id = B.cust_id;
+
+-- 3. Residuals
+--    If the residuals show substantial variance between
+--    the predicted value and the actual, you can consider
+--    changing the algorithm parameters.
+--
+SELECT TO_CHAR(ROUND(pred, 4)) prediction, residual
+  FROM (SELECT A.pred, (A.pred - B.age) residual
+          FROM (SELECT cust_id, prediction(GLMR_SH_Regr_sample using *) pred
+                  FROM mining_data_test_v) A,
+               mining_data_test_v B
+         WHERE A.cust_id = B.cust_id
+        ORDER BY A.pred ASC)
+ WHERE pred <= 18.388
+ ORDER BY pred;
+
+-----------------------------------------------------------------------
+--                               SCORE NEW DATA
+-----------------------------------------------------------------------
+
+-- Now that the model has an invalid covariance matrix, it is 
+-- no longer possible to obtain confidence bounds.
+-- So the lower (PL) and upper (PU) confidence bounds are NULL
+set long 20000
+set line 200
+set pagesize 100
+SELECT CUST_ID,
+       PREDICTION(GLMR_SH_Regr_sample USING *) pr,
+       PREDICTION_BOUNDS(GLMR_SH_Regr_sample USING *).lower pl,
+       PREDICTION_BOUNDS(GLMR_SH_Regr_sample USING *).upper pu,
+       PREDICTION_DETAILS(GLMR_SH_Regr_sample USING *) pd
+  FROM mining_data_apply_v
+ WHERE CUST_ID < 100010
+ ORDER BY CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-regression-neural-networks.sql b/machine-learning/sql/26ai/oml4sql-regression-neural-networks.sql
new file mode 100644
index 00000000..b37e21a5
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-regression-neural-networks.sql
@@ -0,0 +1,223 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Regression - Neural Networks Algorithm - dmnnrdem.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic, purchase, and affinity card membership data for a 
+-- set of customers, predict customer's age. Since age is a continuous 
+-- variable, this is a regression problem.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in the SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_v (build data)
+-- mining_data_test_v  (test data)
+-- mining_data_apply_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+-----------
+-- ANALYSIS
+-----------
+-- For regression using NN, perform the following on mining data.
+--
+-- 1. Use Auto Data Preparation
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name (if any)
+BEGIN DBMS_DATA_MINING.DROP_MODEL('NNR_SH_Regr_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE nnr_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create settings for neural network
+--
+SET echo off
+CREATE TABLE nnr_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+SET echo on 
+BEGIN       
+  -- Populate settings table
+  INSERT INTO nnr_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.algo_name,dbms_data_mining.algo_neural_network);
+  INSERT INTO nnr_sh_sample_settings (setting_name, setting_value) VALUES 
+  (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
+  INSERT INTO nnr_sh_sample_settings (setting_name, setting_value) VALUES 
+  (dbms_data_mining.odms_random_seed, '12');
+
+  -- Examples of other possible settings are:
+  --(dbms_data_mining.nnet_hidden_layers, '2');
+  --(dbms_data_mining.nnet_nodes_per_layer, '10, 30');
+  --(dbms_data_mining.nnet_iterations, '100');
+  --(dbms_data_mining.nnet_tolerance, '0.0001');
+  --(dbms_data_mining.nnet_activations, 
+  --   ''''|| dbms_data_mining.nnet_activations_log_sig ||'''');
+  --(dbms_data_mining.nnet_regularizer, 
+  --        dbms_data_mining.nnet_regularizer_heldaside);
+  --(dbms_data_mining.nnet_heldaside_ratio, '0.3');
+  --(dbms_data_mining.nnet_heldaside_max_fail, '5');
+  --(dbms_data_mining.nnet_regularizer,
+  --        dbms_data_mining.nnet_regularizer_l2);
+  --(dbms_data_mining.nnet_reg_lambda, '0.5');
+  --(dbms_data_mining.nnet_weight_upper_bound, '0.7');
+  --(dbms_data_mining.nnet_weight_lower_bound, '-0.6')
+  --(dbms_data_mining.lbfgs_history_depth, '20');
+  --(dbms_data_mining.lbfgs_scale_hessian, 
+  --        dbms_data_mining.lbfgs_scale_hessian_disable);
+  --(dbms_data_mining.lbfgs_gradient_tolerance, '0.0001');
+  
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'NNR_SH_Regr_sample',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'age',
+    settings_table_name => 'nnr_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'NNR_SH_REGR_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+col attribute_name format a30
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'NNR_SH_REGR_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+WHERE model_name='NNR_SH_REGR_SAMPLE'
+ORDER BY view_name;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+
+-- 1. Root Mean Square Error - Sqrt(Mean((y - y')^2))
+--
+column rmse format 9999.99
+SELECT SQRT(AVG((prediction - age) * (prediction - age))) rmse
+  FROM (select age, PREDICTION(nnr_sh_regr_sample USING *) prediction
+        from mining_data_test_v);
+
+-- 2. Mean Absolute Error - Mean(|(y - y')|)
+--
+column mae format 9999.99
+SELECT AVG(ABS(prediction - age)) mae
+  FROM (select age, PREDICTION(nnr_sh_regr_sample USING *) prediction
+        from mining_data_test_v);
+
+-- 3. Residuals
+--    If the residuals show substantial variance between
+--    the predicted value and the actual, you can consider
+--    changing the algorithm parameters.
+--
+column prediction format 99.9999
+SELECT prediction, (prediction - age) residual
+  FROM (select age, PREDICTION(nnr_sh_regr_sample USING *) prediction
+        from mining_data_test_v)
+ WHERE prediction < 17.5
+ ORDER BY prediction;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- Predict the average age of customers, broken out by gender.
+--
+column cust_gender format a12
+SELECT A.cust_gender,
+       COUNT(*) AS cnt,
+       ROUND(
+       AVG(PREDICTION(nnr_sh_regr_sample USING A.*)),4)
+       AS avg_age
+  FROM mining_data_apply_v A
+GROUP BY cust_gender
+ORDER BY cust_gender;
+
+------------------
+-- BUSINESS CASE 2
+-- Create a 10 bucket histogram of customers from Italy based on their age
+-- and return each customer's age group.
+--
+column pred_age format 999.99
+SELECT cust_id,
+       PREDICTION(nnr_sh_regr_sample USING *) pred_age,
+       WIDTH_BUCKET(
+        PREDICTION(nnr_sh_regr_sample USING *), 10, 100, 10) "Age Group"
+  FROM mining_data_apply_v
+ WHERE country_name = 'Italy'
+ORDER BY pred_age;
+
+------------------
+-- BUSINESS CASE 3
+-- Find the reasons (8 attributes with the most impact) for the
+-- predicted age of customer 100001.
+--
+set long 2000
+set line 200
+set pagesize 100
+SELECT PREDICTION_DETAILS(nnr_sh_regr_sample, null, 8 USING *) prediction_details
+  FROM mining_data_apply_v
+ WHERE cust_id = 100001;
+
diff --git a/machine-learning/sql/26ai/oml4sql-regression-random-forest.sql b/machine-learning/sql/26ai/oml4sql-regression-random-forest.sql
new file mode 100644
index 00000000..91607c37
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-regression-random-forest.sql
@@ -0,0 +1,391 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Regression - Random Forest Algorithm - dmrrfdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+SET LONG 10000
+
+-------------------------------------------------------------------------------
+--                         RANDOMFOREST REGRESSION DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the random forest regression algorithm in 
+-- Oracle Data Mining using R nnet algorithm.
+
+-- Cleanup old output table for repeat runs -----------------------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE RF_RDEMO_SETTINGS_RE';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+  sys.rqScriptDrop('RF_RDEMO_BUILD_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('RF_RDEMO_SCORE_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('RF_RDEMO_DETAILS_REGRESSION', v_silent => TRUE);
+  sys.rqScriptDrop('RF_RDEMO_WEIGHT_REGRESSION', v_silent => TRUE);
+END;
+/
+
+-- Model Settings -------------------------------------------------------------
+CREATE TABLE RF_RDEMO_SETTINGS_RE (
+        setting_name varchar2(30),
+        setting_value varchar2(4000));
+
+BEGIN
+ INSERT INTO RF_RDEMO_SETTINGS_RE VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+-- Build R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named RF_RDEMO_BUILD_REGRESSION is defined. This 
+-- function builds and returns a random forest regression model using R
+-- randomForest algorithm. User can also choose other R algorithm to implement 
+-- the random forest regression algorithm.
+
+  sys.rqScriptCreate('RF_RDEMO_BUILD_REGRESSION', 'function(dat, form) {
+   require(randomForest); 
+   set.seed(1234); 
+   mod <- randomForest(formula = formula(form), data=dat); 
+   mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named RF_RDEMO_SCORE_REGRESSION is defined. 
+-- This function creates and returns an R data.frame containing the target 
+-- predictions. User can also define other prediction functions with different 
+-- settings. Note that the randomForest function in R requires types and levels 
+-- of the scoring data be exactly same with types and levels of training data
+
+  sys.rqScriptCreate('RF_RDEMO_SCORE_REGRESSION', 'function(mod, dat) {
+   require(randomForest);
+
+   for(i in 1:length(names(dat))) {
+       if(is.numeric(dat[1,i])) {
+         dat[,i] = as.numeric(dat[,i]);} 
+       else {
+         dat[,i] = factor(dat[,i], levels=mod$forest$xlevels[[i]]);
+       } 
+   }
+
+   res <- predict(mod, newdata = dat);
+   data.frame(pred=res)}');
+
+-- Detail R Function ----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named RF_RDEMO_DETAILS_REGRESSION
+-- is defined. This function creates and returns an R data.frame containing the 
+-- attribute importance of the built random forest regression model. User can 
+-- also display other details.
+
+  sys.rqScriptCreate('RF_RDEMO_DETAILS_REGRESSION', 'function(object, x) {
+   mod <- object;
+   data.frame(row_name=row.names(mod$importance), importance=mod$importance)}');
+
+   INSERT INTO RF_RDEMO_SETTINGS_RE 
+     VALUES(dbms_data_mining.ralg_build_function, 'RF_RDEMO_BUILD_REGRESSION');
+   INSERT INTO RF_RDEMO_SETTINGS_RE 
+     VALUES(dbms_data_mining.ralg_score_function, 'RF_RDEMO_SCORE_REGRESSION');
+   INSERT INTO RF_RDEMO_SETTINGS_RE 
+     VALUES(dbms_data_mining.ralg_details_function, 'RF_RDEMO_DETAILS_REGRESSION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- attribute names and the corresponding importance.
+
+   INSERT INTO RF_RDEMO_SETTINGS_RE 
+     VALUES(dbms_data_mining.ralg_details_format,
+     'select cast(''a'' as varchar2(100)) name, 1 importance from dual');
+
+-- In this setting, a formula is specified,  which will be passed as a parameter 
+-- to the model build function to build the model.
+
+  INSERT INTO RF_RDEMO_SETTINGS_RE 
+    VALUES(dbms_data_mining.ralg_build_parameter, 
+           'select ''AGE ~ .'' "form" from dual');
+END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_RDEMO_REGRESSION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- RF_RDEMO_BUILD_REGRESSION will be used to create the random forest 
+-- regression model RF_RDEMO_REGRESSION using dataset mining_data_build_v.
+
+BEGIN
+  dbms_data_mining.create_model(
+    model_name          => 'RF_RDEMO_REGRESSION',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AGE',
+    settings_table_name => 'RF_RDEMO_SETTINGS_RE');
+END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Display the details of the built model using the R script user defined. 
+-- Here R script RF_RDEMO_DETAIL_REGRESSION will be used to display the model 
+-- details.
+
+column name format a30;
+SELECT name, round(importance, 3) as importance, 
+rank() OVER (ORDER BY importance DESC) rank 
+from DM$VDRF_RDEMO_REGRESSION order by importance desc;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. Here R script 
+-- RF_RDEMO_SCORE_REGRESSION will be used to do the scoring. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Show actual target value and predicted target values.
+
+SELECT CUST_ID, AGE as AGE_act, 
+round(PREDICTION(RF_RDEMO_REGRESSION USING *), 3) as AGE_pred 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
+
+-------------------------------------------------------------------------------
+--                        RANDOM FOREST CLASSIFICATION DEMO
+-------------------------------------------------------------------------------
+-- Explaination:
+-- This demo shows how to implement the random forest classification algorithm
+-- in Oracle Data Mining using R randomForest algorithm.
+
+-- Cleanup old output table for repeat runs -----------------------------------
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE RF_RDEMO_SETTINGS_CL';  
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+BEGIN
+  sys.rqScriptDrop('RF_RDEMO_BUILD_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('RF_RDEMO_SCORE_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('RF_RDEMO_DETAILS_CLASSIFICATION', v_silent => TRUE);
+  sys.rqScriptDrop('RF_RDEMO_WEIGHT_CLASSIFICATION', v_silent => TRUE);
+END;
+/
+
+-- Model Settings -------------------------------------------------------------
+create table RF_RDEMO_SETTINGS_CL (
+        setting_name varchar2(30),
+        setting_value varchar2(4000));
+
+BEGIN
+ INSERT INTO RF_RDEMO_SETTINGS_CL VALUES
+  ('ALGO_EXTENSIBLE_LANG', 'R');
+END;
+/
+
+BEGIN
+-- Build Function -------------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to build the model they want. 
+-- For example, here a script named RF_RDEMO_BUILD_CLASSIFICATION is defined. 
+-- This function builds and returns a random forest classification model using 
+-- R randomForest algorithm. User can also choose other R algorithm to 
+-- implement the random forest classification algorithm.
+
+  sys.rqScriptCreate('RF_RDEMO_BUILD_CLASSIFICATION', 'function(dat) {
+   require(randomForest); 
+   set.seed(1234); 
+   dat$AFFINITY_CARD=as.factor(dat$AFFINITY_CARD); 
+   mod <- randomForest(AFFINITY_CARD ~ ., data=dat); 
+   mod}');
+
+-- Score R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to do the scoring using the built
+-- model. For example, here a script named RF_RDEMO_SCORE_CLASSIFICATION is 
+-- defined. This function creates and returns an R data.frame containing the 
+-- target predictions with type vote. User can also define other prediction 
+-- functions with other types. Note that the randomForest function in R 
+-- requires types and levels of the scoring data be exactly same with types 
+-- and levels of training data
+
+  sys.rqScriptCreate('RF_RDEMO_SCORE_CLASSIFICATION', 'function(mod, dat) {
+  require(randomForest); 
+
+  for(i in 1:length(names(dat))) {
+      if(is.numeric(dat[1,i])) {
+        dat[,i] = as.numeric(dat[,i]);} 
+      else {
+        dat[,i] = factor(dat[,i], levels=mod$forest$xlevels[[i]]);
+      }  
+  }
+
+  res <- data.frame(predict(mod, newdata=dat, type="vote"));
+  names(res) <- c("0", "1");
+  res}');
+  
+-- Detail R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to show the model details they
+-- want to display. For example, here a script named 
+-- RF_RDEMO_DETAILS_CLASSIFICATION is defined. This function creates and 
+-- returns an R data.frame containing the attribute importance of the built
+-- random forest classification model. User can also display other details.
+
+  sys.rqScriptCreate('RF_RDEMO_DETAILS_CLASSIFICATION', 'function(object, x) {
+   mod <- object; 
+   data.frame(row_name=row.names(mod$importance), importance=mod$importance)}');
+
+-- Weight R Function -----------------------------------------------------------
+-- Explanation:
+-- User can define their own R script function to provide the attribute weights
+-- of the scoring data. For example, here a script named 
+-- RF_RDEMO_WEIGHT_CLASSIFICATION is defined. This function creates and returns
+-- an R data.frame containing the weights of each attribute of the scoring data.
+-- Here we simply use the ratio of the predicted target probability with all
+-- attribute values present to the predicted target probability with one 
+-- attribute value missing as the weight of the missing attribute. User can 
+-- define their own method to calculate the attribute weight. Note that the 
+-- randomForest function in R requires types and levels of the scoring data be 
+-- exactly same with types and levels of training data.
+
+  sys.rqScriptCreate('RF_RDEMO_WEIGHT_CLASSIFICATION', 'function(mod, dat, clas) {
+   require(randomForest); 
+
+   for(i in 1:length(names(dat))) {
+       if(is.numeric(dat[,i])) {
+         dat[,i] = as.numeric(dat[,i]);} 
+       else {
+         dat[,i] = factor(dat[,i], levels=mod$forest$xlevels[[i]]);
+       }  
+   }
+
+   v0 <- as.data.frame(predict(mod, newdata=dat, type = "prob"));
+   res <- data.frame(lapply(seq_along(dat),
+   function(x, dat) {
+   if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(0)
+   else {dat[,x] <- factor(NA, levels = mod$forest$xlevels[[x]]);};
+   vv <- as.data.frame(predict(mod, newdata = dat, type = "prob"));
+   v0[[clas]] / vv[[clas]]}, dat = dat));
+   names(res) <- names(dat);
+   res}');
+
+  INSERT INTO RF_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_build_function, 'RF_RDEMO_BUILD_CLASSIFICATION');
+  INSERT INTO RF_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_score_function, 'RF_RDEMO_SCORE_CLASSIFICATION');
+  INSERT INTO RF_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_function, 'RF_RDEMO_DETAILS_CLASSIFICATION');
+  INSERT INTO RF_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_weight_function, 'RF_RDEMO_WEIGHT_CLASSIFICATION');
+
+-- Once this setting is specified, a model view will be created. This model
+-- view will be generated to display the model details, which contains the 
+-- attribute names and the corresponding importance.
+
+  INSERT INTO RF_RDEMO_SETTINGS_CL 
+    VALUES(dbms_data_mining.ralg_details_format,
+    'select cast(''a'' as varchar2(100)) name, 1 importance from dual');
+END;
+/
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('RF_RDEMO_CLASSIFICATION');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL BUILD
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Build the model using the R script user defined. Here R script 
+-- RF_RDEMO_BUILD_CLASSIFICATION will be used to create the random forest 
+-- classification model RF_RDEMO_CLASSIFICATION using dataset mining_data_build_v.
+
+begin
+  dbms_data_mining.create_model(
+    model_name          => 'RF_RDEMO_CLASSIFICATION',
+    mining_function     => dbms_data_mining.classification,
+    data_table_name     => 'mining_data_build_v',
+    case_id_column_name => 'CUST_ID',
+    target_column_name  => 'AFFINITY_CARD',
+    settings_table_name => 'RF_RDEMO_SETTINGS_CL');
+end;
+/
+
+-------------------------------------------------------------------------------
+--                              MODEL DETAIL
+-------------------------------------------------------------------------------
+-- Display the details of the built model using the R script user defined. 
+-- Here R script RF_RDEMO_DETAIL_CLASSIFICATION will be used to display the 
+-- model details.
+
+column name format a30;
+select name, round(importance, 3) as importance, 
+rank() OVER (ORDER BY importance DESC) rank 
+from DM$VDRF_RDEMO_CLASSIFICATION order by importance desc;
+
+-------------------------------------------------------------------------------
+--                              MODEL SCORE
+-------------------------------------------------------------------------------
+-- Explanation:
+-- Score the model using the R script user defined. 
+
+-- PREDICTION/PREDICTION_PROBABILITY ------------------------------------------
+-- Explanation:
+-- Here R script RF_RDEMO_SCORE_CLASSIFICATION is used to get the prediction 
+-- value and the prediction probability. Actual target value and predicted 
+-- target values are provided.
+
+SELECT cust_id, affinity_card as affinity_card_act, 
+PREDICTION(RF_RDEMO_CLASSIFICATION USING *) affinity_card_pred,
+round(PREDICTION_PROBABILITY(RF_RDEMO_CLASSIFICATION USING *), 3) 
+as affinity_card_prob 
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by cust_id;
+
+-- PREDICTION_SET -------------------------------------------------------------
+-- Explanation:
+-- Here R script RF_RDEMO_SCORE_CLASSIFICATION is used to get the 
+-- prediction set. Actual target value and predicted target values are provided.
+
+select T.CUST_ID, T.affinity_card, S.prediction, 
+round(S.probability, 3) as probability  
+from (select CUST_ID, affinity_card, 
+PREDICTION_SET(RF_RDEMO_CLASSIFICATION USING *) pset 
+from mining_data_apply_v where CUST_ID <= 100010) T, TABLE(T.pset) S
+where S.probability > 0 
+order by T.CUST_ID, S.prediction;
+
+-- PREDICTION_DETAILS ---------------------------------------------------------
+-- Explanation:
+-- The R script RF_RDEMO_WEIGHT_CLASSIFICATION is used to get the prediction 
+-- details. The AFFINITY_CARD and the prediction details with the weight of each 
+-- attribute are provided.
+
+column pred_det format a65;
+SELECT CUST_ID, PREDICTION_DETAILS(RF_RDEMO_CLASSIFICATION, '1' USING *) pred_det
+FROM mining_data_apply_v where CUST_ID <= 100010 
+order by CUST_ID;
diff --git a/machine-learning/sql/26ai/oml4sql-regression-svm.sql b/machine-learning/sql/26ai/oml4sql-regression-svm.sql
new file mode 100644
index 00000000..1491f4af
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-regression-svm.sql
@@ -0,0 +1,237 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Regression - SVM Algorithm - dmsvrdem.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------  
+SET serveroutput ON
+SET trimspool ON  
+SET pages 10000
+SET echo ON
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Given demographic, purchase, and affinity card membership data for a 
+-- set of customers, predict customer's age. Since age is a continuous 
+-- variable, this is a regression problem.
+
+-----------------------------------------------------------------------
+--                            SET UP AND ANALYZE DATA
+-----------------------------------------------------------------------
+
+-- The data for this sample is composed from base tables in the SH Schema
+-- (See Sample Schema Documentation) and presented through these views:
+-- mining_data_build_parallel_v (build data)
+-- mining_data_test_parallel_v  (test data)
+-- mining_data_apply_parallel_v (apply data)
+-- (See dmsh.sql for view definitions).
+--
+-----------
+-- ANALYSIS
+-----------
+-- For regression using SVM, perform the following on mining data.
+--
+-- 1. Use Auto Data Preparation
+--
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-- Cleanup old model with same name (if any)
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVMR_SH_Regr_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-------------------
+-- SPECIFY SETTINGS
+--
+-- Cleanup old settings table for repeat runs
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svmr_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- The default algorithm for regression is SVM.
+-- see dmsvcdem.sql on choice of kernel function.
+-- 
+SET echo off
+CREATE TABLE svmr_sh_sample_settings (
+  setting_name  VARCHAR2(30),
+  setting_value VARCHAR2(4000));
+SET echo on 
+BEGIN       
+  -- Populate settings table
+  INSERT INTO svmr_sh_sample_settings (setting_name, setting_value) VALUES
+  (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_gaussian);
+
+  INSERT INTO svmr_sh_sample_settings (setting_name, setting_value) VALUES 
+  (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
+  
+  -- Examples of other possible overrides are:
+  --(dbms_data_mining.svms_conv_tolerance,0.01);
+  --(dbms_data_mining.svms_epsilon,0.1);
+  --(dbms_data_mining.svms_kernel_function,dbms_data_mining.svms_linear);
+END;
+/
+
+---------------------
+-- CREATE A NEW MODEL
+--
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVMR_SH_Regr_sample',
+    mining_function     => dbms_data_mining.regression,
+    data_table_name     => 'mining_data_build_parallel_v',
+    case_id_column_name => 'cust_id',
+    target_column_name  => 'age',
+    settings_table_name => 'svmr_sh_sample_settings');
+END;
+/
+
+-------------------------
+-- DISPLAY MODEL SETTINGS
+--
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVMR_SH_REGR_SAMPLE'
+ORDER BY setting_name;
+
+--------------------------
+-- DISPLAY MODEL SIGNATURE
+--
+col attribute_name format a30
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'SVMR_SH_REGR_SAMPLE'
+ORDER BY attribute_name;
+
+------------------------
+-- DISPLAY MODEL DETAILS
+--
+-- SVM model details are provided only for Linear Kernels.
+-- The current model is built using a Gaussian Kernel (see dmsvcdem.sql).
+--
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+WHERE model_name='SVMR_SH_REGR_SAMPLE'
+ORDER BY view_name;
+
+-----------------------------------------------------------------------
+--                               TEST THE MODEL
+-----------------------------------------------------------------------
+
+------------------------------------
+-- COMPUTE METRICS TO TEST THE MODEL
+--
+
+-- 1. Root Mean Square Error - Sqrt(Mean((y - y')^2))
+--
+column rmse format 9999.99
+SELECT SQRT(AVG((prediction - age) * (prediction - age))) rmse
+  FROM (select age, PREDICTION(svmr_sh_regr_sample USING *) prediction
+        from mining_data_test_parallel_v);
+
+-- 2. Mean Absolute Error - Mean(|(y - y')|)
+--
+column mae format 9999.99
+SELECT AVG(ABS(prediction - age)) mae
+  FROM (select age, PREDICTION(svmr_sh_regr_sample USING *) prediction
+        from mining_data_test_parallel_v);
+
+-- 3. Residuals
+--    If the residuals show substantial variance between
+--    the predicted value and the actual, you can consider
+--    changing the algorithm parameters.
+--
+column prediction format 99.9999
+SELECT prediction, (prediction - age) residual
+  FROM (select age, PREDICTION(svmr_sh_regr_sample USING *) prediction
+        from mining_data_test_parallel_v)
+ WHERE prediction < 17.5
+ ORDER BY prediction;
+
+-----------------------------------------------------------------------
+--                               APPLY THE MODEL
+-----------------------------------------------------------------------
+-------------------------------------------------
+-- SCORE NEW DATA USING SQL DATA MINING FUNCTIONS
+--
+------------------
+-- BUSINESS CASE 1
+-- Predict the average age of customers, broken out by gender.
+--
+column cust_gender format a12
+SELECT A.cust_gender,
+       COUNT(*) AS cnt,
+       ROUND(
+       AVG(PREDICTION(svmr_sh_regr_sample USING A.*)),4)
+       AS avg_age
+  FROM mining_data_apply_parallel_v A
+GROUP BY cust_gender
+ORDER BY cust_gender;
+
+------------------
+-- BUSINESS CASE 2
+-- Create a 10 bucket histogram of customers from Italy based on their age
+-- and return each customer's age group.
+--
+column pred_age format 999.99
+SELECT cust_id,
+       PREDICTION(svmr_sh_regr_sample USING *) pred_age,
+       WIDTH_BUCKET(
+        PREDICTION(svmr_sh_regr_sample USING *), 10, 100, 10) "Age Group"
+  FROM mining_data_apply_parallel_v
+ WHERE country_name = 'Italy'
+ORDER BY pred_age;
+
+------------------
+-- BUSINESS CASE 3
+-- Find the reasons (8 attributes with the most impact) for the
+-- predicted age of customer 100001.
+--
+set long 2000
+set line 200
+set pagesize 100
+SELECT PREDICTION_DETAILS(svmr_sh_regr_sample, null, 8 USING *) prediction_details
+  FROM mining_data_apply_parallel_v
+ WHERE cust_id = 100001;
+
+-----------------------------------------------------------------------
+--    BUILD and APPLY a transient model using analytic functions
+-----------------------------------------------------------------------
+-- In addition to creating a persistent model that is stored as a schema
+-- object, models can be built and scored on data on the fly using
+-- Oracle's analytic function syntax.
+
+----------------------
+-- BUSINESS USE CASE 4
+-- 
+-- Identify rows for which the provided value of the age column
+-- does not match the expected value based on patterns in the data.
+-- This could indicate bad data entry.
+-- All necessary data preparation steps are automatically performed.
+-- In addition, provide information as to what attributes most effect the
+-- predicted value, where positive weights are pushing towards a larger
+-- age and negative weights towards a smaller age.
+set long 2000
+set pagesize 100
+col age_diff format 99.99
+select cust_id, age, pred_age, age-pred_age age_diff, pred_det from
+(select cust_id, age, pred_age, pred_det,
+        rank() over (order by abs(age-pred_age) desc) rnk from
+ (select cust_id, age, 
+         prediction(for age using *) over () pred_age,
+         prediction_details(for age ABS using *) over () pred_det
+  from mining_data_apply_parallel_v))
+where rnk <= 5;
diff --git a/machine-learning/sql/26ai/oml4sql-singular-value-decomposition.sql b/machine-learning/sql/26ai/oml4sql-singular-value-decomposition.sql
new file mode 100644
index 00000000..51a13824
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-singular-value-decomposition.sql
@@ -0,0 +1,341 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Singular Value Decomposition Demo - dmsvddemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEMS
+-----------------------------------------------------------------------
+-- Extract SVD features for:
+-- 1. Visualization 
+-- 2. Data compression
+-- Each use case will be illustrated separately.  
+--
+  
+-----------------------------------------------------------------------
+-----------------------------------------------------------------------
+-- VISUALIZATIION USE CASE
+--
+-- Goal: Produce the top two PCA projections to visualize the data  
+-----------------------------------------------------------------------  
+-- Cleanup for repeat runs
+-- Cleanup old data view
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW svd_sh_sample_build_num';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_SH_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create a build data view with only numerical columns
+CREATE VIEW svd_sh_sample_build_num AS
+  SELECT CUST_ID, AGE, YRS_RESIDENCE, AFFINITY_CARD, BULK_PACK_DISKETTES,
+    FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, BOOKKEEPING_APPLICATION,
+    PRINTER_SUPPLIES, Y_BOX_GAMES, OS_DOC_SET_KANJI 
+  FROM MINING_DATA_BUILD_V;
+
+-- Setting specification
+-- Create settings table
+set echo off
+ CREATE TABLE svd_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+set echo on
+  
+-- Populate settings table
+BEGIN       
+  -- Select SVD as the Feature Extraction algorithm
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_singular_value_decomp);
+  -- Turn on automatic data preparation
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_off);  
+  -- Select PCA mode of scoring
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.svds_scoring_mode, dbms_data_mining.svds_scoring_pca);
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_shift_2dnum, dbms_data_mining.prep_shift_mean); 
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_scale_2dnum, dbms_data_mining.prep_scale_stddev);   
+  -- Examples of possible overrides are:
+  -- (dbms_data_mining.feat_num_features, 10);
+  -- (dbms_data_mining.svds_u_matrix_output, dbms_data_mining.svds_u_matrix_enable);
+  -- (dbms_data_mining.odms_approximate_computation, dbms_data_mining.odms_appr_comp_enable);
+END;
+/
+
+-- Build an SVD model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVD_SH_sample',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'svd_sh_sample_build_num',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'svd_sh_sample_settings');
+END;
+/
+
+-- Display model settings
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY setting_name;
+
+-- Display model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY attribute_name;
+
+-- Display model details
+--
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='SVD_SH_SAMPLE'
+  ORDER BY view_name;
+
+-- The model details return the SVD decomposition matrices.
+-- The user can specify the type of matrix. If no matrix type is provided
+-- all stored matrices are returned.
+-- In the current use case only the S matrix (singular values and variances)
+-- and the V matrix (PCA bases) are stored in the model.
+column value format 9999999.99
+column variance format 999999999999.9
+column pct_cum_variance format 999999.9
+  
+---- S matrix
+select feature_id, VALUE, variance, pct_cum_variance 
+FROM DM$VESVD_SH_SAMPLE;
+
+---- V matrix
+select feature_id, attribute_name, value
+from DM$VVSVD_SH_sample
+ORDER BY feature_id, attribute_name;  
+
+-- Display the high-level model details 
+column name format a30;
+column string_value format 99999.99;
+column string_value format a20;
+SELECT name, string_value, numeric_value
+FROM DM$VGSVD_SH_SAMPLE
+ORDER BY name;
+
+-- Compute the top two PCA projections that will be used for visualization
+column proj1 format 9.9999999
+column proj2 format 9.9999999
+SELECT FEATURE_VALUE(svd_sh_sample, 1 USING *) proj1,
+       FEATURE_VALUE(svd_sh_sample, 2 USING *) proj2
+FROM svd_sh_sample_build_num
+WHERE CUST_ID <= 101510
+ORDER BY 1, 2;
+
+-- Identify the three input attributes that most impact the top PCA projection
+-- for customer 101501
+set long 10000
+SELECT FEATURE_DETAILS(svd_sh_sample, 1, 3 USING *) proj1det
+FROM svd_sh_sample_build_num
+WHERE CUST_ID = 101501;
+
+-----------------------------------------------------------------------
+-----------------------------------------------------------------------
+-- Compression USE CASE
+--
+-- Goal: Compress the data and measure the reconstruction error.
+-----------------------------------------------------------------------  
+-- Cleanup for repeat runs
+-- Cleanup old transactional table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_build_num_piv';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old reconstruction table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_build_num_recon';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE svd_sh_sample_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_SH_sample');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Setting specification
+-- Create settings table
+set echo off
+ CREATE TABLE svd_sh_sample_settings (
+   setting_name  VARCHAR2(30),
+   setting_value VARCHAR2(4000));
+set echo on
+  
+-- Populate settings table
+BEGIN       
+  -- Select SVD as the Feature Extraction algorithm
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.algo_name, dbms_data_mining.algo_singular_value_decomp);
+  -- Request the output of the U matrix values (not produced by default)
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.svds_u_matrix_output, dbms_data_mining.svds_u_matrix_enable);
+  INSERT INTO svd_sh_sample_settings (setting_name, setting_value) VALUES
+    (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_off);    
+  -- Examples of possible overrides are:
+  -- (dbms_data_mining.feat_num_features, 10);
+  -- (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
+  -- (dbms_data_mining.svds_scoring_mode, dbms_data_mining.svds_scoring_pca);
+  -- (dbms_data_mining.odms_approximate_computation, dbms_data_mining.odms_appr_comp_enable);
+END;
+/
+
+-- Build an SVD model
+BEGIN
+  DBMS_DATA_MINING.CREATE_MODEL(
+    model_name          => 'SVD_SH_sample',
+    mining_function     => dbms_data_mining.feature_extraction,
+    data_table_name     => 'svd_sh_sample_build_num',
+    case_id_column_name => 'cust_id',
+    settings_table_name => 'svd_sh_sample_settings');
+END;
+/
+
+-- Display model settings
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY setting_name;
+
+-- Display model signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+  FROM user_mining_model_attributes
+ WHERE model_name = 'SVD_SH_SAMPLE'
+ORDER BY attribute_name;
+
+-- Display model details
+--
+-- The model details return the SVD decomposition matrices.
+-- The user can specify the type of matrix. If no matrix type is provided
+-- all stored matrices are returned.
+-- The S matrix represents the singular values.
+-- The V and U matrices represent two new sets of orthonormal bases.
+-- Usually, V is chosen as the new coordinate system and U represents
+-- the projection of the data in the new coordinates.
+column case_id format a10
+column attribute_name format a30
+column value format 9999999.99
+  
+---- S matrix
+select feature_id, VALUE FROM DM$VESVD_SH_SAMPLE;
+
+---- V matrix
+select feature_id, attribute_name, value
+from DM$VVSVD_SH_SAMPLE;  
+
+---- U matrix
+select feature_id, value 
+FROM DM$VUSVD_SH_sample
+WHERE case_id = 101501
+ORDER BY feature_id;
+
+-- To compress the data and reduce storage only a few of the projections
+-- in the new coordinate system need to be stored.
+-- In this use case, we  use only the top 5 projections. This results in
+-- two-fold compression. The SVD projection values can be obtained
+-- either by invoking the FEATURE_VALUE operator (see previous 
+-- use case) or using the get_model_details_svd U matrix output.
+
+-- Here, we compute the average reconstruction error due to compression.
+-- To facilitate the computation, we first pivot the original data into
+-- transactional format.
+
+-- Make the data transactional
+CREATE TABLE svd_sh_sample_build_num_piv as
+select * from svd_sh_sample_build_num
+  unpivot (value for attribute_name in("AGE", "YRS_RESIDENCE", "AFFINITY_CARD", 
+  "BULK_PACK_DISKETTES", "FLAT_PANEL_MONITOR", "HOME_THEATER_PACKAGE",
+  "BOOKKEEPING_APPLICATION", "PRINTER_SUPPLIES", "Y_BOX_GAMES", "OS_DOC_SET_KANJI"));
+
+
+-- Compute the average reconstruction error using the top 5 projections
+-- First compute the data reconstruction as U*S*V' using only the top five
+-- projections.
+CREATE TABLE svd_sh_sample_build_num_recon as
+WITH
+  s_mat AS (
+  SELECT feature_id, value FROM DM$VESVD_SH_SAMPLE 
+  WHERE feature_id<=5),
+  v_mat AS (
+  SELECT feature_id, attribute_name, value FROM DM$VVSVD_SH_SAMPLE
+  WHERE feature_id<=5),
+  u_mat AS (
+  SELECT feature_id, case_id, value FROM DM$VUSVD_SH_SAMPLE
+  WHERE feature_id<=5)
+SELECT case_id cust_id, attribute_name, sum(c.value*b.VALUE*a.value) value
+  FROM s_mat a, v_mat b, u_mat c
+  WHERE a.feature_id=b.feature_id AND a.feature_id=c.feature_id
+  GROUP BY case_id, attribute_name;
+
+column mae format 9999999.999
+-- Compute the mean absolute error.
+SELECT avg(abs(a.value-b.value)) mae
+FROM svd_sh_sample_build_num_recon a, svd_sh_sample_build_num_piv b
+WHERE a.cust_id=b.cust_id AND a.attribute_name=b.attribute_name; 
+
+column mape format 9999999.999
+-- Compute the mean absolute percentage error.
+SELECT avg(abs((a.value-b.value)/
+                CASE WHEN b.VALUE=0 THEN 1 ELSE b.VALUE END)) mape
+FROM svd_sh_sample_build_num_recon a, svd_sh_sample_build_num_piv b
+WHERE a.cust_id=b.cust_id AND a.attribute_name=b.attribute_name; 
+
+-----------------------------------------------------------------------
+--    BUILD and APPLY a transient model using analytic functions
+-----------------------------------------------------------------------
+-- In addition to creating a persistent model that is stored as a schema
+-- object, models can be built and scored on data on the fly using
+-- Oracle's analytic function syntax.
+
+--------------------
+-- BUSINESS USE CASE
+-- 
+-- Map customer attributes into six features and return the feature
+-- mapping for customer 100001.
+-- All data in the apply view is used to construct the feature mappings.
+-- All necessary data preparation steps are automatically performed.
+column feature_id format 999
+column value format 999.999
+select feature_id, value 
+from (
+ select cust_id, feature_set(into 6 using *) over () fset
+ from mining_data_apply_v),
+table(fset)
+where cust_id = 100001
+order by feature_id;
diff --git a/machine-learning/sql/26ai/oml4sql-survival-analysis-xgboost.sql b/machine-learning/sql/26ai/oml4sql-survival-analysis-xgboost.sql
new file mode 100644
index 00000000..afbabdd2
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-survival-analysis-xgboost.sql
@@ -0,0 +1,451 @@
+-----------------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+--
+--   Survival Analysis Modeling using XGBoost
+--  
+--   Copyright (c) 2025 Oracle Corporation and/or its affiliates.
+--   The Universal Permissive License (UPL), Version 1.0
+--
+--   https://oss.oracle.com/licenses/upl/
+
+-----------------------------------------------------------------------------
+-- For more information...
+
+-- Oracle ADW Documentation: 
+-- https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html
+
+-- OML Folder on Github: 
+-- https://github.com/oracle/oracle-db-examples/tree/master/machine-learning
+
+-- OML Web Page: 
+-- https://www.oracle.com/machine-learning
+
+-- OML Regression:
+-- https://www.oracle.com/goto/ml-regression
+
+-- OML XGBoost: 
+-- https://http://www.oracle.com/goto/ml-xgboost
+ 
+-----------------------------------------------------------------------------
+--                   EXAMPLES IN THIS SCRIPT
+-----------------------------------------------------------------------------
+-- Create a Survival Analysis XGBoost Model using CREATE_MODEL2
+-- Walk through XGB algorithm settings with the model
+-- Survival analysis with AFT model
+
+-----------------------------------------------------------------------------
+--         Examples of Setting Overrides for XGBoost 
+-----------------------------------------------------------------------------
+
+-- If the user does not override the default settings, relevant settings 
+-- are determined by the algorithm. 
+
+-- A complete list of settings can be found in the documentation link:
+-- https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/
+-- DBMS_DATA_MINING.html#GUID-443B3D58-8B74-422E-8E51-C8F609249B2C
+
+-- Set evaluation metric:
+--   v_setlst('eval_metric') := 'cox-nloglik';
+--   v_setlst('eval_metric') := 'aft-nloglik';
+--   v_setlst('eval_metric') := 'c-index';
+--   v_setlst('eval_metric') := 'ndcg';
+
+-- Regularization parameters for all boosters:
+--   v_setlst('alpha')  := '0.1';
+--   v_setlst('eta')    := '0.3';
+--   v_setlst('lambda') := '0.3';
+
+-- Subsampling methods:
+--   v_setlst('max_depth')         := '6';
+--   v_setlst('min_child_weight')  := '1';
+--   v_setlst('colsample_bytree')  := '0.9';
+
+-- Set a tree method:
+--   v_setlst('tree_method')  := 'hist';
+--   v_setlst('tree_method')  := 'exact';
+--   v_setlst('tree_method')  := 'approx';
+--   v_setlst('tree_method')  := 'gpu_exact';
+--   v_setlst('tree_method')  := 'gpu_hist';
+
+-- Number of iteration rounds:
+--   v_setlst('num_round') := '100';
+
+-- For XGBoost survival tasks, the objective is:
+--   v_setlst('objective') := 'survival:aft';
+--   v_setlst('objective') := 'survival:cox';
+--   v_setlst('objective') := 'survival:cox-nloglik';
+--   v_setlst('objective') := 'survival:cox-gamma';
+
+-- Specify loss distribution:
+--   v_setlst('aft_loss_distribution') := 'normal';
+--   v_setlst('aft_loss_distribution') := 'logistic';
+--   v_setlst('aft_loss_distribution') := 'extreme';
+
+-----------------------------------------------------------------------------
+--         Create a data table with left and right bound columns
+-----------------------------------------------------------------------------
+
+-- The data table 'SURVIVAL_DATA' contains both exact data point and 
+-- right-censored data point. The left bound column is set by 
+-- parameter target_column_name. The right bound column is set 
+-- by setting aft_right_bound_column_name.
+
+-- For right censored data point, the right bound is infinity,
+-- which is represented as NULL in the right bound column.
+
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE SURVIVAL_DATA';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/ 
+CREATE TABLE SURVIVAL_DATA (INST NUMBER, LBOUND NUMBER, AGE NUMBER, 
+                            SEX NUMBER, PHECOG NUMBER, PHKARNO NUMBER, 
+                            PATKARNO NUMBER, MEALCAL NUMBER, WTLOSS NUMBER, 
+                            RBOUND NUMBER);                
+INSERT INTO SURVIVAL_DATA VALUES(26, 235, 63, 2, 0, 100,  90,  413,  0,   NULL);
+INSERT INTO SURVIVAL_DATA VALUES(22, 444, 75, 2, 2,  70,  70,  438,  8,   444);
+INSERT INTO SURVIVAL_DATA VALUES(16, 806, 44, 1, 1,  80,  80, 1025,  1,   NULL);
+INSERT INTO SURVIVAL_DATA VALUES(16, 551, 77, 2, 2,  80,  60,  750, 28,   NULL);
+INSERT INTO SURVIVAL_DATA VALUES(3,  202, 50, 2, 0, 100, 100,  635,  1,   NULL);
+INSERT INTO SURVIVAL_DATA VALUES(7,  583, 68, 1, 1,  60,  70, 1025,  7,   583);
+INSERT INTO SURVIVAL_DATA VALUES(32, 135, 60, 1, 1,  90,  70, 1275,  0,   135);
+INSERT INTO SURVIVAL_DATA VALUES(21, 237, 69, 1, 1,  80,  70, NULL, NULL, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(26, 356, 53, 2, 1,  90,  90, NULL,   2,  NULL);
+INSERT INTO SURVIVAL_DATA VALUES(13, 387, 56, 1, 2,  80,  60, 1075, NULL, 387);
+
+-----------------------------------------------------------------------------
+--             Build an XGBoost survival model with survival:aft
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('eval_metric')                  := 'aft-nloglik';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'normal';
+    v_setlst('aft_loss_distribution_scale')  := '1.20';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_DATA',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+--  NULL value in rbound (aft_right_bound_column_name) column 
+--    is intepreted as infinity.
+
+COLUMN PRED FORMAT 99999
+SET LONG 20000
+SET LINES 100
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL  USING *),3) PRED 
+FROM   SURVIVAL_DATA;
+
+-----------------------------------------------------------------------------
+--              Nest data into numerical values
+-----------------------------------------------------------------------------
+
+CREATE OR REPLACE VIEW SURVIVAL_NUMERIC AS 
+SELECT LBOUND, RBOUND, WTLOSS,
+       DM_NESTED_NUMERICALS(
+         DM_NESTED_NUMERICAL('INST', INST),
+         DM_NESTED_NUMERICAL('AGE', AGE),
+         DM_NESTED_NUMERICAL('SEX', SEX),
+         DM_NESTED_NUMERICAL('PHECOG', PHECOG),
+         DM_NESTED_NUMERICAL('PHKARNO', PHKARNO),
+         DM_NESTED_NUMERICAL('PATKARNO', PATKARNO),
+         DM_NESTED_NUMERICAL('MEALCAL', MEALCAL)) NNUM 
+FROM  SURVIVAL_DATA;
+
+-----------------------------------------------------------------------------
+-- Build an XGBoost model using nested numeric data
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('eval_metric')                  := 'aft-nloglik';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'normal';
+    v_setlst('aft_loss_distribution_scale')  := '1.20';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_NUMERIC',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+-- NULL value in rbound (aft_right_bound_column_name) column
+--   is intepreted as infinity.
+
+COLUMN PRED FORMAT 99999
+SET LONG 20000
+SET LINES 100
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL  USING *),3) PRED 
+FROM   SURVIVAL_NUMERIC;
+
+-----------------------------------------------------------------------------
+--    Build an XGBoost model with no eval_metric specified
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+begin
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'normal';
+    v_setlst('aft_loss_distribution_scale')  := '1.20';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_DATA',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL  USING *),3) PRED 
+FROM   SURVIVAL_DATA;
+
+-----------------------------------------------------------------------------
+--    Build an XGBoost model with aft_loss_distribution = logistic
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('eval_metric')                  := 'aft-nloglik';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'logistic';
+    v_setlst('aft_loss_distribution_scale')  := '1.20';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_DATA',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED 
+FROM   SURVIVAL_DATA;
+
+-----------------------------------------------------------------------------
+--    Build an XGBoost model with aft_loss_distribution = extreme
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('eval_metric')                  := 'aft-nloglik';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'extreme';
+    v_setlst('aft_loss_distribution_scale')  := '1.20';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_DATA',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL  USING *),3) PRED 
+FROM   SURVIVAL_DATA;
+
+-----------------------------------------------------------------------------
+--    Build an XGBoost model with aft_loss_distribution_scale = 0 
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('eval_metric')                  := 'aft-nloglik';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'extreme';
+    v_setlst('aft_loss_distribution_scale')  := '0';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+    
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_DATA',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL  USING *),3) PRED 
+From   SURVIVAL_DATA;
+
+-----------------------------------------------------------------------------
+--          Create a table with only one numerical column
+-----------------------------------------------------------------------------
+
+DROP TABLE SURVIVAL_DATA;
+CREATE TABLE SURVIVAL_DATA (LBOUND NUMBER, RBOUND NUMBER);                
+INSERT INTO SURVIVAL_DATA VALUES(235, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(444, 444);
+INSERT INTO SURVIVAL_DATA VALUES(806, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(551, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(202, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(583, 583);
+INSERT INTO SURVIVAL_DATA VALUES(135, 135);
+INSERT INTO SURVIVAL_DATA VALUES(237, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(356, NULL);
+INSERT INTO SURVIVAL_DATA VALUES(387, 387);
+
+-----------------------------------------------------------------------------
+--        Build an XGBoost model using numerical table  
+-----------------------------------------------------------------------------
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')                    := 'ALGO_XGBOOST';
+    v_setlst('max_depth')                    := '6';
+    v_setlst('eval_metric')                  := 'aft-nloglik';
+    v_setlst('num_round')                    := '100';
+    v_setlst('objective')                    := 'survival:aft';
+    v_setlst('aft_right_bound_column_name')  := 'rbound';
+    v_setlst('aft_loss_distribution')        := 'normal';
+    v_setlst('aft_loss_distribution_scale')  := '1.20';
+    v_setlst('eta')                          := '0.05';
+    v_setlst('lambda')                       := '0.01';
+    v_setlst('alpha')                        := '0.02';
+    v_setlst('tree_method')                  := 'hist';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'XGB_SURVIVAL_MODEL',
+        MINING_FUNCTION     => 'REGRESSION',
+        DATA_QUERY          => 'SELECT * FROM SURVIVAL_DATA',
+        TARGET_COLUMN_NAME  => 'LBOUND',
+        CASE_ID_COLUMN_NAME =>  NULL,
+        SET_LIST            =>  v_setlst);
+END;
+/
+
+-----------------------------------------------------------------------------
+--                    Get Prediction Details 
+-----------------------------------------------------------------------------
+
+SELECT LBOUND, RBOUND, 
+       ROUND(PREDICTION(XGB_SURVIVAL_MODEL  USING *),3) PRED 
+FROM   SURVIVAL_DATA;
+
+-----------------------------------------------------------------------
+--   End of script
+-----------------------------------------------------------------------
\ No newline at end of file
diff --git a/machine-learning/sql/26ai/oml4sql-time-series-esm-auto-model-search.sql b/machine-learning/sql/26ai/oml4sql-time-series-esm-auto-model-search.sql
new file mode 100644
index 00000000..829e6a72
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-time-series-esm-auto-model-search.sql
@@ -0,0 +1,88 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+--
+--   Automated Model Search- Time Series Algorithm ESM
+--
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--   The Universal Permissive License (UPL), Version 1.0
+--
+--   https://oss.oracle.com/licenses/upl
+-----------------------------------------------------------------------
+
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Create an ESM Time Series Model with Automated Model Search, which is
+--   also the default behavior when no ESM model type is specified
+
+-----------------------------------------------------------------------
+--                            EXAMPLE IN THIS SCRIPT
+-----------------------------------------------------------------------
+-- Create an ESM model with CREATE_MODEL2 and Model Search Enabled
+-- Evaluate the model 
+
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-------------------------
+-- CREATE VIEW
+--
+
+CREATE OR REPLACE VIEW ESM_SH_DATA AS 
+SELECT TIME_ID, AMOUNT_SOLD 
+FROM   SH.SALES;
+
+
+-------------------------
+-- CREATE MODEL
+--
+
+BEGIN DBMS_DATA_MINING.DROP_MODEL('ESM_SALES_FORECAST_1');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN 
+    v_setlst('ALGO_NAME')            := 'ALGO_EXPONENTIAL_SMOOTHING';
+    v_setlst('EXSM_INTERVAL')       := 'EXSM_INTERVAL_DAY';
+
+    DBMS_DATA_MINING.CREATE_MODEL2(
+        MODEL_NAME          => 'ESM_SALES_FORECAST_1',
+        MINING_FUNCTION     => 'TIME_SERIES',
+        DATA_QUERY          => 'select * from ESM_SH_DATA',
+        SET_LIST            => v_setlst,
+        CASE_ID_COLUMN_NAME => 'TIME_ID',
+        TARGET_COLUMN_NAME  => 'AMOUNT_SOLD');
+END;
+/
+
+-----------------------------------------------------------------------
+--                            ANALYZE THE MODEL
+-----------------------------------------------------------------------
+-------------------------
+-- GET MODEL DETAILS
+--
+
+SELECT setting_name, setting_value, setting_type
+FROM   user_mining_model_settings
+WHERE  (setting_type != 'DEFAULT' or setting_name like 'EXSM%') 
+AND    model_name = upper('ESM_SALES_FORECAST_1')
+ORDER BY setting_name;
+/
+
+
+-------------------------
+-- COMPUTED SETTINGS AND OTHER GLOBAL STATISTICS
+--
+
+SELECT name, ROUND(numeric_value,3) numeric_value, string_value 
+FROM DM$VGESM_SALES_FORECAST_1
+ORDER BY name;
+/
+
+-----------------------------------------------------------------------
+--   End of script
+-----------------------------------------------------------------------
\ No newline at end of file
diff --git a/machine-learning/sql/26ai/oml4sql-time-series-exponential-smoothing.sql b/machine-learning/sql/26ai/oml4sql-time-series-exponential-smoothing.sql
new file mode 100644
index 00000000..3c7b6aa4
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-time-series-exponential-smoothing.sql
@@ -0,0 +1,112 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Time Series - Exponential Smoothing Algorithm - dmesmdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+-----------------------------------------------------------------------
+--                            SET UP THE DATA
+-----------------------------------------------------------------------
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE esm_sh_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('ESM_SH_SAMPLE');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create input time series
+create or replace view esm_sh_data 
+       as select time_id, amount_sold 
+       from sh.sales;
+
+CREATE TABLE esm_sh_settings(setting_name VARCHAR2(30), 
+                             setting_value VARCHAR2(128));
+begin
+  -- Select ESM as the algorithm
+  insert into esm_sh_settings 
+         values(dbms_data_mining.algo_name,
+                dbms_data_mining.algo_exponential_smoothing);
+  -- Set accumulation interval to be quarter
+  insert into esm_sh_settings 
+         values(dbms_data_mining.exsm_interval,
+                dbms_data_mining.exsm_interval_qtr);
+  -- Set prediction step to be 4 quarters (one year)
+  INSERT INTO esm_sh_settings 
+         VALUES(dbms_data_mining.exsm_prediction_step,
+                '4');
+  -- Set ESM model to be Holt-Winters
+  INSERT INTO esm_sh_settings 
+         VALUES(dbms_data_mining.exsm_model,
+                dbms_data_mining.exsm_hw);
+  -- Set seasonal cycle to be 4 quarters
+  insert into esm_sh_settings 
+         values(dbms_data_mining.exsm_seasonality,
+                '4');
+end;
+/
+
+-- Build the ESM model
+BEGIN
+  dbms_data_mining.create_model(model_name => 'ESM_SH_SAMPLE',
+               mining_function   => 'TIME_SERIES',
+               data_table_name => 'esm_sh_data',
+               case_id_column_name => 'time_id',
+               target_column_name => 'amount_sold',
+               settings_table_name => 'ESM_SH_SETTINGS');
+END;
+/
+
+-- output setting table
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = upper('ESM_SH_SAMPLE')
+ORDER BY setting_name;
+
+-- get signature
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+FROM   user_mining_model_attributes
+  WHERE  model_name=upper('ESM_SH_SAMPLE')
+  ORDER BY attribute_name;
+
+
+-- get global diagnostics
+column name format a20
+column numeric_value format a20
+column string_value format a15
+SELECT name, 
+to_char(numeric_value, '99999.99EEEE') numeric_value, 
+string_value FROM DM$VGESM_SH_SAMPLE
+  ORDER BY name;
+
+-- get predictions
+set heading on
+SET LINES 100
+SET PAGES 105
+COLUMN CASE_ID FORMAT A30
+COLUMN VALUE FORMAT 9999999
+COLUMN PREDICTION FORMAT 99999999
+COLUMN LOWER FORMAT 99999999
+COLUMN UPPER FORMAT 99999999
+select case_id, value, prediction, lower, upper 
+from DM$VPESM_SH_SAMPLE
+ORDER BY case_id;
diff --git a/machine-learning/sql/26ai/oml4sql-time-series-mset.sql b/machine-learning/sql/26ai/oml4sql-time-series-mset.sql
new file mode 100644
index 00000000..133bf51e
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-time-series-mset.sql
@@ -0,0 +1,180 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+-- 
+--   Time Series - Multivariate State Estimation Technique Algorithm - dmmsetdemo.sql
+--   
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--  The Universal Permissive License (UPL), Version 1.0
+--
+--  https://oss.oracle.com/licenses/upl/
+-----------------------------------------------------------------------
+SET ECHO ON
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100  
+  
+-----------------------------------------------------------------------  
+--  MSET-SPRT algorithm for multi-variate time-series anomaly detection
+-----------------------------------------------------------------------
+--                      SAMPLE PROBLEM
+--  Given the two-variate time series data, the daily sold 
+--  quantity and amount from January 1, 1998 to December 31, 1999, 
+--  predict the anomalies of sold data from January 1, 2000 to  
+--  December 31 2001.
+-----------------------------------------------------------------------  
+--                     (1)   SET UP THE DATA
+--  The data for this sample is from SH Schema, specifically, subtables 
+--  of sh.sales.
+--  Two views are created based on sh.sales:
+--  mset_build_sh_data (build data)
+--  mset_test_sh_data  (test data)
+-----------------------------------------------------------------------
+-- Cleanup old settings table
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE mset_sh_settings';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Cleanup old model with the same name
+BEGIN DBMS_DATA_MINING.DROP_MODEL('MSET_SH_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+-- Create training multivariate time series data from year 1998 and 1999
+CREATE OR replace VIEW mset_build_sh_data 
+  AS SELECT time_id, sum(quantity_sold) quantity, 
+  sum(amount_sold) amount from (SELECT * FROM sh.sales WHERE 
+  time_id <= '30-DEC-99') GROUP BY time_id ORDER BY time_id;
+       
+-- Create testing multivariate time series data from year 2000-2001 
+CREATE OR replace VIEW mset_test_sh_data 
+  AS SELECT time_id, sum(quantity_sold) quantity, sum(amount_sold) 
+    amount FROM (SELECT * FROM sh.sales WHERE time_id > '30-DEC-99') 
+    GROUP BY time_id ORDER BY time_id;      
+              
+-- Create setting table        
+CREATE TABLE mset_sh_settings(setting_name VARCHAR2(30), 
+                             setting_value VARCHAR2(128));
+-- Populate setting table
+BEGIN
+  -- Select MSET-SPRT as the algorithm
+  INSERT  INTO mset_sh_settings
+         VALUES(dbms_data_mining.algo_name,
+                dbms_data_mining.algo_mset_sprt);
+  -- Turn on automatic data preparation   
+  INSERT INTO mset_sh_settings
+         VALUES(dbms_data_mining.prep_auto,
+                dbms_data_mining.prep_auto_on);
+  -- Set memory vector
+  INSERT INTO mset_sh_settings
+    VALUES(dbms_data_mining.mset_memory_vectors, 100);
+  -- Set alpha
+  INSERT INTO mset_sh_settings
+    VALUES(dbms_data_mining.MSET_ALPHA_PROB, 0.1);
+  -- Set alert count
+  INSERT INTO mset_sh_settings
+    VALUES(dbms_data_mining.MSET_ALERT_COUNT, 3);
+  -- Set alert window
+  INSERT INTO mset_sh_settings
+    VALUES(dbms_data_mining.MSET_ALERT_WINDOW, 5);  
+  -- Examples of other possible settings are:
+  -- (dbms_data_mining.mset_beta_prob, 0.1)
+  -- (dbms_data_mining.mset_adb_height, 0.01)
+  -- (dbms_data_mining.mset_std_tolerance, 3)
+  -- (dbms_data_mining.mset_heldaside, 500)
+  commit;  
+END;
+/
+
+-----------------------------------------------------------------
+--                (2)  Build the MSET model
+-----------------------------------------------------------------
+BEGIN
+  dbms_data_mining.create_model(model_name => 'MSET_SH_MODEL',
+               mining_function   => 'CLASSIFICATION',
+               data_table_name => 'mset_build_sh_data',
+               case_id_column_name => 'time_id',
+               target_column_name => '',
+               settings_table_name => 'MSET_SH_SETTINGS');
+END;
+/
+
+------------------------
+-- DISPLAY MODEL SETTINGS
+column setting_name format a30
+column setting_value format a30
+SELECT setting_name, setting_value
+  FROM user_mining_model_settings
+ WHERE model_name = 'MSET_SH_MODEL'
+ORDER BY setting_name;
+
+-------------------------
+-- DISPLAY MODEL SIGNITURE
+column attribute_name format a40
+column attribute_type format a20
+SELECT attribute_name, attribute_type
+FROM   user_mining_model_attributes
+  WHERE  model_name = 'MSET_SH_MODEL'
+  ORDER BY attribute_name;
+
+-- Get a list of model views
+col view_name format a30
+col view_type format a50
+SELECT view_name, view_type FROM user_mining_model_views
+  WHERE model_name='MSET_SH_MODEL'
+  ORDER BY view_name;
+
+-- get global diagnostics
+column name format a20
+column numeric_value format a20
+column string_value format a15
+SELECT name, 
+to_char(numeric_value, '99999') numeric_value, 
+string_value FROM DM$VGMSET_SH_MODEL
+  ORDER BY name;
+
+-----------------------------------------------------------------------
+--                         (3)   TEST THE MODEL
+-----------------------------------------------------------------------
+-- Predicting anomalies of daily sold data from year 2000 to 2001
+-----------------------------------------------------------------------
+-- display predictions and probabilities of a sample list of data
+col prob format 0.999   
+col pred format 9999
+col time_id format a20  
+SELECT time_id, prediction(mset_sh_model using *) over 
+  (ORDER BY time_id) pred, prediction_probability (mset_sh_model using *) 
+  over (ORDER BY time_id) prob
+  FROM (SELECT * FROM mset_test_sh_data  WHERE time_id > '15-DEC-01' AND 
+  time_id <= '25-DEC-01' ) ORDER BY time_id;
+
+-- display all dates of year 2000 to 2001 when anomalies occur
+SELECT time_id, pred FROM (SELECT time_id, prediction(mset_sh_model using *) 
+  over (ORDER BY time_id) pred FROM mset_test_sh_data) WHERE pred = 0;  
+
+-- display total anomaly count for year 2000 to 2001
+col min(prob) format 0.999
+col max(prob) format 0.999
+SELECT pred, count(pred), min(prob), max(prob) FROM (
+  SELECT prediction(mset_sh_model using *) over (ORDER BY time_id) pred, 
+  prediction_probability(mset_sh_model using *) over (ORDER BY time_id) 
+  prob FROM mset_test_sh_data ) GROUP BY pred ORDER BY pred;
+
+-- display anomly rate: number of anomaly/total number of data
+col anomalyrate format 9.999
+SELECT 1-sum(correct)/count(*) AS anomalyrate
+  FROM (SELECT decode(prediction(mset_sh_model using *) over 
+  (ORDER BY time_id), 1, 1) AS correct FROM mset_test_sh_data );
+  
+-- display prediction details 
+SET long 1000;  
+col anomalydetails format a80
+SELECT time_id, prediction(mset_sh_model using *) over (ORDER BY time_id) 
+  pred, prediction_details(mset_sh_model using *) over (ORDER BY time_id) 
+  anomalyDetails FROM mset_test_sh_data  WHERE time_id > '15-DEC-01' AND 
+  time_id <= '25-DEC-01' ORDER BY time_id;
+  
+ 
diff --git a/machine-learning/sql/26ai/oml4sql-time-series-regression-dataset.sql b/machine-learning/sql/26ai/oml4sql-time-series-regression-dataset.sql
new file mode 100644
index 00000000..3ade220f
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-time-series-regression-dataset.sql
@@ -0,0 +1,1889 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+--
+--   Time Series Regression - dataset creation
+--
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--   The Universal Permissive License (UPL), Version 1.0
+--
+--   https://oss.oracle.com/licenses/upl
+-----------------------------------------------------------------------
+
+
+DROP TABLE EUSTOCK;
+  CREATE TABLE EUSTOCK 
+   (	"DAX" NUMBER(38,2), 
+	"SMI" NUMBER(38,1), 
+	"CAC" NUMBER(38,1), 
+	"FTSE" NUMBER(38,1), 
+	"DATES" DATE
+   ) SEGMENT CREATION IMMEDIATE 
+  PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 
+ NOCOMPRESS LOGGING
+  STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645
+  PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1
+  BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)
+  TABLESPACE "USERS" ;
+REM INSERTING into EUSTOCK
+SET DEFINE OFF;
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.69,2062.7,1818.8,2748.7,to_date('06-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1580.64,2089.9,1812.2,2773.3,to_date('07-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1593.35,2102.9,1820.4,2781.2,to_date('08-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.28,2086,1779.9,2771.9,to_date('09-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1575.59,2085.9,1792.6,2835.7,to_date('10-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.78,2064.1,1777.4,2832.5,to_date('11-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.68,2072.7,1780.6,2816.9,to_date('12-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1574.04,2091,1772.2,2807.2,to_date('13-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.33,2120.2,1785.9,2851.6,to_date('14-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1584.14,2120.4,1787.3,2834.4,to_date('15-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.91,2117.6,1824.1,2873.8,to_date('16-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1615.98,2123.7,1854.4,2865.9,to_date('17-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1643.83,2132.2,1908.2,2862.9,to_date('18-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1646.85,2137,1904.7,2870,to_date('19-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1639.12,2134.8,1894.1,2831.3,to_date('20-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1642.8,2121.4,1893.3,2816.4,to_date('21-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1659.07,2127.5,1905.6,2834.3,to_date('22-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1649.64,2135.5,1912,2843,to_date('23-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.93,2144.8,1899.5,2845.9,to_date('24-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1651.6,2131,1878.2,2812.2,to_date('25-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.35,2112.9,1905,2814,to_date('26-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1670.9,2131.3,1926.5,2837.7,to_date('27-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.3,2117.8,1937.2,2840,to_date('28-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.41,2096.1,1959.2,2838.3,to_date('29-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.09,2051.5,1944.1,2818,to_date('30-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.92,2065.7,1953.4,2817,to_date('01-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1661.96,2061,1944.6,2828.7,to_date('02-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.02,2100.6,1983.7,2868,to_date('03-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4781.62,7273,3446.7,5820.6,to_date('04-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4759.62,7259.5,3414.9,5807.7,to_date('05-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4690.52,7130.5,3381.3,5733.1,to_date('06-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4676.42,7077.3,3395.8,5695.6,to_date('07-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4762.71,7197.2,3483.2,5782.9,to_date('08-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4828.89,7187.5,3525.9,5818.9,to_date('09-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4852.22,7246.5,3521.5,5828.5,to_date('10-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4862.41,7276.7,3539.4,5829.8,to_date('11-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4838.67,7267.9,3526.6,5794.8,to_date('12-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4872.24,7328,3540.2,5782.3,to_date('13-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4905.59,7261.2,3598.3,5785.1,to_date('14-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4945.91,7236.5,3661.3,5834.9,to_date('15-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4908.55,7132.4,3652.5,5903.6,to_date('16-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4949.91,7143.8,3688.7,5997.9,to_date('17-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5045.16,7300.5,3688.9,5956.3,to_date('18-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5014.13,7341,3680.1,5947,to_date('19-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5064.35,7407.4,3738.5,5983.7,to_date('20-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5114.13,7472.1,3818.7,5967.8,to_date('21-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5029,7415.9,3783.8,5905.6,to_date('22-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5066.9,7530.3,3810.2,5939.3,to_date('23-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5069.89,7536.3,3800.2,5911.9,to_date('24-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5097.25,7585.5,3875.3,5932.2,to_date('25-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5135.35,7615.5,3883.3,6017.6,to_date('26-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5179.04,7638.8,3935.9,6052.8,to_date('27-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5254.32,7725.9,3932,6064.2,to_date('28-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5345.89,7827.7,3986.8,6105.8,to_date('29-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5309.67,7744.3,3903.3,6094,to_date('30-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5267.35,7588.1,3873.9,6055.2,to_date('31-JUL-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('01-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('02-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5312.25,7624.1,3894.5,6105.5,to_date('03-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5367.98,7662.9,3867.7,6104.1,to_date('04-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5359.24,7616.3,3884.6,6074.1,to_date('05-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5292.97,7500.1,3845.9,6002,to_date('06-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5326.63,7453.7,3861.6,5922.2,to_date('07-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5407.93,7500.1,3885.7,5954.1,to_date('08-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2173.6,2674.5,1998.2,3241.5,to_date('09-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2158.07,2652.4,1961.5,3205.4,to_date('10-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2167.68,2662.5,1964.2,3203.9,to_date('11-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2178.1,2664.1,1983.4,3180,to_date('12-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2155.58,2642.1,1948.8,3139.3,to_date('13-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2155.81,2643,1966.8,3128.8,to_date('14-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2157.15,2658.8,1969.4,3121.4,to_date('15-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.17,2637.7,1952.9,3079.8,to_date('16-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.36,2628.6,1977.3,3112.7,to_date('17-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.45,2603.3,1924.6,3065.1,to_date('18-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.55,2614.7,1922.9,3079.1,to_date('19-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2059.15,2593,1919.3,3037.3,to_date('20-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2067.17,2594,1897.2,3014.8,to_date('21-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.81,2602.9,1899.4,3021.2,to_date('22-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.33,2609.1,1927.4,3028.2,to_date('23-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.83,2586.1,1902.7,2999.8,to_date('24-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.89,2581,1901.3,3008.5,to_date('25-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.36,2590,1905,3038.7,to_date('26-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.08,2558,1876.2,2992.5,to_date('27-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2002.3,2534.4,1879.3,3026.3,to_date('28-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2002.3,2499.5,1852.8,2983.5,to_date('29-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1988.67,2522.5,1876.1,3001.8,to_date('30-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1946.49,2480.4,1833.7,2956.3,to_date('31-AUG-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1965.41,2484.8,1843.4,2984.4,to_date('01-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1977.67,2496.9,1856.4,2998.7,to_date('02-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.56,2553.4,1898.3,3032.3,to_date('03-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2087.71,2570.2,1919,3073,to_date('04-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.68,2562.7,1918.1,3100.5,to_date('05-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2108.08,2593.3,1955.6,3141.9,to_date('06-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.52,2585.3,1933,3106.7,to_date('07-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.58,2575.6,1906.4,3120.2,to_date('08-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.58,2542.5,1898.6,3085.3,to_date('09-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2055.94,2529.8,1876.3,3060.8,to_date('10-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.15,2530.9,1867.4,3063.2,to_date('11-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.6,2508.6,1842.1,3032.8,to_date('12-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.64,2525.2,1841.6,3029.1,to_date('13-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.85,2494.7,1824.4,3000.9,to_date('14-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2009.45,2477.2,1831.5,2999.9,to_date('15-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.37,2458.6,1858.1,3029.6,to_date('16-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.86,2490.5,1905.7,3083.8,to_date('17-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.58,2506.5,1905.7,3097.4,to_date('18-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2066.18,2503.9,1905.7,3096.3,to_date('19-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2039.91,2500,1873.6,3081.3,to_date('20-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.37,2534.8,1911.1,3104.4,to_date('21-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.39,2541,1931.7,3097.6,to_date('22-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2043.3,2557.9,1906.1,3065.8,to_date('23-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.36,2543.9,1921.5,3063.8,to_date('24-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2090.78,2590.4,1943.9,3099.6,to_date('25-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2098.04,2602.7,1948.4,3103.5,to_date('26-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.67,2597.8,1948.4,3075.9,to_date('27-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.04,2590.9,1941.1,3095.3,to_date('28-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.21,2591.9,1954.5,3135.4,to_date('29-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.21,2609.4,1950.2,3146.5,to_date('30-SEP-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.94,2600.9,1927.5,3127.5,to_date('01-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.48,2606.5,1926.5,3131,to_date('02-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.2,2607,1927.8,3121,to_date('03-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.29,2571.8,1911.4,3078.7,to_date('04-OCT-95','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2058.79,2568.6,1934.7,3036.6,to_date('06-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2056.27,2572.5,1945.9,3033.5,to_date('07-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.82,2579.4,1952.4,3047.1,to_date('08-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.24,2584.7,1940,3061.1,to_date('09-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.08,2594.9,1975.9,3081.4,to_date('10-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2045.54,2576,1964,3039.6,to_date('11-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2042.38,2577.5,1982.7,3017.3,to_date('12-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2067.26,2611,1973.7,3033.5,to_date('13-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2046.99,2589.1,1969,3016.1,to_date('14-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2044.04,2584.3,1969.8,3012.5,to_date('15-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2041.85,2589.3,1954.1,3013.8,to_date('16-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.19,2576,1937,2977.3,to_date('17-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2003.64,2562.8,1919.3,2943.4,to_date('18-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2019.13,2566,1917.1,2946.4,to_date('19-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.94,2581.4,1930.2,2980.6,to_date('20-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2054.23,2598,1931.1,2973.4,to_date('21-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.9,2604.7,1924.2,3013.6,to_date('22-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2066.59,2600.7,1928.1,3034.4,to_date('23-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2080.16,2614.7,1924.7,3058.1,to_date('24-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2095.5,2636.1,1940.9,3070.4,to_date('25-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2102.25,2649.8,1952.1,3091.7,to_date('26-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.98,2651,1949.9,3083.4,to_date('27-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2100.98,2651,1966.6,3083.4,to_date('28-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2110.77,2673.5,1956,3083.4,to_date('29-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.34,2656.2,1927.8,3095.8,to_date('30-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2074.68,2628.8,1894.2,3065.6,to_date('31-OCT-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.51,2628.8,1881.2,3065.5,to_date('01-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2079.19,2628.8,1881.2,3065.5,to_date('02-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2068.92,2612.3,1885.9,3065.7,to_date('03-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2072.9,2632.4,1901.8,3051.6,to_date('04-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2051.46,2613.1,1871.5,3032.3,to_date('05-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2058.2,2622.3,1886.4,3065,to_date('06-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2053.41,2617.3,1864.2,3055.8,to_date('07-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2062.08,2600.4,1859.2,3060.4,to_date('08-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.76,2597,1849.1,3049.4,to_date('09-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2059.68,2600.5,1844.1,3033.2,to_date('10-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.14,2600.2,1854,3048.3,to_date('11-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.25,2591.6,1872.8,3076.7,to_date('12-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2081.39,2582.8,1856.9,3054,to_date('13-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2085.62,2593.6,1860.3,3054.9,to_date('14-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2079.6,2595.5,1837.1,3028.6,to_date('15-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.86,2574.6,1813.3,2995.9,to_date('16-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2013.03,2525.3,1772.8,2954.2,to_date('17-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2027.67,2534.8,1780.2,2969,to_date('18-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.97,2525.5,1802.5,2982.2,to_date('19-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.79,2554.7,1826.3,3007.3,to_date('20-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2030.56,2552.4,1814.1,3022.2,to_date('21-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2026.68,2540.2,1813.4,2995.9,to_date('22-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.82,2536.9,1797.9,2991.6,to_date('23-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.44,2560.8,1827.8,3017.3,to_date('24-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2038.46,2563.7,1816.1,3034.7,to_date('25-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2077.94,2589.4,1842.4,3059.7,to_date('26-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2085.08,2621.4,1872.1,3062,to_date('27-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2093.01,2628,1870.4,3072.7,to_date('28-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2087.78,2617.4,1850.9,3072.5,to_date('29-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.8,2636.9,1874.4,3099,to_date('30-NOV-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.56,2635.4,1869.4,3109.9,to_date('01-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2118.96,2636,1850.1,3081.1,to_date('02-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.33,2642.4,1856.1,3071.3,to_date('03-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2135.49,2636,1861.9,3074.9,to_date('04-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.06,2621,1835,3051.1,to_date('05-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.29,2601,1822.5,3044.2,to_date('06-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.98,2597.2,1802.2,3018.6,to_date('07-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2101.82,2609.6,1805.6,3023.4,to_date('08-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2096.26,2610,1804.4,3019.5,to_date('09-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2126.44,2632.1,1827,3049.3,to_date('10-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.59,2623.3,1805.7,3037.7,to_date('11-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2106.05,2607.5,1802.2,3025.3,to_date('12-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2097.85,2600.4,1776.9,3009.3,to_date('13-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.21,2619.2,1808.4,3041.2,to_date('14-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.64,2617.2,1807.4,3038.2,to_date('15-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2094.16,2586.5,1795.2,3025.1,to_date('16-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.95,2553.2,1773.3,3001.9,to_date('17-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2041.26,2543.1,1748.6,2977,to_date('18-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2029.38,2515.8,1756.8,2992.1,to_date('19-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1989.2,2461.5,1727.1,2986.9,to_date('20-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2008.85,2467.7,1743.1,3021.1,to_date('21-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1974.14,2450.3,1721.8,3011.8,to_date('22-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.5,2463.3,1769,3050.6,to_date('23-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.22,2475,1738.6,3047,to_date('24-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2016.15,2491.7,1785.8,3094.1,to_date('25-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1979.52,2497.5,1788.8,3089.3,to_date('26-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1984.99,2516,1811.6,3124.2,to_date('27-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1984.15,2505.5,1813.8,3135,to_date('28-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.07,2501.9,1818,3139.7,to_date('29-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1935.08,2480.7,1795.7,3136.4,to_date('30-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1935.87,2526.6,1817.3,3153.4,to_date('31-DEC-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1928.82,2518.1,1836.1,3149.8,to_date('01-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1911.7,2495.6,1837.2,3128.3,to_date('02-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1918.85,2495,1852.4,3142.3,to_date('03-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1949.76,2533.1,1893,3176.2,to_date('04-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1914.69,2508.9,1859.5,3137.9,to_date('05-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1934.96,2511.1,1864,3143.1,to_date('06-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1965.68,2536.2,1882.8,3188.1,to_date('07-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1972.59,2536.3,1872.9,3190.2,to_date('08-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1976.52,2556.1,1890.2,3200.9,to_date('09-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.71,2559.3,1900.4,3210.9,to_date('10-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1980.81,2561.2,1880.9,3204.2,to_date('11-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1979.26,2563.1,1869.3,3190.9,to_date('12-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1991.08,2562.4,1871.9,3209.8,to_date('13-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1691.37,2120.5,1998.8,2882.6,to_date('01-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1701.46,2130.9,2001.5,2882.3,to_date('02-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.48,2142.4,1995.2,2918.6,to_date('03-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.46,2139.5,1986.8,2904.8,to_date('04-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.15,2134.6,1995.1,2916.6,to_date('05-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1702.27,2132.2,2004.3,2957.3,to_date('06-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.91,2150.1,2009.7,2949.9,to_date('07-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.48,2157,1992.4,2956.7,to_date('08-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1708.65,2165,1988.9,2953.4,to_date('09-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1688.74,2127.2,1965.2,2915.9,to_date('10-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1705.05,2157.2,1986,2922.4,to_date('11-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1700.28,2150.8,1975.3,2919.3,to_date('12-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1689.71,2139.1,1967.3,2889.9,to_date('13-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1696.38,2154.7,1963.5,2883.3,to_date('14-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.57,2182.4,1962.7,2900.1,to_date('15-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.59,2161.3,1939.3,2863.9,to_date('16-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1653.2,2166.3,1952.2,2861.1,to_date('17-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1666.72,2146.8,1954.6,2860.6,to_date('18-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1663.27,2135.7,2001.4,2852.8,to_date('19-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1667.26,2160.9,2025.8,2852.9,to_date('20-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1675.18,2175.7,2033.9,2846.5,to_date('21-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.64,2188.9,2035.9,2861,to_date('22-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1676.84,2190.3,2031.4,2878.7,to_date('23-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1670.04,2188.4,2005.9,2878.4,to_date('24-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1657.06,2190.3,1990.8,2869.9,to_date('25-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.36,2184,1974.7,2838.8,to_date('26-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1667.64,2196,1995.3,2832.2,to_date('27-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.6,2184.9,1984,2822.1,to_date('28-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('29-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('30-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1658.13,2188.3,1986.9,2821.8,to_date('31-JAN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.54,2181.7,2018.1,2846.8,to_date('01-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.95,2165.7,2015.4,2842.1,to_date('02-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1674.67,2160.5,1988.6,2839.7,to_date('03-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1678.65,2162.9,1986.7,2824.4,to_date('04-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.14,2166.3,1968.9,2830,to_date('05-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.06,2170.8,1949.3,2856.1,to_date('06-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1666.49,2178.1,1931.9,2869.6,to_date('07-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.01,2177.3,1944.5,2881.1,to_date('08-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1656.03,2162.3,1916.6,2843.8,to_date('09-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1643.53,2140.4,1911.6,2822.3,to_date('10-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.59,2124.7,1927.4,2832.7,to_date('11-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.88,2138.2,1942.5,2797.3,to_date('12-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.6,2123.2,1920.6,2786.8,to_date('13-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.83,2129.7,1939,2813.1,to_date('14-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632,2152.8,1937,2813.1,to_date('15-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.92,2160,1923.6,2812.6,to_date('16-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.88,2165.8,1926.3,2796.5,to_date('17-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.74,2165.4,1920.5,2786.3,to_date('18-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.7,2162.6,1878.6,2793.7,to_date('19-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.45,2179.1,1877.2,2829.7,to_date('20-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.46,2191.1,1854.5,2836.1,to_date('21-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.99,2191.5,1872.7,2860.8,to_date('22-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.02,2183.3,1879.9,2849.3,to_date('23-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.35,2186.3,1851.7,2847,to_date('24-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.37,2205.2,1835.7,2858.1,to_date('25-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1619.26,2227.2,1846.4,2847.3,to_date('26-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.64,2227,1836.8,2819.7,to_date('27-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1606.64,2227,1836.8,2816.8,to_date('28-FEB-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.98,2232.9,1836.8,2812.2,to_date('01-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1608.58,2237.4,1861.4,2825.6,to_date('02-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.05,2243.5,1891.1,2837.7,to_date('03-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.18,2247.5,1890.4,2846.9,to_date('04-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.18,2267.1,1904.6,2855.3,to_date('05-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.21,2271.6,1888.7,2840.7,to_date('06-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.21,2271.6,1888.7,2840.7,to_date('07-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.59,2253.8,1872.8,2849.2,to_date('08-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1628.53,2259.8,1875.8,2863,to_date('09-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1630.56,2269,1867.9,2852.8,to_date('10-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1638.47,2284.2,1859.7,2829.9,to_date('11-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1660.88,2309.7,1887.9,2844.8,to_date('12-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1662.28,2294.5,1893.7,2844.4,to_date('13-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.69,2312.5,1915.2,2866.9,to_date('14-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.69,2309.2,1911.2,2860,to_date('15-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.85,2308.6,1920.4,2861.8,to_date('16-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.44,2293.8,1916.8,2885.5,to_date('17-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1684.57,2274,1897.9,2870,to_date('18-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1689.93,2294.6,1918.8,2883,to_date('19-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1681.47,2323.4,1900.3,2875.7,to_date('20-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.14,2318,1910.3,2879.4,to_date('21-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1697.26,2329.5,1929.2,2903.4,to_date('22-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.33,2335,1935.3,2907.6,to_date('23-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.96,2323.9,1942.4,2900.7,to_date('24-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1692.16,2335.8,1963.3,2894.7,to_date('25-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1699.52,2347.3,1960.8,2887.5,to_date('26-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1712.33,2369.8,1991,2897,to_date('27-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1703.05,2371.9,1977.5,2886,to_date('28-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1700.93,2376.2,1971.9,2900,to_date('29-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.36,2375.5,1960.2,2888.8,to_date('30-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1697.39,2368.8,1941.2,2857.7,to_date('31-MAR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1694.83,2364.4,1925.4,2838.5,to_date('01-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1705.66,2390.9,1935.1,2848.1,to_date('02-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1739.48,2372,1943.7,2848.3,to_date('03-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.63,2397.1,1980.4,2845.9,to_date('04-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.36,2403,1985.7,2843.2,to_date('05-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1808.74,2408.7,1992.2,2830.9,to_date('06-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1806.52,2418.1,1991.2,2837.1,to_date('07-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1815.63,2410.5,1991.2,2832.3,to_date('08-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1807.12,2399.9,1963.1,2831.7,to_date('09-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1829.36,2396.4,1974.9,2833,to_date('10-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1835.09,2381.8,1981.7,2842.9,to_date('11-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1826.45,2324.5,1968.4,2823.9,to_date('12-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1821.28,2313.7,1947.5,2814.1,to_date('13-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1828.53,2340.2,1965.7,2820.1,to_date('14-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1830.61,2350.5,1995,2827.7,to_date('15-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1859.49,2388.7,2006.2,2844.2,to_date('16-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1846.02,2398,1998.1,2879.4,to_date('17-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1832.2,2408.7,1989.5,2884.2,to_date('18-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1823.4,2401.5,2036,2917.6,to_date('19-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1823.07,2400.9,2085.9,2926.5,to_date('20-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1818.1,2400.7,2129,2941.7,to_date('21-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1857.36,2429.8,2110.6,2945,to_date('22-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1861.22,2431.5,2101.4,2941.3,to_date('23-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.8,2424.9,2115.3,2943.4,to_date('24-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1878.94,2423.3,2149.8,2969.8,to_date('25-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.24,2420.5,2138.5,2986.4,to_date('26-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1864.51,2384.5,2139.8,2971.6,to_date('27-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1894.56,2411.1,2167.4,3006.1,to_date('28-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1908.69,2449.5,2161.9,3009.1,to_date('29-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1917.69,2461,2148,3010.1,to_date('30-APR-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1903.44,2478.7,2148,3008.3,to_date('01-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1918.75,2464.9,2136.3,3025,to_date('02-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1930.29,2488,2160.8,3073.6,to_date('03-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1937.77,2480.6,2139.2,3065.5,to_date('04-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1909.53,2474.5,2128.2,3057.6,to_date('05-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1893.48,2467.3,2111.4,3042,to_date('06-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1907.65,2472.4,2123.4,3049.3,to_date('07-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1915.59,2499.7,2159.3,3079.2,to_date('08-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1890.58,2475.2,2173.6,3079.2,to_date('09-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1909.54,2478.6,2183.9,3100.6,to_date('10-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1929.56,2481,2205.7,3100.6,to_date('11-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1931.88,2488.5,2216.5,3100,to_date('12-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1923.67,2470.4,2191.9,3085.1,to_date('13-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1928.63,2463.8,2185.1,3072.6,to_date('14-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1920.43,2438.8,2156.1,3057.3,to_date('15-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1911.15,2392.5,2158,3055.4,to_date('16-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1878.77,2403.4,2137.3,3038.6,to_date('17-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.32,2397.9,2129.4,3035.4,to_date('18-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1870.46,2382.1,2108.8,3031.2,to_date('19-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1868.28,2363.6,2108.4,3037,to_date('20-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1874.38,2365.6,2119.1,3024.8,to_date('21-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1869.25,2388.3,2134.1,3028,to_date('22-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1852.81,2361.8,2078.5,2989.4,to_date('23-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1862.62,2374.4,2075.6,3003.9,to_date('24-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1884.67,2385.2,2099.5,3005.5,to_date('25-MAY-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1922.05,2431.8,2094.4,3001.6,to_date('27-MAY-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1902.14,2425.8,2057.5,3001.3,to_date('29-MAY-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1890.18,2445.9,2092.6,3005.2,to_date('30-MAY-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1991.95,2528,2147.4,3092.4,to_date('08-JUN-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2009.28,2548.5,2138.7,3102.2,to_date('10-JUN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2004.11,2552.5,2126.9,3094.7,to_date('11-JUN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1999.6,2571,2127.3,3080.9,to_date('12-JUN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1995.35,2586.4,2113.9,3086.3,to_date('13-JUN-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2057.1,2706.6,2136,3149,to_date('28-JUN-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2013.65,2654.2,2081,3085.6,to_date('29-JUN-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.11,2742.1,2087.3,3098.5,to_date('02-JUL-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5460.43,7721.3,3939.5,5587.6,to_date('28-SEP-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5285.78,7447.9,3846,5432.8,to_date('29-SEP-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5386.94,7607.5,3945.7,5462.2,to_date('30-SEP-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (5473.72,7676.3,3995,5455,to_date('02-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2224.85,2884,2145.3,3149.4,to_date('03-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.19,2887.8,2148.6,3159.1,to_date('04-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2215.19,2877.7,2152.4,3145.8,to_date('05-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2198.24,2843.7,2139.1,3131.7,to_date('06-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.92,2872.9,2159.6,3168.3,to_date('07-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.37,2869.1,2160.1,3138.2,to_date('08-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.89,2837.1,2136,3128,to_date('09-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.09,2818.7,2102.7,3098.3,to_date('10-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.41,2781.6,2092,3101.2,to_date('11-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.13,2787.9,2135.2,3133.7,to_date('12-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2208.68,2763.5,2116.3,3106.1,to_date('13-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.36,2776.2,2130.9,3125.3,to_date('14-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2256.98,2791.3,2147.3,3150,to_date('15-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2237.82,2768.9,2150.3,3129.9,to_date('16-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2252.51,2736.3,2166,3125.3,to_date('17-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.72,2765.2,2186.2,3125.3,to_date('18-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2261.71,2755.9,2179,3100,to_date('19-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.85,2705.6,2141.6,3070.5,to_date('20-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.78,2682.2,2162.6,3106,to_date('21-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2233.55,2641.4,2158.2,3106,to_date('22-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2218.77,2569.5,2139.4,3097.8,to_date('23-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.34,2629,2165,3136.3,to_date('24-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2248.02,2633,2176.7,3130.5,to_date('25-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2248.02,2633,2176.7,3137.8,to_date('26-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2257.33,2678.7,2187,3119.2,to_date('27-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2272.96,2709,2187.8,3115.6,to_date('28-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.11,2727.1,2195.2,3123.5,to_date('29-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2254.21,2740.6,2184,3116.5,to_date('30-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2245.79,2725.8,2165.4,3122.8,to_date('31-OCT-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2238.97,2732.7,2155.4,3127.3,to_date('01-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2238.97,2732.7,2155.4,3108.4,to_date('02-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.01,2692,2133.3,3089.1,to_date('03-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.56,2673.1,2084.4,3020.7,to_date('04-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2146,2689.2,2091.9,3019.7,to_date('05-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.8,2711.9,2050.7,2966.4,to_date('06-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.76,2742.9,2052.5,2966.4,to_date('07-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.34,2722.9,2029.9,2970.5,to_date('08-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2113.62,2731.5,1979.7,2931.9,to_date('09-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2120.23,2725.9,2007.4,2980.8,to_date('10-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2158.88,2728.4,2041.7,2997.8,to_date('11-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.59,2781.4,2037.2,3009.4,to_date('12-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2131.8,2778.1,2024.7,3004.8,to_date('13-NOV-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2131.14,2777.2,2028.4,3028.9,to_date('15-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.58,2763.8,2020.7,3055.9,to_date('16-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2084.42,2744.2,1977.7,3016.3,to_date('17-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.44,2740,1992,3039.6,to_date('18-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.21,2701.8,1966.4,3045.8,to_date('19-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.29,2666.6,1942.8,3030.1,to_date('20-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2031.8,2627.6,1936,3022.9,to_date('21-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1986.42,2545,1903,2971.1,to_date('22-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1957.08,2544.2,1890.8,2940.2,to_date('23-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2004.93,2595.7,1917,2960.4,to_date('24-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2032.52,2626.7,1939,2942.4,to_date('25-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2005.07,2577.2,1907,2876.6,to_date('26-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2000.48,2561.4,1911.6,2899.9,to_date('27-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2022.25,2604.4,1925.8,2909,to_date('28-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2042.45,2631.2,1936.3,2946.3,to_date('29-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2020.85,2608.8,1892,2919.2,to_date('30-NOV-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2040.69,2588.9,1872.9,2936.4,to_date('01-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2061.7,2636.4,1866.2,2970.4,to_date('02-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2034.64,2609.7,1878.7,2965,to_date('03-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2031.33,2598.9,1889,2946.7,to_date('04-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2049.1,2590.5,1920.8,2964.4,to_date('05-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2047.83,2560.3,1920.8,2962.4,to_date('06-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.46,2562.5,1949.8,2983.8,to_date('07-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2048.57,2508,1942.1,2963.9,to_date('08-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2051.25,2474.5,1974.6,3005.3,to_date('09-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2070.71,2528.3,1974.6,3050.4,to_date('10-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2103.54,2514.9,1974.6,3074.8,to_date('11-DEC-93','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.96,2494.7,2025.1,3082,to_date('12-DEC-93','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2143.84,2557,2001.3,3191.4,to_date('05-JAN-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.38,2672,2020.4,3222.7,to_date('15-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2027.99,2702.1,2071.5,3069.3,to_date('16-JAN-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.39,2834.8,2176.1,3237.3,to_date('26-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.95,2843.8,2205.3,3277.4,to_date('27-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2166.58,2858.4,2211.4,3271.6,to_date('28-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.5,2861.2,2198.1,3261.3,to_date('29-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.83,2876.9,2196.3,3254.6,to_date('30-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.07,2867.8,2156.5,3248.4,to_date('31-JAN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2129.52,2850.6,2162.6,3278.8,to_date('01-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.03,2867.7,2160.3,3311.2,to_date('02-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.76,2889.5,2196.4,3337.1,to_date('03-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2188.18,2909,2223.5,3364.9,to_date('04-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.78,2918,2215.9,3342.4,to_date('05-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2209.17,2942.4,2225.8,3355.7,to_date('06-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2227.63,2967.4,2243,3396.5,to_date('07-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2227.63,2967.4,2251.5,3412.3,to_date('08-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.7,2972.6,2276.6,3412.3,to_date('09-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.91,2930.9,2264.6,3412.3,to_date('10-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2229.62,2934.2,2281.9,3462,to_date('11-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2255.29,2957.6,2281.2,3428.8,to_date('12-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2255.29,2957.6,2268.2,3418.4,to_date('13-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2274.62,2996.2,2290.6,3418.4,to_date('14-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.85,2999.2,2274.3,3408.5,to_date('15-FEB-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2220.63,3021.9,2275.1,3403,to_date('17-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2224.95,3042.9,2307.6,3446,to_date('18-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2225,3015.9,2317.3,3440.6,to_date('19-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2228.1,3026.6,2331.3,3413.8,to_date('20-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.06,2999,2281.9,3372,to_date('21-FEB-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.35,3037.5,2247.4,3437,to_date('25-FEB-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2098.36,3045.9,2257.8,3470,to_date('27-FEB-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2073.94,3039.3,2244,3484.2,to_date('28-FEB-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.71,2947.1,2258,3393.2,to_date('17-MAR-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2133.85,3025.9,2251.8,3382.6,to_date('20-MAR-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2018.69,2768.5,2144.7,3248.1,to_date('28-MAR-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.8,1708.7,1840.5,2630.8,to_date('15-JUN-94','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1632.22,1713.5,1861.2,2641.9,to_date('17-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1633.65,1718,1876.2,2625.8,to_date('18-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1631.17,1701.7,1878.3,2606,to_date('19-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1635.8,1701.7,1878.4,2594.4,to_date('20-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1621.27,1684.9,1869.4,2583.6,to_date('21-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.7,1687.2,1880.4,2588.7,to_date('22-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1616.13,1690.6,1885.5,2600.3,to_date('23-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.12,1684.3,1888.4,2579.5,to_date('24-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.8,1679.9,1885.2,2576.6,to_date('25-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1625.79,1672.9,1877.9,2597.8,to_date('26-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1614.8,1663.1,1876.5,2595.6,to_date('27-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1612.8,1669.3,1883.8,2599,to_date('28-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.47,1664.7,1880.6,2621.7,to_date('29-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1609.32,1672.3,1887.4,2645.6,to_date('30-JUN-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.48,1687.7,1878.3,2644.2,to_date('01-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1607.48,1686.8,1867.1,2625.6,to_date('02-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.89,1686.6,1851.9,2624.6,to_date('03-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.12,1675.8,1843.6,2596.2,to_date('04-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1582.27,1677.4,1848.1,2599.5,to_date('05-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1567.99,1673.2,1843.4,2584.1,to_date('06-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1568.16,1665,1843.6,2570.8,to_date('07-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1569.71,1671.3,1833.8,2555,to_date('08-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.74,1672.4,1833.4,2574.5,to_date('09-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1585.41,1676.2,1856.9,2576.7,to_date('10-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.01,1692.6,1863.4,2579,to_date('11-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.89,1696.5,1855.5,2588.7,to_date('12-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1565.18,1716.1,1864.2,2601.1,to_date('13-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.34,1713.3,1846,2575.7,to_date('14-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577,1705.1,1836.8,2559.5,to_date('15-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.29,1711.3,1830.4,2561.1,to_date('16-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.72,1709.8,1831.6,2528.3,to_date('17-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.07,1688.6,1834.8,2514.7,to_date('18-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.19,1698.9,1852.1,2558.5,to_date('19-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1588.73,1700,1849.8,2553.3,to_date('20-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1586.01,1693,1861.8,2577.1,to_date('21-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.77,1683.9,1856.7,2566,to_date('22-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.58,1679.2,1856.7,2549.5,to_date('23-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1568.09,1673.9,1841.5,2527.8,to_date('24-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1578.21,1683.9,1846.9,2540.9,to_date('25-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1573.94,1688.4,1836.1,2534.2,to_date('26-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1582.06,1693.9,1838.6,2538,to_date('27-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1610.18,1720.9,1857.6,2559,to_date('28-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.16,1717.9,1857.6,2554.9,to_date('29-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1623.84,1733.6,1858.4,2575.5,to_date('30-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1615.26,1729.7,1846.8,2546.5,to_date('31-JUL-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1627.08,1735.6,1868.5,2561.6,to_date('01-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1626.97,1734.1,1863.2,2546.6,to_date('02-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.7,1699.3,1808.3,2502.9,to_date('03-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.7,1678.6,1765.1,2463.1,to_date('04-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.7,1675.5,1763.5,2472.6,to_date('05-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.26,1670.1,1766,2463.5,to_date('06-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1599.75,1652.2,1741.3,2446.3,to_date('07-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1590.86,1635,1743.3,2456.2,to_date('08-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1603.5,1654.9,1769,2471.5,to_date('09-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1589.86,1642,1757.9,2447.5,to_date('10-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1587.92,1638.7,1754.9,2428.6,to_date('11-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1571.06,1622.6,1739.7,2420.2,to_date('12-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1549.81,1596.1,1708.8,2414.9,to_date('13-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1549.36,1612.4,1722.2,2420.2,to_date('14-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.65,1625,1713.9,2423.8,to_date('15-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.52,1610.5,1703.2,2407,to_date('16-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1555.31,1606.6,1685.7,2388.7,to_date('17-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1559.76,1610.7,1663.4,2409.6,to_date('18-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1548.44,1603.1,1636.9,2392,to_date('19-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.99,1591.5,1645.6,2380.2,to_date('20-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.21,1605.2,1671.6,2423.3,to_date('21-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.03,1621.4,1688.3,2451.6,to_date('22-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1551.78,1622.5,1696.8,2440.8,to_date('23-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1562.89,1626.6,1711.7,2432.9,to_date('24-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1570.28,1627.4,1706.2,2413.6,to_date('25-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1559.26,1614.9,1684.2,2391.6,to_date('26-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.87,1602.3,1648.5,2358.1,to_date('27-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1598.3,1633.6,2345.4,to_date('28-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1699.1,2384.4,to_date('29-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1699.1,2384.4,to_date('30-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.77,1627,1722.5,2384.4,to_date('31-AUG-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1564.27,1655.7,1720.7,2418.7,to_date('01-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1741.9,2420,to_date('02-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1765.7,2493.1,to_date('03-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1577.26,1670.1,1765.7,2493.1,to_date('04-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1598.19,1670.1,1749.9,2492.8,to_date('05-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.05,1704,1770.3,2504.1,to_date('06-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1604.69,1711.8,1787.6,2493.2,to_date('07-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1593.65,1700.5,1778.7,2482.9,to_date('08-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1581.68,1690.3,1785.6,2467.1,to_date('09-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1599.14,1715.4,1833.9,2497.9,to_date('10-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.82,1723.5,1837.4,2477.9,to_date('11-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1620.45,1719.4,1824.3,2490.1,to_date('12-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1629.51,1734.4,1843.8,2516.3,to_date('13-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1663.7,1772.8,1873.6,2537.1,to_date('14-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1664.09,1760.3,1860.2,2541.6,to_date('15-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1669.29,1747.2,1860.2,2536.7,to_date('16-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.14,1750.2,1865.9,2544.9,to_date('17-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1687.07,1755.3,1867.9,2543.4,to_date('18-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.13,1754.6,1841.3,2522,to_date('19-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.84,1751.2,1838.7,2525.3,to_date('20-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1669.52,1752.5,1849.9,2510.4,to_date('21-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.71,1769.4,1869.3,2539.9,to_date('22-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.51,1767.6,1890.6,2552,to_date('23-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1671.01,1750,1879.6,2546.5,to_date('24-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.06,1747.1,1873.9,2550.8,to_date('25-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.7,1753.5,1875.3,2571.2,to_date('26-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.66,1752.8,1857,2560.2,to_date('27-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1678.77,1752.9,1856.5,2556.8,to_date('28-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.85,1764.7,1865.8,2547.1,to_date('29-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.71,1776.8,1860.6,2534.3,to_date('30-SEP-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1686.59,1779.3,1861.6,2517.2,to_date('01-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1683.73,1785.1,1865.6,2538.4,to_date('02-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1679.14,1798.2,1864.1,2537.1,to_date('03-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1685.03,1794.1,1861.6,2523.7,to_date('04-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1680.81,1795.2,1876.5,2522.6,to_date('05-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1676.17,1780.4,1865.1,2513.9,to_date('06-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1688.46,1789.5,1882.1,2541,to_date('07-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1696.55,1794.2,1912.2,2555.9,to_date('08-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1690.24,1784.4,1915.4,2536.7,to_date('09-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.35,1800.1,1951.2,2543.4,to_date('10-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1711.29,1804,1962.4,2542.3,to_date('11-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.86,1816.2,1976.5,2559.7,to_date('12-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.63,1810.5,1953.5,2546.8,to_date('13-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1743.36,1821.9,1981.3,2565,to_date('14-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.17,1828.2,1985.1,2562,to_date('15-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.76,1840.6,1983.4,2562.1,to_date('16-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1749.29,1841.1,1979.7,2554.3,to_date('17-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1763.86,1846.3,1983.8,2565.4,to_date('18-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.27,1850,1988.1,2558.4,to_date('19-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.29,1839,1973,2538.3,to_date('20-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.77,1820.2,1966.9,2533.1,to_date('21-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.5,1815.2,1976.3,2550.7,to_date('22-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.21,1820.6,1993.9,2574.8,to_date('23-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1739.88,1807.1,1968,2522.4,to_date('24-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.92,1791.4,1941.8,2493.3,to_date('25-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1734.42,1806.2,1947.1,2476,to_date('26-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.13,1798.7,1929.2,2470.7,to_date('27-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1732.92,1818.2,1943.6,2491.2,to_date('28-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.89,1820.5,1928.2,2464.7,to_date('29-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1725.74,1833.3,1922,2467.6,to_date('30-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1730.9,1837.1,1919.1,2456.6,to_date('31-OCT-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.17,1818.2,1884.6,2441,to_date('01-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.2,1824.1,1896.3,2458.7,to_date('02-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1719.06,1830.1,1928.3,2464.9,to_date('03-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1718.21,1835.6,1934.8,2472.2,to_date('04-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1698.84,1828.7,1923.5,2447.9,to_date('05-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.76,1839.2,1943.8,2452.9,to_date('06-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1718.35,1837.2,1942.4,2440.1,to_date('07-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1706.69,1826.7,1928.1,2408.6,to_date('08-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1723.37,1838,1942,2405.4,to_date('09-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1716.18,1829.1,1942.7,2382.7,to_date('10-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1738.78,1843.1,1974.8,2400.9,to_date('11-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1737.41,1850.5,1975.4,2404.2,to_date('12-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1714.77,1827.1,1907.5,2393.2,to_date('13-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1724.24,1829.1,1943.6,2436.4,to_date('14-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.77,1848,1974.1,2572.6,to_date('15-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1729.96,1840.5,1963.3,2591,to_date('16-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1734.46,1853.8,1972.3,2600.5,to_date('17-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1744.35,1874.1,1990.7,2640.2,to_date('18-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('19-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('20-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1746.88,1871.3,1978.2,2638.6,to_date('21-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1747.47,1860.5,1980.4,2625.8,to_date('22-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1753.1,1874.7,1983.7,2607.8,to_date('23-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.17,1880.1,1978.1,2609.8,to_date('24-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.72,1874.7,1984.9,2643,to_date('25-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1742.92,1875.6,1995.7,2658.2,to_date('26-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.68,1859.5,2006.6,2651,to_date('27-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.18,1874.2,2036.7,2664.9,to_date('28-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1728.09,1880.1,2031.1,2654.1,to_date('29-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1728.09,1880.1,2031.1,2659.8,to_date('30-NOV-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.29,1907.7,2041.6,2659.8,to_date('01-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.82,1920.5,2046.9,2662.2,to_date('02-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.78,1937.3,2047.2,2698.7,to_date('03-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.57,1936.8,2063.4,2701.9,to_date('04-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1748.13,1949.1,2063.4,2725.7,to_date('05-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1750.7,1963.7,2077.5,2737.8,to_date('06-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1747.91,1950.8,2063.6,2722.4,to_date('07-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1745.79,1953.5,2053.2,2720.5,to_date('08-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1735.34,1945,2017,2694.7,to_date('09-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1719.92,1921.1,2024,2682.6,to_date('10-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1763.59,1939.1,2051.6,2703.6,to_date('11-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1766.76,1928,2024.1,2700.6,to_date('12-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1785.4,1933.4,2030.8,2711.9,to_date('13-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1783.56,1925.7,2016.8,2702,to_date('14-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1804.42,1931.7,2045.1,2715,to_date('15-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1812.33,1928.7,2046.3,2715,to_date('16-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1799.51,1924.5,2029.6,2704.6,to_date('17-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1792.8,1914.2,2014.1,2698.6,to_date('18-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1792.8,1914.2,2014.1,2694.2,to_date('19-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1806.36,1920.6,2033.3,2707.6,to_date('20-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1798.23,1923.3,2017.4,2697.6,to_date('21-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1800.62,1930.4,2024.9,2705.9,to_date('22-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1786.19,1915.2,1992.6,2680.9,to_date('23-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1791.35,1916.9,1994.9,2681.9,to_date('24-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.05,1913.8,1981.6,2668.5,to_date('25-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.05,1913.8,1981.6,2645.8,to_date('26-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1784.71,1899.7,1962.2,2635.4,to_date('27-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1789.45,1888,1953.7,2636.1,to_date('28-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1779.74,1868.8,1928.8,2614.1,to_date('29-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1786.97,1879.9,1928.3,2603.7,to_date('30-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.25,1865.7,1918.1,2593.6,to_date('31-DEC-94','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1781.62,1881.3,1931.4,2616.3,to_date('01-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.75,1873.1,1908.8,2598.4,to_date('02-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1773.75,1862.5,1891.8,2562.7,to_date('03-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1776.34,1869.3,1913.9,2584.8,to_date('04-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1770.72,1846.9,1885.8,2550.3,to_date('05-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1772.39,1847.1,1895.8,2560.6,to_date('06-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1762.55,1838.3,1899.6,2532.6,to_date('07-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1764.35,1845.8,1920.3,2557.3,to_date('08-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.83,1835.5,1915.3,2534.1,to_date('09-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1755.98,1846.6,1907.3,2515.8,to_date('10-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1754.95,1854.8,1900.6,2521.2,to_date('11-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1759.9,1845.3,1880.9,2493.9,to_date('12-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1759.84,1854.5,1873.5,2476.1,to_date('13-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1776.5,1870.5,1883.6,2497.1,to_date('14-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1769.98,1862.6,1868.5,2469,to_date('15-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1766.98,1856.6,1879.1,2493.7,to_date('16-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1752.29,1837.6,1847.8,2472.6,to_date('17-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1760.17,1846.7,1861.8,2497.9,to_date('18-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1750.32,1856.5,1859.4,2490.8,to_date('19-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1731.44,1841.8,1859.4,2478.3,to_date('20-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1735.51,1835,1859.4,2484,to_date('21-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1733.84,1844.4,1853.3,2486.4,to_date('22-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1730.78,1838.9,1851.2,2483.4,to_date('23-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1699.46,1805.6,1801.8,2431.9,to_date('24-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1652.71,1756.6,1767.9,2403.7,to_date('25-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1654.09,1786.1,1762.7,2415.6,to_date('26-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1636.81,1757.1,1727.5,2387.9,to_date('27-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.81,1762.8,1734.6,2399.5,to_date('28-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1613.36,1756.8,1734.6,2377.2,to_date('29-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.78,1761.9,1755.4,2348,to_date('30-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1617.18,1778.5,1769,2373.4,to_date('31-JAN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1637.62,1812.7,1801.6,2423.2,to_date('01-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1622.2,1806.1,1782.6,2411.6,to_date('02-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1608.49,1798.1,1754.7,2399.6,to_date('03-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1605.11,1794.9,1784.4,2420.2,to_date('04-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1609.61,1805.4,1787.6,2407.5,to_date('05-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1624.94,1820.3,1798,2392.8,to_date('06-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1618.07,1819.6,1793.8,2377.6,to_date('07-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1611.96,1809.6,1777.3,2350.1,to_date('08-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1578.95,1799.9,1755.2,2325.7,to_date('09-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1561.39,1800.3,1737.8,2309.6,to_date('10-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.87,1793.3,1730.1,2303.1,to_date('11-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1548.63,1784.8,1722.4,2318,to_date('12-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1560.16,1791.7,1753.5,2356.8,to_date('13-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.76,1800.2,1757.3,2376.1,to_date('14-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1531.87,1788.6,1736.7,2354.7,to_date('15-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.14,1775.7,1734.2,2363.5,to_date('16-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1509.03,1753.5,1724.2,2359.4,to_date('17-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.03,1768.2,1744.2,2365.7,to_date('18-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.97,1727.9,1689.7,2311.1,to_date('19-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1464.03,1709.6,1667.7,2281,to_date('20-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1475.11,1704.6,1667.8,2285,to_date('21-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1516.12,1740.6,1687.6,2311.6,to_date('22-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1519.69,1745.7,1687.5,2312.6,to_date('23-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.97,1751.7,1684.9,2312.6,to_date('24-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1516.44,1747.3,1674.2,2298.4,to_date('25-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1515.53,1757.8,1711.4,2313,to_date('26-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.89,1774.2,1780.5,2381.9,to_date('27-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1534.72,1774.4,1779,2362.2,to_date('28-FEB-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.66,1788.3,1779.3,2372.2,to_date('01-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.71,1788,1763.7,2337.7,to_date('02-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.83,1779.1,1756.8,2327.5,to_date('03-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.1,1792.8,1774.2,2340.6,to_date('04-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.2,1812,1802,2370.9,to_date('05-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1601.5,1872.1,1873.6,2422.1,to_date('06-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1580.29,1851.4,1836.2,2370,to_date('07-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1595.09,1873.4,1859.8,2378.3,to_date('08-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1579.47,1889.6,1852.7,2483.9,to_date('09-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1600.59,1897.5,1882.9,2567,to_date('10-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1566,1888.8,1826.1,2560.1,to_date('11-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1557.01,1900.4,1832.8,2586,to_date('12-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.74,1913.4,1828.9,2580.5,to_date('13-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.3,1909.9,1829.5,2621.2,to_date('14-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1510.66,1910.8,1843.5,2601,to_date('15-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1481.03,1879.2,1770.3,2560,to_date('16-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1483.83,1880.2,1731.9,2565.5,to_date('17-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1470.09,1878.3,1736.7,2553,to_date('18-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.78,1885.2,1724,2572.3,to_date('19-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1475.41,1867.6,1683.3,2549.7,to_date('20-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1402.34,1788,1611,2446.3,to_date('21-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1421.49,1820.5,1612.5,2488.4,to_date('22-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1434.61,1858.2,1654.2,2517.1,to_date('23-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1446.32,1870.3,1673.9,2538.8,to_date('24-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1437.65,1878.4,1657.3,2541.2,to_date('25-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1441.57,1881.5,1655.1,2557.2,to_date('26-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1471.64,1893.2,1685.1,2584.7,to_date('27-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1453.95,1889.3,1667.9,2574.7,to_date('28-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1453.79,1877.3,1650,2546.6,to_date('29-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1458.02,1884,1664.2,2563.9,to_date('30-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1479.59,1904.7,1679.1,2562.2,to_date('31-MAR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1504.89,1922.7,1731.3,2617,to_date('01-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1496.54,1908.5,1722.2,2645.7,to_date('02-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1511,1911.4,1730.7,2658.1,to_date('03-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1528.86,1921.1,1766.4,2669.7,to_date('04-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1534.02,1930.8,1770.7,2661.6,to_date('05-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1536.6,1927.8,1774.5,2669.8,to_date('06-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1508.19,1908.3,1749.9,2650.4,to_date('07-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1493.54,1905.9,1730.9,2642.3,to_date('08-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1489.68,1911.1,1742.4,2658.3,to_date('09-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1482.44,1921.6,1742.4,2687.8,to_date('10-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1483.34,1933.6,1786.9,2705.6,to_date('11-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1470.57,1942,1804.1,2691.7,to_date('12-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1484.84,1951.5,1804.7,2711.1,to_date('13-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1487.71,1955.7,1793.6,2702.7,to_date('14-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1508.63,1957.4,1786.7,2695.4,to_date('15-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1515.27,1962.3,1798.5,2714.6,to_date('16-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1509.84,1946.1,1798.5,2696.8,to_date('17-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.28,1950.2,1821.5,2726.4,to_date('18-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1541.79,1929.7,1796.8,2697.5,to_date('19-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1542.48,1913.4,1772.7,2679.6,to_date('20-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.27,1889.5,1764.4,2679.2,to_date('21-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1550.27,1882.8,1759.2,2704,to_date('22-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1543.37,1895.4,1722.3,2706.2,to_date('23-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.84,1897.9,1724.2,2732.4,to_date('24-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.62,1891.5,1674.8,2722.9,to_date('25-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.68,1880.1,1720.6,2727.1,to_date('26-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1513.42,1887,1721,2709.6,to_date('27-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1523.02,1891.4,1739.7,2741.8,to_date('28-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.69,1914.6,1749.7,2760.1,to_date('29-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.12,1931.2,1771.4,2778.8,to_date('30-APR-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1546.82,1929.2,1792.3,2792,to_date('01-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1528.12,1924.3,1783.3,2764.1,to_date('02-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1530.65,1927,1799.4,2771,to_date('03-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.25,1935,1781.7,2759.4,to_date('04-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1519.48,1955.4,1788.6,2754.5,to_date('05-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1506.65,1962.2,1765.9,2769.8,to_date('06-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1504.3,1980.7,1791.2,2750.7,to_date('07-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1480.65,1987.7,1769.5,2726.5,to_date('08-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1476.7,1993.7,1758.7,2716.2,to_date('09-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1478.07,2015.7,1738.3,2721.8,to_date('10-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1479.62,2005,1744.8,2717.9,to_date('11-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1477.55,2024.9,1736.7,2732.8,to_date('12-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1472.59,2028.5,1735.2,2740.3,to_date('13-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1495.6,2044.9,1760.1,2789.7,to_date('14-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1517.45,2045.8,1786.3,2807.7,to_date('15-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1520.93,2057.3,1824.4,2842,to_date('16-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1821.1,2827.4,to_date('17-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1854.6,2827.5,to_date('18-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1527.06,2061.7,1854.6,2827.5,to_date('19-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1547.51,2092.3,1857.5,2827.5,to_date('20-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1545.82,2090.1,1870.3,2847.8,to_date('21-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1858.8,2832.5,to_date('22-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1857.8,2846.5,to_date('23-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.43,2105.4,1857.8,2846.5,to_date('24-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.04,2117.7,1843.1,2861.5,to_date('25-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1554.03,2128.2,1850.8,2833.6,to_date('26-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1551.17,2124.7,1859.6,2826,to_date('27-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1538.37,2079.9,1844.5,2816.5,to_date('28-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1529.1,2074.9,1852.6,2799.2,to_date('29-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1522.26,2046.4,1814.6,2773.4,to_date('30-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1533.79,2079.8,1796.8,2757.9,to_date('31-MAY-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1510.18,2076.7,1782.5,2745.3,to_date('01-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1526.91,2104.5,1803.5,2759.2,to_date('02-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1555.52,2101.3,1827.1,2765.1,to_date('03-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1581.49,2084,1837.5,2763.1,to_date('04-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1572.61,2063.9,1837.7,2737.6,to_date('05-JUN-95','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2387.6,3270.6,1939.7,3744.3,to_date('23-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2373.01,3243.5,1932.4,3714.6,to_date('24-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2401.59,3253,1936.9,3725.6,to_date('25-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2421.93,3264.4,1953.3,3740,to_date('26-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.52,3296.2,1976.9,3740.3,to_date('27-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2438.73,3298.6,1960.9,3704.2,to_date('28-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.09,3309.9,1974.5,3715.9,to_date('29-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2486.95,3360.5,1996.9,3738.2,to_date('30-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2485.18,3354.8,1990.8,3727.6,to_date('31-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2488.85,3385.5,2017.2,3752.7,to_date('01-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2486.83,3400.6,2015.3,3768.6,to_date('02-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.52,3383.4,2001.9,3777.1,to_date('03-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2471.38,3408.3,2005.9,3758.9,to_date('04-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2479.38,3581.9,2008,3758.2,to_date('05-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2448.8,3549.7,1975.5,3710.3,to_date('06-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2419.72,3506.2,1948.9,3674.5,to_date('07-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2415.29,3540.5,1932.1,3639.5,to_date('08-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2417,3558.3,1944.3,3640.3,to_date('09-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2432.46,3578.4,1962.4,3681.8,to_date('10-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2458,3561.1,1950.1,3644.8,to_date('11-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.55,3583.6,1965,3669.6,to_date('12-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2484.74,3605.1,1967.2,3693,to_date('13-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2491.73,3611.1,1969.8,3685.4,to_date('14-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.32,3629.3,1976.9,3698.3,to_date('15-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2479.84,3647.7,1974.4,3707,to_date('16-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2514.8,3677,2003.8,3681.9,to_date('17-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.78,3658.1,2008,3660.9,to_date('18-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2523.81,3671,2030.5,3672.4,to_date('19-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2502.94,3640.3,2020,3672.6,to_date('20-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2489.35,3646.5,2044.8,3699.7,to_date('21-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2500.75,3654.3,2055.6,3718.4,to_date('22-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2508.11,3648.3,2070.4,3728.5,to_date('23-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2489.52,3606.7,2064,3725.1,to_date('24-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('25-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('26-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.75,3611.1,2075,3755.6,to_date('27-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2510.81,3594.4,2081.7,3758.6,to_date('28-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2525.59,3601.5,2093.9,3767.4,to_date('29-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2508.12,3566.9,2072.5,3744.2,to_date('30-SEP-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2526.74,3575.5,2074.7,3766.8,to_date('01-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2540.79,3589.6,2080.6,3790.5,to_date('02-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2547.32,3607.5,2097.3,3825.3,to_date('03-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2519.82,3603.9,2075.1,3805.6,to_date('04-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2524.18,3601.2,2086,3820.7,to_date('05-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2535.86,3628.1,2092.5,3857.1,to_date('06-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.27,3673.2,2116.5,3852.7,to_date('07-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.12,3647.3,2112,3833,to_date('08-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2524.84,3668.4,2122.1,3817.6,to_date('09-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2538.68,3668.1,2116.4,3819.3,to_date('10-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2539.88,3696,2138.4,3832.8,to_date('11-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.97,3658.2,2130.8,3809.2,to_date('12-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2492.63,3651,2146.8,3817.9,to_date('13-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2492.63,3651,2146.8,3806,to_date('14-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2465.49,3635.8,2136.8,3776.4,to_date('15-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.43,3611.2,2115.4,3751.6,to_date('16-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2473.52,3591.6,2090.4,3751.6,to_date('17-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2476.79,3583.6,2083.7,3723,to_date('18-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2466.21,3534.1,2083.7,3707.3,to_date('19-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2470.57,3521.6,2085.4,3728.3,to_date('20-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2499.02,3556.9,2114.8,3754.4,to_date('21-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2496.33,3557.8,2100.9,3739.2,to_date('22-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2528.2,3582.4,2122.1,3759.7,to_date('23-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.4,3572.5,2124.6,3776.2,to_date('24-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.4,3572.5,2124.6,3753.6,to_date('25-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2552.29,3593,2136.9,3789.6,to_date('26-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2541.98,3584,2120.6,3778.2,to_date('27-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2564.12,3587.3,2129.7,3789.4,to_date('28-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.79,3550,2103.5,3764.2,to_date('29-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2546.55,3547.4,2114.5,3747,to_date('30-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.53,3572.2,2117.7,3752.1,to_date('31-OCT-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.53,3572.2,2117.7,3752.1,to_date('01-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.15,3583.8,2132.9,3760.2,to_date('02-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.53,3558.2,2117.1,3775.7,to_date('03-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2535.78,3537.7,2108.4,3746.7,to_date('04-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2523.81,3556.1,2110.1,3747.8,to_date('05-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2543.99,3544.6,2121.1,3739.2,to_date('06-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2550.42,3568.1,2111,3755.2,to_date('07-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2551.03,3572,2115.4,3753.4,to_date('08-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.02,3585.5,2133.2,3760.3,to_date('09-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2536.03,3547.9,2102,3706.8,to_date('10-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2552.96,3575.8,2120.8,3728.8,to_date('11-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2571.1,3575.6,2137.5,3755.7,to_date('12-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2569.9,3600.9,2137.3,3769.2,to_date('13-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2566.13,3607.4,2126.2,3761.7,to_date('14-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2544.9,3593.9,2111.8,3753.6,to_date('15-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.71,3600.1,2113,3761.5,to_date('16-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2554.12,3628.3,2107.9,3756.4,to_date('17-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2546.04,3643.6,2100.7,3753.2,to_date('18-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.22,3655.4,2077.1,3727.5,to_date('19-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2547.78,3687.3,2084.2,3722.3,to_date('20-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.19,3701.5,2097.6,3710.8,to_date('21-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2578.36,3699.6,2118.4,3679.5,to_date('22-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2572.06,3723.5,2113.3,3695.5,to_date('23-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2551.8,3703,2112.9,3678.8,to_date('24-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2570.44,3732.9,2123.7,3711,to_date('25-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2573.44,3728.2,2118.8,3725.6,to_date('26-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2565.32,3750.8,2111.8,3725.7,to_date('27-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2566.32,3757,2114,3714.1,to_date('28-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2578.74,3771.3,2126.8,3760.6,to_date('29-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.79,3731.2,2098.8,3743.2,to_date('30-NOV-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2561.51,3732.8,2079.1,3741.5,to_date('01-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.24,3773.4,2076.6,3752.3,to_date('02-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2573,3788,2081.9,3765.8,to_date('03-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2561.95,3810,2073.4,3749,to_date('04-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.97,3785.8,2050.6,3728.3,to_date('05-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2529.5,3755.4,2029.5,3698.3,to_date('06-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2475.98,3678.1,1989.5,3632.3,to_date('07-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2497.69,3656.2,1995.1,3658.2,to_date('08-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2505.56,3649.2,2007.3,3693.4,to_date('09-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2498.35,3661.5,1992.8,3710.5,to_date('10-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2468.32,3566.1,1960.3,3681.3,to_date('11-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2488.82,3585.2,1982.2,3708.4,to_date('12-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2459.13,3482.6,1954.1,3668.8,to_date('13-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2464.51,3537.4,1974.7,3684.7,to_date('14-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2469.51,3537.8,1962.9,3673.3,to_date('15-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.25,3549.5,1961.1,3678.8,to_date('16-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2466.84,3518.2,1968.5,3668.5,to_date('17-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2491.5,3494.4,1995.9,3703.2,to_date('18-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2504.16,3494.4,2009.9,3734.4,to_date('19-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2520.26,3604.6,2024.4,3770.6,to_date('20-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2520.52,3623.3,2013.2,3788.3,to_date('21-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2527.73,3629,1999.3,3788.4,to_date('22-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2541.41,3662.6,1996.7,3811.1,to_date('23-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.63,3653.4,1997.4,3811.4,to_date('24-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.96,3643.2,1985.4,3810.7,to_date('25-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2529.14,3623.1,1978.2,3803.3,to_date('26-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2545.65,3630.2,1980.6,3823.4,to_date('27-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2544.61,3650.7,1979.5,3830.3,to_date('28-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2542.75,3646.3,1979.5,3837.4,to_date('29-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2567.96,3670.5,1979.5,3872.9,to_date('30-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2556.25,3664.4,1986.2,3863.7,to_date('31-DEC-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.12,3698.2,2019.3,3883.2,to_date('01-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.44,3683.8,2000.6,3872.1,to_date('02-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2559.42,3713,2017.8,3891.1,to_date('03-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2562.12,3725.4,2020.8,3907.5,to_date('04-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2554.42,3714.6,2020.4,3907.5,to_date('05-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2563.59,3722.4,2018,3905.7,to_date('06-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2556.64,3692.6,2002.9,3918.7,to_date('07-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2548.84,3682.6,1977.6,3885,to_date('08-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2534.49,3646.3,1970.6,3867.6,to_date('09-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2538.34,3654.7,1977,3884.4,to_date('10-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2528.73,3623.9,1971.4,3855.9,to_date('11-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2526.18,3634.2,1984.8,3872.7,to_date('12-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2532.55,3627.1,1996.2,3887.2,to_date('13-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2542.74,3637.8,2004.8,3893,to_date('14-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2549.71,3684.4,2020.3,3910.8,to_date('15-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.77,3694.2,2042.1,3916.1,to_date('16-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2568.7,3678.9,2038.1,3905.6,to_date('17-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2588.04,3687.2,2065.4,3932.6,to_date('18-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2614.5,3716.8,2080.4,3967.9,to_date('19-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2630.24,3722.7,2086.2,3977.2,to_date('20-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2626.43,3690,2080.9,3972.3,to_date('21-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2621.2,3690.1,2072.7,3955.7,to_date('22-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2625.46,3694.9,2082.3,3974.3,to_date('23-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2641.5,3694.9,2079.5,3964.1,to_date('24-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2624.18,3635.5,2067.1,3919.7,to_date('25-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2643.42,3661.2,2081.5,3910.5,to_date('26-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2663.1,3697.7,2103.4,3935.7,to_date('27-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2664.96,3699.5,2104.1,3933.2,to_date('28-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2659.86,3696.6,2107.1,3946.4,to_date('29-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2655.49,3736.4,2132.8,3953.7,to_date('30-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2654.34,3737.5,2123.2,3992.2,to_date('31-JAN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.29,3760.2,2141.8,4015.1,to_date('01-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.29,3771.6,2136.1,4000,to_date('02-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2704.25,3771.6,2152.7,4024.8,to_date('03-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2705.21,3813.7,2151.6,4031.5,to_date('04-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2702.6,3818.1,2161.5,4035.6,to_date('05-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2685.23,3807.9,2146.7,4009.3,to_date('06-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2683.52,3797.3,2135.6,3994.7,to_date('07-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2692.69,3805.2,2147.1,4028.1,to_date('08-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2700.83,3813.7,2143.6,4038.7,to_date('09-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2718.73,3822.2,2168.3,4050.8,to_date('10-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2705.06,3790.2,2158.5,4024.4,to_date('11-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2717.5,3793.2,2165.3,4042.1,to_date('12-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2727.56,3803.3,2185.2,4053.1,to_date('13-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2733.67,3788.2,2180.2,4073.1,to_date('14-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2721.74,3775.3,2175.6,4057.2,to_date('15-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2678.89,3742.9,2148.9,4028.4,to_date('16-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2681.94,3753.6,2151.6,3999.4,to_date('17-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2690.79,3758.7,2162.4,4022.4,to_date('18-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2703.33,3763.5,2150.4,4025.3,to_date('19-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2675.5,3732,2125.7,3993.5,to_date('20-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2664.72,3723.1,2124.8,3963.9,to_date('21-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2671.4,3725.3,2140.5,3979.1,to_date('22-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2670.19,3736.1,2140.5,3948.5,to_date('23-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2678.73,3749.2,2142.2,3928.1,to_date('24-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2716.16,3784.8,2187.3,3921.1,to_date('25-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2735.28,3815.4,2213.4,3935.7,to_date('26-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2724.25,3811.5,2211.8,3900.4,to_date('27-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2732.29,3812,2205.2,3910.8,to_date('28-FEB-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2730.44,3827.6,2205.2,3914.4,to_date('01-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2764,3829.8,2229.1,3934.3,to_date('02-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2770.61,3825.7,2217.2,3926.9,to_date('03-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2784.39,3856.4,2218.2,3926.1,to_date('04-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2800.6,3897.9,2240.3,3958.2,to_date('05-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2768.68,3888.3,2228.8,3962.1,to_date('06-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2781.54,3892.6,2240.1,3978.1,to_date('07-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2769.47,3882.8,2255.5,4018.7,to_date('10-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2793.86,3845.3,2270.8,4049.2,to_date('13-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2848.84,3902.5,2315.7,4058,to_date('15-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2853.46,3874,2318.6,4038.5,to_date('16-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2900.76,3924.5,2349.1,4061.5,to_date('17-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2894.43,3913.8,2292.5,4051.2,to_date('19-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2832.53,3835.5,2240.7,3963,to_date('20-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2870.3,3874,2255.8,4011.6,to_date('21-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2836.36,3857.6,2213.3,3982.5,to_date('23-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2846.94,3860,2212.1,3990.7,to_date('24-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2815.77,3839.3,2203.4,3972.4,to_date('25-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2808.5,3850.4,2193.7,3979.6,to_date('27-MAR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2814.23,3874,2218.9,4018.2,to_date('28-MAR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2844.09,3869.8,2303.8,4092.5,to_date('03-APR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2820.81,3942.2,2257,4057.4,to_date('08-APR-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2976.56,3979.6,2388,4158.9,to_date('17-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2996.12,4007.1,2407.8,4197.5,to_date('18-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3006.87,4019.9,2425.1,4207.7,to_date('19-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2999.19,4009.5,2406.1,4194,to_date('20-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3000.66,4023.1,2409.9,4195.5,to_date('21-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3026.63,4115.4,2442.5,4219.1,to_date('22-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3037.28,4161,2461.3,4219.1,to_date('23-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2982.63,4125.5,2430.3,4218.8,to_date('24-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2992.55,4127.3,2435.2,4212,to_date('25-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3028.27,4182.3,2482.8,4237.4,to_date('26-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2997.95,4169.7,2465,4207.5,to_date('27-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3018.58,4209.1,2503.1,4228.4,to_date('28-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3037.7,4272.2,2516.6,4275.8,to_date('29-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3064.7,4282.8,2508.6,4257.8,to_date('30-APR-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3067.48,4296.5,2503.1,4260.9,to_date('01-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3114.73,4305.5,2541.3,4281.5,to_date('02-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3124.78,4309.8,2558.4,4265.9,to_date('03-MAY-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3191.45,4408.4,2582.1,4304.3,to_date('06-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3211.01,4444.1,2599.3,4304.3,to_date('07-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3256.86,4436.3,2628.4,4327.1,to_date('08-MAY-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3260.3,4514.6,2634.5,4337.8,to_date('10-MAY-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3203.79,4522.5,2562.8,4336.8,to_date('14-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3180.63,4463.2,2567.9,4331.1,to_date('15-MAY-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3245.02,4539,2602.2,4329.3,to_date('17-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3272.58,4519.7,2629.4,4339.2,to_date('18-MAY-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3258.74,4460.1,2600.3,4307.1,to_date('20-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3345.09,4513.7,2651.7,4357.7,to_date('21-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3375.45,4547.1,2666.2,4360.1,to_date('22-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3396.55,4605.2,2698.9,4399.3,to_date('23-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3419.51,4638.9,2708.3,4420.3,to_date('24-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3426.77,4684.4,2709.2,4437.4,to_date('25-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3430.95,4677.1,2686.2,4444.3,to_date('26-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3382.4,4676.2,2641.7,4422.5,to_date('27-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3367.82,4609.9,2632.1,4397.7,to_date('28-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3404.29,4636.2,2645.6,4424.3,to_date('29-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3337.11,4556.5,2588.4,4373.3,to_date('30-MAY-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3289.59,4519.9,2574,4356.8,to_date('31-MAY-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3247.03,4442.9,2553.7,4258.1,to_date('02-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3288.52,4491.3,2587.1,4254.8,to_date('03-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3302.57,4497.3,2579.3,4214.8,to_date('04-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3374.93,4558.6,2624.3,4270.7,to_date('05-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3439.22,4620.5,2648.7,4301.5,to_date('06-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('07-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('08-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3407.83,4659.2,2656.7,4312.9,to_date('09-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3281.46,4501.7,2581.8,4248.1,to_date('10-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3210.94,4488.7,2530.3,4236.6,to_date('11-JUN-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3235.35,4471.5,2518,4236.6,to_date('13-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3342.77,4588,2572.3,4271.7,to_date('14-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3328.13,4582.6,2579,4269.3,to_date('15-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3364.76,4634.9,2617.6,4292.3,to_date('16-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3352.58,4626.6,2608,4313.2,to_date('17-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3319.24,4604.2,2574.6,4270.7,to_date('18-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3297.52,4586.3,2566.1,4251.7,to_date('19-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3369.26,4643.4,2620.6,4286.8,to_date('20-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3347.54,4625.6,2621,4294.6,to_date('21-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3361.8,4665.7,2615.2,4298.9,to_date('22-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3361.2,4699.1,2547.6,4310.5,to_date('23-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3328.41,4740.1,2522.7,4328.7,to_date('24-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3348.9,4752.3,2514.7,4346.1,to_date('25-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3366.87,4781.1,2533.6,4387.7,to_date('26-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3396.49,4836.1,2539.8,4388.5,to_date('27-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3357.57,4772.3,2536.3,4369.7,to_date('28-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3372.96,4793.3,2550.3,4389.7,to_date('29-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3425.86,4855.1,2602.9,4433.2,to_date('30-JUN-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3438.09,4897.6,2639.5,4436,to_date('01-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3438.09,4897.6,2639.5,4445,to_date('02-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3491.08,4953.5,2655.3,4455.6,to_date('03-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3565.69,5029.6,2672.8,4455.6,to_date('04-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3548.52,4988.4,2651.9,4519.3,to_date('05-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3537.45,5016,2643.3,4537.5,to_date('06-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3537.45,5016,2643.3,4580.4,to_date('07-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3533.21,5004.7,2633.9,4630.9,to_date('08-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3593.14,5042.5,2693.1,4669.6,to_date('09-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3559.29,5084.2,2719.6,4691,to_date('10-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3588.57,5134.3,2774.6,4686.9,to_date('11-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3564.85,5141.7,2776,4681.2,to_date('12-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3569.26,5157.5,2784.3,4693.9,to_date('13-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3569.26,5157.5,2784.3,4645.2,to_date('14-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3516.2,5081,2751.1,4607.5,to_date('15-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3600.4,5178.6,2786.4,4642,to_date('16-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3575.44,5176.4,2741.7,4651.8,to_date('17-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3621.72,5181,2762.9,4661.8,to_date('18-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3669.31,5196.7,2654.7,4661.8,to_date('19-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3665.43,5190,2680.3,4681.6,to_date('20-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3626.6,5133.1,2583.2,4677.5,to_date('21-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3635.38,5132.1,2579.2,4672.3,to_date('22-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3562.73,5041.6,2583.9,4621.3,to_date('23-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3596.4,5150,2601.5,4562.8,to_date('24-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3655.59,5207.2,2624.5,4557.8,to_date('25-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3651.59,5238.5,2635.4,4557.1,to_date('26-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3684.6,5251.2,2690.9,4576.2,to_date('27-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3700.53,5320,2719.3,4645,to_date('28-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3668.61,5368.8,2686.2,4686.7,to_date('29-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3671.16,5361.9,2664.2,4739.6,to_date('30-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3671.87,5308.6,2696.2,4724.8,to_date('31-JUL-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3737.16,5364.2,2760.3,4757.4,to_date('01-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3752.37,5384.6,2808.5,4783.1,to_date('02-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3750.02,5362,2795.9,4745.1,to_date('03-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3721.18,5345.9,2762.6,4682.2,to_date('04-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3730.56,5405,2751.7,4657,to_date('05-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3777.56,5510.3,2739.7,4653.7,to_date('06-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3788.54,5561.8,2757.1,4593.9,to_date('07-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3748.79,5587.8,2762.2,4575.8,to_date('08-AUG-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3819.52,5662.4,2867.4,4640,to_date('10-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3820.16,5669.9,2893.6,4657.9,to_date('11-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3809.92,5700.3,2891,4640.3,to_date('12-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3766.89,5620.6,2858.3,4604.6,to_date('13-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3834.84,5654.8,2944,4728.3,to_date('14-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3867.53,5674.3,2909.5,4751.4,to_date('15-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3939.73,5804.9,2937,4831.7,to_date('16-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3946.73,5846.5,2934.5,4812.8,to_date('17-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4003.35,5947,2947.7,4810.7,to_date('18-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4030.1,6012.6,2929.8,4758.5,to_date('19-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4026.97,5977.1,2950.6,4762.4,to_date('20-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4000.65,5885.4,2929.1,4767.8,to_date('21-AUG-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4142.19,5845.8,2941.6,4857.4,to_date('23-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4139.68,5844.7,2950.7,4899.3,to_date('24-AUG-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4203.91,5868.3,2958.6,4949,to_date('26-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4131.94,5737.1,2876.7,4877.2,to_date('27-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4139.96,5620.5,2874.1,4805.7,to_date('28-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4297.64,5677.1,2921.1,4846.7,to_date('29-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4384.82,5869.9,3003.5,4874.5,to_date('30-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4320.52,5849.2,2973.5,4862.9,to_date('31-AUG-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4368.54,5847,3025.9,4851.5,to_date('01-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4400.3,5888,3022.2,4862.6,to_date('02-SEP-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4458.66,5929.5,3069.3,4927.3,to_date('04-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4405.52,5898.2,3075.7,4907.5,to_date('05-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4336.98,5898.2,3049.5,4899.3,to_date('06-SEP-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4428.08,5922.1,3056.3,5086.8,to_date('10-SEP-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4377.51,5808.4,2998.6,5075.8,to_date('13-SEP-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4195.53,5579.5,2921.8,4991.3,to_date('15-SEP-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4080.55,5405.6,2870.1,4835,to_date('17-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4190.45,5580.1,2936.2,4914.2,to_date('18-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4251.93,5690.1,2979.3,4958.4,to_date('19-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4204.81,5668.8,2957.2,4978,to_date('20-SEP-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4076.75,5473.9,2898.6,4901.1,to_date('22-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3993.7,5363.3,2869.3,4886.3,to_date('23-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3992.03,5409.6,2871.7,4906.9,to_date('24-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3897.43,5217.3,2828.4,4845.4,to_date('25-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3919.79,5216.7,2770.5,4817.5,to_date('26-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4001.81,5271.5,2805.8,4870.2,to_date('27-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4127.28,5447.5,2921.2,4952.2,to_date('28-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4062.13,5478.6,2918,4976.9,to_date('29-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4093.43,5478.1,2927,4991.3,to_date('30-SEP-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4073.71,5532.9,2924.5,4994.2,to_date('01-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4131.26,5505.3,2940.9,4985.2,to_date('02-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4104.57,5445.1,2919.7,4950.5,to_date('03-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4028,5356.7,2874.6,4905.2,to_date('04-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3890.24,5280.8,2843.6,4854.8,to_date('05-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3796.61,5281.9,2834.1,4848.2,to_date('06-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3869.53,5321.7,2898.6,4902.9,to_date('07-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3995.69,5417.8,2940.6,4976.4,to_date('08-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3970.44,5550.4,2944,5013.1,to_date('09-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4004.04,5629,2978.4,5046.2,to_date('10-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3983.06,5611,2977.2,5023.8,to_date('11-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4096.85,5705.1,3017.5,5075.7,to_date('12-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4091.77,5730.4,2997.2,5027.5,to_date('13-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4150.95,5732.5,3023.7,5077.2,to_date('14-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4104.93,5667.1,3005.4,5065.5,to_date('15-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4135.09,5716.6,2985.6,5226.3,to_date('16-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4116.52,5691.8,2989,5220.3,to_date('17-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4154.89,5673.6,3008.3,5244.2,to_date('18-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4262.98,5754.7,3054.9,5317.1,to_date('19-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.17,5825,3052.1,5296.1,to_date('20-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4266.17,5929,3094,5330.8,to_date('21-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4326.35,5897.4,3078,5300,to_date('22-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4311.13,5846.9,3064.4,5305.6,to_date('23-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4267.4,5822.3,3024.1,5262.1,to_date('24-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4179.92,5732.2,2960.7,5217.8,to_date('25-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4164.62,5699.5,2955.1,5227.3,to_date('26-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4225.27,5792.8,3002.9,5300.1,to_date('27-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4215.23,5836.3,3002.5,5298.9,to_date('28-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4168.62,5815.9,2992.2,5263.7,to_date('29-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4149.92,5806.8,2992.9,5287.9,to_date('30-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4049.16,5751.6,2958,5271.1,to_date('31-OCT-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4069.25,5777.2,2946.7,5211,to_date('01-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4172.47,5862.9,2989.9,5225.9,to_date('02-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4124.86,5803.2,2958.1,5148.8,to_date('03-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3976.38,5651.8,2856.9,4991.5,to_date('04-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3981.44,5689.5,2849,4970.2,to_date('05-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3871.39,5533.5,2769.6,4840.7,to_date('06-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3645.69,5279.7,2651.3,4755.4,to_date('07-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3806.66,5479,2818,4871.8,to_date('08-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3748.88,5370.9,2739.5,4801.9,to_date('09-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3753.66,5467.2,2739.3,4842.3,to_date('10-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3847.73,5581.6,2788,4906.4,to_date('11-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3784.8,5538.2,2774.9,4897.4,to_date('12-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3841.39,5601.6,2822.4,4908.3,to_date('13-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3813.88,5557.4,2781.8,4863.8,to_date('14-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3715.38,5438.6,2707.1,4764.3,to_date('15-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3728.37,5459.7,2707.1,4806.8,to_date('16-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3734.79,5483.9,2707.1,4793.7,to_date('17-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3697.48,5434,2694.5,4720.4,to_date('18-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3701.94,5418.2,2700.7,4711,to_date('19-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3676.65,5437,2698.9,4741.8,to_date('20-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3816.71,5565,2773,4867,to_date('21-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3844.14,5574.2,2782.6,4845.4,to_date('22-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3876.9,5571.7,2790.6,4830.1,to_date('23-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3931.81,5650.4,2821.2,4908.4,to_date('24-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3941.91,5725.5,2861.7,4985.8,to_date('25-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3832.1,5645.7,2802.5,4898.6,to_date('26-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3850.14,5666.3,2786.3,4863.5,to_date('27-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3926.93,5738.3,2811.7,4891.2,to_date('28-NOV-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (3961.97,5772.4,2829,4889,to_date('29-NOV-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4339.98,6390,3006.7,5224.1,to_date('28-DEC-97','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4293.64,6330.2,2954.9,5237.1,to_date('29-DEC-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4134.64,6062.1,2862.5,5068.8,to_date('31-DEC-97','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4385.29,6508.7,3088.3,5372.6,to_date('12-JAN-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4494.72,6772,3189.6,5606.4,to_date('18-JAN-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4519.56,6828.4,3220.9,5600.9,to_date('20-JAN-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4552.46,6931.6,3240,5607.9,to_date('22-JAN-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4535.56,6905.3,3225.1,5619.9,to_date('25-JAN-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4581.08,6953.2,3250.6,5718.5,to_date('28-JAN-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4704.58,7065.4,3348.2,5745.1,to_date('01-FEB-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (4693.86,7153.1,3421.9,5767.3,to_date('03-FEB-98','DD-MON-RR'));
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+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1954.62,2547.1,1855.7,3194.5,to_date('17-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1943.88,2516.8,1874.9,3170.1,to_date('18-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1955.33,2544.2,1882,3174.7,to_date('19-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1987.7,2560.8,1928.4,3199.9,to_date('20-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (1978.97,2554.8,1918.5,3209.3,to_date('21-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2007.57,2561.1,1945.9,3214.9,to_date('22-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2028.52,2574.4,1942.3,3226.2,to_date('23-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2024.25,2567,1931,3217.6,to_date('24-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2017.95,2597.2,1918.5,3216.7,to_date('25-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2017.95,2597.2,1918.5,3220.4,to_date('26-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2036.47,2621.4,1936.9,3248.2,to_date('27-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2037.99,2618.6,1971.1,3262.6,to_date('28-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2034.15,2641.7,1946.2,3264.3,to_date('29-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2021.22,2650.5,1927.4,3251.7,to_date('30-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2030.65,2658.8,1927.4,3251.7,to_date('31-JAN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2050.66,2655.6,1988.7,3261.2,to_date('01-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.41,2661.4,1996.7,3290.1,to_date('02-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2086.41,2701,2003.6,3317.9,to_date('03-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2102.4,2728.2,2017.3,3310.3,to_date('04-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2092,2739,1991.5,3310.7,to_date('05-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2109.36,2746.6,2001.3,3300.8,to_date('06-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2088.99,2739.8,2004.7,3297.4,to_date('07-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.8,2727.7,1989.8,3285.8,to_date('08-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2064.42,2701,1965.5,3261,to_date('09-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2096.87,2751.2,1979.5,3284.5,to_date('10-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2083.55,2750.4,1965.3,3291.8,to_date('11-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2105.88,2778.5,1960.7,3327.3,to_date('12-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2105.88,2778.5,1960.7,3328.2,to_date('13-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2065.71,2751.7,1919.1,3311.1,to_date('14-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2069.26,2762.5,1927.8,3311.1,to_date('15-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2081.46,2779.4,1927.3,3309.9,to_date('16-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.95,2786.4,1948,3319.4,to_date('17-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2119.24,2807.8,1960.8,3340.6,to_date('18-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.72,2805.3,1971.3,3345,to_date('19-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2132.72,2805.3,1971.3,3376.6,to_date('20-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.17,2809.7,1965.9,3380,to_date('21-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2136.72,2819.6,1974.7,3370.8,to_date('22-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.21,2806.8,1951.2,3380.8,to_date('23-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2107.16,2795.3,1897,3337.7,to_date('24-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2127.79,2802.3,1907.8,3344.6,to_date('25-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.25,2800.9,1922.8,3348,to_date('26-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2124.84,2798.1,1893.7,3339.8,to_date('27-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2130.78,2800.2,1920.7,3370.4,to_date('28-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2125.06,2775.4,1905,3366.1,to_date('29-FEB-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2154.13,2803,1918.4,3381.3,to_date('01-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.36,2805.6,1896.1,3377.2,to_date('02-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2816.8,1902.8,3378.3,to_date('03-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2843.3,1925.2,3403.8,to_date('04-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2144.88,2837.7,1895.1,3379.4,to_date('05-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.36,2830.3,1902.4,3309.2,to_date('06-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2128.99,2845.1,1877.7,3313.2,to_date('07-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2091.3,2836.8,1865.5,3282.7,to_date('08-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2094.68,2845.6,1861.9,3294,to_date('09-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2089.04,2825.3,1858.8,3314.6,to_date('10-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2099.68,2827.5,1879.7,3323.7,to_date('11-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2110.13,2847.5,1889.4,3349.2,to_date('12-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2117.63,2843.7,1907.8,3394.9,to_date('13-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2111.35,2833.7,1889.5,3388.3,to_date('14-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2163.37,2848.4,1950.8,3462.9,to_date('15-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.39,2842.9,1949.4,3455,to_date('16-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.15,2847.3,1961.3,3464,to_date('17-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2200.32,2867.9,1961.7,3450.6,to_date('18-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.72,2869.7,1948.3,3447.2,to_date('19-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2191.25,2863.4,1948.3,3429.2,to_date('20-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.36,2892.3,1951.5,3442.6,to_date('21-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.71,2875.1,1929,3420.7,to_date('22-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2195.31,2856.2,1931.2,3405.3,to_date('23-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2183.52,2822.2,1905.9,3400.4,to_date('24-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.54,2830.2,1908.9,3413.1,to_date('25-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.02,2836.6,1926.6,3431.6,to_date('26-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.23,2833.2,1941.3,3432.9,to_date('27-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2237.73,2827.1,1942.5,3454.3,to_date('28-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2239.36,2824.7,1947.9,3458.3,to_date('29-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2217.91,2833.1,1932.8,3468.9,to_date('30-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2222.51,2826.8,1920,3463.3,to_date('31-MAR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.26,2826.8,1917.8,3449.9,to_date('01-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.6,2852.3,1960,3499.9,to_date('02-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2234.86,2814.3,1950.8,3475.6,to_date('03-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.68,2807.1,1957.1,3482.4,to_date('04-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2244.56,2800.6,1950.4,3483.5,to_date('05-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.89,2797.4,1954.7,3468.8,to_date('06-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2222.28,2781.3,1943.7,3468.3,to_date('07-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.72,2802.6,1949.2,3474.7,to_date('08-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2228.44,2820.1,1946.2,3467.5,to_date('09-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2208.41,2807.2,1946.2,3441.4,to_date('10-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2236.73,2838.4,1946.2,3444.4,to_date('11-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2251.64,2880.6,1970.4,3465.1,to_date('12-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2253.93,2880,1967.3,3470.6,to_date('13-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2265.86,2889.5,1971.6,3509.8,to_date('14-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2269.6,2886.7,1984.4,3535.7,to_date('15-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2258.97,2900.8,1963.3,3530.2,to_date('16-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2262.66,2896.8,1957.4,3515.9,to_date('17-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2253.91,2887.1,1939.2,3520,to_date('18-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2257.34,2893.8,1937.8,3524.9,to_date('19-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2240.31,2883.3,1890.1,3524.9,to_date('20-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2230.27,2887.3,1899.5,3502.6,to_date('21-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2251.3,2915.7,1921.4,3504,to_date('22-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2234.23,2908.8,1883.4,3477.8,to_date('23-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2242.76,2918.3,1881.7,3509.4,to_date('24-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2263.08,2952.7,1900.5,3522.7,to_date('25-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.57,2971.1,1902.5,3532.4,to_date('26-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.37,2999.6,1884.8,3557.7,to_date('27-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2263.27,2989.1,1861.7,3545.6,to_date('28-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.77,2989.2,1854.9,3554.5,to_date('29-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.26,2986.3,1869.7,3549.3,to_date('30-APR-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.07,2990.5,1873.6,3535.9,to_date('01-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2300.72,3016.8,1898.2,3570.8,to_date('02-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2300.29,3011.6,1892.4,3565.4,to_date('03-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2305.58,3025.4,1880,3564.6,to_date('04-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2289.49,3008.1,1871.7,3533.3,to_date('05-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2306.66,3033.5,1882.5,3541.4,to_date('06-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2294.15,3029,1863.8,3561.5,to_date('07-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2275.72,3039.1,1853.9,3557.9,to_date('08-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2204.44,2994.7,1790.7,3514.8,to_date('09-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2212.97,3015.3,1800.6,3507,to_date('10-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2232.02,3045.5,1816.3,3523.3,to_date('11-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2190.9,3009.8,1791.8,3485,to_date('12-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.24,3000.6,1767.6,3479,to_date('13-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.27,3014.8,1788.4,3508.2,to_date('14-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.01,3039,1780.7,3520.2,to_date('15-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.01,3049,1800.7,3524.2,to_date('16-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2211.6,3036.5,1803.9,3544.1,to_date('17-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2191.54,3036.6,1800.3,3544.4,to_date('18-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.35,3054,1809.6,3526.5,to_date('19-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2157.41,3064,1785.7,3510.3,to_date('20-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2137.01,3050,1778,3460.1,to_date('21-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2153.56,3073,1794.4,3474.3,to_date('22-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2159.73,3083.7,1803.8,3523.8,to_date('23-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2206.11,3109.2,1817,3568,to_date('24-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2193.85,3116.3,1790.5,3557.3,to_date('25-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2185.52,3112.3,1779.7,3562.2,to_date('26-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.57,3135.4,1770.7,3593,to_date('27-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2176.09,3124.5,1757.3,3578.6,to_date('28-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2148.12,3120.4,1740.7,3551.4,to_date('29-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2116.86,3041.7,1721.1,3531.5,to_date('30-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2114.88,3062.3,1724.2,3535.3,to_date('31-MAY-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2140.74,3078.9,1764.1,3537.8,to_date('01-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2142.74,3080,1754.1,3519.6,to_date('02-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2112.01,3026.6,1742.4,3497.9,to_date('03-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2149.71,3057.9,1795.3,3510,to_date('04-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2165.76,3108.2,1814,3529.1,to_date('05-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.11,3128.5,1814,3518.7,to_date('06-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2180.49,3123.4,1828.7,3523,to_date('07-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2169.69,3137.6,1832.1,3500.4,to_date('08-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2168.57,3123.2,1822.6,3514.8,to_date('09-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2174.27,3133.8,1857.4,3522.4,to_date('10-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2184.05,3132.6,1866.3,3537.1,to_date('11-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2182.47,3150.1,1852.6,3541.6,to_date('12-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2174.78,3130.4,1839.8,3523.4,to_date('13-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2195.15,3121.4,1838.2,3536.8,to_date('14-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.34,3133,1838.2,3547.9,to_date('15-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.64,3124.8,1875.2,3571.4,to_date('16-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2199.29,3148.4,1905.1,3610.8,to_date('17-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2196.59,3152.5,1890.5,3609.2,to_date('18-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2204.01,3182.2,1881.4,3628.8,to_date('19-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2201.9,3183.9,1872.9,3604.1,to_date('20-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2194.53,3201,1875.5,3632.4,to_date('21-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2197.26,3209.2,1867.1,3602.5,to_date('22-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2210.92,3220.1,1891,3624,to_date('23-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2247.97,3251,1889.8,3649,to_date('24-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2242.91,3229.5,1870.3,3648.8,to_date('25-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2249.75,3245.9,1857.3,3655.5,to_date('26-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2254.95,3251.6,1828.3,3664.3,to_date('27-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2266.56,3261.2,1820.9,3664.3,to_date('28-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2261.08,3278.4,1774.9,3669.7,to_date('29-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2269.34,3278.5,1814.9,3664.2,to_date('30-JUN-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2274.94,3281.5,1834.8,3662.8,to_date('01-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.62,3245.7,1846.8,3639.5,to_date('02-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2277.7,3246.6,1856.3,3630,to_date('03-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2277.7,3261.6,1849.6,3652.1,to_date('04-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2278.64,3280.9,1848.9,3654.9,to_date('05-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2283.26,3273.4,1833.8,3662.4,to_date('06-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2286.21,3272.2,1875,3671.6,to_date('07-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2283.84,3289.8,1859.3,3642.6,to_date('08-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2240.24,3251.6,1820.5,3596.1,to_date('09-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2241.43,3217.7,1810.2,3576.9,to_date('10-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.07,3254.3,1831.2,3613.7,to_date('11-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2268.35,3255,1834.4,3633.3,to_date('12-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1873.4,3658.3,to_date('13-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1873.4,3658.3,to_date('14-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.81,3277.9,1866.7,3658.3,to_date('15-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2280.44,3317.1,1877,3676.4,to_date('16-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2273.9,3297.7,1879.1,3676.7,to_date('17-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.69,3297.7,1872,3689.3,to_date('18-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2260.69,3297.7,1872,3689.3,to_date('19-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2307.7,3297.7,1908.4,3687.9,to_date('20-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2326.18,3384.5,1943,3715.6,to_date('21-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2332.81,3395.8,1931.2,3714.1,to_date('22-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2315.66,3376.1,1917.7,3704.5,to_date('23-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2336.76,3368.1,1916.6,3720.6,to_date('24-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2351.47,3361.5,1916.3,3720.6,to_date('25-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2340.31,3314.8,1910.1,3671.5,to_date('26-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2330.98,3284.1,1897.9,3654.9,to_date('27-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2353.89,3292.9,1907.6,3657.3,to_date('28-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2361.38,3233.9,1924.4,3662.7,to_date('29-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2379.43,3254.8,1952.1,3710.6,to_date('30-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2375.63,3219.3,1966,3704.2,to_date('31-JUL-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2389.62,3257.8,1960.1,3748.7,to_date('01-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2400.58,3253.1,1964.3,3748.4,to_date('02-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2391.74,3249.9,1954.4,3754.2,to_date('03-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2393.12,3219,1934.1,3735,to_date('04-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2436.68,3221.3,1946,3758.2,to_date('05-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.02,3211.2,1950.2,3734.2,to_date('06-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2449.71,3191.4,1966.7,3734.7,to_date('07-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.7,3198.2,1980.7,3734.6,to_date('08-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2456.09,3236.8,2003.1,3735.3,to_date('09-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2463,3248.7,2021,3759.3,to_date('10-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2472.53,3279.9,2024.1,3752.8,to_date('11-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2459.81,3270,2022.2,3781.3,to_date('12-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2416.84,3221.7,1985.6,3746.6,to_date('13-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2437.98,3253.8,1988.5,3747.5,to_date('14-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2442.43,3262.7,1983.3,3726.1,to_date('15-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2411.49,3245.6,1956.2,3708.4,to_date('16-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2435.07,3242.7,1960.7,3716.3,to_date('17-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2428.59,3253.1,1967.7,3726.6,to_date('18-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2436.09,3260.1,1983.3,3747.6,to_date('19-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2427.77,3266.1,1956.4,3745,to_date('20-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2426.51,3272.2,1964.2,3779.8,to_date('21-AUG-96','DD-MON-RR'));
+Insert into EUSTOCK (DAX,SMI,CAC,FTSE,DATES) values (2423.6,3280.3,1952.5,3770.9,to_date('22-AUG-96','DD-MON-RR'));
diff --git a/machine-learning/sql/26ai/oml4sql-time-series-regression.sql b/machine-learning/sql/26ai/oml4sql-time-series-regression.sql
new file mode 100644
index 00000000..76846ac7
--- /dev/null
+++ b/machine-learning/sql/26ai/oml4sql-time-series-regression.sql
@@ -0,0 +1,262 @@
+-----------------------------------------------------------------------
+--   Oracle Machine Learning for SQL (OML4SQL) 26ai
+--
+--   Time Series Regression - Using Exponential Smoothing with 
+--      Generalized Linear Model and Extreme Gradient Boosting
+--
+--   Copyright (c) 2025 Oracle Corporation and/or its affilitiates.
+--
+--   The Universal Permissive License (UPL), Version 1.0
+--
+--   https://oss.oracle.com/licenses/upl
+-----------------------------------------------------------------------
+
+SET ECHO ON 
+SET FEEDBACK 1
+SET NUMWIDTH 10
+SET LINESIZE 80
+SET TRIMSPOOL ON
+SET TAB OFF
+SET PAGESIZE 100
+
+SET echo off;  
+
+col setting_name format a30
+col setting_value format a45
+col setting_type format a15  
+col data_type format a15
+column model_name format a20
+col numeric_value format 9990.9999    
+col string_value format a10  
+  
+column mining_function format a15
+column attribute_name format a15
+column attribute_type format a15 
+  
+col target format 990.99999
+col x1 format 990.99999
+col x2 format 990.99999
+
+-----------------------------------------------------------------------
+--                            SAMPLE PROBLEM
+-----------------------------------------------------------------------
+-- Perform Time Series Regression using a combination of ESM and GLM algorithms.
+-- See documentation on "Multiple Time Seriess Models" at 
+-- https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmcon/exponential-smoothing.html
+​
+-----------------------------------------------------------------------
+--                            EXAMPLE IN THIS SCRIPT
+-----------------------------------------------------------------------
+-- Create the time series dataset
+-- Build an ESM model using multiple time series forecasting
+-- Build a GLM model using the ESM result
+-- Explore the model views
+-- Compare the regression forecast to the baseline (ESM) forecast
+-- Build an XGBOOST model and compare to ESM forecast
+​
+-----------------------------------------------------------------------
+-- Create Time Series Dataset-- Invoke this script 
+--
+
+@time-series-regression-dataset.sql
+/
+-----------------------------------------------------------------------
+--                            BUILD THE MODEL
+-----------------------------------------------------------------------
+
+-----------------------------------------------------------------------
+-- Multiple time series model (MSDEMO_MODEL) is built using an interval 
+-- of days, which corresponds to the dataset interval. By specifying
+-- a series list (parameter EXSM_SERIES_LIST), we can include additional 
+-- "target" columns, i.e., columns for which ESM should create backcasts.
+--
+
+-------------------------
+-- BUILD ESM MODEL
+--
+
+SET echo OFF;
+BEGIN DBMS_DATA_MINING.DROP_MODEL('MSDEMO_MODEL'); 
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+SET echo ON;
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')            := 'ALGO_EXPONENTIAL_SMOOTHING';
+    v_setlst('EXSM_INTERVAL')        := 'EXSM_INTERVAL_DAY';
+    v_setlst('EXSM_MODEL')           := 'EXSM_ADDWINTERS_DAMPED';
+    v_setlst('EXSM_SEASONALITY')     := '7';
+    v_setlst('EXSM_PREDICTION_STEP') := '1';
+    v_setlst('EXSM_SERIES_LIST')     := 'SMI,CAC,FTSE';
+	 
+    dbms_data_mining.create_model2(
+               MODEL_NAME          => 'MSDEMO_MODEL',
+               MINING_FUNCTION     => 'TIME_SERIES',
+               DATA_QUERY          => 'SELECT * FROM EUSTOCK',
+               CASE_ID_COLUMN_NAME => 'DATES',
+               TARGET_COLUMN_NAME  => 'DAX',
+               SET_LIST            => v_setlst);
+END;
+/
+
+-------------------------
+-- Model Results:
+-- DM$VRNSDEMO compares the actuals to the prediction
+--
+
+SELECT * FROM DM$VRMSDEMO_MODEL 
+FETCH FIRST 10 ROWS ONLY;
+/
+
+-------------------------
+-- Create Trianing, Actual, and Test Datasets:
+-- DM models show the forcast for the target column
+--
+
+BEGIN DROP TABLE tmesm_ms_train;
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+CREATE TABLE tmesm_ms_train as
+SELECT CASE_ID, DAX, DM$DAX, DM$SMI, DM$CAC, DM$FTSE, 
+       CASE WHEN case_id < to_date('1998-02-03','YYYY-MM-DD') 
+       THEN 0 ELSE 1 END AS prod 
+FROM DM$VRMSDEMO_Model order by 1;
+/
+-------------------------
+-- By inserting the actual values into the prediction dataset, 
+-- a comparison of the accuracy for the ESM and regression
+-- models can be possible.
+--
+
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE tmesm_ms_actual';
+EXCEPTION WHEN OTHERS THEN NULL; END; 
+/
+CREATE TABLE tmesm_ms_actual (case_id DATE, DAX binary_double);
+INSERT INTO tmesm_ms_actual VALUES(DATE '1998-02-04', 4633.008);
+commit;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE tmesm_ms_test';
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+CREATE TABLE tmesm_ms_test as 
+SELECT a.case_id, b.DAX, DM$DAX, DM$SMI, DM$CAC, DM$FTSE, 1 prod 
+FROM   DM$VTMSDEMO_model a, tmesm_ms_actual b
+WHERE  a.case_id=b.case_id;
+/
+-------------------------
+-- Column 'prod' is a binary variable indicating a change in the environment,
+-- e.g., the introduction of a new product.
+-- It is an example of an auxillary, or flag, variable that is used by the regression
+-- to improve accuracy. It may represent the presence of a holiday, or encodw
+-- an alternate seasonality (multi-seasonal model)
+--
+
+-------------------------
+-- BUILD GLM MODEL
+--
+
+SET echo OFF;
+BEGIN DBMS_DATA_MINING.DROP_MODEL('MS_GLM_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+SET echo ON;
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')          := 'ALGO_GENERALIZED_LINEAR_MODEL';
+	
+    DBMS_DATA_MINING.CREATE_MODEL2(
+               MODEL_NAME          => 'MS_GLM_MODEL',
+               MINING_FUNCTION     => dbms_data_mining.regression,
+                DATA_QUERY         => 'SELECT * FROM tmesm_ms_train',
+               CASE_ID_COLUMN_NAME => 'CASE_ID',
+               TARGET_COLUMN_NAME  => 'DAX',
+               SET_LIST            => v_setlst);    
+END;
+/
+
+-----------------------------------------------------------------------
+--                            ANALYZE THE MODEL
+-----------------------------------------------------------------------
+
+-------------------------
+-- DISPLAY MODEL VIEWS
+--
+
+SELECT VIEW_NAME, VIEW_TYPE 
+FROM   USER_MINING_MODEL_VIEWS
+WHERE  MODEL_NAME='MSDEMO_MODEL'
+ORDER BY VIEW_NAME;
+/
+-------------------------
+-- DISPLAY BACKCASTS
+--
+
+SELECT * 
+FROM   DM$VRMSDEMO_MODEL
+ORDER BY CASE_ID
+FETCH FIRST 10 ROWS ONLY;
+/
+-------------------------
+-- DISPLAY FORECASTS
+--
+
+SELECT * 
+FROM DM$VTMSDEMO_MODEL
+ORDER BY CASE_ID 
+FETCH FIRST 10 ROWS ONLY;
+/
+-------------------------
+-- COMPARE REGRESSION FORECAST (GLM) TO BASELINE (EXPONENTIAL SMOOTHING) FORECAST
+--
+
+SELECT CASE_ID, DAX, regression_forecast, baseline_forecast,
+       DAX - regression_forecast AS regression_error,
+       DAX - baseline_forecast AS baseline_error 
+FROM (SELECT CASE_ID, DAX, 
+             PREDICTION(MS_GLM_model using *) regression_forecast, 
+             DM$DAX baseline_forecast
+      FROM   tmesm_ms_test 
+      ORDER BY CASE_ID);
+/
+-------------------------
+-- BUILD XGBOOST MODEL
+--
+
+SET echo OFF;
+BEGIN DBMS_DATA_MINING.DROP_MODEL('MS_XGB_MODEL');
+EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+SET echo ON;
+DECLARE
+    v_setlst DBMS_DATA_MINING.SETTING_LIST;
+BEGIN
+    v_setlst('ALGO_NAME')          := 'ALGO_XGBOOST';
+	
+    DBMS_DATA_MINING.CREATE_MODEL2(
+               MODEL_NAME          => 'MS_XGB_MODEL',
+               MINING_FUNCTION     => dbms_data_mining.regression,
+               DATA_QUERY          => 'SELECT * FROM tmesm_ms_train',
+               CASE_ID_COLUMN_NAME => 'CASE_ID',
+               TARGET_COLUMN_NAME  => 'DAX',
+               SET_LIST            => v_setlst);    
+END;
+/
+	  
+-------------------------
+-- COMPARE REGRESSION FORECAST (XGB) TO BASELINE (EXPONENTIAL SMOOTHING) FORECAST
+--
+
+SELECT CASE_ID, DAX, regression_forecast, baseline_forecast,
+       DAX - regression_forecast AS regression_error,
+       DAX - baseline_forecast AS baseline_error 
+FROM (SELECT CASE_ID, DAX, 
+             PREDICTION(MS_XGB_model using *) regression_forecast, 
+             DM$DAX baseline_forecast
+      FROM   tmesm_ms_test 
+      ORDER BY CASE_ID);
+/
+-----------------------------------------------------------------------
+--   End of script
+-------------------------------------------------------------------------
\ No newline at end of file